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@@ -33,3 +33,16 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+dataset/_train.xlsx filter=lfs diff=lfs merge=lfs -text
+dataset/test_.xlsx filter=lfs diff=lfs merge=lfs -text
+dataset/train.xlsx filter=lfs diff=lfs merge=lfs -text
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diff --git a/ImageMolEncoder.pth b/ImageMolEncoder.pth
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+version https://git-lfs.github.com/spec/v1
+oid sha256:85eebbe81192401d0b4337f89e0eea507092c396909ff83bd6b569fd89d49750
+size 44782591
diff --git a/README.md b/README.md
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+# AI-based D-amino acid substitution for optimizing antimicrobial peptides to treat multidrug-resistant bacterial infection
+This repository contains the code for the paper "AI-based D-amino acid substitution for optimizing antimicrobial peptides to treat multidrug-resistant bacterial infection"
+
+## Requirements
+```
+mamba_ssm==2.2.4
+numpy==1.26.3
+pandas==2.1.4
+rdkit==2024.3.5
+scikit_learn==1.4.1.post1
+scipy==1.13.0
+torch==2.2.0
+torchmetrics==1.3.1
+torchvision==0.17.0
+```
+You can install them with `pip install -r requirements.txt`
+
+Additionally, `mamba_ssm` is optional since it is not used for our final method.
+You can comment `mamba_ssm==2.2.4` in `requirements.txt` and `from mamba_ssm import Mamba` in `network.py` out if you don't want to install it and avoid use `--q-encoder mamba`.
+
+## Training
+There are two .py file for training: `main.py` and `main_simple.py`.
+
+`main.py`: Can train model with Classification and Regression tasks. Prefered with regression task.
+
+`main_simple.py`: Can ONLY train model with Classification task. Prefered with classification task. `simple` means a simple dataset that direct loads pre-processed data.
+
+example: 
+```
+python main-simple.py \
+    --q-encoder cnn \ # Encoder, can be cnn, lstm, gru, mamba, mha
+    --channels 16 \ # Encoder channels
+    --side-enc lstm \ # Side sequence Encoder, only lstm implemented, only use with cnn encoder
+    --fusion att \ # Fusion method, can be att, mlp or diff
+    --task cls \ # Task, can be cls or reg
+    --loss ce \ # Loss, can be ce or mse, some other losses can be found in code
+    --batch-size 32 \ # Batch size
+    --epochs 35 \ # Epochs
+    --gpu 0 \ # GPU index to use, -1 for cpu
+# ===CNN only options=== \
+    --pcs \ # Enable protease cleavage site dyeing for input pictures
+    --resize 768 \ # Resize input pictures, can be 1 or 2 numbers like 768 or 768 512
+# ===main_simple.py only options=== \
+    --llm-data # Use LLM augmented training data
+```
+Corresponding model weight checkpoints will be saved in the subdirectory of `run-cls` or `run-reg`, e.g. `/run-cls/cnn-att-16-lstm-pcs-simple-llm-768-oneway-ce-32-0.001-35/`
+
+For more arguments, please refer to the code of `main.py` or `main_simple.py`
+
+## Inference
+You can simple replace `main.py` with `infer.py` in your training command to do inference. Remember to add `--simple` if you used checkpoints trained from `main_simple.py`
+
+For case study scanning, please use `infer_case.py` with an additional argument `--case r2` or `--case YOUR_PEPTIDE_SEQUENCE`
+
+Inference results will be saved in the weights directory in `csv` format, e.g. `/run-cls/cnn-att-16-lstm-pcs-simple-llm-768-oneway-ce-32-0.001-35/preds_test.csv`
\ No newline at end of file
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diff --git a/dataset.py b/dataset.py
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+++ b/dataset.py
@@ -0,0 +1,865 @@
+import pandas as pd
+import numpy as np
+import itertools
+import torch
+from torch.utils.data import Dataset
+import re
+import json
+from typing import Literal
+import os
+# import io
+from rdkit import Chem
+from rdkit.Chem import AllChem
+from rdkit.Chem.Draw import rdMolDraw2D
+# from PIL import Image
+import torchvision.io as tvio
+# import torchvision.transforms as tvt
+import torchvision.transforms.v2.functional as tvtF
+
+# --- 辅助函数 ---
+
+# 定义20种常见氨基酸字母（按字母顺序）
+AMINO_ACIDS = ['A', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'K', 'L', 
+               'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'Y']
+AA_to_index = {aa: i for i, aa in enumerate(AMINO_ACIDS)}
+valid_aa = set(AMINO_ACIDS)
+
+def is_valid_sequence(seq):
+    """
+    判断序列是否只包含标准氨基酸字符（允许大写或小写，
+    对于小写表示 D 型氨基酸也视为合法）
+    """
+    for ch in seq:
+        if not ch.isalpha():
+            return False
+        if ch.upper() not in valid_aa:
+            return False
+    return True
+
+def parse_mic(mic_str):
+    """
+    解析 MIC 数据，支持以下几种格式：
+      1. 数字，例如 "5" -> 5.0 
+      2. ">{数字}" 或 "≥{数字}"（例如 ">4" 或 "≥ 4"）→ 数值乘以 1.5
+      3. 平均值±标准差，例如 "3.2 ± 0.4" → 取平均值 3.2
+      4. 范围形式，例如 "2.0 - 4.0" → (2.0 + 4.0)/2
+
+    注：符号与数字之间可能存在空格，大于等于符号为 "≥" 而非 ">="
+    """
+    if not isinstance(mic_str, str):
+        return float(mic_str)
+    
+    mic_str = mic_str.strip()
+    mic_str = re.sub(r'\s+', '', mic_str)
+    
+    # 匹配纯数字
+    if re.fullmatch(r'\d+(\.\d+)?', mic_str):
+        return float(mic_str)
+    
+    # 匹配 >{数字} 或 ≥{数字}
+    m = re.fullmatch(r'[>≥](\d+(\.\d+)?)', mic_str)
+    if m:
+        num = float(m.group(1))
+        return num * 1.5
+    
+    # 匹配 <{数字} 或 ≤{数字}
+    m = re.fullmatch(r'[<≤](\d+(\.\d+)?)', mic_str)
+    if m:
+        num = float(m.group(1))
+        return num * 0.75
+    
+    # 匹配 {数字}±{数字}
+    m = re.fullmatch(r'(\d+(\.\d+)?)[±](\d+(\.\d+)?)', mic_str)
+    if m:
+        return float(m.group(1))
+    
+    # 匹配 {数字}-{数字}
+    m = re.fullmatch(r'(\d+(\.\d+)?)-(\d+(\.\d+)?)', mic_str)
+    if m:
+        num1 = float(m.group(1))
+        num2 = float(m.group(3))
+        return (num1 + num2) / 2.0
+    
+    print(f"Warning: 无法解析 MIC 值 {mic_str}")
+    return np.nan
+
+def encode_sequence(seq, pad_length):
+    """
+    将多肽序列转换为固定大小 (pad_length, 21) 的张量：
+      - 每个残基对应一行；
+      - 第1列: 表示是否为 D 型氨基酸（若字符为小写，则置 1，否则为 0）；
+      - 后20列: 20种常见氨基酸的独热编码（先转为大写匹配）。
+    若序列长度小于 pad_length，则在末尾填充全 0 行。
+    """
+    n = len(seq)
+    arr = np.zeros((pad_length, 21), dtype=np.float32)
+    
+    # 对实际序列部分进行编码
+    for i, char in enumerate(seq):
+        if i >= pad_length:
+            break  # 超出部分不处理（数据集构造时已过滤掉长序列）
+        if char.islower():
+            d_indicator = 1.0
+            aa = char.upper()
+        else:
+            d_indicator = 0.0
+            aa = char
+        arr[i, 0] = d_indicator
+        if aa in AA_to_index:
+            idx = AA_to_index[aa]
+            arr[i, idx + 1] = 1.0
+        else:
+            print(f"Warning: 氨基酸 {aa} 不在标准列表中")
+    return torch.tensor(arr)
+
+def geometric_mean(values):
+    """
+    计算数值序列的几何平均值
+    """
+    log_vals = np.log(np.array(values))
+    return float(np.exp(log_vals.mean()))
+
+def process_label(ratio, task):
+    """
+    对比值 ratio 进行 log2 变换，并根据 task 参数返回最终标签：
+     - task="reg": 返回 log₂比值，并转换为 np.float32；
+     - task="cls": 根据 log₂比值进行分类：
+                  如果 x <= -0.5 返回 1，
+                  否则返回 0.
+    若 ratio 非正，返回 np.nan。
+    """
+    if ratio <= 0:
+        return np.nan
+    ratio_log = np.log2(ratio)
+    if task == "reg":
+        return np.float32(ratio_log)
+    elif task == "cls":
+        if ratio_log < 0.:
+            return 1
+        else:
+            return 0
+    else:
+        raise ValueError("未知的 task 类型，请使用 'reg' 或 'cls'")
+
+# --- 数据预处理与构建数据集 ---
+
+def load_data(xlsx_file, condition=None):
+    """
+    从 xlsx 文件中读取数据，将每个具体变种（同一原型-变种）对应的 MIC 值取几何平均，
+    并按照原型分组。对于原型和变种序列，若存在非标准氨基酸或非字母字符，则过滤掉该行数据。
+
+    返回：
+      groups: dict，其中 key 为原型序列，
+              value 为 dict，其 key 为变种序列（"SEQUENCE - D-type amino acid substitution"），
+              value 为该变种所有 MIC 值的几何平均
+    """
+    df = pd.read_excel(xlsx_file)
+    # df = df[df['TARGET ACTIVITY - ACTIVITY MEASURE VALUE'] != 'MBC']
+    
+    groups = {}
+    for _, row in df.iterrows():
+        orig = row["SEQUENCE - Original"]
+        variant = row["SEQUENCE - D-type amino acid substitution"]
+        mic_raw = row["TARGET ACTIVITY - CONCENTRATION"]
+        
+        # 过滤包含非标准氨基酸或非字母字符的序列（原型和变种均检查）
+        if not (isinstance(orig, str) and is_valid_sequence(orig)):
+            continue
+        if not (isinstance(variant, str) and is_valid_sequence(variant)):
+            continue
+        
+        mic_val = parse_mic(mic_raw)
+        
+        if orig not in groups:
+            groups[orig] = {}
+        if variant not in groups[orig]:
+            groups[orig][variant] = []
+        groups[orig][variant].append(mic_val)
+    
+    # 对每个变种计算几何平均（过滤掉 NaN 值）
+    groups_avg = {}
+    for orig, var_dict in groups.items():
+        groups_avg[orig] = {}
+        for variant, mic_list in var_dict.items():
+            mic_list = [x for x in mic_list if not np.isnan(x)]
+            if len(mic_list) == 0:
+                continue
+            groups_avg[orig][variant] = geometric_mean(mic_list)
+    return groups_avg
+
+
+def load_data_stability(xlsx_file, condition):
+    """
+    从 xlsx 文件中读取数据，将每个具体变种（同一原型-变种）对应的 MIC 值取几何平均，
+    并按照原型分组。对于原型和变种序列，若存在非标准氨基酸或非字母字符，则过滤掉该行数据。
+
+    返回：
+      groups: dict，其中 key 为原型序列，
+              value 为 dict，其 key 为变种序列（"SEQUENCE - D-type amino acid substitution"），
+              value 为该变种所有 MIC 值的几何平均
+    """
+    map_dict = {
+        '125fbs': '12.5% FBS',
+        '25fbs': '25% FBS',
+        'mhb': 'MHB',
+        'nacl': '150mM NaCl'
+    }
+    df = pd.read_excel(xlsx_file)
+    df = df[df['Condition'] == map_dict[condition]]
+    
+    groups = {}
+    for _, row in df.iterrows():
+        variant = row["SEQUENCE"]
+        orig = variant.upper()
+        mic_raw = row["Activity"]
+        
+        # 过滤包含非标准氨基酸或非字母字符的序列（原型和变种均检查）
+        if not (isinstance(orig, str) and is_valid_sequence(orig)):
+            continue
+        if not (isinstance(variant, str) and is_valid_sequence(variant)):
+            continue
+        
+        mic_val = parse_mic(mic_raw)
+        
+        if orig not in groups:
+            groups[orig] = {}
+        if variant not in groups[orig]:
+            groups[orig][variant] = []
+        groups[orig][variant].append(mic_val)
+    
+    # 对每个变种计算几何平均（过滤掉 NaN 值）
+    groups_avg = {}
+    for orig, var_dict in groups.items():
+        groups_avg[orig] = {}
+        for variant, mic_list in var_dict.items():
+            mic_list = [x for x in mic_list if not np.isnan(x)]
+            if len(mic_list) == 0:
+                continue
+            groups_avg[orig][variant] = geometric_mean(mic_list)
+    return groups_avg
+
+class PeptidePairDataset(Dataset):
+    def __init__(self, mode=Literal['train', 'test', '125fbs', 'nacl', '25fbs', 'mhb'], pad_length=30, task="cls", 
+                 include_reverse=False, include_self=False, one_way=False, gf=False) :
+        """
+        构建数据集：
+          - 从 xlsx 文件中读取数据，并按照原型分组，
+            同时过滤包含非标准氨基酸或非字母字符的行，以及变种序列长度超过 pad_length 的样本；
+          - 对于同一原型下不同变种构成配对；
+          - 参数 include_reverse: 是否启用正反组合（同时添加 (A, B) 和 (B, A)）；
+          - 参数 include_self: 是否启用自组合（添加 (A, A)，标签为 log₂(1)=0）；
+          - 参数 task: "reg" 表示回归任务（输出 32 位浮点数标签），"cls" 表示分类任务，
+                      将 log₂比值转为整数标签，规则为：
+                          log₂比值 ≤ -0.5 → 1，
+                          log₂比值 ≥ 0.5 → 2，
+                          -0.5 < log₂比值 < 0.5 → 0.
+                          
+        每个数据项返回：
+          - 变种多肽序列编码后的张量，形状为 (pad_length, 21)
+          - 另一个变种多肽序列编码后的张量，形状为 (pad_length, 21)
+          - 标签：根据 task 不同分别为 32 位浮点数或整数
+        """
+        if mode == "train":
+            loader = load_data
+            xlsx_file = os.path.join(os.path.dirname(__file__), 'dataset', 'train.xlsx')
+        elif mode in ["test", "r2_case", 'r2_case_', "125fbs", "nacl", "25fbs", "mhb"]:
+            one_way = True
+            if mode in ["test", "r2_case", 'r2_case_']:
+                loader = load_data
+                xlsx_file = os.path.join(os.path.dirname(__file__), 'dataset', f'{mode}.xlsx')
+            else:
+                loader = load_data_stability
+                xlsx_file = os.path.join(os.path.dirname(__file__), 'dataset', 'stability.xlsx')
+        else:
+            raise ValueError("未知的 mode，请使用 'train' 或 'test'")
+
+        self.data = []
+        self.pad_length = pad_length
+        self.task = task
+        groups_avg = loader(xlsx_file, mode)
+        if gf:
+            gf_dict = torch.load(os.path.join(os.path.dirname(__file__), 'dataset', 'protbert.pth'))
+        
+        # 针对每个原型，过滤掉长度超过 pad_length 的变种
+        for orig, variant_dict in groups_avg.items():
+            # a = len(self.data)
+            filtered_variants = {variant: mic for variant, mic in variant_dict.items() 
+                                 if len(variant) <= pad_length}
+            variants = list(filtered_variants.keys())
+            n_variants = len(variants)
+            if n_variants == 0:
+                continue
+            
+            if gf:
+                glob_feat = gf_dict[orig.upper()]
+
+            # 若启用自组合，则添加 (A, A) 样本，标签为 process_label(1, task) → log2(1)=0（再分类也为 0）
+            if include_self and (not one_way):
+                for variant in variants:
+                    encoded_seq = encode_sequence(variant, pad_length)
+                    label = process_label(1.0, task)  # log2(1)=0
+                    if gf:
+                        self.data.append(((encoded_seq, encoded_seq, glob_feat), label))
+                    else:
+                        self.data.append(((encoded_seq, encoded_seq), label))
+            
+            # 添加不同变种之间的样本
+            for i in [0] if one_way else range(n_variants):
+                for j in range(i + 1, n_variants):
+                    var1 = variants[i]
+                    var2 = variants[j]
+                    mic1 = filtered_variants[var1]
+                    mic2 = filtered_variants[var2]
+                    
+                    # 正向组合: (var1, var2) 标签为 log₂(mic2/mic1)
+                    ratio = mic2 / mic1 if mic1 != 0 else np.nan
+                    label = process_label(ratio, task)
+                    if np.isnan(label):
+                        continue
+                    encoded_var1 = encode_sequence(var1, pad_length)
+                    encoded_var2 = encode_sequence(var2, pad_length)
+                    if gf:
+                        self.data.append(((encoded_var1, encoded_var2, glob_feat), label))
+                    else:
+                        self.data.append(((encoded_var1, encoded_var2), label))
+                    
+                    # 若启用正反组合，则添加 (var2, var1)
+                    if include_reverse and (not one_way):
+                        rev_ratio = mic1 / mic2 if mic2 != 0 else np.nan
+                        rev_label = process_label(rev_ratio, task)
+                        if gf:
+                            self.data.append(((encoded_var2, encoded_var1, glob_feat), rev_label))
+                        else:
+                            self.data.append(((encoded_var2, encoded_var1), rev_label))
+            # b = len(self.data)
+            # print(f"{orig},{b - a}")
+    
+    def reg_sample_weight(self):
+        y = []
+        for _, label in self.data:
+            y.append(label)
+        y = np.array(y)
+        mu = np.mean(y)
+        sigma = np.std(y)
+        p = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-((y - mu) ** 2) / (2 * sigma ** 2))
+        
+        # 如果未提供 C，则使用 p 的中位数作为基准常数
+        C = np.median(p)
+        epsilon = 1e-6
+        
+        # 使用对数转化计算采样权重： p 值越低权重越高
+        weights = np.log(C / (p + epsilon))
+        
+        # 可选：对权重进行归一化处理，使得权重均值为1
+        weights_normalized = weights / np.mean(weights)
+        positive_weights = np.exp(weights_normalized)
+        
+        return torch.tensor(positive_weights, dtype=torch.float32)
+    
+    def __len__(self):
+        return len(self.data)
+    
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+
+class PeptidePairPicDataset(Dataset):
+    def __init__(self, mode=Literal['train', 'test', '125fbs', 'nacl', '25fbs', 'mhb'], pad_length=30, task="reg", 
+                 include_reverse=False, include_self=False, one_way=False, gf=False,
+                 side_enc=None, pcs=False, resize=None) :
+        """
+        构建数据集：
+          - 从 xlsx 文件中读取数据，并按照原型分组，
+            同时过滤包含非标准氨基酸或非字母字符的行，以及变种序列长度超过 pad_length 的样本；
+          - 对于同一原型下不同变种构成配对；
+          - 参数 include_reverse: 是否启用正反组合（同时添加 (A, B) 和 (B, A)）；
+          - 参数 include_self: 是否启用自组合（添加 (A, A)，标签为 log₂(1)=0）；
+          - 参数 task: "reg" 表示回归任务（输出 32 位浮点数标签），"cls" 表示分类任务，
+                      将 log₂比值转为整数标签，规则为：
+                          log₂比值 ≤ -0.5 → 1，
+                          log₂比值 ≥ 0.5 → 2，
+                          -0.5 < log₂比值 < 0.5 → 0.
+                          
+        每个数据项返回：
+          - 变种多肽序列编码后的张量，形状为 (pad_length, 21)
+          - 另一个变种多肽序列编码后的张量，形状为 (pad_length, 21)
+          - 标签：根据 task 不同分别为 32 位浮点数或整数
+        """
+        if mode == "train":
+            loader = load_data
+            xlsx_file = os.path.join(os.path.dirname(__file__), 'dataset', 'train.xlsx')
+        elif mode in ["test", "r2_case", 'r2_case_', "125fbs", "nacl", "25fbs", "mhb"]:
+            one_way = True
+            if mode in ["test", "r2_case", 'r2_case_']:
+                loader = load_data
+                xlsx_file = os.path.join(os.path.dirname(__file__), 'dataset', f'{mode}.xlsx')
+            else:
+                loader = load_data_stability
+                xlsx_file = os.path.join(os.path.dirname(__file__), 'dataset', 'stability.xlsx')
+        else:
+            raise ValueError("未知的 mode，请使用 'train' 或 'test'")
+
+        self.data = []
+        self.pics = {}
+        self.pad_length = pad_length
+        self.task = task
+        self.gf = gf
+        self.side_enc = True if side_enc else False
+        self.pcs = pcs
+        self.resize = resize
+        groups_avg = loader(xlsx_file, mode)
+        if gf:
+            gf_dict = torch.load(os.path.join(os.path.dirname(__file__), 'dataset', 'protbert.pth'))
+        
+        # 针对每个原型，过滤掉长度超过 pad_length 的变种
+        for orig, variant_dict in groups_avg.items():
+            # a = len(self.data)
+            filtered_variants = {variant: mic for variant, mic in variant_dict.items() 
+                                 if len(variant) <= pad_length}
+            variants = list(filtered_variants.keys())
+            for variant in variants:
+                if self.pcs == 'mix' and variant == orig:
+                    self.pics[variant] = self.read_img(variant, False)
+                else:
+                    self.pics[variant] = self.read_img(variant, self.pcs)
+            n_variants = len(variants)
+            if n_variants == 0:
+                continue
+            
+            if gf:
+                glob_feat = gf_dict[orig.upper()]
+
+            # 若启用自组合，则添加 (A, A) 样本，标签为 process_label(1, task) → log2(1)=0（再分类也为 0）
+            if include_self and (not one_way):
+                for variant in variants:
+                    label = process_label(1.0, task)  # log2(1)=0
+                    if gf:
+                        self.data.append((variant, variant, glob_feat, label))
+                    else:
+                        self.data.append((variant, variant, label))
+            
+            # 添加不同变种之间的样本
+            for i in [0] if one_way else range(n_variants):
+                for j in range(i + 1, n_variants):
+                    var1 = variants[i]
+                    var2 = variants[j]
+                    mic1 = filtered_variants[var1]
+                    mic2 = filtered_variants[var2]
+                    
+                    # 正向组合: (var1, var2) 标签为 log₂(mic2/mic1)
+                    ratio = mic2 / mic1 if mic1 != 0 else np.nan
+                    label = process_label(ratio, task)
+                    if np.isnan(label):
+                        continue
+                    if gf:
+                        self.data.append((var1, var2, glob_feat, label))
+                    else:
+                        self.data.append((var1, var2, label))
+                    
+                    # 若启用正反组合，则添加 (var2, var1)
+                    if include_reverse and (not one_way):
+                        rev_ratio = mic1 / mic2 if mic2 != 0 else np.nan
+                        rev_label = process_label(rev_ratio, task)
+                        if gf:
+                            self.data.append((var2, var1, glob_feat, rev_label))
+                        else:
+                            self.data.append((var2, var1, rev_label))
+            # b = len(self.data)
+            # print(f"{orig},{b - a}")
+    
+    def reg_sample_weight(self):
+        y = []
+        for d in self.data:
+            label = d[-1]
+            y.append(label)
+        y = np.array(y)
+        mu = np.mean(y)
+        sigma = np.std(y)
+        p = 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-((y - mu) ** 2) / (2 * sigma ** 2))
+        
+        # 如果未提供 C，则使用 p 的中位数作为基准常数
+        C = np.median(p)
+        epsilon = 1e-6
+        
+        # 使用对数转化计算采样权重： p 值越低权重越高
+        weights = np.log(C / (p + epsilon))
+        
+        # 可选：对权重进行归一化处理，使得权重均值为1
+        weights_normalized = weights / np.mean(weights)
+        positive_weights = np.exp(weights_normalized)
+        
+        return torch.tensor(positive_weights, dtype=torch.float32)
+    
+    def read_img(self, peptide, pcs):
+        image = draw_peptide(peptide, self.resize, pcs)
+        return image
+    
+    def __len__(self):
+        return len(self.data)
+    
+    def __getitem__(self, idx):
+        if self.gf:
+            seq1, seq2, glob_feat, label = self.data[idx]
+        else:
+            seq1, seq2, label = self.data[idx]
+        img1 = self.pics[seq1]
+        img2 = self.pics[seq2]
+
+        if self.side_enc:
+            img1 = (img1, encode_sequence(seq1, self.pad_length))
+            img2 = (img2, encode_sequence(seq2, self.pad_length))
+
+        if self.gf:
+            return (img1, img2, glob_feat), label
+        else:
+            return (img1, img2), label
+        
+
+class SimplePairClsDataset(Dataset):
+    def __init__(self, pad_length=30, llm=False, ftr2=False, gf=False,
+                 q_encoder=None, side_enc=None, pcs=False, resize=None):
+        if llm:
+            file_path = os.path.join(os.path.dirname(__file__), 'dataset', 'train_set_llm_aug.json')
+        elif ftr2:
+            file_path = os.path.join(os.path.dirname(__file__), 'dataset', 'finetune_for_r2_llm.json')
+        else:
+            file_path = os.path.join(os.path.dirname(__file__), 'dataset', 'train_set.json')
+        with open(file_path, 'r', encoding='utf-8') as f:
+            dataset = json.load(f)   
+
+        self.data = []
+        self.pics = {}
+        self.pad_length = pad_length
+        self.gf = gf
+        self.q_encoder = q_encoder
+        self.side_enc = True if side_enc else False
+        self.pcs = pcs
+        self.resize = resize
+        if gf:
+            self.gf_dict = torch.load(os.path.join(os.path.dirname(__file__), 'dataset', 'protbert.pth'))
+        
+        all_seqs = []
+        for orig, variants in dataset.items():
+            if len(orig) > pad_length:
+                continue
+            all_seqs.append(orig)
+            for label in ["1", "0"]:
+                for variant in variants[label]:
+                    self.data.append((orig, variant, int(label)))
+                    all_seqs.append(variant)
+        if q_encoder in ['cnn', 'rn18']:
+            for i in all_seqs:
+                if self.pcs == 'mix' and i.isupper():
+                    self.pics[i] = self.read_img(i, False)
+                else:
+                    self.pics[i] = self.read_img(i, self.pcs)
+    
+    def read_img(self, peptide, pcs):
+        image = draw_peptide(peptide, self.resize, pcs)
+        return image
+    
+    def __len__(self):
+        return len(self.data)
+    
+    def __getitem__(self, idx):
+        seq1, seq2, label = self.data[idx]
+        if self.q_encoder in ['cnn', 'rn18']:
+            img1 = self.pics[seq1]
+            img2 = self.pics[seq2]
+
+            if self.side_enc:
+                img1 = (img1, encode_sequence(seq1, self.pad_length))
+                img2 = (img2, encode_sequence(seq2, self.pad_length))
+        
+        else:
+            img1 = encode_sequence(seq1, self.pad_length)
+            img2 = encode_sequence(seq2, self.pad_length)
+
+        if self.gf:
+            return (img1, img2, self.gf_dict[seq1]), label
+        else:
+            return (img1, img2), label
+
+
+class PeptidePairCaseDataset(Dataset):
+    def __init__(self, case:str ='r2', pad_length=30, gf=False):
+
+        if case == 'r2':
+            self.template = 'KWKIKWPVKWFKML'
+        elif case == 'Indolicidin':
+            self.template = 'ILPWKWPWWPWRR'
+        elif case == 'Temporin-A':
+            self.template = 'FLPLIGRVLSGIL'
+        elif case == 'Melittin':
+            self.template = 'GIGAVLKVLTTGLPALISWIKRKRQQ'
+        elif case == 'Anoplin':
+            self.template = 'GLLKRIKTLL'
+        else:
+            self.template = case.upper().strip()
+        self.data = []
+        self.pad_length = pad_length
+        self.gf = gf
+
+        if gf:
+            self.glob_feat = torch.load(os.path.join(os.path.dirname(__file__), 'dataset', 'protbert.pth'))[self.template]
+        
+        pools = [(ch.upper(), ch.lower()) if ch != 'G' else (ch.upper(),) for ch in self.template]
+        # 笛卡尔积，即所有组合
+        self.variants = [''.join(chars) for chars in itertools.product(*pools)][1:]
+
+        self.template_seq = encode_sequence(self.template, self.pad_length)
+    
+    def __len__(self):
+        return len(self.variants)
+    
+    def __getitem__(self, idx):
+        variant  = self.variants[idx]
+        seq2, label = variant, variant
+        enc_seq1 = self.template_seq
+        enc_seq2 = encode_sequence(seq2, self.pad_length)
+
+        if self.gf:
+            return (enc_seq1, enc_seq2, self.glob_feat), label
+        else:
+            return (enc_seq1, enc_seq2), label
+
+
+
+class PeptidePairPicCaseDataset(Dataset):
+    def __init__(self, case:str ='r2', pad_length=30, side_enc=None, pcs=False, resize=None, gf=False):
+
+        if case == 'r2':
+            self.template = 'KWKIKWPVKWFKML'
+        elif case == 'Indolicidin':
+            self.template = 'ILPWKWPWWPWRR'
+        elif case == 'Temporin-A':
+            self.template = 'FLPLIGRVLSGIL'
+        elif case == 'Melittin':
+            self.template = 'GIGAVLKVLTTGLPALISWIKRKRQQ'
+        elif case == 'Anoplin':
+            self.template = 'GLLKRIKTLL'
+        else:
+            self.template = case.upper().strip()
+        self.data = []
+        self.pad_length = pad_length
+        self.side_enc = True if side_enc else False
+        self.pcs = pcs
+        self.resize = resize
+        self.gf = gf
+
+        if gf:
+            self.glob_feat = torch.load(os.path.join(os.path.dirname(__file__), 'dataset', 'protbert.pth'))[self.template]
+        
+        pools = [(ch.upper(), ch.lower()) if ch != 'G' else (ch.upper(),) for ch in self.template]
+        # 笛卡尔积，即所有组合
+        self.variants = [''.join(chars) for chars in itertools.product(*pools)][1:]
+
+        self.template_pic = self.read_img(self.template)
+        if self.side_enc:
+            self.template_seq = encode_sequence(self.template, self.pad_length)
+    
+    def read_img(self, peptide):
+        image = draw_peptide(peptide, self.resize, self.pcs)
+        return image
+    
+    def __len__(self):
+        return len(self.variants)
+    
+    def __getitem__(self, idx):
+        variant  = self.variants[idx]
+        seq2, label = variant, variant
+        img1 = self.template_pic
+        img2 = self.read_img(variant)
+
+        if self.side_enc:
+            img1 = (img1, self.template_seq)
+            img2 = (img2, encode_sequence(seq2, self.pad_length))
+
+        if self.gf:
+            return (img1, img2, self.glob_feat), label
+        else:
+            return (img1, img2), label
+
+
+aa_side = {
+    "A": "C", "R": "CCCNC(N)=N", "N": "CC(=O)N", "D": "CC(=O)O", "C": "CS",
+    "E": "CCC(=O)O", "Q": "CCC(=O)N", "G": "", "H": "Cc1cnc[nH]1", "I": "C(C)CC",
+    "L": "CC(C)C", "K": "CCCCN", "M": "CCSC", "F": "Cc1ccccc1", "P": "C1CCN1",
+    "S": "CO", "T": "C(C)O", "W": "Cc1c[nH]c2ccccc12", "Y": "Cc1ccc(O)cc1", "V": "C(C)C"
+}
+
+aa_tpl = {}
+for aa, R in aa_side.items():
+    for stereo, chir in (("L", "@"), ("D", "@@")):
+        if aa == "G":  # Gly 没手性
+            backbone = "N[C:{idx}]C"          # N-CA(带编号)-C
+        else:
+            backbone = f"N[C{chir}H:{'{idx}'}]({R})C"  # N-[C@H:idx](R)-C
+        aa_tpl[f"{aa}_{stereo}"]       = backbone + "(=O)"     # 中间残基
+        aa_tpl[f"{aa}_{stereo}_term"]  = backbone + "(=O)O"    # C 端
+
+def build_peptide_smiles(seq: str) -> str:
+    """
+    给定单字母序列，返回 backbone 带 [atom_map] 的 SMILES。
+    大写 = L 型， 小写 = D 型。编号 = 残基序号（1,2,3...） -> α-碳。
+    """
+    if not seq:
+        return ""
+
+    out = []
+    n = len(seq)
+    for i, aa in enumerate(seq, start=1):
+        key = f"{aa.upper()}_{'L' if aa.isupper() else 'D'}"
+        if i == n:
+            key += "_term"
+        out.append(aa_tpl[key].format(idx=i))
+    return "".join(out)
+
+protease_patterns = {
+    'trypsin':       re.compile(r'(?<=[KR])(?!P)'),
+    'chymotrypsin':  re.compile(r'(?<=[FYWL])(?!P)'),
+    'elastase':      re.compile(r'(?<=[AVSGT])(?!P)'),
+    'enterokinase':  re.compile(r'D{4}K(?=[^P])'),
+    'caspase':       re.compile(r'(?<=D)(?=[GSA])'),
+}
+
+def draw_peptide(sequence, size=[768], pcs=False):
+    """
+    根据输入序列生成多肽结构图，并基于常见蛋白酶识别模式高亮酶切位点肽键（红色）。
+    支持的酶及其正则模式（P1--P1'）：
+      • trypsin:       (?<=[KR])(?!P)
+      • chymotrypsin:  (?<=[FYWL])(?!P)
+      • elastase:      (?<=[AVSGT])(?!P)
+      • enterokinase:  D{4}K(?=[^P])
+      • caspase:       (?<=D)(?=[GSA])
+    """
+
+    # # 1. 生成带 atom map 的 SMILES（现在序号标注在α-碳上）
+    smiles = build_peptide_smiles(sequence)
+    mol = Chem.MolFromSmiles(smiles)
+    # if mol is None:
+    #     raise ValueError("SMILES 解析失败，请检查输入序列和侧链字典。")
+    AllChem.Compute2DCoords(mol)
+
+    highlight_bonds = []
+    bond_colors = {}
+    
+    # ----------------------------------------------------
+    # 2. 先标 D 型残基：高亮与α-碳相连的键为蓝色
+    d_positions = {i for i, aa in enumerate(sequence, start=1) if aa.islower()}
+
+    for atom in mol.GetAtoms():
+        if atom.GetAtomMapNum() in d_positions:
+            # 这个atom就是α-碳，高亮与它相连的所有键
+            for b in atom.GetBonds():
+                idx = b.GetIdx()
+                if idx not in highlight_bonds:
+                    highlight_bonds.append(idx)
+                bond_colors[idx] = (0.0, 0.0, 1.0)
+
+    # ----------------------------------------------------
+    # 3. 再标酶切键：红色（覆盖之前的蓝色）
+    if pcs:
+        cleavage_sites = set()
+        for pat in protease_patterns.values():
+            for m in pat.finditer(sequence):
+                cut = m.end()  # 切在 cut 之后
+                if 1 <= cut < len(sequence):
+                    cleavage_sites.add(cut)
+
+        for pos in cleavage_sites:
+            # 先找 P1 残基的 α-C
+            ca = next((a for a in mol.GetAtoms()
+                       if a.GetAtomMapNum() == pos), None)
+            if ca is None:
+                continue
+
+            # 找同残基的羧基碳 (sp², 含 O 双键)
+            carbonyl_c = None
+            for nb in ca.GetNeighbors():
+                if nb.GetSymbol() != "C":
+                    continue
+                # 判断是否有 "=O"
+                if any(bond.GetBondType() == Chem.BondType.DOUBLE and
+                       o.GetSymbol() == "O"
+                       for bond in nb.GetBonds()
+                       for o in (bond.GetBeginAtom(), bond.GetEndAtom())):
+                    carbonyl_c = nb
+                    break
+            if carbonyl_c is None:
+                continue
+
+            # 羧基碳连到的 N 就是下一残基的氮
+            peptide_bond = None
+            for b in carbonyl_c.GetBonds():
+                o_atom = b.GetOtherAtom(carbonyl_c)
+                if o_atom.GetSymbol() == "N":
+                    peptide_bond = b
+                    break
+            if peptide_bond is None:
+                continue
+
+            bidx = peptide_bond.GetIdx()
+            if bidx not in highlight_bonds:
+                highlight_bonds.append(bidx)
+            bond_colors[bidx] = (1.0, 0.0, 0.0)  # 红
+    
+    # 4. 设置画布大小
+    if len(size) == 1:
+        w = h = size[0]
+    else:
+        w, h = size
+
+    # 5. MolDraw2DCairo 接收 highlightBondColors
+    drawer = rdMolDraw2D.MolDraw2DCairo(w, h)
+    # 你也可以通过 drawer.drawOptions() 调整一些样式：bond line width、atom font 等
+    drawer.DrawMolecule(
+        mol,
+        highlightAtoms=[],
+        highlightBonds=highlight_bonds,
+        highlightAtomColors={},
+        highlightBondColors=bond_colors
+    )
+    drawer.FinishDrawing()
+
+    # 6. 把输出的 PNG bytes 转成 Tensor
+    png_bytes = bytearray(drawer.GetDrawingText())
+    byte_tensor = torch.frombuffer(png_bytes, dtype=torch.uint8)
+    img = tvio.decode_png(byte_tensor, mode=tvio.ImageReadMode.RGB)       # [3, H, W], uint8
+    img = tvtF.to_dtype(img, torch.float32)
+    img = tvtF.normalize(img, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    return img
+
+if __name__ == '__main__':
+    # 假设 xlsx 文件路径为 "data.xlsx"
+    # 设置 pad_length 为 50，同时启用正反组合和自组合
+    pad_length = 30
+    dataset = PeptidePairDataset('r2_case', pad_length, "cls", include_reverse=False, include_self=False, one_way=True)
+    
+    # 打印第一个数据项
+    if len(dataset) > 0:
+        (encoded_seq1, encoded_seq2), ratio = dataset[0]
+        print("第一个样本：")
+        print("变种1的编码张量形状：", encoded_seq1.shape)
+        print("变种2的编码张量形状：", encoded_seq2.shape)
+        print("标签比值（变种2/变种1）：", ratio)
+        print(f"数据集大小：{len(dataset)}")
+        label_pos = 0
+        for (_, _), i in dataset:
+            label_pos += i
+        print(label_pos)
+
+    else:
+        print("未读入组合数据！")
+
+    # # 测试 PeptidesDataset
+    # pad_length = 30
+    # dataset = PeptidesDataset(xlsx_file="./dataset/train.xlsx", pad_length=pad_length)
+    # print(f"PeptidesDataset 样本总数: {len(dataset)}")
+    # if len(dataset) > 0:
+    #     encoded_seq, label = dataset[0]
+    #     print("第一个样本：")
+    #     print("多肽编码张量形状：", encoded_seq.shape)
+    #     print("标签浓度值（几何平均后）：", label)
+    # else:
+    #     print("未读取到有效数据！")
\ No newline at end of file
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+version https://git-lfs.github.com/spec/v1
+oid sha256:03bfa373ecd3e21fd68313c0917ba5201985b1453ea8eedcf2e3fe0da8b911eb
+size 150386
diff --git a/dataset/finetune_for_r2_llm copy.json b/dataset/finetune_for_r2_llm copy.json
new file mode 100644
index 0000000000000000000000000000000000000000..e60c6b20eaa1c445acb2428c5aa70625d12f9365
--- /dev/null
+++ b/dataset/finetune_for_r2_llm copy.json	
@@ -0,0 +1,215 @@
+{
+    "KWKIKWPVKWFKML": {
+        "1": [
+            "kwkikwpvkwfkml",
+            "Kwkikwpvkwfkml",
+            "kWkikwpvkwfkml",
+            "kwKikwpvkwfkml",
+            "kwkiKwpvkwfkml",
+            "kwkikWpvkwfkml",
+            "kwkikwPvkwfkml",
+            "kwkikwpVkwfkml",
+            "kwkikwpvKwfkml",
+            "kwkikwpvkWfkml",
+            "kwkikwpvkwFkml",
+            "kwkikwpvkwfKml",
+            "kwkikwpvkwfkMl",
+            "kwkikwpvkwfkmL",
+            "KWkikwpvkwfkml",
+            "KwkIkwpvkwfkml",
+            "KWKikwpvkwfkml",
+            "KWKiKwpvkwfkml",
+            "KWKikWpvkwfkml",
+            "KWKikwPvkwfkml",
+            "KWKikwpVkwfkml",
+            "KWKikwpvKwfkml",
+            "KWKikwpvkWfkml",
+            "KWKikwpvkwFkml",
+            "KWKikwpvkwfKml",
+            "KWKikwpvkwfkMl",
+            "KWKikwpvkwfkmL",
+            "kwKiKwpvkwFkml",
+            "kwKiKwpvkwfKml",
+            "kwKiKwpvkwfkMl",
+            "kwKiKwpvkwfkmL",
+            "kWkIkwpvKwFkml",
+            "kWkIkwpvKwfKml",
+            "kWkIkwpvKwfkMl",
+            "kWkIkwpvKwfkmL",
+            "kWKikWpvkwFkml",
+            "kWKikWpvkwfKml",
+            "kWKikWpvkwfkMl",
+            "kWKikWpvkwfkmL",
+            "kwKikwPvKwFkml",
+            "kwKikwPvKwfKml",
+            "kwKikwPvKwfkMl",
+            "kwKikwPvKwfkmL",
+            "KWKikwpVkwFkml",
+            "KWKikwpVkwfKml",
+            "KWKikwpVkwfkMl",
+            "KWKikwpVkwfkmL",
+            "KWkikwpvkWFkml",
+            "KWkikwpvkWfKml",
+            "KWkikwpvkWfkMl",
+            "KWkikwpvkWfkmL",
+            "kwkikwpvkwfKML",
+            "KWKIKWPVKWFKML",
+            "kwKiKWpVkwfkml",
+            "kWKiKWpVkwfkml",
+            "kwkIKWpVkwfkml",
+            "kWkIKWpVkwfkml",
+            "kwKiKWpVkwfKml",
+            "kWKiKWpVkwfKml",
+            "kwkIKWpVkwfKml",
+            "kWkIKWpVkwfKml",
+            "kwKiKWpVkwfkMl",
+            "kWKiKWpVkwfkMl",
+            "kwkIKWpVkwfkMl",
+            "kWkIKWpVkwfkMl",
+            "kwKiKWpVkwfkmL",
+            "kWKiKWpVkwfkmL",
+            "kwkIKWpVkwfkmL",
+            "kWkIKWpVkwfkmL",
+            "kwKiKWpVkwfKML",
+            "kWKiKWpVkwfKML",
+            "kwkIKWpVkwfKML",
+            "kWkIKWpVkwfKML",
+            "kWKIKWpVkwfkml",
+            "kWKIKWpVkwfKml",
+            "kWKIKWpVkwfkMl",
+            "kWKIKWpVkwfkmL",
+            "kWKIKWpVkwfKML",
+            "KWKiKWpVkwfkml",
+            "KWKiKWpVkwfKml",
+            "KWKiKWpVkwfkMl",
+            "KWKiKWpVkwfkmL",
+            "KWKiKWpVkwfKML",
+            "KWKIKWpVkwfkml",
+            "KWKIKWpVkwfKml",
+            "KWKIKWpVkwfkMl",
+            "KWKIKWpVkwfkmL",
+            "KWKIKWpVkwfKML",
+            "kwkikWPvkwFKML",
+            "kWkikWPvkwFKML",
+            "kwKikWPvkwFKML",
+            "kWKikWPvkwFKML",
+            "kwkikWPvkwfKML",
+            "kWkikWPvkwfKML",
+            "kwKikWPvkwfKML",
+            "kWKikWPvkwfKML",
+            "kwkikWPvkwfkML",
+            "kWkikWPvkwfkML",
+            "kwKikWPvkwfkML",
+            "kWKikWPvkwfkML",
+            "kwkikWPvkwfkmL"
+        ],
+        "0": [
+            "KWKIKWPVKWFKML",
+            "kWKIKWPVKWFKML",
+            "KwKIKWPVKWFKML",
+            "KWkIKWPVKWFKML",
+            "KWKIkwPVKWFKML",
+            "KWKIKWpVKWFKML",
+            "KWKIKWPvKWFKML",
+            "KWKIKWPVKWFkML",
+            "KWKIKWPVKWfKML",
+            "KWKIKWPVKWFkMl",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWFKMl",
+            "KWKIKWPVKWFKmL",
+            "KWKIKWPVKWFKmL",
+            "kWKIKWPVKWFKMl",
+            "KWkIKWPVKWFKMl",
+            "KWKIkwPVKWFKMl",
+            "KWKIKWpVKWFKMl",
+            "KWKIKWPvKWFKMl",
+            "KWKIKWPVKWFkMl",
+            "KWKIKWPVKWfKMl",
+            "KWKIKWPVKWFkMl",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWFKmL",
+            "kWKIKWPVKWFKmL",
+            "KWkIKWPVKWFKmL",
+            "KWKIkwPVKWFKmL",
+            "KWKIKWpVKWFKmL",
+            "KWKIKWPvKWFKmL",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWfKML",
+            "kWKIKWPVKWfKML",
+            "KWkIKWPVKWfKML",
+            "KWKIkwPVKWfKML",
+            "KWKIKWpVKWfKML",
+            "KWKIKWPvKWfKML",
+            "KWKIKWPVKWFkML",
+            "KWKIKWPVKWfkML",
+            "KWKIKWPVKWfkmL",
+            "kWKIKWPVKWfkmL",
+            "KWkIKWPVKWfkmL",
+            "KWKIkwPVKWfkmL",
+            "KWKIKWpVKWfkmL",
+            "KWKIKWPvKWfkmL",
+            "KWKIKWPVKWFkMl",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWfKml",
+            "KWKIKWPVKWFkml",
+            "KWKIKWPVKWFkMl",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWFKml",
+            "KWKIKWPVKWFkmL",
+            "kWKIKWPVKWFkmL",
+            "KWkIKWPVKWFkmL",
+            "KWKIkwPVKWFkmL",
+            "KWKIKWpVKWFkmL",
+            "KWKIKWPvKWFkmL",
+            "KWKIKWPVKWFkml",
+            "KWKIKWPVKWfkml",
+            "KWKIKWPVKWfkmL",
+            "kWKIKWPVKWfkml",
+            "KWkIKWPVKWfkml",
+            "KWKIkwPVKWfkml",
+            "KWKIKWpVKWfkml",
+            "KWKIKWPvKWfkml",
+            "KWKIKWPVKWFkMl",
+            "KWKIKWPVKWFkml",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWfKml",
+            "KWKIKWPVKWFkml",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWFKml",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWFkml",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWfkmL",
+            "kWKIKWPVKWFKML",
+            "KWkIKWPVKWFKML",
+            "KWKIkwPVKWFKML",
+            "KWKIKWpVKWFKML",
+            "KWKIKWPvKWFKML",
+            "KWKIKWPVKWFKmL",
+            "KWKIKWPVKWFKMl",
+            "KWKIKWPVKWFkML",
+            "KWKIKWPVKWfKML",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWFkML",
+            "KWKIKWPvKWFKmL",
+            "kWKIKWPVKWFkmL",
+            "KWkIKWPVKWFkML",
+            "KWKIKWPVKWfkMl",
+            "KWkIKWPVKWFkmL",
+            "KWKIKWPVKWFkml",
+            "KWKIKWPVKWfkml",
+            "KWKIkwPVKWfkMl",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWfkmL",
+            "KWKIKWPVKWFKml",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWFkml",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWfkml",
+            "KWKIKWPVKWfkmL",
+            "KWKIKWPVKWFkmL",
+            "KWKIKWPVKWFkml"
+        ]
+    }
+}
\ No newline at end of file
diff --git a/dataset/finetune_for_r2_llm.json b/dataset/finetune_for_r2_llm.json
new file mode 100644
index 0000000000000000000000000000000000000000..861e089838aca0b8f87e7396c11053c47273faec
--- /dev/null
+++ b/dataset/finetune_for_r2_llm.json
@@ -0,0 +1,197 @@
+{
+    "KWKIKWPVKWFKML": {
+        "1": [
+            "kwKIKWPVKWFKML",
+            "kWkIKWPVKWFKML",
+            "kWKIkWPVKWFKML",
+            "kWKIKwPVKWFKML",
+            "kWKIKWPvKWFKML",
+            "kWKIKWPVkWFKML",
+            "kWKIKWPVKwFKML",
+            "kWKIKWPVKWfKML",
+            "kWKIKWPVKWFKmL",
+            "KwkIKWPVKWFKML",
+            "KwKIkWPVKWFKML",
+            "KwKIKwPVKWFKML",
+            "KwKIKWPvKWFKML",
+            "KwKIKWPVkWFKML",
+            "KwKIKWPVKwFKML",
+            "KwKIKWPVKWfKML",
+            "KwKIKWPVKWFKmL",
+            "KWkIkWPVKWFKML",
+            "KWkIKwPVKWFKML",
+            "KWkIKWPvKWFKML",
+            "KWkIKWPVkWFKML",
+            "KWkIKWPVKwFKML",
+            "KWkIKWPVKWfKML",
+            "KWkIKWPVKWFKmL",
+            "KWKIkwPVKWFKML",
+            "KWKIkWPvKWFKML",
+            "KWKIkWPVkWFKML",
+            "KWKIkWPVKwFKML",
+            "KWKIkWPVKWfKML",
+            "KWKIkWPVKWFKmL",
+            "KWKIKwPvKWFKML",
+            "KWKIKwPVkWFKML",
+            "KWKIKwPVKwFKML",
+            "KWKIKwPVKWfKML",
+            "KWKIKwPVKWFKmL",
+            "KWKIKWPvkWFKML",
+            "KWKIKWPvKwFKML",
+            "KWKIKWPvKWFKmL",
+            "KWKIKWPVkwFKML",
+            "KWKIKWPVkWFKmL",
+            "KWKIKWPVKwfKML",
+            "KWKIKWPVKwFKmL",
+            "KWKIKWPVKWfKmL",
+            "kwkIKWPVKWFKML",
+            "kwKIkWPVKWFKML",
+            "kwKIKwPVKWFKML",
+            "kwKIKWPvKWFKML",
+            "kwKIKWPVkWFKML",
+            "kwKIKWPVKwFKML",
+            "kwKIKWPVKWfKML",
+            "kwKIKWPVKWFKmL",
+            "kWkIkWPVKWFKML",
+            "kWkIKwPVKWFKML",
+            "kWkIKWPvKWFKML",
+            "kWkIKWPVkWFKML",
+            "kWkIKWPVKwFKML",
+            "kWkIKWPVKWfKML",
+            "kWkIKWPVKWFKmL",
+            "kWKIkwPVKWFKML",
+            "kWKIkWPvKWFKML",
+            "kWKIkWPVkWFKML",
+            "kWKIkWPVKwFKML",
+            "kWKIkWPVKWfKML",
+            "kWKIkWPVKWFKmL",
+            "kWKIKwPvKWFKML",
+            "kWKIKwPVkWFKML",
+            "kWKIKwPVKwFKML",
+            "kWKIKwPVKWfKML",
+            "kWKIKwPVKWFKmL",
+            "kWKIKWPvkWFKML",
+            "kWKIKWPvKwFKML",
+            "kWKIKWPvKWFKmL",
+            "kWKIKWPVkwFKML",
+            "kWKIKWPVkWFKmL",
+            "kWKIKWPVKwfKML",
+            "kWKIKWPVKwFKmL",
+            "kWKIKWPVKWfKmL",
+            "kwkIKwPVKWFKML",
+            "kwkIKWPvKWFKML",
+            "kwkIKWPVkWFKML",
+            "kwkIKWPVKwFKML",
+            "kwkIKWPVKWfKML",
+            "kwkIKWPVKWFKmL",
+            "KwKIkwPVKWFKML",
+            "KwKIKwPvKWFKML",
+            "KwKIKwPVkWFKML",
+            "KwKIKwPVKwFKML",
+            "KwKIKwPVKWfKML",
+            "KwKIKwPVKWFKmL",
+            "KwKIKWPvkWFKML",
+            "KwKIKWPvKwFKML",
+            "KwKIKWPvKWFKmL",
+            "KwKIKWPVkwFKML",
+            "KwKIKWPVkWFKmL",
+            "KwKIKWPVKwfKML",
+            "KwKIKWPVKwFKmL",
+            "KwKIKWPVKWfKmL"
+        ],
+        "0": [
+            "KWKiKWPVKWfKML",
+            "KWKiKWPVKWFKmL",
+            "KWKiKWPVkWFKML",
+            "KWKiKWPVKwFKML",
+            "KWKiKWPvKWFKML",
+            "KWKiKwPVKWFKML",
+            "kWKiKWPVKWFKML",
+            "kWKiKWPvKWFKML",
+            "kWKiKWPVkWFKML",
+            "kWKiKWPVKwFKML",
+            "KWKIKWpVKWfKML",
+            "KWKIKWpVKWFKmL",
+            "KWKIKWpVKwFKML",
+            "KWKIKWpVkWFKML",
+            "KWKIKWpVkwFKML",
+            "KWKIKWpVKWFkML",
+            "KWKIKWpVKWFkMl",
+            "kWKIKWpVKWFKML",
+            "kWKIKWpVKWfKML",
+            "kWKIKWpVkWFKML",
+            "kWKIKWpVKwFKML",
+            "kWKIKWpVKWFKmL",
+            "KWKIKWPVKWFkML",
+            "KWKIKWPVKWFkML",
+            "KWKIKWPVKWfkML",
+            "KWKIKWPVKWfkMl",
+            "KWKIKWPVKWfKMl",
+            "KWKIKWPVKWFkMl",
+            "KWKIKWPVKwFkML",
+            "KWKIKWPVKwFkMl",
+            "KWKIKWPVkwFkML",
+            "KWKIKWPVkwFkMl",
+            "KWKIKWpVkwFkML",
+            "kWKIKWpVKWFkML",
+            "kWKIKWpVKWFkMl",
+            "kWKIKWpVkwFkML",
+            "KWKiKWPVKWFkML",
+            "KWKiKWPVKWFkMl",
+            "KWKiKWPVkwFkML",
+            "KWKiKWPVkwFkMl",
+            "KWKIKWpVKWfkMl",
+            "KWKIKWpVKWfkML",
+            "KWKIkWPvKWfkML",
+            "kWKIKWPvKWfkML",
+            "KWKIKwPvKWfKML",
+            "KWKIKWPvkWfKML",
+            "KWKIKWPvKwfKML",
+            "KWKIKWPvKWfkML",
+            "KWKIKWPvKWfkMl",
+            "KWKIKWPvkWFkMl",
+            "KWKIKWPvKWFkMl",
+            "KWKIKWpvKWFkML",
+            "KWKIKWpvKWFkMl",
+            "KWKiKWPvKWFkMl",
+            "kWKIKWPVKWFkMl",
+            "KwKiKWPVKWFkML",
+            "KwKiKWPVKWFkMl",
+            "KwKiKWPVKWfkML",
+            "KwKiKWPVKWfkMl",
+            "KWkIkWpVKWFkML",
+            "KWkIkWpVKWFkMl",
+            "KWkIkWpVKWfkML",
+            "KWkIkWpVKWfkMl",
+            "KWKiKWpVKWFKML",
+            "KWKiKWpVKWfKML",
+            "KWKiKWpVKwFKML",
+            "KWKiKWpVKWFkML",
+            "KWKiKWpVKWFKmL",
+            "KWKIKWpVKWFKMl",
+            "kWKIKWpVKWFKMl",
+            "KwKIKWpVKWFKMl",
+            "KWkIKWpVKWFKMl",
+            "KWKIkWpVKWFKMl",
+            "KWKIKwPvKWFKMl",
+            "KWKIKWpVkWFKMl",
+            "KWKIKWpVKwFKMl",
+            "kWKIKWpVKwFKMl",
+            "kWKIKWpVKWfkML",
+            "kWKIKWpVKWfkMl",
+            "KwKiKWPVkwfkML",
+            "KwKiKWPVkwfkMl",
+            "KWKIKWPvkwfKML",
+            "KWKIKWPVkwfkML",
+            "kWKIKWPVkwfkML",
+            "KWKiKWPVkwfkML",
+            "KWKiKWPVkwfkMl",
+            "KWKIKWpVkwfkML",
+            "KWKIKWpVkwfkMl",
+            "kWKIKWpVkwfkML",
+            "kWKIKWpVkwfkMl",
+            "KWKIKWPVkWFkML",
+            "KWKIKWPVkWFkMl"
+        ]
+    }
+}
\ No newline at end of file
diff --git a/dataset/r2_case.xlsx b/dataset/r2_case.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..eaedca4c220066882d9eaed1bd439d0c361f0911
Binary files /dev/null and b/dataset/r2_case.xlsx differ
diff --git a/dataset/stability.xlsx b/dataset/stability.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..f16f33ae4239c1c9f4d3c9d5d33ddeef371a5b06
Binary files /dev/null and b/dataset/stability.xlsx differ
diff --git a/dataset/test.xlsx b/dataset/test.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..86305af35f19e09aafdcd366d4d6914cfd454aab
Binary files /dev/null and b/dataset/test.xlsx differ
diff --git a/dataset/test_.xlsx b/dataset/test_.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..eb03080349a055770d8018989feea09686c7a053
--- /dev/null
+++ b/dataset/test_.xlsx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb17d223ff62391058b5c257977abf929a9cbb6c8cf29c7d7f15aeb6a585b7b9
+size 101949
diff --git a/dataset/test__.xlsx b/dataset/test__.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..9e4790494e436ecdb3c0c2236722a279e3221b6b
Binary files /dev/null and b/dataset/test__.xlsx differ
diff --git a/dataset/train.xlsx b/dataset/train.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..0701f17c7bdfb0fad6276b4fcb2b0f51fad6a956
--- /dev/null
+++ b/dataset/train.xlsx
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f2f19bd66bb781214298e586c07500e911fa8d50666a4918d91b656e397632a9
+size 228312
diff --git a/dataset/train_set.json b/dataset/train_set.json
new file mode 100644
index 0000000000000000000000000000000000000000..9c60a1f39e68be330006a202d438f3eaf4b46cca
--- /dev/null
+++ b/dataset/train_set.json
@@ -0,0 +1,1736 @@
+{
+    "GIMSSLMKKLAAHIAK": {
+        "1": [
+            "GIMSSLMkKLAAHIAK",
+            "GIMSSLMKkLAAHIAK",
+            "GIMSSLMKKLAAHIAk",
+            "GIMSSLMkkLAAHIAK",
+            "GIMSSLMkKLAAHIAk",
+            "GIMSSLMKkLAAHIAk",
+            "GIMSSLMkkLAAHIAk"
+        ],
+        "0": []
+    },
+    "ILGTILGLLKSL": {
+        "1": [],
+        "0": [
+            "ILGTILGLLkSL",
+            "ilgtilgllksl"
+        ]
+    },
+    "KRLFKKLLKYLRKF": {
+        "1": [
+            "KRLFkkLLKYLRkF",
+            "krLFkkLLKYLRkF",
+            "krLFkkLLkYLrkF"
+        ],
+        "0": [
+            "KRLFKKLLKYLRkF"
+        ]
+    },
+    "ILGTILGLLKGL": {
+        "1": [
+            "ilgtilgllkgl"
+        ],
+        "0": [
+            "ILGTILGLLkGL"
+        ]
+    },
+    "IDWKKLLDAAKQIL": {
+        "1": [
+            "idwkklldaakqil"
+        ],
+        "0": [
+            "IDWkkLLDAAkQIL"
+        ]
+    },
+    "VWRRWRRFWRR": {
+        "1": [],
+        "0": [
+            "vwrrwrrfwrr",
+            "VWrrWrrFWrr"
+        ]
+    },
+    "FLKLLKKLL": {
+        "1": [
+            "fLKLLKKLL",
+            "FlKLLKKLL",
+            "FLkLLKKLL",
+            "flkllkkll"
+        ],
+        "0": [
+            "FLKlLKKLL",
+            "FLKLlKKLL",
+            "FLKLLkKLL",
+            "FLKLLKkLL",
+            "FLKLLKKlL",
+            "FLKLLKKLl"
+        ]
+    },
+    "KKVVFWVKFK": {
+        "1": [
+            "KKVVFWVKFk"
+        ],
+        "0": [
+            "KKVVFWVKfK",
+            "KKVVFWVkFK",
+            "KKVVFWvKFK",
+            "KKVVFwVKFK",
+            "KKVVfWVKFK",
+            "KKVvFWVKFK",
+            "KKvVFWVKFK",
+            "KkVVFWVKFK",
+            "kKVVFWVKFK"
+        ]
+    },
+    "KRIVKLILKWLR": {
+        "1": [
+            "KRIVkLILKWLR",
+            "KRIVKlILKWLR"
+        ],
+        "0": []
+    },
+    "KKVVFKVKFKK": {
+        "1": [
+            "kKVVFKVKFKk"
+        ],
+        "0": [
+            "kkVVFKVKFKK",
+            "KKVVFKVKFkk",
+            "kkVVFKVKFkk",
+            "KKVVFkVKFKK",
+            "kkVVFkVKFkk",
+            "kkvvfkvkfkk"
+        ]
+    },
+    "KWKSFLKTFKSAKKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKSAkKTVLHTALKAISS"
+        ],
+        "0": [
+            "KWKSFLKTFKSAKkTVLHTALKAISS",
+            "KWKSFLKTFKsAKkTVLHTALKAISS",
+            "KWKSFLKTFKSAKktVLHTALKAISS",
+            "KWKSFLKTFKsAKktVLHTALKAISS",
+            "KWKSFLKTFKSaKKTVLHTALKAISS",
+            "KWKSFLKTfKSaKKTVLHTALKAISS",
+            "KWKSFLKTFKSaKKTvLHTALKAISS",
+            "KWKSFLKTfKSaKKTvLHTALKAISS",
+            "kwksflktfksakktvlhtalkaiss"
+        ]
+    },
+    "FLPLIIGALSSLLPKIF": {
+        "1": [],
+        "0": [
+            "FLPLIIGALSSLLPKiF",
+            "FLPLiiGALSSLLPKiF"
+        ]
+    },
+    "KLKKLLKKWLKLLKKLLK": {
+        "1": [
+            "KLKKLlKKWLKlLKKLLk",
+            "KLKKlLKKWlKLLKkLLK",
+            "KLKkLLKkWLKlLKKlLK",
+            "KLkKLlKKwLKlLKkLLk",
+            "KlKkLlKkWlKlLkKlLk",
+            "KLKKLLKKWlkllkkllk"
+        ],
+        "0": []
+    },
+    "KKAAAAAAAAAAAAWAAAAAAKKKK": {
+        "1": [
+            "kkAAAAAAAAAAAAWAAAAAAKKKK",
+            "KKAAAAAAAAAAAAwaAAAAAKKKK",
+            "KKAAAAAAAAAAAAWAaaAAAKKKK",
+            "KKAAAAAAAAAAAAWAAAaaAKKKK"
+        ],
+        "0": [
+            "KKaaAAAAAAAAAAWAAAAAAKKKK",
+            "KKAAaaAAAAAAAAWAAAAAAKKKK",
+            "KKAAAAaaAAAAAAWAAAAAAKKKK",
+            "KKAAAAAAaaAAAAWAAAAAAKKKK",
+            "KKAAAAAAAAaaAAWAAAAAAKKKK",
+            "KKAAAAAAAAAAaaWAAAAAAKKKK",
+            "KKAAAAAAAAAAAAWAAAAAakKKK",
+            "KKAAAAAAAAAAAAWAAAAAAKkkK"
+        ]
+    },
+    "FVPWFSKFLGRIL": {
+        "1": [],
+        "0": [
+            "FVPWFSkFLGRIL",
+            "FVPWFSKfLGRIL",
+            "FVPWFSKFlGRIL",
+            "FVPWFSKFLGrIL",
+            "FVPWFSKFLGRiL",
+            "FVPWFSKFLGRIl"
+        ]
+    },
+    "IRIKIRIK": {
+        "1": [
+            "irikirik",
+            "IRIkIrIK"
+        ],
+        "0": [
+            "IrIkIrIk"
+        ]
+    },
+    "IIRKIIRK": {
+        "1": [
+            "iirkiirk",
+            "IirKIirK"
+        ],
+        "0": []
+    },
+    "KKLFKKILKYL": {
+        "1": [
+            "KKLfKKILKYL",
+            "KKLFKKILkYL",
+            "KKLFKKIlKYL",
+            "KKLFkKILKYL",
+            "KKlFKKILKYL",
+            "KkLFKKILKYL",
+            "KKLFKkILKYL",
+            "kKLFKKILKYL",
+            "KKLFKKIlkYL",
+            "KKlFKkILkYL",
+            "KKLFKKilkYL",
+            "kklfkkilkyl",
+            "kkLfKKILKYL",
+            "KKLFKKilkyl",
+            "KKLFkkilkyl",
+            "KKLfkkilkyl",
+            "KKlfkkilkyl",
+            "Kklfkkilkyl",
+            "kklfKKILKYL",
+            "kklfkKILKYL",
+            "kklfkkILKYL",
+            "kklfkkiLKYL",
+            "kklfkkilKYL",
+            "kklfkkilkYL",
+            "kklfkkilkyL"
+        ],
+        "0": [
+            "KKLFKkilkyl",
+            "KKLFKKiLKYL",
+            "KKLFKKILKyL",
+            "KKLFKKILKYl",
+            "KKlFKKILkYL",
+            "KKLFKKIlkyl"
+        ]
+    },
+    "KFFKRLLKSVRRAVKKFRK": {
+        "1": [],
+        "0": [
+            "kFFkrLLkSVrrAVkkFrk",
+            "kffkrllksvrravkkfrk"
+        ]
+    },
+    "RWRWRWK": {
+        "1": [
+            "rWRWRWK",
+            "rWRWRwK",
+            "rWRWrWK",
+            "rWRwRWK",
+            "rWrWRWK",
+            "rwRWRWK",
+            "rWRWrwK",
+            "rWRwRwK",
+            "rWrWRwK",
+            "rwRWRwK",
+            "rWRwrWK",
+            "rWrWrWK",
+            "rwRWrWK",
+            "rWrwRWK",
+            "rwRwRWK",
+            "rwrWRWK",
+            "rWRwrwK",
+            "rWrWrwK",
+            "rwRWrwK",
+            "rWrwRwK",
+            "rwrWRwK",
+            "rWrwrWK",
+            "rwRwrWK",
+            "rwrWrWK",
+            "rwrwRWK",
+            "rWrwrwK",
+            "rwRwrwK",
+            "rwrWrwK",
+            "rwrwRwK",
+            "rwrwrWK",
+            "rwrwrwK"
+        ],
+        "0": [
+            "rwRwRwK"
+        ]
+    },
+    "KWKSFLKTFKSLKKTVLHTLLKAISS": {
+        "1": [
+            "KWKSFLkTFKSLKKTVLHTLLKAISS",
+            "KWKSFLKTFKSLKkTVLHTLLKAISS",
+            "KWKSFLKTFKSLKKTVLHTLLkAISS",
+            "KWKSFLkTFKSLKkTVLHTLLKAISS",
+            "KWKSFLKTFKSLKkTVLHTLLkAISS",
+            "KWKSFlKTFKSLKKTVLHTLLKAISS",
+            "KWKSFLKTFKSlKKTVLHTLLKAISS",
+            "KWKSFLKTFKSLKKTVLHTlLKAISS",
+            "KWKSFLKTFKSlKKTVLHTlLKAISS",
+            "KWKSFlKTFKSlKKTVLHTlLKAISS"
+        ],
+        "0": [
+            "KWKSFLkTFKSLKkTVLHTLLkAISS",
+            "KWKSFLkTFkSLKkTVLHTLLkAISS",
+            "KWkSFLkTFkSLKkTVLHTLLkAISS",
+            "kWkSFLkTFkSLKkTVLHTLLkAISS",
+            "KWKSFlKTFKSlKKTVLHTLLKAISS",
+            "KWKSFlKTFKSlKKTVlHTlLKAISS",
+            "KWKSFlKTFKSlKKTVlHTllKAISS"
+        ]
+    },
+    "GWLDVAKKIGKAAFNVAKNFL": {
+        "1": [],
+        "0": [
+            "GWLDvAKKIGKAAFNvAKNFL",
+            "GWLDVAKKIGKAAFNvAKNFL"
+        ]
+    },
+    "GFGMALKLLKKVL": {
+        "1": [
+            "GfGmalkllkkvl",
+            "GfGMALKLLKKVL"
+        ],
+        "0": [
+            "GFGMALKLLKKVl",
+            "GFGMALKLLKKvL",
+            "GFGMALKLLKkVL",
+            "GFGMALKLLkKVL",
+            "GFGMALKLlKKVL",
+            "GFGMALKlLKKVL",
+            "GFGMALkLLKKVL",
+            "GFGMAlKLLKKVL",
+            "GFGMaLKLLKKVL",
+            "GFGmALKLLKKVL"
+        ]
+    },
+    "RGLRRLGRKIAHGVKKYGPTVLRIIRIA": {
+        "1": [],
+        "0": [
+            "rglrrlgrkiahgvkkygptvlriiria",
+            "RGLRRLGRKIAHGVKKYGptvlriiria"
+        ]
+    },
+    "KVLGRLVKVLGRLV": {
+        "1": [
+            "kVLGRLVKVLGRLV"
+        ],
+        "0": [
+            "KVLGRLVkVLGRLV",
+            "kVLGRLVkVLGRLV"
+        ]
+    },
+    "RRLFRRILRWL": {
+        "1": [
+            "RRLfRRILRWL",
+            "RRLFrRILRWL",
+            "rrlfrrilrwl"
+        ],
+        "0": [
+            "rRLFRRILRWL",
+            "RrLFRRILRWL",
+            "RRlFRRILRWL",
+            "RRLFRrILRWL",
+            "RRLFRRiLRWL",
+            "RRLFRRIlRWL",
+            "RRLFRRILrWL",
+            "RRLFRRILRwL",
+            "RRLFRRILRWl"
+        ]
+    },
+    "KWKSFLKTFKSAVKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKSAvKTVLHTALKAISS",
+            "KWKSFLKTFKsAVKTVLHTALKAISS"
+        ],
+        "0": [
+            "kwksflktfksavktvlhtalkaiss"
+        ]
+    },
+    "RRWVRRVRRVWRRVVRVVRRWVRR": {
+        "1": [],
+        "0": [
+            "RRWVRRvRRVWRRVvRvVRRWvRR",
+            "RRWVRRvRRvWRRVvRvvRRWvRR",
+            "RRWvRRvRRvWRRvvRvvRRWvRR"
+        ]
+    },
+    "TVGGLVKWILKTVKKFA": {
+        "1": [
+            "tvgglvkwilktvkkfa",
+            "TVGGLVKWILkTVKKFA"
+        ],
+        "0": [
+            "TVGGLVkWILkTVKkFA"
+        ]
+    },
+    "INLKALAALAKKIL": {
+        "1": [],
+        "0": [
+            "iNLKALAALAKKIL",
+            "InLKALAALAKKIL",
+            "inLKALAALAKKIL",
+            "inlkalaalakkil"
+        ]
+    },
+    "FLSLIPKAIKAVGVKAKKF": {
+        "1": [],
+        "0": [
+            "FLSLIPkAIkAVGVkAkkF",
+            "FLSLIPkAIKAVGVKAKKF"
+        ]
+    },
+    "KKLLKLLKLLL": {
+        "1": [
+            "kkllkllklll",
+            "KkLLKLLKLLL",
+            "KkLLkLLKLLL",
+            "KkLlKLLKLLL",
+            "kKLLKLLKLLl",
+            "kkLLKLLKLLl",
+            "KkllKLLKLLL",
+            "kkLLkLLKLLL",
+            "KkllKLlKLLL"
+        ],
+        "0": [
+            "kkLLKLLKLLL",
+            "KKLLKllKLLL",
+            "KKLLkllKLLL",
+            "KkllKlLKLLL",
+            "KKLLkllkLLL",
+            "KKllKllKLLL",
+            "KKlLkLlKlLL",
+            "KKLlkLLklLL",
+            "KklLKLLKllL",
+            "kkLLKLLKLll",
+            "kkLLkLLKLLl",
+            "KKllKLLklLL",
+            "KklLKlLKlLL",
+            "KKllKLlKLlL",
+            "KKLlkLLkLLl",
+            "KkllKllKLLL",
+            "KKllKllKlLL",
+            "kkLLkLLKLll",
+            "kkLLkLLkLLl",
+            "kKLLkllKLLl",
+            "KKLlkllkLLL",
+            "KkLlKlLkLlL",
+            "kKlLkLlKlLl"
+        ]
+    },
+    "KKVVFKVKFK": {
+        "1": [],
+        "0": [
+            "KKVVFKVKFk",
+            "kKVVFKVKFk",
+            "kkVVFKVKFk",
+            "KKVVfkvKFK",
+            "kKVVfkvKFk"
+        ]
+    },
+    "LKLLKKLLKKLLKLL": {
+        "1": [
+            "LKlLKkLlkKLLkLL"
+        ],
+        "0": []
+    },
+    "KLKLLKLLKLLKLLK": {
+        "1": [],
+        "0": [
+            "KLkLLkLlkLLKlLK"
+        ]
+    },
+    "KKKLLLLLLLLLKKK": {
+        "1": [
+            "KKkLLlLllLLLkKK"
+        ],
+        "0": []
+    },
+    "KKFKKTAKWLIKSAWLLLKSLALKMK": {
+        "1": [
+            "kkfkktakwliksawlllkslalkmk"
+        ],
+        "0": []
+    },
+    "WWWLRRRW": {
+        "1": [],
+        "0": [
+            "wwwlrrrw"
+        ]
+    },
+    "RRRWWWWV": {
+        "1": [],
+        "0": [
+            "rrrwwwwv"
+        ]
+    },
+    "KWFRVYRGIYRRR": {
+        "1": [],
+        "0": [
+            "kwfrvyrgiyrrr"
+        ]
+    },
+    "RRRYIGRYVRFWK": {
+        "1": [],
+        "0": [
+            "rrryigryvrfwk"
+        ]
+    },
+    "GKIIKLKASLKLL": {
+        "1": [
+            "gkiiklkaslkll"
+        ],
+        "0": []
+    },
+    "KLFKKLFKKLFK": {
+        "1": [],
+        "0": [
+            "kLFkkLFkkLFk"
+        ]
+    },
+    "GFFALIPKIISSPLFKTLLSAV": {
+        "1": [],
+        "0": [
+            "GFFALIpKIISSPLFKTllSAV"
+        ]
+    },
+    "KGFFALIPKIISSPLFKTLLSAV": {
+        "1": [],
+        "0": [
+            "KGFFALIpKIISSPLFKTllSAV"
+        ]
+    },
+    "RGLRRLGRKIAHGVKKYG": {
+        "1": [
+            "rglrrlgrkiahgvkkyg"
+        ],
+        "0": []
+    },
+    "FLGGLIKIVPAMICAVTKKC": {
+        "1": [
+            "flGGlikivpamicavtkkc"
+        ],
+        "0": []
+    },
+    "AKRLKKLAKKIWKWK": {
+        "1": [],
+        "0": [
+            "AkRLkkLAkkIWkWk"
+        ]
+    },
+    "VDKPPYLPRPRPIRRPGGR": {
+        "1": [
+            "VDkPPYLPrPrPIrrPGGr"
+        ],
+        "0": [
+            "VDKPPYLPrPRPIrRPGGR",
+            "VDKPPYLPrPRPIRrPGGR",
+            "VDKPPYLPrPRPIRRPGGr",
+            "VDKPPYLPRPrPIrRPGGR",
+            "VDKPPYLPRPrPIRrPGGR",
+            "VDKPPYLPRPrPIRRPGGr",
+            "VDKPPYLPRPRPIrRPGGr",
+            "VDKPPYLPRPRPIRrPGGr"
+        ]
+    },
+    "GIGAVLKVLTTGLPALISWIKRKRQQ": {
+        "1": [
+            "GIGAVlKVLTTGlPALISWiKRKRQQ",
+            "gigavlkvlttglpaliswikrkrqq"
+        ],
+        "0": [
+            "GIGAvLKvLTTGLPALiSWIkRKRQQ"
+        ]
+    },
+    "FWGALAKGALKLIPSLFSSFSKKD": {
+        "1": [
+            "fwGalakGalklipslfssfskkd"
+        ],
+        "0": []
+    },
+    "IRVKIRVKIRVK": {
+        "1": [
+            "irvkirvkirvk"
+        ],
+        "0": []
+    },
+    "LIKKALAALAKLNI": {
+        "1": [],
+        "0": [
+            "likkalaalaklni"
+        ]
+    },
+    "RSMRLSFRARGYGFR": {
+        "1": [
+            "rsmrlsfrarGyGfr"
+        ],
+        "0": []
+    },
+    "GLLKRIKTLL": {
+        "1": [],
+        "0": [
+            "GLLkRIkTLL",
+            "Gllkriktll"
+        ]
+    },
+    "KKLFKKILRYL": {
+        "1": [
+            "KKLfKKILRYL"
+        ],
+        "0": [
+            "KKLFKkilryl",
+            "kklfkkilryl"
+        ]
+    },
+    "FQWQRNMRKVR": {
+        "1": [
+            "fqwqrnmrkvr"
+        ],
+        "0": []
+    },
+    "KKKKKKAAFAAWAAFAA": {
+        "1": [],
+        "0": [
+            "kkkkkkaafaawaafaa"
+        ]
+    },
+    "RRWWRF": {
+        "1": [],
+        "0": [
+            "rrwwrf"
+        ]
+    },
+    "KWKSFLKTFKSALKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKSAlKTVLHTALKAISS"
+        ],
+        "0": []
+    },
+    "KWKSFLKTFKSAAKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKSAaKTVLHTALKAISS"
+        ],
+        "0": []
+    },
+    "KWKSFLKTFKSASKTVLHTALKAISS": {
+        "1": [],
+        "0": [
+            "KWKSFLKTFKSAsKTVLHTALKAISS"
+        ]
+    },
+    "KWKSFLKTFKLAVKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKlAVKTVLHTALKAISS"
+        ],
+        "0": []
+    },
+    "KWKSFLKTFKVAVKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKvAVKTVLHTALKAISS"
+        ],
+        "0": []
+    },
+    "KWKSFLKTFKAAVKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKaAVKTVLHTALKAISS"
+        ],
+        "0": []
+    },
+    "KWKSFLKTFKKAVKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKkAVKTVLHTALKAISS"
+        ],
+        "0": []
+    },
+    "GFKMALKLLKKVL": {
+        "1": [],
+        "0": [
+            "GFkMALKLLKKVL",
+            "GfkMALKLLKKVL"
+        ]
+    },
+    "AFGMALKLLKKVL": {
+        "1": [],
+        "0": [
+            "aFGMALKLLKKVL"
+        ]
+    },
+    "RRLLRLLRLLL": {
+        "1": [
+            "rrLLrLLrLLL"
+        ],
+        "0": []
+    },
+    "KKIIKIIKIII": {
+        "1": [
+            "kkIIkIIkIII"
+        ],
+        "0": []
+    },
+    "RRIIRIIRIII": {
+        "1": [
+            "rrIIrIIrIII"
+        ],
+        "0": []
+    },
+    "KRFKKFFKKVKKSVKKRLKKIFKKPMVIGVTIPF": {
+        "1": [],
+        "0": [
+            "krfkkffkkvkksvkkrlkkifkkpmviGvtipf"
+        ]
+    },
+    "KKRLKKIFKKPMVIGVTIPF": {
+        "1": [],
+        "0": [
+            "kkrlkkifkkpmviGvtipf"
+        ]
+    },
+    "RLFRRVKKVAGKIAKRIWK": {
+        "1": [],
+        "0": [
+            "rlfrrvkkvagkiakriwk"
+        ]
+    },
+    "FIRRIARLLRRIF": {
+        "1": [],
+        "0": [
+            "firriarllrrif"
+        ]
+    },
+    "GIGAVLKVLALISWIKRKR": {
+        "1": [],
+        "0": [
+            "GIGAvLKvLAlISWIkRKR"
+        ]
+    },
+    "WKKLKKLLKKLKKL": {
+        "1": [],
+        "0": [
+            "Wkklkkllkklkkl"
+        ]
+    },
+    "KFWSLLKKALRLWANVL": {
+        "1": [
+            "kFwSLLkKALRLwANVL"
+        ],
+        "0": []
+    },
+    "KFWKLLKKALRLWAKVL": {
+        "1": [
+            "kFwKLLkKALrLwAkVL"
+        ],
+        "0": [
+            "kFWKlLKkAlrLWAkVL"
+        ]
+    },
+    "WFKKLLKKALRLWKKVL": {
+        "1": [
+            "wFKKlLKkAlrLWKkVL"
+        ],
+        "0": []
+    },
+    "ILLKKLLKKI": {
+        "1": [
+            "illkkllkki"
+        ],
+        "0": []
+    },
+    "GRFKRFRKKFKKLFKKLS": {
+        "1": [
+            "GRfKRfRKKfKKLfKKLS"
+        ],
+        "0": [
+            "grfkrfrkkfkklfkkls"
+        ]
+    },
+    "RAGLQFPVGRVHRLLRK": {
+        "1": [
+            "raglqfpvgrvhrllrk"
+        ],
+        "0": []
+    },
+    "KLKLLLLLKLK": {
+        "1": [
+            "klklllllklk"
+        ],
+        "0": []
+    },
+    "KLKLLLKLK": {
+        "1": [
+            "klklllklk"
+        ],
+        "0": []
+    },
+    "FIKRIARLLRKIF": {
+        "1": [],
+        "0": [
+            "fikriarllrkif"
+        ]
+    },
+    "INLKAIAALAKKLL": {
+        "1": [],
+        "0": [
+            "inlkaiaalakkll"
+        ]
+    },
+    "FLPLIGRVLSGIL": {
+        "1": [],
+        "0": [
+            "flpligrvlsgil"
+        ]
+    },
+    "KLLKKAGKLLKKAGKLLKKAG": {
+        "1": [],
+        "0": [
+            "KlLkKaGkLlKkAGKlLkKaG"
+        ]
+    },
+    "LLAKKKGLLAKKKGLLAKKKG": {
+        "1": [
+            "LlAkKkGlLaKkKgLlAkKkG"
+        ],
+        "0": []
+    },
+    "RPFTRAQWFAIQHISPRTIAMRAINNYRWR": {
+        "1": [],
+        "0": [
+            "rpftraqwfaiqhisprtiamrainnyrwr"
+        ]
+    },
+    "RLWLAIWRR": {
+        "1": [
+            "rlwlaiwrr"
+        ],
+        "0": []
+    },
+    "KLWLAIWKK": {
+        "1": [
+            "klwlaiwkk"
+        ],
+        "0": []
+    },
+    "FLKLLKKLLFLKLLKKLL": {
+        "1": [
+            "fLKLLKKLLfLKLLKKLL"
+        ],
+        "0": []
+    },
+    "VDKPPYLPRPRPPRRIYNR": {
+        "1": [
+            "VDKPPYLPRPrpprriynr",
+            "VDKPPYLPRPRpPRRIYNR",
+            "VDKPPYLPRPRPPRrIYNr"
+        ],
+        "0": [
+            "VDKPPYLPRPRPPRriynr",
+            "VDKPPYLPRPRpprriynr",
+            "VDKPPYLPRPrPPRRIYNR",
+            "VDKPPYLPRpRPPRRIYNR",
+            "VDKPPYLPrPRPPRRIYNR",
+            "VDKPPYLpRPRPPRRIYNR",
+            "VDKPPYlPRPRPPRRIYNR",
+            "VDKPPyLPRPRPPRRIYNR",
+            "VDKPpYLPRPRPPRRIYNR",
+            "VDKppYLPRPRPPRRIYNR",
+            "VDKpPYLPRPRPPRRIYNR",
+            "vdkppylprprpprriynr",
+            "VDKPPYLPRPRPPRRIYNr",
+            "VDKPPYLPRPRPPRrIYNR"
+        ]
+    },
+    "VRLIVAVRIWRR": {
+        "1": [],
+        "0": [
+            "vrlivavriwrr"
+        ]
+    },
+    "VRLRWWRRRWRR": {
+        "1": [],
+        "0": [
+            "vrlrwwrrrwrr"
+        ]
+    },
+    "RRW": {
+        "1": [],
+        "0": [
+            "rRW",
+            "RrW",
+            "RRw",
+            "rrW",
+            "Rrw",
+            "rRw",
+            "rrw"
+        ]
+    },
+    "FLGTVLKVAAKVLPAALCQIFKKC": {
+        "1": [
+            "FlGTVlKVAAKVlPAAlCQIFKKC"
+        ],
+        "0": [
+            "FLGTVLkVAAkVLPAALCQIFkkC"
+        ]
+    },
+    "FLGTVLKVLAKVLPAALCQIFKKC": {
+        "1": [
+            "FlGTVlKVlAKVlPAAlCQIFKKC"
+        ],
+        "0": []
+    },
+    "FLGTVLRVAARVLPAALCQIFRRC": {
+        "1": [],
+        "0": [
+            "FLGTVLrVAArVLPAALCQIFrrC"
+        ]
+    },
+    "RWKIFKKIEKMGRNIRDGIVKAGPAIQVLGSAKAI": {
+        "1": [],
+        "0": [
+            "rwkifkkiekmgrnirdgivkagpaiqvlgsakai"
+        ]
+    },
+    "GPLGVRGKRLWDIVRRWVGWL": {
+        "1": [
+            "GPlGvRGKRLWDIVRRWVGWL"
+        ],
+        "0": []
+    },
+    "RIVQRIKKWLR": {
+        "1": [
+            "rivqrikkwlr"
+        ],
+        "0": []
+    },
+    "KRIWQRIK": {
+        "1": [
+            "kriwqrik"
+        ],
+        "0": []
+    },
+    "KRIWQRIKDF": {
+        "1": [
+            "kriwqrikdf"
+        ],
+        "0": []
+    },
+    "KYKKALKKLAKLL": {
+        "1": [
+            "kykkalkklakll"
+        ],
+        "0": []
+    },
+    "VQWRAIRVRVIR": {
+        "1": [
+            "vqwrairvrvir"
+        ],
+        "0": []
+    },
+    "GFAWNVCVYRNGVRVCHRRAN": {
+        "1": [],
+        "0": [
+            "GfawnvcvyrnGvrvchrran"
+        ]
+    },
+    "RKRWWRWWKWWKR": {
+        "1": [],
+        "0": [
+            "RKrWWrWwkWWkR"
+        ]
+    },
+    "WRWWKWW": {
+        "1": [],
+        "0": [
+            "WrWwkWW"
+        ]
+    },
+    "WWRWWKWW": {
+        "1": [],
+        "0": [
+            "WWrWwkWW"
+        ]
+    },
+    "RRGKKLLLLLKKKG": {
+        "1": [
+            "rrgkklllllkkkg"
+        ],
+        "0": []
+    },
+    "LLWIALRKK": {
+        "1": [
+            "llwialrkk"
+        ],
+        "0": []
+    },
+    "PRPRPRP": {
+        "1": [],
+        "0": [
+            "prprprp"
+        ]
+    },
+    "KWLKKWLKWLKK": {
+        "1": [],
+        "0": [
+            "kwLkkwLkwLkk"
+        ]
+    },
+    "ILRWPWWPWRRK": {
+        "1": [],
+        "0": [
+            "ilrwpwwpwrrk"
+        ]
+    },
+    "KRKIFLRTKILV": {
+        "1": [
+            "KrKiFlRtKiLv"
+        ],
+        "0": [
+            "kRkIfLrTkIlV"
+        ]
+    },
+    "VLIKTRLFIKRK": {
+        "1": [
+            "vLiKtRlFiKrK"
+        ],
+        "0": []
+    },
+    "KWKLFKKIEKVGQNIRDGIIKAGPAVAVVGQATQIAK": {
+        "1": [],
+        "0": [
+            "kwklfkkiekvgqnirdgiikagpavavvgqatqiak"
+        ]
+    },
+    "GIGKFLHSAKKFGKAFVGEIMNS": {
+        "1": [
+            "gigkflhsakkfgkafvgeimns"
+        ],
+        "0": []
+    },
+    "KWKLFKKIEKVGQGIGAVLKVLTTGL": {
+        "1": [],
+        "0": [
+            "kwklfkkiekvgqgigavlkvlttgl"
+        ]
+    },
+    "KWKLFKKIGIGAVLKVLTTGLPALIS": {
+        "1": [
+            "kwklfkkigigavlkvlttglpalis"
+        ],
+        "0": []
+    },
+    "KWKLFKKGIGAVLKV": {
+        "1": [
+            "kwklfkkgigavlkv"
+        ],
+        "0": []
+    },
+    "KWKLFKKIGAVLKVL": {
+        "1": [
+            "kwklfkkigavlkvl"
+        ],
+        "0": []
+    },
+    "KWKLFKKGAVLKVLT": {
+        "1": [
+            "kwklfkkgavlkvlt"
+        ],
+        "0": []
+    },
+    "KWKLFKKAVLKVLTT": {
+        "1": [
+            "kwklfkkavlkvltt"
+        ],
+        "0": []
+    },
+    "KWKLFKKVLKVLTTG": {
+        "1": [
+            "kwklfkkvlkvlttg"
+        ],
+        "0": []
+    },
+    "GSKKPVPIIYCNRRTGKCQRM": {
+        "1": [],
+        "0": [
+            "gskkpvpiiycnrrtgkcqrm"
+        ]
+    },
+    "RRWQWRMKK": {
+        "1": [
+            "rrwqwrmkk"
+        ],
+        "0": []
+    },
+    "FKCRRWQWRMKKLGA": {
+        "1": [
+            "fkcrrwqwrmkklga"
+        ],
+        "0": []
+    },
+    "PKLLKTFLSKWIG": {
+        "1": [],
+        "0": [
+            "pkllktflskwig",
+            "pkllktflskwiG"
+        ]
+    },
+    "KLPLIGRVLSGIL": {
+        "1": [
+            "klpligrvlsgil"
+        ],
+        "0": []
+    },
+    "KKHRKHRKHRKHGGSGGSKNLRRIIRKGIHIIKKYG": {
+        "1": [],
+        "0": [
+            "kkhrkhrkhrkhggsggsknlrriirkgihiikkyg"
+        ]
+    },
+    "FKRIVQRIKDFLRNLV": {
+        "1": [],
+        "0": [
+            "FKRiVQRiKDFlRNLV"
+        ]
+    },
+    "GWGSFFKKAAHVGKHVGKAALTHYL": {
+        "1": [],
+        "0": [
+            "gwgsffkkaahvgkhvgkaalthyl",
+            "GwGsffkkaahvGkhvGkaalthyl"
+        ]
+    },
+    "RRGWVLALVLRYGRR": {
+        "1": [],
+        "0": [
+            "RRGWVLALVlRYGRR"
+        ]
+    },
+    "RRGWVLALYLRYGRR": {
+        "1": [],
+        "0": [
+            "RRGWVLALYlRYGRR"
+        ]
+    },
+    "RRGWALRLVLAY": {
+        "1": [],
+        "0": [
+            "RRGWALRLVlAY"
+        ]
+    },
+    "KWKKLLKKPLLKKLLKKL": {
+        "1": [
+            "kwkkllkkpllkkllkkl"
+        ],
+        "0": []
+    },
+    "NKKAGLFVVQFPKKY": {
+        "1": [
+            "nkkaglfvvqfpkky"
+        ],
+        "0": []
+    },
+    "LVKKLLKLAMGFG": {
+        "1": [
+            "lvkkllklamgfg"
+        ],
+        "0": []
+    },
+    "WLRRIKAWLRRIKA": {
+        "1": [
+            "wlrrikawlrrika"
+        ],
+        "0": []
+    },
+    "RRGWARRLAFAFGRR": {
+        "1": [
+            "rrgwarrlafafgrr"
+        ],
+        "0": []
+    },
+    "GKKLLKKLKKLLKKG": {
+        "1": [],
+        "0": [
+            "GKKllKKlKKllKKG"
+        ]
+    },
+    "GLLSVLGSVAKHVLPHVVPVIAEHL": {
+        "1": [],
+        "0": [
+            "GllsvlGsvakhvlphvvpviaehl"
+        ]
+    },
+    "EFKRIVQRIKDFLRNLV": {
+        "1": [],
+        "0": [
+            "EfKRiVQRiKDfLRNLV"
+        ]
+    },
+    "GLFDVIKKVASVIGGL": {
+        "1": [
+            "GlfdvikkvasviGGl"
+        ],
+        "0": []
+    },
+    "GIGKFLKKAKKFGKAFVKILKK": {
+        "1": [
+            "GiGkflkkakkfGkafvkilkk"
+        ],
+        "0": []
+    },
+    "GFKKLLKGAAKALVKTVLF": {
+        "1": [],
+        "0": [
+            "GFKkLLKGAAKALVKTVLF"
+        ]
+    },
+    "GFKDLLKKAAKALVKTVLF": {
+        "1": [],
+        "0": [
+            "GFKDLLKkAAKALVKTVLF"
+        ]
+    },
+    "GFKDLLKGAKKALVKTVLF": {
+        "1": [],
+        "0": [
+            "GFKDLLKGAKkALVKTVLF"
+        ]
+    },
+    "GFKDLLKGAAKALKKTVLF": {
+        "1": [],
+        "0": [
+            "GFKDLLKGAAKALkKTVLF"
+        ]
+    },
+    "GFKDLLKGAAKALVKTVKF": {
+        "1": [],
+        "0": [
+            "GFKDLLKGAAKALVKTVkF"
+        ]
+    },
+    "KLWKKWKKWLK": {
+        "1": [],
+        "0": [
+            "klwkkwkkwlk"
+        ]
+    },
+    "RLWRRWRRWLR": {
+        "1": [
+            "rlwrrwrrwlr"
+        ],
+        "0": []
+    },
+    "GMWSKILGHLIR": {
+        "1": [
+            "GmwskilGhlir"
+        ],
+        "0": [
+            "GMWSKIlGHLIR",
+            "GMWSKiLGHLIR",
+            "GMWSkILGHLIR"
+        ]
+    },
+    "GKWMSLLKHILK": {
+        "1": [
+            "Gkwmsllkhilk"
+        ],
+        "0": [
+            "GKWMSLLKhILK",
+            "GKwMSLLKHILK"
+        ]
+    },
+    "GVCRCVCRRGVCRCVCRR": {
+        "1": [
+            "GvcrcvcrrGvcrcvcrr"
+        ],
+        "0": []
+    },
+    "RGGRLCYCRRRFCVCVGR": {
+        "1": [
+            "rGGrlcycrrrfcvcvGr"
+        ],
+        "0": []
+    },
+    "RRWCFRVCYRGFCYRKCR": {
+        "1": [],
+        "0": [
+            "rrwcfrvcyrGfcyrkcr"
+        ]
+    },
+    "GLFVGLAKVAAHVVPAIAEHF": {
+        "1": [],
+        "0": [
+            "GlfvGlakvaahvvpaiaehf"
+        ]
+    },
+    "ILGKLLKTAAGLLSNL": {
+        "1": [],
+        "0": [
+            "ILGKLLkTAAGLLSNL"
+        ]
+    },
+    "ILGKLLSTAAKLLSNL": {
+        "1": [],
+        "0": [
+            "ILGKLLSTAAkLLSNL"
+        ]
+    },
+    "ILGKLLKTAAKLLSNL": {
+        "1": [],
+        "0": [
+            "ILGKLLkTAAkLLSNL"
+        ]
+    },
+    "WLLKRWKKLL": {
+        "1": [
+            "wllkrwkkll"
+        ],
+        "0": []
+    },
+    "KLLKWWKKLL": {
+        "1": [
+            "kllkwwkkll"
+        ],
+        "0": []
+    },
+    "RRIRPRPPRLPRPRPRPLPYPRP": {
+        "1": [],
+        "0": [
+            "rrIRPRPPRLPRPRPRPLPYPRP"
+        ]
+    },
+    "KRWWKWWRR": {
+        "1": [
+            "krwwkwwrr"
+        ],
+        "0": []
+    },
+    "GIMSSLMKKLKKIIAK": {
+        "1": [
+            "Gimsslmkklkkiiak"
+        ],
+        "0": []
+    },
+    "GILSSLLKKLKKIIAK": {
+        "1": [
+            "Gilssllkklkkiiak"
+        ],
+        "0": []
+    },
+    "GILSSLWKKLKKIIAK": {
+        "1": [],
+        "0": [
+            "Gilsslwkklkkiiak"
+        ]
+    },
+    "FFFLSRIF": {
+        "1": [],
+        "0": [
+            "ffflsrif"
+        ]
+    },
+    "FIRSLFFF": {
+        "1": [
+            "firslfff"
+        ],
+        "0": []
+    },
+    "IKIPSFFRNILKKVGKEAVSLIAGALKQS": {
+        "1": [],
+        "0": [
+            "IKIPSFFrNILKKVGKEAVSLIAGALKQS"
+        ]
+    },
+    "WWWLRKIW": {
+        "1": [
+            "wwwlrkiw"
+        ],
+        "0": []
+    },
+    "LLGMIPVAIKAISALSKL": {
+        "1": [
+            "LlGMIPVAIKAISALSKL"
+        ],
+        "0": []
+    },
+    "RLLRKFFRKLKKSV": {
+        "1": [],
+        "0": [
+            "rllrkffrklkksv"
+        ]
+    },
+    "GGLRSLGRKILRAWKKYGPIIVPIIRIG": {
+        "1": [
+            "GGlrslGrkilrawkkyGpiivpiiriG"
+        ],
+        "0": []
+    },
+    "WKIVFWWRR": {
+        "1": [],
+        "0": [
+            "wkivfwwrr"
+        ]
+    },
+    "RRWRIVVIRVRR": {
+        "1": [
+            "rrwrivvirvrr"
+        ],
+        "0": []
+    },
+    "GFGSLLGKALRLGANVL": {
+        "1": [
+            "GfGsllGkalrlGanvl"
+        ],
+        "0": []
+    },
+    "GFGSLLGKALRLWKKVL": {
+        "1": [],
+        "0": [
+            "GFGSLLGKALRLwKkVL",
+            "GFGSLLGKAlrLwKkVL"
+        ]
+    },
+    "GKWKKILGKLIR": {
+        "1": [],
+        "0": [
+            "GkwkkilGklir"
+        ]
+    },
+    "KKWRKWLKWLAKK": {
+        "1": [],
+        "0": [
+            "kkwrkwlkwlakk"
+        ]
+    },
+    "KWRRWIRWL": {
+        "1": [],
+        "0": [
+            "kwrrwirwl"
+        ]
+    },
+    "RRWVRRVRRWVRRVVRVVRRWVRR": {
+        "1": [
+            "RRWvRRvRRWvRRvvRvvRRWvRR"
+        ],
+        "0": []
+    },
+    "VFRLKKWIQKVI": {
+        "1": [
+            "vfrlkkwiqkvi"
+        ],
+        "0": []
+    },
+    "IVKQIWKKLRFV": {
+        "1": [
+            "ivkqiwkklrfv"
+        ],
+        "0": []
+    },
+    "LPLIAGLWGKIW": {
+        "1": [],
+        "0": [
+            "LPLIAGLwGKIw"
+        ]
+    },
+    "FVQWFSKFLGRIL": {
+        "1": [],
+        "0": [
+            "fqvqwfskflgril"
+        ]
+    },
+    "FVPWFSKFLPRIL": {
+        "1": [],
+        "0": [
+            "FVPWFSKFLpRIL"
+        ]
+    },
+    "FFHHIFRAIVHVAKTIHRLVTG": {
+        "1": [
+            "FFHHIFRaIVHVaKTIHRLVTG"
+        ],
+        "0": []
+    },
+    "HFLKTLVNLAKKIL": {
+        "1": [],
+        "0": [
+            "HFLkTLVNLAKKIL"
+        ]
+    },
+    "HFLGKLVNLAKKIL": {
+        "1": [],
+        "0": [
+            "HFLGkLVNLAKKIL"
+        ]
+    },
+    "HFLGTLKNLAKKIL": {
+        "1": [],
+        "0": [
+            "HFLGTLkNLAKKIL"
+        ]
+    },
+    "HFLGTLVKLAKKIL": {
+        "1": [],
+        "0": [
+            "HFLGTLVkLAKKIL"
+        ]
+    },
+    "HFLGTLVNLAKKIL": {
+        "1": [],
+        "0": [
+            "HFLGTLVNLAkKIL",
+            "HFLGTLVNLAKkIL"
+        ]
+    },
+    "ACPIFTKIQGTYRGRAKCR": {
+        "1": [],
+        "0": [
+            "ACPiFTKiQGTYrGrAKCR"
+        ]
+    },
+    "KLALKLALKALKAAKLA": {
+        "1": [
+            "KLalKLALKALKAAKLA"
+        ],
+        "0": [
+            "klALKLALKALKAAKLA",
+            "KLaLKLALKALKAAKLA",
+            "KLALklALKALKAAKlA",
+            "KLALklALKALKAAKLA",
+            "KLALKLalKALKAALKLA",
+            "KLALKLALkaLKAALKLA",
+            "KLALKLALKAlkAALKLA",
+            "KLALKLALKALKaaLKLA",
+            "KLALKLALKALKAAlkLA",
+            "KLALKLALKALKAALKla"
+        ]
+    },
+    "KWKLFKKIPKFLHLAKKF": {
+        "1": [],
+        "0": [
+            "KWKLFKKIpKFLHLAKKF"
+        ]
+    },
+    "FFGSVLKLIPKIL": {
+        "1": [],
+        "0": [
+            "ffGsvlklipkil"
+        ]
+    },
+    "IKLSPKTKDNLKKVLKGAIKGAIAVAKMV": {
+        "1": [
+            "IKLSPkTKDNLKKVLKGAIKGAIAVAKMV"
+        ],
+        "0": []
+    },
+    "IKLSPETKKNLKKVLKGAIKGAIAVAKMV": {
+        "1": [
+            "IKLSPETKkNLKKVLKGAIKGAIAVAKMV"
+        ],
+        "0": []
+    },
+    "IKLSPKTKKNLKKVLKGAIKGAIAVAKMV": {
+        "1": [
+            "IKLSPkTKkNLKKVLKGAIKGAIAVAKMV"
+        ],
+        "0": []
+    },
+    "GLKKIFKAGLGSLVKGIAAHVAS": {
+        "1": [],
+        "0": [
+            "GLKkIFKAGLGSLVKGIAAHVAS"
+        ]
+    },
+    "GLKKIFKKGLGSLVKGIAAHVAS": {
+        "1": [
+            "GLKkIFKKGLGSLVKGIAAHVAS"
+        ],
+        "0": []
+    },
+    "GLKKIFKAGLGSLKKGIAAHVAS": {
+        "1": [],
+        "0": [
+            "GLKkIFKAGLGSLKKGIAAHVAS"
+        ]
+    },
+    "GLKKIFKAGLGSLVKGIKAHVAS": {
+        "1": [],
+        "0": [
+            "GLKkIFKAGLGSLVKGIKAHVAS"
+        ]
+    },
+    "ILGKLLSTAAGLLSKL": {
+        "1": [
+            "ILGKLLSTAAGLLSkL"
+        ],
+        "0": []
+    },
+    "ILGKLLSTAAKLLSKL": {
+        "1": [],
+        "0": [
+            "ILGKLLSTAAkLLSKL"
+        ]
+    },
+    "GFKRIVQRIKDFLRNLV": {
+        "1": [],
+        "0": [
+            "GFKRiVQRiKDFlRNLV"
+        ]
+    },
+    "GLKALKKVFKGIHKAIKLINNHVQ": {
+        "1": [],
+        "0": [
+            "GLkALKKVFkGIHkAIKLINNHVQ"
+        ]
+    },
+    "KFFKKLKNSVKKRAKKFFKKPRVIGVSIPF": {
+        "1": [],
+        "0": [
+            "kffkklknsvkkrakkffkkprvigvsipf"
+        ]
+    },
+    "KFFKKLKKAVKKGFKKFAKV": {
+        "1": [],
+        "0": [
+            "kffkklkkavkkGfkkfakv"
+        ]
+    },
+    "WGIRRILKYGKRS": {
+        "1": [
+            "wglrrllkygkrs"
+        ],
+        "0": []
+    },
+    "IKKILSKIKKLL": {
+        "1": [],
+        "0": [
+            "IKKILSkIKKLL"
+        ]
+    },
+    "IKKIVSKIKKVLK": {
+        "1": [],
+        "0": [
+            "IkKIVSKIKKVLK"
+        ]
+    },
+    "KGKPRPYPPRPPPHPRPIRV": {
+        "1": [],
+        "0": [
+            "kgkprpypprppphprpirv"
+        ]
+    },
+    "GKWMKLLKKILK": {
+        "1": [],
+        "0": [
+            "Gkwmkllkkilk"
+        ]
+    },
+    "GKWVKLLKKILK": {
+        "1": [],
+        "0": [
+            "Gkwvkllkkilk"
+        ]
+    },
+    "KWMKLLKKILK": {
+        "1": [],
+        "0": [
+            "kwmkllkkilk"
+        ]
+    },
+    "LRRLLlRWLRRLLRR": {
+        "1": [],
+        "0": [
+            "LRRllRWlRRLLRR"
+        ]
+    },
+    "ILKKIWKPIKKLF": {
+        "1": [],
+        "0": [
+            "ILKKIWKpIKKLF"
+        ]
+    },
+    "RWLKLPGRWLKL": {
+        "1": [],
+        "0": [
+            "RWLKLpGRWLKL"
+        ]
+    },
+    "RWFKFPGRWFKF": {
+        "1": [],
+        "0": [
+            "RWFKFpGRWFKF"
+        ]
+    },
+    "RWLRLPGRWLRL": {
+        "1": [],
+        "0": [
+            "RWLRLpGRWLRL"
+        ]
+    },
+    "RWFRFPGRWFRF": {
+        "1": [],
+        "0": [
+            "RWFRFpGRWFRF"
+        ]
+    },
+    "RWLHLPGRWLHL": {
+        "1": [],
+        "0": [
+            "RWLHLpGRWLHL"
+        ]
+    },
+    "RWFHFPGRWFHF": {
+        "1": [],
+        "0": [
+            "RWFHFpGRWFHF"
+        ]
+    },
+    "GIFSKLAPKKIKNLLISGLKG": {
+        "1": [],
+        "0": [
+            "GIFSKLApKKIKNLLISGLKG"
+        ]
+    },
+    "WGRRGWGPGRRYVRW": {
+        "1": [
+            "WGRRGWGpGRRYVRW"
+        ],
+        "0": []
+    },
+    "KKYRYHLKPF": {
+        "1": [
+            "kkyryhlkpf"
+        ],
+        "0": []
+    },
+    "RFLRRIFFFF": {
+        "1": [],
+        "0": [
+            "rflrriffff"
+        ]
+    },
+    "FFFFLRRIF": {
+        "1": [
+            "FFFFLrrIF"
+        ],
+        "0": [
+            "FFFFLrRIF",
+            "FFFFLRrIF"
+        ]
+    },
+    "WLLWIALRKKR": {
+        "1": [
+            "wllwialrkkr"
+        ],
+        "0": []
+    },
+    "WLVWIWRRR": {
+        "1": [
+            "wlvwiwrrr"
+        ],
+        "0": []
+    }
+}
\ No newline at end of file
diff --git a/dataset/train_set_llm_aug.json b/dataset/train_set_llm_aug.json
new file mode 100644
index 0000000000000000000000000000000000000000..c2ed1d079d30a1c07955f32486ca9c2ba2f4025a
--- /dev/null
+++ b/dataset/train_set_llm_aug.json
@@ -0,0 +1,2719 @@
+{
+    "GIMSSLMKKLAAHIAK": {
+        "1": [
+            "GIMSSLMkKLAAHIAK",
+            "GIMSSLMKkLAAHIAK",
+            "GIMSSLMKKLAAHIAk",
+            "GIMSSLMkkLAAHIAK",
+            "GIMSSLMkKLAAHIAk",
+            "GIMSSLMKkLAAHIAk",
+            "GIMSSLMkkLAAHIAk",
+            "gIMSSLMkKLAAHIAK",
+            "GiMSSLMKkLAAHIAK",
+            "GIMsSLMKKlAAHIAK",
+            "GIMSSlmKkLAAHIAK",
+            "GIMSsLMkKLAAHIaK"
+        ],
+        "0": [
+            "gIMSSLMKKLAAHIAK",
+            "GImSSLMKKLAAHIAK",
+            "GIMsSLMKKLAAHIAK",
+            "GIMSSlMKKLAAHIAK",
+            "GIMSSLMkklAAHIAK"
+        ]
+    },
+    "ILGTILGLLKSL": {
+        "1": [
+            "iLGTILGLLKSL",
+            "ILgTILGLLKSL",
+            "ILGtILGLLKSL",
+            "ILGTILGLLKsL",
+            "ILGTILGLLKSl"
+        ],
+        "0": [
+            "ILGTILGLLkSL",
+            "ilgtilgllksl",
+            "ILGTILGLLksL",
+            "ILGTILGLLkSl",
+            "ILGTIlGLLkSL",
+            "ILGTiLGLLkSL"
+        ]
+    },
+    "KRLFKKLLKYLRKF": {
+        "1": [
+            "KRLFkkLLKYLRkF",
+            "krLFkkLLKYLRkF",
+            "krLFkkLLkYLrkF",
+            "KRlFkkLLKYLRkF",
+            "KRLfkkLLKYLRkF",
+            "KRLFkkllKYLRkF",
+            "KRLFkkLLkYLrkF",
+            "KRLFkkLLKyLRkF"
+        ],
+        "0": [
+            "KRLFKKLLKYLRkF",
+            "KRLFkKLLKYLRkF",
+            "KRLFKkLLKYLRkF",
+            "KRlFKKLLKYLRkF",
+            "kRLFkKLLKYLRKF",
+            "KRLFKKLLkYLRKF"
+        ]
+    },
+    "ILGTILGLLKGL": {
+        "1": [
+            "ilgtilgllkgl",
+            "IlGtiLgllkgl",
+            "ILgTilgllkgl",
+            "ilgtiLgllkgL",
+            "ilGTilgllkgl",
+            "ILgtilGllkgl"
+        ],
+        "0": [
+            "ILGTILGLLkGL",
+            "ILGTiLgLLKGL",
+            "ilgTILGLLKGL",
+            "ILgtiLGLLKGL",
+            "ILGTILGlLKGL",
+            "ILGTILgLLKGl"
+        ]
+    },
+    "IDWKKLLDAAKQIL": {
+        "1": [
+            "idwkklldaakqil",
+            "IDwkkllDaakQil",
+            "IDwkkllDAaKQIl",
+            "idwKKlldaaKqil",
+            "iDwkklLDAakqil",
+            "IDwkkLldaakqIl"
+        ],
+        "0": [
+            "IDWkkLLDAAkQIL",
+            "iDWKKLLDAAKQIL",
+            "IdWKKLLDAAKQIL",
+            "IDWKKLLdAAKQIL",
+            "IDWKKLLDAaKQIL",
+            "IDWKKLLDAAkQIL"
+        ]
+    },
+    "VWRRWRRFWRR": {
+        "1": [
+            "vWRRWRRFWRR",
+            "VwRRWRRFWRR",
+            "VWRRwRRFWRR",
+            "VWRRWRRfWRR",
+            "VWRRWRRFwRR"
+        ],
+        "0": [
+            "vwrrwrrfwrr",
+            "VWrrWrrFWrr",
+            "VWrrWrrFWRR",
+            "VWRRWrrFWrr",
+            "VWrrWRRFWrr",
+            "VwrrWrrFWrr",
+            "VWrrwrrFWrr"
+        ]
+    },
+    "FLKLLKKLL": {
+        "1": [
+            "fLKLLKKLL",
+            "FlKLLKKLL",
+            "FLkLLKKLL",
+            "flkllkkll",
+            "flKLLKKLL",
+            "fLkLLKKLL",
+            "FlkLLKKLL",
+            "flkLLKKLL",
+            "flkLLKKll"
+        ],
+        "0": [
+            "FLKlLKKLL",
+            "FLKLlKKLL",
+            "FLKLLkKLL",
+            "FLKLLKkLL",
+            "FLKLLKKlL",
+            "FLKLLKKLl",
+            "FLKllKKLL",
+            "FLKlLkKLL",
+            "FLKLlkKLL",
+            "FLKllkKLL",
+            "FLKllKKll"
+        ]
+    },
+    "KKVVFWVKFK": {
+        "1": [
+            "KKVVFWVKFk",
+            "KKVVFWVKfk",
+            "KKVVFWVkFk",
+            "KKVVFWvKFk",
+            "KKVVFwVKFk",
+            "KKVVfWVKFk"
+        ],
+        "0": [
+            "KKVVFWVKfK",
+            "KKVVFWVkFK",
+            "KKVVFWvKFK",
+            "KKVVFwVKFK",
+            "KKVVfWVKFK",
+            "KKVvFWVKFK",
+            "KKvVFWVKFK",
+            "KkVVFWVKFK",
+            "kKVVFWVKFK",
+            "kkVVFWVKFK",
+            "KKvvFWVKFK",
+            "KKVVfWVKfK",
+            "KKVVFwvKFK",
+            "KKVVFWVkfK"
+        ]
+    },
+    "KRIVKLILKWLR": {
+        "1": [
+            "KRIVkLILKWLR",
+            "KRIVKlILKWLR",
+            "KRIVklILKWLR",
+            "KRIVkLIlKWLR",
+            "KRIVkLILkWLR",
+            "KRIVKlilKWLR",
+            "KRIVkLiLkWLR"
+        ],
+        "0": [
+            "kRIVKLILKWLR",
+            "KRivKLILKWLR",
+            "KrIVKLILKWLR",
+            "KRIVkliLKWLR",
+            "KRIVKLiLKWLR"
+        ]
+    },
+    "KKVVFKVKFKK": {
+        "1": [
+            "kKVVFKVKFKk",
+            "kKVVFKVKFKK",
+            "KKVVFKVKFKk",
+            "KkVVFKVKFKK",
+            "KKVVFKVKFkK",
+            "kKVVFKVKFkK"
+        ],
+        "0": [
+            "kkVVFKVKFKK",
+            "KKVVFKVKFkk",
+            "kkVVFKVKFkk",
+            "KKVVFkVKFKK",
+            "kkVVFkVKFkk",
+            "kkvvfkvkfkk",
+            "KKvVFKVKFKK",
+            "KKVVfKVKFKK",
+            "KkVvFKVKFKK",
+            "KKVvFKVkFKK"
+        ]
+    },
+    "KWKSFLKTFKSAKKTVLHTALKAISS": {
+        "1": [
+            "kWKSFLKTFKSAKKTVLHTALKAISS",
+            "KwKSFLKTFKSAKKTVLHTALKAISS",
+            "KWkSFLKTFKSAKKTVLHTALKAISS",
+            "KWKsFLKTFKSAKKTVLHTALKAISS",
+            "KWKSFLkTFKSAKKTVLHTALKAISS"
+        ],
+        "0": [
+            "KWKSFLKTFKSAKkTVLHTALKAISS",
+            "KWKSFLKTFKsAKkTVLHTALKAISS",
+            "KWKSFLKTFKSAKktVLHTALKAISS",
+            "KWKSFLKTFKsAKktVLHTALKAISS",
+            "KWKSFLKTFKSaKKTVLHTALKAISS",
+            "KWKSFLKTfKSaKKTVLHTALKAISS",
+            "KWKSFLKTFKSaKKTvLHTALKAISS",
+            "KWKSFLKTfKSaKKTvLHTALKAISS",
+            "KWKSFLKTFKSAkKTVLHTALKAISS",
+            "kwksflktfksakktvlhtalkaiss",
+            "KWKSFLKTfKSAKKTVLHTALKAISS",
+            "KWKSFLKTFkSAKKTVLHTALKAISS",
+            "KWKSFLKTFKSAKKtVLHTALKAISS",
+            "KWKSFLKTFKSAKKTvLHTALKAISS",
+            "KWKSFLKTfkSAKKTVLHTALKAISS"
+        ]
+    },
+    "FLPLIIGALSSLLPKIF": {
+        "1": [
+            "fLPLIIGALSSLLPKIF",
+            "FLPLIIGALsSLLPKIF",
+            "FLPLIIGALSSLLPkIF",
+            "FLPLiIGALSSLLPKIF",
+            "FLPLIIgaLSSLLPKIF"
+        ],
+        "0": [
+            "FLPLIIGALSSLLPKiF",
+            "FLPLiiGALSSLLPKiF",
+            "FLPLIIGALSSLLPkiF",
+            "FLPLiIGALSSLLPKiF",
+            "FLPLIIGaLSSLLPKiF",
+            "FLPLIIGALSSllPKiF",
+            "FLPLiiGALSSLLPkiF"
+        ]
+    },
+    "KLKKLLKKWLKLLKKLLK": {
+        "1": [
+            "KLKKLlKKWLKlLKKLLk",
+            "KLKKlLKKWlKLLKkLLK",
+            "KLKkLLKkWLKlLKKlLK",
+            "KLkKLlKKwLKlLKkLLk",
+            "KlKkLlKkWlKlLkKlLk",
+            "KLKKLLKKWlkllkkllk",
+            "klKKLLKKWLKLLKKLLK",
+            "KLKKLLkkWLKLLKKLLK",
+            "kLKKLLKKWLKLLKKLLk",
+            "KLKKLLKKWLkLLKKLLK"
+        ],
+        "0": [
+            "KlkKLLKKWLKLLKKLLK",
+            "KLKklLKKWLKLLKKLLK",
+            "KLKKLLKKWLKLLKKlLK",
+            "klkKLLKKWLKLLKKLLK",
+            "KLKKLLKKWLKLLKKLlk"
+        ]
+    },
+    "KKAAAAAAAAAAAAWAAAAAAKKKK": {
+        "1": [
+            "kkAAAAAAAAAAAAWAAAAAAKKKK",
+            "KKAAAAAAAAAAAAwaAAAAAKKKK",
+            "KKAAAAAAAAAAAAWAaaAAAKKKK",
+            "KKAAAAAAAAAAAAWAAAaaAKKKK",
+            "KKAAAAAAAAAAAAWAAAAaaKKKK",
+            "kKAAAAAAAAAAAAwAAAAAAKKKK",
+            "KKAAAAAAAAAAAAwAAAaAAKKKK"
+        ],
+        "0": [
+            "KKaaAAAAAAAAAAWAAAAAAKKKK",
+            "KKAAaaAAAAAAAAWAAAAAAKKKK",
+            "KKAAAAaaAAAAAAWAAAAAAKKKK",
+            "KKAAAAAAaaAAAAWAAAAAAKKKK",
+            "KKAAAAAAAAaaAAWAAAAAAKKKK",
+            "KKAAAAAAAAAAaaWAAAAAAKKKK",
+            "KKAAAAAAAAAAAAWAAAAAakKKK",
+            "KKAAAAAAAAAAAAWAAAAAAKkkK",
+            "KKAAAAAAAaaAAAWAAAAAAKKKK",
+            "KKAAAAAAAAAaaAWAAAAAAKKKK"
+        ]
+    },
+    "FVPWFSKFLGRIL": {
+        "1": [
+            "fVPWFSKFLGRIL",
+            "FvPWFSKFLGRIL",
+            "FVpWFSKFLGRIL",
+            "FVPwFSKFLGRIL",
+            "FVPWfSKFLGRIL"
+        ],
+        "0": [
+            "FVPWFSkFLGRIL",
+            "FVPWFSKfLGRIL",
+            "FVPWFSKFlGRIL",
+            "FVPWFSKFLGrIL",
+            "FVPWFSKFLGRiL",
+            "FVPWFSKFLGRIl",
+            "FVPWFSKflGRIL",
+            "FVPWFSKfLGrIL",
+            "FVPWFSKFlGrIL",
+            "FVPWFSKFLgriL"
+        ]
+    },
+    "IRIKIRIK": {
+        "1": [
+            "irikirik",
+            "IRIkIrIK",
+            "irikIRIK",
+            "IRIKirik",
+            "IRikirIK",
+            "IRIkiriK",
+            "irIKIRik"
+        ],
+        "0": [
+            "IrIkIrIk",
+            "iRiKiRiK",
+            "iRIkIriK",
+            "IRiKirIk",
+            "iRIKIRIK",
+            "IRIKIRIk"
+        ]
+    },
+    "IIRKIIRK": {
+        "1": [
+            "iirkiirk",
+            "IirKIirK",
+            "IIRKiirk",
+            "IiRkIiRk",
+            "iIrKiIrK",
+            "Iirkiirk",
+            "iirkiirK"
+        ],
+        "0": [
+            "iIRKIIRK",
+            "IIRkIIRK",
+            "IirKIIRK",
+            "IIRKiiRK",
+            "iiRKiirk"
+        ]
+    },
+    "KKLFKKILKYL": {
+        "1": [
+            "KKLfKKILKYL",
+            "KKLFKKILkYL",
+            "KKLFKKIlKYL",
+            "KKLFkKILKYL",
+            "KKlFKKILKYL",
+            "KkLFKKILKYL",
+            "KKLFKkILKYL",
+            "kKLFKKILKYL",
+            "KKLFKKIlkYL",
+            "KKlFKkILkYL",
+            "KKLFKKilkYL",
+            "kklfkkilkyl",
+            "kkLfKKILKYL",
+            "KKLFKKilkyl",
+            "KKLFkkilkyl",
+            "KKLfkkilkyl",
+            "KKlfkkilkyl",
+            "Kklfkkilkyl",
+            "kklfKKILKYL",
+            "kklfkKILKYL",
+            "kklfkkILKYL",
+            "kklfkkiLKYL",
+            "kklfkkilKYL",
+            "kklfkkilkYL",
+            "kklfkkilkyL",
+            "KkLFkKILKYL",
+            "kKLFKKILkYL",
+            "KKLFKKIlKyL",
+            "KKLfKkILKYL",
+            "KKlFKKiLKYL"
+        ],
+        "0": [
+            "KKLFKkilkyl",
+            "KKLFKKiLKYL",
+            "KKLFKKILKyL",
+            "KKLFKKILKYl",
+            "KKlFKKILkYL",
+            "KKLFKKIlkyl",
+            "KKLFKKILkyL",
+            "kKLFKKILKYl",
+            "KkLFKKILKyL",
+            "KKLfKKILkyl",
+            "KKLFKKiLkyL"
+        ]
+    },
+    "KFFKRLLKSVRRAVKKFRK": {
+        "1": [
+            "KffkRLLKSVRRAVKKFRK",
+            "kFfkrLLkSVrrAVKKfrK",
+            "KFfKRLlKSvrRAVKkFRK",
+            "kFFKrLLkSVRravKkFrK",
+            "KfFKRlLKSVRRAVKKfRK"
+        ],
+        "0": [
+            "kFFkrLLkSVrrAVkkFrk",
+            "kffkrllksvrravkkfrk",
+            "kffkrLLksvRRaVKKfrk",
+            "KFFKrlLkSvrrAVKKfRk",
+            "kffKrlLkSVrravkKFRk",
+            "KfFkRllKSVrrAvkKfrK",
+            "kFFKrlLksvrraVkkfRk"
+        ]
+    },
+    "KWKSFLKTFKSLKKTVLHTLLKAISS": {
+        "1": [
+            "KWKSFLkTFKSLKKTVLHTLLKAISS",
+            "KWKSFLKTFKSLKkTVLHTLLKAISS",
+            "KWKSFLKTFKSLKKTVLHTLLkAISS",
+            "KWKSFLkTFKSLKkTVLHTLLKAISS",
+            "KWKSFLKTFKSLKkTVLHTLLkAISS",
+            "KWKSFlKTFKSLKKTVLHTLLKAISS",
+            "KWKSFLKTFKSlKKTVLHTLLKAISS",
+            "KWKSFLKTFKSLKKTVLHTlLKAISS",
+            "KWKSFLKTFKSlKKTVLHTlLKAISS",
+            "KWKSFlkTFKSLKKTVLHTLLKAISS",
+            "KWKSFLkTFKSLKKTVLHTLLkAISS",
+            "KWKSFLKTFKSlKKTVLHTLLkAISS",
+            "KWKSFLKTFKSLkKTVLHTlLKAISS",
+            "KWKSFLKTFKSLKKTVLHTlLkAISS"
+        ],
+        "0": [
+            "KWKSFLkTFKSLKkTVLHTLLkAISS",
+            "KWKSFLkTFkSLKkTVLHTLLkAISS",
+            "KWkSFLkTFkSLKkTVLHTLLkAISS",
+            "kWkSFLkTFkSLKkTVLHTLLkAISS",
+            "KWKSFlKTFKSlKKTVLHTLLKAISS",
+            "KWKSFlKTFKSlKKTVLHTlLKAISS",
+            "KWKSFlKTFKSlKKTVlHTlLKAISS",
+            "KWKSFlKTFKSlKKTVlHTllKAISS",
+            "KWKSFlkTFKSlKKTVLHTLLKAISS",
+            "KWKSFlKTFkSLKKTVLHTLLKAISS",
+            "KWKSFLKTFkSlkKTVLHTLLKAISS",
+            "KWKSFLKTFKsLkKTVLHTLLKAISS",
+            "kwkSFLKTFKSLKKTVLHTLLKAISS"
+        ]
+    },
+    "GWLDVAKKIGKAAFNVAKNFL": {
+        "1": [
+            "gWLDVAKKIGKAAFNVAKNFL",
+            "GwLDVAKKIGKAAFNVAKNFL",
+            "GWlDVAKKIGKAAFNVAKNFL",
+            "GWLdVAKKIGKAAFNVAKNFL",
+            "GWLDvAKKIGKAAFNVAKNFL"
+        ],
+        "0": [
+            "GWLDvAKKIGKAAFNvAKNFL",
+            "GWLDVAKKIGKAAFNvAKNFL",
+            "gWLDVAKKIGKAAFNvAKNFL",
+            "GwLDVAKKIGKAAFNvAKNFL",
+            "GWlDVAKKIGKAAFNvAKNFL",
+            "GWLdVAKKIGKAAFNvAKNFL",
+            "GWLDVaKKIGKAAFNvAKNFL"
+        ]
+    },
+    "GFGMALKLLKKVL": {
+        "1": [
+            "GfGmalkllkkvl",
+            "GfGMALKLLKKVL",
+            "gfGMALKLLKKVL",
+            "GfgMALKLLKKVL",
+            "GfGmALKLLKKVL",
+            "GfGMalkllkkvl",
+            "GfGMALKLLKKvl"
+        ],
+        "0": [
+            "GFGMALKLLKKVl",
+            "GFGMALKLLKKvL",
+            "GFGMALKLLKkVL",
+            "GFGMALKLLkKVL",
+            "GFGMALKLlKKVL",
+            "GFGMALKlLKKVL",
+            "GFGMALkLLKKVL",
+            "GFGMAlKLLKKVL",
+            "GFGMaLKLLKKVL",
+            "GFGmALKLLKKVL",
+            "gFGMALKLLKKVL",
+            "GFgMALKLLKKVL",
+            "gFgMALKLLKKVL",
+            "GFGmaLKLLKKVL",
+            "gFgmalkllkkvl"
+        ]
+    },
+    "RGLRRLGRKIAHGVKKYGPTVLRIIRIA": {
+        "1": [
+            "rGLRRLGRKIAHGVKKYGPTVLRIIRIA",
+            "RGLRRLGRKiAHGVKKYGPTVLRIIRIA",
+            "RGLRRLGRKIAHGVkkYGPTVLRIIRIA",
+            "RGLRRLGRKIAHGVKKYGPtVLRIIRIa",
+            "RGLRrLGRKiahGVKKYGPTVLRIIRIA"
+        ],
+        "0": [
+            "rglrrlgrkiahgvkkygptvlriiria",
+            "RGLRRLGRKIAHGVKKYGptvlriiria",
+            "RGLRRLGRKIAHgvkkygptvlriiria",
+            "RGLRRLGRKIAHGVKKYgpTVLRIIRIA",
+            "rglrrlGRKIAHGVKKYGPTVLRIIRIA",
+            "RGLRRlgrkiahgvkkYGptvlriiria"
+        ]
+    },
+    "KVLGRLVKVLGRLV": {
+        "1": [
+            "kVLGRLVKVLGRLV",
+            "kvLGRLVKVLGRLV",
+            "kVlGRLVKVLGRLV",
+            "kVLgRLVKVLGRLV",
+            "kVLGrLVKVLGRLV",
+            "kVLGRlVKVLGRLV"
+        ],
+        "0": [
+            "KVLGRLVkVLGRLV",
+            "kVLGRLVkVLGRLV",
+            "KvLGRLVkVLGRLV",
+            "KVlGRLVkVLGRLV",
+            "KVLgRLVkVLGRLV",
+            "KVLGrLVkVLGRLV",
+            "KVLGRlVkVLGRLV"
+        ]
+    },
+    "RRLFRRILRWL": {
+        "1": [
+            "RRLfRRILRWL",
+            "RRLFrRILRWL",
+            "rrlfrrilrwl",
+            "RRLfrRILRWL",
+            "RRLfRRILRwL",
+            "RRLFrRILrWL",
+            "rrlfrRILRWL",
+            "RRLfrRILRwL"
+        ],
+        "0": [
+            "rRLFRRILRWL",
+            "RrLFRRILRWL",
+            "RRlFRRILRWL",
+            "RRLFRrILRWL",
+            "RRLFRRiLRWL",
+            "RRLFRRIlRWL",
+            "RRLFRRILrWL",
+            "RRLFRRILRwL",
+            "RRLFRRILRWl",
+            "rRLFRrILRWL",
+            "RrLFRRiLRWL",
+            "RRlFRRIlRWL",
+            "RRLFRrILrWL",
+            "RRLFRRILRwl"
+        ]
+    },
+    "KWKSFLKTFKSAVKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKSAvKTVLHTALKAISS",
+            "KWKSFLKTFKsAVKTVLHTALKAISS",
+            "KWKSFLkTfKSAVKTVLHTALKAISS",
+            "kWKSFLKTFKSAvkTVLHTALKAISS",
+            "KWKSFLKTFKSAVKTVLhTaLKAISS",
+            "KwKSfLKTFKsavKTVLHTALKAISS",
+            "KWKsFLKtFKSAVKtVLHTALKAISS"
+        ],
+        "0": [
+            "kwksflktfksavktvlhtalkaiss",
+            "KWKSFLKTFKSAvKTVLhtaLKAISS",
+            "kwksfLKTFKSAVKTVLHTALKAISS",
+            "KWKSFlktfkSAVKTVLHTALKAISS",
+            "kWKsfLKTFKSAVKTVLHTalkaiss",
+            "KwKsFLKTFksAVKtVLHTaLKAISs"
+        ]
+    },
+    "RRWVRRVRRVWRRVVRVVRRWVRR": {
+        "1": [
+            "rRWVRRVRRVWRRVVRVVRRWVrR",
+            "RRwVRRVRRVwRRVVRVVRRWVRR",
+            "RRWVRrVRRVWRRVVrVVRRWVrR",
+            "RRWVrRVRRVWRRVVRVVRRwVRR",
+            "rRWVRRVRRVWrrVVRVVRRWVRr"
+        ],
+        "0": [
+            "RRWVRRvRRVWRRVvRvVRRWvRR",
+            "RRWVRRvRRvWRRVvRvvRRWvRR",
+            "RRWvRRvRRvWRRvvRvvRRWvRR",
+            "RRWvRRVRRVWRRVvRVvRRWvRR",
+            "RRWVRRVRRvWRRvVRvvRRWvRR"
+        ]
+    },
+    "TVGGLVKWILKTVKKFA": {
+        "1": [
+            "tvgglvkwilktvkkfa",
+            "TVGGLVKWILkTVKKFA",
+            "tVGGLVKWILKTVKKFA",
+            "TVgGLVKWILKTVKKFA",
+            "TVGGlVKWILKTVKKFA",
+            "TVGGLvKWILKTVKKFA",
+            "TVGGLVKWILKTVkKFA"
+        ],
+        "0": [
+            "TVGGLVkWILkTVKkFA",
+            "TVGGLVkWILkTVKKFA",
+            "TVGGLVkWILKTVKkFA",
+            "TVGGLVKWILkTVKkFA",
+            "TVGGLVkWILkTVKkfA",
+            "tVGGLVkWILkTVKkFA"
+        ]
+    },
+    "INLKALAALAKKIL": {
+        "1": [
+            "INLKAlAALAKKIL",
+            "INLKALaALAKKIL",
+            "INLKALAALaKKIL",
+            "INLKaLAALAKKIL",
+            "INLkALaALAKKIL"
+        ],
+        "0": [
+            "iNLKALAALAKKIL",
+            "InLKALAALAKKIL",
+            "inLKALAALAKKIL",
+            "inlkalaalakkil",
+            "iNlKALAALAKKIL",
+            "iNLKAaAALAKKIL",
+            "iNlkALAALAKKIL",
+            "InLkALAALAKKIL",
+            "inlKALAALAKKIL"
+        ]
+    },
+    "FLSLIPKAIKAVGVKAKKF": {
+        "1": [
+            "FlSLIPKAIKAVGVKAKKF",
+            "FLsLIPKAIKAVGVKAKKF",
+            "FLSLiPKAIKAVGVKAKKF",
+            "FLSLIPkAkKAVGVKAKKF"
+        ],
+        "0": [
+            "FLSLIPkAIkAVGVkAkkF",
+            "FLSLIPkAIKAVGVKAKKF",
+            "FLSLIPkAIkAVGVKAkKF",
+            "FLSLIPKaiKAVGVKAKKF",
+            "FLSLIPKAIkAVgVKAKKF",
+            "FLSLIPkAIKAVGvkAKKF",
+            "fLSLIPKAIKAVGVKAKkF"
+        ]
+    },
+    "KKLLKLLKLLL": {
+        "1": [
+            "kkllkllklll",
+            "KkLLKLLKLLL",
+            "KkLLkLLKLLL",
+            "KkLlKLLKLLL",
+            "kKLLKLLKLLl",
+            "kkLLKLLKLLl",
+            "KkllKLLKLLL",
+            "kkLLkLLKLLL",
+            "KkllKLlKLLL",
+            "KkLLKLLkLLL",
+            "kklLKLLKLLL",
+            "KkLLkLLKLLl",
+            "kkLLKLlKLLL",
+            "kKlLKLLKLLL"
+        ],
+        "0": [
+            "kkLLKLLKLLL",
+            "KKLLKllKLLL",
+            "KKLLkllKLLL",
+            "KkllKlLKLLL",
+            "KKLLkllkLLL",
+            "KKllKllKLLL",
+            "KKlLkLlKlLL",
+            "KKLlkLLklLL",
+            "KklLKLLKllL",
+            "kkLLKLLKLll",
+            "kkLLkLLKLLl",
+            "KKllKLLklLL",
+            "KklLKlLKlLL",
+            "KKllKLlKLlL",
+            "KKLlkLLkLLl",
+            "KkllKllKLLL",
+            "KKllKllKlLL",
+            "kkLLkLLKLll",
+            "kkLLkLLkLLl",
+            "kKLLkllKLLl",
+            "KKLlkllkLLL",
+            "KkLlKlLkLlL",
+            "kKlLkLlKlLl",
+            "kKLLKLLKLLL",
+            "KKLLKLLKLLl",
+            "KKllKLLKLLL",
+            "KKLLkLLKLLL",
+            "KKLLKlLKLLL"
+        ]
+    },
+    "KKVVFKVKFK": {
+        "1": [
+            "KKVVfKvKFK",
+            "KKVvFKVKFK",
+            "kKVVFkVKFK",
+            "KKVvFkVkFK",
+            "kKvvFKVKFK"
+        ],
+        "0": [
+            "KKVVFKVKFk",
+            "kKVVFKVKFk",
+            "kkVVFKVKFk",
+            "KKVVfkvKFK",
+            "kKVVfkvKFK",
+            "kkVVFkVkFK",
+            "KKVVFKVKfK",
+            "kKVVfkKFKk",
+            "KKVvFkKfKf",
+            "kKVvFkvKfK"
+        ]
+    },
+    "LKLLKKLLKKLLKLL": {
+        "1": [
+            "LKlLKkLlkKLLkLL",
+            "lkLlKKlLKkLLKLL",
+            "LkLLkKLlKKlLKLl",
+            "LKLlKkLlKkLlKlL",
+            "lKLLkKLLkKLLkLL"
+        ],
+        "0": [
+            "KKkLLlLllLLLkKK",
+            "LKllkkllKKLLKLL",
+            "LkLlKkLlKkLLKLL",
+            "lKLLkKLLkklLKLL",
+            "lklLKKLLKKLlkll",
+            "LKLlkkllkKLLKLL"
+        ]
+    },
+    "KLKLLKLLKLLKLLK": {
+        "1": [
+            "kLKLLKLLKLLKLLK",
+            "KlKLLKLLKLLKLLK",
+            "KLKLLkLLKLLKLLK",
+            "KLKLLKLLkLLKLLK",
+            "KLKLLKLLKLLkLLK"
+        ],
+        "0": [
+            "KLkLLkLlkLLKlLK",
+            "KLKLLKLLKLLKLLk",
+            "kLkLlKLKlLKLKLL",
+            "KkLLkLLlkLkLLKk",
+            "KlkLkLKLLkLlKLk",
+            "klKLklKLLklKLLk"
+        ]
+    },
+    "KKKLLLLLLLLLKKK": {
+        "1": [
+            "KKKLLLLlllllKKK",
+            "kkkLLLlllLLLkkk"
+        ],
+        "0": [
+            "KKKLLlLLlLLlKKK",
+            "kkklllLLLLLLkkk"
+        ]
+    },
+    "KKFKKTAKWLIKSAWLLLKSLALKMK": {
+        "1": [
+            "kkfkktakwliksawlllkslalkmk",
+            "KKFkktaKwliksawlllkslalkmk",
+            "kkfKKtaKwliksawlllkslalkmk",
+            "kkfkkTAKwliksawlllkslalkmk",
+            "kkfkktaKWliksawlllkslalkmk",
+            "kkfkktaKwLIksawlllkslalkmk"
+        ],
+        "0": [
+            "KKFKKTAkwliksawlllkslalkmk",
+            "KKFKKTAKwliksawlllkslalkmk",
+            "KKFKKTAKwlIksawlllkslalkmk",
+            "KKFKKTAKwliKSAWLLLKSLALKMK",
+            "KKFKKTAKwliksawlllKSLALKMK"
+        ]
+    },
+    "WWWLRRRW": {
+        "1": [
+            "wWWLRRRW",
+            "WwWLRRRW",
+            "WWwLRRRW",
+            "WWWlRRRW",
+            "WWWLrRRW"
+        ],
+        "0": [
+            "wwwlrrrw",
+            "Wwwlrrrw",
+            "wWwlrrrw",
+            "wwWlrrrw",
+            "wwwLrrrw",
+            "wwwlRrrw"
+        ]
+    },
+    "RRRWWWWV": {
+        "1": [
+            "rRRWWWWV",
+            "RRRWWWWv",
+            "RRRwWWWV",
+            "RrrWWWWV",
+            "RRRwwWWv"
+        ],
+        "0": [
+            "rrrwwwwv",
+            "rrRWWWWv",
+            "RRRwwwwV",
+            "rrrWWWwv",
+            "RrwWWWWv",
+            "rrRwWWwv"
+        ]
+    },
+    "KWFRVYRGIYRRR": {
+        "1": [
+            "KwFRVYRGIYRRR",
+            "KWfRVYRGIYRRR",
+            "KWFRvYRGIYRRR",
+            "KWFRVyrGIYRRR",
+            "KWFrVYRGiYRRR"
+        ],
+        "0": [
+            "kwfrvyrgiyrrr",
+            "kwfrvyrgiyrrR",
+            "kWfrvyrgiyrrr",
+            "kwfRvyrgiyrrr",
+            "kwfrvyRgiyrrr",
+            "kwfrvyrgIyRrr"
+        ]
+    },
+    "RRRYIGRYVRFWK": {
+        "1": [
+            "RRRYIGRYvRFWK",
+            "rRRYIGRYVRFWK",
+            "RRRyIGRYVRFWK",
+            "RRRYIGRyVRFWK",
+            "RRRYIGRYVrFWK"
+        ],
+        "0": [
+            "rrryigryvrfwk",
+            "rrryigrYVRFWK",
+            "RRRYIgryVRFWK",
+            "rrryiGRYvRFWK",
+            "rRRyIGRYVrfwK",
+            "RRRYIGRyVrFwK"
+        ]
+    },
+    "GKIIKLKASLKLL": {
+        "1": [
+            "gkiiklkaslkll",
+            "GkIIKLKASLKLL",
+            "GKiIKLKASLKLL",
+            "GKIiKLKASLKLL",
+            "GKIIkLKASLKLL",
+            "GKIIKlKASLKLL"
+        ],
+        "0": [
+            "GKIIKLkASLKLL",
+            "GKIIKLKaSLKLL",
+            "GKIIKLKAsLKLL",
+            "GKIIKLKASlKLL",
+            "GKIIKLKASLkLL"
+        ]
+    },
+    "KLFKKLFKKLFK": {
+        "1": [
+            "KlFkKlFkKlFk",
+            "KlfKKlFKKlFk",
+            "kLfKkLfKkLfK",
+            "KLFkKLFkKLFk",
+            "kLFKkLFKkLFK"
+        ],
+        "0": [
+            "kLFkkLFkkLFk",
+            "klfkkfkkfkkk",
+            "KlfklfklfklK",
+            "klkklkkklkkk"
+        ]
+    },
+    "GFFALIPKIISSPLFKTLLSAV": {
+        "1": [
+            "gFFALIPKIISSPLFKTLLSAV",
+            "GFFALIPkiISSPLFKTLLSAV",
+            "GFFALIPKIIsSPLFKTLLSAV",
+            "GFFALiPKIISSPLFKTLLSAV",
+            "GFFALIPKIISSPLFKtLLSAV"
+        ],
+        "0": [
+            "GFFALIpKIISSPLFKTllSAV",
+            "GFFALIPKIISSPLFKTLLsaV",
+            "GFFALIPKIISSPlFKTLLSAV",
+            "GfFALIPKIISSPLfKTLLSAV",
+            "GFFALIPKIISSPLFKTlLSAV",
+            "GFFALIPkIISSPLFKTLLSAV"
+        ]
+    },
+    "KGFFALIPKIISSPLFKTLLSAV": {
+        "1": [
+            "kGFFALIPKIISSPLFKTLLSAV",
+            "KGfFALIPKIISSPLFKTLLSAV",
+            "KGFFALIPkIISSPLFKTLLSAV",
+            "KGFFALIPKIISsPLFKTLLSAV",
+            "KGFFALIPKIISSPLfKTLLSAV"
+        ],
+        "0": [
+            "KGFFALIpKIISSPLFKTllSAV",
+            "KGFFALIPKIISSPLFKTllSAv",
+            "KGFFALIPKIISSPLFKTlLSAV",
+            "KGFFALIpKIISSPLFKTlLSAV",
+            "KGFFALIpKIISSPLFktLLSAV",
+            "KGfFALIPKIISsPLFKTllSAV"
+        ]
+    },
+    "RGLRRLGRKIAHGVKKYG": {
+        "1": [
+            "rglrrlgrkiahgvkkyg",
+            "rGLRRLGRKIAHGVKKYG",
+            "RgLRRLGRKIAHGVKKYG",
+            "RGlRRLGRKIAHGVKKYG",
+            "RGLrRLGRKIAHGVKKYG",
+            "RGLRrLGRKIAHGVKKYG"
+        ],
+        "0": [
+            "RGLRRlGRKIAHGVKKYG",
+            "RGLRRLgRKIAHGVKKYG",
+            "RGLRRLGrKIAHGVKKYG",
+            "RGLRRLGRkIAHGVKKYG",
+            "RGLRRLGRKiAHGVKKYG"
+        ]
+    },
+    "FLGGLIKIVPAMICAVTKKC": {
+        "1": [
+            "flGGlikivpamicavtkkc",
+            "flGGLikivpamicavtkkc",
+            "flGGliKivpamicavtkkc",
+            "flGGlikivpamicavtKkc",
+            "flGGlikivpamicavtkkC"
+        ],
+        "0": [
+            "FLGGlikivpamicavtkkc",
+            "flgglikivpamicavtkkc",
+            "flGgLIKivpamicavtkkc",
+            "fLGGlikivpamicavtkkc",
+            "FlGGLikivpamicavtkkc"
+        ]
+    },
+    "AKRLKKLAKKIWKWK": {
+        "1": [
+            "aKRLKKLAKKIWKWK",
+            "AKRlKKLAKKIWKWK",
+            "AKRLKKLAKkIWKWK",
+            "AKRLKklAKKIWKWK",
+            "aKRLKKlAKKIWKkK"
+        ],
+        "0": [
+            "AkRLkkLAkkIWkWk",
+            "AKrLkkkAKkIWkWk",
+            "AkRLKkLAKKkwKWK",
+            "akRLkKLAkkIWKWK",
+            "AkRLkklAKKIWKWk",
+            "akrLkKkAKkIWKWk"
+        ]
+    },
+    "VDKPPYLPRPRPIRRPGGR": {
+        "1": [
+            "VDkPPYLPRPRPIRRPGGR",
+            "VDKpPYLPRPRPIRRPGGR",
+            "VDKPPyLPRPRPIRRPGGR",
+            "VDKPPYLPRPRPIRRPGgR",
+            "VDKPPYLPRPRPIRRPgGR"
+        ],
+        "0": [
+            "VDKPPYLPrPRPIrRPGGR",
+            "VDKPPYLPrPRPIRrPGGR",
+            "VDKPPYLPrPRPIRRPGGr",
+            "VDKPPYLPRPrPIrRPGGR",
+            "VDKPPYLPRPrPIRrPGGR",
+            "VDKPPYLPRPrPIRRPGGr",
+            "VDKPPYLPRPRPIrRPGGr",
+            "VDKPPYLPRPRPIRrPGGr",
+            "VDkPPYLPrPrPIrrPGGr",
+            "VDKPPYLPRPRPIrrPGGR",
+            "VDKPPYLPRPRPIrrPGGr",
+            "VDKPPYLPrPrPIRrPGGR",
+            "VDKPPYLPRPrPIrrPGGR"
+        ]
+    },
+    "GIGAVLKVLTTGLPALISWIKRKRQQ": {
+        "1": [
+            "GIGAVlKVLTTGlPALISWiKRKRQQ",
+            "gigavlkvlttglpaliswikrkrqq",
+            "GIGAvLKVLTTgLPALISwIKRKRQQ",
+            "GIGAVLKVlTTGLPALISWIKRkRQQ",
+            "GIGAVLKvLTTGLPAlISWiKRKRQQ",
+            "gIGAVLkVLTTGLPALiSWIKRKRQQ",
+            "GIGaVlKVLTTGlPALISWikRKRQQ"
+        ],
+        "0": [
+            "GIGAVLKVLTTgLPALIsWIKRKRQQ",
+            "GIGAVLKvLTTGLpALISWIKRKRqQ",
+            "gIgAVLKVLTTGLPALISWiKRKRQQ",
+            "GIGAVLKVLTtGLPALISWIKrKRQQ",
+            "GIGAVlKVLTtGLPALiSWIKRKRQq"
+        ]
+    },
+    "FWGALAKGALKLIPSLFSSFSKKD": {
+        "1": [
+            "fwGalakGalklipslfssfskkd",
+            "fwgalakgalklipslfssfskkd",
+            "FwGalakGalklIPSLFSSFSKKD",
+            "FWgALaKGALKliPSlFssfskkd",
+            "fwGAlakgalKLIPsLfSSFSKkD",
+            "FwgaLAkgaLKlipsLfssfSKKd"
+        ],
+        "0": [
+            "FwgalakGAlklIpslFsSFSkKd",
+            "FWGALaKGalkLIPsLFSSfSkkD",
+            "fWGalAKgaLklIpSLfssFSKKd",
+            "FWgALAkgaLkliPSLFSsfSkkD",
+            "FWgaLAKgaLKLIpslFSSfskkd"
+        ]
+    },
+    "IRVKIRVKIRVK": {
+        "1": [
+            "irvkirvkirvk",
+            "irvkirvKirvk",
+            "iRvKiRvKiRvK",
+            "IRvkIRvkIRvk",
+            "IRVkiRVkiRVk",
+            "irvkiRvKirVK"
+        ],
+        "0": [
+            "IrVkIrVkIrVk",
+            "irVKIRVKIRVK",
+            "iRvkIrVKIRVk",
+            "IRvKIrVKiRvK",
+            "iRvKirvKIRVk"
+        ]
+    },
+    "LIKKALAALAKLNI": {
+        "1": [
+            "lIKKALAALAKLNI",
+            "LIkKALAALAKLNI",
+            "LIKkALAALAKLNI",
+            "LIKKAlAALAKLNI",
+            "LIKKALAAlAKLNI"
+        ],
+        "0": [
+            "likkalaalaklni",
+            "likkaLAALAKLNI",
+            "LIKKalAALAKLNI",
+            "LIKKALaaLAKLNI",
+            "LIKKALAALaKLNI",
+            "lIKKALAALAKLNi"
+        ]
+    },
+    "RSMRLSFRARGYGFR": {
+        "1": [
+            "rsmrlsfrarGyGfr",
+            "rsmrlSfRARGyGfR",
+            "RSmRLSFRARGyGfr",
+            "RSMRLsfRaRGygFR",
+            "RSmRLsfRARgyGFR",
+            "RSmrLSFRaRGYGfR"
+        ],
+        "0": [
+            "RSMRLSFRaRgYGFR",
+            "rsmRLSFRARGygFr",
+            "RSmrlSFRARgYgFr",
+            "RSMRLsFrarGyGFr",
+            "rsmRLSFrARGygfR"
+        ]
+    },
+    "GLLKRIKTLL": {
+        "1": [
+            "Gllkriktll",
+            "gllkriktll",
+            "gLlkriktll",
+            "gllkriKtll",
+            "gllkrikTlL",
+            "gllkRikTll"
+        ],
+        "0": [
+            "GLLkRIkTLL",
+            "GLlKRIKTLL",
+            "GLLKRIkTLl",
+            "GLLKrIKTLL",
+            "GllkriktLl",
+            "GLLKriKTLL"
+        ]
+    },
+    "KKLFKKILRYL": {
+        "1": [
+            "KKLfKKILRYL",
+            "KKLFKKiLRYL",
+            "KKLFKKILRyL",
+            "KKlFKKILRYL",
+            "KKLFKKIlRYL",
+            "KKLFKKILRYl"
+        ],
+        "0": [
+            "KKLFKkilryl",
+            "kklfkkilryl",
+            "kkLFKKILRYL",
+            "KKLFKKILryL",
+            "KKlfkkiLRYL",
+            "kKlFKKILRYL",
+            "kklFkkilryl"
+        ]
+    },
+    "FQWQRNMRKVR": {
+        "1": [
+            "fqwqrnmrkvr",
+            "Fqwqrnmrkvr",
+            "fQwqrnmrkvr",
+            "fqWqrnmrkvr",
+            "fqwQrnmrkvr",
+            "fqwqRnmrkvr"
+        ],
+        "0": [
+            "fQWQRNMRKVR",
+            "FqWQRNMRKVR",
+            "FQwQRNMRKVR",
+            "FQWqRNMRKVR",
+            "FQWQrNMRKVR"
+        ]
+    },
+    "KKKKKKAAFAAWAAFAA": {
+        "1": [
+            "kkkkkkAAFAAWAAFAA",
+            "KKKKKKaafaaWaafaa",
+            "KkKkKkAaFaAwAaFaA",
+            "KKKKKKAAFAAwaafaa",
+            "KKKKKKAAfaawaafAA"
+        ],
+        "0": [
+            "kkkkkkaafaawaafaa",
+            "KKKKKKAAFAAwAAFAA",
+            "kkKKKKAAFAAWAAFAA",
+            "KKKKKKAAFAAWAAFaa",
+            "KKKKKKaaFaaWaaFaa",
+            "KKkkkkAAFAAWAAFAA"
+        ]
+    },
+    "RRWWRF": {
+        "1": [
+            "rRWWRF",
+            "RrWWRF",
+            "RRWwRF",
+            "RRWWrF",
+            "RRWWRf",
+            "rrWWRF",
+            "rRWwRF",
+            "rRWWrF",
+            "rRWWRf",
+            "RrWwRF"
+        ],
+        "0": [
+            "rrwwrf",
+            "RRwWRF",
+            "rRwWRF",
+            "RrwWRF",
+            "RRwwRF",
+            "RRwWrF",
+            "RRwWRf"
+        ]
+    },
+    "KWKSFLKTFKSALKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKSAlKTVLHTALKAISS",
+            "KWKSFlKTFKSALKTVLHTALKAISS",
+            "KWKSFLKtFKSALKTVLHTALKAISS",
+            "KWKSFLKTFKSaLKTVLHTALKAISS",
+            "KWKSFLKTFKSALKTVlHTALKAISS",
+            "KWKSFLKTFKSALKTVLHtALKAISS"
+        ],
+        "0": [
+            "kWKSFLKTFKSALKTVLHTALKAISS",
+            "KwKSFLKTFKSALKTVLHTALKAISS",
+            "KWkSFLKTFKSALKTVLHTALKAISS",
+            "KWKSfLKTFKSALKTVLHTALKAISS"
+        ]
+    },
+    "KWKSFLKTFKSAAKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKSAaKTVLHTALKAISS",
+            "KWKSFLKTFKsaAKTVLHTALKAISS",
+            "KWKSFLKTFKSAAkTVLHTALKAISS",
+            "KWKSFLKTFKSAAKTvLHTALKAISS",
+            "KWKSFLKTFKSAAKTVLHTaLKAISS",
+            "KWKSFLKTFKSAAKTVLHTALKaISS"
+        ],
+        "0": [
+            "kWKSFLKTFKSAAKTVLHTALKAISS",
+            "KwKSFLKTFKSAAKTVLHTALKAISS",
+            "KWKSfLKTFKSAAKTVLHTALKAISS",
+            "KWKSFLKTFKSAAKTVLHTALKAIsS",
+            "KWKSFLKTFKSAAKTVLHTALKAISs"
+        ]
+    },
+    "KWKSFLKTFKSASKTVLHTALKAISS": {
+        "1": [
+            "kWKSFLKTFKSASKTVLHTALKAISS",
+            "KwKSFLKTFKSASKTVLHTALKAISS",
+            "KWKsFLKTFKSASKTVLHTALKAISS",
+            "KWKSFlKTFKSASKTVLHTALKAISS",
+            "KWKSFLKtFKSASKTVLHTALKAISS"
+        ],
+        "0": [
+            "KWKSFLKTFKSAsKTVLHTALKAISS",
+            "kWKSFLKTFKSAsKTVLHTALKAISS",
+            "KWkSFLKTFKSAsKTVLHTALKAISS",
+            "KWKSfLKTFKSAsKTVLHTALKAISS",
+            "KWKSFLkTFKSAsKTVLHTALKAISS",
+            "KWKSFLKTfKSAsKTVLHTALKAISS"
+        ]
+    },
+    "KWKSFLKTFKLAVKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKlAVKTVLHTALKAISS",
+            "KWKSFLKtFKLAVKTVLHTALKAISS",
+            "KWKSFLKTFKLAVKtvLHTALKAISS",
+            "KWKSFLKTFKLAvKTVLHTALKAISS",
+            "kWKSFLKTFKLAVKTVLHTALKAISS",
+            "KWKsFLKTFKLAVKTVLHTALKAISS"
+        ],
+        "0": [
+            "KWKSFLKTFkLAVKTVLHTALKAISS",
+            "KWKSFLKTFKLAVKTVLhTALKAISS",
+            "KWKSFlKTFKLAVKTVLHTALKAISS",
+            "KWKSFLKTFKLAVKTVLHTALkAISS",
+            "KWKSFLKTFKLAVKTVLHTAlKAISS"
+        ]
+    },
+    "KWKSFLKTFKVAVKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKvAVKTVLHTALKAISS",
+            "kWKSFLKTFKVAVKTVLHTALKAISS",
+            "KWKSfLKTFKVAVKTVLHTALKAISS",
+            "KWKSFLKTFKVaVKTVLHTALKAISS",
+            "KWKSFLKTFKVAVKtVLHTALKAISS",
+            "KWKSFLKTFKVAVKTVLHtALKAISS"
+        ],
+        "0": [
+            "KWKSFlKTFKVAVKTVLHTALKAISS",
+            "KWKSFLkTfKVAVKTVLHTALKAISS",
+            "KWKSFLKTFKVavKTVLHTALKAISS",
+            "KWKSFLKTFKVAVKTVlHTALKAISS",
+            "KWKSFLKTFKVAVKTVLHTALKaiSS"
+        ]
+    },
+    "KWKSFLKTFKAAVKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKaAVKTVLHTALKAISS",
+            "KWKSFLKtFKAAVKTVLHTALKAISS",
+            "KWKSFLKTFkAAVKTVLHTALKAISS",
+            "KWKSFLKTFKAvVKTVLHTALKAISS",
+            "KWKSFLKTFKAAVKtVLHTALKAISS",
+            "KWKSFLKTFKAAVKTvLHTALKAISS"
+        ],
+        "0": [
+            "kWKSFLKTFKAAVKTVLHTALKAISS",
+            "KWKsFLKTFKAAVKTVLHTALKAISS",
+            "KWKSFlKTFKAAVKTVLHTALKAISS",
+            "KWKSFLKTfKAAVKTVLHTALKAISS",
+            "KWKSFLKTFKAAVKTVLhTALKAISS"
+        ]
+    },
+    "KWKSFLKTFKKAVKTVLHTALKAISS": {
+        "1": [
+            "KWKSFLKTFKkAVKTVLHTALKAISS",
+            "kWKSFLKTFKKAVKTVLHTALKAISS",
+            "KWkSFLKTFKKAVKTVLHTALKAISS",
+            "KWKSFLkTFKKAVKTVLHTALKAISS",
+            "KWKSFLKTFKKAVkTVLHTALKAISS"
+        ],
+        "0": [
+            "KwKSFLKTFKKAVKTVLHTALKAISS",
+            "KWKsFLKTFKKAVKTVLHTALKAISS",
+            "KWKSfLKTFKKAVKTVLHTALKAISS",
+            "KWKSFlKTFKKAVKTVLHTALKAISS",
+            "KWKSFLKtFKKAVKTVLHTALKAISS"
+        ]
+    },
+    "GFKMALKLLKKVL": {
+        "1": [
+            "GFKMALKLLKKvl",
+            "GFKMALklLKKVL",
+            "GFKMALKLLKkVl",
+            "GFKMALKLLKkvl"
+        ],
+        "0": [
+            "GFkMALKLLKKVL",
+            "GfkMALKLLKKVL",
+            "GfKMALKLLKKVL",
+            "GFkMaLKLLKKVL",
+            "GFKMALKLLkKVL",
+            "GfkMaLKLLKKVL",
+            "GfKmaLKLLKKVL"
+        ]
+    },
+    "AFGMALKLLKKVL": {
+        "1": [
+            "AFGMALKLLKKvL",
+            "AFGMALKLLKKVl",
+            "AFGmALKLLKKVL",
+            "AFGMaLKLLKKVL",
+            "AFGMAlKLLKKVL"
+        ],
+        "0": [
+            "aFGMALKLLKKVL",
+            "aFGMALkLLKKVL",
+            "AFgMALKLLKKVL",
+            "aFGMALKLLKKvL",
+            "AFGMALKllKKVL",
+            "AFGMALKLLkkVL"
+        ]
+    },
+    "RRLLRLLRLLL": {
+        "1": [
+            "rrLLrLLrLLL",
+            "rrlLrLLrLLL",
+            "rrLlrLLrLLL",
+            "rRLLrlLrLLL",
+            "rrLLrLlrLLL",
+            "rRlLrLLrLLL"
+        ],
+        "0": [
+            "rRLLRLLRLLL",
+            "RrLLRLLRLLL",
+            "RRlLRLLRLLL",
+            "rRlLRLLRLLL",
+            "RRLLrLLRLLL"
+        ]
+    },
+    "KKIIKIIKIII": {
+        "1": [
+            "kkIIkIIkIII",
+            "kkiIkIIkIII",
+            "kkIikIIkIII",
+            "kkIIkiIkIII",
+            "kkIIkIikIII",
+            "kkIIkIIkiII"
+        ],
+        "0": [
+            "kKIIKIIKIII",
+            "KkIIKIIKIII",
+            "KKIIKIIkIII",
+            "KKiiKIIKIII",
+            "KKIIKIIKIIi"
+        ]
+    },
+    "RRIIRIIRIII": {
+        "1": [
+            "RRIIRIIRIII",
+            "RRIIRIIRIIi",
+            "RRIiRIIRIII",
+            "RRIIRiIRIII",
+            "RRIIRIiRIII"
+        ],
+        "0": [
+            "rrIIrIIrIII",
+            "rRIIRIIRIII",
+            "RrIIRIIRIII",
+            "RRIIrIIRIII",
+            "RRIIRIIrIII",
+            "rRIIrIIRIII"
+        ]
+    },
+    "ALWKKLLKK": {
+        "1": [
+            "AlWkkllkk",
+            "aLWkkllkk",
+            "AlWkkllkK",
+            "ALWkkllkk",
+            "alwkkllKK",
+            "alWkkllKk"
+        ],
+        "0": [
+            "ALwkkLLKK",
+            "aLwKkLLKK",
+            "ALWKKllKK",
+            "ALWKKLLkk",
+            "ALwKKLLKK"
+        ]
+    },
+    "KRFKKFFKKVKKSVKKRLKKIFKKPMVIGVTIPF": {
+        "1": [
+            "kRFKKFFKKVKKSVKKRLKKIFKKPMVIGVTIPF",
+            "KRFKKFFKKVKKSVKKRLKKIFKKPMVIGVTIpF",
+            "KRFKKFFKKVKKSVKKRlKKIFKKPMVIGVTIPF",
+            "KRFKKFFKKvKKSVKKRLKkIFKKPMVIGVTIPF",
+            "KRFKKFFKKVKKSVKKRLKKIFKKPMVIGvtIPF"
+        ],
+        "0": [
+            "krfkkffkkvkksvkkrlkkifkkpmviGvtipf",
+            "Krfkkffkkvkksvkkrlkkifkkpmvigvtipf",
+            "krfkkffkkvkksvkkrLkkifkkpmviGvtipf",
+            "krfkkffkkVkksvkkrlkkifkkpmvigvtipF",
+            "krfkkffkkvkksvKkrlkkifkkpmviGvtipf"
+        ]
+    },
+    "KKRLKKIFKKPMVIGVTIPF": {
+        "1": [
+            "kKRLKKIFKKPMVIGVTIPF",
+            "KKRLKKIFKKPMVIGVTIPf",
+            "kkRLKKIFKKPMVIGVTIPF",
+            "KKRLKKIFKKPMVIGVTIpf",
+            "kKRLKKIFKKPMVIGVTIPf"
+        ],
+        "0": [
+            "kkrlkkifkkpmviGvtipf",
+            "Kkrlkkifkkpmvigvtipf",
+            "kkrlKkifkkpmvigvtipf",
+            "kkrlkkifkKpmvigvtipf",
+            "kkrlkkifkkpmvIgvtipf",
+            "kkrlkkifkkpmvigVtipf"
+        ]
+    },
+    "RLFRRVKKVAGKIAKRIWK": {
+        "1": [
+            "rLFRRVKKVAGKIAKRIWK",
+            "RLfrRVKKVAGKIAKRIWK",
+            "RLFRRVKKVAGKiAKRIWK",
+            "RLFRRVKKVAGKIAKrIWK",
+            "RLFRRvkkVAGKIAKRIWK"
+        ],
+        "0": [
+            "rlfrrvkkvagkiakriwk",
+            "rlFrrVKKVAGKIAKRIWK",
+            "RLFrRVKKVAGKIAkRIWK",
+            "RLFRRvKKVagkIAKRIWK",
+            "RLFRRVKKVAgkiakriWK",
+            "RLFRRVKKvAGKIAKrIwK"
+        ]
+    },
+    "FIRRIARLLRRIF": {
+        "1": [
+            "fIRRIARLLRRIF",
+            "FiRRIARLLRRIF",
+            "FIrRIARLLRRIF",
+            "FIrrIARLLRRIF",
+            "FIRRiARLLRRIF"
+        ],
+        "0": [
+            "firriarllrrif",
+            "fiRRIARLLRRIF",
+            "firRIARLLRRIF",
+            "firrIARLLRRIF",
+            "firriARLLRRIF",
+            "firriaRLLRRIF"
+        ]
+    },
+    "GIGAVLKVLALISWIKRKR": {
+        "1": [
+            "gIGAVLKVLALISWIKRKR",
+            "GIGaVLKVLALISWIKRKR",
+            "GIGAVLkVLALISWIKRKR",
+            "GIGAVLKVLAlISWIKRKR",
+            "GIGAVLKVLALISWiKRKR"
+        ],
+        "0": [
+            "GIGAvLKvLAlISWIkRKR",
+            "GIGAvLKVLALISWIKRKR",
+            "GIGAVLKvLALISWIKRKR",
+            "GIGAVLKVLAlISWIKRKR"
+        ]
+    },
+    "FKCRRWQWRMKKLG": {
+        "1": [
+            "fkcrrwqwrmkklg",
+            "Fkcrrwqwrmkklg",
+            "fKcrrwqwrmkklg",
+            "fkCrrwqwrmkklg",
+            "fkcRrwqwrmkklg",
+            "fkcrRwqwrmkklg"
+        ],
+        "0": [
+            "fKCRRWQWRMKKLG",
+            "FkCRRWQWRMKKLG",
+            "FKcRRWQWRMKKLG",
+            "FKCrRWQWRMKKLG",
+            "FKCRrWQWRMKKLG"
+        ]
+    },
+    "WKKLKKLLKKLKKL": {
+        "1": [
+            "WKKlKKLLKKLKKL",
+            "WKKLKKlLKKLKKL",
+            "WKKLKKLlKKLKKL",
+            "WKKLKKLLKKlKKL",
+            "WKKLKKLLKKLKKl"
+        ],
+        "0": [
+            "Wkklkkllkklkkl",
+            "wKKLKKLLKKLKKL",
+            "wkKLKKLLKKLKKL",
+            "wkkLKKLLKKLKKL",
+            "wkklKKLLKKLKKL",
+            "wkklkKLLKKLKKL"
+        ]
+    },
+    "KFWSLLKKALRLWANVL": {
+        "1": [
+            "kFwSLLkKALRLwANVL",
+            "kFwSLLkKALRLwANvL",
+            "KFwSLLkKALRLwANVL",
+            "kFwSLLKKALRLwANVL",
+            "kFWSLLkKALRLwANVL",
+            "kFWsLLkKALRLwANVL"
+        ],
+        "0": [
+            "KFWSLLKKALRLWANVL",
+            "kFWSLLKKALRLWANVL",
+            "kfWSLLKKALRLWANVL",
+            "KFWSLLKKALRLWANvL",
+            "KFWSllKKALRLWANVL"
+        ]
+    },
+    "KFWKLLKKALRLWAKVL": {
+        "1": [
+            "kFwKLLkKALrLwAkVL",
+            "KfWkLlKkAlRlWAKVL",
+            "kFWKLLkKAlRLwAKvL",
+            "KfWKLLKkALrLWaKVl",
+            "KFwKLlKKaLRlWAkvL",
+            "kfWKLLkkALrLWAKvL"
+        ],
+        "0": [
+            "kFWKlLKkAlrLWAkVL",
+            "kFWKLlkkalRLWAKVL",
+            "KfwkllKKALRLWAKvL",
+            "KFwkllkKALRLWAKVl",
+            "kfwKLLKKALRLWAkvl",
+            "KFWKLLKKalrlwaKVL"
+        ]
+    },
+    "WFKKLLKKALRLWKKVL": {
+        "1": [
+            "wFKKlLKkAlrLWKkVL",
+            "wFKKlLKKAlrlWKkVL",
+            "wFKKlLKkAlRlWKkVL",
+            "wFKKlLkkAlrLWKkVL",
+            "wfKKlLKkAlrLWKkVL",
+            "wFKKlLKkALrLWkkVL"
+        ],
+        "0": [
+            "WFKKLlKKALRLWKKVL",
+            "WFKKLLKkaLRLWKKVL",
+            "WFkKLLKKALRLWKKVL",
+            "WFKKlLKKALRLWKKVL",
+            "WFKKLLKKALrlWKkVL"
+        ]
+    },
+    "ACPIFTKIQGTYRGRAKCR": {
+        "1": [
+            "aCPIFTKIQGTYRGRAKCR",
+            "AcPIFTKIQGTYRGRAKCR",
+            "ACpIFTKIQGTYRGRAKCR",
+            "ACPIfTKIQGTYRGRAKCR",
+            "ACPIFtKIQGTYRGRAKCR"
+        ],
+        "0": [
+            "ACPiFTKiQGTYrGrAKCR",
+            "ACPiFTKiQGTYrGrAKCr",
+            "aCPiFTKiQGTYrGrAKCR",
+            "AcPiFTKiQGTYrGrAKCR",
+            "ACPifTKiQGTYrGrAKCR",
+            "ACPiFTKiQGTYrGrAkCR"
+        ]
+    },
+    "ILLKKLLKKI": {
+        "1": [
+            "illkkllkki",
+            "Illkkllkki",
+            "iLlkkllkki",
+            "ilLkkllkki",
+            "illKkllkki",
+            "illkKllkki"
+        ],
+        "0": [
+            "iLLKKLLKKI",
+            "IlLKKLLKKI",
+            "ILlKKLLKKI",
+            "ILLkKLLKKI",
+            "ILLKkLLKKI"
+        ]
+    },
+    "GRFKRFRKKFKKLFKKLS": {
+        "1": [
+            "grfkrfrkkfkklfkkls",
+            "Grfkrfrkkfkklfkkls",
+            "gRfkrfrkkfkklfkkls",
+            "grfkrfrkkfkklfkklS",
+            "grfkrFrkkfKklfkkls"
+        ],
+        "0": [
+            "gRFKRFRKKFKKLFKKLS",
+            "GRFKRFRKKFKKLFKKLs",
+            "gRFKRFRKKFKKLFKKLs",
+            "grfKRFRKKFKKLFKKLS",
+            "grfkRFRKKFKKLFKKLS"
+        ]
+    },
+    "RAGLQFPVGRVHRLLRK": {
+        "1": [
+            "raglqfpvgrvhrllrk",
+            "Raglqfpvgrvhrllrk",
+            "rAglqfpvgrvhrllrk",
+            "rAgLqfpvgrvhrllrk",
+            "RaglqfpvgrVhrllrk",
+            "raglqfpvgrVhrllrk"
+        ],
+        "0": [
+            "rAGLQFPVGRVHRLLRK",
+            "RaglQFPVGRVHRLLRK",
+            "RAGLQfpVGRVHRLLRK",
+            "RAGLQFPvgRVHRLLRK",
+            "RAGLQFPVGRvhRLLRK"
+        ]
+    },
+    "KLKLLLLLKLK": {
+        "1": [
+            "klklllllklk",
+            "KLklllllklk",
+            "klKLllllklk",
+            "KLKlLllLklk",
+            "klkllLlklKk",
+            "klKlLlLLKlk"
+        ],
+        "0": [
+            "kLklLLlllkK",
+            "KLkLlllLkLk",
+            "KlklllllKLK",
+            "kLKLLlLKkLk",
+            "KLKLllLLkKk"
+        ]
+    },
+    "KLKLLLKLK": {
+        "1": [
+            "klklllklk",
+            "kLKLLLKLK",
+            "KLKllLKLK",
+            "kLkLlLkLk",
+            "kLKlllkLK"
+        ],
+        "0": [
+            "KLkLLLKLK",
+            "KlKlllklK",
+            "kLKLllKLk",
+            "KLKLLkLKk"
+        ]
+    },
+    "FIKRIARLLRKIF": {
+        "1": [
+            "fIKRIARLLRKIF",
+            "FIKRIArLLRKIF",
+            "FIKrIARLLRKIF",
+            "fIKRIARLLRKIf",
+            "FIkRIARLLrKIF"
+        ],
+        "0": [
+            "fikriarllrkif",
+            "Fikriarllrkif",
+            "fikriarllrkiF",
+            "fikriArllrkif",
+            "fIkrIarllrkif",
+            "fiKriarLlrkif"
+        ]
+    },
+    "INLKAIAALAKKLL": {
+        "1": [
+            "iNLKAIAALAKKLL",
+            "InLKAIAALAKKLL",
+            "INlKAIAALAKKLL",
+            "INLkAIAALAKKLL",
+            "INLKaIAALAKKLL"
+        ],
+        "0": [
+            "inlkaiaalakkll",
+            "Inlkaiaalakkll",
+            "iNlkaiaalakkll",
+            "inLkaiaalakkll",
+            "inlKaiaalakkll",
+            "inlkAiaalaakll"
+        ]
+    },
+    "FLPLIGRVLSGIL": {
+        "1": [
+            "fLPLIGRVLSGIL",
+            "FLPlIGRVLSGIL",
+            "FLPliGRVLSGIL",
+            "FLpLIGRVLSGIL",
+            "FlPLIGRvLSGIL"
+        ],
+        "0": [
+            "flpligrvlsgil",
+            "FLPLiGRVLSGIL",
+            "FLPLIGrVLSGIL",
+            "FLPLigrvLSGIL",
+            "FLPLIGRVLsGIL",
+            "flPLIGRvlsGIL"
+        ]
+    },
+    "KLLKKAGKLLKKAGKLLKKAG": {
+        "1": [
+            "KlLkKaGkLlKkAGkLlKkAG",
+            "kLlKkAGKlLkKaGkLlKkAG",
+            "KLLkkaGKLLkkaGKLLkkaG",
+            "KlkKKAGKlkKKAGKlkKKAG",
+            "KkLKKAGKkLKKAGKkLKKAG"
+        ],
+        "0": [
+            "KlLkKaGkLlKkAGKlLkKaG",
+            "KlLkKaGKLLKKAGkLlKkAG",
+            "KkLKKAGKlLKKAGkLlKkAG",
+            "kLlKkAGKlLKKAGKlLkKaG",
+            "KlLkKaGkLLKKAGkLlKkAG",
+            "KlLKKAGkLlKkAGKlLkKaG"
+        ]
+    },
+    "LLAKKKGLLAKKKGLLAKKKG": {
+        "1": [
+            "LlAkKkGlLaKkKgLlAkKkG",
+            "LlAkKkGlLaKkKgLlAkKKg",
+            "LlAkKkGlLaKkKgLlAKkkG",
+            "LlAkKkGlLaKkKgllAkKkG",
+            "lLAkKkGlLaKkKgLlAkKkG",
+            "LlAkKkGllaKkKgLlAkKkG"
+        ],
+        "0": [
+            "llakkkgllaKKKGLLAKKKG",
+            "LLAKKKGLLAKkkgllakkkg",
+            "LlAKkkgLLaKkKgLlAkKkg",
+            "llAkKKglLAkKKglLAkKkG"
+        ]
+    },
+    "RPFTRAQWFAIQHISPRTIAMRAINNYRWR": {
+        "1": [
+            "rPFTRAQWFAIQHISPRTIAMRAINNYRWR",
+            "RpFTRAQWFAIQHISPRTIAMRAINNYRWR",
+            "RPFTRaQWFAIQHISPRTIAMRAINNYRWR",
+            "RPFTRAQWFAiQHISPRTIAMRAINNYRWR",
+            "RPftRAQWFAIQHISPRTIAMRAINNYRWR"
+        ],
+        "0": [
+            "rpftraqwfaiqhisprtiamrainnyrwr",
+            "rpftraqwfaIQHISPRTIAMRAINNYRWR",
+            "RPFTRaqwfaiqhisprtiamrainNYRWR",
+            "rpftraqwfaIQHISPRTIAmrainnyrwr",
+            "rPfrAqWfAiQhIsPrTiAmRainNynRwR",
+            "RpFtRaQwFaIqHispRtIaMRAINNYRWR"
+        ]
+    },
+    "RLWLAIWRR": {
+        "1": [
+            "rlwlaiwrr",
+            "rLwlaiwrr",
+            "rlwLaiwrr",
+            "rlwlAiwrr",
+            "rlwlaIwrr",
+            "rlwlaiwRr"
+        ],
+        "0": [
+            "rLWLAIWRR",
+            "RlWLAIWRR",
+            "RLwLAIWRR",
+            "RLWlAIWRR",
+            "RLWLaIWRR"
+        ]
+    },
+    "KLWLAIWKK": {
+        "1": [
+            "klwlaiwkk",
+            "klwlaIWKK",
+            "KLWLAiWKK",
+            "KlWLAIwKK",
+            "klWLAIWKK",
+            "KLwlaIWKK"
+        ],
+        "0": [
+            "KLWLAIwKK",
+            "KlwlAIWKK",
+            "KLWLaiWKK",
+            "kLWlAIWKK",
+            "kLWLalWKK"
+        ]
+    },
+    "LKWLKKL": {
+        "1": [
+            "lkwlkkl",
+            "LKWlKKL",
+            "lKwLKKl",
+            "LkWLKKl",
+            "LKWlkkl",
+            "lkwLKkl"
+        ],
+        "0": [
+            "LkWLkkL",
+            "lKwlKKl",
+            "LKWLKKl",
+            "lkwlKKL",
+            "lKWLKkL",
+            "LkWLKkl",
+            "lKwLkKL"
+        ]
+    },
+    "LRWLRRL": {
+        "1": [
+            "lrwlrrl",
+            "lRwlrrl",
+            "lrwlRrl",
+            "lrwlrRl",
+            "lRwlRrl",
+            "lRwlrRl"
+        ],
+        "0": [
+            "LrWLrrL",
+            "lRwlRRl",
+            "Lrwlrrl",
+            "lrWlrrl",
+            "lrwLrrl",
+            "lrwlrrL",
+            "LrwLrrl"
+        ]
+    },
+    "FLKLLKKLLFLKLLKKLL": {
+        "1": [
+            "fLKLLKKLLfLKLLKKLL",
+            "fLKLlKKLLfLKLLKKLL",
+            "FLkLLKKLLflKLLKKLL",
+            "flKLlKKLLfLKLLKKLL",
+            "fLKLLkkLLfLKLLKKLL",
+            "fLKLLKKLLfLkLLKKLL"
+        ],
+        "0": [
+            "FLklLKKLLFLKLLKKLL",
+            "FLKLLKKllFLKLLKKLL",
+            "FlkLLKKLLFLKLLKKLL",
+            "FLKllKKLLfLKLLKKLL",
+            "FLKLLKKLLFLKLLkKLL"
+        ]
+    },
+    "VDKPPYLPRPRPPRRIYNR": {
+        "1": [
+            "VDKPPYLPRPRPPRriynr",
+            "VDKPPYLPRPRpprriynr",
+            "VDKPPYLPRPrpprriynr",
+            "VDKPPYLPRPRpPRRIYNR",
+            "VDKPPYLPRPrPPRRIYNR",
+            "VDKPPyLPRPRPPRRIYNR",
+            "VDKPPYLPRPRPPrriynr",
+            "VDKPPYLPRpRPPrriynr",
+            "VDKPPYLPRPRPprriynr",
+            "VDKPPyLPRPRPPrRIYNR",
+            "VDKPPYLPrpRPPRRIYNR"
+        ],
+        "0": [
+            "VDKPPYLPRpRPPRRIYNR",
+            "VDKPPYLPrPRPPRRIYNR",
+            "VDKPPYLpRPRPPRRIYNR",
+            "VDKPPYlPRPRPPRRIYNR",
+            "VDKPpYLPRPRPPRRIYNR",
+            "VDKppYLPRPRPPRRIYNR",
+            "VDKpPYLPRPRPPRRIYNR",
+            "vdkppylprprpprriynr",
+            "vDKPPYLPRPRPPRRIYNR",
+            "VDKpPYLPRPRPPRRIYNr",
+            "VDKPpyLPRPRPPRRIYNR",
+            "VDKPPYLpRPRPpRRIYNR",
+            "VDKPPYLPRpRPPRRIYnR"
+        ]
+    },
+    "VRLIVAVRIWRR": {
+        "1": [
+            "VRLIVAVRIWRR",
+            "vRLIVAVRIWRR",
+            "VRlIVAVRIWRR",
+            "VRLIvAVRIWRR",
+            "VRLIVAvRIWRR"
+        ],
+        "0": [
+            "vrlivavriwrr",
+            "Vrlivavriwrr",
+            "vRlivavriwrr",
+            "vrLivavriwrr",
+            "vrlIvavriwrr",
+            "vrliVavriwrr"
+        ]
+    },
+    "VRLRWWRRRWRR": {
+        "1": [
+            "vRLRWWRRRWRR",
+            "VRlRWWRRRWRR",
+            "VRLRwWRRRWRR",
+            "VRLRWwRRRWRR",
+            "vRlRwwRRRWRR"
+        ],
+        "0": [
+            "vrlrwwrrrwrr",
+            "vrlrwwrrrwrR",
+            "vrlrwwrrrwRr",
+            "Vrlrwwrrrwrr",
+            "VRlrwwrrrwrr",
+            "VrLrWWrrrWrr"
+        ]
+    },
+    "RRW": {
+        "1": [],
+        "0": [
+            "rRW",
+            "RrW",
+            "RRw",
+            "rrW",
+            "Rrw",
+            "rRw",
+            "rrw"
+        ]
+    },
+    "FLGTVLKVAAKVLPAALCQIFKKC": {
+        "1": [
+            "FlGTVlKVAAKVlPAAlCQIFKKC",
+            "FlGTVlKVAAKVlPAALCQIFKKC",
+            "FlGTVlKVAAKVLPAAlCQIFKKC",
+            "FlGTVLKVAAKVlPAAlCQIFKKC",
+            "FLGTVlKVAAKVlPAAlCQIFKKC",
+            "FlGTVlKVAAKVLPAALCQIFKKC"
+        ],
+        "0": [
+            "FLGTVLkVAAkVLPAALCQIFkkC",
+            "FLGTVLkVAAkVLPAALCQIFKkC",
+            "FLGTVLkVAAKVLPAALCQIFkkC",
+            "FLGTVLKVAAkVLPAALCQIFkkC",
+            "FLGTVLkVAAkVLPAALCQIFKKC"
+        ]
+    },
+    "FLGTVLKVLAKVLPAALCQIFKKC": {
+        "1": [
+            "FlGTVlKVlAKVlPAAlCQIFKKC",
+            "FLGTVlKVlAKVlPAAlCQIFKKC",
+            "FlGTVLKVlAKVlPAAlCQIFKKC",
+            "FlGTVlKVLAKVlPAAlCQIFKKC",
+            "FlGTVlKVlAKVLPAAlCQIFKKC",
+            "FlGTVlKVlAKVlPAALCQIFKKC"
+        ],
+        "0": [
+            "fLGTVLKVLAKVLPAALCQIFKKC",
+            "FLgTVLKVLAKVLPAALCQIFKKC",
+            "FLGtVLKVLAKVLPAALCQIFKKC",
+            "FLGTvLKVLAKVLPAALCQIFKKC",
+            "FLGTVLkVLAKVLPAALCQIFKKC"
+        ]
+    },
+    "FLGTVLRVAARVLPAALCQIFRRC": {
+        "1": [
+            "FLGtvLRVAARVLPAALCQIFRRC",
+            "FLGTVLRvaarVLPAALCQIFRRC",
+            "FLGTVLRVAARvlpAALCQIFRRC",
+            "fLGTVLRVAARVLPAALcqIFRRC",
+            "FLGTVLrvAARVLPAALCQiFRRC"
+        ],
+        "0": [
+            "FLGTVLrVAArVLPAALCQIFrrC",
+            "FLGTVlrVAARVLPAALCQIFRRC",
+            "FLGTVLrVaaRVLPAALCQIFRRC",
+            "FLGTVLrVAARVLPAALCQIFrRC",
+            "FLGTVLRVAaRVLPAALCQIFrrC",
+            "FLGTVLRVAARVLPAALCqifrRC"
+        ]
+    },
+    "RWKIFKKIEKMGRNIRDGIVKAGPAIQVLGSAKAI": {
+        "1": [
+            "rWKIFKKIEKMGRNIRDGIVKAGPAIQVLGSAKAI",
+            "RWKIFKKIEKMGRNIRDGIVKAGPAIQVLGSAKAi",
+            "RWKIFKKIEKmGRNIRDGIVKAGPAIQVLGSAKAI",
+            "rWKIFKKIEKMGRNIRDGIVKAGPAIQVLGSAKAi"
+        ],
+        "0": [
+            "rwkifkkiekmgrnirdgivkagpaiqvlgsakai",
+            "Rwkifkkiekmgrnirdgivkagpaiqvlgsakai",
+            "rwkifkkiekmgrnirdgivkagpaiqvlgsakaI",
+            "rwkifkkiekMgrnirdgivKagpaiqvlgsakai",
+            "RWKIFKKIEKmgrnirdgivkagpaiqvlgsakai",
+            "RwKiFkKiEkMgRnIrDgIvKaGpAiQvLgSaKaI"
+        ]
+    },
+    "GPLGVRGKRLWDIVRRWVGWL": {
+        "1": [
+            "GPlGvRGKRLWDIVRRWVGWL",
+            "GPlGvRGKRLWDIvRRWVGWL",
+            "GPLGvRGKRlWDIVRRWVGWL",
+            "GPLGVRGKRlWDIVRRWVGWl",
+            "GPLGVRGKRLWDIvRRWvGWL",
+            "GPlGVRGKRlWDIvRRWVgWL"
+        ],
+        "0": [
+            "gPLGVRGKRLWDIVRRWVGWL",
+            "GPLGVRgKRLWDIVRRWVGWL",
+            "GPLGVRGKRLWDIvRrWVGWL",
+            "GPLGVRGKRLWDIVrRwVGWL",
+            "GPLGVRGKRLWDIVRRWVgWl"
+        ]
+    },
+    "RIVQRIKKWLR": {
+        "1": [
+            "rivqrikkwlr",
+            "rIVQRIKKwlr",
+            "riVqRIKKWLR",
+            "RivqRIKKwlr",
+            "rivQRiKKWLR",
+            "RIvQrIKKWLr"
+        ],
+        "0": [
+            "RIVqRIKKWLr",
+            "riVQRiKKwlR",
+            "RiVQRIkKwLr",
+            "rIVQrIKKwLR",
+            "rivQrIKKwLR"
+        ]
+    },
+    "KRIWQRIK": {
+        "1": [
+            "kriwqrik",
+            "KrIWQRIK",
+            "KRIwQRIK",
+            "kRIWQRiK",
+            "kriwqRIK",
+            "KriwqRIk"
+        ],
+        "0": [
+            "KRIWqRIK",
+            "kRIwQRIk",
+            "KRIWqriK",
+            "kRIWqRIK",
+            "KRIWQrIk"
+        ]
+    },
+    "KRIWQRIKDF": {
+        "1": [
+            "kriwqrikdf",
+            "Kriwqrikdf",
+            "krIwqrikdf",
+            "kriwQrikdf",
+            "kriwqrIkdf",
+            "kriwqrikDf"
+        ],
+        "0": [
+            "kRIWQRIKDF",
+            "KRiWQRIKDF",
+            "KRIWQRIKDf",
+            "KrIWqRIKDF",
+            "KRIwQrIKDF"
+        ]
+    },
+    "KYKKALKKLAKLL": {
+        "1": [
+            "kykkalkklakll",
+            "Kykkalkklakll",
+            "kYkkalkklakll",
+            "kyKkalkklakll",
+            "kykKalkklakll",
+            "kykkAlkklakll"
+        ],
+        "0": [
+            "kYKKALKKLAKLL",
+            "KyKKALKKLAKLL",
+            "KYkKALKKLAKLL",
+            "KYKkALKKLAKLL",
+            "KYKKaLKKLAKLL"
+        ]
+    },
+    "VQWRAIRVRVIR": {
+        "1": [
+            "vqwrairvrvir",
+            "vQWRAIRVRVIR",
+            "vqWRAIRVRVIR",
+            "vqwRAIRVRVIR",
+            "vqwrAIRVRVIR",
+            "vqwraIRVRVIR"
+        ],
+        "0": [
+            "vqwraiRVRVIR",
+            "vqwrairVRVIR",
+            "vqwrairvRVIR",
+            "vqwrairvrVIR",
+            "vqwrairvrvIR"
+        ]
+    },
+    "GFAWNVCVYRNGVRVCHRRAN": {
+        "1": [
+            "gFAWNVCVYRNGVRVCHRRAN",
+            "GfAWNVCVYRNGVRVCHRRAN",
+            "GFawNVCVYRNGVRVCHRRAN",
+            "GFAWNVCVYRNGVRVCHRRAn",
+            "GFAWNVCVyRNGVRVCHRRAN"
+        ],
+        "0": [
+            "GfawnvcvyrnGvrvchrran",
+            "gfawnvcvyrngvrvchrran",
+            "Gfawnvcvyrngvrvchrran",
+            "GfawnvcvyrngvrvchrraN",
+            "gfawnvcvyrNgvrvChrran",
+            "gfawnvcvyRNgvrvchrran"
+        ]
+    },
+    "LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES": {
+        "1": [
+            "llgdffrkskekigkefkrivqrikdflrnlvprtes",
+            "LLGDFFRkskeKIGKEFKRIVQRIKDFLRNLVPRTES",
+            "LLGDffrkskeKIGKEFKRIVQRIKDFLRNLVPRTES",
+            "LLGDFFRKSKEKIGKefkrIVQRIKDFLRNLVPRTES",
+            "LLGDFFRKSKEKigkeFKRIvqrikdflrnLVPRTES",
+            "llgdFFRKSKEKIGKEFKRIVQrikdflrnlvprtes"
+        ],
+        "0": [
+            "LLgDfFRKsKEkIgKeFkRiVqRIKdFlRnLvPRtEs",
+            "lLGDFfRkSKeKIGKeFkRIvQRIkDfLrnlVPrTeS",
+            "LlGdFfRkSKEkIGKeFkRIVQRIKdflRNLvPRTeS",
+            "LLgDFFRKSkEkIgKeFKRivQRIkdfLrnlVPrTeS",
+            "LLgDFfRksKekIGkEfKrivQrIKdflRNlVpRtEs"
+        ]
+    },
+    "LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNL": {
+        "1": [
+            "llgdffrkskekigkefkrivqrikdflrnl",
+            "Llgdffrkskekigkefkrivqrikdflrnl",
+            "llgdfFrkskekigkefkrivqrikdflrnl",
+            "llgdffrkskEkigkefkrivqrikdflrnl",
+            "llgdffrkskekigkEfkrivqrikdflrnl",
+            "llgdffrkskekigkefkriVqrikdflrnl"
+        ],
+        "0": [
+            "lLGDFFRKSKEKIGKEFKRIVQRIKDFLRNL",
+            "LLGDFFRKSKeKIGKEFKRIVQRIKDFLRNL",
+            "llgDFFRKSKEKIGKEFKRIVQRIKDFLRNL",
+            "LLGDFFRKSKeKIGKEFKRIvQRIKDFLRNl",
+            "LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNl"
+        ]
+    },
+    "RKRWWRWWKWWKR": {
+        "1": [],
+        "0": [
+            "RKrWWrWwkWWkR"
+        ]
+    },
+    "WRWWKWW": {
+        "1": [
+            "wRWWKWW",
+            "WRwWKWW",
+            "wRWwKWW",
+            "WRWWKwW",
+            "wrWwKWW"
+        ],
+        "0": [
+            "WrWwkWW",
+            "WrWWKWW",
+            "WRWwKWW",
+            "WRWWkWW",
+            "WrWWkWW",
+            "WRWwkWW"
+        ]
+    },
+    "WWRWWKWW": {
+        "1": [
+            "wWRWWKWW",
+            "WwRWWKWW",
+            "WWRwWKWW",
+            "WWRWWKwW",
+            "wWRWWKWw"
+        ],
+        "0": [
+            "WWrWwkWW",
+            "WWrWWKWW",
+            "WWRWwKWW",
+            "WWRWWkWW",
+            "WWrWwKWW",
+            "WWRWwkWW"
+        ]
+    },
+    "RRGKKLLLLLKKKG": {
+        "1": [
+            "rrgkklllllkkkg",
+            "RRGKKlllllKKKG",
+            "rrGKKlllllKKKG",
+            "RRgKKlllllKKKG",
+            "RRGKKlllllkkkG",
+            "RRGkklllllKKKG"
+        ],
+        "0": [
+            "rrgkkLLLLLKKKG",
+            "RRGKKLLLLLkkkg",
+            "rRGKKllllLKKKG",
+            "RrGKKLllllKKKG",
+            "RRGkkLLLllKKKG"
+        ]
+    },
+    "LLWIALRKK": {
+        "1": [
+            "llwialrkk",
+            "llwIaLRKK",
+            "LLwiaLRKK",
+            "LLWIALrKK",
+            "llWialRKK",
+            "LLwiAlrKK"
+        ],
+        "0": [
+            "lLwIALRKK",
+            "LLWiaLrKK",
+            "LLwiALRKK",
+            "llwIaLrkK",
+            "LLWIAlrKk"
+        ]
+    },
+    "PRPRPRP": {
+        "1": [
+            "PrPrPrP",
+            "pRpRpRp",
+            "PRpRPRP",
+            "PrPRPRP",
+            "pRPRPRP"
+        ],
+        "0": [
+            "prprprp",
+            "prPRPRP",
+            "PRprPRP",
+            "PRPRprP",
+            "prprprP",
+            "pRPRPRp"
+        ]
+    },
+    "KWLKKWLKWLKK": {
+        "1": [
+            "kwlKkWLKWLKK",
+            "KWlKkKWLKWLK",
+            "KWLKkwLKWLKk",
+            "KWlKKwLkwLKK",
+            "kWLKKWLKwLkK"
+        ],
+        "0": [
+            "kwLkkwLkwLkk",
+            "kwlkkwLkwLkk",
+            "kwLKKwlkwlkk",
+            "kWLkkwLkWLkk",
+            "kwLKKwLkWLkk",
+            "KwLKKwLkwlkk"
+        ]
+    },
+    "ILRWPWWPWRRK": {
+        "1": [
+            "iLRWPWWPWRRK",
+            "ILrWPWWPWRRK",
+            "ILRwPWWPWRRK",
+            "ILRWPwWPWRRK",
+            "ILRWPWWpWRRK"
+        ],
+        "0": [
+            "ilrwpwwpwrrk",
+            "Ilrwpwwpwrrk",
+            "ilRwpwwpwrrk",
+            "ilrWpwwpwrrk",
+            "ilrwPwwpwrrk",
+            "ilrwpwWpwrrk"
+        ]
+    },
+    "KRKIFLRTKILV": {
+        "1": [
+            "KrKiFlRtKiLv",
+            "KrKiFLRTKILV",
+            "KrKIFlRTKILV",
+            "KRKiFlRTKILV",
+            "KrKIFLRTKILv",
+            "KRKiFLRTKILv"
+        ],
+        "0": [
+            "kRkIfLrTkIlV",
+            "kRKIFLRTKILV",
+            "KRkIFLRTKILV",
+            "kRkIFLRTKILV",
+            "kRKIfLRTKILV",
+            "KRKIFLrTKILV"
+        ]
+    },
+    "VLIKTRLFIKRK": {
+        "1": [
+            "vLiKtRlFiKrK",
+            "vLiKtRLFIKrK",
+            "VLIKtRlFiKrK",
+            "vLIKtRlFiKrK",
+            "VLiKtRLfiKRk",
+            "VLIKTlFirKrk"
+        ],
+        "0": [
+            "VliKTrlfiKRK",
+            "vLIkTrLfIkRK",
+            "VLkTrLFiKrkK",
+            "vlIKtrLFikRk",
+            "VLiKTlFiKrkk"
+        ]
+    },
+    "KWKLFKKIEKVGQNIRDGIIKAGPAVAVVGQATQIAK": {
+        "1": [
+            "kWKLFKKIEKVGQNIRDGIIKAGPAVAVVGQATQIAK",
+            "KwKLFKKIEKVGQNIRDGIIKAGPAVAVVGQATQIAK",
+            "KWkLFKKIEKVGQNIRDGIIKAGPAVAVVGQATQIAK",
+            "kWkLFKKIEKVGQNIRDGIIKAGPAVAVVGQATQIAK",
+            "KWKLFKKIEKVGQNIRDGIIKAGPAVAVVGQATQIAk"
+        ],
+        "0": [
+            "kwklfkkiekvgqnirdgiikagpavavvgqatqiak",
+            "Kwklfkkiekvgqnirdgiikagpavavvgqatqiak",
+            "kwklfkkiekvgqnirdgiikagpavavvgqatqiAk",
+            "kwklfkkiekvgqnirdgiiKAGPAVAVVGQATQIAK",
+            "KwKlFkKiEkVgQnIrDgIiKaGpAvAvVgQaTqIaK"
+        ]
+    },
+    "GIGKFLHSAKKFGKAFVGEIMNS": {
+        "1": [
+            "gigkflhsakkfgkafvgeimns",
+            "gIgKFLHSAKKFGKAFVGEIMNS",
+            "GIgKFLHSAKKFGKAFVGEIMNS",
+            "GIGkfLHSAKKFGKAFVGEIMNS",
+            "GIGKFlHSAKKFGKaFVGEIMNS",
+            "GIGKFLHSaKKFGKAFVGEiMNS"
+        ],
+        "0": [
+            "GIGkfLHSaKKFGKAFVGEIMNS",
+            "GIGKFLHsakkfgKAFVGEIMNS",
+            "GIGKFLHSAKKfGKafvgeimns",
+            "GigkflhsakKfGKAFVGEIMNS",
+            "GIGKFLHSaKKFGKAFVGEImnS"
+        ]
+    },
+    "KWKLFKKIEKVGQGIGAVLKVLTTGL": {
+        "1": [
+            "KWKLfKKIEKVGQGIGAVLKVLTTGL",
+            "kKWLFKKIEKVGQGIGAVLKVLTTGL",
+            "KWKkFKKIEKVGQGIGAVLKVLTTGL",
+            "KWKlFKKiEKVGQGIGAVLKVLTTGL",
+            "KwKLFKKIEkVGQGIGAVLKVLTTGL"
+        ],
+        "0": [
+            "kwklfkkiekvgqgigavlkvlttgl",
+            "kwkLfkkiekvgqgigavlkvlttgl",
+            "kwklfkkiekvgqgigavlkVLttgl",
+            "KwklfkkiekvgqgigavLKVlttgl",
+            "kwklfKkiekvgqgigavlkvlttgl",
+            "kWkLfkkiekvgqgigavlkvlttgl"
+        ]
+    },
+    "KWKLFKKIGIGAVLKVLTTGLPALIS": {
+        "1": [
+            "kwklfkkigigavlkvlttglpalis",
+            "kwklfkkigigavlkvlttgLPALIS",
+            "KWKLFKkigigavlkvlttglpalis",
+            "KwklfkkigIgavlkvlttGlpalis",
+            "kwklFkkigigavlKvlttglpalIs"
+        ],
+        "0": [
+            "kWKLFKKIGIGAVLKVLTTGLPALIS",
+            "kwKLFKKIGIGAVLKVLTTGLPALIS",
+            "KWKLFKKIGiGAVLKVLTTGLPALIS",
+            "KWKLFKKIGIGAVLKVLTTgLPALIS",
+            "KWKLfKKIGIGAVLKVLTTGLPALIS"
+        ]
+    },
+    "KWKLFKKGIGAVLKV": {
+        "1": [
+            "kwklfkkgigavlkv",
+            "kWKLFKKGIGAVLKv",
+            "kwKLFKKGIGAVLKV",
+            "kwkLFKKGIGAVLKV",
+            "KWKLFKKGIGAVLkv",
+            "KWKLFKKgIGAVlkV"
+        ],
+        "0": [
+            "KWKlfKKGIGAVLKV",
+            "KWKLFKKGiGAVLKV",
+            "KWKlFKKGiGAVLKV",
+            "KWKLfKKGiGAVLKV"
+        ]
+    },
+    "KWKLFKKIGAVLKVL": {
+        "1": [
+            "kwklfkkigavlkvl",
+            "kWKLFKKIGAVLKVL",
+            "KwKLFKKIGAVLKVL",
+            "kwKLFKKIGAVLKVL",
+            "KWKLFKKIGAVLKVl",
+            "kWkLfKkIgAvLkVl"
+        ],
+        "0": [
+            "KWklFKKIGAVLKVL",
+            "kwkLFKKIGAVLKVL",
+            "KWKLFKKIGAVLkvl",
+            "KwKlFkKiGaVlKvL",
+            "kwKlFKkIGAvLKVL"
+        ]
+    },
+    "KWKLFKKGAVLKVLT": {
+        "1": [
+            "kwklfkkgavlkvlt",
+            "KWKlfkkgavlkvlt",
+            "kwklFKKgavlkvlt",
+            "Kwklfkkgavlkvlt",
+            "kwklfkkkgVLkvlt"
+        ],
+        "0": [
+            "kWKLFKKGAVLKVLT",
+            "kWKLFKKGAVLKVLt",
+            "kwkLFKKGAVLKVLT",
+            "KWKlfkkGAVLKVLT",
+            "kWkLfKkGaVLKVLT"
+        ]
+    },
+    "KWKLFKKAVLKVLTT": {
+        "1": [
+            "kwklfkkavlkvltt",
+            "Kwklfkkavlkvltt",
+            "kWklfkkavlkvltt",
+            "kwKlfkkavlkvltt",
+            "kwkLfkkavlkvltt",
+            "kwklFkkavlkvltt"
+        ],
+        "0": [
+            "KWKLFkKAVLKVLTT",
+            "KWKLFKkAVLKVLTT",
+            "KWKLFKKaVLKVLTT",
+            "KWKLFKKAvLKVLTT",
+            "KWKLFKKAVlKVLTT"
+        ]
+    },
+    "KWKLFKKVLKVLTTG": {
+        "1": [
+            "kwklfkkvlkvlttg",
+            "Kwklfkkvlkvlttg",
+            "kWklfkkvlkvlttg",
+            "kwKlfkkvlkvlttg",
+            "kwkLfkkvlkvlttg",
+            "kwklFkkvlkvlttg"
+        ],
+        "0": [
+            "kWKLFKKVLKVLTTG",
+            "kwKLFKKVLKVLTTG",
+            "kwkLFKKVLKVLTTG",
+            "kwklFKKVLKVLTTG",
+            "kwklfKKVLKVLTTG"
+        ]
+    },
+    "GSKKPVPIIYCNRRTGKCQRM": {
+        "1": [
+            "GsKKPVPIIYCNRRTGKCQRM",
+            "GSKkpvpiiyCNRRTGKCQRM",
+            "GSKKPVPIIYCNrRTgKCQRM",
+            "gSKKPVPIIYCNRRTGkCQRM",
+            "GSKKPVPIIycnrrTGKCQRM"
+        ],
+        "0": [
+            "gskkpvpiiycnrrtgkcqrm",
+            "gskkpvpiiycnrrtgkCQRM",
+            "gskkpvpiIYCNRRTGKcqrM",
+            "GSKKPVPiiycnrrtgkcqrm",
+            "gskkpVPIIYCNRRTgkcqrm",
+            "gskkPVPIIYcnrrtgkcqrm"
+        ]
+    },
+    "RRWQWRMKK": {
+        "1": [
+            "rrwqwrmkk",
+            "Rrwqwrmkk",
+            "rRwqwrmkk",
+            "rrwQwrmkk",
+            "rrwqWrmkk",
+            "rrwqwRmkk"
+        ],
+        "0": [
+            "rRWQWRMKK",
+            "RrWQWRMKK",
+            "RRwQWRMKK",
+            "RRWqWRMKK",
+            "RRWQwRMKK"
+        ]
+    },
+    "FKCRRWQWRMKKLGA": {
+        "1": [
+            "fkcrrwqwrmkklga",
+            "fkcrrwqwRMKKLGA",
+            "fKcRrWqWrMkKlGa",
+            "FKCRRwQwRMKKLGA",
+            "fkCrrwqwrmkklga"
+        ],
+        "0": [
+            "fKCRRWQWRMKKLGA",
+            "FkCRRWQWRMKKLGA",
+            "FKCrRWQWRMKKLGA",
+            "FKCRRWqWRMKKLGA",
+            "FKCRRWQWRMKKLGa"
+        ]
+    },
+    "PKLLKTFLSKWIG": {
+        "1": [
+            "pKLLKTFLSKWIG",
+            "PKlLKTFLSKWIG",
+            "PKLLkTFLSKWIG",
+            "PKLLKTfLSKWIG",
+            "PKLLKTFLsKWIG"
+        ],
+        "0": [
+            "pkllktflskwig",
+            "pkllktflskwiG",
+            "pkllktflskwIg",
+            "pkllktflsKwIg",
+            "pkllktfLSkwiG",
+            "pkllkTflskwiG"
+        ]
+    },
+    "KLPLIGRVLSGIL": {
+        "1": [
+            "klpligrvlsgil",
+            "KLPLigrvlsgil",
+            "kLPLigrvlsgil",
+            "klPLigrvlsgil",
+            "klpLIGRvlsgil",
+            "klpliGrVLSGIL"
+        ],
+        "0": [
+            "KlPLigrvlsgil",
+            "klpLIGRVlSGIL",
+            "KLPLigRvLSGIl",
+            "klPLIGRvlsgil",
+            "KlpligRVLSGiL"
+        ]
+    },
+    "KKHRKHRKHRKHGGSGGSKNLRRIIRKGIHIIKKYG": {
+        "1": [
+            "KKHRKHRKHRKHGGSGGSKNLRRIIRKGIHIIKKYG",
+            "kKHRKHRKHRKHGGSGGSKNLRRIIRKGIHIIKKYG",
+            "KKHRKHRKHRKHGGSGGSKNLRRIIRKGIHIIKKYg",
+            "KKHRKHRKHRKHGGsGGSKNLRRIIRKGIHIIKKYG"
+        ],
+        "0": [
+            "kkhrkhrkhrkhggsggsknlrriirkgihiikkyg",
+            "kkhrkhrkhrkhggsggsKnlrriirkgihiikkyg",
+            "KKHRKhrkhrkhggsggsknlrriirkgihiikkyg",
+            "KkHrKhRkHrKhGgSgGsKnLrRiIrKgIhIiKkYg",
+            "KKHRKHRKHRKHGGSGGSknlrriirkgihiikkyg"
+        ]
+    },
+    "FKRIVQRIKDFLRNLV": {
+        "1": [
+            "fKRIVQRIKDFLRNLV",
+            "FkRIVQRIKDFLRNLV",
+            "FKrIVQRIKDFLRNLV",
+            "FKRIVQRIKDFLRrLV",
+            "FKRIVQRIKdFLRNLV"
+        ],
+        "0": [
+            "FKRiVQRiKDFlRNLV",
+            "FKRIvQRiKDFlRNLV",
+            "FKRIVQrIKDFLRNlV",
+            "fKRiVQRIKDFLRNLV",
+            "FKRIVQRikDFLRnLV",
+            "FKRIVQRIKDfLRNLV"
+        ]
+    },
+    "GWGSFFKKAAHVGKHVGKAALTHYL": {
+        "1": [
+            "gwgsffkKAAHVGKHVGKAALTHYL",
+            "GWGSFFKKAAhvgkhvgkaalTHYL",
+            "gWgSfFkKaAhVgKhVgKaAlThYl",
+            "GwGSffKKaaHvGKHvGKaalTHyl",
+            "GWGSFFkkAAHVGKHVGKAALTHYL"
+        ],
+        "0": [
+            "gwgsffkkaahvgkhvgkaalthyl",
+            "Gwgsffkkaahvgkhvgkaalthyl",
+            "GWGSFFkkAAHVGkHVGkAALTHYL",
+            "gwgsffkkaahvgKHVGKAALTHYL",
+            "gwgsffkkaahvgKhvgKaalthyl",
+            "GwGsffkkaahvGkhvGkaalthyl"
+        ]
+    },
+    "RRGWVLALVLRYGRR": {
+        "1": [
+            "rRGWVLALVLRYGRR",
+            "RrGWVLALVLRYGRR",
+            "RRgWVLALVLRYGRR",
+            "RRGwVLALVLRYGRR",
+            "RRGWvLALVLRYGRR"
+        ],
+        "0": [
+            "RRGWVLALVlRYGRR",
+            "rRGWVLALVlRYGRR",
+            "RrGWVLALVlRYGRR",
+            "RRgWVLALVlRYGRR",
+            "RRGwVLALVlRYGRR",
+            "RRGWvLALVlRYGRR"
+        ]
+    },
+    "RRGWVLALYLRYGRR": {
+        "1": [
+            "rRGWVLALYLRYGRR",
+            "RRgWVLALYLRYGRR",
+            "RRGWVLalYLRyGRR",
+            "RrGWvLALYlRYgRR"
+        ],
+        "0": [
+            "RRGWVLALYlRYGRR",
+            "RRGWVLALyLrYGRR",
+            "RRGwVLalYLRYGRR",
+            "rRGWVLALYLryGRR",
+            "RRGWvLALyLRYGrR",
+            "RRgWVLalYLRyGRr"
+        ]
+    },
+    "RRGWALRLVLAY": {
+        "1": [
+            "rRGWALRLVLAY",
+            "RrGWALRLVLAY",
+            "RRgWALRLVLAY",
+            "RRGWaLRLVLAY",
+            "RRGWALrLVLAY"
+        ],
+        "0": [
+            "RRGWALRLVlAY",
+            "RRGwALRLVLAY",
+            "RRGWAlRLVLAY",
+            "RRGWALRlVLAY",
+            "RRGWALRLVLaY",
+            "RRGWALRLVLAy"
+        ]
+    },
+    "KWKKLLKKPLLKKLLKKL": {
+        "1": [
+            "kwkkllkkpllkkllkkl",
+            "Kwkkllkkpllkkllkkl",
+            "kWkkllkkpllkkllkkl",
+            "kwkkllkkpLLkkllkkl",
+            "kwkkllkkpllkkllkkL",
+            "KWkkllkkpllkkllkkl"
+        ],
+        "0": [
+            "kWKKLLKKPLLKKLLKKL",
+            "KWKKLLKKPLLKKLLKKl",
+            "KWKKLLKKpLLKKLLKKL",
+            "kwKKLLKKPLLKKLLKKL",
+            "KWKKLLKKPllKKLLKKL"
+        ]
+    },
+    "NKKAGLFVVQFPKKY": {
+        "1": [
+            "nkkaglfvvqfpkky",
+            "nKkAGLFVVQFPKKY",
+            "NkKAGLFVVQFPKKy",
+            "NKkaglfVVQFPKKY",
+            "NkkaGlfVVQFPKKY",
+            "nKKaGLfvvqFPkky"
+        ],
+        "0": [
+            "NKkAGlFVVQfPKKy",
+            "NkkAglFvvQFPKkY",
+            "nkkaglFVVqfpKKY",
+            "NKKAGLfVvQfPKkY",
+            "nkKAGlFvVQFPkKy"
+        ]
+    },
+    "LVKKLLKLAMGFG": {
+        "1": [
+            "lvkkllklamgfg",
+            "Lvkkllklamgfg",
+            "lVkkllklamgfg",
+            "lvKkllklamgfg",
+            "lvkKllklamgfg",
+            "lvkkLlklamgfg"
+        ],
+        "0": [
+            "VkKKLLKLAMGFG",
+            "LvKLLKLAMGFGg",
+            "LVKKllKLaMGFG",
+            "LVKKLLkLAmGfG"
+        ]
+    },
+    "WLRRIKAWLRRIKA": {
+        "1": [
+            "wlrrikawlrrika",
+            "Wlrrikawlrrika",
+            "wLrrikawlrrika",
+            "wlRrikawlrrika",
+            "wlrRikawlrrika",
+            "WLrrikawlrrika"
+        ],
+        "0": [
+            "wLRRIKAWLRRIKA",
+            "WlRRIKAWLRRIKA",
+            "WLrRIKAWLRRIKA",
+            "WLRrIKAWLRRIKA",
+            "WLrrIKAWLRRIKA"
+        ]
+    },
+    "RRGWARRLAFAFGRR": {
+        "1": [
+            "rrgwarrlafafgrr",
+            "Rrgwarrlafafgrr",
+            "rrgwarRLafafgrr",
+            "RrGWarRLafafgrr",
+            "rrgWarrlafafgRR",
+            "rrgwarrlafafgRr"
+        ],
+        "0": [
+            "rRGWARRLAFAFGRR",
+            "rrGWARRLAFAFGRR",
+            "RRGWARRLAFaFGRR",
+            "RRGWaRRLAFAFGRR",
+            "RRGWARRLafAFGRR"
+        ]
+    }
+}
\ No newline at end of file
diff --git a/finetune.py b/finetune.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf38acaadb216a74cab31f70eba76dfa76039a27
--- /dev/null
+++ b/finetune.py
@@ -0,0 +1,201 @@
+import argparse
+import json
+import logging
+import os
+import time
+
+from dataset import PeptidePairDataset, PeptidePairPicDataset, SimplePairClsDataset
+from network import DMutaPeptide, DMutaPeptideCNN#, DMutaPeptideWiden
+from sklearn.model_selection import KFold
+from train import train_cls
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, WeightedRandomSampler, RandomSampler, Subset
+import numpy as np
+from loss import MLCE, SuperLoss, LogCoshLoss, BMCLoss
+from utils import set_seed
+
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='cnn',
+                    help='lstm mamba mla')
+parser.add_argument("--side-enc", dest='side_enc', type=str, default='lstm',
+                    help="use side features")
+parser.add_argument('--channels', type=int, default=16)
+parser.add_argument('--fusion', type=str, default='att',
+                    help='mlp att diff')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+parser.add_argument('--widen', action='store_true', default=False,
+                    help='use widen non-siamese architecture')
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='cls',
+                    help='reg or cls')
+parser.add_argument('--pdb-src', type=str, dest='pdb_src', default='af',
+                    help='af or hf')
+parser.add_argument('--data-ver', type=str, dest='data_ver', default='250228',
+                    help='data version')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=True,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--run-folds', type=int, dest='run_folds', nargs='+', default=-1,
+                    help='specify which folds to run')
+parser.add_argument('--seed', type=int, default=1,
+                    help="Seed (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+parser.add_argument('--llm-data', action='store_true', default=False,
+                    help='Use LLM augmentation data')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--warm-steps', type=int, dest='warm_steps', default=0,
+                    help='number of warm start steps for learning rate (default: 10)')
+parser.add_argument('--patience', type=int, default=10,
+                    help='patience for early stopping (default: 10)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='ce',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+parser.add_argument('--bias-curri', dest='bias_curri', action='store_true', default=False,
+                    help='directly use loss as the training data (biased) or not (unbiased)')
+parser.add_argument('--anti-curri', dest='anti_curri', action='store_true', default=False,
+                    help='easy to hard (curri), hard to easy (anti)')
+parser.add_argument('--std-coff', dest='std_coff', type=float, default=1,
+                    help='the hyper-parameter of std')
+
+parser.add_argument('--ft-epochs', dest='ft_epochs', type=int, default=15,
+                    help='fine-tune epochs')
+parser.add_argument('--ft-lr', dest='ft_lr', type=float, default=0.0002,
+                    help='fine-tune learning rate')
+
+parser.add_argument('--simple', dest='simple', action='store_true', default=False)
+
+args = parser.parse_args()
+
+if args.llm_data:
+    args.simple = True
+
+if args.simple:
+    args.one_way = True
+
+if args.run_folds == -1:
+    args.run_folds = list(range(args.split))
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+        if args.loss == "mse" or args.loss in ['ce']:
+            args.loss = 'mse'
+            criterion = nn.MSELoss()
+        elif args.loss == "smoothl1":
+            criterion = nn.SmoothL1Loss()
+        elif args.loss == "super":
+            criterion = SuperLoss()
+        elif args.loss in ["bmc", "bmc_ln"]:
+            criterion = BMCLoss()
+        else:
+            raise NotImplementedError("unimplemented regression task loss function")
+    elif args.task == 'cls':
+        args.classes = 2
+        if args.loss == 'ce' or args.loss in ['mse', 'smoothl1', 'super']:
+            args.loss = 'ce'
+            criterion = nn.CrossEntropyLoss()
+        else:
+            raise NotImplementedError("unimplemented classification task loss function")
+    else:
+        raise NotImplementedError("unimplemented task")
+    
+    if args.q_encoder in ['cnn', 'rn18']:
+        weight_dir = f'./run-{args.task}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+    else:
+        weight_dir = f'./run-{args.task}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+    
+    logging.basicConfig(handlers=[
+        logging.FileHandler(filename=os.path.join(weight_dir, "finetune.log"), encoding='utf-8', mode='w+'),
+        logging.StreamHandler()],
+        format="%(asctime)s: %(message)s", datefmt="%F %T", level=logging.INFO)
+    
+    logging.info(f'Finetuning: {weight_dir}')
+
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+
+    logging.info(f'Loading Training Dataset')
+    train_set = SimplePairClsDataset(pad_length=args.max_length, ftr2=True, gf=args.glob_feat, q_encoder=args.q_encoder, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+
+    logging.info('Loading Test Dataset')
+    if args.q_encoder in ['cnn', 'rn18']:
+        test_set = PeptidePairPicDataset(mode='r2_case', pad_length=args.max_length, task=args.task, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+    else:
+        test_set = PeptidePairDataset(mode='r2_case', pad_length=args.max_length, task=args.task, gf=args.glob_feat)
+
+    train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=8, pin_memory=True)
+    test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True)
+
+    best_perform_list = [[] for i in range(5)]
+
+    for fold in range(args.split):
+        logging.info(f'Finetuning Fold {fold}')
+        logging.info(f'Fold {fold}  Train set:{len(train_set)}, Test set: {len(test_set)}')
+        # if args.widen:
+        #     model = DMutaPeptideWiden(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, side_enc=args.side_enc)
+        # else:
+        if args.q_encoder in ['cnn', 'rn18']:
+            model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese)
+        else:
+            model = DMutaPeptide(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, non_siamese=args.non_siamese)
+
+        weights_path = f"{weight_dir}/model_{fold}.pth"
+
+        model.to(device)
+        # model.load_state_dict(torch.load(weights_path.replace('.pth', '_test.pth'), map_location=device), strict=False)
+        model.load_state_dict(torch.load(weights_path, map_location=device), strict=False)
+
+        optimizer = torch.optim.AdamW(model.parameters(), lr=args.ft_lr)
+
+        best_metric = -float('inf')
+
+        if args.task == 'cls':
+            for epoch in range(1, args.ft_epochs + 1):
+                train_loss, ap, auc, f1, acc = train_cls(args, epoch, model, train_loader, test_loader, device, criterion, optimizer)
+                logging.info(f'Epoch: {epoch:03d} Train Loss: {train_loss:.3f}, ap: {ap:.3f}, auc: {auc:.3f}, f1: {f1:.3f}, acc: {acc:.3f}')
+                avg_metric = ap + auc #+ f1 + acc
+                if avg_metric > best_metric:
+                    logging.info(f'Epoch: {epoch:03d} New best VALIDATION metrics')
+                    best_metric = avg_metric
+                    best_perform_list[fold] = np.asarray([ap, auc, f1, acc])
+                    torch.save(model.state_dict(), weights_path.replace('.pth', '_ft.pth'))
+
+
+
+if __name__ == "__main__":
+    main()
diff --git a/gradcam.py b/gradcam.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc87daceab24d0c251c4f892d4100ff83aeb2bdc
--- /dev/null
+++ b/gradcam.py
@@ -0,0 +1,407 @@
+import torch
+import torch.nn.functional as F
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+from torchvision import transforms
+from network import DMutaPeptideCNN
+from dataset import draw_peptide, encode_sequence
+
+class GradCAMMulti:
+    def __init__(self, model):
+        self.model = model
+        self.has_side_enc = hasattr(model, 'side_encoder') and model.side_encoder is not None
+
+    def generate(self, img1, img2, seq1=None, seq2=None, target_class=1):
+        self.model.eval()
+        
+        # 先计算两个图的原始CAM（未归一化）
+        cam1_raw = self._compute_cam_for_input(img1, img2, seq1, seq2, target_class, analyze_idx=0, normalize=False)
+        cam2_raw = self._compute_cam_for_input(img1, img2, seq1, seq2, target_class, analyze_idx=1, normalize=False)
+        
+        # 使用全局最大最小值进行归一化
+        global_min = min(cam1_raw.min(), cam2_raw.min())
+        global_max = max(cam1_raw.max(), cam2_raw.max())
+        
+        hm_cnn1 = self._normalize_cam(cam1_raw, global_min, global_max)
+        hm_cnn2 = self._normalize_cam(cam2_raw, global_min, global_max)
+        
+        if not self.has_side_enc:
+            return hm_cnn1, hm_cnn2
+        
+        # 序列热力图也使用相同的策略
+        seq1_raw = self._compute_seq_cam_for_input(img1, img2, seq1, seq2, target_class, analyze_idx=0, normalize=False)
+        seq2_raw = self._compute_seq_cam_for_input(img1, img2, seq1, seq2, target_class, analyze_idx=1, normalize=False)
+        
+        seq_global_min = min(seq1_raw.min(), seq2_raw.min())
+        seq_global_max = max(seq1_raw.max(), seq2_raw.max())
+        
+        hm_seq1 = self._normalize_cam(seq1_raw, seq_global_min, seq_global_max)
+        hm_seq2 = self._normalize_cam(seq2_raw, seq_global_min, seq_global_max)
+        
+        return hm_cnn1, hm_cnn2, hm_seq1, hm_seq2
+    
+    def _normalize_cam(self, cam, global_min, global_max):
+        """使用全局最大最小值归一化"""
+        cam_norm = (cam - global_min) / (global_max - global_min + 1e-8)
+        return np.uint8(cam_norm * 255)
+
+    def _compute_cam_for_input(self, img1, img2, seq1, seq2, target_class, analyze_idx, normalize=True):
+        """
+        analyze_idx: 0 分析 img1, 1 分析 img2
+        normalize: 是否在此函数内归一化（False时返回原始numpy数组）
+        """
+        if analyze_idx == 0:
+            img_analyze = img1.clone().requires_grad_(True)
+            img_other = img2.detach()
+        else:
+            img_analyze = img2.clone().requires_grad_(True)
+            img_other = img1.detach()
+        
+        activations = []
+        gradients = []
+        
+        def fwd_hook(mod, inp, out):
+            activations.append(out)
+            return out
+        
+        def bwd_hook(mod, grad_in, grad_out):
+            gradients.append(grad_out[0])
+        
+        last_conv = self.model.q_encoder[7][-1].conv2
+        fwd_h = last_conv.register_forward_hook(fwd_hook)
+        bwd_h = last_conv.register_full_backward_hook(bwd_hook)
+        
+        try:
+            if self.has_side_enc:
+                if analyze_idx == 0:
+                    inputs = ((img_analyze, seq1), (img_other, seq2))
+                else:
+                    inputs = ((img_other, seq1), (img_analyze, seq2))
+            else:
+                if analyze_idx == 0:
+                    inputs = (img_analyze, img_other)
+                else:
+                    inputs = (img_other, img_analyze)
+            
+            logits = self.model(inputs)
+            if isinstance(logits, tuple):
+                logits = logits[0]
+            score = logits[0, target_class]
+            
+            self.model.zero_grad()
+            score.backward()
+            
+            act = activations[analyze_idx]
+            grad = gradients[-(analyze_idx + 1)]
+            
+            if grad is None:
+                cam = np.zeros((img1.shape[2], img1.shape[3]), dtype=np.float32)
+                return np.uint8(cam * 255) if normalize else cam
+            
+            # 使用梯度的绝对值来计算权重
+            α = grad.abs().mean(dim=(2, 3), keepdim=True)
+            cam = (α * act).sum(dim=1, keepdim=True)
+            cam = cam.abs()  # 取绝对值
+            
+            cam = F.interpolate(cam, size=img1.shape[2:], mode='bilinear', align_corners=False)
+            cam = cam.squeeze().detach().cpu().numpy()
+            
+            if normalize:
+                cam = (cam - cam.min()) / (cam.max() - cam.min() + 1e-8)
+                return np.uint8(cam * 255)
+            else:
+                return cam  # 返回原始float数组
+            
+        finally:
+            fwd_h.remove()
+            bwd_h.remove()
+
+    def _compute_seq_cam_for_input(self, img1, img2, seq1, seq2, target_class, analyze_idx, normalize=True):
+        """序列CAM计算"""
+        if analyze_idx == 0:
+            seq_analyze = seq1.clone().requires_grad_(True)
+            seq_other = seq2.detach()
+        else:
+            seq_analyze = seq2.clone().requires_grad_(True)
+            seq_other = seq1.detach()
+        
+        activations = []
+        gradients = []
+        
+        def fwd_hook(mod, inp, out):
+            activations.append(out)
+            return out
+        
+        def bwd_hook(mod, grad_in, grad_out):
+            gradients.append(grad_out[0])
+        
+        fwd_h = self.model.side_encoder.mamba.register_forward_hook(fwd_hook)
+        bwd_h = self.model.side_encoder.mamba.register_full_backward_hook(bwd_hook)
+        
+        try:
+            if analyze_idx == 0:
+                inputs = ((img1.detach(), seq_analyze), (img2.detach(), seq_other))
+            else:
+                inputs = ((img1.detach(), seq_other), (img2.detach(), seq_analyze))
+            
+            logits = self.model(inputs)
+            if isinstance(logits, tuple):
+                logits = logits[0]
+            score = logits[0, target_class]
+            
+            self.model.zero_grad()
+            score.backward()
+            
+            act = activations[analyze_idx]
+            grad = gradients[-(analyze_idx + 1)]
+            
+            if grad is None:
+                cam_seq = np.zeros(seq1.shape[1], dtype=np.float32)
+                return np.uint8(cam_seq * 255) if normalize else cam_seq
+            
+            # 使用绝对值
+            α = grad.abs().mean(dim=1, keepdim=True)
+            cam_seq = (act * α).sum(dim=2).abs()
+            
+            cam_seq = cam_seq.squeeze().detach().cpu().numpy()
+            
+            if normalize:
+                cam_seq = (cam_seq - cam_seq.min()) / (cam_seq.max() - cam_seq.min() + 1e-8)
+                return np.uint8(cam_seq * 255)
+            else:
+                return cam_seq  # 返回原始float数组
+            
+        finally:
+            fwd_h.remove()
+            bwd_h.remove()
+
+
+def plot_seq_heat_tailpad(
+    seq: str,
+    heatmap: np.ndarray,
+    keep_pad: int = 2,
+    ax=None,
+    cmap='Oranges',
+    border_width: float = 2.0,
+    figsize_per_base: float = 0.3
+):
+    """
+    seq:             原始氨基酸序列，不含 padding
+    heatmap:         np.uint8 数组，长度 N = L + padding_length
+    keep_pad:        在末端保留的 padding 方块数
+    ax:              matplotlib Axes
+    cmap:            配色方案
+    border_width:    最外圈边框宽度
+    figsize_per_base: 每个位置宽度，用于自动计算 figsize
+    """
+    N = len(heatmap)
+    L = len(seq)
+    # 实际要显示的长度：0 ~ end_pos
+    end_pos = min(L + keep_pad, N)
+    data = heatmap[:end_pos].astype(np.float32) / 255.0  # 归一化到 [0,1]
+    M = end_pos
+
+    # 构造 x 轴标签：前 L 位显示字母，后面 keep_pad 位留空
+    xticks = [seq[i] if i < L else '' for i in range(M)]
+
+    if ax is None:
+        fig, ax = plt.subplots(
+            figsize=(figsize_per_base * M, 1.5),
+            dpi=100
+        )
+    im = ax.imshow(
+        data[np.newaxis, :],      # 变为 shape (1, M)
+        cmap=cmap,
+        aspect='auto',
+        interpolation='nearest',
+        vmin=0, vmax=1
+    )
+
+    # x 轴在顶部显示
+    ax.set_xticks(np.arange(M))
+    ax.set_xticklabels(xticks, fontsize=12)
+    ax.xaxis.set_ticks_position('top')
+    ax.xaxis.set_label_position('top')
+
+    # 隐藏 y 轴
+    ax.set_yticks([])
+
+    # 四周画一圈粗边框
+    for spine in ax.spines.values():
+        spine.set_visible(True)
+        spine.set_linewidth(border_width)
+        spine.set_edgecolor('black')
+
+    return im, ax
+
+
+def inv_norm(tensor: torch.Tensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
+    tensor = tensor.clone()
+    for t, m, s in zip(tensor, mean, std):
+        t.mul_(s).add_(m)
+    return -tensor
+
+
+def diff_hm(hm1, hm2):
+    diff = hm2.astype(np.float32) - hm1.astype(np.float32) + 127.
+    return np.clip(diff, 0, 255).astype(np.uint8)
+
+def get_resnet18_last_conv(model):
+    """
+    获取 ResNet18 的最后一个卷积层
+    从打印的结构可知：
+    - model.q_encoder[7] 是 layer4 (Sequential with 2 BasicBlocks)
+    - model.q_encoder[7][-1] 是最后一个 BasicBlock
+    - model.q_encoder[7][-1].conv2 是最后一个卷积层
+    """
+    return model.q_encoder[7][-1].conv2
+
+def add_alpha_to_cmap(base_cmap='RdBu_r', name='RdBu_alpha', center_alpha=0.0):
+    """
+    给已有的colormap添加alpha通道
+    
+    Args:
+        base_cmap: 基础colormap名称
+        name: 新colormap名称
+        center_alpha: 中心透明度
+    """
+    from matplotlib import colormaps as cm
+    
+    # 获取基础colormap
+    base = cm.get_cmap(base_cmap)
+    
+    # 创建新的颜色数组
+    n = 256
+    colors = base(np.linspace(0, 1, n))
+    
+    # 修改alpha通道：中心透明，两端不透明
+    alpha_values = np.abs(np.linspace(-1, 1, n))  # V型曲线
+    alpha_values = alpha_values ** 0.7  # 调整曲线形状
+    alpha_values = alpha_values * (1 - center_alpha) + center_alpha
+    
+    colors[:, 3] = alpha_values
+    
+    return ListedColormap(colors, name=name)
+
+def main(sequence1, sequence2, model):
+    img1 = draw_peptide(sequence1, pcs=True)
+    img2 = draw_peptide(sequence2, pcs=True)
+    img1_raw = transforms.ToPILImage()(inv_norm(img1))
+    img2_raw = transforms.ToPILImage()(inv_norm(img2))
+
+    # img1_raw.save('./gradcam/img1.png')
+
+    img1 = img1.unsqueeze(0).to(torch.device('cuda'))
+    img2 = img2.unsqueeze(0).to(torch.device('cuda'))
+
+    has_side_enc = hasattr(model, 'side_enc') and model.side_enc
+    
+    if has_side_enc:
+        # 假设序列已 one-hot 或 embedding，直接作 tensor
+        seq1 = encode_sequence(sequence1, 30).unsqueeze(0).to(torch.device('cuda'))
+        seq2 = encode_sequence(sequence2, 30).unsqueeze(0).to(torch.device('cuda'))
+
+        # 挂 hook - ResNet18 的最后一个卷积层
+        cam = GradCAMMulti(model)
+
+        # 生成热力图
+        hm_c1, hm_c2, hm_s1, hm_s2 = cam.generate(
+            img1, img2, seq1, seq2,
+            target_class=1
+        )
+    else:
+        seq1 = seq2 = None
+        cam = GradCAMMulti(model)
+        
+        hm_c1, hm_c2 = cam.generate(
+            img1, img2, seq1, seq2,
+            target_class=1
+        )
+
+    # 可视化 CNN 热力图
+    def show_img_heat(img_pil, hm, name, cmap='jet', alpha=0.4):
+        plt.figure(figsize=(5, 5))
+        img = np.array(img_pil.resize(hm.shape[::-1]))
+        plt.imshow(img, alpha=0.8)
+        plt.imshow(hm, cmap=cmap, alpha=alpha)
+        plt.axis('off')
+        plt.savefig(f'{name}.png',
+                    bbox_inches='tight',
+                    pad_inches=0,
+                    dpi=200)
+        plt.close()
+
+    diff_cmap = add_alpha_to_cmap()
+
+    hm_diff = diff_hm(hm_c1, hm_c2)
+
+    show_img_heat(img1_raw, hm_c1, f'./gradcam/{sequence1}-temp')
+    show_img_heat(img2_raw, hm_c2, f'./gradcam/{sequence2}-muta')
+    show_img_heat(img2_raw, hm_diff, f'./gradcam/{sequence2}-diff', cmap=diff_cmap, alpha=0.8)
+
+    # 可视化序列热力图（如果有）
+    if has_side_enc:
+        fig, axes = plt.subplots(
+            2, 1,
+            figsize=(len(sequence1) * 0.3, 1.25),
+            constrained_layout=True
+        )
+        plot_seq_heat_tailpad(
+            sequence1, hm_s1,
+            keep_pad=0,
+            ax=axes[0],
+            cmap='jet'
+        )
+        plot_seq_heat_tailpad(
+            sequence2, hm_s2,
+            keep_pad=0,
+            ax=axes[1],
+            cmap='jet'
+        )
+        plt.savefig(f'./gradcam/{sequence1}_seq.svg')
+        plt.close()
+
+        fig, ax = plt.subplots(
+            1, 1,
+            figsize=(len(sequence1) * 0.3, 0.625),
+            constrained_layout=True
+        )
+        plot_seq_heat_tailpad(
+            sequence2, diff_hm(hm_s1, hm_s2),
+            keep_pad=0,
+            ax=ax,
+            cmap=diff_cmap
+        )
+        plt.savefig(f'./gradcam/{sequence2}_diff.svg')
+        plt.close()
+
+
+# —— 使用示例 —— #
+if __name__ == "__main__":
+    # 1) load model
+    model = DMutaPeptideCNN(
+        q_encoder='rn18',
+        classes=2,
+        channels=16,
+        dir=False,
+        gf=False,
+        side_enc='mamba',
+        fusion='diff'
+    )
+    model.eval().to(torch.device('cuda'))
+    model.load_state_dict(
+        torch.load("run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/model_0.pth",
+                   map_location=torch.device('cuda')),
+        strict=True
+    )
+
+    # 2) 准备数据
+    sequence1 = "KWKIKWPVKWFKML"
+    sequence2 = "KWKIKWPVKWfKML"
+    main(sequence1, sequence2, model)
+    
+    sequence1 = "KKLFKKILKYL"
+    sequence2 = "KKLFKKiLKYL"
+    main(sequence1, sequence2, model)
\ No newline at end of file
diff --git a/gradcam/KKLFKKILKYL-temp.png b/gradcam/KKLFKKILKYL-temp.png
new file mode 100644
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--- /dev/null
+++ b/gradcam/KKLFKKILKYL-temp.png
@@ -0,0 +1,3 @@
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+size 237582
diff --git a/gradcam/KKLFKKILKYL_seq.svg b/gradcam/KKLFKKILKYL_seq.svg
new file mode 100644
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+   <cc:Work>
+    <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
+    <dc:date>2025-11-21T00:56:42.306670</dc:date>
+    <dc:format>image/svg+xml</dc:format>
+    <dc:creator>
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diff --git a/gradcam/img1.png b/gradcam/img1.png
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diff --git a/infer.py b/infer.py
new file mode 100644
index 0000000000000000000000000000000000000000..1064b0181d6e52122c75b3f2f3b118270fe29d91
--- /dev/null
+++ b/infer.py
@@ -0,0 +1,201 @@
+import argparse
+from dataset import PeptidePairDataset, PeptidePairPicDataset
+from network import DMutaPeptide, DMutaPeptideCNN
+from train import move_to_device
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+import numpy as np
+from utils import set_seed
+import pandas as pd
+from torchmetrics import MeanAbsoluteError, RelativeSquaredError, PearsonCorrCoef, KendallRankCorrCoef, F1Score, Accuracy, AveragePrecision, AUROC
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='cnn',
+                    help='lstm mamba mla')
+parser.add_argument('--channels', type=int, default=16)
+parser.add_argument("--side-enc", dest='side_enc', type=str, default=None,
+                    help="use side features")
+parser.add_argument('--fusion', type=str, default='mlp',
+                    help='mlp att')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='cls',
+                    help='reg or cls')
+parser.add_argument('--pdb-src', type=str, dest='pdb_src', default='af',
+                    help='af or hf')
+parser.add_argument('--data-ver', type=str, dest='data_ver', default='250228',
+                    help='data version')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=1,
+                    help="Seed (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--warm-steps', type=int, dest='warm_steps', default=0,
+                    help='number of warm start steps for learning rate (default: 10)')
+parser.add_argument('--patience', type=int, default=10,
+                    help='patience for early stopping (default: 10)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')  # /home/duadua/Desktop/fetal/3dpretrain/runs/e50.pth
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='ce',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+parser.add_argument('--simple', dest='simple', action='store_true', default=False)
+parser.add_argument('--llm-data', dest='llm_data', action='store_true', default=False)
+parser.add_argument('--uda', type=str, default=None)
+
+args = parser.parse_args()
+
+if args.llm_data:
+    args.simple = True
+
+if args.simple:
+    args.one_way = True
+
+if args.mix_pcs:
+    args.pcs = 'mix'
+
+if args.q_encoder in ['cnn', 'rn18']:
+    weight_dir = f'./run-{args.task}/{f"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+else:
+    weight_dir = f'./run-{args.task}/{f"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+
+if args.uda:
+    weight_dir += f'/uda_{args.uda}'
+
+print(weight_dir)
+
+def metrics(preds, gt, task):
+    avg = 'marco'
+    device = preds.device
+    if task == 'cls':
+        metric_1 = AveragePrecision(average=avg, task='binary').to(device)
+        metric_2 = AUROC(average=avg, task='binary').to(device)
+        metric_3 = F1Score(average=avg, task='binary').to(device)
+        metric_4 = Accuracy(average=avg, task='binary').to(device)
+        all_metrics = [metric_1(preds, gt).item(), 
+                metric_2(preds, gt).item(), 
+                metric_3(preds, gt).item(), 
+                metric_4(preds, gt).item()]
+    
+    elif task == 'reg':
+        metric_1 = MeanAbsoluteError().to(device)
+        metric_2 = RelativeSquaredError(num_outputs=1).to(device)
+        metric_3 = PearsonCorrCoef(num_outputs=1).to(device)
+        metric_4 = KendallRankCorrCoef(num_outputs=1).to(device)
+        all_metrics = [metric_1(preds, gt).item(), 
+                metric_2(preds, gt).item(), 
+                metric_3(preds.squeeze(), gt.squeeze()).mean().item(), 
+                metric_4(preds.squeeze(), gt.squeeze()).mean().item()]
+
+    return [f'{i * 100:.2f}' for i in all_metrics]
+    
+
+def main(dataset):
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+    elif args.task == 'cls':
+        args.classes = 2
+    else:
+        raise NotImplementedError("unimplemented task")
+
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+    
+    if args.q_encoder in ['cnn', 'rn18']:
+        model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
+        test_set = PeptidePairPicDataset(mode=dataset, pad_length=args.max_length, task=args.task, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+    else:
+        model = DMutaPeptide(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
+        test_set = PeptidePairDataset(mode=dataset, pad_length=args.max_length, task=args.task, gf=args.glob_feat)
+    
+    test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False)
+
+    df = pd.DataFrame()
+    raw_preds = []
+    ckpt_names = ['model_uda_teacher'] if args.uda else [f'model_{i}_test' for i in range(5)]
+    for i in ckpt_names:
+        model.load_state_dict(torch.load(f'{weight_dir}/{i}.pth', map_location=device))
+        preds = []
+        gt_list_valid = []
+        with torch.no_grad():
+            for data in test_loader:
+                x, gt = data
+                gt_list_valid.append(gt.to(device))
+                out = model(move_to_device(x, device))
+                if args.dir:
+                    out, _ = out
+                preds.append(out)
+        r_pred = torch.cat(preds, dim=0)
+        if args.task == 'reg':
+            preds = r_pred.cpu().numpy()
+        elif args.task == 'cls':
+            preds = torch.softmax(r_pred, dim=-1)[:, 1].cpu().numpy()
+        gt_tensor = torch.cat(gt_list_valid, dim=0)
+        gt_list_valid = gt_tensor.cpu().numpy()
+        df[f'{i}'] = preds
+        raw_preds.append(r_pred)
+    if args.task == 'cls':
+        preds_tensor = torch.softmax(torch.stack(raw_preds, 0).mean(0), dim=-1)[:, 1]
+    elif args.task == 'reg':
+        preds_tensor = torch.stack(raw_preds, 0).mean(0)
+    df['fusion'] = preds_tensor.cpu().numpy()
+    df['gt'] = gt_list_valid
+    df.to_csv(f'{weight_dir}/preds_{dataset}.csv', index=False)
+    return metrics(preds_tensor, gt_tensor, args.task)
+
+
+if __name__ == '__main__':
+    if args.task == 'cls':
+        df = pd.DataFrame(columns=['dataset', 'AUPRC', 'AUROC', 'F1', 'ACC'])
+    elif args.task == 'reg':
+        df = pd.DataFrame(columns=['dataset', 'MAE', 'RSE', 'PCC', 'KCC'])
+
+    datasets = [
+        'r2_case',
+        # 'r2_case_'
+        "test", 
+        # "mhb", 
+        # "nacl", 
+        # "125fbs", 
+        # "25fbs",
+        ]
+
+    for dataset in datasets:
+        results = main(dataset)
+        df.loc[len(df) + 1] = [dataset] + results
+    df.to_csv(f'{weight_dir}/inference_results.csv', index=False)
+    print(df)
diff --git a/infer_case.py b/infer_case.py
new file mode 100644
index 0000000000000000000000000000000000000000..b515c6a05becd099c00eda52efe61e6eef4df4d0
--- /dev/null
+++ b/infer_case.py
@@ -0,0 +1,245 @@
+import argparse
+import time
+from dataset import PeptidePairPicCaseDataset, encode_sequence
+from network import DMutaPeptideCNN
+from train import move_to_device
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+import numpy as np
+from utils import set_seed
+import pandas as pd
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='cnn',
+                    help='lstm mamba mla')
+parser.add_argument('--channels', type=int, default=16)
+parser.add_argument("--side-enc", dest='side_enc', type=str, default='lstm',
+                    help="use side features")
+parser.add_argument('--fusion', type=str, default='att',
+                    help='mlp att')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='cls',
+                    help='reg or cls')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=1,
+                    help="Seed for model initialization (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='ce',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+parser.add_argument('--simple', dest='simple', action='store_true', default=False)
+parser.add_argument('--llm-data', dest='llm_data', action='store_true', default=False)
+
+# Case Study Specific
+parser.add_argument('--case', type=str, default='r2',
+                    help='case to infer')
+parser.add_argument('--use-ft', dest='use_ft', type=str, default='')
+
+args = parser.parse_args()
+
+if args.llm_data:
+    args.simple = True
+
+if args.simple:
+    args.one_way = True
+
+if args.mix_pcs:
+    args.pcs = 'mix'
+
+if args.gpu != -1:
+    torch.backends.cudnn.benchmark = True
+    torch.set_float32_matmul_precision('high')
+
+
+class FasterModelForCase(DMutaPeptideCNN):
+    def cache_temp_vector(self, seq):
+        if self.side_enc:
+            seq_seq = seq[1]
+            seq = seq[0]
+            if self.side_encoder.__class__.__name__ == 'MambaModel':
+                self.temp_seq_vector = self.norm(self.side_encoder(seq_seq))
+            else:
+                self.temp_seq_vector = self.norm(self.side_encoder(seq_seq)[0][:, -1, :])
+        self.temp_vector = self.norm(self.q_encoder(seq))
+
+    def forward(self, x, labels=None, epoch=0):
+        seq2 = x
+
+        if self.side_enc:
+            seq2_seq = seq2[1]
+            seq2 = seq2[0]
+
+        batch_size = seq2.shape[0]
+
+        fusion = []
+
+        # 获取两个序列的编码结果
+        fusion.append(self.temp_vector.expand(batch_size, -1))
+        fusion.append(self.norm(self.q_encoder_2(seq2)))
+        if self.side_enc:
+            fusion.append(self.temp_seq_vector.expand(batch_size, -1))
+            if self.side_encoder.__class__.__name__ == 'MambaModel':
+                fusion.append(self.norm(self.side_encoder_2(seq2_seq)))
+            else:
+                fusion.append(self.norm(self.side_encoder_2(seq2_seq)[0][:, -1, :]))
+
+        # 根据 fusion_method 决定融合方式
+        if self.fusion_method == 'mlp':
+            # 维持原有行为：拼接两个向量
+            fusion = torch.cat(fusion, dim=-1)
+        elif self.fusion_method == 'diff':
+            if not self.side_enc:
+                fusion = torch.cat([fusion[1] - fusion[0]] + fusion[2:], dim=-1)
+            else:
+                fusion = torch.cat([fusion[1] - fusion[0], fusion[3] - fusion[2]] + fusion[4:], dim=-1)
+        elif self.fusion_method == 'att':
+            # 使用 attention 融合:
+            # 先将两个向量堆叠成“tokens”，形状：(batch, 2, embed_dim)
+            tokens = torch.stack(fusion, dim=1)  # embed_dim 应该为 final_dim//2
+            # 利用 MultiheadAttention 进行自注意力计算
+            # 注意：因为采用 batch_first=True，所以输入形状为 (batch, seq_len, embed_dim)
+            attn_output, _ = self.attn(tokens, tokens, tokens)
+            # 将 attention 输出展平，得到形状 (batch, 2 * embed_dim)，即 (batch, final_dim)
+            fusion = attn_output.reshape(attn_output.size(0), -1)
+        else:
+            raise ValueError("Invalid fusion method: choose either 'mse' or 'att'.")
+
+        # 如果启用 DIR 模块，保留传入 FDS 前的特征表示
+        if self.DIR:
+            features = fusion
+            fusion = self.FDS.smooth(fusion, labels, epoch)
+        
+        pred = self.fc(fusion)
+
+        if self.DIR:
+            return pred, features
+        else:
+            return pred
+
+class CustomDataset(PeptidePairPicCaseDataset):
+    def __getitem__(self, idx):
+        variant  = self.variants[idx]
+        seq2, label = variant, variant
+        img2 = self.read_img(variant)
+
+        if self.side_enc:
+            img2 = (img2, encode_sequence(seq2, self.pad_length))
+
+        return img2, label
+
+def load_model(args, weight_path, device, temp_batch):
+    model = FasterModelForCase(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
+    model.load_state_dict(torch.load(weight_path, map_location=device), strict=False)
+    model.cache_temp_vector(move_to_device(temp_batch, device))
+    model.compile()
+    return model
+
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+    elif args.task == 'cls':
+        args.classes = 2
+    else:
+        raise NotImplementedError("unimplemented task")
+    weight_dir = f'./run-{args.task}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+    print(weight_dir)
+    print(device)
+    
+    test_set = CustomDataset(case=args.case, pad_length=args.max_length, side_enc=args.side_enc, pcs=True, resize=args.resize, gf=args.glob_feat)
+    test_loader = DataLoader(test_set, batch_size=192, shuffle=False, num_workers=16, pin_memory=True)
+    # test_loader = DataLoader(test_set, batch_size=192, shuffle=False, num_workers=8)
+
+    temp_batch = test_set.template_pic.unsqueeze(0)
+    if args.side_enc:
+        temp_batch = (temp_batch, test_set.template_seq.unsqueeze(0))
+    
+    models = [load_model(args, f'{weight_dir}/model_{i}{f"_{args.use_ft}" if args.use_ft else ""}.pth', device, temp_batch) for i in range(args.split)]
+    # models = [load_model(args, f'{weight_dir}/model_{i}{"_ft" if args.use_ft else ""}.pth', device, temp_batch) for i in [0]]
+
+    all_seqs = []
+    logits_batches = []  # 存放每个 batch 的 [m,B,2] avg_logits (CPU 上)
+
+    start_time = time.time()
+
+    with torch.no_grad():
+        for x, gt in test_loader:
+            # x: [B, ...] on CPU pin memory，gt: tuple of B strings
+            x = move_to_device(x, device, non_blocking=True)
+            # x = move_to_device(x, device)
+
+            # 1) 记录 5 个模型的 logits
+            # logits: [m,B,2]
+            logits = torch.zeros(len(models), len(gt), args.classes, device=device)
+            for i, m in enumerate(models):
+                logits[i] = m(x)
+            # avg_logits = sum_logits.div_(len(models))
+
+            # 3) 立刻搬到 CPU（pin_memory 下可以 non_blocking）
+            logits_batches.append(logits.cpu())
+            all_seqs.extend(gt)
+
+    # 拼接成 [n,2]，n = sum(batch_size)
+    all_logits = torch.cat(logits_batches, dim=1)  # [m,n,2]
+    
+    if args.task == 'reg':
+        preds = all_logits.mean(0).squeeze().tolist()
+    elif args.task == 'cls':
+        # 最后一次性 softmax，取正类概率
+        preds = torch.softmax(all_logits, dim=-1)[:, :, 1].mean(0).tolist()
+
+    consumed_time = time.time() - start_time
+    print(f'total consumed time: {consumed_time} s')
+    print(f'time per sample: {consumed_time / len(test_set)} s')
+
+    # 保存到 CSV
+    df = pd.DataFrame({
+        "seq":  all_seqs,
+        "pred": preds,
+    })
+
+    df.to_csv(f'{weight_dir}/preds_case{f"_{args.use_ft}" if args.use_ft else ""}.csv', index=False)
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/infer_case_feature.py b/infer_case_feature.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa97a71a76cb3f5ec43ff3a775799c570d72fac4
--- /dev/null
+++ b/infer_case_feature.py
@@ -0,0 +1,223 @@
+import argparse
+import time
+from dataset import PeptidePairPicCaseDataset, encode_sequence
+from network import DMutaPeptideCNN
+from train import move_to_device
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+import numpy as np
+from utils import set_seed
+import pandas as pd
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='cnn',
+                    help='lstm mamba mla')
+parser.add_argument('--channels', type=int, default=16)
+parser.add_argument("--side-enc", dest='side_enc', type=str, default=None,
+                    help="use side features")
+parser.add_argument('--fusion', type=str, default='att',
+                    help='mlp att')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='cls',
+                    help='reg or cls')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=1,
+                    help="Seed for model initialization (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='ce',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+parser.add_argument('--simple', dest='simple', action='store_true', default=False)
+parser.add_argument('--llm-data', dest='llm_data', action='store_true', default=False)
+
+# Case Study Specific
+parser.add_argument('--case', type=str, default='r2',
+                    help='case to infer')
+parser.add_argument('--uda', action='store_true', default=False)
+
+args = parser.parse_args()
+
+if args.llm_data:
+    args.simple = True
+
+if args.simple:
+    args.one_way = True
+
+if args.mix_pcs:
+    args.pcs = 'mix'
+
+if args.gpu != -1:
+    torch.backends.cudnn.benchmark = True
+    torch.set_float32_matmul_precision('high')
+
+
+class FasterModelForCase(DMutaPeptideCNN):
+    def cache_temp_vector(self, seq):
+        if self.side_enc:
+            seq_seq = seq[1]
+            seq = seq[0]
+            if self.side_encoder.__class__.__name__ == 'MambaModel':
+                self.temp_seq_vector = self.norm(self.side_encoder(seq_seq))
+            else:
+                self.temp_seq_vector = self.norm(self.side_encoder(seq_seq)[0][:, -1, :])
+        self.temp_vector = self.norm(self.q_encoder(seq))
+
+    def forward(self, x, labels=None, epoch=0):
+        seq2 = x
+
+        if self.side_enc:
+            seq2_seq = seq2[1]
+            seq2 = seq2[0]
+
+        batch_size = seq2.shape[0]
+
+        fusion = []
+
+        # 获取两个序列的编码结果
+        fusion.append(self.temp_vector.expand(batch_size, -1))
+        fusion.append(self.norm(self.q_encoder_2(seq2)))
+        if self.side_enc:
+            fusion.append(self.temp_seq_vector.expand(batch_size, -1))
+            if self.side_encoder.__class__.__name__ == 'MambaModel':
+                fusion.append(self.norm(self.side_encoder_2(seq2_seq)))
+            else:
+                fusion.append(self.norm(self.side_encoder_2(seq2_seq)[0][:, -1, :]))
+
+        # 根据 fusion_method 决定融合方式
+        if self.fusion_method == 'mlp':
+            # 维持原有行为：拼接两个向量
+            fusion = torch.cat(fusion, dim=-1)
+        elif self.fusion_method == 'diff':
+            if not self.side_enc:
+                fusion = torch.cat([fusion[1] - fusion[0]] + fusion[2:], dim=-1)
+            else:
+                fusion = torch.cat([fusion[1] - fusion[0], fusion[3] - fusion[2]] + fusion[4:], dim=-1)
+        elif self.fusion_method == 'att':
+            # 使用 attention 融合:
+            # 先将两个向量堆叠成“tokens”，形状：(batch, 2, embed_dim)
+            tokens = torch.stack(fusion, dim=1)  # embed_dim 应该为 final_dim//2
+            # 利用 MultiheadAttention 进行自注意力计算
+            # 注意：因为采用 batch_first=True，所以输入形状为 (batch, seq_len, embed_dim)
+            attn_output, _ = self.attn(tokens, tokens, tokens)
+            # 将 attention 输出展平，得到形状 (batch, 2 * embed_dim)，即 (batch, final_dim)
+            fusion = attn_output.reshape(attn_output.size(0), -1)
+        else:
+            raise ValueError("Invalid fusion method: choose either 'mse' or 'att'.")
+        
+        feature = self.fc[:-1](fusion)
+        pred = self.fc[-1](feature)
+
+        return pred, feature
+
+class CustomDataset(PeptidePairPicCaseDataset):
+    def __getitem__(self, idx):
+        variant  = self.variants[idx]
+        seq2, label = variant, variant
+        img2 = self.read_img(variant)
+
+        if self.side_enc:
+            img2 = (img2, encode_sequence(seq2, self.pad_length))
+
+        return img2, label
+
+def load_model(args, weight_path, device, temp_batch):
+    model = FasterModelForCase(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
+    model.load_state_dict(torch.load(weight_path, map_location=device), strict=False)
+    model.cache_temp_vector(move_to_device(temp_batch, device))
+    model.compile()
+    return model
+
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+    elif args.task == 'cls':
+        args.classes = 2
+    else:
+        raise NotImplementedError("unimplemented task")
+    weight_dir = f'./run-{args.task}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+    if args.uda:
+        weight_dir += f'/uda_{args.case}'
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+    print(weight_dir)
+    print(device)
+    
+    test_set = CustomDataset(case=args.case, pad_length=args.max_length, side_enc=args.side_enc, pcs=True, resize=args.resize, gf=args.glob_feat)
+    test_loader = DataLoader(test_set, batch_size=192, shuffle=False, num_workers=16, pin_memory=True)
+    # test_loader = DataLoader(test_set, batch_size=192, shuffle=False, num_workers=8)
+
+    temp_batch = test_set.template_pic.unsqueeze(0)
+    if args.side_enc:
+        temp_batch = (temp_batch, test_set.template_seq.unsqueeze(0))
+
+    pth_path = f'{weight_dir}/model_uda_teacher.pth' if args.uda else f'{weight_dir}/model_0_test.pth'
+    
+    model = load_model(args, pth_path, device, temp_batch)
+    # models = [load_model(args, f'{weight_dir}/model_{i}{"_ft" if args.use_ft else ""}.pth', device, temp_batch) for i in [0]]
+
+    all_features = {}
+    all_preds = {}
+
+    start_time = time.time()
+
+    with torch.no_grad():
+        for x, gt in test_loader:
+            # x: [B, ...] on CPU pin memory，gt: tuple of B strings
+            x = move_to_device(x, device, non_blocking=True)
+            preds, feats = model(x)
+            if args.task == 'cls':
+                preds = torch.softmax(preds, dim=1)[:, 1]
+            for name, feat, pred in zip(gt, feats, preds):
+                all_features[name] = feat.cpu()
+                all_preds[name] = pred.item()
+
+    consumed_time = time.time() - start_time
+    print(f'total consumed time: {consumed_time} s')
+    print(f'time per sample: {consumed_time / len(test_set)} s')
+
+    torch.save(all_features, f'{weight_dir}/features.pth')
+
+    df = pd.DataFrame(list(all_preds.items()), columns=['seq', 'pred'])
+    df.to_csv(f'{weight_dir}/feature_preds.csv', index=False)
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/infer_case_uda.py b/infer_case_uda.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f621aa4c3aab871394e342391be30936b31d77f
--- /dev/null
+++ b/infer_case_uda.py
@@ -0,0 +1,247 @@
+import argparse
+import time
+from dataset import PeptidePairPicCaseDataset, encode_sequence
+from network import DMutaPeptideCNN
+from train import move_to_device
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+import numpy as np
+from utils import set_seed
+import pandas as pd
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='cnn',
+                    help='lstm mamba mla')
+parser.add_argument('--channels', type=int, default=16)
+parser.add_argument("--side-enc", dest='side_enc', type=str, default=None,
+                    help="use side features")
+parser.add_argument('--fusion', type=str, default='att',
+                    help='mlp att')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='cls',
+                    help='reg or cls')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=1,
+                    help="Seed for model initialization (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='ce',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+parser.add_argument('--simple', dest='simple', action='store_true', default=False)
+parser.add_argument('--llm-data', dest='llm_data', action='store_true', default=False)
+
+# Case Study Specific
+parser.add_argument('--case', type=str, default='r2',
+                    help='case to infer')
+parser.add_argument('--use-variant', dest='use_variant', type=str, default='')
+
+args = parser.parse_args()
+
+if args.llm_data:
+    args.simple = True
+
+if args.simple:
+    args.one_way = True
+
+if args.mix_pcs:
+    args.pcs = 'mix'
+
+if args.gpu != -1:
+    torch.backends.cudnn.benchmark = True
+    torch.set_float32_matmul_precision('high')
+
+
+class FasterModelForCase(DMutaPeptideCNN):
+    def cache_temp_vector(self, seq):
+        if self.side_enc:
+            seq_seq = seq[1]
+            seq = seq[0]
+            if self.side_encoder.__class__.__name__ == 'MambaModel':
+                self.temp_seq_vector = self.norm(self.side_encoder(seq_seq))
+            else:
+                self.temp_seq_vector = self.norm(self.side_encoder(seq_seq)[0][:, -1, :])
+        self.temp_vector = self.norm(self.q_encoder(seq))
+
+    def forward(self, x, labels=None, epoch=0):
+        seq2 = x
+
+        if self.side_enc:
+            seq2_seq = seq2[1]
+            seq2 = seq2[0]
+
+        batch_size = seq2.shape[0]
+
+        fusion = []
+
+        # 获取两个序列的编码结果
+        fusion.append(self.temp_vector.expand(batch_size, -1))
+        fusion.append(self.norm(self.q_encoder_2(seq2)))
+        if self.side_enc:
+            fusion.append(self.temp_seq_vector.expand(batch_size, -1))
+            if self.side_encoder.__class__.__name__ == 'MambaModel':
+                fusion.append(self.norm(self.side_encoder_2(seq2_seq)))
+            else:
+                fusion.append(self.norm(self.side_encoder_2(seq2_seq)[0][:, -1, :]))
+
+        # 根据 fusion_method 决定融合方式
+        if self.fusion_method == 'mlp':
+            # 维持原有行为：拼接两个向量
+            fusion = torch.cat(fusion, dim=-1)
+        elif self.fusion_method == 'diff':
+            if not self.side_enc:
+                fusion = torch.cat([fusion[1] - fusion[0]] + fusion[2:], dim=-1)
+            else:
+                fusion = torch.cat([fusion[1] - fusion[0], fusion[3] - fusion[2]] + fusion[4:], dim=-1)
+        elif self.fusion_method == 'att':
+            # 使用 attention 融合:
+            # 先将两个向量堆叠成“tokens”，形状：(batch, 2, embed_dim)
+            tokens = torch.stack(fusion, dim=1)  # embed_dim 应该为 final_dim//2
+            # 利用 MultiheadAttention 进行自注意力计算
+            # 注意：因为采用 batch_first=True，所以输入形状为 (batch, seq_len, embed_dim)
+            attn_output, _ = self.attn(tokens, tokens, tokens)
+            # 将 attention 输出展平，得到形状 (batch, 2 * embed_dim)，即 (batch, final_dim)
+            fusion = attn_output.reshape(attn_output.size(0), -1)
+        else:
+            raise ValueError("Invalid fusion method: choose either 'mse' or 'att'.")
+
+        # 如果启用 DIR 模块，保留传入 FDS 前的特征表示
+        if self.DIR:
+            features = fusion
+            fusion = self.FDS.smooth(fusion, labels, epoch)
+        
+        pred = self.fc(fusion)
+
+        if self.DIR:
+            return pred, features
+        else:
+            return pred
+
+
+class CustomDataset(PeptidePairPicCaseDataset):
+    def __getitem__(self, idx):
+        variant  = self.variants[idx]
+        seq2, label = variant, variant
+        img2 = self.read_img(variant)
+
+        if self.side_enc:
+            img2 = (img2, encode_sequence(seq2, self.pad_length))
+
+        return img2, label
+
+
+def load_model(args, weight_path, device, temp_batch):
+    model = FasterModelForCase(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
+    model.load_state_dict(torch.load(weight_path, map_location=device), strict=False)
+    model.cache_temp_vector(move_to_device(temp_batch, device))
+    model.compile()
+    return model
+
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+    elif args.task == 'cls':
+        args.classes = 2
+    else:
+        raise NotImplementedError("unimplemented task")
+    weight_dir = f'./run-{args.task}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}/uda_{args.case}'
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+    print(weight_dir)
+    print(device)
+    
+    test_set = CustomDataset(case=args.case, pad_length=args.max_length, side_enc=args.side_enc, pcs=True, resize=args.resize, gf=args.glob_feat)
+    test_loader = DataLoader(test_set, batch_size=192, shuffle=False, num_workers=16, pin_memory=True)
+    # test_loader = DataLoader(test_set, batch_size=192, shuffle=False, num_workers=8)
+
+    temp_batch = test_set.template_pic.unsqueeze(0)
+    if args.side_enc:
+        temp_batch = (temp_batch, test_set.template_seq.unsqueeze(0))
+    
+    models = [load_model(args, f'{weight_dir}/model_uda_{role}{f"_{args.use_variant}" if args.use_variant else ""}.pth', device, temp_batch) for role in ('teacher',)]
+    # models = [load_model(args, f'{weight_dir}/model_{i}{"_ft" if args.use_ft else ""}.pth', device, temp_batch) for i in [0]]
+
+    all_seqs = []
+    logits_batches = []  # 存放每个 batch 的 [m,B,2] avg_logits (CPU 上)
+
+    start_time = time.time()
+
+    with torch.no_grad():#, torch.autocast(device_type=device.type):
+        for x, gt in test_loader:
+            # x: [B, ...] on CPU pin memory，gt: tuple of B strings
+            x = move_to_device(x, device, non_blocking=True)
+            # x = move_to_device(x, device)
+
+            # 1) 记录 5 个模型的 logits
+            # logits: [m,B,2]
+            logits = torch.zeros(len(models), len(gt), args.classes, device=device)
+            for i, m in enumerate(models):
+                logits[i] = m(x)
+            # avg_logits = sum_logits.div_(len(models))
+
+            # 3) 立刻搬到 CPU（pin_memory 下可以 non_blocking）
+            logits_batches.append(logits.cpu())
+            all_seqs.extend(gt)
+
+    # 拼接成 [n,2]，n = sum(batch_size)
+    all_logits = torch.cat(logits_batches, dim=1)  # [m,n,2]
+    
+    if args.task == 'reg':
+        preds = all_logits.mean(0).squeeze().tolist()
+    elif args.task == 'cls':
+        # 最后一次性 softmax，取正类概率
+        preds = torch.softmax(all_logits, dim=-1)[:, :, 1].mean(0).tolist()
+
+    consumed_time = time.time() - start_time
+    print(f'total consumed time: {consumed_time} s')
+    print(f'time per sample: {consumed_time / len(test_set)} s')
+
+    # 保存到 CSV
+    df = pd.DataFrame({
+        "seq":  all_seqs,
+        "pred": preds,
+    })
+
+    df.to_csv(f'{weight_dir}/preds_case{f"_{args.use_variant}" if args.use_variant else ""}.csv', index=False)
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/infer_case_unoptimized.py b/infer_case_unoptimized.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba6a34b8af9e663381b21d9bf4012f3a66af9976
--- /dev/null
+++ b/infer_case_unoptimized.py
@@ -0,0 +1,164 @@
+import argparse
+import time
+from dataset import PeptidePairPicCaseDataset, encode_sequence
+from network import DMutaPeptideCNN
+from train import move_to_device
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+import numpy as np
+from utils import set_seed
+import pandas as pd
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='cnn',
+                    help='lstm mamba mla')
+parser.add_argument('--channels', type=int, default=16)
+parser.add_argument("--side-enc", dest='side_enc', type=str, default='lstm',
+                    help="use side features")
+parser.add_argument('--fusion', type=str, default='att',
+                    help='mlp att')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='cls',
+                    help='reg or cls')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=1,
+                    help="Seed for model initialization (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='ce',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+parser.add_argument('--simple', dest='simple', action='store_true', default=False)
+parser.add_argument('--llm-data', dest='llm_data', action='store_true', default=False)
+
+# Case Study Specific
+parser.add_argument('--case', type=str, default='r2',
+                    help='case to infer')
+parser.add_argument('--use-ft', dest='use_ft', action='store_true', default=False)
+
+args = parser.parse_args()
+
+if args.llm_data:
+    args.simple = True
+
+if args.simple:
+    args.one_way = True
+
+if args.mix_pcs:
+    args.pcs = 'mix'
+
+if args.gpu != -1:
+    torch.backends.cudnn.benchmark = True
+    torch.set_float32_matmul_precision('high')
+
+
+def load_model(args, weight_path, device):
+    model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
+    model.load_state_dict(torch.load(weight_path, map_location=device), strict=False)
+    model.compile()
+    return model
+
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+    elif args.task == 'cls':
+        args.classes = 2
+    else:
+        raise NotImplementedError("unimplemented task")
+    weight_dir = f'./run-{args.task}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+    print(weight_dir)
+    print(device)
+    
+    test_set = PeptidePairPicCaseDataset(case=args.case, pad_length=args.max_length, side_enc=args.side_enc, pcs=True, resize=args.resize, gf=args.glob_feat)
+    test_loader = DataLoader(test_set, batch_size=128, shuffle=False, num_workers=16, pin_memory=True)
+    # test_loader = DataLoader(test_set, batch_size=192, shuffle=False, num_workers=8)
+    
+    models = [load_model(args, f'{weight_dir}/model_{i}{"_ft" if args.use_ft else ""}.pth', device) for i in range(args.split)]
+
+    all_seqs = []
+    logits_batches = []  # 存放每个 batch 的 [m,B,2] avg_logits (CPU 上)
+
+    start_time = time.time()
+
+    with torch.no_grad():
+        for x, gt in test_loader:
+            # x: [B, ...] on CPU pin memory，gt: tuple of B strings
+            x = move_to_device(x, device, non_blocking=True)
+            # x = move_to_device(x, device)
+
+            # 1) 记录 5 个模型的 logits
+            # logits: [m,B,2]
+            logits = torch.zeros(len(models), len(gt), args.classes, device=device)
+            for i, m in enumerate(models):
+                logits[i] = m(x)
+
+            # 3) 立刻搬到 CPU（pin_memory 下可以 non_blocking）
+            logits_batches.append(logits.cpu())
+            all_seqs.extend(gt)
+
+    # 拼接成 [n,2]，n = sum(batch_size)
+    all_logits = torch.cat(logits_batches, dim=1)  # [m,n,2]
+    
+    if args.task == 'reg':
+        preds = all_logits.mean(0).squeeze().tolist()
+    elif args.task == 'cls':
+        # 最后一次性 softmax，取正类概率
+        preds = torch.softmax(all_logits, dim=-1)[:, :, 1].mean(0).tolist()
+
+    consumed_time = time.time() - start_time
+    print(f'total consumed time: {consumed_time} s')
+    print(f'time per sample: {consumed_time / len(test_set)} s')
+
+    # 保存到 CSV
+    df = pd.DataFrame({
+        "seq":  all_seqs,
+        "pred": preds,
+    })
+
+    df.to_csv(f'{weight_dir}/preds_case.csv', index=False)
+
+
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/infer_cf.py b/infer_cf.py
new file mode 100644
index 0000000000000000000000000000000000000000..581c60f8a8a11bbc01c6603355b9c1d2b0074799
--- /dev/null
+++ b/infer_cf.py
@@ -0,0 +1,187 @@
+import argparse
+from dataset import PeptidePairDataset, PeptidePairPicDataset
+from network import DMutaPeptide, DMutaPeptideCNN
+from train import move_to_device
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+import numpy as np
+from utils import set_seed
+import pandas as pd
+from torchmetrics import MeanAbsoluteError, RelativeSquaredError, PearsonCorrCoef, KendallRankCorrCoef, F1Score, Accuracy, AveragePrecision, AUROC
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='cnn',
+                    help='lstm mamba mla')
+parser.add_argument('--channels', type=int, default=16)
+parser.add_argument("--side-enc", dest='side_enc', type=str, default=None,
+                    help="use side features")
+parser.add_argument('--fusion', type=str, default='diff',
+                    help='mlp att')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='cls',
+                    help='reg or cls')
+parser.add_argument('--pdb-src', type=str, dest='pdb_src', default='af',
+                    help='af or hf')
+parser.add_argument('--data-ver', type=str, dest='data_ver', default='250228',
+                    help='data version')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=1,
+                    help="Seed (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--warm-steps', type=int, dest='warm_steps', default=0,
+                    help='number of warm start steps for learning rate (default: 10)')
+parser.add_argument('--patience', type=int, default=10,
+                    help='patience for early stopping (default: 10)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')  # /home/duadua/Desktop/fetal/3dpretrain/runs/e50.pth
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='ce',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+parser.add_argument('--simple', dest='simple', action='store_true', default=False)
+parser.add_argument('--llm-data', dest='llm_data', action='store_true', default=False)
+parser.add_argument('--uda', type=str, default=None)
+
+args = parser.parse_args()
+
+if args.llm_data:
+    args.simple = True
+
+if args.simple:
+    args.one_way = True
+
+if args.mix_pcs:
+    args.pcs = 'mix'
+
+if args.q_encoder in ['cnn', 'rn18']:
+    weight_dir = f'./run-{args.task}/{f"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+else:
+    weight_dir = f'./run-{args.task}/{f"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+
+if args.uda:
+    weight_dir += f'/uda_{args.uda}'
+
+print(weight_dir)
+
+def metrics(preds, gt, task):
+    avg = 'marco'
+    device = preds.device
+    if task == 'cls':
+        metric_1 = AveragePrecision(average=avg, task='binary').to(device)
+        metric_2 = AUROC(average=avg, task='binary').to(device)
+        metric_3 = F1Score(average=avg, task='binary').to(device)
+        metric_4 = Accuracy(average=avg, task='binary').to(device)
+        all_metrics = [metric_1(preds, gt).item(), 
+                metric_2(preds, gt).item(), 
+                metric_3(preds, gt).item(), 
+                metric_4(preds, gt).item()]
+    
+    elif task == 'reg':
+        metric_1 = MeanAbsoluteError().to(device)
+        metric_2 = RelativeSquaredError(num_outputs=1).to(device)
+        metric_3 = PearsonCorrCoef(num_outputs=1).to(device)
+        metric_4 = KendallRankCorrCoef(num_outputs=1).to(device)
+        all_metrics = [metric_1(preds, gt).item(), 
+                metric_2(preds, gt).item(), 
+                metric_3(preds.squeeze(), gt.squeeze()).mean().item(), 
+                metric_4(preds.squeeze(), gt.squeeze()).mean().item()]
+
+    return [f'{i * 100:.2f}' for i in all_metrics]
+    
+
+def main(dataset):
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+    elif args.task == 'cls':
+        args.classes = 2
+    else:
+        raise NotImplementedError("unimplemented task")
+
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+    
+    if args.q_encoder in ['cnn', 'rn18']:
+        model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
+        test_set = PeptidePairPicDataset(mode=dataset, pad_length=args.max_length, task=args.task, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+    else:
+        model = DMutaPeptide(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
+        test_set = PeptidePairDataset(mode=dataset, pad_length=args.max_length, task=args.task, gf=args.glob_feat)
+    
+    test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False)
+
+    raw_preds = []
+    ckpt_names = ['model_uda_teacher'] if args.uda else [f'model_{i}_test' for i in range(5)]
+    for i in ckpt_names:
+        model.load_state_dict(torch.load(f'{weight_dir}/{i}.pth', map_location=device))
+        preds = []
+        gt_list_valid = []
+        with torch.no_grad():
+            for data in test_loader:
+                x, gt = data
+                gt_list_valid.append(gt.to(device))
+                out = model(move_to_device(x, device))
+                if args.dir:
+                    out, _ = out
+                preds.append(out)
+        r_pred = torch.cat(preds, dim=0)
+        if args.task == 'reg':
+            preds = r_pred.cpu().numpy()
+        elif args.task == 'cls':
+            preds = torch.softmax(r_pred, dim=-1)[:, 1].cpu().numpy()
+        gt_tensor = torch.cat(gt_list_valid, dim=0)
+        gt_list_valid = gt_tensor.cpu().numpy()
+        raw_preds.append(r_pred)
+    if args.task == 'cls':
+        preds_tensor = torch.softmax(torch.stack(raw_preds, 0), dim=-1)[:, :, 1]
+    elif args.task == 'reg':
+        preds_tensor = torch.stack(raw_preds, 0)
+
+    return [metrics(preds_tensor[i], gt_tensor, args.task) for i in range(len(ckpt_names))]
+
+
+if __name__ == '__main__':
+    if args.task == 'cls':
+        # df = pd.DataFrame(columns=['dataset', 'AUPRC', 'AUROC', 'F1', 'ACC'])
+        print(','.join(['AUPRC', 'AUROC', 'F1', 'ACC']))
+    elif args.task == 'reg':
+        # df = pd.DataFrame(columns=['dataset', 'MAE', 'RSE', 'PCC', 'KCC'])
+        print(','.join(['MAE', 'RSE', 'PCC', 'KCC']))
+
+    results = main('r2_case')
+    for result in results:
+        print(','.join(result))
diff --git a/inferthro.sh b/inferthro.sh
new file mode 100644
index 0000000000000000000000000000000000000000..cbb8c21b9ca07b43c283f184650b3d40605e24d3
--- /dev/null
+++ b/inferthro.sh
@@ -0,0 +1,13 @@
+# !/bin/bash
+python infer.py --task cls --loss ce --q-encoder lstm --channels 256 --fusion diff
+python infer.py --task cls --loss ce --q-encoder mamba --channels 256 --fusion diff
+python infer.py --task cls --loss ce --q-encoder mha --channels 256 --fusion diff
+python infer.py --task cls --loss ce --q-encoder gru --channels 256 --fusion diff
+python infer.py --task cls --loss ce --q-encoder rn18 --channels 16 --fusion diff --pcs --side-enc mamba
+python infer.py --task cls --loss ce --q-encoder rn18 --channels 16 --fusion diff --pcs --side-enc mamba --uda r2
+python infer.py --task reg --loss mse --q-encoder lstm --channels 256 --fusion diff
+python infer.py --task reg --loss mse --q-encoder mamba --channels 256 --fusion diff
+python infer.py --task reg --loss mse --q-encoder mha --channels 256 --fusion diff
+python infer.py --task reg --loss mse --q-encoder gru --channels 256 --fusion diff
+python infer.py --task reg --loss mse --q-encoder rn18 --channels 16 --fusion diff --pcs --side-enc mamba
+python infer.py --task reg --loss mse --q-encoder rn18 --channels 16 --fusion diff --pcs --side-enc mamba --uda r2
diff --git a/loss.py b/loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..e5401b93bce8c2661fd16cb8f5b970a8720c92dd
--- /dev/null
+++ b/loss.py
@@ -0,0 +1,164 @@
+import torch
+from torch import nn
+from torch.nn.modules.loss import _Loss
+import torch.nn.functional as F
+from math import cos, pi, sin
+import math
+import numpy as np
+from scipy.special import lambertw
+
+
+
+def mixup_criterion(criterion, pred, y_a, y_b, lam, pow=2):
+    y = lam ** pow * y_a + (1 - lam) ** pow * y_b
+    return criterion(pred, y)
+
+
+def mixup_data(v, q, a):
+    '''Returns mixed inputs, pairs of targets, and lambda without organ constraint'''
+    lam = np.random.beta(1, 1)
+
+    batch_size = v.shape[0]
+    index = torch.randperm(batch_size)
+
+    mixed_v = lam * v + (1 - lam) * v[index, :]
+    mixed_q = lam * q + (1 - lam) * q[index, :]
+
+    a_1, a_2 = a, a[index]
+    return mixed_v, mixed_q, a_1, a_2, lam
+
+
+def linear(epoch, nepoch):
+    return 1 - epoch / nepoch
+
+
+def convex(epoch, nepoch):
+    return epoch / (2 - nepoch)
+
+
+def concave(epoch, nepoch):
+    return 1 - sin((epoch / nepoch) * (pi / 2))
+
+
+def composite(epoch, nepoch):
+    return 0.5 * cos((epoch / nepoch) * pi) + 0.5
+
+
+class LogCoshLoss(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, y_t, y_prime_t):
+        ey_t = y_t - y_prime_t
+        return torch.mean(torch.log(torch.cosh(ey_t + 1e-12)))+F.mse_loss(y_t, y_prime_t)
+    
+
+class WeightedMSELoss(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, y, y_t, weights=None):
+        loss = (y - y_t) ** 2
+        if weights is not None:
+            loss *= weights.expand_as(loss)
+        return torch.mean(loss)
+
+
+class MLCE(nn.Module):
+    def __init__(self):
+        super(MLCE, self).__init__()
+
+    def _mlcce(self, y_pred, y_true):
+        y_pred = (1 - 2 * y_true) * y_pred
+        y_pred_neg = y_pred - y_true * 1e12
+        y_pred_pos = y_pred - (1 - y_true) * 1e12
+        zeros = torch.zeros_like(y_pred[..., :1])
+        y_pred_neg = torch.cat([y_pred_neg, zeros], dim=-1)
+        y_pred_pos = torch.cat([y_pred_pos, zeros], dim=-1)
+        neg_loss = torch.logsumexp(y_pred_neg, dim=-1)
+        pos_loss = torch.logsumexp(y_pred_pos, dim=-1)
+        loss = torch.mean(neg_loss + pos_loss)
+        return loss
+
+    def __call__(self, y_pred, y_true):
+        return self._mlcce(y_pred, y_true)
+
+
+class SuperLoss(nn.Module):
+    def __init__(self, C=10, lam=1, batch_size=256):
+        super(SuperLoss, self).__init__()
+        self.tau = math.log(C)
+        self.lam = lam  # set to 1 for CIFAR10 and 0.25 for CIFAR100
+        self.batch_size = batch_size
+
+    def forward(self, logits, targets):
+        l_i = F.mse_loss(logits, targets, reduction='none').detach()
+        sigma = self.sigma(l_i)
+        loss = (F.mse_loss(logits, targets, reduction='none') - self.tau) * sigma + self.lam * (
+                torch.log(sigma) ** 2)
+        loss = loss.sum() / self.batch_size
+        return loss
+
+    def sigma(self, l_i):
+        x = torch.ones_like(l_i) * (-2 / math.exp(1.))
+        y = 0.5 * torch.max(x, (l_i - self.tau) / self.lam)
+        y = y.cpu().numpy()
+        sigma = np.exp(-lambertw(y))
+        sigma = sigma.real.astype(np.float32)
+        sigma = torch.from_numpy(sigma).to(l_i.device)
+        return sigma
+
+
+def unbiased_curriculum_loss(out, data, args, epoch, epochs, scheduler='linear'):
+    losses = []
+    scheduler = linear if scheduler == 'linear' else concave
+
+    # calculate difficulty measurement function
+    adjusted_losses = []
+    for idx in range(out.shape[0]):
+        ground_truth = max(1, abs(data[idx].item()))
+        loss = F.mse_loss(out[idx], data[idx])
+        losses.append(loss)
+        adjusted_losses.append(loss.item() / ground_truth)
+
+    mean_loss, std_loss = np.mean(adjusted_losses), np.std(adjusted_losses)
+
+    # re-weight losses
+    total_loss = 0
+    for i, loss in enumerate(losses):
+        if adjusted_losses[i] > mean_loss + 1 * std_loss:
+            schedule_factor = scheduler(epoch, args.epochs)
+            total_loss += schedule_factor * loss
+        else:
+            total_loss += loss
+
+    return total_loss
+
+
+class BMCLoss(_Loss):
+    def __init__(self, init_noise_sigma=1.0):
+        super(BMCLoss, self).__init__()
+        self.noise_sigma = torch.nn.Parameter(torch.tensor(init_noise_sigma))
+
+    def bmc_loss(self, pred, target, noise_var):
+        """Compute the Balanced MSE Loss (BMC) between `pred` and the ground truth `targets`.
+        Args:
+        pred: A float tensor of size [batch, 1].
+        target: A float tensor of size [batch, 1].
+        noise_var: A float number or tensor.
+        Returns:
+        loss: A float tensor. Balanced MSE Loss.
+        """
+        if len(pred.shape) == 1:
+            pred = pred.unsqueeze(1)
+        if len(target.shape) == 1:
+            target = target.unsqueeze(1)
+        logits = - (pred - target.T).pow(2) / (2 * noise_var)   # logit size: [batch, batch]
+        loss = F.cross_entropy(logits, torch.arange(pred.shape[0], device=pred.device))     # contrastive-like loss
+        loss = loss * (2 * noise_var).detach()  # optional: restore the loss scale, 'detach' when noise is learnable 
+
+        return loss
+
+    def forward(self, pred, target):
+        noise_var = self.noise_sigma ** 2
+        return self.bmc_loss(pred, target, noise_var)
diff --git a/main.py b/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e03a45226b8547bde8071b256c2c66a51b3e47b
--- /dev/null
+++ b/main.py
@@ -0,0 +1,245 @@
+import argparse
+import json
+import logging
+import os
+import time
+
+from dataset import PeptidePairDataset, PeptidePairPicDataset
+from network import DMutaPeptide, DMutaPeptideCNN
+from sklearn.model_selection import KFold
+from train import train, train_cls
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, Subset
+import numpy as np
+from loss import MLCE, SuperLoss, LogCoshLoss, BMCLoss
+from utils import set_seed
+
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='lstm',
+                    help='lstm mamba mla')
+parser.add_argument("--side-enc", dest='side_enc', type=str, default=None,
+                    help="use side features")
+parser.add_argument('--channels', type=int, default=256)
+parser.add_argument('--fusion', type=str, default='att',
+                    help='mlp att diff')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='reg',
+                    help='reg or cls')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=42,
+                    help="Seed (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cleavage site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cleavage site')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+# parser.add_argument('--llm-data', action='store_true', default=False,
+#                     help='Use LLM augmentation data')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='mse',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+args = parser.parse_args()
+
+if args.mix_pcs:
+    args.pcs = 'mix'
+
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+        trainer = train
+        if args.loss == "mse" or args.loss in ['ce']:
+            args.loss = 'mse'
+            criterion = nn.MSELoss()
+        elif args.loss == "smoothl1":
+            criterion = nn.SmoothL1Loss()
+        elif args.loss == "super":
+            criterion = SuperLoss()
+        elif args.loss in ["bmc", "bmc_ln"]:
+            criterion = BMCLoss()
+        else:
+            raise NotImplementedError("unimplemented regression task loss function")
+    elif args.task == 'cls':
+        trainer = train_cls
+        args.classes = 2
+        if args.loss == 'ce' or args.loss in ['mse', 'smoothl1', 'super']:
+            args.loss = 'ce'
+            criterion = nn.CrossEntropyLoss()
+        else:
+            raise NotImplementedError("unimplemented classification task loss function")
+    else:
+        raise NotImplementedError("unimplemented task")
+    
+    if args.q_encoder in ['cnn', 'rn18']:
+        weight_dir = f'./run-{args.task}/{"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+    else:
+        weight_dir = f'./run-{args.task}/{"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+
+    if not os.path.exists(weight_dir):
+        os.makedirs(weight_dir)
+    
+    logging.basicConfig(handlers=[
+        logging.FileHandler(filename=os.path.join(weight_dir, "training.log"), encoding='utf-8', mode='w+'),
+        logging.StreamHandler()],
+        format="%(asctime)s: %(message)s", datefmt="%F %T", level=logging.INFO)
+
+    logging.info(f'saving_dir: {weight_dir}')
+    
+    with open(os.path.join(weight_dir, "config.json"), "w") as f:
+        f.write(json.dumps(vars(args)))
+
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+
+    if args.q_encoder in ['cnn', 'rn18']:
+        logging.info('Loading Training Dataset')
+        all_set = PeptidePairPicDataset(mode='train', pad_length=args.max_length, task=args.task, one_way=args.one_way, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+        logging.info('Loading Test Dataset')
+        test_set = PeptidePairPicDataset(mode='test', pad_length=args.max_length, task=args.task, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+    else:
+        logging.info('Loading Train Dataset')
+        all_set = PeptidePairDataset(mode='train', pad_length=args.max_length, task=args.task, one_way=args.one_way, gf=args.glob_feat)
+        logging.info('Loading Test Dataset')
+        test_set = PeptidePairDataset(mode='test', pad_length=args.max_length, task=args.task, gf=args.glob_feat)
+
+    test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True)
+
+    best_perform_list = [[] for i in range(5)]
+    test_perform_list = [[] for i in range(5)]
+
+    kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+    for fold, (train_idx, val_idx) in enumerate(kf.split(all_set)):
+        train_set= Subset(all_set, train_idx)
+        valid_set = Subset(all_set, val_idx)
+
+        train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=8, pin_memory=True)
+        valid_loader = DataLoader(valid_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True)
+
+        if args.q_encoder in ['cnn', 'rn18']:
+            model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese)
+        else:
+            model = DMutaPeptide(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, non_siamese=args.non_siamese)
+        if len(args.pretrain) != 0: #TODO: load pretrain
+            pass
+        model.to(device)
+        # model.compile()
+        
+        optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.decay)
+        # optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.decay)
+
+        # scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10], gamma=0.5)
+        if args.q_encoder == 'cnn':
+            scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
+        else:
+            scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)
+
+        if args.loss == 'bmc_ln':
+            optimizer.add_param_group({'params': criterion.noise_sigma, 'lr': args.lr, 'name': 'noise_sigma'})
+        weights_path = f"{weight_dir}/model_{fold}.pth"
+        # early_stopping = EarlyStopping(patience=args.patience, path=weights_path)
+        logging.info(f'Running Cross Validation {fold}')
+        logging.info(f'Fold {fold}  Train set:{len(train_set)}, Valid set:{len(valid_set)}, Test set: {len(test_set)}')
+        best_metric = -float('inf')
+        best_test = -float('inf')
+        start_time = time.time()
+        if args.task == 'reg':
+            for epoch in range(1, args.epochs + 1):
+                train_loss, mae, rse, pcc, kcc = trainer(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer)
+                logging.info(f'Epoch: {epoch:03d} Train Loss: {train_loss:.3f}, mae: {mae:.3f}, rse: {rse:.3f}, pcc: {pcc:.3f}, kcc: {kcc:.3f}')
+                scheduler.step()
+                avg_metric = (pcc + kcc) - (mae + rse)
+                if avg_metric > best_metric:
+                    logging.info(f'Epoch: {epoch:03d} New best VALIDATION metrics')
+                    torch.save(model.state_dict(), weights_path)
+                    best_metric = avg_metric
+                    best_perform_list[fold] = np.asarray([mae, rse, pcc, kcc])
+                
+                _, test_mae, test_rse, test_pcc, test_kcc = trainer(args, epoch, model, None, test_loader, device, None, None)
+                logging.info(f'Epoch: {epoch:03d} Test results, ap: mae: {test_mae:.3f}, rse: {test_rse:.3f}, pcc: {test_pcc:.3f}, kcc: {test_kcc:.3f}')
+                test_metric = (test_pcc + test_kcc) - (test_mae + test_rse)
+                if test_metric > best_test and epoch > 10:
+                    logging.info(f'Epoch: {epoch:03d} New best TEST metrics')
+                    best_test = test_metric
+                    test_perform_list[fold] = np.asarray([test_mae, test_rse, test_pcc, test_kcc])
+                    torch.save(model.state_dict(), weights_path.replace('.pth', '_test.pth'))
+        
+        elif args.task == 'cls':
+            for epoch in range(1, args.epochs + 1):
+                train_loss, ap, auc, f1, acc = trainer(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer)
+                logging.info(f'Epoch: {epoch:03d} Train Loss: {train_loss:.3f}, ap: {ap:.3f}, auc: {auc:.3f}, f1: {f1:.3f}, acc: {acc:.3f}')
+                scheduler.step()
+                avg_metric = ap + auc #+ f1 + acc
+                if avg_metric > best_metric:
+                    logging.info(f'Epoch: {epoch:03d} New best VALIDATION metrics')
+                    torch.save(model.state_dict(), weights_path)
+                    best_metric = avg_metric
+                    best_perform_list[fold] = np.asarray([ap, auc, f1, acc])
+                
+                _, test_ap, test_auc, test_f1, test_acc = trainer(args, epoch, model, None, test_loader, device, None, None)
+                logging.info(f'Epoch: {epoch:03d} Test results, ap: {test_ap:.3f}, auc: {test_auc:.3f}, f1: {test_f1:.3f}, acc: {test_acc:.3f}')
+                test_metric = test_ap + test_auc #+ test_f1 + test_acc
+                if test_metric > best_test and epoch > 10:
+                    logging.info(f'Epoch: {epoch:03d} New best TEST metrics')
+                    best_test = test_metric
+                    test_perform_list[fold] = np.asarray([test_ap, test_auc, test_f1, test_acc])
+                    torch.save(model.state_dict(), weights_path.replace('.pth', '_test.pth'))
+        
+        torch.save(model.state_dict(), weights_path.replace('.pth', '_last.pth'))
+        logging.info(f'used time {(time.time()-start_time)/3600:.2f}h')
+
+    logging.info(f'Cross Validation Finished!')
+    best_perform_list = np.asarray(best_perform_list)
+    test_perform_list = np.asarray(test_perform_list)
+    logging.info('Best validation perform list\n%s', best_perform_list)
+    logging.info('mean: %s', np.round(np.mean(best_perform_list, 0), 3))
+    logging.info('std: %s', np.round(np.std(best_perform_list, 0), 3))
+    logging.info('Best test perform list\n%s', test_perform_list)
+    logging.info('mean: %s', np.round(np.mean(test_perform_list, 0), 3))
+    logging.info('std: %s', np.round(np.std(test_perform_list, 0), 3))
+    perform = open(weight_dir+'/result.txt', 'w')
+    perform.write('Valid\n')
+    perform.write(','.join([str(i) for i in np.mean(best_perform_list, 0)])+'\n')
+    perform.write(','.join([str(i) for i in np.std(best_perform_list, 0)])+'\n')
+    perform.write('Test\n')
+    perform.write(','.join([str(i) for i in np.mean(test_perform_list, 0)])+'\n')
+    perform.write(','.join([str(i) for i in np.std(test_perform_list, 0)])+'\n')
+
+
+if __name__ == "__main__":
+    main()
diff --git a/main_aug.py b/main_aug.py
new file mode 100644
index 0000000000000000000000000000000000000000..42d2afbbed12f35ddc9a3c99aef7b61f47b32b32
--- /dev/null
+++ b/main_aug.py
@@ -0,0 +1,412 @@
+import argparse
+import json
+import logging
+import os
+import time
+
+from dataset import PeptidePairDataset, PeptidePairPicDataset
+from network import DMutaPeptide, DMutaPeptideCNN
+from sklearn.model_selection import KFold
+from torchmetrics import MeanAbsoluteError, RelativeSquaredError, PearsonCorrCoef, KendallRankCorrCoef, F1Score, Accuracy, AveragePrecision, AUROC
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, Subset
+import torchvision.transforms.v2 as T
+import numpy as np
+from loss import MLCE, SuperLoss, LogCoshLoss, BMCLoss
+from utils import set_seed
+
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='lstm',
+                    help='lstm mamba mla')
+parser.add_argument("--side-enc", dest='side_enc', type=str, default=None,
+                    help="use side features")
+parser.add_argument('--channels', type=int, default=256)
+parser.add_argument('--fusion', type=str, default='att',
+                    help='mlp att diff')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='reg',
+                    help='reg or cls')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=42,
+                    help="Seed (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cleavage site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cleavage site')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+# parser.add_argument('--llm-data', action='store_true', default=False,
+#                     help='Use LLM augmentation data')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='mse',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+args = parser.parse_args()
+
+if args.mix_pcs:
+    args.pcs = 'mix'
+
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+        trainer = train
+        if args.loss == "mse" or args.loss in ['ce']:
+            args.loss = 'mse'
+            criterion = nn.MSELoss()
+        elif args.loss == "smoothl1":
+            criterion = nn.SmoothL1Loss()
+        elif args.loss == "super":
+            criterion = SuperLoss()
+        elif args.loss in ["bmc", "bmc_ln"]:
+            criterion = BMCLoss()
+        else:
+            raise NotImplementedError("unimplemented regression task loss function")
+    elif args.task == 'cls':
+        trainer = train_cls
+        args.classes = 2
+        if args.loss == 'ce' or args.loss in ['mse', 'smoothl1', 'super']:
+            args.loss = 'ce'
+            criterion = nn.CrossEntropyLoss()
+        else:
+            raise NotImplementedError("unimplemented classification task loss function")
+    else:
+        raise NotImplementedError("unimplemented task")
+    
+    if args.q_encoder in ['cnn', 'rn18']:
+        weight_dir = f'./run-{args.task}/{"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}_aug'
+    else:
+        weight_dir = f'./run-{args.task}/{"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}_aug'
+
+    if not os.path.exists(weight_dir):
+        os.makedirs(weight_dir)
+    
+    logging.basicConfig(handlers=[
+        logging.FileHandler(filename=os.path.join(weight_dir, "training.log"), encoding='utf-8', mode='w+'),
+        logging.StreamHandler()],
+        format="%(asctime)s: %(message)s", datefmt="%F %T", level=logging.INFO)
+
+    logging.info(f'saving_dir: {weight_dir}')
+    
+    with open(os.path.join(weight_dir, "config.json"), "w") as f:
+        f.write(json.dumps(vars(args)))
+
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+
+    if args.q_encoder in ['cnn', 'rn18']:
+        logging.info('Loading Training Dataset')
+        all_set = PeptidePairPicDataset(mode='train', pad_length=args.max_length, task=args.task, one_way=args.one_way, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+        logging.info('Loading Test Dataset')
+        test_set = PeptidePairPicDataset(mode='test', pad_length=args.max_length, task=args.task, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+    else:
+        logging.info('Loading Train Dataset')
+        all_set = PeptidePairDataset(mode='train', pad_length=args.max_length, task=args.task, one_way=args.one_way, gf=args.glob_feat)
+        logging.info('Loading Test Dataset')
+        test_set = PeptidePairDataset(mode='test', pad_length=args.max_length, task=args.task, gf=args.glob_feat)
+
+    test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True)
+
+    best_perform_list = [[] for i in range(5)]
+    test_perform_list = [[] for i in range(5)]
+
+    kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+    for fold, (train_idx, val_idx) in enumerate(kf.split(all_set)):
+        train_set= Subset(all_set, train_idx)
+        valid_set = Subset(all_set, val_idx)
+
+        train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=8, pin_memory=True)
+        valid_loader = DataLoader(valid_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True)
+
+        if args.q_encoder in ['cnn', 'rn18']:
+            model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese)
+        else:
+            model = DMutaPeptide(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, non_siamese=args.non_siamese)
+        if len(args.pretrain) != 0: #TODO: load pretrain
+            pass
+        model.to(device)
+        # model.compile()
+        
+        optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.decay)
+        # optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.decay)
+
+        # scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10], gamma=0.5)
+        if args.q_encoder == 'cnn':
+            scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
+        else:
+            scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)
+
+        if args.loss == 'bmc_ln':
+            optimizer.add_param_group({'params': criterion.noise_sigma, 'lr': args.lr, 'name': 'noise_sigma'})
+        weights_path = f"{weight_dir}/model_{fold}.pth"
+        # early_stopping = EarlyStopping(patience=args.patience, path=weights_path)
+        logging.info(f'Running Cross Validation {fold}')
+        logging.info(f'Fold {fold}  Train set:{len(train_set)}, Valid set:{len(valid_set)}, Test set: {len(test_set)}')
+        best_metric = -float('inf')
+        best_test = -float('inf')
+        start_time = time.time()
+        if args.task == 'reg':
+            for epoch in range(1, args.epochs + 1):
+                train_loss, mae, rse, pcc, kcc = trainer(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer)
+                logging.info(f'Epoch: {epoch:03d} Train Loss: {train_loss:.3f}, mae: {mae:.3f}, rse: {rse:.3f}, pcc: {pcc:.3f}, kcc: {kcc:.3f}')
+                scheduler.step()
+                avg_metric = (pcc + kcc) - (mae + rse)
+                if avg_metric > best_metric:
+                    logging.info(f'Epoch: {epoch:03d} New best VALIDATION metrics')
+                    torch.save(model.state_dict(), weights_path)
+                    best_metric = avg_metric
+                    best_perform_list[fold] = np.asarray([mae, rse, pcc, kcc])
+                
+                _, test_mae, test_rse, test_pcc, test_kcc = trainer(args, epoch, model, None, test_loader, device, None, None)
+                logging.info(f'Epoch: {epoch:03d} Test results, ap: mae: {test_mae:.3f}, rse: {test_rse:.3f}, pcc: {test_pcc:.3f}, kcc: {test_kcc:.3f}')
+                test_metric = (test_pcc + test_kcc) - (test_mae + test_rse)
+                if test_metric > best_test and epoch > 10:
+                    logging.info(f'Epoch: {epoch:03d} New best TEST metrics')
+                    best_test = test_metric
+                    test_perform_list[fold] = np.asarray([test_mae, test_rse, test_pcc, test_kcc])
+                    torch.save(model.state_dict(), weights_path.replace('.pth', '_test.pth'))
+        
+        elif args.task == 'cls':
+            for epoch in range(1, args.epochs + 1):
+                train_loss, ap, auc, f1, acc = trainer(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer)
+                logging.info(f'Epoch: {epoch:03d} Train Loss: {train_loss:.3f}, ap: {ap:.3f}, auc: {auc:.3f}, f1: {f1:.3f}, acc: {acc:.3f}')
+                scheduler.step()
+                avg_metric = ap + auc #+ f1 + acc
+                if avg_metric > best_metric:
+                    logging.info(f'Epoch: {epoch:03d} New best VALIDATION metrics')
+                    torch.save(model.state_dict(), weights_path)
+                    best_metric = avg_metric
+                    best_perform_list[fold] = np.asarray([ap, auc, f1, acc])
+                
+                _, test_ap, test_auc, test_f1, test_acc = trainer(args, epoch, model, None, test_loader, device, None, None)
+                logging.info(f'Epoch: {epoch:03d} Test results, ap: {test_ap:.3f}, auc: {test_auc:.3f}, f1: {test_f1:.3f}, acc: {test_acc:.3f}')
+                test_metric = test_ap + test_auc #+ test_f1 + test_acc
+                if test_metric > best_test and epoch > 10:
+                    logging.info(f'Epoch: {epoch:03d} New best TEST metrics')
+                    best_test = test_metric
+                    test_perform_list[fold] = np.asarray([test_ap, test_auc, test_f1, test_acc])
+                    torch.save(model.state_dict(), weights_path.replace('.pth', '_test.pth'))
+        
+        torch.save(model.state_dict(), weights_path.replace('.pth', '_last.pth'))
+        logging.info(f'used time {(time.time()-start_time)/3600:.2f}h')
+
+    logging.info(f'Cross Validation Finished!')
+    best_perform_list = np.asarray(best_perform_list)
+    test_perform_list = np.asarray(test_perform_list)
+    logging.info('Best validation perform list\n%s', best_perform_list)
+    logging.info('mean: %s', np.round(np.mean(best_perform_list, 0), 3))
+    logging.info('std: %s', np.round(np.std(best_perform_list, 0), 3))
+    logging.info('Best test perform list\n%s', test_perform_list)
+    logging.info('mean: %s', np.round(np.mean(test_perform_list, 0), 3))
+    logging.info('std: %s', np.round(np.std(test_perform_list, 0), 3))
+    perform = open(weight_dir+'/result.txt', 'w')
+    perform.write('Valid\n')
+    perform.write(','.join([str(i) for i in np.mean(best_perform_list, 0)])+'\n')
+    perform.write(','.join([str(i) for i in np.std(best_perform_list, 0)])+'\n')
+    perform.write('Test\n')
+    perform.write(','.join([str(i) for i in np.mean(test_perform_list, 0)])+'\n')
+    perform.write(','.join([str(i) for i in np.std(test_perform_list, 0)])+'\n')
+
+
+def move_to_device(batch, device, non_blocking=False):
+    if isinstance(batch, (list, tuple)):
+        return type(batch)(move_to_device(item, device, non_blocking) for item in batch)
+    return batch.to(device, non_blocking=non_blocking)
+
+
+def move_and_aug(batch, device, transforms, non_blocking=False):
+    batch = move_to_device(batch, device, non_blocking)
+    if not isinstance(batch[0][0], (list, tuple)):
+        return batch
+    
+    for i in range(batch[0][0][0].shape[0]):
+        img_pair = torch.stack((batch[0][0][0][i], batch[0][1][0][i]), dim=0)
+        img_pair = transforms(img_pair)
+        batch[0][0][0][i] = img_pair[0]
+        batch[0][1][0][i] = img_pair[1]
+    return batch
+
+
+class GaussianNoise(nn.Module):
+    def __init__(self, mean=0., sigma=0.15):
+        super(GaussianNoise, self).__init__()
+        self.mean = mean
+        self.sigma = sigma
+
+    def forward(self, x):
+        return x + torch.randn_like(x) * self.sigma + self.mean
+
+
+Transforms = T.Compose([
+    T.RandomResizedCrop(args.resize, scale=(0.9, 1.0)),
+    T.RandomRotation(degrees=30),
+    GaussianNoise(0., 0.05),
+])
+
+def train(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer):
+    train_loss = 0
+    num_labels = model.classes
+    metric_mae = MeanAbsoluteError().to(device)
+    metric_rse = RelativeSquaredError(num_outputs=num_labels).to(device)
+    metric_pcc = PearsonCorrCoef(num_outputs=num_labels).to(device)
+    metric_kcc = KendallRankCorrCoef(num_outputs=num_labels).to(device)
+
+    if args.dir:
+        encodings, labels = [], []
+
+    if train_loader is not None:
+        model.train()
+        for data in train_loader:
+            x, gt = data
+            x = move_and_aug(x, device, Transforms)
+            if args.dir:
+                out, features = model(x,
+                                      gt.to(device),
+                                      epoch)
+                encodings.append(features.detach().cpu())
+                labels.append(gt.cpu())
+            else:
+                out = model(x)
+            loss = criterion(out, gt.to(device))
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+            train_loss += loss.item()
+        train_loss /= len(train_loader)
+
+        if args.dir:
+            encodings, labels = torch.cat(encodings), torch.cat(labels)
+            model.FDS.update_last_epoch_stats(epoch)
+            model.FDS.update_running_stats(encodings, labels, epoch)
+            encodings, labels = [], []
+            
+
+    model.eval()
+    preds = []
+    gt_list_valid = []
+    with torch.no_grad():
+        for data in valid_loader:
+            x, gt = data
+            x = move_to_device(x, device)
+            gt_list_valid.append(gt.to(device))
+            out = model(x)
+            if args.dir:
+                out, _ = out
+            preds.append(out)
+
+    # calculate metrics
+    preds = torch.cat(preds, dim=0)
+    gt_list_valid = torch.cat(gt_list_valid, dim=0)
+
+    mae = metric_mae(preds, gt_list_valid).item()
+    rse = metric_rse(preds, gt_list_valid).item()
+    pcc = metric_pcc(preds.squeeze(), gt_list_valid.squeeze()).mean().item()
+    kcc = metric_kcc(preds.squeeze(), gt_list_valid.squeeze()).mean().item()
+    return train_loss, mae, rse, pcc, kcc
+
+
+def update_ce_loss_weight(loss_fn: torch.nn.CrossEntropyLoss, gt: torch.Tensor, num_classes: int, device):
+    """
+    根据当前 batch 的 ground truth 标签更新 nn.CrossEntropyLoss 对象中的 weight 缓冲区，
+    使用逆频率方法计算新权重，并通过 register_buffer 进行原地更新。
+    
+    参数:
+      loss_fn (nn.CrossEntropyLoss): 已初始化的 nn.CrossEntropyLoss 对象，
+                                      要求在初始化时已经注册了 weight 缓冲区。
+      gt (torch.Tensor): 当前 batch 的 ground truth 标签，1D整数张量，标签取值范围 [0, num_classes-1]。
+    """
+    class_counts = torch.bincount(gt, minlength=num_classes).float()
+    epsilon = 1e-6
+    new_weights = 1.0 / (class_counts + epsilon)
+    new_weights = new_weights / new_weights.sum() * num_classes
+    # 使用 register_buffer 来更新 loss_fn 内部的 weight 缓冲区
+    loss_fn.register_buffer('weight', new_weights.to(device))
+
+def train_cls(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer):
+    train_loss = 0
+    num_labels = model.classes
+    avg = args.metric_avg
+    if num_labels == 1 or num_labels == 2:
+        task = 'binary'
+    else:
+        task = 'multiclass'
+    metric_acc = Accuracy(average=avg, task=task, num_classes=num_labels).to(device)
+    metric_f1 = F1Score(average=avg, task=task, num_classes=num_labels).to(device)
+    metric_ap = AveragePrecision(average=avg, task=task, num_classes=num_labels).to(device)
+    metric_auc = AUROC(average=avg, task=task, num_classes=num_labels).to(device)
+
+    if train_loader is not None:
+        model.train()
+        for data in train_loader:
+            x, gt = data
+            x = move_to_device(x, device)
+            out = model(x)
+            update_ce_loss_weight(criterion, gt, num_classes=num_labels, device=device)
+            loss = criterion(out, gt.to(device))
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+            train_loss += loss.item()
+        train_loss /= len(train_loader)
+
+    model.eval()
+    preds = []
+    gt_list_valid = []
+    with torch.no_grad():
+        for data in valid_loader:
+            x, gt = data
+            x = move_to_device(x, device)
+            gt_list_valid.append(gt.to(device))
+            out = model(x)
+            preds.append(out)
+
+    # calculate metrics
+    preds = torch.softmax(torch.cat(preds, dim=0), dim=-1).squeeze()
+    gt_list_valid = torch.cat(gt_list_valid, dim=0).int().squeeze()
+
+    if num_labels == 2:
+        preds = preds[:, 1]
+
+    ap = metric_ap(preds, gt_list_valid).item()
+    auc = metric_auc(preds, gt_list_valid).item()
+    f1 = metric_f1(preds, gt_list_valid).item()
+    acc = metric_acc(preds, gt_list_valid).item()
+    return train_loss, ap, auc, f1, acc
+
+
+if __name__ == "__main__":
+    main()
diff --git a/main_imagemol.py b/main_imagemol.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbf07646ad33efdf2b1f13e4700bf02b88cc39aa
--- /dev/null
+++ b/main_imagemol.py
@@ -0,0 +1,246 @@
+import argparse
+import json
+import logging
+import os
+import time
+
+from dataset import PeptidePairDataset, PeptidePairPicDataset
+from network import DMutaPeptide, DMutaPeptideCNN
+from sklearn.model_selection import KFold
+from train import train, train_cls
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, Subset
+import numpy as np
+from loss import MLCE, SuperLoss, LogCoshLoss, BMCLoss
+from utils import set_seed
+
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='rn18',
+                    help='lstm mamba mla')
+parser.add_argument("--side-enc", dest='side_enc', type=str, default=None,
+                    help="use side features")
+parser.add_argument('--channels', type=int, default=256)
+parser.add_argument('--fusion', type=str, default='att',
+                    help='mlp att diff')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='reg',
+                    help='reg or cls')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=42,
+                    help="Seed (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cleavage site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cleavage site')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+# parser.add_argument('--llm-data', action='store_true', default=False,
+#                     help='Use LLM augmentation data')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='mse',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+args = parser.parse_args()
+
+if args.mix_pcs:
+    args.pcs = 'mix'
+
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+        trainer = train
+        if args.loss == "mse" or args.loss in ['ce']:
+            args.loss = 'mse'
+            criterion = nn.MSELoss()
+        elif args.loss == "smoothl1":
+            criterion = nn.SmoothL1Loss()
+        elif args.loss == "super":
+            criterion = SuperLoss()
+        elif args.loss in ["bmc", "bmc_ln"]:
+            criterion = BMCLoss()
+        else:
+            raise NotImplementedError("unimplemented regression task loss function")
+    elif args.task == 'cls':
+        trainer = train_cls
+        args.classes = 2
+        if args.loss == 'ce' or args.loss in ['mse', 'smoothl1', 'super']:
+            args.loss = 'ce'
+            criterion = nn.CrossEntropyLoss()
+        else:
+            raise NotImplementedError("unimplemented classification task loss function")
+    else:
+        raise NotImplementedError("unimplemented task")
+    
+    if args.q_encoder in ['cnn', 'rn18']:
+        weight_dir = f'./run-{args.task}/{"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}_ImageMol'
+    else:
+        weight_dir = f'./run-{args.task}/{"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}_ImageMol'
+
+    if not os.path.exists(weight_dir):
+        os.makedirs(weight_dir)
+    
+    logging.basicConfig(handlers=[
+        logging.FileHandler(filename=os.path.join(weight_dir, "training.log"), encoding='utf-8', mode='w+'),
+        logging.StreamHandler()],
+        format="%(asctime)s: %(message)s", datefmt="%F %T", level=logging.INFO)
+
+    logging.info(f'saving_dir: {weight_dir}')
+    
+    with open(os.path.join(weight_dir, "config.json"), "w") as f:
+        f.write(json.dumps(vars(args)))
+
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+
+    if args.q_encoder in ['cnn', 'rn18']:
+        logging.info('Loading Training Dataset')
+        all_set = PeptidePairPicDataset(mode='train', pad_length=args.max_length, task=args.task, one_way=args.one_way, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+        logging.info('Loading Test Dataset')
+        test_set = PeptidePairPicDataset(mode='test', pad_length=args.max_length, task=args.task, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+    else:
+        logging.info('Loading Train Dataset')
+        all_set = PeptidePairDataset(mode='train', pad_length=args.max_length, task=args.task, one_way=args.one_way, gf=args.glob_feat)
+        logging.info('Loading Test Dataset')
+        test_set = PeptidePairDataset(mode='test', pad_length=args.max_length, task=args.task, gf=args.glob_feat)
+
+    test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True)
+
+    best_perform_list = [[] for i in range(5)]
+    test_perform_list = [[] for i in range(5)]
+
+    kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+    for fold, (train_idx, val_idx) in enumerate(kf.split(all_set)):
+        train_set= Subset(all_set, train_idx)
+        valid_set = Subset(all_set, val_idx)
+
+        train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=8, pin_memory=True)
+        valid_loader = DataLoader(valid_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True)
+
+        if args.q_encoder in ['cnn', 'rn18']:
+            model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese)
+            model.q_encoder.load_state_dict(torch.load('./ImageMolEncoder.pth', map_location=device))
+        else:
+            model = DMutaPeptide(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, non_siamese=args.non_siamese)
+        if len(args.pretrain) != 0: #TODO: load pretrain
+            pass
+        model.to(device)
+        # model.compile()
+        
+        optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.decay)
+        # optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.decay)
+
+        # scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10], gamma=0.5)
+        if args.q_encoder == 'cnn':
+            scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
+        else:
+            scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)
+
+        if args.loss == 'bmc_ln':
+            optimizer.add_param_group({'params': criterion.noise_sigma, 'lr': args.lr, 'name': 'noise_sigma'})
+        weights_path = f"{weight_dir}/model_{fold}.pth"
+        # early_stopping = EarlyStopping(patience=args.patience, path=weights_path)
+        logging.info(f'Running Cross Validation {fold}')
+        logging.info(f'Fold {fold}  Train set:{len(train_set)}, Valid set:{len(valid_set)}, Test set: {len(test_set)}')
+        best_metric = -float('inf')
+        best_test = -float('inf')
+        start_time = time.time()
+        if args.task == 'reg':
+            for epoch in range(1, args.epochs + 1):
+                train_loss, mae, rse, pcc, kcc = trainer(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer)
+                logging.info(f'Epoch: {epoch:03d} Train Loss: {train_loss:.3f}, mae: {mae:.3f}, rse: {rse:.3f}, pcc: {pcc:.3f}, kcc: {kcc:.3f}')
+                scheduler.step()
+                avg_metric = (pcc + kcc) - (mae + rse)
+                if avg_metric > best_metric:
+                    logging.info(f'Epoch: {epoch:03d} New best VALIDATION metrics')
+                    torch.save(model.state_dict(), weights_path)
+                    best_metric = avg_metric
+                    best_perform_list[fold] = np.asarray([mae, rse, pcc, kcc])
+                
+                _, test_mae, test_rse, test_pcc, test_kcc = trainer(args, epoch, model, None, test_loader, device, None, None)
+                logging.info(f'Epoch: {epoch:03d} Test results, ap: mae: {test_mae:.3f}, rse: {test_rse:.3f}, pcc: {test_pcc:.3f}, kcc: {test_kcc:.3f}')
+                test_metric = (test_pcc + test_kcc) - (test_mae + test_rse)
+                if test_metric > best_test and epoch > 10:
+                    logging.info(f'Epoch: {epoch:03d} New best TEST metrics')
+                    best_test = test_metric
+                    test_perform_list[fold] = np.asarray([test_mae, test_rse, test_pcc, test_kcc])
+                    torch.save(model.state_dict(), weights_path.replace('.pth', '_test.pth'))
+        
+        elif args.task == 'cls':
+            for epoch in range(1, args.epochs + 1):
+                train_loss, ap, auc, f1, acc = trainer(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer)
+                logging.info(f'Epoch: {epoch:03d} Train Loss: {train_loss:.3f}, ap: {ap:.3f}, auc: {auc:.3f}, f1: {f1:.3f}, acc: {acc:.3f}')
+                scheduler.step()
+                avg_metric = ap + auc #+ f1 + acc
+                if avg_metric > best_metric:
+                    logging.info(f'Epoch: {epoch:03d} New best VALIDATION metrics')
+                    torch.save(model.state_dict(), weights_path)
+                    best_metric = avg_metric
+                    best_perform_list[fold] = np.asarray([ap, auc, f1, acc])
+                
+                _, test_ap, test_auc, test_f1, test_acc = trainer(args, epoch, model, None, test_loader, device, None, None)
+                logging.info(f'Epoch: {epoch:03d} Test results, ap: {test_ap:.3f}, auc: {test_auc:.3f}, f1: {test_f1:.3f}, acc: {test_acc:.3f}')
+                test_metric = test_ap + test_auc #+ test_f1 + test_acc
+                if test_metric > best_test and epoch > 10:
+                    logging.info(f'Epoch: {epoch:03d} New best TEST metrics')
+                    best_test = test_metric
+                    test_perform_list[fold] = np.asarray([test_ap, test_auc, test_f1, test_acc])
+                    torch.save(model.state_dict(), weights_path.replace('.pth', '_test.pth'))
+        
+        torch.save(model.state_dict(), weights_path.replace('.pth', '_last.pth'))
+        logging.info(f'used time {(time.time()-start_time)/3600:.2f}h')
+
+    logging.info(f'Cross Validation Finished!')
+    best_perform_list = np.asarray(best_perform_list)
+    test_perform_list = np.asarray(test_perform_list)
+    logging.info('Best validation perform list\n%s', best_perform_list)
+    logging.info('mean: %s', np.round(np.mean(best_perform_list, 0), 3))
+    logging.info('std: %s', np.round(np.std(best_perform_list, 0), 3))
+    logging.info('Best test perform list\n%s', test_perform_list)
+    logging.info('mean: %s', np.round(np.mean(test_perform_list, 0), 3))
+    logging.info('std: %s', np.round(np.std(test_perform_list, 0), 3))
+    perform = open(weight_dir+'/result.txt', 'w')
+    perform.write('Valid\n')
+    perform.write(','.join([str(i) for i in np.mean(best_perform_list, 0)])+'\n')
+    perform.write(','.join([str(i) for i in np.std(best_perform_list, 0)])+'\n')
+    perform.write('Test\n')
+    perform.write(','.join([str(i) for i in np.mean(test_perform_list, 0)])+'\n')
+    perform.write(','.join([str(i) for i in np.std(test_perform_list, 0)])+'\n')
+
+
+if __name__ == "__main__":
+    main()
diff --git a/main_simple.py b/main_simple.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2469f1c5ad994fe410570451cbaa521cbe847ba
--- /dev/null
+++ b/main_simple.py
@@ -0,0 +1,208 @@
+import argparse
+import json
+import logging
+import os
+import time
+
+from dataset import PeptidePairDataset, PeptidePairPicDataset, SimplePairClsDataset
+from network import DMutaPeptide, DMutaPeptideCNN
+from sklearn.model_selection import KFold
+from train import train, train_cls
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, Subset
+import numpy as np
+from loss import MLCE, SuperLoss, LogCoshLoss, BMCLoss
+from utils import set_seed
+
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='lstm',
+                    help='lstm mamba mla')
+parser.add_argument("--side-enc", dest='side_enc', type=str, default=None,
+                    help="use side features")
+parser.add_argument('--channels', type=int, default=256)
+parser.add_argument('--fusion', type=str, default='att',
+                    help='mlp att diff')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='cls',
+                    help='reg or cls')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=True,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=1,
+                    help="Seed (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+parser.add_argument('--llm-data', action='store_true', default=False,
+                    help='Use LLM augmentation data')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='ce',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+args = parser.parse_args()
+
+if args.mix_pcs:
+    args.pcs = 'mix'
+
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        raise NotImplementedError("unimplemented regression task")
+    elif args.task == 'cls':
+        trainer = train_cls
+        args.classes = 2
+        if args.loss == 'ce' or args.loss in ['mse', 'smoothl1', 'super']:
+            args.loss = 'ce'
+            criterion = nn.CrossEntropyLoss()
+        else:
+            raise NotImplementedError("unimplemented classification task loss function")
+    else:
+        raise NotImplementedError("unimplemented task")
+    
+    if args.q_encoder in ['cnn', 'rn18']:
+        weight_dir = f'./run-{args.task}/{"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}-simple{"-llm" if args.llm_data else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+    else:
+        weight_dir = f'./run-{args.task}/{f"non-siamese-" if args.non_siamese else ""}{args.q_encoder}-{args.fusion}-{args.channels}-simple{"-llm" if args.llm_data else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+
+    if not os.path.exists(weight_dir):
+        os.makedirs(weight_dir)
+    
+    logging.basicConfig(handlers=[
+        logging.FileHandler(filename=os.path.join(weight_dir, "training.log"), encoding='utf-8', mode='w+'),
+        logging.StreamHandler()],
+        format="%(asctime)s: %(message)s", datefmt="%F %T", level=logging.INFO)
+
+    logging.info(f'saving_dir: {weight_dir}')
+    
+    with open(os.path.join(weight_dir, "config.json"), "w") as f:
+        f.write(json.dumps(vars(args)))
+
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+
+    logging.info('Loading Training Dataset')
+    all_set = SimplePairClsDataset(pad_length=args.max_length, llm=args.llm_data, gf=args.glob_feat, q_encoder=args.q_encoder, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+
+    logging.info('Loading Test Dataset')
+    if args.q_encoder in ['cnn', 'rn18']:
+        test_set = PeptidePairPicDataset(mode='test', pad_length=args.max_length, task=args.task, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+    else:
+        test_set = PeptidePairDataset(mode='test', pad_length=args.max_length, task=args.task, gf=args.glob_feat)
+
+    test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True)
+
+    best_perform_list = [[] for i in range(5)]
+    test_perform_list = [[] for i in range(5)]
+
+    kf = KFold(n_splits=5, shuffle=True, random_state=42)
+
+    for fold, (train_idx, val_idx) in enumerate(kf.split(all_set)):
+        train_set= Subset(all_set, train_idx)
+        valid_set = Subset(all_set, val_idx)
+
+        train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=8, pin_memory=True)
+        valid_loader = DataLoader(valid_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True)
+
+        if args.q_encoder in ['cnn', 'rn18']:
+            model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese)
+        else:
+            model = DMutaPeptide(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, non_siamese=args.non_siamese)
+        if len(args.pretrain) != 0: #TODO: load pretrain
+            pass
+        model.to(device)
+        # model.compile()
+        
+        optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.decay)
+
+        if args.q_encoder == 'cnn':
+            scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)
+        else:
+            scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.5)
+
+        if args.loss == 'bmc_ln':
+            optimizer.add_param_group({'params': criterion.noise_sigma, 'lr': args.lr, 'name': 'noise_sigma'})
+        weights_path = f"{weight_dir}/model_{fold}.pth"
+        # early_stopping = EarlyStopping(patience=args.patience, path=weights_path)
+        logging.info(f'Running Cross Validation {fold}')
+        logging.info(f'Fold {fold}  Train set:{len(train_set)}, Valid set:{len(valid_set)}, Test set: {len(test_set)}')
+        best_metric = -float('inf')
+        best_test = -float('inf')
+        start_time = time.time()
+        if args.task == 'cls':
+            for epoch in range(1, args.epochs + 1):
+                train_loss, ap, auc, f1, acc = trainer(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer)
+                logging.info(f'Epoch: {epoch:03d} Train Loss: {train_loss:.3f}, ap: {ap:.3f}, auc: {auc:.3f}, f1: {f1:.3f}, acc: {acc:.3f}')
+                scheduler.step()
+                avg_metric = ap + auc #+ f1 + acc
+                if avg_metric > best_metric:
+                    logging.info(f'Epoch: {epoch:03d} New best VALIDATION metrics')
+                    torch.save(model.state_dict(), weights_path)
+                    best_metric = avg_metric
+                    best_perform_list[fold] = np.asarray([ap, auc, f1, acc])
+                
+                _, test_ap, test_auc, test_f1, test_acc = trainer(args, epoch, model, None, test_loader, device, None, None)
+                logging.info(f'Epoch: {epoch:03d} Test results, ap: {test_ap:.3f}, auc: {test_auc:.3f}, f1: {test_f1:.3f}, acc: {test_acc:.3f}')
+                test_metric = test_ap + test_auc #+ test_f1 + test_acc
+                if test_metric > best_test and epoch > 10:
+                    logging.info(f'Epoch: {epoch:03d} New best TEST metrics')
+                    best_test = test_metric
+                    test_perform_list[fold] = np.asarray([test_ap, test_auc, test_f1, test_acc])
+                    torch.save(model.state_dict(), weights_path.replace('.pth', '_test.pth'))
+        
+        torch.save(model.state_dict(), weights_path.replace('.pth', '_last.pth'))
+        logging.info(f'used time {(time.time()-start_time)/3600:.2f}h')
+
+    logging.info(f'Cross Validation Finished!')
+    best_perform_list = np.asarray(best_perform_list)
+    test_perform_list = np.asarray(test_perform_list)
+    logging.info('Best validation perform list\n%s', best_perform_list)
+    logging.info('mean: %s', np.round(np.mean(best_perform_list, 0), 3))
+    logging.info('std: %s', np.round(np.std(best_perform_list, 0), 3))
+    logging.info('Best test perform list\n%s', test_perform_list)
+    logging.info('mean: %s', np.round(np.mean(test_perform_list, 0), 3))
+    logging.info('std: %s', np.round(np.std(test_perform_list, 0), 3))
+    perform = open(weight_dir+'/result.txt', 'w')
+    perform.write('Valid\n')
+    perform.write(','.join([str(i) for i in np.mean(best_perform_list, 0)])+'\n')
+    perform.write(','.join([str(i) for i in np.std(best_perform_list, 0)])+'\n')
+    perform.write('Test\n')
+    perform.write(','.join([str(i) for i in np.mean(test_perform_list, 0)])+'\n')
+    perform.write(','.join([str(i) for i in np.std(test_perform_list, 0)])+'\n')
+
+
+if __name__ == "__main__":
+    main()
diff --git a/network.py b/network.py
new file mode 100644
index 0000000000000000000000000000000000000000..4344e33d82101e9099aad417865a418d387b74c2
--- /dev/null
+++ b/network.py
@@ -0,0 +1,586 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from copy import deepcopy
+from mamba_ssm import Mamba
+from utils import FDS
+from torchvision.models import resnet18
+
+class MambaModel(nn.Module):
+    def __init__(self, d_model, max_length=30):
+        super(MambaModel, self).__init__()
+        self.linear = nn.Linear(in_features=21, out_features=d_model)
+        self.pos_encoder = PositionalEncoding(d_model, max_length)
+        self.mamba = Mamba(d_model=d_model, d_state=32, expand=4)
+        self.global_pool = nn.AdaptiveAvgPool1d(1)
+
+    def forward(self, x: torch.Tensor):
+        x = self.pos_encoder(self.linear(x))
+        y = self.mamba(x)
+        y_flip = self.mamba(x.flip([-2])).flip([-2])
+        y = torch.cat((y, y_flip), dim=-1)
+        y = self.global_pool(y.permute(0, 2, 1)).squeeze(-1)
+        return y
+
+
+class MLP(nn.Module):
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers=3, dropout_rate=0.1):
+        super(MLP, self).__init__()
+        if isinstance(hidden_dim, int):
+            hidden_dim = [hidden_dim] * num_layers
+        
+        layers = []
+        layers.append(nn.Linear(input_dim, hidden_dim[0]))
+        layers.append(nn.ReLU())
+        layers.append(nn.Dropout(dropout_rate))
+        
+        for i in range(len(hidden_dim) - 1):
+            layers.append(nn.Linear(hidden_dim[i], hidden_dim[i + 1]))
+            layers.append(nn.ReLU())
+            layers.append(nn.Dropout(dropout_rate))
+        
+        layers.append(nn.Linear(hidden_dim[-1], output_dim))
+        
+        self.network = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.network(x)
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(self, d_model, max_len=50):
+        super(PositionalEncoding, self).__init__()
+        
+        pe = torch.zeros(max_len, d_model)  # (max_len, d_model)
+        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)  # (max_len, 1)
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * 
+                             (-torch.log(torch.FloatTensor([10000.0])) / d_model))  # (d_model/2,)
+        pe[:, 0::2] = torch.sin(position * div_term)  # 偶数维
+        pe[:, 1::2] = torch.cos(position * div_term)  # 奇数维
+        pe = pe.unsqueeze(0)  # (1, max_len, d_model)
+        self.register_buffer('pe', pe)
+        
+    def forward(self, x):
+        """
+        x: (B, N, d_model)
+        """
+        x = x + self.pe[:, :x.size(1), :]
+        return x
+
+
+class MHAModel(nn.Module):
+    def __init__(self, d_model, max_length=50):
+        super(MHAModel, self).__init__()
+        self.linear = nn.Linear(in_features=21, out_features=d_model)
+        self.pos_encoder = PositionalEncoding(d_model, max_length)
+        self.self_attn = nn.MultiheadAttention(d_model, num_heads=8, batch_first=True)
+        self.global_pool = nn.AdaptiveAvgPool1d(1)
+
+    def forward(self, x: torch.Tensor):
+        # 线性变换 + 位置编码
+        x = self.pos_encoder(self.linear(x))  # [batch, seq_len, d_model]
+        
+        # 正向自注意力
+        y, _ = self.self_attn(x, x, x)  # [batch, seq_len, d_model]
+        
+        # 反向自注意力
+        x_flip = x.flip([-2])  # 沿序列维度翻转
+        y_flip, _ = self.self_attn(x_flip, x_flip, x_flip)
+        y_flip = y_flip.flip([-2])  # 翻转回原顺序
+        
+        # 拼接正反向结果
+        y = torch.cat((y, y_flip), dim=-1)  # [batch, seq_len, 2*d_model]
+        
+        # 全局池化
+        y = self.global_pool(y.permute(0, 2, 1))  # [batch, 2*d_model, 1]
+        return y.squeeze(-1)  # [batch, 2*d_model]  
+    
+
+class MLAModel(nn.Module):
+    def __init__(self, d_model, max_length=50):
+        super(MLAModel, self).__init__()
+        self.linear = nn.Linear(in_features=21, out_features=d_model)
+        self.pos_encoder = PositionalEncoding(d_model, max_length)
+        self.MLA = MLA(d_model, n_heads=8, max_len=max_length)
+        self.global_pool = nn.AdaptiveAvgPool1d(1)
+
+    def forward(self, x: torch.Tensor):
+        x = self.pos_encoder(self.linear(x))
+        y = self.MLA(x)
+        y_flip = self.MLA(x.flip([-2])).flip([-2])
+        y = torch.cat((y, y_flip), dim=-1)
+        y = self.global_pool(y.permute(0, 2, 1)).squeeze(-1)
+        return y
+
+
+class MLA(nn.Module):
+    def __init__(self, d_model, n_heads, max_len=50, rope_theta=10000.0):
+        super().__init__()
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.dh = d_model // n_heads
+        self.q_proj_dim = d_model // 2
+        self.kv_proj_dim = (2*d_model) // 3
+
+        self.qk_nope_dim = self.dh // 2
+        self.qk_rope_dim = self.dh // 2
+        
+        ## Q projections
+        # Lora
+        self.W_dq = nn.Parameter(0.01*torch.randn((d_model, self.q_proj_dim)))
+        self.W_uq = nn.Parameter(0.01*torch.randn((self.q_proj_dim, self.d_model)))
+        self.q_layernorm = nn.LayerNorm(self.q_proj_dim)
+        
+        ## KV projections
+        # Lora
+        self.W_dkv = nn.Parameter(0.01*torch.randn((d_model, self.kv_proj_dim + self.qk_rope_dim)))
+        self.W_ukv = nn.Parameter(0.01*torch.randn((self.kv_proj_dim,
+                                                          self.d_model + (self.n_heads * self.qk_nope_dim))))
+        self.kv_layernorm = nn.LayerNorm(self.kv_proj_dim)
+        
+        # output projection
+        self.W_o = nn.Parameter(0.01*torch.randn((d_model, d_model)))
+
+        # RoPE
+        self.max_seq_len = max_len
+        self.rope_theta = rope_theta
+
+        # https://github.com/lucidrains/rotary-embedding-torch/tree/main
+        # visualize emb later to make sure it looks ok
+        # we do self.dh here instead of self.qk_rope_dim because its better
+        freqs = 1.0 / (rope_theta ** (torch.arange(0, self.dh, 2).float() / self.dh))
+        emb = torch.outer(torch.arange(self.max_seq_len).float(), freqs)
+        cos_cached = emb.cos()[None, None, :, :]
+        sin_cached = emb.sin()[None, None, :, :]
+
+        # https://pytorch.org/docs/stable/generated/torch.nn.Module.html#torch.nn.Module.register_buffer
+        # This is like a parameter but its a constant so we can use register_buffer
+        self.register_buffer("cos_cached", cos_cached)
+        self.register_buffer("sin_cached", sin_cached)
+    
+    def apply_rope_x(self, x, cos, sin):
+        return (x * cos) + (self.rotate_half(x) * sin)
+    
+    @staticmethod
+    def rotate_half(x):
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+
+    def forward(self, x, kv_cache=None, past_length=0):
+        B, S, D = x.size()
+
+        # Q Projections
+        compressed_q = x @ self.W_dq
+        compressed_q = self.q_layernorm(compressed_q)
+        Q = compressed_q @ self.W_uq
+        Q = Q.view(B, -1, self.n_heads, self.dh).transpose(1,2)
+        Q, Q_for_rope = torch.split(Q, [self.qk_nope_dim, self.qk_rope_dim], dim=-1)
+
+        # Q Decoupled RoPE
+        cos_q = self.cos_cached[:, :, past_length:past_length+S, :self.qk_rope_dim//2].repeat(1, 1, 1, 2)
+        sin_q = self.sin_cached[:, :, past_length:past_length+S, :self.qk_rope_dim//2].repeat(1, 1, 1, 2)
+        Q_for_rope = self.apply_rope_x(Q_for_rope, cos_q, sin_q)
+
+        # KV Projections
+        if kv_cache is None:
+            compressed_kv = x @ self.W_dkv
+            KV_for_lora, K_for_rope = torch.split(compressed_kv,
+                                                  [self.kv_proj_dim, self.qk_rope_dim],
+                                                  dim=-1)
+            KV_for_lora = self.kv_layernorm(KV_for_lora)
+        else:
+            new_kv = x @ self.W_dkv
+            compressed_kv = torch.cat([kv_cache, new_kv], dim=1)
+            new_kv, new_K_for_rope = torch.split(new_kv,
+                                                 [self.kv_proj_dim, self.qk_rope_dim],
+                                                 dim=-1)
+            old_kv, old_K_for_rope = torch.split(kv_cache,
+                                                 [self.kv_proj_dim, self.qk_rope_dim],
+                                                 dim=-1)
+            new_kv = self.kv_layernorm(new_kv)
+            old_kv = self.kv_layernorm(old_kv)
+            KV_for_lora = torch.cat([old_kv, new_kv], dim=1)
+            K_for_rope = torch.cat([old_K_for_rope, new_K_for_rope], dim=1)
+            
+
+        KV = KV_for_lora @ self.W_ukv
+        KV = KV.view(B, -1, self.n_heads, self.dh+self.qk_nope_dim).transpose(1,2)
+        K, V = torch.split(KV, [self.qk_nope_dim, self.dh], dim=-1)
+        S_full = K.size(2)        
+
+        # K Rope
+        K_for_rope = K_for_rope.view(B, -1, 1, self.qk_rope_dim).transpose(1,2)
+        cos_k = self.cos_cached[:, :, :S_full, :self.qk_rope_dim//2].repeat(1, 1, 1, 2)
+        sin_k = self.sin_cached[:, :, :S_full, :self.qk_rope_dim//2].repeat(1, 1, 1, 2)
+        K_for_rope = self.apply_rope_x(K_for_rope, cos_k, sin_k)
+
+        # apply position encoding to each head
+        K_for_rope = K_for_rope.repeat(1, self.n_heads, 1, 1)
+
+        # split into multiple heads
+        q_heads = torch.cat([Q, Q_for_rope], dim=-1)
+        k_heads = torch.cat([K, K_for_rope], dim=-1)
+        v_heads = V # already reshaped before the split
+
+        # make attention mask
+        mask = torch.ones((S,S_full), device=x.device)
+        mask = torch.tril(mask, diagonal=past_length)
+        mask = mask[None, None, :, :]
+
+        sq_mask = mask == 1
+
+        # attention
+        x = nn.functional.scaled_dot_product_attention(
+            q_heads, k_heads, v_heads,
+            attn_mask=sq_mask
+        )
+
+        x = x.transpose(1, 2).reshape(B, S, D)
+
+        # apply projection
+        x = x @ self.W_o.T
+
+        return x
+    
+
+class DMutaPeptide(nn.Module):
+    def __init__(self, q_encoder='lstm', classes=1, channels=128, dir=False, gf=False, fusion='mlp', non_siamese=False):
+        """
+        参数:
+            q_encoder: 使用的编码器类型，支持 'lstm', 'mamba', 'mla', 'mha'
+            classes: 输出类别数
+            channels: 通道数量，影响隐藏状态维度
+            dir: 是否使用 DIR 模块
+            fusion: 融合方法，可选 'mlp'（默认，直接拼接）或 'att'（使用 attention 融合）
+        """
+        super().__init__()
+        self.classes = classes
+        self.DIR = dir
+        self.gf = gf
+        self.fusion_method = fusion  # 融合方式
+        self.non_siamese = non_siamese
+        # 拼接后维度设定为 channels * 4
+        final_dim = channels * 4
+
+
+        # 初始化编码器
+        if q_encoder == 'lstm':
+            self.q_encoder = nn.LSTM(
+                input_size=21,
+                hidden_size=channels,
+                num_layers=2,
+                batch_first=True,  # 输入和输出均以 (batch, time_step, input_size) 表示
+                dropout=0.1,
+                bidirectional=True
+            )
+        elif q_encoder == 'gru':
+            self.q_encoder = nn.GRU(
+                input_size=21,
+                hidden_size=channels,
+                num_layers=2,
+                batch_first=True,  # 输入和输出均以 (batch, time_step, input_size) 表示
+                dropout=0.1,
+                bidirectional=True
+            )
+        elif q_encoder == 'mamba':
+            self.q_encoder = MambaModel(channels, 30)
+        elif q_encoder == 'mla':
+            self.q_encoder = MLAModel(channels, 30)
+        elif q_encoder == 'mha':
+            self.q_encoder = MHAModel(channels, 30)
+        else:
+            raise NotImplementedError
+        
+        if non_siamese:
+            self.q_encoder_2 = deepcopy(self.q_encoder)
+        else:
+            self.q_encoder_2 = self.q_encoder
+        
+        if self.fusion_method == 'diff':
+            final_dim //= 2
+
+        if gf:
+            self.g_encoder = MLP(1024, [512, 256, 128], channels * 2, dropout_rate=0.3)
+            final_dim += channels * 2
+
+        # 如果 fusion 模式为 'att' ，则使用 MultiheadAttention 对两个向量进行融合
+        if self.fusion_method == 'att':
+            # 假设每个编码器输出的向量维度为 final_dim // 2
+            embed_dim = channels * 2
+            self.attn = nn.MultiheadAttention(embed_dim=embed_dim, num_heads=4 if gf else 2, batch_first=True)
+            
+        if self.DIR:
+            self.FDS = FDS(final_dim)
+
+        self.fc = nn.Sequential(
+            nn.Linear(final_dim, 128),
+            nn.Mish(),
+            nn.Dropout(0.3),
+            nn.Linear(128, 64),
+            nn.Mish(),
+            nn.Dropout(0.3),
+            nn.Linear(64, self.classes)
+        )
+
+    def norm(self, x, dim=-1, p=2):
+        return F.normalize(x, p=p, dim=dim)
+
+    def forward(self, x, labels=None, epoch=0):
+        if self.gf:
+            seq1, seq2, gf = x
+        else:
+            seq1, seq2 = x
+        fusion = []
+
+        # 获取两个序列的编码结果
+        if self.q_encoder.__class__.__name__ in ['LSTM', 'GRU']:
+            # 对于 LSTM, 取序列最后时刻的输出，其维度应为 channels*2 (bidirectional)
+            fusion.append(self.norm(self.q_encoder(seq1)[0][:, -1, :]))
+            fusion.append(self.norm(self.q_encoder_2(seq2)[0][:, -1, :]))
+        # elif self.q_encoder.__class__.__name__ in ['MambaModel', 'MLAModel', 'MHAModel']:
+        else:
+            fusion.append(self.norm(self.q_encoder(seq1)))
+            fusion.append(self.norm(self.q_encoder_2(seq2)))
+        
+        if self.gf:
+            fusion.append(self.g_encoder(gf))
+
+        # 根据 fusion_method 决定融合方式
+        if self.fusion_method == 'mlp':
+            # 维持原有行为：拼接两个向量
+            fusion = torch.cat(fusion, dim=-1)
+        elif self.fusion_method == 'diff':
+            fusion = torch.cat([fusion[1] - fusion[0]] + fusion[2:], dim=-1)
+        elif self.fusion_method == 'att':
+            # 使用 attention 融合:
+            # 先将两个向量堆叠成“tokens”，形状：(batch, 2, embed_dim)
+            tokens = torch.stack(fusion, dim=1)  # embed_dim 应该为 final_dim//2
+            # 利用 MultiheadAttention 进行自注意力计算
+            # 注意：因为采用 batch_first=True，所以输入形状为 (batch, seq_len, embed_dim)
+            attn_output, _ = self.attn(tokens, tokens, tokens)
+            # 将 attention 输出展平，得到形状 (batch, 2 * embed_dim)，即 (batch, final_dim)
+            fusion = attn_output.reshape(attn_output.size(0), -1)
+        else:
+            raise ValueError("Invalid fusion method: choose either 'mse' or 'att'.")
+
+        # 如果启用 DIR 模块，保留传入 FDS 前的特征表示
+        if self.DIR:
+            features = fusion
+            fusion = self.FDS.smooth(fusion, labels, epoch)
+        
+        pred = self.fc(fusion).squeeze(-1)
+
+        if self.DIR:
+            return pred, features
+        else:
+            return pred
+
+
+class CNNEncoder(nn.Module):
+    def __init__(self, feature_dim=256, base_channels=16, in_dim=3):
+        """
+        feature_dim: 输出的一维特征向量维度
+        base_channels: 基础卷积模块的通道数
+        """
+        super(CNNEncoder, self).__init__()
+        
+        # 卷积层
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_dim, base_channels, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(base_channels),
+            # nn.ReLU(inplace=True),
+            nn.Mish(inplace=True),
+            nn.MaxPool2d(kernel_size=2),
+
+            nn.Conv2d(base_channels, base_channels * 2, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(base_channels * 2),
+            # nn.ReLU(inplace=True),
+            nn.Mish(inplace=True),
+            nn.MaxPool2d(kernel_size=2),
+            
+            nn.Conv2d(base_channels * 2, base_channels * 4, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(base_channels * 4),
+            # nn.ReLU(inplace=True),
+            nn.Mish(inplace=True),
+            nn.MaxPool2d(kernel_size=2)
+        )
+        
+        # 自适应池化，得到固定尺寸（1x1）的特征图
+        self.adaptive_pool = nn.AdaptiveAvgPool2d((1, 1))
+        
+        # 全连接层将卷积特征转换为一维特征向量
+        self.fc = nn.Linear(base_channels * 4, feature_dim)
+        
+    def forward(self, img):
+        """
+        img: [B, 3, 1024, 1024] 输入的 RGB 图像张量
+        """
+        # 融合后进一步进行卷积、池化处理
+        fused_conv = self.conv(img)
+        pooled = self.adaptive_pool(fused_conv)  # [B, base_channels*4, 1, 1]
+        
+        # 展平并经过全连接层输出特征向量
+        flattened = pooled.view(pooled.size(0), -1)  # [B, base_channels*4]
+        feature_vector = self.fc(flattened)          # [B, feature_dim]
+        return feature_vector
+
+
+class DMutaPeptideCNN(nn.Module):
+    def __init__(self, q_encoder='cnn', classes=1, channels=16, dir=False, gf=False, side_enc=None, fusion='mlp', non_siamese=False):
+        """
+        参数:
+            q_encoder: 使用的编码器类型，支持 'lstm', 'mamba', 'mla', 'mha'
+            classes: 输出类别数
+            channels: 通道数量，影响隐藏状态维度
+            dir: 是否使用 DIR 模块
+            fusion: 融合方法，可选 'mlp'（默认，直接拼接）或 'att'（使用 attention 融合）
+        """
+        super().__init__()
+        self.classes = classes
+        self.DIR = dir
+        self.gf = gf
+        self.fusion_method = fusion  # 融合方式
+        self.non_siamese = non_siamese
+        # 拼接后维度设定为 channels * 4
+        vector_dim = 512
+        final_dim = vector_dim * 2
+
+        # 初始化编码器
+        if q_encoder == 'cnn':
+            self.q_encoder = CNNEncoder(feature_dim=vector_dim, base_channels=channels)
+        elif q_encoder == 'rn18':
+            self.q_encoder = resnet18_backbone(pretrained=True)
+        if non_siamese:
+            self.q_encoder_2 = deepcopy(self.q_encoder)
+        else:
+            self.q_encoder_2 = self.q_encoder
+
+        if side_enc:
+            self.side_enc = True
+            if side_enc == 'lstm':
+                self.side_encoder = nn.LSTM(
+                    input_size=21,
+                    hidden_size=256,
+                    num_layers=2,
+                    batch_first=True,  # 输入和输出均以 (batch, time_step, input_size) 表示
+                    dropout=0.1,
+                    bidirectional=True
+                )
+            elif side_enc == 'mamba':
+                self.side_encoder = MambaModel(256, 30)
+            else:
+                raise NotImplementedError
+            
+            final_dim += vector_dim * 2
+
+            if non_siamese:
+                self.side_encoder_2 = deepcopy(self.side_encoder)
+            else:
+                self.side_encoder_2 = self.side_encoder
+        else:
+            self.side_enc = False
+        
+        if self.fusion_method == 'diff':
+            final_dim //= 2
+        
+        if gf:
+            self.g_encoder = MLP(1024, [512, 256, 128], vector_dim, dropout_rate=0.3)
+            final_dim += vector_dim
+
+        # 如果 fusion 模式为 'att' ，则使用 MultiheadAttention 对两个向量进行融合
+        if self.fusion_method == 'att':
+            # 假设每个编码器输出的向量维度为 final_dim // 2
+            embed_dim = vector_dim
+            self.attn = nn.MultiheadAttention(embed_dim=embed_dim, num_heads=4 if gf else 2, batch_first=True)
+  
+        if self.DIR:
+            self.FDS = FDS(final_dim)
+
+        self.fc = nn.Sequential(
+            nn.Linear(final_dim, 128),
+            nn.Mish(),
+            nn.Dropout(0.3),
+            nn.Linear(128, 64),
+            nn.Mish(),
+            nn.Dropout(0.3),
+            nn.Linear(64, self.classes)
+        )
+
+    def norm(self, x, dim=-1, p=2):
+        return F.normalize(x, p=p, dim=dim)
+
+    def forward(self, x, labels=None, epoch=0):
+        if self.gf:
+            seq1, seq2, gf = x
+        else:
+            seq1, seq2 = x
+
+        if self.side_enc:
+            seq1_seq = seq1[1]
+            seq1 = seq1[0]
+            seq2_seq = seq2[1]
+            seq2 = seq2[0]
+
+        fusion = []
+
+        # 获取两个序列的编码结果
+        fusion.append(self.norm(self.q_encoder(seq1)))
+        fusion.append(self.norm(self.q_encoder_2(seq2)))
+        if self.side_enc:
+            if self.side_encoder.__class__.__name__ == 'MambaModel':
+                fusion.append(self.norm(self.side_encoder(seq1_seq)))
+                fusion.append(self.norm(self.side_encoder_2(seq2_seq)))
+            # elif self.side_encoder.__class__.__name__ == 'LSTM':
+            else:
+                fusion.append(self.norm(self.side_encoder(seq1_seq)[0][:, -1, :]))
+                fusion.append(self.norm(self.side_encoder_2(seq2_seq)[0][:, -1, :]))
+        
+        if self.gf:
+            fusion.append(self.g_encoder(gf))
+
+        # 根据 fusion_method 决定融合方式
+        if self.fusion_method == 'mlp':
+            # 维持原有行为：拼接两个向量
+            fusion = torch.cat(fusion, dim=-1)
+        elif self.fusion_method == 'diff':
+            if not self.side_enc:
+                fusion = torch.cat([fusion[1] - fusion[0]] + fusion[2:], dim=-1)
+            else:
+                fusion = torch.cat([fusion[1] - fusion[0], fusion[3] - fusion[2]] + fusion[4:], dim=-1)
+        elif self.fusion_method == 'att':
+            # 使用 attention 融合:
+            # 先将两个向量堆叠成“tokens”，形状：(batch, 2, embed_dim)
+            tokens = torch.stack(fusion, dim=1)  # embed_dim 应该为 final_dim//2
+            # 利用 MultiheadAttention 进行自注意力计算
+            # 注意：因为采用 batch_first=True，所以输入形状为 (batch, seq_len, embed_dim)
+            attn_output, _ = self.attn(tokens, tokens, tokens)
+            # 将 attention 输出展平，得到形状 (batch, 2 * embed_dim)，即 (batch, final_dim)
+            fusion = attn_output.reshape(attn_output.size(0), -1)
+        else:
+            raise ValueError("Invalid fusion method: choose either 'mse' or 'att'.")
+
+        # 如果启用 DIR 模块，保留传入 FDS 前的特征表示
+        if self.DIR:
+            features = fusion
+            fusion = self.FDS.smooth(fusion, labels, epoch)
+        
+        pred = self.fc(fusion).squeeze(-1)
+
+        if self.DIR:
+            return pred, features
+        else:
+            return pred
+        
+
+def resnet18_backbone(pretrained=False):
+    weights = None
+    if pretrained:
+        weights = 'IMAGENET1K_V1'
+    model = resnet18(weights=weights, progress=False)
+    return torch.nn.Sequential(*list(model.children())[:-1], nn.Flatten())
+
+
+if __name__ == "__main__":
+    model = resnet18_backbone(pretrained=True)
+    print(model)
+    pass
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ed1b2419773c11699b777d8ba7d2f2743898828d
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,9 @@
+mamba_ssm==2.2.4
+numpy==1.26.3
+pandas==2.1.4
+rdkit==2024.3.5
+scikit_learn==1.4.1.post1
+scipy==1.13.0
+torch==2.2.0
+torchmetrics==1.3.1
+torchvision==0.17.0
diff --git a/run-cls-simple/_rn18-mlp-16-mamba-768-oneway-ce-32-0.001-30/uda_r2/config.json b/run-cls-simple/_rn18-mlp-16-mamba-768-oneway-ce-32-0.001-30/uda_r2/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d040ebecb882f620d65fcbf71332539018bbb65d
--- /dev/null
+++ b/run-cls-simple/_rn18-mlp-16-mamba-768-oneway-ce-32-0.001-30/uda_r2/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": "mamba", "channels": 16, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 46, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 1, "batch_size": 32, "epochs": 30, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "case": "r2", "classes": 2}
\ No newline at end of file
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+2025-08-07 14:22:51: Loading Validation Dataset
+2025-08-07 14:22:52: Start UDA training
+2025-08-07 14:24:15: Epoch 1 Train Loss: 0.6730 Val: ap: 0.2109 auc: 0.1028 f1: 0.0000 acc: 0.6765
+2025-08-07 14:24:15: Epoch: 1 New best VAL metrics
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+2025-08-07 14:25:36: Epoch: 2 New best VAL metrics
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+2025-08-07 14:26:58: Epoch: 3 New best VAL metrics
+2025-08-07 14:28:19: Epoch 4 Train Loss: 0.6813 Val: ap: 0.2506 auc: 0.3043 f1: 0.4889 acc: 0.3235
+2025-08-07 14:28:19: Epoch: 4 New best VAL metrics
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+2025-08-07 14:32:23: Epoch: 7 New best VAL metrics
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+2025-08-07 14:33:45: Epoch: 8 New best VAL metrics
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+2025-08-07 14:35:06: Epoch: 9 New best VAL metrics
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+2025-08-07 14:37:49: Epoch 11 Train Loss: 0.6509 Val: ap: 0.4061 auc: 0.5850 f1: 0.5116 acc: 0.3824
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+2025-08-07 14:40:32: Epoch 13 Train Loss: 0.4216 Val: ap: 0.4133 auc: 0.5850 f1: 0.4444 acc: 0.4118
+2025-08-07 14:41:53: Epoch 14 Train Loss: 0.3033 Val: ap: 0.4527 auc: 0.6285 f1: 0.4828 acc: 0.5588
+2025-08-07 14:43:13: Epoch 15 Train Loss: 0.2602 Val: ap: 0.5063 auc: 0.6680 f1: 0.5000 acc: 0.7059
+2025-08-07 14:44:35: Epoch 16 Train Loss: 0.2552 Val: ap: 0.6082 auc: 0.6877 f1: 0.5000 acc: 0.6471
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+2025-08-07 14:47:17: Epoch 18 Train Loss: 0.2075 Val: ap: 0.6180 auc: 0.7036 f1: 0.4286 acc: 0.7647
+2025-08-07 14:48:39: Epoch 19 Train Loss: 0.2190 Val: ap: 0.6180 auc: 0.7036 f1: 0.0000 acc: 0.6765
+2025-08-07 14:50:00: Epoch 20 Train Loss: 0.2027 Val: ap: 0.6350 auc: 0.7115 f1: 0.4444 acc: 0.7059
+2025-08-07 14:50:00: Epoch: 20 New best VAL metrics
+2025-08-07 14:51:22: Epoch 21 Train Loss: 0.1994 Val: ap: 0.6394 auc: 0.7194 f1: 0.0000 acc: 0.6765
+2025-08-07 14:51:22: Epoch: 21 New best VAL metrics
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+2025-08-07 14:52:43: Epoch: 22 New best VAL metrics
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+2025-08-06 19:03:52: saving_dir: ./run-cls/rn18-mlp-16-mamba-simple-768-oneway-ce-32-0.001-50
+2025-08-06 19:03:52: Loading Training Dataset
+2025-08-06 19:04:15: Loading Test Dataset
+2025-08-06 19:04:19: Running Cross Validation 0
+2025-08-06 19:04:19: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-06 19:04:27: Epoch: 001 Train Loss: 0.687, ap: 0.692, auc: 0.729, f1: 0.000, acc: 0.544
+2025-08-06 19:04:27: Epoch: 001 New best VALIDATION metrics
+2025-08-06 19:04:28: Epoch: 001 Test results, ap: 0.174, auc: 0.299, f1: 0.000, acc: 0.759
+2025-08-06 19:04:35: Epoch: 002 Train Loss: 0.638, ap: 0.720, auc: 0.734, f1: 0.693, acc: 0.699
+2025-08-06 19:04:35: Epoch: 002 New best VALIDATION metrics
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+2025-08-06 19:04:43: Epoch: 003 Train Loss: 0.575, ap: 0.671, auc: 0.713, f1: 0.538, acc: 0.650
+2025-08-06 19:04:44: Epoch: 003 Test results, ap: 0.267, auc: 0.534, f1: 0.000, acc: 0.722
+2025-08-06 19:04:51: Epoch: 004 Train Loss: 0.560, ap: 0.679, auc: 0.744, f1: 0.185, acc: 0.573
+2025-08-06 19:04:52: Epoch: 004 Test results, ap: 0.204, auc: 0.404, f1: 0.000, acc: 0.759
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+2025-08-06 19:05:00: Epoch: 005 Test results, ap: 0.276, auc: 0.429, f1: 0.000, acc: 0.759
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+2025-08-06 19:05:07: Epoch: 006 New best VALIDATION metrics
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+2025-08-06 19:05:15: Epoch: 007 Train Loss: 0.446, ap: 0.728, auc: 0.735, f1: 0.682, acc: 0.602
+2025-08-06 19:05:16: Epoch: 007 Test results, ap: 0.388, auc: 0.483, f1: 0.300, acc: 0.481
+2025-08-06 19:05:23: Epoch: 008 Train Loss: 0.425, ap: 0.697, auc: 0.729, f1: 0.369, acc: 0.602
+2025-08-06 19:05:24: Epoch: 008 Test results, ap: 0.202, auc: 0.399, f1: 0.000, acc: 0.759
+2025-08-06 19:05:31: Epoch: 009 Train Loss: 0.378, ap: 0.610, auc: 0.552, f1: 0.627, acc: 0.456
+2025-08-06 19:05:32: Epoch: 009 Test results, ap: 0.243, auc: 0.438, f1: 0.388, acc: 0.241
+2025-08-06 19:05:39: Epoch: 010 Train Loss: 0.413, ap: 0.741, auc: 0.763, f1: 0.464, acc: 0.641
+2025-08-06 19:05:40: Epoch: 010 Test results, ap: 0.187, auc: 0.344, f1: 0.000, acc: 0.750
+2025-08-06 19:05:47: Epoch: 011 Train Loss: 0.318, ap: 0.731, auc: 0.753, f1: 0.673, acc: 0.680
+2025-08-06 19:05:48: Epoch: 011 Test results, ap: 0.184, auc: 0.338, f1: 0.171, acc: 0.463
+2025-08-06 19:05:48: Epoch: 011 New best TEST metrics
+2025-08-06 19:05:55: Epoch: 012 Train Loss: 0.289, ap: 0.708, auc: 0.731, f1: 0.684, acc: 0.650
+2025-08-06 19:05:56: Epoch: 012 Test results, ap: 0.196, auc: 0.361, f1: 0.241, acc: 0.417
+2025-08-06 19:05:56: Epoch: 012 New best TEST metrics
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+2025-08-06 19:06:04: Epoch: 013 Test results, ap: 0.200, auc: 0.387, f1: 0.061, acc: 0.713
+2025-08-06 19:06:04: Epoch: 013 New best TEST metrics
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+2025-08-06 19:06:12: Epoch: 014 Test results, ap: 0.257, auc: 0.424, f1: 0.300, acc: 0.352
+2025-08-06 19:06:12: Epoch: 014 New best TEST metrics
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+2025-08-06 19:06:35: Epoch: 017 Train Loss: 0.234, ap: 0.715, auc: 0.718, f1: 0.585, acc: 0.670
+2025-08-06 19:06:36: Epoch: 017 Test results, ap: 0.183, auc: 0.335, f1: 0.000, acc: 0.685
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+2025-08-06 19:06:51: Epoch: 019 Train Loss: 0.211, ap: 0.713, auc: 0.720, f1: 0.557, acc: 0.660
+2025-08-06 19:06:52: Epoch: 019 Test results, ap: 0.180, auc: 0.332, f1: 0.000, acc: 0.583
+2025-08-06 19:06:59: Epoch: 020 Train Loss: 0.194, ap: 0.709, auc: 0.706, f1: 0.571, acc: 0.680
+2025-08-06 19:07:00: Epoch: 020 Test results, ap: 0.180, auc: 0.333, f1: 0.000, acc: 0.741
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+2025-08-06 19:07:08: Epoch: 021 Test results, ap: 0.176, auc: 0.314, f1: 0.000, acc: 0.574
+2025-08-06 19:07:15: Epoch: 022 Train Loss: 0.164, ap: 0.711, auc: 0.720, f1: 0.652, acc: 0.689
+2025-08-06 19:07:16: Epoch: 022 Test results, ap: 0.177, auc: 0.320, f1: 0.133, acc: 0.519
+2025-08-06 19:07:23: Epoch: 023 Train Loss: 0.153, ap: 0.711, auc: 0.726, f1: 0.561, acc: 0.650
+2025-08-06 19:07:24: Epoch: 023 Test results, ap: 0.181, auc: 0.339, f1: 0.000, acc: 0.583
+2025-08-06 19:07:31: Epoch: 024 Train Loss: 0.154, ap: 0.707, auc: 0.728, f1: 0.621, acc: 0.680
+2025-08-06 19:07:32: Epoch: 024 Test results, ap: 0.182, auc: 0.344, f1: 0.040, acc: 0.556
+2025-08-06 19:07:39: Epoch: 025 Train Loss: 0.155, ap: 0.711, auc: 0.700, f1: 0.600, acc: 0.689
+2025-08-06 19:07:40: Epoch: 025 Test results, ap: 0.192, auc: 0.384, f1: 0.000, acc: 0.583
+2025-08-06 19:07:47: Epoch: 026 Train Loss: 0.142, ap: 0.726, auc: 0.774, f1: 0.701, acc: 0.718
+2025-08-06 19:07:48: Epoch: 026 Test results, ap: 0.188, auc: 0.360, f1: 0.182, acc: 0.417
+2025-08-06 19:07:55: Epoch: 027 Train Loss: 0.140, ap: 0.713, auc: 0.697, f1: 0.634, acc: 0.709
+2025-08-06 19:07:56: Epoch: 027 Test results, ap: 0.199, auc: 0.404, f1: 0.000, acc: 0.667
+2025-08-06 19:08:03: Epoch: 028 Train Loss: 0.134, ap: 0.711, auc: 0.713, f1: 0.627, acc: 0.699
+2025-08-06 19:08:04: Epoch: 028 Test results, ap: 0.182, auc: 0.343, f1: 0.000, acc: 0.593
+2025-08-06 19:08:11: Epoch: 029 Train Loss: 0.129, ap: 0.707, auc: 0.727, f1: 0.660, acc: 0.680
+2025-08-06 19:08:12: Epoch: 029 Test results, ap: 0.193, auc: 0.382, f1: 0.111, acc: 0.556
+2025-08-06 19:08:19: Epoch: 030 Train Loss: 0.142, ap: 0.718, auc: 0.730, f1: 0.602, acc: 0.680
+2025-08-06 19:08:20: Epoch: 030 Test results, ap: 0.188, auc: 0.369, f1: 0.000, acc: 0.583
+2025-08-06 19:08:27: Epoch: 031 Train Loss: 0.129, ap: 0.719, auc: 0.727, f1: 0.635, acc: 0.699
+2025-08-06 19:08:28: Epoch: 031 Test results, ap: 0.186, auc: 0.357, f1: 0.083, acc: 0.593
+2025-08-06 19:08:35: Epoch: 032 Train Loss: 0.131, ap: 0.725, auc: 0.729, f1: 0.635, acc: 0.699
+2025-08-06 19:08:36: Epoch: 032 Test results, ap: 0.182, auc: 0.344, f1: 0.043, acc: 0.593
+2025-08-06 19:08:43: Epoch: 033 Train Loss: 0.121, ap: 0.728, auc: 0.749, f1: 0.628, acc: 0.689
+2025-08-06 19:08:44: Epoch: 033 Test results, ap: 0.182, auc: 0.343, f1: 0.040, acc: 0.556
+2025-08-06 19:08:51: Epoch: 034 Train Loss: 0.115, ap: 0.729, auc: 0.738, f1: 0.659, acc: 0.709
+2025-08-06 19:08:52: Epoch: 034 Test results, ap: 0.181, auc: 0.341, f1: 0.038, acc: 0.537
+2025-08-06 19:08:59: Epoch: 035 Train Loss: 0.112, ap: 0.724, auc: 0.742, f1: 0.628, acc: 0.689
+2025-08-06 19:09:00: Epoch: 035 Test results, ap: 0.183, auc: 0.350, f1: 0.074, acc: 0.537
+2025-08-06 19:09:07: Epoch: 036 Train Loss: 0.123, ap: 0.723, auc: 0.737, f1: 0.674, acc: 0.718
+2025-08-06 19:09:08: Epoch: 036 Test results, ap: 0.184, auc: 0.352, f1: 0.073, acc: 0.528
+2025-08-06 19:09:15: Epoch: 037 Train Loss: 0.107, ap: 0.732, auc: 0.741, f1: 0.619, acc: 0.689
+2025-08-06 19:09:16: Epoch: 037 Test results, ap: 0.184, auc: 0.349, f1: 0.039, acc: 0.546
+2025-08-06 19:09:23: Epoch: 038 Train Loss: 0.109, ap: 0.730, auc: 0.753, f1: 0.659, acc: 0.699
+2025-08-06 19:09:24: Epoch: 038 Test results, ap: 0.185, auc: 0.352, f1: 0.169, acc: 0.546
+2025-08-06 19:09:31: Epoch: 039 Train Loss: 0.102, ap: 0.725, auc: 0.738, f1: 0.628, acc: 0.689
+2025-08-06 19:09:32: Epoch: 039 Test results, ap: 0.185, auc: 0.356, f1: 0.075, acc: 0.546
+2025-08-06 19:09:39: Epoch: 040 Train Loss: 0.099, ap: 0.725, auc: 0.737, f1: 0.644, acc: 0.699
+2025-08-06 19:09:40: Epoch: 040 Test results, ap: 0.187, auc: 0.363, f1: 0.077, acc: 0.556
+2025-08-06 19:09:47: Epoch: 041 Train Loss: 0.096, ap: 0.720, auc: 0.743, f1: 0.628, acc: 0.689
+2025-08-06 19:09:48: Epoch: 041 Test results, ap: 0.183, auc: 0.349, f1: 0.073, acc: 0.528
+2025-08-06 19:09:55: Epoch: 042 Train Loss: 0.094, ap: 0.721, auc: 0.739, f1: 0.659, acc: 0.709
+2025-08-06 19:09:56: Epoch: 042 Test results, ap: 0.182, auc: 0.345, f1: 0.073, acc: 0.528
+2025-08-06 19:10:03: Epoch: 043 Train Loss: 0.104, ap: 0.718, auc: 0.732, f1: 0.659, acc: 0.709
+2025-08-06 19:10:04: Epoch: 043 Test results, ap: 0.182, auc: 0.343, f1: 0.073, acc: 0.528
+2025-08-06 19:10:11: Epoch: 044 Train Loss: 0.087, ap: 0.724, auc: 0.735, f1: 0.659, acc: 0.709
+2025-08-06 19:10:12: Epoch: 044 Test results, ap: 0.182, auc: 0.342, f1: 0.037, acc: 0.519
+2025-08-06 19:10:19: Epoch: 045 Train Loss: 0.094, ap: 0.722, auc: 0.743, f1: 0.659, acc: 0.709
+2025-08-06 19:10:20: Epoch: 045 Test results, ap: 0.180, auc: 0.336, f1: 0.037, acc: 0.519
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+2025-08-06 19:10:28: Epoch: 046 Test results, ap: 0.180, auc: 0.336, f1: 0.073, acc: 0.528
+2025-08-06 19:10:35: Epoch: 047 Train Loss: 0.094, ap: 0.708, auc: 0.734, f1: 0.644, acc: 0.699
+2025-08-06 19:10:36: Epoch: 047 Test results, ap: 0.180, auc: 0.337, f1: 0.073, acc: 0.528
+2025-08-06 19:10:43: Epoch: 048 Train Loss: 0.090, ap: 0.720, auc: 0.737, f1: 0.659, acc: 0.709
+2025-08-06 19:10:44: Epoch: 048 Test results, ap: 0.182, auc: 0.344, f1: 0.073, acc: 0.528
+2025-08-06 19:10:51: Epoch: 049 Train Loss: 0.082, ap: 0.720, auc: 0.741, f1: 0.659, acc: 0.709
+2025-08-06 19:10:52: Epoch: 049 Test results, ap: 0.182, auc: 0.346, f1: 0.038, acc: 0.528
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+2025-08-06 19:11:00: Epoch: 050 Test results, ap: 0.181, auc: 0.341, f1: 0.073, acc: 0.528
+2025-08-06 19:11:00: used time 0.11h
+2025-08-06 19:11:00: Running Cross Validation 1
+2025-08-06 19:11:00: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-06 19:11:07: Epoch: 001 Train Loss: 0.692, ap: 0.499, auc: 0.618, f1: 0.555, acc: 0.485
+2025-08-06 19:11:07: Epoch: 001 New best VALIDATION metrics
+2025-08-06 19:11:08: Epoch: 001 Test results, ap: 0.231, auc: 0.467, f1: 0.000, acc: 0.731
+2025-08-06 19:11:15: Epoch: 002 Train Loss: 0.656, ap: 0.429, auc: 0.582, f1: 0.529, acc: 0.359
+2025-08-06 19:11:16: Epoch: 002 Test results, ap: 0.494, auc: 0.653, f1: 0.388, acc: 0.241
+2025-08-06 19:11:23: Epoch: 003 Train Loss: 0.588, ap: 0.537, auc: 0.675, f1: 0.357, acc: 0.650
+2025-08-06 19:11:23: Epoch: 003 New best VALIDATION metrics
+2025-08-06 19:11:24: Epoch: 003 Test results, ap: 0.254, auc: 0.487, f1: 0.000, acc: 0.759
+2025-08-06 19:11:31: Epoch: 004 Train Loss: 0.546, ap: 0.578, auc: 0.700, f1: 0.000, acc: 0.641
+2025-08-06 19:11:31: Epoch: 004 New best VALIDATION metrics
+2025-08-06 19:11:32: Epoch: 004 Test results, ap: 0.272, auc: 0.386, f1: 0.000, acc: 0.759
+2025-08-06 19:11:39: Epoch: 005 Train Loss: 0.495, ap: 0.548, auc: 0.672, f1: 0.598, acc: 0.544
+2025-08-06 19:11:40: Epoch: 005 Test results, ap: 0.337, auc: 0.581, f1: 0.320, acc: 0.528
+2025-08-06 19:11:47: Epoch: 006 Train Loss: 0.468, ap: 0.566, auc: 0.638, f1: 0.053, acc: 0.650
+2025-08-06 19:11:48: Epoch: 006 Test results, ap: 0.251, auc: 0.397, f1: 0.000, acc: 0.759
+2025-08-06 19:11:55: Epoch: 007 Train Loss: 0.434, ap: 0.596, auc: 0.651, f1: 0.547, acc: 0.534
+2025-08-06 19:11:56: Epoch: 007 Test results, ap: 0.352, auc: 0.590, f1: 0.388, acc: 0.241
+2025-08-06 19:12:03: Epoch: 008 Train Loss: 0.453, ap: 0.622, auc: 0.712, f1: 0.415, acc: 0.699
+2025-08-06 19:12:03: Epoch: 008 New best VALIDATION metrics
+2025-08-06 19:12:04: Epoch: 008 Test results, ap: 0.296, auc: 0.480, f1: 0.000, acc: 0.759
+2025-08-06 19:12:11: Epoch: 009 Train Loss: 0.432, ap: 0.605, auc: 0.676, f1: 0.550, acc: 0.650
+2025-08-06 19:12:13: Epoch: 009 Test results, ap: 0.335, auc: 0.576, f1: 0.296, acc: 0.648
+2025-08-06 19:12:19: Epoch: 010 Train Loss: 0.361, ap: 0.595, auc: 0.704, f1: 0.604, acc: 0.631
+2025-08-06 19:12:21: Epoch: 010 Test results, ap: 0.178, auc: 0.326, f1: 0.000, acc: 0.657
+2025-08-06 19:12:27: Epoch: 011 Train Loss: 0.335, ap: 0.661, auc: 0.713, f1: 0.559, acc: 0.709
+2025-08-06 19:12:27: Epoch: 011 New best VALIDATION metrics
+2025-08-06 19:12:29: Epoch: 011 Test results, ap: 0.182, auc: 0.345, f1: 0.000, acc: 0.741
+2025-08-06 19:12:29: Epoch: 011 New best TEST metrics
+2025-08-06 19:12:35: Epoch: 012 Train Loss: 0.310, ap: 0.599, auc: 0.678, f1: 0.459, acc: 0.612
+2025-08-06 19:12:37: Epoch: 012 Test results, ap: 0.199, auc: 0.395, f1: 0.000, acc: 0.731
+2025-08-06 19:12:37: Epoch: 012 New best TEST metrics
+2025-08-06 19:12:43: Epoch: 013 Train Loss: 0.302, ap: 0.620, auc: 0.696, f1: 0.553, acc: 0.670
+2025-08-06 19:12:45: Epoch: 013 Test results, ap: 0.257, auc: 0.422, f1: 0.229, acc: 0.750
+2025-08-06 19:12:45: Epoch: 013 New best TEST metrics
+2025-08-06 19:12:52: Epoch: 014 Train Loss: 0.290, ap: 0.584, auc: 0.676, f1: 0.580, acc: 0.592
+2025-08-06 19:12:53: Epoch: 014 Test results, ap: 0.182, auc: 0.343, f1: 0.268, acc: 0.343
+2025-08-06 19:13:00: Epoch: 015 Train Loss: 0.297, ap: 0.629, auc: 0.710, f1: 0.486, acc: 0.650
+2025-08-06 19:13:01: Epoch: 015 Test results, ap: 0.192, auc: 0.377, f1: 0.000, acc: 0.750
+2025-08-06 19:13:07: Epoch: 016 Train Loss: 0.289, ap: 0.587, auc: 0.683, f1: 0.563, acc: 0.563
+2025-08-06 19:13:09: Epoch: 016 Test results, ap: 0.187, auc: 0.360, f1: 0.065, acc: 0.463
+2025-08-06 19:13:16: Epoch: 017 Train Loss: 0.265, ap: 0.504, auc: 0.638, f1: 0.505, acc: 0.563
+2025-08-06 19:13:17: Epoch: 017 Test results, ap: 0.200, auc: 0.406, f1: 0.189, acc: 0.444
+2025-08-06 19:13:23: Epoch: 018 Train Loss: 0.254, ap: 0.614, auc: 0.705, f1: 0.479, acc: 0.641
+2025-08-06 19:13:25: Epoch: 018 Test results, ap: 0.197, auc: 0.373, f1: 0.000, acc: 0.750
+2025-08-06 19:13:31: Epoch: 019 Train Loss: 0.279, ap: 0.646, auc: 0.713, f1: 0.540, acc: 0.718
+2025-08-06 19:13:33: Epoch: 019 Test results, ap: 0.178, auc: 0.324, f1: 0.000, acc: 0.750
+2025-08-06 19:13:39: Epoch: 020 Train Loss: 0.262, ap: 0.598, auc: 0.667, f1: 0.538, acc: 0.650
+2025-08-06 19:13:41: Epoch: 020 Test results, ap: 0.179, auc: 0.332, f1: 0.000, acc: 0.519
+2025-08-06 19:13:48: Epoch: 021 Train Loss: 0.246, ap: 0.605, auc: 0.682, f1: 0.553, acc: 0.670
+2025-08-06 19:13:49: Epoch: 021 Test results, ap: 0.182, auc: 0.338, f1: 0.000, acc: 0.648
+2025-08-06 19:13:56: Epoch: 022 Train Loss: 0.198, ap: 0.619, auc: 0.687, f1: 0.514, acc: 0.650
+2025-08-06 19:13:57: Epoch: 022 Test results, ap: 0.183, auc: 0.345, f1: 0.000, acc: 0.713
+2025-08-06 19:14:04: Epoch: 023 Train Loss: 0.199, ap: 0.610, auc: 0.679, f1: 0.500, acc: 0.631
+2025-08-06 19:14:05: Epoch: 023 Test results, ap: 0.183, auc: 0.342, f1: 0.000, acc: 0.713
+2025-08-06 19:14:12: Epoch: 024 Train Loss: 0.203, ap: 0.557, auc: 0.659, f1: 0.529, acc: 0.602
+2025-08-06 19:14:13: Epoch: 024 Test results, ap: 0.184, auc: 0.352, f1: 0.000, acc: 0.537
+2025-08-06 19:14:20: Epoch: 025 Train Loss: 0.194, ap: 0.597, auc: 0.683, f1: 0.543, acc: 0.641
+2025-08-06 19:14:21: Epoch: 025 Test results, ap: 0.189, auc: 0.376, f1: 0.000, acc: 0.713
+2025-08-06 19:14:28: Epoch: 026 Train Loss: 0.200, ap: 0.623, auc: 0.691, f1: 0.537, acc: 0.699
+2025-08-06 19:14:29: Epoch: 026 Test results, ap: 0.211, auc: 0.411, f1: 0.000, acc: 0.750
+2025-08-06 19:14:36: Epoch: 027 Train Loss: 0.205, ap: 0.594, auc: 0.677, f1: 0.507, acc: 0.660
+2025-08-06 19:14:37: Epoch: 027 Test results, ap: 0.185, auc: 0.360, f1: 0.000, acc: 0.741
+2025-08-06 19:14:44: Epoch: 028 Train Loss: 0.191, ap: 0.614, auc: 0.680, f1: 0.548, acc: 0.680
+2025-08-06 19:14:45: Epoch: 028 Test results, ap: 0.186, auc: 0.364, f1: 0.000, acc: 0.741
+2025-08-06 19:14:52: Epoch: 029 Train Loss: 0.193, ap: 0.618, auc: 0.688, f1: 0.548, acc: 0.680
+2025-08-06 19:14:53: Epoch: 029 Test results, ap: 0.185, auc: 0.358, f1: 0.000, acc: 0.731
+2025-08-06 19:15:00: Epoch: 030 Train Loss: 0.185, ap: 0.594, auc: 0.661, f1: 0.494, acc: 0.583
+2025-08-06 19:15:01: Epoch: 030 Test results, ap: 0.190, auc: 0.376, f1: 0.000, acc: 0.741
+2025-08-06 19:15:08: Epoch: 031 Train Loss: 0.178, ap: 0.597, auc: 0.673, f1: 0.545, acc: 0.660
+2025-08-06 19:15:09: Epoch: 031 Test results, ap: 0.194, auc: 0.393, f1: 0.000, acc: 0.731
+2025-08-06 19:15:16: Epoch: 032 Train Loss: 0.163, ap: 0.611, auc: 0.683, f1: 0.522, acc: 0.680
+2025-08-06 19:15:17: Epoch: 032 Test results, ap: 0.235, auc: 0.422, f1: 0.000, acc: 0.750
+2025-08-06 19:15:24: Epoch: 033 Train Loss: 0.150, ap: 0.603, auc: 0.680, f1: 0.556, acc: 0.689
+2025-08-06 19:15:25: Epoch: 033 Test results, ap: 0.194, auc: 0.390, f1: 0.000, acc: 0.750
+2025-08-06 19:15:32: Epoch: 034 Train Loss: 0.157, ap: 0.600, auc: 0.672, f1: 0.538, acc: 0.650
+2025-08-06 19:15:33: Epoch: 034 Test results, ap: 0.190, auc: 0.376, f1: 0.000, acc: 0.731
+2025-08-06 19:15:40: Epoch: 035 Train Loss: 0.131, ap: 0.591, auc: 0.667, f1: 0.553, acc: 0.670
+2025-08-06 19:15:41: Epoch: 035 Test results, ap: 0.189, auc: 0.373, f1: 0.000, acc: 0.731
+2025-08-06 19:15:48: Epoch: 036 Train Loss: 0.169, ap: 0.593, auc: 0.662, f1: 0.553, acc: 0.670
+2025-08-06 19:15:49: Epoch: 036 Test results, ap: 0.192, auc: 0.387, f1: 0.000, acc: 0.731
+2025-08-06 19:15:56: Epoch: 037 Train Loss: 0.136, ap: 0.606, auc: 0.681, f1: 0.548, acc: 0.680
+2025-08-06 19:15:57: Epoch: 037 Test results, ap: 0.198, auc: 0.396, f1: 0.000, acc: 0.741
+2025-08-06 19:16:04: Epoch: 038 Train Loss: 0.147, ap: 0.593, auc: 0.677, f1: 0.537, acc: 0.631
+2025-08-06 19:16:05: Epoch: 038 Test results, ap: 0.188, auc: 0.370, f1: 0.000, acc: 0.722
+2025-08-06 19:16:12: Epoch: 039 Train Loss: 0.129, ap: 0.583, auc: 0.664, f1: 0.538, acc: 0.650
+2025-08-06 19:16:13: Epoch: 039 Test results, ap: 0.189, auc: 0.374, f1: 0.000, acc: 0.722
+2025-08-06 19:16:20: Epoch: 040 Train Loss: 0.146, ap: 0.550, auc: 0.669, f1: 0.553, acc: 0.670
+2025-08-06 19:16:21: Epoch: 040 Test results, ap: 0.189, auc: 0.373, f1: 0.000, acc: 0.750
+2025-08-06 19:16:28: Epoch: 041 Train Loss: 0.128, ap: 0.546, auc: 0.666, f1: 0.560, acc: 0.680
+2025-08-06 19:16:29: Epoch: 041 Test results, ap: 0.188, auc: 0.370, f1: 0.000, acc: 0.741
+2025-08-06 19:16:36: Epoch: 042 Train Loss: 0.128, ap: 0.574, auc: 0.681, f1: 0.560, acc: 0.680
+2025-08-06 19:16:37: Epoch: 042 Test results, ap: 0.194, auc: 0.390, f1: 0.000, acc: 0.741
+2025-08-06 19:16:44: Epoch: 043 Train Loss: 0.124, ap: 0.584, auc: 0.686, f1: 0.553, acc: 0.670
+2025-08-06 19:16:45: Epoch: 043 Test results, ap: 0.206, auc: 0.410, f1: 0.000, acc: 0.750
+2025-08-06 19:16:52: Epoch: 044 Train Loss: 0.127, ap: 0.589, auc: 0.683, f1: 0.538, acc: 0.650
+2025-08-06 19:16:53: Epoch: 044 Test results, ap: 0.204, auc: 0.412, f1: 0.000, acc: 0.731
+2025-08-06 19:17:00: Epoch: 045 Train Loss: 0.135, ap: 0.549, auc: 0.669, f1: 0.553, acc: 0.670
+2025-08-06 19:17:01: Epoch: 045 Test results, ap: 0.194, auc: 0.389, f1: 0.000, acc: 0.741
+2025-08-06 19:17:08: Epoch: 046 Train Loss: 0.107, ap: 0.548, auc: 0.671, f1: 0.538, acc: 0.650
+2025-08-06 19:17:09: Epoch: 046 Test results, ap: 0.194, auc: 0.386, f1: 0.000, acc: 0.750
+2025-08-06 19:17:16: Epoch: 047 Train Loss: 0.116, ap: 0.562, auc: 0.679, f1: 0.553, acc: 0.670
+2025-08-06 19:17:17: Epoch: 047 Test results, ap: 0.204, auc: 0.408, f1: 0.000, acc: 0.750
+2025-08-06 19:17:24: Epoch: 048 Train Loss: 0.138, ap: 0.576, auc: 0.686, f1: 0.538, acc: 0.650
+2025-08-06 19:17:25: Epoch: 048 Test results, ap: 0.192, auc: 0.386, f1: 0.000, acc: 0.750
+2025-08-06 19:17:32: Epoch: 049 Train Loss: 0.104, ap: 0.541, auc: 0.667, f1: 0.533, acc: 0.660
+2025-08-06 19:17:33: Epoch: 049 Test results, ap: 0.190, auc: 0.380, f1: 0.000, acc: 0.750
+2025-08-06 19:17:40: Epoch: 050 Train Loss: 0.119, ap: 0.582, auc: 0.686, f1: 0.553, acc: 0.670
+2025-08-06 19:17:41: Epoch: 050 Test results, ap: 0.202, auc: 0.408, f1: 0.000, acc: 0.750
+2025-08-06 19:17:41: used time 0.11h
+2025-08-06 19:17:41: Running Cross Validation 2
+2025-08-06 19:17:41: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-06 19:17:48: Epoch: 001 Train Loss: 0.690, ap: 0.568, auc: 0.562, f1: 0.634, acc: 0.563
+2025-08-06 19:17:48: Epoch: 001 New best VALIDATION metrics
+2025-08-06 19:17:50: Epoch: 001 Test results, ap: 0.232, auc: 0.486, f1: 0.000, acc: 0.759
+2025-08-06 19:17:56: Epoch: 002 Train Loss: 0.688, ap: 0.666, auc: 0.586, f1: 0.549, acc: 0.505
+2025-08-06 19:17:56: Epoch: 002 New best VALIDATION metrics
+2025-08-06 19:17:58: Epoch: 002 Test results, ap: 0.161, auc: 0.238, f1: 0.000, acc: 0.750
+2025-08-06 19:18:04: Epoch: 003 Train Loss: 0.660, ap: 0.639, auc: 0.643, f1: 0.695, acc: 0.583
+2025-08-06 19:18:04: Epoch: 003 New best VALIDATION metrics
+2025-08-06 19:18:05: Epoch: 003 Test results, ap: 0.237, auc: 0.476, f1: 0.196, acc: 0.620
+2025-08-06 19:18:12: Epoch: 004 Train Loss: 0.610, ap: 0.721, auc: 0.676, f1: 0.680, acc: 0.524
+2025-08-06 19:18:12: Epoch: 004 New best VALIDATION metrics
+2025-08-06 19:18:13: Epoch: 004 Test results, ap: 0.243, auc: 0.448, f1: 0.388, acc: 0.241
+2025-08-06 19:18:20: Epoch: 005 Train Loss: 0.566, ap: 0.769, auc: 0.707, f1: 0.707, acc: 0.621
+2025-08-06 19:18:20: Epoch: 005 New best VALIDATION metrics
+2025-08-06 19:18:21: Epoch: 005 Test results, ap: 0.239, auc: 0.532, f1: 0.000, acc: 0.611
+2025-08-06 19:18:28: Epoch: 006 Train Loss: 0.574, ap: 0.608, auc: 0.620, f1: 0.000, acc: 0.495
+2025-08-06 19:18:29: Epoch: 006 Test results, ap: 0.180, auc: 0.335, f1: 0.000, acc: 0.759
+2025-08-06 19:18:36: Epoch: 007 Train Loss: 0.514, ap: 0.807, auc: 0.764, f1: 0.748, acc: 0.718
+2025-08-06 19:18:36: Epoch: 007 New best VALIDATION metrics
+2025-08-06 19:18:37: Epoch: 007 Test results, ap: 0.226, auc: 0.484, f1: 0.115, acc: 0.574
+2025-08-06 19:18:44: Epoch: 008 Train Loss: 0.497, ap: 0.696, auc: 0.684, f1: 0.671, acc: 0.524
+2025-08-06 19:18:45: Epoch: 008 Test results, ap: 0.189, auc: 0.366, f1: 0.367, acc: 0.296
+2025-08-06 19:18:52: Epoch: 009 Train Loss: 0.482, ap: 0.779, auc: 0.764, f1: 0.682, acc: 0.728
+2025-08-06 19:18:53: Epoch: 009 Test results, ap: 0.265, auc: 0.488, f1: 0.000, acc: 0.759
+2025-08-06 19:19:00: Epoch: 010 Train Loss: 0.445, ap: 0.801, auc: 0.764, f1: 0.730, acc: 0.699
+2025-08-06 19:19:01: Epoch: 010 Test results, ap: 0.219, auc: 0.388, f1: 0.091, acc: 0.630
+2025-08-06 19:19:08: Epoch: 011 Train Loss: 0.368, ap: 0.788, auc: 0.739, f1: 0.672, acc: 0.602
+2025-08-06 19:19:09: Epoch: 011 Test results, ap: 0.193, auc: 0.341, f1: 0.111, acc: 0.556
+2025-08-06 19:19:09: Epoch: 011 New best TEST metrics
+2025-08-06 19:19:16: Epoch: 012 Train Loss: 0.355, ap: 0.796, auc: 0.766, f1: 0.627, acc: 0.699
+2025-08-06 19:19:17: Epoch: 012 Test results, ap: 0.216, auc: 0.406, f1: 0.000, acc: 0.759
+2025-08-06 19:19:17: Epoch: 012 New best TEST metrics
+2025-08-06 19:19:24: Epoch: 013 Train Loss: 0.325, ap: 0.764, auc: 0.758, f1: 0.718, acc: 0.680
+2025-08-06 19:19:25: Epoch: 013 Test results, ap: 0.202, auc: 0.384, f1: 0.108, acc: 0.694
+2025-08-06 19:19:32: Epoch: 014 Train Loss: 0.281, ap: 0.771, auc: 0.767, f1: 0.698, acc: 0.689
+2025-08-06 19:19:33: Epoch: 014 Test results, ap: 0.197, auc: 0.371, f1: 0.108, acc: 0.694
+2025-08-06 19:19:40: Epoch: 015 Train Loss: 0.264, ap: 0.732, auc: 0.751, f1: 0.667, acc: 0.670
+2025-08-06 19:19:41: Epoch: 015 Test results, ap: 0.222, auc: 0.405, f1: 0.067, acc: 0.741
+2025-08-06 19:19:41: Epoch: 015 New best TEST metrics
+2025-08-06 19:19:48: Epoch: 016 Train Loss: 0.284, ap: 0.783, auc: 0.779, f1: 0.707, acc: 0.670
+2025-08-06 19:19:49: Epoch: 016 Test results, ap: 0.237, auc: 0.429, f1: 0.129, acc: 0.750
+2025-08-06 19:19:49: Epoch: 016 New best TEST metrics
+2025-08-06 19:19:56: Epoch: 017 Train Loss: 0.262, ap: 0.759, auc: 0.779, f1: 0.730, acc: 0.670
+2025-08-06 19:19:57: Epoch: 017 Test results, ap: 0.216, auc: 0.367, f1: 0.105, acc: 0.685
+2025-08-06 19:20:04: Epoch: 018 Train Loss: 0.249, ap: 0.774, auc: 0.771, f1: 0.537, acc: 0.631
+2025-08-06 19:20:05: Epoch: 018 Test results, ap: 0.253, auc: 0.419, f1: 0.000, acc: 0.750
+2025-08-06 19:20:05: Epoch: 018 New best TEST metrics
+2025-08-06 19:20:12: Epoch: 019 Train Loss: 0.238, ap: 0.818, auc: 0.796, f1: 0.741, acc: 0.709
+2025-08-06 19:20:12: Epoch: 019 New best VALIDATION metrics
+2025-08-06 19:20:13: Epoch: 019 Test results, ap: 0.283, auc: 0.424, f1: 0.182, acc: 0.750
+2025-08-06 19:20:13: Epoch: 019 New best TEST metrics
+2025-08-06 19:20:20: Epoch: 020 Train Loss: 0.235, ap: 0.732, auc: 0.739, f1: 0.711, acc: 0.660
+2025-08-06 19:20:21: Epoch: 020 Test results, ap: 0.231, auc: 0.428, f1: 0.121, acc: 0.731
+2025-08-06 19:20:28: Epoch: 021 Train Loss: 0.205, ap: 0.789, auc: 0.783, f1: 0.718, acc: 0.718
+2025-08-06 19:20:29: Epoch: 021 Test results, ap: 0.290, auc: 0.446, f1: 0.138, acc: 0.769
+2025-08-06 19:20:29: Epoch: 021 New best TEST metrics
+2025-08-06 19:20:36: Epoch: 022 Train Loss: 0.177, ap: 0.763, auc: 0.767, f1: 0.752, acc: 0.718
+2025-08-06 19:20:37: Epoch: 022 Test results, ap: 0.307, auc: 0.472, f1: 0.133, acc: 0.759
+2025-08-06 19:20:37: Epoch: 022 New best TEST metrics
+2025-08-06 19:20:44: Epoch: 023 Train Loss: 0.199, ap: 0.744, auc: 0.745, f1: 0.726, acc: 0.670
+2025-08-06 19:20:45: Epoch: 023 Test results, ap: 0.251, auc: 0.411, f1: 0.167, acc: 0.722
+2025-08-06 19:20:52: Epoch: 024 Train Loss: 0.169, ap: 0.753, auc: 0.771, f1: 0.736, acc: 0.728
+2025-08-06 19:20:53: Epoch: 024 Test results, ap: 0.280, auc: 0.413, f1: 0.133, acc: 0.759
+2025-08-06 19:21:00: Epoch: 025 Train Loss: 0.207, ap: 0.763, auc: 0.775, f1: 0.759, acc: 0.728
+2025-08-06 19:21:01: Epoch: 025 Test results, ap: 0.287, auc: 0.432, f1: 0.125, acc: 0.741
+2025-08-06 19:21:08: Epoch: 026 Train Loss: 0.181, ap: 0.769, auc: 0.767, f1: 0.712, acc: 0.670
+2025-08-06 19:21:09: Epoch: 026 Test results, ap: 0.233, auc: 0.437, f1: 0.125, acc: 0.741
+2025-08-06 19:21:16: Epoch: 027 Train Loss: 0.183, ap: 0.758, auc: 0.763, f1: 0.735, acc: 0.699
+2025-08-06 19:21:17: Epoch: 027 Test results, ap: 0.293, auc: 0.436, f1: 0.125, acc: 0.741
+2025-08-06 19:21:24: Epoch: 028 Train Loss: 0.182, ap: 0.760, auc: 0.771, f1: 0.719, acc: 0.689
+2025-08-06 19:21:25: Epoch: 028 Test results, ap: 0.278, auc: 0.416, f1: 0.125, acc: 0.741
+2025-08-06 19:21:32: Epoch: 029 Train Loss: 0.156, ap: 0.762, auc: 0.781, f1: 0.743, acc: 0.718
+2025-08-06 19:21:33: Epoch: 029 Test results, ap: 0.283, auc: 0.415, f1: 0.125, acc: 0.741
+2025-08-06 19:21:40: Epoch: 030 Train Loss: 0.154, ap: 0.765, auc: 0.785, f1: 0.741, acc: 0.709
+2025-08-06 19:21:42: Epoch: 030 Test results, ap: 0.273, auc: 0.411, f1: 0.125, acc: 0.741
+2025-08-06 19:21:48: Epoch: 031 Train Loss: 0.145, ap: 0.756, auc: 0.770, f1: 0.726, acc: 0.699
+2025-08-06 19:21:50: Epoch: 031 Test results, ap: 0.279, auc: 0.425, f1: 0.133, acc: 0.759
+2025-08-06 19:21:56: Epoch: 032 Train Loss: 0.123, ap: 0.763, auc: 0.773, f1: 0.752, acc: 0.718
+2025-08-06 19:21:58: Epoch: 032 Test results, ap: 0.288, auc: 0.445, f1: 0.129, acc: 0.750
+2025-08-06 19:22:04: Epoch: 033 Train Loss: 0.121, ap: 0.772, auc: 0.778, f1: 0.746, acc: 0.709
+2025-08-06 19:22:06: Epoch: 033 Test results, ap: 0.283, auc: 0.431, f1: 0.129, acc: 0.750
+2025-08-06 19:22:13: Epoch: 034 Train Loss: 0.122, ap: 0.762, auc: 0.775, f1: 0.735, acc: 0.699
+2025-08-06 19:22:14: Epoch: 034 Test results, ap: 0.277, auc: 0.422, f1: 0.125, acc: 0.741
+2025-08-06 19:22:20: Epoch: 035 Train Loss: 0.127, ap: 0.761, auc: 0.771, f1: 0.724, acc: 0.689
+2025-08-06 19:22:22: Epoch: 035 Test results, ap: 0.278, auc: 0.423, f1: 0.125, acc: 0.741
+2025-08-06 19:22:28: Epoch: 036 Train Loss: 0.129, ap: 0.764, auc: 0.775, f1: 0.729, acc: 0.689
+2025-08-06 19:22:30: Epoch: 036 Test results, ap: 0.274, auc: 0.411, f1: 0.125, acc: 0.741
+2025-08-06 19:22:37: Epoch: 037 Train Loss: 0.122, ap: 0.781, auc: 0.790, f1: 0.739, acc: 0.718
+2025-08-06 19:22:38: Epoch: 037 Test results, ap: 0.285, auc: 0.441, f1: 0.138, acc: 0.769
+2025-08-06 19:22:45: Epoch: 038 Train Loss: 0.122, ap: 0.783, auc: 0.788, f1: 0.739, acc: 0.699
+2025-08-06 19:22:46: Epoch: 038 Test results, ap: 0.272, auc: 0.409, f1: 0.125, acc: 0.741
+2025-08-06 19:22:53: Epoch: 039 Train Loss: 0.125, ap: 0.786, auc: 0.797, f1: 0.724, acc: 0.689
+2025-08-06 19:22:54: Epoch: 039 Test results, ap: 0.279, auc: 0.414, f1: 0.125, acc: 0.741
+2025-08-06 19:23:01: Epoch: 040 Train Loss: 0.126, ap: 0.771, auc: 0.772, f1: 0.726, acc: 0.670
+2025-08-06 19:23:02: Epoch: 040 Test results, ap: 0.272, auc: 0.408, f1: 0.125, acc: 0.741
+2025-08-06 19:23:09: Epoch: 041 Train Loss: 0.108, ap: 0.779, auc: 0.786, f1: 0.724, acc: 0.689
+2025-08-06 19:23:10: Epoch: 041 Test results, ap: 0.272, auc: 0.410, f1: 0.125, acc: 0.741
+2025-08-06 19:23:17: Epoch: 042 Train Loss: 0.112, ap: 0.785, auc: 0.795, f1: 0.754, acc: 0.728
+2025-08-06 19:23:18: Epoch: 042 Test results, ap: 0.270, auc: 0.411, f1: 0.129, acc: 0.750
+2025-08-06 19:23:25: Epoch: 043 Train Loss: 0.108, ap: 0.785, auc: 0.792, f1: 0.748, acc: 0.718
+2025-08-06 19:23:26: Epoch: 043 Test results, ap: 0.272, auc: 0.411, f1: 0.133, acc: 0.759
+2025-08-06 19:23:33: Epoch: 044 Train Loss: 0.106, ap: 0.777, auc: 0.784, f1: 0.735, acc: 0.699
+2025-08-06 19:23:34: Epoch: 044 Test results, ap: 0.275, auc: 0.414, f1: 0.125, acc: 0.741
+2025-08-06 19:23:41: Epoch: 045 Train Loss: 0.103, ap: 0.776, auc: 0.783, f1: 0.735, acc: 0.699
+2025-08-06 19:23:42: Epoch: 045 Test results, ap: 0.271, auc: 0.410, f1: 0.125, acc: 0.741
+2025-08-06 19:23:49: Epoch: 046 Train Loss: 0.100, ap: 0.776, auc: 0.784, f1: 0.735, acc: 0.699
+2025-08-06 19:23:50: Epoch: 046 Test results, ap: 0.269, auc: 0.408, f1: 0.129, acc: 0.750
+2025-08-06 19:23:57: Epoch: 047 Train Loss: 0.105, ap: 0.782, auc: 0.793, f1: 0.748, acc: 0.718
+2025-08-06 19:23:58: Epoch: 047 Test results, ap: 0.269, auc: 0.407, f1: 0.129, acc: 0.750
+2025-08-06 19:24:05: Epoch: 048 Train Loss: 0.099, ap: 0.785, auc: 0.797, f1: 0.735, acc: 0.699
+2025-08-06 19:24:06: Epoch: 048 Test results, ap: 0.268, auc: 0.407, f1: 0.125, acc: 0.741
+2025-08-06 19:24:13: Epoch: 049 Train Loss: 0.090, ap: 0.782, auc: 0.790, f1: 0.735, acc: 0.699
+2025-08-06 19:24:14: Epoch: 049 Test results, ap: 0.279, auc: 0.425, f1: 0.125, acc: 0.741
+2025-08-06 19:24:21: Epoch: 050 Train Loss: 0.106, ap: 0.787, auc: 0.796, f1: 0.757, acc: 0.738
+2025-08-06 19:24:22: Epoch: 050 Test results, ap: 0.292, auc: 0.443, f1: 0.129, acc: 0.750
+2025-08-06 19:24:22: used time 0.11h
+2025-08-06 19:24:22: Running Cross Validation 3
+2025-08-06 19:24:22: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-06 19:24:29: Epoch: 001 Train Loss: 0.689, ap: 0.668, auc: 0.755, f1: 0.660, acc: 0.689
+2025-08-06 19:24:29: Epoch: 001 New best VALIDATION metrics
+2025-08-06 19:24:30: Epoch: 001 Test results, ap: 0.206, auc: 0.397, f1: 0.000, acc: 0.722
+2025-08-06 19:24:37: Epoch: 002 Train Loss: 0.638, ap: 0.681, auc: 0.773, f1: 0.235, acc: 0.621
+2025-08-06 19:24:37: Epoch: 002 New best VALIDATION metrics
+2025-08-06 19:24:38: Epoch: 002 Test results, ap: 0.201, auc: 0.369, f1: 0.000, acc: 0.759
+2025-08-06 19:24:45: Epoch: 003 Train Loss: 0.595, ap: 0.504, auc: 0.605, f1: 0.593, acc: 0.466
+2025-08-06 19:24:46: Epoch: 003 Test results, ap: 0.290, auc: 0.608, f1: 0.391, acc: 0.250
+2025-08-06 19:24:53: Epoch: 004 Train Loss: 0.571, ap: 0.772, auc: 0.793, f1: 0.491, acc: 0.718
+2025-08-06 19:24:53: Epoch: 004 New best VALIDATION metrics
+2025-08-06 19:24:54: Epoch: 004 Test results, ap: 0.214, auc: 0.380, f1: 0.000, acc: 0.750
+2025-08-06 19:25:01: Epoch: 005 Train Loss: 0.513, ap: 0.764, auc: 0.788, f1: 0.680, acc: 0.689
+2025-08-06 19:25:02: Epoch: 005 Test results, ap: 0.235, auc: 0.506, f1: 0.000, acc: 0.694
+2025-08-06 19:25:09: Epoch: 006 Train Loss: 0.495, ap: 0.690, auc: 0.710, f1: 0.634, acc: 0.563
+2025-08-06 19:25:10: Epoch: 006 Test results, ap: 0.245, auc: 0.413, f1: 0.388, acc: 0.241
+2025-08-06 19:25:17: Epoch: 007 Train Loss: 0.469, ap: 0.680, auc: 0.760, f1: 0.364, acc: 0.660
+2025-08-06 19:25:18: Epoch: 007 Test results, ap: 0.230, auc: 0.471, f1: 0.000, acc: 0.759
+2025-08-06 19:25:25: Epoch: 008 Train Loss: 0.443, ap: 0.708, auc: 0.747, f1: 0.556, acc: 0.689
+2025-08-06 19:25:26: Epoch: 008 Test results, ap: 0.301, auc: 0.447, f1: 0.267, acc: 0.694
+2025-08-06 19:25:33: Epoch: 009 Train Loss: 0.468, ap: 0.730, auc: 0.760, f1: 0.571, acc: 0.680
+2025-08-06 19:25:34: Epoch: 009 Test results, ap: 0.217, auc: 0.332, f1: 0.000, acc: 0.741
+2025-08-06 19:25:41: Epoch: 010 Train Loss: 0.414, ap: 0.757, auc: 0.814, f1: 0.705, acc: 0.748
+2025-08-06 19:25:41: Epoch: 010 New best VALIDATION metrics
+2025-08-06 19:25:42: Epoch: 010 Test results, ap: 0.297, auc: 0.596, f1: 0.000, acc: 0.750
+2025-08-06 19:25:49: Epoch: 011 Train Loss: 0.390, ap: 0.754, auc: 0.778, f1: 0.642, acc: 0.631
+2025-08-06 19:25:50: Epoch: 011 Test results, ap: 0.400, auc: 0.604, f1: 0.217, acc: 0.667
+2025-08-06 19:25:50: Epoch: 011 New best TEST metrics
+2025-08-06 19:25:57: Epoch: 012 Train Loss: 0.343, ap: 0.770, auc: 0.804, f1: 0.674, acc: 0.718
+2025-08-06 19:25:57: Epoch: 012 New best VALIDATION metrics
+2025-08-06 19:25:58: Epoch: 012 Test results, ap: 0.348, auc: 0.580, f1: 0.250, acc: 0.778
+2025-08-06 19:26:05: Epoch: 013 Train Loss: 0.285, ap: 0.752, auc: 0.804, f1: 0.688, acc: 0.709
+2025-08-06 19:26:06: Epoch: 013 Test results, ap: 0.349, auc: 0.575, f1: 0.244, acc: 0.713
+2025-08-06 19:26:13: Epoch: 014 Train Loss: 0.352, ap: 0.815, auc: 0.837, f1: 0.649, acc: 0.748
+2025-08-06 19:26:13: Epoch: 014 New best VALIDATION metrics
+2025-08-06 19:26:14: Epoch: 014 Test results, ap: 0.263, auc: 0.483, f1: 0.000, acc: 0.750
+2025-08-06 19:26:21: Epoch: 015 Train Loss: 0.297, ap: 0.758, auc: 0.813, f1: 0.725, acc: 0.728
+2025-08-06 19:26:22: Epoch: 015 Test results, ap: 0.249, auc: 0.427, f1: 0.125, acc: 0.741
+2025-08-06 19:26:29: Epoch: 016 Train Loss: 0.297, ap: 0.743, auc: 0.814, f1: 0.731, acc: 0.757
+2025-08-06 19:26:30: Epoch: 016 Test results, ap: 0.295, auc: 0.548, f1: 0.121, acc: 0.731
+2025-08-06 19:26:37: Epoch: 017 Train Loss: 0.280, ap: 0.765, auc: 0.827, f1: 0.705, acc: 0.748
+2025-08-06 19:26:38: Epoch: 017 Test results, ap: 0.335, auc: 0.595, f1: 0.200, acc: 0.704
+2025-08-06 19:26:45: Epoch: 018 Train Loss: 0.250, ap: 0.763, auc: 0.817, f1: 0.658, acc: 0.748
+2025-08-06 19:26:46: Epoch: 018 Test results, ap: 0.271, auc: 0.535, f1: 0.000, acc: 0.731
+2025-08-06 19:26:53: Epoch: 019 Train Loss: 0.249, ap: 0.736, auc: 0.817, f1: 0.753, acc: 0.777
+2025-08-06 19:26:54: Epoch: 019 Test results, ap: 0.303, auc: 0.549, f1: 0.242, acc: 0.769
+2025-08-06 19:27:01: Epoch: 020 Train Loss: 0.223, ap: 0.763, auc: 0.833, f1: 0.698, acc: 0.748
+2025-08-06 19:27:02: Epoch: 020 Test results, ap: 0.273, auc: 0.556, f1: 0.000, acc: 0.731
+2025-08-06 19:27:09: Epoch: 021 Train Loss: 0.201, ap: 0.768, auc: 0.828, f1: 0.707, acc: 0.767
+2025-08-06 19:27:11: Epoch: 021 Test results, ap: 0.378, auc: 0.574, f1: 0.238, acc: 0.704
+2025-08-06 19:27:17: Epoch: 022 Train Loss: 0.172, ap: 0.784, auc: 0.842, f1: 0.736, acc: 0.777
+2025-08-06 19:27:19: Epoch: 022 Test results, ap: 0.255, auc: 0.547, f1: 0.105, acc: 0.685
+2025-08-06 19:27:25: Epoch: 023 Train Loss: 0.157, ap: 0.736, auc: 0.814, f1: 0.714, acc: 0.767
+2025-08-06 19:27:27: Epoch: 023 Test results, ap: 0.249, auc: 0.535, f1: 0.000, acc: 0.713
+2025-08-06 19:27:33: Epoch: 024 Train Loss: 0.152, ap: 0.738, auc: 0.816, f1: 0.698, acc: 0.748
+2025-08-06 19:27:34: Epoch: 024 Test results, ap: 0.251, auc: 0.542, f1: 0.000, acc: 0.722
+2025-08-06 19:27:41: Epoch: 025 Train Loss: 0.162, ap: 0.743, auc: 0.820, f1: 0.675, acc: 0.748
+2025-08-06 19:27:42: Epoch: 025 Test results, ap: 0.254, auc: 0.554, f1: 0.000, acc: 0.731
+2025-08-06 19:27:49: Epoch: 026 Train Loss: 0.160, ap: 0.724, auc: 0.799, f1: 0.681, acc: 0.718
+2025-08-06 19:27:50: Epoch: 026 Test results, ap: 0.260, auc: 0.568, f1: 0.000, acc: 0.667
+2025-08-06 19:27:57: Epoch: 027 Train Loss: 0.140, ap: 0.731, auc: 0.813, f1: 0.711, acc: 0.748
+2025-08-06 19:27:58: Epoch: 027 Test results, ap: 0.251, auc: 0.539, f1: 0.240, acc: 0.648
+2025-08-06 19:28:05: Epoch: 028 Train Loss: 0.169, ap: 0.704, auc: 0.792, f1: 0.642, acc: 0.718
+2025-08-06 19:28:06: Epoch: 028 Test results, ap: 0.249, auc: 0.530, f1: 0.000, acc: 0.750
+2025-08-06 19:28:13: Epoch: 029 Train Loss: 0.174, ap: 0.712, auc: 0.809, f1: 0.729, acc: 0.777
+2025-08-06 19:28:14: Epoch: 029 Test results, ap: 0.232, auc: 0.499, f1: 0.000, acc: 0.713
+2025-08-06 19:28:21: Epoch: 030 Train Loss: 0.146, ap: 0.703, auc: 0.796, f1: 0.667, acc: 0.738
+2025-08-06 19:28:22: Epoch: 030 Test results, ap: 0.228, auc: 0.476, f1: 0.000, acc: 0.731
+2025-08-06 19:28:29: Epoch: 031 Train Loss: 0.134, ap: 0.710, auc: 0.799, f1: 0.717, acc: 0.748
+2025-08-06 19:28:30: Epoch: 031 Test results, ap: 0.229, auc: 0.484, f1: 0.053, acc: 0.667
+2025-08-06 19:28:37: Epoch: 032 Train Loss: 0.115, ap: 0.731, auc: 0.809, f1: 0.721, acc: 0.767
+2025-08-06 19:28:38: Epoch: 032 Test results, ap: 0.225, auc: 0.483, f1: 0.000, acc: 0.620
+2025-08-06 19:28:45: Epoch: 033 Train Loss: 0.109, ap: 0.721, auc: 0.805, f1: 0.707, acc: 0.767
+2025-08-06 19:28:46: Epoch: 033 Test results, ap: 0.219, auc: 0.462, f1: 0.000, acc: 0.704
+2025-08-06 19:28:53: Epoch: 034 Train Loss: 0.115, ap: 0.670, auc: 0.779, f1: 0.667, acc: 0.728
+2025-08-06 19:28:55: Epoch: 034 Test results, ap: 0.220, auc: 0.449, f1: 0.059, acc: 0.704
+2025-08-06 19:29:01: Epoch: 035 Train Loss: 0.105, ap: 0.710, auc: 0.800, f1: 0.691, acc: 0.757
+2025-08-06 19:29:02: Epoch: 035 Test results, ap: 0.213, auc: 0.444, f1: 0.000, acc: 0.741
+2025-08-06 19:29:09: Epoch: 036 Train Loss: 0.100, ap: 0.718, auc: 0.803, f1: 0.691, acc: 0.757
+2025-08-06 19:29:11: Epoch: 036 Test results, ap: 0.226, auc: 0.463, f1: 0.000, acc: 0.741
+2025-08-06 19:29:17: Epoch: 037 Train Loss: 0.110, ap: 0.698, auc: 0.790, f1: 0.615, acc: 0.709
+2025-08-06 19:29:19: Epoch: 037 Test results, ap: 0.217, auc: 0.428, f1: 0.000, acc: 0.759
+2025-08-06 19:29:25: Epoch: 038 Train Loss: 0.082, ap: 0.709, auc: 0.806, f1: 0.703, acc: 0.738
+2025-08-06 19:29:27: Epoch: 038 Test results, ap: 0.214, auc: 0.439, f1: 0.087, acc: 0.611
+2025-08-06 19:29:34: Epoch: 039 Train Loss: 0.126, ap: 0.727, auc: 0.812, f1: 0.700, acc: 0.767
+2025-08-06 19:29:35: Epoch: 039 Test results, ap: 0.249, auc: 0.536, f1: 0.000, acc: 0.741
+2025-08-06 19:29:42: Epoch: 040 Train Loss: 0.094, ap: 0.730, auc: 0.819, f1: 0.684, acc: 0.757
+2025-08-06 19:29:43: Epoch: 040 Test results, ap: 0.255, auc: 0.541, f1: 0.000, acc: 0.759
+2025-08-06 19:29:50: Epoch: 041 Train Loss: 0.087, ap: 0.716, auc: 0.811, f1: 0.690, acc: 0.748
+2025-08-06 19:29:51: Epoch: 041 Test results, ap: 0.252, auc: 0.522, f1: 0.000, acc: 0.759
+2025-08-06 19:29:58: Epoch: 042 Train Loss: 0.088, ap: 0.712, auc: 0.805, f1: 0.698, acc: 0.748
+2025-08-06 19:29:59: Epoch: 042 Test results, ap: 0.255, auc: 0.525, f1: 0.111, acc: 0.704
+2025-08-06 19:30:06: Epoch: 043 Train Loss: 0.078, ap: 0.716, auc: 0.811, f1: 0.658, acc: 0.738
+2025-08-06 19:30:07: Epoch: 043 Test results, ap: 0.253, auc: 0.516, f1: 0.000, acc: 0.731
+2025-08-06 19:30:14: Epoch: 044 Train Loss: 0.068, ap: 0.720, auc: 0.809, f1: 0.691, acc: 0.757
+2025-08-06 19:30:15: Epoch: 044 Test results, ap: 0.253, auc: 0.517, f1: 0.000, acc: 0.722
+2025-08-06 19:30:22: Epoch: 045 Train Loss: 0.076, ap: 0.712, auc: 0.803, f1: 0.707, acc: 0.767
+2025-08-06 19:30:23: Epoch: 045 Test results, ap: 0.256, auc: 0.511, f1: 0.062, acc: 0.722
+2025-08-06 19:30:30: Epoch: 046 Train Loss: 0.083, ap: 0.706, auc: 0.799, f1: 0.707, acc: 0.767
+2025-08-06 19:30:31: Epoch: 046 Test results, ap: 0.248, auc: 0.503, f1: 0.000, acc: 0.731
+2025-08-06 19:30:38: Epoch: 047 Train Loss: 0.067, ap: 0.708, auc: 0.808, f1: 0.711, acc: 0.748
+2025-08-06 19:30:39: Epoch: 047 Test results, ap: 0.236, auc: 0.495, f1: 0.103, acc: 0.676
+2025-08-06 19:30:46: Epoch: 048 Train Loss: 0.068, ap: 0.712, auc: 0.807, f1: 0.675, acc: 0.748
+2025-08-06 19:30:47: Epoch: 048 Test results, ap: 0.242, auc: 0.503, f1: 0.000, acc: 0.731
+2025-08-06 19:30:54: Epoch: 049 Train Loss: 0.065, ap: 0.716, auc: 0.810, f1: 0.700, acc: 0.767
+2025-08-06 19:30:55: Epoch: 049 Test results, ap: 0.242, auc: 0.505, f1: 0.000, acc: 0.731
+2025-08-06 19:31:02: Epoch: 050 Train Loss: 0.062, ap: 0.711, auc: 0.804, f1: 0.683, acc: 0.748
+2025-08-06 19:31:03: Epoch: 050 Test results, ap: 0.237, auc: 0.493, f1: 0.000, acc: 0.722
+2025-08-06 19:31:03: used time 0.11h
+2025-08-06 19:31:03: Running Cross Validation 4
+2025-08-06 19:31:03: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-06 19:31:10: Epoch: 001 Train Loss: 0.686, ap: 0.702, auc: 0.745, f1: 0.292, acc: 0.670
+2025-08-06 19:31:10: Epoch: 001 New best VALIDATION metrics
+2025-08-06 19:31:12: Epoch: 001 Test results, ap: 0.173, auc: 0.300, f1: 0.000, acc: 0.759
+2025-08-06 19:31:18: Epoch: 002 Train Loss: 0.632, ap: 0.480, auc: 0.593, f1: 0.542, acc: 0.573
+2025-08-06 19:31:20: Epoch: 002 Test results, ap: 0.177, auc: 0.322, f1: 0.000, acc: 0.750
+2025-08-06 19:31:27: Epoch: 003 Train Loss: 0.577, ap: 0.690, auc: 0.718, f1: 0.541, acc: 0.621
+2025-08-06 19:31:28: Epoch: 003 Test results, ap: 0.174, auc: 0.308, f1: 0.000, acc: 0.583
+2025-08-06 19:31:35: Epoch: 004 Train Loss: 0.512, ap: 0.705, auc: 0.721, f1: 0.353, acc: 0.680
+2025-08-06 19:31:36: Epoch: 004 Test results, ap: 0.171, auc: 0.294, f1: 0.000, acc: 0.759
+2025-08-06 19:31:43: Epoch: 005 Train Loss: 0.453, ap: 0.691, auc: 0.718, f1: 0.508, acc: 0.718
+2025-08-06 19:31:44: Epoch: 005 Test results, ap: 0.175, auc: 0.303, f1: 0.000, acc: 0.759
+2025-08-06 19:31:51: Epoch: 006 Train Loss: 0.426, ap: 0.665, auc: 0.688, f1: 0.553, acc: 0.670
+2025-08-06 19:31:52: Epoch: 006 Test results, ap: 0.365, auc: 0.529, f1: 0.370, acc: 0.528
+2025-08-06 19:31:59: Epoch: 007 Train Loss: 0.440, ap: 0.714, auc: 0.731, f1: 0.629, acc: 0.621
+2025-08-06 19:32:00: Epoch: 007 Test results, ap: 0.179, auc: 0.316, f1: 0.291, acc: 0.324
+2025-08-06 19:32:07: Epoch: 008 Train Loss: 0.379, ap: 0.665, auc: 0.725, f1: 0.130, acc: 0.612
+2025-08-06 19:32:08: Epoch: 008 Test results, ap: 0.173, auc: 0.298, f1: 0.000, acc: 0.759
+2025-08-06 19:32:15: Epoch: 009 Train Loss: 0.360, ap: 0.711, auc: 0.738, f1: 0.563, acc: 0.699
+2025-08-06 19:32:15: Epoch: 009 New best VALIDATION metrics
+2025-08-06 19:32:16: Epoch: 009 Test results, ap: 0.168, auc: 0.268, f1: 0.000, acc: 0.685
+2025-08-06 19:32:23: Epoch: 010 Train Loss: 0.362, ap: 0.706, auc: 0.765, f1: 0.674, acc: 0.709
+2025-08-06 19:32:23: Epoch: 010 New best VALIDATION metrics
+2025-08-06 19:32:24: Epoch: 010 Test results, ap: 0.172, auc: 0.289, f1: 0.209, acc: 0.370
+2025-08-06 19:32:31: Epoch: 011 Train Loss: 0.297, ap: 0.685, auc: 0.738, f1: 0.619, acc: 0.689
+2025-08-06 19:32:32: Epoch: 011 Test results, ap: 0.167, auc: 0.270, f1: 0.000, acc: 0.444
+2025-08-06 19:32:32: Epoch: 011 New best TEST metrics
+2025-08-06 19:32:39: Epoch: 012 Train Loss: 0.264, ap: 0.644, auc: 0.725, f1: 0.646, acc: 0.670
+2025-08-06 19:32:40: Epoch: 012 Test results, ap: 0.178, auc: 0.313, f1: 0.160, acc: 0.417
+2025-08-06 19:32:40: Epoch: 012 New best TEST metrics
+2025-08-06 19:32:47: Epoch: 013 Train Loss: 0.250, ap: 0.643, auc: 0.694, f1: 0.386, acc: 0.660
+2025-08-06 19:32:48: Epoch: 013 Test results, ap: 0.189, auc: 0.345, f1: 0.000, acc: 0.741
+2025-08-06 19:32:48: Epoch: 013 New best TEST metrics
+2025-08-06 19:32:55: Epoch: 014 Train Loss: 0.277, ap: 0.668, auc: 0.747, f1: 0.660, acc: 0.680
+2025-08-06 19:32:56: Epoch: 014 Test results, ap: 0.204, auc: 0.370, f1: 0.222, acc: 0.417
+2025-08-06 19:32:56: Epoch: 014 New best TEST metrics
+2025-08-06 19:33:03: Epoch: 015 Train Loss: 0.258, ap: 0.630, auc: 0.690, f1: 0.528, acc: 0.670
+2025-08-06 19:33:05: Epoch: 015 Test results, ap: 0.191, auc: 0.355, f1: 0.100, acc: 0.667
+2025-08-06 19:33:11: Epoch: 016 Train Loss: 0.215, ap: 0.640, auc: 0.713, f1: 0.636, acc: 0.621
+2025-08-06 19:33:12: Epoch: 016 Test results, ap: 0.190, auc: 0.368, f1: 0.179, acc: 0.491
+2025-08-06 19:33:19: Epoch: 017 Train Loss: 0.227, ap: 0.623, auc: 0.692, f1: 0.413, acc: 0.641
+2025-08-06 19:33:20: Epoch: 017 Test results, ap: 0.237, auc: 0.444, f1: 0.111, acc: 0.704
+2025-08-06 19:33:20: Epoch: 017 New best TEST metrics
+2025-08-06 19:33:27: Epoch: 018 Train Loss: 0.217, ap: 0.612, auc: 0.720, f1: 0.619, acc: 0.534
+2025-08-06 19:33:29: Epoch: 018 Test results, ap: 0.179, auc: 0.307, f1: 0.366, acc: 0.231
+2025-08-06 19:33:35: Epoch: 019 Train Loss: 0.200, ap: 0.618, auc: 0.675, f1: 0.493, acc: 0.641
+2025-08-06 19:33:37: Epoch: 019 Test results, ap: 0.210, auc: 0.364, f1: 0.059, acc: 0.704
+2025-08-06 19:33:44: Epoch: 020 Train Loss: 0.161, ap: 0.664, auc: 0.723, f1: 0.640, acc: 0.650
+2025-08-06 19:33:45: Epoch: 020 Test results, ap: 0.241, auc: 0.451, f1: 0.246, acc: 0.602
+2025-08-06 19:33:45: Epoch: 020 New best TEST metrics
+2025-08-06 19:33:52: Epoch: 021 Train Loss: 0.195, ap: 0.632, auc: 0.696, f1: 0.519, acc: 0.641
+2025-08-06 19:33:53: Epoch: 021 Test results, ap: 0.270, auc: 0.450, f1: 0.222, acc: 0.611
+2025-08-06 19:33:53: Epoch: 021 New best TEST metrics
+2025-08-06 19:34:00: Epoch: 022 Train Loss: 0.148, ap: 0.653, auc: 0.721, f1: 0.612, acc: 0.631
+2025-08-06 19:34:01: Epoch: 022 Test results, ap: 0.237, auc: 0.426, f1: 0.197, acc: 0.546
+2025-08-06 19:34:08: Epoch: 023 Train Loss: 0.167, ap: 0.634, auc: 0.705, f1: 0.574, acc: 0.612
+2025-08-06 19:34:09: Epoch: 023 Test results, ap: 0.234, auc: 0.430, f1: 0.226, acc: 0.556
+2025-08-06 19:34:16: Epoch: 024 Train Loss: 0.173, ap: 0.645, auc: 0.711, f1: 0.640, acc: 0.650
+2025-08-06 19:34:17: Epoch: 024 Test results, ap: 0.263, auc: 0.468, f1: 0.254, acc: 0.565
+2025-08-06 19:34:17: Epoch: 024 New best TEST metrics
+2025-08-06 19:34:24: Epoch: 025 Train Loss: 0.162, ap: 0.629, auc: 0.691, f1: 0.580, acc: 0.592
+2025-08-06 19:34:25: Epoch: 025 Test results, ap: 0.282, auc: 0.456, f1: 0.295, acc: 0.426
+2025-08-06 19:34:25: Epoch: 025 New best TEST metrics
+2025-08-06 19:34:32: Epoch: 026 Train Loss: 0.154, ap: 0.638, auc: 0.710, f1: 0.585, acc: 0.670
+2025-08-06 19:34:33: Epoch: 026 Test results, ap: 0.260, auc: 0.452, f1: 0.218, acc: 0.602
+2025-08-06 19:34:40: Epoch: 027 Train Loss: 0.144, ap: 0.661, auc: 0.734, f1: 0.587, acc: 0.631
+2025-08-06 19:34:41: Epoch: 027 Test results, ap: 0.241, auc: 0.442, f1: 0.235, acc: 0.639
+2025-08-06 19:34:48: Epoch: 028 Train Loss: 0.123, ap: 0.645, auc: 0.717, f1: 0.654, acc: 0.641
+2025-08-06 19:34:49: Epoch: 028 Test results, ap: 0.242, auc: 0.450, f1: 0.232, acc: 0.509
+2025-08-06 19:34:56: Epoch: 029 Train Loss: 0.105, ap: 0.644, auc: 0.718, f1: 0.589, acc: 0.621
+2025-08-06 19:34:57: Epoch: 029 Test results, ap: 0.232, auc: 0.457, f1: 0.269, acc: 0.648
+2025-08-06 19:35:04: Epoch: 030 Train Loss: 0.116, ap: 0.634, auc: 0.698, f1: 0.595, acc: 0.670
+2025-08-06 19:35:05: Epoch: 030 Test results, ap: 0.253, auc: 0.467, f1: 0.280, acc: 0.667
+2025-08-06 19:35:12: Epoch: 031 Train Loss: 0.086, ap: 0.641, auc: 0.718, f1: 0.631, acc: 0.602
+2025-08-06 19:35:13: Epoch: 031 Test results, ap: 0.236, auc: 0.453, f1: 0.282, acc: 0.481
+2025-08-06 19:35:20: Epoch: 032 Train Loss: 0.088, ap: 0.648, auc: 0.714, f1: 0.602, acc: 0.641
+2025-08-06 19:35:21: Epoch: 032 Test results, ap: 0.235, auc: 0.451, f1: 0.281, acc: 0.620
+2025-08-06 19:35:28: Epoch: 033 Train Loss: 0.094, ap: 0.640, auc: 0.704, f1: 0.566, acc: 0.583
+2025-08-06 19:35:29: Epoch: 033 Test results, ap: 0.253, auc: 0.471, f1: 0.303, acc: 0.574
+2025-08-06 19:35:36: Epoch: 034 Train Loss: 0.073, ap: 0.655, auc: 0.720, f1: 0.615, acc: 0.612
+2025-08-06 19:35:37: Epoch: 034 Test results, ap: 0.243, auc: 0.464, f1: 0.273, acc: 0.556
+2025-08-06 19:35:44: Epoch: 035 Train Loss: 0.078, ap: 0.651, auc: 0.722, f1: 0.580, acc: 0.592
+2025-08-06 19:35:45: Epoch: 035 Test results, ap: 0.244, auc: 0.457, f1: 0.299, acc: 0.565
+2025-08-06 19:35:52: Epoch: 036 Train Loss: 0.077, ap: 0.635, auc: 0.698, f1: 0.592, acc: 0.612
+2025-08-06 19:35:53: Epoch: 036 Test results, ap: 0.247, auc: 0.468, f1: 0.278, acc: 0.519
+2025-08-06 19:36:00: Epoch: 037 Train Loss: 0.074, ap: 0.656, auc: 0.722, f1: 0.577, acc: 0.602
+2025-08-06 19:36:01: Epoch: 037 Test results, ap: 0.247, auc: 0.483, f1: 0.281, acc: 0.620
+2025-08-06 19:36:08: Epoch: 038 Train Loss: 0.068, ap: 0.654, auc: 0.731, f1: 0.648, acc: 0.631
+2025-08-06 19:36:09: Epoch: 038 Test results, ap: 0.233, auc: 0.463, f1: 0.273, acc: 0.556
+2025-08-06 19:36:16: Epoch: 039 Train Loss: 0.061, ap: 0.652, auc: 0.731, f1: 0.608, acc: 0.612
+2025-08-06 19:36:17: Epoch: 039 Test results, ap: 0.225, auc: 0.452, f1: 0.276, acc: 0.611
+2025-08-06 19:36:24: Epoch: 040 Train Loss: 0.076, ap: 0.636, auc: 0.701, f1: 0.596, acc: 0.631
+2025-08-06 19:36:25: Epoch: 040 Test results, ap: 0.228, auc: 0.458, f1: 0.276, acc: 0.611
+2025-08-06 19:36:32: Epoch: 041 Train Loss: 0.057, ap: 0.651, auc: 0.713, f1: 0.594, acc: 0.602
+2025-08-06 19:36:33: Epoch: 041 Test results, ap: 0.231, auc: 0.462, f1: 0.261, acc: 0.528
+2025-08-06 19:36:40: Epoch: 042 Train Loss: 0.051, ap: 0.646, auc: 0.718, f1: 0.596, acc: 0.592
+2025-08-06 19:36:41: Epoch: 042 Test results, ap: 0.226, auc: 0.455, f1: 0.239, acc: 0.528
+2025-08-06 19:36:48: Epoch: 043 Train Loss: 0.048, ap: 0.647, auc: 0.719, f1: 0.602, acc: 0.602
+2025-08-06 19:36:49: Epoch: 043 Test results, ap: 0.224, auc: 0.450, f1: 0.239, acc: 0.528
+2025-08-06 19:36:56: Epoch: 044 Train Loss: 0.041, ap: 0.650, auc: 0.718, f1: 0.588, acc: 0.592
+2025-08-06 19:36:58: Epoch: 044 Test results, ap: 0.225, auc: 0.453, f1: 0.258, acc: 0.574
+2025-08-06 19:37:04: Epoch: 045 Train Loss: 0.048, ap: 0.650, auc: 0.715, f1: 0.588, acc: 0.592
+2025-08-06 19:37:06: Epoch: 045 Test results, ap: 0.226, auc: 0.453, f1: 0.239, acc: 0.528
+2025-08-06 19:37:12: Epoch: 046 Train Loss: 0.048, ap: 0.645, auc: 0.716, f1: 0.608, acc: 0.612
+2025-08-06 19:37:14: Epoch: 046 Test results, ap: 0.223, auc: 0.450, f1: 0.250, acc: 0.556
+2025-08-06 19:37:20: Epoch: 047 Train Loss: 0.046, ap: 0.651, auc: 0.708, f1: 0.592, acc: 0.612
+2025-08-06 19:37:22: Epoch: 047 Test results, ap: 0.222, auc: 0.442, f1: 0.250, acc: 0.556
+2025-08-06 19:37:28: Epoch: 048 Train Loss: 0.047, ap: 0.641, auc: 0.710, f1: 0.602, acc: 0.602
+2025-08-06 19:37:30: Epoch: 048 Test results, ap: 0.214, auc: 0.428, f1: 0.225, acc: 0.491
+2025-08-06 19:37:36: Epoch: 049 Train Loss: 0.047, ap: 0.657, auc: 0.717, f1: 0.583, acc: 0.612
+2025-08-06 19:37:38: Epoch: 049 Test results, ap: 0.217, auc: 0.435, f1: 0.258, acc: 0.574
+2025-08-06 19:37:45: Epoch: 050 Train Loss: 0.038, ap: 0.652, auc: 0.719, f1: 0.586, acc: 0.602
+2025-08-06 19:37:46: Epoch: 050 Test results, ap: 0.218, auc: 0.437, f1: 0.250, acc: 0.556
+2025-08-06 19:37:46: used time 0.11h
+2025-08-06 19:37:46: Cross Validation Finished!
+2025-08-06 19:37:46: Best validation perform list
+[[0.77821231 0.77469599 0.6506024  0.71844661]
+ [0.66057336 0.7133497  0.55882353 0.70873785]
+ [0.81826639 0.7963801  0.74137932 0.70873785]
+ [0.81482285 0.83723652 0.64864862 0.74757284]
+ [0.70587182 0.76514554 0.67391306 0.70873785]]
+2025-08-06 19:37:46: mean: [0.756 0.777 0.655 0.718]
+2025-08-06 19:37:46: std: [0.062 0.04  0.059 0.015]
+2025-08-06 19:37:46: Best test perform list
+[[0.25706989 0.42448404 0.30000001 0.35185185]
+ [0.25719953 0.42166981 0.22857143 0.75      ]
+ [0.30668312 0.47185743 0.13333334 0.75925928]
+ [0.39988172 0.60365856 0.2173913  0.66666669]
+ [0.28163761 0.45590997 0.29545453 0.42592594]]
+2025-08-06 19:37:46: mean: [0.3   0.476 0.235 0.591]
+2025-08-06 19:37:46: std: [0.053 0.067 0.061 0.17 ]
diff --git a/run-cls-simple/gru-att-256-simple-oneway-ce-32-0.001-50/config.json b/run-cls-simple/gru-att-256-simple-oneway-ce-32-0.001-50/config.json
new file mode 100644
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "gru", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
diff --git a/run-cls-simple/gru-att-256-simple-oneway-ce-32-0.001-50/inference_results.csv b/run-cls-simple/gru-att-256-simple-oneway-ce-32-0.001-50/inference_results.csv
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+2025-08-07 19:42:41: saving_dir: ./run-cls/gru-att-256-simple-oneway-ce-32-0.001-50
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+2025-08-07 19:42:53: Epoch: 023 New best VALIDATION metrics
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+2025-08-07 19:43:01: Epoch: 038 Test results, ap: 0.233, auc: 0.471, f1: 0.000, acc: 0.731
+2025-08-07 19:43:01: Epoch: 039 Train Loss: 0.468, ap: 0.699, auc: 0.752, f1: 0.667, acc: 0.709
+2025-08-07 19:43:01: Epoch: 039 Test results, ap: 0.255, auc: 0.543, f1: 0.000, acc: 0.759
+2025-08-07 19:43:01: Epoch: 040 Train Loss: 0.434, ap: 0.705, auc: 0.756, f1: 0.600, acc: 0.689
+2025-08-07 19:43:02: Epoch: 040 Test results, ap: 0.256, auc: 0.546, f1: 0.000, acc: 0.759
+2025-08-07 19:43:02: Epoch: 041 Train Loss: 0.414, ap: 0.732, auc: 0.761, f1: 0.674, acc: 0.718
+2025-08-07 19:43:02: Epoch: 041 Test results, ap: 0.248, auc: 0.529, f1: 0.000, acc: 0.759
+2025-08-07 19:43:03: Epoch: 042 Train Loss: 0.397, ap: 0.704, auc: 0.754, f1: 0.667, acc: 0.728
+2025-08-07 19:43:03: Epoch: 042 Test results, ap: 0.250, auc: 0.531, f1: 0.000, acc: 0.759
+2025-08-07 19:43:03: Epoch: 043 Train Loss: 0.391, ap: 0.723, auc: 0.765, f1: 0.688, acc: 0.718
+2025-08-07 19:43:03: Epoch: 043 Test results, ap: 0.250, auc: 0.532, f1: 0.000, acc: 0.759
+2025-08-07 19:43:04: Epoch: 044 Train Loss: 0.392, ap: 0.708, auc: 0.758, f1: 0.667, acc: 0.728
+2025-08-07 19:43:04: Epoch: 044 Test results, ap: 0.235, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:43:04: Epoch: 045 Train Loss: 0.409, ap: 0.702, auc: 0.755, f1: 0.681, acc: 0.718
+2025-08-07 19:43:04: Epoch: 045 Test results, ap: 0.238, auc: 0.508, f1: 0.000, acc: 0.759
+2025-08-07 19:43:04: Epoch: 046 Train Loss: 0.403, ap: 0.699, auc: 0.751, f1: 0.689, acc: 0.728
+2025-08-07 19:43:05: Epoch: 046 Test results, ap: 0.237, auc: 0.505, f1: 0.000, acc: 0.759
+2025-08-07 19:43:05: Epoch: 047 Train Loss: 0.390, ap: 0.704, auc: 0.760, f1: 0.674, acc: 0.728
+2025-08-07 19:43:05: Epoch: 047 Test results, ap: 0.248, auc: 0.526, f1: 0.000, acc: 0.759
+2025-08-07 19:43:05: Epoch: 048 Train Loss: 0.379, ap: 0.700, auc: 0.755, f1: 0.689, acc: 0.728
+2025-08-07 19:43:06: Epoch: 048 Test results, ap: 0.248, auc: 0.527, f1: 0.000, acc: 0.759
+2025-08-07 19:43:06: Epoch: 049 Train Loss: 0.392, ap: 0.690, auc: 0.743, f1: 0.667, acc: 0.699
+2025-08-07 19:43:06: Epoch: 049 Test results, ap: 0.234, auc: 0.457, f1: 0.000, acc: 0.759
+2025-08-07 19:43:06: Epoch: 050 Train Loss: 0.371, ap: 0.701, auc: 0.761, f1: 0.689, acc: 0.728
+2025-08-07 19:43:06: Epoch: 050 Test results, ap: 0.250, auc: 0.534, f1: 0.000, acc: 0.759
+2025-08-07 19:43:06: used time 0.01h
+2025-08-07 19:43:06: Running Cross Validation 1
+2025-08-07 19:43:06: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:43:07: Epoch: 001 Train Loss: 0.695, ap: 0.340, auc: 0.434, f1: 0.000, acc: 0.641
+2025-08-07 19:43:07: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:43:07: Epoch: 001 Test results, ap: 0.362, auc: 0.576, f1: 0.000, acc: 0.759
+2025-08-07 19:43:07: Epoch: 002 Train Loss: 0.695, ap: 0.335, auc: 0.404, f1: 0.529, acc: 0.359
+2025-08-07 19:43:07: Epoch: 002 Test results, ap: 0.400, auc: 0.577, f1: 0.388, acc: 0.241
+2025-08-07 19:43:08: Epoch: 003 Train Loss: 0.697, ap: 0.344, auc: 0.416, f1: 0.000, acc: 0.641
+2025-08-07 19:43:08: Epoch: 003 Test results, ap: 0.428, auc: 0.597, f1: 0.000, acc: 0.759
+2025-08-07 19:43:08: Epoch: 004 Train Loss: 0.695, ap: 0.372, auc: 0.426, f1: 0.529, acc: 0.359
+2025-08-07 19:43:08: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:43:08: Epoch: 004 Test results, ap: 0.531, auc: 0.610, f1: 0.388, acc: 0.241
+2025-08-07 19:43:09: Epoch: 005 Train Loss: 0.692, ap: 0.378, auc: 0.463, f1: 0.496, acc: 0.388
+2025-08-07 19:43:09: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:43:09: Epoch: 005 Test results, ap: 0.552, auc: 0.608, f1: 0.388, acc: 0.241
+2025-08-07 19:43:09: Epoch: 006 Train Loss: 0.693, ap: 0.409, auc: 0.486, f1: 0.508, acc: 0.398
+2025-08-07 19:43:09: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:43:09: Epoch: 006 Test results, ap: 0.295, auc: 0.505, f1: 0.388, acc: 0.241
+2025-08-07 19:43:10: Epoch: 007 Train Loss: 0.699, ap: 0.388, auc: 0.497, f1: 0.478, acc: 0.427
+2025-08-07 19:43:10: Epoch: 007 Test results, ap: 0.172, auc: 0.295, f1: 0.388, acc: 0.241
+2025-08-07 19:43:10: Epoch: 008 Train Loss: 0.681, ap: 0.409, auc: 0.541, f1: 0.454, acc: 0.485
+2025-08-07 19:43:10: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:43:10: Epoch: 008 Test results, ap: 0.241, auc: 0.502, f1: 0.376, acc: 0.417
+2025-08-07 19:43:11: Epoch: 009 Train Loss: 0.660, ap: 0.389, auc: 0.539, f1: 0.509, acc: 0.456
+2025-08-07 19:43:11: Epoch: 009 Test results, ap: 0.309, auc: 0.557, f1: 0.388, acc: 0.241
+2025-08-07 19:43:11: Epoch: 010 Train Loss: 0.632, ap: 0.419, auc: 0.597, f1: 0.549, acc: 0.553
+2025-08-07 19:43:11: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:43:11: Epoch: 010 Test results, ap: 0.206, auc: 0.435, f1: 0.388, acc: 0.241
+2025-08-07 19:43:12: Epoch: 011 Train Loss: 0.643, ap: 0.418, auc: 0.592, f1: 0.557, acc: 0.583
+2025-08-07 19:43:12: Epoch: 011 Test results, ap: 0.542, auc: 0.705, f1: 0.388, acc: 0.241
+2025-08-07 19:43:12: Epoch: 011 New best TEST metrics
+2025-08-07 19:43:12: Epoch: 012 Train Loss: 0.609, ap: 0.463, auc: 0.593, f1: 0.521, acc: 0.553
+2025-08-07 19:43:12: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:43:12: Epoch: 012 Test results, ap: 0.448, auc: 0.677, f1: 0.388, acc: 0.241
+2025-08-07 19:43:12: Epoch: 013 Train Loss: 0.635, ap: 0.434, auc: 0.600, f1: 0.388, acc: 0.602
+2025-08-07 19:43:13: Epoch: 013 Test results, ap: 0.257, auc: 0.565, f1: 0.174, acc: 0.648
+2025-08-07 19:43:13: Epoch: 014 Train Loss: 0.628, ap: 0.455, auc: 0.621, f1: 0.539, acc: 0.602
+2025-08-07 19:43:13: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:43:13: Epoch: 014 Test results, ap: 0.263, auc: 0.571, f1: 0.409, acc: 0.306
+2025-08-07 19:43:13: Epoch: 015 Train Loss: 0.629, ap: 0.523, auc: 0.615, f1: 0.476, acc: 0.573
+2025-08-07 19:43:13: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:43:14: Epoch: 015 Test results, ap: 0.391, auc: 0.619, f1: 0.388, acc: 0.241
+2025-08-07 19:43:14: Epoch: 016 Train Loss: 0.604, ap: 0.473, auc: 0.623, f1: 0.480, acc: 0.621
+2025-08-07 19:43:14: Epoch: 016 Test results, ap: 0.271, auc: 0.594, f1: 0.388, acc: 0.241
+2025-08-07 19:43:14: Epoch: 017 Train Loss: 0.598, ap: 0.450, auc: 0.615, f1: 0.553, acc: 0.592
+2025-08-07 19:43:15: Epoch: 017 Test results, ap: 0.264, auc: 0.587, f1: 0.388, acc: 0.241
+2025-08-07 19:43:15: Epoch: 018 Train Loss: 0.571, ap: 0.444, auc: 0.615, f1: 0.462, acc: 0.592
+2025-08-07 19:43:15: Epoch: 018 Test results, ap: 0.257, auc: 0.568, f1: 0.388, acc: 0.241
+2025-08-07 19:43:15: Epoch: 019 Train Loss: 0.601, ap: 0.520, auc: 0.625, f1: 0.480, acc: 0.621
+2025-08-07 19:43:15: Epoch: 019 New best VALIDATION metrics
+2025-08-07 19:43:16: Epoch: 019 Test results, ap: 0.229, auc: 0.487, f1: 0.388, acc: 0.241
+2025-08-07 19:43:16: Epoch: 020 Train Loss: 0.582, ap: 0.494, auc: 0.611, f1: 0.453, acc: 0.602
+2025-08-07 19:43:16: Epoch: 020 Test results, ap: 0.201, auc: 0.393, f1: 0.388, acc: 0.241
+2025-08-07 19:43:16: Epoch: 021 Train Loss: 0.584, ap: 0.503, auc: 0.632, f1: 0.480, acc: 0.621
+2025-08-07 19:43:17: Epoch: 021 Test results, ap: 0.228, auc: 0.481, f1: 0.388, acc: 0.241
+2025-08-07 19:43:17: Epoch: 022 Train Loss: 0.580, ap: 0.480, auc: 0.598, f1: 0.377, acc: 0.583
+2025-08-07 19:43:17: Epoch: 022 Test results, ap: 0.186, auc: 0.353, f1: 0.388, acc: 0.241
+2025-08-07 19:43:17: Epoch: 023 Train Loss: 0.595, ap: 0.471, auc: 0.622, f1: 0.468, acc: 0.602
+2025-08-07 19:43:17: Epoch: 023 Test results, ap: 0.219, auc: 0.459, f1: 0.388, acc: 0.241
+2025-08-07 19:43:18: Epoch: 024 Train Loss: 0.586, ap: 0.497, auc: 0.639, f1: 0.459, acc: 0.612
+2025-08-07 19:43:18: Epoch: 024 Test results, ap: 0.225, auc: 0.491, f1: 0.388, acc: 0.241
+2025-08-07 19:43:18: Epoch: 025 Train Loss: 0.573, ap: 0.470, auc: 0.630, f1: 0.457, acc: 0.631
+2025-08-07 19:43:18: Epoch: 025 Test results, ap: 0.233, auc: 0.517, f1: 0.388, acc: 0.241
+2025-08-07 19:43:19: Epoch: 026 Train Loss: 0.571, ap: 0.480, auc: 0.635, f1: 0.457, acc: 0.631
+2025-08-07 19:43:19: Epoch: 026 Test results, ap: 0.227, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:43:19: Epoch: 027 Train Loss: 0.571, ap: 0.479, auc: 0.633, f1: 0.451, acc: 0.621
+2025-08-07 19:43:19: Epoch: 027 Test results, ap: 0.228, auc: 0.503, f1: 0.388, acc: 0.241
+2025-08-07 19:43:20: Epoch: 028 Train Loss: 0.562, ap: 0.468, auc: 0.627, f1: 0.459, acc: 0.612
+2025-08-07 19:43:20: Epoch: 028 Test results, ap: 0.221, auc: 0.478, f1: 0.388, acc: 0.241
+2025-08-07 19:43:20: Epoch: 029 Train Loss: 0.563, ap: 0.467, auc: 0.619, f1: 0.444, acc: 0.612
+2025-08-07 19:43:20: Epoch: 029 Test results, ap: 0.225, auc: 0.491, f1: 0.388, acc: 0.241
+2025-08-07 19:43:21: Epoch: 030 Train Loss: 0.570, ap: 0.468, auc: 0.622, f1: 0.444, acc: 0.612
+2025-08-07 19:43:21: Epoch: 030 Test results, ap: 0.220, auc: 0.477, f1: 0.388, acc: 0.241
+2025-08-07 19:43:21: Epoch: 031 Train Loss: 0.547, ap: 0.467, auc: 0.624, f1: 0.466, acc: 0.621
+2025-08-07 19:43:21: Epoch: 031 Test results, ap: 0.219, auc: 0.472, f1: 0.388, acc: 0.241
+2025-08-07 19:43:22: Epoch: 032 Train Loss: 0.558, ap: 0.465, auc: 0.623, f1: 0.444, acc: 0.612
+2025-08-07 19:43:22: Epoch: 032 Test results, ap: 0.220, auc: 0.477, f1: 0.388, acc: 0.241
+2025-08-07 19:43:22: Epoch: 033 Train Loss: 0.561, ap: 0.473, auc: 0.627, f1: 0.444, acc: 0.612
+2025-08-07 19:43:22: Epoch: 033 Test results, ap: 0.224, auc: 0.492, f1: 0.388, acc: 0.241
+2025-08-07 19:43:23: Epoch: 034 Train Loss: 0.558, ap: 0.462, auc: 0.623, f1: 0.459, acc: 0.612
+2025-08-07 19:43:23: Epoch: 034 Test results, ap: 0.220, auc: 0.474, f1: 0.388, acc: 0.241
+2025-08-07 19:43:23: Epoch: 035 Train Loss: 0.583, ap: 0.469, auc: 0.626, f1: 0.447, acc: 0.592
+2025-08-07 19:43:23: Epoch: 035 Test results, ap: 0.215, auc: 0.457, f1: 0.388, acc: 0.241
+2025-08-07 19:43:24: Epoch: 036 Train Loss: 0.553, ap: 0.469, auc: 0.625, f1: 0.453, acc: 0.602
+2025-08-07 19:43:24: Epoch: 036 Test results, ap: 0.217, auc: 0.468, f1: 0.388, acc: 0.241
+2025-08-07 19:43:24: Epoch: 037 Train Loss: 0.556, ap: 0.471, auc: 0.625, f1: 0.453, acc: 0.602
+2025-08-07 19:43:24: Epoch: 037 Test results, ap: 0.218, auc: 0.468, f1: 0.388, acc: 0.241
+2025-08-07 19:43:25: Epoch: 038 Train Loss: 0.552, ap: 0.472, auc: 0.624, f1: 0.447, acc: 0.592
+2025-08-07 19:43:25: Epoch: 038 Test results, ap: 0.220, auc: 0.477, f1: 0.388, acc: 0.241
+2025-08-07 19:43:25: Epoch: 039 Train Loss: 0.564, ap: 0.471, auc: 0.627, f1: 0.442, acc: 0.583
+2025-08-07 19:43:25: Epoch: 039 Test results, ap: 0.218, auc: 0.471, f1: 0.388, acc: 0.241
+2025-08-07 19:43:26: Epoch: 040 Train Loss: 0.553, ap: 0.473, auc: 0.633, f1: 0.442, acc: 0.583
+2025-08-07 19:43:26: Epoch: 040 Test results, ap: 0.217, auc: 0.470, f1: 0.388, acc: 0.241
+2025-08-07 19:43:26: Epoch: 041 Train Loss: 0.567, ap: 0.479, auc: 0.636, f1: 0.447, acc: 0.592
+2025-08-07 19:43:26: Epoch: 041 Test results, ap: 0.219, auc: 0.475, f1: 0.388, acc: 0.241
+2025-08-07 19:43:27: Epoch: 042 Train Loss: 0.547, ap: 0.478, auc: 0.640, f1: 0.456, acc: 0.583
+2025-08-07 19:43:27: Epoch: 042 Test results, ap: 0.217, auc: 0.469, f1: 0.388, acc: 0.241
+2025-08-07 19:43:27: Epoch: 043 Train Loss: 0.556, ap: 0.482, auc: 0.640, f1: 0.474, acc: 0.612
+2025-08-07 19:43:27: Epoch: 043 Test results, ap: 0.218, auc: 0.470, f1: 0.388, acc: 0.241
+2025-08-07 19:43:28: Epoch: 044 Train Loss: 0.556, ap: 0.483, auc: 0.642, f1: 0.450, acc: 0.573
+2025-08-07 19:43:28: Epoch: 044 Test results, ap: 0.214, auc: 0.454, f1: 0.388, acc: 0.241
+2025-08-07 19:43:28: Epoch: 045 Train Loss: 0.559, ap: 0.486, auc: 0.646, f1: 0.500, acc: 0.592
+2025-08-07 19:43:28: Epoch: 045 Test results, ap: 0.217, auc: 0.465, f1: 0.388, acc: 0.241
+2025-08-07 19:43:29: Epoch: 046 Train Loss: 0.533, ap: 0.491, auc: 0.650, f1: 0.500, acc: 0.592
+2025-08-07 19:43:29: Epoch: 046 Test results, ap: 0.220, auc: 0.474, f1: 0.388, acc: 0.241
+2025-08-07 19:43:29: Epoch: 047 Train Loss: 0.565, ap: 0.488, auc: 0.650, f1: 0.500, acc: 0.592
+2025-08-07 19:43:29: Epoch: 047 Test results, ap: 0.216, auc: 0.458, f1: 0.388, acc: 0.241
+2025-08-07 19:43:29: Epoch: 048 Train Loss: 0.553, ap: 0.490, auc: 0.651, f1: 0.500, acc: 0.592
+2025-08-07 19:43:30: Epoch: 048 Test results, ap: 0.215, auc: 0.454, f1: 0.388, acc: 0.241
+2025-08-07 19:43:30: Epoch: 049 Train Loss: 0.544, ap: 0.492, auc: 0.654, f1: 0.512, acc: 0.612
+2025-08-07 19:43:30: Epoch: 049 New best VALIDATION metrics
+2025-08-07 19:43:30: Epoch: 049 Test results, ap: 0.218, auc: 0.465, f1: 0.388, acc: 0.241
+2025-08-07 19:43:30: Epoch: 050 Train Loss: 0.544, ap: 0.495, auc: 0.656, f1: 0.512, acc: 0.612
+2025-08-07 19:43:30: Epoch: 050 New best VALIDATION metrics
+2025-08-07 19:43:30: Epoch: 050 Test results, ap: 0.218, auc: 0.469, f1: 0.388, acc: 0.241
+2025-08-07 19:43:30: used time 0.01h
+2025-08-07 19:43:30: Running Cross Validation 2
+2025-08-07 19:43:30: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:43:31: Epoch: 001 Train Loss: 0.695, ap: 0.561, auc: 0.484, f1: 0.671, acc: 0.505
+2025-08-07 19:43:31: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:43:31: Epoch: 001 Test results, ap: 0.170, auc: 0.256, f1: 0.388, acc: 0.241
+2025-08-07 19:43:31: Epoch: 002 Train Loss: 0.695, ap: 0.599, auc: 0.562, f1: 0.671, acc: 0.505
+2025-08-07 19:43:31: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:43:31: Epoch: 002 Test results, ap: 0.170, auc: 0.266, f1: 0.388, acc: 0.241
+2025-08-07 19:43:32: Epoch: 003 Train Loss: 0.691, ap: 0.638, auc: 0.613, f1: 0.671, acc: 0.505
+2025-08-07 19:43:32: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:43:32: Epoch: 003 Test results, ap: 0.291, auc: 0.482, f1: 0.388, acc: 0.241
+2025-08-07 19:43:32: Epoch: 004 Train Loss: 0.692, ap: 0.616, auc: 0.590, f1: 0.000, acc: 0.495
+2025-08-07 19:43:32: Epoch: 004 Test results, ap: 0.355, auc: 0.526, f1: 0.000, acc: 0.759
+2025-08-07 19:43:33: Epoch: 005 Train Loss: 0.700, ap: 0.624, auc: 0.626, f1: 0.169, acc: 0.524
+2025-08-07 19:43:33: Epoch: 005 Test results, ap: 0.333, auc: 0.523, f1: 0.000, acc: 0.759
+2025-08-07 19:43:33: Epoch: 006 Train Loss: 0.693, ap: 0.530, auc: 0.581, f1: 0.625, acc: 0.592
+2025-08-07 19:43:33: Epoch: 006 Test results, ap: 0.477, auc: 0.569, f1: 0.333, acc: 0.296
+2025-08-07 19:43:34: Epoch: 007 Train Loss: 0.687, ap: 0.579, auc: 0.633, f1: 0.690, acc: 0.563
+2025-08-07 19:43:34: Epoch: 007 Test results, ap: 0.206, auc: 0.414, f1: 0.388, acc: 0.241
+2025-08-07 19:43:34: Epoch: 008 Train Loss: 0.664, ap: 0.614, auc: 0.692, f1: 0.704, acc: 0.689
+2025-08-07 19:43:34: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:43:34: Epoch: 008 Test results, ap: 0.427, auc: 0.612, f1: 0.388, acc: 0.241
+2025-08-07 19:43:35: Epoch: 009 Train Loss: 0.651, ap: 0.646, auc: 0.698, f1: 0.742, acc: 0.689
+2025-08-07 19:43:35: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:43:35: Epoch: 009 Test results, ap: 0.415, auc: 0.641, f1: 0.388, acc: 0.241
+2025-08-07 19:43:35: Epoch: 010 Train Loss: 0.657, ap: 0.595, auc: 0.667, f1: 0.000, acc: 0.495
+2025-08-07 19:43:35: Epoch: 010 Test results, ap: 0.204, auc: 0.406, f1: 0.000, acc: 0.759
+2025-08-07 19:43:36: Epoch: 011 Train Loss: 0.682, ap: 0.651, auc: 0.713, f1: 0.627, acc: 0.631
+2025-08-07 19:43:36: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:43:36: Epoch: 011 Test results, ap: 0.225, auc: 0.474, f1: 0.048, acc: 0.630
+2025-08-07 19:43:36: Epoch: 011 New best TEST metrics
+2025-08-07 19:43:36: Epoch: 012 Train Loss: 0.656, ap: 0.639, auc: 0.702, f1: 0.600, acc: 0.612
+2025-08-07 19:43:36: Epoch: 012 Test results, ap: 0.239, auc: 0.504, f1: 0.000, acc: 0.630
+2025-08-07 19:43:36: Epoch: 012 New best TEST metrics
+2025-08-07 19:43:37: Epoch: 013 Train Loss: 0.653, ap: 0.604, auc: 0.672, f1: 0.589, acc: 0.621
+2025-08-07 19:43:37: Epoch: 013 Test results, ap: 0.235, auc: 0.497, f1: 0.000, acc: 0.676
+2025-08-07 19:43:37: Epoch: 014 Train Loss: 0.666, ap: 0.651, auc: 0.715, f1: 0.621, acc: 0.621
+2025-08-07 19:43:37: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:43:37: Epoch: 014 Test results, ap: 0.243, auc: 0.511, f1: 0.048, acc: 0.630
+2025-08-07 19:43:37: Epoch: 014 New best TEST metrics
+2025-08-07 19:43:38: Epoch: 015 Train Loss: 0.645, ap: 0.647, auc: 0.715, f1: 0.627, acc: 0.631
+2025-08-07 19:43:38: Epoch: 015 Test results, ap: 0.242, auc: 0.509, f1: 0.133, acc: 0.639
+2025-08-07 19:43:38: Epoch: 016 Train Loss: 0.671, ap: 0.629, auc: 0.708, f1: 0.627, acc: 0.631
+2025-08-07 19:43:38: Epoch: 016 Test results, ap: 0.244, auc: 0.515, f1: 0.125, acc: 0.611
+2025-08-07 19:43:38: Epoch: 016 New best TEST metrics
+2025-08-07 19:43:38: Epoch: 017 Train Loss: 0.648, ap: 0.639, auc: 0.711, f1: 0.614, acc: 0.621
+2025-08-07 19:43:39: Epoch: 017 Test results, ap: 0.247, auc: 0.522, f1: 0.130, acc: 0.630
+2025-08-07 19:43:39: Epoch: 017 New best TEST metrics
+2025-08-07 19:43:39: Epoch: 018 Train Loss: 0.642, ap: 0.662, auc: 0.708, f1: 0.754, acc: 0.689
+2025-08-07 19:43:39: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:43:39: Epoch: 018 Test results, ap: 0.253, auc: 0.532, f1: 0.416, acc: 0.583
+2025-08-07 19:43:39: Epoch: 018 New best TEST metrics
+2025-08-07 19:43:40: Epoch: 019 Train Loss: 0.614, ap: 0.643, auc: 0.698, f1: 0.559, acc: 0.602
+2025-08-07 19:43:40: Epoch: 019 Test results, ap: 0.264, auc: 0.553, f1: 0.000, acc: 0.639
+2025-08-07 19:43:40: Epoch: 019 New best TEST metrics
+2025-08-07 19:43:40: Epoch: 020 Train Loss: 0.615, ap: 0.621, auc: 0.705, f1: 0.690, acc: 0.650
+2025-08-07 19:43:40: Epoch: 020 Test results, ap: 0.266, auc: 0.567, f1: 0.419, acc: 0.537
+2025-08-07 19:43:40: Epoch: 020 New best TEST metrics
+2025-08-07 19:43:41: Epoch: 021 Train Loss: 0.601, ap: 0.664, auc: 0.724, f1: 0.742, acc: 0.689
+2025-08-07 19:43:41: Epoch: 021 New best VALIDATION metrics
+2025-08-07 19:43:41: Epoch: 021 Test results, ap: 0.264, auc: 0.575, f1: 0.409, acc: 0.519
+2025-08-07 19:43:41: Epoch: 021 New best TEST metrics
+2025-08-07 19:43:41: Epoch: 022 Train Loss: 0.585, ap: 0.690, auc: 0.757, f1: 0.739, acc: 0.699
+2025-08-07 19:43:41: Epoch: 022 New best VALIDATION metrics
+2025-08-07 19:43:41: Epoch: 022 Test results, ap: 0.274, auc: 0.593, f1: 0.432, acc: 0.611
+2025-08-07 19:43:41: Epoch: 022 New best TEST metrics
+2025-08-07 19:43:41: Epoch: 023 Train Loss: 0.601, ap: 0.758, auc: 0.762, f1: 0.720, acc: 0.660
+2025-08-07 19:43:41: Epoch: 023 New best VALIDATION metrics
+2025-08-07 19:43:42: Epoch: 023 Test results, ap: 0.283, auc: 0.609, f1: 0.429, acc: 0.481
+2025-08-07 19:43:42: Epoch: 023 New best TEST metrics
+2025-08-07 19:43:42: Epoch: 024 Train Loss: 0.589, ap: 0.707, auc: 0.783, f1: 0.745, acc: 0.728
+2025-08-07 19:43:42: Epoch: 024 Test results, ap: 0.288, auc: 0.617, f1: 0.424, acc: 0.648
+2025-08-07 19:43:42: Epoch: 024 New best TEST metrics
+2025-08-07 19:43:43: Epoch: 025 Train Loss: 0.585, ap: 0.764, auc: 0.801, f1: 0.760, acc: 0.718
+2025-08-07 19:43:43: Epoch: 025 New best VALIDATION metrics
+2025-08-07 19:43:43: Epoch: 025 Test results, ap: 0.278, auc: 0.605, f1: 0.429, acc: 0.407
+2025-08-07 19:43:43: Epoch: 026 Train Loss: 0.583, ap: 0.794, auc: 0.809, f1: 0.756, acc: 0.718
+2025-08-07 19:43:43: Epoch: 026 New best VALIDATION metrics
+2025-08-07 19:43:43: Epoch: 026 Test results, ap: 0.277, auc: 0.603, f1: 0.429, acc: 0.407
+2025-08-07 19:43:44: Epoch: 027 Train Loss: 0.572, ap: 0.797, auc: 0.807, f1: 0.757, acc: 0.738
+2025-08-07 19:43:44: Epoch: 027 New best VALIDATION metrics
+2025-08-07 19:43:44: Epoch: 027 Test results, ap: 0.290, auc: 0.611, f1: 0.370, acc: 0.685
+2025-08-07 19:43:44: Epoch: 028 Train Loss: 0.561, ap: 0.809, auc: 0.807, f1: 0.754, acc: 0.728
+2025-08-07 19:43:44: Epoch: 028 New best VALIDATION metrics
+2025-08-07 19:43:44: Epoch: 028 Test results, ap: 0.293, auc: 0.606, f1: 0.244, acc: 0.713
+2025-08-07 19:43:45: Epoch: 029 Train Loss: 0.541, ap: 0.699, auc: 0.759, f1: 0.693, acc: 0.699
+2025-08-07 19:43:45: Epoch: 029 Test results, ap: 0.296, auc: 0.603, f1: 0.348, acc: 0.722
+2025-08-07 19:43:45: Epoch: 030 Train Loss: 0.584, ap: 0.795, auc: 0.809, f1: 0.769, acc: 0.738
+2025-08-07 19:43:45: Epoch: 030 Test results, ap: 0.272, auc: 0.583, f1: 0.384, acc: 0.287
+2025-08-07 19:43:46: Epoch: 031 Train Loss: 0.537, ap: 0.783, auc: 0.801, f1: 0.775, acc: 0.757
+2025-08-07 19:43:46: Epoch: 031 Test results, ap: 0.281, auc: 0.597, f1: 0.403, acc: 0.343
+2025-08-07 19:43:46: Epoch: 032 Train Loss: 0.542, ap: 0.777, auc: 0.801, f1: 0.757, acc: 0.738
+2025-08-07 19:43:46: Epoch: 032 Test results, ap: 0.282, auc: 0.596, f1: 0.397, acc: 0.324
+2025-08-07 19:43:47: Epoch: 033 Train Loss: 0.536, ap: 0.772, auc: 0.807, f1: 0.796, acc: 0.777
+2025-08-07 19:43:47: Epoch: 033 Test results, ap: 0.283, auc: 0.605, f1: 0.432, acc: 0.417
+2025-08-07 19:43:47: Epoch: 034 Train Loss: 0.539, ap: 0.792, auc: 0.805, f1: 0.752, acc: 0.738
+2025-08-07 19:43:47: Epoch: 034 Test results, ap: 0.274, auc: 0.587, f1: 0.366, acc: 0.454
+2025-08-07 19:43:47: Epoch: 035 Train Loss: 0.528, ap: 0.796, auc: 0.810, f1: 0.768, acc: 0.748
+2025-08-07 19:43:47: Epoch: 035 Test results, ap: 0.278, auc: 0.597, f1: 0.420, acc: 0.463
+2025-08-07 19:43:48: Epoch: 036 Train Loss: 0.527, ap: 0.809, auc: 0.814, f1: 0.741, acc: 0.709
+2025-08-07 19:43:48: Epoch: 036 New best VALIDATION metrics
+2025-08-07 19:43:48: Epoch: 036 Test results, ap: 0.277, auc: 0.592, f1: 0.417, acc: 0.481
+2025-08-07 19:43:48: Epoch: 037 Train Loss: 0.519, ap: 0.797, auc: 0.813, f1: 0.772, acc: 0.748
+2025-08-07 19:43:49: Epoch: 037 Test results, ap: 0.284, auc: 0.608, f1: 0.430, acc: 0.435
+2025-08-07 19:43:49: Epoch: 038 Train Loss: 0.533, ap: 0.784, auc: 0.799, f1: 0.768, acc: 0.748
+2025-08-07 19:43:49: Epoch: 038 Test results, ap: 0.281, auc: 0.592, f1: 0.349, acc: 0.481
+2025-08-07 19:43:49: Epoch: 039 Train Loss: 0.517, ap: 0.781, auc: 0.809, f1: 0.793, acc: 0.777
+2025-08-07 19:43:50: Epoch: 039 Test results, ap: 0.288, auc: 0.612, f1: 0.404, acc: 0.454
+2025-08-07 19:43:50: Epoch: 040 Train Loss: 0.526, ap: 0.788, auc: 0.809, f1: 0.775, acc: 0.757
+2025-08-07 19:43:50: Epoch: 040 Test results, ap: 0.282, auc: 0.606, f1: 0.436, acc: 0.426
+2025-08-07 19:43:50: Epoch: 041 Train Loss: 0.501, ap: 0.791, auc: 0.810, f1: 0.779, acc: 0.757
+2025-08-07 19:43:51: Epoch: 041 Test results, ap: 0.288, auc: 0.614, f1: 0.447, acc: 0.472
+2025-08-07 19:43:51: Epoch: 042 Train Loss: 0.496, ap: 0.780, auc: 0.805, f1: 0.793, acc: 0.777
+2025-08-07 19:43:51: Epoch: 042 Test results, ap: 0.289, auc: 0.614, f1: 0.451, acc: 0.481
+2025-08-07 19:43:51: Epoch: 043 Train Loss: 0.487, ap: 0.779, auc: 0.805, f1: 0.786, acc: 0.767
+2025-08-07 19:43:52: Epoch: 043 Test results, ap: 0.296, auc: 0.626, f1: 0.438, acc: 0.454
+2025-08-07 19:43:52: Epoch: 043 New best TEST metrics
+2025-08-07 19:43:52: Epoch: 044 Train Loss: 0.499, ap: 0.784, auc: 0.807, f1: 0.768, acc: 0.748
+2025-08-07 19:43:52: Epoch: 044 Test results, ap: 0.295, auc: 0.625, f1: 0.444, acc: 0.444
+2025-08-07 19:43:52: Epoch: 045 Train Loss: 0.484, ap: 0.780, auc: 0.805, f1: 0.786, acc: 0.767
+2025-08-07 19:43:53: Epoch: 045 Test results, ap: 0.294, auc: 0.623, f1: 0.444, acc: 0.444
+2025-08-07 19:43:53: Epoch: 046 Train Loss: 0.475, ap: 0.779, auc: 0.797, f1: 0.779, acc: 0.757
+2025-08-07 19:43:53: Epoch: 046 Test results, ap: 0.296, auc: 0.624, f1: 0.434, acc: 0.444
+2025-08-07 19:43:54: Epoch: 047 Train Loss: 0.482, ap: 0.772, auc: 0.794, f1: 0.764, acc: 0.748
+2025-08-07 19:43:54: Epoch: 047 Test results, ap: 0.292, auc: 0.616, f1: 0.424, acc: 0.472
+2025-08-07 19:43:54: Epoch: 048 Train Loss: 0.496, ap: 0.778, auc: 0.797, f1: 0.768, acc: 0.748
+2025-08-07 19:43:54: Epoch: 048 Test results, ap: 0.277, auc: 0.598, f1: 0.417, acc: 0.481
+2025-08-07 19:43:54: Epoch: 049 Train Loss: 0.456, ap: 0.770, auc: 0.788, f1: 0.761, acc: 0.738
+2025-08-07 19:43:55: Epoch: 049 Test results, ap: 0.268, auc: 0.588, f1: 0.436, acc: 0.426
+2025-08-07 19:43:55: Epoch: 050 Train Loss: 0.478, ap: 0.763, auc: 0.785, f1: 0.757, acc: 0.738
+2025-08-07 19:43:55: Epoch: 050 Test results, ap: 0.289, auc: 0.603, f1: 0.386, acc: 0.528
+2025-08-07 19:43:55: used time 0.01h
+2025-08-07 19:43:55: Running Cross Validation 3
+2025-08-07 19:43:55: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:43:55: Epoch: 001 Train Loss: 0.694, ap: 0.484, auc: 0.527, f1: 0.579, acc: 0.408
+2025-08-07 19:43:55: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:43:56: Epoch: 001 Test results, ap: 0.197, auc: 0.365, f1: 0.388, acc: 0.241
+2025-08-07 19:43:56: Epoch: 002 Train Loss: 0.694, ap: 0.436, auc: 0.508, f1: 0.000, acc: 0.583
+2025-08-07 19:43:56: Epoch: 002 Test results, ap: 0.175, auc: 0.278, f1: 0.000, acc: 0.759
+2025-08-07 19:43:56: Epoch: 003 Train Loss: 0.695, ap: 0.473, auc: 0.561, f1: 0.000, acc: 0.592
+2025-08-07 19:43:56: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:43:57: Epoch: 003 Test results, ap: 0.168, auc: 0.248, f1: 0.000, acc: 0.759
+2025-08-07 19:43:57: Epoch: 004 Train Loss: 0.693, ap: 0.467, auc: 0.548, f1: 0.579, acc: 0.408
+2025-08-07 19:43:57: Epoch: 004 Test results, ap: 0.374, auc: 0.656, f1: 0.388, acc: 0.241
+2025-08-07 19:43:57: Epoch: 005 Train Loss: 0.695, ap: 0.491, auc: 0.572, f1: 0.000, acc: 0.592
+2025-08-07 19:43:57: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:43:58: Epoch: 005 Test results, ap: 0.238, auc: 0.438, f1: 0.000, acc: 0.759
+2025-08-07 19:43:58: Epoch: 006 Train Loss: 0.692, ap: 0.474, auc: 0.529, f1: 0.579, acc: 0.408
+2025-08-07 19:43:58: Epoch: 006 Test results, ap: 0.259, auc: 0.467, f1: 0.388, acc: 0.241
+2025-08-07 19:43:58: Epoch: 007 Train Loss: 0.695, ap: 0.577, auc: 0.655, f1: 0.579, acc: 0.408
+2025-08-07 19:43:58: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:43:58: Epoch: 007 Test results, ap: 0.224, auc: 0.463, f1: 0.388, acc: 0.241
+2025-08-07 19:43:59: Epoch: 008 Train Loss: 0.694, ap: 0.567, auc: 0.665, f1: 0.579, acc: 0.408
+2025-08-07 19:43:59: Epoch: 008 Test results, ap: 0.251, auc: 0.561, f1: 0.388, acc: 0.241
+2025-08-07 19:43:59: Epoch: 009 Train Loss: 0.696, ap: 0.545, auc: 0.643, f1: 0.579, acc: 0.408
+2025-08-07 19:43:59: Epoch: 009 Test results, ap: 0.221, auc: 0.490, f1: 0.388, acc: 0.241
+2025-08-07 19:44:00: Epoch: 010 Train Loss: 0.695, ap: 0.593, auc: 0.681, f1: 0.163, acc: 0.602
+2025-08-07 19:44:00: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:44:00: Epoch: 010 Test results, ap: 0.222, auc: 0.389, f1: 0.000, acc: 0.759
+2025-08-07 19:44:00: Epoch: 011 Train Loss: 0.692, ap: 0.536, auc: 0.665, f1: 0.636, acc: 0.612
+2025-08-07 19:44:00: Epoch: 011 Test results, ap: 0.212, auc: 0.390, f1: 0.192, acc: 0.454
+2025-08-07 19:44:00: Epoch: 011 New best TEST metrics
+2025-08-07 19:44:01: Epoch: 012 Train Loss: 0.689, ap: 0.487, auc: 0.615, f1: 0.595, acc: 0.563
+2025-08-07 19:44:01: Epoch: 012 Test results, ap: 0.271, auc: 0.409, f1: 0.391, acc: 0.250
+2025-08-07 19:44:01: Epoch: 012 New best TEST metrics
+2025-08-07 19:44:01: Epoch: 013 Train Loss: 0.673, ap: 0.590, auc: 0.587, f1: 0.431, acc: 0.641
+2025-08-07 19:44:01: Epoch: 013 Test results, ap: 0.276, auc: 0.582, f1: 0.000, acc: 0.704
+2025-08-07 19:44:01: Epoch: 013 New best TEST metrics
+2025-08-07 19:44:02: Epoch: 014 Train Loss: 0.657, ap: 0.588, auc: 0.660, f1: 0.413, acc: 0.641
+2025-08-07 19:44:02: Epoch: 014 Test results, ap: 0.204, auc: 0.375, f1: 0.000, acc: 0.694
+2025-08-07 19:44:02: Epoch: 015 Train Loss: 0.648, ap: 0.631, auc: 0.665, f1: 0.444, acc: 0.660
+2025-08-07 19:44:02: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:44:02: Epoch: 015 Test results, ap: 0.284, auc: 0.447, f1: 0.222, acc: 0.676
+2025-08-07 19:44:03: Epoch: 016 Train Loss: 0.650, ap: 0.659, auc: 0.692, f1: 0.575, acc: 0.670
+2025-08-07 19:44:03: Epoch: 016 New best VALIDATION metrics
+2025-08-07 19:44:03: Epoch: 016 Test results, ap: 0.355, auc: 0.489, f1: 0.389, acc: 0.361
+2025-08-07 19:44:03: Epoch: 017 Train Loss: 0.635, ap: 0.561, auc: 0.573, f1: 0.438, acc: 0.650
+2025-08-07 19:44:03: Epoch: 017 Test results, ap: 0.205, auc: 0.424, f1: 0.051, acc: 0.657
+2025-08-07 19:44:04: Epoch: 018 Train Loss: 0.626, ap: 0.561, auc: 0.578, f1: 0.419, acc: 0.650
+2025-08-07 19:44:04: Epoch: 018 Test results, ap: 0.190, auc: 0.357, f1: 0.098, acc: 0.657
+2025-08-07 19:44:04: Epoch: 019 Train Loss: 0.616, ap: 0.671, auc: 0.713, f1: 0.579, acc: 0.689
+2025-08-07 19:44:04: Epoch: 019 New best VALIDATION metrics
+2025-08-07 19:44:04: Epoch: 019 Test results, ap: 0.306, auc: 0.582, f1: 0.178, acc: 0.657
+2025-08-07 19:44:04: Epoch: 019 New best TEST metrics
+2025-08-07 19:44:05: Epoch: 020 Train Loss: 0.624, ap: 0.605, auc: 0.740, f1: 0.321, acc: 0.631
+2025-08-07 19:44:05: Epoch: 020 Test results, ap: 0.316, auc: 0.539, f1: 0.154, acc: 0.694
+2025-08-07 19:44:05: Epoch: 021 Train Loss: 0.618, ap: 0.679, auc: 0.736, f1: 0.619, acc: 0.689
+2025-08-07 19:44:05: Epoch: 021 New best VALIDATION metrics
+2025-08-07 19:44:05: Epoch: 021 Test results, ap: 0.283, auc: 0.600, f1: 0.163, acc: 0.620
+2025-08-07 19:44:06: Epoch: 022 Train Loss: 0.629, ap: 0.668, auc: 0.756, f1: 0.551, acc: 0.699
+2025-08-07 19:44:06: Epoch: 022 New best VALIDATION metrics
+2025-08-07 19:44:06: Epoch: 022 Test results, ap: 0.315, auc: 0.572, f1: 0.238, acc: 0.704
+2025-08-07 19:44:06: Epoch: 023 Train Loss: 0.605, ap: 0.700, auc: 0.758, f1: 0.568, acc: 0.689
+2025-08-07 19:44:06: Epoch: 023 New best VALIDATION metrics
+2025-08-07 19:44:07: Epoch: 023 Test results, ap: 0.393, auc: 0.615, f1: 0.208, acc: 0.648
+2025-08-07 19:44:07: Epoch: 023 New best TEST metrics
+2025-08-07 19:44:07: Epoch: 024 Train Loss: 0.594, ap: 0.701, auc: 0.759, f1: 0.575, acc: 0.670
+2025-08-07 19:44:07: Epoch: 024 New best VALIDATION metrics
+2025-08-07 19:44:07: Epoch: 024 Test results, ap: 0.404, auc: 0.626, f1: 0.178, acc: 0.657
+2025-08-07 19:44:07: Epoch: 024 New best TEST metrics
+2025-08-07 19:44:07: Epoch: 025 Train Loss: 0.591, ap: 0.714, auc: 0.781, f1: 0.563, acc: 0.699
+2025-08-07 19:44:07: Epoch: 025 New best VALIDATION metrics
+2025-08-07 19:44:08: Epoch: 025 Test results, ap: 0.383, auc: 0.624, f1: 0.174, acc: 0.648
+2025-08-07 19:44:08: Epoch: 026 Train Loss: 0.576, ap: 0.724, auc: 0.796, f1: 0.617, acc: 0.699
+2025-08-07 19:44:08: Epoch: 026 New best VALIDATION metrics
+2025-08-07 19:44:08: Epoch: 026 Test results, ap: 0.333, auc: 0.630, f1: 0.178, acc: 0.657
+2025-08-07 19:44:08: Epoch: 027 Train Loss: 0.566, ap: 0.737, auc: 0.808, f1: 0.667, acc: 0.728
+2025-08-07 19:44:08: Epoch: 027 New best VALIDATION metrics
+2025-08-07 19:44:09: Epoch: 027 Test results, ap: 0.390, auc: 0.581, f1: 0.182, acc: 0.667
+2025-08-07 19:44:09: Epoch: 028 Train Loss: 0.550, ap: 0.745, auc: 0.802, f1: 0.575, acc: 0.699
+2025-08-07 19:44:09: Epoch: 028 New best VALIDATION metrics
+2025-08-07 19:44:09: Epoch: 028 Test results, ap: 0.335, auc: 0.601, f1: 0.182, acc: 0.667
+2025-08-07 19:44:09: Epoch: 029 Train Loss: 0.574, ap: 0.738, auc: 0.797, f1: 0.644, acc: 0.699
+2025-08-07 19:44:10: Epoch: 029 Test results, ap: 0.410, auc: 0.638, f1: 0.178, acc: 0.657
+2025-08-07 19:44:10: Epoch: 029 New best TEST metrics
+2025-08-07 19:44:10: Epoch: 030 Train Loss: 0.561, ap: 0.754, auc: 0.805, f1: 0.683, acc: 0.748
+2025-08-07 19:44:10: Epoch: 030 New best VALIDATION metrics
+2025-08-07 19:44:10: Epoch: 030 Test results, ap: 0.373, auc: 0.538, f1: 0.186, acc: 0.676
+2025-08-07 19:44:10: Epoch: 031 Train Loss: 0.537, ap: 0.759, auc: 0.810, f1: 0.698, acc: 0.748
+2025-08-07 19:44:10: Epoch: 031 New best VALIDATION metrics
+2025-08-07 19:44:11: Epoch: 031 Test results, ap: 0.322, auc: 0.501, f1: 0.182, acc: 0.667
+2025-08-07 19:44:11: Epoch: 032 Train Loss: 0.535, ap: 0.760, auc: 0.811, f1: 0.706, acc: 0.757
+2025-08-07 19:44:11: Epoch: 032 New best VALIDATION metrics
+2025-08-07 19:44:11: Epoch: 032 Test results, ap: 0.393, auc: 0.591, f1: 0.182, acc: 0.667
+2025-08-07 19:44:11: Epoch: 033 Train Loss: 0.544, ap: 0.754, auc: 0.800, f1: 0.697, acc: 0.738
+2025-08-07 19:44:12: Epoch: 033 Test results, ap: 0.381, auc: 0.559, f1: 0.178, acc: 0.657
+2025-08-07 19:44:12: Epoch: 034 Train Loss: 0.524, ap: 0.761, auc: 0.808, f1: 0.683, acc: 0.748
+2025-08-07 19:44:12: Epoch: 034 Test results, ap: 0.272, auc: 0.545, f1: 0.190, acc: 0.685
+2025-08-07 19:44:12: Epoch: 035 Train Loss: 0.517, ap: 0.754, auc: 0.808, f1: 0.698, acc: 0.748
+2025-08-07 19:44:13: Epoch: 035 Test results, ap: 0.379, auc: 0.558, f1: 0.182, acc: 0.667
+2025-08-07 19:44:13: Epoch: 036 Train Loss: 0.531, ap: 0.760, auc: 0.807, f1: 0.706, acc: 0.757
+2025-08-07 19:44:13: Epoch: 036 Test results, ap: 0.394, auc: 0.597, f1: 0.186, acc: 0.676
+2025-08-07 19:44:13: Epoch: 037 Train Loss: 0.512, ap: 0.759, auc: 0.803, f1: 0.698, acc: 0.748
+2025-08-07 19:44:13: Epoch: 037 Test results, ap: 0.366, auc: 0.524, f1: 0.182, acc: 0.667
+2025-08-07 19:44:14: Epoch: 038 Train Loss: 0.525, ap: 0.732, auc: 0.807, f1: 0.706, acc: 0.757
+2025-08-07 19:44:14: Epoch: 038 Test results, ap: 0.404, auc: 0.625, f1: 0.182, acc: 0.667
+2025-08-07 19:44:14: Epoch: 039 Train Loss: 0.507, ap: 0.761, auc: 0.801, f1: 0.690, acc: 0.738
+2025-08-07 19:44:14: Epoch: 039 Test results, ap: 0.397, auc: 0.606, f1: 0.174, acc: 0.648
+2025-08-07 19:44:15: Epoch: 040 Train Loss: 0.509, ap: 0.766, auc: 0.806, f1: 0.682, acc: 0.738
+2025-08-07 19:44:15: Epoch: 040 New best VALIDATION metrics
+2025-08-07 19:44:15: Epoch: 040 Test results, ap: 0.389, auc: 0.588, f1: 0.190, acc: 0.685
+2025-08-07 19:44:15: Epoch: 041 Train Loss: 0.493, ap: 0.768, auc: 0.808, f1: 0.690, acc: 0.738
+2025-08-07 19:44:15: Epoch: 041 New best VALIDATION metrics
+2025-08-07 19:44:15: Epoch: 041 Test results, ap: 0.391, auc: 0.594, f1: 0.182, acc: 0.667
+2025-08-07 19:44:16: Epoch: 042 Train Loss: 0.499, ap: 0.755, auc: 0.801, f1: 0.690, acc: 0.738
+2025-08-07 19:44:16: Epoch: 042 Test results, ap: 0.378, auc: 0.558, f1: 0.186, acc: 0.676
+2025-08-07 19:44:16: Epoch: 043 Train Loss: 0.500, ap: 0.755, auc: 0.804, f1: 0.690, acc: 0.738
+2025-08-07 19:44:16: Epoch: 043 Test results, ap: 0.380, auc: 0.563, f1: 0.190, acc: 0.685
+2025-08-07 19:44:17: Epoch: 044 Train Loss: 0.506, ap: 0.752, auc: 0.796, f1: 0.667, acc: 0.709
+2025-08-07 19:44:17: Epoch: 044 Test results, ap: 0.370, auc: 0.535, f1: 0.190, acc: 0.685
+2025-08-07 19:44:17: Epoch: 045 Train Loss: 0.499, ap: 0.755, auc: 0.805, f1: 0.699, acc: 0.757
+2025-08-07 19:44:17: Epoch: 045 Test results, ap: 0.375, auc: 0.545, f1: 0.190, acc: 0.685
+2025-08-07 19:44:18: Epoch: 046 Train Loss: 0.501, ap: 0.749, auc: 0.788, f1: 0.674, acc: 0.718
+2025-08-07 19:44:18: Epoch: 046 Test results, ap: 0.340, auc: 0.439, f1: 0.190, acc: 0.685
+2025-08-07 19:44:18: Epoch: 047 Train Loss: 0.487, ap: 0.754, auc: 0.799, f1: 0.698, acc: 0.748
+2025-08-07 19:44:18: Epoch: 047 Test results, ap: 0.349, auc: 0.470, f1: 0.186, acc: 0.676
+2025-08-07 19:44:19: Epoch: 048 Train Loss: 0.491, ap: 0.736, auc: 0.783, f1: 0.681, acc: 0.718
+2025-08-07 19:44:19: Epoch: 048 Test results, ap: 0.343, auc: 0.482, f1: 0.186, acc: 0.676
+2025-08-07 19:44:19: Epoch: 049 Train Loss: 0.505, ap: 0.740, auc: 0.790, f1: 0.698, acc: 0.748
+2025-08-07 19:44:19: Epoch: 049 Test results, ap: 0.365, auc: 0.525, f1: 0.182, acc: 0.667
+2025-08-07 19:44:20: Epoch: 050 Train Loss: 0.482, ap: 0.757, auc: 0.807, f1: 0.723, acc: 0.777
+2025-08-07 19:44:20: Epoch: 050 Test results, ap: 0.390, auc: 0.594, f1: 0.190, acc: 0.685
+2025-08-07 19:44:20: used time 0.01h
+2025-08-07 19:44:20: Running Cross Validation 4
+2025-08-07 19:44:20: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:44:20: Epoch: 001 Train Loss: 0.695, ap: 0.353, auc: 0.440, f1: 0.000, acc: 0.602
+2025-08-07 19:44:20: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:44:20: Epoch: 001 Test results, ap: 0.342, auc: 0.599, f1: 0.000, acc: 0.759
+2025-08-07 19:44:21: Epoch: 002 Train Loss: 0.697, ap: 0.396, auc: 0.526, f1: 0.000, acc: 0.602
+2025-08-07 19:44:21: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:44:21: Epoch: 002 Test results, ap: 0.480, auc: 0.702, f1: 0.000, acc: 0.759
+2025-08-07 19:44:21: Epoch: 003 Train Loss: 0.693, ap: 0.366, auc: 0.464, f1: 0.569, acc: 0.398
+2025-08-07 19:44:21: Epoch: 003 Test results, ap: 0.477, auc: 0.622, f1: 0.388, acc: 0.241
+2025-08-07 19:44:22: Epoch: 004 Train Loss: 0.692, ap: 0.347, auc: 0.411, f1: 0.569, acc: 0.398
+2025-08-07 19:44:22: Epoch: 004 Test results, ap: 0.233, auc: 0.453, f1: 0.388, acc: 0.241
+2025-08-07 19:44:22: Epoch: 005 Train Loss: 0.703, ap: 0.423, auc: 0.545, f1: 0.000, acc: 0.602
+2025-08-07 19:44:22: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:44:22: Epoch: 005 Test results, ap: 0.265, auc: 0.569, f1: 0.000, acc: 0.759
+2025-08-07 19:44:23: Epoch: 006 Train Loss: 0.695, ap: 0.368, auc: 0.416, f1: 0.000, acc: 0.602
+2025-08-07 19:44:23: Epoch: 006 Test results, ap: 0.451, auc: 0.553, f1: 0.000, acc: 0.759
+2025-08-07 19:44:23: Epoch: 007 Train Loss: 0.696, ap: 0.350, auc: 0.413, f1: 0.000, acc: 0.592
+2025-08-07 19:44:23: Epoch: 007 Test results, ap: 0.325, auc: 0.593, f1: 0.000, acc: 0.759
+2025-08-07 19:44:24: Epoch: 008 Train Loss: 0.693, ap: 0.377, auc: 0.474, f1: 0.435, acc: 0.495
+2025-08-07 19:44:24: Epoch: 008 Test results, ap: 0.294, auc: 0.574, f1: 0.388, acc: 0.241
+2025-08-07 19:44:24: Epoch: 009 Train Loss: 0.692, ap: 0.420, auc: 0.549, f1: 0.246, acc: 0.524
+2025-08-07 19:44:24: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:44:24: Epoch: 009 Test results, ap: 0.376, auc: 0.593, f1: 0.415, acc: 0.426
+2025-08-07 19:44:25: Epoch: 010 Train Loss: 0.697, ap: 0.465, auc: 0.550, f1: 0.569, acc: 0.398
+2025-08-07 19:44:25: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:44:25: Epoch: 010 Test results, ap: 0.220, auc: 0.477, f1: 0.388, acc: 0.241
+2025-08-07 19:44:25: Epoch: 011 Train Loss: 0.694, ap: 0.403, auc: 0.539, f1: 0.465, acc: 0.553
+2025-08-07 19:44:25: Epoch: 011 Test results, ap: 0.234, auc: 0.487, f1: 0.388, acc: 0.241
+2025-08-07 19:44:25: Epoch: 011 New best TEST metrics
+2025-08-07 19:44:26: Epoch: 012 Train Loss: 0.695, ap: 0.396, auc: 0.491, f1: 0.215, acc: 0.505
+2025-08-07 19:44:26: Epoch: 012 Test results, ap: 0.236, auc: 0.500, f1: 0.392, acc: 0.426
+2025-08-07 19:44:26: Epoch: 012 New best TEST metrics
+2025-08-07 19:44:26: Epoch: 013 Train Loss: 0.690, ap: 0.434, auc: 0.575, f1: 0.465, acc: 0.553
+2025-08-07 19:44:26: Epoch: 013 Test results, ap: 0.240, auc: 0.499, f1: 0.388, acc: 0.241
+2025-08-07 19:44:26: Epoch: 013 New best TEST metrics
+2025-08-07 19:44:27: Epoch: 014 Train Loss: 0.691, ap: 0.436, auc: 0.580, f1: 0.465, acc: 0.553
+2025-08-07 19:44:27: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:44:27: Epoch: 014 Test results, ap: 0.244, auc: 0.508, f1: 0.388, acc: 0.241
+2025-08-07 19:44:27: Epoch: 014 New best TEST metrics
+2025-08-07 19:44:27: Epoch: 015 Train Loss: 0.688, ap: 0.426, auc: 0.564, f1: 0.465, acc: 0.553
+2025-08-07 19:44:27: Epoch: 015 Test results, ap: 0.242, auc: 0.499, f1: 0.388, acc: 0.241
+2025-08-07 19:44:28: Epoch: 016 Train Loss: 0.697, ap: 0.548, auc: 0.580, f1: 0.491, acc: 0.476
+2025-08-07 19:44:28: Epoch: 016 New best VALIDATION metrics
+2025-08-07 19:44:28: Epoch: 016 Test results, ap: 0.227, auc: 0.482, f1: 0.388, acc: 0.241
+2025-08-07 19:44:28: Epoch: 017 Train Loss: 0.687, ap: 0.533, auc: 0.611, f1: 0.514, acc: 0.485
+2025-08-07 19:44:28: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:44:28: Epoch: 017 Test results, ap: 0.218, auc: 0.460, f1: 0.388, acc: 0.241
+2025-08-07 19:44:29: Epoch: 018 Train Loss: 0.683, ap: 0.534, auc: 0.622, f1: 0.413, acc: 0.641
+2025-08-07 19:44:29: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:44:29: Epoch: 018 Test results, ap: 0.260, auc: 0.554, f1: 0.000, acc: 0.731
+2025-08-07 19:44:29: Epoch: 018 New best TEST metrics
+2025-08-07 19:44:29: Epoch: 019 Train Loss: 0.672, ap: 0.585, auc: 0.605, f1: 0.531, acc: 0.485
+2025-08-07 19:44:29: Epoch: 019 New best VALIDATION metrics
+2025-08-07 19:44:29: Epoch: 019 Test results, ap: 0.339, auc: 0.679, f1: 0.388, acc: 0.241
+2025-08-07 19:44:29: Epoch: 019 New best TEST metrics
+2025-08-07 19:44:30: Epoch: 020 Train Loss: 0.652, ap: 0.511, auc: 0.609, f1: 0.560, acc: 0.466
+2025-08-07 19:44:30: Epoch: 020 Test results, ap: 0.267, auc: 0.534, f1: 0.388, acc: 0.241
+2025-08-07 19:44:30: Epoch: 021 Train Loss: 0.643, ap: 0.550, auc: 0.611, f1: 0.500, acc: 0.612
+2025-08-07 19:44:30: Epoch: 021 Test results, ap: 0.239, auc: 0.481, f1: 0.000, acc: 0.722
+2025-08-07 19:44:31: Epoch: 022 Train Loss: 0.625, ap: 0.485, auc: 0.583, f1: 0.565, acc: 0.476
+2025-08-07 19:44:31: Epoch: 022 Test results, ap: 0.187, auc: 0.365, f1: 0.388, acc: 0.241
+2025-08-07 19:44:31: Epoch: 023 Train Loss: 0.609, ap: 0.495, auc: 0.578, f1: 0.500, acc: 0.592
+2025-08-07 19:44:31: Epoch: 023 Test results, ap: 0.184, auc: 0.354, f1: 0.000, acc: 0.685
+2025-08-07 19:44:32: Epoch: 024 Train Loss: 0.619, ap: 0.568, auc: 0.629, f1: 0.565, acc: 0.476
+2025-08-07 19:44:32: Epoch: 024 New best VALIDATION metrics
+2025-08-07 19:44:32: Epoch: 024 Test results, ap: 0.188, auc: 0.371, f1: 0.388, acc: 0.241
+2025-08-07 19:44:32: Epoch: 025 Train Loss: 0.609, ap: 0.536, auc: 0.594, f1: 0.540, acc: 0.437
+2025-08-07 19:44:32: Epoch: 025 Test results, ap: 0.188, auc: 0.374, f1: 0.388, acc: 0.241
+2025-08-07 19:44:33: Epoch: 026 Train Loss: 0.625, ap: 0.506, auc: 0.584, f1: 0.506, acc: 0.602
+2025-08-07 19:44:33: Epoch: 026 Test results, ap: 0.185, auc: 0.358, f1: 0.000, acc: 0.685
+2025-08-07 19:44:33: Epoch: 027 Train Loss: 0.598, ap: 0.549, auc: 0.617, f1: 0.556, acc: 0.456
+2025-08-07 19:44:33: Epoch: 027 Test results, ap: 0.189, auc: 0.377, f1: 0.388, acc: 0.241
+2025-08-07 19:44:34: Epoch: 028 Train Loss: 0.593, ap: 0.483, auc: 0.576, f1: 0.500, acc: 0.592
+2025-08-07 19:44:34: Epoch: 028 Test results, ap: 0.187, auc: 0.362, f1: 0.000, acc: 0.694
+2025-08-07 19:44:34: Epoch: 029 Train Loss: 0.603, ap: 0.527, auc: 0.619, f1: 0.525, acc: 0.631
+2025-08-07 19:44:34: Epoch: 029 Test results, ap: 0.184, auc: 0.355, f1: 0.000, acc: 0.685
+2025-08-07 19:44:35: Epoch: 030 Train Loss: 0.597, ap: 0.515, auc: 0.596, f1: 0.512, acc: 0.612
+2025-08-07 19:44:35: Epoch: 030 Test results, ap: 0.184, auc: 0.355, f1: 0.000, acc: 0.685
+2025-08-07 19:44:35: Epoch: 031 Train Loss: 0.598, ap: 0.532, auc: 0.613, f1: 0.519, acc: 0.621
+2025-08-07 19:44:35: Epoch: 031 Test results, ap: 0.188, auc: 0.371, f1: 0.000, acc: 0.685
+2025-08-07 19:44:36: Epoch: 032 Train Loss: 0.591, ap: 0.531, auc: 0.620, f1: 0.525, acc: 0.631
+2025-08-07 19:44:36: Epoch: 032 Test results, ap: 0.187, auc: 0.368, f1: 0.000, acc: 0.685
+2025-08-07 19:44:36: Epoch: 033 Train Loss: 0.588, ap: 0.485, auc: 0.580, f1: 0.512, acc: 0.612
+2025-08-07 19:44:36: Epoch: 033 Test results, ap: 0.187, auc: 0.365, f1: 0.000, acc: 0.685
+2025-08-07 19:44:37: Epoch: 034 Train Loss: 0.601, ap: 0.527, auc: 0.617, f1: 0.525, acc: 0.631
+2025-08-07 19:44:37: Epoch: 034 Test results, ap: 0.189, auc: 0.377, f1: 0.000, acc: 0.685
+2025-08-07 19:44:37: Epoch: 035 Train Loss: 0.596, ap: 0.513, auc: 0.602, f1: 0.519, acc: 0.621
+2025-08-07 19:44:37: Epoch: 035 Test results, ap: 0.189, auc: 0.375, f1: 0.000, acc: 0.685
+2025-08-07 19:44:38: Epoch: 036 Train Loss: 0.578, ap: 0.510, auc: 0.598, f1: 0.519, acc: 0.621
+2025-08-07 19:44:38: Epoch: 036 Test results, ap: 0.190, auc: 0.379, f1: 0.000, acc: 0.685
+2025-08-07 19:44:38: Epoch: 037 Train Loss: 0.557, ap: 0.506, auc: 0.595, f1: 0.512, acc: 0.612
+2025-08-07 19:44:38: Epoch: 037 Test results, ap: 0.189, auc: 0.371, f1: 0.000, acc: 0.685
+2025-08-07 19:44:39: Epoch: 038 Train Loss: 0.570, ap: 0.501, auc: 0.590, f1: 0.513, acc: 0.631
+2025-08-07 19:44:39: Epoch: 038 Test results, ap: 0.190, auc: 0.377, f1: 0.000, acc: 0.685
+2025-08-07 19:44:39: Epoch: 039 Train Loss: 0.580, ap: 0.540, auc: 0.590, f1: 0.512, acc: 0.612
+2025-08-07 19:44:39: Epoch: 039 Test results, ap: 0.188, auc: 0.368, f1: 0.000, acc: 0.685
+2025-08-07 19:44:40: Epoch: 040 Train Loss: 0.573, ap: 0.519, auc: 0.601, f1: 0.519, acc: 0.641
+2025-08-07 19:44:40: Epoch: 040 Test results, ap: 0.191, auc: 0.379, f1: 0.000, acc: 0.685
+2025-08-07 19:44:40: Epoch: 041 Train Loss: 0.571, ap: 0.536, auc: 0.580, f1: 0.506, acc: 0.621
+2025-08-07 19:44:40: Epoch: 041 Test results, ap: 0.193, auc: 0.388, f1: 0.000, acc: 0.694
+2025-08-07 19:44:41: Epoch: 042 Train Loss: 0.572, ap: 0.554, auc: 0.604, f1: 0.532, acc: 0.641
+2025-08-07 19:44:41: Epoch: 042 Test results, ap: 0.193, auc: 0.382, f1: 0.000, acc: 0.694
+2025-08-07 19:44:41: Epoch: 043 Train Loss: 0.569, ap: 0.545, auc: 0.603, f1: 0.532, acc: 0.641
+2025-08-07 19:44:41: Epoch: 043 Test results, ap: 0.192, auc: 0.380, f1: 0.000, acc: 0.694
+2025-08-07 19:44:42: Epoch: 044 Train Loss: 0.545, ap: 0.555, auc: 0.613, f1: 0.532, acc: 0.641
+2025-08-07 19:44:42: Epoch: 044 Test results, ap: 0.193, auc: 0.382, f1: 0.000, acc: 0.694
+2025-08-07 19:44:42: Epoch: 045 Train Loss: 0.560, ap: 0.535, auc: 0.586, f1: 0.525, acc: 0.631
+2025-08-07 19:44:42: Epoch: 045 Test results, ap: 0.193, auc: 0.381, f1: 0.000, acc: 0.694
+2025-08-07 19:44:43: Epoch: 046 Train Loss: 0.553, ap: 0.541, auc: 0.598, f1: 0.525, acc: 0.631
+2025-08-07 19:44:43: Epoch: 046 Test results, ap: 0.193, auc: 0.382, f1: 0.000, acc: 0.694
+2025-08-07 19:44:43: Epoch: 047 Train Loss: 0.561, ap: 0.543, auc: 0.603, f1: 0.525, acc: 0.631
+2025-08-07 19:44:43: Epoch: 047 Test results, ap: 0.194, auc: 0.383, f1: 0.000, acc: 0.694
+2025-08-07 19:44:44: Epoch: 048 Train Loss: 0.547, ap: 0.545, auc: 0.603, f1: 0.525, acc: 0.631
+2025-08-07 19:44:44: Epoch: 048 Test results, ap: 0.194, auc: 0.384, f1: 0.000, acc: 0.694
+2025-08-07 19:44:44: Epoch: 049 Train Loss: 0.548, ap: 0.544, auc: 0.603, f1: 0.525, acc: 0.631
+2025-08-07 19:44:44: Epoch: 049 Test results, ap: 0.193, auc: 0.376, f1: 0.000, acc: 0.694
+2025-08-07 19:44:45: Epoch: 050 Train Loss: 0.554, ap: 0.535, auc: 0.600, f1: 0.525, acc: 0.631
+2025-08-07 19:44:45: Epoch: 050 Test results, ap: 0.193, auc: 0.378, f1: 0.000, acc: 0.694
+2025-08-07 19:44:45: used time 0.01h
+2025-08-07 19:44:45: Cross Validation Finished!
+2025-08-07 19:44:45: Best validation perform list
+[[0.74124646 0.7606383  0.67415732 0.71844661]
+ [0.49513796 0.65601969 0.51219511 0.61165047]
+ [0.80921638 0.81372547 0.74137932 0.70873785]
+ [0.7684046  0.80796248 0.68965518 0.73786408]
+ [0.56787348 0.62942564 0.56451613 0.47572815]]
+2025-08-07 19:44:45: mean: [0.676 0.734 0.636 0.65 ]
+2025-08-07 19:44:45: std: [0.122 0.077 0.085 0.098]
+2025-08-07 19:44:45: Best test perform list
+[[0.45190972 0.62242025 0.         0.75925928]
+ [0.54183054 0.70544088 0.38805971 0.24074075]
+ [0.29641813 0.62617266 0.43809524 0.4537037 ]
+ [0.41014984 0.63836777 0.17777778 0.6574074 ]
+ [0.33936068 0.67917448 0.38805971 0.24074075]]
+2025-08-07 19:44:45: mean: [0.408 0.654 0.278 0.47 ]
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@@ -0,0 +1 @@
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diff --git a/run-cls-simple/gru-diff-256-simple-oneway-ce-32-0.001-50/training.log b/run-cls-simple/gru-diff-256-simple-oneway-ce-32-0.001-50/training.log
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+2025-08-07 19:44:47: saving_dir: ./run-cls/gru-diff-256-simple-oneway-ce-32-0.001-50
+2025-08-07 19:44:47: Loading Training Dataset
+2025-08-07 19:44:47: Loading Test Dataset
+2025-08-07 19:44:48: Running Cross Validation 0
+2025-08-07 19:44:48: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:44:48: Epoch: 001 Train Loss: 0.693, ap: 0.491, auc: 0.517, f1: 0.624, acc: 0.485
+2025-08-07 19:44:48: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:44:48: Epoch: 001 Test results, ap: 0.434, auc: 0.691, f1: 0.388, acc: 0.241
+2025-08-07 19:44:49: Epoch: 002 Train Loss: 0.692, ap: 0.475, auc: 0.504, f1: 0.622, acc: 0.456
+2025-08-07 19:44:49: Epoch: 002 Test results, ap: 0.423, auc: 0.646, f1: 0.388, acc: 0.241
+2025-08-07 19:44:49: Epoch: 003 Train Loss: 0.693, ap: 0.428, auc: 0.471, f1: 0.156, acc: 0.476
+2025-08-07 19:44:49: Epoch: 003 Test results, ap: 0.434, auc: 0.660, f1: 0.387, acc: 0.648
+2025-08-07 19:44:49: Epoch: 004 Train Loss: 0.696, ap: 0.444, auc: 0.483, f1: 0.627, acc: 0.456
+2025-08-07 19:44:49: Epoch: 004 Test results, ap: 0.191, auc: 0.362, f1: 0.388, acc: 0.241
+2025-08-07 19:44:50: Epoch: 005 Train Loss: 0.694, ap: 0.435, auc: 0.452, f1: 0.627, acc: 0.456
+2025-08-07 19:44:50: Epoch: 005 Test results, ap: 0.191, auc: 0.338, f1: 0.388, acc: 0.241
+2025-08-07 19:44:50: Epoch: 006 Train Loss: 0.693, ap: 0.456, auc: 0.468, f1: 0.000, acc: 0.544
+2025-08-07 19:44:50: Epoch: 006 Test results, ap: 0.282, auc: 0.496, f1: 0.000, acc: 0.759
+2025-08-07 19:44:51: Epoch: 007 Train Loss: 0.694, ap: 0.494, auc: 0.501, f1: 0.000, acc: 0.544
+2025-08-07 19:44:51: Epoch: 007 Test results, ap: 0.322, auc: 0.623, f1: 0.000, acc: 0.759
+2025-08-07 19:44:51: Epoch: 008 Train Loss: 0.693, ap: 0.449, auc: 0.501, f1: 0.631, acc: 0.466
+2025-08-07 19:44:51: Epoch: 008 Test results, ap: 0.328, auc: 0.629, f1: 0.388, acc: 0.241
+2025-08-07 19:44:52: Epoch: 009 Train Loss: 0.694, ap: 0.473, auc: 0.513, f1: 0.000, acc: 0.544
+2025-08-07 19:44:52: Epoch: 009 Test results, ap: 0.415, auc: 0.652, f1: 0.000, acc: 0.759
+2025-08-07 19:44:52: Epoch: 010 Train Loss: 0.693, ap: 0.456, auc: 0.500, f1: 0.000, acc: 0.544
+2025-08-07 19:44:52: Epoch: 010 Test results, ap: 0.413, auc: 0.651, f1: 0.000, acc: 0.759
+2025-08-07 19:44:53: Epoch: 011 Train Loss: 0.693, ap: 0.464, auc: 0.505, f1: 0.495, acc: 0.466
+2025-08-07 19:44:53: Epoch: 011 Test results, ap: 0.471, auc: 0.661, f1: 0.415, acc: 0.426
+2025-08-07 19:44:53: Epoch: 011 New best TEST metrics
+2025-08-07 19:44:53: Epoch: 012 Train Loss: 0.692, ap: 0.449, auc: 0.498, f1: 0.578, acc: 0.476
+2025-08-07 19:44:53: Epoch: 012 Test results, ap: 0.430, auc: 0.649, f1: 0.400, acc: 0.278
+2025-08-07 19:44:54: Epoch: 013 Train Loss: 0.693, ap: 0.469, auc: 0.508, f1: 0.609, acc: 0.476
+2025-08-07 19:44:54: Epoch: 013 Test results, ap: 0.375, auc: 0.635, f1: 0.388, acc: 0.241
+2025-08-07 19:44:54: Epoch: 014 Train Loss: 0.692, ap: 0.483, auc: 0.488, f1: 0.634, acc: 0.485
+2025-08-07 19:44:54: Epoch: 014 Test results, ap: 0.233, auc: 0.501, f1: 0.388, acc: 0.241
+2025-08-07 19:44:55: Epoch: 015 Train Loss: 0.692, ap: 0.513, auc: 0.565, f1: 0.624, acc: 0.485
+2025-08-07 19:44:55: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:44:55: Epoch: 015 Test results, ap: 0.205, auc: 0.413, f1: 0.388, acc: 0.241
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+2025-08-07 19:44:55: Epoch: 016 New best VALIDATION metrics
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+2025-08-07 19:44:55: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:44:56: Epoch: 017 Test results, ap: 0.220, auc: 0.476, f1: 0.388, acc: 0.241
+2025-08-07 19:44:56: Epoch: 018 Train Loss: 0.686, ap: 0.609, auc: 0.606, f1: 0.619, acc: 0.485
+2025-08-07 19:44:56: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:44:56: Epoch: 018 Test results, ap: 0.204, auc: 0.427, f1: 0.388, acc: 0.241
+2025-08-07 19:44:56: Epoch: 019 Train Loss: 0.678, ap: 0.578, auc: 0.590, f1: 0.604, acc: 0.631
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+2025-08-07 19:44:57: Epoch: 020 Train Loss: 0.674, ap: 0.600, auc: 0.611, f1: 0.554, acc: 0.515
+2025-08-07 19:44:57: Epoch: 020 Test results, ap: 0.221, auc: 0.479, f1: 0.360, acc: 0.472
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+2025-08-07 19:44:57: Epoch: 021 New best VALIDATION metrics
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+2025-08-07 19:44:58: Epoch: 022 Train Loss: 0.632, ap: 0.639, auc: 0.631, f1: 0.566, acc: 0.553
+2025-08-07 19:44:58: Epoch: 022 New best VALIDATION metrics
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+2025-08-07 19:44:58: Epoch: 023 Train Loss: 0.621, ap: 0.613, auc: 0.627, f1: 0.521, acc: 0.553
+2025-08-07 19:44:58: Epoch: 023 Test results, ap: 0.217, auc: 0.469, f1: 0.083, acc: 0.593
+2025-08-07 19:44:59: Epoch: 024 Train Loss: 0.625, ap: 0.640, auc: 0.658, f1: 0.562, acc: 0.621
+2025-08-07 19:44:59: Epoch: 024 New best VALIDATION metrics
+2025-08-07 19:44:59: Epoch: 024 Test results, ap: 0.204, auc: 0.429, f1: 0.260, acc: 0.472
+2025-08-07 19:44:59: Epoch: 025 Train Loss: 0.609, ap: 0.657, auc: 0.648, f1: 0.543, acc: 0.592
+2025-08-07 19:44:59: Epoch: 025 New best VALIDATION metrics
+2025-08-07 19:44:59: Epoch: 025 Test results, ap: 0.248, auc: 0.524, f1: 0.337, acc: 0.491
+2025-08-07 19:45:00: Epoch: 026 Train Loss: 0.611, ap: 0.678, auc: 0.678, f1: 0.587, acc: 0.631
+2025-08-07 19:45:00: Epoch: 026 New best VALIDATION metrics
+2025-08-07 19:45:00: Epoch: 026 Test results, ap: 0.230, auc: 0.499, f1: 0.271, acc: 0.602
+2025-08-07 19:45:00: Epoch: 027 Train Loss: 0.589, ap: 0.690, auc: 0.692, f1: 0.652, acc: 0.699
+2025-08-07 19:45:00: Epoch: 027 New best VALIDATION metrics
+2025-08-07 19:45:00: Epoch: 027 Test results, ap: 0.226, auc: 0.480, f1: 0.277, acc: 0.565
+2025-08-07 19:45:01: Epoch: 028 Train Loss: 0.565, ap: 0.690, auc: 0.720, f1: 0.628, acc: 0.689
+2025-08-07 19:45:01: Epoch: 028 New best VALIDATION metrics
+2025-08-07 19:45:01: Epoch: 028 Test results, ap: 0.232, auc: 0.478, f1: 0.278, acc: 0.519
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+2025-08-07 19:45:01: Epoch: 029 New best VALIDATION metrics
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+2025-08-07 19:45:02: Epoch: 030 Test results, ap: 0.226, auc: 0.474, f1: 0.269, acc: 0.546
+2025-08-07 19:45:02: Epoch: 031 Train Loss: 0.539, ap: 0.702, auc: 0.733, f1: 0.575, acc: 0.670
+2025-08-07 19:45:02: Epoch: 031 Test results, ap: 0.255, auc: 0.482, f1: 0.275, acc: 0.657
+2025-08-07 19:45:02: Epoch: 032 Train Loss: 0.520, ap: 0.710, auc: 0.724, f1: 0.628, acc: 0.689
+2025-08-07 19:45:03: Epoch: 032 Test results, ap: 0.238, auc: 0.480, f1: 0.267, acc: 0.593
+2025-08-07 19:45:03: Epoch: 033 Train Loss: 0.495, ap: 0.709, auc: 0.736, f1: 0.602, acc: 0.680
+2025-08-07 19:45:03: Epoch: 033 Test results, ap: 0.256, auc: 0.490, f1: 0.281, acc: 0.574
+2025-08-07 19:45:03: Epoch: 034 Train Loss: 0.518, ap: 0.711, auc: 0.736, f1: 0.614, acc: 0.670
+2025-08-07 19:45:03: Epoch: 034 Test results, ap: 0.237, auc: 0.482, f1: 0.242, acc: 0.537
+2025-08-07 19:45:04: Epoch: 035 Train Loss: 0.500, ap: 0.723, auc: 0.758, f1: 0.585, acc: 0.670
+2025-08-07 19:45:04: Epoch: 035 New best VALIDATION metrics
+2025-08-07 19:45:04: Epoch: 035 Test results, ap: 0.268, auc: 0.505, f1: 0.302, acc: 0.657
+2025-08-07 19:45:04: Epoch: 036 Train Loss: 0.518, ap: 0.717, auc: 0.744, f1: 0.667, acc: 0.709
+2025-08-07 19:45:04: Epoch: 036 Test results, ap: 0.236, auc: 0.495, f1: 0.306, acc: 0.537
+2025-08-07 19:45:05: Epoch: 037 Train Loss: 0.498, ap: 0.738, auc: 0.772, f1: 0.617, acc: 0.699
+2025-08-07 19:45:05: Epoch: 037 New best VALIDATION metrics
+2025-08-07 19:45:05: Epoch: 037 Test results, ap: 0.266, auc: 0.511, f1: 0.304, acc: 0.704
+2025-08-07 19:45:05: Epoch: 038 Train Loss: 0.501, ap: 0.728, auc: 0.763, f1: 0.621, acc: 0.680
+2025-08-07 19:45:05: Epoch: 038 Test results, ap: 0.256, auc: 0.502, f1: 0.267, acc: 0.593
+2025-08-07 19:45:06: Epoch: 039 Train Loss: 0.475, ap: 0.711, auc: 0.755, f1: 0.636, acc: 0.689
+2025-08-07 19:45:06: Epoch: 039 Test results, ap: 0.250, auc: 0.496, f1: 0.277, acc: 0.565
+2025-08-07 19:45:06: Epoch: 040 Train Loss: 0.489, ap: 0.724, auc: 0.759, f1: 0.628, acc: 0.689
+2025-08-07 19:45:06: Epoch: 040 Test results, ap: 0.256, auc: 0.494, f1: 0.259, acc: 0.630
+2025-08-07 19:45:07: Epoch: 041 Train Loss: 0.457, ap: 0.721, auc: 0.776, f1: 0.667, acc: 0.709
+2025-08-07 19:45:07: Epoch: 041 Test results, ap: 0.249, auc: 0.481, f1: 0.262, acc: 0.583
+2025-08-07 19:45:07: Epoch: 042 Train Loss: 0.441, ap: 0.724, auc: 0.778, f1: 0.636, acc: 0.689
+2025-08-07 19:45:07: Epoch: 042 Test results, ap: 0.247, auc: 0.485, f1: 0.258, acc: 0.574
+2025-08-07 19:45:07: Epoch: 043 Train Loss: 0.441, ap: 0.717, auc: 0.776, f1: 0.612, acc: 0.680
+2025-08-07 19:45:08: Epoch: 043 Test results, ap: 0.261, auc: 0.500, f1: 0.291, acc: 0.639
+2025-08-07 19:45:08: Epoch: 044 Train Loss: 0.445, ap: 0.726, auc: 0.778, f1: 0.659, acc: 0.699
+2025-08-07 19:45:08: Epoch: 044 Test results, ap: 0.249, auc: 0.494, f1: 0.281, acc: 0.574
+2025-08-07 19:45:08: Epoch: 045 Train Loss: 0.445, ap: 0.721, auc: 0.778, f1: 0.621, acc: 0.680
+2025-08-07 19:45:08: Epoch: 045 Test results, ap: 0.253, auc: 0.495, f1: 0.276, acc: 0.611
+2025-08-07 19:45:09: Epoch: 046 Train Loss: 0.426, ap: 0.726, auc: 0.786, f1: 0.652, acc: 0.699
+2025-08-07 19:45:09: Epoch: 046 New best VALIDATION metrics
+2025-08-07 19:45:09: Epoch: 046 Test results, ap: 0.255, auc: 0.493, f1: 0.267, acc: 0.593
+2025-08-07 19:45:09: Epoch: 047 Train Loss: 0.416, ap: 0.727, auc: 0.788, f1: 0.659, acc: 0.699
+2025-08-07 19:45:09: Epoch: 047 New best VALIDATION metrics
+2025-08-07 19:45:09: Epoch: 047 Test results, ap: 0.246, auc: 0.486, f1: 0.312, acc: 0.593
+2025-08-07 19:45:10: Epoch: 048 Train Loss: 0.408, ap: 0.732, auc: 0.781, f1: 0.636, acc: 0.689
+2025-08-07 19:45:10: Epoch: 048 Test results, ap: 0.258, auc: 0.501, f1: 0.276, acc: 0.611
+2025-08-07 19:45:10: Epoch: 049 Train Loss: 0.434, ap: 0.723, auc: 0.781, f1: 0.644, acc: 0.689
+2025-08-07 19:45:10: Epoch: 049 Test results, ap: 0.249, auc: 0.493, f1: 0.276, acc: 0.611
+2025-08-07 19:45:11: Epoch: 050 Train Loss: 0.423, ap: 0.741, auc: 0.784, f1: 0.667, acc: 0.718
+2025-08-07 19:45:11: Epoch: 050 New best VALIDATION metrics
+2025-08-07 19:45:11: Epoch: 050 Test results, ap: 0.257, auc: 0.499, f1: 0.276, acc: 0.611
+2025-08-07 19:45:11: used time 0.01h
+2025-08-07 19:45:11: Running Cross Validation 1
+2025-08-07 19:45:11: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:45:11: Epoch: 001 Train Loss: 0.694, ap: 0.359, auc: 0.389, f1: 0.526, acc: 0.369
+2025-08-07 19:45:11: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:45:11: Epoch: 001 Test results, ap: 0.339, auc: 0.636, f1: 0.388, acc: 0.241
+2025-08-07 19:45:12: Epoch: 002 Train Loss: 0.693, ap: 0.331, auc: 0.398, f1: 0.000, acc: 0.641
+2025-08-07 19:45:12: Epoch: 002 Test results, ap: 0.377, auc: 0.639, f1: 0.000, acc: 0.759
+2025-08-07 19:45:12: Epoch: 003 Train Loss: 0.693, ap: 0.422, auc: 0.484, f1: 0.000, acc: 0.641
+2025-08-07 19:45:12: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:45:12: Epoch: 003 Test results, ap: 0.216, auc: 0.448, f1: 0.000, acc: 0.759
+2025-08-07 19:45:13: Epoch: 004 Train Loss: 0.693, ap: 0.488, auc: 0.578, f1: 0.529, acc: 0.379
+2025-08-07 19:45:13: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:45:13: Epoch: 004 Test results, ap: 0.194, auc: 0.393, f1: 0.388, acc: 0.241
+2025-08-07 19:45:13: Epoch: 005 Train Loss: 0.692, ap: 0.492, auc: 0.579, f1: 0.519, acc: 0.369
+2025-08-07 19:45:13: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:45:13: Epoch: 005 Test results, ap: 0.189, auc: 0.356, f1: 0.388, acc: 0.241
+2025-08-07 19:45:13: Epoch: 006 Train Loss: 0.692, ap: 0.422, auc: 0.487, f1: 0.414, acc: 0.369
+2025-08-07 19:45:14: Epoch: 006 Test results, ap: 0.240, auc: 0.519, f1: 0.407, acc: 0.380
+2025-08-07 19:45:14: Epoch: 007 Train Loss: 0.690, ap: 0.462, auc: 0.523, f1: 0.529, acc: 0.379
+2025-08-07 19:45:14: Epoch: 007 Test results, ap: 0.221, auc: 0.487, f1: 0.388, acc: 0.241
+2025-08-07 19:45:14: Epoch: 008 Train Loss: 0.693, ap: 0.447, auc: 0.569, f1: 0.471, acc: 0.388
+2025-08-07 19:45:14: Epoch: 008 Test results, ap: 0.214, auc: 0.465, f1: 0.419, acc: 0.333
+2025-08-07 19:45:15: Epoch: 009 Train Loss: 0.694, ap: 0.352, auc: 0.440, f1: 0.000, acc: 0.641
+2025-08-07 19:45:15: Epoch: 009 Test results, ap: 0.330, auc: 0.562, f1: 0.000, acc: 0.759
+2025-08-07 19:45:15: Epoch: 010 Train Loss: 0.692, ap: 0.486, auc: 0.615, f1: 0.526, acc: 0.369
+2025-08-07 19:45:15: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:45:15: Epoch: 010 Test results, ap: 0.203, auc: 0.416, f1: 0.364, acc: 0.222
+2025-08-07 19:45:16: Epoch: 011 Train Loss: 0.672, ap: 0.460, auc: 0.616, f1: 0.523, acc: 0.505
+2025-08-07 19:45:16: Epoch: 011 Test results, ap: 0.238, auc: 0.524, f1: 0.254, acc: 0.509
+2025-08-07 19:45:16: Epoch: 011 New best TEST metrics
+2025-08-07 19:45:16: Epoch: 012 Train Loss: 0.690, ap: 0.470, auc: 0.597, f1: 0.493, acc: 0.641
+2025-08-07 19:45:16: Epoch: 012 Test results, ap: 0.212, auc: 0.451, f1: 0.048, acc: 0.630
+2025-08-07 19:45:17: Epoch: 013 Train Loss: 0.667, ap: 0.518, auc: 0.609, f1: 0.538, acc: 0.650
+2025-08-07 19:45:17: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:45:17: Epoch: 013 Test results, ap: 0.215, auc: 0.450, f1: 0.048, acc: 0.630
+2025-08-07 19:45:17: Epoch: 014 Train Loss: 0.658, ap: 0.504, auc: 0.616, f1: 0.457, acc: 0.631
+2025-08-07 19:45:17: Epoch: 014 Test results, ap: 0.230, auc: 0.488, f1: 0.281, acc: 0.620
+2025-08-07 19:45:18: Epoch: 015 Train Loss: 0.636, ap: 0.562, auc: 0.608, f1: 0.486, acc: 0.631
+2025-08-07 19:45:18: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:45:18: Epoch: 015 Test results, ap: 0.210, auc: 0.429, f1: 0.098, acc: 0.657
+2025-08-07 19:45:18: Epoch: 016 Train Loss: 0.620, ap: 0.538, auc: 0.629, f1: 0.431, acc: 0.641
+2025-08-07 19:45:18: Epoch: 016 Test results, ap: 0.197, auc: 0.396, f1: 0.000, acc: 0.694
+2025-08-07 19:45:19: Epoch: 017 Train Loss: 0.645, ap: 0.391, auc: 0.536, f1: 0.269, acc: 0.631
+2025-08-07 19:45:19: Epoch: 017 Test results, ap: 0.268, auc: 0.496, f1: 0.000, acc: 0.750
+2025-08-07 19:45:19: Epoch: 017 New best TEST metrics
+2025-08-07 19:45:19: Epoch: 018 Train Loss: 0.656, ap: 0.481, auc: 0.605, f1: 0.361, acc: 0.621
+2025-08-07 19:45:19: Epoch: 018 Test results, ap: 0.219, auc: 0.452, f1: 0.057, acc: 0.694
+2025-08-07 19:45:20: Epoch: 019 Train Loss: 0.637, ap: 0.496, auc: 0.636, f1: 0.424, acc: 0.631
+2025-08-07 19:45:20: Epoch: 019 Test results, ap: 0.195, auc: 0.374, f1: 0.100, acc: 0.667
+2025-08-07 19:45:20: Epoch: 020 Train Loss: 0.640, ap: 0.455, auc: 0.592, f1: 0.333, acc: 0.612
+2025-08-07 19:45:20: Epoch: 020 Test results, ap: 0.192, auc: 0.383, f1: 0.000, acc: 0.722
+2025-08-07 19:45:21: Epoch: 021 Train Loss: 0.645, ap: 0.451, auc: 0.565, f1: 0.381, acc: 0.621
+2025-08-07 19:45:21: Epoch: 021 Test results, ap: 0.198, auc: 0.389, f1: 0.100, acc: 0.667
+2025-08-07 19:45:21: Epoch: 022 Train Loss: 0.626, ap: 0.456, auc: 0.581, f1: 0.328, acc: 0.602
+2025-08-07 19:45:21: Epoch: 022 Test results, ap: 0.195, auc: 0.401, f1: 0.000, acc: 0.722
+2025-08-07 19:45:21: Epoch: 023 Train Loss: 0.613, ap: 0.462, auc: 0.571, f1: 0.364, acc: 0.592
+2025-08-07 19:45:22: Epoch: 023 Test results, ap: 0.198, auc: 0.386, f1: 0.146, acc: 0.676
+2025-08-07 19:45:22: Epoch: 024 Train Loss: 0.614, ap: 0.480, auc: 0.611, f1: 0.381, acc: 0.621
+2025-08-07 19:45:22: Epoch: 024 Test results, ap: 0.197, auc: 0.393, f1: 0.061, acc: 0.713
+2025-08-07 19:45:22: Epoch: 025 Train Loss: 0.611, ap: 0.477, auc: 0.600, f1: 0.375, acc: 0.612
+2025-08-07 19:45:22: Epoch: 025 Test results, ap: 0.190, auc: 0.351, f1: 0.146, acc: 0.676
+2025-08-07 19:45:23: Epoch: 026 Train Loss: 0.605, ap: 0.489, auc: 0.621, f1: 0.400, acc: 0.621
+2025-08-07 19:45:23: Epoch: 026 Test results, ap: 0.199, auc: 0.385, f1: 0.150, acc: 0.685
+2025-08-07 19:45:23: Epoch: 027 Train Loss: 0.581, ap: 0.471, auc: 0.611, f1: 0.418, acc: 0.621
+2025-08-07 19:45:23: Epoch: 027 Test results, ap: 0.212, auc: 0.427, f1: 0.133, acc: 0.639
+2025-08-07 19:45:24: Epoch: 028 Train Loss: 0.594, ap: 0.509, auc: 0.638, f1: 0.418, acc: 0.621
+2025-08-07 19:45:24: Epoch: 028 Test results, ap: 0.217, auc: 0.440, f1: 0.150, acc: 0.685
+2025-08-07 19:45:24: Epoch: 029 Train Loss: 0.580, ap: 0.508, auc: 0.597, f1: 0.435, acc: 0.621
+2025-08-07 19:45:24: Epoch: 029 Test results, ap: 0.267, auc: 0.555, f1: 0.258, acc: 0.574
+2025-08-07 19:45:24: Epoch: 029 New best TEST metrics
+2025-08-07 19:45:25: Epoch: 030 Train Loss: 0.573, ap: 0.541, auc: 0.632, f1: 0.419, acc: 0.650
+2025-08-07 19:45:25: Epoch: 030 New best VALIDATION metrics
+2025-08-07 19:45:25: Epoch: 030 Test results, ap: 0.235, auc: 0.469, f1: 0.146, acc: 0.676
+2025-08-07 19:45:25: Epoch: 031 Train Loss: 0.563, ap: 0.523, auc: 0.613, f1: 0.424, acc: 0.631
+2025-08-07 19:45:25: Epoch: 031 Test results, ap: 0.324, auc: 0.537, f1: 0.323, acc: 0.611
+2025-08-07 19:45:25: Epoch: 031 New best TEST metrics
+2025-08-07 19:45:26: Epoch: 032 Train Loss: 0.569, ap: 0.519, auc: 0.606, f1: 0.424, acc: 0.631
+2025-08-07 19:45:26: Epoch: 032 Test results, ap: 0.296, auc: 0.525, f1: 0.286, acc: 0.630
+2025-08-07 19:45:26: Epoch: 033 Train Loss: 0.549, ap: 0.560, auc: 0.636, f1: 0.406, acc: 0.631
+2025-08-07 19:45:26: Epoch: 033 New best VALIDATION metrics
+2025-08-07 19:45:26: Epoch: 033 Test results, ap: 0.229, auc: 0.447, f1: 0.195, acc: 0.694
+2025-08-07 19:45:27: Epoch: 034 Train Loss: 0.550, ap: 0.552, auc: 0.609, f1: 0.455, acc: 0.650
+2025-08-07 19:45:27: Epoch: 034 Test results, ap: 0.316, auc: 0.537, f1: 0.281, acc: 0.620
+2025-08-07 19:45:27: Epoch: 035 Train Loss: 0.554, ap: 0.522, auc: 0.600, f1: 0.493, acc: 0.660
+2025-08-07 19:45:27: Epoch: 035 Test results, ap: 0.363, auc: 0.578, f1: 0.378, acc: 0.574
+2025-08-07 19:45:27: Epoch: 035 New best TEST metrics
+2025-08-07 19:45:28: Epoch: 036 Train Loss: 0.553, ap: 0.583, auc: 0.627, f1: 0.438, acc: 0.650
+2025-08-07 19:45:28: Epoch: 036 New best VALIDATION metrics
+2025-08-07 19:45:28: Epoch: 036 Test results, ap: 0.237, auc: 0.466, f1: 0.195, acc: 0.694
+2025-08-07 19:45:28: Epoch: 037 Train Loss: 0.529, ap: 0.582, auc: 0.646, f1: 0.455, acc: 0.650
+2025-08-07 19:45:28: Epoch: 037 New best VALIDATION metrics
+2025-08-07 19:45:28: Epoch: 037 Test results, ap: 0.232, auc: 0.449, f1: 0.154, acc: 0.694
+2025-08-07 19:45:29: Epoch: 038 Train Loss: 0.550, ap: 0.557, auc: 0.618, f1: 0.448, acc: 0.641
+2025-08-07 19:45:29: Epoch: 038 Test results, ap: 0.343, auc: 0.530, f1: 0.222, acc: 0.611
+2025-08-07 19:45:29: Epoch: 039 Train Loss: 0.530, ap: 0.536, auc: 0.636, f1: 0.514, acc: 0.670
+2025-08-07 19:45:29: Epoch: 039 Test results, ap: 0.375, auc: 0.573, f1: 0.386, acc: 0.528
+2025-08-07 19:45:29: Epoch: 039 New best TEST metrics
+2025-08-07 19:45:29: Epoch: 040 Train Loss: 0.534, ap: 0.577, auc: 0.654, f1: 0.448, acc: 0.641
+2025-08-07 19:45:29: Epoch: 040 New best VALIDATION metrics
+2025-08-07 19:45:30: Epoch: 040 Test results, ap: 0.219, auc: 0.426, f1: 0.154, acc: 0.694
+2025-08-07 19:45:30: Epoch: 041 Train Loss: 0.528, ap: 0.545, auc: 0.619, f1: 0.435, acc: 0.621
+2025-08-07 19:45:30: Epoch: 041 Test results, ap: 0.298, auc: 0.468, f1: 0.190, acc: 0.685
+2025-08-07 19:45:30: Epoch: 042 Train Loss: 0.526, ap: 0.554, auc: 0.624, f1: 0.435, acc: 0.621
+2025-08-07 19:45:30: Epoch: 042 Test results, ap: 0.300, auc: 0.470, f1: 0.217, acc: 0.667
+2025-08-07 19:45:31: Epoch: 043 Train Loss: 0.530, ap: 0.557, auc: 0.625, f1: 0.435, acc: 0.621
+2025-08-07 19:45:31: Epoch: 043 Test results, ap: 0.301, auc: 0.470, f1: 0.182, acc: 0.667
+2025-08-07 19:45:31: Epoch: 044 Train Loss: 0.517, ap: 0.560, auc: 0.630, f1: 0.429, acc: 0.612
+2025-08-07 19:45:31: Epoch: 044 Test results, ap: 0.316, auc: 0.470, f1: 0.213, acc: 0.657
+2025-08-07 19:45:32: Epoch: 045 Train Loss: 0.515, ap: 0.574, auc: 0.640, f1: 0.457, acc: 0.631
+2025-08-07 19:45:32: Epoch: 045 Test results, ap: 0.289, auc: 0.447, f1: 0.195, acc: 0.694
+2025-08-07 19:45:32: Epoch: 046 Train Loss: 0.509, ap: 0.565, auc: 0.629, f1: 0.438, acc: 0.602
+2025-08-07 19:45:32: Epoch: 046 Test results, ap: 0.296, auc: 0.459, f1: 0.222, acc: 0.676
+2025-08-07 19:45:33: Epoch: 047 Train Loss: 0.496, ap: 0.571, auc: 0.642, f1: 0.486, acc: 0.631
+2025-08-07 19:45:33: Epoch: 047 Test results, ap: 0.290, auc: 0.448, f1: 0.190, acc: 0.685
+2025-08-07 19:45:33: Epoch: 048 Train Loss: 0.499, ap: 0.572, auc: 0.646, f1: 0.472, acc: 0.631
+2025-08-07 19:45:33: Epoch: 048 Test results, ap: 0.288, auc: 0.446, f1: 0.150, acc: 0.685
+2025-08-07 19:45:34: Epoch: 049 Train Loss: 0.556, ap: 0.553, auc: 0.653, f1: 0.507, acc: 0.641
+2025-08-07 19:45:34: Epoch: 049 Test results, ap: 0.220, auc: 0.431, f1: 0.171, acc: 0.731
+2025-08-07 19:45:34: Epoch: 050 Train Loss: 0.535, ap: 0.566, auc: 0.629, f1: 0.451, acc: 0.621
+2025-08-07 19:45:34: Epoch: 050 Test results, ap: 0.324, auc: 0.465, f1: 0.217, acc: 0.667
+2025-08-07 19:45:34: used time 0.01h
+2025-08-07 19:45:34: Running Cross Validation 2
+2025-08-07 19:45:34: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:45:34: Epoch: 001 Train Loss: 0.693, ap: 0.499, auc: 0.495, f1: 0.000, acc: 0.495
+2025-08-07 19:45:34: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:45:35: Epoch: 001 Test results, ap: 0.335, auc: 0.623, f1: 0.000, acc: 0.759
+2025-08-07 19:45:35: Epoch: 002 Train Loss: 0.693, ap: 0.499, auc: 0.497, f1: 0.000, acc: 0.495
+2025-08-07 19:45:35: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:45:35: Epoch: 002 Test results, ap: 0.205, auc: 0.420, f1: 0.000, acc: 0.759
+2025-08-07 19:45:35: Epoch: 003 Train Loss: 0.693, ap: 0.534, auc: 0.533, f1: 0.621, acc: 0.515
+2025-08-07 19:45:35: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:45:36: Epoch: 003 Test results, ap: 0.410, auc: 0.649, f1: 0.400, acc: 0.278
+2025-08-07 19:45:36: Epoch: 004 Train Loss: 0.693, ap: 0.493, auc: 0.464, f1: 0.675, acc: 0.524
+2025-08-07 19:45:36: Epoch: 004 Test results, ap: 0.193, auc: 0.381, f1: 0.388, acc: 0.241
+2025-08-07 19:45:36: Epoch: 005 Train Loss: 0.693, ap: 0.507, auc: 0.507, f1: 0.036, acc: 0.485
+2025-08-07 19:45:36: Epoch: 005 Test results, ap: 0.185, auc: 0.352, f1: 0.000, acc: 0.759
+2025-08-07 19:45:37: Epoch: 006 Train Loss: 0.693, ap: 0.507, auc: 0.504, f1: 0.671, acc: 0.524
+2025-08-07 19:45:37: Epoch: 006 Test results, ap: 0.189, auc: 0.359, f1: 0.388, acc: 0.241
+2025-08-07 19:45:37: Epoch: 007 Train Loss: 0.695, ap: 0.552, auc: 0.608, f1: 0.675, acc: 0.515
+2025-08-07 19:45:37: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:45:37: Epoch: 007 Test results, ap: 0.195, auc: 0.392, f1: 0.388, acc: 0.241
+2025-08-07 19:45:38: Epoch: 008 Train Loss: 0.692, ap: 0.584, auc: 0.624, f1: 0.686, acc: 0.583
+2025-08-07 19:45:38: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:45:38: Epoch: 008 Test results, ap: 0.209, auc: 0.449, f1: 0.371, acc: 0.435
+2025-08-07 19:45:38: Epoch: 009 Train Loss: 0.691, ap: 0.570, auc: 0.615, f1: 0.689, acc: 0.631
+2025-08-07 19:45:39: Epoch: 009 Test results, ap: 0.217, auc: 0.476, f1: 0.317, acc: 0.481
+2025-08-07 19:45:39: Epoch: 010 Train Loss: 0.673, ap: 0.688, auc: 0.687, f1: 0.558, acc: 0.631
+2025-08-07 19:45:39: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:45:39: Epoch: 010 Test results, ap: 0.203, auc: 0.404, f1: 0.000, acc: 0.685
+2025-08-07 19:45:39: Epoch: 011 Train Loss: 0.654, ap: 0.629, auc: 0.662, f1: 0.660, acc: 0.650
+2025-08-07 19:45:39: Epoch: 011 Test results, ap: 0.225, auc: 0.490, f1: 0.286, acc: 0.537
+2025-08-07 19:45:39: Epoch: 011 New best TEST metrics
+2025-08-07 19:45:40: Epoch: 012 Train Loss: 0.647, ap: 0.619, auc: 0.638, f1: 0.655, acc: 0.612
+2025-08-07 19:45:40: Epoch: 012 Test results, ap: 0.226, auc: 0.500, f1: 0.321, acc: 0.491
+2025-08-07 19:45:40: Epoch: 012 New best TEST metrics
+2025-08-07 19:45:40: Epoch: 013 Train Loss: 0.663, ap: 0.659, auc: 0.684, f1: 0.584, acc: 0.641
+2025-08-07 19:45:41: Epoch: 013 Test results, ap: 0.209, auc: 0.421, f1: 0.000, acc: 0.667
+2025-08-07 19:45:41: Epoch: 014 Train Loss: 0.646, ap: 0.659, auc: 0.690, f1: 0.638, acc: 0.670
+2025-08-07 19:45:41: Epoch: 014 Test results, ap: 0.209, auc: 0.431, f1: 0.091, acc: 0.630
+2025-08-07 19:45:41: Epoch: 015 Train Loss: 0.635, ap: 0.691, auc: 0.715, f1: 0.673, acc: 0.680
+2025-08-07 19:45:41: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:45:41: Epoch: 015 Test results, ap: 0.210, auc: 0.431, f1: 0.258, acc: 0.574
+2025-08-07 19:45:42: Epoch: 016 Train Loss: 0.623, ap: 0.699, auc: 0.727, f1: 0.634, acc: 0.641
+2025-08-07 19:45:42: Epoch: 016 New best VALIDATION metrics
+2025-08-07 19:45:42: Epoch: 016 Test results, ap: 0.214, auc: 0.417, f1: 0.312, acc: 0.593
+2025-08-07 19:45:42: Epoch: 017 Train Loss: 0.592, ap: 0.733, auc: 0.742, f1: 0.735, acc: 0.699
+2025-08-07 19:45:42: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:45:42: Epoch: 017 Test results, ap: 0.252, auc: 0.470, f1: 0.355, acc: 0.630
+2025-08-07 19:45:43: Epoch: 018 Train Loss: 0.574, ap: 0.757, auc: 0.742, f1: 0.667, acc: 0.680
+2025-08-07 19:45:43: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:45:43: Epoch: 018 Test results, ap: 0.245, auc: 0.433, f1: 0.308, acc: 0.583
+2025-08-07 19:45:43: Epoch: 019 Train Loss: 0.582, ap: 0.768, auc: 0.758, f1: 0.694, acc: 0.641
+2025-08-07 19:45:43: Epoch: 019 New best VALIDATION metrics
+2025-08-07 19:45:43: Epoch: 019 Test results, ap: 0.208, auc: 0.394, f1: 0.275, acc: 0.463
+2025-08-07 19:45:44: Epoch: 020 Train Loss: 0.573, ap: 0.734, auc: 0.743, f1: 0.720, acc: 0.660
+2025-08-07 19:45:44: Epoch: 020 Test results, ap: 0.224, auc: 0.491, f1: 0.396, acc: 0.463
+2025-08-07 19:45:44: Epoch: 021 Train Loss: 0.566, ap: 0.804, auc: 0.794, f1: 0.637, acc: 0.680
+2025-08-07 19:45:44: Epoch: 021 New best VALIDATION metrics
+2025-08-07 19:45:44: Epoch: 021 Test results, ap: 0.223, auc: 0.417, f1: 0.225, acc: 0.491
+2025-08-07 19:45:45: Epoch: 022 Train Loss: 0.554, ap: 0.743, auc: 0.763, f1: 0.660, acc: 0.689
+2025-08-07 19:45:45: Epoch: 022 Test results, ap: 0.238, auc: 0.407, f1: 0.205, acc: 0.426
+2025-08-07 19:45:45: Epoch: 023 Train Loss: 0.527, ap: 0.747, auc: 0.761, f1: 0.646, acc: 0.670
+2025-08-07 19:45:45: Epoch: 023 Test results, ap: 0.226, auc: 0.394, f1: 0.121, acc: 0.731
+2025-08-07 19:45:46: Epoch: 024 Train Loss: 0.558, ap: 0.707, auc: 0.716, f1: 0.621, acc: 0.621
+2025-08-07 19:45:46: Epoch: 024 Test results, ap: 0.231, auc: 0.396, f1: 0.267, acc: 0.389
+2025-08-07 19:45:46: Epoch: 025 Train Loss: 0.521, ap: 0.756, auc: 0.767, f1: 0.605, acc: 0.670
+2025-08-07 19:45:46: Epoch: 025 Test results, ap: 0.246, auc: 0.425, f1: 0.164, acc: 0.528
+2025-08-07 19:45:46: Epoch: 026 Train Loss: 0.521, ap: 0.749, auc: 0.755, f1: 0.680, acc: 0.699
+2025-08-07 19:45:47: Epoch: 026 Test results, ap: 0.218, auc: 0.394, f1: 0.239, acc: 0.528
+2025-08-07 19:45:47: Epoch: 027 Train Loss: 0.512, ap: 0.762, auc: 0.767, f1: 0.667, acc: 0.670
+2025-08-07 19:45:47: Epoch: 027 Test results, ap: 0.209, auc: 0.388, f1: 0.151, acc: 0.583
+2025-08-07 19:45:47: Epoch: 028 Train Loss: 0.498, ap: 0.783, auc: 0.773, f1: 0.638, acc: 0.670
+2025-08-07 19:45:47: Epoch: 028 Test results, ap: 0.232, auc: 0.413, f1: 0.186, acc: 0.676
+2025-08-07 19:45:48: Epoch: 029 Train Loss: 0.490, ap: 0.768, auc: 0.767, f1: 0.667, acc: 0.680
+2025-08-07 19:45:48: Epoch: 029 Test results, ap: 0.226, auc: 0.415, f1: 0.190, acc: 0.528
+2025-08-07 19:45:48: Epoch: 030 Train Loss: 0.497, ap: 0.793, auc: 0.768, f1: 0.645, acc: 0.680
+2025-08-07 19:45:48: Epoch: 030 Test results, ap: 0.240, auc: 0.433, f1: 0.190, acc: 0.528
+2025-08-07 19:45:49: Epoch: 031 Train Loss: 0.489, ap: 0.774, auc: 0.770, f1: 0.646, acc: 0.660
+2025-08-07 19:45:49: Epoch: 031 Test results, ap: 0.251, auc: 0.439, f1: 0.215, acc: 0.528
+2025-08-07 19:45:49: Epoch: 032 Train Loss: 0.476, ap: 0.779, auc: 0.772, f1: 0.680, acc: 0.689
+2025-08-07 19:45:49: Epoch: 032 Test results, ap: 0.276, auc: 0.448, f1: 0.215, acc: 0.528
+2025-08-07 19:45:50: Epoch: 033 Train Loss: 0.465, ap: 0.776, auc: 0.776, f1: 0.632, acc: 0.660
+2025-08-07 19:45:50: Epoch: 033 Test results, ap: 0.312, auc: 0.476, f1: 0.229, acc: 0.750
+2025-08-07 19:45:50: Epoch: 033 New best TEST metrics
+2025-08-07 19:45:50: Epoch: 034 Train Loss: 0.471, ap: 0.779, auc: 0.778, f1: 0.646, acc: 0.670
+2025-08-07 19:45:50: Epoch: 034 Test results, ap: 0.287, auc: 0.467, f1: 0.241, acc: 0.593
+2025-08-07 19:45:51: Epoch: 035 Train Loss: 0.460, ap: 0.798, auc: 0.783, f1: 0.638, acc: 0.670
+2025-08-07 19:45:51: Epoch: 035 Test results, ap: 0.288, auc: 0.471, f1: 0.211, acc: 0.583
+2025-08-07 19:45:51: Epoch: 036 Train Loss: 0.463, ap: 0.769, auc: 0.784, f1: 0.693, acc: 0.699
+2025-08-07 19:45:51: Epoch: 036 Test results, ap: 0.312, auc: 0.491, f1: 0.194, acc: 0.769
+2025-08-07 19:45:51: Epoch: 036 New best TEST metrics
+2025-08-07 19:45:52: Epoch: 037 Train Loss: 0.466, ap: 0.785, auc: 0.775, f1: 0.630, acc: 0.670
+2025-08-07 19:45:52: Epoch: 037 Test results, ap: 0.296, auc: 0.487, f1: 0.182, acc: 0.750
+2025-08-07 19:45:52: Epoch: 038 Train Loss: 0.455, ap: 0.792, auc: 0.774, f1: 0.646, acc: 0.670
+2025-08-07 19:45:52: Epoch: 038 Test results, ap: 0.286, auc: 0.468, f1: 0.209, acc: 0.509
+2025-08-07 19:45:52: Epoch: 039 Train Loss: 0.443, ap: 0.754, auc: 0.764, f1: 0.653, acc: 0.680
+2025-08-07 19:45:53: Epoch: 039 Test results, ap: 0.316, auc: 0.515, f1: 0.188, acc: 0.759
+2025-08-07 19:45:53: Epoch: 039 New best TEST metrics
+2025-08-07 19:45:53: Epoch: 040 Train Loss: 0.453, ap: 0.767, auc: 0.770, f1: 0.638, acc: 0.670
+2025-08-07 19:45:53: Epoch: 040 Test results, ap: 0.316, auc: 0.513, f1: 0.242, acc: 0.769
+2025-08-07 19:45:53: Epoch: 041 Train Loss: 0.433, ap: 0.797, auc: 0.775, f1: 0.638, acc: 0.670
+2025-08-07 19:45:54: Epoch: 041 Test results, ap: 0.310, auc: 0.501, f1: 0.233, acc: 0.574
+2025-08-07 19:45:54: Epoch: 042 Train Loss: 0.442, ap: 0.762, auc: 0.770, f1: 0.630, acc: 0.670
+2025-08-07 19:45:54: Epoch: 042 Test results, ap: 0.307, auc: 0.517, f1: 0.242, acc: 0.769
+2025-08-07 19:45:54: Epoch: 043 Train Loss: 0.411, ap: 0.759, auc: 0.774, f1: 0.653, acc: 0.680
+2025-08-07 19:45:54: Epoch: 043 Test results, ap: 0.310, auc: 0.520, f1: 0.242, acc: 0.769
+2025-08-07 19:45:55: Epoch: 044 Train Loss: 0.420, ap: 0.760, auc: 0.764, f1: 0.630, acc: 0.670
+2025-08-07 19:45:55: Epoch: 044 Test results, ap: 0.308, auc: 0.521, f1: 0.229, acc: 0.750
+2025-08-07 19:45:55: Epoch: 045 Train Loss: 0.424, ap: 0.755, auc: 0.768, f1: 0.638, acc: 0.670
+2025-08-07 19:45:55: Epoch: 045 Test results, ap: 0.305, auc: 0.522, f1: 0.229, acc: 0.750
+2025-08-07 19:45:56: Epoch: 046 Train Loss: 0.423, ap: 0.751, auc: 0.764, f1: 0.653, acc: 0.680
+2025-08-07 19:45:56: Epoch: 046 Test results, ap: 0.306, auc: 0.523, f1: 0.229, acc: 0.750
+2025-08-07 19:45:56: Epoch: 047 Train Loss: 0.414, ap: 0.753, auc: 0.765, f1: 0.645, acc: 0.680
+2025-08-07 19:45:56: Epoch: 047 Test results, ap: 0.308, auc: 0.522, f1: 0.242, acc: 0.769
+2025-08-07 19:45:57: Epoch: 048 Train Loss: 0.423, ap: 0.753, auc: 0.762, f1: 0.624, acc: 0.660
+2025-08-07 19:45:57: Epoch: 048 Test results, ap: 0.309, auc: 0.524, f1: 0.242, acc: 0.769
+2025-08-07 19:45:57: Epoch: 048 New best TEST metrics
+2025-08-07 19:45:57: Epoch: 049 Train Loss: 0.420, ap: 0.758, auc: 0.760, f1: 0.617, acc: 0.650
+2025-08-07 19:45:57: Epoch: 049 Test results, ap: 0.301, auc: 0.521, f1: 0.242, acc: 0.769
+2025-08-07 19:45:58: Epoch: 050 Train Loss: 0.397, ap: 0.762, auc: 0.771, f1: 0.624, acc: 0.660
+2025-08-07 19:45:58: Epoch: 050 Test results, ap: 0.303, auc: 0.528, f1: 0.242, acc: 0.769
+2025-08-07 19:45:58: used time 0.01h
+2025-08-07 19:45:58: Running Cross Validation 3
+2025-08-07 19:45:58: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:45:58: Epoch: 001 Train Loss: 0.693, ap: 0.409, auc: 0.485, f1: 0.000, acc: 0.592
+2025-08-07 19:45:58: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:45:58: Epoch: 001 Test results, ap: 0.393, auc: 0.654, f1: 0.000, acc: 0.759
+2025-08-07 19:45:59: Epoch: 002 Train Loss: 0.693, ap: 0.489, auc: 0.559, f1: 0.582, acc: 0.427
+2025-08-07 19:45:59: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:45:59: Epoch: 002 Test results, ap: 0.190, auc: 0.358, f1: 0.388, acc: 0.241
+2025-08-07 19:45:59: Epoch: 003 Train Loss: 0.692, ap: 0.401, auc: 0.476, f1: 0.585, acc: 0.476
+2025-08-07 19:45:59: Epoch: 003 Test results, ap: 0.398, auc: 0.643, f1: 0.388, acc: 0.241
+2025-08-07 19:45:59: Epoch: 004 Train Loss: 0.693, ap: 0.457, auc: 0.528, f1: 0.579, acc: 0.408
+2025-08-07 19:46:00: Epoch: 004 Test results, ap: 0.190, auc: 0.363, f1: 0.388, acc: 0.241
+2025-08-07 19:46:00: Epoch: 005 Train Loss: 0.694, ap: 0.398, auc: 0.435, f1: 0.583, acc: 0.417
+2025-08-07 19:46:00: Epoch: 005 Test results, ap: 0.385, auc: 0.646, f1: 0.388, acc: 0.241
+2025-08-07 19:46:00: Epoch: 006 Train Loss: 0.694, ap: 0.444, auc: 0.519, f1: 0.583, acc: 0.417
+2025-08-07 19:46:00: Epoch: 006 Test results, ap: 0.236, auc: 0.495, f1: 0.388, acc: 0.241
+2025-08-07 19:46:01: Epoch: 007 Train Loss: 0.693, ap: 0.539, auc: 0.656, f1: 0.000, acc: 0.592
+2025-08-07 19:46:01: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:46:01: Epoch: 007 Test results, ap: 0.214, auc: 0.458, f1: 0.000, acc: 0.759
+2025-08-07 19:46:01: Epoch: 008 Train Loss: 0.693, ap: 0.565, auc: 0.686, f1: 0.000, acc: 0.592
+2025-08-07 19:46:01: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:46:01: Epoch: 008 Test results, ap: 0.213, auc: 0.455, f1: 0.000, acc: 0.759
+2025-08-07 19:46:02: Epoch: 009 Train Loss: 0.694, ap: 0.568, auc: 0.685, f1: 0.000, acc: 0.592
+2025-08-07 19:46:02: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:46:02: Epoch: 009 Test results, ap: 0.214, auc: 0.458, f1: 0.000, acc: 0.759
+2025-08-07 19:46:02: Epoch: 010 Train Loss: 0.692, ap: 0.548, auc: 0.662, f1: 0.573, acc: 0.408
+2025-08-07 19:46:02: Epoch: 010 Test results, ap: 0.206, auc: 0.439, f1: 0.388, acc: 0.241
+2025-08-07 19:46:03: Epoch: 011 Train Loss: 0.688, ap: 0.519, auc: 0.671, f1: 0.000, acc: 0.592
+2025-08-07 19:46:03: Epoch: 011 Test results, ap: 0.211, auc: 0.453, f1: 0.000, acc: 0.759
+2025-08-07 19:46:03: Epoch: 011 New best TEST metrics
+2025-08-07 19:46:03: Epoch: 012 Train Loss: 0.692, ap: 0.543, auc: 0.677, f1: 0.513, acc: 0.631
+2025-08-07 19:46:03: Epoch: 012 Test results, ap: 0.214, auc: 0.462, f1: 0.000, acc: 0.630
+2025-08-07 19:46:03: Epoch: 012 New best TEST metrics
+2025-08-07 19:46:04: Epoch: 013 Train Loss: 0.689, ap: 0.551, auc: 0.682, f1: 0.614, acc: 0.573
+2025-08-07 19:46:04: Epoch: 013 Test results, ap: 0.202, auc: 0.417, f1: 0.289, acc: 0.454
+2025-08-07 19:46:04: Epoch: 014 Train Loss: 0.676, ap: 0.538, auc: 0.682, f1: 0.621, acc: 0.621
+2025-08-07 19:46:04: Epoch: 014 Test results, ap: 0.209, auc: 0.426, f1: 0.247, acc: 0.491
+2025-08-07 19:46:05: Epoch: 015 Train Loss: 0.670, ap: 0.539, auc: 0.646, f1: 0.525, acc: 0.631
+2025-08-07 19:46:05: Epoch: 015 Test results, ap: 0.215, auc: 0.451, f1: 0.125, acc: 0.611
+2025-08-07 19:46:05: Epoch: 016 Train Loss: 0.665, ap: 0.557, auc: 0.663, f1: 0.545, acc: 0.612
+2025-08-07 19:46:05: Epoch: 016 Test results, ap: 0.209, auc: 0.425, f1: 0.276, acc: 0.611
+2025-08-07 19:46:06: Epoch: 017 Train Loss: 0.644, ap: 0.642, auc: 0.682, f1: 0.562, acc: 0.621
+2025-08-07 19:46:06: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:46:06: Epoch: 017 Test results, ap: 0.211, auc: 0.405, f1: 0.291, acc: 0.639
+2025-08-07 19:46:06: Epoch: 018 Train Loss: 0.614, ap: 0.654, auc: 0.666, f1: 0.575, acc: 0.641
+2025-08-07 19:46:06: Epoch: 018 Test results, ap: 0.232, auc: 0.460, f1: 0.239, acc: 0.528
+2025-08-07 19:46:06: Epoch: 018 New best TEST metrics
+2025-08-07 19:46:07: Epoch: 019 Train Loss: 0.645, ap: 0.585, auc: 0.662, f1: 0.584, acc: 0.641
+2025-08-07 19:46:07: Epoch: 019 Test results, ap: 0.232, auc: 0.455, f1: 0.050, acc: 0.648
+2025-08-07 19:46:07: Epoch: 020 Train Loss: 0.630, ap: 0.610, auc: 0.703, f1: 0.550, acc: 0.650
+2025-08-07 19:46:07: Epoch: 020 Test results, ap: 0.221, auc: 0.430, f1: 0.136, acc: 0.648
+2025-08-07 19:46:08: Epoch: 021 Train Loss: 0.603, ap: 0.615, auc: 0.702, f1: 0.552, acc: 0.621
+2025-08-07 19:46:08: Epoch: 021 Test results, ap: 0.210, auc: 0.409, f1: 0.286, acc: 0.583
+2025-08-07 19:46:08: Epoch: 022 Train Loss: 0.594, ap: 0.682, auc: 0.710, f1: 0.598, acc: 0.660
+2025-08-07 19:46:08: Epoch: 022 New best VALIDATION metrics
+2025-08-07 19:46:08: Epoch: 022 Test results, ap: 0.263, auc: 0.414, f1: 0.143, acc: 0.667
+2025-08-07 19:46:09: Epoch: 023 Train Loss: 0.549, ap: 0.687, auc: 0.737, f1: 0.593, acc: 0.680
+2025-08-07 19:46:09: Epoch: 023 New best VALIDATION metrics
+2025-08-07 19:46:09: Epoch: 023 Test results, ap: 0.237, auc: 0.425, f1: 0.146, acc: 0.676
+2025-08-07 19:46:09: Epoch: 024 Train Loss: 0.527, ap: 0.738, auc: 0.740, f1: 0.639, acc: 0.660
+2025-08-07 19:46:09: Epoch: 024 New best VALIDATION metrics
+2025-08-07 19:46:09: Epoch: 024 Test results, ap: 0.227, auc: 0.430, f1: 0.186, acc: 0.676
+2025-08-07 19:46:10: Epoch: 025 Train Loss: 0.504, ap: 0.709, auc: 0.753, f1: 0.635, acc: 0.699
+2025-08-07 19:46:10: Epoch: 025 Test results, ap: 0.227, auc: 0.470, f1: 0.186, acc: 0.676
+2025-08-07 19:46:10: Epoch: 025 New best TEST metrics
+2025-08-07 19:46:10: Epoch: 026 Train Loss: 0.475, ap: 0.752, auc: 0.759, f1: 0.644, acc: 0.689
+2025-08-07 19:46:10: Epoch: 026 New best VALIDATION metrics
+2025-08-07 19:46:10: Epoch: 026 Test results, ap: 0.225, auc: 0.469, f1: 0.186, acc: 0.676
+2025-08-07 19:46:11: Epoch: 027 Train Loss: 0.469, ap: 0.749, auc: 0.756, f1: 0.680, acc: 0.699
+2025-08-07 19:46:11: Epoch: 027 Test results, ap: 0.211, auc: 0.419, f1: 0.145, acc: 0.565
+2025-08-07 19:46:11: Epoch: 028 Train Loss: 0.461, ap: 0.729, auc: 0.758, f1: 0.627, acc: 0.699
+2025-08-07 19:46:11: Epoch: 028 Test results, ap: 0.200, auc: 0.394, f1: 0.182, acc: 0.667
+2025-08-07 19:46:12: Epoch: 029 Train Loss: 0.434, ap: 0.778, auc: 0.792, f1: 0.681, acc: 0.718
+2025-08-07 19:46:12: Epoch: 029 New best VALIDATION metrics
+2025-08-07 19:46:12: Epoch: 029 Test results, ap: 0.212, auc: 0.435, f1: 0.186, acc: 0.676
+2025-08-07 19:46:12: Epoch: 030 Train Loss: 0.435, ap: 0.748, auc: 0.779, f1: 0.682, acc: 0.738
+2025-08-07 19:46:12: Epoch: 030 Test results, ap: 0.205, auc: 0.416, f1: 0.186, acc: 0.676
+2025-08-07 19:46:13: Epoch: 031 Train Loss: 0.393, ap: 0.757, auc: 0.786, f1: 0.674, acc: 0.718
+2025-08-07 19:46:13: Epoch: 031 Test results, ap: 0.205, auc: 0.415, f1: 0.178, acc: 0.657
+2025-08-07 19:46:13: Epoch: 032 Train Loss: 0.395, ap: 0.743, auc: 0.778, f1: 0.674, acc: 0.718
+2025-08-07 19:46:13: Epoch: 032 Test results, ap: 0.204, auc: 0.409, f1: 0.145, acc: 0.565
+2025-08-07 19:46:14: Epoch: 033 Train Loss: 0.385, ap: 0.767, auc: 0.795, f1: 0.697, acc: 0.738
+2025-08-07 19:46:14: Epoch: 033 Test results, ap: 0.207, auc: 0.421, f1: 0.182, acc: 0.667
+2025-08-07 19:46:14: Epoch: 034 Train Loss: 0.392, ap: 0.730, auc: 0.777, f1: 0.667, acc: 0.718
+2025-08-07 19:46:14: Epoch: 034 Test results, ap: 0.203, auc: 0.407, f1: 0.145, acc: 0.565
+2025-08-07 19:46:15: Epoch: 035 Train Loss: 0.399, ap: 0.738, auc: 0.776, f1: 0.659, acc: 0.709
+2025-08-07 19:46:15: Epoch: 035 Test results, ap: 0.207, auc: 0.420, f1: 0.178, acc: 0.657
+2025-08-07 19:46:15: Epoch: 036 Train Loss: 0.373, ap: 0.759, auc: 0.792, f1: 0.682, acc: 0.738
+2025-08-07 19:46:15: Epoch: 036 Test results, ap: 0.207, auc: 0.420, f1: 0.186, acc: 0.676
+2025-08-07 19:46:16: Epoch: 037 Train Loss: 0.375, ap: 0.773, auc: 0.787, f1: 0.659, acc: 0.699
+2025-08-07 19:46:16: Epoch: 037 Test results, ap: 0.202, auc: 0.401, f1: 0.143, acc: 0.556
+2025-08-07 19:46:16: Epoch: 038 Train Loss: 0.344, ap: 0.751, auc: 0.785, f1: 0.682, acc: 0.738
+2025-08-07 19:46:16: Epoch: 038 Test results, ap: 0.208, auc: 0.424, f1: 0.182, acc: 0.667
+2025-08-07 19:46:16: Epoch: 039 Train Loss: 0.392, ap: 0.751, auc: 0.788, f1: 0.667, acc: 0.718
+2025-08-07 19:46:17: Epoch: 039 Test results, ap: 0.202, auc: 0.402, f1: 0.140, acc: 0.546
+2025-08-07 19:46:17: Epoch: 040 Train Loss: 0.375, ap: 0.776, auc: 0.783, f1: 0.674, acc: 0.718
+2025-08-07 19:46:17: Epoch: 040 Test results, ap: 0.205, auc: 0.414, f1: 0.186, acc: 0.676
+2025-08-07 19:46:17: Epoch: 041 Train Loss: 0.354, ap: 0.759, auc: 0.779, f1: 0.674, acc: 0.709
+2025-08-07 19:46:17: Epoch: 041 Test results, ap: 0.200, auc: 0.393, f1: 0.143, acc: 0.556
+2025-08-07 19:46:18: Epoch: 042 Train Loss: 0.358, ap: 0.746, auc: 0.779, f1: 0.690, acc: 0.738
+2025-08-07 19:46:18: Epoch: 042 Test results, ap: 0.203, auc: 0.406, f1: 0.148, acc: 0.574
+2025-08-07 19:46:18: Epoch: 043 Train Loss: 0.354, ap: 0.750, auc: 0.783, f1: 0.690, acc: 0.738
+2025-08-07 19:46:18: Epoch: 043 Test results, ap: 0.205, auc: 0.416, f1: 0.151, acc: 0.583
+2025-08-07 19:46:19: Epoch: 044 Train Loss: 0.331, ap: 0.755, auc: 0.782, f1: 0.697, acc: 0.738
+2025-08-07 19:46:19: Epoch: 044 Test results, ap: 0.203, auc: 0.407, f1: 0.143, acc: 0.556
+2025-08-07 19:46:19: Epoch: 045 Train Loss: 0.349, ap: 0.749, auc: 0.781, f1: 0.705, acc: 0.748
+2025-08-07 19:46:19: Epoch: 045 Test results, ap: 0.204, auc: 0.409, f1: 0.143, acc: 0.556
+2025-08-07 19:46:20: Epoch: 046 Train Loss: 0.343, ap: 0.745, auc: 0.783, f1: 0.713, acc: 0.757
+2025-08-07 19:46:20: Epoch: 046 Test results, ap: 0.205, auc: 0.415, f1: 0.143, acc: 0.556
+2025-08-07 19:46:20: Epoch: 047 Train Loss: 0.351, ap: 0.743, auc: 0.783, f1: 0.713, acc: 0.757
+2025-08-07 19:46:20: Epoch: 047 Test results, ap: 0.206, auc: 0.417, f1: 0.148, acc: 0.574
+2025-08-07 19:46:21: Epoch: 048 Train Loss: 0.333, ap: 0.754, auc: 0.784, f1: 0.697, acc: 0.738
+2025-08-07 19:46:21: Epoch: 048 Test results, ap: 0.205, auc: 0.414, f1: 0.143, acc: 0.556
+2025-08-07 19:46:21: Epoch: 049 Train Loss: 0.342, ap: 0.740, auc: 0.781, f1: 0.705, acc: 0.748
+2025-08-07 19:46:21: Epoch: 049 Test results, ap: 0.206, auc: 0.417, f1: 0.143, acc: 0.556
+2025-08-07 19:46:22: Epoch: 050 Train Loss: 0.345, ap: 0.750, auc: 0.793, f1: 0.713, acc: 0.757
+2025-08-07 19:46:22: Epoch: 050 Test results, ap: 0.208, auc: 0.424, f1: 0.182, acc: 0.667
+2025-08-07 19:46:22: used time 0.01h
+2025-08-07 19:46:22: Running Cross Validation 4
+2025-08-07 19:46:22: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:46:22: Epoch: 001 Train Loss: 0.695, ap: 0.452, auc: 0.567, f1: 0.569, acc: 0.398
+2025-08-07 19:46:22: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:46:22: Epoch: 001 Test results, ap: 0.305, auc: 0.621, f1: 0.388, acc: 0.241
+2025-08-07 19:46:23: Epoch: 002 Train Loss: 0.693, ap: 0.505, auc: 0.634, f1: 0.576, acc: 0.456
+2025-08-07 19:46:23: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:46:23: Epoch: 002 Test results, ap: 0.296, auc: 0.608, f1: 0.388, acc: 0.241
+2025-08-07 19:46:23: Epoch: 003 Train Loss: 0.693, ap: 0.578, auc: 0.668, f1: 0.000, acc: 0.602
+2025-08-07 19:46:23: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:46:23: Epoch: 003 Test results, ap: 0.219, auc: 0.462, f1: 0.000, acc: 0.759
+2025-08-07 19:46:24: Epoch: 004 Train Loss: 0.692, ap: 0.328, auc: 0.354, f1: 0.109, acc: 0.524
+2025-08-07 19:46:24: Epoch: 004 Test results, ap: 0.284, auc: 0.564, f1: 0.000, acc: 0.759
+2025-08-07 19:46:24: Epoch: 005 Train Loss: 0.691, ap: 0.399, auc: 0.498, f1: 0.569, acc: 0.398
+2025-08-07 19:46:24: Epoch: 005 Test results, ap: 0.190, auc: 0.376, f1: 0.388, acc: 0.241
+2025-08-07 19:46:25: Epoch: 006 Train Loss: 0.691, ap: 0.531, auc: 0.702, f1: 0.567, acc: 0.437
+2025-08-07 19:46:25: Epoch: 006 Test results, ap: 0.261, auc: 0.561, f1: 0.388, acc: 0.241
+2025-08-07 19:46:25: Epoch: 007 Train Loss: 0.690, ap: 0.535, auc: 0.700, f1: 0.597, acc: 0.699
+2025-08-07 19:46:25: Epoch: 007 Test results, ap: 0.242, auc: 0.509, f1: 0.000, acc: 0.694
+2025-08-07 19:46:25: Epoch: 008 Train Loss: 0.689, ap: 0.539, auc: 0.666, f1: 0.560, acc: 0.680
+2025-08-07 19:46:25: Epoch: 008 Test results, ap: 0.205, auc: 0.390, f1: 0.000, acc: 0.667
+2025-08-07 19:46:26: Epoch: 009 Train Loss: 0.675, ap: 0.534, auc: 0.552, f1: 0.245, acc: 0.641
+2025-08-07 19:46:26: Epoch: 009 Test results, ap: 0.242, auc: 0.485, f1: 0.000, acc: 0.731
+2025-08-07 19:46:26: Epoch: 010 Train Loss: 0.694, ap: 0.442, auc: 0.561, f1: 0.000, acc: 0.602
+2025-08-07 19:46:26: Epoch: 010 Test results, ap: 0.194, auc: 0.365, f1: 0.000, acc: 0.759
+2025-08-07 19:46:27: Epoch: 011 Train Loss: 0.690, ap: 0.533, auc: 0.679, f1: 0.507, acc: 0.660
+2025-08-07 19:46:27: Epoch: 011 Test results, ap: 0.220, auc: 0.483, f1: 0.000, acc: 0.694
+2025-08-07 19:46:27: Epoch: 011 New best TEST metrics
+2025-08-07 19:46:27: Epoch: 012 Train Loss: 0.678, ap: 0.620, auc: 0.714, f1: 0.553, acc: 0.670
+2025-08-07 19:46:27: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:46:27: Epoch: 012 Test results, ap: 0.217, auc: 0.445, f1: 0.247, acc: 0.491
+2025-08-07 19:46:28: Epoch: 013 Train Loss: 0.640, ap: 0.675, auc: 0.731, f1: 0.600, acc: 0.650
+2025-08-07 19:46:28: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:46:28: Epoch: 013 Test results, ap: 0.218, auc: 0.455, f1: 0.313, acc: 0.472
+2025-08-07 19:46:28: Epoch: 014 Train Loss: 0.637, ap: 0.619, auc: 0.637, f1: 0.507, acc: 0.680
+2025-08-07 19:46:28: Epoch: 014 Test results, ap: 0.245, auc: 0.467, f1: 0.305, acc: 0.620
+2025-08-07 19:46:28: Epoch: 014 New best TEST metrics
+2025-08-07 19:46:29: Epoch: 015 Train Loss: 0.605, ap: 0.633, auc: 0.667, f1: 0.587, acc: 0.699
+2025-08-07 19:46:29: Epoch: 015 Test results, ap: 0.230, auc: 0.423, f1: 0.257, acc: 0.519
+2025-08-07 19:46:29: Epoch: 016 Train Loss: 0.566, ap: 0.669, auc: 0.682, f1: 0.587, acc: 0.699
+2025-08-07 19:46:29: Epoch: 016 Test results, ap: 0.230, auc: 0.426, f1: 0.259, acc: 0.630
+2025-08-07 19:46:30: Epoch: 017 Train Loss: 0.600, ap: 0.582, auc: 0.643, f1: 0.579, acc: 0.689
+2025-08-07 19:46:30: Epoch: 017 Test results, ap: 0.223, auc: 0.419, f1: 0.222, acc: 0.676
+2025-08-07 19:46:30: Epoch: 018 Train Loss: 0.583, ap: 0.668, auc: 0.699, f1: 0.564, acc: 0.670
+2025-08-07 19:46:30: Epoch: 018 Test results, ap: 0.239, auc: 0.468, f1: 0.269, acc: 0.546
+2025-08-07 19:46:31: Epoch: 019 Train Loss: 0.553, ap: 0.650, auc: 0.665, f1: 0.600, acc: 0.689
+2025-08-07 19:46:31: Epoch: 019 Test results, ap: 0.222, auc: 0.422, f1: 0.192, acc: 0.611
+2025-08-07 19:46:31: Epoch: 020 Train Loss: 0.542, ap: 0.683, auc: 0.699, f1: 0.611, acc: 0.728
+2025-08-07 19:46:31: Epoch: 020 Test results, ap: 0.221, auc: 0.419, f1: 0.255, acc: 0.620
+2025-08-07 19:46:31: Epoch: 021 Train Loss: 0.533, ap: 0.691, auc: 0.700, f1: 0.580, acc: 0.718
+2025-08-07 19:46:31: Epoch: 021 Test results, ap: 0.232, auc: 0.437, f1: 0.255, acc: 0.676
+2025-08-07 19:46:32: Epoch: 022 Train Loss: 0.494, ap: 0.701, auc: 0.707, f1: 0.567, acc: 0.718
+2025-08-07 19:46:32: Epoch: 022 New best VALIDATION metrics
+2025-08-07 19:46:32: Epoch: 022 Test results, ap: 0.236, auc: 0.432, f1: 0.245, acc: 0.657
+2025-08-07 19:46:32: Epoch: 023 Train Loss: 0.476, ap: 0.700, auc: 0.718, f1: 0.580, acc: 0.718
+2025-08-07 19:46:32: Epoch: 023 New best VALIDATION metrics
+2025-08-07 19:46:32: Epoch: 023 Test results, ap: 0.238, auc: 0.436, f1: 0.130, acc: 0.630
+2025-08-07 19:46:33: Epoch: 024 Train Loss: 0.477, ap: 0.684, auc: 0.692, f1: 0.600, acc: 0.728
+2025-08-07 19:46:33: Epoch: 024 Test results, ap: 0.240, auc: 0.441, f1: 0.130, acc: 0.630
+2025-08-07 19:46:33: Epoch: 025 Train Loss: 0.472, ap: 0.664, auc: 0.669, f1: 0.597, acc: 0.738
+2025-08-07 19:46:33: Epoch: 025 Test results, ap: 0.254, auc: 0.437, f1: 0.154, acc: 0.694
+2025-08-07 19:46:34: Epoch: 026 Train Loss: 0.450, ap: 0.676, auc: 0.683, f1: 0.630, acc: 0.738
+2025-08-07 19:46:34: Epoch: 026 Test results, ap: 0.227, auc: 0.426, f1: 0.130, acc: 0.630
+2025-08-07 19:46:34: Epoch: 027 Train Loss: 0.453, ap: 0.688, auc: 0.708, f1: 0.622, acc: 0.728
+2025-08-07 19:46:34: Epoch: 027 Test results, ap: 0.228, auc: 0.431, f1: 0.133, acc: 0.639
+2025-08-07 19:46:35: Epoch: 028 Train Loss: 0.491, ap: 0.663, auc: 0.660, f1: 0.554, acc: 0.718
+2025-08-07 19:46:35: Epoch: 028 Test results, ap: 0.237, auc: 0.433, f1: 0.130, acc: 0.630
+2025-08-07 19:46:35: Epoch: 029 Train Loss: 0.479, ap: 0.669, auc: 0.677, f1: 0.629, acc: 0.748
+2025-08-07 19:46:35: Epoch: 029 Test results, ap: 0.238, auc: 0.435, f1: 0.136, acc: 0.648
+2025-08-07 19:46:35: Epoch: 030 Train Loss: 0.429, ap: 0.681, auc: 0.693, f1: 0.571, acc: 0.709
+2025-08-07 19:46:36: Epoch: 030 Test results, ap: 0.226, auc: 0.422, f1: 0.130, acc: 0.630
+2025-08-07 19:46:36: Epoch: 031 Train Loss: 0.434, ap: 0.682, auc: 0.690, f1: 0.592, acc: 0.718
+2025-08-07 19:46:36: Epoch: 031 Test results, ap: 0.228, auc: 0.428, f1: 0.130, acc: 0.630
+2025-08-07 19:46:36: Epoch: 032 Train Loss: 0.407, ap: 0.692, auc: 0.703, f1: 0.600, acc: 0.728
+2025-08-07 19:46:37: Epoch: 032 Test results, ap: 0.242, auc: 0.435, f1: 0.143, acc: 0.667
+2025-08-07 19:46:37: Epoch: 033 Train Loss: 0.413, ap: 0.679, auc: 0.686, f1: 0.571, acc: 0.709
+2025-08-07 19:46:37: Epoch: 033 Test results, ap: 0.237, auc: 0.441, f1: 0.121, acc: 0.731
+2025-08-07 19:46:37: Epoch: 034 Train Loss: 0.402, ap: 0.681, auc: 0.692, f1: 0.580, acc: 0.718
+2025-08-07 19:46:38: Epoch: 034 Test results, ap: 0.233, auc: 0.438, f1: 0.171, acc: 0.731
+2025-08-07 19:46:38: Epoch: 035 Train Loss: 0.405, ap: 0.689, auc: 0.701, f1: 0.600, acc: 0.728
+2025-08-07 19:46:38: Epoch: 035 Test results, ap: 0.232, auc: 0.432, f1: 0.140, acc: 0.657
+2025-08-07 19:46:38: Epoch: 036 Train Loss: 0.384, ap: 0.658, auc: 0.686, f1: 0.571, acc: 0.709
+2025-08-07 19:46:38: Epoch: 036 Test results, ap: 0.228, auc: 0.439, f1: 0.167, acc: 0.722
+2025-08-07 19:46:39: Epoch: 037 Train Loss: 0.394, ap: 0.640, auc: 0.677, f1: 0.571, acc: 0.709
+2025-08-07 19:46:39: Epoch: 037 Test results, ap: 0.229, auc: 0.439, f1: 0.114, acc: 0.713
+2025-08-07 19:46:39: Epoch: 038 Train Loss: 0.394, ap: 0.650, auc: 0.691, f1: 0.571, acc: 0.709
+2025-08-07 19:46:39: Epoch: 038 Test results, ap: 0.237, auc: 0.440, f1: 0.121, acc: 0.731
+2025-08-07 19:46:40: Epoch: 039 Train Loss: 0.372, ap: 0.623, auc: 0.659, f1: 0.580, acc: 0.718
+2025-08-07 19:46:40: Epoch: 039 Test results, ap: 0.228, auc: 0.437, f1: 0.000, acc: 0.713
+2025-08-07 19:46:40: Epoch: 040 Train Loss: 0.367, ap: 0.646, auc: 0.685, f1: 0.611, acc: 0.728
+2025-08-07 19:46:40: Epoch: 040 Test results, ap: 0.231, auc: 0.434, f1: 0.162, acc: 0.713
+2025-08-07 19:46:41: Epoch: 041 Train Loss: 0.375, ap: 0.639, auc: 0.688, f1: 0.592, acc: 0.718
+2025-08-07 19:46:41: Epoch: 041 Test results, ap: 0.231, auc: 0.438, f1: 0.114, acc: 0.713
+2025-08-07 19:46:41: Epoch: 042 Train Loss: 0.372, ap: 0.644, auc: 0.688, f1: 0.592, acc: 0.718
+2025-08-07 19:46:41: Epoch: 042 Test results, ap: 0.231, auc: 0.440, f1: 0.062, acc: 0.722
+2025-08-07 19:46:42: Epoch: 043 Train Loss: 0.379, ap: 0.641, auc: 0.672, f1: 0.559, acc: 0.709
+2025-08-07 19:46:42: Epoch: 043 Test results, ap: 0.231, auc: 0.436, f1: 0.000, acc: 0.722
+2025-08-07 19:46:42: Epoch: 044 Train Loss: 0.366, ap: 0.642, auc: 0.681, f1: 0.571, acc: 0.709
+2025-08-07 19:46:42: Epoch: 044 Test results, ap: 0.230, auc: 0.430, f1: 0.000, acc: 0.722
+2025-08-07 19:46:43: Epoch: 045 Train Loss: 0.368, ap: 0.625, auc: 0.677, f1: 0.571, acc: 0.709
+2025-08-07 19:46:43: Epoch: 045 Test results, ap: 0.229, auc: 0.431, f1: 0.121, acc: 0.731
+2025-08-07 19:46:43: Epoch: 046 Train Loss: 0.362, ap: 0.622, auc: 0.672, f1: 0.559, acc: 0.709
+2025-08-07 19:46:43: Epoch: 046 Test results, ap: 0.229, auc: 0.432, f1: 0.062, acc: 0.722
+2025-08-07 19:46:43: Epoch: 047 Train Loss: 0.362, ap: 0.624, auc: 0.672, f1: 0.580, acc: 0.718
+2025-08-07 19:46:44: Epoch: 047 Test results, ap: 0.225, auc: 0.428, f1: 0.000, acc: 0.713
+2025-08-07 19:46:44: Epoch: 048 Train Loss: 0.364, ap: 0.637, auc: 0.695, f1: 0.551, acc: 0.699
+2025-08-07 19:46:44: Epoch: 048 Test results, ap: 0.224, auc: 0.424, f1: 0.000, acc: 0.722
+2025-08-07 19:46:44: Epoch: 049 Train Loss: 0.353, ap: 0.624, auc: 0.675, f1: 0.559, acc: 0.709
+2025-08-07 19:46:44: Epoch: 049 Test results, ap: 0.228, auc: 0.428, f1: 0.000, acc: 0.722
+2025-08-07 19:46:45: Epoch: 050 Train Loss: 0.339, ap: 0.631, auc: 0.682, f1: 0.537, acc: 0.699
+2025-08-07 19:46:45: Epoch: 050 Test results, ap: 0.227, auc: 0.427, f1: 0.000, acc: 0.731
+2025-08-07 19:46:45: used time 0.01h
+2025-08-07 19:46:45: Cross Validation Finished!
+2025-08-07 19:46:45: Best validation perform list
+[[0.74054968 0.78419453 0.66666669 0.71844661]
+ [0.57669091 0.65397215 0.44776121 0.64077669]
+ [0.80416226 0.79411769 0.63736266 0.67961162]
+ [0.77799666 0.79234976 0.6813187  0.71844661]
+ [0.70043141 0.71754527 0.57971013 0.71844661]]
+2025-08-07 19:46:45: mean: [0.72  0.748 0.603 0.695]
+2025-08-07 19:46:45: std: [0.08  0.055 0.085 0.031]
+2025-08-07 19:46:45: Best test perform list
+[[0.47133482 0.66135085 0.41509435 0.42592594]
+ [0.37490061 0.57270169 0.38554215 0.52777779]
+ [0.30861205 0.52392119 0.24242425 0.76851851]
+ [0.22747995 0.47045028 0.18604651 0.67592591]
+ [0.24485071 0.46716699 0.30508474 0.62037039]]
+2025-08-07 19:46:45: mean: [0.325 0.539 0.307 0.604]
+2025-08-07 19:46:45: std: [0.09  0.072 0.086 0.118]
diff --git a/run-cls-simple/gru-mlp-256-simple-oneway-ce-32-0.001-50/config.json b/run-cls-simple/gru-mlp-256-simple-oneway-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
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\ No newline at end of file
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@@ -0,0 +1,6 @@
+Valid
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+0.09803023422480489,0.06664112806244168,0.22568371852865815,0.048504845239488274
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diff --git a/run-cls-simple/gru-mlp-256-simple-oneway-ce-32-0.001-50/training.log b/run-cls-simple/gru-mlp-256-simple-oneway-ce-32-0.001-50/training.log
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+2025-08-07 19:40:40: saving_dir: ./run-cls/gru-mlp-256-simple-oneway-ce-32-0.001-50
+2025-08-07 19:40:40: Loading Training Dataset
+2025-08-07 19:40:40: Loading Test Dataset
+2025-08-07 19:40:41: Running Cross Validation 0
+2025-08-07 19:40:41: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:40:41: Epoch: 001 Train Loss: 0.694, ap: 0.528, auc: 0.538, f1: 0.000, acc: 0.544
+2025-08-07 19:40:41: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:40:41: Epoch: 001 Test results, ap: 0.277, auc: 0.559, f1: 0.000, acc: 0.759
+2025-08-07 19:40:42: Epoch: 002 Train Loss: 0.694, ap: 0.537, auc: 0.544, f1: 0.627, acc: 0.456
+2025-08-07 19:40:42: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:40:42: Epoch: 002 Test results, ap: 0.360, auc: 0.623, f1: 0.388, acc: 0.241
+2025-08-07 19:40:42: Epoch: 003 Train Loss: 0.694, ap: 0.469, auc: 0.520, f1: 0.000, acc: 0.544
+2025-08-07 19:40:42: Epoch: 003 Test results, ap: 0.444, auc: 0.607, f1: 0.000, acc: 0.759
+2025-08-07 19:40:43: Epoch: 004 Train Loss: 0.693, ap: 0.513, auc: 0.504, f1: 0.627, acc: 0.456
+2025-08-07 19:40:43: Epoch: 004 Test results, ap: 0.411, auc: 0.561, f1: 0.388, acc: 0.241
+2025-08-07 19:40:43: Epoch: 005 Train Loss: 0.693, ap: 0.469, auc: 0.526, f1: 0.000, acc: 0.544
+2025-08-07 19:40:43: Epoch: 005 Test results, ap: 0.440, auc: 0.606, f1: 0.000, acc: 0.759
+2025-08-07 19:40:44: Epoch: 006 Train Loss: 0.691, ap: 0.462, auc: 0.514, f1: 0.000, acc: 0.544
+2025-08-07 19:40:44: Epoch: 006 Test results, ap: 0.475, auc: 0.625, f1: 0.000, acc: 0.759
+2025-08-07 19:40:44: Epoch: 007 Train Loss: 0.695, ap: 0.449, auc: 0.486, f1: 0.440, acc: 0.456
+2025-08-07 19:40:44: Epoch: 007 Test results, ap: 0.494, auc: 0.588, f1: 0.371, acc: 0.278
+2025-08-07 19:40:44: Epoch: 008 Train Loss: 0.694, ap: 0.468, auc: 0.501, f1: 0.627, acc: 0.456
+2025-08-07 19:40:45: Epoch: 008 Test results, ap: 0.244, auc: 0.499, f1: 0.388, acc: 0.241
+2025-08-07 19:40:45: Epoch: 009 Train Loss: 0.691, ap: 0.541, auc: 0.573, f1: 0.629, acc: 0.553
+2025-08-07 19:40:45: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:40:45: Epoch: 009 Test results, ap: 0.238, auc: 0.499, f1: 0.328, acc: 0.278
+2025-08-07 19:40:45: Epoch: 010 Train Loss: 0.688, ap: 0.528, auc: 0.596, f1: 0.529, acc: 0.534
+2025-08-07 19:40:45: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:40:46: Epoch: 010 Test results, ap: 0.426, auc: 0.611, f1: 0.388, acc: 0.241
+2025-08-07 19:40:46: Epoch: 011 Train Loss: 0.694, ap: 0.508, auc: 0.591, f1: 0.579, acc: 0.563
+2025-08-07 19:40:46: Epoch: 011 Test results, ap: 0.421, auc: 0.644, f1: 0.388, acc: 0.241
+2025-08-07 19:40:46: Epoch: 011 New best TEST metrics
+2025-08-07 19:40:46: Epoch: 012 Train Loss: 0.693, ap: 0.513, auc: 0.595, f1: 0.613, acc: 0.583
+2025-08-07 19:40:46: Epoch: 012 Test results, ap: 0.460, auc: 0.629, f1: 0.388, acc: 0.241
+2025-08-07 19:40:46: Epoch: 012 New best TEST metrics
+2025-08-07 19:40:47: Epoch: 013 Train Loss: 0.684, ap: 0.560, auc: 0.604, f1: 0.613, acc: 0.583
+2025-08-07 19:40:47: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:40:47: Epoch: 013 Test results, ap: 0.361, auc: 0.630, f1: 0.388, acc: 0.241
+2025-08-07 19:40:47: Epoch: 014 Train Loss: 0.685, ap: 0.598, auc: 0.609, f1: 0.600, acc: 0.573
+2025-08-07 19:40:47: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:40:48: Epoch: 014 Test results, ap: 0.265, auc: 0.548, f1: 0.388, acc: 0.241
+2025-08-07 19:40:48: Epoch: 015 Train Loss: 0.685, ap: 0.586, auc: 0.605, f1: 0.613, acc: 0.583
+2025-08-07 19:40:48: Epoch: 015 Test results, ap: 0.237, auc: 0.520, f1: 0.388, acc: 0.241
+2025-08-07 19:40:48: Epoch: 016 Train Loss: 0.687, ap: 0.594, auc: 0.630, f1: 0.494, acc: 0.563
+2025-08-07 19:40:48: Epoch: 016 New best VALIDATION metrics
+2025-08-07 19:40:48: Epoch: 016 Test results, ap: 0.238, auc: 0.527, f1: 0.388, acc: 0.241
+2025-08-07 19:40:49: Epoch: 017 Train Loss: 0.696, ap: 0.622, auc: 0.634, f1: 0.120, acc: 0.573
+2025-08-07 19:40:49: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:40:49: Epoch: 017 Test results, ap: 0.230, auc: 0.508, f1: 0.388, acc: 0.241
+2025-08-07 19:40:49: Epoch: 018 Train Loss: 0.692, ap: 0.515, auc: 0.594, f1: 0.617, acc: 0.447
+2025-08-07 19:40:49: Epoch: 018 Test results, ap: 0.202, auc: 0.348, f1: 0.391, acc: 0.250
+2025-08-07 19:40:50: Epoch: 019 Train Loss: 0.694, ap: 0.470, auc: 0.520, f1: 0.042, acc: 0.553
+2025-08-07 19:40:50: Epoch: 019 Test results, ap: 0.239, auc: 0.530, f1: 0.000, acc: 0.750
+2025-08-07 19:40:50: Epoch: 020 Train Loss: 0.692, ap: 0.485, auc: 0.560, f1: 0.617, acc: 0.447
+2025-08-07 19:40:50: Epoch: 020 Test results, ap: 0.281, auc: 0.551, f1: 0.388, acc: 0.241
+2025-08-07 19:40:51: Epoch: 021 Train Loss: 0.690, ap: 0.491, auc: 0.567, f1: 0.617, acc: 0.447
+2025-08-07 19:40:51: Epoch: 021 Test results, ap: 0.273, auc: 0.545, f1: 0.388, acc: 0.241
+2025-08-07 19:40:51: Epoch: 022 Train Loss: 0.693, ap: 0.465, auc: 0.532, f1: 0.608, acc: 0.437
+2025-08-07 19:40:51: Epoch: 022 Test results, ap: 0.265, auc: 0.540, f1: 0.388, acc: 0.241
+2025-08-07 19:40:52: Epoch: 023 Train Loss: 0.692, ap: 0.471, auc: 0.542, f1: 0.608, acc: 0.437
+2025-08-07 19:40:52: Epoch: 023 Test results, ap: 0.268, auc: 0.547, f1: 0.388, acc: 0.241
+2025-08-07 19:40:52: Epoch: 024 Train Loss: 0.694, ap: 0.485, auc: 0.562, f1: 0.617, acc: 0.447
+2025-08-07 19:40:52: Epoch: 024 Test results, ap: 0.272, auc: 0.549, f1: 0.388, acc: 0.241
+2025-08-07 19:40:52: Epoch: 025 Train Loss: 0.690, ap: 0.489, auc: 0.566, f1: 0.617, acc: 0.447
+2025-08-07 19:40:53: Epoch: 025 Test results, ap: 0.272, auc: 0.548, f1: 0.388, acc: 0.241
+2025-08-07 19:40:53: Epoch: 026 Train Loss: 0.693, ap: 0.496, auc: 0.581, f1: 0.000, acc: 0.544
+2025-08-07 19:40:53: Epoch: 026 Test results, ap: 0.270, auc: 0.548, f1: 0.000, acc: 0.759
+2025-08-07 19:40:53: Epoch: 027 Train Loss: 0.691, ap: 0.491, auc: 0.582, f1: 0.000, acc: 0.544
+2025-08-07 19:40:53: Epoch: 027 Test results, ap: 0.269, auc: 0.543, f1: 0.000, acc: 0.759
+2025-08-07 19:40:54: Epoch: 028 Train Loss: 0.691, ap: 0.490, auc: 0.585, f1: 0.000, acc: 0.544
+2025-08-07 19:40:54: Epoch: 028 Test results, ap: 0.266, auc: 0.540, f1: 0.000, acc: 0.759
+2025-08-07 19:40:54: Epoch: 029 Train Loss: 0.692, ap: 0.493, auc: 0.594, f1: 0.000, acc: 0.544
+2025-08-07 19:40:54: Epoch: 029 Test results, ap: 0.262, auc: 0.537, f1: 0.000, acc: 0.759
+2025-08-07 19:40:55: Epoch: 030 Train Loss: 0.691, ap: 0.493, auc: 0.593, f1: 0.629, acc: 0.485
+2025-08-07 19:40:55: Epoch: 030 Test results, ap: 0.262, auc: 0.537, f1: 0.388, acc: 0.241
+2025-08-07 19:40:55: Epoch: 031 Train Loss: 0.691, ap: 0.493, auc: 0.595, f1: 0.617, acc: 0.447
+2025-08-07 19:40:55: Epoch: 031 Test results, ap: 0.264, auc: 0.538, f1: 0.388, acc: 0.241
+2025-08-07 19:40:56: Epoch: 032 Train Loss: 0.691, ap: 0.499, auc: 0.603, f1: 0.000, acc: 0.544
+2025-08-07 19:40:56: Epoch: 032 Test results, ap: 0.261, auc: 0.540, f1: 0.000, acc: 0.759
+2025-08-07 19:40:56: Epoch: 033 Train Loss: 0.691, ap: 0.503, auc: 0.606, f1: 0.000, acc: 0.544
+2025-08-07 19:40:56: Epoch: 033 Test results, ap: 0.260, auc: 0.539, f1: 0.000, acc: 0.759
+2025-08-07 19:40:57: Epoch: 034 Train Loss: 0.690, ap: 0.501, auc: 0.602, f1: 0.000, acc: 0.544
+2025-08-07 19:40:57: Epoch: 034 Test results, ap: 0.257, auc: 0.538, f1: 0.000, acc: 0.759
+2025-08-07 19:40:57: Epoch: 035 Train Loss: 0.691, ap: 0.503, auc: 0.605, f1: 0.000, acc: 0.534
+2025-08-07 19:40:57: Epoch: 035 Test results, ap: 0.255, auc: 0.536, f1: 0.000, acc: 0.759
+2025-08-07 19:40:58: Epoch: 036 Train Loss: 0.690, ap: 0.502, auc: 0.603, f1: 0.622, acc: 0.456
+2025-08-07 19:40:58: Epoch: 036 Test results, ap: 0.258, auc: 0.540, f1: 0.388, acc: 0.241
+2025-08-07 19:40:58: Epoch: 037 Train Loss: 0.689, ap: 0.499, auc: 0.598, f1: 0.617, acc: 0.447
+2025-08-07 19:40:58: Epoch: 037 Test results, ap: 0.255, auc: 0.537, f1: 0.388, acc: 0.241
+2025-08-07 19:40:58: Epoch: 038 Train Loss: 0.692, ap: 0.498, auc: 0.595, f1: 0.617, acc: 0.447
+2025-08-07 19:40:59: Epoch: 038 Test results, ap: 0.253, auc: 0.534, f1: 0.388, acc: 0.241
+2025-08-07 19:40:59: Epoch: 039 Train Loss: 0.690, ap: 0.499, auc: 0.597, f1: 0.107, acc: 0.515
+2025-08-07 19:40:59: Epoch: 039 Test results, ap: 0.251, auc: 0.534, f1: 0.000, acc: 0.759
+2025-08-07 19:40:59: Epoch: 040 Train Loss: 0.690, ap: 0.507, auc: 0.608, f1: 0.000, acc: 0.534
+2025-08-07 19:40:59: Epoch: 040 Test results, ap: 0.247, auc: 0.530, f1: 0.000, acc: 0.759
+2025-08-07 19:41:00: Epoch: 041 Train Loss: 0.691, ap: 0.501, auc: 0.599, f1: 0.000, acc: 0.534
+2025-08-07 19:41:00: Epoch: 041 Test results, ap: 0.251, auc: 0.538, f1: 0.000, acc: 0.759
+2025-08-07 19:41:00: Epoch: 042 Train Loss: 0.691, ap: 0.498, auc: 0.594, f1: 0.000, acc: 0.524
+2025-08-07 19:41:01: Epoch: 042 Test results, ap: 0.252, auc: 0.538, f1: 0.000, acc: 0.759
+2025-08-07 19:41:01: Epoch: 043 Train Loss: 0.692, ap: 0.497, auc: 0.593, f1: 0.140, acc: 0.524
+2025-08-07 19:41:01: Epoch: 043 Test results, ap: 0.251, auc: 0.537, f1: 0.133, acc: 0.639
+2025-08-07 19:41:01: Epoch: 044 Train Loss: 0.691, ap: 0.500, auc: 0.597, f1: 0.074, acc: 0.515
+2025-08-07 19:41:01: Epoch: 044 Test results, ap: 0.247, auc: 0.533, f1: 0.000, acc: 0.759
+2025-08-07 19:41:02: Epoch: 045 Train Loss: 0.691, ap: 0.501, auc: 0.598, f1: 0.074, acc: 0.515
+2025-08-07 19:41:02: Epoch: 045 Test results, ap: 0.247, auc: 0.535, f1: 0.000, acc: 0.759
+2025-08-07 19:41:02: Epoch: 046 Train Loss: 0.693, ap: 0.498, auc: 0.592, f1: 0.040, acc: 0.534
+2025-08-07 19:41:02: Epoch: 046 Test results, ap: 0.250, auc: 0.539, f1: 0.000, acc: 0.759
+2025-08-07 19:41:03: Epoch: 047 Train Loss: 0.690, ap: 0.516, auc: 0.595, f1: 0.000, acc: 0.544
+2025-08-07 19:41:03: Epoch: 047 Test results, ap: 0.247, auc: 0.538, f1: 0.000, acc: 0.759
+2025-08-07 19:41:03: Epoch: 048 Train Loss: 0.690, ap: 0.519, auc: 0.598, f1: 0.000, acc: 0.544
+2025-08-07 19:41:03: Epoch: 048 Test results, ap: 0.243, auc: 0.534, f1: 0.000, acc: 0.759
+2025-08-07 19:41:04: Epoch: 049 Train Loss: 0.690, ap: 0.519, auc: 0.598, f1: 0.000, acc: 0.544
+2025-08-07 19:41:04: Epoch: 049 Test results, ap: 0.246, auc: 0.538, f1: 0.000, acc: 0.759
+2025-08-07 19:41:04: Epoch: 050 Train Loss: 0.691, ap: 0.519, auc: 0.597, f1: 0.000, acc: 0.544
+2025-08-07 19:41:04: Epoch: 050 Test results, ap: 0.246, auc: 0.542, f1: 0.000, acc: 0.759
+2025-08-07 19:41:04: used time 0.01h
+2025-08-07 19:41:04: Running Cross Validation 1
+2025-08-07 19:41:04: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:41:05: Epoch: 001 Train Loss: 0.694, ap: 0.391, auc: 0.459, f1: 0.529, acc: 0.359
+2025-08-07 19:41:05: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:41:05: Epoch: 001 Test results, ap: 0.314, auc: 0.568, f1: 0.388, acc: 0.241
+2025-08-07 19:41:05: Epoch: 002 Train Loss: 0.694, ap: 0.398, auc: 0.462, f1: 0.000, acc: 0.641
+2025-08-07 19:41:05: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:41:05: Epoch: 002 Test results, ap: 0.347, auc: 0.579, f1: 0.000, acc: 0.759
+2025-08-07 19:41:06: Epoch: 003 Train Loss: 0.694, ap: 0.383, auc: 0.447, f1: 0.000, acc: 0.641
+2025-08-07 19:41:06: Epoch: 003 Test results, ap: 0.454, auc: 0.631, f1: 0.000, acc: 0.759
+2025-08-07 19:41:06: Epoch: 004 Train Loss: 0.692, ap: 0.366, auc: 0.436, f1: 0.529, acc: 0.379
+2025-08-07 19:41:06: Epoch: 004 Test results, ap: 0.426, auc: 0.623, f1: 0.388, acc: 0.241
+2025-08-07 19:41:06: Epoch: 005 Train Loss: 0.692, ap: 0.354, auc: 0.425, f1: 0.000, acc: 0.641
+2025-08-07 19:41:07: Epoch: 005 Test results, ap: 0.506, auc: 0.640, f1: 0.000, acc: 0.759
+2025-08-07 19:41:07: Epoch: 006 Train Loss: 0.694, ap: 0.345, auc: 0.424, f1: 0.532, acc: 0.369
+2025-08-07 19:41:07: Epoch: 006 Test results, ap: 0.504, auc: 0.620, f1: 0.388, acc: 0.241
+2025-08-07 19:41:07: Epoch: 007 Train Loss: 0.696, ap: 0.299, auc: 0.396, f1: 0.519, acc: 0.369
+2025-08-07 19:41:07: Epoch: 007 Test results, ap: 0.525, auc: 0.625, f1: 0.388, acc: 0.241
+2025-08-07 19:41:08: Epoch: 008 Train Loss: 0.692, ap: 0.331, auc: 0.459, f1: 0.538, acc: 0.417
+2025-08-07 19:41:08: Epoch: 008 Test results, ap: 0.375, auc: 0.560, f1: 0.388, acc: 0.241
+2025-08-07 19:41:08: Epoch: 009 Train Loss: 0.677, ap: 0.382, auc: 0.501, f1: 0.525, acc: 0.437
+2025-08-07 19:41:08: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:41:08: Epoch: 009 Test results, ap: 0.224, auc: 0.471, f1: 0.388, acc: 0.241
+2025-08-07 19:41:09: Epoch: 010 Train Loss: 0.700, ap: 0.462, auc: 0.552, f1: 0.000, acc: 0.641
+2025-08-07 19:41:09: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:41:09: Epoch: 010 Test results, ap: 0.212, auc: 0.451, f1: 0.000, acc: 0.759
+2025-08-07 19:41:09: Epoch: 011 Train Loss: 0.693, ap: 0.528, auc: 0.620, f1: 0.000, acc: 0.641
+2025-08-07 19:41:09: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:41:09: Epoch: 011 Test results, ap: 0.212, auc: 0.451, f1: 0.000, acc: 0.759
+2025-08-07 19:41:09: Epoch: 011 New best TEST metrics
+2025-08-07 19:41:10: Epoch: 012 Train Loss: 0.694, ap: 0.524, auc: 0.636, f1: 0.529, acc: 0.359
+2025-08-07 19:41:10: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:41:10: Epoch: 012 Test results, ap: 0.215, auc: 0.469, f1: 0.388, acc: 0.241
+2025-08-07 19:41:10: Epoch: 012 New best TEST metrics
+2025-08-07 19:41:10: Epoch: 013 Train Loss: 0.693, ap: 0.516, auc: 0.635, f1: 0.529, acc: 0.359
+2025-08-07 19:41:10: Epoch: 013 Test results, ap: 0.215, auc: 0.466, f1: 0.388, acc: 0.241
+2025-08-07 19:41:11: Epoch: 014 Train Loss: 0.692, ap: 0.520, auc: 0.640, f1: 0.537, acc: 0.631
+2025-08-07 19:41:11: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:41:11: Epoch: 014 Test results, ap: 0.216, auc: 0.469, f1: 0.388, acc: 0.241
+2025-08-07 19:41:11: Epoch: 014 New best TEST metrics
+2025-08-07 19:41:11: Epoch: 015 Train Loss: 0.691, ap: 0.511, auc: 0.640, f1: 0.000, acc: 0.641
+2025-08-07 19:41:11: Epoch: 015 Test results, ap: 0.218, auc: 0.476, f1: 0.000, acc: 0.759
+2025-08-07 19:41:11: Epoch: 015 New best TEST metrics
+2025-08-07 19:41:12: Epoch: 016 Train Loss: 0.692, ap: 0.472, auc: 0.635, f1: 0.518, acc: 0.350
+2025-08-07 19:41:12: Epoch: 016 Test results, ap: 0.227, auc: 0.505, f1: 0.388, acc: 0.241
+2025-08-07 19:41:12: Epoch: 016 New best TEST metrics
+2025-08-07 19:41:12: Epoch: 017 Train Loss: 0.695, ap: 0.426, auc: 0.625, f1: 0.529, acc: 0.359
+2025-08-07 19:41:12: Epoch: 017 Test results, ap: 0.233, auc: 0.520, f1: 0.388, acc: 0.241
+2025-08-07 19:41:12: Epoch: 017 New best TEST metrics
+2025-08-07 19:41:13: Epoch: 018 Train Loss: 0.692, ap: 0.456, auc: 0.618, f1: 0.000, acc: 0.641
+2025-08-07 19:41:13: Epoch: 018 Test results, ap: 0.240, auc: 0.516, f1: 0.000, acc: 0.759
+2025-08-07 19:41:13: Epoch: 018 New best TEST metrics
+2025-08-07 19:41:13: Epoch: 019 Train Loss: 0.692, ap: 0.442, auc: 0.622, f1: 0.471, acc: 0.650
+2025-08-07 19:41:13: Epoch: 019 Test results, ap: 0.234, auc: 0.492, f1: 0.411, acc: 0.602
+2025-08-07 19:41:14: Epoch: 020 Train Loss: 0.685, ap: 0.415, auc: 0.576, f1: 0.494, acc: 0.563
+2025-08-07 19:41:14: Epoch: 020 Test results, ap: 0.236, auc: 0.473, f1: 0.368, acc: 0.269
+2025-08-07 19:41:14: Epoch: 021 Train Loss: 0.680, ap: 0.436, auc: 0.569, f1: 0.487, acc: 0.612
+2025-08-07 19:41:14: Epoch: 021 Test results, ap: 0.332, auc: 0.520, f1: 0.333, acc: 0.333
+2025-08-07 19:41:14: Epoch: 021 New best TEST metrics
+2025-08-07 19:41:15: Epoch: 022 Train Loss: 0.678, ap: 0.418, auc: 0.579, f1: 0.500, acc: 0.573
+2025-08-07 19:41:15: Epoch: 022 Test results, ap: 0.248, auc: 0.481, f1: 0.372, acc: 0.250
+2025-08-07 19:41:15: Epoch: 023 Train Loss: 0.664, ap: 0.462, auc: 0.609, f1: 0.444, acc: 0.612
+2025-08-07 19:41:15: Epoch: 023 Test results, ap: 0.322, auc: 0.506, f1: 0.343, acc: 0.361
+2025-08-07 19:41:16: Epoch: 024 Train Loss: 0.645, ap: 0.439, auc: 0.576, f1: 0.431, acc: 0.641
+2025-08-07 19:41:16: Epoch: 024 Test results, ap: 0.315, auc: 0.495, f1: 0.341, acc: 0.463
+2025-08-07 19:41:16: Epoch: 025 Train Loss: 0.592, ap: 0.427, auc: 0.600, f1: 0.418, acc: 0.621
+2025-08-07 19:41:16: Epoch: 025 Test results, ap: 0.236, auc: 0.475, f1: 0.375, acc: 0.537
+2025-08-07 19:41:17: Epoch: 026 Train Loss: 0.570, ap: 0.437, auc: 0.622, f1: 0.506, acc: 0.621
+2025-08-07 19:41:17: Epoch: 026 Test results, ap: 0.360, auc: 0.520, f1: 0.323, acc: 0.417
+2025-08-07 19:41:17: Epoch: 026 New best TEST metrics
+2025-08-07 19:41:17: Epoch: 027 Train Loss: 0.558, ap: 0.464, auc: 0.636, f1: 0.532, acc: 0.641
+2025-08-07 19:41:17: Epoch: 027 Test results, ap: 0.369, auc: 0.512, f1: 0.345, acc: 0.472
+2025-08-07 19:41:17: Epoch: 027 New best TEST metrics
+2025-08-07 19:41:17: Epoch: 028 Train Loss: 0.567, ap: 0.474, auc: 0.677, f1: 0.524, acc: 0.612
+2025-08-07 19:41:18: Epoch: 028 Test results, ap: 0.396, auc: 0.540, f1: 0.370, acc: 0.528
+2025-08-07 19:41:18: Epoch: 028 New best TEST metrics
+2025-08-07 19:41:18: Epoch: 029 Train Loss: 0.569, ap: 0.538, auc: 0.663, f1: 0.593, acc: 0.641
+2025-08-07 19:41:18: Epoch: 029 New best VALIDATION metrics
+2025-08-07 19:41:18: Epoch: 029 Test results, ap: 0.485, auc: 0.609, f1: 0.388, acc: 0.241
+2025-08-07 19:41:18: Epoch: 029 New best TEST metrics
+2025-08-07 19:41:18: Epoch: 030 Train Loss: 0.578, ap: 0.494, auc: 0.656, f1: 0.464, acc: 0.641
+2025-08-07 19:41:19: Epoch: 030 Test results, ap: 0.381, auc: 0.533, f1: 0.374, acc: 0.287
+2025-08-07 19:41:19: Epoch: 031 Train Loss: 0.571, ap: 0.503, auc: 0.659, f1: 0.513, acc: 0.631
+2025-08-07 19:41:19: Epoch: 031 Test results, ap: 0.387, auc: 0.539, f1: 0.388, acc: 0.241
+2025-08-07 19:41:19: Epoch: 032 Train Loss: 0.562, ap: 0.505, auc: 0.658, f1: 0.535, acc: 0.612
+2025-08-07 19:41:19: Epoch: 032 Test results, ap: 0.388, auc: 0.543, f1: 0.388, acc: 0.241
+2025-08-07 19:41:20: Epoch: 033 Train Loss: 0.552, ap: 0.504, auc: 0.655, f1: 0.500, acc: 0.592
+2025-08-07 19:41:20: Epoch: 033 Test results, ap: 0.386, auc: 0.540, f1: 0.388, acc: 0.241
+2025-08-07 19:41:20: Epoch: 034 Train Loss: 0.566, ap: 0.523, auc: 0.658, f1: 0.535, acc: 0.612
+2025-08-07 19:41:20: Epoch: 034 Test results, ap: 0.376, auc: 0.543, f1: 0.388, acc: 0.241
+2025-08-07 19:41:21: Epoch: 035 Train Loss: 0.545, ap: 0.506, auc: 0.661, f1: 0.500, acc: 0.592
+2025-08-07 19:41:21: Epoch: 035 Test results, ap: 0.380, auc: 0.545, f1: 0.375, acc: 0.259
+2025-08-07 19:41:21: Epoch: 036 Train Loss: 0.539, ap: 0.531, auc: 0.665, f1: 0.518, acc: 0.602
+2025-08-07 19:41:21: Epoch: 036 Test results, ap: 0.375, auc: 0.548, f1: 0.388, acc: 0.241
+2025-08-07 19:41:22: Epoch: 037 Train Loss: 0.550, ap: 0.538, auc: 0.673, f1: 0.506, acc: 0.602
+2025-08-07 19:41:22: Epoch: 037 New best VALIDATION metrics
+2025-08-07 19:41:22: Epoch: 037 Test results, ap: 0.373, auc: 0.553, f1: 0.342, acc: 0.287
+2025-08-07 19:41:22: Epoch: 038 Train Loss: 0.556, ap: 0.530, auc: 0.668, f1: 0.535, acc: 0.612
+2025-08-07 19:41:22: Epoch: 038 Test results, ap: 0.409, auc: 0.582, f1: 0.388, acc: 0.241
+2025-08-07 19:41:23: Epoch: 039 Train Loss: 0.544, ap: 0.533, auc: 0.672, f1: 0.506, acc: 0.602
+2025-08-07 19:41:23: Epoch: 039 Test results, ap: 0.380, auc: 0.563, f1: 0.342, acc: 0.287
+2025-08-07 19:41:23: Epoch: 040 Train Loss: 0.557, ap: 0.529, auc: 0.673, f1: 0.506, acc: 0.602
+2025-08-07 19:41:23: Epoch: 040 Test results, ap: 0.395, auc: 0.577, f1: 0.342, acc: 0.287
+2025-08-07 19:41:24: Epoch: 041 Train Loss: 0.519, ap: 0.535, auc: 0.676, f1: 0.524, acc: 0.612
+2025-08-07 19:41:24: Epoch: 041 Test results, ap: 0.435, auc: 0.604, f1: 0.339, acc: 0.278
+2025-08-07 19:41:24: Epoch: 042 Train Loss: 0.544, ap: 0.533, auc: 0.674, f1: 0.506, acc: 0.602
+2025-08-07 19:41:24: Epoch: 042 Test results, ap: 0.402, auc: 0.582, f1: 0.348, acc: 0.306
+2025-08-07 19:41:25: Epoch: 043 Train Loss: 0.531, ap: 0.536, auc: 0.677, f1: 0.524, acc: 0.612
+2025-08-07 19:41:25: Epoch: 043 New best VALIDATION metrics
+2025-08-07 19:41:25: Epoch: 043 Test results, ap: 0.422, auc: 0.596, f1: 0.342, acc: 0.287
+2025-08-07 19:41:25: Epoch: 044 Train Loss: 0.545, ap: 0.533, auc: 0.676, f1: 0.506, acc: 0.602
+2025-08-07 19:41:25: Epoch: 044 Test results, ap: 0.406, auc: 0.587, f1: 0.373, acc: 0.407
+2025-08-07 19:41:26: Epoch: 045 Train Loss: 0.545, ap: 0.532, auc: 0.676, f1: 0.524, acc: 0.612
+2025-08-07 19:41:26: Epoch: 045 Test results, ap: 0.426, auc: 0.602, f1: 0.380, acc: 0.426
+2025-08-07 19:41:26: Epoch: 046 Train Loss: 0.536, ap: 0.547, auc: 0.684, f1: 0.524, acc: 0.612
+2025-08-07 19:41:26: Epoch: 046 New best VALIDATION metrics
+2025-08-07 19:41:26: Epoch: 046 Test results, ap: 0.441, auc: 0.619, f1: 0.342, acc: 0.287
+2025-08-07 19:41:27: Epoch: 047 Train Loss: 0.525, ap: 0.547, auc: 0.686, f1: 0.524, acc: 0.612
+2025-08-07 19:41:27: Epoch: 047 New best VALIDATION metrics
+2025-08-07 19:41:27: Epoch: 047 Test results, ap: 0.443, auc: 0.622, f1: 0.354, acc: 0.324
+2025-08-07 19:41:27: Epoch: 048 Train Loss: 0.539, ap: 0.534, auc: 0.680, f1: 0.488, acc: 0.592
+2025-08-07 19:41:27: Epoch: 048 Test results, ap: 0.433, auc: 0.615, f1: 0.381, acc: 0.519
+2025-08-07 19:41:28: Epoch: 049 Train Loss: 0.527, ap: 0.537, auc: 0.680, f1: 0.524, acc: 0.612
+2025-08-07 19:41:28: Epoch: 049 Test results, ap: 0.437, auc: 0.622, f1: 0.386, acc: 0.500
+2025-08-07 19:41:28: Epoch: 050 Train Loss: 0.536, ap: 0.550, auc: 0.685, f1: 0.524, acc: 0.612
+2025-08-07 19:41:28: Epoch: 050 New best VALIDATION metrics
+2025-08-07 19:41:28: Epoch: 050 Test results, ap: 0.413, auc: 0.621, f1: 0.385, acc: 0.407
+2025-08-07 19:41:28: used time 0.01h
+2025-08-07 19:41:28: Running Cross Validation 2
+2025-08-07 19:41:28: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:41:28: Epoch: 001 Train Loss: 0.694, ap: 0.606, auc: 0.565, f1: 0.250, acc: 0.534
+2025-08-07 19:41:28: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:41:29: Epoch: 001 Test results, ap: 0.256, auc: 0.501, f1: 0.000, acc: 0.759
+2025-08-07 19:41:29: Epoch: 002 Train Loss: 0.694, ap: 0.609, auc: 0.563, f1: 0.671, acc: 0.505
+2025-08-07 19:41:29: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:41:29: Epoch: 002 Test results, ap: 0.286, auc: 0.529, f1: 0.388, acc: 0.241
+2025-08-07 19:41:29: Epoch: 003 Train Loss: 0.695, ap: 0.687, auc: 0.685, f1: 0.000, acc: 0.495
+2025-08-07 19:41:29: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:41:30: Epoch: 003 Test results, ap: 0.186, auc: 0.352, f1: 0.000, acc: 0.759
+2025-08-07 19:41:30: Epoch: 004 Train Loss: 0.694, ap: 0.577, auc: 0.531, f1: 0.671, acc: 0.505
+2025-08-07 19:41:30: Epoch: 004 Test results, ap: 0.292, auc: 0.631, f1: 0.388, acc: 0.241
+2025-08-07 19:41:30: Epoch: 005 Train Loss: 0.694, ap: 0.567, auc: 0.542, f1: 0.000, acc: 0.495
+2025-08-07 19:41:30: Epoch: 005 Test results, ap: 0.400, auc: 0.574, f1: 0.000, acc: 0.759
+2025-08-07 19:41:31: Epoch: 006 Train Loss: 0.694, ap: 0.578, auc: 0.561, f1: 0.667, acc: 0.505
+2025-08-07 19:41:31: Epoch: 006 Test results, ap: 0.391, auc: 0.581, f1: 0.388, acc: 0.241
+2025-08-07 19:41:31: Epoch: 007 Train Loss: 0.693, ap: 0.596, auc: 0.599, f1: 0.671, acc: 0.515
+2025-08-07 19:41:31: Epoch: 007 Test results, ap: 0.262, auc: 0.468, f1: 0.388, acc: 0.241
+2025-08-07 19:41:32: Epoch: 008 Train Loss: 0.692, ap: 0.565, auc: 0.627, f1: 0.696, acc: 0.602
+2025-08-07 19:41:32: Epoch: 008 Test results, ap: 0.193, auc: 0.322, f1: 0.388, acc: 0.241
+2025-08-07 19:41:32: Epoch: 009 Train Loss: 0.693, ap: 0.495, auc: 0.466, f1: 0.671, acc: 0.505
+2025-08-07 19:41:32: Epoch: 009 Test results, ap: 0.435, auc: 0.755, f1: 0.388, acc: 0.241
+2025-08-07 19:41:33: Epoch: 010 Train Loss: 0.696, ap: 0.603, auc: 0.573, f1: 0.000, acc: 0.495
+2025-08-07 19:41:33: Epoch: 010 Test results, ap: 0.480, auc: 0.577, f1: 0.000, acc: 0.759
+2025-08-07 19:41:33: Epoch: 011 Train Loss: 0.693, ap: 0.598, auc: 0.567, f1: 0.671, acc: 0.505
+2025-08-07 19:41:33: Epoch: 011 Test results, ap: 0.425, auc: 0.562, f1: 0.388, acc: 0.241
+2025-08-07 19:41:33: Epoch: 011 New best TEST metrics
+2025-08-07 19:41:34: Epoch: 012 Train Loss: 0.694, ap: 0.649, auc: 0.719, f1: 0.000, acc: 0.495
+2025-08-07 19:41:34: Epoch: 012 Test results, ap: 0.212, auc: 0.432, f1: 0.000, acc: 0.759
+2025-08-07 19:41:34: Epoch: 013 Train Loss: 0.692, ap: 0.474, auc: 0.491, f1: 0.672, acc: 0.573
+2025-08-07 19:41:34: Epoch: 013 Test results, ap: 0.263, auc: 0.549, f1: 0.394, acc: 0.287
+2025-08-07 19:41:34: Epoch: 014 Train Loss: 0.689, ap: 0.721, auc: 0.682, f1: 0.681, acc: 0.553
+2025-08-07 19:41:34: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:41:35: Epoch: 014 Test results, ap: 0.253, auc: 0.514, f1: 0.391, acc: 0.250
+2025-08-07 19:41:35: Epoch: 015 Train Loss: 0.689, ap: 0.575, auc: 0.626, f1: 0.689, acc: 0.544
+2025-08-07 19:41:35: Epoch: 015 Test results, ap: 0.211, auc: 0.437, f1: 0.388, acc: 0.241
+2025-08-07 19:41:35: Epoch: 016 Train Loss: 0.688, ap: 0.590, auc: 0.641, f1: 0.680, acc: 0.534
+2025-08-07 19:41:36: Epoch: 016 Test results, ap: 0.215, auc: 0.437, f1: 0.388, acc: 0.241
+2025-08-07 19:41:36: Epoch: 017 Train Loss: 0.681, ap: 0.578, auc: 0.633, f1: 0.681, acc: 0.553
+2025-08-07 19:41:36: Epoch: 017 Test results, ap: 0.215, auc: 0.432, f1: 0.388, acc: 0.241
+2025-08-07 19:41:36: Epoch: 018 Train Loss: 0.675, ap: 0.592, auc: 0.632, f1: 0.691, acc: 0.583
+2025-08-07 19:41:37: Epoch: 018 Test results, ap: 0.212, auc: 0.434, f1: 0.367, acc: 0.296
+2025-08-07 19:41:37: Epoch: 019 Train Loss: 0.665, ap: 0.535, auc: 0.591, f1: 0.574, acc: 0.583
+2025-08-07 19:41:37: Epoch: 019 Test results, ap: 0.215, auc: 0.455, f1: 0.000, acc: 0.620
+2025-08-07 19:41:37: Epoch: 020 Train Loss: 0.680, ap: 0.517, auc: 0.575, f1: 0.694, acc: 0.641
+2025-08-07 19:41:37: Epoch: 020 Test results, ap: 0.226, auc: 0.474, f1: 0.388, acc: 0.241
+2025-08-07 19:41:38: Epoch: 021 Train Loss: 0.632, ap: 0.608, auc: 0.697, f1: 0.719, acc: 0.621
+2025-08-07 19:41:38: Epoch: 021 Test results, ap: 0.224, auc: 0.495, f1: 0.388, acc: 0.241
+2025-08-07 19:41:38: Epoch: 022 Train Loss: 0.662, ap: 0.599, auc: 0.693, f1: 0.735, acc: 0.650
+2025-08-07 19:41:38: Epoch: 022 Test results, ap: 0.226, auc: 0.504, f1: 0.388, acc: 0.241
+2025-08-07 19:41:39: Epoch: 023 Train Loss: 0.624, ap: 0.568, auc: 0.659, f1: 0.734, acc: 0.670
+2025-08-07 19:41:39: Epoch: 023 Test results, ap: 0.232, auc: 0.521, f1: 0.388, acc: 0.241
+2025-08-07 19:41:39: Epoch: 024 Train Loss: 0.628, ap: 0.644, auc: 0.726, f1: 0.735, acc: 0.650
+2025-08-07 19:41:39: Epoch: 024 Test results, ap: 0.232, auc: 0.520, f1: 0.388, acc: 0.241
+2025-08-07 19:41:39: Epoch: 025 Train Loss: 0.640, ap: 0.594, auc: 0.690, f1: 0.737, acc: 0.660
+2025-08-07 19:41:40: Epoch: 025 Test results, ap: 0.229, auc: 0.513, f1: 0.388, acc: 0.241
+2025-08-07 19:41:40: Epoch: 026 Train Loss: 0.621, ap: 0.570, auc: 0.662, f1: 0.723, acc: 0.650
+2025-08-07 19:41:40: Epoch: 026 Test results, ap: 0.238, auc: 0.537, f1: 0.388, acc: 0.241
+2025-08-07 19:41:40: Epoch: 027 Train Loss: 0.618, ap: 0.552, auc: 0.639, f1: 0.734, acc: 0.670
+2025-08-07 19:41:40: Epoch: 027 Test results, ap: 0.236, auc: 0.525, f1: 0.388, acc: 0.241
+2025-08-07 19:41:41: Epoch: 028 Train Loss: 0.623, ap: 0.601, auc: 0.679, f1: 0.737, acc: 0.660
+2025-08-07 19:41:41: Epoch: 028 Test results, ap: 0.207, auc: 0.414, f1: 0.388, acc: 0.241
+2025-08-07 19:41:41: Epoch: 029 Train Loss: 0.627, ap: 0.568, auc: 0.661, f1: 0.748, acc: 0.699
+2025-08-07 19:41:41: Epoch: 029 Test results, ap: 0.252, auc: 0.553, f1: 0.388, acc: 0.241
+2025-08-07 19:41:42: Epoch: 030 Train Loss: 0.591, ap: 0.760, auc: 0.744, f1: 0.748, acc: 0.680
+2025-08-07 19:41:42: Epoch: 030 New best VALIDATION metrics
+2025-08-07 19:41:42: Epoch: 030 Test results, ap: 0.200, auc: 0.382, f1: 0.388, acc: 0.241
+2025-08-07 19:41:42: Epoch: 031 Train Loss: 0.596, ap: 0.779, auc: 0.761, f1: 0.744, acc: 0.680
+2025-08-07 19:41:42: Epoch: 031 New best VALIDATION metrics
+2025-08-07 19:41:42: Epoch: 031 Test results, ap: 0.203, auc: 0.380, f1: 0.388, acc: 0.241
+2025-08-07 19:41:43: Epoch: 032 Train Loss: 0.597, ap: 0.764, auc: 0.752, f1: 0.744, acc: 0.680
+2025-08-07 19:41:43: Epoch: 032 Test results, ap: 0.204, auc: 0.382, f1: 0.388, acc: 0.241
+2025-08-07 19:41:43: Epoch: 033 Train Loss: 0.596, ap: 0.747, auc: 0.741, f1: 0.756, acc: 0.699
+2025-08-07 19:41:43: Epoch: 033 Test results, ap: 0.203, auc: 0.387, f1: 0.388, acc: 0.241
+2025-08-07 19:41:44: Epoch: 034 Train Loss: 0.596, ap: 0.597, auc: 0.673, f1: 0.750, acc: 0.689
+2025-08-07 19:41:44: Epoch: 034 Test results, ap: 0.202, auc: 0.377, f1: 0.388, acc: 0.241
+2025-08-07 19:41:44: Epoch: 035 Train Loss: 0.595, ap: 0.783, auc: 0.754, f1: 0.756, acc: 0.699
+2025-08-07 19:41:44: Epoch: 035 Test results, ap: 0.201, auc: 0.367, f1: 0.388, acc: 0.241
+2025-08-07 19:41:45: Epoch: 036 Train Loss: 0.587, ap: 0.797, auc: 0.770, f1: 0.756, acc: 0.699
+2025-08-07 19:41:45: Epoch: 036 New best VALIDATION metrics
+2025-08-07 19:41:45: Epoch: 036 Test results, ap: 0.205, auc: 0.371, f1: 0.388, acc: 0.241
+2025-08-07 19:41:45: Epoch: 037 Train Loss: 0.592, ap: 0.682, auc: 0.707, f1: 0.762, acc: 0.709
+2025-08-07 19:41:45: Epoch: 037 Test results, ap: 0.205, auc: 0.370, f1: 0.388, acc: 0.241
+2025-08-07 19:41:45: Epoch: 038 Train Loss: 0.578, ap: 0.779, auc: 0.762, f1: 0.744, acc: 0.680
+2025-08-07 19:41:46: Epoch: 038 Test results, ap: 0.203, auc: 0.369, f1: 0.388, acc: 0.241
+2025-08-07 19:41:46: Epoch: 039 Train Loss: 0.584, ap: 0.829, auc: 0.801, f1: 0.754, acc: 0.709
+2025-08-07 19:41:46: Epoch: 039 New best VALIDATION metrics
+2025-08-07 19:41:46: Epoch: 039 Test results, ap: 0.206, auc: 0.363, f1: 0.388, acc: 0.241
+2025-08-07 19:41:46: Epoch: 040 Train Loss: 0.591, ap: 0.814, auc: 0.784, f1: 0.756, acc: 0.699
+2025-08-07 19:41:47: Epoch: 040 Test results, ap: 0.198, auc: 0.363, f1: 0.388, acc: 0.241
+2025-08-07 19:41:47: Epoch: 041 Train Loss: 0.583, ap: 0.813, auc: 0.786, f1: 0.744, acc: 0.680
+2025-08-07 19:41:47: Epoch: 041 Test results, ap: 0.198, auc: 0.360, f1: 0.388, acc: 0.241
+2025-08-07 19:41:47: Epoch: 042 Train Loss: 0.587, ap: 0.775, auc: 0.787, f1: 0.758, acc: 0.709
+2025-08-07 19:41:47: Epoch: 042 Test results, ap: 0.203, auc: 0.364, f1: 0.388, acc: 0.241
+2025-08-07 19:41:48: Epoch: 043 Train Loss: 0.588, ap: 0.834, auc: 0.807, f1: 0.756, acc: 0.699
+2025-08-07 19:41:48: Epoch: 043 New best VALIDATION metrics
+2025-08-07 19:41:48: Epoch: 043 Test results, ap: 0.209, auc: 0.363, f1: 0.388, acc: 0.241
+2025-08-07 19:41:48: Epoch: 044 Train Loss: 0.586, ap: 0.839, auc: 0.812, f1: 0.746, acc: 0.689
+2025-08-07 19:41:48: Epoch: 044 New best VALIDATION metrics
+2025-08-07 19:41:48: Epoch: 044 Test results, ap: 0.211, auc: 0.365, f1: 0.388, acc: 0.241
+2025-08-07 19:41:49: Epoch: 045 Train Loss: 0.578, ap: 0.815, auc: 0.789, f1: 0.762, acc: 0.709
+2025-08-07 19:41:49: Epoch: 045 Test results, ap: 0.209, auc: 0.363, f1: 0.388, acc: 0.241
+2025-08-07 19:41:49: Epoch: 046 Train Loss: 0.590, ap: 0.827, auc: 0.799, f1: 0.754, acc: 0.689
+2025-08-07 19:41:49: Epoch: 046 Test results, ap: 0.207, auc: 0.369, f1: 0.388, acc: 0.241
+2025-08-07 19:41:50: Epoch: 047 Train Loss: 0.587, ap: 0.830, auc: 0.801, f1: 0.758, acc: 0.709
+2025-08-07 19:41:50: Epoch: 047 Test results, ap: 0.209, auc: 0.370, f1: 0.388, acc: 0.241
+2025-08-07 19:41:50: Epoch: 048 Train Loss: 0.589, ap: 0.831, auc: 0.807, f1: 0.758, acc: 0.709
+2025-08-07 19:41:50: Epoch: 048 Test results, ap: 0.213, auc: 0.368, f1: 0.388, acc: 0.241
+2025-08-07 19:41:51: Epoch: 049 Train Loss: 0.582, ap: 0.826, auc: 0.798, f1: 0.746, acc: 0.689
+2025-08-07 19:41:51: Epoch: 049 Test results, ap: 0.205, auc: 0.368, f1: 0.388, acc: 0.241
+2025-08-07 19:41:51: Epoch: 050 Train Loss: 0.573, ap: 0.842, auc: 0.814, f1: 0.752, acc: 0.699
+2025-08-07 19:41:51: Epoch: 050 New best VALIDATION metrics
+2025-08-07 19:41:51: Epoch: 050 Test results, ap: 0.218, auc: 0.373, f1: 0.388, acc: 0.241
+2025-08-07 19:41:51: used time 0.01h
+2025-08-07 19:41:51: Running Cross Validation 3
+2025-08-07 19:41:51: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:41:51: Epoch: 001 Train Loss: 0.695, ap: 0.449, auc: 0.526, f1: 0.557, acc: 0.476
+2025-08-07 19:41:51: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:41:52: Epoch: 001 Test results, ap: 0.177, auc: 0.295, f1: 0.388, acc: 0.241
+2025-08-07 19:41:52: Epoch: 002 Train Loss: 0.695, ap: 0.449, auc: 0.504, f1: 0.587, acc: 0.427
+2025-08-07 19:41:52: Epoch: 002 Test results, ap: 0.234, auc: 0.471, f1: 0.388, acc: 0.241
+2025-08-07 19:41:52: Epoch: 003 Train Loss: 0.693, ap: 0.504, auc: 0.546, f1: 0.579, acc: 0.408
+2025-08-07 19:41:52: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:41:52: Epoch: 003 Test results, ap: 0.564, auc: 0.679, f1: 0.388, acc: 0.241
+2025-08-07 19:41:53: Epoch: 004 Train Loss: 0.696, ap: 0.515, auc: 0.538, f1: 0.000, acc: 0.592
+2025-08-07 19:41:53: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:41:53: Epoch: 004 Test results, ap: 0.251, auc: 0.403, f1: 0.000, acc: 0.759
+2025-08-07 19:41:53: Epoch: 005 Train Loss: 0.697, ap: 0.570, auc: 0.623, f1: 0.200, acc: 0.612
+2025-08-07 19:41:53: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:41:53: Epoch: 005 Test results, ap: 0.165, auc: 0.262, f1: 0.000, acc: 0.759
+2025-08-07 19:41:54: Epoch: 006 Train Loss: 0.691, ap: 0.634, auc: 0.708, f1: 0.597, acc: 0.515
+2025-08-07 19:41:54: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:41:54: Epoch: 006 Test results, ap: 0.169, auc: 0.283, f1: 0.328, acc: 0.278
+2025-08-07 19:41:54: Epoch: 007 Train Loss: 0.694, ap: 0.426, auc: 0.543, f1: 0.542, acc: 0.476
+2025-08-07 19:41:54: Epoch: 007 Test results, ap: 0.270, auc: 0.484, f1: 0.388, acc: 0.241
+2025-08-07 19:41:55: Epoch: 008 Train Loss: 0.688, ap: 0.491, auc: 0.645, f1: 0.586, acc: 0.437
+2025-08-07 19:41:55: Epoch: 008 Test results, ap: 0.228, auc: 0.479, f1: 0.388, acc: 0.241
+2025-08-07 19:41:55: Epoch: 009 Train Loss: 0.689, ap: 0.505, auc: 0.624, f1: 0.645, acc: 0.573
+2025-08-07 19:41:55: Epoch: 009 Test results, ap: 0.222, auc: 0.475, f1: 0.373, acc: 0.315
+2025-08-07 19:41:56: Epoch: 010 Train Loss: 0.668, ap: 0.545, auc: 0.701, f1: 0.655, acc: 0.621
+2025-08-07 19:41:56: Epoch: 010 Test results, ap: 0.221, auc: 0.476, f1: 0.289, acc: 0.454
+2025-08-07 19:41:56: Epoch: 011 Train Loss: 0.645, ap: 0.496, auc: 0.669, f1: 0.578, acc: 0.631
+2025-08-07 19:41:56: Epoch: 011 Test results, ap: 0.221, auc: 0.475, f1: 0.241, acc: 0.593
+2025-08-07 19:41:56: Epoch: 011 New best TEST metrics
+2025-08-07 19:41:56: Epoch: 012 Train Loss: 0.668, ap: 0.566, auc: 0.695, f1: 0.673, acc: 0.641
+2025-08-07 19:41:57: Epoch: 012 Test results, ap: 0.226, auc: 0.488, f1: 0.400, acc: 0.306
+2025-08-07 19:41:57: Epoch: 012 New best TEST metrics
+2025-08-07 19:41:57: Epoch: 013 Train Loss: 0.627, ap: 0.582, auc: 0.706, f1: 0.684, acc: 0.650
+2025-08-07 19:41:57: Epoch: 013 Test results, ap: 0.228, auc: 0.494, f1: 0.403, acc: 0.287
+2025-08-07 19:41:57: Epoch: 013 New best TEST metrics
+2025-08-07 19:41:57: Epoch: 014 Train Loss: 0.643, ap: 0.517, auc: 0.677, f1: 0.646, acc: 0.670
+2025-08-07 19:41:58: Epoch: 014 Test results, ap: 0.225, auc: 0.487, f1: 0.246, acc: 0.546
+2025-08-07 19:41:58: Epoch: 015 Train Loss: 0.640, ap: 0.580, auc: 0.695, f1: 0.639, acc: 0.573
+2025-08-07 19:41:58: Epoch: 015 Test results, ap: 0.229, auc: 0.495, f1: 0.388, acc: 0.241
+2025-08-07 19:41:58: Epoch: 015 New best TEST metrics
+2025-08-07 19:41:58: Epoch: 016 Train Loss: 0.625, ap: 0.580, auc: 0.706, f1: 0.647, acc: 0.650
+2025-08-07 19:41:58: Epoch: 016 Test results, ap: 0.242, auc: 0.498, f1: 0.297, acc: 0.519
+2025-08-07 19:41:58: Epoch: 016 New best TEST metrics
+2025-08-07 19:41:59: Epoch: 017 Train Loss: 0.606, ap: 0.588, auc: 0.725, f1: 0.667, acc: 0.660
+2025-08-07 19:41:59: Epoch: 017 Test results, ap: 0.260, auc: 0.489, f1: 0.308, acc: 0.500
+2025-08-07 19:41:59: Epoch: 017 New best TEST metrics
+2025-08-07 19:41:59: Epoch: 018 Train Loss: 0.598, ap: 0.598, auc: 0.736, f1: 0.685, acc: 0.670
+2025-08-07 19:41:59: Epoch: 018 Test results, ap: 0.280, auc: 0.507, f1: 0.333, acc: 0.481
+2025-08-07 19:41:59: Epoch: 018 New best TEST metrics
+2025-08-07 19:42:00: Epoch: 019 Train Loss: 0.598, ap: 0.573, auc: 0.715, f1: 0.629, acc: 0.553
+2025-08-07 19:42:00: Epoch: 019 Test results, ap: 0.260, auc: 0.507, f1: 0.388, acc: 0.241
+2025-08-07 19:42:00: Epoch: 020 Train Loss: 0.642, ap: 0.552, auc: 0.691, f1: 0.673, acc: 0.670
+2025-08-07 19:42:00: Epoch: 020 Test results, ap: 0.293, auc: 0.453, f1: 0.297, acc: 0.519
+2025-08-07 19:42:01: Epoch: 021 Train Loss: 0.609, ap: 0.618, auc: 0.738, f1: 0.679, acc: 0.650
+2025-08-07 19:42:01: Epoch: 021 New best VALIDATION metrics
+2025-08-07 19:42:01: Epoch: 021 Test results, ap: 0.248, auc: 0.512, f1: 0.322, acc: 0.454
+2025-08-07 19:42:01: Epoch: 022 Train Loss: 0.599, ap: 0.532, auc: 0.675, f1: 0.632, acc: 0.660
+2025-08-07 19:42:01: Epoch: 022 Test results, ap: 0.318, auc: 0.444, f1: 0.303, acc: 0.574
+2025-08-07 19:42:02: Epoch: 023 Train Loss: 0.612, ap: 0.645, auc: 0.752, f1: 0.697, acc: 0.680
+2025-08-07 19:42:02: Epoch: 023 New best VALIDATION metrics
+2025-08-07 19:42:02: Epoch: 023 Test results, ap: 0.354, auc: 0.506, f1: 0.289, acc: 0.500
+2025-08-07 19:42:02: Epoch: 023 New best TEST metrics
+2025-08-07 19:42:02: Epoch: 024 Train Loss: 0.597, ap: 0.599, auc: 0.717, f1: 0.673, acc: 0.660
+2025-08-07 19:42:02: Epoch: 024 Test results, ap: 0.358, auc: 0.462, f1: 0.308, acc: 0.583
+2025-08-07 19:42:03: Epoch: 025 Train Loss: 0.585, ap: 0.641, auc: 0.738, f1: 0.673, acc: 0.660
+2025-08-07 19:42:03: Epoch: 025 Test results, ap: 0.336, auc: 0.458, f1: 0.226, acc: 0.556
+2025-08-07 19:42:03: Epoch: 026 Train Loss: 0.594, ap: 0.647, auc: 0.766, f1: 0.690, acc: 0.660
+2025-08-07 19:42:03: Epoch: 026 New best VALIDATION metrics
+2025-08-07 19:42:03: Epoch: 026 Test results, ap: 0.294, auc: 0.532, f1: 0.293, acc: 0.509
+2025-08-07 19:42:04: Epoch: 027 Train Loss: 0.599, ap: 0.630, auc: 0.742, f1: 0.697, acc: 0.680
+2025-08-07 19:42:04: Epoch: 027 Test results, ap: 0.357, auc: 0.491, f1: 0.303, acc: 0.574
+2025-08-07 19:42:04: Epoch: 028 Train Loss: 0.590, ap: 0.589, auc: 0.721, f1: 0.667, acc: 0.670
+2025-08-07 19:42:04: Epoch: 028 Test results, ap: 0.321, auc: 0.459, f1: 0.281, acc: 0.574
+2025-08-07 19:42:05: Epoch: 029 Train Loss: 0.600, ap: 0.624, auc: 0.756, f1: 0.685, acc: 0.660
+2025-08-07 19:42:05: Epoch: 029 Test results, ap: 0.345, auc: 0.537, f1: 0.314, acc: 0.556
+2025-08-07 19:42:05: Epoch: 029 New best TEST metrics
+2025-08-07 19:42:05: Epoch: 030 Train Loss: 0.592, ap: 0.650, auc: 0.750, f1: 0.655, acc: 0.602
+2025-08-07 19:42:05: Epoch: 030 Test results, ap: 0.264, auc: 0.516, f1: 0.282, acc: 0.528
+2025-08-07 19:42:06: Epoch: 031 Train Loss: 0.583, ap: 0.629, auc: 0.745, f1: 0.647, acc: 0.650
+2025-08-07 19:42:06: Epoch: 031 Test results, ap: 0.308, auc: 0.453, f1: 0.179, acc: 0.574
+2025-08-07 19:42:06: Epoch: 032 Train Loss: 0.585, ap: 0.658, auc: 0.774, f1: 0.703, acc: 0.680
+2025-08-07 19:42:06: Epoch: 032 New best VALIDATION metrics
+2025-08-07 19:42:06: Epoch: 032 Test results, ap: 0.313, auc: 0.405, f1: 0.172, acc: 0.556
+2025-08-07 19:42:07: Epoch: 033 Train Loss: 0.563, ap: 0.664, auc: 0.760, f1: 0.704, acc: 0.689
+2025-08-07 19:42:07: Epoch: 033 Test results, ap: 0.329, auc: 0.420, f1: 0.175, acc: 0.565
+2025-08-07 19:42:07: Epoch: 034 Train Loss: 0.568, ap: 0.663, auc: 0.763, f1: 0.704, acc: 0.689
+2025-08-07 19:42:07: Epoch: 034 Test results, ap: 0.325, auc: 0.428, f1: 0.169, acc: 0.546
+2025-08-07 19:42:08: Epoch: 035 Train Loss: 0.561, ap: 0.656, auc: 0.767, f1: 0.697, acc: 0.680
+2025-08-07 19:42:08: Epoch: 035 Test results, ap: 0.319, auc: 0.417, f1: 0.172, acc: 0.556
+2025-08-07 19:42:08: Epoch: 036 Train Loss: 0.551, ap: 0.662, auc: 0.770, f1: 0.710, acc: 0.699
+2025-08-07 19:42:08: Epoch: 036 Test results, ap: 0.324, auc: 0.448, f1: 0.179, acc: 0.574
+2025-08-07 19:42:08: Epoch: 037 Train Loss: 0.564, ap: 0.697, auc: 0.781, f1: 0.698, acc: 0.689
+2025-08-07 19:42:08: Epoch: 037 New best VALIDATION metrics
+2025-08-07 19:42:09: Epoch: 037 Test results, ap: 0.325, auc: 0.447, f1: 0.179, acc: 0.574
+2025-08-07 19:42:09: Epoch: 038 Train Loss: 0.560, ap: 0.649, auc: 0.755, f1: 0.686, acc: 0.680
+2025-08-07 19:42:09: Epoch: 038 Test results, ap: 0.328, auc: 0.454, f1: 0.179, acc: 0.574
+2025-08-07 19:42:09: Epoch: 039 Train Loss: 0.551, ap: 0.690, auc: 0.767, f1: 0.698, acc: 0.689
+2025-08-07 19:42:10: Epoch: 039 Test results, ap: 0.328, auc: 0.456, f1: 0.182, acc: 0.583
+2025-08-07 19:42:10: Epoch: 040 Train Loss: 0.539, ap: 0.717, auc: 0.792, f1: 0.698, acc: 0.689
+2025-08-07 19:42:10: Epoch: 040 New best VALIDATION metrics
+2025-08-07 19:42:10: Epoch: 040 Test results, ap: 0.326, auc: 0.455, f1: 0.182, acc: 0.583
+2025-08-07 19:42:10: Epoch: 041 Train Loss: 0.550, ap: 0.642, auc: 0.752, f1: 0.705, acc: 0.699
+2025-08-07 19:42:11: Epoch: 041 Test results, ap: 0.329, auc: 0.458, f1: 0.182, acc: 0.583
+2025-08-07 19:42:11: Epoch: 042 Train Loss: 0.516, ap: 0.675, auc: 0.778, f1: 0.698, acc: 0.689
+2025-08-07 19:42:11: Epoch: 042 Test results, ap: 0.328, auc: 0.457, f1: 0.182, acc: 0.583
+2025-08-07 19:42:11: Epoch: 043 Train Loss: 0.532, ap: 0.676, auc: 0.777, f1: 0.705, acc: 0.699
+2025-08-07 19:42:11: Epoch: 043 Test results, ap: 0.329, auc: 0.463, f1: 0.182, acc: 0.583
+2025-08-07 19:42:12: Epoch: 044 Train Loss: 0.519, ap: 0.669, auc: 0.767, f1: 0.705, acc: 0.699
+2025-08-07 19:42:12: Epoch: 044 Test results, ap: 0.332, auc: 0.466, f1: 0.182, acc: 0.583
+2025-08-07 19:42:12: Epoch: 045 Train Loss: 0.531, ap: 0.669, auc: 0.770, f1: 0.705, acc: 0.699
+2025-08-07 19:42:12: Epoch: 045 Test results, ap: 0.331, auc: 0.465, f1: 0.182, acc: 0.583
+2025-08-07 19:42:13: Epoch: 046 Train Loss: 0.521, ap: 0.643, auc: 0.764, f1: 0.705, acc: 0.699
+2025-08-07 19:42:13: Epoch: 046 Test results, ap: 0.330, auc: 0.465, f1: 0.192, acc: 0.611
+2025-08-07 19:42:13: Epoch: 047 Train Loss: 0.522, ap: 0.658, auc: 0.778, f1: 0.698, acc: 0.689
+2025-08-07 19:42:13: Epoch: 047 Test results, ap: 0.331, auc: 0.466, f1: 0.185, acc: 0.593
+2025-08-07 19:42:14: Epoch: 048 Train Loss: 0.530, ap: 0.654, auc: 0.768, f1: 0.705, acc: 0.699
+2025-08-07 19:42:14: Epoch: 048 Test results, ap: 0.331, auc: 0.466, f1: 0.189, acc: 0.602
+2025-08-07 19:42:14: Epoch: 049 Train Loss: 0.533, ap: 0.625, auc: 0.754, f1: 0.705, acc: 0.699
+2025-08-07 19:42:14: Epoch: 049 Test results, ap: 0.224, auc: 0.456, f1: 0.192, acc: 0.611
+2025-08-07 19:42:15: Epoch: 050 Train Loss: 0.528, ap: 0.650, auc: 0.773, f1: 0.705, acc: 0.699
+2025-08-07 19:42:15: Epoch: 050 Test results, ap: 0.320, auc: 0.467, f1: 0.192, acc: 0.611
+2025-08-07 19:42:15: used time 0.01h
+2025-08-07 19:42:15: Running Cross Validation 4
+2025-08-07 19:42:15: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:42:15: Epoch: 001 Train Loss: 0.693, ap: 0.367, auc: 0.448, f1: 0.000, acc: 0.602
+2025-08-07 19:42:15: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:42:15: Epoch: 001 Test results, ap: 0.232, auc: 0.497, f1: 0.000, acc: 0.759
+2025-08-07 19:42:16: Epoch: 002 Train Loss: 0.694, ap: 0.353, auc: 0.433, f1: 0.569, acc: 0.398
+2025-08-07 19:42:16: Epoch: 002 Test results, ap: 0.316, auc: 0.538, f1: 0.388, acc: 0.241
+2025-08-07 19:42:16: Epoch: 003 Train Loss: 0.695, ap: 0.379, auc: 0.443, f1: 0.000, acc: 0.602
+2025-08-07 19:42:16: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:42:16: Epoch: 003 Test results, ap: 0.183, auc: 0.336, f1: 0.000, acc: 0.759
+2025-08-07 19:42:16: Epoch: 004 Train Loss: 0.694, ap: 0.453, auc: 0.571, f1: 0.569, acc: 0.398
+2025-08-07 19:42:16: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:42:17: Epoch: 004 Test results, ap: 0.274, auc: 0.581, f1: 0.388, acc: 0.241
+2025-08-07 19:42:17: Epoch: 005 Train Loss: 0.694, ap: 0.405, auc: 0.499, f1: 0.573, acc: 0.408
+2025-08-07 19:42:17: Epoch: 005 Test results, ap: 0.190, auc: 0.360, f1: 0.388, acc: 0.241
+2025-08-07 19:42:17: Epoch: 006 Train Loss: 0.686, ap: 0.562, auc: 0.620, f1: 0.566, acc: 0.524
+2025-08-07 19:42:17: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:42:18: Epoch: 006 Test results, ap: 0.202, auc: 0.387, f1: 0.388, acc: 0.241
+2025-08-07 19:42:18: Epoch: 007 Train Loss: 0.637, ap: 0.503, auc: 0.651, f1: 0.556, acc: 0.612
+2025-08-07 19:42:18: Epoch: 007 Test results, ap: 0.222, auc: 0.437, f1: 0.391, acc: 0.250
+2025-08-07 19:42:18: Epoch: 008 Train Loss: 0.682, ap: 0.430, auc: 0.558, f1: 0.343, acc: 0.553
+2025-08-07 19:42:19: Epoch: 008 Test results, ap: 0.212, auc: 0.447, f1: 0.391, acc: 0.250
+2025-08-07 19:42:19: Epoch: 009 Train Loss: 0.653, ap: 0.538, auc: 0.663, f1: 0.607, acc: 0.660
+2025-08-07 19:42:19: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:42:19: Epoch: 009 Test results, ap: 0.270, auc: 0.452, f1: 0.388, acc: 0.241
+2025-08-07 19:42:19: Epoch: 010 Train Loss: 0.618, ap: 0.483, auc: 0.600, f1: 0.482, acc: 0.583
+2025-08-07 19:42:20: Epoch: 010 Test results, ap: 0.172, auc: 0.300, f1: 0.222, acc: 0.352
+2025-08-07 19:42:20: Epoch: 011 Train Loss: 0.601, ap: 0.500, auc: 0.645, f1: 0.610, acc: 0.689
+2025-08-07 19:42:20: Epoch: 011 Test results, ap: 0.183, auc: 0.334, f1: 0.388, acc: 0.241
+2025-08-07 19:42:20: Epoch: 011 New best TEST metrics
+2025-08-07 19:42:20: Epoch: 012 Train Loss: 0.594, ap: 0.532, auc: 0.650, f1: 0.602, acc: 0.680
+2025-08-07 19:42:21: Epoch: 012 Test results, ap: 0.191, auc: 0.364, f1: 0.388, acc: 0.241
+2025-08-07 19:42:21: Epoch: 012 New best TEST metrics
+2025-08-07 19:42:21: Epoch: 013 Train Loss: 0.592, ap: 0.586, auc: 0.651, f1: 0.619, acc: 0.689
+2025-08-07 19:42:21: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:42:21: Epoch: 013 Test results, ap: 0.179, auc: 0.331, f1: 0.388, acc: 0.241
+2025-08-07 19:42:21: Epoch: 014 Train Loss: 0.561, ap: 0.605, auc: 0.674, f1: 0.602, acc: 0.680
+2025-08-07 19:42:21: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:42:22: Epoch: 014 Test results, ap: 0.174, auc: 0.303, f1: 0.388, acc: 0.241
+2025-08-07 19:42:22: Epoch: 015 Train Loss: 0.542, ap: 0.636, auc: 0.678, f1: 0.625, acc: 0.709
+2025-08-07 19:42:22: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:42:22: Epoch: 015 Test results, ap: 0.172, auc: 0.297, f1: 0.388, acc: 0.241
+2025-08-07 19:42:22: Epoch: 016 Train Loss: 0.546, ap: 0.616, auc: 0.660, f1: 0.538, acc: 0.650
+2025-08-07 19:42:22: Epoch: 016 Test results, ap: 0.172, auc: 0.299, f1: 0.388, acc: 0.241
+2025-08-07 19:42:23: Epoch: 017 Train Loss: 0.516, ap: 0.617, auc: 0.679, f1: 0.557, acc: 0.660
+2025-08-07 19:42:23: Epoch: 017 Test results, ap: 0.173, auc: 0.300, f1: 0.388, acc: 0.241
+2025-08-07 19:42:23: Epoch: 018 Train Loss: 0.547, ap: 0.615, auc: 0.708, f1: 0.612, acc: 0.680
+2025-08-07 19:42:23: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:42:23: Epoch: 018 Test results, ap: 0.217, auc: 0.413, f1: 0.388, acc: 0.241
+2025-08-07 19:42:23: Epoch: 018 New best TEST metrics
+2025-08-07 19:42:24: Epoch: 019 Train Loss: 0.540, ap: 0.612, auc: 0.699, f1: 0.549, acc: 0.602
+2025-08-07 19:42:24: Epoch: 019 Test results, ap: 0.224, auc: 0.417, f1: 0.388, acc: 0.241
+2025-08-07 19:42:24: Epoch: 019 New best TEST metrics
+2025-08-07 19:42:24: Epoch: 020 Train Loss: 0.522, ap: 0.653, auc: 0.731, f1: 0.629, acc: 0.680
+2025-08-07 19:42:24: Epoch: 020 New best VALIDATION metrics
+2025-08-07 19:42:24: Epoch: 020 Test results, ap: 0.213, auc: 0.402, f1: 0.388, acc: 0.241
+2025-08-07 19:42:25: Epoch: 021 Train Loss: 0.492, ap: 0.638, auc: 0.720, f1: 0.643, acc: 0.709
+2025-08-07 19:42:25: Epoch: 021 Test results, ap: 0.190, auc: 0.365, f1: 0.388, acc: 0.241
+2025-08-07 19:42:25: Epoch: 022 Train Loss: 0.498, ap: 0.663, auc: 0.745, f1: 0.637, acc: 0.680
+2025-08-07 19:42:25: Epoch: 022 New best VALIDATION metrics
+2025-08-07 19:42:25: Epoch: 022 Test results, ap: 0.205, auc: 0.394, f1: 0.388, acc: 0.241
+2025-08-07 19:42:26: Epoch: 023 Train Loss: 0.483, ap: 0.650, auc: 0.735, f1: 0.650, acc: 0.728
+2025-08-07 19:42:26: Epoch: 023 Test results, ap: 0.184, auc: 0.348, f1: 0.369, acc: 0.241
+2025-08-07 19:42:26: Epoch: 024 Train Loss: 0.492, ap: 0.658, auc: 0.740, f1: 0.651, acc: 0.718
+2025-08-07 19:42:26: Epoch: 024 Test results, ap: 0.191, auc: 0.366, f1: 0.391, acc: 0.250
+2025-08-07 19:42:27: Epoch: 025 Train Loss: 0.481, ap: 0.666, auc: 0.742, f1: 0.621, acc: 0.680
+2025-08-07 19:42:27: Epoch: 025 Test results, ap: 0.183, auc: 0.344, f1: 0.372, acc: 0.250
+2025-08-07 19:42:27: Epoch: 026 Train Loss: 0.462, ap: 0.664, auc: 0.737, f1: 0.612, acc: 0.680
+2025-08-07 19:42:27: Epoch: 026 Test results, ap: 0.180, auc: 0.334, f1: 0.362, acc: 0.250
+2025-08-07 19:42:28: Epoch: 027 Train Loss: 0.487, ap: 0.662, auc: 0.746, f1: 0.652, acc: 0.689
+2025-08-07 19:42:28: Epoch: 027 New best VALIDATION metrics
+2025-08-07 19:42:28: Epoch: 027 Test results, ap: 0.186, auc: 0.355, f1: 0.391, acc: 0.250
+2025-08-07 19:42:28: Epoch: 028 Train Loss: 0.452, ap: 0.645, auc: 0.736, f1: 0.615, acc: 0.660
+2025-08-07 19:42:28: Epoch: 028 Test results, ap: 0.189, auc: 0.369, f1: 0.391, acc: 0.250
+2025-08-07 19:42:29: Epoch: 029 Train Loss: 0.448, ap: 0.669, auc: 0.742, f1: 0.605, acc: 0.670
+2025-08-07 19:42:29: Epoch: 029 New best VALIDATION metrics
+2025-08-07 19:42:29: Epoch: 029 Test results, ap: 0.177, auc: 0.321, f1: 0.339, acc: 0.278
+2025-08-07 19:42:29: Epoch: 030 Train Loss: 0.447, ap: 0.656, auc: 0.738, f1: 0.611, acc: 0.641
+2025-08-07 19:42:29: Epoch: 030 Test results, ap: 0.187, auc: 0.361, f1: 0.391, acc: 0.250
+2025-08-07 19:42:30: Epoch: 031 Train Loss: 0.461, ap: 0.679, auc: 0.742, f1: 0.630, acc: 0.670
+2025-08-07 19:42:30: Epoch: 031 New best VALIDATION metrics
+2025-08-07 19:42:30: Epoch: 031 Test results, ap: 0.181, auc: 0.340, f1: 0.382, acc: 0.250
+2025-08-07 19:42:30: Epoch: 032 Train Loss: 0.429, ap: 0.643, auc: 0.708, f1: 0.598, acc: 0.660
+2025-08-07 19:42:30: Epoch: 032 Test results, ap: 0.179, auc: 0.332, f1: 0.333, acc: 0.333
+2025-08-07 19:42:31: Epoch: 033 Train Loss: 0.417, ap: 0.656, auc: 0.734, f1: 0.621, acc: 0.680
+2025-08-07 19:42:31: Epoch: 033 Test results, ap: 0.178, auc: 0.329, f1: 0.330, acc: 0.287
+2025-08-07 19:42:31: Epoch: 034 Train Loss: 0.418, ap: 0.658, auc: 0.732, f1: 0.652, acc: 0.689
+2025-08-07 19:42:31: Epoch: 034 Test results, ap: 0.177, auc: 0.321, f1: 0.333, acc: 0.296
+2025-08-07 19:42:32: Epoch: 035 Train Loss: 0.420, ap: 0.639, auc: 0.710, f1: 0.621, acc: 0.680
+2025-08-07 19:42:32: Epoch: 035 Test results, ap: 0.180, auc: 0.332, f1: 0.258, acc: 0.361
+2025-08-07 19:42:32: Epoch: 036 Train Loss: 0.414, ap: 0.646, auc: 0.715, f1: 0.674, acc: 0.718
+2025-08-07 19:42:32: Epoch: 036 Test results, ap: 0.178, auc: 0.326, f1: 0.286, acc: 0.352
+2025-08-07 19:42:33: Epoch: 037 Train Loss: 0.402, ap: 0.633, auc: 0.705, f1: 0.652, acc: 0.699
+2025-08-07 19:42:33: Epoch: 037 Test results, ap: 0.177, auc: 0.323, f1: 0.271, acc: 0.352
+2025-08-07 19:42:33: Epoch: 038 Train Loss: 0.397, ap: 0.656, auc: 0.706, f1: 0.605, acc: 0.670
+2025-08-07 19:42:33: Epoch: 038 Test results, ap: 0.181, auc: 0.337, f1: 0.276, acc: 0.417
+2025-08-07 19:42:34: Epoch: 039 Train Loss: 0.377, ap: 0.655, auc: 0.723, f1: 0.645, acc: 0.680
+2025-08-07 19:42:34: Epoch: 039 Test results, ap: 0.178, auc: 0.326, f1: 0.294, acc: 0.333
+2025-08-07 19:42:34: Epoch: 040 Train Loss: 0.394, ap: 0.647, auc: 0.707, f1: 0.652, acc: 0.689
+2025-08-07 19:42:34: Epoch: 040 Test results, ap: 0.178, auc: 0.324, f1: 0.274, acc: 0.361
+2025-08-07 19:42:35: Epoch: 041 Train Loss: 0.370, ap: 0.644, auc: 0.708, f1: 0.636, acc: 0.689
+2025-08-07 19:42:35: Epoch: 041 Test results, ap: 0.179, auc: 0.331, f1: 0.264, acc: 0.380
+2025-08-07 19:42:35: Epoch: 042 Train Loss: 0.384, ap: 0.643, auc: 0.712, f1: 0.636, acc: 0.689
+2025-08-07 19:42:35: Epoch: 042 Test results, ap: 0.180, auc: 0.334, f1: 0.261, acc: 0.370
+2025-08-07 19:42:35: Epoch: 043 Train Loss: 0.362, ap: 0.653, auc: 0.718, f1: 0.652, acc: 0.699
+2025-08-07 19:42:36: Epoch: 043 Test results, ap: 0.179, auc: 0.333, f1: 0.274, acc: 0.361
+2025-08-07 19:42:36: Epoch: 044 Train Loss: 0.385, ap: 0.657, auc: 0.701, f1: 0.605, acc: 0.670
+2025-08-07 19:42:36: Epoch: 044 Test results, ap: 0.182, auc: 0.341, f1: 0.203, acc: 0.417
+2025-08-07 19:42:36: Epoch: 045 Train Loss: 0.365, ap: 0.648, auc: 0.717, f1: 0.636, acc: 0.689
+2025-08-07 19:42:36: Epoch: 045 Test results, ap: 0.181, auc: 0.340, f1: 0.264, acc: 0.380
+2025-08-07 19:42:37: Epoch: 046 Train Loss: 0.358, ap: 0.672, auc: 0.712, f1: 0.621, acc: 0.680
+2025-08-07 19:42:37: Epoch: 046 Test results, ap: 0.181, auc: 0.338, f1: 0.276, acc: 0.417
+2025-08-07 19:42:37: Epoch: 047 Train Loss: 0.367, ap: 0.641, auc: 0.708, f1: 0.605, acc: 0.670
+2025-08-07 19:42:37: Epoch: 047 Test results, ap: 0.181, auc: 0.338, f1: 0.259, acc: 0.417
+2025-08-07 19:42:38: Epoch: 048 Train Loss: 0.351, ap: 0.656, auc: 0.719, f1: 0.636, acc: 0.689
+2025-08-07 19:42:38: Epoch: 048 Test results, ap: 0.181, auc: 0.339, f1: 0.273, acc: 0.407
+2025-08-07 19:42:38: Epoch: 049 Train Loss: 0.339, ap: 0.648, auc: 0.716, f1: 0.621, acc: 0.680
+2025-08-07 19:42:38: Epoch: 049 Test results, ap: 0.181, auc: 0.341, f1: 0.265, acc: 0.435
+2025-08-07 19:42:39: Epoch: 050 Train Loss: 0.357, ap: 0.662, auc: 0.735, f1: 0.652, acc: 0.699
+2025-08-07 19:42:39: Epoch: 050 Test results, ap: 0.181, auc: 0.338, f1: 0.261, acc: 0.370
+2025-08-07 19:42:39: used time 0.01h
+2025-08-07 19:42:39: Cross Validation Finished!
+2025-08-07 19:42:39: Best validation perform list
+[[0.62162852 0.63411856 0.12       0.57281554]
+ [0.54996133 0.68509424 0.52380955 0.61165047]
+ [0.84247327 0.81410253 0.75199997 0.69902915]
+ [0.71675265 0.7919594  0.6981132  0.68932039]
+ [0.67863059 0.74193549 0.63043481 0.66990292]]
+2025-08-07 19:42:39: mean: [0.682 0.733 0.545 0.649]
+2025-08-07 19:42:39: std: [0.098 0.067 0.226 0.049]
+2025-08-07 19:42:39: Best test perform list
+[[0.45967847 0.6289869  0.38805971 0.24074075]
+ [0.4848671  0.60881805 0.38805971 0.24074075]
+ [0.42523122 0.56238276 0.38805971 0.24074075]
+ [0.34498996 0.53705442 0.31428573 0.55555558]
+ [0.22368318 0.4174484  0.38805971 0.24074075]]
+2025-08-07 19:42:39: mean: [0.388 0.551 0.373 0.304]
+2025-08-07 19:42:39: std: [0.095 0.074 0.03  0.126]
diff --git a/run-cls-simple/lstm-att-256-simple-oneway-ce-32-0.001-50/config.json b/run-cls-simple/lstm-att-256-simple-oneway-ce-32-0.001-50/config.json
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+++ b/run-cls-simple/lstm-att-256-simple-oneway-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
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@@ -0,0 +1,6 @@
+Valid
+0.5927080392837525,0.658204996585846,0.6015851736068726,0.4815533995628357
+0.09750186905390205,0.04132737311964437,0.04747816047714281,0.110287147908446
+Test
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+0.06516296551175646,0.04179312236777989,0.0,0.0
diff --git a/run-cls-simple/lstm-att-256-simple-oneway-ce-32-0.001-50/training.log b/run-cls-simple/lstm-att-256-simple-oneway-ce-32-0.001-50/training.log
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@@ -0,0 +1,576 @@
+2025-08-07 19:31:02: saving_dir: ./run-cls/lstm-att-256-simple-oneway-ce-32-0.001-50
+2025-08-07 19:31:02: Loading Training Dataset
+2025-08-07 19:31:02: Loading Test Dataset
+2025-08-07 19:31:03: Running Cross Validation 0
+2025-08-07 19:31:03: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:31:03: Epoch: 001 Train Loss: 0.695, ap: 0.500, auc: 0.509, f1: 0.627, acc: 0.456
+2025-08-07 19:31:03: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:31:04: Epoch: 001 Test results, ap: 0.371, auc: 0.739, f1: 0.388, acc: 0.241
+2025-08-07 19:31:04: Epoch: 002 Train Loss: 0.694, ap: 0.544, auc: 0.588, f1: 0.627, acc: 0.456
+2025-08-07 19:31:04: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:31:04: Epoch: 002 Test results, ap: 0.246, auc: 0.496, f1: 0.388, acc: 0.241
+2025-08-07 19:31:04: Epoch: 003 Train Loss: 0.695, ap: 0.471, auc: 0.514, f1: 0.627, acc: 0.456
+2025-08-07 19:31:05: Epoch: 003 Test results, ap: 0.294, auc: 0.612, f1: 0.388, acc: 0.241
+2025-08-07 19:31:05: Epoch: 004 Train Loss: 0.695, ap: 0.572, auc: 0.625, f1: 0.627, acc: 0.456
+2025-08-07 19:31:05: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:31:05: Epoch: 004 Test results, ap: 0.288, auc: 0.598, f1: 0.388, acc: 0.241
+2025-08-07 19:31:05: Epoch: 005 Train Loss: 0.698, ap: 0.495, auc: 0.517, f1: 0.627, acc: 0.456
+2025-08-07 19:31:05: Epoch: 005 Test results, ap: 0.219, auc: 0.378, f1: 0.388, acc: 0.241
+2025-08-07 19:31:06: Epoch: 006 Train Loss: 0.694, ap: 0.489, auc: 0.542, f1: 0.000, acc: 0.544
+2025-08-07 19:31:06: Epoch: 006 Test results, ap: 0.205, auc: 0.389, f1: 0.000, acc: 0.759
+2025-08-07 19:31:06: Epoch: 007 Train Loss: 0.697, ap: 0.498, auc: 0.506, f1: 0.627, acc: 0.456
+2025-08-07 19:31:06: Epoch: 007 Test results, ap: 0.210, auc: 0.408, f1: 0.388, acc: 0.241
+2025-08-07 19:31:07: Epoch: 008 Train Loss: 0.694, ap: 0.484, auc: 0.575, f1: 0.627, acc: 0.456
+2025-08-07 19:31:07: Epoch: 008 Test results, ap: 0.255, auc: 0.487, f1: 0.388, acc: 0.241
+2025-08-07 19:31:07: Epoch: 009 Train Loss: 0.692, ap: 0.525, auc: 0.607, f1: 0.627, acc: 0.456
+2025-08-07 19:31:07: Epoch: 009 Test results, ap: 0.273, auc: 0.555, f1: 0.388, acc: 0.241
+2025-08-07 19:31:08: Epoch: 010 Train Loss: 0.697, ap: 0.520, auc: 0.598, f1: 0.000, acc: 0.544
+2025-08-07 19:31:08: Epoch: 010 Test results, ap: 0.279, auc: 0.607, f1: 0.000, acc: 0.759
+2025-08-07 19:31:08: Epoch: 011 Train Loss: 0.694, ap: 0.533, auc: 0.603, f1: 0.627, acc: 0.456
+2025-08-07 19:31:08: Epoch: 011 Test results, ap: 0.278, auc: 0.568, f1: 0.388, acc: 0.241
+2025-08-07 19:31:08: Epoch: 011 New best TEST metrics
+2025-08-07 19:31:08: Epoch: 012 Train Loss: 0.695, ap: 0.473, auc: 0.543, f1: 0.000, acc: 0.544
+2025-08-07 19:31:09: Epoch: 012 Test results, ap: 0.233, auc: 0.471, f1: 0.000, acc: 0.759
+2025-08-07 19:31:09: Epoch: 013 Train Loss: 0.693, ap: 0.513, auc: 0.582, f1: 0.627, acc: 0.456
+2025-08-07 19:31:09: Epoch: 013 Test results, ap: 0.214, auc: 0.401, f1: 0.388, acc: 0.241
+2025-08-07 19:31:09: Epoch: 014 Train Loss: 0.694, ap: 0.540, auc: 0.612, f1: 0.000, acc: 0.544
+2025-08-07 19:31:10: Epoch: 014 Test results, ap: 0.213, auc: 0.373, f1: 0.000, acc: 0.759
+2025-08-07 19:31:10: Epoch: 015 Train Loss: 0.694, ap: 0.525, auc: 0.600, f1: 0.627, acc: 0.456
+2025-08-07 19:31:10: Epoch: 015 Test results, ap: 0.222, auc: 0.409, f1: 0.388, acc: 0.241
+2025-08-07 19:31:10: Epoch: 016 Train Loss: 0.694, ap: 0.510, auc: 0.608, f1: 0.000, acc: 0.544
+2025-08-07 19:31:10: Epoch: 016 Test results, ap: 0.216, auc: 0.385, f1: 0.000, acc: 0.759
+2025-08-07 19:31:11: Epoch: 017 Train Loss: 0.694, ap: 0.504, auc: 0.594, f1: 0.000, acc: 0.544
+2025-08-07 19:31:11: Epoch: 017 Test results, ap: 0.225, auc: 0.433, f1: 0.000, acc: 0.759
+2025-08-07 19:31:11: Epoch: 018 Train Loss: 0.693, ap: 0.515, auc: 0.574, f1: 0.000, acc: 0.544
+2025-08-07 19:31:11: Epoch: 018 Test results, ap: 0.219, auc: 0.422, f1: 0.000, acc: 0.759
+2025-08-07 19:31:12: Epoch: 019 Train Loss: 0.693, ap: 0.541, auc: 0.602, f1: 0.000, acc: 0.544
+2025-08-07 19:31:12: Epoch: 019 Test results, ap: 0.268, auc: 0.551, f1: 0.000, acc: 0.759
+2025-08-07 19:31:12: Epoch: 020 Train Loss: 0.693, ap: 0.513, auc: 0.557, f1: 0.627, acc: 0.456
+2025-08-07 19:31:12: Epoch: 020 Test results, ap: 0.244, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:31:13: Epoch: 021 Train Loss: 0.695, ap: 0.556, auc: 0.613, f1: 0.627, acc: 0.456
+2025-08-07 19:31:13: Epoch: 021 Test results, ap: 0.241, auc: 0.507, f1: 0.388, acc: 0.241
+2025-08-07 19:31:13: Epoch: 022 Train Loss: 0.693, ap: 0.502, auc: 0.581, f1: 0.000, acc: 0.544
+2025-08-07 19:31:13: Epoch: 022 Test results, ap: 0.282, auc: 0.585, f1: 0.000, acc: 0.759
+2025-08-07 19:31:13: Epoch: 022 New best TEST metrics
+2025-08-07 19:31:14: Epoch: 023 Train Loss: 0.694, ap: 0.513, auc: 0.605, f1: 0.000, acc: 0.544
+2025-08-07 19:31:14: Epoch: 023 Test results, ap: 0.258, auc: 0.542, f1: 0.000, acc: 0.759
+2025-08-07 19:31:14: Epoch: 024 Train Loss: 0.693, ap: 0.529, auc: 0.599, f1: 0.000, acc: 0.544
+2025-08-07 19:31:14: Epoch: 024 Test results, ap: 0.208, auc: 0.395, f1: 0.000, acc: 0.759
+2025-08-07 19:31:15: Epoch: 025 Train Loss: 0.695, ap: 0.504, auc: 0.548, f1: 0.000, acc: 0.544
+2025-08-07 19:31:15: Epoch: 025 Test results, ap: 0.219, auc: 0.420, f1: 0.000, acc: 0.759
+2025-08-07 19:31:15: Epoch: 026 Train Loss: 0.693, ap: 0.504, auc: 0.571, f1: 0.000, acc: 0.544
+2025-08-07 19:31:15: Epoch: 026 Test results, ap: 0.246, auc: 0.457, f1: 0.000, acc: 0.759
+2025-08-07 19:31:15: Epoch: 027 Train Loss: 0.694, ap: 0.518, auc: 0.572, f1: 0.000, acc: 0.544
+2025-08-07 19:31:16: Epoch: 027 Test results, ap: 0.230, auc: 0.444, f1: 0.000, acc: 0.759
+2025-08-07 19:31:16: Epoch: 028 Train Loss: 0.694, ap: 0.503, auc: 0.589, f1: 0.000, acc: 0.544
+2025-08-07 19:31:16: Epoch: 028 Test results, ap: 0.216, auc: 0.413, f1: 0.000, acc: 0.759
+2025-08-07 19:31:16: Epoch: 029 Train Loss: 0.694, ap: 0.525, auc: 0.617, f1: 0.000, acc: 0.544
+2025-08-07 19:31:17: Epoch: 029 Test results, ap: 0.257, auc: 0.525, f1: 0.000, acc: 0.759
+2025-08-07 19:31:17: Epoch: 030 Train Loss: 0.694, ap: 0.478, auc: 0.550, f1: 0.000, acc: 0.544
+2025-08-07 19:31:17: Epoch: 030 Test results, ap: 0.243, auc: 0.497, f1: 0.000, acc: 0.759
+2025-08-07 19:31:17: Epoch: 031 Train Loss: 0.694, ap: 0.479, auc: 0.558, f1: 0.627, acc: 0.456
+2025-08-07 19:31:18: Epoch: 031 Test results, ap: 0.225, auc: 0.455, f1: 0.388, acc: 0.241
+2025-08-07 19:31:18: Epoch: 032 Train Loss: 0.693, ap: 0.494, auc: 0.580, f1: 0.627, acc: 0.456
+2025-08-07 19:31:18: Epoch: 032 Test results, ap: 0.232, auc: 0.479, f1: 0.388, acc: 0.241
+2025-08-07 19:31:18: Epoch: 033 Train Loss: 0.693, ap: 0.477, auc: 0.553, f1: 0.000, acc: 0.544
+2025-08-07 19:31:18: Epoch: 033 Test results, ap: 0.228, auc: 0.443, f1: 0.000, acc: 0.759
+2025-08-07 19:31:19: Epoch: 034 Train Loss: 0.693, ap: 0.509, auc: 0.555, f1: 0.627, acc: 0.456
+2025-08-07 19:31:19: Epoch: 034 Test results, ap: 0.252, auc: 0.458, f1: 0.388, acc: 0.241
+2025-08-07 19:31:19: Epoch: 035 Train Loss: 0.693, ap: 0.515, auc: 0.561, f1: 0.627, acc: 0.456
+2025-08-07 19:31:19: Epoch: 035 Test results, ap: 0.238, auc: 0.437, f1: 0.388, acc: 0.241
+2025-08-07 19:31:20: Epoch: 036 Train Loss: 0.693, ap: 0.508, auc: 0.552, f1: 0.000, acc: 0.544
+2025-08-07 19:31:20: Epoch: 036 Test results, ap: 0.251, auc: 0.468, f1: 0.000, acc: 0.759
+2025-08-07 19:31:20: Epoch: 037 Train Loss: 0.693, ap: 0.506, auc: 0.541, f1: 0.000, acc: 0.544
+2025-08-07 19:31:20: Epoch: 037 Test results, ap: 0.245, auc: 0.477, f1: 0.000, acc: 0.759
+2025-08-07 19:31:21: Epoch: 038 Train Loss: 0.692, ap: 0.485, auc: 0.517, f1: 0.627, acc: 0.456
+2025-08-07 19:31:21: Epoch: 038 Test results, ap: 0.270, auc: 0.551, f1: 0.388, acc: 0.241
+2025-08-07 19:31:21: Epoch: 039 Train Loss: 0.693, ap: 0.515, auc: 0.565, f1: 0.627, acc: 0.456
+2025-08-07 19:31:21: Epoch: 039 Test results, ap: 0.264, auc: 0.536, f1: 0.388, acc: 0.241
+2025-08-07 19:31:22: Epoch: 040 Train Loss: 0.694, ap: 0.522, auc: 0.570, f1: 0.627, acc: 0.456
+2025-08-07 19:31:22: Epoch: 040 Test results, ap: 0.235, auc: 0.480, f1: 0.388, acc: 0.241
+2025-08-07 19:31:22: Epoch: 041 Train Loss: 0.693, ap: 0.506, auc: 0.541, f1: 0.000, acc: 0.544
+2025-08-07 19:31:22: Epoch: 041 Test results, ap: 0.253, auc: 0.517, f1: 0.000, acc: 0.759
+2025-08-07 19:31:22: Epoch: 042 Train Loss: 0.693, ap: 0.521, auc: 0.561, f1: 0.627, acc: 0.456
+2025-08-07 19:31:23: Epoch: 042 Test results, ap: 0.283, auc: 0.571, f1: 0.388, acc: 0.241
+2025-08-07 19:31:23: Epoch: 043 Train Loss: 0.693, ap: 0.510, auc: 0.536, f1: 0.627, acc: 0.456
+2025-08-07 19:31:23: Epoch: 043 Test results, ap: 0.251, auc: 0.512, f1: 0.388, acc: 0.241
+2025-08-07 19:31:23: Epoch: 044 Train Loss: 0.693, ap: 0.512, auc: 0.538, f1: 0.627, acc: 0.456
+2025-08-07 19:31:24: Epoch: 044 Test results, ap: 0.308, auc: 0.579, f1: 0.388, acc: 0.241
+2025-08-07 19:31:24: Epoch: 044 New best TEST metrics
+2025-08-07 19:31:24: Epoch: 045 Train Loss: 0.694, ap: 0.514, auc: 0.546, f1: 0.000, acc: 0.544
+2025-08-07 19:31:24: Epoch: 045 Test results, ap: 0.355, auc: 0.575, f1: 0.000, acc: 0.759
+2025-08-07 19:31:24: Epoch: 045 New best TEST metrics
+2025-08-07 19:31:24: Epoch: 046 Train Loss: 0.694, ap: 0.502, auc: 0.524, f1: 0.627, acc: 0.456
+2025-08-07 19:31:24: Epoch: 046 Test results, ap: 0.312, auc: 0.537, f1: 0.388, acc: 0.241
+2025-08-07 19:31:25: Epoch: 047 Train Loss: 0.694, ap: 0.494, auc: 0.505, f1: 0.627, acc: 0.456
+2025-08-07 19:31:25: Epoch: 047 Test results, ap: 0.280, auc: 0.545, f1: 0.388, acc: 0.241
+2025-08-07 19:31:25: Epoch: 048 Train Loss: 0.693, ap: 0.509, auc: 0.510, f1: 0.627, acc: 0.456
+2025-08-07 19:31:25: Epoch: 048 Test results, ap: 0.313, auc: 0.556, f1: 0.388, acc: 0.241
+2025-08-07 19:31:26: Epoch: 049 Train Loss: 0.694, ap: 0.495, auc: 0.503, f1: 0.627, acc: 0.456
+2025-08-07 19:31:26: Epoch: 049 Test results, ap: 0.284, auc: 0.523, f1: 0.388, acc: 0.241
+2025-08-07 19:31:26: Epoch: 050 Train Loss: 0.693, ap: 0.509, auc: 0.509, f1: 0.627, acc: 0.456
+2025-08-07 19:31:26: Epoch: 050 Test results, ap: 0.302, auc: 0.543, f1: 0.388, acc: 0.241
+2025-08-07 19:31:26: used time 0.01h
+2025-08-07 19:31:26: Running Cross Validation 1
+2025-08-07 19:31:26: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:31:27: Epoch: 001 Train Loss: 0.695, ap: 0.431, auc: 0.559, f1: 0.529, acc: 0.359
+2025-08-07 19:31:27: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:31:27: Epoch: 001 Test results, ap: 0.203, auc: 0.337, f1: 0.388, acc: 0.241
+2025-08-07 19:31:27: Epoch: 002 Train Loss: 0.693, ap: 0.431, auc: 0.537, f1: 0.529, acc: 0.359
+2025-08-07 19:31:27: Epoch: 002 Test results, ap: 0.281, auc: 0.484, f1: 0.388, acc: 0.241
+2025-08-07 19:31:28: Epoch: 003 Train Loss: 0.696, ap: 0.385, auc: 0.449, f1: 0.529, acc: 0.359
+2025-08-07 19:31:28: Epoch: 003 Test results, ap: 0.317, auc: 0.613, f1: 0.388, acc: 0.241
+2025-08-07 19:31:28: Epoch: 004 Train Loss: 0.697, ap: 0.406, auc: 0.553, f1: 0.529, acc: 0.359
+2025-08-07 19:31:28: Epoch: 004 Test results, ap: 0.241, auc: 0.451, f1: 0.388, acc: 0.241
+2025-08-07 19:31:29: Epoch: 005 Train Loss: 0.692, ap: 0.400, auc: 0.528, f1: 0.000, acc: 0.641
+2025-08-07 19:31:29: Epoch: 005 Test results, ap: 0.269, auc: 0.536, f1: 0.000, acc: 0.759
+2025-08-07 19:31:29: Epoch: 006 Train Loss: 0.696, ap: 0.419, auc: 0.572, f1: 0.529, acc: 0.359
+2025-08-07 19:31:29: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:31:29: Epoch: 006 Test results, ap: 0.230, auc: 0.469, f1: 0.388, acc: 0.241
+2025-08-07 19:31:29: Epoch: 007 Train Loss: 0.695, ap: 0.425, auc: 0.588, f1: 0.529, acc: 0.359
+2025-08-07 19:31:29: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:31:30: Epoch: 007 Test results, ap: 0.229, auc: 0.430, f1: 0.388, acc: 0.241
+2025-08-07 19:31:30: Epoch: 008 Train Loss: 0.696, ap: 0.378, auc: 0.492, f1: 0.000, acc: 0.641
+2025-08-07 19:31:30: Epoch: 008 Test results, ap: 0.241, auc: 0.482, f1: 0.000, acc: 0.759
+2025-08-07 19:31:30: Epoch: 009 Train Loss: 0.697, ap: 0.380, auc: 0.463, f1: 0.000, acc: 0.641
+2025-08-07 19:31:31: Epoch: 009 Test results, ap: 0.270, auc: 0.570, f1: 0.000, acc: 0.759
+2025-08-07 19:31:31: Epoch: 010 Train Loss: 0.695, ap: 0.339, auc: 0.381, f1: 0.529, acc: 0.359
+2025-08-07 19:31:31: Epoch: 010 Test results, ap: 0.327, auc: 0.571, f1: 0.388, acc: 0.241
+2025-08-07 19:31:31: Epoch: 011 Train Loss: 0.693, ap: 0.394, auc: 0.536, f1: 0.529, acc: 0.359
+2025-08-07 19:31:31: Epoch: 011 Test results, ap: 0.257, auc: 0.541, f1: 0.388, acc: 0.241
+2025-08-07 19:31:31: Epoch: 011 New best TEST metrics
+2025-08-07 19:31:32: Epoch: 012 Train Loss: 0.697, ap: 0.389, auc: 0.548, f1: 0.000, acc: 0.641
+2025-08-07 19:31:32: Epoch: 012 Test results, ap: 0.271, auc: 0.553, f1: 0.000, acc: 0.759
+2025-08-07 19:31:32: Epoch: 012 New best TEST metrics
+2025-08-07 19:31:32: Epoch: 013 Train Loss: 0.694, ap: 0.384, auc: 0.528, f1: 0.000, acc: 0.641
+2025-08-07 19:31:32: Epoch: 013 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:31:33: Epoch: 014 Train Loss: 0.693, ap: 0.411, auc: 0.569, f1: 0.529, acc: 0.359
+2025-08-07 19:31:33: Epoch: 014 Test results, ap: 0.255, auc: 0.535, f1: 0.388, acc: 0.241
+2025-08-07 19:31:33: Epoch: 015 Train Loss: 0.693, ap: 0.387, auc: 0.508, f1: 0.529, acc: 0.359
+2025-08-07 19:31:33: Epoch: 015 Test results, ap: 0.221, auc: 0.437, f1: 0.388, acc: 0.241
+2025-08-07 19:31:34: Epoch: 016 Train Loss: 0.692, ap: 0.384, auc: 0.534, f1: 0.000, acc: 0.641
+2025-08-07 19:31:34: Epoch: 016 Test results, ap: 0.241, auc: 0.463, f1: 0.000, acc: 0.759
+2025-08-07 19:31:34: Epoch: 017 Train Loss: 0.695, ap: 0.398, auc: 0.518, f1: 0.000, acc: 0.641
+2025-08-07 19:31:34: Epoch: 017 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:31:35: Epoch: 018 Train Loss: 0.695, ap: 0.394, auc: 0.540, f1: 0.000, acc: 0.641
+2025-08-07 19:31:35: Epoch: 018 Test results, ap: 0.251, auc: 0.513, f1: 0.000, acc: 0.759
+2025-08-07 19:31:35: Epoch: 019 Train Loss: 0.693, ap: 0.401, auc: 0.547, f1: 0.000, acc: 0.641
+2025-08-07 19:31:35: Epoch: 019 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:31:35: Epoch: 020 Train Loss: 0.693, ap: 0.415, auc: 0.565, f1: 0.000, acc: 0.641
+2025-08-07 19:31:36: Epoch: 020 Test results, ap: 0.322, auc: 0.643, f1: 0.000, acc: 0.759
+2025-08-07 19:31:36: Epoch: 020 New best TEST metrics
+2025-08-07 19:31:36: Epoch: 021 Train Loss: 0.691, ap: 0.434, auc: 0.578, f1: 0.529, acc: 0.359
+2025-08-07 19:31:36: Epoch: 021 Test results, ap: 0.220, auc: 0.437, f1: 0.388, acc: 0.241
+2025-08-07 19:31:36: Epoch: 022 Train Loss: 0.691, ap: 0.394, auc: 0.538, f1: 0.529, acc: 0.359
+2025-08-07 19:31:36: Epoch: 022 Test results, ap: 0.243, auc: 0.505, f1: 0.388, acc: 0.241
+2025-08-07 19:31:37: Epoch: 023 Train Loss: 0.692, ap: 0.402, auc: 0.544, f1: 0.000, acc: 0.641
+2025-08-07 19:31:37: Epoch: 023 Test results, ap: 0.222, auc: 0.350, f1: 0.000, acc: 0.759
+2025-08-07 19:31:37: Epoch: 024 Train Loss: 0.697, ap: 0.449, auc: 0.627, f1: 0.529, acc: 0.359
+2025-08-07 19:31:37: Epoch: 024 New best VALIDATION metrics
+2025-08-07 19:31:37: Epoch: 024 Test results, ap: 0.256, auc: 0.521, f1: 0.388, acc: 0.241
+2025-08-07 19:31:38: Epoch: 025 Train Loss: 0.696, ap: 0.382, auc: 0.490, f1: 0.529, acc: 0.359
+2025-08-07 19:31:38: Epoch: 025 Test results, ap: 0.238, auc: 0.492, f1: 0.388, acc: 0.241
+2025-08-07 19:31:38: Epoch: 026 Train Loss: 0.695, ap: 0.376, auc: 0.506, f1: 0.529, acc: 0.359
+2025-08-07 19:31:38: Epoch: 026 Test results, ap: 0.241, auc: 0.494, f1: 0.388, acc: 0.241
+2025-08-07 19:31:39: Epoch: 027 Train Loss: 0.694, ap: 0.404, auc: 0.532, f1: 0.000, acc: 0.641
+2025-08-07 19:31:39: Epoch: 027 Test results, ap: 0.252, auc: 0.530, f1: 0.000, acc: 0.759
+2025-08-07 19:31:39: Epoch: 028 Train Loss: 0.694, ap: 0.392, auc: 0.534, f1: 0.000, acc: 0.641
+2025-08-07 19:31:39: Epoch: 028 Test results, ap: 0.286, auc: 0.506, f1: 0.000, acc: 0.759
+2025-08-07 19:31:40: Epoch: 029 Train Loss: 0.693, ap: 0.389, auc: 0.532, f1: 0.529, acc: 0.359
+2025-08-07 19:31:40: Epoch: 029 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:31:40: Epoch: 030 Train Loss: 0.694, ap: 0.408, auc: 0.574, f1: 0.529, acc: 0.359
+2025-08-07 19:31:40: Epoch: 030 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:31:41: Epoch: 031 Train Loss: 0.692, ap: 0.397, auc: 0.526, f1: 0.529, acc: 0.359
+2025-08-07 19:31:41: Epoch: 031 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:31:41: Epoch: 032 Train Loss: 0.694, ap: 0.401, auc: 0.568, f1: 0.000, acc: 0.641
+2025-08-07 19:31:41: Epoch: 032 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:31:42: Epoch: 033 Train Loss: 0.694, ap: 0.419, auc: 0.564, f1: 0.000, acc: 0.641
+2025-08-07 19:31:42: Epoch: 033 Test results, ap: 0.268, auc: 0.559, f1: 0.000, acc: 0.759
+2025-08-07 19:31:42: Epoch: 034 Train Loss: 0.693, ap: 0.385, auc: 0.535, f1: 0.000, acc: 0.641
+2025-08-07 19:31:42: Epoch: 034 Test results, ap: 0.268, auc: 0.559, f1: 0.000, acc: 0.759
+2025-08-07 19:31:43: Epoch: 035 Train Loss: 0.694, ap: 0.394, auc: 0.539, f1: 0.000, acc: 0.641
+2025-08-07 19:31:43: Epoch: 035 Test results, ap: 0.270, auc: 0.519, f1: 0.000, acc: 0.759
+2025-08-07 19:31:43: Epoch: 036 Train Loss: 0.694, ap: 0.395, auc: 0.538, f1: 0.000, acc: 0.641
+2025-08-07 19:31:43: Epoch: 036 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:31:44: Epoch: 037 Train Loss: 0.694, ap: 0.388, auc: 0.530, f1: 0.529, acc: 0.359
+2025-08-07 19:31:44: Epoch: 037 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:31:44: Epoch: 038 Train Loss: 0.693, ap: 0.442, auc: 0.607, f1: 0.529, acc: 0.359
+2025-08-07 19:31:44: Epoch: 038 Test results, ap: 0.332, auc: 0.584, f1: 0.388, acc: 0.241
+2025-08-07 19:31:45: Epoch: 039 Train Loss: 0.693, ap: 0.397, auc: 0.548, f1: 0.529, acc: 0.359
+2025-08-07 19:31:45: Epoch: 039 Test results, ap: 0.241, auc: 0.502, f1: 0.388, acc: 0.241
+2025-08-07 19:31:45: Epoch: 040 Train Loss: 0.693, ap: 0.405, auc: 0.538, f1: 0.000, acc: 0.641
+2025-08-07 19:31:45: Epoch: 040 Test results, ap: 0.268, auc: 0.559, f1: 0.000, acc: 0.759
+2025-08-07 19:31:46: Epoch: 041 Train Loss: 0.693, ap: 0.392, auc: 0.534, f1: 0.529, acc: 0.359
+2025-08-07 19:31:46: Epoch: 041 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:31:46: Epoch: 042 Train Loss: 0.694, ap: 0.402, auc: 0.553, f1: 0.529, acc: 0.359
+2025-08-07 19:31:46: Epoch: 042 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:31:47: Epoch: 043 Train Loss: 0.693, ap: 0.388, auc: 0.530, f1: 0.529, acc: 0.359
+2025-08-07 19:31:47: Epoch: 043 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:31:47: Epoch: 044 Train Loss: 0.694, ap: 0.389, auc: 0.526, f1: 0.000, acc: 0.641
+2025-08-07 19:31:47: Epoch: 044 Test results, ap: 0.248, auc: 0.518, f1: 0.000, acc: 0.759
+2025-08-07 19:31:47: Epoch: 045 Train Loss: 0.694, ap: 0.400, auc: 0.542, f1: 0.529, acc: 0.359
+2025-08-07 19:31:48: Epoch: 045 Test results, ap: 0.275, auc: 0.572, f1: 0.388, acc: 0.241
+2025-08-07 19:31:48: Epoch: 046 Train Loss: 0.693, ap: 0.394, auc: 0.535, f1: 0.529, acc: 0.359
+2025-08-07 19:31:48: Epoch: 046 Test results, ap: 0.251, auc: 0.523, f1: 0.388, acc: 0.241
+2025-08-07 19:31:48: Epoch: 047 Train Loss: 0.694, ap: 0.386, auc: 0.529, f1: 0.529, acc: 0.359
+2025-08-07 19:31:49: Epoch: 047 Test results, ap: 0.258, auc: 0.541, f1: 0.388, acc: 0.241
+2025-08-07 19:31:49: Epoch: 048 Train Loss: 0.693, ap: 0.413, auc: 0.569, f1: 0.529, acc: 0.359
+2025-08-07 19:31:49: Epoch: 048 Test results, ap: 0.228, auc: 0.462, f1: 0.388, acc: 0.241
+2025-08-07 19:31:49: Epoch: 049 Train Loss: 0.693, ap: 0.401, auc: 0.547, f1: 0.529, acc: 0.359
+2025-08-07 19:31:50: Epoch: 049 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:31:50: Epoch: 050 Train Loss: 0.693, ap: 0.391, auc: 0.530, f1: 0.529, acc: 0.359
+2025-08-07 19:31:50: Epoch: 050 Test results, ap: 0.241, auc: 0.494, f1: 0.388, acc: 0.241
+2025-08-07 19:31:50: used time 0.01h
+2025-08-07 19:31:50: Running Cross Validation 2
+2025-08-07 19:31:50: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:31:50: Epoch: 001 Train Loss: 0.693, ap: 0.605, auc: 0.519, f1: 0.000, acc: 0.495
+2025-08-07 19:31:50: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:31:51: Epoch: 001 Test results, ap: 0.340, auc: 0.539, f1: 0.000, acc: 0.759
+2025-08-07 19:31:51: Epoch: 002 Train Loss: 0.699, ap: 0.690, auc: 0.673, f1: 0.671, acc: 0.505
+2025-08-07 19:31:51: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:31:51: Epoch: 002 Test results, ap: 0.334, auc: 0.513, f1: 0.388, acc: 0.241
+2025-08-07 19:31:51: Epoch: 003 Train Loss: 0.695, ap: 0.556, auc: 0.520, f1: 0.000, acc: 0.495
+2025-08-07 19:31:52: Epoch: 003 Test results, ap: 0.336, auc: 0.567, f1: 0.000, acc: 0.759
+2025-08-07 19:31:52: Epoch: 004 Train Loss: 0.692, ap: 0.559, auc: 0.531, f1: 0.000, acc: 0.495
+2025-08-07 19:31:52: Epoch: 004 Test results, ap: 0.406, auc: 0.579, f1: 0.000, acc: 0.759
+2025-08-07 19:31:52: Epoch: 005 Train Loss: 0.695, ap: 0.582, auc: 0.531, f1: 0.671, acc: 0.505
+2025-08-07 19:31:53: Epoch: 005 Test results, ap: 0.264, auc: 0.405, f1: 0.388, acc: 0.241
+2025-08-07 19:31:53: Epoch: 006 Train Loss: 0.697, ap: 0.543, auc: 0.461, f1: 0.671, acc: 0.505
+2025-08-07 19:31:53: Epoch: 006 Test results, ap: 0.324, auc: 0.589, f1: 0.388, acc: 0.241
+2025-08-07 19:31:53: Epoch: 007 Train Loss: 0.694, ap: 0.529, auc: 0.435, f1: 0.671, acc: 0.505
+2025-08-07 19:31:53: Epoch: 007 Test results, ap: 0.430, auc: 0.676, f1: 0.388, acc: 0.241
+2025-08-07 19:31:54: Epoch: 008 Train Loss: 0.694, ap: 0.613, auc: 0.593, f1: 0.000, acc: 0.495
+2025-08-07 19:31:54: Epoch: 008 Test results, ap: 0.329, auc: 0.538, f1: 0.000, acc: 0.759
+2025-08-07 19:31:54: Epoch: 009 Train Loss: 0.693, ap: 0.600, auc: 0.560, f1: 0.000, acc: 0.495
+2025-08-07 19:31:54: Epoch: 009 Test results, ap: 0.405, auc: 0.639, f1: 0.000, acc: 0.759
+2025-08-07 19:31:55: Epoch: 010 Train Loss: 0.692, ap: 0.582, auc: 0.560, f1: 0.273, acc: 0.534
+2025-08-07 19:31:55: Epoch: 010 Test results, ap: 0.509, auc: 0.624, f1: 0.444, acc: 0.769
+2025-08-07 19:31:55: Epoch: 011 Train Loss: 0.698, ap: 0.595, auc: 0.612, f1: 0.671, acc: 0.505
+2025-08-07 19:31:56: Epoch: 011 Test results, ap: 0.209, auc: 0.417, f1: 0.388, acc: 0.241
+2025-08-07 19:31:56: Epoch: 011 New best TEST metrics
+2025-08-07 19:31:56: Epoch: 012 Train Loss: 0.694, ap: 0.600, auc: 0.643, f1: 0.000, acc: 0.495
+2025-08-07 19:31:56: Epoch: 012 Test results, ap: 0.225, auc: 0.448, f1: 0.000, acc: 0.759
+2025-08-07 19:31:56: Epoch: 012 New best TEST metrics
+2025-08-07 19:31:56: Epoch: 013 Train Loss: 0.698, ap: 0.492, auc: 0.467, f1: 0.671, acc: 0.505
+2025-08-07 19:31:57: Epoch: 013 Test results, ap: 0.241, auc: 0.488, f1: 0.388, acc: 0.241
+2025-08-07 19:31:57: Epoch: 013 New best TEST metrics
+2025-08-07 19:31:57: Epoch: 014 Train Loss: 0.693, ap: 0.547, auc: 0.561, f1: 0.000, acc: 0.495
+2025-08-07 19:31:57: Epoch: 014 Test results, ap: 0.220, auc: 0.433, f1: 0.000, acc: 0.759
+2025-08-07 19:31:57: Epoch: 015 Train Loss: 0.694, ap: 0.553, auc: 0.572, f1: 0.671, acc: 0.505
+2025-08-07 19:31:57: Epoch: 015 Test results, ap: 0.228, auc: 0.462, f1: 0.388, acc: 0.241
+2025-08-07 19:31:58: Epoch: 016 Train Loss: 0.697, ap: 0.568, auc: 0.597, f1: 0.671, acc: 0.505
+2025-08-07 19:31:58: Epoch: 016 Test results, ap: 0.228, auc: 0.462, f1: 0.388, acc: 0.241
+2025-08-07 19:31:58: Epoch: 017 Train Loss: 0.693, ap: 0.550, auc: 0.574, f1: 0.000, acc: 0.495
+2025-08-07 19:31:58: Epoch: 017 Test results, ap: 0.244, auc: 0.509, f1: 0.000, acc: 0.759
+2025-08-07 19:31:58: Epoch: 017 New best TEST metrics
+2025-08-07 19:31:59: Epoch: 018 Train Loss: 0.699, ap: 0.600, auc: 0.645, f1: 0.000, acc: 0.495
+2025-08-07 19:31:59: Epoch: 018 Test results, ap: 0.241, auc: 0.451, f1: 0.000, acc: 0.759
+2025-08-07 19:31:59: Epoch: 019 Train Loss: 0.695, ap: 0.493, auc: 0.463, f1: 0.000, acc: 0.495
+2025-08-07 19:31:59: Epoch: 019 Test results, ap: 0.323, auc: 0.634, f1: 0.000, acc: 0.759
+2025-08-07 19:31:59: Epoch: 019 New best TEST metrics
+2025-08-07 19:32:00: Epoch: 020 Train Loss: 0.693, ap: 0.453, auc: 0.389, f1: 0.000, acc: 0.495
+2025-08-07 19:32:00: Epoch: 020 Test results, ap: 0.257, auc: 0.541, f1: 0.000, acc: 0.759
+2025-08-07 19:32:00: Epoch: 021 Train Loss: 0.693, ap: 0.449, auc: 0.386, f1: 0.000, acc: 0.495
+2025-08-07 19:32:00: Epoch: 021 Test results, ap: 0.252, auc: 0.528, f1: 0.000, acc: 0.759
+2025-08-07 19:32:01: Epoch: 022 Train Loss: 0.697, ap: 0.478, auc: 0.417, f1: 0.000, acc: 0.495
+2025-08-07 19:32:01: Epoch: 022 Test results, ap: 0.270, auc: 0.552, f1: 0.000, acc: 0.759
+2025-08-07 19:32:01: Epoch: 023 Train Loss: 0.698, ap: 0.486, auc: 0.438, f1: 0.671, acc: 0.505
+2025-08-07 19:32:01: Epoch: 023 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:32:02: Epoch: 024 Train Loss: 0.694, ap: 0.490, auc: 0.462, f1: 0.000, acc: 0.495
+2025-08-07 19:32:02: Epoch: 024 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:32:02: Epoch: 025 Train Loss: 0.693, ap: 0.491, auc: 0.440, f1: 0.000, acc: 0.495
+2025-08-07 19:32:02: Epoch: 025 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:32:03: Epoch: 026 Train Loss: 0.693, ap: 0.482, auc: 0.422, f1: 0.000, acc: 0.495
+2025-08-07 19:32:03: Epoch: 026 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:32:03: Epoch: 027 Train Loss: 0.693, ap: 0.491, auc: 0.472, f1: 0.000, acc: 0.495
+2025-08-07 19:32:03: Epoch: 027 Test results, ap: 0.247, auc: 0.517, f1: 0.000, acc: 0.759
+2025-08-07 19:32:04: Epoch: 028 Train Loss: 0.693, ap: 0.439, auc: 0.325, f1: 0.000, acc: 0.495
+2025-08-07 19:32:04: Epoch: 028 Test results, ap: 0.254, auc: 0.535, f1: 0.000, acc: 0.759
+2025-08-07 19:32:04: Epoch: 029 Train Loss: 0.694, ap: 0.495, auc: 0.478, f1: 0.671, acc: 0.505
+2025-08-07 19:32:04: Epoch: 029 Test results, ap: 0.243, auc: 0.506, f1: 0.388, acc: 0.241
+2025-08-07 19:32:05: Epoch: 030 Train Loss: 0.693, ap: 0.491, auc: 0.439, f1: 0.671, acc: 0.505
+2025-08-07 19:32:05: Epoch: 030 Test results, ap: 0.241, auc: 0.494, f1: 0.388, acc: 0.241
+2025-08-07 19:32:05: Epoch: 031 Train Loss: 0.694, ap: 0.491, auc: 0.472, f1: 0.671, acc: 0.505
+2025-08-07 19:32:05: Epoch: 031 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:32:05: Epoch: 032 Train Loss: 0.694, ap: 0.491, auc: 0.473, f1: 0.000, acc: 0.495
+2025-08-07 19:32:06: Epoch: 032 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:32:06: Epoch: 033 Train Loss: 0.694, ap: 0.452, auc: 0.346, f1: 0.000, acc: 0.495
+2025-08-07 19:32:06: Epoch: 033 Test results, ap: 0.276, auc: 0.581, f1: 0.000, acc: 0.759
+2025-08-07 19:32:06: Epoch: 034 Train Loss: 0.696, ap: 0.592, auc: 0.586, f1: 0.000, acc: 0.495
+2025-08-07 19:32:07: Epoch: 034 Test results, ap: 0.241, auc: 0.494, f1: 0.000, acc: 0.759
+2025-08-07 19:32:07: Epoch: 035 Train Loss: 0.693, ap: 0.486, auc: 0.462, f1: 0.000, acc: 0.495
+2025-08-07 19:32:07: Epoch: 035 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:32:07: Epoch: 036 Train Loss: 0.692, ap: 0.486, auc: 0.461, f1: 0.000, acc: 0.495
+2025-08-07 19:32:08: Epoch: 036 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:32:08: Epoch: 037 Train Loss: 0.694, ap: 0.487, auc: 0.463, f1: 0.000, acc: 0.495
+2025-08-07 19:32:08: Epoch: 037 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:32:08: Epoch: 038 Train Loss: 0.693, ap: 0.491, auc: 0.470, f1: 0.671, acc: 0.505
+2025-08-07 19:32:08: Epoch: 038 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:32:09: Epoch: 039 Train Loss: 0.693, ap: 0.525, auc: 0.532, f1: 0.671, acc: 0.505
+2025-08-07 19:32:09: Epoch: 039 Test results, ap: 0.243, auc: 0.506, f1: 0.388, acc: 0.241
+2025-08-07 19:32:09: Epoch: 040 Train Loss: 0.695, ap: 0.539, auc: 0.545, f1: 0.671, acc: 0.505
+2025-08-07 19:32:09: Epoch: 040 Test results, ap: 0.241, auc: 0.476, f1: 0.388, acc: 0.241
+2025-08-07 19:32:10: Epoch: 041 Train Loss: 0.693, ap: 0.460, auc: 0.326, f1: 0.671, acc: 0.505
+2025-08-07 19:32:10: Epoch: 041 Test results, ap: 0.261, auc: 0.544, f1: 0.388, acc: 0.241
+2025-08-07 19:32:10: Epoch: 042 Train Loss: 0.694, ap: 0.487, auc: 0.463, f1: 0.671, acc: 0.505
+2025-08-07 19:32:10: Epoch: 042 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:32:11: Epoch: 043 Train Loss: 0.694, ap: 0.488, auc: 0.432, f1: 0.671, acc: 0.505
+2025-08-07 19:32:11: Epoch: 043 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:32:11: Epoch: 044 Train Loss: 0.694, ap: 0.482, auc: 0.454, f1: 0.671, acc: 0.505
+2025-08-07 19:32:11: Epoch: 044 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:32:12: Epoch: 045 Train Loss: 0.694, ap: 0.487, auc: 0.464, f1: 0.671, acc: 0.505
+2025-08-07 19:32:12: Epoch: 045 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:32:12: Epoch: 046 Train Loss: 0.693, ap: 0.487, auc: 0.447, f1: 0.671, acc: 0.505
+2025-08-07 19:32:12: Epoch: 046 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:32:12: Epoch: 047 Train Loss: 0.693, ap: 0.480, auc: 0.382, f1: 0.671, acc: 0.505
+2025-08-07 19:32:13: Epoch: 047 Test results, ap: 0.228, auc: 0.450, f1: 0.388, acc: 0.241
+2025-08-07 19:32:13: Epoch: 048 Train Loss: 0.692, ap: 0.500, auc: 0.488, f1: 0.671, acc: 0.505
+2025-08-07 19:32:13: Epoch: 048 Test results, ap: 0.218, auc: 0.345, f1: 0.388, acc: 0.241
+2025-08-07 19:32:13: Epoch: 049 Train Loss: 0.694, ap: 0.495, auc: 0.479, f1: 0.671, acc: 0.505
+2025-08-07 19:32:14: Epoch: 049 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:32:14: Epoch: 050 Train Loss: 0.693, ap: 0.481, auc: 0.390, f1: 0.671, acc: 0.505
+2025-08-07 19:32:14: Epoch: 050 Test results, ap: 0.274, auc: 0.532, f1: 0.388, acc: 0.241
+2025-08-07 19:32:14: used time 0.01h
+2025-08-07 19:32:14: Running Cross Validation 3
+2025-08-07 19:32:14: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:32:14: Epoch: 001 Train Loss: 0.694, ap: 0.466, auc: 0.510, f1: 0.000, acc: 0.592
+2025-08-07 19:32:14: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:32:15: Epoch: 001 Test results, ap: 0.371, auc: 0.595, f1: 0.000, acc: 0.759
+2025-08-07 19:32:15: Epoch: 002 Train Loss: 0.697, ap: 0.488, auc: 0.531, f1: 0.000, acc: 0.592
+2025-08-07 19:32:15: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:32:15: Epoch: 002 Test results, ap: 0.470, auc: 0.642, f1: 0.000, acc: 0.759
+2025-08-07 19:32:15: Epoch: 003 Train Loss: 0.689, ap: 0.398, auc: 0.462, f1: 0.000, acc: 0.592
+2025-08-07 19:32:15: Epoch: 003 Test results, ap: 0.268, auc: 0.438, f1: 0.000, acc: 0.759
+2025-08-07 19:32:16: Epoch: 004 Train Loss: 0.699, ap: 0.412, auc: 0.491, f1: 0.000, acc: 0.592
+2025-08-07 19:32:16: Epoch: 004 Test results, ap: 0.309, auc: 0.507, f1: 0.000, acc: 0.759
+2025-08-07 19:32:16: Epoch: 005 Train Loss: 0.694, ap: 0.427, auc: 0.524, f1: 0.579, acc: 0.408
+2025-08-07 19:32:16: Epoch: 005 Test results, ap: 0.262, auc: 0.553, f1: 0.388, acc: 0.241
+2025-08-07 19:32:17: Epoch: 006 Train Loss: 0.694, ap: 0.452, auc: 0.519, f1: 0.000, acc: 0.592
+2025-08-07 19:32:17: Epoch: 006 Test results, ap: 0.259, auc: 0.506, f1: 0.000, acc: 0.759
+2025-08-07 19:32:17: Epoch: 007 Train Loss: 0.693, ap: 0.482, auc: 0.537, f1: 0.579, acc: 0.408
+2025-08-07 19:32:17: Epoch: 007 Test results, ap: 0.331, auc: 0.593, f1: 0.388, acc: 0.241
+2025-08-07 19:32:18: Epoch: 008 Train Loss: 0.695, ap: 0.482, auc: 0.529, f1: 0.000, acc: 0.592
+2025-08-07 19:32:18: Epoch: 008 Test results, ap: 0.258, auc: 0.505, f1: 0.000, acc: 0.759
+2025-08-07 19:32:18: Epoch: 009 Train Loss: 0.695, ap: 0.461, auc: 0.502, f1: 0.579, acc: 0.408
+2025-08-07 19:32:18: Epoch: 009 Test results, ap: 0.251, auc: 0.509, f1: 0.388, acc: 0.241
+2025-08-07 19:32:19: Epoch: 010 Train Loss: 0.694, ap: 0.443, auc: 0.472, f1: 0.579, acc: 0.408
+2025-08-07 19:32:19: Epoch: 010 Test results, ap: 0.244, auc: 0.509, f1: 0.388, acc: 0.241
+2025-08-07 19:32:19: Epoch: 011 Train Loss: 0.694, ap: 0.483, auc: 0.581, f1: 0.000, acc: 0.592
+2025-08-07 19:32:19: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:32:19: Epoch: 011 Test results, ap: 0.280, auc: 0.582, f1: 0.000, acc: 0.759
+2025-08-07 19:32:19: Epoch: 011 New best TEST metrics
+2025-08-07 19:32:20: Epoch: 012 Train Loss: 0.693, ap: 0.480, auc: 0.550, f1: 0.000, acc: 0.592
+2025-08-07 19:32:20: Epoch: 012 Test results, ap: 0.232, auc: 0.474, f1: 0.000, acc: 0.759
+2025-08-07 19:32:20: Epoch: 013 Train Loss: 0.694, ap: 0.458, auc: 0.506, f1: 0.000, acc: 0.592
+2025-08-07 19:32:20: Epoch: 013 Test results, ap: 0.268, auc: 0.545, f1: 0.000, acc: 0.759
+2025-08-07 19:32:20: Epoch: 014 Train Loss: 0.695, ap: 0.465, auc: 0.517, f1: 0.000, acc: 0.592
+2025-08-07 19:32:21: Epoch: 014 Test results, ap: 0.300, auc: 0.576, f1: 0.000, acc: 0.759
+2025-08-07 19:32:21: Epoch: 014 New best TEST metrics
+2025-08-07 19:32:21: Epoch: 015 Train Loss: 0.694, ap: 0.496, auc: 0.557, f1: 0.000, acc: 0.592
+2025-08-07 19:32:21: Epoch: 015 Test results, ap: 0.272, auc: 0.506, f1: 0.000, acc: 0.759
+2025-08-07 19:32:21: Epoch: 016 Train Loss: 0.692, ap: 0.488, auc: 0.551, f1: 0.000, acc: 0.592
+2025-08-07 19:32:22: Epoch: 016 Test results, ap: 0.272, auc: 0.519, f1: 0.000, acc: 0.759
+2025-08-07 19:32:22: Epoch: 017 Train Loss: 0.693, ap: 0.511, auc: 0.602, f1: 0.579, acc: 0.408
+2025-08-07 19:32:22: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:32:22: Epoch: 017 Test results, ap: 0.285, auc: 0.512, f1: 0.388, acc: 0.241
+2025-08-07 19:32:22: Epoch: 018 Train Loss: 0.694, ap: 0.540, auc: 0.633, f1: 0.579, acc: 0.408
+2025-08-07 19:32:22: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:32:23: Epoch: 018 Test results, ap: 0.286, auc: 0.544, f1: 0.388, acc: 0.241
+2025-08-07 19:32:23: Epoch: 019 Train Loss: 0.692, ap: 0.510, auc: 0.582, f1: 0.000, acc: 0.592
+2025-08-07 19:32:23: Epoch: 019 Test results, ap: 0.292, auc: 0.550, f1: 0.000, acc: 0.759
+2025-08-07 19:32:23: Epoch: 020 Train Loss: 0.693, ap: 0.505, auc: 0.561, f1: 0.000, acc: 0.592
+2025-08-07 19:32:24: Epoch: 020 Test results, ap: 0.355, auc: 0.543, f1: 0.000, acc: 0.759
+2025-08-07 19:32:24: Epoch: 020 New best TEST metrics
+2025-08-07 19:32:24: Epoch: 021 Train Loss: 0.696, ap: 0.500, auc: 0.571, f1: 0.579, acc: 0.408
+2025-08-07 19:32:24: Epoch: 021 Test results, ap: 0.278, auc: 0.542, f1: 0.388, acc: 0.241
+2025-08-07 19:32:24: Epoch: 022 Train Loss: 0.695, ap: 0.492, auc: 0.565, f1: 0.579, acc: 0.408
+2025-08-07 19:32:24: Epoch: 022 Test results, ap: 0.287, auc: 0.587, f1: 0.388, acc: 0.241
+2025-08-07 19:32:25: Epoch: 023 Train Loss: 0.694, ap: 0.479, auc: 0.527, f1: 0.579, acc: 0.408
+2025-08-07 19:32:25: Epoch: 023 Test results, ap: 0.272, auc: 0.561, f1: 0.388, acc: 0.241
+2025-08-07 19:32:25: Epoch: 024 Train Loss: 0.692, ap: 0.479, auc: 0.520, f1: 0.579, acc: 0.408
+2025-08-07 19:32:26: Epoch: 024 Test results, ap: 0.299, auc: 0.581, f1: 0.388, acc: 0.241
+2025-08-07 19:32:26: Epoch: 025 Train Loss: 0.695, ap: 0.493, auc: 0.557, f1: 0.579, acc: 0.408
+2025-08-07 19:32:26: Epoch: 025 Test results, ap: 0.280, auc: 0.542, f1: 0.388, acc: 0.241
+2025-08-07 19:32:26: Epoch: 026 Train Loss: 0.694, ap: 0.491, auc: 0.562, f1: 0.579, acc: 0.408
+2025-08-07 19:32:26: Epoch: 026 Test results, ap: 0.300, auc: 0.583, f1: 0.388, acc: 0.241
+2025-08-07 19:32:27: Epoch: 027 Train Loss: 0.693, ap: 0.487, auc: 0.518, f1: 0.000, acc: 0.592
+2025-08-07 19:32:27: Epoch: 027 Test results, ap: 0.297, auc: 0.575, f1: 0.000, acc: 0.759
+2025-08-07 19:32:27: Epoch: 028 Train Loss: 0.693, ap: 0.491, auc: 0.540, f1: 0.579, acc: 0.408
+2025-08-07 19:32:27: Epoch: 028 Test results, ap: 0.293, auc: 0.585, f1: 0.388, acc: 0.241
+2025-08-07 19:32:28: Epoch: 029 Train Loss: 0.694, ap: 0.474, auc: 0.505, f1: 0.579, acc: 0.408
+2025-08-07 19:32:28: Epoch: 029 Test results, ap: 0.285, auc: 0.557, f1: 0.388, acc: 0.241
+2025-08-07 19:32:28: Epoch: 030 Train Loss: 0.693, ap: 0.479, auc: 0.496, f1: 0.579, acc: 0.408
+2025-08-07 19:32:28: Epoch: 030 Test results, ap: 0.293, auc: 0.590, f1: 0.388, acc: 0.241
+2025-08-07 19:32:29: Epoch: 031 Train Loss: 0.693, ap: 0.478, auc: 0.498, f1: 0.000, acc: 0.592
+2025-08-07 19:32:29: Epoch: 031 Test results, ap: 0.312, auc: 0.562, f1: 0.000, acc: 0.759
+2025-08-07 19:32:29: Epoch: 032 Train Loss: 0.693, ap: 0.482, auc: 0.501, f1: 0.000, acc: 0.592
+2025-08-07 19:32:29: Epoch: 032 Test results, ap: 0.392, auc: 0.603, f1: 0.000, acc: 0.759
+2025-08-07 19:32:29: Epoch: 032 New best TEST metrics
+2025-08-07 19:32:30: Epoch: 033 Train Loss: 0.693, ap: 0.482, auc: 0.519, f1: 0.000, acc: 0.592
+2025-08-07 19:32:30: Epoch: 033 Test results, ap: 0.323, auc: 0.582, f1: 0.000, acc: 0.759
+2025-08-07 19:32:30: Epoch: 034 Train Loss: 0.694, ap: 0.477, auc: 0.504, f1: 0.000, acc: 0.592
+2025-08-07 19:32:30: Epoch: 034 Test results, ap: 0.401, auc: 0.599, f1: 0.000, acc: 0.759
+2025-08-07 19:32:30: Epoch: 034 New best TEST metrics
+2025-08-07 19:32:31: Epoch: 035 Train Loss: 0.695, ap: 0.482, auc: 0.521, f1: 0.579, acc: 0.408
+2025-08-07 19:32:31: Epoch: 035 Test results, ap: 0.292, auc: 0.553, f1: 0.388, acc: 0.241
+2025-08-07 19:32:31: Epoch: 036 Train Loss: 0.693, ap: 0.486, auc: 0.513, f1: 0.579, acc: 0.408
+2025-08-07 19:32:31: Epoch: 036 Test results, ap: 0.342, auc: 0.576, f1: 0.388, acc: 0.241
+2025-08-07 19:32:32: Epoch: 037 Train Loss: 0.695, ap: 0.471, auc: 0.498, f1: 0.579, acc: 0.408
+2025-08-07 19:32:32: Epoch: 037 Test results, ap: 0.292, auc: 0.591, f1: 0.388, acc: 0.241
+2025-08-07 19:32:32: Epoch: 038 Train Loss: 0.694, ap: 0.482, auc: 0.527, f1: 0.579, acc: 0.408
+2025-08-07 19:32:32: Epoch: 038 Test results, ap: 0.331, auc: 0.595, f1: 0.388, acc: 0.241
+2025-08-07 19:32:33: Epoch: 039 Train Loss: 0.693, ap: 0.478, auc: 0.522, f1: 0.579, acc: 0.408
+2025-08-07 19:32:33: Epoch: 039 Test results, ap: 0.297, auc: 0.572, f1: 0.388, acc: 0.241
+2025-08-07 19:32:33: Epoch: 040 Train Loss: 0.692, ap: 0.475, auc: 0.501, f1: 0.579, acc: 0.408
+2025-08-07 19:32:33: Epoch: 040 Test results, ap: 0.294, auc: 0.572, f1: 0.388, acc: 0.241
+2025-08-07 19:32:33: Epoch: 041 Train Loss: 0.692, ap: 0.477, auc: 0.508, f1: 0.579, acc: 0.408
+2025-08-07 19:32:34: Epoch: 041 Test results, ap: 0.370, auc: 0.599, f1: 0.388, acc: 0.241
+2025-08-07 19:32:34: Epoch: 042 Train Loss: 0.694, ap: 0.479, auc: 0.523, f1: 0.579, acc: 0.408
+2025-08-07 19:32:34: Epoch: 042 Test results, ap: 0.353, auc: 0.598, f1: 0.388, acc: 0.241
+2025-08-07 19:32:34: Epoch: 043 Train Loss: 0.693, ap: 0.480, auc: 0.507, f1: 0.579, acc: 0.408
+2025-08-07 19:32:35: Epoch: 043 Test results, ap: 0.342, auc: 0.569, f1: 0.388, acc: 0.241
+2025-08-07 19:32:35: Epoch: 044 Train Loss: 0.693, ap: 0.482, auc: 0.526, f1: 0.579, acc: 0.408
+2025-08-07 19:32:35: Epoch: 044 Test results, ap: 0.306, auc: 0.595, f1: 0.388, acc: 0.241
+2025-08-07 19:32:35: Epoch: 045 Train Loss: 0.694, ap: 0.482, auc: 0.528, f1: 0.583, acc: 0.417
+2025-08-07 19:32:36: Epoch: 045 Test results, ap: 0.382, auc: 0.598, f1: 0.388, acc: 0.241
+2025-08-07 19:32:36: Epoch: 046 Train Loss: 0.694, ap: 0.479, auc: 0.519, f1: 0.587, acc: 0.427
+2025-08-07 19:32:36: Epoch: 046 Test results, ap: 0.301, auc: 0.557, f1: 0.388, acc: 0.241
+2025-08-07 19:32:36: Epoch: 047 Train Loss: 0.694, ap: 0.475, auc: 0.514, f1: 0.579, acc: 0.408
+2025-08-07 19:32:36: Epoch: 047 Test results, ap: 0.325, auc: 0.573, f1: 0.388, acc: 0.241
+2025-08-07 19:32:37: Epoch: 048 Train Loss: 0.693, ap: 0.477, auc: 0.511, f1: 0.047, acc: 0.602
+2025-08-07 19:32:37: Epoch: 048 Test results, ap: 0.387, auc: 0.586, f1: 0.000, acc: 0.759
+2025-08-07 19:32:37: Epoch: 049 Train Loss: 0.693, ap: 0.483, auc: 0.522, f1: 0.000, acc: 0.592
+2025-08-07 19:32:37: Epoch: 049 Test results, ap: 0.400, auc: 0.591, f1: 0.000, acc: 0.759
+2025-08-07 19:32:38: Epoch: 050 Train Loss: 0.693, ap: 0.482, auc: 0.525, f1: 0.000, acc: 0.592
+2025-08-07 19:32:38: Epoch: 050 Test results, ap: 0.337, auc: 0.588, f1: 0.000, acc: 0.759
+2025-08-07 19:32:38: used time 0.01h
+2025-08-07 19:32:38: Running Cross Validation 4
+2025-08-07 19:32:38: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:32:38: Epoch: 001 Train Loss: 0.694, ap: 0.520, auc: 0.634, f1: 0.000, acc: 0.602
+2025-08-07 19:32:38: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:32:38: Epoch: 001 Test results, ap: 0.526, auc: 0.688, f1: 0.000, acc: 0.759
+2025-08-07 19:32:39: Epoch: 002 Train Loss: 0.694, ap: 0.369, auc: 0.471, f1: 0.000, acc: 0.602
+2025-08-07 19:32:39: Epoch: 002 Test results, ap: 0.465, auc: 0.597, f1: 0.000, acc: 0.759
+2025-08-07 19:32:39: Epoch: 003 Train Loss: 0.696, ap: 0.387, auc: 0.470, f1: 0.569, acc: 0.398
+2025-08-07 19:32:39: Epoch: 003 Test results, ap: 0.204, auc: 0.347, f1: 0.388, acc: 0.241
+2025-08-07 19:32:40: Epoch: 004 Train Loss: 0.695, ap: 0.414, auc: 0.551, f1: 0.569, acc: 0.398
+2025-08-07 19:32:40: Epoch: 004 Test results, ap: 0.210, auc: 0.380, f1: 0.388, acc: 0.241
+2025-08-07 19:32:40: Epoch: 005 Train Loss: 0.692, ap: 0.388, auc: 0.476, f1: 0.569, acc: 0.398
+2025-08-07 19:32:40: Epoch: 005 Test results, ap: 0.203, auc: 0.386, f1: 0.388, acc: 0.241
+2025-08-07 19:32:41: Epoch: 006 Train Loss: 0.696, ap: 0.435, auc: 0.585, f1: 0.569, acc: 0.398
+2025-08-07 19:32:41: Epoch: 006 Test results, ap: 0.226, auc: 0.409, f1: 0.388, acc: 0.241
+2025-08-07 19:32:41: Epoch: 007 Train Loss: 0.692, ap: 0.385, auc: 0.461, f1: 0.000, acc: 0.602
+2025-08-07 19:32:41: Epoch: 007 Test results, ap: 0.182, auc: 0.324, f1: 0.000, acc: 0.759
+2025-08-07 19:32:42: Epoch: 008 Train Loss: 0.695, ap: 0.360, auc: 0.439, f1: 0.569, acc: 0.398
+2025-08-07 19:32:42: Epoch: 008 Test results, ap: 0.239, auc: 0.485, f1: 0.388, acc: 0.241
+2025-08-07 19:32:42: Epoch: 009 Train Loss: 0.695, ap: 0.353, auc: 0.423, f1: 0.569, acc: 0.398
+2025-08-07 19:32:42: Epoch: 009 Test results, ap: 0.418, auc: 0.581, f1: 0.388, acc: 0.241
+2025-08-07 19:32:42: Epoch: 010 Train Loss: 0.694, ap: 0.451, auc: 0.578, f1: 0.000, acc: 0.602
+2025-08-07 19:32:43: Epoch: 010 Test results, ap: 0.356, auc: 0.696, f1: 0.000, acc: 0.759
+2025-08-07 19:32:43: Epoch: 011 Train Loss: 0.693, ap: 0.498, auc: 0.649, f1: 0.148, acc: 0.553
+2025-08-07 19:32:43: Epoch: 011 Test results, ap: 0.496, auc: 0.697, f1: 0.000, acc: 0.759
+2025-08-07 19:32:43: Epoch: 011 New best TEST metrics
+2025-08-07 19:32:43: Epoch: 012 Train Loss: 0.685, ap: 0.475, auc: 0.621, f1: 0.619, acc: 0.583
+2025-08-07 19:32:43: Epoch: 012 Test results, ap: 0.191, auc: 0.365, f1: 0.300, acc: 0.352
+2025-08-07 19:32:44: Epoch: 013 Train Loss: 0.693, ap: 0.502, auc: 0.661, f1: 0.603, acc: 0.476
+2025-08-07 19:32:44: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:32:44: Epoch: 013 Test results, ap: 0.187, auc: 0.364, f1: 0.388, acc: 0.241
+2025-08-07 19:32:44: Epoch: 014 Train Loss: 0.692, ap: 0.530, auc: 0.670, f1: 0.643, acc: 0.602
+2025-08-07 19:32:44: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:32:45: Epoch: 014 Test results, ap: 0.186, auc: 0.322, f1: 0.320, acc: 0.213
+2025-08-07 19:32:45: Epoch: 015 Train Loss: 0.673, ap: 0.544, auc: 0.698, f1: 0.634, acc: 0.641
+2025-08-07 19:32:45: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:32:45: Epoch: 015 Test results, ap: 0.182, auc: 0.347, f1: 0.388, acc: 0.241
+2025-08-07 19:32:45: Epoch: 016 Train Loss: 0.664, ap: 0.544, auc: 0.695, f1: 0.638, acc: 0.670
+2025-08-07 19:32:46: Epoch: 016 Test results, ap: 0.202, auc: 0.386, f1: 0.394, acc: 0.259
+2025-08-07 19:32:46: Epoch: 017 Train Loss: 0.648, ap: 0.553, auc: 0.702, f1: 0.645, acc: 0.680
+2025-08-07 19:32:46: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:32:46: Epoch: 017 Test results, ap: 0.184, auc: 0.345, f1: 0.388, acc: 0.241
+2025-08-07 19:32:46: Epoch: 018 Train Loss: 0.642, ap: 0.623, auc: 0.710, f1: 0.667, acc: 0.680
+2025-08-07 19:32:46: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:32:46: Epoch: 018 Test results, ap: 0.266, auc: 0.402, f1: 0.388, acc: 0.241
+2025-08-07 19:32:47: Epoch: 019 Train Loss: 0.628, ap: 0.561, auc: 0.692, f1: 0.636, acc: 0.689
+2025-08-07 19:32:47: Epoch: 019 Test results, ap: 0.256, auc: 0.507, f1: 0.338, acc: 0.204
+2025-08-07 19:32:47: Epoch: 020 Train Loss: 0.617, ap: 0.620, auc: 0.734, f1: 0.632, acc: 0.660
+2025-08-07 19:32:47: Epoch: 020 New best VALIDATION metrics
+2025-08-07 19:32:47: Epoch: 020 Test results, ap: 0.187, auc: 0.366, f1: 0.388, acc: 0.241
+2025-08-07 19:32:48: Epoch: 021 Train Loss: 0.589, ap: 0.669, auc: 0.735, f1: 0.646, acc: 0.670
+2025-08-07 19:32:48: Epoch: 021 New best VALIDATION metrics
+2025-08-07 19:32:48: Epoch: 021 Test results, ap: 0.274, auc: 0.412, f1: 0.388, acc: 0.241
+2025-08-07 19:32:48: Epoch: 022 Train Loss: 0.574, ap: 0.606, auc: 0.694, f1: 0.635, acc: 0.699
+2025-08-07 19:32:48: Epoch: 022 Test results, ap: 0.210, auc: 0.411, f1: 0.388, acc: 0.241
+2025-08-07 19:32:49: Epoch: 023 Train Loss: 0.579, ap: 0.680, auc: 0.721, f1: 0.619, acc: 0.689
+2025-08-07 19:32:49: Epoch: 023 Test results, ap: 0.200, auc: 0.381, f1: 0.388, acc: 0.241
+2025-08-07 19:32:49: Epoch: 024 Train Loss: 0.555, ap: 0.712, auc: 0.733, f1: 0.602, acc: 0.680
+2025-08-07 19:32:49: Epoch: 024 New best VALIDATION metrics
+2025-08-07 19:32:49: Epoch: 024 Test results, ap: 0.207, auc: 0.407, f1: 0.388, acc: 0.241
+2025-08-07 19:32:50: Epoch: 025 Train Loss: 0.573, ap: 0.693, auc: 0.741, f1: 0.635, acc: 0.631
+2025-08-07 19:32:50: Epoch: 025 Test results, ap: 0.330, auc: 0.417, f1: 0.388, acc: 0.241
+2025-08-07 19:32:50: Epoch: 026 Train Loss: 0.591, ap: 0.680, auc: 0.726, f1: 0.621, acc: 0.680
+2025-08-07 19:32:50: Epoch: 026 Test results, ap: 0.288, auc: 0.557, f1: 0.351, acc: 0.213
+2025-08-07 19:32:51: Epoch: 027 Train Loss: 0.585, ap: 0.703, auc: 0.709, f1: 0.610, acc: 0.689
+2025-08-07 19:32:51: Epoch: 027 Test results, ap: 0.213, auc: 0.422, f1: 0.388, acc: 0.241
+2025-08-07 19:32:51: Epoch: 028 Train Loss: 0.553, ap: 0.718, auc: 0.710, f1: 0.582, acc: 0.680
+2025-08-07 19:32:51: Epoch: 028 Test results, ap: 0.225, auc: 0.450, f1: 0.388, acc: 0.241
+2025-08-07 19:32:52: Epoch: 029 Train Loss: 0.531, ap: 0.707, auc: 0.691, f1: 0.575, acc: 0.670
+2025-08-07 19:32:52: Epoch: 029 Test results, ap: 0.289, auc: 0.559, f1: 0.388, acc: 0.241
+2025-08-07 19:32:52: Epoch: 030 Train Loss: 0.522, ap: 0.709, auc: 0.712, f1: 0.582, acc: 0.680
+2025-08-07 19:32:52: Epoch: 030 Test results, ap: 0.357, auc: 0.598, f1: 0.388, acc: 0.241
+2025-08-07 19:32:53: Epoch: 031 Train Loss: 0.514, ap: 0.708, auc: 0.693, f1: 0.575, acc: 0.670
+2025-08-07 19:32:53: Epoch: 031 Test results, ap: 0.314, auc: 0.566, f1: 0.388, acc: 0.241
+2025-08-07 19:32:53: Epoch: 032 Train Loss: 0.505, ap: 0.706, auc: 0.672, f1: 0.564, acc: 0.670
+2025-08-07 19:32:53: Epoch: 032 Test results, ap: 0.346, auc: 0.586, f1: 0.388, acc: 0.241
+2025-08-07 19:32:54: Epoch: 033 Train Loss: 0.487, ap: 0.710, auc: 0.690, f1: 0.575, acc: 0.670
+2025-08-07 19:32:54: Epoch: 033 Test results, ap: 0.337, auc: 0.594, f1: 0.391, acc: 0.250
+2025-08-07 19:32:54: Epoch: 034 Train Loss: 0.523, ap: 0.703, auc: 0.687, f1: 0.575, acc: 0.670
+2025-08-07 19:32:54: Epoch: 034 Test results, ap: 0.330, auc: 0.592, f1: 0.391, acc: 0.250
+2025-08-07 19:32:54: Epoch: 035 Train Loss: 0.493, ap: 0.701, auc: 0.673, f1: 0.590, acc: 0.689
+2025-08-07 19:32:55: Epoch: 035 Test results, ap: 0.347, auc: 0.605, f1: 0.397, acc: 0.296
+2025-08-07 19:32:55: Epoch: 036 Train Loss: 0.501, ap: 0.697, auc: 0.677, f1: 0.571, acc: 0.680
+2025-08-07 19:32:55: Epoch: 036 Test results, ap: 0.363, auc: 0.622, f1: 0.391, acc: 0.250
+2025-08-07 19:32:55: Epoch: 037 Train Loss: 0.501, ap: 0.696, auc: 0.678, f1: 0.579, acc: 0.689
+2025-08-07 19:32:56: Epoch: 037 Test results, ap: 0.357, auc: 0.620, f1: 0.394, acc: 0.259
+2025-08-07 19:32:56: Epoch: 038 Train Loss: 0.504, ap: 0.702, auc: 0.676, f1: 0.579, acc: 0.689
+2025-08-07 19:32:56: Epoch: 038 Test results, ap: 0.320, auc: 0.583, f1: 0.394, acc: 0.259
+2025-08-07 19:32:56: Epoch: 039 Train Loss: 0.501, ap: 0.699, auc: 0.677, f1: 0.605, acc: 0.709
+2025-08-07 19:32:56: Epoch: 039 Test results, ap: 0.363, auc: 0.623, f1: 0.394, acc: 0.259
+2025-08-07 19:32:57: Epoch: 040 Train Loss: 0.497, ap: 0.704, auc: 0.689, f1: 0.575, acc: 0.670
+2025-08-07 19:32:57: Epoch: 040 Test results, ap: 0.289, auc: 0.570, f1: 0.393, acc: 0.315
+2025-08-07 19:32:57: Epoch: 041 Train Loss: 0.496, ap: 0.707, auc: 0.680, f1: 0.575, acc: 0.670
+2025-08-07 19:32:57: Epoch: 041 Test results, ap: 0.295, auc: 0.572, f1: 0.413, acc: 0.315
+2025-08-07 19:32:58: Epoch: 042 Train Loss: 0.465, ap: 0.704, auc: 0.673, f1: 0.571, acc: 0.680
+2025-08-07 19:32:58: Epoch: 042 Test results, ap: 0.297, auc: 0.576, f1: 0.413, acc: 0.315
+2025-08-07 19:32:58: Epoch: 043 Train Loss: 0.466, ap: 0.698, auc: 0.673, f1: 0.571, acc: 0.680
+2025-08-07 19:32:58: Epoch: 043 Test results, ap: 0.285, auc: 0.570, f1: 0.406, acc: 0.296
+2025-08-07 19:32:59: Epoch: 044 Train Loss: 0.474, ap: 0.698, auc: 0.672, f1: 0.571, acc: 0.680
+2025-08-07 19:32:59: Epoch: 044 Test results, ap: 0.288, auc: 0.573, f1: 0.400, acc: 0.278
+2025-08-07 19:32:59: Epoch: 045 Train Loss: 0.489, ap: 0.702, auc: 0.672, f1: 0.571, acc: 0.680
+2025-08-07 19:32:59: Epoch: 045 Test results, ap: 0.287, auc: 0.572, f1: 0.406, acc: 0.296
+2025-08-07 19:33:00: Epoch: 046 Train Loss: 0.486, ap: 0.697, auc: 0.662, f1: 0.571, acc: 0.680
+2025-08-07 19:33:00: Epoch: 046 Test results, ap: 0.286, auc: 0.574, f1: 0.409, acc: 0.306
+2025-08-07 19:33:00: Epoch: 047 Train Loss: 0.476, ap: 0.689, auc: 0.663, f1: 0.587, acc: 0.699
+2025-08-07 19:33:00: Epoch: 047 Test results, ap: 0.282, auc: 0.568, f1: 0.416, acc: 0.324
+2025-08-07 19:33:01: Epoch: 048 Train Loss: 0.471, ap: 0.695, auc: 0.666, f1: 0.571, acc: 0.680
+2025-08-07 19:33:01: Epoch: 048 Test results, ap: 0.287, auc: 0.572, f1: 0.419, acc: 0.333
+2025-08-07 19:33:01: Epoch: 049 Train Loss: 0.482, ap: 0.695, auc: 0.670, f1: 0.579, acc: 0.689
+2025-08-07 19:33:01: Epoch: 049 Test results, ap: 0.282, auc: 0.568, f1: 0.413, acc: 0.343
+2025-08-07 19:33:02: Epoch: 050 Train Loss: 0.484, ap: 0.699, auc: 0.672, f1: 0.587, acc: 0.699
+2025-08-07 19:33:02: Epoch: 050 Test results, ap: 0.275, auc: 0.564, f1: 0.413, acc: 0.315
+2025-08-07 19:33:02: used time 0.01h
+2025-08-07 19:33:02: Cross Validation Finished!
+2025-08-07 19:33:02: Best validation perform list
+[[0.57233167 0.62462002 0.62666667 0.45631069]
+ [0.4490608  0.62735462 0.52857143 0.35922331]
+ [0.6899091  0.67345399 0.67096776 0.50485438]
+ [0.5400883  0.63270885 0.57931036 0.407767  ]
+ [0.71215034 0.73288751 0.60240966 0.67961162]]
+2025-08-07 19:33:02: mean: [0.593 0.658 0.602 0.482]
+2025-08-07 19:33:02: std: [0.098 0.041 0.047 0.11 ]
+2025-08-07 19:33:02: Best test perform list
+[[0.35461581 0.57481241 0.         0.75925928]
+ [0.32157293 0.64305812 0.         0.75925928]
+ [0.32349539 0.63414633 0.         0.75925928]
+ [0.4014506  0.59920263 0.         0.75925928]
+ [0.49628162 0.69746715 0.         0.75925928]]
+2025-08-07 19:33:02: mean: [0.379 0.63  0.    0.759]
+2025-08-07 19:33:02: std: [0.065 0.042 0.    0.   ]
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@@ -0,0 +1 @@
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diff --git a/run-cls-simple/lstm-diff-256-simple-oneway-ce-32-0.001-50/training.log b/run-cls-simple/lstm-diff-256-simple-oneway-ce-32-0.001-50/training.log
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+2025-08-07 19:33:04: saving_dir: ./run-cls/lstm-diff-256-simple-oneway-ce-32-0.001-50
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+2025-08-07 19:33:05: Epoch: 001 Train Loss: 0.695, ap: 0.449, auc: 0.498, f1: 0.627, acc: 0.456
+2025-08-07 19:33:05: Epoch: 001 New best VALIDATION metrics
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+2025-08-07 19:33:06: Epoch: 002 Train Loss: 0.693, ap: 0.460, auc: 0.509, f1: 0.627, acc: 0.456
+2025-08-07 19:33:06: Epoch: 002 New best VALIDATION metrics
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+2025-08-07 19:33:06: Epoch: 003 Train Loss: 0.693, ap: 0.483, auc: 0.512, f1: 0.591, acc: 0.476
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+2025-08-07 19:33:07: Epoch: 004 Test results, ap: 0.418, auc: 0.650, f1: 0.000, acc: 0.759
+2025-08-07 19:33:07: Epoch: 005 Train Loss: 0.693, ap: 0.554, auc: 0.613, f1: 0.000, acc: 0.544
+2025-08-07 19:33:07: Epoch: 005 New best VALIDATION metrics
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+2025-08-07 19:33:07: Epoch: 006 Train Loss: 0.693, ap: 0.510, auc: 0.534, f1: 0.627, acc: 0.456
+2025-08-07 19:33:07: Epoch: 006 Test results, ap: 0.181, auc: 0.328, f1: 0.388, acc: 0.241
+2025-08-07 19:33:08: Epoch: 007 Train Loss: 0.693, ap: 0.582, auc: 0.610, f1: 0.627, acc: 0.456
+2025-08-07 19:33:08: Epoch: 007 New best VALIDATION metrics
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+2025-08-07 19:33:08: Epoch: 008 Train Loss: 0.693, ap: 0.559, auc: 0.590, f1: 0.627, acc: 0.456
+2025-08-07 19:33:08: Epoch: 008 Test results, ap: 0.189, auc: 0.355, f1: 0.388, acc: 0.241
+2025-08-07 19:33:09: Epoch: 009 Train Loss: 0.693, ap: 0.543, auc: 0.536, f1: 0.582, acc: 0.456
+2025-08-07 19:33:09: Epoch: 009 Test results, ap: 0.327, auc: 0.616, f1: 0.391, acc: 0.250
+2025-08-07 19:33:09: Epoch: 010 Train Loss: 0.692, ap: 0.554, auc: 0.594, f1: 0.521, acc: 0.553
+2025-08-07 19:33:09: Epoch: 010 Test results, ap: 0.324, auc: 0.594, f1: 0.275, acc: 0.657
+2025-08-07 19:33:10: Epoch: 011 Train Loss: 0.690, ap: 0.528, auc: 0.554, f1: 0.368, acc: 0.534
+2025-08-07 19:33:10: Epoch: 011 Test results, ap: 0.264, auc: 0.553, f1: 0.000, acc: 0.722
+2025-08-07 19:33:10: Epoch: 011 New best TEST metrics
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+2025-08-07 19:33:10: Epoch: 012 Test results, ap: 0.301, auc: 0.592, f1: 0.000, acc: 0.713
+2025-08-07 19:33:10: Epoch: 012 New best TEST metrics
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+2025-08-07 19:33:11: Epoch: 013 Test results, ap: 0.305, auc: 0.609, f1: 0.000, acc: 0.759
+2025-08-07 19:33:11: Epoch: 013 New best TEST metrics
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+2025-08-07 19:33:11: Epoch: 014 New best VALIDATION metrics
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+2025-08-07 19:33:12: Epoch: 015 New best VALIDATION metrics
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+2025-08-07 19:33:13: Epoch: 017 New best VALIDATION metrics
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+2025-08-07 19:33:14: Epoch: 021 Test results, ap: 0.269, auc: 0.504, f1: 0.388, acc: 0.241
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+2025-08-07 19:33:15: Epoch: 022 Test results, ap: 0.275, auc: 0.513, f1: 0.388, acc: 0.241
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+2025-08-07 19:33:15: Epoch: 023 Test results, ap: 0.276, auc: 0.502, f1: 0.388, acc: 0.241
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+2025-08-07 19:33:16: Epoch: 024 Test results, ap: 0.268, auc: 0.494, f1: 0.388, acc: 0.241
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+2025-08-07 19:33:17: Epoch: 027 Test results, ap: 0.251, auc: 0.466, f1: 0.388, acc: 0.241
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+2025-08-07 19:33:18: Epoch: 030 New best VALIDATION metrics
+2025-08-07 19:33:19: Epoch: 030 Test results, ap: 0.256, auc: 0.477, f1: 0.000, acc: 0.759
+2025-08-07 19:33:19: Epoch: 031 Train Loss: 0.693, ap: 0.638, auc: 0.648, f1: 0.000, acc: 0.544
+2025-08-07 19:33:19: Epoch: 031 Test results, ap: 0.267, auc: 0.473, f1: 0.000, acc: 0.759
+2025-08-07 19:33:19: Epoch: 032 Train Loss: 0.693, ap: 0.632, auc: 0.637, f1: 0.000, acc: 0.544
+2025-08-07 19:33:19: Epoch: 032 Test results, ap: 0.262, auc: 0.473, f1: 0.000, acc: 0.759
+2025-08-07 19:33:20: Epoch: 033 Train Loss: 0.693, ap: 0.644, auc: 0.660, f1: 0.000, acc: 0.544
+2025-08-07 19:33:20: Epoch: 033 Test results, ap: 0.251, auc: 0.460, f1: 0.000, acc: 0.759
+2025-08-07 19:33:20: Epoch: 034 Train Loss: 0.693, ap: 0.649, auc: 0.665, f1: 0.000, acc: 0.544
+2025-08-07 19:33:20: Epoch: 034 Test results, ap: 0.273, auc: 0.513, f1: 0.000, acc: 0.759
+2025-08-07 19:33:21: Epoch: 035 Train Loss: 0.694, ap: 0.638, auc: 0.653, f1: 0.000, acc: 0.544
+2025-08-07 19:33:21: Epoch: 035 Test results, ap: 0.262, auc: 0.509, f1: 0.000, acc: 0.759
+2025-08-07 19:33:21: Epoch: 036 Train Loss: 0.693, ap: 0.634, auc: 0.653, f1: 0.000, acc: 0.544
+2025-08-07 19:33:21: Epoch: 036 Test results, ap: 0.260, auc: 0.497, f1: 0.000, acc: 0.759
+2025-08-07 19:33:22: Epoch: 037 Train Loss: 0.693, ap: 0.630, auc: 0.632, f1: 0.000, acc: 0.544
+2025-08-07 19:33:22: Epoch: 037 Test results, ap: 0.275, auc: 0.520, f1: 0.000, acc: 0.759
+2025-08-07 19:33:22: Epoch: 038 Train Loss: 0.693, ap: 0.639, auc: 0.649, f1: 0.000, acc: 0.544
+2025-08-07 19:33:22: Epoch: 038 Test results, ap: 0.264, auc: 0.499, f1: 0.000, acc: 0.759
+2025-08-07 19:33:23: Epoch: 039 Train Loss: 0.693, ap: 0.643, auc: 0.658, f1: 0.000, acc: 0.544
+2025-08-07 19:33:23: Epoch: 039 Test results, ap: 0.268, auc: 0.525, f1: 0.000, acc: 0.759
+2025-08-07 19:33:23: Epoch: 040 Train Loss: 0.694, ap: 0.637, auc: 0.653, f1: 0.627, acc: 0.456
+2025-08-07 19:33:23: Epoch: 040 Test results, ap: 0.264, auc: 0.491, f1: 0.388, acc: 0.241
+2025-08-07 19:33:24: Epoch: 041 Train Loss: 0.693, ap: 0.638, auc: 0.652, f1: 0.627, acc: 0.456
+2025-08-07 19:33:24: Epoch: 041 Test results, ap: 0.256, auc: 0.471, f1: 0.388, acc: 0.241
+2025-08-07 19:33:24: Epoch: 042 Train Loss: 0.694, ap: 0.629, auc: 0.646, f1: 0.627, acc: 0.456
+2025-08-07 19:33:24: Epoch: 042 Test results, ap: 0.257, auc: 0.473, f1: 0.388, acc: 0.241
+2025-08-07 19:33:25: Epoch: 043 Train Loss: 0.694, ap: 0.643, auc: 0.647, f1: 0.627, acc: 0.456
+2025-08-07 19:33:25: Epoch: 043 Test results, ap: 0.268, auc: 0.513, f1: 0.388, acc: 0.241
+2025-08-07 19:33:25: Epoch: 044 Train Loss: 0.693, ap: 0.630, auc: 0.634, f1: 0.627, acc: 0.456
+2025-08-07 19:33:25: Epoch: 044 Test results, ap: 0.255, auc: 0.461, f1: 0.388, acc: 0.241
+2025-08-07 19:33:25: Epoch: 045 Train Loss: 0.693, ap: 0.638, auc: 0.653, f1: 0.627, acc: 0.456
+2025-08-07 19:33:26: Epoch: 045 Test results, ap: 0.263, auc: 0.489, f1: 0.388, acc: 0.241
+2025-08-07 19:33:26: Epoch: 046 Train Loss: 0.693, ap: 0.641, auc: 0.649, f1: 0.627, acc: 0.456
+2025-08-07 19:33:26: Epoch: 046 Test results, ap: 0.272, auc: 0.486, f1: 0.388, acc: 0.241
+2025-08-07 19:33:26: Epoch: 047 Train Loss: 0.693, ap: 0.638, auc: 0.653, f1: 0.627, acc: 0.456
+2025-08-07 19:33:26: Epoch: 047 Test results, ap: 0.266, auc: 0.515, f1: 0.388, acc: 0.241
+2025-08-07 19:33:27: Epoch: 048 Train Loss: 0.694, ap: 0.643, auc: 0.663, f1: 0.627, acc: 0.456
+2025-08-07 19:33:27: Epoch: 048 Test results, ap: 0.259, auc: 0.475, f1: 0.388, acc: 0.241
+2025-08-07 19:33:27: Epoch: 049 Train Loss: 0.693, ap: 0.633, auc: 0.638, f1: 0.627, acc: 0.456
+2025-08-07 19:33:27: Epoch: 049 Test results, ap: 0.257, auc: 0.495, f1: 0.388, acc: 0.241
+2025-08-07 19:33:28: Epoch: 050 Train Loss: 0.693, ap: 0.625, auc: 0.647, f1: 0.627, acc: 0.456
+2025-08-07 19:33:28: Epoch: 050 Test results, ap: 0.283, auc: 0.532, f1: 0.388, acc: 0.241
+2025-08-07 19:33:28: used time 0.01h
+2025-08-07 19:33:28: Running Cross Validation 1
+2025-08-07 19:33:28: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:33:28: Epoch: 001 Train Loss: 0.695, ap: 0.347, auc: 0.399, f1: 0.000, acc: 0.641
+2025-08-07 19:33:28: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:33:28: Epoch: 001 Test results, ap: 0.306, auc: 0.614, f1: 0.000, acc: 0.759
+2025-08-07 19:33:29: Epoch: 002 Train Loss: 0.694, ap: 0.302, auc: 0.378, f1: 0.529, acc: 0.379
+2025-08-07 19:33:29: Epoch: 002 Test results, ap: 0.379, auc: 0.652, f1: 0.388, acc: 0.241
+2025-08-07 19:33:29: Epoch: 003 Train Loss: 0.693, ap: 0.416, auc: 0.478, f1: 0.532, acc: 0.369
+2025-08-07 19:33:29: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:33:29: Epoch: 003 Test results, ap: 0.203, auc: 0.400, f1: 0.388, acc: 0.241
+2025-08-07 19:33:30: Epoch: 004 Train Loss: 0.694, ap: 0.312, auc: 0.376, f1: 0.526, acc: 0.388
+2025-08-07 19:33:30: Epoch: 004 Test results, ap: 0.410, auc: 0.644, f1: 0.388, acc: 0.241
+2025-08-07 19:33:30: Epoch: 005 Train Loss: 0.693, ap: 0.312, auc: 0.370, f1: 0.185, acc: 0.485
+2025-08-07 19:33:30: Epoch: 005 Test results, ap: 0.396, auc: 0.645, f1: 0.387, acc: 0.648
+2025-08-07 19:33:31: Epoch: 006 Train Loss: 0.692, ap: 0.559, auc: 0.673, f1: 0.529, acc: 0.379
+2025-08-07 19:33:31: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:33:31: Epoch: 006 Test results, ap: 0.185, auc: 0.344, f1: 0.388, acc: 0.241
+2025-08-07 19:33:31: Epoch: 007 Train Loss: 0.692, ap: 0.426, auc: 0.539, f1: 0.526, acc: 0.369
+2025-08-07 19:33:31: Epoch: 007 Test results, ap: 0.239, auc: 0.515, f1: 0.388, acc: 0.241
+2025-08-07 19:33:32: Epoch: 008 Train Loss: 0.693, ap: 0.466, auc: 0.574, f1: 0.529, acc: 0.379
+2025-08-07 19:33:32: Epoch: 008 Test results, ap: 0.207, auc: 0.429, f1: 0.388, acc: 0.241
+2025-08-07 19:33:32: Epoch: 009 Train Loss: 0.692, ap: 0.472, auc: 0.569, f1: 0.000, acc: 0.641
+2025-08-07 19:33:32: Epoch: 009 Test results, ap: 0.200, auc: 0.414, f1: 0.000, acc: 0.759
+2025-08-07 19:33:32: Epoch: 010 Train Loss: 0.690, ap: 0.511, auc: 0.625, f1: 0.529, acc: 0.379
+2025-08-07 19:33:33: Epoch: 010 Test results, ap: 0.190, auc: 0.362, f1: 0.388, acc: 0.241
+2025-08-07 19:33:33: Epoch: 011 Train Loss: 0.691, ap: 0.496, auc: 0.573, f1: 0.529, acc: 0.379
+2025-08-07 19:33:33: Epoch: 011 Test results, ap: 0.203, auc: 0.410, f1: 0.388, acc: 0.241
+2025-08-07 19:33:33: Epoch: 011 New best TEST metrics
+2025-08-07 19:33:33: Epoch: 012 Train Loss: 0.690, ap: 0.451, auc: 0.493, f1: 0.529, acc: 0.379
+2025-08-07 19:33:34: Epoch: 012 Test results, ap: 0.198, auc: 0.384, f1: 0.388, acc: 0.241
+2025-08-07 19:33:34: Epoch: 013 Train Loss: 0.690, ap: 0.527, auc: 0.624, f1: 0.529, acc: 0.379
+2025-08-07 19:33:34: Epoch: 013 Test results, ap: 0.193, auc: 0.366, f1: 0.388, acc: 0.241
+2025-08-07 19:33:34: Epoch: 014 Train Loss: 0.690, ap: 0.433, auc: 0.486, f1: 0.530, acc: 0.398
+2025-08-07 19:33:35: Epoch: 014 Test results, ap: 0.211, auc: 0.455, f1: 0.388, acc: 0.241
+2025-08-07 19:33:35: Epoch: 014 New best TEST metrics
+2025-08-07 19:33:35: Epoch: 015 Train Loss: 0.685, ap: 0.497, auc: 0.561, f1: 0.429, acc: 0.379
+2025-08-07 19:33:35: Epoch: 015 Test results, ap: 0.209, auc: 0.453, f1: 0.404, acc: 0.426
+2025-08-07 19:33:35: Epoch: 016 Train Loss: 0.691, ap: 0.475, auc: 0.559, f1: 0.479, acc: 0.408
+2025-08-07 19:33:35: Epoch: 016 Test results, ap: 0.211, auc: 0.417, f1: 0.342, acc: 0.324
+2025-08-07 19:33:36: Epoch: 017 Train Loss: 0.686, ap: 0.514, auc: 0.610, f1: 0.519, acc: 0.621
+2025-08-07 19:33:36: Epoch: 017 Test results, ap: 0.227, auc: 0.487, f1: 0.085, acc: 0.602
+2025-08-07 19:33:36: Epoch: 017 New best TEST metrics
+2025-08-07 19:33:36: Epoch: 018 Train Loss: 0.685, ap: 0.471, auc: 0.565, f1: 0.448, acc: 0.641
+2025-08-07 19:33:36: Epoch: 018 Test results, ap: 0.205, auc: 0.435, f1: 0.080, acc: 0.574
+2025-08-07 19:33:37: Epoch: 019 Train Loss: 0.674, ap: 0.473, auc: 0.609, f1: 0.533, acc: 0.524
+2025-08-07 19:33:37: Epoch: 019 Test results, ap: 0.235, auc: 0.438, f1: 0.293, acc: 0.509
+2025-08-07 19:33:37: Epoch: 020 Train Loss: 0.676, ap: 0.468, auc: 0.563, f1: 0.466, acc: 0.621
+2025-08-07 19:33:37: Epoch: 020 Test results, ap: 0.213, auc: 0.456, f1: 0.105, acc: 0.528
+2025-08-07 19:33:38: Epoch: 021 Train Loss: 0.654, ap: 0.462, auc: 0.578, f1: 0.526, acc: 0.563
+2025-08-07 19:33:38: Epoch: 021 Test results, ap: 0.215, auc: 0.458, f1: 0.296, acc: 0.472
+2025-08-07 19:33:38: Epoch: 022 Train Loss: 0.650, ap: 0.512, auc: 0.607, f1: 0.457, acc: 0.631
+2025-08-07 19:33:38: Epoch: 022 Test results, ap: 0.239, auc: 0.451, f1: 0.250, acc: 0.667
+2025-08-07 19:33:39: Epoch: 023 Train Loss: 0.640, ap: 0.468, auc: 0.604, f1: 0.488, acc: 0.573
+2025-08-07 19:33:39: Epoch: 023 Test results, ap: 0.223, auc: 0.419, f1: 0.267, acc: 0.491
+2025-08-07 19:33:39: Epoch: 024 Train Loss: 0.620, ap: 0.484, auc: 0.600, f1: 0.539, acc: 0.602
+2025-08-07 19:33:39: Epoch: 024 Test results, ap: 0.206, auc: 0.423, f1: 0.268, acc: 0.444
+2025-08-07 19:33:39: Epoch: 025 Train Loss: 0.624, ap: 0.590, auc: 0.658, f1: 0.532, acc: 0.641
+2025-08-07 19:33:39: Epoch: 025 New best VALIDATION metrics
+2025-08-07 19:33:40: Epoch: 025 Test results, ap: 0.305, auc: 0.582, f1: 0.000, acc: 0.741
+2025-08-07 19:33:40: Epoch: 025 New best TEST metrics
+2025-08-07 19:33:40: Epoch: 026 Train Loss: 0.614, ap: 0.506, auc: 0.596, f1: 0.480, acc: 0.621
+2025-08-07 19:33:40: Epoch: 026 Test results, ap: 0.214, auc: 0.354, f1: 0.250, acc: 0.611
+2025-08-07 19:33:40: Epoch: 027 Train Loss: 0.583, ap: 0.487, auc: 0.572, f1: 0.400, acc: 0.534
+2025-08-07 19:33:40: Epoch: 027 Test results, ap: 0.192, auc: 0.325, f1: 0.214, acc: 0.593
+2025-08-07 19:33:41: Epoch: 028 Train Loss: 0.574, ap: 0.524, auc: 0.621, f1: 0.435, acc: 0.621
+2025-08-07 19:33:41: Epoch: 028 Test results, ap: 0.387, auc: 0.673, f1: 0.000, acc: 0.759
+2025-08-07 19:33:41: Epoch: 028 New best TEST metrics
+2025-08-07 19:33:41: Epoch: 029 Train Loss: 0.592, ap: 0.381, auc: 0.525, f1: 0.406, acc: 0.602
+2025-08-07 19:33:41: Epoch: 029 Test results, ap: 0.188, auc: 0.332, f1: 0.122, acc: 0.602
+2025-08-07 19:33:42: Epoch: 030 Train Loss: 0.573, ap: 0.549, auc: 0.646, f1: 0.424, acc: 0.631
+2025-08-07 19:33:42: Epoch: 030 Test results, ap: 0.320, auc: 0.573, f1: 0.000, acc: 0.741
+2025-08-07 19:33:42: Epoch: 031 Train Loss: 0.559, ap: 0.529, auc: 0.604, f1: 0.405, acc: 0.573
+2025-08-07 19:33:42: Epoch: 031 Test results, ap: 0.208, auc: 0.373, f1: 0.178, acc: 0.657
+2025-08-07 19:33:43: Epoch: 032 Train Loss: 0.556, ap: 0.528, auc: 0.608, f1: 0.388, acc: 0.602
+2025-08-07 19:33:43: Epoch: 032 Test results, ap: 0.244, auc: 0.458, f1: 0.146, acc: 0.676
+2025-08-07 19:33:43: Epoch: 033 Train Loss: 0.553, ap: 0.540, auc: 0.609, f1: 0.435, acc: 0.621
+2025-08-07 19:33:43: Epoch: 033 Test results, ap: 0.254, auc: 0.481, f1: 0.121, acc: 0.731
+2025-08-07 19:33:44: Epoch: 034 Train Loss: 0.551, ap: 0.542, auc: 0.615, f1: 0.441, acc: 0.631
+2025-08-07 19:33:44: Epoch: 034 Test results, ap: 0.262, auc: 0.508, f1: 0.000, acc: 0.731
+2025-08-07 19:33:44: Epoch: 035 Train Loss: 0.537, ap: 0.530, auc: 0.605, f1: 0.412, acc: 0.612
+2025-08-07 19:33:44: Epoch: 035 Test results, ap: 0.250, auc: 0.498, f1: 0.065, acc: 0.731
+2025-08-07 19:33:44: Epoch: 036 Train Loss: 0.530, ap: 0.541, auc: 0.617, f1: 0.435, acc: 0.621
+2025-08-07 19:33:45: Epoch: 036 Test results, ap: 0.277, auc: 0.547, f1: 0.000, acc: 0.759
+2025-08-07 19:33:45: Epoch: 037 Train Loss: 0.526, ap: 0.521, auc: 0.603, f1: 0.394, acc: 0.583
+2025-08-07 19:33:45: Epoch: 037 Test results, ap: 0.238, auc: 0.494, f1: 0.000, acc: 0.722
+2025-08-07 19:33:45: Epoch: 038 Train Loss: 0.525, ap: 0.534, auc: 0.611, f1: 0.377, acc: 0.583
+2025-08-07 19:33:46: Epoch: 038 Test results, ap: 0.272, auc: 0.542, f1: 0.000, acc: 0.759
+2025-08-07 19:33:46: Epoch: 039 Train Loss: 0.529, ap: 0.526, auc: 0.604, f1: 0.364, acc: 0.592
+2025-08-07 19:33:46: Epoch: 039 Test results, ap: 0.258, auc: 0.521, f1: 0.000, acc: 0.750
+2025-08-07 19:33:46: Epoch: 040 Train Loss: 0.528, ap: 0.524, auc: 0.613, f1: 0.388, acc: 0.602
+2025-08-07 19:33:46: Epoch: 040 Test results, ap: 0.310, auc: 0.567, f1: 0.000, acc: 0.759
+2025-08-07 19:33:47: Epoch: 041 Train Loss: 0.517, ap: 0.537, auc: 0.623, f1: 0.412, acc: 0.612
+2025-08-07 19:33:47: Epoch: 041 Test results, ap: 0.326, auc: 0.576, f1: 0.000, acc: 0.759
+2025-08-07 19:33:47: Epoch: 042 Train Loss: 0.517, ap: 0.534, auc: 0.615, f1: 0.353, acc: 0.573
+2025-08-07 19:33:47: Epoch: 042 Test results, ap: 0.326, auc: 0.575, f1: 0.000, acc: 0.759
+2025-08-07 19:33:48: Epoch: 043 Train Loss: 0.511, ap: 0.513, auc: 0.626, f1: 0.364, acc: 0.592
+2025-08-07 19:33:48: Epoch: 043 Test results, ap: 0.351, auc: 0.585, f1: 0.000, acc: 0.759
+2025-08-07 19:33:48: Epoch: 044 Train Loss: 0.503, ap: 0.528, auc: 0.616, f1: 0.364, acc: 0.592
+2025-08-07 19:33:48: Epoch: 044 Test results, ap: 0.334, auc: 0.580, f1: 0.000, acc: 0.759
+2025-08-07 19:33:49: Epoch: 045 Train Loss: 0.512, ap: 0.530, auc: 0.622, f1: 0.369, acc: 0.602
+2025-08-07 19:33:49: Epoch: 045 Test results, ap: 0.344, auc: 0.584, f1: 0.000, acc: 0.759
+2025-08-07 19:33:49: Epoch: 046 Train Loss: 0.503, ap: 0.518, auc: 0.611, f1: 0.343, acc: 0.553
+2025-08-07 19:33:49: Epoch: 046 Test results, ap: 0.300, auc: 0.572, f1: 0.000, acc: 0.759
+2025-08-07 19:33:49: Epoch: 047 Train Loss: 0.509, ap: 0.541, auc: 0.631, f1: 0.400, acc: 0.592
+2025-08-07 19:33:50: Epoch: 047 Test results, ap: 0.353, auc: 0.585, f1: 0.000, acc: 0.759
+2025-08-07 19:33:50: Epoch: 048 Train Loss: 0.505, ap: 0.538, auc: 0.634, f1: 0.406, acc: 0.602
+2025-08-07 19:33:50: Epoch: 048 Test results, ap: 0.360, auc: 0.582, f1: 0.000, acc: 0.759
+2025-08-07 19:33:50: Epoch: 049 Train Loss: 0.495, ap: 0.538, auc: 0.636, f1: 0.412, acc: 0.612
+2025-08-07 19:33:50: Epoch: 049 Test results, ap: 0.362, auc: 0.583, f1: 0.000, acc: 0.759
+2025-08-07 19:33:51: Epoch: 050 Train Loss: 0.498, ap: 0.528, auc: 0.619, f1: 0.364, acc: 0.592
+2025-08-07 19:33:51: Epoch: 050 Test results, ap: 0.355, auc: 0.584, f1: 0.000, acc: 0.759
+2025-08-07 19:33:51: used time 0.01h
+2025-08-07 19:33:51: Running Cross Validation 2
+2025-08-07 19:33:51: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:33:51: Epoch: 001 Train Loss: 0.693, ap: 0.480, auc: 0.466, f1: 0.671, acc: 0.505
+2025-08-07 19:33:51: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:33:51: Epoch: 001 Test results, ap: 0.187, auc: 0.348, f1: 0.388, acc: 0.241
+2025-08-07 19:33:52: Epoch: 002 Train Loss: 0.694, ap: 0.539, auc: 0.512, f1: 0.675, acc: 0.524
+2025-08-07 19:33:52: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:33:52: Epoch: 002 Test results, ap: 0.188, auc: 0.345, f1: 0.388, acc: 0.241
+2025-08-07 19:33:52: Epoch: 003 Train Loss: 0.694, ap: 0.532, auc: 0.538, f1: 0.671, acc: 0.524
+2025-08-07 19:33:52: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:33:52: Epoch: 003 Test results, ap: 0.398, auc: 0.650, f1: 0.388, acc: 0.241
+2025-08-07 19:33:53: Epoch: 004 Train Loss: 0.693, ap: 0.572, auc: 0.554, f1: 0.000, acc: 0.495
+2025-08-07 19:33:53: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:33:53: Epoch: 004 Test results, ap: 0.194, auc: 0.374, f1: 0.000, acc: 0.759
+2025-08-07 19:33:53: Epoch: 005 Train Loss: 0.693, ap: 0.618, auc: 0.614, f1: 0.671, acc: 0.534
+2025-08-07 19:33:53: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:33:53: Epoch: 005 Test results, ap: 0.232, auc: 0.513, f1: 0.388, acc: 0.241
+2025-08-07 19:33:54: Epoch: 006 Train Loss: 0.692, ap: 0.518, auc: 0.497, f1: 0.675, acc: 0.524
+2025-08-07 19:33:54: Epoch: 006 Test results, ap: 0.190, auc: 0.363, f1: 0.388, acc: 0.241
+2025-08-07 19:33:54: Epoch: 007 Train Loss: 0.693, ap: 0.524, auc: 0.544, f1: 0.667, acc: 0.524
+2025-08-07 19:33:54: Epoch: 007 Test results, ap: 0.187, auc: 0.350, f1: 0.388, acc: 0.241
+2025-08-07 19:33:55: Epoch: 008 Train Loss: 0.692, ap: 0.716, auc: 0.650, f1: 0.000, acc: 0.495
+2025-08-07 19:33:55: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:33:55: Epoch: 008 Test results, ap: 0.261, auc: 0.562, f1: 0.000, acc: 0.759
+2025-08-07 19:33:55: Epoch: 009 Train Loss: 0.693, ap: 0.491, auc: 0.438, f1: 0.000, acc: 0.495
+2025-08-07 19:33:55: Epoch: 009 Test results, ap: 0.304, auc: 0.587, f1: 0.000, acc: 0.759
+2025-08-07 19:33:55: Epoch: 010 Train Loss: 0.693, ap: 0.709, auc: 0.662, f1: 0.000, acc: 0.495
+2025-08-07 19:33:55: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:33:56: Epoch: 010 Test results, ap: 0.258, auc: 0.534, f1: 0.000, acc: 0.759
+2025-08-07 19:33:56: Epoch: 011 Train Loss: 0.693, ap: 0.723, auc: 0.673, f1: 0.000, acc: 0.495
+2025-08-07 19:33:56: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:33:56: Epoch: 011 Test results, ap: 0.253, auc: 0.521, f1: 0.000, acc: 0.759
+2025-08-07 19:33:56: Epoch: 011 New best TEST metrics
+2025-08-07 19:33:56: Epoch: 012 Train Loss: 0.693, ap: 0.736, auc: 0.685, f1: 0.000, acc: 0.495
+2025-08-07 19:33:56: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:33:57: Epoch: 012 Test results, ap: 0.243, auc: 0.505, f1: 0.000, acc: 0.759
+2025-08-07 19:33:57: Epoch: 013 Train Loss: 0.694, ap: 0.736, auc: 0.698, f1: 0.000, acc: 0.495
+2025-08-07 19:33:57: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:33:57: Epoch: 013 Test results, ap: 0.228, auc: 0.489, f1: 0.000, acc: 0.759
+2025-08-07 19:33:57: Epoch: 014 Train Loss: 0.694, ap: 0.723, auc: 0.697, f1: 0.000, acc: 0.495
+2025-08-07 19:33:57: Epoch: 014 Test results, ap: 0.223, auc: 0.485, f1: 0.000, acc: 0.759
+2025-08-07 19:33:58: Epoch: 015 Train Loss: 0.694, ap: 0.624, auc: 0.658, f1: 0.655, acc: 0.621
+2025-08-07 19:33:58: Epoch: 015 Test results, ap: 0.215, auc: 0.465, f1: 0.111, acc: 0.556
+2025-08-07 19:33:58: Epoch: 016 Train Loss: 0.692, ap: 0.609, auc: 0.640, f1: 0.676, acc: 0.544
+2025-08-07 19:33:58: Epoch: 016 Test results, ap: 0.209, auc: 0.439, f1: 0.372, acc: 0.343
+2025-08-07 19:33:59: Epoch: 017 Train Loss: 0.694, ap: 0.426, auc: 0.323, f1: 0.000, acc: 0.495
+2025-08-07 19:33:59: Epoch: 017 Test results, ap: 0.471, auc: 0.634, f1: 0.000, acc: 0.759
+2025-08-07 19:33:59: Epoch: 017 New best TEST metrics
+2025-08-07 19:33:59: Epoch: 018 Train Loss: 0.693, ap: 0.482, auc: 0.455, f1: 0.000, acc: 0.495
+2025-08-07 19:33:59: Epoch: 018 Test results, ap: 0.483, auc: 0.682, f1: 0.000, acc: 0.759
+2025-08-07 19:33:59: Epoch: 018 New best TEST metrics
+2025-08-07 19:34:00: Epoch: 019 Train Loss: 0.694, ap: 0.520, auc: 0.511, f1: 0.671, acc: 0.505
+2025-08-07 19:34:00: Epoch: 019 Test results, ap: 0.446, auc: 0.637, f1: 0.388, acc: 0.241
+2025-08-07 19:34:00: Epoch: 020 Train Loss: 0.692, ap: 0.444, auc: 0.418, f1: 0.671, acc: 0.505
+2025-08-07 19:34:00: Epoch: 020 Test results, ap: 0.431, auc: 0.683, f1: 0.388, acc: 0.241
+2025-08-07 19:34:01: Epoch: 021 Train Loss: 0.693, ap: 0.432, auc: 0.418, f1: 0.671, acc: 0.505
+2025-08-07 19:34:01: Epoch: 021 Test results, ap: 0.514, auc: 0.750, f1: 0.388, acc: 0.241
+2025-08-07 19:34:01: Epoch: 021 New best TEST metrics
+2025-08-07 19:34:01: Epoch: 022 Train Loss: 0.693, ap: 0.516, auc: 0.459, f1: 0.675, acc: 0.515
+2025-08-07 19:34:01: Epoch: 022 Test results, ap: 0.278, auc: 0.598, f1: 0.388, acc: 0.241
+2025-08-07 19:34:02: Epoch: 023 Train Loss: 0.693, ap: 0.504, auc: 0.490, f1: 0.000, acc: 0.495
+2025-08-07 19:34:02: Epoch: 023 Test results, ap: 0.425, auc: 0.689, f1: 0.000, acc: 0.759
+2025-08-07 19:34:02: Epoch: 024 Train Loss: 0.693, ap: 0.501, auc: 0.507, f1: 0.000, acc: 0.495
+2025-08-07 19:34:02: Epoch: 024 Test results, ap: 0.358, auc: 0.644, f1: 0.000, acc: 0.759
+2025-08-07 19:34:03: Epoch: 025 Train Loss: 0.693, ap: 0.475, auc: 0.453, f1: 0.000, acc: 0.495
+2025-08-07 19:34:03: Epoch: 025 Test results, ap: 0.391, auc: 0.694, f1: 0.000, acc: 0.759
+2025-08-07 19:34:03: Epoch: 026 Train Loss: 0.694, ap: 0.466, auc: 0.478, f1: 0.000, acc: 0.495
+2025-08-07 19:34:03: Epoch: 026 Test results, ap: 0.466, auc: 0.685, f1: 0.000, acc: 0.759
+2025-08-07 19:34:04: Epoch: 027 Train Loss: 0.692, ap: 0.577, auc: 0.514, f1: 0.000, acc: 0.495
+2025-08-07 19:34:04: Epoch: 027 Test results, ap: 0.395, auc: 0.666, f1: 0.000, acc: 0.759
+2025-08-07 19:34:04: Epoch: 028 Train Loss: 0.694, ap: 0.439, auc: 0.422, f1: 0.671, acc: 0.505
+2025-08-07 19:34:04: Epoch: 028 Test results, ap: 0.554, auc: 0.770, f1: 0.388, acc: 0.241
+2025-08-07 19:34:04: Epoch: 028 New best TEST metrics
+2025-08-07 19:34:04: Epoch: 029 Train Loss: 0.694, ap: 0.503, auc: 0.458, f1: 0.675, acc: 0.515
+2025-08-07 19:34:05: Epoch: 029 Test results, ap: 0.236, auc: 0.460, f1: 0.388, acc: 0.241
+2025-08-07 19:34:05: Epoch: 030 Train Loss: 0.693, ap: 0.535, auc: 0.463, f1: 0.675, acc: 0.515
+2025-08-07 19:34:05: Epoch: 030 Test results, ap: 0.262, auc: 0.511, f1: 0.388, acc: 0.241
+2025-08-07 19:34:05: Epoch: 031 Train Loss: 0.693, ap: 0.513, auc: 0.469, f1: 0.675, acc: 0.515
+2025-08-07 19:34:05: Epoch: 031 Test results, ap: 0.249, auc: 0.478, f1: 0.388, acc: 0.241
+2025-08-07 19:34:06: Epoch: 032 Train Loss: 0.693, ap: 0.514, auc: 0.469, f1: 0.675, acc: 0.515
+2025-08-07 19:34:06: Epoch: 032 Test results, ap: 0.253, auc: 0.496, f1: 0.388, acc: 0.241
+2025-08-07 19:34:06: Epoch: 033 Train Loss: 0.693, ap: 0.499, auc: 0.480, f1: 0.675, acc: 0.515
+2025-08-07 19:34:06: Epoch: 033 Test results, ap: 0.265, auc: 0.519, f1: 0.388, acc: 0.241
+2025-08-07 19:34:07: Epoch: 034 Train Loss: 0.693, ap: 0.518, auc: 0.476, f1: 0.675, acc: 0.515
+2025-08-07 19:34:07: Epoch: 034 Test results, ap: 0.246, auc: 0.481, f1: 0.388, acc: 0.241
+2025-08-07 19:34:07: Epoch: 035 Train Loss: 0.694, ap: 0.567, auc: 0.489, f1: 0.675, acc: 0.515
+2025-08-07 19:34:07: Epoch: 035 Test results, ap: 0.239, auc: 0.481, f1: 0.388, acc: 0.241
+2025-08-07 19:34:08: Epoch: 036 Train Loss: 0.693, ap: 0.487, auc: 0.456, f1: 0.675, acc: 0.515
+2025-08-07 19:34:08: Epoch: 036 Test results, ap: 0.256, auc: 0.497, f1: 0.388, acc: 0.241
+2025-08-07 19:34:08: Epoch: 037 Train Loss: 0.693, ap: 0.484, auc: 0.445, f1: 0.675, acc: 0.515
+2025-08-07 19:34:08: Epoch: 037 Test results, ap: 0.239, auc: 0.478, f1: 0.388, acc: 0.241
+2025-08-07 19:34:09: Epoch: 038 Train Loss: 0.693, ap: 0.489, auc: 0.459, f1: 0.667, acc: 0.505
+2025-08-07 19:34:09: Epoch: 038 Test results, ap: 0.251, auc: 0.494, f1: 0.388, acc: 0.241
+2025-08-07 19:34:09: Epoch: 039 Train Loss: 0.693, ap: 0.530, auc: 0.470, f1: 0.000, acc: 0.495
+2025-08-07 19:34:09: Epoch: 039 Test results, ap: 0.237, auc: 0.468, f1: 0.000, acc: 0.759
+2025-08-07 19:34:09: Epoch: 040 Train Loss: 0.692, ap: 0.488, auc: 0.460, f1: 0.000, acc: 0.495
+2025-08-07 19:34:10: Epoch: 040 Test results, ap: 0.248, auc: 0.482, f1: 0.000, acc: 0.759
+2025-08-07 19:34:10: Epoch: 041 Train Loss: 0.693, ap: 0.527, auc: 0.482, f1: 0.138, acc: 0.515
+2025-08-07 19:34:10: Epoch: 041 Test results, ap: 0.239, auc: 0.469, f1: 0.000, acc: 0.731
+2025-08-07 19:34:10: Epoch: 042 Train Loss: 0.693, ap: 0.535, auc: 0.470, f1: 0.324, acc: 0.553
+2025-08-07 19:34:11: Epoch: 042 Test results, ap: 0.225, auc: 0.450, f1: 0.000, acc: 0.722
+2025-08-07 19:34:11: Epoch: 043 Train Loss: 0.693, ap: 0.513, auc: 0.537, f1: 0.229, acc: 0.476
+2025-08-07 19:34:11: Epoch: 043 Test results, ap: 0.274, auc: 0.554, f1: 0.000, acc: 0.750
+2025-08-07 19:34:11: Epoch: 044 Train Loss: 0.693, ap: 0.518, auc: 0.495, f1: 0.312, acc: 0.485
+2025-08-07 19:34:12: Epoch: 044 Test results, ap: 0.246, auc: 0.480, f1: 0.000, acc: 0.722
+2025-08-07 19:34:12: Epoch: 045 Train Loss: 0.693, ap: 0.547, auc: 0.500, f1: 0.400, acc: 0.505
+2025-08-07 19:34:12: Epoch: 045 Test results, ap: 0.232, auc: 0.453, f1: 0.062, acc: 0.722
+2025-08-07 19:34:12: Epoch: 046 Train Loss: 0.694, ap: 0.489, auc: 0.494, f1: 0.098, acc: 0.466
+2025-08-07 19:34:12: Epoch: 046 Test results, ap: 0.297, auc: 0.589, f1: 0.000, acc: 0.759
+2025-08-07 19:34:13: Epoch: 047 Train Loss: 0.694, ap: 0.494, auc: 0.499, f1: 0.000, acc: 0.495
+2025-08-07 19:34:13: Epoch: 047 Test results, ap: 0.395, auc: 0.706, f1: 0.000, acc: 0.759
+2025-08-07 19:34:13: Epoch: 048 Train Loss: 0.693, ap: 0.627, auc: 0.565, f1: 0.000, acc: 0.495
+2025-08-07 19:34:13: Epoch: 048 Test results, ap: 0.215, auc: 0.466, f1: 0.000, acc: 0.759
+2025-08-07 19:34:14: Epoch: 049 Train Loss: 0.694, ap: 0.535, auc: 0.528, f1: 0.000, acc: 0.495
+2025-08-07 19:34:14: Epoch: 049 Test results, ap: 0.372, auc: 0.673, f1: 0.000, acc: 0.759
+2025-08-07 19:34:14: Epoch: 050 Train Loss: 0.694, ap: 0.483, auc: 0.507, f1: 0.000, acc: 0.495
+2025-08-07 19:34:14: Epoch: 050 Test results, ap: 0.283, auc: 0.607, f1: 0.000, acc: 0.759
+2025-08-07 19:34:14: used time 0.01h
+2025-08-07 19:34:14: Running Cross Validation 3
+2025-08-07 19:34:14: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:34:15: Epoch: 001 Train Loss: 0.695, ap: 0.366, auc: 0.434, f1: 0.000, acc: 0.592
+2025-08-07 19:34:15: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:34:15: Epoch: 001 Test results, ap: 0.311, auc: 0.633, f1: 0.000, acc: 0.759
+2025-08-07 19:34:15: Epoch: 002 Train Loss: 0.694, ap: 0.408, auc: 0.478, f1: 0.000, acc: 0.592
+2025-08-07 19:34:15: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:34:15: Epoch: 002 Test results, ap: 0.376, auc: 0.643, f1: 0.000, acc: 0.759
+2025-08-07 19:34:16: Epoch: 003 Train Loss: 0.693, ap: 0.447, auc: 0.502, f1: 0.571, acc: 0.417
+2025-08-07 19:34:16: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:34:16: Epoch: 003 Test results, ap: 0.404, auc: 0.652, f1: 0.388, acc: 0.241
+2025-08-07 19:34:16: Epoch: 004 Train Loss: 0.694, ap: 0.477, auc: 0.512, f1: 0.582, acc: 0.456
+2025-08-07 19:34:16: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:34:16: Epoch: 004 Test results, ap: 0.234, auc: 0.517, f1: 0.388, acc: 0.241
+2025-08-07 19:34:17: Epoch: 005 Train Loss: 0.693, ap: 0.482, auc: 0.599, f1: 0.576, acc: 0.456
+2025-08-07 19:34:17: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:34:17: Epoch: 005 Test results, ap: 0.195, auc: 0.391, f1: 0.388, acc: 0.241
+2025-08-07 19:34:17: Epoch: 006 Train Loss: 0.693, ap: 0.478, auc: 0.594, f1: 0.371, acc: 0.573
+2025-08-07 19:34:17: Epoch: 006 Test results, ap: 0.202, auc: 0.423, f1: 0.000, acc: 0.648
+2025-08-07 19:34:18: Epoch: 007 Train Loss: 0.692, ap: 0.491, auc: 0.568, f1: 0.511, acc: 0.573
+2025-08-07 19:34:18: Epoch: 007 Test results, ap: 0.204, auc: 0.425, f1: 0.215, acc: 0.528
+2025-08-07 19:34:18: Epoch: 008 Train Loss: 0.680, ap: 0.341, auc: 0.361, f1: 0.000, acc: 0.592
+2025-08-07 19:34:18: Epoch: 008 Test results, ap: 0.379, auc: 0.594, f1: 0.000, acc: 0.759
+2025-08-07 19:34:19: Epoch: 009 Train Loss: 0.694, ap: 0.394, auc: 0.472, f1: 0.000, acc: 0.592
+2025-08-07 19:34:19: Epoch: 009 Test results, ap: 0.205, auc: 0.427, f1: 0.000, acc: 0.759
+2025-08-07 19:34:19: Epoch: 010 Train Loss: 0.692, ap: 0.434, auc: 0.495, f1: 0.000, acc: 0.592
+2025-08-07 19:34:19: Epoch: 010 Test results, ap: 0.223, auc: 0.489, f1: 0.000, acc: 0.759
+2025-08-07 19:34:20: Epoch: 011 Train Loss: 0.693, ap: 0.427, auc: 0.506, f1: 0.000, acc: 0.592
+2025-08-07 19:34:20: Epoch: 011 Test results, ap: 0.217, auc: 0.464, f1: 0.000, acc: 0.759
+2025-08-07 19:34:20: Epoch: 011 New best TEST metrics
+2025-08-07 19:34:20: Epoch: 012 Train Loss: 0.694, ap: 0.432, auc: 0.502, f1: 0.000, acc: 0.592
+2025-08-07 19:34:20: Epoch: 012 Test results, ap: 0.284, auc: 0.593, f1: 0.000, acc: 0.759
+2025-08-07 19:34:20: Epoch: 012 New best TEST metrics
+2025-08-07 19:34:20: Epoch: 013 Train Loss: 0.693, ap: 0.405, auc: 0.456, f1: 0.000, acc: 0.592
+2025-08-07 19:34:21: Epoch: 013 Test results, ap: 0.224, auc: 0.493, f1: 0.000, acc: 0.759
+2025-08-07 19:34:21: Epoch: 014 Train Loss: 0.693, ap: 0.416, auc: 0.487, f1: 0.000, acc: 0.592
+2025-08-07 19:34:21: Epoch: 014 Test results, ap: 0.222, auc: 0.477, f1: 0.000, acc: 0.759
+2025-08-07 19:34:21: Epoch: 015 Train Loss: 0.694, ap: 0.419, auc: 0.491, f1: 0.000, acc: 0.592
+2025-08-07 19:34:21: Epoch: 015 Test results, ap: 0.226, auc: 0.477, f1: 0.000, acc: 0.759
+2025-08-07 19:34:22: Epoch: 016 Train Loss: 0.694, ap: 0.444, auc: 0.524, f1: 0.000, acc: 0.592
+2025-08-07 19:34:22: Epoch: 016 Test results, ap: 0.255, auc: 0.554, f1: 0.000, acc: 0.759
+2025-08-07 19:34:22: Epoch: 017 Train Loss: 0.694, ap: 0.461, auc: 0.538, f1: 0.000, acc: 0.592
+2025-08-07 19:34:22: Epoch: 017 Test results, ap: 0.213, auc: 0.455, f1: 0.000, acc: 0.759
+2025-08-07 19:34:23: Epoch: 018 Train Loss: 0.693, ap: 0.483, auc: 0.566, f1: 0.000, acc: 0.592
+2025-08-07 19:34:23: Epoch: 018 Test results, ap: 0.244, auc: 0.516, f1: 0.000, acc: 0.759
+2025-08-07 19:34:23: Epoch: 019 Train Loss: 0.692, ap: 0.447, auc: 0.521, f1: 0.587, acc: 0.427
+2025-08-07 19:34:23: Epoch: 019 Test results, ap: 0.266, auc: 0.551, f1: 0.388, acc: 0.241
+2025-08-07 19:34:24: Epoch: 020 Train Loss: 0.693, ap: 0.446, auc: 0.521, f1: 0.583, acc: 0.417
+2025-08-07 19:34:24: Epoch: 020 Test results, ap: 0.287, auc: 0.532, f1: 0.388, acc: 0.241
+2025-08-07 19:34:24: Epoch: 021 Train Loss: 0.694, ap: 0.450, auc: 0.526, f1: 0.587, acc: 0.427
+2025-08-07 19:34:24: Epoch: 021 Test results, ap: 0.348, auc: 0.584, f1: 0.388, acc: 0.241
+2025-08-07 19:34:24: Epoch: 021 New best TEST metrics
+2025-08-07 19:34:25: Epoch: 022 Train Loss: 0.694, ap: 0.441, auc: 0.520, f1: 0.582, acc: 0.427
+2025-08-07 19:34:25: Epoch: 022 Test results, ap: 0.308, auc: 0.550, f1: 0.388, acc: 0.241
+2025-08-07 19:34:25: Epoch: 023 Train Loss: 0.693, ap: 0.424, auc: 0.503, f1: 0.571, acc: 0.417
+2025-08-07 19:34:25: Epoch: 023 Test results, ap: 0.368, auc: 0.587, f1: 0.388, acc: 0.241
+2025-08-07 19:34:25: Epoch: 023 New best TEST metrics
+2025-08-07 19:34:26: Epoch: 024 Train Loss: 0.692, ap: 0.402, auc: 0.475, f1: 0.582, acc: 0.456
+2025-08-07 19:34:26: Epoch: 024 Test results, ap: 0.367, auc: 0.599, f1: 0.388, acc: 0.241
+2025-08-07 19:34:26: Epoch: 024 New best TEST metrics
+2025-08-07 19:34:26: Epoch: 025 Train Loss: 0.693, ap: 0.407, auc: 0.470, f1: 0.483, acc: 0.417
+2025-08-07 19:34:26: Epoch: 025 Test results, ap: 0.312, auc: 0.573, f1: 0.394, acc: 0.259
+2025-08-07 19:34:27: Epoch: 026 Train Loss: 0.693, ap: 0.417, auc: 0.475, f1: 0.000, acc: 0.592
+2025-08-07 19:34:27: Epoch: 026 Test results, ap: 0.341, auc: 0.599, f1: 0.000, acc: 0.759
+2025-08-07 19:34:27: Epoch: 027 Train Loss: 0.693, ap: 0.400, auc: 0.465, f1: 0.000, acc: 0.592
+2025-08-07 19:34:27: Epoch: 027 Test results, ap: 0.351, auc: 0.617, f1: 0.000, acc: 0.759
+2025-08-07 19:34:27: Epoch: 027 New best TEST metrics
+2025-08-07 19:34:28: Epoch: 028 Train Loss: 0.694, ap: 0.413, auc: 0.466, f1: 0.000, acc: 0.592
+2025-08-07 19:34:28: Epoch: 028 Test results, ap: 0.358, auc: 0.612, f1: 0.000, acc: 0.759
+2025-08-07 19:34:28: Epoch: 028 New best TEST metrics
+2025-08-07 19:34:28: Epoch: 029 Train Loss: 0.694, ap: 0.419, auc: 0.476, f1: 0.000, acc: 0.592
+2025-08-07 19:34:28: Epoch: 029 Test results, ap: 0.389, auc: 0.625, f1: 0.000, acc: 0.759
+2025-08-07 19:34:28: Epoch: 029 New best TEST metrics
+2025-08-07 19:34:29: Epoch: 030 Train Loss: 0.694, ap: 0.417, auc: 0.475, f1: 0.000, acc: 0.592
+2025-08-07 19:34:29: Epoch: 030 Test results, ap: 0.419, auc: 0.635, f1: 0.000, acc: 0.759
+2025-08-07 19:34:29: Epoch: 030 New best TEST metrics
+2025-08-07 19:34:29: Epoch: 031 Train Loss: 0.693, ap: 0.412, auc: 0.473, f1: 0.000, acc: 0.592
+2025-08-07 19:34:29: Epoch: 031 Test results, ap: 0.403, auc: 0.635, f1: 0.000, acc: 0.759
+2025-08-07 19:34:29: Epoch: 032 Train Loss: 0.694, ap: 0.414, auc: 0.474, f1: 0.000, acc: 0.592
+2025-08-07 19:34:30: Epoch: 032 Test results, ap: 0.447, auc: 0.638, f1: 0.000, acc: 0.759
+2025-08-07 19:34:30: Epoch: 032 New best TEST metrics
+2025-08-07 19:34:30: Epoch: 033 Train Loss: 0.693, ap: 0.413, auc: 0.474, f1: 0.000, acc: 0.592
+2025-08-07 19:34:30: Epoch: 033 Test results, ap: 0.424, auc: 0.636, f1: 0.000, acc: 0.759
+2025-08-07 19:34:30: Epoch: 034 Train Loss: 0.693, ap: 0.416, auc: 0.469, f1: 0.000, acc: 0.592
+2025-08-07 19:34:31: Epoch: 034 Test results, ap: 0.459, auc: 0.646, f1: 0.000, acc: 0.759
+2025-08-07 19:34:31: Epoch: 034 New best TEST metrics
+2025-08-07 19:34:31: Epoch: 035 Train Loss: 0.694, ap: 0.414, auc: 0.467, f1: 0.000, acc: 0.592
+2025-08-07 19:34:31: Epoch: 035 Test results, ap: 0.445, auc: 0.647, f1: 0.000, acc: 0.759
+2025-08-07 19:34:31: Epoch: 036 Train Loss: 0.692, ap: 0.413, auc: 0.466, f1: 0.000, acc: 0.592
+2025-08-07 19:34:31: Epoch: 036 Test results, ap: 0.459, auc: 0.647, f1: 0.000, acc: 0.759
+2025-08-07 19:34:32: Epoch: 037 Train Loss: 0.693, ap: 0.436, auc: 0.472, f1: 0.000, acc: 0.592
+2025-08-07 19:34:32: Epoch: 037 Test results, ap: 0.451, auc: 0.628, f1: 0.000, acc: 0.759
+2025-08-07 19:34:32: Epoch: 038 Train Loss: 0.693, ap: 0.436, auc: 0.474, f1: 0.000, acc: 0.592
+2025-08-07 19:34:32: Epoch: 038 Test results, ap: 0.439, auc: 0.632, f1: 0.000, acc: 0.759
+2025-08-07 19:34:33: Epoch: 039 Train Loss: 0.694, ap: 0.439, auc: 0.470, f1: 0.000, acc: 0.592
+2025-08-07 19:34:33: Epoch: 039 Test results, ap: 0.450, auc: 0.635, f1: 0.000, acc: 0.759
+2025-08-07 19:34:33: Epoch: 040 Train Loss: 0.692, ap: 0.436, auc: 0.482, f1: 0.329, acc: 0.524
+2025-08-07 19:34:33: Epoch: 040 Test results, ap: 0.425, auc: 0.621, f1: 0.333, acc: 0.704
+2025-08-07 19:34:34: Epoch: 041 Train Loss: 0.693, ap: 0.435, auc: 0.496, f1: 0.438, acc: 0.427
+2025-08-07 19:34:34: Epoch: 041 Test results, ap: 0.374, auc: 0.601, f1: 0.339, acc: 0.639
+2025-08-07 19:34:34: Epoch: 042 Train Loss: 0.693, ap: 0.437, auc: 0.494, f1: 0.442, acc: 0.485
+2025-08-07 19:34:34: Epoch: 042 Test results, ap: 0.377, auc: 0.592, f1: 0.340, acc: 0.676
+2025-08-07 19:34:34: Epoch: 043 Train Loss: 0.693, ap: 0.449, auc: 0.511, f1: 0.463, acc: 0.505
+2025-08-07 19:34:35: Epoch: 043 Test results, ap: 0.338, auc: 0.572, f1: 0.308, acc: 0.667
+2025-08-07 19:34:35: Epoch: 044 Train Loss: 0.692, ap: 0.470, auc: 0.554, f1: 0.515, acc: 0.524
+2025-08-07 19:34:35: Epoch: 044 Test results, ap: 0.282, auc: 0.535, f1: 0.316, acc: 0.639
+2025-08-07 19:34:35: Epoch: 045 Train Loss: 0.691, ap: 0.492, auc: 0.562, f1: 0.517, acc: 0.583
+2025-08-07 19:34:35: Epoch: 045 Test results, ap: 0.309, auc: 0.564, f1: 0.377, acc: 0.602
+2025-08-07 19:34:36: Epoch: 046 Train Loss: 0.691, ap: 0.494, auc: 0.604, f1: 0.584, acc: 0.544
+2025-08-07 19:34:36: Epoch: 046 New best VALIDATION metrics
+2025-08-07 19:34:36: Epoch: 046 Test results, ap: 0.236, auc: 0.460, f1: 0.289, acc: 0.500
+2025-08-07 19:34:36: Epoch: 047 Train Loss: 0.688, ap: 0.544, auc: 0.671, f1: 0.643, acc: 0.612
+2025-08-07 19:34:36: Epoch: 047 New best VALIDATION metrics
+2025-08-07 19:34:36: Epoch: 047 Test results, ap: 0.234, auc: 0.474, f1: 0.337, acc: 0.491
+2025-08-07 19:34:37: Epoch: 048 Train Loss: 0.687, ap: 0.463, auc: 0.452, f1: 0.346, acc: 0.485
+2025-08-07 19:34:37: Epoch: 048 Test results, ap: 0.227, auc: 0.464, f1: 0.282, acc: 0.481
+2025-08-07 19:34:37: Epoch: 049 Train Loss: 0.688, ap: 0.513, auc: 0.560, f1: 0.513, acc: 0.466
+2025-08-07 19:34:37: Epoch: 049 Test results, ap: 0.209, auc: 0.420, f1: 0.317, acc: 0.481
+2025-08-07 19:34:38: Epoch: 050 Train Loss: 0.686, ap: 0.524, auc: 0.641, f1: 0.505, acc: 0.563
+2025-08-07 19:34:38: Epoch: 050 Test results, ap: 0.240, auc: 0.478, f1: 0.118, acc: 0.722
+2025-08-07 19:34:38: used time 0.01h
+2025-08-07 19:34:38: Running Cross Validation 4
+2025-08-07 19:34:38: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:34:38: Epoch: 001 Train Loss: 0.693, ap: 0.458, auc: 0.575, f1: 0.569, acc: 0.398
+2025-08-07 19:34:38: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:34:38: Epoch: 001 Test results, ap: 0.332, auc: 0.598, f1: 0.388, acc: 0.241
+2025-08-07 19:34:39: Epoch: 002 Train Loss: 0.692, ap: 0.449, auc: 0.566, f1: 0.567, acc: 0.408
+2025-08-07 19:34:39: Epoch: 002 Test results, ap: 0.327, auc: 0.629, f1: 0.388, acc: 0.241
+2025-08-07 19:34:39: Epoch: 003 Train Loss: 0.694, ap: 0.420, auc: 0.511, f1: 0.569, acc: 0.398
+2025-08-07 19:34:39: Epoch: 003 Test results, ap: 0.193, auc: 0.366, f1: 0.388, acc: 0.241
+2025-08-07 19:34:40: Epoch: 004 Train Loss: 0.692, ap: 0.303, auc: 0.277, f1: 0.000, acc: 0.602
+2025-08-07 19:34:40: Epoch: 004 Test results, ap: 0.204, auc: 0.428, f1: 0.000, acc: 0.759
+2025-08-07 19:34:40: Epoch: 005 Train Loss: 0.693, ap: 0.559, auc: 0.642, f1: 0.000, acc: 0.602
+2025-08-07 19:34:40: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:34:40: Epoch: 005 Test results, ap: 0.202, auc: 0.395, f1: 0.000, acc: 0.759
+2025-08-07 19:34:41: Epoch: 006 Train Loss: 0.693, ap: 0.459, auc: 0.573, f1: 0.569, acc: 0.398
+2025-08-07 19:34:41: Epoch: 006 Test results, ap: 0.230, auc: 0.489, f1: 0.388, acc: 0.241
+2025-08-07 19:34:41: Epoch: 007 Train Loss: 0.693, ap: 0.499, auc: 0.516, f1: 0.569, acc: 0.398
+2025-08-07 19:34:41: Epoch: 007 Test results, ap: 0.245, auc: 0.476, f1: 0.388, acc: 0.241
+2025-08-07 19:34:42: Epoch: 008 Train Loss: 0.693, ap: 0.301, auc: 0.309, f1: 0.000, acc: 0.602
+2025-08-07 19:34:42: Epoch: 008 Test results, ap: 0.328, auc: 0.600, f1: 0.000, acc: 0.759
+2025-08-07 19:34:42: Epoch: 009 Train Loss: 0.693, ap: 0.345, auc: 0.351, f1: 0.000, acc: 0.602
+2025-08-07 19:34:42: Epoch: 009 Test results, ap: 0.192, auc: 0.361, f1: 0.000, acc: 0.759
+2025-08-07 19:34:43: Epoch: 010 Train Loss: 0.694, ap: 0.487, auc: 0.613, f1: 0.000, acc: 0.602
+2025-08-07 19:34:43: Epoch: 010 Test results, ap: 0.234, auc: 0.491, f1: 0.000, acc: 0.759
+2025-08-07 19:34:43: Epoch: 011 Train Loss: 0.694, ap: 0.518, auc: 0.608, f1: 0.569, acc: 0.398
+2025-08-07 19:34:43: Epoch: 011 Test results, ap: 0.294, auc: 0.584, f1: 0.388, acc: 0.241
+2025-08-07 19:34:43: Epoch: 011 New best TEST metrics
+2025-08-07 19:34:44: Epoch: 012 Train Loss: 0.694, ap: 0.434, auc: 0.515, f1: 0.000, acc: 0.602
+2025-08-07 19:34:44: Epoch: 012 Test results, ap: 0.398, auc: 0.651, f1: 0.000, acc: 0.759
+2025-08-07 19:34:44: Epoch: 012 New best TEST metrics
+2025-08-07 19:34:44: Epoch: 013 Train Loss: 0.694, ap: 0.465, auc: 0.599, f1: 0.569, acc: 0.398
+2025-08-07 19:34:44: Epoch: 013 Test results, ap: 0.313, auc: 0.574, f1: 0.388, acc: 0.241
+2025-08-07 19:34:45: Epoch: 014 Train Loss: 0.692, ap: 0.530, auc: 0.596, f1: 0.569, acc: 0.398
+2025-08-07 19:34:45: Epoch: 014 Test results, ap: 0.247, auc: 0.526, f1: 0.388, acc: 0.241
+2025-08-07 19:34:45: Epoch: 015 Train Loss: 0.693, ap: 0.542, auc: 0.611, f1: 0.000, acc: 0.602
+2025-08-07 19:34:45: Epoch: 015 Test results, ap: 0.255, auc: 0.527, f1: 0.000, acc: 0.759
+2025-08-07 19:34:45: Epoch: 016 Train Loss: 0.695, ap: 0.385, auc: 0.483, f1: 0.567, acc: 0.466
+2025-08-07 19:34:46: Epoch: 016 Test results, ap: 0.382, auc: 0.654, f1: 0.388, acc: 0.241
+2025-08-07 19:34:46: Epoch: 017 Train Loss: 0.693, ap: 0.443, auc: 0.504, f1: 0.559, acc: 0.388
+2025-08-07 19:34:46: Epoch: 017 Test results, ap: 0.398, auc: 0.706, f1: 0.388, acc: 0.241
+2025-08-07 19:34:46: Epoch: 017 New best TEST metrics
+2025-08-07 19:34:46: Epoch: 018 Train Loss: 0.694, ap: 0.380, auc: 0.487, f1: 0.563, acc: 0.398
+2025-08-07 19:34:47: Epoch: 018 Test results, ap: 0.321, auc: 0.652, f1: 0.388, acc: 0.241
+2025-08-07 19:34:47: Epoch: 019 Train Loss: 0.694, ap: 0.409, auc: 0.536, f1: 0.559, acc: 0.388
+2025-08-07 19:34:47: Epoch: 019 Test results, ap: 0.379, auc: 0.650, f1: 0.388, acc: 0.241
+2025-08-07 19:34:47: Epoch: 020 Train Loss: 0.693, ap: 0.588, auc: 0.676, f1: 0.000, acc: 0.602
+2025-08-07 19:34:47: Epoch: 020 New best VALIDATION metrics
+2025-08-07 19:34:48: Epoch: 020 Test results, ap: 0.249, auc: 0.550, f1: 0.000, acc: 0.759
+2025-08-07 19:34:48: Epoch: 021 Train Loss: 0.693, ap: 0.600, auc: 0.685, f1: 0.000, acc: 0.602
+2025-08-07 19:34:48: Epoch: 021 New best VALIDATION metrics
+2025-08-07 19:34:48: Epoch: 021 Test results, ap: 0.241, auc: 0.506, f1: 0.000, acc: 0.759
+2025-08-07 19:34:48: Epoch: 022 Train Loss: 0.693, ap: 0.604, auc: 0.689, f1: 0.000, acc: 0.602
+2025-08-07 19:34:48: Epoch: 022 New best VALIDATION metrics
+2025-08-07 19:34:49: Epoch: 022 Test results, ap: 0.227, auc: 0.483, f1: 0.000, acc: 0.759
+2025-08-07 19:34:49: Epoch: 023 Train Loss: 0.693, ap: 0.555, auc: 0.651, f1: 0.000, acc: 0.602
+2025-08-07 19:34:49: Epoch: 023 Test results, ap: 0.258, auc: 0.541, f1: 0.000, acc: 0.759
+2025-08-07 19:34:49: Epoch: 024 Train Loss: 0.692, ap: 0.527, auc: 0.656, f1: 0.580, acc: 0.437
+2025-08-07 19:34:49: Epoch: 024 Test results, ap: 0.242, auc: 0.522, f1: 0.388, acc: 0.241
+2025-08-07 19:34:50: Epoch: 025 Train Loss: 0.692, ap: 0.536, auc: 0.661, f1: 0.576, acc: 0.427
+2025-08-07 19:34:50: Epoch: 025 Test results, ap: 0.253, auc: 0.544, f1: 0.388, acc: 0.241
+2025-08-07 19:34:50: Epoch: 026 Train Loss: 0.693, ap: 0.327, auc: 0.383, f1: 0.569, acc: 0.398
+2025-08-07 19:34:50: Epoch: 026 Test results, ap: 0.262, auc: 0.545, f1: 0.388, acc: 0.241
+2025-08-07 19:34:51: Epoch: 027 Train Loss: 0.694, ap: 0.578, auc: 0.680, f1: 0.559, acc: 0.388
+2025-08-07 19:34:51: Epoch: 027 Test results, ap: 0.223, auc: 0.474, f1: 0.388, acc: 0.241
+2025-08-07 19:34:51: Epoch: 028 Train Loss: 0.694, ap: 0.459, auc: 0.537, f1: 0.559, acc: 0.388
+2025-08-07 19:34:51: Epoch: 028 Test results, ap: 0.261, auc: 0.563, f1: 0.388, acc: 0.241
+2025-08-07 19:34:52: Epoch: 029 Train Loss: 0.694, ap: 0.551, auc: 0.634, f1: 0.569, acc: 0.398
+2025-08-07 19:34:52: Epoch: 029 Test results, ap: 0.294, auc: 0.612, f1: 0.388, acc: 0.241
+2025-08-07 19:34:52: Epoch: 030 Train Loss: 0.693, ap: 0.382, auc: 0.464, f1: 0.563, acc: 0.398
+2025-08-07 19:34:52: Epoch: 030 Test results, ap: 0.320, auc: 0.628, f1: 0.388, acc: 0.241
+2025-08-07 19:34:53: Epoch: 031 Train Loss: 0.694, ap: 0.465, auc: 0.501, f1: 0.563, acc: 0.398
+2025-08-07 19:34:53: Epoch: 031 Test results, ap: 0.350, auc: 0.622, f1: 0.388, acc: 0.241
+2025-08-07 19:34:53: Epoch: 032 Train Loss: 0.694, ap: 0.481, auc: 0.609, f1: 0.552, acc: 0.417
+2025-08-07 19:34:53: Epoch: 032 Test results, ap: 0.322, auc: 0.630, f1: 0.388, acc: 0.241
+2025-08-07 19:34:54: Epoch: 033 Train Loss: 0.693, ap: 0.481, auc: 0.607, f1: 0.574, acc: 0.466
+2025-08-07 19:34:54: Epoch: 033 Test results, ap: 0.316, auc: 0.624, f1: 0.388, acc: 0.241
+2025-08-07 19:34:54: Epoch: 034 Train Loss: 0.693, ap: 0.487, auc: 0.617, f1: 0.569, acc: 0.456
+2025-08-07 19:34:54: Epoch: 034 Test results, ap: 0.310, auc: 0.619, f1: 0.388, acc: 0.241
+2025-08-07 19:34:55: Epoch: 035 Train Loss: 0.692, ap: 0.508, auc: 0.638, f1: 0.578, acc: 0.476
+2025-08-07 19:34:55: Epoch: 035 Test results, ap: 0.258, auc: 0.552, f1: 0.388, acc: 0.241
+2025-08-07 19:34:55: Epoch: 036 Train Loss: 0.692, ap: 0.536, auc: 0.665, f1: 0.562, acc: 0.456
+2025-08-07 19:34:55: Epoch: 036 Test results, ap: 0.252, auc: 0.548, f1: 0.388, acc: 0.241
+2025-08-07 19:34:56: Epoch: 037 Train Loss: 0.692, ap: 0.541, auc: 0.669, f1: 0.574, acc: 0.466
+2025-08-07 19:34:56: Epoch: 037 Test results, ap: 0.263, auc: 0.561, f1: 0.388, acc: 0.241
+2025-08-07 19:34:56: Epoch: 038 Train Loss: 0.693, ap: 0.520, auc: 0.657, f1: 0.574, acc: 0.437
+2025-08-07 19:34:56: Epoch: 038 Test results, ap: 0.215, auc: 0.463, f1: 0.388, acc: 0.241
+2025-08-07 19:34:57: Epoch: 039 Train Loss: 0.692, ap: 0.548, auc: 0.667, f1: 0.578, acc: 0.476
+2025-08-07 19:34:57: Epoch: 039 Test results, ap: 0.216, auc: 0.468, f1: 0.388, acc: 0.241
+2025-08-07 19:34:57: Epoch: 040 Train Loss: 0.692, ap: 0.570, auc: 0.672, f1: 0.578, acc: 0.476
+2025-08-07 19:34:57: Epoch: 040 Test results, ap: 0.215, auc: 0.465, f1: 0.388, acc: 0.241
+2025-08-07 19:34:57: Epoch: 041 Train Loss: 0.691, ap: 0.553, auc: 0.678, f1: 0.562, acc: 0.456
+2025-08-07 19:34:58: Epoch: 041 Test results, ap: 0.215, auc: 0.466, f1: 0.388, acc: 0.241
+2025-08-07 19:34:58: Epoch: 042 Train Loss: 0.693, ap: 0.553, auc: 0.673, f1: 0.569, acc: 0.485
+2025-08-07 19:34:58: Epoch: 042 Test results, ap: 0.213, auc: 0.458, f1: 0.388, acc: 0.241
+2025-08-07 19:34:58: Epoch: 043 Train Loss: 0.692, ap: 0.600, auc: 0.696, f1: 0.582, acc: 0.427
+2025-08-07 19:34:58: Epoch: 043 New best VALIDATION metrics
+2025-08-07 19:34:58: Epoch: 043 Test results, ap: 0.207, auc: 0.432, f1: 0.388, acc: 0.241
+2025-08-07 19:34:59: Epoch: 044 Train Loss: 0.692, ap: 0.594, auc: 0.685, f1: 0.565, acc: 0.417
+2025-08-07 19:34:59: Epoch: 044 Test results, ap: 0.214, auc: 0.456, f1: 0.388, acc: 0.241
+2025-08-07 19:34:59: Epoch: 045 Train Loss: 0.691, ap: 0.582, auc: 0.682, f1: 0.569, acc: 0.485
+2025-08-07 19:34:59: Epoch: 045 Test results, ap: 0.212, auc: 0.455, f1: 0.388, acc: 0.241
+2025-08-07 19:35:00: Epoch: 046 Train Loss: 0.690, ap: 0.592, auc: 0.683, f1: 0.587, acc: 0.495
+2025-08-07 19:35:00: Epoch: 046 Test results, ap: 0.215, auc: 0.464, f1: 0.388, acc: 0.241
+2025-08-07 19:35:00: Epoch: 047 Train Loss: 0.690, ap: 0.590, auc: 0.684, f1: 0.578, acc: 0.476
+2025-08-07 19:35:00: Epoch: 047 Test results, ap: 0.214, auc: 0.462, f1: 0.388, acc: 0.241
+2025-08-07 19:35:01: Epoch: 048 Train Loss: 0.691, ap: 0.592, auc: 0.694, f1: 0.567, acc: 0.466
+2025-08-07 19:35:01: Epoch: 048 Test results, ap: 0.215, auc: 0.467, f1: 0.388, acc: 0.241
+2025-08-07 19:35:01: Epoch: 049 Train Loss: 0.691, ap: 0.579, auc: 0.684, f1: 0.574, acc: 0.466
+2025-08-07 19:35:01: Epoch: 049 Test results, ap: 0.216, auc: 0.466, f1: 0.388, acc: 0.241
+2025-08-07 19:35:02: Epoch: 050 Train Loss: 0.687, ap: 0.597, auc: 0.689, f1: 0.565, acc: 0.447
+2025-08-07 19:35:02: Epoch: 050 Test results, ap: 0.215, auc: 0.462, f1: 0.388, acc: 0.241
+2025-08-07 19:35:02: used time 0.01h
+2025-08-07 19:35:02: Cross Validation Finished!
+2025-08-07 19:35:02: Best validation perform list
+[[0.65865934 0.67021281 0.         0.54368931]
+ [0.59022868 0.65765762 0.5316456  0.64077669]
+ [0.7360279  0.69834089 0.         0.49514562]
+ [0.54442036 0.6705699  0.64285713 0.61165047]
+ [0.60009766 0.69610542 0.58156025 0.42718446]]
+2025-08-07 19:35:02: mean: [0.626 0.679 0.351 0.544]
+2025-08-07 19:35:02: std: [0.066 0.016 0.289 0.077]
+2025-08-07 19:35:02: Best test perform list
+[[0.30522531 0.60881805 0.         0.75925928]
+ [0.38651866 0.6726079  0.         0.75925928]
+ [0.55352503 0.77016884 0.38805971 0.24074075]
+ [0.45911044 0.64634144 0.         0.75925928]
+ [0.39819705 0.70614451 0.38805971 0.24074075]]
+2025-08-07 19:35:02: mean: [0.421 0.681 0.155 0.552]
+2025-08-07 19:35:02: std: [0.083 0.055 0.19  0.254]
diff --git a/run-cls-simple/lstm-mlp-256-simple-oneway-ce-32-0.001-50/config.json b/run-cls-simple/lstm-mlp-256-simple-oneway-ce-32-0.001-50/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..d4b213996dc953511f00ab47934a1868a3179c66
--- /dev/null
+++ b/run-cls-simple/lstm-mlp-256-simple-oneway-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
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diff --git a/run-cls-simple/lstm-mlp-256-simple-oneway-ce-32-0.001-50/training.log b/run-cls-simple/lstm-mlp-256-simple-oneway-ce-32-0.001-50/training.log
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+2025-08-07 19:29:02: Loading Test Dataset
+2025-08-07 19:29:02: Running Cross Validation 0
+2025-08-07 19:29:02: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:29:03: Epoch: 001 Train Loss: 0.695, ap: 0.481, auc: 0.529, f1: 0.000, acc: 0.544
+2025-08-07 19:29:03: Epoch: 001 New best VALIDATION metrics
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+2025-08-07 19:29:03: Epoch: 002 Train Loss: 0.693, ap: 0.482, auc: 0.544, f1: 0.627, acc: 0.456
+2025-08-07 19:29:03: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:29:03: Epoch: 002 Test results, ap: 0.207, auc: 0.403, f1: 0.388, acc: 0.241
+2025-08-07 19:29:04: Epoch: 003 Train Loss: 0.692, ap: 0.457, auc: 0.512, f1: 0.627, acc: 0.456
+2025-08-07 19:29:04: Epoch: 003 Test results, ap: 0.324, auc: 0.498, f1: 0.388, acc: 0.241
+2025-08-07 19:29:04: Epoch: 004 Train Loss: 0.693, ap: 0.460, auc: 0.512, f1: 0.000, acc: 0.544
+2025-08-07 19:29:04: Epoch: 004 Test results, ap: 0.301, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:29:04: Epoch: 005 Train Loss: 0.692, ap: 0.473, auc: 0.518, f1: 0.000, acc: 0.544
+2025-08-07 19:29:05: Epoch: 005 Test results, ap: 0.193, auc: 0.359, f1: 0.000, acc: 0.759
+2025-08-07 19:29:05: Epoch: 006 Train Loss: 0.686, ap: 0.452, auc: 0.501, f1: 0.613, acc: 0.485
+2025-08-07 19:29:05: Epoch: 006 Test results, ap: 0.310, auc: 0.503, f1: 0.388, acc: 0.241
+2025-08-07 19:29:05: Epoch: 007 Train Loss: 0.699, ap: 0.426, auc: 0.464, f1: 0.627, acc: 0.456
+2025-08-07 19:29:06: Epoch: 007 Test results, ap: 0.336, auc: 0.632, f1: 0.388, acc: 0.241
+2025-08-07 19:29:06: Epoch: 008 Train Loss: 0.699, ap: 0.495, auc: 0.492, f1: 0.627, acc: 0.456
+2025-08-07 19:29:06: Epoch: 008 Test results, ap: 0.368, auc: 0.656, f1: 0.388, acc: 0.241
+2025-08-07 19:29:06: Epoch: 009 Train Loss: 0.692, ap: 0.445, auc: 0.414, f1: 0.627, acc: 0.456
+2025-08-07 19:29:06: Epoch: 009 Test results, ap: 0.250, auc: 0.524, f1: 0.388, acc: 0.241
+2025-08-07 19:29:07: Epoch: 010 Train Loss: 0.693, ap: 0.427, auc: 0.472, f1: 0.000, acc: 0.544
+2025-08-07 19:29:07: Epoch: 010 Test results, ap: 0.264, auc: 0.553, f1: 0.000, acc: 0.759
+2025-08-07 19:29:07: Epoch: 011 Train Loss: 0.692, ap: 0.457, auc: 0.479, f1: 0.000, acc: 0.544
+2025-08-07 19:29:07: Epoch: 011 Test results, ap: 0.243, auc: 0.485, f1: 0.000, acc: 0.759
+2025-08-07 19:29:07: Epoch: 011 New best TEST metrics
+2025-08-07 19:29:07: Epoch: 012 Train Loss: 0.693, ap: 0.424, auc: 0.441, f1: 0.627, acc: 0.456
+2025-08-07 19:29:08: Epoch: 012 Test results, ap: 0.265, auc: 0.553, f1: 0.388, acc: 0.241
+2025-08-07 19:29:08: Epoch: 012 New best TEST metrics
+2025-08-07 19:29:08: Epoch: 013 Train Loss: 0.691, ap: 0.458, auc: 0.490, f1: 0.000, acc: 0.544
+2025-08-07 19:29:08: Epoch: 013 Test results, ap: 0.263, auc: 0.548, f1: 0.000, acc: 0.759
+2025-08-07 19:29:08: Epoch: 014 Train Loss: 0.695, ap: 0.460, auc: 0.506, f1: 0.627, acc: 0.456
+2025-08-07 19:29:08: Epoch: 014 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:29:09: Epoch: 015 Train Loss: 0.693, ap: 0.472, auc: 0.526, f1: 0.627, acc: 0.456
+2025-08-07 19:29:09: Epoch: 015 Test results, ap: 0.225, auc: 0.452, f1: 0.388, acc: 0.241
+2025-08-07 19:29:09: Epoch: 016 Train Loss: 0.695, ap: 0.467, auc: 0.510, f1: 0.627, acc: 0.456
+2025-08-07 19:29:09: Epoch: 016 Test results, ap: 0.227, auc: 0.458, f1: 0.388, acc: 0.241
+2025-08-07 19:29:10: Epoch: 017 Train Loss: 0.693, ap: 0.431, auc: 0.448, f1: 0.627, acc: 0.456
+2025-08-07 19:29:10: Epoch: 017 Test results, ap: 0.236, auc: 0.470, f1: 0.388, acc: 0.241
+2025-08-07 19:29:10: Epoch: 018 Train Loss: 0.691, ap: 0.449, auc: 0.489, f1: 0.627, acc: 0.456
+2025-08-07 19:29:10: Epoch: 018 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:29:11: Epoch: 019 Train Loss: 0.692, ap: 0.472, auc: 0.529, f1: 0.000, acc: 0.544
+2025-08-07 19:29:11: Epoch: 019 Test results, ap: 0.252, auc: 0.529, f1: 0.000, acc: 0.759
+2025-08-07 19:29:11: Epoch: 020 Train Loss: 0.694, ap: 0.441, auc: 0.468, f1: 0.627, acc: 0.456
+2025-08-07 19:29:11: Epoch: 020 Test results, ap: 0.305, auc: 0.562, f1: 0.388, acc: 0.241
+2025-08-07 19:29:11: Epoch: 020 New best TEST metrics
+2025-08-07 19:29:11: Epoch: 021 Train Loss: 0.692, ap: 0.443, auc: 0.482, f1: 0.627, acc: 0.456
+2025-08-07 19:29:11: Epoch: 021 Test results, ap: 0.219, auc: 0.428, f1: 0.388, acc: 0.241
+2025-08-07 19:29:12: Epoch: 022 Train Loss: 0.690, ap: 0.455, auc: 0.515, f1: 0.000, acc: 0.544
+2025-08-07 19:29:12: Epoch: 022 Test results, ap: 0.241, auc: 0.445, f1: 0.000, acc: 0.759
+2025-08-07 19:29:12: Epoch: 023 Train Loss: 0.696, ap: 0.475, auc: 0.490, f1: 0.000, acc: 0.544
+2025-08-07 19:29:12: Epoch: 023 Test results, ap: 0.201, auc: 0.331, f1: 0.000, acc: 0.759
+2025-08-07 19:29:13: Epoch: 024 Train Loss: 0.696, ap: 0.490, auc: 0.555, f1: 0.000, acc: 0.544
+2025-08-07 19:29:13: Epoch: 024 New best VALIDATION metrics
+2025-08-07 19:29:13: Epoch: 024 Test results, ap: 0.241, auc: 0.501, f1: 0.000, acc: 0.759
+2025-08-07 19:29:13: Epoch: 025 Train Loss: 0.695, ap: 0.519, auc: 0.589, f1: 0.627, acc: 0.456
+2025-08-07 19:29:13: Epoch: 025 New best VALIDATION metrics
+2025-08-07 19:29:13: Epoch: 025 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:29:14: Epoch: 026 Train Loss: 0.697, ap: 0.448, auc: 0.489, f1: 0.000, acc: 0.544
+2025-08-07 19:29:14: Epoch: 026 Test results, ap: 0.245, auc: 0.512, f1: 0.000, acc: 0.759
+2025-08-07 19:29:14: Epoch: 027 Train Loss: 0.688, ap: 0.466, auc: 0.523, f1: 0.000, acc: 0.544
+2025-08-07 19:29:14: Epoch: 027 Test results, ap: 0.302, auc: 0.613, f1: 0.000, acc: 0.759
+2025-08-07 19:29:14: Epoch: 027 New best TEST metrics
+2025-08-07 19:29:14: Epoch: 028 Train Loss: 0.693, ap: 0.463, auc: 0.513, f1: 0.627, acc: 0.456
+2025-08-07 19:29:15: Epoch: 028 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:29:15: Epoch: 029 Train Loss: 0.695, ap: 0.468, auc: 0.519, f1: 0.627, acc: 0.456
+2025-08-07 19:29:15: Epoch: 029 Test results, ap: 0.241, auc: 0.501, f1: 0.388, acc: 0.241
+2025-08-07 19:29:15: Epoch: 030 Train Loss: 0.691, ap: 0.512, auc: 0.566, f1: 0.627, acc: 0.456
+2025-08-07 19:29:15: Epoch: 030 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:29:16: Epoch: 031 Train Loss: 0.694, ap: 0.474, auc: 0.526, f1: 0.627, acc: 0.456
+2025-08-07 19:29:16: Epoch: 031 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:29:16: Epoch: 032 Train Loss: 0.693, ap: 0.443, auc: 0.471, f1: 0.627, acc: 0.456
+2025-08-07 19:29:16: Epoch: 032 Test results, ap: 0.279, auc: 0.568, f1: 0.388, acc: 0.241
+2025-08-07 19:29:17: Epoch: 033 Train Loss: 0.694, ap: 0.471, auc: 0.521, f1: 0.627, acc: 0.456
+2025-08-07 19:29:17: Epoch: 033 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:29:17: Epoch: 034 Train Loss: 0.696, ap: 0.453, auc: 0.496, f1: 0.627, acc: 0.456
+2025-08-07 19:29:17: Epoch: 034 Test results, ap: 0.242, auc: 0.504, f1: 0.388, acc: 0.241
+2025-08-07 19:29:18: Epoch: 035 Train Loss: 0.694, ap: 0.457, auc: 0.503, f1: 0.627, acc: 0.456
+2025-08-07 19:29:18: Epoch: 035 Test results, ap: 0.257, auc: 0.541, f1: 0.388, acc: 0.241
+2025-08-07 19:29:18: Epoch: 036 Train Loss: 0.693, ap: 0.493, auc: 0.561, f1: 0.627, acc: 0.456
+2025-08-07 19:29:18: Epoch: 036 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:29:19: Epoch: 037 Train Loss: 0.694, ap: 0.501, auc: 0.551, f1: 0.627, acc: 0.456
+2025-08-07 19:29:19: Epoch: 037 Test results, ap: 0.244, auc: 0.507, f1: 0.388, acc: 0.241
+2025-08-07 19:29:19: Epoch: 038 Train Loss: 0.694, ap: 0.461, auc: 0.514, f1: 0.000, acc: 0.544
+2025-08-07 19:29:19: Epoch: 038 Test results, ap: 0.229, auc: 0.410, f1: 0.000, acc: 0.759
+2025-08-07 19:29:19: Epoch: 039 Train Loss: 0.696, ap: 0.435, auc: 0.461, f1: 0.000, acc: 0.544
+2025-08-07 19:29:20: Epoch: 039 Test results, ap: 0.273, auc: 0.557, f1: 0.000, acc: 0.759
+2025-08-07 19:29:20: Epoch: 040 Train Loss: 0.692, ap: 0.511, auc: 0.577, f1: 0.000, acc: 0.544
+2025-08-07 19:29:20: Epoch: 040 Test results, ap: 0.282, auc: 0.575, f1: 0.000, acc: 0.759
+2025-08-07 19:29:20: Epoch: 041 Train Loss: 0.694, ap: 0.457, auc: 0.486, f1: 0.000, acc: 0.544
+2025-08-07 19:29:21: Epoch: 041 Test results, ap: 0.242, auc: 0.499, f1: 0.000, acc: 0.759
+2025-08-07 19:29:21: Epoch: 042 Train Loss: 0.693, ap: 0.470, auc: 0.528, f1: 0.000, acc: 0.544
+2025-08-07 19:29:21: Epoch: 042 Test results, ap: 0.253, auc: 0.477, f1: 0.000, acc: 0.759
+2025-08-07 19:29:21: Epoch: 043 Train Loss: 0.693, ap: 0.480, auc: 0.499, f1: 0.000, acc: 0.544
+2025-08-07 19:29:22: Epoch: 043 Test results, ap: 0.210, auc: 0.413, f1: 0.000, acc: 0.759
+2025-08-07 19:29:22: Epoch: 044 Train Loss: 0.695, ap: 0.514, auc: 0.571, f1: 0.000, acc: 0.544
+2025-08-07 19:29:22: Epoch: 044 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:29:22: Epoch: 045 Train Loss: 0.696, ap: 0.479, auc: 0.540, f1: 0.627, acc: 0.456
+2025-08-07 19:29:23: Epoch: 045 Test results, ap: 0.333, auc: 0.632, f1: 0.388, acc: 0.241
+2025-08-07 19:29:23: Epoch: 045 New best TEST metrics
+2025-08-07 19:29:23: Epoch: 046 Train Loss: 0.693, ap: 0.518, auc: 0.587, f1: 0.627, acc: 0.456
+2025-08-07 19:29:23: Epoch: 046 Test results, ap: 0.342, auc: 0.623, f1: 0.388, acc: 0.241
+2025-08-07 19:29:23: Epoch: 046 New best TEST metrics
+2025-08-07 19:29:23: Epoch: 047 Train Loss: 0.692, ap: 0.479, auc: 0.537, f1: 0.627, acc: 0.456
+2025-08-07 19:29:23: Epoch: 047 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:29:24: Epoch: 048 Train Loss: 0.691, ap: 0.461, auc: 0.509, f1: 0.627, acc: 0.456
+2025-08-07 19:29:24: Epoch: 048 Test results, ap: 0.259, auc: 0.547, f1: 0.388, acc: 0.241
+2025-08-07 19:29:24: Epoch: 049 Train Loss: 0.693, ap: 0.462, auc: 0.506, f1: 0.627, acc: 0.456
+2025-08-07 19:29:24: Epoch: 049 Test results, ap: 0.241, auc: 0.482, f1: 0.388, acc: 0.241
+2025-08-07 19:29:25: Epoch: 050 Train Loss: 0.694, ap: 0.445, auc: 0.484, f1: 0.627, acc: 0.456
+2025-08-07 19:29:25: Epoch: 050 Test results, ap: 0.292, auc: 0.573, f1: 0.388, acc: 0.241
+2025-08-07 19:29:25: used time 0.01h
+2025-08-07 19:29:25: Running Cross Validation 1
+2025-08-07 19:29:25: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:29:25: Epoch: 001 Train Loss: 0.694, ap: 0.390, auc: 0.503, f1: 0.529, acc: 0.359
+2025-08-07 19:29:25: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:29:25: Epoch: 001 Test results, ap: 0.243, auc: 0.478, f1: 0.388, acc: 0.241
+2025-08-07 19:29:26: Epoch: 002 Train Loss: 0.692, ap: 0.323, auc: 0.407, f1: 0.000, acc: 0.641
+2025-08-07 19:29:26: Epoch: 002 Test results, ap: 0.480, auc: 0.617, f1: 0.000, acc: 0.759
+2025-08-07 19:29:26: Epoch: 003 Train Loss: 0.693, ap: 0.346, auc: 0.430, f1: 0.529, acc: 0.359
+2025-08-07 19:29:26: Epoch: 003 Test results, ap: 0.496, auc: 0.593, f1: 0.388, acc: 0.241
+2025-08-07 19:29:27: Epoch: 004 Train Loss: 0.695, ap: 0.422, auc: 0.516, f1: 0.526, acc: 0.369
+2025-08-07 19:29:27: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:29:27: Epoch: 004 Test results, ap: 0.173, auc: 0.299, f1: 0.388, acc: 0.241
+2025-08-07 19:29:27: Epoch: 005 Train Loss: 0.693, ap: 0.463, auc: 0.515, f1: 0.000, acc: 0.641
+2025-08-07 19:29:27: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:29:27: Epoch: 005 Test results, ap: 0.174, auc: 0.300, f1: 0.000, acc: 0.759
+2025-08-07 19:29:28: Epoch: 006 Train Loss: 0.678, ap: 0.476, auc: 0.670, f1: 0.526, acc: 0.650
+2025-08-07 19:29:28: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:29:28: Epoch: 006 Test results, ap: 0.287, auc: 0.617, f1: 0.000, acc: 0.694
+2025-08-07 19:29:28: Epoch: 007 Train Loss: 0.679, ap: 0.444, auc: 0.571, f1: 0.393, acc: 0.641
+2025-08-07 19:29:28: Epoch: 007 Test results, ap: 0.284, auc: 0.331, f1: 0.205, acc: 0.713
+2025-08-07 19:29:29: Epoch: 008 Train Loss: 0.666, ap: 0.476, auc: 0.633, f1: 0.462, acc: 0.592
+2025-08-07 19:29:29: Epoch: 008 Test results, ap: 0.230, auc: 0.488, f1: 0.125, acc: 0.611
+2025-08-07 19:29:29: Epoch: 009 Train Loss: 0.642, ap: 0.466, auc: 0.618, f1: 0.562, acc: 0.592
+2025-08-07 19:29:29: Epoch: 009 Test results, ap: 0.205, auc: 0.419, f1: 0.379, acc: 0.333
+2025-08-07 19:29:29: Epoch: 010 Train Loss: 0.635, ap: 0.467, auc: 0.652, f1: 0.426, acc: 0.660
+2025-08-07 19:29:30: Epoch: 010 Test results, ap: 0.257, auc: 0.525, f1: 0.240, acc: 0.648
+2025-08-07 19:29:30: Epoch: 011 Train Loss: 0.655, ap: 0.403, auc: 0.577, f1: 0.395, acc: 0.553
+2025-08-07 19:29:30: Epoch: 011 Test results, ap: 0.240, auc: 0.383, f1: 0.216, acc: 0.731
+2025-08-07 19:29:30: Epoch: 011 New best TEST metrics
+2025-08-07 19:29:30: Epoch: 012 Train Loss: 0.632, ap: 0.542, auc: 0.630, f1: 0.432, acc: 0.592
+2025-08-07 19:29:30: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:29:31: Epoch: 012 Test results, ap: 0.216, auc: 0.387, f1: 0.273, acc: 0.407
+2025-08-07 19:29:31: Epoch: 013 Train Loss: 0.620, ap: 0.415, auc: 0.536, f1: 0.388, acc: 0.602
+2025-08-07 19:29:31: Epoch: 013 Test results, ap: 0.222, auc: 0.389, f1: 0.174, acc: 0.648
+2025-08-07 19:29:31: Epoch: 014 Train Loss: 0.611, ap: 0.444, auc: 0.580, f1: 0.406, acc: 0.602
+2025-08-07 19:29:32: Epoch: 014 Test results, ap: 0.217, auc: 0.383, f1: 0.170, acc: 0.639
+2025-08-07 19:29:32: Epoch: 015 Train Loss: 0.608, ap: 0.438, auc: 0.607, f1: 0.463, acc: 0.573
+2025-08-07 19:29:32: Epoch: 015 Test results, ap: 0.214, auc: 0.377, f1: 0.139, acc: 0.426
+2025-08-07 19:29:32: Epoch: 016 Train Loss: 0.581, ap: 0.450, auc: 0.597, f1: 0.517, acc: 0.583
+2025-08-07 19:29:32: Epoch: 016 Test results, ap: 0.208, auc: 0.369, f1: 0.330, acc: 0.287
+2025-08-07 19:29:33: Epoch: 017 Train Loss: 0.581, ap: 0.453, auc: 0.602, f1: 0.500, acc: 0.612
+2025-08-07 19:29:33: Epoch: 017 Test results, ap: 0.217, auc: 0.380, f1: 0.125, acc: 0.481
+2025-08-07 19:29:33: Epoch: 018 Train Loss: 0.583, ap: 0.483, auc: 0.644, f1: 0.441, acc: 0.631
+2025-08-07 19:29:33: Epoch: 018 Test results, ap: 0.237, auc: 0.434, f1: 0.065, acc: 0.731
+2025-08-07 19:29:33: Epoch: 018 New best TEST metrics
+2025-08-07 19:29:34: Epoch: 019 Train Loss: 0.583, ap: 0.446, auc: 0.644, f1: 0.413, acc: 0.641
+2025-08-07 19:29:34: Epoch: 019 Test results, ap: 0.254, auc: 0.485, f1: 0.000, acc: 0.731
+2025-08-07 19:29:34: Epoch: 019 New best TEST metrics
+2025-08-07 19:29:34: Epoch: 020 Train Loss: 0.577, ap: 0.521, auc: 0.661, f1: 0.424, acc: 0.631
+2025-08-07 19:29:34: Epoch: 020 New best VALIDATION metrics
+2025-08-07 19:29:34: Epoch: 020 Test results, ap: 0.239, auc: 0.442, f1: 0.065, acc: 0.731
+2025-08-07 19:29:35: Epoch: 021 Train Loss: 0.545, ap: 0.477, auc: 0.644, f1: 0.506, acc: 0.621
+2025-08-07 19:29:35: Epoch: 021 Test results, ap: 0.222, auc: 0.407, f1: 0.216, acc: 0.731
+2025-08-07 19:29:35: Epoch: 022 Train Loss: 0.531, ap: 0.461, auc: 0.657, f1: 0.494, acc: 0.621
+2025-08-07 19:29:35: Epoch: 022 Test results, ap: 0.233, auc: 0.425, f1: 0.222, acc: 0.741
+2025-08-07 19:29:36: Epoch: 023 Train Loss: 0.560, ap: 0.454, auc: 0.652, f1: 0.480, acc: 0.621
+2025-08-07 19:29:36: Epoch: 023 Test results, ap: 0.234, auc: 0.427, f1: 0.222, acc: 0.741
+2025-08-07 19:29:36: Epoch: 024 Train Loss: 0.531, ap: 0.448, auc: 0.646, f1: 0.493, acc: 0.641
+2025-08-07 19:29:36: Epoch: 024 Test results, ap: 0.232, auc: 0.432, f1: 0.121, acc: 0.731
+2025-08-07 19:29:37: Epoch: 025 Train Loss: 0.537, ap: 0.444, auc: 0.638, f1: 0.465, acc: 0.553
+2025-08-07 19:29:37: Epoch: 025 Test results, ap: 0.245, auc: 0.418, f1: 0.205, acc: 0.713
+2025-08-07 19:29:37: Epoch: 026 Train Loss: 0.528, ap: 0.454, auc: 0.651, f1: 0.457, acc: 0.631
+2025-08-07 19:29:37: Epoch: 026 Test results, ap: 0.264, auc: 0.530, f1: 0.000, acc: 0.722
+2025-08-07 19:29:37: Epoch: 026 New best TEST metrics
+2025-08-07 19:29:38: Epoch: 027 Train Loss: 0.530, ap: 0.455, auc: 0.636, f1: 0.500, acc: 0.612
+2025-08-07 19:29:38: Epoch: 027 Test results, ap: 0.257, auc: 0.492, f1: 0.182, acc: 0.750
+2025-08-07 19:29:38: Epoch: 028 Train Loss: 0.502, ap: 0.448, auc: 0.648, f1: 0.459, acc: 0.612
+2025-08-07 19:29:38: Epoch: 028 Test results, ap: 0.276, auc: 0.582, f1: 0.000, acc: 0.722
+2025-08-07 19:29:38: Epoch: 028 New best TEST metrics
+2025-08-07 19:29:39: Epoch: 029 Train Loss: 0.552, ap: 0.474, auc: 0.646, f1: 0.475, acc: 0.592
+2025-08-07 19:29:39: Epoch: 029 Test results, ap: 0.231, auc: 0.434, f1: 0.216, acc: 0.731
+2025-08-07 19:29:39: Epoch: 030 Train Loss: 0.516, ap: 0.442, auc: 0.640, f1: 0.494, acc: 0.621
+2025-08-07 19:29:39: Epoch: 030 Test results, ap: 0.273, auc: 0.507, f1: 0.176, acc: 0.741
+2025-08-07 19:29:40: Epoch: 031 Train Loss: 0.524, ap: 0.441, auc: 0.639, f1: 0.459, acc: 0.612
+2025-08-07 19:29:40: Epoch: 031 Test results, ap: 0.270, auc: 0.531, f1: 0.000, acc: 0.722
+2025-08-07 19:29:40: Epoch: 032 Train Loss: 0.505, ap: 0.464, auc: 0.648, f1: 0.459, acc: 0.612
+2025-08-07 19:29:40: Epoch: 032 Test results, ap: 0.267, auc: 0.521, f1: 0.065, acc: 0.731
+2025-08-07 19:29:41: Epoch: 033 Train Loss: 0.507, ap: 0.450, auc: 0.647, f1: 0.451, acc: 0.621
+2025-08-07 19:29:41: Epoch: 033 Test results, ap: 0.265, auc: 0.579, f1: 0.000, acc: 0.722
+2025-08-07 19:29:41: Epoch: 034 Train Loss: 0.522, ap: 0.432, auc: 0.629, f1: 0.513, acc: 0.631
+2025-08-07 19:29:41: Epoch: 034 Test results, ap: 0.274, auc: 0.507, f1: 0.171, acc: 0.731
+2025-08-07 19:29:42: Epoch: 035 Train Loss: 0.507, ap: 0.452, auc: 0.646, f1: 0.466, acc: 0.621
+2025-08-07 19:29:42: Epoch: 035 Test results, ap: 0.259, auc: 0.534, f1: 0.000, acc: 0.722
+2025-08-07 19:29:42: Epoch: 036 Train Loss: 0.507, ap: 0.448, auc: 0.643, f1: 0.474, acc: 0.612
+2025-08-07 19:29:42: Epoch: 036 Test results, ap: 0.267, auc: 0.525, f1: 0.176, acc: 0.741
+2025-08-07 19:29:43: Epoch: 037 Train Loss: 0.506, ap: 0.444, auc: 0.640, f1: 0.480, acc: 0.621
+2025-08-07 19:29:43: Epoch: 037 Test results, ap: 0.266, auc: 0.539, f1: 0.000, acc: 0.722
+2025-08-07 19:29:43: Epoch: 038 Train Loss: 0.489, ap: 0.453, auc: 0.649, f1: 0.493, acc: 0.641
+2025-08-07 19:29:43: Epoch: 038 Test results, ap: 0.253, auc: 0.554, f1: 0.000, acc: 0.722
+2025-08-07 19:29:43: Epoch: 039 Train Loss: 0.513, ap: 0.441, auc: 0.642, f1: 0.480, acc: 0.621
+2025-08-07 19:29:44: Epoch: 039 Test results, ap: 0.257, auc: 0.556, f1: 0.000, acc: 0.722
+2025-08-07 19:29:44: Epoch: 040 Train Loss: 0.499, ap: 0.440, auc: 0.639, f1: 0.480, acc: 0.621
+2025-08-07 19:29:44: Epoch: 040 Test results, ap: 0.256, auc: 0.553, f1: 0.000, acc: 0.722
+2025-08-07 19:29:44: Epoch: 041 Train Loss: 0.478, ap: 0.440, auc: 0.635, f1: 0.494, acc: 0.621
+2025-08-07 19:29:45: Epoch: 041 Test results, ap: 0.269, auc: 0.558, f1: 0.000, acc: 0.722
+2025-08-07 19:29:45: Epoch: 042 Train Loss: 0.481, ap: 0.445, auc: 0.640, f1: 0.466, acc: 0.621
+2025-08-07 19:29:45: Epoch: 042 Test results, ap: 0.257, auc: 0.559, f1: 0.000, acc: 0.722
+2025-08-07 19:29:45: Epoch: 043 Train Loss: 0.492, ap: 0.442, auc: 0.639, f1: 0.474, acc: 0.612
+2025-08-07 19:29:45: Epoch: 043 Test results, ap: 0.271, auc: 0.566, f1: 0.000, acc: 0.722
+2025-08-07 19:29:46: Epoch: 044 Train Loss: 0.476, ap: 0.444, auc: 0.640, f1: 0.480, acc: 0.621
+2025-08-07 19:29:46: Epoch: 044 Test results, ap: 0.258, auc: 0.561, f1: 0.000, acc: 0.722
+2025-08-07 19:29:46: Epoch: 045 Train Loss: 0.485, ap: 0.445, auc: 0.643, f1: 0.494, acc: 0.621
+2025-08-07 19:29:46: Epoch: 045 Test results, ap: 0.260, auc: 0.565, f1: 0.000, acc: 0.722
+2025-08-07 19:29:47: Epoch: 046 Train Loss: 0.471, ap: 0.446, auc: 0.645, f1: 0.500, acc: 0.631
+2025-08-07 19:29:47: Epoch: 046 Test results, ap: 0.260, auc: 0.568, f1: 0.000, acc: 0.722
+2025-08-07 19:29:47: Epoch: 047 Train Loss: 0.467, ap: 0.443, auc: 0.645, f1: 0.494, acc: 0.621
+2025-08-07 19:29:47: Epoch: 047 Test results, ap: 0.262, auc: 0.571, f1: 0.000, acc: 0.722
+2025-08-07 19:29:48: Epoch: 048 Train Loss: 0.465, ap: 0.446, auc: 0.648, f1: 0.500, acc: 0.631
+2025-08-07 19:29:48: Epoch: 048 Test results, ap: 0.254, auc: 0.560, f1: 0.000, acc: 0.722
+2025-08-07 19:29:48: Epoch: 049 Train Loss: 0.468, ap: 0.447, auc: 0.650, f1: 0.500, acc: 0.631
+2025-08-07 19:29:48: Epoch: 049 Test results, ap: 0.253, auc: 0.559, f1: 0.000, acc: 0.722
+2025-08-07 19:29:49: Epoch: 050 Train Loss: 0.453, ap: 0.447, auc: 0.653, f1: 0.480, acc: 0.621
+2025-08-07 19:29:49: Epoch: 050 Test results, ap: 0.253, auc: 0.555, f1: 0.000, acc: 0.722
+2025-08-07 19:29:49: used time 0.01h
+2025-08-07 19:29:49: Running Cross Validation 2
+2025-08-07 19:29:49: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:29:49: Epoch: 001 Train Loss: 0.695, ap: 0.596, auc: 0.569, f1: 0.000, acc: 0.495
+2025-08-07 19:29:49: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:29:49: Epoch: 001 Test results, ap: 0.233, auc: 0.481, f1: 0.000, acc: 0.759
+2025-08-07 19:29:50: Epoch: 002 Train Loss: 0.694, ap: 0.551, auc: 0.589, f1: 0.671, acc: 0.505
+2025-08-07 19:29:50: Epoch: 002 Test results, ap: 0.439, auc: 0.594, f1: 0.388, acc: 0.241
+2025-08-07 19:29:50: Epoch: 003 Train Loss: 0.693, ap: 0.565, auc: 0.582, f1: 0.000, acc: 0.495
+2025-08-07 19:29:50: Epoch: 003 Test results, ap: 0.436, auc: 0.554, f1: 0.000, acc: 0.759
+2025-08-07 19:29:51: Epoch: 004 Train Loss: 0.696, ap: 0.624, auc: 0.606, f1: 0.671, acc: 0.505
+2025-08-07 19:29:51: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:29:51: Epoch: 004 Test results, ap: 0.328, auc: 0.515, f1: 0.388, acc: 0.241
+2025-08-07 19:29:51: Epoch: 005 Train Loss: 0.694, ap: 0.590, auc: 0.622, f1: 0.000, acc: 0.495
+2025-08-07 19:29:51: Epoch: 005 Test results, ap: 0.211, auc: 0.380, f1: 0.000, acc: 0.759
+2025-08-07 19:29:52: Epoch: 006 Train Loss: 0.693, ap: 0.565, auc: 0.614, f1: 0.696, acc: 0.670
+2025-08-07 19:29:52: Epoch: 006 Test results, ap: 0.236, auc: 0.486, f1: 0.328, acc: 0.278
+2025-08-07 19:29:52: Epoch: 007 Train Loss: 0.692, ap: 0.449, auc: 0.434, f1: 0.671, acc: 0.505
+2025-08-07 19:29:52: Epoch: 007 Test results, ap: 0.239, auc: 0.460, f1: 0.388, acc: 0.241
+2025-08-07 19:29:53: Epoch: 008 Train Loss: 0.696, ap: 0.526, auc: 0.525, f1: 0.000, acc: 0.495
+2025-08-07 19:29:53: Epoch: 008 Test results, ap: 0.260, auc: 0.511, f1: 0.000, acc: 0.759
+2025-08-07 19:29:53: Epoch: 009 Train Loss: 0.695, ap: 0.551, auc: 0.554, f1: 0.671, acc: 0.505
+2025-08-07 19:29:53: Epoch: 009 Test results, ap: 0.287, auc: 0.582, f1: 0.388, acc: 0.241
+2025-08-07 19:29:54: Epoch: 010 Train Loss: 0.694, ap: 0.511, auc: 0.468, f1: 0.000, acc: 0.495
+2025-08-07 19:29:54: Epoch: 010 Test results, ap: 0.320, auc: 0.618, f1: 0.000, acc: 0.759
+2025-08-07 19:29:54: Epoch: 011 Train Loss: 0.692, ap: 0.526, auc: 0.491, f1: 0.000, acc: 0.495
+2025-08-07 19:29:54: Epoch: 011 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:29:54: Epoch: 011 New best TEST metrics
+2025-08-07 19:29:54: Epoch: 012 Train Loss: 0.695, ap: 0.547, auc: 0.546, f1: 0.000, acc: 0.495
+2025-08-07 19:29:55: Epoch: 012 Test results, ap: 0.245, auc: 0.511, f1: 0.000, acc: 0.759
+2025-08-07 19:29:55: Epoch: 012 New best TEST metrics
+2025-08-07 19:29:55: Epoch: 013 Train Loss: 0.695, ap: 0.567, auc: 0.579, f1: 0.671, acc: 0.505
+2025-08-07 19:29:55: Epoch: 013 Test results, ap: 0.276, auc: 0.557, f1: 0.388, acc: 0.241
+2025-08-07 19:29:55: Epoch: 013 New best TEST metrics
+2025-08-07 19:29:55: Epoch: 014 Train Loss: 0.691, ap: 0.584, auc: 0.551, f1: 0.671, acc: 0.505
+2025-08-07 19:29:55: Epoch: 014 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:29:56: Epoch: 015 Train Loss: 0.695, ap: 0.573, auc: 0.530, f1: 0.671, acc: 0.505
+2025-08-07 19:29:56: Epoch: 015 Test results, ap: 0.245, auc: 0.511, f1: 0.388, acc: 0.241
+2025-08-07 19:29:56: Epoch: 016 Train Loss: 0.696, ap: 0.523, auc: 0.489, f1: 0.671, acc: 0.505
+2025-08-07 19:29:56: Epoch: 016 Test results, ap: 0.216, auc: 0.418, f1: 0.388, acc: 0.241
+2025-08-07 19:29:57: Epoch: 017 Train Loss: 0.695, ap: 0.572, auc: 0.571, f1: 0.000, acc: 0.495
+2025-08-07 19:29:57: Epoch: 017 Test results, ap: 0.250, auc: 0.524, f1: 0.000, acc: 0.759
+2025-08-07 19:29:57: Epoch: 018 Train Loss: 0.692, ap: 0.519, auc: 0.482, f1: 0.000, acc: 0.495
+2025-08-07 19:29:57: Epoch: 018 Test results, ap: 0.235, auc: 0.470, f1: 0.000, acc: 0.759
+2025-08-07 19:29:58: Epoch: 019 Train Loss: 0.694, ap: 0.515, auc: 0.480, f1: 0.000, acc: 0.495
+2025-08-07 19:29:58: Epoch: 019 Test results, ap: 0.244, auc: 0.506, f1: 0.000, acc: 0.759
+2025-08-07 19:29:58: Epoch: 020 Train Loss: 0.691, ap: 0.557, auc: 0.560, f1: 0.671, acc: 0.505
+2025-08-07 19:29:58: Epoch: 020 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:29:59: Epoch: 021 Train Loss: 0.695, ap: 0.520, auc: 0.444, f1: 0.671, acc: 0.505
+2025-08-07 19:29:59: Epoch: 021 Test results, ap: 0.236, auc: 0.487, f1: 0.388, acc: 0.241
+2025-08-07 19:29:59: Epoch: 022 Train Loss: 0.695, ap: 0.527, auc: 0.519, f1: 0.671, acc: 0.505
+2025-08-07 19:29:59: Epoch: 022 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:30:00: Epoch: 023 Train Loss: 0.690, ap: 0.609, auc: 0.622, f1: 0.671, acc: 0.505
+2025-08-07 19:30:00: Epoch: 023 New best VALIDATION metrics
+2025-08-07 19:30:00: Epoch: 023 Test results, ap: 0.241, auc: 0.488, f1: 0.388, acc: 0.241
+2025-08-07 19:30:00: Epoch: 024 Train Loss: 0.695, ap: 0.535, auc: 0.496, f1: 0.000, acc: 0.495
+2025-08-07 19:30:00: Epoch: 024 Test results, ap: 0.238, auc: 0.493, f1: 0.000, acc: 0.759
+2025-08-07 19:30:01: Epoch: 025 Train Loss: 0.694, ap: 0.521, auc: 0.484, f1: 0.000, acc: 0.495
+2025-08-07 19:30:01: Epoch: 025 Test results, ap: 0.243, auc: 0.506, f1: 0.000, acc: 0.759
+2025-08-07 19:30:01: Epoch: 026 Train Loss: 0.692, ap: 0.547, auc: 0.551, f1: 0.671, acc: 0.505
+2025-08-07 19:30:01: Epoch: 026 Test results, ap: 0.241, auc: 0.488, f1: 0.388, acc: 0.241
+2025-08-07 19:30:01: Epoch: 027 Train Loss: 0.693, ap: 0.525, auc: 0.504, f1: 0.671, acc: 0.505
+2025-08-07 19:30:02: Epoch: 027 Test results, ap: 0.253, auc: 0.520, f1: 0.388, acc: 0.241
+2025-08-07 19:30:02: Epoch: 028 Train Loss: 0.693, ap: 0.512, auc: 0.460, f1: 0.000, acc: 0.495
+2025-08-07 19:30:02: Epoch: 028 Test results, ap: 0.301, auc: 0.579, f1: 0.000, acc: 0.759
+2025-08-07 19:30:02: Epoch: 028 New best TEST metrics
+2025-08-07 19:30:03: Epoch: 029 Train Loss: 0.691, ap: 0.548, auc: 0.534, f1: 0.000, acc: 0.495
+2025-08-07 19:30:03: Epoch: 029 Test results, ap: 0.238, auc: 0.486, f1: 0.000, acc: 0.759
+2025-08-07 19:30:03: Epoch: 030 Train Loss: 0.692, ap: 0.534, auc: 0.516, f1: 0.000, acc: 0.495
+2025-08-07 19:30:03: Epoch: 030 Test results, ap: 0.261, auc: 0.548, f1: 0.000, acc: 0.759
+2025-08-07 19:30:03: Epoch: 031 Train Loss: 0.693, ap: 0.567, auc: 0.570, f1: 0.000, acc: 0.495
+2025-08-07 19:30:04: Epoch: 031 Test results, ap: 0.278, auc: 0.581, f1: 0.000, acc: 0.759
+2025-08-07 19:30:04: Epoch: 032 Train Loss: 0.694, ap: 0.545, auc: 0.527, f1: 0.000, acc: 0.495
+2025-08-07 19:30:04: Epoch: 032 Test results, ap: 0.228, auc: 0.439, f1: 0.000, acc: 0.759
+2025-08-07 19:30:04: Epoch: 033 Train Loss: 0.694, ap: 0.511, auc: 0.456, f1: 0.000, acc: 0.495
+2025-08-07 19:30:04: Epoch: 033 Test results, ap: 0.224, auc: 0.430, f1: 0.000, acc: 0.759
+2025-08-07 19:30:05: Epoch: 034 Train Loss: 0.693, ap: 0.554, auc: 0.545, f1: 0.000, acc: 0.495
+2025-08-07 19:30:05: Epoch: 034 Test results, ap: 0.238, auc: 0.487, f1: 0.000, acc: 0.759
+2025-08-07 19:30:05: Epoch: 035 Train Loss: 0.690, ap: 0.508, auc: 0.473, f1: 0.000, acc: 0.495
+2025-08-07 19:30:05: Epoch: 035 Test results, ap: 0.245, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:30:06: Epoch: 036 Train Loss: 0.694, ap: 0.563, auc: 0.515, f1: 0.671, acc: 0.505
+2025-08-07 19:30:06: Epoch: 036 Test results, ap: 0.236, auc: 0.487, f1: 0.388, acc: 0.241
+2025-08-07 19:30:06: Epoch: 037 Train Loss: 0.693, ap: 0.534, auc: 0.521, f1: 0.671, acc: 0.505
+2025-08-07 19:30:06: Epoch: 037 Test results, ap: 0.241, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:30:07: Epoch: 038 Train Loss: 0.694, ap: 0.549, auc: 0.548, f1: 0.671, acc: 0.505
+2025-08-07 19:30:07: Epoch: 038 Test results, ap: 0.248, auc: 0.518, f1: 0.388, acc: 0.241
+2025-08-07 19:30:07: Epoch: 039 Train Loss: 0.695, ap: 0.506, auc: 0.466, f1: 0.000, acc: 0.495
+2025-08-07 19:30:07: Epoch: 039 Test results, ap: 0.227, auc: 0.424, f1: 0.000, acc: 0.759
+2025-08-07 19:30:08: Epoch: 040 Train Loss: 0.691, ap: 0.511, auc: 0.474, f1: 0.000, acc: 0.495
+2025-08-07 19:30:08: Epoch: 040 Test results, ap: 0.273, auc: 0.572, f1: 0.000, acc: 0.759
+2025-08-07 19:30:08: Epoch: 041 Train Loss: 0.695, ap: 0.532, auc: 0.524, f1: 0.000, acc: 0.495
+2025-08-07 19:30:08: Epoch: 041 Test results, ap: 0.259, auc: 0.542, f1: 0.000, acc: 0.759
+2025-08-07 19:30:09: Epoch: 042 Train Loss: 0.693, ap: 0.496, auc: 0.439, f1: 0.000, acc: 0.495
+2025-08-07 19:30:09: Epoch: 042 Test results, ap: 0.263, auc: 0.555, f1: 0.000, acc: 0.759
+2025-08-07 19:30:09: Epoch: 043 Train Loss: 0.693, ap: 0.516, auc: 0.501, f1: 0.000, acc: 0.495
+2025-08-07 19:30:09: Epoch: 043 Test results, ap: 0.252, auc: 0.528, f1: 0.000, acc: 0.759
+2025-08-07 19:30:09: Epoch: 044 Train Loss: 0.692, ap: 0.564, auc: 0.549, f1: 0.000, acc: 0.495
+2025-08-07 19:30:10: Epoch: 044 Test results, ap: 0.246, auc: 0.515, f1: 0.000, acc: 0.759
+2025-08-07 19:30:10: Epoch: 045 Train Loss: 0.693, ap: 0.565, auc: 0.526, f1: 0.000, acc: 0.495
+2025-08-07 19:30:10: Epoch: 045 Test results, ap: 0.260, auc: 0.524, f1: 0.000, acc: 0.759
+2025-08-07 19:30:10: Epoch: 046 Train Loss: 0.693, ap: 0.540, auc: 0.526, f1: 0.000, acc: 0.495
+2025-08-07 19:30:10: Epoch: 046 Test results, ap: 0.236, auc: 0.476, f1: 0.000, acc: 0.759
+2025-08-07 19:30:11: Epoch: 047 Train Loss: 0.693, ap: 0.532, auc: 0.512, f1: 0.000, acc: 0.495
+2025-08-07 19:30:11: Epoch: 047 Test results, ap: 0.245, auc: 0.492, f1: 0.000, acc: 0.759
+2025-08-07 19:30:11: Epoch: 048 Train Loss: 0.690, ap: 0.606, auc: 0.602, f1: 0.000, acc: 0.495
+2025-08-07 19:30:11: Epoch: 048 Test results, ap: 0.252, auc: 0.530, f1: 0.000, acc: 0.759
+2025-08-07 19:30:12: Epoch: 049 Train Loss: 0.694, ap: 0.560, auc: 0.553, f1: 0.675, acc: 0.515
+2025-08-07 19:30:12: Epoch: 049 Test results, ap: 0.291, auc: 0.598, f1: 0.388, acc: 0.241
+2025-08-07 19:30:12: Epoch: 049 New best TEST metrics
+2025-08-07 19:30:12: Epoch: 050 Train Loss: 0.692, ap: 0.539, auc: 0.536, f1: 0.671, acc: 0.505
+2025-08-07 19:30:12: Epoch: 050 Test results, ap: 0.255, auc: 0.537, f1: 0.388, acc: 0.241
+2025-08-07 19:30:12: used time 0.01h
+2025-08-07 19:30:12: Running Cross Validation 3
+2025-08-07 19:30:12: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:30:13: Epoch: 001 Train Loss: 0.694, ap: 0.482, auc: 0.573, f1: 0.579, acc: 0.408
+2025-08-07 19:30:13: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:30:13: Epoch: 001 Test results, ap: 0.218, auc: 0.427, f1: 0.388, acc: 0.241
+2025-08-07 19:30:13: Epoch: 002 Train Loss: 0.693, ap: 0.474, auc: 0.566, f1: 0.000, acc: 0.592
+2025-08-07 19:30:13: Epoch: 002 Test results, ap: 0.343, auc: 0.594, f1: 0.000, acc: 0.759
+2025-08-07 19:30:14: Epoch: 003 Train Loss: 0.697, ap: 0.501, auc: 0.560, f1: 0.000, acc: 0.592
+2025-08-07 19:30:14: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:30:14: Epoch: 003 Test results, ap: 0.523, auc: 0.651, f1: 0.000, acc: 0.759
+2025-08-07 19:30:14: Epoch: 004 Train Loss: 0.695, ap: 0.505, auc: 0.540, f1: 0.579, acc: 0.408
+2025-08-07 19:30:14: Epoch: 004 Test results, ap: 0.501, auc: 0.637, f1: 0.388, acc: 0.241
+2025-08-07 19:30:15: Epoch: 005 Train Loss: 0.692, ap: 0.515, auc: 0.554, f1: 0.588, acc: 0.524
+2025-08-07 19:30:15: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:30:15: Epoch: 005 Test results, ap: 0.191, auc: 0.378, f1: 0.388, acc: 0.241
+2025-08-07 19:30:15: Epoch: 006 Train Loss: 0.694, ap: 0.516, auc: 0.481, f1: 0.000, acc: 0.592
+2025-08-07 19:30:15: Epoch: 006 Test results, ap: 0.582, auc: 0.749, f1: 0.000, acc: 0.759
+2025-08-07 19:30:16: Epoch: 007 Train Loss: 0.693, ap: 0.509, auc: 0.606, f1: 0.579, acc: 0.408
+2025-08-07 19:30:16: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:30:16: Epoch: 007 Test results, ap: 0.232, auc: 0.487, f1: 0.388, acc: 0.241
+2025-08-07 19:30:16: Epoch: 008 Train Loss: 0.690, ap: 0.479, auc: 0.625, f1: 0.599, acc: 0.466
+2025-08-07 19:30:16: Epoch: 008 Test results, ap: 0.373, auc: 0.561, f1: 0.388, acc: 0.241
+2025-08-07 19:30:16: Epoch: 009 Train Loss: 0.673, ap: 0.374, auc: 0.479, f1: 0.579, acc: 0.505
+2025-08-07 19:30:17: Epoch: 009 Test results, ap: 0.209, auc: 0.413, f1: 0.388, acc: 0.241
+2025-08-07 19:30:17: Epoch: 010 Train Loss: 0.667, ap: 0.415, auc: 0.474, f1: 0.592, acc: 0.437
+2025-08-07 19:30:17: Epoch: 010 Test results, ap: 0.238, auc: 0.469, f1: 0.388, acc: 0.241
+2025-08-07 19:30:17: Epoch: 011 Train Loss: 0.696, ap: 0.432, auc: 0.507, f1: 0.000, acc: 0.592
+2025-08-07 19:30:17: Epoch: 011 Test results, ap: 0.269, auc: 0.532, f1: 0.000, acc: 0.759
+2025-08-07 19:30:17: Epoch: 011 New best TEST metrics
+2025-08-07 19:30:18: Epoch: 012 Train Loss: 0.689, ap: 0.400, auc: 0.438, f1: 0.579, acc: 0.408
+2025-08-07 19:30:18: Epoch: 012 Test results, ap: 0.245, auc: 0.501, f1: 0.388, acc: 0.241
+2025-08-07 19:30:18: Epoch: 013 Train Loss: 0.692, ap: 0.415, auc: 0.431, f1: 0.000, acc: 0.592
+2025-08-07 19:30:18: Epoch: 013 Test results, ap: 0.223, auc: 0.409, f1: 0.000, acc: 0.759
+2025-08-07 19:30:19: Epoch: 014 Train Loss: 0.692, ap: 0.409, auc: 0.404, f1: 0.579, acc: 0.408
+2025-08-07 19:30:19: Epoch: 014 Test results, ap: 0.259, auc: 0.513, f1: 0.388, acc: 0.241
+2025-08-07 19:30:19: Epoch: 015 Train Loss: 0.694, ap: 0.433, auc: 0.458, f1: 0.579, acc: 0.408
+2025-08-07 19:30:19: Epoch: 015 Test results, ap: 0.240, auc: 0.489, f1: 0.388, acc: 0.241
+2025-08-07 19:30:20: Epoch: 016 Train Loss: 0.697, ap: 0.448, auc: 0.499, f1: 0.000, acc: 0.592
+2025-08-07 19:30:20: Epoch: 016 Test results, ap: 0.273, auc: 0.541, f1: 0.000, acc: 0.759
+2025-08-07 19:30:20: Epoch: 016 New best TEST metrics
+2025-08-07 19:30:20: Epoch: 017 Train Loss: 0.694, ap: 0.427, auc: 0.466, f1: 0.579, acc: 0.408
+2025-08-07 19:30:20: Epoch: 017 Test results, ap: 0.220, auc: 0.409, f1: 0.388, acc: 0.241
+2025-08-07 19:30:21: Epoch: 018 Train Loss: 0.694, ap: 0.406, auc: 0.409, f1: 0.579, acc: 0.408
+2025-08-07 19:30:21: Epoch: 018 Test results, ap: 0.284, auc: 0.585, f1: 0.388, acc: 0.241
+2025-08-07 19:30:21: Epoch: 018 New best TEST metrics
+2025-08-07 19:30:21: Epoch: 019 Train Loss: 0.695, ap: 0.389, auc: 0.384, f1: 0.579, acc: 0.408
+2025-08-07 19:30:21: Epoch: 019 Test results, ap: 0.235, auc: 0.480, f1: 0.388, acc: 0.241
+2025-08-07 19:30:22: Epoch: 020 Train Loss: 0.693, ap: 0.407, auc: 0.427, f1: 0.000, acc: 0.592
+2025-08-07 19:30:22: Epoch: 020 Test results, ap: 0.334, auc: 0.608, f1: 0.000, acc: 0.759
+2025-08-07 19:30:22: Epoch: 020 New best TEST metrics
+2025-08-07 19:30:22: Epoch: 021 Train Loss: 0.693, ap: 0.445, auc: 0.514, f1: 0.000, acc: 0.592
+2025-08-07 19:30:22: Epoch: 021 Test results, ap: 0.331, auc: 0.534, f1: 0.000, acc: 0.759
+2025-08-07 19:30:22: Epoch: 022 Train Loss: 0.695, ap: 0.426, auc: 0.451, f1: 0.579, acc: 0.408
+2025-08-07 19:30:23: Epoch: 022 Test results, ap: 0.254, auc: 0.535, f1: 0.388, acc: 0.241
+2025-08-07 19:30:23: Epoch: 023 Train Loss: 0.695, ap: 0.425, auc: 0.451, f1: 0.579, acc: 0.408
+2025-08-07 19:30:23: Epoch: 023 Test results, ap: 0.271, auc: 0.562, f1: 0.388, acc: 0.241
+2025-08-07 19:30:23: Epoch: 024 Train Loss: 0.694, ap: 0.408, auc: 0.430, f1: 0.000, acc: 0.592
+2025-08-07 19:30:24: Epoch: 024 Test results, ap: 0.249, auc: 0.479, f1: 0.000, acc: 0.759
+2025-08-07 19:30:24: Epoch: 025 Train Loss: 0.697, ap: 0.409, auc: 0.438, f1: 0.000, acc: 0.592
+2025-08-07 19:30:24: Epoch: 025 Test results, ap: 0.234, auc: 0.410, f1: 0.000, acc: 0.759
+2025-08-07 19:30:24: Epoch: 026 Train Loss: 0.691, ap: 0.394, auc: 0.413, f1: 0.000, acc: 0.592
+2025-08-07 19:30:24: Epoch: 026 Test results, ap: 0.300, auc: 0.605, f1: 0.000, acc: 0.759
+2025-08-07 19:30:25: Epoch: 027 Train Loss: 0.691, ap: 0.429, auc: 0.463, f1: 0.579, acc: 0.408
+2025-08-07 19:30:25: Epoch: 027 Test results, ap: 0.281, auc: 0.565, f1: 0.388, acc: 0.241
+2025-08-07 19:30:25: Epoch: 028 Train Loss: 0.691, ap: 0.424, auc: 0.433, f1: 0.579, acc: 0.408
+2025-08-07 19:30:25: Epoch: 028 Test results, ap: 0.270, auc: 0.519, f1: 0.388, acc: 0.241
+2025-08-07 19:30:26: Epoch: 029 Train Loss: 0.695, ap: 0.478, auc: 0.543, f1: 0.000, acc: 0.592
+2025-08-07 19:30:26: Epoch: 029 Test results, ap: 0.321, auc: 0.621, f1: 0.000, acc: 0.759
+2025-08-07 19:30:26: Epoch: 029 New best TEST metrics
+2025-08-07 19:30:26: Epoch: 030 Train Loss: 0.693, ap: 0.446, auc: 0.506, f1: 0.000, acc: 0.592
+2025-08-07 19:30:26: Epoch: 030 Test results, ap: 0.278, auc: 0.581, f1: 0.000, acc: 0.759
+2025-08-07 19:30:27: Epoch: 031 Train Loss: 0.693, ap: 0.416, auc: 0.451, f1: 0.000, acc: 0.592
+2025-08-07 19:30:27: Epoch: 031 Test results, ap: 0.234, auc: 0.456, f1: 0.000, acc: 0.759
+2025-08-07 19:30:27: Epoch: 032 Train Loss: 0.696, ap: 0.423, auc: 0.467, f1: 0.000, acc: 0.592
+2025-08-07 19:30:27: Epoch: 032 Test results, ap: 0.228, auc: 0.447, f1: 0.000, acc: 0.759
+2025-08-07 19:30:28: Epoch: 033 Train Loss: 0.693, ap: 0.428, auc: 0.461, f1: 0.000, acc: 0.592
+2025-08-07 19:30:28: Epoch: 033 Test results, ap: 0.234, auc: 0.484, f1: 0.000, acc: 0.759
+2025-08-07 19:30:28: Epoch: 034 Train Loss: 0.696, ap: 0.403, auc: 0.416, f1: 0.000, acc: 0.592
+2025-08-07 19:30:28: Epoch: 034 Test results, ap: 0.252, auc: 0.547, f1: 0.000, acc: 0.759
+2025-08-07 19:30:28: Epoch: 035 Train Loss: 0.692, ap: 0.403, auc: 0.414, f1: 0.000, acc: 0.592
+2025-08-07 19:30:29: Epoch: 035 Test results, ap: 0.212, auc: 0.419, f1: 0.000, acc: 0.759
+2025-08-07 19:30:29: Epoch: 036 Train Loss: 0.692, ap: 0.414, auc: 0.444, f1: 0.000, acc: 0.592
+2025-08-07 19:30:29: Epoch: 036 Test results, ap: 0.247, auc: 0.489, f1: 0.000, acc: 0.759
+2025-08-07 19:30:29: Epoch: 037 Train Loss: 0.692, ap: 0.411, auc: 0.434, f1: 0.579, acc: 0.408
+2025-08-07 19:30:29: Epoch: 037 Test results, ap: 0.238, auc: 0.474, f1: 0.388, acc: 0.241
+2025-08-07 19:30:30: Epoch: 038 Train Loss: 0.693, ap: 0.421, auc: 0.430, f1: 0.579, acc: 0.408
+2025-08-07 19:30:30: Epoch: 038 Test results, ap: 0.322, auc: 0.566, f1: 0.388, acc: 0.241
+2025-08-07 19:30:30: Epoch: 039 Train Loss: 0.695, ap: 0.425, auc: 0.452, f1: 0.579, acc: 0.408
+2025-08-07 19:30:30: Epoch: 039 Test results, ap: 0.268, auc: 0.537, f1: 0.388, acc: 0.241
+2025-08-07 19:30:31: Epoch: 040 Train Loss: 0.696, ap: 0.425, auc: 0.458, f1: 0.583, acc: 0.417
+2025-08-07 19:30:31: Epoch: 040 Test results, ap: 0.276, auc: 0.578, f1: 0.388, acc: 0.241
+2025-08-07 19:30:31: Epoch: 041 Train Loss: 0.695, ap: 0.389, auc: 0.380, f1: 0.000, acc: 0.592
+2025-08-07 19:30:31: Epoch: 041 Test results, ap: 0.242, auc: 0.462, f1: 0.000, acc: 0.759
+2025-08-07 19:30:32: Epoch: 042 Train Loss: 0.693, ap: 0.411, auc: 0.425, f1: 0.000, acc: 0.592
+2025-08-07 19:30:32: Epoch: 042 Test results, ap: 0.255, auc: 0.534, f1: 0.000, acc: 0.759
+2025-08-07 19:30:32: Epoch: 043 Train Loss: 0.693, ap: 0.396, auc: 0.391, f1: 0.000, acc: 0.592
+2025-08-07 19:30:32: Epoch: 043 Test results, ap: 0.310, auc: 0.633, f1: 0.000, acc: 0.759
+2025-08-07 19:30:33: Epoch: 044 Train Loss: 0.691, ap: 0.413, auc: 0.428, f1: 0.579, acc: 0.408
+2025-08-07 19:30:33: Epoch: 044 Test results, ap: 0.306, auc: 0.572, f1: 0.388, acc: 0.241
+2025-08-07 19:30:33: Epoch: 045 Train Loss: 0.694, ap: 0.424, auc: 0.457, f1: 0.579, acc: 0.408
+2025-08-07 19:30:33: Epoch: 045 Test results, ap: 0.245, auc: 0.512, f1: 0.388, acc: 0.241
+2025-08-07 19:30:34: Epoch: 046 Train Loss: 0.690, ap: 0.409, auc: 0.435, f1: 0.000, acc: 0.592
+2025-08-07 19:30:34: Epoch: 046 Test results, ap: 0.287, auc: 0.551, f1: 0.000, acc: 0.759
+2025-08-07 19:30:34: Epoch: 047 Train Loss: 0.692, ap: 0.405, auc: 0.412, f1: 0.000, acc: 0.592
+2025-08-07 19:30:34: Epoch: 047 Test results, ap: 0.353, auc: 0.629, f1: 0.000, acc: 0.759
+2025-08-07 19:30:34: Epoch: 047 New best TEST metrics
+2025-08-07 19:30:35: Epoch: 048 Train Loss: 0.692, ap: 0.403, auc: 0.405, f1: 0.000, acc: 0.592
+2025-08-07 19:30:35: Epoch: 048 Test results, ap: 0.360, auc: 0.626, f1: 0.000, acc: 0.759
+2025-08-07 19:30:35: Epoch: 048 New best TEST metrics
+2025-08-07 19:30:35: Epoch: 049 Train Loss: 0.694, ap: 0.398, auc: 0.418, f1: 0.000, acc: 0.592
+2025-08-07 19:30:35: Epoch: 049 Test results, ap: 0.311, auc: 0.575, f1: 0.000, acc: 0.759
+2025-08-07 19:30:35: Epoch: 050 Train Loss: 0.692, ap: 0.405, auc: 0.441, f1: 0.000, acc: 0.592
+2025-08-07 19:30:36: Epoch: 050 Test results, ap: 0.247, auc: 0.509, f1: 0.000, acc: 0.759
+2025-08-07 19:30:36: used time 0.01h
+2025-08-07 19:30:36: Running Cross Validation 4
+2025-08-07 19:30:36: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:30:36: Epoch: 001 Train Loss: 0.694, ap: 0.345, auc: 0.411, f1: 0.569, acc: 0.398
+2025-08-07 19:30:36: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:30:36: Epoch: 001 Test results, ap: 0.205, auc: 0.364, f1: 0.388, acc: 0.241
+2025-08-07 19:30:36: Epoch: 002 Train Loss: 0.693, ap: 0.367, auc: 0.476, f1: 0.000, acc: 0.602
+2025-08-07 19:30:36: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:30:37: Epoch: 002 Test results, ap: 0.481, auc: 0.596, f1: 0.000, acc: 0.759
+2025-08-07 19:30:37: Epoch: 003 Train Loss: 0.694, ap: 0.361, auc: 0.458, f1: 0.000, acc: 0.602
+2025-08-07 19:30:37: Epoch: 003 Test results, ap: 0.245, auc: 0.486, f1: 0.000, acc: 0.759
+2025-08-07 19:30:37: Epoch: 004 Train Loss: 0.694, ap: 0.359, auc: 0.437, f1: 0.148, acc: 0.553
+2025-08-07 19:30:38: Epoch: 004 Test results, ap: 0.464, auc: 0.542, f1: 0.000, acc: 0.759
+2025-08-07 19:30:38: Epoch: 005 Train Loss: 0.692, ap: 0.383, auc: 0.452, f1: 0.120, acc: 0.573
+2025-08-07 19:30:38: Epoch: 005 Test results, ap: 0.333, auc: 0.563, f1: 0.000, acc: 0.759
+2025-08-07 19:30:38: Epoch: 006 Train Loss: 0.691, ap: 0.386, auc: 0.454, f1: 0.347, acc: 0.524
+2025-08-07 19:30:38: Epoch: 006 Test results, ap: 0.220, auc: 0.431, f1: 0.000, acc: 0.759
+2025-08-07 19:30:39: Epoch: 007 Train Loss: 0.692, ap: 0.425, auc: 0.540, f1: 0.429, acc: 0.534
+2025-08-07 19:30:39: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:30:39: Epoch: 007 Test results, ap: 0.186, auc: 0.352, f1: 0.048, acc: 0.630
+2025-08-07 19:30:39: Epoch: 008 Train Loss: 0.673, ap: 0.468, auc: 0.612, f1: 0.562, acc: 0.621
+2025-08-07 19:30:39: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:30:39: Epoch: 008 Test results, ap: 0.218, auc: 0.465, f1: 0.089, acc: 0.620
+2025-08-07 19:30:40: Epoch: 009 Train Loss: 0.626, ap: 0.555, auc: 0.674, f1: 0.646, acc: 0.670
+2025-08-07 19:30:40: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:30:40: Epoch: 009 Test results, ap: 0.281, auc: 0.516, f1: 0.367, acc: 0.296
+2025-08-07 19:30:40: Epoch: 010 Train Loss: 0.658, ap: 0.386, auc: 0.508, f1: 0.576, acc: 0.515
+2025-08-07 19:30:40: Epoch: 010 Test results, ap: 0.197, auc: 0.384, f1: 0.388, acc: 0.241
+2025-08-07 19:30:41: Epoch: 011 Train Loss: 0.643, ap: 0.372, auc: 0.482, f1: 0.571, acc: 0.534
+2025-08-07 19:30:41: Epoch: 011 Test results, ap: 0.208, auc: 0.384, f1: 0.388, acc: 0.241
+2025-08-07 19:30:41: Epoch: 011 New best TEST metrics
+2025-08-07 19:30:41: Epoch: 012 Train Loss: 0.626, ap: 0.362, auc: 0.451, f1: 0.586, acc: 0.534
+2025-08-07 19:30:41: Epoch: 012 Test results, ap: 0.201, auc: 0.384, f1: 0.388, acc: 0.241
+2025-08-07 19:30:42: Epoch: 013 Train Loss: 0.654, ap: 0.357, auc: 0.433, f1: 0.567, acc: 0.495
+2025-08-07 19:30:42: Epoch: 013 Test results, ap: 0.201, auc: 0.382, f1: 0.388, acc: 0.241
+2025-08-07 19:30:42: Epoch: 014 Train Loss: 0.653, ap: 0.363, auc: 0.446, f1: 0.571, acc: 0.505
+2025-08-07 19:30:42: Epoch: 014 Test results, ap: 0.201, auc: 0.383, f1: 0.388, acc: 0.241
+2025-08-07 19:30:43: Epoch: 015 Train Loss: 0.654, ap: 0.369, auc: 0.457, f1: 0.571, acc: 0.505
+2025-08-07 19:30:43: Epoch: 015 Test results, ap: 0.202, auc: 0.387, f1: 0.388, acc: 0.241
+2025-08-07 19:30:43: Epoch: 016 Train Loss: 0.643, ap: 0.402, auc: 0.515, f1: 0.579, acc: 0.563
+2025-08-07 19:30:43: Epoch: 016 Test results, ap: 0.201, auc: 0.383, f1: 0.388, acc: 0.241
+2025-08-07 19:30:44: Epoch: 017 Train Loss: 0.640, ap: 0.420, auc: 0.537, f1: 0.584, acc: 0.544
+2025-08-07 19:30:44: Epoch: 017 Test results, ap: 0.202, auc: 0.385, f1: 0.388, acc: 0.241
+2025-08-07 19:30:44: Epoch: 018 Train Loss: 0.626, ap: 0.416, auc: 0.535, f1: 0.577, acc: 0.544
+2025-08-07 19:30:44: Epoch: 018 Test results, ap: 0.214, auc: 0.431, f1: 0.388, acc: 0.241
+2025-08-07 19:30:44: Epoch: 018 New best TEST metrics
+2025-08-07 19:30:45: Epoch: 019 Train Loss: 0.631, ap: 0.408, auc: 0.518, f1: 0.582, acc: 0.553
+2025-08-07 19:30:45: Epoch: 019 Test results, ap: 0.391, auc: 0.661, f1: 0.388, acc: 0.241
+2025-08-07 19:30:45: Epoch: 019 New best TEST metrics
+2025-08-07 19:30:45: Epoch: 020 Train Loss: 0.621, ap: 0.434, auc: 0.563, f1: 0.569, acc: 0.544
+2025-08-07 19:30:45: Epoch: 020 Test results, ap: 0.250, auc: 0.466, f1: 0.388, acc: 0.241
+2025-08-07 19:30:46: Epoch: 021 Train Loss: 0.617, ap: 0.422, auc: 0.548, f1: 0.569, acc: 0.544
+2025-08-07 19:30:46: Epoch: 021 Test results, ap: 0.268, auc: 0.547, f1: 0.388, acc: 0.241
+2025-08-07 19:30:46: Epoch: 022 Train Loss: 0.622, ap: 0.398, auc: 0.526, f1: 0.564, acc: 0.534
+2025-08-07 19:30:46: Epoch: 022 Test results, ap: 0.224, auc: 0.438, f1: 0.388, acc: 0.241
+2025-08-07 19:30:47: Epoch: 023 Train Loss: 0.607, ap: 0.414, auc: 0.542, f1: 0.577, acc: 0.544
+2025-08-07 19:30:47: Epoch: 023 Test results, ap: 0.217, auc: 0.438, f1: 0.388, acc: 0.241
+2025-08-07 19:30:47: Epoch: 024 Train Loss: 0.625, ap: 0.418, auc: 0.561, f1: 0.574, acc: 0.553
+2025-08-07 19:30:47: Epoch: 024 Test results, ap: 0.210, auc: 0.410, f1: 0.388, acc: 0.241
+2025-08-07 19:30:48: Epoch: 025 Train Loss: 0.609, ap: 0.426, auc: 0.570, f1: 0.582, acc: 0.553
+2025-08-07 19:30:48: Epoch: 025 Test results, ap: 0.203, auc: 0.389, f1: 0.388, acc: 0.241
+2025-08-07 19:30:48: Epoch: 026 Train Loss: 0.612, ap: 0.423, auc: 0.569, f1: 0.582, acc: 0.553
+2025-08-07 19:30:48: Epoch: 026 Test results, ap: 0.199, auc: 0.388, f1: 0.388, acc: 0.241
+2025-08-07 19:30:49: Epoch: 027 Train Loss: 0.633, ap: 0.371, auc: 0.496, f1: 0.579, acc: 0.534
+2025-08-07 19:30:49: Epoch: 027 Test results, ap: 0.203, auc: 0.409, f1: 0.388, acc: 0.241
+2025-08-07 19:30:49: Epoch: 028 Train Loss: 0.648, ap: 0.344, auc: 0.445, f1: 0.571, acc: 0.505
+2025-08-07 19:30:49: Epoch: 028 Test results, ap: 0.198, auc: 0.396, f1: 0.388, acc: 0.241
+2025-08-07 19:30:50: Epoch: 029 Train Loss: 0.634, ap: 0.363, auc: 0.442, f1: 0.571, acc: 0.505
+2025-08-07 19:30:50: Epoch: 029 Test results, ap: 0.200, auc: 0.403, f1: 0.388, acc: 0.241
+2025-08-07 19:30:50: Epoch: 030 Train Loss: 0.630, ap: 0.420, auc: 0.550, f1: 0.559, acc: 0.495
+2025-08-07 19:30:50: Epoch: 030 Test results, ap: 0.231, auc: 0.498, f1: 0.388, acc: 0.241
+2025-08-07 19:30:51: Epoch: 031 Train Loss: 0.626, ap: 0.427, auc: 0.559, f1: 0.559, acc: 0.495
+2025-08-07 19:30:51: Epoch: 031 Test results, ap: 0.225, auc: 0.477, f1: 0.388, acc: 0.241
+2025-08-07 19:30:51: Epoch: 032 Train Loss: 0.622, ap: 0.432, auc: 0.573, f1: 0.571, acc: 0.505
+2025-08-07 19:30:51: Epoch: 032 Test results, ap: 0.266, auc: 0.567, f1: 0.388, acc: 0.241
+2025-08-07 19:30:52: Epoch: 033 Train Loss: 0.622, ap: 0.486, auc: 0.600, f1: 0.564, acc: 0.505
+2025-08-07 19:30:52: Epoch: 033 Test results, ap: 0.305, auc: 0.637, f1: 0.388, acc: 0.241
+2025-08-07 19:30:52: Epoch: 034 Train Loss: 0.615, ap: 0.479, auc: 0.598, f1: 0.579, acc: 0.534
+2025-08-07 19:30:52: Epoch: 034 Test results, ap: 0.310, auc: 0.648, f1: 0.388, acc: 0.241
+2025-08-07 19:30:53: Epoch: 035 Train Loss: 0.609, ap: 0.459, auc: 0.595, f1: 0.596, acc: 0.553
+2025-08-07 19:30:53: Epoch: 035 Test results, ap: 0.297, auc: 0.610, f1: 0.388, acc: 0.241
+2025-08-07 19:30:53: Epoch: 036 Train Loss: 0.619, ap: 0.471, auc: 0.584, f1: 0.583, acc: 0.583
+2025-08-07 19:30:53: Epoch: 036 Test results, ap: 0.294, auc: 0.634, f1: 0.391, acc: 0.250
+2025-08-07 19:30:54: Epoch: 037 Train Loss: 0.620, ap: 0.431, auc: 0.576, f1: 0.571, acc: 0.534
+2025-08-07 19:30:54: Epoch: 037 Test results, ap: 0.286, auc: 0.621, f1: 0.388, acc: 0.241
+2025-08-07 19:30:54: Epoch: 038 Train Loss: 0.599, ap: 0.431, auc: 0.561, f1: 0.500, acc: 0.515
+2025-08-07 19:30:54: Epoch: 038 Test results, ap: 0.269, auc: 0.584, f1: 0.397, acc: 0.269
+2025-08-07 19:30:55: Epoch: 039 Train Loss: 0.607, ap: 0.440, auc: 0.589, f1: 0.500, acc: 0.553
+2025-08-07 19:30:55: Epoch: 039 Test results, ap: 0.244, auc: 0.531, f1: 0.394, acc: 0.259
+2025-08-07 19:30:55: Epoch: 040 Train Loss: 0.618, ap: 0.444, auc: 0.596, f1: 0.595, acc: 0.563
+2025-08-07 19:30:55: Epoch: 040 Test results, ap: 0.253, auc: 0.543, f1: 0.388, acc: 0.241
+2025-08-07 19:30:55: Epoch: 041 Train Loss: 0.592, ap: 0.449, auc: 0.604, f1: 0.527, acc: 0.583
+2025-08-07 19:30:56: Epoch: 041 Test results, ap: 0.229, auc: 0.483, f1: 0.400, acc: 0.278
+2025-08-07 19:30:56: Epoch: 042 Train Loss: 0.591, ap: 0.440, auc: 0.600, f1: 0.505, acc: 0.563
+2025-08-07 19:30:56: Epoch: 042 Test results, ap: 0.229, auc: 0.483, f1: 0.397, acc: 0.269
+2025-08-07 19:30:56: Epoch: 043 Train Loss: 0.581, ap: 0.470, auc: 0.605, f1: 0.527, acc: 0.583
+2025-08-07 19:30:57: Epoch: 043 Test results, ap: 0.229, auc: 0.488, f1: 0.388, acc: 0.241
+2025-08-07 19:30:57: Epoch: 044 Train Loss: 0.585, ap: 0.453, auc: 0.609, f1: 0.471, acc: 0.563
+2025-08-07 19:30:57: Epoch: 044 Test results, ap: 0.218, auc: 0.443, f1: 0.403, acc: 0.287
+2025-08-07 19:30:57: Epoch: 045 Train Loss: 0.591, ap: 0.454, auc: 0.612, f1: 0.488, acc: 0.592
+2025-08-07 19:30:58: Epoch: 045 Test results, ap: 0.229, auc: 0.471, f1: 0.400, acc: 0.278
+2025-08-07 19:30:58: Epoch: 046 Train Loss: 0.580, ap: 0.467, auc: 0.630, f1: 0.552, acc: 0.621
+2025-08-07 19:30:58: Epoch: 046 Test results, ap: 0.232, auc: 0.470, f1: 0.385, acc: 0.259
+2025-08-07 19:30:58: Epoch: 047 Train Loss: 0.589, ap: 0.465, auc: 0.620, f1: 0.541, acc: 0.621
+2025-08-07 19:30:59: Epoch: 047 Test results, ap: 0.222, auc: 0.449, f1: 0.394, acc: 0.287
+2025-08-07 19:30:59: Epoch: 048 Train Loss: 0.570, ap: 0.454, auc: 0.607, f1: 0.488, acc: 0.573
+2025-08-07 19:30:59: Epoch: 048 Test results, ap: 0.228, auc: 0.473, f1: 0.394, acc: 0.287
+2025-08-07 19:30:59: Epoch: 049 Train Loss: 0.562, ap: 0.475, auc: 0.609, f1: 0.465, acc: 0.553
+2025-08-07 19:30:59: Epoch: 049 Test results, ap: 0.239, auc: 0.497, f1: 0.388, acc: 0.241
+2025-08-07 19:31:00: Epoch: 050 Train Loss: 0.587, ap: 0.472, auc: 0.608, f1: 0.447, acc: 0.544
+2025-08-07 19:31:00: Epoch: 050 Test results, ap: 0.245, auc: 0.495, f1: 0.388, acc: 0.241
+2025-08-07 19:31:00: used time 0.01h
+2025-08-07 19:31:00: Cross Validation Finished!
+2025-08-07 19:31:00: Best validation perform list
+[[0.51902699 0.58947569 0.62666667 0.45631069]
+ [0.52111119 0.66093361 0.42424244 0.63106793]
+ [0.60931545 0.62198341 0.67096776 0.50485438]
+ [0.50947905 0.60616708 0.57931036 0.407767  ]
+ [0.55522573 0.67427224 0.64583331 0.66990292]]
+2025-08-07 19:31:00: mean: [0.543 0.631 0.589 0.534]
+2025-08-07 19:31:00: std: [0.037 0.032 0.088 0.101]
+2025-08-07 19:31:00: Best test perform list
+[[0.34223646 0.62312382 0.38805971 0.24074075]
+ [0.27590978 0.58161348 0.         0.72222221]
+ [0.2905983  0.59849906 0.38805971 0.24074075]
+ [0.36006042 0.62640715 0.         0.75925928]
+ [0.39110807 0.66088182 0.38805971 0.24074075]]
+2025-08-07 19:31:00: mean: [0.332 0.618 0.233 0.441]
+2025-08-07 19:31:00: std: [0.043 0.027 0.19  0.245]
diff --git a/run-cls-simple/mamba-att-256-simple-oneway-ce-32-0.001-50/config.json b/run-cls-simple/mamba-att-256-simple-oneway-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/run-cls-simple/mamba-att-256-simple-oneway-ce-32-0.001-50/inference_results.csv b/run-cls-simple/mamba-att-256-simple-oneway-ce-32-0.001-50/inference_results.csv
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+0.5030504,0.5625758,0.50268537,0.25633,0.39864108,0.44020277,1
diff --git a/run-cls-simple/mamba-att-256-simple-oneway-ce-32-0.001-50/result.txt b/run-cls-simple/mamba-att-256-simple-oneway-ce-32-0.001-50/result.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d72c10bb75e4b9cb5072f4933af9953a22a142da
--- /dev/null
+++ b/run-cls-simple/mamba-att-256-simple-oneway-ce-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
+Valid
+0.7044971704483032,0.7465046167373657,0.4971423625946045,0.646601927280426
+0.09079762143253169,0.05245907039926837,0.25858951637930605,0.06687058031753045
+Test
+0.26849261820316317,0.5188555419445038,0.2059273511171341,0.5018518567085266
+0.05271410246250333,0.08137183468361718,0.18099787423690727,0.19914081194514616
diff --git a/run-cls-simple/mamba-att-256-simple-oneway-ce-32-0.001-50/training.log b/run-cls-simple/mamba-att-256-simple-oneway-ce-32-0.001-50/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..ae0e5d7679fa035deabe7d092843dd3b5861dd18
--- /dev/null
+++ b/run-cls-simple/mamba-att-256-simple-oneway-ce-32-0.001-50/training.log
@@ -0,0 +1,598 @@
+2025-08-07 19:21:43: saving_dir: ./run-cls/mamba-att-256-simple-oneway-ce-32-0.001-50
+2025-08-07 19:21:43: Loading Training Dataset
+2025-08-07 19:21:43: Loading Test Dataset
+2025-08-07 19:21:44: Running Cross Validation 0
+2025-08-07 19:21:44: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:21:44: Epoch: 001 Train Loss: 0.693, ap: 0.595, auc: 0.666, f1: 0.000, acc: 0.544
+2025-08-07 19:21:44: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:21:45: Epoch: 001 Test results, ap: 0.205, auc: 0.418, f1: 0.000, acc: 0.759
+2025-08-07 19:21:45: Epoch: 002 Train Loss: 0.693, ap: 0.609, auc: 0.682, f1: 0.000, acc: 0.544
+2025-08-07 19:21:45: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:21:45: Epoch: 002 Test results, ap: 0.210, auc: 0.436, f1: 0.000, acc: 0.759
+2025-08-07 19:21:46: Epoch: 003 Train Loss: 0.694, ap: 0.574, auc: 0.661, f1: 0.000, acc: 0.544
+2025-08-07 19:21:46: Epoch: 003 Test results, ap: 0.207, auc: 0.428, f1: 0.000, acc: 0.759
+2025-08-07 19:21:46: Epoch: 004 Train Loss: 0.696, ap: 0.590, auc: 0.674, f1: 0.627, acc: 0.456
+2025-08-07 19:21:46: Epoch: 004 Test results, ap: 0.212, auc: 0.437, f1: 0.388, acc: 0.241
+2025-08-07 19:21:47: Epoch: 005 Train Loss: 0.697, ap: 0.588, auc: 0.675, f1: 0.000, acc: 0.544
+2025-08-07 19:21:47: Epoch: 005 Test results, ap: 0.199, auc: 0.401, f1: 0.000, acc: 0.759
+2025-08-07 19:21:47: Epoch: 006 Train Loss: 0.694, ap: 0.470, auc: 0.554, f1: 0.000, acc: 0.544
+2025-08-07 19:21:47: Epoch: 006 Test results, ap: 0.281, auc: 0.592, f1: 0.000, acc: 0.759
+2025-08-07 19:21:48: Epoch: 007 Train Loss: 0.697, ap: 0.538, auc: 0.618, f1: 0.627, acc: 0.456
+2025-08-07 19:21:48: Epoch: 007 Test results, ap: 0.229, auc: 0.493, f1: 0.388, acc: 0.241
+2025-08-07 19:21:48: Epoch: 008 Train Loss: 0.695, ap: 0.527, auc: 0.585, f1: 0.627, acc: 0.456
+2025-08-07 19:21:48: Epoch: 008 Test results, ap: 0.163, auc: 0.208, f1: 0.388, acc: 0.241
+2025-08-07 19:21:49: Epoch: 009 Train Loss: 0.694, ap: 0.576, auc: 0.668, f1: 0.000, acc: 0.544
+2025-08-07 19:21:49: Epoch: 009 Test results, ap: 0.216, auc: 0.456, f1: 0.000, acc: 0.759
+2025-08-07 19:21:49: Epoch: 010 Train Loss: 0.700, ap: 0.360, auc: 0.320, f1: 0.000, acc: 0.544
+2025-08-07 19:21:49: Epoch: 010 Test results, ap: 0.196, auc: 0.400, f1: 0.000, acc: 0.759
+2025-08-07 19:21:50: Epoch: 011 Train Loss: 0.697, ap: 0.449, auc: 0.477, f1: 0.000, acc: 0.544
+2025-08-07 19:21:50: Epoch: 011 Test results, ap: 0.165, auc: 0.261, f1: 0.000, acc: 0.759
+2025-08-07 19:21:50: Epoch: 011 New best TEST metrics
+2025-08-07 19:21:50: Epoch: 012 Train Loss: 0.694, ap: 0.461, auc: 0.478, f1: 0.627, acc: 0.456
+2025-08-07 19:21:50: Epoch: 012 Test results, ap: 0.370, auc: 0.642, f1: 0.388, acc: 0.241
+2025-08-07 19:21:50: Epoch: 012 New best TEST metrics
+2025-08-07 19:21:51: Epoch: 013 Train Loss: 0.695, ap: 0.404, auc: 0.404, f1: 0.627, acc: 0.456
+2025-08-07 19:21:51: Epoch: 013 Test results, ap: 0.382, auc: 0.627, f1: 0.388, acc: 0.241
+2025-08-07 19:21:51: Epoch: 014 Train Loss: 0.697, ap: 0.367, auc: 0.348, f1: 0.000, acc: 0.544
+2025-08-07 19:21:51: Epoch: 014 Test results, ap: 0.211, auc: 0.441, f1: 0.000, acc: 0.759
+2025-08-07 19:21:52: Epoch: 015 Train Loss: 0.695, ap: 0.403, auc: 0.420, f1: 0.000, acc: 0.544
+2025-08-07 19:21:52: Epoch: 015 Test results, ap: 0.171, auc: 0.291, f1: 0.000, acc: 0.759
+2025-08-07 19:21:52: Epoch: 016 Train Loss: 0.693, ap: 0.491, auc: 0.530, f1: 0.000, acc: 0.544
+2025-08-07 19:21:52: Epoch: 016 Test results, ap: 0.161, auc: 0.235, f1: 0.000, acc: 0.759
+2025-08-07 19:21:53: Epoch: 017 Train Loss: 0.693, ap: 0.482, auc: 0.526, f1: 0.627, acc: 0.456
+2025-08-07 19:21:53: Epoch: 017 Test results, ap: 0.162, auc: 0.244, f1: 0.388, acc: 0.241
+2025-08-07 19:21:53: Epoch: 018 Train Loss: 0.693, ap: 0.559, auc: 0.598, f1: 0.627, acc: 0.456
+2025-08-07 19:21:53: Epoch: 018 Test results, ap: 0.164, auc: 0.250, f1: 0.388, acc: 0.241
+2025-08-07 19:21:54: Epoch: 019 Train Loss: 0.694, ap: 0.578, auc: 0.638, f1: 0.000, acc: 0.544
+2025-08-07 19:21:54: Epoch: 019 Test results, ap: 0.195, auc: 0.390, f1: 0.000, acc: 0.759
+2025-08-07 19:21:54: Epoch: 020 Train Loss: 0.692, ap: 0.576, auc: 0.649, f1: 0.000, acc: 0.544
+2025-08-07 19:21:54: Epoch: 020 Test results, ap: 0.217, auc: 0.471, f1: 0.000, acc: 0.759
+2025-08-07 19:21:55: Epoch: 021 Train Loss: 0.693, ap: 0.572, auc: 0.650, f1: 0.627, acc: 0.456
+2025-08-07 19:21:55: Epoch: 021 Test results, ap: 0.227, auc: 0.498, f1: 0.388, acc: 0.241
+2025-08-07 19:21:55: Epoch: 022 Train Loss: 0.692, ap: 0.577, auc: 0.650, f1: 0.627, acc: 0.456
+2025-08-07 19:21:55: Epoch: 022 Test results, ap: 0.226, auc: 0.493, f1: 0.388, acc: 0.241
+2025-08-07 19:21:56: Epoch: 023 Train Loss: 0.694, ap: 0.576, auc: 0.652, f1: 0.000, acc: 0.544
+2025-08-07 19:21:56: Epoch: 023 Test results, ap: 0.230, auc: 0.501, f1: 0.000, acc: 0.759
+2025-08-07 19:21:56: Epoch: 024 Train Loss: 0.696, ap: 0.578, auc: 0.652, f1: 0.000, acc: 0.544
+2025-08-07 19:21:57: Epoch: 024 Test results, ap: 0.225, auc: 0.491, f1: 0.000, acc: 0.759
+2025-08-07 19:21:57: Epoch: 025 Train Loss: 0.694, ap: 0.580, auc: 0.651, f1: 0.000, acc: 0.544
+2025-08-07 19:21:57: Epoch: 025 Test results, ap: 0.225, auc: 0.491, f1: 0.000, acc: 0.759
+2025-08-07 19:21:57: Epoch: 026 Train Loss: 0.694, ap: 0.578, auc: 0.653, f1: 0.000, acc: 0.544
+2025-08-07 19:21:58: Epoch: 026 Test results, ap: 0.230, auc: 0.509, f1: 0.000, acc: 0.759
+2025-08-07 19:21:58: Epoch: 027 Train Loss: 0.693, ap: 0.588, auc: 0.657, f1: 0.000, acc: 0.544
+2025-08-07 19:21:58: Epoch: 027 Test results, ap: 0.236, auc: 0.520, f1: 0.000, acc: 0.759
+2025-08-07 19:21:59: Epoch: 028 Train Loss: 0.693, ap: 0.589, auc: 0.660, f1: 0.627, acc: 0.456
+2025-08-07 19:21:59: Epoch: 028 Test results, ap: 0.236, auc: 0.522, f1: 0.388, acc: 0.241
+2025-08-07 19:21:59: Epoch: 029 Train Loss: 0.693, ap: 0.593, auc: 0.661, f1: 0.000, acc: 0.544
+2025-08-07 19:21:59: Epoch: 029 Test results, ap: 0.236, auc: 0.521, f1: 0.000, acc: 0.759
+2025-08-07 19:22:00: Epoch: 030 Train Loss: 0.695, ap: 0.587, auc: 0.664, f1: 0.042, acc: 0.553
+2025-08-07 19:22:00: Epoch: 030 Test results, ap: 0.241, auc: 0.533, f1: 0.000, acc: 0.759
+2025-08-07 19:22:00: Epoch: 031 Train Loss: 0.692, ap: 0.588, auc: 0.665, f1: 0.000, acc: 0.544
+2025-08-07 19:22:00: Epoch: 031 Test results, ap: 0.239, auc: 0.530, f1: 0.000, acc: 0.759
+2025-08-07 19:22:01: Epoch: 032 Train Loss: 0.692, ap: 0.589, auc: 0.666, f1: 0.000, acc: 0.544
+2025-08-07 19:22:01: Epoch: 032 Test results, ap: 0.240, auc: 0.531, f1: 0.000, acc: 0.759
+2025-08-07 19:22:01: Epoch: 033 Train Loss: 0.693, ap: 0.590, auc: 0.668, f1: 0.000, acc: 0.544
+2025-08-07 19:22:01: Epoch: 033 Test results, ap: 0.241, auc: 0.532, f1: 0.000, acc: 0.759
+2025-08-07 19:22:02: Epoch: 034 Train Loss: 0.693, ap: 0.590, auc: 0.669, f1: 0.627, acc: 0.456
+2025-08-07 19:22:02: Epoch: 034 Test results, ap: 0.243, auc: 0.535, f1: 0.388, acc: 0.241
+2025-08-07 19:22:02: Epoch: 035 Train Loss: 0.693, ap: 0.590, auc: 0.670, f1: 0.627, acc: 0.456
+2025-08-07 19:22:02: Epoch: 035 Test results, ap: 0.246, auc: 0.539, f1: 0.388, acc: 0.241
+2025-08-07 19:22:03: Epoch: 036 Train Loss: 0.693, ap: 0.592, auc: 0.671, f1: 0.627, acc: 0.456
+2025-08-07 19:22:03: Epoch: 036 Test results, ap: 0.245, auc: 0.541, f1: 0.388, acc: 0.241
+2025-08-07 19:22:03: Epoch: 037 Train Loss: 0.693, ap: 0.588, auc: 0.671, f1: 0.000, acc: 0.544
+2025-08-07 19:22:03: Epoch: 037 Test results, ap: 0.243, auc: 0.537, f1: 0.000, acc: 0.759
+2025-08-07 19:22:04: Epoch: 038 Train Loss: 0.694, ap: 0.590, auc: 0.671, f1: 0.627, acc: 0.456
+2025-08-07 19:22:04: Epoch: 038 Test results, ap: 0.243, auc: 0.538, f1: 0.388, acc: 0.241
+2025-08-07 19:22:04: Epoch: 039 Train Loss: 0.691, ap: 0.593, auc: 0.672, f1: 0.627, acc: 0.456
+2025-08-07 19:22:04: Epoch: 039 Test results, ap: 0.243, auc: 0.539, f1: 0.388, acc: 0.241
+2025-08-07 19:22:05: Epoch: 040 Train Loss: 0.692, ap: 0.589, auc: 0.672, f1: 0.627, acc: 0.456
+2025-08-07 19:22:05: Epoch: 040 Test results, ap: 0.242, auc: 0.537, f1: 0.388, acc: 0.241
+2025-08-07 19:22:05: Epoch: 041 Train Loss: 0.695, ap: 0.589, auc: 0.671, f1: 0.627, acc: 0.456
+2025-08-07 19:22:05: Epoch: 041 Test results, ap: 0.242, auc: 0.537, f1: 0.388, acc: 0.241
+2025-08-07 19:22:06: Epoch: 042 Train Loss: 0.694, ap: 0.592, auc: 0.673, f1: 0.627, acc: 0.456
+2025-08-07 19:22:06: Epoch: 042 Test results, ap: 0.245, auc: 0.538, f1: 0.388, acc: 0.241
+2025-08-07 19:22:06: Epoch: 043 Train Loss: 0.692, ap: 0.594, auc: 0.674, f1: 0.627, acc: 0.456
+2025-08-07 19:22:06: Epoch: 043 Test results, ap: 0.241, auc: 0.537, f1: 0.388, acc: 0.241
+2025-08-07 19:22:07: Epoch: 044 Train Loss: 0.695, ap: 0.600, auc: 0.676, f1: 0.635, acc: 0.631
+2025-08-07 19:22:07: Epoch: 044 Test results, ap: 0.241, auc: 0.536, f1: 0.388, acc: 0.241
+2025-08-07 19:22:07: Epoch: 045 Train Loss: 0.693, ap: 0.601, auc: 0.676, f1: 0.628, acc: 0.505
+2025-08-07 19:22:07: Epoch: 045 Test results, ap: 0.242, auc: 0.537, f1: 0.388, acc: 0.241
+2025-08-07 19:22:08: Epoch: 046 Train Loss: 0.695, ap: 0.600, auc: 0.676, f1: 0.000, acc: 0.544
+2025-08-07 19:22:08: Epoch: 046 Test results, ap: 0.241, auc: 0.537, f1: 0.000, acc: 0.759
+2025-08-07 19:22:08: Epoch: 047 Train Loss: 0.694, ap: 0.602, auc: 0.678, f1: 0.000, acc: 0.544
+2025-08-07 19:22:08: Epoch: 047 Test results, ap: 0.242, auc: 0.539, f1: 0.000, acc: 0.759
+2025-08-07 19:22:09: Epoch: 048 Train Loss: 0.693, ap: 0.602, auc: 0.677, f1: 0.000, acc: 0.544
+2025-08-07 19:22:09: Epoch: 048 Test results, ap: 0.243, auc: 0.541, f1: 0.000, acc: 0.759
+2025-08-07 19:22:09: Epoch: 049 Train Loss: 0.693, ap: 0.600, auc: 0.676, f1: 0.000, acc: 0.544
+2025-08-07 19:22:09: Epoch: 049 Test results, ap: 0.243, auc: 0.542, f1: 0.000, acc: 0.759
+2025-08-07 19:22:10: Epoch: 050 Train Loss: 0.694, ap: 0.602, auc: 0.678, f1: 0.000, acc: 0.544
+2025-08-07 19:22:10: Epoch: 050 Test results, ap: 0.243, auc: 0.542, f1: 0.000, acc: 0.759
+2025-08-07 19:22:10: used time 0.01h
+2025-08-07 19:22:10: Running Cross Validation 1
+2025-08-07 19:22:10: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:22:10: Epoch: 001 Train Loss: 0.695, ap: 0.454, auc: 0.600, f1: 0.000, acc: 0.641
+2025-08-07 19:22:10: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:22:10: Epoch: 001 Test results, ap: 0.199, auc: 0.382, f1: 0.000, acc: 0.759
+2025-08-07 19:22:11: Epoch: 002 Train Loss: 0.694, ap: 0.461, auc: 0.614, f1: 0.529, acc: 0.359
+2025-08-07 19:22:11: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:22:11: Epoch: 002 Test results, ap: 0.202, auc: 0.406, f1: 0.388, acc: 0.241
+2025-08-07 19:22:11: Epoch: 003 Train Loss: 0.692, ap: 0.458, auc: 0.635, f1: 0.291, acc: 0.621
+2025-08-07 19:22:11: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:22:12: Epoch: 003 Test results, ap: 0.245, auc: 0.550, f1: 0.000, acc: 0.713
+2025-08-07 19:22:12: Epoch: 004 Train Loss: 0.693, ap: 0.460, auc: 0.635, f1: 0.000, acc: 0.641
+2025-08-07 19:22:12: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:22:12: Epoch: 004 Test results, ap: 0.235, auc: 0.519, f1: 0.000, acc: 0.759
+2025-08-07 19:22:13: Epoch: 005 Train Loss: 0.695, ap: 0.461, auc: 0.636, f1: 0.000, acc: 0.641
+2025-08-07 19:22:13: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:22:13: Epoch: 005 Test results, ap: 0.230, auc: 0.507, f1: 0.000, acc: 0.759
+2025-08-07 19:22:13: Epoch: 006 Train Loss: 0.698, ap: 0.485, auc: 0.622, f1: 0.000, acc: 0.641
+2025-08-07 19:22:13: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:22:13: Epoch: 006 Test results, ap: 0.220, auc: 0.484, f1: 0.000, acc: 0.759
+2025-08-07 19:22:14: Epoch: 007 Train Loss: 0.696, ap: 0.465, auc: 0.618, f1: 0.529, acc: 0.359
+2025-08-07 19:22:14: Epoch: 007 Test results, ap: 0.217, auc: 0.470, f1: 0.388, acc: 0.241
+2025-08-07 19:22:14: Epoch: 008 Train Loss: 0.694, ap: 0.450, auc: 0.618, f1: 0.000, acc: 0.641
+2025-08-07 19:22:14: Epoch: 008 Test results, ap: 0.217, auc: 0.476, f1: 0.000, acc: 0.759
+2025-08-07 19:22:15: Epoch: 009 Train Loss: 0.648, ap: 0.455, auc: 0.615, f1: 0.523, acc: 0.592
+2025-08-07 19:22:15: Epoch: 009 Test results, ap: 0.210, auc: 0.446, f1: 0.388, acc: 0.241
+2025-08-07 19:22:15: Epoch: 010 Train Loss: 0.701, ap: 0.457, auc: 0.622, f1: 0.525, acc: 0.631
+2025-08-07 19:22:15: Epoch: 010 Test results, ap: 0.198, auc: 0.397, f1: 0.388, acc: 0.241
+2025-08-07 19:22:16: Epoch: 011 Train Loss: 0.604, ap: 0.445, auc: 0.616, f1: 0.547, acc: 0.583
+2025-08-07 19:22:16: Epoch: 011 Test results, ap: 0.196, auc: 0.379, f1: 0.388, acc: 0.241
+2025-08-07 19:22:16: Epoch: 011 New best TEST metrics
+2025-08-07 19:22:16: Epoch: 012 Train Loss: 0.588, ap: 0.445, auc: 0.613, f1: 0.543, acc: 0.592
+2025-08-07 19:22:16: Epoch: 012 Test results, ap: 0.192, auc: 0.357, f1: 0.388, acc: 0.241
+2025-08-07 19:22:17: Epoch: 013 Train Loss: 0.571, ap: 0.472, auc: 0.638, f1: 0.530, acc: 0.621
+2025-08-07 19:22:17: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:22:17: Epoch: 013 Test results, ap: 0.192, auc: 0.322, f1: 0.328, acc: 0.204
+2025-08-07 19:22:17: Epoch: 014 Train Loss: 0.581, ap: 0.445, auc: 0.616, f1: 0.523, acc: 0.592
+2025-08-07 19:22:17: Epoch: 014 Test results, ap: 0.200, auc: 0.356, f1: 0.388, acc: 0.241
+2025-08-07 19:22:18: Epoch: 015 Train Loss: 0.575, ap: 0.453, auc: 0.624, f1: 0.411, acc: 0.583
+2025-08-07 19:22:18: Epoch: 015 Test results, ap: 0.205, auc: 0.366, f1: 0.274, acc: 0.361
+2025-08-07 19:22:18: Epoch: 016 Train Loss: 0.560, ap: 0.455, auc: 0.631, f1: 0.523, acc: 0.592
+2025-08-07 19:22:18: Epoch: 016 Test results, ap: 0.215, auc: 0.371, f1: 0.369, acc: 0.241
+2025-08-07 19:22:18: Epoch: 016 New best TEST metrics
+2025-08-07 19:22:19: Epoch: 017 Train Loss: 0.561, ap: 0.457, auc: 0.622, f1: 0.488, acc: 0.573
+2025-08-07 19:22:19: Epoch: 017 Test results, ap: 0.214, auc: 0.362, f1: 0.159, acc: 0.509
+2025-08-07 19:22:19: Epoch: 018 Train Loss: 0.566, ap: 0.479, auc: 0.633, f1: 0.423, acc: 0.602
+2025-08-07 19:22:19: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:22:19: Epoch: 018 Test results, ap: 0.210, auc: 0.340, f1: 0.195, acc: 0.694
+2025-08-07 19:22:20: Epoch: 019 Train Loss: 0.545, ap: 0.511, auc: 0.644, f1: 0.524, acc: 0.612
+2025-08-07 19:22:20: Epoch: 019 New best VALIDATION metrics
+2025-08-07 19:22:20: Epoch: 019 Test results, ap: 0.208, auc: 0.319, f1: 0.229, acc: 0.750
+2025-08-07 19:22:20: Epoch: 020 Train Loss: 0.549, ap: 0.530, auc: 0.648, f1: 0.527, acc: 0.583
+2025-08-07 19:22:20: Epoch: 020 New best VALIDATION metrics
+2025-08-07 19:22:20: Epoch: 020 Test results, ap: 0.210, auc: 0.327, f1: 0.216, acc: 0.731
+2025-08-07 19:22:21: Epoch: 021 Train Loss: 0.541, ap: 0.552, auc: 0.660, f1: 0.533, acc: 0.592
+2025-08-07 19:22:21: Epoch: 021 New best VALIDATION metrics
+2025-08-07 19:22:21: Epoch: 021 Test results, ap: 0.208, auc: 0.321, f1: 0.000, acc: 0.722
+2025-08-07 19:22:21: Epoch: 022 Train Loss: 0.539, ap: 0.550, auc: 0.668, f1: 0.480, acc: 0.621
+2025-08-07 19:22:21: Epoch: 022 New best VALIDATION metrics
+2025-08-07 19:22:22: Epoch: 022 Test results, ap: 0.195, auc: 0.296, f1: 0.000, acc: 0.731
+2025-08-07 19:22:22: Epoch: 023 Train Loss: 0.520, ap: 0.529, auc: 0.666, f1: 0.535, acc: 0.612
+2025-08-07 19:22:22: Epoch: 023 Test results, ap: 0.185, auc: 0.294, f1: 0.000, acc: 0.759
+2025-08-07 19:22:22: Epoch: 024 Train Loss: 0.508, ap: 0.527, auc: 0.667, f1: 0.518, acc: 0.602
+2025-08-07 19:22:23: Epoch: 024 Test results, ap: 0.195, auc: 0.315, f1: 0.000, acc: 0.722
+2025-08-07 19:22:23: Epoch: 025 Train Loss: 0.502, ap: 0.522, auc: 0.669, f1: 0.535, acc: 0.612
+2025-08-07 19:22:23: Epoch: 025 Test results, ap: 0.181, auc: 0.298, f1: 0.000, acc: 0.741
+2025-08-07 19:22:24: Epoch: 026 Train Loss: 0.494, ap: 0.530, auc: 0.672, f1: 0.529, acc: 0.602
+2025-08-07 19:22:24: Epoch: 026 Test results, ap: 0.193, auc: 0.319, f1: 0.000, acc: 0.722
+2025-08-07 19:22:24: Epoch: 027 Train Loss: 0.519, ap: 0.525, auc: 0.683, f1: 0.494, acc: 0.602
+2025-08-07 19:22:24: Epoch: 027 Test results, ap: 0.178, auc: 0.310, f1: 0.000, acc: 0.759
+2025-08-07 19:22:25: Epoch: 028 Train Loss: 0.509, ap: 0.580, auc: 0.674, f1: 0.462, acc: 0.592
+2025-08-07 19:22:25: Epoch: 028 New best VALIDATION metrics
+2025-08-07 19:22:25: Epoch: 028 Test results, ap: 0.212, auc: 0.366, f1: 0.121, acc: 0.731
+2025-08-07 19:22:25: Epoch: 029 Train Loss: 0.508, ap: 0.581, auc: 0.679, f1: 0.523, acc: 0.592
+2025-08-07 19:22:25: Epoch: 029 New best VALIDATION metrics
+2025-08-07 19:22:25: Epoch: 029 Test results, ap: 0.224, auc: 0.391, f1: 0.222, acc: 0.481
+2025-08-07 19:22:25: Epoch: 029 New best TEST metrics
+2025-08-07 19:22:26: Epoch: 030 Train Loss: 0.508, ap: 0.576, auc: 0.682, f1: 0.512, acc: 0.592
+2025-08-07 19:22:26: Epoch: 030 Test results, ap: 0.201, auc: 0.368, f1: 0.108, acc: 0.694
+2025-08-07 19:22:26: Epoch: 031 Train Loss: 0.475, ap: 0.583, auc: 0.683, f1: 0.488, acc: 0.592
+2025-08-07 19:22:26: Epoch: 031 New best VALIDATION metrics
+2025-08-07 19:22:26: Epoch: 031 Test results, ap: 0.195, auc: 0.362, f1: 0.000, acc: 0.722
+2025-08-07 19:22:27: Epoch: 032 Train Loss: 0.500, ap: 0.581, auc: 0.683, f1: 0.524, acc: 0.612
+2025-08-07 19:22:27: Epoch: 032 Test results, ap: 0.194, auc: 0.359, f1: 0.000, acc: 0.722
+2025-08-07 19:22:27: Epoch: 033 Train Loss: 0.501, ap: 0.577, auc: 0.681, f1: 0.512, acc: 0.592
+2025-08-07 19:22:27: Epoch: 033 Test results, ap: 0.197, auc: 0.367, f1: 0.098, acc: 0.657
+2025-08-07 19:22:28: Epoch: 034 Train Loss: 0.479, ap: 0.577, auc: 0.679, f1: 0.524, acc: 0.612
+2025-08-07 19:22:28: Epoch: 034 Test results, ap: 0.192, auc: 0.354, f1: 0.000, acc: 0.648
+2025-08-07 19:22:28: Epoch: 035 Train Loss: 0.491, ap: 0.578, auc: 0.680, f1: 0.518, acc: 0.602
+2025-08-07 19:22:28: Epoch: 035 Test results, ap: 0.193, auc: 0.359, f1: 0.170, acc: 0.639
+2025-08-07 19:22:29: Epoch: 036 Train Loss: 0.479, ap: 0.577, auc: 0.678, f1: 0.512, acc: 0.592
+2025-08-07 19:22:29: Epoch: 036 Test results, ap: 0.192, auc: 0.355, f1: 0.214, acc: 0.593
+2025-08-07 19:22:29: Epoch: 037 Train Loss: 0.463, ap: 0.577, auc: 0.679, f1: 0.518, acc: 0.602
+2025-08-07 19:22:29: Epoch: 037 Test results, ap: 0.190, auc: 0.352, f1: 0.000, acc: 0.620
+2025-08-07 19:22:30: Epoch: 038 Train Loss: 0.457, ap: 0.577, auc: 0.677, f1: 0.524, acc: 0.612
+2025-08-07 19:22:30: Epoch: 038 Test results, ap: 0.184, auc: 0.335, f1: 0.000, acc: 0.630
+2025-08-07 19:22:30: Epoch: 039 Train Loss: 0.479, ap: 0.578, auc: 0.677, f1: 0.512, acc: 0.592
+2025-08-07 19:22:30: Epoch: 039 Test results, ap: 0.185, auc: 0.335, f1: 0.207, acc: 0.574
+2025-08-07 19:22:31: Epoch: 040 Train Loss: 0.468, ap: 0.578, auc: 0.677, f1: 0.518, acc: 0.602
+2025-08-07 19:22:31: Epoch: 040 Test results, ap: 0.184, auc: 0.331, f1: 0.218, acc: 0.602
+2025-08-07 19:22:31: Epoch: 041 Train Loss: 0.470, ap: 0.567, auc: 0.672, f1: 0.518, acc: 0.602
+2025-08-07 19:22:31: Epoch: 041 Test results, ap: 0.184, auc: 0.330, f1: 0.233, acc: 0.574
+2025-08-07 19:22:32: Epoch: 042 Train Loss: 0.444, ap: 0.566, auc: 0.676, f1: 0.518, acc: 0.602
+2025-08-07 19:22:32: Epoch: 042 Test results, ap: 0.185, auc: 0.333, f1: 0.247, acc: 0.324
+2025-08-07 19:22:32: Epoch: 043 Train Loss: 0.455, ap: 0.563, auc: 0.676, f1: 0.518, acc: 0.602
+2025-08-07 19:22:33: Epoch: 043 Test results, ap: 0.184, auc: 0.331, f1: 0.265, acc: 0.333
+2025-08-07 19:22:33: Epoch: 044 Train Loss: 0.434, ap: 0.545, auc: 0.668, f1: 0.524, acc: 0.612
+2025-08-07 19:22:33: Epoch: 044 Test results, ap: 0.184, auc: 0.329, f1: 0.264, acc: 0.278
+2025-08-07 19:22:33: Epoch: 045 Train Loss: 0.471, ap: 0.547, auc: 0.669, f1: 0.535, acc: 0.612
+2025-08-07 19:22:34: Epoch: 045 Test results, ap: 0.183, auc: 0.327, f1: 0.288, acc: 0.269
+2025-08-07 19:22:34: Epoch: 046 Train Loss: 0.469, ap: 0.548, auc: 0.670, f1: 0.524, acc: 0.612
+2025-08-07 19:22:34: Epoch: 046 Test results, ap: 0.179, auc: 0.317, f1: 0.298, acc: 0.259
+2025-08-07 19:22:35: Epoch: 047 Train Loss: 0.454, ap: 0.548, auc: 0.670, f1: 0.524, acc: 0.612
+2025-08-07 19:22:35: Epoch: 047 Test results, ap: 0.180, auc: 0.319, f1: 0.298, acc: 0.259
+2025-08-07 19:22:35: Epoch: 048 Train Loss: 0.469, ap: 0.546, auc: 0.667, f1: 0.535, acc: 0.612
+2025-08-07 19:22:35: Epoch: 048 Test results, ap: 0.182, auc: 0.327, f1: 0.328, acc: 0.241
+2025-08-07 19:22:36: Epoch: 049 Train Loss: 0.463, ap: 0.545, auc: 0.667, f1: 0.541, acc: 0.621
+2025-08-07 19:22:36: Epoch: 049 Test results, ap: 0.178, auc: 0.316, f1: 0.309, acc: 0.296
+2025-08-07 19:22:36: Epoch: 050 Train Loss: 0.480, ap: 0.545, auc: 0.667, f1: 0.524, acc: 0.612
+2025-08-07 19:22:36: Epoch: 050 Test results, ap: 0.179, auc: 0.319, f1: 0.255, acc: 0.296
+2025-08-07 19:22:36: used time 0.01h
+2025-08-07 19:22:36: Running Cross Validation 2
+2025-08-07 19:22:36: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:22:37: Epoch: 001 Train Loss: 0.694, ap: 0.537, auc: 0.605, f1: 0.671, acc: 0.505
+2025-08-07 19:22:37: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:22:37: Epoch: 001 Test results, ap: 0.209, auc: 0.427, f1: 0.388, acc: 0.241
+2025-08-07 19:22:37: Epoch: 002 Train Loss: 0.695, ap: 0.571, auc: 0.590, f1: 0.000, acc: 0.495
+2025-08-07 19:22:37: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:22:37: Epoch: 002 Test results, ap: 0.223, auc: 0.485, f1: 0.000, acc: 0.759
+2025-08-07 19:22:38: Epoch: 003 Train Loss: 0.693, ap: 0.576, auc: 0.615, f1: 0.000, acc: 0.495
+2025-08-07 19:22:38: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:22:38: Epoch: 003 Test results, ap: 0.225, auc: 0.495, f1: 0.000, acc: 0.759
+2025-08-07 19:22:38: Epoch: 004 Train Loss: 0.697, ap: 0.440, auc: 0.429, f1: 0.671, acc: 0.505
+2025-08-07 19:22:38: Epoch: 004 Test results, ap: 0.212, auc: 0.430, f1: 0.388, acc: 0.241
+2025-08-07 19:22:39: Epoch: 005 Train Loss: 0.693, ap: 0.584, auc: 0.674, f1: 0.000, acc: 0.495
+2025-08-07 19:22:39: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:22:39: Epoch: 005 Test results, ap: 0.209, auc: 0.432, f1: 0.000, acc: 0.759
+2025-08-07 19:22:39: Epoch: 006 Train Loss: 0.696, ap: 0.564, auc: 0.635, f1: 0.000, acc: 0.495
+2025-08-07 19:22:39: Epoch: 006 Test results, ap: 0.230, auc: 0.491, f1: 0.000, acc: 0.759
+2025-08-07 19:22:40: Epoch: 007 Train Loss: 0.694, ap: 0.557, auc: 0.621, f1: 0.000, acc: 0.495
+2025-08-07 19:22:40: Epoch: 007 Test results, ap: 0.226, auc: 0.487, f1: 0.000, acc: 0.759
+2025-08-07 19:22:40: Epoch: 008 Train Loss: 0.694, ap: 0.632, auc: 0.552, f1: 0.000, acc: 0.495
+2025-08-07 19:22:40: Epoch: 008 Test results, ap: 0.219, auc: 0.440, f1: 0.000, acc: 0.759
+2025-08-07 19:22:41: Epoch: 009 Train Loss: 0.698, ap: 0.574, auc: 0.645, f1: 0.000, acc: 0.495
+2025-08-07 19:22:41: Epoch: 009 Test results, ap: 0.231, auc: 0.502, f1: 0.000, acc: 0.759
+2025-08-07 19:22:41: Epoch: 010 Train Loss: 0.697, ap: 0.578, auc: 0.626, f1: 0.000, acc: 0.495
+2025-08-07 19:22:41: Epoch: 010 Test results, ap: 0.230, auc: 0.502, f1: 0.000, acc: 0.759
+2025-08-07 19:22:42: Epoch: 011 Train Loss: 0.692, ap: 0.573, auc: 0.627, f1: 0.703, acc: 0.631
+2025-08-07 19:22:42: Epoch: 011 Test results, ap: 0.234, auc: 0.520, f1: 0.419, acc: 0.333
+2025-08-07 19:22:42: Epoch: 011 New best TEST metrics
+2025-08-07 19:22:42: Epoch: 012 Train Loss: 0.688, ap: 0.599, auc: 0.669, f1: 0.684, acc: 0.641
+2025-08-07 19:22:42: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:22:42: Epoch: 012 Test results, ap: 0.219, auc: 0.476, f1: 0.222, acc: 0.546
+2025-08-07 19:22:43: Epoch: 013 Train Loss: 0.659, ap: 0.634, auc: 0.715, f1: 0.704, acc: 0.592
+2025-08-07 19:22:43: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:22:43: Epoch: 013 Test results, ap: 0.228, auc: 0.505, f1: 0.388, acc: 0.241
+2025-08-07 19:22:43: Epoch: 014 Train Loss: 0.632, ap: 0.701, auc: 0.744, f1: 0.696, acc: 0.670
+2025-08-07 19:22:43: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:22:43: Epoch: 014 Test results, ap: 0.230, auc: 0.510, f1: 0.320, acc: 0.528
+2025-08-07 19:22:44: Epoch: 015 Train Loss: 0.616, ap: 0.733, auc: 0.756, f1: 0.756, acc: 0.718
+2025-08-07 19:22:44: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:22:44: Epoch: 015 Test results, ap: 0.227, auc: 0.502, f1: 0.239, acc: 0.528
+2025-08-07 19:22:44: Epoch: 016 Train Loss: 0.604, ap: 0.765, auc: 0.762, f1: 0.685, acc: 0.670
+2025-08-07 19:22:44: Epoch: 016 New best VALIDATION metrics
+2025-08-07 19:22:44: Epoch: 016 Test results, ap: 0.219, auc: 0.480, f1: 0.000, acc: 0.657
+2025-08-07 19:22:45: Epoch: 017 Train Loss: 0.580, ap: 0.763, auc: 0.753, f1: 0.694, acc: 0.631
+2025-08-07 19:22:45: Epoch: 017 Test results, ap: 0.220, auc: 0.485, f1: 0.000, acc: 0.620
+2025-08-07 19:22:45: Epoch: 018 Train Loss: 0.573, ap: 0.781, auc: 0.775, f1: 0.660, acc: 0.689
+2025-08-07 19:22:45: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:22:45: Epoch: 018 Test results, ap: 0.203, auc: 0.430, f1: 0.000, acc: 0.741
+2025-08-07 19:22:46: Epoch: 019 Train Loss: 0.595, ap: 0.762, auc: 0.765, f1: 0.543, acc: 0.641
+2025-08-07 19:22:46: Epoch: 019 Test results, ap: 0.200, auc: 0.409, f1: 0.000, acc: 0.759
+2025-08-07 19:22:46: Epoch: 020 Train Loss: 0.580, ap: 0.779, auc: 0.769, f1: 0.652, acc: 0.689
+2025-08-07 19:22:46: Epoch: 020 Test results, ap: 0.203, auc: 0.415, f1: 0.000, acc: 0.759
+2025-08-07 19:22:47: Epoch: 021 Train Loss: 0.570, ap: 0.759, auc: 0.768, f1: 0.719, acc: 0.650
+2025-08-07 19:22:47: Epoch: 021 Test results, ap: 0.206, auc: 0.431, f1: 0.235, acc: 0.519
+2025-08-07 19:22:47: Epoch: 022 Train Loss: 0.562, ap: 0.791, auc: 0.781, f1: 0.748, acc: 0.738
+2025-08-07 19:22:47: Epoch: 022 New best VALIDATION metrics
+2025-08-07 19:22:47: Epoch: 022 Test results, ap: 0.212, auc: 0.433, f1: 0.000, acc: 0.722
+2025-08-07 19:22:48: Epoch: 023 Train Loss: 0.562, ap: 0.779, auc: 0.767, f1: 0.712, acc: 0.670
+2025-08-07 19:22:48: Epoch: 023 Test results, ap: 0.215, auc: 0.446, f1: 0.000, acc: 0.722
+2025-08-07 19:22:48: Epoch: 024 Train Loss: 0.542, ap: 0.780, auc: 0.771, f1: 0.687, acc: 0.699
+2025-08-07 19:22:48: Epoch: 024 Test results, ap: 0.209, auc: 0.422, f1: 0.000, acc: 0.759
+2025-08-07 19:22:49: Epoch: 025 Train Loss: 0.534, ap: 0.783, auc: 0.771, f1: 0.729, acc: 0.718
+2025-08-07 19:22:49: Epoch: 025 Test results, ap: 0.205, auc: 0.405, f1: 0.000, acc: 0.750
+2025-08-07 19:22:49: Epoch: 026 Train Loss: 0.534, ap: 0.788, auc: 0.769, f1: 0.701, acc: 0.718
+2025-08-07 19:22:49: Epoch: 026 Test results, ap: 0.204, auc: 0.397, f1: 0.000, acc: 0.759
+2025-08-07 19:22:50: Epoch: 027 Train Loss: 0.524, ap: 0.785, auc: 0.766, f1: 0.709, acc: 0.689
+2025-08-07 19:22:50: Epoch: 027 Test results, ap: 0.201, auc: 0.381, f1: 0.000, acc: 0.750
+2025-08-07 19:22:50: Epoch: 028 Train Loss: 0.522, ap: 0.771, auc: 0.758, f1: 0.732, acc: 0.709
+2025-08-07 19:22:50: Epoch: 028 Test results, ap: 0.198, auc: 0.387, f1: 0.000, acc: 0.731
+2025-08-07 19:22:51: Epoch: 029 Train Loss: 0.518, ap: 0.785, auc: 0.769, f1: 0.694, acc: 0.709
+2025-08-07 19:22:51: Epoch: 029 Test results, ap: 0.201, auc: 0.386, f1: 0.000, acc: 0.741
+2025-08-07 19:22:51: Epoch: 030 Train Loss: 0.515, ap: 0.799, auc: 0.770, f1: 0.688, acc: 0.709
+2025-08-07 19:22:51: Epoch: 030 Test results, ap: 0.199, auc: 0.387, f1: 0.000, acc: 0.759
+2025-08-07 19:22:52: Epoch: 031 Train Loss: 0.510, ap: 0.787, auc: 0.766, f1: 0.745, acc: 0.728
+2025-08-07 19:22:52: Epoch: 031 Test results, ap: 0.199, auc: 0.381, f1: 0.000, acc: 0.731
+2025-08-07 19:22:52: Epoch: 032 Train Loss: 0.513, ap: 0.791, auc: 0.767, f1: 0.745, acc: 0.728
+2025-08-07 19:22:52: Epoch: 032 Test results, ap: 0.200, auc: 0.382, f1: 0.000, acc: 0.741
+2025-08-07 19:22:53: Epoch: 033 Train Loss: 0.492, ap: 0.805, auc: 0.782, f1: 0.694, acc: 0.709
+2025-08-07 19:22:53: Epoch: 033 New best VALIDATION metrics
+2025-08-07 19:22:53: Epoch: 033 Test results, ap: 0.198, auc: 0.371, f1: 0.000, acc: 0.759
+2025-08-07 19:22:53: Epoch: 034 Train Loss: 0.503, ap: 0.788, auc: 0.761, f1: 0.739, acc: 0.718
+2025-08-07 19:22:54: Epoch: 034 Test results, ap: 0.201, auc: 0.384, f1: 0.000, acc: 0.741
+2025-08-07 19:22:54: Epoch: 035 Train Loss: 0.508, ap: 0.805, auc: 0.773, f1: 0.681, acc: 0.709
+2025-08-07 19:22:54: Epoch: 035 Test results, ap: 0.202, auc: 0.393, f1: 0.000, acc: 0.759
+2025-08-07 19:22:54: Epoch: 036 Train Loss: 0.478, ap: 0.786, auc: 0.755, f1: 0.713, acc: 0.680
+2025-08-07 19:22:55: Epoch: 036 Test results, ap: 0.206, auc: 0.406, f1: 0.000, acc: 0.750
+2025-08-07 19:22:55: Epoch: 037 Train Loss: 0.498, ap: 0.798, auc: 0.774, f1: 0.708, acc: 0.728
+2025-08-07 19:22:55: Epoch: 037 Test results, ap: 0.209, auc: 0.411, f1: 0.000, acc: 0.759
+2025-08-07 19:22:55: Epoch: 038 Train Loss: 0.478, ap: 0.804, auc: 0.771, f1: 0.710, acc: 0.699
+2025-08-07 19:22:56: Epoch: 038 Test results, ap: 0.212, auc: 0.422, f1: 0.000, acc: 0.759
+2025-08-07 19:22:56: Epoch: 039 Train Loss: 0.487, ap: 0.785, auc: 0.763, f1: 0.727, acc: 0.709
+2025-08-07 19:22:56: Epoch: 039 Test results, ap: 0.213, auc: 0.429, f1: 0.000, acc: 0.759
+2025-08-07 19:22:56: Epoch: 040 Train Loss: 0.461, ap: 0.781, auc: 0.767, f1: 0.734, acc: 0.718
+2025-08-07 19:22:57: Epoch: 040 Test results, ap: 0.214, auc: 0.425, f1: 0.000, acc: 0.759
+2025-08-07 19:22:57: Epoch: 041 Train Loss: 0.458, ap: 0.791, auc: 0.770, f1: 0.710, acc: 0.699
+2025-08-07 19:22:57: Epoch: 041 Test results, ap: 0.215, auc: 0.429, f1: 0.000, acc: 0.759
+2025-08-07 19:22:58: Epoch: 042 Train Loss: 0.440, ap: 0.775, auc: 0.766, f1: 0.722, acc: 0.709
+2025-08-07 19:22:58: Epoch: 042 Test results, ap: 0.217, auc: 0.435, f1: 0.000, acc: 0.759
+2025-08-07 19:22:58: Epoch: 043 Train Loss: 0.447, ap: 0.770, auc: 0.761, f1: 0.722, acc: 0.709
+2025-08-07 19:22:58: Epoch: 043 Test results, ap: 0.218, auc: 0.438, f1: 0.000, acc: 0.759
+2025-08-07 19:22:59: Epoch: 044 Train Loss: 0.447, ap: 0.772, auc: 0.764, f1: 0.710, acc: 0.699
+2025-08-07 19:22:59: Epoch: 044 Test results, ap: 0.221, auc: 0.447, f1: 0.000, acc: 0.759
+2025-08-07 19:22:59: Epoch: 045 Train Loss: 0.439, ap: 0.782, auc: 0.761, f1: 0.709, acc: 0.689
+2025-08-07 19:22:59: Epoch: 045 Test results, ap: 0.223, auc: 0.447, f1: 0.000, acc: 0.759
+2025-08-07 19:23:00: Epoch: 046 Train Loss: 0.457, ap: 0.774, auc: 0.778, f1: 0.745, acc: 0.748
+2025-08-07 19:23:00: Epoch: 046 Test results, ap: 0.225, auc: 0.451, f1: 0.000, acc: 0.759
+2025-08-07 19:23:00: Epoch: 047 Train Loss: 0.456, ap: 0.774, auc: 0.762, f1: 0.727, acc: 0.709
+2025-08-07 19:23:00: Epoch: 047 Test results, ap: 0.225, auc: 0.454, f1: 0.000, acc: 0.759
+2025-08-07 19:23:01: Epoch: 048 Train Loss: 0.448, ap: 0.774, auc: 0.762, f1: 0.727, acc: 0.709
+2025-08-07 19:23:01: Epoch: 048 Test results, ap: 0.230, auc: 0.467, f1: 0.000, acc: 0.759
+2025-08-07 19:23:01: Epoch: 049 Train Loss: 0.430, ap: 0.748, auc: 0.753, f1: 0.727, acc: 0.709
+2025-08-07 19:23:01: Epoch: 049 Test results, ap: 0.228, auc: 0.467, f1: 0.000, acc: 0.759
+2025-08-07 19:23:02: Epoch: 050 Train Loss: 0.449, ap: 0.750, auc: 0.761, f1: 0.710, acc: 0.699
+2025-08-07 19:23:02: Epoch: 050 Test results, ap: 0.231, auc: 0.476, f1: 0.000, acc: 0.759
+2025-08-07 19:23:02: used time 0.01h
+2025-08-07 19:23:02: Running Cross Validation 3
+2025-08-07 19:23:02: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:23:02: Epoch: 001 Train Loss: 0.693, ap: 0.614, auc: 0.746, f1: 0.000, acc: 0.592
+2025-08-07 19:23:02: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:23:02: Epoch: 001 Test results, ap: 0.229, auc: 0.492, f1: 0.000, acc: 0.759
+2025-08-07 19:23:03: Epoch: 002 Train Loss: 0.695, ap: 0.590, auc: 0.620, f1: 0.000, acc: 0.592
+2025-08-07 19:23:03: Epoch: 002 Test results, ap: 0.185, auc: 0.346, f1: 0.000, acc: 0.759
+2025-08-07 19:23:03: Epoch: 003 Train Loss: 0.694, ap: 0.580, auc: 0.726, f1: 0.000, acc: 0.592
+2025-08-07 19:23:04: Epoch: 003 Test results, ap: 0.236, auc: 0.529, f1: 0.000, acc: 0.759
+2025-08-07 19:23:04: Epoch: 004 Train Loss: 0.694, ap: 0.551, auc: 0.706, f1: 0.000, acc: 0.592
+2025-08-07 19:23:04: Epoch: 004 Test results, ap: 0.235, auc: 0.529, f1: 0.000, acc: 0.759
+2025-08-07 19:23:04: Epoch: 005 Train Loss: 0.689, ap: 0.557, auc: 0.700, f1: 0.579, acc: 0.408
+2025-08-07 19:23:05: Epoch: 005 Test results, ap: 0.233, auc: 0.521, f1: 0.388, acc: 0.241
+2025-08-07 19:23:05: Epoch: 006 Train Loss: 0.701, ap: 0.555, auc: 0.696, f1: 0.000, acc: 0.592
+2025-08-07 19:23:05: Epoch: 006 Test results, ap: 0.229, auc: 0.508, f1: 0.000, acc: 0.759
+2025-08-07 19:23:06: Epoch: 007 Train Loss: 0.669, ap: 0.627, auc: 0.732, f1: 0.565, acc: 0.641
+2025-08-07 19:23:06: Epoch: 007 Test results, ap: 0.224, auc: 0.496, f1: 0.185, acc: 0.593
+2025-08-07 19:23:06: Epoch: 008 Train Loss: 0.647, ap: 0.624, auc: 0.753, f1: 0.696, acc: 0.660
+2025-08-07 19:23:06: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:23:06: Epoch: 008 Test results, ap: 0.230, auc: 0.506, f1: 0.388, acc: 0.241
+2025-08-07 19:23:07: Epoch: 009 Train Loss: 0.648, ap: 0.618, auc: 0.751, f1: 0.541, acc: 0.670
+2025-08-07 19:23:07: Epoch: 009 Test results, ap: 0.224, auc: 0.482, f1: 0.095, acc: 0.648
+2025-08-07 19:23:07: Epoch: 010 Train Loss: 0.632, ap: 0.621, auc: 0.744, f1: 0.672, acc: 0.621
+2025-08-07 19:23:07: Epoch: 010 Test results, ap: 0.235, auc: 0.462, f1: 0.388, acc: 0.241
+2025-08-07 19:23:08: Epoch: 011 Train Loss: 0.616, ap: 0.629, auc: 0.753, f1: 0.684, acc: 0.641
+2025-08-07 19:23:08: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:23:08: Epoch: 011 Test results, ap: 0.236, auc: 0.482, f1: 0.388, acc: 0.241
+2025-08-07 19:23:08: Epoch: 011 New best TEST metrics
+2025-08-07 19:23:08: Epoch: 012 Train Loss: 0.606, ap: 0.631, auc: 0.754, f1: 0.692, acc: 0.680
+2025-08-07 19:23:08: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:23:08: Epoch: 012 Test results, ap: 0.246, auc: 0.466, f1: 0.179, acc: 0.574
+2025-08-07 19:23:09: Epoch: 013 Train Loss: 0.588, ap: 0.643, auc: 0.708, f1: 0.608, acc: 0.524
+2025-08-07 19:23:09: Epoch: 013 Test results, ap: 0.248, auc: 0.372, f1: 0.222, acc: 0.741
+2025-08-07 19:23:09: Epoch: 014 Train Loss: 0.609, ap: 0.639, auc: 0.772, f1: 0.675, acc: 0.738
+2025-08-07 19:23:09: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:23:09: Epoch: 014 Test results, ap: 0.249, auc: 0.492, f1: 0.000, acc: 0.731
+2025-08-07 19:23:09: Epoch: 014 New best TEST metrics
+2025-08-07 19:23:10: Epoch: 015 Train Loss: 0.596, ap: 0.646, auc: 0.772, f1: 0.600, acc: 0.728
+2025-08-07 19:23:10: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:23:10: Epoch: 015 Test results, ap: 0.242, auc: 0.471, f1: 0.000, acc: 0.750
+2025-08-07 19:23:10: Epoch: 016 Train Loss: 0.573, ap: 0.662, auc: 0.770, f1: 0.667, acc: 0.718
+2025-08-07 19:23:10: Epoch: 016 New best VALIDATION metrics
+2025-08-07 19:23:10: Epoch: 016 Test results, ap: 0.246, auc: 0.478, f1: 0.000, acc: 0.750
+2025-08-07 19:23:11: Epoch: 017 Train Loss: 0.576, ap: 0.688, auc: 0.771, f1: 0.642, acc: 0.718
+2025-08-07 19:23:11: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:23:11: Epoch: 017 Test results, ap: 0.234, auc: 0.423, f1: 0.000, acc: 0.759
+2025-08-07 19:23:11: Epoch: 018 Train Loss: 0.582, ap: 0.729, auc: 0.774, f1: 0.660, acc: 0.689
+2025-08-07 19:23:11: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:23:12: Epoch: 018 Test results, ap: 0.243, auc: 0.457, f1: 0.000, acc: 0.750
+2025-08-07 19:23:12: Epoch: 019 Train Loss: 0.555, ap: 0.746, auc: 0.785, f1: 0.682, acc: 0.728
+2025-08-07 19:23:12: Epoch: 019 New best VALIDATION metrics
+2025-08-07 19:23:12: Epoch: 019 Test results, ap: 0.216, auc: 0.396, f1: 0.000, acc: 0.759
+2025-08-07 19:23:12: Epoch: 020 Train Loss: 0.535, ap: 0.748, auc: 0.785, f1: 0.645, acc: 0.680
+2025-08-07 19:23:12: Epoch: 020 New best VALIDATION metrics
+2025-08-07 19:23:13: Epoch: 020 Test results, ap: 0.213, auc: 0.390, f1: 0.000, acc: 0.759
+2025-08-07 19:23:13: Epoch: 021 Train Loss: 0.541, ap: 0.752, auc: 0.788, f1: 0.693, acc: 0.699
+2025-08-07 19:23:13: Epoch: 021 New best VALIDATION metrics
+2025-08-07 19:23:13: Epoch: 021 Test results, ap: 0.223, auc: 0.400, f1: 0.000, acc: 0.750
+2025-08-07 19:23:13: Epoch: 022 Train Loss: 0.529, ap: 0.753, auc: 0.790, f1: 0.667, acc: 0.709
+2025-08-07 19:23:13: Epoch: 022 New best VALIDATION metrics
+2025-08-07 19:23:14: Epoch: 022 Test results, ap: 0.207, auc: 0.377, f1: 0.000, acc: 0.759
+2025-08-07 19:23:14: Epoch: 023 Train Loss: 0.531, ap: 0.751, auc: 0.777, f1: 0.635, acc: 0.631
+2025-08-07 19:23:14: Epoch: 023 Test results, ap: 0.216, auc: 0.399, f1: 0.000, acc: 0.759
+2025-08-07 19:23:14: Epoch: 024 Train Loss: 0.537, ap: 0.750, auc: 0.783, f1: 0.619, acc: 0.689
+2025-08-07 19:23:15: Epoch: 024 Test results, ap: 0.201, auc: 0.378, f1: 0.000, acc: 0.759
+2025-08-07 19:23:15: Epoch: 025 Train Loss: 0.505, ap: 0.747, auc: 0.781, f1: 0.621, acc: 0.680
+2025-08-07 19:23:15: Epoch: 025 Test results, ap: 0.201, auc: 0.381, f1: 0.000, acc: 0.759
+2025-08-07 19:23:16: Epoch: 026 Train Loss: 0.504, ap: 0.752, auc: 0.778, f1: 0.660, acc: 0.670
+2025-08-07 19:23:16: Epoch: 026 Test results, ap: 0.202, auc: 0.380, f1: 0.000, acc: 0.759
+2025-08-07 19:23:16: Epoch: 027 Train Loss: 0.510, ap: 0.750, auc: 0.781, f1: 0.653, acc: 0.670
+2025-08-07 19:23:16: Epoch: 027 Test results, ap: 0.212, auc: 0.397, f1: 0.000, acc: 0.759
+2025-08-07 19:23:17: Epoch: 028 Train Loss: 0.510, ap: 0.750, auc: 0.784, f1: 0.639, acc: 0.660
+2025-08-07 19:23:17: Epoch: 028 Test results, ap: 0.214, auc: 0.403, f1: 0.000, acc: 0.759
+2025-08-07 19:23:17: Epoch: 029 Train Loss: 0.497, ap: 0.743, auc: 0.776, f1: 0.642, acc: 0.631
+2025-08-07 19:23:17: Epoch: 029 Test results, ap: 0.211, auc: 0.384, f1: 0.000, acc: 0.759
+2025-08-07 19:23:18: Epoch: 030 Train Loss: 0.503, ap: 0.737, auc: 0.771, f1: 0.619, acc: 0.689
+2025-08-07 19:23:18: Epoch: 030 Test results, ap: 0.199, auc: 0.379, f1: 0.000, acc: 0.759
+2025-08-07 19:23:18: Epoch: 031 Train Loss: 0.496, ap: 0.728, auc: 0.772, f1: 0.660, acc: 0.680
+2025-08-07 19:23:18: Epoch: 031 Test results, ap: 0.210, auc: 0.394, f1: 0.000, acc: 0.759
+2025-08-07 19:23:18: Epoch: 032 Train Loss: 0.469, ap: 0.738, auc: 0.776, f1: 0.646, acc: 0.670
+2025-08-07 19:23:19: Epoch: 032 Test results, ap: 0.210, auc: 0.405, f1: 0.000, acc: 0.759
+2025-08-07 19:23:19: Epoch: 033 Train Loss: 0.469, ap: 0.735, auc: 0.772, f1: 0.652, acc: 0.699
+2025-08-07 19:23:19: Epoch: 033 Test results, ap: 0.213, auc: 0.413, f1: 0.000, acc: 0.759
+2025-08-07 19:23:20: Epoch: 034 Train Loss: 0.464, ap: 0.727, auc: 0.767, f1: 0.652, acc: 0.689
+2025-08-07 19:23:20: Epoch: 034 Test results, ap: 0.213, auc: 0.412, f1: 0.000, acc: 0.759
+2025-08-07 19:23:20: Epoch: 035 Train Loss: 0.472, ap: 0.716, auc: 0.761, f1: 0.632, acc: 0.660
+2025-08-07 19:23:20: Epoch: 035 Test results, ap: 0.213, auc: 0.405, f1: 0.000, acc: 0.759
+2025-08-07 19:23:21: Epoch: 036 Train Loss: 0.470, ap: 0.723, auc: 0.762, f1: 0.610, acc: 0.689
+2025-08-07 19:23:21: Epoch: 036 Test results, ap: 0.213, auc: 0.408, f1: 0.000, acc: 0.759
+2025-08-07 19:23:21: Epoch: 037 Train Loss: 0.433, ap: 0.716, auc: 0.756, f1: 0.624, acc: 0.660
+2025-08-07 19:23:21: Epoch: 037 Test results, ap: 0.214, auc: 0.408, f1: 0.000, acc: 0.759
+2025-08-07 19:23:22: Epoch: 038 Train Loss: 0.450, ap: 0.718, auc: 0.759, f1: 0.630, acc: 0.670
+2025-08-07 19:23:22: Epoch: 038 Test results, ap: 0.215, auc: 0.409, f1: 0.000, acc: 0.759
+2025-08-07 19:23:22: Epoch: 039 Train Loss: 0.451, ap: 0.728, auc: 0.763, f1: 0.617, acc: 0.699
+2025-08-07 19:23:22: Epoch: 039 Test results, ap: 0.209, auc: 0.403, f1: 0.000, acc: 0.759
+2025-08-07 19:23:23: Epoch: 040 Train Loss: 0.458, ap: 0.695, auc: 0.740, f1: 0.606, acc: 0.621
+2025-08-07 19:23:23: Epoch: 040 Test results, ap: 0.212, auc: 0.403, f1: 0.000, acc: 0.759
+2025-08-07 19:23:23: Epoch: 041 Train Loss: 0.425, ap: 0.705, auc: 0.743, f1: 0.627, acc: 0.699
+2025-08-07 19:23:23: Epoch: 041 Test results, ap: 0.211, auc: 0.406, f1: 0.000, acc: 0.759
+2025-08-07 19:23:24: Epoch: 042 Train Loss: 0.427, ap: 0.698, auc: 0.742, f1: 0.615, acc: 0.660
+2025-08-07 19:23:24: Epoch: 042 Test results, ap: 0.213, auc: 0.408, f1: 0.000, acc: 0.759
+2025-08-07 19:23:24: Epoch: 043 Train Loss: 0.428, ap: 0.692, auc: 0.740, f1: 0.600, acc: 0.650
+2025-08-07 19:23:24: Epoch: 043 Test results, ap: 0.214, auc: 0.409, f1: 0.000, acc: 0.759
+2025-08-07 19:23:25: Epoch: 044 Train Loss: 0.417, ap: 0.697, auc: 0.747, f1: 0.605, acc: 0.670
+2025-08-07 19:23:25: Epoch: 044 Test results, ap: 0.215, auc: 0.411, f1: 0.000, acc: 0.759
+2025-08-07 19:23:25: Epoch: 045 Train Loss: 0.431, ap: 0.704, auc: 0.747, f1: 0.595, acc: 0.670
+2025-08-07 19:23:25: Epoch: 045 Test results, ap: 0.215, auc: 0.411, f1: 0.000, acc: 0.759
+2025-08-07 19:23:26: Epoch: 046 Train Loss: 0.416, ap: 0.698, auc: 0.735, f1: 0.593, acc: 0.641
+2025-08-07 19:23:26: Epoch: 046 Test results, ap: 0.212, auc: 0.412, f1: 0.000, acc: 0.759
+2025-08-07 19:23:26: Epoch: 047 Train Loss: 0.421, ap: 0.695, auc: 0.733, f1: 0.600, acc: 0.650
+2025-08-07 19:23:26: Epoch: 047 Test results, ap: 0.212, auc: 0.411, f1: 0.000, acc: 0.759
+2025-08-07 19:23:27: Epoch: 048 Train Loss: 0.419, ap: 0.695, auc: 0.738, f1: 0.595, acc: 0.670
+2025-08-07 19:23:27: Epoch: 048 Test results, ap: 0.214, auc: 0.408, f1: 0.000, acc: 0.759
+2025-08-07 19:23:27: Epoch: 049 Train Loss: 0.397, ap: 0.695, auc: 0.743, f1: 0.622, acc: 0.670
+2025-08-07 19:23:27: Epoch: 049 Test results, ap: 0.214, auc: 0.408, f1: 0.000, acc: 0.759
+2025-08-07 19:23:28: Epoch: 050 Train Loss: 0.402, ap: 0.704, auc: 0.740, f1: 0.621, acc: 0.680
+2025-08-07 19:23:28: Epoch: 050 Test results, ap: 0.214, auc: 0.411, f1: 0.000, acc: 0.759
+2025-08-07 19:23:28: used time 0.01h
+2025-08-07 19:23:28: Running Cross Validation 4
+2025-08-07 19:23:28: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:23:28: Epoch: 001 Train Loss: 0.695, ap: 0.557, auc: 0.703, f1: 0.000, acc: 0.602
+2025-08-07 19:23:28: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:23:28: Epoch: 001 Test results, ap: 0.176, auc: 0.320, f1: 0.000, acc: 0.759
+2025-08-07 19:23:29: Epoch: 002 Train Loss: 0.692, ap: 0.585, auc: 0.704, f1: 0.569, acc: 0.398
+2025-08-07 19:23:29: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:23:29: Epoch: 002 Test results, ap: 0.210, auc: 0.439, f1: 0.388, acc: 0.241
+2025-08-07 19:23:29: Epoch: 003 Train Loss: 0.694, ap: 0.576, auc: 0.691, f1: 0.000, acc: 0.602
+2025-08-07 19:23:30: Epoch: 003 Test results, ap: 0.219, auc: 0.467, f1: 0.000, acc: 0.759
+2025-08-07 19:23:30: Epoch: 004 Train Loss: 0.697, ap: 0.616, auc: 0.735, f1: 0.000, acc: 0.602
+2025-08-07 19:23:30: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:23:30: Epoch: 004 Test results, ap: 0.237, auc: 0.520, f1: 0.000, acc: 0.759
+2025-08-07 19:23:30: Epoch: 005 Train Loss: 0.692, ap: 0.626, auc: 0.741, f1: 0.641, acc: 0.728
+2025-08-07 19:23:30: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:23:31: Epoch: 005 Test results, ap: 0.246, auc: 0.543, f1: 0.397, acc: 0.269
+2025-08-07 19:23:31: Epoch: 006 Train Loss: 0.671, ap: 0.653, auc: 0.758, f1: 0.548, acc: 0.728
+2025-08-07 19:23:31: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:23:31: Epoch: 006 Test results, ap: 0.234, auc: 0.503, f1: 0.000, acc: 0.722
+2025-08-07 19:23:31: Epoch: 007 Train Loss: 0.653, ap: 0.669, auc: 0.757, f1: 0.634, acc: 0.641
+2025-08-07 19:23:31: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:23:32: Epoch: 007 Test results, ap: 0.226, auc: 0.484, f1: 0.388, acc: 0.241
+2025-08-07 19:23:32: Epoch: 008 Train Loss: 0.645, ap: 0.706, auc: 0.766, f1: 0.638, acc: 0.670
+2025-08-07 19:23:32: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:23:32: Epoch: 008 Test results, ap: 0.248, auc: 0.508, f1: 0.400, acc: 0.417
+2025-08-07 19:23:33: Epoch: 009 Train Loss: 0.626, ap: 0.725, auc: 0.785, f1: 0.652, acc: 0.689
+2025-08-07 19:23:33: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:23:33: Epoch: 009 Test results, ap: 0.256, auc: 0.506, f1: 0.229, acc: 0.750
+2025-08-07 19:23:33: Epoch: 010 Train Loss: 0.595, ap: 0.716, auc: 0.771, f1: 0.674, acc: 0.728
+2025-08-07 19:23:33: Epoch: 010 Test results, ap: 0.266, auc: 0.533, f1: 0.000, acc: 0.722
+2025-08-07 19:23:34: Epoch: 011 Train Loss: 0.549, ap: 0.715, auc: 0.769, f1: 0.697, acc: 0.738
+2025-08-07 19:23:34: Epoch: 011 Test results, ap: 0.265, auc: 0.550, f1: 0.000, acc: 0.722
+2025-08-07 19:23:34: Epoch: 011 New best TEST metrics
+2025-08-07 19:23:34: Epoch: 012 Train Loss: 0.519, ap: 0.726, auc: 0.780, f1: 0.688, acc: 0.709
+2025-08-07 19:23:34: Epoch: 012 Test results, ap: 0.248, auc: 0.484, f1: 0.229, acc: 0.750
+2025-08-07 19:23:35: Epoch: 013 Train Loss: 0.550, ap: 0.738, auc: 0.777, f1: 0.681, acc: 0.709
+2025-08-07 19:23:35: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:23:35: Epoch: 013 Test results, ap: 0.244, auc: 0.510, f1: 0.000, acc: 0.731
+2025-08-07 19:23:35: Epoch: 014 Train Loss: 0.533, ap: 0.771, auc: 0.781, f1: 0.681, acc: 0.709
+2025-08-07 19:23:35: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:23:35: Epoch: 014 Test results, ap: 0.231, auc: 0.494, f1: 0.000, acc: 0.750
+2025-08-07 19:23:36: Epoch: 015 Train Loss: 0.514, ap: 0.744, auc: 0.779, f1: 0.689, acc: 0.728
+2025-08-07 19:23:36: Epoch: 015 Test results, ap: 0.250, auc: 0.550, f1: 0.000, acc: 0.759
+2025-08-07 19:23:36: Epoch: 016 Train Loss: 0.524, ap: 0.732, auc: 0.764, f1: 0.625, acc: 0.709
+2025-08-07 19:23:36: Epoch: 016 Test results, ap: 0.210, auc: 0.437, f1: 0.000, acc: 0.759
+2025-08-07 19:23:37: Epoch: 017 Train Loss: 0.493, ap: 0.740, auc: 0.768, f1: 0.674, acc: 0.728
+2025-08-07 19:23:37: Epoch: 017 Test results, ap: 0.233, auc: 0.505, f1: 0.000, acc: 0.759
+2025-08-07 19:23:37: Epoch: 018 Train Loss: 0.489, ap: 0.786, auc: 0.784, f1: 0.695, acc: 0.718
+2025-08-07 19:23:37: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:23:37: Epoch: 018 Test results, ap: 0.188, auc: 0.364, f1: 0.000, acc: 0.750
+2025-08-07 19:23:38: Epoch: 019 Train Loss: 0.498, ap: 0.745, auc: 0.780, f1: 0.703, acc: 0.738
+2025-08-07 19:23:38: Epoch: 019 Test results, ap: 0.205, auc: 0.425, f1: 0.000, acc: 0.750
+2025-08-07 19:23:38: Epoch: 020 Train Loss: 0.472, ap: 0.753, auc: 0.788, f1: 0.673, acc: 0.680
+2025-08-07 19:23:39: Epoch: 020 Test results, ap: 0.222, auc: 0.483, f1: 0.000, acc: 0.676
+2025-08-07 19:23:39: Epoch: 021 Train Loss: 0.460, ap: 0.732, auc: 0.778, f1: 0.689, acc: 0.728
+2025-08-07 19:23:39: Epoch: 021 Test results, ap: 0.202, auc: 0.416, f1: 0.000, acc: 0.750
+2025-08-07 19:23:40: Epoch: 022 Train Loss: 0.453, ap: 0.758, auc: 0.789, f1: 0.681, acc: 0.709
+2025-08-07 19:23:40: Epoch: 022 Test results, ap: 0.178, auc: 0.323, f1: 0.000, acc: 0.750
+2025-08-07 19:23:40: Epoch: 023 Train Loss: 0.424, ap: 0.732, auc: 0.777, f1: 0.681, acc: 0.709
+2025-08-07 19:23:40: Epoch: 023 Test results, ap: 0.199, auc: 0.400, f1: 0.000, acc: 0.750
+2025-08-07 19:23:41: Epoch: 024 Train Loss: 0.448, ap: 0.753, auc: 0.792, f1: 0.667, acc: 0.699
+2025-08-07 19:23:41: Epoch: 024 Test results, ap: 0.189, auc: 0.364, f1: 0.000, acc: 0.750
+2025-08-07 19:23:41: Epoch: 025 Train Loss: 0.446, ap: 0.721, auc: 0.777, f1: 0.652, acc: 0.699
+2025-08-07 19:23:41: Epoch: 025 Test results, ap: 0.175, auc: 0.308, f1: 0.000, acc: 0.722
+2025-08-07 19:23:42: Epoch: 026 Train Loss: 0.432, ap: 0.737, auc: 0.772, f1: 0.681, acc: 0.709
+2025-08-07 19:23:42: Epoch: 026 Test results, ap: 0.255, auc: 0.552, f1: 0.000, acc: 0.676
+2025-08-07 19:23:42: Epoch: 027 Train Loss: 0.412, ap: 0.750, auc: 0.788, f1: 0.654, acc: 0.641
+2025-08-07 19:23:42: Epoch: 027 Test results, ap: 0.247, auc: 0.539, f1: 0.093, acc: 0.639
+2025-08-07 19:23:43: Epoch: 028 Train Loss: 0.436, ap: 0.731, auc: 0.779, f1: 0.667, acc: 0.699
+2025-08-07 19:23:43: Epoch: 028 Test results, ap: 0.176, auc: 0.304, f1: 0.000, acc: 0.750
+2025-08-07 19:23:43: Epoch: 029 Train Loss: 0.392, ap: 0.737, auc: 0.775, f1: 0.628, acc: 0.689
+2025-08-07 19:23:43: Epoch: 029 Test results, ap: 0.202, auc: 0.405, f1: 0.000, acc: 0.750
+2025-08-07 19:23:44: Epoch: 030 Train Loss: 0.416, ap: 0.728, auc: 0.777, f1: 0.625, acc: 0.650
+2025-08-07 19:23:44: Epoch: 030 Test results, ap: 0.173, auc: 0.287, f1: 0.000, acc: 0.731
+2025-08-07 19:23:44: Epoch: 031 Train Loss: 0.379, ap: 0.732, auc: 0.780, f1: 0.638, acc: 0.670
+2025-08-07 19:23:45: Epoch: 031 Test results, ap: 0.171, auc: 0.279, f1: 0.000, acc: 0.750
+2025-08-07 19:23:45: Epoch: 032 Train Loss: 0.381, ap: 0.737, auc: 0.779, f1: 0.645, acc: 0.680
+2025-08-07 19:23:45: Epoch: 032 Test results, ap: 0.175, auc: 0.295, f1: 0.000, acc: 0.750
+2025-08-07 19:23:45: Epoch: 033 Train Loss: 0.374, ap: 0.729, auc: 0.779, f1: 0.660, acc: 0.680
+2025-08-07 19:23:46: Epoch: 033 Test results, ap: 0.173, auc: 0.281, f1: 0.000, acc: 0.750
+2025-08-07 19:23:46: Epoch: 034 Train Loss: 0.364, ap: 0.738, auc: 0.780, f1: 0.632, acc: 0.660
+2025-08-07 19:23:46: Epoch: 034 Test results, ap: 0.170, auc: 0.270, f1: 0.000, acc: 0.750
+2025-08-07 19:23:46: Epoch: 035 Train Loss: 0.354, ap: 0.739, auc: 0.780, f1: 0.607, acc: 0.660
+2025-08-07 19:23:47: Epoch: 035 Test results, ap: 0.171, auc: 0.278, f1: 0.000, acc: 0.759
+2025-08-07 19:23:47: Epoch: 036 Train Loss: 0.346, ap: 0.739, auc: 0.790, f1: 0.700, acc: 0.709
+2025-08-07 19:23:47: Epoch: 036 Test results, ap: 0.171, auc: 0.274, f1: 0.000, acc: 0.750
+2025-08-07 19:23:48: Epoch: 037 Train Loss: 0.348, ap: 0.740, auc: 0.786, f1: 0.622, acc: 0.670
+2025-08-07 19:23:48: Epoch: 037 Test results, ap: 0.170, auc: 0.273, f1: 0.000, acc: 0.759
+2025-08-07 19:23:48: Epoch: 038 Train Loss: 0.333, ap: 0.745, auc: 0.790, f1: 0.653, acc: 0.680
+2025-08-07 19:23:48: Epoch: 038 Test results, ap: 0.173, auc: 0.283, f1: 0.000, acc: 0.750
+2025-08-07 19:23:49: Epoch: 039 Train Loss: 0.326, ap: 0.740, auc: 0.790, f1: 0.674, acc: 0.699
+2025-08-07 19:23:49: Epoch: 039 Test results, ap: 0.169, auc: 0.263, f1: 0.000, acc: 0.759
+2025-08-07 19:23:49: Epoch: 040 Train Loss: 0.334, ap: 0.756, auc: 0.800, f1: 0.653, acc: 0.670
+2025-08-07 19:23:49: Epoch: 040 Test results, ap: 0.160, auc: 0.232, f1: 0.000, acc: 0.750
+2025-08-07 19:23:50: Epoch: 041 Train Loss: 0.328, ap: 0.756, auc: 0.792, f1: 0.674, acc: 0.699
+2025-08-07 19:23:50: Epoch: 041 Test results, ap: 0.162, auc: 0.241, f1: 0.000, acc: 0.759
+2025-08-07 19:23:50: Epoch: 042 Train Loss: 0.331, ap: 0.758, auc: 0.795, f1: 0.681, acc: 0.718
+2025-08-07 19:23:50: Epoch: 042 Test results, ap: 0.164, auc: 0.249, f1: 0.000, acc: 0.759
+2025-08-07 19:23:51: Epoch: 043 Train Loss: 0.304, ap: 0.760, auc: 0.796, f1: 0.667, acc: 0.689
+2025-08-07 19:23:51: Epoch: 043 Test results, ap: 0.164, auc: 0.248, f1: 0.000, acc: 0.759
+2025-08-07 19:23:51: Epoch: 044 Train Loss: 0.288, ap: 0.762, auc: 0.795, f1: 0.667, acc: 0.709
+2025-08-07 19:23:51: Epoch: 044 Test results, ap: 0.164, auc: 0.250, f1: 0.000, acc: 0.759
+2025-08-07 19:23:52: Epoch: 045 Train Loss: 0.321, ap: 0.764, auc: 0.799, f1: 0.667, acc: 0.689
+2025-08-07 19:23:52: Epoch: 045 Test results, ap: 0.164, auc: 0.246, f1: 0.000, acc: 0.759
+2025-08-07 19:23:52: Epoch: 046 Train Loss: 0.326, ap: 0.762, auc: 0.792, f1: 0.667, acc: 0.718
+2025-08-07 19:23:52: Epoch: 046 Test results, ap: 0.166, auc: 0.254, f1: 0.000, acc: 0.759
+2025-08-07 19:23:53: Epoch: 047 Train Loss: 0.296, ap: 0.766, auc: 0.801, f1: 0.694, acc: 0.709
+2025-08-07 19:23:53: Epoch: 047 Test results, ap: 0.165, auc: 0.255, f1: 0.000, acc: 0.759
+2025-08-07 19:23:53: Epoch: 048 Train Loss: 0.307, ap: 0.765, auc: 0.797, f1: 0.667, acc: 0.689
+2025-08-07 19:23:53: Epoch: 048 Test results, ap: 0.166, auc: 0.260, f1: 0.000, acc: 0.759
+2025-08-07 19:23:54: Epoch: 049 Train Loss: 0.290, ap: 0.760, auc: 0.793, f1: 0.667, acc: 0.709
+2025-08-07 19:23:54: Epoch: 049 Test results, ap: 0.179, auc: 0.301, f1: 0.000, acc: 0.759
+2025-08-07 19:23:54: Epoch: 050 Train Loss: 0.294, ap: 0.773, auc: 0.796, f1: 0.637, acc: 0.680
+2025-08-07 19:23:54: Epoch: 050 New best VALIDATION metrics
+2025-08-07 19:23:54: Epoch: 050 Test results, ap: 0.169, auc: 0.274, f1: 0.000, acc: 0.759
+2025-08-07 19:23:54: used time 0.01h
+2025-08-07 19:23:54: Cross Validation Finished!
+2025-08-07 19:23:54: Best validation perform list
+[[0.60862464 0.68237078 0.         0.54368931]
+ [0.58253944 0.68263721 0.48780489 0.592233  ]
+ [0.80462766 0.78167421 0.69387758 0.70873785]
+ [0.75327045 0.78961748 0.66666669 0.70873785]
+ [0.77342367 0.7962234  0.63736266 0.67961162]]
+2025-08-07 19:23:54: mean: [0.704 0.747 0.497 0.647]
+2025-08-07 19:23:54: std: [0.091 0.052 0.259 0.067]
+2025-08-07 19:23:54: Best test perform list
+[[0.37027615 0.64165103 0.38805971 0.24074075]
+ [0.22406469 0.39118201 0.22222222 0.48148149]
+ [0.23442096 0.51969981 0.41935483 0.33333334]
+ [0.24884424 0.49155721 0.         0.73148149]
+ [0.26485705 0.55018765 0.         0.72222221]]
+2025-08-07 19:23:54: mean: [0.268 0.519 0.206 0.502]
+2025-08-07 19:23:54: std: [0.053 0.081 0.181 0.199]
diff --git a/run-cls-simple/mamba-diff-256-simple-oneway-ce-32-0.001-50/config.json b/run-cls-simple/mamba-diff-256-simple-oneway-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mamba", "side_enc": null, "channels": 256, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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diff --git a/run-cls-simple/mamba-diff-256-simple-oneway-ce-32-0.001-50/result.txt b/run-cls-simple/mamba-diff-256-simple-oneway-ce-32-0.001-50/result.txt
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diff --git a/run-cls-simple/mamba-diff-256-simple-oneway-ce-32-0.001-50/training.log b/run-cls-simple/mamba-diff-256-simple-oneway-ce-32-0.001-50/training.log
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+2025-08-07 19:23:57: saving_dir: ./run-cls/mamba-diff-256-simple-oneway-ce-32-0.001-50
+2025-08-07 19:23:57: Loading Training Dataset
+2025-08-07 19:23:57: Loading Test Dataset
+2025-08-07 19:23:57: Running Cross Validation 0
+2025-08-07 19:23:57: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:23:58: Epoch: 001 Train Loss: 0.692, ap: 0.496, auc: 0.507, f1: 0.359, acc: 0.515
+2025-08-07 19:23:58: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:23:58: Epoch: 001 Test results, ap: 0.350, auc: 0.629, f1: 0.375, acc: 0.630
+2025-08-07 19:23:58: Epoch: 002 Train Loss: 0.687, ap: 0.551, auc: 0.593, f1: 0.576, acc: 0.485
+2025-08-07 19:23:58: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:23:58: Epoch: 002 Test results, ap: 0.202, auc: 0.425, f1: 0.375, acc: 0.352
+2025-08-07 19:23:59: Epoch: 003 Train Loss: 0.680, ap: 0.550, auc: 0.579, f1: 0.571, acc: 0.592
+2025-08-07 19:23:59: Epoch: 003 Test results, ap: 0.201, auc: 0.424, f1: 0.082, acc: 0.583
+2025-08-07 19:23:59: Epoch: 004 Train Loss: 0.673, ap: 0.571, auc: 0.585, f1: 0.580, acc: 0.592
+2025-08-07 19:23:59: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:23:59: Epoch: 004 Test results, ap: 0.207, auc: 0.442, f1: 0.115, acc: 0.574
+2025-08-07 19:24:00: Epoch: 005 Train Loss: 0.669, ap: 0.595, auc: 0.607, f1: 0.571, acc: 0.592
+2025-08-07 19:24:00: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:24:00: Epoch: 005 Test results, ap: 0.209, auc: 0.443, f1: 0.115, acc: 0.574
+2025-08-07 19:24:00: Epoch: 006 Train Loss: 0.658, ap: 0.582, auc: 0.613, f1: 0.519, acc: 0.621
+2025-08-07 19:24:00: Epoch: 006 Test results, ap: 0.208, auc: 0.431, f1: 0.048, acc: 0.630
+2025-08-07 19:24:01: Epoch: 007 Train Loss: 0.654, ap: 0.615, auc: 0.620, f1: 0.551, acc: 0.573
+2025-08-07 19:24:01: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:24:01: Epoch: 007 Test results, ap: 0.217, auc: 0.446, f1: 0.250, acc: 0.556
+2025-08-07 19:24:01: Epoch: 008 Train Loss: 0.638, ap: 0.609, auc: 0.611, f1: 0.557, acc: 0.660
+2025-08-07 19:24:01: Epoch: 008 Test results, ap: 0.224, auc: 0.462, f1: 0.047, acc: 0.620
+2025-08-07 19:24:02: Epoch: 009 Train Loss: 0.653, ap: 0.590, auc: 0.608, f1: 0.276, acc: 0.592
+2025-08-07 19:24:02: Epoch: 009 Test results, ap: 0.220, auc: 0.457, f1: 0.000, acc: 0.722
+2025-08-07 19:24:02: Epoch: 010 Train Loss: 0.642, ap: 0.580, auc: 0.598, f1: 0.349, acc: 0.602
+2025-08-07 19:24:02: Epoch: 010 Test results, ap: 0.215, auc: 0.454, f1: 0.000, acc: 0.713
+2025-08-07 19:24:03: Epoch: 011 Train Loss: 0.639, ap: 0.598, auc: 0.584, f1: 0.542, acc: 0.573
+2025-08-07 19:24:03: Epoch: 011 Test results, ap: 0.220, auc: 0.471, f1: 0.258, acc: 0.574
+2025-08-07 19:24:03: Epoch: 011 New best TEST metrics
+2025-08-07 19:24:03: Epoch: 012 Train Loss: 0.625, ap: 0.601, auc: 0.581, f1: 0.480, acc: 0.621
+2025-08-07 19:24:03: Epoch: 012 Test results, ap: 0.217, auc: 0.458, f1: 0.167, acc: 0.630
+2025-08-07 19:24:04: Epoch: 013 Train Loss: 0.601, ap: 0.620, auc: 0.600, f1: 0.559, acc: 0.602
+2025-08-07 19:24:04: Epoch: 013 Test results, ap: 0.214, auc: 0.452, f1: 0.258, acc: 0.574
+2025-08-07 19:24:04: Epoch: 014 Train Loss: 0.612, ap: 0.617, auc: 0.600, f1: 0.518, acc: 0.602
+2025-08-07 19:24:04: Epoch: 014 Test results, ap: 0.217, auc: 0.459, f1: 0.267, acc: 0.593
+2025-08-07 19:24:05: Epoch: 015 Train Loss: 0.598, ap: 0.607, auc: 0.590, f1: 0.494, acc: 0.602
+2025-08-07 19:24:05: Epoch: 015 Test results, ap: 0.229, auc: 0.480, f1: 0.157, acc: 0.602
+2025-08-07 19:24:05: Epoch: 015 New best TEST metrics
+2025-08-07 19:24:05: Epoch: 016 Train Loss: 0.577, ap: 0.609, auc: 0.604, f1: 0.522, acc: 0.573
+2025-08-07 19:24:05: Epoch: 016 Test results, ap: 0.234, auc: 0.492, f1: 0.250, acc: 0.556
+2025-08-07 19:24:05: Epoch: 016 New best TEST metrics
+2025-08-07 19:24:06: Epoch: 017 Train Loss: 0.580, ap: 0.619, auc: 0.606, f1: 0.527, acc: 0.583
+2025-08-07 19:24:06: Epoch: 017 Test results, ap: 0.218, auc: 0.464, f1: 0.267, acc: 0.593
+2025-08-07 19:24:06: Epoch: 018 Train Loss: 0.582, ap: 0.599, auc: 0.593, f1: 0.475, acc: 0.592
+2025-08-07 19:24:07: Epoch: 018 Test results, ap: 0.247, auc: 0.505, f1: 0.267, acc: 0.593
+2025-08-07 19:24:07: Epoch: 018 New best TEST metrics
+2025-08-07 19:24:07: Epoch: 019 Train Loss: 0.574, ap: 0.612, auc: 0.604, f1: 0.500, acc: 0.592
+2025-08-07 19:24:07: Epoch: 019 Test results, ap: 0.231, auc: 0.489, f1: 0.226, acc: 0.556
+2025-08-07 19:24:07: Epoch: 020 Train Loss: 0.571, ap: 0.631, auc: 0.625, f1: 0.523, acc: 0.592
+2025-08-07 19:24:07: Epoch: 020 New best VALIDATION metrics
+2025-08-07 19:24:08: Epoch: 020 Test results, ap: 0.212, auc: 0.449, f1: 0.188, acc: 0.519
+2025-08-07 19:24:08: Epoch: 021 Train Loss: 0.553, ap: 0.627, auc: 0.615, f1: 0.483, acc: 0.563
+2025-08-07 19:24:08: Epoch: 021 Test results, ap: 0.227, auc: 0.466, f1: 0.261, acc: 0.528
+2025-08-07 19:24:08: Epoch: 022 Train Loss: 0.550, ap: 0.618, auc: 0.611, f1: 0.559, acc: 0.602
+2025-08-07 19:24:09: Epoch: 022 Test results, ap: 0.231, auc: 0.475, f1: 0.329, acc: 0.509
+2025-08-07 19:24:09: Epoch: 023 Train Loss: 0.517, ap: 0.636, auc: 0.636, f1: 0.553, acc: 0.592
+2025-08-07 19:24:09: Epoch: 023 New best VALIDATION metrics
+2025-08-07 19:24:09: Epoch: 023 Test results, ap: 0.215, auc: 0.450, f1: 0.286, acc: 0.491
+2025-08-07 19:24:09: Epoch: 024 Train Loss: 0.509, ap: 0.654, auc: 0.660, f1: 0.535, acc: 0.612
+2025-08-07 19:24:09: Epoch: 024 New best VALIDATION metrics
+2025-08-07 19:24:10: Epoch: 024 Test results, ap: 0.229, auc: 0.477, f1: 0.282, acc: 0.528
+2025-08-07 19:24:10: Epoch: 025 Train Loss: 0.474, ap: 0.670, auc: 0.675, f1: 0.543, acc: 0.641
+2025-08-07 19:24:10: Epoch: 025 New best VALIDATION metrics
+2025-08-07 19:24:10: Epoch: 025 Test results, ap: 0.241, auc: 0.502, f1: 0.294, acc: 0.556
+2025-08-07 19:24:11: Epoch: 026 Train Loss: 0.447, ap: 0.658, auc: 0.658, f1: 0.539, acc: 0.602
+2025-08-07 19:24:11: Epoch: 026 Test results, ap: 0.242, auc: 0.502, f1: 0.306, acc: 0.537
+2025-08-07 19:24:11: Epoch: 027 Train Loss: 0.429, ap: 0.663, auc: 0.668, f1: 0.577, acc: 0.602
+2025-08-07 19:24:11: Epoch: 027 Test results, ap: 0.237, auc: 0.484, f1: 0.359, acc: 0.537
+2025-08-07 19:24:12: Epoch: 028 Train Loss: 0.473, ap: 0.663, auc: 0.676, f1: 0.518, acc: 0.602
+2025-08-07 19:24:12: Epoch: 028 Test results, ap: 0.237, auc: 0.486, f1: 0.273, acc: 0.556
+2025-08-07 19:24:12: Epoch: 029 Train Loss: 0.444, ap: 0.682, auc: 0.677, f1: 0.581, acc: 0.650
+2025-08-07 19:24:12: Epoch: 029 New best VALIDATION metrics
+2025-08-07 19:24:12: Epoch: 029 Test results, ap: 0.252, auc: 0.513, f1: 0.294, acc: 0.556
+2025-08-07 19:24:12: Epoch: 029 New best TEST metrics
+2025-08-07 19:24:13: Epoch: 030 Train Loss: 0.412, ap: 0.684, auc: 0.699, f1: 0.584, acc: 0.641
+2025-08-07 19:24:13: Epoch: 030 New best VALIDATION metrics
+2025-08-07 19:24:13: Epoch: 030 Test results, ap: 0.262, auc: 0.535, f1: 0.319, acc: 0.565
+2025-08-07 19:24:13: Epoch: 030 New best TEST metrics
+2025-08-07 19:24:13: Epoch: 031 Train Loss: 0.369, ap: 0.702, auc: 0.713, f1: 0.619, acc: 0.689
+2025-08-07 19:24:13: Epoch: 031 New best VALIDATION metrics
+2025-08-07 19:24:13: Epoch: 031 Test results, ap: 0.271, auc: 0.550, f1: 0.328, acc: 0.620
+2025-08-07 19:24:13: Epoch: 031 New best TEST metrics
+2025-08-07 19:24:14: Epoch: 032 Train Loss: 0.341, ap: 0.702, auc: 0.720, f1: 0.614, acc: 0.670
+2025-08-07 19:24:14: Epoch: 032 New best VALIDATION metrics
+2025-08-07 19:24:14: Epoch: 032 Test results, ap: 0.259, auc: 0.544, f1: 0.312, acc: 0.593
+2025-08-07 19:24:14: Epoch: 033 Train Loss: 0.335, ap: 0.705, auc: 0.723, f1: 0.635, acc: 0.699
+2025-08-07 19:24:14: Epoch: 033 New best VALIDATION metrics
+2025-08-07 19:24:14: Epoch: 033 Test results, ap: 0.276, auc: 0.565, f1: 0.387, acc: 0.648
+2025-08-07 19:24:14: Epoch: 033 New best TEST metrics
+2025-08-07 19:24:15: Epoch: 034 Train Loss: 0.325, ap: 0.695, auc: 0.716, f1: 0.660, acc: 0.689
+2025-08-07 19:24:15: Epoch: 034 Test results, ap: 0.272, auc: 0.555, f1: 0.377, acc: 0.602
+2025-08-07 19:24:15: Epoch: 035 Train Loss: 0.294, ap: 0.711, auc: 0.726, f1: 0.636, acc: 0.689
+2025-08-07 19:24:15: Epoch: 035 New best VALIDATION metrics
+2025-08-07 19:24:15: Epoch: 035 Test results, ap: 0.283, auc: 0.579, f1: 0.400, acc: 0.639
+2025-08-07 19:24:15: Epoch: 035 New best TEST metrics
+2025-08-07 19:24:16: Epoch: 036 Train Loss: 0.291, ap: 0.717, auc: 0.736, f1: 0.615, acc: 0.660
+2025-08-07 19:24:16: Epoch: 036 New best VALIDATION metrics
+2025-08-07 19:24:16: Epoch: 036 Test results, ap: 0.276, auc: 0.565, f1: 0.381, acc: 0.639
+2025-08-07 19:24:16: Epoch: 037 Train Loss: 0.281, ap: 0.716, auc: 0.728, f1: 0.622, acc: 0.670
+2025-08-07 19:24:16: Epoch: 037 Test results, ap: 0.274, auc: 0.563, f1: 0.338, acc: 0.602
+2025-08-07 19:24:17: Epoch: 038 Train Loss: 0.270, ap: 0.731, auc: 0.756, f1: 0.621, acc: 0.680
+2025-08-07 19:24:17: Epoch: 038 New best VALIDATION metrics
+2025-08-07 19:24:17: Epoch: 038 Test results, ap: 0.269, auc: 0.544, f1: 0.305, acc: 0.620
+2025-08-07 19:24:17: Epoch: 039 Train Loss: 0.256, ap: 0.729, auc: 0.745, f1: 0.622, acc: 0.670
+2025-08-07 19:24:17: Epoch: 039 Test results, ap: 0.274, auc: 0.560, f1: 0.353, acc: 0.593
+2025-08-07 19:24:18: Epoch: 040 Train Loss: 0.231, ap: 0.732, auc: 0.748, f1: 0.622, acc: 0.670
+2025-08-07 19:24:18: Epoch: 040 Test results, ap: 0.274, auc: 0.555, f1: 0.344, acc: 0.611
+2025-08-07 19:24:18: Epoch: 041 Train Loss: 0.208, ap: 0.731, auc: 0.746, f1: 0.621, acc: 0.680
+2025-08-07 19:24:18: Epoch: 041 Test results, ap: 0.282, auc: 0.560, f1: 0.361, acc: 0.639
+2025-08-07 19:24:19: Epoch: 042 Train Loss: 0.207, ap: 0.741, auc: 0.747, f1: 0.612, acc: 0.680
+2025-08-07 19:24:19: Epoch: 042 New best VALIDATION metrics
+2025-08-07 19:24:19: Epoch: 042 Test results, ap: 0.275, auc: 0.561, f1: 0.361, acc: 0.639
+2025-08-07 19:24:19: Epoch: 043 Train Loss: 0.208, ap: 0.730, auc: 0.749, f1: 0.652, acc: 0.689
+2025-08-07 19:24:19: Epoch: 043 Test results, ap: 0.270, auc: 0.543, f1: 0.369, acc: 0.620
+2025-08-07 19:24:20: Epoch: 044 Train Loss: 0.203, ap: 0.744, auc: 0.756, f1: 0.644, acc: 0.689
+2025-08-07 19:24:20: Epoch: 044 New best VALIDATION metrics
+2025-08-07 19:24:20: Epoch: 044 Test results, ap: 0.266, auc: 0.543, f1: 0.333, acc: 0.630
+2025-08-07 19:24:20: Epoch: 045 Train Loss: 0.179, ap: 0.741, auc: 0.759, f1: 0.644, acc: 0.689
+2025-08-07 19:24:20: Epoch: 045 New best VALIDATION metrics
+2025-08-07 19:24:21: Epoch: 045 Test results, ap: 0.272, auc: 0.547, f1: 0.361, acc: 0.639
+2025-08-07 19:24:21: Epoch: 046 Train Loss: 0.176, ap: 0.742, auc: 0.760, f1: 0.659, acc: 0.699
+2025-08-07 19:24:21: Epoch: 046 New best VALIDATION metrics
+2025-08-07 19:24:21: Epoch: 046 Test results, ap: 0.276, auc: 0.545, f1: 0.355, acc: 0.630
+2025-08-07 19:24:21: Epoch: 047 Train Loss: 0.190, ap: 0.747, auc: 0.758, f1: 0.636, acc: 0.689
+2025-08-07 19:24:21: Epoch: 047 New best VALIDATION metrics
+2025-08-07 19:24:22: Epoch: 047 Test results, ap: 0.268, auc: 0.546, f1: 0.328, acc: 0.620
+2025-08-07 19:24:22: Epoch: 048 Train Loss: 0.183, ap: 0.748, auc: 0.764, f1: 0.629, acc: 0.680
+2025-08-07 19:24:22: Epoch: 048 New best VALIDATION metrics
+2025-08-07 19:24:22: Epoch: 048 Test results, ap: 0.279, auc: 0.546, f1: 0.355, acc: 0.630
+2025-08-07 19:24:22: Epoch: 049 Train Loss: 0.167, ap: 0.737, auc: 0.751, f1: 0.667, acc: 0.689
+2025-08-07 19:24:23: Epoch: 049 Test results, ap: 0.263, auc: 0.530, f1: 0.281, acc: 0.574
+2025-08-07 19:24:23: Epoch: 050 Train Loss: 0.185, ap: 0.745, auc: 0.754, f1: 0.644, acc: 0.689
+2025-08-07 19:24:23: Epoch: 050 Test results, ap: 0.266, auc: 0.542, f1: 0.305, acc: 0.620
+2025-08-07 19:24:23: used time 0.01h
+2025-08-07 19:24:23: Running Cross Validation 1
+2025-08-07 19:24:23: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:24:23: Epoch: 001 Train Loss: 0.692, ap: 0.482, auc: 0.651, f1: 0.519, acc: 0.641
+2025-08-07 19:24:23: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:24:24: Epoch: 001 Test results, ap: 0.199, auc: 0.412, f1: 0.048, acc: 0.630
+2025-08-07 19:24:24: Epoch: 002 Train Loss: 0.685, ap: 0.486, auc: 0.663, f1: 0.596, acc: 0.631
+2025-08-07 19:24:24: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:24:24: Epoch: 002 Test results, ap: 0.196, auc: 0.402, f1: 0.159, acc: 0.509
+2025-08-07 19:24:25: Epoch: 003 Train Loss: 0.683, ap: 0.481, auc: 0.652, f1: 0.533, acc: 0.660
+2025-08-07 19:24:25: Epoch: 003 Test results, ap: 0.204, auc: 0.431, f1: 0.048, acc: 0.630
+2025-08-07 19:24:25: Epoch: 004 Train Loss: 0.677, ap: 0.437, auc: 0.497, f1: 0.000, acc: 0.641
+2025-08-07 19:24:25: Epoch: 004 Test results, ap: 0.274, auc: 0.519, f1: 0.000, acc: 0.759
+2025-08-07 19:24:26: Epoch: 005 Train Loss: 0.691, ap: 0.621, auc: 0.675, f1: 0.538, acc: 0.650
+2025-08-07 19:24:26: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:24:26: Epoch: 005 Test results, ap: 0.205, auc: 0.423, f1: 0.085, acc: 0.602
+2025-08-07 19:24:26: Epoch: 006 Train Loss: 0.669, ap: 0.570, auc: 0.623, f1: 0.549, acc: 0.602
+2025-08-07 19:24:26: Epoch: 006 Test results, ap: 0.219, auc: 0.455, f1: 0.278, acc: 0.472
+2025-08-07 19:24:27: Epoch: 007 Train Loss: 0.655, ap: 0.543, auc: 0.606, f1: 0.500, acc: 0.553
+2025-08-07 19:24:27: Epoch: 007 Test results, ap: 0.229, auc: 0.462, f1: 0.341, acc: 0.500
+2025-08-07 19:24:27: Epoch: 008 Train Loss: 0.659, ap: 0.610, auc: 0.673, f1: 0.469, acc: 0.670
+2025-08-07 19:24:27: Epoch: 008 Test results, ap: 0.206, auc: 0.412, f1: 0.051, acc: 0.657
+2025-08-07 19:24:28: Epoch: 009 Train Loss: 0.660, ap: 0.594, auc: 0.670, f1: 0.571, acc: 0.680
+2025-08-07 19:24:28: Epoch: 009 Test results, ap: 0.209, auc: 0.420, f1: 0.237, acc: 0.583
+2025-08-07 19:24:28: Epoch: 010 Train Loss: 0.640, ap: 0.605, auc: 0.695, f1: 0.521, acc: 0.660
+2025-08-07 19:24:28: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:24:28: Epoch: 010 Test results, ap: 0.205, auc: 0.413, f1: 0.049, acc: 0.639
+2025-08-07 19:24:29: Epoch: 011 Train Loss: 0.632, ap: 0.603, auc: 0.687, f1: 0.484, acc: 0.689
+2025-08-07 19:24:29: Epoch: 011 Test results, ap: 0.206, auc: 0.413, f1: 0.000, acc: 0.685
+2025-08-07 19:24:29: Epoch: 011 New best TEST metrics
+2025-08-07 19:24:29: Epoch: 012 Train Loss: 0.611, ap: 0.570, auc: 0.647, f1: 0.500, acc: 0.670
+2025-08-07 19:24:29: Epoch: 012 Test results, ap: 0.215, auc: 0.433, f1: 0.125, acc: 0.611
+2025-08-07 19:24:29: Epoch: 012 New best TEST metrics
+2025-08-07 19:24:30: Epoch: 013 Train Loss: 0.623, ap: 0.577, auc: 0.637, f1: 0.467, acc: 0.689
+2025-08-07 19:24:30: Epoch: 013 Test results, ap: 0.224, auc: 0.450, f1: 0.000, acc: 0.667
+2025-08-07 19:24:30: Epoch: 013 New best TEST metrics
+2025-08-07 19:24:30: Epoch: 014 Train Loss: 0.610, ap: 0.551, auc: 0.625, f1: 0.471, acc: 0.650
+2025-08-07 19:24:30: Epoch: 014 Test results, ap: 0.229, auc: 0.452, f1: 0.222, acc: 0.611
+2025-08-07 19:24:30: Epoch: 014 New best TEST metrics
+2025-08-07 19:24:31: Epoch: 015 Train Loss: 0.622, ap: 0.497, auc: 0.583, f1: 0.493, acc: 0.641
+2025-08-07 19:24:31: Epoch: 015 Test results, ap: 0.197, auc: 0.386, f1: 0.047, acc: 0.620
+2025-08-07 19:24:31: Epoch: 016 Train Loss: 0.647, ap: 0.456, auc: 0.582, f1: 0.479, acc: 0.641
+2025-08-07 19:24:31: Epoch: 016 Test results, ap: 0.200, auc: 0.407, f1: 0.048, acc: 0.630
+2025-08-07 19:24:32: Epoch: 017 Train Loss: 0.643, ap: 0.464, auc: 0.586, f1: 0.493, acc: 0.641
+2025-08-07 19:24:32: Epoch: 017 Test results, ap: 0.200, auc: 0.402, f1: 0.118, acc: 0.583
+2025-08-07 19:24:32: Epoch: 018 Train Loss: 0.638, ap: 0.499, auc: 0.628, f1: 0.493, acc: 0.641
+2025-08-07 19:24:32: Epoch: 018 Test results, ap: 0.206, auc: 0.413, f1: 0.120, acc: 0.593
+2025-08-07 19:24:33: Epoch: 019 Train Loss: 0.626, ap: 0.499, auc: 0.584, f1: 0.507, acc: 0.660
+2025-08-07 19:24:33: Epoch: 019 Test results, ap: 0.214, auc: 0.426, f1: 0.140, acc: 0.657
+2025-08-07 19:24:33: Epoch: 020 Train Loss: 0.604, ap: 0.531, auc: 0.617, f1: 0.508, acc: 0.718
+2025-08-07 19:24:33: Epoch: 020 Test results, ap: 0.226, auc: 0.458, f1: 0.146, acc: 0.676
+2025-08-07 19:24:33: Epoch: 020 New best TEST metrics
+2025-08-07 19:24:34: Epoch: 021 Train Loss: 0.597, ap: 0.546, auc: 0.628, f1: 0.492, acc: 0.680
+2025-08-07 19:24:34: Epoch: 021 Test results, ap: 0.221, auc: 0.445, f1: 0.170, acc: 0.639
+2025-08-07 19:24:34: Epoch: 022 Train Loss: 0.582, ap: 0.508, auc: 0.623, f1: 0.529, acc: 0.689
+2025-08-07 19:24:34: Epoch: 022 Test results, ap: 0.230, auc: 0.452, f1: 0.222, acc: 0.611
+2025-08-07 19:24:35: Epoch: 023 Train Loss: 0.596, ap: 0.510, auc: 0.584, f1: 0.508, acc: 0.699
+2025-08-07 19:24:35: Epoch: 023 Test results, ap: 0.223, auc: 0.463, f1: 0.098, acc: 0.657
+2025-08-07 19:24:35: Epoch: 023 New best TEST metrics
+2025-08-07 19:24:35: Epoch: 024 Train Loss: 0.570, ap: 0.521, auc: 0.591, f1: 0.400, acc: 0.680
+2025-08-07 19:24:35: Epoch: 024 Test results, ap: 0.237, auc: 0.497, f1: 0.114, acc: 0.713
+2025-08-07 19:24:35: Epoch: 024 New best TEST metrics
+2025-08-07 19:24:36: Epoch: 025 Train Loss: 0.540, ap: 0.518, auc: 0.612, f1: 0.471, acc: 0.650
+2025-08-07 19:24:36: Epoch: 025 Test results, ap: 0.280, auc: 0.441, f1: 0.200, acc: 0.630
+2025-08-07 19:24:36: Epoch: 026 Train Loss: 0.549, ap: 0.540, auc: 0.641, f1: 0.515, acc: 0.689
+2025-08-07 19:24:37: Epoch: 026 Test results, ap: 0.234, auc: 0.492, f1: 0.174, acc: 0.648
+2025-08-07 19:24:37: Epoch: 027 Train Loss: 0.529, ap: 0.510, auc: 0.595, f1: 0.492, acc: 0.699
+2025-08-07 19:24:37: Epoch: 027 Test results, ap: 0.248, auc: 0.520, f1: 0.190, acc: 0.685
+2025-08-07 19:24:37: Epoch: 027 New best TEST metrics
+2025-08-07 19:24:37: Epoch: 028 Train Loss: 0.555, ap: 0.475, auc: 0.592, f1: 0.364, acc: 0.660
+2025-08-07 19:24:38: Epoch: 028 Test results, ap: 0.257, auc: 0.489, f1: 0.146, acc: 0.676
+2025-08-07 19:24:38: Epoch: 029 Train Loss: 0.518, ap: 0.521, auc: 0.659, f1: 0.553, acc: 0.670
+2025-08-07 19:24:38: Epoch: 029 Test results, ap: 0.226, auc: 0.455, f1: 0.172, acc: 0.556
+2025-08-07 19:24:38: Epoch: 030 Train Loss: 0.515, ap: 0.497, auc: 0.617, f1: 0.407, acc: 0.660
+2025-08-07 19:24:39: Epoch: 030 Test results, ap: 0.222, auc: 0.477, f1: 0.051, acc: 0.657
+2025-08-07 19:24:39: Epoch: 031 Train Loss: 0.478, ap: 0.520, auc: 0.659, f1: 0.506, acc: 0.621
+2025-08-07 19:24:39: Epoch: 031 Test results, ap: 0.250, auc: 0.485, f1: 0.203, acc: 0.565
+2025-08-07 19:24:39: Epoch: 032 Train Loss: 0.469, ap: 0.522, auc: 0.653, f1: 0.486, acc: 0.650
+2025-08-07 19:24:40: Epoch: 032 Test results, ap: 0.236, auc: 0.496, f1: 0.175, acc: 0.565
+2025-08-07 19:24:40: Epoch: 033 Train Loss: 0.457, ap: 0.490, auc: 0.636, f1: 0.494, acc: 0.621
+2025-08-07 19:24:40: Epoch: 033 Test results, ap: 0.249, auc: 0.503, f1: 0.175, acc: 0.565
+2025-08-07 19:24:40: Epoch: 034 Train Loss: 0.444, ap: 0.488, auc: 0.645, f1: 0.500, acc: 0.612
+2025-08-07 19:24:41: Epoch: 034 Test results, ap: 0.243, auc: 0.490, f1: 0.169, acc: 0.546
+2025-08-07 19:24:41: Epoch: 035 Train Loss: 0.420, ap: 0.469, auc: 0.633, f1: 0.494, acc: 0.621
+2025-08-07 19:24:41: Epoch: 035 Test results, ap: 0.268, auc: 0.514, f1: 0.286, acc: 0.583
+2025-08-07 19:24:41: Epoch: 035 New best TEST metrics
+2025-08-07 19:24:42: Epoch: 036 Train Loss: 0.411, ap: 0.524, auc: 0.654, f1: 0.521, acc: 0.660
+2025-08-07 19:24:42: Epoch: 036 Test results, ap: 0.232, auc: 0.495, f1: 0.115, acc: 0.574
+2025-08-07 19:24:42: Epoch: 037 Train Loss: 0.430, ap: 0.491, auc: 0.655, f1: 0.518, acc: 0.602
+2025-08-07 19:24:42: Epoch: 037 Test results, ap: 0.246, auc: 0.489, f1: 0.301, acc: 0.528
+2025-08-07 19:24:43: Epoch: 038 Train Loss: 0.413, ap: 0.485, auc: 0.631, f1: 0.526, acc: 0.650
+2025-08-07 19:24:43: Epoch: 038 Test results, ap: 0.226, auc: 0.445, f1: 0.138, acc: 0.537
+2025-08-07 19:24:43: Epoch: 039 Train Loss: 0.395, ap: 0.496, auc: 0.653, f1: 0.511, acc: 0.573
+2025-08-07 19:24:43: Epoch: 039 Test results, ap: 0.257, auc: 0.505, f1: 0.306, acc: 0.537
+2025-08-07 19:24:44: Epoch: 040 Train Loss: 0.397, ap: 0.479, auc: 0.637, f1: 0.532, acc: 0.641
+2025-08-07 19:24:44: Epoch: 040 Test results, ap: 0.240, auc: 0.494, f1: 0.172, acc: 0.556
+2025-08-07 19:24:44: Epoch: 041 Train Loss: 0.379, ap: 0.475, auc: 0.633, f1: 0.512, acc: 0.612
+2025-08-07 19:24:44: Epoch: 041 Test results, ap: 0.254, auc: 0.505, f1: 0.246, acc: 0.602
+2025-08-07 19:24:45: Epoch: 042 Train Loss: 0.356, ap: 0.502, auc: 0.656, f1: 0.524, acc: 0.612
+2025-08-07 19:24:45: Epoch: 042 Test results, ap: 0.229, auc: 0.467, f1: 0.197, acc: 0.546
+2025-08-07 19:24:45: Epoch: 043 Train Loss: 0.360, ap: 0.466, auc: 0.627, f1: 0.545, acc: 0.660
+2025-08-07 19:24:45: Epoch: 043 Test results, ap: 0.241, auc: 0.497, f1: 0.218, acc: 0.602
+2025-08-07 19:24:46: Epoch: 044 Train Loss: 0.352, ap: 0.478, auc: 0.639, f1: 0.524, acc: 0.612
+2025-08-07 19:24:46: Epoch: 044 Test results, ap: 0.229, auc: 0.469, f1: 0.226, acc: 0.556
+2025-08-07 19:24:46: Epoch: 045 Train Loss: 0.328, ap: 0.481, auc: 0.640, f1: 0.525, acc: 0.631
+2025-08-07 19:24:46: Epoch: 045 Test results, ap: 0.246, auc: 0.509, f1: 0.211, acc: 0.583
+2025-08-07 19:24:47: Epoch: 046 Train Loss: 0.319, ap: 0.490, auc: 0.655, f1: 0.543, acc: 0.641
+2025-08-07 19:24:47: Epoch: 046 Test results, ap: 0.226, auc: 0.473, f1: 0.175, acc: 0.565
+2025-08-07 19:24:47: Epoch: 047 Train Loss: 0.318, ap: 0.478, auc: 0.637, f1: 0.537, acc: 0.631
+2025-08-07 19:24:47: Epoch: 047 Test results, ap: 0.240, auc: 0.494, f1: 0.207, acc: 0.574
+2025-08-07 19:24:48: Epoch: 048 Train Loss: 0.317, ap: 0.480, auc: 0.642, f1: 0.538, acc: 0.650
+2025-08-07 19:24:48: Epoch: 048 Test results, ap: 0.232, auc: 0.492, f1: 0.148, acc: 0.574
+2025-08-07 19:24:48: Epoch: 049 Train Loss: 0.307, ap: 0.498, auc: 0.658, f1: 0.524, acc: 0.612
+2025-08-07 19:24:48: Epoch: 049 Test results, ap: 0.226, auc: 0.462, f1: 0.127, acc: 0.491
+2025-08-07 19:24:49: Epoch: 050 Train Loss: 0.317, ap: 0.486, auc: 0.648, f1: 0.550, acc: 0.650
+2025-08-07 19:24:49: Epoch: 050 Test results, ap: 0.232, auc: 0.474, f1: 0.222, acc: 0.546
+2025-08-07 19:24:49: used time 0.01h
+2025-08-07 19:24:49: Running Cross Validation 2
+2025-08-07 19:24:49: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:24:49: Epoch: 001 Train Loss: 0.695, ap: 0.696, auc: 0.613, f1: 0.671, acc: 0.505
+2025-08-07 19:24:49: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:24:49: Epoch: 001 Test results, ap: 0.284, auc: 0.576, f1: 0.388, acc: 0.241
+2025-08-07 19:24:50: Epoch: 002 Train Loss: 0.693, ap: 0.545, auc: 0.587, f1: 0.671, acc: 0.505
+2025-08-07 19:24:50: Epoch: 002 Test results, ap: 0.197, auc: 0.406, f1: 0.388, acc: 0.241
+2025-08-07 19:24:50: Epoch: 003 Train Loss: 0.693, ap: 0.519, auc: 0.563, f1: 0.678, acc: 0.621
+2025-08-07 19:24:50: Epoch: 003 Test results, ap: 0.199, auc: 0.409, f1: 0.197, acc: 0.546
+2025-08-07 19:24:51: Epoch: 004 Train Loss: 0.689, ap: 0.621, auc: 0.621, f1: 0.577, acc: 0.602
+2025-08-07 19:24:51: Epoch: 004 Test results, ap: 0.198, auc: 0.400, f1: 0.045, acc: 0.611
+2025-08-07 19:24:51: Epoch: 005 Train Loss: 0.669, ap: 0.710, auc: 0.654, f1: 0.612, acc: 0.631
+2025-08-07 19:24:51: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:24:51: Epoch: 005 Test results, ap: 0.201, auc: 0.408, f1: 0.122, acc: 0.602
+2025-08-07 19:24:52: Epoch: 006 Train Loss: 0.656, ap: 0.722, auc: 0.667, f1: 0.604, acc: 0.631
+2025-08-07 19:24:52: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:24:52: Epoch: 006 Test results, ap: 0.204, auc: 0.412, f1: 0.182, acc: 0.583
+2025-08-07 19:24:52: Epoch: 007 Train Loss: 0.659, ap: 0.730, auc: 0.667, f1: 0.609, acc: 0.650
+2025-08-07 19:24:52: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:24:52: Epoch: 007 Test results, ap: 0.205, auc: 0.410, f1: 0.128, acc: 0.620
+2025-08-07 19:24:53: Epoch: 008 Train Loss: 0.664, ap: 0.600, auc: 0.623, f1: 0.654, acc: 0.641
+2025-08-07 19:24:53: Epoch: 008 Test results, ap: 0.213, auc: 0.424, f1: 0.265, acc: 0.537
+2025-08-07 19:24:53: Epoch: 009 Train Loss: 0.654, ap: 0.626, auc: 0.652, f1: 0.667, acc: 0.660
+2025-08-07 19:24:53: Epoch: 009 Test results, ap: 0.205, auc: 0.404, f1: 0.255, acc: 0.620
+2025-08-07 19:24:54: Epoch: 010 Train Loss: 0.632, ap: 0.722, auc: 0.675, f1: 0.641, acc: 0.641
+2025-08-07 19:24:54: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:24:54: Epoch: 010 Test results, ap: 0.205, auc: 0.411, f1: 0.233, acc: 0.574
+2025-08-07 19:24:54: Epoch: 011 Train Loss: 0.618, ap: 0.730, auc: 0.687, f1: 0.667, acc: 0.660
+2025-08-07 19:24:54: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:24:54: Epoch: 011 Test results, ap: 0.202, auc: 0.405, f1: 0.151, acc: 0.583
+2025-08-07 19:24:54: Epoch: 011 New best TEST metrics
+2025-08-07 19:24:55: Epoch: 012 Train Loss: 0.594, ap: 0.736, auc: 0.705, f1: 0.679, acc: 0.670
+2025-08-07 19:24:55: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:24:55: Epoch: 012 Test results, ap: 0.209, auc: 0.428, f1: 0.148, acc: 0.574
+2025-08-07 19:24:55: Epoch: 012 New best TEST metrics
+2025-08-07 19:24:55: Epoch: 013 Train Loss: 0.586, ap: 0.755, auc: 0.696, f1: 0.654, acc: 0.650
+2025-08-07 19:24:55: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:24:55: Epoch: 013 Test results, ap: 0.204, auc: 0.403, f1: 0.145, acc: 0.565
+2025-08-07 19:24:56: Epoch: 014 Train Loss: 0.574, ap: 0.727, auc: 0.694, f1: 0.654, acc: 0.641
+2025-08-07 19:24:56: Epoch: 014 Test results, ap: 0.203, auc: 0.416, f1: 0.145, acc: 0.565
+2025-08-07 19:24:56: Epoch: 015 Train Loss: 0.535, ap: 0.763, auc: 0.706, f1: 0.646, acc: 0.660
+2025-08-07 19:24:56: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:24:57: Epoch: 015 Test results, ap: 0.204, auc: 0.394, f1: 0.154, acc: 0.593
+2025-08-07 19:24:57: Epoch: 016 Train Loss: 0.529, ap: 0.775, auc: 0.727, f1: 0.660, acc: 0.670
+2025-08-07 19:24:57: Epoch: 016 New best VALIDATION metrics
+2025-08-07 19:24:57: Epoch: 016 Test results, ap: 0.210, auc: 0.408, f1: 0.138, acc: 0.537
+2025-08-07 19:24:57: Epoch: 017 Train Loss: 0.509, ap: 0.763, auc: 0.719, f1: 0.692, acc: 0.680
+2025-08-07 19:24:58: Epoch: 017 Test results, ap: 0.202, auc: 0.403, f1: 0.169, acc: 0.546
+2025-08-07 19:24:58: Epoch: 018 Train Loss: 0.511, ap: 0.762, auc: 0.724, f1: 0.617, acc: 0.650
+2025-08-07 19:24:58: Epoch: 018 Test results, ap: 0.218, auc: 0.445, f1: 0.148, acc: 0.574
+2025-08-07 19:24:58: Epoch: 018 New best TEST metrics
+2025-08-07 19:24:58: Epoch: 019 Train Loss: 0.484, ap: 0.763, auc: 0.726, f1: 0.673, acc: 0.680
+2025-08-07 19:24:59: Epoch: 019 Test results, ap: 0.212, auc: 0.427, f1: 0.148, acc: 0.574
+2025-08-07 19:24:59: Epoch: 020 Train Loss: 0.447, ap: 0.740, auc: 0.733, f1: 0.647, acc: 0.650
+2025-08-07 19:24:59: Epoch: 020 Test results, ap: 0.200, auc: 0.392, f1: 0.143, acc: 0.556
+2025-08-07 19:24:59: Epoch: 021 Train Loss: 0.434, ap: 0.739, auc: 0.728, f1: 0.706, acc: 0.709
+2025-08-07 19:25:00: Epoch: 021 Test results, ap: 0.203, auc: 0.399, f1: 0.113, acc: 0.565
+2025-08-07 19:25:00: Epoch: 022 Train Loss: 0.431, ap: 0.739, auc: 0.734, f1: 0.692, acc: 0.680
+2025-08-07 19:25:00: Epoch: 022 Test results, ap: 0.199, auc: 0.394, f1: 0.143, acc: 0.556
+2025-08-07 19:25:01: Epoch: 023 Train Loss: 0.409, ap: 0.710, auc: 0.684, f1: 0.641, acc: 0.641
+2025-08-07 19:25:01: Epoch: 023 Test results, ap: 0.200, auc: 0.385, f1: 0.182, acc: 0.583
+2025-08-07 19:25:01: Epoch: 024 Train Loss: 0.406, ap: 0.722, auc: 0.722, f1: 0.660, acc: 0.660
+2025-08-07 19:25:01: Epoch: 024 Test results, ap: 0.213, auc: 0.413, f1: 0.145, acc: 0.565
+2025-08-07 19:25:01: Epoch: 025 Train Loss: 0.412, ap: 0.737, auc: 0.696, f1: 0.673, acc: 0.680
+2025-08-07 19:25:02: Epoch: 025 Test results, ap: 0.209, auc: 0.421, f1: 0.154, acc: 0.593
+2025-08-07 19:25:02: Epoch: 026 Train Loss: 0.381, ap: 0.720, auc: 0.684, f1: 0.654, acc: 0.641
+2025-08-07 19:25:02: Epoch: 026 Test results, ap: 0.209, auc: 0.414, f1: 0.154, acc: 0.593
+2025-08-07 19:25:02: Epoch: 027 Train Loss: 0.393, ap: 0.713, auc: 0.694, f1: 0.673, acc: 0.660
+2025-08-07 19:25:03: Epoch: 027 Test results, ap: 0.207, auc: 0.405, f1: 0.154, acc: 0.593
+2025-08-07 19:25:03: Epoch: 028 Train Loss: 0.375, ap: 0.713, auc: 0.693, f1: 0.667, acc: 0.660
+2025-08-07 19:25:03: Epoch: 028 Test results, ap: 0.206, auc: 0.397, f1: 0.151, acc: 0.583
+2025-08-07 19:25:03: Epoch: 029 Train Loss: 0.367, ap: 0.706, auc: 0.693, f1: 0.703, acc: 0.680
+2025-08-07 19:25:04: Epoch: 029 Test results, ap: 0.202, auc: 0.387, f1: 0.143, acc: 0.556
+2025-08-07 19:25:04: Epoch: 030 Train Loss: 0.344, ap: 0.727, auc: 0.681, f1: 0.667, acc: 0.660
+2025-08-07 19:25:04: Epoch: 030 Test results, ap: 0.208, auc: 0.409, f1: 0.154, acc: 0.593
+2025-08-07 19:25:05: Epoch: 031 Train Loss: 0.343, ap: 0.722, auc: 0.693, f1: 0.667, acc: 0.650
+2025-08-07 19:25:05: Epoch: 031 Test results, ap: 0.209, auc: 0.407, f1: 0.154, acc: 0.593
+2025-08-07 19:25:05: Epoch: 032 Train Loss: 0.321, ap: 0.721, auc: 0.708, f1: 0.685, acc: 0.670
+2025-08-07 19:25:05: Epoch: 032 Test results, ap: 0.209, auc: 0.410, f1: 0.189, acc: 0.602
+2025-08-07 19:25:06: Epoch: 033 Train Loss: 0.324, ap: 0.740, auc: 0.705, f1: 0.660, acc: 0.660
+2025-08-07 19:25:06: Epoch: 033 Test results, ap: 0.212, auc: 0.422, f1: 0.154, acc: 0.593
+2025-08-07 19:25:06: Epoch: 034 Train Loss: 0.330, ap: 0.728, auc: 0.700, f1: 0.654, acc: 0.650
+2025-08-07 19:25:06: Epoch: 034 Test results, ap: 0.209, auc: 0.411, f1: 0.154, acc: 0.593
+2025-08-07 19:25:07: Epoch: 035 Train Loss: 0.306, ap: 0.731, auc: 0.694, f1: 0.594, acc: 0.602
+2025-08-07 19:25:07: Epoch: 035 Test results, ap: 0.213, auc: 0.421, f1: 0.151, acc: 0.583
+2025-08-07 19:25:07: Epoch: 036 Train Loss: 0.312, ap: 0.730, auc: 0.714, f1: 0.704, acc: 0.689
+2025-08-07 19:25:07: Epoch: 036 Test results, ap: 0.205, auc: 0.400, f1: 0.148, acc: 0.574
+2025-08-07 19:25:08: Epoch: 037 Train Loss: 0.315, ap: 0.744, auc: 0.712, f1: 0.647, acc: 0.650
+2025-08-07 19:25:08: Epoch: 037 Test results, ap: 0.213, auc: 0.419, f1: 0.157, acc: 0.602
+2025-08-07 19:25:08: Epoch: 038 Train Loss: 0.297, ap: 0.755, auc: 0.724, f1: 0.667, acc: 0.660
+2025-08-07 19:25:08: Epoch: 038 Test results, ap: 0.207, auc: 0.406, f1: 0.148, acc: 0.574
+2025-08-07 19:25:09: Epoch: 039 Train Loss: 0.291, ap: 0.742, auc: 0.711, f1: 0.654, acc: 0.650
+2025-08-07 19:25:09: Epoch: 039 Test results, ap: 0.210, auc: 0.414, f1: 0.182, acc: 0.583
+2025-08-07 19:25:09: Epoch: 040 Train Loss: 0.271, ap: 0.727, auc: 0.697, f1: 0.673, acc: 0.660
+2025-08-07 19:25:09: Epoch: 040 Test results, ap: 0.204, auc: 0.401, f1: 0.172, acc: 0.556
+2025-08-07 19:25:10: Epoch: 041 Train Loss: 0.269, ap: 0.735, auc: 0.707, f1: 0.673, acc: 0.660
+2025-08-07 19:25:10: Epoch: 041 Test results, ap: 0.206, auc: 0.402, f1: 0.179, acc: 0.574
+2025-08-07 19:25:10: Epoch: 042 Train Loss: 0.263, ap: 0.744, auc: 0.716, f1: 0.673, acc: 0.670
+2025-08-07 19:25:10: Epoch: 042 Test results, ap: 0.207, auc: 0.407, f1: 0.182, acc: 0.583
+2025-08-07 19:25:11: Epoch: 043 Train Loss: 0.260, ap: 0.729, auc: 0.701, f1: 0.660, acc: 0.660
+2025-08-07 19:25:11: Epoch: 043 Test results, ap: 0.208, auc: 0.409, f1: 0.185, acc: 0.593
+2025-08-07 19:25:11: Epoch: 044 Train Loss: 0.250, ap: 0.729, auc: 0.710, f1: 0.679, acc: 0.670
+2025-08-07 19:25:11: Epoch: 044 Test results, ap: 0.206, auc: 0.405, f1: 0.175, acc: 0.565
+2025-08-07 19:25:12: Epoch: 045 Train Loss: 0.254, ap: 0.728, auc: 0.709, f1: 0.679, acc: 0.670
+2025-08-07 19:25:12: Epoch: 045 Test results, ap: 0.205, auc: 0.406, f1: 0.172, acc: 0.556
+2025-08-07 19:25:12: Epoch: 046 Train Loss: 0.254, ap: 0.734, auc: 0.710, f1: 0.713, acc: 0.718
+2025-08-07 19:25:12: Epoch: 046 Test results, ap: 0.208, auc: 0.409, f1: 0.185, acc: 0.593
+2025-08-07 19:25:13: Epoch: 047 Train Loss: 0.228, ap: 0.727, auc: 0.703, f1: 0.686, acc: 0.680
+2025-08-07 19:25:13: Epoch: 047 Test results, ap: 0.204, auc: 0.400, f1: 0.172, acc: 0.556
+2025-08-07 19:25:13: Epoch: 048 Train Loss: 0.250, ap: 0.732, auc: 0.715, f1: 0.692, acc: 0.689
+2025-08-07 19:25:13: Epoch: 048 Test results, ap: 0.205, auc: 0.403, f1: 0.172, acc: 0.556
+2025-08-07 19:25:14: Epoch: 049 Train Loss: 0.231, ap: 0.729, auc: 0.710, f1: 0.692, acc: 0.689
+2025-08-07 19:25:14: Epoch: 049 Test results, ap: 0.205, auc: 0.402, f1: 0.203, acc: 0.565
+2025-08-07 19:25:14: Epoch: 050 Train Loss: 0.235, ap: 0.728, auc: 0.715, f1: 0.692, acc: 0.689
+2025-08-07 19:25:14: Epoch: 050 Test results, ap: 0.205, auc: 0.405, f1: 0.230, acc: 0.565
+2025-08-07 19:25:14: used time 0.01h
+2025-08-07 19:25:14: Running Cross Validation 3
+2025-08-07 19:25:14: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:25:15: Epoch: 001 Train Loss: 0.692, ap: 0.475, auc: 0.503, f1: 0.000, acc: 0.592
+2025-08-07 19:25:15: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:25:15: Epoch: 001 Test results, ap: 0.317, auc: 0.615, f1: 0.000, acc: 0.759
+2025-08-07 19:25:15: Epoch: 002 Train Loss: 0.693, ap: 0.564, auc: 0.682, f1: 0.679, acc: 0.650
+2025-08-07 19:25:15: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:25:15: Epoch: 002 Test results, ap: 0.213, auc: 0.462, f1: 0.345, acc: 0.472
+2025-08-07 19:25:16: Epoch: 003 Train Loss: 0.687, ap: 0.526, auc: 0.658, f1: 0.656, acc: 0.592
+2025-08-07 19:25:16: Epoch: 003 Test results, ap: 0.201, auc: 0.422, f1: 0.371, acc: 0.435
+2025-08-07 19:25:16: Epoch: 004 Train Loss: 0.679, ap: 0.564, auc: 0.679, f1: 0.618, acc: 0.592
+2025-08-07 19:25:17: Epoch: 004 Test results, ap: 0.206, auc: 0.417, f1: 0.293, acc: 0.463
+2025-08-07 19:25:17: Epoch: 005 Train Loss: 0.670, ap: 0.520, auc: 0.665, f1: 0.554, acc: 0.641
+2025-08-07 19:25:17: Epoch: 005 Test results, ap: 0.210, auc: 0.449, f1: 0.048, acc: 0.630
+2025-08-07 19:25:17: Epoch: 006 Train Loss: 0.663, ap: 0.514, auc: 0.668, f1: 0.558, acc: 0.631
+2025-08-07 19:25:18: Epoch: 006 Test results, ap: 0.212, auc: 0.451, f1: 0.047, acc: 0.620
+2025-08-07 19:25:18: Epoch: 007 Train Loss: 0.651, ap: 0.543, auc: 0.678, f1: 0.464, acc: 0.641
+2025-08-07 19:25:18: Epoch: 007 Test results, ap: 0.212, auc: 0.434, f1: 0.000, acc: 0.685
+2025-08-07 19:25:18: Epoch: 008 Train Loss: 0.667, ap: 0.545, auc: 0.662, f1: 0.327, acc: 0.641
+2025-08-07 19:25:19: Epoch: 008 Test results, ap: 0.216, auc: 0.439, f1: 0.000, acc: 0.731
+2025-08-07 19:25:19: Epoch: 009 Train Loss: 0.654, ap: 0.541, auc: 0.671, f1: 0.506, acc: 0.621
+2025-08-07 19:25:19: Epoch: 009 Test results, ap: 0.216, auc: 0.441, f1: 0.047, acc: 0.620
+2025-08-07 19:25:20: Epoch: 010 Train Loss: 0.656, ap: 0.532, auc: 0.649, f1: 0.591, acc: 0.650
+2025-08-07 19:25:20: Epoch: 010 Test results, ap: 0.206, auc: 0.414, f1: 0.125, acc: 0.611
+2025-08-07 19:25:20: Epoch: 011 Train Loss: 0.646, ap: 0.592, auc: 0.664, f1: 0.591, acc: 0.650
+2025-08-07 19:25:20: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:25:20: Epoch: 011 Test results, ap: 0.198, auc: 0.376, f1: 0.125, acc: 0.611
+2025-08-07 19:25:20: Epoch: 011 New best TEST metrics
+2025-08-07 19:25:21: Epoch: 012 Train Loss: 0.638, ap: 0.622, auc: 0.662, f1: 0.479, acc: 0.641
+2025-08-07 19:25:21: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:25:21: Epoch: 012 Test results, ap: 0.200, auc: 0.386, f1: 0.000, acc: 0.667
+2025-08-07 19:25:21: Epoch: 012 New best TEST metrics
+2025-08-07 19:25:21: Epoch: 013 Train Loss: 0.632, ap: 0.639, auc: 0.669, f1: 0.561, acc: 0.650
+2025-08-07 19:25:21: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:25:21: Epoch: 013 Test results, ap: 0.203, auc: 0.397, f1: 0.125, acc: 0.611
+2025-08-07 19:25:21: Epoch: 013 New best TEST metrics
+2025-08-07 19:25:22: Epoch: 014 Train Loss: 0.615, ap: 0.645, auc: 0.678, f1: 0.588, acc: 0.660
+2025-08-07 19:25:22: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:25:22: Epoch: 014 Test results, ap: 0.199, auc: 0.381, f1: 0.163, acc: 0.620
+2025-08-07 19:25:22: Epoch: 015 Train Loss: 0.614, ap: 0.646, auc: 0.665, f1: 0.561, acc: 0.650
+2025-08-07 19:25:22: Epoch: 015 Test results, ap: 0.203, auc: 0.395, f1: 0.222, acc: 0.611
+2025-08-07 19:25:23: Epoch: 016 Train Loss: 0.609, ap: 0.652, auc: 0.676, f1: 0.525, acc: 0.631
+2025-08-07 19:25:23: Epoch: 016 New best VALIDATION metrics
+2025-08-07 19:25:23: Epoch: 016 Test results, ap: 0.207, auc: 0.407, f1: 0.231, acc: 0.630
+2025-08-07 19:25:23: Epoch: 016 New best TEST metrics
+2025-08-07 19:25:23: Epoch: 017 Train Loss: 0.587, ap: 0.666, auc: 0.695, f1: 0.588, acc: 0.660
+2025-08-07 19:25:23: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:25:23: Epoch: 017 Test results, ap: 0.202, auc: 0.389, f1: 0.211, acc: 0.583
+2025-08-07 19:25:24: Epoch: 018 Train Loss: 0.588, ap: 0.587, auc: 0.682, f1: 0.588, acc: 0.660
+2025-08-07 19:25:24: Epoch: 018 Test results, ap: 0.224, auc: 0.432, f1: 0.254, acc: 0.565
+2025-08-07 19:25:24: Epoch: 018 New best TEST metrics
+2025-08-07 19:25:24: Epoch: 019 Train Loss: 0.585, ap: 0.642, auc: 0.684, f1: 0.507, acc: 0.641
+2025-08-07 19:25:24: Epoch: 019 Test results, ap: 0.227, auc: 0.429, f1: 0.235, acc: 0.639
+2025-08-07 19:25:25: Epoch: 020 Train Loss: 0.554, ap: 0.653, auc: 0.708, f1: 0.486, acc: 0.631
+2025-08-07 19:25:25: Epoch: 020 Test results, ap: 0.224, auc: 0.443, f1: 0.240, acc: 0.648
+2025-08-07 19:25:25: Epoch: 020 New best TEST metrics
+2025-08-07 19:25:25: Epoch: 021 Train Loss: 0.557, ap: 0.616, auc: 0.685, f1: 0.526, acc: 0.650
+2025-08-07 19:25:25: Epoch: 021 Test results, ap: 0.218, auc: 0.429, f1: 0.136, acc: 0.648
+2025-08-07 19:25:26: Epoch: 022 Train Loss: 0.541, ap: 0.648, auc: 0.689, f1: 0.608, acc: 0.699
+2025-08-07 19:25:26: Epoch: 022 Test results, ap: 0.219, auc: 0.425, f1: 0.100, acc: 0.667
+2025-08-07 19:25:26: Epoch: 023 Train Loss: 0.523, ap: 0.638, auc: 0.694, f1: 0.563, acc: 0.699
+2025-08-07 19:25:26: Epoch: 023 Test results, ap: 0.226, auc: 0.432, f1: 0.100, acc: 0.667
+2025-08-07 19:25:27: Epoch: 024 Train Loss: 0.505, ap: 0.636, auc: 0.697, f1: 0.543, acc: 0.689
+2025-08-07 19:25:27: Epoch: 024 Test results, ap: 0.224, auc: 0.421, f1: 0.227, acc: 0.685
+2025-08-07 19:25:27: Epoch: 025 Train Loss: 0.499, ap: 0.616, auc: 0.674, f1: 0.469, acc: 0.670
+2025-08-07 19:25:27: Epoch: 025 Test results, ap: 0.255, auc: 0.447, f1: 0.114, acc: 0.713
+2025-08-07 19:25:27: Epoch: 025 New best TEST metrics
+2025-08-07 19:25:28: Epoch: 026 Train Loss: 0.490, ap: 0.646, auc: 0.702, f1: 0.587, acc: 0.699
+2025-08-07 19:25:28: Epoch: 026 Test results, ap: 0.244, auc: 0.432, f1: 0.233, acc: 0.694
+2025-08-07 19:25:28: Epoch: 027 Train Loss: 0.475, ap: 0.646, auc: 0.701, f1: 0.568, acc: 0.631
+2025-08-07 19:25:28: Epoch: 027 Test results, ap: 0.242, auc: 0.434, f1: 0.213, acc: 0.657
+2025-08-07 19:25:29: Epoch: 028 Train Loss: 0.467, ap: 0.622, auc: 0.706, f1: 0.582, acc: 0.680
+2025-08-07 19:25:29: Epoch: 028 Test results, ap: 0.236, auc: 0.427, f1: 0.103, acc: 0.676
+2025-08-07 19:25:29: Epoch: 029 Train Loss: 0.443, ap: 0.613, auc: 0.677, f1: 0.487, acc: 0.612
+2025-08-07 19:25:29: Epoch: 029 Test results, ap: 0.251, auc: 0.447, f1: 0.238, acc: 0.704
+2025-08-07 19:25:30: Epoch: 030 Train Loss: 0.413, ap: 0.618, auc: 0.662, f1: 0.525, acc: 0.631
+2025-08-07 19:25:30: Epoch: 030 Test results, ap: 0.248, auc: 0.466, f1: 0.154, acc: 0.694
+2025-08-07 19:25:30: Epoch: 030 New best TEST metrics
+2025-08-07 19:25:30: Epoch: 031 Train Loss: 0.402, ap: 0.607, auc: 0.669, f1: 0.500, acc: 0.650
+2025-08-07 19:25:30: Epoch: 031 Test results, ap: 0.234, auc: 0.468, f1: 0.118, acc: 0.722
+2025-08-07 19:25:31: Epoch: 032 Train Loss: 0.401, ap: 0.631, auc: 0.686, f1: 0.537, acc: 0.631
+2025-08-07 19:25:31: Epoch: 032 Test results, ap: 0.238, auc: 0.454, f1: 0.136, acc: 0.648
+2025-08-07 19:25:31: Epoch: 033 Train Loss: 0.403, ap: 0.608, auc: 0.676, f1: 0.584, acc: 0.641
+2025-08-07 19:25:31: Epoch: 033 Test results, ap: 0.228, auc: 0.442, f1: 0.226, acc: 0.620
+2025-08-07 19:25:32: Epoch: 034 Train Loss: 0.391, ap: 0.625, auc: 0.671, f1: 0.548, acc: 0.631
+2025-08-07 19:25:32: Epoch: 034 Test results, ap: 0.252, auc: 0.466, f1: 0.218, acc: 0.602
+2025-08-07 19:25:32: Epoch: 034 New best TEST metrics
+2025-08-07 19:25:32: Epoch: 035 Train Loss: 0.371, ap: 0.598, auc: 0.670, f1: 0.543, acc: 0.641
+2025-08-07 19:25:32: Epoch: 035 Test results, ap: 0.240, auc: 0.462, f1: 0.143, acc: 0.667
+2025-08-07 19:25:33: Epoch: 036 Train Loss: 0.364, ap: 0.581, auc: 0.639, f1: 0.550, acc: 0.650
+2025-08-07 19:25:33: Epoch: 036 Test results, ap: 0.267, auc: 0.478, f1: 0.241, acc: 0.593
+2025-08-07 19:25:33: Epoch: 036 New best TEST metrics
+2025-08-07 19:25:33: Epoch: 037 Train Loss: 0.379, ap: 0.593, auc: 0.664, f1: 0.543, acc: 0.641
+2025-08-07 19:25:33: Epoch: 037 Test results, ap: 0.244, auc: 0.462, f1: 0.143, acc: 0.667
+2025-08-07 19:25:34: Epoch: 038 Train Loss: 0.344, ap: 0.626, auc: 0.676, f1: 0.541, acc: 0.621
+2025-08-07 19:25:34: Epoch: 038 Test results, ap: 0.265, auc: 0.486, f1: 0.231, acc: 0.630
+2025-08-07 19:25:34: Epoch: 038 New best TEST metrics
+2025-08-07 19:25:34: Epoch: 039 Train Loss: 0.321, ap: 0.608, auc: 0.663, f1: 0.548, acc: 0.631
+2025-08-07 19:25:34: Epoch: 039 Test results, ap: 0.267, auc: 0.490, f1: 0.231, acc: 0.630
+2025-08-07 19:25:34: Epoch: 039 New best TEST metrics
+2025-08-07 19:25:35: Epoch: 040 Train Loss: 0.305, ap: 0.634, auc: 0.679, f1: 0.554, acc: 0.641
+2025-08-07 19:25:35: Epoch: 040 Test results, ap: 0.259, auc: 0.474, f1: 0.273, acc: 0.556
+2025-08-07 19:25:35: Epoch: 041 Train Loss: 0.288, ap: 0.619, auc: 0.684, f1: 0.565, acc: 0.641
+2025-08-07 19:25:36: Epoch: 041 Test results, ap: 0.248, auc: 0.460, f1: 0.157, acc: 0.602
+2025-08-07 19:25:36: Epoch: 042 Train Loss: 0.299, ap: 0.626, auc: 0.676, f1: 0.554, acc: 0.641
+2025-08-07 19:25:36: Epoch: 042 Test results, ap: 0.258, auc: 0.479, f1: 0.262, acc: 0.583
+2025-08-07 19:25:36: Epoch: 043 Train Loss: 0.289, ap: 0.621, auc: 0.662, f1: 0.554, acc: 0.641
+2025-08-07 19:25:36: Epoch: 043 Test results, ap: 0.264, auc: 0.487, f1: 0.271, acc: 0.602
+2025-08-07 19:25:37: Epoch: 044 Train Loss: 0.283, ap: 0.624, auc: 0.667, f1: 0.561, acc: 0.650
+2025-08-07 19:25:37: Epoch: 044 Test results, ap: 0.273, auc: 0.497, f1: 0.286, acc: 0.630
+2025-08-07 19:25:37: Epoch: 044 New best TEST metrics
+2025-08-07 19:25:37: Epoch: 045 Train Loss: 0.266, ap: 0.631, auc: 0.677, f1: 0.568, acc: 0.660
+2025-08-07 19:25:37: Epoch: 045 Test results, ap: 0.256, auc: 0.477, f1: 0.295, acc: 0.602
+2025-08-07 19:25:38: Epoch: 046 Train Loss: 0.262, ap: 0.619, auc: 0.661, f1: 0.554, acc: 0.641
+2025-08-07 19:25:38: Epoch: 046 Test results, ap: 0.255, auc: 0.479, f1: 0.267, acc: 0.593
+2025-08-07 19:25:38: Epoch: 047 Train Loss: 0.265, ap: 0.601, auc: 0.656, f1: 0.558, acc: 0.631
+2025-08-07 19:25:38: Epoch: 047 Test results, ap: 0.248, auc: 0.479, f1: 0.226, acc: 0.620
+2025-08-07 19:25:39: Epoch: 048 Train Loss: 0.259, ap: 0.613, auc: 0.665, f1: 0.548, acc: 0.631
+2025-08-07 19:25:39: Epoch: 048 Test results, ap: 0.253, auc: 0.477, f1: 0.241, acc: 0.593
+2025-08-07 19:25:39: Epoch: 049 Train Loss: 0.267, ap: 0.625, auc: 0.669, f1: 0.561, acc: 0.650
+2025-08-07 19:25:39: Epoch: 049 Test results, ap: 0.264, auc: 0.491, f1: 0.281, acc: 0.620
+2025-08-07 19:25:40: Epoch: 050 Train Loss: 0.259, ap: 0.623, auc: 0.665, f1: 0.585, acc: 0.670
+2025-08-07 19:25:40: Epoch: 050 Test results, ap: 0.266, auc: 0.490, f1: 0.291, acc: 0.639
+2025-08-07 19:25:40: used time 0.01h
+2025-08-07 19:25:40: Running Cross Validation 4
+2025-08-07 19:25:40: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:25:40: Epoch: 001 Train Loss: 0.693, ap: 0.625, auc: 0.690, f1: 0.000, acc: 0.602
+2025-08-07 19:25:40: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:25:40: Epoch: 001 Test results, ap: 0.214, auc: 0.463, f1: 0.000, acc: 0.759
+2025-08-07 19:25:41: Epoch: 002 Train Loss: 0.693, ap: 0.588, auc: 0.672, f1: 0.000, acc: 0.602
+2025-08-07 19:25:41: Epoch: 002 Test results, ap: 0.206, auc: 0.437, f1: 0.000, acc: 0.759
+2025-08-07 19:25:41: Epoch: 003 Train Loss: 0.693, ap: 0.490, auc: 0.630, f1: 0.567, acc: 0.408
+2025-08-07 19:25:41: Epoch: 003 Test results, ap: 0.189, auc: 0.369, f1: 0.388, acc: 0.241
+2025-08-07 19:25:42: Epoch: 004 Train Loss: 0.692, ap: 0.501, auc: 0.611, f1: 0.518, acc: 0.476
+2025-08-07 19:25:42: Epoch: 004 Test results, ap: 0.192, auc: 0.372, f1: 0.200, acc: 0.481
+2025-08-07 19:25:42: Epoch: 005 Train Loss: 0.696, ap: 0.516, auc: 0.642, f1: 0.581, acc: 0.621
+2025-08-07 19:25:42: Epoch: 005 Test results, ap: 0.204, auc: 0.430, f1: 0.115, acc: 0.574
+2025-08-07 19:25:43: Epoch: 006 Train Loss: 0.689, ap: 0.557, auc: 0.655, f1: 0.000, acc: 0.602
+2025-08-07 19:25:43: Epoch: 006 Test results, ap: 0.213, auc: 0.415, f1: 0.000, acc: 0.759
+2025-08-07 19:25:43: Epoch: 007 Train Loss: 0.692, ap: 0.591, auc: 0.689, f1: 0.614, acc: 0.621
+2025-08-07 19:25:43: Epoch: 007 Test results, ap: 0.204, auc: 0.423, f1: 0.250, acc: 0.500
+2025-08-07 19:25:44: Epoch: 008 Train Loss: 0.679, ap: 0.600, auc: 0.696, f1: 0.610, acc: 0.602
+2025-08-07 19:25:44: Epoch: 008 Test results, ap: 0.203, auc: 0.415, f1: 0.296, acc: 0.472
+2025-08-07 19:25:44: Epoch: 009 Train Loss: 0.665, ap: 0.619, auc: 0.700, f1: 0.602, acc: 0.641
+2025-08-07 19:25:44: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:25:44: Epoch: 009 Test results, ap: 0.205, auc: 0.416, f1: 0.250, acc: 0.500
+2025-08-07 19:25:45: Epoch: 010 Train Loss: 0.656, ap: 0.605, auc: 0.682, f1: 0.585, acc: 0.670
+2025-08-07 19:25:45: Epoch: 010 Test results, ap: 0.210, auc: 0.427, f1: 0.265, acc: 0.537
+2025-08-07 19:25:45: Epoch: 011 Train Loss: 0.632, ap: 0.632, auc: 0.689, f1: 0.568, acc: 0.660
+2025-08-07 19:25:45: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:25:45: Epoch: 011 Test results, ap: 0.209, auc: 0.425, f1: 0.273, acc: 0.556
+2025-08-07 19:25:45: Epoch: 011 New best TEST metrics
+2025-08-07 19:25:46: Epoch: 012 Train Loss: 0.626, ap: 0.607, auc: 0.682, f1: 0.522, acc: 0.680
+2025-08-07 19:25:46: Epoch: 012 Test results, ap: 0.212, auc: 0.435, f1: 0.154, acc: 0.593
+2025-08-07 19:25:46: Epoch: 012 New best TEST metrics
+2025-08-07 19:25:46: Epoch: 013 Train Loss: 0.622, ap: 0.632, auc: 0.696, f1: 0.519, acc: 0.641
+2025-08-07 19:25:46: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:25:46: Epoch: 013 Test results, ap: 0.215, auc: 0.445, f1: 0.179, acc: 0.574
+2025-08-07 19:25:46: Epoch: 013 New best TEST metrics
+2025-08-07 19:25:47: Epoch: 014 Train Loss: 0.607, ap: 0.597, auc: 0.671, f1: 0.541, acc: 0.670
+2025-08-07 19:25:47: Epoch: 014 Test results, ap: 0.219, auc: 0.455, f1: 0.207, acc: 0.574
+2025-08-07 19:25:47: Epoch: 014 New best TEST metrics
+2025-08-07 19:25:47: Epoch: 015 Train Loss: 0.608, ap: 0.620, auc: 0.684, f1: 0.526, acc: 0.650
+2025-08-07 19:25:47: Epoch: 015 Test results, ap: 0.223, auc: 0.470, f1: 0.185, acc: 0.593
+2025-08-07 19:25:47: Epoch: 015 New best TEST metrics
+2025-08-07 19:25:48: Epoch: 016 Train Loss: 0.614, ap: 0.621, auc: 0.679, f1: 0.541, acc: 0.670
+2025-08-07 19:25:48: Epoch: 016 Test results, ap: 0.221, auc: 0.462, f1: 0.179, acc: 0.574
+2025-08-07 19:25:48: Epoch: 017 Train Loss: 0.587, ap: 0.598, auc: 0.670, f1: 0.560, acc: 0.680
+2025-08-07 19:25:48: Epoch: 017 Test results, ap: 0.225, auc: 0.470, f1: 0.200, acc: 0.556
+2025-08-07 19:25:48: Epoch: 017 New best TEST metrics
+2025-08-07 19:25:49: Epoch: 018 Train Loss: 0.591, ap: 0.624, auc: 0.679, f1: 0.550, acc: 0.650
+2025-08-07 19:25:49: Epoch: 018 Test results, ap: 0.222, auc: 0.468, f1: 0.290, acc: 0.546
+2025-08-07 19:25:49: Epoch: 019 Train Loss: 0.574, ap: 0.605, auc: 0.677, f1: 0.485, acc: 0.670
+2025-08-07 19:25:50: Epoch: 019 Test results, ap: 0.227, auc: 0.480, f1: 0.230, acc: 0.565
+2025-08-07 19:25:50: Epoch: 019 New best TEST metrics
+2025-08-07 19:25:50: Epoch: 020 Train Loss: 0.547, ap: 0.612, auc: 0.672, f1: 0.533, acc: 0.660
+2025-08-07 19:25:50: Epoch: 020 Test results, ap: 0.220, auc: 0.467, f1: 0.125, acc: 0.611
+2025-08-07 19:25:50: Epoch: 021 Train Loss: 0.544, ap: 0.614, auc: 0.674, f1: 0.413, acc: 0.641
+2025-08-07 19:25:51: Epoch: 021 Test results, ap: 0.234, auc: 0.501, f1: 0.056, acc: 0.685
+2025-08-07 19:25:51: Epoch: 021 New best TEST metrics
+2025-08-07 19:25:51: Epoch: 022 Train Loss: 0.542, ap: 0.617, auc: 0.691, f1: 0.548, acc: 0.680
+2025-08-07 19:25:51: Epoch: 022 Test results, ap: 0.229, auc: 0.487, f1: 0.192, acc: 0.611
+2025-08-07 19:25:52: Epoch: 023 Train Loss: 0.501, ap: 0.624, auc: 0.686, f1: 0.500, acc: 0.670
+2025-08-07 19:25:52: Epoch: 023 Test results, ap: 0.238, auc: 0.510, f1: 0.089, acc: 0.620
+2025-08-07 19:25:52: Epoch: 023 New best TEST metrics
+2025-08-07 19:25:52: Epoch: 024 Train Loss: 0.490, ap: 0.626, auc: 0.693, f1: 0.522, acc: 0.680
+2025-08-07 19:25:52: Epoch: 024 Test results, ap: 0.235, auc: 0.497, f1: 0.095, acc: 0.648
+2025-08-07 19:25:53: Epoch: 025 Train Loss: 0.483, ap: 0.626, auc: 0.696, f1: 0.535, acc: 0.680
+2025-08-07 19:25:53: Epoch: 025 Test results, ap: 0.234, auc: 0.491, f1: 0.182, acc: 0.667
+2025-08-07 19:25:53: Epoch: 026 Train Loss: 0.477, ap: 0.634, auc: 0.701, f1: 0.493, acc: 0.641
+2025-08-07 19:25:53: Epoch: 026 New best VALIDATION metrics
+2025-08-07 19:25:53: Epoch: 026 Test results, ap: 0.231, auc: 0.493, f1: 0.170, acc: 0.639
+2025-08-07 19:25:54: Epoch: 027 Train Loss: 0.459, ap: 0.616, auc: 0.696, f1: 0.548, acc: 0.680
+2025-08-07 19:25:54: Epoch: 027 Test results, ap: 0.247, auc: 0.516, f1: 0.182, acc: 0.667
+2025-08-07 19:25:54: Epoch: 027 New best TEST metrics
+2025-08-07 19:25:54: Epoch: 028 Train Loss: 0.466, ap: 0.625, auc: 0.719, f1: 0.551, acc: 0.699
+2025-08-07 19:25:54: Epoch: 028 New best VALIDATION metrics
+2025-08-07 19:25:54: Epoch: 028 Test results, ap: 0.233, auc: 0.491, f1: 0.186, acc: 0.676
+2025-08-07 19:25:55: Epoch: 029 Train Loss: 0.456, ap: 0.594, auc: 0.688, f1: 0.514, acc: 0.650
+2025-08-07 19:25:55: Epoch: 029 Test results, ap: 0.240, auc: 0.508, f1: 0.250, acc: 0.667
+2025-08-07 19:25:55: Epoch: 030 Train Loss: 0.429, ap: 0.616, auc: 0.716, f1: 0.568, acc: 0.689
+2025-08-07 19:25:55: Epoch: 030 Test results, ap: 0.236, auc: 0.488, f1: 0.195, acc: 0.694
+2025-08-07 19:25:56: Epoch: 031 Train Loss: 0.416, ap: 0.605, auc: 0.693, f1: 0.528, acc: 0.670
+2025-08-07 19:25:56: Epoch: 031 Test results, ap: 0.235, auc: 0.492, f1: 0.182, acc: 0.667
+2025-08-07 19:25:56: Epoch: 032 Train Loss: 0.389, ap: 0.607, auc: 0.699, f1: 0.556, acc: 0.689
+2025-08-07 19:25:56: Epoch: 032 Test results, ap: 0.228, auc: 0.477, f1: 0.186, acc: 0.676
+2025-08-07 19:25:57: Epoch: 033 Train Loss: 0.397, ap: 0.608, auc: 0.702, f1: 0.556, acc: 0.689
+2025-08-07 19:25:57: Epoch: 033 Test results, ap: 0.228, auc: 0.475, f1: 0.186, acc: 0.676
+2025-08-07 19:25:57: Epoch: 034 Train Loss: 0.384, ap: 0.607, auc: 0.700, f1: 0.548, acc: 0.680
+2025-08-07 19:25:57: Epoch: 034 Test results, ap: 0.233, auc: 0.487, f1: 0.182, acc: 0.667
+2025-08-07 19:25:58: Epoch: 035 Train Loss: 0.367, ap: 0.596, auc: 0.702, f1: 0.560, acc: 0.680
+2025-08-07 19:25:58: Epoch: 035 Test results, ap: 0.231, auc: 0.482, f1: 0.186, acc: 0.676
+2025-08-07 19:25:58: Epoch: 036 Train Loss: 0.362, ap: 0.593, auc: 0.685, f1: 0.535, acc: 0.680
+2025-08-07 19:25:58: Epoch: 036 Test results, ap: 0.232, auc: 0.482, f1: 0.186, acc: 0.676
+2025-08-07 19:25:59: Epoch: 037 Train Loss: 0.363, ap: 0.577, auc: 0.694, f1: 0.560, acc: 0.680
+2025-08-07 19:25:59: Epoch: 037 Test results, ap: 0.230, auc: 0.479, f1: 0.186, acc: 0.676
+2025-08-07 19:25:59: Epoch: 038 Train Loss: 0.352, ap: 0.589, auc: 0.692, f1: 0.541, acc: 0.670
+2025-08-07 19:25:59: Epoch: 038 Test results, ap: 0.232, auc: 0.478, f1: 0.190, acc: 0.685
+2025-08-07 19:26:00: Epoch: 039 Train Loss: 0.356, ap: 0.575, auc: 0.687, f1: 0.541, acc: 0.670
+2025-08-07 19:26:00: Epoch: 039 Test results, ap: 0.235, auc: 0.477, f1: 0.190, acc: 0.685
+2025-08-07 19:26:00: Epoch: 040 Train Loss: 0.343, ap: 0.560, auc: 0.678, f1: 0.548, acc: 0.680
+2025-08-07 19:26:00: Epoch: 040 Test results, ap: 0.233, auc: 0.469, f1: 0.190, acc: 0.685
+2025-08-07 19:26:01: Epoch: 041 Train Loss: 0.349, ap: 0.539, auc: 0.653, f1: 0.435, acc: 0.621
+2025-08-07 19:26:01: Epoch: 041 Test results, ap: 0.233, auc: 0.471, f1: 0.195, acc: 0.694
+2025-08-07 19:26:01: Epoch: 042 Train Loss: 0.337, ap: 0.568, auc: 0.686, f1: 0.541, acc: 0.670
+2025-08-07 19:26:01: Epoch: 042 Test results, ap: 0.233, auc: 0.474, f1: 0.190, acc: 0.685
+2025-08-07 19:26:02: Epoch: 043 Train Loss: 0.337, ap: 0.550, auc: 0.672, f1: 0.493, acc: 0.641
+2025-08-07 19:26:02: Epoch: 043 Test results, ap: 0.234, auc: 0.474, f1: 0.190, acc: 0.685
+2025-08-07 19:26:02: Epoch: 044 Train Loss: 0.330, ap: 0.542, auc: 0.654, f1: 0.479, acc: 0.641
+2025-08-07 19:26:02: Epoch: 044 Test results, ap: 0.236, auc: 0.475, f1: 0.195, acc: 0.694
+2025-08-07 19:26:03: Epoch: 045 Train Loss: 0.329, ap: 0.547, auc: 0.670, f1: 0.493, acc: 0.641
+2025-08-07 19:26:03: Epoch: 045 Test results, ap: 0.236, auc: 0.477, f1: 0.195, acc: 0.694
+2025-08-07 19:26:03: Epoch: 046 Train Loss: 0.333, ap: 0.538, auc: 0.655, f1: 0.479, acc: 0.641
+2025-08-07 19:26:03: Epoch: 046 Test results, ap: 0.236, auc: 0.475, f1: 0.195, acc: 0.694
+2025-08-07 19:26:04: Epoch: 047 Train Loss: 0.313, ap: 0.546, auc: 0.667, f1: 0.486, acc: 0.650
+2025-08-07 19:26:04: Epoch: 047 Test results, ap: 0.235, auc: 0.470, f1: 0.195, acc: 0.694
+2025-08-07 19:26:04: Epoch: 048 Train Loss: 0.319, ap: 0.535, auc: 0.647, f1: 0.486, acc: 0.650
+2025-08-07 19:26:04: Epoch: 048 Test results, ap: 0.237, auc: 0.474, f1: 0.195, acc: 0.694
+2025-08-07 19:26:05: Epoch: 049 Train Loss: 0.309, ap: 0.527, auc: 0.646, f1: 0.457, acc: 0.631
+2025-08-07 19:26:05: Epoch: 049 Test results, ap: 0.238, auc: 0.478, f1: 0.195, acc: 0.694
+2025-08-07 19:26:05: Epoch: 050 Train Loss: 0.304, ap: 0.541, auc: 0.656, f1: 0.493, acc: 0.641
+2025-08-07 19:26:05: Epoch: 050 Test results, ap: 0.238, auc: 0.477, f1: 0.195, acc: 0.694
+2025-08-07 19:26:05: used time 0.01h
+2025-08-07 19:26:05: Cross Validation Finished!
+2025-08-07 19:26:05: Best validation perform list
+[[0.74803126 0.76443768 0.62921351 0.67961162]
+ [0.6052146  0.69492221 0.52054793 0.66019416]
+ [0.77486187 0.72699845 0.66000003 0.66990292]
+ [0.66594911 0.69516003 0.58823532 0.66019416]
+ [0.62470341 0.71911883 0.55072463 0.69902915]]
+2025-08-07 19:26:05: mean: [0.684 0.72  0.59  0.674]
+2025-08-07 19:26:05: std: [0.067 0.026 0.051 0.015]
+2025-08-07 19:26:05: Best test perform list
+[[0.28345585 0.57879925 0.40000001 0.6388889 ]
+ [0.26815426 0.51407129 0.2857143  0.58333331]
+ [0.21833059 0.44465292 0.14814815 0.57407409]
+ [0.27313215 0.49671671 0.2857143  0.62962961]
+ [0.24728853 0.51641655 0.18181819 0.66666669]]
+2025-08-07 19:26:05: mean: [0.258 0.51  0.26  0.619]
+2025-08-07 19:26:05: std: [0.023 0.043 0.089 0.035]
diff --git a/run-cls-simple/mamba-mlp-256-simple-oneway-ce-32-0.001-50/config.json b/run-cls-simple/mamba-mlp-256-simple-oneway-ce-32-0.001-50/config.json
new file mode 100644
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+++ b/run-cls-simple/mamba-mlp-256-simple-oneway-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mamba", "side_enc": null, "channels": 256, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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+2025-08-07 19:19:32: saving_dir: ./run-cls/mamba-mlp-256-simple-oneway-ce-32-0.001-50
+2025-08-07 19:19:32: Loading Training Dataset
+2025-08-07 19:19:32: Loading Test Dataset
+2025-08-07 19:19:32: Running Cross Validation 0
+2025-08-07 19:19:32: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:19:33: Epoch: 001 Train Loss: 0.693, ap: 0.596, auc: 0.658, f1: 0.627, acc: 0.456
+2025-08-07 19:19:33: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:19:33: Epoch: 001 Test results, ap: 0.216, auc: 0.462, f1: 0.388, acc: 0.241
+2025-08-07 19:19:34: Epoch: 002 Train Loss: 0.690, ap: 0.602, auc: 0.665, f1: 0.627, acc: 0.456
+2025-08-07 19:19:34: Epoch: 002 New best VALIDATION metrics
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+2025-08-07 19:19:34: Epoch: 003 Train Loss: 0.694, ap: 0.585, auc: 0.661, f1: 0.237, acc: 0.563
+2025-08-07 19:19:34: Epoch: 003 Test results, ap: 0.232, auc: 0.516, f1: 0.000, acc: 0.722
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+2025-08-07 19:19:35: Epoch: 004 Test results, ap: 0.229, auc: 0.514, f1: 0.044, acc: 0.602
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+2025-08-07 19:19:35: Epoch: 005 New best VALIDATION metrics
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+2025-08-07 19:19:36: Epoch: 006 Train Loss: 0.636, ap: 0.647, auc: 0.699, f1: 0.554, acc: 0.641
+2025-08-07 19:19:36: Epoch: 006 New best VALIDATION metrics
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+2025-08-07 19:19:36: Epoch: 007 Train Loss: 0.636, ap: 0.671, auc: 0.700, f1: 0.602, acc: 0.641
+2025-08-07 19:19:36: Epoch: 007 New best VALIDATION metrics
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+2025-08-07 19:19:37: Epoch: 008 Train Loss: 0.613, ap: 0.691, auc: 0.717, f1: 0.609, acc: 0.650
+2025-08-07 19:19:37: Epoch: 008 New best VALIDATION metrics
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+2025-08-07 19:19:37: Epoch: 009 Train Loss: 0.598, ap: 0.721, auc: 0.742, f1: 0.673, acc: 0.660
+2025-08-07 19:19:37: Epoch: 009 New best VALIDATION metrics
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+2025-08-07 19:19:38: Epoch: 010 Train Loss: 0.576, ap: 0.773, auc: 0.759, f1: 0.681, acc: 0.718
+2025-08-07 19:19:38: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:19:38: Epoch: 010 Test results, ap: 0.234, auc: 0.509, f1: 0.163, acc: 0.620
+2025-08-07 19:19:38: Epoch: 011 Train Loss: 0.559, ap: 0.766, auc: 0.755, f1: 0.652, acc: 0.699
+2025-08-07 19:19:38: Epoch: 011 Test results, ap: 0.230, auc: 0.493, f1: 0.100, acc: 0.667
+2025-08-07 19:19:38: Epoch: 011 New best TEST metrics
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+2025-08-07 19:19:39: Epoch: 012 Test results, ap: 0.223, auc: 0.462, f1: 0.000, acc: 0.722
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+2025-08-07 19:19:40: Epoch: 014 New best VALIDATION metrics
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+2025-08-07 19:19:40: Epoch: 015 New best VALIDATION metrics
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+2025-08-07 19:19:41: Epoch: 016 Test results, ap: 0.230, auc: 0.489, f1: 0.000, acc: 0.722
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+2025-08-07 19:19:42: Epoch: 017 Test results, ap: 0.228, auc: 0.482, f1: 0.000, acc: 0.731
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+2025-08-07 19:19:42: Epoch: 018 New best TEST metrics
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+2025-08-07 19:19:42: Epoch: 019 New best TEST metrics
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+2025-08-07 19:19:43: Epoch: 020 Test results, ap: 0.234, auc: 0.505, f1: 0.000, acc: 0.759
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+2025-08-07 19:19:43: Epoch: 021 New best TEST metrics
+2025-08-07 19:19:44: Epoch: 022 Train Loss: 0.492, ap: 0.779, auc: 0.781, f1: 0.597, acc: 0.699
+2025-08-07 19:19:44: Epoch: 022 Test results, ap: 0.233, auc: 0.500, f1: 0.000, acc: 0.759
+2025-08-07 19:19:44: Epoch: 023 Train Loss: 0.498, ap: 0.764, auc: 0.772, f1: 0.717, acc: 0.748
+2025-08-07 19:19:44: Epoch: 023 Test results, ap: 0.239, auc: 0.517, f1: 0.000, acc: 0.750
+2025-08-07 19:19:45: Epoch: 024 Train Loss: 0.507, ap: 0.774, auc: 0.778, f1: 0.597, acc: 0.699
+2025-08-07 19:19:45: Epoch: 024 Test results, ap: 0.232, auc: 0.492, f1: 0.000, acc: 0.759
+2025-08-07 19:19:45: Epoch: 025 Train Loss: 0.502, ap: 0.776, auc: 0.778, f1: 0.725, acc: 0.757
+2025-08-07 19:19:46: Epoch: 025 Test results, ap: 0.237, auc: 0.507, f1: 0.000, acc: 0.750
+2025-08-07 19:19:46: Epoch: 026 Train Loss: 0.492, ap: 0.765, auc: 0.774, f1: 0.710, acc: 0.738
+2025-08-07 19:19:46: Epoch: 026 Test results, ap: 0.242, auc: 0.520, f1: 0.000, acc: 0.750
+2025-08-07 19:19:46: Epoch: 027 Train Loss: 0.489, ap: 0.776, auc: 0.778, f1: 0.590, acc: 0.689
+2025-08-07 19:19:47: Epoch: 027 Test results, ap: 0.230, auc: 0.485, f1: 0.000, acc: 0.759
+2025-08-07 19:19:47: Epoch: 028 Train Loss: 0.465, ap: 0.781, auc: 0.781, f1: 0.710, acc: 0.738
+2025-08-07 19:19:47: Epoch: 028 Test results, ap: 0.240, auc: 0.513, f1: 0.000, acc: 0.759
+2025-08-07 19:19:47: Epoch: 029 Train Loss: 0.476, ap: 0.778, auc: 0.779, f1: 0.733, acc: 0.767
+2025-08-07 19:19:48: Epoch: 029 Test results, ap: 0.238, auc: 0.503, f1: 0.000, acc: 0.759
+2025-08-07 19:19:48: Epoch: 030 Train Loss: 0.463, ap: 0.778, auc: 0.779, f1: 0.733, acc: 0.767
+2025-08-07 19:19:48: Epoch: 030 Test results, ap: 0.237, auc: 0.501, f1: 0.000, acc: 0.759
+2025-08-07 19:19:48: Epoch: 031 Train Loss: 0.459, ap: 0.772, auc: 0.779, f1: 0.617, acc: 0.699
+2025-08-07 19:19:49: Epoch: 031 Test results, ap: 0.232, auc: 0.483, f1: 0.000, acc: 0.759
+2025-08-07 19:19:49: Epoch: 032 Train Loss: 0.467, ap: 0.770, auc: 0.781, f1: 0.733, acc: 0.767
+2025-08-07 19:19:49: Epoch: 032 Test results, ap: 0.237, auc: 0.499, f1: 0.000, acc: 0.759
+2025-08-07 19:19:49: Epoch: 033 Train Loss: 0.450, ap: 0.768, auc: 0.777, f1: 0.733, acc: 0.767
+2025-08-07 19:19:50: Epoch: 033 Test results, ap: 0.235, auc: 0.492, f1: 0.000, acc: 0.759
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+2025-08-07 19:19:50: Epoch: 034 Test results, ap: 0.230, auc: 0.476, f1: 0.000, acc: 0.759
+2025-08-07 19:19:51: Epoch: 035 Train Loss: 0.434, ap: 0.766, auc: 0.781, f1: 0.733, acc: 0.767
+2025-08-07 19:19:51: Epoch: 035 Test results, ap: 0.233, auc: 0.482, f1: 0.000, acc: 0.759
+2025-08-07 19:19:51: Epoch: 036 Train Loss: 0.444, ap: 0.759, auc: 0.779, f1: 0.719, acc: 0.757
+2025-08-07 19:19:51: Epoch: 036 Test results, ap: 0.228, auc: 0.466, f1: 0.000, acc: 0.759
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+2025-08-07 19:19:52: Epoch: 037 Test results, ap: 0.233, auc: 0.471, f1: 0.000, acc: 0.759
+2025-08-07 19:19:52: Epoch: 038 Train Loss: 0.439, ap: 0.762, auc: 0.783, f1: 0.742, acc: 0.777
+2025-08-07 19:19:52: Epoch: 038 Test results, ap: 0.228, auc: 0.459, f1: 0.000, acc: 0.759
+2025-08-07 19:19:53: Epoch: 039 Train Loss: 0.414, ap: 0.768, auc: 0.783, f1: 0.719, acc: 0.757
+2025-08-07 19:19:53: Epoch: 039 Test results, ap: 0.228, auc: 0.454, f1: 0.000, acc: 0.759
+2025-08-07 19:19:53: Epoch: 040 Train Loss: 0.424, ap: 0.783, auc: 0.801, f1: 0.711, acc: 0.748
+2025-08-07 19:19:53: Epoch: 040 New best VALIDATION metrics
+2025-08-07 19:19:53: Epoch: 040 Test results, ap: 0.234, auc: 0.465, f1: 0.000, acc: 0.759
+2025-08-07 19:19:53: Epoch: 041 Train Loss: 0.422, ap: 0.776, auc: 0.793, f1: 0.697, acc: 0.738
+2025-08-07 19:19:54: Epoch: 041 Test results, ap: 0.229, auc: 0.453, f1: 0.000, acc: 0.759
+2025-08-07 19:19:54: Epoch: 042 Train Loss: 0.407, ap: 0.778, auc: 0.793, f1: 0.703, acc: 0.738
+2025-08-07 19:19:54: Epoch: 042 Test results, ap: 0.227, auc: 0.448, f1: 0.000, acc: 0.759
+2025-08-07 19:19:54: Epoch: 043 Train Loss: 0.408, ap: 0.773, auc: 0.788, f1: 0.711, acc: 0.748
+2025-08-07 19:19:55: Epoch: 043 Test results, ap: 0.223, auc: 0.432, f1: 0.000, acc: 0.759
+2025-08-07 19:19:55: Epoch: 044 Train Loss: 0.393, ap: 0.775, auc: 0.798, f1: 0.697, acc: 0.738
+2025-08-07 19:19:55: Epoch: 044 Test results, ap: 0.224, auc: 0.437, f1: 0.000, acc: 0.759
+2025-08-07 19:19:56: Epoch: 045 Train Loss: 0.400, ap: 0.776, auc: 0.798, f1: 0.711, acc: 0.748
+2025-08-07 19:19:56: Epoch: 045 Test results, ap: 0.219, auc: 0.427, f1: 0.000, acc: 0.759
+2025-08-07 19:19:56: Epoch: 046 Train Loss: 0.389, ap: 0.776, auc: 0.797, f1: 0.690, acc: 0.738
+2025-08-07 19:19:56: Epoch: 046 Test results, ap: 0.217, auc: 0.421, f1: 0.000, acc: 0.759
+2025-08-07 19:19:57: Epoch: 047 Train Loss: 0.388, ap: 0.779, auc: 0.805, f1: 0.710, acc: 0.738
+2025-08-07 19:19:57: Epoch: 047 New best VALIDATION metrics
+2025-08-07 19:19:57: Epoch: 047 Test results, ap: 0.222, auc: 0.435, f1: 0.000, acc: 0.759
+2025-08-07 19:19:57: Epoch: 048 Train Loss: 0.406, ap: 0.772, auc: 0.797, f1: 0.719, acc: 0.757
+2025-08-07 19:19:57: Epoch: 048 Test results, ap: 0.215, auc: 0.418, f1: 0.000, acc: 0.759
+2025-08-07 19:19:58: Epoch: 049 Train Loss: 0.399, ap: 0.775, auc: 0.797, f1: 0.719, acc: 0.757
+2025-08-07 19:19:58: Epoch: 049 Test results, ap: 0.215, auc: 0.417, f1: 0.000, acc: 0.759
+2025-08-07 19:19:58: Epoch: 050 Train Loss: 0.386, ap: 0.772, auc: 0.799, f1: 0.696, acc: 0.728
+2025-08-07 19:19:58: Epoch: 050 Test results, ap: 0.216, auc: 0.420, f1: 0.000, acc: 0.759
+2025-08-07 19:19:58: used time 0.01h
+2025-08-07 19:19:58: Running Cross Validation 1
+2025-08-07 19:19:58: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:19:59: Epoch: 001 Train Loss: 0.694, ap: 0.435, auc: 0.602, f1: 0.000, acc: 0.641
+2025-08-07 19:19:59: Epoch: 001 New best VALIDATION metrics
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+2025-08-07 19:19:59: Epoch: 002 New best VALIDATION metrics
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+2025-08-07 19:20:00: Epoch: 003 New best VALIDATION metrics
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+2025-08-07 19:20:00: Epoch: 004 New best VALIDATION metrics
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+2025-08-07 19:20:04: Epoch: 010 Test results, ap: 0.220, auc: 0.474, f1: 0.000, acc: 0.759
+2025-08-07 19:20:04: Epoch: 011 Train Loss: 0.701, ap: 0.491, auc: 0.600, f1: 0.529, acc: 0.359
+2025-08-07 19:20:04: Epoch: 011 Test results, ap: 0.221, auc: 0.478, f1: 0.388, acc: 0.241
+2025-08-07 19:20:04: Epoch: 011 New best TEST metrics
+2025-08-07 19:20:05: Epoch: 012 Train Loss: 0.695, ap: 0.501, auc: 0.618, f1: 0.529, acc: 0.359
+2025-08-07 19:20:05: Epoch: 012 Test results, ap: 0.219, auc: 0.478, f1: 0.388, acc: 0.241
+2025-08-07 19:20:05: Epoch: 013 Train Loss: 0.693, ap: 0.485, auc: 0.609, f1: 0.000, acc: 0.641
+2025-08-07 19:20:05: Epoch: 013 Test results, ap: 0.217, auc: 0.473, f1: 0.000, acc: 0.759
+2025-08-07 19:20:06: Epoch: 014 Train Loss: 0.695, ap: 0.503, auc: 0.630, f1: 0.529, acc: 0.359
+2025-08-07 19:20:06: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:20:06: Epoch: 014 Test results, ap: 0.214, auc: 0.455, f1: 0.388, acc: 0.241
+2025-08-07 19:20:06: Epoch: 015 Train Loss: 0.691, ap: 0.521, auc: 0.644, f1: 0.186, acc: 0.660
+2025-08-07 19:20:06: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:20:06: Epoch: 015 Test results, ap: 0.212, auc: 0.454, f1: 0.000, acc: 0.750
+2025-08-07 19:20:07: Epoch: 016 Train Loss: 0.663, ap: 0.474, auc: 0.610, f1: 0.565, acc: 0.476
+2025-08-07 19:20:07: Epoch: 016 Test results, ap: 0.201, auc: 0.411, f1: 0.388, acc: 0.241
+2025-08-07 19:20:07: Epoch: 017 Train Loss: 0.652, ap: 0.479, auc: 0.634, f1: 0.525, acc: 0.631
+2025-08-07 19:20:07: Epoch: 017 Test results, ap: 0.214, auc: 0.466, f1: 0.400, acc: 0.278
+2025-08-07 19:20:08: Epoch: 018 Train Loss: 0.634, ap: 0.469, auc: 0.637, f1: 0.539, acc: 0.602
+2025-08-07 19:20:08: Epoch: 018 Test results, ap: 0.213, auc: 0.462, f1: 0.388, acc: 0.241
+2025-08-07 19:20:08: Epoch: 019 Train Loss: 0.626, ap: 0.457, auc: 0.629, f1: 0.533, acc: 0.592
+2025-08-07 19:20:08: Epoch: 019 Test results, ap: 0.210, auc: 0.452, f1: 0.388, acc: 0.241
+2025-08-07 19:20:09: Epoch: 020 Train Loss: 0.601, ap: 0.465, auc: 0.636, f1: 0.565, acc: 0.612
+2025-08-07 19:20:09: Epoch: 020 Test results, ap: 0.213, auc: 0.458, f1: 0.388, acc: 0.241
+2025-08-07 19:20:09: Epoch: 021 Train Loss: 0.568, ap: 0.460, auc: 0.626, f1: 0.543, acc: 0.592
+2025-08-07 19:20:09: Epoch: 021 Test results, ap: 0.215, auc: 0.462, f1: 0.388, acc: 0.241
+2025-08-07 19:20:10: Epoch: 022 Train Loss: 0.584, ap: 0.464, auc: 0.625, f1: 0.558, acc: 0.631
+2025-08-07 19:20:10: Epoch: 022 Test results, ap: 0.220, auc: 0.470, f1: 0.388, acc: 0.241
+2025-08-07 19:20:10: Epoch: 023 Train Loss: 0.579, ap: 0.491, auc: 0.622, f1: 0.535, acc: 0.612
+2025-08-07 19:20:10: Epoch: 023 Test results, ap: 0.219, auc: 0.461, f1: 0.388, acc: 0.241
+2025-08-07 19:20:11: Epoch: 024 Train Loss: 0.536, ap: 0.500, auc: 0.624, f1: 0.541, acc: 0.621
+2025-08-07 19:20:11: Epoch: 024 Test results, ap: 0.219, auc: 0.456, f1: 0.388, acc: 0.241
+2025-08-07 19:20:11: Epoch: 025 Train Loss: 0.557, ap: 0.512, auc: 0.639, f1: 0.444, acc: 0.612
+2025-08-07 19:20:11: Epoch: 025 Test results, ap: 0.219, auc: 0.454, f1: 0.388, acc: 0.241
+2025-08-07 19:20:12: Epoch: 026 Train Loss: 0.585, ap: 0.516, auc: 0.622, f1: 0.536, acc: 0.563
+2025-08-07 19:20:12: Epoch: 026 Test results, ap: 0.211, auc: 0.427, f1: 0.388, acc: 0.241
+2025-08-07 19:20:12: Epoch: 027 Train Loss: 0.546, ap: 0.515, auc: 0.648, f1: 0.442, acc: 0.583
+2025-08-07 19:20:12: Epoch: 027 Test results, ap: 0.206, auc: 0.405, f1: 0.388, acc: 0.241
+2025-08-07 19:20:13: Epoch: 028 Train Loss: 0.531, ap: 0.537, auc: 0.635, f1: 0.476, acc: 0.573
+2025-08-07 19:20:13: Epoch: 028 New best VALIDATION metrics
+2025-08-07 19:20:13: Epoch: 028 Test results, ap: 0.206, auc: 0.406, f1: 0.388, acc: 0.241
+2025-08-07 19:20:13: Epoch: 029 Train Loss: 0.517, ap: 0.539, auc: 0.636, f1: 0.488, acc: 0.592
+2025-08-07 19:20:13: Epoch: 029 New best VALIDATION metrics
+2025-08-07 19:20:13: Epoch: 029 Test results, ap: 0.202, auc: 0.396, f1: 0.388, acc: 0.241
+2025-08-07 19:20:14: Epoch: 030 Train Loss: 0.531, ap: 0.531, auc: 0.626, f1: 0.494, acc: 0.583
+2025-08-07 19:20:14: Epoch: 030 Test results, ap: 0.210, auc: 0.410, f1: 0.388, acc: 0.241
+2025-08-07 19:20:14: Epoch: 031 Train Loss: 0.512, ap: 0.553, auc: 0.657, f1: 0.514, acc: 0.650
+2025-08-07 19:20:14: Epoch: 031 New best VALIDATION metrics
+2025-08-07 19:20:14: Epoch: 031 Test results, ap: 0.209, auc: 0.402, f1: 0.388, acc: 0.241
+2025-08-07 19:20:15: Epoch: 032 Train Loss: 0.520, ap: 0.552, auc: 0.643, f1: 0.494, acc: 0.602
+2025-08-07 19:20:15: Epoch: 032 Test results, ap: 0.208, auc: 0.412, f1: 0.388, acc: 0.241
+2025-08-07 19:20:15: Epoch: 033 Train Loss: 0.494, ap: 0.567, auc: 0.664, f1: 0.521, acc: 0.660
+2025-08-07 19:20:15: Epoch: 033 New best VALIDATION metrics
+2025-08-07 19:20:16: Epoch: 033 Test results, ap: 0.204, auc: 0.405, f1: 0.388, acc: 0.241
+2025-08-07 19:20:16: Epoch: 034 Train Loss: 0.507, ap: 0.568, auc: 0.666, f1: 0.514, acc: 0.650
+2025-08-07 19:20:16: Epoch: 034 New best VALIDATION metrics
+2025-08-07 19:20:16: Epoch: 034 Test results, ap: 0.205, auc: 0.409, f1: 0.388, acc: 0.241
+2025-08-07 19:20:16: Epoch: 035 Train Loss: 0.489, ap: 0.555, auc: 0.642, f1: 0.512, acc: 0.612
+2025-08-07 19:20:17: Epoch: 035 Test results, ap: 0.207, auc: 0.414, f1: 0.388, acc: 0.241
+2025-08-07 19:20:17: Epoch: 036 Train Loss: 0.505, ap: 0.566, auc: 0.658, f1: 0.494, acc: 0.621
+2025-08-07 19:20:17: Epoch: 036 Test results, ap: 0.208, auc: 0.415, f1: 0.388, acc: 0.241
+2025-08-07 19:20:17: Epoch: 037 Train Loss: 0.522, ap: 0.571, auc: 0.669, f1: 0.507, acc: 0.641
+2025-08-07 19:20:17: Epoch: 037 New best VALIDATION metrics
+2025-08-07 19:20:18: Epoch: 037 Test results, ap: 0.210, auc: 0.412, f1: 0.388, acc: 0.241
+2025-08-07 19:20:18: Epoch: 038 Train Loss: 0.485, ap: 0.561, auc: 0.659, f1: 0.507, acc: 0.641
+2025-08-07 19:20:18: Epoch: 038 Test results, ap: 0.216, auc: 0.418, f1: 0.388, acc: 0.241
+2025-08-07 19:20:18: Epoch: 039 Train Loss: 0.483, ap: 0.552, auc: 0.656, f1: 0.513, acc: 0.631
+2025-08-07 19:20:19: Epoch: 039 Test results, ap: 0.214, auc: 0.418, f1: 0.388, acc: 0.241
+2025-08-07 19:20:19: Epoch: 040 Train Loss: 0.475, ap: 0.557, auc: 0.654, f1: 0.481, acc: 0.602
+2025-08-07 19:20:19: Epoch: 040 Test results, ap: 0.215, auc: 0.421, f1: 0.388, acc: 0.241
+2025-08-07 19:20:19: Epoch: 041 Train Loss: 0.490, ap: 0.566, auc: 0.660, f1: 0.500, acc: 0.631
+2025-08-07 19:20:20: Epoch: 041 Test results, ap: 0.213, auc: 0.417, f1: 0.388, acc: 0.241
+2025-08-07 19:20:20: Epoch: 042 Train Loss: 0.485, ap: 0.567, auc: 0.659, f1: 0.500, acc: 0.631
+2025-08-07 19:20:20: Epoch: 042 Test results, ap: 0.216, auc: 0.422, f1: 0.388, acc: 0.241
+2025-08-07 19:20:21: Epoch: 043 Train Loss: 0.492, ap: 0.568, auc: 0.663, f1: 0.500, acc: 0.631
+2025-08-07 19:20:21: Epoch: 043 Test results, ap: 0.213, auc: 0.416, f1: 0.388, acc: 0.241
+2025-08-07 19:20:21: Epoch: 044 Train Loss: 0.472, ap: 0.560, auc: 0.657, f1: 0.481, acc: 0.602
+2025-08-07 19:20:21: Epoch: 044 Test results, ap: 0.214, auc: 0.419, f1: 0.388, acc: 0.241
+2025-08-07 19:20:22: Epoch: 045 Train Loss: 0.487, ap: 0.562, auc: 0.666, f1: 0.500, acc: 0.631
+2025-08-07 19:20:22: Epoch: 045 Test results, ap: 0.215, auc: 0.419, f1: 0.388, acc: 0.241
+2025-08-07 19:20:22: Epoch: 046 Train Loss: 0.481, ap: 0.562, auc: 0.661, f1: 0.506, acc: 0.621
+2025-08-07 19:20:22: Epoch: 046 Test results, ap: 0.216, auc: 0.420, f1: 0.388, acc: 0.241
+2025-08-07 19:20:23: Epoch: 047 Train Loss: 0.477, ap: 0.562, auc: 0.672, f1: 0.507, acc: 0.641
+2025-08-07 19:20:23: Epoch: 047 Test results, ap: 0.214, auc: 0.416, f1: 0.388, acc: 0.241
+2025-08-07 19:20:23: Epoch: 048 Train Loss: 0.468, ap: 0.560, auc: 0.668, f1: 0.507, acc: 0.641
+2025-08-07 19:20:23: Epoch: 048 Test results, ap: 0.211, auc: 0.413, f1: 0.388, acc: 0.241
+2025-08-07 19:20:24: Epoch: 049 Train Loss: 0.481, ap: 0.559, auc: 0.661, f1: 0.500, acc: 0.612
+2025-08-07 19:20:24: Epoch: 049 Test results, ap: 0.215, auc: 0.420, f1: 0.388, acc: 0.241
+2025-08-07 19:20:24: Epoch: 050 Train Loss: 0.469, ap: 0.562, auc: 0.675, f1: 0.486, acc: 0.631
+2025-08-07 19:20:24: Epoch: 050 Test results, ap: 0.212, auc: 0.409, f1: 0.388, acc: 0.241
+2025-08-07 19:20:24: used time 0.01h
+2025-08-07 19:20:24: Running Cross Validation 2
+2025-08-07 19:20:24: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:20:25: Epoch: 001 Train Loss: 0.693, ap: 0.536, auc: 0.587, f1: 0.000, acc: 0.495
+2025-08-07 19:20:25: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:20:25: Epoch: 001 Test results, ap: 0.216, auc: 0.453, f1: 0.000, acc: 0.759
+2025-08-07 19:20:25: Epoch: 002 Train Loss: 0.695, ap: 0.540, auc: 0.602, f1: 0.671, acc: 0.505
+2025-08-07 19:20:25: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:20:25: Epoch: 002 Test results, ap: 0.221, auc: 0.471, f1: 0.388, acc: 0.241
+2025-08-07 19:20:26: Epoch: 003 Train Loss: 0.694, ap: 0.540, auc: 0.610, f1: 0.671, acc: 0.505
+2025-08-07 19:20:26: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:20:26: Epoch: 003 Test results, ap: 0.224, auc: 0.484, f1: 0.388, acc: 0.241
+2025-08-07 19:20:26: Epoch: 004 Train Loss: 0.693, ap: 0.632, auc: 0.686, f1: 0.671, acc: 0.505
+2025-08-07 19:20:26: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:20:26: Epoch: 004 Test results, ap: 0.230, auc: 0.511, f1: 0.388, acc: 0.241
+2025-08-07 19:20:27: Epoch: 005 Train Loss: 0.689, ap: 0.568, auc: 0.635, f1: 0.455, acc: 0.534
+2025-08-07 19:20:27: Epoch: 005 Test results, ap: 0.232, auc: 0.515, f1: 0.000, acc: 0.676
+2025-08-07 19:20:27: Epoch: 006 Train Loss: 0.671, ap: 0.586, auc: 0.686, f1: 0.735, acc: 0.650
+2025-08-07 19:20:27: Epoch: 006 Test results, ap: 0.218, auc: 0.478, f1: 0.388, acc: 0.241
+2025-08-07 19:20:28: Epoch: 007 Train Loss: 0.641, ap: 0.608, auc: 0.706, f1: 0.735, acc: 0.650
+2025-08-07 19:20:28: Epoch: 007 Test results, ap: 0.227, auc: 0.498, f1: 0.388, acc: 0.241
+2025-08-07 19:20:28: Epoch: 008 Train Loss: 0.627, ap: 0.644, auc: 0.720, f1: 0.712, acc: 0.670
+2025-08-07 19:20:28: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:20:28: Epoch: 008 Test results, ap: 0.215, auc: 0.453, f1: 0.091, acc: 0.630
+2025-08-07 19:20:29: Epoch: 009 Train Loss: 0.582, ap: 0.777, auc: 0.764, f1: 0.742, acc: 0.689
+2025-08-07 19:20:29: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:20:29: Epoch: 009 Test results, ap: 0.204, auc: 0.370, f1: 0.000, acc: 0.722
+2025-08-07 19:20:29: Epoch: 010 Train Loss: 0.615, ap: 0.626, auc: 0.717, f1: 0.754, acc: 0.709
+2025-08-07 19:20:29: Epoch: 010 Test results, ap: 0.223, auc: 0.398, f1: 0.211, acc: 0.722
+2025-08-07 19:20:30: Epoch: 011 Train Loss: 0.596, ap: 0.627, auc: 0.711, f1: 0.744, acc: 0.699
+2025-08-07 19:20:30: Epoch: 011 Test results, ap: 0.217, auc: 0.379, f1: 0.114, acc: 0.713
+2025-08-07 19:20:30: Epoch: 011 New best TEST metrics
+2025-08-07 19:20:30: Epoch: 012 Train Loss: 0.595, ap: 0.720, auc: 0.746, f1: 0.738, acc: 0.689
+2025-08-07 19:20:30: Epoch: 012 Test results, ap: 0.219, auc: 0.376, f1: 0.229, acc: 0.750
+2025-08-07 19:20:31: Epoch: 013 Train Loss: 0.563, ap: 0.774, auc: 0.757, f1: 0.719, acc: 0.650
+2025-08-07 19:20:31: Epoch: 013 Test results, ap: 0.217, auc: 0.368, f1: 0.229, acc: 0.750
+2025-08-07 19:20:31: Epoch: 014 Train Loss: 0.581, ap: 0.785, auc: 0.762, f1: 0.729, acc: 0.689
+2025-08-07 19:20:31: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:20:31: Epoch: 014 Test results, ap: 0.227, auc: 0.382, f1: 0.000, acc: 0.722
+2025-08-07 19:20:31: Epoch: 014 New best TEST metrics
+2025-08-07 19:20:32: Epoch: 015 Train Loss: 0.559, ap: 0.819, auc: 0.785, f1: 0.752, acc: 0.718
+2025-08-07 19:20:32: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:20:32: Epoch: 015 Test results, ap: 0.219, auc: 0.376, f1: 0.000, acc: 0.722
+2025-08-07 19:20:32: Epoch: 016 Train Loss: 0.557, ap: 0.820, auc: 0.792, f1: 0.756, acc: 0.718
+2025-08-07 19:20:32: Epoch: 016 New best VALIDATION metrics
+2025-08-07 19:20:32: Epoch: 016 Test results, ap: 0.230, auc: 0.396, f1: 0.000, acc: 0.722
+2025-08-07 19:20:32: Epoch: 016 New best TEST metrics
+2025-08-07 19:20:33: Epoch: 017 Train Loss: 0.547, ap: 0.833, auc: 0.787, f1: 0.704, acc: 0.689
+2025-08-07 19:20:33: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:20:33: Epoch: 017 Test results, ap: 0.226, auc: 0.394, f1: 0.000, acc: 0.722
+2025-08-07 19:20:33: Epoch: 018 Train Loss: 0.538, ap: 0.822, auc: 0.787, f1: 0.757, acc: 0.738
+2025-08-07 19:20:33: Epoch: 018 Test results, ap: 0.228, auc: 0.392, f1: 0.000, acc: 0.722
+2025-08-07 19:20:34: Epoch: 019 Train Loss: 0.533, ap: 0.839, auc: 0.810, f1: 0.750, acc: 0.728
+2025-08-07 19:20:34: Epoch: 019 New best VALIDATION metrics
+2025-08-07 19:20:34: Epoch: 019 Test results, ap: 0.223, auc: 0.384, f1: 0.000, acc: 0.722
+2025-08-07 19:20:34: Epoch: 020 Train Loss: 0.490, ap: 0.851, auc: 0.813, f1: 0.754, acc: 0.728
+2025-08-07 19:20:34: Epoch: 020 New best VALIDATION metrics
+2025-08-07 19:20:35: Epoch: 020 Test results, ap: 0.227, auc: 0.388, f1: 0.000, acc: 0.722
+2025-08-07 19:20:35: Epoch: 021 Train Loss: 0.488, ap: 0.842, auc: 0.803, f1: 0.750, acc: 0.728
+2025-08-07 19:20:35: Epoch: 021 Test results, ap: 0.231, auc: 0.387, f1: 0.000, acc: 0.722
+2025-08-07 19:20:35: Epoch: 022 Train Loss: 0.489, ap: 0.844, auc: 0.804, f1: 0.757, acc: 0.738
+2025-08-07 19:20:36: Epoch: 022 Test results, ap: 0.236, auc: 0.395, f1: 0.000, acc: 0.741
+2025-08-07 19:20:36: Epoch: 022 New best TEST metrics
+2025-08-07 19:20:36: Epoch: 023 Train Loss: 0.452, ap: 0.842, auc: 0.805, f1: 0.754, acc: 0.728
+2025-08-07 19:20:36: Epoch: 023 Test results, ap: 0.237, auc: 0.399, f1: 0.000, acc: 0.731
+2025-08-07 19:20:36: Epoch: 023 New best TEST metrics
+2025-08-07 19:20:36: Epoch: 024 Train Loss: 0.463, ap: 0.828, auc: 0.791, f1: 0.743, acc: 0.738
+2025-08-07 19:20:37: Epoch: 024 Test results, ap: 0.243, auc: 0.403, f1: 0.000, acc: 0.741
+2025-08-07 19:20:37: Epoch: 024 New best TEST metrics
+2025-08-07 19:20:37: Epoch: 025 Train Loss: 0.444, ap: 0.843, auc: 0.808, f1: 0.750, acc: 0.728
+2025-08-07 19:20:37: Epoch: 025 Test results, ap: 0.248, auc: 0.409, f1: 0.000, acc: 0.741
+2025-08-07 19:20:37: Epoch: 025 New best TEST metrics
+2025-08-07 19:20:38: Epoch: 026 Train Loss: 0.454, ap: 0.834, auc: 0.804, f1: 0.761, acc: 0.738
+2025-08-07 19:20:38: Epoch: 026 Test results, ap: 0.239, auc: 0.402, f1: 0.000, acc: 0.722
+2025-08-07 19:20:38: Epoch: 027 Train Loss: 0.451, ap: 0.843, auc: 0.813, f1: 0.765, acc: 0.738
+2025-08-07 19:20:38: Epoch: 027 Test results, ap: 0.239, auc: 0.410, f1: 0.000, acc: 0.722
+2025-08-07 19:20:39: Epoch: 028 Train Loss: 0.452, ap: 0.836, auc: 0.817, f1: 0.757, acc: 0.757
+2025-08-07 19:20:39: Epoch: 028 Test results, ap: 0.236, auc: 0.410, f1: 0.000, acc: 0.722
+2025-08-07 19:20:39: Epoch: 029 Train Loss: 0.450, ap: 0.860, auc: 0.833, f1: 0.771, acc: 0.757
+2025-08-07 19:20:39: Epoch: 029 New best VALIDATION metrics
+2025-08-07 19:20:39: Epoch: 029 Test results, ap: 0.234, auc: 0.402, f1: 0.000, acc: 0.722
+2025-08-07 19:20:39: Epoch: 030 Train Loss: 0.416, ap: 0.849, auc: 0.826, f1: 0.755, acc: 0.748
+2025-08-07 19:20:40: Epoch: 030 Test results, ap: 0.229, auc: 0.415, f1: 0.000, acc: 0.731
+2025-08-07 19:20:40: Epoch: 031 Train Loss: 0.411, ap: 0.851, auc: 0.826, f1: 0.768, acc: 0.748
+2025-08-07 19:20:40: Epoch: 031 Test results, ap: 0.232, auc: 0.421, f1: 0.000, acc: 0.722
+2025-08-07 19:20:40: Epoch: 032 Train Loss: 0.419, ap: 0.847, auc: 0.819, f1: 0.750, acc: 0.748
+2025-08-07 19:20:41: Epoch: 032 Test results, ap: 0.229, auc: 0.410, f1: 0.000, acc: 0.731
+2025-08-07 19:20:41: Epoch: 033 Train Loss: 0.415, ap: 0.846, auc: 0.820, f1: 0.755, acc: 0.748
+2025-08-07 19:20:41: Epoch: 033 Test results, ap: 0.231, auc: 0.412, f1: 0.000, acc: 0.722
+2025-08-07 19:20:41: Epoch: 034 Train Loss: 0.389, ap: 0.842, auc: 0.814, f1: 0.725, acc: 0.728
+2025-08-07 19:20:42: Epoch: 034 Test results, ap: 0.237, auc: 0.410, f1: 0.000, acc: 0.731
+2025-08-07 19:20:42: Epoch: 035 Train Loss: 0.383, ap: 0.852, auc: 0.822, f1: 0.750, acc: 0.748
+2025-08-07 19:20:42: Epoch: 035 Test results, ap: 0.241, auc: 0.417, f1: 0.000, acc: 0.731
+2025-08-07 19:20:42: Epoch: 035 New best TEST metrics
+2025-08-07 19:20:43: Epoch: 036 Train Loss: 0.395, ap: 0.848, auc: 0.820, f1: 0.725, acc: 0.728
+2025-08-07 19:20:43: Epoch: 036 Test results, ap: 0.240, auc: 0.415, f1: 0.000, acc: 0.731
+2025-08-07 19:20:43: Epoch: 037 Train Loss: 0.387, ap: 0.847, auc: 0.821, f1: 0.731, acc: 0.728
+2025-08-07 19:20:43: Epoch: 037 Test results, ap: 0.239, auc: 0.416, f1: 0.000, acc: 0.731
+2025-08-07 19:20:44: Epoch: 038 Train Loss: 0.386, ap: 0.849, auc: 0.821, f1: 0.755, acc: 0.767
+2025-08-07 19:20:44: Epoch: 038 Test results, ap: 0.241, auc: 0.419, f1: 0.000, acc: 0.731
+2025-08-07 19:20:44: Epoch: 038 New best TEST metrics
+2025-08-07 19:20:44: Epoch: 039 Train Loss: 0.374, ap: 0.849, auc: 0.823, f1: 0.725, acc: 0.728
+2025-08-07 19:20:44: Epoch: 039 Test results, ap: 0.243, auc: 0.420, f1: 0.129, acc: 0.750
+2025-08-07 19:20:44: Epoch: 039 New best TEST metrics
+2025-08-07 19:20:45: Epoch: 040 Train Loss: 0.365, ap: 0.839, auc: 0.812, f1: 0.731, acc: 0.728
+2025-08-07 19:20:45: Epoch: 040 Test results, ap: 0.246, auc: 0.421, f1: 0.125, acc: 0.741
+2025-08-07 19:20:45: Epoch: 040 New best TEST metrics
+2025-08-07 19:20:45: Epoch: 041 Train Loss: 0.354, ap: 0.838, auc: 0.808, f1: 0.729, acc: 0.748
+2025-08-07 19:20:45: Epoch: 041 Test results, ap: 0.246, auc: 0.420, f1: 0.000, acc: 0.731
+2025-08-07 19:20:46: Epoch: 042 Train Loss: 0.367, ap: 0.840, auc: 0.811, f1: 0.722, acc: 0.738
+2025-08-07 19:20:46: Epoch: 042 Test results, ap: 0.248, auc: 0.422, f1: 0.125, acc: 0.741
+2025-08-07 19:20:46: Epoch: 042 New best TEST metrics
+2025-08-07 19:20:46: Epoch: 043 Train Loss: 0.323, ap: 0.840, auc: 0.808, f1: 0.700, acc: 0.709
+2025-08-07 19:20:46: Epoch: 043 Test results, ap: 0.248, auc: 0.420, f1: 0.129, acc: 0.750
+2025-08-07 19:20:47: Epoch: 044 Train Loss: 0.363, ap: 0.835, auc: 0.806, f1: 0.729, acc: 0.748
+2025-08-07 19:20:47: Epoch: 044 Test results, ap: 0.245, auc: 0.418, f1: 0.129, acc: 0.750
+2025-08-07 19:20:47: Epoch: 045 Train Loss: 0.354, ap: 0.839, auc: 0.808, f1: 0.713, acc: 0.718
+2025-08-07 19:20:47: Epoch: 045 Test results, ap: 0.247, auc: 0.420, f1: 0.182, acc: 0.750
+2025-08-07 19:20:48: Epoch: 046 Train Loss: 0.342, ap: 0.839, auc: 0.808, f1: 0.701, acc: 0.718
+2025-08-07 19:20:48: Epoch: 046 Test results, ap: 0.247, auc: 0.423, f1: 0.125, acc: 0.741
+2025-08-07 19:20:48: Epoch: 046 New best TEST metrics
+2025-08-07 19:20:48: Epoch: 047 Train Loss: 0.344, ap: 0.838, auc: 0.807, f1: 0.694, acc: 0.709
+2025-08-07 19:20:48: Epoch: 047 Test results, ap: 0.253, auc: 0.427, f1: 0.176, acc: 0.741
+2025-08-07 19:20:48: Epoch: 047 New best TEST metrics
+2025-08-07 19:20:49: Epoch: 048 Train Loss: 0.331, ap: 0.832, auc: 0.801, f1: 0.708, acc: 0.728
+2025-08-07 19:20:49: Epoch: 048 Test results, ap: 0.248, auc: 0.425, f1: 0.176, acc: 0.741
+2025-08-07 19:20:49: Epoch: 049 Train Loss: 0.343, ap: 0.832, auc: 0.803, f1: 0.701, acc: 0.718
+2025-08-07 19:20:49: Epoch: 049 Test results, ap: 0.253, auc: 0.429, f1: 0.176, acc: 0.741
+2025-08-07 19:20:49: Epoch: 049 New best TEST metrics
+2025-08-07 19:20:50: Epoch: 050 Train Loss: 0.331, ap: 0.827, auc: 0.796, f1: 0.695, acc: 0.718
+2025-08-07 19:20:50: Epoch: 050 Test results, ap: 0.242, auc: 0.424, f1: 0.176, acc: 0.741
+2025-08-07 19:20:50: used time 0.01h
+2025-08-07 19:20:50: Running Cross Validation 3
+2025-08-07 19:20:50: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:20:50: Epoch: 001 Train Loss: 0.694, ap: 0.545, auc: 0.667, f1: 0.579, acc: 0.408
+2025-08-07 19:20:50: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:20:50: Epoch: 001 Test results, ap: 0.183, auc: 0.342, f1: 0.388, acc: 0.241
+2025-08-07 19:20:51: Epoch: 002 Train Loss: 0.695, ap: 0.437, auc: 0.456, f1: 0.579, acc: 0.408
+2025-08-07 19:20:51: Epoch: 002 Test results, ap: 0.489, auc: 0.562, f1: 0.388, acc: 0.241
+2025-08-07 19:20:51: Epoch: 003 Train Loss: 0.693, ap: 0.614, auc: 0.644, f1: 0.000, acc: 0.592
+2025-08-07 19:20:51: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:20:51: Epoch: 003 Test results, ap: 0.219, auc: 0.398, f1: 0.000, acc: 0.759
+2025-08-07 19:20:52: Epoch: 004 Train Loss: 0.695, ap: 0.633, auc: 0.678, f1: 0.579, acc: 0.408
+2025-08-07 19:20:52: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:20:52: Epoch: 004 Test results, ap: 0.200, auc: 0.379, f1: 0.388, acc: 0.241
+2025-08-07 19:20:52: Epoch: 005 Train Loss: 0.691, ap: 0.634, auc: 0.695, f1: 0.000, acc: 0.592
+2025-08-07 19:20:52: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:20:53: Epoch: 005 Test results, ap: 0.210, auc: 0.421, f1: 0.000, acc: 0.759
+2025-08-07 19:20:53: Epoch: 006 Train Loss: 0.693, ap: 0.600, auc: 0.708, f1: 0.579, acc: 0.408
+2025-08-07 19:20:53: Epoch: 006 Test results, ap: 0.229, auc: 0.479, f1: 0.388, acc: 0.241
+2025-08-07 19:20:53: Epoch: 007 Train Loss: 0.695, ap: 0.542, auc: 0.670, f1: 0.579, acc: 0.408
+2025-08-07 19:20:54: Epoch: 007 Test results, ap: 0.219, auc: 0.457, f1: 0.388, acc: 0.241
+2025-08-07 19:20:54: Epoch: 008 Train Loss: 0.693, ap: 0.583, auc: 0.703, f1: 0.000, acc: 0.592
+2025-08-07 19:20:54: Epoch: 008 Test results, ap: 0.231, auc: 0.491, f1: 0.000, acc: 0.759
+2025-08-07 19:20:54: Epoch: 009 Train Loss: 0.693, ap: 0.582, auc: 0.701, f1: 0.000, acc: 0.592
+2025-08-07 19:20:54: Epoch: 009 Test results, ap: 0.231, auc: 0.489, f1: 0.000, acc: 0.759
+2025-08-07 19:20:55: Epoch: 010 Train Loss: 0.696, ap: 0.578, auc: 0.701, f1: 0.000, acc: 0.592
+2025-08-07 19:20:55: Epoch: 010 Test results, ap: 0.224, auc: 0.466, f1: 0.000, acc: 0.759
+2025-08-07 19:20:55: Epoch: 011 Train Loss: 0.694, ap: 0.579, auc: 0.706, f1: 0.579, acc: 0.408
+2025-08-07 19:20:55: Epoch: 011 Test results, ap: 0.222, auc: 0.464, f1: 0.388, acc: 0.241
+2025-08-07 19:20:55: Epoch: 011 New best TEST metrics
+2025-08-07 19:20:56: Epoch: 012 Train Loss: 0.696, ap: 0.582, auc: 0.708, f1: 0.579, acc: 0.408
+2025-08-07 19:20:56: Epoch: 012 Test results, ap: 0.210, auc: 0.432, f1: 0.388, acc: 0.241
+2025-08-07 19:20:56: Epoch: 013 Train Loss: 0.693, ap: 0.585, auc: 0.710, f1: 0.579, acc: 0.408
+2025-08-07 19:20:57: Epoch: 013 Test results, ap: 0.205, auc: 0.417, f1: 0.388, acc: 0.241
+2025-08-07 19:20:57: Epoch: 014 Train Loss: 0.694, ap: 0.573, auc: 0.713, f1: 0.000, acc: 0.592
+2025-08-07 19:20:57: Epoch: 014 Test results, ap: 0.207, auc: 0.424, f1: 0.000, acc: 0.759
+2025-08-07 19:20:57: Epoch: 015 Train Loss: 0.692, ap: 0.586, auc: 0.721, f1: 0.579, acc: 0.408
+2025-08-07 19:20:58: Epoch: 015 Test results, ap: 0.215, auc: 0.450, f1: 0.388, acc: 0.241
+2025-08-07 19:20:58: Epoch: 016 Train Loss: 0.697, ap: 0.597, auc: 0.728, f1: 0.579, acc: 0.408
+2025-08-07 19:20:58: Epoch: 016 Test results, ap: 0.214, auc: 0.445, f1: 0.388, acc: 0.241
+2025-08-07 19:20:58: Epoch: 017 Train Loss: 0.692, ap: 0.593, auc: 0.732, f1: 0.367, acc: 0.631
+2025-08-07 19:20:59: Epoch: 017 Test results, ap: 0.218, auc: 0.455, f1: 0.000, acc: 0.722
+2025-08-07 19:20:59: Epoch: 018 Train Loss: 0.691, ap: 0.587, auc: 0.731, f1: 0.673, acc: 0.660
+2025-08-07 19:20:59: Epoch: 018 Test results, ap: 0.215, auc: 0.451, f1: 0.388, acc: 0.241
+2025-08-07 19:20:59: Epoch: 019 Train Loss: 0.675, ap: 0.633, auc: 0.738, f1: 0.641, acc: 0.553
+2025-08-07 19:20:59: Epoch: 019 New best VALIDATION metrics
+2025-08-07 19:21:00: Epoch: 019 Test results, ap: 0.224, auc: 0.417, f1: 0.388, acc: 0.241
+2025-08-07 19:21:00: Epoch: 020 Train Loss: 0.641, ap: 0.667, auc: 0.761, f1: 0.667, acc: 0.621
+2025-08-07 19:21:00: Epoch: 020 New best VALIDATION metrics
+2025-08-07 19:21:00: Epoch: 020 Test results, ap: 0.226, auc: 0.424, f1: 0.388, acc: 0.241
+2025-08-07 19:21:00: Epoch: 021 Train Loss: 0.607, ap: 0.650, auc: 0.764, f1: 0.673, acc: 0.650
+2025-08-07 19:21:01: Epoch: 021 Test results, ap: 0.237, auc: 0.428, f1: 0.388, acc: 0.241
+2025-08-07 19:21:01: Epoch: 022 Train Loss: 0.594, ap: 0.642, auc: 0.765, f1: 0.660, acc: 0.650
+2025-08-07 19:21:01: Epoch: 022 Test results, ap: 0.231, auc: 0.402, f1: 0.388, acc: 0.241
+2025-08-07 19:21:01: Epoch: 023 Train Loss: 0.584, ap: 0.652, auc: 0.760, f1: 0.578, acc: 0.660
+2025-08-07 19:21:02: Epoch: 023 Test results, ap: 0.227, auc: 0.383, f1: 0.336, acc: 0.231
+2025-08-07 19:21:02: Epoch: 024 Train Loss: 0.583, ap: 0.655, auc: 0.772, f1: 0.653, acc: 0.680
+2025-08-07 19:21:02: Epoch: 024 Test results, ap: 0.229, auc: 0.391, f1: 0.388, acc: 0.241
+2025-08-07 19:21:02: Epoch: 025 Train Loss: 0.594, ap: 0.669, auc: 0.775, f1: 0.679, acc: 0.660
+2025-08-07 19:21:02: Epoch: 025 New best VALIDATION metrics
+2025-08-07 19:21:03: Epoch: 025 Test results, ap: 0.229, auc: 0.393, f1: 0.388, acc: 0.241
+2025-08-07 19:21:03: Epoch: 026 Train Loss: 0.590, ap: 0.662, auc: 0.773, f1: 0.673, acc: 0.680
+2025-08-07 19:21:03: Epoch: 026 Test results, ap: 0.219, auc: 0.367, f1: 0.388, acc: 0.241
+2025-08-07 19:21:04: Epoch: 027 Train Loss: 0.578, ap: 0.660, auc: 0.770, f1: 0.646, acc: 0.670
+2025-08-07 19:21:04: Epoch: 027 Test results, ap: 0.220, auc: 0.351, f1: 0.194, acc: 0.463
+2025-08-07 19:21:04: Epoch: 028 Train Loss: 0.561, ap: 0.656, auc: 0.767, f1: 0.622, acc: 0.670
+2025-08-07 19:21:04: Epoch: 028 Test results, ap: 0.218, auc: 0.343, f1: 0.175, acc: 0.565
+2025-08-07 19:21:04: Epoch: 029 Train Loss: 0.548, ap: 0.672, auc: 0.776, f1: 0.660, acc: 0.680
+2025-08-07 19:21:04: Epoch: 029 New best VALIDATION metrics
+2025-08-07 19:21:05: Epoch: 029 Test results, ap: 0.218, auc: 0.344, f1: 0.185, acc: 0.509
+2025-08-07 19:21:05: Epoch: 030 Train Loss: 0.576, ap: 0.677, auc: 0.777, f1: 0.673, acc: 0.660
+2025-08-07 19:21:05: Epoch: 030 New best VALIDATION metrics
+2025-08-07 19:21:05: Epoch: 030 Test results, ap: 0.219, auc: 0.348, f1: 0.242, acc: 0.306
+2025-08-07 19:21:05: Epoch: 031 Train Loss: 0.571, ap: 0.667, auc: 0.770, f1: 0.622, acc: 0.670
+2025-08-07 19:21:06: Epoch: 031 Test results, ap: 0.214, auc: 0.337, f1: 0.174, acc: 0.648
+2025-08-07 19:21:06: Epoch: 032 Train Loss: 0.547, ap: 0.673, auc: 0.774, f1: 0.630, acc: 0.670
+2025-08-07 19:21:06: Epoch: 032 Test results, ap: 0.215, auc: 0.335, f1: 0.192, acc: 0.611
+2025-08-07 19:21:06: Epoch: 033 Train Loss: 0.553, ap: 0.676, auc: 0.776, f1: 0.653, acc: 0.680
+2025-08-07 19:21:07: Epoch: 033 Test results, ap: 0.218, auc: 0.340, f1: 0.179, acc: 0.491
+2025-08-07 19:21:07: Epoch: 034 Train Loss: 0.541, ap: 0.666, auc: 0.776, f1: 0.630, acc: 0.670
+2025-08-07 19:21:07: Epoch: 034 Test results, ap: 0.218, auc: 0.339, f1: 0.192, acc: 0.611
+2025-08-07 19:21:08: Epoch: 035 Train Loss: 0.555, ap: 0.665, auc: 0.773, f1: 0.630, acc: 0.670
+2025-08-07 19:21:08: Epoch: 035 Test results, ap: 0.218, auc: 0.336, f1: 0.167, acc: 0.630
+2025-08-07 19:21:08: Epoch: 036 Train Loss: 0.553, ap: 0.666, auc: 0.774, f1: 0.629, acc: 0.680
+2025-08-07 19:21:08: Epoch: 036 Test results, ap: 0.218, auc: 0.336, f1: 0.178, acc: 0.657
+2025-08-07 19:21:09: Epoch: 037 Train Loss: 0.542, ap: 0.670, auc: 0.776, f1: 0.638, acc: 0.670
+2025-08-07 19:21:09: Epoch: 037 Test results, ap: 0.218, auc: 0.341, f1: 0.179, acc: 0.574
+2025-08-07 19:21:09: Epoch: 038 Train Loss: 0.545, ap: 0.671, auc: 0.776, f1: 0.629, acc: 0.680
+2025-08-07 19:21:09: Epoch: 038 Test results, ap: 0.218, auc: 0.337, f1: 0.178, acc: 0.657
+2025-08-07 19:21:10: Epoch: 039 Train Loss: 0.545, ap: 0.678, auc: 0.776, f1: 0.637, acc: 0.680
+2025-08-07 19:21:10: Epoch: 039 New best VALIDATION metrics
+2025-08-07 19:21:10: Epoch: 039 Test results, ap: 0.217, auc: 0.333, f1: 0.186, acc: 0.676
+2025-08-07 19:21:10: Epoch: 040 Train Loss: 0.543, ap: 0.695, auc: 0.780, f1: 0.644, acc: 0.689
+2025-08-07 19:21:10: Epoch: 040 New best VALIDATION metrics
+2025-08-07 19:21:10: Epoch: 040 Test results, ap: 0.218, auc: 0.336, f1: 0.205, acc: 0.713
+2025-08-07 19:21:11: Epoch: 041 Train Loss: 0.541, ap: 0.696, auc: 0.781, f1: 0.637, acc: 0.680
+2025-08-07 19:21:11: Epoch: 041 New best VALIDATION metrics
+2025-08-07 19:21:11: Epoch: 041 Test results, ap: 0.219, auc: 0.338, f1: 0.200, acc: 0.704
+2025-08-07 19:21:11: Epoch: 042 Train Loss: 0.549, ap: 0.696, auc: 0.782, f1: 0.628, acc: 0.689
+2025-08-07 19:21:11: Epoch: 042 New best VALIDATION metrics
+2025-08-07 19:21:11: Epoch: 042 Test results, ap: 0.219, auc: 0.338, f1: 0.216, acc: 0.731
+2025-08-07 19:21:12: Epoch: 043 Train Loss: 0.532, ap: 0.698, auc: 0.783, f1: 0.644, acc: 0.689
+2025-08-07 19:21:12: Epoch: 043 New best VALIDATION metrics
+2025-08-07 19:21:12: Epoch: 043 Test results, ap: 0.220, auc: 0.343, f1: 0.190, acc: 0.685
+2025-08-07 19:21:12: Epoch: 044 Train Loss: 0.539, ap: 0.712, auc: 0.786, f1: 0.636, acc: 0.689
+2025-08-07 19:21:12: Epoch: 044 New best VALIDATION metrics
+2025-08-07 19:21:12: Epoch: 044 Test results, ap: 0.221, auc: 0.341, f1: 0.211, acc: 0.722
+2025-08-07 19:21:13: Epoch: 045 Train Loss: 0.544, ap: 0.710, auc: 0.783, f1: 0.636, acc: 0.689
+2025-08-07 19:21:13: Epoch: 045 Test results, ap: 0.221, auc: 0.342, f1: 0.216, acc: 0.731
+2025-08-07 19:21:13: Epoch: 046 Train Loss: 0.526, ap: 0.713, auc: 0.785, f1: 0.644, acc: 0.689
+2025-08-07 19:21:13: Epoch: 046 Test results, ap: 0.224, auc: 0.350, f1: 0.211, acc: 0.722
+2025-08-07 19:21:14: Epoch: 047 Train Loss: 0.530, ap: 0.712, auc: 0.785, f1: 0.636, acc: 0.689
+2025-08-07 19:21:14: Epoch: 047 Test results, ap: 0.224, auc: 0.352, f1: 0.216, acc: 0.731
+2025-08-07 19:21:14: Epoch: 048 Train Loss: 0.537, ap: 0.710, auc: 0.782, f1: 0.612, acc: 0.680
+2025-08-07 19:21:14: Epoch: 048 Test results, ap: 0.225, auc: 0.352, f1: 0.125, acc: 0.741
+2025-08-07 19:21:15: Epoch: 049 Train Loss: 0.534, ap: 0.712, auc: 0.783, f1: 0.636, acc: 0.689
+2025-08-07 19:21:15: Epoch: 049 Test results, ap: 0.226, auc: 0.354, f1: 0.235, acc: 0.759
+2025-08-07 19:21:15: Epoch: 050 Train Loss: 0.533, ap: 0.715, auc: 0.785, f1: 0.652, acc: 0.699
+2025-08-07 19:21:15: Epoch: 050 New best VALIDATION metrics
+2025-08-07 19:21:15: Epoch: 050 Test results, ap: 0.228, auc: 0.364, f1: 0.216, acc: 0.731
+2025-08-07 19:21:15: used time 0.01h
+2025-08-07 19:21:15: Running Cross Validation 4
+2025-08-07 19:21:15: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:21:16: Epoch: 001 Train Loss: 0.693, ap: 0.546, auc: 0.703, f1: 0.000, acc: 0.602
+2025-08-07 19:21:16: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:21:16: Epoch: 001 Test results, ap: 0.197, auc: 0.407, f1: 0.000, acc: 0.759
+2025-08-07 19:21:16: Epoch: 002 Train Loss: 0.693, ap: 0.532, auc: 0.664, f1: 0.000, acc: 0.602
+2025-08-07 19:21:16: Epoch: 002 Test results, ap: 0.217, auc: 0.468, f1: 0.000, acc: 0.759
+2025-08-07 19:21:17: Epoch: 003 Train Loss: 0.694, ap: 0.560, auc: 0.700, f1: 0.569, acc: 0.398
+2025-08-07 19:21:17: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:21:17: Epoch: 003 Test results, ap: 0.224, auc: 0.495, f1: 0.388, acc: 0.241
+2025-08-07 19:21:17: Epoch: 004 Train Loss: 0.693, ap: 0.571, auc: 0.704, f1: 0.478, acc: 0.660
+2025-08-07 19:21:17: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:21:17: Epoch: 004 Test results, ap: 0.228, auc: 0.508, f1: 0.000, acc: 0.704
+2025-08-07 19:21:18: Epoch: 005 Train Loss: 0.690, ap: 0.609, auc: 0.726, f1: 0.275, acc: 0.641
+2025-08-07 19:21:18: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:21:18: Epoch: 005 Test results, ap: 0.215, auc: 0.470, f1: 0.000, acc: 0.704
+2025-08-07 19:21:18: Epoch: 006 Train Loss: 0.670, ap: 0.633, auc: 0.736, f1: 0.620, acc: 0.631
+2025-08-07 19:21:18: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:21:18: Epoch: 006 Test results, ap: 0.214, auc: 0.469, f1: 0.388, acc: 0.241
+2025-08-07 19:21:19: Epoch: 007 Train Loss: 0.685, ap: 0.648, auc: 0.729, f1: 0.583, acc: 0.612
+2025-08-07 19:21:19: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:21:19: Epoch: 007 Test results, ap: 0.206, auc: 0.435, f1: 0.388, acc: 0.241
+2025-08-07 19:21:19: Epoch: 008 Train Loss: 0.651, ap: 0.685, auc: 0.744, f1: 0.620, acc: 0.631
+2025-08-07 19:21:19: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:21:19: Epoch: 008 Test results, ap: 0.204, auc: 0.423, f1: 0.388, acc: 0.241
+2025-08-07 19:21:20: Epoch: 009 Train Loss: 0.630, ap: 0.692, auc: 0.742, f1: 0.605, acc: 0.709
+2025-08-07 19:21:20: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:21:20: Epoch: 009 Test results, ap: 0.211, auc: 0.436, f1: 0.203, acc: 0.565
+2025-08-07 19:21:20: Epoch: 010 Train Loss: 0.605, ap: 0.653, auc: 0.732, f1: 0.634, acc: 0.641
+2025-08-07 19:21:20: Epoch: 010 Test results, ap: 0.213, auc: 0.435, f1: 0.388, acc: 0.241
+2025-08-07 19:21:21: Epoch: 011 Train Loss: 0.591, ap: 0.651, auc: 0.734, f1: 0.645, acc: 0.680
+2025-08-07 19:21:21: Epoch: 011 Test results, ap: 0.217, auc: 0.430, f1: 0.394, acc: 0.259
+2025-08-07 19:21:21: Epoch: 011 New best TEST metrics
+2025-08-07 19:21:21: Epoch: 012 Train Loss: 0.582, ap: 0.661, auc: 0.743, f1: 0.641, acc: 0.641
+2025-08-07 19:21:22: Epoch: 012 Test results, ap: 0.229, auc: 0.429, f1: 0.388, acc: 0.241
+2025-08-07 19:21:22: Epoch: 012 New best TEST metrics
+2025-08-07 19:21:22: Epoch: 013 Train Loss: 0.568, ap: 0.647, auc: 0.736, f1: 0.641, acc: 0.641
+2025-08-07 19:21:22: Epoch: 013 Test results, ap: 0.224, auc: 0.435, f1: 0.388, acc: 0.241
+2025-08-07 19:21:22: Epoch: 013 New best TEST metrics
+2025-08-07 19:21:22: Epoch: 014 Train Loss: 0.562, ap: 0.656, auc: 0.744, f1: 0.646, acc: 0.660
+2025-08-07 19:21:23: Epoch: 014 Test results, ap: 0.222, auc: 0.422, f1: 0.382, acc: 0.250
+2025-08-07 19:21:23: Epoch: 015 Train Loss: 0.543, ap: 0.683, auc: 0.753, f1: 0.667, acc: 0.689
+2025-08-07 19:21:23: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:21:23: Epoch: 015 Test results, ap: 0.210, auc: 0.395, f1: 0.342, acc: 0.287
+2025-08-07 19:21:23: Epoch: 016 Train Loss: 0.539, ap: 0.686, auc: 0.741, f1: 0.653, acc: 0.680
+2025-08-07 19:21:24: Epoch: 016 Test results, ap: 0.201, auc: 0.385, f1: 0.315, acc: 0.315
+2025-08-07 19:21:24: Epoch: 017 Train Loss: 0.530, ap: 0.680, auc: 0.736, f1: 0.653, acc: 0.670
+2025-08-07 19:21:24: Epoch: 017 Test results, ap: 0.206, auc: 0.407, f1: 0.394, acc: 0.259
+2025-08-07 19:21:24: Epoch: 018 Train Loss: 0.522, ap: 0.731, auc: 0.771, f1: 0.660, acc: 0.660
+2025-08-07 19:21:24: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:21:25: Epoch: 018 Test results, ap: 0.200, auc: 0.389, f1: 0.374, acc: 0.287
+2025-08-07 19:21:25: Epoch: 019 Train Loss: 0.510, ap: 0.736, auc: 0.768, f1: 0.653, acc: 0.660
+2025-08-07 19:21:25: Epoch: 019 New best VALIDATION metrics
+2025-08-07 19:21:25: Epoch: 019 Test results, ap: 0.205, auc: 0.416, f1: 0.382, acc: 0.250
+2025-08-07 19:21:25: Epoch: 020 Train Loss: 0.501, ap: 0.717, auc: 0.756, f1: 0.647, acc: 0.650
+2025-08-07 19:21:26: Epoch: 020 Test results, ap: 0.222, auc: 0.468, f1: 0.388, acc: 0.241
+2025-08-07 19:21:26: Epoch: 020 New best TEST metrics
+2025-08-07 19:21:26: Epoch: 021 Train Loss: 0.487, ap: 0.743, auc: 0.790, f1: 0.673, acc: 0.689
+2025-08-07 19:21:26: Epoch: 021 New best VALIDATION metrics
+2025-08-07 19:21:26: Epoch: 021 Test results, ap: 0.210, auc: 0.424, f1: 0.253, acc: 0.343
+2025-08-07 19:21:26: Epoch: 022 Train Loss: 0.500, ap: 0.716, auc: 0.773, f1: 0.673, acc: 0.680
+2025-08-07 19:21:27: Epoch: 022 Test results, ap: 0.216, auc: 0.439, f1: 0.382, acc: 0.250
+2025-08-07 19:21:27: Epoch: 023 Train Loss: 0.480, ap: 0.718, auc: 0.776, f1: 0.667, acc: 0.680
+2025-08-07 19:21:27: Epoch: 023 Test results, ap: 0.216, auc: 0.439, f1: 0.381, acc: 0.278
+2025-08-07 19:21:27: Epoch: 024 Train Loss: 0.466, ap: 0.726, auc: 0.786, f1: 0.690, acc: 0.738
+2025-08-07 19:21:28: Epoch: 024 Test results, ap: 0.211, auc: 0.420, f1: 0.289, acc: 0.500
+2025-08-07 19:21:28: Epoch: 025 Train Loss: 0.474, ap: 0.712, auc: 0.773, f1: 0.621, acc: 0.680
+2025-08-07 19:21:28: Epoch: 025 Test results, ap: 0.206, auc: 0.404, f1: 0.254, acc: 0.565
+2025-08-07 19:21:28: Epoch: 026 Train Loss: 0.439, ap: 0.707, auc: 0.776, f1: 0.667, acc: 0.718
+2025-08-07 19:21:29: Epoch: 026 Test results, ap: 0.208, auc: 0.405, f1: 0.294, acc: 0.333
+2025-08-07 19:21:29: Epoch: 027 Train Loss: 0.461, ap: 0.716, auc: 0.784, f1: 0.688, acc: 0.718
+2025-08-07 19:21:29: Epoch: 027 Test results, ap: 0.219, auc: 0.422, f1: 0.372, acc: 0.250
+2025-08-07 19:21:29: Epoch: 028 Train Loss: 0.446, ap: 0.721, auc: 0.790, f1: 0.690, acc: 0.738
+2025-08-07 19:21:30: Epoch: 028 Test results, ap: 0.209, auc: 0.389, f1: 0.247, acc: 0.435
+2025-08-07 19:21:30: Epoch: 029 Train Loss: 0.425, ap: 0.681, auc: 0.760, f1: 0.659, acc: 0.709
+2025-08-07 19:21:30: Epoch: 029 Test results, ap: 0.218, auc: 0.409, f1: 0.375, acc: 0.352
+2025-08-07 19:21:31: Epoch: 030 Train Loss: 0.416, ap: 0.699, auc: 0.775, f1: 0.652, acc: 0.699
+2025-08-07 19:21:31: Epoch: 030 Test results, ap: 0.215, auc: 0.402, f1: 0.318, acc: 0.324
+2025-08-07 19:21:31: Epoch: 031 Train Loss: 0.410, ap: 0.718, auc: 0.786, f1: 0.659, acc: 0.709
+2025-08-07 19:21:31: Epoch: 031 Test results, ap: 0.214, auc: 0.405, f1: 0.365, acc: 0.324
+2025-08-07 19:21:32: Epoch: 032 Train Loss: 0.415, ap: 0.711, auc: 0.781, f1: 0.651, acc: 0.709
+2025-08-07 19:21:32: Epoch: 032 Test results, ap: 0.216, auc: 0.406, f1: 0.362, acc: 0.315
+2025-08-07 19:21:32: Epoch: 033 Train Loss: 0.416, ap: 0.696, auc: 0.769, f1: 0.651, acc: 0.709
+2025-08-07 19:21:32: Epoch: 033 Test results, ap: 0.221, auc: 0.401, f1: 0.302, acc: 0.315
+2025-08-07 19:21:33: Epoch: 034 Train Loss: 0.428, ap: 0.693, auc: 0.768, f1: 0.644, acc: 0.689
+2025-08-07 19:21:33: Epoch: 034 Test results, ap: 0.221, auc: 0.423, f1: 0.368, acc: 0.269
+2025-08-07 19:21:33: Epoch: 035 Train Loss: 0.415, ap: 0.711, auc: 0.776, f1: 0.659, acc: 0.718
+2025-08-07 19:21:33: Epoch: 035 Test results, ap: 0.222, auc: 0.395, f1: 0.227, acc: 0.306
+2025-08-07 19:21:34: Epoch: 036 Train Loss: 0.415, ap: 0.719, auc: 0.789, f1: 0.644, acc: 0.689
+2025-08-07 19:21:34: Epoch: 036 Test results, ap: 0.221, auc: 0.410, f1: 0.333, acc: 0.296
+2025-08-07 19:21:34: Epoch: 037 Train Loss: 0.420, ap: 0.713, auc: 0.782, f1: 0.667, acc: 0.718
+2025-08-07 19:21:34: Epoch: 037 Test results, ap: 0.223, auc: 0.408, f1: 0.327, acc: 0.315
+2025-08-07 19:21:35: Epoch: 038 Train Loss: 0.409, ap: 0.708, auc: 0.779, f1: 0.667, acc: 0.709
+2025-08-07 19:21:35: Epoch: 038 Test results, ap: 0.226, auc: 0.435, f1: 0.372, acc: 0.250
+2025-08-07 19:21:35: Epoch: 039 Train Loss: 0.423, ap: 0.710, auc: 0.775, f1: 0.644, acc: 0.699
+2025-08-07 19:21:35: Epoch: 039 Test results, ap: 0.229, auc: 0.418, f1: 0.333, acc: 0.296
+2025-08-07 19:21:36: Epoch: 040 Train Loss: 0.428, ap: 0.713, auc: 0.779, f1: 0.651, acc: 0.718
+2025-08-07 19:21:36: Epoch: 040 Test results, ap: 0.237, auc: 0.402, f1: 0.217, acc: 0.333
+2025-08-07 19:21:36: Epoch: 041 Train Loss: 0.389, ap: 0.705, auc: 0.773, f1: 0.659, acc: 0.709
+2025-08-07 19:21:36: Epoch: 041 Test results, ap: 0.233, auc: 0.418, f1: 0.370, acc: 0.306
+2025-08-07 19:21:37: Epoch: 042 Train Loss: 0.418, ap: 0.703, auc: 0.772, f1: 0.652, acc: 0.699
+2025-08-07 19:21:37: Epoch: 042 Test results, ap: 0.232, auc: 0.415, f1: 0.345, acc: 0.296
+2025-08-07 19:21:37: Epoch: 043 Train Loss: 0.400, ap: 0.707, auc: 0.774, f1: 0.628, acc: 0.689
+2025-08-07 19:21:37: Epoch: 043 Test results, ap: 0.232, auc: 0.407, f1: 0.294, acc: 0.287
+2025-08-07 19:21:38: Epoch: 044 Train Loss: 0.395, ap: 0.716, auc: 0.781, f1: 0.644, acc: 0.699
+2025-08-07 19:21:38: Epoch: 044 Test results, ap: 0.232, auc: 0.410, f1: 0.294, acc: 0.287
+2025-08-07 19:21:38: Epoch: 045 Train Loss: 0.391, ap: 0.715, auc: 0.780, f1: 0.659, acc: 0.709
+2025-08-07 19:21:38: Epoch: 045 Test results, ap: 0.234, auc: 0.415, f1: 0.330, acc: 0.287
+2025-08-07 19:21:39: Epoch: 046 Train Loss: 0.384, ap: 0.713, auc: 0.778, f1: 0.651, acc: 0.709
+2025-08-07 19:21:39: Epoch: 046 Test results, ap: 0.231, auc: 0.406, f1: 0.299, acc: 0.306
+2025-08-07 19:21:39: Epoch: 047 Train Loss: 0.401, ap: 0.714, auc: 0.779, f1: 0.652, acc: 0.699
+2025-08-07 19:21:39: Epoch: 047 Test results, ap: 0.228, auc: 0.405, f1: 0.339, acc: 0.315
+2025-08-07 19:21:40: Epoch: 048 Train Loss: 0.408, ap: 0.713, auc: 0.779, f1: 0.635, acc: 0.699
+2025-08-07 19:21:40: Epoch: 048 Test results, ap: 0.231, auc: 0.405, f1: 0.302, acc: 0.315
+2025-08-07 19:21:40: Epoch: 049 Train Loss: 0.402, ap: 0.712, auc: 0.778, f1: 0.644, acc: 0.699
+2025-08-07 19:21:40: Epoch: 049 Test results, ap: 0.227, auc: 0.401, f1: 0.302, acc: 0.315
+2025-08-07 19:21:41: Epoch: 050 Train Loss: 0.388, ap: 0.714, auc: 0.779, f1: 0.644, acc: 0.699
+2025-08-07 19:21:41: Epoch: 050 Test results, ap: 0.228, auc: 0.404, f1: 0.302, acc: 0.315
+2025-08-07 19:21:41: used time 0.01h
+2025-08-07 19:21:41: Cross Validation Finished!
+2025-08-07 19:21:41: Best validation perform list
+[[0.77946419 0.80471122 0.7096774  0.73786408]
+ [0.57056081 0.66912365 0.50666666 0.64077669]
+ [0.86036819 0.8325792  0.77064222 0.75728154]
+ [0.71478868 0.78454334 0.65168542 0.69902915]
+ [0.74279141 0.7903226  0.67346936 0.68932039]]
+2025-08-07 19:21:41: mean: [0.734 0.776 0.662 0.705]
+2025-08-07 19:21:41: std: [0.095 0.056 0.088 0.041]
+2025-08-07 19:21:41: Best test perform list
+[[0.24014005 0.52532834 0.         0.75      ]
+ [0.2213383  0.47842401 0.38805971 0.24074075]
+ [0.25316638 0.42870545 0.17647059 0.74074072]
+ [0.22183074 0.4638837  0.38805971 0.24074075]
+ [0.22158125 0.46810505 0.38805971 0.24074075]]
+2025-08-07 19:21:41: mean: [0.232 0.473 0.268 0.443]
+2025-08-07 19:21:41: std: [0.013 0.031 0.157 0.247]
diff --git a/run-cls-simple/mha-att-256-simple-oneway-ce-32-0.001-50/config.json b/run-cls-simple/mha-att-256-simple-oneway-ce-32-0.001-50/config.json
new file mode 100644
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--- /dev/null
+++ b/run-cls-simple/mha-att-256-simple-oneway-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mha", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
diff --git a/run-cls-simple/mha-att-256-simple-oneway-ce-32-0.001-50/inference_results.csv b/run-cls-simple/mha-att-256-simple-oneway-ce-32-0.001-50/inference_results.csv
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+++ b/run-cls-simple/mha-att-256-simple-oneway-ce-32-0.001-50/inference_results.csv
@@ -0,0 +1,2 @@
+dataset,AUPRC,AUROC,F1,ACC
+r2_case,30.86,49.80,0.00,69.39
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+2025-08-07 19:37:09: Epoch: 032 Test results, ap: 0.180, auc: 0.318, f1: 0.000, acc: 0.750
+2025-08-07 19:37:10: Epoch: 033 Train Loss: 0.312, ap: 0.735, auc: 0.768, f1: 0.643, acc: 0.709
+2025-08-07 19:37:10: Epoch: 033 Test results, ap: 0.179, auc: 0.311, f1: 0.000, acc: 0.750
+2025-08-07 19:37:10: Epoch: 034 Train Loss: 0.294, ap: 0.738, auc: 0.767, f1: 0.651, acc: 0.718
+2025-08-07 19:37:10: Epoch: 034 Test results, ap: 0.176, auc: 0.302, f1: 0.000, acc: 0.750
+2025-08-07 19:37:11: Epoch: 035 Train Loss: 0.303, ap: 0.713, auc: 0.766, f1: 0.651, acc: 0.718
+2025-08-07 19:37:11: Epoch: 035 Test results, ap: 0.176, auc: 0.303, f1: 0.000, acc: 0.750
+2025-08-07 19:37:11: Epoch: 036 Train Loss: 0.295, ap: 0.726, auc: 0.769, f1: 0.634, acc: 0.709
+2025-08-07 19:37:11: Epoch: 036 Test results, ap: 0.184, auc: 0.333, f1: 0.000, acc: 0.750
+2025-08-07 19:37:11: Epoch: 037 Train Loss: 0.279, ap: 0.730, auc: 0.767, f1: 0.659, acc: 0.718
+2025-08-07 19:37:12: Epoch: 037 Test results, ap: 0.189, auc: 0.353, f1: 0.000, acc: 0.750
+2025-08-07 19:37:12: Epoch: 037 New best TEST metrics
+2025-08-07 19:37:12: Epoch: 038 Train Loss: 0.276, ap: 0.725, auc: 0.766, f1: 0.644, acc: 0.699
+2025-08-07 19:37:12: Epoch: 038 Test results, ap: 0.181, auc: 0.324, f1: 0.000, acc: 0.741
+2025-08-07 19:37:12: Epoch: 039 Train Loss: 0.283, ap: 0.718, auc: 0.774, f1: 0.674, acc: 0.728
+2025-08-07 19:37:12: Epoch: 039 Test results, ap: 0.193, auc: 0.363, f1: 0.000, acc: 0.741
+2025-08-07 19:37:12: Epoch: 039 New best TEST metrics
+2025-08-07 19:37:13: Epoch: 040 Train Loss: 0.301, ap: 0.704, auc: 0.763, f1: 0.667, acc: 0.718
+2025-08-07 19:37:13: Epoch: 040 Test results, ap: 0.190, auc: 0.361, f1: 0.000, acc: 0.750
+2025-08-07 19:37:13: Epoch: 041 Train Loss: 0.272, ap: 0.706, auc: 0.760, f1: 0.634, acc: 0.709
+2025-08-07 19:37:13: Epoch: 041 Test results, ap: 0.200, auc: 0.406, f1: 0.000, acc: 0.750
+2025-08-07 19:37:13: Epoch: 041 New best TEST metrics
+2025-08-07 19:37:14: Epoch: 042 Train Loss: 0.234, ap: 0.714, auc: 0.764, f1: 0.659, acc: 0.718
+2025-08-07 19:37:14: Epoch: 042 Test results, ap: 0.208, auc: 0.430, f1: 0.000, acc: 0.741
+2025-08-07 19:37:14: Epoch: 042 New best TEST metrics
+2025-08-07 19:37:14: Epoch: 043 Train Loss: 0.257, ap: 0.710, auc: 0.762, f1: 0.627, acc: 0.699
+2025-08-07 19:37:14: Epoch: 043 Test results, ap: 0.211, auc: 0.444, f1: 0.000, acc: 0.750
+2025-08-07 19:37:14: Epoch: 043 New best TEST metrics
+2025-08-07 19:37:15: Epoch: 044 Train Loss: 0.244, ap: 0.708, auc: 0.759, f1: 0.627, acc: 0.699
+2025-08-07 19:37:15: Epoch: 044 Test results, ap: 0.214, auc: 0.455, f1: 0.000, acc: 0.750
+2025-08-07 19:37:15: Epoch: 044 New best TEST metrics
+2025-08-07 19:37:15: Epoch: 045 Train Loss: 0.261, ap: 0.704, auc: 0.755, f1: 0.627, acc: 0.699
+2025-08-07 19:37:15: Epoch: 045 Test results, ap: 0.215, auc: 0.459, f1: 0.000, acc: 0.750
+2025-08-07 19:37:15: Epoch: 045 New best TEST metrics
+2025-08-07 19:37:15: Epoch: 046 Train Loss: 0.244, ap: 0.708, auc: 0.756, f1: 0.602, acc: 0.680
+2025-08-07 19:37:16: Epoch: 046 Test results, ap: 0.220, auc: 0.470, f1: 0.000, acc: 0.741
+2025-08-07 19:37:16: Epoch: 046 New best TEST metrics
+2025-08-07 19:37:16: Epoch: 047 Train Loss: 0.247, ap: 0.702, auc: 0.751, f1: 0.602, acc: 0.680
+2025-08-07 19:37:16: Epoch: 047 Test results, ap: 0.221, auc: 0.473, f1: 0.000, acc: 0.750
+2025-08-07 19:37:16: Epoch: 047 New best TEST metrics
+2025-08-07 19:37:16: Epoch: 048 Train Loss: 0.249, ap: 0.702, auc: 0.745, f1: 0.615, acc: 0.709
+2025-08-07 19:37:16: Epoch: 048 Test results, ap: 0.246, auc: 0.539, f1: 0.000, acc: 0.750
+2025-08-07 19:37:16: Epoch: 048 New best TEST metrics
+2025-08-07 19:37:17: Epoch: 049 Train Loss: 0.249, ap: 0.704, auc: 0.754, f1: 0.644, acc: 0.699
+2025-08-07 19:37:17: Epoch: 049 Test results, ap: 0.220, auc: 0.474, f1: 0.000, acc: 0.741
+2025-08-07 19:37:17: Epoch: 050 Train Loss: 0.244, ap: 0.699, auc: 0.744, f1: 0.635, acc: 0.699
+2025-08-07 19:37:17: Epoch: 050 Test results, ap: 0.239, auc: 0.524, f1: 0.000, acc: 0.750
+2025-08-07 19:37:17: used time 0.01h
+2025-08-07 19:37:17: Running Cross Validation 1
+2025-08-07 19:37:17: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:37:18: Epoch: 001 Train Loss: 0.691, ap: 0.505, auc: 0.652, f1: 0.000, acc: 0.641
+2025-08-07 19:37:18: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:37:18: Epoch: 001 Test results, ap: 0.225, auc: 0.485, f1: 0.000, acc: 0.759
+2025-08-07 19:37:18: Epoch: 002 Train Loss: 0.696, ap: 0.515, auc: 0.622, f1: 0.529, acc: 0.359
+2025-08-07 19:37:18: Epoch: 002 Test results, ap: 0.224, auc: 0.474, f1: 0.388, acc: 0.241
+2025-08-07 19:37:18: Epoch: 003 Train Loss: 0.673, ap: 0.508, auc: 0.640, f1: 0.533, acc: 0.660
+2025-08-07 19:37:19: Epoch: 003 Test results, ap: 0.224, auc: 0.478, f1: 0.349, acc: 0.481
+2025-08-07 19:37:19: Epoch: 004 Train Loss: 0.636, ap: 0.539, auc: 0.654, f1: 0.518, acc: 0.476
+2025-08-07 19:37:19: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:37:19: Epoch: 004 Test results, ap: 0.222, auc: 0.461, f1: 0.388, acc: 0.241
+2025-08-07 19:37:19: Epoch: 005 Train Loss: 0.623, ap: 0.579, auc: 0.649, f1: 0.582, acc: 0.680
+2025-08-07 19:37:19: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:37:20: Epoch: 005 Test results, ap: 0.211, auc: 0.430, f1: 0.000, acc: 0.722
+2025-08-07 19:37:20: Epoch: 006 Train Loss: 0.582, ap: 0.551, auc: 0.640, f1: 0.521, acc: 0.553
+2025-08-07 19:37:20: Epoch: 006 Test results, ap: 0.191, auc: 0.379, f1: 0.352, acc: 0.352
+2025-08-07 19:37:20: Epoch: 007 Train Loss: 0.598, ap: 0.543, auc: 0.639, f1: 0.444, acc: 0.612
+2025-08-07 19:37:20: Epoch: 007 Test results, ap: 0.187, auc: 0.365, f1: 0.000, acc: 0.731
+2025-08-07 19:37:21: Epoch: 008 Train Loss: 0.561, ap: 0.573, auc: 0.649, f1: 0.500, acc: 0.631
+2025-08-07 19:37:21: Epoch: 008 Test results, ap: 0.213, auc: 0.429, f1: 0.000, acc: 0.722
+2025-08-07 19:37:21: Epoch: 009 Train Loss: 0.535, ap: 0.576, auc: 0.647, f1: 0.511, acc: 0.573
+2025-08-07 19:37:21: Epoch: 009 Test results, ap: 0.222, auc: 0.459, f1: 0.154, acc: 0.593
+2025-08-07 19:37:22: Epoch: 010 Train Loss: 0.536, ap: 0.569, auc: 0.633, f1: 0.542, acc: 0.573
+2025-08-07 19:37:22: Epoch: 010 Test results, ap: 0.209, auc: 0.438, f1: 0.361, acc: 0.278
+2025-08-07 19:37:22: Epoch: 011 Train Loss: 0.524, ap: 0.585, auc: 0.660, f1: 0.512, acc: 0.612
+2025-08-07 19:37:22: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:37:22: Epoch: 011 Test results, ap: 0.225, auc: 0.477, f1: 0.000, acc: 0.685
+2025-08-07 19:37:22: Epoch: 011 New best TEST metrics
+2025-08-07 19:37:22: Epoch: 012 Train Loss: 0.484, ap: 0.598, auc: 0.672, f1: 0.525, acc: 0.631
+2025-08-07 19:37:22: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:37:23: Epoch: 012 Test results, ap: 0.217, auc: 0.459, f1: 0.000, acc: 0.630
+2025-08-07 19:37:23: Epoch: 013 Train Loss: 0.515, ap: 0.600, auc: 0.679, f1: 0.525, acc: 0.631
+2025-08-07 19:37:23: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:37:23: Epoch: 013 Test results, ap: 0.219, auc: 0.465, f1: 0.000, acc: 0.667
+2025-08-07 19:37:23: Epoch: 014 Train Loss: 0.482, ap: 0.603, auc: 0.670, f1: 0.517, acc: 0.583
+2025-08-07 19:37:24: Epoch: 014 Test results, ap: 0.220, auc: 0.473, f1: 0.047, acc: 0.620
+2025-08-07 19:37:24: Epoch: 015 Train Loss: 0.534, ap: 0.573, auc: 0.626, f1: 0.510, acc: 0.534
+2025-08-07 19:37:24: Epoch: 015 Test results, ap: 0.248, auc: 0.522, f1: 0.372, acc: 0.500
+2025-08-07 19:37:24: Epoch: 015 New best TEST metrics
+2025-08-07 19:37:24: Epoch: 016 Train Loss: 0.488, ap: 0.597, auc: 0.670, f1: 0.533, acc: 0.660
+2025-08-07 19:37:24: Epoch: 016 Test results, ap: 0.238, auc: 0.500, f1: 0.000, acc: 0.750
+2025-08-07 19:37:25: Epoch: 017 Train Loss: 0.458, ap: 0.605, auc: 0.685, f1: 0.541, acc: 0.670
+2025-08-07 19:37:25: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:37:25: Epoch: 017 Test results, ap: 0.241, auc: 0.505, f1: 0.000, acc: 0.750
+2025-08-07 19:37:25: Epoch: 018 Train Loss: 0.426, ap: 0.585, auc: 0.683, f1: 0.500, acc: 0.631
+2025-08-07 19:37:25: Epoch: 018 Test results, ap: 0.245, auc: 0.516, f1: 0.000, acc: 0.750
+2025-08-07 19:37:26: Epoch: 019 Train Loss: 0.457, ap: 0.560, auc: 0.646, f1: 0.535, acc: 0.612
+2025-08-07 19:37:26: Epoch: 019 Test results, ap: 0.249, auc: 0.520, f1: 0.133, acc: 0.639
+2025-08-07 19:37:26: Epoch: 020 Train Loss: 0.453, ap: 0.585, auc: 0.675, f1: 0.519, acc: 0.641
+2025-08-07 19:37:26: Epoch: 020 Test results, ap: 0.238, auc: 0.505, f1: 0.000, acc: 0.704
+2025-08-07 19:37:26: Epoch: 021 Train Loss: 0.418, ap: 0.519, auc: 0.676, f1: 0.560, acc: 0.680
+2025-08-07 19:37:27: Epoch: 021 Test results, ap: 0.237, auc: 0.502, f1: 0.000, acc: 0.704
+2025-08-07 19:37:27: Epoch: 022 Train Loss: 0.427, ap: 0.513, auc: 0.682, f1: 0.521, acc: 0.660
+2025-08-07 19:37:27: Epoch: 022 Test results, ap: 0.241, auc: 0.508, f1: 0.133, acc: 0.639
+2025-08-07 19:37:27: Epoch: 023 Train Loss: 0.399, ap: 0.548, auc: 0.681, f1: 0.543, acc: 0.641
+2025-08-07 19:37:27: Epoch: 023 Test results, ap: 0.241, auc: 0.510, f1: 0.231, acc: 0.630
+2025-08-07 19:37:28: Epoch: 024 Train Loss: 0.421, ap: 0.546, auc: 0.675, f1: 0.578, acc: 0.660
+2025-08-07 19:37:28: Epoch: 024 Test results, ap: 0.259, auc: 0.523, f1: 0.286, acc: 0.583
+2025-08-07 19:37:28: Epoch: 024 New best TEST metrics
+2025-08-07 19:37:28: Epoch: 025 Train Loss: 0.402, ap: 0.550, auc: 0.697, f1: 0.526, acc: 0.650
+2025-08-07 19:37:28: Epoch: 025 Test results, ap: 0.259, auc: 0.520, f1: 0.226, acc: 0.620
+2025-08-07 19:37:29: Epoch: 026 Train Loss: 0.399, ap: 0.536, auc: 0.701, f1: 0.507, acc: 0.660
+2025-08-07 19:37:29: Epoch: 026 Test results, ap: 0.262, auc: 0.520, f1: 0.163, acc: 0.620
+2025-08-07 19:37:29: Epoch: 027 Train Loss: 0.401, ap: 0.552, auc: 0.686, f1: 0.585, acc: 0.670
+2025-08-07 19:37:29: Epoch: 027 Test results, ap: 0.263, auc: 0.521, f1: 0.271, acc: 0.602
+2025-08-07 19:37:29: Epoch: 027 New best TEST metrics
+2025-08-07 19:37:29: Epoch: 028 Train Loss: 0.379, ap: 0.527, auc: 0.680, f1: 0.591, acc: 0.650
+2025-08-07 19:37:29: Epoch: 028 Test results, ap: 0.271, auc: 0.526, f1: 0.395, acc: 0.519
+2025-08-07 19:37:29: Epoch: 028 New best TEST metrics
+2025-08-07 19:37:30: Epoch: 029 Train Loss: 0.376, ap: 0.531, auc: 0.672, f1: 0.584, acc: 0.641
+2025-08-07 19:37:30: Epoch: 029 Test results, ap: 0.271, auc: 0.523, f1: 0.364, acc: 0.481
+2025-08-07 19:37:30: Epoch: 030 Train Loss: 0.388, ap: 0.544, auc: 0.694, f1: 0.591, acc: 0.650
+2025-08-07 19:37:30: Epoch: 030 Test results, ap: 0.268, auc: 0.514, f1: 0.255, acc: 0.620
+2025-08-07 19:37:31: Epoch: 031 Train Loss: 0.373, ap: 0.529, auc: 0.683, f1: 0.525, acc: 0.631
+2025-08-07 19:37:31: Epoch: 031 Test results, ap: 0.272, auc: 0.523, f1: 0.278, acc: 0.519
+2025-08-07 19:37:31: Epoch: 032 Train Loss: 0.340, ap: 0.558, auc: 0.708, f1: 0.571, acc: 0.650
+2025-08-07 19:37:31: Epoch: 032 Test results, ap: 0.268, auc: 0.519, f1: 0.222, acc: 0.611
+2025-08-07 19:37:32: Epoch: 033 Train Loss: 0.373, ap: 0.535, auc: 0.686, f1: 0.578, acc: 0.660
+2025-08-07 19:37:32: Epoch: 033 Test results, ap: 0.273, auc: 0.526, f1: 0.293, acc: 0.509
+2025-08-07 19:37:32: Epoch: 033 New best TEST metrics
+2025-08-07 19:37:32: Epoch: 034 Train Loss: 0.376, ap: 0.540, auc: 0.698, f1: 0.602, acc: 0.680
+2025-08-07 19:37:32: Epoch: 034 Test results, ap: 0.269, auc: 0.520, f1: 0.293, acc: 0.509
+2025-08-07 19:37:33: Epoch: 035 Train Loss: 0.366, ap: 0.525, auc: 0.690, f1: 0.588, acc: 0.660
+2025-08-07 19:37:33: Epoch: 035 Test results, ap: 0.271, auc: 0.520, f1: 0.312, acc: 0.509
+2025-08-07 19:37:33: Epoch: 036 Train Loss: 0.346, ap: 0.526, auc: 0.686, f1: 0.525, acc: 0.631
+2025-08-07 19:37:33: Epoch: 036 Test results, ap: 0.273, auc: 0.515, f1: 0.286, acc: 0.583
+2025-08-07 19:37:33: Epoch: 037 Train Loss: 0.340, ap: 0.541, auc: 0.697, f1: 0.612, acc: 0.680
+2025-08-07 19:37:34: Epoch: 037 Test results, ap: 0.269, auc: 0.517, f1: 0.293, acc: 0.509
+2025-08-07 19:37:34: Epoch: 038 Train Loss: 0.349, ap: 0.533, auc: 0.688, f1: 0.591, acc: 0.650
+2025-08-07 19:37:34: Epoch: 038 Test results, ap: 0.272, auc: 0.520, f1: 0.357, acc: 0.500
+2025-08-07 19:37:34: Epoch: 039 Train Loss: 0.337, ap: 0.522, auc: 0.691, f1: 0.538, acc: 0.650
+2025-08-07 19:37:34: Epoch: 039 Test results, ap: 0.273, auc: 0.515, f1: 0.290, acc: 0.593
+2025-08-07 19:37:35: Epoch: 040 Train Loss: 0.361, ap: 0.518, auc: 0.684, f1: 0.578, acc: 0.660
+2025-08-07 19:37:35: Epoch: 040 Test results, ap: 0.274, auc: 0.518, f1: 0.278, acc: 0.519
+2025-08-07 19:37:35: Epoch: 041 Train Loss: 0.335, ap: 0.530, auc: 0.695, f1: 0.578, acc: 0.660
+2025-08-07 19:37:35: Epoch: 041 Test results, ap: 0.272, auc: 0.517, f1: 0.274, acc: 0.509
+2025-08-07 19:37:36: Epoch: 042 Train Loss: 0.328, ap: 0.522, auc: 0.686, f1: 0.578, acc: 0.660
+2025-08-07 19:37:36: Epoch: 042 Test results, ap: 0.274, auc: 0.519, f1: 0.289, acc: 0.500
+2025-08-07 19:37:36: Epoch: 043 Train Loss: 0.313, ap: 0.528, auc: 0.693, f1: 0.578, acc: 0.660
+2025-08-07 19:37:36: Epoch: 043 Test results, ap: 0.273, auc: 0.517, f1: 0.270, acc: 0.500
+2025-08-07 19:37:37: Epoch: 044 Train Loss: 0.312, ap: 0.526, auc: 0.691, f1: 0.585, acc: 0.670
+2025-08-07 19:37:37: Epoch: 044 Test results, ap: 0.275, auc: 0.517, f1: 0.278, acc: 0.519
+2025-08-07 19:37:37: Epoch: 045 Train Loss: 0.303, ap: 0.523, auc: 0.688, f1: 0.581, acc: 0.650
+2025-08-07 19:37:37: Epoch: 045 Test results, ap: 0.280, auc: 0.522, f1: 0.352, acc: 0.454
+2025-08-07 19:37:37: Epoch: 045 New best TEST metrics
+2025-08-07 19:37:38: Epoch: 046 Train Loss: 0.319, ap: 0.537, auc: 0.700, f1: 0.575, acc: 0.670
+2025-08-07 19:37:38: Epoch: 046 Test results, ap: 0.273, auc: 0.515, f1: 0.278, acc: 0.519
+2025-08-07 19:37:38: Epoch: 047 Train Loss: 0.321, ap: 0.525, auc: 0.691, f1: 0.575, acc: 0.641
+2025-08-07 19:37:38: Epoch: 047 Test results, ap: 0.276, auc: 0.515, f1: 0.352, acc: 0.454
+2025-08-07 19:37:39: Epoch: 048 Train Loss: 0.328, ap: 0.516, auc: 0.688, f1: 0.585, acc: 0.670
+2025-08-07 19:37:39: Epoch: 048 Test results, ap: 0.282, auc: 0.508, f1: 0.281, acc: 0.574
+2025-08-07 19:37:39: Epoch: 049 Train Loss: 0.317, ap: 0.528, auc: 0.692, f1: 0.571, acc: 0.650
+2025-08-07 19:37:39: Epoch: 049 Test results, ap: 0.281, auc: 0.508, f1: 0.267, acc: 0.491
+2025-08-07 19:37:39: Epoch: 050 Train Loss: 0.301, ap: 0.529, auc: 0.692, f1: 0.578, acc: 0.660
+2025-08-07 19:37:40: Epoch: 050 Test results, ap: 0.282, auc: 0.505, f1: 0.257, acc: 0.519
+2025-08-07 19:37:40: used time 0.01h
+2025-08-07 19:37:40: Running Cross Validation 2
+2025-08-07 19:37:40: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:37:40: Epoch: 001 Train Loss: 0.694, ap: 0.543, auc: 0.616, f1: 0.684, acc: 0.534
+2025-08-07 19:37:40: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:37:40: Epoch: 001 Test results, ap: 0.231, auc: 0.500, f1: 0.388, acc: 0.241
+2025-08-07 19:37:40: Epoch: 002 Train Loss: 0.693, ap: 0.608, auc: 0.676, f1: 0.000, acc: 0.495
+2025-08-07 19:37:40: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:37:40: Epoch: 002 Test results, ap: 0.230, auc: 0.511, f1: 0.000, acc: 0.759
+2025-08-07 19:37:41: Epoch: 003 Train Loss: 0.691, ap: 0.663, auc: 0.719, f1: 0.671, acc: 0.505
+2025-08-07 19:37:41: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:37:41: Epoch: 003 Test results, ap: 0.243, auc: 0.545, f1: 0.388, acc: 0.241
+2025-08-07 19:37:41: Epoch: 004 Train Loss: 0.694, ap: 0.696, auc: 0.721, f1: 0.633, acc: 0.650
+2025-08-07 19:37:41: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:37:41: Epoch: 004 Test results, ap: 0.245, auc: 0.523, f1: 0.277, acc: 0.565
+2025-08-07 19:37:42: Epoch: 005 Train Loss: 0.651, ap: 0.707, auc: 0.721, f1: 0.548, acc: 0.631
+2025-08-07 19:37:42: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:37:42: Epoch: 005 Test results, ap: 0.246, auc: 0.524, f1: 0.000, acc: 0.722
+2025-08-07 19:37:42: Epoch: 006 Train Loss: 0.634, ap: 0.713, auc: 0.733, f1: 0.645, acc: 0.680
+2025-08-07 19:37:42: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:37:42: Epoch: 006 Test results, ap: 0.268, auc: 0.579, f1: 0.000, acc: 0.685
+2025-08-07 19:37:43: Epoch: 007 Train Loss: 0.594, ap: 0.690, auc: 0.713, f1: 0.712, acc: 0.670
+2025-08-07 19:37:43: Epoch: 007 Test results, ap: 0.258, auc: 0.546, f1: 0.111, acc: 0.704
+2025-08-07 19:37:43: Epoch: 008 Train Loss: 0.578, ap: 0.729, auc: 0.747, f1: 0.700, acc: 0.650
+2025-08-07 19:37:43: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:37:43: Epoch: 008 Test results, ap: 0.268, auc: 0.517, f1: 0.286, acc: 0.537
+2025-08-07 19:37:44: Epoch: 009 Train Loss: 0.593, ap: 0.715, auc: 0.723, f1: 0.660, acc: 0.660
+2025-08-07 19:37:44: Epoch: 009 Test results, ap: 0.264, auc: 0.523, f1: 0.000, acc: 0.722
+2025-08-07 19:37:44: Epoch: 010 Train Loss: 0.558, ap: 0.767, auc: 0.730, f1: 0.636, acc: 0.612
+2025-08-07 19:37:44: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:37:44: Epoch: 010 Test results, ap: 0.246, auc: 0.506, f1: 0.182, acc: 0.667
+2025-08-07 19:37:45: Epoch: 011 Train Loss: 0.540, ap: 0.789, auc: 0.756, f1: 0.723, acc: 0.680
+2025-08-07 19:37:45: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:37:45: Epoch: 011 Test results, ap: 0.249, auc: 0.514, f1: 0.190, acc: 0.685
+2025-08-07 19:37:45: Epoch: 011 New best TEST metrics
+2025-08-07 19:37:45: Epoch: 012 Train Loss: 0.512, ap: 0.790, auc: 0.763, f1: 0.712, acc: 0.670
+2025-08-07 19:37:45: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:37:45: Epoch: 012 Test results, ap: 0.274, auc: 0.564, f1: 0.190, acc: 0.685
+2025-08-07 19:37:45: Epoch: 012 New best TEST metrics
+2025-08-07 19:37:45: Epoch: 013 Train Loss: 0.505, ap: 0.794, auc: 0.764, f1: 0.717, acc: 0.709
+2025-08-07 19:37:45: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:37:46: Epoch: 013 Test results, ap: 0.284, auc: 0.594, f1: 0.000, acc: 0.713
+2025-08-07 19:37:46: Epoch: 013 New best TEST metrics
+2025-08-07 19:37:46: Epoch: 014 Train Loss: 0.475, ap: 0.799, auc: 0.768, f1: 0.722, acc: 0.709
+2025-08-07 19:37:46: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:37:46: Epoch: 014 Test results, ap: 0.292, auc: 0.611, f1: 0.000, acc: 0.713
+2025-08-07 19:37:46: Epoch: 014 New best TEST metrics
+2025-08-07 19:37:46: Epoch: 015 Train Loss: 0.451, ap: 0.784, auc: 0.756, f1: 0.734, acc: 0.718
+2025-08-07 19:37:46: Epoch: 015 Test results, ap: 0.280, auc: 0.584, f1: 0.000, acc: 0.722
+2025-08-07 19:37:47: Epoch: 016 Train Loss: 0.449, ap: 0.733, auc: 0.750, f1: 0.743, acc: 0.738
+2025-08-07 19:37:47: Epoch: 016 Test results, ap: 0.290, auc: 0.608, f1: 0.000, acc: 0.741
+2025-08-07 19:37:47: Epoch: 017 Train Loss: 0.446, ap: 0.778, auc: 0.748, f1: 0.732, acc: 0.709
+2025-08-07 19:37:47: Epoch: 017 Test results, ap: 0.277, auc: 0.577, f1: 0.000, acc: 0.741
+2025-08-07 19:37:48: Epoch: 018 Train Loss: 0.505, ap: 0.765, auc: 0.745, f1: 0.673, acc: 0.689
+2025-08-07 19:37:48: Epoch: 018 Test results, ap: 0.274, auc: 0.545, f1: 0.000, acc: 0.759
+2025-08-07 19:37:48: Epoch: 019 Train Loss: 0.484, ap: 0.794, auc: 0.752, f1: 0.709, acc: 0.689
+2025-08-07 19:37:48: Epoch: 019 Test results, ap: 0.272, auc: 0.551, f1: 0.000, acc: 0.722
+2025-08-07 19:37:48: Epoch: 020 Train Loss: 0.438, ap: 0.728, auc: 0.752, f1: 0.737, acc: 0.709
+2025-08-07 19:37:49: Epoch: 020 Test results, ap: 0.262, auc: 0.541, f1: 0.000, acc: 0.722
+2025-08-07 19:37:49: Epoch: 021 Train Loss: 0.473, ap: 0.763, auc: 0.761, f1: 0.755, acc: 0.748
+2025-08-07 19:37:49: Epoch: 021 Test results, ap: 0.246, auc: 0.499, f1: 0.000, acc: 0.741
+2025-08-07 19:37:49: Epoch: 022 Train Loss: 0.424, ap: 0.758, auc: 0.753, f1: 0.624, acc: 0.660
+2025-08-07 19:37:49: Epoch: 022 Test results, ap: 0.259, auc: 0.525, f1: 0.000, acc: 0.759
+2025-08-07 19:37:50: Epoch: 023 Train Loss: 0.447, ap: 0.755, auc: 0.754, f1: 0.727, acc: 0.709
+2025-08-07 19:37:50: Epoch: 023 Test results, ap: 0.249, auc: 0.514, f1: 0.000, acc: 0.713
+2025-08-07 19:37:50: Epoch: 024 Train Loss: 0.416, ap: 0.747, auc: 0.750, f1: 0.667, acc: 0.689
+2025-08-07 19:37:50: Epoch: 024 Test results, ap: 0.259, auc: 0.515, f1: 0.000, acc: 0.759
+2025-08-07 19:37:51: Epoch: 025 Train Loss: 0.413, ap: 0.744, auc: 0.755, f1: 0.721, acc: 0.699
+2025-08-07 19:37:51: Epoch: 025 Test results, ap: 0.262, auc: 0.529, f1: 0.000, acc: 0.731
+2025-08-07 19:37:51: Epoch: 026 Train Loss: 0.420, ap: 0.757, auc: 0.751, f1: 0.706, acc: 0.709
+2025-08-07 19:37:51: Epoch: 026 Test results, ap: 0.281, auc: 0.583, f1: 0.000, acc: 0.759
+2025-08-07 19:37:52: Epoch: 027 Train Loss: 0.411, ap: 0.748, auc: 0.752, f1: 0.667, acc: 0.680
+2025-08-07 19:37:52: Epoch: 027 Test results, ap: 0.288, auc: 0.603, f1: 0.000, acc: 0.759
+2025-08-07 19:37:52: Epoch: 028 Train Loss: 0.407, ap: 0.746, auc: 0.740, f1: 0.686, acc: 0.680
+2025-08-07 19:37:52: Epoch: 028 Test results, ap: 0.288, auc: 0.598, f1: 0.000, acc: 0.759
+2025-08-07 19:37:52: Epoch: 029 Train Loss: 0.404, ap: 0.726, auc: 0.739, f1: 0.706, acc: 0.709
+2025-08-07 19:37:52: Epoch: 029 Test results, ap: 0.289, auc: 0.597, f1: 0.000, acc: 0.759
+2025-08-07 19:37:53: Epoch: 030 Train Loss: 0.389, ap: 0.719, auc: 0.739, f1: 0.718, acc: 0.718
+2025-08-07 19:37:53: Epoch: 030 Test results, ap: 0.295, auc: 0.606, f1: 0.000, acc: 0.759
+2025-08-07 19:37:53: Epoch: 031 Train Loss: 0.363, ap: 0.716, auc: 0.740, f1: 0.680, acc: 0.689
+2025-08-07 19:37:53: Epoch: 031 Test results, ap: 0.306, auc: 0.606, f1: 0.000, acc: 0.759
+2025-08-07 19:37:53: Epoch: 031 New best TEST metrics
+2025-08-07 19:37:54: Epoch: 032 Train Loss: 0.348, ap: 0.705, auc: 0.739, f1: 0.731, acc: 0.728
+2025-08-07 19:37:54: Epoch: 032 Test results, ap: 0.307, auc: 0.608, f1: 0.000, acc: 0.759
+2025-08-07 19:37:54: Epoch: 032 New best TEST metrics
+2025-08-07 19:37:54: Epoch: 033 Train Loss: 0.335, ap: 0.707, auc: 0.738, f1: 0.725, acc: 0.728
+2025-08-07 19:37:54: Epoch: 033 Test results, ap: 0.310, auc: 0.611, f1: 0.000, acc: 0.759
+2025-08-07 19:37:54: Epoch: 033 New best TEST metrics
+2025-08-07 19:37:55: Epoch: 034 Train Loss: 0.359, ap: 0.688, auc: 0.727, f1: 0.693, acc: 0.699
+2025-08-07 19:37:55: Epoch: 034 Test results, ap: 0.311, auc: 0.613, f1: 0.000, acc: 0.759
+2025-08-07 19:37:55: Epoch: 034 New best TEST metrics
+2025-08-07 19:37:55: Epoch: 035 Train Loss: 0.329, ap: 0.678, auc: 0.725, f1: 0.707, acc: 0.718
+2025-08-07 19:37:55: Epoch: 035 Test results, ap: 0.313, auc: 0.611, f1: 0.000, acc: 0.759
+2025-08-07 19:37:56: Epoch: 036 Train Loss: 0.352, ap: 0.694, auc: 0.730, f1: 0.733, acc: 0.738
+2025-08-07 19:37:56: Epoch: 036 Test results, ap: 0.319, auc: 0.625, f1: 0.000, acc: 0.759
+2025-08-07 19:37:56: Epoch: 036 New best TEST metrics
+2025-08-07 19:37:56: Epoch: 037 Train Loss: 0.340, ap: 0.700, auc: 0.732, f1: 0.667, acc: 0.689
+2025-08-07 19:37:56: Epoch: 037 Test results, ap: 0.328, auc: 0.637, f1: 0.000, acc: 0.759
+2025-08-07 19:37:56: Epoch: 037 New best TEST metrics
+2025-08-07 19:37:56: Epoch: 038 Train Loss: 0.327, ap: 0.707, auc: 0.726, f1: 0.720, acc: 0.728
+2025-08-07 19:37:57: Epoch: 038 Test results, ap: 0.321, auc: 0.625, f1: 0.000, acc: 0.759
+2025-08-07 19:37:57: Epoch: 039 Train Loss: 0.344, ap: 0.695, auc: 0.722, f1: 0.694, acc: 0.709
+2025-08-07 19:37:57: Epoch: 039 Test results, ap: 0.307, auc: 0.608, f1: 0.000, acc: 0.759
+2025-08-07 19:37:57: Epoch: 040 Train Loss: 0.318, ap: 0.691, auc: 0.723, f1: 0.707, acc: 0.718
+2025-08-07 19:37:57: Epoch: 040 Test results, ap: 0.320, auc: 0.620, f1: 0.000, acc: 0.759
+2025-08-07 19:37:58: Epoch: 041 Train Loss: 0.314, ap: 0.689, auc: 0.722, f1: 0.680, acc: 0.699
+2025-08-07 19:37:58: Epoch: 041 Test results, ap: 0.328, auc: 0.630, f1: 0.000, acc: 0.759
+2025-08-07 19:37:58: Epoch: 042 Train Loss: 0.330, ap: 0.682, auc: 0.719, f1: 0.694, acc: 0.709
+2025-08-07 19:37:58: Epoch: 042 Test results, ap: 0.337, auc: 0.635, f1: 0.000, acc: 0.759
+2025-08-07 19:37:58: Epoch: 042 New best TEST metrics
+2025-08-07 19:37:59: Epoch: 043 Train Loss: 0.309, ap: 0.675, auc: 0.720, f1: 0.667, acc: 0.689
+2025-08-07 19:37:59: Epoch: 043 Test results, ap: 0.336, auc: 0.631, f1: 0.000, acc: 0.759
+2025-08-07 19:37:59: Epoch: 044 Train Loss: 0.303, ap: 0.679, auc: 0.713, f1: 0.720, acc: 0.728
+2025-08-07 19:37:59: Epoch: 044 Test results, ap: 0.336, auc: 0.627, f1: 0.000, acc: 0.759
+2025-08-07 19:38:00: Epoch: 045 Train Loss: 0.287, ap: 0.684, auc: 0.720, f1: 0.653, acc: 0.680
+2025-08-07 19:38:00: Epoch: 045 Test results, ap: 0.335, auc: 0.631, f1: 0.000, acc: 0.759
+2025-08-07 19:38:00: Epoch: 046 Train Loss: 0.277, ap: 0.685, auc: 0.713, f1: 0.667, acc: 0.689
+2025-08-07 19:38:00: Epoch: 046 Test results, ap: 0.350, auc: 0.625, f1: 0.000, acc: 0.759
+2025-08-07 19:38:00: Epoch: 046 New best TEST metrics
+2025-08-07 19:38:01: Epoch: 047 Train Loss: 0.296, ap: 0.685, auc: 0.718, f1: 0.667, acc: 0.689
+2025-08-07 19:38:01: Epoch: 047 Test results, ap: 0.337, auc: 0.624, f1: 0.000, acc: 0.759
+2025-08-07 19:38:01: Epoch: 048 Train Loss: 0.308, ap: 0.693, auc: 0.731, f1: 0.688, acc: 0.709
+2025-08-07 19:38:01: Epoch: 048 Test results, ap: 0.359, auc: 0.644, f1: 0.000, acc: 0.759
+2025-08-07 19:38:01: Epoch: 048 New best TEST metrics
+2025-08-07 19:38:01: Epoch: 049 Train Loss: 0.296, ap: 0.686, auc: 0.718, f1: 0.707, acc: 0.718
+2025-08-07 19:38:02: Epoch: 049 Test results, ap: 0.350, auc: 0.621, f1: 0.000, acc: 0.759
+2025-08-07 19:38:02: Epoch: 050 Train Loss: 0.304, ap: 0.691, auc: 0.710, f1: 0.694, acc: 0.709
+2025-08-07 19:38:02: Epoch: 050 Test results, ap: 0.344, auc: 0.611, f1: 0.000, acc: 0.759
+2025-08-07 19:38:02: used time 0.01h
+2025-08-07 19:38:02: Running Cross Validation 3
+2025-08-07 19:38:02: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:38:02: Epoch: 001 Train Loss: 0.695, ap: 0.564, auc: 0.695, f1: 0.579, acc: 0.408
+2025-08-07 19:38:02: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:38:02: Epoch: 001 Test results, ap: 0.201, auc: 0.418, f1: 0.388, acc: 0.241
+2025-08-07 19:38:03: Epoch: 002 Train Loss: 0.694, ap: 0.580, auc: 0.704, f1: 0.000, acc: 0.592
+2025-08-07 19:38:03: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:38:03: Epoch: 002 Test results, ap: 0.220, auc: 0.477, f1: 0.000, acc: 0.759
+2025-08-07 19:38:03: Epoch: 003 Train Loss: 0.669, ap: 0.640, auc: 0.719, f1: 0.557, acc: 0.660
+2025-08-07 19:38:03: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:38:03: Epoch: 003 Test results, ap: 0.228, auc: 0.483, f1: 0.182, acc: 0.667
+2025-08-07 19:38:04: Epoch: 004 Train Loss: 0.671, ap: 0.650, auc: 0.735, f1: 0.609, acc: 0.476
+2025-08-07 19:38:04: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:38:04: Epoch: 004 Test results, ap: 0.256, auc: 0.510, f1: 0.388, acc: 0.241
+2025-08-07 19:38:04: Epoch: 005 Train Loss: 0.656, ap: 0.651, auc: 0.735, f1: 0.529, acc: 0.689
+2025-08-07 19:38:04: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:38:04: Epoch: 005 Test results, ap: 0.265, auc: 0.530, f1: 0.000, acc: 0.722
+2025-08-07 19:38:05: Epoch: 006 Train Loss: 0.608, ap: 0.676, auc: 0.749, f1: 0.591, acc: 0.650
+2025-08-07 19:38:05: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:38:05: Epoch: 006 Test results, ap: 0.251, auc: 0.506, f1: 0.000, acc: 0.722
+2025-08-07 19:38:05: Epoch: 007 Train Loss: 0.621, ap: 0.669, auc: 0.773, f1: 0.648, acc: 0.641
+2025-08-07 19:38:05: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:38:05: Epoch: 007 Test results, ap: 0.273, auc: 0.552, f1: 0.267, acc: 0.694
+2025-08-07 19:38:06: Epoch: 008 Train Loss: 0.601, ap: 0.679, auc: 0.779, f1: 0.660, acc: 0.660
+2025-08-07 19:38:06: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:38:06: Epoch: 008 Test results, ap: 0.286, auc: 0.579, f1: 0.000, acc: 0.722
+2025-08-07 19:38:06: Epoch: 009 Train Loss: 0.567, ap: 0.729, auc: 0.797, f1: 0.660, acc: 0.670
+2025-08-07 19:38:06: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:38:06: Epoch: 009 Test results, ap: 0.271, auc: 0.563, f1: 0.000, acc: 0.722
+2025-08-07 19:38:07: Epoch: 010 Train Loss: 0.547, ap: 0.720, auc: 0.786, f1: 0.653, acc: 0.670
+2025-08-07 19:38:07: Epoch: 010 Test results, ap: 0.270, auc: 0.576, f1: 0.000, acc: 0.694
+2025-08-07 19:38:07: Epoch: 011 Train Loss: 0.536, ap: 0.731, auc: 0.794, f1: 0.634, acc: 0.641
+2025-08-07 19:38:07: Epoch: 011 Test results, ap: 0.257, auc: 0.539, f1: 0.000, acc: 0.713
+2025-08-07 19:38:07: Epoch: 011 New best TEST metrics
+2025-08-07 19:38:07: Epoch: 012 Train Loss: 0.516, ap: 0.760, auc: 0.797, f1: 0.646, acc: 0.670
+2025-08-07 19:38:07: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:38:08: Epoch: 012 Test results, ap: 0.252, auc: 0.526, f1: 0.000, acc: 0.741
+2025-08-07 19:38:08: Epoch: 013 Train Loss: 0.498, ap: 0.766, auc: 0.793, f1: 0.632, acc: 0.660
+2025-08-07 19:38:08: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:38:08: Epoch: 013 Test results, ap: 0.254, auc: 0.518, f1: 0.000, acc: 0.741
+2025-08-07 19:38:08: Epoch: 014 Train Loss: 0.480, ap: 0.754, auc: 0.783, f1: 0.667, acc: 0.680
+2025-08-07 19:38:08: Epoch: 014 Test results, ap: 0.275, auc: 0.568, f1: 0.000, acc: 0.731
+2025-08-07 19:38:08: Epoch: 014 New best TEST metrics
+2025-08-07 19:38:09: Epoch: 015 Train Loss: 0.484, ap: 0.743, auc: 0.774, f1: 0.646, acc: 0.660
+2025-08-07 19:38:09: Epoch: 015 Test results, ap: 0.269, auc: 0.551, f1: 0.053, acc: 0.667
+2025-08-07 19:38:09: Epoch: 016 Train Loss: 0.447, ap: 0.757, auc: 0.777, f1: 0.624, acc: 0.660
+2025-08-07 19:38:09: Epoch: 016 Test results, ap: 0.251, auc: 0.516, f1: 0.061, acc: 0.713
+2025-08-07 19:38:10: Epoch: 017 Train Loss: 0.454, ap: 0.782, auc: 0.793, f1: 0.681, acc: 0.709
+2025-08-07 19:38:10: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:38:10: Epoch: 017 Test results, ap: 0.243, auc: 0.499, f1: 0.000, acc: 0.750
+2025-08-07 19:38:10: Epoch: 018 Train Loss: 0.441, ap: 0.782, auc: 0.804, f1: 0.605, acc: 0.709
+2025-08-07 19:38:10: Epoch: 018 New best VALIDATION metrics
+2025-08-07 19:38:10: Epoch: 018 Test results, ap: 0.236, auc: 0.485, f1: 0.000, acc: 0.759
+2025-08-07 19:38:10: Epoch: 019 Train Loss: 0.488, ap: 0.785, auc: 0.805, f1: 0.650, acc: 0.728
+2025-08-07 19:38:10: Epoch: 019 New best VALIDATION metrics
+2025-08-07 19:38:11: Epoch: 019 Test results, ap: 0.236, auc: 0.477, f1: 0.000, acc: 0.759
+2025-08-07 19:38:11: Epoch: 020 Train Loss: 0.424, ap: 0.766, auc: 0.790, f1: 0.652, acc: 0.689
+2025-08-07 19:38:11: Epoch: 020 Test results, ap: 0.234, auc: 0.475, f1: 0.000, acc: 0.750
+2025-08-07 19:38:11: Epoch: 021 Train Loss: 0.374, ap: 0.770, auc: 0.790, f1: 0.659, acc: 0.699
+2025-08-07 19:38:12: Epoch: 021 Test results, ap: 0.232, auc: 0.477, f1: 0.000, acc: 0.731
+2025-08-07 19:38:12: Epoch: 022 Train Loss: 0.354, ap: 0.757, auc: 0.785, f1: 0.644, acc: 0.689
+2025-08-07 19:38:12: Epoch: 022 Test results, ap: 0.234, auc: 0.483, f1: 0.000, acc: 0.731
+2025-08-07 19:38:12: Epoch: 023 Train Loss: 0.375, ap: 0.756, auc: 0.786, f1: 0.673, acc: 0.689
+2025-08-07 19:38:12: Epoch: 023 Test results, ap: 0.234, auc: 0.475, f1: 0.061, acc: 0.713
+2025-08-07 19:38:13: Epoch: 024 Train Loss: 0.332, ap: 0.750, auc: 0.780, f1: 0.621, acc: 0.680
+2025-08-07 19:38:13: Epoch: 024 Test results, ap: 0.226, auc: 0.466, f1: 0.000, acc: 0.722
+2025-08-07 19:38:13: Epoch: 025 Train Loss: 0.332, ap: 0.737, auc: 0.776, f1: 0.644, acc: 0.689
+2025-08-07 19:38:13: Epoch: 025 Test results, ap: 0.219, auc: 0.452, f1: 0.000, acc: 0.713
+2025-08-07 19:38:14: Epoch: 026 Train Loss: 0.326, ap: 0.760, auc: 0.786, f1: 0.635, acc: 0.699
+2025-08-07 19:38:14: Epoch: 026 Test results, ap: 0.214, auc: 0.446, f1: 0.000, acc: 0.722
+2025-08-07 19:38:14: Epoch: 027 Train Loss: 0.314, ap: 0.666, auc: 0.771, f1: 0.641, acc: 0.728
+2025-08-07 19:38:14: Epoch: 027 Test results, ap: 0.207, auc: 0.429, f1: 0.000, acc: 0.741
+2025-08-07 19:38:15: Epoch: 028 Train Loss: 0.328, ap: 0.760, auc: 0.783, f1: 0.688, acc: 0.718
+2025-08-07 19:38:15: Epoch: 028 Test results, ap: 0.215, auc: 0.452, f1: 0.000, acc: 0.722
+2025-08-07 19:38:15: Epoch: 029 Train Loss: 0.329, ap: 0.739, auc: 0.774, f1: 0.633, acc: 0.718
+2025-08-07 19:38:15: Epoch: 029 Test results, ap: 0.220, auc: 0.461, f1: 0.000, acc: 0.731
+2025-08-07 19:38:16: Epoch: 030 Train Loss: 0.291, ap: 0.737, auc: 0.770, f1: 0.625, acc: 0.709
+2025-08-07 19:38:16: Epoch: 030 Test results, ap: 0.218, auc: 0.457, f1: 0.000, acc: 0.722
+2025-08-07 19:38:16: Epoch: 031 Train Loss: 0.270, ap: 0.728, auc: 0.770, f1: 0.614, acc: 0.670
+2025-08-07 19:38:16: Epoch: 031 Test results, ap: 0.216, auc: 0.454, f1: 0.057, acc: 0.694
+2025-08-07 19:38:16: Epoch: 032 Train Loss: 0.286, ap: 0.723, auc: 0.762, f1: 0.612, acc: 0.680
+2025-08-07 19:38:16: Epoch: 032 Test results, ap: 0.215, auc: 0.450, f1: 0.057, acc: 0.694
+2025-08-07 19:38:17: Epoch: 033 Train Loss: 0.253, ap: 0.733, auc: 0.765, f1: 0.612, acc: 0.680
+2025-08-07 19:38:17: Epoch: 033 Test results, ap: 0.210, auc: 0.436, f1: 0.000, acc: 0.694
+2025-08-07 19:38:17: Epoch: 034 Train Loss: 0.277, ap: 0.733, auc: 0.775, f1: 0.674, acc: 0.728
+2025-08-07 19:38:17: Epoch: 034 Test results, ap: 0.209, auc: 0.432, f1: 0.000, acc: 0.704
+2025-08-07 19:38:18: Epoch: 035 Train Loss: 0.243, ap: 0.747, auc: 0.776, f1: 0.644, acc: 0.699
+2025-08-07 19:38:18: Epoch: 035 Test results, ap: 0.212, auc: 0.439, f1: 0.000, acc: 0.704
+2025-08-07 19:38:18: Epoch: 036 Train Loss: 0.239, ap: 0.754, auc: 0.780, f1: 0.667, acc: 0.709
+2025-08-07 19:38:18: Epoch: 036 Test results, ap: 0.211, auc: 0.438, f1: 0.057, acc: 0.694
+2025-08-07 19:38:19: Epoch: 037 Train Loss: 0.235, ap: 0.755, auc: 0.785, f1: 0.659, acc: 0.718
+2025-08-07 19:38:19: Epoch: 037 Test results, ap: 0.208, auc: 0.430, f1: 0.000, acc: 0.694
+2025-08-07 19:38:19: Epoch: 038 Train Loss: 0.228, ap: 0.727, auc: 0.770, f1: 0.644, acc: 0.699
+2025-08-07 19:38:19: Epoch: 038 Test results, ap: 0.208, auc: 0.429, f1: 0.000, acc: 0.694
+2025-08-07 19:38:19: Epoch: 039 Train Loss: 0.256, ap: 0.695, auc: 0.769, f1: 0.659, acc: 0.709
+2025-08-07 19:38:19: Epoch: 039 Test results, ap: 0.206, auc: 0.426, f1: 0.000, acc: 0.694
+2025-08-07 19:38:20: Epoch: 040 Train Loss: 0.213, ap: 0.726, auc: 0.768, f1: 0.619, acc: 0.689
+2025-08-07 19:38:20: Epoch: 040 Test results, ap: 0.206, auc: 0.424, f1: 0.000, acc: 0.713
+2025-08-07 19:38:20: Epoch: 041 Train Loss: 0.233, ap: 0.735, auc: 0.773, f1: 0.627, acc: 0.699
+2025-08-07 19:38:20: Epoch: 041 Test results, ap: 0.201, auc: 0.412, f1: 0.000, acc: 0.713
+2025-08-07 19:38:21: Epoch: 042 Train Loss: 0.194, ap: 0.736, auc: 0.780, f1: 0.667, acc: 0.718
+2025-08-07 19:38:21: Epoch: 042 Test results, ap: 0.207, auc: 0.426, f1: 0.000, acc: 0.694
+2025-08-07 19:38:21: Epoch: 043 Train Loss: 0.228, ap: 0.729, auc: 0.773, f1: 0.642, acc: 0.718
+2025-08-07 19:38:21: Epoch: 043 Test results, ap: 0.202, auc: 0.416, f1: 0.000, acc: 0.713
+2025-08-07 19:38:22: Epoch: 044 Train Loss: 0.195, ap: 0.725, auc: 0.778, f1: 0.651, acc: 0.709
+2025-08-07 19:38:22: Epoch: 044 Test results, ap: 0.208, auc: 0.429, f1: 0.000, acc: 0.694
+2025-08-07 19:38:22: Epoch: 045 Train Loss: 0.226, ap: 0.721, auc: 0.770, f1: 0.643, acc: 0.709
+2025-08-07 19:38:22: Epoch: 045 Test results, ap: 0.204, auc: 0.421, f1: 0.000, acc: 0.694
+2025-08-07 19:38:22: Epoch: 046 Train Loss: 0.212, ap: 0.717, auc: 0.776, f1: 0.667, acc: 0.728
+2025-08-07 19:38:23: Epoch: 046 Test results, ap: 0.200, auc: 0.411, f1: 0.000, acc: 0.704
+2025-08-07 19:38:23: Epoch: 047 Train Loss: 0.208, ap: 0.705, auc: 0.774, f1: 0.651, acc: 0.709
+2025-08-07 19:38:23: Epoch: 047 Test results, ap: 0.206, auc: 0.424, f1: 0.000, acc: 0.694
+2025-08-07 19:38:23: Epoch: 048 Train Loss: 0.195, ap: 0.696, auc: 0.769, f1: 0.675, acc: 0.738
+2025-08-07 19:38:23: Epoch: 048 Test results, ap: 0.204, auc: 0.419, f1: 0.000, acc: 0.694
+2025-08-07 19:38:24: Epoch: 049 Train Loss: 0.205, ap: 0.694, auc: 0.768, f1: 0.651, acc: 0.709
+2025-08-07 19:38:24: Epoch: 049 Test results, ap: 0.206, auc: 0.424, f1: 0.000, acc: 0.694
+2025-08-07 19:38:24: Epoch: 050 Train Loss: 0.201, ap: 0.686, auc: 0.767, f1: 0.644, acc: 0.699
+2025-08-07 19:38:24: Epoch: 050 Test results, ap: 0.206, auc: 0.424, f1: 0.051, acc: 0.657
+2025-08-07 19:38:24: used time 0.01h
+2025-08-07 19:38:24: Running Cross Validation 4
+2025-08-07 19:38:24: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:38:25: Epoch: 001 Train Loss: 0.696, ap: 0.517, auc: 0.656, f1: 0.000, acc: 0.602
+2025-08-07 19:38:25: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:38:25: Epoch: 001 Test results, ap: 0.192, auc: 0.374, f1: 0.000, acc: 0.759
+2025-08-07 19:38:25: Epoch: 002 Train Loss: 0.694, ap: 0.602, auc: 0.712, f1: 0.000, acc: 0.602
+2025-08-07 19:38:25: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:38:25: Epoch: 002 Test results, ap: 0.209, auc: 0.443, f1: 0.000, acc: 0.759
+2025-08-07 19:38:26: Epoch: 003 Train Loss: 0.691, ap: 0.647, auc: 0.704, f1: 0.561, acc: 0.650
+2025-08-07 19:38:26: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:38:26: Epoch: 003 Test results, ap: 0.220, auc: 0.468, f1: 0.261, acc: 0.528
+2025-08-07 19:38:26: Epoch: 004 Train Loss: 0.676, ap: 0.654, auc: 0.709, f1: 0.314, acc: 0.660
+2025-08-07 19:38:26: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:38:26: Epoch: 004 Test results, ap: 0.225, auc: 0.476, f1: 0.000, acc: 0.713
+2025-08-07 19:38:27: Epoch: 005 Train Loss: 0.639, ap: 0.671, auc: 0.730, f1: 0.606, acc: 0.621
+2025-08-07 19:38:27: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:38:27: Epoch: 005 Test results, ap: 0.258, auc: 0.478, f1: 0.250, acc: 0.556
+2025-08-07 19:38:27: Epoch: 006 Train Loss: 0.598, ap: 0.639, auc: 0.727, f1: 0.629, acc: 0.621
+2025-08-07 19:38:27: Epoch: 006 Test results, ap: 0.291, auc: 0.465, f1: 0.325, acc: 0.500
+2025-08-07 19:38:27: Epoch: 007 Train Loss: 0.573, ap: 0.611, auc: 0.706, f1: 0.578, acc: 0.660
+2025-08-07 19:38:28: Epoch: 007 Test results, ap: 0.288, auc: 0.459, f1: 0.140, acc: 0.657
+2025-08-07 19:38:28: Epoch: 008 Train Loss: 0.542, ap: 0.597, auc: 0.689, f1: 0.565, acc: 0.612
+2025-08-07 19:38:28: Epoch: 008 Test results, ap: 0.271, auc: 0.429, f1: 0.312, acc: 0.593
+2025-08-07 19:38:28: Epoch: 009 Train Loss: 0.545, ap: 0.622, auc: 0.686, f1: 0.507, acc: 0.641
+2025-08-07 19:38:29: Epoch: 009 Test results, ap: 0.273, auc: 0.397, f1: 0.130, acc: 0.630
+2025-08-07 19:38:29: Epoch: 010 Train Loss: 0.501, ap: 0.540, auc: 0.666, f1: 0.568, acc: 0.631
+2025-08-07 19:38:29: Epoch: 010 Test results, ap: 0.287, auc: 0.445, f1: 0.303, acc: 0.574
+2025-08-07 19:38:29: Epoch: 011 Train Loss: 0.469, ap: 0.585, auc: 0.696, f1: 0.525, acc: 0.631
+2025-08-07 19:38:29: Epoch: 011 Test results, ap: 0.283, auc: 0.427, f1: 0.226, acc: 0.620
+2025-08-07 19:38:29: Epoch: 011 New best TEST metrics
+2025-08-07 19:38:30: Epoch: 012 Train Loss: 0.465, ap: 0.585, auc: 0.691, f1: 0.584, acc: 0.641
+2025-08-07 19:38:30: Epoch: 012 Test results, ap: 0.290, auc: 0.440, f1: 0.281, acc: 0.620
+2025-08-07 19:38:30: Epoch: 012 New best TEST metrics
+2025-08-07 19:38:30: Epoch: 013 Train Loss: 0.463, ap: 0.585, auc: 0.690, f1: 0.459, acc: 0.612
+2025-08-07 19:38:30: Epoch: 013 Test results, ap: 0.282, auc: 0.431, f1: 0.140, acc: 0.657
+2025-08-07 19:38:31: Epoch: 014 Train Loss: 0.428, ap: 0.589, auc: 0.691, f1: 0.565, acc: 0.641
+2025-08-07 19:38:31: Epoch: 014 Test results, ap: 0.278, auc: 0.421, f1: 0.130, acc: 0.630
+2025-08-07 19:38:31: Epoch: 015 Train Loss: 0.427, ap: 0.584, auc: 0.700, f1: 0.507, acc: 0.641
+2025-08-07 19:38:31: Epoch: 015 Test results, ap: 0.233, auc: 0.352, f1: 0.143, acc: 0.667
+2025-08-07 19:38:31: Epoch: 016 Train Loss: 0.446, ap: 0.599, auc: 0.697, f1: 0.448, acc: 0.641
+2025-08-07 19:38:32: Epoch: 016 Test results, ap: 0.217, auc: 0.365, f1: 0.108, acc: 0.694
+2025-08-07 19:38:32: Epoch: 017 Train Loss: 0.418, ap: 0.585, auc: 0.693, f1: 0.568, acc: 0.631
+2025-08-07 19:38:32: Epoch: 017 Test results, ap: 0.275, auc: 0.388, f1: 0.264, acc: 0.639
+2025-08-07 19:38:32: Epoch: 018 Train Loss: 0.375, ap: 0.599, auc: 0.714, f1: 0.584, acc: 0.641
+2025-08-07 19:38:33: Epoch: 018 Test results, ap: 0.296, auc: 0.408, f1: 0.286, acc: 0.630
+2025-08-07 19:38:33: Epoch: 019 Train Loss: 0.405, ap: 0.592, auc: 0.713, f1: 0.638, acc: 0.670
+2025-08-07 19:38:33: Epoch: 019 Test results, ap: 0.267, auc: 0.388, f1: 0.276, acc: 0.611
+2025-08-07 19:38:33: Epoch: 020 Train Loss: 0.379, ap: 0.587, auc: 0.701, f1: 0.548, acc: 0.631
+2025-08-07 19:38:33: Epoch: 020 Test results, ap: 0.226, auc: 0.392, f1: 0.170, acc: 0.639
+2025-08-07 19:38:34: Epoch: 021 Train Loss: 0.332, ap: 0.578, auc: 0.702, f1: 0.581, acc: 0.650
+2025-08-07 19:38:34: Epoch: 021 Test results, ap: 0.224, auc: 0.384, f1: 0.170, acc: 0.639
+2025-08-07 19:38:34: Epoch: 022 Train Loss: 0.371, ap: 0.580, auc: 0.704, f1: 0.525, acc: 0.631
+2025-08-07 19:38:34: Epoch: 022 Test results, ap: 0.245, auc: 0.393, f1: 0.182, acc: 0.667
+2025-08-07 19:38:35: Epoch: 023 Train Loss: 0.348, ap: 0.593, auc: 0.710, f1: 0.451, acc: 0.621
+2025-08-07 19:38:35: Epoch: 023 Test results, ap: 0.247, auc: 0.402, f1: 0.200, acc: 0.704
+2025-08-07 19:38:35: Epoch: 024 Train Loss: 0.345, ap: 0.582, auc: 0.705, f1: 0.519, acc: 0.621
+2025-08-07 19:38:35: Epoch: 024 Test results, ap: 0.250, auc: 0.405, f1: 0.264, acc: 0.639
+2025-08-07 19:38:36: Epoch: 025 Train Loss: 0.316, ap: 0.587, auc: 0.714, f1: 0.537, acc: 0.631
+2025-08-07 19:38:36: Epoch: 025 Test results, ap: 0.248, auc: 0.405, f1: 0.170, acc: 0.639
+2025-08-07 19:38:36: Epoch: 026 Train Loss: 0.293, ap: 0.590, auc: 0.721, f1: 0.543, acc: 0.641
+2025-08-07 19:38:36: Epoch: 026 Test results, ap: 0.247, auc: 0.402, f1: 0.178, acc: 0.657
+2025-08-07 19:38:36: Epoch: 027 Train Loss: 0.284, ap: 0.567, auc: 0.702, f1: 0.494, acc: 0.621
+2025-08-07 19:38:37: Epoch: 027 Test results, ap: 0.228, auc: 0.397, f1: 0.154, acc: 0.694
+2025-08-07 19:38:37: Epoch: 028 Train Loss: 0.302, ap: 0.591, auc: 0.714, f1: 0.500, acc: 0.631
+2025-08-07 19:38:37: Epoch: 028 Test results, ap: 0.227, auc: 0.409, f1: 0.105, acc: 0.685
+2025-08-07 19:38:37: Epoch: 029 Train Loss: 0.308, ap: 0.574, auc: 0.697, f1: 0.507, acc: 0.641
+2025-08-07 19:38:38: Epoch: 029 Test results, ap: 0.255, auc: 0.410, f1: 0.105, acc: 0.685
+2025-08-07 19:38:38: Epoch: 030 Train Loss: 0.290, ap: 0.549, auc: 0.682, f1: 0.507, acc: 0.641
+2025-08-07 19:38:38: Epoch: 030 Test results, ap: 0.251, auc: 0.408, f1: 0.065, acc: 0.731
+2025-08-07 19:38:38: Epoch: 031 Train Loss: 0.278, ap: 0.572, auc: 0.714, f1: 0.541, acc: 0.621
+2025-08-07 19:38:38: Epoch: 031 Test results, ap: 0.247, auc: 0.393, f1: 0.200, acc: 0.704
+2025-08-07 19:38:39: Epoch: 032 Train Loss: 0.265, ap: 0.590, auc: 0.725, f1: 0.561, acc: 0.650
+2025-08-07 19:38:39: Epoch: 032 Test results, ap: 0.228, auc: 0.397, f1: 0.195, acc: 0.694
+2025-08-07 19:38:39: Epoch: 033 Train Loss: 0.269, ap: 0.580, auc: 0.714, f1: 0.545, acc: 0.660
+2025-08-07 19:38:39: Epoch: 033 Test results, ap: 0.218, auc: 0.393, f1: 0.150, acc: 0.685
+2025-08-07 19:38:40: Epoch: 034 Train Loss: 0.229, ap: 0.590, auc: 0.714, f1: 0.507, acc: 0.641
+2025-08-07 19:38:40: Epoch: 034 Test results, ap: 0.217, auc: 0.393, f1: 0.150, acc: 0.685
+2025-08-07 19:38:40: Epoch: 035 Train Loss: 0.261, ap: 0.582, auc: 0.714, f1: 0.538, acc: 0.650
+2025-08-07 19:38:40: Epoch: 035 Test results, ap: 0.219, auc: 0.400, f1: 0.195, acc: 0.694
+2025-08-07 19:38:40: Epoch: 036 Train Loss: 0.247, ap: 0.567, auc: 0.708, f1: 0.538, acc: 0.650
+2025-08-07 19:38:41: Epoch: 036 Test results, ap: 0.220, auc: 0.402, f1: 0.150, acc: 0.685
+2025-08-07 19:38:41: Epoch: 037 Train Loss: 0.238, ap: 0.575, auc: 0.707, f1: 0.557, acc: 0.660
+2025-08-07 19:38:41: Epoch: 037 Test results, ap: 0.224, auc: 0.409, f1: 0.195, acc: 0.694
+2025-08-07 19:38:41: Epoch: 038 Train Loss: 0.234, ap: 0.571, auc: 0.721, f1: 0.593, acc: 0.680
+2025-08-07 19:38:41: Epoch: 038 Test results, ap: 0.223, auc: 0.404, f1: 0.195, acc: 0.694
+2025-08-07 19:38:42: Epoch: 039 Train Loss: 0.238, ap: 0.564, auc: 0.717, f1: 0.621, acc: 0.680
+2025-08-07 19:38:42: Epoch: 039 Test results, ap: 0.222, auc: 0.406, f1: 0.195, acc: 0.694
+2025-08-07 19:38:42: Epoch: 040 Train Loss: 0.238, ap: 0.571, auc: 0.714, f1: 0.571, acc: 0.650
+2025-08-07 19:38:42: Epoch: 040 Test results, ap: 0.231, auc: 0.403, f1: 0.195, acc: 0.694
+2025-08-07 19:38:43: Epoch: 041 Train Loss: 0.266, ap: 0.582, auc: 0.712, f1: 0.538, acc: 0.650
+2025-08-07 19:38:43: Epoch: 041 Test results, ap: 0.222, auc: 0.401, f1: 0.114, acc: 0.713
+2025-08-07 19:38:43: Epoch: 042 Train Loss: 0.218, ap: 0.583, auc: 0.723, f1: 0.538, acc: 0.650
+2025-08-07 19:38:43: Epoch: 042 Test results, ap: 0.224, auc: 0.409, f1: 0.195, acc: 0.694
+2025-08-07 19:38:44: Epoch: 043 Train Loss: 0.206, ap: 0.578, auc: 0.719, f1: 0.538, acc: 0.650
+2025-08-07 19:38:44: Epoch: 043 Test results, ap: 0.224, auc: 0.409, f1: 0.200, acc: 0.704
+2025-08-07 19:38:44: Epoch: 044 Train Loss: 0.216, ap: 0.575, auc: 0.716, f1: 0.557, acc: 0.660
+2025-08-07 19:38:44: Epoch: 044 Test results, ap: 0.223, auc: 0.410, f1: 0.195, acc: 0.694
+2025-08-07 19:38:45: Epoch: 045 Train Loss: 0.209, ap: 0.566, auc: 0.710, f1: 0.557, acc: 0.660
+2025-08-07 19:38:45: Epoch: 045 Test results, ap: 0.223, auc: 0.407, f1: 0.195, acc: 0.694
+2025-08-07 19:38:45: Epoch: 046 Train Loss: 0.213, ap: 0.564, auc: 0.707, f1: 0.575, acc: 0.670
+2025-08-07 19:38:45: Epoch: 046 Test results, ap: 0.223, auc: 0.407, f1: 0.195, acc: 0.694
+2025-08-07 19:38:45: Epoch: 047 Train Loss: 0.211, ap: 0.566, auc: 0.709, f1: 0.575, acc: 0.670
+2025-08-07 19:38:46: Epoch: 047 Test results, ap: 0.225, auc: 0.408, f1: 0.200, acc: 0.704
+2025-08-07 19:38:46: Epoch: 048 Train Loss: 0.213, ap: 0.563, auc: 0.706, f1: 0.593, acc: 0.680
+2025-08-07 19:38:46: Epoch: 048 Test results, ap: 0.222, auc: 0.411, f1: 0.205, acc: 0.713
+2025-08-07 19:38:46: Epoch: 049 Train Loss: 0.211, ap: 0.564, auc: 0.706, f1: 0.593, acc: 0.680
+2025-08-07 19:38:46: Epoch: 049 Test results, ap: 0.226, auc: 0.416, f1: 0.205, acc: 0.713
+2025-08-07 19:38:47: Epoch: 050 Train Loss: 0.218, ap: 0.566, auc: 0.708, f1: 0.575, acc: 0.670
+2025-08-07 19:38:47: Epoch: 050 Test results, ap: 0.226, auc: 0.418, f1: 0.205, acc: 0.713
+2025-08-07 19:38:47: used time 0.01h
+2025-08-07 19:38:47: Cross Validation Finished!
+2025-08-07 19:38:47: Best validation perform list
+[[0.74600112 0.76899695 0.68041235 0.69902915]
+ [0.60544658 0.68468469 0.54054052 0.66990292]
+ [0.79941893 0.76809955 0.72222221 0.70873785]
+ [0.78471053 0.80523026 0.64999998 0.72815531]
+ [0.67148149 0.73013377 0.60606062 0.62135923]]
+2025-08-07 19:38:47: mean: [0.721 0.751 0.64  0.685]
+2025-08-07 19:38:47: std: [0.073 0.041 0.063 0.037]
+2025-08-07 19:38:47: Best test perform list
+[[0.24558017 0.53893059 0.         0.75      ]
+ [0.28034586 0.52204502 0.35164836 0.4537037 ]
+ [0.3585906  0.64352721 0.         0.75925928]
+ [0.27525982 0.56754225 0.         0.73148149]
+ [0.28975064 0.44043151 0.28070176 0.62037039]]
+2025-08-07 19:38:47: mean: [0.29  0.542 0.126 0.663]
+2025-08-07 19:38:47: std: [0.037 0.066 0.157 0.116]
diff --git a/run-cls-simple/mha-diff-256-simple-oneway-ce-32-0.001-50/config.json b/run-cls-simple/mha-diff-256-simple-oneway-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
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+Valid
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+0.056117260027478626,0.026238560010593902,0.04972104462180674,0.025760187844204994
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diff --git a/run-cls-simple/mha-diff-256-simple-oneway-ce-32-0.001-50/training.log b/run-cls-simple/mha-diff-256-simple-oneway-ce-32-0.001-50/training.log
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+2025-08-07 19:38:49: saving_dir: ./run-cls/mha-diff-256-simple-oneway-ce-32-0.001-50
+2025-08-07 19:38:49: Loading Training Dataset
+2025-08-07 19:38:49: Loading Test Dataset
+2025-08-07 19:38:50: Running Cross Validation 0
+2025-08-07 19:38:50: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:38:50: Epoch: 001 Train Loss: 0.688, ap: 0.537, auc: 0.583, f1: 0.566, acc: 0.583
+2025-08-07 19:38:50: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:38:50: Epoch: 001 Test results, ap: 0.204, auc: 0.426, f1: 0.115, acc: 0.574
+2025-08-07 19:38:51: Epoch: 002 Train Loss: 0.674, ap: 0.583, auc: 0.622, f1: 0.338, acc: 0.583
+2025-08-07 19:38:51: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:38:51: Epoch: 002 Test results, ap: 0.216, auc: 0.457, f1: 0.000, acc: 0.685
+2025-08-07 19:38:51: Epoch: 003 Train Loss: 0.671, ap: 0.555, auc: 0.594, f1: 0.542, acc: 0.573
+2025-08-07 19:38:51: Epoch: 003 Test results, ap: 0.218, auc: 0.449, f1: 0.254, acc: 0.565
+2025-08-07 19:38:51: Epoch: 004 Train Loss: 0.653, ap: 0.616, auc: 0.614, f1: 0.541, acc: 0.621
+2025-08-07 19:38:51: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:38:52: Epoch: 004 Test results, ap: 0.217, auc: 0.446, f1: 0.100, acc: 0.667
+2025-08-07 19:38:52: Epoch: 005 Train Loss: 0.644, ap: 0.616, auc: 0.620, f1: 0.375, acc: 0.612
+2025-08-07 19:38:52: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:38:52: Epoch: 005 Test results, ap: 0.220, auc: 0.454, f1: 0.000, acc: 0.759
+2025-08-07 19:38:52: Epoch: 006 Train Loss: 0.652, ap: 0.628, auc: 0.613, f1: 0.538, acc: 0.583
+2025-08-07 19:38:52: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:38:53: Epoch: 006 Test results, ap: 0.210, auc: 0.407, f1: 0.254, acc: 0.565
+2025-08-07 19:38:53: Epoch: 007 Train Loss: 0.633, ap: 0.627, auc: 0.614, f1: 0.533, acc: 0.592
+2025-08-07 19:38:53: Epoch: 007 Test results, ap: 0.212, auc: 0.406, f1: 0.254, acc: 0.565
+2025-08-07 19:38:53: Epoch: 008 Train Loss: 0.630, ap: 0.616, auc: 0.612, f1: 0.481, acc: 0.602
+2025-08-07 19:38:53: Epoch: 008 Test results, ap: 0.206, auc: 0.392, f1: 0.000, acc: 0.676
+2025-08-07 19:38:54: Epoch: 009 Train Loss: 0.625, ap: 0.619, auc: 0.607, f1: 0.488, acc: 0.592
+2025-08-07 19:38:54: Epoch: 009 Test results, ap: 0.205, auc: 0.392, f1: 0.100, acc: 0.667
+2025-08-07 19:38:54: Epoch: 010 Train Loss: 0.620, ap: 0.614, auc: 0.598, f1: 0.518, acc: 0.602
+2025-08-07 19:38:54: Epoch: 010 Test results, ap: 0.209, auc: 0.410, f1: 0.000, acc: 0.676
+2025-08-07 19:38:55: Epoch: 011 Train Loss: 0.604, ap: 0.625, auc: 0.597, f1: 0.481, acc: 0.602
+2025-08-07 19:38:55: Epoch: 011 Test results, ap: 0.213, auc: 0.422, f1: 0.000, acc: 0.694
+2025-08-07 19:38:55: Epoch: 011 New best TEST metrics
+2025-08-07 19:38:55: Epoch: 012 Train Loss: 0.613, ap: 0.635, auc: 0.609, f1: 0.494, acc: 0.621
+2025-08-07 19:38:55: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:38:55: Epoch: 012 Test results, ap: 0.210, auc: 0.409, f1: 0.000, acc: 0.694
+2025-08-07 19:38:55: Epoch: 013 Train Loss: 0.600, ap: 0.637, auc: 0.613, f1: 0.530, acc: 0.621
+2025-08-07 19:38:55: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:38:55: Epoch: 013 Test results, ap: 0.213, auc: 0.409, f1: 0.000, acc: 0.676
+2025-08-07 19:38:56: Epoch: 014 Train Loss: 0.593, ap: 0.651, auc: 0.638, f1: 0.475, acc: 0.592
+2025-08-07 19:38:56: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:38:56: Epoch: 014 Test results, ap: 0.201, auc: 0.392, f1: 0.000, acc: 0.694
+2025-08-07 19:38:56: Epoch: 015 Train Loss: 0.589, ap: 0.646, auc: 0.646, f1: 0.459, acc: 0.612
+2025-08-07 19:38:56: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:38:56: Epoch: 015 Test results, ap: 0.196, auc: 0.382, f1: 0.000, acc: 0.731
+2025-08-07 19:38:57: Epoch: 016 Train Loss: 0.589, ap: 0.642, auc: 0.632, f1: 0.468, acc: 0.602
+2025-08-07 19:38:57: Epoch: 016 Test results, ap: 0.193, auc: 0.372, f1: 0.000, acc: 0.722
+2025-08-07 19:38:57: Epoch: 017 Train Loss: 0.584, ap: 0.651, auc: 0.660, f1: 0.459, acc: 0.612
+2025-08-07 19:38:57: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:38:57: Epoch: 017 Test results, ap: 0.200, auc: 0.392, f1: 0.000, acc: 0.722
+2025-08-07 19:38:58: Epoch: 018 Train Loss: 0.564, ap: 0.649, auc: 0.650, f1: 0.513, acc: 0.631
+2025-08-07 19:38:58: Epoch: 018 Test results, ap: 0.198, auc: 0.382, f1: 0.000, acc: 0.722
+2025-08-07 19:38:58: Epoch: 019 Train Loss: 0.577, ap: 0.653, auc: 0.655, f1: 0.453, acc: 0.602
+2025-08-07 19:38:58: Epoch: 019 Test results, ap: 0.201, auc: 0.416, f1: 0.000, acc: 0.759
+2025-08-07 19:38:59: Epoch: 020 Train Loss: 0.586, ap: 0.644, auc: 0.646, f1: 0.466, acc: 0.621
+2025-08-07 19:38:59: Epoch: 020 Test results, ap: 0.231, auc: 0.477, f1: 0.000, acc: 0.722
+2025-08-07 19:38:59: Epoch: 020 New best TEST metrics
+2025-08-07 19:38:59: Epoch: 021 Train Loss: 0.579, ap: 0.649, auc: 0.651, f1: 0.519, acc: 0.621
+2025-08-07 19:38:59: Epoch: 021 Test results, ap: 0.215, auc: 0.429, f1: 0.000, acc: 0.722
+2025-08-07 19:38:59: Epoch: 022 Train Loss: 0.538, ap: 0.646, auc: 0.650, f1: 0.480, acc: 0.621
+2025-08-07 19:39:00: Epoch: 022 Test results, ap: 0.214, auc: 0.434, f1: 0.000, acc: 0.722
+2025-08-07 19:39:00: Epoch: 023 Train Loss: 0.527, ap: 0.667, auc: 0.678, f1: 0.543, acc: 0.641
+2025-08-07 19:39:00: Epoch: 023 New best VALIDATION metrics
+2025-08-07 19:39:00: Epoch: 023 Test results, ap: 0.220, auc: 0.442, f1: 0.000, acc: 0.722
+2025-08-07 19:39:00: Epoch: 024 Train Loss: 0.541, ap: 0.679, auc: 0.692, f1: 0.507, acc: 0.641
+2025-08-07 19:39:00: Epoch: 024 New best VALIDATION metrics
+2025-08-07 19:39:00: Epoch: 024 Test results, ap: 0.228, auc: 0.455, f1: 0.000, acc: 0.722
+2025-08-07 19:39:01: Epoch: 025 Train Loss: 0.522, ap: 0.666, auc: 0.691, f1: 0.525, acc: 0.631
+2025-08-07 19:39:01: Epoch: 025 Test results, ap: 0.217, auc: 0.430, f1: 0.000, acc: 0.722
+2025-08-07 19:39:01: Epoch: 026 Train Loss: 0.494, ap: 0.672, auc: 0.688, f1: 0.545, acc: 0.660
+2025-08-07 19:39:01: Epoch: 026 Test results, ap: 0.219, auc: 0.429, f1: 0.000, acc: 0.722
+2025-08-07 19:39:02: Epoch: 027 Train Loss: 0.507, ap: 0.680, auc: 0.687, f1: 0.480, acc: 0.621
+2025-08-07 19:39:02: Epoch: 027 Test results, ap: 0.235, auc: 0.479, f1: 0.000, acc: 0.722
+2025-08-07 19:39:02: Epoch: 027 New best TEST metrics
+2025-08-07 19:39:02: Epoch: 028 Train Loss: 0.516, ap: 0.664, auc: 0.672, f1: 0.548, acc: 0.631
+2025-08-07 19:39:02: Epoch: 028 Test results, ap: 0.215, auc: 0.412, f1: 0.000, acc: 0.704
+2025-08-07 19:39:02: Epoch: 029 Train Loss: 0.503, ap: 0.693, auc: 0.703, f1: 0.582, acc: 0.680
+2025-08-07 19:39:02: Epoch: 029 New best VALIDATION metrics
+2025-08-07 19:39:02: Epoch: 029 Test results, ap: 0.227, auc: 0.459, f1: 0.000, acc: 0.713
+2025-08-07 19:39:03: Epoch: 030 Train Loss: 0.478, ap: 0.693, auc: 0.711, f1: 0.621, acc: 0.680
+2025-08-07 19:39:03: Epoch: 030 New best VALIDATION metrics
+2025-08-07 19:39:03: Epoch: 030 Test results, ap: 0.228, auc: 0.470, f1: 0.059, acc: 0.704
+2025-08-07 19:39:03: Epoch: 031 Train Loss: 0.481, ap: 0.696, auc: 0.704, f1: 0.575, acc: 0.670
+2025-08-07 19:39:03: Epoch: 031 Test results, ap: 0.245, auc: 0.495, f1: 0.000, acc: 0.704
+2025-08-07 19:39:03: Epoch: 031 New best TEST metrics
+2025-08-07 19:39:04: Epoch: 032 Train Loss: 0.467, ap: 0.693, auc: 0.701, f1: 0.612, acc: 0.680
+2025-08-07 19:39:04: Epoch: 032 Test results, ap: 0.238, auc: 0.470, f1: 0.000, acc: 0.694
+2025-08-07 19:39:04: Epoch: 033 Train Loss: 0.477, ap: 0.699, auc: 0.710, f1: 0.602, acc: 0.680
+2025-08-07 19:39:04: Epoch: 033 New best VALIDATION metrics
+2025-08-07 19:39:04: Epoch: 033 Test results, ap: 0.252, auc: 0.498, f1: 0.000, acc: 0.694
+2025-08-07 19:39:04: Epoch: 033 New best TEST metrics
+2025-08-07 19:39:04: Epoch: 034 Train Loss: 0.468, ap: 0.713, auc: 0.714, f1: 0.568, acc: 0.689
+2025-08-07 19:39:04: Epoch: 034 New best VALIDATION metrics
+2025-08-07 19:39:04: Epoch: 034 Test results, ap: 0.253, auc: 0.499, f1: 0.000, acc: 0.722
+2025-08-07 19:39:04: Epoch: 034 New best TEST metrics
+2025-08-07 19:39:05: Epoch: 035 Train Loss: 0.458, ap: 0.702, auc: 0.720, f1: 0.637, acc: 0.680
+2025-08-07 19:39:05: Epoch: 035 Test results, ap: 0.251, auc: 0.495, f1: 0.059, acc: 0.704
+2025-08-07 19:39:05: Epoch: 036 Train Loss: 0.470, ap: 0.713, auc: 0.718, f1: 0.617, acc: 0.699
+2025-08-07 19:39:05: Epoch: 036 New best VALIDATION metrics
+2025-08-07 19:39:05: Epoch: 036 Test results, ap: 0.254, auc: 0.501, f1: 0.000, acc: 0.722
+2025-08-07 19:39:05: Epoch: 036 New best TEST metrics
+2025-08-07 19:39:06: Epoch: 037 Train Loss: 0.454, ap: 0.711, auc: 0.729, f1: 0.644, acc: 0.699
+2025-08-07 19:39:06: Epoch: 037 New best VALIDATION metrics
+2025-08-07 19:39:06: Epoch: 037 Test results, ap: 0.258, auc: 0.505, f1: 0.061, acc: 0.713
+2025-08-07 19:39:06: Epoch: 037 New best TEST metrics
+2025-08-07 19:39:06: Epoch: 038 Train Loss: 0.452, ap: 0.717, auc: 0.734, f1: 0.635, acc: 0.699
+2025-08-07 19:39:06: Epoch: 038 New best VALIDATION metrics
+2025-08-07 19:39:06: Epoch: 038 Test results, ap: 0.254, auc: 0.511, f1: 0.061, acc: 0.713
+2025-08-07 19:39:06: Epoch: 038 New best TEST metrics
+2025-08-07 19:39:06: Epoch: 039 Train Loss: 0.451, ap: 0.719, auc: 0.733, f1: 0.659, acc: 0.709
+2025-08-07 19:39:07: Epoch: 039 Test results, ap: 0.255, auc: 0.515, f1: 0.000, acc: 0.704
+2025-08-07 19:39:07: Epoch: 039 New best TEST metrics
+2025-08-07 19:39:07: Epoch: 040 Train Loss: 0.435, ap: 0.719, auc: 0.736, f1: 0.643, acc: 0.709
+2025-08-07 19:39:07: Epoch: 040 New best VALIDATION metrics
+2025-08-07 19:39:07: Epoch: 040 Test results, ap: 0.264, auc: 0.519, f1: 0.000, acc: 0.704
+2025-08-07 19:39:07: Epoch: 040 New best TEST metrics
+2025-08-07 19:39:07: Epoch: 041 Train Loss: 0.442, ap: 0.719, auc: 0.735, f1: 0.634, acc: 0.709
+2025-08-07 19:39:07: Epoch: 041 Test results, ap: 0.278, auc: 0.552, f1: 0.000, acc: 0.704
+2025-08-07 19:39:07: Epoch: 041 New best TEST metrics
+2025-08-07 19:39:08: Epoch: 042 Train Loss: 0.437, ap: 0.721, auc: 0.739, f1: 0.625, acc: 0.709
+2025-08-07 19:39:08: Epoch: 042 New best VALIDATION metrics
+2025-08-07 19:39:08: Epoch: 042 Test results, ap: 0.277, auc: 0.549, f1: 0.000, acc: 0.704
+2025-08-07 19:39:08: Epoch: 043 Train Loss: 0.453, ap: 0.721, auc: 0.734, f1: 0.627, acc: 0.699
+2025-08-07 19:39:08: Epoch: 043 Test results, ap: 0.274, auc: 0.544, f1: 0.000, acc: 0.694
+2025-08-07 19:39:09: Epoch: 044 Train Loss: 0.438, ap: 0.720, auc: 0.737, f1: 0.634, acc: 0.709
+2025-08-07 19:39:09: Epoch: 044 Test results, ap: 0.275, auc: 0.547, f1: 0.000, acc: 0.704
+2025-08-07 19:39:09: Epoch: 045 Train Loss: 0.421, ap: 0.728, auc: 0.740, f1: 0.634, acc: 0.709
+2025-08-07 19:39:09: Epoch: 045 New best VALIDATION metrics
+2025-08-07 19:39:09: Epoch: 045 Test results, ap: 0.278, auc: 0.553, f1: 0.000, acc: 0.713
+2025-08-07 19:39:09: Epoch: 045 New best TEST metrics
+2025-08-07 19:39:10: Epoch: 046 Train Loss: 0.428, ap: 0.728, auc: 0.741, f1: 0.635, acc: 0.699
+2025-08-07 19:39:10: Epoch: 046 New best VALIDATION metrics
+2025-08-07 19:39:10: Epoch: 046 Test results, ap: 0.273, auc: 0.550, f1: 0.000, acc: 0.713
+2025-08-07 19:39:10: Epoch: 047 Train Loss: 0.419, ap: 0.728, auc: 0.741, f1: 0.642, acc: 0.718
+2025-08-07 19:39:10: Epoch: 047 Test results, ap: 0.280, auc: 0.555, f1: 0.000, acc: 0.713
+2025-08-07 19:39:10: Epoch: 047 New best TEST metrics
+2025-08-07 19:39:10: Epoch: 048 Train Loss: 0.415, ap: 0.727, auc: 0.736, f1: 0.667, acc: 0.718
+2025-08-07 19:39:11: Epoch: 048 Test results, ap: 0.276, auc: 0.551, f1: 0.000, acc: 0.713
+2025-08-07 19:39:11: Epoch: 049 Train Loss: 0.420, ap: 0.732, auc: 0.744, f1: 0.634, acc: 0.709
+2025-08-07 19:39:11: Epoch: 049 New best VALIDATION metrics
+2025-08-07 19:39:11: Epoch: 049 Test results, ap: 0.277, auc: 0.551, f1: 0.000, acc: 0.713
+2025-08-07 19:39:11: Epoch: 050 Train Loss: 0.391, ap: 0.734, auc: 0.742, f1: 0.636, acc: 0.689
+2025-08-07 19:39:11: Epoch: 050 Test results, ap: 0.272, auc: 0.547, f1: 0.000, acc: 0.713
+2025-08-07 19:39:11: used time 0.01h
+2025-08-07 19:39:11: Running Cross Validation 1
+2025-08-07 19:39:11: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:39:12: Epoch: 001 Train Loss: 0.693, ap: 0.513, auc: 0.654, f1: 0.521, acc: 0.660
+2025-08-07 19:39:12: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:39:12: Epoch: 001 Test results, ap: 0.210, auc: 0.449, f1: 0.048, acc: 0.630
+2025-08-07 19:39:12: Epoch: 002 Train Loss: 0.679, ap: 0.487, auc: 0.640, f1: 0.559, acc: 0.602
+2025-08-07 19:39:12: Epoch: 002 Test results, ap: 0.226, auc: 0.485, f1: 0.247, acc: 0.491
+2025-08-07 19:39:13: Epoch: 003 Train Loss: 0.679, ap: 0.547, auc: 0.651, f1: 0.553, acc: 0.670
+2025-08-07 19:39:13: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:39:13: Epoch: 003 Test results, ap: 0.226, auc: 0.492, f1: 0.211, acc: 0.583
+2025-08-07 19:39:13: Epoch: 004 Train Loss: 0.658, ap: 0.598, auc: 0.678, f1: 0.584, acc: 0.641
+2025-08-07 19:39:13: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:39:13: Epoch: 004 Test results, ap: 0.226, auc: 0.491, f1: 0.254, acc: 0.565
+2025-08-07 19:39:13: Epoch: 005 Train Loss: 0.660, ap: 0.569, auc: 0.671, f1: 0.537, acc: 0.631
+2025-08-07 19:39:14: Epoch: 005 Test results, ap: 0.225, auc: 0.486, f1: 0.100, acc: 0.667
+2025-08-07 19:39:14: Epoch: 006 Train Loss: 0.647, ap: 0.575, auc: 0.672, f1: 0.538, acc: 0.650
+2025-08-07 19:39:14: Epoch: 006 Test results, ap: 0.218, auc: 0.464, f1: 0.100, acc: 0.667
+2025-08-07 19:39:14: Epoch: 007 Train Loss: 0.650, ap: 0.584, auc: 0.643, f1: 0.533, acc: 0.660
+2025-08-07 19:39:14: Epoch: 007 Test results, ap: 0.216, auc: 0.461, f1: 0.100, acc: 0.667
+2025-08-07 19:39:15: Epoch: 008 Train Loss: 0.633, ap: 0.547, auc: 0.640, f1: 0.563, acc: 0.699
+2025-08-07 19:39:15: Epoch: 008 Test results, ap: 0.219, auc: 0.470, f1: 0.000, acc: 0.685
+2025-08-07 19:39:15: Epoch: 009 Train Loss: 0.602, ap: 0.580, auc: 0.665, f1: 0.583, acc: 0.709
+2025-08-07 19:39:15: Epoch: 009 Test results, ap: 0.228, auc: 0.432, f1: 0.100, acc: 0.667
+2025-08-07 19:39:16: Epoch: 010 Train Loss: 0.618, ap: 0.591, auc: 0.658, f1: 0.514, acc: 0.670
+2025-08-07 19:39:16: Epoch: 010 Test results, ap: 0.225, auc: 0.426, f1: 0.100, acc: 0.667
+2025-08-07 19:39:16: Epoch: 011 Train Loss: 0.627, ap: 0.602, auc: 0.665, f1: 0.592, acc: 0.718
+2025-08-07 19:39:16: Epoch: 011 Test results, ap: 0.214, auc: 0.423, f1: 0.000, acc: 0.722
+2025-08-07 19:39:16: Epoch: 011 New best TEST metrics
+2025-08-07 19:39:17: Epoch: 012 Train Loss: 0.591, ap: 0.588, auc: 0.664, f1: 0.529, acc: 0.689
+2025-08-07 19:39:17: Epoch: 012 Test results, ap: 0.211, auc: 0.426, f1: 0.000, acc: 0.759
+2025-08-07 19:39:17: Epoch: 012 New best TEST metrics
+2025-08-07 19:39:17: Epoch: 013 Train Loss: 0.585, ap: 0.584, auc: 0.674, f1: 0.583, acc: 0.709
+2025-08-07 19:39:17: Epoch: 013 Test results, ap: 0.231, auc: 0.445, f1: 0.100, acc: 0.667
+2025-08-07 19:39:17: Epoch: 013 New best TEST metrics
+2025-08-07 19:39:18: Epoch: 014 Train Loss: 0.656, ap: 0.517, auc: 0.617, f1: 0.433, acc: 0.670
+2025-08-07 19:39:18: Epoch: 014 Test results, ap: 0.219, auc: 0.459, f1: 0.000, acc: 0.759
+2025-08-07 19:39:18: Epoch: 014 New best TEST metrics
+2025-08-07 19:39:18: Epoch: 015 Train Loss: 0.622, ap: 0.523, auc: 0.630, f1: 0.560, acc: 0.680
+2025-08-07 19:39:18: Epoch: 015 Test results, ap: 0.225, auc: 0.488, f1: 0.098, acc: 0.657
+2025-08-07 19:39:18: Epoch: 015 New best TEST metrics
+2025-08-07 19:39:18: Epoch: 016 Train Loss: 0.597, ap: 0.623, auc: 0.654, f1: 0.583, acc: 0.709
+2025-08-07 19:39:18: Epoch: 016 New best VALIDATION metrics
+2025-08-07 19:39:19: Epoch: 016 Test results, ap: 0.217, auc: 0.457, f1: 0.000, acc: 0.676
+2025-08-07 19:39:19: Epoch: 017 Train Loss: 0.583, ap: 0.614, auc: 0.677, f1: 0.571, acc: 0.709
+2025-08-07 19:39:19: Epoch: 017 New best VALIDATION metrics
+2025-08-07 19:39:19: Epoch: 017 Test results, ap: 0.218, auc: 0.464, f1: 0.000, acc: 0.722
+2025-08-07 19:39:19: Epoch: 018 Train Loss: 0.579, ap: 0.587, auc: 0.671, f1: 0.580, acc: 0.718
+2025-08-07 19:39:19: Epoch: 018 Test results, ap: 0.225, auc: 0.476, f1: 0.000, acc: 0.722
+2025-08-07 19:39:20: Epoch: 019 Train Loss: 0.564, ap: 0.635, auc: 0.687, f1: 0.571, acc: 0.709
+2025-08-07 19:39:20: Epoch: 019 New best VALIDATION metrics
+2025-08-07 19:39:20: Epoch: 019 Test results, ap: 0.219, auc: 0.461, f1: 0.000, acc: 0.676
+2025-08-07 19:39:20: Epoch: 020 Train Loss: 0.573, ap: 0.636, auc: 0.698, f1: 0.580, acc: 0.718
+2025-08-07 19:39:20: Epoch: 020 New best VALIDATION metrics
+2025-08-07 19:39:20: Epoch: 020 Test results, ap: 0.204, auc: 0.416, f1: 0.000, acc: 0.704
+2025-08-07 19:39:21: Epoch: 021 Train Loss: 0.552, ap: 0.617, auc: 0.701, f1: 0.571, acc: 0.709
+2025-08-07 19:39:21: Epoch: 021 Test results, ap: 0.208, auc: 0.429, f1: 0.000, acc: 0.704
+2025-08-07 19:39:21: Epoch: 022 Train Loss: 0.560, ap: 0.608, auc: 0.682, f1: 0.588, acc: 0.728
+2025-08-07 19:39:21: Epoch: 022 Test results, ap: 0.204, auc: 0.413, f1: 0.000, acc: 0.704
+2025-08-07 19:39:22: Epoch: 023 Train Loss: 0.566, ap: 0.606, auc: 0.684, f1: 0.600, acc: 0.728
+2025-08-07 19:39:22: Epoch: 023 Test results, ap: 0.215, auc: 0.444, f1: 0.000, acc: 0.704
+2025-08-07 19:39:22: Epoch: 024 Train Loss: 0.525, ap: 0.614, auc: 0.708, f1: 0.588, acc: 0.728
+2025-08-07 19:39:22: Epoch: 024 Test results, ap: 0.212, auc: 0.433, f1: 0.000, acc: 0.722
+2025-08-07 19:39:22: Epoch: 025 Train Loss: 0.543, ap: 0.613, auc: 0.686, f1: 0.571, acc: 0.709
+2025-08-07 19:39:23: Epoch: 025 Test results, ap: 0.227, auc: 0.472, f1: 0.000, acc: 0.704
+2025-08-07 19:39:23: Epoch: 026 Train Loss: 0.556, ap: 0.614, auc: 0.721, f1: 0.484, acc: 0.689
+2025-08-07 19:39:23: Epoch: 026 New best VALIDATION metrics
+2025-08-07 19:39:23: Epoch: 026 Test results, ap: 0.230, auc: 0.474, f1: 0.000, acc: 0.722
+2025-08-07 19:39:23: Epoch: 027 Train Loss: 0.529, ap: 0.592, auc: 0.701, f1: 0.583, acc: 0.709
+2025-08-07 19:39:23: Epoch: 027 Test results, ap: 0.218, auc: 0.458, f1: 0.000, acc: 0.704
+2025-08-07 19:39:24: Epoch: 028 Train Loss: 0.518, ap: 0.627, auc: 0.723, f1: 0.559, acc: 0.709
+2025-08-07 19:39:24: Epoch: 028 New best VALIDATION metrics
+2025-08-07 19:39:24: Epoch: 028 Test results, ap: 0.226, auc: 0.463, f1: 0.000, acc: 0.722
+2025-08-07 19:39:24: Epoch: 029 Train Loss: 0.524, ap: 0.603, auc: 0.712, f1: 0.583, acc: 0.709
+2025-08-07 19:39:24: Epoch: 029 Test results, ap: 0.227, auc: 0.453, f1: 0.000, acc: 0.704
+2025-08-07 19:39:25: Epoch: 030 Train Loss: 0.536, ap: 0.594, auc: 0.719, f1: 0.492, acc: 0.699
+2025-08-07 19:39:25: Epoch: 030 Test results, ap: 0.228, auc: 0.459, f1: 0.000, acc: 0.722
+2025-08-07 19:39:25: Epoch: 031 Train Loss: 0.533, ap: 0.601, auc: 0.713, f1: 0.592, acc: 0.718
+2025-08-07 19:39:25: Epoch: 031 Test results, ap: 0.226, auc: 0.459, f1: 0.000, acc: 0.704
+2025-08-07 19:39:25: Epoch: 032 Train Loss: 0.507, ap: 0.595, auc: 0.708, f1: 0.622, acc: 0.728
+2025-08-07 19:39:26: Epoch: 032 Test results, ap: 0.223, auc: 0.453, f1: 0.000, acc: 0.704
+2025-08-07 19:39:26: Epoch: 033 Train Loss: 0.507, ap: 0.612, auc: 0.725, f1: 0.639, acc: 0.748
+2025-08-07 19:39:26: Epoch: 033 Test results, ap: 0.225, auc: 0.458, f1: 0.000, acc: 0.704
+2025-08-07 19:39:26: Epoch: 034 Train Loss: 0.497, ap: 0.605, auc: 0.723, f1: 0.648, acc: 0.757
+2025-08-07 19:39:26: Epoch: 034 Test results, ap: 0.222, auc: 0.446, f1: 0.000, acc: 0.704
+2025-08-07 19:39:27: Epoch: 035 Train Loss: 0.479, ap: 0.599, auc: 0.731, f1: 0.622, acc: 0.728
+2025-08-07 19:39:27: Epoch: 035 Test results, ap: 0.231, auc: 0.468, f1: 0.000, acc: 0.704
+2025-08-07 19:39:27: Epoch: 036 Train Loss: 0.496, ap: 0.611, auc: 0.732, f1: 0.648, acc: 0.757
+2025-08-07 19:39:27: Epoch: 036 Test results, ap: 0.216, auc: 0.427, f1: 0.000, acc: 0.704
+2025-08-07 19:39:28: Epoch: 037 Train Loss: 0.503, ap: 0.617, auc: 0.739, f1: 0.648, acc: 0.757
+2025-08-07 19:39:28: Epoch: 037 New best VALIDATION metrics
+2025-08-07 19:39:28: Epoch: 037 Test results, ap: 0.216, auc: 0.435, f1: 0.000, acc: 0.704
+2025-08-07 19:39:28: Epoch: 038 Train Loss: 0.465, ap: 0.606, auc: 0.748, f1: 0.639, acc: 0.748
+2025-08-07 19:39:28: Epoch: 038 Test results, ap: 0.225, auc: 0.451, f1: 0.000, acc: 0.704
+2025-08-07 19:39:28: Epoch: 039 Train Loss: 0.470, ap: 0.617, auc: 0.757, f1: 0.639, acc: 0.748
+2025-08-07 19:39:28: Epoch: 039 New best VALIDATION metrics
+2025-08-07 19:39:29: Epoch: 039 Test results, ap: 0.230, auc: 0.448, f1: 0.000, acc: 0.704
+2025-08-07 19:39:29: Epoch: 040 Train Loss: 0.456, ap: 0.620, auc: 0.753, f1: 0.605, acc: 0.709
+2025-08-07 19:39:29: Epoch: 040 Test results, ap: 0.236, auc: 0.472, f1: 0.000, acc: 0.704
+2025-08-07 19:39:29: Epoch: 041 Train Loss: 0.467, ap: 0.623, auc: 0.759, f1: 0.613, acc: 0.718
+2025-08-07 19:39:29: Epoch: 041 New best VALIDATION metrics
+2025-08-07 19:39:29: Epoch: 041 Test results, ap: 0.229, auc: 0.453, f1: 0.000, acc: 0.704
+2025-08-07 19:39:30: Epoch: 042 Train Loss: 0.457, ap: 0.626, auc: 0.760, f1: 0.622, acc: 0.728
+2025-08-07 19:39:30: Epoch: 042 New best VALIDATION metrics
+2025-08-07 19:39:30: Epoch: 042 Test results, ap: 0.227, auc: 0.447, f1: 0.000, acc: 0.704
+2025-08-07 19:39:30: Epoch: 043 Train Loss: 0.462, ap: 0.630, auc: 0.765, f1: 0.597, acc: 0.699
+2025-08-07 19:39:30: Epoch: 043 New best VALIDATION metrics
+2025-08-07 19:39:30: Epoch: 043 Test results, ap: 0.231, auc: 0.455, f1: 0.000, acc: 0.704
+2025-08-07 19:39:31: Epoch: 044 Train Loss: 0.468, ap: 0.623, auc: 0.753, f1: 0.595, acc: 0.709
+2025-08-07 19:39:31: Epoch: 044 Test results, ap: 0.230, auc: 0.447, f1: 0.000, acc: 0.704
+2025-08-07 19:39:31: Epoch: 045 Train Loss: 0.448, ap: 0.646, auc: 0.771, f1: 0.605, acc: 0.709
+2025-08-07 19:39:31: Epoch: 045 New best VALIDATION metrics
+2025-08-07 19:39:31: Epoch: 045 Test results, ap: 0.232, auc: 0.459, f1: 0.105, acc: 0.685
+2025-08-07 19:39:32: Epoch: 046 Train Loss: 0.465, ap: 0.636, auc: 0.764, f1: 0.587, acc: 0.699
+2025-08-07 19:39:32: Epoch: 046 Test results, ap: 0.228, auc: 0.446, f1: 0.105, acc: 0.685
+2025-08-07 19:39:32: Epoch: 047 Train Loss: 0.448, ap: 0.636, auc: 0.763, f1: 0.605, acc: 0.709
+2025-08-07 19:39:32: Epoch: 047 Test results, ap: 0.226, auc: 0.443, f1: 0.103, acc: 0.676
+2025-08-07 19:39:32: Epoch: 048 Train Loss: 0.438, ap: 0.632, auc: 0.766, f1: 0.605, acc: 0.709
+2025-08-07 19:39:33: Epoch: 048 Test results, ap: 0.226, auc: 0.450, f1: 0.103, acc: 0.676
+2025-08-07 19:39:33: Epoch: 049 Train Loss: 0.441, ap: 0.635, auc: 0.767, f1: 0.605, acc: 0.709
+2025-08-07 19:39:33: Epoch: 049 Test results, ap: 0.225, auc: 0.443, f1: 0.103, acc: 0.676
+2025-08-07 19:39:33: Epoch: 050 Train Loss: 0.445, ap: 0.635, auc: 0.769, f1: 0.597, acc: 0.699
+2025-08-07 19:39:33: Epoch: 050 Test results, ap: 0.224, auc: 0.439, f1: 0.103, acc: 0.676
+2025-08-07 19:39:33: used time 0.01h
+2025-08-07 19:39:33: Running Cross Validation 2
+2025-08-07 19:39:33: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:39:34: Epoch: 001 Train Loss: 0.690, ap: 0.541, auc: 0.601, f1: 0.655, acc: 0.612
+2025-08-07 19:39:34: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:39:34: Epoch: 001 Test results, ap: 0.195, auc: 0.389, f1: 0.188, acc: 0.519
+2025-08-07 19:39:34: Epoch: 002 Train Loss: 0.681, ap: 0.661, auc: 0.671, f1: 0.678, acc: 0.621
+2025-08-07 19:39:34: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:39:34: Epoch: 002 Test results, ap: 0.207, auc: 0.416, f1: 0.247, acc: 0.491
+2025-08-07 19:39:34: Epoch: 003 Train Loss: 0.664, ap: 0.664, auc: 0.678, f1: 0.655, acc: 0.631
+2025-08-07 19:39:34: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:39:35: Epoch: 003 Test results, ap: 0.215, auc: 0.436, f1: 0.254, acc: 0.565
+2025-08-07 19:39:35: Epoch: 004 Train Loss: 0.653, ap: 0.722, auc: 0.689, f1: 0.655, acc: 0.631
+2025-08-07 19:39:35: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:39:35: Epoch: 004 Test results, ap: 0.221, auc: 0.442, f1: 0.254, acc: 0.565
+2025-08-07 19:39:35: Epoch: 005 Train Loss: 0.671, ap: 0.731, auc: 0.692, f1: 0.581, acc: 0.621
+2025-08-07 19:39:35: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:39:35: Epoch: 005 Test results, ap: 0.224, auc: 0.443, f1: 0.000, acc: 0.685
+2025-08-07 19:39:36: Epoch: 006 Train Loss: 0.642, ap: 0.741, auc: 0.705, f1: 0.667, acc: 0.650
+2025-08-07 19:39:36: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:39:36: Epoch: 006 Test results, ap: 0.226, auc: 0.451, f1: 0.254, acc: 0.565
+2025-08-07 19:39:36: Epoch: 007 Train Loss: 0.648, ap: 0.757, auc: 0.722, f1: 0.615, acc: 0.660
+2025-08-07 19:39:36: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:39:36: Epoch: 007 Test results, ap: 0.226, auc: 0.459, f1: 0.000, acc: 0.685
+2025-08-07 19:39:37: Epoch: 008 Train Loss: 0.638, ap: 0.656, auc: 0.690, f1: 0.625, acc: 0.650
+2025-08-07 19:39:37: Epoch: 008 Test results, ap: 0.219, auc: 0.411, f1: 0.000, acc: 0.685
+2025-08-07 19:39:37: Epoch: 009 Train Loss: 0.616, ap: 0.689, auc: 0.707, f1: 0.652, acc: 0.689
+2025-08-07 19:39:37: Epoch: 009 Test results, ap: 0.217, auc: 0.414, f1: 0.103, acc: 0.676
+2025-08-07 19:39:38: Epoch: 010 Train Loss: 0.616, ap: 0.736, auc: 0.705, f1: 0.639, acc: 0.660
+2025-08-07 19:39:38: Epoch: 010 Test results, ap: 0.218, auc: 0.425, f1: 0.267, acc: 0.593
+2025-08-07 19:39:38: Epoch: 011 Train Loss: 0.614, ap: 0.774, auc: 0.727, f1: 0.667, acc: 0.660
+2025-08-07 19:39:38: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:39:38: Epoch: 011 Test results, ap: 0.215, auc: 0.420, f1: 0.265, acc: 0.537
+2025-08-07 19:39:38: Epoch: 011 New best TEST metrics
+2025-08-07 19:39:38: Epoch: 012 Train Loss: 0.591, ap: 0.788, auc: 0.744, f1: 0.679, acc: 0.670
+2025-08-07 19:39:38: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:39:39: Epoch: 012 Test results, ap: 0.209, auc: 0.410, f1: 0.273, acc: 0.556
+2025-08-07 19:39:39: Epoch: 013 Train Loss: 0.605, ap: 0.787, auc: 0.739, f1: 0.660, acc: 0.660
+2025-08-07 19:39:39: Epoch: 013 Test results, ap: 0.208, auc: 0.403, f1: 0.133, acc: 0.639
+2025-08-07 19:39:39: Epoch: 014 Train Loss: 0.586, ap: 0.788, auc: 0.749, f1: 0.679, acc: 0.670
+2025-08-07 19:39:39: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:39:39: Epoch: 014 Test results, ap: 0.212, auc: 0.410, f1: 0.133, acc: 0.639
+2025-08-07 19:39:40: Epoch: 015 Train Loss: 0.581, ap: 0.791, auc: 0.748, f1: 0.697, acc: 0.680
+2025-08-07 19:39:40: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:39:40: Epoch: 015 Test results, ap: 0.203, auc: 0.390, f1: 0.261, acc: 0.528
+2025-08-07 19:39:40: Epoch: 016 Train Loss: 0.579, ap: 0.796, auc: 0.761, f1: 0.714, acc: 0.689
+2025-08-07 19:39:40: Epoch: 016 New best VALIDATION metrics
+2025-08-07 19:39:40: Epoch: 016 Test results, ap: 0.203, auc: 0.396, f1: 0.275, acc: 0.463
+2025-08-07 19:39:41: Epoch: 017 Train Loss: 0.571, ap: 0.798, auc: 0.759, f1: 0.702, acc: 0.670
+2025-08-07 19:39:41: Epoch: 017 Test results, ap: 0.204, auc: 0.394, f1: 0.240, acc: 0.472
+2025-08-07 19:39:41: Epoch: 018 Train Loss: 0.559, ap: 0.790, auc: 0.750, f1: 0.714, acc: 0.689
+2025-08-07 19:39:41: Epoch: 018 Test results, ap: 0.206, auc: 0.409, f1: 0.265, acc: 0.435
+2025-08-07 19:39:42: Epoch: 019 Train Loss: 0.557, ap: 0.778, auc: 0.722, f1: 0.661, acc: 0.631
+2025-08-07 19:39:42: Epoch: 019 Test results, ap: 0.189, auc: 0.350, f1: 0.115, acc: 0.574
+2025-08-07 19:39:42: Epoch: 020 Train Loss: 0.564, ap: 0.769, auc: 0.719, f1: 0.690, acc: 0.650
+2025-08-07 19:39:42: Epoch: 020 Test results, ap: 0.194, auc: 0.375, f1: 0.253, acc: 0.398
+2025-08-07 19:39:42: Epoch: 021 Train Loss: 0.547, ap: 0.762, auc: 0.714, f1: 0.685, acc: 0.660
+2025-08-07 19:39:43: Epoch: 021 Test results, ap: 0.202, auc: 0.399, f1: 0.344, acc: 0.435
+2025-08-07 19:39:43: Epoch: 022 Train Loss: 0.528, ap: 0.772, auc: 0.726, f1: 0.690, acc: 0.660
+2025-08-07 19:39:43: Epoch: 022 Test results, ap: 0.196, auc: 0.386, f1: 0.351, acc: 0.417
+2025-08-07 19:39:43: Epoch: 023 Train Loss: 0.525, ap: 0.780, auc: 0.731, f1: 0.661, acc: 0.641
+2025-08-07 19:39:43: Epoch: 023 Test results, ap: 0.193, auc: 0.375, f1: 0.318, acc: 0.444
+2025-08-07 19:39:44: Epoch: 024 Train Loss: 0.494, ap: 0.770, auc: 0.729, f1: 0.699, acc: 0.699
+2025-08-07 19:39:44: Epoch: 024 Test results, ap: 0.203, auc: 0.380, f1: 0.329, acc: 0.472
+2025-08-07 19:39:44: Epoch: 025 Train Loss: 0.506, ap: 0.774, auc: 0.718, f1: 0.636, acc: 0.621
+2025-08-07 19:39:44: Epoch: 025 Test results, ap: 0.200, auc: 0.367, f1: 0.296, acc: 0.472
+2025-08-07 19:39:45: Epoch: 026 Train Loss: 0.500, ap: 0.781, auc: 0.736, f1: 0.673, acc: 0.689
+2025-08-07 19:39:45: Epoch: 026 Test results, ap: 0.214, auc: 0.400, f1: 0.311, acc: 0.426
+2025-08-07 19:39:45: Epoch: 027 Train Loss: 0.494, ap: 0.773, auc: 0.718, f1: 0.654, acc: 0.650
+2025-08-07 19:39:45: Epoch: 027 Test results, ap: 0.218, auc: 0.407, f1: 0.240, acc: 0.472
+2025-08-07 19:39:45: Epoch: 028 Train Loss: 0.505, ap: 0.788, auc: 0.732, f1: 0.667, acc: 0.660
+2025-08-07 19:39:46: Epoch: 028 Test results, ap: 0.222, auc: 0.400, f1: 0.282, acc: 0.435
+2025-08-07 19:39:46: Epoch: 029 Train Loss: 0.505, ap: 0.783, auc: 0.738, f1: 0.699, acc: 0.699
+2025-08-07 19:39:46: Epoch: 029 Test results, ap: 0.214, auc: 0.417, f1: 0.311, acc: 0.426
+2025-08-07 19:39:46: Epoch: 030 Train Loss: 0.478, ap: 0.772, auc: 0.719, f1: 0.661, acc: 0.641
+2025-08-07 19:39:46: Epoch: 030 Test results, ap: 0.209, auc: 0.401, f1: 0.194, acc: 0.537
+2025-08-07 19:39:47: Epoch: 031 Train Loss: 0.488, ap: 0.787, auc: 0.733, f1: 0.673, acc: 0.680
+2025-08-07 19:39:47: Epoch: 031 Test results, ap: 0.229, auc: 0.409, f1: 0.282, acc: 0.435
+2025-08-07 19:39:47: Epoch: 031 New best TEST metrics
+2025-08-07 19:39:47: Epoch: 032 Train Loss: 0.479, ap: 0.794, auc: 0.742, f1: 0.680, acc: 0.689
+2025-08-07 19:39:47: Epoch: 032 Test results, ap: 0.224, auc: 0.411, f1: 0.282, acc: 0.435
+2025-08-07 19:39:48: Epoch: 033 Train Loss: 0.460, ap: 0.789, auc: 0.734, f1: 0.692, acc: 0.689
+2025-08-07 19:39:48: Epoch: 033 Test results, ap: 0.226, auc: 0.409, f1: 0.282, acc: 0.435
+2025-08-07 19:39:48: Epoch: 034 Train Loss: 0.447, ap: 0.788, auc: 0.735, f1: 0.660, acc: 0.660
+2025-08-07 19:39:48: Epoch: 034 Test results, ap: 0.227, auc: 0.405, f1: 0.247, acc: 0.491
+2025-08-07 19:39:48: Epoch: 035 Train Loss: 0.461, ap: 0.781, auc: 0.730, f1: 0.667, acc: 0.660
+2025-08-07 19:39:48: Epoch: 035 Test results, ap: 0.227, auc: 0.394, f1: 0.194, acc: 0.537
+2025-08-07 19:39:49: Epoch: 036 Train Loss: 0.448, ap: 0.787, auc: 0.741, f1: 0.686, acc: 0.689
+2025-08-07 19:39:49: Epoch: 036 Test results, ap: 0.228, auc: 0.400, f1: 0.194, acc: 0.537
+2025-08-07 19:39:49: Epoch: 037 Train Loss: 0.452, ap: 0.787, auc: 0.742, f1: 0.685, acc: 0.670
+2025-08-07 19:39:49: Epoch: 037 Test results, ap: 0.208, auc: 0.386, f1: 0.194, acc: 0.537
+2025-08-07 19:39:50: Epoch: 038 Train Loss: 0.450, ap: 0.779, auc: 0.732, f1: 0.667, acc: 0.660
+2025-08-07 19:39:50: Epoch: 038 Test results, ap: 0.216, auc: 0.387, f1: 0.194, acc: 0.537
+2025-08-07 19:39:50: Epoch: 039 Train Loss: 0.444, ap: 0.782, auc: 0.735, f1: 0.686, acc: 0.689
+2025-08-07 19:39:50: Epoch: 039 Test results, ap: 0.216, auc: 0.382, f1: 0.194, acc: 0.537
+2025-08-07 19:39:51: Epoch: 040 Train Loss: 0.440, ap: 0.779, auc: 0.735, f1: 0.686, acc: 0.680
+2025-08-07 19:39:51: Epoch: 040 Test results, ap: 0.210, auc: 0.380, f1: 0.194, acc: 0.537
+2025-08-07 19:39:51: Epoch: 041 Train Loss: 0.426, ap: 0.783, auc: 0.735, f1: 0.699, acc: 0.699
+2025-08-07 19:39:51: Epoch: 041 Test results, ap: 0.210, auc: 0.380, f1: 0.182, acc: 0.500
+2025-08-07 19:39:52: Epoch: 042 Train Loss: 0.425, ap: 0.777, auc: 0.731, f1: 0.680, acc: 0.680
+2025-08-07 19:39:52: Epoch: 042 Test results, ap: 0.210, auc: 0.380, f1: 0.182, acc: 0.500
+2025-08-07 19:39:52: Epoch: 043 Train Loss: 0.431, ap: 0.778, auc: 0.731, f1: 0.680, acc: 0.680
+2025-08-07 19:39:52: Epoch: 043 Test results, ap: 0.207, auc: 0.380, f1: 0.182, acc: 0.500
+2025-08-07 19:39:52: Epoch: 044 Train Loss: 0.421, ap: 0.765, auc: 0.725, f1: 0.679, acc: 0.670
+2025-08-07 19:39:52: Epoch: 044 Test results, ap: 0.207, auc: 0.370, f1: 0.182, acc: 0.500
+2025-08-07 19:39:53: Epoch: 045 Train Loss: 0.410, ap: 0.767, auc: 0.726, f1: 0.680, acc: 0.680
+2025-08-07 19:39:53: Epoch: 045 Test results, ap: 0.214, auc: 0.372, f1: 0.182, acc: 0.500
+2025-08-07 19:39:53: Epoch: 046 Train Loss: 0.409, ap: 0.768, auc: 0.722, f1: 0.660, acc: 0.650
+2025-08-07 19:39:53: Epoch: 046 Test results, ap: 0.216, auc: 0.383, f1: 0.182, acc: 0.500
+2025-08-07 19:39:54: Epoch: 047 Train Loss: 0.432, ap: 0.769, auc: 0.726, f1: 0.660, acc: 0.650
+2025-08-07 19:39:54: Epoch: 047 Test results, ap: 0.214, auc: 0.373, f1: 0.182, acc: 0.500
+2025-08-07 19:39:54: Epoch: 048 Train Loss: 0.428, ap: 0.766, auc: 0.721, f1: 0.660, acc: 0.650
+2025-08-07 19:39:54: Epoch: 048 Test results, ap: 0.218, auc: 0.392, f1: 0.182, acc: 0.500
+2025-08-07 19:39:54: Epoch: 049 Train Loss: 0.423, ap: 0.766, auc: 0.722, f1: 0.654, acc: 0.650
+2025-08-07 19:39:55: Epoch: 049 Test results, ap: 0.214, auc: 0.376, f1: 0.182, acc: 0.500
+2025-08-07 19:39:55: Epoch: 050 Train Loss: 0.416, ap: 0.762, auc: 0.723, f1: 0.667, acc: 0.660
+2025-08-07 19:39:55: Epoch: 050 Test results, ap: 0.214, auc: 0.375, f1: 0.182, acc: 0.500
+2025-08-07 19:39:55: used time 0.01h
+2025-08-07 19:39:55: Running Cross Validation 3
+2025-08-07 19:39:55: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:39:55: Epoch: 001 Train Loss: 0.693, ap: 0.519, auc: 0.656, f1: 0.626, acc: 0.583
+2025-08-07 19:39:55: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:39:55: Epoch: 001 Test results, ap: 0.203, auc: 0.423, f1: 0.306, acc: 0.454
+2025-08-07 19:39:56: Epoch: 002 Train Loss: 0.687, ap: 0.553, auc: 0.658, f1: 0.635, acc: 0.631
+2025-08-07 19:39:56: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:39:56: Epoch: 002 Test results, ap: 0.223, auc: 0.484, f1: 0.247, acc: 0.491
+2025-08-07 19:39:56: Epoch: 003 Train Loss: 0.674, ap: 0.550, auc: 0.667, f1: 0.633, acc: 0.650
+2025-08-07 19:39:56: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:39:56: Epoch: 003 Test results, ap: 0.239, auc: 0.505, f1: 0.261, acc: 0.528
+2025-08-07 19:39:57: Epoch: 004 Train Loss: 0.665, ap: 0.551, auc: 0.656, f1: 0.259, acc: 0.612
+2025-08-07 19:39:57: Epoch: 004 Test results, ap: 0.237, auc: 0.500, f1: 0.000, acc: 0.722
+2025-08-07 19:39:57: Epoch: 005 Train Loss: 0.667, ap: 0.555, auc: 0.688, f1: 0.519, acc: 0.641
+2025-08-07 19:39:57: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:39:57: Epoch: 005 Test results, ap: 0.232, auc: 0.469, f1: 0.211, acc: 0.583
+2025-08-07 19:39:57: Epoch: 006 Train Loss: 0.650, ap: 0.568, auc: 0.670, f1: 0.598, acc: 0.660
+2025-08-07 19:39:58: Epoch: 006 Test results, ap: 0.228, auc: 0.465, f1: 0.254, acc: 0.565
+2025-08-07 19:39:58: Epoch: 007 Train Loss: 0.623, ap: 0.579, auc: 0.681, f1: 0.486, acc: 0.631
+2025-08-07 19:39:58: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:39:58: Epoch: 007 Test results, ap: 0.208, auc: 0.415, f1: 0.211, acc: 0.583
+2025-08-07 19:39:58: Epoch: 008 Train Loss: 0.617, ap: 0.610, auc: 0.702, f1: 0.448, acc: 0.641
+2025-08-07 19:39:58: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:39:59: Epoch: 008 Test results, ap: 0.202, auc: 0.402, f1: 0.174, acc: 0.648
+2025-08-07 19:39:59: Epoch: 009 Train Loss: 0.612, ap: 0.625, auc: 0.715, f1: 0.545, acc: 0.660
+2025-08-07 19:39:59: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:39:59: Epoch: 009 Test results, ap: 0.208, auc: 0.406, f1: 0.254, acc: 0.565
+2025-08-07 19:39:59: Epoch: 010 Train Loss: 0.604, ap: 0.612, auc: 0.698, f1: 0.553, acc: 0.670
+2025-08-07 19:39:59: Epoch: 010 Test results, ap: 0.213, auc: 0.429, f1: 0.262, acc: 0.583
+2025-08-07 19:40:00: Epoch: 011 Train Loss: 0.595, ap: 0.628, auc: 0.703, f1: 0.514, acc: 0.650
+2025-08-07 19:40:00: Epoch: 011 Test results, ap: 0.203, auc: 0.385, f1: 0.100, acc: 0.667
+2025-08-07 19:40:00: Epoch: 011 New best TEST metrics
+2025-08-07 19:40:00: Epoch: 012 Train Loss: 0.588, ap: 0.627, auc: 0.699, f1: 0.541, acc: 0.670
+2025-08-07 19:40:00: Epoch: 012 Test results, ap: 0.201, auc: 0.371, f1: 0.160, acc: 0.611
+2025-08-07 19:40:01: Epoch: 013 Train Loss: 0.594, ap: 0.610, auc: 0.683, f1: 0.513, acc: 0.631
+2025-08-07 19:40:01: Epoch: 013 Test results, ap: 0.199, auc: 0.362, f1: 0.098, acc: 0.657
+2025-08-07 19:40:01: Epoch: 014 Train Loss: 0.553, ap: 0.623, auc: 0.681, f1: 0.507, acc: 0.641
+2025-08-07 19:40:01: Epoch: 014 Test results, ap: 0.194, auc: 0.351, f1: 0.098, acc: 0.657
+2025-08-07 19:40:02: Epoch: 015 Train Loss: 0.537, ap: 0.600, auc: 0.670, f1: 0.475, acc: 0.592
+2025-08-07 19:40:02: Epoch: 015 Test results, ap: 0.188, auc: 0.344, f1: 0.136, acc: 0.648
+2025-08-07 19:40:02: Epoch: 016 Train Loss: 0.531, ap: 0.603, auc: 0.685, f1: 0.519, acc: 0.641
+2025-08-07 19:40:02: Epoch: 016 Test results, ap: 0.196, auc: 0.368, f1: 0.160, acc: 0.611
+2025-08-07 19:40:02: Epoch: 017 Train Loss: 0.559, ap: 0.587, auc: 0.669, f1: 0.530, acc: 0.621
+2025-08-07 19:40:03: Epoch: 017 Test results, ap: 0.201, auc: 0.371, f1: 0.233, acc: 0.574
+2025-08-07 19:40:03: Epoch: 018 Train Loss: 0.516, ap: 0.602, auc: 0.678, f1: 0.554, acc: 0.641
+2025-08-07 19:40:03: Epoch: 018 Test results, ap: 0.195, auc: 0.369, f1: 0.190, acc: 0.528
+2025-08-07 19:40:03: Epoch: 019 Train Loss: 0.499, ap: 0.576, auc: 0.656, f1: 0.532, acc: 0.641
+2025-08-07 19:40:03: Epoch: 019 Test results, ap: 0.189, auc: 0.370, f1: 0.133, acc: 0.519
+2025-08-07 19:40:04: Epoch: 020 Train Loss: 0.527, ap: 0.680, auc: 0.686, f1: 0.578, acc: 0.631
+2025-08-07 19:40:04: Epoch: 020 New best VALIDATION metrics
+2025-08-07 19:40:04: Epoch: 020 Test results, ap: 0.194, auc: 0.379, f1: 0.289, acc: 0.361
+2025-08-07 19:40:04: Epoch: 021 Train Loss: 0.469, ap: 0.649, auc: 0.685, f1: 0.548, acc: 0.631
+2025-08-07 19:40:04: Epoch: 021 Test results, ap: 0.200, auc: 0.402, f1: 0.293, acc: 0.463
+2025-08-07 19:40:04: Epoch: 021 New best TEST metrics
+2025-08-07 19:40:05: Epoch: 022 Train Loss: 0.447, ap: 0.679, auc: 0.696, f1: 0.589, acc: 0.621
+2025-08-07 19:40:05: Epoch: 022 New best VALIDATION metrics
+2025-08-07 19:40:05: Epoch: 022 Test results, ap: 0.209, auc: 0.413, f1: 0.303, acc: 0.361
+2025-08-07 19:40:05: Epoch: 022 New best TEST metrics
+2025-08-07 19:40:05: Epoch: 023 Train Loss: 0.431, ap: 0.636, auc: 0.697, f1: 0.619, acc: 0.641
+2025-08-07 19:40:05: Epoch: 023 Test results, ap: 0.213, auc: 0.417, f1: 0.311, acc: 0.343
+2025-08-07 19:40:05: Epoch: 023 New best TEST metrics
+2025-08-07 19:40:06: Epoch: 024 Train Loss: 0.412, ap: 0.653, auc: 0.701, f1: 0.598, acc: 0.621
+2025-08-07 19:40:06: Epoch: 024 Test results, ap: 0.221, auc: 0.420, f1: 0.305, acc: 0.324
+2025-08-07 19:40:06: Epoch: 024 New best TEST metrics
+2025-08-07 19:40:06: Epoch: 025 Train Loss: 0.404, ap: 0.648, auc: 0.692, f1: 0.604, acc: 0.631
+2025-08-07 19:40:06: Epoch: 025 Test results, ap: 0.223, auc: 0.432, f1: 0.314, acc: 0.352
+2025-08-07 19:40:06: Epoch: 025 New best TEST metrics
+2025-08-07 19:40:06: Epoch: 026 Train Loss: 0.404, ap: 0.668, auc: 0.701, f1: 0.604, acc: 0.631
+2025-08-07 19:40:06: Epoch: 026 Test results, ap: 0.224, auc: 0.427, f1: 0.305, acc: 0.324
+2025-08-07 19:40:07: Epoch: 027 Train Loss: 0.418, ap: 0.653, auc: 0.694, f1: 0.598, acc: 0.621
+2025-08-07 19:40:07: Epoch: 027 Test results, ap: 0.249, auc: 0.443, f1: 0.327, acc: 0.352
+2025-08-07 19:40:07: Epoch: 027 New best TEST metrics
+2025-08-07 19:40:07: Epoch: 028 Train Loss: 0.367, ap: 0.638, auc: 0.679, f1: 0.611, acc: 0.641
+2025-08-07 19:40:07: Epoch: 028 Test results, ap: 0.229, auc: 0.433, f1: 0.308, acc: 0.333
+2025-08-07 19:40:08: Epoch: 029 Train Loss: 0.362, ap: 0.677, auc: 0.706, f1: 0.604, acc: 0.631
+2025-08-07 19:40:08: Epoch: 029 New best VALIDATION metrics
+2025-08-07 19:40:08: Epoch: 029 Test results, ap: 0.254, auc: 0.452, f1: 0.311, acc: 0.343
+2025-08-07 19:40:08: Epoch: 029 New best TEST metrics
+2025-08-07 19:40:08: Epoch: 030 Train Loss: 0.345, ap: 0.681, auc: 0.698, f1: 0.589, acc: 0.621
+2025-08-07 19:40:08: Epoch: 030 Test results, ap: 0.250, auc: 0.421, f1: 0.311, acc: 0.343
+2025-08-07 19:40:08: Epoch: 031 Train Loss: 0.335, ap: 0.672, auc: 0.698, f1: 0.604, acc: 0.631
+2025-08-07 19:40:09: Epoch: 031 Test results, ap: 0.244, auc: 0.440, f1: 0.317, acc: 0.361
+2025-08-07 19:40:09: Epoch: 032 Train Loss: 0.360, ap: 0.693, auc: 0.708, f1: 0.611, acc: 0.641
+2025-08-07 19:40:09: Epoch: 032 New best VALIDATION metrics
+2025-08-07 19:40:09: Epoch: 032 Test results, ap: 0.240, auc: 0.421, f1: 0.314, acc: 0.352
+2025-08-07 19:40:09: Epoch: 033 Train Loss: 0.325, ap: 0.628, auc: 0.692, f1: 0.609, acc: 0.650
+2025-08-07 19:40:09: Epoch: 033 Test results, ap: 0.249, auc: 0.443, f1: 0.314, acc: 0.352
+2025-08-07 19:40:10: Epoch: 034 Train Loss: 0.331, ap: 0.666, auc: 0.696, f1: 0.617, acc: 0.650
+2025-08-07 19:40:10: Epoch: 034 Test results, ap: 0.249, auc: 0.423, f1: 0.311, acc: 0.343
+2025-08-07 19:40:10: Epoch: 035 Train Loss: 0.325, ap: 0.673, auc: 0.700, f1: 0.596, acc: 0.631
+2025-08-07 19:40:10: Epoch: 035 Test results, ap: 0.242, auc: 0.430, f1: 0.317, acc: 0.361
+2025-08-07 19:40:11: Epoch: 036 Train Loss: 0.301, ap: 0.665, auc: 0.699, f1: 0.602, acc: 0.641
+2025-08-07 19:40:11: Epoch: 036 Test results, ap: 0.247, auc: 0.435, f1: 0.317, acc: 0.361
+2025-08-07 19:40:11: Epoch: 037 Train Loss: 0.312, ap: 0.680, auc: 0.697, f1: 0.596, acc: 0.631
+2025-08-07 19:40:11: Epoch: 037 Test results, ap: 0.239, auc: 0.413, f1: 0.265, acc: 0.333
+2025-08-07 19:40:11: Epoch: 038 Train Loss: 0.313, ap: 0.630, auc: 0.686, f1: 0.615, acc: 0.660
+2025-08-07 19:40:12: Epoch: 038 Test results, ap: 0.257, auc: 0.432, f1: 0.306, acc: 0.370
+2025-08-07 19:40:12: Epoch: 039 Train Loss: 0.311, ap: 0.693, auc: 0.700, f1: 0.602, acc: 0.641
+2025-08-07 19:40:12: Epoch: 039 Test results, ap: 0.260, auc: 0.414, f1: 0.253, acc: 0.398
+2025-08-07 19:40:12: Epoch: 040 Train Loss: 0.303, ap: 0.631, auc: 0.683, f1: 0.615, acc: 0.660
+2025-08-07 19:40:12: Epoch: 040 Test results, ap: 0.283, auc: 0.448, f1: 0.306, acc: 0.370
+2025-08-07 19:40:12: Epoch: 040 New best TEST metrics
+2025-08-07 19:40:13: Epoch: 041 Train Loss: 0.296, ap: 0.681, auc: 0.693, f1: 0.596, acc: 0.631
+2025-08-07 19:40:13: Epoch: 041 Test results, ap: 0.248, auc: 0.413, f1: 0.253, acc: 0.398
+2025-08-07 19:40:13: Epoch: 042 Train Loss: 0.271, ap: 0.653, auc: 0.686, f1: 0.609, acc: 0.650
+2025-08-07 19:40:13: Epoch: 042 Test results, ap: 0.256, auc: 0.424, f1: 0.277, acc: 0.370
+2025-08-07 19:40:13: Epoch: 043 Train Loss: 0.273, ap: 0.660, auc: 0.688, f1: 0.609, acc: 0.650
+2025-08-07 19:40:14: Epoch: 043 Test results, ap: 0.242, auc: 0.411, f1: 0.253, acc: 0.398
+2025-08-07 19:40:14: Epoch: 044 Train Loss: 0.285, ap: 0.661, auc: 0.688, f1: 0.602, acc: 0.641
+2025-08-07 19:40:14: Epoch: 044 Test results, ap: 0.237, auc: 0.405, f1: 0.262, acc: 0.426
+2025-08-07 19:40:14: Epoch: 045 Train Loss: 0.255, ap: 0.641, auc: 0.683, f1: 0.609, acc: 0.650
+2025-08-07 19:40:14: Epoch: 045 Test results, ap: 0.237, auc: 0.403, f1: 0.247, acc: 0.380
+2025-08-07 19:40:15: Epoch: 046 Train Loss: 0.260, ap: 0.655, auc: 0.688, f1: 0.609, acc: 0.650
+2025-08-07 19:40:15: Epoch: 046 Test results, ap: 0.238, auc: 0.408, f1: 0.256, acc: 0.407
+2025-08-07 19:40:15: Epoch: 047 Train Loss: 0.282, ap: 0.648, auc: 0.688, f1: 0.602, acc: 0.641
+2025-08-07 19:40:15: Epoch: 047 Test results, ap: 0.265, auc: 0.424, f1: 0.250, acc: 0.389
+2025-08-07 19:40:16: Epoch: 048 Train Loss: 0.272, ap: 0.634, auc: 0.683, f1: 0.602, acc: 0.641
+2025-08-07 19:40:16: Epoch: 048 Test results, ap: 0.269, auc: 0.432, f1: 0.250, acc: 0.389
+2025-08-07 19:40:16: Epoch: 049 Train Loss: 0.251, ap: 0.654, auc: 0.684, f1: 0.617, acc: 0.650
+2025-08-07 19:40:16: Epoch: 049 Test results, ap: 0.234, auc: 0.404, f1: 0.253, acc: 0.398
+2025-08-07 19:40:17: Epoch: 050 Train Loss: 0.267, ap: 0.635, auc: 0.681, f1: 0.624, acc: 0.660
+2025-08-07 19:40:17: Epoch: 050 Test results, ap: 0.237, auc: 0.405, f1: 0.256, acc: 0.407
+2025-08-07 19:40:17: used time 0.01h
+2025-08-07 19:40:17: Running Cross Validation 4
+2025-08-07 19:40:17: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:40:17: Epoch: 001 Train Loss: 0.689, ap: 0.538, auc: 0.664, f1: 0.604, acc: 0.631
+2025-08-07 19:40:17: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:40:17: Epoch: 001 Test results, ap: 0.209, auc: 0.439, f1: 0.175, acc: 0.565
+2025-08-07 19:40:18: Epoch: 002 Train Loss: 0.686, ap: 0.620, auc: 0.676, f1: 0.604, acc: 0.592
+2025-08-07 19:40:18: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:40:18: Epoch: 002 Test results, ap: 0.216, auc: 0.463, f1: 0.247, acc: 0.491
+2025-08-07 19:40:18: Epoch: 003 Train Loss: 0.676, ap: 0.623, auc: 0.689, f1: 0.609, acc: 0.650
+2025-08-07 19:40:18: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:40:18: Epoch: 003 Test results, ap: 0.219, auc: 0.462, f1: 0.254, acc: 0.565
+2025-08-07 19:40:18: Epoch: 004 Train Loss: 0.668, ap: 0.610, auc: 0.697, f1: 0.581, acc: 0.650
+2025-08-07 19:40:18: Epoch: 004 Test results, ap: 0.220, auc: 0.433, f1: 0.254, acc: 0.565
+2025-08-07 19:40:19: Epoch: 005 Train Loss: 0.653, ap: 0.636, auc: 0.700, f1: 0.571, acc: 0.680
+2025-08-07 19:40:19: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:40:19: Epoch: 005 Test results, ap: 0.215, auc: 0.417, f1: 0.100, acc: 0.667
+2025-08-07 19:40:19: Epoch: 006 Train Loss: 0.640, ap: 0.526, auc: 0.668, f1: 0.568, acc: 0.631
+2025-08-07 19:40:19: Epoch: 006 Test results, ap: 0.223, auc: 0.428, f1: 0.254, acc: 0.565
+2025-08-07 19:40:20: Epoch: 007 Train Loss: 0.636, ap: 0.650, auc: 0.708, f1: 0.564, acc: 0.670
+2025-08-07 19:40:20: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:40:20: Epoch: 007 Test results, ap: 0.225, auc: 0.431, f1: 0.103, acc: 0.676
+2025-08-07 19:40:20: Epoch: 008 Train Loss: 0.638, ap: 0.620, auc: 0.703, f1: 0.553, acc: 0.670
+2025-08-07 19:40:20: Epoch: 008 Test results, ap: 0.231, auc: 0.436, f1: 0.103, acc: 0.676
+2025-08-07 19:40:20: Epoch: 009 Train Loss: 0.620, ap: 0.577, auc: 0.700, f1: 0.600, acc: 0.650
+2025-08-07 19:40:21: Epoch: 009 Test results, ap: 0.257, auc: 0.439, f1: 0.282, acc: 0.481
+2025-08-07 19:40:21: Epoch: 010 Train Loss: 0.578, ap: 0.495, auc: 0.624, f1: 0.551, acc: 0.573
+2025-08-07 19:40:21: Epoch: 010 Test results, ap: 0.235, auc: 0.406, f1: 0.256, acc: 0.463
+2025-08-07 19:40:21: Epoch: 011 Train Loss: 0.529, ap: 0.484, auc: 0.630, f1: 0.527, acc: 0.583
+2025-08-07 19:40:21: Epoch: 011 Test results, ap: 0.222, auc: 0.399, f1: 0.232, acc: 0.509
+2025-08-07 19:40:21: Epoch: 011 New best TEST metrics
+2025-08-07 19:40:22: Epoch: 012 Train Loss: 0.528, ap: 0.523, auc: 0.651, f1: 0.571, acc: 0.621
+2025-08-07 19:40:22: Epoch: 012 Test results, ap: 0.234, auc: 0.398, f1: 0.190, acc: 0.528
+2025-08-07 19:40:22: Epoch: 012 New best TEST metrics
+2025-08-07 19:40:22: Epoch: 013 Train Loss: 0.514, ap: 0.566, auc: 0.688, f1: 0.584, acc: 0.641
+2025-08-07 19:40:22: Epoch: 013 Test results, ap: 0.195, auc: 0.381, f1: 0.188, acc: 0.519
+2025-08-07 19:40:23: Epoch: 014 Train Loss: 0.503, ap: 0.521, auc: 0.654, f1: 0.518, acc: 0.602
+2025-08-07 19:40:23: Epoch: 014 Test results, ap: 0.228, auc: 0.381, f1: 0.194, acc: 0.537
+2025-08-07 19:40:23: Epoch: 015 Train Loss: 0.481, ap: 0.475, auc: 0.635, f1: 0.545, acc: 0.612
+2025-08-07 19:40:23: Epoch: 015 Test results, ap: 0.208, auc: 0.374, f1: 0.188, acc: 0.519
+2025-08-07 19:40:23: Epoch: 016 Train Loss: 0.446, ap: 0.508, auc: 0.665, f1: 0.565, acc: 0.641
+2025-08-07 19:40:24: Epoch: 016 Test results, ap: 0.184, auc: 0.354, f1: 0.111, acc: 0.556
+2025-08-07 19:40:24: Epoch: 017 Train Loss: 0.463, ap: 0.524, auc: 0.637, f1: 0.523, acc: 0.592
+2025-08-07 19:40:24: Epoch: 017 Test results, ap: 0.188, auc: 0.365, f1: 0.107, acc: 0.537
+2025-08-07 19:40:24: Epoch: 018 Train Loss: 0.421, ap: 0.517, auc: 0.655, f1: 0.512, acc: 0.592
+2025-08-07 19:40:24: Epoch: 018 Test results, ap: 0.200, auc: 0.399, f1: 0.200, acc: 0.556
+2025-08-07 19:40:25: Epoch: 019 Train Loss: 0.428, ap: 0.549, auc: 0.655, f1: 0.578, acc: 0.660
+2025-08-07 19:40:25: Epoch: 019 Test results, ap: 0.193, auc: 0.369, f1: 0.182, acc: 0.583
+2025-08-07 19:40:25: Epoch: 020 Train Loss: 0.402, ap: 0.512, auc: 0.636, f1: 0.519, acc: 0.621
+2025-08-07 19:40:25: Epoch: 020 Test results, ap: 0.206, auc: 0.371, f1: 0.172, acc: 0.556
+2025-08-07 19:40:26: Epoch: 021 Train Loss: 0.384, ap: 0.557, auc: 0.661, f1: 0.562, acc: 0.621
+2025-08-07 19:40:26: Epoch: 021 Test results, ap: 0.194, auc: 0.381, f1: 0.190, acc: 0.528
+2025-08-07 19:40:26: Epoch: 022 Train Loss: 0.366, ap: 0.542, auc: 0.676, f1: 0.562, acc: 0.621
+2025-08-07 19:40:26: Epoch: 022 Test results, ap: 0.203, auc: 0.382, f1: 0.154, acc: 0.491
+2025-08-07 19:40:26: Epoch: 023 Train Loss: 0.381, ap: 0.553, auc: 0.648, f1: 0.541, acc: 0.621
+2025-08-07 19:40:26: Epoch: 023 Test results, ap: 0.190, auc: 0.372, f1: 0.113, acc: 0.565
+2025-08-07 19:40:27: Epoch: 024 Train Loss: 0.334, ap: 0.548, auc: 0.664, f1: 0.529, acc: 0.602
+2025-08-07 19:40:27: Epoch: 024 Test results, ap: 0.192, auc: 0.378, f1: 0.197, acc: 0.546
+2025-08-07 19:40:27: Epoch: 025 Train Loss: 0.323, ap: 0.541, auc: 0.662, f1: 0.535, acc: 0.612
+2025-08-07 19:40:27: Epoch: 025 Test results, ap: 0.190, auc: 0.371, f1: 0.188, acc: 0.519
+2025-08-07 19:40:28: Epoch: 026 Train Loss: 0.305, ap: 0.543, auc: 0.660, f1: 0.558, acc: 0.631
+2025-08-07 19:40:28: Epoch: 026 Test results, ap: 0.195, auc: 0.395, f1: 0.140, acc: 0.546
+2025-08-07 19:40:28: Epoch: 027 Train Loss: 0.309, ap: 0.508, auc: 0.624, f1: 0.541, acc: 0.621
+2025-08-07 19:40:28: Epoch: 027 Test results, ap: 0.204, auc: 0.415, f1: 0.197, acc: 0.546
+2025-08-07 19:40:28: Epoch: 028 Train Loss: 0.308, ap: 0.510, auc: 0.628, f1: 0.463, acc: 0.573
+2025-08-07 19:40:29: Epoch: 028 Test results, ap: 0.199, auc: 0.406, f1: 0.111, acc: 0.556
+2025-08-07 19:40:29: Epoch: 029 Train Loss: 0.291, ap: 0.536, auc: 0.649, f1: 0.529, acc: 0.602
+2025-08-07 19:40:29: Epoch: 029 Test results, ap: 0.207, auc: 0.433, f1: 0.136, acc: 0.528
+2025-08-07 19:40:29: Epoch: 029 New best TEST metrics
+2025-08-07 19:40:29: Epoch: 030 Train Loss: 0.294, ap: 0.546, auc: 0.678, f1: 0.548, acc: 0.631
+2025-08-07 19:40:29: Epoch: 030 Test results, ap: 0.222, auc: 0.452, f1: 0.182, acc: 0.500
+2025-08-07 19:40:29: Epoch: 030 New best TEST metrics
+2025-08-07 19:40:30: Epoch: 031 Train Loss: 0.306, ap: 0.515, auc: 0.632, f1: 0.506, acc: 0.602
+2025-08-07 19:40:30: Epoch: 031 Test results, ap: 0.219, auc: 0.462, f1: 0.131, acc: 0.509
+2025-08-07 19:40:30: Epoch: 031 New best TEST metrics
+2025-08-07 19:40:30: Epoch: 032 Train Loss: 0.281, ap: 0.531, auc: 0.644, f1: 0.488, acc: 0.592
+2025-08-07 19:40:30: Epoch: 032 Test results, ap: 0.211, auc: 0.443, f1: 0.140, acc: 0.546
+2025-08-07 19:40:30: Epoch: 033 Train Loss: 0.278, ap: 0.522, auc: 0.651, f1: 0.494, acc: 0.602
+2025-08-07 19:40:30: Epoch: 033 Test results, ap: 0.211, auc: 0.440, f1: 0.138, acc: 0.537
+2025-08-07 19:40:31: Epoch: 034 Train Loss: 0.248, ap: 0.512, auc: 0.624, f1: 0.494, acc: 0.602
+2025-08-07 19:40:31: Epoch: 034 Test results, ap: 0.208, auc: 0.439, f1: 0.140, acc: 0.546
+2025-08-07 19:40:31: Epoch: 035 Train Loss: 0.255, ap: 0.523, auc: 0.646, f1: 0.469, acc: 0.583
+2025-08-07 19:40:31: Epoch: 035 Test results, ap: 0.213, auc: 0.444, f1: 0.138, acc: 0.537
+2025-08-07 19:40:32: Epoch: 036 Train Loss: 0.251, ap: 0.515, auc: 0.630, f1: 0.500, acc: 0.592
+2025-08-07 19:40:32: Epoch: 036 Test results, ap: 0.208, auc: 0.436, f1: 0.140, acc: 0.546
+2025-08-07 19:40:32: Epoch: 037 Train Loss: 0.256, ap: 0.527, auc: 0.650, f1: 0.512, acc: 0.612
+2025-08-07 19:40:32: Epoch: 037 Test results, ap: 0.217, auc: 0.440, f1: 0.140, acc: 0.546
+2025-08-07 19:40:32: Epoch: 038 Train Loss: 0.253, ap: 0.515, auc: 0.627, f1: 0.488, acc: 0.592
+2025-08-07 19:40:33: Epoch: 038 Test results, ap: 0.207, auc: 0.436, f1: 0.200, acc: 0.556
+2025-08-07 19:40:33: Epoch: 039 Train Loss: 0.239, ap: 0.536, auc: 0.650, f1: 0.500, acc: 0.592
+2025-08-07 19:40:33: Epoch: 039 Test results, ap: 0.215, auc: 0.438, f1: 0.140, acc: 0.546
+2025-08-07 19:40:33: Epoch: 040 Train Loss: 0.233, ap: 0.522, auc: 0.639, f1: 0.494, acc: 0.602
+2025-08-07 19:40:33: Epoch: 040 Test results, ap: 0.212, auc: 0.447, f1: 0.140, acc: 0.546
+2025-08-07 19:40:34: Epoch: 041 Train Loss: 0.238, ap: 0.534, auc: 0.646, f1: 0.488, acc: 0.592
+2025-08-07 19:40:34: Epoch: 041 Test results, ap: 0.213, auc: 0.447, f1: 0.138, acc: 0.537
+2025-08-07 19:40:34: Epoch: 042 Train Loss: 0.236, ap: 0.529, auc: 0.639, f1: 0.500, acc: 0.592
+2025-08-07 19:40:34: Epoch: 042 Test results, ap: 0.210, auc: 0.442, f1: 0.140, acc: 0.546
+2025-08-07 19:40:35: Epoch: 043 Train Loss: 0.228, ap: 0.540, auc: 0.653, f1: 0.506, acc: 0.602
+2025-08-07 19:40:35: Epoch: 043 Test results, ap: 0.216, auc: 0.447, f1: 0.138, acc: 0.537
+2025-08-07 19:40:35: Epoch: 044 Train Loss: 0.226, ap: 0.533, auc: 0.642, f1: 0.476, acc: 0.573
+2025-08-07 19:40:35: Epoch: 044 Test results, ap: 0.214, auc: 0.451, f1: 0.140, acc: 0.546
+2025-08-07 19:40:36: Epoch: 045 Train Loss: 0.222, ap: 0.530, auc: 0.642, f1: 0.476, acc: 0.573
+2025-08-07 19:40:36: Epoch: 045 Test results, ap: 0.215, auc: 0.453, f1: 0.140, acc: 0.546
+2025-08-07 19:40:36: Epoch: 046 Train Loss: 0.223, ap: 0.531, auc: 0.647, f1: 0.482, acc: 0.583
+2025-08-07 19:40:36: Epoch: 046 Test results, ap: 0.217, auc: 0.450, f1: 0.138, acc: 0.537
+2025-08-07 19:40:36: Epoch: 047 Train Loss: 0.233, ap: 0.523, auc: 0.634, f1: 0.494, acc: 0.583
+2025-08-07 19:40:37: Epoch: 047 Test results, ap: 0.213, auc: 0.448, f1: 0.211, acc: 0.583
+2025-08-07 19:40:37: Epoch: 048 Train Loss: 0.218, ap: 0.531, auc: 0.646, f1: 0.518, acc: 0.602
+2025-08-07 19:40:37: Epoch: 048 Test results, ap: 0.215, auc: 0.450, f1: 0.140, acc: 0.546
+2025-08-07 19:40:37: Epoch: 049 Train Loss: 0.214, ap: 0.531, auc: 0.647, f1: 0.500, acc: 0.592
+2025-08-07 19:40:37: Epoch: 049 Test results, ap: 0.216, auc: 0.452, f1: 0.200, acc: 0.556
+2025-08-07 19:40:38: Epoch: 050 Train Loss: 0.224, ap: 0.528, auc: 0.637, f1: 0.488, acc: 0.573
+2025-08-07 19:40:38: Epoch: 050 Test results, ap: 0.214, auc: 0.450, f1: 0.211, acc: 0.583
+2025-08-07 19:40:38: used time 0.01h
+2025-08-07 19:40:38: Cross Validation Finished!
+2025-08-07 19:40:38: Best validation perform list
+[[0.73233581 0.7443009  0.63414633 0.70873785]
+ [0.64636159 0.77108926 0.60526317 0.70873785]
+ [0.79643834 0.7607466  0.71428573 0.68932039]
+ [0.69327044 0.7080406  0.61052632 0.64077669]
+ [0.6495806  0.7081039  0.56410259 0.66990292]]
+2025-08-07 19:40:38: mean: [0.704 0.738 0.626 0.683]
+2025-08-07 19:40:38: std: [0.056 0.026 0.05  0.026]
+2025-08-07 19:40:38: Best test perform list
+[[0.27970797 0.55534708 0.         0.71296299]
+ [0.22532222 0.48757038 0.09756097 0.6574074 ]
+ [0.2286312  0.40900564 0.28235295 0.43518519]
+ [0.28289223 0.44840527 0.30612245 0.37037036]
+ [0.21904576 0.46200749 0.13114753 0.50925928]]
+2025-08-07 19:40:38: mean: [0.247 0.472 0.163 0.537]
+2025-08-07 19:40:38: std: [0.028 0.049 0.115 0.13 ]
diff --git a/run-cls-simple/mha-mlp-256-simple-oneway-ce-32-0.001-50/config.json b/run-cls-simple/mha-mlp-256-simple-oneway-ce-32-0.001-50/config.json
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+++ b/run-cls-simple/mha-mlp-256-simple-oneway-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mha", "side_enc": null, "channels": 256, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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diff --git a/run-cls-simple/mha-mlp-256-simple-oneway-ce-32-0.001-50/result.txt b/run-cls-simple/mha-mlp-256-simple-oneway-ce-32-0.001-50/result.txt
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@@ -0,0 +1,6 @@
+Valid
+0.7238998293876648,0.7579486012458801,0.6413166642189025,0.6330097019672394
+0.07212089462842786,0.0286726121008566,0.05089957468107851,0.11833512952304052
+Test
+0.25025349855422974,0.49362100958824157,0.19908447265625,0.620370376110077
+0.015797406817023157,0.026606939976974716,0.11732606785148329,0.08725348524026369
diff --git a/run-cls-simple/mha-mlp-256-simple-oneway-ce-32-0.001-50/training.log b/run-cls-simple/mha-mlp-256-simple-oneway-ce-32-0.001-50/training.log
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@@ -0,0 +1,600 @@
+2025-08-07 19:35:04: saving_dir: ./run-cls/mha-mlp-256-simple-oneway-ce-32-0.001-50
+2025-08-07 19:35:04: Loading Training Dataset
+2025-08-07 19:35:04: Loading Test Dataset
+2025-08-07 19:35:05: Running Cross Validation 0
+2025-08-07 19:35:05: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:35:05: Epoch: 001 Train Loss: 0.692, ap: 0.594, auc: 0.650, f1: 0.627, acc: 0.456
+2025-08-07 19:35:05: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:35:05: Epoch: 001 Test results, ap: 0.218, auc: 0.480, f1: 0.388, acc: 0.241
+2025-08-07 19:35:06: Epoch: 002 Train Loss: 0.692, ap: 0.625, auc: 0.653, f1: 0.646, acc: 0.553
+2025-08-07 19:35:06: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:35:06: Epoch: 002 Test results, ap: 0.230, auc: 0.497, f1: 0.388, acc: 0.241
+2025-08-07 19:35:06: Epoch: 003 Train Loss: 0.680, ap: 0.626, auc: 0.656, f1: 0.529, acc: 0.602
+2025-08-07 19:35:06: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:35:06: Epoch: 003 Test results, ap: 0.221, auc: 0.450, f1: 0.254, acc: 0.565
+2025-08-07 19:35:06: Epoch: 004 Train Loss: 0.643, ap: 0.600, auc: 0.657, f1: 0.594, acc: 0.602
+2025-08-07 19:35:07: Epoch: 004 Test results, ap: 0.218, auc: 0.436, f1: 0.312, acc: 0.389
+2025-08-07 19:35:07: Epoch: 005 Train Loss: 0.630, ap: 0.584, auc: 0.653, f1: 0.571, acc: 0.592
+2025-08-07 19:35:07: Epoch: 005 Test results, ap: 0.228, auc: 0.455, f1: 0.317, acc: 0.481
+2025-08-07 19:35:07: Epoch: 006 Train Loss: 0.586, ap: 0.579, auc: 0.649, f1: 0.505, acc: 0.563
+2025-08-07 19:35:07: Epoch: 006 Test results, ap: 0.251, auc: 0.487, f1: 0.247, acc: 0.491
+2025-08-07 19:35:08: Epoch: 007 Train Loss: 0.580, ap: 0.618, auc: 0.685, f1: 0.631, acc: 0.602
+2025-08-07 19:35:08: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:35:08: Epoch: 007 Test results, ap: 0.242, auc: 0.474, f1: 0.343, acc: 0.361
+2025-08-07 19:35:08: Epoch: 008 Train Loss: 0.545, ap: 0.594, auc: 0.665, f1: 0.548, acc: 0.631
+2025-08-07 19:35:08: Epoch: 008 Test results, ap: 0.278, auc: 0.469, f1: 0.257, acc: 0.519
+2025-08-07 19:35:09: Epoch: 009 Train Loss: 0.589, ap: 0.629, auc: 0.700, f1: 0.581, acc: 0.650
+2025-08-07 19:35:09: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:35:09: Epoch: 009 Test results, ap: 0.261, auc: 0.430, f1: 0.261, acc: 0.528
+2025-08-07 19:35:09: Epoch: 010 Train Loss: 0.535, ap: 0.669, auc: 0.708, f1: 0.609, acc: 0.650
+2025-08-07 19:35:09: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:35:09: Epoch: 010 Test results, ap: 0.258, auc: 0.439, f1: 0.257, acc: 0.519
+2025-08-07 19:35:09: Epoch: 011 Train Loss: 0.510, ap: 0.694, auc: 0.722, f1: 0.673, acc: 0.670
+2025-08-07 19:35:09: Epoch: 011 New best VALIDATION metrics
+2025-08-07 19:35:10: Epoch: 011 Test results, ap: 0.235, auc: 0.425, f1: 0.358, acc: 0.269
+2025-08-07 19:35:10: Epoch: 011 New best TEST metrics
+2025-08-07 19:35:10: Epoch: 012 Train Loss: 0.483, ap: 0.715, auc: 0.740, f1: 0.680, acc: 0.680
+2025-08-07 19:35:10: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:35:10: Epoch: 012 Test results, ap: 0.217, auc: 0.406, f1: 0.344, acc: 0.259
+2025-08-07 19:35:10: Epoch: 013 Train Loss: 0.454, ap: 0.696, auc: 0.728, f1: 0.686, acc: 0.689
+2025-08-07 19:35:10: Epoch: 013 Test results, ap: 0.228, auc: 0.414, f1: 0.316, acc: 0.278
+2025-08-07 19:35:11: Epoch: 014 Train Loss: 0.472, ap: 0.696, auc: 0.726, f1: 0.679, acc: 0.670
+2025-08-07 19:35:11: Epoch: 014 Test results, ap: 0.221, auc: 0.394, f1: 0.358, acc: 0.269
+2025-08-07 19:35:11: Epoch: 015 Train Loss: 0.456, ap: 0.706, auc: 0.731, f1: 0.680, acc: 0.680
+2025-08-07 19:35:11: Epoch: 015 Test results, ap: 0.227, auc: 0.412, f1: 0.358, acc: 0.269
+2025-08-07 19:35:11: Epoch: 016 Train Loss: 0.454, ap: 0.698, auc: 0.727, f1: 0.667, acc: 0.689
+2025-08-07 19:35:12: Epoch: 016 Test results, ap: 0.249, auc: 0.473, f1: 0.314, acc: 0.352
+2025-08-07 19:35:12: Epoch: 016 New best TEST metrics
+2025-08-07 19:35:12: Epoch: 017 Train Loss: 0.465, ap: 0.682, auc: 0.728, f1: 0.607, acc: 0.660
+2025-08-07 19:35:12: Epoch: 017 Test results, ap: 0.261, auc: 0.509, f1: 0.324, acc: 0.537
+2025-08-07 19:35:12: Epoch: 017 New best TEST metrics
+2025-08-07 19:35:12: Epoch: 018 Train Loss: 0.434, ap: 0.702, auc: 0.723, f1: 0.607, acc: 0.660
+2025-08-07 19:35:12: Epoch: 018 Test results, ap: 0.234, auc: 0.439, f1: 0.286, acc: 0.583
+2025-08-07 19:35:13: Epoch: 019 Train Loss: 0.438, ap: 0.688, auc: 0.721, f1: 0.660, acc: 0.689
+2025-08-07 19:35:13: Epoch: 019 Test results, ap: 0.239, auc: 0.435, f1: 0.314, acc: 0.352
+2025-08-07 19:35:13: Epoch: 020 Train Loss: 0.414, ap: 0.682, auc: 0.728, f1: 0.680, acc: 0.699
+2025-08-07 19:35:13: Epoch: 020 Test results, ap: 0.250, auc: 0.494, f1: 0.356, acc: 0.463
+2025-08-07 19:35:14: Epoch: 021 Train Loss: 0.402, ap: 0.673, auc: 0.728, f1: 0.652, acc: 0.689
+2025-08-07 19:35:14: Epoch: 021 Test results, ap: 0.256, auc: 0.498, f1: 0.340, acc: 0.426
+2025-08-07 19:35:14: Epoch: 022 Train Loss: 0.409, ap: 0.695, auc: 0.726, f1: 0.674, acc: 0.699
+2025-08-07 19:35:14: Epoch: 022 Test results, ap: 0.252, auc: 0.484, f1: 0.340, acc: 0.426
+2025-08-07 19:35:15: Epoch: 023 Train Loss: 0.386, ap: 0.695, auc: 0.728, f1: 0.700, acc: 0.709
+2025-08-07 19:35:15: Epoch: 023 Test results, ap: 0.226, auc: 0.432, f1: 0.314, acc: 0.352
+2025-08-07 19:35:15: Epoch: 024 Train Loss: 0.341, ap: 0.703, auc: 0.732, f1: 0.673, acc: 0.689
+2025-08-07 19:35:15: Epoch: 024 Test results, ap: 0.231, auc: 0.447, f1: 0.330, acc: 0.398
+2025-08-07 19:35:15: Epoch: 025 Train Loss: 0.344, ap: 0.664, auc: 0.723, f1: 0.568, acc: 0.660
+2025-08-07 19:35:16: Epoch: 025 Test results, ap: 0.250, auc: 0.498, f1: 0.381, acc: 0.519
+2025-08-07 19:35:16: Epoch: 026 Train Loss: 0.357, ap: 0.681, auc: 0.724, f1: 0.695, acc: 0.718
+2025-08-07 19:35:16: Epoch: 026 Test results, ap: 0.233, auc: 0.451, f1: 0.323, acc: 0.380
+2025-08-07 19:35:16: Epoch: 027 Train Loss: 0.345, ap: 0.700, auc: 0.733, f1: 0.701, acc: 0.718
+2025-08-07 19:35:16: Epoch: 027 Test results, ap: 0.240, auc: 0.476, f1: 0.344, acc: 0.435
+2025-08-07 19:35:17: Epoch: 028 Train Loss: 0.347, ap: 0.667, auc: 0.731, f1: 0.681, acc: 0.718
+2025-08-07 19:35:17: Epoch: 028 Test results, ap: 0.247, auc: 0.499, f1: 0.364, acc: 0.481
+2025-08-07 19:35:17: Epoch: 029 Train Loss: 0.315, ap: 0.696, auc: 0.725, f1: 0.674, acc: 0.718
+2025-08-07 19:35:17: Epoch: 029 Test results, ap: 0.243, auc: 0.486, f1: 0.360, acc: 0.472
+2025-08-07 19:35:18: Epoch: 030 Train Loss: 0.307, ap: 0.697, auc: 0.727, f1: 0.644, acc: 0.699
+2025-08-07 19:35:18: Epoch: 030 Test results, ap: 0.246, auc: 0.490, f1: 0.368, acc: 0.491
+2025-08-07 19:35:18: Epoch: 031 Train Loss: 0.305, ap: 0.704, auc: 0.731, f1: 0.674, acc: 0.718
+2025-08-07 19:35:18: Epoch: 031 Test results, ap: 0.248, auc: 0.496, f1: 0.368, acc: 0.491
+2025-08-07 19:35:19: Epoch: 032 Train Loss: 0.301, ap: 0.703, auc: 0.732, f1: 0.644, acc: 0.699
+2025-08-07 19:35:19: Epoch: 032 Test results, ap: 0.248, auc: 0.499, f1: 0.372, acc: 0.500
+2025-08-07 19:35:19: Epoch: 033 Train Loss: 0.299, ap: 0.669, auc: 0.730, f1: 0.550, acc: 0.650
+2025-08-07 19:35:19: Epoch: 033 Test results, ap: 0.252, auc: 0.514, f1: 0.381, acc: 0.519
+2025-08-07 19:35:19: Epoch: 034 Train Loss: 0.286, ap: 0.701, auc: 0.729, f1: 0.703, acc: 0.738
+2025-08-07 19:35:19: Epoch: 034 Test results, ap: 0.249, auc: 0.497, f1: 0.368, acc: 0.491
+2025-08-07 19:35:20: Epoch: 035 Train Loss: 0.287, ap: 0.698, auc: 0.724, f1: 0.696, acc: 0.728
+2025-08-07 19:35:20: Epoch: 035 Test results, ap: 0.250, auc: 0.500, f1: 0.364, acc: 0.481
+2025-08-07 19:35:20: Epoch: 036 Train Loss: 0.277, ap: 0.693, auc: 0.721, f1: 0.550, acc: 0.650
+2025-08-07 19:35:20: Epoch: 036 Test results, ap: 0.248, auc: 0.499, f1: 0.376, acc: 0.509
+2025-08-07 19:35:21: Epoch: 037 Train Loss: 0.287, ap: 0.696, auc: 0.722, f1: 0.612, acc: 0.680
+2025-08-07 19:35:21: Epoch: 037 Test results, ap: 0.246, auc: 0.493, f1: 0.372, acc: 0.500
+2025-08-07 19:35:21: Epoch: 038 Train Loss: 0.301, ap: 0.667, auc: 0.723, f1: 0.700, acc: 0.709
+2025-08-07 19:35:21: Epoch: 038 Test results, ap: 0.242, auc: 0.481, f1: 0.337, acc: 0.417
+2025-08-07 19:35:21: Epoch: 039 Train Loss: 0.281, ap: 0.693, auc: 0.724, f1: 0.612, acc: 0.680
+2025-08-07 19:35:21: Epoch: 039 Test results, ap: 0.243, auc: 0.484, f1: 0.372, acc: 0.500
+2025-08-07 19:35:22: Epoch: 040 Train Loss: 0.288, ap: 0.696, auc: 0.724, f1: 0.550, acc: 0.650
+2025-08-07 19:35:22: Epoch: 040 Test results, ap: 0.251, auc: 0.509, f1: 0.289, acc: 0.500
+2025-08-07 19:35:22: Epoch: 041 Train Loss: 0.243, ap: 0.700, auc: 0.729, f1: 0.659, acc: 0.699
+2025-08-07 19:35:22: Epoch: 041 Test results, ap: 0.242, auc: 0.481, f1: 0.356, acc: 0.463
+2025-08-07 19:35:23: Epoch: 042 Train Loss: 0.259, ap: 0.700, auc: 0.728, f1: 0.568, acc: 0.660
+2025-08-07 19:35:23: Epoch: 042 Test results, ap: 0.250, auc: 0.501, f1: 0.376, acc: 0.509
+2025-08-07 19:35:23: Epoch: 043 Train Loss: 0.231, ap: 0.696, auc: 0.725, f1: 0.595, acc: 0.670
+2025-08-07 19:35:23: Epoch: 043 Test results, ap: 0.249, auc: 0.500, f1: 0.376, acc: 0.509
+2025-08-07 19:35:24: Epoch: 044 Train Loss: 0.247, ap: 0.695, auc: 0.724, f1: 0.543, acc: 0.641
+2025-08-07 19:35:24: Epoch: 044 Test results, ap: 0.251, auc: 0.507, f1: 0.376, acc: 0.509
+2025-08-07 19:35:24: Epoch: 045 Train Loss: 0.235, ap: 0.694, auc: 0.723, f1: 0.595, acc: 0.670
+2025-08-07 19:35:24: Epoch: 045 Test results, ap: 0.252, auc: 0.509, f1: 0.376, acc: 0.509
+2025-08-07 19:35:24: Epoch: 046 Train Loss: 0.243, ap: 0.688, auc: 0.719, f1: 0.595, acc: 0.670
+2025-08-07 19:35:25: Epoch: 046 Test results, ap: 0.251, auc: 0.514, f1: 0.376, acc: 0.509
+2025-08-07 19:35:25: Epoch: 047 Train Loss: 0.226, ap: 0.691, auc: 0.723, f1: 0.644, acc: 0.699
+2025-08-07 19:35:25: Epoch: 047 Test results, ap: 0.252, auc: 0.516, f1: 0.376, acc: 0.509
+2025-08-07 19:35:25: Epoch: 048 Train Loss: 0.247, ap: 0.694, auc: 0.723, f1: 0.595, acc: 0.670
+2025-08-07 19:35:25: Epoch: 048 Test results, ap: 0.252, auc: 0.518, f1: 0.376, acc: 0.509
+2025-08-07 19:35:26: Epoch: 049 Train Loss: 0.225, ap: 0.687, auc: 0.722, f1: 0.578, acc: 0.660
+2025-08-07 19:35:26: Epoch: 049 Test results, ap: 0.253, auc: 0.521, f1: 0.361, acc: 0.509
+2025-08-07 19:35:26: Epoch: 049 New best TEST metrics
+2025-08-07 19:35:26: Epoch: 050 Train Loss: 0.224, ap: 0.684, auc: 0.722, f1: 0.578, acc: 0.660
+2025-08-07 19:35:26: Epoch: 050 Test results, ap: 0.254, auc: 0.525, f1: 0.341, acc: 0.500
+2025-08-07 19:35:26: Epoch: 050 New best TEST metrics
+2025-08-07 19:35:26: used time 0.01h
+2025-08-07 19:35:26: Running Cross Validation 1
+2025-08-07 19:35:26: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:35:27: Epoch: 001 Train Loss: 0.694, ap: 0.402, auc: 0.545, f1: 0.529, acc: 0.359
+2025-08-07 19:35:27: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:35:27: Epoch: 001 Test results, ap: 0.263, auc: 0.536, f1: 0.388, acc: 0.241
+2025-08-07 19:35:27: Epoch: 002 Train Loss: 0.694, ap: 0.465, auc: 0.612, f1: 0.529, acc: 0.359
+2025-08-07 19:35:27: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:35:27: Epoch: 002 Test results, ap: 0.244, auc: 0.526, f1: 0.388, acc: 0.241
+2025-08-07 19:35:27: Epoch: 003 Train Loss: 0.693, ap: 0.514, auc: 0.653, f1: 0.000, acc: 0.641
+2025-08-07 19:35:27: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:35:28: Epoch: 003 Test results, ap: 0.250, auc: 0.510, f1: 0.000, acc: 0.759
+2025-08-07 19:35:28: Epoch: 004 Train Loss: 0.688, ap: 0.502, auc: 0.640, f1: 0.591, acc: 0.650
+2025-08-07 19:35:28: Epoch: 004 Test results, ap: 0.318, auc: 0.497, f1: 0.326, acc: 0.463
+2025-08-07 19:35:28: Epoch: 005 Train Loss: 0.639, ap: 0.493, auc: 0.633, f1: 0.524, acc: 0.612
+2025-08-07 19:35:28: Epoch: 005 Test results, ap: 0.316, auc: 0.502, f1: 0.235, acc: 0.759
+2025-08-07 19:35:29: Epoch: 006 Train Loss: 0.576, ap: 0.549, auc: 0.658, f1: 0.506, acc: 0.621
+2025-08-07 19:35:29: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:35:29: Epoch: 006 Test results, ap: 0.257, auc: 0.532, f1: 0.000, acc: 0.741
+2025-08-07 19:35:29: Epoch: 007 Train Loss: 0.551, ap: 0.576, auc: 0.662, f1: 0.587, acc: 0.631
+2025-08-07 19:35:29: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:35:29: Epoch: 007 Test results, ap: 0.290, auc: 0.510, f1: 0.138, acc: 0.769
+2025-08-07 19:35:30: Epoch: 008 Train Loss: 0.545, ap: 0.548, auc: 0.669, f1: 0.519, acc: 0.621
+2025-08-07 19:35:30: Epoch: 008 Test results, ap: 0.258, auc: 0.535, f1: 0.121, acc: 0.731
+2025-08-07 19:35:30: Epoch: 009 Train Loss: 0.532, ap: 0.539, auc: 0.642, f1: 0.568, acc: 0.602
+2025-08-07 19:35:30: Epoch: 009 Test results, ap: 0.289, auc: 0.503, f1: 0.205, acc: 0.713
+2025-08-07 19:35:30: Epoch: 010 Train Loss: 0.544, ap: 0.611, auc: 0.688, f1: 0.478, acc: 0.660
+2025-08-07 19:35:30: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:35:31: Epoch: 010 Test results, ap: 0.235, auc: 0.487, f1: 0.000, acc: 0.750
+2025-08-07 19:35:31: Epoch: 011 Train Loss: 0.537, ap: 0.589, auc: 0.681, f1: 0.581, acc: 0.621
+2025-08-07 19:35:31: Epoch: 011 Test results, ap: 0.230, auc: 0.476, f1: 0.108, acc: 0.694
+2025-08-07 19:35:31: Epoch: 011 New best TEST metrics
+2025-08-07 19:35:31: Epoch: 012 Train Loss: 0.502, ap: 0.546, auc: 0.684, f1: 0.596, acc: 0.631
+2025-08-07 19:35:32: Epoch: 012 Test results, ap: 0.235, auc: 0.474, f1: 0.182, acc: 0.667
+2025-08-07 19:35:32: Epoch: 012 New best TEST metrics
+2025-08-07 19:35:32: Epoch: 013 Train Loss: 0.489, ap: 0.556, auc: 0.688, f1: 0.593, acc: 0.641
+2025-08-07 19:35:32: Epoch: 013 Test results, ap: 0.237, auc: 0.481, f1: 0.182, acc: 0.667
+2025-08-07 19:35:32: Epoch: 013 New best TEST metrics
+2025-08-07 19:35:32: Epoch: 014 Train Loss: 0.473, ap: 0.563, auc: 0.698, f1: 0.621, acc: 0.680
+2025-08-07 19:35:32: Epoch: 014 Test results, ap: 0.235, auc: 0.486, f1: 0.182, acc: 0.667
+2025-08-07 19:35:32: Epoch: 014 New best TEST metrics
+2025-08-07 19:35:33: Epoch: 015 Train Loss: 0.457, ap: 0.556, auc: 0.679, f1: 0.607, acc: 0.660
+2025-08-07 19:35:33: Epoch: 015 Test results, ap: 0.226, auc: 0.469, f1: 0.000, acc: 0.713
+2025-08-07 19:35:33: Epoch: 016 Train Loss: 0.447, ap: 0.528, auc: 0.677, f1: 0.575, acc: 0.641
+2025-08-07 19:35:33: Epoch: 016 Test results, ap: 0.228, auc: 0.464, f1: 0.051, acc: 0.657
+2025-08-07 19:35:33: Epoch: 017 Train Loss: 0.448, ap: 0.564, auc: 0.704, f1: 0.644, acc: 0.699
+2025-08-07 19:35:34: Epoch: 017 Test results, ap: 0.208, auc: 0.418, f1: 0.051, acc: 0.657
+2025-08-07 19:35:34: Epoch: 018 Train Loss: 0.436, ap: 0.563, auc: 0.692, f1: 0.602, acc: 0.641
+2025-08-07 19:35:34: Epoch: 018 Test results, ap: 0.215, auc: 0.435, f1: 0.226, acc: 0.620
+2025-08-07 19:35:34: Epoch: 019 Train Loss: 0.427, ap: 0.556, auc: 0.677, f1: 0.587, acc: 0.631
+2025-08-07 19:35:34: Epoch: 019 Test results, ap: 0.204, auc: 0.405, f1: 0.095, acc: 0.648
+2025-08-07 19:35:35: Epoch: 020 Train Loss: 0.412, ap: 0.579, auc: 0.692, f1: 0.550, acc: 0.650
+2025-08-07 19:35:35: Epoch: 020 Test results, ap: 0.209, auc: 0.417, f1: 0.095, acc: 0.648
+2025-08-07 19:35:35: Epoch: 021 Train Loss: 0.411, ap: 0.558, auc: 0.688, f1: 0.581, acc: 0.650
+2025-08-07 19:35:35: Epoch: 021 Test results, ap: 0.206, auc: 0.410, f1: 0.093, acc: 0.639
+2025-08-07 19:35:36: Epoch: 022 Train Loss: 0.388, ap: 0.567, auc: 0.695, f1: 0.578, acc: 0.660
+2025-08-07 19:35:36: Epoch: 022 Test results, ap: 0.205, auc: 0.407, f1: 0.098, acc: 0.657
+2025-08-07 19:35:36: Epoch: 023 Train Loss: 0.385, ap: 0.563, auc: 0.690, f1: 0.571, acc: 0.650
+2025-08-07 19:35:36: Epoch: 023 Test results, ap: 0.204, auc: 0.407, f1: 0.098, acc: 0.657
+2025-08-07 19:35:36: Epoch: 024 Train Loss: 0.387, ap: 0.533, auc: 0.681, f1: 0.598, acc: 0.660
+2025-08-07 19:35:37: Epoch: 024 Test results, ap: 0.204, auc: 0.404, f1: 0.093, acc: 0.639
+2025-08-07 19:35:37: Epoch: 025 Train Loss: 0.386, ap: 0.603, auc: 0.708, f1: 0.619, acc: 0.689
+2025-08-07 19:35:37: Epoch: 025 New best VALIDATION metrics
+2025-08-07 19:35:37: Epoch: 025 Test results, ap: 0.204, auc: 0.402, f1: 0.204, acc: 0.639
+2025-08-07 19:35:37: Epoch: 026 Train Loss: 0.397, ap: 0.607, auc: 0.703, f1: 0.595, acc: 0.670
+2025-08-07 19:35:37: Epoch: 026 Test results, ap: 0.204, auc: 0.404, f1: 0.098, acc: 0.657
+2025-08-07 19:35:38: Epoch: 027 Train Loss: 0.381, ap: 0.596, auc: 0.696, f1: 0.578, acc: 0.660
+2025-08-07 19:35:38: Epoch: 027 Test results, ap: 0.205, auc: 0.402, f1: 0.098, acc: 0.657
+2025-08-07 19:35:38: Epoch: 028 Train Loss: 0.361, ap: 0.613, auc: 0.701, f1: 0.564, acc: 0.670
+2025-08-07 19:35:38: Epoch: 028 New best VALIDATION metrics
+2025-08-07 19:35:38: Epoch: 028 Test results, ap: 0.206, auc: 0.408, f1: 0.051, acc: 0.657
+2025-08-07 19:35:39: Epoch: 029 Train Loss: 0.376, ap: 0.588, auc: 0.702, f1: 0.602, acc: 0.680
+2025-08-07 19:35:39: Epoch: 029 Test results, ap: 0.204, auc: 0.399, f1: 0.178, acc: 0.657
+2025-08-07 19:35:39: Epoch: 030 Train Loss: 0.345, ap: 0.617, auc: 0.719, f1: 0.582, acc: 0.680
+2025-08-07 19:35:39: Epoch: 030 New best VALIDATION metrics
+2025-08-07 19:35:39: Epoch: 030 Test results, ap: 0.204, auc: 0.403, f1: 0.050, acc: 0.648
+2025-08-07 19:35:39: Epoch: 031 Train Loss: 0.346, ap: 0.597, auc: 0.710, f1: 0.619, acc: 0.689
+2025-08-07 19:35:40: Epoch: 031 Test results, ap: 0.204, auc: 0.400, f1: 0.095, acc: 0.648
+2025-08-07 19:35:40: Epoch: 032 Train Loss: 0.339, ap: 0.598, auc: 0.712, f1: 0.593, acc: 0.680
+2025-08-07 19:35:40: Epoch: 032 Test results, ap: 0.203, auc: 0.399, f1: 0.095, acc: 0.648
+2025-08-07 19:35:40: Epoch: 033 Train Loss: 0.339, ap: 0.599, auc: 0.706, f1: 0.605, acc: 0.670
+2025-08-07 19:35:40: Epoch: 033 Test results, ap: 0.203, auc: 0.395, f1: 0.204, acc: 0.639
+2025-08-07 19:35:41: Epoch: 034 Train Loss: 0.353, ap: 0.608, auc: 0.714, f1: 0.533, acc: 0.660
+2025-08-07 19:35:41: Epoch: 034 Test results, ap: 0.210, auc: 0.416, f1: 0.053, acc: 0.667
+2025-08-07 19:35:41: Epoch: 035 Train Loss: 0.345, ap: 0.585, auc: 0.706, f1: 0.595, acc: 0.670
+2025-08-07 19:35:41: Epoch: 035 Test results, ap: 0.204, auc: 0.398, f1: 0.204, acc: 0.639
+2025-08-07 19:35:41: Epoch: 036 Train Loss: 0.320, ap: 0.597, auc: 0.706, f1: 0.595, acc: 0.670
+2025-08-07 19:35:42: Epoch: 036 Test results, ap: 0.206, auc: 0.407, f1: 0.204, acc: 0.639
+2025-08-07 19:35:42: Epoch: 037 Train Loss: 0.326, ap: 0.588, auc: 0.709, f1: 0.595, acc: 0.670
+2025-08-07 19:35:42: Epoch: 037 Test results, ap: 0.205, auc: 0.403, f1: 0.204, acc: 0.639
+2025-08-07 19:35:42: Epoch: 038 Train Loss: 0.336, ap: 0.614, auc: 0.713, f1: 0.595, acc: 0.670
+2025-08-07 19:35:42: Epoch: 038 Test results, ap: 0.204, auc: 0.402, f1: 0.196, acc: 0.620
+2025-08-07 19:35:43: Epoch: 039 Train Loss: 0.339, ap: 0.620, auc: 0.718, f1: 0.590, acc: 0.689
+2025-08-07 19:35:43: Epoch: 039 New best VALIDATION metrics
+2025-08-07 19:35:43: Epoch: 039 Test results, ap: 0.208, auc: 0.411, f1: 0.208, acc: 0.648
+2025-08-07 19:35:43: Epoch: 040 Train Loss: 0.316, ap: 0.603, auc: 0.714, f1: 0.579, acc: 0.689
+2025-08-07 19:35:43: Epoch: 040 Test results, ap: 0.205, auc: 0.402, f1: 0.217, acc: 0.667
+2025-08-07 19:35:44: Epoch: 041 Train Loss: 0.327, ap: 0.608, auc: 0.714, f1: 0.628, acc: 0.689
+2025-08-07 19:35:44: Epoch: 041 Test results, ap: 0.201, auc: 0.391, f1: 0.192, acc: 0.611
+2025-08-07 19:35:44: Epoch: 042 Train Loss: 0.313, ap: 0.600, auc: 0.713, f1: 0.595, acc: 0.670
+2025-08-07 19:35:44: Epoch: 042 Test results, ap: 0.202, auc: 0.393, f1: 0.204, acc: 0.639
+2025-08-07 19:35:44: Epoch: 043 Train Loss: 0.307, ap: 0.599, auc: 0.711, f1: 0.628, acc: 0.689
+2025-08-07 19:35:45: Epoch: 043 Test results, ap: 0.201, auc: 0.390, f1: 0.185, acc: 0.593
+2025-08-07 19:35:45: Epoch: 044 Train Loss: 0.306, ap: 0.589, auc: 0.713, f1: 0.575, acc: 0.670
+2025-08-07 19:35:45: Epoch: 044 Test results, ap: 0.204, auc: 0.397, f1: 0.208, acc: 0.648
+2025-08-07 19:35:45: Epoch: 045 Train Loss: 0.305, ap: 0.610, auc: 0.715, f1: 0.612, acc: 0.680
+2025-08-07 19:35:46: Epoch: 045 Test results, ap: 0.205, auc: 0.401, f1: 0.196, acc: 0.620
+2025-08-07 19:35:46: Epoch: 046 Train Loss: 0.308, ap: 0.600, auc: 0.714, f1: 0.610, acc: 0.689
+2025-08-07 19:35:46: Epoch: 046 Test results, ap: 0.207, auc: 0.406, f1: 0.204, acc: 0.639
+2025-08-07 19:35:46: Epoch: 047 Train Loss: 0.303, ap: 0.599, auc: 0.716, f1: 0.602, acc: 0.680
+2025-08-07 19:35:46: Epoch: 047 Test results, ap: 0.205, auc: 0.402, f1: 0.204, acc: 0.639
+2025-08-07 19:35:47: Epoch: 048 Train Loss: 0.308, ap: 0.604, auc: 0.714, f1: 0.619, acc: 0.689
+2025-08-07 19:35:47: Epoch: 048 Test results, ap: 0.206, auc: 0.403, f1: 0.204, acc: 0.639
+2025-08-07 19:35:47: Epoch: 049 Train Loss: 0.295, ap: 0.599, auc: 0.713, f1: 0.610, acc: 0.689
+2025-08-07 19:35:47: Epoch: 049 Test results, ap: 0.204, auc: 0.397, f1: 0.200, acc: 0.630
+2025-08-07 19:35:48: Epoch: 050 Train Loss: 0.307, ap: 0.600, auc: 0.713, f1: 0.619, acc: 0.689
+2025-08-07 19:35:48: Epoch: 050 Test results, ap: 0.205, auc: 0.402, f1: 0.185, acc: 0.593
+2025-08-07 19:35:48: used time 0.01h
+2025-08-07 19:35:48: Running Cross Validation 2
+2025-08-07 19:35:48: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:35:48: Epoch: 001 Train Loss: 0.692, ap: 0.550, auc: 0.616, f1: 0.000, acc: 0.495
+2025-08-07 19:35:48: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:35:48: Epoch: 001 Test results, ap: 0.220, auc: 0.476, f1: 0.000, acc: 0.759
+2025-08-07 19:35:48: Epoch: 002 Train Loss: 0.693, ap: 0.610, auc: 0.676, f1: 0.000, acc: 0.495
+2025-08-07 19:35:48: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:35:48: Epoch: 002 Test results, ap: 0.236, auc: 0.495, f1: 0.000, acc: 0.759
+2025-08-07 19:35:49: Epoch: 003 Train Loss: 0.691, ap: 0.685, auc: 0.722, f1: 0.722, acc: 0.641
+2025-08-07 19:35:49: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:35:49: Epoch: 003 Test results, ap: 0.264, auc: 0.508, f1: 0.178, acc: 0.657
+2025-08-07 19:35:49: Epoch: 004 Train Loss: 0.658, ap: 0.746, auc: 0.747, f1: 0.702, acc: 0.670
+2025-08-07 19:35:49: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:35:49: Epoch: 004 Test results, ap: 0.276, auc: 0.546, f1: 0.000, acc: 0.722
+2025-08-07 19:35:50: Epoch: 005 Train Loss: 0.597, ap: 0.745, auc: 0.765, f1: 0.724, acc: 0.660
+2025-08-07 19:35:50: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:35:50: Epoch: 005 Test results, ap: 0.259, auc: 0.515, f1: 0.000, acc: 0.722
+2025-08-07 19:35:50: Epoch: 006 Train Loss: 0.605, ap: 0.768, auc: 0.762, f1: 0.729, acc: 0.689
+2025-08-07 19:35:50: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:35:50: Epoch: 006 Test results, ap: 0.229, auc: 0.488, f1: 0.000, acc: 0.722
+2025-08-07 19:35:51: Epoch: 007 Train Loss: 0.602, ap: 0.757, auc: 0.753, f1: 0.653, acc: 0.660
+2025-08-07 19:35:51: Epoch: 007 Test results, ap: 0.218, auc: 0.451, f1: 0.000, acc: 0.722
+2025-08-07 19:35:51: Epoch: 008 Train Loss: 0.584, ap: 0.796, auc: 0.763, f1: 0.743, acc: 0.718
+2025-08-07 19:35:51: Epoch: 008 New best VALIDATION metrics
+2025-08-07 19:35:51: Epoch: 008 Test results, ap: 0.236, auc: 0.493, f1: 0.000, acc: 0.750
+2025-08-07 19:35:51: Epoch: 009 Train Loss: 0.562, ap: 0.798, auc: 0.767, f1: 0.679, acc: 0.670
+2025-08-07 19:35:51: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:35:52: Epoch: 009 Test results, ap: 0.225, auc: 0.466, f1: 0.000, acc: 0.750
+2025-08-07 19:35:52: Epoch: 010 Train Loss: 0.576, ap: 0.774, auc: 0.756, f1: 0.727, acc: 0.680
+2025-08-07 19:35:52: Epoch: 010 Test results, ap: 0.228, auc: 0.442, f1: 0.100, acc: 0.667
+2025-08-07 19:35:52: Epoch: 011 Train Loss: 0.560, ap: 0.792, auc: 0.755, f1: 0.726, acc: 0.699
+2025-08-07 19:35:52: Epoch: 011 Test results, ap: 0.222, auc: 0.428, f1: 0.054, acc: 0.676
+2025-08-07 19:35:52: Epoch: 011 New best TEST metrics
+2025-08-07 19:35:53: Epoch: 012 Train Loss: 0.527, ap: 0.808, auc: 0.763, f1: 0.685, acc: 0.660
+2025-08-07 19:35:53: Epoch: 012 New best VALIDATION metrics
+2025-08-07 19:35:53: Epoch: 012 Test results, ap: 0.240, auc: 0.427, f1: 0.000, acc: 0.759
+2025-08-07 19:35:53: Epoch: 012 New best TEST metrics
+2025-08-07 19:35:53: Epoch: 013 Train Loss: 0.503, ap: 0.808, auc: 0.766, f1: 0.716, acc: 0.699
+2025-08-07 19:35:53: Epoch: 013 New best VALIDATION metrics
+2025-08-07 19:35:53: Epoch: 013 Test results, ap: 0.250, auc: 0.448, f1: 0.000, acc: 0.759
+2025-08-07 19:35:53: Epoch: 013 New best TEST metrics
+2025-08-07 19:35:54: Epoch: 014 Train Loss: 0.492, ap: 0.808, auc: 0.770, f1: 0.660, acc: 0.689
+2025-08-07 19:35:54: Epoch: 014 New best VALIDATION metrics
+2025-08-07 19:35:54: Epoch: 014 Test results, ap: 0.235, auc: 0.437, f1: 0.000, acc: 0.759
+2025-08-07 19:35:54: Epoch: 015 Train Loss: 0.484, ap: 0.815, auc: 0.769, f1: 0.680, acc: 0.680
+2025-08-07 19:35:54: Epoch: 015 New best VALIDATION metrics
+2025-08-07 19:35:54: Epoch: 015 Test results, ap: 0.240, auc: 0.419, f1: 0.000, acc: 0.759
+2025-08-07 19:35:54: Epoch: 016 Train Loss: 0.493, ap: 0.807, auc: 0.762, f1: 0.653, acc: 0.670
+2025-08-07 19:35:55: Epoch: 016 Test results, ap: 0.258, auc: 0.415, f1: 0.000, acc: 0.759
+2025-08-07 19:35:55: Epoch: 017 Train Loss: 0.459, ap: 0.782, auc: 0.769, f1: 0.644, acc: 0.689
+2025-08-07 19:35:55: Epoch: 017 Test results, ap: 0.240, auc: 0.399, f1: 0.000, acc: 0.759
+2025-08-07 19:35:55: Epoch: 018 Train Loss: 0.459, ap: 0.802, auc: 0.764, f1: 0.673, acc: 0.680
+2025-08-07 19:35:55: Epoch: 018 Test results, ap: 0.249, auc: 0.398, f1: 0.000, acc: 0.759
+2025-08-07 19:35:56: Epoch: 019 Train Loss: 0.449, ap: 0.797, auc: 0.760, f1: 0.646, acc: 0.660
+2025-08-07 19:35:56: Epoch: 019 Test results, ap: 0.247, auc: 0.399, f1: 0.000, acc: 0.759
+2025-08-07 19:35:56: Epoch: 020 Train Loss: 0.441, ap: 0.808, auc: 0.758, f1: 0.686, acc: 0.689
+2025-08-07 19:35:56: Epoch: 020 Test results, ap: 0.228, auc: 0.408, f1: 0.000, acc: 0.759
+2025-08-07 19:35:57: Epoch: 021 Train Loss: 0.415, ap: 0.788, auc: 0.757, f1: 0.625, acc: 0.650
+2025-08-07 19:35:57: Epoch: 021 Test results, ap: 0.220, auc: 0.402, f1: 0.000, acc: 0.759
+2025-08-07 19:35:57: Epoch: 022 Train Loss: 0.402, ap: 0.783, auc: 0.760, f1: 0.733, acc: 0.738
+2025-08-07 19:35:57: Epoch: 022 Test results, ap: 0.218, auc: 0.407, f1: 0.000, acc: 0.759
+2025-08-07 19:35:57: Epoch: 023 Train Loss: 0.405, ap: 0.801, auc: 0.758, f1: 0.712, acc: 0.709
+2025-08-07 19:35:58: Epoch: 023 Test results, ap: 0.224, auc: 0.413, f1: 0.000, acc: 0.759
+2025-08-07 19:35:58: Epoch: 024 Train Loss: 0.398, ap: 0.773, auc: 0.748, f1: 0.632, acc: 0.660
+2025-08-07 19:35:58: Epoch: 024 Test results, ap: 0.242, auc: 0.412, f1: 0.000, acc: 0.759
+2025-08-07 19:35:58: Epoch: 025 Train Loss: 0.389, ap: 0.768, auc: 0.752, f1: 0.712, acc: 0.709
+2025-08-07 19:35:59: Epoch: 025 Test results, ap: 0.223, auc: 0.417, f1: 0.000, acc: 0.759
+2025-08-07 19:35:59: Epoch: 026 Train Loss: 0.393, ap: 0.754, auc: 0.745, f1: 0.712, acc: 0.709
+2025-08-07 19:35:59: Epoch: 026 Test results, ap: 0.218, auc: 0.407, f1: 0.000, acc: 0.759
+2025-08-07 19:35:59: Epoch: 027 Train Loss: 0.381, ap: 0.778, auc: 0.750, f1: 0.731, acc: 0.728
+2025-08-07 19:35:59: Epoch: 027 Test results, ap: 0.217, auc: 0.402, f1: 0.000, acc: 0.759
+2025-08-07 19:36:00: Epoch: 028 Train Loss: 0.382, ap: 0.764, auc: 0.752, f1: 0.718, acc: 0.718
+2025-08-07 19:36:00: Epoch: 028 Test results, ap: 0.238, auc: 0.395, f1: 0.000, acc: 0.759
+2025-08-07 19:36:00: Epoch: 029 Train Loss: 0.367, ap: 0.761, auc: 0.749, f1: 0.638, acc: 0.670
+2025-08-07 19:36:00: Epoch: 029 Test results, ap: 0.237, auc: 0.386, f1: 0.000, acc: 0.759
+2025-08-07 19:36:01: Epoch: 030 Train Loss: 0.386, ap: 0.767, auc: 0.758, f1: 0.731, acc: 0.728
+2025-08-07 19:36:01: Epoch: 030 Test results, ap: 0.237, auc: 0.383, f1: 0.000, acc: 0.759
+2025-08-07 19:36:01: Epoch: 031 Train Loss: 0.372, ap: 0.768, auc: 0.758, f1: 0.700, acc: 0.709
+2025-08-07 19:36:01: Epoch: 031 Test results, ap: 0.214, auc: 0.387, f1: 0.000, acc: 0.759
+2025-08-07 19:36:01: Epoch: 032 Train Loss: 0.363, ap: 0.758, auc: 0.752, f1: 0.729, acc: 0.718
+2025-08-07 19:36:01: Epoch: 032 Test results, ap: 0.218, auc: 0.389, f1: 0.000, acc: 0.759
+2025-08-07 19:36:02: Epoch: 033 Train Loss: 0.352, ap: 0.767, auc: 0.756, f1: 0.713, acc: 0.718
+2025-08-07 19:36:02: Epoch: 033 Test results, ap: 0.217, auc: 0.389, f1: 0.000, acc: 0.759
+2025-08-07 19:36:02: Epoch: 034 Train Loss: 0.347, ap: 0.768, auc: 0.755, f1: 0.725, acc: 0.728
+2025-08-07 19:36:02: Epoch: 034 Test results, ap: 0.221, auc: 0.395, f1: 0.000, acc: 0.759
+2025-08-07 19:36:03: Epoch: 035 Train Loss: 0.359, ap: 0.752, auc: 0.744, f1: 0.724, acc: 0.718
+2025-08-07 19:36:03: Epoch: 035 Test results, ap: 0.223, auc: 0.395, f1: 0.000, acc: 0.759
+2025-08-07 19:36:03: Epoch: 036 Train Loss: 0.343, ap: 0.765, auc: 0.753, f1: 0.680, acc: 0.699
+2025-08-07 19:36:03: Epoch: 036 Test results, ap: 0.220, auc: 0.395, f1: 0.000, acc: 0.759
+2025-08-07 19:36:03: Epoch: 037 Train Loss: 0.342, ap: 0.748, auc: 0.745, f1: 0.717, acc: 0.709
+2025-08-07 19:36:04: Epoch: 037 Test results, ap: 0.218, auc: 0.389, f1: 0.000, acc: 0.759
+2025-08-07 19:36:04: Epoch: 038 Train Loss: 0.352, ap: 0.763, auc: 0.753, f1: 0.667, acc: 0.680
+2025-08-07 19:36:04: Epoch: 038 Test results, ap: 0.218, auc: 0.388, f1: 0.000, acc: 0.759
+2025-08-07 19:36:04: Epoch: 039 Train Loss: 0.338, ap: 0.750, auc: 0.745, f1: 0.724, acc: 0.718
+2025-08-07 19:36:04: Epoch: 039 Test results, ap: 0.228, auc: 0.390, f1: 0.000, acc: 0.759
+2025-08-07 19:36:05: Epoch: 040 Train Loss: 0.359, ap: 0.750, auc: 0.745, f1: 0.712, acc: 0.709
+2025-08-07 19:36:05: Epoch: 040 Test results, ap: 0.230, auc: 0.386, f1: 0.000, acc: 0.759
+2025-08-07 19:36:05: Epoch: 041 Train Loss: 0.341, ap: 0.759, auc: 0.752, f1: 0.693, acc: 0.699
+2025-08-07 19:36:05: Epoch: 041 Test results, ap: 0.228, auc: 0.387, f1: 0.000, acc: 0.759
+2025-08-07 19:36:06: Epoch: 042 Train Loss: 0.324, ap: 0.750, auc: 0.744, f1: 0.712, acc: 0.709
+2025-08-07 19:36:06: Epoch: 042 Test results, ap: 0.229, auc: 0.388, f1: 0.000, acc: 0.759
+2025-08-07 19:36:06: Epoch: 043 Train Loss: 0.345, ap: 0.758, auc: 0.748, f1: 0.686, acc: 0.689
+2025-08-07 19:36:06: Epoch: 043 Test results, ap: 0.228, auc: 0.389, f1: 0.000, acc: 0.759
+2025-08-07 19:36:06: Epoch: 044 Train Loss: 0.344, ap: 0.758, auc: 0.748, f1: 0.699, acc: 0.699
+2025-08-07 19:36:07: Epoch: 044 Test results, ap: 0.223, auc: 0.386, f1: 0.000, acc: 0.759
+2025-08-07 19:36:07: Epoch: 045 Train Loss: 0.323, ap: 0.751, auc: 0.744, f1: 0.712, acc: 0.709
+2025-08-07 19:36:07: Epoch: 045 Test results, ap: 0.224, auc: 0.386, f1: 0.000, acc: 0.759
+2025-08-07 19:36:07: Epoch: 046 Train Loss: 0.334, ap: 0.753, auc: 0.746, f1: 0.699, acc: 0.699
+2025-08-07 19:36:07: Epoch: 046 Test results, ap: 0.222, auc: 0.386, f1: 0.000, acc: 0.759
+2025-08-07 19:36:08: Epoch: 047 Train Loss: 0.312, ap: 0.753, auc: 0.747, f1: 0.699, acc: 0.699
+2025-08-07 19:36:08: Epoch: 047 Test results, ap: 0.222, auc: 0.385, f1: 0.000, acc: 0.759
+2025-08-07 19:36:08: Epoch: 048 Train Loss: 0.327, ap: 0.752, auc: 0.747, f1: 0.712, acc: 0.709
+2025-08-07 19:36:08: Epoch: 048 Test results, ap: 0.222, auc: 0.381, f1: 0.000, acc: 0.759
+2025-08-07 19:36:09: Epoch: 049 Train Loss: 0.330, ap: 0.757, auc: 0.748, f1: 0.699, acc: 0.699
+2025-08-07 19:36:09: Epoch: 049 Test results, ap: 0.222, auc: 0.383, f1: 0.000, acc: 0.759
+2025-08-07 19:36:09: Epoch: 050 Train Loss: 0.341, ap: 0.751, auc: 0.743, f1: 0.724, acc: 0.718
+2025-08-07 19:36:09: Epoch: 050 Test results, ap: 0.222, auc: 0.384, f1: 0.000, acc: 0.759
+2025-08-07 19:36:09: used time 0.01h
+2025-08-07 19:36:09: Running Cross Validation 3
+2025-08-07 19:36:09: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:36:09: Epoch: 001 Train Loss: 0.693, ap: 0.550, auc: 0.683, f1: 0.000, acc: 0.592
+2025-08-07 19:36:09: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:36:10: Epoch: 001 Test results, ap: 0.209, auc: 0.440, f1: 0.000, acc: 0.759
+2025-08-07 19:36:10: Epoch: 002 Train Loss: 0.692, ap: 0.592, auc: 0.709, f1: 0.579, acc: 0.408
+2025-08-07 19:36:10: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:36:10: Epoch: 002 Test results, ap: 0.213, auc: 0.457, f1: 0.388, acc: 0.241
+2025-08-07 19:36:10: Epoch: 003 Train Loss: 0.689, ap: 0.652, auc: 0.731, f1: 0.632, acc: 0.524
+2025-08-07 19:36:10: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:36:11: Epoch: 003 Test results, ap: 0.214, auc: 0.455, f1: 0.388, acc: 0.241
+2025-08-07 19:36:11: Epoch: 004 Train Loss: 0.655, ap: 0.638, auc: 0.750, f1: 0.438, acc: 0.650
+2025-08-07 19:36:11: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:36:11: Epoch: 004 Test results, ap: 0.206, auc: 0.424, f1: 0.000, acc: 0.722
+2025-08-07 19:36:11: Epoch: 005 Train Loss: 0.634, ap: 0.657, auc: 0.753, f1: 0.617, acc: 0.699
+2025-08-07 19:36:11: Epoch: 005 New best VALIDATION metrics
+2025-08-07 19:36:11: Epoch: 005 Test results, ap: 0.207, auc: 0.438, f1: 0.100, acc: 0.667
+2025-08-07 19:36:12: Epoch: 006 Train Loss: 0.581, ap: 0.729, auc: 0.772, f1: 0.614, acc: 0.573
+2025-08-07 19:36:12: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:36:12: Epoch: 006 Test results, ap: 0.217, auc: 0.454, f1: 0.000, acc: 0.722
+2025-08-07 19:36:12: Epoch: 007 Train Loss: 0.572, ap: 0.765, auc: 0.811, f1: 0.681, acc: 0.709
+2025-08-07 19:36:12: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:36:12: Epoch: 007 Test results, ap: 0.222, auc: 0.478, f1: 0.057, acc: 0.694
+2025-08-07 19:36:13: Epoch: 008 Train Loss: 0.550, ap: 0.769, auc: 0.801, f1: 0.690, acc: 0.738
+2025-08-07 19:36:13: Epoch: 008 Test results, ap: 0.242, auc: 0.502, f1: 0.000, acc: 0.759
+2025-08-07 19:36:13: Epoch: 009 Train Loss: 0.516, ap: 0.793, auc: 0.804, f1: 0.688, acc: 0.718
+2025-08-07 19:36:13: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:36:13: Epoch: 009 Test results, ap: 0.257, auc: 0.543, f1: 0.000, acc: 0.694
+2025-08-07 19:36:13: Epoch: 010 Train Loss: 0.498, ap: 0.782, auc: 0.799, f1: 0.642, acc: 0.718
+2025-08-07 19:36:13: Epoch: 010 Test results, ap: 0.229, auc: 0.497, f1: 0.000, acc: 0.750
+2025-08-07 19:36:14: Epoch: 011 Train Loss: 0.515, ap: 0.783, auc: 0.804, f1: 0.659, acc: 0.728
+2025-08-07 19:36:14: Epoch: 011 Test results, ap: 0.222, auc: 0.479, f1: 0.000, acc: 0.722
+2025-08-07 19:36:14: Epoch: 011 New best TEST metrics
+2025-08-07 19:36:14: Epoch: 012 Train Loss: 0.475, ap: 0.773, auc: 0.801, f1: 0.658, acc: 0.738
+2025-08-07 19:36:14: Epoch: 012 Test results, ap: 0.224, auc: 0.470, f1: 0.000, acc: 0.759
+2025-08-07 19:36:15: Epoch: 013 Train Loss: 0.463, ap: 0.788, auc: 0.801, f1: 0.690, acc: 0.748
+2025-08-07 19:36:15: Epoch: 013 Test results, ap: 0.216, auc: 0.457, f1: 0.000, acc: 0.722
+2025-08-07 19:36:15: Epoch: 014 Train Loss: 0.463, ap: 0.783, auc: 0.794, f1: 0.681, acc: 0.718
+2025-08-07 19:36:15: Epoch: 014 Test results, ap: 0.215, auc: 0.455, f1: 0.091, acc: 0.630
+2025-08-07 19:36:16: Epoch: 015 Train Loss: 0.430, ap: 0.784, auc: 0.795, f1: 0.696, acc: 0.728
+2025-08-07 19:36:16: Epoch: 015 Test results, ap: 0.230, auc: 0.495, f1: 0.091, acc: 0.630
+2025-08-07 19:36:16: Epoch: 015 New best TEST metrics
+2025-08-07 19:36:16: Epoch: 016 Train Loss: 0.436, ap: 0.789, auc: 0.804, f1: 0.682, acc: 0.728
+2025-08-07 19:36:16: Epoch: 016 Test results, ap: 0.226, auc: 0.475, f1: 0.111, acc: 0.704
+2025-08-07 19:36:17: Epoch: 017 Train Loss: 0.424, ap: 0.771, auc: 0.785, f1: 0.642, acc: 0.718
+2025-08-07 19:36:17: Epoch: 017 Test results, ap: 0.219, auc: 0.470, f1: 0.000, acc: 0.704
+2025-08-07 19:36:17: Epoch: 018 Train Loss: 0.444, ap: 0.765, auc: 0.790, f1: 0.667, acc: 0.709
+2025-08-07 19:36:17: Epoch: 018 Test results, ap: 0.221, auc: 0.476, f1: 0.000, acc: 0.676
+2025-08-07 19:36:17: Epoch: 019 Train Loss: 0.431, ap: 0.766, auc: 0.783, f1: 0.667, acc: 0.709
+2025-08-07 19:36:18: Epoch: 019 Test results, ap: 0.236, auc: 0.480, f1: 0.114, acc: 0.713
+2025-08-07 19:36:18: Epoch: 020 Train Loss: 0.424, ap: 0.756, auc: 0.781, f1: 0.688, acc: 0.709
+2025-08-07 19:36:18: Epoch: 020 Test results, ap: 0.229, auc: 0.488, f1: 0.093, acc: 0.639
+2025-08-07 19:36:18: Epoch: 021 Train Loss: 0.404, ap: 0.765, auc: 0.789, f1: 0.696, acc: 0.728
+2025-08-07 19:36:18: Epoch: 021 Test results, ap: 0.224, auc: 0.479, f1: 0.103, acc: 0.676
+2025-08-07 19:36:19: Epoch: 022 Train Loss: 0.379, ap: 0.720, auc: 0.776, f1: 0.667, acc: 0.728
+2025-08-07 19:36:19: Epoch: 022 Test results, ap: 0.226, auc: 0.480, f1: 0.000, acc: 0.750
+2025-08-07 19:36:19: Epoch: 023 Train Loss: 0.374, ap: 0.682, auc: 0.772, f1: 0.644, acc: 0.689
+2025-08-07 19:36:19: Epoch: 023 Test results, ap: 0.222, auc: 0.472, f1: 0.105, acc: 0.685
+2025-08-07 19:36:20: Epoch: 024 Train Loss: 0.371, ap: 0.684, auc: 0.772, f1: 0.674, acc: 0.728
+2025-08-07 19:36:20: Epoch: 024 Test results, ap: 0.222, auc: 0.472, f1: 0.000, acc: 0.731
+2025-08-07 19:36:20: Epoch: 025 Train Loss: 0.352, ap: 0.662, auc: 0.766, f1: 0.652, acc: 0.699
+2025-08-07 19:36:20: Epoch: 025 Test results, ap: 0.224, auc: 0.478, f1: 0.000, acc: 0.685
+2025-08-07 19:36:21: Epoch: 026 Train Loss: 0.363, ap: 0.692, auc: 0.774, f1: 0.702, acc: 0.728
+2025-08-07 19:36:21: Epoch: 026 Test results, ap: 0.224, auc: 0.478, f1: 0.093, acc: 0.639
+2025-08-07 19:36:21: Epoch: 027 Train Loss: 0.354, ap: 0.667, auc: 0.767, f1: 0.651, acc: 0.718
+2025-08-07 19:36:21: Epoch: 027 Test results, ap: 0.223, auc: 0.473, f1: 0.105, acc: 0.685
+2025-08-07 19:36:21: Epoch: 028 Train Loss: 0.344, ap: 0.669, auc: 0.762, f1: 0.667, acc: 0.738
+2025-08-07 19:36:22: Epoch: 028 Test results, ap: 0.224, auc: 0.475, f1: 0.000, acc: 0.722
+2025-08-07 19:36:22: Epoch: 029 Train Loss: 0.366, ap: 0.680, auc: 0.758, f1: 0.682, acc: 0.728
+2025-08-07 19:36:22: Epoch: 029 Test results, ap: 0.226, auc: 0.484, f1: 0.093, acc: 0.639
+2025-08-07 19:36:22: Epoch: 030 Train Loss: 0.359, ap: 0.650, auc: 0.754, f1: 0.643, acc: 0.709
+2025-08-07 19:36:22: Epoch: 030 Test results, ap: 0.223, auc: 0.477, f1: 0.000, acc: 0.685
+2025-08-07 19:36:23: Epoch: 031 Train Loss: 0.356, ap: 0.631, auc: 0.752, f1: 0.674, acc: 0.728
+2025-08-07 19:36:23: Epoch: 031 Test results, ap: 0.221, auc: 0.471, f1: 0.093, acc: 0.639
+2025-08-07 19:36:23: Epoch: 032 Train Loss: 0.340, ap: 0.645, auc: 0.757, f1: 0.674, acc: 0.728
+2025-08-07 19:36:23: Epoch: 032 Test results, ap: 0.223, auc: 0.477, f1: 0.093, acc: 0.639
+2025-08-07 19:36:24: Epoch: 033 Train Loss: 0.318, ap: 0.646, auc: 0.758, f1: 0.659, acc: 0.718
+2025-08-07 19:36:24: Epoch: 033 Test results, ap: 0.231, auc: 0.488, f1: 0.103, acc: 0.676
+2025-08-07 19:36:24: Epoch: 034 Train Loss: 0.334, ap: 0.634, auc: 0.751, f1: 0.659, acc: 0.718
+2025-08-07 19:36:24: Epoch: 034 Test results, ap: 0.232, auc: 0.489, f1: 0.169, acc: 0.546
+2025-08-07 19:36:24: Epoch: 035 Train Loss: 0.315, ap: 0.638, auc: 0.751, f1: 0.659, acc: 0.718
+2025-08-07 19:36:24: Epoch: 035 Test results, ap: 0.230, auc: 0.489, f1: 0.204, acc: 0.639
+2025-08-07 19:36:25: Epoch: 036 Train Loss: 0.345, ap: 0.638, auc: 0.750, f1: 0.667, acc: 0.718
+2025-08-07 19:36:25: Epoch: 036 Test results, ap: 0.235, auc: 0.493, f1: 0.182, acc: 0.583
+2025-08-07 19:36:25: Epoch: 036 New best TEST metrics
+2025-08-07 19:36:25: Epoch: 037 Train Loss: 0.322, ap: 0.643, auc: 0.749, f1: 0.659, acc: 0.709
+2025-08-07 19:36:25: Epoch: 037 Test results, ap: 0.223, auc: 0.480, f1: 0.182, acc: 0.583
+2025-08-07 19:36:26: Epoch: 038 Train Loss: 0.322, ap: 0.632, auc: 0.746, f1: 0.674, acc: 0.728
+2025-08-07 19:36:26: Epoch: 038 Test results, ap: 0.223, auc: 0.479, f1: 0.182, acc: 0.583
+2025-08-07 19:36:26: Epoch: 039 Train Loss: 0.283, ap: 0.611, auc: 0.735, f1: 0.644, acc: 0.699
+2025-08-07 19:36:26: Epoch: 039 Test results, ap: 0.225, auc: 0.486, f1: 0.169, acc: 0.546
+2025-08-07 19:36:26: Epoch: 040 Train Loss: 0.296, ap: 0.611, auc: 0.733, f1: 0.659, acc: 0.718
+2025-08-07 19:36:27: Epoch: 040 Test results, ap: 0.230, auc: 0.489, f1: 0.169, acc: 0.546
+2025-08-07 19:36:27: Epoch: 041 Train Loss: 0.303, ap: 0.621, auc: 0.734, f1: 0.651, acc: 0.709
+2025-08-07 19:36:27: Epoch: 041 Test results, ap: 0.227, auc: 0.485, f1: 0.167, acc: 0.537
+2025-08-07 19:36:27: Epoch: 042 Train Loss: 0.293, ap: 0.621, auc: 0.735, f1: 0.644, acc: 0.699
+2025-08-07 19:36:27: Epoch: 042 Test results, ap: 0.225, auc: 0.483, f1: 0.167, acc: 0.537
+2025-08-07 19:36:28: Epoch: 043 Train Loss: 0.286, ap: 0.615, auc: 0.730, f1: 0.659, acc: 0.718
+2025-08-07 19:36:28: Epoch: 043 Test results, ap: 0.226, auc: 0.484, f1: 0.182, acc: 0.583
+2025-08-07 19:36:28: Epoch: 044 Train Loss: 0.279, ap: 0.611, auc: 0.730, f1: 0.651, acc: 0.709
+2025-08-07 19:36:28: Epoch: 044 Test results, ap: 0.225, auc: 0.485, f1: 0.167, acc: 0.537
+2025-08-07 19:36:29: Epoch: 045 Train Loss: 0.301, ap: 0.609, auc: 0.728, f1: 0.659, acc: 0.718
+2025-08-07 19:36:29: Epoch: 045 Test results, ap: 0.225, auc: 0.485, f1: 0.167, acc: 0.537
+2025-08-07 19:36:29: Epoch: 046 Train Loss: 0.272, ap: 0.616, auc: 0.729, f1: 0.644, acc: 0.699
+2025-08-07 19:36:29: Epoch: 046 Test results, ap: 0.228, auc: 0.490, f1: 0.167, acc: 0.537
+2025-08-07 19:36:29: Epoch: 047 Train Loss: 0.318, ap: 0.619, auc: 0.729, f1: 0.644, acc: 0.699
+2025-08-07 19:36:30: Epoch: 047 Test results, ap: 0.225, auc: 0.485, f1: 0.167, acc: 0.537
+2025-08-07 19:36:30: Epoch: 048 Train Loss: 0.287, ap: 0.609, auc: 0.725, f1: 0.636, acc: 0.689
+2025-08-07 19:36:30: Epoch: 048 Test results, ap: 0.225, auc: 0.485, f1: 0.167, acc: 0.537
+2025-08-07 19:36:30: Epoch: 049 Train Loss: 0.269, ap: 0.592, auc: 0.722, f1: 0.644, acc: 0.699
+2025-08-07 19:36:30: Epoch: 049 Test results, ap: 0.226, auc: 0.488, f1: 0.167, acc: 0.537
+2025-08-07 19:36:31: Epoch: 050 Train Loss: 0.288, ap: 0.602, auc: 0.722, f1: 0.644, acc: 0.699
+2025-08-07 19:36:31: Epoch: 050 Test results, ap: 0.225, auc: 0.487, f1: 0.167, acc: 0.537
+2025-08-07 19:36:31: used time 0.01h
+2025-08-07 19:36:31: Running Cross Validation 4
+2025-08-07 19:36:31: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:36:31: Epoch: 001 Train Loss: 0.694, ap: 0.677, auc: 0.758, f1: 0.569, acc: 0.398
+2025-08-07 19:36:31: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:36:31: Epoch: 001 Test results, ap: 0.224, auc: 0.478, f1: 0.388, acc: 0.241
+2025-08-07 19:36:32: Epoch: 002 Train Loss: 0.694, ap: 0.659, auc: 0.718, f1: 0.000, acc: 0.602
+2025-08-07 19:36:32: Epoch: 002 Test results, ap: 0.193, auc: 0.385, f1: 0.000, acc: 0.759
+2025-08-07 19:36:32: Epoch: 003 Train Loss: 0.692, ap: 0.692, auc: 0.727, f1: 0.569, acc: 0.398
+2025-08-07 19:36:32: Epoch: 003 Test results, ap: 0.192, auc: 0.379, f1: 0.388, acc: 0.241
+2025-08-07 19:36:32: Epoch: 004 Train Loss: 0.686, ap: 0.673, auc: 0.718, f1: 0.585, acc: 0.670
+2025-08-07 19:36:33: Epoch: 004 Test results, ap: 0.200, auc: 0.403, f1: 0.308, acc: 0.333
+2025-08-07 19:36:33: Epoch: 005 Train Loss: 0.640, ap: 0.657, auc: 0.699, f1: 0.571, acc: 0.621
+2025-08-07 19:36:33: Epoch: 005 Test results, ap: 0.198, auc: 0.394, f1: 0.388, acc: 0.241
+2025-08-07 19:36:33: Epoch: 006 Train Loss: 0.609, ap: 0.648, auc: 0.730, f1: 0.576, acc: 0.515
+2025-08-07 19:36:33: Epoch: 006 Test results, ap: 0.199, auc: 0.403, f1: 0.388, acc: 0.241
+2025-08-07 19:36:34: Epoch: 007 Train Loss: 0.610, ap: 0.634, auc: 0.717, f1: 0.635, acc: 0.631
+2025-08-07 19:36:34: Epoch: 007 Test results, ap: 0.196, auc: 0.389, f1: 0.388, acc: 0.241
+2025-08-07 19:36:34: Epoch: 008 Train Loss: 0.573, ap: 0.658, auc: 0.727, f1: 0.597, acc: 0.699
+2025-08-07 19:36:34: Epoch: 008 Test results, ap: 0.225, auc: 0.390, f1: 0.382, acc: 0.250
+2025-08-07 19:36:35: Epoch: 009 Train Loss: 0.563, ap: 0.626, auc: 0.721, f1: 0.605, acc: 0.670
+2025-08-07 19:36:35: Epoch: 009 Test results, ap: 0.188, auc: 0.363, f1: 0.388, acc: 0.241
+2025-08-07 19:36:35: Epoch: 010 Train Loss: 0.530, ap: 0.634, auc: 0.716, f1: 0.630, acc: 0.670
+2025-08-07 19:36:35: Epoch: 010 Test results, ap: 0.188, auc: 0.359, f1: 0.388, acc: 0.241
+2025-08-07 19:36:35: Epoch: 011 Train Loss: 0.512, ap: 0.659, auc: 0.725, f1: 0.674, acc: 0.699
+2025-08-07 19:36:36: Epoch: 011 Test results, ap: 0.215, auc: 0.385, f1: 0.388, acc: 0.241
+2025-08-07 19:36:36: Epoch: 011 New best TEST metrics
+2025-08-07 19:36:36: Epoch: 012 Train Loss: 0.493, ap: 0.641, auc: 0.715, f1: 0.660, acc: 0.689
+2025-08-07 19:36:36: Epoch: 012 Test results, ap: 0.194, auc: 0.370, f1: 0.388, acc: 0.241
+2025-08-07 19:36:36: Epoch: 013 Train Loss: 0.510, ap: 0.631, auc: 0.701, f1: 0.637, acc: 0.680
+2025-08-07 19:36:36: Epoch: 013 Test results, ap: 0.194, auc: 0.368, f1: 0.355, acc: 0.259
+2025-08-07 19:36:37: Epoch: 014 Train Loss: 0.484, ap: 0.630, auc: 0.705, f1: 0.638, acc: 0.670
+2025-08-07 19:36:37: Epoch: 014 Test results, ap: 0.197, auc: 0.355, f1: 0.388, acc: 0.241
+2025-08-07 19:36:37: Epoch: 015 Train Loss: 0.478, ap: 0.625, auc: 0.699, f1: 0.653, acc: 0.680
+2025-08-07 19:36:37: Epoch: 015 Test results, ap: 0.201, auc: 0.360, f1: 0.388, acc: 0.241
+2025-08-07 19:36:38: Epoch: 016 Train Loss: 0.477, ap: 0.609, auc: 0.687, f1: 0.667, acc: 0.680
+2025-08-07 19:36:38: Epoch: 016 Test results, ap: 0.215, auc: 0.380, f1: 0.388, acc: 0.241
+2025-08-07 19:36:38: Epoch: 017 Train Loss: 0.511, ap: 0.621, auc: 0.691, f1: 0.568, acc: 0.660
+2025-08-07 19:36:38: Epoch: 017 Test results, ap: 0.210, auc: 0.365, f1: 0.196, acc: 0.620
+2025-08-07 19:36:38: Epoch: 018 Train Loss: 0.472, ap: 0.621, auc: 0.703, f1: 0.638, acc: 0.670
+2025-08-07 19:36:39: Epoch: 018 Test results, ap: 0.209, auc: 0.361, f1: 0.316, acc: 0.278
+2025-08-07 19:36:39: Epoch: 019 Train Loss: 0.455, ap: 0.630, auc: 0.708, f1: 0.645, acc: 0.680
+2025-08-07 19:36:39: Epoch: 019 Test results, ap: 0.242, auc: 0.362, f1: 0.316, acc: 0.278
+2025-08-07 19:36:39: Epoch: 019 New best TEST metrics
+2025-08-07 19:36:39: Epoch: 020 Train Loss: 0.452, ap: 0.620, auc: 0.686, f1: 0.632, acc: 0.660
+2025-08-07 19:36:39: Epoch: 020 Test results, ap: 0.212, auc: 0.367, f1: 0.316, acc: 0.278
+2025-08-07 19:36:40: Epoch: 021 Train Loss: 0.456, ap: 0.633, auc: 0.695, f1: 0.598, acc: 0.660
+2025-08-07 19:36:40: Epoch: 021 Test results, ap: 0.221, auc: 0.405, f1: 0.194, acc: 0.537
+2025-08-07 19:36:40: Epoch: 021 New best TEST metrics
+2025-08-07 19:36:40: Epoch: 022 Train Loss: 0.435, ap: 0.620, auc: 0.697, f1: 0.630, acc: 0.670
+2025-08-07 19:36:40: Epoch: 022 Test results, ap: 0.230, auc: 0.434, f1: 0.316, acc: 0.278
+2025-08-07 19:36:40: Epoch: 022 New best TEST metrics
+2025-08-07 19:36:41: Epoch: 023 Train Loss: 0.440, ap: 0.617, auc: 0.694, f1: 0.652, acc: 0.699
+2025-08-07 19:36:41: Epoch: 023 Test results, ap: 0.236, auc: 0.439, f1: 0.316, acc: 0.278
+2025-08-07 19:36:41: Epoch: 023 New best TEST metrics
+2025-08-07 19:36:41: Epoch: 024 Train Loss: 0.408, ap: 0.622, auc: 0.695, f1: 0.605, acc: 0.670
+2025-08-07 19:36:41: Epoch: 024 Test results, ap: 0.237, auc: 0.441, f1: 0.293, acc: 0.509
+2025-08-07 19:36:41: Epoch: 024 New best TEST metrics
+2025-08-07 19:36:41: Epoch: 025 Train Loss: 0.409, ap: 0.618, auc: 0.694, f1: 0.578, acc: 0.660
+2025-08-07 19:36:42: Epoch: 025 Test results, ap: 0.259, auc: 0.432, f1: 0.192, acc: 0.611
+2025-08-07 19:36:42: Epoch: 025 New best TEST metrics
+2025-08-07 19:36:42: Epoch: 026 Train Loss: 0.393, ap: 0.619, auc: 0.694, f1: 0.624, acc: 0.660
+2025-08-07 19:36:42: Epoch: 026 Test results, ap: 0.268, auc: 0.453, f1: 0.319, acc: 0.287
+2025-08-07 19:36:42: Epoch: 026 New best TEST metrics
+2025-08-07 19:36:42: Epoch: 027 Train Loss: 0.399, ap: 0.625, auc: 0.709, f1: 0.630, acc: 0.670
+2025-08-07 19:36:43: Epoch: 027 Test results, ap: 0.255, auc: 0.465, f1: 0.312, acc: 0.389
+2025-08-07 19:36:43: Epoch: 028 Train Loss: 0.397, ap: 0.613, auc: 0.687, f1: 0.600, acc: 0.650
+2025-08-07 19:36:43: Epoch: 028 Test results, ap: 0.262, auc: 0.489, f1: 0.362, acc: 0.444
+2025-08-07 19:36:43: Epoch: 028 New best TEST metrics
+2025-08-07 19:36:43: Epoch: 029 Train Loss: 0.393, ap: 0.620, auc: 0.695, f1: 0.595, acc: 0.670
+2025-08-07 19:36:43: Epoch: 029 Test results, ap: 0.257, auc: 0.482, f1: 0.200, acc: 0.630
+2025-08-07 19:36:44: Epoch: 030 Train Loss: 0.370, ap: 0.630, auc: 0.710, f1: 0.621, acc: 0.680
+2025-08-07 19:36:44: Epoch: 030 Test results, ap: 0.293, auc: 0.490, f1: 0.196, acc: 0.620
+2025-08-07 19:36:44: Epoch: 030 New best TEST metrics
+2025-08-07 19:36:44: Epoch: 031 Train Loss: 0.354, ap: 0.641, auc: 0.717, f1: 0.635, acc: 0.699
+2025-08-07 19:36:44: Epoch: 031 Test results, ap: 0.259, auc: 0.487, f1: 0.233, acc: 0.694
+2025-08-07 19:36:45: Epoch: 032 Train Loss: 0.374, ap: 0.634, auc: 0.711, f1: 0.629, acc: 0.680
+2025-08-07 19:36:45: Epoch: 032 Test results, ap: 0.256, auc: 0.495, f1: 0.281, acc: 0.574
+2025-08-07 19:36:45: Epoch: 033 Train Loss: 0.368, ap: 0.643, auc: 0.718, f1: 0.628, acc: 0.689
+2025-08-07 19:36:45: Epoch: 033 Test results, ap: 0.252, auc: 0.489, f1: 0.200, acc: 0.630
+2025-08-07 19:36:45: Epoch: 034 Train Loss: 0.339, ap: 0.640, auc: 0.714, f1: 0.622, acc: 0.670
+2025-08-07 19:36:45: Epoch: 034 Test results, ap: 0.263, auc: 0.492, f1: 0.286, acc: 0.630
+2025-08-07 19:36:46: Epoch: 035 Train Loss: 0.350, ap: 0.632, auc: 0.712, f1: 0.614, acc: 0.670
+2025-08-07 19:36:46: Epoch: 035 Test results, ap: 0.261, auc: 0.499, f1: 0.286, acc: 0.630
+2025-08-07 19:36:46: Epoch: 036 Train Loss: 0.349, ap: 0.641, auc: 0.718, f1: 0.621, acc: 0.680
+2025-08-07 19:36:46: Epoch: 036 Test results, ap: 0.267, auc: 0.498, f1: 0.204, acc: 0.639
+2025-08-07 19:36:47: Epoch: 037 Train Loss: 0.354, ap: 0.641, auc: 0.718, f1: 0.629, acc: 0.680
+2025-08-07 19:36:47: Epoch: 037 Test results, ap: 0.269, auc: 0.493, f1: 0.200, acc: 0.630
+2025-08-07 19:36:47: Epoch: 038 Train Loss: 0.334, ap: 0.641, auc: 0.719, f1: 0.644, acc: 0.689
+2025-08-07 19:36:47: Epoch: 038 Test results, ap: 0.261, auc: 0.500, f1: 0.290, acc: 0.593
+2025-08-07 19:36:48: Epoch: 039 Train Loss: 0.322, ap: 0.636, auc: 0.718, f1: 0.588, acc: 0.660
+2025-08-07 19:36:48: Epoch: 039 Test results, ap: 0.261, auc: 0.507, f1: 0.291, acc: 0.639
+2025-08-07 19:36:48: Epoch: 040 Train Loss: 0.321, ap: 0.639, auc: 0.713, f1: 0.605, acc: 0.670
+2025-08-07 19:36:48: Epoch: 040 Test results, ap: 0.256, auc: 0.492, f1: 0.208, acc: 0.648
+2025-08-07 19:36:48: Epoch: 041 Train Loss: 0.310, ap: 0.640, auc: 0.717, f1: 0.629, acc: 0.680
+2025-08-07 19:36:48: Epoch: 041 Test results, ap: 0.256, auc: 0.499, f1: 0.300, acc: 0.611
+2025-08-07 19:36:49: Epoch: 042 Train Loss: 0.324, ap: 0.646, auc: 0.721, f1: 0.614, acc: 0.670
+2025-08-07 19:36:49: Epoch: 042 Test results, ap: 0.260, auc: 0.500, f1: 0.208, acc: 0.648
+2025-08-07 19:36:49: Epoch: 043 Train Loss: 0.313, ap: 0.640, auc: 0.718, f1: 0.629, acc: 0.680
+2025-08-07 19:36:49: Epoch: 043 Test results, ap: 0.261, auc: 0.508, f1: 0.295, acc: 0.602
+2025-08-07 19:36:50: Epoch: 044 Train Loss: 0.320, ap: 0.638, auc: 0.720, f1: 0.598, acc: 0.660
+2025-08-07 19:36:50: Epoch: 044 Test results, ap: 0.265, auc: 0.508, f1: 0.235, acc: 0.639
+2025-08-07 19:36:50: Epoch: 045 Train Loss: 0.312, ap: 0.645, auc: 0.721, f1: 0.629, acc: 0.680
+2025-08-07 19:36:50: Epoch: 045 Test results, ap: 0.261, auc: 0.506, f1: 0.295, acc: 0.602
+2025-08-07 19:36:50: Epoch: 046 Train Loss: 0.299, ap: 0.648, auc: 0.727, f1: 0.605, acc: 0.670
+2025-08-07 19:36:51: Epoch: 046 Test results, ap: 0.259, auc: 0.505, f1: 0.136, acc: 0.648
+2025-08-07 19:36:51: Epoch: 047 Train Loss: 0.333, ap: 0.646, auc: 0.725, f1: 0.629, acc: 0.680
+2025-08-07 19:36:51: Epoch: 047 Test results, ap: 0.261, auc: 0.503, f1: 0.246, acc: 0.602
+2025-08-07 19:36:51: Epoch: 048 Train Loss: 0.315, ap: 0.647, auc: 0.725, f1: 0.644, acc: 0.689
+2025-08-07 19:36:51: Epoch: 048 Test results, ap: 0.278, auc: 0.515, f1: 0.290, acc: 0.593
+2025-08-07 19:36:51: Epoch: 048 New best TEST metrics
+2025-08-07 19:36:52: Epoch: 049 Train Loss: 0.303, ap: 0.647, auc: 0.730, f1: 0.565, acc: 0.641
+2025-08-07 19:36:52: Epoch: 049 Test results, ap: 0.264, auc: 0.506, f1: 0.261, acc: 0.685
+2025-08-07 19:36:52: Epoch: 050 Train Loss: 0.317, ap: 0.645, auc: 0.725, f1: 0.629, acc: 0.680
+2025-08-07 19:36:52: Epoch: 050 Test results, ap: 0.252, auc: 0.485, f1: 0.246, acc: 0.602
+2025-08-07 19:36:52: used time 0.01h
+2025-08-07 19:36:52: Cross Validation Finished!
+2025-08-07 19:36:52: Best validation perform list
+[[0.71509498 0.7401216  0.67961162 0.67961162]
+ [0.62028706 0.71826375 0.58974361 0.68932039]
+ [0.81459934 0.76923072 0.67961162 0.67961162]
+ [0.79277503 0.80366904 0.68817204 0.71844661]
+ [0.67674273 0.7584579  0.56944442 0.39805827]]
+2025-08-07 19:36:52: mean: [0.724 0.758 0.641 0.633]
+2025-08-07 19:36:52: std: [0.072 0.029 0.051 0.118]
+2025-08-07 19:36:52: Best test perform list
+[[0.25440264 0.52532834 0.34146342 0.5       ]
+ [0.2345998  0.48639774 0.18181819 0.66666669]
+ [0.2496099  0.44840527 0.         0.75925928]
+ [0.23498122 0.49343339 0.18181819 0.58333331]
+ [0.27767393 0.51454031 0.29032257 0.5925926 ]]
+2025-08-07 19:36:52: mean: [0.25  0.494 0.199 0.62 ]
+2025-08-07 19:36:52: std: [0.016 0.027 0.117 0.087]
diff --git a/run-cls-simple/non-siamese-rn18-att-16-simple-768-oneway-ce-32-0.001-50/config.json b/run-cls-simple/non-siamese-rn18-att-16-simple-768-oneway-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": null, "channels": 16, "fusion": "att", "glob_feat": false, "non_siamese": true, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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diff --git a/run-cls-simple/non-siamese-rn18-att-16-simple-768-oneway-ce-32-0.001-50/result.txt b/run-cls-simple/non-siamese-rn18-att-16-simple-768-oneway-ce-32-0.001-50/result.txt
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@@ -0,0 +1,6 @@
+Valid
+0.735260808467865,0.7675035119056701,0.4909443974494934,0.7048543691635132
+0.0911251633639917,0.05078466967955672,0.281694195602699,0.06630435351490913
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+0.04579890978809086,0.03929098569932664,0.0834275868906813,0.10871229014848059
diff --git a/run-cls-simple/non-siamese-rn18-att-16-simple-768-oneway-ce-32-0.001-50/training.log b/run-cls-simple/non-siamese-rn18-att-16-simple-768-oneway-ce-32-0.001-50/training.log
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+2025-08-25 21:51:27: saving_dir: ./run-cls/non-siamese-rn18-att-16-simple-768-oneway-ce-32-0.001-50
+2025-08-25 21:51:27: Loading Training Dataset
+2025-08-25 21:51:51: Loading Test Dataset
+2025-08-25 21:51:55: Running Cross Validation 0
+2025-08-25 21:51:55: Fold 0  Train set:412, Valid set:103, Test set: 123
+2025-08-25 21:52:02: Epoch: 001 Train Loss: 0.682, ap: 0.405, auc: 0.440, f1: 0.586, acc: 0.437
+2025-08-25 21:52:02: Epoch: 001 New best VALIDATION metrics
+2025-08-25 21:52:03: Epoch: 001 Test results, ap: 0.253, auc: 0.467, f1: 0.392, acc: 0.244
+2025-08-25 21:52:09: Epoch: 002 Train Loss: 0.609, ap: 0.534, auc: 0.551, f1: 0.627, acc: 0.456
+2025-08-25 21:52:09: Epoch: 002 New best VALIDATION metrics
+2025-08-25 21:52:11: Epoch: 002 Test results, ap: 0.257, auc: 0.545, f1: 0.392, acc: 0.244
+2025-08-25 21:52:17: Epoch: 003 Train Loss: 0.590, ap: 0.652, auc: 0.688, f1: 0.189, acc: 0.583
+2025-08-25 21:52:17: Epoch: 003 New best VALIDATION metrics
+2025-08-25 21:52:18: Epoch: 003 Test results, ap: 0.224, auc: 0.444, f1: 0.000, acc: 0.756
+2025-08-25 21:52:24: Epoch: 004 Train Loss: 0.546, ap: 0.620, auc: 0.633, f1: 0.000, acc: 0.544
+2025-08-25 21:52:25: Epoch: 004 Test results, ap: 0.194, auc: 0.391, f1: 0.000, acc: 0.756
+2025-08-25 21:52:31: Epoch: 005 Train Loss: 0.505, ap: 0.699, auc: 0.756, f1: 0.522, acc: 0.680
+2025-08-25 21:52:31: Epoch: 005 New best VALIDATION metrics
+2025-08-25 21:52:32: Epoch: 005 Test results, ap: 0.192, auc: 0.370, f1: 0.000, acc: 0.740
+2025-08-25 21:52:39: Epoch: 006 Train Loss: 0.427, ap: 0.605, auc: 0.697, f1: 0.667, acc: 0.670
+2025-08-25 21:52:40: Epoch: 006 Test results, ap: 0.181, auc: 0.332, f1: 0.198, acc: 0.472
+2025-08-25 21:52:46: Epoch: 007 Train Loss: 0.441, ap: 0.618, auc: 0.644, f1: 0.000, acc: 0.544
+2025-08-25 21:52:47: Epoch: 007 Test results, ap: 0.360, auc: 0.617, f1: 0.000, acc: 0.756
+2025-08-25 21:52:53: Epoch: 008 Train Loss: 0.417, ap: 0.639, auc: 0.677, f1: 0.627, acc: 0.456
+2025-08-25 21:52:54: Epoch: 008 Test results, ap: 0.321, auc: 0.527, f1: 0.392, acc: 0.244
+2025-08-25 21:53:01: Epoch: 009 Train Loss: 0.396, ap: 0.623, auc: 0.657, f1: 0.558, acc: 0.631
+2025-08-25 21:53:02: Epoch: 009 Test results, ap: 0.308, auc: 0.472, f1: 0.324, acc: 0.626
+2025-08-25 21:53:08: Epoch: 010 Train Loss: 0.353, ap: 0.668, auc: 0.731, f1: 0.660, acc: 0.680
+2025-08-25 21:53:09: Epoch: 010 Test results, ap: 0.290, auc: 0.529, f1: 0.000, acc: 0.748
+2025-08-25 21:53:15: Epoch: 011 Train Loss: 0.368, ap: 0.679, auc: 0.715, f1: 0.632, acc: 0.660
+2025-08-25 21:53:17: Epoch: 011 Test results, ap: 0.363, auc: 0.491, f1: 0.327, acc: 0.699
+2025-08-25 21:53:17: Epoch: 011 New best TEST metrics
+2025-08-25 21:53:23: Epoch: 012 Train Loss: 0.296, ap: 0.683, auc: 0.745, f1: 0.533, acc: 0.660
+2025-08-25 21:53:24: Epoch: 012 Test results, ap: 0.187, auc: 0.353, f1: 0.000, acc: 0.707
+2025-08-25 21:53:31: Epoch: 013 Train Loss: 0.250, ap: 0.655, auc: 0.714, f1: 0.637, acc: 0.680
+2025-08-25 21:53:32: Epoch: 013 Test results, ap: 0.190, auc: 0.371, f1: 0.068, acc: 0.553
+2025-08-25 21:53:38: Epoch: 014 Train Loss: 0.218, ap: 0.670, auc: 0.711, f1: 0.622, acc: 0.670
+2025-08-25 21:53:39: Epoch: 014 Test results, ap: 0.197, auc: 0.394, f1: 0.050, acc: 0.691
+2025-08-25 21:53:46: Epoch: 015 Train Loss: 0.228, ap: 0.654, auc: 0.697, f1: 0.565, acc: 0.641
+2025-08-25 21:53:47: Epoch: 015 Test results, ap: 0.216, auc: 0.415, f1: 0.067, acc: 0.545
+2025-08-25 21:53:53: Epoch: 016 Train Loss: 0.221, ap: 0.676, auc: 0.723, f1: 0.624, acc: 0.660
+2025-08-25 21:53:54: Epoch: 016 Test results, ap: 0.198, auc: 0.388, f1: 0.082, acc: 0.634
+2025-08-25 21:54:00: Epoch: 017 Train Loss: 0.196, ap: 0.692, auc: 0.721, f1: 0.525, acc: 0.631
+2025-08-25 21:54:01: Epoch: 017 Test results, ap: 0.210, auc: 0.386, f1: 0.056, acc: 0.724
+2025-08-25 21:54:08: Epoch: 018 Train Loss: 0.208, ap: 0.665, auc: 0.696, f1: 0.519, acc: 0.621
+2025-08-25 21:54:09: Epoch: 018 Test results, ap: 0.215, auc: 0.389, f1: 0.189, acc: 0.650
+2025-08-25 21:54:15: Epoch: 019 Train Loss: 0.228, ap: 0.650, auc: 0.690, f1: 0.552, acc: 0.621
+2025-08-25 21:54:16: Epoch: 019 Test results, ap: 0.220, auc: 0.427, f1: 0.175, acc: 0.618
+2025-08-25 21:54:22: Epoch: 020 Train Loss: 0.183, ap: 0.638, auc: 0.687, f1: 0.550, acc: 0.650
+2025-08-25 21:54:23: Epoch: 020 Test results, ap: 0.216, auc: 0.428, f1: 0.050, acc: 0.691
+2025-08-25 21:54:30: Epoch: 021 Train Loss: 0.166, ap: 0.654, auc: 0.688, f1: 0.607, acc: 0.660
+2025-08-25 21:54:31: Epoch: 021 Test results, ap: 0.207, auc: 0.415, f1: 0.071, acc: 0.577
+2025-08-25 21:54:37: Epoch: 022 Train Loss: 0.160, ap: 0.672, auc: 0.715, f1: 0.615, acc: 0.660
+2025-08-25 21:54:38: Epoch: 022 Test results, ap: 0.220, auc: 0.422, f1: 0.182, acc: 0.561
+2025-08-25 21:54:44: Epoch: 023 Train Loss: 0.143, ap: 0.675, auc: 0.709, f1: 0.605, acc: 0.670
+2025-08-25 21:54:45: Epoch: 023 Test results, ap: 0.237, auc: 0.456, f1: 0.049, acc: 0.683
+2025-08-25 21:54:52: Epoch: 024 Train Loss: 0.150, ap: 0.694, auc: 0.712, f1: 0.607, acc: 0.660
+2025-08-25 21:54:53: Epoch: 024 Test results, ap: 0.214, auc: 0.428, f1: 0.051, acc: 0.699
+2025-08-25 21:54:59: Epoch: 025 Train Loss: 0.132, ap: 0.694, auc: 0.721, f1: 0.652, acc: 0.689
+2025-08-25 21:55:00: Epoch: 025 Test results, ap: 0.262, auc: 0.464, f1: 0.226, acc: 0.610
+2025-08-25 21:55:06: Epoch: 026 Train Loss: 0.146, ap: 0.674, auc: 0.712, f1: 0.630, acc: 0.670
+2025-08-25 21:55:07: Epoch: 026 Test results, ap: 0.258, auc: 0.473, f1: 0.214, acc: 0.642
+2025-08-25 21:55:14: Epoch: 027 Train Loss: 0.147, ap: 0.681, auc: 0.721, f1: 0.615, acc: 0.660
+2025-08-25 21:55:15: Epoch: 027 Test results, ap: 0.236, auc: 0.451, f1: 0.215, acc: 0.585
+2025-08-25 21:55:21: Epoch: 028 Train Loss: 0.138, ap: 0.706, auc: 0.742, f1: 0.681, acc: 0.709
+2025-08-25 21:55:22: Epoch: 028 Test results, ap: 0.263, auc: 0.459, f1: 0.296, acc: 0.537
+2025-08-25 21:55:28: Epoch: 029 Train Loss: 0.121, ap: 0.682, auc: 0.715, f1: 0.681, acc: 0.718
+2025-08-25 21:55:29: Epoch: 029 Test results, ap: 0.244, auc: 0.457, f1: 0.130, acc: 0.675
+2025-08-25 21:55:36: Epoch: 030 Train Loss: 0.124, ap: 0.679, auc: 0.708, f1: 0.635, acc: 0.699
+2025-08-25 21:55:37: Epoch: 030 Test results, ap: 0.212, auc: 0.439, f1: 0.051, acc: 0.699
+2025-08-25 21:55:43: Epoch: 031 Train Loss: 0.124, ap: 0.683, auc: 0.721, f1: 0.568, acc: 0.660
+2025-08-25 21:55:44: Epoch: 031 Test results, ap: 0.226, auc: 0.447, f1: 0.100, acc: 0.561
+2025-08-25 21:55:50: Epoch: 032 Train Loss: 0.101, ap: 0.684, auc: 0.719, f1: 0.578, acc: 0.660
+2025-08-25 21:55:51: Epoch: 032 Test results, ap: 0.231, auc: 0.456, f1: 0.113, acc: 0.618
+2025-08-25 21:55:57: Epoch: 033 Train Loss: 0.124, ap: 0.690, auc: 0.725, f1: 0.607, acc: 0.660
+2025-08-25 21:55:58: Epoch: 033 Test results, ap: 0.261, auc: 0.463, f1: 0.179, acc: 0.553
+2025-08-25 21:56:04: Epoch: 034 Train Loss: 0.103, ap: 0.693, auc: 0.732, f1: 0.660, acc: 0.689
+2025-08-25 21:56:06: Epoch: 034 Test results, ap: 0.265, auc: 0.446, f1: 0.197, acc: 0.537
+2025-08-25 21:56:12: Epoch: 035 Train Loss: 0.098, ap: 0.679, auc: 0.722, f1: 0.637, acc: 0.680
+2025-08-25 21:56:13: Epoch: 035 Test results, ap: 0.231, auc: 0.439, f1: 0.164, acc: 0.585
+2025-08-25 21:56:19: Epoch: 036 Train Loss: 0.088, ap: 0.674, auc: 0.723, f1: 0.621, acc: 0.680
+2025-08-25 21:56:20: Epoch: 036 Test results, ap: 0.226, auc: 0.437, f1: 0.164, acc: 0.585
+2025-08-25 21:56:27: Epoch: 037 Train Loss: 0.088, ap: 0.689, auc: 0.723, f1: 0.628, acc: 0.689
+2025-08-25 21:56:28: Epoch: 037 Test results, ap: 0.291, auc: 0.444, f1: 0.172, acc: 0.610
+2025-08-25 21:56:34: Epoch: 038 Train Loss: 0.078, ap: 0.687, auc: 0.741, f1: 0.695, acc: 0.718
+2025-08-25 21:56:35: Epoch: 038 Test results, ap: 0.265, auc: 0.430, f1: 0.197, acc: 0.537
+2025-08-25 21:56:41: Epoch: 039 Train Loss: 0.122, ap: 0.660, auc: 0.708, f1: 0.575, acc: 0.670
+2025-08-25 21:56:42: Epoch: 039 Test results, ap: 0.214, auc: 0.434, f1: 0.091, acc: 0.675
+2025-08-25 21:56:48: Epoch: 040 Train Loss: 0.097, ap: 0.686, auc: 0.738, f1: 0.681, acc: 0.709
+2025-08-25 21:56:49: Epoch: 040 Test results, ap: 0.212, auc: 0.436, f1: 0.229, acc: 0.561
+2025-08-25 21:56:56: Epoch: 041 Train Loss: 0.097, ap: 0.686, auc: 0.737, f1: 0.674, acc: 0.709
+2025-08-25 21:56:57: Epoch: 041 Test results, ap: 0.222, auc: 0.443, f1: 0.152, acc: 0.545
+2025-08-25 21:57:03: Epoch: 042 Train Loss: 0.082, ap: 0.688, auc: 0.740, f1: 0.681, acc: 0.709
+2025-08-25 21:57:04: Epoch: 042 Test results, ap: 0.230, auc: 0.433, f1: 0.179, acc: 0.553
+2025-08-25 21:57:10: Epoch: 043 Train Loss: 0.076, ap: 0.682, auc: 0.734, f1: 0.607, acc: 0.660
+2025-08-25 21:57:11: Epoch: 043 Test results, ap: 0.241, auc: 0.433, f1: 0.164, acc: 0.585
+2025-08-25 21:57:18: Epoch: 044 Train Loss: 0.091, ap: 0.681, auc: 0.734, f1: 0.681, acc: 0.709
+2025-08-25 21:57:19: Epoch: 044 Test results, ap: 0.224, auc: 0.432, f1: 0.161, acc: 0.577
+2025-08-25 21:57:25: Epoch: 045 Train Loss: 0.081, ap: 0.694, auc: 0.739, f1: 0.621, acc: 0.680
+2025-08-25 21:57:26: Epoch: 045 Test results, ap: 0.220, auc: 0.428, f1: 0.169, acc: 0.602
+2025-08-25 21:57:32: Epoch: 046 Train Loss: 0.084, ap: 0.691, auc: 0.738, f1: 0.619, acc: 0.689
+2025-08-25 21:57:34: Epoch: 046 Test results, ap: 0.211, auc: 0.431, f1: 0.172, acc: 0.610
+2025-08-25 21:57:40: Epoch: 047 Train Loss: 0.072, ap: 0.690, auc: 0.740, f1: 0.674, acc: 0.718
+2025-08-25 21:57:41: Epoch: 047 Test results, ap: 0.220, auc: 0.436, f1: 0.169, acc: 0.602
+2025-08-25 21:57:47: Epoch: 048 Train Loss: 0.067, ap: 0.695, auc: 0.744, f1: 0.674, acc: 0.718
+2025-08-25 21:57:49: Epoch: 048 Test results, ap: 0.212, auc: 0.432, f1: 0.164, acc: 0.585
+2025-08-25 21:57:55: Epoch: 049 Train Loss: 0.065, ap: 0.689, auc: 0.737, f1: 0.602, acc: 0.680
+2025-08-25 21:57:56: Epoch: 049 Test results, ap: 0.211, auc: 0.433, f1: 0.172, acc: 0.610
+2025-08-25 21:58:02: Epoch: 050 Train Loss: 0.071, ap: 0.691, auc: 0.739, f1: 0.635, acc: 0.699
+2025-08-25 21:58:03: Epoch: 050 Test results, ap: 0.215, auc: 0.440, f1: 0.167, acc: 0.593
+2025-08-25 21:58:04: used time 0.10h
+2025-08-25 21:58:04: Running Cross Validation 1
+2025-08-25 21:58:04: Fold 1  Train set:412, Valid set:103, Test set: 123
+2025-08-25 21:58:10: Epoch: 001 Train Loss: 0.671, ap: 0.413, auc: 0.557, f1: 0.529, acc: 0.359
+2025-08-25 21:58:10: Epoch: 001 New best VALIDATION metrics
+2025-08-25 21:58:11: Epoch: 001 Test results, ap: 0.320, auc: 0.543, f1: 0.392, acc: 0.244
+2025-08-25 21:58:17: Epoch: 002 Train Loss: 0.594, ap: 0.421, auc: 0.549, f1: 0.456, acc: 0.583
+2025-08-25 21:58:19: Epoch: 002 Test results, ap: 0.307, auc: 0.510, f1: 0.222, acc: 0.772
+2025-08-25 21:58:25: Epoch: 003 Train Loss: 0.522, ap: 0.557, auc: 0.641, f1: 0.535, acc: 0.544
+2025-08-25 21:58:25: Epoch: 003 New best VALIDATION metrics
+2025-08-25 21:58:26: Epoch: 003 Test results, ap: 0.314, auc: 0.557, f1: 0.426, acc: 0.341
+2025-08-25 21:58:32: Epoch: 004 Train Loss: 0.491, ap: 0.531, auc: 0.672, f1: 0.531, acc: 0.485
+2025-08-25 21:58:32: Epoch: 004 New best VALIDATION metrics
+2025-08-25 21:58:33: Epoch: 004 Test results, ap: 0.341, auc: 0.621, f1: 0.421, acc: 0.374
+2025-08-25 21:58:39: Epoch: 005 Train Loss: 0.476, ap: 0.601, auc: 0.700, f1: 0.581, acc: 0.495
+2025-08-25 21:58:39: Epoch: 005 New best VALIDATION metrics
+2025-08-25 21:58:41: Epoch: 005 Test results, ap: 0.245, auc: 0.529, f1: 0.392, acc: 0.244
+2025-08-25 21:58:47: Epoch: 006 Train Loss: 0.431, ap: 0.529, auc: 0.681, f1: 0.098, acc: 0.641
+2025-08-25 21:58:48: Epoch: 006 Test results, ap: 0.309, auc: 0.559, f1: 0.000, acc: 0.756
+2025-08-25 21:58:54: Epoch: 007 Train Loss: 0.415, ap: 0.571, auc: 0.672, f1: 0.588, acc: 0.524
+2025-08-25 21:58:55: Epoch: 007 Test results, ap: 0.386, auc: 0.554, f1: 0.289, acc: 0.439
+2025-08-25 21:59:01: Epoch: 008 Train Loss: 0.368, ap: 0.625, auc: 0.686, f1: 0.545, acc: 0.563
+2025-08-25 21:59:01: Epoch: 008 New best VALIDATION metrics
+2025-08-25 21:59:02: Epoch: 008 Test results, ap: 0.429, auc: 0.594, f1: 0.355, acc: 0.675
+2025-08-25 21:59:09: Epoch: 009 Train Loss: 0.364, ap: 0.498, auc: 0.599, f1: 0.472, acc: 0.544
+2025-08-25 21:59:10: Epoch: 009 Test results, ap: 0.347, auc: 0.515, f1: 0.316, acc: 0.789
+2025-08-25 21:59:16: Epoch: 010 Train Loss: 0.300, ap: 0.616, auc: 0.682, f1: 0.500, acc: 0.670
+2025-08-25 21:59:17: Epoch: 010 Test results, ap: 0.419, auc: 0.523, f1: 0.316, acc: 0.789
+2025-08-25 21:59:23: Epoch: 011 Train Loss: 0.231, ap: 0.525, auc: 0.631, f1: 0.514, acc: 0.485
+2025-08-25 21:59:24: Epoch: 011 Test results, ap: 0.333, auc: 0.505, f1: 0.330, acc: 0.439
+2025-08-25 21:59:24: Epoch: 011 New best TEST metrics
+2025-08-25 21:59:31: Epoch: 012 Train Loss: 0.189, ap: 0.604, auc: 0.718, f1: 0.407, acc: 0.689
+2025-08-25 21:59:31: Epoch: 012 New best VALIDATION metrics
+2025-08-25 21:59:32: Epoch: 012 Test results, ap: 0.339, auc: 0.501, f1: 0.000, acc: 0.756
+2025-08-25 21:59:32: Epoch: 012 New best TEST metrics
+2025-08-25 21:59:38: Epoch: 013 Train Loss: 0.254, ap: 0.531, auc: 0.688, f1: 0.487, acc: 0.612
+2025-08-25 21:59:39: Epoch: 013 Test results, ap: 0.358, auc: 0.470, f1: 0.323, acc: 0.659
+2025-08-25 21:59:45: Epoch: 014 Train Loss: 0.204, ap: 0.511, auc: 0.672, f1: 0.579, acc: 0.505
+2025-08-25 21:59:47: Epoch: 014 Test results, ap: 0.383, auc: 0.524, f1: 0.345, acc: 0.260
+2025-08-25 21:59:47: Epoch: 014 New best TEST metrics
+2025-08-25 21:59:53: Epoch: 015 Train Loss: 0.166, ap: 0.567, auc: 0.692, f1: 0.506, acc: 0.602
+2025-08-25 21:59:54: Epoch: 015 Test results, ap: 0.285, auc: 0.447, f1: 0.190, acc: 0.724
+2025-08-25 22:00:00: Epoch: 016 Train Loss: 0.130, ap: 0.598, auc: 0.687, f1: 0.557, acc: 0.660
+2025-08-25 22:00:01: Epoch: 016 Test results, ap: 0.369, auc: 0.467, f1: 0.300, acc: 0.772
+2025-08-25 22:00:07: Epoch: 017 Train Loss: 0.158, ap: 0.543, auc: 0.674, f1: 0.527, acc: 0.583
+2025-08-25 22:00:08: Epoch: 017 Test results, ap: 0.249, auc: 0.425, f1: 0.133, acc: 0.683
+2025-08-25 22:00:15: Epoch: 018 Train Loss: 0.167, ap: 0.571, auc: 0.685, f1: 0.471, acc: 0.650
+2025-08-25 22:00:16: Epoch: 018 Test results, ap: 0.386, auc: 0.573, f1: 0.125, acc: 0.772
+2025-08-25 22:00:16: Epoch: 018 New best TEST metrics
+2025-08-25 22:00:22: Epoch: 019 Train Loss: 0.132, ap: 0.583, auc: 0.701, f1: 0.562, acc: 0.621
+2025-08-25 22:00:23: Epoch: 019 Test results, ap: 0.346, auc: 0.523, f1: 0.267, acc: 0.732
+2025-08-25 22:00:30: Epoch: 020 Train Loss: 0.129, ap: 0.540, auc: 0.688, f1: 0.554, acc: 0.641
+2025-08-25 22:00:31: Epoch: 020 Test results, ap: 0.357, auc: 0.521, f1: 0.261, acc: 0.724
+2025-08-25 22:00:37: Epoch: 021 Train Loss: 0.099, ap: 0.575, auc: 0.695, f1: 0.487, acc: 0.612
+2025-08-25 22:00:38: Epoch: 021 Test results, ap: 0.426, auc: 0.572, f1: 0.121, acc: 0.764
+2025-08-25 22:00:38: Epoch: 021 New best TEST metrics
+2025-08-25 22:00:44: Epoch: 022 Train Loss: 0.117, ap: 0.590, auc: 0.701, f1: 0.568, acc: 0.660
+2025-08-25 22:00:45: Epoch: 022 Test results, ap: 0.429, auc: 0.579, f1: 0.121, acc: 0.764
+2025-08-25 22:00:45: Epoch: 022 New best TEST metrics
+2025-08-25 22:00:52: Epoch: 023 Train Loss: 0.103, ap: 0.574, auc: 0.696, f1: 0.568, acc: 0.660
+2025-08-25 22:00:53: Epoch: 023 Test results, ap: 0.427, auc: 0.587, f1: 0.375, acc: 0.756
+2025-08-25 22:00:53: Epoch: 023 New best TEST metrics
+2025-08-25 22:00:59: Epoch: 024 Train Loss: 0.086, ap: 0.551, auc: 0.686, f1: 0.500, acc: 0.631
+2025-08-25 22:01:00: Epoch: 024 Test results, ap: 0.454, auc: 0.592, f1: 0.369, acc: 0.667
+2025-08-25 22:01:00: Epoch: 024 New best TEST metrics
+2025-08-25 22:01:06: Epoch: 025 Train Loss: 0.092, ap: 0.530, auc: 0.682, f1: 0.500, acc: 0.573
+2025-08-25 22:01:07: Epoch: 025 Test results, ap: 0.334, auc: 0.496, f1: 0.295, acc: 0.496
+2025-08-25 22:01:13: Epoch: 026 Train Loss: 0.093, ap: 0.565, auc: 0.677, f1: 0.493, acc: 0.641
+2025-08-25 22:01:14: Epoch: 026 Test results, ap: 0.388, auc: 0.569, f1: 0.377, acc: 0.732
+2025-08-25 22:01:21: Epoch: 027 Train Loss: 0.071, ap: 0.552, auc: 0.666, f1: 0.512, acc: 0.612
+2025-08-25 22:01:22: Epoch: 027 Test results, ap: 0.377, auc: 0.559, f1: 0.333, acc: 0.675
+2025-08-25 22:01:28: Epoch: 028 Train Loss: 0.091, ap: 0.558, auc: 0.666, f1: 0.456, acc: 0.583
+2025-08-25 22:01:29: Epoch: 028 Test results, ap: 0.352, auc: 0.550, f1: 0.320, acc: 0.585
+2025-08-25 22:01:35: Epoch: 029 Train Loss: 0.082, ap: 0.548, auc: 0.670, f1: 0.453, acc: 0.602
+2025-08-25 22:01:37: Epoch: 029 Test results, ap: 0.448, auc: 0.596, f1: 0.341, acc: 0.528
+2025-08-25 22:01:43: Epoch: 030 Train Loss: 0.077, ap: 0.551, auc: 0.673, f1: 0.487, acc: 0.612
+2025-08-25 22:01:44: Epoch: 030 Test results, ap: 0.427, auc: 0.575, f1: 0.323, acc: 0.488
+2025-08-25 22:01:50: Epoch: 031 Train Loss: 0.092, ap: 0.517, auc: 0.645, f1: 0.476, acc: 0.573
+2025-08-25 22:01:51: Epoch: 031 Test results, ap: 0.328, auc: 0.515, f1: 0.394, acc: 0.650
+2025-08-25 22:01:57: Epoch: 032 Train Loss: 0.052, ap: 0.511, auc: 0.644, f1: 0.462, acc: 0.592
+2025-08-25 22:01:58: Epoch: 032 Test results, ap: 0.313, auc: 0.516, f1: 0.338, acc: 0.650
+2025-08-25 22:02:05: Epoch: 033 Train Loss: 0.066, ap: 0.515, auc: 0.648, f1: 0.519, acc: 0.621
+2025-08-25 22:02:06: Epoch: 033 Test results, ap: 0.295, auc: 0.508, f1: 0.364, acc: 0.602
+2025-08-25 22:02:12: Epoch: 034 Train Loss: 0.056, ap: 0.527, auc: 0.655, f1: 0.500, acc: 0.592
+2025-08-25 22:02:13: Epoch: 034 Test results, ap: 0.306, auc: 0.510, f1: 0.390, acc: 0.593
+2025-08-25 22:02:19: Epoch: 035 Train Loss: 0.051, ap: 0.526, auc: 0.656, f1: 0.525, acc: 0.631
+2025-08-25 22:02:21: Epoch: 035 Test results, ap: 0.323, auc: 0.525, f1: 0.320, acc: 0.585
+2025-08-25 22:02:27: Epoch: 036 Train Loss: 0.045, ap: 0.534, auc: 0.664, f1: 0.519, acc: 0.621
+2025-08-25 22:02:28: Epoch: 036 Test results, ap: 0.323, auc: 0.523, f1: 0.270, acc: 0.472
+2025-08-25 22:02:34: Epoch: 037 Train Loss: 0.052, ap: 0.548, auc: 0.657, f1: 0.506, acc: 0.621
+2025-08-25 22:02:35: Epoch: 037 Test results, ap: 0.343, auc: 0.544, f1: 0.324, acc: 0.593
+2025-08-25 22:02:41: Epoch: 038 Train Loss: 0.045, ap: 0.531, auc: 0.641, f1: 0.519, acc: 0.621
+2025-08-25 22:02:42: Epoch: 038 Test results, ap: 0.321, auc: 0.526, f1: 0.295, acc: 0.496
+2025-08-25 22:02:49: Epoch: 039 Train Loss: 0.045, ap: 0.529, auc: 0.645, f1: 0.487, acc: 0.612
+2025-08-25 22:02:50: Epoch: 039 Test results, ap: 0.324, auc: 0.529, f1: 0.276, acc: 0.488
+2025-08-25 22:02:56: Epoch: 040 Train Loss: 0.049, ap: 0.498, auc: 0.644, f1: 0.474, acc: 0.612
+2025-08-25 22:02:57: Epoch: 040 Test results, ap: 0.324, auc: 0.537, f1: 0.289, acc: 0.480
+2025-08-25 22:03:04: Epoch: 041 Train Loss: 0.043, ap: 0.499, auc: 0.639, f1: 0.506, acc: 0.621
+2025-08-25 22:03:05: Epoch: 041 Test results, ap: 0.308, auc: 0.530, f1: 0.337, acc: 0.488
+2025-08-25 22:03:11: Epoch: 042 Train Loss: 0.033, ap: 0.509, auc: 0.640, f1: 0.519, acc: 0.621
+2025-08-25 22:03:12: Epoch: 042 Test results, ap: 0.308, auc: 0.527, f1: 0.320, acc: 0.447
+2025-08-25 22:03:18: Epoch: 043 Train Loss: 0.047, ap: 0.504, auc: 0.634, f1: 0.500, acc: 0.612
+2025-08-25 22:03:19: Epoch: 043 Test results, ap: 0.332, auc: 0.530, f1: 0.312, acc: 0.463
+2025-08-25 22:03:25: Epoch: 044 Train Loss: 0.036, ap: 0.510, auc: 0.645, f1: 0.519, acc: 0.621
+2025-08-25 22:03:27: Epoch: 044 Test results, ap: 0.319, auc: 0.528, f1: 0.306, acc: 0.447
+2025-08-25 22:03:33: Epoch: 045 Train Loss: 0.030, ap: 0.516, auc: 0.642, f1: 0.525, acc: 0.631
+2025-08-25 22:03:34: Epoch: 045 Test results, ap: 0.331, auc: 0.543, f1: 0.283, acc: 0.463
+2025-08-25 22:03:40: Epoch: 046 Train Loss: 0.033, ap: 0.511, auc: 0.644, f1: 0.519, acc: 0.621
+2025-08-25 22:03:41: Epoch: 046 Test results, ap: 0.344, auc: 0.540, f1: 0.283, acc: 0.463
+2025-08-25 22:03:48: Epoch: 047 Train Loss: 0.030, ap: 0.514, auc: 0.654, f1: 0.512, acc: 0.612
+2025-08-25 22:03:49: Epoch: 047 Test results, ap: 0.296, auc: 0.514, f1: 0.300, acc: 0.431
+2025-08-25 22:03:55: Epoch: 048 Train Loss: 0.018, ap: 0.515, auc: 0.656, f1: 0.512, acc: 0.612
+2025-08-25 22:03:56: Epoch: 048 Test results, ap: 0.321, auc: 0.518, f1: 0.271, acc: 0.431
+2025-08-25 22:04:02: Epoch: 049 Train Loss: 0.035, ap: 0.520, auc: 0.650, f1: 0.487, acc: 0.612
+2025-08-25 22:04:04: Epoch: 049 Test results, ap: 0.335, auc: 0.533, f1: 0.295, acc: 0.496
+2025-08-25 22:04:10: Epoch: 050 Train Loss: 0.042, ap: 0.514, auc: 0.634, f1: 0.507, acc: 0.641
+2025-08-25 22:04:11: Epoch: 050 Test results, ap: 0.351, auc: 0.533, f1: 0.289, acc: 0.480
+2025-08-25 22:04:11: used time 0.10h
+2025-08-25 22:04:11: Running Cross Validation 2
+2025-08-25 22:04:11: Fold 2  Train set:412, Valid set:103, Test set: 123
+2025-08-25 22:04:17: Epoch: 001 Train Loss: 0.686, ap: 0.480, auc: 0.428, f1: 0.000, acc: 0.495
+2025-08-25 22:04:17: Epoch: 001 New best VALIDATION metrics
+2025-08-25 22:04:19: Epoch: 001 Test results, ap: 0.282, auc: 0.476, f1: 0.000, acc: 0.756
+2025-08-25 22:04:25: Epoch: 002 Train Loss: 0.655, ap: 0.616, auc: 0.648, f1: 0.635, acc: 0.553
+2025-08-25 22:04:25: Epoch: 002 New best VALIDATION metrics
+2025-08-25 22:04:26: Epoch: 002 Test results, ap: 0.190, auc: 0.358, f1: 0.000, acc: 0.724
+2025-08-25 22:04:32: Epoch: 003 Train Loss: 0.612, ap: 0.631, auc: 0.659, f1: 0.000, acc: 0.495
+2025-08-25 22:04:32: Epoch: 003 New best VALIDATION metrics
+2025-08-25 22:04:34: Epoch: 003 Test results, ap: 0.182, auc: 0.337, f1: 0.000, acc: 0.756
+2025-08-25 22:04:40: Epoch: 004 Train Loss: 0.577, ap: 0.724, auc: 0.709, f1: 0.675, acc: 0.524
+2025-08-25 22:04:40: Epoch: 004 New best VALIDATION metrics
+2025-08-25 22:04:41: Epoch: 004 Test results, ap: 0.238, auc: 0.471, f1: 0.392, acc: 0.244
+2025-08-25 22:04:47: Epoch: 005 Train Loss: 0.546, ap: 0.596, auc: 0.610, f1: 0.000, acc: 0.495
+2025-08-25 22:04:48: Epoch: 005 Test results, ap: 0.212, auc: 0.435, f1: 0.000, acc: 0.756
+2025-08-25 22:04:54: Epoch: 006 Train Loss: 0.508, ap: 0.729, auc: 0.721, f1: 0.687, acc: 0.602
+2025-08-25 22:04:54: Epoch: 006 New best VALIDATION metrics
+2025-08-25 22:04:55: Epoch: 006 Test results, ap: 0.193, auc: 0.379, f1: 0.341, acc: 0.309
+2025-08-25 22:05:02: Epoch: 007 Train Loss: 0.494, ap: 0.782, auc: 0.726, f1: 0.600, acc: 0.689
+2025-08-25 22:05:02: Epoch: 007 New best VALIDATION metrics
+2025-08-25 22:05:03: Epoch: 007 Test results, ap: 0.188, auc: 0.364, f1: 0.000, acc: 0.756
+2025-08-25 22:05:09: Epoch: 008 Train Loss: 0.464, ap: 0.655, auc: 0.642, f1: 0.512, acc: 0.592
+2025-08-25 22:05:10: Epoch: 008 Test results, ap: 0.196, auc: 0.391, f1: 0.000, acc: 0.756
+2025-08-25 22:05:16: Epoch: 009 Train Loss: 0.423, ap: 0.792, auc: 0.749, f1: 0.684, acc: 0.534
+2025-08-25 22:05:16: Epoch: 009 New best VALIDATION metrics
+2025-08-25 22:05:17: Epoch: 009 Test results, ap: 0.169, auc: 0.277, f1: 0.385, acc: 0.325
+2025-08-25 22:05:24: Epoch: 010 Train Loss: 0.434, ap: 0.765, auc: 0.692, f1: 0.349, acc: 0.602
+2025-08-25 22:05:25: Epoch: 010 Test results, ap: 0.194, auc: 0.385, f1: 0.000, acc: 0.756
+2025-08-25 22:05:31: Epoch: 011 Train Loss: 0.372, ap: 0.821, auc: 0.774, f1: 0.714, acc: 0.728
+2025-08-25 22:05:31: Epoch: 011 New best VALIDATION metrics
+2025-08-25 22:05:32: Epoch: 011 Test results, ap: 0.167, auc: 0.256, f1: 0.000, acc: 0.756
+2025-08-25 22:05:32: Epoch: 011 New best TEST metrics
+2025-08-25 22:05:38: Epoch: 012 Train Loss: 0.323, ap: 0.838, auc: 0.799, f1: 0.705, acc: 0.748
+2025-08-25 22:05:38: Epoch: 012 New best VALIDATION metrics
+2025-08-25 22:05:39: Epoch: 012 Test results, ap: 0.167, auc: 0.265, f1: 0.000, acc: 0.756
+2025-08-25 22:05:39: Epoch: 012 New best TEST metrics
+2025-08-25 22:05:46: Epoch: 013 Train Loss: 0.343, ap: 0.802, auc: 0.782, f1: 0.667, acc: 0.718
+2025-08-25 22:05:47: Epoch: 013 Test results, ap: 0.175, auc: 0.303, f1: 0.000, acc: 0.732
+2025-08-25 22:05:47: Epoch: 013 New best TEST metrics
+2025-08-25 22:05:53: Epoch: 014 Train Loss: 0.309, ap: 0.839, auc: 0.805, f1: 0.711, acc: 0.748
+2025-08-25 22:05:53: Epoch: 014 New best VALIDATION metrics
+2025-08-25 22:05:54: Epoch: 014 Test results, ap: 0.169, auc: 0.266, f1: 0.000, acc: 0.756
+2025-08-25 22:06:01: Epoch: 015 Train Loss: 0.279, ap: 0.798, auc: 0.795, f1: 0.746, acc: 0.709
+2025-08-25 22:06:02: Epoch: 015 Test results, ap: 0.170, auc: 0.278, f1: 0.000, acc: 0.667
+2025-08-25 22:06:08: Epoch: 016 Train Loss: 0.235, ap: 0.798, auc: 0.775, f1: 0.699, acc: 0.699
+2025-08-25 22:06:09: Epoch: 016 Test results, ap: 0.169, auc: 0.271, f1: 0.000, acc: 0.740
+2025-08-25 22:06:15: Epoch: 017 Train Loss: 0.231, ap: 0.813, auc: 0.779, f1: 0.717, acc: 0.670
+2025-08-25 22:06:17: Epoch: 017 Test results, ap: 0.182, auc: 0.332, f1: 0.000, acc: 0.512
+2025-08-25 22:06:17: Epoch: 017 New best TEST metrics
+2025-08-25 22:06:23: Epoch: 018 Train Loss: 0.244, ap: 0.858, auc: 0.808, f1: 0.765, acc: 0.767
+2025-08-25 22:06:23: Epoch: 018 New best VALIDATION metrics
+2025-08-25 22:06:24: Epoch: 018 Test results, ap: 0.177, auc: 0.312, f1: 0.000, acc: 0.740
+2025-08-25 22:06:30: Epoch: 019 Train Loss: 0.232, ap: 0.771, auc: 0.745, f1: 0.667, acc: 0.631
+2025-08-25 22:06:31: Epoch: 019 Test results, ap: 0.328, auc: 0.569, f1: 0.375, acc: 0.675
+2025-08-25 22:06:31: Epoch: 019 New best TEST metrics
+2025-08-25 22:06:37: Epoch: 020 Train Loss: 0.215, ap: 0.798, auc: 0.768, f1: 0.667, acc: 0.650
+2025-08-25 22:06:38: Epoch: 020 Test results, ap: 0.183, auc: 0.335, f1: 0.000, acc: 0.756
+2025-08-25 22:06:45: Epoch: 021 Train Loss: 0.224, ap: 0.815, auc: 0.775, f1: 0.674, acc: 0.728
+2025-08-25 22:06:46: Epoch: 021 Test results, ap: 0.178, auc: 0.319, f1: 0.000, acc: 0.756
+2025-08-25 22:06:52: Epoch: 022 Train Loss: 0.171, ap: 0.837, auc: 0.802, f1: 0.690, acc: 0.738
+2025-08-25 22:06:53: Epoch: 022 Test results, ap: 0.174, auc: 0.299, f1: 0.000, acc: 0.756
+2025-08-25 22:06:59: Epoch: 023 Train Loss: 0.154, ap: 0.835, auc: 0.798, f1: 0.792, acc: 0.786
+2025-08-25 22:07:00: Epoch: 023 Test results, ap: 0.179, auc: 0.319, f1: 0.000, acc: 0.756
+2025-08-25 22:07:06: Epoch: 024 Train Loss: 0.169, ap: 0.815, auc: 0.783, f1: 0.781, acc: 0.777
+2025-08-25 22:07:08: Epoch: 024 Test results, ap: 0.181, auc: 0.328, f1: 0.000, acc: 0.756
+2025-08-25 22:07:14: Epoch: 025 Train Loss: 0.161, ap: 0.804, auc: 0.773, f1: 0.705, acc: 0.699
+2025-08-25 22:07:15: Epoch: 025 Test results, ap: 0.188, auc: 0.355, f1: 0.000, acc: 0.520
+2025-08-25 22:07:21: Epoch: 026 Train Loss: 0.162, ap: 0.776, auc: 0.739, f1: 0.653, acc: 0.670
+2025-08-25 22:07:22: Epoch: 026 Test results, ap: 0.185, auc: 0.344, f1: 0.000, acc: 0.740
+2025-08-25 22:07:29: Epoch: 027 Train Loss: 0.136, ap: 0.809, auc: 0.775, f1: 0.760, acc: 0.767
+2025-08-25 22:07:30: Epoch: 027 Test results, ap: 0.181, auc: 0.329, f1: 0.000, acc: 0.740
+2025-08-25 22:07:36: Epoch: 028 Train Loss: 0.124, ap: 0.812, auc: 0.784, f1: 0.782, acc: 0.767
+2025-08-25 22:07:37: Epoch: 028 Test results, ap: 0.175, auc: 0.303, f1: 0.000, acc: 0.740
+2025-08-25 22:07:43: Epoch: 029 Train Loss: 0.105, ap: 0.827, auc: 0.790, f1: 0.752, acc: 0.757
+2025-08-25 22:07:44: Epoch: 029 Test results, ap: 0.177, auc: 0.311, f1: 0.000, acc: 0.756
+2025-08-25 22:07:51: Epoch: 030 Train Loss: 0.109, ap: 0.819, auc: 0.776, f1: 0.733, acc: 0.738
+2025-08-25 22:07:52: Epoch: 030 Test results, ap: 0.184, auc: 0.342, f1: 0.000, acc: 0.748
+2025-08-25 22:07:58: Epoch: 031 Train Loss: 0.120, ap: 0.815, auc: 0.774, f1: 0.739, acc: 0.718
+2025-08-25 22:07:59: Epoch: 031 Test results, ap: 0.176, auc: 0.307, f1: 0.000, acc: 0.732
+2025-08-25 22:08:05: Epoch: 032 Train Loss: 0.084, ap: 0.811, auc: 0.766, f1: 0.740, acc: 0.748
+2025-08-25 22:08:07: Epoch: 032 Test results, ap: 0.182, auc: 0.330, f1: 0.000, acc: 0.675
+2025-08-25 22:08:13: Epoch: 033 Train Loss: 0.084, ap: 0.820, auc: 0.764, f1: 0.731, acc: 0.757
+2025-08-25 22:08:14: Epoch: 033 Test results, ap: 0.184, auc: 0.341, f1: 0.000, acc: 0.756
+2025-08-25 22:08:20: Epoch: 034 Train Loss: 0.074, ap: 0.805, auc: 0.771, f1: 0.722, acc: 0.738
+2025-08-25 22:08:22: Epoch: 034 Test results, ap: 0.184, auc: 0.339, f1: 0.000, acc: 0.756
+2025-08-25 22:08:28: Epoch: 035 Train Loss: 0.075, ap: 0.814, auc: 0.770, f1: 0.723, acc: 0.748
+2025-08-25 22:08:29: Epoch: 035 Test results, ap: 0.184, auc: 0.339, f1: 0.000, acc: 0.756
+2025-08-25 22:08:35: Epoch: 036 Train Loss: 0.088, ap: 0.815, auc: 0.787, f1: 0.747, acc: 0.757
+2025-08-25 22:08:36: Epoch: 036 Test results, ap: 0.182, auc: 0.334, f1: 0.000, acc: 0.756
+2025-08-25 22:08:43: Epoch: 037 Train Loss: 0.078, ap: 0.816, auc: 0.773, f1: 0.716, acc: 0.738
+2025-08-25 22:08:44: Epoch: 037 Test results, ap: 0.180, auc: 0.325, f1: 0.000, acc: 0.756
+2025-08-25 22:08:50: Epoch: 038 Train Loss: 0.082, ap: 0.803, auc: 0.756, f1: 0.700, acc: 0.709
+2025-08-25 22:08:51: Epoch: 038 Test results, ap: 0.188, auc: 0.349, f1: 0.000, acc: 0.569
+2025-08-25 22:08:57: Epoch: 039 Train Loss: 0.074, ap: 0.794, auc: 0.748, f1: 0.701, acc: 0.718
+2025-08-25 22:08:58: Epoch: 039 Test results, ap: 0.188, auc: 0.351, f1: 0.000, acc: 0.650
+2025-08-25 22:09:05: Epoch: 040 Train Loss: 0.058, ap: 0.809, auc: 0.762, f1: 0.752, acc: 0.757
+2025-08-25 22:09:06: Epoch: 040 Test results, ap: 0.182, auc: 0.332, f1: 0.000, acc: 0.756
+2025-08-25 22:09:12: Epoch: 041 Train Loss: 0.065, ap: 0.805, auc: 0.757, f1: 0.752, acc: 0.757
+2025-08-25 22:09:13: Epoch: 041 Test results, ap: 0.182, auc: 0.332, f1: 0.000, acc: 0.715
+2025-08-25 22:09:19: Epoch: 042 Train Loss: 0.049, ap: 0.799, auc: 0.750, f1: 0.740, acc: 0.748
+2025-08-25 22:09:21: Epoch: 042 Test results, ap: 0.184, auc: 0.338, f1: 0.000, acc: 0.756
+2025-08-25 22:09:27: Epoch: 043 Train Loss: 0.060, ap: 0.806, auc: 0.758, f1: 0.714, acc: 0.728
+2025-08-25 22:09:28: Epoch: 043 Test results, ap: 0.185, auc: 0.342, f1: 0.000, acc: 0.756
+2025-08-25 22:09:34: Epoch: 044 Train Loss: 0.053, ap: 0.805, auc: 0.759, f1: 0.740, acc: 0.748
+2025-08-25 22:09:35: Epoch: 044 Test results, ap: 0.186, auc: 0.344, f1: 0.000, acc: 0.756
+2025-08-25 22:09:41: Epoch: 045 Train Loss: 0.053, ap: 0.805, auc: 0.763, f1: 0.752, acc: 0.757
+2025-08-25 22:09:42: Epoch: 045 Test results, ap: 0.187, auc: 0.346, f1: 0.000, acc: 0.707
+2025-08-25 22:09:49: Epoch: 046 Train Loss: 0.055, ap: 0.806, auc: 0.768, f1: 0.768, acc: 0.777
+2025-08-25 22:09:50: Epoch: 046 Test results, ap: 0.184, auc: 0.336, f1: 0.000, acc: 0.756
+2025-08-25 22:09:56: Epoch: 047 Train Loss: 0.059, ap: 0.799, auc: 0.758, f1: 0.722, acc: 0.738
+2025-08-25 22:09:57: Epoch: 047 Test results, ap: 0.184, auc: 0.337, f1: 0.000, acc: 0.756
+2025-08-25 22:10:04: Epoch: 048 Train Loss: 0.062, ap: 0.809, auc: 0.763, f1: 0.755, acc: 0.767
+2025-08-25 22:10:05: Epoch: 048 Test results, ap: 0.183, auc: 0.333, f1: 0.000, acc: 0.756
+2025-08-25 22:10:11: Epoch: 049 Train Loss: 0.068, ap: 0.807, auc: 0.752, f1: 0.750, acc: 0.767
+2025-08-25 22:10:12: Epoch: 049 Test results, ap: 0.184, auc: 0.338, f1: 0.000, acc: 0.756
+2025-08-25 22:10:18: Epoch: 050 Train Loss: 0.057, ap: 0.805, auc: 0.756, f1: 0.729, acc: 0.748
+2025-08-25 22:10:19: Epoch: 050 Test results, ap: 0.181, auc: 0.327, f1: 0.000, acc: 0.756
+2025-08-25 22:10:19: used time 0.10h
+2025-08-25 22:10:19: Running Cross Validation 3
+2025-08-25 22:10:19: Fold 3  Train set:412, Valid set:103, Test set: 123
+2025-08-25 22:10:26: Epoch: 001 Train Loss: 0.688, ap: 0.420, auc: 0.529, f1: 0.579, acc: 0.408
+2025-08-25 22:10:26: Epoch: 001 New best VALIDATION metrics
+2025-08-25 22:10:27: Epoch: 001 Test results, ap: 0.369, auc: 0.622, f1: 0.392, acc: 0.244
+2025-08-25 22:10:33: Epoch: 002 Train Loss: 0.624, ap: 0.735, auc: 0.776, f1: 0.693, acc: 0.699
+2025-08-25 22:10:33: Epoch: 002 New best VALIDATION metrics
+2025-08-25 22:10:35: Epoch: 002 Test results, ap: 0.239, auc: 0.482, f1: 0.000, acc: 0.724
+2025-08-25 22:10:41: Epoch: 003 Train Loss: 0.562, ap: 0.574, auc: 0.681, f1: 0.393, acc: 0.670
+2025-08-25 22:10:42: Epoch: 003 Test results, ap: 0.247, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-25 22:10:48: Epoch: 004 Train Loss: 0.600, ap: 0.673, auc: 0.710, f1: 0.635, acc: 0.699
+2025-08-25 22:10:49: Epoch: 004 Test results, ap: 0.241, auc: 0.500, f1: 0.000, acc: 0.724
+2025-08-25 22:10:56: Epoch: 005 Train Loss: 0.540, ap: 0.621, auc: 0.654, f1: 0.587, acc: 0.427
+2025-08-25 22:10:57: Epoch: 005 Test results, ap: 0.183, auc: 0.346, f1: 0.392, acc: 0.244
+2025-08-25 22:11:03: Epoch: 006 Train Loss: 0.492, ap: 0.694, auc: 0.755, f1: 0.646, acc: 0.660
+2025-08-25 22:11:04: Epoch: 006 Test results, ap: 0.235, auc: 0.491, f1: 0.000, acc: 0.724
+2025-08-25 22:11:10: Epoch: 007 Train Loss: 0.463, ap: 0.766, auc: 0.793, f1: 0.645, acc: 0.583
+2025-08-25 22:11:10: Epoch: 007 New best VALIDATION metrics
+2025-08-25 22:11:11: Epoch: 007 Test results, ap: 0.315, auc: 0.527, f1: 0.317, acc: 0.650
+2025-08-25 22:11:18: Epoch: 008 Train Loss: 0.426, ap: 0.732, auc: 0.786, f1: 0.523, acc: 0.699
+2025-08-25 22:11:19: Epoch: 008 Test results, ap: 0.241, auc: 0.429, f1: 0.000, acc: 0.756
+2025-08-25 22:11:25: Epoch: 009 Train Loss: 0.375, ap: 0.676, auc: 0.779, f1: 0.691, acc: 0.757
+2025-08-25 22:11:26: Epoch: 009 Test results, ap: 0.339, auc: 0.571, f1: 0.000, acc: 0.756
+2025-08-25 22:11:32: Epoch: 010 Train Loss: 0.372, ap: 0.766, auc: 0.819, f1: 0.588, acc: 0.728
+2025-08-25 22:11:32: Epoch: 010 New best VALIDATION metrics
+2025-08-25 22:11:33: Epoch: 010 Test results, ap: 0.194, auc: 0.352, f1: 0.000, acc: 0.756
+2025-08-25 22:11:40: Epoch: 011 Train Loss: 0.325, ap: 0.787, auc: 0.823, f1: 0.716, acc: 0.738
+2025-08-25 22:11:40: Epoch: 011 New best VALIDATION metrics
+2025-08-25 22:11:41: Epoch: 011 Test results, ap: 0.223, auc: 0.405, f1: 0.000, acc: 0.732
+2025-08-25 22:11:41: Epoch: 011 New best TEST metrics
+2025-08-25 22:11:47: Epoch: 012 Train Loss: 0.282, ap: 0.788, auc: 0.827, f1: 0.742, acc: 0.757
+2025-08-25 22:11:47: Epoch: 012 New best VALIDATION metrics
+2025-08-25 22:11:49: Epoch: 012 Test results, ap: 0.233, auc: 0.465, f1: 0.000, acc: 0.748
+2025-08-25 22:11:49: Epoch: 012 New best TEST metrics
+2025-08-25 22:11:55: Epoch: 013 Train Loss: 0.269, ap: 0.816, auc: 0.842, f1: 0.761, acc: 0.786
+2025-08-25 22:11:55: Epoch: 013 New best VALIDATION metrics
+2025-08-25 22:11:56: Epoch: 013 Test results, ap: 0.260, auc: 0.482, f1: 0.000, acc: 0.748
+2025-08-25 22:11:56: Epoch: 013 New best TEST metrics
+2025-08-25 22:12:02: Epoch: 014 Train Loss: 0.240, ap: 0.816, auc: 0.827, f1: 0.713, acc: 0.757
+2025-08-25 22:12:03: Epoch: 014 Test results, ap: 0.223, auc: 0.418, f1: 0.000, acc: 0.748
+2025-08-25 22:12:10: Epoch: 015 Train Loss: 0.234, ap: 0.791, auc: 0.824, f1: 0.719, acc: 0.757
+2025-08-25 22:12:11: Epoch: 015 Test results, ap: 0.226, auc: 0.402, f1: 0.000, acc: 0.740
+2025-08-25 22:12:17: Epoch: 016 Train Loss: 0.243, ap: 0.784, auc: 0.819, f1: 0.716, acc: 0.738
+2025-08-25 22:12:18: Epoch: 016 Test results, ap: 0.252, auc: 0.448, f1: 0.000, acc: 0.740
+2025-08-25 22:12:24: Epoch: 017 Train Loss: 0.198, ap: 0.805, auc: 0.828, f1: 0.718, acc: 0.786
+2025-08-25 22:12:26: Epoch: 017 Test results, ap: 0.234, auc: 0.421, f1: 0.000, acc: 0.756
+2025-08-25 22:12:32: Epoch: 018 Train Loss: 0.205, ap: 0.791, auc: 0.814, f1: 0.694, acc: 0.709
+2025-08-25 22:12:33: Epoch: 018 Test results, ap: 0.323, auc: 0.467, f1: 0.176, acc: 0.772
+2025-08-25 22:12:33: Epoch: 018 New best TEST metrics
+2025-08-25 22:12:39: Epoch: 019 Train Loss: 0.185, ap: 0.733, auc: 0.815, f1: 0.734, acc: 0.796
+2025-08-25 22:12:40: Epoch: 019 Test results, ap: 0.225, auc: 0.436, f1: 0.000, acc: 0.748
+2025-08-25 22:12:46: Epoch: 020 Train Loss: 0.165, ap: 0.749, auc: 0.774, f1: 0.646, acc: 0.670
+2025-08-25 22:12:47: Epoch: 020 Test results, ap: 0.351, auc: 0.458, f1: 0.125, acc: 0.772
+2025-08-25 22:12:47: Epoch: 020 New best TEST metrics
+2025-08-25 22:12:54: Epoch: 021 Train Loss: 0.186, ap: 0.819, auc: 0.833, f1: 0.701, acc: 0.777
+2025-08-25 22:12:55: Epoch: 021 Test results, ap: 0.256, auc: 0.444, f1: 0.000, acc: 0.756
+2025-08-25 22:13:01: Epoch: 022 Train Loss: 0.160, ap: 0.773, auc: 0.819, f1: 0.713, acc: 0.757
+2025-08-25 22:13:02: Epoch: 022 Test results, ap: 0.285, auc: 0.457, f1: 0.121, acc: 0.764
+2025-08-25 22:13:09: Epoch: 023 Train Loss: 0.133, ap: 0.780, auc: 0.823, f1: 0.713, acc: 0.757
+2025-08-25 22:13:10: Epoch: 023 Test results, ap: 0.254, auc: 0.445, f1: 0.121, acc: 0.764
+2025-08-25 22:13:16: Epoch: 024 Train Loss: 0.145, ap: 0.721, auc: 0.802, f1: 0.649, acc: 0.738
+2025-08-25 22:13:17: Epoch: 024 Test results, ap: 0.231, auc: 0.434, f1: 0.000, acc: 0.748
+2025-08-25 22:13:23: Epoch: 025 Train Loss: 0.147, ap: 0.765, auc: 0.816, f1: 0.733, acc: 0.767
+2025-08-25 22:13:24: Epoch: 025 Test results, ap: 0.256, auc: 0.454, f1: 0.118, acc: 0.756
+2025-08-25 22:13:31: Epoch: 026 Train Loss: 0.140, ap: 0.773, auc: 0.816, f1: 0.733, acc: 0.767
+2025-08-25 22:13:32: Epoch: 026 Test results, ap: 0.253, auc: 0.444, f1: 0.111, acc: 0.740
+2025-08-25 22:13:38: Epoch: 027 Train Loss: 0.115, ap: 0.763, auc: 0.803, f1: 0.713, acc: 0.757
+2025-08-25 22:13:39: Epoch: 027 Test results, ap: 0.290, auc: 0.445, f1: 0.121, acc: 0.764
+2025-08-25 22:13:46: Epoch: 028 Train Loss: 0.114, ap: 0.762, auc: 0.813, f1: 0.731, acc: 0.757
+2025-08-25 22:13:47: Epoch: 028 Test results, ap: 0.247, auc: 0.438, f1: 0.114, acc: 0.748
+2025-08-25 22:13:53: Epoch: 029 Train Loss: 0.151, ap: 0.780, auc: 0.820, f1: 0.727, acc: 0.796
+2025-08-25 22:13:54: Epoch: 029 Test results, ap: 0.239, auc: 0.409, f1: 0.000, acc: 0.756
+2025-08-25 22:14:00: Epoch: 030 Train Loss: 0.121, ap: 0.772, auc: 0.808, f1: 0.692, acc: 0.767
+2025-08-25 22:14:01: Epoch: 030 Test results, ap: 0.301, auc: 0.453, f1: 0.000, acc: 0.756
+2025-08-25 22:14:08: Epoch: 031 Train Loss: 0.127, ap: 0.765, auc: 0.809, f1: 0.682, acc: 0.728
+2025-08-25 22:14:09: Epoch: 031 Test results, ap: 0.308, auc: 0.478, f1: 0.118, acc: 0.756
+2025-08-25 22:14:15: Epoch: 032 Train Loss: 0.109, ap: 0.752, auc: 0.799, f1: 0.703, acc: 0.738
+2025-08-25 22:14:17: Epoch: 032 Test results, ap: 0.295, auc: 0.453, f1: 0.111, acc: 0.740
+2025-08-25 22:14:23: Epoch: 033 Train Loss: 0.102, ap: 0.756, auc: 0.801, f1: 0.703, acc: 0.738
+2025-08-25 22:14:24: Epoch: 033 Test results, ap: 0.315, auc: 0.482, f1: 0.162, acc: 0.748
+2025-08-25 22:14:30: Epoch: 034 Train Loss: 0.092, ap: 0.776, auc: 0.816, f1: 0.714, acc: 0.767
+2025-08-25 22:14:31: Epoch: 034 Test results, ap: 0.260, auc: 0.451, f1: 0.118, acc: 0.756
+2025-08-25 22:14:37: Epoch: 035 Train Loss: 0.082, ap: 0.768, auc: 0.813, f1: 0.716, acc: 0.777
+2025-08-25 22:14:38: Epoch: 035 Test results, ap: 0.239, auc: 0.433, f1: 0.000, acc: 0.748
+2025-08-25 22:14:45: Epoch: 036 Train Loss: 0.087, ap: 0.759, auc: 0.808, f1: 0.706, acc: 0.757
+2025-08-25 22:14:46: Epoch: 036 Test results, ap: 0.283, auc: 0.437, f1: 0.118, acc: 0.756
+2025-08-25 22:14:52: Epoch: 037 Train Loss: 0.077, ap: 0.739, auc: 0.790, f1: 0.738, acc: 0.786
+2025-08-25 22:14:53: Epoch: 037 Test results, ap: 0.302, auc: 0.469, f1: 0.121, acc: 0.764
+2025-08-25 22:14:59: Epoch: 038 Train Loss: 0.080, ap: 0.741, auc: 0.790, f1: 0.705, acc: 0.748
+2025-08-25 22:15:01: Epoch: 038 Test results, ap: 0.273, auc: 0.458, f1: 0.114, acc: 0.748
+2025-08-25 22:15:07: Epoch: 039 Train Loss: 0.074, ap: 0.761, auc: 0.802, f1: 0.719, acc: 0.757
+2025-08-25 22:15:08: Epoch: 039 Test results, ap: 0.295, auc: 0.475, f1: 0.118, acc: 0.756
+2025-08-25 22:15:14: Epoch: 040 Train Loss: 0.077, ap: 0.718, auc: 0.796, f1: 0.744, acc: 0.786
+2025-08-25 22:15:15: Epoch: 040 Test results, ap: 0.337, auc: 0.490, f1: 0.121, acc: 0.764
+2025-08-25 22:15:15: Epoch: 040 New best TEST metrics
+2025-08-25 22:15:22: Epoch: 041 Train Loss: 0.095, ap: 0.727, auc: 0.791, f1: 0.714, acc: 0.767
+2025-08-25 22:15:23: Epoch: 041 Test results, ap: 0.328, auc: 0.502, f1: 0.118, acc: 0.756
+2025-08-25 22:15:23: Epoch: 041 New best TEST metrics
+2025-08-25 22:15:29: Epoch: 042 Train Loss: 0.060, ap: 0.760, auc: 0.786, f1: 0.703, acc: 0.738
+2025-08-25 22:15:31: Epoch: 042 Test results, ap: 0.322, auc: 0.509, f1: 0.114, acc: 0.748
+2025-08-25 22:15:31: Epoch: 042 New best TEST metrics
+2025-08-25 22:15:37: Epoch: 043 Train Loss: 0.061, ap: 0.765, auc: 0.792, f1: 0.659, acc: 0.728
+2025-08-25 22:15:38: Epoch: 043 Test results, ap: 0.322, auc: 0.514, f1: 0.118, acc: 0.756
+2025-08-25 22:15:38: Epoch: 043 New best TEST metrics
+2025-08-25 22:15:44: Epoch: 044 Train Loss: 0.073, ap: 0.737, auc: 0.798, f1: 0.721, acc: 0.767
+2025-08-25 22:15:46: Epoch: 044 Test results, ap: 0.325, auc: 0.502, f1: 0.114, acc: 0.748
+2025-08-25 22:15:52: Epoch: 045 Train Loss: 0.072, ap: 0.720, auc: 0.794, f1: 0.721, acc: 0.767
+2025-08-25 22:15:53: Epoch: 045 Test results, ap: 0.329, auc: 0.499, f1: 0.118, acc: 0.756
+2025-08-25 22:15:59: Epoch: 046 Train Loss: 0.051, ap: 0.705, auc: 0.783, f1: 0.705, acc: 0.748
+2025-08-25 22:16:00: Epoch: 046 Test results, ap: 0.334, auc: 0.509, f1: 0.162, acc: 0.748
+2025-08-25 22:16:00: Epoch: 046 New best TEST metrics
+2025-08-25 22:16:07: Epoch: 047 Train Loss: 0.049, ap: 0.709, auc: 0.782, f1: 0.699, acc: 0.757
+2025-08-25 22:16:08: Epoch: 047 Test results, ap: 0.341, auc: 0.519, f1: 0.167, acc: 0.756
+2025-08-25 22:16:08: Epoch: 047 New best TEST metrics
+2025-08-25 22:16:15: Epoch: 048 Train Loss: 0.048, ap: 0.721, auc: 0.787, f1: 0.699, acc: 0.757
+2025-08-25 22:16:16: Epoch: 048 Test results, ap: 0.334, auc: 0.514, f1: 0.167, acc: 0.756
+2025-08-25 22:16:22: Epoch: 049 Train Loss: 0.053, ap: 0.728, auc: 0.788, f1: 0.721, acc: 0.767
+2025-08-25 22:16:23: Epoch: 049 Test results, ap: 0.341, auc: 0.519, f1: 0.158, acc: 0.740
+2025-08-25 22:16:23: Epoch: 049 New best TEST metrics
+2025-08-25 22:16:29: Epoch: 050 Train Loss: 0.068, ap: 0.727, auc: 0.785, f1: 0.713, acc: 0.757
+2025-08-25 22:16:31: Epoch: 050 Test results, ap: 0.335, auc: 0.520, f1: 0.200, acc: 0.740
+2025-08-25 22:16:31: used time 0.10h
+2025-08-25 22:16:31: Running Cross Validation 4
+2025-08-25 22:16:31: Fold 4  Train set:412, Valid set:103, Test set: 123
+2025-08-25 22:16:37: Epoch: 001 Train Loss: 0.676, ap: 0.465, auc: 0.554, f1: 0.569, acc: 0.398
+2025-08-25 22:16:37: Epoch: 001 New best VALIDATION metrics
+2025-08-25 22:16:38: Epoch: 001 Test results, ap: 0.181, auc: 0.333, f1: 0.392, acc: 0.244
+2025-08-25 22:16:44: Epoch: 002 Train Loss: 0.628, ap: 0.453, auc: 0.486, f1: 0.569, acc: 0.398
+2025-08-25 22:16:45: Epoch: 002 Test results, ap: 0.332, auc: 0.691, f1: 0.392, acc: 0.244
+2025-08-25 22:16:52: Epoch: 003 Train Loss: 0.607, ap: 0.517, auc: 0.566, f1: 0.569, acc: 0.398
+2025-08-25 22:16:52: Epoch: 003 New best VALIDATION metrics
+2025-08-25 22:16:53: Epoch: 003 Test results, ap: 0.366, auc: 0.562, f1: 0.392, acc: 0.244
+2025-08-25 22:16:59: Epoch: 004 Train Loss: 0.611, ap: 0.474, auc: 0.562, f1: 0.558, acc: 0.447
+2025-08-25 22:17:00: Epoch: 004 Test results, ap: 0.330, auc: 0.636, f1: 0.504, acc: 0.537
+2025-08-25 22:17:07: Epoch: 005 Train Loss: 0.570, ap: 0.548, auc: 0.653, f1: 0.566, acc: 0.524
+2025-08-25 22:17:07: Epoch: 005 New best VALIDATION metrics
+2025-08-25 22:17:08: Epoch: 005 Test results, ap: 0.262, auc: 0.586, f1: 0.471, acc: 0.488
+2025-08-25 22:17:14: Epoch: 006 Train Loss: 0.533, ap: 0.584, auc: 0.659, f1: 0.587, acc: 0.563
+2025-08-25 22:17:14: Epoch: 006 New best VALIDATION metrics
+2025-08-25 22:17:15: Epoch: 006 Test results, ap: 0.273, auc: 0.556, f1: 0.190, acc: 0.724
+2025-08-25 22:17:21: Epoch: 007 Train Loss: 0.486, ap: 0.406, auc: 0.476, f1: 0.495, acc: 0.466
+2025-08-25 22:17:23: Epoch: 007 Test results, ap: 0.391, auc: 0.616, f1: 0.382, acc: 0.317
+2025-08-25 22:17:29: Epoch: 008 Train Loss: 0.486, ap: 0.505, auc: 0.629, f1: 0.167, acc: 0.612
+2025-08-25 22:17:30: Epoch: 008 Test results, ap: 0.326, auc: 0.524, f1: 0.000, acc: 0.756
+2025-08-25 22:17:36: Epoch: 009 Train Loss: 0.450, ap: 0.583, auc: 0.644, f1: 0.506, acc: 0.602
+2025-08-25 22:17:37: Epoch: 009 Test results, ap: 0.232, auc: 0.360, f1: 0.167, acc: 0.756
+2025-08-25 22:17:43: Epoch: 010 Train Loss: 0.418, ap: 0.700, auc: 0.712, f1: 0.000, acc: 0.602
+2025-08-25 22:17:43: Epoch: 010 New best VALIDATION metrics
+2025-08-25 22:17:44: Epoch: 010 Test results, ap: 0.197, auc: 0.384, f1: 0.000, acc: 0.756
+2025-08-25 22:17:51: Epoch: 011 Train Loss: 0.351, ap: 0.606, auc: 0.657, f1: 0.275, acc: 0.641
+2025-08-25 22:17:52: Epoch: 011 Test results, ap: 0.257, auc: 0.413, f1: 0.000, acc: 0.756
+2025-08-25 22:17:52: Epoch: 011 New best TEST metrics
+2025-08-25 22:17:58: Epoch: 012 Train Loss: 0.334, ap: 0.659, auc: 0.727, f1: 0.567, acc: 0.718
+2025-08-25 22:17:59: Epoch: 012 Test results, ap: 0.227, auc: 0.349, f1: 0.000, acc: 0.740
+2025-08-25 22:18:06: Epoch: 013 Train Loss: 0.282, ap: 0.505, auc: 0.585, f1: 0.580, acc: 0.466
+2025-08-25 22:18:07: Epoch: 013 Test results, ap: 0.340, auc: 0.501, f1: 0.375, acc: 0.431
+2025-08-25 22:18:07: Epoch: 013 New best TEST metrics
+2025-08-25 22:18:13: Epoch: 014 Train Loss: 0.283, ap: 0.650, auc: 0.712, f1: 0.548, acc: 0.728
+2025-08-25 22:18:14: Epoch: 014 Test results, ap: 0.189, auc: 0.364, f1: 0.000, acc: 0.756
+2025-08-25 22:18:21: Epoch: 015 Train Loss: 0.308, ap: 0.585, auc: 0.701, f1: 0.438, acc: 0.650
+2025-08-25 22:18:22: Epoch: 015 Test results, ap: 0.186, auc: 0.330, f1: 0.000, acc: 0.748
+2025-08-25 22:18:28: Epoch: 016 Train Loss: 0.255, ap: 0.502, auc: 0.600, f1: 0.204, acc: 0.621
+2025-08-25 22:18:29: Epoch: 016 Test results, ap: 0.224, auc: 0.472, f1: 0.000, acc: 0.756
+2025-08-25 22:18:35: Epoch: 017 Train Loss: 0.254, ap: 0.581, auc: 0.681, f1: 0.562, acc: 0.621
+2025-08-25 22:18:36: Epoch: 017 Test results, ap: 0.225, auc: 0.354, f1: 0.000, acc: 0.740
+2025-08-25 22:18:43: Epoch: 018 Train Loss: 0.282, ap: 0.644, auc: 0.736, f1: 0.612, acc: 0.680
+2025-08-25 22:18:44: Epoch: 018 Test results, ap: 0.221, auc: 0.443, f1: 0.000, acc: 0.756
+2025-08-25 22:18:50: Epoch: 019 Train Loss: 0.232, ap: 0.556, auc: 0.681, f1: 0.444, acc: 0.660
+2025-08-25 22:18:51: Epoch: 019 Test results, ap: 0.220, auc: 0.356, f1: 0.000, acc: 0.740
+2025-08-25 22:18:58: Epoch: 020 Train Loss: 0.238, ap: 0.568, auc: 0.679, f1: 0.479, acc: 0.641
+2025-08-25 22:18:59: Epoch: 020 Test results, ap: 0.208, auc: 0.369, f1: 0.000, acc: 0.756
+2025-08-25 22:19:05: Epoch: 021 Train Loss: 0.205, ap: 0.567, auc: 0.680, f1: 0.563, acc: 0.699
+2025-08-25 22:19:06: Epoch: 021 Test results, ap: 0.210, auc: 0.369, f1: 0.000, acc: 0.748
+2025-08-25 22:19:12: Epoch: 022 Train Loss: 0.202, ap: 0.514, auc: 0.646, f1: 0.441, acc: 0.631
+2025-08-25 22:19:13: Epoch: 022 Test results, ap: 0.231, auc: 0.383, f1: 0.000, acc: 0.740
+2025-08-25 22:19:19: Epoch: 023 Train Loss: 0.188, ap: 0.558, auc: 0.641, f1: 0.514, acc: 0.670
+2025-08-25 22:19:20: Epoch: 023 Test results, ap: 0.188, auc: 0.364, f1: 0.000, acc: 0.756
+2025-08-25 22:19:27: Epoch: 024 Train Loss: 0.189, ap: 0.577, auc: 0.649, f1: 0.486, acc: 0.650
+2025-08-25 22:19:28: Epoch: 024 Test results, ap: 0.197, auc: 0.393, f1: 0.000, acc: 0.756
+2025-08-25 22:19:34: Epoch: 025 Train Loss: 0.179, ap: 0.571, auc: 0.700, f1: 0.590, acc: 0.689
+2025-08-25 22:19:35: Epoch: 025 Test results, ap: 0.188, auc: 0.344, f1: 0.000, acc: 0.732
+2025-08-25 22:19:42: Epoch: 026 Train Loss: 0.159, ap: 0.585, auc: 0.703, f1: 0.629, acc: 0.680
+2025-08-25 22:19:43: Epoch: 026 Test results, ap: 0.206, auc: 0.365, f1: 0.000, acc: 0.740
+2025-08-25 22:19:49: Epoch: 027 Train Loss: 0.136, ap: 0.550, auc: 0.656, f1: 0.500, acc: 0.631
+2025-08-25 22:19:50: Epoch: 027 Test results, ap: 0.217, auc: 0.396, f1: 0.000, acc: 0.756
+2025-08-25 22:19:56: Epoch: 028 Train Loss: 0.135, ap: 0.512, auc: 0.642, f1: 0.494, acc: 0.583
+2025-08-25 22:19:57: Epoch: 028 Test results, ap: 0.206, auc: 0.380, f1: 0.000, acc: 0.748
+2025-08-25 22:20:03: Epoch: 029 Train Loss: 0.150, ap: 0.588, auc: 0.697, f1: 0.533, acc: 0.660
+2025-08-25 22:20:05: Epoch: 029 Test results, ap: 0.192, auc: 0.380, f1: 0.000, acc: 0.740
+2025-08-25 22:20:11: Epoch: 030 Train Loss: 0.125, ap: 0.607, auc: 0.704, f1: 0.545, acc: 0.660
+2025-08-25 22:20:12: Epoch: 030 Test results, ap: 0.194, auc: 0.377, f1: 0.000, acc: 0.740
+2025-08-25 22:20:18: Epoch: 031 Train Loss: 0.113, ap: 0.539, auc: 0.677, f1: 0.558, acc: 0.631
+2025-08-25 22:20:19: Epoch: 031 Test results, ap: 0.207, auc: 0.431, f1: 0.000, acc: 0.691
+2025-08-25 22:20:26: Epoch: 032 Train Loss: 0.097, ap: 0.526, auc: 0.644, f1: 0.519, acc: 0.641
+2025-08-25 22:20:27: Epoch: 032 Test results, ap: 0.210, auc: 0.433, f1: 0.000, acc: 0.724
+2025-08-25 22:20:33: Epoch: 033 Train Loss: 0.093, ap: 0.537, auc: 0.627, f1: 0.462, acc: 0.592
+2025-08-25 22:20:34: Epoch: 033 Test results, ap: 0.211, auc: 0.430, f1: 0.000, acc: 0.748
+2025-08-25 22:20:40: Epoch: 034 Train Loss: 0.076, ap: 0.539, auc: 0.663, f1: 0.558, acc: 0.631
+2025-08-25 22:20:41: Epoch: 034 Test results, ap: 0.217, auc: 0.456, f1: 0.000, acc: 0.691
+2025-08-25 22:20:48: Epoch: 035 Train Loss: 0.086, ap: 0.541, auc: 0.651, f1: 0.519, acc: 0.621
+2025-08-25 22:20:49: Epoch: 035 Test results, ap: 0.211, auc: 0.439, f1: 0.000, acc: 0.732
+2025-08-25 22:20:55: Epoch: 036 Train Loss: 0.068, ap: 0.521, auc: 0.577, f1: 0.429, acc: 0.612
+2025-08-25 22:20:56: Epoch: 036 Test results, ap: 0.212, auc: 0.439, f1: 0.000, acc: 0.756
+2025-08-25 22:21:02: Epoch: 037 Train Loss: 0.070, ap: 0.561, auc: 0.675, f1: 0.500, acc: 0.650
+2025-08-25 22:21:04: Epoch: 037 Test results, ap: 0.208, auc: 0.420, f1: 0.000, acc: 0.740
+2025-08-25 22:21:10: Epoch: 038 Train Loss: 0.110, ap: 0.552, auc: 0.653, f1: 0.500, acc: 0.592
+2025-08-25 22:21:11: Epoch: 038 Test results, ap: 0.206, auc: 0.418, f1: 0.000, acc: 0.732
+2025-08-25 22:21:17: Epoch: 039 Train Loss: 0.080, ap: 0.600, auc: 0.664, f1: 0.533, acc: 0.660
+2025-08-25 22:21:18: Epoch: 039 Test results, ap: 0.208, auc: 0.423, f1: 0.000, acc: 0.724
+2025-08-25 22:21:24: Epoch: 040 Train Loss: 0.068, ap: 0.574, auc: 0.656, f1: 0.548, acc: 0.631
+2025-08-25 22:21:26: Epoch: 040 Test results, ap: 0.209, auc: 0.427, f1: 0.000, acc: 0.699
+2025-08-25 22:21:32: Epoch: 041 Train Loss: 0.090, ap: 0.562, auc: 0.660, f1: 0.537, acc: 0.631
+2025-08-25 22:21:33: Epoch: 041 Test results, ap: 0.211, auc: 0.430, f1: 0.000, acc: 0.724
+2025-08-25 22:21:39: Epoch: 042 Train Loss: 0.054, ap: 0.595, auc: 0.642, f1: 0.500, acc: 0.650
+2025-08-25 22:21:41: Epoch: 042 Test results, ap: 0.216, auc: 0.445, f1: 0.000, acc: 0.756
+2025-08-25 22:21:47: Epoch: 043 Train Loss: 0.061, ap: 0.587, auc: 0.665, f1: 0.521, acc: 0.660
+2025-08-25 22:21:48: Epoch: 043 Test results, ap: 0.216, auc: 0.442, f1: 0.000, acc: 0.756
+2025-08-25 22:21:54: Epoch: 044 Train Loss: 0.051, ap: 0.568, auc: 0.664, f1: 0.561, acc: 0.650
+2025-08-25 22:21:55: Epoch: 044 Test results, ap: 0.221, auc: 0.443, f1: 0.000, acc: 0.724
+2025-08-25 22:22:01: Epoch: 045 Train Loss: 0.050, ap: 0.570, auc: 0.664, f1: 0.543, acc: 0.641
+2025-08-25 22:22:03: Epoch: 045 Test results, ap: 0.213, auc: 0.433, f1: 0.000, acc: 0.724
+2025-08-25 22:22:09: Epoch: 046 Train Loss: 0.049, ap: 0.549, auc: 0.655, f1: 0.513, acc: 0.631
+2025-08-25 22:22:10: Epoch: 046 Test results, ap: 0.214, auc: 0.433, f1: 0.000, acc: 0.715
+2025-08-25 22:22:16: Epoch: 047 Train Loss: 0.039, ap: 0.554, auc: 0.655, f1: 0.564, acc: 0.670
+2025-08-25 22:22:17: Epoch: 047 Test results, ap: 0.214, auc: 0.435, f1: 0.000, acc: 0.732
+2025-08-25 22:22:23: Epoch: 048 Train Loss: 0.042, ap: 0.576, auc: 0.660, f1: 0.507, acc: 0.641
+2025-08-25 22:22:24: Epoch: 048 Test results, ap: 0.213, auc: 0.434, f1: 0.000, acc: 0.732
+2025-08-25 22:22:31: Epoch: 049 Train Loss: 0.040, ap: 0.571, auc: 0.663, f1: 0.557, acc: 0.660
+2025-08-25 22:22:32: Epoch: 049 Test results, ap: 0.214, auc: 0.433, f1: 0.000, acc: 0.748
+2025-08-25 22:22:38: Epoch: 050 Train Loss: 0.037, ap: 0.597, auc: 0.694, f1: 0.529, acc: 0.689
+2025-08-25 22:22:40: Epoch: 050 Test results, ap: 0.221, auc: 0.452, f1: 0.000, acc: 0.748
+2025-08-25 22:22:40: used time 0.10h
+2025-08-25 22:22:40: Cross Validation Finished!
+2025-08-25 22:22:40: Best validation perform list
+[[0.69865072 0.75645894 0.52173913 0.67961162]
+ [0.60372388 0.71826375 0.4074074  0.68932039]
+ [0.85783309 0.80844641 0.7647059  0.7669903 ]
+ [0.81641865 0.84231067 0.76086956 0.78640777]
+ [0.69967771 0.7120378  0.         0.60194176]]
+2025-08-25 22:22:40: mean: [0.735 0.768 0.491 0.705]
+2025-08-25 22:22:40: std: [0.091 0.051 0.282 0.066]
+2025-08-25 22:22:40: Best test perform list
+[[0.36292094 0.49139786 0.32727271 0.69918698]
+ [0.45410472 0.59247309 0.36923078 0.66666669]
+ [0.32828522 0.5688172  0.375      0.67479676]
+ [0.34138894 0.51935482 0.15789473 0.73983741]
+ [0.33972794 0.50143373 0.375      0.43089432]]
+2025-08-25 22:22:40: mean: [0.365 0.535 0.321 0.642]
+2025-08-25 22:22:40: std: [0.046 0.039 0.083 0.109]
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+2025-08-10 20:33:26: saving_dir: ./run-cls/rn18-att-16-768-ce-32-0.001-50/uda_r2
+2025-08-10 20:33:26: Loading Training Dataset
+2025-08-10 20:33:51: Loading Validation Dataset
+2025-08-10 20:33:52: Start UDA training
+2025-08-10 20:35:15: Epoch 1 Train Loss: 0.6765 Val: ap: 0.3074 auc: 0.4407 f1: 0.4889 acc: 0.3235
+2025-08-10 20:35:15: Epoch: 1 New best VAL metrics
+2025-08-10 20:36:36: Epoch 2 Train Loss: 0.6678 Val: ap: 0.3522 auc: 0.5731 f1: 0.0000 acc: 0.6765
+2025-08-10 20:36:36: Epoch: 2 New best VAL metrics
+2025-08-10 20:37:57: Epoch 3 Train Loss: 0.6720 Val: ap: 0.5400 auc: 0.7727 f1: 0.0000 acc: 0.6765
+2025-08-10 20:37:57: Epoch: 3 New best VAL metrics
+2025-08-10 20:39:18: Epoch 4 Train Loss: 0.6546 Val: ap: 0.5215 auc: 0.7352 f1: 0.0000 acc: 0.6765
+2025-08-10 20:40:39: Epoch 5 Train Loss: 0.6290 Val: ap: 0.5714 auc: 0.7826 f1: 0.0000 acc: 0.6765
+2025-08-10 20:40:39: Epoch: 5 New best VAL metrics
+2025-08-10 20:42:00: Epoch 6 Train Loss: 0.6197 Val: ap: 0.5135 auc: 0.7668 f1: 0.0000 acc: 0.6765
+2025-08-10 20:43:21: Epoch 7 Train Loss: 0.6047 Val: ap: 0.5815 auc: 0.7747 f1: 0.0000 acc: 0.6765
+2025-08-10 20:43:21: Epoch: 7 New best VAL metrics
+2025-08-10 20:44:42: Epoch 8 Train Loss: 0.5925 Val: ap: 0.5997 auc: 0.8379 f1: 0.0000 acc: 0.6765
+2025-08-10 20:44:42: Epoch: 8 New best VAL metrics
+2025-08-10 20:46:04: Epoch 9 Train Loss: 0.5775 Val: ap: 0.6620 auc: 0.8735 f1: 0.0000 acc: 0.6765
+2025-08-10 20:46:04: Epoch: 9 New best VAL metrics
+2025-08-10 20:47:24: Epoch 10 Train Loss: 0.5684 Val: ap: 0.6196 auc: 0.8538 f1: 0.0000 acc: 0.6765
+2025-08-10 20:48:45: Epoch 11 Train Loss: 0.5465 Val: ap: 0.2267 auc: 0.2055 f1: 0.0000 acc: 0.6765
+2025-08-10 20:50:06: Epoch 12 Train Loss: 0.5358 Val: ap: 0.4522 auc: 0.6838 f1: 0.0000 acc: 0.6765
+2025-08-10 20:51:27: Epoch 13 Train Loss: 0.5100 Val: ap: 0.6324 auc: 0.8696 f1: 0.0000 acc: 0.6765
+2025-08-10 20:52:49: Epoch 14 Train Loss: 0.5395 Val: ap: 0.6426 auc: 0.8538 f1: 0.0000 acc: 0.6765
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+++ b/run-cls-simple/rn18-att-16-mamba-simple-768-oneway-ce-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
+Valid
+0.7588983297348022,0.7713648200035095,0.440481461584568,0.6893203854560852
+0.06282677100563092,0.04424979496949819,0.23400483590310775,0.048737475798288174
+Test
+0.2897914946079254,0.5786116421222687,0.17430925667285918,0.7092592716217041
+0.030501957852533134,0.06283120305984151,0.17412519101048382,0.07913280843945016
diff --git a/run-cls-simple/rn18-att-16-mamba-simple-768-oneway-ce-32-0.001-50/training.log b/run-cls-simple/rn18-att-16-mamba-simple-768-oneway-ce-32-0.001-50/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..9710aebd4f99fef982b9973e0c41ea8b816beda4
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-mamba-simple-768-oneway-ce-32-0.001-50/training.log
@@ -0,0 +1,591 @@
+2025-08-07 21:39:03: saving_dir: ./run-cls/rn18-att-16-mamba-simple-768-oneway-ce-32-0.001-50
+2025-08-07 21:39:04: Loading Training Dataset
+2025-08-07 21:39:27: Loading Test Dataset
+2025-08-07 21:39:31: Running Cross Validation 0
+2025-08-07 21:39:31: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 21:39:39: Epoch: 001 Train Loss: 0.693, ap: 0.615, auc: 0.623, f1: 0.627, acc: 0.456
+2025-08-07 21:39:39: Epoch: 001 New best VALIDATION metrics
+2025-08-07 21:39:41: Epoch: 001 Test results, ap: 0.223, auc: 0.458, f1: 0.388, acc: 0.241
+2025-08-07 21:39:48: Epoch: 002 Train Loss: 0.662, ap: 0.544, auc: 0.524, f1: 0.000, acc: 0.544
+2025-08-07 21:39:50: Epoch: 002 Test results, ap: 0.287, auc: 0.621, f1: 0.000, acc: 0.759
+2025-08-07 21:39:57: Epoch: 003 Train Loss: 0.648, ap: 0.697, auc: 0.734, f1: 0.548, acc: 0.680
+2025-08-07 21:39:57: Epoch: 003 New best VALIDATION metrics
+2025-08-07 21:39:58: Epoch: 003 Test results, ap: 0.215, auc: 0.422, f1: 0.000, acc: 0.759
+2025-08-07 21:40:06: Epoch: 004 Train Loss: 0.584, ap: 0.648, auc: 0.657, f1: 0.000, acc: 0.544
+2025-08-07 21:40:07: Epoch: 004 Test results, ap: 0.233, auc: 0.510, f1: 0.000, acc: 0.759
+2025-08-07 21:40:14: Epoch: 005 Train Loss: 0.581, ap: 0.672, auc: 0.695, f1: 0.627, acc: 0.456
+2025-08-07 21:40:16: Epoch: 005 Test results, ap: 0.229, auc: 0.507, f1: 0.388, acc: 0.241
+2025-08-07 21:40:23: Epoch: 006 Train Loss: 0.538, ap: 0.702, auc: 0.684, f1: 0.543, acc: 0.689
+2025-08-07 21:40:24: Epoch: 006 Test results, ap: 0.282, auc: 0.625, f1: 0.000, acc: 0.759
+2025-08-07 21:40:32: Epoch: 007 Train Loss: 0.539, ap: 0.733, auc: 0.708, f1: 0.637, acc: 0.680
+2025-08-07 21:40:32: Epoch: 007 New best VALIDATION metrics
+2025-08-07 21:40:33: Epoch: 007 Test results, ap: 0.173, auc: 0.306, f1: 0.000, acc: 0.722
+2025-08-07 21:40:41: Epoch: 008 Train Loss: 0.482, ap: 0.690, auc: 0.678, f1: 0.000, acc: 0.544
+2025-08-07 21:40:42: Epoch: 008 Test results, ap: 0.173, auc: 0.291, f1: 0.000, acc: 0.759
+2025-08-07 21:40:49: Epoch: 009 Train Loss: 0.467, ap: 0.663, auc: 0.696, f1: 0.647, acc: 0.534
+2025-08-07 21:40:51: Epoch: 009 Test results, ap: 0.245, auc: 0.551, f1: 0.388, acc: 0.241
+2025-08-07 21:40:58: Epoch: 010 Train Loss: 0.424, ap: 0.671, auc: 0.670, f1: 0.400, acc: 0.650
+2025-08-07 21:41:00: Epoch: 010 Test results, ap: 0.242, auc: 0.520, f1: 0.000, acc: 0.759
+2025-08-07 21:41:07: Epoch: 011 Train Loss: 0.347, ap: 0.707, auc: 0.723, f1: 0.522, acc: 0.680
+2025-08-07 21:41:08: Epoch: 011 Test results, ap: 0.254, auc: 0.566, f1: 0.000, acc: 0.759
+2025-08-07 21:41:08: Epoch: 011 New best TEST metrics
+2025-08-07 21:41:16: Epoch: 012 Train Loss: 0.344, ap: 0.728, auc: 0.739, f1: 0.610, acc: 0.689
+2025-08-07 21:41:16: Epoch: 012 New best VALIDATION metrics
+2025-08-07 21:41:17: Epoch: 012 Test results, ap: 0.255, auc: 0.574, f1: 0.000, acc: 0.759
+2025-08-07 21:41:17: Epoch: 012 New best TEST metrics
+2025-08-07 21:41:24: Epoch: 013 Train Loss: 0.300, ap: 0.707, auc: 0.700, f1: 0.593, acc: 0.680
+2025-08-07 21:41:26: Epoch: 013 Test results, ap: 0.204, auc: 0.382, f1: 0.207, acc: 0.361
+2025-08-07 21:41:33: Epoch: 014 Train Loss: 0.303, ap: 0.681, auc: 0.712, f1: 0.589, acc: 0.621
+2025-08-07 21:41:35: Epoch: 014 Test results, ap: 0.200, auc: 0.418, f1: 0.000, acc: 0.713
+2025-08-07 21:41:42: Epoch: 015 Train Loss: 0.317, ap: 0.742, auc: 0.729, f1: 0.571, acc: 0.709
+2025-08-07 21:41:42: Epoch: 015 New best VALIDATION metrics
+2025-08-07 21:41:43: Epoch: 015 Test results, ap: 0.181, auc: 0.342, f1: 0.000, acc: 0.759
+2025-08-07 21:41:51: Epoch: 016 Train Loss: 0.278, ap: 0.697, auc: 0.704, f1: 0.636, acc: 0.612
+2025-08-07 21:41:52: Epoch: 016 Test results, ap: 0.191, auc: 0.380, f1: 0.179, acc: 0.491
+2025-08-07 21:41:59: Epoch: 017 Train Loss: 0.279, ap: 0.683, auc: 0.684, f1: 0.575, acc: 0.641
+2025-08-07 21:42:01: Epoch: 017 Test results, ap: 0.227, auc: 0.475, f1: 0.000, acc: 0.704
+2025-08-07 21:42:08: Epoch: 018 Train Loss: 0.289, ap: 0.732, auc: 0.729, f1: 0.587, acc: 0.699
+2025-08-07 21:42:09: Epoch: 018 Test results, ap: 0.214, auc: 0.466, f1: 0.000, acc: 0.759
+2025-08-07 21:42:17: Epoch: 019 Train Loss: 0.318, ap: 0.667, auc: 0.687, f1: 0.630, acc: 0.544
+2025-08-07 21:42:18: Epoch: 019 Test results, ap: 0.180, auc: 0.324, f1: 0.118, acc: 0.444
+2025-08-07 21:42:25: Epoch: 020 Train Loss: 0.278, ap: 0.724, auc: 0.712, f1: 0.560, acc: 0.680
+2025-08-07 21:42:27: Epoch: 020 Test results, ap: 0.200, auc: 0.411, f1: 0.000, acc: 0.750
+2025-08-07 21:42:34: Epoch: 021 Train Loss: 0.225, ap: 0.706, auc: 0.698, f1: 0.568, acc: 0.660
+2025-08-07 21:42:36: Epoch: 021 Test results, ap: 0.226, auc: 0.507, f1: 0.000, acc: 0.741
+2025-08-07 21:42:43: Epoch: 022 Train Loss: 0.215, ap: 0.709, auc: 0.712, f1: 0.632, acc: 0.660
+2025-08-07 21:42:44: Epoch: 022 Test results, ap: 0.235, auc: 0.529, f1: 0.000, acc: 0.741
+2025-08-07 21:42:52: Epoch: 023 Train Loss: 0.205, ap: 0.722, auc: 0.719, f1: 0.590, acc: 0.689
+2025-08-07 21:42:53: Epoch: 023 Test results, ap: 0.236, auc: 0.533, f1: 0.000, acc: 0.750
+2025-08-07 21:43:00: Epoch: 024 Train Loss: 0.212, ap: 0.720, auc: 0.710, f1: 0.650, acc: 0.728
+2025-08-07 21:43:02: Epoch: 024 Test results, ap: 0.254, auc: 0.570, f1: 0.000, acc: 0.750
+2025-08-07 21:43:09: Epoch: 025 Train Loss: 0.214, ap: 0.707, auc: 0.702, f1: 0.605, acc: 0.670
+2025-08-07 21:43:11: Epoch: 025 Test results, ap: 0.239, auc: 0.530, f1: 0.000, acc: 0.741
+2025-08-07 21:43:18: Epoch: 026 Train Loss: 0.220, ap: 0.704, auc: 0.690, f1: 0.619, acc: 0.641
+2025-08-07 21:43:19: Epoch: 026 Test results, ap: 0.261, auc: 0.583, f1: 0.000, acc: 0.741
+2025-08-07 21:43:19: Epoch: 026 New best TEST metrics
+2025-08-07 21:43:27: Epoch: 027 Train Loss: 0.229, ap: 0.713, auc: 0.718, f1: 0.629, acc: 0.680
+2025-08-07 21:43:28: Epoch: 027 Test results, ap: 0.248, auc: 0.544, f1: 0.000, acc: 0.741
+2025-08-07 21:43:35: Epoch: 028 Train Loss: 0.210, ap: 0.698, auc: 0.676, f1: 0.579, acc: 0.689
+2025-08-07 21:43:37: Epoch: 028 Test results, ap: 0.259, auc: 0.574, f1: 0.000, acc: 0.750
+2025-08-07 21:43:44: Epoch: 029 Train Loss: 0.211, ap: 0.695, auc: 0.704, f1: 0.617, acc: 0.650
+2025-08-07 21:43:46: Epoch: 029 Test results, ap: 0.230, auc: 0.489, f1: 0.000, acc: 0.741
+2025-08-07 21:43:53: Epoch: 030 Train Loss: 0.213, ap: 0.727, auc: 0.725, f1: 0.615, acc: 0.709
+2025-08-07 21:43:54: Epoch: 030 Test results, ap: 0.237, auc: 0.511, f1: 0.000, acc: 0.741
+2025-08-07 21:44:02: Epoch: 031 Train Loss: 0.176, ap: 0.716, auc: 0.706, f1: 0.625, acc: 0.709
+2025-08-07 21:44:03: Epoch: 031 Test results, ap: 0.259, auc: 0.556, f1: 0.000, acc: 0.741
+2025-08-07 21:44:10: Epoch: 032 Train Loss: 0.186, ap: 0.722, auc: 0.712, f1: 0.674, acc: 0.728
+2025-08-07 21:44:12: Epoch: 032 Test results, ap: 0.270, auc: 0.590, f1: 0.000, acc: 0.741
+2025-08-07 21:44:12: Epoch: 032 New best TEST metrics
+2025-08-07 21:44:19: Epoch: 033 Train Loss: 0.170, ap: 0.708, auc: 0.703, f1: 0.638, acc: 0.670
+2025-08-07 21:44:21: Epoch: 033 Test results, ap: 0.265, auc: 0.576, f1: 0.000, acc: 0.741
+2025-08-07 21:44:28: Epoch: 034 Train Loss: 0.182, ap: 0.717, auc: 0.706, f1: 0.650, acc: 0.728
+2025-08-07 21:44:29: Epoch: 034 Test results, ap: 0.296, auc: 0.609, f1: 0.000, acc: 0.741
+2025-08-07 21:44:29: Epoch: 034 New best TEST metrics
+2025-08-07 21:44:37: Epoch: 035 Train Loss: 0.160, ap: 0.722, auc: 0.709, f1: 0.650, acc: 0.728
+2025-08-07 21:44:38: Epoch: 035 Test results, ap: 0.276, auc: 0.599, f1: 0.000, acc: 0.741
+2025-08-07 21:44:45: Epoch: 036 Train Loss: 0.161, ap: 0.726, auc: 0.708, f1: 0.623, acc: 0.718
+2025-08-07 21:44:47: Epoch: 036 Test results, ap: 0.284, auc: 0.606, f1: 0.000, acc: 0.750
+2025-08-07 21:44:54: Epoch: 037 Train Loss: 0.153, ap: 0.715, auc: 0.712, f1: 0.650, acc: 0.728
+2025-08-07 21:44:55: Epoch: 037 Test results, ap: 0.264, auc: 0.590, f1: 0.000, acc: 0.741
+2025-08-07 21:45:03: Epoch: 038 Train Loss: 0.153, ap: 0.693, auc: 0.699, f1: 0.629, acc: 0.680
+2025-08-07 21:45:04: Epoch: 038 Test results, ap: 0.286, auc: 0.629, f1: 0.000, acc: 0.741
+2025-08-07 21:45:04: Epoch: 038 New best TEST metrics
+2025-08-07 21:45:11: Epoch: 039 Train Loss: 0.156, ap: 0.722, auc: 0.713, f1: 0.625, acc: 0.709
+2025-08-07 21:45:13: Epoch: 039 Test results, ap: 0.279, auc: 0.625, f1: 0.000, acc: 0.741
+2025-08-07 21:45:20: Epoch: 040 Train Loss: 0.163, ap: 0.690, auc: 0.701, f1: 0.667, acc: 0.680
+2025-08-07 21:45:22: Epoch: 040 Test results, ap: 0.267, auc: 0.595, f1: 0.000, acc: 0.713
+2025-08-07 21:45:29: Epoch: 041 Train Loss: 0.157, ap: 0.705, auc: 0.702, f1: 0.608, acc: 0.699
+2025-08-07 21:45:30: Epoch: 041 Test results, ap: 0.278, auc: 0.620, f1: 0.000, acc: 0.741
+2025-08-07 21:45:38: Epoch: 042 Train Loss: 0.161, ap: 0.710, auc: 0.701, f1: 0.623, acc: 0.718
+2025-08-07 21:45:39: Epoch: 042 Test results, ap: 0.293, auc: 0.652, f1: 0.000, acc: 0.750
+2025-08-07 21:45:39: Epoch: 042 New best TEST metrics
+2025-08-07 21:45:46: Epoch: 043 Train Loss: 0.133, ap: 0.707, auc: 0.711, f1: 0.643, acc: 0.709
+2025-08-07 21:45:48: Epoch: 043 Test results, ap: 0.284, auc: 0.634, f1: 0.000, acc: 0.741
+2025-08-07 21:45:55: Epoch: 044 Train Loss: 0.138, ap: 0.704, auc: 0.707, f1: 0.628, acc: 0.689
+2025-08-07 21:45:57: Epoch: 044 Test results, ap: 0.281, auc: 0.629, f1: 0.000, acc: 0.741
+2025-08-07 21:46:04: Epoch: 045 Train Loss: 0.127, ap: 0.711, auc: 0.712, f1: 0.595, acc: 0.670
+2025-08-07 21:46:05: Epoch: 045 Test results, ap: 0.284, auc: 0.633, f1: 0.000, acc: 0.741
+2025-08-07 21:46:13: Epoch: 046 Train Loss: 0.147, ap: 0.713, auc: 0.708, f1: 0.633, acc: 0.718
+2025-08-07 21:46:14: Epoch: 046 Test results, ap: 0.287, auc: 0.633, f1: 0.000, acc: 0.741
+2025-08-07 21:46:21: Epoch: 047 Train Loss: 0.133, ap: 0.707, auc: 0.698, f1: 0.641, acc: 0.728
+2025-08-07 21:46:23: Epoch: 047 Test results, ap: 0.294, auc: 0.651, f1: 0.000, acc: 0.741
+2025-08-07 21:46:30: Epoch: 048 Train Loss: 0.143, ap: 0.703, auc: 0.707, f1: 0.650, acc: 0.728
+2025-08-07 21:46:31: Epoch: 048 Test results, ap: 0.289, auc: 0.642, f1: 0.000, acc: 0.741
+2025-08-07 21:46:39: Epoch: 049 Train Loss: 0.146, ap: 0.707, auc: 0.705, f1: 0.627, acc: 0.699
+2025-08-07 21:46:40: Epoch: 049 Test results, ap: 0.291, auc: 0.647, f1: 0.000, acc: 0.741
+2025-08-07 21:46:47: Epoch: 050 Train Loss: 0.120, ap: 0.697, auc: 0.705, f1: 0.579, acc: 0.689
+2025-08-07 21:46:49: Epoch: 050 Test results, ap: 0.286, auc: 0.635, f1: 0.000, acc: 0.741
+2025-08-07 21:46:49: used time 0.12h
+2025-08-07 21:46:49: Running Cross Validation 1
+2025-08-07 21:46:49: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 21:46:56: Epoch: 001 Train Loss: 0.665, ap: 0.471, auc: 0.649, f1: 0.545, acc: 0.515
+2025-08-07 21:46:56: Epoch: 001 New best VALIDATION metrics
+2025-08-07 21:46:58: Epoch: 001 Test results, ap: 0.172, auc: 0.298, f1: 0.000, acc: 0.509
+2025-08-07 21:47:05: Epoch: 002 Train Loss: 0.612, ap: 0.531, auc: 0.657, f1: 0.540, acc: 0.388
+2025-08-07 21:47:05: Epoch: 002 New best VALIDATION metrics
+2025-08-07 21:47:07: Epoch: 002 Test results, ap: 0.242, auc: 0.534, f1: 0.388, acc: 0.241
+2025-08-07 21:47:14: Epoch: 003 Train Loss: 0.593, ap: 0.469, auc: 0.556, f1: 0.529, acc: 0.359
+2025-08-07 21:47:15: Epoch: 003 Test results, ap: 0.212, auc: 0.425, f1: 0.388, acc: 0.241
+2025-08-07 21:47:23: Epoch: 004 Train Loss: 0.554, ap: 0.559, auc: 0.658, f1: 0.531, acc: 0.709
+2025-08-07 21:47:23: Epoch: 004 New best VALIDATION metrics
+2025-08-07 21:47:24: Epoch: 004 Test results, ap: 0.218, auc: 0.451, f1: 0.000, acc: 0.759
+2025-08-07 21:47:32: Epoch: 005 Train Loss: 0.500, ap: 0.602, auc: 0.677, f1: 0.568, acc: 0.602
+2025-08-07 21:47:32: Epoch: 005 New best VALIDATION metrics
+2025-08-07 21:47:33: Epoch: 005 Test results, ap: 0.176, auc: 0.320, f1: 0.000, acc: 0.620
+2025-08-07 21:47:40: Epoch: 006 Train Loss: 0.460, ap: 0.528, auc: 0.656, f1: 0.532, acc: 0.369
+2025-08-07 21:47:42: Epoch: 006 Test results, ap: 0.378, auc: 0.600, f1: 0.388, acc: 0.241
+2025-08-07 21:47:49: Epoch: 007 Train Loss: 0.457, ap: 0.612, auc: 0.656, f1: 0.469, acc: 0.670
+2025-08-07 21:47:51: Epoch: 007 Test results, ap: 0.249, auc: 0.518, f1: 0.000, acc: 0.750
+2025-08-07 21:47:58: Epoch: 008 Train Loss: 0.391, ap: 0.651, auc: 0.725, f1: 0.565, acc: 0.641
+2025-08-07 21:47:58: Epoch: 008 New best VALIDATION metrics
+2025-08-07 21:48:00: Epoch: 008 Test results, ap: 0.238, auc: 0.501, f1: 0.233, acc: 0.574
+2025-08-07 21:48:07: Epoch: 009 Train Loss: 0.354, ap: 0.548, auc: 0.647, f1: 0.488, acc: 0.592
+2025-08-07 21:48:08: Epoch: 009 Test results, ap: 0.247, auc: 0.447, f1: 0.213, acc: 0.657
+2025-08-07 21:48:16: Epoch: 010 Train Loss: 0.387, ap: 0.544, auc: 0.646, f1: 0.527, acc: 0.583
+2025-08-07 21:48:17: Epoch: 010 Test results, ap: 0.282, auc: 0.516, f1: 0.324, acc: 0.343
+2025-08-07 21:48:24: Epoch: 011 Train Loss: 0.357, ap: 0.645, auc: 0.717, f1: 0.605, acc: 0.709
+2025-08-07 21:48:26: Epoch: 011 Test results, ap: 0.197, auc: 0.338, f1: 0.000, acc: 0.722
+2025-08-07 21:48:26: Epoch: 011 New best TEST metrics
+2025-08-07 21:48:33: Epoch: 012 Train Loss: 0.317, ap: 0.604, auc: 0.693, f1: 0.584, acc: 0.641
+2025-08-07 21:48:34: Epoch: 012 Test results, ap: 0.262, auc: 0.505, f1: 0.333, acc: 0.630
+2025-08-07 21:48:34: Epoch: 012 New best TEST metrics
+2025-08-07 21:48:42: Epoch: 013 Train Loss: 0.314, ap: 0.630, auc: 0.688, f1: 0.538, acc: 0.650
+2025-08-07 21:48:43: Epoch: 013 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.648
+2025-08-07 21:48:50: Epoch: 014 Train Loss: 0.305, ap: 0.614, auc: 0.675, f1: 0.571, acc: 0.680
+2025-08-07 21:48:52: Epoch: 014 Test results, ap: 0.266, auc: 0.549, f1: 0.355, acc: 0.630
+2025-08-07 21:48:52: Epoch: 014 New best TEST metrics
+2025-08-07 21:48:59: Epoch: 015 Train Loss: 0.266, ap: 0.587, auc: 0.661, f1: 0.562, acc: 0.592
+2025-08-07 21:49:01: Epoch: 015 Test results, ap: 0.271, auc: 0.535, f1: 0.330, acc: 0.287
+2025-08-07 21:49:08: Epoch: 016 Train Loss: 0.278, ap: 0.584, auc: 0.670, f1: 0.500, acc: 0.631
+2025-08-07 21:49:10: Epoch: 016 Test results, ap: 0.263, auc: 0.523, f1: 0.447, acc: 0.565
+2025-08-07 21:49:17: Epoch: 017 Train Loss: 0.286, ap: 0.669, auc: 0.713, f1: 0.536, acc: 0.748
+2025-08-07 21:49:17: Epoch: 017 New best VALIDATION metrics
+2025-08-07 21:49:18: Epoch: 017 Test results, ap: 0.246, auc: 0.503, f1: 0.000, acc: 0.741
+2025-08-07 21:49:26: Epoch: 018 Train Loss: 0.289, ap: 0.503, auc: 0.643, f1: 0.578, acc: 0.631
+2025-08-07 21:49:27: Epoch: 018 Test results, ap: 0.343, auc: 0.590, f1: 0.352, acc: 0.352
+2025-08-07 21:49:27: Epoch: 018 New best TEST metrics
+2025-08-07 21:49:35: Epoch: 019 Train Loss: 0.276, ap: 0.631, auc: 0.691, f1: 0.507, acc: 0.680
+2025-08-07 21:49:36: Epoch: 019 Test results, ap: 0.253, auc: 0.506, f1: 0.000, acc: 0.741
+2025-08-07 21:49:43: Epoch: 020 Train Loss: 0.263, ap: 0.571, auc: 0.655, f1: 0.550, acc: 0.650
+2025-08-07 21:49:45: Epoch: 020 Test results, ap: 0.282, auc: 0.573, f1: 0.437, acc: 0.546
+2025-08-07 21:49:52: Epoch: 021 Train Loss: 0.267, ap: 0.576, auc: 0.665, f1: 0.557, acc: 0.660
+2025-08-07 21:49:53: Epoch: 021 Test results, ap: 0.251, auc: 0.548, f1: 0.447, acc: 0.565
+2025-08-07 21:50:01: Epoch: 022 Train Loss: 0.230, ap: 0.567, auc: 0.670, f1: 0.554, acc: 0.641
+2025-08-07 21:50:02: Epoch: 022 Test results, ap: 0.250, auc: 0.535, f1: 0.415, acc: 0.556
+2025-08-07 21:50:10: Epoch: 023 Train Loss: 0.238, ap: 0.553, auc: 0.667, f1: 0.548, acc: 0.631
+2025-08-07 21:50:11: Epoch: 023 Test results, ap: 0.285, auc: 0.621, f1: 0.447, acc: 0.565
+2025-08-07 21:50:18: Epoch: 024 Train Loss: 0.254, ap: 0.556, auc: 0.654, f1: 0.545, acc: 0.660
+2025-08-07 21:50:20: Epoch: 024 Test results, ap: 0.260, auc: 0.540, f1: 0.296, acc: 0.648
+2025-08-07 21:50:27: Epoch: 025 Train Loss: 0.232, ap: 0.577, auc: 0.667, f1: 0.548, acc: 0.631
+2025-08-07 21:50:29: Epoch: 025 Test results, ap: 0.297, auc: 0.560, f1: 0.389, acc: 0.593
+2025-08-07 21:50:36: Epoch: 026 Train Loss: 0.233, ap: 0.556, auc: 0.662, f1: 0.507, acc: 0.660
+2025-08-07 21:50:37: Epoch: 026 Test results, ap: 0.298, auc: 0.545, f1: 0.200, acc: 0.630
+2025-08-07 21:50:45: Epoch: 027 Train Loss: 0.231, ap: 0.514, auc: 0.649, f1: 0.500, acc: 0.631
+2025-08-07 21:50:46: Epoch: 027 Test results, ap: 0.318, auc: 0.579, f1: 0.452, acc: 0.574
+2025-08-07 21:50:53: Epoch: 028 Train Loss: 0.242, ap: 0.529, auc: 0.652, f1: 0.480, acc: 0.621
+2025-08-07 21:50:55: Epoch: 028 Test results, ap: 0.294, auc: 0.544, f1: 0.213, acc: 0.657
+2025-08-07 21:51:02: Epoch: 029 Train Loss: 0.220, ap: 0.588, auc: 0.660, f1: 0.521, acc: 0.660
+2025-08-07 21:51:03: Epoch: 029 Test results, ap: 0.303, auc: 0.572, f1: 0.189, acc: 0.602
+2025-08-07 21:51:11: Epoch: 030 Train Loss: 0.227, ap: 0.585, auc: 0.675, f1: 0.545, acc: 0.660
+2025-08-07 21:51:12: Epoch: 030 Test results, ap: 0.321, auc: 0.637, f1: 0.455, acc: 0.556
+2025-08-07 21:51:12: Epoch: 030 New best TEST metrics
+2025-08-07 21:51:19: Epoch: 031 Train Loss: 0.210, ap: 0.570, auc: 0.672, f1: 0.533, acc: 0.660
+2025-08-07 21:51:21: Epoch: 031 Test results, ap: 0.320, auc: 0.618, f1: 0.432, acc: 0.537
+2025-08-07 21:51:28: Epoch: 032 Train Loss: 0.212, ap: 0.568, auc: 0.665, f1: 0.537, acc: 0.631
+2025-08-07 21:51:30: Epoch: 032 Test results, ap: 0.312, auc: 0.584, f1: 0.430, acc: 0.509
+2025-08-07 21:51:37: Epoch: 033 Train Loss: 0.202, ap: 0.566, auc: 0.667, f1: 0.543, acc: 0.641
+2025-08-07 21:51:39: Epoch: 033 Test results, ap: 0.315, auc: 0.611, f1: 0.442, acc: 0.509
+2025-08-07 21:51:46: Epoch: 034 Train Loss: 0.197, ap: 0.556, auc: 0.663, f1: 0.537, acc: 0.631
+2025-08-07 21:51:47: Epoch: 034 Test results, ap: 0.322, auc: 0.636, f1: 0.410, acc: 0.333
+2025-08-07 21:51:55: Epoch: 035 Train Loss: 0.182, ap: 0.580, auc: 0.668, f1: 0.525, acc: 0.631
+2025-08-07 21:51:56: Epoch: 035 Test results, ap: 0.291, auc: 0.585, f1: 0.218, acc: 0.602
+2025-08-07 21:52:03: Epoch: 036 Train Loss: 0.201, ap: 0.546, auc: 0.645, f1: 0.494, acc: 0.602
+2025-08-07 21:52:05: Epoch: 036 Test results, ap: 0.305, auc: 0.604, f1: 0.432, acc: 0.417
+2025-08-07 21:52:12: Epoch: 037 Train Loss: 0.175, ap: 0.556, auc: 0.656, f1: 0.513, acc: 0.631
+2025-08-07 21:52:14: Epoch: 037 Test results, ap: 0.300, auc: 0.583, f1: 0.438, acc: 0.620
+2025-08-07 21:52:21: Epoch: 038 Train Loss: 0.169, ap: 0.545, auc: 0.654, f1: 0.457, acc: 0.631
+2025-08-07 21:52:22: Epoch: 038 Test results, ap: 0.321, auc: 0.616, f1: 0.200, acc: 0.630
+2025-08-07 21:52:30: Epoch: 039 Train Loss: 0.148, ap: 0.561, auc: 0.670, f1: 0.548, acc: 0.631
+2025-08-07 21:52:31: Epoch: 039 Test results, ap: 0.283, auc: 0.554, f1: 0.409, acc: 0.491
+2025-08-07 21:52:38: Epoch: 040 Train Loss: 0.158, ap: 0.527, auc: 0.644, f1: 0.526, acc: 0.650
+2025-08-07 21:52:40: Epoch: 040 Test results, ap: 0.292, auc: 0.565, f1: 0.294, acc: 0.556
+2025-08-07 21:52:47: Epoch: 041 Train Loss: 0.145, ap: 0.527, auc: 0.647, f1: 0.554, acc: 0.641
+2025-08-07 21:52:48: Epoch: 041 Test results, ap: 0.298, auc: 0.581, f1: 0.397, acc: 0.352
+2025-08-07 21:52:56: Epoch: 042 Train Loss: 0.140, ap: 0.538, auc: 0.658, f1: 0.519, acc: 0.641
+2025-08-07 21:52:57: Epoch: 042 Test results, ap: 0.282, auc: 0.555, f1: 0.420, acc: 0.463
+2025-08-07 21:53:05: Epoch: 043 Train Loss: 0.148, ap: 0.520, auc: 0.650, f1: 0.545, acc: 0.660
+2025-08-07 21:53:06: Epoch: 043 Test results, ap: 0.281, auc: 0.554, f1: 0.393, acc: 0.370
+2025-08-07 21:53:13: Epoch: 044 Train Loss: 0.139, ap: 0.530, auc: 0.657, f1: 0.545, acc: 0.660
+2025-08-07 21:53:15: Epoch: 044 Test results, ap: 0.276, auc: 0.544, f1: 0.393, acc: 0.370
+2025-08-07 21:53:22: Epoch: 045 Train Loss: 0.143, ap: 0.497, auc: 0.643, f1: 0.541, acc: 0.670
+2025-08-07 21:53:24: Epoch: 045 Test results, ap: 0.284, auc: 0.561, f1: 0.435, acc: 0.519
+2025-08-07 21:53:31: Epoch: 046 Train Loss: 0.129, ap: 0.494, auc: 0.638, f1: 0.538, acc: 0.650
+2025-08-07 21:53:32: Epoch: 046 Test results, ap: 0.298, auc: 0.575, f1: 0.396, acc: 0.407
+2025-08-07 21:53:40: Epoch: 047 Train Loss: 0.117, ap: 0.494, auc: 0.636, f1: 0.494, acc: 0.602
+2025-08-07 21:53:41: Epoch: 047 Test results, ap: 0.285, auc: 0.542, f1: 0.400, acc: 0.556
+2025-08-07 21:53:49: Epoch: 048 Train Loss: 0.116, ap: 0.506, auc: 0.648, f1: 0.537, acc: 0.631
+2025-08-07 21:53:50: Epoch: 048 Test results, ap: 0.296, auc: 0.577, f1: 0.404, acc: 0.370
+2025-08-07 21:53:57: Epoch: 049 Train Loss: 0.128, ap: 0.518, auc: 0.649, f1: 0.525, acc: 0.631
+2025-08-07 21:53:59: Epoch: 049 Test results, ap: 0.268, auc: 0.500, f1: 0.301, acc: 0.528
+2025-08-07 21:54:06: Epoch: 050 Train Loss: 0.140, ap: 0.515, auc: 0.652, f1: 0.538, acc: 0.650
+2025-08-07 21:54:07: Epoch: 050 Test results, ap: 0.289, auc: 0.553, f1: 0.409, acc: 0.491
+2025-08-07 21:54:07: used time 0.12h
+2025-08-07 21:54:08: Running Cross Validation 2
+2025-08-07 21:54:08: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 21:54:15: Epoch: 001 Train Loss: 0.682, ap: 0.666, auc: 0.644, f1: 0.038, acc: 0.505
+2025-08-07 21:54:15: Epoch: 001 New best VALIDATION metrics
+2025-08-07 21:54:16: Epoch: 001 Test results, ap: 0.228, auc: 0.480, f1: 0.000, acc: 0.759
+2025-08-07 21:54:24: Epoch: 002 Train Loss: 0.619, ap: 0.812, auc: 0.784, f1: 0.671, acc: 0.505
+2025-08-07 21:54:24: Epoch: 002 New best VALIDATION metrics
+2025-08-07 21:54:25: Epoch: 002 Test results, ap: 0.337, auc: 0.491, f1: 0.388, acc: 0.241
+2025-08-07 21:54:32: Epoch: 003 Train Loss: 0.579, ap: 0.775, auc: 0.726, f1: 0.585, acc: 0.670
+2025-08-07 21:54:34: Epoch: 003 Test results, ap: 0.202, auc: 0.399, f1: 0.000, acc: 0.759
+2025-08-07 21:54:41: Epoch: 004 Train Loss: 0.563, ap: 0.837, auc: 0.806, f1: 0.741, acc: 0.660
+2025-08-07 21:54:41: Epoch: 004 New best VALIDATION metrics
+2025-08-07 21:54:42: Epoch: 004 Test results, ap: 0.209, auc: 0.422, f1: 0.133, acc: 0.639
+2025-08-07 21:54:50: Epoch: 005 Train Loss: 0.492, ap: 0.835, auc: 0.799, f1: 0.713, acc: 0.718
+2025-08-07 21:54:51: Epoch: 005 Test results, ap: 0.185, auc: 0.358, f1: 0.000, acc: 0.741
+2025-08-07 21:54:59: Epoch: 006 Train Loss: 0.476, ap: 0.700, auc: 0.701, f1: 0.619, acc: 0.641
+2025-08-07 21:55:00: Epoch: 006 Test results, ap: 0.217, auc: 0.455, f1: 0.000, acc: 0.741
+2025-08-07 21:55:07: Epoch: 007 Train Loss: 0.506, ap: 0.630, auc: 0.702, f1: 0.671, acc: 0.505
+2025-08-07 21:55:09: Epoch: 007 Test results, ap: 0.297, auc: 0.486, f1: 0.388, acc: 0.241
+2025-08-07 21:55:16: Epoch: 008 Train Loss: 0.475, ap: 0.851, auc: 0.827, f1: 0.725, acc: 0.728
+2025-08-07 21:55:16: Epoch: 008 New best VALIDATION metrics
+2025-08-07 21:55:18: Epoch: 008 Test results, ap: 0.278, auc: 0.534, f1: 0.000, acc: 0.759
+2025-08-07 21:55:25: Epoch: 009 Train Loss: 0.502, ap: 0.796, auc: 0.779, f1: 0.748, acc: 0.699
+2025-08-07 21:55:26: Epoch: 009 Test results, ap: 0.231, auc: 0.463, f1: 0.093, acc: 0.639
+2025-08-07 21:55:34: Epoch: 010 Train Loss: 0.435, ap: 0.853, auc: 0.824, f1: 0.766, acc: 0.757
+2025-08-07 21:55:35: Epoch: 010 Test results, ap: 0.222, auc: 0.421, f1: 0.000, acc: 0.741
+2025-08-07 21:55:42: Epoch: 011 Train Loss: 0.361, ap: 0.825, auc: 0.786, f1: 0.694, acc: 0.709
+2025-08-07 21:55:44: Epoch: 011 Test results, ap: 0.187, auc: 0.357, f1: 0.000, acc: 0.741
+2025-08-07 21:55:44: Epoch: 011 New best TEST metrics
+2025-08-07 21:55:51: Epoch: 012 Train Loss: 0.350, ap: 0.834, auc: 0.813, f1: 0.739, acc: 0.718
+2025-08-07 21:55:53: Epoch: 012 Test results, ap: 0.193, auc: 0.372, f1: 0.000, acc: 0.722
+2025-08-07 21:55:53: Epoch: 012 New best TEST metrics
+2025-08-07 21:56:00: Epoch: 013 Train Loss: 0.324, ap: 0.838, auc: 0.811, f1: 0.720, acc: 0.728
+2025-08-07 21:56:01: Epoch: 013 Test results, ap: 0.192, auc: 0.352, f1: 0.000, acc: 0.741
+2025-08-07 21:56:09: Epoch: 014 Train Loss: 0.318, ap: 0.818, auc: 0.829, f1: 0.758, acc: 0.689
+2025-08-07 21:56:10: Epoch: 014 Test results, ap: 0.223, auc: 0.453, f1: 0.136, acc: 0.648
+2025-08-07 21:56:10: Epoch: 014 New best TEST metrics
+2025-08-07 21:56:18: Epoch: 015 Train Loss: 0.336, ap: 0.859, auc: 0.821, f1: 0.722, acc: 0.709
+2025-08-07 21:56:18: Epoch: 015 New best VALIDATION metrics
+2025-08-07 21:56:19: Epoch: 015 Test results, ap: 0.221, auc: 0.435, f1: 0.000, acc: 0.722
+2025-08-07 21:56:26: Epoch: 016 Train Loss: 0.294, ap: 0.849, auc: 0.809, f1: 0.703, acc: 0.680
+2025-08-07 21:56:28: Epoch: 016 Test results, ap: 0.229, auc: 0.445, f1: 0.062, acc: 0.722
+2025-08-07 21:56:35: Epoch: 017 Train Loss: 0.318, ap: 0.846, auc: 0.821, f1: 0.702, acc: 0.728
+2025-08-07 21:56:37: Epoch: 017 Test results, ap: 0.223, auc: 0.447, f1: 0.000, acc: 0.731
+2025-08-07 21:56:44: Epoch: 018 Train Loss: 0.313, ap: 0.800, auc: 0.816, f1: 0.718, acc: 0.680
+2025-08-07 21:56:46: Epoch: 018 Test results, ap: 0.203, auc: 0.412, f1: 0.000, acc: 0.713
+2025-08-07 21:56:53: Epoch: 019 Train Loss: 0.275, ap: 0.804, auc: 0.760, f1: 0.703, acc: 0.680
+2025-08-07 21:56:54: Epoch: 019 Test results, ap: 0.198, auc: 0.393, f1: 0.000, acc: 0.722
+2025-08-07 21:57:02: Epoch: 020 Train Loss: 0.265, ap: 0.831, auc: 0.792, f1: 0.667, acc: 0.699
+2025-08-07 21:57:03: Epoch: 020 Test results, ap: 0.223, auc: 0.405, f1: 0.000, acc: 0.759
+2025-08-07 21:57:10: Epoch: 021 Train Loss: 0.250, ap: 0.826, auc: 0.811, f1: 0.708, acc: 0.680
+2025-08-07 21:57:12: Epoch: 021 Test results, ap: 0.215, auc: 0.421, f1: 0.000, acc: 0.722
+2025-08-07 21:57:19: Epoch: 022 Train Loss: 0.219, ap: 0.796, auc: 0.790, f1: 0.721, acc: 0.699
+2025-08-07 21:57:21: Epoch: 022 Test results, ap: 0.212, auc: 0.414, f1: 0.000, acc: 0.722
+2025-08-07 21:57:28: Epoch: 023 Train Loss: 0.217, ap: 0.829, auc: 0.805, f1: 0.750, acc: 0.728
+2025-08-07 21:57:29: Epoch: 023 Test results, ap: 0.208, auc: 0.408, f1: 0.000, acc: 0.722
+2025-08-07 21:57:37: Epoch: 024 Train Loss: 0.238, ap: 0.801, auc: 0.768, f1: 0.697, acc: 0.680
+2025-08-07 21:57:38: Epoch: 024 Test results, ap: 0.224, auc: 0.446, f1: 0.118, acc: 0.722
+2025-08-07 21:57:46: Epoch: 025 Train Loss: 0.234, ap: 0.818, auc: 0.776, f1: 0.710, acc: 0.650
+2025-08-07 21:57:47: Epoch: 025 Test results, ap: 0.217, auc: 0.447, f1: 0.128, acc: 0.620
+2025-08-07 21:57:54: Epoch: 026 Train Loss: 0.208, ap: 0.811, auc: 0.759, f1: 0.692, acc: 0.680
+2025-08-07 21:57:56: Epoch: 026 Test results, ap: 0.218, auc: 0.439, f1: 0.000, acc: 0.722
+2025-08-07 21:58:03: Epoch: 027 Train Loss: 0.215, ap: 0.790, auc: 0.768, f1: 0.701, acc: 0.718
+2025-08-07 21:58:05: Epoch: 027 Test results, ap: 0.231, auc: 0.435, f1: 0.000, acc: 0.722
+2025-08-07 21:58:12: Epoch: 028 Train Loss: 0.181, ap: 0.795, auc: 0.768, f1: 0.708, acc: 0.680
+2025-08-07 21:58:13: Epoch: 028 Test results, ap: 0.232, auc: 0.476, f1: 0.217, acc: 0.667
+2025-08-07 21:58:13: Epoch: 028 New best TEST metrics
+2025-08-07 21:58:21: Epoch: 029 Train Loss: 0.231, ap: 0.775, auc: 0.756, f1: 0.684, acc: 0.641
+2025-08-07 21:58:22: Epoch: 029 Test results, ap: 0.213, auc: 0.429, f1: 0.174, acc: 0.648
+2025-08-07 21:58:30: Epoch: 030 Train Loss: 0.246, ap: 0.760, auc: 0.758, f1: 0.719, acc: 0.689
+2025-08-07 21:58:31: Epoch: 030 Test results, ap: 0.221, auc: 0.448, f1: 0.103, acc: 0.676
+2025-08-07 21:58:38: Epoch: 031 Train Loss: 0.187, ap: 0.780, auc: 0.757, f1: 0.691, acc: 0.670
+2025-08-07 21:58:40: Epoch: 031 Test results, ap: 0.216, auc: 0.435, f1: 0.053, acc: 0.667
+2025-08-07 21:58:47: Epoch: 032 Train Loss: 0.169, ap: 0.790, auc: 0.757, f1: 0.692, acc: 0.689
+2025-08-07 21:58:48: Epoch: 032 Test results, ap: 0.223, auc: 0.443, f1: 0.000, acc: 0.704
+2025-08-07 21:58:56: Epoch: 033 Train Loss: 0.163, ap: 0.774, auc: 0.772, f1: 0.741, acc: 0.709
+2025-08-07 21:58:57: Epoch: 033 Test results, ap: 0.239, auc: 0.471, f1: 0.222, acc: 0.676
+2025-08-07 21:58:57: Epoch: 033 New best TEST metrics
+2025-08-07 21:59:05: Epoch: 034 Train Loss: 0.157, ap: 0.788, auc: 0.760, f1: 0.694, acc: 0.641
+2025-08-07 21:59:06: Epoch: 034 Test results, ap: 0.231, auc: 0.477, f1: 0.231, acc: 0.630
+2025-08-07 21:59:13: Epoch: 035 Train Loss: 0.163, ap: 0.797, auc: 0.761, f1: 0.699, acc: 0.641
+2025-08-07 21:59:15: Epoch: 035 Test results, ap: 0.234, auc: 0.487, f1: 0.231, acc: 0.630
+2025-08-07 21:59:15: Epoch: 035 New best TEST metrics
+2025-08-07 21:59:22: Epoch: 036 Train Loss: 0.145, ap: 0.807, auc: 0.767, f1: 0.709, acc: 0.689
+2025-08-07 21:59:23: Epoch: 036 Test results, ap: 0.260, auc: 0.515, f1: 0.261, acc: 0.685
+2025-08-07 21:59:23: Epoch: 036 New best TEST metrics
+2025-08-07 21:59:31: Epoch: 037 Train Loss: 0.161, ap: 0.833, auc: 0.789, f1: 0.734, acc: 0.718
+2025-08-07 21:59:32: Epoch: 037 Test results, ap: 0.250, auc: 0.503, f1: 0.227, acc: 0.685
+2025-08-07 21:59:40: Epoch: 038 Train Loss: 0.130, ap: 0.807, auc: 0.764, f1: 0.703, acc: 0.680
+2025-08-07 21:59:41: Epoch: 038 Test results, ap: 0.248, auc: 0.481, f1: 0.255, acc: 0.676
+2025-08-07 21:59:48: Epoch: 039 Train Loss: 0.139, ap: 0.825, auc: 0.787, f1: 0.737, acc: 0.709
+2025-08-07 21:59:50: Epoch: 039 Test results, ap: 0.254, auc: 0.492, f1: 0.275, acc: 0.657
+2025-08-07 21:59:57: Epoch: 040 Train Loss: 0.124, ap: 0.839, auc: 0.795, f1: 0.715, acc: 0.660
+2025-08-07 21:59:59: Epoch: 040 Test results, ap: 0.244, auc: 0.487, f1: 0.255, acc: 0.620
+2025-08-07 22:00:06: Epoch: 041 Train Loss: 0.124, ap: 0.820, auc: 0.779, f1: 0.712, acc: 0.670
+2025-08-07 22:00:07: Epoch: 041 Test results, ap: 0.260, auc: 0.517, f1: 0.264, acc: 0.639
+2025-08-07 22:00:07: Epoch: 041 New best TEST metrics
+2025-08-07 22:00:15: Epoch: 042 Train Loss: 0.106, ap: 0.824, auc: 0.781, f1: 0.719, acc: 0.689
+2025-08-07 22:00:16: Epoch: 042 Test results, ap: 0.264, auc: 0.515, f1: 0.279, acc: 0.713
+2025-08-07 22:00:16: Epoch: 042 New best TEST metrics
+2025-08-07 22:00:23: Epoch: 043 Train Loss: 0.086, ap: 0.825, auc: 0.782, f1: 0.718, acc: 0.680
+2025-08-07 22:00:25: Epoch: 043 Test results, ap: 0.242, auc: 0.467, f1: 0.267, acc: 0.694
+2025-08-07 22:00:32: Epoch: 044 Train Loss: 0.086, ap: 0.823, auc: 0.781, f1: 0.720, acc: 0.660
+2025-08-07 22:00:34: Epoch: 044 Test results, ap: 0.237, auc: 0.467, f1: 0.250, acc: 0.667
+2025-08-07 22:00:41: Epoch: 045 Train Loss: 0.081, ap: 0.815, auc: 0.779, f1: 0.721, acc: 0.670
+2025-08-07 22:00:42: Epoch: 045 Test results, ap: 0.259, auc: 0.500, f1: 0.308, acc: 0.667
+2025-08-07 22:00:50: Epoch: 046 Train Loss: 0.093, ap: 0.831, auc: 0.789, f1: 0.712, acc: 0.670
+2025-08-07 22:00:51: Epoch: 046 Test results, ap: 0.243, auc: 0.464, f1: 0.267, acc: 0.694
+2025-08-07 22:00:59: Epoch: 047 Train Loss: 0.096, ap: 0.824, auc: 0.785, f1: 0.712, acc: 0.670
+2025-08-07 22:01:00: Epoch: 047 Test results, ap: 0.254, auc: 0.482, f1: 0.286, acc: 0.676
+2025-08-07 22:01:07: Epoch: 048 Train Loss: 0.093, ap: 0.827, auc: 0.780, f1: 0.688, acc: 0.612
+2025-08-07 22:01:09: Epoch: 048 Test results, ap: 0.245, auc: 0.483, f1: 0.281, acc: 0.620
+2025-08-07 22:01:16: Epoch: 049 Train Loss: 0.074, ap: 0.799, auc: 0.758, f1: 0.715, acc: 0.660
+2025-08-07 22:01:17: Epoch: 049 Test results, ap: 0.253, auc: 0.501, f1: 0.269, acc: 0.648
+2025-08-07 22:01:25: Epoch: 050 Train Loss: 0.067, ap: 0.798, auc: 0.757, f1: 0.688, acc: 0.612
+2025-08-07 22:01:26: Epoch: 050 Test results, ap: 0.246, auc: 0.498, f1: 0.259, acc: 0.630
+2025-08-07 22:01:26: used time 0.12h
+2025-08-07 22:01:26: Running Cross Validation 3
+2025-08-07 22:01:26: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 22:01:34: Epoch: 001 Train Loss: 0.692, ap: 0.536, auc: 0.630, f1: 0.631, acc: 0.602
+2025-08-07 22:01:34: Epoch: 001 New best VALIDATION metrics
+2025-08-07 22:01:35: Epoch: 001 Test results, ap: 0.204, auc: 0.379, f1: 0.000, acc: 0.759
+2025-08-07 22:01:42: Epoch: 002 Train Loss: 0.643, ap: 0.654, auc: 0.714, f1: 0.667, acc: 0.699
+2025-08-07 22:01:42: Epoch: 002 New best VALIDATION metrics
+2025-08-07 22:01:44: Epoch: 002 Test results, ap: 0.253, auc: 0.516, f1: 0.000, acc: 0.759
+2025-08-07 22:01:51: Epoch: 003 Train Loss: 0.609, ap: 0.725, auc: 0.772, f1: 0.583, acc: 0.417
+2025-08-07 22:01:51: Epoch: 003 New best VALIDATION metrics
+2025-08-07 22:01:53: Epoch: 003 Test results, ap: 0.260, auc: 0.548, f1: 0.388, acc: 0.241
+2025-08-07 22:02:00: Epoch: 004 Train Loss: 0.582, ap: 0.739, auc: 0.784, f1: 0.644, acc: 0.699
+2025-08-07 22:02:00: Epoch: 004 New best VALIDATION metrics
+2025-08-07 22:02:01: Epoch: 004 Test results, ap: 0.227, auc: 0.469, f1: 0.000, acc: 0.731
+2025-08-07 22:02:09: Epoch: 005 Train Loss: 0.546, ap: 0.789, auc: 0.816, f1: 0.047, acc: 0.602
+2025-08-07 22:02:09: Epoch: 005 New best VALIDATION metrics
+2025-08-07 22:02:10: Epoch: 005 Test results, ap: 0.194, auc: 0.330, f1: 0.000, acc: 0.759
+2025-08-07 22:02:17: Epoch: 006 Train Loss: 0.475, ap: 0.644, auc: 0.658, f1: 0.579, acc: 0.408
+2025-08-07 22:02:19: Epoch: 006 Test results, ap: 0.217, auc: 0.392, f1: 0.388, acc: 0.241
+2025-08-07 22:02:26: Epoch: 007 Train Loss: 0.474, ap: 0.745, auc: 0.787, f1: 0.632, acc: 0.728
+2025-08-07 22:02:27: Epoch: 007 Test results, ap: 0.192, auc: 0.345, f1: 0.000, acc: 0.741
+2025-08-07 22:02:35: Epoch: 008 Train Loss: 0.471, ap: 0.706, auc: 0.788, f1: 0.000, acc: 0.592
+2025-08-07 22:02:36: Epoch: 008 Test results, ap: 0.216, auc: 0.457, f1: 0.000, acc: 0.759
+2025-08-07 22:02:43: Epoch: 009 Train Loss: 0.502, ap: 0.709, auc: 0.741, f1: 0.340, acc: 0.660
+2025-08-07 22:02:45: Epoch: 009 Test results, ap: 0.233, auc: 0.502, f1: 0.000, acc: 0.759
+2025-08-07 22:02:52: Epoch: 010 Train Loss: 0.428, ap: 0.702, auc: 0.751, f1: 0.655, acc: 0.631
+2025-08-07 22:02:54: Epoch: 010 Test results, ap: 0.200, auc: 0.394, f1: 0.179, acc: 0.574
+2025-08-07 22:03:01: Epoch: 011 Train Loss: 0.377, ap: 0.755, auc: 0.783, f1: 0.658, acc: 0.757
+2025-08-07 22:03:03: Epoch: 011 Test results, ap: 0.173, auc: 0.291, f1: 0.000, acc: 0.750
+2025-08-07 22:03:03: Epoch: 011 New best TEST metrics
+2025-08-07 22:03:10: Epoch: 012 Train Loss: 0.346, ap: 0.757, auc: 0.814, f1: 0.719, acc: 0.689
+2025-08-07 22:03:11: Epoch: 012 Test results, ap: 0.214, auc: 0.384, f1: 0.273, acc: 0.407
+2025-08-07 22:03:11: Epoch: 012 New best TEST metrics
+2025-08-07 22:03:19: Epoch: 013 Train Loss: 0.367, ap: 0.782, auc: 0.787, f1: 0.603, acc: 0.757
+2025-08-07 22:03:20: Epoch: 013 Test results, ap: 0.247, auc: 0.497, f1: 0.000, acc: 0.759
+2025-08-07 22:03:20: Epoch: 013 New best TEST metrics
+2025-08-07 22:03:27: Epoch: 014 Train Loss: 0.330, ap: 0.739, auc: 0.793, f1: 0.659, acc: 0.699
+2025-08-07 22:03:29: Epoch: 014 Test results, ap: 0.204, auc: 0.415, f1: 0.000, acc: 0.713
+2025-08-07 22:03:36: Epoch: 015 Train Loss: 0.333, ap: 0.716, auc: 0.802, f1: 0.673, acc: 0.689
+2025-08-07 22:03:38: Epoch: 015 Test results, ap: 0.160, auc: 0.228, f1: 0.000, acc: 0.630
+2025-08-07 22:03:45: Epoch: 016 Train Loss: 0.284, ap: 0.738, auc: 0.772, f1: 0.667, acc: 0.767
+2025-08-07 22:03:46: Epoch: 016 Test results, ap: 0.177, auc: 0.319, f1: 0.000, acc: 0.759
+2025-08-07 22:03:54: Epoch: 017 Train Loss: 0.320, ap: 0.775, auc: 0.798, f1: 0.621, acc: 0.680
+2025-08-07 22:03:55: Epoch: 017 Test results, ap: 0.175, auc: 0.306, f1: 0.000, acc: 0.685
+2025-08-07 22:04:02: Epoch: 018 Train Loss: 0.294, ap: 0.763, auc: 0.804, f1: 0.625, acc: 0.534
+2025-08-07 22:04:04: Epoch: 018 Test results, ap: 0.228, auc: 0.422, f1: 0.366, acc: 0.231
+2025-08-07 22:04:11: Epoch: 019 Train Loss: 0.286, ap: 0.674, auc: 0.763, f1: 0.537, acc: 0.699
+2025-08-07 22:04:13: Epoch: 019 Test results, ap: 0.176, auc: 0.287, f1: 0.000, acc: 0.759
+2025-08-07 22:04:20: Epoch: 020 Train Loss: 0.291, ap: 0.730, auc: 0.770, f1: 0.610, acc: 0.689
+2025-08-07 22:04:22: Epoch: 020 Test results, ap: 0.164, auc: 0.254, f1: 0.000, acc: 0.648
+2025-08-07 22:04:29: Epoch: 021 Train Loss: 0.243, ap: 0.711, auc: 0.788, f1: 0.707, acc: 0.718
+2025-08-07 22:04:30: Epoch: 021 Test results, ap: 0.178, auc: 0.283, f1: 0.143, acc: 0.667
+2025-08-07 22:04:38: Epoch: 022 Train Loss: 0.194, ap: 0.708, auc: 0.786, f1: 0.603, acc: 0.718
+2025-08-07 22:04:39: Epoch: 022 Test results, ap: 0.178, auc: 0.313, f1: 0.000, acc: 0.741
+2025-08-07 22:04:46: Epoch: 023 Train Loss: 0.198, ap: 0.739, auc: 0.781, f1: 0.700, acc: 0.767
+2025-08-07 22:04:48: Epoch: 023 Test results, ap: 0.166, auc: 0.262, f1: 0.000, acc: 0.741
+2025-08-07 22:04:55: Epoch: 024 Train Loss: 0.186, ap: 0.698, auc: 0.749, f1: 0.588, acc: 0.660
+2025-08-07 22:04:57: Epoch: 024 Test results, ap: 0.181, auc: 0.333, f1: 0.077, acc: 0.556
+2025-08-07 22:05:04: Epoch: 025 Train Loss: 0.207, ap: 0.692, auc: 0.745, f1: 0.568, acc: 0.689
+2025-08-07 22:05:05: Epoch: 025 Test results, ap: 0.190, auc: 0.340, f1: 0.000, acc: 0.750
+2025-08-07 22:05:13: Epoch: 026 Train Loss: 0.162, ap: 0.661, auc: 0.758, f1: 0.587, acc: 0.699
+2025-08-07 22:05:14: Epoch: 026 Test results, ap: 0.193, auc: 0.356, f1: 0.000, acc: 0.731
+2025-08-07 22:05:22: Epoch: 027 Train Loss: 0.163, ap: 0.690, auc: 0.750, f1: 0.603, acc: 0.718
+2025-08-07 22:05:23: Epoch: 027 Test results, ap: 0.184, auc: 0.333, f1: 0.000, acc: 0.750
+2025-08-07 22:05:30: Epoch: 028 Train Loss: 0.165, ap: 0.703, auc: 0.739, f1: 0.638, acc: 0.670
+2025-08-07 22:05:32: Epoch: 028 Test results, ap: 0.230, auc: 0.439, f1: 0.232, acc: 0.509
+2025-08-07 22:05:39: Epoch: 029 Train Loss: 0.120, ap: 0.667, auc: 0.750, f1: 0.686, acc: 0.680
+2025-08-07 22:05:41: Epoch: 029 Test results, ap: 0.197, auc: 0.387, f1: 0.203, acc: 0.491
+2025-08-07 22:05:48: Epoch: 030 Train Loss: 0.119, ap: 0.660, auc: 0.743, f1: 0.469, acc: 0.670
+2025-08-07 22:05:49: Epoch: 030 Test results, ap: 0.218, auc: 0.383, f1: 0.065, acc: 0.731
+2025-08-07 22:05:57: Epoch: 031 Train Loss: 0.101, ap: 0.691, auc: 0.757, f1: 0.646, acc: 0.660
+2025-08-07 22:05:58: Epoch: 031 Test results, ap: 0.201, auc: 0.360, f1: 0.244, acc: 0.426
+2025-08-07 22:06:06: Epoch: 032 Train Loss: 0.092, ap: 0.663, auc: 0.751, f1: 0.686, acc: 0.689
+2025-08-07 22:06:07: Epoch: 032 Test results, ap: 0.204, auc: 0.386, f1: 0.154, acc: 0.491
+2025-08-07 22:06:14: Epoch: 033 Train Loss: 0.082, ap: 0.631, auc: 0.725, f1: 0.629, acc: 0.680
+2025-08-07 22:06:16: Epoch: 033 Test results, ap: 0.209, auc: 0.375, f1: 0.085, acc: 0.602
+2025-08-07 22:06:23: Epoch: 034 Train Loss: 0.083, ap: 0.620, auc: 0.717, f1: 0.578, acc: 0.660
+2025-08-07 22:06:25: Epoch: 034 Test results, ap: 0.219, auc: 0.393, f1: 0.145, acc: 0.565
+2025-08-07 22:06:32: Epoch: 035 Train Loss: 0.082, ap: 0.642, auc: 0.733, f1: 0.660, acc: 0.670
+2025-08-07 22:06:33: Epoch: 035 Test results, ap: 0.201, auc: 0.370, f1: 0.174, acc: 0.472
+2025-08-07 22:06:41: Epoch: 036 Train Loss: 0.079, ap: 0.669, auc: 0.752, f1: 0.714, acc: 0.728
+2025-08-07 22:06:42: Epoch: 036 Test results, ap: 0.189, auc: 0.348, f1: 0.073, acc: 0.528
+2025-08-07 22:06:50: Epoch: 037 Train Loss: 0.077, ap: 0.646, auc: 0.732, f1: 0.571, acc: 0.680
+2025-08-07 22:06:51: Epoch: 037 Test results, ap: 0.208, auc: 0.354, f1: 0.129, acc: 0.750
+2025-08-07 22:06:58: Epoch: 038 Train Loss: 0.088, ap: 0.676, auc: 0.756, f1: 0.700, acc: 0.709
+2025-08-07 22:07:00: Epoch: 038 Test results, ap: 0.214, auc: 0.413, f1: 0.232, acc: 0.509
+2025-08-07 22:07:07: Epoch: 039 Train Loss: 0.097, ap: 0.657, auc: 0.755, f1: 0.693, acc: 0.699
+2025-08-07 22:07:09: Epoch: 039 Test results, ap: 0.196, auc: 0.370, f1: 0.091, acc: 0.630
+2025-08-07 22:07:16: Epoch: 040 Train Loss: 0.079, ap: 0.632, auc: 0.730, f1: 0.607, acc: 0.660
+2025-08-07 22:07:18: Epoch: 040 Test results, ap: 0.185, auc: 0.337, f1: 0.083, acc: 0.593
+2025-08-07 22:07:25: Epoch: 041 Train Loss: 0.049, ap: 0.640, auc: 0.725, f1: 0.617, acc: 0.650
+2025-08-07 22:07:26: Epoch: 041 Test results, ap: 0.188, auc: 0.341, f1: 0.100, acc: 0.500
+2025-08-07 22:07:34: Epoch: 042 Train Loss: 0.062, ap: 0.652, auc: 0.744, f1: 0.686, acc: 0.689
+2025-08-07 22:07:35: Epoch: 042 Test results, ap: 0.179, auc: 0.323, f1: 0.143, acc: 0.444
+2025-08-07 22:07:42: Epoch: 043 Train Loss: 0.043, ap: 0.645, auc: 0.736, f1: 0.660, acc: 0.670
+2025-08-07 22:07:44: Epoch: 043 Test results, ap: 0.185, auc: 0.332, f1: 0.103, acc: 0.519
+2025-08-07 22:07:51: Epoch: 044 Train Loss: 0.042, ap: 0.634, auc: 0.723, f1: 0.617, acc: 0.650
+2025-08-07 22:07:53: Epoch: 044 Test results, ap: 0.191, auc: 0.336, f1: 0.083, acc: 0.593
+2025-08-07 22:08:00: Epoch: 045 Train Loss: 0.041, ap: 0.649, auc: 0.738, f1: 0.646, acc: 0.670
+2025-08-07 22:08:02: Epoch: 045 Test results, ap: 0.193, auc: 0.339, f1: 0.091, acc: 0.630
+2025-08-07 22:08:09: Epoch: 046 Train Loss: 0.034, ap: 0.656, auc: 0.741, f1: 0.660, acc: 0.680
+2025-08-07 22:08:10: Epoch: 046 Test results, ap: 0.196, auc: 0.343, f1: 0.085, acc: 0.602
+2025-08-07 22:08:18: Epoch: 047 Train Loss: 0.039, ap: 0.633, auc: 0.731, f1: 0.645, acc: 0.680
+2025-08-07 22:08:19: Epoch: 047 Test results, ap: 0.202, auc: 0.347, f1: 0.103, acc: 0.676
+2025-08-07 22:08:27: Epoch: 048 Train Loss: 0.038, ap: 0.632, auc: 0.733, f1: 0.660, acc: 0.680
+2025-08-07 22:08:28: Epoch: 048 Test results, ap: 0.202, auc: 0.347, f1: 0.083, acc: 0.593
+2025-08-07 22:08:35: Epoch: 049 Train Loss: 0.038, ap: 0.633, auc: 0.733, f1: 0.653, acc: 0.670
+2025-08-07 22:08:37: Epoch: 049 Test results, ap: 0.201, auc: 0.356, f1: 0.078, acc: 0.565
+2025-08-07 22:08:44: Epoch: 050 Train Loss: 0.054, ap: 0.669, auc: 0.751, f1: 0.694, acc: 0.709
+2025-08-07 22:08:45: Epoch: 050 Test results, ap: 0.202, auc: 0.378, f1: 0.136, acc: 0.528
+2025-08-07 22:08:45: used time 0.12h
+2025-08-07 22:08:45: Running Cross Validation 4
+2025-08-07 22:08:45: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 22:08:53: Epoch: 001 Train Loss: 0.691, ap: 0.500, auc: 0.639, f1: 0.000, acc: 0.602
+2025-08-07 22:08:53: Epoch: 001 New best VALIDATION metrics
+2025-08-07 22:08:54: Epoch: 001 Test results, ap: 0.189, auc: 0.340, f1: 0.000, acc: 0.759
+2025-08-07 22:09:02: Epoch: 002 Train Loss: 0.644, ap: 0.654, auc: 0.681, f1: 0.530, acc: 0.621
+2025-08-07 22:09:02: Epoch: 002 New best VALIDATION metrics
+2025-08-07 22:09:03: Epoch: 002 Test results, ap: 0.227, auc: 0.368, f1: 0.176, acc: 0.741
+2025-08-07 22:09:10: Epoch: 003 Train Loss: 0.587, ap: 0.705, auc: 0.752, f1: 0.464, acc: 0.709
+2025-08-07 22:09:10: Epoch: 003 New best VALIDATION metrics
+2025-08-07 22:09:12: Epoch: 003 Test results, ap: 0.179, auc: 0.327, f1: 0.000, acc: 0.759
+2025-08-07 22:09:19: Epoch: 004 Train Loss: 0.556, ap: 0.719, auc: 0.759, f1: 0.644, acc: 0.699
+2025-08-07 22:09:19: Epoch: 004 New best VALIDATION metrics
+2025-08-07 22:09:21: Epoch: 004 Test results, ap: 0.189, auc: 0.301, f1: 0.000, acc: 0.759
+2025-08-07 22:09:28: Epoch: 005 Train Loss: 0.552, ap: 0.687, auc: 0.720, f1: 0.414, acc: 0.670
+2025-08-07 22:09:30: Epoch: 005 Test results, ap: 0.186, auc: 0.292, f1: 0.000, acc: 0.759
+2025-08-07 22:09:37: Epoch: 006 Train Loss: 0.493, ap: 0.678, auc: 0.683, f1: 0.554, acc: 0.718
+2025-08-07 22:09:38: Epoch: 006 Test results, ap: 0.270, auc: 0.442, f1: 0.129, acc: 0.750
+2025-08-07 22:09:46: Epoch: 007 Train Loss: 0.502, ap: 0.672, auc: 0.659, f1: 0.574, acc: 0.495
+2025-08-07 22:09:47: Epoch: 007 Test results, ap: 0.160, auc: 0.229, f1: 0.184, acc: 0.343
+2025-08-07 22:09:54: Epoch: 008 Train Loss: 0.463, ap: 0.736, auc: 0.779, f1: 0.327, acc: 0.680
+2025-08-07 22:09:54: Epoch: 008 New best VALIDATION metrics
+2025-08-07 22:09:56: Epoch: 008 Test results, ap: 0.211, auc: 0.416, f1: 0.000, acc: 0.759
+2025-08-07 22:10:03: Epoch: 009 Train Loss: 0.435, ap: 0.704, auc: 0.731, f1: 0.585, acc: 0.738
+2025-08-07 22:10:05: Epoch: 009 Test results, ap: 0.197, auc: 0.368, f1: 0.000, acc: 0.759
+2025-08-07 22:10:12: Epoch: 010 Train Loss: 0.424, ap: 0.659, auc: 0.710, f1: 0.554, acc: 0.641
+2025-08-07 22:10:13: Epoch: 010 Test results, ap: 0.295, auc: 0.456, f1: 0.222, acc: 0.676
+2025-08-07 22:10:21: Epoch: 011 Train Loss: 0.377, ap: 0.692, auc: 0.734, f1: 0.519, acc: 0.641
+2025-08-07 22:10:22: Epoch: 011 Test results, ap: 0.258, auc: 0.446, f1: 0.000, acc: 0.731
+2025-08-07 22:10:22: Epoch: 011 New best TEST metrics
+2025-08-07 22:10:30: Epoch: 012 Train Loss: 0.320, ap: 0.659, auc: 0.738, f1: 0.485, acc: 0.670
+2025-08-07 22:10:31: Epoch: 012 Test results, ap: 0.235, auc: 0.417, f1: 0.000, acc: 0.759
+2025-08-07 22:10:38: Epoch: 013 Train Loss: 0.319, ap: 0.631, auc: 0.695, f1: 0.533, acc: 0.660
+2025-08-07 22:10:40: Epoch: 013 Test results, ap: 0.274, auc: 0.476, f1: 0.000, acc: 0.759
+2025-08-07 22:10:40: Epoch: 013 New best TEST metrics
+2025-08-07 22:10:47: Epoch: 014 Train Loss: 0.346, ap: 0.684, auc: 0.740, f1: 0.528, acc: 0.670
+2025-08-07 22:10:48: Epoch: 014 Test results, ap: 0.257, auc: 0.455, f1: 0.000, acc: 0.759
+2025-08-07 22:10:56: Epoch: 015 Train Loss: 0.290, ap: 0.660, auc: 0.740, f1: 0.615, acc: 0.660
+2025-08-07 22:10:57: Epoch: 015 Test results, ap: 0.233, auc: 0.441, f1: 0.061, acc: 0.713
+2025-08-07 22:11:05: Epoch: 016 Train Loss: 0.232, ap: 0.661, auc: 0.733, f1: 0.587, acc: 0.699
+2025-08-07 22:11:06: Epoch: 016 Test results, ap: 0.254, auc: 0.474, f1: 0.000, acc: 0.759
+2025-08-07 22:11:13: Epoch: 017 Train Loss: 0.289, ap: 0.633, auc: 0.703, f1: 0.578, acc: 0.631
+2025-08-07 22:11:15: Epoch: 017 Test results, ap: 0.290, auc: 0.583, f1: 0.000, acc: 0.713
+2025-08-07 22:11:15: Epoch: 017 New best TEST metrics
+2025-08-07 22:11:22: Epoch: 018 Train Loss: 0.248, ap: 0.652, auc: 0.728, f1: 0.632, acc: 0.660
+2025-08-07 22:11:24: Epoch: 018 Test results, ap: 0.260, auc: 0.467, f1: 0.061, acc: 0.713
+2025-08-07 22:11:31: Epoch: 019 Train Loss: 0.266, ap: 0.683, auc: 0.757, f1: 0.625, acc: 0.709
+2025-08-07 22:11:32: Epoch: 019 Test results, ap: 0.265, auc: 0.472, f1: 0.000, acc: 0.741
+2025-08-07 22:11:39: Epoch: 020 Train Loss: 0.269, ap: 0.658, auc: 0.732, f1: 0.591, acc: 0.650
+2025-08-07 22:11:41: Epoch: 020 Test results, ap: 0.258, auc: 0.506, f1: 0.162, acc: 0.713
+2025-08-07 22:11:48: Epoch: 021 Train Loss: 0.232, ap: 0.709, auc: 0.769, f1: 0.611, acc: 0.728
+2025-08-07 22:11:50: Epoch: 021 Test results, ap: 0.250, auc: 0.493, f1: 0.000, acc: 0.750
+2025-08-07 22:11:57: Epoch: 022 Train Loss: 0.205, ap: 0.677, auc: 0.745, f1: 0.550, acc: 0.650
+2025-08-07 22:11:58: Epoch: 022 Test results, ap: 0.263, auc: 0.497, f1: 0.000, acc: 0.741
+2025-08-07 22:12:06: Epoch: 023 Train Loss: 0.192, ap: 0.669, auc: 0.742, f1: 0.645, acc: 0.680
+2025-08-07 22:12:07: Epoch: 023 Test results, ap: 0.256, auc: 0.524, f1: 0.114, acc: 0.713
+2025-08-07 22:12:14: Epoch: 024 Train Loss: 0.202, ap: 0.669, auc: 0.741, f1: 0.575, acc: 0.699
+2025-08-07 22:12:16: Epoch: 024 Test results, ap: 0.277, auc: 0.554, f1: 0.000, acc: 0.741
+2025-08-07 22:12:23: Epoch: 025 Train Loss: 0.188, ap: 0.648, auc: 0.716, f1: 0.635, acc: 0.631
+2025-08-07 22:12:25: Epoch: 025 Test results, ap: 0.279, auc: 0.535, f1: 0.276, acc: 0.611
+2025-08-07 22:12:32: Epoch: 026 Train Loss: 0.178, ap: 0.663, auc: 0.735, f1: 0.564, acc: 0.670
+2025-08-07 22:12:33: Epoch: 026 Test results, ap: 0.247, auc: 0.516, f1: 0.000, acc: 0.713
+2025-08-07 22:12:41: Epoch: 027 Train Loss: 0.163, ap: 0.643, auc: 0.713, f1: 0.579, acc: 0.689
+2025-08-07 22:12:42: Epoch: 027 Test results, ap: 0.281, auc: 0.539, f1: 0.222, acc: 0.741
+2025-08-07 22:12:49: Epoch: 028 Train Loss: 0.155, ap: 0.636, auc: 0.712, f1: 0.636, acc: 0.621
+2025-08-07 22:12:51: Epoch: 028 Test results, ap: 0.298, auc: 0.566, f1: 0.246, acc: 0.602
+2025-08-07 22:12:58: Epoch: 029 Train Loss: 0.152, ap: 0.664, auc: 0.726, f1: 0.600, acc: 0.689
+2025-08-07 22:13:00: Epoch: 029 Test results, ap: 0.282, auc: 0.566, f1: 0.125, acc: 0.741
+2025-08-07 22:13:07: Epoch: 030 Train Loss: 0.168, ap: 0.630, auc: 0.702, f1: 0.613, acc: 0.583
+2025-08-07 22:13:09: Epoch: 030 Test results, ap: 0.294, auc: 0.586, f1: 0.405, acc: 0.593
+2025-08-07 22:13:09: Epoch: 030 New best TEST metrics
+2025-08-07 22:13:16: Epoch: 031 Train Loss: 0.134, ap: 0.636, auc: 0.710, f1: 0.571, acc: 0.680
+2025-08-07 22:13:17: Epoch: 031 Test results, ap: 0.297, auc: 0.577, f1: 0.227, acc: 0.685
+2025-08-07 22:13:25: Epoch: 032 Train Loss: 0.126, ap: 0.632, auc: 0.708, f1: 0.585, acc: 0.670
+2025-08-07 22:13:26: Epoch: 032 Test results, ap: 0.304, auc: 0.571, f1: 0.238, acc: 0.704
+2025-08-07 22:13:33: Epoch: 033 Train Loss: 0.113, ap: 0.631, auc: 0.708, f1: 0.556, acc: 0.689
+2025-08-07 22:13:35: Epoch: 033 Test results, ap: 0.306, auc: 0.572, f1: 0.125, acc: 0.741
+2025-08-07 22:13:42: Epoch: 034 Train Loss: 0.097, ap: 0.589, auc: 0.679, f1: 0.519, acc: 0.621
+2025-08-07 22:13:44: Epoch: 034 Test results, ap: 0.318, auc: 0.587, f1: 0.182, acc: 0.667
+2025-08-07 22:13:44: Epoch: 034 New best TEST metrics
+2025-08-07 22:13:51: Epoch: 035 Train Loss: 0.098, ap: 0.621, auc: 0.715, f1: 0.568, acc: 0.631
+2025-08-07 22:13:53: Epoch: 035 Test results, ap: 0.282, auc: 0.550, f1: 0.108, acc: 0.694
+2025-08-07 22:14:00: Epoch: 036 Train Loss: 0.100, ap: 0.596, auc: 0.700, f1: 0.557, acc: 0.660
+2025-08-07 22:14:01: Epoch: 036 Test results, ap: 0.310, auc: 0.568, f1: 0.211, acc: 0.722
+2025-08-07 22:14:09: Epoch: 037 Train Loss: 0.118, ap: 0.616, auc: 0.701, f1: 0.493, acc: 0.660
+2025-08-07 22:14:10: Epoch: 037 Test results, ap: 0.324, auc: 0.591, f1: 0.138, acc: 0.769
+2025-08-07 22:14:10: Epoch: 037 New best TEST metrics
+2025-08-07 22:14:18: Epoch: 038 Train Loss: 0.115, ap: 0.550, auc: 0.663, f1: 0.598, acc: 0.583
+2025-08-07 22:14:19: Epoch: 038 Test results, ap: 0.322, auc: 0.590, f1: 0.344, acc: 0.611
+2025-08-07 22:14:26: Epoch: 039 Train Loss: 0.108, ap: 0.617, auc: 0.707, f1: 0.514, acc: 0.670
+2025-08-07 22:14:28: Epoch: 039 Test results, ap: 0.320, auc: 0.572, f1: 0.129, acc: 0.750
+2025-08-07 22:14:35: Epoch: 040 Train Loss: 0.101, ap: 0.563, auc: 0.671, f1: 0.552, acc: 0.621
+2025-08-07 22:14:37: Epoch: 040 Test results, ap: 0.313, auc: 0.568, f1: 0.238, acc: 0.704
+2025-08-07 22:14:44: Epoch: 041 Train Loss: 0.080, ap: 0.571, auc: 0.679, f1: 0.543, acc: 0.641
+2025-08-07 22:14:45: Epoch: 041 Test results, ap: 0.312, auc: 0.569, f1: 0.121, acc: 0.731
+2025-08-07 22:14:53: Epoch: 042 Train Loss: 0.073, ap: 0.563, auc: 0.669, f1: 0.507, acc: 0.641
+2025-08-07 22:14:54: Epoch: 042 Test results, ap: 0.323, auc: 0.585, f1: 0.125, acc: 0.741
+2025-08-07 22:15:02: Epoch: 043 Train Loss: 0.076, ap: 0.567, auc: 0.679, f1: 0.550, acc: 0.650
+2025-08-07 22:15:03: Epoch: 043 Test results, ap: 0.317, auc: 0.580, f1: 0.171, acc: 0.731
+2025-08-07 22:15:10: Epoch: 044 Train Loss: 0.069, ap: 0.551, auc: 0.667, f1: 0.530, acc: 0.621
+2025-08-07 22:15:12: Epoch: 044 Test results, ap: 0.304, auc: 0.566, f1: 0.176, acc: 0.741
+2025-08-07 22:15:19: Epoch: 045 Train Loss: 0.070, ap: 0.551, auc: 0.667, f1: 0.525, acc: 0.631
+2025-08-07 22:15:20: Epoch: 045 Test results, ap: 0.306, auc: 0.568, f1: 0.125, acc: 0.741
+2025-08-07 22:15:28: Epoch: 046 Train Loss: 0.063, ap: 0.553, auc: 0.672, f1: 0.528, acc: 0.670
+2025-08-07 22:15:29: Epoch: 046 Test results, ap: 0.318, auc: 0.576, f1: 0.176, acc: 0.741
+2025-08-07 22:15:37: Epoch: 047 Train Loss: 0.085, ap: 0.538, auc: 0.659, f1: 0.481, acc: 0.602
+2025-08-07 22:15:38: Epoch: 047 Test results, ap: 0.311, auc: 0.561, f1: 0.216, acc: 0.731
+2025-08-07 22:15:45: Epoch: 048 Train Loss: 0.080, ap: 0.544, auc: 0.659, f1: 0.519, acc: 0.641
+2025-08-07 22:15:47: Epoch: 048 Test results, ap: 0.298, auc: 0.563, f1: 0.182, acc: 0.750
+2025-08-07 22:15:54: Epoch: 049 Train Loss: 0.070, ap: 0.584, auc: 0.686, f1: 0.500, acc: 0.650
+2025-08-07 22:15:56: Epoch: 049 Test results, ap: 0.302, auc: 0.559, f1: 0.129, acc: 0.750
+2025-08-07 22:16:03: Epoch: 050 Train Loss: 0.051, ap: 0.565, auc: 0.679, f1: 0.543, acc: 0.641
+2025-08-07 22:16:05: Epoch: 050 Test results, ap: 0.303, auc: 0.565, f1: 0.171, acc: 0.731
+2025-08-07 22:16:05: used time 0.12h
+2025-08-07 22:16:05: Cross Validation Finished!
+2025-08-07 22:16:05: Best validation perform list
+[[0.74243701 0.72872341 0.5714286  0.70873785]
+ [0.66894197 0.71253073 0.53571427 0.74757284]
+ [0.85854769 0.82088989 0.72222221 0.70873785]
+ [0.78892827 0.81615925 0.04651163 0.60194176]
+ [0.73563671 0.77852082 0.32653061 0.67961162]]
+2025-08-07 22:16:05: mean: [0.759 0.771 0.44  0.689]
+2025-08-07 22:16:05: std: [0.063 0.044 0.234 0.049]
+2025-08-07 22:16:05: Best test perform list
+[[0.29326925 0.65243906 0.         0.75      ]
+ [0.32092667 0.63742965 0.45454547 0.55555558]
+ [0.26351118 0.5150094  0.27906978 0.71296299]
+ [0.24725214 0.49718574 0.         0.75925928]
+ [0.32399824 0.59099436 0.13793103 0.76851851]]
+2025-08-07 22:16:05: mean: [0.29  0.579 0.174 0.709]
+2025-08-07 22:16:05: std: [0.031 0.063 0.174 0.079]
diff --git a/run-cls-simple/rn18-att-16-pcs-simple-768-oneway-ce-32-0.001-50/config.json b/run-cls-simple/rn18-att-16-pcs-simple-768-oneway-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": null, "channels": 16, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": true, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
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+++ b/run-cls-simple/rn18-att-16-pcs-simple-768-oneway-ce-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
+Valid
+0.7452430725097656,0.7606262564659119,0.632979953289032,0.7029126167297364
+0.06589720939041566,0.02902777911625941,0.0737449046349041,0.04700472684543068
+Test
+0.2568772852420807,0.48198875188827517,0.06600221693515777,0.7111111164093018
+0.03380071065033077,0.08087510131215318,0.08242536381151835,0.03857901851441094
diff --git a/run-cls-simple/rn18-att-16-pcs-simple-768-oneway-ce-32-0.001-50/training.log b/run-cls-simple/rn18-att-16-pcs-simple-768-oneway-ce-32-0.001-50/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..7dadc37dd2815676e2cedd89bbf2fd467d4d6b26
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-pcs-simple-768-oneway-ce-32-0.001-50/training.log
@@ -0,0 +1,576 @@
+2025-08-07 22:40:43: saving_dir: ./run-cls/rn18-att-16-pcs-simple-768-oneway-ce-32-0.001-50
+2025-08-07 22:40:43: Loading Training Dataset
+2025-08-07 22:41:07: Loading Test Dataset
+2025-08-07 22:41:11: Running Cross Validation 0
+2025-08-07 22:41:11: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 22:41:19: Epoch: 001 Train Loss: 0.692, ap: 0.467, auc: 0.544, f1: 0.000, acc: 0.544
+2025-08-07 22:41:19: Epoch: 001 New best VALIDATION metrics
+2025-08-07 22:41:21: Epoch: 001 Test results, ap: 0.150, auc: 0.161, f1: 0.000, acc: 0.759
+2025-08-07 22:41:28: Epoch: 002 Train Loss: 0.658, ap: 0.710, auc: 0.736, f1: 0.345, acc: 0.631
+2025-08-07 22:41:28: Epoch: 002 New best VALIDATION metrics
+2025-08-07 22:41:29: Epoch: 002 Test results, ap: 0.197, auc: 0.402, f1: 0.000, acc: 0.759
+2025-08-07 22:41:37: Epoch: 003 Train Loss: 0.616, ap: 0.586, auc: 0.600, f1: 0.595, acc: 0.485
+2025-08-07 22:41:38: Epoch: 003 Test results, ap: 0.277, auc: 0.432, f1: 0.388, acc: 0.241
+2025-08-07 22:41:45: Epoch: 004 Train Loss: 0.601, ap: 0.734, auc: 0.744, f1: 0.000, acc: 0.544
+2025-08-07 22:41:45: Epoch: 004 New best VALIDATION metrics
+2025-08-07 22:41:47: Epoch: 004 Test results, ap: 0.221, auc: 0.389, f1: 0.000, acc: 0.759
+2025-08-07 22:41:54: Epoch: 005 Train Loss: 0.580, ap: 0.718, auc: 0.722, f1: 0.042, acc: 0.553
+2025-08-07 22:41:55: Epoch: 005 Test results, ap: 0.214, auc: 0.427, f1: 0.000, acc: 0.759
+2025-08-07 22:42:03: Epoch: 006 Train Loss: 0.508, ap: 0.659, auc: 0.677, f1: 0.662, acc: 0.544
+2025-08-07 22:42:04: Epoch: 006 Test results, ap: 0.183, auc: 0.306, f1: 0.388, acc: 0.241
+2025-08-07 22:42:11: Epoch: 007 Train Loss: 0.489, ap: 0.740, auc: 0.755, f1: 0.310, acc: 0.612
+2025-08-07 22:42:11: Epoch: 007 New best VALIDATION metrics
+2025-08-07 22:42:13: Epoch: 007 Test results, ap: 0.255, auc: 0.566, f1: 0.000, acc: 0.759
+2025-08-07 22:42:20: Epoch: 008 Train Loss: 0.448, ap: 0.729, auc: 0.740, f1: 0.548, acc: 0.680
+2025-08-07 22:42:21: Epoch: 008 Test results, ap: 0.243, auc: 0.432, f1: 0.000, acc: 0.759
+2025-08-07 22:42:29: Epoch: 009 Train Loss: 0.384, ap: 0.704, auc: 0.699, f1: 0.345, acc: 0.631
+2025-08-07 22:42:30: Epoch: 009 Test results, ap: 0.204, auc: 0.344, f1: 0.000, acc: 0.759
+2025-08-07 22:42:37: Epoch: 010 Train Loss: 0.396, ap: 0.717, auc: 0.713, f1: 0.556, acc: 0.689
+2025-08-07 22:42:39: Epoch: 010 Test results, ap: 0.232, auc: 0.431, f1: 0.000, acc: 0.759
+2025-08-07 22:42:46: Epoch: 011 Train Loss: 0.340, ap: 0.735, auc: 0.756, f1: 0.717, acc: 0.709
+2025-08-07 22:42:47: Epoch: 011 Test results, ap: 0.209, auc: 0.380, f1: 0.000, acc: 0.722
+2025-08-07 22:42:47: Epoch: 011 New best TEST metrics
+2025-08-07 22:42:55: Epoch: 012 Train Loss: 0.301, ap: 0.721, auc: 0.734, f1: 0.667, acc: 0.709
+2025-08-07 22:42:56: Epoch: 012 Test results, ap: 0.210, auc: 0.399, f1: 0.000, acc: 0.722
+2025-08-07 22:42:56: Epoch: 012 New best TEST metrics
+2025-08-07 22:43:03: Epoch: 013 Train Loss: 0.305, ap: 0.730, auc: 0.754, f1: 0.690, acc: 0.738
+2025-08-07 22:43:05: Epoch: 013 Test results, ap: 0.187, auc: 0.340, f1: 0.000, acc: 0.722
+2025-08-07 22:43:12: Epoch: 014 Train Loss: 0.276, ap: 0.759, auc: 0.766, f1: 0.556, acc: 0.689
+2025-08-07 22:43:12: Epoch: 014 New best VALIDATION metrics
+2025-08-07 22:43:13: Epoch: 014 Test results, ap: 0.201, auc: 0.385, f1: 0.000, acc: 0.759
+2025-08-07 22:43:21: Epoch: 015 Train Loss: 0.277, ap: 0.727, auc: 0.736, f1: 0.612, acc: 0.680
+2025-08-07 22:43:22: Epoch: 015 Test results, ap: 0.189, auc: 0.366, f1: 0.000, acc: 0.731
+2025-08-07 22:43:29: Epoch: 016 Train Loss: 0.318, ap: 0.710, auc: 0.724, f1: 0.621, acc: 0.680
+2025-08-07 22:43:31: Epoch: 016 Test results, ap: 0.195, auc: 0.383, f1: 0.000, acc: 0.676
+2025-08-07 22:43:38: Epoch: 017 Train Loss: 0.269, ap: 0.726, auc: 0.747, f1: 0.608, acc: 0.699
+2025-08-07 22:43:39: Epoch: 017 Test results, ap: 0.211, auc: 0.413, f1: 0.000, acc: 0.759
+2025-08-07 22:43:39: Epoch: 017 New best TEST metrics
+2025-08-07 22:43:47: Epoch: 018 Train Loss: 0.292, ap: 0.742, auc: 0.753, f1: 0.680, acc: 0.680
+2025-08-07 22:43:48: Epoch: 018 Test results, ap: 0.217, auc: 0.466, f1: 0.000, acc: 0.667
+2025-08-07 22:43:48: Epoch: 018 New best TEST metrics
+2025-08-07 22:43:55: Epoch: 019 Train Loss: 0.292, ap: 0.742, auc: 0.729, f1: 0.593, acc: 0.680
+2025-08-07 22:43:57: Epoch: 019 Test results, ap: 0.220, auc: 0.460, f1: 0.000, acc: 0.741
+2025-08-07 22:44:04: Epoch: 020 Train Loss: 0.246, ap: 0.725, auc: 0.734, f1: 0.653, acc: 0.670
+2025-08-07 22:44:05: Epoch: 020 Test results, ap: 0.197, auc: 0.393, f1: 0.000, acc: 0.704
+2025-08-07 22:44:13: Epoch: 021 Train Loss: 0.239, ap: 0.734, auc: 0.733, f1: 0.652, acc: 0.699
+2025-08-07 22:44:14: Epoch: 021 Test results, ap: 0.215, auc: 0.429, f1: 0.000, acc: 0.722
+2025-08-07 22:44:21: Epoch: 022 Train Loss: 0.236, ap: 0.726, auc: 0.718, f1: 0.571, acc: 0.680
+2025-08-07 22:44:23: Epoch: 022 Test results, ap: 0.201, auc: 0.403, f1: 0.000, acc: 0.759
+2025-08-07 22:44:30: Epoch: 023 Train Loss: 0.218, ap: 0.729, auc: 0.718, f1: 0.610, acc: 0.689
+2025-08-07 22:44:31: Epoch: 023 Test results, ap: 0.206, auc: 0.422, f1: 0.000, acc: 0.731
+2025-08-07 22:44:39: Epoch: 024 Train Loss: 0.201, ap: 0.734, auc: 0.734, f1: 0.667, acc: 0.680
+2025-08-07 22:44:40: Epoch: 024 Test results, ap: 0.236, auc: 0.516, f1: 0.000, acc: 0.704
+2025-08-07 22:44:40: Epoch: 024 New best TEST metrics
+2025-08-07 22:44:47: Epoch: 025 Train Loss: 0.199, ap: 0.731, auc: 0.713, f1: 0.612, acc: 0.680
+2025-08-07 22:44:49: Epoch: 025 Test results, ap: 0.225, auc: 0.473, f1: 0.000, acc: 0.722
+2025-08-07 22:44:56: Epoch: 026 Train Loss: 0.200, ap: 0.735, auc: 0.741, f1: 0.692, acc: 0.689
+2025-08-07 22:44:58: Epoch: 026 Test results, ap: 0.228, auc: 0.493, f1: 0.000, acc: 0.713
+2025-08-07 22:45:05: Epoch: 027 Train Loss: 0.227, ap: 0.739, auc: 0.733, f1: 0.635, acc: 0.699
+2025-08-07 22:45:06: Epoch: 027 Test results, ap: 0.228, auc: 0.486, f1: 0.000, acc: 0.741
+2025-08-07 22:45:13: Epoch: 028 Train Loss: 0.202, ap: 0.725, auc: 0.720, f1: 0.659, acc: 0.699
+2025-08-07 22:45:15: Epoch: 028 Test results, ap: 0.244, auc: 0.538, f1: 0.000, acc: 0.741
+2025-08-07 22:45:15: Epoch: 028 New best TEST metrics
+2025-08-07 22:45:22: Epoch: 029 Train Loss: 0.195, ap: 0.732, auc: 0.724, f1: 0.660, acc: 0.689
+2025-08-07 22:45:23: Epoch: 029 Test results, ap: 0.247, auc: 0.543, f1: 0.000, acc: 0.713
+2025-08-07 22:45:23: Epoch: 029 New best TEST metrics
+2025-08-07 22:45:31: Epoch: 030 Train Loss: 0.197, ap: 0.724, auc: 0.690, f1: 0.615, acc: 0.709
+2025-08-07 22:45:32: Epoch: 030 Test results, ap: 0.221, auc: 0.455, f1: 0.000, acc: 0.759
+2025-08-07 22:45:39: Epoch: 031 Train Loss: 0.188, ap: 0.725, auc: 0.715, f1: 0.667, acc: 0.699
+2025-08-07 22:45:41: Epoch: 031 Test results, ap: 0.253, auc: 0.561, f1: 0.000, acc: 0.704
+2025-08-07 22:45:41: Epoch: 031 New best TEST metrics
+2025-08-07 22:45:48: Epoch: 032 Train Loss: 0.167, ap: 0.721, auc: 0.696, f1: 0.575, acc: 0.670
+2025-08-07 22:45:50: Epoch: 032 Test results, ap: 0.244, auc: 0.540, f1: 0.000, acc: 0.741
+2025-08-07 22:45:57: Epoch: 033 Train Loss: 0.175, ap: 0.723, auc: 0.710, f1: 0.653, acc: 0.670
+2025-08-07 22:45:58: Epoch: 033 Test results, ap: 0.250, auc: 0.552, f1: 0.000, acc: 0.694
+2025-08-07 22:46:06: Epoch: 034 Train Loss: 0.161, ap: 0.720, auc: 0.705, f1: 0.629, acc: 0.680
+2025-08-07 22:46:07: Epoch: 034 Test results, ap: 0.234, auc: 0.510, f1: 0.000, acc: 0.713
+2025-08-07 22:46:14: Epoch: 035 Train Loss: 0.175, ap: 0.714, auc: 0.694, f1: 0.653, acc: 0.680
+2025-08-07 22:46:16: Epoch: 035 Test results, ap: 0.249, auc: 0.545, f1: 0.000, acc: 0.713
+2025-08-07 22:46:23: Epoch: 036 Train Loss: 0.145, ap: 0.730, auc: 0.722, f1: 0.674, acc: 0.699
+2025-08-07 22:46:24: Epoch: 036 Test results, ap: 0.240, auc: 0.522, f1: 0.000, acc: 0.694
+2025-08-07 22:46:32: Epoch: 037 Train Loss: 0.153, ap: 0.718, auc: 0.697, f1: 0.652, acc: 0.689
+2025-08-07 22:46:33: Epoch: 037 Test results, ap: 0.245, auc: 0.524, f1: 0.000, acc: 0.722
+2025-08-07 22:46:40: Epoch: 038 Train Loss: 0.155, ap: 0.726, auc: 0.726, f1: 0.686, acc: 0.680
+2025-08-07 22:46:42: Epoch: 038 Test results, ap: 0.229, auc: 0.503, f1: 0.000, acc: 0.630
+2025-08-07 22:46:49: Epoch: 039 Train Loss: 0.150, ap: 0.719, auc: 0.702, f1: 0.610, acc: 0.689
+2025-08-07 22:46:51: Epoch: 039 Test results, ap: 0.250, auc: 0.547, f1: 0.000, acc: 0.704
+2025-08-07 22:46:58: Epoch: 040 Train Loss: 0.151, ap: 0.722, auc: 0.709, f1: 0.629, acc: 0.680
+2025-08-07 22:46:59: Epoch: 040 Test results, ap: 0.255, auc: 0.557, f1: 0.000, acc: 0.713
+2025-08-07 22:47:06: Epoch: 041 Train Loss: 0.141, ap: 0.709, auc: 0.691, f1: 0.652, acc: 0.689
+2025-08-07 22:47:08: Epoch: 041 Test results, ap: 0.263, auc: 0.568, f1: 0.000, acc: 0.704
+2025-08-07 22:47:08: Epoch: 041 New best TEST metrics
+2025-08-07 22:47:15: Epoch: 042 Train Loss: 0.138, ap: 0.717, auc: 0.705, f1: 0.632, acc: 0.660
+2025-08-07 22:47:17: Epoch: 042 Test results, ap: 0.253, auc: 0.553, f1: 0.000, acc: 0.657
+2025-08-07 22:47:24: Epoch: 043 Train Loss: 0.116, ap: 0.723, auc: 0.712, f1: 0.660, acc: 0.689
+2025-08-07 22:47:25: Epoch: 043 Test results, ap: 0.248, auc: 0.546, f1: 0.000, acc: 0.667
+2025-08-07 22:47:32: Epoch: 044 Train Loss: 0.135, ap: 0.723, auc: 0.701, f1: 0.644, acc: 0.689
+2025-08-07 22:47:34: Epoch: 044 Test results, ap: 0.256, auc: 0.554, f1: 0.000, acc: 0.713
+2025-08-07 22:47:41: Epoch: 045 Train Loss: 0.120, ap: 0.707, auc: 0.683, f1: 0.629, acc: 0.680
+2025-08-07 22:47:43: Epoch: 045 Test results, ap: 0.251, auc: 0.539, f1: 0.000, acc: 0.694
+2025-08-07 22:47:50: Epoch: 046 Train Loss: 0.127, ap: 0.705, auc: 0.665, f1: 0.568, acc: 0.660
+2025-08-07 22:47:51: Epoch: 046 Test results, ap: 0.268, auc: 0.560, f1: 0.000, acc: 0.731
+2025-08-07 22:47:58: Epoch: 047 Train Loss: 0.114, ap: 0.710, auc: 0.695, f1: 0.645, acc: 0.680
+2025-08-07 22:48:00: Epoch: 047 Test results, ap: 0.253, auc: 0.552, f1: 0.000, acc: 0.657
+2025-08-07 22:48:07: Epoch: 048 Train Loss: 0.112, ap: 0.717, auc: 0.714, f1: 0.638, acc: 0.670
+2025-08-07 22:48:09: Epoch: 048 Test results, ap: 0.244, auc: 0.540, f1: 0.000, acc: 0.657
+2025-08-07 22:48:16: Epoch: 049 Train Loss: 0.108, ap: 0.713, auc: 0.687, f1: 0.614, acc: 0.670
+2025-08-07 22:48:17: Epoch: 049 Test results, ap: 0.248, auc: 0.536, f1: 0.000, acc: 0.694
+2025-08-07 22:48:25: Epoch: 050 Train Loss: 0.114, ap: 0.718, auc: 0.699, f1: 0.637, acc: 0.680
+2025-08-07 22:48:26: Epoch: 050 Test results, ap: 0.240, auc: 0.519, f1: 0.000, acc: 0.667
+2025-08-07 22:48:26: used time 0.12h
+2025-08-07 22:48:26: Running Cross Validation 1
+2025-08-07 22:48:26: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 22:48:33: Epoch: 001 Train Loss: 0.679, ap: 0.507, auc: 0.593, f1: 0.529, acc: 0.359
+2025-08-07 22:48:33: Epoch: 001 New best VALIDATION metrics
+2025-08-07 22:48:35: Epoch: 001 Test results, ap: 0.275, auc: 0.619, f1: 0.388, acc: 0.241
+2025-08-07 22:48:42: Epoch: 002 Train Loss: 0.602, ap: 0.509, auc: 0.576, f1: 0.529, acc: 0.359
+2025-08-07 22:48:44: Epoch: 002 Test results, ap: 0.421, auc: 0.509, f1: 0.388, acc: 0.241
+2025-08-07 22:48:51: Epoch: 003 Train Loss: 0.583, ap: 0.585, auc: 0.703, f1: 0.327, acc: 0.680
+2025-08-07 22:48:51: Epoch: 003 New best VALIDATION metrics
+2025-08-07 22:48:52: Epoch: 003 Test results, ap: 0.180, auc: 0.330, f1: 0.000, acc: 0.759
+2025-08-07 22:49:00: Epoch: 004 Train Loss: 0.518, ap: 0.493, auc: 0.612, f1: 0.529, acc: 0.359
+2025-08-07 22:49:01: Epoch: 004 Test results, ap: 0.169, auc: 0.269, f1: 0.388, acc: 0.241
+2025-08-07 22:49:08: Epoch: 005 Train Loss: 0.517, ap: 0.615, auc: 0.718, f1: 0.233, acc: 0.680
+2025-08-07 22:49:08: Epoch: 005 New best VALIDATION metrics
+2025-08-07 22:49:10: Epoch: 005 Test results, ap: 0.183, auc: 0.332, f1: 0.000, acc: 0.759
+2025-08-07 22:49:17: Epoch: 006 Train Loss: 0.452, ap: 0.513, auc: 0.626, f1: 0.000, acc: 0.641
+2025-08-07 22:49:18: Epoch: 006 Test results, ap: 0.219, auc: 0.427, f1: 0.000, acc: 0.759
+2025-08-07 22:49:26: Epoch: 007 Train Loss: 0.449, ap: 0.464, auc: 0.621, f1: 0.536, acc: 0.379
+2025-08-07 22:49:27: Epoch: 007 Test results, ap: 0.174, auc: 0.301, f1: 0.388, acc: 0.241
+2025-08-07 22:49:34: Epoch: 008 Train Loss: 0.417, ap: 0.593, auc: 0.652, f1: 0.518, acc: 0.476
+2025-08-07 22:49:36: Epoch: 008 Test results, ap: 0.182, auc: 0.334, f1: 0.388, acc: 0.241
+2025-08-07 22:49:43: Epoch: 009 Train Loss: 0.385, ap: 0.590, auc: 0.660, f1: 0.563, acc: 0.563
+2025-08-07 22:49:44: Epoch: 009 Test results, ap: 0.213, auc: 0.365, f1: 0.288, acc: 0.315
+2025-08-07 22:49:52: Epoch: 010 Train Loss: 0.366, ap: 0.624, auc: 0.668, f1: 0.441, acc: 0.631
+2025-08-07 22:49:53: Epoch: 010 Test results, ap: 0.175, auc: 0.313, f1: 0.000, acc: 0.704
+2025-08-07 22:50:00: Epoch: 011 Train Loss: 0.328, ap: 0.615, auc: 0.686, f1: 0.000, acc: 0.641
+2025-08-07 22:50:02: Epoch: 011 Test results, ap: 0.269, auc: 0.417, f1: 0.000, acc: 0.759
+2025-08-07 22:50:02: Epoch: 011 New best TEST metrics
+2025-08-07 22:50:09: Epoch: 012 Train Loss: 0.316, ap: 0.591, auc: 0.696, f1: 0.469, acc: 0.670
+2025-08-07 22:50:10: Epoch: 012 Test results, ap: 0.243, auc: 0.357, f1: 0.000, acc: 0.741
+2025-08-07 22:50:18: Epoch: 013 Train Loss: 0.306, ap: 0.613, auc: 0.683, f1: 0.568, acc: 0.689
+2025-08-07 22:50:19: Epoch: 013 Test results, ap: 0.230, auc: 0.367, f1: 0.216, acc: 0.731
+2025-08-07 22:50:26: Epoch: 014 Train Loss: 0.293, ap: 0.596, auc: 0.690, f1: 0.519, acc: 0.621
+2025-08-07 22:50:28: Epoch: 014 Test results, ap: 0.213, auc: 0.347, f1: 0.131, acc: 0.509
+2025-08-07 22:50:35: Epoch: 015 Train Loss: 0.272, ap: 0.625, auc: 0.709, f1: 0.559, acc: 0.709
+2025-08-07 22:50:35: Epoch: 015 New best VALIDATION metrics
+2025-08-07 22:50:36: Epoch: 015 Test results, ap: 0.186, auc: 0.349, f1: 0.000, acc: 0.694
+2025-08-07 22:50:43: Epoch: 016 Train Loss: 0.290, ap: 0.600, auc: 0.699, f1: 0.561, acc: 0.650
+2025-08-07 22:50:45: Epoch: 016 Test results, ap: 0.227, auc: 0.373, f1: 0.131, acc: 0.509
+2025-08-07 22:50:52: Epoch: 017 Train Loss: 0.255, ap: 0.608, auc: 0.706, f1: 0.514, acc: 0.670
+2025-08-07 22:50:53: Epoch: 017 Test results, ap: 0.202, auc: 0.365, f1: 0.000, acc: 0.713
+2025-08-07 22:51:01: Epoch: 018 Train Loss: 0.257, ap: 0.576, auc: 0.693, f1: 0.532, acc: 0.641
+2025-08-07 22:51:02: Epoch: 018 Test results, ap: 0.204, auc: 0.379, f1: 0.195, acc: 0.694
+2025-08-07 22:51:09: Epoch: 019 Train Loss: 0.247, ap: 0.582, auc: 0.681, f1: 0.521, acc: 0.660
+2025-08-07 22:51:11: Epoch: 019 Test results, ap: 0.229, auc: 0.358, f1: 0.186, acc: 0.676
+2025-08-07 22:51:18: Epoch: 020 Train Loss: 0.242, ap: 0.554, auc: 0.692, f1: 0.476, acc: 0.680
+2025-08-07 22:51:19: Epoch: 020 Test results, ap: 0.250, auc: 0.391, f1: 0.000, acc: 0.750
+2025-08-07 22:51:27: Epoch: 021 Train Loss: 0.250, ap: 0.611, auc: 0.719, f1: 0.553, acc: 0.670
+2025-08-07 22:51:28: Epoch: 021 Test results, ap: 0.221, auc: 0.386, f1: 0.119, acc: 0.454
+2025-08-07 22:51:35: Epoch: 022 Train Loss: 0.248, ap: 0.606, auc: 0.710, f1: 0.528, acc: 0.670
+2025-08-07 22:51:37: Epoch: 022 Test results, ap: 0.248, auc: 0.380, f1: 0.235, acc: 0.759
+2025-08-07 22:51:44: Epoch: 023 Train Loss: 0.212, ap: 0.580, auc: 0.701, f1: 0.529, acc: 0.689
+2025-08-07 22:51:45: Epoch: 023 Test results, ap: 0.249, auc: 0.386, f1: 0.242, acc: 0.769
+2025-08-07 22:51:53: Epoch: 024 Train Loss: 0.200, ap: 0.573, auc: 0.688, f1: 0.537, acc: 0.699
+2025-08-07 22:51:54: Epoch: 024 Test results, ap: 0.246, auc: 0.391, f1: 0.235, acc: 0.759
+2025-08-07 22:52:01: Epoch: 025 Train Loss: 0.220, ap: 0.578, auc: 0.698, f1: 0.550, acc: 0.650
+2025-08-07 22:52:03: Epoch: 025 Test results, ap: 0.241, auc: 0.384, f1: 0.121, acc: 0.463
+2025-08-07 22:52:10: Epoch: 026 Train Loss: 0.205, ap: 0.564, auc: 0.701, f1: 0.571, acc: 0.709
+2025-08-07 22:52:11: Epoch: 026 Test results, ap: 0.239, auc: 0.402, f1: 0.178, acc: 0.657
+2025-08-07 22:52:19: Epoch: 027 Train Loss: 0.191, ap: 0.562, auc: 0.690, f1: 0.563, acc: 0.699
+2025-08-07 22:52:20: Epoch: 027 Test results, ap: 0.223, auc: 0.376, f1: 0.211, acc: 0.722
+2025-08-07 22:52:27: Epoch: 028 Train Loss: 0.231, ap: 0.564, auc: 0.694, f1: 0.567, acc: 0.718
+2025-08-07 22:52:29: Epoch: 028 Test results, ap: 0.216, auc: 0.377, f1: 0.000, acc: 0.722
+2025-08-07 22:52:36: Epoch: 029 Train Loss: 0.180, ap: 0.575, auc: 0.715, f1: 0.556, acc: 0.689
+2025-08-07 22:52:38: Epoch: 029 Test results, ap: 0.224, auc: 0.379, f1: 0.216, acc: 0.731
+2025-08-07 22:52:45: Epoch: 030 Train Loss: 0.176, ap: 0.574, auc: 0.699, f1: 0.522, acc: 0.680
+2025-08-07 22:52:46: Epoch: 030 Test results, ap: 0.269, auc: 0.394, f1: 0.242, acc: 0.769
+2025-08-07 22:52:53: Epoch: 031 Train Loss: 0.187, ap: 0.580, auc: 0.712, f1: 0.548, acc: 0.631
+2025-08-07 22:52:55: Epoch: 031 Test results, ap: 0.242, auc: 0.403, f1: 0.167, acc: 0.630
+2025-08-07 22:53:02: Epoch: 032 Train Loss: 0.143, ap: 0.592, auc: 0.709, f1: 0.537, acc: 0.699
+2025-08-07 22:53:04: Epoch: 032 Test results, ap: 0.236, auc: 0.391, f1: 0.242, acc: 0.769
+2025-08-07 22:53:11: Epoch: 033 Train Loss: 0.147, ap: 0.580, auc: 0.707, f1: 0.521, acc: 0.660
+2025-08-07 22:53:12: Epoch: 033 Test results, ap: 0.237, auc: 0.398, f1: 0.229, acc: 0.750
+2025-08-07 22:53:19: Epoch: 034 Train Loss: 0.134, ap: 0.569, auc: 0.695, f1: 0.580, acc: 0.718
+2025-08-07 22:53:21: Epoch: 034 Test results, ap: 0.245, auc: 0.401, f1: 0.229, acc: 0.750
+2025-08-07 22:53:28: Epoch: 035 Train Loss: 0.156, ap: 0.573, auc: 0.687, f1: 0.533, acc: 0.660
+2025-08-07 22:53:30: Epoch: 035 Test results, ap: 0.224, auc: 0.389, f1: 0.229, acc: 0.750
+2025-08-07 22:53:37: Epoch: 036 Train Loss: 0.127, ap: 0.566, auc: 0.690, f1: 0.571, acc: 0.709
+2025-08-07 22:53:38: Epoch: 036 Test results, ap: 0.245, auc: 0.400, f1: 0.229, acc: 0.750
+2025-08-07 22:53:46: Epoch: 037 Train Loss: 0.129, ap: 0.575, auc: 0.695, f1: 0.551, acc: 0.699
+2025-08-07 22:53:47: Epoch: 037 Test results, ap: 0.258, auc: 0.424, f1: 0.242, acc: 0.769
+2025-08-07 22:53:54: Epoch: 038 Train Loss: 0.128, ap: 0.580, auc: 0.708, f1: 0.564, acc: 0.670
+2025-08-07 22:53:56: Epoch: 038 Test results, ap: 0.216, auc: 0.390, f1: 0.171, acc: 0.731
+2025-08-07 22:54:03: Epoch: 039 Train Loss: 0.131, ap: 0.573, auc: 0.695, f1: 0.548, acc: 0.680
+2025-08-07 22:54:04: Epoch: 039 Test results, ap: 0.207, auc: 0.384, f1: 0.114, acc: 0.713
+2025-08-07 22:54:11: Epoch: 040 Train Loss: 0.126, ap: 0.572, auc: 0.695, f1: 0.526, acc: 0.650
+2025-08-07 22:54:13: Epoch: 040 Test results, ap: 0.209, auc: 0.383, f1: 0.205, acc: 0.713
+2025-08-07 22:54:20: Epoch: 041 Train Loss: 0.116, ap: 0.582, auc: 0.704, f1: 0.538, acc: 0.650
+2025-08-07 22:54:21: Epoch: 041 Test results, ap: 0.213, auc: 0.386, f1: 0.200, acc: 0.704
+2025-08-07 22:54:29: Epoch: 042 Train Loss: 0.102, ap: 0.573, auc: 0.689, f1: 0.519, acc: 0.641
+2025-08-07 22:54:30: Epoch: 042 Test results, ap: 0.230, auc: 0.400, f1: 0.205, acc: 0.713
+2025-08-07 22:54:37: Epoch: 043 Train Loss: 0.117, ap: 0.574, auc: 0.692, f1: 0.559, acc: 0.709
+2025-08-07 22:54:39: Epoch: 043 Test results, ap: 0.236, auc: 0.412, f1: 0.067, acc: 0.741
+2025-08-07 22:54:46: Epoch: 044 Train Loss: 0.101, ap: 0.578, auc: 0.695, f1: 0.556, acc: 0.689
+2025-08-07 22:54:47: Epoch: 044 Test results, ap: 0.223, auc: 0.406, f1: 0.062, acc: 0.722
+2025-08-07 22:54:55: Epoch: 045 Train Loss: 0.079, ap: 0.572, auc: 0.695, f1: 0.563, acc: 0.699
+2025-08-07 22:54:56: Epoch: 045 Test results, ap: 0.218, auc: 0.404, f1: 0.000, acc: 0.722
+2025-08-07 22:55:03: Epoch: 046 Train Loss: 0.086, ap: 0.575, auc: 0.698, f1: 0.551, acc: 0.699
+2025-08-07 22:55:05: Epoch: 046 Test results, ap: 0.218, auc: 0.401, f1: 0.000, acc: 0.722
+2025-08-07 22:55:12: Epoch: 047 Train Loss: 0.084, ap: 0.569, auc: 0.684, f1: 0.526, acc: 0.650
+2025-08-07 22:55:13: Epoch: 047 Test results, ap: 0.230, auc: 0.410, f1: 0.222, acc: 0.741
+2025-08-07 22:55:21: Epoch: 048 Train Loss: 0.083, ap: 0.572, auc: 0.697, f1: 0.563, acc: 0.699
+2025-08-07 22:55:22: Epoch: 048 Test results, ap: 0.224, auc: 0.412, f1: 0.000, acc: 0.722
+2025-08-07 22:55:29: Epoch: 049 Train Loss: 0.102, ap: 0.571, auc: 0.692, f1: 0.568, acc: 0.689
+2025-08-07 22:55:31: Epoch: 049 Test results, ap: 0.221, auc: 0.409, f1: 0.062, acc: 0.722
+2025-08-07 22:55:38: Epoch: 050 Train Loss: 0.095, ap: 0.571, auc: 0.688, f1: 0.548, acc: 0.680
+2025-08-07 22:55:39: Epoch: 050 Test results, ap: 0.229, auc: 0.402, f1: 0.065, acc: 0.731
+2025-08-07 22:55:39: used time 0.12h
+2025-08-07 22:55:39: Running Cross Validation 2
+2025-08-07 22:55:39: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 22:55:46: Epoch: 001 Train Loss: 0.689, ap: 0.687, auc: 0.653, f1: 0.671, acc: 0.505
+2025-08-07 22:55:46: Epoch: 001 New best VALIDATION metrics
+2025-08-07 22:55:48: Epoch: 001 Test results, ap: 0.202, auc: 0.398, f1: 0.388, acc: 0.241
+2025-08-07 22:55:55: Epoch: 002 Train Loss: 0.680, ap: 0.772, auc: 0.684, f1: 0.671, acc: 0.505
+2025-08-07 22:55:55: Epoch: 002 New best VALIDATION metrics
+2025-08-07 22:55:57: Epoch: 002 Test results, ap: 0.205, auc: 0.403, f1: 0.388, acc: 0.241
+2025-08-07 22:56:04: Epoch: 003 Train Loss: 0.628, ap: 0.708, auc: 0.647, f1: 0.565, acc: 0.612
+2025-08-07 22:56:05: Epoch: 003 Test results, ap: 0.181, auc: 0.339, f1: 0.000, acc: 0.759
+2025-08-07 22:56:13: Epoch: 004 Train Loss: 0.637, ap: 0.783, auc: 0.752, f1: 0.713, acc: 0.680
+2025-08-07 22:56:13: Epoch: 004 New best VALIDATION metrics
+2025-08-07 22:56:14: Epoch: 004 Test results, ap: 0.223, auc: 0.485, f1: 0.045, acc: 0.611
+2025-08-07 22:56:21: Epoch: 005 Train Loss: 0.559, ap: 0.653, auc: 0.678, f1: 0.073, acc: 0.505
+2025-08-07 22:56:23: Epoch: 005 Test results, ap: 0.176, auc: 0.318, f1: 0.000, acc: 0.759
+2025-08-07 22:56:30: Epoch: 006 Train Loss: 0.561, ap: 0.818, auc: 0.769, f1: 0.693, acc: 0.621
+2025-08-07 22:56:30: Epoch: 006 New best VALIDATION metrics
+2025-08-07 22:56:31: Epoch: 006 Test results, ap: 0.216, auc: 0.424, f1: 0.000, acc: 0.741
+2025-08-07 22:56:39: Epoch: 007 Train Loss: 0.531, ap: 0.708, auc: 0.666, f1: 0.526, acc: 0.563
+2025-08-07 22:56:40: Epoch: 007 Test results, ap: 0.181, auc: 0.303, f1: 0.000, acc: 0.759
+2025-08-07 22:56:47: Epoch: 008 Train Loss: 0.481, ap: 0.802, auc: 0.736, f1: 0.722, acc: 0.612
+2025-08-07 22:56:49: Epoch: 008 Test results, ap: 0.163, auc: 0.243, f1: 0.341, acc: 0.213
+2025-08-07 22:56:56: Epoch: 009 Train Loss: 0.491, ap: 0.809, auc: 0.755, f1: 0.689, acc: 0.544
+2025-08-07 22:56:57: Epoch: 009 Test results, ap: 0.214, auc: 0.394, f1: 0.388, acc: 0.241
+2025-08-07 22:57:05: Epoch: 010 Train Loss: 0.465, ap: 0.750, auc: 0.736, f1: 0.733, acc: 0.689
+2025-08-07 22:57:06: Epoch: 010 Test results, ap: 0.230, auc: 0.455, f1: 0.000, acc: 0.759
+2025-08-07 22:57:13: Epoch: 011 Train Loss: 0.437, ap: 0.818, auc: 0.776, f1: 0.689, acc: 0.728
+2025-08-07 22:57:13: Epoch: 011 New best VALIDATION metrics
+2025-08-07 22:57:15: Epoch: 011 Test results, ap: 0.255, auc: 0.532, f1: 0.000, acc: 0.759
+2025-08-07 22:57:15: Epoch: 011 New best TEST metrics
+2025-08-07 22:57:22: Epoch: 012 Train Loss: 0.386, ap: 0.814, auc: 0.782, f1: 0.715, acc: 0.660
+2025-08-07 22:57:22: Epoch: 012 New best VALIDATION metrics
+2025-08-07 22:57:24: Epoch: 012 Test results, ap: 0.201, auc: 0.398, f1: 0.000, acc: 0.694
+2025-08-07 22:57:31: Epoch: 013 Train Loss: 0.366, ap: 0.806, auc: 0.773, f1: 0.709, acc: 0.689
+2025-08-07 22:57:32: Epoch: 013 Test results, ap: 0.184, auc: 0.319, f1: 0.000, acc: 0.731
+2025-08-07 22:57:39: Epoch: 014 Train Loss: 0.355, ap: 0.817, auc: 0.757, f1: 0.659, acc: 0.709
+2025-08-07 22:57:41: Epoch: 014 Test results, ap: 0.214, auc: 0.439, f1: 0.000, acc: 0.759
+2025-08-07 22:57:48: Epoch: 015 Train Loss: 0.341, ap: 0.824, auc: 0.777, f1: 0.738, acc: 0.738
+2025-08-07 22:57:48: Epoch: 015 New best VALIDATION metrics
+2025-08-07 22:57:50: Epoch: 015 Test results, ap: 0.218, auc: 0.452, f1: 0.000, acc: 0.759
+2025-08-07 22:57:57: Epoch: 016 Train Loss: 0.314, ap: 0.805, auc: 0.778, f1: 0.732, acc: 0.680
+2025-08-07 22:57:58: Epoch: 016 Test results, ap: 0.196, auc: 0.372, f1: 0.000, acc: 0.731
+2025-08-07 22:58:05: Epoch: 017 Train Loss: 0.288, ap: 0.743, auc: 0.760, f1: 0.734, acc: 0.718
+2025-08-07 22:58:07: Epoch: 017 Test results, ap: 0.194, auc: 0.344, f1: 0.065, acc: 0.731
+2025-08-07 22:58:14: Epoch: 018 Train Loss: 0.343, ap: 0.751, auc: 0.745, f1: 0.712, acc: 0.670
+2025-08-07 22:58:15: Epoch: 018 Test results, ap: 0.188, auc: 0.356, f1: 0.000, acc: 0.722
+2025-08-07 22:58:23: Epoch: 019 Train Loss: 0.323, ap: 0.779, auc: 0.732, f1: 0.673, acc: 0.660
+2025-08-07 22:58:24: Epoch: 019 Test results, ap: 0.244, auc: 0.490, f1: 0.000, acc: 0.750
+2025-08-07 22:58:31: Epoch: 020 Train Loss: 0.284, ap: 0.788, auc: 0.764, f1: 0.760, acc: 0.718
+2025-08-07 22:58:33: Epoch: 020 Test results, ap: 0.201, auc: 0.391, f1: 0.000, acc: 0.685
+2025-08-07 22:58:40: Epoch: 021 Train Loss: 0.235, ap: 0.797, auc: 0.756, f1: 0.727, acc: 0.680
+2025-08-07 22:58:41: Epoch: 021 Test results, ap: 0.208, auc: 0.412, f1: 0.000, acc: 0.694
+2025-08-07 22:58:49: Epoch: 022 Train Loss: 0.228, ap: 0.809, auc: 0.765, f1: 0.707, acc: 0.718
+2025-08-07 22:58:50: Epoch: 022 Test results, ap: 0.233, auc: 0.448, f1: 0.000, acc: 0.759
+2025-08-07 22:58:57: Epoch: 023 Train Loss: 0.228, ap: 0.819, auc: 0.765, f1: 0.689, acc: 0.728
+2025-08-07 22:58:59: Epoch: 023 Test results, ap: 0.250, auc: 0.479, f1: 0.000, acc: 0.759
+2025-08-07 22:59:06: Epoch: 024 Train Loss: 0.235, ap: 0.767, auc: 0.755, f1: 0.712, acc: 0.670
+2025-08-07 22:59:08: Epoch: 024 Test results, ap: 0.245, auc: 0.490, f1: 0.000, acc: 0.722
+2025-08-07 22:59:15: Epoch: 025 Train Loss: 0.219, ap: 0.767, auc: 0.768, f1: 0.734, acc: 0.718
+2025-08-07 22:59:16: Epoch: 025 Test results, ap: 0.226, auc: 0.445, f1: 0.000, acc: 0.694
+2025-08-07 22:59:23: Epoch: 026 Train Loss: 0.201, ap: 0.810, auc: 0.772, f1: 0.702, acc: 0.728
+2025-08-07 22:59:25: Epoch: 026 Test results, ap: 0.249, auc: 0.472, f1: 0.105, acc: 0.685
+2025-08-07 22:59:32: Epoch: 027 Train Loss: 0.214, ap: 0.732, auc: 0.735, f1: 0.699, acc: 0.641
+2025-08-07 22:59:33: Epoch: 027 Test results, ap: 0.229, auc: 0.471, f1: 0.051, acc: 0.657
+2025-08-07 22:59:41: Epoch: 028 Train Loss: 0.192, ap: 0.784, auc: 0.758, f1: 0.688, acc: 0.709
+2025-08-07 22:59:42: Epoch: 028 Test results, ap: 0.232, auc: 0.467, f1: 0.000, acc: 0.741
+2025-08-07 22:59:49: Epoch: 029 Train Loss: 0.178, ap: 0.800, auc: 0.781, f1: 0.730, acc: 0.699
+2025-08-07 22:59:51: Epoch: 029 Test results, ap: 0.240, auc: 0.487, f1: 0.100, acc: 0.667
+2025-08-07 22:59:58: Epoch: 030 Train Loss: 0.192, ap: 0.747, auc: 0.749, f1: 0.748, acc: 0.738
+2025-08-07 23:00:00: Epoch: 030 Test results, ap: 0.254, auc: 0.523, f1: 0.000, acc: 0.741
+2025-08-07 23:00:07: Epoch: 031 Train Loss: 0.150, ap: 0.712, auc: 0.751, f1: 0.694, acc: 0.641
+2025-08-07 23:00:08: Epoch: 031 Test results, ap: 0.243, auc: 0.489, f1: 0.195, acc: 0.694
+2025-08-07 23:00:15: Epoch: 032 Train Loss: 0.134, ap: 0.741, auc: 0.766, f1: 0.737, acc: 0.709
+2025-08-07 23:00:17: Epoch: 032 Test results, ap: 0.254, auc: 0.503, f1: 0.200, acc: 0.704
+2025-08-07 23:00:24: Epoch: 033 Train Loss: 0.137, ap: 0.764, auc: 0.779, f1: 0.750, acc: 0.728
+2025-08-07 23:00:25: Epoch: 033 Test results, ap: 0.245, auc: 0.513, f1: 0.108, acc: 0.694
+2025-08-07 23:00:33: Epoch: 034 Train Loss: 0.131, ap: 0.792, auc: 0.784, f1: 0.716, acc: 0.699
+2025-08-07 23:00:34: Epoch: 034 Test results, ap: 0.250, auc: 0.519, f1: 0.118, acc: 0.722
+2025-08-07 23:00:42: Epoch: 035 Train Loss: 0.113, ap: 0.786, auc: 0.788, f1: 0.752, acc: 0.718
+2025-08-07 23:00:43: Epoch: 035 Test results, ap: 0.264, auc: 0.528, f1: 0.200, acc: 0.704
+2025-08-07 23:00:43: Epoch: 035 New best TEST metrics
+2025-08-07 23:00:50: Epoch: 036 Train Loss: 0.139, ap: 0.775, auc: 0.787, f1: 0.748, acc: 0.718
+2025-08-07 23:00:52: Epoch: 036 Test results, ap: 0.248, auc: 0.510, f1: 0.108, acc: 0.694
+2025-08-07 23:00:59: Epoch: 037 Train Loss: 0.130, ap: 0.767, auc: 0.756, f1: 0.710, acc: 0.650
+2025-08-07 23:01:00: Epoch: 037 Test results, ap: 0.261, auc: 0.539, f1: 0.190, acc: 0.685
+2025-08-07 23:01:00: Epoch: 037 New best TEST metrics
+2025-08-07 23:01:08: Epoch: 038 Train Loss: 0.134, ap: 0.755, auc: 0.762, f1: 0.741, acc: 0.728
+2025-08-07 23:01:09: Epoch: 038 Test results, ap: 0.235, auc: 0.499, f1: 0.000, acc: 0.713
+2025-08-07 23:01:16: Epoch: 039 Train Loss: 0.124, ap: 0.721, auc: 0.743, f1: 0.723, acc: 0.680
+2025-08-07 23:01:18: Epoch: 039 Test results, ap: 0.242, auc: 0.508, f1: 0.057, acc: 0.694
+2025-08-07 23:01:25: Epoch: 040 Train Loss: 0.106, ap: 0.765, auc: 0.788, f1: 0.737, acc: 0.709
+2025-08-07 23:01:27: Epoch: 040 Test results, ap: 0.254, auc: 0.515, f1: 0.111, acc: 0.704
+2025-08-07 23:01:34: Epoch: 041 Train Loss: 0.085, ap: 0.768, auc: 0.787, f1: 0.737, acc: 0.709
+2025-08-07 23:01:35: Epoch: 041 Test results, ap: 0.246, auc: 0.508, f1: 0.057, acc: 0.694
+2025-08-07 23:01:42: Epoch: 042 Train Loss: 0.080, ap: 0.763, auc: 0.785, f1: 0.719, acc: 0.689
+2025-08-07 23:01:44: Epoch: 042 Test results, ap: 0.246, auc: 0.498, f1: 0.108, acc: 0.694
+2025-08-07 23:01:51: Epoch: 043 Train Loss: 0.075, ap: 0.750, auc: 0.768, f1: 0.737, acc: 0.709
+2025-08-07 23:01:52: Epoch: 043 Test results, ap: 0.250, auc: 0.517, f1: 0.057, acc: 0.694
+2025-08-07 23:02:00: Epoch: 044 Train Loss: 0.086, ap: 0.749, auc: 0.766, f1: 0.726, acc: 0.699
+2025-08-07 23:02:01: Epoch: 044 Test results, ap: 0.249, auc: 0.515, f1: 0.000, acc: 0.685
+2025-08-07 23:02:08: Epoch: 045 Train Loss: 0.067, ap: 0.733, auc: 0.763, f1: 0.732, acc: 0.709
+2025-08-07 23:02:10: Epoch: 045 Test results, ap: 0.247, auc: 0.511, f1: 0.057, acc: 0.694
+2025-08-07 23:02:17: Epoch: 046 Train Loss: 0.073, ap: 0.741, auc: 0.766, f1: 0.735, acc: 0.699
+2025-08-07 23:02:18: Epoch: 046 Test results, ap: 0.250, auc: 0.512, f1: 0.150, acc: 0.685
+2025-08-07 23:02:25: Epoch: 047 Train Loss: 0.078, ap: 0.733, auc: 0.760, f1: 0.735, acc: 0.699
+2025-08-07 23:02:27: Epoch: 047 Test results, ap: 0.252, auc: 0.517, f1: 0.103, acc: 0.676
+2025-08-07 23:02:34: Epoch: 048 Train Loss: 0.065, ap: 0.759, auc: 0.778, f1: 0.739, acc: 0.718
+2025-08-07 23:02:36: Epoch: 048 Test results, ap: 0.251, auc: 0.514, f1: 0.061, acc: 0.713
+2025-08-07 23:02:43: Epoch: 049 Train Loss: 0.084, ap: 0.757, auc: 0.773, f1: 0.716, acc: 0.699
+2025-08-07 23:02:44: Epoch: 049 Test results, ap: 0.247, auc: 0.510, f1: 0.000, acc: 0.713
+2025-08-07 23:02:52: Epoch: 050 Train Loss: 0.088, ap: 0.736, auc: 0.753, f1: 0.701, acc: 0.660
+2025-08-07 23:02:53: Epoch: 050 Test results, ap: 0.247, auc: 0.513, f1: 0.057, acc: 0.694
+2025-08-07 23:02:53: used time 0.12h
+2025-08-07 23:02:53: Running Cross Validation 3
+2025-08-07 23:02:53: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 23:03:00: Epoch: 001 Train Loss: 0.686, ap: 0.760, auc: 0.778, f1: 0.644, acc: 0.699
+2025-08-07 23:03:00: Epoch: 001 New best VALIDATION metrics
+2025-08-07 23:03:02: Epoch: 001 Test results, ap: 0.244, auc: 0.469, f1: 0.000, acc: 0.759
+2025-08-07 23:03:09: Epoch: 002 Train Loss: 0.631, ap: 0.575, auc: 0.710, f1: 0.000, acc: 0.592
+2025-08-07 23:03:11: Epoch: 002 Test results, ap: 0.292, auc: 0.518, f1: 0.000, acc: 0.759
+2025-08-07 23:03:18: Epoch: 003 Train Loss: 0.615, ap: 0.691, auc: 0.727, f1: 0.533, acc: 0.660
+2025-08-07 23:03:19: Epoch: 003 Test results, ap: 0.224, auc: 0.479, f1: 0.000, acc: 0.759
+2025-08-07 23:03:26: Epoch: 004 Train Loss: 0.558, ap: 0.696, auc: 0.709, f1: 0.622, acc: 0.670
+2025-08-07 23:03:28: Epoch: 004 Test results, ap: 0.256, auc: 0.530, f1: 0.182, acc: 0.583
+2025-08-07 23:03:35: Epoch: 005 Train Loss: 0.590, ap: 0.703, auc: 0.748, f1: 0.602, acc: 0.602
+2025-08-07 23:03:36: Epoch: 005 Test results, ap: 0.256, auc: 0.569, f1: 0.209, acc: 0.509
+2025-08-07 23:03:44: Epoch: 006 Train Loss: 0.605, ap: 0.723, auc: 0.741, f1: 0.602, acc: 0.602
+2025-08-07 23:03:45: Epoch: 006 Test results, ap: 0.257, auc: 0.532, f1: 0.404, acc: 0.481
+2025-08-07 23:03:52: Epoch: 007 Train Loss: 0.557, ap: 0.710, auc: 0.718, f1: 0.467, acc: 0.689
+2025-08-07 23:03:54: Epoch: 007 Test results, ap: 0.286, auc: 0.619, f1: 0.000, acc: 0.759
+2025-08-07 23:04:01: Epoch: 008 Train Loss: 0.521, ap: 0.612, auc: 0.730, f1: 0.000, acc: 0.592
+2025-08-07 23:04:02: Epoch: 008 Test results, ap: 0.172, auc: 0.288, f1: 0.000, acc: 0.759
+2025-08-07 23:04:10: Epoch: 009 Train Loss: 0.521, ap: 0.755, auc: 0.738, f1: 0.608, acc: 0.612
+2025-08-07 23:04:11: Epoch: 009 Test results, ap: 0.189, auc: 0.328, f1: 0.170, acc: 0.639
+2025-08-07 23:04:18: Epoch: 010 Train Loss: 0.485, ap: 0.726, auc: 0.759, f1: 0.655, acc: 0.631
+2025-08-07 23:04:20: Epoch: 010 Test results, ap: 0.165, auc: 0.259, f1: 0.394, acc: 0.259
+2025-08-07 23:04:27: Epoch: 011 Train Loss: 0.431, ap: 0.759, auc: 0.763, f1: 0.608, acc: 0.699
+2025-08-07 23:04:28: Epoch: 011 Test results, ap: 0.195, auc: 0.357, f1: 0.140, acc: 0.657
+2025-08-07 23:04:28: Epoch: 011 New best TEST metrics
+2025-08-07 23:04:36: Epoch: 012 Train Loss: 0.418, ap: 0.732, auc: 0.751, f1: 0.654, acc: 0.641
+2025-08-07 23:04:37: Epoch: 012 Test results, ap: 0.190, auc: 0.349, f1: 0.351, acc: 0.315
+2025-08-07 23:04:44: Epoch: 013 Train Loss: 0.402, ap: 0.757, auc: 0.774, f1: 0.622, acc: 0.728
+2025-08-07 23:04:46: Epoch: 013 Test results, ap: 0.174, auc: 0.295, f1: 0.000, acc: 0.759
+2025-08-07 23:04:53: Epoch: 014 Train Loss: 0.376, ap: 0.746, auc: 0.763, f1: 0.659, acc: 0.699
+2025-08-07 23:04:54: Epoch: 014 Test results, ap: 0.171, auc: 0.288, f1: 0.000, acc: 0.694
+2025-08-07 23:05:02: Epoch: 015 Train Loss: 0.376, ap: 0.757, auc: 0.770, f1: 0.688, acc: 0.709
+2025-08-07 23:05:03: Epoch: 015 Test results, ap: 0.186, auc: 0.329, f1: 0.110, acc: 0.398
+2025-08-07 23:05:10: Epoch: 016 Train Loss: 0.389, ap: 0.766, auc: 0.799, f1: 0.649, acc: 0.738
+2025-08-07 23:05:10: Epoch: 016 New best VALIDATION metrics
+2025-08-07 23:05:12: Epoch: 016 Test results, ap: 0.170, auc: 0.286, f1: 0.000, acc: 0.759
+2025-08-07 23:05:19: Epoch: 017 Train Loss: 0.344, ap: 0.765, auc: 0.785, f1: 0.611, acc: 0.728
+2025-08-07 23:05:20: Epoch: 017 Test results, ap: 0.166, auc: 0.259, f1: 0.000, acc: 0.759
+2025-08-07 23:05:28: Epoch: 018 Train Loss: 0.370, ap: 0.725, auc: 0.767, f1: 0.644, acc: 0.699
+2025-08-07 23:05:29: Epoch: 018 Test results, ap: 0.175, auc: 0.306, f1: 0.000, acc: 0.741
+2025-08-07 23:05:36: Epoch: 019 Train Loss: 0.348, ap: 0.742, auc: 0.764, f1: 0.642, acc: 0.631
+2025-08-07 23:05:38: Epoch: 019 Test results, ap: 0.186, auc: 0.334, f1: 0.222, acc: 0.352
+2025-08-07 23:05:45: Epoch: 020 Train Loss: 0.338, ap: 0.773, auc: 0.795, f1: 0.581, acc: 0.748
+2025-08-07 23:05:45: Epoch: 020 New best VALIDATION metrics
+2025-08-07 23:05:46: Epoch: 020 Test results, ap: 0.183, auc: 0.303, f1: 0.000, acc: 0.759
+2025-08-07 23:05:54: Epoch: 021 Train Loss: 0.341, ap: 0.775, auc: 0.795, f1: 0.699, acc: 0.757
+2025-08-07 23:05:54: Epoch: 021 New best VALIDATION metrics
+2025-08-07 23:05:55: Epoch: 021 Test results, ap: 0.179, auc: 0.296, f1: 0.000, acc: 0.731
+2025-08-07 23:06:02: Epoch: 022 Train Loss: 0.319, ap: 0.736, auc: 0.771, f1: 0.674, acc: 0.709
+2025-08-07 23:06:04: Epoch: 022 Test results, ap: 0.182, auc: 0.299, f1: 0.170, acc: 0.639
+2025-08-07 23:06:11: Epoch: 023 Train Loss: 0.289, ap: 0.732, auc: 0.764, f1: 0.659, acc: 0.718
+2025-08-07 23:06:12: Epoch: 023 Test results, ap: 0.186, auc: 0.313, f1: 0.178, acc: 0.657
+2025-08-07 23:06:20: Epoch: 024 Train Loss: 0.303, ap: 0.687, auc: 0.756, f1: 0.674, acc: 0.699
+2025-08-07 23:06:21: Epoch: 024 Test results, ap: 0.182, auc: 0.302, f1: 0.170, acc: 0.639
+2025-08-07 23:06:28: Epoch: 025 Train Loss: 0.303, ap: 0.706, auc: 0.761, f1: 0.651, acc: 0.718
+2025-08-07 23:06:30: Epoch: 025 Test results, ap: 0.183, auc: 0.303, f1: 0.000, acc: 0.759
+2025-08-07 23:06:37: Epoch: 026 Train Loss: 0.304, ap: 0.695, auc: 0.762, f1: 0.652, acc: 0.689
+2025-08-07 23:06:38: Epoch: 026 Test results, ap: 0.183, auc: 0.314, f1: 0.182, acc: 0.667
+2025-08-07 23:06:46: Epoch: 027 Train Loss: 0.288, ap: 0.689, auc: 0.770, f1: 0.667, acc: 0.670
+2025-08-07 23:06:47: Epoch: 027 Test results, ap: 0.184, auc: 0.315, f1: 0.141, acc: 0.324
+2025-08-07 23:06:54: Epoch: 028 Train Loss: 0.274, ap: 0.674, auc: 0.745, f1: 0.597, acc: 0.699
+2025-08-07 23:06:56: Epoch: 028 Test results, ap: 0.187, auc: 0.297, f1: 0.000, acc: 0.759
+2025-08-07 23:07:03: Epoch: 029 Train Loss: 0.258, ap: 0.701, auc: 0.766, f1: 0.667, acc: 0.670
+2025-08-07 23:07:04: Epoch: 029 Test results, ap: 0.176, auc: 0.286, f1: 0.116, acc: 0.435
+2025-08-07 23:07:12: Epoch: 030 Train Loss: 0.271, ap: 0.690, auc: 0.760, f1: 0.646, acc: 0.670
+2025-08-07 23:07:13: Epoch: 030 Test results, ap: 0.186, auc: 0.332, f1: 0.157, acc: 0.602
+2025-08-07 23:07:20: Epoch: 031 Train Loss: 0.253, ap: 0.674, auc: 0.759, f1: 0.674, acc: 0.718
+2025-08-07 23:07:22: Epoch: 031 Test results, ap: 0.183, auc: 0.305, f1: 0.000, acc: 0.704
+2025-08-07 23:07:29: Epoch: 032 Train Loss: 0.224, ap: 0.652, auc: 0.751, f1: 0.632, acc: 0.660
+2025-08-07 23:07:31: Epoch: 032 Test results, ap: 0.178, auc: 0.288, f1: 0.091, acc: 0.630
+2025-08-07 23:07:38: Epoch: 033 Train Loss: 0.215, ap: 0.679, auc: 0.755, f1: 0.638, acc: 0.670
+2025-08-07 23:07:39: Epoch: 033 Test results, ap: 0.184, auc: 0.320, f1: 0.182, acc: 0.667
+2025-08-07 23:07:47: Epoch: 034 Train Loss: 0.197, ap: 0.675, auc: 0.749, f1: 0.640, acc: 0.650
+2025-08-07 23:07:48: Epoch: 034 Test results, ap: 0.188, auc: 0.336, f1: 0.157, acc: 0.602
+2025-08-07 23:07:55: Epoch: 035 Train Loss: 0.199, ap: 0.645, auc: 0.741, f1: 0.653, acc: 0.670
+2025-08-07 23:07:57: Epoch: 035 Test results, ap: 0.183, auc: 0.311, f1: 0.178, acc: 0.657
+2025-08-07 23:08:04: Epoch: 036 Train Loss: 0.204, ap: 0.661, auc: 0.742, f1: 0.621, acc: 0.680
+2025-08-07 23:08:05: Epoch: 036 Test results, ap: 0.184, auc: 0.317, f1: 0.000, acc: 0.741
+2025-08-07 23:08:13: Epoch: 037 Train Loss: 0.198, ap: 0.660, auc: 0.737, f1: 0.661, acc: 0.641
+2025-08-07 23:08:14: Epoch: 037 Test results, ap: 0.186, auc: 0.326, f1: 0.111, acc: 0.407
+2025-08-07 23:08:21: Epoch: 038 Train Loss: 0.201, ap: 0.682, auc: 0.747, f1: 0.614, acc: 0.670
+2025-08-07 23:08:23: Epoch: 038 Test results, ap: 0.181, auc: 0.304, f1: 0.000, acc: 0.731
+2025-08-07 23:08:30: Epoch: 039 Train Loss: 0.181, ap: 0.640, auc: 0.733, f1: 0.630, acc: 0.670
+2025-08-07 23:08:31: Epoch: 039 Test results, ap: 0.189, auc: 0.335, f1: 0.103, acc: 0.676
+2025-08-07 23:08:39: Epoch: 040 Train Loss: 0.181, ap: 0.628, auc: 0.724, f1: 0.646, acc: 0.660
+2025-08-07 23:08:40: Epoch: 040 Test results, ap: 0.183, auc: 0.314, f1: 0.182, acc: 0.667
+2025-08-07 23:08:47: Epoch: 041 Train Loss: 0.191, ap: 0.657, auc: 0.737, f1: 0.653, acc: 0.660
+2025-08-07 23:08:49: Epoch: 041 Test results, ap: 0.187, auc: 0.326, f1: 0.154, acc: 0.593
+2025-08-07 23:08:56: Epoch: 042 Train Loss: 0.164, ap: 0.648, auc: 0.731, f1: 0.653, acc: 0.680
+2025-08-07 23:08:57: Epoch: 042 Test results, ap: 0.187, auc: 0.331, f1: 0.182, acc: 0.667
+2025-08-07 23:09:05: Epoch: 043 Train Loss: 0.150, ap: 0.646, auc: 0.730, f1: 0.667, acc: 0.680
+2025-08-07 23:09:06: Epoch: 043 Test results, ap: 0.187, auc: 0.328, f1: 0.182, acc: 0.667
+2025-08-07 23:09:13: Epoch: 044 Train Loss: 0.145, ap: 0.659, auc: 0.736, f1: 0.646, acc: 0.670
+2025-08-07 23:09:15: Epoch: 044 Test results, ap: 0.182, auc: 0.301, f1: 0.182, acc: 0.667
+2025-08-07 23:09:22: Epoch: 045 Train Loss: 0.157, ap: 0.665, auc: 0.734, f1: 0.640, acc: 0.650
+2025-08-07 23:09:23: Epoch: 045 Test results, ap: 0.183, auc: 0.310, f1: 0.182, acc: 0.667
+2025-08-07 23:09:31: Epoch: 046 Train Loss: 0.153, ap: 0.666, auc: 0.741, f1: 0.653, acc: 0.680
+2025-08-07 23:09:32: Epoch: 046 Test results, ap: 0.184, auc: 0.313, f1: 0.182, acc: 0.667
+2025-08-07 23:09:39: Epoch: 047 Train Loss: 0.155, ap: 0.642, auc: 0.725, f1: 0.638, acc: 0.670
+2025-08-07 23:09:41: Epoch: 047 Test results, ap: 0.187, auc: 0.328, f1: 0.182, acc: 0.667
+2025-08-07 23:09:48: Epoch: 048 Train Loss: 0.156, ap: 0.637, auc: 0.726, f1: 0.646, acc: 0.670
+2025-08-07 23:09:49: Epoch: 048 Test results, ap: 0.186, auc: 0.323, f1: 0.182, acc: 0.667
+2025-08-07 23:09:56: Epoch: 049 Train Loss: 0.164, ap: 0.647, auc: 0.735, f1: 0.643, acc: 0.709
+2025-08-07 23:09:58: Epoch: 049 Test results, ap: 0.188, auc: 0.326, f1: 0.100, acc: 0.667
+2025-08-07 23:10:05: Epoch: 050 Train Loss: 0.162, ap: 0.644, auc: 0.734, f1: 0.667, acc: 0.689
+2025-08-07 23:10:07: Epoch: 050 Test results, ap: 0.183, auc: 0.311, f1: 0.182, acc: 0.667
+2025-08-07 23:10:07: used time 0.12h
+2025-08-07 23:10:07: Running Cross Validation 4
+2025-08-07 23:10:07: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 23:10:14: Epoch: 001 Train Loss: 0.693, ap: 0.721, auc: 0.727, f1: 0.569, acc: 0.398
+2025-08-07 23:10:14: Epoch: 001 New best VALIDATION metrics
+2025-08-07 23:10:15: Epoch: 001 Test results, ap: 0.281, auc: 0.538, f1: 0.388, acc: 0.241
+2025-08-07 23:10:23: Epoch: 002 Train Loss: 0.612, ap: 0.657, auc: 0.772, f1: 0.648, acc: 0.631
+2025-08-07 23:10:24: Epoch: 002 Test results, ap: 0.258, auc: 0.568, f1: 0.000, acc: 0.741
+2025-08-07 23:10:31: Epoch: 003 Train Loss: 0.579, ap: 0.654, auc: 0.671, f1: 0.379, acc: 0.650
+2025-08-07 23:10:33: Epoch: 003 Test results, ap: 0.477, auc: 0.750, f1: 0.000, acc: 0.759
+2025-08-07 23:10:40: Epoch: 004 Train Loss: 0.563, ap: 0.604, auc: 0.717, f1: 0.617, acc: 0.699
+2025-08-07 23:10:41: Epoch: 004 Test results, ap: 0.364, auc: 0.634, f1: 0.291, acc: 0.639
+2025-08-07 23:10:49: Epoch: 005 Train Loss: 0.543, ap: 0.515, auc: 0.616, f1: 0.567, acc: 0.437
+2025-08-07 23:10:50: Epoch: 005 Test results, ap: 0.367, auc: 0.623, f1: 0.388, acc: 0.241
+2025-08-07 23:10:57: Epoch: 006 Train Loss: 0.483, ap: 0.700, auc: 0.740, f1: 0.360, acc: 0.689
+2025-08-07 23:10:59: Epoch: 006 Test results, ap: 0.240, auc: 0.428, f1: 0.000, acc: 0.759
+2025-08-07 23:11:06: Epoch: 007 Train Loss: 0.410, ap: 0.568, auc: 0.588, f1: 0.538, acc: 0.417
+2025-08-07 23:11:07: Epoch: 007 Test results, ap: 0.350, auc: 0.556, f1: 0.388, acc: 0.241
+2025-08-07 23:11:15: Epoch: 008 Train Loss: 0.479, ap: 0.681, auc: 0.717, f1: 0.537, acc: 0.699
+2025-08-07 23:11:16: Epoch: 008 Test results, ap: 0.312, auc: 0.528, f1: 0.000, acc: 0.759
+2025-08-07 23:11:23: Epoch: 009 Train Loss: 0.443, ap: 0.589, auc: 0.651, f1: 0.136, acc: 0.631
+2025-08-07 23:11:25: Epoch: 009 Test results, ap: 0.240, auc: 0.478, f1: 0.000, acc: 0.759
+2025-08-07 23:11:32: Epoch: 010 Train Loss: 0.404, ap: 0.698, auc: 0.724, f1: 0.415, acc: 0.699
+2025-08-07 23:11:34: Epoch: 010 Test results, ap: 0.240, auc: 0.485, f1: 0.000, acc: 0.759
+2025-08-07 23:11:41: Epoch: 011 Train Loss: 0.351, ap: 0.639, auc: 0.698, f1: 0.605, acc: 0.709
+2025-08-07 23:11:42: Epoch: 011 Test results, ap: 0.239, auc: 0.484, f1: 0.000, acc: 0.731
+2025-08-07 23:11:42: Epoch: 011 New best TEST metrics
+2025-08-07 23:11:50: Epoch: 012 Train Loss: 0.321, ap: 0.668, auc: 0.726, f1: 0.588, acc: 0.592
+2025-08-07 23:11:51: Epoch: 012 Test results, ap: 0.208, auc: 0.420, f1: 0.120, acc: 0.593
+2025-08-07 23:11:58: Epoch: 013 Train Loss: 0.307, ap: 0.638, auc: 0.673, f1: 0.478, acc: 0.660
+2025-08-07 23:12:00: Epoch: 013 Test results, ap: 0.268, auc: 0.510, f1: 0.000, acc: 0.750
+2025-08-07 23:12:00: Epoch: 013 New best TEST metrics
+2025-08-07 23:12:07: Epoch: 014 Train Loss: 0.296, ap: 0.638, auc: 0.712, f1: 0.619, acc: 0.689
+2025-08-07 23:12:08: Epoch: 014 Test results, ap: 0.262, auc: 0.502, f1: 0.125, acc: 0.741
+2025-08-07 23:12:16: Epoch: 015 Train Loss: 0.317, ap: 0.642, auc: 0.730, f1: 0.630, acc: 0.670
+2025-08-07 23:12:17: Epoch: 015 Test results, ap: 0.254, auc: 0.524, f1: 0.054, acc: 0.676
+2025-08-07 23:12:17: Epoch: 015 New best TEST metrics
+2025-08-07 23:12:24: Epoch: 016 Train Loss: 0.282, ap: 0.682, auc: 0.735, f1: 0.585, acc: 0.670
+2025-08-07 23:12:26: Epoch: 016 Test results, ap: 0.297, auc: 0.529, f1: 0.000, acc: 0.750
+2025-08-07 23:12:26: Epoch: 016 New best TEST metrics
+2025-08-07 23:12:33: Epoch: 017 Train Loss: 0.271, ap: 0.641, auc: 0.719, f1: 0.578, acc: 0.631
+2025-08-07 23:12:34: Epoch: 017 Test results, ap: 0.241, auc: 0.485, f1: 0.105, acc: 0.685
+2025-08-07 23:12:42: Epoch: 018 Train Loss: 0.267, ap: 0.645, auc: 0.715, f1: 0.563, acc: 0.699
+2025-08-07 23:12:43: Epoch: 018 Test results, ap: 0.231, auc: 0.464, f1: 0.000, acc: 0.750
+2025-08-07 23:12:50: Epoch: 019 Train Loss: 0.224, ap: 0.695, auc: 0.734, f1: 0.636, acc: 0.689
+2025-08-07 23:12:52: Epoch: 019 Test results, ap: 0.252, auc: 0.504, f1: 0.146, acc: 0.676
+2025-08-07 23:12:59: Epoch: 020 Train Loss: 0.242, ap: 0.658, auc: 0.723, f1: 0.619, acc: 0.689
+2025-08-07 23:13:00: Epoch: 020 Test results, ap: 0.238, auc: 0.466, f1: 0.000, acc: 0.731
+2025-08-07 23:13:08: Epoch: 021 Train Loss: 0.211, ap: 0.692, auc: 0.725, f1: 0.554, acc: 0.641
+2025-08-07 23:13:09: Epoch: 021 Test results, ap: 0.231, auc: 0.473, f1: 0.000, acc: 0.685
+2025-08-07 23:13:16: Epoch: 022 Train Loss: 0.195, ap: 0.718, auc: 0.732, f1: 0.565, acc: 0.641
+2025-08-07 23:13:16: Epoch: 022 New best VALIDATION metrics
+2025-08-07 23:13:18: Epoch: 022 Test results, ap: 0.227, auc: 0.475, f1: 0.136, acc: 0.648
+2025-08-07 23:13:25: Epoch: 023 Train Loss: 0.183, ap: 0.675, auc: 0.709, f1: 0.575, acc: 0.699
+2025-08-07 23:13:26: Epoch: 023 Test results, ap: 0.236, auc: 0.488, f1: 0.000, acc: 0.741
+2025-08-07 23:13:34: Epoch: 024 Train Loss: 0.201, ap: 0.743, auc: 0.756, f1: 0.614, acc: 0.621
+2025-08-07 23:13:34: Epoch: 024 New best VALIDATION metrics
+2025-08-07 23:13:35: Epoch: 024 Test results, ap: 0.225, auc: 0.478, f1: 0.125, acc: 0.611
+2025-08-07 23:13:43: Epoch: 025 Train Loss: 0.194, ap: 0.641, auc: 0.694, f1: 0.478, acc: 0.660
+2025-08-07 23:13:44: Epoch: 025 Test results, ap: 0.265, auc: 0.499, f1: 0.000, acc: 0.759
+2025-08-07 23:13:51: Epoch: 026 Train Loss: 0.182, ap: 0.653, auc: 0.705, f1: 0.528, acc: 0.670
+2025-08-07 23:13:53: Epoch: 026 Test results, ap: 0.257, auc: 0.502, f1: 0.000, acc: 0.759
+2025-08-07 23:14:00: Epoch: 027 Train Loss: 0.161, ap: 0.619, auc: 0.710, f1: 0.564, acc: 0.670
+2025-08-07 23:14:01: Epoch: 027 Test results, ap: 0.204, auc: 0.396, f1: 0.000, acc: 0.704
+2025-08-07 23:14:08: Epoch: 028 Train Loss: 0.173, ap: 0.627, auc: 0.703, f1: 0.525, acc: 0.631
+2025-08-07 23:14:10: Epoch: 028 Test results, ap: 0.244, auc: 0.469, f1: 0.105, acc: 0.685
+2025-08-07 23:14:17: Epoch: 029 Train Loss: 0.180, ap: 0.601, auc: 0.687, f1: 0.486, acc: 0.650
+2025-08-07 23:14:18: Epoch: 029 Test results, ap: 0.271, auc: 0.485, f1: 0.000, acc: 0.759
+2025-08-07 23:14:26: Epoch: 030 Train Loss: 0.168, ap: 0.632, auc: 0.712, f1: 0.541, acc: 0.670
+2025-08-07 23:14:27: Epoch: 030 Test results, ap: 0.216, auc: 0.427, f1: 0.105, acc: 0.685
+2025-08-07 23:14:34: Epoch: 031 Train Loss: 0.155, ap: 0.644, auc: 0.720, f1: 0.563, acc: 0.699
+2025-08-07 23:14:36: Epoch: 031 Test results, ap: 0.218, auc: 0.435, f1: 0.054, acc: 0.676
+2025-08-07 23:14:43: Epoch: 032 Train Loss: 0.126, ap: 0.634, auc: 0.712, f1: 0.563, acc: 0.699
+2025-08-07 23:14:44: Epoch: 032 Test results, ap: 0.221, auc: 0.436, f1: 0.000, acc: 0.713
+2025-08-07 23:14:51: Epoch: 033 Train Loss: 0.133, ap: 0.601, auc: 0.698, f1: 0.514, acc: 0.670
+2025-08-07 23:14:53: Epoch: 033 Test results, ap: 0.227, auc: 0.431, f1: 0.000, acc: 0.750
+2025-08-07 23:15:00: Epoch: 034 Train Loss: 0.118, ap: 0.568, auc: 0.688, f1: 0.514, acc: 0.670
+2025-08-07 23:15:02: Epoch: 034 Test results, ap: 0.237, auc: 0.437, f1: 0.065, acc: 0.731
+2025-08-07 23:15:09: Epoch: 035 Train Loss: 0.135, ap: 0.544, auc: 0.654, f1: 0.468, acc: 0.602
+2025-08-07 23:15:10: Epoch: 035 Test results, ap: 0.266, auc: 0.490, f1: 0.125, acc: 0.741
+2025-08-07 23:15:17: Epoch: 036 Train Loss: 0.167, ap: 0.602, auc: 0.697, f1: 0.548, acc: 0.680
+2025-08-07 23:15:19: Epoch: 036 Test results, ap: 0.247, auc: 0.507, f1: 0.000, acc: 0.713
+2025-08-07 23:15:26: Epoch: 037 Train Loss: 0.137, ap: 0.646, auc: 0.728, f1: 0.613, acc: 0.718
+2025-08-07 23:15:27: Epoch: 037 Test results, ap: 0.241, auc: 0.482, f1: 0.000, acc: 0.704
+2025-08-07 23:15:35: Epoch: 038 Train Loss: 0.114, ap: 0.649, auc: 0.728, f1: 0.590, acc: 0.689
+2025-08-07 23:15:36: Epoch: 038 Test results, ap: 0.222, auc: 0.445, f1: 0.103, acc: 0.676
+2025-08-07 23:15:43: Epoch: 039 Train Loss: 0.124, ap: 0.632, auc: 0.709, f1: 0.575, acc: 0.699
+2025-08-07 23:15:45: Epoch: 039 Test results, ap: 0.224, auc: 0.435, f1: 0.057, acc: 0.694
+2025-08-07 23:15:52: Epoch: 040 Train Loss: 0.112, ap: 0.600, auc: 0.686, f1: 0.514, acc: 0.650
+2025-08-07 23:15:54: Epoch: 040 Test results, ap: 0.241, auc: 0.455, f1: 0.061, acc: 0.713
+2025-08-07 23:16:01: Epoch: 041 Train Loss: 0.117, ap: 0.593, auc: 0.701, f1: 0.568, acc: 0.689
+2025-08-07 23:16:02: Epoch: 041 Test results, ap: 0.237, auc: 0.452, f1: 0.065, acc: 0.731
+2025-08-07 23:16:10: Epoch: 042 Train Loss: 0.095, ap: 0.609, auc: 0.705, f1: 0.507, acc: 0.680
+2025-08-07 23:16:11: Epoch: 042 Test results, ap: 0.253, auc: 0.454, f1: 0.071, acc: 0.759
+2025-08-07 23:16:18: Epoch: 043 Train Loss: 0.102, ap: 0.596, auc: 0.688, f1: 0.579, acc: 0.689
+2025-08-07 23:16:20: Epoch: 043 Test results, ap: 0.241, auc: 0.452, f1: 0.061, acc: 0.713
+2025-08-07 23:16:27: Epoch: 044 Train Loss: 0.092, ap: 0.600, auc: 0.706, f1: 0.568, acc: 0.689
+2025-08-07 23:16:28: Epoch: 044 Test results, ap: 0.241, auc: 0.468, f1: 0.000, acc: 0.722
+2025-08-07 23:16:36: Epoch: 045 Train Loss: 0.096, ap: 0.578, auc: 0.696, f1: 0.486, acc: 0.650
+2025-08-07 23:16:37: Epoch: 045 Test results, ap: 0.238, auc: 0.447, f1: 0.067, acc: 0.741
+2025-08-07 23:16:44: Epoch: 046 Train Loss: 0.087, ap: 0.579, auc: 0.699, f1: 0.548, acc: 0.680
+2025-08-07 23:16:46: Epoch: 046 Test results, ap: 0.232, auc: 0.441, f1: 0.000, acc: 0.722
+2025-08-07 23:16:53: Epoch: 047 Train Loss: 0.087, ap: 0.580, auc: 0.695, f1: 0.431, acc: 0.641
+2025-08-07 23:16:55: Epoch: 047 Test results, ap: 0.249, auc: 0.441, f1: 0.000, acc: 0.759
+2025-08-07 23:17:02: Epoch: 048 Train Loss: 0.084, ap: 0.560, auc: 0.680, f1: 0.493, acc: 0.660
+2025-08-07 23:17:03: Epoch: 048 Test results, ap: 0.288, auc: 0.431, f1: 0.000, acc: 0.759
+2025-08-07 23:17:11: Epoch: 049 Train Loss: 0.078, ap: 0.564, auc: 0.687, f1: 0.472, acc: 0.631
+2025-08-07 23:17:12: Epoch: 049 Test results, ap: 0.234, auc: 0.427, f1: 0.067, acc: 0.741
+2025-08-07 23:17:19: Epoch: 050 Train Loss: 0.077, ap: 0.574, auc: 0.688, f1: 0.493, acc: 0.660
+2025-08-07 23:17:21: Epoch: 050 Test results, ap: 0.232, auc: 0.435, f1: 0.000, acc: 0.741
+2025-08-07 23:17:21: used time 0.12h
+2025-08-07 23:17:21: Cross Validation Finished!
+2025-08-07 23:17:21: Best validation perform list
+[[0.75897467 0.76633739 0.55555558 0.68932039]
+ [0.62524414 0.7088452  0.55882353 0.70873785]
+ [0.82433081 0.77677226 0.73786408 0.73786408]
+ [0.77463913 0.79547226 0.6987952  0.75728154]
+ [0.74302661 0.75570416 0.61386138 0.62135923]]
+2025-08-07 23:17:21: mean: [0.745 0.761 0.633 0.703]
+2025-08-07 23:17:21: std: [0.066 0.029 0.074 0.047]
+2025-08-07 23:17:21: Best test perform list
+[[0.26280934 0.56848025 0.         0.7037037 ]
+ [0.26902258 0.41651034 0.         0.75925928]
+ [0.26054946 0.53893059 0.19047619 0.68518519]
+ [0.19462287 0.35741091 0.13953489 0.6574074 ]
+ [0.29738218 0.52861166 0.         0.75      ]]
+2025-08-07 23:17:21: mean: [0.257 0.482 0.066 0.711]
+2025-08-07 23:17:21: std: [0.034 0.081 0.082 0.039]
diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/config.json b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..9aad041823eef0fe4400e8624cf06bca660f3707
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": null, "channels": 16, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/feature_preds.csv b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/feature_preds.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cdcb01b1a7e0f435b2b081be7b63d8b8efe19e60
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/feature_preds.csv
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diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/training.log b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..2852b2a3bffd9c732cc1fe59fa0391581b08345b
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/training.log
@@ -0,0 +1,589 @@
+2025-08-07 18:41:29: saving_dir: ./run-cls/rn18-att-16-simple-768-oneway-ce-32-0.001-50
+2025-08-07 18:41:29: Loading Training Dataset
+2025-08-07 18:41:52: Loading Test Dataset
+2025-08-07 18:41:56: Running Cross Validation 0
+2025-08-07 18:41:56: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 18:42:04: Epoch: 001 Train Loss: 0.689, ap: 0.620, auc: 0.631, f1: 0.000, acc: 0.544
+2025-08-07 18:42:04: Epoch: 001 New best VALIDATION metrics
+2025-08-07 18:42:06: Epoch: 001 Test results, ap: 0.169, auc: 0.279, f1: 0.000, acc: 0.759
+2025-08-07 18:42:13: Epoch: 002 Train Loss: 0.660, ap: 0.631, auc: 0.656, f1: 0.677, acc: 0.602
+2025-08-07 18:42:13: Epoch: 002 New best VALIDATION metrics
+2025-08-07 18:42:14: Epoch: 002 Test results, ap: 0.240, auc: 0.363, f1: 0.000, acc: 0.759
+2025-08-07 18:42:22: Epoch: 003 Train Loss: 0.603, ap: 0.666, auc: 0.672, f1: 0.630, acc: 0.612
+2025-08-07 18:42:22: Epoch: 003 New best VALIDATION metrics
+2025-08-07 18:42:23: Epoch: 003 Test results, ap: 0.306, auc: 0.477, f1: 0.388, acc: 0.241
+2025-08-07 18:42:30: Epoch: 004 Train Loss: 0.601, ap: 0.630, auc: 0.695, f1: 0.211, acc: 0.563
+2025-08-07 18:42:31: Epoch: 004 Test results, ap: 0.356, auc: 0.512, f1: 0.000, acc: 0.759
+2025-08-07 18:42:39: Epoch: 005 Train Loss: 0.518, ap: 0.681, auc: 0.728, f1: 0.367, acc: 0.631
+2025-08-07 18:42:39: Epoch: 005 New best VALIDATION metrics
+2025-08-07 18:42:40: Epoch: 005 Test results, ap: 0.185, auc: 0.343, f1: 0.000, acc: 0.759
+2025-08-07 18:42:47: Epoch: 006 Train Loss: 0.478, ap: 0.695, auc: 0.725, f1: 0.652, acc: 0.544
+2025-08-07 18:42:47: Epoch: 006 New best VALIDATION metrics
+2025-08-07 18:42:49: Epoch: 006 Test results, ap: 0.169, auc: 0.284, f1: 0.317, acc: 0.241
+2025-08-07 18:42:56: Epoch: 007 Train Loss: 0.436, ap: 0.611, auc: 0.677, f1: 0.082, acc: 0.563
+2025-08-07 18:42:57: Epoch: 007 Test results, ap: 0.315, auc: 0.530, f1: 0.000, acc: 0.759
+2025-08-07 18:43:04: Epoch: 008 Train Loss: 0.460, ap: 0.697, auc: 0.748, f1: 0.579, acc: 0.689
+2025-08-07 18:43:04: Epoch: 008 New best VALIDATION metrics
+2025-08-07 18:43:06: Epoch: 008 Test results, ap: 0.330, auc: 0.510, f1: 0.000, acc: 0.759
+2025-08-07 18:43:13: Epoch: 009 Train Loss: 0.399, ap: 0.703, auc: 0.725, f1: 0.605, acc: 0.709
+2025-08-07 18:43:14: Epoch: 009 Test results, ap: 0.205, auc: 0.390, f1: 0.000, acc: 0.759
+2025-08-07 18:43:21: Epoch: 010 Train Loss: 0.409, ap: 0.681, auc: 0.727, f1: 0.654, acc: 0.641
+2025-08-07 18:43:23: Epoch: 010 Test results, ap: 0.315, auc: 0.501, f1: 0.368, acc: 0.778
+2025-08-07 18:43:30: Epoch: 011 Train Loss: 0.357, ap: 0.718, auc: 0.745, f1: 0.681, acc: 0.718
+2025-08-07 18:43:30: Epoch: 011 New best VALIDATION metrics
+2025-08-07 18:43:31: Epoch: 011 Test results, ap: 0.242, auc: 0.452, f1: 0.000, acc: 0.741
+2025-08-07 18:43:31: Epoch: 011 New best TEST metrics
+2025-08-07 18:43:38: Epoch: 012 Train Loss: 0.309, ap: 0.705, auc: 0.735, f1: 0.617, acc: 0.699
+2025-08-07 18:43:39: Epoch: 012 Test results, ap: 0.339, auc: 0.468, f1: 0.000, acc: 0.750
+2025-08-07 18:43:39: Epoch: 012 New best TEST metrics
+2025-08-07 18:43:46: Epoch: 013 Train Loss: 0.301, ap: 0.699, auc: 0.735, f1: 0.582, acc: 0.680
+2025-08-07 18:43:48: Epoch: 013 Test results, ap: 0.317, auc: 0.457, f1: 0.000, acc: 0.750
+2025-08-07 18:43:55: Epoch: 014 Train Loss: 0.247, ap: 0.746, auc: 0.789, f1: 0.674, acc: 0.718
+2025-08-07 18:43:55: Epoch: 014 New best VALIDATION metrics
+2025-08-07 18:43:56: Epoch: 014 Test results, ap: 0.192, auc: 0.356, f1: 0.000, acc: 0.722
+2025-08-07 18:44:04: Epoch: 015 Train Loss: 0.275, ap: 0.726, auc: 0.746, f1: 0.533, acc: 0.660
+2025-08-07 18:44:05: Epoch: 015 Test results, ap: 0.293, auc: 0.453, f1: 0.000, acc: 0.750
+2025-08-07 18:44:12: Epoch: 016 Train Loss: 0.295, ap: 0.703, auc: 0.739, f1: 0.642, acc: 0.718
+2025-08-07 18:44:13: Epoch: 016 Test results, ap: 0.287, auc: 0.441, f1: 0.000, acc: 0.750
+2025-08-07 18:44:20: Epoch: 017 Train Loss: 0.261, ap: 0.710, auc: 0.767, f1: 0.673, acc: 0.689
+2025-08-07 18:44:22: Epoch: 017 Test results, ap: 0.235, auc: 0.415, f1: 0.000, acc: 0.741
+2025-08-07 18:44:29: Epoch: 018 Train Loss: 0.249, ap: 0.713, auc: 0.752, f1: 0.602, acc: 0.680
+2025-08-07 18:44:30: Epoch: 018 Test results, ap: 0.225, auc: 0.411, f1: 0.000, acc: 0.741
+2025-08-07 18:44:37: Epoch: 019 Train Loss: 0.282, ap: 0.714, auc: 0.732, f1: 0.608, acc: 0.699
+2025-08-07 18:44:39: Epoch: 019 Test results, ap: 0.282, auc: 0.483, f1: 0.000, acc: 0.750
+2025-08-07 18:44:46: Epoch: 020 Train Loss: 0.248, ap: 0.701, auc: 0.742, f1: 0.629, acc: 0.680
+2025-08-07 18:44:47: Epoch: 020 Test results, ap: 0.239, auc: 0.417, f1: 0.000, acc: 0.741
+2025-08-07 18:44:54: Epoch: 021 Train Loss: 0.232, ap: 0.712, auc: 0.731, f1: 0.637, acc: 0.680
+2025-08-07 18:44:56: Epoch: 021 Test results, ap: 0.241, auc: 0.418, f1: 0.000, acc: 0.731
+2025-08-07 18:45:03: Epoch: 022 Train Loss: 0.211, ap: 0.711, auc: 0.726, f1: 0.630, acc: 0.670
+2025-08-07 18:45:04: Epoch: 022 Test results, ap: 0.243, auc: 0.424, f1: 0.000, acc: 0.741
+2025-08-07 18:45:11: Epoch: 023 Train Loss: 0.206, ap: 0.717, auc: 0.733, f1: 0.628, acc: 0.689
+2025-08-07 18:45:13: Epoch: 023 Test results, ap: 0.216, auc: 0.409, f1: 0.000, acc: 0.750
+2025-08-07 18:45:20: Epoch: 024 Train Loss: 0.192, ap: 0.707, auc: 0.736, f1: 0.635, acc: 0.699
+2025-08-07 18:45:21: Epoch: 024 Test results, ap: 0.273, auc: 0.437, f1: 0.188, acc: 0.759
+2025-08-07 18:45:28: Epoch: 025 Train Loss: 0.203, ap: 0.699, auc: 0.734, f1: 0.643, acc: 0.709
+2025-08-07 18:45:29: Epoch: 025 Test results, ap: 0.233, auc: 0.427, f1: 0.108, acc: 0.694
+2025-08-07 18:45:36: Epoch: 026 Train Loss: 0.196, ap: 0.690, auc: 0.735, f1: 0.564, acc: 0.670
+2025-08-07 18:45:38: Epoch: 026 Test results, ap: 0.195, auc: 0.385, f1: 0.000, acc: 0.741
+2025-08-07 18:45:45: Epoch: 027 Train Loss: 0.237, ap: 0.729, auc: 0.738, f1: 0.585, acc: 0.670
+2025-08-07 18:45:46: Epoch: 027 Test results, ap: 0.226, auc: 0.416, f1: 0.000, acc: 0.750
+2025-08-07 18:45:53: Epoch: 028 Train Loss: 0.208, ap: 0.730, auc: 0.742, f1: 0.635, acc: 0.699
+2025-08-07 18:45:55: Epoch: 028 Test results, ap: 0.226, auc: 0.410, f1: 0.000, acc: 0.750
+2025-08-07 18:46:02: Epoch: 029 Train Loss: 0.189, ap: 0.698, auc: 0.744, f1: 0.635, acc: 0.699
+2025-08-07 18:46:03: Epoch: 029 Test results, ap: 0.237, auc: 0.435, f1: 0.000, acc: 0.750
+2025-08-07 18:46:10: Epoch: 030 Train Loss: 0.200, ap: 0.681, auc: 0.728, f1: 0.674, acc: 0.709
+2025-08-07 18:46:12: Epoch: 030 Test results, ap: 0.312, auc: 0.492, f1: 0.242, acc: 0.769
+2025-08-07 18:46:19: Epoch: 031 Train Loss: 0.189, ap: 0.722, auc: 0.731, f1: 0.627, acc: 0.699
+2025-08-07 18:46:20: Epoch: 031 Test results, ap: 0.225, auc: 0.456, f1: 0.000, acc: 0.750
+2025-08-07 18:46:27: Epoch: 032 Train Loss: 0.159, ap: 0.726, auc: 0.730, f1: 0.635, acc: 0.699
+2025-08-07 18:46:29: Epoch: 032 Test results, ap: 0.197, auc: 0.401, f1: 0.000, acc: 0.741
+2025-08-07 18:46:36: Epoch: 033 Train Loss: 0.173, ap: 0.718, auc: 0.744, f1: 0.635, acc: 0.699
+2025-08-07 18:46:37: Epoch: 033 Test results, ap: 0.195, auc: 0.393, f1: 0.000, acc: 0.685
+2025-08-07 18:46:44: Epoch: 034 Train Loss: 0.158, ap: 0.684, auc: 0.730, f1: 0.690, acc: 0.738
+2025-08-07 18:46:46: Epoch: 034 Test results, ap: 0.196, auc: 0.395, f1: 0.000, acc: 0.722
+2025-08-07 18:46:53: Epoch: 035 Train Loss: 0.168, ap: 0.715, auc: 0.732, f1: 0.593, acc: 0.680
+2025-08-07 18:46:54: Epoch: 035 Test results, ap: 0.194, auc: 0.386, f1: 0.000, acc: 0.750
+2025-08-07 18:47:01: Epoch: 036 Train Loss: 0.152, ap: 0.712, auc: 0.719, f1: 0.600, acc: 0.650
+2025-08-07 18:47:03: Epoch: 036 Test results, ap: 0.201, auc: 0.408, f1: 0.000, acc: 0.704
+2025-08-07 18:47:10: Epoch: 037 Train Loss: 0.147, ap: 0.694, auc: 0.715, f1: 0.629, acc: 0.680
+2025-08-07 18:47:11: Epoch: 037 Test results, ap: 0.200, auc: 0.407, f1: 0.000, acc: 0.593
+2025-08-07 18:47:18: Epoch: 038 Train Loss: 0.153, ap: 0.709, auc: 0.740, f1: 0.710, acc: 0.738
+2025-08-07 18:47:20: Epoch: 038 Test results, ap: 0.204, auc: 0.421, f1: 0.268, acc: 0.444
+2025-08-07 18:47:27: Epoch: 039 Train Loss: 0.151, ap: 0.706, auc: 0.719, f1: 0.593, acc: 0.680
+2025-08-07 18:47:28: Epoch: 039 Test results, ap: 0.242, auc: 0.463, f1: 0.167, acc: 0.722
+2025-08-07 18:47:36: Epoch: 040 Train Loss: 0.168, ap: 0.715, auc: 0.731, f1: 0.602, acc: 0.680
+2025-08-07 18:47:37: Epoch: 040 Test results, ap: 0.199, auc: 0.401, f1: 0.000, acc: 0.694
+2025-08-07 18:47:44: Epoch: 041 Train Loss: 0.133, ap: 0.686, auc: 0.735, f1: 0.635, acc: 0.699
+2025-08-07 18:47:46: Epoch: 041 Test results, ap: 0.196, auc: 0.394, f1: 0.000, acc: 0.583
+2025-08-07 18:47:53: Epoch: 042 Train Loss: 0.130, ap: 0.692, auc: 0.736, f1: 0.637, acc: 0.680
+2025-08-07 18:47:54: Epoch: 042 Test results, ap: 0.191, auc: 0.380, f1: 0.074, acc: 0.537
+2025-08-07 18:48:01: Epoch: 043 Train Loss: 0.116, ap: 0.690, auc: 0.733, f1: 0.628, acc: 0.689
+2025-08-07 18:48:02: Epoch: 043 Test results, ap: 0.196, auc: 0.398, f1: 0.190, acc: 0.528
+2025-08-07 18:48:10: Epoch: 044 Train Loss: 0.125, ap: 0.704, auc: 0.728, f1: 0.644, acc: 0.699
+2025-08-07 18:48:11: Epoch: 044 Test results, ap: 0.201, auc: 0.416, f1: 0.254, acc: 0.509
+2025-08-07 18:48:18: Epoch: 045 Train Loss: 0.119, ap: 0.706, auc: 0.722, f1: 0.635, acc: 0.699
+2025-08-07 18:48:20: Epoch: 045 Test results, ap: 0.200, auc: 0.414, f1: 0.232, acc: 0.509
+2025-08-07 18:48:27: Epoch: 046 Train Loss: 0.117, ap: 0.714, auc: 0.718, f1: 0.619, acc: 0.689
+2025-08-07 18:48:28: Epoch: 046 Test results, ap: 0.224, auc: 0.441, f1: 0.278, acc: 0.472
+2025-08-07 18:48:35: Epoch: 047 Train Loss: 0.100, ap: 0.708, auc: 0.726, f1: 0.635, acc: 0.699
+2025-08-07 18:48:37: Epoch: 047 Test results, ap: 0.201, auc: 0.411, f1: 0.222, acc: 0.481
+2025-08-07 18:48:44: Epoch: 048 Train Loss: 0.113, ap: 0.707, auc: 0.732, f1: 0.593, acc: 0.680
+2025-08-07 18:48:45: Epoch: 048 Test results, ap: 0.201, auc: 0.412, f1: 0.206, acc: 0.500
+2025-08-07 18:48:52: Epoch: 049 Train Loss: 0.105, ap: 0.714, auc: 0.725, f1: 0.651, acc: 0.709
+2025-08-07 18:48:54: Epoch: 049 Test results, ap: 0.227, auc: 0.441, f1: 0.225, acc: 0.491
+2025-08-07 18:49:01: Epoch: 050 Train Loss: 0.103, ap: 0.713, auc: 0.738, f1: 0.595, acc: 0.670
+2025-08-07 18:49:02: Epoch: 050 Test results, ap: 0.192, auc: 0.386, f1: 0.133, acc: 0.519
+2025-08-07 18:49:02: used time 0.12h
+2025-08-07 18:49:02: Running Cross Validation 1
+2025-08-07 18:49:02: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 18:49:09: Epoch: 001 Train Loss: 0.676, ap: 0.387, auc: 0.488, f1: 0.529, acc: 0.359
+2025-08-07 18:49:09: Epoch: 001 New best VALIDATION metrics
+2025-08-07 18:49:11: Epoch: 001 Test results, ap: 0.265, auc: 0.519, f1: 0.388, acc: 0.241
+2025-08-07 18:49:18: Epoch: 002 Train Loss: 0.601, ap: 0.486, auc: 0.645, f1: 0.455, acc: 0.650
+2025-08-07 18:49:18: Epoch: 002 New best VALIDATION metrics
+2025-08-07 18:49:19: Epoch: 002 Test results, ap: 0.249, auc: 0.385, f1: 0.133, acc: 0.759
+2025-08-07 18:49:26: Epoch: 003 Train Loss: 0.556, ap: 0.610, auc: 0.725, f1: 0.528, acc: 0.670
+2025-08-07 18:49:26: Epoch: 003 New best VALIDATION metrics
+2025-08-07 18:49:28: Epoch: 003 Test results, ap: 0.271, auc: 0.382, f1: 0.229, acc: 0.750
+2025-08-07 18:49:35: Epoch: 004 Train Loss: 0.522, ap: 0.661, auc: 0.719, f1: 0.464, acc: 0.709
+2025-08-07 18:49:35: Epoch: 004 New best VALIDATION metrics
+2025-08-07 18:49:36: Epoch: 004 Test results, ap: 0.191, auc: 0.340, f1: 0.000, acc: 0.759
+2025-08-07 18:49:43: Epoch: 005 Train Loss: 0.487, ap: 0.674, auc: 0.760, f1: 0.523, acc: 0.699
+2025-08-07 18:49:43: Epoch: 005 New best VALIDATION metrics
+2025-08-07 18:49:45: Epoch: 005 Test results, ap: 0.213, auc: 0.428, f1: 0.000, acc: 0.722
+2025-08-07 18:49:52: Epoch: 006 Train Loss: 0.439, ap: 0.650, auc: 0.749, f1: 0.615, acc: 0.612
+2025-08-07 18:49:53: Epoch: 006 Test results, ap: 0.252, auc: 0.558, f1: 0.395, acc: 0.546
+2025-08-07 18:50:00: Epoch: 007 Train Loss: 0.400, ap: 0.585, auc: 0.673, f1: 0.459, acc: 0.680
+2025-08-07 18:50:02: Epoch: 007 Test results, ap: 0.181, auc: 0.331, f1: 0.000, acc: 0.759
+2025-08-07 18:50:09: Epoch: 008 Train Loss: 0.403, ap: 0.607, auc: 0.672, f1: 0.524, acc: 0.417
+2025-08-07 18:50:10: Epoch: 008 Test results, ap: 0.218, auc: 0.463, f1: 0.388, acc: 0.241
+2025-08-07 18:50:17: Epoch: 009 Train Loss: 0.393, ap: 0.544, auc: 0.643, f1: 0.444, acc: 0.660
+2025-08-07 18:50:19: Epoch: 009 Test results, ap: 0.241, auc: 0.367, f1: 0.000, acc: 0.759
+2025-08-07 18:50:26: Epoch: 010 Train Loss: 0.486, ap: 0.648, auc: 0.697, f1: 0.526, acc: 0.650
+2025-08-07 18:50:27: Epoch: 010 Test results, ap: 0.241, auc: 0.384, f1: 0.000, acc: 0.759
+2025-08-07 18:50:34: Epoch: 011 Train Loss: 0.357, ap: 0.663, auc: 0.747, f1: 0.561, acc: 0.757
+2025-08-07 18:50:36: Epoch: 011 Test results, ap: 0.214, auc: 0.333, f1: 0.000, acc: 0.759
+2025-08-07 18:50:36: Epoch: 011 New best TEST metrics
+2025-08-07 18:50:43: Epoch: 012 Train Loss: 0.342, ap: 0.630, auc: 0.747, f1: 0.507, acc: 0.680
+2025-08-07 18:50:44: Epoch: 012 Test results, ap: 0.228, auc: 0.384, f1: 0.000, acc: 0.731
+2025-08-07 18:50:44: Epoch: 012 New best TEST metrics
+2025-08-07 18:50:51: Epoch: 013 Train Loss: 0.311, ap: 0.638, auc: 0.720, f1: 0.608, acc: 0.699
+2025-08-07 18:50:53: Epoch: 013 Test results, ap: 0.219, auc: 0.454, f1: 0.232, acc: 0.509
+2025-08-07 18:50:53: Epoch: 013 New best TEST metrics
+2025-08-07 18:51:00: Epoch: 014 Train Loss: 0.310, ap: 0.648, auc: 0.726, f1: 0.500, acc: 0.650
+2025-08-07 18:51:01: Epoch: 014 Test results, ap: 0.218, auc: 0.394, f1: 0.057, acc: 0.694
+2025-08-07 18:51:08: Epoch: 015 Train Loss: 0.295, ap: 0.666, auc: 0.744, f1: 0.592, acc: 0.718
+2025-08-07 18:51:10: Epoch: 015 Test results, ap: 0.240, auc: 0.417, f1: 0.000, acc: 0.731
+2025-08-07 18:51:17: Epoch: 016 Train Loss: 0.301, ap: 0.632, auc: 0.745, f1: 0.587, acc: 0.699
+2025-08-07 18:51:18: Epoch: 016 Test results, ap: 0.242, auc: 0.426, f1: 0.125, acc: 0.741
+2025-08-07 18:51:25: Epoch: 017 Train Loss: 0.280, ap: 0.654, auc: 0.728, f1: 0.600, acc: 0.689
+2025-08-07 18:51:27: Epoch: 017 Test results, ap: 0.222, auc: 0.393, f1: 0.211, acc: 0.722
+2025-08-07 18:51:34: Epoch: 018 Train Loss: 0.263, ap: 0.638, auc: 0.717, f1: 0.610, acc: 0.689
+2025-08-07 18:51:35: Epoch: 018 Test results, ap: 0.214, auc: 0.401, f1: 0.111, acc: 0.704
+2025-08-07 18:51:42: Epoch: 019 Train Loss: 0.261, ap: 0.685, auc: 0.734, f1: 0.515, acc: 0.689
+2025-08-07 18:51:44: Epoch: 019 Test results, ap: 0.240, auc: 0.418, f1: 0.000, acc: 0.750
+2025-08-07 18:51:51: Epoch: 020 Train Loss: 0.255, ap: 0.621, auc: 0.696, f1: 0.529, acc: 0.689
+2025-08-07 18:51:52: Epoch: 020 Test results, ap: 0.248, auc: 0.422, f1: 0.000, acc: 0.741
+2025-08-07 18:51:59: Epoch: 021 Train Loss: 0.266, ap: 0.640, auc: 0.693, f1: 0.580, acc: 0.718
+2025-08-07 18:52:01: Epoch: 021 Test results, ap: 0.248, auc: 0.429, f1: 0.000, acc: 0.731
+2025-08-07 18:52:01: Epoch: 021 New best TEST metrics
+2025-08-07 18:52:08: Epoch: 022 Train Loss: 0.252, ap: 0.618, auc: 0.701, f1: 0.543, acc: 0.641
+2025-08-07 18:52:09: Epoch: 022 Test results, ap: 0.237, auc: 0.411, f1: 0.121, acc: 0.731
+2025-08-07 18:52:16: Epoch: 023 Train Loss: 0.228, ap: 0.607, auc: 0.694, f1: 0.558, acc: 0.631
+2025-08-07 18:52:18: Epoch: 023 Test results, ap: 0.238, auc: 0.410, f1: 0.217, acc: 0.667
+2025-08-07 18:52:25: Epoch: 024 Train Loss: 0.218, ap: 0.622, auc: 0.710, f1: 0.623, acc: 0.718
+2025-08-07 18:52:26: Epoch: 024 Test results, ap: 0.234, auc: 0.411, f1: 0.211, acc: 0.722
+2025-08-07 18:52:33: Epoch: 025 Train Loss: 0.221, ap: 0.628, auc: 0.726, f1: 0.563, acc: 0.699
+2025-08-07 18:52:35: Epoch: 025 Test results, ap: 0.235, auc: 0.420, f1: 0.067, acc: 0.741
+2025-08-07 18:52:42: Epoch: 026 Train Loss: 0.216, ap: 0.584, auc: 0.695, f1: 0.592, acc: 0.718
+2025-08-07 18:52:43: Epoch: 026 Test results, ap: 0.249, auc: 0.418, f1: 0.129, acc: 0.750
+2025-08-07 18:52:50: Epoch: 027 Train Loss: 0.189, ap: 0.603, auc: 0.704, f1: 0.585, acc: 0.670
+2025-08-07 18:52:52: Epoch: 027 Test results, ap: 0.248, auc: 0.432, f1: 0.229, acc: 0.750
+2025-08-07 18:52:52: Epoch: 027 New best TEST metrics
+2025-08-07 18:52:59: Epoch: 028 Train Loss: 0.234, ap: 0.614, auc: 0.715, f1: 0.571, acc: 0.680
+2025-08-07 18:53:00: Epoch: 028 Test results, ap: 0.243, auc: 0.430, f1: 0.000, acc: 0.731
+2025-08-07 18:53:07: Epoch: 029 Train Loss: 0.192, ap: 0.589, auc: 0.715, f1: 0.568, acc: 0.689
+2025-08-07 18:53:09: Epoch: 029 Test results, ap: 0.227, auc: 0.394, f1: 0.121, acc: 0.731
+2025-08-07 18:53:16: Epoch: 030 Train Loss: 0.200, ap: 0.597, auc: 0.704, f1: 0.563, acc: 0.699
+2025-08-07 18:53:17: Epoch: 030 Test results, ap: 0.277, auc: 0.514, f1: 0.000, acc: 0.731
+2025-08-07 18:53:17: Epoch: 030 New best TEST metrics
+2025-08-07 18:53:24: Epoch: 031 Train Loss: 0.203, ap: 0.596, auc: 0.701, f1: 0.532, acc: 0.641
+2025-08-07 18:53:26: Epoch: 031 Test results, ap: 0.258, auc: 0.433, f1: 0.235, acc: 0.759
+2025-08-07 18:53:33: Epoch: 032 Train Loss: 0.155, ap: 0.606, auc: 0.694, f1: 0.548, acc: 0.680
+2025-08-07 18:53:34: Epoch: 032 Test results, ap: 0.254, auc: 0.436, f1: 0.129, acc: 0.750
+2025-08-07 18:53:41: Epoch: 033 Train Loss: 0.167, ap: 0.618, auc: 0.705, f1: 0.571, acc: 0.680
+2025-08-07 18:53:43: Epoch: 033 Test results, ap: 0.253, auc: 0.449, f1: 0.129, acc: 0.750
+2025-08-07 18:53:50: Epoch: 034 Train Loss: 0.157, ap: 0.606, auc: 0.698, f1: 0.575, acc: 0.699
+2025-08-07 18:53:51: Epoch: 034 Test results, ap: 0.248, auc: 0.439, f1: 0.129, acc: 0.750
+2025-08-07 18:53:58: Epoch: 035 Train Loss: 0.161, ap: 0.587, auc: 0.697, f1: 0.587, acc: 0.699
+2025-08-07 18:54:00: Epoch: 035 Test results, ap: 0.245, auc: 0.430, f1: 0.129, acc: 0.750
+2025-08-07 18:54:07: Epoch: 036 Train Loss: 0.157, ap: 0.594, auc: 0.699, f1: 0.595, acc: 0.709
+2025-08-07 18:54:08: Epoch: 036 Test results, ap: 0.252, auc: 0.450, f1: 0.129, acc: 0.750
+2025-08-07 18:54:15: Epoch: 037 Train Loss: 0.153, ap: 0.589, auc: 0.702, f1: 0.592, acc: 0.718
+2025-08-07 18:54:17: Epoch: 037 Test results, ap: 0.259, auc: 0.455, f1: 0.129, acc: 0.750
+2025-08-07 18:54:24: Epoch: 038 Train Loss: 0.145, ap: 0.587, auc: 0.696, f1: 0.559, acc: 0.709
+2025-08-07 18:54:25: Epoch: 038 Test results, ap: 0.238, auc: 0.435, f1: 0.000, acc: 0.750
+2025-08-07 18:54:32: Epoch: 039 Train Loss: 0.141, ap: 0.582, auc: 0.690, f1: 0.588, acc: 0.728
+2025-08-07 18:54:33: Epoch: 039 Test results, ap: 0.262, auc: 0.448, f1: 0.000, acc: 0.741
+2025-08-07 18:54:41: Epoch: 040 Train Loss: 0.140, ap: 0.565, auc: 0.685, f1: 0.556, acc: 0.689
+2025-08-07 18:54:42: Epoch: 040 Test results, ap: 0.275, auc: 0.449, f1: 0.133, acc: 0.759
+2025-08-07 18:54:49: Epoch: 041 Train Loss: 0.129, ap: 0.561, auc: 0.677, f1: 0.543, acc: 0.689
+2025-08-07 18:54:50: Epoch: 041 Test results, ap: 0.262, auc: 0.430, f1: 0.133, acc: 0.759
+2025-08-07 18:54:57: Epoch: 042 Train Loss: 0.113, ap: 0.539, auc: 0.657, f1: 0.535, acc: 0.680
+2025-08-07 18:54:59: Epoch: 042 Test results, ap: 0.297, auc: 0.430, f1: 0.138, acc: 0.769
+2025-08-07 18:55:06: Epoch: 043 Train Loss: 0.127, ap: 0.546, auc: 0.659, f1: 0.535, acc: 0.680
+2025-08-07 18:55:07: Epoch: 043 Test results, ap: 0.298, auc: 0.432, f1: 0.133, acc: 0.759
+2025-08-07 18:55:15: Epoch: 044 Train Loss: 0.101, ap: 0.571, auc: 0.684, f1: 0.583, acc: 0.709
+2025-08-07 18:55:16: Epoch: 044 Test results, ap: 0.253, auc: 0.420, f1: 0.129, acc: 0.750
+2025-08-07 18:55:23: Epoch: 045 Train Loss: 0.089, ap: 0.562, auc: 0.679, f1: 0.551, acc: 0.699
+2025-08-07 18:55:24: Epoch: 045 Test results, ap: 0.251, auc: 0.414, f1: 0.000, acc: 0.750
+2025-08-07 18:55:31: Epoch: 046 Train Loss: 0.089, ap: 0.546, auc: 0.661, f1: 0.543, acc: 0.689
+2025-08-07 18:55:33: Epoch: 046 Test results, ap: 0.239, auc: 0.402, f1: 0.000, acc: 0.759
+2025-08-07 18:55:40: Epoch: 047 Train Loss: 0.080, ap: 0.558, auc: 0.689, f1: 0.548, acc: 0.680
+2025-08-07 18:55:41: Epoch: 047 Test results, ap: 0.217, auc: 0.367, f1: 0.121, acc: 0.731
+2025-08-07 18:55:48: Epoch: 048 Train Loss: 0.075, ap: 0.536, auc: 0.665, f1: 0.535, acc: 0.680
+2025-08-07 18:55:49: Epoch: 048 Test results, ap: 0.242, auc: 0.396, f1: 0.000, acc: 0.750
+2025-08-07 18:55:56: Epoch: 049 Train Loss: 0.078, ap: 0.550, auc: 0.663, f1: 0.571, acc: 0.709
+2025-08-07 18:55:58: Epoch: 049 Test results, ap: 0.230, auc: 0.416, f1: 0.000, acc: 0.741
+2025-08-07 18:56:05: Epoch: 050 Train Loss: 0.104, ap: 0.538, auc: 0.663, f1: 0.543, acc: 0.689
+2025-08-07 18:56:06: Epoch: 050 Test results, ap: 0.213, auc: 0.395, f1: 0.000, acc: 0.759
+2025-08-07 18:56:06: used time 0.12h
+2025-08-07 18:56:06: Running Cross Validation 2
+2025-08-07 18:56:06: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 18:56:13: Epoch: 001 Train Loss: 0.690, ap: 0.759, auc: 0.706, f1: 0.675, acc: 0.524
+2025-08-07 18:56:13: Epoch: 001 New best VALIDATION metrics
+2025-08-07 18:56:15: Epoch: 001 Test results, ap: 0.351, auc: 0.611, f1: 0.388, acc: 0.241
+2025-08-07 18:56:22: Epoch: 002 Train Loss: 0.661, ap: 0.747, auc: 0.718, f1: 0.571, acc: 0.650
+2025-08-07 18:56:22: Epoch: 002 New best VALIDATION metrics
+2025-08-07 18:56:23: Epoch: 002 Test results, ap: 0.193, auc: 0.317, f1: 0.000, acc: 0.759
+2025-08-07 18:56:31: Epoch: 003 Train Loss: 0.576, ap: 0.561, auc: 0.592, f1: 0.636, acc: 0.612
+2025-08-07 18:56:32: Epoch: 003 Test results, ap: 0.203, auc: 0.374, f1: 0.000, acc: 0.759
+2025-08-07 18:56:39: Epoch: 004 Train Loss: 0.581, ap: 0.826, auc: 0.784, f1: 0.680, acc: 0.699
+2025-08-07 18:56:39: Epoch: 004 New best VALIDATION metrics
+2025-08-07 18:56:40: Epoch: 004 Test results, ap: 0.305, auc: 0.537, f1: 0.211, acc: 0.722
+2025-08-07 18:56:48: Epoch: 005 Train Loss: 0.562, ap: 0.787, auc: 0.752, f1: 0.553, acc: 0.670
+2025-08-07 18:56:49: Epoch: 005 Test results, ap: 0.221, auc: 0.364, f1: 0.000, acc: 0.759
+2025-08-07 18:56:56: Epoch: 006 Train Loss: 0.500, ap: 0.822, auc: 0.801, f1: 0.667, acc: 0.505
+2025-08-07 18:56:56: Epoch: 006 New best VALIDATION metrics
+2025-08-07 18:56:58: Epoch: 006 Test results, ap: 0.199, auc: 0.335, f1: 0.388, acc: 0.241
+2025-08-07 18:57:05: Epoch: 007 Train Loss: 0.522, ap: 0.689, auc: 0.651, f1: 0.000, acc: 0.495
+2025-08-07 18:57:06: Epoch: 007 Test results, ap: 0.224, auc: 0.389, f1: 0.000, acc: 0.759
+2025-08-07 18:57:13: Epoch: 008 Train Loss: 0.500, ap: 0.765, auc: 0.729, f1: 0.673, acc: 0.650
+2025-08-07 18:57:15: Epoch: 008 Test results, ap: 0.187, auc: 0.343, f1: 0.000, acc: 0.750
+2025-08-07 18:57:22: Epoch: 009 Train Loss: 0.469, ap: 0.816, auc: 0.789, f1: 0.764, acc: 0.748
+2025-08-07 18:57:23: Epoch: 009 Test results, ap: 0.268, auc: 0.447, f1: 0.000, acc: 0.722
+2025-08-07 18:57:30: Epoch: 010 Train Loss: 0.463, ap: 0.699, auc: 0.635, f1: 0.639, acc: 0.495
+2025-08-07 18:57:31: Epoch: 010 Test results, ap: 0.215, auc: 0.387, f1: 0.263, acc: 0.481
+2025-08-07 18:57:39: Epoch: 011 Train Loss: 0.421, ap: 0.816, auc: 0.776, f1: 0.745, acc: 0.748
+2025-08-07 18:57:40: Epoch: 011 Test results, ap: 0.193, auc: 0.368, f1: 0.000, acc: 0.741
+2025-08-07 18:57:40: Epoch: 011 New best TEST metrics
+2025-08-07 18:57:47: Epoch: 012 Train Loss: 0.378, ap: 0.809, auc: 0.762, f1: 0.680, acc: 0.699
+2025-08-07 18:57:49: Epoch: 012 Test results, ap: 0.195, auc: 0.354, f1: 0.000, acc: 0.741
+2025-08-07 18:57:56: Epoch: 013 Train Loss: 0.380, ap: 0.822, auc: 0.797, f1: 0.644, acc: 0.699
+2025-08-07 18:57:57: Epoch: 013 Test results, ap: 0.239, auc: 0.459, f1: 0.000, acc: 0.759
+2025-08-07 18:57:57: Epoch: 013 New best TEST metrics
+2025-08-07 18:58:04: Epoch: 014 Train Loss: 0.346, ap: 0.834, auc: 0.797, f1: 0.725, acc: 0.728
+2025-08-07 18:58:04: Epoch: 014 New best VALIDATION metrics
+2025-08-07 18:58:06: Epoch: 014 Test results, ap: 0.234, auc: 0.409, f1: 0.000, acc: 0.759
+2025-08-07 18:58:13: Epoch: 015 Train Loss: 0.363, ap: 0.782, auc: 0.731, f1: 0.614, acc: 0.621
+2025-08-07 18:58:14: Epoch: 015 Test results, ap: 0.229, auc: 0.440, f1: 0.000, acc: 0.759
+2025-08-07 18:58:21: Epoch: 016 Train Loss: 0.317, ap: 0.776, auc: 0.784, f1: 0.712, acc: 0.670
+2025-08-07 18:58:23: Epoch: 016 Test results, ap: 0.292, auc: 0.514, f1: 0.000, acc: 0.759
+2025-08-07 18:58:23: Epoch: 016 New best TEST metrics
+2025-08-07 18:58:30: Epoch: 017 Train Loss: 0.297, ap: 0.819, auc: 0.790, f1: 0.682, acc: 0.728
+2025-08-07 18:58:31: Epoch: 017 Test results, ap: 0.320, auc: 0.457, f1: 0.000, acc: 0.759
+2025-08-07 18:58:38: Epoch: 018 Train Loss: 0.339, ap: 0.733, auc: 0.687, f1: 0.661, acc: 0.641
+2025-08-07 18:58:40: Epoch: 018 Test results, ap: 0.234, auc: 0.474, f1: 0.000, acc: 0.759
+2025-08-07 18:58:47: Epoch: 019 Train Loss: 0.355, ap: 0.795, auc: 0.758, f1: 0.695, acc: 0.650
+2025-08-07 18:58:48: Epoch: 019 Test results, ap: 0.251, auc: 0.517, f1: 0.000, acc: 0.722
+2025-08-07 18:58:55: Epoch: 020 Train Loss: 0.311, ap: 0.832, auc: 0.796, f1: 0.722, acc: 0.738
+2025-08-07 18:58:57: Epoch: 020 Test results, ap: 0.282, auc: 0.515, f1: 0.000, acc: 0.759
+2025-08-07 18:59:04: Epoch: 021 Train Loss: 0.269, ap: 0.796, auc: 0.774, f1: 0.741, acc: 0.709
+2025-08-07 18:59:05: Epoch: 021 Test results, ap: 0.348, auc: 0.558, f1: 0.000, acc: 0.750
+2025-08-07 18:59:05: Epoch: 021 New best TEST metrics
+2025-08-07 18:59:12: Epoch: 022 Train Loss: 0.240, ap: 0.798, auc: 0.780, f1: 0.735, acc: 0.699
+2025-08-07 18:59:14: Epoch: 022 Test results, ap: 0.350, auc: 0.589, f1: 0.000, acc: 0.750
+2025-08-07 18:59:14: Epoch: 022 New best TEST metrics
+2025-08-07 18:59:21: Epoch: 023 Train Loss: 0.239, ap: 0.802, auc: 0.787, f1: 0.743, acc: 0.718
+2025-08-07 18:59:22: Epoch: 023 Test results, ap: 0.379, auc: 0.656, f1: 0.000, acc: 0.759
+2025-08-07 18:59:22: Epoch: 023 New best TEST metrics
+2025-08-07 18:59:30: Epoch: 024 Train Loss: 0.233, ap: 0.764, auc: 0.766, f1: 0.725, acc: 0.728
+2025-08-07 18:59:31: Epoch: 024 Test results, ap: 0.352, auc: 0.670, f1: 0.000, acc: 0.759
+2025-08-07 18:59:38: Epoch: 025 Train Loss: 0.236, ap: 0.774, auc: 0.744, f1: 0.678, acc: 0.631
+2025-08-07 18:59:39: Epoch: 025 Test results, ap: 0.470, auc: 0.790, f1: 0.074, acc: 0.769
+2025-08-07 18:59:39: Epoch: 025 New best TEST metrics
+2025-08-07 18:59:47: Epoch: 026 Train Loss: 0.244, ap: 0.781, auc: 0.768, f1: 0.727, acc: 0.738
+2025-08-07 18:59:48: Epoch: 026 Test results, ap: 0.375, auc: 0.651, f1: 0.000, acc: 0.759
+2025-08-07 18:59:55: Epoch: 027 Train Loss: 0.211, ap: 0.795, auc: 0.775, f1: 0.739, acc: 0.699
+2025-08-07 18:59:57: Epoch: 027 Test results, ap: 0.475, auc: 0.704, f1: 0.250, acc: 0.778
+2025-08-07 19:00:04: Epoch: 028 Train Loss: 0.202, ap: 0.798, auc: 0.785, f1: 0.765, acc: 0.738
+2025-08-07 19:00:05: Epoch: 028 Test results, ap: 0.482, auc: 0.750, f1: 0.138, acc: 0.769
+2025-08-07 19:00:12: Epoch: 029 Train Loss: 0.190, ap: 0.771, auc: 0.786, f1: 0.782, acc: 0.767
+2025-08-07 19:00:14: Epoch: 029 Test results, ap: 0.462, auc: 0.750, f1: 0.333, acc: 0.741
+2025-08-07 19:00:21: Epoch: 030 Train Loss: 0.192, ap: 0.727, auc: 0.740, f1: 0.718, acc: 0.680
+2025-08-07 19:00:22: Epoch: 030 Test results, ap: 0.393, auc: 0.632, f1: 0.304, acc: 0.704
+2025-08-07 19:00:29: Epoch: 031 Train Loss: 0.196, ap: 0.788, auc: 0.768, f1: 0.762, acc: 0.757
+2025-08-07 19:00:31: Epoch: 031 Test results, ap: 0.416, auc: 0.655, f1: 0.000, acc: 0.759
+2025-08-07 19:00:38: Epoch: 032 Train Loss: 0.177, ap: 0.785, auc: 0.770, f1: 0.775, acc: 0.757
+2025-08-07 19:00:39: Epoch: 032 Test results, ap: 0.391, auc: 0.675, f1: 0.000, acc: 0.759
+2025-08-07 19:00:46: Epoch: 033 Train Loss: 0.174, ap: 0.789, auc: 0.779, f1: 0.746, acc: 0.709
+2025-08-07 19:00:48: Epoch: 033 Test results, ap: 0.474, auc: 0.732, f1: 0.074, acc: 0.769
+2025-08-07 19:00:55: Epoch: 034 Train Loss: 0.159, ap: 0.788, auc: 0.778, f1: 0.747, acc: 0.757
+2025-08-07 19:00:56: Epoch: 034 Test results, ap: 0.533, auc: 0.824, f1: 0.235, acc: 0.759
+2025-08-07 19:00:56: Epoch: 034 New best TEST metrics
+2025-08-07 19:01:03: Epoch: 035 Train Loss: 0.174, ap: 0.779, auc: 0.785, f1: 0.765, acc: 0.738
+2025-08-07 19:01:05: Epoch: 035 Test results, ap: 0.438, auc: 0.677, f1: 0.311, acc: 0.713
+2025-08-07 19:01:12: Epoch: 036 Train Loss: 0.176, ap: 0.792, auc: 0.776, f1: 0.734, acc: 0.718
+2025-08-07 19:01:13: Epoch: 036 Test results, ap: 0.493, auc: 0.790, f1: 0.188, acc: 0.759
+2025-08-07 19:01:20: Epoch: 037 Train Loss: 0.163, ap: 0.786, auc: 0.776, f1: 0.761, acc: 0.738
+2025-08-07 19:01:22: Epoch: 037 Test results, ap: 0.394, auc: 0.619, f1: 0.229, acc: 0.750
+2025-08-07 19:01:29: Epoch: 038 Train Loss: 0.160, ap: 0.790, auc: 0.791, f1: 0.759, acc: 0.728
+2025-08-07 19:01:30: Epoch: 038 Test results, ap: 0.438, auc: 0.682, f1: 0.311, acc: 0.713
+2025-08-07 19:01:37: Epoch: 039 Train Loss: 0.149, ap: 0.782, auc: 0.784, f1: 0.776, acc: 0.748
+2025-08-07 19:01:39: Epoch: 039 Test results, ap: 0.412, auc: 0.689, f1: 0.311, acc: 0.713
+2025-08-07 19:01:46: Epoch: 040 Train Loss: 0.133, ap: 0.784, auc: 0.778, f1: 0.779, acc: 0.757
+2025-08-07 19:01:47: Epoch: 040 Test results, ap: 0.388, auc: 0.670, f1: 0.229, acc: 0.750
+2025-08-07 19:01:54: Epoch: 041 Train Loss: 0.127, ap: 0.785, auc: 0.785, f1: 0.783, acc: 0.757
+2025-08-07 19:01:56: Epoch: 041 Test results, ap: 0.408, auc: 0.708, f1: 0.318, acc: 0.722
+2025-08-07 19:02:03: Epoch: 042 Train Loss: 0.136, ap: 0.780, auc: 0.779, f1: 0.756, acc: 0.718
+2025-08-07 19:02:04: Epoch: 042 Test results, ap: 0.363, auc: 0.631, f1: 0.267, acc: 0.694
+2025-08-07 19:02:11: Epoch: 043 Train Loss: 0.119, ap: 0.784, auc: 0.783, f1: 0.765, acc: 0.738
+2025-08-07 19:02:13: Epoch: 043 Test results, ap: 0.345, auc: 0.638, f1: 0.216, acc: 0.731
+2025-08-07 19:02:20: Epoch: 044 Train Loss: 0.126, ap: 0.791, auc: 0.776, f1: 0.750, acc: 0.728
+2025-08-07 19:02:21: Epoch: 044 Test results, ap: 0.322, auc: 0.678, f1: 0.000, acc: 0.759
+2025-08-07 19:02:28: Epoch: 045 Train Loss: 0.117, ap: 0.790, auc: 0.774, f1: 0.717, acc: 0.709
+2025-08-07 19:02:29: Epoch: 045 Test results, ap: 0.347, auc: 0.691, f1: 0.000, acc: 0.759
+2025-08-07 19:02:37: Epoch: 046 Train Loss: 0.114, ap: 0.794, auc: 0.789, f1: 0.772, acc: 0.748
+2025-08-07 19:02:38: Epoch: 046 Test results, ap: 0.318, auc: 0.652, f1: 0.125, acc: 0.741
+2025-08-07 19:02:45: Epoch: 047 Train Loss: 0.117, ap: 0.796, auc: 0.790, f1: 0.775, acc: 0.757
+2025-08-07 19:02:46: Epoch: 047 Test results, ap: 0.303, auc: 0.629, f1: 0.121, acc: 0.731
+2025-08-07 19:02:54: Epoch: 048 Train Loss: 0.105, ap: 0.802, auc: 0.793, f1: 0.779, acc: 0.757
+2025-08-07 19:02:55: Epoch: 048 Test results, ap: 0.315, auc: 0.654, f1: 0.065, acc: 0.731
+2025-08-07 19:03:02: Epoch: 049 Train Loss: 0.125, ap: 0.790, auc: 0.784, f1: 0.737, acc: 0.709
+2025-08-07 19:03:03: Epoch: 049 Test results, ap: 0.299, auc: 0.623, f1: 0.108, acc: 0.694
+2025-08-07 19:03:11: Epoch: 050 Train Loss: 0.126, ap: 0.786, auc: 0.781, f1: 0.752, acc: 0.718
+2025-08-07 19:03:12: Epoch: 050 Test results, ap: 0.310, auc: 0.643, f1: 0.158, acc: 0.704
+2025-08-07 19:03:12: used time 0.12h
+2025-08-07 19:03:12: Running Cross Validation 3
+2025-08-07 19:03:12: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:03:19: Epoch: 001 Train Loss: 0.687, ap: 0.658, auc: 0.663, f1: 0.583, acc: 0.417
+2025-08-07 19:03:19: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:03:21: Epoch: 001 Test results, ap: 0.261, auc: 0.486, f1: 0.388, acc: 0.241
+2025-08-07 19:03:28: Epoch: 002 Train Loss: 0.630, ap: 0.654, auc: 0.704, f1: 0.619, acc: 0.641
+2025-08-07 19:03:28: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:03:29: Epoch: 002 Test results, ap: 0.207, auc: 0.409, f1: 0.000, acc: 0.731
+2025-08-07 19:03:36: Epoch: 003 Train Loss: 0.589, ap: 0.686, auc: 0.704, f1: 0.594, acc: 0.456
+2025-08-07 19:03:36: Epoch: 003 New best VALIDATION metrics
+2025-08-07 19:03:38: Epoch: 003 Test results, ap: 0.319, auc: 0.372, f1: 0.286, acc: 0.630
+2025-08-07 19:03:45: Epoch: 004 Train Loss: 0.547, ap: 0.720, auc: 0.786, f1: 0.170, acc: 0.621
+2025-08-07 19:03:45: Epoch: 004 New best VALIDATION metrics
+2025-08-07 19:03:46: Epoch: 004 Test results, ap: 0.181, auc: 0.310, f1: 0.000, acc: 0.759
+2025-08-07 19:03:53: Epoch: 005 Train Loss: 0.543, ap: 0.584, auc: 0.714, f1: 0.648, acc: 0.641
+2025-08-07 19:03:55: Epoch: 005 Test results, ap: 0.219, auc: 0.473, f1: 0.000, acc: 0.593
+2025-08-07 19:04:02: Epoch: 006 Train Loss: 0.511, ap: 0.753, auc: 0.779, f1: 0.377, acc: 0.680
+2025-08-07 19:04:02: Epoch: 006 New best VALIDATION metrics
+2025-08-07 19:04:03: Epoch: 006 Test results, ap: 0.217, auc: 0.469, f1: 0.000, acc: 0.759
+2025-08-07 19:04:10: Epoch: 007 Train Loss: 0.478, ap: 0.764, auc: 0.801, f1: 0.714, acc: 0.767
+2025-08-07 19:04:10: Epoch: 007 New best VALIDATION metrics
+2025-08-07 19:04:12: Epoch: 007 Test results, ap: 0.202, auc: 0.328, f1: 0.000, acc: 0.759
+2025-08-07 19:04:19: Epoch: 008 Train Loss: 0.464, ap: 0.736, auc: 0.764, f1: 0.686, acc: 0.689
+2025-08-07 19:04:20: Epoch: 008 Test results, ap: 0.231, auc: 0.508, f1: 0.303, acc: 0.574
+2025-08-07 19:04:27: Epoch: 009 Train Loss: 0.445, ap: 0.798, auc: 0.817, f1: 0.729, acc: 0.777
+2025-08-07 19:04:27: Epoch: 009 New best VALIDATION metrics
+2025-08-07 19:04:29: Epoch: 009 Test results, ap: 0.259, auc: 0.569, f1: 0.000, acc: 0.685
+2025-08-07 19:04:36: Epoch: 010 Train Loss: 0.428, ap: 0.632, auc: 0.706, f1: 0.438, acc: 0.650
+2025-08-07 19:04:37: Epoch: 010 Test results, ap: 0.175, auc: 0.309, f1: 0.000, acc: 0.676
+2025-08-07 19:04:44: Epoch: 011 Train Loss: 0.397, ap: 0.747, auc: 0.780, f1: 0.673, acc: 0.689
+2025-08-07 19:04:46: Epoch: 011 Test results, ap: 0.213, auc: 0.445, f1: 0.314, acc: 0.556
+2025-08-07 19:04:46: Epoch: 011 New best TEST metrics
+2025-08-07 19:04:53: Epoch: 012 Train Loss: 0.353, ap: 0.740, auc: 0.778, f1: 0.673, acc: 0.650
+2025-08-07 19:04:54: Epoch: 012 Test results, ap: 0.220, auc: 0.471, f1: 0.290, acc: 0.546
+2025-08-07 19:04:54: Epoch: 012 New best TEST metrics
+2025-08-07 19:05:01: Epoch: 013 Train Loss: 0.334, ap: 0.723, auc: 0.765, f1: 0.592, acc: 0.718
+2025-08-07 19:05:03: Epoch: 013 Test results, ap: 0.244, auc: 0.549, f1: 0.000, acc: 0.704
+2025-08-07 19:05:03: Epoch: 013 New best TEST metrics
+2025-08-07 19:05:10: Epoch: 014 Train Loss: 0.341, ap: 0.708, auc: 0.765, f1: 0.681, acc: 0.718
+2025-08-07 19:05:11: Epoch: 014 Test results, ap: 0.202, auc: 0.411, f1: 0.074, acc: 0.537
+2025-08-07 19:05:19: Epoch: 015 Train Loss: 0.320, ap: 0.726, auc: 0.767, f1: 0.642, acc: 0.718
+2025-08-07 19:05:20: Epoch: 015 Test results, ap: 0.217, auc: 0.463, f1: 0.000, acc: 0.593
+2025-08-07 19:05:27: Epoch: 016 Train Loss: 0.335, ap: 0.743, auc: 0.776, f1: 0.680, acc: 0.699
+2025-08-07 19:05:28: Epoch: 016 Test results, ap: 0.227, auc: 0.491, f1: 0.333, acc: 0.556
+2025-08-07 19:05:35: Epoch: 017 Train Loss: 0.284, ap: 0.740, auc: 0.776, f1: 0.713, acc: 0.757
+2025-08-07 19:05:37: Epoch: 017 Test results, ap: 0.222, auc: 0.471, f1: 0.219, acc: 0.537
+2025-08-07 19:05:44: Epoch: 018 Train Loss: 0.290, ap: 0.696, auc: 0.762, f1: 0.648, acc: 0.641
+2025-08-07 19:05:45: Epoch: 018 Test results, ap: 0.217, auc: 0.457, f1: 0.341, acc: 0.500
+2025-08-07 19:05:52: Epoch: 019 Train Loss: 0.250, ap: 0.671, auc: 0.762, f1: 0.675, acc: 0.738
+2025-08-07 19:05:54: Epoch: 019 Test results, ap: 0.272, auc: 0.505, f1: 0.300, acc: 0.611
+2025-08-07 19:06:01: Epoch: 020 Train Loss: 0.259, ap: 0.699, auc: 0.754, f1: 0.642, acc: 0.631
+2025-08-07 19:06:02: Epoch: 020 Test results, ap: 0.234, auc: 0.502, f1: 0.338, acc: 0.565
+2025-08-07 19:06:09: Epoch: 021 Train Loss: 0.270, ap: 0.687, auc: 0.735, f1: 0.493, acc: 0.660
+2025-08-07 19:06:10: Epoch: 021 Test results, ap: 0.280, auc: 0.504, f1: 0.098, acc: 0.657
+2025-08-07 19:06:17: Epoch: 022 Train Loss: 0.205, ap: 0.675, auc: 0.753, f1: 0.705, acc: 0.748
+2025-08-07 19:06:19: Epoch: 022 Test results, ap: 0.235, auc: 0.458, f1: 0.319, acc: 0.565
+2025-08-07 19:06:26: Epoch: 023 Train Loss: 0.214, ap: 0.709, auc: 0.749, f1: 0.690, acc: 0.748
+2025-08-07 19:06:27: Epoch: 023 Test results, ap: 0.260, auc: 0.472, f1: 0.314, acc: 0.556
+2025-08-07 19:06:35: Epoch: 024 Train Loss: 0.218, ap: 0.635, auc: 0.751, f1: 0.708, acc: 0.728
+2025-08-07 19:06:36: Epoch: 024 Test results, ap: 0.240, auc: 0.514, f1: 0.308, acc: 0.583
+2025-08-07 19:06:43: Epoch: 025 Train Loss: 0.201, ap: 0.679, auc: 0.754, f1: 0.603, acc: 0.718
+2025-08-07 19:06:45: Epoch: 025 Test results, ap: 0.308, auc: 0.575, f1: 0.250, acc: 0.667
+2025-08-07 19:06:45: Epoch: 025 New best TEST metrics
+2025-08-07 19:06:52: Epoch: 026 Train Loss: 0.178, ap: 0.672, auc: 0.715, f1: 0.567, acc: 0.718
+2025-08-07 19:06:53: Epoch: 026 Test results, ap: 0.361, auc: 0.525, f1: 0.293, acc: 0.731
+2025-08-07 19:06:53: Epoch: 026 New best TEST metrics
+2025-08-07 19:07:00: Epoch: 027 Train Loss: 0.176, ap: 0.709, auc: 0.763, f1: 0.659, acc: 0.709
+2025-08-07 19:07:01: Epoch: 027 Test results, ap: 0.320, auc: 0.520, f1: 0.319, acc: 0.565
+2025-08-07 19:07:09: Epoch: 028 Train Loss: 0.169, ap: 0.683, auc: 0.745, f1: 0.582, acc: 0.680
+2025-08-07 19:07:10: Epoch: 028 Test results, ap: 0.314, auc: 0.501, f1: 0.269, acc: 0.648
+2025-08-07 19:07:17: Epoch: 029 Train Loss: 0.162, ap: 0.691, auc: 0.744, f1: 0.659, acc: 0.699
+2025-08-07 19:07:19: Epoch: 029 Test results, ap: 0.299, auc: 0.482, f1: 0.314, acc: 0.556
+2025-08-07 19:07:26: Epoch: 030 Train Loss: 0.158, ap: 0.704, auc: 0.770, f1: 0.707, acc: 0.718
+2025-08-07 19:07:27: Epoch: 030 Test results, ap: 0.269, auc: 0.482, f1: 0.310, acc: 0.463
+2025-08-07 19:07:34: Epoch: 031 Train Loss: 0.138, ap: 0.666, auc: 0.728, f1: 0.590, acc: 0.689
+2025-08-07 19:07:36: Epoch: 031 Test results, ap: 0.333, auc: 0.498, f1: 0.250, acc: 0.556
+2025-08-07 19:07:43: Epoch: 032 Train Loss: 0.111, ap: 0.677, auc: 0.740, f1: 0.643, acc: 0.709
+2025-08-07 19:07:44: Epoch: 032 Test results, ap: 0.323, auc: 0.506, f1: 0.329, acc: 0.546
+2025-08-07 19:07:51: Epoch: 033 Train Loss: 0.110, ap: 0.682, auc: 0.748, f1: 0.634, acc: 0.709
+2025-08-07 19:07:53: Epoch: 033 Test results, ap: 0.319, auc: 0.499, f1: 0.246, acc: 0.546
+2025-08-07 19:08:00: Epoch: 034 Train Loss: 0.116, ap: 0.672, auc: 0.742, f1: 0.651, acc: 0.709
+2025-08-07 19:08:01: Epoch: 034 Test results, ap: 0.336, auc: 0.522, f1: 0.329, acc: 0.546
+2025-08-07 19:08:08: Epoch: 035 Train Loss: 0.108, ap: 0.711, auc: 0.768, f1: 0.667, acc: 0.728
+2025-08-07 19:08:10: Epoch: 035 Test results, ap: 0.283, auc: 0.528, f1: 0.277, acc: 0.565
+2025-08-07 19:08:17: Epoch: 036 Train Loss: 0.111, ap: 0.669, auc: 0.738, f1: 0.659, acc: 0.718
+2025-08-07 19:08:18: Epoch: 036 Test results, ap: 0.329, auc: 0.530, f1: 0.324, acc: 0.574
+2025-08-07 19:08:25: Epoch: 037 Train Loss: 0.098, ap: 0.709, auc: 0.774, f1: 0.653, acc: 0.670
+2025-08-07 19:08:26: Epoch: 037 Test results, ap: 0.281, auc: 0.524, f1: 0.324, acc: 0.537
+2025-08-07 19:08:34: Epoch: 038 Train Loss: 0.093, ap: 0.669, auc: 0.756, f1: 0.690, acc: 0.738
+2025-08-07 19:08:35: Epoch: 038 Test results, ap: 0.309, auc: 0.523, f1: 0.310, acc: 0.546
+2025-08-07 19:08:42: Epoch: 039 Train Loss: 0.098, ap: 0.696, auc: 0.754, f1: 0.682, acc: 0.728
+2025-08-07 19:08:43: Epoch: 039 Test results, ap: 0.339, auc: 0.500, f1: 0.269, acc: 0.546
+2025-08-07 19:08:51: Epoch: 040 Train Loss: 0.086, ap: 0.684, auc: 0.732, f1: 0.587, acc: 0.699
+2025-08-07 19:08:52: Epoch: 040 Test results, ap: 0.347, auc: 0.485, f1: 0.291, acc: 0.639
+2025-08-07 19:08:59: Epoch: 041 Train Loss: 0.095, ap: 0.671, auc: 0.746, f1: 0.634, acc: 0.709
+2025-08-07 19:09:01: Epoch: 041 Test results, ap: 0.337, auc: 0.500, f1: 0.250, acc: 0.556
+2025-08-07 19:09:08: Epoch: 042 Train Loss: 0.079, ap: 0.668, auc: 0.755, f1: 0.697, acc: 0.738
+2025-08-07 19:09:09: Epoch: 042 Test results, ap: 0.278, auc: 0.504, f1: 0.306, acc: 0.537
+2025-08-07 19:09:16: Epoch: 043 Train Loss: 0.061, ap: 0.681, auc: 0.754, f1: 0.713, acc: 0.757
+2025-08-07 19:09:18: Epoch: 043 Test results, ap: 0.292, auc: 0.502, f1: 0.235, acc: 0.519
+2025-08-07 19:09:25: Epoch: 044 Train Loss: 0.064, ap: 0.666, auc: 0.743, f1: 0.651, acc: 0.718
+2025-08-07 19:09:26: Epoch: 044 Test results, ap: 0.334, auc: 0.499, f1: 0.242, acc: 0.537
+2025-08-07 19:09:33: Epoch: 045 Train Loss: 0.062, ap: 0.676, auc: 0.746, f1: 0.634, acc: 0.709
+2025-08-07 19:09:35: Epoch: 045 Test results, ap: 0.351, auc: 0.489, f1: 0.242, acc: 0.537
+2025-08-07 19:09:42: Epoch: 046 Train Loss: 0.046, ap: 0.681, auc: 0.749, f1: 0.705, acc: 0.748
+2025-08-07 19:09:43: Epoch: 046 Test results, ap: 0.308, auc: 0.497, f1: 0.297, acc: 0.519
+2025-08-07 19:09:50: Epoch: 047 Train Loss: 0.066, ap: 0.675, auc: 0.749, f1: 0.682, acc: 0.738
+2025-08-07 19:09:52: Epoch: 047 Test results, ap: 0.306, auc: 0.488, f1: 0.265, acc: 0.537
+2025-08-07 19:09:59: Epoch: 048 Train Loss: 0.052, ap: 0.677, auc: 0.748, f1: 0.698, acc: 0.748
+2025-08-07 19:10:00: Epoch: 048 Test results, ap: 0.304, auc: 0.484, f1: 0.257, acc: 0.519
+2025-08-07 19:10:07: Epoch: 049 Train Loss: 0.048, ap: 0.679, auc: 0.744, f1: 0.667, acc: 0.728
+2025-08-07 19:10:09: Epoch: 049 Test results, ap: 0.326, auc: 0.481, f1: 0.269, acc: 0.546
+2025-08-07 19:10:16: Epoch: 050 Train Loss: 0.051, ap: 0.698, auc: 0.757, f1: 0.713, acc: 0.757
+2025-08-07 19:10:17: Epoch: 050 Test results, ap: 0.323, auc: 0.471, f1: 0.243, acc: 0.481
+2025-08-07 19:10:17: used time 0.12h
+2025-08-07 19:10:17: Running Cross Validation 4
+2025-08-07 19:10:17: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 19:10:24: Epoch: 001 Train Loss: 0.677, ap: 0.653, auc: 0.673, f1: 0.353, acc: 0.680
+2025-08-07 19:10:24: Epoch: 001 New best VALIDATION metrics
+2025-08-07 19:10:26: Epoch: 001 Test results, ap: 0.264, auc: 0.567, f1: 0.000, acc: 0.759
+2025-08-07 19:10:33: Epoch: 002 Train Loss: 0.607, ap: 0.717, auc: 0.761, f1: 0.641, acc: 0.728
+2025-08-07 19:10:33: Epoch: 002 New best VALIDATION metrics
+2025-08-07 19:10:34: Epoch: 002 Test results, ap: 0.194, auc: 0.362, f1: 0.000, acc: 0.722
+2025-08-07 19:10:41: Epoch: 003 Train Loss: 0.544, ap: 0.640, auc: 0.649, f1: 0.506, acc: 0.602
+2025-08-07 19:10:43: Epoch: 003 Test results, ap: 0.259, auc: 0.324, f1: 0.222, acc: 0.741
+2025-08-07 19:10:50: Epoch: 004 Train Loss: 0.527, ap: 0.656, auc: 0.689, f1: 0.407, acc: 0.660
+2025-08-07 19:10:51: Epoch: 004 Test results, ap: 0.161, auc: 0.228, f1: 0.000, acc: 0.741
+2025-08-07 19:10:58: Epoch: 005 Train Loss: 0.491, ap: 0.611, auc: 0.673, f1: 0.590, acc: 0.447
+2025-08-07 19:11:00: Epoch: 005 Test results, ap: 0.178, auc: 0.279, f1: 0.388, acc: 0.241
+2025-08-07 19:11:07: Epoch: 006 Train Loss: 0.509, ap: 0.614, auc: 0.644, f1: 0.559, acc: 0.388
+2025-08-07 19:11:08: Epoch: 006 Test results, ap: 0.168, auc: 0.278, f1: 0.388, acc: 0.241
+2025-08-07 19:11:15: Epoch: 007 Train Loss: 0.434, ap: 0.596, auc: 0.659, f1: 0.316, acc: 0.621
+2025-08-07 19:11:17: Epoch: 007 Test results, ap: 0.242, auc: 0.345, f1: 0.000, acc: 0.759
+2025-08-07 19:11:24: Epoch: 008 Train Loss: 0.431, ap: 0.668, auc: 0.723, f1: 0.000, acc: 0.602
+2025-08-07 19:11:25: Epoch: 008 Test results, ap: 0.223, auc: 0.464, f1: 0.000, acc: 0.759
+2025-08-07 19:11:32: Epoch: 009 Train Loss: 0.429, ap: 0.588, auc: 0.683, f1: 0.586, acc: 0.437
+2025-08-07 19:11:34: Epoch: 009 Test results, ap: 0.296, auc: 0.526, f1: 0.300, acc: 0.481
+2025-08-07 19:11:41: Epoch: 010 Train Loss: 0.362, ap: 0.769, auc: 0.797, f1: 0.481, acc: 0.728
+2025-08-07 19:11:41: Epoch: 010 New best VALIDATION metrics
+2025-08-07 19:11:42: Epoch: 010 Test results, ap: 0.233, auc: 0.510, f1: 0.000, acc: 0.759
+2025-08-07 19:11:49: Epoch: 011 Train Loss: 0.358, ap: 0.675, auc: 0.718, f1: 0.637, acc: 0.680
+2025-08-07 19:11:51: Epoch: 011 Test results, ap: 0.269, auc: 0.548, f1: 0.291, acc: 0.639
+2025-08-07 19:11:51: Epoch: 011 New best TEST metrics
+2025-08-07 19:11:58: Epoch: 012 Train Loss: 0.323, ap: 0.674, auc: 0.734, f1: 0.660, acc: 0.650
+2025-08-07 19:11:59: Epoch: 012 Test results, ap: 0.258, auc: 0.561, f1: 0.146, acc: 0.676
+2025-08-07 19:11:59: Epoch: 012 New best TEST metrics
+2025-08-07 19:12:06: Epoch: 013 Train Loss: 0.283, ap: 0.705, auc: 0.748, f1: 0.492, acc: 0.680
+2025-08-07 19:12:08: Epoch: 013 Test results, ap: 0.261, auc: 0.546, f1: 0.000, acc: 0.759
+2025-08-07 19:12:15: Epoch: 014 Train Loss: 0.283, ap: 0.684, auc: 0.739, f1: 0.603, acc: 0.718
+2025-08-07 19:12:16: Epoch: 014 Test results, ap: 0.230, auc: 0.496, f1: 0.000, acc: 0.759
+2025-08-07 19:12:23: Epoch: 015 Train Loss: 0.306, ap: 0.686, auc: 0.736, f1: 0.582, acc: 0.680
+2025-08-07 19:12:25: Epoch: 015 Test results, ap: 0.219, auc: 0.467, f1: 0.000, acc: 0.750
+2025-08-07 19:12:32: Epoch: 016 Train Loss: 0.274, ap: 0.685, auc: 0.744, f1: 0.575, acc: 0.670
+2025-08-07 19:12:33: Epoch: 016 Test results, ap: 0.227, auc: 0.487, f1: 0.000, acc: 0.750
+2025-08-07 19:12:40: Epoch: 017 Train Loss: 0.258, ap: 0.693, auc: 0.734, f1: 0.651, acc: 0.718
+2025-08-07 19:12:42: Epoch: 017 Test results, ap: 0.242, auc: 0.506, f1: 0.000, acc: 0.759
+2025-08-07 19:12:49: Epoch: 018 Train Loss: 0.275, ap: 0.716, auc: 0.770, f1: 0.576, acc: 0.728
+2025-08-07 19:12:50: Epoch: 018 Test results, ap: 0.227, auc: 0.507, f1: 0.000, acc: 0.759
+2025-08-07 19:12:57: Epoch: 019 Train Loss: 0.227, ap: 0.665, auc: 0.721, f1: 0.484, acc: 0.689
+2025-08-07 19:12:59: Epoch: 019 Test results, ap: 0.262, auc: 0.546, f1: 0.000, acc: 0.759
+2025-08-07 19:13:06: Epoch: 020 Train Loss: 0.253, ap: 0.657, auc: 0.718, f1: 0.626, acc: 0.641
+2025-08-07 19:13:07: Epoch: 020 Test results, ap: 0.235, auc: 0.523, f1: 0.000, acc: 0.713
+2025-08-07 19:13:14: Epoch: 021 Train Loss: 0.223, ap: 0.679, auc: 0.732, f1: 0.593, acc: 0.680
+2025-08-07 19:13:15: Epoch: 021 Test results, ap: 0.241, auc: 0.535, f1: 0.000, acc: 0.759
+2025-08-07 19:13:23: Epoch: 022 Train Loss: 0.217, ap: 0.671, auc: 0.719, f1: 0.493, acc: 0.641
+2025-08-07 19:13:24: Epoch: 022 Test results, ap: 0.241, auc: 0.510, f1: 0.000, acc: 0.759
+2025-08-07 19:13:31: Epoch: 023 Train Loss: 0.211, ap: 0.641, auc: 0.709, f1: 0.507, acc: 0.641
+2025-08-07 19:13:33: Epoch: 023 Test results, ap: 0.246, auc: 0.519, f1: 0.000, acc: 0.759
+2025-08-07 19:13:40: Epoch: 024 Train Loss: 0.207, ap: 0.662, auc: 0.730, f1: 0.614, acc: 0.670
+2025-08-07 19:13:41: Epoch: 024 Test results, ap: 0.234, auc: 0.524, f1: 0.000, acc: 0.741
+2025-08-07 19:13:48: Epoch: 025 Train Loss: 0.195, ap: 0.643, auc: 0.722, f1: 0.543, acc: 0.689
+2025-08-07 19:13:50: Epoch: 025 Test results, ap: 0.231, auc: 0.513, f1: 0.000, acc: 0.759
+2025-08-07 19:13:57: Epoch: 026 Train Loss: 0.189, ap: 0.644, auc: 0.713, f1: 0.429, acc: 0.612
+2025-08-07 19:13:58: Epoch: 026 Test results, ap: 0.241, auc: 0.529, f1: 0.000, acc: 0.759
+2025-08-07 19:14:05: Epoch: 027 Train Loss: 0.177, ap: 0.649, auc: 0.714, f1: 0.479, acc: 0.641
+2025-08-07 19:14:06: Epoch: 027 Test results, ap: 0.245, auc: 0.521, f1: 0.000, acc: 0.759
+2025-08-07 19:14:14: Epoch: 028 Train Loss: 0.189, ap: 0.653, auc: 0.722, f1: 0.614, acc: 0.670
+2025-08-07 19:14:15: Epoch: 028 Test results, ap: 0.249, auc: 0.550, f1: 0.000, acc: 0.741
+2025-08-07 19:14:22: Epoch: 029 Train Loss: 0.206, ap: 0.598, auc: 0.695, f1: 0.523, acc: 0.699
+2025-08-07 19:14:24: Epoch: 029 Test results, ap: 0.260, auc: 0.565, f1: 0.000, acc: 0.759
+2025-08-07 19:14:24: Epoch: 029 New best TEST metrics
+2025-08-07 19:14:31: Epoch: 030 Train Loss: 0.179, ap: 0.658, auc: 0.729, f1: 0.638, acc: 0.670
+2025-08-07 19:14:32: Epoch: 030 Test results, ap: 0.235, auc: 0.523, f1: 0.000, acc: 0.722
+2025-08-07 19:14:39: Epoch: 031 Train Loss: 0.180, ap: 0.659, auc: 0.728, f1: 0.568, acc: 0.689
+2025-08-07 19:14:40: Epoch: 031 Test results, ap: 0.244, auc: 0.544, f1: 0.000, acc: 0.750
+2025-08-07 19:14:48: Epoch: 032 Train Loss: 0.155, ap: 0.645, auc: 0.716, f1: 0.548, acc: 0.680
+2025-08-07 19:14:49: Epoch: 032 Test results, ap: 0.246, auc: 0.543, f1: 0.000, acc: 0.759
+2025-08-07 19:14:56: Epoch: 033 Train Loss: 0.148, ap: 0.619, auc: 0.701, f1: 0.532, acc: 0.641
+2025-08-07 19:14:58: Epoch: 033 Test results, ap: 0.249, auc: 0.552, f1: 0.000, acc: 0.759
+2025-08-07 19:15:05: Epoch: 034 Train Loss: 0.158, ap: 0.587, auc: 0.689, f1: 0.514, acc: 0.650
+2025-08-07 19:15:06: Epoch: 034 Test results, ap: 0.256, auc: 0.560, f1: 0.000, acc: 0.759
+2025-08-07 19:15:13: Epoch: 035 Train Loss: 0.152, ap: 0.599, auc: 0.696, f1: 0.560, acc: 0.680
+2025-08-07 19:15:15: Epoch: 035 Test results, ap: 0.249, auc: 0.532, f1: 0.000, acc: 0.759
+2025-08-07 19:15:22: Epoch: 036 Train Loss: 0.156, ap: 0.582, auc: 0.683, f1: 0.493, acc: 0.641
+2025-08-07 19:15:23: Epoch: 036 Test results, ap: 0.251, auc: 0.547, f1: 0.000, acc: 0.759
+2025-08-07 19:15:30: Epoch: 037 Train Loss: 0.120, ap: 0.586, auc: 0.682, f1: 0.551, acc: 0.699
+2025-08-07 19:15:32: Epoch: 037 Test results, ap: 0.255, auc: 0.548, f1: 0.000, acc: 0.759
+2025-08-07 19:15:39: Epoch: 038 Train Loss: 0.126, ap: 0.607, auc: 0.701, f1: 0.557, acc: 0.660
+2025-08-07 19:15:40: Epoch: 038 Test results, ap: 0.246, auc: 0.538, f1: 0.000, acc: 0.741
+2025-08-07 19:15:47: Epoch: 039 Train Loss: 0.139, ap: 0.567, auc: 0.679, f1: 0.535, acc: 0.680
+2025-08-07 19:15:49: Epoch: 039 Test results, ap: 0.280, auc: 0.527, f1: 0.000, acc: 0.759
+2025-08-07 19:15:56: Epoch: 040 Train Loss: 0.120, ap: 0.557, auc: 0.676, f1: 0.479, acc: 0.641
+2025-08-07 19:15:57: Epoch: 040 Test results, ap: 0.260, auc: 0.553, f1: 0.000, acc: 0.759
+2025-08-07 19:16:04: Epoch: 041 Train Loss: 0.102, ap: 0.566, auc: 0.676, f1: 0.478, acc: 0.660
+2025-08-07 19:16:06: Epoch: 041 Test results, ap: 0.272, auc: 0.562, f1: 0.000, acc: 0.759
+2025-08-07 19:16:06: Epoch: 041 New best TEST metrics
+2025-08-07 19:16:13: Epoch: 042 Train Loss: 0.093, ap: 0.540, auc: 0.644, f1: 0.529, acc: 0.689
+2025-08-07 19:16:14: Epoch: 042 Test results, ap: 0.319, auc: 0.567, f1: 0.074, acc: 0.769
+2025-08-07 19:16:14: Epoch: 042 New best TEST metrics
+2025-08-07 19:16:21: Epoch: 043 Train Loss: 0.101, ap: 0.584, auc: 0.699, f1: 0.593, acc: 0.680
+2025-08-07 19:16:23: Epoch: 043 Test results, ap: 0.254, auc: 0.529, f1: 0.100, acc: 0.667
+2025-08-07 19:16:30: Epoch: 044 Train Loss: 0.079, ap: 0.577, auc: 0.692, f1: 0.514, acc: 0.650
+2025-08-07 19:16:31: Epoch: 044 Test results, ap: 0.256, auc: 0.530, f1: 0.121, acc: 0.731
+2025-08-07 19:16:38: Epoch: 045 Train Loss: 0.087, ap: 0.536, auc: 0.660, f1: 0.471, acc: 0.650
+2025-08-07 19:16:40: Epoch: 045 Test results, ap: 0.305, auc: 0.537, f1: 0.121, acc: 0.731
+2025-08-07 19:16:47: Epoch: 046 Train Loss: 0.077, ap: 0.561, auc: 0.674, f1: 0.485, acc: 0.670
+2025-08-07 19:16:48: Epoch: 046 Test results, ap: 0.268, auc: 0.515, f1: 0.121, acc: 0.731
+2025-08-07 19:16:55: Epoch: 047 Train Loss: 0.069, ap: 0.540, auc: 0.642, f1: 0.493, acc: 0.660
+2025-08-07 19:16:57: Epoch: 047 Test results, ap: 0.314, auc: 0.537, f1: 0.067, acc: 0.741
+2025-08-07 19:17:04: Epoch: 048 Train Loss: 0.078, ap: 0.518, auc: 0.626, f1: 0.514, acc: 0.650
+2025-08-07 19:17:05: Epoch: 048 Test results, ap: 0.305, auc: 0.533, f1: 0.286, acc: 0.722
+2025-08-07 19:17:12: Epoch: 049 Train Loss: 0.079, ap: 0.559, auc: 0.678, f1: 0.553, acc: 0.670
+2025-08-07 19:17:14: Epoch: 049 Test results, ap: 0.262, auc: 0.528, f1: 0.105, acc: 0.685
+2025-08-07 19:17:21: Epoch: 050 Train Loss: 0.069, ap: 0.608, auc: 0.714, f1: 0.592, acc: 0.718
+2025-08-07 19:17:22: Epoch: 050 Test results, ap: 0.244, auc: 0.519, f1: 0.062, acc: 0.722
+2025-08-07 19:17:22: used time 0.12h
+2025-08-07 19:17:22: Cross Validation Finished!
+2025-08-07 19:17:22: Best validation perform list
+[[0.74579835 0.78913379 0.67415732 0.71844661]
+ [0.67364138 0.76003277 0.52307695 0.69902915]
+ [0.83353961 0.79675716 0.72549021 0.72815531]
+ [0.7978577  0.81693989 0.72941178 0.77669901]
+ [0.76878643 0.79661679 0.48148149 0.72815531]]
+2025-08-07 19:17:22: mean: [0.764 0.792 0.627 0.73 ]
+2025-08-07 19:17:22: std: [0.054 0.018 0.104 0.026]
+2025-08-07 19:17:22: Best test perform list
+[[0.33938497 0.46810508 0.         0.75      ]
+ [0.27696303 0.51407135 0.         0.73148149]
+ [0.53292429 0.82363975 0.23529412 0.75925928]
+ [0.36059269 0.52532834 0.29268292 0.73148149]
+ [0.31876886 0.56660414 0.07407407 0.76851851]]
+2025-08-07 19:17:22: mean: [0.366 0.58  0.12  0.748]
+2025-08-07 19:17:22: std: [0.088 0.126 0.122 0.015]
diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Anoplin/config.json b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Anoplin/config.json
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@@ -0,0 +1 @@
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diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Anoplin/training.log b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Anoplin/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..26b591aff95b29acb42af17a0110500e88f24f5b
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Anoplin/training.log
@@ -0,0 +1,21 @@
+2025-08-10 22:12:31: saving_dir: ./run-cls/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Anoplin
+2025-08-10 22:12:31: Loading Training Dataset
+2025-08-10 22:12:55: Start UDA training
+2025-08-10 22:13:17: Epoch 1 Train Loss: 0.6959
+2025-08-10 22:13:36: Epoch 2 Train Loss: 0.6435
+2025-08-10 22:13:55: Epoch 3 Train Loss: 0.532
+2025-08-10 22:14:15: Epoch 4 Train Loss: 0.5701
+2025-08-10 22:14:34: Epoch 5 Train Loss: 0.5022
+2025-08-10 22:14:54: Epoch 6 Train Loss: 0.4047
+2025-08-10 22:15:13: Epoch 7 Train Loss: 0.4162
+2025-08-10 22:15:32: Epoch 8 Train Loss: 0.3863
+2025-08-10 22:15:52: Epoch 9 Train Loss: 0.3775
+2025-08-10 22:16:11: Epoch 10 Train Loss: 0.3637
+2025-08-10 22:16:31: Epoch 11 Train Loss: 0.3093
+2025-08-10 22:16:50: Epoch 12 Train Loss: 0.3059
+2025-08-10 22:17:09: Epoch 13 Train Loss: 0.3156
+2025-08-10 22:17:29: Epoch 14 Train Loss: 0.2926
+2025-08-10 22:17:48: Epoch 15 Train Loss: 0.2594
+2025-08-10 22:18:07: Epoch 16 Train Loss: 0.2654
+2025-08-10 22:18:27: Epoch 17 Train Loss: 0.2334
+2025-08-10 22:18:27: UDA training finished
diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Indolicidin/config.json b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Indolicidin/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..a4639e063f1b6352fa12f7bf2e38d18a20dfcb67
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Indolicidin/config.json
@@ -0,0 +1 @@
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diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Indolicidin/training.log b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Indolicidin/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..163d173e43d7e3f022be49ad2254ad585a8d091c
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Indolicidin/training.log
@@ -0,0 +1,21 @@
+2025-08-10 22:00:50: saving_dir: ./run-cls/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Indolicidin
+2025-08-10 22:00:50: Loading Training Dataset
+2025-08-10 22:01:15: Start UDA training
+2025-08-10 22:01:35: Epoch 1 Train Loss: 0.6959
+2025-08-10 22:01:51: Epoch 2 Train Loss: 0.6526
+2025-08-10 22:02:09: Epoch 3 Train Loss: 0.5432
+2025-08-10 22:02:26: Epoch 4 Train Loss: 0.5841
+2025-08-10 22:02:43: Epoch 5 Train Loss: 0.5081
+2025-08-10 22:02:59: Epoch 6 Train Loss: 0.4485
+2025-08-10 22:03:16: Epoch 7 Train Loss: 0.4143
+2025-08-10 22:03:33: Epoch 8 Train Loss: 0.3874
+2025-08-10 22:03:50: Epoch 9 Train Loss: 0.3558
+2025-08-10 22:04:07: Epoch 10 Train Loss: 0.3766
+2025-08-10 22:04:25: Epoch 11 Train Loss: 0.3147
+2025-08-10 22:04:42: Epoch 12 Train Loss: 0.3349
+2025-08-10 22:04:59: Epoch 13 Train Loss: 0.3267
+2025-08-10 22:05:16: Epoch 14 Train Loss: 0.2863
+2025-08-10 22:05:34: Epoch 15 Train Loss: 0.2982
+2025-08-10 22:05:50: Epoch 16 Train Loss: 0.2629
+2025-08-10 22:06:07: Epoch 17 Train Loss: 0.2545
+2025-08-10 22:06:07: UDA training finished
diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Temporin-A/config.json b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Temporin-A/config.json
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diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Temporin-A/training.log b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Temporin-A/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..6ff4bcf6438f2b484f6c32fe439183537a78844e
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Temporin-A/training.log
@@ -0,0 +1,25 @@
+2025-08-10 22:06:11: saving_dir: ./run-cls/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_Temporin-A
+2025-08-10 22:06:11: Loading Training Dataset
+2025-08-10 22:06:35: Start UDA training
+2025-08-10 22:06:54: Epoch 1 Train Loss: 0.6959
+2025-08-10 22:07:10: Epoch 2 Train Loss: 0.6523
+2025-08-10 22:07:27: Epoch 3 Train Loss: 0.5442
+2025-08-10 22:07:44: Epoch 4 Train Loss: 0.5768
+2025-08-10 22:08:00: Epoch 5 Train Loss: 0.511
+2025-08-10 22:08:17: Epoch 6 Train Loss: 0.4448
+2025-08-10 22:08:34: Epoch 7 Train Loss: 0.4144
+2025-08-10 22:08:50: Epoch 8 Train Loss: 0.3858
+2025-08-10 22:09:07: Epoch 9 Train Loss: 0.3639
+2025-08-10 22:09:24: Epoch 10 Train Loss: 0.3804
+2025-08-10 22:09:41: Epoch 11 Train Loss: 0.3226
+2025-08-10 22:09:58: Epoch 12 Train Loss: 0.3415
+2025-08-10 22:10:14: Epoch 13 Train Loss: 0.3355
+2025-08-10 22:10:31: Epoch 14 Train Loss: 0.287
+2025-08-10 22:10:47: Epoch 15 Train Loss: 0.3177
+2025-08-10 22:11:03: Epoch 16 Train Loss: 0.2738
+2025-08-10 22:11:20: Epoch 17 Train Loss: 0.2618
+2025-08-10 22:11:37: Epoch 18 Train Loss: 0.2975
+2025-08-10 22:11:53: Epoch 19 Train Loss: 0.2625
+2025-08-10 22:12:10: Epoch 20 Train Loss: 0.2749
+2025-08-10 22:12:27: Epoch 21 Train Loss: 0.2512
+2025-08-10 22:12:27: UDA training finished
diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/config.json b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..ef518d71fdce0191342205ce50470154337347a4
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": null, "channels": 16, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 42, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "case": "r2", "simple": true, "pt": false, "classes": 2}
\ No newline at end of file
diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/feature_preds.csv b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/feature_preds.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2b881b287e4d55c0e7fe2e14df9e1f055b4af851
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/feature_preds.csv
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diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/preds_r2_case.csv b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/preds_r2_case.csv
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diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/preds_test.csv b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/preds_test.csv
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diff --git a/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/training.log b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..6fe78846573b57b7214640df28dfb3341b130712
--- /dev/null
+++ b/run-cls-simple/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2/training.log
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+2025-08-24 22:40:06: saving_dir: ./run-cls/rn18-att-16-simple-768-oneway-ce-32-0.001-50/uda_r2
+2025-08-24 22:40:06: Loading Training Dataset
+2025-08-24 22:40:30: Loading Validation Dataset
+2025-08-24 22:40:32: Start UDA training
+2025-08-24 22:40:53: Epoch 1 Train Loss: 0.6959 Val: ap: 0.2278 auc: 0.2735 f1: 0.0000 acc: 0.6939
+2025-08-24 22:40:53: Epoch: 1 New best VAL metrics
+2025-08-24 22:41:11: Epoch 2 Train Loss: 0.6401 Val: ap: 0.2326 auc: 0.3020 f1: 0.0000 acc: 0.6939
+2025-08-24 22:41:11: Epoch: 2 New best VAL metrics
+2025-08-24 22:41:30: Epoch 3 Train Loss: 0.5481 Val: ap: 0.2199 auc: 0.2618 f1: 0.0000 acc: 0.6939
+2025-08-24 22:41:48: Epoch 4 Train Loss: 0.4825 Val: ap: 0.2643 auc: 0.3804 f1: 0.0000 acc: 0.6939
+2025-08-24 22:41:48: Epoch: 4 New best VAL metrics
+2025-08-24 22:42:06: Epoch 5 Train Loss: 0.4367 Val: ap: 0.2977 auc: 0.4980 f1: 0.0000 acc: 0.6939
+2025-08-24 22:42:06: Epoch: 5 New best VAL metrics
+2025-08-24 22:42:25: Epoch 6 Train Loss: 0.4144 Val: ap: 0.3986 auc: 0.6353 f1: 0.0000 acc: 0.6939
+2025-08-24 22:42:25: Epoch: 6 New best VAL metrics
+2025-08-24 22:42:43: Epoch 7 Train Loss: 0.3896 Val: ap: 0.5458 auc: 0.7255 f1: 0.0000 acc: 0.6939
+2025-08-24 22:42:43: Epoch: 7 New best VAL metrics
+2025-08-24 22:43:02: Epoch 8 Train Loss: 0.3441 Val: ap: 0.5815 auc: 0.7549 f1: 0.0000 acc: 0.6939
+2025-08-24 22:43:02: Epoch: 8 New best VAL metrics
+2025-08-24 22:43:20: Epoch 9 Train Loss: 0.3541 Val: ap: 0.5474 auc: 0.7510 f1: 0.0000 acc: 0.6939
+2025-08-24 22:43:38: Epoch 10 Train Loss: 0.3216 Val: ap: 0.5510 auc: 0.7647 f1: 0.0000 acc: 0.6939
+2025-08-24 22:43:56: Epoch 11 Train Loss: 0.3504 Val: ap: 0.5044 auc: 0.7657 f1: 0.0000 acc: 0.6939
+2025-08-24 22:44:15: Epoch 12 Train Loss: 0.3273 Val: ap: 0.5691 auc: 0.7608 f1: 0.0000 acc: 0.6939
+2025-08-24 22:44:34: Epoch 13 Train Loss: 0.3014 Val: ap: 0.5497 auc: 0.7529 f1: 0.0000 acc: 0.6939
+2025-08-24 22:44:52: Epoch 14 Train Loss: 0.2931 Val: ap: 0.5422 auc: 0.7667 f1: 0.0000 acc: 0.6939
+2025-08-24 22:45:10: Epoch 15 Train Loss: 0.2880 Val: ap: 0.5433 auc: 0.7510 f1: 0.0000 acc: 0.6939
+2025-08-24 22:45:28: Epoch 16 Train Loss: 0.2648 Val: ap: 0.5808 auc: 0.7588 f1: 0.0000 acc: 0.6939
+2025-08-24 22:45:28: Epoch: 16 New best VAL metrics
+2025-08-24 22:45:46: Epoch 17 Train Loss: 0.2109 Val: ap: 0.5778 auc: 0.7608 f1: 0.0000 acc: 0.6939
+2025-08-24 22:46:05: Epoch 18 Train Loss: 0.2491 Val: ap: 0.6412 auc: 0.7588 f1: 0.0000 acc: 0.6939
+2025-08-24 22:46:05: Epoch: 18 New best VAL metrics
+2025-08-24 22:46:23: Epoch 19 Train Loss: 0.2473 Val: ap: 0.5687 auc: 0.7000 f1: 0.0000 acc: 0.6939
+2025-08-24 22:46:42: Epoch 20 Train Loss: 0.2072 Val: ap: 0.5534 auc: 0.6765 f1: 0.0000 acc: 0.6939
+2025-08-24 22:47:00: Epoch 21 Train Loss: 0.1964 Val: ap: 0.5104 auc: 0.6098 f1: 0.0000 acc: 0.6939
+2025-08-24 22:47:18: Epoch 22 Train Loss: 0.1779 Val: ap: 0.5590 auc: 0.6412 f1: 0.0000 acc: 0.6939
+2025-08-24 22:47:37: Epoch 23 Train Loss: 0.1886 Val: ap: 0.5452 auc: 0.6137 f1: 0.0000 acc: 0.6939
+2025-08-24 22:47:56: Epoch 24 Train Loss: 0.1930 Val: ap: 0.5575 auc: 0.6412 f1: 0.0000 acc: 0.6939
+2025-08-24 22:48:14: Epoch 25 Train Loss: 0.1506 Val: ap: 0.5755 auc: 0.7059 f1: 0.0000 acc: 0.6939
+2025-08-24 22:48:32: Epoch 26 Train Loss: 0.1270 Val: ap: 0.6224 auc: 0.7255 f1: 0.0000 acc: 0.6939
+2025-08-24 22:48:51: Epoch 27 Train Loss: 0.1203 Val: ap: 0.5749 auc: 0.7275 f1: 0.0000 acc: 0.6939
+2025-08-24 22:49:09: Epoch 28 Train Loss: 0.1002 Val: ap: 0.5876 auc: 0.7333 f1: 0.0000 acc: 0.6939
+2025-08-24 22:49:27: Epoch 29 Train Loss: 0.1083 Val: ap: 0.5153 auc: 0.6745 f1: 0.0000 acc: 0.6939
+2025-08-24 22:49:46: Epoch 30 Train Loss: 0.0808 Val: ap: 0.4675 auc: 0.6275 f1: 0.0000 acc: 0.6939
+2025-08-24 22:50:04: Epoch 31 Train Loss: 0.1008 Val: ap: 0.4996 auc: 0.7118 f1: 0.0000 acc: 0.6939
+2025-08-24 22:50:22: Epoch 32 Train Loss: 0.1116 Val: ap: 0.4818 auc: 0.6549 f1: 0.0000 acc: 0.6939
+2025-08-24 22:50:40: Epoch 33 Train Loss: 0.1361 Val: ap: 0.4102 auc: 0.6196 f1: 0.0000 acc: 0.6939
+2025-08-24 22:50:59: Epoch 34 Train Loss: 0.0885 Val: ap: 0.5341 auc: 0.7314 f1: 0.0000 acc: 0.6939
+2025-08-24 22:51:17: Epoch 35 Train Loss: 0.1379 Val: ap: 0.5852 auc: 0.7480 f1: 0.0000 acc: 0.6939
+2025-08-24 22:51:35: Epoch 36 Train Loss: 0.0842 Val: ap: 0.5437 auc: 0.6667 f1: 0.0000 acc: 0.6939
+2025-08-24 22:51:54: Epoch 37 Train Loss: 0.0822 Val: ap: 0.3438 auc: 0.5098 f1: 0.0000 acc: 0.6939
+2025-08-24 22:52:12: Epoch 38 Train Loss: 0.0906 Val: ap: 0.3487 auc: 0.5529 f1: 0.0000 acc: 0.6939
+2025-08-24 22:52:30: Epoch 39 Train Loss: 0.0849 Val: ap: 0.3311 auc: 0.5216 f1: 0.0000 acc: 0.6939
+2025-08-24 22:52:49: Epoch 40 Train Loss: 0.0599 Val: ap: 0.3452 auc: 0.5608 f1: 0.0000 acc: 0.6939
+2025-08-24 22:53:07: Epoch 41 Train Loss: 0.0635 Val: ap: 0.3164 auc: 0.5294 f1: 0.0000 acc: 0.6939
+2025-08-24 22:53:25: Epoch 42 Train Loss: 0.0725 Val: ap: 0.3021 auc: 0.4706 f1: 0.0000 acc: 0.6939
+2025-08-24 22:53:44: Epoch 43 Train Loss: 0.0627 Val: ap: 0.3098 auc: 0.4373 f1: 0.0000 acc: 0.6939
+2025-08-24 22:54:02: Epoch 44 Train Loss: 0.0553 Val: ap: 0.3162 auc: 0.4412 f1: 0.0000 acc: 0.6939
+2025-08-24 22:54:20: Epoch 45 Train Loss: 0.0622 Val: ap: 0.2689 auc: 0.3961 f1: 0.0000 acc: 0.6939
+2025-08-24 22:54:38: Epoch 46 Train Loss: 0.0470 Val: ap: 0.2475 auc: 0.3529 f1: 0.0000 acc: 0.6939
+2025-08-24 22:54:57: Epoch 47 Train Loss: 0.0487 Val: ap: 0.2348 auc: 0.3137 f1: 0.0000 acc: 0.6939
+2025-08-24 22:55:15: Epoch 48 Train Loss: 0.0493 Val: ap: 0.2055 auc: 0.1824 f1: 0.0000 acc: 0.6939
+2025-08-24 22:55:33: Epoch 49 Train Loss: 0.0394 Val: ap: 0.2297 auc: 0.2980 f1: 0.0000 acc: 0.6939
+2025-08-24 22:55:52: Epoch 50 Train Loss: 0.0328 Val: ap: 0.2500 auc: 0.3569 f1: 0.0000 acc: 0.6939
+2025-08-24 22:55:52: UDA training finished
diff --git a/run-cls-simple/rn18-diff-16-simple-768-oneway-ce-32-0.001-50/config.json b/run-cls-simple/rn18-diff-16-simple-768-oneway-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
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+Valid
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+0.06881862728085901,0.03127604290090347,0.05748124165996658,0.04254156098124675
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diff --git a/run-cls-simple/rn18-diff-16-simple-768-oneway-ce-32-0.001-50/training.log b/run-cls-simple/rn18-diff-16-simple-768-oneway-ce-32-0.001-50/training.log
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+2025-07-18 21:50:49: saving_dir: ./run-cls/rn18-diff-16-simple-768-oneway-ce-32-0.001-50
+2025-07-18 21:50:50: Loading Training Dataset
+2025-07-18 21:51:13: Loading Test Dataset
+2025-07-18 21:51:17: Running Cross Validation 0
+2025-07-18 21:51:17: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-07-18 21:51:26: Epoch: 001 Train Loss: 0.680, ap: 0.622, auc: 0.594, f1: 0.627, acc: 0.456
+2025-07-18 21:51:26: Epoch: 001 New best VALIDATION metrics
+2025-07-18 21:51:28: Epoch: 001 Test results, ap: 0.227, auc: 0.495, f1: 0.388, acc: 0.241
+2025-07-18 21:51:35: Epoch: 002 Train Loss: 0.616, ap: 0.553, auc: 0.606, f1: 0.616, acc: 0.456
+2025-07-18 21:51:37: Epoch: 002 Test results, ap: 0.217, auc: 0.469, f1: 0.388, acc: 0.241
+2025-07-18 21:51:44: Epoch: 003 Train Loss: 0.541, ap: 0.604, auc: 0.617, f1: 0.603, acc: 0.437
+2025-07-18 21:51:44: Epoch: 003 New best VALIDATION metrics
+2025-07-18 21:51:46: Epoch: 003 Test results, ap: 0.266, auc: 0.564, f1: 0.388, acc: 0.241
+2025-07-18 21:51:53: Epoch: 004 Train Loss: 0.497, ap: 0.654, auc: 0.625, f1: 0.631, acc: 0.466
+2025-07-18 21:51:53: Epoch: 004 New best VALIDATION metrics
+2025-07-18 21:51:55: Epoch: 004 Test results, ap: 0.246, auc: 0.515, f1: 0.388, acc: 0.241
+2025-07-18 21:52:03: Epoch: 005 Train Loss: 0.403, ap: 0.510, auc: 0.511, f1: 0.606, acc: 0.495
+2025-07-18 21:52:04: Epoch: 005 Test results, ap: 0.316, auc: 0.577, f1: 0.394, acc: 0.259
+2025-07-18 21:52:12: Epoch: 006 Train Loss: 0.440, ap: 0.667, auc: 0.657, f1: 0.648, acc: 0.505
+2025-07-18 21:52:12: Epoch: 006 New best VALIDATION metrics
+2025-07-18 21:52:13: Epoch: 006 Test results, ap: 0.247, auc: 0.518, f1: 0.391, acc: 0.250
+2025-07-18 21:52:21: Epoch: 007 Train Loss: 0.380, ap: 0.637, auc: 0.611, f1: 0.644, acc: 0.495
+2025-07-18 21:52:22: Epoch: 007 Test results, ap: 0.330, auc: 0.534, f1: 0.379, acc: 0.333
+2025-07-18 21:52:30: Epoch: 008 Train Loss: 0.269, ap: 0.626, auc: 0.630, f1: 0.627, acc: 0.456
+2025-07-18 21:52:31: Epoch: 008 Test results, ap: 0.235, auc: 0.507, f1: 0.388, acc: 0.241
+2025-07-18 21:52:39: Epoch: 009 Train Loss: 0.234, ap: 0.522, auc: 0.559, f1: 0.612, acc: 0.447
+2025-07-18 21:52:40: Epoch: 009 Test results, ap: 0.287, auc: 0.517, f1: 0.388, acc: 0.241
+2025-07-18 21:52:48: Epoch: 010 Train Loss: 0.260, ap: 0.603, auc: 0.593, f1: 0.547, acc: 0.485
+2025-07-18 21:52:49: Epoch: 010 Test results, ap: 0.248, auc: 0.460, f1: 0.397, acc: 0.324
+2025-07-18 21:52:57: Epoch: 011 Train Loss: 0.231, ap: 0.625, auc: 0.630, f1: 0.622, acc: 0.456
+2025-07-18 21:52:58: Epoch: 011 Test results, ap: 0.259, auc: 0.552, f1: 0.391, acc: 0.250
+2025-07-18 21:52:58: Epoch: 011 New best TEST metrics
+2025-07-18 21:53:06: Epoch: 012 Train Loss: 0.176, ap: 0.627, auc: 0.609, f1: 0.613, acc: 0.485
+2025-07-18 21:53:08: Epoch: 012 Test results, ap: 0.262, auc: 0.491, f1: 0.388, acc: 0.269
+2025-07-18 21:53:15: Epoch: 013 Train Loss: 0.143, ap: 0.634, auc: 0.611, f1: 0.638, acc: 0.505
+2025-07-18 21:53:17: Epoch: 013 Test results, ap: 0.225, auc: 0.488, f1: 0.388, acc: 0.241
+2025-07-18 21:53:24: Epoch: 014 Train Loss: 0.157, ap: 0.627, auc: 0.635, f1: 0.639, acc: 0.495
+2025-07-18 21:53:26: Epoch: 014 Test results, ap: 0.253, auc: 0.522, f1: 0.388, acc: 0.241
+2025-07-18 21:53:33: Epoch: 015 Train Loss: 0.131, ap: 0.595, auc: 0.657, f1: 0.662, acc: 0.563
+2025-07-18 21:53:35: Epoch: 015 Test results, ap: 0.248, auc: 0.501, f1: 0.376, acc: 0.231
+2025-07-18 21:53:42: Epoch: 016 Train Loss: 0.119, ap: 0.660, auc: 0.655, f1: 0.638, acc: 0.515
+2025-07-18 21:53:44: Epoch: 016 Test results, ap: 0.239, auc: 0.490, f1: 0.388, acc: 0.241
+2025-07-18 21:53:51: Epoch: 017 Train Loss: 0.115, ap: 0.640, auc: 0.625, f1: 0.617, acc: 0.505
+2025-07-18 21:53:53: Epoch: 017 Test results, ap: 0.270, auc: 0.515, f1: 0.372, acc: 0.250
+2025-07-18 21:54:00: Epoch: 018 Train Loss: 0.111, ap: 0.618, auc: 0.602, f1: 0.596, acc: 0.447
+2025-07-18 21:54:02: Epoch: 018 Test results, ap: 0.239, auc: 0.493, f1: 0.369, acc: 0.241
+2025-07-18 21:54:09: Epoch: 019 Train Loss: 0.120, ap: 0.638, auc: 0.619, f1: 0.629, acc: 0.485
+2025-07-18 21:54:11: Epoch: 019 Test results, ap: 0.238, auc: 0.519, f1: 0.376, acc: 0.231
+2025-07-18 21:54:18: Epoch: 020 Train Loss: 0.101, ap: 0.602, auc: 0.591, f1: 0.625, acc: 0.476
+2025-07-18 21:54:20: Epoch: 020 Test results, ap: 0.242, auc: 0.509, f1: 0.366, acc: 0.231
+2025-07-18 21:54:28: Epoch: 021 Train Loss: 0.105, ap: 0.610, auc: 0.594, f1: 0.627, acc: 0.515
+2025-07-18 21:54:29: Epoch: 021 Test results, ap: 0.273, auc: 0.533, f1: 0.369, acc: 0.241
+2025-07-18 21:54:37: Epoch: 022 Train Loss: 0.085, ap: 0.624, auc: 0.593, f1: 0.638, acc: 0.505
+2025-07-18 21:54:38: Epoch: 022 Test results, ap: 0.245, auc: 0.513, f1: 0.375, acc: 0.259
+2025-07-18 21:54:46: Epoch: 023 Train Loss: 0.085, ap: 0.623, auc: 0.607, f1: 0.620, acc: 0.476
+2025-07-18 21:54:47: Epoch: 023 Test results, ap: 0.272, auc: 0.526, f1: 0.376, acc: 0.231
+2025-07-18 21:54:55: Epoch: 024 Train Loss: 0.078, ap: 0.632, auc: 0.617, f1: 0.592, acc: 0.437
+2025-07-18 21:54:56: Epoch: 024 Test results, ap: 0.241, auc: 0.511, f1: 0.366, acc: 0.231
+2025-07-18 21:55:04: Epoch: 025 Train Loss: 0.076, ap: 0.581, auc: 0.595, f1: 0.583, acc: 0.417
+2025-07-18 21:55:05: Epoch: 025 Test results, ap: 0.244, auc: 0.513, f1: 0.369, acc: 0.241
+2025-07-18 21:55:13: Epoch: 026 Train Loss: 0.077, ap: 0.605, auc: 0.603, f1: 0.587, acc: 0.427
+2025-07-18 21:55:14: Epoch: 026 Test results, ap: 0.221, auc: 0.477, f1: 0.366, acc: 0.231
+2025-07-18 21:55:22: Epoch: 027 Train Loss: 0.079, ap: 0.572, auc: 0.626, f1: 0.574, acc: 0.495
+2025-07-18 21:55:23: Epoch: 027 Test results, ap: 0.325, auc: 0.533, f1: 0.379, acc: 0.333
+2025-07-18 21:55:23: Epoch: 027 New best TEST metrics
+2025-07-18 21:55:31: Epoch: 028 Train Loss: 0.073, ap: 0.564, auc: 0.610, f1: 0.609, acc: 0.476
+2025-07-18 21:55:33: Epoch: 028 Test results, ap: 0.263, auc: 0.516, f1: 0.387, acc: 0.324
+2025-07-18 21:55:40: Epoch: 029 Train Loss: 0.080, ap: 0.499, auc: 0.569, f1: 0.594, acc: 0.456
+2025-07-18 21:55:42: Epoch: 029 Test results, ap: 0.263, auc: 0.500, f1: 0.377, acc: 0.296
+2025-07-18 21:55:49: Epoch: 030 Train Loss: 0.073, ap: 0.587, auc: 0.608, f1: 0.573, acc: 0.408
+2025-07-18 21:55:51: Epoch: 030 Test results, ap: 0.252, auc: 0.493, f1: 0.369, acc: 0.241
+2025-07-18 21:55:58: Epoch: 031 Train Loss: 0.076, ap: 0.554, auc: 0.607, f1: 0.584, acc: 0.447
+2025-07-18 21:56:00: Epoch: 031 Test results, ap: 0.262, auc: 0.505, f1: 0.387, acc: 0.296
+2025-07-18 21:56:07: Epoch: 032 Train Loss: 0.058, ap: 0.577, auc: 0.615, f1: 0.594, acc: 0.456
+2025-07-18 21:56:09: Epoch: 032 Test results, ap: 0.256, auc: 0.502, f1: 0.372, acc: 0.250
+2025-07-18 21:56:17: Epoch: 033 Train Loss: 0.059, ap: 0.576, auc: 0.602, f1: 0.583, acc: 0.417
+2025-07-18 21:56:18: Epoch: 033 Test results, ap: 0.244, auc: 0.494, f1: 0.369, acc: 0.241
+2025-07-18 21:56:26: Epoch: 034 Train Loss: 0.051, ap: 0.568, auc: 0.616, f1: 0.584, acc: 0.447
+2025-07-18 21:56:27: Epoch: 034 Test results, ap: 0.270, auc: 0.517, f1: 0.381, acc: 0.278
+2025-07-18 21:56:35: Epoch: 035 Train Loss: 0.060, ap: 0.563, auc: 0.624, f1: 0.607, acc: 0.485
+2025-07-18 21:56:36: Epoch: 035 Test results, ap: 0.281, auc: 0.521, f1: 0.380, acc: 0.306
+2025-07-18 21:56:44: Epoch: 036 Train Loss: 0.065, ap: 0.552, auc: 0.592, f1: 0.583, acc: 0.417
+2025-07-18 21:56:45: Epoch: 036 Test results, ap: 0.247, auc: 0.498, f1: 0.369, acc: 0.241
+2025-07-18 21:56:53: Epoch: 037 Train Loss: 0.060, ap: 0.594, auc: 0.619, f1: 0.583, acc: 0.417
+2025-07-18 21:56:54: Epoch: 037 Test results, ap: 0.238, auc: 0.491, f1: 0.369, acc: 0.241
+2025-07-18 21:57:02: Epoch: 038 Train Loss: 0.056, ap: 0.566, auc: 0.623, f1: 0.607, acc: 0.485
+2025-07-18 21:57:03: Epoch: 038 Test results, ap: 0.290, auc: 0.517, f1: 0.378, acc: 0.269
+2025-07-18 21:57:11: Epoch: 039 Train Loss: 0.054, ap: 0.569, auc: 0.619, f1: 0.590, acc: 0.447
+2025-07-18 21:57:12: Epoch: 039 Test results, ap: 0.277, auc: 0.513, f1: 0.369, acc: 0.241
+2025-07-18 21:57:20: Epoch: 040 Train Loss: 0.051, ap: 0.562, auc: 0.624, f1: 0.594, acc: 0.456
+2025-07-18 21:57:22: Epoch: 040 Test results, ap: 0.270, auc: 0.504, f1: 0.369, acc: 0.241
+2025-07-18 21:57:29: Epoch: 041 Train Loss: 0.044, ap: 0.571, auc: 0.629, f1: 0.612, acc: 0.495
+2025-07-18 21:57:31: Epoch: 041 Test results, ap: 0.287, auc: 0.516, f1: 0.372, acc: 0.250
+2025-07-18 21:57:38: Epoch: 042 Train Loss: 0.043, ap: 0.574, auc: 0.632, f1: 0.600, acc: 0.456
+2025-07-18 21:57:40: Epoch: 042 Test results, ap: 0.260, auc: 0.508, f1: 0.369, acc: 0.241
+2025-07-18 21:57:47: Epoch: 043 Train Loss: 0.050, ap: 0.566, auc: 0.622, f1: 0.604, acc: 0.466
+2025-07-18 21:57:49: Epoch: 043 Test results, ap: 0.259, auc: 0.508, f1: 0.369, acc: 0.241
+2025-07-18 21:57:57: Epoch: 044 Train Loss: 0.038, ap: 0.568, auc: 0.622, f1: 0.587, acc: 0.427
+2025-07-18 21:57:58: Epoch: 044 Test results, ap: 0.256, auc: 0.506, f1: 0.369, acc: 0.241
+2025-07-18 21:58:06: Epoch: 045 Train Loss: 0.041, ap: 0.573, auc: 0.627, f1: 0.600, acc: 0.456
+2025-07-18 21:58:07: Epoch: 045 Test results, ap: 0.261, auc: 0.508, f1: 0.369, acc: 0.241
+2025-07-18 21:58:15: Epoch: 046 Train Loss: 0.037, ap: 0.578, auc: 0.630, f1: 0.613, acc: 0.485
+2025-07-18 21:58:16: Epoch: 046 Test results, ap: 0.268, auc: 0.520, f1: 0.369, acc: 0.241
+2025-07-18 21:58:24: Epoch: 047 Train Loss: 0.043, ap: 0.583, auc: 0.637, f1: 0.613, acc: 0.485
+2025-07-18 21:58:25: Epoch: 047 Test results, ap: 0.268, auc: 0.518, f1: 0.369, acc: 0.241
+2025-07-18 21:58:33: Epoch: 048 Train Loss: 0.035, ap: 0.583, auc: 0.635, f1: 0.613, acc: 0.485
+2025-07-18 21:58:34: Epoch: 048 Test results, ap: 0.265, auc: 0.513, f1: 0.369, acc: 0.241
+2025-07-18 21:58:42: Epoch: 049 Train Loss: 0.034, ap: 0.583, auc: 0.634, f1: 0.609, acc: 0.476
+2025-07-18 21:58:43: Epoch: 049 Test results, ap: 0.267, auc: 0.513, f1: 0.369, acc: 0.241
+2025-07-18 21:58:51: Epoch: 050 Train Loss: 0.040, ap: 0.578, auc: 0.632, f1: 0.609, acc: 0.476
+2025-07-18 21:58:52: Epoch: 050 Test results, ap: 0.261, auc: 0.512, f1: 0.369, acc: 0.241
+2025-07-18 21:58:52: used time 0.13h
+2025-07-18 21:58:52: Running Cross Validation 1
+2025-07-18 21:58:52: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-07-18 21:59:00: Epoch: 001 Train Loss: 0.683, ap: 0.422, auc: 0.543, f1: 0.000, acc: 0.641
+2025-07-18 21:59:00: Epoch: 001 New best VALIDATION metrics
+2025-07-18 21:59:01: Epoch: 001 Test results, ap: 0.218, auc: 0.477, f1: 0.000, acc: 0.759
+2025-07-18 21:59:09: Epoch: 002 Train Loss: 0.631, ap: 0.451, auc: 0.593, f1: 0.529, acc: 0.359
+2025-07-18 21:59:09: Epoch: 002 New best VALIDATION metrics
+2025-07-18 21:59:10: Epoch: 002 Test results, ap: 0.285, auc: 0.443, f1: 0.388, acc: 0.241
+2025-07-18 21:59:18: Epoch: 003 Train Loss: 0.551, ap: 0.479, auc: 0.594, f1: 0.529, acc: 0.359
+2025-07-18 21:59:18: Epoch: 003 New best VALIDATION metrics
+2025-07-18 21:59:19: Epoch: 003 Test results, ap: 0.199, auc: 0.386, f1: 0.388, acc: 0.241
+2025-07-18 21:59:27: Epoch: 004 Train Loss: 0.530, ap: 0.443, auc: 0.552, f1: 0.529, acc: 0.359
+2025-07-18 21:59:29: Epoch: 004 Test results, ap: 0.231, auc: 0.510, f1: 0.388, acc: 0.241
+2025-07-18 21:59:36: Epoch: 005 Train Loss: 0.526, ap: 0.468, auc: 0.600, f1: 0.529, acc: 0.359
+2025-07-18 21:59:38: Epoch: 005 Test results, ap: 0.226, auc: 0.501, f1: 0.388, acc: 0.241
+2025-07-18 21:59:45: Epoch: 006 Train Loss: 0.461, ap: 0.567, auc: 0.637, f1: 0.529, acc: 0.359
+2025-07-18 21:59:45: Epoch: 006 New best VALIDATION metrics
+2025-07-18 21:59:47: Epoch: 006 Test results, ap: 0.227, auc: 0.484, f1: 0.403, acc: 0.315
+2025-07-18 21:59:54: Epoch: 007 Train Loss: 0.460, ap: 0.528, auc: 0.609, f1: 0.522, acc: 0.359
+2025-07-18 21:59:56: Epoch: 007 Test results, ap: 0.351, auc: 0.595, f1: 0.394, acc: 0.259
+2025-07-18 22:00:04: Epoch: 008 Train Loss: 0.409, ap: 0.473, auc: 0.618, f1: 0.536, acc: 0.379
+2025-07-18 22:00:05: Epoch: 008 Test results, ap: 0.402, auc: 0.458, f1: 0.388, acc: 0.241
+2025-07-18 22:00:13: Epoch: 009 Train Loss: 0.348, ap: 0.528, auc: 0.672, f1: 0.533, acc: 0.388
+2025-07-18 22:00:14: Epoch: 009 Test results, ap: 0.268, auc: 0.552, f1: 0.388, acc: 0.241
+2025-07-18 22:00:22: Epoch: 010 Train Loss: 0.345, ap: 0.544, auc: 0.613, f1: 0.525, acc: 0.544
+2025-07-18 22:00:23: Epoch: 010 Test results, ap: 0.285, auc: 0.502, f1: 0.348, acc: 0.444
+2025-07-18 22:00:31: Epoch: 011 Train Loss: 0.254, ap: 0.459, auc: 0.619, f1: 0.544, acc: 0.398
+2025-07-18 22:00:32: Epoch: 011 Test results, ap: 0.250, auc: 0.523, f1: 0.391, acc: 0.278
+2025-07-18 22:00:32: Epoch: 011 New best TEST metrics
+2025-07-18 22:00:40: Epoch: 012 Train Loss: 0.199, ap: 0.477, auc: 0.645, f1: 0.529, acc: 0.447
+2025-07-18 22:00:41: Epoch: 012 Test results, ap: 0.267, auc: 0.553, f1: 0.385, acc: 0.407
+2025-07-18 22:00:41: Epoch: 012 New best TEST metrics
+2025-07-18 22:00:49: Epoch: 013 Train Loss: 0.198, ap: 0.474, auc: 0.647, f1: 0.567, acc: 0.466
+2025-07-18 22:00:50: Epoch: 013 Test results, ap: 0.248, auc: 0.497, f1: 0.383, acc: 0.315
+2025-07-18 22:00:58: Epoch: 014 Train Loss: 0.167, ap: 0.494, auc: 0.653, f1: 0.558, acc: 0.447
+2025-07-18 22:00:59: Epoch: 014 Test results, ap: 0.269, auc: 0.511, f1: 0.370, acc: 0.306
+2025-07-18 22:01:07: Epoch: 015 Train Loss: 0.130, ap: 0.491, auc: 0.674, f1: 0.541, acc: 0.456
+2025-07-18 22:01:08: Epoch: 015 Test results, ap: 0.298, auc: 0.555, f1: 0.364, acc: 0.352
+2025-07-18 22:01:08: Epoch: 015 New best TEST metrics
+2025-07-18 22:01:16: Epoch: 016 Train Loss: 0.161, ap: 0.504, auc: 0.630, f1: 0.523, acc: 0.398
+2025-07-18 22:01:18: Epoch: 016 Test results, ap: 0.290, auc: 0.545, f1: 0.369, acc: 0.241
+2025-07-18 22:01:25: Epoch: 017 Train Loss: 0.142, ap: 0.452, auc: 0.618, f1: 0.534, acc: 0.408
+2025-07-18 22:01:27: Epoch: 017 Test results, ap: 0.278, auc: 0.530, f1: 0.397, acc: 0.269
+2025-07-18 22:01:34: Epoch: 018 Train Loss: 0.124, ap: 0.508, auc: 0.639, f1: 0.531, acc: 0.417
+2025-07-18 22:01:36: Epoch: 018 Test results, ap: 0.239, auc: 0.454, f1: 0.377, acc: 0.296
+2025-07-18 22:01:44: Epoch: 019 Train Loss: 0.116, ap: 0.433, auc: 0.621, f1: 0.602, acc: 0.602
+2025-07-18 22:01:45: Epoch: 019 Test results, ap: 0.348, auc: 0.597, f1: 0.366, acc: 0.583
+2025-07-18 22:01:45: Epoch: 019 New best TEST metrics
+2025-07-18 22:01:53: Epoch: 020 Train Loss: 0.097, ap: 0.585, auc: 0.665, f1: 0.527, acc: 0.408
+2025-07-18 22:01:53: Epoch: 020 New best VALIDATION metrics
+2025-07-18 22:01:54: Epoch: 020 Test results, ap: 0.227, auc: 0.464, f1: 0.387, acc: 0.296
+2025-07-18 22:02:02: Epoch: 021 Train Loss: 0.083, ap: 0.453, auc: 0.637, f1: 0.564, acc: 0.505
+2025-07-18 22:02:03: Epoch: 021 Test results, ap: 0.242, auc: 0.453, f1: 0.324, acc: 0.306
+2025-07-18 22:02:11: Epoch: 022 Train Loss: 0.065, ap: 0.446, auc: 0.624, f1: 0.556, acc: 0.612
+2025-07-18 22:02:12: Epoch: 022 Test results, ap: 0.308, auc: 0.540, f1: 0.377, acc: 0.602
+2025-07-18 22:02:20: Epoch: 023 Train Loss: 0.063, ap: 0.491, auc: 0.637, f1: 0.559, acc: 0.495
+2025-07-18 22:02:21: Epoch: 023 Test results, ap: 0.266, auc: 0.495, f1: 0.362, acc: 0.315
+2025-07-18 22:02:29: Epoch: 024 Train Loss: 0.051, ap: 0.476, auc: 0.642, f1: 0.559, acc: 0.495
+2025-07-18 22:02:30: Epoch: 024 Test results, ap: 0.248, auc: 0.491, f1: 0.393, acc: 0.343
+2025-07-18 22:02:38: Epoch: 025 Train Loss: 0.046, ap: 0.458, auc: 0.636, f1: 0.555, acc: 0.485
+2025-07-18 22:02:39: Epoch: 025 Test results, ap: 0.243, auc: 0.483, f1: 0.376, acc: 0.324
+2025-07-18 22:02:47: Epoch: 026 Train Loss: 0.052, ap: 0.458, auc: 0.637, f1: 0.545, acc: 0.466
+2025-07-18 22:02:49: Epoch: 026 Test results, ap: 0.236, auc: 0.477, f1: 0.374, acc: 0.287
+2025-07-18 22:02:56: Epoch: 027 Train Loss: 0.049, ap: 0.462, auc: 0.639, f1: 0.541, acc: 0.456
+2025-07-18 22:02:58: Epoch: 027 Test results, ap: 0.238, auc: 0.476, f1: 0.383, acc: 0.315
+2025-07-18 22:03:05: Epoch: 028 Train Loss: 0.060, ap: 0.447, auc: 0.637, f1: 0.579, acc: 0.534
+2025-07-18 22:03:07: Epoch: 028 Test results, ap: 0.275, auc: 0.476, f1: 0.367, acc: 0.361
+2025-07-18 22:03:14: Epoch: 029 Train Loss: 0.048, ap: 0.474, auc: 0.651, f1: 0.564, acc: 0.505
+2025-07-18 22:03:16: Epoch: 029 Test results, ap: 0.246, auc: 0.465, f1: 0.370, acc: 0.306
+2025-07-18 22:03:23: Epoch: 030 Train Loss: 0.046, ap: 0.461, auc: 0.641, f1: 0.545, acc: 0.466
+2025-07-18 22:03:25: Epoch: 030 Test results, ap: 0.237, auc: 0.466, f1: 0.368, acc: 0.269
+2025-07-18 22:03:32: Epoch: 031 Train Loss: 0.042, ap: 0.462, auc: 0.647, f1: 0.569, acc: 0.515
+2025-07-18 22:03:34: Epoch: 031 Test results, ap: 0.255, auc: 0.475, f1: 0.362, acc: 0.315
+2025-07-18 22:03:42: Epoch: 032 Train Loss: 0.040, ap: 0.473, auc: 0.658, f1: 0.574, acc: 0.524
+2025-07-18 22:03:43: Epoch: 032 Test results, ap: 0.262, auc: 0.477, f1: 0.378, acc: 0.361
+2025-07-18 22:03:51: Epoch: 033 Train Loss: 0.037, ap: 0.470, auc: 0.653, f1: 0.574, acc: 0.524
+2025-07-18 22:03:52: Epoch: 033 Test results, ap: 0.260, auc: 0.478, f1: 0.362, acc: 0.315
+2025-07-18 22:04:00: Epoch: 034 Train Loss: 0.035, ap: 0.465, auc: 0.648, f1: 0.545, acc: 0.466
+2025-07-18 22:04:01: Epoch: 034 Test results, ap: 0.251, auc: 0.477, f1: 0.358, acc: 0.269
+2025-07-18 22:04:09: Epoch: 035 Train Loss: 0.035, ap: 0.452, auc: 0.643, f1: 0.555, acc: 0.485
+2025-07-18 22:04:10: Epoch: 035 Test results, ap: 0.247, auc: 0.478, f1: 0.367, acc: 0.296
+2025-07-18 22:04:18: Epoch: 036 Train Loss: 0.043, ap: 0.462, auc: 0.650, f1: 0.584, acc: 0.544
+2025-07-18 22:04:19: Epoch: 036 Test results, ap: 0.262, auc: 0.476, f1: 0.352, acc: 0.352
+2025-07-18 22:04:27: Epoch: 037 Train Loss: 0.041, ap: 0.470, auc: 0.651, f1: 0.564, acc: 0.505
+2025-07-18 22:04:28: Epoch: 037 Test results, ap: 0.260, auc: 0.481, f1: 0.376, acc: 0.324
+2025-07-18 22:04:36: Epoch: 038 Train Loss: 0.038, ap: 0.457, auc: 0.643, f1: 0.541, acc: 0.456
+2025-07-18 22:04:38: Epoch: 038 Test results, ap: 0.255, auc: 0.488, f1: 0.367, acc: 0.296
+2025-07-18 22:04:45: Epoch: 039 Train Loss: 0.044, ap: 0.449, auc: 0.643, f1: 0.555, acc: 0.485
+2025-07-18 22:04:47: Epoch: 039 Test results, ap: 0.257, auc: 0.492, f1: 0.376, acc: 0.324
+2025-07-18 22:04:54: Epoch: 040 Train Loss: 0.037, ap: 0.457, auc: 0.647, f1: 0.564, acc: 0.505
+2025-07-18 22:04:56: Epoch: 040 Test results, ap: 0.268, auc: 0.483, f1: 0.386, acc: 0.352
+2025-07-18 22:05:04: Epoch: 041 Train Loss: 0.038, ap: 0.457, auc: 0.646, f1: 0.569, acc: 0.515
+2025-07-18 22:05:05: Epoch: 041 Test results, ap: 0.267, auc: 0.483, f1: 0.379, acc: 0.333
+2025-07-18 22:05:13: Epoch: 042 Train Loss: 0.035, ap: 0.461, auc: 0.648, f1: 0.564, acc: 0.505
+2025-07-18 22:05:14: Epoch: 042 Test results, ap: 0.258, auc: 0.478, f1: 0.373, acc: 0.315
+2025-07-18 22:05:22: Epoch: 043 Train Loss: 0.033, ap: 0.461, auc: 0.647, f1: 0.559, acc: 0.495
+2025-07-18 22:05:23: Epoch: 043 Test results, ap: 0.262, auc: 0.478, f1: 0.373, acc: 0.315
+2025-07-18 22:05:31: Epoch: 044 Train Loss: 0.025, ap: 0.459, auc: 0.647, f1: 0.569, acc: 0.515
+2025-07-18 22:05:32: Epoch: 044 Test results, ap: 0.272, auc: 0.475, f1: 0.376, acc: 0.324
+2025-07-18 22:05:40: Epoch: 045 Train Loss: 0.031, ap: 0.456, auc: 0.641, f1: 0.555, acc: 0.485
+2025-07-18 22:05:41: Epoch: 045 Test results, ap: 0.262, auc: 0.474, f1: 0.370, acc: 0.306
+2025-07-18 22:05:49: Epoch: 046 Train Loss: 0.031, ap: 0.453, auc: 0.637, f1: 0.555, acc: 0.485
+2025-07-18 22:05:50: Epoch: 046 Test results, ap: 0.263, auc: 0.476, f1: 0.370, acc: 0.306
+2025-07-18 22:05:58: Epoch: 047 Train Loss: 0.030, ap: 0.454, auc: 0.640, f1: 0.559, acc: 0.495
+2025-07-18 22:05:59: Epoch: 047 Test results, ap: 0.268, auc: 0.477, f1: 0.370, acc: 0.306
+2025-07-18 22:06:07: Epoch: 048 Train Loss: 0.038, ap: 0.455, auc: 0.643, f1: 0.564, acc: 0.505
+2025-07-18 22:06:08: Epoch: 048 Test results, ap: 0.276, auc: 0.477, f1: 0.373, acc: 0.315
+2025-07-18 22:06:16: Epoch: 049 Train Loss: 0.027, ap: 0.451, auc: 0.640, f1: 0.574, acc: 0.524
+2025-07-18 22:06:17: Epoch: 049 Test results, ap: 0.325, auc: 0.474, f1: 0.360, acc: 0.343
+2025-07-18 22:06:25: Epoch: 050 Train Loss: 0.040, ap: 0.441, auc: 0.633, f1: 0.559, acc: 0.495
+2025-07-18 22:06:27: Epoch: 050 Test results, ap: 0.278, auc: 0.475, f1: 0.376, acc: 0.324
+2025-07-18 22:06:27: used time 0.13h
+2025-07-18 22:06:27: Running Cross Validation 2
+2025-07-18 22:06:27: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-07-18 22:06:34: Epoch: 001 Train Loss: 0.681, ap: 0.737, auc: 0.722, f1: 0.671, acc: 0.505
+2025-07-18 22:06:34: Epoch: 001 New best VALIDATION metrics
+2025-07-18 22:06:36: Epoch: 001 Test results, ap: 0.233, auc: 0.472, f1: 0.388, acc: 0.241
+2025-07-18 22:06:44: Epoch: 002 Train Loss: 0.635, ap: 0.584, auc: 0.596, f1: 0.681, acc: 0.553
+2025-07-18 22:06:45: Epoch: 002 Test results, ap: 0.272, auc: 0.549, f1: 0.376, acc: 0.417
+2025-07-18 22:06:52: Epoch: 003 Train Loss: 0.620, ap: 0.751, auc: 0.692, f1: 0.647, acc: 0.544
+2025-07-18 22:06:54: Epoch: 003 Test results, ap: 0.220, auc: 0.416, f1: 0.356, acc: 0.296
+2025-07-18 22:07:02: Epoch: 004 Train Loss: 0.579, ap: 0.768, auc: 0.706, f1: 0.680, acc: 0.524
+2025-07-18 22:07:02: Epoch: 004 New best VALIDATION metrics
+2025-07-18 22:07:03: Epoch: 004 Test results, ap: 0.249, auc: 0.517, f1: 0.388, acc: 0.241
+2025-07-18 22:07:10: Epoch: 005 Train Loss: 0.563, ap: 0.749, auc: 0.712, f1: 0.676, acc: 0.534
+2025-07-18 22:07:12: Epoch: 005 Test results, ap: 0.198, auc: 0.384, f1: 0.388, acc: 0.241
+2025-07-18 22:07:20: Epoch: 006 Train Loss: 0.575, ap: 0.753, auc: 0.673, f1: 0.675, acc: 0.515
+2025-07-18 22:07:21: Epoch: 006 Test results, ap: 0.242, auc: 0.512, f1: 0.376, acc: 0.231
+2025-07-18 22:07:29: Epoch: 007 Train Loss: 0.501, ap: 0.800, auc: 0.735, f1: 0.680, acc: 0.524
+2025-07-18 22:07:29: Epoch: 007 New best VALIDATION metrics
+2025-07-18 22:07:30: Epoch: 007 Test results, ap: 0.362, auc: 0.496, f1: 0.391, acc: 0.278
+2025-07-18 22:07:38: Epoch: 008 Train Loss: 0.461, ap: 0.755, auc: 0.669, f1: 0.649, acc: 0.495
+2025-07-18 22:07:39: Epoch: 008 Test results, ap: 0.234, auc: 0.487, f1: 0.383, acc: 0.463
+2025-07-18 22:07:47: Epoch: 009 Train Loss: 0.429, ap: 0.749, auc: 0.668, f1: 0.662, acc: 0.544
+2025-07-18 22:07:48: Epoch: 009 Test results, ap: 0.224, auc: 0.474, f1: 0.370, acc: 0.306
+2025-07-18 22:07:56: Epoch: 010 Train Loss: 0.392, ap: 0.734, auc: 0.687, f1: 0.696, acc: 0.602
+2025-07-18 22:07:57: Epoch: 010 Test results, ap: 0.235, auc: 0.513, f1: 0.378, acc: 0.361
+2025-07-18 22:08:05: Epoch: 011 Train Loss: 0.295, ap: 0.751, auc: 0.709, f1: 0.711, acc: 0.621
+2025-07-18 22:08:06: Epoch: 011 Test results, ap: 0.261, auc: 0.489, f1: 0.337, acc: 0.491
+2025-07-18 22:08:06: Epoch: 011 New best TEST metrics
+2025-07-18 22:08:14: Epoch: 012 Train Loss: 0.258, ap: 0.768, auc: 0.700, f1: 0.703, acc: 0.583
+2025-07-18 22:08:15: Epoch: 012 Test results, ap: 0.274, auc: 0.579, f1: 0.397, acc: 0.324
+2025-07-18 22:08:15: Epoch: 012 New best TEST metrics
+2025-07-18 22:08:23: Epoch: 013 Train Loss: 0.258, ap: 0.724, auc: 0.686, f1: 0.727, acc: 0.650
+2025-07-18 22:08:24: Epoch: 013 Test results, ap: 0.255, auc: 0.500, f1: 0.365, acc: 0.324
+2025-07-18 22:08:32: Epoch: 014 Train Loss: 0.247, ap: 0.750, auc: 0.696, f1: 0.713, acc: 0.602
+2025-07-18 22:08:33: Epoch: 014 Test results, ap: 0.252, auc: 0.527, f1: 0.368, acc: 0.269
+2025-07-18 22:08:41: Epoch: 015 Train Loss: 0.212, ap: 0.763, auc: 0.707, f1: 0.711, acc: 0.621
+2025-07-18 22:08:42: Epoch: 015 Test results, ap: 0.290, auc: 0.602, f1: 0.438, acc: 0.454
+2025-07-18 22:08:42: Epoch: 015 New best TEST metrics
+2025-07-18 22:08:50: Epoch: 016 Train Loss: 0.190, ap: 0.751, auc: 0.683, f1: 0.685, acc: 0.553
+2025-07-18 22:08:51: Epoch: 016 Test results, ap: 0.255, auc: 0.544, f1: 0.393, acc: 0.315
+2025-07-18 22:08:59: Epoch: 017 Train Loss: 0.167, ap: 0.721, auc: 0.676, f1: 0.699, acc: 0.573
+2025-07-18 22:09:00: Epoch: 017 Test results, ap: 0.271, auc: 0.551, f1: 0.390, acc: 0.306
+2025-07-18 22:09:08: Epoch: 018 Train Loss: 0.169, ap: 0.777, auc: 0.712, f1: 0.651, acc: 0.573
+2025-07-18 22:09:09: Epoch: 018 Test results, ap: 0.384, auc: 0.621, f1: 0.420, acc: 0.463
+2025-07-18 22:09:09: Epoch: 018 New best TEST metrics
+2025-07-18 22:09:17: Epoch: 019 Train Loss: 0.161, ap: 0.747, auc: 0.700, f1: 0.701, acc: 0.602
+2025-07-18 22:09:19: Epoch: 019 Test results, ap: 0.296, auc: 0.557, f1: 0.396, acc: 0.380
+2025-07-18 22:09:26: Epoch: 020 Train Loss: 0.129, ap: 0.727, auc: 0.683, f1: 0.687, acc: 0.602
+2025-07-18 22:09:28: Epoch: 020 Test results, ap: 0.296, auc: 0.603, f1: 0.455, acc: 0.556
+2025-07-18 22:09:35: Epoch: 021 Train Loss: 0.137, ap: 0.709, auc: 0.661, f1: 0.646, acc: 0.563
+2025-07-18 22:09:37: Epoch: 021 Test results, ap: 0.311, auc: 0.604, f1: 0.439, acc: 0.574
+2025-07-18 22:09:44: Epoch: 022 Train Loss: 0.143, ap: 0.733, auc: 0.692, f1: 0.662, acc: 0.544
+2025-07-18 22:09:46: Epoch: 022 Test results, ap: 0.256, auc: 0.531, f1: 0.370, acc: 0.306
+2025-07-18 22:09:53: Epoch: 023 Train Loss: 0.113, ap: 0.702, auc: 0.704, f1: 0.677, acc: 0.612
+2025-07-18 22:09:55: Epoch: 023 Test results, ap: 0.337, auc: 0.623, f1: 0.400, acc: 0.556
+2025-07-18 22:10:03: Epoch: 024 Train Loss: 0.117, ap: 0.713, auc: 0.692, f1: 0.667, acc: 0.592
+2025-07-18 22:10:04: Epoch: 024 Test results, ap: 0.308, auc: 0.617, f1: 0.419, acc: 0.537
+2025-07-18 22:10:12: Epoch: 025 Train Loss: 0.100, ap: 0.659, auc: 0.646, f1: 0.641, acc: 0.553
+2025-07-18 22:10:13: Epoch: 025 Test results, ap: 0.317, auc: 0.620, f1: 0.405, acc: 0.537
+2025-07-18 22:10:21: Epoch: 026 Train Loss: 0.149, ap: 0.698, auc: 0.667, f1: 0.685, acc: 0.563
+2025-07-18 22:10:22: Epoch: 026 Test results, ap: 0.304, auc: 0.603, f1: 0.404, acc: 0.398
+2025-07-18 22:10:30: Epoch: 027 Train Loss: 0.116, ap: 0.713, auc: 0.675, f1: 0.652, acc: 0.534
+2025-07-18 22:10:31: Epoch: 027 Test results, ap: 0.261, auc: 0.557, f1: 0.412, acc: 0.444
+2025-07-18 22:10:39: Epoch: 028 Train Loss: 0.112, ap: 0.722, auc: 0.681, f1: 0.629, acc: 0.553
+2025-07-18 22:10:40: Epoch: 028 Test results, ap: 0.311, auc: 0.581, f1: 0.381, acc: 0.519
+2025-07-18 22:10:48: Epoch: 029 Train Loss: 0.098, ap: 0.716, auc: 0.676, f1: 0.646, acc: 0.553
+2025-07-18 22:10:49: Epoch: 029 Test results, ap: 0.300, auc: 0.603, f1: 0.400, acc: 0.528
+2025-07-18 22:10:57: Epoch: 030 Train Loss: 0.108, ap: 0.669, auc: 0.658, f1: 0.639, acc: 0.573
+2025-07-18 22:10:58: Epoch: 030 Test results, ap: 0.334, auc: 0.606, f1: 0.348, acc: 0.583
+2025-07-18 22:11:06: Epoch: 031 Train Loss: 0.103, ap: 0.684, auc: 0.667, f1: 0.657, acc: 0.553
+2025-07-18 22:11:07: Epoch: 031 Test results, ap: 0.269, auc: 0.575, f1: 0.404, acc: 0.509
+2025-07-18 22:11:15: Epoch: 032 Train Loss: 0.083, ap: 0.684, auc: 0.669, f1: 0.667, acc: 0.573
+2025-07-18 22:11:16: Epoch: 032 Test results, ap: 0.278, auc: 0.593, f1: 0.390, acc: 0.537
+2025-07-18 22:11:24: Epoch: 033 Train Loss: 0.069, ap: 0.685, auc: 0.667, f1: 0.657, acc: 0.553
+2025-07-18 22:11:26: Epoch: 033 Test results, ap: 0.274, auc: 0.588, f1: 0.409, acc: 0.519
+2025-07-18 22:11:33: Epoch: 034 Train Loss: 0.073, ap: 0.675, auc: 0.649, f1: 0.647, acc: 0.544
+2025-07-18 22:11:35: Epoch: 034 Test results, ap: 0.290, auc: 0.616, f1: 0.409, acc: 0.519
+2025-07-18 22:11:42: Epoch: 035 Train Loss: 0.085, ap: 0.670, auc: 0.652, f1: 0.657, acc: 0.553
+2025-07-18 22:11:44: Epoch: 035 Test results, ap: 0.278, auc: 0.590, f1: 0.405, acc: 0.537
+2025-07-18 22:11:51: Epoch: 036 Train Loss: 0.077, ap: 0.664, auc: 0.651, f1: 0.657, acc: 0.553
+2025-07-18 22:11:53: Epoch: 036 Test results, ap: 0.284, auc: 0.594, f1: 0.405, acc: 0.537
+2025-07-18 22:12:01: Epoch: 037 Train Loss: 0.078, ap: 0.667, auc: 0.656, f1: 0.652, acc: 0.544
+2025-07-18 22:12:02: Epoch: 037 Test results, ap: 0.269, auc: 0.570, f1: 0.422, acc: 0.519
+2025-07-18 22:12:10: Epoch: 038 Train Loss: 0.070, ap: 0.640, auc: 0.632, f1: 0.620, acc: 0.524
+2025-07-18 22:12:11: Epoch: 038 Test results, ap: 0.297, auc: 0.610, f1: 0.416, acc: 0.583
+2025-07-18 22:12:19: Epoch: 039 Train Loss: 0.058, ap: 0.639, auc: 0.638, f1: 0.636, acc: 0.544
+2025-07-18 22:12:20: Epoch: 039 Test results, ap: 0.293, auc: 0.606, f1: 0.416, acc: 0.583
+2025-07-18 22:12:28: Epoch: 040 Train Loss: 0.060, ap: 0.641, auc: 0.639, f1: 0.652, acc: 0.544
+2025-07-18 22:12:29: Epoch: 040 Test results, ap: 0.271, auc: 0.584, f1: 0.396, acc: 0.491
+2025-07-18 22:12:37: Epoch: 041 Train Loss: 0.065, ap: 0.641, auc: 0.640, f1: 0.647, acc: 0.534
+2025-07-18 22:12:39: Epoch: 041 Test results, ap: 0.272, auc: 0.585, f1: 0.418, acc: 0.509
+2025-07-18 22:12:46: Epoch: 042 Train Loss: 0.061, ap: 0.633, auc: 0.631, f1: 0.652, acc: 0.553
+2025-07-18 22:12:48: Epoch: 042 Test results, ap: 0.297, auc: 0.613, f1: 0.429, acc: 0.556
+2025-07-18 22:12:55: Epoch: 043 Train Loss: 0.055, ap: 0.620, auc: 0.620, f1: 0.647, acc: 0.544
+2025-07-18 22:12:57: Epoch: 043 Test results, ap: 0.301, auc: 0.616, f1: 0.456, acc: 0.602
+2025-07-18 22:13:04: Epoch: 044 Train Loss: 0.053, ap: 0.631, auc: 0.625, f1: 0.652, acc: 0.544
+2025-07-18 22:13:06: Epoch: 044 Test results, ap: 0.281, auc: 0.599, f1: 0.404, acc: 0.509
+2025-07-18 22:13:13: Epoch: 045 Train Loss: 0.053, ap: 0.639, auc: 0.632, f1: 0.647, acc: 0.534
+2025-07-18 22:13:15: Epoch: 045 Test results, ap: 0.291, auc: 0.601, f1: 0.429, acc: 0.556
+2025-07-18 22:13:23: Epoch: 046 Train Loss: 0.052, ap: 0.639, auc: 0.634, f1: 0.642, acc: 0.534
+2025-07-18 22:13:24: Epoch: 046 Test results, ap: 0.313, auc: 0.615, f1: 0.444, acc: 0.583
+2025-07-18 22:13:32: Epoch: 047 Train Loss: 0.058, ap: 0.638, auc: 0.632, f1: 0.636, acc: 0.534
+2025-07-18 22:13:33: Epoch: 047 Test results, ap: 0.298, auc: 0.617, f1: 0.425, acc: 0.574
+2025-07-18 22:13:41: Epoch: 048 Train Loss: 0.051, ap: 0.643, auc: 0.636, f1: 0.652, acc: 0.544
+2025-07-18 22:13:42: Epoch: 048 Test results, ap: 0.285, auc: 0.601, f1: 0.447, acc: 0.565
+2025-07-18 22:13:50: Epoch: 049 Train Loss: 0.050, ap: 0.649, auc: 0.643, f1: 0.652, acc: 0.544
+2025-07-18 22:13:51: Epoch: 049 Test results, ap: 0.278, auc: 0.595, f1: 0.427, acc: 0.528
+2025-07-18 22:13:59: Epoch: 050 Train Loss: 0.050, ap: 0.606, auc: 0.613, f1: 0.642, acc: 0.534
+2025-07-18 22:14:00: Epoch: 050 Test results, ap: 0.299, auc: 0.614, f1: 0.439, acc: 0.574
+2025-07-18 22:14:00: used time 0.13h
+2025-07-18 22:14:00: Running Cross Validation 3
+2025-07-18 22:14:00: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-07-18 22:14:08: Epoch: 001 Train Loss: 0.683, ap: 0.578, auc: 0.636, f1: 0.579, acc: 0.408
+2025-07-18 22:14:08: Epoch: 001 New best VALIDATION metrics
+2025-07-18 22:14:09: Epoch: 001 Test results, ap: 0.231, auc: 0.503, f1: 0.388, acc: 0.241
+2025-07-18 22:14:17: Epoch: 002 Train Loss: 0.628, ap: 0.655, auc: 0.663, f1: 0.579, acc: 0.408
+2025-07-18 22:14:17: Epoch: 002 New best VALIDATION metrics
+2025-07-18 22:14:19: Epoch: 002 Test results, ap: 0.211, auc: 0.455, f1: 0.388, acc: 0.241
+2025-07-18 22:14:26: Epoch: 003 Train Loss: 0.625, ap: 0.578, auc: 0.607, f1: 0.574, acc: 0.583
+2025-07-18 22:14:28: Epoch: 003 Test results, ap: 0.209, auc: 0.451, f1: 0.045, acc: 0.611
+2025-07-18 22:14:35: Epoch: 004 Train Loss: 0.567, ap: 0.594, auc: 0.631, f1: 0.555, acc: 0.408
+2025-07-18 22:14:37: Epoch: 004 Test results, ap: 0.235, auc: 0.500, f1: 0.370, acc: 0.306
+2025-07-18 22:14:44: Epoch: 005 Train Loss: 0.559, ap: 0.595, auc: 0.697, f1: 0.612, acc: 0.495
+2025-07-18 22:14:46: Epoch: 005 Test results, ap: 0.251, auc: 0.438, f1: 0.373, acc: 0.407
+2025-07-18 22:14:54: Epoch: 006 Train Loss: 0.528, ap: 0.612, auc: 0.640, f1: 0.569, acc: 0.427
+2025-07-18 22:14:55: Epoch: 006 Test results, ap: 0.210, auc: 0.440, f1: 0.387, acc: 0.324
+2025-07-18 22:15:03: Epoch: 007 Train Loss: 0.467, ap: 0.637, auc: 0.623, f1: 0.532, acc: 0.505
+2025-07-18 22:15:04: Epoch: 007 Test results, ap: 0.212, auc: 0.444, f1: 0.347, acc: 0.407
+2025-07-18 22:15:12: Epoch: 008 Train Loss: 0.423, ap: 0.595, auc: 0.663, f1: 0.577, acc: 0.417
+2025-07-18 22:15:13: Epoch: 008 Test results, ap: 0.190, auc: 0.381, f1: 0.387, acc: 0.296
+2025-07-18 22:15:21: Epoch: 009 Train Loss: 0.419, ap: 0.667, auc: 0.662, f1: 0.571, acc: 0.447
+2025-07-18 22:15:21: Epoch: 009 New best VALIDATION metrics
+2025-07-18 22:15:22: Epoch: 009 Test results, ap: 0.214, auc: 0.449, f1: 0.378, acc: 0.361
+2025-07-18 22:15:30: Epoch: 010 Train Loss: 0.346, ap: 0.647, auc: 0.679, f1: 0.571, acc: 0.447
+2025-07-18 22:15:31: Epoch: 010 Test results, ap: 0.214, auc: 0.447, f1: 0.417, acc: 0.352
+2025-07-18 22:15:39: Epoch: 011 Train Loss: 0.314, ap: 0.606, auc: 0.657, f1: 0.561, acc: 0.437
+2025-07-18 22:15:40: Epoch: 011 Test results, ap: 0.225, auc: 0.493, f1: 0.346, acc: 0.370
+2025-07-18 22:15:40: Epoch: 011 New best TEST metrics
+2025-07-18 22:15:48: Epoch: 012 Train Loss: 0.289, ap: 0.632, auc: 0.676, f1: 0.588, acc: 0.524
+2025-07-18 22:15:49: Epoch: 012 Test results, ap: 0.228, auc: 0.480, f1: 0.374, acc: 0.472
+2025-07-18 22:15:57: Epoch: 013 Train Loss: 0.189, ap: 0.600, auc: 0.662, f1: 0.565, acc: 0.447
+2025-07-18 22:15:59: Epoch: 013 Test results, ap: 0.224, auc: 0.482, f1: 0.393, acc: 0.370
+2025-07-18 22:16:06: Epoch: 014 Train Loss: 0.166, ap: 0.575, auc: 0.618, f1: 0.544, acc: 0.495
+2025-07-18 22:16:08: Epoch: 014 Test results, ap: 0.236, auc: 0.491, f1: 0.352, acc: 0.454
+2025-07-18 22:16:08: Epoch: 014 New best TEST metrics
+2025-07-18 22:16:15: Epoch: 015 Train Loss: 0.139, ap: 0.549, auc: 0.602, f1: 0.528, acc: 0.427
+2025-07-18 22:16:17: Epoch: 015 Test results, ap: 0.238, auc: 0.512, f1: 0.385, acc: 0.407
+2025-07-18 22:16:17: Epoch: 015 New best TEST metrics
+2025-07-18 22:16:25: Epoch: 016 Train Loss: 0.131, ap: 0.623, auc: 0.637, f1: 0.548, acc: 0.456
+2025-07-18 22:16:26: Epoch: 016 Test results, ap: 0.217, auc: 0.470, f1: 0.404, acc: 0.398
+2025-07-18 22:16:34: Epoch: 017 Train Loss: 0.111, ap: 0.550, auc: 0.635, f1: 0.562, acc: 0.485
+2025-07-18 22:16:35: Epoch: 017 Test results, ap: 0.219, auc: 0.471, f1: 0.365, acc: 0.389
+2025-07-18 22:16:43: Epoch: 018 Train Loss: 0.115, ap: 0.488, auc: 0.569, f1: 0.517, acc: 0.456
+2025-07-18 22:16:44: Epoch: 018 Test results, ap: 0.265, auc: 0.502, f1: 0.388, acc: 0.417
+2025-07-18 22:16:44: Epoch: 018 New best TEST metrics
+2025-07-18 22:16:52: Epoch: 019 Train Loss: 0.120, ap: 0.605, auc: 0.647, f1: 0.533, acc: 0.524
+2025-07-18 22:16:53: Epoch: 019 Test results, ap: 0.226, auc: 0.476, f1: 0.316, acc: 0.398
+2025-07-18 22:17:01: Epoch: 020 Train Loss: 0.129, ap: 0.493, auc: 0.553, f1: 0.513, acc: 0.447
+2025-07-18 22:17:02: Epoch: 020 Test results, ap: 0.223, auc: 0.477, f1: 0.330, acc: 0.398
+2025-07-18 22:17:10: Epoch: 021 Train Loss: 0.092, ap: 0.510, auc: 0.576, f1: 0.504, acc: 0.447
+2025-07-18 22:17:11: Epoch: 021 Test results, ap: 0.240, auc: 0.471, f1: 0.301, acc: 0.398
+2025-07-18 22:17:19: Epoch: 022 Train Loss: 0.089, ap: 0.516, auc: 0.583, f1: 0.530, acc: 0.466
+2025-07-18 22:17:20: Epoch: 022 Test results, ap: 0.248, auc: 0.511, f1: 0.351, acc: 0.417
+2025-07-18 22:17:28: Epoch: 023 Train Loss: 0.094, ap: 0.493, auc: 0.565, f1: 0.538, acc: 0.466
+2025-07-18 22:17:30: Epoch: 023 Test results, ap: 0.229, auc: 0.476, f1: 0.351, acc: 0.417
+2025-07-18 22:17:37: Epoch: 024 Train Loss: 0.068, ap: 0.531, auc: 0.597, f1: 0.525, acc: 0.437
+2025-07-18 22:17:39: Epoch: 024 Test results, ap: 0.217, auc: 0.465, f1: 0.360, acc: 0.407
+2025-07-18 22:17:46: Epoch: 025 Train Loss: 0.073, ap: 0.517, auc: 0.568, f1: 0.505, acc: 0.505
+2025-07-18 22:17:48: Epoch: 025 Test results, ap: 0.259, auc: 0.505, f1: 0.306, acc: 0.454
+2025-07-18 22:17:55: Epoch: 026 Train Loss: 0.075, ap: 0.511, auc: 0.567, f1: 0.491, acc: 0.456
+2025-07-18 22:17:57: Epoch: 026 Test results, ap: 0.250, auc: 0.480, f1: 0.299, acc: 0.435
+2025-07-18 22:18:05: Epoch: 027 Train Loss: 0.055, ap: 0.488, auc: 0.529, f1: 0.510, acc: 0.515
+2025-07-18 22:18:06: Epoch: 027 Test results, ap: 0.310, auc: 0.499, f1: 0.333, acc: 0.481
+2025-07-18 22:18:06: Epoch: 027 New best TEST metrics
+2025-07-18 22:18:14: Epoch: 028 Train Loss: 0.068, ap: 0.504, auc: 0.579, f1: 0.525, acc: 0.437
+2025-07-18 22:18:15: Epoch: 028 Test results, ap: 0.223, auc: 0.462, f1: 0.347, acc: 0.407
+2025-07-18 22:18:23: Epoch: 029 Train Loss: 0.056, ap: 0.471, auc: 0.572, f1: 0.520, acc: 0.427
+2025-07-18 22:18:24: Epoch: 029 Test results, ap: 0.226, auc: 0.472, f1: 0.347, acc: 0.407
+2025-07-18 22:18:32: Epoch: 030 Train Loss: 0.052, ap: 0.494, auc: 0.557, f1: 0.495, acc: 0.485
+2025-07-18 22:18:33: Epoch: 030 Test results, ap: 0.240, auc: 0.487, f1: 0.308, acc: 0.417
+2025-07-18 22:18:41: Epoch: 031 Train Loss: 0.060, ap: 0.497, auc: 0.559, f1: 0.505, acc: 0.466
+2025-07-18 22:18:42: Epoch: 031 Test results, ap: 0.237, auc: 0.478, f1: 0.308, acc: 0.417
+2025-07-18 22:18:50: Epoch: 032 Train Loss: 0.047, ap: 0.465, auc: 0.554, f1: 0.525, acc: 0.456
+2025-07-18 22:18:52: Epoch: 032 Test results, ap: 0.236, auc: 0.479, f1: 0.319, acc: 0.407
+2025-07-18 22:18:59: Epoch: 033 Train Loss: 0.043, ap: 0.464, auc: 0.557, f1: 0.539, acc: 0.485
+2025-07-18 22:19:01: Epoch: 033 Test results, ap: 0.238, auc: 0.479, f1: 0.319, acc: 0.407
+2025-07-18 22:19:08: Epoch: 034 Train Loss: 0.046, ap: 0.471, auc: 0.558, f1: 0.564, acc: 0.534
+2025-07-18 22:19:10: Epoch: 034 Test results, ap: 0.242, auc: 0.487, f1: 0.337, acc: 0.454
+2025-07-18 22:19:17: Epoch: 035 Train Loss: 0.041, ap: 0.458, auc: 0.549, f1: 0.545, acc: 0.515
+2025-07-18 22:19:19: Epoch: 035 Test results, ap: 0.247, auc: 0.495, f1: 0.333, acc: 0.444
+2025-07-18 22:19:26: Epoch: 036 Train Loss: 0.030, ap: 0.463, auc: 0.558, f1: 0.539, acc: 0.485
+2025-07-18 22:19:28: Epoch: 036 Test results, ap: 0.241, auc: 0.488, f1: 0.323, acc: 0.417
+2025-07-18 22:19:35: Epoch: 037 Train Loss: 0.040, ap: 0.454, auc: 0.538, f1: 0.514, acc: 0.505
+2025-07-18 22:19:37: Epoch: 037 Test results, ap: 0.246, auc: 0.490, f1: 0.337, acc: 0.454
+2025-07-18 22:19:45: Epoch: 038 Train Loss: 0.039, ap: 0.455, auc: 0.541, f1: 0.532, acc: 0.505
+2025-07-18 22:19:46: Epoch: 038 Test results, ap: 0.237, auc: 0.483, f1: 0.333, acc: 0.444
+2025-07-18 22:19:54: Epoch: 039 Train Loss: 0.047, ap: 0.480, auc: 0.561, f1: 0.541, acc: 0.505
+2025-07-18 22:19:55: Epoch: 039 Test results, ap: 0.217, auc: 0.450, f1: 0.326, acc: 0.426
+2025-07-18 22:20:03: Epoch: 040 Train Loss: 0.043, ap: 0.477, auc: 0.556, f1: 0.532, acc: 0.505
+2025-07-18 22:20:04: Epoch: 040 Test results, ap: 0.231, auc: 0.461, f1: 0.308, acc: 0.417
+2025-07-18 22:20:12: Epoch: 041 Train Loss: 0.029, ap: 0.466, auc: 0.550, f1: 0.536, acc: 0.495
+2025-07-18 22:20:13: Epoch: 041 Test results, ap: 0.229, auc: 0.466, f1: 0.323, acc: 0.417
+2025-07-18 22:20:21: Epoch: 042 Train Loss: 0.027, ap: 0.468, auc: 0.543, f1: 0.537, acc: 0.515
+2025-07-18 22:20:22: Epoch: 042 Test results, ap: 0.249, auc: 0.470, f1: 0.330, acc: 0.435
+2025-07-18 22:20:30: Epoch: 043 Train Loss: 0.027, ap: 0.463, auc: 0.538, f1: 0.537, acc: 0.515
+2025-07-18 22:20:32: Epoch: 043 Test results, ap: 0.247, auc: 0.473, f1: 0.330, acc: 0.435
+2025-07-18 22:20:39: Epoch: 044 Train Loss: 0.032, ap: 0.466, auc: 0.542, f1: 0.527, acc: 0.495
+2025-07-18 22:20:41: Epoch: 044 Test results, ap: 0.244, auc: 0.470, f1: 0.326, acc: 0.426
+2025-07-18 22:20:48: Epoch: 045 Train Loss: 0.029, ap: 0.456, auc: 0.536, f1: 0.532, acc: 0.505
+2025-07-18 22:20:50: Epoch: 045 Test results, ap: 0.248, auc: 0.474, f1: 0.330, acc: 0.435
+2025-07-18 22:20:57: Epoch: 046 Train Loss: 0.030, ap: 0.455, auc: 0.541, f1: 0.531, acc: 0.485
+2025-07-18 22:20:59: Epoch: 046 Test results, ap: 0.238, auc: 0.473, f1: 0.323, acc: 0.417
+2025-07-18 22:21:06: Epoch: 047 Train Loss: 0.028, ap: 0.452, auc: 0.536, f1: 0.527, acc: 0.495
+2025-07-18 22:21:08: Epoch: 047 Test results, ap: 0.239, auc: 0.469, f1: 0.326, acc: 0.426
+2025-07-18 22:21:15: Epoch: 048 Train Loss: 0.025, ap: 0.442, auc: 0.523, f1: 0.519, acc: 0.495
+2025-07-18 22:21:17: Epoch: 048 Test results, ap: 0.242, auc: 0.479, f1: 0.330, acc: 0.435
+2025-07-18 22:21:24: Epoch: 049 Train Loss: 0.026, ap: 0.444, auc: 0.520, f1: 0.509, acc: 0.495
+2025-07-18 22:21:26: Epoch: 049 Test results, ap: 0.250, auc: 0.489, f1: 0.333, acc: 0.444
+2025-07-18 22:21:34: Epoch: 050 Train Loss: 0.019, ap: 0.441, auc: 0.528, f1: 0.531, acc: 0.485
+2025-07-18 22:21:35: Epoch: 050 Test results, ap: 0.244, auc: 0.494, f1: 0.326, acc: 0.426
+2025-07-18 22:21:35: used time 0.13h
+2025-07-18 22:21:35: Running Cross Validation 4
+2025-07-18 22:21:35: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-07-18 22:21:43: Epoch: 001 Train Loss: 0.672, ap: 0.566, auc: 0.565, f1: 0.569, acc: 0.398
+2025-07-18 22:21:43: Epoch: 001 New best VALIDATION metrics
+2025-07-18 22:21:44: Epoch: 001 Test results, ap: 0.227, auc: 0.500, f1: 0.388, acc: 0.241
+2025-07-18 22:21:52: Epoch: 002 Train Loss: 0.625, ap: 0.677, auc: 0.710, f1: 0.558, acc: 0.447
+2025-07-18 22:21:52: Epoch: 002 New best VALIDATION metrics
+2025-07-18 22:21:53: Epoch: 002 Test results, ap: 0.242, auc: 0.538, f1: 0.388, acc: 0.241
+2025-07-18 22:22:01: Epoch: 003 Train Loss: 0.566, ap: 0.641, auc: 0.675, f1: 0.467, acc: 0.689
+2025-07-18 22:22:02: Epoch: 003 Test results, ap: 0.302, auc: 0.543, f1: 0.000, acc: 0.759
+2025-07-18 22:22:10: Epoch: 004 Train Loss: 0.533, ap: 0.688, auc: 0.683, f1: 0.569, acc: 0.398
+2025-07-18 22:22:11: Epoch: 004 Test results, ap: 0.219, auc: 0.475, f1: 0.385, acc: 0.259
+2025-07-18 22:22:19: Epoch: 005 Train Loss: 0.453, ap: 0.614, auc: 0.667, f1: 0.576, acc: 0.515
+2025-07-18 22:22:21: Epoch: 005 Test results, ap: 0.268, auc: 0.498, f1: 0.387, acc: 0.296
+2025-07-18 22:22:28: Epoch: 006 Train Loss: 0.421, ap: 0.635, auc: 0.682, f1: 0.565, acc: 0.447
+2025-07-18 22:22:30: Epoch: 006 Test results, ap: 0.216, auc: 0.464, f1: 0.389, acc: 0.361
+2025-07-18 22:22:37: Epoch: 007 Train Loss: 0.351, ap: 0.625, auc: 0.707, f1: 0.611, acc: 0.592
+2025-07-18 22:22:39: Epoch: 007 Test results, ap: 0.322, auc: 0.568, f1: 0.424, acc: 0.546
+2025-07-18 22:22:47: Epoch: 008 Train Loss: 0.341, ap: 0.582, auc: 0.655, f1: 0.569, acc: 0.398
+2025-07-18 22:22:48: Epoch: 008 Test results, ap: 0.222, auc: 0.491, f1: 0.388, acc: 0.241
+2025-07-18 22:22:56: Epoch: 009 Train Loss: 0.354, ap: 0.618, auc: 0.673, f1: 0.578, acc: 0.476
+2025-07-18 22:22:57: Epoch: 009 Test results, ap: 0.360, auc: 0.590, f1: 0.389, acc: 0.361
+2025-07-18 22:23:05: Epoch: 010 Train Loss: 0.264, ap: 0.615, auc: 0.669, f1: 0.574, acc: 0.466
+2025-07-18 22:23:06: Epoch: 010 Test results, ap: 0.253, auc: 0.545, f1: 0.393, acc: 0.370
+2025-07-18 22:23:14: Epoch: 011 Train Loss: 0.212, ap: 0.555, auc: 0.727, f1: 0.667, acc: 0.641
+2025-07-18 22:23:15: Epoch: 011 Test results, ap: 0.327, auc: 0.511, f1: 0.352, acc: 0.454
+2025-07-18 22:23:15: Epoch: 011 New best TEST metrics
+2025-07-18 22:23:23: Epoch: 012 Train Loss: 0.197, ap: 0.604, auc: 0.694, f1: 0.598, acc: 0.505
+2025-07-18 22:23:24: Epoch: 012 Test results, ap: 0.286, auc: 0.515, f1: 0.356, acc: 0.296
+2025-07-18 22:23:32: Epoch: 013 Train Loss: 0.144, ap: 0.579, auc: 0.684, f1: 0.578, acc: 0.476
+2025-07-18 22:23:34: Epoch: 013 Test results, ap: 0.265, auc: 0.540, f1: 0.378, acc: 0.361
+2025-07-18 22:23:41: Epoch: 014 Train Loss: 0.138, ap: 0.564, auc: 0.653, f1: 0.561, acc: 0.437
+2025-07-18 22:23:43: Epoch: 014 Test results, ap: 0.300, auc: 0.516, f1: 0.371, acc: 0.278
+2025-07-18 22:23:50: Epoch: 015 Train Loss: 0.136, ap: 0.588, auc: 0.686, f1: 0.592, acc: 0.505
+2025-07-18 22:23:52: Epoch: 015 Test results, ap: 0.272, auc: 0.498, f1: 0.310, acc: 0.259
+2025-07-18 22:23:59: Epoch: 016 Train Loss: 0.121, ap: 0.596, auc: 0.715, f1: 0.610, acc: 0.553
+2025-07-18 22:24:01: Epoch: 016 Test results, ap: 0.274, auc: 0.536, f1: 0.347, acc: 0.407
+2025-07-18 22:24:09: Epoch: 017 Train Loss: 0.102, ap: 0.615, auc: 0.691, f1: 0.581, acc: 0.495
+2025-07-18 22:24:10: Epoch: 017 Test results, ap: 0.306, auc: 0.542, f1: 0.377, acc: 0.296
+2025-07-18 22:24:10: Epoch: 017 New best TEST metrics
+2025-07-18 22:24:18: Epoch: 018 Train Loss: 0.092, ap: 0.550, auc: 0.682, f1: 0.603, acc: 0.553
+2025-07-18 22:24:19: Epoch: 018 Test results, ap: 0.317, auc: 0.504, f1: 0.345, acc: 0.296
+2025-07-18 22:24:27: Epoch: 019 Train Loss: 0.106, ap: 0.562, auc: 0.681, f1: 0.574, acc: 0.524
+2025-07-18 22:24:28: Epoch: 019 Test results, ap: 0.248, auc: 0.520, f1: 0.426, acc: 0.352
+2025-07-18 22:24:36: Epoch: 020 Train Loss: 0.094, ap: 0.606, auc: 0.682, f1: 0.585, acc: 0.573
+2025-07-18 22:24:37: Epoch: 020 Test results, ap: 0.382, auc: 0.565, f1: 0.358, acc: 0.435
+2025-07-18 22:24:37: Epoch: 020 New best TEST metrics
+2025-07-18 22:24:45: Epoch: 021 Train Loss: 0.083, ap: 0.576, auc: 0.690, f1: 0.611, acc: 0.592
+2025-07-18 22:24:46: Epoch: 021 Test results, ap: 0.355, auc: 0.521, f1: 0.347, acc: 0.407
+2025-07-18 22:24:54: Epoch: 022 Train Loss: 0.061, ap: 0.558, auc: 0.672, f1: 0.574, acc: 0.524
+2025-07-18 22:24:56: Epoch: 022 Test results, ap: 0.358, auc: 0.558, f1: 0.389, acc: 0.361
+2025-07-18 22:25:03: Epoch: 023 Train Loss: 0.065, ap: 0.536, auc: 0.679, f1: 0.606, acc: 0.583
+2025-07-18 22:25:05: Epoch: 023 Test results, ap: 0.380, auc: 0.524, f1: 0.317, acc: 0.361
+2025-07-18 22:25:12: Epoch: 024 Train Loss: 0.054, ap: 0.545, auc: 0.685, f1: 0.595, acc: 0.563
+2025-07-18 22:25:13: Epoch: 024 Test results, ap: 0.370, auc: 0.539, f1: 0.333, acc: 0.333
+2025-07-18 22:25:21: Epoch: 025 Train Loss: 0.060, ap: 0.546, auc: 0.674, f1: 0.613, acc: 0.583
+2025-07-18 22:25:22: Epoch: 025 Test results, ap: 0.391, auc: 0.531, f1: 0.324, acc: 0.343
+2025-07-18 22:25:30: Epoch: 026 Train Loss: 0.061, ap: 0.549, auc: 0.696, f1: 0.614, acc: 0.621
+2025-07-18 22:25:31: Epoch: 026 Test results, ap: 0.427, auc: 0.553, f1: 0.380, acc: 0.546
+2025-07-18 22:25:31: Epoch: 026 New best TEST metrics
+2025-07-18 22:25:39: Epoch: 027 Train Loss: 0.055, ap: 0.565, auc: 0.674, f1: 0.579, acc: 0.505
+2025-07-18 22:25:41: Epoch: 027 Test results, ap: 0.335, auc: 0.556, f1: 0.389, acc: 0.361
+2025-07-18 22:25:48: Epoch: 028 Train Loss: 0.045, ap: 0.525, auc: 0.664, f1: 0.596, acc: 0.592
+2025-07-18 22:25:50: Epoch: 028 Test results, ap: 0.403, auc: 0.531, f1: 0.337, acc: 0.417
+2025-07-18 22:25:57: Epoch: 029 Train Loss: 0.050, ap: 0.513, auc: 0.652, f1: 0.582, acc: 0.553
+2025-07-18 22:25:59: Epoch: 029 Test results, ap: 0.367, auc: 0.561, f1: 0.389, acc: 0.389
+2025-07-18 22:26:06: Epoch: 030 Train Loss: 0.034, ap: 0.523, auc: 0.657, f1: 0.582, acc: 0.553
+2025-07-18 22:26:08: Epoch: 030 Test results, ap: 0.381, auc: 0.558, f1: 0.377, acc: 0.389
+2025-07-18 22:26:15: Epoch: 031 Train Loss: 0.037, ap: 0.522, auc: 0.652, f1: 0.581, acc: 0.524
+2025-07-18 22:26:17: Epoch: 031 Test results, ap: 0.348, auc: 0.557, f1: 0.383, acc: 0.343
+2025-07-18 22:26:24: Epoch: 032 Train Loss: 0.033, ap: 0.520, auc: 0.645, f1: 0.571, acc: 0.505
+2025-07-18 22:26:26: Epoch: 032 Test results, ap: 0.310, auc: 0.543, f1: 0.379, acc: 0.333
+2025-07-18 22:26:33: Epoch: 033 Train Loss: 0.033, ap: 0.529, auc: 0.657, f1: 0.581, acc: 0.524
+2025-07-18 22:26:35: Epoch: 033 Test results, ap: 0.376, auc: 0.552, f1: 0.386, acc: 0.352
+2025-07-18 22:26:42: Epoch: 034 Train Loss: 0.028, ap: 0.517, auc: 0.646, f1: 0.582, acc: 0.553
+2025-07-18 22:26:44: Epoch: 034 Test results, ap: 0.386, auc: 0.554, f1: 0.364, acc: 0.352
+2025-07-18 22:26:52: Epoch: 035 Train Loss: 0.035, ap: 0.528, auc: 0.657, f1: 0.613, acc: 0.583
+2025-07-18 22:26:53: Epoch: 035 Test results, ap: 0.403, auc: 0.563, f1: 0.385, acc: 0.380
+2025-07-18 22:27:01: Epoch: 036 Train Loss: 0.033, ap: 0.533, auc: 0.659, f1: 0.591, acc: 0.544
+2025-07-18 22:27:02: Epoch: 036 Test results, ap: 0.368, auc: 0.557, f1: 0.386, acc: 0.352
+2025-07-18 22:27:10: Epoch: 037 Train Loss: 0.023, ap: 0.530, auc: 0.658, f1: 0.596, acc: 0.553
+2025-07-18 22:27:11: Epoch: 037 Test results, ap: 0.384, auc: 0.566, f1: 0.389, acc: 0.361
+2025-07-18 22:27:19: Epoch: 038 Train Loss: 0.032, ap: 0.532, auc: 0.654, f1: 0.596, acc: 0.553
+2025-07-18 22:27:20: Epoch: 038 Test results, ap: 0.406, auc: 0.567, f1: 0.383, acc: 0.343
+2025-07-18 22:27:28: Epoch: 039 Train Loss: 0.027, ap: 0.518, auc: 0.636, f1: 0.569, acc: 0.515
+2025-07-18 22:27:29: Epoch: 039 Test results, ap: 0.341, auc: 0.552, f1: 0.367, acc: 0.296
+2025-07-18 22:27:37: Epoch: 040 Train Loss: 0.036, ap: 0.514, auc: 0.651, f1: 0.602, acc: 0.602
+2025-07-18 22:27:38: Epoch: 040 Test results, ap: 0.438, auc: 0.548, f1: 0.337, acc: 0.380
+2025-07-18 22:27:38: Epoch: 040 New best TEST metrics
+2025-07-18 22:27:46: Epoch: 041 Train Loss: 0.028, ap: 0.511, auc: 0.648, f1: 0.569, acc: 0.544
+2025-07-18 22:27:48: Epoch: 041 Test results, ap: 0.422, auc: 0.568, f1: 0.367, acc: 0.361
+2025-07-18 22:27:48: Epoch: 041 New best TEST metrics
+2025-07-18 22:27:55: Epoch: 042 Train Loss: 0.023, ap: 0.510, auc: 0.634, f1: 0.582, acc: 0.553
+2025-07-18 22:27:57: Epoch: 042 Test results, ap: 0.403, auc: 0.568, f1: 0.378, acc: 0.361
+2025-07-18 22:28:04: Epoch: 043 Train Loss: 0.028, ap: 0.513, auc: 0.656, f1: 0.606, acc: 0.583
+2025-07-18 22:28:06: Epoch: 043 Test results, ap: 0.435, auc: 0.559, f1: 0.362, acc: 0.380
+2025-07-18 22:28:06: Epoch: 043 New best TEST metrics
+2025-07-18 22:28:13: Epoch: 044 Train Loss: 0.020, ap: 0.519, auc: 0.653, f1: 0.589, acc: 0.553
+2025-07-18 22:28:15: Epoch: 044 Test results, ap: 0.428, auc: 0.575, f1: 0.386, acc: 0.352
+2025-07-18 22:28:15: Epoch: 044 New best TEST metrics
+2025-07-18 22:28:23: Epoch: 045 Train Loss: 0.024, ap: 0.508, auc: 0.631, f1: 0.564, acc: 0.534
+2025-07-18 22:28:24: Epoch: 045 Test results, ap: 0.410, auc: 0.566, f1: 0.389, acc: 0.361
+2025-07-18 22:28:32: Epoch: 046 Train Loss: 0.016, ap: 0.507, auc: 0.627, f1: 0.571, acc: 0.534
+2025-07-18 22:28:33: Epoch: 046 Test results, ap: 0.357, auc: 0.562, f1: 0.386, acc: 0.352
+2025-07-18 22:28:41: Epoch: 047 Train Loss: 0.022, ap: 0.504, auc: 0.639, f1: 0.589, acc: 0.553
+2025-07-18 22:28:42: Epoch: 047 Test results, ap: 0.393, auc: 0.568, f1: 0.389, acc: 0.361
+2025-07-18 22:28:50: Epoch: 048 Train Loss: 0.013, ap: 0.501, auc: 0.633, f1: 0.577, acc: 0.544
+2025-07-18 22:28:51: Epoch: 048 Test results, ap: 0.384, auc: 0.561, f1: 0.389, acc: 0.361
+2025-07-18 22:28:59: Epoch: 049 Train Loss: 0.023, ap: 0.491, auc: 0.623, f1: 0.564, acc: 0.534
+2025-07-18 22:29:00: Epoch: 049 Test results, ap: 0.391, auc: 0.561, f1: 0.372, acc: 0.343
+2025-07-18 22:29:08: Epoch: 050 Train Loss: 0.021, ap: 0.486, auc: 0.617, f1: 0.564, acc: 0.534
+2025-07-18 22:29:09: Epoch: 050 Test results, ap: 0.394, auc: 0.558, f1: 0.372, acc: 0.343
+2025-07-18 22:29:09: used time 0.13h
+2025-07-18 22:29:09: Cross Validation Finished!
+2025-07-18 22:29:09: Best validation perform list
+[[0.66684878 0.65691489 0.64827585 0.50485438]
+ [0.58543634 0.66502869 0.5271318  0.407767  ]
+ [0.79996973 0.7352941  0.67973858 0.52427185]
+ [0.6671012  0.66198277 0.5714286  0.44660193]
+ [0.67715442 0.71046424 0.55813956 0.44660193]]
+2025-07-18 22:29:09: mean: [0.679 0.686 0.597 0.466]
+2025-07-18 22:29:09: std: [0.069 0.031 0.057 0.043]
+2025-07-18 22:29:09: Best test perform list
+[[0.32534167 0.53283298 0.37931034 0.33333334]
+ [0.34786993 0.59662288 0.36619717 0.58333331]
+ [0.38370675 0.62054408 0.41999999 0.46296296]
+ [0.31047478 0.49906194 0.33333334 0.48148149]
+ [0.42822814 0.57457787 0.3859649  0.35185185]]
+2025-07-18 22:29:09: mean: [0.359 0.565 0.377 0.443]
+2025-07-18 22:29:09: std: [0.042 0.044 0.028 0.092]
diff --git a/run-cls-simple/rn18-mlp-16-mamba-pcs-simple-768-oneway-ce-32-0.001-50/config.json b/run-cls-simple/rn18-mlp-16-mamba-pcs-simple-768-oneway-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": "mamba", "channels": 16, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": true, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
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@@ -0,0 +1,6 @@
+Valid
+0.7559389710426331,0.7794676780700683,0.6119301378726959,0.7145631074905395
+0.0627359674812611,0.03555148332544521,0.16235060831352538,0.0226444789555471
+Test
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+0.021761779276477627,0.03873103846701763,0.14543662471874966,0.08518520023511847
diff --git a/run-cls-simple/rn18-mlp-16-mamba-pcs-simple-768-oneway-ce-32-0.001-50/training.log b/run-cls-simple/rn18-mlp-16-mamba-pcs-simple-768-oneway-ce-32-0.001-50/training.log
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+2025-08-07 17:29:54: saving_dir: ./run-cls/rn18-mlp-16-mamba-pcs-simple-768-oneway-ce-32-0.001-50
+2025-08-07 17:29:54: Loading Training Dataset
+2025-08-07 17:30:18: Loading Test Dataset
+2025-08-07 17:30:22: Running Cross Validation 0
+2025-08-07 17:30:22: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 17:30:31: Epoch: 001 Train Loss: 0.687, ap: 0.740, auc: 0.757, f1: 0.716, acc: 0.738
+2025-08-07 17:30:31: Epoch: 001 New best VALIDATION metrics
+2025-08-07 17:30:32: Epoch: 001 Test results, ap: 0.206, auc: 0.435, f1: 0.000, acc: 0.731
+2025-08-07 17:30:39: Epoch: 002 Train Loss: 0.624, ap: 0.666, auc: 0.668, f1: 0.565, acc: 0.641
+2025-08-07 17:30:41: Epoch: 002 Test results, ap: 0.195, auc: 0.375, f1: 0.000, acc: 0.722
+2025-08-07 17:30:48: Epoch: 003 Train Loss: 0.552, ap: 0.422, auc: 0.474, f1: 0.457, acc: 0.515
+2025-08-07 17:30:50: Epoch: 003 Test results, ap: 0.223, auc: 0.443, f1: 0.303, acc: 0.361
+2025-08-07 17:30:57: Epoch: 004 Train Loss: 0.520, ap: 0.668, auc: 0.633, f1: 0.644, acc: 0.495
+2025-08-07 17:30:58: Epoch: 004 Test results, ap: 0.341, auc: 0.432, f1: 0.388, acc: 0.241
+2025-08-07 17:31:06: Epoch: 005 Train Loss: 0.479, ap: 0.684, auc: 0.711, f1: 0.598, acc: 0.660
+2025-08-07 17:31:07: Epoch: 005 Test results, ap: 0.185, auc: 0.348, f1: 0.000, acc: 0.481
+2025-08-07 17:31:14: Epoch: 006 Train Loss: 0.420, ap: 0.609, auc: 0.652, f1: 0.000, acc: 0.544
+2025-08-07 17:31:16: Epoch: 006 Test results, ap: 0.337, auc: 0.537, f1: 0.000, acc: 0.759
+2025-08-07 17:31:23: Epoch: 007 Train Loss: 0.398, ap: 0.646, auc: 0.672, f1: 0.621, acc: 0.621
+2025-08-07 17:31:24: Epoch: 007 Test results, ap: 0.179, auc: 0.330, f1: 0.291, acc: 0.324
+2025-08-07 17:31:32: Epoch: 008 Train Loss: 0.373, ap: 0.568, auc: 0.594, f1: 0.469, acc: 0.583
+2025-08-07 17:31:33: Epoch: 008 Test results, ap: 0.290, auc: 0.514, f1: 0.346, acc: 0.370
+2025-08-07 17:31:40: Epoch: 009 Train Loss: 0.337, ap: 0.654, auc: 0.669, f1: 0.650, acc: 0.592
+2025-08-07 17:31:42: Epoch: 009 Test results, ap: 0.233, auc: 0.373, f1: 0.331, acc: 0.250
+2025-08-07 17:31:49: Epoch: 010 Train Loss: 0.348, ap: 0.626, auc: 0.645, f1: 0.676, acc: 0.573
+2025-08-07 17:31:50: Epoch: 010 Test results, ap: 0.388, auc: 0.515, f1: 0.400, acc: 0.278
+2025-08-07 17:31:58: Epoch: 011 Train Loss: 0.290, ap: 0.671, auc: 0.709, f1: 0.525, acc: 0.631
+2025-08-07 17:31:59: Epoch: 011 Test results, ap: 0.299, auc: 0.504, f1: 0.188, acc: 0.759
+2025-08-07 17:31:59: Epoch: 011 New best TEST metrics
+2025-08-07 17:32:06: Epoch: 012 Train Loss: 0.259, ap: 0.698, auc: 0.726, f1: 0.568, acc: 0.631
+2025-08-07 17:32:08: Epoch: 012 Test results, ap: 0.268, auc: 0.454, f1: 0.211, acc: 0.722
+2025-08-07 17:32:15: Epoch: 013 Train Loss: 0.242, ap: 0.699, auc: 0.718, f1: 0.629, acc: 0.680
+2025-08-07 17:32:17: Epoch: 013 Test results, ap: 0.237, auc: 0.434, f1: 0.118, acc: 0.722
+2025-08-07 17:32:24: Epoch: 014 Train Loss: 0.242, ap: 0.691, auc: 0.728, f1: 0.667, acc: 0.699
+2025-08-07 17:32:25: Epoch: 014 Test results, ap: 0.236, auc: 0.446, f1: 0.065, acc: 0.731
+2025-08-07 17:32:32: Epoch: 015 Train Loss: 0.257, ap: 0.675, auc: 0.688, f1: 0.654, acc: 0.650
+2025-08-07 17:32:34: Epoch: 015 Test results, ap: 0.310, auc: 0.445, f1: 0.246, acc: 0.602
+2025-08-07 17:32:41: Epoch: 016 Train Loss: 0.227, ap: 0.688, auc: 0.710, f1: 0.695, acc: 0.718
+2025-08-07 17:32:43: Epoch: 016 Test results, ap: 0.236, auc: 0.441, f1: 0.213, acc: 0.657
+2025-08-07 17:32:50: Epoch: 017 Train Loss: 0.202, ap: 0.689, auc: 0.715, f1: 0.554, acc: 0.641
+2025-08-07 17:32:51: Epoch: 017 Test results, ap: 0.267, auc: 0.505, f1: 0.200, acc: 0.704
+2025-08-07 17:32:59: Epoch: 018 Train Loss: 0.195, ap: 0.698, auc: 0.717, f1: 0.637, acc: 0.680
+2025-08-07 17:33:00: Epoch: 018 Test results, ap: 0.281, auc: 0.515, f1: 0.222, acc: 0.676
+2025-08-07 17:33:07: Epoch: 019 Train Loss: 0.179, ap: 0.686, auc: 0.704, f1: 0.686, acc: 0.689
+2025-08-07 17:33:09: Epoch: 019 Test results, ap: 0.268, auc: 0.465, f1: 0.286, acc: 0.676
+2025-08-07 17:33:16: Epoch: 020 Train Loss: 0.186, ap: 0.687, auc: 0.704, f1: 0.667, acc: 0.680
+2025-08-07 17:33:17: Epoch: 020 Test results, ap: 0.270, auc: 0.483, f1: 0.286, acc: 0.676
+2025-08-07 17:33:25: Epoch: 021 Train Loss: 0.163, ap: 0.678, auc: 0.700, f1: 0.644, acc: 0.689
+2025-08-07 17:33:26: Epoch: 021 Test results, ap: 0.234, auc: 0.414, f1: 0.245, acc: 0.657
+2025-08-07 17:33:33: Epoch: 022 Train Loss: 0.142, ap: 0.676, auc: 0.688, f1: 0.611, acc: 0.641
+2025-08-07 17:33:35: Epoch: 022 Test results, ap: 0.267, auc: 0.487, f1: 0.292, acc: 0.685
+2025-08-07 17:33:42: Epoch: 023 Train Loss: 0.146, ap: 0.704, auc: 0.718, f1: 0.696, acc: 0.728
+2025-08-07 17:33:44: Epoch: 023 Test results, ap: 0.241, auc: 0.422, f1: 0.217, acc: 0.667
+2025-08-07 17:33:51: Epoch: 024 Train Loss: 0.130, ap: 0.707, auc: 0.724, f1: 0.681, acc: 0.718
+2025-08-07 17:33:52: Epoch: 024 Test results, ap: 0.260, auc: 0.410, f1: 0.292, acc: 0.685
+2025-08-07 17:34:00: Epoch: 025 Train Loss: 0.138, ap: 0.693, auc: 0.713, f1: 0.695, acc: 0.718
+2025-08-07 17:34:01: Epoch: 025 Test results, ap: 0.251, auc: 0.379, f1: 0.292, acc: 0.685
+2025-08-07 17:34:08: Epoch: 026 Train Loss: 0.155, ap: 0.707, auc: 0.704, f1: 0.680, acc: 0.680
+2025-08-07 17:34:10: Epoch: 026 Test results, ap: 0.261, auc: 0.480, f1: 0.267, acc: 0.593
+2025-08-07 17:34:17: Epoch: 027 Train Loss: 0.127, ap: 0.695, auc: 0.703, f1: 0.674, acc: 0.699
+2025-08-07 17:34:19: Epoch: 027 Test results, ap: 0.265, auc: 0.415, f1: 0.292, acc: 0.685
+2025-08-07 17:34:26: Epoch: 028 Train Loss: 0.119, ap: 0.706, auc: 0.719, f1: 0.652, acc: 0.699
+2025-08-07 17:34:27: Epoch: 028 Test results, ap: 0.231, auc: 0.418, f1: 0.190, acc: 0.685
+2025-08-07 17:34:35: Epoch: 029 Train Loss: 0.115, ap: 0.690, auc: 0.710, f1: 0.667, acc: 0.680
+2025-08-07 17:34:36: Epoch: 029 Test results, ap: 0.248, auc: 0.439, f1: 0.292, acc: 0.685
+2025-08-07 17:34:43: Epoch: 030 Train Loss: 0.124, ap: 0.713, auc: 0.737, f1: 0.695, acc: 0.718
+2025-08-07 17:34:45: Epoch: 030 Test results, ap: 0.240, auc: 0.405, f1: 0.275, acc: 0.657
+2025-08-07 17:34:52: Epoch: 031 Train Loss: 0.104, ap: 0.716, auc: 0.740, f1: 0.702, acc: 0.728
+2025-08-07 17:34:54: Epoch: 031 Test results, ap: 0.233, auc: 0.418, f1: 0.235, acc: 0.639
+2025-08-07 17:35:01: Epoch: 032 Train Loss: 0.113, ap: 0.714, auc: 0.739, f1: 0.628, acc: 0.689
+2025-08-07 17:35:02: Epoch: 032 Test results, ap: 0.236, auc: 0.386, f1: 0.255, acc: 0.676
+2025-08-07 17:35:10: Epoch: 033 Train Loss: 0.101, ap: 0.717, auc: 0.731, f1: 0.674, acc: 0.709
+2025-08-07 17:35:11: Epoch: 033 Test results, ap: 0.228, auc: 0.369, f1: 0.250, acc: 0.667
+2025-08-07 17:35:18: Epoch: 034 Train Loss: 0.109, ap: 0.710, auc: 0.734, f1: 0.652, acc: 0.699
+2025-08-07 17:35:20: Epoch: 034 Test results, ap: 0.218, auc: 0.372, f1: 0.190, acc: 0.685
+2025-08-07 17:35:27: Epoch: 035 Train Loss: 0.092, ap: 0.710, auc: 0.738, f1: 0.651, acc: 0.709
+2025-08-07 17:35:29: Epoch: 035 Test results, ap: 0.219, auc: 0.372, f1: 0.162, acc: 0.713
+2025-08-07 17:35:36: Epoch: 036 Train Loss: 0.105, ap: 0.713, auc: 0.727, f1: 0.621, acc: 0.680
+2025-08-07 17:35:37: Epoch: 036 Test results, ap: 0.235, auc: 0.407, f1: 0.213, acc: 0.657
+2025-08-07 17:35:45: Epoch: 037 Train Loss: 0.105, ap: 0.719, auc: 0.717, f1: 0.615, acc: 0.660
+2025-08-07 17:35:46: Epoch: 037 Test results, ap: 0.270, auc: 0.504, f1: 0.222, acc: 0.676
+2025-08-07 17:35:54: Epoch: 038 Train Loss: 0.089, ap: 0.709, auc: 0.729, f1: 0.674, acc: 0.709
+2025-08-07 17:35:55: Epoch: 038 Test results, ap: 0.247, auc: 0.390, f1: 0.213, acc: 0.657
+2025-08-07 17:36:02: Epoch: 039 Train Loss: 0.100, ap: 0.721, auc: 0.724, f1: 0.667, acc: 0.709
+2025-08-07 17:36:04: Epoch: 039 Test results, ap: 0.270, auc: 0.464, f1: 0.217, acc: 0.667
+2025-08-07 17:36:11: Epoch: 040 Train Loss: 0.090, ap: 0.726, auc: 0.742, f1: 0.688, acc: 0.718
+2025-08-07 17:36:12: Epoch: 040 Test results, ap: 0.247, auc: 0.421, f1: 0.178, acc: 0.657
+2025-08-07 17:36:20: Epoch: 041 Train Loss: 0.074, ap: 0.727, auc: 0.752, f1: 0.659, acc: 0.699
+2025-08-07 17:36:21: Epoch: 041 Test results, ap: 0.223, auc: 0.375, f1: 0.213, acc: 0.657
+2025-08-07 17:36:28: Epoch: 042 Train Loss: 0.082, ap: 0.724, auc: 0.741, f1: 0.674, acc: 0.718
+2025-08-07 17:36:30: Epoch: 042 Test results, ap: 0.237, auc: 0.387, f1: 0.213, acc: 0.657
+2025-08-07 17:36:37: Epoch: 043 Train Loss: 0.077, ap: 0.730, auc: 0.734, f1: 0.667, acc: 0.709
+2025-08-07 17:36:39: Epoch: 043 Test results, ap: 0.250, auc: 0.437, f1: 0.217, acc: 0.667
+2025-08-07 17:36:46: Epoch: 044 Train Loss: 0.083, ap: 0.725, auc: 0.735, f1: 0.674, acc: 0.718
+2025-08-07 17:36:47: Epoch: 044 Test results, ap: 0.250, auc: 0.417, f1: 0.182, acc: 0.667
+2025-08-07 17:36:55: Epoch: 045 Train Loss: 0.078, ap: 0.705, auc: 0.732, f1: 0.636, acc: 0.689
+2025-08-07 17:36:56: Epoch: 045 Test results, ap: 0.225, auc: 0.370, f1: 0.178, acc: 0.657
+2025-08-07 17:37:03: Epoch: 046 Train Loss: 0.079, ap: 0.710, auc: 0.733, f1: 0.659, acc: 0.699
+2025-08-07 17:37:05: Epoch: 046 Test results, ap: 0.243, auc: 0.392, f1: 0.250, acc: 0.667
+2025-08-07 17:37:12: Epoch: 047 Train Loss: 0.077, ap: 0.728, auc: 0.731, f1: 0.644, acc: 0.699
+2025-08-07 17:37:13: Epoch: 047 Test results, ap: 0.248, auc: 0.412, f1: 0.178, acc: 0.657
+2025-08-07 17:37:21: Epoch: 048 Train Loss: 0.065, ap: 0.712, auc: 0.738, f1: 0.652, acc: 0.689
+2025-08-07 17:37:22: Epoch: 048 Test results, ap: 0.226, auc: 0.382, f1: 0.182, acc: 0.667
+2025-08-07 17:37:29: Epoch: 049 Train Loss: 0.059, ap: 0.699, auc: 0.739, f1: 0.659, acc: 0.699
+2025-08-07 17:37:31: Epoch: 049 Test results, ap: 0.208, auc: 0.346, f1: 0.170, acc: 0.639
+2025-08-07 17:37:38: Epoch: 050 Train Loss: 0.069, ap: 0.705, auc: 0.733, f1: 0.652, acc: 0.699
+2025-08-07 17:37:39: Epoch: 050 Test results, ap: 0.215, auc: 0.363, f1: 0.174, acc: 0.648
+2025-08-07 17:37:39: used time 0.12h
+2025-08-07 17:37:39: Running Cross Validation 1
+2025-08-07 17:37:39: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 17:37:47: Epoch: 001 Train Loss: 0.692, ap: 0.516, auc: 0.654, f1: 0.379, acc: 0.650
+2025-08-07 17:37:47: Epoch: 001 New best VALIDATION metrics
+2025-08-07 17:37:48: Epoch: 001 Test results, ap: 0.450, auc: 0.737, f1: 0.000, acc: 0.759
+2025-08-07 17:37:55: Epoch: 002 Train Loss: 0.658, ap: 0.633, auc: 0.659, f1: 0.492, acc: 0.680
+2025-08-07 17:37:55: Epoch: 002 New best VALIDATION metrics
+2025-08-07 17:37:57: Epoch: 002 Test results, ap: 0.275, auc: 0.616, f1: 0.000, acc: 0.722
+2025-08-07 17:38:04: Epoch: 003 Train Loss: 0.597, ap: 0.614, auc: 0.687, f1: 0.238, acc: 0.689
+2025-08-07 17:38:04: Epoch: 003 New best VALIDATION metrics
+2025-08-07 17:38:05: Epoch: 003 Test results, ap: 0.306, auc: 0.506, f1: 0.000, acc: 0.759
+2025-08-07 17:38:13: Epoch: 004 Train Loss: 0.555, ap: 0.596, auc: 0.625, f1: 0.456, acc: 0.699
+2025-08-07 17:38:14: Epoch: 004 Test results, ap: 0.243, auc: 0.358, f1: 0.000, acc: 0.750
+2025-08-07 17:38:21: Epoch: 005 Train Loss: 0.479, ap: 0.517, auc: 0.601, f1: 0.529, acc: 0.359
+2025-08-07 17:38:23: Epoch: 005 Test results, ap: 0.276, auc: 0.551, f1: 0.388, acc: 0.241
+2025-08-07 17:38:30: Epoch: 006 Train Loss: 0.435, ap: 0.530, auc: 0.631, f1: 0.532, acc: 0.641
+2025-08-07 17:38:31: Epoch: 006 Test results, ap: 0.223, auc: 0.368, f1: 0.125, acc: 0.741
+2025-08-07 17:38:39: Epoch: 007 Train Loss: 0.407, ap: 0.646, auc: 0.726, f1: 0.553, acc: 0.670
+2025-08-07 17:38:39: Epoch: 007 New best VALIDATION metrics
+2025-08-07 17:38:40: Epoch: 007 Test results, ap: 0.218, auc: 0.439, f1: 0.000, acc: 0.750
+2025-08-07 17:38:48: Epoch: 008 Train Loss: 0.382, ap: 0.559, auc: 0.663, f1: 0.379, acc: 0.650
+2025-08-07 17:38:49: Epoch: 008 Test results, ap: 0.210, auc: 0.348, f1: 0.000, acc: 0.759
+2025-08-07 17:38:56: Epoch: 009 Train Loss: 0.377, ap: 0.574, auc: 0.657, f1: 0.541, acc: 0.621
+2025-08-07 17:38:58: Epoch: 009 Test results, ap: 0.223, auc: 0.458, f1: 0.370, acc: 0.528
+2025-08-07 17:39:05: Epoch: 010 Train Loss: 0.338, ap: 0.568, auc: 0.655, f1: 0.505, acc: 0.505
+2025-08-07 17:39:07: Epoch: 010 Test results, ap: 0.325, auc: 0.540, f1: 0.330, acc: 0.287
+2025-08-07 17:39:14: Epoch: 011 Train Loss: 0.279, ap: 0.654, auc: 0.726, f1: 0.537, acc: 0.699
+2025-08-07 17:39:14: Epoch: 011 New best VALIDATION metrics
+2025-08-07 17:39:15: Epoch: 011 Test results, ap: 0.218, auc: 0.371, f1: 0.000, acc: 0.741
+2025-08-07 17:39:15: Epoch: 011 New best TEST metrics
+2025-08-07 17:39:22: Epoch: 012 Train Loss: 0.292, ap: 0.563, auc: 0.693, f1: 0.471, acc: 0.650
+2025-08-07 17:39:24: Epoch: 012 Test results, ap: 0.260, auc: 0.512, f1: 0.000, acc: 0.685
+2025-08-07 17:39:24: Epoch: 012 New best TEST metrics
+2025-08-07 17:39:31: Epoch: 013 Train Loss: 0.293, ap: 0.614, auc: 0.696, f1: 0.537, acc: 0.699
+2025-08-07 17:39:33: Epoch: 013 Test results, ap: 0.256, auc: 0.504, f1: 0.000, acc: 0.731
+2025-08-07 17:39:40: Epoch: 014 Train Loss: 0.267, ap: 0.491, auc: 0.643, f1: 0.535, acc: 0.612
+2025-08-07 17:39:41: Epoch: 014 Test results, ap: 0.250, auc: 0.500, f1: 0.370, acc: 0.528
+2025-08-07 17:39:49: Epoch: 015 Train Loss: 0.283, ap: 0.581, auc: 0.681, f1: 0.575, acc: 0.641
+2025-08-07 17:39:50: Epoch: 015 Test results, ap: 0.212, auc: 0.397, f1: 0.301, acc: 0.398
+2025-08-07 17:39:57: Epoch: 016 Train Loss: 0.276, ap: 0.525, auc: 0.676, f1: 0.589, acc: 0.621
+2025-08-07 17:39:59: Epoch: 016 Test results, ap: 0.243, auc: 0.491, f1: 0.400, acc: 0.583
+2025-08-07 17:40:06: Epoch: 017 Train Loss: 0.272, ap: 0.563, auc: 0.701, f1: 0.514, acc: 0.670
+2025-08-07 17:40:08: Epoch: 017 Test results, ap: 0.235, auc: 0.482, f1: 0.000, acc: 0.676
+2025-08-07 17:40:15: Epoch: 018 Train Loss: 0.238, ap: 0.579, auc: 0.691, f1: 0.506, acc: 0.621
+2025-08-07 17:40:16: Epoch: 018 Test results, ap: 0.235, auc: 0.486, f1: 0.000, acc: 0.630
+2025-08-07 17:40:24: Epoch: 019 Train Loss: 0.257, ap: 0.617, auc: 0.715, f1: 0.522, acc: 0.680
+2025-08-07 17:40:25: Epoch: 019 Test results, ap: 0.222, auc: 0.457, f1: 0.000, acc: 0.694
+2025-08-07 17:40:32: Epoch: 020 Train Loss: 0.230, ap: 0.606, auc: 0.702, f1: 0.613, acc: 0.718
+2025-08-07 17:40:34: Epoch: 020 Test results, ap: 0.291, auc: 0.496, f1: 0.229, acc: 0.750
+2025-08-07 17:40:34: Epoch: 020 New best TEST metrics
+2025-08-07 17:40:41: Epoch: 021 Train Loss: 0.218, ap: 0.568, auc: 0.673, f1: 0.550, acc: 0.650
+2025-08-07 17:40:43: Epoch: 021 Test results, ap: 0.252, auc: 0.507, f1: 0.195, acc: 0.694
+2025-08-07 17:40:50: Epoch: 022 Train Loss: 0.178, ap: 0.570, auc: 0.684, f1: 0.541, acc: 0.670
+2025-08-07 17:40:51: Epoch: 022 Test results, ap: 0.248, auc: 0.511, f1: 0.108, acc: 0.694
+2025-08-07 17:40:59: Epoch: 023 Train Loss: 0.190, ap: 0.578, auc: 0.683, f1: 0.560, acc: 0.680
+2025-08-07 17:41:00: Epoch: 023 Test results, ap: 0.247, auc: 0.512, f1: 0.057, acc: 0.694
+2025-08-07 17:41:08: Epoch: 024 Train Loss: 0.175, ap: 0.566, auc: 0.668, f1: 0.590, acc: 0.689
+2025-08-07 17:41:09: Epoch: 024 Test results, ap: 0.265, auc: 0.544, f1: 0.105, acc: 0.685
+2025-08-07 17:41:09: Epoch: 024 New best TEST metrics
+2025-08-07 17:41:16: Epoch: 025 Train Loss: 0.173, ap: 0.571, auc: 0.682, f1: 0.561, acc: 0.650
+2025-08-07 17:41:18: Epoch: 025 Test results, ap: 0.275, auc: 0.568, f1: 0.200, acc: 0.704
+2025-08-07 17:41:18: Epoch: 025 New best TEST metrics
+2025-08-07 17:41:25: Epoch: 026 Train Loss: 0.179, ap: 0.574, auc: 0.671, f1: 0.507, acc: 0.660
+2025-08-07 17:41:27: Epoch: 026 Test results, ap: 0.267, auc: 0.542, f1: 0.000, acc: 0.741
+2025-08-07 17:41:34: Epoch: 027 Train Loss: 0.176, ap: 0.567, auc: 0.683, f1: 0.561, acc: 0.650
+2025-08-07 17:41:35: Epoch: 027 Test results, ap: 0.274, auc: 0.571, f1: 0.108, acc: 0.694
+2025-08-07 17:41:35: Epoch: 027 New best TEST metrics
+2025-08-07 17:41:43: Epoch: 028 Train Loss: 0.155, ap: 0.535, auc: 0.647, f1: 0.526, acc: 0.650
+2025-08-07 17:41:44: Epoch: 028 Test results, ap: 0.291, auc: 0.600, f1: 0.105, acc: 0.685
+2025-08-07 17:41:44: Epoch: 028 New best TEST metrics
+2025-08-07 17:41:51: Epoch: 029 Train Loss: 0.167, ap: 0.585, auc: 0.679, f1: 0.528, acc: 0.670
+2025-08-07 17:41:53: Epoch: 029 Test results, ap: 0.241, auc: 0.501, f1: 0.000, acc: 0.722
+2025-08-07 17:42:00: Epoch: 030 Train Loss: 0.160, ap: 0.539, auc: 0.631, f1: 0.506, acc: 0.583
+2025-08-07 17:42:02: Epoch: 030 Test results, ap: 0.271, auc: 0.552, f1: 0.150, acc: 0.685
+2025-08-07 17:42:09: Epoch: 031 Train Loss: 0.141, ap: 0.573, auc: 0.673, f1: 0.526, acc: 0.650
+2025-08-07 17:42:10: Epoch: 031 Test results, ap: 0.264, auc: 0.549, f1: 0.000, acc: 0.694
+2025-08-07 17:42:18: Epoch: 032 Train Loss: 0.125, ap: 0.567, auc: 0.659, f1: 0.528, acc: 0.670
+2025-08-07 17:42:19: Epoch: 032 Test results, ap: 0.275, auc: 0.551, f1: 0.000, acc: 0.704
+2025-08-07 17:42:26: Epoch: 033 Train Loss: 0.136, ap: 0.545, auc: 0.632, f1: 0.528, acc: 0.670
+2025-08-07 17:42:28: Epoch: 033 Test results, ap: 0.292, auc: 0.573, f1: 0.114, acc: 0.713
+2025-08-07 17:42:35: Epoch: 034 Train Loss: 0.119, ap: 0.556, auc: 0.650, f1: 0.575, acc: 0.670
+2025-08-07 17:42:36: Epoch: 034 Test results, ap: 0.256, auc: 0.525, f1: 0.111, acc: 0.704
+2025-08-07 17:42:44: Epoch: 035 Train Loss: 0.103, ap: 0.550, auc: 0.644, f1: 0.537, acc: 0.631
+2025-08-07 17:42:45: Epoch: 035 Test results, ap: 0.274, auc: 0.554, f1: 0.190, acc: 0.685
+2025-08-07 17:42:53: Epoch: 036 Train Loss: 0.120, ap: 0.575, auc: 0.668, f1: 0.553, acc: 0.670
+2025-08-07 17:42:54: Epoch: 036 Test results, ap: 0.254, auc: 0.525, f1: 0.000, acc: 0.704
+2025-08-07 17:43:01: Epoch: 037 Train Loss: 0.115, ap: 0.554, auc: 0.640, f1: 0.514, acc: 0.650
+2025-08-07 17:43:03: Epoch: 037 Test results, ap: 0.292, auc: 0.586, f1: 0.108, acc: 0.694
+2025-08-07 17:43:10: Epoch: 038 Train Loss: 0.113, ap: 0.545, auc: 0.636, f1: 0.543, acc: 0.641
+2025-08-07 17:43:12: Epoch: 038 Test results, ap: 0.296, auc: 0.587, f1: 0.111, acc: 0.704
+2025-08-07 17:43:19: Epoch: 039 Train Loss: 0.091, ap: 0.546, auc: 0.642, f1: 0.519, acc: 0.621
+2025-08-07 17:43:20: Epoch: 039 Test results, ap: 0.270, auc: 0.549, f1: 0.000, acc: 0.704
+2025-08-07 17:43:28: Epoch: 040 Train Loss: 0.095, ap: 0.548, auc: 0.637, f1: 0.525, acc: 0.631
+2025-08-07 17:43:29: Epoch: 040 Test results, ap: 0.264, auc: 0.540, f1: 0.056, acc: 0.685
+2025-08-07 17:43:36: Epoch: 041 Train Loss: 0.093, ap: 0.543, auc: 0.633, f1: 0.506, acc: 0.621
+2025-08-07 17:43:38: Epoch: 041 Test results, ap: 0.291, auc: 0.562, f1: 0.158, acc: 0.704
+2025-08-07 17:43:45: Epoch: 042 Train Loss: 0.086, ap: 0.551, auc: 0.641, f1: 0.513, acc: 0.631
+2025-08-07 17:43:47: Epoch: 042 Test results, ap: 0.282, auc: 0.552, f1: 0.162, acc: 0.713
+2025-08-07 17:43:54: Epoch: 043 Train Loss: 0.077, ap: 0.543, auc: 0.635, f1: 0.525, acc: 0.631
+2025-08-07 17:43:55: Epoch: 043 Test results, ap: 0.279, auc: 0.547, f1: 0.108, acc: 0.694
+2025-08-07 17:44:03: Epoch: 044 Train Loss: 0.083, ap: 0.545, auc: 0.637, f1: 0.537, acc: 0.631
+2025-08-07 17:44:04: Epoch: 044 Test results, ap: 0.279, auc: 0.552, f1: 0.158, acc: 0.704
+2025-08-07 17:44:11: Epoch: 045 Train Loss: 0.072, ap: 0.528, auc: 0.622, f1: 0.543, acc: 0.641
+2025-08-07 17:44:13: Epoch: 045 Test results, ap: 0.309, auc: 0.556, f1: 0.129, acc: 0.750
+2025-08-07 17:44:20: Epoch: 046 Train Loss: 0.068, ap: 0.531, auc: 0.625, f1: 0.513, acc: 0.631
+2025-08-07 17:44:21: Epoch: 046 Test results, ap: 0.294, auc: 0.565, f1: 0.061, acc: 0.713
+2025-08-07 17:44:29: Epoch: 047 Train Loss: 0.065, ap: 0.538, auc: 0.628, f1: 0.519, acc: 0.641
+2025-08-07 17:44:30: Epoch: 047 Test results, ap: 0.301, auc: 0.552, f1: 0.069, acc: 0.750
+2025-08-07 17:44:37: Epoch: 048 Train Loss: 0.079, ap: 0.551, auc: 0.643, f1: 0.553, acc: 0.670
+2025-08-07 17:44:39: Epoch: 048 Test results, ap: 0.289, auc: 0.541, f1: 0.069, acc: 0.750
+2025-08-07 17:44:46: Epoch: 049 Train Loss: 0.066, ap: 0.543, auc: 0.636, f1: 0.550, acc: 0.650
+2025-08-07 17:44:48: Epoch: 049 Test results, ap: 0.284, auc: 0.546, f1: 0.069, acc: 0.750
+2025-08-07 17:44:55: Epoch: 050 Train Loss: 0.059, ap: 0.534, auc: 0.633, f1: 0.554, acc: 0.641
+2025-08-07 17:44:56: Epoch: 050 Test results, ap: 0.290, auc: 0.539, f1: 0.121, acc: 0.731
+2025-08-07 17:44:56: used time 0.12h
+2025-08-07 17:44:57: Running Cross Validation 2
+2025-08-07 17:44:57: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 17:45:04: Epoch: 001 Train Loss: 0.689, ap: 0.717, auc: 0.663, f1: 0.672, acc: 0.612
+2025-08-07 17:45:04: Epoch: 001 New best VALIDATION metrics
+2025-08-07 17:45:05: Epoch: 001 Test results, ap: 0.419, auc: 0.584, f1: 0.000, acc: 0.759
+2025-08-07 17:45:12: Epoch: 002 Train Loss: 0.674, ap: 0.631, auc: 0.662, f1: 0.697, acc: 0.612
+2025-08-07 17:45:14: Epoch: 002 Test results, ap: 0.246, auc: 0.492, f1: 0.000, acc: 0.722
+2025-08-07 17:45:21: Epoch: 003 Train Loss: 0.623, ap: 0.791, auc: 0.787, f1: 0.718, acc: 0.680
+2025-08-07 17:45:21: Epoch: 003 New best VALIDATION metrics
+2025-08-07 17:45:23: Epoch: 003 Test results, ap: 0.244, auc: 0.545, f1: 0.000, acc: 0.704
+2025-08-07 17:45:30: Epoch: 004 Train Loss: 0.585, ap: 0.695, auc: 0.742, f1: 0.564, acc: 0.670
+2025-08-07 17:45:31: Epoch: 004 Test results, ap: 0.209, auc: 0.408, f1: 0.000, acc: 0.759
+2025-08-07 17:45:39: Epoch: 005 Train Loss: 0.545, ap: 0.807, auc: 0.774, f1: 0.712, acc: 0.709
+2025-08-07 17:45:39: Epoch: 005 New best VALIDATION metrics
+2025-08-07 17:45:40: Epoch: 005 Test results, ap: 0.201, auc: 0.391, f1: 0.000, acc: 0.694
+2025-08-07 17:45:47: Epoch: 006 Train Loss: 0.521, ap: 0.757, auc: 0.730, f1: 0.723, acc: 0.680
+2025-08-07 17:45:49: Epoch: 006 Test results, ap: 0.181, auc: 0.341, f1: 0.361, acc: 0.278
+2025-08-07 17:45:56: Epoch: 007 Train Loss: 0.464, ap: 0.721, auc: 0.724, f1: 0.681, acc: 0.553
+2025-08-07 17:45:58: Epoch: 007 Test results, ap: 0.192, auc: 0.369, f1: 0.217, acc: 0.398
+2025-08-07 17:46:05: Epoch: 008 Train Loss: 0.453, ap: 0.785, auc: 0.735, f1: 0.521, acc: 0.660
+2025-08-07 17:46:06: Epoch: 008 Test results, ap: 0.238, auc: 0.451, f1: 0.000, acc: 0.759
+2025-08-07 17:46:14: Epoch: 009 Train Loss: 0.425, ap: 0.851, auc: 0.811, f1: 0.720, acc: 0.728
+2025-08-07 17:46:14: Epoch: 009 New best VALIDATION metrics
+2025-08-07 17:46:15: Epoch: 009 Test results, ap: 0.237, auc: 0.511, f1: 0.000, acc: 0.722
+2025-08-07 17:46:22: Epoch: 010 Train Loss: 0.367, ap: 0.768, auc: 0.710, f1: 0.652, acc: 0.553
+2025-08-07 17:46:24: Epoch: 010 Test results, ap: 0.185, auc: 0.360, f1: 0.253, acc: 0.398
+2025-08-07 17:46:31: Epoch: 011 Train Loss: 0.339, ap: 0.789, auc: 0.727, f1: 0.591, acc: 0.650
+2025-08-07 17:46:33: Epoch: 011 Test results, ap: 0.220, auc: 0.452, f1: 0.000, acc: 0.741
+2025-08-07 17:46:33: Epoch: 011 New best TEST metrics
+2025-08-07 17:46:40: Epoch: 012 Train Loss: 0.333, ap: 0.847, auc: 0.819, f1: 0.764, acc: 0.718
+2025-08-07 17:46:40: Epoch: 012 New best VALIDATION metrics
+2025-08-07 17:46:41: Epoch: 012 Test results, ap: 0.222, auc: 0.467, f1: 0.160, acc: 0.611
+2025-08-07 17:46:41: Epoch: 012 New best TEST metrics
+2025-08-07 17:46:49: Epoch: 013 Train Loss: 0.327, ap: 0.833, auc: 0.794, f1: 0.708, acc: 0.680
+2025-08-07 17:46:50: Epoch: 013 Test results, ap: 0.223, auc: 0.446, f1: 0.103, acc: 0.676
+2025-08-07 17:46:57: Epoch: 014 Train Loss: 0.262, ap: 0.805, auc: 0.774, f1: 0.732, acc: 0.709
+2025-08-07 17:46:59: Epoch: 014 Test results, ap: 0.214, auc: 0.451, f1: 0.049, acc: 0.639
+2025-08-07 17:47:06: Epoch: 015 Train Loss: 0.264, ap: 0.823, auc: 0.765, f1: 0.702, acc: 0.728
+2025-08-07 17:47:08: Epoch: 015 Test results, ap: 0.192, auc: 0.355, f1: 0.000, acc: 0.741
+2025-08-07 17:47:15: Epoch: 016 Train Loss: 0.279, ap: 0.833, auc: 0.805, f1: 0.738, acc: 0.670
+2025-08-07 17:47:16: Epoch: 016 Test results, ap: 0.207, auc: 0.428, f1: 0.304, acc: 0.491
+2025-08-07 17:47:24: Epoch: 017 Train Loss: 0.246, ap: 0.820, auc: 0.783, f1: 0.686, acc: 0.689
+2025-08-07 17:47:25: Epoch: 017 Test results, ap: 0.214, auc: 0.432, f1: 0.089, acc: 0.620
+2025-08-07 17:47:32: Epoch: 018 Train Loss: 0.227, ap: 0.805, auc: 0.766, f1: 0.688, acc: 0.709
+2025-08-07 17:47:34: Epoch: 018 Test results, ap: 0.223, auc: 0.427, f1: 0.000, acc: 0.741
+2025-08-07 17:47:41: Epoch: 019 Train Loss: 0.229, ap: 0.816, auc: 0.768, f1: 0.735, acc: 0.699
+2025-08-07 17:47:43: Epoch: 019 Test results, ap: 0.203, auc: 0.389, f1: 0.197, acc: 0.472
+2025-08-07 17:47:50: Epoch: 020 Train Loss: 0.246, ap: 0.767, auc: 0.696, f1: 0.575, acc: 0.641
+2025-08-07 17:47:51: Epoch: 020 Test results, ap: 0.215, auc: 0.417, f1: 0.000, acc: 0.750
+2025-08-07 17:47:59: Epoch: 021 Train Loss: 0.200, ap: 0.843, auc: 0.807, f1: 0.752, acc: 0.718
+2025-08-07 17:48:00: Epoch: 021 Test results, ap: 0.228, auc: 0.456, f1: 0.093, acc: 0.639
+2025-08-07 17:48:08: Epoch: 022 Train Loss: 0.178, ap: 0.835, auc: 0.801, f1: 0.748, acc: 0.718
+2025-08-07 17:48:09: Epoch: 022 Test results, ap: 0.247, auc: 0.466, f1: 0.316, acc: 0.639
+2025-08-07 17:48:09: Epoch: 022 New best TEST metrics
+2025-08-07 17:48:16: Epoch: 023 Train Loss: 0.189, ap: 0.822, auc: 0.788, f1: 0.733, acc: 0.689
+2025-08-07 17:48:18: Epoch: 023 Test results, ap: 0.265, auc: 0.488, f1: 0.255, acc: 0.620
+2025-08-07 17:48:18: Epoch: 023 New best TEST metrics
+2025-08-07 17:48:25: Epoch: 024 Train Loss: 0.186, ap: 0.822, auc: 0.774, f1: 0.724, acc: 0.718
+2025-08-07 17:48:27: Epoch: 024 Test results, ap: 0.241, auc: 0.432, f1: 0.216, acc: 0.731
+2025-08-07 17:48:34: Epoch: 025 Train Loss: 0.171, ap: 0.814, auc: 0.771, f1: 0.717, acc: 0.709
+2025-08-07 17:48:35: Epoch: 025 Test results, ap: 0.289, auc: 0.432, f1: 0.167, acc: 0.630
+2025-08-07 17:48:43: Epoch: 026 Train Loss: 0.144, ap: 0.817, auc: 0.781, f1: 0.709, acc: 0.689
+2025-08-07 17:48:44: Epoch: 026 Test results, ap: 0.203, auc: 0.403, f1: 0.118, acc: 0.583
+2025-08-07 17:48:51: Epoch: 027 Train Loss: 0.148, ap: 0.814, auc: 0.773, f1: 0.704, acc: 0.689
+2025-08-07 17:48:53: Epoch: 027 Test results, ap: 0.200, auc: 0.398, f1: 0.085, acc: 0.602
+2025-08-07 17:49:00: Epoch: 028 Train Loss: 0.130, ap: 0.822, auc: 0.781, f1: 0.733, acc: 0.738
+2025-08-07 17:49:02: Epoch: 028 Test results, ap: 0.218, auc: 0.409, f1: 0.103, acc: 0.676
+2025-08-07 17:49:09: Epoch: 029 Train Loss: 0.117, ap: 0.820, auc: 0.780, f1: 0.710, acc: 0.699
+2025-08-07 17:49:10: Epoch: 029 Test results, ap: 0.211, auc: 0.399, f1: 0.100, acc: 0.667
+2025-08-07 17:49:18: Epoch: 030 Train Loss: 0.112, ap: 0.835, auc: 0.787, f1: 0.710, acc: 0.699
+2025-08-07 17:49:19: Epoch: 030 Test results, ap: 0.204, auc: 0.402, f1: 0.098, acc: 0.657
+2025-08-07 17:49:26: Epoch: 031 Train Loss: 0.091, ap: 0.826, auc: 0.787, f1: 0.716, acc: 0.699
+2025-08-07 17:49:28: Epoch: 031 Test results, ap: 0.212, auc: 0.408, f1: 0.163, acc: 0.620
+2025-08-07 17:49:35: Epoch: 032 Train Loss: 0.088, ap: 0.825, auc: 0.784, f1: 0.721, acc: 0.699
+2025-08-07 17:49:36: Epoch: 032 Test results, ap: 0.214, auc: 0.408, f1: 0.160, acc: 0.611
+2025-08-07 17:49:44: Epoch: 033 Train Loss: 0.086, ap: 0.831, auc: 0.790, f1: 0.716, acc: 0.699
+2025-08-07 17:49:45: Epoch: 033 Test results, ap: 0.207, auc: 0.408, f1: 0.160, acc: 0.611
+2025-08-07 17:49:53: Epoch: 034 Train Loss: 0.083, ap: 0.825, auc: 0.779, f1: 0.719, acc: 0.689
+2025-08-07 17:49:54: Epoch: 034 Test results, ap: 0.213, auc: 0.409, f1: 0.154, acc: 0.593
+2025-08-07 17:50:01: Epoch: 035 Train Loss: 0.076, ap: 0.824, auc: 0.781, f1: 0.697, acc: 0.680
+2025-08-07 17:50:03: Epoch: 035 Test results, ap: 0.201, auc: 0.383, f1: 0.154, acc: 0.593
+2025-08-07 17:50:10: Epoch: 036 Train Loss: 0.081, ap: 0.825, auc: 0.785, f1: 0.714, acc: 0.689
+2025-08-07 17:50:11: Epoch: 036 Test results, ap: 0.198, auc: 0.386, f1: 0.151, acc: 0.583
+2025-08-07 17:50:19: Epoch: 037 Train Loss: 0.075, ap: 0.832, auc: 0.785, f1: 0.734, acc: 0.718
+2025-08-07 17:50:20: Epoch: 037 Test results, ap: 0.213, auc: 0.398, f1: 0.136, acc: 0.648
+2025-08-07 17:50:27: Epoch: 038 Train Loss: 0.083, ap: 0.819, auc: 0.780, f1: 0.724, acc: 0.718
+2025-08-07 17:50:29: Epoch: 038 Test results, ap: 0.209, auc: 0.388, f1: 0.095, acc: 0.648
+2025-08-07 17:50:36: Epoch: 039 Train Loss: 0.088, ap: 0.828, auc: 0.792, f1: 0.710, acc: 0.699
+2025-08-07 17:50:38: Epoch: 039 Test results, ap: 0.202, auc: 0.379, f1: 0.120, acc: 0.593
+2025-08-07 17:50:45: Epoch: 040 Train Loss: 0.071, ap: 0.826, auc: 0.789, f1: 0.743, acc: 0.718
+2025-08-07 17:50:46: Epoch: 040 Test results, ap: 0.196, auc: 0.376, f1: 0.145, acc: 0.565
+2025-08-07 17:50:53: Epoch: 041 Train Loss: 0.066, ap: 0.826, auc: 0.791, f1: 0.716, acc: 0.699
+2025-08-07 17:50:55: Epoch: 041 Test results, ap: 0.203, auc: 0.391, f1: 0.167, acc: 0.630
+2025-08-07 17:51:02: Epoch: 042 Train Loss: 0.074, ap: 0.830, auc: 0.789, f1: 0.717, acc: 0.709
+2025-08-07 17:51:04: Epoch: 042 Test results, ap: 0.214, auc: 0.384, f1: 0.140, acc: 0.657
+2025-08-07 17:51:11: Epoch: 043 Train Loss: 0.056, ap: 0.829, auc: 0.791, f1: 0.717, acc: 0.709
+2025-08-07 17:51:12: Epoch: 043 Test results, ap: 0.211, auc: 0.386, f1: 0.120, acc: 0.593
+2025-08-07 17:51:20: Epoch: 044 Train Loss: 0.057, ap: 0.827, auc: 0.792, f1: 0.722, acc: 0.709
+2025-08-07 17:51:21: Epoch: 044 Test results, ap: 0.207, auc: 0.389, f1: 0.154, acc: 0.593
+2025-08-07 17:51:28: Epoch: 045 Train Loss: 0.053, ap: 0.825, auc: 0.788, f1: 0.739, acc: 0.718
+2025-08-07 17:51:30: Epoch: 045 Test results, ap: 0.209, auc: 0.389, f1: 0.154, acc: 0.593
+2025-08-07 17:51:37: Epoch: 046 Train Loss: 0.067, ap: 0.833, auc: 0.791, f1: 0.699, acc: 0.699
+2025-08-07 17:51:38: Epoch: 046 Test results, ap: 0.213, auc: 0.389, f1: 0.100, acc: 0.667
+2025-08-07 17:51:46: Epoch: 047 Train Loss: 0.058, ap: 0.827, auc: 0.788, f1: 0.712, acc: 0.709
+2025-08-07 17:51:47: Epoch: 047 Test results, ap: 0.213, auc: 0.390, f1: 0.105, acc: 0.685
+2025-08-07 17:51:54: Epoch: 048 Train Loss: 0.052, ap: 0.821, auc: 0.784, f1: 0.729, acc: 0.718
+2025-08-07 17:51:56: Epoch: 048 Test results, ap: 0.206, auc: 0.375, f1: 0.120, acc: 0.593
+2025-08-07 17:52:03: Epoch: 049 Train Loss: 0.043, ap: 0.819, auc: 0.784, f1: 0.716, acc: 0.699
+2025-08-07 17:52:05: Epoch: 049 Test results, ap: 0.209, auc: 0.386, f1: 0.151, acc: 0.583
+2025-08-07 17:52:12: Epoch: 050 Train Loss: 0.045, ap: 0.825, auc: 0.790, f1: 0.717, acc: 0.709
+2025-08-07 17:52:13: Epoch: 050 Test results, ap: 0.215, auc: 0.404, f1: 0.130, acc: 0.630
+2025-08-07 17:52:13: used time 0.12h
+2025-08-07 17:52:13: Running Cross Validation 3
+2025-08-07 17:52:13: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 17:52:21: Epoch: 001 Train Loss: 0.690, ap: 0.665, auc: 0.720, f1: 0.584, acc: 0.447
+2025-08-07 17:52:21: Epoch: 001 New best VALIDATION metrics
+2025-08-07 17:52:22: Epoch: 001 Test results, ap: 0.211, auc: 0.402, f1: 0.321, acc: 0.296
+2025-08-07 17:52:29: Epoch: 002 Train Loss: 0.636, ap: 0.522, auc: 0.598, f1: 0.047, acc: 0.602
+2025-08-07 17:52:31: Epoch: 002 Test results, ap: 0.414, auc: 0.771, f1: 0.000, acc: 0.759
+2025-08-07 17:52:38: Epoch: 003 Train Loss: 0.601, ap: 0.549, auc: 0.649, f1: 0.000, acc: 0.592
+2025-08-07 17:52:40: Epoch: 003 Test results, ap: 0.187, auc: 0.359, f1: 0.000, acc: 0.759
+2025-08-07 17:52:47: Epoch: 004 Train Loss: 0.576, ap: 0.583, auc: 0.615, f1: 0.579, acc: 0.408
+2025-08-07 17:52:49: Epoch: 004 Test results, ap: 0.320, auc: 0.582, f1: 0.388, acc: 0.241
+2025-08-07 17:52:56: Epoch: 005 Train Loss: 0.502, ap: 0.702, auc: 0.729, f1: 0.609, acc: 0.476
+2025-08-07 17:52:56: Epoch: 005 New best VALIDATION metrics
+2025-08-07 17:52:57: Epoch: 005 Test results, ap: 0.232, auc: 0.446, f1: 0.324, acc: 0.343
+2025-08-07 17:53:05: Epoch: 006 Train Loss: 0.487, ap: 0.710, auc: 0.767, f1: 0.637, acc: 0.680
+2025-08-07 17:53:05: Epoch: 006 New best VALIDATION metrics
+2025-08-07 17:53:06: Epoch: 006 Test results, ap: 0.235, auc: 0.520, f1: 0.000, acc: 0.611
+2025-08-07 17:53:13: Epoch: 007 Train Loss: 0.473, ap: 0.719, auc: 0.749, f1: 0.478, acc: 0.660
+2025-08-07 17:53:15: Epoch: 007 Test results, ap: 0.243, auc: 0.545, f1: 0.000, acc: 0.759
+2025-08-07 17:53:22: Epoch: 008 Train Loss: 0.456, ap: 0.628, auc: 0.735, f1: 0.688, acc: 0.709
+2025-08-07 17:53:24: Epoch: 008 Test results, ap: 0.233, auc: 0.474, f1: 0.158, acc: 0.704
+2025-08-07 17:53:31: Epoch: 009 Train Loss: 0.404, ap: 0.761, auc: 0.791, f1: 0.724, acc: 0.718
+2025-08-07 17:53:31: Epoch: 009 New best VALIDATION metrics
+2025-08-07 17:53:32: Epoch: 009 Test results, ap: 0.230, auc: 0.449, f1: 0.367, acc: 0.296
+2025-08-07 17:53:40: Epoch: 010 Train Loss: 0.364, ap: 0.732, auc: 0.790, f1: 0.688, acc: 0.709
+2025-08-07 17:53:41: Epoch: 010 Test results, ap: 0.220, auc: 0.470, f1: 0.000, acc: 0.648
+2025-08-07 17:53:49: Epoch: 011 Train Loss: 0.378, ap: 0.710, auc: 0.784, f1: 0.708, acc: 0.728
+2025-08-07 17:53:50: Epoch: 011 Test results, ap: 0.264, auc: 0.497, f1: 0.214, acc: 0.593
+2025-08-07 17:53:50: Epoch: 011 New best TEST metrics
+2025-08-07 17:53:57: Epoch: 012 Train Loss: 0.301, ap: 0.785, auc: 0.816, f1: 0.716, acc: 0.738
+2025-08-07 17:53:57: Epoch: 012 New best VALIDATION metrics
+2025-08-07 17:53:59: Epoch: 012 Test results, ap: 0.247, auc: 0.520, f1: 0.301, acc: 0.528
+2025-08-07 17:53:59: Epoch: 012 New best TEST metrics
+2025-08-07 17:54:06: Epoch: 013 Train Loss: 0.271, ap: 0.732, auc: 0.799, f1: 0.680, acc: 0.680
+2025-08-07 17:54:08: Epoch: 013 Test results, ap: 0.241, auc: 0.519, f1: 0.219, acc: 0.537
+2025-08-07 17:54:15: Epoch: 014 Train Loss: 0.327, ap: 0.728, auc: 0.786, f1: 0.661, acc: 0.592
+2025-08-07 17:54:16: Epoch: 014 Test results, ap: 0.249, auc: 0.541, f1: 0.432, acc: 0.537
+2025-08-07 17:54:16: Epoch: 014 New best TEST metrics
+2025-08-07 17:54:24: Epoch: 015 Train Loss: 0.278, ap: 0.778, auc: 0.822, f1: 0.567, acc: 0.718
+2025-08-07 17:54:25: Epoch: 015 Test results, ap: 0.240, auc: 0.525, f1: 0.000, acc: 0.694
+2025-08-07 17:54:33: Epoch: 016 Train Loss: 0.249, ap: 0.766, auc: 0.797, f1: 0.659, acc: 0.728
+2025-08-07 17:54:34: Epoch: 016 Test results, ap: 0.248, auc: 0.541, f1: 0.222, acc: 0.611
+2025-08-07 17:54:41: Epoch: 017 Train Loss: 0.275, ap: 0.745, auc: 0.798, f1: 0.702, acc: 0.670
+2025-08-07 17:54:43: Epoch: 017 Test results, ap: 0.233, auc: 0.495, f1: 0.326, acc: 0.463
+2025-08-07 17:54:50: Epoch: 018 Train Loss: 0.212, ap: 0.751, auc: 0.796, f1: 0.667, acc: 0.728
+2025-08-07 17:54:52: Epoch: 018 Test results, ap: 0.242, auc: 0.515, f1: 0.226, acc: 0.556
+2025-08-07 17:54:59: Epoch: 019 Train Loss: 0.231, ap: 0.747, auc: 0.769, f1: 0.610, acc: 0.689
+2025-08-07 17:55:00: Epoch: 019 Test results, ap: 0.235, auc: 0.520, f1: 0.000, acc: 0.741
+2025-08-07 17:55:08: Epoch: 020 Train Loss: 0.244, ap: 0.748, auc: 0.796, f1: 0.673, acc: 0.660
+2025-08-07 17:55:09: Epoch: 020 Test results, ap: 0.233, auc: 0.478, f1: 0.333, acc: 0.444
+2025-08-07 17:55:16: Epoch: 021 Train Loss: 0.181, ap: 0.767, auc: 0.819, f1: 0.690, acc: 0.748
+2025-08-07 17:55:18: Epoch: 021 Test results, ap: 0.246, auc: 0.522, f1: 0.160, acc: 0.611
+2025-08-07 17:55:25: Epoch: 022 Train Loss: 0.153, ap: 0.743, auc: 0.801, f1: 0.696, acc: 0.728
+2025-08-07 17:55:27: Epoch: 022 Test results, ap: 0.234, auc: 0.489, f1: 0.192, acc: 0.611
+2025-08-07 17:55:34: Epoch: 023 Train Loss: 0.137, ap: 0.756, auc: 0.810, f1: 0.702, acc: 0.728
+2025-08-07 17:55:35: Epoch: 023 Test results, ap: 0.226, auc: 0.482, f1: 0.118, acc: 0.583
+2025-08-07 17:55:42: Epoch: 024 Train Loss: 0.138, ap: 0.746, auc: 0.790, f1: 0.699, acc: 0.699
+2025-08-07 17:55:44: Epoch: 024 Test results, ap: 0.229, auc: 0.488, f1: 0.267, acc: 0.593
+2025-08-07 17:55:51: Epoch: 025 Train Loss: 0.129, ap: 0.733, auc: 0.781, f1: 0.659, acc: 0.709
+2025-08-07 17:55:53: Epoch: 025 Test results, ap: 0.242, auc: 0.500, f1: 0.089, acc: 0.620
+2025-08-07 17:56:00: Epoch: 026 Train Loss: 0.137, ap: 0.708, auc: 0.765, f1: 0.688, acc: 0.718
+2025-08-07 17:56:01: Epoch: 026 Test results, ap: 0.267, auc: 0.519, f1: 0.246, acc: 0.602
+2025-08-07 17:56:09: Epoch: 027 Train Loss: 0.126, ap: 0.729, auc: 0.781, f1: 0.667, acc: 0.660
+2025-08-07 17:56:10: Epoch: 027 Test results, ap: 0.211, auc: 0.444, f1: 0.207, acc: 0.574
+2025-08-07 17:56:18: Epoch: 028 Train Loss: 0.117, ap: 0.726, auc: 0.779, f1: 0.659, acc: 0.699
+2025-08-07 17:56:19: Epoch: 028 Test results, ap: 0.232, auc: 0.469, f1: 0.226, acc: 0.620
+2025-08-07 17:56:26: Epoch: 029 Train Loss: 0.147, ap: 0.729, auc: 0.772, f1: 0.698, acc: 0.689
+2025-08-07 17:56:28: Epoch: 029 Test results, ap: 0.243, auc: 0.501, f1: 0.269, acc: 0.546
+2025-08-07 17:56:35: Epoch: 030 Train Loss: 0.134, ap: 0.746, auc: 0.777, f1: 0.615, acc: 0.660
+2025-08-07 17:56:37: Epoch: 030 Test results, ap: 0.232, auc: 0.502, f1: 0.170, acc: 0.639
+2025-08-07 17:56:44: Epoch: 031 Train Loss: 0.125, ap: 0.739, auc: 0.778, f1: 0.694, acc: 0.709
+2025-08-07 17:56:45: Epoch: 031 Test results, ap: 0.231, auc: 0.496, f1: 0.167, acc: 0.630
+2025-08-07 17:56:53: Epoch: 032 Train Loss: 0.101, ap: 0.728, auc: 0.776, f1: 0.706, acc: 0.709
+2025-08-07 17:56:54: Epoch: 032 Test results, ap: 0.231, auc: 0.495, f1: 0.207, acc: 0.574
+2025-08-07 17:57:02: Epoch: 033 Train Loss: 0.111, ap: 0.729, auc: 0.779, f1: 0.667, acc: 0.680
+2025-08-07 17:57:03: Epoch: 033 Test results, ap: 0.230, auc: 0.496, f1: 0.207, acc: 0.574
+2025-08-07 17:57:10: Epoch: 034 Train Loss: 0.110, ap: 0.729, auc: 0.778, f1: 0.646, acc: 0.670
+2025-08-07 17:57:12: Epoch: 034 Test results, ap: 0.227, auc: 0.487, f1: 0.185, acc: 0.593
+2025-08-07 17:57:19: Epoch: 035 Train Loss: 0.103, ap: 0.725, auc: 0.777, f1: 0.607, acc: 0.660
+2025-08-07 17:57:20: Epoch: 035 Test results, ap: 0.227, auc: 0.484, f1: 0.196, acc: 0.620
+2025-08-07 17:57:28: Epoch: 036 Train Loss: 0.094, ap: 0.719, auc: 0.775, f1: 0.645, acc: 0.680
+2025-08-07 17:57:29: Epoch: 036 Test results, ap: 0.229, auc: 0.486, f1: 0.196, acc: 0.620
+2025-08-07 17:57:36: Epoch: 037 Train Loss: 0.097, ap: 0.727, auc: 0.781, f1: 0.622, acc: 0.670
+2025-08-07 17:57:38: Epoch: 037 Test results, ap: 0.234, auc: 0.481, f1: 0.196, acc: 0.620
+2025-08-07 17:57:45: Epoch: 038 Train Loss: 0.081, ap: 0.714, auc: 0.769, f1: 0.622, acc: 0.670
+2025-08-07 17:57:47: Epoch: 038 Test results, ap: 0.244, auc: 0.480, f1: 0.196, acc: 0.620
+2025-08-07 17:57:54: Epoch: 039 Train Loss: 0.096, ap: 0.731, auc: 0.781, f1: 0.696, acc: 0.728
+2025-08-07 17:57:55: Epoch: 039 Test results, ap: 0.243, auc: 0.478, f1: 0.231, acc: 0.630
+2025-08-07 17:58:02: Epoch: 040 Train Loss: 0.076, ap: 0.730, auc: 0.783, f1: 0.714, acc: 0.728
+2025-08-07 17:58:04: Epoch: 040 Test results, ap: 0.225, auc: 0.476, f1: 0.189, acc: 0.602
+2025-08-07 17:58:11: Epoch: 041 Train Loss: 0.065, ap: 0.742, auc: 0.792, f1: 0.708, acc: 0.728
+2025-08-07 17:58:13: Epoch: 041 Test results, ap: 0.226, auc: 0.474, f1: 0.185, acc: 0.593
+2025-08-07 17:58:20: Epoch: 042 Train Loss: 0.088, ap: 0.730, auc: 0.783, f1: 0.688, acc: 0.718
+2025-08-07 17:58:21: Epoch: 042 Test results, ap: 0.230, auc: 0.471, f1: 0.185, acc: 0.593
+2025-08-07 17:58:29: Epoch: 043 Train Loss: 0.075, ap: 0.730, auc: 0.784, f1: 0.652, acc: 0.699
+2025-08-07 17:58:30: Epoch: 043 Test results, ap: 0.231, auc: 0.468, f1: 0.122, acc: 0.602
+2025-08-07 17:58:37: Epoch: 044 Train Loss: 0.076, ap: 0.733, auc: 0.786, f1: 0.714, acc: 0.728
+2025-08-07 17:58:39: Epoch: 044 Test results, ap: 0.229, auc: 0.462, f1: 0.120, acc: 0.593
+2025-08-07 17:58:46: Epoch: 045 Train Loss: 0.083, ap: 0.728, auc: 0.786, f1: 0.681, acc: 0.718
+2025-08-07 17:58:48: Epoch: 045 Test results, ap: 0.224, auc: 0.453, f1: 0.157, acc: 0.602
+2025-08-07 17:58:55: Epoch: 046 Train Loss: 0.077, ap: 0.730, auc: 0.787, f1: 0.651, acc: 0.709
+2025-08-07 17:58:56: Epoch: 046 Test results, ap: 0.229, auc: 0.463, f1: 0.122, acc: 0.602
+2025-08-07 17:59:04: Epoch: 047 Train Loss: 0.072, ap: 0.740, auc: 0.795, f1: 0.727, acc: 0.738
+2025-08-07 17:59:05: Epoch: 047 Test results, ap: 0.226, auc: 0.456, f1: 0.157, acc: 0.602
+2025-08-07 17:59:12: Epoch: 048 Train Loss: 0.071, ap: 0.735, auc: 0.788, f1: 0.696, acc: 0.728
+2025-08-07 17:59:14: Epoch: 048 Test results, ap: 0.226, auc: 0.455, f1: 0.218, acc: 0.602
+2025-08-07 17:59:21: Epoch: 049 Train Loss: 0.083, ap: 0.737, auc: 0.790, f1: 0.689, acc: 0.728
+2025-08-07 17:59:23: Epoch: 049 Test results, ap: 0.226, auc: 0.456, f1: 0.160, acc: 0.611
+2025-08-07 17:59:30: Epoch: 050 Train Loss: 0.073, ap: 0.737, auc: 0.790, f1: 0.717, acc: 0.748
+2025-08-07 17:59:31: Epoch: 050 Test results, ap: 0.224, auc: 0.450, f1: 0.160, acc: 0.611
+2025-08-07 17:59:31: used time 0.12h
+2025-08-07 17:59:32: Running Cross Validation 4
+2025-08-07 17:59:32: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 17:59:39: Epoch: 001 Train Loss: 0.687, ap: 0.638, auc: 0.693, f1: 0.413, acc: 0.641
+2025-08-07 17:59:39: Epoch: 001 New best VALIDATION metrics
+2025-08-07 17:59:40: Epoch: 001 Test results, ap: 0.178, auc: 0.328, f1: 0.000, acc: 0.759
+2025-08-07 17:59:48: Epoch: 002 Train Loss: 0.644, ap: 0.500, auc: 0.536, f1: 0.573, acc: 0.408
+2025-08-07 17:59:49: Epoch: 002 Test results, ap: 0.277, auc: 0.520, f1: 0.388, acc: 0.241
+2025-08-07 17:59:56: Epoch: 003 Train Loss: 0.604, ap: 0.657, auc: 0.731, f1: 0.414, acc: 0.670
+2025-08-07 17:59:56: Epoch: 003 New best VALIDATION metrics
+2025-08-07 17:59:58: Epoch: 003 Test results, ap: 0.196, auc: 0.390, f1: 0.000, acc: 0.759
+2025-08-07 18:00:05: Epoch: 004 Train Loss: 0.515, ap: 0.693, auc: 0.699, f1: 0.552, acc: 0.495
+2025-08-07 18:00:05: Epoch: 004 New best VALIDATION metrics
+2025-08-07 18:00:06: Epoch: 004 Test results, ap: 0.211, auc: 0.425, f1: 0.388, acc: 0.241
+2025-08-07 18:00:14: Epoch: 005 Train Loss: 0.465, ap: 0.588, auc: 0.668, f1: 0.174, acc: 0.631
+2025-08-07 18:00:15: Epoch: 005 Test results, ap: 0.305, auc: 0.494, f1: 0.000, acc: 0.759
+2025-08-07 18:00:23: Epoch: 006 Train Loss: 0.465, ap: 0.706, auc: 0.733, f1: 0.543, acc: 0.689
+2025-08-07 18:00:23: Epoch: 006 New best VALIDATION metrics
+2025-08-07 18:00:24: Epoch: 006 Test results, ap: 0.193, auc: 0.372, f1: 0.000, acc: 0.759
+2025-08-07 18:00:31: Epoch: 007 Train Loss: 0.448, ap: 0.717, auc: 0.708, f1: 0.444, acc: 0.709
+2025-08-07 18:00:33: Epoch: 007 Test results, ap: 0.171, auc: 0.290, f1: 0.000, acc: 0.759
+2025-08-07 18:00:40: Epoch: 008 Train Loss: 0.392, ap: 0.597, auc: 0.734, f1: 0.612, acc: 0.680
+2025-08-07 18:00:42: Epoch: 008 Test results, ap: 0.190, auc: 0.363, f1: 0.000, acc: 0.704
+2025-08-07 18:00:49: Epoch: 009 Train Loss: 0.394, ap: 0.703, auc: 0.712, f1: 0.571, acc: 0.621
+2025-08-07 18:00:50: Epoch: 009 Test results, ap: 0.234, auc: 0.502, f1: 0.211, acc: 0.583
+2025-08-07 18:00:58: Epoch: 010 Train Loss: 0.408, ap: 0.753, auc: 0.780, f1: 0.327, acc: 0.680
+2025-08-07 18:00:58: Epoch: 010 New best VALIDATION metrics
+2025-08-07 18:00:59: Epoch: 010 Test results, ap: 0.226, auc: 0.488, f1: 0.000, acc: 0.759
+2025-08-07 18:01:06: Epoch: 011 Train Loss: 0.347, ap: 0.662, auc: 0.730, f1: 0.630, acc: 0.670
+2025-08-07 18:01:08: Epoch: 011 Test results, ap: 0.171, auc: 0.294, f1: 0.043, acc: 0.583
+2025-08-07 18:01:08: Epoch: 011 New best TEST metrics
+2025-08-07 18:01:15: Epoch: 012 Train Loss: 0.289, ap: 0.692, auc: 0.755, f1: 0.659, acc: 0.728
+2025-08-07 18:01:17: Epoch: 012 Test results, ap: 0.187, auc: 0.353, f1: 0.000, acc: 0.722
+2025-08-07 18:01:17: Epoch: 012 New best TEST metrics
+2025-08-07 18:01:24: Epoch: 013 Train Loss: 0.282, ap: 0.653, auc: 0.729, f1: 0.608, acc: 0.699
+2025-08-07 18:01:26: Epoch: 013 Test results, ap: 0.185, auc: 0.343, f1: 0.000, acc: 0.731
+2025-08-07 18:01:33: Epoch: 014 Train Loss: 0.286, ap: 0.656, auc: 0.740, f1: 0.600, acc: 0.689
+2025-08-07 18:01:34: Epoch: 014 Test results, ap: 0.182, auc: 0.341, f1: 0.000, acc: 0.741
+2025-08-07 18:01:42: Epoch: 015 Train Loss: 0.249, ap: 0.656, auc: 0.734, f1: 0.642, acc: 0.718
+2025-08-07 18:01:43: Epoch: 015 Test results, ap: 0.195, auc: 0.388, f1: 0.000, acc: 0.685
+2025-08-07 18:01:43: Epoch: 015 New best TEST metrics
+2025-08-07 18:01:50: Epoch: 016 Train Loss: 0.223, ap: 0.671, auc: 0.745, f1: 0.478, acc: 0.660
+2025-08-07 18:01:52: Epoch: 016 Test results, ap: 0.246, auc: 0.524, f1: 0.000, acc: 0.759
+2025-08-07 18:01:52: Epoch: 016 New best TEST metrics
+2025-08-07 18:01:59: Epoch: 017 Train Loss: 0.222, ap: 0.602, auc: 0.700, f1: 0.538, acc: 0.650
+2025-08-07 18:02:01: Epoch: 017 Test results, ap: 0.219, auc: 0.465, f1: 0.000, acc: 0.750
+2025-08-07 18:02:08: Epoch: 018 Train Loss: 0.200, ap: 0.651, auc: 0.729, f1: 0.632, acc: 0.660
+2025-08-07 18:02:10: Epoch: 018 Test results, ap: 0.211, auc: 0.447, f1: 0.000, acc: 0.611
+2025-08-07 18:02:17: Epoch: 019 Train Loss: 0.192, ap: 0.593, auc: 0.720, f1: 0.500, acc: 0.670
+2025-08-07 18:02:18: Epoch: 019 Test results, ap: 0.211, auc: 0.441, f1: 0.000, acc: 0.759
+2025-08-07 18:02:26: Epoch: 020 Train Loss: 0.171, ap: 0.634, auc: 0.721, f1: 0.667, acc: 0.650
+2025-08-07 18:02:27: Epoch: 020 Test results, ap: 0.177, auc: 0.317, f1: 0.139, acc: 0.426
+2025-08-07 18:02:34: Epoch: 021 Train Loss: 0.176, ap: 0.661, auc: 0.718, f1: 0.597, acc: 0.699
+2025-08-07 18:02:36: Epoch: 021 Test results, ap: 0.207, auc: 0.425, f1: 0.000, acc: 0.731
+2025-08-07 18:02:43: Epoch: 022 Train Loss: 0.159, ap: 0.645, auc: 0.734, f1: 0.590, acc: 0.689
+2025-08-07 18:02:45: Epoch: 022 Test results, ap: 0.209, auc: 0.429, f1: 0.000, acc: 0.741
+2025-08-07 18:02:52: Epoch: 023 Train Loss: 0.153, ap: 0.615, auc: 0.717, f1: 0.571, acc: 0.680
+2025-08-07 18:02:54: Epoch: 023 Test results, ap: 0.196, auc: 0.398, f1: 0.000, acc: 0.741
+2025-08-07 18:03:01: Epoch: 024 Train Loss: 0.156, ap: 0.624, auc: 0.721, f1: 0.557, acc: 0.660
+2025-08-07 18:03:02: Epoch: 024 Test results, ap: 0.215, auc: 0.404, f1: 0.000, acc: 0.759
+2025-08-07 18:03:10: Epoch: 025 Train Loss: 0.130, ap: 0.601, auc: 0.707, f1: 0.598, acc: 0.621
+2025-08-07 18:03:11: Epoch: 025 Test results, ap: 0.184, auc: 0.351, f1: 0.071, acc: 0.519
+2025-08-07 18:03:18: Epoch: 026 Train Loss: 0.127, ap: 0.626, auc: 0.688, f1: 0.485, acc: 0.670
+2025-08-07 18:03:20: Epoch: 026 Test results, ap: 0.212, auc: 0.399, f1: 0.000, acc: 0.759
+2025-08-07 18:03:27: Epoch: 027 Train Loss: 0.128, ap: 0.616, auc: 0.701, f1: 0.588, acc: 0.660
+2025-08-07 18:03:29: Epoch: 027 Test results, ap: 0.186, auc: 0.356, f1: 0.000, acc: 0.639
+2025-08-07 18:03:36: Epoch: 028 Train Loss: 0.104, ap: 0.643, auc: 0.729, f1: 0.602, acc: 0.641
+2025-08-07 18:03:37: Epoch: 028 Test results, ap: 0.196, auc: 0.394, f1: 0.048, acc: 0.630
+2025-08-07 18:03:45: Epoch: 029 Train Loss: 0.110, ap: 0.598, auc: 0.674, f1: 0.514, acc: 0.650
+2025-08-07 18:03:46: Epoch: 029 Test results, ap: 0.201, auc: 0.371, f1: 0.000, acc: 0.759
+2025-08-07 18:03:53: Epoch: 030 Train Loss: 0.105, ap: 0.583, auc: 0.643, f1: 0.400, acc: 0.592
+2025-08-07 18:03:55: Epoch: 030 Test results, ap: 0.198, auc: 0.364, f1: 0.000, acc: 0.759
+2025-08-07 18:04:02: Epoch: 031 Train Loss: 0.099, ap: 0.616, auc: 0.673, f1: 0.519, acc: 0.641
+2025-08-07 18:04:04: Epoch: 031 Test results, ap: 0.195, auc: 0.371, f1: 0.000, acc: 0.713
+2025-08-07 18:04:11: Epoch: 032 Train Loss: 0.086, ap: 0.630, auc: 0.709, f1: 0.610, acc: 0.689
+2025-08-07 18:04:12: Epoch: 032 Test results, ap: 0.200, auc: 0.373, f1: 0.059, acc: 0.704
+2025-08-07 18:04:20: Epoch: 033 Train Loss: 0.094, ap: 0.618, auc: 0.688, f1: 0.541, acc: 0.670
+2025-08-07 18:04:21: Epoch: 033 Test results, ap: 0.252, auc: 0.404, f1: 0.074, acc: 0.769
+2025-08-07 18:04:29: Epoch: 034 Train Loss: 0.082, ap: 0.617, auc: 0.688, f1: 0.526, acc: 0.650
+2025-08-07 18:04:30: Epoch: 034 Test results, ap: 0.246, auc: 0.401, f1: 0.071, acc: 0.759
+2025-08-07 18:04:37: Epoch: 035 Train Loss: 0.087, ap: 0.623, auc: 0.677, f1: 0.507, acc: 0.660
+2025-08-07 18:04:39: Epoch: 035 Test results, ap: 0.242, auc: 0.394, f1: 0.000, acc: 0.759
+2025-08-07 18:04:46: Epoch: 036 Train Loss: 0.108, ap: 0.607, auc: 0.688, f1: 0.507, acc: 0.641
+2025-08-07 18:04:48: Epoch: 036 Test results, ap: 0.206, auc: 0.369, f1: 0.071, acc: 0.759
+2025-08-07 18:04:55: Epoch: 037 Train Loss: 0.083, ap: 0.618, auc: 0.686, f1: 0.471, acc: 0.650
+2025-08-07 18:04:56: Epoch: 037 Test results, ap: 0.230, auc: 0.388, f1: 0.000, acc: 0.759
+2025-08-07 18:05:04: Epoch: 038 Train Loss: 0.078, ap: 0.619, auc: 0.697, f1: 0.545, acc: 0.660
+2025-08-07 18:05:05: Epoch: 038 Test results, ap: 0.204, auc: 0.368, f1: 0.062, acc: 0.722
+2025-08-07 18:05:13: Epoch: 039 Train Loss: 0.071, ap: 0.632, auc: 0.690, f1: 0.528, acc: 0.670
+2025-08-07 18:05:14: Epoch: 039 Test results, ap: 0.232, auc: 0.379, f1: 0.000, acc: 0.759
+2025-08-07 18:05:21: Epoch: 040 Train Loss: 0.093, ap: 0.628, auc: 0.684, f1: 0.486, acc: 0.650
+2025-08-07 18:05:23: Epoch: 040 Test results, ap: 0.214, auc: 0.395, f1: 0.000, acc: 0.759
+2025-08-07 18:05:30: Epoch: 041 Train Loss: 0.066, ap: 0.628, auc: 0.680, f1: 0.507, acc: 0.660
+2025-08-07 18:05:32: Epoch: 041 Test results, ap: 0.201, auc: 0.391, f1: 0.000, acc: 0.741
+2025-08-07 18:05:39: Epoch: 042 Train Loss: 0.071, ap: 0.622, auc: 0.685, f1: 0.533, acc: 0.660
+2025-08-07 18:05:40: Epoch: 042 Test results, ap: 0.191, auc: 0.370, f1: 0.057, acc: 0.694
+2025-08-07 18:05:48: Epoch: 043 Train Loss: 0.074, ap: 0.635, auc: 0.703, f1: 0.553, acc: 0.670
+2025-08-07 18:05:49: Epoch: 043 Test results, ap: 0.196, auc: 0.362, f1: 0.069, acc: 0.750
+2025-08-07 18:05:56: Epoch: 044 Train Loss: 0.053, ap: 0.641, auc: 0.699, f1: 0.500, acc: 0.650
+2025-08-07 18:05:58: Epoch: 044 Test results, ap: 0.196, auc: 0.357, f1: 0.000, acc: 0.759
+2025-08-07 18:06:05: Epoch: 045 Train Loss: 0.066, ap: 0.631, auc: 0.697, f1: 0.541, acc: 0.670
+2025-08-07 18:06:06: Epoch: 045 Test results, ap: 0.193, auc: 0.353, f1: 0.000, acc: 0.741
+2025-08-07 18:06:14: Epoch: 046 Train Loss: 0.056, ap: 0.634, auc: 0.700, f1: 0.548, acc: 0.680
+2025-08-07 18:06:15: Epoch: 046 Test results, ap: 0.195, auc: 0.360, f1: 0.067, acc: 0.741
+2025-08-07 18:06:23: Epoch: 047 Train Loss: 0.064, ap: 0.630, auc: 0.690, f1: 0.521, acc: 0.660
+2025-08-07 18:06:24: Epoch: 047 Test results, ap: 0.188, auc: 0.357, f1: 0.062, acc: 0.722
+2025-08-07 18:06:31: Epoch: 048 Train Loss: 0.064, ap: 0.631, auc: 0.699, f1: 0.541, acc: 0.670
+2025-08-07 18:06:33: Epoch: 048 Test results, ap: 0.192, auc: 0.362, f1: 0.059, acc: 0.704
+2025-08-07 18:06:40: Epoch: 049 Train Loss: 0.063, ap: 0.631, auc: 0.692, f1: 0.486, acc: 0.650
+2025-08-07 18:06:41: Epoch: 049 Test results, ap: 0.204, auc: 0.386, f1: 0.000, acc: 0.750
+2025-08-07 18:06:49: Epoch: 050 Train Loss: 0.053, ap: 0.633, auc: 0.683, f1: 0.457, acc: 0.631
+2025-08-07 18:06:50: Epoch: 050 Test results, ap: 0.192, auc: 0.365, f1: 0.000, acc: 0.741
+2025-08-07 18:06:50: used time 0.12h
+2025-08-07 18:06:50: Cross Validation Finished!
+2025-08-07 18:06:50: Best validation perform list
+[[0.74039924 0.75683892 0.7157895  0.73786408]
+ [0.65418124 0.72563469 0.53731346 0.69902915]
+ [0.8465544  0.81900454 0.76422763 0.71844661]
+ [0.78533876 0.81615925 0.7157895  0.73786408]
+ [0.75322121 0.77970099 0.32653061 0.67961162]]
+2025-08-07 18:06:50: mean: [0.756 0.779 0.612 0.715]
+2025-08-07 18:06:50: std: [0.063 0.036 0.162 0.023]
+2025-08-07 18:06:50: Best test perform list
+[[0.29923779 0.50422138 0.1875     0.75925928]
+ [0.29134548 0.60037524 0.10526316 0.68518519]
+ [0.26479831 0.48827392 0.25454545 0.62037039]
+ [0.24876711 0.54080677 0.43181819 0.53703701]
+ [0.24592708 0.52392125 0.         0.75925928]]
+2025-08-07 18:06:50: mean: [0.27  0.532 0.196 0.672]
+2025-08-07 18:06:50: std: [0.022 0.039 0.145 0.085]
diff --git a/run-cls-simple/rn18-mlp-16-pcs-simple-768-oneway-ce-32-0.001-50/config.json b/run-cls-simple/rn18-mlp-16-pcs-simple-768-oneway-ce-32-0.001-50/config.json
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+++ b/run-cls-simple/rn18-mlp-16-pcs-simple-768-oneway-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": null, "channels": 16, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": true, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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diff --git a/run-cls-simple/rn18-mlp-16-pcs-simple-768-oneway-ce-32-0.001-50/result.txt b/run-cls-simple/rn18-mlp-16-pcs-simple-768-oneway-ce-32-0.001-50/result.txt
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@@ -0,0 +1,6 @@
+Valid
+0.7547462582588196,0.776338267326355,0.6810154438018798,0.6932038784027099
+0.06639154613188748,0.03614147358734211,0.05494382373436784,0.0903695335131998
+Test
+0.2932529658079147,0.5239211976528168,0.23540616184473037,0.5851851761341095
+0.05822028428321391,0.09025005198293931,0.14283547168020566,0.10977592310642756
diff --git a/run-cls-simple/rn18-mlp-16-pcs-simple-768-oneway-ce-32-0.001-50/training.log b/run-cls-simple/rn18-mlp-16-pcs-simple-768-oneway-ce-32-0.001-50/training.log
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@@ -0,0 +1,583 @@
+2025-08-07 16:51:49: saving_dir: ./run-cls/rn18-mlp-16-pcs-simple-768-oneway-ce-32-0.001-50
+2025-08-07 16:51:49: Loading Training Dataset
+2025-08-07 16:52:16: Loading Test Dataset
+2025-08-07 16:52:21: Running Cross Validation 0
+2025-08-07 16:52:21: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 16:52:29: Epoch: 001 Train Loss: 0.689, ap: 0.672, auc: 0.684, f1: 0.643, acc: 0.612
+2025-08-07 16:52:29: Epoch: 001 New best VALIDATION metrics
+2025-08-07 16:52:30: Epoch: 001 Test results, ap: 0.261, auc: 0.378, f1: 0.000, acc: 0.759
+2025-08-07 16:52:36: Epoch: 002 Train Loss: 0.646, ap: 0.651, auc: 0.687, f1: 0.000, acc: 0.544
+2025-08-07 16:52:37: Epoch: 002 Test results, ap: 0.296, auc: 0.652, f1: 0.000, acc: 0.759
+2025-08-07 16:52:44: Epoch: 003 Train Loss: 0.592, ap: 0.692, auc: 0.723, f1: 0.000, acc: 0.544
+2025-08-07 16:52:44: Epoch: 003 New best VALIDATION metrics
+2025-08-07 16:52:45: Epoch: 003 Test results, ap: 0.243, auc: 0.402, f1: 0.000, acc: 0.759
+2025-08-07 16:52:51: Epoch: 004 Train Loss: 0.582, ap: 0.618, auc: 0.722, f1: 0.678, acc: 0.631
+2025-08-07 16:52:53: Epoch: 004 Test results, ap: 0.232, auc: 0.497, f1: 0.364, acc: 0.287
+2025-08-07 16:52:59: Epoch: 005 Train Loss: 0.498, ap: 0.611, auc: 0.622, f1: 0.623, acc: 0.553
+2025-08-07 16:53:00: Epoch: 005 Test results, ap: 0.229, auc: 0.444, f1: 0.172, acc: 0.556
+2025-08-07 16:53:07: Epoch: 006 Train Loss: 0.542, ap: 0.687, auc: 0.747, f1: 0.612, acc: 0.680
+2025-08-07 16:53:07: Epoch: 006 New best VALIDATION metrics
+2025-08-07 16:53:08: Epoch: 006 Test results, ap: 0.200, auc: 0.383, f1: 0.000, acc: 0.759
+2025-08-07 16:53:14: Epoch: 007 Train Loss: 0.456, ap: 0.697, auc: 0.713, f1: 0.680, acc: 0.680
+2025-08-07 16:53:16: Epoch: 007 Test results, ap: 0.260, auc: 0.539, f1: 0.275, acc: 0.657
+2025-08-07 16:53:22: Epoch: 008 Train Loss: 0.390, ap: 0.627, auc: 0.638, f1: 0.343, acc: 0.553
+2025-08-07 16:53:23: Epoch: 008 Test results, ap: 0.416, auc: 0.567, f1: 0.000, acc: 0.759
+2025-08-07 16:53:30: Epoch: 009 Train Loss: 0.350, ap: 0.736, auc: 0.756, f1: 0.444, acc: 0.660
+2025-08-07 16:53:30: Epoch: 009 New best VALIDATION metrics
+2025-08-07 16:53:31: Epoch: 009 Test results, ap: 0.286, auc: 0.440, f1: 0.000, acc: 0.750
+2025-08-07 16:53:37: Epoch: 010 Train Loss: 0.409, ap: 0.714, auc: 0.757, f1: 0.333, acc: 0.612
+2025-08-07 16:53:38: Epoch: 010 Test results, ap: 0.246, auc: 0.443, f1: 0.000, acc: 0.759
+2025-08-07 16:53:45: Epoch: 011 Train Loss: 0.336, ap: 0.702, auc: 0.754, f1: 0.667, acc: 0.680
+2025-08-07 16:53:46: Epoch: 011 Test results, ap: 0.242, auc: 0.413, f1: 0.000, acc: 0.750
+2025-08-07 16:53:46: Epoch: 011 New best TEST metrics
+2025-08-07 16:53:52: Epoch: 012 Train Loss: 0.273, ap: 0.703, auc: 0.745, f1: 0.686, acc: 0.689
+2025-08-07 16:53:54: Epoch: 012 Test results, ap: 0.303, auc: 0.480, f1: 0.000, acc: 0.731
+2025-08-07 16:53:54: Epoch: 012 New best TEST metrics
+2025-08-07 16:54:00: Epoch: 013 Train Loss: 0.241, ap: 0.668, auc: 0.726, f1: 0.625, acc: 0.650
+2025-08-07 16:54:01: Epoch: 013 Test results, ap: 0.368, auc: 0.538, f1: 0.235, acc: 0.759
+2025-08-07 16:54:01: Epoch: 013 New best TEST metrics
+2025-08-07 16:54:08: Epoch: 014 Train Loss: 0.238, ap: 0.720, auc: 0.743, f1: 0.635, acc: 0.699
+2025-08-07 16:54:09: Epoch: 014 Test results, ap: 0.335, auc: 0.544, f1: 0.000, acc: 0.741
+2025-08-07 16:54:16: Epoch: 015 Train Loss: 0.252, ap: 0.699, auc: 0.733, f1: 0.659, acc: 0.699
+2025-08-07 16:54:17: Epoch: 015 Test results, ap: 0.291, auc: 0.483, f1: 0.000, acc: 0.731
+2025-08-07 16:54:24: Epoch: 016 Train Loss: 0.247, ap: 0.687, auc: 0.725, f1: 0.639, acc: 0.660
+2025-08-07 16:54:25: Epoch: 016 Test results, ap: 0.340, auc: 0.519, f1: 0.000, acc: 0.750
+2025-08-07 16:54:31: Epoch: 017 Train Loss: 0.224, ap: 0.727, auc: 0.772, f1: 0.688, acc: 0.718
+2025-08-07 16:54:31: Epoch: 017 New best VALIDATION metrics
+2025-08-07 16:54:32: Epoch: 017 Test results, ap: 0.307, auc: 0.520, f1: 0.000, acc: 0.731
+2025-08-07 16:54:39: Epoch: 018 Train Loss: 0.187, ap: 0.719, auc: 0.718, f1: 0.644, acc: 0.689
+2025-08-07 16:54:40: Epoch: 018 Test results, ap: 0.351, auc: 0.562, f1: 0.278, acc: 0.759
+2025-08-07 16:54:40: Epoch: 018 New best TEST metrics
+2025-08-07 16:54:47: Epoch: 019 Train Loss: 0.190, ap: 0.722, auc: 0.744, f1: 0.673, acc: 0.641
+2025-08-07 16:54:48: Epoch: 019 Test results, ap: 0.328, auc: 0.524, f1: 0.229, acc: 0.750
+2025-08-07 16:54:54: Epoch: 020 Train Loss: 0.200, ap: 0.726, auc: 0.775, f1: 0.708, acc: 0.728
+2025-08-07 16:54:54: Epoch: 020 New best VALIDATION metrics
+2025-08-07 16:54:56: Epoch: 020 Test results, ap: 0.256, auc: 0.473, f1: 0.000, acc: 0.722
+2025-08-07 16:55:02: Epoch: 021 Train Loss: 0.179, ap: 0.730, auc: 0.754, f1: 0.681, acc: 0.718
+2025-08-07 16:55:03: Epoch: 021 Test results, ap: 0.293, auc: 0.505, f1: 0.000, acc: 0.741
+2025-08-07 16:55:10: Epoch: 022 Train Loss: 0.167, ap: 0.732, auc: 0.752, f1: 0.660, acc: 0.689
+2025-08-07 16:55:11: Epoch: 022 Test results, ap: 0.294, auc: 0.506, f1: 0.000, acc: 0.741
+2025-08-07 16:55:18: Epoch: 023 Train Loss: 0.159, ap: 0.724, auc: 0.750, f1: 0.673, acc: 0.670
+2025-08-07 16:55:19: Epoch: 023 Test results, ap: 0.306, auc: 0.517, f1: 0.000, acc: 0.722
+2025-08-07 16:55:25: Epoch: 024 Train Loss: 0.152, ap: 0.735, auc: 0.763, f1: 0.644, acc: 0.689
+2025-08-07 16:55:27: Epoch: 024 Test results, ap: 0.316, auc: 0.528, f1: 0.000, acc: 0.750
+2025-08-07 16:55:33: Epoch: 025 Train Loss: 0.144, ap: 0.718, auc: 0.757, f1: 0.629, acc: 0.680
+2025-08-07 16:55:34: Epoch: 025 Test results, ap: 0.294, auc: 0.534, f1: 0.000, acc: 0.722
+2025-08-07 16:55:41: Epoch: 026 Train Loss: 0.139, ap: 0.704, auc: 0.743, f1: 0.625, acc: 0.650
+2025-08-07 16:55:42: Epoch: 026 Test results, ap: 0.309, auc: 0.556, f1: 0.000, acc: 0.722
+2025-08-07 16:55:49: Epoch: 027 Train Loss: 0.132, ap: 0.739, auc: 0.780, f1: 0.711, acc: 0.748
+2025-08-07 16:55:49: Epoch: 027 New best VALIDATION metrics
+2025-08-07 16:55:50: Epoch: 027 Test results, ap: 0.271, auc: 0.530, f1: 0.000, acc: 0.722
+2025-08-07 16:55:56: Epoch: 028 Train Loss: 0.121, ap: 0.712, auc: 0.750, f1: 0.585, acc: 0.670
+2025-08-07 16:55:57: Epoch: 028 Test results, ap: 0.313, auc: 0.580, f1: 0.000, acc: 0.759
+2025-08-07 16:56:04: Epoch: 029 Train Loss: 0.122, ap: 0.734, auc: 0.761, f1: 0.667, acc: 0.680
+2025-08-07 16:56:05: Epoch: 029 Test results, ap: 0.297, auc: 0.512, f1: 0.222, acc: 0.741
+2025-08-07 16:56:12: Epoch: 030 Train Loss: 0.107, ap: 0.742, auc: 0.772, f1: 0.693, acc: 0.699
+2025-08-07 16:56:13: Epoch: 030 Test results, ap: 0.271, auc: 0.513, f1: 0.114, acc: 0.713
+2025-08-07 16:56:20: Epoch: 031 Train Loss: 0.088, ap: 0.738, auc: 0.764, f1: 0.689, acc: 0.728
+2025-08-07 16:56:21: Epoch: 031 Test results, ap: 0.278, auc: 0.516, f1: 0.000, acc: 0.731
+2025-08-07 16:56:28: Epoch: 032 Train Loss: 0.105, ap: 0.730, auc: 0.759, f1: 0.612, acc: 0.680
+2025-08-07 16:56:29: Epoch: 032 Test results, ap: 0.277, auc: 0.501, f1: 0.000, acc: 0.741
+2025-08-07 16:56:35: Epoch: 033 Train Loss: 0.115, ap: 0.736, auc: 0.756, f1: 0.690, acc: 0.738
+2025-08-07 16:56:36: Epoch: 033 Test results, ap: 0.297, auc: 0.505, f1: 0.000, acc: 0.750
+2025-08-07 16:56:43: Epoch: 034 Train Loss: 0.092, ap: 0.728, auc: 0.754, f1: 0.638, acc: 0.670
+2025-08-07 16:56:44: Epoch: 034 Test results, ap: 0.318, auc: 0.507, f1: 0.000, acc: 0.750
+2025-08-07 16:56:51: Epoch: 035 Train Loss: 0.087, ap: 0.737, auc: 0.768, f1: 0.680, acc: 0.699
+2025-08-07 16:56:52: Epoch: 035 Test results, ap: 0.283, auc: 0.518, f1: 0.125, acc: 0.741
+2025-08-07 16:56:58: Epoch: 036 Train Loss: 0.103, ap: 0.744, auc: 0.781, f1: 0.723, acc: 0.748
+2025-08-07 16:56:58: Epoch: 036 New best VALIDATION metrics
+2025-08-07 16:57:00: Epoch: 036 Test results, ap: 0.275, auc: 0.508, f1: 0.125, acc: 0.741
+2025-08-07 16:57:06: Epoch: 037 Train Loss: 0.095, ap: 0.724, auc: 0.761, f1: 0.680, acc: 0.699
+2025-08-07 16:57:07: Epoch: 037 Test results, ap: 0.306, auc: 0.532, f1: 0.125, acc: 0.741
+2025-08-07 16:57:14: Epoch: 038 Train Loss: 0.090, ap: 0.732, auc: 0.778, f1: 0.681, acc: 0.718
+2025-08-07 16:57:15: Epoch: 038 Test results, ap: 0.268, auc: 0.477, f1: 0.000, acc: 0.731
+2025-08-07 16:57:22: Epoch: 039 Train Loss: 0.074, ap: 0.732, auc: 0.767, f1: 0.674, acc: 0.728
+2025-08-07 16:57:23: Epoch: 039 Test results, ap: 0.316, auc: 0.513, f1: 0.000, acc: 0.750
+2025-08-07 16:57:29: Epoch: 040 Train Loss: 0.089, ap: 0.743, auc: 0.772, f1: 0.696, acc: 0.728
+2025-08-07 16:57:31: Epoch: 040 Test results, ap: 0.313, auc: 0.513, f1: 0.121, acc: 0.731
+2025-08-07 16:57:37: Epoch: 041 Train Loss: 0.076, ap: 0.723, auc: 0.763, f1: 0.674, acc: 0.718
+2025-08-07 16:57:38: Epoch: 041 Test results, ap: 0.308, auc: 0.514, f1: 0.125, acc: 0.741
+2025-08-07 16:57:45: Epoch: 042 Train Loss: 0.075, ap: 0.729, auc: 0.764, f1: 0.697, acc: 0.738
+2025-08-07 16:57:46: Epoch: 042 Test results, ap: 0.314, auc: 0.531, f1: 0.125, acc: 0.741
+2025-08-07 16:57:53: Epoch: 043 Train Loss: 0.062, ap: 0.724, auc: 0.765, f1: 0.697, acc: 0.738
+2025-08-07 16:57:54: Epoch: 043 Test results, ap: 0.316, auc: 0.526, f1: 0.125, acc: 0.741
+2025-08-07 16:58:00: Epoch: 044 Train Loss: 0.065, ap: 0.724, auc: 0.766, f1: 0.682, acc: 0.728
+2025-08-07 16:58:01: Epoch: 044 Test results, ap: 0.298, auc: 0.531, f1: 0.125, acc: 0.741
+2025-08-07 16:58:08: Epoch: 045 Train Loss: 0.065, ap: 0.724, auc: 0.765, f1: 0.719, acc: 0.757
+2025-08-07 16:58:10: Epoch: 045 Test results, ap: 0.319, auc: 0.530, f1: 0.125, acc: 0.741
+2025-08-07 16:58:16: Epoch: 046 Train Loss: 0.073, ap: 0.726, auc: 0.769, f1: 0.703, acc: 0.738
+2025-08-07 16:58:17: Epoch: 046 Test results, ap: 0.314, auc: 0.536, f1: 0.125, acc: 0.741
+2025-08-07 16:58:24: Epoch: 047 Train Loss: 0.061, ap: 0.726, auc: 0.773, f1: 0.689, acc: 0.728
+2025-08-07 16:58:25: Epoch: 047 Test results, ap: 0.284, auc: 0.516, f1: 0.121, acc: 0.731
+2025-08-07 16:58:31: Epoch: 048 Train Loss: 0.055, ap: 0.727, auc: 0.771, f1: 0.696, acc: 0.728
+2025-08-07 16:58:32: Epoch: 048 Test results, ap: 0.320, auc: 0.541, f1: 0.176, acc: 0.741
+2025-08-07 16:58:39: Epoch: 049 Train Loss: 0.056, ap: 0.724, auc: 0.763, f1: 0.674, acc: 0.718
+2025-08-07 16:58:40: Epoch: 049 Test results, ap: 0.377, auc: 0.566, f1: 0.194, acc: 0.769
+2025-08-07 16:58:40: Epoch: 049 New best TEST metrics
+2025-08-07 16:58:47: Epoch: 050 Train Loss: 0.075, ap: 0.725, auc: 0.777, f1: 0.717, acc: 0.748
+2025-08-07 16:58:48: Epoch: 050 Test results, ap: 0.288, auc: 0.530, f1: 0.171, acc: 0.731
+2025-08-07 16:58:48: used time 0.11h
+2025-08-07 16:58:48: Running Cross Validation 1
+2025-08-07 16:58:48: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 16:58:55: Epoch: 001 Train Loss: 0.691, ap: 0.571, auc: 0.628, f1: 0.545, acc: 0.466
+2025-08-07 16:58:55: Epoch: 001 New best VALIDATION metrics
+2025-08-07 16:58:56: Epoch: 001 Test results, ap: 0.200, auc: 0.418, f1: 0.388, acc: 0.241
+2025-08-07 16:59:03: Epoch: 002 Train Loss: 0.655, ap: 0.480, auc: 0.618, f1: 0.518, acc: 0.350
+2025-08-07 16:59:04: Epoch: 002 Test results, ap: 0.418, auc: 0.610, f1: 0.388, acc: 0.241
+2025-08-07 16:59:11: Epoch: 003 Train Loss: 0.566, ap: 0.563, auc: 0.662, f1: 0.500, acc: 0.650
+2025-08-07 16:59:11: Epoch: 003 New best VALIDATION metrics
+2025-08-07 16:59:12: Epoch: 003 Test results, ap: 0.213, auc: 0.460, f1: 0.000, acc: 0.722
+2025-08-07 16:59:18: Epoch: 004 Train Loss: 0.525, ap: 0.571, auc: 0.714, f1: 0.600, acc: 0.650
+2025-08-07 16:59:18: Epoch: 004 New best VALIDATION metrics
+2025-08-07 16:59:20: Epoch: 004 Test results, ap: 0.211, auc: 0.402, f1: 0.000, acc: 0.713
+2025-08-07 16:59:26: Epoch: 005 Train Loss: 0.482, ap: 0.575, auc: 0.675, f1: 0.393, acc: 0.670
+2025-08-07 16:59:27: Epoch: 005 Test results, ap: 0.347, auc: 0.570, f1: 0.000, acc: 0.759
+2025-08-07 16:59:34: Epoch: 006 Train Loss: 0.465, ap: 0.498, auc: 0.627, f1: 0.533, acc: 0.388
+2025-08-07 16:59:35: Epoch: 006 Test results, ap: 0.161, auc: 0.239, f1: 0.388, acc: 0.241
+2025-08-07 16:59:42: Epoch: 007 Train Loss: 0.411, ap: 0.661, auc: 0.735, f1: 0.583, acc: 0.515
+2025-08-07 16:59:42: Epoch: 007 New best VALIDATION metrics
+2025-08-07 16:59:43: Epoch: 007 Test results, ap: 0.327, auc: 0.577, f1: 0.409, acc: 0.306
+2025-08-07 16:59:49: Epoch: 008 Train Loss: 0.379, ap: 0.633, auc: 0.686, f1: 0.514, acc: 0.650
+2025-08-07 16:59:51: Epoch: 008 Test results, ap: 0.204, auc: 0.316, f1: 0.190, acc: 0.685
+2025-08-07 16:59:57: Epoch: 009 Train Loss: 0.362, ap: 0.630, auc: 0.724, f1: 0.550, acc: 0.650
+2025-08-07 16:59:58: Epoch: 009 Test results, ap: 0.280, auc: 0.413, f1: 0.205, acc: 0.713
+2025-08-07 17:00:05: Epoch: 010 Train Loss: 0.330, ap: 0.621, auc: 0.701, f1: 0.532, acc: 0.641
+2025-08-07 17:00:06: Epoch: 010 Test results, ap: 0.247, auc: 0.439, f1: 0.222, acc: 0.741
+2025-08-07 17:00:13: Epoch: 011 Train Loss: 0.293, ap: 0.638, auc: 0.702, f1: 0.548, acc: 0.680
+2025-08-07 17:00:14: Epoch: 011 Test results, ap: 0.254, auc: 0.403, f1: 0.211, acc: 0.722
+2025-08-07 17:00:14: Epoch: 011 New best TEST metrics
+2025-08-07 17:00:20: Epoch: 012 Train Loss: 0.260, ap: 0.624, auc: 0.714, f1: 0.557, acc: 0.583
+2025-08-07 17:00:22: Epoch: 012 Test results, ap: 0.203, auc: 0.404, f1: 0.179, acc: 0.407
+2025-08-07 17:00:28: Epoch: 013 Train Loss: 0.241, ap: 0.581, auc: 0.680, f1: 0.603, acc: 0.718
+2025-08-07 17:00:29: Epoch: 013 Test results, ap: 0.274, auc: 0.389, f1: 0.250, acc: 0.778
+2025-08-07 17:00:29: Epoch: 013 New best TEST metrics
+2025-08-07 17:00:36: Epoch: 014 Train Loss: 0.223, ap: 0.616, auc: 0.713, f1: 0.581, acc: 0.621
+2025-08-07 17:00:37: Epoch: 014 Test results, ap: 0.216, auc: 0.399, f1: 0.192, acc: 0.611
+2025-08-07 17:00:43: Epoch: 015 Train Loss: 0.215, ap: 0.529, auc: 0.667, f1: 0.561, acc: 0.515
+2025-08-07 17:00:45: Epoch: 015 Test results, ap: 0.237, auc: 0.462, f1: 0.208, acc: 0.435
+2025-08-07 17:00:45: Epoch: 015 New best TEST metrics
+2025-08-07 17:00:51: Epoch: 016 Train Loss: 0.249, ap: 0.624, auc: 0.729, f1: 0.575, acc: 0.641
+2025-08-07 17:00:53: Epoch: 016 Test results, ap: 0.206, auc: 0.429, f1: 0.000, acc: 0.667
+2025-08-07 17:00:59: Epoch: 017 Train Loss: 0.171, ap: 0.630, auc: 0.709, f1: 0.535, acc: 0.680
+2025-08-07 17:01:00: Epoch: 017 Test results, ap: 0.282, auc: 0.422, f1: 0.250, acc: 0.778
+2025-08-07 17:01:00: Epoch: 017 New best TEST metrics
+2025-08-07 17:01:07: Epoch: 018 Train Loss: 0.187, ap: 0.564, auc: 0.688, f1: 0.547, acc: 0.534
+2025-08-07 17:01:08: Epoch: 018 Test results, ap: 0.235, auc: 0.463, f1: 0.306, acc: 0.454
+2025-08-07 17:01:15: Epoch: 019 Train Loss: 0.153, ap: 0.612, auc: 0.711, f1: 0.612, acc: 0.680
+2025-08-07 17:01:16: Epoch: 019 Test results, ap: 0.177, auc: 0.322, f1: 0.222, acc: 0.481
+2025-08-07 17:01:23: Epoch: 020 Train Loss: 0.158, ap: 0.574, auc: 0.708, f1: 0.602, acc: 0.680
+2025-08-07 17:01:24: Epoch: 020 Test results, ap: 0.179, auc: 0.329, f1: 0.229, acc: 0.500
+2025-08-07 17:01:30: Epoch: 021 Train Loss: 0.133, ap: 0.554, auc: 0.699, f1: 0.587, acc: 0.631
+2025-08-07 17:01:31: Epoch: 021 Test results, ap: 0.197, auc: 0.401, f1: 0.237, acc: 0.463
+2025-08-07 17:01:38: Epoch: 022 Train Loss: 0.113, ap: 0.580, auc: 0.699, f1: 0.612, acc: 0.680
+2025-08-07 17:01:39: Epoch: 022 Test results, ap: 0.211, auc: 0.399, f1: 0.174, acc: 0.472
+2025-08-07 17:01:46: Epoch: 023 Train Loss: 0.112, ap: 0.550, auc: 0.696, f1: 0.577, acc: 0.602
+2025-08-07 17:01:47: Epoch: 023 Test results, ap: 0.225, auc: 0.398, f1: 0.205, acc: 0.352
+2025-08-07 17:01:54: Epoch: 024 Train Loss: 0.096, ap: 0.553, auc: 0.694, f1: 0.568, acc: 0.602
+2025-08-07 17:01:55: Epoch: 024 Test results, ap: 0.265, auc: 0.406, f1: 0.227, acc: 0.370
+2025-08-07 17:02:01: Epoch: 025 Train Loss: 0.102, ap: 0.559, auc: 0.706, f1: 0.614, acc: 0.670
+2025-08-07 17:02:03: Epoch: 025 Test results, ap: 0.202, auc: 0.392, f1: 0.195, acc: 0.389
+2025-08-07 17:02:09: Epoch: 026 Train Loss: 0.100, ap: 0.615, auc: 0.691, f1: 0.560, acc: 0.680
+2025-08-07 17:02:10: Epoch: 026 Test results, ap: 0.257, auc: 0.404, f1: 0.195, acc: 0.694
+2025-08-07 17:02:17: Epoch: 027 Train Loss: 0.102, ap: 0.587, auc: 0.712, f1: 0.615, acc: 0.660
+2025-08-07 17:02:18: Epoch: 027 Test results, ap: 0.247, auc: 0.424, f1: 0.235, acc: 0.398
+2025-08-07 17:02:24: Epoch: 028 Train Loss: 0.093, ap: 0.596, auc: 0.707, f1: 0.614, acc: 0.670
+2025-08-07 17:02:26: Epoch: 028 Test results, ap: 0.267, auc: 0.423, f1: 0.211, acc: 0.444
+2025-08-07 17:02:32: Epoch: 029 Train Loss: 0.096, ap: 0.585, auc: 0.704, f1: 0.607, acc: 0.660
+2025-08-07 17:02:33: Epoch: 029 Test results, ap: 0.225, auc: 0.398, f1: 0.198, acc: 0.398
+2025-08-07 17:02:40: Epoch: 030 Train Loss: 0.096, ap: 0.583, auc: 0.686, f1: 0.568, acc: 0.631
+2025-08-07 17:02:41: Epoch: 030 Test results, ap: 0.327, auc: 0.483, f1: 0.237, acc: 0.463
+2025-08-07 17:02:41: Epoch: 030 New best TEST metrics
+2025-08-07 17:02:48: Epoch: 031 Train Loss: 0.081, ap: 0.580, auc: 0.690, f1: 0.578, acc: 0.631
+2025-08-07 17:02:49: Epoch: 031 Test results, ap: 0.228, auc: 0.450, f1: 0.275, acc: 0.463
+2025-08-07 17:02:55: Epoch: 032 Train Loss: 0.075, ap: 0.549, auc: 0.695, f1: 0.593, acc: 0.641
+2025-08-07 17:02:56: Epoch: 032 Test results, ap: 0.219, auc: 0.432, f1: 0.250, acc: 0.389
+2025-08-07 17:03:03: Epoch: 033 Train Loss: 0.077, ap: 0.584, auc: 0.693, f1: 0.571, acc: 0.621
+2025-08-07 17:03:04: Epoch: 033 Test results, ap: 0.239, auc: 0.443, f1: 0.235, acc: 0.398
+2025-08-07 17:03:11: Epoch: 034 Train Loss: 0.077, ap: 0.586, auc: 0.695, f1: 0.598, acc: 0.660
+2025-08-07 17:03:12: Epoch: 034 Test results, ap: 0.223, auc: 0.434, f1: 0.241, acc: 0.417
+2025-08-07 17:03:19: Epoch: 035 Train Loss: 0.055, ap: 0.584, auc: 0.697, f1: 0.612, acc: 0.680
+2025-08-07 17:03:20: Epoch: 035 Test results, ap: 0.205, auc: 0.403, f1: 0.211, acc: 0.444
+2025-08-07 17:03:26: Epoch: 036 Train Loss: 0.059, ap: 0.591, auc: 0.695, f1: 0.595, acc: 0.670
+2025-08-07 17:03:27: Epoch: 036 Test results, ap: 0.211, auc: 0.421, f1: 0.256, acc: 0.463
+2025-08-07 17:03:34: Epoch: 037 Train Loss: 0.039, ap: 0.585, auc: 0.686, f1: 0.575, acc: 0.641
+2025-08-07 17:03:35: Epoch: 037 Test results, ap: 0.231, auc: 0.453, f1: 0.235, acc: 0.398
+2025-08-07 17:03:42: Epoch: 038 Train Loss: 0.045, ap: 0.582, auc: 0.681, f1: 0.581, acc: 0.650
+2025-08-07 17:03:43: Epoch: 038 Test results, ap: 0.251, auc: 0.474, f1: 0.282, acc: 0.435
+2025-08-07 17:03:50: Epoch: 039 Train Loss: 0.056, ap: 0.578, auc: 0.696, f1: 0.605, acc: 0.670
+2025-08-07 17:03:51: Epoch: 039 Test results, ap: 0.222, auc: 0.432, f1: 0.244, acc: 0.426
+2025-08-07 17:03:57: Epoch: 040 Train Loss: 0.057, ap: 0.571, auc: 0.687, f1: 0.581, acc: 0.650
+2025-08-07 17:03:58: Epoch: 040 Test results, ap: 0.243, auc: 0.438, f1: 0.233, acc: 0.389
+2025-08-07 17:04:05: Epoch: 041 Train Loss: 0.039, ap: 0.575, auc: 0.687, f1: 0.591, acc: 0.650
+2025-08-07 17:04:06: Epoch: 041 Test results, ap: 0.234, auc: 0.444, f1: 0.230, acc: 0.380
+2025-08-07 17:04:12: Epoch: 042 Train Loss: 0.041, ap: 0.585, auc: 0.687, f1: 0.581, acc: 0.650
+2025-08-07 17:04:13: Epoch: 042 Test results, ap: 0.245, auc: 0.457, f1: 0.235, acc: 0.398
+2025-08-07 17:04:20: Epoch: 043 Train Loss: 0.037, ap: 0.578, auc: 0.686, f1: 0.588, acc: 0.660
+2025-08-07 17:04:21: Epoch: 043 Test results, ap: 0.247, auc: 0.457, f1: 0.235, acc: 0.398
+2025-08-07 17:04:28: Epoch: 044 Train Loss: 0.043, ap: 0.572, auc: 0.686, f1: 0.588, acc: 0.660
+2025-08-07 17:04:29: Epoch: 044 Test results, ap: 0.246, auc: 0.447, f1: 0.233, acc: 0.389
+2025-08-07 17:04:36: Epoch: 045 Train Loss: 0.045, ap: 0.575, auc: 0.685, f1: 0.588, acc: 0.660
+2025-08-07 17:04:37: Epoch: 045 Test results, ap: 0.245, auc: 0.449, f1: 0.217, acc: 0.398
+2025-08-07 17:04:44: Epoch: 046 Train Loss: 0.046, ap: 0.580, auc: 0.694, f1: 0.581, acc: 0.650
+2025-08-07 17:04:45: Epoch: 046 Test results, ap: 0.219, auc: 0.427, f1: 0.230, acc: 0.380
+2025-08-07 17:04:51: Epoch: 047 Train Loss: 0.034, ap: 0.578, auc: 0.695, f1: 0.581, acc: 0.650
+2025-08-07 17:04:52: Epoch: 047 Test results, ap: 0.219, auc: 0.424, f1: 0.214, acc: 0.389
+2025-08-07 17:04:59: Epoch: 048 Train Loss: 0.031, ap: 0.572, auc: 0.686, f1: 0.581, acc: 0.650
+2025-08-07 17:05:00: Epoch: 048 Test results, ap: 0.227, auc: 0.435, f1: 0.234, acc: 0.454
+2025-08-07 17:05:06: Epoch: 049 Train Loss: 0.035, ap: 0.573, auc: 0.686, f1: 0.581, acc: 0.650
+2025-08-07 17:05:07: Epoch: 049 Test results, ap: 0.227, auc: 0.434, f1: 0.234, acc: 0.454
+2025-08-07 17:05:14: Epoch: 050 Train Loss: 0.036, ap: 0.575, auc: 0.686, f1: 0.581, acc: 0.650
+2025-08-07 17:05:15: Epoch: 050 Test results, ap: 0.225, auc: 0.438, f1: 0.247, acc: 0.380
+2025-08-07 17:05:15: used time 0.11h
+2025-08-07 17:05:15: Running Cross Validation 2
+2025-08-07 17:05:15: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 17:05:22: Epoch: 001 Train Loss: 0.688, ap: 0.691, auc: 0.645, f1: 0.661, acc: 0.612
+2025-08-07 17:05:22: Epoch: 001 New best VALIDATION metrics
+2025-08-07 17:05:23: Epoch: 001 Test results, ap: 0.351, auc: 0.525, f1: 0.000, acc: 0.759
+2025-08-07 17:05:30: Epoch: 002 Train Loss: 0.668, ap: 0.613, auc: 0.641, f1: 0.577, acc: 0.602
+2025-08-07 17:05:31: Epoch: 002 Test results, ap: 0.214, auc: 0.440, f1: 0.000, acc: 0.759
+2025-08-07 17:05:38: Epoch: 003 Train Loss: 0.621, ap: 0.836, auc: 0.794, f1: 0.706, acc: 0.612
+2025-08-07 17:05:38: Epoch: 003 New best VALIDATION metrics
+2025-08-07 17:05:39: Epoch: 003 Test results, ap: 0.245, auc: 0.476, f1: 0.277, acc: 0.565
+2025-08-07 17:05:46: Epoch: 004 Train Loss: 0.581, ap: 0.761, auc: 0.706, f1: 0.578, acc: 0.660
+2025-08-07 17:05:47: Epoch: 004 Test results, ap: 0.272, auc: 0.559, f1: 0.000, acc: 0.759
+2025-08-07 17:05:53: Epoch: 005 Train Loss: 0.568, ap: 0.777, auc: 0.731, f1: 0.701, acc: 0.612
+2025-08-07 17:05:54: Epoch: 005 Test results, ap: 0.211, auc: 0.444, f1: 0.000, acc: 0.630
+2025-08-07 17:06:01: Epoch: 006 Train Loss: 0.526, ap: 0.772, auc: 0.720, f1: 0.349, acc: 0.602
+2025-08-07 17:06:02: Epoch: 006 Test results, ap: 0.198, auc: 0.335, f1: 0.000, acc: 0.759
+2025-08-07 17:06:09: Epoch: 007 Train Loss: 0.515, ap: 0.741, auc: 0.780, f1: 0.698, acc: 0.563
+2025-08-07 17:06:10: Epoch: 007 Test results, ap: 0.204, auc: 0.422, f1: 0.179, acc: 0.491
+2025-08-07 17:06:17: Epoch: 008 Train Loss: 0.455, ap: 0.778, auc: 0.780, f1: 0.705, acc: 0.699
+2025-08-07 17:06:18: Epoch: 008 Test results, ap: 0.245, auc: 0.475, f1: 0.000, acc: 0.759
+2025-08-07 17:06:24: Epoch: 009 Train Loss: 0.418, ap: 0.800, auc: 0.763, f1: 0.660, acc: 0.670
+2025-08-07 17:06:26: Epoch: 009 Test results, ap: 0.213, auc: 0.409, f1: 0.000, acc: 0.750
+2025-08-07 17:06:32: Epoch: 010 Train Loss: 0.389, ap: 0.812, auc: 0.788, f1: 0.719, acc: 0.621
+2025-08-07 17:06:33: Epoch: 010 Test results, ap: 0.199, auc: 0.355, f1: 0.174, acc: 0.648
+2025-08-07 17:06:40: Epoch: 011 Train Loss: 0.334, ap: 0.840, auc: 0.805, f1: 0.708, acc: 0.728
+2025-08-07 17:06:40: Epoch: 011 New best VALIDATION metrics
+2025-08-07 17:06:41: Epoch: 011 Test results, ap: 0.227, auc: 0.474, f1: 0.000, acc: 0.759
+2025-08-07 17:06:41: Epoch: 011 New best TEST metrics
+2025-08-07 17:06:47: Epoch: 012 Train Loss: 0.286, ap: 0.800, auc: 0.777, f1: 0.750, acc: 0.728
+2025-08-07 17:06:49: Epoch: 012 Test results, ap: 0.225, auc: 0.438, f1: 0.061, acc: 0.713
+2025-08-07 17:06:55: Epoch: 013 Train Loss: 0.251, ap: 0.734, auc: 0.769, f1: 0.723, acc: 0.680
+2025-08-07 17:06:57: Epoch: 013 Test results, ap: 0.179, auc: 0.306, f1: 0.000, acc: 0.713
+2025-08-07 17:07:03: Epoch: 014 Train Loss: 0.228, ap: 0.822, auc: 0.797, f1: 0.700, acc: 0.709
+2025-08-07 17:07:04: Epoch: 014 Test results, ap: 0.225, auc: 0.454, f1: 0.000, acc: 0.741
+2025-08-07 17:07:11: Epoch: 015 Train Loss: 0.204, ap: 0.759, auc: 0.778, f1: 0.738, acc: 0.689
+2025-08-07 17:07:12: Epoch: 015 Test results, ap: 0.251, auc: 0.533, f1: 0.174, acc: 0.648
+2025-08-07 17:07:12: Epoch: 015 New best TEST metrics
+2025-08-07 17:07:19: Epoch: 016 Train Loss: 0.243, ap: 0.747, auc: 0.738, f1: 0.680, acc: 0.699
+2025-08-07 17:07:20: Epoch: 016 Test results, ap: 0.246, auc: 0.520, f1: 0.000, acc: 0.759
+2025-08-07 17:07:26: Epoch: 017 Train Loss: 0.228, ap: 0.782, auc: 0.800, f1: 0.700, acc: 0.709
+2025-08-07 17:07:27: Epoch: 017 Test results, ap: 0.250, auc: 0.514, f1: 0.062, acc: 0.722
+2025-08-07 17:07:34: Epoch: 018 Train Loss: 0.198, ap: 0.828, auc: 0.796, f1: 0.713, acc: 0.718
+2025-08-07 17:07:36: Epoch: 018 Test results, ap: 0.259, auc: 0.515, f1: 0.125, acc: 0.741
+2025-08-07 17:07:42: Epoch: 019 Train Loss: 0.171, ap: 0.821, auc: 0.787, f1: 0.696, acc: 0.670
+2025-08-07 17:07:43: Epoch: 019 Test results, ap: 0.236, auc: 0.491, f1: 0.062, acc: 0.722
+2025-08-07 17:07:50: Epoch: 020 Train Loss: 0.180, ap: 0.722, auc: 0.781, f1: 0.744, acc: 0.699
+2025-08-07 17:07:51: Epoch: 020 Test results, ap: 0.225, auc: 0.465, f1: 0.054, acc: 0.676
+2025-08-07 17:07:58: Epoch: 021 Train Loss: 0.181, ap: 0.800, auc: 0.782, f1: 0.724, acc: 0.718
+2025-08-07 17:07:59: Epoch: 021 Test results, ap: 0.242, auc: 0.509, f1: 0.000, acc: 0.750
+2025-08-07 17:08:05: Epoch: 022 Train Loss: 0.149, ap: 0.776, auc: 0.778, f1: 0.722, acc: 0.709
+2025-08-07 17:08:06: Epoch: 022 Test results, ap: 0.244, auc: 0.515, f1: 0.065, acc: 0.731
+2025-08-07 17:08:13: Epoch: 023 Train Loss: 0.127, ap: 0.762, auc: 0.784, f1: 0.734, acc: 0.718
+2025-08-07 17:08:14: Epoch: 023 Test results, ap: 0.239, auc: 0.511, f1: 0.065, acc: 0.731
+2025-08-07 17:08:21: Epoch: 024 Train Loss: 0.133, ap: 0.765, auc: 0.779, f1: 0.722, acc: 0.709
+2025-08-07 17:08:22: Epoch: 024 Test results, ap: 0.239, auc: 0.511, f1: 0.067, acc: 0.741
+2025-08-07 17:08:28: Epoch: 025 Train Loss: 0.115, ap: 0.751, auc: 0.765, f1: 0.686, acc: 0.689
+2025-08-07 17:08:29: Epoch: 025 Test results, ap: 0.238, auc: 0.514, f1: 0.000, acc: 0.750
+2025-08-07 17:08:36: Epoch: 026 Train Loss: 0.136, ap: 0.779, auc: 0.768, f1: 0.646, acc: 0.670
+2025-08-07 17:08:37: Epoch: 026 Test results, ap: 0.233, auc: 0.458, f1: 0.000, acc: 0.759
+2025-08-07 17:08:44: Epoch: 027 Train Loss: 0.119, ap: 0.738, auc: 0.773, f1: 0.708, acc: 0.680
+2025-08-07 17:08:45: Epoch: 027 Test results, ap: 0.238, auc: 0.504, f1: 0.065, acc: 0.731
+2025-08-07 17:08:51: Epoch: 028 Train Loss: 0.145, ap: 0.753, auc: 0.768, f1: 0.667, acc: 0.680
+2025-08-07 17:08:52: Epoch: 028 Test results, ap: 0.243, auc: 0.528, f1: 0.067, acc: 0.741
+2025-08-07 17:08:59: Epoch: 029 Train Loss: 0.110, ap: 0.786, auc: 0.776, f1: 0.693, acc: 0.699
+2025-08-07 17:09:00: Epoch: 029 Test results, ap: 0.232, auc: 0.467, f1: 0.000, acc: 0.750
+2025-08-07 17:09:07: Epoch: 030 Train Loss: 0.113, ap: 0.757, auc: 0.773, f1: 0.680, acc: 0.680
+2025-08-07 17:09:08: Epoch: 030 Test results, ap: 0.229, auc: 0.469, f1: 0.065, acc: 0.731
+2025-08-07 17:09:14: Epoch: 031 Train Loss: 0.106, ap: 0.795, auc: 0.776, f1: 0.693, acc: 0.699
+2025-08-07 17:09:16: Epoch: 031 Test results, ap: 0.226, auc: 0.458, f1: 0.065, acc: 0.731
+2025-08-07 17:09:22: Epoch: 032 Train Loss: 0.102, ap: 0.762, auc: 0.771, f1: 0.686, acc: 0.689
+2025-08-07 17:09:23: Epoch: 032 Test results, ap: 0.226, auc: 0.456, f1: 0.065, acc: 0.731
+2025-08-07 17:09:30: Epoch: 033 Train Loss: 0.077, ap: 0.761, auc: 0.781, f1: 0.698, acc: 0.689
+2025-08-07 17:09:31: Epoch: 033 Test results, ap: 0.240, auc: 0.512, f1: 0.065, acc: 0.731
+2025-08-07 17:09:38: Epoch: 034 Train Loss: 0.085, ap: 0.759, auc: 0.780, f1: 0.699, acc: 0.699
+2025-08-07 17:09:39: Epoch: 034 Test results, ap: 0.237, auc: 0.507, f1: 0.065, acc: 0.731
+2025-08-07 17:09:45: Epoch: 035 Train Loss: 0.095, ap: 0.750, auc: 0.776, f1: 0.692, acc: 0.689
+2025-08-07 17:09:46: Epoch: 035 Test results, ap: 0.238, auc: 0.511, f1: 0.065, acc: 0.731
+2025-08-07 17:09:53: Epoch: 036 Train Loss: 0.093, ap: 0.759, auc: 0.773, f1: 0.694, acc: 0.709
+2025-08-07 17:09:54: Epoch: 036 Test results, ap: 0.238, auc: 0.503, f1: 0.069, acc: 0.750
+2025-08-07 17:10:00: Epoch: 037 Train Loss: 0.108, ap: 0.771, auc: 0.789, f1: 0.717, acc: 0.709
+2025-08-07 17:10:02: Epoch: 037 Test results, ap: 0.242, auc: 0.504, f1: 0.065, acc: 0.731
+2025-08-07 17:10:08: Epoch: 038 Train Loss: 0.101, ap: 0.745, auc: 0.777, f1: 0.699, acc: 0.699
+2025-08-07 17:10:09: Epoch: 038 Test results, ap: 0.244, auc: 0.518, f1: 0.065, acc: 0.731
+2025-08-07 17:10:16: Epoch: 039 Train Loss: 0.096, ap: 0.742, auc: 0.775, f1: 0.705, acc: 0.699
+2025-08-07 17:10:17: Epoch: 039 Test results, ap: 0.234, auc: 0.484, f1: 0.065, acc: 0.731
+2025-08-07 17:10:24: Epoch: 040 Train Loss: 0.081, ap: 0.727, auc: 0.769, f1: 0.709, acc: 0.689
+2025-08-07 17:10:25: Epoch: 040 Test results, ap: 0.228, auc: 0.479, f1: 0.061, acc: 0.713
+2025-08-07 17:10:31: Epoch: 041 Train Loss: 0.087, ap: 0.724, auc: 0.761, f1: 0.705, acc: 0.699
+2025-08-07 17:10:33: Epoch: 041 Test results, ap: 0.227, auc: 0.475, f1: 0.067, acc: 0.741
+2025-08-07 17:10:39: Epoch: 042 Train Loss: 0.084, ap: 0.736, auc: 0.766, f1: 0.713, acc: 0.718
+2025-08-07 17:10:40: Epoch: 042 Test results, ap: 0.233, auc: 0.485, f1: 0.069, acc: 0.750
+2025-08-07 17:10:47: Epoch: 043 Train Loss: 0.083, ap: 0.737, auc: 0.768, f1: 0.712, acc: 0.709
+2025-08-07 17:10:48: Epoch: 043 Test results, ap: 0.231, auc: 0.478, f1: 0.067, acc: 0.741
+2025-08-07 17:10:55: Epoch: 044 Train Loss: 0.073, ap: 0.737, auc: 0.764, f1: 0.718, acc: 0.718
+2025-08-07 17:10:56: Epoch: 044 Test results, ap: 0.234, auc: 0.492, f1: 0.069, acc: 0.750
+2025-08-07 17:11:02: Epoch: 045 Train Loss: 0.072, ap: 0.730, auc: 0.760, f1: 0.718, acc: 0.718
+2025-08-07 17:11:03: Epoch: 045 Test results, ap: 0.234, auc: 0.492, f1: 0.069, acc: 0.750
+2025-08-07 17:11:10: Epoch: 046 Train Loss: 0.071, ap: 0.736, auc: 0.761, f1: 0.718, acc: 0.718
+2025-08-07 17:11:11: Epoch: 046 Test results, ap: 0.229, auc: 0.462, f1: 0.069, acc: 0.750
+2025-08-07 17:11:18: Epoch: 047 Train Loss: 0.074, ap: 0.736, auc: 0.763, f1: 0.712, acc: 0.709
+2025-08-07 17:11:19: Epoch: 047 Test results, ap: 0.234, auc: 0.495, f1: 0.067, acc: 0.741
+2025-08-07 17:11:26: Epoch: 048 Train Loss: 0.069, ap: 0.731, auc: 0.761, f1: 0.725, acc: 0.728
+2025-08-07 17:11:27: Epoch: 048 Test results, ap: 0.238, auc: 0.505, f1: 0.069, acc: 0.750
+2025-08-07 17:11:33: Epoch: 049 Train Loss: 0.068, ap: 0.733, auc: 0.763, f1: 0.725, acc: 0.728
+2025-08-07 17:11:35: Epoch: 049 Test results, ap: 0.234, auc: 0.488, f1: 0.069, acc: 0.750
+2025-08-07 17:11:41: Epoch: 050 Train Loss: 0.061, ap: 0.745, auc: 0.767, f1: 0.745, acc: 0.748
+2025-08-07 17:11:42: Epoch: 050 Test results, ap: 0.238, auc: 0.489, f1: 0.069, acc: 0.750
+2025-08-07 17:11:42: used time 0.11h
+2025-08-07 17:11:42: Running Cross Validation 3
+2025-08-07 17:11:42: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 17:11:49: Epoch: 001 Train Loss: 0.690, ap: 0.689, auc: 0.721, f1: 0.565, acc: 0.641
+2025-08-07 17:11:49: Epoch: 001 New best VALIDATION metrics
+2025-08-07 17:11:51: Epoch: 001 Test results, ap: 0.245, auc: 0.544, f1: 0.000, acc: 0.704
+2025-08-07 17:11:57: Epoch: 002 Train Loss: 0.659, ap: 0.711, auc: 0.763, f1: 0.250, acc: 0.650
+2025-08-07 17:11:57: Epoch: 002 New best VALIDATION metrics
+2025-08-07 17:11:58: Epoch: 002 Test results, ap: 0.203, auc: 0.363, f1: 0.000, acc: 0.759
+2025-08-07 17:12:05: Epoch: 003 Train Loss: 0.591, ap: 0.501, auc: 0.562, f1: 0.218, acc: 0.583
+2025-08-07 17:12:06: Epoch: 003 Test results, ap: 0.181, auc: 0.337, f1: 0.000, acc: 0.759
+2025-08-07 17:12:13: Epoch: 004 Train Loss: 0.584, ap: 0.680, auc: 0.765, f1: 0.340, acc: 0.660
+2025-08-07 17:12:14: Epoch: 004 Test results, ap: 0.181, auc: 0.318, f1: 0.000, acc: 0.759
+2025-08-07 17:12:21: Epoch: 005 Train Loss: 0.503, ap: 0.676, auc: 0.714, f1: 0.588, acc: 0.456
+2025-08-07 17:12:22: Epoch: 005 Test results, ap: 0.206, auc: 0.363, f1: 0.388, acc: 0.241
+2025-08-07 17:12:28: Epoch: 006 Train Loss: 0.469, ap: 0.683, auc: 0.772, f1: 0.433, acc: 0.670
+2025-08-07 17:12:30: Epoch: 006 Test results, ap: 0.179, auc: 0.326, f1: 0.000, acc: 0.759
+2025-08-07 17:12:36: Epoch: 007 Train Loss: 0.447, ap: 0.668, auc: 0.785, f1: 0.611, acc: 0.505
+2025-08-07 17:12:37: Epoch: 007 Test results, ap: 0.185, auc: 0.328, f1: 0.388, acc: 0.241
+2025-08-07 17:12:44: Epoch: 008 Train Loss: 0.420, ap: 0.732, auc: 0.772, f1: 0.583, acc: 0.417
+2025-08-07 17:12:44: Epoch: 008 New best VALIDATION metrics
+2025-08-07 17:12:45: Epoch: 008 Test results, ap: 0.217, auc: 0.391, f1: 0.388, acc: 0.241
+2025-08-07 17:12:52: Epoch: 009 Train Loss: 0.421, ap: 0.730, auc: 0.794, f1: 0.476, acc: 0.680
+2025-08-07 17:12:52: Epoch: 009 New best VALIDATION metrics
+2025-08-07 17:12:53: Epoch: 009 Test results, ap: 0.174, auc: 0.300, f1: 0.000, acc: 0.759
+2025-08-07 17:12:59: Epoch: 010 Train Loss: 0.394, ap: 0.717, auc: 0.810, f1: 0.651, acc: 0.718
+2025-08-07 17:12:59: Epoch: 010 New best VALIDATION metrics
+2025-08-07 17:13:01: Epoch: 010 Test results, ap: 0.177, auc: 0.304, f1: 0.000, acc: 0.759
+2025-08-07 17:13:07: Epoch: 011 Train Loss: 0.343, ap: 0.770, auc: 0.797, f1: 0.680, acc: 0.689
+2025-08-07 17:13:07: Epoch: 011 New best VALIDATION metrics
+2025-08-07 17:13:09: Epoch: 011 Test results, ap: 0.194, auc: 0.331, f1: 0.059, acc: 0.704
+2025-08-07 17:13:09: Epoch: 011 New best TEST metrics
+2025-08-07 17:13:15: Epoch: 012 Train Loss: 0.324, ap: 0.795, auc: 0.817, f1: 0.706, acc: 0.709
+2025-08-07 17:13:15: Epoch: 012 New best VALIDATION metrics
+2025-08-07 17:13:17: Epoch: 012 Test results, ap: 0.186, auc: 0.292, f1: 0.034, acc: 0.481
+2025-08-07 17:13:23: Epoch: 013 Train Loss: 0.295, ap: 0.818, auc: 0.825, f1: 0.731, acc: 0.757
+2025-08-07 17:13:23: Epoch: 013 New best VALIDATION metrics
+2025-08-07 17:13:24: Epoch: 013 Test results, ap: 0.170, auc: 0.291, f1: 0.000, acc: 0.583
+2025-08-07 17:13:31: Epoch: 014 Train Loss: 0.277, ap: 0.789, auc: 0.819, f1: 0.727, acc: 0.738
+2025-08-07 17:13:32: Epoch: 014 Test results, ap: 0.208, auc: 0.341, f1: 0.163, acc: 0.620
+2025-08-07 17:13:32: Epoch: 014 New best TEST metrics
+2025-08-07 17:13:39: Epoch: 015 Train Loss: 0.276, ap: 0.798, auc: 0.822, f1: 0.737, acc: 0.757
+2025-08-07 17:13:40: Epoch: 015 Test results, ap: 0.168, auc: 0.273, f1: 0.040, acc: 0.556
+2025-08-07 17:13:47: Epoch: 016 Train Loss: 0.280, ap: 0.766, auc: 0.798, f1: 0.683, acc: 0.748
+2025-08-07 17:13:48: Epoch: 016 Test results, ap: 0.178, auc: 0.325, f1: 0.000, acc: 0.583
+2025-08-07 17:13:54: Epoch: 017 Train Loss: 0.234, ap: 0.755, auc: 0.821, f1: 0.716, acc: 0.738
+2025-08-07 17:13:56: Epoch: 017 Test results, ap: 0.188, auc: 0.363, f1: 0.000, acc: 0.556
+2025-08-07 17:13:56: Epoch: 017 New best TEST metrics
+2025-08-07 17:14:02: Epoch: 018 Train Loss: 0.236, ap: 0.786, auc: 0.811, f1: 0.697, acc: 0.738
+2025-08-07 17:14:03: Epoch: 018 Test results, ap: 0.187, auc: 0.366, f1: 0.000, acc: 0.630
+2025-08-07 17:14:03: Epoch: 018 New best TEST metrics
+2025-08-07 17:14:10: Epoch: 019 Train Loss: 0.210, ap: 0.732, auc: 0.819, f1: 0.731, acc: 0.757
+2025-08-07 17:14:11: Epoch: 019 Test results, ap: 0.196, auc: 0.337, f1: 0.000, acc: 0.759
+2025-08-07 17:14:18: Epoch: 020 Train Loss: 0.207, ap: 0.734, auc: 0.792, f1: 0.727, acc: 0.738
+2025-08-07 17:14:19: Epoch: 020 Test results, ap: 0.182, auc: 0.341, f1: 0.000, acc: 0.731
+2025-08-07 17:14:26: Epoch: 021 Train Loss: 0.211, ap: 0.714, auc: 0.790, f1: 0.720, acc: 0.728
+2025-08-07 17:14:27: Epoch: 021 Test results, ap: 0.182, auc: 0.331, f1: 0.042, acc: 0.574
+2025-08-07 17:14:33: Epoch: 022 Train Loss: 0.179, ap: 0.723, auc: 0.803, f1: 0.711, acc: 0.748
+2025-08-07 17:14:35: Epoch: 022 Test results, ap: 0.179, auc: 0.304, f1: 0.000, acc: 0.731
+2025-08-07 17:14:41: Epoch: 023 Train Loss: 0.167, ap: 0.712, auc: 0.805, f1: 0.753, acc: 0.777
+2025-08-07 17:14:42: Epoch: 023 Test results, ap: 0.182, auc: 0.303, f1: 0.000, acc: 0.750
+2025-08-07 17:14:49: Epoch: 024 Train Loss: 0.153, ap: 0.728, auc: 0.806, f1: 0.731, acc: 0.757
+2025-08-07 17:14:50: Epoch: 024 Test results, ap: 0.177, auc: 0.292, f1: 0.000, acc: 0.750
+2025-08-07 17:14:57: Epoch: 025 Train Loss: 0.151, ap: 0.748, auc: 0.813, f1: 0.758, acc: 0.777
+2025-08-07 17:14:58: Epoch: 025 Test results, ap: 0.179, auc: 0.286, f1: 0.067, acc: 0.741
+2025-08-07 17:15:04: Epoch: 026 Train Loss: 0.147, ap: 0.711, auc: 0.788, f1: 0.702, acc: 0.728
+2025-08-07 17:15:06: Epoch: 026 Test results, ap: 0.175, auc: 0.311, f1: 0.000, acc: 0.546
+2025-08-07 17:15:12: Epoch: 027 Train Loss: 0.130, ap: 0.722, auc: 0.801, f1: 0.697, acc: 0.738
+2025-08-07 17:15:13: Epoch: 027 Test results, ap: 0.174, auc: 0.310, f1: 0.000, acc: 0.713
+2025-08-07 17:15:20: Epoch: 028 Train Loss: 0.125, ap: 0.723, auc: 0.788, f1: 0.697, acc: 0.738
+2025-08-07 17:15:21: Epoch: 028 Test results, ap: 0.210, auc: 0.382, f1: 0.073, acc: 0.528
+2025-08-07 17:15:21: Epoch: 028 New best TEST metrics
+2025-08-07 17:15:28: Epoch: 029 Train Loss: 0.130, ap: 0.689, auc: 0.790, f1: 0.722, acc: 0.738
+2025-08-07 17:15:29: Epoch: 029 Test results, ap: 0.186, auc: 0.287, f1: 0.059, acc: 0.704
+2025-08-07 17:15:36: Epoch: 030 Train Loss: 0.123, ap: 0.685, auc: 0.782, f1: 0.747, acc: 0.777
+2025-08-07 17:15:37: Epoch: 030 Test results, ap: 0.206, auc: 0.370, f1: 0.069, acc: 0.750
+2025-08-07 17:15:43: Epoch: 031 Train Loss: 0.097, ap: 0.708, auc: 0.792, f1: 0.747, acc: 0.777
+2025-08-07 17:15:44: Epoch: 031 Test results, ap: 0.198, auc: 0.342, f1: 0.071, acc: 0.759
+2025-08-07 17:15:51: Epoch: 032 Train Loss: 0.089, ap: 0.714, auc: 0.797, f1: 0.747, acc: 0.777
+2025-08-07 17:15:52: Epoch: 032 Test results, ap: 0.188, auc: 0.297, f1: 0.071, acc: 0.759
+2025-08-07 17:15:59: Epoch: 033 Train Loss: 0.088, ap: 0.686, auc: 0.781, f1: 0.739, acc: 0.767
+2025-08-07 17:16:00: Epoch: 033 Test results, ap: 0.186, auc: 0.285, f1: 0.071, acc: 0.759
+2025-08-07 17:16:07: Epoch: 034 Train Loss: 0.066, ap: 0.688, auc: 0.774, f1: 0.717, acc: 0.748
+2025-08-07 17:16:08: Epoch: 034 Test results, ap: 0.190, auc: 0.310, f1: 0.057, acc: 0.694
+2025-08-07 17:16:15: Epoch: 035 Train Loss: 0.079, ap: 0.725, auc: 0.791, f1: 0.731, acc: 0.757
+2025-08-07 17:16:16: Epoch: 035 Test results, ap: 0.183, auc: 0.273, f1: 0.069, acc: 0.750
+2025-08-07 17:16:22: Epoch: 036 Train Loss: 0.076, ap: 0.719, auc: 0.793, f1: 0.745, acc: 0.767
+2025-08-07 17:16:24: Epoch: 036 Test results, ap: 0.181, auc: 0.260, f1: 0.069, acc: 0.750
+2025-08-07 17:16:30: Epoch: 037 Train Loss: 0.071, ap: 0.695, auc: 0.779, f1: 0.719, acc: 0.757
+2025-08-07 17:16:31: Epoch: 037 Test results, ap: 0.189, auc: 0.298, f1: 0.069, acc: 0.750
+2025-08-07 17:16:38: Epoch: 038 Train Loss: 0.061, ap: 0.692, auc: 0.777, f1: 0.731, acc: 0.757
+2025-08-07 17:16:39: Epoch: 038 Test results, ap: 0.185, auc: 0.284, f1: 0.057, acc: 0.694
+2025-08-07 17:16:45: Epoch: 039 Train Loss: 0.064, ap: 0.709, auc: 0.786, f1: 0.731, acc: 0.757
+2025-08-07 17:16:47: Epoch: 039 Test results, ap: 0.203, auc: 0.275, f1: 0.067, acc: 0.741
+2025-08-07 17:16:53: Epoch: 040 Train Loss: 0.074, ap: 0.737, auc: 0.795, f1: 0.723, acc: 0.777
+2025-08-07 17:16:55: Epoch: 040 Test results, ap: 0.207, auc: 0.295, f1: 0.071, acc: 0.759
+2025-08-07 17:17:01: Epoch: 041 Train Loss: 0.059, ap: 0.743, auc: 0.790, f1: 0.742, acc: 0.777
+2025-08-07 17:17:02: Epoch: 041 Test results, ap: 0.204, auc: 0.282, f1: 0.061, acc: 0.713
+2025-08-07 17:17:09: Epoch: 042 Train Loss: 0.047, ap: 0.742, auc: 0.789, f1: 0.733, acc: 0.767
+2025-08-07 17:17:10: Epoch: 042 Test results, ap: 0.204, auc: 0.285, f1: 0.054, acc: 0.676
+2025-08-07 17:17:17: Epoch: 043 Train Loss: 0.049, ap: 0.734, auc: 0.790, f1: 0.725, acc: 0.757
+2025-08-07 17:17:18: Epoch: 043 Test results, ap: 0.208, auc: 0.295, f1: 0.067, acc: 0.741
+2025-08-07 17:17:24: Epoch: 044 Train Loss: 0.049, ap: 0.727, auc: 0.785, f1: 0.742, acc: 0.777
+2025-08-07 17:17:25: Epoch: 044 Test results, ap: 0.203, auc: 0.275, f1: 0.067, acc: 0.741
+2025-08-07 17:17:32: Epoch: 045 Train Loss: 0.043, ap: 0.721, auc: 0.784, f1: 0.733, acc: 0.767
+2025-08-07 17:17:33: Epoch: 045 Test results, ap: 0.204, auc: 0.280, f1: 0.065, acc: 0.731
+2025-08-07 17:17:40: Epoch: 046 Train Loss: 0.045, ap: 0.725, auc: 0.787, f1: 0.733, acc: 0.767
+2025-08-07 17:17:41: Epoch: 046 Test results, ap: 0.206, auc: 0.287, f1: 0.071, acc: 0.759
+2025-08-07 17:17:48: Epoch: 047 Train Loss: 0.045, ap: 0.730, auc: 0.784, f1: 0.733, acc: 0.767
+2025-08-07 17:17:49: Epoch: 047 Test results, ap: 0.206, auc: 0.297, f1: 0.053, acc: 0.667
+2025-08-07 17:17:56: Epoch: 048 Train Loss: 0.050, ap: 0.739, auc: 0.788, f1: 0.725, acc: 0.757
+2025-08-07 17:17:57: Epoch: 048 Test results, ap: 0.208, auc: 0.300, f1: 0.069, acc: 0.750
+2025-08-07 17:18:03: Epoch: 049 Train Loss: 0.055, ap: 0.724, auc: 0.785, f1: 0.733, acc: 0.767
+2025-08-07 17:18:04: Epoch: 049 Test results, ap: 0.204, auc: 0.280, f1: 0.057, acc: 0.694
+2025-08-07 17:18:11: Epoch: 050 Train Loss: 0.035, ap: 0.740, auc: 0.793, f1: 0.750, acc: 0.786
+2025-08-07 17:18:12: Epoch: 050 Test results, ap: 0.205, auc: 0.289, f1: 0.062, acc: 0.722
+2025-08-07 17:18:12: used time 0.11h
+2025-08-07 17:18:12: Running Cross Validation 4
+2025-08-07 17:18:12: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 17:18:19: Epoch: 001 Train Loss: 0.689, ap: 0.436, auc: 0.511, f1: 0.000, acc: 0.602
+2025-08-07 17:18:19: Epoch: 001 New best VALIDATION metrics
+2025-08-07 17:18:20: Epoch: 001 Test results, ap: 0.181, auc: 0.330, f1: 0.000, acc: 0.759
+2025-08-07 17:18:27: Epoch: 002 Train Loss: 0.661, ap: 0.504, auc: 0.480, f1: 0.569, acc: 0.398
+2025-08-07 17:18:27: Epoch: 002 New best VALIDATION metrics
+2025-08-07 17:18:28: Epoch: 002 Test results, ap: 0.366, auc: 0.672, f1: 0.388, acc: 0.241
+2025-08-07 17:18:34: Epoch: 003 Train Loss: 0.600, ap: 0.530, auc: 0.674, f1: 0.000, acc: 0.602
+2025-08-07 17:18:34: Epoch: 003 New best VALIDATION metrics
+2025-08-07 17:18:36: Epoch: 003 Test results, ap: 0.264, auc: 0.464, f1: 0.000, acc: 0.759
+2025-08-07 17:18:42: Epoch: 004 Train Loss: 0.574, ap: 0.651, auc: 0.696, f1: 0.586, acc: 0.437
+2025-08-07 17:18:42: Epoch: 004 New best VALIDATION metrics
+2025-08-07 17:18:43: Epoch: 004 Test results, ap: 0.341, auc: 0.590, f1: 0.388, acc: 0.241
+2025-08-07 17:18:50: Epoch: 005 Train Loss: 0.526, ap: 0.657, auc: 0.705, f1: 0.346, acc: 0.670
+2025-08-07 17:18:50: Epoch: 005 New best VALIDATION metrics
+2025-08-07 17:18:51: Epoch: 005 Test results, ap: 0.285, auc: 0.541, f1: 0.000, acc: 0.750
+2025-08-07 17:18:58: Epoch: 006 Train Loss: 0.453, ap: 0.600, auc: 0.674, f1: 0.240, acc: 0.631
+2025-08-07 17:18:59: Epoch: 006 Test results, ap: 0.458, auc: 0.746, f1: 0.000, acc: 0.759
+2025-08-07 17:19:05: Epoch: 007 Train Loss: 0.461, ap: 0.606, auc: 0.649, f1: 0.000, acc: 0.602
+2025-08-07 17:19:07: Epoch: 007 Test results, ap: 0.287, auc: 0.417, f1: 0.000, acc: 0.759
+2025-08-07 17:19:13: Epoch: 008 Train Loss: 0.409, ap: 0.590, auc: 0.679, f1: 0.595, acc: 0.563
+2025-08-07 17:19:14: Epoch: 008 Test results, ap: 0.371, auc: 0.645, f1: 0.500, acc: 0.519
+2025-08-07 17:19:21: Epoch: 009 Train Loss: 0.400, ap: 0.684, auc: 0.740, f1: 0.484, acc: 0.689
+2025-08-07 17:19:21: Epoch: 009 New best VALIDATION metrics
+2025-08-07 17:19:22: Epoch: 009 Test results, ap: 0.276, auc: 0.548, f1: 0.000, acc: 0.750
+2025-08-07 17:19:29: Epoch: 010 Train Loss: 0.332, ap: 0.586, auc: 0.688, f1: 0.639, acc: 0.660
+2025-08-07 17:19:30: Epoch: 010 Test results, ap: 0.302, auc: 0.653, f1: 0.435, acc: 0.639
+2025-08-07 17:19:37: Epoch: 011 Train Loss: 0.302, ap: 0.580, auc: 0.663, f1: 0.613, acc: 0.583
+2025-08-07 17:19:38: Epoch: 011 Test results, ap: 0.302, auc: 0.655, f1: 0.500, acc: 0.519
+2025-08-07 17:19:38: Epoch: 011 New best TEST metrics
+2025-08-07 17:19:45: Epoch: 012 Train Loss: 0.293, ap: 0.684, auc: 0.697, f1: 0.292, acc: 0.670
+2025-08-07 17:19:46: Epoch: 012 Test results, ap: 0.278, auc: 0.395, f1: 0.000, acc: 0.759
+2025-08-07 17:19:52: Epoch: 013 Train Loss: 0.269, ap: 0.712, auc: 0.736, f1: 0.659, acc: 0.718
+2025-08-07 17:19:52: Epoch: 013 New best VALIDATION metrics
+2025-08-07 17:19:53: Epoch: 013 Test results, ap: 0.264, auc: 0.595, f1: 0.000, acc: 0.704
+2025-08-07 17:20:00: Epoch: 014 Train Loss: 0.233, ap: 0.665, auc: 0.722, f1: 0.612, acc: 0.631
+2025-08-07 17:20:01: Epoch: 014 Test results, ap: 0.242, auc: 0.547, f1: 0.354, acc: 0.528
+2025-08-07 17:20:08: Epoch: 015 Train Loss: 0.225, ap: 0.649, auc: 0.687, f1: 0.610, acc: 0.689
+2025-08-07 17:20:09: Epoch: 015 Test results, ap: 0.266, auc: 0.587, f1: 0.143, acc: 0.667
+2025-08-07 17:20:15: Epoch: 016 Train Loss: 0.211, ap: 0.664, auc: 0.731, f1: 0.636, acc: 0.689
+2025-08-07 17:20:17: Epoch: 016 Test results, ap: 0.244, auc: 0.534, f1: 0.246, acc: 0.546
+2025-08-07 17:20:23: Epoch: 017 Train Loss: 0.183, ap: 0.663, auc: 0.720, f1: 0.515, acc: 0.689
+2025-08-07 17:20:24: Epoch: 017 Test results, ap: 0.219, auc: 0.447, f1: 0.000, acc: 0.722
+2025-08-07 17:20:31: Epoch: 018 Train Loss: 0.166, ap: 0.620, auc: 0.700, f1: 0.632, acc: 0.660
+2025-08-07 17:20:32: Epoch: 018 Test results, ap: 0.290, auc: 0.614, f1: 0.432, acc: 0.611
+2025-08-07 17:20:39: Epoch: 019 Train Loss: 0.169, ap: 0.641, auc: 0.707, f1: 0.622, acc: 0.670
+2025-08-07 17:20:40: Epoch: 019 Test results, ap: 0.258, auc: 0.554, f1: 0.364, acc: 0.611
+2025-08-07 17:20:47: Epoch: 020 Train Loss: 0.140, ap: 0.672, auc: 0.714, f1: 0.528, acc: 0.670
+2025-08-07 17:20:48: Epoch: 020 Test results, ap: 0.273, auc: 0.560, f1: 0.000, acc: 0.713
+2025-08-07 17:20:54: Epoch: 021 Train Loss: 0.135, ap: 0.622, auc: 0.686, f1: 0.588, acc: 0.660
+2025-08-07 17:20:55: Epoch: 021 Test results, ap: 0.284, auc: 0.577, f1: 0.430, acc: 0.583
+2025-08-07 17:21:02: Epoch: 022 Train Loss: 0.133, ap: 0.645, auc: 0.722, f1: 0.582, acc: 0.680
+2025-08-07 17:21:03: Epoch: 022 Test results, ap: 0.256, auc: 0.545, f1: 0.324, acc: 0.574
+2025-08-07 17:21:10: Epoch: 023 Train Loss: 0.130, ap: 0.567, auc: 0.689, f1: 0.632, acc: 0.660
+2025-08-07 17:21:11: Epoch: 023 Test results, ap: 0.325, auc: 0.624, f1: 0.417, acc: 0.611
+2025-08-07 17:21:17: Epoch: 024 Train Loss: 0.112, ap: 0.662, auc: 0.737, f1: 0.595, acc: 0.709
+2025-08-07 17:21:19: Epoch: 024 Test results, ap: 0.274, auc: 0.597, f1: 0.275, acc: 0.657
+2025-08-07 17:21:25: Epoch: 025 Train Loss: 0.133, ap: 0.574, auc: 0.667, f1: 0.552, acc: 0.621
+2025-08-07 17:21:26: Epoch: 025 Test results, ap: 0.290, auc: 0.621, f1: 0.425, acc: 0.574
+2025-08-07 17:21:33: Epoch: 026 Train Loss: 0.108, ap: 0.666, auc: 0.726, f1: 0.605, acc: 0.709
+2025-08-07 17:21:34: Epoch: 026 Test results, ap: 0.257, auc: 0.568, f1: 0.300, acc: 0.611
+2025-08-07 17:21:41: Epoch: 027 Train Loss: 0.104, ap: 0.622, auc: 0.675, f1: 0.575, acc: 0.641
+2025-08-07 17:21:42: Epoch: 027 Test results, ap: 0.294, auc: 0.609, f1: 0.420, acc: 0.565
+2025-08-07 17:21:48: Epoch: 028 Train Loss: 0.106, ap: 0.657, auc: 0.727, f1: 0.652, acc: 0.689
+2025-08-07 17:21:50: Epoch: 028 Test results, ap: 0.290, auc: 0.611, f1: 0.421, acc: 0.593
+2025-08-07 17:21:56: Epoch: 029 Train Loss: 0.110, ap: 0.631, auc: 0.720, f1: 0.500, acc: 0.670
+2025-08-07 17:21:57: Epoch: 029 Test results, ap: 0.290, auc: 0.617, f1: 0.000, acc: 0.685
+2025-08-07 17:22:04: Epoch: 030 Train Loss: 0.094, ap: 0.622, auc: 0.724, f1: 0.674, acc: 0.718
+2025-08-07 17:22:05: Epoch: 030 Test results, ap: 0.291, auc: 0.622, f1: 0.436, acc: 0.593
+2025-08-07 17:22:11: Epoch: 031 Train Loss: 0.077, ap: 0.634, auc: 0.731, f1: 0.674, acc: 0.728
+2025-08-07 17:22:13: Epoch: 031 Test results, ap: 0.282, auc: 0.608, f1: 0.328, acc: 0.583
+2025-08-07 17:22:19: Epoch: 032 Train Loss: 0.087, ap: 0.638, auc: 0.735, f1: 0.651, acc: 0.718
+2025-08-07 17:22:20: Epoch: 032 Test results, ap: 0.284, auc: 0.608, f1: 0.300, acc: 0.611
+2025-08-07 17:22:27: Epoch: 033 Train Loss: 0.078, ap: 0.613, auc: 0.713, f1: 0.595, acc: 0.709
+2025-08-07 17:22:28: Epoch: 033 Test results, ap: 0.317, auc: 0.623, f1: 0.323, acc: 0.611
+2025-08-07 17:22:35: Epoch: 034 Train Loss: 0.073, ap: 0.638, auc: 0.732, f1: 0.595, acc: 0.709
+2025-08-07 17:22:36: Epoch: 034 Test results, ap: 0.290, auc: 0.606, f1: 0.255, acc: 0.620
+2025-08-07 17:22:43: Epoch: 035 Train Loss: 0.078, ap: 0.624, auc: 0.721, f1: 0.659, acc: 0.728
+2025-08-07 17:22:44: Epoch: 035 Test results, ap: 0.303, auc: 0.621, f1: 0.317, acc: 0.602
+2025-08-07 17:22:50: Epoch: 036 Train Loss: 0.070, ap: 0.630, auc: 0.721, f1: 0.642, acc: 0.718
+2025-08-07 17:22:52: Epoch: 036 Test results, ap: 0.288, auc: 0.609, f1: 0.295, acc: 0.602
+2025-08-07 17:22:58: Epoch: 037 Train Loss: 0.059, ap: 0.634, auc: 0.719, f1: 0.582, acc: 0.680
+2025-08-07 17:22:59: Epoch: 037 Test results, ap: 0.301, auc: 0.606, f1: 0.286, acc: 0.583
+2025-08-07 17:23:06: Epoch: 038 Train Loss: 0.057, ap: 0.640, auc: 0.730, f1: 0.690, acc: 0.748
+2025-08-07 17:23:07: Epoch: 038 Test results, ap: 0.296, auc: 0.612, f1: 0.344, acc: 0.611
+2025-08-07 17:23:14: Epoch: 039 Train Loss: 0.077, ap: 0.644, auc: 0.736, f1: 0.683, acc: 0.748
+2025-08-07 17:23:15: Epoch: 039 Test results, ap: 0.281, auc: 0.599, f1: 0.250, acc: 0.611
+2025-08-07 17:23:22: Epoch: 040 Train Loss: 0.070, ap: 0.653, auc: 0.740, f1: 0.675, acc: 0.738
+2025-08-07 17:23:23: Epoch: 040 Test results, ap: 0.288, auc: 0.603, f1: 0.271, acc: 0.602
+2025-08-07 17:23:29: Epoch: 041 Train Loss: 0.065, ap: 0.638, auc: 0.732, f1: 0.698, acc: 0.748
+2025-08-07 17:23:31: Epoch: 041 Test results, ap: 0.283, auc: 0.606, f1: 0.364, acc: 0.611
+2025-08-07 17:23:37: Epoch: 042 Train Loss: 0.060, ap: 0.630, auc: 0.726, f1: 0.683, acc: 0.748
+2025-08-07 17:23:38: Epoch: 042 Test results, ap: 0.288, auc: 0.610, f1: 0.317, acc: 0.602
+2025-08-07 17:23:45: Epoch: 043 Train Loss: 0.052, ap: 0.632, auc: 0.722, f1: 0.667, acc: 0.738
+2025-08-07 17:23:46: Epoch: 043 Test results, ap: 0.284, auc: 0.603, f1: 0.295, acc: 0.602
+2025-08-07 17:23:53: Epoch: 044 Train Loss: 0.052, ap: 0.634, auc: 0.723, f1: 0.613, acc: 0.718
+2025-08-07 17:23:54: Epoch: 044 Test results, ap: 0.306, auc: 0.613, f1: 0.295, acc: 0.602
+2025-08-07 17:24:00: Epoch: 045 Train Loss: 0.051, ap: 0.619, auc: 0.715, f1: 0.633, acc: 0.718
+2025-08-07 17:24:02: Epoch: 045 Test results, ap: 0.318, auc: 0.606, f1: 0.317, acc: 0.602
+2025-08-07 17:24:08: Epoch: 046 Train Loss: 0.042, ap: 0.626, auc: 0.728, f1: 0.714, acc: 0.767
+2025-08-07 17:24:09: Epoch: 046 Test results, ap: 0.283, auc: 0.589, f1: 0.323, acc: 0.611
+2025-08-07 17:24:16: Epoch: 047 Train Loss: 0.049, ap: 0.630, auc: 0.728, f1: 0.699, acc: 0.757
+2025-08-07 17:24:17: Epoch: 047 Test results, ap: 0.286, auc: 0.597, f1: 0.300, acc: 0.611
+2025-08-07 17:24:24: Epoch: 048 Train Loss: 0.037, ap: 0.632, auc: 0.729, f1: 0.691, acc: 0.757
+2025-08-07 17:24:25: Epoch: 048 Test results, ap: 0.282, auc: 0.595, f1: 0.323, acc: 0.611
+2025-08-07 17:24:31: Epoch: 049 Train Loss: 0.053, ap: 0.630, auc: 0.719, f1: 0.605, acc: 0.709
+2025-08-07 17:24:33: Epoch: 049 Test results, ap: 0.311, auc: 0.601, f1: 0.300, acc: 0.611
+2025-08-07 17:24:39: Epoch: 050 Train Loss: 0.044, ap: 0.625, auc: 0.714, f1: 0.597, acc: 0.699
+2025-08-07 17:24:40: Epoch: 050 Test results, ap: 0.343, auc: 0.605, f1: 0.333, acc: 0.630
+2025-08-07 17:24:40: used time 0.11h
+2025-08-07 17:24:40: Cross Validation Finished!
+2025-08-07 17:24:40: Best validation perform list
+[[0.74366575 0.78077507 0.72340423 0.74757284]
+ [0.66071236 0.73546273 0.58333331 0.51456308]
+ [0.84003997 0.8046757  0.70833331 0.72815531]
+ [0.81758386 0.8251366  0.73118281 0.75728154]
+ [0.71172935 0.73564124 0.65882355 0.71844661]]
+2025-08-07 17:24:40: mean: [0.755 0.776 0.681 0.693]
+2025-08-07 17:24:40: std: [0.066 0.036 0.055 0.09 ]
+2025-08-07 17:24:40: Best test perform list
+[[0.37676236 0.56613505 0.19354838 0.76851851]
+ [0.32685554 0.48311445 0.23684211 0.46296296]
+ [0.25132385 0.53330207 0.17391305 0.64814812]
+ [0.20950542 0.38227016 0.07272727 0.52777779]
+ [0.30181766 0.65478426 0.5        0.51851851]]
+2025-08-07 17:24:40: mean: [0.293 0.524 0.235 0.585]
+2025-08-07 17:24:40: std: [0.058 0.09  0.143 0.11 ]
diff --git a/run-cls-simple/rn18-mlp-16-simple-768-oneway-ce-32-0.001-50/config.json b/run-cls-simple/rn18-mlp-16-simple-768-oneway-ce-32-0.001-50/config.json
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+++ b/run-cls-simple/rn18-mlp-16-simple-768-oneway-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": null, "channels": 16, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "llm_data": false, "gpu": 1, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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diff --git a/run-cls-simple/rn18-mlp-16-simple-768-oneway-ce-32-0.001-50/result.txt b/run-cls-simple/rn18-mlp-16-simple-768-oneway-ce-32-0.001-50/result.txt
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@@ -0,0 +1,6 @@
+Valid
+0.7533769130706787,0.7935755491256714,0.5770086139440537,0.7029126167297364
+0.060652326302279985,0.035742074210977616,0.20971300748577193,0.04896902960405458
+Test
+0.36708807945251465,0.6177298188209533,0.1612804889678955,0.7370370388031006
+0.07102768250133738,0.05904880042267585,0.19148768732743998,0.0171733689173386
diff --git a/run-cls-simple/rn18-mlp-16-simple-768-oneway-ce-32-0.001-50/training.log b/run-cls-simple/rn18-mlp-16-simple-768-oneway-ce-32-0.001-50/training.log
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@@ -0,0 +1,597 @@
+2025-08-07 16:51:49: saving_dir: ./run-cls/rn18-mlp-16-simple-768-oneway-ce-32-0.001-50
+2025-08-07 16:51:49: Loading Training Dataset
+2025-08-07 16:52:16: Loading Test Dataset
+2025-08-07 16:52:20: Running Cross Validation 0
+2025-08-07 16:52:20: Fold 0  Train set:412, Valid set:103, Test set: 108
+2025-08-07 16:52:29: Epoch: 001 Train Loss: 0.689, ap: 0.517, auc: 0.570, f1: 0.000, acc: 0.544
+2025-08-07 16:52:29: Epoch: 001 New best VALIDATION metrics
+2025-08-07 16:52:31: Epoch: 001 Test results, ap: 0.192, auc: 0.356, f1: 0.000, acc: 0.759
+2025-08-07 16:52:38: Epoch: 002 Train Loss: 0.644, ap: 0.711, auc: 0.739, f1: 0.689, acc: 0.728
+2025-08-07 16:52:38: Epoch: 002 New best VALIDATION metrics
+2025-08-07 16:52:40: Epoch: 002 Test results, ap: 0.206, auc: 0.428, f1: 0.000, acc: 0.713
+2025-08-07 16:52:47: Epoch: 003 Train Loss: 0.576, ap: 0.727, auc: 0.738, f1: 0.500, acc: 0.670
+2025-08-07 16:52:47: Epoch: 003 New best VALIDATION metrics
+2025-08-07 16:52:49: Epoch: 003 Test results, ap: 0.258, auc: 0.489, f1: 0.000, acc: 0.741
+2025-08-07 16:52:56: Epoch: 004 Train Loss: 0.566, ap: 0.663, auc: 0.649, f1: 0.000, acc: 0.544
+2025-08-07 16:52:57: Epoch: 004 Test results, ap: 0.279, auc: 0.570, f1: 0.000, acc: 0.759
+2025-08-07 16:53:05: Epoch: 005 Train Loss: 0.541, ap: 0.724, auc: 0.707, f1: 0.526, acc: 0.650
+2025-08-07 16:53:06: Epoch: 005 Test results, ap: 0.403, auc: 0.668, f1: 0.000, acc: 0.759
+2025-08-07 16:53:14: Epoch: 006 Train Loss: 0.519, ap: 0.694, auc: 0.760, f1: 0.533, acc: 0.660
+2025-08-07 16:53:15: Epoch: 006 Test results, ap: 0.238, auc: 0.495, f1: 0.000, acc: 0.759
+2025-08-07 16:53:23: Epoch: 007 Train Loss: 0.467, ap: 0.726, auc: 0.753, f1: 0.400, acc: 0.621
+2025-08-07 16:53:23: Epoch: 007 New best VALIDATION metrics
+2025-08-07 16:53:24: Epoch: 007 Test results, ap: 0.237, auc: 0.519, f1: 0.000, acc: 0.759
+2025-08-07 16:53:32: Epoch: 008 Train Loss: 0.428, ap: 0.731, auc: 0.751, f1: 0.471, acc: 0.650
+2025-08-07 16:53:32: Epoch: 008 New best VALIDATION metrics
+2025-08-07 16:53:33: Epoch: 008 Test results, ap: 0.336, auc: 0.646, f1: 0.000, acc: 0.759
+2025-08-07 16:53:41: Epoch: 009 Train Loss: 0.405, ap: 0.721, auc: 0.759, f1: 0.634, acc: 0.485
+2025-08-07 16:53:42: Epoch: 009 Test results, ap: 0.202, auc: 0.421, f1: 0.333, acc: 0.444
+2025-08-07 16:53:50: Epoch: 010 Train Loss: 0.374, ap: 0.719, auc: 0.754, f1: 0.674, acc: 0.718
+2025-08-07 16:53:51: Epoch: 010 Test results, ap: 0.219, auc: 0.482, f1: 0.000, acc: 0.741
+2025-08-07 16:53:59: Epoch: 011 Train Loss: 0.303, ap: 0.740, auc: 0.754, f1: 0.642, acc: 0.718
+2025-08-07 16:53:59: Epoch: 011 New best VALIDATION metrics
+2025-08-07 16:54:00: Epoch: 011 Test results, ap: 0.270, auc: 0.567, f1: 0.000, acc: 0.750
+2025-08-07 16:54:00: Epoch: 011 New best TEST metrics
+2025-08-07 16:54:08: Epoch: 012 Train Loss: 0.259, ap: 0.712, auc: 0.740, f1: 0.695, acc: 0.718
+2025-08-07 16:54:09: Epoch: 012 Test results, ap: 0.216, auc: 0.462, f1: 0.000, acc: 0.722
+2025-08-07 16:54:17: Epoch: 013 Train Loss: 0.218, ap: 0.711, auc: 0.750, f1: 0.660, acc: 0.680
+2025-08-07 16:54:18: Epoch: 013 Test results, ap: 0.258, auc: 0.572, f1: 0.000, acc: 0.750
+2025-08-07 16:54:25: Epoch: 014 Train Loss: 0.208, ap: 0.744, auc: 0.764, f1: 0.675, acc: 0.738
+2025-08-07 16:54:25: Epoch: 014 New best VALIDATION metrics
+2025-08-07 16:54:27: Epoch: 014 Test results, ap: 0.291, auc: 0.579, f1: 0.000, acc: 0.750
+2025-08-07 16:54:27: Epoch: 014 New best TEST metrics
+2025-08-07 16:54:34: Epoch: 015 Train Loss: 0.243, ap: 0.715, auc: 0.736, f1: 0.686, acc: 0.680
+2025-08-07 16:54:36: Epoch: 015 Test results, ap: 0.222, auc: 0.487, f1: 0.000, acc: 0.593
+2025-08-07 16:54:43: Epoch: 016 Train Loss: 0.220, ap: 0.745, auc: 0.753, f1: 0.605, acc: 0.709
+2025-08-07 16:54:45: Epoch: 016 Test results, ap: 0.265, auc: 0.556, f1: 0.000, acc: 0.750
+2025-08-07 16:54:52: Epoch: 017 Train Loss: 0.189, ap: 0.728, auc: 0.755, f1: 0.718, acc: 0.718
+2025-08-07 16:54:53: Epoch: 017 Test results, ap: 0.242, auc: 0.538, f1: 0.045, acc: 0.611
+2025-08-07 16:55:01: Epoch: 018 Train Loss: 0.153, ap: 0.754, auc: 0.733, f1: 0.537, acc: 0.699
+2025-08-07 16:55:02: Epoch: 018 Test results, ap: 0.323, auc: 0.635, f1: 0.000, acc: 0.759
+2025-08-07 16:55:02: Epoch: 018 New best TEST metrics
+2025-08-07 16:55:10: Epoch: 019 Train Loss: 0.153, ap: 0.747, auc: 0.774, f1: 0.697, acc: 0.680
+2025-08-07 16:55:10: Epoch: 019 New best VALIDATION metrics
+2025-08-07 16:55:11: Epoch: 019 Test results, ap: 0.220, auc: 0.480, f1: 0.000, acc: 0.676
+2025-08-07 16:55:19: Epoch: 020 Train Loss: 0.145, ap: 0.717, auc: 0.733, f1: 0.568, acc: 0.689
+2025-08-07 16:55:21: Epoch: 020 Test results, ap: 0.301, auc: 0.601, f1: 0.000, acc: 0.759
+2025-08-07 16:55:28: Epoch: 021 Train Loss: 0.148, ap: 0.736, auc: 0.771, f1: 0.731, acc: 0.728
+2025-08-07 16:55:29: Epoch: 021 Test results, ap: 0.248, auc: 0.525, f1: 0.000, acc: 0.750
+2025-08-07 16:55:37: Epoch: 022 Train Loss: 0.129, ap: 0.723, auc: 0.750, f1: 0.636, acc: 0.689
+2025-08-07 16:55:38: Epoch: 022 Test results, ap: 0.277, auc: 0.553, f1: 0.000, acc: 0.750
+2025-08-07 16:55:46: Epoch: 023 Train Loss: 0.124, ap: 0.725, auc: 0.756, f1: 0.644, acc: 0.689
+2025-08-07 16:55:47: Epoch: 023 Test results, ap: 0.265, auc: 0.558, f1: 0.000, acc: 0.750
+2025-08-07 16:55:55: Epoch: 024 Train Loss: 0.121, ap: 0.726, auc: 0.756, f1: 0.659, acc: 0.699
+2025-08-07 16:55:56: Epoch: 024 Test results, ap: 0.268, auc: 0.559, f1: 0.000, acc: 0.750
+2025-08-07 16:56:04: Epoch: 025 Train Loss: 0.104, ap: 0.707, auc: 0.726, f1: 0.638, acc: 0.670
+2025-08-07 16:56:05: Epoch: 025 Test results, ap: 0.270, auc: 0.572, f1: 0.000, acc: 0.731
+2025-08-07 16:56:13: Epoch: 026 Train Loss: 0.100, ap: 0.720, auc: 0.746, f1: 0.680, acc: 0.699
+2025-08-07 16:56:14: Epoch: 026 Test results, ap: 0.260, auc: 0.552, f1: 0.000, acc: 0.731
+2025-08-07 16:56:22: Epoch: 027 Train Loss: 0.117, ap: 0.715, auc: 0.744, f1: 0.622, acc: 0.670
+2025-08-07 16:56:23: Epoch: 027 Test results, ap: 0.283, auc: 0.579, f1: 0.000, acc: 0.750
+2025-08-07 16:56:31: Epoch: 028 Train Loss: 0.103, ap: 0.726, auc: 0.758, f1: 0.673, acc: 0.689
+2025-08-07 16:56:32: Epoch: 028 Test results, ap: 0.241, auc: 0.520, f1: 0.000, acc: 0.722
+2025-08-07 16:56:40: Epoch: 029 Train Loss: 0.101, ap: 0.729, auc: 0.744, f1: 0.610, acc: 0.689
+2025-08-07 16:56:41: Epoch: 029 Test results, ap: 0.316, auc: 0.590, f1: 0.000, acc: 0.750
+2025-08-07 16:56:48: Epoch: 030 Train Loss: 0.082, ap: 0.719, auc: 0.736, f1: 0.652, acc: 0.699
+2025-08-07 16:56:50: Epoch: 030 Test results, ap: 0.315, auc: 0.598, f1: 0.000, acc: 0.750
+2025-08-07 16:56:57: Epoch: 031 Train Loss: 0.073, ap: 0.728, auc: 0.753, f1: 0.645, acc: 0.680
+2025-08-07 16:56:59: Epoch: 031 Test results, ap: 0.312, auc: 0.594, f1: 0.000, acc: 0.731
+2025-08-07 16:57:06: Epoch: 032 Train Loss: 0.091, ap: 0.723, auc: 0.754, f1: 0.687, acc: 0.699
+2025-08-07 16:57:08: Epoch: 032 Test results, ap: 0.263, auc: 0.552, f1: 0.000, acc: 0.713
+2025-08-07 16:57:15: Epoch: 033 Train Loss: 0.094, ap: 0.723, auc: 0.748, f1: 0.659, acc: 0.709
+2025-08-07 16:57:17: Epoch: 033 Test results, ap: 0.287, auc: 0.571, f1: 0.000, acc: 0.750
+2025-08-07 16:57:24: Epoch: 034 Train Loss: 0.076, ap: 0.723, auc: 0.752, f1: 0.605, acc: 0.670
+2025-08-07 16:57:26: Epoch: 034 Test results, ap: 0.330, auc: 0.606, f1: 0.000, acc: 0.750
+2025-08-07 16:57:33: Epoch: 035 Train Loss: 0.063, ap: 0.717, auc: 0.742, f1: 0.667, acc: 0.689
+2025-08-07 16:57:35: Epoch: 035 Test results, ap: 0.304, auc: 0.592, f1: 0.000, acc: 0.731
+2025-08-07 16:57:42: Epoch: 036 Train Loss: 0.075, ap: 0.718, auc: 0.747, f1: 0.667, acc: 0.689
+2025-08-07 16:57:43: Epoch: 036 Test results, ap: 0.295, auc: 0.591, f1: 0.061, acc: 0.713
+2025-08-07 16:57:51: Epoch: 037 Train Loss: 0.072, ap: 0.718, auc: 0.744, f1: 0.636, acc: 0.689
+2025-08-07 16:57:52: Epoch: 037 Test results, ap: 0.339, auc: 0.624, f1: 0.000, acc: 0.750
+2025-08-07 16:57:52: Epoch: 037 New best TEST metrics
+2025-08-07 16:58:00: Epoch: 038 Train Loss: 0.072, ap: 0.717, auc: 0.748, f1: 0.714, acc: 0.728
+2025-08-07 16:58:01: Epoch: 038 Test results, ap: 0.289, auc: 0.583, f1: 0.061, acc: 0.713
+2025-08-07 16:58:09: Epoch: 039 Train Loss: 0.061, ap: 0.721, auc: 0.746, f1: 0.659, acc: 0.709
+2025-08-07 16:58:11: Epoch: 039 Test results, ap: 0.283, auc: 0.576, f1: 0.000, acc: 0.750
+2025-08-07 16:58:18: Epoch: 040 Train Loss: 0.064, ap: 0.721, auc: 0.733, f1: 0.619, acc: 0.689
+2025-08-07 16:58:20: Epoch: 040 Test results, ap: 0.328, auc: 0.612, f1: 0.000, acc: 0.750
+2025-08-07 16:58:27: Epoch: 041 Train Loss: 0.055, ap: 0.715, auc: 0.735, f1: 0.644, acc: 0.689
+2025-08-07 16:58:28: Epoch: 041 Test results, ap: 0.311, auc: 0.593, f1: 0.065, acc: 0.731
+2025-08-07 16:58:36: Epoch: 042 Train Loss: 0.063, ap: 0.712, auc: 0.733, f1: 0.644, acc: 0.689
+2025-08-07 16:58:37: Epoch: 042 Test results, ap: 0.302, auc: 0.586, f1: 0.065, acc: 0.731
+2025-08-07 16:58:45: Epoch: 043 Train Loss: 0.056, ap: 0.709, auc: 0.732, f1: 0.653, acc: 0.680
+2025-08-07 16:58:46: Epoch: 043 Test results, ap: 0.306, auc: 0.581, f1: 0.121, acc: 0.731
+2025-08-07 16:58:54: Epoch: 044 Train Loss: 0.052, ap: 0.709, auc: 0.729, f1: 0.644, acc: 0.689
+2025-08-07 16:58:56: Epoch: 044 Test results, ap: 0.304, auc: 0.577, f1: 0.065, acc: 0.731
+2025-08-07 16:59:03: Epoch: 045 Train Loss: 0.053, ap: 0.715, auc: 0.733, f1: 0.659, acc: 0.709
+2025-08-07 16:59:05: Epoch: 045 Test results, ap: 0.321, auc: 0.594, f1: 0.067, acc: 0.741
+2025-08-07 16:59:12: Epoch: 046 Train Loss: 0.064, ap: 0.709, auc: 0.731, f1: 0.630, acc: 0.670
+2025-08-07 16:59:14: Epoch: 046 Test results, ap: 0.327, auc: 0.597, f1: 0.067, acc: 0.741
+2025-08-07 16:59:21: Epoch: 047 Train Loss: 0.058, ap: 0.711, auc: 0.738, f1: 0.667, acc: 0.689
+2025-08-07 16:59:22: Epoch: 047 Test results, ap: 0.307, auc: 0.582, f1: 0.121, acc: 0.731
+2025-08-07 16:59:30: Epoch: 048 Train Loss: 0.054, ap: 0.712, auc: 0.737, f1: 0.638, acc: 0.670
+2025-08-07 16:59:31: Epoch: 048 Test results, ap: 0.297, auc: 0.579, f1: 0.065, acc: 0.731
+2025-08-07 16:59:39: Epoch: 049 Train Loss: 0.052, ap: 0.706, auc: 0.728, f1: 0.622, acc: 0.670
+2025-08-07 16:59:40: Epoch: 049 Test results, ap: 0.308, auc: 0.591, f1: 0.067, acc: 0.741
+2025-08-07 16:59:48: Epoch: 050 Train Loss: 0.059, ap: 0.709, auc: 0.732, f1: 0.629, acc: 0.680
+2025-08-07 16:59:49: Epoch: 050 Test results, ap: 0.303, auc: 0.579, f1: 0.067, acc: 0.741
+2025-08-07 16:59:49: used time 0.12h
+2025-08-07 16:59:49: Running Cross Validation 1
+2025-08-07 16:59:49: Fold 1  Train set:412, Valid set:103, Test set: 108
+2025-08-07 16:59:57: Epoch: 001 Train Loss: 0.690, ap: 0.521, auc: 0.673, f1: 0.545, acc: 0.563
+2025-08-07 16:59:57: Epoch: 001 New best VALIDATION metrics
+2025-08-07 16:59:58: Epoch: 001 Test results, ap: 0.183, auc: 0.342, f1: 0.388, acc: 0.241
+2025-08-07 17:00:06: Epoch: 002 Train Loss: 0.655, ap: 0.532, auc: 0.647, f1: 0.544, acc: 0.398
+2025-08-07 17:00:07: Epoch: 002 Test results, ap: 0.204, auc: 0.353, f1: 0.388, acc: 0.241
+2025-08-07 17:00:15: Epoch: 003 Train Loss: 0.583, ap: 0.633, auc: 0.757, f1: 0.146, acc: 0.660
+2025-08-07 17:00:15: Epoch: 003 New best VALIDATION metrics
+2025-08-07 17:00:16: Epoch: 003 Test results, ap: 0.303, auc: 0.513, f1: 0.000, acc: 0.759
+2025-08-07 17:00:24: Epoch: 004 Train Loss: 0.544, ap: 0.630, auc: 0.679, f1: 0.529, acc: 0.359
+2025-08-07 17:00:25: Epoch: 004 Test results, ap: 0.255, auc: 0.533, f1: 0.388, acc: 0.241
+2025-08-07 17:00:32: Epoch: 005 Train Loss: 0.490, ap: 0.623, auc: 0.672, f1: 0.563, acc: 0.563
+2025-08-07 17:00:34: Epoch: 005 Test results, ap: 0.349, auc: 0.530, f1: 0.235, acc: 0.759
+2025-08-07 17:00:41: Epoch: 006 Train Loss: 0.470, ap: 0.583, auc: 0.720, f1: 0.483, acc: 0.709
+2025-08-07 17:00:43: Epoch: 006 Test results, ap: 0.225, auc: 0.416, f1: 0.000, acc: 0.759
+2025-08-07 17:00:50: Epoch: 007 Train Loss: 0.419, ap: 0.636, auc: 0.725, f1: 0.606, acc: 0.621
+2025-08-07 17:00:52: Epoch: 007 Test results, ap: 0.180, auc: 0.331, f1: 0.138, acc: 0.537
+2025-08-07 17:00:59: Epoch: 008 Train Loss: 0.426, ap: 0.644, auc: 0.720, f1: 0.531, acc: 0.709
+2025-08-07 17:01:01: Epoch: 008 Test results, ap: 0.371, auc: 0.663, f1: 0.000, acc: 0.759
+2025-08-07 17:01:08: Epoch: 009 Train Loss: 0.402, ap: 0.664, auc: 0.735, f1: 0.103, acc: 0.660
+2025-08-07 17:01:08: Epoch: 009 New best VALIDATION metrics
+2025-08-07 17:01:10: Epoch: 009 Test results, ap: 0.293, auc: 0.491, f1: 0.000, acc: 0.759
+2025-08-07 17:01:18: Epoch: 010 Train Loss: 0.351, ap: 0.587, auc: 0.677, f1: 0.569, acc: 0.515
+2025-08-07 17:01:19: Epoch: 010 Test results, ap: 0.252, auc: 0.503, f1: 0.391, acc: 0.250
+2025-08-07 17:01:26: Epoch: 011 Train Loss: 0.324, ap: 0.637, auc: 0.727, f1: 0.589, acc: 0.621
+2025-08-07 17:01:28: Epoch: 011 Test results, ap: 0.258, auc: 0.472, f1: 0.344, acc: 0.435
+2025-08-07 17:01:28: Epoch: 011 New best TEST metrics
+2025-08-07 17:01:35: Epoch: 012 Train Loss: 0.282, ap: 0.638, auc: 0.733, f1: 0.548, acc: 0.631
+2025-08-07 17:01:37: Epoch: 012 Test results, ap: 0.305, auc: 0.525, f1: 0.286, acc: 0.676
+2025-08-07 17:01:37: Epoch: 012 New best TEST metrics
+2025-08-07 17:01:44: Epoch: 013 Train Loss: 0.276, ap: 0.616, auc: 0.718, f1: 0.583, acc: 0.709
+2025-08-07 17:01:46: Epoch: 013 Test results, ap: 0.304, auc: 0.614, f1: 0.067, acc: 0.741
+2025-08-07 17:01:46: Epoch: 013 New best TEST metrics
+2025-08-07 17:01:53: Epoch: 014 Train Loss: 0.258, ap: 0.665, auc: 0.733, f1: 0.594, acc: 0.748
+2025-08-07 17:01:55: Epoch: 014 Test results, ap: 0.261, auc: 0.567, f1: 0.000, acc: 0.741
+2025-08-07 17:02:02: Epoch: 015 Train Loss: 0.265, ap: 0.576, auc: 0.700, f1: 0.533, acc: 0.660
+2025-08-07 17:02:04: Epoch: 015 Test results, ap: 0.318, auc: 0.581, f1: 0.263, acc: 0.741
+2025-08-07 17:02:11: Epoch: 016 Train Loss: 0.260, ap: 0.621, auc: 0.717, f1: 0.587, acc: 0.631
+2025-08-07 17:02:13: Epoch: 016 Test results, ap: 0.266, auc: 0.549, f1: 0.208, acc: 0.648
+2025-08-07 17:02:20: Epoch: 017 Train Loss: 0.215, ap: 0.572, auc: 0.682, f1: 0.486, acc: 0.631
+2025-08-07 17:02:22: Epoch: 017 Test results, ap: 0.373, auc: 0.670, f1: 0.350, acc: 0.759
+2025-08-07 17:02:22: Epoch: 017 New best TEST metrics
+2025-08-07 17:02:29: Epoch: 018 Train Loss: 0.221, ap: 0.594, auc: 0.704, f1: 0.592, acc: 0.612
+2025-08-07 17:02:30: Epoch: 018 Test results, ap: 0.402, auc: 0.627, f1: 0.350, acc: 0.519
+2025-08-07 17:02:38: Epoch: 019 Train Loss: 0.210, ap: 0.574, auc: 0.676, f1: 0.507, acc: 0.680
+2025-08-07 17:02:40: Epoch: 019 Test results, ap: 0.407, auc: 0.654, f1: 0.372, acc: 0.750
+2025-08-07 17:02:40: Epoch: 019 New best TEST metrics
+2025-08-07 17:02:47: Epoch: 020 Train Loss: 0.210, ap: 0.680, auc: 0.739, f1: 0.579, acc: 0.689
+2025-08-07 17:02:47: Epoch: 020 New best VALIDATION metrics
+2025-08-07 17:02:49: Epoch: 020 Test results, ap: 0.271, auc: 0.555, f1: 0.103, acc: 0.676
+2025-08-07 17:02:57: Epoch: 021 Train Loss: 0.168, ap: 0.597, auc: 0.703, f1: 0.571, acc: 0.621
+2025-08-07 17:02:58: Epoch: 021 Test results, ap: 0.292, auc: 0.588, f1: 0.308, acc: 0.583
+2025-08-07 17:03:06: Epoch: 022 Train Loss: 0.172, ap: 0.587, auc: 0.687, f1: 0.500, acc: 0.631
+2025-08-07 17:03:07: Epoch: 022 Test results, ap: 0.400, auc: 0.634, f1: 0.341, acc: 0.750
+2025-08-07 17:03:15: Epoch: 023 Train Loss: 0.152, ap: 0.587, auc: 0.699, f1: 0.553, acc: 0.592
+2025-08-07 17:03:16: Epoch: 023 Test results, ap: 0.402, auc: 0.650, f1: 0.382, acc: 0.611
+2025-08-07 17:03:23: Epoch: 024 Train Loss: 0.139, ap: 0.598, auc: 0.705, f1: 0.514, acc: 0.670
+2025-08-07 17:03:25: Epoch: 024 Test results, ap: 0.413, auc: 0.659, f1: 0.311, acc: 0.713
+2025-08-07 17:03:25: Epoch: 024 New best TEST metrics
+2025-08-07 17:03:32: Epoch: 025 Train Loss: 0.145, ap: 0.584, auc: 0.688, f1: 0.561, acc: 0.650
+2025-08-07 17:03:34: Epoch: 025 Test results, ap: 0.437, auc: 0.684, f1: 0.418, acc: 0.639
+2025-08-07 17:03:34: Epoch: 025 New best TEST metrics
+2025-08-07 17:03:41: Epoch: 026 Train Loss: 0.134, ap: 0.646, auc: 0.729, f1: 0.609, acc: 0.738
+2025-08-07 17:03:43: Epoch: 026 Test results, ap: 0.383, auc: 0.616, f1: 0.300, acc: 0.741
+2025-08-07 17:03:51: Epoch: 027 Train Loss: 0.150, ap: 0.632, auc: 0.724, f1: 0.525, acc: 0.631
+2025-08-07 17:03:52: Epoch: 027 Test results, ap: 0.396, auc: 0.633, f1: 0.406, acc: 0.620
+2025-08-07 17:04:00: Epoch: 028 Train Loss: 0.147, ap: 0.622, auc: 0.710, f1: 0.521, acc: 0.660
+2025-08-07 17:04:01: Epoch: 028 Test results, ap: 0.414, auc: 0.663, f1: 0.407, acc: 0.676
+2025-08-07 17:04:09: Epoch: 029 Train Loss: 0.138, ap: 0.574, auc: 0.681, f1: 0.553, acc: 0.592
+2025-08-07 17:04:10: Epoch: 029 Test results, ap: 0.391, auc: 0.659, f1: 0.416, acc: 0.583
+2025-08-07 17:04:18: Epoch: 030 Train Loss: 0.134, ap: 0.616, auc: 0.706, f1: 0.519, acc: 0.641
+2025-08-07 17:04:19: Epoch: 030 Test results, ap: 0.392, auc: 0.628, f1: 0.327, acc: 0.657
+2025-08-07 17:04:26: Epoch: 031 Train Loss: 0.099, ap: 0.606, auc: 0.696, f1: 0.525, acc: 0.631
+2025-08-07 17:04:28: Epoch: 031 Test results, ap: 0.404, auc: 0.643, f1: 0.393, acc: 0.685
+2025-08-07 17:04:35: Epoch: 032 Train Loss: 0.102, ap: 0.598, auc: 0.698, f1: 0.525, acc: 0.631
+2025-08-07 17:04:37: Epoch: 032 Test results, ap: 0.416, auc: 0.650, f1: 0.429, acc: 0.704
+2025-08-07 17:04:45: Epoch: 033 Train Loss: 0.094, ap: 0.602, auc: 0.704, f1: 0.561, acc: 0.650
+2025-08-07 17:04:46: Epoch: 033 Test results, ap: 0.426, auc: 0.649, f1: 0.394, acc: 0.630
+2025-08-07 17:04:54: Epoch: 034 Train Loss: 0.087, ap: 0.615, auc: 0.715, f1: 0.553, acc: 0.670
+2025-08-07 17:04:55: Epoch: 034 Test results, ap: 0.397, auc: 0.639, f1: 0.419, acc: 0.667
+2025-08-07 17:05:02: Epoch: 035 Train Loss: 0.075, ap: 0.604, auc: 0.708, f1: 0.568, acc: 0.660
+2025-08-07 17:05:04: Epoch: 035 Test results, ap: 0.412, auc: 0.657, f1: 0.394, acc: 0.602
+2025-08-07 17:05:11: Epoch: 036 Train Loss: 0.071, ap: 0.605, auc: 0.708, f1: 0.550, acc: 0.650
+2025-08-07 17:05:13: Epoch: 036 Test results, ap: 0.430, auc: 0.648, f1: 0.371, acc: 0.593
+2025-08-07 17:05:20: Epoch: 037 Train Loss: 0.070, ap: 0.612, auc: 0.715, f1: 0.564, acc: 0.670
+2025-08-07 17:05:22: Epoch: 037 Test results, ap: 0.457, auc: 0.669, f1: 0.418, acc: 0.639
+2025-08-07 17:05:22: Epoch: 037 New best TEST metrics
+2025-08-07 17:05:29: Epoch: 038 Train Loss: 0.082, ap: 0.620, auc: 0.723, f1: 0.628, acc: 0.689
+2025-08-07 17:05:31: Epoch: 038 Test results, ap: 0.462, auc: 0.667, f1: 0.438, acc: 0.620
+2025-08-07 17:05:31: Epoch: 038 New best TEST metrics
+2025-08-07 17:05:39: Epoch: 039 Train Loss: 0.069, ap: 0.625, auc: 0.724, f1: 0.548, acc: 0.680
+2025-08-07 17:05:40: Epoch: 039 Test results, ap: 0.434, auc: 0.621, f1: 0.345, acc: 0.648
+2025-08-07 17:05:48: Epoch: 040 Train Loss: 0.085, ap: 0.600, auc: 0.699, f1: 0.525, acc: 0.631
+2025-08-07 17:05:49: Epoch: 040 Test results, ap: 0.492, auc: 0.701, f1: 0.479, acc: 0.657
+2025-08-07 17:05:49: Epoch: 040 New best TEST metrics
+2025-08-07 17:05:57: Epoch: 041 Train Loss: 0.065, ap: 0.611, auc: 0.700, f1: 0.545, acc: 0.660
+2025-08-07 17:05:58: Epoch: 041 Test results, ap: 0.506, auc: 0.706, f1: 0.500, acc: 0.741
+2025-08-07 17:05:58: Epoch: 041 New best TEST metrics
+2025-08-07 17:06:05: Epoch: 042 Train Loss: 0.059, ap: 0.616, auc: 0.708, f1: 0.582, acc: 0.680
+2025-08-07 17:06:07: Epoch: 042 Test results, ap: 0.485, auc: 0.688, f1: 0.469, acc: 0.685
+2025-08-07 17:06:14: Epoch: 043 Train Loss: 0.057, ap: 0.609, auc: 0.710, f1: 0.581, acc: 0.650
+2025-08-07 17:06:16: Epoch: 043 Test results, ap: 0.399, auc: 0.635, f1: 0.416, acc: 0.583
+2025-08-07 17:06:23: Epoch: 044 Train Loss: 0.058, ap: 0.604, auc: 0.703, f1: 0.548, acc: 0.631
+2025-08-07 17:06:25: Epoch: 044 Test results, ap: 0.464, auc: 0.663, f1: 0.400, acc: 0.611
+2025-08-07 17:06:32: Epoch: 045 Train Loss: 0.060, ap: 0.615, auc: 0.708, f1: 0.545, acc: 0.660
+2025-08-07 17:06:34: Epoch: 045 Test results, ap: 0.469, auc: 0.674, f1: 0.393, acc: 0.657
+2025-08-07 17:06:41: Epoch: 046 Train Loss: 0.065, ap: 0.612, auc: 0.705, f1: 0.575, acc: 0.641
+2025-08-07 17:06:43: Epoch: 046 Test results, ap: 0.470, auc: 0.690, f1: 0.455, acc: 0.667
+2025-08-07 17:06:51: Epoch: 047 Train Loss: 0.054, ap: 0.609, auc: 0.708, f1: 0.550, acc: 0.650
+2025-08-07 17:06:52: Epoch: 047 Test results, ap: 0.488, auc: 0.694, f1: 0.523, acc: 0.713
+2025-08-07 17:06:59: Epoch: 048 Train Loss: 0.050, ap: 0.604, auc: 0.706, f1: 0.568, acc: 0.660
+2025-08-07 17:07:01: Epoch: 048 Test results, ap: 0.478, auc: 0.687, f1: 0.469, acc: 0.685
+2025-08-07 17:07:08: Epoch: 049 Train Loss: 0.050, ap: 0.605, auc: 0.706, f1: 0.554, acc: 0.641
+2025-08-07 17:07:09: Epoch: 049 Test results, ap: 0.480, auc: 0.702, f1: 0.476, acc: 0.694
+2025-08-07 17:07:17: Epoch: 050 Train Loss: 0.052, ap: 0.602, auc: 0.707, f1: 0.588, acc: 0.660
+2025-08-07 17:07:18: Epoch: 050 Test results, ap: 0.458, auc: 0.682, f1: 0.472, acc: 0.648
+2025-08-07 17:07:19: used time 0.12h
+2025-08-07 17:07:19: Running Cross Validation 2
+2025-08-07 17:07:19: Fold 2  Train set:412, Valid set:103, Test set: 108
+2025-08-07 17:07:26: Epoch: 001 Train Loss: 0.687, ap: 0.687, auc: 0.641, f1: 0.388, acc: 0.602
+2025-08-07 17:07:26: Epoch: 001 New best VALIDATION metrics
+2025-08-07 17:07:28: Epoch: 001 Test results, ap: 0.290, auc: 0.471, f1: 0.000, acc: 0.759
+2025-08-07 17:07:35: Epoch: 002 Train Loss: 0.663, ap: 0.807, auc: 0.765, f1: 0.514, acc: 0.650
+2025-08-07 17:07:35: Epoch: 002 New best VALIDATION metrics
+2025-08-07 17:07:37: Epoch: 002 Test results, ap: 0.262, auc: 0.490, f1: 0.000, acc: 0.759
+2025-08-07 17:07:44: Epoch: 003 Train Loss: 0.609, ap: 0.783, auc: 0.753, f1: 0.412, acc: 0.612
+2025-08-07 17:07:45: Epoch: 003 Test results, ap: 0.168, auc: 0.258, f1: 0.000, acc: 0.759
+2025-08-07 17:07:53: Epoch: 004 Train Loss: 0.574, ap: 0.745, auc: 0.742, f1: 0.671, acc: 0.505
+2025-08-07 17:07:54: Epoch: 004 Test results, ap: 0.231, auc: 0.365, f1: 0.388, acc: 0.241
+2025-08-07 17:08:02: Epoch: 005 Train Loss: 0.553, ap: 0.682, auc: 0.685, f1: 0.000, acc: 0.495
+2025-08-07 17:08:03: Epoch: 005 Test results, ap: 0.200, auc: 0.393, f1: 0.000, acc: 0.759
+2025-08-07 17:08:11: Epoch: 006 Train Loss: 0.579, ap: 0.806, auc: 0.749, f1: 0.671, acc: 0.505
+2025-08-07 17:08:12: Epoch: 006 Test results, ap: 0.198, auc: 0.407, f1: 0.388, acc: 0.241
+2025-08-07 17:08:20: Epoch: 007 Train Loss: 0.547, ap: 0.754, auc: 0.706, f1: 0.418, acc: 0.621
+2025-08-07 17:08:21: Epoch: 007 Test results, ap: 0.172, auc: 0.298, f1: 0.000, acc: 0.759
+2025-08-07 17:08:29: Epoch: 008 Train Loss: 0.537, ap: 0.848, auc: 0.816, f1: 0.671, acc: 0.505
+2025-08-07 17:08:29: Epoch: 008 New best VALIDATION metrics
+2025-08-07 17:08:31: Epoch: 008 Test results, ap: 0.211, auc: 0.392, f1: 0.388, acc: 0.241
+2025-08-07 17:08:38: Epoch: 009 Train Loss: 0.467, ap: 0.830, auc: 0.803, f1: 0.579, acc: 0.689
+2025-08-07 17:08:40: Epoch: 009 Test results, ap: 0.200, auc: 0.361, f1: 0.000, acc: 0.759
+2025-08-07 17:08:47: Epoch: 010 Train Loss: 0.461, ap: 0.818, auc: 0.780, f1: 0.709, acc: 0.641
+2025-08-07 17:08:49: Epoch: 010 Test results, ap: 0.212, auc: 0.392, f1: 0.000, acc: 0.722
+2025-08-07 17:08:56: Epoch: 011 Train Loss: 0.412, ap: 0.816, auc: 0.776, f1: 0.677, acc: 0.612
+2025-08-07 17:08:57: Epoch: 011 Test results, ap: 0.203, auc: 0.382, f1: 0.000, acc: 0.722
+2025-08-07 17:08:57: Epoch: 011 New best TEST metrics
+2025-08-07 17:09:05: Epoch: 012 Train Loss: 0.397, ap: 0.856, auc: 0.826, f1: 0.745, acc: 0.728
+2025-08-07 17:09:05: Epoch: 012 New best VALIDATION metrics
+2025-08-07 17:09:07: Epoch: 012 Test results, ap: 0.232, auc: 0.379, f1: 0.129, acc: 0.750
+2025-08-07 17:09:07: Epoch: 012 New best TEST metrics
+2025-08-07 17:09:14: Epoch: 013 Train Loss: 0.386, ap: 0.858, auc: 0.829, f1: 0.759, acc: 0.748
+2025-08-07 17:09:14: Epoch: 013 New best VALIDATION metrics
+2025-08-07 17:09:16: Epoch: 013 Test results, ap: 0.256, auc: 0.357, f1: 0.235, acc: 0.759
+2025-08-07 17:09:16: Epoch: 013 New best TEST metrics
+2025-08-07 17:09:23: Epoch: 014 Train Loss: 0.341, ap: 0.813, auc: 0.813, f1: 0.737, acc: 0.757
+2025-08-07 17:09:25: Epoch: 014 Test results, ap: 0.297, auc: 0.417, f1: 0.235, acc: 0.759
+2025-08-07 17:09:25: Epoch: 014 New best TEST metrics
+2025-08-07 17:09:32: Epoch: 015 Train Loss: 0.336, ap: 0.845, auc: 0.812, f1: 0.727, acc: 0.680
+2025-08-07 17:09:34: Epoch: 015 Test results, ap: 0.218, auc: 0.372, f1: 0.190, acc: 0.685
+2025-08-07 17:09:41: Epoch: 016 Train Loss: 0.329, ap: 0.815, auc: 0.788, f1: 0.659, acc: 0.718
+2025-08-07 17:09:43: Epoch: 016 Test results, ap: 0.219, auc: 0.398, f1: 0.000, acc: 0.731
+2025-08-07 17:09:50: Epoch: 017 Train Loss: 0.331, ap: 0.830, auc: 0.826, f1: 0.756, acc: 0.718
+2025-08-07 17:09:51: Epoch: 017 Test results, ap: 0.313, auc: 0.402, f1: 0.187, acc: 0.435
+2025-08-07 17:09:51: Epoch: 017 New best TEST metrics
+2025-08-07 17:09:59: Epoch: 018 Train Loss: 0.303, ap: 0.843, auc: 0.793, f1: 0.706, acc: 0.757
+2025-08-07 17:10:00: Epoch: 018 Test results, ap: 0.220, auc: 0.390, f1: 0.000, acc: 0.759
+2025-08-07 17:10:08: Epoch: 019 Train Loss: 0.291, ap: 0.845, auc: 0.801, f1: 0.721, acc: 0.670
+2025-08-07 17:10:10: Epoch: 019 Test results, ap: 0.316, auc: 0.467, f1: 0.242, acc: 0.769
+2025-08-07 17:10:10: Epoch: 019 New best TEST metrics
+2025-08-07 17:10:17: Epoch: 020 Train Loss: 0.309, ap: 0.754, auc: 0.760, f1: 0.438, acc: 0.602
+2025-08-07 17:10:19: Epoch: 020 Test results, ap: 0.330, auc: 0.526, f1: 0.000, acc: 0.759
+2025-08-07 17:10:19: Epoch: 020 New best TEST metrics
+2025-08-07 17:10:26: Epoch: 021 Train Loss: 0.301, ap: 0.841, auc: 0.791, f1: 0.714, acc: 0.728
+2025-08-07 17:10:28: Epoch: 021 Test results, ap: 0.274, auc: 0.461, f1: 0.000, acc: 0.750
+2025-08-07 17:10:35: Epoch: 022 Train Loss: 0.255, ap: 0.840, auc: 0.801, f1: 0.723, acc: 0.680
+2025-08-07 17:10:37: Epoch: 022 Test results, ap: 0.280, auc: 0.427, f1: 0.222, acc: 0.741
+2025-08-07 17:10:44: Epoch: 023 Train Loss: 0.240, ap: 0.827, auc: 0.796, f1: 0.771, acc: 0.757
+2025-08-07 17:10:45: Epoch: 023 Test results, ap: 0.305, auc: 0.440, f1: 0.235, acc: 0.759
+2025-08-07 17:10:53: Epoch: 024 Train Loss: 0.252, ap: 0.806, auc: 0.806, f1: 0.746, acc: 0.709
+2025-08-07 17:10:54: Epoch: 024 Test results, ap: 0.340, auc: 0.461, f1: 0.211, acc: 0.722
+2025-08-07 17:11:02: Epoch: 025 Train Loss: 0.202, ap: 0.761, auc: 0.779, f1: 0.704, acc: 0.689
+2025-08-07 17:11:03: Epoch: 025 Test results, ap: 0.335, auc: 0.440, f1: 0.229, acc: 0.750
+2025-08-07 17:11:11: Epoch: 026 Train Loss: 0.235, ap: 0.816, auc: 0.798, f1: 0.758, acc: 0.777
+2025-08-07 17:11:13: Epoch: 026 Test results, ap: 0.306, auc: 0.451, f1: 0.250, acc: 0.778
+2025-08-07 17:11:20: Epoch: 027 Train Loss: 0.199, ap: 0.790, auc: 0.771, f1: 0.731, acc: 0.728
+2025-08-07 17:11:22: Epoch: 027 Test results, ap: 0.342, auc: 0.455, f1: 0.242, acc: 0.769
+2025-08-07 17:11:29: Epoch: 028 Train Loss: 0.218, ap: 0.824, auc: 0.785, f1: 0.718, acc: 0.718
+2025-08-07 17:11:31: Epoch: 028 Test results, ap: 0.291, auc: 0.461, f1: 0.235, acc: 0.759
+2025-08-07 17:11:38: Epoch: 029 Train Loss: 0.222, ap: 0.802, auc: 0.780, f1: 0.724, acc: 0.718
+2025-08-07 17:11:39: Epoch: 029 Test results, ap: 0.333, auc: 0.439, f1: 0.242, acc: 0.769
+2025-08-07 17:11:47: Epoch: 030 Train Loss: 0.193, ap: 0.794, auc: 0.776, f1: 0.717, acc: 0.709
+2025-08-07 17:11:48: Epoch: 030 Test results, ap: 0.340, auc: 0.451, f1: 0.250, acc: 0.722
+2025-08-07 17:11:56: Epoch: 031 Train Loss: 0.165, ap: 0.797, auc: 0.785, f1: 0.713, acc: 0.718
+2025-08-07 17:11:57: Epoch: 031 Test results, ap: 0.333, auc: 0.440, f1: 0.250, acc: 0.722
+2025-08-07 17:12:05: Epoch: 032 Train Loss: 0.163, ap: 0.781, auc: 0.779, f1: 0.724, acc: 0.718
+2025-08-07 17:12:06: Epoch: 032 Test results, ap: 0.335, auc: 0.447, f1: 0.256, acc: 0.731
+2025-08-07 17:12:14: Epoch: 033 Train Loss: 0.144, ap: 0.787, auc: 0.774, f1: 0.714, acc: 0.728
+2025-08-07 17:12:16: Epoch: 033 Test results, ap: 0.371, auc: 0.468, f1: 0.242, acc: 0.769
+2025-08-07 17:12:23: Epoch: 034 Train Loss: 0.149, ap: 0.784, auc: 0.775, f1: 0.705, acc: 0.699
+2025-08-07 17:12:24: Epoch: 034 Test results, ap: 0.354, auc: 0.456, f1: 0.263, acc: 0.741
+2025-08-07 17:12:32: Epoch: 035 Train Loss: 0.160, ap: 0.788, auc: 0.778, f1: 0.740, acc: 0.748
+2025-08-07 17:12:33: Epoch: 035 Test results, ap: 0.361, auc: 0.463, f1: 0.263, acc: 0.741
+2025-08-07 17:12:41: Epoch: 036 Train Loss: 0.147, ap: 0.782, auc: 0.775, f1: 0.750, acc: 0.767
+2025-08-07 17:12:42: Epoch: 036 Test results, ap: 0.360, auc: 0.461, f1: 0.250, acc: 0.778
+2025-08-07 17:12:50: Epoch: 037 Train Loss: 0.149, ap: 0.788, auc: 0.795, f1: 0.752, acc: 0.738
+2025-08-07 17:12:51: Epoch: 037 Test results, ap: 0.339, auc: 0.470, f1: 0.280, acc: 0.667
+2025-08-07 17:12:59: Epoch: 038 Train Loss: 0.148, ap: 0.777, auc: 0.773, f1: 0.725, acc: 0.728
+2025-08-07 17:13:00: Epoch: 038 Test results, ap: 0.351, auc: 0.451, f1: 0.216, acc: 0.731
+2025-08-07 17:13:08: Epoch: 039 Train Loss: 0.131, ap: 0.773, auc: 0.770, f1: 0.714, acc: 0.728
+2025-08-07 17:13:09: Epoch: 039 Test results, ap: 0.350, auc: 0.453, f1: 0.222, acc: 0.741
+2025-08-07 17:13:17: Epoch: 040 Train Loss: 0.131, ap: 0.786, auc: 0.785, f1: 0.731, acc: 0.728
+2025-08-07 17:13:19: Epoch: 040 Test results, ap: 0.352, auc: 0.451, f1: 0.233, acc: 0.694
+2025-08-07 17:13:26: Epoch: 041 Train Loss: 0.127, ap: 0.781, auc: 0.775, f1: 0.747, acc: 0.757
+2025-08-07 17:13:27: Epoch: 041 Test results, ap: 0.356, auc: 0.462, f1: 0.216, acc: 0.731
+2025-08-07 17:13:35: Epoch: 042 Train Loss: 0.136, ap: 0.800, auc: 0.785, f1: 0.752, acc: 0.757
+2025-08-07 17:13:36: Epoch: 042 Test results, ap: 0.355, auc: 0.481, f1: 0.250, acc: 0.722
+2025-08-07 17:13:44: Epoch: 043 Train Loss: 0.128, ap: 0.793, auc: 0.777, f1: 0.717, acc: 0.709
+2025-08-07 17:13:45: Epoch: 043 Test results, ap: 0.364, auc: 0.485, f1: 0.222, acc: 0.676
+2025-08-07 17:13:53: Epoch: 044 Train Loss: 0.114, ap: 0.790, auc: 0.772, f1: 0.724, acc: 0.718
+2025-08-07 17:13:54: Epoch: 044 Test results, ap: 0.360, auc: 0.468, f1: 0.256, acc: 0.731
+2025-08-07 17:14:02: Epoch: 045 Train Loss: 0.119, ap: 0.805, auc: 0.777, f1: 0.738, acc: 0.738
+2025-08-07 17:14:03: Epoch: 045 Test results, ap: 0.359, auc: 0.470, f1: 0.238, acc: 0.704
+2025-08-07 17:14:11: Epoch: 046 Train Loss: 0.122, ap: 0.803, auc: 0.779, f1: 0.740, acc: 0.748
+2025-08-07 17:14:13: Epoch: 046 Test results, ap: 0.361, auc: 0.472, f1: 0.263, acc: 0.741
+2025-08-07 17:14:20: Epoch: 047 Train Loss: 0.109, ap: 0.806, auc: 0.784, f1: 0.733, acc: 0.738
+2025-08-07 17:14:22: Epoch: 047 Test results, ap: 0.346, auc: 0.481, f1: 0.256, acc: 0.731
+2025-08-07 17:14:29: Epoch: 048 Train Loss: 0.120, ap: 0.792, auc: 0.774, f1: 0.710, acc: 0.699
+2025-08-07 17:14:30: Epoch: 048 Test results, ap: 0.346, auc: 0.481, f1: 0.244, acc: 0.713
+2025-08-07 17:14:38: Epoch: 049 Train Loss: 0.116, ap: 0.787, auc: 0.770, f1: 0.731, acc: 0.728
+2025-08-07 17:14:39: Epoch: 049 Test results, ap: 0.355, auc: 0.475, f1: 0.244, acc: 0.713
+2025-08-07 17:14:47: Epoch: 050 Train Loss: 0.122, ap: 0.782, auc: 0.770, f1: 0.727, acc: 0.738
+2025-08-07 17:14:49: Epoch: 050 Test results, ap: 0.357, auc: 0.477, f1: 0.250, acc: 0.722
+2025-08-07 17:14:49: used time 0.13h
+2025-08-07 17:14:49: Running Cross Validation 3
+2025-08-07 17:14:49: Fold 3  Train set:412, Valid set:103, Test set: 108
+2025-08-07 17:14:56: Epoch: 001 Train Loss: 0.691, ap: 0.572, auc: 0.693, f1: 0.000, acc: 0.592
+2025-08-07 17:14:56: Epoch: 001 New best VALIDATION metrics
+2025-08-07 17:14:58: Epoch: 001 Test results, ap: 0.272, auc: 0.503, f1: 0.000, acc: 0.759
+2025-08-07 17:15:05: Epoch: 002 Train Loss: 0.664, ap: 0.716, auc: 0.764, f1: 0.563, acc: 0.699
+2025-08-07 17:15:05: Epoch: 002 New best VALIDATION metrics
+2025-08-07 17:15:07: Epoch: 002 Test results, ap: 0.220, auc: 0.457, f1: 0.000, acc: 0.759
+2025-08-07 17:15:14: Epoch: 003 Train Loss: 0.602, ap: 0.655, auc: 0.744, f1: 0.659, acc: 0.699
+2025-08-07 17:15:16: Epoch: 003 Test results, ap: 0.196, auc: 0.397, f1: 0.000, acc: 0.657
+2025-08-07 17:15:23: Epoch: 004 Train Loss: 0.539, ap: 0.674, auc: 0.722, f1: 0.568, acc: 0.660
+2025-08-07 17:15:25: Epoch: 004 Test results, ap: 0.261, auc: 0.488, f1: 0.000, acc: 0.722
+2025-08-07 17:15:32: Epoch: 005 Train Loss: 0.502, ap: 0.627, auc: 0.646, f1: 0.579, acc: 0.408
+2025-08-07 17:15:34: Epoch: 005 Test results, ap: 0.245, auc: 0.445, f1: 0.388, acc: 0.241
+2025-08-07 17:15:41: Epoch: 006 Train Loss: 0.464, ap: 0.782, auc: 0.816, f1: 0.639, acc: 0.748
+2025-08-07 17:15:41: Epoch: 006 New best VALIDATION metrics
+2025-08-07 17:15:43: Epoch: 006 Test results, ap: 0.187, auc: 0.355, f1: 0.000, acc: 0.759
+2025-08-07 17:15:50: Epoch: 007 Train Loss: 0.452, ap: 0.684, auc: 0.759, f1: 0.667, acc: 0.621
+2025-08-07 17:15:51: Epoch: 007 Test results, ap: 0.188, auc: 0.355, f1: 0.304, acc: 0.278
+2025-08-07 17:15:59: Epoch: 008 Train Loss: 0.430, ap: 0.526, auc: 0.690, f1: 0.650, acc: 0.592
+2025-08-07 17:16:01: Epoch: 008 Test results, ap: 0.186, auc: 0.359, f1: 0.265, acc: 0.333
+2025-08-07 17:16:08: Epoch: 009 Train Loss: 0.435, ap: 0.723, auc: 0.807, f1: 0.658, acc: 0.738
+2025-08-07 17:16:10: Epoch: 009 Test results, ap: 0.204, auc: 0.374, f1: 0.000, acc: 0.759
+2025-08-07 17:16:17: Epoch: 010 Train Loss: 0.404, ap: 0.712, auc: 0.762, f1: 0.630, acc: 0.670
+2025-08-07 17:16:19: Epoch: 010 Test results, ap: 0.240, auc: 0.445, f1: 0.267, acc: 0.491
+2025-08-07 17:16:26: Epoch: 011 Train Loss: 0.363, ap: 0.703, auc: 0.787, f1: 0.703, acc: 0.738
+2025-08-07 17:16:28: Epoch: 011 Test results, ap: 0.333, auc: 0.476, f1: 0.292, acc: 0.685
+2025-08-07 17:16:28: Epoch: 011 New best TEST metrics
+2025-08-07 17:16:35: Epoch: 012 Train Loss: 0.349, ap: 0.748, auc: 0.815, f1: 0.688, acc: 0.718
+2025-08-07 17:16:36: Epoch: 012 Test results, ap: 0.186, auc: 0.358, f1: 0.145, acc: 0.565
+2025-08-07 17:16:44: Epoch: 013 Train Loss: 0.325, ap: 0.736, auc: 0.770, f1: 0.660, acc: 0.670
+2025-08-07 17:16:45: Epoch: 013 Test results, ap: 0.269, auc: 0.485, f1: 0.345, acc: 0.472
+2025-08-07 17:16:53: Epoch: 014 Train Loss: 0.306, ap: 0.777, auc: 0.810, f1: 0.667, acc: 0.738
+2025-08-07 17:16:54: Epoch: 014 Test results, ap: 0.240, auc: 0.477, f1: 0.000, acc: 0.741
+2025-08-07 17:17:02: Epoch: 015 Train Loss: 0.294, ap: 0.713, auc: 0.790, f1: 0.667, acc: 0.660
+2025-08-07 17:17:03: Epoch: 015 Test results, ap: 0.189, auc: 0.373, f1: 0.228, acc: 0.435
+2025-08-07 17:17:11: Epoch: 016 Train Loss: 0.295, ap: 0.727, auc: 0.813, f1: 0.698, acc: 0.748
+2025-08-07 17:17:12: Epoch: 016 Test results, ap: 0.194, auc: 0.387, f1: 0.000, acc: 0.648
+2025-08-07 17:17:20: Epoch: 017 Train Loss: 0.265, ap: 0.770, auc: 0.835, f1: 0.676, acc: 0.767
+2025-08-07 17:17:20: Epoch: 017 New best VALIDATION metrics
+2025-08-07 17:17:21: Epoch: 017 Test results, ap: 0.247, auc: 0.499, f1: 0.000, acc: 0.759
+2025-08-07 17:17:29: Epoch: 018 Train Loss: 0.237, ap: 0.738, auc: 0.799, f1: 0.679, acc: 0.670
+2025-08-07 17:17:30: Epoch: 018 Test results, ap: 0.229, auc: 0.495, f1: 0.277, acc: 0.565
+2025-08-07 17:17:38: Epoch: 019 Train Loss: 0.227, ap: 0.702, auc: 0.775, f1: 0.673, acc: 0.660
+2025-08-07 17:17:39: Epoch: 019 Test results, ap: 0.209, auc: 0.433, f1: 0.364, acc: 0.352
+2025-08-07 17:17:47: Epoch: 020 Train Loss: 0.230, ap: 0.724, auc: 0.802, f1: 0.689, acc: 0.728
+2025-08-07 17:17:49: Epoch: 020 Test results, ap: 0.210, auc: 0.436, f1: 0.293, acc: 0.463
+2025-08-07 17:17:57: Epoch: 021 Train Loss: 0.200, ap: 0.687, auc: 0.786, f1: 0.692, acc: 0.680
+2025-08-07 17:17:58: Epoch: 021 Test results, ap: 0.224, auc: 0.473, f1: 0.367, acc: 0.426
+2025-08-07 17:18:06: Epoch: 022 Train Loss: 0.185, ap: 0.729, auc: 0.810, f1: 0.691, acc: 0.757
+2025-08-07 17:18:07: Epoch: 022 Test results, ap: 0.223, auc: 0.465, f1: 0.140, acc: 0.546
+2025-08-07 17:18:15: Epoch: 023 Train Loss: 0.170, ap: 0.731, auc: 0.806, f1: 0.714, acc: 0.767
+2025-08-07 17:18:16: Epoch: 023 Test results, ap: 0.248, auc: 0.487, f1: 0.208, acc: 0.648
+2025-08-07 17:18:24: Epoch: 024 Train Loss: 0.167, ap: 0.712, auc: 0.799, f1: 0.674, acc: 0.699
+2025-08-07 17:18:25: Epoch: 024 Test results, ap: 0.232, auc: 0.477, f1: 0.275, acc: 0.463
+2025-08-07 17:18:33: Epoch: 025 Train Loss: 0.163, ap: 0.732, auc: 0.808, f1: 0.718, acc: 0.718
+2025-08-07 17:18:34: Epoch: 025 Test results, ap: 0.245, auc: 0.507, f1: 0.375, acc: 0.444
+2025-08-07 17:18:42: Epoch: 026 Train Loss: 0.178, ap: 0.727, auc: 0.788, f1: 0.611, acc: 0.728
+2025-08-07 17:18:43: Epoch: 026 Test results, ap: 0.351, auc: 0.592, f1: 0.244, acc: 0.713
+2025-08-07 17:18:43: Epoch: 026 New best TEST metrics
+2025-08-07 17:18:51: Epoch: 027 Train Loss: 0.145, ap: 0.733, auc: 0.803, f1: 0.723, acc: 0.748
+2025-08-07 17:18:53: Epoch: 027 Test results, ap: 0.240, auc: 0.496, f1: 0.262, acc: 0.583
+2025-08-07 17:19:00: Epoch: 028 Train Loss: 0.144, ap: 0.724, auc: 0.802, f1: 0.667, acc: 0.689
+2025-08-07 17:19:01: Epoch: 028 Test results, ap: 0.256, auc: 0.529, f1: 0.300, acc: 0.611
+2025-08-07 17:19:09: Epoch: 029 Train Loss: 0.137, ap: 0.719, auc: 0.803, f1: 0.713, acc: 0.757
+2025-08-07 17:19:10: Epoch: 029 Test results, ap: 0.300, auc: 0.579, f1: 0.375, acc: 0.630
+2025-08-07 17:19:18: Epoch: 030 Train Loss: 0.134, ap: 0.719, auc: 0.808, f1: 0.703, acc: 0.738
+2025-08-07 17:19:19: Epoch: 030 Test results, ap: 0.249, auc: 0.519, f1: 0.261, acc: 0.528
+2025-08-07 17:19:27: Epoch: 031 Train Loss: 0.126, ap: 0.717, auc: 0.805, f1: 0.713, acc: 0.757
+2025-08-07 17:19:28: Epoch: 031 Test results, ap: 0.285, auc: 0.558, f1: 0.333, acc: 0.741
+2025-08-07 17:19:36: Epoch: 032 Train Loss: 0.118, ap: 0.713, auc: 0.792, f1: 0.705, acc: 0.748
+2025-08-07 17:19:37: Epoch: 032 Test results, ap: 0.275, auc: 0.553, f1: 0.250, acc: 0.667
+2025-08-07 17:19:45: Epoch: 033 Train Loss: 0.123, ap: 0.729, auc: 0.804, f1: 0.689, acc: 0.728
+2025-08-07 17:19:47: Epoch: 033 Test results, ap: 0.257, auc: 0.526, f1: 0.255, acc: 0.676
+2025-08-07 17:19:54: Epoch: 034 Train Loss: 0.101, ap: 0.706, auc: 0.790, f1: 0.714, acc: 0.767
+2025-08-07 17:19:56: Epoch: 034 Test results, ap: 0.278, auc: 0.561, f1: 0.273, acc: 0.704
+2025-08-07 17:20:03: Epoch: 035 Train Loss: 0.085, ap: 0.717, auc: 0.801, f1: 0.705, acc: 0.748
+2025-08-07 17:20:05: Epoch: 035 Test results, ap: 0.261, auc: 0.535, f1: 0.240, acc: 0.648
+2025-08-07 17:20:12: Epoch: 036 Train Loss: 0.098, ap: 0.729, auc: 0.802, f1: 0.703, acc: 0.738
+2025-08-07 17:20:14: Epoch: 036 Test results, ap: 0.277, auc: 0.563, f1: 0.264, acc: 0.639
+2025-08-07 17:20:21: Epoch: 037 Train Loss: 0.101, ap: 0.707, auc: 0.782, f1: 0.684, acc: 0.757
+2025-08-07 17:20:23: Epoch: 037 Test results, ap: 0.297, auc: 0.578, f1: 0.279, acc: 0.713
+2025-08-07 17:20:31: Epoch: 038 Train Loss: 0.097, ap: 0.697, auc: 0.787, f1: 0.699, acc: 0.757
+2025-08-07 17:20:32: Epoch: 038 Test results, ap: 0.289, auc: 0.578, f1: 0.261, acc: 0.685
+2025-08-07 17:20:40: Epoch: 039 Train Loss: 0.100, ap: 0.709, auc: 0.795, f1: 0.703, acc: 0.738
+2025-08-07 17:20:41: Epoch: 039 Test results, ap: 0.264, auc: 0.546, f1: 0.275, acc: 0.657
+2025-08-07 17:20:49: Epoch: 040 Train Loss: 0.093, ap: 0.699, auc: 0.792, f1: 0.708, acc: 0.728
+2025-08-07 17:20:50: Epoch: 040 Test results, ap: 0.275, auc: 0.552, f1: 0.300, acc: 0.611
+2025-08-07 17:20:58: Epoch: 041 Train Loss: 0.088, ap: 0.715, auc: 0.799, f1: 0.696, acc: 0.728
+2025-08-07 17:20:59: Epoch: 041 Test results, ap: 0.263, auc: 0.543, f1: 0.275, acc: 0.657
+2025-08-07 17:21:07: Epoch: 042 Train Loss: 0.065, ap: 0.709, auc: 0.797, f1: 0.736, acc: 0.777
+2025-08-07 17:21:08: Epoch: 042 Test results, ap: 0.275, auc: 0.566, f1: 0.245, acc: 0.657
+2025-08-07 17:21:16: Epoch: 043 Train Loss: 0.069, ap: 0.703, auc: 0.793, f1: 0.727, acc: 0.767
+2025-08-07 17:21:17: Epoch: 043 Test results, ap: 0.270, auc: 0.548, f1: 0.286, acc: 0.676
+2025-08-07 17:21:25: Epoch: 044 Train Loss: 0.072, ap: 0.715, auc: 0.800, f1: 0.731, acc: 0.757
+2025-08-07 17:21:26: Epoch: 044 Test results, ap: 0.258, auc: 0.533, f1: 0.240, acc: 0.648
+2025-08-07 17:21:34: Epoch: 045 Train Loss: 0.068, ap: 0.713, auc: 0.799, f1: 0.711, acc: 0.748
+2025-08-07 17:21:36: Epoch: 045 Test results, ap: 0.269, auc: 0.556, f1: 0.245, acc: 0.657
+2025-08-07 17:21:43: Epoch: 046 Train Loss: 0.071, ap: 0.713, auc: 0.797, f1: 0.725, acc: 0.757
+2025-08-07 17:21:45: Epoch: 046 Test results, ap: 0.256, auc: 0.523, f1: 0.250, acc: 0.667
+2025-08-07 17:21:52: Epoch: 047 Train Loss: 0.053, ap: 0.720, auc: 0.803, f1: 0.711, acc: 0.748
+2025-08-07 17:21:54: Epoch: 047 Test results, ap: 0.250, auc: 0.508, f1: 0.222, acc: 0.676
+2025-08-07 17:22:01: Epoch: 048 Train Loss: 0.063, ap: 0.716, auc: 0.800, f1: 0.717, acc: 0.748
+2025-08-07 17:22:03: Epoch: 048 Test results, ap: 0.267, auc: 0.543, f1: 0.269, acc: 0.648
+2025-08-07 17:22:10: Epoch: 049 Train Loss: 0.055, ap: 0.709, auc: 0.798, f1: 0.742, acc: 0.777
+2025-08-07 17:22:12: Epoch: 049 Test results, ap: 0.263, auc: 0.540, f1: 0.245, acc: 0.657
+2025-08-07 17:22:19: Epoch: 050 Train Loss: 0.060, ap: 0.712, auc: 0.798, f1: 0.742, acc: 0.777
+2025-08-07 17:22:21: Epoch: 050 Test results, ap: 0.257, auc: 0.525, f1: 0.245, acc: 0.657
+2025-08-07 17:22:21: used time 0.13h
+2025-08-07 17:22:21: Running Cross Validation 4
+2025-08-07 17:22:21: Fold 4  Train set:412, Valid set:103, Test set: 108
+2025-08-07 17:22:28: Epoch: 001 Train Loss: 0.685, ap: 0.453, auc: 0.512, f1: 0.048, acc: 0.612
+2025-08-07 17:22:28: Epoch: 001 New best VALIDATION metrics
+2025-08-07 17:22:30: Epoch: 001 Test results, ap: 0.187, auc: 0.354, f1: 0.000, acc: 0.759
+2025-08-07 17:22:37: Epoch: 002 Train Loss: 0.641, ap: 0.581, auc: 0.643, f1: 0.093, acc: 0.621
+2025-08-07 17:22:37: Epoch: 002 New best VALIDATION metrics
+2025-08-07 17:22:39: Epoch: 002 Test results, ap: 0.247, auc: 0.553, f1: 0.000, acc: 0.759
+2025-08-07 17:22:46: Epoch: 003 Train Loss: 0.603, ap: 0.712, auc: 0.792, f1: 0.174, acc: 0.631
+2025-08-07 17:22:46: Epoch: 003 New best VALIDATION metrics
+2025-08-07 17:22:48: Epoch: 003 Test results, ap: 0.226, auc: 0.389, f1: 0.000, acc: 0.759
+2025-08-07 17:22:55: Epoch: 004 Train Loss: 0.556, ap: 0.604, auc: 0.657, f1: 0.549, acc: 0.553
+2025-08-07 17:22:57: Epoch: 004 Test results, ap: 0.234, auc: 0.427, f1: 0.388, acc: 0.241
+2025-08-07 17:23:04: Epoch: 005 Train Loss: 0.506, ap: 0.525, auc: 0.663, f1: 0.493, acc: 0.660
+2025-08-07 17:23:06: Epoch: 005 Test results, ap: 0.241, auc: 0.526, f1: 0.000, acc: 0.759
+2025-08-07 17:23:13: Epoch: 006 Train Loss: 0.459, ap: 0.623, auc: 0.696, f1: 0.217, acc: 0.650
+2025-08-07 17:23:15: Epoch: 006 Test results, ap: 0.251, auc: 0.414, f1: 0.000, acc: 0.759
+2025-08-07 17:23:22: Epoch: 007 Train Loss: 0.434, ap: 0.675, auc: 0.680, f1: 0.255, acc: 0.660
+2025-08-07 17:23:24: Epoch: 007 Test results, ap: 0.200, auc: 0.320, f1: 0.000, acc: 0.759
+2025-08-07 17:23:32: Epoch: 008 Train Loss: 0.433, ap: 0.619, auc: 0.691, f1: 0.568, acc: 0.602
+2025-08-07 17:23:33: Epoch: 008 Test results, ap: 0.232, auc: 0.488, f1: 0.327, acc: 0.389
+2025-08-07 17:23:41: Epoch: 009 Train Loss: 0.400, ap: 0.554, auc: 0.638, f1: 0.529, acc: 0.602
+2025-08-07 17:23:42: Epoch: 009 Test results, ap: 0.317, auc: 0.585, f1: 0.375, acc: 0.259
+2025-08-07 17:23:50: Epoch: 010 Train Loss: 0.345, ap: 0.645, auc: 0.708, f1: 0.585, acc: 0.670
+2025-08-07 17:23:51: Epoch: 010 Test results, ap: 0.212, auc: 0.443, f1: 0.000, acc: 0.694
+2025-08-07 17:23:59: Epoch: 011 Train Loss: 0.312, ap: 0.605, auc: 0.691, f1: 0.626, acc: 0.641
+2025-08-07 17:24:00: Epoch: 011 Test results, ap: 0.220, auc: 0.467, f1: 0.151, acc: 0.583
+2025-08-07 17:24:00: Epoch: 011 New best TEST metrics
+2025-08-07 17:24:08: Epoch: 012 Train Loss: 0.307, ap: 0.630, auc: 0.649, f1: 0.462, acc: 0.660
+2025-08-07 17:24:09: Epoch: 012 Test results, ap: 0.213, auc: 0.412, f1: 0.000, acc: 0.759
+2025-08-07 17:24:17: Epoch: 013 Train Loss: 0.301, ap: 0.667, auc: 0.714, f1: 0.548, acc: 0.680
+2025-08-07 17:24:18: Epoch: 013 Test results, ap: 0.176, auc: 0.310, f1: 0.000, acc: 0.741
+2025-08-07 17:24:26: Epoch: 014 Train Loss: 0.247, ap: 0.596, auc: 0.653, f1: 0.424, acc: 0.631
+2025-08-07 17:24:27: Epoch: 014 Test results, ap: 0.224, auc: 0.470, f1: 0.000, acc: 0.759
+2025-08-07 17:24:27: Epoch: 014 New best TEST metrics
+2025-08-07 17:24:35: Epoch: 015 Train Loss: 0.257, ap: 0.637, auc: 0.641, f1: 0.484, acc: 0.689
+2025-08-07 17:24:36: Epoch: 015 Test results, ap: 0.179, auc: 0.323, f1: 0.000, acc: 0.759
+2025-08-07 17:24:44: Epoch: 016 Train Loss: 0.229, ap: 0.666, auc: 0.723, f1: 0.560, acc: 0.680
+2025-08-07 17:24:45: Epoch: 016 Test results, ap: 0.206, auc: 0.426, f1: 0.000, acc: 0.731
+2025-08-07 17:24:53: Epoch: 017 Train Loss: 0.210, ap: 0.592, auc: 0.638, f1: 0.541, acc: 0.670
+2025-08-07 17:24:54: Epoch: 017 Test results, ap: 0.203, auc: 0.386, f1: 0.000, acc: 0.750
+2025-08-07 17:25:02: Epoch: 018 Train Loss: 0.198, ap: 0.619, auc: 0.646, f1: 0.500, acc: 0.631
+2025-08-07 17:25:03: Epoch: 018 Test results, ap: 0.238, auc: 0.474, f1: 0.000, acc: 0.731
+2025-08-07 17:25:03: Epoch: 018 New best TEST metrics
+2025-08-07 17:25:11: Epoch: 019 Train Loss: 0.192, ap: 0.624, auc: 0.663, f1: 0.575, acc: 0.699
+2025-08-07 17:25:12: Epoch: 019 Test results, ap: 0.210, auc: 0.379, f1: 0.162, acc: 0.713
+2025-08-07 17:25:20: Epoch: 020 Train Loss: 0.173, ap: 0.653, auc: 0.658, f1: 0.551, acc: 0.699
+2025-08-07 17:25:21: Epoch: 020 Test results, ap: 0.223, auc: 0.357, f1: 0.000, acc: 0.750
+2025-08-07 17:25:29: Epoch: 021 Train Loss: 0.146, ap: 0.648, auc: 0.657, f1: 0.535, acc: 0.680
+2025-08-07 17:25:30: Epoch: 021 Test results, ap: 0.252, auc: 0.420, f1: 0.071, acc: 0.759
+2025-08-07 17:25:38: Epoch: 022 Train Loss: 0.152, ap: 0.626, auc: 0.643, f1: 0.535, acc: 0.680
+2025-08-07 17:25:39: Epoch: 022 Test results, ap: 0.231, auc: 0.389, f1: 0.000, acc: 0.741
+2025-08-07 17:25:47: Epoch: 023 Train Loss: 0.152, ap: 0.622, auc: 0.643, f1: 0.551, acc: 0.699
+2025-08-07 17:25:48: Epoch: 023 Test results, ap: 0.243, auc: 0.384, f1: 0.138, acc: 0.769
+2025-08-07 17:25:56: Epoch: 024 Train Loss: 0.155, ap: 0.629, auc: 0.636, f1: 0.532, acc: 0.641
+2025-08-07 17:25:57: Epoch: 024 Test results, ap: 0.196, auc: 0.376, f1: 0.000, acc: 0.741
+2025-08-07 17:26:04: Epoch: 025 Train Loss: 0.140, ap: 0.610, auc: 0.637, f1: 0.563, acc: 0.699
+2025-08-07 17:26:06: Epoch: 025 Test results, ap: 0.212, auc: 0.402, f1: 0.158, acc: 0.704
+2025-08-07 17:26:13: Epoch: 026 Train Loss: 0.123, ap: 0.666, auc: 0.679, f1: 0.568, acc: 0.660
+2025-08-07 17:26:15: Epoch: 026 Test results, ap: 0.197, auc: 0.390, f1: 0.050, acc: 0.648
+2025-08-07 17:26:22: Epoch: 027 Train Loss: 0.128, ap: 0.685, auc: 0.679, f1: 0.615, acc: 0.757
+2025-08-07 17:26:24: Epoch: 027 Test results, ap: 0.248, auc: 0.467, f1: 0.000, acc: 0.759
+2025-08-07 17:26:24: Epoch: 027 New best TEST metrics
+2025-08-07 17:26:32: Epoch: 028 Train Loss: 0.120, ap: 0.672, auc: 0.683, f1: 0.571, acc: 0.680
+2025-08-07 17:26:33: Epoch: 028 Test results, ap: 0.208, auc: 0.411, f1: 0.158, acc: 0.704
+2025-08-07 17:26:41: Epoch: 029 Train Loss: 0.119, ap: 0.649, auc: 0.646, f1: 0.571, acc: 0.738
+2025-08-07 17:26:42: Epoch: 029 Test results, ap: 0.244, auc: 0.447, f1: 0.000, acc: 0.750
+2025-08-07 17:26:50: Epoch: 030 Train Loss: 0.095, ap: 0.681, auc: 0.699, f1: 0.561, acc: 0.650
+2025-08-07 17:26:51: Epoch: 030 Test results, ap: 0.231, auc: 0.467, f1: 0.059, acc: 0.704
+2025-08-07 17:26:58: Epoch: 031 Train Loss: 0.097, ap: 0.685, auc: 0.689, f1: 0.592, acc: 0.718
+2025-08-07 17:27:00: Epoch: 031 Test results, ap: 0.244, auc: 0.462, f1: 0.000, acc: 0.731
+2025-08-07 17:27:07: Epoch: 032 Train Loss: 0.083, ap: 0.671, auc: 0.689, f1: 0.580, acc: 0.718
+2025-08-07 17:27:09: Epoch: 032 Test results, ap: 0.227, auc: 0.421, f1: 0.000, acc: 0.750
+2025-08-07 17:27:16: Epoch: 033 Train Loss: 0.086, ap: 0.649, auc: 0.651, f1: 0.548, acc: 0.728
+2025-08-07 17:27:18: Epoch: 033 Test results, ap: 0.256, auc: 0.462, f1: 0.000, acc: 0.759
+2025-08-07 17:27:18: Epoch: 033 New best TEST metrics
+2025-08-07 17:27:25: Epoch: 034 Train Loss: 0.073, ap: 0.650, auc: 0.659, f1: 0.559, acc: 0.709
+2025-08-07 17:27:27: Epoch: 034 Test results, ap: 0.260, auc: 0.492, f1: 0.000, acc: 0.750
+2025-08-07 17:27:27: Epoch: 034 New best TEST metrics
+2025-08-07 17:27:34: Epoch: 035 Train Loss: 0.081, ap: 0.651, auc: 0.681, f1: 0.548, acc: 0.680
+2025-08-07 17:27:36: Epoch: 035 Test results, ap: 0.248, auc: 0.485, f1: 0.000, acc: 0.713
+2025-08-07 17:27:44: Epoch: 036 Train Loss: 0.075, ap: 0.665, auc: 0.675, f1: 0.594, acc: 0.748
+2025-08-07 17:27:45: Epoch: 036 Test results, ap: 0.266, auc: 0.489, f1: 0.000, acc: 0.759
+2025-08-07 17:27:45: Epoch: 036 New best TEST metrics
+2025-08-07 17:27:53: Epoch: 037 Train Loss: 0.080, ap: 0.657, auc: 0.668, f1: 0.597, acc: 0.738
+2025-08-07 17:27:54: Epoch: 037 Test results, ap: 0.296, auc: 0.568, f1: 0.000, acc: 0.741
+2025-08-07 17:27:54: Epoch: 037 New best TEST metrics
+2025-08-07 17:28:02: Epoch: 038 Train Loss: 0.059, ap: 0.682, auc: 0.694, f1: 0.609, acc: 0.738
+2025-08-07 17:28:03: Epoch: 038 Test results, ap: 0.265, auc: 0.530, f1: 0.000, acc: 0.713
+2025-08-07 17:28:11: Epoch: 039 Train Loss: 0.090, ap: 0.666, auc: 0.664, f1: 0.581, acc: 0.748
+2025-08-07 17:28:12: Epoch: 039 Test results, ap: 0.269, auc: 0.528, f1: 0.000, acc: 0.759
+2025-08-07 17:28:20: Epoch: 040 Train Loss: 0.103, ap: 0.651, auc: 0.660, f1: 0.579, acc: 0.689
+2025-08-07 17:28:21: Epoch: 040 Test results, ap: 0.290, auc: 0.577, f1: 0.279, acc: 0.713
+2025-08-07 17:28:21: Epoch: 040 New best TEST metrics
+2025-08-07 17:28:29: Epoch: 041 Train Loss: 0.089, ap: 0.673, auc: 0.694, f1: 0.553, acc: 0.670
+2025-08-07 17:28:30: Epoch: 041 Test results, ap: 0.255, auc: 0.520, f1: 0.059, acc: 0.704
+2025-08-07 17:28:38: Epoch: 042 Train Loss: 0.074, ap: 0.678, auc: 0.689, f1: 0.575, acc: 0.699
+2025-08-07 17:28:39: Epoch: 042 Test results, ap: 0.267, auc: 0.522, f1: 0.000, acc: 0.713
+2025-08-07 17:28:47: Epoch: 043 Train Loss: 0.064, ap: 0.677, auc: 0.702, f1: 0.595, acc: 0.709
+2025-08-07 17:28:48: Epoch: 043 Test results, ap: 0.268, auc: 0.531, f1: 0.000, acc: 0.713
+2025-08-07 17:28:56: Epoch: 044 Train Loss: 0.055, ap: 0.670, auc: 0.686, f1: 0.588, acc: 0.728
+2025-08-07 17:28:57: Epoch: 044 Test results, ap: 0.281, auc: 0.581, f1: 0.000, acc: 0.713
+2025-08-07 17:29:05: Epoch: 045 Train Loss: 0.055, ap: 0.663, auc: 0.684, f1: 0.571, acc: 0.709
+2025-08-07 17:29:06: Epoch: 045 Test results, ap: 0.289, auc: 0.618, f1: 0.057, acc: 0.694
+2025-08-07 17:29:06: Epoch: 045 New best TEST metrics
+2025-08-07 17:29:14: Epoch: 046 Train Loss: 0.055, ap: 0.690, auc: 0.701, f1: 0.571, acc: 0.709
+2025-08-07 17:29:15: Epoch: 046 Test results, ap: 0.294, auc: 0.619, f1: 0.000, acc: 0.713
+2025-08-07 17:29:15: Epoch: 046 New best TEST metrics
+2025-08-07 17:29:23: Epoch: 047 Train Loss: 0.061, ap: 0.697, auc: 0.708, f1: 0.575, acc: 0.699
+2025-08-07 17:29:24: Epoch: 047 Test results, ap: 0.299, auc: 0.632, f1: 0.000, acc: 0.713
+2025-08-07 17:29:24: Epoch: 047 New best TEST metrics
+2025-08-07 17:29:32: Epoch: 048 Train Loss: 0.041, ap: 0.686, auc: 0.697, f1: 0.568, acc: 0.689
+2025-08-07 17:29:33: Epoch: 048 Test results, ap: 0.297, auc: 0.625, f1: 0.000, acc: 0.713
+2025-08-07 17:29:41: Epoch: 049 Train Loss: 0.052, ap: 0.684, auc: 0.701, f1: 0.571, acc: 0.709
+2025-08-07 17:29:42: Epoch: 049 Test results, ap: 0.302, auc: 0.633, f1: 0.062, acc: 0.722
+2025-08-07 17:29:42: Epoch: 049 New best TEST metrics
+2025-08-07 17:29:50: Epoch: 050 Train Loss: 0.054, ap: 0.672, auc: 0.691, f1: 0.600, acc: 0.728
+2025-08-07 17:29:51: Epoch: 050 Test results, ap: 0.309, auc: 0.640, f1: 0.062, acc: 0.722
+2025-08-07 17:29:51: Epoch: 050 New best TEST metrics
+2025-08-07 17:29:51: used time 0.13h
+2025-08-07 17:29:51: Cross Validation Finished!
+2025-08-07 17:29:51: Best validation perform list
+[[0.74682003 0.77355623 0.69724768 0.67961162]
+ [0.68009746 0.73873872 0.57894737 0.68932039]
+ [0.85801035 0.82918549 0.75925928 0.74757284]
+ [0.77034712 0.8348946  0.67567569 0.7669903 ]
+ [0.7116096  0.79150271 0.17391305 0.63106793]]
+2025-08-07 17:29:51: mean: [0.753 0.794 0.577 0.703]
+2025-08-07 17:29:51: std: [0.061 0.036 0.21  0.049]
+2025-08-07 17:29:51: Best test perform list
+[[0.3389641  0.6238274  0.         0.75      ]
+ [0.50646311 0.706379   0.5        0.74074072]
+ [0.32962123 0.5262664  0.         0.75925928]
+ [0.35120171 0.59193242 0.24390244 0.71296299]
+ [0.30919024 0.64024389 0.0625     0.72222221]]
+2025-08-07 17:29:51: mean: [0.367 0.618 0.161 0.737]
+2025-08-07 17:29:51: std: [0.071 0.059 0.191 0.017]
diff --git a/run-cls/gru-att-256-ce-32-0.001-50/config.json b/run-cls/gru-att-256-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "gru", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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@@ -0,0 +1,6 @@
+Valid
+0.6630676627159119,0.7111135840415954,0.5206708669662475,0.648241925239563
+0.08769546795719477,0.09065419010060403,0.2672250864774429,0.08686235850921079
+Test
+0.3952899217605591,0.6397132515907288,0.29646825790405273,0.5268292605876923
+0.09723253407204156,0.08306798634069686,0.15898332273396398,0.20447759486048872
diff --git a/run-cls/gru-att-256-ce-32-0.001-50/training.log b/run-cls/gru-att-256-ce-32-0.001-50/training.log
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@@ -0,0 +1,625 @@
+2025-08-26 01:38:16: saving_dir: ./run-cls/gru-att-256-ce-32-0.001-50
+2025-08-26 01:38:16: Loading Train Dataset
+2025-08-26 01:38:16: Loading Test Dataset
+2025-08-26 01:38:17: Running Cross Validation 0
+2025-08-26 01:38:17: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 01:38:19: Epoch: 001 Train Loss: 0.694, ap: 0.528, auc: 0.568, f1: 0.649, acc: 0.481
+2025-08-26 01:38:19: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:38:19: Epoch: 001 Test results, ap: 0.289, auc: 0.588, f1: 0.392, acc: 0.244
+2025-08-26 01:38:20: Epoch: 002 Train Loss: 0.695, ap: 0.520, auc: 0.594, f1: 0.649, acc: 0.481
+2025-08-26 01:38:20: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:38:20: Epoch: 002 Test results, ap: 0.297, auc: 0.619, f1: 0.392, acc: 0.244
+2025-08-26 01:38:22: Epoch: 003 Train Loss: 0.694, ap: 0.549, auc: 0.588, f1: 0.649, acc: 0.481
+2025-08-26 01:38:22: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:38:22: Epoch: 003 Test results, ap: 0.262, auc: 0.582, f1: 0.392, acc: 0.244
+2025-08-26 01:38:23: Epoch: 004 Train Loss: 0.697, ap: 0.557, auc: 0.593, f1: 0.007, acc: 0.518
+2025-08-26 01:38:23: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:38:23: Epoch: 004 Test results, ap: 0.367, auc: 0.569, f1: 0.000, acc: 0.756
+2025-08-26 01:38:25: Epoch: 005 Train Loss: 0.695, ap: 0.546, auc: 0.577, f1: 0.000, acc: 0.519
+2025-08-26 01:38:25: Epoch: 005 Test results, ap: 0.272, auc: 0.518, f1: 0.000, acc: 0.756
+2025-08-26 01:38:26: Epoch: 006 Train Loss: 0.695, ap: 0.459, auc: 0.489, f1: 0.649, acc: 0.481
+2025-08-26 01:38:26: Epoch: 006 Test results, ap: 0.279, auc: 0.513, f1: 0.392, acc: 0.244
+2025-08-26 01:38:28: Epoch: 007 Train Loss: 0.694, ap: 0.465, auc: 0.458, f1: 0.007, acc: 0.519
+2025-08-26 01:38:28: Epoch: 007 Test results, ap: 0.238, auc: 0.489, f1: 0.000, acc: 0.756
+2025-08-26 01:38:29: Epoch: 008 Train Loss: 0.694, ap: 0.485, auc: 0.528, f1: 0.000, acc: 0.519
+2025-08-26 01:38:29: Epoch: 008 Test results, ap: 0.324, auc: 0.558, f1: 0.000, acc: 0.756
+2025-08-26 01:38:31: Epoch: 009 Train Loss: 0.695, ap: 0.523, auc: 0.552, f1: 0.649, acc: 0.481
+2025-08-26 01:38:31: Epoch: 009 Test results, ap: 0.252, auc: 0.538, f1: 0.392, acc: 0.244
+2025-08-26 01:38:32: Epoch: 010 Train Loss: 0.694, ap: 0.534, auc: 0.564, f1: 0.649, acc: 0.481
+2025-08-26 01:38:32: Epoch: 010 Test results, ap: 0.248, auc: 0.529, f1: 0.392, acc: 0.244
+2025-08-26 01:38:34: Epoch: 011 Train Loss: 0.694, ap: 0.491, auc: 0.529, f1: 0.649, acc: 0.481
+2025-08-26 01:38:34: Epoch: 011 Test results, ap: 0.226, auc: 0.481, f1: 0.392, acc: 0.244
+2025-08-26 01:38:34: Epoch: 011 New best TEST metrics
+2025-08-26 01:38:35: Epoch: 012 Train Loss: 0.694, ap: 0.522, auc: 0.543, f1: 0.007, acc: 0.519
+2025-08-26 01:38:35: Epoch: 012 Test results, ap: 0.240, auc: 0.508, f1: 0.000, acc: 0.756
+2025-08-26 01:38:35: Epoch: 012 New best TEST metrics
+2025-08-26 01:38:37: Epoch: 013 Train Loss: 0.693, ap: 0.515, auc: 0.532, f1: 0.648, acc: 0.481
+2025-08-26 01:38:37: Epoch: 013 Test results, ap: 0.255, auc: 0.548, f1: 0.392, acc: 0.244
+2025-08-26 01:38:37: Epoch: 013 New best TEST metrics
+2025-08-26 01:38:38: Epoch: 014 Train Loss: 0.693, ap: 0.516, auc: 0.545, f1: 0.649, acc: 0.481
+2025-08-26 01:38:39: Epoch: 014 Test results, ap: 0.226, auc: 0.475, f1: 0.392, acc: 0.244
+2025-08-26 01:38:40: Epoch: 015 Train Loss: 0.693, ap: 0.546, auc: 0.566, f1: 0.649, acc: 0.481
+2025-08-26 01:38:40: Epoch: 015 Test results, ap: 0.269, auc: 0.553, f1: 0.392, acc: 0.244
+2025-08-26 01:38:40: Epoch: 015 New best TEST metrics
+2025-08-26 01:38:41: Epoch: 016 Train Loss: 0.693, ap: 0.530, auc: 0.554, f1: 0.649, acc: 0.481
+2025-08-26 01:38:42: Epoch: 016 Test results, ap: 0.275, auc: 0.564, f1: 0.392, acc: 0.244
+2025-08-26 01:38:42: Epoch: 016 New best TEST metrics
+2025-08-26 01:38:43: Epoch: 017 Train Loss: 0.694, ap: 0.568, auc: 0.601, f1: 0.000, acc: 0.519
+2025-08-26 01:38:43: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:38:43: Epoch: 017 Test results, ap: 0.260, auc: 0.552, f1: 0.000, acc: 0.756
+2025-08-26 01:38:44: Epoch: 018 Train Loss: 0.693, ap: 0.536, auc: 0.591, f1: 0.000, acc: 0.519
+2025-08-26 01:38:45: Epoch: 018 Test results, ap: 0.273, auc: 0.556, f1: 0.000, acc: 0.756
+2025-08-26 01:38:46: Epoch: 019 Train Loss: 0.693, ap: 0.558, auc: 0.611, f1: 0.649, acc: 0.481
+2025-08-26 01:38:46: Epoch: 019 Test results, ap: 0.304, auc: 0.573, f1: 0.392, acc: 0.244
+2025-08-26 01:38:46: Epoch: 019 New best TEST metrics
+2025-08-26 01:38:47: Epoch: 020 Train Loss: 0.694, ap: 0.570, auc: 0.607, f1: 0.000, acc: 0.519
+2025-08-26 01:38:47: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:38:48: Epoch: 020 Test results, ap: 0.242, auc: 0.504, f1: 0.000, acc: 0.756
+2025-08-26 01:38:49: Epoch: 021 Train Loss: 0.693, ap: 0.538, auc: 0.562, f1: 0.000, acc: 0.519
+2025-08-26 01:38:49: Epoch: 021 Test results, ap: 0.250, auc: 0.536, f1: 0.000, acc: 0.756
+2025-08-26 01:38:51: Epoch: 022 Train Loss: 0.693, ap: 0.535, auc: 0.575, f1: 0.649, acc: 0.481
+2025-08-26 01:38:51: Epoch: 022 Test results, ap: 0.244, auc: 0.510, f1: 0.392, acc: 0.244
+2025-08-26 01:38:52: Epoch: 023 Train Loss: 0.693, ap: 0.538, auc: 0.570, f1: 0.000, acc: 0.519
+2025-08-26 01:38:52: Epoch: 023 Test results, ap: 0.248, auc: 0.543, f1: 0.000, acc: 0.756
+2025-08-26 01:38:54: Epoch: 024 Train Loss: 0.693, ap: 0.508, auc: 0.518, f1: 0.000, acc: 0.519
+2025-08-26 01:38:54: Epoch: 024 Test results, ap: 0.248, auc: 0.511, f1: 0.000, acc: 0.756
+2025-08-26 01:38:55: Epoch: 025 Train Loss: 0.693, ap: 0.521, auc: 0.540, f1: 0.649, acc: 0.481
+2025-08-26 01:38:55: Epoch: 025 Test results, ap: 0.283, auc: 0.565, f1: 0.392, acc: 0.244
+2025-08-26 01:38:57: Epoch: 026 Train Loss: 0.693, ap: 0.496, auc: 0.503, f1: 0.649, acc: 0.481
+2025-08-26 01:38:57: Epoch: 026 Test results, ap: 0.232, auc: 0.449, f1: 0.392, acc: 0.244
+2025-08-26 01:38:58: Epoch: 027 Train Loss: 0.693, ap: 0.507, auc: 0.517, f1: 0.000, acc: 0.519
+2025-08-26 01:38:58: Epoch: 027 Test results, ap: 0.285, auc: 0.574, f1: 0.000, acc: 0.756
+2025-08-26 01:39:00: Epoch: 028 Train Loss: 0.693, ap: 0.514, auc: 0.544, f1: 0.649, acc: 0.481
+2025-08-26 01:39:00: Epoch: 028 Test results, ap: 0.283, auc: 0.545, f1: 0.392, acc: 0.244
+2025-08-26 01:39:01: Epoch: 029 Train Loss: 0.693, ap: 0.524, auc: 0.564, f1: 0.000, acc: 0.519
+2025-08-26 01:39:01: Epoch: 029 Test results, ap: 0.265, auc: 0.540, f1: 0.000, acc: 0.756
+2025-08-26 01:39:03: Epoch: 030 Train Loss: 0.693, ap: 0.533, auc: 0.580, f1: 0.649, acc: 0.481
+2025-08-26 01:39:03: Epoch: 030 Test results, ap: 0.246, auc: 0.503, f1: 0.392, acc: 0.244
+2025-08-26 01:39:04: Epoch: 031 Train Loss: 0.693, ap: 0.529, auc: 0.579, f1: 0.649, acc: 0.481
+2025-08-26 01:39:04: Epoch: 031 Test results, ap: 0.251, auc: 0.504, f1: 0.392, acc: 0.244
+2025-08-26 01:39:06: Epoch: 032 Train Loss: 0.693, ap: 0.525, auc: 0.585, f1: 0.649, acc: 0.481
+2025-08-26 01:39:06: Epoch: 032 Test results, ap: 0.286, auc: 0.567, f1: 0.392, acc: 0.244
+2025-08-26 01:39:07: Epoch: 033 Train Loss: 0.693, ap: 0.505, auc: 0.560, f1: 0.000, acc: 0.519
+2025-08-26 01:39:07: Epoch: 033 Test results, ap: 0.261, auc: 0.554, f1: 0.000, acc: 0.756
+2025-08-26 01:39:09: Epoch: 034 Train Loss: 0.693, ap: 0.503, auc: 0.559, f1: 0.000, acc: 0.519
+2025-08-26 01:39:09: Epoch: 034 Test results, ap: 0.266, auc: 0.554, f1: 0.000, acc: 0.756
+2025-08-26 01:39:10: Epoch: 035 Train Loss: 0.693, ap: 0.519, auc: 0.568, f1: 0.649, acc: 0.481
+2025-08-26 01:39:10: Epoch: 035 Test results, ap: 0.271, auc: 0.567, f1: 0.392, acc: 0.244
+2025-08-26 01:39:12: Epoch: 036 Train Loss: 0.693, ap: 0.533, auc: 0.576, f1: 0.649, acc: 0.481
+2025-08-26 01:39:12: Epoch: 036 Test results, ap: 0.262, auc: 0.560, f1: 0.392, acc: 0.244
+2025-08-26 01:39:13: Epoch: 037 Train Loss: 0.693, ap: 0.529, auc: 0.575, f1: 0.000, acc: 0.519
+2025-08-26 01:39:13: Epoch: 037 Test results, ap: 0.272, auc: 0.551, f1: 0.000, acc: 0.756
+2025-08-26 01:39:15: Epoch: 038 Train Loss: 0.693, ap: 0.531, auc: 0.576, f1: 0.000, acc: 0.519
+2025-08-26 01:39:15: Epoch: 038 Test results, ap: 0.264, auc: 0.552, f1: 0.000, acc: 0.756
+2025-08-26 01:39:16: Epoch: 039 Train Loss: 0.693, ap: 0.542, auc: 0.582, f1: 0.649, acc: 0.481
+2025-08-26 01:39:16: Epoch: 039 Test results, ap: 0.262, auc: 0.565, f1: 0.392, acc: 0.244
+2025-08-26 01:39:18: Epoch: 040 Train Loss: 0.693, ap: 0.541, auc: 0.583, f1: 0.649, acc: 0.481
+2025-08-26 01:39:18: Epoch: 040 Test results, ap: 0.257, auc: 0.551, f1: 0.392, acc: 0.244
+2025-08-26 01:39:19: Epoch: 041 Train Loss: 0.693, ap: 0.538, auc: 0.581, f1: 0.000, acc: 0.519
+2025-08-26 01:39:19: Epoch: 041 Test results, ap: 0.261, auc: 0.557, f1: 0.000, acc: 0.756
+2025-08-26 01:39:21: Epoch: 042 Train Loss: 0.693, ap: 0.541, auc: 0.583, f1: 0.000, acc: 0.519
+2025-08-26 01:39:21: Epoch: 042 Test results, ap: 0.262, auc: 0.555, f1: 0.000, acc: 0.756
+2025-08-26 01:39:22: Epoch: 043 Train Loss: 0.693, ap: 0.539, auc: 0.582, f1: 0.000, acc: 0.519
+2025-08-26 01:39:22: Epoch: 043 Test results, ap: 0.259, auc: 0.559, f1: 0.000, acc: 0.756
+2025-08-26 01:39:24: Epoch: 044 Train Loss: 0.693, ap: 0.540, auc: 0.582, f1: 0.000, acc: 0.519
+2025-08-26 01:39:24: Epoch: 044 Test results, ap: 0.266, auc: 0.557, f1: 0.000, acc: 0.756
+2025-08-26 01:39:25: Epoch: 045 Train Loss: 0.693, ap: 0.540, auc: 0.582, f1: 0.626, acc: 0.549
+2025-08-26 01:39:25: Epoch: 045 Test results, ap: 0.265, auc: 0.558, f1: 0.392, acc: 0.244
+2025-08-26 01:39:27: Epoch: 046 Train Loss: 0.693, ap: 0.541, auc: 0.583, f1: 0.649, acc: 0.481
+2025-08-26 01:39:27: Epoch: 046 Test results, ap: 0.261, auc: 0.561, f1: 0.392, acc: 0.244
+2025-08-26 01:39:28: Epoch: 047 Train Loss: 0.693, ap: 0.543, auc: 0.586, f1: 0.649, acc: 0.481
+2025-08-26 01:39:28: Epoch: 047 Test results, ap: 0.263, auc: 0.556, f1: 0.392, acc: 0.244
+2025-08-26 01:39:30: Epoch: 048 Train Loss: 0.693, ap: 0.543, auc: 0.585, f1: 0.649, acc: 0.481
+2025-08-26 01:39:30: Epoch: 048 Test results, ap: 0.262, auc: 0.567, f1: 0.392, acc: 0.244
+2025-08-26 01:39:31: Epoch: 049 Train Loss: 0.693, ap: 0.544, auc: 0.586, f1: 0.649, acc: 0.481
+2025-08-26 01:39:31: Epoch: 049 Test results, ap: 0.257, auc: 0.549, f1: 0.392, acc: 0.244
+2025-08-26 01:39:33: Epoch: 050 Train Loss: 0.693, ap: 0.545, auc: 0.587, f1: 0.649, acc: 0.481
+2025-08-26 01:39:33: Epoch: 050 Test results, ap: 0.260, auc: 0.552, f1: 0.392, acc: 0.244
+2025-08-26 01:39:33: used time 0.02h
+2025-08-26 01:39:33: Running Cross Validation 1
+2025-08-26 01:39:33: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 01:39:34: Epoch: 001 Train Loss: 0.695, ap: 0.392, auc: 0.448, f1: 0.000, acc: 0.563
+2025-08-26 01:39:34: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:39:35: Epoch: 001 Test results, ap: 0.230, auc: 0.369, f1: 0.000, acc: 0.756
+2025-08-26 01:39:36: Epoch: 002 Train Loss: 0.694, ap: 0.395, auc: 0.435, f1: 0.608, acc: 0.437
+2025-08-26 01:39:36: Epoch: 002 Test results, ap: 0.248, auc: 0.462, f1: 0.392, acc: 0.244
+2025-08-26 01:39:37: Epoch: 003 Train Loss: 0.695, ap: 0.449, auc: 0.542, f1: 0.000, acc: 0.563
+2025-08-26 01:39:37: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:39:38: Epoch: 003 Test results, ap: 0.555, auc: 0.728, f1: 0.000, acc: 0.756
+2025-08-26 01:39:39: Epoch: 004 Train Loss: 0.696, ap: 0.440, auc: 0.515, f1: 0.000, acc: 0.563
+2025-08-26 01:39:39: Epoch: 004 Test results, ap: 0.581, auc: 0.776, f1: 0.000, acc: 0.756
+2025-08-26 01:39:40: Epoch: 005 Train Loss: 0.694, ap: 0.433, auc: 0.526, f1: 0.608, acc: 0.437
+2025-08-26 01:39:41: Epoch: 005 Test results, ap: 0.605, auc: 0.763, f1: 0.392, acc: 0.244
+2025-08-26 01:39:42: Epoch: 006 Train Loss: 0.695, ap: 0.580, auc: 0.638, f1: 0.000, acc: 0.563
+2025-08-26 01:39:42: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:39:42: Epoch: 006 Test results, ap: 0.408, auc: 0.622, f1: 0.000, acc: 0.756
+2025-08-26 01:39:43: Epoch: 007 Train Loss: 0.681, ap: 0.591, auc: 0.669, f1: 0.554, acc: 0.660
+2025-08-26 01:39:43: Epoch: 007 New best VALIDATION metrics
+2025-08-26 01:39:44: Epoch: 007 Test results, ap: 0.293, auc: 0.552, f1: 0.200, acc: 0.740
+2025-08-26 01:39:45: Epoch: 008 Train Loss: 0.665, ap: 0.601, auc: 0.686, f1: 0.529, acc: 0.667
+2025-08-26 01:39:45: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:39:45: Epoch: 008 Test results, ap: 0.232, auc: 0.467, f1: 0.000, acc: 0.699
+2025-08-26 01:39:46: Epoch: 009 Train Loss: 0.667, ap: 0.614, auc: 0.688, f1: 0.492, acc: 0.655
+2025-08-26 01:39:46: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:39:47: Epoch: 009 Test results, ap: 0.231, auc: 0.490, f1: 0.000, acc: 0.740
+2025-08-26 01:39:48: Epoch: 010 Train Loss: 0.661, ap: 0.519, auc: 0.586, f1: 0.217, acc: 0.555
+2025-08-26 01:39:48: Epoch: 010 Test results, ap: 0.257, auc: 0.498, f1: 0.000, acc: 0.756
+2025-08-26 01:39:49: Epoch: 011 Train Loss: 0.663, ap: 0.632, auc: 0.684, f1: 0.600, acc: 0.676
+2025-08-26 01:39:49: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:39:50: Epoch: 011 Test results, ap: 0.251, auc: 0.475, f1: 0.267, acc: 0.463
+2025-08-26 01:39:50: Epoch: 011 New best TEST metrics
+2025-08-26 01:39:51: Epoch: 012 Train Loss: 0.647, ap: 0.644, auc: 0.705, f1: 0.572, acc: 0.671
+2025-08-26 01:39:51: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:39:51: Epoch: 012 Test results, ap: 0.245, auc: 0.508, f1: 0.246, acc: 0.602
+2025-08-26 01:39:51: Epoch: 012 New best TEST metrics
+2025-08-26 01:39:52: Epoch: 013 Train Loss: 0.645, ap: 0.619, auc: 0.699, f1: 0.610, acc: 0.669
+2025-08-26 01:39:53: Epoch: 013 Test results, ap: 0.250, auc: 0.506, f1: 0.397, acc: 0.333
+2025-08-26 01:39:53: Epoch: 013 New best TEST metrics
+2025-08-26 01:39:54: Epoch: 014 Train Loss: 0.639, ap: 0.617, auc: 0.699, f1: 0.588, acc: 0.680
+2025-08-26 01:39:54: Epoch: 014 Test results, ap: 0.250, auc: 0.505, f1: 0.292, acc: 0.488
+2025-08-26 01:39:56: Epoch: 015 Train Loss: 0.640, ap: 0.615, auc: 0.699, f1: 0.603, acc: 0.676
+2025-08-26 01:39:56: Epoch: 015 Test results, ap: 0.243, auc: 0.494, f1: 0.316, acc: 0.577
+2025-08-26 01:39:57: Epoch: 016 Train Loss: 0.638, ap: 0.658, auc: 0.713, f1: 0.601, acc: 0.671
+2025-08-26 01:39:57: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:39:57: Epoch: 016 Test results, ap: 0.238, auc: 0.490, f1: 0.286, acc: 0.512
+2025-08-26 01:39:59: Epoch: 017 Train Loss: 0.633, ap: 0.619, auc: 0.714, f1: 0.608, acc: 0.688
+2025-08-26 01:39:59: Epoch: 017 Test results, ap: 0.251, auc: 0.503, f1: 0.265, acc: 0.593
+2025-08-26 01:40:00: Epoch: 018 Train Loss: 0.634, ap: 0.628, auc: 0.709, f1: 0.618, acc: 0.678
+2025-08-26 01:40:00: Epoch: 018 Test results, ap: 0.245, auc: 0.511, f1: 0.384, acc: 0.374
+2025-08-26 01:40:00: Epoch: 018 New best TEST metrics
+2025-08-26 01:40:02: Epoch: 019 Train Loss: 0.628, ap: 0.622, auc: 0.694, f1: 0.626, acc: 0.664
+2025-08-26 01:40:02: Epoch: 019 Test results, ap: 0.246, auc: 0.479, f1: 0.391, acc: 0.366
+2025-08-26 01:40:03: Epoch: 020 Train Loss: 0.624, ap: 0.637, auc: 0.709, f1: 0.583, acc: 0.685
+2025-08-26 01:40:03: Epoch: 020 Test results, ap: 0.242, auc: 0.512, f1: 0.289, acc: 0.520
+2025-08-26 01:40:05: Epoch: 021 Train Loss: 0.615, ap: 0.663, auc: 0.726, f1: 0.626, acc: 0.680
+2025-08-26 01:40:05: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:40:05: Epoch: 021 Test results, ap: 0.237, auc: 0.481, f1: 0.391, acc: 0.341
+2025-08-26 01:40:06: Epoch: 022 Train Loss: 0.606, ap: 0.652, auc: 0.734, f1: 0.629, acc: 0.685
+2025-08-26 01:40:06: Epoch: 022 Test results, ap: 0.243, auc: 0.486, f1: 0.351, acc: 0.398
+2025-08-26 01:40:08: Epoch: 023 Train Loss: 0.598, ap: 0.611, auc: 0.722, f1: 0.647, acc: 0.690
+2025-08-26 01:40:08: Epoch: 023 Test results, ap: 0.273, auc: 0.484, f1: 0.368, acc: 0.358
+2025-08-26 01:40:08: Epoch: 023 New best TEST metrics
+2025-08-26 01:40:09: Epoch: 024 Train Loss: 0.602, ap: 0.657, auc: 0.725, f1: 0.644, acc: 0.681
+2025-08-26 01:40:09: Epoch: 024 Test results, ap: 0.245, auc: 0.481, f1: 0.371, acc: 0.366
+2025-08-26 01:40:11: Epoch: 025 Train Loss: 0.598, ap: 0.624, auc: 0.723, f1: 0.660, acc: 0.694
+2025-08-26 01:40:11: Epoch: 025 Test results, ap: 0.240, auc: 0.443, f1: 0.339, acc: 0.333
+2025-08-26 01:40:12: Epoch: 026 Train Loss: 0.590, ap: 0.651, auc: 0.732, f1: 0.674, acc: 0.699
+2025-08-26 01:40:12: Epoch: 026 Test results, ap: 0.213, auc: 0.415, f1: 0.372, acc: 0.341
+2025-08-26 01:40:14: Epoch: 027 Train Loss: 0.590, ap: 0.620, auc: 0.721, f1: 0.669, acc: 0.681
+2025-08-26 01:40:14: Epoch: 027 Test results, ap: 0.231, auc: 0.426, f1: 0.370, acc: 0.309
+2025-08-26 01:40:15: Epoch: 028 Train Loss: 0.579, ap: 0.698, auc: 0.756, f1: 0.649, acc: 0.699
+2025-08-26 01:40:15: Epoch: 028 New best VALIDATION metrics
+2025-08-26 01:40:16: Epoch: 028 Test results, ap: 0.259, auc: 0.510, f1: 0.261, acc: 0.585
+2025-08-26 01:40:16: Epoch: 028 New best TEST metrics
+2025-08-26 01:40:17: Epoch: 029 Train Loss: 0.574, ap: 0.671, auc: 0.753, f1: 0.647, acc: 0.685
+2025-08-26 01:40:17: Epoch: 029 Test results, ap: 0.216, auc: 0.439, f1: 0.317, acc: 0.439
+2025-08-26 01:40:18: Epoch: 030 Train Loss: 0.580, ap: 0.692, auc: 0.757, f1: 0.651, acc: 0.694
+2025-08-26 01:40:19: Epoch: 030 Test results, ap: 0.243, auc: 0.493, f1: 0.167, acc: 0.593
+2025-08-26 01:40:20: Epoch: 031 Train Loss: 0.565, ap: 0.667, auc: 0.739, f1: 0.673, acc: 0.683
+2025-08-26 01:40:20: Epoch: 031 Test results, ap: 0.224, auc: 0.443, f1: 0.383, acc: 0.423
+2025-08-26 01:40:21: Epoch: 032 Train Loss: 0.556, ap: 0.719, auc: 0.776, f1: 0.665, acc: 0.699
+2025-08-26 01:40:21: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:40:22: Epoch: 032 Test results, ap: 0.223, auc: 0.456, f1: 0.270, acc: 0.472
+2025-08-26 01:40:23: Epoch: 033 Train Loss: 0.546, ap: 0.701, auc: 0.771, f1: 0.667, acc: 0.717
+2025-08-26 01:40:23: Epoch: 033 Test results, ap: 0.229, auc: 0.475, f1: 0.203, acc: 0.553
+2025-08-26 01:40:25: Epoch: 034 Train Loss: 0.547, ap: 0.710, auc: 0.775, f1: 0.662, acc: 0.727
+2025-08-26 01:40:25: Epoch: 034 Test results, ap: 0.229, auc: 0.466, f1: 0.174, acc: 0.691
+2025-08-26 01:40:26: Epoch: 035 Train Loss: 0.537, ap: 0.730, auc: 0.789, f1: 0.675, acc: 0.724
+2025-08-26 01:40:26: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:40:26: Epoch: 035 Test results, ap: 0.229, auc: 0.458, f1: 0.197, acc: 0.602
+2025-08-26 01:40:28: Epoch: 036 Train Loss: 0.541, ap: 0.713, auc: 0.782, f1: 0.669, acc: 0.701
+2025-08-26 01:40:28: Epoch: 036 Test results, ap: 0.225, auc: 0.460, f1: 0.283, acc: 0.463
+2025-08-26 01:40:29: Epoch: 037 Train Loss: 0.535, ap: 0.717, auc: 0.785, f1: 0.677, acc: 0.701
+2025-08-26 01:40:29: Epoch: 037 Test results, ap: 0.246, auc: 0.498, f1: 0.143, acc: 0.610
+2025-08-26 01:40:31: Epoch: 038 Train Loss: 0.539, ap: 0.719, auc: 0.784, f1: 0.669, acc: 0.701
+2025-08-26 01:40:31: Epoch: 038 Test results, ap: 0.225, auc: 0.459, f1: 0.222, acc: 0.602
+2025-08-26 01:40:32: Epoch: 039 Train Loss: 0.523, ap: 0.734, auc: 0.779, f1: 0.680, acc: 0.708
+2025-08-26 01:40:33: Epoch: 039 Test results, ap: 0.255, auc: 0.444, f1: 0.176, acc: 0.545
+2025-08-26 01:40:34: Epoch: 040 Train Loss: 0.516, ap: 0.736, auc: 0.788, f1: 0.686, acc: 0.711
+2025-08-26 01:40:34: Epoch: 040 New best VALIDATION metrics
+2025-08-26 01:40:34: Epoch: 040 Test results, ap: 0.220, auc: 0.444, f1: 0.164, acc: 0.585
+2025-08-26 01:40:35: Epoch: 041 Train Loss: 0.508, ap: 0.744, auc: 0.792, f1: 0.682, acc: 0.713
+2025-08-26 01:40:35: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:40:36: Epoch: 041 Test results, ap: 0.220, auc: 0.434, f1: 0.289, acc: 0.520
+2025-08-26 01:40:37: Epoch: 042 Train Loss: 0.507, ap: 0.735, auc: 0.785, f1: 0.680, acc: 0.690
+2025-08-26 01:40:37: Epoch: 042 Test results, ap: 0.261, auc: 0.452, f1: 0.302, acc: 0.512
+2025-08-26 01:40:38: Epoch: 043 Train Loss: 0.502, ap: 0.735, auc: 0.788, f1: 0.686, acc: 0.715
+2025-08-26 01:40:39: Epoch: 043 Test results, ap: 0.230, auc: 0.452, f1: 0.182, acc: 0.634
+2025-08-26 01:40:40: Epoch: 044 Train Loss: 0.502, ap: 0.748, auc: 0.797, f1: 0.701, acc: 0.724
+2025-08-26 01:40:40: Epoch: 044 New best VALIDATION metrics
+2025-08-26 01:40:40: Epoch: 044 Test results, ap: 0.232, auc: 0.455, f1: 0.188, acc: 0.577
+2025-08-26 01:40:42: Epoch: 045 Train Loss: 0.500, ap: 0.750, auc: 0.796, f1: 0.695, acc: 0.715
+2025-08-26 01:40:42: Epoch: 045 New best VALIDATION metrics
+2025-08-26 01:40:42: Epoch: 045 Test results, ap: 0.241, auc: 0.472, f1: 0.154, acc: 0.642
+2025-08-26 01:40:43: Epoch: 046 Train Loss: 0.494, ap: 0.738, auc: 0.790, f1: 0.683, acc: 0.704
+2025-08-26 01:40:43: Epoch: 046 Test results, ap: 0.237, auc: 0.463, f1: 0.229, acc: 0.561
+2025-08-26 01:40:45: Epoch: 047 Train Loss: 0.494, ap: 0.732, auc: 0.787, f1: 0.694, acc: 0.701
+2025-08-26 01:40:45: Epoch: 047 Test results, ap: 0.270, auc: 0.470, f1: 0.299, acc: 0.504
+2025-08-26 01:40:46: Epoch: 048 Train Loss: 0.496, ap: 0.743, auc: 0.794, f1: 0.692, acc: 0.710
+2025-08-26 01:40:46: Epoch: 048 Test results, ap: 0.246, auc: 0.459, f1: 0.239, acc: 0.585
+2025-08-26 01:40:48: Epoch: 049 Train Loss: 0.488, ap: 0.728, auc: 0.790, f1: 0.690, acc: 0.713
+2025-08-26 01:40:48: Epoch: 049 Test results, ap: 0.226, auc: 0.445, f1: 0.182, acc: 0.634
+2025-08-26 01:40:49: Epoch: 050 Train Loss: 0.482, ap: 0.743, auc: 0.797, f1: 0.700, acc: 0.718
+2025-08-26 01:40:49: Epoch: 050 Test results, ap: 0.229, auc: 0.456, f1: 0.179, acc: 0.626
+2025-08-26 01:40:49: used time 0.02h
+2025-08-26 01:40:49: Running Cross Validation 2
+2025-08-26 01:40:49: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:40:51: Epoch: 001 Train Loss: 0.694, ap: 0.463, auc: 0.545, f1: 0.000, acc: 0.559
+2025-08-26 01:40:51: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:40:51: Epoch: 001 Test results, ap: 0.451, auc: 0.659, f1: 0.000, acc: 0.756
+2025-08-26 01:40:52: Epoch: 002 Train Loss: 0.694, ap: 0.488, auc: 0.584, f1: 0.000, acc: 0.559
+2025-08-26 01:40:52: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:40:53: Epoch: 002 Test results, ap: 0.530, auc: 0.670, f1: 0.000, acc: 0.756
+2025-08-26 01:40:54: Epoch: 003 Train Loss: 0.694, ap: 0.397, auc: 0.439, f1: 0.000, acc: 0.559
+2025-08-26 01:40:54: Epoch: 003 Test results, ap: 0.337, auc: 0.564, f1: 0.000, acc: 0.756
+2025-08-26 01:40:55: Epoch: 004 Train Loss: 0.695, ap: 0.511, auc: 0.608, f1: 0.000, acc: 0.559
+2025-08-26 01:40:55: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:40:56: Epoch: 004 Test results, ap: 0.322, auc: 0.602, f1: 0.000, acc: 0.756
+2025-08-26 01:40:57: Epoch: 005 Train Loss: 0.691, ap: 0.442, auc: 0.531, f1: 0.000, acc: 0.559
+2025-08-26 01:40:57: Epoch: 005 Test results, ap: 0.307, auc: 0.501, f1: 0.000, acc: 0.756
+2025-08-26 01:40:58: Epoch: 006 Train Loss: 0.690, ap: 0.576, auc: 0.649, f1: 0.563, acc: 0.598
+2025-08-26 01:40:58: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:40:59: Epoch: 006 Test results, ap: 0.276, auc: 0.588, f1: 0.392, acc: 0.244
+2025-08-26 01:41:00: Epoch: 007 Train Loss: 0.672, ap: 0.593, auc: 0.650, f1: 0.568, acc: 0.614
+2025-08-26 01:41:00: Epoch: 007 New best VALIDATION metrics
+2025-08-26 01:41:00: Epoch: 007 Test results, ap: 0.331, auc: 0.616, f1: 0.392, acc: 0.244
+2025-08-26 01:41:02: Epoch: 008 Train Loss: 0.651, ap: 0.648, auc: 0.695, f1: 0.607, acc: 0.660
+2025-08-26 01:41:02: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:41:02: Epoch: 008 Test results, ap: 0.345, auc: 0.641, f1: 0.392, acc: 0.244
+2025-08-26 01:41:03: Epoch: 009 Train Loss: 0.644, ap: 0.626, auc: 0.666, f1: 0.593, acc: 0.612
+2025-08-26 01:41:03: Epoch: 009 Test results, ap: 0.334, auc: 0.625, f1: 0.405, acc: 0.285
+2025-08-26 01:41:05: Epoch: 010 Train Loss: 0.644, ap: 0.631, auc: 0.677, f1: 0.547, acc: 0.653
+2025-08-26 01:41:05: Epoch: 010 Test results, ap: 0.340, auc: 0.634, f1: 0.432, acc: 0.593
+2025-08-26 01:41:06: Epoch: 011 Train Loss: 0.631, ap: 0.648, auc: 0.679, f1: 0.601, acc: 0.649
+2025-08-26 01:41:06: Epoch: 011 Test results, ap: 0.340, auc: 0.640, f1: 0.392, acc: 0.244
+2025-08-26 01:41:06: Epoch: 011 New best TEST metrics
+2025-08-26 01:41:08: Epoch: 012 Train Loss: 0.623, ap: 0.649, auc: 0.680, f1: 0.608, acc: 0.654
+2025-08-26 01:41:08: Epoch: 012 Test results, ap: 0.381, auc: 0.652, f1: 0.392, acc: 0.244
+2025-08-26 01:41:08: Epoch: 012 New best TEST metrics
+2025-08-26 01:41:09: Epoch: 013 Train Loss: 0.622, ap: 0.661, auc: 0.699, f1: 0.649, acc: 0.658
+2025-08-26 01:41:09: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:41:10: Epoch: 013 Test results, ap: 0.347, auc: 0.685, f1: 0.392, acc: 0.244
+2025-08-26 01:41:11: Epoch: 014 Train Loss: 0.617, ap: 0.613, auc: 0.685, f1: 0.581, acc: 0.649
+2025-08-26 01:41:11: Epoch: 014 Test results, ap: 0.267, auc: 0.568, f1: 0.439, acc: 0.398
+2025-08-26 01:41:12: Epoch: 015 Train Loss: 0.601, ap: 0.688, auc: 0.728, f1: 0.656, acc: 0.691
+2025-08-26 01:41:12: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:41:13: Epoch: 015 Test results, ap: 0.323, auc: 0.655, f1: 0.397, acc: 0.260
+2025-08-26 01:41:14: Epoch: 016 Train Loss: 0.595, ap: 0.699, auc: 0.739, f1: 0.667, acc: 0.695
+2025-08-26 01:41:14: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:41:14: Epoch: 016 Test results, ap: 0.439, auc: 0.696, f1: 0.423, acc: 0.333
+2025-08-26 01:41:14: Epoch: 016 New best TEST metrics
+2025-08-26 01:41:15: Epoch: 017 Train Loss: 0.597, ap: 0.683, auc: 0.731, f1: 0.626, acc: 0.677
+2025-08-26 01:41:16: Epoch: 017 Test results, ap: 0.404, auc: 0.706, f1: 0.408, acc: 0.293
+2025-08-26 01:41:17: Epoch: 018 Train Loss: 0.581, ap: 0.702, auc: 0.748, f1: 0.645, acc: 0.693
+2025-08-26 01:41:17: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:41:17: Epoch: 018 Test results, ap: 0.387, auc: 0.691, f1: 0.417, acc: 0.317
+2025-08-26 01:41:19: Epoch: 019 Train Loss: 0.585, ap: 0.698, auc: 0.748, f1: 0.665, acc: 0.700
+2025-08-26 01:41:19: Epoch: 019 Test results, ap: 0.395, auc: 0.685, f1: 0.480, acc: 0.472
+2025-08-26 01:41:20: Epoch: 020 Train Loss: 0.567, ap: 0.703, auc: 0.745, f1: 0.663, acc: 0.681
+2025-08-26 01:41:20: Epoch: 020 Test results, ap: 0.366, auc: 0.654, f1: 0.432, acc: 0.358
+2025-08-26 01:41:22: Epoch: 021 Train Loss: 0.550, ap: 0.707, auc: 0.767, f1: 0.681, acc: 0.714
+2025-08-26 01:41:22: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:41:22: Epoch: 021 Test results, ap: 0.377, auc: 0.660, f1: 0.441, acc: 0.382
+2025-08-26 01:41:23: Epoch: 022 Train Loss: 0.542, ap: 0.714, auc: 0.774, f1: 0.686, acc: 0.727
+2025-08-26 01:41:23: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:41:24: Epoch: 022 Test results, ap: 0.403, auc: 0.669, f1: 0.433, acc: 0.382
+2025-08-26 01:41:25: Epoch: 023 Train Loss: 0.542, ap: 0.728, auc: 0.779, f1: 0.689, acc: 0.732
+2025-08-26 01:41:25: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:41:25: Epoch: 023 Test results, ap: 0.393, auc: 0.664, f1: 0.438, acc: 0.415
+2025-08-26 01:41:26: Epoch: 024 Train Loss: 0.545, ap: 0.721, auc: 0.775, f1: 0.685, acc: 0.711
+2025-08-26 01:41:27: Epoch: 024 Test results, ap: 0.421, auc: 0.703, f1: 0.465, acc: 0.439
+2025-08-26 01:41:28: Epoch: 025 Train Loss: 0.533, ap: 0.726, auc: 0.771, f1: 0.684, acc: 0.702
+2025-08-26 01:41:28: Epoch: 025 Test results, ap: 0.417, auc: 0.694, f1: 0.426, acc: 0.366
+2025-08-26 01:41:29: Epoch: 026 Train Loss: 0.534, ap: 0.718, auc: 0.777, f1: 0.687, acc: 0.714
+2025-08-26 01:41:30: Epoch: 026 Test results, ap: 0.408, auc: 0.682, f1: 0.453, acc: 0.431
+2025-08-26 01:41:31: Epoch: 027 Train Loss: 0.523, ap: 0.727, auc: 0.780, f1: 0.701, acc: 0.737
+2025-08-26 01:41:31: Epoch: 027 Test results, ap: 0.390, auc: 0.663, f1: 0.439, acc: 0.398
+2025-08-26 01:41:32: Epoch: 028 Train Loss: 0.521, ap: 0.718, auc: 0.778, f1: 0.678, acc: 0.704
+2025-08-26 01:41:33: Epoch: 028 Test results, ap: 0.336, auc: 0.634, f1: 0.433, acc: 0.382
+2025-08-26 01:41:34: Epoch: 029 Train Loss: 0.528, ap: 0.723, auc: 0.784, f1: 0.683, acc: 0.707
+2025-08-26 01:41:34: Epoch: 029 Test results, ap: 0.372, auc: 0.670, f1: 0.429, acc: 0.350
+2025-08-26 01:41:36: Epoch: 030 Train Loss: 0.505, ap: 0.709, auc: 0.766, f1: 0.670, acc: 0.683
+2025-08-26 01:41:36: Epoch: 030 Test results, ap: 0.345, auc: 0.616, f1: 0.406, acc: 0.309
+2025-08-26 01:41:37: Epoch: 031 Train Loss: 0.497, ap: 0.732, auc: 0.790, f1: 0.698, acc: 0.714
+2025-08-26 01:41:37: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:41:37: Epoch: 031 Test results, ap: 0.325, auc: 0.619, f1: 0.420, acc: 0.350
+2025-08-26 01:41:39: Epoch: 032 Train Loss: 0.488, ap: 0.720, auc: 0.785, f1: 0.697, acc: 0.707
+2025-08-26 01:41:39: Epoch: 032 Test results, ap: 0.314, auc: 0.583, f1: 0.409, acc: 0.341
+2025-08-26 01:41:40: Epoch: 033 Train Loss: 0.477, ap: 0.723, auc: 0.787, f1: 0.693, acc: 0.709
+2025-08-26 01:41:40: Epoch: 033 Test results, ap: 0.315, auc: 0.576, f1: 0.420, acc: 0.350
+2025-08-26 01:41:42: Epoch: 034 Train Loss: 0.477, ap: 0.723, auc: 0.788, f1: 0.711, acc: 0.718
+2025-08-26 01:41:42: Epoch: 034 Test results, ap: 0.307, auc: 0.596, f1: 0.408, acc: 0.317
+2025-08-26 01:41:43: Epoch: 035 Train Loss: 0.478, ap: 0.726, auc: 0.788, f1: 0.698, acc: 0.718
+2025-08-26 01:41:44: Epoch: 035 Test results, ap: 0.306, auc: 0.561, f1: 0.432, acc: 0.488
+2025-08-26 01:41:45: Epoch: 036 Train Loss: 0.469, ap: 0.715, auc: 0.787, f1: 0.700, acc: 0.725
+2025-08-26 01:41:45: Epoch: 036 Test results, ap: 0.318, auc: 0.569, f1: 0.434, acc: 0.407
+2025-08-26 01:41:46: Epoch: 037 Train Loss: 0.467, ap: 0.725, auc: 0.790, f1: 0.702, acc: 0.716
+2025-08-26 01:41:47: Epoch: 037 Test results, ap: 0.319, auc: 0.574, f1: 0.454, acc: 0.472
+2025-08-26 01:41:48: Epoch: 038 Train Loss: 0.459, ap: 0.715, auc: 0.784, f1: 0.717, acc: 0.723
+2025-08-26 01:41:48: Epoch: 038 Test results, ap: 0.320, auc: 0.575, f1: 0.415, acc: 0.358
+2025-08-26 01:41:49: Epoch: 039 Train Loss: 0.461, ap: 0.716, auc: 0.789, f1: 0.715, acc: 0.728
+2025-08-26 01:41:50: Epoch: 039 Test results, ap: 0.321, auc: 0.604, f1: 0.423, acc: 0.358
+2025-08-26 01:41:51: Epoch: 040 Train Loss: 0.457, ap: 0.707, auc: 0.782, f1: 0.702, acc: 0.711
+2025-08-26 01:41:51: Epoch: 040 Test results, ap: 0.324, auc: 0.580, f1: 0.379, acc: 0.520
+2025-08-26 01:41:52: Epoch: 041 Train Loss: 0.433, ap: 0.715, auc: 0.788, f1: 0.716, acc: 0.727
+2025-08-26 01:41:53: Epoch: 041 Test results, ap: 0.312, auc: 0.567, f1: 0.381, acc: 0.472
+2025-08-26 01:41:54: Epoch: 042 Train Loss: 0.433, ap: 0.710, auc: 0.781, f1: 0.708, acc: 0.714
+2025-08-26 01:41:54: Epoch: 042 Test results, ap: 0.305, auc: 0.552, f1: 0.424, acc: 0.447
+2025-08-26 01:41:56: Epoch: 043 Train Loss: 0.431, ap: 0.707, auc: 0.782, f1: 0.712, acc: 0.725
+2025-08-26 01:41:56: Epoch: 043 Test results, ap: 0.306, auc: 0.556, f1: 0.373, acc: 0.480
+2025-08-26 01:41:57: Epoch: 044 Train Loss: 0.427, ap: 0.707, auc: 0.786, f1: 0.707, acc: 0.720
+2025-08-26 01:41:57: Epoch: 044 Test results, ap: 0.315, auc: 0.578, f1: 0.429, acc: 0.480
+2025-08-26 01:41:59: Epoch: 045 Train Loss: 0.422, ap: 0.711, auc: 0.784, f1: 0.710, acc: 0.709
+2025-08-26 01:41:59: Epoch: 045 Test results, ap: 0.312, auc: 0.570, f1: 0.389, acc: 0.439
+2025-08-26 01:42:00: Epoch: 046 Train Loss: 0.428, ap: 0.710, auc: 0.784, f1: 0.708, acc: 0.716
+2025-08-26 01:42:00: Epoch: 046 Test results, ap: 0.311, auc: 0.568, f1: 0.400, acc: 0.488
+2025-08-26 01:42:02: Epoch: 047 Train Loss: 0.423, ap: 0.707, auc: 0.784, f1: 0.706, acc: 0.713
+2025-08-26 01:42:02: Epoch: 047 Test results, ap: 0.317, auc: 0.575, f1: 0.381, acc: 0.472
+2025-08-26 01:42:03: Epoch: 048 Train Loss: 0.414, ap: 0.702, auc: 0.783, f1: 0.713, acc: 0.725
+2025-08-26 01:42:04: Epoch: 048 Test results, ap: 0.322, auc: 0.586, f1: 0.379, acc: 0.520
+2025-08-26 01:42:05: Epoch: 049 Train Loss: 0.417, ap: 0.712, auc: 0.788, f1: 0.709, acc: 0.720
+2025-08-26 01:42:05: Epoch: 049 Test results, ap: 0.319, auc: 0.578, f1: 0.367, acc: 0.496
+2025-08-26 01:42:06: Epoch: 050 Train Loss: 0.411, ap: 0.709, auc: 0.786, f1: 0.708, acc: 0.709
+2025-08-26 01:42:07: Epoch: 050 Test results, ap: 0.318, auc: 0.578, f1: 0.371, acc: 0.504
+2025-08-26 01:42:07: used time 0.02h
+2025-08-26 01:42:07: Running Cross Validation 3
+2025-08-26 01:42:07: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:42:08: Epoch: 001 Train Loss: 0.694, ap: 0.381, auc: 0.439, f1: 0.000, acc: 0.570
+2025-08-26 01:42:08: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:42:08: Epoch: 001 Test results, ap: 0.230, auc: 0.478, f1: 0.000, acc: 0.756
+2025-08-26 01:42:09: Epoch: 002 Train Loss: 0.694, ap: 0.540, auc: 0.638, f1: 0.602, acc: 0.430
+2025-08-26 01:42:09: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:42:10: Epoch: 002 Test results, ap: 0.249, auc: 0.509, f1: 0.392, acc: 0.244
+2025-08-26 01:42:11: Epoch: 003 Train Loss: 0.695, ap: 0.486, auc: 0.579, f1: 0.000, acc: 0.570
+2025-08-26 01:42:11: Epoch: 003 Test results, ap: 0.382, auc: 0.663, f1: 0.000, acc: 0.756
+2025-08-26 01:42:13: Epoch: 004 Train Loss: 0.694, ap: 0.398, auc: 0.481, f1: 0.000, acc: 0.570
+2025-08-26 01:42:13: Epoch: 004 Test results, ap: 0.248, auc: 0.498, f1: 0.000, acc: 0.756
+2025-08-26 01:42:14: Epoch: 005 Train Loss: 0.694, ap: 0.486, auc: 0.579, f1: 0.602, acc: 0.430
+2025-08-26 01:42:14: Epoch: 005 Test results, ap: 0.285, auc: 0.529, f1: 0.392, acc: 0.244
+2025-08-26 01:42:16: Epoch: 006 Train Loss: 0.694, ap: 0.409, auc: 0.483, f1: 0.602, acc: 0.430
+2025-08-26 01:42:16: Epoch: 006 Test results, ap: 0.229, auc: 0.419, f1: 0.392, acc: 0.244
+2025-08-26 01:42:17: Epoch: 007 Train Loss: 0.694, ap: 0.384, auc: 0.450, f1: 0.603, acc: 0.467
+2025-08-26 01:42:17: Epoch: 007 Test results, ap: 0.214, auc: 0.422, f1: 0.392, acc: 0.244
+2025-08-26 01:42:19: Epoch: 008 Train Loss: 0.694, ap: 0.541, auc: 0.611, f1: 0.602, acc: 0.430
+2025-08-26 01:42:19: Epoch: 008 Test results, ap: 0.318, auc: 0.592, f1: 0.392, acc: 0.244
+2025-08-26 01:42:20: Epoch: 009 Train Loss: 0.695, ap: 0.525, auc: 0.636, f1: 0.602, acc: 0.430
+2025-08-26 01:42:20: Epoch: 009 Test results, ap: 0.313, auc: 0.585, f1: 0.392, acc: 0.244
+2025-08-26 01:42:22: Epoch: 010 Train Loss: 0.689, ap: 0.547, auc: 0.650, f1: 0.608, acc: 0.526
+2025-08-26 01:42:22: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:42:22: Epoch: 010 Test results, ap: 0.337, auc: 0.628, f1: 0.392, acc: 0.244
+2025-08-26 01:42:23: Epoch: 011 Train Loss: 0.665, ap: 0.563, auc: 0.645, f1: 0.555, acc: 0.624
+2025-08-26 01:42:23: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:42:24: Epoch: 011 Test results, ap: 0.413, auc: 0.665, f1: 0.442, acc: 0.610
+2025-08-26 01:42:24: Epoch: 011 New best TEST metrics
+2025-08-26 01:42:25: Epoch: 012 Train Loss: 0.657, ap: 0.578, auc: 0.665, f1: 0.548, acc: 0.651
+2025-08-26 01:42:25: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:42:25: Epoch: 012 Test results, ap: 0.427, auc: 0.691, f1: 0.463, acc: 0.642
+2025-08-26 01:42:25: Epoch: 012 New best TEST metrics
+2025-08-26 01:42:26: Epoch: 013 Train Loss: 0.650, ap: 0.567, auc: 0.658, f1: 0.577, acc: 0.668
+2025-08-26 01:42:27: Epoch: 013 Test results, ap: 0.395, auc: 0.698, f1: 0.444, acc: 0.634
+2025-08-26 01:42:28: Epoch: 014 Train Loss: 0.644, ap: 0.573, auc: 0.665, f1: 0.585, acc: 0.654
+2025-08-26 01:42:28: Epoch: 014 Test results, ap: 0.440, auc: 0.701, f1: 0.444, acc: 0.675
+2025-08-26 01:42:28: Epoch: 014 New best TEST metrics
+2025-08-26 01:42:29: Epoch: 015 Train Loss: 0.638, ap: 0.577, auc: 0.675, f1: 0.570, acc: 0.646
+2025-08-26 01:42:29: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:42:30: Epoch: 015 Test results, ap: 0.427, auc: 0.692, f1: 0.371, acc: 0.642
+2025-08-26 01:42:31: Epoch: 016 Train Loss: 0.640, ap: 0.584, auc: 0.664, f1: 0.576, acc: 0.644
+2025-08-26 01:42:31: Epoch: 016 Test results, ap: 0.442, auc: 0.706, f1: 0.453, acc: 0.667
+2025-08-26 01:42:31: Epoch: 016 New best TEST metrics
+2025-08-26 01:42:33: Epoch: 017 Train Loss: 0.641, ap: 0.586, auc: 0.669, f1: 0.480, acc: 0.640
+2025-08-26 01:42:33: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:42:33: Epoch: 017 Test results, ap: 0.446, auc: 0.713, f1: 0.407, acc: 0.715
+2025-08-26 01:42:33: Epoch: 017 New best TEST metrics
+2025-08-26 01:42:34: Epoch: 018 Train Loss: 0.640, ap: 0.593, auc: 0.672, f1: 0.450, acc: 0.624
+2025-08-26 01:42:34: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:42:34: Epoch: 018 Test results, ap: 0.446, auc: 0.714, f1: 0.393, acc: 0.699
+2025-08-26 01:42:34: Epoch: 018 New best TEST metrics
+2025-08-26 01:42:36: Epoch: 019 Train Loss: 0.631, ap: 0.590, auc: 0.665, f1: 0.573, acc: 0.642
+2025-08-26 01:42:36: Epoch: 019 Test results, ap: 0.407, auc: 0.719, f1: 0.486, acc: 0.691
+2025-08-26 01:42:37: Epoch: 020 Train Loss: 0.633, ap: 0.603, auc: 0.667, f1: 0.603, acc: 0.672
+2025-08-26 01:42:37: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:42:38: Epoch: 020 Test results, ap: 0.451, auc: 0.708, f1: 0.444, acc: 0.675
+2025-08-26 01:42:39: Epoch: 021 Train Loss: 0.626, ap: 0.603, auc: 0.670, f1: 0.596, acc: 0.623
+2025-08-26 01:42:39: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:42:39: Epoch: 021 Test results, ap: 0.457, auc: 0.714, f1: 0.480, acc: 0.683
+2025-08-26 01:42:39: Epoch: 021 New best TEST metrics
+2025-08-26 01:42:40: Epoch: 022 Train Loss: 0.624, ap: 0.602, auc: 0.676, f1: 0.599, acc: 0.686
+2025-08-26 01:42:40: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:42:41: Epoch: 022 Test results, ap: 0.449, auc: 0.713, f1: 0.419, acc: 0.707
+2025-08-26 01:42:42: Epoch: 023 Train Loss: 0.624, ap: 0.609, auc: 0.691, f1: 0.619, acc: 0.674
+2025-08-26 01:42:42: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:42:42: Epoch: 023 Test results, ap: 0.457, auc: 0.715, f1: 0.407, acc: 0.715
+2025-08-26 01:42:42: Epoch: 023 New best TEST metrics
+2025-08-26 01:42:43: Epoch: 024 Train Loss: 0.613, ap: 0.644, auc: 0.703, f1: 0.596, acc: 0.697
+2025-08-26 01:42:43: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:42:44: Epoch: 024 Test results, ap: 0.397, auc: 0.700, f1: 0.400, acc: 0.707
+2025-08-26 01:42:45: Epoch: 025 Train Loss: 0.598, ap: 0.670, auc: 0.727, f1: 0.626, acc: 0.686
+2025-08-26 01:42:45: Epoch: 025 New best VALIDATION metrics
+2025-08-26 01:42:45: Epoch: 025 Test results, ap: 0.384, auc: 0.691, f1: 0.400, acc: 0.634
+2025-08-26 01:42:47: Epoch: 026 Train Loss: 0.597, ap: 0.698, auc: 0.724, f1: 0.627, acc: 0.681
+2025-08-26 01:42:47: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:42:47: Epoch: 026 Test results, ap: 0.383, auc: 0.685, f1: 0.442, acc: 0.610
+2025-08-26 01:42:48: Epoch: 027 Train Loss: 0.588, ap: 0.675, auc: 0.723, f1: 0.623, acc: 0.684
+2025-08-26 01:42:48: Epoch: 027 Test results, ap: 0.411, auc: 0.702, f1: 0.442, acc: 0.610
+2025-08-26 01:42:50: Epoch: 028 Train Loss: 0.588, ap: 0.695, auc: 0.733, f1: 0.637, acc: 0.693
+2025-08-26 01:42:50: Epoch: 028 New best VALIDATION metrics
+2025-08-26 01:42:50: Epoch: 028 Test results, ap: 0.309, auc: 0.661, f1: 0.442, acc: 0.610
+2025-08-26 01:42:51: Epoch: 029 Train Loss: 0.583, ap: 0.654, auc: 0.718, f1: 0.595, acc: 0.695
+2025-08-26 01:42:51: Epoch: 029 Test results, ap: 0.338, auc: 0.675, f1: 0.442, acc: 0.610
+2025-08-26 01:42:53: Epoch: 030 Train Loss: 0.576, ap: 0.699, auc: 0.748, f1: 0.650, acc: 0.700
+2025-08-26 01:42:53: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:42:53: Epoch: 030 Test results, ap: 0.301, auc: 0.650, f1: 0.442, acc: 0.610
+2025-08-26 01:42:54: Epoch: 031 Train Loss: 0.572, ap: 0.692, auc: 0.745, f1: 0.646, acc: 0.713
+2025-08-26 01:42:55: Epoch: 031 Test results, ap: 0.299, auc: 0.649, f1: 0.442, acc: 0.610
+2025-08-26 01:42:56: Epoch: 032 Train Loss: 0.565, ap: 0.693, auc: 0.749, f1: 0.668, acc: 0.718
+2025-08-26 01:42:56: Epoch: 032 Test results, ap: 0.307, auc: 0.657, f1: 0.442, acc: 0.610
+2025-08-26 01:42:57: Epoch: 033 Train Loss: 0.559, ap: 0.707, auc: 0.758, f1: 0.665, acc: 0.713
+2025-08-26 01:42:57: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:42:58: Epoch: 033 Test results, ap: 0.298, auc: 0.633, f1: 0.442, acc: 0.610
+2025-08-26 01:42:59: Epoch: 034 Train Loss: 0.559, ap: 0.685, auc: 0.742, f1: 0.639, acc: 0.695
+2025-08-26 01:42:59: Epoch: 034 Test results, ap: 0.296, auc: 0.629, f1: 0.442, acc: 0.610
+2025-08-26 01:43:00: Epoch: 035 Train Loss: 0.562, ap: 0.683, auc: 0.740, f1: 0.656, acc: 0.700
+2025-08-26 01:43:01: Epoch: 035 Test results, ap: 0.303, auc: 0.655, f1: 0.442, acc: 0.610
+2025-08-26 01:43:02: Epoch: 036 Train Loss: 0.565, ap: 0.680, auc: 0.732, f1: 0.629, acc: 0.713
+2025-08-26 01:43:02: Epoch: 036 Test results, ap: 0.319, auc: 0.648, f1: 0.442, acc: 0.610
+2025-08-26 01:43:04: Epoch: 037 Train Loss: 0.553, ap: 0.693, auc: 0.751, f1: 0.665, acc: 0.704
+2025-08-26 01:43:04: Epoch: 037 Test results, ap: 0.304, auc: 0.655, f1: 0.442, acc: 0.610
+2025-08-26 01:43:05: Epoch: 038 Train Loss: 0.556, ap: 0.706, auc: 0.746, f1: 0.635, acc: 0.716
+2025-08-26 01:43:05: Epoch: 038 Test results, ap: 0.295, auc: 0.630, f1: 0.442, acc: 0.610
+2025-08-26 01:43:07: Epoch: 039 Train Loss: 0.552, ap: 0.715, auc: 0.763, f1: 0.668, acc: 0.704
+2025-08-26 01:43:07: Epoch: 039 New best VALIDATION metrics
+2025-08-26 01:43:07: Epoch: 039 Test results, ap: 0.295, auc: 0.630, f1: 0.442, acc: 0.610
+2025-08-26 01:43:08: Epoch: 040 Train Loss: 0.551, ap: 0.720, auc: 0.768, f1: 0.675, acc: 0.725
+2025-08-26 01:43:08: Epoch: 040 New best VALIDATION metrics
+2025-08-26 01:43:08: Epoch: 040 Test results, ap: 0.294, auc: 0.628, f1: 0.442, acc: 0.610
+2025-08-26 01:43:10: Epoch: 041 Train Loss: 0.548, ap: 0.716, auc: 0.763, f1: 0.669, acc: 0.718
+2025-08-26 01:43:10: Epoch: 041 Test results, ap: 0.296, auc: 0.631, f1: 0.442, acc: 0.610
+2025-08-26 01:43:11: Epoch: 042 Train Loss: 0.540, ap: 0.719, auc: 0.768, f1: 0.674, acc: 0.723
+2025-08-26 01:43:12: Epoch: 042 Test results, ap: 0.296, auc: 0.630, f1: 0.442, acc: 0.610
+2025-08-26 01:43:13: Epoch: 043 Train Loss: 0.547, ap: 0.706, auc: 0.758, f1: 0.671, acc: 0.728
+2025-08-26 01:43:13: Epoch: 043 Test results, ap: 0.296, auc: 0.629, f1: 0.442, acc: 0.610
+2025-08-26 01:43:14: Epoch: 044 Train Loss: 0.543, ap: 0.707, auc: 0.767, f1: 0.668, acc: 0.723
+2025-08-26 01:43:15: Epoch: 044 Test results, ap: 0.298, auc: 0.632, f1: 0.442, acc: 0.610
+2025-08-26 01:43:16: Epoch: 045 Train Loss: 0.535, ap: 0.715, auc: 0.768, f1: 0.668, acc: 0.718
+2025-08-26 01:43:16: Epoch: 045 Test results, ap: 0.293, auc: 0.626, f1: 0.442, acc: 0.610
+2025-08-26 01:43:17: Epoch: 046 Train Loss: 0.536, ap: 0.707, auc: 0.763, f1: 0.668, acc: 0.727
+2025-08-26 01:43:18: Epoch: 046 Test results, ap: 0.294, auc: 0.627, f1: 0.442, acc: 0.610
+2025-08-26 01:43:19: Epoch: 047 Train Loss: 0.541, ap: 0.715, auc: 0.772, f1: 0.671, acc: 0.723
+2025-08-26 01:43:19: Epoch: 047 Test results, ap: 0.295, auc: 0.628, f1: 0.442, acc: 0.610
+2025-08-26 01:43:21: Epoch: 048 Train Loss: 0.536, ap: 0.713, auc: 0.770, f1: 0.671, acc: 0.727
+2025-08-26 01:43:21: Epoch: 048 Test results, ap: 0.298, auc: 0.632, f1: 0.442, acc: 0.610
+2025-08-26 01:43:22: Epoch: 049 Train Loss: 0.537, ap: 0.704, auc: 0.760, f1: 0.665, acc: 0.727
+2025-08-26 01:43:22: Epoch: 049 Test results, ap: 0.297, auc: 0.631, f1: 0.442, acc: 0.610
+2025-08-26 01:43:24: Epoch: 050 Train Loss: 0.537, ap: 0.710, auc: 0.767, f1: 0.684, acc: 0.728
+2025-08-26 01:43:24: Epoch: 050 Test results, ap: 0.297, auc: 0.630, f1: 0.442, acc: 0.610
+2025-08-26 01:43:24: used time 0.02h
+2025-08-26 01:43:24: Running Cross Validation 4
+2025-08-26 01:43:24: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:43:25: Epoch: 001 Train Loss: 0.693, ap: 0.433, auc: 0.482, f1: 0.614, acc: 0.443
+2025-08-26 01:43:25: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:43:25: Epoch: 001 Test results, ap: 0.227, auc: 0.466, f1: 0.392, acc: 0.244
+2025-08-26 01:43:27: Epoch: 002 Train Loss: 0.694, ap: 0.483, auc: 0.546, f1: 0.000, acc: 0.557
+2025-08-26 01:43:27: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:43:27: Epoch: 002 Test results, ap: 0.355, auc: 0.560, f1: 0.000, acc: 0.756
+2025-08-26 01:43:28: Epoch: 003 Train Loss: 0.694, ap: 0.523, auc: 0.590, f1: 0.614, acc: 0.443
+2025-08-26 01:43:28: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:43:29: Epoch: 003 Test results, ap: 0.249, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 01:43:30: Epoch: 004 Train Loss: 0.694, ap: 0.528, auc: 0.599, f1: 0.000, acc: 0.557
+2025-08-26 01:43:30: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:43:30: Epoch: 004 Test results, ap: 0.359, auc: 0.600, f1: 0.000, acc: 0.756
+2025-08-26 01:43:31: Epoch: 005 Train Loss: 0.693, ap: 0.516, auc: 0.599, f1: 0.599, acc: 0.460
+2025-08-26 01:43:32: Epoch: 005 Test results, ap: 0.271, auc: 0.581, f1: 0.392, acc: 0.244
+2025-08-26 01:43:33: Epoch: 006 Train Loss: 0.689, ap: 0.465, auc: 0.506, f1: 0.473, acc: 0.469
+2025-08-26 01:43:33: Epoch: 006 Test results, ap: 0.346, auc: 0.578, f1: 0.394, acc: 0.350
+2025-08-26 01:43:35: Epoch: 007 Train Loss: 0.696, ap: 0.427, auc: 0.475, f1: 0.000, acc: 0.557
+2025-08-26 01:43:35: Epoch: 007 Test results, ap: 0.322, auc: 0.584, f1: 0.000, acc: 0.756
+2025-08-26 01:43:36: Epoch: 008 Train Loss: 0.692, ap: 0.498, auc: 0.577, f1: 0.591, acc: 0.541
+2025-08-26 01:43:36: Epoch: 008 Test results, ap: 0.278, auc: 0.491, f1: 0.392, acc: 0.244
+2025-08-26 01:43:38: Epoch: 009 Train Loss: 0.694, ap: 0.431, auc: 0.479, f1: 0.538, acc: 0.478
+2025-08-26 01:43:38: Epoch: 009 Test results, ap: 0.345, auc: 0.620, f1: 0.397, acc: 0.260
+2025-08-26 01:43:39: Epoch: 010 Train Loss: 0.695, ap: 0.438, auc: 0.506, f1: 0.570, acc: 0.489
+2025-08-26 01:43:39: Epoch: 010 Test results, ap: 0.284, auc: 0.570, f1: 0.443, acc: 0.447
+2025-08-26 01:43:41: Epoch: 011 Train Loss: 0.693, ap: 0.468, auc: 0.505, f1: 0.483, acc: 0.494
+2025-08-26 01:43:41: Epoch: 011 Test results, ap: 0.239, auc: 0.382, f1: 0.133, acc: 0.683
+2025-08-26 01:43:41: Epoch: 011 New best TEST metrics
+2025-08-26 01:43:42: Epoch: 012 Train Loss: 0.694, ap: 0.481, auc: 0.549, f1: 0.000, acc: 0.549
+2025-08-26 01:43:42: Epoch: 012 Test results, ap: 0.308, auc: 0.526, f1: 0.000, acc: 0.756
+2025-08-26 01:43:42: Epoch: 012 New best TEST metrics
+2025-08-26 01:43:44: Epoch: 013 Train Loss: 0.693, ap: 0.475, auc: 0.542, f1: 0.052, acc: 0.549
+2025-08-26 01:43:44: Epoch: 013 Test results, ap: 0.517, auc: 0.705, f1: 0.000, acc: 0.756
+2025-08-26 01:43:44: Epoch: 013 New best TEST metrics
+2025-08-26 01:43:45: Epoch: 014 Train Loss: 0.693, ap: 0.493, auc: 0.543, f1: 0.352, acc: 0.533
+2025-08-26 01:43:46: Epoch: 014 Test results, ap: 0.238, auc: 0.512, f1: 0.044, acc: 0.650
+2025-08-26 01:43:47: Epoch: 015 Train Loss: 0.693, ap: 0.511, auc: 0.566, f1: 0.443, acc: 0.526
+2025-08-26 01:43:47: Epoch: 015 Test results, ap: 0.348, auc: 0.587, f1: 0.420, acc: 0.439
+2025-08-26 01:43:48: Epoch: 016 Train Loss: 0.693, ap: 0.503, auc: 0.533, f1: 0.499, acc: 0.508
+2025-08-26 01:43:49: Epoch: 016 Test results, ap: 0.247, auc: 0.487, f1: 0.388, acc: 0.309
+2025-08-26 01:43:50: Epoch: 017 Train Loss: 0.693, ap: 0.493, auc: 0.561, f1: 0.471, acc: 0.545
+2025-08-26 01:43:50: Epoch: 017 Test results, ap: 0.254, auc: 0.538, f1: 0.407, acc: 0.480
+2025-08-26 01:43:51: Epoch: 018 Train Loss: 0.693, ap: 0.479, auc: 0.552, f1: 0.172, acc: 0.559
+2025-08-26 01:43:52: Epoch: 018 Test results, ap: 0.215, auc: 0.409, f1: 0.197, acc: 0.602
+2025-08-26 01:43:53: Epoch: 019 Train Loss: 0.693, ap: 0.508, auc: 0.589, f1: 0.613, acc: 0.453
+2025-08-26 01:43:53: Epoch: 019 Test results, ap: 0.321, auc: 0.578, f1: 0.392, acc: 0.244
+2025-08-26 01:43:55: Epoch: 020 Train Loss: 0.693, ap: 0.514, auc: 0.592, f1: 0.206, acc: 0.550
+2025-08-26 01:43:55: Epoch: 020 Test results, ap: 0.475, auc: 0.662, f1: 0.421, acc: 0.374
+2025-08-26 01:43:56: Epoch: 021 Train Loss: 0.692, ap: 0.434, auc: 0.496, f1: 0.554, acc: 0.455
+2025-08-26 01:43:56: Epoch: 021 Test results, ap: 0.265, auc: 0.417, f1: 0.283, acc: 0.423
+2025-08-26 01:43:58: Epoch: 022 Train Loss: 0.688, ap: 0.441, auc: 0.497, f1: 0.407, acc: 0.522
+2025-08-26 01:43:58: Epoch: 022 Test results, ap: 0.249, auc: 0.472, f1: 0.270, acc: 0.561
+2025-08-26 01:43:59: Epoch: 023 Train Loss: 0.694, ap: 0.495, auc: 0.541, f1: 0.594, acc: 0.487
+2025-08-26 01:43:59: Epoch: 023 Test results, ap: 0.342, auc: 0.454, f1: 0.300, acc: 0.431
+2025-08-26 01:44:01: Epoch: 024 Train Loss: 0.689, ap: 0.467, auc: 0.511, f1: 0.534, acc: 0.492
+2025-08-26 01:44:01: Epoch: 024 Test results, ap: 0.444, auc: 0.532, f1: 0.274, acc: 0.439
+2025-08-26 01:44:02: Epoch: 025 Train Loss: 0.691, ap: 0.469, auc: 0.539, f1: 0.551, acc: 0.515
+2025-08-26 01:44:02: Epoch: 025 Test results, ap: 0.224, auc: 0.388, f1: 0.111, acc: 0.740
+2025-08-26 01:44:04: Epoch: 026 Train Loss: 0.693, ap: 0.466, auc: 0.536, f1: 0.582, acc: 0.483
+2025-08-26 01:44:04: Epoch: 026 Test results, ap: 0.260, auc: 0.558, f1: 0.408, acc: 0.293
+2025-08-26 01:44:05: Epoch: 027 Train Loss: 0.692, ap: 0.470, auc: 0.553, f1: 0.552, acc: 0.508
+2025-08-26 01:44:06: Epoch: 027 Test results, ap: 0.267, auc: 0.534, f1: 0.260, acc: 0.537
+2025-08-26 01:44:07: Epoch: 028 Train Loss: 0.685, ap: 0.456, auc: 0.535, f1: 0.510, acc: 0.503
+2025-08-26 01:44:07: Epoch: 028 Test results, ap: 0.239, auc: 0.509, f1: 0.315, acc: 0.504
+2025-08-26 01:44:08: Epoch: 029 Train Loss: 0.690, ap: 0.473, auc: 0.533, f1: 0.552, acc: 0.481
+2025-08-26 01:44:09: Epoch: 029 Test results, ap: 0.367, auc: 0.549, f1: 0.273, acc: 0.740
+2025-08-26 01:44:10: Epoch: 030 Train Loss: 0.692, ap: 0.469, auc: 0.542, f1: 0.257, acc: 0.561
+2025-08-26 01:44:10: Epoch: 030 Test results, ap: 0.341, auc: 0.576, f1: 0.400, acc: 0.341
+2025-08-26 01:44:11: Epoch: 031 Train Loss: 0.691, ap: 0.458, auc: 0.522, f1: 0.539, acc: 0.499
+2025-08-26 01:44:12: Epoch: 031 Test results, ap: 0.292, auc: 0.558, f1: 0.446, acc: 0.496
+2025-08-26 01:44:13: Epoch: 032 Train Loss: 0.685, ap: 0.492, auc: 0.550, f1: 0.555, acc: 0.517
+2025-08-26 01:44:13: Epoch: 032 Test results, ap: 0.300, auc: 0.506, f1: 0.392, acc: 0.244
+2025-08-26 01:44:15: Epoch: 033 Train Loss: 0.692, ap: 0.443, auc: 0.483, f1: 0.116, acc: 0.570
+2025-08-26 01:44:15: Epoch: 033 Test results, ap: 0.217, auc: 0.416, f1: 0.000, acc: 0.756
+2025-08-26 01:44:16: Epoch: 034 Train Loss: 0.691, ap: 0.477, auc: 0.516, f1: 0.443, acc: 0.526
+2025-08-26 01:44:16: Epoch: 034 Test results, ap: 0.263, auc: 0.400, f1: 0.128, acc: 0.667
+2025-08-26 01:44:18: Epoch: 035 Train Loss: 0.686, ap: 0.474, auc: 0.534, f1: 0.544, acc: 0.501
+2025-08-26 01:44:18: Epoch: 035 Test results, ap: 0.226, auc: 0.428, f1: 0.128, acc: 0.667
+2025-08-26 01:44:19: Epoch: 036 Train Loss: 0.680, ap: 0.469, auc: 0.526, f1: 0.496, acc: 0.496
+2025-08-26 01:44:19: Epoch: 036 Test results, ap: 0.259, auc: 0.556, f1: 0.301, acc: 0.585
+2025-08-26 01:44:21: Epoch: 037 Train Loss: 0.675, ap: 0.490, auc: 0.554, f1: 0.501, acc: 0.526
+2025-08-26 01:44:21: Epoch: 037 Test results, ap: 0.243, auc: 0.495, f1: 0.125, acc: 0.659
+2025-08-26 01:44:22: Epoch: 038 Train Loss: 0.673, ap: 0.472, auc: 0.538, f1: 0.495, acc: 0.533
+2025-08-26 01:44:22: Epoch: 038 Test results, ap: 0.216, auc: 0.416, f1: 0.275, acc: 0.528
+2025-08-26 01:44:24: Epoch: 039 Train Loss: 0.672, ap: 0.496, auc: 0.560, f1: 0.527, acc: 0.515
+2025-08-26 01:44:24: Epoch: 039 Test results, ap: 0.302, auc: 0.504, f1: 0.229, acc: 0.561
+2025-08-26 01:44:25: Epoch: 040 Train Loss: 0.668, ap: 0.507, auc: 0.586, f1: 0.527, acc: 0.531
+2025-08-26 01:44:26: Epoch: 040 Test results, ap: 0.242, auc: 0.501, f1: 0.250, acc: 0.610
+2025-08-26 01:44:27: Epoch: 041 Train Loss: 0.665, ap: 0.523, auc: 0.596, f1: 0.533, acc: 0.531
+2025-08-26 01:44:27: Epoch: 041 Test results, ap: 0.265, auc: 0.557, f1: 0.226, acc: 0.610
+2025-08-26 01:44:28: Epoch: 042 Train Loss: 0.669, ap: 0.524, auc: 0.590, f1: 0.529, acc: 0.533
+2025-08-26 01:44:29: Epoch: 042 Test results, ap: 0.243, auc: 0.509, f1: 0.087, acc: 0.659
+2025-08-26 01:44:30: Epoch: 043 Train Loss: 0.667, ap: 0.537, auc: 0.590, f1: 0.535, acc: 0.540
+2025-08-26 01:44:30: Epoch: 043 New best VALIDATION metrics
+2025-08-26 01:44:30: Epoch: 043 Test results, ap: 0.257, auc: 0.530, f1: 0.087, acc: 0.659
+2025-08-26 01:44:31: Epoch: 044 Train Loss: 0.659, ap: 0.536, auc: 0.591, f1: 0.533, acc: 0.533
+2025-08-26 01:44:31: Epoch: 044 New best VALIDATION metrics
+2025-08-26 01:44:32: Epoch: 044 Test results, ap: 0.256, auc: 0.549, f1: 0.145, acc: 0.618
+2025-08-26 01:44:33: Epoch: 045 Train Loss: 0.659, ap: 0.524, auc: 0.586, f1: 0.528, acc: 0.520
+2025-08-26 01:44:33: Epoch: 045 Test results, ap: 0.253, auc: 0.487, f1: 0.182, acc: 0.634
+2025-08-26 01:44:35: Epoch: 046 Train Loss: 0.662, ap: 0.514, auc: 0.578, f1: 0.526, acc: 0.540
+2025-08-26 01:44:35: Epoch: 046 Test results, ap: 0.292, auc: 0.586, f1: 0.255, acc: 0.715
+2025-08-26 01:44:36: Epoch: 047 Train Loss: 0.660, ap: 0.512, auc: 0.590, f1: 0.530, acc: 0.563
+2025-08-26 01:44:36: Epoch: 047 Test results, ap: 0.255, auc: 0.535, f1: 0.204, acc: 0.683
+2025-08-26 01:44:38: Epoch: 048 Train Loss: 0.656, ap: 0.536, auc: 0.588, f1: 0.515, acc: 0.552
+2025-08-26 01:44:38: Epoch: 048 Test results, ap: 0.300, auc: 0.614, f1: 0.178, acc: 0.699
+2025-08-26 01:44:39: Epoch: 049 Train Loss: 0.654, ap: 0.543, auc: 0.594, f1: 0.535, acc: 0.568
+2025-08-26 01:44:39: Epoch: 049 New best VALIDATION metrics
+2025-08-26 01:44:39: Epoch: 049 Test results, ap: 0.279, auc: 0.570, f1: 0.196, acc: 0.667
+2025-08-26 01:44:41: Epoch: 050 Train Loss: 0.658, ap: 0.523, auc: 0.589, f1: 0.528, acc: 0.564
+2025-08-26 01:44:41: Epoch: 050 Test results, ap: 0.241, auc: 0.509, f1: 0.196, acc: 0.667
+2025-08-26 01:44:41: used time 0.02h
+2025-08-26 01:44:41: Cross Validation Finished!
+2025-08-26 01:44:41: Best validation perform list
+[[0.57019985 0.60747504 0.         0.5193662 ]
+ [0.74963057 0.79634577 0.69548875 0.71478873]
+ [0.73201805 0.79004419 0.69776118 0.71428573]
+ [0.72011495 0.76752269 0.67500001 0.7248677 ]
+ [0.5433749  0.59418023 0.53510439 0.56790125]]
+2025-08-26 01:44:41: mean: [0.663 0.711 0.521 0.648]
+2025-08-26 01:44:41: std: [0.088 0.091 0.267 0.087]
+2025-08-26 01:44:41: Best test perform list
+[[0.30440924 0.57275987 0.39215687 0.24390244]
+ [0.25918308 0.50967741 0.26086956 0.58536583]
+ [0.43897092 0.69641578 0.42253521 0.33333334]
+ [0.45712888 0.71505374 0.40677965 0.71544713]
+ [0.51675749 0.70465946 0.         0.75609756]]
+2025-08-26 01:44:41: mean: [0.395 0.64  0.296 0.527]
+2025-08-26 01:44:41: std: [0.097 0.083 0.159 0.204]
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\ No newline at end of file
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+2025-08-26 01:44:46: saving_dir: ./run-cls/gru-diff-256-ce-32-0.001-50
+2025-08-26 01:44:46: Loading Train Dataset
+2025-08-26 01:44:46: Loading Test Dataset
+2025-08-26 01:44:47: Running Cross Validation 0
+2025-08-26 01:44:47: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 01:44:49: Epoch: 001 Train Loss: 0.694, ap: 0.496, auc: 0.525, f1: 0.000, acc: 0.519
+2025-08-26 01:44:49: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:44:49: Epoch: 001 Test results, ap: 0.332, auc: 0.637, f1: 0.000, acc: 0.756
+2025-08-26 01:44:50: Epoch: 002 Train Loss: 0.693, ap: 0.584, auc: 0.625, f1: 0.648, acc: 0.486
+2025-08-26 01:44:50: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:44:50: Epoch: 002 Test results, ap: 0.224, auc: 0.477, f1: 0.392, acc: 0.244
+2025-08-26 01:44:52: Epoch: 003 Train Loss: 0.692, ap: 0.613, auc: 0.667, f1: 0.537, acc: 0.606
+2025-08-26 01:44:52: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:44:52: Epoch: 003 Test results, ap: 0.229, auc: 0.483, f1: 0.316, acc: 0.577
+2025-08-26 01:44:53: Epoch: 004 Train Loss: 0.664, ap: 0.662, auc: 0.664, f1: 0.537, acc: 0.618
+2025-08-26 01:44:53: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:44:53: Epoch: 004 Test results, ap: 0.243, auc: 0.499, f1: 0.215, acc: 0.585
+2025-08-26 01:44:54: Epoch: 005 Train Loss: 0.653, ap: 0.665, auc: 0.688, f1: 0.575, acc: 0.641
+2025-08-26 01:44:54: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:44:55: Epoch: 005 Test results, ap: 0.230, auc: 0.488, f1: 0.312, acc: 0.569
+2025-08-26 01:44:56: Epoch: 006 Train Loss: 0.645, ap: 0.660, auc: 0.707, f1: 0.594, acc: 0.658
+2025-08-26 01:44:56: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:44:56: Epoch: 006 Test results, ap: 0.227, auc: 0.477, f1: 0.261, acc: 0.585
+2025-08-26 01:44:57: Epoch: 007 Train Loss: 0.627, ap: 0.712, auc: 0.741, f1: 0.590, acc: 0.653
+2025-08-26 01:44:57: Epoch: 007 New best VALIDATION metrics
+2025-08-26 01:44:58: Epoch: 007 Test results, ap: 0.218, auc: 0.414, f1: 0.293, acc: 0.528
+2025-08-26 01:44:59: Epoch: 008 Train Loss: 0.598, ap: 0.787, auc: 0.797, f1: 0.690, acc: 0.717
+2025-08-26 01:44:59: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:44:59: Epoch: 008 Test results, ap: 0.263, auc: 0.536, f1: 0.296, acc: 0.537
+2025-08-26 01:45:00: Epoch: 009 Train Loss: 0.601, ap: 0.747, auc: 0.761, f1: 0.645, acc: 0.692
+2025-08-26 01:45:01: Epoch: 009 Test results, ap: 0.209, auc: 0.420, f1: 0.323, acc: 0.488
+2025-08-26 01:45:02: Epoch: 010 Train Loss: 0.560, ap: 0.751, auc: 0.770, f1: 0.680, acc: 0.695
+2025-08-26 01:45:02: Epoch: 010 Test results, ap: 0.236, auc: 0.451, f1: 0.333, acc: 0.480
+2025-08-26 01:45:03: Epoch: 011 Train Loss: 0.534, ap: 0.818, auc: 0.823, f1: 0.736, acc: 0.759
+2025-08-26 01:45:03: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:45:04: Epoch: 011 Test results, ap: 0.357, auc: 0.624, f1: 0.302, acc: 0.699
+2025-08-26 01:45:04: Epoch: 011 New best TEST metrics
+2025-08-26 01:45:05: Epoch: 012 Train Loss: 0.524, ap: 0.828, auc: 0.836, f1: 0.759, acc: 0.766
+2025-08-26 01:45:05: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:45:05: Epoch: 012 Test results, ap: 0.280, auc: 0.552, f1: 0.366, acc: 0.634
+2025-08-26 01:45:06: Epoch: 013 Train Loss: 0.484, ap: 0.850, auc: 0.856, f1: 0.760, acc: 0.766
+2025-08-26 01:45:06: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:45:07: Epoch: 013 Test results, ap: 0.306, auc: 0.571, f1: 0.385, acc: 0.610
+2025-08-26 01:45:08: Epoch: 014 Train Loss: 0.464, ap: 0.860, auc: 0.864, f1: 0.764, acc: 0.778
+2025-08-26 01:45:08: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:45:08: Epoch: 014 Test results, ap: 0.338, auc: 0.572, f1: 0.317, acc: 0.650
+2025-08-26 01:45:09: Epoch: 015 Train Loss: 0.463, ap: 0.868, auc: 0.877, f1: 0.788, acc: 0.789
+2025-08-26 01:45:09: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:45:10: Epoch: 015 Test results, ap: 0.308, auc: 0.551, f1: 0.342, acc: 0.593
+2025-08-26 01:45:11: Epoch: 016 Train Loss: 0.437, ap: 0.866, auc: 0.885, f1: 0.790, acc: 0.794
+2025-08-26 01:45:11: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:45:11: Epoch: 016 Test results, ap: 0.300, auc: 0.572, f1: 0.419, acc: 0.593
+2025-08-26 01:45:12: Epoch: 017 Train Loss: 0.425, ap: 0.875, auc: 0.882, f1: 0.788, acc: 0.796
+2025-08-26 01:45:12: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:45:12: Epoch: 017 Test results, ap: 0.304, auc: 0.543, f1: 0.289, acc: 0.561
+2025-08-26 01:45:14: Epoch: 018 Train Loss: 0.409, ap: 0.872, auc: 0.887, f1: 0.793, acc: 0.799
+2025-08-26 01:45:14: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:45:14: Epoch: 018 Test results, ap: 0.301, auc: 0.533, f1: 0.347, acc: 0.480
+2025-08-26 01:45:15: Epoch: 019 Train Loss: 0.388, ap: 0.892, auc: 0.902, f1: 0.802, acc: 0.810
+2025-08-26 01:45:15: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:45:15: Epoch: 019 Test results, ap: 0.330, auc: 0.616, f1: 0.420, acc: 0.618
+2025-08-26 01:45:17: Epoch: 020 Train Loss: 0.365, ap: 0.895, auc: 0.907, f1: 0.808, acc: 0.806
+2025-08-26 01:45:17: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:45:17: Epoch: 020 Test results, ap: 0.329, auc: 0.595, f1: 0.419, acc: 0.593
+2025-08-26 01:45:18: Epoch: 021 Train Loss: 0.345, ap: 0.910, auc: 0.920, f1: 0.828, acc: 0.833
+2025-08-26 01:45:18: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:45:18: Epoch: 021 Test results, ap: 0.334, auc: 0.597, f1: 0.400, acc: 0.634
+2025-08-26 01:45:20: Epoch: 022 Train Loss: 0.316, ap: 0.919, auc: 0.917, f1: 0.829, acc: 0.840
+2025-08-26 01:45:20: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:45:20: Epoch: 022 Test results, ap: 0.358, auc: 0.646, f1: 0.400, acc: 0.634
+2025-08-26 01:45:20: Epoch: 022 New best TEST metrics
+2025-08-26 01:45:21: Epoch: 023 Train Loss: 0.311, ap: 0.920, auc: 0.925, f1: 0.836, acc: 0.842
+2025-08-26 01:45:21: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:45:22: Epoch: 023 Test results, ap: 0.333, auc: 0.616, f1: 0.395, acc: 0.602
+2025-08-26 01:45:23: Epoch: 024 Train Loss: 0.297, ap: 0.911, auc: 0.918, f1: 0.829, acc: 0.831
+2025-08-26 01:45:23: Epoch: 024 Test results, ap: 0.322, auc: 0.580, f1: 0.333, acc: 0.577
+2025-08-26 01:45:24: Epoch: 025 Train Loss: 0.290, ap: 0.892, auc: 0.915, f1: 0.837, acc: 0.836
+2025-08-26 01:45:24: Epoch: 025 Test results, ap: 0.300, auc: 0.555, f1: 0.346, acc: 0.569
+2025-08-26 01:45:26: Epoch: 026 Train Loss: 0.285, ap: 0.897, auc: 0.918, f1: 0.833, acc: 0.840
+2025-08-26 01:45:26: Epoch: 026 Test results, ap: 0.330, auc: 0.617, f1: 0.366, acc: 0.634
+2025-08-26 01:45:27: Epoch: 027 Train Loss: 0.264, ap: 0.927, auc: 0.933, f1: 0.860, acc: 0.864
+2025-08-26 01:45:27: Epoch: 027 New best VALIDATION metrics
+2025-08-26 01:45:27: Epoch: 027 Test results, ap: 0.299, auc: 0.584, f1: 0.354, acc: 0.585
+2025-08-26 01:45:29: Epoch: 028 Train Loss: 0.262, ap: 0.912, auc: 0.930, f1: 0.860, acc: 0.861
+2025-08-26 01:45:29: Epoch: 028 Test results, ap: 0.334, auc: 0.609, f1: 0.366, acc: 0.577
+2025-08-26 01:45:30: Epoch: 029 Train Loss: 0.257, ap: 0.925, auc: 0.931, f1: 0.856, acc: 0.857
+2025-08-26 01:45:30: Epoch: 029 Test results, ap: 0.328, auc: 0.603, f1: 0.405, acc: 0.642
+2025-08-26 01:45:32: Epoch: 030 Train Loss: 0.245, ap: 0.898, auc: 0.916, f1: 0.826, acc: 0.827
+2025-08-26 01:45:32: Epoch: 030 Test results, ap: 0.333, auc: 0.596, f1: 0.374, acc: 0.537
+2025-08-26 01:45:33: Epoch: 031 Train Loss: 0.220, ap: 0.928, auc: 0.937, f1: 0.869, acc: 0.871
+2025-08-26 01:45:33: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:45:33: Epoch: 031 Test results, ap: 0.329, auc: 0.599, f1: 0.390, acc: 0.618
+2025-08-26 01:45:34: Epoch: 032 Train Loss: 0.209, ap: 0.929, auc: 0.935, f1: 0.862, acc: 0.868
+2025-08-26 01:45:35: Epoch: 032 Test results, ap: 0.327, auc: 0.606, f1: 0.377, acc: 0.650
+2025-08-26 01:45:36: Epoch: 033 Train Loss: 0.212, ap: 0.924, auc: 0.934, f1: 0.860, acc: 0.866
+2025-08-26 01:45:36: Epoch: 033 Test results, ap: 0.330, auc: 0.603, f1: 0.384, acc: 0.634
+2025-08-26 01:45:37: Epoch: 034 Train Loss: 0.200, ap: 0.935, auc: 0.938, f1: 0.865, acc: 0.871
+2025-08-26 01:45:37: Epoch: 034 New best VALIDATION metrics
+2025-08-26 01:45:38: Epoch: 034 Test results, ap: 0.355, auc: 0.621, f1: 0.427, acc: 0.650
+2025-08-26 01:45:39: Epoch: 035 Train Loss: 0.200, ap: 0.910, auc: 0.929, f1: 0.868, acc: 0.871
+2025-08-26 01:45:39: Epoch: 035 Test results, ap: 0.327, auc: 0.600, f1: 0.405, acc: 0.593
+2025-08-26 01:45:40: Epoch: 036 Train Loss: 0.201, ap: 0.929, auc: 0.939, f1: 0.875, acc: 0.877
+2025-08-26 01:45:41: Epoch: 036 Test results, ap: 0.341, auc: 0.598, f1: 0.405, acc: 0.642
+2025-08-26 01:45:42: Epoch: 037 Train Loss: 0.180, ap: 0.925, auc: 0.935, f1: 0.873, acc: 0.879
+2025-08-26 01:45:42: Epoch: 037 Test results, ap: 0.339, auc: 0.606, f1: 0.427, acc: 0.650
+2025-08-26 01:45:43: Epoch: 038 Train Loss: 0.175, ap: 0.931, auc: 0.939, f1: 0.884, acc: 0.886
+2025-08-26 01:45:44: Epoch: 038 Test results, ap: 0.304, auc: 0.571, f1: 0.359, acc: 0.593
+2025-08-26 01:45:45: Epoch: 039 Train Loss: 0.183, ap: 0.929, auc: 0.935, f1: 0.848, acc: 0.854
+2025-08-26 01:45:45: Epoch: 039 Test results, ap: 0.318, auc: 0.584, f1: 0.384, acc: 0.634
+2025-08-26 01:45:46: Epoch: 040 Train Loss: 0.176, ap: 0.915, auc: 0.934, f1: 0.872, acc: 0.877
+2025-08-26 01:45:46: Epoch: 040 Test results, ap: 0.343, auc: 0.594, f1: 0.395, acc: 0.602
+2025-08-26 01:45:48: Epoch: 041 Train Loss: 0.154, ap: 0.934, auc: 0.939, f1: 0.862, acc: 0.870
+2025-08-26 01:45:48: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:45:48: Epoch: 041 Test results, ap: 0.339, auc: 0.603, f1: 0.394, acc: 0.650
+2025-08-26 01:45:49: Epoch: 042 Train Loss: 0.154, ap: 0.923, auc: 0.936, f1: 0.883, acc: 0.887
+2025-08-26 01:45:49: Epoch: 042 Test results, ap: 0.324, auc: 0.590, f1: 0.405, acc: 0.618
+2025-08-26 01:45:51: Epoch: 043 Train Loss: 0.145, ap: 0.929, auc: 0.938, f1: 0.880, acc: 0.886
+2025-08-26 01:45:51: Epoch: 043 Test results, ap: 0.327, auc: 0.585, f1: 0.416, acc: 0.634
+2025-08-26 01:45:52: Epoch: 044 Train Loss: 0.143, ap: 0.932, auc: 0.939, f1: 0.888, acc: 0.893
+2025-08-26 01:45:52: Epoch: 044 Test results, ap: 0.320, auc: 0.584, f1: 0.390, acc: 0.618
+2025-08-26 01:45:53: Epoch: 045 Train Loss: 0.136, ap: 0.932, auc: 0.935, f1: 0.878, acc: 0.886
+2025-08-26 01:45:54: Epoch: 045 Test results, ap: 0.309, auc: 0.589, f1: 0.368, acc: 0.610
+2025-08-26 01:45:55: Epoch: 046 Train Loss: 0.134, ap: 0.937, auc: 0.939, f1: 0.878, acc: 0.884
+2025-08-26 01:45:55: Epoch: 046 New best VALIDATION metrics
+2025-08-26 01:45:55: Epoch: 046 Test results, ap: 0.301, auc: 0.576, f1: 0.347, acc: 0.602
+2025-08-26 01:45:56: Epoch: 047 Train Loss: 0.131, ap: 0.925, auc: 0.936, f1: 0.875, acc: 0.880
+2025-08-26 01:45:57: Epoch: 047 Test results, ap: 0.286, auc: 0.562, f1: 0.380, acc: 0.602
+2025-08-26 01:45:58: Epoch: 048 Train Loss: 0.134, ap: 0.938, auc: 0.939, f1: 0.879, acc: 0.886
+2025-08-26 01:45:58: Epoch: 048 New best VALIDATION metrics
+2025-08-26 01:45:58: Epoch: 048 Test results, ap: 0.303, auc: 0.582, f1: 0.394, acc: 0.650
+2025-08-26 01:45:59: Epoch: 049 Train Loss: 0.134, ap: 0.934, auc: 0.939, f1: 0.887, acc: 0.893
+2025-08-26 01:46:00: Epoch: 049 Test results, ap: 0.300, auc: 0.575, f1: 0.329, acc: 0.602
+2025-08-26 01:46:01: Epoch: 050 Train Loss: 0.126, ap: 0.930, auc: 0.939, f1: 0.884, acc: 0.889
+2025-08-26 01:46:01: Epoch: 050 Test results, ap: 0.289, auc: 0.561, f1: 0.301, acc: 0.585
+2025-08-26 01:46:01: used time 0.02h
+2025-08-26 01:46:01: Running Cross Validation 1
+2025-08-26 01:46:01: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 01:46:02: Epoch: 001 Train Loss: 0.693, ap: 0.439, auc: 0.474, f1: 0.608, acc: 0.437
+2025-08-26 01:46:02: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:46:03: Epoch: 001 Test results, ap: 0.180, auc: 0.319, f1: 0.392, acc: 0.244
+2025-08-26 01:46:04: Epoch: 002 Train Loss: 0.690, ap: 0.596, auc: 0.679, f1: 0.510, acc: 0.637
+2025-08-26 01:46:04: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:46:04: Epoch: 002 Test results, ap: 0.251, auc: 0.522, f1: 0.049, acc: 0.683
+2025-08-26 01:46:05: Epoch: 003 Train Loss: 0.686, ap: 0.495, auc: 0.580, f1: 0.118, acc: 0.551
+2025-08-26 01:46:06: Epoch: 003 Test results, ap: 0.249, auc: 0.533, f1: 0.000, acc: 0.732
+2025-08-26 01:46:07: Epoch: 004 Train Loss: 0.672, ap: 0.562, auc: 0.608, f1: 0.576, acc: 0.585
+2025-08-26 01:46:07: Epoch: 004 Test results, ap: 0.239, auc: 0.477, f1: 0.315, acc: 0.504
+2025-08-26 01:46:08: Epoch: 005 Train Loss: 0.687, ap: 0.395, auc: 0.440, f1: 0.024, acc: 0.565
+2025-08-26 01:46:08: Epoch: 005 Test results, ap: 0.276, auc: 0.548, f1: 0.000, acc: 0.756
+2025-08-26 01:46:10: Epoch: 006 Train Loss: 0.675, ap: 0.591, auc: 0.639, f1: 0.510, acc: 0.593
+2025-08-26 01:46:10: Epoch: 006 Test results, ap: 0.238, auc: 0.453, f1: 0.268, acc: 0.512
+2025-08-26 01:46:11: Epoch: 007 Train Loss: 0.652, ap: 0.560, auc: 0.631, f1: 0.396, acc: 0.613
+2025-08-26 01:46:11: Epoch: 007 Test results, ap: 0.233, auc: 0.475, f1: 0.286, acc: 0.553
+2025-08-26 01:46:13: Epoch: 008 Train Loss: 0.661, ap: 0.609, auc: 0.659, f1: 0.519, acc: 0.614
+2025-08-26 01:46:13: Epoch: 008 Test results, ap: 0.253, auc: 0.490, f1: 0.337, acc: 0.488
+2025-08-26 01:46:14: Epoch: 009 Train Loss: 0.645, ap: 0.624, auc: 0.667, f1: 0.561, acc: 0.634
+2025-08-26 01:46:14: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:46:14: Epoch: 009 Test results, ap: 0.231, auc: 0.473, f1: 0.337, acc: 0.488
+2025-08-26 01:46:15: Epoch: 010 Train Loss: 0.648, ap: 0.599, auc: 0.649, f1: 0.499, acc: 0.632
+2025-08-26 01:46:16: Epoch: 010 Test results, ap: 0.234, auc: 0.470, f1: 0.301, acc: 0.472
+2025-08-26 01:46:17: Epoch: 011 Train Loss: 0.637, ap: 0.612, auc: 0.657, f1: 0.533, acc: 0.586
+2025-08-26 01:46:17: Epoch: 011 Test results, ap: 0.280, auc: 0.507, f1: 0.337, acc: 0.488
+2025-08-26 01:46:17: Epoch: 011 New best TEST metrics
+2025-08-26 01:46:18: Epoch: 012 Train Loss: 0.639, ap: 0.605, auc: 0.666, f1: 0.528, acc: 0.644
+2025-08-26 01:46:19: Epoch: 012 Test results, ap: 0.279, auc: 0.521, f1: 0.349, acc: 0.545
+2025-08-26 01:46:19: Epoch: 012 New best TEST metrics
+2025-08-26 01:46:20: Epoch: 013 Train Loss: 0.631, ap: 0.634, auc: 0.672, f1: 0.564, acc: 0.611
+2025-08-26 01:46:20: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:46:20: Epoch: 013 Test results, ap: 0.283, auc: 0.522, f1: 0.337, acc: 0.488
+2025-08-26 01:46:20: Epoch: 013 New best TEST metrics
+2025-08-26 01:46:21: Epoch: 014 Train Loss: 0.638, ap: 0.616, auc: 0.684, f1: 0.556, acc: 0.632
+2025-08-26 01:46:22: Epoch: 014 Test results, ap: 0.254, auc: 0.513, f1: 0.316, acc: 0.577
+2025-08-26 01:46:23: Epoch: 015 Train Loss: 0.634, ap: 0.662, auc: 0.709, f1: 0.571, acc: 0.637
+2025-08-26 01:46:23: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:46:23: Epoch: 015 Test results, ap: 0.258, auc: 0.505, f1: 0.312, acc: 0.569
+2025-08-26 01:46:24: Epoch: 016 Train Loss: 0.602, ap: 0.671, auc: 0.726, f1: 0.590, acc: 0.662
+2025-08-26 01:46:24: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:46:25: Epoch: 016 Test results, ap: 0.281, auc: 0.523, f1: 0.353, acc: 0.553
+2025-08-26 01:46:26: Epoch: 017 Train Loss: 0.599, ap: 0.693, auc: 0.732, f1: 0.601, acc: 0.643
+2025-08-26 01:46:26: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:46:26: Epoch: 017 Test results, ap: 0.312, auc: 0.527, f1: 0.357, acc: 0.561
+2025-08-26 01:46:26: Epoch: 017 New best TEST metrics
+2025-08-26 01:46:27: Epoch: 018 Train Loss: 0.578, ap: 0.694, auc: 0.755, f1: 0.639, acc: 0.697
+2025-08-26 01:46:27: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:46:27: Epoch: 018 Test results, ap: 0.353, auc: 0.520, f1: 0.364, acc: 0.602
+2025-08-26 01:46:27: Epoch: 018 New best TEST metrics
+2025-08-26 01:46:29: Epoch: 019 Train Loss: 0.569, ap: 0.708, auc: 0.766, f1: 0.613, acc: 0.687
+2025-08-26 01:46:29: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:46:29: Epoch: 019 Test results, ap: 0.371, auc: 0.528, f1: 0.347, acc: 0.602
+2025-08-26 01:46:29: Epoch: 019 New best TEST metrics
+2025-08-26 01:46:30: Epoch: 020 Train Loss: 0.546, ap: 0.720, auc: 0.773, f1: 0.654, acc: 0.680
+2025-08-26 01:46:30: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:46:30: Epoch: 020 Test results, ap: 0.329, auc: 0.498, f1: 0.278, acc: 0.537
+2025-08-26 01:46:32: Epoch: 021 Train Loss: 0.518, ap: 0.754, auc: 0.810, f1: 0.704, acc: 0.732
+2025-08-26 01:46:32: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:46:32: Epoch: 021 Test results, ap: 0.388, auc: 0.538, f1: 0.364, acc: 0.602
+2025-08-26 01:46:32: Epoch: 021 New best TEST metrics
+2025-08-26 01:46:33: Epoch: 022 Train Loss: 0.498, ap: 0.776, auc: 0.822, f1: 0.718, acc: 0.743
+2025-08-26 01:46:33: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:46:33: Epoch: 022 Test results, ap: 0.387, auc: 0.542, f1: 0.321, acc: 0.553
+2025-08-26 01:46:33: Epoch: 022 New best TEST metrics
+2025-08-26 01:46:35: Epoch: 023 Train Loss: 0.471, ap: 0.786, auc: 0.833, f1: 0.731, acc: 0.754
+2025-08-26 01:46:35: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:46:35: Epoch: 023 Test results, ap: 0.369, auc: 0.524, f1: 0.313, acc: 0.537
+2025-08-26 01:46:36: Epoch: 024 Train Loss: 0.478, ap: 0.801, auc: 0.840, f1: 0.736, acc: 0.757
+2025-08-26 01:46:36: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:46:36: Epoch: 024 Test results, ap: 0.368, auc: 0.548, f1: 0.313, acc: 0.537
+2025-08-26 01:46:38: Epoch: 025 Train Loss: 0.586, ap: 0.758, auc: 0.806, f1: 0.714, acc: 0.732
+2025-08-26 01:46:38: Epoch: 025 Test results, ap: 0.363, auc: 0.535, f1: 0.313, acc: 0.537
+2025-08-26 01:46:39: Epoch: 026 Train Loss: 0.504, ap: 0.807, auc: 0.840, f1: 0.722, acc: 0.750
+2025-08-26 01:46:39: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:46:39: Epoch: 026 Test results, ap: 0.358, auc: 0.567, f1: 0.361, acc: 0.569
+2025-08-26 01:46:41: Epoch: 027 Train Loss: 0.471, ap: 0.825, auc: 0.856, f1: 0.748, acc: 0.771
+2025-08-26 01:46:41: Epoch: 027 New best VALIDATION metrics
+2025-08-26 01:46:41: Epoch: 027 Test results, ap: 0.341, auc: 0.564, f1: 0.378, acc: 0.545
+2025-08-26 01:46:42: Epoch: 028 Train Loss: 0.449, ap: 0.828, auc: 0.864, f1: 0.761, acc: 0.783
+2025-08-26 01:46:42: Epoch: 028 New best VALIDATION metrics
+2025-08-26 01:46:42: Epoch: 028 Test results, ap: 0.357, auc: 0.566, f1: 0.361, acc: 0.569
+2025-08-26 01:46:44: Epoch: 029 Train Loss: 0.451, ap: 0.833, auc: 0.862, f1: 0.743, acc: 0.773
+2025-08-26 01:46:44: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:46:44: Epoch: 029 Test results, ap: 0.305, auc: 0.544, f1: 0.329, acc: 0.569
+2025-08-26 01:46:45: Epoch: 030 Train Loss: 0.414, ap: 0.834, auc: 0.866, f1: 0.761, acc: 0.780
+2025-08-26 01:46:45: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:46:45: Epoch: 030 Test results, ap: 0.342, auc: 0.572, f1: 0.385, acc: 0.610
+2025-08-26 01:46:46: Epoch: 031 Train Loss: 0.402, ap: 0.853, auc: 0.880, f1: 0.778, acc: 0.798
+2025-08-26 01:46:46: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:46:47: Epoch: 031 Test results, ap: 0.316, auc: 0.557, f1: 0.322, acc: 0.520
+2025-08-26 01:46:48: Epoch: 032 Train Loss: 0.398, ap: 0.854, auc: 0.879, f1: 0.775, acc: 0.796
+2025-08-26 01:46:48: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:46:48: Epoch: 032 Test results, ap: 0.346, auc: 0.565, f1: 0.341, acc: 0.528
+2025-08-26 01:46:49: Epoch: 033 Train Loss: 0.376, ap: 0.857, auc: 0.886, f1: 0.794, acc: 0.812
+2025-08-26 01:46:49: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:46:50: Epoch: 033 Test results, ap: 0.354, auc: 0.561, f1: 0.341, acc: 0.528
+2025-08-26 01:46:51: Epoch: 034 Train Loss: 0.387, ap: 0.866, auc: 0.893, f1: 0.799, acc: 0.815
+2025-08-26 01:46:51: Epoch: 034 New best VALIDATION metrics
+2025-08-26 01:46:51: Epoch: 034 Test results, ap: 0.357, auc: 0.567, f1: 0.322, acc: 0.520
+2025-08-26 01:46:52: Epoch: 035 Train Loss: 0.365, ap: 0.874, auc: 0.898, f1: 0.812, acc: 0.831
+2025-08-26 01:46:52: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:46:53: Epoch: 035 Test results, ap: 0.366, auc: 0.584, f1: 0.326, acc: 0.528
+2025-08-26 01:46:53: Epoch: 035 New best TEST metrics
+2025-08-26 01:46:54: Epoch: 036 Train Loss: 0.353, ap: 0.879, auc: 0.902, f1: 0.811, acc: 0.827
+2025-08-26 01:46:54: Epoch: 036 New best VALIDATION metrics
+2025-08-26 01:46:54: Epoch: 036 Test results, ap: 0.368, auc: 0.576, f1: 0.326, acc: 0.528
+2025-08-26 01:46:55: Epoch: 037 Train Loss: 0.354, ap: 0.880, auc: 0.901, f1: 0.808, acc: 0.827
+2025-08-26 01:46:56: Epoch: 037 Test results, ap: 0.332, auc: 0.570, f1: 0.326, acc: 0.528
+2025-08-26 01:46:57: Epoch: 038 Train Loss: 0.346, ap: 0.874, auc: 0.901, f1: 0.810, acc: 0.827
+2025-08-26 01:46:57: Epoch: 038 Test results, ap: 0.366, auc: 0.567, f1: 0.362, acc: 0.512
+2025-08-26 01:46:58: Epoch: 039 Train Loss: 0.339, ap: 0.890, auc: 0.911, f1: 0.820, acc: 0.838
+2025-08-26 01:46:58: Epoch: 039 New best VALIDATION metrics
+2025-08-26 01:46:59: Epoch: 039 Test results, ap: 0.355, auc: 0.583, f1: 0.310, acc: 0.528
+2025-08-26 01:47:00: Epoch: 040 Train Loss: 0.338, ap: 0.887, auc: 0.910, f1: 0.809, acc: 0.831
+2025-08-26 01:47:00: Epoch: 040 Test results, ap: 0.371, auc: 0.584, f1: 0.378, acc: 0.545
+2025-08-26 01:47:00: Epoch: 040 New best TEST metrics
+2025-08-26 01:47:01: Epoch: 041 Train Loss: 0.319, ap: 0.892, auc: 0.914, f1: 0.823, acc: 0.840
+2025-08-26 01:47:01: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:47:01: Epoch: 041 Test results, ap: 0.369, auc: 0.580, f1: 0.362, acc: 0.512
+2025-08-26 01:47:03: Epoch: 042 Train Loss: 0.308, ap: 0.889, auc: 0.913, f1: 0.828, acc: 0.843
+2025-08-26 01:47:03: Epoch: 042 Test results, ap: 0.364, auc: 0.576, f1: 0.362, acc: 0.512
+2025-08-26 01:47:04: Epoch: 043 Train Loss: 0.323, ap: 0.892, auc: 0.915, f1: 0.824, acc: 0.842
+2025-08-26 01:47:04: Epoch: 043 New best VALIDATION metrics
+2025-08-26 01:47:04: Epoch: 043 Test results, ap: 0.367, auc: 0.582, f1: 0.378, acc: 0.545
+2025-08-26 01:47:06: Epoch: 044 Train Loss: 0.311, ap: 0.889, auc: 0.913, f1: 0.828, acc: 0.842
+2025-08-26 01:47:06: Epoch: 044 Test results, ap: 0.363, auc: 0.583, f1: 0.370, acc: 0.528
+2025-08-26 01:47:07: Epoch: 045 Train Loss: 0.321, ap: 0.877, auc: 0.904, f1: 0.810, acc: 0.829
+2025-08-26 01:47:07: Epoch: 045 Test results, ap: 0.334, auc: 0.590, f1: 0.349, acc: 0.545
+2025-08-26 01:47:09: Epoch: 046 Train Loss: 0.304, ap: 0.891, auc: 0.915, f1: 0.830, acc: 0.847
+2025-08-26 01:47:09: Epoch: 046 Test results, ap: 0.376, auc: 0.583, f1: 0.374, acc: 0.537
+2025-08-26 01:47:09: Epoch: 046 New best TEST metrics
+2025-08-26 01:47:10: Epoch: 047 Train Loss: 0.301, ap: 0.883, auc: 0.908, f1: 0.827, acc: 0.843
+2025-08-26 01:47:10: Epoch: 047 Test results, ap: 0.373, auc: 0.572, f1: 0.348, acc: 0.512
+2025-08-26 01:47:12: Epoch: 048 Train Loss: 0.317, ap: 0.889, auc: 0.912, f1: 0.817, acc: 0.838
+2025-08-26 01:47:12: Epoch: 048 Test results, ap: 0.383, auc: 0.594, f1: 0.379, acc: 0.520
+2025-08-26 01:47:12: Epoch: 048 New best TEST metrics
+2025-08-26 01:47:13: Epoch: 049 Train Loss: 0.316, ap: 0.886, auc: 0.912, f1: 0.820, acc: 0.840
+2025-08-26 01:47:13: Epoch: 049 Test results, ap: 0.383, auc: 0.600, f1: 0.388, acc: 0.512
+2025-08-26 01:47:13: Epoch: 049 New best TEST metrics
+2025-08-26 01:47:14: Epoch: 050 Train Loss: 0.300, ap: 0.895, auc: 0.921, f1: 0.837, acc: 0.856
+2025-08-26 01:47:14: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:47:15: Epoch: 050 Test results, ap: 0.370, auc: 0.592, f1: 0.366, acc: 0.520
+2025-08-26 01:47:15: used time 0.02h
+2025-08-26 01:47:15: Running Cross Validation 2
+2025-08-26 01:47:15: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:47:16: Epoch: 001 Train Loss: 0.693, ap: 0.457, auc: 0.538, f1: 0.610, acc: 0.444
+2025-08-26 01:47:16: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:47:16: Epoch: 001 Test results, ap: 0.326, auc: 0.651, f1: 0.392, acc: 0.244
+2025-08-26 01:47:17: Epoch: 002 Train Loss: 0.693, ap: 0.600, auc: 0.673, f1: 0.597, acc: 0.624
+2025-08-26 01:47:17: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:47:18: Epoch: 002 Test results, ap: 0.242, auc: 0.522, f1: 0.302, acc: 0.512
+2025-08-26 01:47:19: Epoch: 003 Train Loss: 0.655, ap: 0.647, auc: 0.719, f1: 0.610, acc: 0.651
+2025-08-26 01:47:19: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:47:19: Epoch: 003 Test results, ap: 0.247, auc: 0.477, f1: 0.316, acc: 0.577
+2025-08-26 01:47:20: Epoch: 004 Train Loss: 0.616, ap: 0.675, auc: 0.701, f1: 0.555, acc: 0.653
+2025-08-26 01:47:20: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:47:21: Epoch: 004 Test results, ap: 0.292, auc: 0.508, f1: 0.328, acc: 0.634
+2025-08-26 01:47:22: Epoch: 005 Train Loss: 0.599, ap: 0.699, auc: 0.729, f1: 0.599, acc: 0.646
+2025-08-26 01:47:22: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:47:22: Epoch: 005 Test results, ap: 0.266, auc: 0.523, f1: 0.306, acc: 0.520
+2025-08-26 01:47:23: Epoch: 006 Train Loss: 0.572, ap: 0.738, auc: 0.781, f1: 0.656, acc: 0.688
+2025-08-26 01:47:23: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:47:24: Epoch: 006 Test results, ap: 0.415, auc: 0.578, f1: 0.349, acc: 0.667
+2025-08-26 01:47:25: Epoch: 007 Train Loss: 0.550, ap: 0.721, auc: 0.793, f1: 0.686, acc: 0.714
+2025-08-26 01:47:25: Epoch: 007 Test results, ap: 0.342, auc: 0.555, f1: 0.345, acc: 0.537
+2025-08-26 01:47:26: Epoch: 008 Train Loss: 0.508, ap: 0.764, auc: 0.814, f1: 0.713, acc: 0.744
+2025-08-26 01:47:26: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:47:27: Epoch: 008 Test results, ap: 0.329, auc: 0.580, f1: 0.337, acc: 0.553
+2025-08-26 01:47:28: Epoch: 009 Train Loss: 0.487, ap: 0.784, auc: 0.815, f1: 0.697, acc: 0.732
+2025-08-26 01:47:28: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:47:28: Epoch: 009 Test results, ap: 0.322, auc: 0.535, f1: 0.267, acc: 0.642
+2025-08-26 01:47:29: Epoch: 010 Train Loss: 0.458, ap: 0.784, auc: 0.845, f1: 0.743, acc: 0.760
+2025-08-26 01:47:29: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:47:30: Epoch: 010 Test results, ap: 0.265, auc: 0.520, f1: 0.329, acc: 0.569
+2025-08-26 01:47:31: Epoch: 011 Train Loss: 0.407, ap: 0.803, auc: 0.844, f1: 0.731, acc: 0.760
+2025-08-26 01:47:31: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:47:31: Epoch: 011 Test results, ap: 0.276, auc: 0.536, f1: 0.346, acc: 0.569
+2025-08-26 01:47:31: Epoch: 011 New best TEST metrics
+2025-08-26 01:47:32: Epoch: 012 Train Loss: 0.395, ap: 0.813, auc: 0.862, f1: 0.763, acc: 0.781
+2025-08-26 01:47:32: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:47:33: Epoch: 012 Test results, ap: 0.280, auc: 0.528, f1: 0.324, acc: 0.593
+2025-08-26 01:47:34: Epoch: 013 Train Loss: 0.378, ap: 0.827, auc: 0.858, f1: 0.755, acc: 0.781
+2025-08-26 01:47:34: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:47:34: Epoch: 013 Test results, ap: 0.255, auc: 0.508, f1: 0.273, acc: 0.610
+2025-08-26 01:47:35: Epoch: 014 Train Loss: 0.372, ap: 0.849, auc: 0.874, f1: 0.771, acc: 0.790
+2025-08-26 01:47:35: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:47:35: Epoch: 014 Test results, ap: 0.262, auc: 0.529, f1: 0.200, acc: 0.610
+2025-08-26 01:47:37: Epoch: 015 Train Loss: 0.360, ap: 0.842, auc: 0.870, f1: 0.781, acc: 0.799
+2025-08-26 01:47:37: Epoch: 015 Test results, ap: 0.324, auc: 0.558, f1: 0.286, acc: 0.634
+2025-08-26 01:47:37: Epoch: 015 New best TEST metrics
+2025-08-26 01:47:38: Epoch: 016 Train Loss: 0.339, ap: 0.857, auc: 0.879, f1: 0.781, acc: 0.792
+2025-08-26 01:47:38: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:47:38: Epoch: 016 Test results, ap: 0.270, auc: 0.509, f1: 0.316, acc: 0.577
+2025-08-26 01:47:40: Epoch: 017 Train Loss: 0.315, ap: 0.862, auc: 0.885, f1: 0.783, acc: 0.806
+2025-08-26 01:47:40: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:47:40: Epoch: 017 Test results, ap: 0.280, auc: 0.514, f1: 0.250, acc: 0.610
+2025-08-26 01:47:41: Epoch: 018 Train Loss: 0.307, ap: 0.869, auc: 0.891, f1: 0.790, acc: 0.806
+2025-08-26 01:47:41: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:47:41: Epoch: 018 Test results, ap: 0.264, auc: 0.503, f1: 0.230, acc: 0.618
+2025-08-26 01:47:43: Epoch: 019 Train Loss: 0.293, ap: 0.890, auc: 0.905, f1: 0.799, acc: 0.815
+2025-08-26 01:47:43: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:47:43: Epoch: 019 Test results, ap: 0.264, auc: 0.512, f1: 0.276, acc: 0.659
+2025-08-26 01:47:44: Epoch: 020 Train Loss: 0.276, ap: 0.872, auc: 0.893, f1: 0.802, acc: 0.822
+2025-08-26 01:47:44: Epoch: 020 Test results, ap: 0.242, auc: 0.498, f1: 0.145, acc: 0.618
+2025-08-26 01:47:45: Epoch: 021 Train Loss: 0.241, ap: 0.894, auc: 0.903, f1: 0.802, acc: 0.825
+2025-08-26 01:47:45: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:47:46: Epoch: 021 Test results, ap: 0.266, auc: 0.501, f1: 0.233, acc: 0.626
+2025-08-26 01:47:47: Epoch: 022 Train Loss: 0.209, ap: 0.906, auc: 0.912, f1: 0.812, acc: 0.834
+2025-08-26 01:47:47: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:47:47: Epoch: 022 Test results, ap: 0.243, auc: 0.501, f1: 0.214, acc: 0.642
+2025-08-26 01:47:48: Epoch: 023 Train Loss: 0.194, ap: 0.915, auc: 0.921, f1: 0.828, acc: 0.848
+2025-08-26 01:47:48: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:47:49: Epoch: 023 Test results, ap: 0.241, auc: 0.496, f1: 0.042, acc: 0.626
+2025-08-26 01:47:50: Epoch: 024 Train Loss: 0.181, ap: 0.920, auc: 0.927, f1: 0.846, acc: 0.864
+2025-08-26 01:47:50: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:47:50: Epoch: 024 Test results, ap: 0.242, auc: 0.499, f1: 0.160, acc: 0.659
+2025-08-26 01:47:51: Epoch: 025 Train Loss: 0.179, ap: 0.915, auc: 0.920, f1: 0.814, acc: 0.836
+2025-08-26 01:47:52: Epoch: 025 Test results, ap: 0.253, auc: 0.507, f1: 0.281, acc: 0.667
+2025-08-26 01:47:53: Epoch: 026 Train Loss: 0.173, ap: 0.910, auc: 0.920, f1: 0.823, acc: 0.840
+2025-08-26 01:47:53: Epoch: 026 Test results, ap: 0.338, auc: 0.516, f1: 0.213, acc: 0.699
+2025-08-26 01:47:54: Epoch: 027 Train Loss: 0.164, ap: 0.916, auc: 0.923, f1: 0.830, acc: 0.850
+2025-08-26 01:47:55: Epoch: 027 Test results, ap: 0.235, auc: 0.493, f1: 0.073, acc: 0.585
+2025-08-26 01:47:56: Epoch: 028 Train Loss: 0.159, ap: 0.917, auc: 0.922, f1: 0.836, acc: 0.857
+2025-08-26 01:47:56: Epoch: 028 Test results, ap: 0.250, auc: 0.511, f1: 0.053, acc: 0.707
+2025-08-26 01:47:57: Epoch: 029 Train Loss: 0.152, ap: 0.929, auc: 0.933, f1: 0.855, acc: 0.871
+2025-08-26 01:47:57: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:47:58: Epoch: 029 Test results, ap: 0.249, auc: 0.503, f1: 0.043, acc: 0.642
+2025-08-26 01:47:59: Epoch: 030 Train Loss: 0.135, ap: 0.928, auc: 0.933, f1: 0.846, acc: 0.861
+2025-08-26 01:47:59: Epoch: 030 Test results, ap: 0.263, auc: 0.530, f1: 0.098, acc: 0.699
+2025-08-26 01:48:00: Epoch: 031 Train Loss: 0.113, ap: 0.938, auc: 0.940, f1: 0.867, acc: 0.882
+2025-08-26 01:48:00: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:48:00: Epoch: 031 Test results, ap: 0.257, auc: 0.525, f1: 0.200, acc: 0.675
+2025-08-26 01:48:02: Epoch: 032 Train Loss: 0.102, ap: 0.938, auc: 0.940, f1: 0.867, acc: 0.882
+2025-08-26 01:48:02: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:48:02: Epoch: 032 Test results, ap: 0.265, auc: 0.533, f1: 0.200, acc: 0.675
+2025-08-26 01:48:03: Epoch: 033 Train Loss: 0.106, ap: 0.937, auc: 0.938, f1: 0.870, acc: 0.885
+2025-08-26 01:48:03: Epoch: 033 Test results, ap: 0.244, auc: 0.509, f1: 0.091, acc: 0.675
+2025-08-26 01:48:05: Epoch: 034 Train Loss: 0.107, ap: 0.938, auc: 0.939, f1: 0.873, acc: 0.889
+2025-08-26 01:48:05: Epoch: 034 Test results, ap: 0.244, auc: 0.510, f1: 0.130, acc: 0.675
+2025-08-26 01:48:06: Epoch: 035 Train Loss: 0.094, ap: 0.937, auc: 0.939, f1: 0.871, acc: 0.887
+2025-08-26 01:48:06: Epoch: 035 Test results, ap: 0.262, auc: 0.514, f1: 0.163, acc: 0.667
+2025-08-26 01:48:08: Epoch: 036 Train Loss: 0.092, ap: 0.942, auc: 0.944, f1: 0.879, acc: 0.892
+2025-08-26 01:48:08: Epoch: 036 New best VALIDATION metrics
+2025-08-26 01:48:08: Epoch: 036 Test results, ap: 0.246, auc: 0.514, f1: 0.163, acc: 0.667
+2025-08-26 01:48:09: Epoch: 037 Train Loss: 0.087, ap: 0.939, auc: 0.943, f1: 0.873, acc: 0.887
+2025-08-26 01:48:09: Epoch: 037 Test results, ap: 0.242, auc: 0.504, f1: 0.118, acc: 0.634
+2025-08-26 01:48:11: Epoch: 038 Train Loss: 0.086, ap: 0.936, auc: 0.940, f1: 0.885, acc: 0.899
+2025-08-26 01:48:11: Epoch: 038 Test results, ap: 0.243, auc: 0.509, f1: 0.160, acc: 0.659
+2025-08-26 01:48:12: Epoch: 039 Train Loss: 0.084, ap: 0.939, auc: 0.940, f1: 0.880, acc: 0.894
+2025-08-26 01:48:12: Epoch: 039 Test results, ap: 0.270, auc: 0.516, f1: 0.178, acc: 0.699
+2025-08-26 01:48:13: Epoch: 040 Train Loss: 0.086, ap: 0.942, auc: 0.944, f1: 0.876, acc: 0.889
+2025-08-26 01:48:14: Epoch: 040 Test results, ap: 0.261, auc: 0.528, f1: 0.174, acc: 0.691
+2025-08-26 01:48:15: Epoch: 041 Train Loss: 0.072, ap: 0.942, auc: 0.944, f1: 0.880, acc: 0.894
+2025-08-26 01:48:15: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:48:15: Epoch: 041 Test results, ap: 0.251, auc: 0.521, f1: 0.167, acc: 0.675
+2025-08-26 01:48:16: Epoch: 042 Train Loss: 0.066, ap: 0.942, auc: 0.943, f1: 0.876, acc: 0.889
+2025-08-26 01:48:17: Epoch: 042 Test results, ap: 0.250, auc: 0.519, f1: 0.160, acc: 0.659
+2025-08-26 01:48:18: Epoch: 043 Train Loss: 0.064, ap: 0.935, auc: 0.944, f1: 0.887, acc: 0.899
+2025-08-26 01:48:18: Epoch: 043 Test results, ap: 0.250, auc: 0.518, f1: 0.160, acc: 0.659
+2025-08-26 01:48:19: Epoch: 044 Train Loss: 0.066, ap: 0.939, auc: 0.943, f1: 0.881, acc: 0.894
+2025-08-26 01:48:20: Epoch: 044 Test results, ap: 0.249, auc: 0.518, f1: 0.163, acc: 0.667
+2025-08-26 01:48:21: Epoch: 045 Train Loss: 0.064, ap: 0.940, auc: 0.944, f1: 0.879, acc: 0.892
+2025-08-26 01:48:21: Epoch: 045 Test results, ap: 0.255, auc: 0.521, f1: 0.196, acc: 0.667
+2025-08-26 01:48:22: Epoch: 046 Train Loss: 0.063, ap: 0.940, auc: 0.943, f1: 0.877, acc: 0.891
+2025-08-26 01:48:23: Epoch: 046 Test results, ap: 0.247, auc: 0.513, f1: 0.196, acc: 0.667
+2025-08-26 01:48:24: Epoch: 047 Train Loss: 0.063, ap: 0.941, auc: 0.945, f1: 0.884, acc: 0.896
+2025-08-26 01:48:24: Epoch: 047 Test results, ap: 0.248, auc: 0.516, f1: 0.160, acc: 0.659
+2025-08-26 01:48:25: Epoch: 048 Train Loss: 0.068, ap: 0.936, auc: 0.944, f1: 0.884, acc: 0.898
+2025-08-26 01:48:25: Epoch: 048 Test results, ap: 0.241, auc: 0.509, f1: 0.189, acc: 0.650
+2025-08-26 01:48:27: Epoch: 049 Train Loss: 0.066, ap: 0.934, auc: 0.942, f1: 0.874, acc: 0.889
+2025-08-26 01:48:27: Epoch: 049 Test results, ap: 0.248, auc: 0.518, f1: 0.160, acc: 0.659
+2025-08-26 01:48:28: Epoch: 050 Train Loss: 0.060, ap: 0.941, auc: 0.946, f1: 0.887, acc: 0.899
+2025-08-26 01:48:28: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:48:28: Epoch: 050 Test results, ap: 0.247, auc: 0.514, f1: 0.160, acc: 0.659
+2025-08-26 01:48:28: used time 0.02h
+2025-08-26 01:48:28: Running Cross Validation 3
+2025-08-26 01:48:28: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:48:30: Epoch: 001 Train Loss: 0.693, ap: 0.406, auc: 0.485, f1: 0.602, acc: 0.430
+2025-08-26 01:48:30: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:48:30: Epoch: 001 Test results, ap: 0.235, auc: 0.502, f1: 0.392, acc: 0.244
+2025-08-26 01:48:31: Epoch: 002 Train Loss: 0.693, ap: 0.600, auc: 0.680, f1: 0.320, acc: 0.617
+2025-08-26 01:48:31: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:48:31: Epoch: 002 Test results, ap: 0.271, auc: 0.555, f1: 0.000, acc: 0.724
+2025-08-26 01:48:33: Epoch: 003 Train Loss: 0.693, ap: 0.554, auc: 0.644, f1: 0.537, acc: 0.610
+2025-08-26 01:48:33: Epoch: 003 Test results, ap: 0.225, auc: 0.476, f1: 0.312, acc: 0.569
+2025-08-26 01:48:34: Epoch: 004 Train Loss: 0.674, ap: 0.590, auc: 0.644, f1: 0.528, acc: 0.575
+2025-08-26 01:48:34: Epoch: 004 Test results, ap: 0.225, auc: 0.474, f1: 0.321, acc: 0.415
+2025-08-26 01:48:35: Epoch: 005 Train Loss: 0.662, ap: 0.592, auc: 0.640, f1: 0.433, acc: 0.607
+2025-08-26 01:48:36: Epoch: 005 Test results, ap: 0.319, auc: 0.560, f1: 0.333, acc: 0.610
+2025-08-26 01:48:37: Epoch: 006 Train Loss: 0.660, ap: 0.588, auc: 0.668, f1: 0.545, acc: 0.621
+2025-08-26 01:48:37: Epoch: 006 Test results, ap: 0.233, auc: 0.500, f1: 0.317, acc: 0.545
+2025-08-26 01:48:38: Epoch: 007 Train Loss: 0.656, ap: 0.606, auc: 0.680, f1: 0.505, acc: 0.651
+2025-08-26 01:48:38: Epoch: 007 New best VALIDATION metrics
+2025-08-26 01:48:39: Epoch: 007 Test results, ap: 0.246, auc: 0.534, f1: 0.312, acc: 0.569
+2025-08-26 01:48:40: Epoch: 008 Train Loss: 0.650, ap: 0.622, auc: 0.699, f1: 0.606, acc: 0.679
+2025-08-26 01:48:40: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:48:40: Epoch: 008 Test results, ap: 0.252, auc: 0.538, f1: 0.312, acc: 0.569
+2025-08-26 01:48:41: Epoch: 009 Train Loss: 0.645, ap: 0.644, auc: 0.721, f1: 0.641, acc: 0.704
+2025-08-26 01:48:41: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:48:42: Epoch: 009 Test results, ap: 0.245, auc: 0.529, f1: 0.312, acc: 0.569
+2025-08-26 01:48:43: Epoch: 010 Train Loss: 0.645, ap: 0.632, auc: 0.705, f1: 0.511, acc: 0.642
+2025-08-26 01:48:43: Epoch: 010 Test results, ap: 0.306, auc: 0.571, f1: 0.316, acc: 0.577
+2025-08-26 01:48:44: Epoch: 011 Train Loss: 0.618, ap: 0.650, auc: 0.731, f1: 0.651, acc: 0.660
+2025-08-26 01:48:44: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:48:45: Epoch: 011 Test results, ap: 0.281, auc: 0.574, f1: 0.404, acc: 0.545
+2025-08-26 01:48:45: Epoch: 011 New best TEST metrics
+2025-08-26 01:48:46: Epoch: 012 Train Loss: 0.601, ap: 0.703, auc: 0.774, f1: 0.682, acc: 0.693
+2025-08-26 01:48:46: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:48:46: Epoch: 012 Test results, ap: 0.294, auc: 0.570, f1: 0.345, acc: 0.537
+2025-08-26 01:48:46: Epoch: 012 New best TEST metrics
+2025-08-26 01:48:47: Epoch: 013 Train Loss: 0.561, ap: 0.764, auc: 0.804, f1: 0.697, acc: 0.720
+2025-08-26 01:48:47: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:48:48: Epoch: 013 Test results, ap: 0.364, auc: 0.623, f1: 0.364, acc: 0.602
+2025-08-26 01:48:48: Epoch: 013 New best TEST metrics
+2025-08-26 01:48:49: Epoch: 014 Train Loss: 0.517, ap: 0.800, auc: 0.822, f1: 0.703, acc: 0.748
+2025-08-26 01:48:49: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:48:49: Epoch: 014 Test results, ap: 0.401, auc: 0.608, f1: 0.406, acc: 0.667
+2025-08-26 01:48:49: Epoch: 014 New best TEST metrics
+2025-08-26 01:48:50: Epoch: 015 Train Loss: 0.496, ap: 0.801, auc: 0.827, f1: 0.708, acc: 0.753
+2025-08-26 01:48:50: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:48:51: Epoch: 015 Test results, ap: 0.399, auc: 0.592, f1: 0.373, acc: 0.699
+2025-08-26 01:48:52: Epoch: 016 Train Loss: 0.471, ap: 0.817, auc: 0.855, f1: 0.723, acc: 0.765
+2025-08-26 01:48:52: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:48:52: Epoch: 016 Test results, ap: 0.360, auc: 0.558, f1: 0.364, acc: 0.602
+2025-08-26 01:48:53: Epoch: 017 Train Loss: 0.425, ap: 0.851, auc: 0.869, f1: 0.771, acc: 0.795
+2025-08-26 01:48:53: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:48:54: Epoch: 017 Test results, ap: 0.329, auc: 0.577, f1: 0.389, acc: 0.642
+2025-08-26 01:48:55: Epoch: 018 Train Loss: 0.425, ap: 0.838, auc: 0.863, f1: 0.753, acc: 0.774
+2025-08-26 01:48:55: Epoch: 018 Test results, ap: 0.345, auc: 0.573, f1: 0.394, acc: 0.650
+2025-08-26 01:48:56: Epoch: 019 Train Loss: 0.391, ap: 0.853, auc: 0.876, f1: 0.750, acc: 0.772
+2025-08-26 01:48:56: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:48:57: Epoch: 019 Test results, ap: 0.345, auc: 0.591, f1: 0.382, acc: 0.659
+2025-08-26 01:48:58: Epoch: 020 Train Loss: 0.380, ap: 0.879, auc: 0.898, f1: 0.780, acc: 0.806
+2025-08-26 01:48:58: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:48:58: Epoch: 020 Test results, ap: 0.332, auc: 0.589, f1: 0.377, acc: 0.650
+2025-08-26 01:48:59: Epoch: 021 Train Loss: 0.338, ap: 0.872, auc: 0.892, f1: 0.779, acc: 0.797
+2025-08-26 01:48:59: Epoch: 021 Test results, ap: 0.309, auc: 0.584, f1: 0.361, acc: 0.626
+2025-08-26 01:49:01: Epoch: 022 Train Loss: 0.330, ap: 0.880, auc: 0.899, f1: 0.789, acc: 0.817
+2025-08-26 01:49:01: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:49:01: Epoch: 022 Test results, ap: 0.320, auc: 0.596, f1: 0.367, acc: 0.691
+2025-08-26 01:49:02: Epoch: 023 Train Loss: 0.314, ap: 0.883, auc: 0.899, f1: 0.787, acc: 0.813
+2025-08-26 01:49:02: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:49:02: Epoch: 023 Test results, ap: 0.303, auc: 0.589, f1: 0.389, acc: 0.642
+2025-08-26 01:49:04: Epoch: 024 Train Loss: 0.304, ap: 0.882, auc: 0.898, f1: 0.785, acc: 0.811
+2025-08-26 01:49:04: Epoch: 024 Test results, ap: 0.305, auc: 0.582, f1: 0.394, acc: 0.650
+2025-08-26 01:49:05: Epoch: 025 Train Loss: 0.294, ap: 0.884, auc: 0.906, f1: 0.797, acc: 0.818
+2025-08-26 01:49:05: Epoch: 025 New best VALIDATION metrics
+2025-08-26 01:49:05: Epoch: 025 Test results, ap: 0.306, auc: 0.590, f1: 0.366, acc: 0.634
+2025-08-26 01:49:07: Epoch: 026 Train Loss: 0.281, ap: 0.895, auc: 0.912, f1: 0.821, acc: 0.841
+2025-08-26 01:49:07: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:49:07: Epoch: 026 Test results, ap: 0.302, auc: 0.590, f1: 0.389, acc: 0.642
+2025-08-26 01:49:08: Epoch: 027 Train Loss: 0.279, ap: 0.890, auc: 0.912, f1: 0.802, acc: 0.824
+2025-08-26 01:49:08: Epoch: 027 Test results, ap: 0.299, auc: 0.589, f1: 0.394, acc: 0.650
+2025-08-26 01:49:10: Epoch: 028 Train Loss: 0.269, ap: 0.902, auc: 0.919, f1: 0.825, acc: 0.845
+2025-08-26 01:49:10: Epoch: 028 New best VALIDATION metrics
+2025-08-26 01:49:10: Epoch: 028 Test results, ap: 0.298, auc: 0.586, f1: 0.371, acc: 0.642
+2025-08-26 01:49:11: Epoch: 029 Train Loss: 0.255, ap: 0.898, auc: 0.915, f1: 0.806, acc: 0.829
+2025-08-26 01:49:11: Epoch: 029 Test results, ap: 0.301, auc: 0.597, f1: 0.377, acc: 0.650
+2025-08-26 01:49:13: Epoch: 030 Train Loss: 0.251, ap: 0.906, auc: 0.923, f1: 0.824, acc: 0.843
+2025-08-26 01:49:13: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:49:13: Epoch: 030 Test results, ap: 0.300, auc: 0.593, f1: 0.382, acc: 0.659
+2025-08-26 01:49:14: Epoch: 031 Train Loss: 0.219, ap: 0.910, auc: 0.923, f1: 0.846, acc: 0.862
+2025-08-26 01:49:14: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:49:14: Epoch: 031 Test results, ap: 0.294, auc: 0.584, f1: 0.361, acc: 0.626
+2025-08-26 01:49:16: Epoch: 032 Train Loss: 0.211, ap: 0.907, auc: 0.923, f1: 0.837, acc: 0.854
+2025-08-26 01:49:16: Epoch: 032 Test results, ap: 0.298, auc: 0.597, f1: 0.382, acc: 0.659
+2025-08-26 01:49:17: Epoch: 033 Train Loss: 0.204, ap: 0.914, auc: 0.928, f1: 0.841, acc: 0.859
+2025-08-26 01:49:17: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:49:17: Epoch: 033 Test results, ap: 0.296, auc: 0.593, f1: 0.351, acc: 0.610
+2025-08-26 01:49:18: Epoch: 034 Train Loss: 0.194, ap: 0.915, auc: 0.929, f1: 0.848, acc: 0.859
+2025-08-26 01:49:18: Epoch: 034 New best VALIDATION metrics
+2025-08-26 01:49:19: Epoch: 034 Test results, ap: 0.287, auc: 0.577, f1: 0.373, acc: 0.618
+2025-08-26 01:49:20: Epoch: 035 Train Loss: 0.193, ap: 0.916, auc: 0.927, f1: 0.843, acc: 0.862
+2025-08-26 01:49:20: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:49:20: Epoch: 035 Test results, ap: 0.306, auc: 0.610, f1: 0.348, acc: 0.634
+2025-08-26 01:49:21: Epoch: 036 Train Loss: 0.194, ap: 0.916, auc: 0.932, f1: 0.849, acc: 0.866
+2025-08-26 01:49:21: Epoch: 036 New best VALIDATION metrics
+2025-08-26 01:49:22: Epoch: 036 Test results, ap: 0.293, auc: 0.595, f1: 0.371, acc: 0.642
+2025-08-26 01:49:23: Epoch: 037 Train Loss: 0.187, ap: 0.916, auc: 0.933, f1: 0.837, acc: 0.850
+2025-08-26 01:49:23: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:49:23: Epoch: 037 Test results, ap: 0.294, auc: 0.592, f1: 0.361, acc: 0.626
+2025-08-26 01:49:24: Epoch: 038 Train Loss: 0.185, ap: 0.916, auc: 0.934, f1: 0.842, acc: 0.855
+2025-08-26 01:49:24: Epoch: 038 New best VALIDATION metrics
+2025-08-26 01:49:25: Epoch: 038 Test results, ap: 0.289, auc: 0.585, f1: 0.361, acc: 0.626
+2025-08-26 01:49:26: Epoch: 039 Train Loss: 0.166, ap: 0.917, auc: 0.931, f1: 0.846, acc: 0.862
+2025-08-26 01:49:26: Epoch: 039 Test results, ap: 0.294, auc: 0.595, f1: 0.371, acc: 0.642
+2025-08-26 01:49:27: Epoch: 040 Train Loss: 0.178, ap: 0.922, auc: 0.934, f1: 0.854, acc: 0.871
+2025-08-26 01:49:27: Epoch: 040 New best VALIDATION metrics
+2025-08-26 01:49:28: Epoch: 040 Test results, ap: 0.294, auc: 0.601, f1: 0.348, acc: 0.634
+2025-08-26 01:49:29: Epoch: 041 Train Loss: 0.152, ap: 0.924, auc: 0.938, f1: 0.864, acc: 0.878
+2025-08-26 01:49:29: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:49:29: Epoch: 041 Test results, ap: 0.293, auc: 0.596, f1: 0.371, acc: 0.642
+2025-08-26 01:49:30: Epoch: 042 Train Loss: 0.148, ap: 0.923, auc: 0.936, f1: 0.852, acc: 0.864
+2025-08-26 01:49:31: Epoch: 042 Test results, ap: 0.293, auc: 0.596, f1: 0.353, acc: 0.642
+2025-08-26 01:49:32: Epoch: 043 Train Loss: 0.136, ap: 0.922, auc: 0.933, f1: 0.840, acc: 0.859
+2025-08-26 01:49:32: Epoch: 043 Test results, ap: 0.302, auc: 0.612, f1: 0.343, acc: 0.626
+2025-08-26 01:49:33: Epoch: 044 Train Loss: 0.140, ap: 0.925, auc: 0.938, f1: 0.865, acc: 0.878
+2025-08-26 01:49:33: Epoch: 044 New best VALIDATION metrics
+2025-08-26 01:49:34: Epoch: 044 Test results, ap: 0.294, auc: 0.599, f1: 0.371, acc: 0.642
+2025-08-26 01:49:35: Epoch: 045 Train Loss: 0.134, ap: 0.925, auc: 0.939, f1: 0.858, acc: 0.873
+2025-08-26 01:49:35: Epoch: 045 New best VALIDATION metrics
+2025-08-26 01:49:35: Epoch: 045 Test results, ap: 0.289, auc: 0.596, f1: 0.343, acc: 0.626
+2025-08-26 01:49:36: Epoch: 046 Train Loss: 0.127, ap: 0.925, auc: 0.939, f1: 0.864, acc: 0.877
+2025-08-26 01:49:37: Epoch: 046 Test results, ap: 0.292, auc: 0.599, f1: 0.371, acc: 0.642
+2025-08-26 01:49:38: Epoch: 047 Train Loss: 0.127, ap: 0.925, auc: 0.938, f1: 0.858, acc: 0.871
+2025-08-26 01:49:38: Epoch: 047 Test results, ap: 0.294, auc: 0.602, f1: 0.377, acc: 0.650
+2025-08-26 01:49:39: Epoch: 048 Train Loss: 0.128, ap: 0.922, auc: 0.940, f1: 0.859, acc: 0.871
+2025-08-26 01:49:39: Epoch: 048 Test results, ap: 0.298, auc: 0.608, f1: 0.343, acc: 0.626
+2025-08-26 01:49:41: Epoch: 049 Train Loss: 0.127, ap: 0.927, auc: 0.941, f1: 0.865, acc: 0.877
+2025-08-26 01:49:41: Epoch: 049 New best VALIDATION metrics
+2025-08-26 01:49:41: Epoch: 049 Test results, ap: 0.293, auc: 0.597, f1: 0.371, acc: 0.642
+2025-08-26 01:49:42: Epoch: 050 Train Loss: 0.119, ap: 0.931, auc: 0.943, f1: 0.862, acc: 0.877
+2025-08-26 01:49:42: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:49:42: Epoch: 050 Test results, ap: 0.295, auc: 0.602, f1: 0.343, acc: 0.626
+2025-08-26 01:49:42: used time 0.02h
+2025-08-26 01:49:42: Running Cross Validation 4
+2025-08-26 01:49:42: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:49:44: Epoch: 001 Train Loss: 0.693, ap: 0.444, auc: 0.486, f1: 0.016, acc: 0.554
+2025-08-26 01:49:44: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:49:44: Epoch: 001 Test results, ap: 0.180, auc: 0.327, f1: 0.000, acc: 0.756
+2025-08-26 01:49:45: Epoch: 002 Train Loss: 0.690, ap: 0.547, auc: 0.593, f1: 0.031, acc: 0.561
+2025-08-26 01:49:45: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:49:45: Epoch: 002 Test results, ap: 0.229, auc: 0.466, f1: 0.000, acc: 0.756
+2025-08-26 01:49:47: Epoch: 003 Train Loss: 0.669, ap: 0.564, auc: 0.643, f1: 0.498, acc: 0.616
+2025-08-26 01:49:47: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:49:47: Epoch: 003 Test results, ap: 0.229, auc: 0.460, f1: 0.265, acc: 0.593
+2025-08-26 01:49:48: Epoch: 004 Train Loss: 0.627, ap: 0.638, auc: 0.704, f1: 0.557, acc: 0.658
+2025-08-26 01:49:48: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:49:48: Epoch: 004 Test results, ap: 0.256, auc: 0.518, f1: 0.310, acc: 0.602
+2025-08-26 01:49:50: Epoch: 005 Train Loss: 0.589, ap: 0.670, auc: 0.732, f1: 0.634, acc: 0.672
+2025-08-26 01:49:50: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:49:50: Epoch: 005 Test results, ap: 0.264, auc: 0.500, f1: 0.322, acc: 0.520
+2025-08-26 01:49:51: Epoch: 006 Train Loss: 0.559, ap: 0.724, auc: 0.771, f1: 0.627, acc: 0.691
+2025-08-26 01:49:51: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:49:51: Epoch: 006 Test results, ap: 0.283, auc: 0.512, f1: 0.349, acc: 0.545
+2025-08-26 01:49:53: Epoch: 007 Train Loss: 0.529, ap: 0.715, auc: 0.763, f1: 0.645, acc: 0.677
+2025-08-26 01:49:53: Epoch: 007 Test results, ap: 0.294, auc: 0.561, f1: 0.353, acc: 0.642
+2025-08-26 01:49:54: Epoch: 008 Train Loss: 0.500, ap: 0.753, auc: 0.808, f1: 0.684, acc: 0.727
+2025-08-26 01:49:54: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:49:54: Epoch: 008 Test results, ap: 0.303, auc: 0.565, f1: 0.356, acc: 0.618
+2025-08-26 01:49:56: Epoch: 009 Train Loss: 0.460, ap: 0.632, auc: 0.725, f1: 0.630, acc: 0.658
+2025-08-26 01:49:56: Epoch: 009 Test results, ap: 0.309, auc: 0.563, f1: 0.442, acc: 0.528
+2025-08-26 01:49:57: Epoch: 010 Train Loss: 0.495, ap: 0.804, auc: 0.848, f1: 0.730, acc: 0.767
+2025-08-26 01:49:57: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:49:57: Epoch: 010 Test results, ap: 0.343, auc: 0.602, f1: 0.379, acc: 0.707
+2025-08-26 01:49:59: Epoch: 011 Train Loss: 0.401, ap: 0.847, auc: 0.877, f1: 0.775, acc: 0.797
+2025-08-26 01:49:59: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:49:59: Epoch: 011 Test results, ap: 0.293, auc: 0.545, f1: 0.319, acc: 0.618
+2025-08-26 01:49:59: Epoch: 011 New best TEST metrics
+2025-08-26 01:50:00: Epoch: 012 Train Loss: 0.391, ap: 0.847, auc: 0.869, f1: 0.768, acc: 0.794
+2025-08-26 01:50:00: Epoch: 012 Test results, ap: 0.382, auc: 0.562, f1: 0.310, acc: 0.602
+2025-08-26 01:50:00: Epoch: 012 New best TEST metrics
+2025-08-26 01:50:01: Epoch: 013 Train Loss: 0.375, ap: 0.851, auc: 0.885, f1: 0.781, acc: 0.799
+2025-08-26 01:50:01: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:50:02: Epoch: 013 Test results, ap: 0.336, auc: 0.596, f1: 0.391, acc: 0.545
+2025-08-26 01:50:03: Epoch: 014 Train Loss: 0.351, ap: 0.837, auc: 0.859, f1: 0.732, acc: 0.758
+2025-08-26 01:50:03: Epoch: 014 Test results, ap: 0.366, auc: 0.610, f1: 0.404, acc: 0.569
+2025-08-26 01:50:03: Epoch: 014 New best TEST metrics
+2025-08-26 01:50:04: Epoch: 015 Train Loss: 0.359, ap: 0.843, auc: 0.873, f1: 0.764, acc: 0.785
+2025-08-26 01:50:05: Epoch: 015 Test results, ap: 0.336, auc: 0.600, f1: 0.415, acc: 0.610
+2025-08-26 01:50:06: Epoch: 016 Train Loss: 0.343, ap: 0.876, auc: 0.899, f1: 0.798, acc: 0.817
+2025-08-26 01:50:06: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:50:06: Epoch: 016 Test results, ap: 0.352, auc: 0.561, f1: 0.299, acc: 0.618
+2025-08-26 01:50:07: Epoch: 017 Train Loss: 0.319, ap: 0.885, auc: 0.905, f1: 0.805, acc: 0.818
+2025-08-26 01:50:07: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:50:08: Epoch: 017 Test results, ap: 0.353, auc: 0.557, f1: 0.354, acc: 0.585
+2025-08-26 01:50:09: Epoch: 018 Train Loss: 0.318, ap: 0.881, auc: 0.906, f1: 0.802, acc: 0.824
+2025-08-26 01:50:09: Epoch: 018 Test results, ap: 0.316, auc: 0.587, f1: 0.373, acc: 0.618
+2025-08-26 01:50:10: Epoch: 019 Train Loss: 0.293, ap: 0.889, auc: 0.913, f1: 0.814, acc: 0.831
+2025-08-26 01:50:10: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:50:11: Epoch: 019 Test results, ap: 0.318, auc: 0.563, f1: 0.294, acc: 0.610
+2025-08-26 01:50:12: Epoch: 020 Train Loss: 0.288, ap: 0.882, auc: 0.912, f1: 0.809, acc: 0.824
+2025-08-26 01:50:12: Epoch: 020 Test results, ap: 0.305, auc: 0.575, f1: 0.425, acc: 0.626
+2025-08-26 01:50:13: Epoch: 021 Train Loss: 0.246, ap: 0.894, auc: 0.918, f1: 0.832, acc: 0.847
+2025-08-26 01:50:13: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:50:13: Epoch: 021 Test results, ap: 0.312, auc: 0.592, f1: 0.444, acc: 0.593
+2025-08-26 01:50:15: Epoch: 022 Train Loss: 0.233, ap: 0.904, auc: 0.925, f1: 0.836, acc: 0.852
+2025-08-26 01:50:15: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:50:15: Epoch: 022 Test results, ap: 0.299, auc: 0.564, f1: 0.368, acc: 0.610
+2025-08-26 01:50:16: Epoch: 023 Train Loss: 0.231, ap: 0.900, auc: 0.924, f1: 0.840, acc: 0.855
+2025-08-26 01:50:16: Epoch: 023 Test results, ap: 0.295, auc: 0.562, f1: 0.342, acc: 0.593
+2025-08-26 01:50:18: Epoch: 024 Train Loss: 0.216, ap: 0.907, auc: 0.927, f1: 0.845, acc: 0.855
+2025-08-26 01:50:18: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:50:18: Epoch: 024 Test results, ap: 0.287, auc: 0.554, f1: 0.386, acc: 0.585
+2025-08-26 01:50:19: Epoch: 025 Train Loss: 0.205, ap: 0.908, auc: 0.927, f1: 0.843, acc: 0.857
+2025-08-26 01:50:19: Epoch: 025 New best VALIDATION metrics
+2025-08-26 01:50:19: Epoch: 025 Test results, ap: 0.276, auc: 0.544, f1: 0.370, acc: 0.585
+2025-08-26 01:50:21: Epoch: 026 Train Loss: 0.193, ap: 0.904, auc: 0.929, f1: 0.849, acc: 0.862
+2025-08-26 01:50:21: Epoch: 026 Test results, ap: 0.264, auc: 0.508, f1: 0.300, acc: 0.545
+2025-08-26 01:50:22: Epoch: 027 Train Loss: 0.185, ap: 0.923, auc: 0.936, f1: 0.849, acc: 0.864
+2025-08-26 01:50:22: Epoch: 027 New best VALIDATION metrics
+2025-08-26 01:50:22: Epoch: 027 Test results, ap: 0.261, auc: 0.508, f1: 0.278, acc: 0.537
+2025-08-26 01:50:24: Epoch: 028 Train Loss: 0.166, ap: 0.925, auc: 0.939, f1: 0.845, acc: 0.862
+2025-08-26 01:50:24: Epoch: 028 New best VALIDATION metrics
+2025-08-26 01:50:24: Epoch: 028 Test results, ap: 0.264, auc: 0.519, f1: 0.289, acc: 0.561
+2025-08-26 01:50:25: Epoch: 029 Train Loss: 0.173, ap: 0.927, auc: 0.940, f1: 0.863, acc: 0.878
+2025-08-26 01:50:25: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:50:25: Epoch: 029 Test results, ap: 0.266, auc: 0.508, f1: 0.260, acc: 0.537
+2025-08-26 01:50:26: Epoch: 030 Train Loss: 0.160, ap: 0.930, auc: 0.941, f1: 0.859, acc: 0.873
+2025-08-26 01:50:26: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:50:27: Epoch: 030 Test results, ap: 0.240, auc: 0.484, f1: 0.267, acc: 0.463
+2025-08-26 01:50:28: Epoch: 031 Train Loss: 0.134, ap: 0.931, auc: 0.944, f1: 0.869, acc: 0.882
+2025-08-26 01:50:28: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:50:28: Epoch: 031 Test results, ap: 0.228, auc: 0.464, f1: 0.265, acc: 0.504
+2025-08-26 01:50:29: Epoch: 032 Train Loss: 0.120, ap: 0.928, auc: 0.941, f1: 0.855, acc: 0.871
+2025-08-26 01:50:30: Epoch: 032 Test results, ap: 0.235, auc: 0.480, f1: 0.272, acc: 0.520
+2025-08-26 01:50:31: Epoch: 033 Train Loss: 0.113, ap: 0.939, auc: 0.947, f1: 0.863, acc: 0.878
+2025-08-26 01:50:31: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:50:31: Epoch: 033 Test results, ap: 0.234, auc: 0.478, f1: 0.272, acc: 0.520
+2025-08-26 01:50:32: Epoch: 034 Train Loss: 0.105, ap: 0.941, auc: 0.950, f1: 0.873, acc: 0.887
+2025-08-26 01:50:32: Epoch: 034 New best VALIDATION metrics
+2025-08-26 01:50:33: Epoch: 034 Test results, ap: 0.225, auc: 0.466, f1: 0.276, acc: 0.488
+2025-08-26 01:50:34: Epoch: 035 Train Loss: 0.107, ap: 0.936, auc: 0.945, f1: 0.859, acc: 0.875
+2025-08-26 01:50:34: Epoch: 035 Test results, ap: 0.232, auc: 0.476, f1: 0.286, acc: 0.512
+2025-08-26 01:50:35: Epoch: 036 Train Loss: 0.102, ap: 0.938, auc: 0.946, f1: 0.861, acc: 0.877
+2025-08-26 01:50:36: Epoch: 036 Test results, ap: 0.242, auc: 0.491, f1: 0.310, acc: 0.528
+2025-08-26 01:50:37: Epoch: 037 Train Loss: 0.104, ap: 0.942, auc: 0.950, f1: 0.867, acc: 0.882
+2025-08-26 01:50:37: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:50:37: Epoch: 037 Test results, ap: 0.232, auc: 0.484, f1: 0.289, acc: 0.520
+2025-08-26 01:50:38: Epoch: 038 Train Loss: 0.095, ap: 0.942, auc: 0.949, f1: 0.873, acc: 0.887
+2025-08-26 01:50:39: Epoch: 038 Test results, ap: 0.218, auc: 0.456, f1: 0.279, acc: 0.496
+2025-08-26 01:50:40: Epoch: 039 Train Loss: 0.086, ap: 0.941, auc: 0.949, f1: 0.870, acc: 0.885
+2025-08-26 01:50:40: Epoch: 039 Test results, ap: 0.230, auc: 0.480, f1: 0.300, acc: 0.545
+2025-08-26 01:50:41: Epoch: 040 Train Loss: 0.085, ap: 0.942, auc: 0.950, f1: 0.880, acc: 0.894
+2025-08-26 01:50:42: Epoch: 040 Test results, ap: 0.230, auc: 0.481, f1: 0.317, acc: 0.545
+2025-08-26 01:50:43: Epoch: 041 Train Loss: 0.073, ap: 0.946, auc: 0.953, f1: 0.882, acc: 0.896
+2025-08-26 01:50:43: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:50:43: Epoch: 041 Test results, ap: 0.220, auc: 0.463, f1: 0.282, acc: 0.504
+2025-08-26 01:50:44: Epoch: 042 Train Loss: 0.072, ap: 0.946, auc: 0.953, f1: 0.882, acc: 0.896
+2025-08-26 01:50:44: Epoch: 042 New best VALIDATION metrics
+2025-08-26 01:50:45: Epoch: 042 Test results, ap: 0.222, auc: 0.463, f1: 0.302, acc: 0.512
+2025-08-26 01:50:46: Epoch: 043 Train Loss: 0.070, ap: 0.947, auc: 0.955, f1: 0.875, acc: 0.889
+2025-08-26 01:50:46: Epoch: 043 New best VALIDATION metrics
+2025-08-26 01:50:46: Epoch: 043 Test results, ap: 0.222, auc: 0.465, f1: 0.289, acc: 0.520
+2025-08-26 01:50:47: Epoch: 044 Train Loss: 0.070, ap: 0.949, auc: 0.955, f1: 0.884, acc: 0.898
+2025-08-26 01:50:47: Epoch: 044 New best VALIDATION metrics
+2025-08-26 01:50:48: Epoch: 044 Test results, ap: 0.219, auc: 0.460, f1: 0.293, acc: 0.528
+2025-08-26 01:50:49: Epoch: 045 Train Loss: 0.062, ap: 0.949, auc: 0.955, f1: 0.874, acc: 0.889
+2025-08-26 01:50:49: Epoch: 045 New best VALIDATION metrics
+2025-08-26 01:50:49: Epoch: 045 Test results, ap: 0.215, auc: 0.448, f1: 0.279, acc: 0.496
+2025-08-26 01:50:50: Epoch: 046 Train Loss: 0.067, ap: 0.946, auc: 0.953, f1: 0.867, acc: 0.882
+2025-08-26 01:50:51: Epoch: 046 Test results, ap: 0.220, auc: 0.461, f1: 0.289, acc: 0.520
+2025-08-26 01:50:52: Epoch: 047 Train Loss: 0.063, ap: 0.948, auc: 0.954, f1: 0.886, acc: 0.899
+2025-08-26 01:50:52: Epoch: 047 Test results, ap: 0.220, auc: 0.461, f1: 0.315, acc: 0.504
+2025-08-26 01:50:53: Epoch: 048 Train Loss: 0.064, ap: 0.948, auc: 0.954, f1: 0.878, acc: 0.892
+2025-08-26 01:50:53: Epoch: 048 Test results, ap: 0.219, auc: 0.462, f1: 0.299, acc: 0.504
+2025-08-26 01:50:55: Epoch: 049 Train Loss: 0.062, ap: 0.948, auc: 0.954, f1: 0.884, acc: 0.896
+2025-08-26 01:50:55: Epoch: 049 Test results, ap: 0.217, auc: 0.454, f1: 0.299, acc: 0.504
+2025-08-26 01:50:56: Epoch: 050 Train Loss: 0.068, ap: 0.949, auc: 0.954, f1: 0.887, acc: 0.899
+2025-08-26 01:50:56: Epoch: 050 Test results, ap: 0.218, auc: 0.453, f1: 0.289, acc: 0.520
+2025-08-26 01:50:56: used time 0.02h
+2025-08-26 01:50:56: Cross Validation Finished!
+2025-08-26 01:50:56: Best validation perform list
+[[0.93754286 0.93864781 0.87895715 0.88556337]
+ [0.89522374 0.92103076 0.83730161 0.8556338 ]
+ [0.94142234 0.94579178 0.88667995 0.89947093]
+ [0.93051958 0.94273078 0.86166006 0.87654322]
+ [0.94884908 0.95477581 0.87425148 0.8888889 ]]
+2025-08-26 01:50:56: mean: [0.931 0.941 0.868 0.881]
+2025-08-26 01:50:56: std: [0.019 0.011 0.017 0.015]
+2025-08-26 01:50:56: Best test perform list
+[[0.35754085 0.64587814 0.40000001 0.63414633]
+ [0.3830983  0.60000002 0.3877551  0.51219511]
+ [0.32428855 0.55842292 0.2857143  0.63414633]
+ [0.40135917 0.60824376 0.40579709 0.66666669]
+ [0.36631978 0.61003578 0.40449437 0.56910568]]
+2025-08-26 01:50:56: mean: [0.367 0.605 0.377 0.603]
+2025-08-26 01:50:56: std: [0.026 0.028 0.046 0.055]
diff --git a/run-cls/gru-mlp-256-ce-32-0.001-50/config.json b/run-cls/gru-mlp-256-ce-32-0.001-50/config.json
new file mode 100644
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--- /dev/null
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "gru", "side_enc": null, "channels": 256, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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+2025-08-26 01:32:08: saving_dir: ./run-cls/gru-mlp-256-ce-32-0.001-50
+2025-08-26 01:32:08: Loading Train Dataset
+2025-08-26 01:32:08: Loading Test Dataset
+2025-08-26 01:32:09: Running Cross Validation 0
+2025-08-26 01:32:09: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 01:32:10: Epoch: 001 Train Loss: 0.693, ap: 0.482, auc: 0.528, f1: 0.648, acc: 0.509
+2025-08-26 01:32:10: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:32:11: Epoch: 001 Test results, ap: 0.484, auc: 0.659, f1: 0.392, acc: 0.244
+2025-08-26 01:32:12: Epoch: 002 Train Loss: 0.694, ap: 0.523, auc: 0.537, f1: 0.649, acc: 0.481
+2025-08-26 01:32:12: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:32:12: Epoch: 002 Test results, ap: 0.329, auc: 0.550, f1: 0.392, acc: 0.244
+2025-08-26 01:32:13: Epoch: 003 Train Loss: 0.694, ap: 0.634, auc: 0.640, f1: 0.000, acc: 0.519
+2025-08-26 01:32:13: Epoch: 003 New best VALIDATION metrics
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+2025-08-26 01:32:15: Epoch: 004 Train Loss: 0.675, ap: 0.647, auc: 0.654, f1: 0.613, acc: 0.567
+2025-08-26 01:32:15: Epoch: 004 New best VALIDATION metrics
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+2025-08-26 01:32:16: Epoch: 005 Train Loss: 0.662, ap: 0.636, auc: 0.672, f1: 0.610, acc: 0.629
+2025-08-26 01:32:16: Epoch: 005 New best VALIDATION metrics
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+2025-08-26 01:32:18: Epoch: 006 Train Loss: 0.656, ap: 0.713, auc: 0.694, f1: 0.622, acc: 0.660
+2025-08-26 01:32:18: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:32:18: Epoch: 006 Test results, ap: 0.270, auc: 0.515, f1: 0.282, acc: 0.545
+2025-08-26 01:32:19: Epoch: 007 Train Loss: 0.627, ap: 0.685, auc: 0.714, f1: 0.656, acc: 0.657
+2025-08-26 01:32:19: Epoch: 007 Test results, ap: 0.256, auc: 0.498, f1: 0.392, acc: 0.244
+2025-08-26 01:32:21: Epoch: 008 Train Loss: 0.614, ap: 0.738, auc: 0.749, f1: 0.656, acc: 0.673
+2025-08-26 01:32:21: Epoch: 008 New best VALIDATION metrics
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+2025-08-26 01:32:22: Epoch: 009 Train Loss: 0.582, ap: 0.734, auc: 0.746, f1: 0.583, acc: 0.665
+2025-08-26 01:32:22: Epoch: 009 Test results, ap: 0.226, auc: 0.401, f1: 0.392, acc: 0.244
+2025-08-26 01:32:23: Epoch: 010 Train Loss: 0.544, ap: 0.791, auc: 0.795, f1: 0.695, acc: 0.722
+2025-08-26 01:32:23: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:32:24: Epoch: 010 Test results, ap: 0.354, auc: 0.533, f1: 0.376, acc: 0.488
+2025-08-26 01:32:25: Epoch: 011 Train Loss: 0.498, ap: 0.797, auc: 0.811, f1: 0.704, acc: 0.731
+2025-08-26 01:32:25: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:32:25: Epoch: 011 Test results, ap: 0.363, auc: 0.545, f1: 0.354, acc: 0.407
+2025-08-26 01:32:25: Epoch: 011 New best TEST metrics
+2025-08-26 01:32:26: Epoch: 012 Train Loss: 0.477, ap: 0.818, auc: 0.816, f1: 0.722, acc: 0.732
+2025-08-26 01:32:26: Epoch: 012 New best VALIDATION metrics
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+2025-08-26 01:32:28: Epoch: 013 Train Loss: 0.458, ap: 0.835, auc: 0.839, f1: 0.747, acc: 0.762
+2025-08-26 01:32:28: Epoch: 013 New best VALIDATION metrics
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+2025-08-26 01:32:28: Epoch: 013 New best TEST metrics
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+2025-08-26 01:32:29: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:32:30: Epoch: 014 Test results, ap: 0.347, auc: 0.575, f1: 0.391, acc: 0.317
+2025-08-26 01:32:31: Epoch: 015 Train Loss: 0.416, ap: 0.858, auc: 0.862, f1: 0.789, acc: 0.789
+2025-08-26 01:32:31: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:32:31: Epoch: 015 Test results, ap: 0.379, auc: 0.581, f1: 0.423, acc: 0.423
+2025-08-26 01:32:31: Epoch: 015 New best TEST metrics
+2025-08-26 01:32:32: Epoch: 016 Train Loss: 0.399, ap: 0.878, auc: 0.879, f1: 0.775, acc: 0.785
+2025-08-26 01:32:32: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:32:33: Epoch: 016 Test results, ap: 0.369, auc: 0.592, f1: 0.409, acc: 0.390
+2025-08-26 01:32:33: Epoch: 016 New best TEST metrics
+2025-08-26 01:32:34: Epoch: 017 Train Loss: 0.389, ap: 0.867, auc: 0.873, f1: 0.799, acc: 0.805
+2025-08-26 01:32:34: Epoch: 017 Test results, ap: 0.374, auc: 0.569, f1: 0.458, acc: 0.480
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+2025-08-26 01:32:35: Epoch: 018 Test results, ap: 0.404, auc: 0.640, f1: 0.412, acc: 0.374
+2025-08-26 01:32:35: Epoch: 018 New best TEST metrics
+2025-08-26 01:32:37: Epoch: 019 Train Loss: 0.389, ap: 0.881, auc: 0.881, f1: 0.784, acc: 0.796
+2025-08-26 01:32:37: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:32:37: Epoch: 019 Test results, ap: 0.375, auc: 0.622, f1: 0.440, acc: 0.504
+2025-08-26 01:32:38: Epoch: 020 Train Loss: 0.351, ap: 0.892, auc: 0.896, f1: 0.810, acc: 0.819
+2025-08-26 01:32:38: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:32:38: Epoch: 020 Test results, ap: 0.355, auc: 0.596, f1: 0.436, acc: 0.537
+2025-08-26 01:32:40: Epoch: 021 Train Loss: 0.316, ap: 0.888, auc: 0.889, f1: 0.795, acc: 0.808
+2025-08-26 01:32:40: Epoch: 021 Test results, ap: 0.362, auc: 0.605, f1: 0.421, acc: 0.553
+2025-08-26 01:32:41: Epoch: 022 Train Loss: 0.312, ap: 0.901, auc: 0.899, f1: 0.807, acc: 0.812
+2025-08-26 01:32:41: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:32:41: Epoch: 022 Test results, ap: 0.343, auc: 0.608, f1: 0.447, acc: 0.537
+2025-08-26 01:32:43: Epoch: 023 Train Loss: 0.295, ap: 0.907, auc: 0.904, f1: 0.812, acc: 0.822
+2025-08-26 01:32:43: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:32:43: Epoch: 023 Test results, ap: 0.357, auc: 0.596, f1: 0.396, acc: 0.553
+2025-08-26 01:32:44: Epoch: 024 Train Loss: 0.278, ap: 0.909, auc: 0.907, f1: 0.815, acc: 0.820
+2025-08-26 01:32:44: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:32:44: Epoch: 024 Test results, ap: 0.369, auc: 0.619, f1: 0.408, acc: 0.504
+2025-08-26 01:32:46: Epoch: 025 Train Loss: 0.271, ap: 0.910, auc: 0.911, f1: 0.836, acc: 0.845
+2025-08-26 01:32:46: Epoch: 025 New best VALIDATION metrics
+2025-08-26 01:32:46: Epoch: 025 Test results, ap: 0.370, auc: 0.619, f1: 0.424, acc: 0.537
+2025-08-26 01:32:47: Epoch: 026 Train Loss: 0.263, ap: 0.919, auc: 0.918, f1: 0.836, acc: 0.842
+2025-08-26 01:32:47: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:32:47: Epoch: 026 Test results, ap: 0.387, auc: 0.627, f1: 0.436, acc: 0.496
+2025-08-26 01:32:48: Epoch: 027 Train Loss: 0.256, ap: 0.910, auc: 0.907, f1: 0.818, acc: 0.831
+2025-08-26 01:32:49: Epoch: 027 Test results, ap: 0.363, auc: 0.610, f1: 0.444, acc: 0.512
+2025-08-26 01:32:50: Epoch: 028 Train Loss: 0.246, ap: 0.916, auc: 0.919, f1: 0.841, acc: 0.850
+2025-08-26 01:32:50: Epoch: 028 Test results, ap: 0.379, auc: 0.626, f1: 0.431, acc: 0.528
+2025-08-26 01:32:51: Epoch: 029 Train Loss: 0.219, ap: 0.916, auc: 0.921, f1: 0.840, acc: 0.845
+2025-08-26 01:32:51: Epoch: 029 Test results, ap: 0.329, auc: 0.579, f1: 0.444, acc: 0.512
+2025-08-26 01:32:53: Epoch: 030 Train Loss: 0.220, ap: 0.918, auc: 0.923, f1: 0.843, acc: 0.847
+2025-08-26 01:32:53: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:32:53: Epoch: 030 Test results, ap: 0.367, auc: 0.604, f1: 0.434, acc: 0.512
+2025-08-26 01:32:54: Epoch: 031 Train Loss: 0.195, ap: 0.926, auc: 0.929, f1: 0.855, acc: 0.861
+2025-08-26 01:32:54: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:32:54: Epoch: 031 Test results, ap: 0.350, auc: 0.601, f1: 0.411, acc: 0.463
+2025-08-26 01:32:55: Epoch: 032 Train Loss: 0.179, ap: 0.927, auc: 0.932, f1: 0.842, acc: 0.850
+2025-08-26 01:32:55: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:32:56: Epoch: 032 Test results, ap: 0.323, auc: 0.563, f1: 0.392, acc: 0.496
+2025-08-26 01:32:57: Epoch: 033 Train Loss: 0.182, ap: 0.923, auc: 0.929, f1: 0.857, acc: 0.861
+2025-08-26 01:32:57: Epoch: 033 Test results, ap: 0.345, auc: 0.597, f1: 0.444, acc: 0.512
+2025-08-26 01:32:58: Epoch: 034 Train Loss: 0.166, ap: 0.927, auc: 0.932, f1: 0.858, acc: 0.861
+2025-08-26 01:32:59: Epoch: 034 Test results, ap: 0.343, auc: 0.581, f1: 0.389, acc: 0.439
+2025-08-26 01:33:00: Epoch: 035 Train Loss: 0.161, ap: 0.928, auc: 0.934, f1: 0.866, acc: 0.875
+2025-08-26 01:33:00: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:33:00: Epoch: 035 Test results, ap: 0.339, auc: 0.587, f1: 0.411, acc: 0.488
+2025-08-26 01:33:01: Epoch: 036 Train Loss: 0.153, ap: 0.925, auc: 0.932, f1: 0.868, acc: 0.875
+2025-08-26 01:33:01: Epoch: 036 Test results, ap: 0.330, auc: 0.575, f1: 0.407, acc: 0.480
+2025-08-26 01:33:03: Epoch: 037 Train Loss: 0.149, ap: 0.928, auc: 0.934, f1: 0.869, acc: 0.875
+2025-08-26 01:33:03: Epoch: 037 Test results, ap: 0.329, auc: 0.568, f1: 0.418, acc: 0.480
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+2025-08-26 01:33:04: Epoch: 038 New best VALIDATION metrics
+2025-08-26 01:33:04: Epoch: 038 Test results, ap: 0.312, auc: 0.553, f1: 0.393, acc: 0.472
+2025-08-26 01:33:06: Epoch: 039 Train Loss: 0.143, ap: 0.928, auc: 0.935, f1: 0.864, acc: 0.868
+2025-08-26 01:33:06: Epoch: 039 Test results, ap: 0.317, auc: 0.553, f1: 0.385, acc: 0.480
+2025-08-26 01:33:07: Epoch: 040 Train Loss: 0.135, ap: 0.931, auc: 0.938, f1: 0.863, acc: 0.871
+2025-08-26 01:33:07: Epoch: 040 New best VALIDATION metrics
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+2025-08-26 01:33:08: Epoch: 041 New best VALIDATION metrics
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+2025-08-26 01:33:14: Epoch: 045 New best VALIDATION metrics
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+2025-08-26 01:33:20: Epoch: 049 New best VALIDATION metrics
+2025-08-26 01:33:20: Epoch: 049 Test results, ap: 0.301, auc: 0.545, f1: 0.362, acc: 0.455
+2025-08-26 01:33:21: Epoch: 050 Train Loss: 0.096, ap: 0.935, auc: 0.942, f1: 0.882, acc: 0.887
+2025-08-26 01:33:21: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:33:22: Epoch: 050 Test results, ap: 0.315, auc: 0.553, f1: 0.400, acc: 0.463
+2025-08-26 01:33:22: used time 0.02h
+2025-08-26 01:33:22: Running Cross Validation 1
+2025-08-26 01:33:22: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 01:33:23: Epoch: 001 Train Loss: 0.694, ap: 0.446, auc: 0.534, f1: 0.000, acc: 0.563
+2025-08-26 01:33:23: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:33:23: Epoch: 001 Test results, ap: 0.510, auc: 0.665, f1: 0.000, acc: 0.756
+2025-08-26 01:33:24: Epoch: 002 Train Loss: 0.694, ap: 0.602, auc: 0.667, f1: 0.000, acc: 0.563
+2025-08-26 01:33:24: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:33:25: Epoch: 002 Test results, ap: 0.226, auc: 0.459, f1: 0.000, acc: 0.756
+2025-08-26 01:33:26: Epoch: 003 Train Loss: 0.693, ap: 0.618, auc: 0.675, f1: 0.601, acc: 0.664
+2025-08-26 01:33:26: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:33:26: Epoch: 003 Test results, ap: 0.347, auc: 0.509, f1: 0.222, acc: 0.772
+2025-08-26 01:33:27: Epoch: 004 Train Loss: 0.670, ap: 0.599, auc: 0.673, f1: 0.606, acc: 0.657
+2025-08-26 01:33:27: Epoch: 004 Test results, ap: 0.260, auc: 0.484, f1: 0.392, acc: 0.244
+2025-08-26 01:33:29: Epoch: 005 Train Loss: 0.655, ap: 0.654, auc: 0.685, f1: 0.618, acc: 0.665
+2025-08-26 01:33:29: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:33:29: Epoch: 005 Test results, ap: 0.265, auc: 0.500, f1: 0.395, acc: 0.252
+2025-08-26 01:33:30: Epoch: 006 Train Loss: 0.650, ap: 0.655, auc: 0.686, f1: 0.590, acc: 0.685
+2025-08-26 01:33:30: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:33:30: Epoch: 006 Test results, ap: 0.268, auc: 0.504, f1: 0.246, acc: 0.602
+2025-08-26 01:33:31: Epoch: 007 Train Loss: 0.647, ap: 0.671, auc: 0.710, f1: 0.568, acc: 0.676
+2025-08-26 01:33:31: Epoch: 007 New best VALIDATION metrics
+2025-08-26 01:33:32: Epoch: 007 Test results, ap: 0.259, auc: 0.503, f1: 0.330, acc: 0.504
+2025-08-26 01:33:33: Epoch: 008 Train Loss: 0.633, ap: 0.710, auc: 0.746, f1: 0.599, acc: 0.687
+2025-08-26 01:33:33: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:33:33: Epoch: 008 Test results, ap: 0.267, auc: 0.507, f1: 0.337, acc: 0.553
+2025-08-26 01:33:34: Epoch: 009 Train Loss: 0.613, ap: 0.728, auc: 0.763, f1: 0.631, acc: 0.720
+2025-08-26 01:33:34: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:33:35: Epoch: 009 Test results, ap: 0.264, auc: 0.510, f1: 0.258, acc: 0.626
+2025-08-26 01:33:36: Epoch: 010 Train Loss: 0.565, ap: 0.797, auc: 0.821, f1: 0.691, acc: 0.743
+2025-08-26 01:33:36: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:33:36: Epoch: 010 Test results, ap: 0.309, auc: 0.543, f1: 0.240, acc: 0.691
+2025-08-26 01:33:37: Epoch: 011 Train Loss: 0.523, ap: 0.796, auc: 0.824, f1: 0.702, acc: 0.750
+2025-08-26 01:33:37: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:33:38: Epoch: 011 Test results, ap: 0.258, auc: 0.490, f1: 0.218, acc: 0.650
+2025-08-26 01:33:38: Epoch: 011 New best TEST metrics
+2025-08-26 01:33:39: Epoch: 012 Train Loss: 0.474, ap: 0.831, auc: 0.859, f1: 0.753, acc: 0.782
+2025-08-26 01:33:39: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:33:39: Epoch: 012 Test results, ap: 0.269, auc: 0.506, f1: 0.286, acc: 0.593
+2025-08-26 01:33:39: Epoch: 012 New best TEST metrics
+2025-08-26 01:33:40: Epoch: 013 Train Loss: 0.432, ap: 0.852, auc: 0.878, f1: 0.770, acc: 0.782
+2025-08-26 01:33:40: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:33:41: Epoch: 013 Test results, ap: 0.275, auc: 0.487, f1: 0.355, acc: 0.350
+2025-08-26 01:33:42: Epoch: 014 Train Loss: 0.406, ap: 0.855, auc: 0.886, f1: 0.766, acc: 0.790
+2025-08-26 01:33:42: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:33:42: Epoch: 014 Test results, ap: 0.270, auc: 0.499, f1: 0.383, acc: 0.423
+2025-08-26 01:33:43: Epoch: 015 Train Loss: 0.397, ap: 0.869, auc: 0.899, f1: 0.796, acc: 0.810
+2025-08-26 01:33:43: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:33:43: Epoch: 015 Test results, ap: 0.336, auc: 0.503, f1: 0.346, acc: 0.325
+2025-08-26 01:33:43: Epoch: 015 New best TEST metrics
+2025-08-26 01:33:45: Epoch: 016 Train Loss: 0.366, ap: 0.877, auc: 0.902, f1: 0.787, acc: 0.803
+2025-08-26 01:33:45: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:33:45: Epoch: 016 Test results, ap: 0.343, auc: 0.519, f1: 0.391, acc: 0.366
+2025-08-26 01:33:45: Epoch: 016 New best TEST metrics
+2025-08-26 01:33:46: Epoch: 017 Train Loss: 0.346, ap: 0.892, auc: 0.910, f1: 0.792, acc: 0.815
+2025-08-26 01:33:46: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:33:46: Epoch: 017 Test results, ap: 0.320, auc: 0.518, f1: 0.362, acc: 0.398
+2025-08-26 01:33:48: Epoch: 018 Train Loss: 0.336, ap: 0.897, auc: 0.917, f1: 0.800, acc: 0.822
+2025-08-26 01:33:48: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:33:48: Epoch: 018 Test results, ap: 0.327, auc: 0.513, f1: 0.374, acc: 0.374
+2025-08-26 01:33:49: Epoch: 019 Train Loss: 0.314, ap: 0.912, auc: 0.931, f1: 0.835, acc: 0.849
+2025-08-26 01:33:49: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:33:49: Epoch: 019 Test results, ap: 0.363, auc: 0.534, f1: 0.397, acc: 0.333
+2025-08-26 01:33:49: Epoch: 019 New best TEST metrics
+2025-08-26 01:33:50: Epoch: 020 Train Loss: 0.313, ap: 0.899, auc: 0.916, f1: 0.779, acc: 0.819
+2025-08-26 01:33:51: Epoch: 020 Test results, ap: 0.312, auc: 0.498, f1: 0.365, acc: 0.350
+2025-08-26 01:33:52: Epoch: 021 Train Loss: 0.266, ap: 0.919, auc: 0.935, f1: 0.846, acc: 0.861
+2025-08-26 01:33:52: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:33:52: Epoch: 021 Test results, ap: 0.360, auc: 0.523, f1: 0.394, acc: 0.350
+2025-08-26 01:33:53: Epoch: 022 Train Loss: 0.239, ap: 0.920, auc: 0.935, f1: 0.822, acc: 0.840
+2025-08-26 01:33:53: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:33:54: Epoch: 022 Test results, ap: 0.362, auc: 0.532, f1: 0.423, acc: 0.423
+2025-08-26 01:33:55: Epoch: 023 Train Loss: 0.223, ap: 0.922, auc: 0.935, f1: 0.822, acc: 0.845
+2025-08-26 01:33:55: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:33:55: Epoch: 023 Test results, ap: 0.361, auc: 0.537, f1: 0.393, acc: 0.447
+2025-08-26 01:33:55: Epoch: 023 New best TEST metrics
+2025-08-26 01:33:56: Epoch: 024 Train Loss: 0.216, ap: 0.932, auc: 0.943, f1: 0.849, acc: 0.866
+2025-08-26 01:33:56: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:33:57: Epoch: 024 Test results, ap: 0.350, auc: 0.548, f1: 0.435, acc: 0.431
+2025-08-26 01:33:57: Epoch: 024 New best TEST metrics
+2025-08-26 01:33:58: Epoch: 025 Train Loss: 0.203, ap: 0.929, auc: 0.940, f1: 0.824, acc: 0.849
+2025-08-26 01:33:58: Epoch: 025 Test results, ap: 0.352, auc: 0.539, f1: 0.411, acc: 0.463
+2025-08-26 01:33:59: Epoch: 026 Train Loss: 0.188, ap: 0.930, auc: 0.943, f1: 0.843, acc: 0.859
+2025-08-26 01:33:59: Epoch: 026 Test results, ap: 0.348, auc: 0.540, f1: 0.415, acc: 0.382
+2025-08-26 01:34:01: Epoch: 027 Train Loss: 0.195, ap: 0.941, auc: 0.950, f1: 0.840, acc: 0.863
+2025-08-26 01:34:01: Epoch: 027 New best VALIDATION metrics
+2025-08-26 01:34:01: Epoch: 027 Test results, ap: 0.344, auc: 0.538, f1: 0.381, acc: 0.472
+2025-08-26 01:34:02: Epoch: 028 Train Loss: 0.188, ap: 0.948, auc: 0.955, f1: 0.854, acc: 0.871
+2025-08-26 01:34:02: Epoch: 028 New best VALIDATION metrics
+2025-08-26 01:34:02: Epoch: 028 Test results, ap: 0.340, auc: 0.529, f1: 0.391, acc: 0.341
+2025-08-26 01:34:04: Epoch: 029 Train Loss: 0.163, ap: 0.948, auc: 0.955, f1: 0.861, acc: 0.880
+2025-08-26 01:34:04: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:34:04: Epoch: 029 Test results, ap: 0.339, auc: 0.528, f1: 0.358, acc: 0.447
+2025-08-26 01:34:05: Epoch: 030 Train Loss: 0.175, ap: 0.950, auc: 0.955, f1: 0.874, acc: 0.887
+2025-08-26 01:34:05: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:34:05: Epoch: 030 Test results, ap: 0.324, auc: 0.521, f1: 0.397, acc: 0.358
+2025-08-26 01:34:06: Epoch: 031 Train Loss: 0.142, ap: 0.954, auc: 0.960, f1: 0.862, acc: 0.882
+2025-08-26 01:34:06: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:34:07: Epoch: 031 Test results, ap: 0.345, auc: 0.533, f1: 0.355, acc: 0.439
+2025-08-26 01:34:08: Epoch: 032 Train Loss: 0.128, ap: 0.953, auc: 0.960, f1: 0.863, acc: 0.884
+2025-08-26 01:34:08: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:34:08: Epoch: 032 Test results, ap: 0.337, auc: 0.538, f1: 0.317, acc: 0.439
+2025-08-26 01:34:09: Epoch: 033 Train Loss: 0.128, ap: 0.952, auc: 0.959, f1: 0.868, acc: 0.882
+2025-08-26 01:34:10: Epoch: 033 Test results, ap: 0.332, auc: 0.527, f1: 0.404, acc: 0.447
+2025-08-26 01:34:11: Epoch: 034 Train Loss: 0.119, ap: 0.957, auc: 0.963, f1: 0.872, acc: 0.889
+2025-08-26 01:34:11: Epoch: 034 New best VALIDATION metrics
+2025-08-26 01:34:11: Epoch: 034 Test results, ap: 0.330, auc: 0.531, f1: 0.346, acc: 0.447
+2025-08-26 01:34:12: Epoch: 035 Train Loss: 0.115, ap: 0.957, auc: 0.964, f1: 0.871, acc: 0.891
+2025-08-26 01:34:12: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:34:12: Epoch: 035 Test results, ap: 0.328, auc: 0.534, f1: 0.309, acc: 0.455
+2025-08-26 01:34:14: Epoch: 036 Train Loss: 0.108, ap: 0.954, auc: 0.963, f1: 0.881, acc: 0.896
+2025-08-26 01:34:14: Epoch: 036 Test results, ap: 0.340, auc: 0.537, f1: 0.327, acc: 0.463
+2025-08-26 01:34:15: Epoch: 037 Train Loss: 0.111, ap: 0.955, auc: 0.963, f1: 0.877, acc: 0.893
+2025-08-26 01:34:15: Epoch: 037 Test results, ap: 0.342, auc: 0.544, f1: 0.312, acc: 0.463
+2025-08-26 01:34:17: Epoch: 038 Train Loss: 0.096, ap: 0.957, auc: 0.964, f1: 0.877, acc: 0.894
+2025-08-26 01:34:17: Epoch: 038 Test results, ap: 0.340, auc: 0.536, f1: 0.319, acc: 0.480
+2025-08-26 01:34:18: Epoch: 039 Train Loss: 0.100, ap: 0.951, auc: 0.960, f1: 0.865, acc: 0.884
+2025-08-26 01:34:18: Epoch: 039 Test results, ap: 0.330, auc: 0.521, f1: 0.337, acc: 0.455
+2025-08-26 01:34:19: Epoch: 040 Train Loss: 0.092, ap: 0.957, auc: 0.964, f1: 0.875, acc: 0.893
+2025-08-26 01:34:20: Epoch: 040 Test results, ap: 0.332, auc: 0.529, f1: 0.333, acc: 0.447
+2025-08-26 01:34:21: Epoch: 041 Train Loss: 0.086, ap: 0.961, auc: 0.967, f1: 0.883, acc: 0.900
+2025-08-26 01:34:21: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:34:21: Epoch: 041 Test results, ap: 0.334, auc: 0.537, f1: 0.309, acc: 0.455
+2025-08-26 01:34:22: Epoch: 042 Train Loss: 0.080, ap: 0.961, auc: 0.967, f1: 0.883, acc: 0.898
+2025-08-26 01:34:22: Epoch: 042 New best VALIDATION metrics
+2025-08-26 01:34:23: Epoch: 042 Test results, ap: 0.331, auc: 0.530, f1: 0.323, acc: 0.455
+2025-08-26 01:34:24: Epoch: 043 Train Loss: 0.078, ap: 0.958, auc: 0.965, f1: 0.882, acc: 0.898
+2025-08-26 01:34:24: Epoch: 043 Test results, ap: 0.336, auc: 0.539, f1: 0.312, acc: 0.463
+2025-08-26 01:34:25: Epoch: 044 Train Loss: 0.078, ap: 0.960, auc: 0.966, f1: 0.879, acc: 0.896
+2025-08-26 01:34:25: Epoch: 044 Test results, ap: 0.335, auc: 0.538, f1: 0.312, acc: 0.463
+2025-08-26 01:34:27: Epoch: 045 Train Loss: 0.077, ap: 0.959, auc: 0.966, f1: 0.888, acc: 0.903
+2025-08-26 01:34:27: Epoch: 045 Test results, ap: 0.335, auc: 0.534, f1: 0.330, acc: 0.472
+2025-08-26 01:34:28: Epoch: 046 Train Loss: 0.077, ap: 0.960, auc: 0.967, f1: 0.887, acc: 0.901
+2025-08-26 01:34:28: Epoch: 046 Test results, ap: 0.333, auc: 0.530, f1: 0.316, acc: 0.472
+2025-08-26 01:34:30: Epoch: 047 Train Loss: 0.073, ap: 0.959, auc: 0.966, f1: 0.888, acc: 0.901
+2025-08-26 01:34:30: Epoch: 047 Test results, ap: 0.341, auc: 0.538, f1: 0.330, acc: 0.472
+2025-08-26 01:34:31: Epoch: 048 Train Loss: 0.069, ap: 0.961, auc: 0.967, f1: 0.892, acc: 0.907
+2025-08-26 01:34:31: Epoch: 048 New best VALIDATION metrics
+2025-08-26 01:34:31: Epoch: 048 Test results, ap: 0.336, auc: 0.534, f1: 0.323, acc: 0.488
+2025-08-26 01:34:32: Epoch: 049 Train Loss: 0.067, ap: 0.960, auc: 0.967, f1: 0.891, acc: 0.907
+2025-08-26 01:34:33: Epoch: 049 Test results, ap: 0.335, auc: 0.539, f1: 0.326, acc: 0.496
+2025-08-26 01:34:34: Epoch: 050 Train Loss: 0.067, ap: 0.959, auc: 0.966, f1: 0.881, acc: 0.900
+2025-08-26 01:34:34: Epoch: 050 Test results, ap: 0.342, auc: 0.538, f1: 0.273, acc: 0.480
+2025-08-26 01:34:34: used time 0.02h
+2025-08-26 01:34:34: Running Cross Validation 2
+2025-08-26 01:34:34: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:34:35: Epoch: 001 Train Loss: 0.693, ap: 0.489, auc: 0.579, f1: 0.612, acc: 0.441
+2025-08-26 01:34:35: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:34:36: Epoch: 001 Test results, ap: 0.512, auc: 0.640, f1: 0.392, acc: 0.244
+2025-08-26 01:34:37: Epoch: 002 Train Loss: 0.694, ap: 0.411, auc: 0.496, f1: 0.000, acc: 0.559
+2025-08-26 01:34:37: Epoch: 002 Test results, ap: 0.422, auc: 0.711, f1: 0.000, acc: 0.756
+2025-08-26 01:34:38: Epoch: 003 Train Loss: 0.694, ap: 0.464, auc: 0.510, f1: 0.597, acc: 0.462
+2025-08-26 01:34:38: Epoch: 003 Test results, ap: 0.382, auc: 0.617, f1: 0.426, acc: 0.341
+2025-08-26 01:34:40: Epoch: 004 Train Loss: 0.692, ap: 0.458, auc: 0.540, f1: 0.612, acc: 0.441
+2025-08-26 01:34:40: Epoch: 004 Test results, ap: 0.245, auc: 0.546, f1: 0.392, acc: 0.244
+2025-08-26 01:34:41: Epoch: 005 Train Loss: 0.694, ap: 0.557, auc: 0.612, f1: 0.202, acc: 0.582
+2025-08-26 01:34:41: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:34:41: Epoch: 005 Test results, ap: 0.244, auc: 0.542, f1: 0.000, acc: 0.756
+2025-08-26 01:34:42: Epoch: 006 Train Loss: 0.693, ap: 0.536, auc: 0.621, f1: 0.609, acc: 0.439
+2025-08-26 01:34:43: Epoch: 006 Test results, ap: 0.209, auc: 0.401, f1: 0.392, acc: 0.244
+2025-08-26 01:34:44: Epoch: 007 Train Loss: 0.693, ap: 0.529, auc: 0.618, f1: 0.588, acc: 0.631
+2025-08-26 01:34:44: Epoch: 007 Test results, ap: 0.275, auc: 0.589, f1: 0.422, acc: 0.577
+2025-08-26 01:34:45: Epoch: 008 Train Loss: 0.689, ap: 0.436, auc: 0.506, f1: 0.000, acc: 0.559
+2025-08-26 01:34:46: Epoch: 008 Test results, ap: 0.227, auc: 0.486, f1: 0.000, acc: 0.756
+2025-08-26 01:34:47: Epoch: 009 Train Loss: 0.694, ap: 0.467, auc: 0.541, f1: 0.008, acc: 0.561
+2025-08-26 01:34:47: Epoch: 009 Test results, ap: 0.236, auc: 0.510, f1: 0.000, acc: 0.756
+2025-08-26 01:34:48: Epoch: 010 Train Loss: 0.693, ap: 0.506, auc: 0.575, f1: 0.000, acc: 0.559
+2025-08-26 01:34:49: Epoch: 010 Test results, ap: 0.249, auc: 0.534, f1: 0.000, acc: 0.756
+2025-08-26 01:34:50: Epoch: 011 Train Loss: 0.693, ap: 0.518, auc: 0.592, f1: 0.000, acc: 0.559
+2025-08-26 01:34:50: Epoch: 011 Test results, ap: 0.245, auc: 0.529, f1: 0.000, acc: 0.756
+2025-08-26 01:34:50: Epoch: 011 New best TEST metrics
+2025-08-26 01:34:51: Epoch: 012 Train Loss: 0.694, ap: 0.518, auc: 0.591, f1: 0.073, acc: 0.554
+2025-08-26 01:34:51: Epoch: 012 Test results, ap: 0.250, auc: 0.538, f1: 0.000, acc: 0.756
+2025-08-26 01:34:51: Epoch: 012 New best TEST metrics
+2025-08-26 01:34:53: Epoch: 013 Train Loss: 0.694, ap: 0.508, auc: 0.583, f1: 0.602, acc: 0.510
+2025-08-26 01:34:53: Epoch: 013 Test results, ap: 0.254, auc: 0.546, f1: 0.424, acc: 0.382
+2025-08-26 01:34:53: Epoch: 013 New best TEST metrics
+2025-08-26 01:34:54: Epoch: 014 Train Loss: 0.693, ap: 0.519, auc: 0.595, f1: 0.613, acc: 0.506
+2025-08-26 01:34:54: Epoch: 014 Test results, ap: 0.243, auc: 0.494, f1: 0.392, acc: 0.244
+2025-08-26 01:34:55: Epoch: 015 Train Loss: 0.693, ap: 0.428, auc: 0.499, f1: 0.494, acc: 0.520
+2025-08-26 01:34:56: Epoch: 015 Test results, ap: 0.493, auc: 0.741, f1: 0.479, acc: 0.488
+2025-08-26 01:34:56: Epoch: 015 New best TEST metrics
+2025-08-26 01:34:57: Epoch: 016 Train Loss: 0.692, ap: 0.520, auc: 0.597, f1: 0.487, acc: 0.607
+2025-08-26 01:34:57: Epoch: 016 Test results, ap: 0.340, auc: 0.666, f1: 0.414, acc: 0.585
+2025-08-26 01:34:58: Epoch: 017 Train Loss: 0.688, ap: 0.467, auc: 0.547, f1: 0.211, acc: 0.550
+2025-08-26 01:34:59: Epoch: 017 Test results, ap: 0.288, auc: 0.484, f1: 0.000, acc: 0.756
+2025-08-26 01:35:00: Epoch: 018 Train Loss: 0.685, ap: 0.561, auc: 0.614, f1: 0.000, acc: 0.557
+2025-08-26 01:35:00: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:35:00: Epoch: 018 Test results, ap: 0.338, auc: 0.528, f1: 0.000, acc: 0.756
+2025-08-26 01:35:01: Epoch: 019 Train Loss: 0.682, ap: 0.468, auc: 0.559, f1: 0.485, acc: 0.563
+2025-08-26 01:35:02: Epoch: 019 Test results, ap: 0.510, auc: 0.671, f1: 0.416, acc: 0.407
+2025-08-26 01:35:03: Epoch: 020 Train Loss: 0.675, ap: 0.592, auc: 0.633, f1: 0.405, acc: 0.631
+2025-08-26 01:35:03: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:35:03: Epoch: 020 Test results, ap: 0.198, auc: 0.405, f1: 0.000, acc: 0.756
+2025-08-26 01:35:04: Epoch: 021 Train Loss: 0.667, ap: 0.584, auc: 0.638, f1: 0.408, acc: 0.631
+2025-08-26 01:35:04: Epoch: 021 Test results, ap: 0.185, auc: 0.347, f1: 0.000, acc: 0.756
+2025-08-26 01:35:06: Epoch: 022 Train Loss: 0.664, ap: 0.588, auc: 0.641, f1: 0.532, acc: 0.607
+2025-08-26 01:35:06: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:35:06: Epoch: 022 Test results, ap: 0.318, auc: 0.433, f1: 0.229, acc: 0.780
+2025-08-26 01:35:07: Epoch: 023 Train Loss: 0.662, ap: 0.638, auc: 0.661, f1: 0.607, acc: 0.623
+2025-08-26 01:35:07: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:35:07: Epoch: 023 Test results, ap: 0.192, auc: 0.383, f1: 0.384, acc: 0.374
+2025-08-26 01:35:08: Epoch: 024 Train Loss: 0.658, ap: 0.627, auc: 0.663, f1: 0.590, acc: 0.603
+2025-08-26 01:35:09: Epoch: 024 Test results, ap: 0.308, auc: 0.421, f1: 0.154, acc: 0.642
+2025-08-26 01:35:10: Epoch: 025 Train Loss: 0.656, ap: 0.632, auc: 0.658, f1: 0.596, acc: 0.630
+2025-08-26 01:35:10: Epoch: 025 Test results, ap: 0.215, auc: 0.416, f1: 0.119, acc: 0.520
+2025-08-26 01:35:11: Epoch: 026 Train Loss: 0.657, ap: 0.622, auc: 0.667, f1: 0.583, acc: 0.594
+2025-08-26 01:35:12: Epoch: 026 Test results, ap: 0.315, auc: 0.422, f1: 0.200, acc: 0.740
+2025-08-26 01:35:13: Epoch: 027 Train Loss: 0.655, ap: 0.633, auc: 0.658, f1: 0.592, acc: 0.621
+2025-08-26 01:35:13: Epoch: 027 Test results, ap: 0.248, auc: 0.428, f1: 0.125, acc: 0.545
+2025-08-26 01:35:14: Epoch: 028 Train Loss: 0.652, ap: 0.622, auc: 0.666, f1: 0.572, acc: 0.638
+2025-08-26 01:35:14: Epoch: 028 Test results, ap: 0.320, auc: 0.443, f1: 0.222, acc: 0.772
+2025-08-26 01:35:16: Epoch: 029 Train Loss: 0.649, ap: 0.615, auc: 0.662, f1: 0.570, acc: 0.593
+2025-08-26 01:35:16: Epoch: 029 Test results, ap: 0.318, auc: 0.435, f1: 0.205, acc: 0.748
+2025-08-26 01:35:17: Epoch: 030 Train Loss: 0.655, ap: 0.635, auc: 0.673, f1: 0.595, acc: 0.623
+2025-08-26 01:35:17: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:35:17: Epoch: 030 Test results, ap: 0.202, auc: 0.417, f1: 0.125, acc: 0.545
+2025-08-26 01:35:19: Epoch: 031 Train Loss: 0.643, ap: 0.649, auc: 0.679, f1: 0.595, acc: 0.628
+2025-08-26 01:35:19: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:35:19: Epoch: 031 Test results, ap: 0.239, auc: 0.434, f1: 0.129, acc: 0.561
+2025-08-26 01:35:20: Epoch: 032 Train Loss: 0.637, ap: 0.648, auc: 0.678, f1: 0.587, acc: 0.633
+2025-08-26 01:35:20: Epoch: 032 Test results, ap: 0.281, auc: 0.453, f1: 0.140, acc: 0.602
+2025-08-26 01:35:21: Epoch: 033 Train Loss: 0.629, ap: 0.630, auc: 0.678, f1: 0.612, acc: 0.623
+2025-08-26 01:35:22: Epoch: 033 Test results, ap: 0.201, auc: 0.414, f1: 0.150, acc: 0.447
+2025-08-26 01:35:23: Epoch: 034 Train Loss: 0.627, ap: 0.664, auc: 0.700, f1: 0.596, acc: 0.638
+2025-08-26 01:35:23: Epoch: 034 New best VALIDATION metrics
+2025-08-26 01:35:23: Epoch: 034 Test results, ap: 0.247, auc: 0.452, f1: 0.143, acc: 0.610
+2025-08-26 01:35:24: Epoch: 035 Train Loss: 0.631, ap: 0.640, auc: 0.697, f1: 0.625, acc: 0.644
+2025-08-26 01:35:25: Epoch: 035 Test results, ap: 0.204, auc: 0.423, f1: 0.129, acc: 0.561
+2025-08-26 01:35:26: Epoch: 036 Train Loss: 0.621, ap: 0.664, auc: 0.708, f1: 0.642, acc: 0.665
+2025-08-26 01:35:26: Epoch: 036 New best VALIDATION metrics
+2025-08-26 01:35:26: Epoch: 036 Test results, ap: 0.251, auc: 0.450, f1: 0.145, acc: 0.618
+2025-08-26 01:35:27: Epoch: 037 Train Loss: 0.609, ap: 0.659, auc: 0.716, f1: 0.635, acc: 0.660
+2025-08-26 01:35:27: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:35:27: Epoch: 037 Test results, ap: 0.279, auc: 0.470, f1: 0.157, acc: 0.650
+2025-08-26 01:35:29: Epoch: 038 Train Loss: 0.605, ap: 0.681, auc: 0.722, f1: 0.647, acc: 0.665
+2025-08-26 01:35:29: Epoch: 038 New best VALIDATION metrics
+2025-08-26 01:35:29: Epoch: 038 Test results, ap: 0.347, auc: 0.498, f1: 0.160, acc: 0.659
+2025-08-26 01:35:30: Epoch: 039 Train Loss: 0.597, ap: 0.682, auc: 0.729, f1: 0.654, acc: 0.677
+2025-08-26 01:35:30: Epoch: 039 New best VALIDATION metrics
+2025-08-26 01:35:30: Epoch: 039 Test results, ap: 0.324, auc: 0.515, f1: 0.216, acc: 0.764
+2025-08-26 01:35:31: Epoch: 040 Train Loss: 0.597, ap: 0.704, auc: 0.746, f1: 0.654, acc: 0.674
+2025-08-26 01:35:31: Epoch: 040 New best VALIDATION metrics
+2025-08-26 01:35:32: Epoch: 040 Test results, ap: 0.349, auc: 0.549, f1: 0.178, acc: 0.699
+2025-08-26 01:35:33: Epoch: 041 Train Loss: 0.578, ap: 0.710, auc: 0.752, f1: 0.672, acc: 0.690
+2025-08-26 01:35:33: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:35:33: Epoch: 041 Test results, ap: 0.357, auc: 0.547, f1: 0.222, acc: 0.772
+2025-08-26 01:35:34: Epoch: 042 Train Loss: 0.577, ap: 0.695, auc: 0.740, f1: 0.665, acc: 0.693
+2025-08-26 01:35:35: Epoch: 042 Test results, ap: 0.334, auc: 0.535, f1: 0.171, acc: 0.764
+2025-08-26 01:35:36: Epoch: 043 Train Loss: 0.575, ap: 0.697, auc: 0.730, f1: 0.634, acc: 0.667
+2025-08-26 01:35:36: Epoch: 043 Test results, ap: 0.307, auc: 0.532, f1: 0.167, acc: 0.675
+2025-08-26 01:35:37: Epoch: 044 Train Loss: 0.571, ap: 0.716, auc: 0.753, f1: 0.683, acc: 0.705
+2025-08-26 01:35:37: Epoch: 044 New best VALIDATION metrics
+2025-08-26 01:35:37: Epoch: 044 Test results, ap: 0.361, auc: 0.562, f1: 0.222, acc: 0.772
+2025-08-26 01:35:39: Epoch: 045 Train Loss: 0.562, ap: 0.723, auc: 0.762, f1: 0.691, acc: 0.707
+2025-08-26 01:35:39: Epoch: 045 New best VALIDATION metrics
+2025-08-26 01:35:39: Epoch: 045 Test results, ap: 0.403, auc: 0.568, f1: 0.316, acc: 0.789
+2025-08-26 01:35:40: Epoch: 046 Train Loss: 0.564, ap: 0.725, auc: 0.769, f1: 0.691, acc: 0.705
+2025-08-26 01:35:40: Epoch: 046 New best VALIDATION metrics
+2025-08-26 01:35:40: Epoch: 046 Test results, ap: 0.415, auc: 0.572, f1: 0.326, acc: 0.764
+2025-08-26 01:35:42: Epoch: 047 Train Loss: 0.554, ap: 0.736, auc: 0.767, f1: 0.690, acc: 0.713
+2025-08-26 01:35:42: Epoch: 047 New best VALIDATION metrics
+2025-08-26 01:35:42: Epoch: 047 Test results, ap: 0.319, auc: 0.545, f1: 0.222, acc: 0.772
+2025-08-26 01:35:43: Epoch: 048 Train Loss: 0.551, ap: 0.737, auc: 0.768, f1: 0.693, acc: 0.711
+2025-08-26 01:35:43: Epoch: 048 New best VALIDATION metrics
+2025-08-26 01:35:43: Epoch: 048 Test results, ap: 0.352, auc: 0.558, f1: 0.190, acc: 0.724
+2025-08-26 01:35:45: Epoch: 049 Train Loss: 0.566, ap: 0.731, auc: 0.759, f1: 0.665, acc: 0.700
+2025-08-26 01:35:45: Epoch: 049 Test results, ap: 0.318, auc: 0.552, f1: 0.178, acc: 0.699
+2025-08-26 01:35:46: Epoch: 050 Train Loss: 0.559, ap: 0.720, auc: 0.769, f1: 0.702, acc: 0.721
+2025-08-26 01:35:46: Epoch: 050 Test results, ap: 0.399, auc: 0.567, f1: 0.229, acc: 0.780
+2025-08-26 01:35:46: used time 0.02h
+2025-08-26 01:35:46: Running Cross Validation 3
+2025-08-26 01:35:46: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:35:47: Epoch: 001 Train Loss: 0.694, ap: 0.564, auc: 0.616, f1: 0.000, acc: 0.570
+2025-08-26 01:35:47: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:35:48: Epoch: 001 Test results, ap: 0.249, auc: 0.551, f1: 0.000, acc: 0.756
+2025-08-26 01:35:49: Epoch: 002 Train Loss: 0.694, ap: 0.497, auc: 0.598, f1: 0.602, acc: 0.430
+2025-08-26 01:35:49: Epoch: 002 Test results, ap: 0.238, auc: 0.509, f1: 0.392, acc: 0.244
+2025-08-26 01:35:50: Epoch: 003 Train Loss: 0.693, ap: 0.477, auc: 0.545, f1: 0.529, acc: 0.469
+2025-08-26 01:35:51: Epoch: 003 Test results, ap: 0.271, auc: 0.551, f1: 0.392, acc: 0.244
+2025-08-26 01:35:52: Epoch: 004 Train Loss: 0.694, ap: 0.507, auc: 0.575, f1: 0.529, acc: 0.469
+2025-08-26 01:35:52: Epoch: 004 Test results, ap: 0.246, auc: 0.537, f1: 0.392, acc: 0.244
+2025-08-26 01:35:53: Epoch: 005 Train Loss: 0.694, ap: 0.499, auc: 0.564, f1: 0.534, acc: 0.473
+2025-08-26 01:35:53: Epoch: 005 Test results, ap: 0.260, auc: 0.525, f1: 0.392, acc: 0.244
+2025-08-26 01:35:55: Epoch: 006 Train Loss: 0.694, ap: 0.516, auc: 0.584, f1: 0.534, acc: 0.473
+2025-08-26 01:35:55: Epoch: 006 Test results, ap: 0.247, auc: 0.509, f1: 0.392, acc: 0.244
+2025-08-26 01:35:56: Epoch: 007 Train Loss: 0.694, ap: 0.428, auc: 0.496, f1: 0.534, acc: 0.473
+2025-08-26 01:35:56: Epoch: 007 Test results, ap: 0.232, auc: 0.475, f1: 0.392, acc: 0.244
+2025-08-26 01:35:57: Epoch: 008 Train Loss: 0.694, ap: 0.504, auc: 0.557, f1: 0.534, acc: 0.473
+2025-08-26 01:35:58: Epoch: 008 Test results, ap: 0.246, auc: 0.508, f1: 0.392, acc: 0.244
+2025-08-26 01:35:59: Epoch: 009 Train Loss: 0.693, ap: 0.495, auc: 0.573, f1: 0.534, acc: 0.473
+2025-08-26 01:35:59: Epoch: 009 Test results, ap: 0.250, auc: 0.531, f1: 0.392, acc: 0.244
+2025-08-26 01:36:00: Epoch: 010 Train Loss: 0.693, ap: 0.498, auc: 0.576, f1: 0.534, acc: 0.473
+2025-08-26 01:36:01: Epoch: 010 Test results, ap: 0.251, auc: 0.534, f1: 0.392, acc: 0.244
+2025-08-26 01:36:02: Epoch: 011 Train Loss: 0.692, ap: 0.514, auc: 0.584, f1: 0.531, acc: 0.471
+2025-08-26 01:36:02: Epoch: 011 Test results, ap: 0.257, auc: 0.537, f1: 0.392, acc: 0.244
+2025-08-26 01:36:02: Epoch: 011 New best TEST metrics
+2025-08-26 01:36:03: Epoch: 012 Train Loss: 0.693, ap: 0.513, auc: 0.581, f1: 0.531, acc: 0.471
+2025-08-26 01:36:03: Epoch: 012 Test results, ap: 0.251, auc: 0.527, f1: 0.392, acc: 0.244
+2025-08-26 01:36:05: Epoch: 013 Train Loss: 0.694, ap: 0.501, auc: 0.579, f1: 0.531, acc: 0.471
+2025-08-26 01:36:05: Epoch: 013 Test results, ap: 0.252, auc: 0.529, f1: 0.392, acc: 0.244
+2025-08-26 01:36:06: Epoch: 014 Train Loss: 0.693, ap: 0.497, auc: 0.577, f1: 0.529, acc: 0.469
+2025-08-26 01:36:06: Epoch: 014 Test results, ap: 0.259, auc: 0.537, f1: 0.392, acc: 0.244
+2025-08-26 01:36:06: Epoch: 014 New best TEST metrics
+2025-08-26 01:36:08: Epoch: 015 Train Loss: 0.693, ap: 0.496, auc: 0.578, f1: 0.523, acc: 0.473
+2025-08-26 01:36:08: Epoch: 015 Test results, ap: 0.274, auc: 0.539, f1: 0.392, acc: 0.244
+2025-08-26 01:36:08: Epoch: 015 New best TEST metrics
+2025-08-26 01:36:09: Epoch: 016 Train Loss: 0.692, ap: 0.504, auc: 0.583, f1: 0.531, acc: 0.471
+2025-08-26 01:36:09: Epoch: 016 Test results, ap: 0.282, auc: 0.551, f1: 0.392, acc: 0.244
+2025-08-26 01:36:09: Epoch: 016 New best TEST metrics
+2025-08-26 01:36:10: Epoch: 017 Train Loss: 0.694, ap: 0.489, auc: 0.602, f1: 0.602, acc: 0.430
+2025-08-26 01:36:11: Epoch: 017 Test results, ap: 0.263, auc: 0.511, f1: 0.392, acc: 0.244
+2025-08-26 01:36:12: Epoch: 018 Train Loss: 0.694, ap: 0.503, auc: 0.619, f1: 0.000, acc: 0.570
+2025-08-26 01:36:12: Epoch: 018 Test results, ap: 0.289, auc: 0.530, f1: 0.000, acc: 0.756
+2025-08-26 01:36:13: Epoch: 019 Train Loss: 0.694, ap: 0.540, auc: 0.639, f1: 0.602, acc: 0.432
+2025-08-26 01:36:14: Epoch: 019 Test results, ap: 0.303, auc: 0.570, f1: 0.392, acc: 0.244
+2025-08-26 01:36:14: Epoch: 019 New best TEST metrics
+2025-08-26 01:36:15: Epoch: 020 Train Loss: 0.691, ap: 0.543, auc: 0.650, f1: 0.590, acc: 0.628
+2025-08-26 01:36:15: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:36:15: Epoch: 020 Test results, ap: 0.299, auc: 0.504, f1: 0.392, acc: 0.244
+2025-08-26 01:36:16: Epoch: 021 Train Loss: 0.675, ap: 0.550, auc: 0.645, f1: 0.520, acc: 0.631
+2025-08-26 01:36:16: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:36:16: Epoch: 021 Test results, ap: 0.303, auc: 0.512, f1: 0.392, acc: 0.244
+2025-08-26 01:36:18: Epoch: 022 Train Loss: 0.654, ap: 0.550, auc: 0.652, f1: 0.557, acc: 0.654
+2025-08-26 01:36:18: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:36:18: Epoch: 022 Test results, ap: 0.300, auc: 0.509, f1: 0.392, acc: 0.244
+2025-08-26 01:36:19: Epoch: 023 Train Loss: 0.648, ap: 0.564, auc: 0.653, f1: 0.544, acc: 0.651
+2025-08-26 01:36:19: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:36:19: Epoch: 023 Test results, ap: 0.302, auc: 0.514, f1: 0.392, acc: 0.244
+2025-08-26 01:36:21: Epoch: 024 Train Loss: 0.651, ap: 0.552, auc: 0.649, f1: 0.511, acc: 0.646
+2025-08-26 01:36:21: Epoch: 024 Test results, ap: 0.298, auc: 0.506, f1: 0.265, acc: 0.504
+2025-08-26 01:36:22: Epoch: 025 Train Loss: 0.647, ap: 0.586, auc: 0.659, f1: 0.579, acc: 0.644
+2025-08-26 01:36:22: Epoch: 025 New best VALIDATION metrics
+2025-08-26 01:36:22: Epoch: 025 Test results, ap: 0.310, auc: 0.529, f1: 0.392, acc: 0.244
+2025-08-26 01:36:24: Epoch: 026 Train Loss: 0.641, ap: 0.613, auc: 0.669, f1: 0.574, acc: 0.637
+2025-08-26 01:36:24: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:36:24: Epoch: 026 Test results, ap: 0.330, auc: 0.516, f1: 0.395, acc: 0.252
+2025-08-26 01:36:25: Epoch: 027 Train Loss: 0.643, ap: 0.616, auc: 0.673, f1: 0.562, acc: 0.654
+2025-08-26 01:36:25: Epoch: 027 New best VALIDATION metrics
+2025-08-26 01:36:25: Epoch: 027 Test results, ap: 0.332, auc: 0.519, f1: 0.301, acc: 0.472
+2025-08-26 01:36:26: Epoch: 028 Train Loss: 0.638, ap: 0.637, auc: 0.682, f1: 0.580, acc: 0.642
+2025-08-26 01:36:26: Epoch: 028 New best VALIDATION metrics
+2025-08-26 01:36:27: Epoch: 028 Test results, ap: 0.338, auc: 0.527, f1: 0.400, acc: 0.268
+2025-08-26 01:36:28: Epoch: 029 Train Loss: 0.635, ap: 0.641, auc: 0.690, f1: 0.571, acc: 0.667
+2025-08-26 01:36:28: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:36:28: Epoch: 029 Test results, ap: 0.321, auc: 0.506, f1: 0.301, acc: 0.472
+2025-08-26 01:36:29: Epoch: 030 Train Loss: 0.628, ap: 0.671, auc: 0.707, f1: 0.585, acc: 0.667
+2025-08-26 01:36:29: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:36:30: Epoch: 030 Test results, ap: 0.345, auc: 0.520, f1: 0.299, acc: 0.504
+2025-08-26 01:36:31: Epoch: 031 Train Loss: 0.619, ap: 0.679, auc: 0.715, f1: 0.581, acc: 0.667
+2025-08-26 01:36:31: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:36:31: Epoch: 031 Test results, ap: 0.359, auc: 0.523, f1: 0.289, acc: 0.520
+2025-08-26 01:36:31: Epoch: 031 New best TEST metrics
+2025-08-26 01:36:32: Epoch: 032 Train Loss: 0.614, ap: 0.694, auc: 0.722, f1: 0.587, acc: 0.651
+2025-08-26 01:36:32: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:36:32: Epoch: 032 Test results, ap: 0.295, auc: 0.523, f1: 0.364, acc: 0.431
+2025-08-26 01:36:34: Epoch: 033 Train Loss: 0.603, ap: 0.686, auc: 0.731, f1: 0.603, acc: 0.693
+2025-08-26 01:36:34: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:36:34: Epoch: 033 Test results, ap: 0.366, auc: 0.535, f1: 0.237, acc: 0.528
+2025-08-26 01:36:34: Epoch: 033 New best TEST metrics
+2025-08-26 01:36:35: Epoch: 034 Train Loss: 0.595, ap: 0.693, auc: 0.740, f1: 0.604, acc: 0.690
+2025-08-26 01:36:35: Epoch: 034 New best VALIDATION metrics
+2025-08-26 01:36:35: Epoch: 034 Test results, ap: 0.398, auc: 0.552, f1: 0.247, acc: 0.553
+2025-08-26 01:36:35: Epoch: 034 New best TEST metrics
+2025-08-26 01:36:37: Epoch: 035 Train Loss: 0.592, ap: 0.715, auc: 0.756, f1: 0.665, acc: 0.702
+2025-08-26 01:36:37: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:36:37: Epoch: 035 Test results, ap: 0.389, auc: 0.554, f1: 0.295, acc: 0.496
+2025-08-26 01:36:38: Epoch: 036 Train Loss: 0.578, ap: 0.723, auc: 0.763, f1: 0.638, acc: 0.700
+2025-08-26 01:36:38: Epoch: 036 New best VALIDATION metrics
+2025-08-26 01:36:38: Epoch: 036 Test results, ap: 0.400, auc: 0.556, f1: 0.289, acc: 0.520
+2025-08-26 01:36:38: Epoch: 036 New best TEST metrics
+2025-08-26 01:36:39: Epoch: 037 Train Loss: 0.566, ap: 0.740, auc: 0.778, f1: 0.672, acc: 0.700
+2025-08-26 01:36:39: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:36:40: Epoch: 037 Test results, ap: 0.431, auc: 0.568, f1: 0.296, acc: 0.537
+2025-08-26 01:36:40: Epoch: 037 New best TEST metrics
+2025-08-26 01:36:41: Epoch: 038 Train Loss: 0.557, ap: 0.753, auc: 0.789, f1: 0.677, acc: 0.720
+2025-08-26 01:36:41: Epoch: 038 New best VALIDATION metrics
+2025-08-26 01:36:41: Epoch: 038 Test results, ap: 0.427, auc: 0.542, f1: 0.296, acc: 0.537
+2025-08-26 01:36:42: Epoch: 039 Train Loss: 0.537, ap: 0.756, auc: 0.787, f1: 0.676, acc: 0.721
+2025-08-26 01:36:42: Epoch: 039 New best VALIDATION metrics
+2025-08-26 01:36:43: Epoch: 039 Test results, ap: 0.420, auc: 0.561, f1: 0.319, acc: 0.618
+2025-08-26 01:36:44: Epoch: 040 Train Loss: 0.536, ap: 0.752, auc: 0.788, f1: 0.679, acc: 0.734
+2025-08-26 01:36:44: Epoch: 040 Test results, ap: 0.303, auc: 0.529, f1: 0.300, acc: 0.659
+2025-08-26 01:36:45: Epoch: 041 Train Loss: 0.521, ap: 0.759, auc: 0.789, f1: 0.685, acc: 0.725
+2025-08-26 01:36:45: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:36:45: Epoch: 041 Test results, ap: 0.330, auc: 0.531, f1: 0.320, acc: 0.585
+2025-08-26 01:36:47: Epoch: 042 Train Loss: 0.516, ap: 0.770, auc: 0.802, f1: 0.685, acc: 0.725
+2025-08-26 01:36:47: Epoch: 042 New best VALIDATION metrics
+2025-08-26 01:36:47: Epoch: 042 Test results, ap: 0.376, auc: 0.564, f1: 0.308, acc: 0.561
+2025-08-26 01:36:48: Epoch: 043 Train Loss: 0.504, ap: 0.778, auc: 0.810, f1: 0.692, acc: 0.739
+2025-08-26 01:36:48: Epoch: 043 New best VALIDATION metrics
+2025-08-26 01:36:48: Epoch: 043 Test results, ap: 0.386, auc: 0.566, f1: 0.316, acc: 0.577
+2025-08-26 01:36:50: Epoch: 044 Train Loss: 0.501, ap: 0.783, auc: 0.814, f1: 0.691, acc: 0.741
+2025-08-26 01:36:50: Epoch: 044 New best VALIDATION metrics
+2025-08-26 01:36:50: Epoch: 044 Test results, ap: 0.395, auc: 0.564, f1: 0.333, acc: 0.610
+2025-08-26 01:36:51: Epoch: 045 Train Loss: 0.495, ap: 0.795, auc: 0.824, f1: 0.707, acc: 0.744
+2025-08-26 01:36:51: Epoch: 045 New best VALIDATION metrics
+2025-08-26 01:36:51: Epoch: 045 Test results, ap: 0.409, auc: 0.562, f1: 0.300, acc: 0.545
+2025-08-26 01:36:52: Epoch: 046 Train Loss: 0.488, ap: 0.803, auc: 0.832, f1: 0.699, acc: 0.746
+2025-08-26 01:36:52: Epoch: 046 New best VALIDATION metrics
+2025-08-26 01:36:53: Epoch: 046 Test results, ap: 0.446, auc: 0.573, f1: 0.308, acc: 0.561
+2025-08-26 01:36:53: Epoch: 046 New best TEST metrics
+2025-08-26 01:36:54: Epoch: 047 Train Loss: 0.479, ap: 0.804, auc: 0.834, f1: 0.708, acc: 0.760
+2025-08-26 01:36:54: Epoch: 047 New best VALIDATION metrics
+2025-08-26 01:36:54: Epoch: 047 Test results, ap: 0.445, auc: 0.595, f1: 0.338, acc: 0.618
+2025-08-26 01:36:54: Epoch: 047 New best TEST metrics
+2025-08-26 01:36:55: Epoch: 048 Train Loss: 0.470, ap: 0.810, auc: 0.839, f1: 0.722, acc: 0.762
+2025-08-26 01:36:55: Epoch: 048 New best VALIDATION metrics
+2025-08-26 01:36:56: Epoch: 048 Test results, ap: 0.418, auc: 0.572, f1: 0.308, acc: 0.561
+2025-08-26 01:36:57: Epoch: 049 Train Loss: 0.470, ap: 0.810, auc: 0.839, f1: 0.718, acc: 0.760
+2025-08-26 01:36:57: Epoch: 049 New best VALIDATION metrics
+2025-08-26 01:36:57: Epoch: 049 Test results, ap: 0.418, auc: 0.572, f1: 0.338, acc: 0.618
+2025-08-26 01:36:58: Epoch: 050 Train Loss: 0.458, ap: 0.804, auc: 0.841, f1: 0.728, acc: 0.769
+2025-08-26 01:36:59: Epoch: 050 Test results, ap: 0.375, auc: 0.571, f1: 0.329, acc: 0.602
+2025-08-26 01:36:59: used time 0.02h
+2025-08-26 01:36:59: Running Cross Validation 4
+2025-08-26 01:36:59: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:37:00: Epoch: 001 Train Loss: 0.694, ap: 0.475, auc: 0.533, f1: 0.000, acc: 0.557
+2025-08-26 01:37:00: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:37:00: Epoch: 001 Test results, ap: 0.333, auc: 0.577, f1: 0.000, acc: 0.756
+2025-08-26 01:37:01: Epoch: 002 Train Loss: 0.685, ap: 0.571, auc: 0.649, f1: 0.627, acc: 0.587
+2025-08-26 01:37:01: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:37:01: Epoch: 002 Test results, ap: 0.295, auc: 0.471, f1: 0.364, acc: 0.545
+2025-08-26 01:37:03: Epoch: 003 Train Loss: 0.695, ap: 0.549, auc: 0.618, f1: 0.592, acc: 0.563
+2025-08-26 01:37:03: Epoch: 003 Test results, ap: 0.214, auc: 0.441, f1: 0.392, acc: 0.244
+2025-08-26 01:37:04: Epoch: 004 Train Loss: 0.682, ap: 0.541, auc: 0.593, f1: 0.482, acc: 0.591
+2025-08-26 01:37:04: Epoch: 004 Test results, ap: 0.279, auc: 0.596, f1: 0.386, acc: 0.585
+2025-08-26 01:37:06: Epoch: 005 Train Loss: 0.667, ap: 0.551, auc: 0.622, f1: 0.605, acc: 0.637
+2025-08-26 01:37:06: Epoch: 005 Test results, ap: 0.295, auc: 0.490, f1: 0.286, acc: 0.390
+2025-08-26 01:37:07: Epoch: 006 Train Loss: 0.654, ap: 0.564, auc: 0.624, f1: 0.583, acc: 0.624
+2025-08-26 01:37:07: Epoch: 006 Test results, ap: 0.263, auc: 0.494, f1: 0.379, acc: 0.333
+2025-08-26 01:37:08: Epoch: 007 Train Loss: 0.646, ap: 0.568, auc: 0.632, f1: 0.569, acc: 0.637
+2025-08-26 01:37:09: Epoch: 007 Test results, ap: 0.319, auc: 0.476, f1: 0.348, acc: 0.390
+2025-08-26 01:37:10: Epoch: 008 Train Loss: 0.635, ap: 0.622, auc: 0.643, f1: 0.547, acc: 0.640
+2025-08-26 01:37:10: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:37:10: Epoch: 008 Test results, ap: 0.419, auc: 0.578, f1: 0.365, acc: 0.463
+2025-08-26 01:37:11: Epoch: 009 Train Loss: 0.628, ap: 0.653, auc: 0.686, f1: 0.589, acc: 0.628
+2025-08-26 01:37:11: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:37:12: Epoch: 009 Test results, ap: 0.243, auc: 0.479, f1: 0.397, acc: 0.407
+2025-08-26 01:37:13: Epoch: 010 Train Loss: 0.599, ap: 0.635, auc: 0.708, f1: 0.630, acc: 0.635
+2025-08-26 01:37:13: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:37:13: Epoch: 010 Test results, ap: 0.228, auc: 0.470, f1: 0.330, acc: 0.439
+2025-08-26 01:37:14: Epoch: 011 Train Loss: 0.556, ap: 0.740, auc: 0.774, f1: 0.674, acc: 0.688
+2025-08-26 01:37:14: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:37:14: Epoch: 011 Test results, ap: 0.258, auc: 0.500, f1: 0.321, acc: 0.415
+2025-08-26 01:37:14: Epoch: 011 New best TEST metrics
+2025-08-26 01:37:16: Epoch: 012 Train Loss: 0.520, ap: 0.773, auc: 0.801, f1: 0.700, acc: 0.716
+2025-08-26 01:37:16: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:37:16: Epoch: 012 Test results, ap: 0.273, auc: 0.532, f1: 0.358, acc: 0.504
+2025-08-26 01:37:16: Epoch: 012 New best TEST metrics
+2025-08-26 01:37:17: Epoch: 013 Train Loss: 0.487, ap: 0.794, auc: 0.826, f1: 0.703, acc: 0.741
+2025-08-26 01:37:17: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:37:17: Epoch: 013 Test results, ap: 0.310, auc: 0.547, f1: 0.365, acc: 0.463
+2025-08-26 01:37:17: Epoch: 013 New best TEST metrics
+2025-08-26 01:37:19: Epoch: 014 Train Loss: 0.451, ap: 0.822, auc: 0.844, f1: 0.722, acc: 0.751
+2025-08-26 01:37:19: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:37:19: Epoch: 014 Test results, ap: 0.288, auc: 0.552, f1: 0.388, acc: 0.512
+2025-08-26 01:37:20: Epoch: 015 Train Loss: 0.430, ap: 0.811, auc: 0.837, f1: 0.740, acc: 0.758
+2025-08-26 01:37:20: Epoch: 015 Test results, ap: 0.334, auc: 0.567, f1: 0.388, acc: 0.358
+2025-08-26 01:37:20: Epoch: 015 New best TEST metrics
+2025-08-26 01:37:22: Epoch: 016 Train Loss: 0.391, ap: 0.829, auc: 0.870, f1: 0.776, acc: 0.787
+2025-08-26 01:37:22: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:37:22: Epoch: 016 Test results, ap: 0.314, auc: 0.575, f1: 0.414, acc: 0.333
+2025-08-26 01:37:23: Epoch: 017 Train Loss: 0.379, ap: 0.856, auc: 0.885, f1: 0.774, acc: 0.781
+2025-08-26 01:37:23: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:37:23: Epoch: 017 Test results, ap: 0.309, auc: 0.561, f1: 0.427, acc: 0.390
+2025-08-26 01:37:25: Epoch: 018 Train Loss: 0.342, ap: 0.862, auc: 0.892, f1: 0.795, acc: 0.804
+2025-08-26 01:37:25: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:37:25: Epoch: 018 Test results, ap: 0.303, auc: 0.571, f1: 0.438, acc: 0.415
+2025-08-26 01:37:26: Epoch: 019 Train Loss: 0.313, ap: 0.864, auc: 0.895, f1: 0.793, acc: 0.801
+2025-08-26 01:37:26: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:37:26: Epoch: 019 Test results, ap: 0.294, auc: 0.563, f1: 0.425, acc: 0.407
+2025-08-26 01:37:27: Epoch: 020 Train Loss: 0.315, ap: 0.886, auc: 0.903, f1: 0.807, acc: 0.815
+2025-08-26 01:37:27: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:37:28: Epoch: 020 Test results, ap: 0.274, auc: 0.542, f1: 0.422, acc: 0.398
+2025-08-26 01:37:29: Epoch: 021 Train Loss: 0.270, ap: 0.892, auc: 0.914, f1: 0.813, acc: 0.829
+2025-08-26 01:37:29: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:37:29: Epoch: 021 Test results, ap: 0.274, auc: 0.516, f1: 0.422, acc: 0.398
+2025-08-26 01:37:30: Epoch: 022 Train Loss: 0.240, ap: 0.913, auc: 0.929, f1: 0.841, acc: 0.852
+2025-08-26 01:37:30: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:37:31: Epoch: 022 Test results, ap: 0.273, auc: 0.525, f1: 0.418, acc: 0.366
+2025-08-26 01:37:32: Epoch: 023 Train Loss: 0.239, ap: 0.913, auc: 0.928, f1: 0.846, acc: 0.857
+2025-08-26 01:37:32: Epoch: 023 Test results, ap: 0.264, auc: 0.524, f1: 0.400, acc: 0.317
+2025-08-26 01:37:33: Epoch: 024 Train Loss: 0.224, ap: 0.917, auc: 0.932, f1: 0.852, acc: 0.862
+2025-08-26 01:37:33: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:37:34: Epoch: 024 Test results, ap: 0.272, auc: 0.519, f1: 0.421, acc: 0.374
+2025-08-26 01:37:35: Epoch: 025 Train Loss: 0.207, ap: 0.921, auc: 0.932, f1: 0.852, acc: 0.862
+2025-08-26 01:37:35: Epoch: 025 New best VALIDATION metrics
+2025-08-26 01:37:35: Epoch: 025 Test results, ap: 0.274, auc: 0.526, f1: 0.419, acc: 0.390
+2025-08-26 01:37:36: Epoch: 026 Train Loss: 0.220, ap: 0.934, auc: 0.944, f1: 0.875, acc: 0.884
+2025-08-26 01:37:36: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:37:36: Epoch: 026 Test results, ap: 0.291, auc: 0.535, f1: 0.415, acc: 0.358
+2025-08-26 01:37:38: Epoch: 027 Train Loss: 0.187, ap: 0.917, auc: 0.936, f1: 0.875, acc: 0.885
+2025-08-26 01:37:38: Epoch: 027 Test results, ap: 0.288, auc: 0.543, f1: 0.412, acc: 0.350
+2025-08-26 01:37:39: Epoch: 028 Train Loss: 0.205, ap: 0.938, auc: 0.945, f1: 0.866, acc: 0.880
+2025-08-26 01:37:39: Epoch: 028 New best VALIDATION metrics
+2025-08-26 01:37:39: Epoch: 028 Test results, ap: 0.290, auc: 0.541, f1: 0.431, acc: 0.398
+2025-08-26 01:37:41: Epoch: 029 Train Loss: 0.190, ap: 0.935, auc: 0.944, f1: 0.866, acc: 0.882
+2025-08-26 01:37:41: Epoch: 029 Test results, ap: 0.291, auc: 0.527, f1: 0.427, acc: 0.390
+2025-08-26 01:37:42: Epoch: 030 Train Loss: 0.181, ap: 0.935, auc: 0.944, f1: 0.877, acc: 0.889
+2025-08-26 01:37:42: Epoch: 030 Test results, ap: 0.280, auc: 0.534, f1: 0.424, acc: 0.382
+2025-08-26 01:37:43: Epoch: 031 Train Loss: 0.145, ap: 0.936, auc: 0.944, f1: 0.864, acc: 0.877
+2025-08-26 01:37:44: Epoch: 031 Test results, ap: 0.280, auc: 0.516, f1: 0.431, acc: 0.398
+2025-08-26 01:37:45: Epoch: 032 Train Loss: 0.145, ap: 0.936, auc: 0.945, f1: 0.868, acc: 0.880
+2025-08-26 01:37:45: Epoch: 032 Test results, ap: 0.285, auc: 0.526, f1: 0.415, acc: 0.358
+2025-08-26 01:37:46: Epoch: 033 Train Loss: 0.135, ap: 0.938, auc: 0.945, f1: 0.876, acc: 0.887
+2025-08-26 01:37:46: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:37:47: Epoch: 033 Test results, ap: 0.280, auc: 0.525, f1: 0.421, acc: 0.374
+2025-08-26 01:37:48: Epoch: 034 Train Loss: 0.128, ap: 0.940, auc: 0.946, f1: 0.868, acc: 0.877
+2025-08-26 01:37:48: Epoch: 034 New best VALIDATION metrics
+2025-08-26 01:37:48: Epoch: 034 Test results, ap: 0.285, auc: 0.536, f1: 0.418, acc: 0.366
+2025-08-26 01:37:49: Epoch: 035 Train Loss: 0.127, ap: 0.935, auc: 0.944, f1: 0.875, acc: 0.887
+2025-08-26 01:37:49: Epoch: 035 Test results, ap: 0.294, auc: 0.537, f1: 0.415, acc: 0.358
+2025-08-26 01:37:51: Epoch: 036 Train Loss: 0.126, ap: 0.941, auc: 0.947, f1: 0.873, acc: 0.882
+2025-08-26 01:37:51: Epoch: 036 New best VALIDATION metrics
+2025-08-26 01:37:51: Epoch: 036 Test results, ap: 0.294, auc: 0.533, f1: 0.418, acc: 0.366
+2025-08-26 01:37:52: Epoch: 037 Train Loss: 0.117, ap: 0.944, auc: 0.950, f1: 0.884, acc: 0.894
+2025-08-26 01:37:52: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:37:52: Epoch: 037 Test results, ap: 0.296, auc: 0.536, f1: 0.418, acc: 0.366
+2025-08-26 01:37:54: Epoch: 038 Train Loss: 0.123, ap: 0.944, auc: 0.949, f1: 0.874, acc: 0.885
+2025-08-26 01:37:54: Epoch: 038 New best VALIDATION metrics
+2025-08-26 01:37:54: Epoch: 038 Test results, ap: 0.293, auc: 0.533, f1: 0.424, acc: 0.382
+2025-08-26 01:37:55: Epoch: 039 Train Loss: 0.108, ap: 0.946, auc: 0.952, f1: 0.874, acc: 0.884
+2025-08-26 01:37:55: Epoch: 039 New best VALIDATION metrics
+2025-08-26 01:37:55: Epoch: 039 Test results, ap: 0.303, auc: 0.549, f1: 0.415, acc: 0.358
+2025-08-26 01:37:56: Epoch: 040 Train Loss: 0.111, ap: 0.944, auc: 0.951, f1: 0.871, acc: 0.882
+2025-08-26 01:37:57: Epoch: 040 Test results, ap: 0.303, auc: 0.550, f1: 0.431, acc: 0.398
+2025-08-26 01:37:58: Epoch: 041 Train Loss: 0.103, ap: 0.943, auc: 0.949, f1: 0.873, acc: 0.884
+2025-08-26 01:37:58: Epoch: 041 Test results, ap: 0.297, auc: 0.539, f1: 0.418, acc: 0.366
+2025-08-26 01:37:59: Epoch: 042 Train Loss: 0.105, ap: 0.943, auc: 0.950, f1: 0.884, acc: 0.894
+2025-08-26 01:38:00: Epoch: 042 Test results, ap: 0.297, auc: 0.544, f1: 0.421, acc: 0.374
+2025-08-26 01:38:01: Epoch: 043 Train Loss: 0.095, ap: 0.944, auc: 0.950, f1: 0.885, acc: 0.894
+2025-08-26 01:38:01: Epoch: 043 Test results, ap: 0.292, auc: 0.538, f1: 0.421, acc: 0.374
+2025-08-26 01:38:02: Epoch: 044 Train Loss: 0.091, ap: 0.943, auc: 0.950, f1: 0.870, acc: 0.882
+2025-08-26 01:38:02: Epoch: 044 Test results, ap: 0.297, auc: 0.546, f1: 0.431, acc: 0.398
+2025-08-26 01:38:04: Epoch: 045 Train Loss: 0.091, ap: 0.945, auc: 0.952, f1: 0.876, acc: 0.885
+2025-08-26 01:38:04: Epoch: 045 Test results, ap: 0.298, auc: 0.549, f1: 0.424, acc: 0.382
+2025-08-26 01:38:05: Epoch: 046 Train Loss: 0.091, ap: 0.943, auc: 0.950, f1: 0.882, acc: 0.892
+2025-08-26 01:38:05: Epoch: 046 Test results, ap: 0.299, auc: 0.548, f1: 0.424, acc: 0.382
+2025-08-26 01:38:07: Epoch: 047 Train Loss: 0.084, ap: 0.944, auc: 0.951, f1: 0.881, acc: 0.891
+2025-08-26 01:38:07: Epoch: 047 Test results, ap: 0.296, auc: 0.547, f1: 0.424, acc: 0.382
+2025-08-26 01:38:08: Epoch: 048 Train Loss: 0.084, ap: 0.944, auc: 0.951, f1: 0.884, acc: 0.894
+2025-08-26 01:38:08: Epoch: 048 Test results, ap: 0.302, auc: 0.553, f1: 0.424, acc: 0.382
+2025-08-26 01:38:09: Epoch: 049 Train Loss: 0.087, ap: 0.946, auc: 0.952, f1: 0.872, acc: 0.884
+2025-08-26 01:38:09: Epoch: 049 New best VALIDATION metrics
+2025-08-26 01:38:10: Epoch: 049 Test results, ap: 0.301, auc: 0.552, f1: 0.427, acc: 0.390
+2025-08-26 01:38:11: Epoch: 050 Train Loss: 0.080, ap: 0.945, auc: 0.951, f1: 0.881, acc: 0.891
+2025-08-26 01:38:11: Epoch: 050 Test results, ap: 0.293, auc: 0.547, f1: 0.424, acc: 0.382
+2025-08-26 01:38:11: used time 0.02h
+2025-08-26 01:38:11: Cross Validation Finished!
+2025-08-26 01:38:11: Best validation perform list
+[[0.93528348 0.9415161  0.88235295 0.88732392]
+ [0.9606486  0.96714973 0.89161557 0.90669012]
+ [0.73677343 0.76797473 0.69288391 0.71075839]
+ [0.8102361  0.83888239 0.71784234 0.76014107]
+ [0.94559801 0.95195174 0.87209302 0.88359791]]
+2025-08-26 01:38:11: mean: [0.878 0.893 0.811 0.83 ]
+2025-08-26 01:38:11: std: [0.089 0.077 0.087 0.079]
+2025-08-26 01:38:11: Best test perform list
+[[0.40374476 0.63978493 0.41221374 0.37398374]
+ [0.35016721 0.54838711 0.43548387 0.43089432]
+ [0.49343312 0.74086022 0.47933885 0.48780489]
+ [0.44495049 0.59498209 0.33802816 0.61788619]
+ [0.33385473 0.56666666 0.38759691 0.35772356]]
+2025-08-26 01:38:11: mean: [0.405 0.618 0.411 0.454]
+2025-08-26 01:38:11: std: [0.059 0.069 0.047 0.094]
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@@ -0,0 +1 @@
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diff --git a/run-cls/lstm-att-256-ce-32-0.001-50/training.log b/run-cls/lstm-att-256-ce-32-0.001-50/training.log
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+2025-08-26 00:24:03: saving_dir: ./run-cls/lstm-att-256-ce-32-0.001-50
+2025-08-26 00:24:03: Loading Train Dataset
+2025-08-26 00:24:04: Loading Test Dataset
+2025-08-26 00:24:05: Running Cross Validation 0
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+2025-08-26 00:24:06: Epoch: 001 Train Loss: 0.694, ap: 0.492, auc: 0.545, f1: 0.649, acc: 0.481
+2025-08-26 00:24:06: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:24:07: Epoch: 001 Test results, ap: 0.177, auc: 0.287, f1: 0.392, acc: 0.244
+2025-08-26 00:24:08: Epoch: 002 Train Loss: 0.695, ap: 0.494, auc: 0.527, f1: 0.649, acc: 0.481
+2025-08-26 00:24:08: Epoch: 002 Test results, ap: 0.306, auc: 0.627, f1: 0.392, acc: 0.244
+2025-08-26 00:24:09: Epoch: 003 Train Loss: 0.696, ap: 0.496, auc: 0.539, f1: 0.649, acc: 0.481
+2025-08-26 00:24:10: Epoch: 003 Test results, ap: 0.385, auc: 0.670, f1: 0.392, acc: 0.244
+2025-08-26 00:24:11: Epoch: 004 Train Loss: 0.695, ap: 0.499, auc: 0.537, f1: 0.000, acc: 0.519
+2025-08-26 00:24:11: Epoch: 004 New best VALIDATION metrics
+2025-08-26 00:24:11: Epoch: 004 Test results, ap: 0.425, auc: 0.667, f1: 0.000, acc: 0.756
+2025-08-26 00:24:12: Epoch: 005 Train Loss: 0.694, ap: 0.489, auc: 0.539, f1: 0.000, acc: 0.519
+2025-08-26 00:24:13: Epoch: 005 Test results, ap: 0.435, auc: 0.660, f1: 0.000, acc: 0.756
+2025-08-26 00:24:14: Epoch: 006 Train Loss: 0.694, ap: 0.461, auc: 0.486, f1: 0.649, acc: 0.481
+2025-08-26 00:24:14: Epoch: 006 Test results, ap: 0.169, auc: 0.260, f1: 0.392, acc: 0.244
+2025-08-26 00:24:16: Epoch: 007 Train Loss: 0.694, ap: 0.500, auc: 0.529, f1: 0.000, acc: 0.519
+2025-08-26 00:24:16: Epoch: 007 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:24:17: Epoch: 008 Train Loss: 0.694, ap: 0.451, auc: 0.468, f1: 0.000, acc: 0.519
+2025-08-26 00:24:17: Epoch: 008 Test results, ap: 0.300, auc: 0.601, f1: 0.000, acc: 0.756
+2025-08-26 00:24:19: Epoch: 009 Train Loss: 0.694, ap: 0.511, auc: 0.553, f1: 0.649, acc: 0.481
+2025-08-26 00:24:19: Epoch: 009 New best VALIDATION metrics
+2025-08-26 00:24:19: Epoch: 009 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:24:20: Epoch: 010 Train Loss: 0.694, ap: 0.499, auc: 0.520, f1: 0.000, acc: 0.519
+2025-08-26 00:24:21: Epoch: 010 Test results, ap: 0.276, auc: 0.595, f1: 0.000, acc: 0.756
+2025-08-26 00:24:22: Epoch: 011 Train Loss: 0.694, ap: 0.491, auc: 0.518, f1: 0.649, acc: 0.481
+2025-08-26 00:24:22: Epoch: 011 Test results, ap: 0.269, auc: 0.559, f1: 0.392, acc: 0.244
+2025-08-26 00:24:22: Epoch: 011 New best TEST metrics
+2025-08-26 00:24:23: Epoch: 012 Train Loss: 0.693, ap: 0.495, auc: 0.525, f1: 0.000, acc: 0.519
+2025-08-26 00:24:24: Epoch: 012 Test results, ap: 0.304, auc: 0.618, f1: 0.000, acc: 0.756
+2025-08-26 00:24:24: Epoch: 012 New best TEST metrics
+2025-08-26 00:24:25: Epoch: 013 Train Loss: 0.693, ap: 0.505, auc: 0.543, f1: 0.649, acc: 0.481
+2025-08-26 00:24:25: Epoch: 013 Test results, ap: 0.270, auc: 0.565, f1: 0.392, acc: 0.244
+2025-08-26 00:24:27: Epoch: 014 Train Loss: 0.693, ap: 0.528, auc: 0.572, f1: 0.649, acc: 0.481
+2025-08-26 00:24:27: Epoch: 014 New best VALIDATION metrics
+2025-08-26 00:24:27: Epoch: 014 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:24:28: Epoch: 015 Train Loss: 0.694, ap: 0.503, auc: 0.538, f1: 0.000, acc: 0.519
+2025-08-26 00:24:28: Epoch: 015 Test results, ap: 0.275, auc: 0.569, f1: 0.000, acc: 0.756
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+2025-08-26 00:24:31: Epoch: 017 Train Loss: 0.694, ap: 0.510, auc: 0.550, f1: 0.649, acc: 0.481
+2025-08-26 00:24:31: Epoch: 017 Test results, ap: 0.304, auc: 0.618, f1: 0.392, acc: 0.244
+2025-08-26 00:24:33: Epoch: 018 Train Loss: 0.693, ap: 0.497, auc: 0.530, f1: 0.649, acc: 0.481
+2025-08-26 00:24:33: Epoch: 018 Test results, ap: 0.248, auc: 0.511, f1: 0.392, acc: 0.244
+2025-08-26 00:24:34: Epoch: 019 Train Loss: 0.694, ap: 0.509, auc: 0.548, f1: 0.649, acc: 0.481
+2025-08-26 00:24:35: Epoch: 019 Test results, ap: 0.301, auc: 0.612, f1: 0.392, acc: 0.244
+2025-08-26 00:24:36: Epoch: 020 Train Loss: 0.693, ap: 0.496, auc: 0.529, f1: 0.649, acc: 0.481
+2025-08-26 00:24:36: Epoch: 020 Test results, ap: 0.296, auc: 0.605, f1: 0.392, acc: 0.244
+2025-08-26 00:24:37: Epoch: 021 Train Loss: 0.693, ap: 0.498, auc: 0.528, f1: 0.649, acc: 0.481
+2025-08-26 00:24:38: Epoch: 021 Test results, ap: 0.301, auc: 0.612, f1: 0.392, acc: 0.244
+2025-08-26 00:24:39: Epoch: 022 Train Loss: 0.694, ap: 0.482, auc: 0.509, f1: 0.649, acc: 0.481
+2025-08-26 00:24:39: Epoch: 022 Test results, ap: 0.285, auc: 0.585, f1: 0.392, acc: 0.244
+2025-08-26 00:24:41: Epoch: 023 Train Loss: 0.693, ap: 0.504, auc: 0.547, f1: 0.649, acc: 0.481
+2025-08-26 00:24:41: Epoch: 023 Test results, ap: 0.299, auc: 0.598, f1: 0.392, acc: 0.244
+2025-08-26 00:24:42: Epoch: 024 Train Loss: 0.693, ap: 0.493, auc: 0.526, f1: 0.649, acc: 0.481
+2025-08-26 00:24:43: Epoch: 024 Test results, ap: 0.294, auc: 0.597, f1: 0.392, acc: 0.244
+2025-08-26 00:24:44: Epoch: 025 Train Loss: 0.693, ap: 0.498, auc: 0.534, f1: 0.649, acc: 0.481
+2025-08-26 00:24:44: Epoch: 025 Test results, ap: 0.280, auc: 0.580, f1: 0.392, acc: 0.244
+2025-08-26 00:24:45: Epoch: 026 Train Loss: 0.693, ap: 0.490, auc: 0.521, f1: 0.000, acc: 0.519
+2025-08-26 00:24:46: Epoch: 026 Test results, ap: 0.298, auc: 0.606, f1: 0.000, acc: 0.756
+2025-08-26 00:24:47: Epoch: 027 Train Loss: 0.694, ap: 0.496, auc: 0.512, f1: 0.649, acc: 0.511
+2025-08-26 00:24:47: Epoch: 027 Test results, ap: 0.378, auc: 0.677, f1: 0.392, acc: 0.244
+2025-08-26 00:24:47: Epoch: 027 New best TEST metrics
+2025-08-26 00:24:49: Epoch: 028 Train Loss: 0.693, ap: 0.500, auc: 0.524, f1: 0.650, acc: 0.484
+2025-08-26 00:24:49: Epoch: 028 Test results, ap: 0.327, auc: 0.647, f1: 0.392, acc: 0.244
+2025-08-26 00:24:50: Epoch: 029 Train Loss: 0.693, ap: 0.490, auc: 0.528, f1: 0.649, acc: 0.481
+2025-08-26 00:24:50: Epoch: 029 Test results, ap: 0.430, auc: 0.700, f1: 0.392, acc: 0.244
+2025-08-26 00:24:50: Epoch: 029 New best TEST metrics
+2025-08-26 00:24:52: Epoch: 030 Train Loss: 0.694, ap: 0.479, auc: 0.524, f1: 0.000, acc: 0.519
+2025-08-26 00:24:52: Epoch: 030 Test results, ap: 0.522, auc: 0.734, f1: 0.000, acc: 0.756
+2025-08-26 00:24:52: Epoch: 030 New best TEST metrics
+2025-08-26 00:24:53: Epoch: 031 Train Loss: 0.693, ap: 0.477, auc: 0.522, f1: 0.000, acc: 0.519
+2025-08-26 00:24:54: Epoch: 031 Test results, ap: 0.610, auc: 0.786, f1: 0.000, acc: 0.756
+2025-08-26 00:24:54: Epoch: 031 New best TEST metrics
+2025-08-26 00:24:55: Epoch: 032 Train Loss: 0.692, ap: 0.503, auc: 0.568, f1: 0.621, acc: 0.593
+2025-08-26 00:24:55: Epoch: 032 Test results, ap: 0.529, auc: 0.773, f1: 0.392, acc: 0.244
+2025-08-26 00:24:56: Epoch: 033 Train Loss: 0.685, ap: 0.432, auc: 0.409, f1: 0.649, acc: 0.481
+2025-08-26 00:24:57: Epoch: 033 Test results, ap: 0.477, auc: 0.727, f1: 0.392, acc: 0.244
+2025-08-26 00:24:58: Epoch: 034 Train Loss: 0.694, ap: 0.424, auc: 0.432, f1: 0.649, acc: 0.481
+2025-08-26 00:24:58: Epoch: 034 Test results, ap: 0.255, auc: 0.377, f1: 0.392, acc: 0.244
+2025-08-26 00:25:00: Epoch: 035 Train Loss: 0.693, ap: 0.424, auc: 0.432, f1: 0.649, acc: 0.481
+2025-08-26 00:25:00: Epoch: 035 Test results, ap: 0.242, auc: 0.376, f1: 0.392, acc: 0.244
+2025-08-26 00:25:01: Epoch: 036 Train Loss: 0.693, ap: 0.424, auc: 0.432, f1: 0.649, acc: 0.481
+2025-08-26 00:25:01: Epoch: 036 Test results, ap: 0.255, auc: 0.378, f1: 0.392, acc: 0.244
+2025-08-26 00:25:03: Epoch: 037 Train Loss: 0.693, ap: 0.423, auc: 0.431, f1: 0.000, acc: 0.518
+2025-08-26 00:25:03: Epoch: 037 Test results, ap: 0.261, auc: 0.379, f1: 0.000, acc: 0.756
+2025-08-26 00:25:04: Epoch: 038 Train Loss: 0.693, ap: 0.427, auc: 0.435, f1: 0.000, acc: 0.518
+2025-08-26 00:25:04: Epoch: 038 Test results, ap: 0.266, auc: 0.380, f1: 0.000, acc: 0.756
+2025-08-26 00:25:06: Epoch: 039 Train Loss: 0.693, ap: 0.424, auc: 0.433, f1: 0.000, acc: 0.518
+2025-08-26 00:25:06: Epoch: 039 Test results, ap: 0.266, auc: 0.382, f1: 0.062, acc: 0.756
+2025-08-26 00:25:07: Epoch: 040 Train Loss: 0.693, ap: 0.425, auc: 0.432, f1: 0.649, acc: 0.481
+2025-08-26 00:25:08: Epoch: 040 Test results, ap: 0.260, auc: 0.378, f1: 0.392, acc: 0.244
+2025-08-26 00:25:09: Epoch: 041 Train Loss: 0.693, ap: 0.424, auc: 0.433, f1: 0.649, acc: 0.481
+2025-08-26 00:25:09: Epoch: 041 Test results, ap: 0.266, auc: 0.380, f1: 0.392, acc: 0.244
+2025-08-26 00:25:10: Epoch: 042 Train Loss: 0.693, ap: 0.426, auc: 0.433, f1: 0.650, acc: 0.482
+2025-08-26 00:25:11: Epoch: 042 Test results, ap: 0.292, auc: 0.481, f1: 0.392, acc: 0.244
+2025-08-26 00:25:12: Epoch: 043 Train Loss: 0.693, ap: 0.426, auc: 0.437, f1: 0.649, acc: 0.481
+2025-08-26 00:25:12: Epoch: 043 Test results, ap: 0.277, auc: 0.435, f1: 0.392, acc: 0.244
+2025-08-26 00:25:14: Epoch: 044 Train Loss: 0.694, ap: 0.421, auc: 0.412, f1: 0.649, acc: 0.481
+2025-08-26 00:25:14: Epoch: 044 Test results, ap: 0.442, auc: 0.618, f1: 0.392, acc: 0.244
+2025-08-26 00:25:15: Epoch: 045 Train Loss: 0.693, ap: 0.421, auc: 0.410, f1: 0.649, acc: 0.481
+2025-08-26 00:25:15: Epoch: 045 Test results, ap: 0.437, auc: 0.609, f1: 0.392, acc: 0.244
+2025-08-26 00:25:17: Epoch: 046 Train Loss: 0.693, ap: 0.429, auc: 0.409, f1: 0.649, acc: 0.481
+2025-08-26 00:25:17: Epoch: 046 Test results, ap: 0.471, auc: 0.725, f1: 0.392, acc: 0.244
+2025-08-26 00:25:18: Epoch: 047 Train Loss: 0.693, ap: 0.414, auc: 0.403, f1: 0.649, acc: 0.481
+2025-08-26 00:25:18: Epoch: 047 Test results, ap: 0.461, auc: 0.708, f1: 0.392, acc: 0.244
+2025-08-26 00:25:20: Epoch: 048 Train Loss: 0.693, ap: 0.469, auc: 0.533, f1: 0.649, acc: 0.481
+2025-08-26 00:25:20: Epoch: 048 Test results, ap: 0.469, auc: 0.720, f1: 0.392, acc: 0.244
+2025-08-26 00:25:21: Epoch: 049 Train Loss: 0.693, ap: 0.467, auc: 0.527, f1: 0.649, acc: 0.481
+2025-08-26 00:25:22: Epoch: 049 Test results, ap: 0.471, auc: 0.726, f1: 0.392, acc: 0.244
+2025-08-26 00:25:23: Epoch: 050 Train Loss: 0.693, ap: 0.461, auc: 0.504, f1: 0.649, acc: 0.481
+2025-08-26 00:25:23: Epoch: 050 Test results, ap: 0.467, auc: 0.717, f1: 0.392, acc: 0.244
+2025-08-26 00:25:23: used time 0.02h
+2025-08-26 00:25:23: Running Cross Validation 1
+2025-08-26 00:25:23: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 00:25:24: Epoch: 001 Train Loss: 0.694, ap: 0.501, auc: 0.545, f1: 0.000, acc: 0.563
+2025-08-26 00:25:24: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:25:25: Epoch: 001 Test results, ap: 0.287, auc: 0.571, f1: 0.000, acc: 0.756
+2025-08-26 00:25:26: Epoch: 002 Train Loss: 0.694, ap: 0.532, auc: 0.563, f1: 0.608, acc: 0.437
+2025-08-26 00:25:26: Epoch: 002 New best VALIDATION metrics
+2025-08-26 00:25:26: Epoch: 002 Test results, ap: 0.262, auc: 0.485, f1: 0.392, acc: 0.244
+2025-08-26 00:25:28: Epoch: 003 Train Loss: 0.695, ap: 0.459, auc: 0.551, f1: 0.608, acc: 0.437
+2025-08-26 00:25:28: Epoch: 003 Test results, ap: 0.531, auc: 0.735, f1: 0.392, acc: 0.244
+2025-08-26 00:25:29: Epoch: 004 Train Loss: 0.694, ap: 0.502, auc: 0.542, f1: 0.608, acc: 0.437
+2025-08-26 00:25:29: Epoch: 004 Test results, ap: 0.196, auc: 0.368, f1: 0.392, acc: 0.244
+2025-08-26 00:25:31: Epoch: 005 Train Loss: 0.695, ap: 0.455, auc: 0.545, f1: 0.309, acc: 0.542
+2025-08-26 00:25:31: Epoch: 005 Test results, ap: 0.315, auc: 0.416, f1: 0.000, acc: 0.756
+2025-08-26 00:25:32: Epoch: 006 Train Loss: 0.697, ap: 0.468, auc: 0.567, f1: 0.608, acc: 0.437
+2025-08-26 00:25:33: Epoch: 006 Test results, ap: 0.243, auc: 0.477, f1: 0.392, acc: 0.244
+2025-08-26 00:25:34: Epoch: 007 Train Loss: 0.695, ap: 0.446, auc: 0.454, f1: 0.608, acc: 0.437
+2025-08-26 00:25:34: Epoch: 007 Test results, ap: 0.272, auc: 0.494, f1: 0.392, acc: 0.244
+2025-08-26 00:25:35: Epoch: 008 Train Loss: 0.695, ap: 0.533, auc: 0.600, f1: 0.582, acc: 0.588
+2025-08-26 00:25:35: Epoch: 008 New best VALIDATION metrics
+2025-08-26 00:25:36: Epoch: 008 Test results, ap: 0.260, auc: 0.499, f1: 0.000, acc: 0.756
+2025-08-26 00:25:37: Epoch: 009 Train Loss: 0.694, ap: 0.516, auc: 0.582, f1: 0.608, acc: 0.437
+2025-08-26 00:25:37: Epoch: 009 Test results, ap: 0.264, auc: 0.537, f1: 0.392, acc: 0.244
+2025-08-26 00:25:39: Epoch: 010 Train Loss: 0.692, ap: 0.488, auc: 0.562, f1: 0.157, acc: 0.585
+2025-08-26 00:25:39: Epoch: 010 Test results, ap: 0.256, auc: 0.546, f1: 0.000, acc: 0.756
+2025-08-26 00:25:40: Epoch: 011 Train Loss: 0.693, ap: 0.500, auc: 0.578, f1: 0.000, acc: 0.563
+2025-08-26 00:25:40: Epoch: 011 Test results, ap: 0.258, auc: 0.516, f1: 0.000, acc: 0.756
+2025-08-26 00:25:40: Epoch: 011 New best TEST metrics
+2025-08-26 00:25:42: Epoch: 012 Train Loss: 0.695, ap: 0.384, auc: 0.421, f1: 0.608, acc: 0.437
+2025-08-26 00:25:42: Epoch: 012 Test results, ap: 0.276, auc: 0.493, f1: 0.392, acc: 0.244
+2025-08-26 00:25:43: Epoch: 013 Train Loss: 0.693, ap: 0.387, auc: 0.419, f1: 0.000, acc: 0.563
+2025-08-26 00:25:44: Epoch: 013 Test results, ap: 0.267, auc: 0.474, f1: 0.000, acc: 0.756
+2025-08-26 00:25:45: Epoch: 014 Train Loss: 0.694, ap: 0.385, auc: 0.423, f1: 0.608, acc: 0.437
+2025-08-26 00:25:45: Epoch: 014 Test results, ap: 0.259, auc: 0.470, f1: 0.392, acc: 0.244
+2025-08-26 00:25:46: Epoch: 015 Train Loss: 0.693, ap: 0.504, auc: 0.581, f1: 0.000, acc: 0.563
+2025-08-26 00:25:47: Epoch: 015 Test results, ap: 0.263, auc: 0.526, f1: 0.000, acc: 0.756
+2025-08-26 00:25:47: Epoch: 015 New best TEST metrics
+2025-08-26 00:25:48: Epoch: 016 Train Loss: 0.694, ap: 0.506, auc: 0.583, f1: 0.608, acc: 0.437
+2025-08-26 00:25:48: Epoch: 016 Test results, ap: 0.302, auc: 0.620, f1: 0.392, acc: 0.244
+2025-08-26 00:25:48: Epoch: 016 New best TEST metrics
+2025-08-26 00:25:50: Epoch: 017 Train Loss: 0.694, ap: 0.507, auc: 0.583, f1: 0.608, acc: 0.437
+2025-08-26 00:25:50: Epoch: 017 Test results, ap: 0.273, auc: 0.570, f1: 0.392, acc: 0.244
+2025-08-26 00:25:51: Epoch: 018 Train Loss: 0.693, ap: 0.385, auc: 0.421, f1: 0.000, acc: 0.563
+2025-08-26 00:25:51: Epoch: 018 Test results, ap: 0.264, auc: 0.491, f1: 0.000, acc: 0.756
+2025-08-26 00:25:53: Epoch: 019 Train Loss: 0.694, ap: 0.510, auc: 0.582, f1: 0.000, acc: 0.563
+2025-08-26 00:25:53: Epoch: 019 Test results, ap: 0.258, auc: 0.501, f1: 0.000, acc: 0.756
+2025-08-26 00:25:54: Epoch: 020 Train Loss: 0.694, ap: 0.506, auc: 0.579, f1: 0.000, acc: 0.563
+2025-08-26 00:25:55: Epoch: 020 Test results, ap: 0.250, auc: 0.479, f1: 0.000, acc: 0.756
+2025-08-26 00:25:56: Epoch: 021 Train Loss: 0.693, ap: 0.506, auc: 0.580, f1: 0.608, acc: 0.437
+2025-08-26 00:25:56: Epoch: 021 Test results, ap: 0.258, auc: 0.509, f1: 0.392, acc: 0.244
+2025-08-26 00:25:57: Epoch: 022 Train Loss: 0.693, ap: 0.506, auc: 0.580, f1: 0.000, acc: 0.563
+2025-08-26 00:25:58: Epoch: 022 Test results, ap: 0.247, auc: 0.481, f1: 0.000, acc: 0.756
+2025-08-26 00:25:59: Epoch: 023 Train Loss: 0.693, ap: 0.508, auc: 0.582, f1: 0.000, acc: 0.563
+2025-08-26 00:25:59: Epoch: 023 Test results, ap: 0.252, auc: 0.493, f1: 0.000, acc: 0.756
+2025-08-26 00:26:01: Epoch: 024 Train Loss: 0.693, ap: 0.508, auc: 0.583, f1: 0.608, acc: 0.437
+2025-08-26 00:26:01: Epoch: 024 Test results, ap: 0.259, auc: 0.513, f1: 0.392, acc: 0.244
+2025-08-26 00:26:02: Epoch: 025 Train Loss: 0.694, ap: 0.507, auc: 0.582, f1: 0.000, acc: 0.563
+2025-08-26 00:26:02: Epoch: 025 Test results, ap: 0.247, auc: 0.471, f1: 0.000, acc: 0.756
+2025-08-26 00:26:04: Epoch: 026 Train Loss: 0.693, ap: 0.507, auc: 0.584, f1: 0.000, acc: 0.563
+2025-08-26 00:26:04: Epoch: 026 Test results, ap: 0.256, auc: 0.490, f1: 0.000, acc: 0.756
+2025-08-26 00:26:05: Epoch: 027 Train Loss: 0.693, ap: 0.507, auc: 0.583, f1: 0.000, acc: 0.563
+2025-08-26 00:26:05: Epoch: 027 Test results, ap: 0.258, auc: 0.494, f1: 0.000, acc: 0.756
+2025-08-26 00:26:07: Epoch: 028 Train Loss: 0.694, ap: 0.506, auc: 0.583, f1: 0.608, acc: 0.437
+2025-08-26 00:26:07: Epoch: 028 Test results, ap: 0.260, auc: 0.492, f1: 0.392, acc: 0.244
+2025-08-26 00:26:08: Epoch: 029 Train Loss: 0.692, ap: 0.505, auc: 0.583, f1: 0.608, acc: 0.437
+2025-08-26 00:26:09: Epoch: 029 Test results, ap: 0.262, auc: 0.466, f1: 0.392, acc: 0.244
+2025-08-26 00:26:10: Epoch: 030 Train Loss: 0.692, ap: 0.494, auc: 0.567, f1: 0.608, acc: 0.437
+2025-08-26 00:26:10: Epoch: 030 Test results, ap: 0.242, auc: 0.431, f1: 0.392, acc: 0.244
+2025-08-26 00:26:11: Epoch: 031 Train Loss: 0.693, ap: 0.504, auc: 0.573, f1: 0.000, acc: 0.563
+2025-08-26 00:26:12: Epoch: 031 Test results, ap: 0.210, auc: 0.432, f1: 0.000, acc: 0.756
+2025-08-26 00:26:13: Epoch: 032 Train Loss: 0.694, ap: 0.497, auc: 0.571, f1: 0.105, acc: 0.579
+2025-08-26 00:26:13: Epoch: 032 Test results, ap: 0.210, auc: 0.439, f1: 0.000, acc: 0.756
+2025-08-26 00:26:15: Epoch: 033 Train Loss: 0.693, ap: 0.509, auc: 0.579, f1: 0.000, acc: 0.563
+2025-08-26 00:26:15: Epoch: 033 Test results, ap: 0.214, auc: 0.442, f1: 0.000, acc: 0.756
+2025-08-26 00:26:16: Epoch: 034 Train Loss: 0.693, ap: 0.512, auc: 0.582, f1: 0.608, acc: 0.437
+2025-08-26 00:26:16: Epoch: 034 Test results, ap: 0.227, auc: 0.463, f1: 0.392, acc: 0.244
+2025-08-26 00:26:18: Epoch: 035 Train Loss: 0.692, ap: 0.518, auc: 0.592, f1: 0.595, acc: 0.461
+2025-08-26 00:26:18: Epoch: 035 Test results, ap: 0.310, auc: 0.546, f1: 0.392, acc: 0.244
+2025-08-26 00:26:19: Epoch: 036 Train Loss: 0.692, ap: 0.517, auc: 0.594, f1: 0.592, acc: 0.592
+2025-08-26 00:26:20: Epoch: 036 Test results, ap: 0.311, auc: 0.571, f1: 0.392, acc: 0.244
+2025-08-26 00:26:21: Epoch: 037 Train Loss: 0.687, ap: 0.526, auc: 0.602, f1: 0.580, acc: 0.590
+2025-08-26 00:26:21: Epoch: 037 Test results, ap: 0.363, auc: 0.604, f1: 0.000, acc: 0.756
+2025-08-26 00:26:21: Epoch: 037 New best TEST metrics
+2025-08-26 00:26:22: Epoch: 038 Train Loss: 0.678, ap: 0.579, auc: 0.631, f1: 0.598, acc: 0.597
+2025-08-26 00:26:22: Epoch: 038 New best VALIDATION metrics
+2025-08-26 00:26:23: Epoch: 038 Test results, ap: 0.343, auc: 0.581, f1: 0.392, acc: 0.244
+2025-08-26 00:26:24: Epoch: 039 Train Loss: 0.676, ap: 0.615, auc: 0.660, f1: 0.385, acc: 0.623
+2025-08-26 00:26:24: Epoch: 039 New best VALIDATION metrics
+2025-08-26 00:26:24: Epoch: 039 Test results, ap: 0.302, auc: 0.544, f1: 0.000, acc: 0.756
+2025-08-26 00:26:26: Epoch: 040 Train Loss: 0.664, ap: 0.597, auc: 0.662, f1: 0.475, acc: 0.650
+2025-08-26 00:26:26: Epoch: 040 Test results, ap: 0.346, auc: 0.593, f1: 0.000, acc: 0.756
+2025-08-26 00:26:27: Epoch: 041 Train Loss: 0.652, ap: 0.615, auc: 0.662, f1: 0.474, acc: 0.644
+2025-08-26 00:26:27: Epoch: 041 New best VALIDATION metrics
+2025-08-26 00:26:27: Epoch: 041 Test results, ap: 0.285, auc: 0.559, f1: 0.000, acc: 0.748
+2025-08-26 00:26:29: Epoch: 042 Train Loss: 0.656, ap: 0.620, auc: 0.667, f1: 0.569, acc: 0.674
+2025-08-26 00:26:29: Epoch: 042 New best VALIDATION metrics
+2025-08-26 00:26:29: Epoch: 042 Test results, ap: 0.270, auc: 0.547, f1: 0.000, acc: 0.724
+2025-08-26 00:26:30: Epoch: 043 Train Loss: 0.656, ap: 0.621, auc: 0.668, f1: 0.591, acc: 0.664
+2025-08-26 00:26:30: Epoch: 043 New best VALIDATION metrics
+2025-08-26 00:26:31: Epoch: 043 Test results, ap: 0.271, auc: 0.548, f1: 0.000, acc: 0.740
+2025-08-26 00:26:32: Epoch: 044 Train Loss: 0.650, ap: 0.624, auc: 0.674, f1: 0.571, acc: 0.680
+2025-08-26 00:26:32: Epoch: 044 New best VALIDATION metrics
+2025-08-26 00:26:32: Epoch: 044 Test results, ap: 0.261, auc: 0.543, f1: 0.000, acc: 0.691
+2025-08-26 00:26:34: Epoch: 045 Train Loss: 0.647, ap: 0.624, auc: 0.675, f1: 0.599, acc: 0.673
+2025-08-26 00:26:34: Epoch: 045 New best VALIDATION metrics
+2025-08-26 00:26:34: Epoch: 045 Test results, ap: 0.256, auc: 0.528, f1: 0.000, acc: 0.732
+2025-08-26 00:26:35: Epoch: 046 Train Loss: 0.642, ap: 0.609, auc: 0.663, f1: 0.592, acc: 0.639
+2025-08-26 00:26:35: Epoch: 046 Test results, ap: 0.254, auc: 0.538, f1: 0.000, acc: 0.683
+2025-08-26 00:26:37: Epoch: 047 Train Loss: 0.645, ap: 0.609, auc: 0.661, f1: 0.613, acc: 0.636
+2025-08-26 00:26:37: Epoch: 047 Test results, ap: 0.247, auc: 0.526, f1: 0.000, acc: 0.659
+2025-08-26 00:26:38: Epoch: 048 Train Loss: 0.645, ap: 0.621, auc: 0.673, f1: 0.598, acc: 0.676
+2025-08-26 00:26:39: Epoch: 048 Test results, ap: 0.254, auc: 0.536, f1: 0.000, acc: 0.724
+2025-08-26 00:26:40: Epoch: 049 Train Loss: 0.645, ap: 0.620, auc: 0.672, f1: 0.585, acc: 0.678
+2025-08-26 00:26:40: Epoch: 049 Test results, ap: 0.255, auc: 0.539, f1: 0.000, acc: 0.732
+2025-08-26 00:26:41: Epoch: 050 Train Loss: 0.641, ap: 0.620, auc: 0.673, f1: 0.574, acc: 0.676
+2025-08-26 00:26:42: Epoch: 050 Test results, ap: 0.255, auc: 0.537, f1: 0.000, acc: 0.724
+2025-08-26 00:26:42: used time 0.02h
+2025-08-26 00:26:42: Running Cross Validation 2
+2025-08-26 00:26:42: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:26:43: Epoch: 001 Train Loss: 0.695, ap: 0.471, auc: 0.566, f1: 0.612, acc: 0.441
+2025-08-26 00:26:43: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:26:43: Epoch: 001 Test results, ap: 0.333, auc: 0.547, f1: 0.392, acc: 0.244
+2025-08-26 00:26:45: Epoch: 002 Train Loss: 0.693, ap: 0.480, auc: 0.564, f1: 0.612, acc: 0.441
+2025-08-26 00:26:45: Epoch: 002 New best VALIDATION metrics
+2025-08-26 00:26:45: Epoch: 002 Test results, ap: 0.243, auc: 0.497, f1: 0.392, acc: 0.244
+2025-08-26 00:26:46: Epoch: 003 Train Loss: 0.696, ap: 0.421, auc: 0.481, f1: 0.000, acc: 0.559
+2025-08-26 00:26:46: Epoch: 003 Test results, ap: 0.288, auc: 0.424, f1: 0.000, acc: 0.756
+2025-08-26 00:26:48: Epoch: 004 Train Loss: 0.694, ap: 0.478, auc: 0.558, f1: 0.000, acc: 0.559
+2025-08-26 00:26:48: Epoch: 004 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:26:49: Epoch: 005 Train Loss: 0.695, ap: 0.470, auc: 0.544, f1: 0.000, acc: 0.559
+2025-08-26 00:26:50: Epoch: 005 Test results, ap: 0.272, auc: 0.565, f1: 0.000, acc: 0.756
+2025-08-26 00:26:51: Epoch: 006 Train Loss: 0.693, ap: 0.486, auc: 0.569, f1: 0.000, acc: 0.559
+2025-08-26 00:26:51: Epoch: 006 New best VALIDATION metrics
+2025-08-26 00:26:51: Epoch: 006 Test results, ap: 0.411, auc: 0.688, f1: 0.000, acc: 0.756
+2025-08-26 00:26:53: Epoch: 007 Train Loss: 0.693, ap: 0.493, auc: 0.583, f1: 0.612, acc: 0.441
+2025-08-26 00:26:53: Epoch: 007 New best VALIDATION metrics
+2025-08-26 00:26:53: Epoch: 007 Test results, ap: 0.471, auc: 0.680, f1: 0.392, acc: 0.244
+2025-08-26 00:26:54: Epoch: 008 Train Loss: 0.694, ap: 0.420, auc: 0.483, f1: 0.000, acc: 0.559
+2025-08-26 00:26:54: Epoch: 008 Test results, ap: 0.473, auc: 0.684, f1: 0.000, acc: 0.756
+2025-08-26 00:26:56: Epoch: 009 Train Loss: 0.694, ap: 0.461, auc: 0.543, f1: 0.000, acc: 0.559
+2025-08-26 00:26:56: Epoch: 009 Test results, ap: 0.209, auc: 0.382, f1: 0.000, acc: 0.756
+2025-08-26 00:26:57: Epoch: 010 Train Loss: 0.696, ap: 0.431, auc: 0.516, f1: 0.000, acc: 0.559
+2025-08-26 00:26:57: Epoch: 010 Test results, ap: 0.310, auc: 0.584, f1: 0.000, acc: 0.756
+2025-08-26 00:26:59: Epoch: 011 Train Loss: 0.693, ap: 0.471, auc: 0.552, f1: 0.000, acc: 0.559
+2025-08-26 00:26:59: Epoch: 011 Test results, ap: 0.207, auc: 0.394, f1: 0.000, acc: 0.756
+2025-08-26 00:26:59: Epoch: 011 New best TEST metrics
+2025-08-26 00:27:00: Epoch: 012 Train Loss: 0.694, ap: 0.479, auc: 0.568, f1: 0.612, acc: 0.441
+2025-08-26 00:27:01: Epoch: 012 Test results, ap: 0.217, auc: 0.411, f1: 0.392, acc: 0.244
+2025-08-26 00:27:01: Epoch: 012 New best TEST metrics
+2025-08-26 00:27:02: Epoch: 013 Train Loss: 0.693, ap: 0.480, auc: 0.566, f1: 0.612, acc: 0.441
+2025-08-26 00:27:02: Epoch: 013 Test results, ap: 0.219, auc: 0.409, f1: 0.392, acc: 0.244
+2025-08-26 00:27:03: Epoch: 014 Train Loss: 0.693, ap: 0.483, auc: 0.568, f1: 0.612, acc: 0.441
+2025-08-26 00:27:04: Epoch: 014 Test results, ap: 0.213, auc: 0.407, f1: 0.392, acc: 0.244
+2025-08-26 00:27:05: Epoch: 015 Train Loss: 0.693, ap: 0.482, auc: 0.557, f1: 0.000, acc: 0.559
+2025-08-26 00:27:05: Epoch: 015 Test results, ap: 0.224, auc: 0.427, f1: 0.000, acc: 0.756
+2025-08-26 00:27:05: Epoch: 015 New best TEST metrics
+2025-08-26 00:27:07: Epoch: 016 Train Loss: 0.693, ap: 0.469, auc: 0.542, f1: 0.612, acc: 0.441
+2025-08-26 00:27:07: Epoch: 016 Test results, ap: 0.223, auc: 0.425, f1: 0.392, acc: 0.244
+2025-08-26 00:27:08: Epoch: 017 Train Loss: 0.693, ap: 0.470, auc: 0.546, f1: 0.612, acc: 0.441
+2025-08-26 00:27:08: Epoch: 017 Test results, ap: 0.275, auc: 0.453, f1: 0.392, acc: 0.244
+2025-08-26 00:27:08: Epoch: 017 New best TEST metrics
+2025-08-26 00:27:10: Epoch: 018 Train Loss: 0.694, ap: 0.482, auc: 0.562, f1: 0.000, acc: 0.559
+2025-08-26 00:27:10: Epoch: 018 Test results, ap: 0.259, auc: 0.538, f1: 0.000, acc: 0.756
+2025-08-26 00:27:10: Epoch: 018 New best TEST metrics
+2025-08-26 00:27:11: Epoch: 019 Train Loss: 0.693, ap: 0.469, auc: 0.548, f1: 0.612, acc: 0.441
+2025-08-26 00:27:12: Epoch: 019 Test results, ap: 0.270, auc: 0.551, f1: 0.392, acc: 0.244
+2025-08-26 00:27:12: Epoch: 019 New best TEST metrics
+2025-08-26 00:27:13: Epoch: 020 Train Loss: 0.693, ap: 0.515, auc: 0.584, f1: 0.612, acc: 0.441
+2025-08-26 00:27:13: Epoch: 020 New best VALIDATION metrics
+2025-08-26 00:27:13: Epoch: 020 Test results, ap: 0.244, auc: 0.484, f1: 0.392, acc: 0.244
+2025-08-26 00:27:15: Epoch: 021 Train Loss: 0.693, ap: 0.507, auc: 0.590, f1: 0.000, acc: 0.559
+2025-08-26 00:27:15: Epoch: 021 Test results, ap: 0.251, auc: 0.518, f1: 0.000, acc: 0.756
+2025-08-26 00:27:16: Epoch: 022 Train Loss: 0.693, ap: 0.479, auc: 0.582, f1: 0.000, acc: 0.559
+2025-08-26 00:27:16: Epoch: 022 Test results, ap: 0.518, auc: 0.650, f1: 0.000, acc: 0.756
+2025-08-26 00:27:16: Epoch: 022 New best TEST metrics
+2025-08-26 00:27:18: Epoch: 023 Train Loss: 0.694, ap: 0.441, auc: 0.491, f1: 0.000, acc: 0.559
+2025-08-26 00:27:18: Epoch: 023 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:27:19: Epoch: 024 Train Loss: 0.695, ap: 0.436, auc: 0.484, f1: 0.000, acc: 0.559
+2025-08-26 00:27:20: Epoch: 024 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:27:21: Epoch: 025 Train Loss: 0.694, ap: 0.440, auc: 0.493, f1: 0.612, acc: 0.441
+2025-08-26 00:27:21: Epoch: 025 Test results, ap: 0.238, auc: 0.420, f1: 0.392, acc: 0.244
+2025-08-26 00:27:22: Epoch: 026 Train Loss: 0.694, ap: 0.461, auc: 0.487, f1: 0.000, acc: 0.559
+2025-08-26 00:27:23: Epoch: 026 Test results, ap: 0.249, auc: 0.513, f1: 0.000, acc: 0.756
+2025-08-26 00:27:24: Epoch: 027 Train Loss: 0.694, ap: 0.477, auc: 0.534, f1: 0.000, acc: 0.559
+2025-08-26 00:27:24: Epoch: 027 Test results, ap: 0.276, auc: 0.566, f1: 0.000, acc: 0.756
+2025-08-26 00:27:26: Epoch: 028 Train Loss: 0.693, ap: 0.448, auc: 0.511, f1: 0.000, acc: 0.559
+2025-08-26 00:27:26: Epoch: 028 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:27:27: Epoch: 029 Train Loss: 0.693, ap: 0.457, auc: 0.517, f1: 0.612, acc: 0.441
+2025-08-26 00:27:27: Epoch: 029 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:29: Epoch: 030 Train Loss: 0.693, ap: 0.454, auc: 0.516, f1: 0.000, acc: 0.559
+2025-08-26 00:27:29: Epoch: 030 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:27:30: Epoch: 031 Train Loss: 0.694, ap: 0.445, auc: 0.504, f1: 0.612, acc: 0.441
+2025-08-26 00:27:31: Epoch: 031 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:32: Epoch: 032 Train Loss: 0.694, ap: 0.441, auc: 0.500, f1: 0.612, acc: 0.441
+2025-08-26 00:27:32: Epoch: 032 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:33: Epoch: 033 Train Loss: 0.693, ap: 0.439, auc: 0.494, f1: 0.612, acc: 0.441
+2025-08-26 00:27:34: Epoch: 033 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:35: Epoch: 034 Train Loss: 0.693, ap: 0.441, auc: 0.500, f1: 0.612, acc: 0.441
+2025-08-26 00:27:35: Epoch: 034 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:37: Epoch: 035 Train Loss: 0.693, ap: 0.452, auc: 0.513, f1: 0.612, acc: 0.441
+2025-08-26 00:27:37: Epoch: 035 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:38: Epoch: 036 Train Loss: 0.694, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.559
+2025-08-26 00:27:38: Epoch: 036 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:27:40: Epoch: 037 Train Loss: 0.694, ap: 0.452, auc: 0.515, f1: 0.612, acc: 0.441
+2025-08-26 00:27:40: Epoch: 037 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:41: Epoch: 038 Train Loss: 0.694, ap: 0.441, auc: 0.500, f1: 0.612, acc: 0.441
+2025-08-26 00:27:41: Epoch: 038 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:43: Epoch: 039 Train Loss: 0.693, ap: 0.441, auc: 0.500, f1: 0.612, acc: 0.441
+2025-08-26 00:27:43: Epoch: 039 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:44: Epoch: 040 Train Loss: 0.694, ap: 0.441, auc: 0.500, f1: 0.612, acc: 0.441
+2025-08-26 00:27:45: Epoch: 040 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:46: Epoch: 041 Train Loss: 0.693, ap: 0.456, auc: 0.518, f1: 0.612, acc: 0.441
+2025-08-26 00:27:46: Epoch: 041 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:47: Epoch: 042 Train Loss: 0.693, ap: 0.441, auc: 0.498, f1: 0.000, acc: 0.559
+2025-08-26 00:27:48: Epoch: 042 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:27:49: Epoch: 043 Train Loss: 0.693, ap: 0.441, auc: 0.500, f1: 0.000, acc: 0.559
+2025-08-26 00:27:49: Epoch: 043 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:27:51: Epoch: 044 Train Loss: 0.693, ap: 0.441, auc: 0.500, f1: 0.612, acc: 0.441
+2025-08-26 00:27:51: Epoch: 044 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:52: Epoch: 045 Train Loss: 0.693, ap: 0.441, auc: 0.500, f1: 0.000, acc: 0.559
+2025-08-26 00:27:52: Epoch: 045 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:27:54: Epoch: 046 Train Loss: 0.693, ap: 0.440, auc: 0.499, f1: 0.000, acc: 0.559
+2025-08-26 00:27:54: Epoch: 046 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:27:55: Epoch: 047 Train Loss: 0.693, ap: 0.441, auc: 0.500, f1: 0.612, acc: 0.441
+2025-08-26 00:27:56: Epoch: 047 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:27:57: Epoch: 048 Train Loss: 0.693, ap: 0.441, auc: 0.500, f1: 0.000, acc: 0.559
+2025-08-26 00:27:57: Epoch: 048 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:27:58: Epoch: 049 Train Loss: 0.693, ap: 0.441, auc: 0.500, f1: 0.000, acc: 0.559
+2025-08-26 00:27:59: Epoch: 049 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:28:00: Epoch: 050 Train Loss: 0.693, ap: 0.441, auc: 0.500, f1: 0.612, acc: 0.441
+2025-08-26 00:28:00: Epoch: 050 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:28:00: used time 0.02h
+2025-08-26 00:28:00: Running Cross Validation 3
+2025-08-26 00:28:00: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:28:02: Epoch: 001 Train Loss: 0.695, ap: 0.420, auc: 0.481, f1: 0.000, acc: 0.570
+2025-08-26 00:28:02: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:28:02: Epoch: 001 Test results, ap: 0.244, auc: 0.478, f1: 0.000, acc: 0.756
+2025-08-26 00:28:03: Epoch: 002 Train Loss: 0.694, ap: 0.449, auc: 0.523, f1: 0.602, acc: 0.430
+2025-08-26 00:28:03: Epoch: 002 New best VALIDATION metrics
+2025-08-26 00:28:03: Epoch: 002 Test results, ap: 0.286, auc: 0.592, f1: 0.392, acc: 0.244
+2025-08-26 00:28:05: Epoch: 003 Train Loss: 0.695, ap: 0.449, auc: 0.536, f1: 0.602, acc: 0.430
+2025-08-26 00:28:05: Epoch: 003 New best VALIDATION metrics
+2025-08-26 00:28:05: Epoch: 003 Test results, ap: 0.335, auc: 0.655, f1: 0.392, acc: 0.244
+2025-08-26 00:28:06: Epoch: 004 Train Loss: 0.694, ap: 0.458, auc: 0.549, f1: 0.602, acc: 0.430
+2025-08-26 00:28:06: Epoch: 004 New best VALIDATION metrics
+2025-08-26 00:28:07: Epoch: 004 Test results, ap: 0.226, auc: 0.445, f1: 0.392, acc: 0.244
+2025-08-26 00:28:08: Epoch: 005 Train Loss: 0.696, ap: 0.464, auc: 0.560, f1: 0.000, acc: 0.570
+2025-08-26 00:28:08: Epoch: 005 New best VALIDATION metrics
+2025-08-26 00:28:08: Epoch: 005 Test results, ap: 0.286, auc: 0.575, f1: 0.000, acc: 0.756
+2025-08-26 00:28:10: Epoch: 006 Train Loss: 0.695, ap: 0.451, auc: 0.540, f1: 0.602, acc: 0.430
+2025-08-26 00:28:10: Epoch: 006 Test results, ap: 0.206, auc: 0.362, f1: 0.392, acc: 0.244
+2025-08-26 00:28:11: Epoch: 007 Train Loss: 0.694, ap: 0.467, auc: 0.558, f1: 0.602, acc: 0.430
+2025-08-26 00:28:11: Epoch: 007 New best VALIDATION metrics
+2025-08-26 00:28:11: Epoch: 007 Test results, ap: 0.320, auc: 0.611, f1: 0.392, acc: 0.244
+2025-08-26 00:28:13: Epoch: 008 Train Loss: 0.695, ap: 0.461, auc: 0.549, f1: 0.602, acc: 0.430
+2025-08-26 00:28:13: Epoch: 008 Test results, ap: 0.243, auc: 0.449, f1: 0.392, acc: 0.244
+2025-08-26 00:28:14: Epoch: 009 Train Loss: 0.694, ap: 0.451, auc: 0.523, f1: 0.000, acc: 0.570
+2025-08-26 00:28:15: Epoch: 009 Test results, ap: 0.438, auc: 0.718, f1: 0.000, acc: 0.756
+2025-08-26 00:28:16: Epoch: 010 Train Loss: 0.693, ap: 0.482, auc: 0.581, f1: 0.000, acc: 0.570
+2025-08-26 00:28:16: Epoch: 010 New best VALIDATION metrics
+2025-08-26 00:28:16: Epoch: 010 Test results, ap: 0.243, auc: 0.492, f1: 0.000, acc: 0.756
+2025-08-26 00:28:18: Epoch: 011 Train Loss: 0.694, ap: 0.451, auc: 0.543, f1: 0.602, acc: 0.430
+2025-08-26 00:28:18: Epoch: 011 Test results, ap: 0.232, auc: 0.466, f1: 0.392, acc: 0.244
+2025-08-26 00:28:18: Epoch: 011 New best TEST metrics
+2025-08-26 00:28:19: Epoch: 012 Train Loss: 0.694, ap: 0.407, auc: 0.438, f1: 0.000, acc: 0.570
+2025-08-26 00:28:19: Epoch: 012 Test results, ap: 0.315, auc: 0.650, f1: 0.000, acc: 0.756
+2025-08-26 00:28:19: Epoch: 012 New best TEST metrics
+2025-08-26 00:28:21: Epoch: 013 Train Loss: 0.694, ap: 0.401, auc: 0.434, f1: 0.000, acc: 0.570
+2025-08-26 00:28:21: Epoch: 013 Test results, ap: 0.313, auc: 0.646, f1: 0.000, acc: 0.756
+2025-08-26 00:28:22: Epoch: 014 Train Loss: 0.694, ap: 0.464, auc: 0.561, f1: 0.602, acc: 0.430
+2025-08-26 00:28:23: Epoch: 014 Test results, ap: 0.201, auc: 0.389, f1: 0.392, acc: 0.244
+2025-08-26 00:28:24: Epoch: 015 Train Loss: 0.693, ap: 0.471, auc: 0.565, f1: 0.604, acc: 0.489
+2025-08-26 00:28:24: Epoch: 015 Test results, ap: 0.281, auc: 0.593, f1: 0.392, acc: 0.244
+2025-08-26 00:28:25: Epoch: 016 Train Loss: 0.694, ap: 0.396, auc: 0.467, f1: 0.599, acc: 0.432
+2025-08-26 00:28:26: Epoch: 016 Test results, ap: 0.197, auc: 0.383, f1: 0.392, acc: 0.244
+2025-08-26 00:28:27: Epoch: 017 Train Loss: 0.696, ap: 0.472, auc: 0.573, f1: 0.110, acc: 0.543
+2025-08-26 00:28:27: Epoch: 017 Test results, ap: 0.233, auc: 0.444, f1: 0.000, acc: 0.756
+2025-08-26 00:28:29: Epoch: 018 Train Loss: 0.694, ap: 0.447, auc: 0.498, f1: 0.531, acc: 0.451
+2025-08-26 00:28:29: Epoch: 018 Test results, ap: 0.315, auc: 0.581, f1: 0.392, acc: 0.244
+2025-08-26 00:28:30: Epoch: 019 Train Loss: 0.693, ap: 0.449, auc: 0.516, f1: 0.520, acc: 0.460
+2025-08-26 00:28:30: Epoch: 019 Test results, ap: 0.309, auc: 0.567, f1: 0.412, acc: 0.350
+2025-08-26 00:28:32: Epoch: 020 Train Loss: 0.694, ap: 0.459, auc: 0.534, f1: 0.599, acc: 0.457
+2025-08-26 00:28:32: Epoch: 020 Test results, ap: 0.307, auc: 0.561, f1: 0.392, acc: 0.244
+2025-08-26 00:28:33: Epoch: 021 Train Loss: 0.692, ap: 0.507, auc: 0.611, f1: 0.506, acc: 0.577
+2025-08-26 00:28:33: Epoch: 021 New best VALIDATION metrics
+2025-08-26 00:28:33: Epoch: 021 Test results, ap: 0.245, auc: 0.502, f1: 0.000, acc: 0.756
+2025-08-26 00:28:35: Epoch: 022 Train Loss: 0.684, ap: 0.529, auc: 0.622, f1: 0.330, acc: 0.600
+2025-08-26 00:28:35: Epoch: 022 New best VALIDATION metrics
+2025-08-26 00:28:35: Epoch: 022 Test results, ap: 0.192, auc: 0.370, f1: 0.000, acc: 0.756
+2025-08-26 00:28:36: Epoch: 023 Train Loss: 0.677, ap: 0.534, auc: 0.608, f1: 0.513, acc: 0.552
+2025-08-26 00:28:37: Epoch: 023 Test results, ap: 0.178, auc: 0.323, f1: 0.000, acc: 0.756
+2025-08-26 00:28:38: Epoch: 024 Train Loss: 0.665, ap: 0.520, auc: 0.592, f1: 0.410, acc: 0.614
+2025-08-26 00:28:38: Epoch: 024 Test results, ap: 0.376, auc: 0.684, f1: 0.000, acc: 0.756
+2025-08-26 00:28:38: Epoch: 024 New best TEST metrics
+2025-08-26 00:28:40: Epoch: 025 Train Loss: 0.665, ap: 0.570, auc: 0.603, f1: 0.578, acc: 0.598
+2025-08-26 00:28:40: Epoch: 025 New best VALIDATION metrics
+2025-08-26 00:28:40: Epoch: 025 Test results, ap: 0.350, auc: 0.665, f1: 0.392, acc: 0.244
+2025-08-26 00:28:41: Epoch: 026 Train Loss: 0.658, ap: 0.571, auc: 0.650, f1: 0.416, acc: 0.619
+2025-08-26 00:28:41: Epoch: 026 New best VALIDATION metrics
+2025-08-26 00:28:41: Epoch: 026 Test results, ap: 0.183, auc: 0.340, f1: 0.000, acc: 0.756
+2025-08-26 00:28:43: Epoch: 027 Train Loss: 0.661, ap: 0.575, auc: 0.666, f1: 0.564, acc: 0.607
+2025-08-26 00:28:43: Epoch: 027 New best VALIDATION metrics
+2025-08-26 00:28:43: Epoch: 027 Test results, ap: 0.184, auc: 0.348, f1: 0.273, acc: 0.350
+2025-08-26 00:28:44: Epoch: 028 Train Loss: 0.654, ap: 0.539, auc: 0.647, f1: 0.500, acc: 0.584
+2025-08-26 00:28:45: Epoch: 028 Test results, ap: 0.187, auc: 0.348, f1: 0.000, acc: 0.756
+2025-08-26 00:28:46: Epoch: 029 Train Loss: 0.664, ap: 0.587, auc: 0.672, f1: 0.434, acc: 0.614
+2025-08-26 00:28:46: Epoch: 029 New best VALIDATION metrics
+2025-08-26 00:28:46: Epoch: 029 Test results, ap: 0.187, auc: 0.356, f1: 0.000, acc: 0.756
+2025-08-26 00:28:48: Epoch: 030 Train Loss: 0.655, ap: 0.579, auc: 0.618, f1: 0.512, acc: 0.580
+2025-08-26 00:28:48: Epoch: 030 Test results, ap: 0.268, auc: 0.503, f1: 0.389, acc: 0.285
+2025-08-26 00:28:49: Epoch: 031 Train Loss: 0.651, ap: 0.589, auc: 0.665, f1: 0.561, acc: 0.619
+2025-08-26 00:28:49: Epoch: 031 Test results, ap: 0.198, auc: 0.359, f1: 0.135, acc: 0.480
+2025-08-26 00:28:51: Epoch: 032 Train Loss: 0.644, ap: 0.557, auc: 0.672, f1: 0.504, acc: 0.635
+2025-08-26 00:28:51: Epoch: 032 Test results, ap: 0.192, auc: 0.354, f1: 0.000, acc: 0.756
+2025-08-26 00:28:52: Epoch: 033 Train Loss: 0.650, ap: 0.549, auc: 0.663, f1: 0.525, acc: 0.610
+2025-08-26 00:28:52: Epoch: 033 Test results, ap: 0.189, auc: 0.350, f1: 0.000, acc: 0.756
+2025-08-26 00:28:54: Epoch: 034 Train Loss: 0.644, ap: 0.604, auc: 0.678, f1: 0.565, acc: 0.619
+2025-08-26 00:28:54: Epoch: 034 New best VALIDATION metrics
+2025-08-26 00:28:54: Epoch: 034 Test results, ap: 0.230, auc: 0.358, f1: 0.125, acc: 0.545
+2025-08-26 00:28:55: Epoch: 035 Train Loss: 0.643, ap: 0.556, auc: 0.667, f1: 0.587, acc: 0.633
+2025-08-26 00:28:56: Epoch: 035 Test results, ap: 0.199, auc: 0.358, f1: 0.108, acc: 0.732
+2025-08-26 00:28:57: Epoch: 036 Train Loss: 0.645, ap: 0.575, auc: 0.674, f1: 0.624, acc: 0.628
+2025-08-26 00:28:57: Epoch: 036 Test results, ap: 0.214, auc: 0.358, f1: 0.200, acc: 0.480
+2025-08-26 00:28:59: Epoch: 037 Train Loss: 0.646, ap: 0.612, auc: 0.673, f1: 0.575, acc: 0.619
+2025-08-26 00:28:59: Epoch: 037 New best VALIDATION metrics
+2025-08-26 00:28:59: Epoch: 037 Test results, ap: 0.259, auc: 0.369, f1: 0.169, acc: 0.439
+2025-08-26 00:29:00: Epoch: 038 Train Loss: 0.644, ap: 0.586, auc: 0.674, f1: 0.490, acc: 0.640
+2025-08-26 00:29:00: Epoch: 038 Test results, ap: 0.217, auc: 0.368, f1: 0.000, acc: 0.748
+2025-08-26 00:29:02: Epoch: 039 Train Loss: 0.643, ap: 0.600, auc: 0.674, f1: 0.589, acc: 0.614
+2025-08-26 00:29:02: Epoch: 039 Test results, ap: 0.255, auc: 0.367, f1: 0.182, acc: 0.415
+2025-08-26 00:29:03: Epoch: 040 Train Loss: 0.641, ap: 0.582, auc: 0.666, f1: 0.573, acc: 0.610
+2025-08-26 00:29:04: Epoch: 040 Test results, ap: 0.260, auc: 0.371, f1: 0.171, acc: 0.447
+2025-08-26 00:29:05: Epoch: 041 Train Loss: 0.639, ap: 0.571, auc: 0.677, f1: 0.600, acc: 0.653
+2025-08-26 00:29:05: Epoch: 041 Test results, ap: 0.207, auc: 0.368, f1: 0.095, acc: 0.691
+2025-08-26 00:29:06: Epoch: 042 Train Loss: 0.637, ap: 0.606, auc: 0.670, f1: 0.579, acc: 0.626
+2025-08-26 00:29:07: Epoch: 042 Test results, ap: 0.265, auc: 0.373, f1: 0.167, acc: 0.512
+2025-08-26 00:29:08: Epoch: 043 Train Loss: 0.635, ap: 0.605, auc: 0.679, f1: 0.615, acc: 0.631
+2025-08-26 00:29:08: Epoch: 043 Test results, ap: 0.254, auc: 0.370, f1: 0.127, acc: 0.553
+2025-08-26 00:29:10: Epoch: 044 Train Loss: 0.637, ap: 0.612, auc: 0.676, f1: 0.580, acc: 0.624
+2025-08-26 00:29:10: Epoch: 044 New best VALIDATION metrics
+2025-08-26 00:29:10: Epoch: 044 Test results, ap: 0.254, auc: 0.370, f1: 0.157, acc: 0.650
+2025-08-26 00:29:11: Epoch: 045 Train Loss: 0.638, ap: 0.614, auc: 0.679, f1: 0.590, acc: 0.628
+2025-08-26 00:29:11: Epoch: 045 New best VALIDATION metrics
+2025-08-26 00:29:11: Epoch: 045 Test results, ap: 0.255, auc: 0.371, f1: 0.154, acc: 0.642
+2025-08-26 00:29:13: Epoch: 046 Train Loss: 0.635, ap: 0.578, auc: 0.679, f1: 0.629, acc: 0.642
+2025-08-26 00:29:13: Epoch: 046 Test results, ap: 0.226, auc: 0.367, f1: 0.138, acc: 0.593
+2025-08-26 00:29:14: Epoch: 047 Train Loss: 0.637, ap: 0.594, auc: 0.678, f1: 0.587, acc: 0.628
+2025-08-26 00:29:15: Epoch: 047 Test results, ap: 0.227, auc: 0.370, f1: 0.136, acc: 0.691
+2025-08-26 00:29:16: Epoch: 048 Train Loss: 0.634, ap: 0.539, auc: 0.614, f1: 0.570, acc: 0.577
+2025-08-26 00:29:16: Epoch: 048 Test results, ap: 0.248, auc: 0.367, f1: 0.358, acc: 0.301
+2025-08-26 00:29:17: Epoch: 049 Train Loss: 0.640, ap: 0.588, auc: 0.661, f1: 0.585, acc: 0.624
+2025-08-26 00:29:18: Epoch: 049 Test results, ap: 0.279, auc: 0.373, f1: 0.220, acc: 0.423
+2025-08-26 00:29:19: Epoch: 050 Train Loss: 0.639, ap: 0.576, auc: 0.678, f1: 0.547, acc: 0.640
+2025-08-26 00:29:19: Epoch: 050 Test results, ap: 0.210, auc: 0.369, f1: 0.114, acc: 0.748
+2025-08-26 00:29:19: used time 0.02h
+2025-08-26 00:29:19: Running Cross Validation 4
+2025-08-26 00:29:19: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:29:20: Epoch: 001 Train Loss: 0.695, ap: 0.440, auc: 0.513, f1: 0.614, acc: 0.443
+2025-08-26 00:29:20: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:29:21: Epoch: 001 Test results, ap: 0.491, auc: 0.695, f1: 0.392, acc: 0.244
+2025-08-26 00:29:22: Epoch: 002 Train Loss: 0.694, ap: 0.439, auc: 0.491, f1: 0.000, acc: 0.557
+2025-08-26 00:29:22: Epoch: 002 Test results, ap: 0.240, auc: 0.485, f1: 0.000, acc: 0.756
+2025-08-26 00:29:24: Epoch: 003 Train Loss: 0.695, ap: 0.429, auc: 0.474, f1: 0.614, acc: 0.443
+2025-08-26 00:29:24: Epoch: 003 Test results, ap: 0.234, auc: 0.465, f1: 0.392, acc: 0.244
+2025-08-26 00:29:25: Epoch: 004 Train Loss: 0.694, ap: 0.438, auc: 0.490, f1: 0.614, acc: 0.443
+2025-08-26 00:29:25: Epoch: 004 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:29:27: Epoch: 005 Train Loss: 0.696, ap: 0.447, auc: 0.508, f1: 0.000, acc: 0.557
+2025-08-26 00:29:27: Epoch: 005 New best VALIDATION metrics
+2025-08-26 00:29:27: Epoch: 005 Test results, ap: 0.244, auc: 0.489, f1: 0.000, acc: 0.756
+2025-08-26 00:29:28: Epoch: 006 Train Loss: 0.694, ap: 0.435, auc: 0.484, f1: 0.614, acc: 0.443
+2025-08-26 00:29:28: Epoch: 006 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:29:30: Epoch: 007 Train Loss: 0.694, ap: 0.436, auc: 0.483, f1: 0.000, acc: 0.557
+2025-08-26 00:29:30: Epoch: 007 Test results, ap: 0.247, auc: 0.506, f1: 0.000, acc: 0.756
+2025-08-26 00:29:31: Epoch: 008 Train Loss: 0.695, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:29:31: Epoch: 008 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:29:33: Epoch: 009 Train Loss: 0.693, ap: 0.468, auc: 0.537, f1: 0.000, acc: 0.557
+2025-08-26 00:29:33: Epoch: 009 New best VALIDATION metrics
+2025-08-26 00:29:33: Epoch: 009 Test results, ap: 0.247, auc: 0.508, f1: 0.000, acc: 0.756
+2025-08-26 00:29:34: Epoch: 010 Train Loss: 0.695, ap: 0.443, auc: 0.498, f1: 0.000, acc: 0.557
+2025-08-26 00:29:35: Epoch: 010 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:29:36: Epoch: 011 Train Loss: 0.694, ap: 0.443, auc: 0.498, f1: 0.000, acc: 0.557
+2025-08-26 00:29:36: Epoch: 011 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:29:36: Epoch: 011 New best TEST metrics
+2025-08-26 00:29:37: Epoch: 012 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:29:38: Epoch: 012 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:29:39: Epoch: 013 Train Loss: 0.694, ap: 0.443, auc: 0.498, f1: 0.000, acc: 0.557
+2025-08-26 00:29:39: Epoch: 013 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:29:40: Epoch: 014 Train Loss: 0.694, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:29:41: Epoch: 014 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:29:42: Epoch: 015 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:29:42: Epoch: 015 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:29:44: Epoch: 016 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:29:44: Epoch: 016 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:29:45: Epoch: 017 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:29:45: Epoch: 017 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:29:47: Epoch: 018 Train Loss: 0.694, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:29:47: Epoch: 018 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:29:48: Epoch: 019 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:29:48: Epoch: 019 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:29:50: Epoch: 020 Train Loss: 0.694, ap: 0.444, auc: 0.503, f1: 0.000, acc: 0.557
+2025-08-26 00:29:50: Epoch: 020 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:29:51: Epoch: 021 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:29:51: Epoch: 021 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:29:53: Epoch: 022 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:29:53: Epoch: 022 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:29:54: Epoch: 023 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:29:55: Epoch: 023 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:29:56: Epoch: 024 Train Loss: 0.694, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:29:56: Epoch: 024 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:29:57: Epoch: 025 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:29:58: Epoch: 025 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:29:59: Epoch: 026 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:29:59: Epoch: 026 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:00: Epoch: 027 Train Loss: 0.694, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:30:01: Epoch: 027 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:02: Epoch: 028 Train Loss: 0.692, ap: 0.443, auc: 0.498, f1: 0.614, acc: 0.443
+2025-08-26 00:30:02: Epoch: 028 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:04: Epoch: 029 Train Loss: 0.694, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:30:04: Epoch: 029 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:05: Epoch: 030 Train Loss: 0.694, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:30:05: Epoch: 030 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:07: Epoch: 031 Train Loss: 0.694, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:30:07: Epoch: 031 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:30:08: Epoch: 032 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:30:08: Epoch: 032 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:10: Epoch: 033 Train Loss: 0.694, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:30:10: Epoch: 033 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:30:11: Epoch: 034 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:30:11: Epoch: 034 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:13: Epoch: 035 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:30:13: Epoch: 035 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:30:14: Epoch: 036 Train Loss: 0.694, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:30:15: Epoch: 036 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:30:16: Epoch: 037 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:30:16: Epoch: 037 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:17: Epoch: 038 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:30:18: Epoch: 038 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:30:19: Epoch: 039 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:30:19: Epoch: 039 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:30:21: Epoch: 040 Train Loss: 0.693, ap: 0.443, auc: 0.500, f1: 0.614, acc: 0.443
+2025-08-26 00:30:21: Epoch: 040 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:22: Epoch: 041 Train Loss: 0.693, ap: 0.443, auc: 0.500, f1: 0.614, acc: 0.443
+2025-08-26 00:30:22: Epoch: 041 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:24: Epoch: 042 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:30:24: Epoch: 042 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:30:25: Epoch: 043 Train Loss: 0.693, ap: 0.443, auc: 0.500, f1: 0.614, acc: 0.443
+2025-08-26 00:30:25: Epoch: 043 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:27: Epoch: 044 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:30:27: Epoch: 044 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:30:28: Epoch: 045 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.000, acc: 0.557
+2025-08-26 00:30:28: Epoch: 045 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:30:30: Epoch: 046 Train Loss: 0.694, ap: 0.443, auc: 0.500, f1: 0.614, acc: 0.443
+2025-08-26 00:30:30: Epoch: 046 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:31: Epoch: 047 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:30:32: Epoch: 047 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:33: Epoch: 048 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:30:33: Epoch: 048 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:34: Epoch: 049 Train Loss: 0.693, ap: 0.432, auc: 0.477, f1: 0.000, acc: 0.557
+2025-08-26 00:30:35: Epoch: 049 Test results, ap: 0.244, auc: 0.501, f1: 0.000, acc: 0.756
+2025-08-26 00:30:35: Epoch: 049 New best TEST metrics
+2025-08-26 00:30:36: Epoch: 050 Train Loss: 0.693, ap: 0.443, auc: 0.500, f1: 0.614, acc: 0.443
+2025-08-26 00:30:36: Epoch: 050 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:30:36: used time 0.02h
+2025-08-26 00:30:36: Cross Validation Finished!
+2025-08-26 00:30:36: Best validation perform list
+[[0.52830774 0.57219225 0.64922708 0.4806338 ]
+ [0.62357605 0.67511344 0.5991379  0.67253524]
+ [0.51538432 0.58351415 0.6119951  0.4409171 ]
+ [0.61363232 0.67935085 0.59029126 0.62786597]
+ [0.46764237 0.53748298 0.         0.55731922]]
+2025-08-26 00:30:36: mean: [0.55  0.61  0.49  0.556]
+2025-08-26 00:30:36: std: [0.06  0.057 0.246 0.087]
+2025-08-26 00:30:36: Best test perform list
+[[0.61041307 0.78602147 0.         0.75609756]
+ [0.36275488 0.60358423 0.         0.75609756]
+ [0.51786554 0.6498208  0.         0.75609756]
+ [0.37592697 0.68387097 0.         0.75609756]
+ [0.24410118 0.50053763 0.         0.75609756]]
+2025-08-26 00:30:36: mean: [0.422 0.645 0.    0.756]
+2025-08-26 00:30:36: std: [0.128 0.094 0.    0.   ]
diff --git a/run-cls/lstm-diff-256-ce-32-0.001-50/config.json b/run-cls/lstm-diff-256-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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diff --git a/run-cls/lstm-diff-256-ce-32-0.001-50/result.txt b/run-cls/lstm-diff-256-ce-32-0.001-50/result.txt
new file mode 100644
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+++ b/run-cls/lstm-diff-256-ce-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
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diff --git a/run-cls/lstm-diff-256-ce-32-0.001-50/training.log b/run-cls/lstm-diff-256-ce-32-0.001-50/training.log
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+2025-08-26 00:30:41: saving_dir: ./run-cls/lstm-diff-256-ce-32-0.001-50
+2025-08-26 00:30:41: Loading Train Dataset
+2025-08-26 00:30:41: Loading Test Dataset
+2025-08-26 00:30:42: Running Cross Validation 0
+2025-08-26 00:30:42: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 00:30:44: Epoch: 001 Train Loss: 0.693, ap: 0.471, auc: 0.467, f1: 0.649, acc: 0.484
+2025-08-26 00:30:44: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:30:44: Epoch: 001 Test results, ap: 0.195, auc: 0.380, f1: 0.392, acc: 0.244
+2025-08-26 00:30:45: Epoch: 002 Train Loss: 0.693, ap: 0.474, auc: 0.500, f1: 0.000, acc: 0.519
+2025-08-26 00:30:45: Epoch: 002 New best VALIDATION metrics
+2025-08-26 00:30:46: Epoch: 002 Test results, ap: 0.191, auc: 0.357, f1: 0.000, acc: 0.756
+2025-08-26 00:30:47: Epoch: 003 Train Loss: 0.694, ap: 0.565, auc: 0.599, f1: 0.649, acc: 0.481
+2025-08-26 00:30:47: Epoch: 003 New best VALIDATION metrics
+2025-08-26 00:30:47: Epoch: 003 Test results, ap: 0.237, auc: 0.504, f1: 0.392, acc: 0.244
+2025-08-26 00:30:48: Epoch: 004 Train Loss: 0.693, ap: 0.489, auc: 0.520, f1: 0.650, acc: 0.482
+2025-08-26 00:30:48: Epoch: 004 Test results, ap: 0.316, auc: 0.560, f1: 0.392, acc: 0.244
+2025-08-26 00:30:50: Epoch: 005 Train Loss: 0.693, ap: 0.474, auc: 0.488, f1: 0.649, acc: 0.481
+2025-08-26 00:30:50: Epoch: 005 Test results, ap: 0.261, auc: 0.545, f1: 0.392, acc: 0.244
+2025-08-26 00:30:51: Epoch: 006 Train Loss: 0.693, ap: 0.456, auc: 0.441, f1: 0.649, acc: 0.481
+2025-08-26 00:30:51: Epoch: 006 Test results, ap: 0.292, auc: 0.615, f1: 0.392, acc: 0.244
+2025-08-26 00:30:53: Epoch: 007 Train Loss: 0.694, ap: 0.496, auc: 0.530, f1: 0.000, acc: 0.519
+2025-08-26 00:30:53: Epoch: 007 Test results, ap: 0.274, auc: 0.571, f1: 0.000, acc: 0.756
+2025-08-26 00:30:54: Epoch: 008 Train Loss: 0.693, ap: 0.483, auc: 0.507, f1: 0.000, acc: 0.519
+2025-08-26 00:30:54: Epoch: 008 Test results, ap: 0.272, auc: 0.566, f1: 0.000, acc: 0.756
+2025-08-26 00:30:55: Epoch: 009 Train Loss: 0.694, ap: 0.457, auc: 0.471, f1: 0.000, acc: 0.519
+2025-08-26 00:30:56: Epoch: 009 Test results, ap: 0.248, auc: 0.484, f1: 0.000, acc: 0.756
+2025-08-26 00:30:57: Epoch: 010 Train Loss: 0.693, ap: 0.516, auc: 0.532, f1: 0.000, acc: 0.519
+2025-08-26 00:30:57: Epoch: 010 Test results, ap: 0.248, auc: 0.487, f1: 0.000, acc: 0.756
+2025-08-26 00:30:58: Epoch: 011 Train Loss: 0.693, ap: 0.481, auc: 0.500, f1: 0.649, acc: 0.481
+2025-08-26 00:30:59: Epoch: 011 Test results, ap: 0.238, auc: 0.480, f1: 0.392, acc: 0.244
+2025-08-26 00:30:59: Epoch: 011 New best TEST metrics
+2025-08-26 00:31:00: Epoch: 012 Train Loss: 0.694, ap: 0.484, auc: 0.497, f1: 0.000, acc: 0.519
+2025-08-26 00:31:00: Epoch: 012 Test results, ap: 0.240, auc: 0.484, f1: 0.000, acc: 0.756
+2025-08-26 00:31:00: Epoch: 012 New best TEST metrics
+2025-08-26 00:31:01: Epoch: 013 Train Loss: 0.693, ap: 0.488, auc: 0.508, f1: 0.000, acc: 0.519
+2025-08-26 00:31:02: Epoch: 013 Test results, ap: 0.214, auc: 0.284, f1: 0.000, acc: 0.756
+2025-08-26 00:31:03: Epoch: 014 Train Loss: 0.693, ap: 0.503, auc: 0.530, f1: 0.649, acc: 0.481
+2025-08-26 00:31:03: Epoch: 014 Test results, ap: 0.238, auc: 0.418, f1: 0.392, acc: 0.244
+2025-08-26 00:31:04: Epoch: 015 Train Loss: 0.693, ap: 0.507, auc: 0.548, f1: 0.649, acc: 0.481
+2025-08-26 00:31:05: Epoch: 015 Test results, ap: 0.272, auc: 0.528, f1: 0.392, acc: 0.244
+2025-08-26 00:31:05: Epoch: 015 New best TEST metrics
+2025-08-26 00:31:06: Epoch: 016 Train Loss: 0.693, ap: 0.484, auc: 0.493, f1: 0.650, acc: 0.482
+2025-08-26 00:31:06: Epoch: 016 Test results, ap: 0.246, auc: 0.505, f1: 0.392, acc: 0.244
+2025-08-26 00:31:07: Epoch: 017 Train Loss: 0.693, ap: 0.469, auc: 0.445, f1: 0.000, acc: 0.519
+2025-08-26 00:31:07: Epoch: 017 Test results, ap: 0.248, auc: 0.511, f1: 0.000, acc: 0.756
+2025-08-26 00:31:09: Epoch: 018 Train Loss: 0.693, ap: 0.547, auc: 0.585, f1: 0.000, acc: 0.519
+2025-08-26 00:31:09: Epoch: 018 Test results, ap: 0.287, auc: 0.595, f1: 0.000, acc: 0.756
+2025-08-26 00:31:09: Epoch: 018 New best TEST metrics
+2025-08-26 00:31:10: Epoch: 019 Train Loss: 0.693, ap: 0.470, auc: 0.478, f1: 0.650, acc: 0.482
+2025-08-26 00:31:10: Epoch: 019 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:31:12: Epoch: 020 Train Loss: 0.693, ap: 0.486, auc: 0.506, f1: 0.000, acc: 0.519
+2025-08-26 00:31:12: Epoch: 020 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:31:13: Epoch: 021 Train Loss: 0.693, ap: 0.475, auc: 0.485, f1: 0.650, acc: 0.482
+2025-08-26 00:31:13: Epoch: 021 Test results, ap: 0.244, auc: 0.484, f1: 0.392, acc: 0.244
+2025-08-26 00:31:15: Epoch: 022 Train Loss: 0.694, ap: 0.519, auc: 0.542, f1: 0.650, acc: 0.482
+2025-08-26 00:31:15: Epoch: 022 Test results, ap: 0.215, auc: 0.372, f1: 0.392, acc: 0.244
+2025-08-26 00:31:16: Epoch: 023 Train Loss: 0.693, ap: 0.475, auc: 0.485, f1: 0.000, acc: 0.519
+2025-08-26 00:31:16: Epoch: 023 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:31:18: Epoch: 024 Train Loss: 0.693, ap: 0.496, auc: 0.504, f1: 0.650, acc: 0.482
+2025-08-26 00:31:18: Epoch: 024 Test results, ap: 0.244, auc: 0.446, f1: 0.392, acc: 0.244
+2025-08-26 00:31:19: Epoch: 025 Train Loss: 0.694, ap: 0.482, auc: 0.513, f1: 0.650, acc: 0.482
+2025-08-26 00:31:19: Epoch: 025 Test results, ap: 0.256, auc: 0.527, f1: 0.392, acc: 0.244
+2025-08-26 00:31:21: Epoch: 026 Train Loss: 0.693, ap: 0.506, auc: 0.546, f1: 0.000, acc: 0.519
+2025-08-26 00:31:21: Epoch: 026 Test results, ap: 0.265, auc: 0.531, f1: 0.000, acc: 0.756
+2025-08-26 00:31:22: Epoch: 027 Train Loss: 0.693, ap: 0.488, auc: 0.491, f1: 0.650, acc: 0.482
+2025-08-26 00:31:22: Epoch: 027 Test results, ap: 0.248, auc: 0.462, f1: 0.392, acc: 0.244
+2025-08-26 00:31:24: Epoch: 028 Train Loss: 0.693, ap: 0.455, auc: 0.454, f1: 0.650, acc: 0.482
+2025-08-26 00:31:24: Epoch: 028 Test results, ap: 0.244, auc: 0.392, f1: 0.392, acc: 0.244
+2025-08-26 00:31:25: Epoch: 029 Train Loss: 0.693, ap: 0.497, auc: 0.536, f1: 0.000, acc: 0.519
+2025-08-26 00:31:25: Epoch: 029 Test results, ap: 0.242, auc: 0.496, f1: 0.000, acc: 0.756
+2025-08-26 00:31:26: Epoch: 030 Train Loss: 0.693, ap: 0.468, auc: 0.451, f1: 0.650, acc: 0.482
+2025-08-26 00:31:27: Epoch: 030 Test results, ap: 0.240, auc: 0.483, f1: 0.392, acc: 0.244
+2025-08-26 00:31:28: Epoch: 031 Train Loss: 0.693, ap: 0.503, auc: 0.529, f1: 0.000, acc: 0.519
+2025-08-26 00:31:28: Epoch: 031 Test results, ap: 0.267, auc: 0.544, f1: 0.000, acc: 0.756
+2025-08-26 00:31:29: Epoch: 032 Train Loss: 0.694, ap: 0.477, auc: 0.488, f1: 0.000, acc: 0.519
+2025-08-26 00:31:30: Epoch: 032 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:31:31: Epoch: 033 Train Loss: 0.693, ap: 0.480, auc: 0.506, f1: 0.000, acc: 0.519
+2025-08-26 00:31:31: Epoch: 033 Test results, ap: 0.263, auc: 0.549, f1: 0.000, acc: 0.756
+2025-08-26 00:31:32: Epoch: 034 Train Loss: 0.693, ap: 0.512, auc: 0.553, f1: 0.000, acc: 0.519
+2025-08-26 00:31:33: Epoch: 034 Test results, ap: 0.273, auc: 0.570, f1: 0.000, acc: 0.756
+2025-08-26 00:31:34: Epoch: 035 Train Loss: 0.694, ap: 0.499, auc: 0.537, f1: 0.000, acc: 0.519
+2025-08-26 00:31:34: Epoch: 035 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:31:35: Epoch: 036 Train Loss: 0.694, ap: 0.485, auc: 0.502, f1: 0.000, acc: 0.519
+2025-08-26 00:31:36: Epoch: 036 Test results, ap: 0.261, auc: 0.523, f1: 0.000, acc: 0.756
+2025-08-26 00:31:37: Epoch: 037 Train Loss: 0.693, ap: 0.472, auc: 0.483, f1: 0.000, acc: 0.519
+2025-08-26 00:31:37: Epoch: 037 Test results, ap: 0.244, auc: 0.484, f1: 0.000, acc: 0.756
+2025-08-26 00:31:38: Epoch: 038 Train Loss: 0.693, ap: 0.475, auc: 0.510, f1: 0.000, acc: 0.519
+2025-08-26 00:31:38: Epoch: 038 Test results, ap: 0.254, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:31:40: Epoch: 039 Train Loss: 0.693, ap: 0.488, auc: 0.508, f1: 0.650, acc: 0.482
+2025-08-26 00:31:40: Epoch: 039 Test results, ap: 0.311, auc: 0.556, f1: 0.392, acc: 0.244
+2025-08-26 00:31:41: Epoch: 040 Train Loss: 0.693, ap: 0.477, auc: 0.497, f1: 0.650, acc: 0.482
+2025-08-26 00:31:41: Epoch: 040 Test results, ap: 0.239, auc: 0.480, f1: 0.392, acc: 0.244
+2025-08-26 00:31:43: Epoch: 041 Train Loss: 0.693, ap: 0.465, auc: 0.469, f1: 0.650, acc: 0.482
+2025-08-26 00:31:43: Epoch: 041 Test results, ap: 0.239, auc: 0.489, f1: 0.392, acc: 0.244
+2025-08-26 00:31:44: Epoch: 042 Train Loss: 0.693, ap: 0.513, auc: 0.552, f1: 0.650, acc: 0.482
+2025-08-26 00:31:44: Epoch: 042 Test results, ap: 0.272, auc: 0.473, f1: 0.392, acc: 0.244
+2025-08-26 00:31:46: Epoch: 043 Train Loss: 0.693, ap: 0.493, auc: 0.527, f1: 0.000, acc: 0.519
+2025-08-26 00:31:46: Epoch: 043 Test results, ap: 0.244, auc: 0.495, f1: 0.000, acc: 0.756
+2025-08-26 00:31:47: Epoch: 044 Train Loss: 0.693, ap: 0.545, auc: 0.580, f1: 0.000, acc: 0.519
+2025-08-26 00:31:47: Epoch: 044 Test results, ap: 0.247, auc: 0.497, f1: 0.000, acc: 0.756
+2025-08-26 00:31:49: Epoch: 045 Train Loss: 0.693, ap: 0.500, auc: 0.534, f1: 0.000, acc: 0.519
+2025-08-26 00:31:49: Epoch: 045 Test results, ap: 0.325, auc: 0.619, f1: 0.000, acc: 0.756
+2025-08-26 00:31:49: Epoch: 045 New best TEST metrics
+2025-08-26 00:31:50: Epoch: 046 Train Loss: 0.693, ap: 0.495, auc: 0.506, f1: 0.650, acc: 0.482
+2025-08-26 00:31:50: Epoch: 046 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:31:51: Epoch: 047 Train Loss: 0.693, ap: 0.475, auc: 0.488, f1: 0.650, acc: 0.482
+2025-08-26 00:31:52: Epoch: 047 Test results, ap: 0.251, auc: 0.517, f1: 0.392, acc: 0.244
+2025-08-26 00:31:53: Epoch: 048 Train Loss: 0.693, ap: 0.485, auc: 0.517, f1: 0.000, acc: 0.519
+2025-08-26 00:31:53: Epoch: 048 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:31:54: Epoch: 049 Train Loss: 0.693, ap: 0.485, auc: 0.508, f1: 0.650, acc: 0.482
+2025-08-26 00:31:55: Epoch: 049 Test results, ap: 0.250, auc: 0.474, f1: 0.392, acc: 0.244
+2025-08-26 00:31:56: Epoch: 050 Train Loss: 0.693, ap: 0.490, auc: 0.497, f1: 0.650, acc: 0.482
+2025-08-26 00:31:56: Epoch: 050 Test results, ap: 0.248, auc: 0.510, f1: 0.392, acc: 0.244
+2025-08-26 00:31:56: used time 0.02h
+2025-08-26 00:31:56: Running Cross Validation 1
+2025-08-26 00:31:56: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 00:31:57: Epoch: 001 Train Loss: 0.693, ap: 0.492, auc: 0.539, f1: 0.604, acc: 0.433
+2025-08-26 00:31:57: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:31:58: Epoch: 001 Test results, ap: 0.237, auc: 0.512, f1: 0.392, acc: 0.244
+2025-08-26 00:31:59: Epoch: 002 Train Loss: 0.693, ap: 0.450, auc: 0.502, f1: 0.608, acc: 0.437
+2025-08-26 00:31:59: Epoch: 002 Test results, ap: 0.210, auc: 0.402, f1: 0.392, acc: 0.244
+2025-08-26 00:32:00: Epoch: 003 Train Loss: 0.693, ap: 0.453, auc: 0.491, f1: 0.000, acc: 0.563
+2025-08-26 00:32:01: Epoch: 003 Test results, ap: 0.259, auc: 0.536, f1: 0.000, acc: 0.756
+2025-08-26 00:32:02: Epoch: 004 Train Loss: 0.694, ap: 0.481, auc: 0.544, f1: 0.000, acc: 0.563
+2025-08-26 00:32:02: Epoch: 004 Test results, ap: 0.189, auc: 0.314, f1: 0.000, acc: 0.756
+2025-08-26 00:32:03: Epoch: 005 Train Loss: 0.693, ap: 0.462, auc: 0.511, f1: 0.000, acc: 0.563
+2025-08-26 00:32:04: Epoch: 005 Test results, ap: 0.261, auc: 0.513, f1: 0.000, acc: 0.756
+2025-08-26 00:32:05: Epoch: 006 Train Loss: 0.693, ap: 0.466, auc: 0.542, f1: 0.000, acc: 0.563
+2025-08-26 00:32:05: Epoch: 006 Test results, ap: 0.240, auc: 0.490, f1: 0.000, acc: 0.756
+2025-08-26 00:32:06: Epoch: 007 Train Loss: 0.694, ap: 0.412, auc: 0.462, f1: 0.608, acc: 0.437
+2025-08-26 00:32:06: Epoch: 007 Test results, ap: 0.286, auc: 0.597, f1: 0.392, acc: 0.244
+2025-08-26 00:32:08: Epoch: 008 Train Loss: 0.693, ap: 0.450, auc: 0.499, f1: 0.000, acc: 0.563
+2025-08-26 00:32:08: Epoch: 008 Test results, ap: 0.357, auc: 0.629, f1: 0.000, acc: 0.756
+2025-08-26 00:32:09: Epoch: 009 Train Loss: 0.693, ap: 0.456, auc: 0.487, f1: 0.608, acc: 0.437
+2025-08-26 00:32:09: Epoch: 009 Test results, ap: 0.211, auc: 0.443, f1: 0.392, acc: 0.244
+2025-08-26 00:32:11: Epoch: 010 Train Loss: 0.693, ap: 0.433, auc: 0.485, f1: 0.000, acc: 0.563
+2025-08-26 00:32:11: Epoch: 010 Test results, ap: 0.290, auc: 0.554, f1: 0.000, acc: 0.756
+2025-08-26 00:32:12: Epoch: 011 Train Loss: 0.684, ap: 0.548, auc: 0.623, f1: 0.413, acc: 0.600
+2025-08-26 00:32:12: Epoch: 011 New best VALIDATION metrics
+2025-08-26 00:32:12: Epoch: 011 Test results, ap: 0.242, auc: 0.473, f1: 0.203, acc: 0.618
+2025-08-26 00:32:12: Epoch: 011 New best TEST metrics
+2025-08-26 00:32:14: Epoch: 012 Train Loss: 0.678, ap: 0.544, auc: 0.567, f1: 0.395, acc: 0.590
+2025-08-26 00:32:14: Epoch: 012 Test results, ap: 0.233, auc: 0.462, f1: 0.200, acc: 0.675
+2025-08-26 00:32:15: Epoch: 013 Train Loss: 0.657, ap: 0.563, auc: 0.618, f1: 0.421, acc: 0.618
+2025-08-26 00:32:15: Epoch: 013 New best VALIDATION metrics
+2025-08-26 00:32:15: Epoch: 013 Test results, ap: 0.246, auc: 0.484, f1: 0.103, acc: 0.715
+2025-08-26 00:32:15: Epoch: 013 New best TEST metrics
+2025-08-26 00:32:17: Epoch: 014 Train Loss: 0.655, ap: 0.572, auc: 0.635, f1: 0.450, acc: 0.634
+2025-08-26 00:32:17: Epoch: 014 New best VALIDATION metrics
+2025-08-26 00:32:17: Epoch: 014 Test results, ap: 0.231, auc: 0.459, f1: 0.103, acc: 0.715
+2025-08-26 00:32:18: Epoch: 015 Train Loss: 0.647, ap: 0.615, auc: 0.632, f1: 0.408, acc: 0.621
+2025-08-26 00:32:18: Epoch: 015 New best VALIDATION metrics
+2025-08-26 00:32:18: Epoch: 015 Test results, ap: 0.225, auc: 0.444, f1: 0.000, acc: 0.756
+2025-08-26 00:32:19: Epoch: 016 Train Loss: 0.644, ap: 0.513, auc: 0.595, f1: 0.460, acc: 0.604
+2025-08-26 00:32:20: Epoch: 016 Test results, ap: 0.241, auc: 0.494, f1: 0.209, acc: 0.569
+2025-08-26 00:32:20: Epoch: 016 New best TEST metrics
+2025-08-26 00:32:21: Epoch: 017 Train Loss: 0.645, ap: 0.549, auc: 0.630, f1: 0.487, acc: 0.629
+2025-08-26 00:32:21: Epoch: 017 Test results, ap: 0.245, auc: 0.487, f1: 0.278, acc: 0.577
+2025-08-26 00:32:22: Epoch: 018 Train Loss: 0.635, ap: 0.626, auc: 0.637, f1: 0.462, acc: 0.643
+2025-08-26 00:32:22: Epoch: 018 New best VALIDATION metrics
+2025-08-26 00:32:23: Epoch: 018 Test results, ap: 0.218, auc: 0.439, f1: 0.103, acc: 0.715
+2025-08-26 00:32:24: Epoch: 019 Train Loss: 0.638, ap: 0.591, auc: 0.611, f1: 0.456, acc: 0.618
+2025-08-26 00:32:24: Epoch: 019 Test results, ap: 0.238, auc: 0.482, f1: 0.274, acc: 0.569
+2025-08-26 00:32:25: Epoch: 020 Train Loss: 0.638, ap: 0.612, auc: 0.621, f1: 0.454, acc: 0.611
+2025-08-26 00:32:26: Epoch: 020 Test results, ap: 0.241, auc: 0.481, f1: 0.333, acc: 0.512
+2025-08-26 00:32:27: Epoch: 021 Train Loss: 0.619, ap: 0.624, auc: 0.632, f1: 0.462, acc: 0.623
+2025-08-26 00:32:27: Epoch: 021 Test results, ap: 0.216, auc: 0.443, f1: 0.149, acc: 0.537
+2025-08-26 00:32:28: Epoch: 022 Train Loss: 0.619, ap: 0.599, auc: 0.625, f1: 0.461, acc: 0.625
+2025-08-26 00:32:29: Epoch: 022 Test results, ap: 0.254, auc: 0.518, f1: 0.000, acc: 0.707
+2025-08-26 00:32:29: Epoch: 022 New best TEST metrics
+2025-08-26 00:32:30: Epoch: 023 Train Loss: 0.615, ap: 0.636, auc: 0.655, f1: 0.465, acc: 0.623
+2025-08-26 00:32:30: Epoch: 023 New best VALIDATION metrics
+2025-08-26 00:32:30: Epoch: 023 Test results, ap: 0.254, auc: 0.477, f1: 0.206, acc: 0.561
+2025-08-26 00:32:31: Epoch: 024 Train Loss: 0.609, ap: 0.592, auc: 0.627, f1: 0.475, acc: 0.618
+2025-08-26 00:32:31: Epoch: 024 Test results, ap: 0.323, auc: 0.564, f1: 0.327, acc: 0.699
+2025-08-26 00:32:31: Epoch: 024 New best TEST metrics
+2025-08-26 00:32:33: Epoch: 025 Train Loss: 0.609, ap: 0.637, auc: 0.662, f1: 0.483, acc: 0.607
+2025-08-26 00:32:33: Epoch: 025 New best VALIDATION metrics
+2025-08-26 00:32:33: Epoch: 025 Test results, ap: 0.312, auc: 0.518, f1: 0.397, acc: 0.431
+2025-08-26 00:32:34: Epoch: 026 Train Loss: 0.602, ap: 0.629, auc: 0.656, f1: 0.484, acc: 0.621
+2025-08-26 00:32:34: Epoch: 026 Test results, ap: 0.349, auc: 0.555, f1: 0.431, acc: 0.398
+2025-08-26 00:32:34: Epoch: 026 New best TEST metrics
+2025-08-26 00:32:36: Epoch: 027 Train Loss: 0.598, ap: 0.614, auc: 0.649, f1: 0.480, acc: 0.611
+2025-08-26 00:32:36: Epoch: 027 Test results, ap: 0.433, auc: 0.631, f1: 0.381, acc: 0.472
+2025-08-26 00:32:36: Epoch: 027 New best TEST metrics
+2025-08-26 00:32:37: Epoch: 028 Train Loss: 0.601, ap: 0.643, auc: 0.662, f1: 0.487, acc: 0.618
+2025-08-26 00:32:37: Epoch: 028 New best VALIDATION metrics
+2025-08-26 00:32:37: Epoch: 028 Test results, ap: 0.396, auc: 0.573, f1: 0.386, acc: 0.561
+2025-08-26 00:32:39: Epoch: 029 Train Loss: 0.588, ap: 0.663, auc: 0.689, f1: 0.483, acc: 0.607
+2025-08-26 00:32:39: Epoch: 029 New best VALIDATION metrics
+2025-08-26 00:32:39: Epoch: 029 Test results, ap: 0.528, auc: 0.636, f1: 0.370, acc: 0.309
+2025-08-26 00:32:39: Epoch: 029 New best TEST metrics
+2025-08-26 00:32:40: Epoch: 030 Train Loss: 0.575, ap: 0.673, auc: 0.706, f1: 0.518, acc: 0.627
+2025-08-26 00:32:40: Epoch: 030 New best VALIDATION metrics
+2025-08-26 00:32:40: Epoch: 030 Test results, ap: 0.516, auc: 0.637, f1: 0.370, acc: 0.447
+2025-08-26 00:32:42: Epoch: 031 Train Loss: 0.563, ap: 0.693, auc: 0.725, f1: 0.566, acc: 0.657
+2025-08-26 00:32:42: Epoch: 031 New best VALIDATION metrics
+2025-08-26 00:32:42: Epoch: 031 Test results, ap: 0.494, auc: 0.605, f1: 0.355, acc: 0.350
+2025-08-26 00:32:43: Epoch: 032 Train Loss: 0.562, ap: 0.677, auc: 0.710, f1: 0.529, acc: 0.639
+2025-08-26 00:32:43: Epoch: 032 Test results, ap: 0.503, auc: 0.612, f1: 0.347, acc: 0.358
+2025-08-26 00:32:45: Epoch: 033 Train Loss: 0.566, ap: 0.684, auc: 0.716, f1: 0.547, acc: 0.641
+2025-08-26 00:32:45: Epoch: 033 Test results, ap: 0.504, auc: 0.634, f1: 0.365, acc: 0.463
+2025-08-26 00:32:46: Epoch: 034 Train Loss: 0.565, ap: 0.693, auc: 0.729, f1: 0.565, acc: 0.650
+2025-08-26 00:32:46: Epoch: 034 New best VALIDATION metrics
+2025-08-26 00:32:46: Epoch: 034 Test results, ap: 0.308, auc: 0.460, f1: 0.230, acc: 0.455
+2025-08-26 00:32:48: Epoch: 035 Train Loss: 0.557, ap: 0.691, auc: 0.731, f1: 0.567, acc: 0.644
+2025-08-26 00:32:48: Epoch: 035 Test results, ap: 0.302, auc: 0.462, f1: 0.308, acc: 0.341
+2025-08-26 00:32:49: Epoch: 036 Train Loss: 0.558, ap: 0.698, auc: 0.729, f1: 0.553, acc: 0.641
+2025-08-26 00:32:49: Epoch: 036 New best VALIDATION metrics
+2025-08-26 00:32:49: Epoch: 036 Test results, ap: 0.453, auc: 0.572, f1: 0.316, acc: 0.366
+2025-08-26 00:32:51: Epoch: 037 Train Loss: 0.548, ap: 0.683, auc: 0.726, f1: 0.561, acc: 0.636
+2025-08-26 00:32:51: Epoch: 037 Test results, ap: 0.470, auc: 0.589, f1: 0.340, acc: 0.431
+2025-08-26 00:32:52: Epoch: 038 Train Loss: 0.555, ap: 0.685, auc: 0.715, f1: 0.554, acc: 0.637
+2025-08-26 00:32:52: Epoch: 038 Test results, ap: 0.508, auc: 0.626, f1: 0.351, acc: 0.398
+2025-08-26 00:32:53: Epoch: 039 Train Loss: 0.551, ap: 0.699, auc: 0.733, f1: 0.560, acc: 0.641
+2025-08-26 00:32:53: Epoch: 039 New best VALIDATION metrics
+2025-08-26 00:32:54: Epoch: 039 Test results, ap: 0.475, auc: 0.593, f1: 0.355, acc: 0.350
+2025-08-26 00:32:55: Epoch: 040 Train Loss: 0.544, ap: 0.700, auc: 0.730, f1: 0.554, acc: 0.643
+2025-08-26 00:32:55: Epoch: 040 Test results, ap: 0.444, auc: 0.578, f1: 0.362, acc: 0.341
+2025-08-26 00:32:56: Epoch: 041 Train Loss: 0.537, ap: 0.702, auc: 0.730, f1: 0.563, acc: 0.650
+2025-08-26 00:32:57: Epoch: 041 Test results, ap: 0.283, auc: 0.444, f1: 0.325, acc: 0.325
+2025-08-26 00:32:58: Epoch: 042 Train Loss: 0.543, ap: 0.700, auc: 0.737, f1: 0.575, acc: 0.653
+2025-08-26 00:32:58: Epoch: 042 New best VALIDATION metrics
+2025-08-26 00:32:58: Epoch: 042 Test results, ap: 0.491, auc: 0.595, f1: 0.322, acc: 0.350
+2025-08-26 00:32:59: Epoch: 043 Train Loss: 0.538, ap: 0.703, auc: 0.740, f1: 0.573, acc: 0.657
+2025-08-26 00:32:59: Epoch: 043 New best VALIDATION metrics
+2025-08-26 00:33:00: Epoch: 043 Test results, ap: 0.440, auc: 0.566, f1: 0.328, acc: 0.333
+2025-08-26 00:33:01: Epoch: 044 Train Loss: 0.533, ap: 0.700, auc: 0.733, f1: 0.567, acc: 0.648
+2025-08-26 00:33:01: Epoch: 044 Test results, ap: 0.420, auc: 0.565, f1: 0.336, acc: 0.325
+2025-08-26 00:33:02: Epoch: 045 Train Loss: 0.532, ap: 0.703, auc: 0.741, f1: 0.581, acc: 0.660
+2025-08-26 00:33:02: Epoch: 045 New best VALIDATION metrics
+2025-08-26 00:33:03: Epoch: 045 Test results, ap: 0.446, auc: 0.572, f1: 0.325, acc: 0.325
+2025-08-26 00:33:04: Epoch: 046 Train Loss: 0.533, ap: 0.697, auc: 0.734, f1: 0.578, acc: 0.658
+2025-08-26 00:33:04: Epoch: 046 Test results, ap: 0.507, auc: 0.610, f1: 0.336, acc: 0.358
+2025-08-26 00:33:05: Epoch: 047 Train Loss: 0.531, ap: 0.699, auc: 0.738, f1: 0.580, acc: 0.664
+2025-08-26 00:33:06: Epoch: 047 Test results, ap: 0.406, auc: 0.544, f1: 0.325, acc: 0.325
+2025-08-26 00:33:07: Epoch: 048 Train Loss: 0.533, ap: 0.703, auc: 0.741, f1: 0.575, acc: 0.658
+2025-08-26 00:33:07: Epoch: 048 New best VALIDATION metrics
+2025-08-26 00:33:07: Epoch: 048 Test results, ap: 0.385, auc: 0.548, f1: 0.328, acc: 0.333
+2025-08-26 00:33:08: Epoch: 049 Train Loss: 0.536, ap: 0.703, auc: 0.738, f1: 0.581, acc: 0.662
+2025-08-26 00:33:08: Epoch: 049 Test results, ap: 0.480, auc: 0.596, f1: 0.316, acc: 0.366
+2025-08-26 00:33:10: Epoch: 050 Train Loss: 0.533, ap: 0.705, auc: 0.740, f1: 0.583, acc: 0.665
+2025-08-26 00:33:10: Epoch: 050 New best VALIDATION metrics
+2025-08-26 00:33:10: Epoch: 050 Test results, ap: 0.470, auc: 0.598, f1: 0.316, acc: 0.366
+2025-08-26 00:33:10: used time 0.02h
+2025-08-26 00:33:10: Running Cross Validation 2
+2025-08-26 00:33:10: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:33:11: Epoch: 001 Train Loss: 0.694, ap: 0.456, auc: 0.533, f1: 0.613, acc: 0.455
+2025-08-26 00:33:11: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:33:11: Epoch: 001 Test results, ap: 0.386, auc: 0.681, f1: 0.392, acc: 0.244
+2025-08-26 00:33:13: Epoch: 002 Train Loss: 0.693, ap: 0.483, auc: 0.558, f1: 0.611, acc: 0.446
+2025-08-26 00:33:13: Epoch: 002 New best VALIDATION metrics
+2025-08-26 00:33:13: Epoch: 002 Test results, ap: 0.299, auc: 0.620, f1: 0.392, acc: 0.244
+2025-08-26 00:33:14: Epoch: 003 Train Loss: 0.693, ap: 0.468, auc: 0.548, f1: 0.612, acc: 0.441
+2025-08-26 00:33:14: Epoch: 003 Test results, ap: 0.367, auc: 0.679, f1: 0.392, acc: 0.244
+2025-08-26 00:33:16: Epoch: 004 Train Loss: 0.693, ap: 0.457, auc: 0.516, f1: 0.613, acc: 0.443
+2025-08-26 00:33:16: Epoch: 004 Test results, ap: 0.331, auc: 0.655, f1: 0.392, acc: 0.244
+2025-08-26 00:33:17: Epoch: 005 Train Loss: 0.693, ap: 0.401, auc: 0.443, f1: 0.612, acc: 0.441
+2025-08-26 00:33:17: Epoch: 005 Test results, ap: 0.205, auc: 0.354, f1: 0.392, acc: 0.244
+2025-08-26 00:33:18: Epoch: 006 Train Loss: 0.693, ap: 0.416, auc: 0.481, f1: 0.612, acc: 0.441
+2025-08-26 00:33:19: Epoch: 006 Test results, ap: 0.210, auc: 0.404, f1: 0.392, acc: 0.244
+2025-08-26 00:33:20: Epoch: 007 Train Loss: 0.692, ap: 0.485, auc: 0.520, f1: 0.612, acc: 0.441
+2025-08-26 00:33:20: Epoch: 007 Test results, ap: 0.250, auc: 0.512, f1: 0.392, acc: 0.244
+2025-08-26 00:33:21: Epoch: 008 Train Loss: 0.694, ap: 0.464, auc: 0.515, f1: 0.612, acc: 0.441
+2025-08-26 00:33:22: Epoch: 008 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:33:23: Epoch: 009 Train Loss: 0.693, ap: 0.446, auc: 0.510, f1: 0.612, acc: 0.441
+2025-08-26 00:33:23: Epoch: 009 Test results, ap: 0.252, auc: 0.522, f1: 0.392, acc: 0.244
+2025-08-26 00:33:24: Epoch: 010 Train Loss: 0.694, ap: 0.411, auc: 0.429, f1: 0.000, acc: 0.559
+2025-08-26 00:33:25: Epoch: 010 Test results, ap: 0.251, auc: 0.482, f1: 0.000, acc: 0.756
+2025-08-26 00:33:26: Epoch: 011 Train Loss: 0.694, ap: 0.461, auc: 0.544, f1: 0.612, acc: 0.441
+2025-08-26 00:33:26: Epoch: 011 Test results, ap: 0.244, auc: 0.495, f1: 0.392, acc: 0.244
+2025-08-26 00:33:26: Epoch: 011 New best TEST metrics
+2025-08-26 00:33:27: Epoch: 012 Train Loss: 0.694, ap: 0.460, auc: 0.532, f1: 0.000, acc: 0.559
+2025-08-26 00:33:28: Epoch: 012 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:33:28: Epoch: 012 New best TEST metrics
+2025-08-26 00:33:29: Epoch: 013 Train Loss: 0.693, ap: 0.447, auc: 0.508, f1: 0.000, acc: 0.559
+2025-08-26 00:33:29: Epoch: 013 Test results, ap: 0.249, auc: 0.511, f1: 0.000, acc: 0.756
+2025-08-26 00:33:29: Epoch: 013 New best TEST metrics
+2025-08-26 00:33:30: Epoch: 014 Train Loss: 0.694, ap: 0.427, auc: 0.469, f1: 0.000, acc: 0.559
+2025-08-26 00:33:31: Epoch: 014 Test results, ap: 0.228, auc: 0.455, f1: 0.000, acc: 0.756
+2025-08-26 00:33:32: Epoch: 015 Train Loss: 0.693, ap: 0.470, auc: 0.556, f1: 0.000, acc: 0.559
+2025-08-26 00:33:32: Epoch: 015 Test results, ap: 0.228, auc: 0.433, f1: 0.000, acc: 0.756
+2025-08-26 00:33:33: Epoch: 016 Train Loss: 0.693, ap: 0.440, auc: 0.489, f1: 0.000, acc: 0.559
+2025-08-26 00:33:33: Epoch: 016 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:33:35: Epoch: 017 Train Loss: 0.693, ap: 0.443, auc: 0.505, f1: 0.612, acc: 0.441
+2025-08-26 00:33:35: Epoch: 017 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:33:36: Epoch: 018 Train Loss: 0.693, ap: 0.457, auc: 0.519, f1: 0.000, acc: 0.559
+2025-08-26 00:33:36: Epoch: 018 Test results, ap: 0.244, auc: 0.478, f1: 0.000, acc: 0.756
+2025-08-26 00:33:38: Epoch: 019 Train Loss: 0.693, ap: 0.421, auc: 0.463, f1: 0.000, acc: 0.559
+2025-08-26 00:33:38: Epoch: 019 Test results, ap: 0.252, auc: 0.522, f1: 0.000, acc: 0.756
+2025-08-26 00:33:38: Epoch: 019 New best TEST metrics
+2025-08-26 00:33:39: Epoch: 020 Train Loss: 0.694, ap: 0.494, auc: 0.576, f1: 0.612, acc: 0.441
+2025-08-26 00:33:39: Epoch: 020 New best VALIDATION metrics
+2025-08-26 00:33:39: Epoch: 020 Test results, ap: 0.278, auc: 0.575, f1: 0.392, acc: 0.244
+2025-08-26 00:33:39: Epoch: 020 New best TEST metrics
+2025-08-26 00:33:41: Epoch: 021 Train Loss: 0.693, ap: 0.431, auc: 0.470, f1: 0.612, acc: 0.441
+2025-08-26 00:33:41: Epoch: 021 Test results, ap: 0.244, auc: 0.489, f1: 0.392, acc: 0.244
+2025-08-26 00:33:42: Epoch: 022 Train Loss: 0.693, ap: 0.457, auc: 0.511, f1: 0.612, acc: 0.441
+2025-08-26 00:33:42: Epoch: 022 Test results, ap: 0.246, auc: 0.506, f1: 0.392, acc: 0.244
+2025-08-26 00:33:44: Epoch: 023 Train Loss: 0.693, ap: 0.457, auc: 0.527, f1: 0.000, acc: 0.559
+2025-08-26 00:33:44: Epoch: 023 Test results, ap: 0.240, auc: 0.484, f1: 0.000, acc: 0.756
+2025-08-26 00:33:45: Epoch: 024 Train Loss: 0.693, ap: 0.446, auc: 0.505, f1: 0.000, acc: 0.559
+2025-08-26 00:33:45: Epoch: 024 Test results, ap: 0.252, auc: 0.522, f1: 0.000, acc: 0.756
+2025-08-26 00:33:47: Epoch: 025 Train Loss: 0.693, ap: 0.455, auc: 0.510, f1: 0.612, acc: 0.441
+2025-08-26 00:33:47: Epoch: 025 Test results, ap: 0.244, auc: 0.499, f1: 0.392, acc: 0.244
+2025-08-26 00:33:48: Epoch: 026 Train Loss: 0.694, ap: 0.459, auc: 0.511, f1: 0.612, acc: 0.441
+2025-08-26 00:33:48: Epoch: 026 Test results, ap: 0.227, auc: 0.434, f1: 0.392, acc: 0.244
+2025-08-26 00:33:49: Epoch: 027 Train Loss: 0.694, ap: 0.451, auc: 0.510, f1: 0.612, acc: 0.441
+2025-08-26 00:33:50: Epoch: 027 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:33:51: Epoch: 028 Train Loss: 0.693, ap: 0.477, auc: 0.527, f1: 0.000, acc: 0.559
+2025-08-26 00:33:51: Epoch: 028 Test results, ap: 0.239, auc: 0.473, f1: 0.000, acc: 0.756
+2025-08-26 00:33:52: Epoch: 029 Train Loss: 0.693, ap: 0.458, auc: 0.500, f1: 0.612, acc: 0.441
+2025-08-26 00:33:53: Epoch: 029 Test results, ap: 0.224, auc: 0.366, f1: 0.392, acc: 0.244
+2025-08-26 00:33:54: Epoch: 030 Train Loss: 0.694, ap: 0.446, auc: 0.473, f1: 0.612, acc: 0.441
+2025-08-26 00:33:54: Epoch: 030 Test results, ap: 0.240, auc: 0.457, f1: 0.392, acc: 0.244
+2025-08-26 00:33:55: Epoch: 031 Train Loss: 0.693, ap: 0.395, auc: 0.391, f1: 0.000, acc: 0.559
+2025-08-26 00:33:56: Epoch: 031 Test results, ap: 0.234, auc: 0.422, f1: 0.000, acc: 0.756
+2025-08-26 00:33:57: Epoch: 032 Train Loss: 0.693, ap: 0.526, auc: 0.600, f1: 0.000, acc: 0.559
+2025-08-26 00:33:57: Epoch: 032 New best VALIDATION metrics
+2025-08-26 00:33:57: Epoch: 032 Test results, ap: 0.226, auc: 0.426, f1: 0.000, acc: 0.756
+2025-08-26 00:33:58: Epoch: 033 Train Loss: 0.693, ap: 0.465, auc: 0.530, f1: 0.612, acc: 0.441
+2025-08-26 00:33:59: Epoch: 033 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:34:00: Epoch: 034 Train Loss: 0.693, ap: 0.504, auc: 0.594, f1: 0.000, acc: 0.559
+2025-08-26 00:34:00: Epoch: 034 Test results, ap: 0.248, auc: 0.504, f1: 0.000, acc: 0.756
+2025-08-26 00:34:01: Epoch: 035 Train Loss: 0.693, ap: 0.470, auc: 0.535, f1: 0.000, acc: 0.559
+2025-08-26 00:34:01: Epoch: 035 Test results, ap: 0.246, auc: 0.473, f1: 0.000, acc: 0.756
+2025-08-26 00:34:03: Epoch: 036 Train Loss: 0.693, ap: 0.469, auc: 0.556, f1: 0.612, acc: 0.441
+2025-08-26 00:34:03: Epoch: 036 Test results, ap: 0.254, auc: 0.527, f1: 0.392, acc: 0.244
+2025-08-26 00:34:04: Epoch: 037 Train Loss: 0.693, ap: 0.449, auc: 0.517, f1: 0.612, acc: 0.441
+2025-08-26 00:34:04: Epoch: 037 Test results, ap: 0.244, auc: 0.452, f1: 0.392, acc: 0.244
+2025-08-26 00:34:06: Epoch: 038 Train Loss: 0.693, ap: 0.472, auc: 0.526, f1: 0.612, acc: 0.441
+2025-08-26 00:34:06: Epoch: 038 Test results, ap: 0.259, auc: 0.538, f1: 0.392, acc: 0.244
+2025-08-26 00:34:07: Epoch: 039 Train Loss: 0.693, ap: 0.450, auc: 0.508, f1: 0.000, acc: 0.559
+2025-08-26 00:34:07: Epoch: 039 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:34:09: Epoch: 040 Train Loss: 0.693, ap: 0.459, auc: 0.536, f1: 0.612, acc: 0.441
+2025-08-26 00:34:09: Epoch: 040 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:34:10: Epoch: 041 Train Loss: 0.693, ap: 0.456, auc: 0.528, f1: 0.000, acc: 0.559
+2025-08-26 00:34:10: Epoch: 041 Test results, ap: 0.244, auc: 0.478, f1: 0.000, acc: 0.756
+2025-08-26 00:34:11: Epoch: 042 Train Loss: 0.693, ap: 0.461, auc: 0.537, f1: 0.000, acc: 0.559
+2025-08-26 00:34:12: Epoch: 042 Test results, ap: 0.276, auc: 0.577, f1: 0.000, acc: 0.756
+2025-08-26 00:34:12: Epoch: 042 New best TEST metrics
+2025-08-26 00:34:13: Epoch: 043 Train Loss: 0.693, ap: 0.446, auc: 0.516, f1: 0.000, acc: 0.559
+2025-08-26 00:34:13: Epoch: 043 Test results, ap: 0.252, auc: 0.522, f1: 0.000, acc: 0.756
+2025-08-26 00:34:14: Epoch: 044 Train Loss: 0.693, ap: 0.483, auc: 0.568, f1: 0.612, acc: 0.441
+2025-08-26 00:34:15: Epoch: 044 Test results, ap: 0.307, auc: 0.633, f1: 0.392, acc: 0.244
+2025-08-26 00:34:15: Epoch: 044 New best TEST metrics
+2025-08-26 00:34:16: Epoch: 045 Train Loss: 0.694, ap: 0.461, auc: 0.534, f1: 0.612, acc: 0.441
+2025-08-26 00:34:16: Epoch: 045 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:34:17: Epoch: 046 Train Loss: 0.693, ap: 0.455, auc: 0.521, f1: 0.000, acc: 0.559
+2025-08-26 00:34:18: Epoch: 046 Test results, ap: 0.252, auc: 0.522, f1: 0.000, acc: 0.756
+2025-08-26 00:34:19: Epoch: 047 Train Loss: 0.693, ap: 0.451, auc: 0.520, f1: 0.612, acc: 0.441
+2025-08-26 00:34:19: Epoch: 047 Test results, ap: 0.245, auc: 0.489, f1: 0.392, acc: 0.244
+2025-08-26 00:34:20: Epoch: 048 Train Loss: 0.693, ap: 0.460, auc: 0.536, f1: 0.000, acc: 0.559
+2025-08-26 00:34:21: Epoch: 048 Test results, ap: 0.248, auc: 0.495, f1: 0.000, acc: 0.756
+2025-08-26 00:34:22: Epoch: 049 Train Loss: 0.693, ap: 0.435, auc: 0.451, f1: 0.000, acc: 0.559
+2025-08-26 00:34:22: Epoch: 049 Test results, ap: 0.275, auc: 0.575, f1: 0.000, acc: 0.756
+2025-08-26 00:34:23: Epoch: 050 Train Loss: 0.693, ap: 0.451, auc: 0.517, f1: 0.612, acc: 0.441
+2025-08-26 00:34:23: Epoch: 050 Test results, ap: 0.253, auc: 0.524, f1: 0.392, acc: 0.244
+2025-08-26 00:34:23: used time 0.02h
+2025-08-26 00:34:24: Running Cross Validation 3
+2025-08-26 00:34:24: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:34:25: Epoch: 001 Train Loss: 0.695, ap: 0.407, auc: 0.466, f1: 0.602, acc: 0.432
+2025-08-26 00:34:25: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:34:25: Epoch: 001 Test results, ap: 0.200, auc: 0.351, f1: 0.392, acc: 0.244
+2025-08-26 00:34:26: Epoch: 002 Train Loss: 0.693, ap: 0.441, auc: 0.515, f1: 0.600, acc: 0.444
+2025-08-26 00:34:26: Epoch: 002 New best VALIDATION metrics
+2025-08-26 00:34:26: Epoch: 002 Test results, ap: 0.443, auc: 0.670, f1: 0.392, acc: 0.244
+2025-08-26 00:34:28: Epoch: 003 Train Loss: 0.693, ap: 0.467, auc: 0.558, f1: 0.000, acc: 0.570
+2025-08-26 00:34:28: Epoch: 003 New best VALIDATION metrics
+2025-08-26 00:34:28: Epoch: 003 Test results, ap: 0.247, auc: 0.529, f1: 0.000, acc: 0.756
+2025-08-26 00:34:29: Epoch: 004 Train Loss: 0.693, ap: 0.417, auc: 0.461, f1: 0.000, acc: 0.570
+2025-08-26 00:34:29: Epoch: 004 Test results, ap: 0.183, auc: 0.340, f1: 0.000, acc: 0.756
+2025-08-26 00:34:31: Epoch: 005 Train Loss: 0.693, ap: 0.423, auc: 0.509, f1: 0.597, acc: 0.429
+2025-08-26 00:34:31: Epoch: 005 Test results, ap: 0.341, auc: 0.646, f1: 0.392, acc: 0.244
+2025-08-26 00:34:32: Epoch: 006 Train Loss: 0.693, ap: 0.444, auc: 0.513, f1: 0.000, acc: 0.570
+2025-08-26 00:34:32: Epoch: 006 Test results, ap: 0.235, auc: 0.426, f1: 0.000, acc: 0.756
+2025-08-26 00:34:34: Epoch: 007 Train Loss: 0.694, ap: 0.394, auc: 0.423, f1: 0.602, acc: 0.430
+2025-08-26 00:34:34: Epoch: 007 Test results, ap: 0.246, auc: 0.513, f1: 0.392, acc: 0.244
+2025-08-26 00:34:35: Epoch: 008 Train Loss: 0.693, ap: 0.382, auc: 0.415, f1: 0.602, acc: 0.430
+2025-08-26 00:34:35: Epoch: 008 Test results, ap: 0.282, auc: 0.575, f1: 0.392, acc: 0.244
+2025-08-26 00:34:36: Epoch: 009 Train Loss: 0.694, ap: 0.452, auc: 0.519, f1: 0.602, acc: 0.430
+2025-08-26 00:34:37: Epoch: 009 Test results, ap: 0.298, auc: 0.615, f1: 0.392, acc: 0.244
+2025-08-26 00:34:38: Epoch: 010 Train Loss: 0.693, ap: 0.488, auc: 0.532, f1: 0.602, acc: 0.430
+2025-08-26 00:34:38: Epoch: 010 Test results, ap: 0.259, auc: 0.514, f1: 0.392, acc: 0.244
+2025-08-26 00:34:39: Epoch: 011 Train Loss: 0.693, ap: 0.468, auc: 0.574, f1: 0.602, acc: 0.430
+2025-08-26 00:34:39: Epoch: 011 New best VALIDATION metrics
+2025-08-26 00:34:40: Epoch: 011 Test results, ap: 0.254, auc: 0.519, f1: 0.392, acc: 0.244
+2025-08-26 00:34:40: Epoch: 011 New best TEST metrics
+2025-08-26 00:34:41: Epoch: 012 Train Loss: 0.693, ap: 0.508, auc: 0.573, f1: 0.000, acc: 0.570
+2025-08-26 00:34:41: Epoch: 012 New best VALIDATION metrics
+2025-08-26 00:34:41: Epoch: 012 Test results, ap: 0.258, auc: 0.519, f1: 0.000, acc: 0.756
+2025-08-26 00:34:41: Epoch: 012 New best TEST metrics
+2025-08-26 00:34:42: Epoch: 013 Train Loss: 0.694, ap: 0.468, auc: 0.565, f1: 0.602, acc: 0.430
+2025-08-26 00:34:43: Epoch: 013 Test results, ap: 0.234, auc: 0.462, f1: 0.392, acc: 0.244
+2025-08-26 00:34:44: Epoch: 014 Train Loss: 0.693, ap: 0.392, auc: 0.407, f1: 0.602, acc: 0.430
+2025-08-26 00:34:44: Epoch: 014 Test results, ap: 0.268, auc: 0.560, f1: 0.392, acc: 0.244
+2025-08-26 00:34:44: Epoch: 014 New best TEST metrics
+2025-08-26 00:34:45: Epoch: 015 Train Loss: 0.694, ap: 0.484, auc: 0.546, f1: 0.602, acc: 0.430
+2025-08-26 00:34:46: Epoch: 015 Test results, ap: 0.235, auc: 0.435, f1: 0.392, acc: 0.244
+2025-08-26 00:34:47: Epoch: 016 Train Loss: 0.693, ap: 0.381, auc: 0.403, f1: 0.602, acc: 0.430
+2025-08-26 00:34:47: Epoch: 016 Test results, ap: 0.241, auc: 0.480, f1: 0.392, acc: 0.244
+2025-08-26 00:34:48: Epoch: 017 Train Loss: 0.693, ap: 0.423, auc: 0.507, f1: 0.000, acc: 0.570
+2025-08-26 00:34:48: Epoch: 017 Test results, ap: 0.222, auc: 0.406, f1: 0.000, acc: 0.756
+2025-08-26 00:34:50: Epoch: 018 Train Loss: 0.693, ap: 0.494, auc: 0.578, f1: 0.000, acc: 0.570
+2025-08-26 00:34:50: Epoch: 018 Test results, ap: 0.274, auc: 0.535, f1: 0.000, acc: 0.756
+2025-08-26 00:34:51: Epoch: 019 Train Loss: 0.693, ap: 0.482, auc: 0.538, f1: 0.602, acc: 0.430
+2025-08-26 00:34:51: Epoch: 019 Test results, ap: 0.227, auc: 0.462, f1: 0.392, acc: 0.244
+2025-08-26 00:34:53: Epoch: 020 Train Loss: 0.694, ap: 0.387, auc: 0.431, f1: 0.602, acc: 0.430
+2025-08-26 00:34:53: Epoch: 020 Test results, ap: 0.251, auc: 0.484, f1: 0.392, acc: 0.244
+2025-08-26 00:34:54: Epoch: 021 Train Loss: 0.693, ap: 0.544, auc: 0.624, f1: 0.602, acc: 0.430
+2025-08-26 00:34:54: Epoch: 021 New best VALIDATION metrics
+2025-08-26 00:34:54: Epoch: 021 Test results, ap: 0.228, auc: 0.451, f1: 0.392, acc: 0.244
+2025-08-26 00:34:56: Epoch: 022 Train Loss: 0.693, ap: 0.439, auc: 0.503, f1: 0.000, acc: 0.570
+2025-08-26 00:34:56: Epoch: 022 Test results, ap: 0.243, auc: 0.474, f1: 0.000, acc: 0.756
+2025-08-26 00:34:57: Epoch: 023 Train Loss: 0.693, ap: 0.473, auc: 0.555, f1: 0.602, acc: 0.430
+2025-08-26 00:34:57: Epoch: 023 Test results, ap: 0.272, auc: 0.536, f1: 0.392, acc: 0.244
+2025-08-26 00:34:59: Epoch: 024 Train Loss: 0.694, ap: 0.526, auc: 0.608, f1: 0.602, acc: 0.430
+2025-08-26 00:34:59: Epoch: 024 Test results, ap: 0.206, auc: 0.380, f1: 0.392, acc: 0.244
+2025-08-26 00:35:00: Epoch: 025 Train Loss: 0.693, ap: 0.506, auc: 0.585, f1: 0.602, acc: 0.430
+2025-08-26 00:35:00: Epoch: 025 Test results, ap: 0.216, auc: 0.413, f1: 0.392, acc: 0.244
+2025-08-26 00:35:01: Epoch: 026 Train Loss: 0.694, ap: 0.499, auc: 0.580, f1: 0.602, acc: 0.430
+2025-08-26 00:35:02: Epoch: 026 Test results, ap: 0.226, auc: 0.443, f1: 0.392, acc: 0.244
+2025-08-26 00:35:03: Epoch: 027 Train Loss: 0.693, ap: 0.475, auc: 0.555, f1: 0.000, acc: 0.570
+2025-08-26 00:35:03: Epoch: 027 Test results, ap: 0.203, auc: 0.365, f1: 0.000, acc: 0.756
+2025-08-26 00:35:04: Epoch: 028 Train Loss: 0.693, ap: 0.493, auc: 0.562, f1: 0.000, acc: 0.570
+2025-08-26 00:35:05: Epoch: 028 Test results, ap: 0.246, auc: 0.496, f1: 0.000, acc: 0.756
+2025-08-26 00:35:06: Epoch: 029 Train Loss: 0.694, ap: 0.519, auc: 0.582, f1: 0.602, acc: 0.430
+2025-08-26 00:35:06: Epoch: 029 Test results, ap: 0.249, auc: 0.467, f1: 0.392, acc: 0.244
+2025-08-26 00:35:07: Epoch: 030 Train Loss: 0.694, ap: 0.557, auc: 0.611, f1: 0.602, acc: 0.430
+2025-08-26 00:35:08: Epoch: 030 Test results, ap: 0.228, auc: 0.438, f1: 0.392, acc: 0.244
+2025-08-26 00:35:09: Epoch: 031 Train Loss: 0.693, ap: 0.481, auc: 0.567, f1: 0.602, acc: 0.430
+2025-08-26 00:35:09: Epoch: 031 Test results, ap: 0.206, auc: 0.401, f1: 0.392, acc: 0.244
+2025-08-26 00:35:10: Epoch: 032 Train Loss: 0.693, ap: 0.491, auc: 0.554, f1: 0.000, acc: 0.570
+2025-08-26 00:35:11: Epoch: 032 Test results, ap: 0.220, auc: 0.449, f1: 0.000, acc: 0.756
+2025-08-26 00:35:12: Epoch: 033 Train Loss: 0.694, ap: 0.500, auc: 0.569, f1: 0.602, acc: 0.430
+2025-08-26 00:35:12: Epoch: 033 Test results, ap: 0.224, auc: 0.418, f1: 0.392, acc: 0.244
+2025-08-26 00:35:13: Epoch: 034 Train Loss: 0.693, ap: 0.495, auc: 0.548, f1: 0.000, acc: 0.570
+2025-08-26 00:35:13: Epoch: 034 Test results, ap: 0.210, auc: 0.420, f1: 0.000, acc: 0.756
+2025-08-26 00:35:15: Epoch: 035 Train Loss: 0.694, ap: 0.538, auc: 0.601, f1: 0.602, acc: 0.430
+2025-08-26 00:35:15: Epoch: 035 Test results, ap: 0.242, auc: 0.489, f1: 0.392, acc: 0.244
+2025-08-26 00:35:16: Epoch: 036 Train Loss: 0.694, ap: 0.495, auc: 0.518, f1: 0.602, acc: 0.430
+2025-08-26 00:35:16: Epoch: 036 Test results, ap: 0.249, auc: 0.526, f1: 0.392, acc: 0.244
+2025-08-26 00:35:18: Epoch: 037 Train Loss: 0.693, ap: 0.513, auc: 0.557, f1: 0.000, acc: 0.570
+2025-08-26 00:35:18: Epoch: 037 Test results, ap: 0.229, auc: 0.430, f1: 0.000, acc: 0.756
+2025-08-26 00:35:19: Epoch: 038 Train Loss: 0.693, ap: 0.494, auc: 0.547, f1: 0.272, acc: 0.612
+2025-08-26 00:35:19: Epoch: 038 Test results, ap: 0.211, auc: 0.426, f1: 0.000, acc: 0.724
+2025-08-26 00:35:21: Epoch: 039 Train Loss: 0.693, ap: 0.552, auc: 0.620, f1: 0.602, acc: 0.430
+2025-08-26 00:35:21: Epoch: 039 New best VALIDATION metrics
+2025-08-26 00:35:21: Epoch: 039 Test results, ap: 0.217, auc: 0.414, f1: 0.392, acc: 0.244
+2025-08-26 00:35:22: Epoch: 040 Train Loss: 0.693, ap: 0.546, auc: 0.595, f1: 0.602, acc: 0.430
+2025-08-26 00:35:22: Epoch: 040 Test results, ap: 0.266, auc: 0.550, f1: 0.392, acc: 0.244
+2025-08-26 00:35:23: Epoch: 041 Train Loss: 0.694, ap: 0.524, auc: 0.564, f1: 0.000, acc: 0.570
+2025-08-26 00:35:24: Epoch: 041 Test results, ap: 0.257, auc: 0.549, f1: 0.000, acc: 0.756
+2025-08-26 00:35:25: Epoch: 042 Train Loss: 0.693, ap: 0.550, auc: 0.618, f1: 0.000, acc: 0.570
+2025-08-26 00:35:25: Epoch: 042 Test results, ap: 0.207, auc: 0.399, f1: 0.000, acc: 0.756
+2025-08-26 00:35:26: Epoch: 043 Train Loss: 0.693, ap: 0.524, auc: 0.572, f1: 0.602, acc: 0.430
+2025-08-26 00:35:27: Epoch: 043 Test results, ap: 0.237, auc: 0.468, f1: 0.392, acc: 0.244
+2025-08-26 00:35:28: Epoch: 044 Train Loss: 0.693, ap: 0.548, auc: 0.606, f1: 0.000, acc: 0.570
+2025-08-26 00:35:28: Epoch: 044 Test results, ap: 0.230, auc: 0.480, f1: 0.000, acc: 0.756
+2025-08-26 00:35:29: Epoch: 045 Train Loss: 0.694, ap: 0.562, auc: 0.629, f1: 0.602, acc: 0.430
+2025-08-26 00:35:29: Epoch: 045 New best VALIDATION metrics
+2025-08-26 00:35:30: Epoch: 045 Test results, ap: 0.218, auc: 0.452, f1: 0.392, acc: 0.244
+2025-08-26 00:35:31: Epoch: 046 Train Loss: 0.693, ap: 0.543, auc: 0.596, f1: 0.602, acc: 0.434
+2025-08-26 00:35:31: Epoch: 046 Test results, ap: 0.222, auc: 0.462, f1: 0.392, acc: 0.244
+2025-08-26 00:35:32: Epoch: 047 Train Loss: 0.694, ap: 0.532, auc: 0.584, f1: 0.602, acc: 0.430
+2025-08-26 00:35:33: Epoch: 047 Test results, ap: 0.234, auc: 0.487, f1: 0.392, acc: 0.244
+2025-08-26 00:35:34: Epoch: 048 Train Loss: 0.693, ap: 0.498, auc: 0.523, f1: 0.452, acc: 0.520
+2025-08-26 00:35:34: Epoch: 048 Test results, ap: 0.271, auc: 0.560, f1: 0.000, acc: 0.650
+2025-08-26 00:35:34: Epoch: 048 New best TEST metrics
+2025-08-26 00:35:35: Epoch: 049 Train Loss: 0.673, ap: 0.542, auc: 0.591, f1: 0.492, acc: 0.511
+2025-08-26 00:35:36: Epoch: 049 Test results, ap: 0.306, auc: 0.585, f1: 0.374, acc: 0.537
+2025-08-26 00:35:36: Epoch: 049 New best TEST metrics
+2025-08-26 00:35:37: Epoch: 050 Train Loss: 0.621, ap: 0.697, auc: 0.749, f1: 0.626, acc: 0.697
+2025-08-26 00:35:37: Epoch: 050 New best VALIDATION metrics
+2025-08-26 00:35:37: Epoch: 050 Test results, ap: 0.301, auc: 0.565, f1: 0.306, acc: 0.593
+2025-08-26 00:35:37: used time 0.02h
+2025-08-26 00:35:37: Running Cross Validation 4
+2025-08-26 00:35:37: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:35:38: Epoch: 001 Train Loss: 0.693, ap: 0.567, auc: 0.602, f1: 0.000, acc: 0.557
+2025-08-26 00:35:38: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:35:39: Epoch: 001 Test results, ap: 0.208, auc: 0.431, f1: 0.000, acc: 0.756
+2025-08-26 00:35:40: Epoch: 002 Train Loss: 0.693, ap: 0.445, auc: 0.495, f1: 0.071, acc: 0.540
+2025-08-26 00:35:40: Epoch: 002 Test results, ap: 0.312, auc: 0.613, f1: 0.000, acc: 0.756
+2025-08-26 00:35:41: Epoch: 003 Train Loss: 0.693, ap: 0.425, auc: 0.474, f1: 0.000, acc: 0.557
+2025-08-26 00:35:41: Epoch: 003 Test results, ap: 0.245, auc: 0.502, f1: 0.000, acc: 0.756
+2025-08-26 00:35:43: Epoch: 004 Train Loss: 0.693, ap: 0.460, auc: 0.531, f1: 0.000, acc: 0.557
+2025-08-26 00:35:43: Epoch: 004 Test results, ap: 0.224, auc: 0.425, f1: 0.000, acc: 0.756
+2025-08-26 00:35:44: Epoch: 005 Train Loss: 0.693, ap: 0.513, auc: 0.572, f1: 0.614, acc: 0.443
+2025-08-26 00:35:44: Epoch: 005 Test results, ap: 0.238, auc: 0.480, f1: 0.392, acc: 0.244
+2025-08-26 00:35:46: Epoch: 006 Train Loss: 0.693, ap: 0.442, auc: 0.485, f1: 0.000, acc: 0.557
+2025-08-26 00:35:46: Epoch: 006 Test results, ap: 0.230, auc: 0.482, f1: 0.000, acc: 0.756
+2025-08-26 00:35:47: Epoch: 007 Train Loss: 0.693, ap: 0.441, auc: 0.511, f1: 0.000, acc: 0.557
+2025-08-26 00:35:47: Epoch: 007 Test results, ap: 0.247, auc: 0.484, f1: 0.000, acc: 0.756
+2025-08-26 00:35:49: Epoch: 008 Train Loss: 0.693, ap: 0.509, auc: 0.609, f1: 0.614, acc: 0.443
+2025-08-26 00:35:49: Epoch: 008 Test results, ap: 0.264, auc: 0.522, f1: 0.392, acc: 0.244
+2025-08-26 00:35:50: Epoch: 009 Train Loss: 0.693, ap: 0.425, auc: 0.475, f1: 0.614, acc: 0.443
+2025-08-26 00:35:50: Epoch: 009 Test results, ap: 0.267, auc: 0.534, f1: 0.392, acc: 0.244
+2025-08-26 00:35:52: Epoch: 010 Train Loss: 0.694, ap: 0.461, auc: 0.531, f1: 0.614, acc: 0.443
+2025-08-26 00:35:52: Epoch: 010 Test results, ap: 0.241, auc: 0.492, f1: 0.392, acc: 0.244
+2025-08-26 00:35:53: Epoch: 011 Train Loss: 0.693, ap: 0.480, auc: 0.540, f1: 0.000, acc: 0.557
+2025-08-26 00:35:53: Epoch: 011 Test results, ap: 0.241, auc: 0.488, f1: 0.000, acc: 0.756
+2025-08-26 00:35:53: Epoch: 011 New best TEST metrics
+2025-08-26 00:35:54: Epoch: 012 Train Loss: 0.693, ap: 0.469, auc: 0.550, f1: 0.614, acc: 0.443
+2025-08-26 00:35:55: Epoch: 012 Test results, ap: 0.244, auc: 0.499, f1: 0.392, acc: 0.244
+2025-08-26 00:35:55: Epoch: 012 New best TEST metrics
+2025-08-26 00:35:56: Epoch: 013 Train Loss: 0.693, ap: 0.441, auc: 0.477, f1: 0.614, acc: 0.443
+2025-08-26 00:35:56: Epoch: 013 Test results, ap: 0.226, auc: 0.444, f1: 0.392, acc: 0.244
+2025-08-26 00:35:57: Epoch: 014 Train Loss: 0.694, ap: 0.424, auc: 0.444, f1: 0.614, acc: 0.443
+2025-08-26 00:35:58: Epoch: 014 Test results, ap: 0.222, auc: 0.391, f1: 0.392, acc: 0.244
+2025-08-26 00:35:59: Epoch: 015 Train Loss: 0.692, ap: 0.493, auc: 0.568, f1: 0.614, acc: 0.443
+2025-08-26 00:35:59: Epoch: 015 Test results, ap: 0.242, auc: 0.487, f1: 0.392, acc: 0.244
+2025-08-26 00:36:00: Epoch: 016 Train Loss: 0.694, ap: 0.422, auc: 0.461, f1: 0.614, acc: 0.443
+2025-08-26 00:36:01: Epoch: 016 Test results, ap: 0.245, auc: 0.496, f1: 0.392, acc: 0.244
+2025-08-26 00:36:02: Epoch: 017 Train Loss: 0.694, ap: 0.399, auc: 0.427, f1: 0.614, acc: 0.443
+2025-08-26 00:36:02: Epoch: 017 Test results, ap: 0.238, auc: 0.484, f1: 0.392, acc: 0.244
+2025-08-26 00:36:03: Epoch: 018 Train Loss: 0.694, ap: 0.448, auc: 0.505, f1: 0.614, acc: 0.443
+2025-08-26 00:36:03: Epoch: 018 Test results, ap: 0.299, auc: 0.588, f1: 0.392, acc: 0.244
+2025-08-26 00:36:03: Epoch: 018 New best TEST metrics
+2025-08-26 00:36:05: Epoch: 019 Train Loss: 0.693, ap: 0.516, auc: 0.591, f1: 0.000, acc: 0.557
+2025-08-26 00:36:05: Epoch: 019 Test results, ap: 0.218, auc: 0.442, f1: 0.000, acc: 0.756
+2025-08-26 00:36:06: Epoch: 020 Train Loss: 0.694, ap: 0.473, auc: 0.543, f1: 0.000, acc: 0.557
+2025-08-26 00:36:06: Epoch: 020 Test results, ap: 0.308, auc: 0.622, f1: 0.000, acc: 0.756
+2025-08-26 00:36:06: Epoch: 020 New best TEST metrics
+2025-08-26 00:36:08: Epoch: 021 Train Loss: 0.694, ap: 0.418, auc: 0.455, f1: 0.614, acc: 0.443
+2025-08-26 00:36:08: Epoch: 021 Test results, ap: 0.219, auc: 0.393, f1: 0.392, acc: 0.244
+2025-08-26 00:36:09: Epoch: 022 Train Loss: 0.693, ap: 0.457, auc: 0.498, f1: 0.000, acc: 0.557
+2025-08-26 00:36:09: Epoch: 022 Test results, ap: 0.312, auc: 0.606, f1: 0.000, acc: 0.756
+2025-08-26 00:36:11: Epoch: 023 Train Loss: 0.694, ap: 0.382, auc: 0.393, f1: 0.614, acc: 0.443
+2025-08-26 00:36:11: Epoch: 023 Test results, ap: 0.288, auc: 0.563, f1: 0.392, acc: 0.244
+2025-08-26 00:36:12: Epoch: 024 Train Loss: 0.693, ap: 0.448, auc: 0.489, f1: 0.000, acc: 0.557
+2025-08-26 00:36:12: Epoch: 024 Test results, ap: 0.267, auc: 0.538, f1: 0.000, acc: 0.756
+2025-08-26 00:36:14: Epoch: 025 Train Loss: 0.693, ap: 0.387, auc: 0.398, f1: 0.000, acc: 0.557
+2025-08-26 00:36:14: Epoch: 025 Test results, ap: 0.286, auc: 0.532, f1: 0.000, acc: 0.756
+2025-08-26 00:36:15: Epoch: 026 Train Loss: 0.693, ap: 0.432, auc: 0.483, f1: 0.000, acc: 0.557
+2025-08-26 00:36:15: Epoch: 026 Test results, ap: 0.251, auc: 0.519, f1: 0.000, acc: 0.756
+2025-08-26 00:36:17: Epoch: 027 Train Loss: 0.694, ap: 0.497, auc: 0.581, f1: 0.614, acc: 0.444
+2025-08-26 00:36:17: Epoch: 027 Test results, ap: 0.214, auc: 0.392, f1: 0.392, acc: 0.244
+2025-08-26 00:36:18: Epoch: 028 Train Loss: 0.693, ap: 0.402, auc: 0.421, f1: 0.614, acc: 0.443
+2025-08-26 00:36:18: Epoch: 028 Test results, ap: 0.241, auc: 0.480, f1: 0.392, acc: 0.244
+2025-08-26 00:36:19: Epoch: 029 Train Loss: 0.693, ap: 0.520, auc: 0.572, f1: 0.000, acc: 0.557
+2025-08-26 00:36:20: Epoch: 029 Test results, ap: 0.230, auc: 0.493, f1: 0.000, acc: 0.756
+2025-08-26 00:36:21: Epoch: 030 Train Loss: 0.692, ap: 0.500, auc: 0.561, f1: 0.000, acc: 0.557
+2025-08-26 00:36:21: Epoch: 030 Test results, ap: 0.262, auc: 0.553, f1: 0.000, acc: 0.756
+2025-08-26 00:36:22: Epoch: 031 Train Loss: 0.693, ap: 0.470, auc: 0.548, f1: 0.000, acc: 0.557
+2025-08-26 00:36:23: Epoch: 031 Test results, ap: 0.272, auc: 0.514, f1: 0.000, acc: 0.756
+2025-08-26 00:36:24: Epoch: 032 Train Loss: 0.693, ap: 0.455, auc: 0.519, f1: 0.000, acc: 0.557
+2025-08-26 00:36:24: Epoch: 032 Test results, ap: 0.248, auc: 0.509, f1: 0.000, acc: 0.756
+2025-08-26 00:36:25: Epoch: 033 Train Loss: 0.693, ap: 0.432, auc: 0.483, f1: 0.614, acc: 0.443
+2025-08-26 00:36:26: Epoch: 033 Test results, ap: 0.283, auc: 0.484, f1: 0.392, acc: 0.244
+2025-08-26 00:36:27: Epoch: 034 Train Loss: 0.693, ap: 0.499, auc: 0.583, f1: 0.614, acc: 0.444
+2025-08-26 00:36:27: Epoch: 034 Test results, ap: 0.235, auc: 0.446, f1: 0.392, acc: 0.244
+2025-08-26 00:36:28: Epoch: 035 Train Loss: 0.694, ap: 0.412, auc: 0.452, f1: 0.000, acc: 0.557
+2025-08-26 00:36:29: Epoch: 035 Test results, ap: 0.275, auc: 0.566, f1: 0.000, acc: 0.756
+2025-08-26 00:36:30: Epoch: 036 Train Loss: 0.693, ap: 0.387, auc: 0.401, f1: 0.000, acc: 0.557
+2025-08-26 00:36:30: Epoch: 036 Test results, ap: 0.259, auc: 0.540, f1: 0.000, acc: 0.756
+2025-08-26 00:36:31: Epoch: 037 Train Loss: 0.693, ap: 0.520, auc: 0.589, f1: 0.614, acc: 0.444
+2025-08-26 00:36:31: Epoch: 037 Test results, ap: 0.238, auc: 0.491, f1: 0.392, acc: 0.244
+2025-08-26 00:36:33: Epoch: 038 Train Loss: 0.693, ap: 0.465, auc: 0.546, f1: 0.614, acc: 0.444
+2025-08-26 00:36:33: Epoch: 038 Test results, ap: 0.209, auc: 0.398, f1: 0.392, acc: 0.244
+2025-08-26 00:36:34: Epoch: 039 Train Loss: 0.693, ap: 0.418, auc: 0.448, f1: 0.000, acc: 0.557
+2025-08-26 00:36:34: Epoch: 039 Test results, ap: 0.249, auc: 0.512, f1: 0.000, acc: 0.756
+2025-08-26 00:36:36: Epoch: 040 Train Loss: 0.693, ap: 0.506, auc: 0.586, f1: 0.614, acc: 0.443
+2025-08-26 00:36:36: Epoch: 040 Test results, ap: 0.241, auc: 0.497, f1: 0.392, acc: 0.244
+2025-08-26 00:36:37: Epoch: 041 Train Loss: 0.693, ap: 0.395, auc: 0.424, f1: 0.614, acc: 0.444
+2025-08-26 00:36:37: Epoch: 041 Test results, ap: 0.236, auc: 0.461, f1: 0.392, acc: 0.244
+2025-08-26 00:36:38: Epoch: 042 Train Loss: 0.694, ap: 0.464, auc: 0.538, f1: 0.614, acc: 0.444
+2025-08-26 00:36:39: Epoch: 042 Test results, ap: 0.328, auc: 0.591, f1: 0.392, acc: 0.244
+2025-08-26 00:36:40: Epoch: 043 Train Loss: 0.693, ap: 0.501, auc: 0.571, f1: 0.000, acc: 0.557
+2025-08-26 00:36:40: Epoch: 043 Test results, ap: 0.223, auc: 0.440, f1: 0.000, acc: 0.756
+2025-08-26 00:36:41: Epoch: 044 Train Loss: 0.694, ap: 0.555, auc: 0.617, f1: 0.614, acc: 0.444
+2025-08-26 00:36:41: Epoch: 044 New best VALIDATION metrics
+2025-08-26 00:36:42: Epoch: 044 Test results, ap: 0.246, auc: 0.506, f1: 0.392, acc: 0.244
+2025-08-26 00:36:43: Epoch: 045 Train Loss: 0.693, ap: 0.496, auc: 0.574, f1: 0.000, acc: 0.557
+2025-08-26 00:36:43: Epoch: 045 Test results, ap: 0.321, auc: 0.561, f1: 0.000, acc: 0.756
+2025-08-26 00:36:44: Epoch: 046 Train Loss: 0.693, ap: 0.514, auc: 0.604, f1: 0.614, acc: 0.444
+2025-08-26 00:36:45: Epoch: 046 Test results, ap: 0.242, auc: 0.484, f1: 0.392, acc: 0.244
+2025-08-26 00:36:46: Epoch: 047 Train Loss: 0.693, ap: 0.468, auc: 0.528, f1: 0.614, acc: 0.444
+2025-08-26 00:36:46: Epoch: 047 Test results, ap: 0.267, auc: 0.514, f1: 0.392, acc: 0.244
+2025-08-26 00:36:47: Epoch: 048 Train Loss: 0.693, ap: 0.496, auc: 0.567, f1: 0.000, acc: 0.557
+2025-08-26 00:36:47: Epoch: 048 Test results, ap: 0.347, auc: 0.509, f1: 0.000, acc: 0.756
+2025-08-26 00:36:49: Epoch: 049 Train Loss: 0.693, ap: 0.518, auc: 0.578, f1: 0.614, acc: 0.444
+2025-08-26 00:36:49: Epoch: 049 Test results, ap: 0.229, auc: 0.420, f1: 0.392, acc: 0.244
+2025-08-26 00:36:50: Epoch: 050 Train Loss: 0.693, ap: 0.526, auc: 0.609, f1: 0.614, acc: 0.444
+2025-08-26 00:36:50: Epoch: 050 Test results, ap: 0.244, auc: 0.496, f1: 0.392, acc: 0.244
+2025-08-26 00:36:50: used time 0.02h
+2025-08-26 00:36:50: Cross Validation Finished!
+2025-08-26 00:36:50: Best validation perform list
+[[0.564722   0.59857202 0.64922708 0.4806338 ]
+ [0.70460749 0.74018395 0.58333331 0.66549295]
+ [0.52579761 0.60005045 0.         0.55908287]
+ [0.6968106  0.74946707 0.62608695 0.69664901]
+ [0.55516577 0.61678606 0.61444306 0.44444445]]
+2025-08-26 00:36:50: mean: [0.609 0.661 0.495 0.569]
+2025-08-26 00:36:50: std: [0.076 0.069 0.248 0.099]
+2025-08-26 00:36:50: Best test perform list
+[[0.32507938 0.61899644 0.         0.75609756]
+ [0.52841443 0.63620067 0.37037036 0.30894309]
+ [0.30663055 0.63333333 0.39215687 0.24390244]
+ [0.30556583 0.58530462 0.37362638 0.53658539]
+ [0.30844709 0.62204301 0.         0.75609756]]
+2025-08-26 00:36:50: mean: [0.355 0.619 0.227 0.52 ]
+2025-08-26 00:36:50: std: [0.087 0.018 0.186 0.216]
diff --git a/run-cls/lstm-mlp-256-ce-32-0.001-50/config.json b/run-cls/lstm-mlp-256-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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diff --git a/run-cls/lstm-mlp-256-ce-32-0.001-50/result.txt b/run-cls/lstm-mlp-256-ce-32-0.001-50/result.txt
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+++ b/run-cls/lstm-mlp-256-ce-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
+Valid
+0.500958114862442,0.5796531558036804,0.48705129623413085,0.45398378372192383
+0.02637282735873211,0.01723983634418505,0.24356058717749643,0.03296703656379193
+Test
+0.2973113775253296,0.5955555438995361,0.0,0.7560975551605225
+0.030823085727692715,0.04618492664582198,0.0,0.0
diff --git a/run-cls/lstm-mlp-256-ce-32-0.001-50/training.log b/run-cls/lstm-mlp-256-ce-32-0.001-50/training.log
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@@ -0,0 +1,558 @@
+2025-08-26 00:17:57: saving_dir: ./run-cls/lstm-mlp-256-ce-32-0.001-50
+2025-08-26 00:17:57: Loading Train Dataset
+2025-08-26 00:17:57: Loading Test Dataset
+2025-08-26 00:17:58: Running Cross Validation 0
+2025-08-26 00:17:58: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 00:18:00: Epoch: 001 Train Loss: 0.694, ap: 0.491, auc: 0.521, f1: 0.000, acc: 0.519
+2025-08-26 00:18:00: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:18:00: Epoch: 001 Test results, ap: 0.199, auc: 0.341, f1: 0.000, acc: 0.756
+2025-08-26 00:18:01: Epoch: 002 Train Loss: 0.694, ap: 0.486, auc: 0.531, f1: 0.649, acc: 0.481
+2025-08-26 00:18:01: Epoch: 002 New best VALIDATION metrics
+2025-08-26 00:18:01: Epoch: 002 Test results, ap: 0.380, auc: 0.708, f1: 0.392, acc: 0.244
+2025-08-26 00:18:02: Epoch: 003 Train Loss: 0.694, ap: 0.492, auc: 0.529, f1: 0.000, acc: 0.519
+2025-08-26 00:18:02: Epoch: 003 New best VALIDATION metrics
+2025-08-26 00:18:03: Epoch: 003 Test results, ap: 0.388, auc: 0.685, f1: 0.000, acc: 0.756
+2025-08-26 00:18:04: Epoch: 004 Train Loss: 0.693, ap: 0.478, auc: 0.516, f1: 0.649, acc: 0.481
+2025-08-26 00:18:04: Epoch: 004 Test results, ap: 0.495, auc: 0.711, f1: 0.392, acc: 0.244
+2025-08-26 00:18:05: Epoch: 005 Train Loss: 0.694, ap: 0.521, auc: 0.551, f1: 0.000, acc: 0.519
+2025-08-26 00:18:05: Epoch: 005 New best VALIDATION metrics
+2025-08-26 00:18:06: Epoch: 005 Test results, ap: 0.185, auc: 0.323, f1: 0.000, acc: 0.756
+2025-08-26 00:18:07: Epoch: 006 Train Loss: 0.695, ap: 0.512, auc: 0.553, f1: 0.000, acc: 0.519
+2025-08-26 00:18:07: Epoch: 006 Test results, ap: 0.446, auc: 0.661, f1: 0.000, acc: 0.756
+2025-08-26 00:18:08: Epoch: 007 Train Loss: 0.694, ap: 0.479, auc: 0.530, f1: 0.649, acc: 0.481
+2025-08-26 00:18:08: Epoch: 007 Test results, ap: 0.535, auc: 0.652, f1: 0.392, acc: 0.244
+2025-08-26 00:18:10: Epoch: 008 Train Loss: 0.695, ap: 0.486, auc: 0.541, f1: 0.649, acc: 0.481
+2025-08-26 00:18:10: Epoch: 008 Test results, ap: 0.442, auc: 0.646, f1: 0.392, acc: 0.244
+2025-08-26 00:18:11: Epoch: 009 Train Loss: 0.693, ap: 0.432, auc: 0.448, f1: 0.000, acc: 0.519
+2025-08-26 00:18:11: Epoch: 009 Test results, ap: 0.178, auc: 0.298, f1: 0.000, acc: 0.756
+2025-08-26 00:18:12: Epoch: 010 Train Loss: 0.694, ap: 0.540, auc: 0.579, f1: 0.649, acc: 0.481
+2025-08-26 00:18:12: Epoch: 010 New best VALIDATION metrics
+2025-08-26 00:18:13: Epoch: 010 Test results, ap: 0.340, auc: 0.506, f1: 0.392, acc: 0.244
+2025-08-26 00:18:14: Epoch: 011 Train Loss: 0.693, ap: 0.438, auc: 0.415, f1: 0.000, acc: 0.519
+2025-08-26 00:18:14: Epoch: 011 Test results, ap: 0.319, auc: 0.623, f1: 0.000, acc: 0.756
+2025-08-26 00:18:14: Epoch: 011 New best TEST metrics
+2025-08-26 00:18:15: Epoch: 012 Train Loss: 0.694, ap: 0.420, auc: 0.369, f1: 0.000, acc: 0.519
+2025-08-26 00:18:15: Epoch: 012 Test results, ap: 0.325, auc: 0.639, f1: 0.000, acc: 0.756
+2025-08-26 00:18:15: Epoch: 012 New best TEST metrics
+2025-08-26 00:18:17: Epoch: 013 Train Loss: 0.694, ap: 0.426, auc: 0.418, f1: 0.649, acc: 0.481
+2025-08-26 00:18:17: Epoch: 013 Test results, ap: 0.245, auc: 0.502, f1: 0.392, acc: 0.244
+2025-08-26 00:18:18: Epoch: 014 Train Loss: 0.694, ap: 0.436, auc: 0.442, f1: 0.000, acc: 0.519
+2025-08-26 00:18:18: Epoch: 014 Test results, ap: 0.227, auc: 0.418, f1: 0.000, acc: 0.756
+2025-08-26 00:18:19: Epoch: 015 Train Loss: 0.693, ap: 0.446, auc: 0.438, f1: 0.649, acc: 0.481
+2025-08-26 00:18:20: Epoch: 015 Test results, ap: 0.255, auc: 0.529, f1: 0.392, acc: 0.244
+2025-08-26 00:18:21: Epoch: 016 Train Loss: 0.693, ap: 0.440, auc: 0.421, f1: 0.649, acc: 0.481
+2025-08-26 00:18:21: Epoch: 016 Test results, ap: 0.248, auc: 0.511, f1: 0.392, acc: 0.244
+2025-08-26 00:18:22: Epoch: 017 Train Loss: 0.694, ap: 0.460, auc: 0.470, f1: 0.000, acc: 0.519
+2025-08-26 00:18:22: Epoch: 017 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:18:24: Epoch: 018 Train Loss: 0.693, ap: 0.445, auc: 0.427, f1: 0.000, acc: 0.519
+2025-08-26 00:18:24: Epoch: 018 Test results, ap: 0.231, auc: 0.443, f1: 0.000, acc: 0.756
+2025-08-26 00:18:25: Epoch: 019 Train Loss: 0.693, ap: 0.460, auc: 0.450, f1: 0.000, acc: 0.519
+2025-08-26 00:18:25: Epoch: 019 Test results, ap: 0.306, auc: 0.625, f1: 0.000, acc: 0.756
+2025-08-26 00:18:26: Epoch: 020 Train Loss: 0.694, ap: 0.490, auc: 0.517, f1: 0.649, acc: 0.481
+2025-08-26 00:18:27: Epoch: 020 Test results, ap: 0.247, auc: 0.506, f1: 0.392, acc: 0.244
+2025-08-26 00:18:28: Epoch: 021 Train Loss: 0.693, ap: 0.465, auc: 0.456, f1: 0.000, acc: 0.519
+2025-08-26 00:18:28: Epoch: 021 Test results, ap: 0.260, auc: 0.541, f1: 0.000, acc: 0.756
+2025-08-26 00:18:29: Epoch: 022 Train Loss: 0.694, ap: 0.494, auc: 0.514, f1: 0.649, acc: 0.481
+2025-08-26 00:18:30: Epoch: 022 Test results, ap: 0.246, auc: 0.505, f1: 0.392, acc: 0.244
+2025-08-26 00:18:31: Epoch: 023 Train Loss: 0.693, ap: 0.500, auc: 0.524, f1: 0.000, acc: 0.519
+2025-08-26 00:18:31: Epoch: 023 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:18:32: Epoch: 024 Train Loss: 0.693, ap: 0.484, auc: 0.505, f1: 0.649, acc: 0.481
+2025-08-26 00:18:33: Epoch: 024 Test results, ap: 0.246, auc: 0.505, f1: 0.392, acc: 0.244
+2025-08-26 00:18:34: Epoch: 025 Train Loss: 0.693, ap: 0.458, auc: 0.469, f1: 0.649, acc: 0.481
+2025-08-26 00:18:34: Epoch: 025 Test results, ap: 0.258, auc: 0.535, f1: 0.392, acc: 0.244
+2025-08-26 00:18:35: Epoch: 026 Train Loss: 0.693, ap: 0.475, auc: 0.484, f1: 0.649, acc: 0.481
+2025-08-26 00:18:35: Epoch: 026 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:18:37: Epoch: 027 Train Loss: 0.693, ap: 0.469, auc: 0.486, f1: 0.000, acc: 0.519
+2025-08-26 00:18:37: Epoch: 027 Test results, ap: 0.244, auc: 0.441, f1: 0.000, acc: 0.756
+2025-08-26 00:18:38: Epoch: 028 Train Loss: 0.693, ap: 0.483, auc: 0.509, f1: 0.000, acc: 0.519
+2025-08-26 00:18:38: Epoch: 028 Test results, ap: 0.330, auc: 0.619, f1: 0.000, acc: 0.756
+2025-08-26 00:18:39: Epoch: 029 Train Loss: 0.693, ap: 0.481, auc: 0.489, f1: 0.649, acc: 0.481
+2025-08-26 00:18:40: Epoch: 029 Test results, ap: 0.238, auc: 0.483, f1: 0.392, acc: 0.244
+2025-08-26 00:18:41: Epoch: 030 Train Loss: 0.693, ap: 0.470, auc: 0.475, f1: 0.649, acc: 0.481
+2025-08-26 00:18:41: Epoch: 030 Test results, ap: 0.247, auc: 0.509, f1: 0.392, acc: 0.244
+2025-08-26 00:18:42: Epoch: 031 Train Loss: 0.693, ap: 0.551, auc: 0.591, f1: 0.000, acc: 0.519
+2025-08-26 00:18:42: Epoch: 031 New best VALIDATION metrics
+2025-08-26 00:18:43: Epoch: 031 Test results, ap: 0.277, auc: 0.578, f1: 0.000, acc: 0.756
+2025-08-26 00:18:44: Epoch: 032 Train Loss: 0.693, ap: 0.512, auc: 0.549, f1: 0.000, acc: 0.519
+2025-08-26 00:18:44: Epoch: 032 Test results, ap: 0.256, auc: 0.530, f1: 0.000, acc: 0.756
+2025-08-26 00:18:45: Epoch: 033 Train Loss: 0.693, ap: 0.489, auc: 0.515, f1: 0.649, acc: 0.481
+2025-08-26 00:18:45: Epoch: 033 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:18:46: Epoch: 034 Train Loss: 0.693, ap: 0.467, auc: 0.464, f1: 0.000, acc: 0.519
+2025-08-26 00:18:47: Epoch: 034 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:18:48: Epoch: 035 Train Loss: 0.693, ap: 0.486, auc: 0.492, f1: 0.649, acc: 0.481
+2025-08-26 00:18:48: Epoch: 035 Test results, ap: 0.259, auc: 0.538, f1: 0.392, acc: 0.244
+2025-08-26 00:18:49: Epoch: 036 Train Loss: 0.694, ap: 0.493, auc: 0.524, f1: 0.649, acc: 0.481
+2025-08-26 00:18:50: Epoch: 036 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:18:51: Epoch: 037 Train Loss: 0.694, ap: 0.467, auc: 0.457, f1: 0.649, acc: 0.481
+2025-08-26 00:18:51: Epoch: 037 Test results, ap: 0.252, auc: 0.522, f1: 0.392, acc: 0.244
+2025-08-26 00:18:52: Epoch: 038 Train Loss: 0.693, ap: 0.491, auc: 0.510, f1: 0.649, acc: 0.481
+2025-08-26 00:18:52: Epoch: 038 Test results, ap: 0.267, auc: 0.557, f1: 0.392, acc: 0.244
+2025-08-26 00:18:54: Epoch: 039 Train Loss: 0.693, ap: 0.468, auc: 0.493, f1: 0.000, acc: 0.519
+2025-08-26 00:18:54: Epoch: 039 Test results, ap: 0.268, auc: 0.558, f1: 0.000, acc: 0.756
+2025-08-26 00:18:55: Epoch: 040 Train Loss: 0.693, ap: 0.486, auc: 0.517, f1: 0.649, acc: 0.481
+2025-08-26 00:18:55: Epoch: 040 Test results, ap: 0.248, auc: 0.511, f1: 0.392, acc: 0.244
+2025-08-26 00:18:56: Epoch: 041 Train Loss: 0.694, ap: 0.463, auc: 0.490, f1: 0.000, acc: 0.519
+2025-08-26 00:18:57: Epoch: 041 Test results, ap: 0.247, auc: 0.506, f1: 0.000, acc: 0.756
+2025-08-26 00:18:58: Epoch: 042 Train Loss: 0.693, ap: 0.514, auc: 0.559, f1: 0.649, acc: 0.481
+2025-08-26 00:18:58: Epoch: 042 Test results, ap: 0.246, auc: 0.505, f1: 0.392, acc: 0.244
+2025-08-26 00:18:59: Epoch: 043 Train Loss: 0.694, ap: 0.503, auc: 0.542, f1: 0.649, acc: 0.481
+2025-08-26 00:19:00: Epoch: 043 Test results, ap: 0.269, auc: 0.517, f1: 0.392, acc: 0.244
+2025-08-26 00:19:01: Epoch: 044 Train Loss: 0.693, ap: 0.492, auc: 0.522, f1: 0.649, acc: 0.481
+2025-08-26 00:19:01: Epoch: 044 Test results, ap: 0.237, auc: 0.377, f1: 0.392, acc: 0.244
+2025-08-26 00:19:02: Epoch: 045 Train Loss: 0.693, ap: 0.501, auc: 0.539, f1: 0.649, acc: 0.481
+2025-08-26 00:19:02: Epoch: 045 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:04: Epoch: 046 Train Loss: 0.693, ap: 0.503, auc: 0.542, f1: 0.000, acc: 0.519
+2025-08-26 00:19:04: Epoch: 046 Test results, ap: 0.237, auc: 0.475, f1: 0.000, acc: 0.756
+2025-08-26 00:19:05: Epoch: 047 Train Loss: 0.693, ap: 0.464, auc: 0.465, f1: 0.000, acc: 0.519
+2025-08-26 00:19:05: Epoch: 047 Test results, ap: 0.264, auc: 0.528, f1: 0.000, acc: 0.756
+2025-08-26 00:19:06: Epoch: 048 Train Loss: 0.693, ap: 0.466, auc: 0.466, f1: 0.649, acc: 0.481
+2025-08-26 00:19:07: Epoch: 048 Test results, ap: 0.229, auc: 0.457, f1: 0.392, acc: 0.244
+2025-08-26 00:19:08: Epoch: 049 Train Loss: 0.694, ap: 0.469, auc: 0.473, f1: 0.649, acc: 0.481
+2025-08-26 00:19:08: Epoch: 049 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:09: Epoch: 050 Train Loss: 0.694, ap: 0.499, auc: 0.535, f1: 0.000, acc: 0.519
+2025-08-26 00:19:10: Epoch: 050 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:19:10: used time 0.02h
+2025-08-26 00:19:10: Running Cross Validation 1
+2025-08-26 00:19:10: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 00:19:11: Epoch: 001 Train Loss: 0.695, ap: 0.492, auc: 0.563, f1: 0.608, acc: 0.437
+2025-08-26 00:19:11: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:19:11: Epoch: 001 Test results, ap: 0.371, auc: 0.612, f1: 0.392, acc: 0.244
+2025-08-26 00:19:12: Epoch: 002 Train Loss: 0.694, ap: 0.442, auc: 0.496, f1: 0.000, acc: 0.563
+2025-08-26 00:19:12: Epoch: 002 Test results, ap: 0.285, auc: 0.507, f1: 0.000, acc: 0.756
+2025-08-26 00:19:14: Epoch: 003 Train Loss: 0.695, ap: 0.443, auc: 0.516, f1: 0.000, acc: 0.563
+2025-08-26 00:19:14: Epoch: 003 Test results, ap: 0.164, auc: 0.242, f1: 0.000, acc: 0.756
+2025-08-26 00:19:15: Epoch: 004 Train Loss: 0.694, ap: 0.449, auc: 0.519, f1: 0.000, acc: 0.563
+2025-08-26 00:19:15: Epoch: 004 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:19:16: Epoch: 005 Train Loss: 0.694, ap: 0.432, auc: 0.484, f1: 0.000, acc: 0.563
+2025-08-26 00:19:17: Epoch: 005 Test results, ap: 0.251, auc: 0.519, f1: 0.000, acc: 0.756
+2025-08-26 00:19:18: Epoch: 006 Train Loss: 0.694, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:18: Epoch: 006 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:19: Epoch: 007 Train Loss: 0.694, ap: 0.439, auc: 0.495, f1: 0.000, acc: 0.563
+2025-08-26 00:19:19: Epoch: 007 Test results, ap: 0.287, auc: 0.562, f1: 0.000, acc: 0.756
+2025-08-26 00:19:21: Epoch: 008 Train Loss: 0.694, ap: 0.451, auc: 0.526, f1: 0.608, acc: 0.437
+2025-08-26 00:19:21: Epoch: 008 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:22: Epoch: 009 Train Loss: 0.694, ap: 0.441, auc: 0.504, f1: 0.608, acc: 0.437
+2025-08-26 00:19:22: Epoch: 009 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:24: Epoch: 010 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.000, acc: 0.563
+2025-08-26 00:19:24: Epoch: 010 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:19:25: Epoch: 011 Train Loss: 0.694, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:25: Epoch: 011 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:25: Epoch: 011 New best TEST metrics
+2025-08-26 00:19:26: Epoch: 012 Train Loss: 0.694, ap: 0.415, auc: 0.448, f1: 0.000, acc: 0.563
+2025-08-26 00:19:27: Epoch: 012 Test results, ap: 0.260, auc: 0.539, f1: 0.000, acc: 0.756
+2025-08-26 00:19:27: Epoch: 012 New best TEST metrics
+2025-08-26 00:19:28: Epoch: 013 Train Loss: 0.694, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:28: Epoch: 013 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:29: Epoch: 014 Train Loss: 0.694, ap: 0.437, auc: 0.500, f1: 0.000, acc: 0.563
+2025-08-26 00:19:29: Epoch: 014 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:19:31: Epoch: 015 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:31: Epoch: 015 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:32: Epoch: 016 Train Loss: 0.694, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:32: Epoch: 016 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:33: Epoch: 017 Train Loss: 0.693, ap: 0.444, auc: 0.514, f1: 0.608, acc: 0.437
+2025-08-26 00:19:34: Epoch: 017 Test results, ap: 0.224, auc: 0.412, f1: 0.392, acc: 0.244
+2025-08-26 00:19:35: Epoch: 018 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:35: Epoch: 018 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:36: Epoch: 019 Train Loss: 0.693, ap: 0.441, auc: 0.504, f1: 0.608, acc: 0.437
+2025-08-26 00:19:37: Epoch: 019 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:38: Epoch: 020 Train Loss: 0.693, ap: 0.441, auc: 0.504, f1: 0.000, acc: 0.563
+2025-08-26 00:19:38: Epoch: 020 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:19:39: Epoch: 021 Train Loss: 0.694, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:39: Epoch: 021 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:41: Epoch: 022 Train Loss: 0.693, ap: 0.453, auc: 0.517, f1: 0.000, acc: 0.563
+2025-08-26 00:19:41: Epoch: 022 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:19:42: Epoch: 023 Train Loss: 0.693, ap: 0.441, auc: 0.507, f1: 0.000, acc: 0.563
+2025-08-26 00:19:42: Epoch: 023 Test results, ap: 0.240, auc: 0.479, f1: 0.000, acc: 0.756
+2025-08-26 00:19:43: Epoch: 024 Train Loss: 0.693, ap: 0.441, auc: 0.504, f1: 0.608, acc: 0.437
+2025-08-26 00:19:44: Epoch: 024 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:45: Epoch: 025 Train Loss: 0.692, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:45: Epoch: 025 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:46: Epoch: 026 Train Loss: 0.694, ap: 0.441, auc: 0.504, f1: 0.000, acc: 0.563
+2025-08-26 00:19:47: Epoch: 026 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:19:48: Epoch: 027 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:48: Epoch: 027 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:49: Epoch: 028 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.000, acc: 0.563
+2025-08-26 00:19:49: Epoch: 028 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:19:51: Epoch: 029 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.000, acc: 0.563
+2025-08-26 00:19:51: Epoch: 029 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:19:52: Epoch: 030 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:52: Epoch: 030 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:53: Epoch: 031 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:54: Epoch: 031 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:55: Epoch: 032 Train Loss: 0.693, ap: 0.435, auc: 0.496, f1: 0.608, acc: 0.437
+2025-08-26 00:19:55: Epoch: 032 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:19:56: Epoch: 033 Train Loss: 0.694, ap: 0.433, auc: 0.492, f1: 0.000, acc: 0.563
+2025-08-26 00:19:57: Epoch: 033 Test results, ap: 0.252, auc: 0.511, f1: 0.000, acc: 0.756
+2025-08-26 00:19:58: Epoch: 034 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.000, acc: 0.563
+2025-08-26 00:19:58: Epoch: 034 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:19:59: Epoch: 035 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:19:59: Epoch: 035 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:01: Epoch: 036 Train Loss: 0.693, ap: 0.441, auc: 0.504, f1: 0.000, acc: 0.563
+2025-08-26 00:20:01: Epoch: 036 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:20:02: Epoch: 037 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.000, acc: 0.563
+2025-08-26 00:20:02: Epoch: 037 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:20:03: Epoch: 038 Train Loss: 0.694, ap: 0.437, auc: 0.500, f1: 0.000, acc: 0.563
+2025-08-26 00:20:04: Epoch: 038 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:20:05: Epoch: 039 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:20:05: Epoch: 039 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:06: Epoch: 040 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:20:07: Epoch: 040 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:08: Epoch: 041 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:20:08: Epoch: 041 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:09: Epoch: 042 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:20:09: Epoch: 042 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:11: Epoch: 043 Train Loss: 0.694, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:20:11: Epoch: 043 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:12: Epoch: 044 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:20:12: Epoch: 044 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:13: Epoch: 045 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:20:14: Epoch: 045 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:15: Epoch: 046 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:20:15: Epoch: 046 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:16: Epoch: 047 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:20:17: Epoch: 047 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:18: Epoch: 048 Train Loss: 0.692, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:20:18: Epoch: 048 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:19: Epoch: 049 Train Loss: 0.693, ap: 0.437, auc: 0.500, f1: 0.608, acc: 0.437
+2025-08-26 00:20:19: Epoch: 049 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:21: Epoch: 050 Train Loss: 0.693, ap: 0.449, auc: 0.525, f1: 0.000, acc: 0.563
+2025-08-26 00:20:21: Epoch: 050 Test results, ap: 0.254, auc: 0.527, f1: 0.000, acc: 0.756
+2025-08-26 00:20:21: used time 0.02h
+2025-08-26 00:20:21: Running Cross Validation 2
+2025-08-26 00:20:21: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:20:22: Epoch: 001 Train Loss: 0.694, ap: 0.488, auc: 0.576, f1: 0.000, acc: 0.559
+2025-08-26 00:20:22: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:20:22: Epoch: 001 Test results, ap: 0.274, auc: 0.572, f1: 0.000, acc: 0.756
+2025-08-26 00:20:23: Epoch: 002 Train Loss: 0.693, ap: 0.484, auc: 0.571, f1: 0.612, acc: 0.441
+2025-08-26 00:20:24: Epoch: 002 Test results, ap: 0.240, auc: 0.489, f1: 0.392, acc: 0.244
+2025-08-26 00:20:25: Epoch: 003 Train Loss: 0.693, ap: 0.501, auc: 0.594, f1: 0.612, acc: 0.441
+2025-08-26 00:20:25: Epoch: 003 New best VALIDATION metrics
+2025-08-26 00:20:25: Epoch: 003 Test results, ap: 0.300, auc: 0.556, f1: 0.392, acc: 0.244
+2025-08-26 00:20:26: Epoch: 004 Train Loss: 0.695, ap: 0.485, auc: 0.569, f1: 0.000, acc: 0.559
+2025-08-26 00:20:27: Epoch: 004 Test results, ap: 0.222, auc: 0.427, f1: 0.000, acc: 0.756
+2025-08-26 00:20:28: Epoch: 005 Train Loss: 0.694, ap: 0.489, auc: 0.578, f1: 0.000, acc: 0.559
+2025-08-26 00:20:28: Epoch: 005 Test results, ap: 0.295, auc: 0.600, f1: 0.000, acc: 0.756
+2025-08-26 00:20:29: Epoch: 006 Train Loss: 0.694, ap: 0.489, auc: 0.585, f1: 0.612, acc: 0.441
+2025-08-26 00:20:29: Epoch: 006 Test results, ap: 0.427, auc: 0.649, f1: 0.392, acc: 0.244
+2025-08-26 00:20:31: Epoch: 007 Train Loss: 0.692, ap: 0.485, auc: 0.538, f1: 0.332, acc: 0.547
+2025-08-26 00:20:31: Epoch: 007 Test results, ap: 0.213, auc: 0.385, f1: 0.000, acc: 0.756
+2025-08-26 00:20:32: Epoch: 008 Train Loss: 0.697, ap: 0.462, auc: 0.518, f1: 0.319, acc: 0.541
+2025-08-26 00:20:32: Epoch: 008 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:20:33: Epoch: 009 Train Loss: 0.694, ap: 0.449, auc: 0.515, f1: 0.338, acc: 0.545
+2025-08-26 00:20:34: Epoch: 009 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:20:35: Epoch: 010 Train Loss: 0.694, ap: 0.436, auc: 0.486, f1: 0.000, acc: 0.559
+2025-08-26 00:20:35: Epoch: 010 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:20:36: Epoch: 011 Train Loss: 0.693, ap: 0.419, auc: 0.459, f1: 0.612, acc: 0.441
+2025-08-26 00:20:36: Epoch: 011 Test results, ap: 0.230, auc: 0.432, f1: 0.392, acc: 0.244
+2025-08-26 00:20:36: Epoch: 011 New best TEST metrics
+2025-08-26 00:20:38: Epoch: 012 Train Loss: 0.694, ap: 0.431, auc: 0.479, f1: 0.000, acc: 0.556
+2025-08-26 00:20:38: Epoch: 012 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:20:38: Epoch: 012 New best TEST metrics
+2025-08-26 00:20:39: Epoch: 013 Train Loss: 0.694, ap: 0.431, auc: 0.480, f1: 0.612, acc: 0.441
+2025-08-26 00:20:39: Epoch: 013 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:41: Epoch: 014 Train Loss: 0.694, ap: 0.430, auc: 0.476, f1: 0.000, acc: 0.556
+2025-08-26 00:20:41: Epoch: 014 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:20:42: Epoch: 015 Train Loss: 0.694, ap: 0.431, auc: 0.475, f1: 0.542, acc: 0.451
+2025-08-26 00:20:42: Epoch: 015 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:43: Epoch: 016 Train Loss: 0.693, ap: 0.432, auc: 0.480, f1: 0.000, acc: 0.559
+2025-08-26 00:20:44: Epoch: 016 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:20:45: Epoch: 017 Train Loss: 0.694, ap: 0.450, auc: 0.510, f1: 0.606, acc: 0.439
+2025-08-26 00:20:45: Epoch: 017 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:46: Epoch: 018 Train Loss: 0.693, ap: 0.452, auc: 0.517, f1: 0.612, acc: 0.441
+2025-08-26 00:20:46: Epoch: 018 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:48: Epoch: 019 Train Loss: 0.694, ap: 0.449, auc: 0.509, f1: 0.606, acc: 0.439
+2025-08-26 00:20:48: Epoch: 019 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:49: Epoch: 020 Train Loss: 0.694, ap: 0.430, auc: 0.475, f1: 0.606, acc: 0.437
+2025-08-26 00:20:49: Epoch: 020 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:51: Epoch: 021 Train Loss: 0.693, ap: 0.451, auc: 0.509, f1: 0.320, acc: 0.543
+2025-08-26 00:20:51: Epoch: 021 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:20:52: Epoch: 022 Train Loss: 0.694, ap: 0.432, auc: 0.480, f1: 0.542, acc: 0.451
+2025-08-26 00:20:52: Epoch: 022 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:53: Epoch: 023 Train Loss: 0.693, ap: 0.427, auc: 0.470, f1: 0.606, acc: 0.439
+2025-08-26 00:20:54: Epoch: 023 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:55: Epoch: 024 Train Loss: 0.694, ap: 0.431, auc: 0.476, f1: 0.606, acc: 0.439
+2025-08-26 00:20:55: Epoch: 024 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:20:56: Epoch: 025 Train Loss: 0.694, ap: 0.431, auc: 0.478, f1: 0.000, acc: 0.556
+2025-08-26 00:20:56: Epoch: 025 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:20:58: Epoch: 026 Train Loss: 0.693, ap: 0.443, auc: 0.486, f1: 0.320, acc: 0.543
+2025-08-26 00:20:58: Epoch: 026 Test results, ap: 0.314, auc: 0.615, f1: 0.000, acc: 0.756
+2025-08-26 00:20:58: Epoch: 026 New best TEST metrics
+2025-08-26 00:20:59: Epoch: 027 Train Loss: 0.693, ap: 0.446, auc: 0.508, f1: 0.320, acc: 0.543
+2025-08-26 00:20:59: Epoch: 027 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:01: Epoch: 028 Train Loss: 0.693, ap: 0.431, auc: 0.477, f1: 0.607, acc: 0.439
+2025-08-26 00:21:01: Epoch: 028 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:21:02: Epoch: 029 Train Loss: 0.693, ap: 0.444, auc: 0.508, f1: 0.000, acc: 0.556
+2025-08-26 00:21:02: Epoch: 029 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:03: Epoch: 030 Train Loss: 0.693, ap: 0.436, auc: 0.492, f1: 0.000, acc: 0.554
+2025-08-26 00:21:04: Epoch: 030 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:05: Epoch: 031 Train Loss: 0.692, ap: 0.453, auc: 0.509, f1: 0.607, acc: 0.439
+2025-08-26 00:21:05: Epoch: 031 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:21:06: Epoch: 032 Train Loss: 0.693, ap: 0.457, auc: 0.509, f1: 0.000, acc: 0.556
+2025-08-26 00:21:07: Epoch: 032 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:08: Epoch: 033 Train Loss: 0.694, ap: 0.452, auc: 0.508, f1: 0.316, acc: 0.541
+2025-08-26 00:21:08: Epoch: 033 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:09: Epoch: 034 Train Loss: 0.694, ap: 0.446, auc: 0.507, f1: 0.316, acc: 0.541
+2025-08-26 00:21:10: Epoch: 034 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:11: Epoch: 035 Train Loss: 0.693, ap: 0.446, auc: 0.506, f1: 0.316, acc: 0.541
+2025-08-26 00:21:11: Epoch: 035 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:12: Epoch: 036 Train Loss: 0.694, ap: 0.452, auc: 0.511, f1: 0.316, acc: 0.541
+2025-08-26 00:21:12: Epoch: 036 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:14: Epoch: 037 Train Loss: 0.693, ap: 0.452, auc: 0.508, f1: 0.607, acc: 0.439
+2025-08-26 00:21:14: Epoch: 037 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:21:15: Epoch: 038 Train Loss: 0.694, ap: 0.446, auc: 0.507, f1: 0.316, acc: 0.541
+2025-08-26 00:21:15: Epoch: 038 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:17: Epoch: 039 Train Loss: 0.694, ap: 0.439, auc: 0.466, f1: 0.316, acc: 0.541
+2025-08-26 00:21:17: Epoch: 039 Test results, ap: 0.250, auc: 0.513, f1: 0.000, acc: 0.756
+2025-08-26 00:21:18: Epoch: 040 Train Loss: 0.694, ap: 0.446, auc: 0.506, f1: 0.316, acc: 0.541
+2025-08-26 00:21:18: Epoch: 040 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:20: Epoch: 041 Train Loss: 0.693, ap: 0.441, auc: 0.505, f1: 0.316, acc: 0.541
+2025-08-26 00:21:20: Epoch: 041 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:21: Epoch: 042 Train Loss: 0.692, ap: 0.445, auc: 0.505, f1: 0.316, acc: 0.541
+2025-08-26 00:21:21: Epoch: 042 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:22: Epoch: 043 Train Loss: 0.695, ap: 0.449, auc: 0.509, f1: 0.316, acc: 0.541
+2025-08-26 00:21:23: Epoch: 043 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:24: Epoch: 044 Train Loss: 0.693, ap: 0.446, auc: 0.506, f1: 0.316, acc: 0.541
+2025-08-26 00:21:24: Epoch: 044 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:25: Epoch: 045 Train Loss: 0.694, ap: 0.445, auc: 0.478, f1: 0.316, acc: 0.541
+2025-08-26 00:21:26: Epoch: 045 Test results, ap: 0.239, auc: 0.474, f1: 0.000, acc: 0.756
+2025-08-26 00:21:27: Epoch: 046 Train Loss: 0.694, ap: 0.445, auc: 0.506, f1: 0.316, acc: 0.541
+2025-08-26 00:21:27: Epoch: 046 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:28: Epoch: 047 Train Loss: 0.693, ap: 0.445, auc: 0.507, f1: 0.316, acc: 0.541
+2025-08-26 00:21:28: Epoch: 047 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:30: Epoch: 048 Train Loss: 0.693, ap: 0.449, auc: 0.508, f1: 0.316, acc: 0.541
+2025-08-26 00:21:30: Epoch: 048 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:31: Epoch: 049 Train Loss: 0.693, ap: 0.446, auc: 0.495, f1: 0.316, acc: 0.541
+2025-08-26 00:21:31: Epoch: 049 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:33: Epoch: 050 Train Loss: 0.694, ap: 0.445, auc: 0.506, f1: 0.607, acc: 0.439
+2025-08-26 00:21:33: Epoch: 050 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:21:33: used time 0.02h
+2025-08-26 00:21:33: Running Cross Validation 3
+2025-08-26 00:21:33: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:21:34: Epoch: 001 Train Loss: 0.694, ap: 0.485, auc: 0.596, f1: 0.602, acc: 0.430
+2025-08-26 00:21:34: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:21:34: Epoch: 001 Test results, ap: 0.273, auc: 0.517, f1: 0.392, acc: 0.244
+2025-08-26 00:21:36: Epoch: 002 Train Loss: 0.694, ap: 0.408, auc: 0.446, f1: 0.602, acc: 0.430
+2025-08-26 00:21:36: Epoch: 002 Test results, ap: 0.255, auc: 0.487, f1: 0.392, acc: 0.244
+2025-08-26 00:21:37: Epoch: 003 Train Loss: 0.694, ap: 0.431, auc: 0.490, f1: 0.000, acc: 0.570
+2025-08-26 00:21:37: Epoch: 003 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:38: Epoch: 004 Train Loss: 0.694, ap: 0.430, auc: 0.495, f1: 0.000, acc: 0.570
+2025-08-26 00:21:39: Epoch: 004 Test results, ap: 0.246, auc: 0.505, f1: 0.000, acc: 0.756
+2025-08-26 00:21:40: Epoch: 005 Train Loss: 0.695, ap: 0.431, auc: 0.501, f1: 0.000, acc: 0.570
+2025-08-26 00:21:40: Epoch: 005 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:41: Epoch: 006 Train Loss: 0.696, ap: 0.430, auc: 0.500, f1: 0.602, acc: 0.430
+2025-08-26 00:21:42: Epoch: 006 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:21:43: Epoch: 007 Train Loss: 0.694, ap: 0.427, auc: 0.492, f1: 0.602, acc: 0.430
+2025-08-26 00:21:43: Epoch: 007 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:21:44: Epoch: 008 Train Loss: 0.694, ap: 0.431, auc: 0.502, f1: 0.000, acc: 0.570
+2025-08-26 00:21:45: Epoch: 008 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:46: Epoch: 009 Train Loss: 0.694, ap: 0.431, auc: 0.502, f1: 0.602, acc: 0.430
+2025-08-26 00:21:46: Epoch: 009 Test results, ap: 0.238, auc: 0.480, f1: 0.392, acc: 0.244
+2025-08-26 00:21:47: Epoch: 010 Train Loss: 0.693, ap: 0.429, auc: 0.496, f1: 0.000, acc: 0.570
+2025-08-26 00:21:47: Epoch: 010 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:49: Epoch: 011 Train Loss: 0.694, ap: 0.431, auc: 0.502, f1: 0.000, acc: 0.570
+2025-08-26 00:21:49: Epoch: 011 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:49: Epoch: 011 New best TEST metrics
+2025-08-26 00:21:50: Epoch: 012 Train Loss: 0.695, ap: 0.436, auc: 0.512, f1: 0.000, acc: 0.570
+2025-08-26 00:21:50: Epoch: 012 Test results, ap: 0.260, auc: 0.541, f1: 0.000, acc: 0.756
+2025-08-26 00:21:50: Epoch: 012 New best TEST metrics
+2025-08-26 00:21:52: Epoch: 013 Train Loss: 0.694, ap: 0.426, auc: 0.483, f1: 0.000, acc: 0.570
+2025-08-26 00:21:52: Epoch: 013 Test results, ap: 0.244, auc: 0.489, f1: 0.000, acc: 0.756
+2025-08-26 00:21:53: Epoch: 014 Train Loss: 0.693, ap: 0.424, auc: 0.487, f1: 0.602, acc: 0.430
+2025-08-26 00:21:53: Epoch: 014 Test results, ap: 0.239, auc: 0.487, f1: 0.392, acc: 0.244
+2025-08-26 00:21:55: Epoch: 015 Train Loss: 0.694, ap: 0.432, auc: 0.503, f1: 0.602, acc: 0.430
+2025-08-26 00:21:55: Epoch: 015 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:21:56: Epoch: 016 Train Loss: 0.694, ap: 0.430, auc: 0.500, f1: 0.000, acc: 0.570
+2025-08-26 00:21:56: Epoch: 016 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:58: Epoch: 017 Train Loss: 0.693, ap: 0.431, auc: 0.502, f1: 0.000, acc: 0.570
+2025-08-26 00:21:58: Epoch: 017 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:21:59: Epoch: 018 Train Loss: 0.694, ap: 0.436, auc: 0.512, f1: 0.000, acc: 0.570
+2025-08-26 00:21:59: Epoch: 018 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:00: Epoch: 019 Train Loss: 0.693, ap: 0.432, auc: 0.503, f1: 0.000, acc: 0.570
+2025-08-26 00:22:01: Epoch: 019 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:02: Epoch: 020 Train Loss: 0.693, ap: 0.430, auc: 0.500, f1: 0.000, acc: 0.570
+2025-08-26 00:22:02: Epoch: 020 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:03: Epoch: 021 Train Loss: 0.694, ap: 0.435, auc: 0.510, f1: 0.602, acc: 0.430
+2025-08-26 00:22:04: Epoch: 021 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:05: Epoch: 022 Train Loss: 0.694, ap: 0.422, auc: 0.476, f1: 0.602, acc: 0.430
+2025-08-26 00:22:05: Epoch: 022 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:06: Epoch: 023 Train Loss: 0.693, ap: 0.435, auc: 0.510, f1: 0.602, acc: 0.430
+2025-08-26 00:22:06: Epoch: 023 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:08: Epoch: 024 Train Loss: 0.694, ap: 0.430, auc: 0.498, f1: 0.000, acc: 0.570
+2025-08-26 00:22:08: Epoch: 024 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:09: Epoch: 025 Train Loss: 0.692, ap: 0.421, auc: 0.480, f1: 0.000, acc: 0.570
+2025-08-26 00:22:09: Epoch: 025 Test results, ap: 0.244, auc: 0.502, f1: 0.000, acc: 0.756
+2025-08-26 00:22:11: Epoch: 026 Train Loss: 0.693, ap: 0.424, auc: 0.480, f1: 0.000, acc: 0.570
+2025-08-26 00:22:11: Epoch: 026 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:12: Epoch: 027 Train Loss: 0.694, ap: 0.429, auc: 0.497, f1: 0.000, acc: 0.570
+2025-08-26 00:22:12: Epoch: 027 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:14: Epoch: 028 Train Loss: 0.693, ap: 0.430, auc: 0.500, f1: 0.602, acc: 0.430
+2025-08-26 00:22:14: Epoch: 028 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:15: Epoch: 029 Train Loss: 0.693, ap: 0.429, auc: 0.497, f1: 0.000, acc: 0.570
+2025-08-26 00:22:15: Epoch: 029 Test results, ap: 0.220, auc: 0.403, f1: 0.000, acc: 0.756
+2025-08-26 00:22:16: Epoch: 030 Train Loss: 0.693, ap: 0.440, auc: 0.519, f1: 0.000, acc: 0.570
+2025-08-26 00:22:17: Epoch: 030 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:18: Epoch: 031 Train Loss: 0.693, ap: 0.430, auc: 0.500, f1: 0.602, acc: 0.430
+2025-08-26 00:22:18: Epoch: 031 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:19: Epoch: 032 Train Loss: 0.694, ap: 0.437, auc: 0.513, f1: 0.000, acc: 0.570
+2025-08-26 00:22:20: Epoch: 032 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:21: Epoch: 033 Train Loss: 0.693, ap: 0.430, auc: 0.500, f1: 0.602, acc: 0.430
+2025-08-26 00:22:21: Epoch: 033 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:22: Epoch: 034 Train Loss: 0.693, ap: 0.430, auc: 0.500, f1: 0.602, acc: 0.430
+2025-08-26 00:22:22: Epoch: 034 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:24: Epoch: 035 Train Loss: 0.693, ap: 0.430, auc: 0.500, f1: 0.602, acc: 0.430
+2025-08-26 00:22:24: Epoch: 035 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:25: Epoch: 036 Train Loss: 0.693, ap: 0.418, auc: 0.474, f1: 0.000, acc: 0.570
+2025-08-26 00:22:25: Epoch: 036 Test results, ap: 0.243, auc: 0.498, f1: 0.000, acc: 0.756
+2025-08-26 00:22:27: Epoch: 037 Train Loss: 0.693, ap: 0.433, auc: 0.502, f1: 0.000, acc: 0.570
+2025-08-26 00:22:27: Epoch: 037 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:28: Epoch: 038 Train Loss: 0.693, ap: 0.431, auc: 0.500, f1: 0.000, acc: 0.570
+2025-08-26 00:22:28: Epoch: 038 Test results, ap: 0.242, auc: 0.495, f1: 0.000, acc: 0.756
+2025-08-26 00:22:29: Epoch: 039 Train Loss: 0.694, ap: 0.435, auc: 0.506, f1: 0.000, acc: 0.570
+2025-08-26 00:22:30: Epoch: 039 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:31: Epoch: 040 Train Loss: 0.692, ap: 0.430, auc: 0.500, f1: 0.602, acc: 0.430
+2025-08-26 00:22:31: Epoch: 040 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:32: Epoch: 041 Train Loss: 0.694, ap: 0.430, auc: 0.500, f1: 0.602, acc: 0.430
+2025-08-26 00:22:33: Epoch: 041 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:34: Epoch: 042 Train Loss: 0.694, ap: 0.430, auc: 0.500, f1: 0.602, acc: 0.430
+2025-08-26 00:22:34: Epoch: 042 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:35: Epoch: 043 Train Loss: 0.694, ap: 0.430, auc: 0.500, f1: 0.000, acc: 0.570
+2025-08-26 00:22:36: Epoch: 043 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:37: Epoch: 044 Train Loss: 0.693, ap: 0.430, auc: 0.500, f1: 0.602, acc: 0.430
+2025-08-26 00:22:37: Epoch: 044 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:38: Epoch: 045 Train Loss: 0.693, ap: 0.430, auc: 0.500, f1: 0.000, acc: 0.570
+2025-08-26 00:22:38: Epoch: 045 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:40: Epoch: 046 Train Loss: 0.693, ap: 0.430, auc: 0.500, f1: 0.602, acc: 0.430
+2025-08-26 00:22:40: Epoch: 046 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:41: Epoch: 047 Train Loss: 0.693, ap: 0.430, auc: 0.500, f1: 0.000, acc: 0.570
+2025-08-26 00:22:41: Epoch: 047 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:43: Epoch: 048 Train Loss: 0.693, ap: 0.431, auc: 0.501, f1: 0.602, acc: 0.430
+2025-08-26 00:22:43: Epoch: 048 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:22:44: Epoch: 049 Train Loss: 0.693, ap: 0.431, auc: 0.502, f1: 0.000, acc: 0.570
+2025-08-26 00:22:44: Epoch: 049 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:46: Epoch: 050 Train Loss: 0.694, ap: 0.431, auc: 0.502, f1: 0.000, acc: 0.570
+2025-08-26 00:22:46: Epoch: 050 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:22:46: used time 0.02h
+2025-08-26 00:22:46: Running Cross Validation 4
+2025-08-26 00:22:46: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:22:47: Epoch: 001 Train Loss: 0.693, ap: 0.444, auc: 0.540, f1: 0.000, acc: 0.557
+2025-08-26 00:22:47: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:22:47: Epoch: 001 Test results, ap: 0.390, auc: 0.588, f1: 0.000, acc: 0.756
+2025-08-26 00:22:48: Epoch: 002 Train Loss: 0.694, ap: 0.475, auc: 0.555, f1: 0.614, acc: 0.443
+2025-08-26 00:22:48: Epoch: 002 New best VALIDATION metrics
+2025-08-26 00:22:49: Epoch: 002 Test results, ap: 0.220, auc: 0.425, f1: 0.392, acc: 0.244
+2025-08-26 00:22:50: Epoch: 003 Train Loss: 0.695, ap: 0.449, auc: 0.504, f1: 0.000, acc: 0.557
+2025-08-26 00:22:50: Epoch: 003 Test results, ap: 0.242, auc: 0.488, f1: 0.000, acc: 0.756
+2025-08-26 00:22:51: Epoch: 004 Train Loss: 0.695, ap: 0.440, auc: 0.488, f1: 0.000, acc: 0.557
+2025-08-26 00:22:52: Epoch: 004 Test results, ap: 0.216, auc: 0.392, f1: 0.000, acc: 0.756
+2025-08-26 00:22:53: Epoch: 005 Train Loss: 0.694, ap: 0.425, auc: 0.449, f1: 0.614, acc: 0.443
+2025-08-26 00:22:53: Epoch: 005 Test results, ap: 0.287, auc: 0.596, f1: 0.392, acc: 0.244
+2025-08-26 00:22:54: Epoch: 006 Train Loss: 0.693, ap: 0.449, auc: 0.490, f1: 0.614, acc: 0.443
+2025-08-26 00:22:54: Epoch: 006 Test results, ap: 0.252, auc: 0.511, f1: 0.392, acc: 0.244
+2025-08-26 00:22:56: Epoch: 007 Train Loss: 0.693, ap: 0.456, auc: 0.513, f1: 0.000, acc: 0.557
+2025-08-26 00:22:56: Epoch: 007 Test results, ap: 0.241, auc: 0.491, f1: 0.000, acc: 0.756
+2025-08-26 00:22:57: Epoch: 008 Train Loss: 0.695, ap: 0.454, auc: 0.515, f1: 0.614, acc: 0.443
+2025-08-26 00:22:57: Epoch: 008 Test results, ap: 0.247, auc: 0.508, f1: 0.392, acc: 0.244
+2025-08-26 00:22:59: Epoch: 009 Train Loss: 0.694, ap: 0.421, auc: 0.450, f1: 0.000, acc: 0.557
+2025-08-26 00:22:59: Epoch: 009 Test results, ap: 0.244, auc: 0.468, f1: 0.000, acc: 0.756
+2025-08-26 00:23:00: Epoch: 010 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:23:00: Epoch: 010 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:02: Epoch: 011 Train Loss: 0.693, ap: 0.458, auc: 0.526, f1: 0.614, acc: 0.443
+2025-08-26 00:23:02: Epoch: 011 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:02: Epoch: 011 New best TEST metrics
+2025-08-26 00:23:03: Epoch: 012 Train Loss: 0.694, ap: 0.450, auc: 0.514, f1: 0.614, acc: 0.443
+2025-08-26 00:23:03: Epoch: 012 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:04: Epoch: 013 Train Loss: 0.694, ap: 0.444, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:23:05: Epoch: 013 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:06: Epoch: 014 Train Loss: 0.694, ap: 0.440, auc: 0.494, f1: 0.000, acc: 0.557
+2025-08-26 00:23:06: Epoch: 014 Test results, ap: 0.327, auc: 0.644, f1: 0.000, acc: 0.756
+2025-08-26 00:23:06: Epoch: 014 New best TEST metrics
+2025-08-26 00:23:07: Epoch: 015 Train Loss: 0.693, ap: 0.442, auc: 0.498, f1: 0.614, acc: 0.443
+2025-08-26 00:23:08: Epoch: 015 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:09: Epoch: 016 Train Loss: 0.692, ap: 0.447, auc: 0.508, f1: 0.000, acc: 0.557
+2025-08-26 00:23:09: Epoch: 016 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:10: Epoch: 017 Train Loss: 0.695, ap: 0.452, auc: 0.518, f1: 0.614, acc: 0.443
+2025-08-26 00:23:10: Epoch: 017 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:12: Epoch: 018 Train Loss: 0.694, ap: 0.455, auc: 0.487, f1: 0.000, acc: 0.557
+2025-08-26 00:23:12: Epoch: 018 Test results, ap: 0.225, auc: 0.444, f1: 0.000, acc: 0.756
+2025-08-26 00:23:13: Epoch: 019 Train Loss: 0.694, ap: 0.436, auc: 0.454, f1: 0.614, acc: 0.443
+2025-08-26 00:23:13: Epoch: 019 Test results, ap: 0.260, auc: 0.529, f1: 0.392, acc: 0.244
+2025-08-26 00:23:15: Epoch: 020 Train Loss: 0.694, ap: 0.451, auc: 0.516, f1: 0.614, acc: 0.443
+2025-08-26 00:23:15: Epoch: 020 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:16: Epoch: 021 Train Loss: 0.693, ap: 0.451, auc: 0.513, f1: 0.614, acc: 0.443
+2025-08-26 00:23:16: Epoch: 021 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:18: Epoch: 022 Train Loss: 0.693, ap: 0.436, auc: 0.486, f1: 0.614, acc: 0.443
+2025-08-26 00:23:18: Epoch: 022 Test results, ap: 0.236, auc: 0.477, f1: 0.392, acc: 0.244
+2025-08-26 00:23:19: Epoch: 023 Train Loss: 0.693, ap: 0.451, auc: 0.514, f1: 0.000, acc: 0.557
+2025-08-26 00:23:19: Epoch: 023 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:20: Epoch: 024 Train Loss: 0.693, ap: 0.438, auc: 0.449, f1: 0.000, acc: 0.557
+2025-08-26 00:23:21: Epoch: 024 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:22: Epoch: 025 Train Loss: 0.693, ap: 0.456, auc: 0.517, f1: 0.000, acc: 0.557
+2025-08-26 00:23:22: Epoch: 025 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:23: Epoch: 026 Train Loss: 0.693, ap: 0.444, auc: 0.503, f1: 0.614, acc: 0.443
+2025-08-26 00:23:24: Epoch: 026 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:25: Epoch: 027 Train Loss: 0.694, ap: 0.450, auc: 0.515, f1: 0.000, acc: 0.557
+2025-08-26 00:23:25: Epoch: 027 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:26: Epoch: 028 Train Loss: 0.694, ap: 0.444, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:23:27: Epoch: 028 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:28: Epoch: 029 Train Loss: 0.694, ap: 0.442, auc: 0.498, f1: 0.000, acc: 0.557
+2025-08-26 00:23:28: Epoch: 029 Test results, ap: 0.234, auc: 0.472, f1: 0.000, acc: 0.756
+2025-08-26 00:23:29: Epoch: 030 Train Loss: 0.694, ap: 0.444, auc: 0.503, f1: 0.614, acc: 0.443
+2025-08-26 00:23:29: Epoch: 030 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:31: Epoch: 031 Train Loss: 0.693, ap: 0.445, auc: 0.504, f1: 0.614, acc: 0.443
+2025-08-26 00:23:31: Epoch: 031 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:32: Epoch: 032 Train Loss: 0.693, ap: 0.438, auc: 0.490, f1: 0.614, acc: 0.443
+2025-08-26 00:23:32: Epoch: 032 Test results, ap: 0.227, auc: 0.450, f1: 0.392, acc: 0.244
+2025-08-26 00:23:34: Epoch: 033 Train Loss: 0.692, ap: 0.448, auc: 0.510, f1: 0.000, acc: 0.557
+2025-08-26 00:23:34: Epoch: 033 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:35: Epoch: 034 Train Loss: 0.693, ap: 0.444, auc: 0.503, f1: 0.614, acc: 0.443
+2025-08-26 00:23:35: Epoch: 034 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:36: Epoch: 035 Train Loss: 0.694, ap: 0.442, auc: 0.498, f1: 0.000, acc: 0.557
+2025-08-26 00:23:37: Epoch: 035 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:38: Epoch: 036 Train Loss: 0.694, ap: 0.442, auc: 0.499, f1: 0.000, acc: 0.557
+2025-08-26 00:23:38: Epoch: 036 Test results, ap: 0.218, auc: 0.398, f1: 0.000, acc: 0.756
+2025-08-26 00:23:39: Epoch: 037 Train Loss: 0.693, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:23:40: Epoch: 037 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:41: Epoch: 038 Train Loss: 0.693, ap: 0.442, auc: 0.498, f1: 0.000, acc: 0.557
+2025-08-26 00:23:41: Epoch: 038 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:42: Epoch: 039 Train Loss: 0.693, ap: 0.433, auc: 0.478, f1: 0.000, acc: 0.557
+2025-08-26 00:23:42: Epoch: 039 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:44: Epoch: 040 Train Loss: 0.694, ap: 0.443, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:23:44: Epoch: 040 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:45: Epoch: 041 Train Loss: 0.693, ap: 0.422, auc: 0.445, f1: 0.000, acc: 0.557
+2025-08-26 00:23:45: Epoch: 041 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:47: Epoch: 042 Train Loss: 0.693, ap: 0.429, auc: 0.472, f1: 0.614, acc: 0.443
+2025-08-26 00:23:47: Epoch: 042 Test results, ap: 0.233, auc: 0.448, f1: 0.392, acc: 0.244
+2025-08-26 00:23:48: Epoch: 043 Train Loss: 0.693, ap: 0.449, auc: 0.511, f1: 0.000, acc: 0.557
+2025-08-26 00:23:48: Epoch: 043 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:50: Epoch: 044 Train Loss: 0.693, ap: 0.450, auc: 0.513, f1: 0.000, acc: 0.557
+2025-08-26 00:23:50: Epoch: 044 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:51: Epoch: 045 Train Loss: 0.693, ap: 0.450, auc: 0.512, f1: 0.000, acc: 0.557
+2025-08-26 00:23:51: Epoch: 045 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:52: Epoch: 046 Train Loss: 0.693, ap: 0.439, auc: 0.492, f1: 0.614, acc: 0.443
+2025-08-26 00:23:53: Epoch: 046 Test results, ap: 0.225, auc: 0.444, f1: 0.392, acc: 0.244
+2025-08-26 00:23:54: Epoch: 047 Train Loss: 0.693, ap: 0.449, auc: 0.511, f1: 0.000, acc: 0.557
+2025-08-26 00:23:54: Epoch: 047 Test results, ap: 0.244, auc: 0.500, f1: 0.000, acc: 0.756
+2025-08-26 00:23:55: Epoch: 048 Train Loss: 0.693, ap: 0.444, auc: 0.502, f1: 0.614, acc: 0.443
+2025-08-26 00:23:56: Epoch: 048 Test results, ap: 0.244, auc: 0.500, f1: 0.392, acc: 0.244
+2025-08-26 00:23:57: Epoch: 049 Train Loss: 0.693, ap: 0.442, auc: 0.493, f1: 0.000, acc: 0.557
+2025-08-26 00:23:57: Epoch: 049 Test results, ap: 0.225, auc: 0.444, f1: 0.000, acc: 0.756
+2025-08-26 00:23:58: Epoch: 050 Train Loss: 0.693, ap: 0.450, auc: 0.511, f1: 0.000, acc: 0.557
+2025-08-26 00:23:59: Epoch: 050 Test results, ap: 0.260, auc: 0.541, f1: 0.000, acc: 0.756
+2025-08-26 00:23:59: used time 0.02h
+2025-08-26 00:23:59: Cross Validation Finished!
+2025-08-26 00:23:59: Best validation perform list
+[[0.55098933 0.59071207 0.         0.5193662 ]
+ [0.49246693 0.56252521 0.60784316 0.43661973]
+ [0.50072902 0.59363407 0.6119951  0.4409171 ]
+ [0.48525533 0.59623533 0.60172629 0.4303351 ]
+ [0.47534996 0.55515909 0.61369193 0.44268078]]
+2025-08-26 00:23:59: mean: [0.501 0.58  0.487 0.454]
+2025-08-26 00:23:59: std: [0.026 0.017 0.244 0.033]
+2025-08-26 00:23:59: Best test perform list
+[[0.3250505  0.63853049 0.         0.75609756]
+ [0.25959256 0.53924727 0.         0.75609756]
+ [0.31425303 0.61451614 0.         0.75609756]
+ [0.26031256 0.54139781 0.         0.75609756]
+ [0.32734823 0.644086   0.         0.75609756]]
+2025-08-26 00:23:59: mean: [0.297 0.596 0.    0.756]
+2025-08-26 00:23:59: std: [0.031 0.046 0.    0.   ]
diff --git a/run-cls/mamba-att-256-ce-32-0.001-50/config.json b/run-cls/mamba-att-256-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mamba", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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@@ -0,0 +1,6 @@
+Valid
+0.8277936220169068,0.8537920594215394,0.7530451059341431,0.7821794986724854
+0.08920128611482761,0.07613688436424394,0.08732112555783038,0.0665245352403388
+Test
+0.3681394815444946,0.6206451654434204,0.3085913360118866,0.5707317113876342
+0.0732621513280911,0.04672581348093429,0.164958815292363,0.16814926622305615
diff --git a/run-cls/mamba-att-256-ce-32-0.001-50/training.log b/run-cls/mamba-att-256-ce-32-0.001-50/training.log
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@@ -0,0 +1,707 @@
+2025-08-26 00:50:15: saving_dir: ./run-cls/mamba-att-256-ce-32-0.001-50
+2025-08-26 00:50:15: Loading Train Dataset
+2025-08-26 00:50:16: Loading Test Dataset
+2025-08-26 00:50:16: Running Cross Validation 0
+2025-08-26 00:50:16: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 00:50:20: Epoch: 001 Train Loss: 0.695, ap: 0.569, auc: 0.612, f1: 0.000, acc: 0.519
+2025-08-26 00:50:20: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:50:20: Epoch: 001 Test results, ap: 0.241, auc: 0.504, f1: 0.000, acc: 0.756
+2025-08-26 00:50:23: Epoch: 002 Train Loss: 0.695, ap: 0.606, auc: 0.630, f1: 0.615, acc: 0.600
+2025-08-26 00:50:23: Epoch: 002 New best VALIDATION metrics
+2025-08-26 00:50:23: Epoch: 002 Test results, ap: 0.253, auc: 0.527, f1: 0.392, acc: 0.244
+2025-08-26 00:50:26: Epoch: 003 Train Loss: 0.680, ap: 0.610, auc: 0.640, f1: 0.438, acc: 0.602
+2025-08-26 00:50:26: Epoch: 003 New best VALIDATION metrics
+2025-08-26 00:50:26: Epoch: 003 Test results, ap: 0.240, auc: 0.502, f1: 0.000, acc: 0.683
+2025-08-26 00:50:29: Epoch: 004 Train Loss: 0.684, ap: 0.638, auc: 0.655, f1: 0.584, acc: 0.614
+2025-08-26 00:50:29: Epoch: 004 New best VALIDATION metrics
+2025-08-26 00:50:29: Epoch: 004 Test results, ap: 0.235, auc: 0.484, f1: 0.254, acc: 0.618
+2025-08-26 00:50:32: Epoch: 005 Train Loss: 0.671, ap: 0.629, auc: 0.645, f1: 0.565, acc: 0.607
+2025-08-26 00:50:32: Epoch: 005 Test results, ap: 0.242, auc: 0.486, f1: 0.207, acc: 0.626
+2025-08-26 00:50:35: Epoch: 006 Train Loss: 0.657, ap: 0.670, auc: 0.675, f1: 0.584, acc: 0.636
+2025-08-26 00:50:35: Epoch: 006 New best VALIDATION metrics
+2025-08-26 00:50:36: Epoch: 006 Test results, ap: 0.236, auc: 0.478, f1: 0.211, acc: 0.634
+2025-08-26 00:50:39: Epoch: 007 Train Loss: 0.652, ap: 0.650, auc: 0.665, f1: 0.576, acc: 0.644
+2025-08-26 00:50:39: Epoch: 007 Test results, ap: 0.243, auc: 0.489, f1: 0.190, acc: 0.724
+2025-08-26 00:50:42: Epoch: 008 Train Loss: 0.655, ap: 0.636, auc: 0.672, f1: 0.501, acc: 0.611
+2025-08-26 00:50:42: Epoch: 008 Test results, ap: 0.269, auc: 0.563, f1: 0.000, acc: 0.732
+2025-08-26 00:50:45: Epoch: 009 Train Loss: 0.643, ap: 0.634, auc: 0.670, f1: 0.619, acc: 0.637
+2025-08-26 00:50:45: Epoch: 009 Test results, ap: 0.236, auc: 0.421, f1: 0.000, acc: 0.699
+2025-08-26 00:50:48: Epoch: 010 Train Loss: 0.642, ap: 0.666, auc: 0.695, f1: 0.632, acc: 0.648
+2025-08-26 00:50:48: Epoch: 010 New best VALIDATION metrics
+2025-08-26 00:50:48: Epoch: 010 Test results, ap: 0.261, auc: 0.525, f1: 0.000, acc: 0.691
+2025-08-26 00:50:51: Epoch: 011 Train Loss: 0.616, ap: 0.677, auc: 0.710, f1: 0.686, acc: 0.678
+2025-08-26 00:50:51: Epoch: 011 New best VALIDATION metrics
+2025-08-26 00:50:51: Epoch: 011 Test results, ap: 0.246, auc: 0.480, f1: 0.051, acc: 0.699
+2025-08-26 00:50:51: Epoch: 011 New best TEST metrics
+2025-08-26 00:50:54: Epoch: 012 Train Loss: 0.595, ap: 0.673, auc: 0.719, f1: 0.678, acc: 0.687
+2025-08-26 00:50:54: Epoch: 012 New best VALIDATION metrics
+2025-08-26 00:50:55: Epoch: 012 Test results, ap: 0.232, auc: 0.456, f1: 0.000, acc: 0.732
+2025-08-26 00:50:58: Epoch: 013 Train Loss: 0.582, ap: 0.680, auc: 0.730, f1: 0.680, acc: 0.692
+2025-08-26 00:50:58: Epoch: 013 New best VALIDATION metrics
+2025-08-26 00:50:58: Epoch: 013 Test results, ap: 0.238, auc: 0.456, f1: 0.000, acc: 0.691
+2025-08-26 00:51:01: Epoch: 014 Train Loss: 0.571, ap: 0.691, auc: 0.738, f1: 0.687, acc: 0.692
+2025-08-26 00:51:01: Epoch: 014 New best VALIDATION metrics
+2025-08-26 00:51:01: Epoch: 014 Test results, ap: 0.263, auc: 0.505, f1: 0.000, acc: 0.691
+2025-08-26 00:51:01: Epoch: 014 New best TEST metrics
+2025-08-26 00:51:04: Epoch: 015 Train Loss: 0.558, ap: 0.731, auc: 0.766, f1: 0.711, acc: 0.702
+2025-08-26 00:51:04: Epoch: 015 New best VALIDATION metrics
+2025-08-26 00:51:04: Epoch: 015 Test results, ap: 0.255, auc: 0.494, f1: 0.000, acc: 0.691
+2025-08-26 00:51:07: Epoch: 016 Train Loss: 0.540, ap: 0.743, auc: 0.777, f1: 0.698, acc: 0.702
+2025-08-26 00:51:07: Epoch: 016 New best VALIDATION metrics
+2025-08-26 00:51:08: Epoch: 016 Test results, ap: 0.265, auc: 0.525, f1: 0.000, acc: 0.691
+2025-08-26 00:51:08: Epoch: 016 New best TEST metrics
+2025-08-26 00:51:10: Epoch: 017 Train Loss: 0.517, ap: 0.776, auc: 0.813, f1: 0.741, acc: 0.750
+2025-08-26 00:51:10: Epoch: 017 New best VALIDATION metrics
+2025-08-26 00:51:11: Epoch: 017 Test results, ap: 0.267, auc: 0.521, f1: 0.000, acc: 0.715
+2025-08-26 00:51:14: Epoch: 018 Train Loss: 0.483, ap: 0.811, auc: 0.828, f1: 0.732, acc: 0.741
+2025-08-26 00:51:14: Epoch: 018 New best VALIDATION metrics
+2025-08-26 00:51:14: Epoch: 018 Test results, ap: 0.264, auc: 0.524, f1: 0.000, acc: 0.699
+2025-08-26 00:51:17: Epoch: 019 Train Loss: 0.444, ap: 0.830, auc: 0.849, f1: 0.750, acc: 0.762
+2025-08-26 00:51:17: Epoch: 019 New best VALIDATION metrics
+2025-08-26 00:51:17: Epoch: 019 Test results, ap: 0.276, auc: 0.544, f1: 0.000, acc: 0.707
+2025-08-26 00:51:17: Epoch: 019 New best TEST metrics
+2025-08-26 00:51:20: Epoch: 020 Train Loss: 0.415, ap: 0.847, auc: 0.848, f1: 0.744, acc: 0.743
+2025-08-26 00:51:20: Epoch: 020 New best VALIDATION metrics
+2025-08-26 00:51:20: Epoch: 020 Test results, ap: 0.292, auc: 0.555, f1: 0.000, acc: 0.691
+2025-08-26 00:51:20: Epoch: 020 New best TEST metrics
+2025-08-26 00:51:23: Epoch: 021 Train Loss: 0.375, ap: 0.854, auc: 0.869, f1: 0.759, acc: 0.785
+2025-08-26 00:51:23: Epoch: 021 New best VALIDATION metrics
+2025-08-26 00:51:23: Epoch: 021 Test results, ap: 0.265, auc: 0.522, f1: 0.000, acc: 0.724
+2025-08-26 00:51:26: Epoch: 022 Train Loss: 0.351, ap: 0.878, auc: 0.883, f1: 0.776, acc: 0.794
+2025-08-26 00:51:26: Epoch: 022 New best VALIDATION metrics
+2025-08-26 00:51:27: Epoch: 022 Test results, ap: 0.289, auc: 0.549, f1: 0.000, acc: 0.707
+2025-08-26 00:51:30: Epoch: 023 Train Loss: 0.331, ap: 0.876, auc: 0.886, f1: 0.783, acc: 0.799
+2025-08-26 00:51:30: Epoch: 023 New best VALIDATION metrics
+2025-08-26 00:51:30: Epoch: 023 Test results, ap: 0.255, auc: 0.514, f1: 0.000, acc: 0.699
+2025-08-26 00:51:33: Epoch: 024 Train Loss: 0.318, ap: 0.884, auc: 0.889, f1: 0.786, acc: 0.808
+2025-08-26 00:51:33: Epoch: 024 New best VALIDATION metrics
+2025-08-26 00:51:33: Epoch: 024 Test results, ap: 0.252, auc: 0.497, f1: 0.000, acc: 0.707
+2025-08-26 00:51:36: Epoch: 025 Train Loss: 0.300, ap: 0.893, auc: 0.892, f1: 0.777, acc: 0.792
+2025-08-26 00:51:36: Epoch: 025 New best VALIDATION metrics
+2025-08-26 00:51:36: Epoch: 025 Test results, ap: 0.270, auc: 0.523, f1: 0.000, acc: 0.715
+2025-08-26 00:51:39: Epoch: 026 Train Loss: 0.284, ap: 0.874, auc: 0.886, f1: 0.773, acc: 0.796
+2025-08-26 00:51:39: Epoch: 026 Test results, ap: 0.266, auc: 0.524, f1: 0.000, acc: 0.699
+2025-08-26 00:51:42: Epoch: 027 Train Loss: 0.279, ap: 0.894, auc: 0.898, f1: 0.802, acc: 0.808
+2025-08-26 00:51:42: Epoch: 027 New best VALIDATION metrics
+2025-08-26 00:51:42: Epoch: 027 Test results, ap: 0.262, auc: 0.512, f1: 0.000, acc: 0.715
+2025-08-26 00:51:45: Epoch: 028 Train Loss: 0.270, ap: 0.898, auc: 0.898, f1: 0.802, acc: 0.822
+2025-08-26 00:51:45: Epoch: 028 New best VALIDATION metrics
+2025-08-26 00:51:46: Epoch: 028 Test results, ap: 0.265, auc: 0.508, f1: 0.059, acc: 0.740
+2025-08-26 00:51:49: Epoch: 029 Train Loss: 0.252, ap: 0.897, auc: 0.896, f1: 0.796, acc: 0.813
+2025-08-26 00:51:49: Epoch: 029 Test results, ap: 0.276, auc: 0.532, f1: 0.059, acc: 0.740
+2025-08-26 00:51:52: Epoch: 030 Train Loss: 0.240, ap: 0.888, auc: 0.890, f1: 0.792, acc: 0.805
+2025-08-26 00:51:52: Epoch: 030 Test results, ap: 0.266, auc: 0.523, f1: 0.054, acc: 0.715
+2025-08-26 00:51:55: Epoch: 031 Train Loss: 0.219, ap: 0.890, auc: 0.900, f1: 0.807, acc: 0.820
+2025-08-26 00:51:55: Epoch: 031 Test results, ap: 0.261, auc: 0.514, f1: 0.000, acc: 0.715
+2025-08-26 00:51:58: Epoch: 032 Train Loss: 0.196, ap: 0.896, auc: 0.905, f1: 0.813, acc: 0.829
+2025-08-26 00:51:58: Epoch: 032 New best VALIDATION metrics
+2025-08-26 00:51:58: Epoch: 032 Test results, ap: 0.260, auc: 0.510, f1: 0.000, acc: 0.732
+2025-08-26 00:52:01: Epoch: 033 Train Loss: 0.193, ap: 0.895, auc: 0.902, f1: 0.803, acc: 0.820
+2025-08-26 00:52:02: Epoch: 033 Test results, ap: 0.266, auc: 0.515, f1: 0.000, acc: 0.740
+2025-08-26 00:52:04: Epoch: 034 Train Loss: 0.187, ap: 0.903, auc: 0.907, f1: 0.803, acc: 0.820
+2025-08-26 00:52:04: Epoch: 034 New best VALIDATION metrics
+2025-08-26 00:52:05: Epoch: 034 Test results, ap: 0.262, auc: 0.513, f1: 0.000, acc: 0.748
+2025-08-26 00:52:08: Epoch: 035 Train Loss: 0.174, ap: 0.894, auc: 0.906, f1: 0.812, acc: 0.829
+2025-08-26 00:52:08: Epoch: 035 Test results, ap: 0.263, auc: 0.511, f1: 0.000, acc: 0.740
+2025-08-26 00:52:11: Epoch: 036 Train Loss: 0.163, ap: 0.901, auc: 0.908, f1: 0.819, acc: 0.827
+2025-08-26 00:52:11: Epoch: 036 Test results, ap: 0.260, auc: 0.516, f1: 0.000, acc: 0.732
+2025-08-26 00:52:14: Epoch: 037 Train Loss: 0.163, ap: 0.900, auc: 0.909, f1: 0.827, acc: 0.842
+2025-08-26 00:52:14: Epoch: 037 Test results, ap: 0.264, auc: 0.517, f1: 0.000, acc: 0.740
+2025-08-26 00:52:17: Epoch: 038 Train Loss: 0.151, ap: 0.903, auc: 0.909, f1: 0.835, acc: 0.847
+2025-08-26 00:52:17: Epoch: 038 New best VALIDATION metrics
+2025-08-26 00:52:17: Epoch: 038 Test results, ap: 0.272, auc: 0.518, f1: 0.000, acc: 0.740
+2025-08-26 00:52:20: Epoch: 039 Train Loss: 0.164, ap: 0.894, auc: 0.907, f1: 0.827, acc: 0.836
+2025-08-26 00:52:21: Epoch: 039 Test results, ap: 0.268, auc: 0.510, f1: 0.000, acc: 0.740
+2025-08-26 00:52:23: Epoch: 040 Train Loss: 0.151, ap: 0.906, auc: 0.911, f1: 0.822, acc: 0.838
+2025-08-26 00:52:23: Epoch: 040 New best VALIDATION metrics
+2025-08-26 00:52:24: Epoch: 040 Test results, ap: 0.267, auc: 0.521, f1: 0.000, acc: 0.740
+2025-08-26 00:52:27: Epoch: 041 Train Loss: 0.144, ap: 0.898, auc: 0.909, f1: 0.828, acc: 0.842
+2025-08-26 00:52:27: Epoch: 041 Test results, ap: 0.270, auc: 0.522, f1: 0.000, acc: 0.740
+2025-08-26 00:52:30: Epoch: 042 Train Loss: 0.135, ap: 0.900, auc: 0.911, f1: 0.822, acc: 0.836
+2025-08-26 00:52:30: Epoch: 042 Test results, ap: 0.268, auc: 0.516, f1: 0.000, acc: 0.740
+2025-08-26 00:52:33: Epoch: 043 Train Loss: 0.125, ap: 0.900, auc: 0.910, f1: 0.830, acc: 0.843
+2025-08-26 00:52:33: Epoch: 043 Test results, ap: 0.269, auc: 0.519, f1: 0.000, acc: 0.748
+2025-08-26 00:52:36: Epoch: 044 Train Loss: 0.119, ap: 0.900, auc: 0.911, f1: 0.822, acc: 0.840
+2025-08-26 00:52:36: Epoch: 044 Test results, ap: 0.273, auc: 0.521, f1: 0.000, acc: 0.748
+2025-08-26 00:52:39: Epoch: 045 Train Loss: 0.123, ap: 0.896, auc: 0.910, f1: 0.834, acc: 0.843
+2025-08-26 00:52:40: Epoch: 045 Test results, ap: 0.270, auc: 0.519, f1: 0.000, acc: 0.748
+2025-08-26 00:52:42: Epoch: 046 Train Loss: 0.120, ap: 0.898, auc: 0.909, f1: 0.823, acc: 0.840
+2025-08-26 00:52:43: Epoch: 046 Test results, ap: 0.273, auc: 0.522, f1: 0.000, acc: 0.748
+2025-08-26 00:52:46: Epoch: 047 Train Loss: 0.118, ap: 0.900, auc: 0.911, f1: 0.806, acc: 0.829
+2025-08-26 00:52:46: Epoch: 047 Test results, ap: 0.291, auc: 0.523, f1: 0.000, acc: 0.748
+2025-08-26 00:52:49: Epoch: 048 Train Loss: 0.115, ap: 0.900, auc: 0.912, f1: 0.834, acc: 0.845
+2025-08-26 00:52:49: Epoch: 048 Test results, ap: 0.285, auc: 0.522, f1: 0.000, acc: 0.748
+2025-08-26 00:52:52: Epoch: 049 Train Loss: 0.115, ap: 0.902, auc: 0.911, f1: 0.838, acc: 0.849
+2025-08-26 00:52:52: Epoch: 049 Test results, ap: 0.272, auc: 0.515, f1: 0.000, acc: 0.748
+2025-08-26 00:52:55: Epoch: 050 Train Loss: 0.106, ap: 0.902, auc: 0.913, f1: 0.836, acc: 0.850
+2025-08-26 00:52:55: Epoch: 050 Test results, ap: 0.269, auc: 0.514, f1: 0.000, acc: 0.748
+2025-08-26 00:52:55: used time 0.04h
+2025-08-26 00:52:55: Running Cross Validation 1
+2025-08-26 00:52:55: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 00:52:58: Epoch: 001 Train Loss: 0.695, ap: 0.568, auc: 0.619, f1: 0.000, acc: 0.563
+2025-08-26 00:52:58: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:52:59: Epoch: 001 Test results, ap: 0.251, auc: 0.487, f1: 0.000, acc: 0.756
+2025-08-26 00:53:01: Epoch: 002 Train Loss: 0.695, ap: 0.427, auc: 0.503, f1: 0.608, acc: 0.437
+2025-08-26 00:53:02: Epoch: 002 Test results, ap: 0.280, auc: 0.531, f1: 0.392, acc: 0.244
+2025-08-26 00:53:05: Epoch: 003 Train Loss: 0.694, ap: 0.406, auc: 0.483, f1: 0.608, acc: 0.437
+2025-08-26 00:53:05: Epoch: 003 Test results, ap: 0.333, auc: 0.564, f1: 0.392, acc: 0.244
+2025-08-26 00:53:08: Epoch: 004 Train Loss: 0.695, ap: 0.401, auc: 0.468, f1: 0.608, acc: 0.437
+2025-08-26 00:53:08: Epoch: 004 Test results, ap: 0.189, auc: 0.361, f1: 0.392, acc: 0.244
+2025-08-26 00:53:11: Epoch: 005 Train Loss: 0.694, ap: 0.447, auc: 0.518, f1: 0.608, acc: 0.437
+2025-08-26 00:53:11: Epoch: 005 Test results, ap: 0.237, auc: 0.475, f1: 0.392, acc: 0.244
+2025-08-26 00:53:14: Epoch: 006 Train Loss: 0.693, ap: 0.449, auc: 0.523, f1: 0.608, acc: 0.437
+2025-08-26 00:53:14: Epoch: 006 Test results, ap: 0.267, auc: 0.475, f1: 0.392, acc: 0.244
+2025-08-26 00:53:17: Epoch: 007 Train Loss: 0.694, ap: 0.470, auc: 0.552, f1: 0.608, acc: 0.437
+2025-08-26 00:53:18: Epoch: 007 Test results, ap: 0.226, auc: 0.446, f1: 0.392, acc: 0.244
+2025-08-26 00:53:20: Epoch: 008 Train Loss: 0.694, ap: 0.486, auc: 0.584, f1: 0.000, acc: 0.563
+2025-08-26 00:53:21: Epoch: 008 Test results, ap: 0.289, auc: 0.568, f1: 0.000, acc: 0.756
+2025-08-26 00:53:24: Epoch: 009 Train Loss: 0.695, ap: 0.451, auc: 0.521, f1: 0.000, acc: 0.563
+2025-08-26 00:53:24: Epoch: 009 Test results, ap: 0.281, auc: 0.584, f1: 0.000, acc: 0.756
+2025-08-26 00:53:27: Epoch: 010 Train Loss: 0.695, ap: 0.401, auc: 0.439, f1: 0.000, acc: 0.563
+2025-08-26 00:53:27: Epoch: 010 Test results, ap: 0.222, auc: 0.394, f1: 0.000, acc: 0.756
+2025-08-26 00:53:30: Epoch: 011 Train Loss: 0.694, ap: 0.424, auc: 0.472, f1: 0.608, acc: 0.437
+2025-08-26 00:53:30: Epoch: 011 Test results, ap: 0.233, auc: 0.453, f1: 0.392, acc: 0.244
+2025-08-26 00:53:30: Epoch: 011 New best TEST metrics
+2025-08-26 00:53:33: Epoch: 012 Train Loss: 0.694, ap: 0.443, auc: 0.514, f1: 0.608, acc: 0.437
+2025-08-26 00:53:34: Epoch: 012 Test results, ap: 0.231, auc: 0.458, f1: 0.392, acc: 0.244
+2025-08-26 00:53:34: Epoch: 012 New best TEST metrics
+2025-08-26 00:53:36: Epoch: 013 Train Loss: 0.694, ap: 0.444, auc: 0.506, f1: 0.608, acc: 0.437
+2025-08-26 00:53:37: Epoch: 013 Test results, ap: 0.252, auc: 0.522, f1: 0.392, acc: 0.244
+2025-08-26 00:53:37: Epoch: 013 New best TEST metrics
+2025-08-26 00:53:40: Epoch: 014 Train Loss: 0.693, ap: 0.423, auc: 0.472, f1: 0.000, acc: 0.563
+2025-08-26 00:53:40: Epoch: 014 Test results, ap: 0.267, auc: 0.543, f1: 0.000, acc: 0.756
+2025-08-26 00:53:40: Epoch: 014 New best TEST metrics
+2025-08-26 00:53:43: Epoch: 015 Train Loss: 0.693, ap: 0.444, auc: 0.512, f1: 0.608, acc: 0.437
+2025-08-26 00:53:43: Epoch: 015 Test results, ap: 0.229, auc: 0.427, f1: 0.392, acc: 0.244
+2025-08-26 00:53:46: Epoch: 016 Train Loss: 0.693, ap: 0.439, auc: 0.504, f1: 0.000, acc: 0.563
+2025-08-26 00:53:46: Epoch: 016 Test results, ap: 0.252, auc: 0.522, f1: 0.000, acc: 0.756
+2025-08-26 00:53:49: Epoch: 017 Train Loss: 0.694, ap: 0.485, auc: 0.575, f1: 0.608, acc: 0.437
+2025-08-26 00:53:49: Epoch: 017 Test results, ap: 0.250, auc: 0.515, f1: 0.392, acc: 0.244
+2025-08-26 00:53:52: Epoch: 018 Train Loss: 0.694, ap: 0.503, auc: 0.600, f1: 0.000, acc: 0.563
+2025-08-26 00:53:53: Epoch: 018 Test results, ap: 0.252, auc: 0.521, f1: 0.000, acc: 0.756
+2025-08-26 00:53:55: Epoch: 019 Train Loss: 0.693, ap: 0.490, auc: 0.576, f1: 0.000, acc: 0.563
+2025-08-26 00:53:56: Epoch: 019 Test results, ap: 0.279, auc: 0.578, f1: 0.000, acc: 0.756
+2025-08-26 00:53:56: Epoch: 019 New best TEST metrics
+2025-08-26 00:53:59: Epoch: 020 Train Loss: 0.693, ap: 0.496, auc: 0.568, f1: 0.608, acc: 0.437
+2025-08-26 00:53:59: Epoch: 020 Test results, ap: 0.268, auc: 0.552, f1: 0.392, acc: 0.244
+2025-08-26 00:54:02: Epoch: 021 Train Loss: 0.693, ap: 0.559, auc: 0.595, f1: 0.016, acc: 0.567
+2025-08-26 00:54:02: Epoch: 021 Test results, ap: 0.354, auc: 0.548, f1: 0.000, acc: 0.756
+2025-08-26 00:54:02: Epoch: 021 New best TEST metrics
+2025-08-26 00:54:05: Epoch: 022 Train Loss: 0.693, ap: 0.519, auc: 0.594, f1: 0.605, acc: 0.463
+2025-08-26 00:54:05: Epoch: 022 Test results, ap: 0.282, auc: 0.477, f1: 0.392, acc: 0.244
+2025-08-26 00:54:08: Epoch: 023 Train Loss: 0.687, ap: 0.569, auc: 0.642, f1: 0.606, acc: 0.611
+2025-08-26 00:54:08: Epoch: 023 New best VALIDATION metrics
+2025-08-26 00:54:09: Epoch: 023 Test results, ap: 0.327, auc: 0.585, f1: 0.373, acc: 0.480
+2025-08-26 00:54:09: Epoch: 023 New best TEST metrics
+2025-08-26 00:54:12: Epoch: 024 Train Loss: 0.682, ap: 0.601, auc: 0.656, f1: 0.608, acc: 0.540
+2025-08-26 00:54:12: Epoch: 024 New best VALIDATION metrics
+2025-08-26 00:54:12: Epoch: 024 Test results, ap: 0.275, auc: 0.532, f1: 0.397, acc: 0.260
+2025-08-26 00:54:15: Epoch: 025 Train Loss: 0.674, ap: 0.592, auc: 0.661, f1: 0.591, acc: 0.650
+2025-08-26 00:54:15: Epoch: 025 Test results, ap: 0.224, auc: 0.428, f1: 0.392, acc: 0.244
+2025-08-26 00:54:18: Epoch: 026 Train Loss: 0.659, ap: 0.629, auc: 0.677, f1: 0.591, acc: 0.658
+2025-08-26 00:54:18: Epoch: 026 New best VALIDATION metrics
+2025-08-26 00:54:18: Epoch: 026 Test results, ap: 0.198, auc: 0.378, f1: 0.000, acc: 0.659
+2025-08-26 00:54:21: Epoch: 027 Train Loss: 0.657, ap: 0.612, auc: 0.676, f1: 0.605, acc: 0.664
+2025-08-26 00:54:21: Epoch: 027 Test results, ap: 0.232, auc: 0.491, f1: 0.423, acc: 0.333
+2025-08-26 00:54:24: Epoch: 028 Train Loss: 0.659, ap: 0.618, auc: 0.675, f1: 0.608, acc: 0.621
+2025-08-26 00:54:25: Epoch: 028 Test results, ap: 0.332, auc: 0.567, f1: 0.392, acc: 0.244
+2025-08-26 00:54:27: Epoch: 029 Train Loss: 0.655, ap: 0.615, auc: 0.677, f1: 0.602, acc: 0.681
+2025-08-26 00:54:28: Epoch: 029 Test results, ap: 0.253, auc: 0.529, f1: 0.392, acc: 0.244
+2025-08-26 00:54:31: Epoch: 030 Train Loss: 0.649, ap: 0.615, auc: 0.690, f1: 0.622, acc: 0.671
+2025-08-26 00:54:31: Epoch: 030 Test results, ap: 0.280, auc: 0.497, f1: 0.405, acc: 0.285
+2025-08-26 00:54:34: Epoch: 031 Train Loss: 0.640, ap: 0.604, auc: 0.665, f1: 0.558, acc: 0.657
+2025-08-26 00:54:34: Epoch: 031 Test results, ap: 0.328, auc: 0.586, f1: 0.417, acc: 0.317
+2025-08-26 00:54:34: Epoch: 031 New best TEST metrics
+2025-08-26 00:54:37: Epoch: 032 Train Loss: 0.631, ap: 0.631, auc: 0.690, f1: 0.607, acc: 0.665
+2025-08-26 00:54:37: Epoch: 032 New best VALIDATION metrics
+2025-08-26 00:54:37: Epoch: 032 Test results, ap: 0.325, auc: 0.573, f1: 0.414, acc: 0.309
+2025-08-26 00:54:40: Epoch: 033 Train Loss: 0.631, ap: 0.620, auc: 0.684, f1: 0.611, acc: 0.653
+2025-08-26 00:54:40: Epoch: 033 Test results, ap: 0.336, auc: 0.579, f1: 0.408, acc: 0.293
+2025-08-26 00:54:40: Epoch: 033 New best TEST metrics
+2025-08-26 00:54:43: Epoch: 034 Train Loss: 0.625, ap: 0.624, auc: 0.678, f1: 0.602, acc: 0.660
+2025-08-26 00:54:44: Epoch: 034 Test results, ap: 0.360, auc: 0.615, f1: 0.411, acc: 0.301
+2025-08-26 00:54:44: Epoch: 034 New best TEST metrics
+2025-08-26 00:54:47: Epoch: 035 Train Loss: 0.617, ap: 0.627, auc: 0.687, f1: 0.616, acc: 0.671
+2025-08-26 00:54:47: Epoch: 035 Test results, ap: 0.415, auc: 0.603, f1: 0.411, acc: 0.301
+2025-08-26 00:54:47: Epoch: 035 New best TEST metrics
+2025-08-26 00:54:50: Epoch: 036 Train Loss: 0.611, ap: 0.623, auc: 0.692, f1: 0.593, acc: 0.664
+2025-08-26 00:54:50: Epoch: 036 Test results, ap: 0.375, auc: 0.600, f1: 0.417, acc: 0.317
+2025-08-26 00:54:53: Epoch: 037 Train Loss: 0.607, ap: 0.599, auc: 0.656, f1: 0.584, acc: 0.653
+2025-08-26 00:54:53: Epoch: 037 Test results, ap: 0.463, auc: 0.610, f1: 0.420, acc: 0.325
+2025-08-26 00:54:53: Epoch: 037 New best TEST metrics
+2025-08-26 00:54:56: Epoch: 038 Train Loss: 0.608, ap: 0.623, auc: 0.689, f1: 0.605, acc: 0.671
+2025-08-26 00:54:56: Epoch: 038 Test results, ap: 0.397, auc: 0.585, f1: 0.420, acc: 0.325
+2025-08-26 00:54:59: Epoch: 039 Train Loss: 0.604, ap: 0.619, auc: 0.693, f1: 0.557, acc: 0.655
+2025-08-26 00:55:00: Epoch: 039 Test results, ap: 0.393, auc: 0.588, f1: 0.423, acc: 0.333
+2025-08-26 00:55:03: Epoch: 040 Train Loss: 0.597, ap: 0.635, auc: 0.692, f1: 0.594, acc: 0.660
+2025-08-26 00:55:03: Epoch: 040 New best VALIDATION metrics
+2025-08-26 00:55:03: Epoch: 040 Test results, ap: 0.419, auc: 0.587, f1: 0.420, acc: 0.325
+2025-08-26 00:55:06: Epoch: 041 Train Loss: 0.581, ap: 0.621, auc: 0.688, f1: 0.575, acc: 0.651
+2025-08-26 00:55:06: Epoch: 041 Test results, ap: 0.393, auc: 0.594, f1: 0.420, acc: 0.325
+2025-08-26 00:55:09: Epoch: 042 Train Loss: 0.575, ap: 0.634, auc: 0.696, f1: 0.573, acc: 0.662
+2025-08-26 00:55:09: Epoch: 042 New best VALIDATION metrics
+2025-08-26 00:55:09: Epoch: 042 Test results, ap: 0.444, auc: 0.628, f1: 0.391, acc: 0.366
+2025-08-26 00:55:12: Epoch: 043 Train Loss: 0.573, ap: 0.647, auc: 0.705, f1: 0.568, acc: 0.655
+2025-08-26 00:55:12: Epoch: 043 New best VALIDATION metrics
+2025-08-26 00:55:12: Epoch: 043 Test results, ap: 0.439, auc: 0.621, f1: 0.409, acc: 0.366
+2025-08-26 00:55:15: Epoch: 044 Train Loss: 0.560, ap: 0.650, auc: 0.703, f1: 0.594, acc: 0.660
+2025-08-26 00:55:15: Epoch: 044 New best VALIDATION metrics
+2025-08-26 00:55:16: Epoch: 044 Test results, ap: 0.400, auc: 0.625, f1: 0.426, acc: 0.341
+2025-08-26 00:55:19: Epoch: 045 Train Loss: 0.556, ap: 0.656, auc: 0.709, f1: 0.587, acc: 0.658
+2025-08-26 00:55:19: Epoch: 045 New best VALIDATION metrics
+2025-08-26 00:55:19: Epoch: 045 Test results, ap: 0.304, auc: 0.605, f1: 0.414, acc: 0.333
+2025-08-26 00:55:22: Epoch: 046 Train Loss: 0.546, ap: 0.667, auc: 0.711, f1: 0.600, acc: 0.662
+2025-08-26 00:55:22: Epoch: 046 New best VALIDATION metrics
+2025-08-26 00:55:22: Epoch: 046 Test results, ap: 0.323, auc: 0.604, f1: 0.397, acc: 0.382
+2025-08-26 00:55:25: Epoch: 047 Train Loss: 0.539, ap: 0.676, auc: 0.719, f1: 0.599, acc: 0.667
+2025-08-26 00:55:25: Epoch: 047 New best VALIDATION metrics
+2025-08-26 00:55:25: Epoch: 047 Test results, ap: 0.450, auc: 0.644, f1: 0.440, acc: 0.545
+2025-08-26 00:55:25: Epoch: 047 New best TEST metrics
+2025-08-26 00:55:28: Epoch: 048 Train Loss: 0.545, ap: 0.676, auc: 0.722, f1: 0.606, acc: 0.671
+2025-08-26 00:55:28: Epoch: 048 New best VALIDATION metrics
+2025-08-26 00:55:28: Epoch: 048 Test results, ap: 0.387, auc: 0.629, f1: 0.436, acc: 0.537
+2025-08-26 00:55:31: Epoch: 049 Train Loss: 0.531, ap: 0.670, auc: 0.722, f1: 0.608, acc: 0.685
+2025-08-26 00:55:32: Epoch: 049 Test results, ap: 0.448, auc: 0.646, f1: 0.418, acc: 0.569
+2025-08-26 00:55:32: Epoch: 049 New best TEST metrics
+2025-08-26 00:55:35: Epoch: 050 Train Loss: 0.525, ap: 0.683, auc: 0.725, f1: 0.598, acc: 0.683
+2025-08-26 00:55:35: Epoch: 050 New best VALIDATION metrics
+2025-08-26 00:55:35: Epoch: 050 Test results, ap: 0.469, auc: 0.650, f1: 0.412, acc: 0.675
+2025-08-26 00:55:35: Epoch: 050 New best TEST metrics
+2025-08-26 00:55:35: used time 0.04h
+2025-08-26 00:55:35: Running Cross Validation 2
+2025-08-26 00:55:35: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:55:38: Epoch: 001 Train Loss: 0.695, ap: 0.434, auc: 0.476, f1: 0.612, acc: 0.441
+2025-08-26 00:55:38: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:55:38: Epoch: 001 Test results, ap: 0.276, auc: 0.491, f1: 0.392, acc: 0.244
+2025-08-26 00:55:41: Epoch: 002 Train Loss: 0.696, ap: 0.521, auc: 0.628, f1: 0.612, acc: 0.441
+2025-08-26 00:55:41: Epoch: 002 New best VALIDATION metrics
+2025-08-26 00:55:41: Epoch: 002 Test results, ap: 0.251, auc: 0.561, f1: 0.392, acc: 0.244
+2025-08-26 00:55:44: Epoch: 003 Train Loss: 0.696, ap: 0.540, auc: 0.621, f1: 0.612, acc: 0.441
+2025-08-26 00:55:44: Epoch: 003 New best VALIDATION metrics
+2025-08-26 00:55:44: Epoch: 003 Test results, ap: 0.255, auc: 0.573, f1: 0.392, acc: 0.244
+2025-08-26 00:55:47: Epoch: 004 Train Loss: 0.693, ap: 0.557, auc: 0.618, f1: 0.000, acc: 0.559
+2025-08-26 00:55:47: Epoch: 004 New best VALIDATION metrics
+2025-08-26 00:55:48: Epoch: 004 Test results, ap: 0.257, auc: 0.566, f1: 0.000, acc: 0.756
+2025-08-26 00:55:51: Epoch: 005 Train Loss: 0.694, ap: 0.553, auc: 0.619, f1: 0.612, acc: 0.441
+2025-08-26 00:55:51: Epoch: 005 Test results, ap: 0.281, auc: 0.617, f1: 0.392, acc: 0.244
+2025-08-26 00:55:54: Epoch: 006 Train Loss: 0.697, ap: 0.563, auc: 0.626, f1: 0.560, acc: 0.607
+2025-08-26 00:55:54: Epoch: 006 New best VALIDATION metrics
+2025-08-26 00:55:54: Epoch: 006 Test results, ap: 0.276, auc: 0.596, f1: 0.392, acc: 0.244
+2025-08-26 00:55:57: Epoch: 007 Train Loss: 0.682, ap: 0.580, auc: 0.647, f1: 0.573, acc: 0.619
+2025-08-26 00:55:57: Epoch: 007 New best VALIDATION metrics
+2025-08-26 00:55:57: Epoch: 007 Test results, ap: 0.222, auc: 0.452, f1: 0.392, acc: 0.244
+2025-08-26 00:56:00: Epoch: 008 Train Loss: 0.675, ap: 0.579, auc: 0.653, f1: 0.457, acc: 0.631
+2025-08-26 00:56:00: Epoch: 008 New best VALIDATION metrics
+2025-08-26 00:56:00: Epoch: 008 Test results, ap: 0.233, auc: 0.492, f1: 0.392, acc: 0.244
+2025-08-26 00:56:03: Epoch: 009 Train Loss: 0.661, ap: 0.579, auc: 0.644, f1: 0.581, acc: 0.626
+2025-08-26 00:56:04: Epoch: 009 Test results, ap: 0.226, auc: 0.461, f1: 0.392, acc: 0.244
+2025-08-26 00:56:06: Epoch: 010 Train Loss: 0.658, ap: 0.581, auc: 0.645, f1: 0.540, acc: 0.651
+2025-08-26 00:56:07: Epoch: 010 Test results, ap: 0.224, auc: 0.451, f1: 0.392, acc: 0.244
+2025-08-26 00:56:10: Epoch: 011 Train Loss: 0.643, ap: 0.586, auc: 0.643, f1: 0.567, acc: 0.644
+2025-08-26 00:56:10: Epoch: 011 Test results, ap: 0.219, auc: 0.442, f1: 0.392, acc: 0.244
+2025-08-26 00:56:10: Epoch: 011 New best TEST metrics
+2025-08-26 00:56:13: Epoch: 012 Train Loss: 0.645, ap: 0.597, auc: 0.650, f1: 0.580, acc: 0.607
+2025-08-26 00:56:13: Epoch: 012 New best VALIDATION metrics
+2025-08-26 00:56:13: Epoch: 012 Test results, ap: 0.224, auc: 0.458, f1: 0.392, acc: 0.244
+2025-08-26 00:56:13: Epoch: 012 New best TEST metrics
+2025-08-26 00:56:16: Epoch: 013 Train Loss: 0.644, ap: 0.595, auc: 0.651, f1: 0.562, acc: 0.635
+2025-08-26 00:56:16: Epoch: 013 Test results, ap: 0.224, auc: 0.463, f1: 0.392, acc: 0.244
+2025-08-26 00:56:16: Epoch: 013 New best TEST metrics
+2025-08-26 00:56:19: Epoch: 014 Train Loss: 0.643, ap: 0.604, auc: 0.649, f1: 0.550, acc: 0.647
+2025-08-26 00:56:19: Epoch: 014 New best VALIDATION metrics
+2025-08-26 00:56:20: Epoch: 014 Test results, ap: 0.245, auc: 0.524, f1: 0.392, acc: 0.244
+2025-08-26 00:56:20: Epoch: 014 New best TEST metrics
+2025-08-26 00:56:22: Epoch: 015 Train Loss: 0.639, ap: 0.600, auc: 0.648, f1: 0.581, acc: 0.642
+2025-08-26 00:56:23: Epoch: 015 Test results, ap: 0.219, auc: 0.449, f1: 0.392, acc: 0.244
+2025-08-26 00:56:26: Epoch: 016 Train Loss: 0.641, ap: 0.606, auc: 0.649, f1: 0.577, acc: 0.633
+2025-08-26 00:56:26: Epoch: 016 New best VALIDATION metrics
+2025-08-26 00:56:26: Epoch: 016 Test results, ap: 0.230, auc: 0.479, f1: 0.392, acc: 0.244
+2025-08-26 00:56:29: Epoch: 017 Train Loss: 0.638, ap: 0.596, auc: 0.647, f1: 0.590, acc: 0.617
+2025-08-26 00:56:29: Epoch: 017 Test results, ap: 0.245, auc: 0.527, f1: 0.392, acc: 0.244
+2025-08-26 00:56:29: Epoch: 017 New best TEST metrics
+2025-08-26 00:56:32: Epoch: 018 Train Loss: 0.643, ap: 0.611, auc: 0.667, f1: 0.593, acc: 0.644
+2025-08-26 00:56:32: Epoch: 018 New best VALIDATION metrics
+2025-08-26 00:56:32: Epoch: 018 Test results, ap: 0.215, auc: 0.437, f1: 0.392, acc: 0.244
+2025-08-26 00:56:35: Epoch: 019 Train Loss: 0.638, ap: 0.591, auc: 0.654, f1: 0.583, acc: 0.642
+2025-08-26 00:56:35: Epoch: 019 Test results, ap: 0.283, auc: 0.588, f1: 0.392, acc: 0.244
+2025-08-26 00:56:35: Epoch: 019 New best TEST metrics
+2025-08-26 00:56:38: Epoch: 020 Train Loss: 0.632, ap: 0.606, auc: 0.663, f1: 0.589, acc: 0.633
+2025-08-26 00:56:39: Epoch: 020 Test results, ap: 0.289, auc: 0.580, f1: 0.392, acc: 0.244
+2025-08-26 00:56:42: Epoch: 021 Train Loss: 0.623, ap: 0.618, auc: 0.669, f1: 0.600, acc: 0.638
+2025-08-26 00:56:42: Epoch: 021 New best VALIDATION metrics
+2025-08-26 00:56:42: Epoch: 021 Test results, ap: 0.245, auc: 0.520, f1: 0.392, acc: 0.244
+2025-08-26 00:56:45: Epoch: 022 Train Loss: 0.620, ap: 0.605, auc: 0.671, f1: 0.613, acc: 0.660
+2025-08-26 00:56:45: Epoch: 022 Test results, ap: 0.280, auc: 0.556, f1: 0.392, acc: 0.244
+2025-08-26 00:56:48: Epoch: 023 Train Loss: 0.619, ap: 0.606, auc: 0.670, f1: 0.605, acc: 0.644
+2025-08-26 00:56:48: Epoch: 023 Test results, ap: 0.257, auc: 0.524, f1: 0.392, acc: 0.244
+2025-08-26 00:56:51: Epoch: 024 Train Loss: 0.617, ap: 0.615, auc: 0.668, f1: 0.604, acc: 0.646
+2025-08-26 00:56:51: Epoch: 024 Test results, ap: 0.253, auc: 0.534, f1: 0.392, acc: 0.244
+2025-08-26 00:56:54: Epoch: 025 Train Loss: 0.613, ap: 0.606, auc: 0.669, f1: 0.592, acc: 0.621
+2025-08-26 00:56:54: Epoch: 025 Test results, ap: 0.251, auc: 0.512, f1: 0.392, acc: 0.244
+2025-08-26 00:56:57: Epoch: 026 Train Loss: 0.612, ap: 0.621, auc: 0.673, f1: 0.598, acc: 0.644
+2025-08-26 00:56:57: Epoch: 026 New best VALIDATION metrics
+2025-08-26 00:56:58: Epoch: 026 Test results, ap: 0.244, auc: 0.483, f1: 0.392, acc: 0.244
+2025-08-26 00:57:01: Epoch: 027 Train Loss: 0.610, ap: 0.618, auc: 0.681, f1: 0.601, acc: 0.651
+2025-08-26 00:57:01: Epoch: 027 New best VALIDATION metrics
+2025-08-26 00:57:01: Epoch: 027 Test results, ap: 0.246, auc: 0.533, f1: 0.392, acc: 0.244
+2025-08-26 00:57:04: Epoch: 028 Train Loss: 0.599, ap: 0.626, auc: 0.690, f1: 0.635, acc: 0.649
+2025-08-26 00:57:04: Epoch: 028 New best VALIDATION metrics
+2025-08-26 00:57:04: Epoch: 028 Test results, ap: 0.238, auc: 0.506, f1: 0.392, acc: 0.244
+2025-08-26 00:57:07: Epoch: 029 Train Loss: 0.596, ap: 0.626, auc: 0.712, f1: 0.670, acc: 0.691
+2025-08-26 00:57:07: Epoch: 029 New best VALIDATION metrics
+2025-08-26 00:57:07: Epoch: 029 Test results, ap: 0.258, auc: 0.542, f1: 0.392, acc: 0.244
+2025-08-26 00:57:10: Epoch: 030 Train Loss: 0.581, ap: 0.666, auc: 0.748, f1: 0.684, acc: 0.697
+2025-08-26 00:57:10: Epoch: 030 New best VALIDATION metrics
+2025-08-26 00:57:10: Epoch: 030 Test results, ap: 0.259, auc: 0.537, f1: 0.392, acc: 0.244
+2025-08-26 00:57:13: Epoch: 031 Train Loss: 0.554, ap: 0.674, auc: 0.742, f1: 0.664, acc: 0.677
+2025-08-26 00:57:13: Epoch: 031 New best VALIDATION metrics
+2025-08-26 00:57:14: Epoch: 031 Test results, ap: 0.241, auc: 0.508, f1: 0.392, acc: 0.244
+2025-08-26 00:57:17: Epoch: 032 Train Loss: 0.547, ap: 0.681, auc: 0.750, f1: 0.691, acc: 0.707
+2025-08-26 00:57:17: Epoch: 032 New best VALIDATION metrics
+2025-08-26 00:57:17: Epoch: 032 Test results, ap: 0.254, auc: 0.522, f1: 0.392, acc: 0.244
+2025-08-26 00:57:20: Epoch: 033 Train Loss: 0.534, ap: 0.684, auc: 0.748, f1: 0.691, acc: 0.702
+2025-08-26 00:57:20: Epoch: 033 New best VALIDATION metrics
+2025-08-26 00:57:20: Epoch: 033 Test results, ap: 0.245, auc: 0.518, f1: 0.392, acc: 0.244
+2025-08-26 00:57:23: Epoch: 034 Train Loss: 0.538, ap: 0.700, auc: 0.764, f1: 0.704, acc: 0.714
+2025-08-26 00:57:23: Epoch: 034 New best VALIDATION metrics
+2025-08-26 00:57:23: Epoch: 034 Test results, ap: 0.258, auc: 0.539, f1: 0.392, acc: 0.244
+2025-08-26 00:57:26: Epoch: 035 Train Loss: 0.519, ap: 0.688, auc: 0.758, f1: 0.705, acc: 0.714
+2025-08-26 00:57:27: Epoch: 035 Test results, ap: 0.256, auc: 0.534, f1: 0.392, acc: 0.244
+2025-08-26 00:57:29: Epoch: 036 Train Loss: 0.508, ap: 0.672, auc: 0.746, f1: 0.684, acc: 0.691
+2025-08-26 00:57:30: Epoch: 036 Test results, ap: 0.251, auc: 0.519, f1: 0.392, acc: 0.244
+2025-08-26 00:57:33: Epoch: 037 Train Loss: 0.502, ap: 0.711, auc: 0.772, f1: 0.706, acc: 0.720
+2025-08-26 00:57:33: Epoch: 037 New best VALIDATION metrics
+2025-08-26 00:57:33: Epoch: 037 Test results, ap: 0.255, auc: 0.531, f1: 0.392, acc: 0.244
+2025-08-26 00:57:36: Epoch: 038 Train Loss: 0.502, ap: 0.701, auc: 0.768, f1: 0.670, acc: 0.697
+2025-08-26 00:57:36: Epoch: 038 Test results, ap: 0.250, auc: 0.523, f1: 0.405, acc: 0.285
+2025-08-26 00:57:39: Epoch: 039 Train Loss: 0.489, ap: 0.710, auc: 0.772, f1: 0.690, acc: 0.697
+2025-08-26 00:57:39: Epoch: 039 Test results, ap: 0.242, auc: 0.499, f1: 0.397, acc: 0.260
+2025-08-26 00:57:42: Epoch: 040 Train Loss: 0.487, ap: 0.721, auc: 0.780, f1: 0.708, acc: 0.713
+2025-08-26 00:57:42: Epoch: 040 New best VALIDATION metrics
+2025-08-26 00:57:42: Epoch: 040 Test results, ap: 0.244, auc: 0.518, f1: 0.392, acc: 0.244
+2025-08-26 00:57:45: Epoch: 041 Train Loss: 0.464, ap: 0.724, auc: 0.781, f1: 0.725, acc: 0.737
+2025-08-26 00:57:45: Epoch: 041 New best VALIDATION metrics
+2025-08-26 00:57:46: Epoch: 041 Test results, ap: 0.244, auc: 0.504, f1: 0.397, acc: 0.260
+2025-08-26 00:57:49: Epoch: 042 Train Loss: 0.460, ap: 0.727, auc: 0.787, f1: 0.707, acc: 0.720
+2025-08-26 00:57:49: Epoch: 042 New best VALIDATION metrics
+2025-08-26 00:57:49: Epoch: 042 Test results, ap: 0.242, auc: 0.499, f1: 0.403, acc: 0.276
+2025-08-26 00:57:52: Epoch: 043 Train Loss: 0.460, ap: 0.738, auc: 0.784, f1: 0.709, acc: 0.709
+2025-08-26 00:57:52: Epoch: 043 New best VALIDATION metrics
+2025-08-26 00:57:52: Epoch: 043 Test results, ap: 0.241, auc: 0.489, f1: 0.395, acc: 0.252
+2025-08-26 00:57:55: Epoch: 044 Train Loss: 0.445, ap: 0.750, auc: 0.797, f1: 0.709, acc: 0.727
+2025-08-26 00:57:55: Epoch: 044 New best VALIDATION metrics
+2025-08-26 00:57:55: Epoch: 044 Test results, ap: 0.235, auc: 0.475, f1: 0.403, acc: 0.276
+2025-08-26 00:57:58: Epoch: 045 Train Loss: 0.441, ap: 0.753, auc: 0.801, f1: 0.715, acc: 0.725
+2025-08-26 00:57:58: Epoch: 045 New best VALIDATION metrics
+2025-08-26 00:57:58: Epoch: 045 Test results, ap: 0.237, auc: 0.474, f1: 0.411, acc: 0.301
+2025-08-26 00:58:01: Epoch: 046 Train Loss: 0.438, ap: 0.755, auc: 0.801, f1: 0.714, acc: 0.721
+2025-08-26 00:58:01: Epoch: 046 New best VALIDATION metrics
+2025-08-26 00:58:02: Epoch: 046 Test results, ap: 0.233, auc: 0.467, f1: 0.408, acc: 0.293
+2025-08-26 00:58:05: Epoch: 047 Train Loss: 0.441, ap: 0.763, auc: 0.807, f1: 0.716, acc: 0.725
+2025-08-26 00:58:05: Epoch: 047 New best VALIDATION metrics
+2025-08-26 00:58:05: Epoch: 047 Test results, ap: 0.232, auc: 0.463, f1: 0.408, acc: 0.293
+2025-08-26 00:58:08: Epoch: 048 Train Loss: 0.427, ap: 0.758, auc: 0.807, f1: 0.714, acc: 0.725
+2025-08-26 00:58:08: Epoch: 048 Test results, ap: 0.234, auc: 0.460, f1: 0.414, acc: 0.309
+2025-08-26 00:58:11: Epoch: 049 Train Loss: 0.421, ap: 0.771, auc: 0.811, f1: 0.726, acc: 0.734
+2025-08-26 00:58:11: Epoch: 049 New best VALIDATION metrics
+2025-08-26 00:58:11: Epoch: 049 Test results, ap: 0.235, auc: 0.470, f1: 0.405, acc: 0.285
+2025-08-26 00:58:14: Epoch: 050 Train Loss: 0.417, ap: 0.772, auc: 0.812, f1: 0.719, acc: 0.728
+2025-08-26 00:58:14: Epoch: 050 New best VALIDATION metrics
+2025-08-26 00:58:14: Epoch: 050 Test results, ap: 0.234, auc: 0.468, f1: 0.411, acc: 0.301
+2025-08-26 00:58:14: used time 0.04h
+2025-08-26 00:58:14: Running Cross Validation 3
+2025-08-26 00:58:14: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:58:17: Epoch: 001 Train Loss: 0.695, ap: 0.531, auc: 0.639, f1: 0.000, acc: 0.570
+2025-08-26 00:58:17: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:58:18: Epoch: 001 Test results, ap: 0.227, auc: 0.480, f1: 0.000, acc: 0.756
+2025-08-26 00:58:20: Epoch: 002 Train Loss: 0.694, ap: 0.524, auc: 0.625, f1: 0.000, acc: 0.570
+2025-08-26 00:58:21: Epoch: 002 Test results, ap: 0.260, auc: 0.567, f1: 0.000, acc: 0.756
+2025-08-26 00:58:24: Epoch: 003 Train Loss: 0.692, ap: 0.545, auc: 0.654, f1: 0.546, acc: 0.628
+2025-08-26 00:58:24: Epoch: 003 New best VALIDATION metrics
+2025-08-26 00:58:24: Epoch: 003 Test results, ap: 0.260, auc: 0.563, f1: 0.200, acc: 0.675
+2025-08-26 00:58:27: Epoch: 004 Train Loss: 0.687, ap: 0.542, auc: 0.651, f1: 0.479, acc: 0.628
+2025-08-26 00:58:27: Epoch: 004 Test results, ap: 0.263, auc: 0.553, f1: 0.167, acc: 0.675
+2025-08-26 00:58:30: Epoch: 005 Train Loss: 0.665, ap: 0.541, auc: 0.647, f1: 0.578, acc: 0.637
+2025-08-26 00:58:30: Epoch: 005 Test results, ap: 0.276, auc: 0.541, f1: 0.430, acc: 0.504
+2025-08-26 00:58:33: Epoch: 006 Train Loss: 0.659, ap: 0.544, auc: 0.646, f1: 0.586, acc: 0.608
+2025-08-26 00:58:33: Epoch: 006 Test results, ap: 0.275, auc: 0.538, f1: 0.429, acc: 0.415
+2025-08-26 00:58:36: Epoch: 007 Train Loss: 0.659, ap: 0.547, auc: 0.649, f1: 0.603, acc: 0.619
+2025-08-26 00:58:37: Epoch: 007 Test results, ap: 0.277, auc: 0.541, f1: 0.443, acc: 0.447
+2025-08-26 00:58:39: Epoch: 008 Train Loss: 0.652, ap: 0.548, auc: 0.656, f1: 0.560, acc: 0.642
+2025-08-26 00:58:39: Epoch: 008 New best VALIDATION metrics
+2025-08-26 00:58:40: Epoch: 008 Test results, ap: 0.278, auc: 0.542, f1: 0.247, acc: 0.553
+2025-08-26 00:58:43: Epoch: 009 Train Loss: 0.651, ap: 0.550, auc: 0.653, f1: 0.570, acc: 0.628
+2025-08-26 00:58:43: Epoch: 009 Test results, ap: 0.283, auc: 0.546, f1: 0.384, acc: 0.504
+2025-08-26 00:58:46: Epoch: 010 Train Loss: 0.645, ap: 0.554, auc: 0.659, f1: 0.588, acc: 0.647
+2025-08-26 00:58:46: Epoch: 010 New best VALIDATION metrics
+2025-08-26 00:58:46: Epoch: 010 Test results, ap: 0.280, auc: 0.546, f1: 0.358, acc: 0.504
+2025-08-26 00:58:49: Epoch: 011 Train Loss: 0.638, ap: 0.580, auc: 0.665, f1: 0.595, acc: 0.616
+2025-08-26 00:58:49: Epoch: 011 New best VALIDATION metrics
+2025-08-26 00:58:49: Epoch: 011 Test results, ap: 0.275, auc: 0.544, f1: 0.442, acc: 0.488
+2025-08-26 00:58:49: Epoch: 011 New best TEST metrics
+2025-08-26 00:58:52: Epoch: 012 Train Loss: 0.635, ap: 0.585, auc: 0.677, f1: 0.565, acc: 0.638
+2025-08-26 00:58:52: Epoch: 012 New best VALIDATION metrics
+2025-08-26 00:58:53: Epoch: 012 Test results, ap: 0.281, auc: 0.553, f1: 0.344, acc: 0.504
+2025-08-26 00:58:53: Epoch: 012 New best TEST metrics
+2025-08-26 00:58:55: Epoch: 013 Train Loss: 0.632, ap: 0.596, auc: 0.698, f1: 0.650, acc: 0.663
+2025-08-26 00:58:55: Epoch: 013 New best VALIDATION metrics
+2025-08-26 00:58:56: Epoch: 013 Test results, ap: 0.306, auc: 0.562, f1: 0.437, acc: 0.455
+2025-08-26 00:58:56: Epoch: 013 New best TEST metrics
+2025-08-26 00:58:59: Epoch: 014 Train Loss: 0.618, ap: 0.578, auc: 0.706, f1: 0.655, acc: 0.653
+2025-08-26 00:58:59: Epoch: 014 Test results, ap: 0.376, auc: 0.599, f1: 0.406, acc: 0.358
+2025-08-26 00:58:59: Epoch: 014 New best TEST metrics
+2025-08-26 00:59:02: Epoch: 015 Train Loss: 0.621, ap: 0.637, auc: 0.737, f1: 0.679, acc: 0.704
+2025-08-26 00:59:02: Epoch: 015 New best VALIDATION metrics
+2025-08-26 00:59:02: Epoch: 015 Test results, ap: 0.312, auc: 0.591, f1: 0.426, acc: 0.496
+2025-08-26 00:59:05: Epoch: 016 Train Loss: 0.613, ap: 0.677, auc: 0.763, f1: 0.676, acc: 0.721
+2025-08-26 00:59:05: Epoch: 016 New best VALIDATION metrics
+2025-08-26 00:59:05: Epoch: 016 Test results, ap: 0.428, auc: 0.682, f1: 0.463, acc: 0.528
+2025-08-26 00:59:05: Epoch: 016 New best TEST metrics
+2025-08-26 00:59:08: Epoch: 017 Train Loss: 0.591, ap: 0.648, auc: 0.738, f1: 0.678, acc: 0.697
+2025-08-26 00:59:09: Epoch: 017 Test results, ap: 0.419, auc: 0.650, f1: 0.431, acc: 0.463
+2025-08-26 00:59:11: Epoch: 018 Train Loss: 0.583, ap: 0.667, auc: 0.756, f1: 0.649, acc: 0.716
+2025-08-26 00:59:12: Epoch: 018 Test results, ap: 0.371, auc: 0.589, f1: 0.370, acc: 0.585
+2025-08-26 00:59:15: Epoch: 019 Train Loss: 0.563, ap: 0.740, auc: 0.791, f1: 0.663, acc: 0.704
+2025-08-26 00:59:15: Epoch: 019 New best VALIDATION metrics
+2025-08-26 00:59:15: Epoch: 019 Test results, ap: 0.388, auc: 0.678, f1: 0.431, acc: 0.528
+2025-08-26 00:59:18: Epoch: 020 Train Loss: 0.575, ap: 0.679, auc: 0.771, f1: 0.699, acc: 0.718
+2025-08-26 00:59:18: Epoch: 020 Test results, ap: 0.408, auc: 0.665, f1: 0.430, acc: 0.504
+2025-08-26 00:59:21: Epoch: 021 Train Loss: 0.534, ap: 0.744, auc: 0.807, f1: 0.734, acc: 0.757
+2025-08-26 00:59:21: Epoch: 021 New best VALIDATION metrics
+2025-08-26 00:59:21: Epoch: 021 Test results, ap: 0.410, auc: 0.670, f1: 0.436, acc: 0.537
+2025-08-26 00:59:24: Epoch: 022 Train Loss: 0.503, ap: 0.776, auc: 0.823, f1: 0.719, acc: 0.741
+2025-08-26 00:59:24: Epoch: 022 New best VALIDATION metrics
+2025-08-26 00:59:24: Epoch: 022 Test results, ap: 0.400, auc: 0.664, f1: 0.429, acc: 0.545
+2025-08-26 00:59:27: Epoch: 023 Train Loss: 0.500, ap: 0.775, auc: 0.828, f1: 0.720, acc: 0.735
+2025-08-26 00:59:27: Epoch: 023 New best VALIDATION metrics
+2025-08-26 00:59:28: Epoch: 023 Test results, ap: 0.408, auc: 0.679, f1: 0.440, acc: 0.504
+2025-08-26 00:59:31: Epoch: 024 Train Loss: 0.490, ap: 0.782, auc: 0.828, f1: 0.736, acc: 0.753
+2025-08-26 00:59:31: Epoch: 024 New best VALIDATION metrics
+2025-08-26 00:59:31: Epoch: 024 Test results, ap: 0.428, auc: 0.671, f1: 0.447, acc: 0.537
+2025-08-26 00:59:34: Epoch: 025 Train Loss: 0.469, ap: 0.785, auc: 0.830, f1: 0.719, acc: 0.741
+2025-08-26 00:59:34: Epoch: 025 New best VALIDATION metrics
+2025-08-26 00:59:34: Epoch: 025 Test results, ap: 0.335, auc: 0.613, f1: 0.400, acc: 0.537
+2025-08-26 00:59:37: Epoch: 026 Train Loss: 0.456, ap: 0.792, auc: 0.838, f1: 0.739, acc: 0.757
+2025-08-26 00:59:37: Epoch: 026 New best VALIDATION metrics
+2025-08-26 00:59:37: Epoch: 026 Test results, ap: 0.312, auc: 0.589, f1: 0.420, acc: 0.528
+2025-08-26 00:59:40: Epoch: 027 Train Loss: 0.438, ap: 0.828, auc: 0.864, f1: 0.755, acc: 0.780
+2025-08-26 00:59:40: Epoch: 027 New best VALIDATION metrics
+2025-08-26 00:59:40: Epoch: 027 Test results, ap: 0.396, auc: 0.660, f1: 0.447, acc: 0.618
+2025-08-26 00:59:43: Epoch: 028 Train Loss: 0.413, ap: 0.840, auc: 0.869, f1: 0.761, acc: 0.778
+2025-08-26 00:59:43: Epoch: 028 New best VALIDATION metrics
+2025-08-26 00:59:44: Epoch: 028 Test results, ap: 0.429, auc: 0.689, f1: 0.458, acc: 0.577
+2025-08-26 00:59:44: Epoch: 028 New best TEST metrics
+2025-08-26 00:59:47: Epoch: 029 Train Loss: 0.391, ap: 0.857, auc: 0.883, f1: 0.754, acc: 0.783
+2025-08-26 00:59:47: Epoch: 029 New best VALIDATION metrics
+2025-08-26 00:59:47: Epoch: 029 Test results, ap: 0.363, auc: 0.637, f1: 0.424, acc: 0.602
+2025-08-26 00:59:50: Epoch: 030 Train Loss: 0.360, ap: 0.870, auc: 0.884, f1: 0.764, acc: 0.787
+2025-08-26 00:59:50: Epoch: 030 New best VALIDATION metrics
+2025-08-26 00:59:50: Epoch: 030 Test results, ap: 0.406, auc: 0.650, f1: 0.460, acc: 0.618
+2025-08-26 00:59:53: Epoch: 031 Train Loss: 0.320, ap: 0.881, auc: 0.899, f1: 0.783, acc: 0.806
+2025-08-26 00:59:53: Epoch: 031 New best VALIDATION metrics
+2025-08-26 00:59:53: Epoch: 031 Test results, ap: 0.377, auc: 0.630, f1: 0.417, acc: 0.545
+2025-08-26 00:59:56: Epoch: 032 Train Loss: 0.302, ap: 0.886, auc: 0.904, f1: 0.781, acc: 0.810
+2025-08-26 00:59:56: Epoch: 032 New best VALIDATION metrics
+2025-08-26 00:59:56: Epoch: 032 Test results, ap: 0.413, auc: 0.648, f1: 0.400, acc: 0.585
+2025-08-26 00:59:59: Epoch: 033 Train Loss: 0.295, ap: 0.896, auc: 0.908, f1: 0.791, acc: 0.815
+2025-08-26 00:59:59: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:00:00: Epoch: 033 Test results, ap: 0.412, auc: 0.662, f1: 0.465, acc: 0.626
+2025-08-26 01:00:02: Epoch: 034 Train Loss: 0.278, ap: 0.899, auc: 0.911, f1: 0.802, acc: 0.820
+2025-08-26 01:00:02: Epoch: 034 New best VALIDATION metrics
+2025-08-26 01:00:03: Epoch: 034 Test results, ap: 0.420, auc: 0.655, f1: 0.471, acc: 0.634
+2025-08-26 01:00:06: Epoch: 035 Train Loss: 0.265, ap: 0.907, auc: 0.920, f1: 0.811, acc: 0.836
+2025-08-26 01:00:06: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:00:06: Epoch: 035 Test results, ap: 0.412, auc: 0.654, f1: 0.447, acc: 0.659
+2025-08-26 01:00:09: Epoch: 036 Train Loss: 0.253, ap: 0.902, auc: 0.916, f1: 0.801, acc: 0.824
+2025-08-26 01:00:09: Epoch: 036 Test results, ap: 0.360, auc: 0.636, f1: 0.400, acc: 0.634
+2025-08-26 01:00:12: Epoch: 037 Train Loss: 0.250, ap: 0.908, auc: 0.921, f1: 0.802, acc: 0.829
+2025-08-26 01:00:12: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:00:12: Epoch: 037 Test results, ap: 0.366, auc: 0.629, f1: 0.405, acc: 0.642
+2025-08-26 01:00:15: Epoch: 038 Train Loss: 0.242, ap: 0.908, auc: 0.921, f1: 0.803, acc: 0.829
+2025-08-26 01:00:15: Epoch: 038 Test results, ap: 0.360, auc: 0.622, f1: 0.394, acc: 0.675
+2025-08-26 01:00:18: Epoch: 039 Train Loss: 0.229, ap: 0.914, auc: 0.925, f1: 0.814, acc: 0.834
+2025-08-26 01:00:18: Epoch: 039 New best VALIDATION metrics
+2025-08-26 01:00:19: Epoch: 039 Test results, ap: 0.369, auc: 0.633, f1: 0.394, acc: 0.650
+2025-08-26 01:00:22: Epoch: 040 Train Loss: 0.232, ap: 0.909, auc: 0.922, f1: 0.807, acc: 0.829
+2025-08-26 01:00:22: Epoch: 040 Test results, ap: 0.382, auc: 0.644, f1: 0.417, acc: 0.659
+2025-08-26 01:00:25: Epoch: 041 Train Loss: 0.204, ap: 0.920, auc: 0.932, f1: 0.831, acc: 0.854
+2025-08-26 01:00:25: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:00:25: Epoch: 041 Test results, ap: 0.362, auc: 0.630, f1: 0.377, acc: 0.650
+2025-08-26 01:00:28: Epoch: 042 Train Loss: 0.192, ap: 0.915, auc: 0.928, f1: 0.835, acc: 0.852
+2025-08-26 01:00:28: Epoch: 042 Test results, ap: 0.351, auc: 0.619, f1: 0.400, acc: 0.659
+2025-08-26 01:00:31: Epoch: 043 Train Loss: 0.183, ap: 0.917, auc: 0.929, f1: 0.833, acc: 0.857
+2025-08-26 01:00:31: Epoch: 043 Test results, ap: 0.355, auc: 0.620, f1: 0.394, acc: 0.675
+2025-08-26 01:00:34: Epoch: 044 Train Loss: 0.181, ap: 0.915, auc: 0.928, f1: 0.824, acc: 0.847
+2025-08-26 01:00:34: Epoch: 044 Test results, ap: 0.352, auc: 0.629, f1: 0.377, acc: 0.650
+2025-08-26 01:00:37: Epoch: 045 Train Loss: 0.179, ap: 0.919, auc: 0.931, f1: 0.831, acc: 0.855
+2025-08-26 01:00:38: Epoch: 045 Test results, ap: 0.357, auc: 0.624, f1: 0.394, acc: 0.675
+2025-08-26 01:00:41: Epoch: 046 Train Loss: 0.171, ap: 0.919, auc: 0.931, f1: 0.833, acc: 0.857
+2025-08-26 01:00:41: Epoch: 046 Test results, ap: 0.358, auc: 0.620, f1: 0.377, acc: 0.650
+2025-08-26 01:00:44: Epoch: 047 Train Loss: 0.159, ap: 0.912, auc: 0.929, f1: 0.827, acc: 0.848
+2025-08-26 01:00:44: Epoch: 047 Test results, ap: 0.321, auc: 0.609, f1: 0.411, acc: 0.650
+2025-08-26 01:00:47: Epoch: 048 Train Loss: 0.158, ap: 0.919, auc: 0.930, f1: 0.826, acc: 0.850
+2025-08-26 01:00:47: Epoch: 048 Test results, ap: 0.370, auc: 0.628, f1: 0.424, acc: 0.691
+2025-08-26 01:00:50: Epoch: 049 Train Loss: 0.159, ap: 0.914, auc: 0.931, f1: 0.836, acc: 0.861
+2025-08-26 01:00:50: Epoch: 049 Test results, ap: 0.338, auc: 0.614, f1: 0.366, acc: 0.634
+2025-08-26 01:00:53: Epoch: 050 Train Loss: 0.151, ap: 0.922, auc: 0.933, f1: 0.836, acc: 0.859
+2025-08-26 01:00:53: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:00:54: Epoch: 050 Test results, ap: 0.356, auc: 0.618, f1: 0.382, acc: 0.659
+2025-08-26 01:00:54: used time 0.04h
+2025-08-26 01:00:54: Running Cross Validation 4
+2025-08-26 01:00:54: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:00:56: Epoch: 001 Train Loss: 0.693, ap: 0.504, auc: 0.584, f1: 0.614, acc: 0.443
+2025-08-26 01:00:56: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:00:57: Epoch: 001 Test results, ap: 0.350, auc: 0.543, f1: 0.392, acc: 0.244
+2025-08-26 01:01:00: Epoch: 002 Train Loss: 0.697, ap: 0.481, auc: 0.566, f1: 0.614, acc: 0.443
+2025-08-26 01:01:00: Epoch: 002 Test results, ap: 0.251, auc: 0.532, f1: 0.392, acc: 0.244
+2025-08-26 01:01:03: Epoch: 003 Train Loss: 0.694, ap: 0.492, auc: 0.574, f1: 0.614, acc: 0.443
+2025-08-26 01:01:03: Epoch: 003 Test results, ap: 0.268, auc: 0.563, f1: 0.392, acc: 0.244
+2025-08-26 01:01:06: Epoch: 004 Train Loss: 0.695, ap: 0.412, auc: 0.472, f1: 0.614, acc: 0.443
+2025-08-26 01:01:06: Epoch: 004 Test results, ap: 0.300, auc: 0.473, f1: 0.392, acc: 0.244
+2025-08-26 01:01:09: Epoch: 005 Train Loss: 0.695, ap: 0.502, auc: 0.588, f1: 0.000, acc: 0.557
+2025-08-26 01:01:09: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:01:09: Epoch: 005 Test results, ap: 0.285, auc: 0.597, f1: 0.000, acc: 0.756
+2025-08-26 01:01:12: Epoch: 006 Train Loss: 0.693, ap: 0.549, auc: 0.626, f1: 0.602, acc: 0.566
+2025-08-26 01:01:12: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:01:13: Epoch: 006 Test results, ap: 0.292, auc: 0.611, f1: 0.392, acc: 0.244
+2025-08-26 01:01:15: Epoch: 007 Train Loss: 0.682, ap: 0.543, auc: 0.611, f1: 0.614, acc: 0.443
+2025-08-26 01:01:16: Epoch: 007 Test results, ap: 0.251, auc: 0.513, f1: 0.392, acc: 0.244
+2025-08-26 01:01:19: Epoch: 008 Train Loss: 0.666, ap: 0.542, auc: 0.606, f1: 0.530, acc: 0.564
+2025-08-26 01:01:19: Epoch: 008 Test results, ap: 0.245, auc: 0.491, f1: 0.175, acc: 0.618
+2025-08-26 01:01:22: Epoch: 009 Train Loss: 0.658, ap: 0.576, auc: 0.621, f1: 0.491, acc: 0.612
+2025-08-26 01:01:22: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:01:22: Epoch: 009 Test results, ap: 0.260, auc: 0.518, f1: 0.111, acc: 0.740
+2025-08-26 01:01:25: Epoch: 010 Train Loss: 0.654, ap: 0.595, auc: 0.648, f1: 0.534, acc: 0.616
+2025-08-26 01:01:25: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:01:25: Epoch: 010 Test results, ap: 0.250, auc: 0.505, f1: 0.195, acc: 0.732
+2025-08-26 01:01:28: Epoch: 011 Train Loss: 0.642, ap: 0.588, auc: 0.653, f1: 0.585, acc: 0.600
+2025-08-26 01:01:28: Epoch: 011 Test results, ap: 0.263, auc: 0.525, f1: 0.240, acc: 0.691
+2025-08-26 01:01:28: Epoch: 011 New best TEST metrics
+2025-08-26 01:01:31: Epoch: 012 Train Loss: 0.631, ap: 0.574, auc: 0.658, f1: 0.582, acc: 0.610
+2025-08-26 01:01:32: Epoch: 012 Test results, ap: 0.273, auc: 0.535, f1: 0.304, acc: 0.553
+2025-08-26 01:01:32: Epoch: 012 New best TEST metrics
+2025-08-26 01:01:34: Epoch: 013 Train Loss: 0.629, ap: 0.603, auc: 0.658, f1: 0.586, acc: 0.633
+2025-08-26 01:01:34: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:01:35: Epoch: 013 Test results, ap: 0.280, auc: 0.547, f1: 0.259, acc: 0.675
+2025-08-26 01:01:35: Epoch: 013 New best TEST metrics
+2025-08-26 01:01:38: Epoch: 014 Train Loss: 0.627, ap: 0.580, auc: 0.658, f1: 0.588, acc: 0.624
+2025-08-26 01:01:38: Epoch: 014 Test results, ap: 0.280, auc: 0.546, f1: 0.301, acc: 0.585
+2025-08-26 01:01:41: Epoch: 015 Train Loss: 0.623, ap: 0.601, auc: 0.663, f1: 0.577, acc: 0.633
+2025-08-26 01:01:41: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:01:41: Epoch: 015 Test results, ap: 0.293, auc: 0.549, f1: 0.254, acc: 0.618
+2025-08-26 01:01:41: Epoch: 015 New best TEST metrics
+2025-08-26 01:01:44: Epoch: 016 Train Loss: 0.614, ap: 0.601, auc: 0.661, f1: 0.590, acc: 0.623
+2025-08-26 01:01:44: Epoch: 016 Test results, ap: 0.296, auc: 0.547, f1: 0.273, acc: 0.610
+2025-08-26 01:01:44: Epoch: 016 New best TEST metrics
+2025-08-26 01:01:47: Epoch: 017 Train Loss: 0.611, ap: 0.607, auc: 0.669, f1: 0.608, acc: 0.638
+2025-08-26 01:01:47: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:01:48: Epoch: 017 Test results, ap: 0.300, auc: 0.561, f1: 0.254, acc: 0.618
+2025-08-26 01:01:48: Epoch: 017 New best TEST metrics
+2025-08-26 01:01:50: Epoch: 018 Train Loss: 0.599, ap: 0.608, auc: 0.679, f1: 0.596, acc: 0.623
+2025-08-26 01:01:50: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:01:51: Epoch: 018 Test results, ap: 0.278, auc: 0.552, f1: 0.306, acc: 0.593
+2025-08-26 01:01:54: Epoch: 019 Train Loss: 0.592, ap: 0.636, auc: 0.707, f1: 0.621, acc: 0.658
+2025-08-26 01:01:54: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:01:54: Epoch: 019 Test results, ap: 0.368, auc: 0.620, f1: 0.281, acc: 0.667
+2025-08-26 01:01:54: Epoch: 019 New best TEST metrics
+2025-08-26 01:01:57: Epoch: 020 Train Loss: 0.571, ap: 0.627, auc: 0.710, f1: 0.647, acc: 0.635
+2025-08-26 01:01:57: Epoch: 020 Test results, ap: 0.331, auc: 0.625, f1: 0.435, acc: 0.683
+2025-08-26 01:02:00: Epoch: 021 Train Loss: 0.565, ap: 0.654, auc: 0.704, f1: 0.608, acc: 0.633
+2025-08-26 01:02:00: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:02:00: Epoch: 021 Test results, ap: 0.320, auc: 0.610, f1: 0.316, acc: 0.683
+2025-08-26 01:02:03: Epoch: 022 Train Loss: 0.537, ap: 0.696, auc: 0.743, f1: 0.639, acc: 0.649
+2025-08-26 01:02:03: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:02:04: Epoch: 022 Test results, ap: 0.303, auc: 0.590, f1: 0.317, acc: 0.650
+2025-08-26 01:02:06: Epoch: 023 Train Loss: 0.516, ap: 0.703, auc: 0.749, f1: 0.656, acc: 0.668
+2025-08-26 01:02:06: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:02:07: Epoch: 023 Test results, ap: 0.300, auc: 0.591, f1: 0.321, acc: 0.691
+2025-08-26 01:02:10: Epoch: 024 Train Loss: 0.492, ap: 0.717, auc: 0.756, f1: 0.627, acc: 0.679
+2025-08-26 01:02:10: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:02:10: Epoch: 024 Test results, ap: 0.308, auc: 0.597, f1: 0.314, acc: 0.715
+2025-08-26 01:02:13: Epoch: 025 Train Loss: 0.483, ap: 0.730, auc: 0.765, f1: 0.627, acc: 0.679
+2025-08-26 01:02:13: Epoch: 025 New best VALIDATION metrics
+2025-08-26 01:02:13: Epoch: 025 Test results, ap: 0.326, auc: 0.626, f1: 0.238, acc: 0.740
+2025-08-26 01:02:16: Epoch: 026 Train Loss: 0.478, ap: 0.739, auc: 0.778, f1: 0.669, acc: 0.702
+2025-08-26 01:02:16: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:02:16: Epoch: 026 Test results, ap: 0.287, auc: 0.574, f1: 0.291, acc: 0.683
+2025-08-26 01:02:19: Epoch: 027 Train Loss: 0.468, ap: 0.755, auc: 0.795, f1: 0.668, acc: 0.695
+2025-08-26 01:02:19: Epoch: 027 New best VALIDATION metrics
+2025-08-26 01:02:19: Epoch: 027 Test results, ap: 0.324, auc: 0.619, f1: 0.316, acc: 0.683
+2025-08-26 01:02:22: Epoch: 028 Train Loss: 0.433, ap: 0.775, auc: 0.809, f1: 0.693, acc: 0.702
+2025-08-26 01:02:22: Epoch: 028 New best VALIDATION metrics
+2025-08-26 01:02:23: Epoch: 028 Test results, ap: 0.297, auc: 0.592, f1: 0.328, acc: 0.634
+2025-08-26 01:02:26: Epoch: 029 Train Loss: 0.422, ap: 0.781, auc: 0.813, f1: 0.681, acc: 0.718
+2025-08-26 01:02:26: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:02:26: Epoch: 029 Test results, ap: 0.291, auc: 0.582, f1: 0.240, acc: 0.691
+2025-08-26 01:02:29: Epoch: 030 Train Loss: 0.398, ap: 0.791, auc: 0.824, f1: 0.706, acc: 0.735
+2025-08-26 01:02:29: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:02:29: Epoch: 030 Test results, ap: 0.308, auc: 0.604, f1: 0.367, acc: 0.691
+2025-08-26 01:02:32: Epoch: 031 Train Loss: 0.369, ap: 0.807, auc: 0.838, f1: 0.734, acc: 0.748
+2025-08-26 01:02:32: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:02:32: Epoch: 031 Test results, ap: 0.305, auc: 0.599, f1: 0.364, acc: 0.659
+2025-08-26 01:02:35: Epoch: 032 Train Loss: 0.350, ap: 0.810, auc: 0.841, f1: 0.735, acc: 0.764
+2025-08-26 01:02:35: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:02:35: Epoch: 032 Test results, ap: 0.301, auc: 0.600, f1: 0.269, acc: 0.691
+2025-08-26 01:02:38: Epoch: 033 Train Loss: 0.339, ap: 0.821, auc: 0.851, f1: 0.738, acc: 0.755
+2025-08-26 01:02:38: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:02:39: Epoch: 033 Test results, ap: 0.310, auc: 0.614, f1: 0.387, acc: 0.691
+2025-08-26 01:02:41: Epoch: 034 Train Loss: 0.324, ap: 0.818, auc: 0.848, f1: 0.728, acc: 0.746
+2025-08-26 01:02:42: Epoch: 034 Test results, ap: 0.306, auc: 0.611, f1: 0.310, acc: 0.675
+2025-08-26 01:02:45: Epoch: 035 Train Loss: 0.319, ap: 0.825, auc: 0.856, f1: 0.753, acc: 0.772
+2025-08-26 01:02:45: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:02:45: Epoch: 035 Test results, ap: 0.315, auc: 0.617, f1: 0.286, acc: 0.675
+2025-08-26 01:02:48: Epoch: 036 Train Loss: 0.295, ap: 0.828, auc: 0.859, f1: 0.763, acc: 0.776
+2025-08-26 01:02:48: Epoch: 036 New best VALIDATION metrics
+2025-08-26 01:02:48: Epoch: 036 Test results, ap: 0.318, auc: 0.619, f1: 0.381, acc: 0.683
+2025-08-26 01:02:51: Epoch: 037 Train Loss: 0.290, ap: 0.835, auc: 0.867, f1: 0.778, acc: 0.794
+2025-08-26 01:02:51: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:02:51: Epoch: 037 Test results, ap: 0.344, auc: 0.641, f1: 0.367, acc: 0.691
+2025-08-26 01:02:54: Epoch: 038 Train Loss: 0.283, ap: 0.841, auc: 0.869, f1: 0.767, acc: 0.785
+2025-08-26 01:02:54: Epoch: 038 New best VALIDATION metrics
+2025-08-26 01:02:54: Epoch: 038 Test results, ap: 0.344, auc: 0.623, f1: 0.310, acc: 0.675
+2025-08-26 01:02:57: Epoch: 039 Train Loss: 0.273, ap: 0.846, auc: 0.877, f1: 0.777, acc: 0.788
+2025-08-26 01:02:57: Epoch: 039 New best VALIDATION metrics
+2025-08-26 01:02:58: Epoch: 039 Test results, ap: 0.343, auc: 0.620, f1: 0.400, acc: 0.659
+2025-08-26 01:03:01: Epoch: 040 Train Loss: 0.266, ap: 0.841, auc: 0.871, f1: 0.782, acc: 0.795
+2025-08-26 01:03:01: Epoch: 040 Test results, ap: 0.347, auc: 0.625, f1: 0.400, acc: 0.683
+2025-08-26 01:03:04: Epoch: 041 Train Loss: 0.250, ap: 0.842, auc: 0.874, f1: 0.777, acc: 0.790
+2025-08-26 01:03:04: Epoch: 041 Test results, ap: 0.344, auc: 0.622, f1: 0.338, acc: 0.650
+2025-08-26 01:03:07: Epoch: 042 Train Loss: 0.242, ap: 0.847, auc: 0.878, f1: 0.781, acc: 0.801
+2025-08-26 01:03:07: Epoch: 042 New best VALIDATION metrics
+2025-08-26 01:03:07: Epoch: 042 Test results, ap: 0.327, auc: 0.620, f1: 0.361, acc: 0.683
+2025-08-26 01:03:10: Epoch: 043 Train Loss: 0.230, ap: 0.850, auc: 0.882, f1: 0.794, acc: 0.806
+2025-08-26 01:03:10: Epoch: 043 New best VALIDATION metrics
+2025-08-26 01:03:10: Epoch: 043 Test results, ap: 0.344, auc: 0.619, f1: 0.338, acc: 0.650
+2025-08-26 01:03:13: Epoch: 044 Train Loss: 0.231, ap: 0.849, auc: 0.881, f1: 0.778, acc: 0.797
+2025-08-26 01:03:14: Epoch: 044 Test results, ap: 0.328, auc: 0.622, f1: 0.349, acc: 0.667
+2025-08-26 01:03:16: Epoch: 045 Train Loss: 0.225, ap: 0.849, auc: 0.882, f1: 0.773, acc: 0.792
+2025-08-26 01:03:17: Epoch: 045 Test results, ap: 0.328, auc: 0.621, f1: 0.349, acc: 0.667
+2025-08-26 01:03:20: Epoch: 046 Train Loss: 0.219, ap: 0.856, auc: 0.887, f1: 0.790, acc: 0.802
+2025-08-26 01:03:20: Epoch: 046 New best VALIDATION metrics
+2025-08-26 01:03:20: Epoch: 046 Test results, ap: 0.329, auc: 0.624, f1: 0.338, acc: 0.650
+2025-08-26 01:03:23: Epoch: 047 Train Loss: 0.216, ap: 0.853, auc: 0.884, f1: 0.780, acc: 0.799
+2025-08-26 01:03:23: Epoch: 047 Test results, ap: 0.320, auc: 0.618, f1: 0.300, acc: 0.659
+2025-08-26 01:03:26: Epoch: 048 Train Loss: 0.205, ap: 0.856, auc: 0.887, f1: 0.791, acc: 0.808
+2025-08-26 01:03:26: Epoch: 048 Test results, ap: 0.333, auc: 0.626, f1: 0.349, acc: 0.667
+2025-08-26 01:03:29: Epoch: 049 Train Loss: 0.209, ap: 0.851, auc: 0.883, f1: 0.792, acc: 0.806
+2025-08-26 01:03:29: Epoch: 049 Test results, ap: 0.323, auc: 0.614, f1: 0.349, acc: 0.667
+2025-08-26 01:03:32: Epoch: 050 Train Loss: 0.193, ap: 0.854, auc: 0.886, f1: 0.789, acc: 0.804
+2025-08-26 01:03:33: Epoch: 050 Test results, ap: 0.328, auc: 0.614, f1: 0.361, acc: 0.683
+2025-08-26 01:03:33: used time 0.04h
+2025-08-26 01:03:33: Cross Validation Finished!
+2025-08-26 01:03:33: Best validation perform list
+[[0.90598518 0.91116905 0.8217054  0.83802819]
+ [0.68315876 0.72522682 0.59821427 0.68309861]
+ [0.77182758 0.81216401 0.71897811 0.72839504]
+ [0.92160934 0.9332906  0.83606559 0.85890651]
+ [0.85638726 0.88710982 0.79026216 0.80246913]]
+2025-08-26 01:03:33: mean: [0.828 0.854 0.753 0.782]
+2025-08-26 01:03:33: std: [0.089 0.076 0.087 0.067]
+2025-08-26 01:03:33: Best test perform list
+[[0.29190296 0.55519712 0.         0.69105691]
+ [0.46892491 0.65017921 0.41176471 0.67479676]
+ [0.28343111 0.58817208 0.39215687 0.24390244]
+ [0.42889425 0.68924731 0.45833334 0.57723576]
+ [0.36754417 0.62043011 0.28070176 0.66666669]]
+2025-08-26 01:03:33: mean: [0.368 0.621 0.309 0.571]
+2025-08-26 01:03:33: std: [0.073 0.047 0.165 0.168]
diff --git a/run-cls/mamba-diff-256-ce-32-0.001-50/config.json b/run-cls/mamba-diff-256-ce-32-0.001-50/config.json
new file mode 100644
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mamba", "side_enc": null, "channels": 256, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 231, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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diff --git a/run-cls/mamba-diff-256-ce-32-0.001-50/preds_test.csv b/run-cls/mamba-diff-256-ce-32-0.001-50/preds_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..abd2e08f531e682493d4c04e7ee54f5dc4349bfc
--- /dev/null
+++ b/run-cls/mamba-diff-256-ce-32-0.001-50/preds_test.csv
@@ -0,0 +1,8 @@
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diff --git a/run-cls/mamba-diff-256-ce-32-0.001-50/result.txt b/run-cls/mamba-diff-256-ce-32-0.001-50/result.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1a5b60a006b273f13197471cc17579868cb1e82d
--- /dev/null
+++ b/run-cls/mamba-diff-256-ce-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
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diff --git a/run-cls/mamba-diff-256-ce-32-0.001-50/training.log b/run-cls/mamba-diff-256-ce-32-0.001-50/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..d0f6c5a71c1521178dc6eb86a5e3261473ff67f5
--- /dev/null
+++ b/run-cls/mamba-diff-256-ce-32-0.001-50/training.log
@@ -0,0 +1,680 @@
+2025-08-27 10:07:36: saving_dir: ./run-cls/mamba-diff-256-ce-32-0.001-50
+2025-08-27 10:07:36: Loading Train Dataset
+2025-08-27 10:07:37: Loading Test Dataset
+2025-08-27 10:07:37: Running Cross Validation 0
+2025-08-27 10:07:37: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-27 10:07:41: Epoch: 001 Train Loss: 0.692, ap: 0.569, auc: 0.601, f1: 0.596, acc: 0.565
+2025-08-27 10:07:41: Epoch: 001 New best VALIDATION metrics
+2025-08-27 10:07:41: Epoch: 001 Test results, ap: 0.218, auc: 0.458, f1: 0.315, acc: 0.398
+2025-08-27 10:07:44: Epoch: 002 Train Loss: 0.689, ap: 0.561, auc: 0.591, f1: 0.000, acc: 0.519
+2025-08-27 10:07:44: Epoch: 002 Test results, ap: 0.332, auc: 0.571, f1: 0.000, acc: 0.756
+2025-08-27 10:07:47: Epoch: 003 Train Loss: 0.662, ap: 0.719, auc: 0.724, f1: 0.657, acc: 0.671
+2025-08-27 10:07:47: Epoch: 003 New best VALIDATION metrics
+2025-08-27 10:07:47: Epoch: 003 Test results, ap: 0.260, auc: 0.506, f1: 0.275, acc: 0.699
+2025-08-27 10:07:50: Epoch: 004 Train Loss: 0.594, ap: 0.745, auc: 0.756, f1: 0.670, acc: 0.692
+2025-08-27 10:07:50: Epoch: 004 New best VALIDATION metrics
+2025-08-27 10:07:51: Epoch: 004 Test results, ap: 0.258, auc: 0.515, f1: 0.327, acc: 0.699
+2025-08-27 10:07:54: Epoch: 005 Train Loss: 0.541, ap: 0.787, auc: 0.799, f1: 0.699, acc: 0.708
+2025-08-27 10:07:54: Epoch: 005 New best VALIDATION metrics
+2025-08-27 10:07:54: Epoch: 005 Test results, ap: 0.331, auc: 0.540, f1: 0.338, acc: 0.618
+2025-08-27 10:07:57: Epoch: 006 Train Loss: 0.517, ap: 0.748, auc: 0.775, f1: 0.691, acc: 0.692
+2025-08-27 10:07:57: Epoch: 006 Test results, ap: 0.296, auc: 0.517, f1: 0.351, acc: 0.488
+2025-08-27 10:08:00: Epoch: 007 Train Loss: 0.504, ap: 0.835, auc: 0.840, f1: 0.753, acc: 0.754
+2025-08-27 10:08:00: Epoch: 007 New best VALIDATION metrics
+2025-08-27 10:08:00: Epoch: 007 Test results, ap: 0.268, auc: 0.545, f1: 0.347, acc: 0.602
+2025-08-27 10:08:03: Epoch: 008 Train Loss: 0.471, ap: 0.833, auc: 0.845, f1: 0.753, acc: 0.766
+2025-08-27 10:08:03: Epoch: 008 New best VALIDATION metrics
+2025-08-27 10:08:04: Epoch: 008 Test results, ap: 0.292, auc: 0.557, f1: 0.294, acc: 0.610
+2025-08-27 10:08:07: Epoch: 009 Train Loss: 0.433, ap: 0.865, auc: 0.868, f1: 0.788, acc: 0.792
+2025-08-27 10:08:07: Epoch: 009 New best VALIDATION metrics
+2025-08-27 10:08:07: Epoch: 009 Test results, ap: 0.267, auc: 0.537, f1: 0.286, acc: 0.593
+2025-08-27 10:08:10: Epoch: 010 Train Loss: 0.402, ap: 0.883, auc: 0.887, f1: 0.780, acc: 0.792
+2025-08-27 10:08:10: Epoch: 010 New best VALIDATION metrics
+2025-08-27 10:08:10: Epoch: 010 Test results, ap: 0.261, auc: 0.492, f1: 0.364, acc: 0.659
+2025-08-27 10:08:13: Epoch: 011 Train Loss: 0.352, ap: 0.882, auc: 0.892, f1: 0.790, acc: 0.799
+2025-08-27 10:08:13: Epoch: 011 New best VALIDATION metrics
+2025-08-27 10:08:13: Epoch: 011 Test results, ap: 0.265, auc: 0.509, f1: 0.373, acc: 0.618
+2025-08-27 10:08:13: Epoch: 011 New best TEST metrics
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+2025-08-27 10:08:17: Epoch: 012 Test results, ap: 0.261, auc: 0.509, f1: 0.333, acc: 0.610
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+2025-08-27 10:08:20: Epoch: 013 New best VALIDATION metrics
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+2025-08-27 10:08:20: Epoch: 013 New best TEST metrics
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+2025-08-27 10:08:23: Epoch: 014 New best VALIDATION metrics
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+2025-08-27 10:08:26: Epoch: 015 New best VALIDATION metrics
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+2025-08-27 10:08:30: Epoch: 016 Train Loss: 0.275, ap: 0.903, auc: 0.915, f1: 0.835, acc: 0.843
+2025-08-27 10:08:30: Epoch: 016 New best VALIDATION metrics
+2025-08-27 10:08:30: Epoch: 016 Test results, ap: 0.268, auc: 0.516, f1: 0.338, acc: 0.650
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+2025-08-27 10:08:33: Epoch: 017 New best VALIDATION metrics
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+2025-08-27 10:08:40: Epoch: 019 New best VALIDATION metrics
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+2025-08-27 10:08:43: Epoch: 020 Train Loss: 0.237, ap: 0.910, auc: 0.918, f1: 0.825, acc: 0.833
+2025-08-27 10:08:43: Epoch: 020 New best VALIDATION metrics
+2025-08-27 10:08:43: Epoch: 020 Test results, ap: 0.317, auc: 0.541, f1: 0.312, acc: 0.642
+2025-08-27 10:08:43: Epoch: 020 New best TEST metrics
+2025-08-27 10:08:46: Epoch: 021 Train Loss: 0.201, ap: 0.916, auc: 0.924, f1: 0.842, acc: 0.850
+2025-08-27 10:08:46: Epoch: 021 New best VALIDATION metrics
+2025-08-27 10:08:47: Epoch: 021 Test results, ap: 0.290, auc: 0.533, f1: 0.295, acc: 0.650
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+2025-08-27 10:08:49: Epoch: 022 New best VALIDATION metrics
+2025-08-27 10:08:50: Epoch: 022 Test results, ap: 0.310, auc: 0.544, f1: 0.290, acc: 0.642
+2025-08-27 10:08:53: Epoch: 023 Train Loss: 0.189, ap: 0.916, auc: 0.928, f1: 0.845, acc: 0.852
+2025-08-27 10:08:53: Epoch: 023 New best VALIDATION metrics
+2025-08-27 10:08:53: Epoch: 023 Test results, ap: 0.306, auc: 0.540, f1: 0.312, acc: 0.642
+2025-08-27 10:08:56: Epoch: 024 Train Loss: 0.180, ap: 0.919, auc: 0.930, f1: 0.858, acc: 0.863
+2025-08-27 10:08:56: Epoch: 024 New best VALIDATION metrics
+2025-08-27 10:08:56: Epoch: 024 Test results, ap: 0.302, auc: 0.525, f1: 0.286, acc: 0.634
+2025-08-27 10:08:59: Epoch: 025 Train Loss: 0.174, ap: 0.918, auc: 0.929, f1: 0.852, acc: 0.861
+2025-08-27 10:09:00: Epoch: 025 Test results, ap: 0.327, auc: 0.530, f1: 0.271, acc: 0.650
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+2025-08-27 10:09:03: Epoch: 026 New best VALIDATION metrics
+2025-08-27 10:09:03: Epoch: 026 Test results, ap: 0.297, auc: 0.535, f1: 0.276, acc: 0.659
+2025-08-27 10:09:06: Epoch: 027 Train Loss: 0.160, ap: 0.920, auc: 0.930, f1: 0.859, acc: 0.863
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+2025-08-27 10:09:06: Epoch: 027 New best TEST metrics
+2025-08-27 10:09:09: Epoch: 028 Train Loss: 0.169, ap: 0.920, auc: 0.931, f1: 0.859, acc: 0.863
+2025-08-27 10:09:10: Epoch: 028 Test results, ap: 0.319, auc: 0.544, f1: 0.281, acc: 0.626
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+2025-08-27 10:09:13: Epoch: 029 Test results, ap: 0.327, auc: 0.551, f1: 0.281, acc: 0.667
+2025-08-27 10:09:13: Epoch: 029 New best TEST metrics
+2025-08-27 10:09:16: Epoch: 030 Train Loss: 0.163, ap: 0.922, auc: 0.933, f1: 0.859, acc: 0.864
+2025-08-27 10:09:16: Epoch: 030 New best VALIDATION metrics
+2025-08-27 10:09:16: Epoch: 030 Test results, ap: 0.328, auc: 0.542, f1: 0.262, acc: 0.634
+2025-08-27 10:09:19: Epoch: 031 Train Loss: 0.137, ap: 0.927, auc: 0.936, f1: 0.868, acc: 0.873
+2025-08-27 10:09:19: Epoch: 031 New best VALIDATION metrics
+2025-08-27 10:09:20: Epoch: 031 Test results, ap: 0.319, auc: 0.549, f1: 0.276, acc: 0.659
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+2025-08-27 10:09:26: Epoch: 033 Test results, ap: 0.329, auc: 0.557, f1: 0.281, acc: 0.667
+2025-08-27 10:09:26: Epoch: 033 New best TEST metrics
+2025-08-27 10:09:29: Epoch: 034 Train Loss: 0.117, ap: 0.929, auc: 0.937, f1: 0.871, acc: 0.877
+2025-08-27 10:09:29: Epoch: 034 New best VALIDATION metrics
+2025-08-27 10:09:30: Epoch: 034 Test results, ap: 0.323, auc: 0.549, f1: 0.291, acc: 0.683
+2025-08-27 10:09:33: Epoch: 035 Train Loss: 0.113, ap: 0.929, auc: 0.936, f1: 0.879, acc: 0.884
+2025-08-27 10:09:33: Epoch: 035 New best VALIDATION metrics
+2025-08-27 10:09:33: Epoch: 035 Test results, ap: 0.326, auc: 0.558, f1: 0.291, acc: 0.683
+2025-08-27 10:09:36: Epoch: 036 Train Loss: 0.112, ap: 0.930, auc: 0.937, f1: 0.883, acc: 0.887
+2025-08-27 10:09:36: Epoch: 036 New best VALIDATION metrics
+2025-08-27 10:09:36: Epoch: 036 Test results, ap: 0.320, auc: 0.549, f1: 0.302, acc: 0.699
+2025-08-27 10:09:39: Epoch: 037 Train Loss: 0.111, ap: 0.930, auc: 0.937, f1: 0.872, acc: 0.877
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+2025-08-27 10:09:40: Epoch: 037 New best TEST metrics
+2025-08-27 10:09:43: Epoch: 038 Train Loss: 0.113, ap: 0.929, auc: 0.936, f1: 0.861, acc: 0.868
+2025-08-27 10:09:43: Epoch: 038 Test results, ap: 0.329, auc: 0.559, f1: 0.310, acc: 0.675
+2025-08-27 10:09:46: Epoch: 039 Train Loss: 0.106, ap: 0.930, auc: 0.936, f1: 0.871, acc: 0.877
+2025-08-27 10:09:46: Epoch: 039 Test results, ap: 0.327, auc: 0.561, f1: 0.291, acc: 0.683
+2025-08-27 10:09:49: Epoch: 040 Train Loss: 0.106, ap: 0.931, auc: 0.938, f1: 0.866, acc: 0.873
+2025-08-27 10:09:49: Epoch: 040 New best VALIDATION metrics
+2025-08-27 10:09:50: Epoch: 040 Test results, ap: 0.322, auc: 0.556, f1: 0.302, acc: 0.699
+2025-08-27 10:09:53: Epoch: 041 Train Loss: 0.101, ap: 0.930, auc: 0.937, f1: 0.876, acc: 0.880
+2025-08-27 10:09:53: Epoch: 041 Test results, ap: 0.323, auc: 0.559, f1: 0.291, acc: 0.683
+2025-08-27 10:09:56: Epoch: 042 Train Loss: 0.098, ap: 0.932, auc: 0.939, f1: 0.869, acc: 0.875
+2025-08-27 10:09:56: Epoch: 042 New best VALIDATION metrics
+2025-08-27 10:09:57: Epoch: 042 Test results, ap: 0.325, auc: 0.558, f1: 0.302, acc: 0.699
+2025-08-27 10:10:00: Epoch: 043 Train Loss: 0.091, ap: 0.933, auc: 0.940, f1: 0.874, acc: 0.880
+2025-08-27 10:10:00: Epoch: 043 New best VALIDATION metrics
+2025-08-27 10:10:00: Epoch: 043 Test results, ap: 0.325, auc: 0.559, f1: 0.333, acc: 0.707
+2025-08-27 10:10:03: Epoch: 044 Train Loss: 0.095, ap: 0.933, auc: 0.940, f1: 0.871, acc: 0.877
+2025-08-27 10:10:03: Epoch: 044 New best VALIDATION metrics
+2025-08-27 10:10:03: Epoch: 044 Test results, ap: 0.326, auc: 0.554, f1: 0.296, acc: 0.691
+2025-08-27 10:10:06: Epoch: 045 Train Loss: 0.091, ap: 0.934, auc: 0.940, f1: 0.874, acc: 0.879
+2025-08-27 10:10:06: Epoch: 045 New best VALIDATION metrics
+2025-08-27 10:10:07: Epoch: 045 Test results, ap: 0.321, auc: 0.559, f1: 0.296, acc: 0.691
+2025-08-27 10:10:10: Epoch: 046 Train Loss: 0.093, ap: 0.934, auc: 0.939, f1: 0.869, acc: 0.875
+2025-08-27 10:10:10: Epoch: 046 Test results, ap: 0.323, auc: 0.561, f1: 0.296, acc: 0.691
+2025-08-27 10:10:13: Epoch: 047 Train Loss: 0.089, ap: 0.933, auc: 0.939, f1: 0.874, acc: 0.880
+2025-08-27 10:10:13: Epoch: 047 Test results, ap: 0.325, auc: 0.563, f1: 0.327, acc: 0.699
+2025-08-27 10:10:16: Epoch: 048 Train Loss: 0.086, ap: 0.934, auc: 0.940, f1: 0.866, acc: 0.873
+2025-08-27 10:10:17: Epoch: 048 Test results, ap: 0.325, auc: 0.565, f1: 0.302, acc: 0.699
+2025-08-27 10:10:20: Epoch: 049 Train Loss: 0.087, ap: 0.935, auc: 0.940, f1: 0.872, acc: 0.877
+2025-08-27 10:10:20: Epoch: 049 New best VALIDATION metrics
+2025-08-27 10:10:20: Epoch: 049 Test results, ap: 0.326, auc: 0.566, f1: 0.302, acc: 0.699
+2025-08-27 10:10:20: Epoch: 049 New best TEST metrics
+2025-08-27 10:10:23: Epoch: 050 Train Loss: 0.082, ap: 0.934, auc: 0.940, f1: 0.875, acc: 0.880
+2025-08-27 10:10:23: Epoch: 050 New best VALIDATION metrics
+2025-08-27 10:10:23: Epoch: 050 Test results, ap: 0.324, auc: 0.560, f1: 0.327, acc: 0.699
+2025-08-27 10:10:23: used time 0.05h
+2025-08-27 10:10:23: Running Cross Validation 1
+2025-08-27 10:10:23: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-27 10:10:26: Epoch: 001 Train Loss: 0.686, ap: 0.565, auc: 0.621, f1: 0.467, acc: 0.618
+2025-08-27 10:10:26: Epoch: 001 New best VALIDATION metrics
+2025-08-27 10:10:27: Epoch: 001 Test results, ap: 0.230, auc: 0.440, f1: 0.316, acc: 0.577
+2025-08-27 10:10:30: Epoch: 002 Train Loss: 0.647, ap: 0.636, auc: 0.696, f1: 0.589, acc: 0.644
+2025-08-27 10:10:30: Epoch: 002 New best VALIDATION metrics
+2025-08-27 10:10:30: Epoch: 002 Test results, ap: 0.255, auc: 0.491, f1: 0.312, acc: 0.569
+2025-08-27 10:10:33: Epoch: 003 Train Loss: 0.587, ap: 0.721, auc: 0.776, f1: 0.667, acc: 0.711
+2025-08-27 10:10:33: Epoch: 003 New best VALIDATION metrics
+2025-08-27 10:10:34: Epoch: 003 Test results, ap: 0.269, auc: 0.551, f1: 0.338, acc: 0.650
+2025-08-27 10:10:37: Epoch: 004 Train Loss: 0.550, ap: 0.767, auc: 0.808, f1: 0.694, acc: 0.724
+2025-08-27 10:10:37: Epoch: 004 New best VALIDATION metrics
+2025-08-27 10:10:37: Epoch: 004 Test results, ap: 0.266, auc: 0.508, f1: 0.295, acc: 0.650
+2025-08-27 10:10:40: Epoch: 005 Train Loss: 0.503, ap: 0.776, auc: 0.830, f1: 0.709, acc: 0.739
+2025-08-27 10:10:40: Epoch: 005 New best VALIDATION metrics
+2025-08-27 10:10:40: Epoch: 005 Test results, ap: 0.310, auc: 0.502, f1: 0.310, acc: 0.602
+2025-08-27 10:10:43: Epoch: 006 Train Loss: 0.477, ap: 0.786, auc: 0.833, f1: 0.727, acc: 0.755
+2025-08-27 10:10:43: Epoch: 006 New best VALIDATION metrics
+2025-08-27 10:10:44: Epoch: 006 Test results, ap: 0.287, auc: 0.461, f1: 0.265, acc: 0.593
+2025-08-27 10:10:47: Epoch: 007 Train Loss: 0.434, ap: 0.817, auc: 0.858, f1: 0.732, acc: 0.775
+2025-08-27 10:10:47: Epoch: 007 New best VALIDATION metrics
+2025-08-27 10:10:47: Epoch: 007 Test results, ap: 0.260, auc: 0.466, f1: 0.290, acc: 0.602
+2025-08-27 10:10:50: Epoch: 008 Train Loss: 0.404, ap: 0.842, auc: 0.877, f1: 0.764, acc: 0.796
+2025-08-27 10:10:50: Epoch: 008 New best VALIDATION metrics
+2025-08-27 10:10:50: Epoch: 008 Test results, ap: 0.258, auc: 0.464, f1: 0.278, acc: 0.577
+2025-08-27 10:10:53: Epoch: 009 Train Loss: 0.379, ap: 0.853, auc: 0.884, f1: 0.748, acc: 0.789
+2025-08-27 10:10:53: Epoch: 009 New best VALIDATION metrics
+2025-08-27 10:10:54: Epoch: 009 Test results, ap: 0.260, auc: 0.478, f1: 0.310, acc: 0.528
+2025-08-27 10:10:57: Epoch: 010 Train Loss: 0.355, ap: 0.856, auc: 0.888, f1: 0.766, acc: 0.794
+2025-08-27 10:10:57: Epoch: 010 New best VALIDATION metrics
+2025-08-27 10:10:57: Epoch: 010 Test results, ap: 0.274, auc: 0.500, f1: 0.333, acc: 0.512
+2025-08-27 10:11:00: Epoch: 011 Train Loss: 0.301, ap: 0.889, auc: 0.907, f1: 0.796, acc: 0.820
+2025-08-27 10:11:00: Epoch: 011 New best VALIDATION metrics
+2025-08-27 10:11:01: Epoch: 011 Test results, ap: 0.259, auc: 0.481, f1: 0.349, acc: 0.545
+2025-08-27 10:11:01: Epoch: 011 New best TEST metrics
+2025-08-27 10:11:04: Epoch: 012 Train Loss: 0.292, ap: 0.883, auc: 0.902, f1: 0.793, acc: 0.815
+2025-08-27 10:11:04: Epoch: 012 Test results, ap: 0.268, auc: 0.496, f1: 0.313, acc: 0.537
+2025-08-27 10:11:04: Epoch: 012 New best TEST metrics
+2025-08-27 10:11:07: Epoch: 013 Train Loss: 0.268, ap: 0.899, auc: 0.915, f1: 0.805, acc: 0.827
+2025-08-27 10:11:07: Epoch: 013 New best VALIDATION metrics
+2025-08-27 10:11:07: Epoch: 013 Test results, ap: 0.253, auc: 0.480, f1: 0.341, acc: 0.561
+2025-08-27 10:11:10: Epoch: 014 Train Loss: 0.245, ap: 0.911, auc: 0.927, f1: 0.828, acc: 0.850
+2025-08-27 10:11:10: Epoch: 014 New best VALIDATION metrics
+2025-08-27 10:11:11: Epoch: 014 Test results, ap: 0.258, auc: 0.464, f1: 0.319, acc: 0.618
+2025-08-27 10:11:14: Epoch: 015 Train Loss: 0.236, ap: 0.907, auc: 0.923, f1: 0.820, acc: 0.843
+2025-08-27 10:11:14: Epoch: 015 Test results, ap: 0.245, auc: 0.466, f1: 0.314, acc: 0.610
+2025-08-27 10:11:17: Epoch: 016 Train Loss: 0.229, ap: 0.920, auc: 0.935, f1: 0.854, acc: 0.871
+2025-08-27 10:11:17: Epoch: 016 New best VALIDATION metrics
+2025-08-27 10:11:17: Epoch: 016 Test results, ap: 0.254, auc: 0.485, f1: 0.348, acc: 0.634
+2025-08-27 10:11:20: Epoch: 017 Train Loss: 0.217, ap: 0.912, auc: 0.927, f1: 0.813, acc: 0.838
+2025-08-27 10:11:21: Epoch: 017 Test results, ap: 0.297, auc: 0.464, f1: 0.294, acc: 0.610
+2025-08-27 10:11:24: Epoch: 018 Train Loss: 0.213, ap: 0.910, auc: 0.928, f1: 0.840, acc: 0.864
+2025-08-27 10:11:24: Epoch: 018 Test results, ap: 0.341, auc: 0.485, f1: 0.286, acc: 0.634
+2025-08-27 10:11:24: Epoch: 018 New best TEST metrics
+2025-08-27 10:11:27: Epoch: 019 Train Loss: 0.197, ap: 0.928, auc: 0.941, f1: 0.841, acc: 0.863
+2025-08-27 10:11:27: Epoch: 019 New best VALIDATION metrics
+2025-08-27 10:11:27: Epoch: 019 Test results, ap: 0.278, auc: 0.475, f1: 0.333, acc: 0.577
+2025-08-27 10:11:30: Epoch: 020 Train Loss: 0.182, ap: 0.919, auc: 0.937, f1: 0.841, acc: 0.864
+2025-08-27 10:11:31: Epoch: 020 Test results, ap: 0.273, auc: 0.481, f1: 0.241, acc: 0.642
+2025-08-27 10:11:34: Epoch: 021 Train Loss: 0.152, ap: 0.927, auc: 0.943, f1: 0.864, acc: 0.886
+2025-08-27 10:11:34: Epoch: 021 New best VALIDATION metrics
+2025-08-27 10:11:34: Epoch: 021 Test results, ap: 0.297, auc: 0.485, f1: 0.258, acc: 0.626
+2025-08-27 10:11:37: Epoch: 022 Train Loss: 0.135, ap: 0.933, auc: 0.948, f1: 0.866, acc: 0.887
+2025-08-27 10:11:37: Epoch: 022 New best VALIDATION metrics
+2025-08-27 10:11:37: Epoch: 022 Test results, ap: 0.302, auc: 0.492, f1: 0.264, acc: 0.683
+2025-08-27 10:11:40: Epoch: 023 Train Loss: 0.131, ap: 0.935, auc: 0.949, f1: 0.874, acc: 0.893
+2025-08-27 10:11:40: Epoch: 023 New best VALIDATION metrics
+2025-08-27 10:11:41: Epoch: 023 Test results, ap: 0.264, auc: 0.483, f1: 0.254, acc: 0.569
+2025-08-27 10:11:44: Epoch: 024 Train Loss: 0.119, ap: 0.934, auc: 0.949, f1: 0.882, acc: 0.898
+2025-08-27 10:11:44: Epoch: 024 Test results, ap: 0.273, auc: 0.484, f1: 0.276, acc: 0.659
+2025-08-27 10:11:47: Epoch: 025 Train Loss: 0.118, ap: 0.937, auc: 0.953, f1: 0.878, acc: 0.894
+2025-08-27 10:11:47: Epoch: 025 New best VALIDATION metrics
+2025-08-27 10:11:48: Epoch: 025 Test results, ap: 0.273, auc: 0.485, f1: 0.262, acc: 0.634
+2025-08-27 10:11:51: Epoch: 026 Train Loss: 0.106, ap: 0.931, auc: 0.946, f1: 0.863, acc: 0.884
+2025-08-27 10:11:51: Epoch: 026 Test results, ap: 0.307, auc: 0.521, f1: 0.240, acc: 0.691
+2025-08-27 10:11:51: Epoch: 026 New best TEST metrics
+2025-08-27 10:11:54: Epoch: 027 Train Loss: 0.114, ap: 0.943, auc: 0.955, f1: 0.885, acc: 0.901
+2025-08-27 10:11:54: Epoch: 027 New best VALIDATION metrics
+2025-08-27 10:11:54: Epoch: 027 Test results, ap: 0.277, auc: 0.496, f1: 0.262, acc: 0.634
+2025-08-27 10:11:57: Epoch: 028 Train Loss: 0.095, ap: 0.940, auc: 0.952, f1: 0.880, acc: 0.896
+2025-08-27 10:11:58: Epoch: 028 Test results, ap: 0.274, auc: 0.491, f1: 0.262, acc: 0.634
+2025-08-27 10:12:01: Epoch: 029 Train Loss: 0.095, ap: 0.939, auc: 0.952, f1: 0.884, acc: 0.901
+2025-08-27 10:12:01: Epoch: 029 Test results, ap: 0.282, auc: 0.498, f1: 0.258, acc: 0.626
+2025-08-27 10:12:04: Epoch: 030 Train Loss: 0.080, ap: 0.941, auc: 0.953, f1: 0.884, acc: 0.901
+2025-08-27 10:12:04: Epoch: 030 Test results, ap: 0.266, auc: 0.491, f1: 0.241, acc: 0.642
+2025-08-27 10:12:07: Epoch: 031 Train Loss: 0.064, ap: 0.939, auc: 0.954, f1: 0.880, acc: 0.898
+2025-08-27 10:12:08: Epoch: 031 Test results, ap: 0.263, auc: 0.484, f1: 0.267, acc: 0.642
+2025-08-27 10:12:11: Epoch: 032 Train Loss: 0.061, ap: 0.940, auc: 0.953, f1: 0.890, acc: 0.907
+2025-08-27 10:12:11: Epoch: 032 Test results, ap: 0.260, auc: 0.485, f1: 0.258, acc: 0.626
+2025-08-27 10:12:14: Epoch: 033 Train Loss: 0.060, ap: 0.937, auc: 0.951, f1: 0.878, acc: 0.896
+2025-08-27 10:12:14: Epoch: 033 Test results, ap: 0.268, auc: 0.509, f1: 0.269, acc: 0.691
+2025-08-27 10:12:18: Epoch: 034 Train Loss: 0.051, ap: 0.936, auc: 0.951, f1: 0.874, acc: 0.893
+2025-08-27 10:12:18: Epoch: 034 Test results, ap: 0.262, auc: 0.501, f1: 0.264, acc: 0.683
+2025-08-27 10:12:21: Epoch: 035 Train Loss: 0.053, ap: 0.939, auc: 0.953, f1: 0.891, acc: 0.907
+2025-08-27 10:12:21: Epoch: 035 Test results, ap: 0.262, auc: 0.492, f1: 0.237, acc: 0.634
+2025-08-27 10:12:24: Epoch: 036 Train Loss: 0.057, ap: 0.937, auc: 0.952, f1: 0.891, acc: 0.907
+2025-08-27 10:12:25: Epoch: 036 Test results, ap: 0.258, auc: 0.485, f1: 0.277, acc: 0.618
+2025-08-27 10:12:28: Epoch: 037 Train Loss: 0.054, ap: 0.933, auc: 0.953, f1: 0.896, acc: 0.910
+2025-08-27 10:12:28: Epoch: 037 Test results, ap: 0.256, auc: 0.491, f1: 0.271, acc: 0.650
+2025-08-27 10:12:31: Epoch: 038 Train Loss: 0.057, ap: 0.940, auc: 0.954, f1: 0.888, acc: 0.905
+2025-08-27 10:12:31: Epoch: 038 Test results, ap: 0.262, auc: 0.490, f1: 0.250, acc: 0.659
+2025-08-27 10:12:34: Epoch: 039 Train Loss: 0.051, ap: 0.937, auc: 0.954, f1: 0.894, acc: 0.908
+2025-08-27 10:12:35: Epoch: 039 Test results, ap: 0.257, auc: 0.484, f1: 0.281, acc: 0.667
+2025-08-27 10:12:38: Epoch: 040 Train Loss: 0.055, ap: 0.935, auc: 0.952, f1: 0.892, acc: 0.907
+2025-08-27 10:12:38: Epoch: 040 Test results, ap: 0.261, auc: 0.491, f1: 0.276, acc: 0.659
+2025-08-27 10:12:41: Epoch: 041 Train Loss: 0.041, ap: 0.936, auc: 0.953, f1: 0.898, acc: 0.912
+2025-08-27 10:12:41: Epoch: 041 Test results, ap: 0.263, auc: 0.492, f1: 0.264, acc: 0.683
+2025-08-27 10:12:44: Epoch: 042 Train Loss: 0.034, ap: 0.936, auc: 0.954, f1: 0.900, acc: 0.914
+2025-08-27 10:12:45: Epoch: 042 Test results, ap: 0.261, auc: 0.492, f1: 0.286, acc: 0.675
+2025-08-27 10:12:48: Epoch: 043 Train Loss: 0.032, ap: 0.935, auc: 0.953, f1: 0.891, acc: 0.907
+2025-08-27 10:12:48: Epoch: 043 Test results, ap: 0.264, auc: 0.497, f1: 0.264, acc: 0.683
+2025-08-27 10:12:51: Epoch: 044 Train Loss: 0.031, ap: 0.934, auc: 0.952, f1: 0.893, acc: 0.908
+2025-08-27 10:12:51: Epoch: 044 Test results, ap: 0.260, auc: 0.496, f1: 0.264, acc: 0.683
+2025-08-27 10:12:54: Epoch: 045 Train Loss: 0.030, ap: 0.934, auc: 0.952, f1: 0.898, acc: 0.912
+2025-08-27 10:12:55: Epoch: 045 Test results, ap: 0.262, auc: 0.494, f1: 0.255, acc: 0.667
+2025-08-27 10:12:58: Epoch: 046 Train Loss: 0.029, ap: 0.933, auc: 0.951, f1: 0.891, acc: 0.907
+2025-08-27 10:12:58: Epoch: 046 Test results, ap: 0.260, auc: 0.493, f1: 0.281, acc: 0.667
+2025-08-27 10:13:01: Epoch: 047 Train Loss: 0.029, ap: 0.935, auc: 0.952, f1: 0.895, acc: 0.910
+2025-08-27 10:13:02: Epoch: 047 Test results, ap: 0.258, auc: 0.493, f1: 0.264, acc: 0.683
+2025-08-27 10:13:05: Epoch: 048 Train Loss: 0.026, ap: 0.933, auc: 0.951, f1: 0.896, acc: 0.910
+2025-08-27 10:13:05: Epoch: 048 Test results, ap: 0.257, auc: 0.491, f1: 0.291, acc: 0.683
+2025-08-27 10:13:08: Epoch: 049 Train Loss: 0.025, ap: 0.933, auc: 0.951, f1: 0.889, acc: 0.905
+2025-08-27 10:13:08: Epoch: 049 Test results, ap: 0.257, auc: 0.493, f1: 0.259, acc: 0.675
+2025-08-27 10:13:11: Epoch: 050 Train Loss: 0.029, ap: 0.929, auc: 0.952, f1: 0.900, acc: 0.914
+2025-08-27 10:13:12: Epoch: 050 Test results, ap: 0.254, auc: 0.491, f1: 0.264, acc: 0.683
+2025-08-27 10:13:12: used time 0.05h
+2025-08-27 10:13:12: Running Cross Validation 2
+2025-08-27 10:13:12: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-27 10:13:15: Epoch: 001 Train Loss: 0.692, ap: 0.603, auc: 0.661, f1: 0.000, acc: 0.559
+2025-08-27 10:13:15: Epoch: 001 New best VALIDATION metrics
+2025-08-27 10:13:15: Epoch: 001 Test results, ap: 0.239, auc: 0.482, f1: 0.000, acc: 0.756
+2025-08-27 10:13:18: Epoch: 002 Train Loss: 0.691, ap: 0.509, auc: 0.567, f1: 0.000, acc: 0.557
+2025-08-27 10:13:18: Epoch: 002 Test results, ap: 0.380, auc: 0.635, f1: 0.000, acc: 0.756
+2025-08-27 10:13:21: Epoch: 003 Train Loss: 0.686, ap: 0.602, auc: 0.671, f1: 0.556, acc: 0.617
+2025-08-27 10:13:21: Epoch: 003 New best VALIDATION metrics
+2025-08-27 10:13:22: Epoch: 003 Test results, ap: 0.266, auc: 0.535, f1: 0.316, acc: 0.577
+2025-08-27 10:13:25: Epoch: 004 Train Loss: 0.642, ap: 0.693, auc: 0.756, f1: 0.663, acc: 0.683
+2025-08-27 10:13:25: Epoch: 004 New best VALIDATION metrics
+2025-08-27 10:13:25: Epoch: 004 Test results, ap: 0.285, auc: 0.541, f1: 0.356, acc: 0.618
+2025-08-27 10:13:28: Epoch: 005 Train Loss: 0.601, ap: 0.644, auc: 0.719, f1: 0.607, acc: 0.667
+2025-08-27 10:13:29: Epoch: 005 Test results, ap: 0.311, auc: 0.563, f1: 0.326, acc: 0.764
+2025-08-27 10:13:32: Epoch: 006 Train Loss: 0.566, ap: 0.747, auc: 0.797, f1: 0.710, acc: 0.720
+2025-08-27 10:13:32: Epoch: 006 New best VALIDATION metrics
+2025-08-27 10:13:32: Epoch: 006 Test results, ap: 0.279, auc: 0.530, f1: 0.354, acc: 0.585
+2025-08-27 10:13:35: Epoch: 007 Train Loss: 0.537, ap: 0.774, auc: 0.817, f1: 0.727, acc: 0.737
+2025-08-27 10:13:35: Epoch: 007 New best VALIDATION metrics
+2025-08-27 10:13:35: Epoch: 007 Test results, ap: 0.278, auc: 0.533, f1: 0.382, acc: 0.659
+2025-08-27 10:13:38: Epoch: 008 Train Loss: 0.505, ap: 0.808, auc: 0.834, f1: 0.729, acc: 0.746
+2025-08-27 10:13:38: Epoch: 008 New best VALIDATION metrics
+2025-08-27 10:13:39: Epoch: 008 Test results, ap: 0.246, auc: 0.521, f1: 0.269, acc: 0.602
+2025-08-27 10:13:42: Epoch: 009 Train Loss: 0.460, ap: 0.836, auc: 0.868, f1: 0.765, acc: 0.780
+2025-08-27 10:13:42: Epoch: 009 New best VALIDATION metrics
+2025-08-27 10:13:42: Epoch: 009 Test results, ap: 0.243, auc: 0.505, f1: 0.241, acc: 0.642
+2025-08-27 10:13:45: Epoch: 010 Train Loss: 0.411, ap: 0.860, auc: 0.886, f1: 0.773, acc: 0.783
+2025-08-27 10:13:45: Epoch: 010 New best VALIDATION metrics
+2025-08-27 10:13:45: Epoch: 010 Test results, ap: 0.268, auc: 0.532, f1: 0.297, acc: 0.577
+2025-08-27 10:13:48: Epoch: 011 Train Loss: 0.363, ap: 0.885, auc: 0.904, f1: 0.784, acc: 0.799
+2025-08-27 10:13:48: Epoch: 011 New best VALIDATION metrics
+2025-08-27 10:13:49: Epoch: 011 Test results, ap: 0.261, auc: 0.521, f1: 0.277, acc: 0.618
+2025-08-27 10:13:49: Epoch: 011 New best TEST metrics
+2025-08-27 10:13:52: Epoch: 012 Train Loss: 0.330, ap: 0.897, auc: 0.909, f1: 0.788, acc: 0.813
+2025-08-27 10:13:52: Epoch: 012 New best VALIDATION metrics
+2025-08-27 10:13:52: Epoch: 012 Test results, ap: 0.289, auc: 0.565, f1: 0.299, acc: 0.618
+2025-08-27 10:13:52: Epoch: 012 New best TEST metrics
+2025-08-27 10:13:55: Epoch: 013 Train Loss: 0.307, ap: 0.911, auc: 0.924, f1: 0.823, acc: 0.840
+2025-08-27 10:13:55: Epoch: 013 New best VALIDATION metrics
+2025-08-27 10:13:55: Epoch: 013 Test results, ap: 0.285, auc: 0.582, f1: 0.312, acc: 0.642
+2025-08-27 10:13:55: Epoch: 013 New best TEST metrics
+2025-08-27 10:13:58: Epoch: 014 Train Loss: 0.284, ap: 0.906, auc: 0.918, f1: 0.788, acc: 0.811
+2025-08-27 10:13:58: Epoch: 014 Test results, ap: 0.290, auc: 0.589, f1: 0.319, acc: 0.618
+2025-08-27 10:13:58: Epoch: 014 New best TEST metrics
+2025-08-27 10:14:01: Epoch: 015 Train Loss: 0.264, ap: 0.917, auc: 0.929, f1: 0.820, acc: 0.834
+2025-08-27 10:14:01: Epoch: 015 New best VALIDATION metrics
+2025-08-27 10:14:02: Epoch: 015 Test results, ap: 0.300, auc: 0.588, f1: 0.299, acc: 0.618
+2025-08-27 10:14:02: Epoch: 015 New best TEST metrics
+2025-08-27 10:14:05: Epoch: 016 Train Loss: 0.251, ap: 0.922, auc: 0.929, f1: 0.818, acc: 0.836
+2025-08-27 10:14:05: Epoch: 016 New best VALIDATION metrics
+2025-08-27 10:14:05: Epoch: 016 Test results, ap: 0.308, auc: 0.586, f1: 0.306, acc: 0.593
+2025-08-27 10:14:05: Epoch: 016 New best TEST metrics
+2025-08-27 10:14:08: Epoch: 017 Train Loss: 0.242, ap: 0.933, auc: 0.942, f1: 0.820, acc: 0.838
+2025-08-27 10:14:08: Epoch: 017 New best VALIDATION metrics
+2025-08-27 10:14:08: Epoch: 017 Test results, ap: 0.274, auc: 0.558, f1: 0.289, acc: 0.561
+2025-08-27 10:14:11: Epoch: 018 Train Loss: 0.216, ap: 0.931, auc: 0.937, f1: 0.838, acc: 0.854
+2025-08-27 10:14:12: Epoch: 018 Test results, ap: 0.302, auc: 0.592, f1: 0.314, acc: 0.610
+2025-08-27 10:14:15: Epoch: 019 Train Loss: 0.211, ap: 0.927, auc: 0.937, f1: 0.825, acc: 0.840
+2025-08-27 10:14:15: Epoch: 019 Test results, ap: 0.290, auc: 0.582, f1: 0.310, acc: 0.602
+2025-08-27 10:14:18: Epoch: 020 Train Loss: 0.214, ap: 0.933, auc: 0.941, f1: 0.848, acc: 0.862
+2025-08-27 10:14:18: Epoch: 020 Test results, ap: 0.302, auc: 0.578, f1: 0.344, acc: 0.659
+2025-08-27 10:14:21: Epoch: 021 Train Loss: 0.174, ap: 0.944, auc: 0.949, f1: 0.853, acc: 0.866
+2025-08-27 10:14:21: Epoch: 021 New best VALIDATION metrics
+2025-08-27 10:14:22: Epoch: 021 Test results, ap: 0.313, auc: 0.600, f1: 0.310, acc: 0.602
+2025-08-27 10:14:22: Epoch: 021 New best TEST metrics
+2025-08-27 10:14:25: Epoch: 022 Train Loss: 0.158, ap: 0.941, auc: 0.950, f1: 0.846, acc: 0.862
+2025-08-27 10:14:25: Epoch: 022 Test results, ap: 0.322, auc: 0.620, f1: 0.328, acc: 0.634
+2025-08-27 10:14:25: Epoch: 022 New best TEST metrics
+2025-08-27 10:14:28: Epoch: 023 Train Loss: 0.156, ap: 0.939, auc: 0.949, f1: 0.852, acc: 0.866
+2025-08-27 10:14:28: Epoch: 023 Test results, ap: 0.322, auc: 0.611, f1: 0.308, acc: 0.561
+2025-08-27 10:14:31: Epoch: 024 Train Loss: 0.145, ap: 0.943, auc: 0.951, f1: 0.871, acc: 0.884
+2025-08-27 10:14:31: Epoch: 024 New best VALIDATION metrics
+2025-08-27 10:14:32: Epoch: 024 Test results, ap: 0.330, auc: 0.632, f1: 0.366, acc: 0.634
+2025-08-27 10:14:32: Epoch: 024 New best TEST metrics
+2025-08-27 10:14:35: Epoch: 025 Train Loss: 0.143, ap: 0.948, auc: 0.958, f1: 0.874, acc: 0.889
+2025-08-27 10:14:35: Epoch: 025 New best VALIDATION metrics
+2025-08-27 10:14:35: Epoch: 025 Test results, ap: 0.328, auc: 0.625, f1: 0.353, acc: 0.642
+2025-08-27 10:14:38: Epoch: 026 Train Loss: 0.127, ap: 0.941, auc: 0.948, f1: 0.854, acc: 0.869
+2025-08-27 10:14:38: Epoch: 026 Test results, ap: 0.306, auc: 0.604, f1: 0.333, acc: 0.642
+2025-08-27 10:14:41: Epoch: 027 Train Loss: 0.120, ap: 0.945, auc: 0.954, f1: 0.865, acc: 0.880
+2025-08-27 10:14:42: Epoch: 027 Test results, ap: 0.320, auc: 0.620, f1: 0.378, acc: 0.626
+2025-08-27 10:14:44: Epoch: 028 Train Loss: 0.121, ap: 0.945, auc: 0.955, f1: 0.860, acc: 0.877
+2025-08-27 10:14:45: Epoch: 028 Test results, ap: 0.323, auc: 0.622, f1: 0.384, acc: 0.634
+2025-08-27 10:14:48: Epoch: 029 Train Loss: 0.122, ap: 0.948, auc: 0.957, f1: 0.872, acc: 0.884
+2025-08-27 10:14:48: Epoch: 029 Test results, ap: 0.322, auc: 0.622, f1: 0.356, acc: 0.618
+2025-08-27 10:14:51: Epoch: 030 Train Loss: 0.112, ap: 0.943, auc: 0.951, f1: 0.864, acc: 0.878
+2025-08-27 10:14:51: Epoch: 030 Test results, ap: 0.311, auc: 0.611, f1: 0.361, acc: 0.626
+2025-08-27 10:14:54: Epoch: 031 Train Loss: 0.090, ap: 0.948, auc: 0.957, f1: 0.867, acc: 0.882
+2025-08-27 10:14:55: Epoch: 031 Test results, ap: 0.315, auc: 0.616, f1: 0.338, acc: 0.618
+2025-08-27 10:14:58: Epoch: 032 Train Loss: 0.073, ap: 0.944, auc: 0.955, f1: 0.870, acc: 0.885
+2025-08-27 10:14:58: Epoch: 032 Test results, ap: 0.317, auc: 0.617, f1: 0.361, acc: 0.626
+2025-08-27 10:15:01: Epoch: 033 Train Loss: 0.076, ap: 0.943, auc: 0.954, f1: 0.871, acc: 0.885
+2025-08-27 10:15:01: Epoch: 033 Test results, ap: 0.316, auc: 0.620, f1: 0.384, acc: 0.634
+2025-08-27 10:15:04: Epoch: 034 Train Loss: 0.068, ap: 0.942, auc: 0.953, f1: 0.876, acc: 0.892
+2025-08-27 10:15:05: Epoch: 034 Test results, ap: 0.321, auc: 0.621, f1: 0.378, acc: 0.626
+2025-08-27 10:15:08: Epoch: 035 Train Loss: 0.067, ap: 0.945, auc: 0.954, f1: 0.872, acc: 0.887
+2025-08-27 10:15:08: Epoch: 035 Test results, ap: 0.322, auc: 0.627, f1: 0.394, acc: 0.650
+2025-08-27 10:15:11: Epoch: 036 Train Loss: 0.061, ap: 0.946, auc: 0.957, f1: 0.880, acc: 0.894
+2025-08-27 10:15:11: Epoch: 036 Test results, ap: 0.320, auc: 0.616, f1: 0.411, acc: 0.650
+2025-08-27 10:15:14: Epoch: 037 Train Loss: 0.060, ap: 0.943, auc: 0.953, f1: 0.869, acc: 0.884
+2025-08-27 10:15:15: Epoch: 037 Test results, ap: 0.309, auc: 0.615, f1: 0.353, acc: 0.642
+2025-08-27 10:15:18: Epoch: 038 Train Loss: 0.059, ap: 0.947, auc: 0.956, f1: 0.882, acc: 0.896
+2025-08-27 10:15:18: Epoch: 038 Test results, ap: 0.320, auc: 0.623, f1: 0.400, acc: 0.634
+2025-08-27 10:15:21: Epoch: 039 Train Loss: 0.056, ap: 0.946, auc: 0.956, f1: 0.875, acc: 0.889
+2025-08-27 10:15:21: Epoch: 039 Test results, ap: 0.321, auc: 0.623, f1: 0.444, acc: 0.675
+2025-08-27 10:15:24: Epoch: 040 Train Loss: 0.059, ap: 0.948, auc: 0.956, f1: 0.873, acc: 0.885
+2025-08-27 10:15:25: Epoch: 040 Test results, ap: 0.317, auc: 0.620, f1: 0.377, acc: 0.650
+2025-08-27 10:15:28: Epoch: 041 Train Loss: 0.039, ap: 0.948, auc: 0.958, f1: 0.879, acc: 0.892
+2025-08-27 10:15:28: Epoch: 041 New best VALIDATION metrics
+2025-08-27 10:15:28: Epoch: 041 Test results, ap: 0.322, auc: 0.620, f1: 0.394, acc: 0.650
+2025-08-27 10:15:31: Epoch: 042 Train Loss: 0.037, ap: 0.948, auc: 0.957, f1: 0.875, acc: 0.889
+2025-08-27 10:15:31: Epoch: 042 Test results, ap: 0.320, auc: 0.628, f1: 0.394, acc: 0.650
+2025-08-27 10:15:34: Epoch: 043 Train Loss: 0.034, ap: 0.950, auc: 0.959, f1: 0.881, acc: 0.894
+2025-08-27 10:15:34: Epoch: 043 New best VALIDATION metrics
+2025-08-27 10:15:35: Epoch: 043 Test results, ap: 0.324, auc: 0.625, f1: 0.394, acc: 0.650
+2025-08-27 10:15:38: Epoch: 044 Train Loss: 0.032, ap: 0.949, auc: 0.958, f1: 0.883, acc: 0.896
+2025-08-27 10:15:38: Epoch: 044 Test results, ap: 0.325, auc: 0.629, f1: 0.394, acc: 0.650
+2025-08-27 10:15:41: Epoch: 045 Train Loss: 0.032, ap: 0.944, auc: 0.956, f1: 0.872, acc: 0.885
+2025-08-27 10:15:41: Epoch: 045 Test results, ap: 0.327, auc: 0.632, f1: 0.394, acc: 0.650
+2025-08-27 10:15:44: Epoch: 046 Train Loss: 0.030, ap: 0.950, auc: 0.958, f1: 0.883, acc: 0.896
+2025-08-27 10:15:45: Epoch: 046 Test results, ap: 0.321, auc: 0.626, f1: 0.394, acc: 0.650
+2025-08-27 10:15:48: Epoch: 047 Train Loss: 0.028, ap: 0.945, auc: 0.957, f1: 0.881, acc: 0.894
+2025-08-27 10:15:48: Epoch: 047 Test results, ap: 0.317, auc: 0.626, f1: 0.394, acc: 0.650
+2025-08-27 10:15:51: Epoch: 048 Train Loss: 0.028, ap: 0.946, auc: 0.958, f1: 0.873, acc: 0.887
+2025-08-27 10:15:51: Epoch: 048 Test results, ap: 0.328, auc: 0.630, f1: 0.394, acc: 0.650
+2025-08-27 10:15:54: Epoch: 049 Train Loss: 0.029, ap: 0.945, auc: 0.957, f1: 0.877, acc: 0.891
+2025-08-27 10:15:55: Epoch: 049 Test results, ap: 0.327, auc: 0.630, f1: 0.371, acc: 0.642
+2025-08-27 10:15:58: Epoch: 050 Train Loss: 0.027, ap: 0.946, auc: 0.958, f1: 0.879, acc: 0.892
+2025-08-27 10:15:58: Epoch: 050 Test results, ap: 0.326, auc: 0.630, f1: 0.371, acc: 0.642
+2025-08-27 10:15:58: used time 0.05h
+2025-08-27 10:15:58: Running Cross Validation 3
+2025-08-27 10:15:58: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-27 10:16:01: Epoch: 001 Train Loss: 0.684, ap: 0.531, auc: 0.608, f1: 0.562, acc: 0.552
+2025-08-27 10:16:01: Epoch: 001 New best VALIDATION metrics
+2025-08-27 10:16:01: Epoch: 001 Test results, ap: 0.223, auc: 0.471, f1: 0.383, acc: 0.423
+2025-08-27 10:16:04: Epoch: 002 Train Loss: 0.661, ap: 0.609, auc: 0.693, f1: 0.596, acc: 0.651
+2025-08-27 10:16:04: Epoch: 002 New best VALIDATION metrics
+2025-08-27 10:16:05: Epoch: 002 Test results, ap: 0.252, auc: 0.495, f1: 0.300, acc: 0.659
+2025-08-27 10:16:08: Epoch: 003 Train Loss: 0.614, ap: 0.708, auc: 0.769, f1: 0.677, acc: 0.698
+2025-08-27 10:16:08: Epoch: 003 New best VALIDATION metrics
+2025-08-27 10:16:08: Epoch: 003 Test results, ap: 0.272, auc: 0.519, f1: 0.393, acc: 0.699
+2025-08-27 10:16:11: Epoch: 004 Train Loss: 0.569, ap: 0.745, auc: 0.803, f1: 0.709, acc: 0.727
+2025-08-27 10:16:11: Epoch: 004 New best VALIDATION metrics
+2025-08-27 10:16:11: Epoch: 004 Test results, ap: 0.321, auc: 0.535, f1: 0.337, acc: 0.553
+2025-08-27 10:16:14: Epoch: 005 Train Loss: 0.527, ap: 0.765, auc: 0.821, f1: 0.720, acc: 0.751
+2025-08-27 10:16:14: Epoch: 005 New best VALIDATION metrics
+2025-08-27 10:16:15: Epoch: 005 Test results, ap: 0.253, auc: 0.522, f1: 0.281, acc: 0.667
+2025-08-27 10:16:17: Epoch: 006 Train Loss: 0.487, ap: 0.808, auc: 0.850, f1: 0.746, acc: 0.760
+2025-08-27 10:16:17: Epoch: 006 New best VALIDATION metrics
+2025-08-27 10:16:18: Epoch: 006 Test results, ap: 0.242, auc: 0.510, f1: 0.115, acc: 0.626
+2025-08-27 10:16:21: Epoch: 007 Train Loss: 0.462, ap: 0.829, auc: 0.858, f1: 0.739, acc: 0.760
+2025-08-27 10:16:21: Epoch: 007 New best VALIDATION metrics
+2025-08-27 10:16:21: Epoch: 007 Test results, ap: 0.291, auc: 0.530, f1: 0.276, acc: 0.659
+2025-08-27 10:16:24: Epoch: 008 Train Loss: 0.453, ap: 0.818, auc: 0.850, f1: 0.744, acc: 0.772
+2025-08-27 10:16:24: Epoch: 008 Test results, ap: 0.332, auc: 0.570, f1: 0.267, acc: 0.642
+2025-08-27 10:16:27: Epoch: 009 Train Loss: 0.400, ap: 0.856, auc: 0.882, f1: 0.758, acc: 0.787
+2025-08-27 10:16:27: Epoch: 009 New best VALIDATION metrics
+2025-08-27 10:16:28: Epoch: 009 Test results, ap: 0.288, auc: 0.570, f1: 0.290, acc: 0.642
+2025-08-27 10:16:31: Epoch: 010 Train Loss: 0.374, ap: 0.841, auc: 0.861, f1: 0.734, acc: 0.755
+2025-08-27 10:16:31: Epoch: 010 Test results, ap: 0.273, auc: 0.538, f1: 0.215, acc: 0.585
+2025-08-27 10:16:34: Epoch: 011 Train Loss: 0.344, ap: 0.879, auc: 0.898, f1: 0.790, acc: 0.810
+2025-08-27 10:16:34: Epoch: 011 New best VALIDATION metrics
+2025-08-27 10:16:34: Epoch: 011 Test results, ap: 0.285, auc: 0.544, f1: 0.328, acc: 0.667
+2025-08-27 10:16:34: Epoch: 011 New best TEST metrics
+2025-08-27 10:16:37: Epoch: 012 Train Loss: 0.309, ap: 0.888, auc: 0.904, f1: 0.796, acc: 0.820
+2025-08-27 10:16:37: Epoch: 012 New best VALIDATION metrics
+2025-08-27 10:16:38: Epoch: 012 Test results, ap: 0.364, auc: 0.581, f1: 0.271, acc: 0.650
+2025-08-27 10:16:38: Epoch: 012 New best TEST metrics
+2025-08-27 10:16:41: Epoch: 013 Train Loss: 0.291, ap: 0.892, auc: 0.910, f1: 0.791, acc: 0.813
+2025-08-27 10:16:41: Epoch: 013 New best VALIDATION metrics
+2025-08-27 10:16:41: Epoch: 013 Test results, ap: 0.323, auc: 0.549, f1: 0.328, acc: 0.667
+2025-08-27 10:16:44: Epoch: 014 Train Loss: 0.274, ap: 0.895, auc: 0.909, f1: 0.794, acc: 0.813
+2025-08-27 10:16:44: Epoch: 014 New best VALIDATION metrics
+2025-08-27 10:16:44: Epoch: 014 Test results, ap: 0.267, auc: 0.500, f1: 0.286, acc: 0.715
+2025-08-27 10:16:47: Epoch: 015 Train Loss: 0.265, ap: 0.897, auc: 0.920, f1: 0.803, acc: 0.824
+2025-08-27 10:16:47: Epoch: 015 New best VALIDATION metrics
+2025-08-27 10:16:48: Epoch: 015 Test results, ap: 0.293, auc: 0.506, f1: 0.233, acc: 0.626
+2025-08-27 10:16:51: Epoch: 016 Train Loss: 0.238, ap: 0.906, auc: 0.921, f1: 0.819, acc: 0.840
+2025-08-27 10:16:51: Epoch: 016 New best VALIDATION metrics
+2025-08-27 10:16:51: Epoch: 016 Test results, ap: 0.280, auc: 0.494, f1: 0.316, acc: 0.683
+2025-08-27 10:16:54: Epoch: 017 Train Loss: 0.231, ap: 0.893, auc: 0.917, f1: 0.815, acc: 0.829
+2025-08-27 10:16:54: Epoch: 017 Test results, ap: 0.369, auc: 0.511, f1: 0.316, acc: 0.683
+2025-08-27 10:16:57: Epoch: 018 Train Loss: 0.231, ap: 0.914, auc: 0.931, f1: 0.826, acc: 0.845
+2025-08-27 10:16:57: Epoch: 018 New best VALIDATION metrics
+2025-08-27 10:16:58: Epoch: 018 Test results, ap: 0.314, auc: 0.542, f1: 0.286, acc: 0.675
+2025-08-27 10:17:01: Epoch: 019 Train Loss: 0.212, ap: 0.919, auc: 0.929, f1: 0.828, acc: 0.850
+2025-08-27 10:17:01: Epoch: 019 New best VALIDATION metrics
+2025-08-27 10:17:01: Epoch: 019 Test results, ap: 0.298, auc: 0.495, f1: 0.250, acc: 0.707
+2025-08-27 10:17:04: Epoch: 020 Train Loss: 0.225, ap: 0.910, auc: 0.926, f1: 0.823, acc: 0.845
+2025-08-27 10:17:04: Epoch: 020 Test results, ap: 0.286, auc: 0.539, f1: 0.286, acc: 0.675
+2025-08-27 10:17:07: Epoch: 021 Train Loss: 0.184, ap: 0.924, auc: 0.941, f1: 0.842, acc: 0.861
+2025-08-27 10:17:07: Epoch: 021 New best VALIDATION metrics
+2025-08-27 10:17:08: Epoch: 021 Test results, ap: 0.302, auc: 0.529, f1: 0.264, acc: 0.683
+2025-08-27 10:17:11: Epoch: 022 Train Loss: 0.153, ap: 0.928, auc: 0.942, f1: 0.850, acc: 0.866
+2025-08-27 10:17:11: Epoch: 022 New best VALIDATION metrics
+2025-08-27 10:17:11: Epoch: 022 Test results, ap: 0.275, auc: 0.480, f1: 0.222, acc: 0.659
+2025-08-27 10:17:14: Epoch: 023 Train Loss: 0.148, ap: 0.922, auc: 0.940, f1: 0.849, acc: 0.864
+2025-08-27 10:17:14: Epoch: 023 Test results, ap: 0.291, auc: 0.516, f1: 0.255, acc: 0.667
+2025-08-27 10:17:17: Epoch: 024 Train Loss: 0.144, ap: 0.928, auc: 0.944, f1: 0.842, acc: 0.861
+2025-08-27 10:17:17: Epoch: 024 New best VALIDATION metrics
+2025-08-27 10:17:18: Epoch: 024 Test results, ap: 0.296, auc: 0.508, f1: 0.259, acc: 0.675
+2025-08-27 10:17:21: Epoch: 025 Train Loss: 0.143, ap: 0.934, auc: 0.949, f1: 0.861, acc: 0.878
+2025-08-27 10:17:21: Epoch: 025 New best VALIDATION metrics
+2025-08-27 10:17:21: Epoch: 025 Test results, ap: 0.319, auc: 0.547, f1: 0.259, acc: 0.675
+2025-08-27 10:17:24: Epoch: 026 Train Loss: 0.126, ap: 0.930, auc: 0.946, f1: 0.847, acc: 0.862
+2025-08-27 10:17:24: Epoch: 026 Test results, ap: 0.292, auc: 0.529, f1: 0.264, acc: 0.683
+2025-08-27 10:17:27: Epoch: 027 Train Loss: 0.138, ap: 0.933, auc: 0.947, f1: 0.841, acc: 0.861
+2025-08-27 10:17:28: Epoch: 027 Test results, ap: 0.295, auc: 0.522, f1: 0.246, acc: 0.650
+2025-08-27 10:17:31: Epoch: 028 Train Loss: 0.122, ap: 0.937, auc: 0.951, f1: 0.866, acc: 0.884
+2025-08-27 10:17:31: Epoch: 028 New best VALIDATION metrics
+2025-08-27 10:17:31: Epoch: 028 Test results, ap: 0.292, auc: 0.490, f1: 0.235, acc: 0.683
+2025-08-27 10:17:34: Epoch: 029 Train Loss: 0.112, ap: 0.935, auc: 0.952, f1: 0.864, acc: 0.880
+2025-08-27 10:17:34: Epoch: 029 Test results, ap: 0.285, auc: 0.513, f1: 0.250, acc: 0.659
+2025-08-27 10:17:37: Epoch: 030 Train Loss: 0.104, ap: 0.937, auc: 0.953, f1: 0.874, acc: 0.891
+2025-08-27 10:17:37: Epoch: 030 New best VALIDATION metrics
+2025-08-27 10:17:38: Epoch: 030 Test results, ap: 0.305, auc: 0.535, f1: 0.255, acc: 0.667
+2025-08-27 10:17:41: Epoch: 031 Train Loss: 0.085, ap: 0.937, auc: 0.954, f1: 0.863, acc: 0.880
+2025-08-27 10:17:41: Epoch: 031 New best VALIDATION metrics
+2025-08-27 10:17:41: Epoch: 031 Test results, ap: 0.288, auc: 0.509, f1: 0.255, acc: 0.667
+2025-08-27 10:17:44: Epoch: 032 Train Loss: 0.084, ap: 0.939, auc: 0.955, f1: 0.866, acc: 0.882
+2025-08-27 10:17:44: Epoch: 032 New best VALIDATION metrics
+2025-08-27 10:17:44: Epoch: 032 Test results, ap: 0.297, auc: 0.515, f1: 0.264, acc: 0.683
+2025-08-27 10:17:47: Epoch: 033 Train Loss: 0.080, ap: 0.937, auc: 0.957, f1: 0.869, acc: 0.884
+2025-08-27 10:17:48: Epoch: 033 Test results, ap: 0.311, auc: 0.525, f1: 0.250, acc: 0.659
+2025-08-27 10:17:51: Epoch: 034 Train Loss: 0.080, ap: 0.937, auc: 0.954, f1: 0.877, acc: 0.892
+2025-08-27 10:17:51: Epoch: 034 Test results, ap: 0.309, auc: 0.508, f1: 0.255, acc: 0.667
+2025-08-27 10:17:54: Epoch: 035 Train Loss: 0.077, ap: 0.938, auc: 0.957, f1: 0.871, acc: 0.885
+2025-08-27 10:17:54: Epoch: 035 New best VALIDATION metrics
+2025-08-27 10:17:54: Epoch: 035 Test results, ap: 0.307, auc: 0.548, f1: 0.250, acc: 0.659
+2025-08-27 10:17:57: Epoch: 036 Train Loss: 0.086, ap: 0.937, auc: 0.955, f1: 0.870, acc: 0.884
+2025-08-27 10:17:58: Epoch: 036 Test results, ap: 0.320, auc: 0.516, f1: 0.259, acc: 0.675
+2025-08-27 10:18:01: Epoch: 037 Train Loss: 0.073, ap: 0.936, auc: 0.954, f1: 0.869, acc: 0.884
+2025-08-27 10:18:01: Epoch: 037 Test results, ap: 0.318, auc: 0.509, f1: 0.231, acc: 0.675
+2025-08-27 10:18:04: Epoch: 038 Train Loss: 0.076, ap: 0.934, auc: 0.957, f1: 0.867, acc: 0.882
+2025-08-27 10:18:04: Epoch: 038 Test results, ap: 0.308, auc: 0.523, f1: 0.250, acc: 0.659
+2025-08-27 10:18:07: Epoch: 039 Train Loss: 0.070, ap: 0.934, auc: 0.957, f1: 0.863, acc: 0.878
+2025-08-27 10:18:08: Epoch: 039 Test results, ap: 0.320, auc: 0.516, f1: 0.241, acc: 0.642
+2025-08-27 10:18:11: Epoch: 040 Train Loss: 0.069, ap: 0.934, auc: 0.956, f1: 0.861, acc: 0.877
+2025-08-27 10:18:11: Epoch: 040 Test results, ap: 0.323, auc: 0.525, f1: 0.218, acc: 0.650
+2025-08-27 10:18:14: Epoch: 041 Train Loss: 0.067, ap: 0.932, auc: 0.958, f1: 0.874, acc: 0.887
+2025-08-27 10:18:14: Epoch: 041 Test results, ap: 0.305, auc: 0.517, f1: 0.246, acc: 0.650
+2025-08-27 10:18:17: Epoch: 042 Train Loss: 0.060, ap: 0.933, auc: 0.958, f1: 0.871, acc: 0.885
+2025-08-27 10:18:18: Epoch: 042 Test results, ap: 0.305, auc: 0.515, f1: 0.214, acc: 0.642
+2025-08-27 10:18:21: Epoch: 043 Train Loss: 0.058, ap: 0.934, auc: 0.959, f1: 0.874, acc: 0.887
+2025-08-27 10:18:21: Epoch: 043 Test results, ap: 0.306, auc: 0.519, f1: 0.241, acc: 0.642
+2025-08-27 10:18:24: Epoch: 044 Train Loss: 0.057, ap: 0.934, auc: 0.958, f1: 0.874, acc: 0.887
+2025-08-27 10:18:24: Epoch: 044 Test results, ap: 0.303, auc: 0.517, f1: 0.241, acc: 0.642
+2025-08-27 10:18:27: Epoch: 045 Train Loss: 0.055, ap: 0.933, auc: 0.958, f1: 0.874, acc: 0.887
+2025-08-27 10:18:28: Epoch: 045 Test results, ap: 0.306, auc: 0.527, f1: 0.237, acc: 0.634
+2025-08-27 10:18:31: Epoch: 046 Train Loss: 0.054, ap: 0.933, auc: 0.957, f1: 0.880, acc: 0.892
+2025-08-27 10:18:31: Epoch: 046 Test results, ap: 0.308, auc: 0.521, f1: 0.211, acc: 0.634
+2025-08-27 10:18:34: Epoch: 047 Train Loss: 0.048, ap: 0.934, auc: 0.958, f1: 0.878, acc: 0.891
+2025-08-27 10:18:34: Epoch: 047 Test results, ap: 0.306, auc: 0.517, f1: 0.211, acc: 0.634
+2025-08-27 10:18:37: Epoch: 048 Train Loss: 0.050, ap: 0.934, auc: 0.958, f1: 0.880, acc: 0.894
+2025-08-27 10:18:38: Epoch: 048 Test results, ap: 0.306, auc: 0.518, f1: 0.214, acc: 0.642
+2025-08-27 10:18:41: Epoch: 049 Train Loss: 0.057, ap: 0.933, auc: 0.957, f1: 0.877, acc: 0.891
+2025-08-27 10:18:41: Epoch: 049 Test results, ap: 0.303, auc: 0.512, f1: 0.211, acc: 0.634
+2025-08-27 10:18:44: Epoch: 050 Train Loss: 0.052, ap: 0.935, auc: 0.959, f1: 0.883, acc: 0.896
+2025-08-27 10:18:44: Epoch: 050 Test results, ap: 0.307, auc: 0.525, f1: 0.211, acc: 0.634
+2025-08-27 10:18:44: used time 0.05h
+2025-08-27 10:18:44: Running Cross Validation 4
+2025-08-27 10:18:44: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-27 10:18:47: Epoch: 001 Train Loss: 0.691, ap: 0.465, auc: 0.515, f1: 0.000, acc: 0.557
+2025-08-27 10:18:47: Epoch: 001 New best VALIDATION metrics
+2025-08-27 10:18:48: Epoch: 001 Test results, ap: 0.248, auc: 0.547, f1: 0.000, acc: 0.756
+2025-08-27 10:18:50: Epoch: 002 Train Loss: 0.684, ap: 0.539, auc: 0.605, f1: 0.512, acc: 0.603
+2025-08-27 10:18:50: Epoch: 002 New best VALIDATION metrics
+2025-08-27 10:18:51: Epoch: 002 Test results, ap: 0.234, auc: 0.509, f1: 0.316, acc: 0.577
+2025-08-27 10:18:54: Epoch: 003 Train Loss: 0.661, ap: 0.600, auc: 0.643, f1: 0.567, acc: 0.591
+2025-08-27 10:18:54: Epoch: 003 New best VALIDATION metrics
+2025-08-27 10:18:54: Epoch: 003 Test results, ap: 0.264, auc: 0.541, f1: 0.299, acc: 0.618
+2025-08-27 10:18:57: Epoch: 004 Train Loss: 0.633, ap: 0.669, auc: 0.733, f1: 0.648, acc: 0.670
+2025-08-27 10:18:57: Epoch: 004 New best VALIDATION metrics
+2025-08-27 10:18:58: Epoch: 004 Test results, ap: 0.286, auc: 0.534, f1: 0.325, acc: 0.561
+2025-08-27 10:19:01: Epoch: 005 Train Loss: 0.581, ap: 0.686, auc: 0.742, f1: 0.659, acc: 0.684
+2025-08-27 10:19:01: Epoch: 005 New best VALIDATION metrics
+2025-08-27 10:19:01: Epoch: 005 Test results, ap: 0.290, auc: 0.579, f1: 0.250, acc: 0.707
+2025-08-27 10:19:04: Epoch: 006 Train Loss: 0.512, ap: 0.754, auc: 0.803, f1: 0.684, acc: 0.714
+2025-08-27 10:19:04: Epoch: 006 New best VALIDATION metrics
+2025-08-27 10:19:04: Epoch: 006 Test results, ap: 0.301, auc: 0.608, f1: 0.245, acc: 0.699
+2025-08-27 10:19:07: Epoch: 007 Train Loss: 0.462, ap: 0.799, auc: 0.852, f1: 0.747, acc: 0.762
+2025-08-27 10:19:07: Epoch: 007 New best VALIDATION metrics
+2025-08-27 10:19:08: Epoch: 007 Test results, ap: 0.311, auc: 0.559, f1: 0.328, acc: 0.634
+2025-08-27 10:19:11: Epoch: 008 Train Loss: 0.427, ap: 0.834, auc: 0.875, f1: 0.774, acc: 0.788
+2025-08-27 10:19:11: Epoch: 008 New best VALIDATION metrics
+2025-08-27 10:19:11: Epoch: 008 Test results, ap: 0.317, auc: 0.578, f1: 0.341, acc: 0.561
+2025-08-27 10:19:14: Epoch: 009 Train Loss: 0.383, ap: 0.870, auc: 0.897, f1: 0.816, acc: 0.831
+2025-08-27 10:19:14: Epoch: 009 New best VALIDATION metrics
+2025-08-27 10:19:14: Epoch: 009 Test results, ap: 0.367, auc: 0.580, f1: 0.400, acc: 0.659
+2025-08-27 10:19:17: Epoch: 010 Train Loss: 0.347, ap: 0.886, auc: 0.903, f1: 0.807, acc: 0.829
+2025-08-27 10:19:17: Epoch: 010 New best VALIDATION metrics
+2025-08-27 10:19:18: Epoch: 010 Test results, ap: 0.407, auc: 0.586, f1: 0.299, acc: 0.618
+2025-08-27 10:19:21: Epoch: 011 Train Loss: 0.304, ap: 0.906, auc: 0.922, f1: 0.839, acc: 0.855
+2025-08-27 10:19:21: Epoch: 011 New best VALIDATION metrics
+2025-08-27 10:19:21: Epoch: 011 Test results, ap: 0.390, auc: 0.605, f1: 0.297, acc: 0.577
+2025-08-27 10:19:21: Epoch: 011 New best TEST metrics
+2025-08-27 10:19:24: Epoch: 012 Train Loss: 0.280, ap: 0.891, auc: 0.910, f1: 0.807, acc: 0.822
+2025-08-27 10:19:24: Epoch: 012 Test results, ap: 0.321, auc: 0.594, f1: 0.310, acc: 0.602
+2025-08-27 10:19:27: Epoch: 013 Train Loss: 0.254, ap: 0.909, auc: 0.927, f1: 0.821, acc: 0.840
+2025-08-27 10:19:27: Epoch: 013 New best VALIDATION metrics
+2025-08-27 10:19:28: Epoch: 013 Test results, ap: 0.376, auc: 0.613, f1: 0.299, acc: 0.618
+2025-08-27 10:19:30: Epoch: 014 Train Loss: 0.243, ap: 0.916, auc: 0.930, f1: 0.843, acc: 0.855
+2025-08-27 10:19:30: Epoch: 014 New best VALIDATION metrics
+2025-08-27 10:19:31: Epoch: 014 Test results, ap: 0.333, auc: 0.611, f1: 0.290, acc: 0.602
+2025-08-27 10:19:34: Epoch: 015 Train Loss: 0.229, ap: 0.914, auc: 0.933, f1: 0.844, acc: 0.859
+2025-08-27 10:19:34: Epoch: 015 New best VALIDATION metrics
+2025-08-27 10:19:34: Epoch: 015 Test results, ap: 0.346, auc: 0.615, f1: 0.290, acc: 0.602
+2025-08-27 10:19:37: Epoch: 016 Train Loss: 0.217, ap: 0.920, auc: 0.934, f1: 0.837, acc: 0.854
+2025-08-27 10:19:37: Epoch: 016 New best VALIDATION metrics
+2025-08-27 10:19:37: Epoch: 016 Test results, ap: 0.352, auc: 0.625, f1: 0.286, acc: 0.593
+2025-08-27 10:19:40: Epoch: 017 Train Loss: 0.199, ap: 0.916, auc: 0.931, f1: 0.837, acc: 0.855
+2025-08-27 10:19:41: Epoch: 017 Test results, ap: 0.335, auc: 0.605, f1: 0.314, acc: 0.610
+2025-08-27 10:19:44: Epoch: 018 Train Loss: 0.197, ap: 0.925, auc: 0.938, f1: 0.853, acc: 0.864
+2025-08-27 10:19:44: Epoch: 018 New best VALIDATION metrics
+2025-08-27 10:19:44: Epoch: 018 Test results, ap: 0.351, auc: 0.618, f1: 0.306, acc: 0.593
+2025-08-27 10:19:47: Epoch: 019 Train Loss: 0.187, ap: 0.935, auc: 0.943, f1: 0.866, acc: 0.880
+2025-08-27 10:19:47: Epoch: 019 New best VALIDATION metrics
+2025-08-27 10:19:47: Epoch: 019 Test results, ap: 0.329, auc: 0.615, f1: 0.319, acc: 0.618
+2025-08-27 10:19:50: Epoch: 020 Train Loss: 0.168, ap: 0.928, auc: 0.940, f1: 0.848, acc: 0.861
+2025-08-27 10:19:51: Epoch: 020 Test results, ap: 0.332, auc: 0.605, f1: 0.286, acc: 0.593
+2025-08-27 10:19:54: Epoch: 021 Train Loss: 0.143, ap: 0.941, auc: 0.951, f1: 0.874, acc: 0.884
+2025-08-27 10:19:54: Epoch: 021 New best VALIDATION metrics
+2025-08-27 10:19:54: Epoch: 021 Test results, ap: 0.353, auc: 0.624, f1: 0.338, acc: 0.618
+2025-08-27 10:19:57: Epoch: 022 Train Loss: 0.125, ap: 0.944, auc: 0.954, f1: 0.867, acc: 0.878
+2025-08-27 10:19:57: Epoch: 022 New best VALIDATION metrics
+2025-08-27 10:19:57: Epoch: 022 Test results, ap: 0.343, auc: 0.617, f1: 0.366, acc: 0.634
+2025-08-27 10:20:00: Epoch: 023 Train Loss: 0.116, ap: 0.945, auc: 0.952, f1: 0.877, acc: 0.885
+2025-08-27 10:20:01: Epoch: 023 Test results, ap: 0.332, auc: 0.609, f1: 0.361, acc: 0.626
+2025-08-27 10:20:04: Epoch: 024 Train Loss: 0.115, ap: 0.943, auc: 0.952, f1: 0.879, acc: 0.889
+2025-08-27 10:20:04: Epoch: 024 Test results, ap: 0.321, auc: 0.605, f1: 0.333, acc: 0.610
+2025-08-27 10:20:07: Epoch: 025 Train Loss: 0.099, ap: 0.944, auc: 0.953, f1: 0.880, acc: 0.891
+2025-08-27 10:20:07: Epoch: 025 Test results, ap: 0.335, auc: 0.607, f1: 0.356, acc: 0.618
+2025-08-27 10:20:10: Epoch: 026 Train Loss: 0.107, ap: 0.944, auc: 0.952, f1: 0.874, acc: 0.884
+2025-08-27 10:20:11: Epoch: 026 Test results, ap: 0.333, auc: 0.606, f1: 0.361, acc: 0.626
+2025-08-27 10:20:14: Epoch: 027 Train Loss: 0.097, ap: 0.943, auc: 0.950, f1: 0.867, acc: 0.878
+2025-08-27 10:20:14: Epoch: 027 Test results, ap: 0.331, auc: 0.614, f1: 0.333, acc: 0.610
+2025-08-27 10:20:17: Epoch: 028 Train Loss: 0.091, ap: 0.939, auc: 0.949, f1: 0.859, acc: 0.869
+2025-08-27 10:20:17: Epoch: 028 Test results, ap: 0.317, auc: 0.600, f1: 0.333, acc: 0.610
+2025-08-27 10:20:20: Epoch: 029 Train Loss: 0.080, ap: 0.945, auc: 0.953, f1: 0.879, acc: 0.891
+2025-08-27 10:20:21: Epoch: 029 Test results, ap: 0.302, auc: 0.597, f1: 0.378, acc: 0.626
+2025-08-27 10:20:24: Epoch: 030 Train Loss: 0.089, ap: 0.950, auc: 0.957, f1: 0.887, acc: 0.896
+2025-08-27 10:20:24: Epoch: 030 New best VALIDATION metrics
+2025-08-27 10:20:24: Epoch: 030 Test results, ap: 0.300, auc: 0.596, f1: 0.351, acc: 0.610
+2025-08-27 10:20:27: Epoch: 031 Train Loss: 0.057, ap: 0.952, auc: 0.959, f1: 0.886, acc: 0.896
+2025-08-27 10:20:27: Epoch: 031 New best VALIDATION metrics
+2025-08-27 10:20:27: Epoch: 031 Test results, ap: 0.305, auc: 0.606, f1: 0.329, acc: 0.602
+2025-08-27 10:20:30: Epoch: 032 Train Loss: 0.053, ap: 0.951, auc: 0.958, f1: 0.894, acc: 0.903
+2025-08-27 10:20:31: Epoch: 032 Test results, ap: 0.305, auc: 0.605, f1: 0.373, acc: 0.618
+2025-08-27 10:20:34: Epoch: 033 Train Loss: 0.053, ap: 0.947, auc: 0.958, f1: 0.898, acc: 0.907
+2025-08-27 10:20:34: Epoch: 033 Test results, ap: 0.299, auc: 0.603, f1: 0.373, acc: 0.618
+2025-08-27 10:20:37: Epoch: 034 Train Loss: 0.055, ap: 0.952, auc: 0.959, f1: 0.896, acc: 0.905
+2025-08-27 10:20:37: Epoch: 034 New best VALIDATION metrics
+2025-08-27 10:20:37: Epoch: 034 Test results, ap: 0.300, auc: 0.605, f1: 0.351, acc: 0.610
+2025-08-27 10:20:40: Epoch: 035 Train Loss: 0.052, ap: 0.947, auc: 0.957, f1: 0.890, acc: 0.899
+2025-08-27 10:20:41: Epoch: 035 Test results, ap: 0.300, auc: 0.609, f1: 0.351, acc: 0.610
+2025-08-27 10:20:44: Epoch: 036 Train Loss: 0.042, ap: 0.947, auc: 0.957, f1: 0.892, acc: 0.901
+2025-08-27 10:20:44: Epoch: 036 Test results, ap: 0.295, auc: 0.607, f1: 0.351, acc: 0.610
+2025-08-27 10:20:47: Epoch: 037 Train Loss: 0.043, ap: 0.946, auc: 0.956, f1: 0.895, acc: 0.907
+2025-08-27 10:20:47: Epoch: 037 Test results, ap: 0.286, auc: 0.606, f1: 0.333, acc: 0.610
+2025-08-27 10:20:50: Epoch: 038 Train Loss: 0.053, ap: 0.947, auc: 0.957, f1: 0.884, acc: 0.894
+2025-08-27 10:20:51: Epoch: 038 Test results, ap: 0.283, auc: 0.602, f1: 0.347, acc: 0.602
+2025-08-27 10:20:54: Epoch: 039 Train Loss: 0.050, ap: 0.947, auc: 0.957, f1: 0.895, acc: 0.907
+2025-08-27 10:20:54: Epoch: 039 Test results, ap: 0.280, auc: 0.596, f1: 0.351, acc: 0.610
+2025-08-27 10:20:57: Epoch: 040 Train Loss: 0.051, ap: 0.948, auc: 0.957, f1: 0.906, acc: 0.915
+2025-08-27 10:20:57: Epoch: 040 Test results, ap: 0.284, auc: 0.598, f1: 0.373, acc: 0.618
+2025-08-27 10:21:00: Epoch: 041 Train Loss: 0.038, ap: 0.951, auc: 0.960, f1: 0.905, acc: 0.914
+2025-08-27 10:21:00: Epoch: 041 New best VALIDATION metrics
+2025-08-27 10:21:01: Epoch: 041 Test results, ap: 0.283, auc: 0.602, f1: 0.351, acc: 0.610
+2025-08-27 10:21:04: Epoch: 042 Train Loss: 0.030, ap: 0.951, auc: 0.960, f1: 0.904, acc: 0.914
+2025-08-27 10:21:04: Epoch: 042 Test results, ap: 0.282, auc: 0.603, f1: 0.368, acc: 0.610
+2025-08-27 10:21:07: Epoch: 043 Train Loss: 0.027, ap: 0.951, auc: 0.959, f1: 0.904, acc: 0.914
+2025-08-27 10:21:07: Epoch: 043 Test results, ap: 0.283, auc: 0.604, f1: 0.368, acc: 0.610
+2025-08-27 10:21:10: Epoch: 044 Train Loss: 0.027, ap: 0.951, auc: 0.959, f1: 0.899, acc: 0.908
+2025-08-27 10:21:10: Epoch: 044 Test results, ap: 0.278, auc: 0.601, f1: 0.395, acc: 0.626
+2025-08-27 10:21:13: Epoch: 045 Train Loss: 0.027, ap: 0.951, auc: 0.959, f1: 0.898, acc: 0.908
+2025-08-27 10:21:14: Epoch: 045 Test results, ap: 0.275, auc: 0.600, f1: 0.390, acc: 0.618
+2025-08-27 10:21:17: Epoch: 046 Train Loss: 0.025, ap: 0.951, auc: 0.960, f1: 0.899, acc: 0.908
+2025-08-27 10:21:17: Epoch: 046 Test results, ap: 0.274, auc: 0.599, f1: 0.385, acc: 0.610
+2025-08-27 10:21:20: Epoch: 047 Train Loss: 0.027, ap: 0.950, auc: 0.959, f1: 0.899, acc: 0.908
+2025-08-27 10:21:20: Epoch: 047 Test results, ap: 0.276, auc: 0.600, f1: 0.368, acc: 0.610
+2025-08-27 10:21:23: Epoch: 048 Train Loss: 0.023, ap: 0.947, auc: 0.959, f1: 0.898, acc: 0.908
+2025-08-27 10:21:24: Epoch: 048 Test results, ap: 0.278, auc: 0.603, f1: 0.364, acc: 0.602
+2025-08-27 10:21:27: Epoch: 049 Train Loss: 0.025, ap: 0.947, auc: 0.959, f1: 0.902, acc: 0.912
+2025-08-27 10:21:27: Epoch: 049 Test results, ap: 0.275, auc: 0.598, f1: 0.385, acc: 0.610
+2025-08-27 10:21:30: Epoch: 050 Train Loss: 0.025, ap: 0.948, auc: 0.959, f1: 0.900, acc: 0.910
+2025-08-27 10:21:30: Epoch: 050 Test results, ap: 0.276, auc: 0.603, f1: 0.364, acc: 0.602
+2025-08-27 10:21:30: used time 0.05h
+2025-08-27 10:21:30: Cross Validation Finished!
+2025-08-27 10:21:30: Best validation perform list
+[[0.93445456 0.94019997 0.87545788 0.88028169]
+ [0.94252288 0.95491433 0.88477367 0.90140843]
+ [0.94967896 0.95875704 0.88095236 0.89417988]
+ [0.93844455 0.95735419 0.87128711 0.88536155]
+ [0.95131356 0.96029806 0.90485436 0.91358024]]
+2025-08-27 10:21:30: mean: [0.943 0.954 0.883 0.895]
+2025-08-27 10:21:30: std: [0.006 0.007 0.012 0.012]
+2025-08-27 10:21:30: Best test perform list
+[[0.3264513  0.5659498  0.3018868  0.69918698]
+ [0.30655631 0.52078855 0.23999999 0.69105691]
+ [0.32996023 0.63154119 0.36619717 0.63414633]
+ [0.3638747  0.5806452  0.27118644 0.65040648]
+ [0.38973784 0.60537636 0.2972973  0.57723576]]
+2025-08-27 10:21:30: mean: [0.343 0.581 0.295 0.65 ]
+2025-08-27 10:21:30: std: [0.03  0.037 0.042 0.044]
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diff --git a/run-cls/mamba-diff-256-ce-32-0.001-50/uda_r2/training.log b/run-cls/mamba-diff-256-ce-32-0.001-50/uda_r2/training.log
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+2025-11-15 16:19:42: saving_dir: ./run-cls/mamba-diff-256-ce-32-0.001-50/uda_r2
+2025-11-15 16:19:42: Loading Training Dataset
+2025-11-15 16:19:42: Loading Validation Dataset
+2025-11-15 16:19:43: Start UDA training
+2025-11-15 16:19:51: Epoch 1 Train Loss: 0.6953 Val: ap: 0.2233 auc: 0.2608 f1: 0.4688 acc: 0.3061
+2025-11-15 16:19:51: Epoch: 1 New best VAL metrics
+2025-11-15 16:19:59: Epoch 2 Train Loss: 0.6791 Val: ap: 0.2644 auc: 0.4020 f1: 0.4000 acc: 0.4490
+2025-11-15 16:19:59: Epoch: 2 New best VAL metrics
+2025-11-15 16:20:06: Epoch 3 Train Loss: 0.6894 Val: ap: 0.2462 auc: 0.3314 f1: 0.3509 acc: 0.2449
+2025-11-15 16:20:14: Epoch 4 Train Loss: 0.6659 Val: ap: 0.2514 auc: 0.3588 f1: 0.3673 acc: 0.3673
+2025-11-15 16:20:22: Epoch 5 Train Loss: 0.6529 Val: ap: 0.2447 auc: 0.3373 f1: 0.3137 acc: 0.2857
+2025-11-15 16:20:29: Epoch 6 Train Loss: 0.6490 Val: ap: 0.2353 auc: 0.3078 f1: 0.3509 acc: 0.2449
+2025-11-15 16:20:37: Epoch 7 Train Loss: 0.6439 Val: ap: 0.2192 auc: 0.2451 f1: 0.4688 acc: 0.3061
+2025-11-15 16:20:45: Epoch 8 Train Loss: 0.6036 Val: ap: 0.2295 auc: 0.2686 f1: 0.4688 acc: 0.3061
+2025-11-15 16:20:52: Epoch 9 Train Loss: 0.5884 Val: ap: 0.2311 auc: 0.2647 f1: 0.4688 acc: 0.3061
+2025-11-15 16:21:00: Epoch 10 Train Loss: 0.5642 Val: ap: 0.2338 auc: 0.2569 f1: 0.4688 acc: 0.3061
+2025-11-15 16:21:08: Epoch 11 Train Loss: 0.5441 Val: ap: 0.2273 auc: 0.2412 f1: 0.4688 acc: 0.3061
+2025-11-15 16:21:15: Epoch 12 Train Loss: 0.5161 Val: ap: 0.2431 auc: 0.2627 f1: 0.4688 acc: 0.3061
+2025-11-15 16:21:23: Epoch 13 Train Loss: 0.4956 Val: ap: 0.2430 auc: 0.2510 f1: 0.4688 acc: 0.3061
+2025-11-15 16:21:30: Epoch 14 Train Loss: 0.4777 Val: ap: 0.2403 auc: 0.2431 f1: 0.4688 acc: 0.3061
+2025-11-15 16:21:38: Epoch 15 Train Loss: 0.4588 Val: ap: 0.2151 auc: 0.2333 f1: 0.4688 acc: 0.3061
+2025-11-15 16:21:46: Epoch 16 Train Loss: 0.4272 Val: ap: 0.2138 auc: 0.2353 f1: 0.4688 acc: 0.3061
+2025-11-15 16:21:54: Epoch 17 Train Loss: 0.4219 Val: ap: 0.2094 auc: 0.2245 f1: 0.4688 acc: 0.3061
+2025-11-15 16:22:02: Epoch 18 Train Loss: 0.3933 Val: ap: 0.2087 auc: 0.2216 f1: 0.4688 acc: 0.3061
+2025-11-15 16:22:09: Epoch 19 Train Loss: 0.3967 Val: ap: 0.2080 auc: 0.2186 f1: 0.4688 acc: 0.3061
+2025-11-15 16:22:17: Epoch 20 Train Loss: 0.3756 Val: ap: 0.2104 auc: 0.2294 f1: 0.4688 acc: 0.3061
+2025-11-15 16:22:24: Epoch 21 Train Loss: 0.3531 Val: ap: 0.2086 auc: 0.2225 f1: 0.4688 acc: 0.3061
+2025-11-15 16:22:32: Epoch 22 Train Loss: 0.3347 Val: ap: 0.2109 auc: 0.2343 f1: 0.4688 acc: 0.3061
+2025-11-15 16:22:40: Epoch 23 Train Loss: 0.3378 Val: ap: 0.2128 auc: 0.2431 f1: 0.4688 acc: 0.3061
+2025-11-15 16:22:47: Epoch 24 Train Loss: 0.3178 Val: ap: 0.2142 auc: 0.2471 f1: 0.4688 acc: 0.3061
+2025-11-15 16:22:55: Epoch 25 Train Loss: 0.3043 Val: ap: 0.2120 auc: 0.2392 f1: 0.4688 acc: 0.3061
+2025-11-15 16:23:02: Epoch 26 Train Loss: 0.3095 Val: ap: 0.2115 auc: 0.2382 f1: 0.4688 acc: 0.3061
+2025-11-15 16:23:10: Epoch 27 Train Loss: 0.2831 Val: ap: 0.2101 auc: 0.2324 f1: 0.4688 acc: 0.3061
+2025-11-15 16:23:18: Epoch 28 Train Loss: 0.2844 Val: ap: 0.2108 auc: 0.2333 f1: 0.4688 acc: 0.3061
+2025-11-15 16:23:25: Epoch 29 Train Loss: 0.2573 Val: ap: 0.2109 auc: 0.2333 f1: 0.4688 acc: 0.3061
+2025-11-15 16:23:33: Epoch 30 Train Loss: 0.2712 Val: ap: 0.2096 auc: 0.2284 f1: 0.4688 acc: 0.3061
+2025-11-15 16:23:41: Epoch 31 Train Loss: 0.2721 Val: ap: 0.2142 auc: 0.2480 f1: 0.4688 acc: 0.3061
+2025-11-15 16:23:49: Epoch 32 Train Loss: 0.2422 Val: ap: 0.2138 auc: 0.2451 f1: 0.4688 acc: 0.3061
+2025-11-15 16:23:56: Epoch 33 Train Loss: 0.2410 Val: ap: 0.2137 auc: 0.2422 f1: 0.4688 acc: 0.3061
+2025-11-15 16:24:04: Epoch 34 Train Loss: 0.2207 Val: ap: 0.2137 auc: 0.2441 f1: 0.4688 acc: 0.3061
+2025-11-15 16:24:12: Epoch 35 Train Loss: 0.2244 Val: ap: 0.2100 auc: 0.2324 f1: 0.4688 acc: 0.3061
+2025-11-15 16:24:19: Epoch 36 Train Loss: 0.2189 Val: ap: 0.2120 auc: 0.2373 f1: 0.4688 acc: 0.3061
+2025-11-15 16:24:27: Epoch 37 Train Loss: 0.1956 Val: ap: 0.2106 auc: 0.2333 f1: 0.4688 acc: 0.3061
+2025-11-15 16:24:34: Epoch 38 Train Loss: 0.2049 Val: ap: 0.2094 auc: 0.2265 f1: 0.4688 acc: 0.3061
+2025-11-15 16:24:42: Epoch 39 Train Loss: 0.1961 Val: ap: 0.2088 auc: 0.2245 f1: 0.4688 acc: 0.3061
+2025-11-15 16:24:50: Epoch 40 Train Loss: 0.1852 Val: ap: 0.2162 auc: 0.2471 f1: 0.4688 acc: 0.3061
+2025-11-15 16:24:57: Epoch 41 Train Loss: 0.1831 Val: ap: 0.2160 auc: 0.2490 f1: 0.4688 acc: 0.3061
+2025-11-15 16:25:05: Epoch 42 Train Loss: 0.1803 Val: ap: 0.2123 auc: 0.2333 f1: 0.4688 acc: 0.3061
+2025-11-15 16:25:12: Epoch 43 Train Loss: 0.1559 Val: ap: 0.2094 auc: 0.2245 f1: 0.4688 acc: 0.3061
+2025-11-15 16:25:20: Epoch 44 Train Loss: 0.1673 Val: ap: 0.2083 auc: 0.2235 f1: 0.4688 acc: 0.3061
+2025-11-15 16:25:28: Epoch 45 Train Loss: 0.1696 Val: ap: 0.2060 auc: 0.2118 f1: 0.4688 acc: 0.3061
+2025-11-15 16:25:35: Epoch 46 Train Loss: 0.1828 Val: ap: 0.2068 auc: 0.2078 f1: 0.4688 acc: 0.3061
+2025-11-15 16:25:43: Epoch 47 Train Loss: 0.1512 Val: ap: 0.2097 auc: 0.2265 f1: 0.4688 acc: 0.3061
+2025-11-15 16:25:50: Epoch 48 Train Loss: 0.1454 Val: ap: 0.2081 auc: 0.2225 f1: 0.4688 acc: 0.3061
+2025-11-15 16:25:58: Epoch 49 Train Loss: 0.1471 Val: ap: 0.2210 auc: 0.2725 f1: 0.4688 acc: 0.3061
+2025-11-15 16:26:06: Epoch 50 Train Loss: 0.1504 Val: ap: 0.2225 auc: 0.2725 f1: 0.4688 acc: 0.3061
+2025-11-15 16:26:06: UDA training finished
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diff --git a/run-cls/mamba-mlp-256-ce-32-0.001-50/training.log b/run-cls/mamba-mlp-256-ce-32-0.001-50/training.log
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+2025-08-27 09:26:22: saving_dir: ./run-cls/mamba-mlp-256-ce-32-0.001-50
+2025-08-27 09:26:22: Loading Train Dataset
+2025-08-27 09:26:23: Loading Test Dataset
+2025-08-27 09:26:23: Running Cross Validation 0
+2025-08-27 09:26:23: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-27 09:26:27: Epoch: 001 Train Loss: 0.693, ap: 0.555, auc: 0.604, f1: 0.000, acc: 0.519
+2025-08-27 09:26:27: Epoch: 001 New best VALIDATION metrics
+2025-08-27 09:26:27: Epoch: 001 Test results, ap: 0.251, auc: 0.546, f1: 0.000, acc: 0.756
+2025-08-27 09:26:30: Epoch: 002 Train Loss: 0.691, ap: 0.614, auc: 0.630, f1: 0.564, acc: 0.616
+2025-08-27 09:26:30: Epoch: 002 New best VALIDATION metrics
+2025-08-27 09:26:30: Epoch: 002 Test results, ap: 0.265, auc: 0.517, f1: 0.174, acc: 0.691
+2025-08-27 09:26:33: Epoch: 003 Train Loss: 0.697, ap: 0.626, auc: 0.626, f1: 0.227, acc: 0.569
+2025-08-27 09:26:33: Epoch: 003 New best VALIDATION metrics
+2025-08-27 09:26:33: Epoch: 003 Test results, ap: 0.277, auc: 0.496, f1: 0.000, acc: 0.756
+2025-08-27 09:26:36: Epoch: 004 Train Loss: 0.674, ap: 0.680, auc: 0.648, f1: 0.581, acc: 0.620
+2025-08-27 09:26:36: Epoch: 004 New best VALIDATION metrics
+2025-08-27 09:26:36: Epoch: 004 Test results, ap: 0.226, auc: 0.453, f1: 0.000, acc: 0.683
+2025-08-27 09:26:39: Epoch: 005 Train Loss: 0.664, ap: 0.692, auc: 0.658, f1: 0.563, acc: 0.634
+2025-08-27 09:26:39: Epoch: 005 New best VALIDATION metrics
+2025-08-27 09:26:39: Epoch: 005 Test results, ap: 0.236, auc: 0.487, f1: 0.000, acc: 0.699
+2025-08-27 09:26:42: Epoch: 006 Train Loss: 0.639, ap: 0.753, auc: 0.750, f1: 0.664, acc: 0.690
+2025-08-27 09:26:42: Epoch: 006 New best VALIDATION metrics
+2025-08-27 09:26:42: Epoch: 006 Test results, ap: 0.275, auc: 0.530, f1: 0.000, acc: 0.707
+2025-08-27 09:26:45: Epoch: 007 Train Loss: 0.663, ap: 0.720, auc: 0.703, f1: 0.605, acc: 0.660
+2025-08-27 09:26:45: Epoch: 007 Test results, ap: 0.212, auc: 0.428, f1: 0.000, acc: 0.707
+2025-08-27 09:26:48: Epoch: 008 Train Loss: 0.598, ap: 0.765, auc: 0.778, f1: 0.696, acc: 0.704
+2025-08-27 09:26:48: Epoch: 008 New best VALIDATION metrics
+2025-08-27 09:26:49: Epoch: 008 Test results, ap: 0.249, auc: 0.487, f1: 0.087, acc: 0.659
+2025-08-27 09:26:51: Epoch: 009 Train Loss: 0.539, ap: 0.764, auc: 0.793, f1: 0.707, acc: 0.699
+2025-08-27 09:26:51: Epoch: 009 New best VALIDATION metrics
+2025-08-27 09:26:52: Epoch: 009 Test results, ap: 0.234, auc: 0.457, f1: 0.230, acc: 0.618
+2025-08-27 09:26:55: Epoch: 010 Train Loss: 0.487, ap: 0.842, auc: 0.843, f1: 0.740, acc: 0.752
+2025-08-27 09:26:55: Epoch: 010 New best VALIDATION metrics
+2025-08-27 09:26:55: Epoch: 010 Test results, ap: 0.263, auc: 0.492, f1: 0.233, acc: 0.626
+2025-08-27 09:26:58: Epoch: 011 Train Loss: 0.437, ap: 0.865, auc: 0.862, f1: 0.755, acc: 0.768
+2025-08-27 09:26:58: Epoch: 011 New best VALIDATION metrics
+2025-08-27 09:26:58: Epoch: 011 Test results, ap: 0.267, auc: 0.502, f1: 0.281, acc: 0.667
+2025-08-27 09:26:58: Epoch: 011 New best TEST metrics
+2025-08-27 09:27:01: Epoch: 012 Train Loss: 0.401, ap: 0.882, auc: 0.889, f1: 0.811, acc: 0.813
+2025-08-27 09:27:01: Epoch: 012 New best VALIDATION metrics
+2025-08-27 09:27:01: Epoch: 012 Test results, ap: 0.302, auc: 0.487, f1: 0.254, acc: 0.618
+2025-08-27 09:27:01: Epoch: 012 New best TEST metrics
+2025-08-27 09:27:04: Epoch: 013 Train Loss: 0.388, ap: 0.881, auc: 0.889, f1: 0.804, acc: 0.808
+2025-08-27 09:27:04: Epoch: 013 Test results, ap: 0.331, auc: 0.515, f1: 0.271, acc: 0.650
+2025-08-27 09:27:04: Epoch: 013 New best TEST metrics
+2025-08-27 09:27:07: Epoch: 014 Train Loss: 0.370, ap: 0.890, auc: 0.899, f1: 0.803, acc: 0.808
+2025-08-27 09:27:07: Epoch: 014 New best VALIDATION metrics
+2025-08-27 09:27:07: Epoch: 014 Test results, ap: 0.358, auc: 0.520, f1: 0.291, acc: 0.683
+2025-08-27 09:27:07: Epoch: 014 New best TEST metrics
+2025-08-27 09:27:10: Epoch: 015 Train Loss: 0.356, ap: 0.911, auc: 0.914, f1: 0.824, acc: 0.827
+2025-08-27 09:27:10: Epoch: 015 New best VALIDATION metrics
+2025-08-27 09:27:11: Epoch: 015 Test results, ap: 0.358, auc: 0.514, f1: 0.281, acc: 0.667
+2025-08-27 09:27:14: Epoch: 016 Train Loss: 0.321, ap: 0.900, auc: 0.907, f1: 0.807, acc: 0.813
+2025-08-27 09:27:14: Epoch: 016 Test results, ap: 0.348, auc: 0.521, f1: 0.235, acc: 0.683
+2025-08-27 09:27:17: Epoch: 017 Train Loss: 0.306, ap: 0.908, auc: 0.915, f1: 0.829, acc: 0.833
+2025-08-27 09:27:18: Epoch: 017 Test results, ap: 0.336, auc: 0.501, f1: 0.200, acc: 0.675
+2025-08-27 09:27:21: Epoch: 018 Train Loss: 0.279, ap: 0.922, auc: 0.925, f1: 0.831, acc: 0.833
+2025-08-27 09:27:21: Epoch: 018 New best VALIDATION metrics
+2025-08-27 09:27:21: Epoch: 018 Test results, ap: 0.347, auc: 0.504, f1: 0.196, acc: 0.667
+2025-08-27 09:27:24: Epoch: 019 Train Loss: 0.271, ap: 0.931, auc: 0.931, f1: 0.832, acc: 0.838
+2025-08-27 09:27:24: Epoch: 019 New best VALIDATION metrics
+2025-08-27 09:27:24: Epoch: 019 Test results, ap: 0.329, auc: 0.510, f1: 0.208, acc: 0.691
+2025-08-27 09:27:27: Epoch: 020 Train Loss: 0.258, ap: 0.922, auc: 0.924, f1: 0.835, acc: 0.840
+2025-08-27 09:27:27: Epoch: 020 Test results, ap: 0.340, auc: 0.500, f1: 0.163, acc: 0.667
+2025-08-27 09:27:30: Epoch: 021 Train Loss: 0.226, ap: 0.935, auc: 0.935, f1: 0.857, acc: 0.861
+2025-08-27 09:27:30: Epoch: 021 New best VALIDATION metrics
+2025-08-27 09:27:30: Epoch: 021 Test results, ap: 0.345, auc: 0.508, f1: 0.170, acc: 0.683
+2025-08-27 09:27:33: Epoch: 022 Train Loss: 0.209, ap: 0.933, auc: 0.933, f1: 0.852, acc: 0.857
+2025-08-27 09:27:33: Epoch: 022 Test results, ap: 0.348, auc: 0.512, f1: 0.178, acc: 0.699
+2025-08-27 09:27:36: Epoch: 023 Train Loss: 0.192, ap: 0.929, auc: 0.929, f1: 0.850, acc: 0.856
+2025-08-27 09:27:37: Epoch: 023 Test results, ap: 0.343, auc: 0.505, f1: 0.170, acc: 0.683
+2025-08-27 09:27:39: Epoch: 024 Train Loss: 0.188, ap: 0.934, auc: 0.936, f1: 0.858, acc: 0.863
+2025-08-27 09:27:39: Epoch: 024 New best VALIDATION metrics
+2025-08-27 09:27:40: Epoch: 024 Test results, ap: 0.344, auc: 0.510, f1: 0.174, acc: 0.691
+2025-08-27 09:27:42: Epoch: 025 Train Loss: 0.184, ap: 0.932, auc: 0.932, f1: 0.854, acc: 0.857
+2025-08-27 09:27:43: Epoch: 025 Test results, ap: 0.349, auc: 0.523, f1: 0.182, acc: 0.707
+2025-08-27 09:27:46: Epoch: 026 Train Loss: 0.178, ap: 0.936, auc: 0.938, f1: 0.871, acc: 0.875
+2025-08-27 09:27:46: Epoch: 026 New best VALIDATION metrics
+2025-08-27 09:27:46: Epoch: 026 Test results, ap: 0.345, auc: 0.515, f1: 0.174, acc: 0.691
+2025-08-27 09:27:49: Epoch: 027 Train Loss: 0.164, ap: 0.937, auc: 0.937, f1: 0.860, acc: 0.861
+2025-08-27 09:27:49: Epoch: 027 Test results, ap: 0.342, auc: 0.501, f1: 0.170, acc: 0.683
+2025-08-27 09:27:52: Epoch: 028 Train Loss: 0.158, ap: 0.936, auc: 0.936, f1: 0.845, acc: 0.850
+2025-08-27 09:27:52: Epoch: 028 Test results, ap: 0.353, auc: 0.529, f1: 0.167, acc: 0.675
+2025-08-27 09:27:52: Epoch: 028 New best TEST metrics
+2025-08-27 09:27:55: Epoch: 029 Train Loss: 0.160, ap: 0.937, auc: 0.939, f1: 0.863, acc: 0.868
+2025-08-27 09:27:55: Epoch: 029 New best VALIDATION metrics
+2025-08-27 09:27:55: Epoch: 029 Test results, ap: 0.337, auc: 0.490, f1: 0.167, acc: 0.675
+2025-08-27 09:27:58: Epoch: 030 Train Loss: 0.147, ap: 0.934, auc: 0.936, f1: 0.853, acc: 0.854
+2025-08-27 09:27:58: Epoch: 030 Test results, ap: 0.344, auc: 0.510, f1: 0.160, acc: 0.659
+2025-08-27 09:28:01: Epoch: 031 Train Loss: 0.129, ap: 0.943, auc: 0.943, f1: 0.868, acc: 0.873
+2025-08-27 09:28:01: Epoch: 031 New best VALIDATION metrics
+2025-08-27 09:28:01: Epoch: 031 Test results, ap: 0.314, auc: 0.488, f1: 0.170, acc: 0.683
+2025-08-27 09:28:04: Epoch: 032 Train Loss: 0.117, ap: 0.941, auc: 0.943, f1: 0.869, acc: 0.873
+2025-08-27 09:28:04: Epoch: 032 Test results, ap: 0.325, auc: 0.484, f1: 0.170, acc: 0.683
+2025-08-27 09:28:07: Epoch: 033 Train Loss: 0.112, ap: 0.941, auc: 0.943, f1: 0.867, acc: 0.871
+2025-08-27 09:28:08: Epoch: 033 Test results, ap: 0.318, auc: 0.506, f1: 0.167, acc: 0.675
+2025-08-27 09:28:10: Epoch: 034 Train Loss: 0.107, ap: 0.943, auc: 0.944, f1: 0.876, acc: 0.882
+2025-08-27 09:28:10: Epoch: 034 New best VALIDATION metrics
+2025-08-27 09:28:11: Epoch: 034 Test results, ap: 0.286, auc: 0.503, f1: 0.163, acc: 0.667
+2025-08-27 09:28:13: Epoch: 035 Train Loss: 0.112, ap: 0.943, auc: 0.944, f1: 0.873, acc: 0.877
+2025-08-27 09:28:14: Epoch: 035 Test results, ap: 0.301, auc: 0.505, f1: 0.192, acc: 0.659
+2025-08-27 09:28:17: Epoch: 036 Train Loss: 0.096, ap: 0.944, auc: 0.945, f1: 0.879, acc: 0.886
+2025-08-27 09:28:17: Epoch: 036 New best VALIDATION metrics
+2025-08-27 09:28:17: Epoch: 036 Test results, ap: 0.278, auc: 0.508, f1: 0.167, acc: 0.675
+2025-08-27 09:28:20: Epoch: 037 Train Loss: 0.098, ap: 0.941, auc: 0.943, f1: 0.876, acc: 0.882
+2025-08-27 09:28:20: Epoch: 037 Test results, ap: 0.304, auc: 0.518, f1: 0.167, acc: 0.675
+2025-08-27 09:28:23: Epoch: 038 Train Loss: 0.099, ap: 0.939, auc: 0.942, f1: 0.866, acc: 0.871
+2025-08-27 09:28:23: Epoch: 038 Test results, ap: 0.294, auc: 0.505, f1: 0.192, acc: 0.659
+2025-08-27 09:28:26: Epoch: 039 Train Loss: 0.099, ap: 0.939, auc: 0.943, f1: 0.872, acc: 0.879
+2025-08-27 09:28:26: Epoch: 039 Test results, ap: 0.330, auc: 0.529, f1: 0.192, acc: 0.659
+2025-08-27 09:28:29: Epoch: 040 Train Loss: 0.093, ap: 0.943, auc: 0.945, f1: 0.885, acc: 0.891
+2025-08-27 09:28:29: Epoch: 040 Test results, ap: 0.297, auc: 0.518, f1: 0.196, acc: 0.667
+2025-08-27 09:28:32: Epoch: 041 Train Loss: 0.079, ap: 0.943, auc: 0.946, f1: 0.883, acc: 0.889
+2025-08-27 09:28:32: Epoch: 041 New best VALIDATION metrics
+2025-08-27 09:28:32: Epoch: 041 Test results, ap: 0.282, auc: 0.514, f1: 0.196, acc: 0.667
+2025-08-27 09:28:35: Epoch: 042 Train Loss: 0.078, ap: 0.943, auc: 0.946, f1: 0.876, acc: 0.882
+2025-08-27 09:28:36: Epoch: 042 Test results, ap: 0.296, auc: 0.522, f1: 0.192, acc: 0.659
+2025-08-27 09:28:38: Epoch: 043 Train Loss: 0.073, ap: 0.942, auc: 0.945, f1: 0.876, acc: 0.882
+2025-08-27 09:28:39: Epoch: 043 Test results, ap: 0.298, auc: 0.520, f1: 0.185, acc: 0.642
+2025-08-27 09:28:41: Epoch: 044 Train Loss: 0.072, ap: 0.942, auc: 0.946, f1: 0.877, acc: 0.882
+2025-08-27 09:28:42: Epoch: 044 Test results, ap: 0.304, auc: 0.520, f1: 0.192, acc: 0.659
+2025-08-27 09:28:45: Epoch: 045 Train Loss: 0.073, ap: 0.941, auc: 0.946, f1: 0.883, acc: 0.891
+2025-08-27 09:28:45: Epoch: 045 Test results, ap: 0.291, auc: 0.515, f1: 0.182, acc: 0.634
+2025-08-27 09:28:48: Epoch: 046 Train Loss: 0.068, ap: 0.941, auc: 0.945, f1: 0.879, acc: 0.886
+2025-08-27 09:28:48: Epoch: 046 Test results, ap: 0.305, auc: 0.517, f1: 0.214, acc: 0.642
+2025-08-27 09:28:51: Epoch: 047 Train Loss: 0.072, ap: 0.940, auc: 0.945, f1: 0.875, acc: 0.882
+2025-08-27 09:28:51: Epoch: 047 Test results, ap: 0.303, auc: 0.529, f1: 0.222, acc: 0.659
+2025-08-27 09:28:54: Epoch: 048 Train Loss: 0.069, ap: 0.940, auc: 0.945, f1: 0.885, acc: 0.891
+2025-08-27 09:28:54: Epoch: 048 Test results, ap: 0.290, auc: 0.519, f1: 0.214, acc: 0.642
+2025-08-27 09:28:57: Epoch: 049 Train Loss: 0.065, ap: 0.940, auc: 0.945, f1: 0.881, acc: 0.887
+2025-08-27 09:28:57: Epoch: 049 Test results, ap: 0.303, auc: 0.528, f1: 0.218, acc: 0.650
+2025-08-27 09:29:00: Epoch: 050 Train Loss: 0.071, ap: 0.941, auc: 0.945, f1: 0.883, acc: 0.889
+2025-08-27 09:29:00: Epoch: 050 Test results, ap: 0.301, auc: 0.525, f1: 0.222, acc: 0.659
+2025-08-27 09:29:00: used time 0.04h
+2025-08-27 09:29:00: Running Cross Validation 1
+2025-08-27 09:29:00: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-27 09:29:03: Epoch: 001 Train Loss: 0.693, ap: 0.583, auc: 0.639, f1: 0.408, acc: 0.627
+2025-08-27 09:29:03: Epoch: 001 New best VALIDATION metrics
+2025-08-27 09:29:04: Epoch: 001 Test results, ap: 0.260, auc: 0.548, f1: 0.000, acc: 0.707
+2025-08-27 09:29:06: Epoch: 002 Train Loss: 0.688, ap: 0.584, auc: 0.649, f1: 0.603, acc: 0.629
+2025-08-27 09:29:06: Epoch: 002 New best VALIDATION metrics
+2025-08-27 09:29:07: Epoch: 002 Test results, ap: 0.259, auc: 0.548, f1: 0.320, acc: 0.585
+2025-08-27 09:29:09: Epoch: 003 Train Loss: 0.662, ap: 0.600, auc: 0.684, f1: 0.376, acc: 0.614
+2025-08-27 09:29:09: Epoch: 003 New best VALIDATION metrics
+2025-08-27 09:29:10: Epoch: 003 Test results, ap: 0.333, auc: 0.601, f1: 0.000, acc: 0.756
+2025-08-27 09:29:13: Epoch: 004 Train Loss: 0.657, ap: 0.569, auc: 0.661, f1: 0.585, acc: 0.630
+2025-08-27 09:29:13: Epoch: 004 Test results, ap: 0.331, auc: 0.590, f1: 0.205, acc: 0.748
+2025-08-27 09:29:16: Epoch: 005 Train Loss: 0.646, ap: 0.638, auc: 0.710, f1: 0.643, acc: 0.609
+2025-08-27 09:29:16: Epoch: 005 New best VALIDATION metrics
+2025-08-27 09:29:16: Epoch: 005 Test results, ap: 0.420, auc: 0.569, f1: 0.360, acc: 0.740
+2025-08-27 09:29:19: Epoch: 006 Train Loss: 0.602, ap: 0.688, auc: 0.756, f1: 0.661, acc: 0.673
+2025-08-27 09:29:19: Epoch: 006 New best VALIDATION metrics
+2025-08-27 09:29:19: Epoch: 006 Test results, ap: 0.255, auc: 0.500, f1: 0.170, acc: 0.683
+2025-08-27 09:29:22: Epoch: 007 Train Loss: 0.550, ap: 0.750, auc: 0.819, f1: 0.715, acc: 0.727
+2025-08-27 09:29:22: Epoch: 007 New best VALIDATION metrics
+2025-08-27 09:29:22: Epoch: 007 Test results, ap: 0.253, auc: 0.500, f1: 0.320, acc: 0.585
+2025-08-27 09:29:25: Epoch: 008 Train Loss: 0.506, ap: 0.694, auc: 0.794, f1: 0.682, acc: 0.713
+2025-08-27 09:29:26: Epoch: 008 Test results, ap: 0.316, auc: 0.570, f1: 0.327, acc: 0.699
+2025-08-27 09:29:28: Epoch: 009 Train Loss: 0.465, ap: 0.779, auc: 0.830, f1: 0.732, acc: 0.745
+2025-08-27 09:29:28: Epoch: 009 New best VALIDATION metrics
+2025-08-27 09:29:29: Epoch: 009 Test results, ap: 0.289, auc: 0.532, f1: 0.270, acc: 0.561
+2025-08-27 09:29:32: Epoch: 010 Train Loss: 0.442, ap: 0.825, auc: 0.867, f1: 0.753, acc: 0.775
+2025-08-27 09:29:32: Epoch: 010 New best VALIDATION metrics
+2025-08-27 09:29:32: Epoch: 010 Test results, ap: 0.267, auc: 0.519, f1: 0.286, acc: 0.593
+2025-08-27 09:29:35: Epoch: 011 Train Loss: 0.375, ap: 0.841, auc: 0.879, f1: 0.750, acc: 0.782
+2025-08-27 09:29:35: Epoch: 011 New best VALIDATION metrics
+2025-08-27 09:29:35: Epoch: 011 Test results, ap: 0.300, auc: 0.541, f1: 0.226, acc: 0.667
+2025-08-27 09:29:35: Epoch: 011 New best TEST metrics
+2025-08-27 09:29:38: Epoch: 012 Train Loss: 0.355, ap: 0.865, auc: 0.893, f1: 0.772, acc: 0.798
+2025-08-27 09:29:38: Epoch: 012 New best VALIDATION metrics
+2025-08-27 09:29:38: Epoch: 012 Test results, ap: 0.312, auc: 0.516, f1: 0.250, acc: 0.707
+2025-08-27 09:29:41: Epoch: 013 Train Loss: 0.323, ap: 0.875, auc: 0.904, f1: 0.805, acc: 0.829
+2025-08-27 09:29:41: Epoch: 013 New best VALIDATION metrics
+2025-08-27 09:29:42: Epoch: 013 Test results, ap: 0.366, auc: 0.589, f1: 0.264, acc: 0.683
+2025-08-27 09:29:42: Epoch: 013 New best TEST metrics
+2025-08-27 09:29:45: Epoch: 014 Train Loss: 0.309, ap: 0.872, auc: 0.904, f1: 0.803, acc: 0.831
+2025-08-27 09:29:45: Epoch: 014 Test results, ap: 0.379, auc: 0.600, f1: 0.267, acc: 0.732
+2025-08-27 09:29:45: Epoch: 014 New best TEST metrics
+2025-08-27 09:29:48: Epoch: 015 Train Loss: 0.285, ap: 0.891, auc: 0.915, f1: 0.794, acc: 0.826
+2025-08-27 09:29:48: Epoch: 015 New best VALIDATION metrics
+2025-08-27 09:29:48: Epoch: 015 Test results, ap: 0.407, auc: 0.614, f1: 0.293, acc: 0.764
+2025-08-27 09:29:48: Epoch: 015 New best TEST metrics
+2025-08-27 09:29:51: Epoch: 016 Train Loss: 0.261, ap: 0.901, auc: 0.924, f1: 0.818, acc: 0.838
+2025-08-27 09:29:51: Epoch: 016 New best VALIDATION metrics
+2025-08-27 09:29:51: Epoch: 016 Test results, ap: 0.354, auc: 0.582, f1: 0.222, acc: 0.715
+2025-08-27 09:29:54: Epoch: 017 Train Loss: 0.224, ap: 0.895, auc: 0.917, f1: 0.796, acc: 0.826
+2025-08-27 09:29:55: Epoch: 017 Test results, ap: 0.376, auc: 0.591, f1: 0.250, acc: 0.707
+2025-08-27 09:29:58: Epoch: 018 Train Loss: 0.231, ap: 0.910, auc: 0.932, f1: 0.845, acc: 0.863
+2025-08-27 09:29:58: Epoch: 018 New best VALIDATION metrics
+2025-08-27 09:29:58: Epoch: 018 Test results, ap: 0.316, auc: 0.571, f1: 0.298, acc: 0.732
+2025-08-27 09:30:01: Epoch: 019 Train Loss: 0.206, ap: 0.906, auc: 0.933, f1: 0.818, acc: 0.843
+2025-08-27 09:30:01: Epoch: 019 Test results, ap: 0.340, auc: 0.567, f1: 0.267, acc: 0.732
+2025-08-27 09:30:04: Epoch: 020 Train Loss: 0.199, ap: 0.915, auc: 0.939, f1: 0.839, acc: 0.864
+2025-08-27 09:30:04: Epoch: 020 New best VALIDATION metrics
+2025-08-27 09:30:04: Epoch: 020 Test results, ap: 0.294, auc: 0.544, f1: 0.190, acc: 0.724
+2025-08-27 09:30:07: Epoch: 021 Train Loss: 0.161, ap: 0.925, auc: 0.944, f1: 0.862, acc: 0.879
+2025-08-27 09:30:07: Epoch: 021 New best VALIDATION metrics
+2025-08-27 09:30:07: Epoch: 021 Test results, ap: 0.334, auc: 0.568, f1: 0.190, acc: 0.724
+2025-08-27 09:30:10: Epoch: 022 Train Loss: 0.140, ap: 0.924, auc: 0.946, f1: 0.869, acc: 0.886
+2025-08-27 09:30:10: Epoch: 022 New best VALIDATION metrics
+2025-08-27 09:30:11: Epoch: 022 Test results, ap: 0.330, auc: 0.550, f1: 0.244, acc: 0.748
+2025-08-27 09:30:13: Epoch: 023 Train Loss: 0.136, ap: 0.924, auc: 0.946, f1: 0.855, acc: 0.877
+2025-08-27 09:30:13: Epoch: 023 New best VALIDATION metrics
+2025-08-27 09:30:14: Epoch: 023 Test results, ap: 0.320, auc: 0.545, f1: 0.233, acc: 0.732
+2025-08-27 09:30:17: Epoch: 024 Train Loss: 0.126, ap: 0.930, auc: 0.949, f1: 0.866, acc: 0.884
+2025-08-27 09:30:17: Epoch: 024 New best VALIDATION metrics
+2025-08-27 09:30:17: Epoch: 024 Test results, ap: 0.335, auc: 0.533, f1: 0.233, acc: 0.732
+2025-08-27 09:30:20: Epoch: 025 Train Loss: 0.120, ap: 0.927, auc: 0.949, f1: 0.869, acc: 0.884
+2025-08-27 09:30:20: Epoch: 025 Test results, ap: 0.274, auc: 0.509, f1: 0.222, acc: 0.715
+2025-08-27 09:30:23: Epoch: 026 Train Loss: 0.122, ap: 0.931, auc: 0.952, f1: 0.862, acc: 0.882
+2025-08-27 09:30:23: Epoch: 026 New best VALIDATION metrics
+2025-08-27 09:30:23: Epoch: 026 Test results, ap: 0.319, auc: 0.551, f1: 0.227, acc: 0.724
+2025-08-27 09:30:26: Epoch: 027 Train Loss: 0.110, ap: 0.927, auc: 0.949, f1: 0.847, acc: 0.873
+2025-08-27 09:30:27: Epoch: 027 Test results, ap: 0.305, auc: 0.547, f1: 0.227, acc: 0.724
+2025-08-27 09:30:30: Epoch: 028 Train Loss: 0.110, ap: 0.937, auc: 0.953, f1: 0.860, acc: 0.882
+2025-08-27 09:30:30: Epoch: 028 New best VALIDATION metrics
+2025-08-27 09:30:30: Epoch: 028 Test results, ap: 0.326, auc: 0.554, f1: 0.195, acc: 0.732
+2025-08-27 09:30:33: Epoch: 029 Train Loss: 0.104, ap: 0.933, auc: 0.952, f1: 0.863, acc: 0.884
+2025-08-27 09:30:33: Epoch: 029 Test results, ap: 0.304, auc: 0.531, f1: 0.233, acc: 0.732
+2025-08-27 09:30:36: Epoch: 030 Train Loss: 0.102, ap: 0.929, auc: 0.951, f1: 0.868, acc: 0.886
+2025-08-27 09:30:36: Epoch: 030 Test results, ap: 0.274, auc: 0.517, f1: 0.186, acc: 0.715
+2025-08-27 09:30:39: Epoch: 031 Train Loss: 0.083, ap: 0.933, auc: 0.955, f1: 0.875, acc: 0.893
+2025-08-27 09:30:39: Epoch: 031 Test results, ap: 0.272, auc: 0.515, f1: 0.208, acc: 0.691
+2025-08-27 09:30:42: Epoch: 032 Train Loss: 0.070, ap: 0.937, auc: 0.956, f1: 0.866, acc: 0.887
+2025-08-27 09:30:42: Epoch: 032 New best VALIDATION metrics
+2025-08-27 09:30:43: Epoch: 032 Test results, ap: 0.308, auc: 0.536, f1: 0.213, acc: 0.699
+2025-08-27 09:30:46: Epoch: 033 Train Loss: 0.068, ap: 0.937, auc: 0.956, f1: 0.875, acc: 0.893
+2025-08-27 09:30:46: Epoch: 033 Test results, ap: 0.280, auc: 0.535, f1: 0.163, acc: 0.667
+2025-08-27 09:30:49: Epoch: 034 Train Loss: 0.065, ap: 0.937, auc: 0.956, f1: 0.877, acc: 0.896
+2025-08-27 09:30:49: Epoch: 034 Test results, ap: 0.282, auc: 0.538, f1: 0.200, acc: 0.675
+2025-08-27 09:30:52: Epoch: 035 Train Loss: 0.067, ap: 0.936, auc: 0.955, f1: 0.863, acc: 0.886
+2025-08-27 09:30:52: Epoch: 035 Test results, ap: 0.308, auc: 0.543, f1: 0.196, acc: 0.667
+2025-08-27 09:30:55: Epoch: 036 Train Loss: 0.059, ap: 0.939, auc: 0.957, f1: 0.871, acc: 0.891
+2025-08-27 09:30:55: Epoch: 036 New best VALIDATION metrics
+2025-08-27 09:30:56: Epoch: 036 Test results, ap: 0.307, auc: 0.544, f1: 0.192, acc: 0.659
+2025-08-27 09:30:59: Epoch: 037 Train Loss: 0.063, ap: 0.939, auc: 0.956, f1: 0.872, acc: 0.891
+2025-08-27 09:30:59: Epoch: 037 Test results, ap: 0.281, auc: 0.550, f1: 0.192, acc: 0.659
+2025-08-27 09:31:02: Epoch: 038 Train Loss: 0.060, ap: 0.935, auc: 0.957, f1: 0.872, acc: 0.893
+2025-08-27 09:31:02: Epoch: 038 Test results, ap: 0.269, auc: 0.530, f1: 0.214, acc: 0.642
+2025-08-27 09:31:05: Epoch: 039 Train Loss: 0.060, ap: 0.937, auc: 0.957, f1: 0.872, acc: 0.893
+2025-08-27 09:31:05: Epoch: 039 Test results, ap: 0.290, auc: 0.544, f1: 0.182, acc: 0.634
+2025-08-27 09:31:08: Epoch: 040 Train Loss: 0.057, ap: 0.936, auc: 0.956, f1: 0.879, acc: 0.898
+2025-08-27 09:31:09: Epoch: 040 Test results, ap: 0.283, auc: 0.531, f1: 0.211, acc: 0.634
+2025-08-27 09:31:12: Epoch: 041 Train Loss: 0.049, ap: 0.939, auc: 0.957, f1: 0.876, acc: 0.896
+2025-08-27 09:31:12: Epoch: 041 New best VALIDATION metrics
+2025-08-27 09:31:12: Epoch: 041 Test results, ap: 0.316, auc: 0.542, f1: 0.218, acc: 0.650
+2025-08-27 09:31:15: Epoch: 042 Train Loss: 0.046, ap: 0.940, auc: 0.958, f1: 0.873, acc: 0.894
+2025-08-27 09:31:15: Epoch: 042 New best VALIDATION metrics
+2025-08-27 09:31:15: Epoch: 042 Test results, ap: 0.320, auc: 0.553, f1: 0.214, acc: 0.642
+2025-08-27 09:31:18: Epoch: 043 Train Loss: 0.043, ap: 0.940, auc: 0.958, f1: 0.874, acc: 0.894
+2025-08-27 09:31:18: Epoch: 043 Test results, ap: 0.317, auc: 0.548, f1: 0.214, acc: 0.642
+2025-08-27 09:31:21: Epoch: 044 Train Loss: 0.043, ap: 0.941, auc: 0.958, f1: 0.885, acc: 0.903
+2025-08-27 09:31:21: Epoch: 044 New best VALIDATION metrics
+2025-08-27 09:31:22: Epoch: 044 Test results, ap: 0.289, auc: 0.547, f1: 0.214, acc: 0.642
+2025-08-27 09:31:24: Epoch: 045 Train Loss: 0.042, ap: 0.941, auc: 0.958, f1: 0.877, acc: 0.896
+2025-08-27 09:31:25: Epoch: 045 Test results, ap: 0.288, auc: 0.546, f1: 0.214, acc: 0.642
+2025-08-27 09:31:28: Epoch: 046 Train Loss: 0.044, ap: 0.941, auc: 0.958, f1: 0.878, acc: 0.898
+2025-08-27 09:31:28: Epoch: 046 Test results, ap: 0.305, auc: 0.547, f1: 0.214, acc: 0.642
+2025-08-27 09:31:31: Epoch: 047 Train Loss: 0.039, ap: 0.941, auc: 0.958, f1: 0.881, acc: 0.900
+2025-08-27 09:31:31: Epoch: 047 Test results, ap: 0.290, auc: 0.549, f1: 0.241, acc: 0.642
+2025-08-27 09:31:34: Epoch: 048 Train Loss: 0.040, ap: 0.941, auc: 0.958, f1: 0.874, acc: 0.894
+2025-08-27 09:31:34: Epoch: 048 New best VALIDATION metrics
+2025-08-27 09:31:34: Epoch: 048 Test results, ap: 0.290, auc: 0.550, f1: 0.276, acc: 0.659
+2025-08-27 09:31:37: Epoch: 049 Train Loss: 0.040, ap: 0.941, auc: 0.957, f1: 0.872, acc: 0.893
+2025-08-27 09:31:38: Epoch: 049 Test results, ap: 0.316, auc: 0.547, f1: 0.211, acc: 0.634
+2025-08-27 09:31:41: Epoch: 050 Train Loss: 0.037, ap: 0.942, auc: 0.958, f1: 0.874, acc: 0.894
+2025-08-27 09:31:41: Epoch: 050 New best VALIDATION metrics
+2025-08-27 09:31:41: Epoch: 050 Test results, ap: 0.321, auc: 0.557, f1: 0.214, acc: 0.642
+2025-08-27 09:31:41: used time 0.04h
+2025-08-27 09:31:41: Running Cross Validation 2
+2025-08-27 09:31:41: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-27 09:31:44: Epoch: 001 Train Loss: 0.694, ap: 0.555, auc: 0.633, f1: 0.612, acc: 0.441
+2025-08-27 09:31:44: Epoch: 001 New best VALIDATION metrics
+2025-08-27 09:31:44: Epoch: 001 Test results, ap: 0.226, auc: 0.475, f1: 0.392, acc: 0.244
+2025-08-27 09:31:47: Epoch: 002 Train Loss: 0.693, ap: 0.559, auc: 0.625, f1: 0.573, acc: 0.549
+2025-08-27 09:31:48: Epoch: 002 Test results, ap: 0.245, auc: 0.537, f1: 0.392, acc: 0.244
+2025-08-27 09:31:51: Epoch: 003 Train Loss: 0.675, ap: 0.585, auc: 0.653, f1: 0.505, acc: 0.647
+2025-08-27 09:31:51: Epoch: 003 New best VALIDATION metrics
+2025-08-27 09:31:51: Epoch: 003 Test results, ap: 0.243, auc: 0.506, f1: 0.283, acc: 0.463
+2025-08-27 09:31:54: Epoch: 004 Train Loss: 0.660, ap: 0.649, auc: 0.675, f1: 0.587, acc: 0.660
+2025-08-27 09:31:54: Epoch: 004 New best VALIDATION metrics
+2025-08-27 09:31:54: Epoch: 004 Test results, ap: 0.246, auc: 0.476, f1: 0.196, acc: 0.667
+2025-08-27 09:31:57: Epoch: 005 Train Loss: 0.651, ap: 0.666, auc: 0.701, f1: 0.585, acc: 0.654
+2025-08-27 09:31:57: Epoch: 005 New best VALIDATION metrics
+2025-08-27 09:31:57: Epoch: 005 Test results, ap: 0.239, auc: 0.491, f1: 0.306, acc: 0.593
+2025-08-27 09:32:00: Epoch: 006 Train Loss: 0.630, ap: 0.723, auc: 0.768, f1: 0.657, acc: 0.707
+2025-08-27 09:32:00: Epoch: 006 New best VALIDATION metrics
+2025-08-27 09:32:01: Epoch: 006 Test results, ap: 0.247, auc: 0.488, f1: 0.333, acc: 0.512
+2025-08-27 09:32:04: Epoch: 007 Train Loss: 0.582, ap: 0.769, auc: 0.813, f1: 0.680, acc: 0.737
+2025-08-27 09:32:04: Epoch: 007 New best VALIDATION metrics
+2025-08-27 09:32:04: Epoch: 007 Test results, ap: 0.222, auc: 0.450, f1: 0.302, acc: 0.512
+2025-08-27 09:32:07: Epoch: 008 Train Loss: 0.536, ap: 0.757, auc: 0.801, f1: 0.705, acc: 0.725
+2025-08-27 09:32:07: Epoch: 008 Test results, ap: 0.244, auc: 0.495, f1: 0.200, acc: 0.675
+2025-08-27 09:32:10: Epoch: 009 Train Loss: 0.491, ap: 0.796, auc: 0.834, f1: 0.746, acc: 0.772
+2025-08-27 09:32:10: Epoch: 009 New best VALIDATION metrics
+2025-08-27 09:32:10: Epoch: 009 Test results, ap: 0.242, auc: 0.489, f1: 0.263, acc: 0.545
+2025-08-27 09:32:13: Epoch: 010 Train Loss: 0.450, ap: 0.836, auc: 0.867, f1: 0.762, acc: 0.790
+2025-08-27 09:32:13: Epoch: 010 New best VALIDATION metrics
+2025-08-27 09:32:13: Epoch: 010 Test results, ap: 0.239, auc: 0.495, f1: 0.246, acc: 0.602
+2025-08-27 09:32:16: Epoch: 011 Train Loss: 0.379, ap: 0.856, auc: 0.883, f1: 0.768, acc: 0.797
+2025-08-27 09:32:16: Epoch: 011 New best VALIDATION metrics
+2025-08-27 09:32:17: Epoch: 011 Test results, ap: 0.229, auc: 0.465, f1: 0.261, acc: 0.585
+2025-08-27 09:32:17: Epoch: 011 New best TEST metrics
+2025-08-27 09:32:20: Epoch: 012 Train Loss: 0.350, ap: 0.857, auc: 0.884, f1: 0.764, acc: 0.776
+2025-08-27 09:32:20: Epoch: 012 New best VALIDATION metrics
+2025-08-27 09:32:20: Epoch: 012 Test results, ap: 0.235, auc: 0.476, f1: 0.294, acc: 0.415
+2025-08-27 09:32:20: Epoch: 012 New best TEST metrics
+2025-08-27 09:32:23: Epoch: 013 Train Loss: 0.319, ap: 0.887, auc: 0.903, f1: 0.782, acc: 0.811
+2025-08-27 09:32:23: Epoch: 013 New best VALIDATION metrics
+2025-08-27 09:32:23: Epoch: 013 Test results, ap: 0.221, auc: 0.403, f1: 0.253, acc: 0.472
+2025-08-27 09:32:26: Epoch: 014 Train Loss: 0.301, ap: 0.893, auc: 0.907, f1: 0.798, acc: 0.818
+2025-08-27 09:32:26: Epoch: 014 New best VALIDATION metrics
+2025-08-27 09:32:26: Epoch: 014 Test results, ap: 0.224, auc: 0.430, f1: 0.184, acc: 0.496
+2025-08-27 09:32:29: Epoch: 015 Train Loss: 0.282, ap: 0.891, auc: 0.906, f1: 0.800, acc: 0.818
+2025-08-27 09:32:30: Epoch: 015 Test results, ap: 0.216, auc: 0.422, f1: 0.209, acc: 0.569
+2025-08-27 09:32:33: Epoch: 016 Train Loss: 0.273, ap: 0.892, auc: 0.907, f1: 0.811, acc: 0.817
+2025-08-27 09:32:33: Epoch: 016 Test results, ap: 0.237, auc: 0.425, f1: 0.244, acc: 0.447
+2025-08-27 09:32:36: Epoch: 017 Train Loss: 0.251, ap: 0.903, auc: 0.914, f1: 0.813, acc: 0.832
+2025-08-27 09:32:36: Epoch: 017 New best VALIDATION metrics
+2025-08-27 09:32:36: Epoch: 017 Test results, ap: 0.253, auc: 0.438, f1: 0.219, acc: 0.537
+2025-08-27 09:32:39: Epoch: 018 Train Loss: 0.246, ap: 0.913, auc: 0.925, f1: 0.819, acc: 0.829
+2025-08-27 09:32:39: Epoch: 018 New best VALIDATION metrics
+2025-08-27 09:32:39: Epoch: 018 Test results, ap: 0.248, auc: 0.453, f1: 0.256, acc: 0.480
+2025-08-27 09:32:42: Epoch: 019 Train Loss: 0.210, ap: 0.896, auc: 0.915, f1: 0.823, acc: 0.841
+2025-08-27 09:32:42: Epoch: 019 Test results, ap: 0.240, auc: 0.449, f1: 0.228, acc: 0.504
+2025-08-27 09:32:45: Epoch: 020 Train Loss: 0.221, ap: 0.916, auc: 0.926, f1: 0.830, acc: 0.847
+2025-08-27 09:32:45: Epoch: 020 New best VALIDATION metrics
+2025-08-27 09:32:46: Epoch: 020 Test results, ap: 0.244, auc: 0.466, f1: 0.209, acc: 0.569
+2025-08-27 09:32:49: Epoch: 021 Train Loss: 0.182, ap: 0.926, auc: 0.934, f1: 0.850, acc: 0.864
+2025-08-27 09:32:49: Epoch: 021 New best VALIDATION metrics
+2025-08-27 09:32:49: Epoch: 021 Test results, ap: 0.251, auc: 0.460, f1: 0.194, acc: 0.528
+2025-08-27 09:32:49: Epoch: 021 New best TEST metrics
+2025-08-27 09:32:52: Epoch: 022 Train Loss: 0.164, ap: 0.923, auc: 0.932, f1: 0.843, acc: 0.855
+2025-08-27 09:32:52: Epoch: 022 Test results, ap: 0.248, auc: 0.469, f1: 0.229, acc: 0.561
+2025-08-27 09:32:52: Epoch: 022 New best TEST metrics
+2025-08-27 09:32:55: Epoch: 023 Train Loss: 0.154, ap: 0.927, auc: 0.935, f1: 0.853, acc: 0.864
+2025-08-27 09:32:55: Epoch: 023 New best VALIDATION metrics
+2025-08-27 09:32:55: Epoch: 023 Test results, ap: 0.241, auc: 0.465, f1: 0.189, acc: 0.512
+2025-08-27 09:32:58: Epoch: 024 Train Loss: 0.137, ap: 0.921, auc: 0.933, f1: 0.848, acc: 0.859
+2025-08-27 09:32:58: Epoch: 024 Test results, ap: 0.237, auc: 0.459, f1: 0.189, acc: 0.512
+2025-08-27 09:33:01: Epoch: 025 Train Loss: 0.138, ap: 0.931, auc: 0.939, f1: 0.850, acc: 0.866
+2025-08-27 09:33:01: Epoch: 025 New best VALIDATION metrics
+2025-08-27 09:33:02: Epoch: 025 Test results, ap: 0.245, auc: 0.466, f1: 0.232, acc: 0.569
+2025-08-27 09:33:05: Epoch: 026 Train Loss: 0.133, ap: 0.918, auc: 0.928, f1: 0.827, acc: 0.848
+2025-08-27 09:33:05: Epoch: 026 Test results, ap: 0.245, auc: 0.482, f1: 0.219, acc: 0.593
+2025-08-27 09:33:05: Epoch: 026 New best TEST metrics
+2025-08-27 09:33:08: Epoch: 027 Train Loss: 0.125, ap: 0.932, auc: 0.940, f1: 0.866, acc: 0.878
+2025-08-27 09:33:08: Epoch: 027 New best VALIDATION metrics
+2025-08-27 09:33:08: Epoch: 027 Test results, ap: 0.242, auc: 0.466, f1: 0.213, acc: 0.520
+2025-08-27 09:33:11: Epoch: 028 Train Loss: 0.112, ap: 0.927, auc: 0.935, f1: 0.842, acc: 0.861
+2025-08-27 09:33:11: Epoch: 028 Test results, ap: 0.241, auc: 0.468, f1: 0.235, acc: 0.577
+2025-08-27 09:33:14: Epoch: 029 Train Loss: 0.108, ap: 0.926, auc: 0.936, f1: 0.858, acc: 0.873
+2025-08-27 09:33:14: Epoch: 029 Test results, ap: 0.249, auc: 0.490, f1: 0.229, acc: 0.561
+2025-08-27 09:33:14: Epoch: 029 New best TEST metrics
+2025-08-27 09:33:17: Epoch: 030 Train Loss: 0.117, ap: 0.921, auc: 0.929, f1: 0.845, acc: 0.857
+2025-08-27 09:33:18: Epoch: 030 Test results, ap: 0.245, auc: 0.483, f1: 0.194, acc: 0.528
+2025-08-27 09:33:21: Epoch: 031 Train Loss: 0.097, ap: 0.931, auc: 0.940, f1: 0.862, acc: 0.875
+2025-08-27 09:33:21: Epoch: 031 Test results, ap: 0.239, auc: 0.478, f1: 0.188, acc: 0.577
+2025-08-27 09:33:24: Epoch: 032 Train Loss: 0.084, ap: 0.929, auc: 0.939, f1: 0.864, acc: 0.875
+2025-08-27 09:33:24: Epoch: 032 Test results, ap: 0.240, auc: 0.483, f1: 0.206, acc: 0.561
+2025-08-27 09:33:27: Epoch: 033 Train Loss: 0.082, ap: 0.931, auc: 0.942, f1: 0.867, acc: 0.878
+2025-08-27 09:33:27: Epoch: 033 New best VALIDATION metrics
+2025-08-27 09:33:27: Epoch: 033 Test results, ap: 0.239, auc: 0.487, f1: 0.192, acc: 0.520
+2025-08-27 09:33:30: Epoch: 034 Train Loss: 0.074, ap: 0.930, auc: 0.941, f1: 0.868, acc: 0.878
+2025-08-27 09:33:30: Epoch: 034 Test results, ap: 0.242, auc: 0.487, f1: 0.203, acc: 0.553
+2025-08-27 09:33:33: Epoch: 035 Train Loss: 0.077, ap: 0.931, auc: 0.942, f1: 0.869, acc: 0.882
+2025-08-27 09:33:34: Epoch: 035 Test results, ap: 0.247, auc: 0.491, f1: 0.209, acc: 0.569
+2025-08-27 09:33:37: Epoch: 036 Train Loss: 0.071, ap: 0.931, auc: 0.943, f1: 0.876, acc: 0.887
+2025-08-27 09:33:37: Epoch: 036 New best VALIDATION metrics
+2025-08-27 09:33:37: Epoch: 036 Test results, ap: 0.243, auc: 0.487, f1: 0.209, acc: 0.569
+2025-08-27 09:33:40: Epoch: 037 Train Loss: 0.069, ap: 0.935, auc: 0.945, f1: 0.874, acc: 0.887
+2025-08-27 09:33:40: Epoch: 037 New best VALIDATION metrics
+2025-08-27 09:33:40: Epoch: 037 Test results, ap: 0.247, auc: 0.492, f1: 0.215, acc: 0.585
+2025-08-27 09:33:43: Epoch: 038 Train Loss: 0.066, ap: 0.931, auc: 0.942, f1: 0.870, acc: 0.882
+2025-08-27 09:33:43: Epoch: 038 Test results, ap: 0.251, auc: 0.488, f1: 0.215, acc: 0.585
+2025-08-27 09:33:46: Epoch: 039 Train Loss: 0.062, ap: 0.931, auc: 0.944, f1: 0.870, acc: 0.884
+2025-08-27 09:33:46: Epoch: 039 Test results, ap: 0.253, auc: 0.492, f1: 0.215, acc: 0.585
+2025-08-27 09:33:46: Epoch: 039 New best TEST metrics
+2025-08-27 09:33:49: Epoch: 040 Train Loss: 0.066, ap: 0.930, auc: 0.944, f1: 0.872, acc: 0.884
+2025-08-27 09:33:50: Epoch: 040 Test results, ap: 0.248, auc: 0.492, f1: 0.185, acc: 0.569
+2025-08-27 09:33:53: Epoch: 041 Train Loss: 0.056, ap: 0.932, auc: 0.944, f1: 0.872, acc: 0.885
+2025-08-27 09:33:53: Epoch: 041 Test results, ap: 0.248, auc: 0.492, f1: 0.188, acc: 0.577
+2025-08-27 09:33:56: Epoch: 042 Train Loss: 0.052, ap: 0.932, auc: 0.945, f1: 0.866, acc: 0.880
+2025-08-27 09:33:56: Epoch: 042 Test results, ap: 0.259, auc: 0.508, f1: 0.188, acc: 0.577
+2025-08-27 09:33:56: Epoch: 042 New best TEST metrics
+2025-08-27 09:33:59: Epoch: 043 Train Loss: 0.051, ap: 0.935, auc: 0.947, f1: 0.873, acc: 0.885
+2025-08-27 09:33:59: Epoch: 043 New best VALIDATION metrics
+2025-08-27 09:33:59: Epoch: 043 Test results, ap: 0.251, auc: 0.501, f1: 0.190, acc: 0.585
+2025-08-27 09:34:02: Epoch: 044 Train Loss: 0.049, ap: 0.933, auc: 0.946, f1: 0.875, acc: 0.887
+2025-08-27 09:34:03: Epoch: 044 Test results, ap: 0.253, auc: 0.501, f1: 0.190, acc: 0.585
+2025-08-27 09:34:05: Epoch: 045 Train Loss: 0.050, ap: 0.933, auc: 0.946, f1: 0.878, acc: 0.889
+2025-08-27 09:34:06: Epoch: 045 Test results, ap: 0.254, auc: 0.499, f1: 0.188, acc: 0.577
+2025-08-27 09:34:09: Epoch: 046 Train Loss: 0.046, ap: 0.935, auc: 0.947, f1: 0.879, acc: 0.891
+2025-08-27 09:34:09: Epoch: 046 New best VALIDATION metrics
+2025-08-27 09:34:09: Epoch: 046 Test results, ap: 0.252, auc: 0.499, f1: 0.190, acc: 0.585
+2025-08-27 09:34:12: Epoch: 047 Train Loss: 0.047, ap: 0.936, auc: 0.948, f1: 0.882, acc: 0.892
+2025-08-27 09:34:12: Epoch: 047 New best VALIDATION metrics
+2025-08-27 09:34:12: Epoch: 047 Test results, ap: 0.256, auc: 0.506, f1: 0.190, acc: 0.585
+2025-08-27 09:34:15: Epoch: 048 Train Loss: 0.047, ap: 0.934, auc: 0.946, f1: 0.875, acc: 0.887
+2025-08-27 09:34:15: Epoch: 048 Test results, ap: 0.258, auc: 0.505, f1: 0.194, acc: 0.593
+2025-08-27 09:34:18: Epoch: 049 Train Loss: 0.044, ap: 0.934, auc: 0.947, f1: 0.870, acc: 0.882
+2025-08-27 09:34:19: Epoch: 049 Test results, ap: 0.255, auc: 0.503, f1: 0.197, acc: 0.602
+2025-08-27 09:34:21: Epoch: 050 Train Loss: 0.041, ap: 0.934, auc: 0.947, f1: 0.873, acc: 0.885
+2025-08-27 09:34:22: Epoch: 050 Test results, ap: 0.254, auc: 0.499, f1: 0.197, acc: 0.602
+2025-08-27 09:34:22: used time 0.04h
+2025-08-27 09:34:22: Running Cross Validation 3
+2025-08-27 09:34:22: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-27 09:34:25: Epoch: 001 Train Loss: 0.694, ap: 0.539, auc: 0.636, f1: 0.602, acc: 0.430
+2025-08-27 09:34:25: Epoch: 001 New best VALIDATION metrics
+2025-08-27 09:34:25: Epoch: 001 Test results, ap: 0.256, auc: 0.556, f1: 0.392, acc: 0.244
+2025-08-27 09:34:28: Epoch: 002 Train Loss: 0.679, ap: 0.598, auc: 0.651, f1: 0.539, acc: 0.617
+2025-08-27 09:34:28: Epoch: 002 New best VALIDATION metrics
+2025-08-27 09:34:28: Epoch: 002 Test results, ap: 0.247, auc: 0.518, f1: 0.157, acc: 0.650
+2025-08-27 09:34:31: Epoch: 003 Train Loss: 0.657, ap: 0.624, auc: 0.688, f1: 0.543, acc: 0.644
+2025-08-27 09:34:31: Epoch: 003 New best VALIDATION metrics
+2025-08-27 09:34:31: Epoch: 003 Test results, ap: 0.232, auc: 0.478, f1: 0.262, acc: 0.634
+2025-08-27 09:34:34: Epoch: 004 Train Loss: 0.631, ap: 0.683, auc: 0.738, f1: 0.604, acc: 0.672
+2025-08-27 09:34:34: Epoch: 004 New best VALIDATION metrics
+2025-08-27 09:34:35: Epoch: 004 Test results, ap: 0.321, auc: 0.567, f1: 0.403, acc: 0.350
+2025-08-27 09:34:38: Epoch: 005 Train Loss: 0.616, ap: 0.679, auc: 0.728, f1: 0.602, acc: 0.675
+2025-08-27 09:34:38: Epoch: 005 Test results, ap: 0.232, auc: 0.470, f1: 0.329, acc: 0.602
+2025-08-27 09:34:41: Epoch: 006 Train Loss: 0.598, ap: 0.708, auc: 0.765, f1: 0.632, acc: 0.700
+2025-08-27 09:34:41: Epoch: 006 New best VALIDATION metrics
+2025-08-27 09:34:41: Epoch: 006 Test results, ap: 0.255, auc: 0.504, f1: 0.312, acc: 0.642
+2025-08-27 09:34:44: Epoch: 007 Train Loss: 0.586, ap: 0.741, auc: 0.782, f1: 0.673, acc: 0.705
+2025-08-27 09:34:44: Epoch: 007 New best VALIDATION metrics
+2025-08-27 09:34:44: Epoch: 007 Test results, ap: 0.259, auc: 0.522, f1: 0.343, acc: 0.626
+2025-08-27 09:34:47: Epoch: 008 Train Loss: 0.565, ap: 0.771, auc: 0.804, f1: 0.669, acc: 0.695
+2025-08-27 09:34:47: Epoch: 008 New best VALIDATION metrics
+2025-08-27 09:34:48: Epoch: 008 Test results, ap: 0.325, auc: 0.513, f1: 0.158, acc: 0.740
+2025-08-27 09:34:51: Epoch: 009 Train Loss: 0.522, ap: 0.782, auc: 0.829, f1: 0.736, acc: 0.753
+2025-08-27 09:34:51: Epoch: 009 New best VALIDATION metrics
+2025-08-27 09:34:51: Epoch: 009 Test results, ap: 0.272, auc: 0.524, f1: 0.316, acc: 0.577
+2025-08-27 09:34:54: Epoch: 010 Train Loss: 0.487, ap: 0.830, auc: 0.863, f1: 0.730, acc: 0.769
+2025-08-27 09:34:54: Epoch: 010 New best VALIDATION metrics
+2025-08-27 09:34:54: Epoch: 010 Test results, ap: 0.325, auc: 0.550, f1: 0.339, acc: 0.683
+2025-08-27 09:34:57: Epoch: 011 Train Loss: 0.439, ap: 0.834, auc: 0.888, f1: 0.783, acc: 0.801
+2025-08-27 09:34:57: Epoch: 011 New best VALIDATION metrics
+2025-08-27 09:34:57: Epoch: 011 Test results, ap: 0.268, auc: 0.513, f1: 0.380, acc: 0.602
+2025-08-27 09:34:57: Epoch: 011 New best TEST metrics
+2025-08-27 09:35:00: Epoch: 012 Train Loss: 0.406, ap: 0.853, auc: 0.884, f1: 0.767, acc: 0.778
+2025-08-27 09:35:00: Epoch: 012 New best VALIDATION metrics
+2025-08-27 09:35:00: Epoch: 012 Test results, ap: 0.240, auc: 0.489, f1: 0.290, acc: 0.642
+2025-08-27 09:35:03: Epoch: 013 Train Loss: 0.384, ap: 0.892, auc: 0.912, f1: 0.797, acc: 0.820
+2025-08-27 09:35:03: Epoch: 013 New best VALIDATION metrics
+2025-08-27 09:35:04: Epoch: 013 Test results, ap: 0.238, auc: 0.478, f1: 0.241, acc: 0.642
+2025-08-27 09:35:07: Epoch: 014 Train Loss: 0.347, ap: 0.913, auc: 0.927, f1: 0.812, acc: 0.825
+2025-08-27 09:35:07: Epoch: 014 New best VALIDATION metrics
+2025-08-27 09:35:07: Epoch: 014 Test results, ap: 0.255, auc: 0.513, f1: 0.281, acc: 0.626
+2025-08-27 09:35:10: Epoch: 015 Train Loss: 0.322, ap: 0.915, auc: 0.930, f1: 0.816, acc: 0.838
+2025-08-27 09:35:10: Epoch: 015 New best VALIDATION metrics
+2025-08-27 09:35:10: Epoch: 015 Test results, ap: 0.216, auc: 0.450, f1: 0.077, acc: 0.610
+2025-08-27 09:35:13: Epoch: 016 Train Loss: 0.299, ap: 0.909, auc: 0.926, f1: 0.806, acc: 0.825
+2025-08-27 09:35:13: Epoch: 016 Test results, ap: 0.203, auc: 0.395, f1: 0.156, acc: 0.561
+2025-08-27 09:35:16: Epoch: 017 Train Loss: 0.281, ap: 0.912, auc: 0.931, f1: 0.813, acc: 0.834
+2025-08-27 09:35:17: Epoch: 017 Test results, ap: 0.203, auc: 0.407, f1: 0.172, acc: 0.610
+2025-08-27 09:35:20: Epoch: 018 Train Loss: 0.267, ap: 0.923, auc: 0.938, f1: 0.826, acc: 0.841
+2025-08-27 09:35:20: Epoch: 018 New best VALIDATION metrics
+2025-08-27 09:35:20: Epoch: 018 Test results, ap: 0.196, auc: 0.385, f1: 0.133, acc: 0.577
+2025-08-27 09:35:23: Epoch: 019 Train Loss: 0.255, ap: 0.916, auc: 0.933, f1: 0.822, acc: 0.843
+2025-08-27 09:35:23: Epoch: 019 Test results, ap: 0.188, auc: 0.353, f1: 0.159, acc: 0.569
+2025-08-27 09:35:26: Epoch: 020 Train Loss: 0.238, ap: 0.916, auc: 0.930, f1: 0.820, acc: 0.838
+2025-08-27 09:35:27: Epoch: 020 Test results, ap: 0.209, auc: 0.429, f1: 0.185, acc: 0.569
+2025-08-27 09:35:30: Epoch: 021 Train Loss: 0.196, ap: 0.926, auc: 0.941, f1: 0.833, acc: 0.850
+2025-08-27 09:35:30: Epoch: 021 New best VALIDATION metrics
+2025-08-27 09:35:30: Epoch: 021 Test results, ap: 0.199, auc: 0.392, f1: 0.197, acc: 0.602
+2025-08-27 09:35:33: Epoch: 022 Train Loss: 0.176, ap: 0.927, auc: 0.945, f1: 0.843, acc: 0.862
+2025-08-27 09:35:33: Epoch: 022 New best VALIDATION metrics
+2025-08-27 09:35:33: Epoch: 022 Test results, ap: 0.193, auc: 0.366, f1: 0.159, acc: 0.569
+2025-08-27 09:35:36: Epoch: 023 Train Loss: 0.168, ap: 0.927, auc: 0.943, f1: 0.836, acc: 0.854
+2025-08-27 09:35:36: Epoch: 023 Test results, ap: 0.200, auc: 0.394, f1: 0.209, acc: 0.569
+2025-08-27 09:35:39: Epoch: 024 Train Loss: 0.156, ap: 0.928, auc: 0.945, f1: 0.841, acc: 0.861
+2025-08-27 09:35:39: Epoch: 024 New best VALIDATION metrics
+2025-08-27 09:35:39: Epoch: 024 Test results, ap: 0.201, auc: 0.393, f1: 0.161, acc: 0.577
+2025-08-27 09:35:42: Epoch: 025 Train Loss: 0.154, ap: 0.927, auc: 0.944, f1: 0.843, acc: 0.857
+2025-08-27 09:35:43: Epoch: 025 Test results, ap: 0.198, auc: 0.374, f1: 0.152, acc: 0.545
+2025-08-27 09:35:46: Epoch: 026 Train Loss: 0.146, ap: 0.924, auc: 0.941, f1: 0.830, acc: 0.847
+2025-08-27 09:35:46: Epoch: 026 Test results, ap: 0.203, auc: 0.380, f1: 0.239, acc: 0.585
+2025-08-27 09:35:49: Epoch: 027 Train Loss: 0.152, ap: 0.925, auc: 0.942, f1: 0.831, acc: 0.852
+2025-08-27 09:35:49: Epoch: 027 Test results, ap: 0.193, auc: 0.355, f1: 0.156, acc: 0.561
+2025-08-27 09:35:52: Epoch: 028 Train Loss: 0.133, ap: 0.926, auc: 0.944, f1: 0.852, acc: 0.869
+2025-08-27 09:35:52: Epoch: 028 Test results, ap: 0.208, auc: 0.418, f1: 0.154, acc: 0.553
+2025-08-27 09:35:55: Epoch: 029 Train Loss: 0.128, ap: 0.927, auc: 0.942, f1: 0.837, acc: 0.855
+2025-08-27 09:35:55: Epoch: 029 Test results, ap: 0.202, auc: 0.395, f1: 0.154, acc: 0.553
+2025-08-27 09:35:58: Epoch: 030 Train Loss: 0.119, ap: 0.924, auc: 0.939, f1: 0.839, acc: 0.857
+2025-08-27 09:35:58: Epoch: 030 Test results, ap: 0.206, auc: 0.393, f1: 0.147, acc: 0.528
+2025-08-27 09:36:01: Epoch: 031 Train Loss: 0.106, ap: 0.931, auc: 0.948, f1: 0.845, acc: 0.862
+2025-08-27 09:36:01: Epoch: 031 New best VALIDATION metrics
+2025-08-27 09:36:02: Epoch: 031 Test results, ap: 0.204, auc: 0.385, f1: 0.197, acc: 0.537
+2025-08-27 09:36:04: Epoch: 032 Train Loss: 0.091, ap: 0.931, auc: 0.948, f1: 0.844, acc: 0.861
+2025-08-27 09:36:05: Epoch: 032 Test results, ap: 0.209, auc: 0.399, f1: 0.274, acc: 0.569
+2025-08-27 09:36:08: Epoch: 033 Train Loss: 0.096, ap: 0.929, auc: 0.945, f1: 0.849, acc: 0.866
+2025-08-27 09:36:08: Epoch: 033 Test results, ap: 0.202, auc: 0.377, f1: 0.149, acc: 0.537
+2025-08-27 09:36:11: Epoch: 034 Train Loss: 0.089, ap: 0.932, auc: 0.946, f1: 0.854, acc: 0.873
+2025-08-27 09:36:11: Epoch: 034 Test results, ap: 0.200, auc: 0.367, f1: 0.152, acc: 0.545
+2025-08-27 09:36:14: Epoch: 035 Train Loss: 0.083, ap: 0.929, auc: 0.946, f1: 0.850, acc: 0.868
+2025-08-27 09:36:14: Epoch: 035 Test results, ap: 0.202, auc: 0.379, f1: 0.174, acc: 0.537
+2025-08-27 09:36:17: Epoch: 036 Train Loss: 0.085, ap: 0.932, auc: 0.948, f1: 0.852, acc: 0.869
+2025-08-27 09:36:17: Epoch: 036 New best VALIDATION metrics
+2025-08-27 09:36:18: Epoch: 036 Test results, ap: 0.206, auc: 0.390, f1: 0.149, acc: 0.537
+2025-08-27 09:36:21: Epoch: 037 Train Loss: 0.080, ap: 0.932, auc: 0.947, f1: 0.851, acc: 0.868
+2025-08-27 09:36:21: Epoch: 037 Test results, ap: 0.206, auc: 0.392, f1: 0.203, acc: 0.553
+2025-08-27 09:36:24: Epoch: 038 Train Loss: 0.080, ap: 0.929, auc: 0.945, f1: 0.857, acc: 0.875
+2025-08-27 09:36:24: Epoch: 038 Test results, ap: 0.209, auc: 0.406, f1: 0.174, acc: 0.537
+2025-08-27 09:36:27: Epoch: 039 Train Loss: 0.072, ap: 0.932, auc: 0.948, f1: 0.850, acc: 0.869
+2025-08-27 09:36:27: Epoch: 039 New best VALIDATION metrics
+2025-08-27 09:36:28: Epoch: 039 Test results, ap: 0.203, auc: 0.381, f1: 0.225, acc: 0.553
+2025-08-27 09:36:31: Epoch: 040 Train Loss: 0.071, ap: 0.933, auc: 0.948, f1: 0.855, acc: 0.873
+2025-08-27 09:36:31: Epoch: 040 Test results, ap: 0.205, auc: 0.391, f1: 0.174, acc: 0.537
+2025-08-27 09:36:34: Epoch: 041 Train Loss: 0.060, ap: 0.934, auc: 0.949, f1: 0.860, acc: 0.878
+2025-08-27 09:36:34: Epoch: 041 New best VALIDATION metrics
+2025-08-27 09:36:34: Epoch: 041 Test results, ap: 0.203, auc: 0.378, f1: 0.225, acc: 0.553
+2025-08-27 09:36:37: Epoch: 042 Train Loss: 0.057, ap: 0.934, auc: 0.949, f1: 0.852, acc: 0.871
+2025-08-27 09:36:37: Epoch: 042 Test results, ap: 0.204, auc: 0.381, f1: 0.225, acc: 0.553
+2025-08-27 09:36:41: Epoch: 043 Train Loss: 0.057, ap: 0.933, auc: 0.949, f1: 0.856, acc: 0.873
+2025-08-27 09:36:41: Epoch: 043 Test results, ap: 0.207, auc: 0.396, f1: 0.225, acc: 0.553
+2025-08-27 09:36:44: Epoch: 044 Train Loss: 0.056, ap: 0.933, auc: 0.949, f1: 0.853, acc: 0.871
+2025-08-27 09:36:44: Epoch: 044 Test results, ap: 0.206, auc: 0.389, f1: 0.225, acc: 0.553
+2025-08-27 09:36:47: Epoch: 045 Train Loss: 0.058, ap: 0.934, auc: 0.950, f1: 0.859, acc: 0.877
+2025-08-27 09:36:47: Epoch: 045 New best VALIDATION metrics
+2025-08-27 09:36:47: Epoch: 045 Test results, ap: 0.207, auc: 0.394, f1: 0.225, acc: 0.553
+2025-08-27 09:36:51: Epoch: 046 Train Loss: 0.057, ap: 0.934, auc: 0.949, f1: 0.859, acc: 0.877
+2025-08-27 09:36:51: Epoch: 046 Test results, ap: 0.206, auc: 0.387, f1: 0.225, acc: 0.553
+2025-08-27 09:36:54: Epoch: 047 Train Loss: 0.053, ap: 0.934, auc: 0.950, f1: 0.861, acc: 0.880
+2025-08-27 09:36:54: Epoch: 047 Test results, ap: 0.209, auc: 0.405, f1: 0.225, acc: 0.553
+2025-08-27 09:36:57: Epoch: 048 Train Loss: 0.053, ap: 0.934, auc: 0.948, f1: 0.862, acc: 0.880
+2025-08-27 09:36:58: Epoch: 048 Test results, ap: 0.208, auc: 0.402, f1: 0.225, acc: 0.553
+2025-08-27 09:37:01: Epoch: 049 Train Loss: 0.055, ap: 0.934, auc: 0.949, f1: 0.861, acc: 0.878
+2025-08-27 09:37:01: Epoch: 049 Test results, ap: 0.207, auc: 0.394, f1: 0.225, acc: 0.553
+2025-08-27 09:37:04: Epoch: 050 Train Loss: 0.048, ap: 0.934, auc: 0.949, f1: 0.864, acc: 0.884
+2025-08-27 09:37:04: Epoch: 050 Test results, ap: 0.206, auc: 0.388, f1: 0.222, acc: 0.545
+2025-08-27 09:37:04: used time 0.05h
+2025-08-27 09:37:04: Running Cross Validation 4
+2025-08-27 09:37:04: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-27 09:37:07: Epoch: 001 Train Loss: 0.694, ap: 0.536, auc: 0.594, f1: 0.614, acc: 0.443
+2025-08-27 09:37:07: Epoch: 001 New best VALIDATION metrics
+2025-08-27 09:37:08: Epoch: 001 Test results, ap: 0.243, auc: 0.533, f1: 0.392, acc: 0.244
+2025-08-27 09:37:11: Epoch: 002 Train Loss: 0.694, ap: 0.418, auc: 0.438, f1: 0.614, acc: 0.443
+2025-08-27 09:37:11: Epoch: 002 Test results, ap: 0.305, auc: 0.627, f1: 0.392, acc: 0.244
+2025-08-27 09:37:14: Epoch: 003 Train Loss: 0.696, ap: 0.433, auc: 0.528, f1: 0.000, acc: 0.557
+2025-08-27 09:37:14: Epoch: 003 Test results, ap: 0.166, auc: 0.243, f1: 0.000, acc: 0.756
+2025-08-27 09:37:17: Epoch: 004 Train Loss: 0.693, ap: 0.485, auc: 0.554, f1: 0.000, acc: 0.557
+2025-08-27 09:37:18: Epoch: 004 Test results, ap: 0.176, auc: 0.311, f1: 0.000, acc: 0.756
+2025-08-27 09:37:21: Epoch: 005 Train Loss: 0.695, ap: 0.487, auc: 0.551, f1: 0.614, acc: 0.443
+2025-08-27 09:37:21: Epoch: 005 Test results, ap: 0.169, auc: 0.274, f1: 0.392, acc: 0.244
+2025-08-27 09:37:24: Epoch: 006 Train Loss: 0.693, ap: 0.523, auc: 0.567, f1: 0.000, acc: 0.557
+2025-08-27 09:37:24: Epoch: 006 Test results, ap: 0.171, auc: 0.288, f1: 0.000, acc: 0.756
+2025-08-27 09:37:27: Epoch: 007 Train Loss: 0.694, ap: 0.530, auc: 0.575, f1: 0.000, acc: 0.557
+2025-08-27 09:37:28: Epoch: 007 Test results, ap: 0.174, auc: 0.304, f1: 0.000, acc: 0.756
+2025-08-27 09:37:31: Epoch: 008 Train Loss: 0.684, ap: 0.543, auc: 0.610, f1: 0.517, acc: 0.598
+2025-08-27 09:37:31: Epoch: 008 New best VALIDATION metrics
+2025-08-27 09:37:31: Epoch: 008 Test results, ap: 0.195, auc: 0.380, f1: 0.182, acc: 0.488
+2025-08-27 09:37:34: Epoch: 009 Train Loss: 0.654, ap: 0.552, auc: 0.636, f1: 0.454, acc: 0.610
+2025-08-27 09:37:34: Epoch: 009 New best VALIDATION metrics
+2025-08-27 09:37:34: Epoch: 009 Test results, ap: 0.202, auc: 0.389, f1: 0.000, acc: 0.756
+2025-08-27 09:37:37: Epoch: 010 Train Loss: 0.652, ap: 0.555, auc: 0.639, f1: 0.574, acc: 0.607
+2025-08-27 09:37:37: Epoch: 010 New best VALIDATION metrics
+2025-08-27 09:37:38: Epoch: 010 Test results, ap: 0.213, auc: 0.395, f1: 0.000, acc: 0.756
+2025-08-27 09:37:41: Epoch: 011 Train Loss: 0.637, ap: 0.599, auc: 0.650, f1: 0.567, acc: 0.628
+2025-08-27 09:37:41: Epoch: 011 New best VALIDATION metrics
+2025-08-27 09:37:41: Epoch: 011 Test results, ap: 0.306, auc: 0.431, f1: 0.190, acc: 0.724
+2025-08-27 09:37:41: Epoch: 011 New best TEST metrics
+2025-08-27 09:37:44: Epoch: 012 Train Loss: 0.612, ap: 0.667, auc: 0.696, f1: 0.621, acc: 0.651
+2025-08-27 09:37:44: Epoch: 012 New best VALIDATION metrics
+2025-08-27 09:37:44: Epoch: 012 Test results, ap: 0.299, auc: 0.444, f1: 0.146, acc: 0.715
+2025-08-27 09:37:44: Epoch: 012 New best TEST metrics
+2025-08-27 09:37:47: Epoch: 013 Train Loss: 0.580, ap: 0.717, auc: 0.759, f1: 0.640, acc: 0.698
+2025-08-27 09:37:47: Epoch: 013 New best VALIDATION metrics
+2025-08-27 09:37:48: Epoch: 013 Test results, ap: 0.265, auc: 0.506, f1: 0.065, acc: 0.764
+2025-08-27 09:37:48: Epoch: 013 New best TEST metrics
+2025-08-27 09:37:51: Epoch: 014 Train Loss: 0.545, ap: 0.759, auc: 0.792, f1: 0.674, acc: 0.730
+2025-08-27 09:37:51: Epoch: 014 New best VALIDATION metrics
+2025-08-27 09:37:51: Epoch: 014 Test results, ap: 0.407, auc: 0.636, f1: 0.267, acc: 0.732
+2025-08-27 09:37:51: Epoch: 014 New best TEST metrics
+2025-08-27 09:37:54: Epoch: 015 Train Loss: 0.507, ap: 0.772, auc: 0.805, f1: 0.703, acc: 0.730
+2025-08-27 09:37:54: Epoch: 015 New best VALIDATION metrics
+2025-08-27 09:37:54: Epoch: 015 Test results, ap: 0.231, auc: 0.490, f1: 0.000, acc: 0.756
+2025-08-27 09:37:57: Epoch: 016 Train Loss: 0.479, ap: 0.776, auc: 0.822, f1: 0.715, acc: 0.753
+2025-08-27 09:37:57: Epoch: 016 New best VALIDATION metrics
+2025-08-27 09:37:58: Epoch: 016 Test results, ap: 0.411, auc: 0.615, f1: 0.353, acc: 0.642
+2025-08-27 09:38:01: Epoch: 017 Train Loss: 0.437, ap: 0.774, auc: 0.826, f1: 0.703, acc: 0.730
+2025-08-27 09:38:01: Epoch: 017 New best VALIDATION metrics
+2025-08-27 09:38:01: Epoch: 017 Test results, ap: 0.382, auc: 0.586, f1: 0.245, acc: 0.699
+2025-08-27 09:38:04: Epoch: 018 Train Loss: 0.403, ap: 0.802, auc: 0.841, f1: 0.730, acc: 0.767
+2025-08-27 09:38:04: Epoch: 018 New best VALIDATION metrics
+2025-08-27 09:38:04: Epoch: 018 Test results, ap: 0.386, auc: 0.629, f1: 0.176, acc: 0.772
+2025-08-27 09:38:07: Epoch: 019 Train Loss: 0.366, ap: 0.831, auc: 0.868, f1: 0.761, acc: 0.787
+2025-08-27 09:38:07: Epoch: 019 New best VALIDATION metrics
+2025-08-27 09:38:08: Epoch: 019 Test results, ap: 0.342, auc: 0.640, f1: 0.000, acc: 0.748
+2025-08-27 09:38:11: Epoch: 020 Train Loss: 0.309, ap: 0.843, auc: 0.880, f1: 0.765, acc: 0.790
+2025-08-27 09:38:11: Epoch: 020 New best VALIDATION metrics
+2025-08-27 09:38:11: Epoch: 020 Test results, ap: 0.357, auc: 0.656, f1: 0.205, acc: 0.748
+2025-08-27 09:38:14: Epoch: 021 Train Loss: 0.261, ap: 0.850, auc: 0.884, f1: 0.782, acc: 0.801
+2025-08-27 09:38:14: Epoch: 021 New best VALIDATION metrics
+2025-08-27 09:38:15: Epoch: 021 Test results, ap: 0.385, auc: 0.657, f1: 0.158, acc: 0.740
+2025-08-27 09:38:18: Epoch: 022 Train Loss: 0.246, ap: 0.875, auc: 0.903, f1: 0.799, acc: 0.817
+2025-08-27 09:38:18: Epoch: 022 New best VALIDATION metrics
+2025-08-27 09:38:18: Epoch: 022 Test results, ap: 0.324, auc: 0.628, f1: 0.154, acc: 0.732
+2025-08-27 09:38:21: Epoch: 023 Train Loss: 0.226, ap: 0.871, auc: 0.906, f1: 0.811, acc: 0.824
+2025-08-27 09:38:21: Epoch: 023 Test results, ap: 0.371, auc: 0.628, f1: 0.190, acc: 0.724
+2025-08-27 09:38:24: Epoch: 024 Train Loss: 0.211, ap: 0.884, auc: 0.910, f1: 0.803, acc: 0.815
+2025-08-27 09:38:24: Epoch: 024 New best VALIDATION metrics
+2025-08-27 09:38:25: Epoch: 024 Test results, ap: 0.369, auc: 0.635, f1: 0.158, acc: 0.740
+2025-08-27 09:38:28: Epoch: 025 Train Loss: 0.186, ap: 0.893, auc: 0.920, f1: 0.821, acc: 0.840
+2025-08-27 09:38:28: Epoch: 025 New best VALIDATION metrics
+2025-08-27 09:38:28: Epoch: 025 Test results, ap: 0.298, auc: 0.605, f1: 0.061, acc: 0.748
+2025-08-27 09:38:31: Epoch: 026 Train Loss: 0.168, ap: 0.898, auc: 0.925, f1: 0.826, acc: 0.841
+2025-08-27 09:38:31: Epoch: 026 New best VALIDATION metrics
+2025-08-27 09:38:31: Epoch: 026 Test results, ap: 0.262, auc: 0.556, f1: 0.051, acc: 0.699
+2025-08-27 09:38:34: Epoch: 027 Train Loss: 0.158, ap: 0.903, auc: 0.927, f1: 0.845, acc: 0.857
+2025-08-27 09:38:34: Epoch: 027 New best VALIDATION metrics
+2025-08-27 09:38:35: Epoch: 027 Test results, ap: 0.279, auc: 0.580, f1: 0.000, acc: 0.715
+2025-08-27 09:38:38: Epoch: 028 Train Loss: 0.151, ap: 0.902, auc: 0.926, f1: 0.844, acc: 0.855
+2025-08-27 09:38:38: Epoch: 028 Test results, ap: 0.264, auc: 0.549, f1: 0.054, acc: 0.715
+2025-08-27 09:38:41: Epoch: 029 Train Loss: 0.144, ap: 0.905, auc: 0.928, f1: 0.848, acc: 0.862
+2025-08-27 09:38:41: Epoch: 029 New best VALIDATION metrics
+2025-08-27 09:38:41: Epoch: 029 Test results, ap: 0.270, auc: 0.570, f1: 0.000, acc: 0.715
+2025-08-27 09:38:44: Epoch: 030 Train Loss: 0.130, ap: 0.904, auc: 0.930, f1: 0.841, acc: 0.854
+2025-08-27 09:38:44: Epoch: 030 New best VALIDATION metrics
+2025-08-27 09:38:45: Epoch: 030 Test results, ap: 0.269, auc: 0.558, f1: 0.000, acc: 0.715
+2025-08-27 09:38:48: Epoch: 031 Train Loss: 0.109, ap: 0.911, auc: 0.936, f1: 0.845, acc: 0.859
+2025-08-27 09:38:48: Epoch: 031 New best VALIDATION metrics
+2025-08-27 09:38:48: Epoch: 031 Test results, ap: 0.264, auc: 0.557, f1: 0.000, acc: 0.707
+2025-08-27 09:38:51: Epoch: 032 Train Loss: 0.096, ap: 0.910, auc: 0.934, f1: 0.852, acc: 0.866
+2025-08-27 09:38:51: Epoch: 032 Test results, ap: 0.272, auc: 0.569, f1: 0.000, acc: 0.724
+2025-08-27 09:38:54: Epoch: 033 Train Loss: 0.097, ap: 0.912, auc: 0.935, f1: 0.854, acc: 0.868
+2025-08-27 09:38:54: Epoch: 033 New best VALIDATION metrics
+2025-08-27 09:38:55: Epoch: 033 Test results, ap: 0.275, auc: 0.575, f1: 0.000, acc: 0.724
+2025-08-27 09:38:58: Epoch: 034 Train Loss: 0.097, ap: 0.913, auc: 0.937, f1: 0.857, acc: 0.869
+2025-08-27 09:38:58: Epoch: 034 New best VALIDATION metrics
+2025-08-27 09:38:58: Epoch: 034 Test results, ap: 0.267, auc: 0.561, f1: 0.000, acc: 0.699
+2025-08-27 09:39:01: Epoch: 035 Train Loss: 0.087, ap: 0.913, auc: 0.938, f1: 0.853, acc: 0.866
+2025-08-27 09:39:01: Epoch: 035 New best VALIDATION metrics
+2025-08-27 09:39:01: Epoch: 035 Test results, ap: 0.271, auc: 0.569, f1: 0.000, acc: 0.724
+2025-08-27 09:39:04: Epoch: 036 Train Loss: 0.082, ap: 0.915, auc: 0.939, f1: 0.855, acc: 0.868
+2025-08-27 09:39:04: Epoch: 036 New best VALIDATION metrics
+2025-08-27 09:39:05: Epoch: 036 Test results, ap: 0.261, auc: 0.549, f1: 0.000, acc: 0.707
+2025-08-27 09:39:08: Epoch: 037 Train Loss: 0.084, ap: 0.915, auc: 0.939, f1: 0.863, acc: 0.875
+2025-08-27 09:39:08: Epoch: 037 Test results, ap: 0.268, auc: 0.559, f1: 0.000, acc: 0.707
+2025-08-27 09:39:11: Epoch: 038 Train Loss: 0.076, ap: 0.918, auc: 0.940, f1: 0.857, acc: 0.871
+2025-08-27 09:39:11: Epoch: 038 New best VALIDATION metrics
+2025-08-27 09:39:11: Epoch: 038 Test results, ap: 0.272, auc: 0.569, f1: 0.000, acc: 0.715
+2025-08-27 09:39:14: Epoch: 039 Train Loss: 0.072, ap: 0.916, auc: 0.939, f1: 0.868, acc: 0.880
+2025-08-27 09:39:15: Epoch: 039 Test results, ap: 0.269, auc: 0.561, f1: 0.000, acc: 0.715
+2025-08-27 09:39:18: Epoch: 040 Train Loss: 0.073, ap: 0.921, auc: 0.942, f1: 0.860, acc: 0.875
+2025-08-27 09:39:18: Epoch: 040 New best VALIDATION metrics
+2025-08-27 09:39:18: Epoch: 040 Test results, ap: 0.269, auc: 0.563, f1: 0.000, acc: 0.707
+2025-08-27 09:39:21: Epoch: 041 Train Loss: 0.066, ap: 0.922, auc: 0.943, f1: 0.862, acc: 0.877
+2025-08-27 09:39:21: Epoch: 041 New best VALIDATION metrics
+2025-08-27 09:39:21: Epoch: 041 Test results, ap: 0.268, auc: 0.563, f1: 0.000, acc: 0.699
+2025-08-27 09:39:24: Epoch: 042 Train Loss: 0.060, ap: 0.922, auc: 0.943, f1: 0.859, acc: 0.873
+2025-08-27 09:39:24: Epoch: 042 New best VALIDATION metrics
+2025-08-27 09:39:25: Epoch: 042 Test results, ap: 0.266, auc: 0.560, f1: 0.000, acc: 0.699
+2025-08-27 09:39:28: Epoch: 043 Train Loss: 0.057, ap: 0.920, auc: 0.942, f1: 0.860, acc: 0.875
+2025-08-27 09:39:28: Epoch: 043 Test results, ap: 0.268, auc: 0.562, f1: 0.000, acc: 0.707
+2025-08-27 09:39:31: Epoch: 044 Train Loss: 0.054, ap: 0.921, auc: 0.942, f1: 0.863, acc: 0.877
+2025-08-27 09:39:31: Epoch: 044 Test results, ap: 0.268, auc: 0.561, f1: 0.000, acc: 0.707
+2025-08-27 09:39:34: Epoch: 045 Train Loss: 0.053, ap: 0.923, auc: 0.944, f1: 0.864, acc: 0.878
+2025-08-27 09:39:34: Epoch: 045 New best VALIDATION metrics
+2025-08-27 09:39:35: Epoch: 045 Test results, ap: 0.265, auc: 0.558, f1: 0.000, acc: 0.699
+2025-08-27 09:39:38: Epoch: 046 Train Loss: 0.049, ap: 0.923, auc: 0.944, f1: 0.866, acc: 0.880
+2025-08-27 09:39:38: Epoch: 046 Test results, ap: 0.268, auc: 0.561, f1: 0.000, acc: 0.707
+2025-08-27 09:39:41: Epoch: 047 Train Loss: 0.056, ap: 0.921, auc: 0.944, f1: 0.866, acc: 0.880
+2025-08-27 09:39:41: Epoch: 047 Test results, ap: 0.267, auc: 0.561, f1: 0.000, acc: 0.707
+2025-08-27 09:39:44: Epoch: 048 Train Loss: 0.053, ap: 0.922, auc: 0.944, f1: 0.867, acc: 0.880
+2025-08-27 09:39:45: Epoch: 048 Test results, ap: 0.273, auc: 0.568, f1: 0.000, acc: 0.707
+2025-08-27 09:39:48: Epoch: 049 Train Loss: 0.049, ap: 0.921, auc: 0.944, f1: 0.862, acc: 0.877
+2025-08-27 09:39:48: Epoch: 049 Test results, ap: 0.267, auc: 0.563, f1: 0.000, acc: 0.707
+2025-08-27 09:39:51: Epoch: 050 Train Loss: 0.049, ap: 0.920, auc: 0.943, f1: 0.866, acc: 0.880
+2025-08-27 09:39:52: Epoch: 050 Test results, ap: 0.269, auc: 0.566, f1: 0.000, acc: 0.707
+2025-08-27 09:39:52: used time 0.05h
+2025-08-27 09:39:52: Cross Validation Finished!
+2025-08-27 09:39:52: Best validation perform list
+[[0.94274819 0.94628429 0.88268155 0.88908452]
+ [0.94166332 0.95822835 0.87394959 0.8943662 ]
+ [0.936324   0.94805676 0.88155341 0.89241624]
+ [0.93377388 0.9500711  0.85887098 0.87654322]
+ [0.92306179 0.94359273 0.86444008 0.87830687]]
+2025-08-27 09:39:52: mean: [0.936 0.949 0.872 0.886]
+2025-08-27 09:39:52: std: [0.007 0.005 0.009 0.007]
+2025-08-27 09:39:52: Best test perform list
+[[0.35282683 0.52939069 0.16666667 0.67479676]
+ [0.40737638 0.6136201  0.29268292 0.76422763]
+ [0.25856349 0.50752687 0.1875     0.57723576]
+ [0.26795179 0.51290327 0.37974682 0.60162604]
+ [0.40710786 0.63620073 0.26666668 0.73170733]]
+2025-08-27 09:39:52: mean: [0.339 0.56  0.259 0.67 ]
+2025-08-27 09:39:52: std: [0.065 0.054 0.077 0.072]
diff --git a/run-cls/mha-att-256-ce-32-0.001-50/config.json b/run-cls/mha-att-256-ce-32-0.001-50/config.json
new file mode 100644
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+2025-08-26 01:21:38: saving_dir: ./run-cls/mha-att-256-ce-32-0.001-50
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+2025-08-26 01:21:41: Epoch: 001 Train Loss: 0.690, ap: 0.630, auc: 0.647, f1: 0.629, acc: 0.574
+2025-08-26 01:21:41: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:21:41: Epoch: 001 Test results, ap: 0.232, auc: 0.466, f1: 0.392, acc: 0.244
+2025-08-26 01:21:42: Epoch: 002 Train Loss: 0.673, ap: 0.642, auc: 0.662, f1: 0.616, acc: 0.614
+2025-08-26 01:21:42: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:21:43: Epoch: 002 Test results, ap: 0.222, auc: 0.424, f1: 0.357, acc: 0.268
+2025-08-26 01:21:44: Epoch: 003 Train Loss: 0.667, ap: 0.632, auc: 0.636, f1: 0.500, acc: 0.634
+2025-08-26 01:21:44: Epoch: 003 Test results, ap: 0.287, auc: 0.548, f1: 0.211, acc: 0.756
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+2025-08-26 01:21:45: Epoch: 004 New best VALIDATION metrics
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+2025-08-26 01:21:46: Epoch: 005 Test results, ap: 0.215, auc: 0.423, f1: 0.185, acc: 0.642
+2025-08-26 01:21:47: Epoch: 006 Train Loss: 0.650, ap: 0.653, auc: 0.678, f1: 0.646, acc: 0.592
+2025-08-26 01:21:47: Epoch: 006 New best VALIDATION metrics
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+2025-08-26 01:21:54: Epoch: 011 New best VALIDATION metrics
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+2025-08-26 01:21:54: Epoch: 011 New best TEST metrics
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+2025-08-26 01:21:55: Epoch: 012 New best VALIDATION metrics
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+2025-08-26 01:21:56: Epoch: 013 New best VALIDATION metrics
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+2025-08-26 01:21:58: Epoch: 014 New best VALIDATION metrics
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+2025-08-26 01:21:59: Epoch: 015 New best TEST metrics
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+2025-08-26 01:22:00: Epoch: 016 New best TEST metrics
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+2025-08-26 01:22:02: Epoch: 017 New best TEST metrics
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+2025-08-26 01:22:03: Epoch: 018 New best VALIDATION metrics
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+2025-08-26 01:22:08: Epoch: 022 New best VALIDATION metrics
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+2025-08-26 01:22:08: Epoch: 022 New best TEST metrics
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+2025-08-26 01:22:09: Epoch: 023 Test results, ap: 0.250, auc: 0.457, f1: 0.281, acc: 0.626
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+2025-08-26 01:22:10: Epoch: 024 New best VALIDATION metrics
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+2025-08-26 01:22:12: Epoch: 025 Test results, ap: 0.269, auc: 0.460, f1: 0.265, acc: 0.593
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+2025-08-26 01:22:13: Epoch: 026 New best TEST metrics
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+2025-08-26 01:22:14: Epoch: 027 New best VALIDATION metrics
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+2025-08-26 01:22:22: Epoch: 033 Train Loss: 0.559, ap: 0.715, auc: 0.716, f1: 0.662, acc: 0.671
+2025-08-26 01:22:22: Epoch: 033 Test results, ap: 0.369, auc: 0.532, f1: 0.299, acc: 0.504
+2025-08-26 01:22:23: Epoch: 034 Train Loss: 0.561, ap: 0.719, auc: 0.718, f1: 0.668, acc: 0.678
+2025-08-26 01:22:23: Epoch: 034 Test results, ap: 0.329, auc: 0.500, f1: 0.282, acc: 0.585
+2025-08-26 01:22:24: Epoch: 035 Train Loss: 0.560, ap: 0.713, auc: 0.713, f1: 0.647, acc: 0.678
+2025-08-26 01:22:24: Epoch: 035 Test results, ap: 0.347, auc: 0.498, f1: 0.281, acc: 0.626
+2025-08-26 01:22:25: Epoch: 036 Train Loss: 0.555, ap: 0.714, auc: 0.716, f1: 0.657, acc: 0.669
+2025-08-26 01:22:26: Epoch: 036 Test results, ap: 0.329, auc: 0.502, f1: 0.286, acc: 0.593
+2025-08-26 01:22:27: Epoch: 037 Train Loss: 0.555, ap: 0.712, auc: 0.717, f1: 0.668, acc: 0.676
+2025-08-26 01:22:27: Epoch: 037 Test results, ap: 0.344, auc: 0.526, f1: 0.302, acc: 0.512
+2025-08-26 01:22:28: Epoch: 038 Train Loss: 0.557, ap: 0.718, auc: 0.720, f1: 0.665, acc: 0.674
+2025-08-26 01:22:28: Epoch: 038 Test results, ap: 0.341, auc: 0.525, f1: 0.278, acc: 0.537
+2025-08-26 01:22:29: Epoch: 039 Train Loss: 0.555, ap: 0.715, auc: 0.720, f1: 0.662, acc: 0.667
+2025-08-26 01:22:29: Epoch: 039 Test results, ap: 0.330, auc: 0.526, f1: 0.302, acc: 0.512
+2025-08-26 01:22:31: Epoch: 040 Train Loss: 0.552, ap: 0.714, auc: 0.717, f1: 0.677, acc: 0.681
+2025-08-26 01:22:31: Epoch: 040 Test results, ap: 0.292, auc: 0.506, f1: 0.274, acc: 0.569
+2025-08-26 01:22:32: Epoch: 041 Train Loss: 0.547, ap: 0.718, auc: 0.720, f1: 0.661, acc: 0.665
+2025-08-26 01:22:32: Epoch: 041 Test results, ap: 0.313, auc: 0.506, f1: 0.274, acc: 0.569
+2025-08-26 01:22:33: Epoch: 042 Train Loss: 0.545, ap: 0.715, auc: 0.717, f1: 0.660, acc: 0.664
+2025-08-26 01:22:33: Epoch: 042 Test results, ap: 0.310, auc: 0.505, f1: 0.293, acc: 0.569
+2025-08-26 01:22:34: Epoch: 043 Train Loss: 0.536, ap: 0.713, auc: 0.715, f1: 0.665, acc: 0.667
+2025-08-26 01:22:35: Epoch: 043 Test results, ap: 0.310, auc: 0.516, f1: 0.272, acc: 0.520
+2025-08-26 01:22:35: Epoch: 044 Train Loss: 0.537, ap: 0.718, auc: 0.718, f1: 0.665, acc: 0.674
+2025-08-26 01:22:36: Epoch: 044 Test results, ap: 0.321, auc: 0.517, f1: 0.310, acc: 0.602
+2025-08-26 01:22:37: Epoch: 045 Train Loss: 0.543, ap: 0.718, auc: 0.718, f1: 0.668, acc: 0.676
+2025-08-26 01:22:37: Epoch: 045 Test results, ap: 0.324, auc: 0.522, f1: 0.282, acc: 0.545
+2025-08-26 01:22:38: Epoch: 046 Train Loss: 0.535, ap: 0.715, auc: 0.720, f1: 0.680, acc: 0.683
+2025-08-26 01:22:38: Epoch: 046 Test results, ap: 0.329, auc: 0.535, f1: 0.293, acc: 0.528
+2025-08-26 01:22:39: Epoch: 047 Train Loss: 0.541, ap: 0.717, auc: 0.719, f1: 0.665, acc: 0.676
+2025-08-26 01:22:40: Epoch: 047 Test results, ap: 0.322, auc: 0.524, f1: 0.282, acc: 0.585
+2025-08-26 01:22:41: Epoch: 048 Train Loss: 0.537, ap: 0.717, auc: 0.722, f1: 0.667, acc: 0.673
+2025-08-26 01:22:41: Epoch: 048 Test results, ap: 0.326, auc: 0.534, f1: 0.293, acc: 0.528
+2025-08-26 01:22:42: Epoch: 049 Train Loss: 0.537, ap: 0.720, auc: 0.719, f1: 0.665, acc: 0.678
+2025-08-26 01:22:42: Epoch: 049 Test results, ap: 0.332, auc: 0.544, f1: 0.289, acc: 0.561
+2025-08-26 01:22:43: Epoch: 050 Train Loss: 0.532, ap: 0.722, auc: 0.720, f1: 0.662, acc: 0.683
+2025-08-26 01:22:43: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:22:43: Epoch: 050 Test results, ap: 0.325, auc: 0.531, f1: 0.294, acc: 0.610
+2025-08-26 01:22:43: used time 0.02h
+2025-08-26 01:22:43: Running Cross Validation 1
+2025-08-26 01:22:43: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 01:22:44: Epoch: 001 Train Loss: 0.695, ap: 0.587, auc: 0.643, f1: 0.608, acc: 0.437
+2025-08-26 01:22:44: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:22:45: Epoch: 001 Test results, ap: 0.225, auc: 0.437, f1: 0.392, acc: 0.244
+2025-08-26 01:22:46: Epoch: 002 Train Loss: 0.680, ap: 0.624, auc: 0.677, f1: 0.591, acc: 0.665
+2025-08-26 01:22:46: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:22:46: Epoch: 002 Test results, ap: 0.210, auc: 0.424, f1: 0.319, acc: 0.341
+2025-08-26 01:22:47: Epoch: 003 Train Loss: 0.666, ap: 0.635, auc: 0.684, f1: 0.473, acc: 0.655
+2025-08-26 01:22:47: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:22:47: Epoch: 003 Test results, ap: 0.218, auc: 0.434, f1: 0.328, acc: 0.366
+2025-08-26 01:22:48: Epoch: 004 Train Loss: 0.654, ap: 0.650, auc: 0.698, f1: 0.594, acc: 0.664
+2025-08-26 01:22:48: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:22:48: Epoch: 004 Test results, ap: 0.196, auc: 0.387, f1: 0.376, acc: 0.244
+2025-08-26 01:22:49: Epoch: 005 Train Loss: 0.651, ap: 0.639, auc: 0.685, f1: 0.595, acc: 0.643
+2025-08-26 01:22:50: Epoch: 005 Test results, ap: 0.230, auc: 0.439, f1: 0.317, acc: 0.333
+2025-08-26 01:22:51: Epoch: 006 Train Loss: 0.657, ap: 0.634, auc: 0.692, f1: 0.602, acc: 0.669
+2025-08-26 01:22:51: Epoch: 006 Test results, ap: 0.200, auc: 0.385, f1: 0.392, acc: 0.244
+2025-08-26 01:22:52: Epoch: 007 Train Loss: 0.646, ap: 0.626, auc: 0.690, f1: 0.634, acc: 0.614
+2025-08-26 01:22:52: Epoch: 007 Test results, ap: 0.229, auc: 0.420, f1: 0.392, acc: 0.244
+2025-08-26 01:22:53: Epoch: 008 Train Loss: 0.653, ap: 0.616, auc: 0.684, f1: 0.615, acc: 0.600
+2025-08-26 01:22:53: Epoch: 008 Test results, ap: 0.205, auc: 0.391, f1: 0.392, acc: 0.244
+2025-08-26 01:22:54: Epoch: 009 Train Loss: 0.645, ap: 0.620, auc: 0.680, f1: 0.544, acc: 0.655
+2025-08-26 01:22:55: Epoch: 009 Test results, ap: 0.200, auc: 0.386, f1: 0.378, acc: 0.276
+2025-08-26 01:22:56: Epoch: 010 Train Loss: 0.627, ap: 0.612, auc: 0.692, f1: 0.583, acc: 0.643
+2025-08-26 01:22:56: Epoch: 010 Test results, ap: 0.215, auc: 0.413, f1: 0.392, acc: 0.244
+2025-08-26 01:22:57: Epoch: 011 Train Loss: 0.607, ap: 0.647, auc: 0.710, f1: 0.592, acc: 0.658
+2025-08-26 01:22:57: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:22:57: Epoch: 011 Test results, ap: 0.264, auc: 0.433, f1: 0.392, acc: 0.244
+2025-08-26 01:22:57: Epoch: 011 New best TEST metrics
+2025-08-26 01:22:58: Epoch: 012 Train Loss: 0.604, ap: 0.647, auc: 0.696, f1: 0.558, acc: 0.651
+2025-08-26 01:22:59: Epoch: 012 Test results, ap: 0.305, auc: 0.528, f1: 0.392, acc: 0.244
+2025-08-26 01:22:59: Epoch: 012 New best TEST metrics
+2025-08-26 01:23:00: Epoch: 013 Train Loss: 0.602, ap: 0.663, auc: 0.719, f1: 0.638, acc: 0.688
+2025-08-26 01:23:00: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:23:00: Epoch: 013 Test results, ap: 0.273, auc: 0.456, f1: 0.392, acc: 0.244
+2025-08-26 01:23:01: Epoch: 014 Train Loss: 0.590, ap: 0.684, auc: 0.728, f1: 0.649, acc: 0.674
+2025-08-26 01:23:01: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:23:01: Epoch: 014 Test results, ap: 0.287, auc: 0.495, f1: 0.392, acc: 0.244
+2025-08-26 01:23:02: Epoch: 015 Train Loss: 0.581, ap: 0.691, auc: 0.732, f1: 0.638, acc: 0.669
+2025-08-26 01:23:02: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:23:02: Epoch: 015 Test results, ap: 0.229, auc: 0.432, f1: 0.392, acc: 0.244
+2025-08-26 01:23:03: Epoch: 016 Train Loss: 0.570, ap: 0.725, auc: 0.762, f1: 0.672, acc: 0.694
+2025-08-26 01:23:03: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:23:04: Epoch: 016 Test results, ap: 0.290, auc: 0.470, f1: 0.392, acc: 0.244
+2025-08-26 01:23:05: Epoch: 017 Train Loss: 0.567, ap: 0.695, auc: 0.724, f1: 0.615, acc: 0.678
+2025-08-26 01:23:05: Epoch: 017 Test results, ap: 0.256, auc: 0.386, f1: 0.392, acc: 0.244
+2025-08-26 01:23:06: Epoch: 018 Train Loss: 0.546, ap: 0.691, auc: 0.724, f1: 0.644, acc: 0.687
+2025-08-26 01:23:06: Epoch: 018 Test results, ap: 0.225, auc: 0.442, f1: 0.392, acc: 0.244
+2025-08-26 01:23:07: Epoch: 019 Train Loss: 0.548, ap: 0.711, auc: 0.750, f1: 0.663, acc: 0.685
+2025-08-26 01:23:07: Epoch: 019 Test results, ap: 0.232, auc: 0.401, f1: 0.392, acc: 0.244
+2025-08-26 01:23:08: Epoch: 020 Train Loss: 0.550, ap: 0.692, auc: 0.744, f1: 0.671, acc: 0.639
+2025-08-26 01:23:09: Epoch: 020 Test results, ap: 0.275, auc: 0.468, f1: 0.392, acc: 0.244
+2025-08-26 01:23:10: Epoch: 021 Train Loss: 0.516, ap: 0.721, auc: 0.760, f1: 0.677, acc: 0.681
+2025-08-26 01:23:10: Epoch: 021 Test results, ap: 0.213, auc: 0.414, f1: 0.392, acc: 0.244
+2025-08-26 01:23:11: Epoch: 022 Train Loss: 0.509, ap: 0.715, auc: 0.768, f1: 0.690, acc: 0.692
+2025-08-26 01:23:11: Epoch: 022 Test results, ap: 0.215, auc: 0.423, f1: 0.392, acc: 0.244
+2025-08-26 01:23:12: Epoch: 023 Train Loss: 0.502, ap: 0.731, auc: 0.778, f1: 0.699, acc: 0.715
+2025-08-26 01:23:12: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:23:12: Epoch: 023 Test results, ap: 0.222, auc: 0.427, f1: 0.361, acc: 0.252
+2025-08-26 01:23:13: Epoch: 024 Train Loss: 0.494, ap: 0.738, auc: 0.784, f1: 0.685, acc: 0.722
+2025-08-26 01:23:13: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:23:14: Epoch: 024 Test results, ap: 0.224, auc: 0.442, f1: 0.338, acc: 0.268
+2025-08-26 01:23:15: Epoch: 025 Train Loss: 0.485, ap: 0.728, auc: 0.784, f1: 0.673, acc: 0.711
+2025-08-26 01:23:15: Epoch: 025 Test results, ap: 0.223, auc: 0.440, f1: 0.333, acc: 0.252
+2025-08-26 01:23:16: Epoch: 026 Train Loss: 0.478, ap: 0.744, auc: 0.793, f1: 0.705, acc: 0.736
+2025-08-26 01:23:16: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:23:16: Epoch: 026 Test results, ap: 0.227, auc: 0.442, f1: 0.333, acc: 0.252
+2025-08-26 01:23:17: Epoch: 027 Train Loss: 0.473, ap: 0.760, auc: 0.805, f1: 0.700, acc: 0.748
+2025-08-26 01:23:17: Epoch: 027 New best VALIDATION metrics
+2025-08-26 01:23:18: Epoch: 027 Test results, ap: 0.227, auc: 0.449, f1: 0.341, acc: 0.276
+2025-08-26 01:23:19: Epoch: 028 Train Loss: 0.469, ap: 0.745, auc: 0.794, f1: 0.710, acc: 0.734
+2025-08-26 01:23:19: Epoch: 028 Test results, ap: 0.223, auc: 0.435, f1: 0.338, acc: 0.268
+2025-08-26 01:23:20: Epoch: 029 Train Loss: 0.470, ap: 0.747, auc: 0.806, f1: 0.723, acc: 0.738
+2025-08-26 01:23:20: Epoch: 029 Test results, ap: 0.217, auc: 0.416, f1: 0.366, acc: 0.268
+2025-08-26 01:23:21: Epoch: 030 Train Loss: 0.464, ap: 0.755, auc: 0.808, f1: 0.726, acc: 0.750
+2025-08-26 01:23:21: Epoch: 030 Test results, ap: 0.221, auc: 0.425, f1: 0.345, acc: 0.260
+2025-08-26 01:23:22: Epoch: 031 Train Loss: 0.437, ap: 0.750, auc: 0.809, f1: 0.732, acc: 0.745
+2025-08-26 01:23:23: Epoch: 031 Test results, ap: 0.227, auc: 0.440, f1: 0.364, acc: 0.260
+2025-08-26 01:23:24: Epoch: 032 Train Loss: 0.439, ap: 0.746, auc: 0.806, f1: 0.699, acc: 0.731
+2025-08-26 01:23:24: Epoch: 032 Test results, ap: 0.236, auc: 0.462, f1: 0.357, acc: 0.268
+2025-08-26 01:23:25: Epoch: 033 Train Loss: 0.431, ap: 0.755, auc: 0.813, f1: 0.695, acc: 0.738
+2025-08-26 01:23:25: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:23:25: Epoch: 033 Test results, ap: 0.230, auc: 0.456, f1: 0.338, acc: 0.268
+2025-08-26 01:23:26: Epoch: 034 Train Loss: 0.432, ap: 0.753, auc: 0.814, f1: 0.715, acc: 0.748
+2025-08-26 01:23:26: Epoch: 034 Test results, ap: 0.237, auc: 0.460, f1: 0.336, acc: 0.260
+2025-08-26 01:23:27: Epoch: 035 Train Loss: 0.426, ap: 0.765, auc: 0.823, f1: 0.722, acc: 0.754
+2025-08-26 01:23:27: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:23:28: Epoch: 035 Test results, ap: 0.232, auc: 0.457, f1: 0.338, acc: 0.268
+2025-08-26 01:23:29: Epoch: 036 Train Loss: 0.420, ap: 0.751, auc: 0.814, f1: 0.717, acc: 0.746
+2025-08-26 01:23:29: Epoch: 036 Test results, ap: 0.230, auc: 0.451, f1: 0.338, acc: 0.268
+2025-08-26 01:23:30: Epoch: 037 Train Loss: 0.423, ap: 0.743, auc: 0.813, f1: 0.713, acc: 0.736
+2025-08-26 01:23:30: Epoch: 037 Test results, ap: 0.237, auc: 0.458, f1: 0.360, acc: 0.276
+2025-08-26 01:23:31: Epoch: 038 Train Loss: 0.420, ap: 0.761, auc: 0.823, f1: 0.736, acc: 0.748
+2025-08-26 01:23:32: Epoch: 038 Test results, ap: 0.237, auc: 0.472, f1: 0.355, acc: 0.260
+2025-08-26 01:23:33: Epoch: 039 Train Loss: 0.417, ap: 0.753, auc: 0.823, f1: 0.724, acc: 0.755
+2025-08-26 01:23:33: Epoch: 039 Test results, ap: 0.234, auc: 0.459, f1: 0.336, acc: 0.260
+2025-08-26 01:23:34: Epoch: 040 Train Loss: 0.413, ap: 0.758, auc: 0.822, f1: 0.713, acc: 0.750
+2025-08-26 01:23:34: Epoch: 040 Test results, ap: 0.237, auc: 0.461, f1: 0.338, acc: 0.268
+2025-08-26 01:23:35: Epoch: 041 Train Loss: 0.409, ap: 0.757, auc: 0.820, f1: 0.723, acc: 0.745
+2025-08-26 01:23:35: Epoch: 041 Test results, ap: 0.240, auc: 0.476, f1: 0.357, acc: 0.268
+2025-08-26 01:23:36: Epoch: 042 Train Loss: 0.396, ap: 0.757, auc: 0.823, f1: 0.727, acc: 0.752
+2025-08-26 01:23:37: Epoch: 042 Test results, ap: 0.233, auc: 0.459, f1: 0.360, acc: 0.276
+2025-08-26 01:23:38: Epoch: 043 Train Loss: 0.400, ap: 0.752, auc: 0.824, f1: 0.724, acc: 0.745
+2025-08-26 01:23:38: Epoch: 043 Test results, ap: 0.235, auc: 0.459, f1: 0.360, acc: 0.276
+2025-08-26 01:23:39: Epoch: 044 Train Loss: 0.394, ap: 0.754, auc: 0.821, f1: 0.711, acc: 0.743
+2025-08-26 01:23:39: Epoch: 044 Test results, ap: 0.234, auc: 0.456, f1: 0.360, acc: 0.276
+2025-08-26 01:23:40: Epoch: 045 Train Loss: 0.391, ap: 0.752, auc: 0.818, f1: 0.708, acc: 0.738
+2025-08-26 01:23:40: Epoch: 045 Test results, ap: 0.234, auc: 0.457, f1: 0.360, acc: 0.276
+2025-08-26 01:23:41: Epoch: 046 Train Loss: 0.395, ap: 0.758, auc: 0.827, f1: 0.725, acc: 0.750
+2025-08-26 01:23:42: Epoch: 046 Test results, ap: 0.233, auc: 0.462, f1: 0.360, acc: 0.276
+2025-08-26 01:23:43: Epoch: 047 Train Loss: 0.389, ap: 0.752, auc: 0.820, f1: 0.706, acc: 0.738
+2025-08-26 01:23:43: Epoch: 047 Test results, ap: 0.233, auc: 0.459, f1: 0.350, acc: 0.276
+2025-08-26 01:23:44: Epoch: 048 Train Loss: 0.392, ap: 0.762, auc: 0.824, f1: 0.726, acc: 0.752
+2025-08-26 01:23:44: Epoch: 048 Test results, ap: 0.232, auc: 0.454, f1: 0.360, acc: 0.276
+2025-08-26 01:23:45: Epoch: 049 Train Loss: 0.392, ap: 0.761, auc: 0.826, f1: 0.742, acc: 0.757
+2025-08-26 01:23:45: Epoch: 049 Test results, ap: 0.238, auc: 0.468, f1: 0.360, acc: 0.276
+2025-08-26 01:23:46: Epoch: 050 Train Loss: 0.390, ap: 0.761, auc: 0.824, f1: 0.718, acc: 0.748
+2025-08-26 01:23:47: Epoch: 050 Test results, ap: 0.233, auc: 0.459, f1: 0.350, acc: 0.276
+2025-08-26 01:23:47: used time 0.02h
+2025-08-26 01:23:47: Running Cross Validation 2
+2025-08-26 01:23:47: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:23:48: Epoch: 001 Train Loss: 0.693, ap: 0.575, auc: 0.635, f1: 0.602, acc: 0.549
+2025-08-26 01:23:48: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:23:48: Epoch: 001 Test results, ap: 0.217, auc: 0.367, f1: 0.392, acc: 0.244
+2025-08-26 01:23:49: Epoch: 002 Train Loss: 0.675, ap: 0.610, auc: 0.652, f1: 0.612, acc: 0.441
+2025-08-26 01:23:49: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:23:49: Epoch: 002 Test results, ap: 0.192, auc: 0.342, f1: 0.392, acc: 0.244
+2025-08-26 01:23:50: Epoch: 003 Train Loss: 0.667, ap: 0.608, auc: 0.660, f1: 0.529, acc: 0.658
+2025-08-26 01:23:50: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:23:50: Epoch: 003 Test results, ap: 0.225, auc: 0.445, f1: 0.000, acc: 0.732
+2025-08-26 01:23:51: Epoch: 004 Train Loss: 0.655, ap: 0.627, auc: 0.679, f1: 0.561, acc: 0.654
+2025-08-26 01:23:51: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:23:52: Epoch: 004 Test results, ap: 0.218, auc: 0.427, f1: 0.000, acc: 0.724
+2025-08-26 01:23:53: Epoch: 005 Train Loss: 0.651, ap: 0.593, auc: 0.663, f1: 0.559, acc: 0.644
+2025-08-26 01:23:53: Epoch: 005 Test results, ap: 0.216, auc: 0.435, f1: 0.000, acc: 0.748
+2025-08-26 01:23:54: Epoch: 006 Train Loss: 0.656, ap: 0.598, auc: 0.666, f1: 0.557, acc: 0.635
+2025-08-26 01:23:54: Epoch: 006 Test results, ap: 0.239, auc: 0.497, f1: 0.000, acc: 0.748
+2025-08-26 01:23:55: Epoch: 007 Train Loss: 0.649, ap: 0.597, auc: 0.669, f1: 0.605, acc: 0.633
+2025-08-26 01:23:56: Epoch: 007 Test results, ap: 0.237, auc: 0.445, f1: 0.000, acc: 0.724
+2025-08-26 01:23:57: Epoch: 008 Train Loss: 0.650, ap: 0.599, auc: 0.673, f1: 0.571, acc: 0.637
+2025-08-26 01:23:57: Epoch: 008 Test results, ap: 0.226, auc: 0.456, f1: 0.000, acc: 0.707
+2025-08-26 01:23:58: Epoch: 009 Train Loss: 0.645, ap: 0.608, auc: 0.669, f1: 0.557, acc: 0.635
+2025-08-26 01:23:58: Epoch: 009 Test results, ap: 0.212, auc: 0.427, f1: 0.000, acc: 0.724
+2025-08-26 01:23:59: Epoch: 010 Train Loss: 0.640, ap: 0.624, auc: 0.682, f1: 0.620, acc: 0.566
+2025-08-26 01:23:59: Epoch: 010 Test results, ap: 0.228, auc: 0.446, f1: 0.344, acc: 0.317
+2025-08-26 01:24:00: Epoch: 011 Train Loss: 0.630, ap: 0.594, auc: 0.677, f1: 0.578, acc: 0.660
+2025-08-26 01:24:01: Epoch: 011 Test results, ap: 0.224, auc: 0.447, f1: 0.000, acc: 0.715
+2025-08-26 01:24:01: Epoch: 011 New best TEST metrics
+2025-08-26 01:24:02: Epoch: 012 Train Loss: 0.623, ap: 0.593, auc: 0.678, f1: 0.585, acc: 0.654
+2025-08-26 01:24:02: Epoch: 012 Test results, ap: 0.229, auc: 0.456, f1: 0.000, acc: 0.732
+2025-08-26 01:24:02: Epoch: 012 New best TEST metrics
+2025-08-26 01:24:03: Epoch: 013 Train Loss: 0.628, ap: 0.612, auc: 0.671, f1: 0.611, acc: 0.637
+2025-08-26 01:24:03: Epoch: 013 Test results, ap: 0.211, auc: 0.427, f1: 0.143, acc: 0.610
+2025-08-26 01:24:04: Epoch: 014 Train Loss: 0.625, ap: 0.615, auc: 0.685, f1: 0.621, acc: 0.631
+2025-08-26 01:24:04: Epoch: 014 Test results, ap: 0.266, auc: 0.445, f1: 0.163, acc: 0.667
+2025-08-26 01:24:04: Epoch: 014 New best TEST metrics
+2025-08-26 01:24:05: Epoch: 015 Train Loss: 0.622, ap: 0.599, auc: 0.685, f1: 0.604, acc: 0.653
+2025-08-26 01:24:06: Epoch: 015 Test results, ap: 0.211, auc: 0.419, f1: 0.000, acc: 0.642
+2025-08-26 01:24:07: Epoch: 016 Train Loss: 0.616, ap: 0.607, auc: 0.682, f1: 0.595, acc: 0.647
+2025-08-26 01:24:07: Epoch: 016 Test results, ap: 0.226, auc: 0.446, f1: 0.049, acc: 0.683
+2025-08-26 01:24:08: Epoch: 017 Train Loss: 0.615, ap: 0.616, auc: 0.691, f1: 0.640, acc: 0.661
+2025-08-26 01:24:08: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:24:08: Epoch: 017 Test results, ap: 0.247, auc: 0.519, f1: 0.047, acc: 0.667
+2025-08-26 01:24:08: Epoch: 017 New best TEST metrics
+2025-08-26 01:24:09: Epoch: 018 Train Loss: 0.614, ap: 0.621, auc: 0.708, f1: 0.646, acc: 0.661
+2025-08-26 01:24:09: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:24:10: Epoch: 018 Test results, ap: 0.243, auc: 0.508, f1: 0.146, acc: 0.715
+2025-08-26 01:24:11: Epoch: 019 Train Loss: 0.609, ap: 0.637, auc: 0.704, f1: 0.637, acc: 0.642
+2025-08-26 01:24:11: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:24:11: Epoch: 019 Test results, ap: 0.264, auc: 0.550, f1: 0.093, acc: 0.683
+2025-08-26 01:24:11: Epoch: 019 New best TEST metrics
+2025-08-26 01:24:12: Epoch: 020 Train Loss: 0.601, ap: 0.617, auc: 0.705, f1: 0.655, acc: 0.679
+2025-08-26 01:24:12: Epoch: 020 Test results, ap: 0.225, auc: 0.477, f1: 0.226, acc: 0.610
+2025-08-26 01:24:13: Epoch: 021 Train Loss: 0.578, ap: 0.634, auc: 0.722, f1: 0.640, acc: 0.677
+2025-08-26 01:24:13: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:24:13: Epoch: 021 Test results, ap: 0.211, auc: 0.441, f1: 0.000, acc: 0.626
+2025-08-26 01:24:14: Epoch: 022 Train Loss: 0.569, ap: 0.625, auc: 0.725, f1: 0.663, acc: 0.653
+2025-08-26 01:24:15: Epoch: 022 Test results, ap: 0.227, auc: 0.473, f1: 0.078, acc: 0.618
+2025-08-26 01:24:16: Epoch: 023 Train Loss: 0.577, ap: 0.633, auc: 0.736, f1: 0.686, acc: 0.691
+2025-08-26 01:24:16: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:24:16: Epoch: 023 Test results, ap: 0.232, auc: 0.487, f1: 0.138, acc: 0.593
+2025-08-26 01:24:17: Epoch: 024 Train Loss: 0.562, ap: 0.674, auc: 0.745, f1: 0.667, acc: 0.688
+2025-08-26 01:24:17: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:24:17: Epoch: 024 Test results, ap: 0.236, auc: 0.464, f1: 0.185, acc: 0.569
+2025-08-26 01:24:18: Epoch: 025 Train Loss: 0.555, ap: 0.640, auc: 0.728, f1: 0.670, acc: 0.637
+2025-08-26 01:24:18: Epoch: 025 Test results, ap: 0.247, auc: 0.488, f1: 0.167, acc: 0.593
+2025-08-26 01:24:19: Epoch: 026 Train Loss: 0.569, ap: 0.669, auc: 0.745, f1: 0.678, acc: 0.679
+2025-08-26 01:24:20: Epoch: 026 Test results, ap: 0.243, auc: 0.513, f1: 0.197, acc: 0.602
+2025-08-26 01:24:21: Epoch: 027 Train Loss: 0.546, ap: 0.664, auc: 0.744, f1: 0.680, acc: 0.684
+2025-08-26 01:24:21: Epoch: 027 Test results, ap: 0.259, auc: 0.460, f1: 0.185, acc: 0.569
+2025-08-26 01:24:22: Epoch: 028 Train Loss: 0.534, ap: 0.662, auc: 0.743, f1: 0.672, acc: 0.667
+2025-08-26 01:24:22: Epoch: 028 Test results, ap: 0.265, auc: 0.437, f1: 0.172, acc: 0.610
+2025-08-26 01:24:23: Epoch: 029 Train Loss: 0.541, ap: 0.685, auc: 0.757, f1: 0.685, acc: 0.721
+2025-08-26 01:24:23: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:24:23: Epoch: 029 Test results, ap: 0.257, auc: 0.424, f1: 0.247, acc: 0.407
+2025-08-26 01:24:24: Epoch: 030 Train Loss: 0.537, ap: 0.681, auc: 0.755, f1: 0.682, acc: 0.675
+2025-08-26 01:24:25: Epoch: 030 Test results, ap: 0.278, auc: 0.480, f1: 0.185, acc: 0.569
+2025-08-26 01:24:26: Epoch: 031 Train Loss: 0.515, ap: 0.682, auc: 0.757, f1: 0.697, acc: 0.691
+2025-08-26 01:24:26: Epoch: 031 Test results, ap: 0.277, auc: 0.443, f1: 0.231, acc: 0.512
+2025-08-26 01:24:27: Epoch: 032 Train Loss: 0.497, ap: 0.681, auc: 0.758, f1: 0.701, acc: 0.721
+2025-08-26 01:24:27: Epoch: 032 Test results, ap: 0.289, auc: 0.446, f1: 0.295, acc: 0.496
+2025-08-26 01:24:28: Epoch: 033 Train Loss: 0.490, ap: 0.708, auc: 0.771, f1: 0.699, acc: 0.707
+2025-08-26 01:24:28: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:24:28: Epoch: 033 Test results, ap: 0.272, auc: 0.439, f1: 0.243, acc: 0.545
+2025-08-26 01:24:29: Epoch: 034 Train Loss: 0.495, ap: 0.706, auc: 0.769, f1: 0.704, acc: 0.700
+2025-08-26 01:24:30: Epoch: 034 Test results, ap: 0.304, auc: 0.464, f1: 0.321, acc: 0.415
+2025-08-26 01:24:31: Epoch: 035 Train Loss: 0.485, ap: 0.707, auc: 0.767, f1: 0.703, acc: 0.704
+2025-08-26 01:24:31: Epoch: 035 Test results, ap: 0.291, auc: 0.454, f1: 0.232, acc: 0.569
+2025-08-26 01:24:32: Epoch: 036 Train Loss: 0.479, ap: 0.718, auc: 0.776, f1: 0.718, acc: 0.718
+2025-08-26 01:24:32: Epoch: 036 New best VALIDATION metrics
+2025-08-26 01:24:32: Epoch: 036 Test results, ap: 0.285, auc: 0.468, f1: 0.302, acc: 0.512
+2025-08-26 01:24:33: Epoch: 037 Train Loss: 0.488, ap: 0.709, auc: 0.768, f1: 0.702, acc: 0.697
+2025-08-26 01:24:33: Epoch: 037 Test results, ap: 0.292, auc: 0.463, f1: 0.267, acc: 0.553
+2025-08-26 01:24:35: Epoch: 038 Train Loss: 0.477, ap: 0.726, auc: 0.781, f1: 0.709, acc: 0.700
+2025-08-26 01:24:35: Epoch: 038 New best VALIDATION metrics
+2025-08-26 01:24:35: Epoch: 038 Test results, ap: 0.273, auc: 0.449, f1: 0.276, acc: 0.488
+2025-08-26 01:24:36: Epoch: 039 Train Loss: 0.474, ap: 0.724, auc: 0.775, f1: 0.713, acc: 0.718
+2025-08-26 01:24:36: Epoch: 039 Test results, ap: 0.272, auc: 0.449, f1: 0.346, acc: 0.293
+2025-08-26 01:24:37: Epoch: 040 Train Loss: 0.471, ap: 0.721, auc: 0.776, f1: 0.723, acc: 0.723
+2025-08-26 01:24:37: Epoch: 040 Test results, ap: 0.284, auc: 0.456, f1: 0.312, acc: 0.390
+2025-08-26 01:24:38: Epoch: 041 Train Loss: 0.457, ap: 0.735, auc: 0.785, f1: 0.727, acc: 0.727
+2025-08-26 01:24:38: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:24:39: Epoch: 041 Test results, ap: 0.281, auc: 0.461, f1: 0.309, acc: 0.382
+2025-08-26 01:24:40: Epoch: 042 Train Loss: 0.451, ap: 0.732, auc: 0.785, f1: 0.730, acc: 0.732
+2025-08-26 01:24:40: Epoch: 042 Test results, ap: 0.281, auc: 0.464, f1: 0.327, acc: 0.398
+2025-08-26 01:24:41: Epoch: 043 Train Loss: 0.446, ap: 0.736, auc: 0.786, f1: 0.716, acc: 0.718
+2025-08-26 01:24:41: Epoch: 043 New best VALIDATION metrics
+2025-08-26 01:24:41: Epoch: 043 Test results, ap: 0.281, auc: 0.472, f1: 0.305, acc: 0.407
+2025-08-26 01:24:42: Epoch: 044 Train Loss: 0.439, ap: 0.738, auc: 0.785, f1: 0.704, acc: 0.711
+2025-08-26 01:24:42: Epoch: 044 New best VALIDATION metrics
+2025-08-26 01:24:42: Epoch: 044 Test results, ap: 0.281, auc: 0.469, f1: 0.299, acc: 0.390
+2025-08-26 01:24:43: Epoch: 045 Train Loss: 0.441, ap: 0.729, auc: 0.780, f1: 0.709, acc: 0.707
+2025-08-26 01:24:44: Epoch: 045 Test results, ap: 0.283, auc: 0.469, f1: 0.383, acc: 0.423
+2025-08-26 01:24:45: Epoch: 046 Train Loss: 0.442, ap: 0.735, auc: 0.788, f1: 0.730, acc: 0.730
+2025-08-26 01:24:45: Epoch: 046 Test results, ap: 0.286, auc: 0.478, f1: 0.356, acc: 0.293
+2025-08-26 01:24:46: Epoch: 047 Train Loss: 0.439, ap: 0.736, auc: 0.785, f1: 0.710, acc: 0.714
+2025-08-26 01:24:46: Epoch: 047 Test results, ap: 0.280, auc: 0.468, f1: 0.309, acc: 0.382
+2025-08-26 01:24:47: Epoch: 048 Train Loss: 0.439, ap: 0.741, auc: 0.790, f1: 0.715, acc: 0.718
+2025-08-26 01:24:47: Epoch: 048 New best VALIDATION metrics
+2025-08-26 01:24:47: Epoch: 048 Test results, ap: 0.274, auc: 0.466, f1: 0.324, acc: 0.390
+2025-08-26 01:24:48: Epoch: 049 Train Loss: 0.435, ap: 0.744, auc: 0.792, f1: 0.724, acc: 0.727
+2025-08-26 01:24:48: Epoch: 049 New best VALIDATION metrics
+2025-08-26 01:24:49: Epoch: 049 Test results, ap: 0.248, auc: 0.475, f1: 0.336, acc: 0.390
+2025-08-26 01:24:50: Epoch: 050 Train Loss: 0.430, ap: 0.746, auc: 0.795, f1: 0.728, acc: 0.732
+2025-08-26 01:24:50: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:24:50: Epoch: 050 Test results, ap: 0.281, auc: 0.476, f1: 0.379, acc: 0.415
+2025-08-26 01:24:50: used time 0.02h
+2025-08-26 01:24:50: Running Cross Validation 3
+2025-08-26 01:24:50: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:24:51: Epoch: 001 Train Loss: 0.695, ap: 0.529, auc: 0.612, f1: 0.000, acc: 0.570
+2025-08-26 01:24:51: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:24:51: Epoch: 001 Test results, ap: 0.217, auc: 0.453, f1: 0.000, acc: 0.756
+2025-08-26 01:24:52: Epoch: 002 Train Loss: 0.679, ap: 0.557, auc: 0.628, f1: 0.411, acc: 0.591
+2025-08-26 01:24:52: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:24:53: Epoch: 002 Test results, ap: 0.215, auc: 0.442, f1: 0.392, acc: 0.244
+2025-08-26 01:24:54: Epoch: 003 Train Loss: 0.667, ap: 0.570, auc: 0.636, f1: 0.487, acc: 0.614
+2025-08-26 01:24:54: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:24:54: Epoch: 003 Test results, ap: 0.221, auc: 0.470, f1: 0.349, acc: 0.545
+2025-08-26 01:24:55: Epoch: 004 Train Loss: 0.657, ap: 0.526, auc: 0.637, f1: 0.583, acc: 0.624
+2025-08-26 01:24:55: Epoch: 004 Test results, ap: 0.272, auc: 0.564, f1: 0.392, acc: 0.244
+2025-08-26 01:24:56: Epoch: 005 Train Loss: 0.663, ap: 0.544, auc: 0.637, f1: 0.415, acc: 0.612
+2025-08-26 01:24:56: Epoch: 005 Test results, ap: 0.246, auc: 0.469, f1: 0.196, acc: 0.667
+2025-08-26 01:24:57: Epoch: 006 Train Loss: 0.653, ap: 0.536, auc: 0.643, f1: 0.499, acc: 0.624
+2025-08-26 01:24:58: Epoch: 006 Test results, ap: 0.199, auc: 0.360, f1: 0.167, acc: 0.512
+2025-08-26 01:24:59: Epoch: 007 Train Loss: 0.645, ap: 0.557, auc: 0.635, f1: 0.567, acc: 0.607
+2025-08-26 01:24:59: Epoch: 007 Test results, ap: 0.227, auc: 0.478, f1: 0.408, acc: 0.504
+2025-08-26 01:25:00: Epoch: 008 Train Loss: 0.654, ap: 0.560, auc: 0.649, f1: 0.500, acc: 0.633
+2025-08-26 01:25:00: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:25:00: Epoch: 008 Test results, ap: 0.186, auc: 0.353, f1: 0.000, acc: 0.634
+2025-08-26 01:25:01: Epoch: 009 Train Loss: 0.656, ap: 0.574, auc: 0.647, f1: 0.479, acc: 0.628
+2025-08-26 01:25:01: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:25:01: Epoch: 009 Test results, ap: 0.207, auc: 0.387, f1: 0.397, acc: 0.260
+2025-08-26 01:25:02: Epoch: 010 Train Loss: 0.651, ap: 0.575, auc: 0.663, f1: 0.542, acc: 0.619
+2025-08-26 01:25:02: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:25:03: Epoch: 010 Test results, ap: 0.175, auc: 0.307, f1: 0.392, acc: 0.244
+2025-08-26 01:25:04: Epoch: 011 Train Loss: 0.642, ap: 0.556, auc: 0.663, f1: 0.536, acc: 0.640
+2025-08-26 01:25:04: Epoch: 011 Test results, ap: 0.184, auc: 0.335, f1: 0.265, acc: 0.325
+2025-08-26 01:25:04: Epoch: 011 New best TEST metrics
+2025-08-26 01:25:05: Epoch: 012 Train Loss: 0.637, ap: 0.577, auc: 0.659, f1: 0.534, acc: 0.633
+2025-08-26 01:25:05: Epoch: 012 Test results, ap: 0.183, auc: 0.329, f1: 0.000, acc: 0.610
+2025-08-26 01:25:06: Epoch: 013 Train Loss: 0.636, ap: 0.573, auc: 0.668, f1: 0.588, acc: 0.635
+2025-08-26 01:25:06: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:25:06: Epoch: 013 Test results, ap: 0.187, auc: 0.352, f1: 0.145, acc: 0.520
+2025-08-26 01:25:06: Epoch: 013 New best TEST metrics
+2025-08-26 01:25:07: Epoch: 014 Train Loss: 0.632, ap: 0.573, auc: 0.674, f1: 0.531, acc: 0.646
+2025-08-26 01:25:07: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:25:08: Epoch: 014 Test results, ap: 0.187, auc: 0.344, f1: 0.000, acc: 0.634
+2025-08-26 01:25:09: Epoch: 015 Train Loss: 0.630, ap: 0.576, auc: 0.675, f1: 0.544, acc: 0.646
+2025-08-26 01:25:09: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:25:09: Epoch: 015 Test results, ap: 0.186, auc: 0.335, f1: 0.000, acc: 0.634
+2025-08-26 01:25:10: Epoch: 016 Train Loss: 0.626, ap: 0.590, auc: 0.678, f1: 0.593, acc: 0.649
+2025-08-26 01:25:10: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:25:10: Epoch: 016 Test results, ap: 0.195, auc: 0.375, f1: 0.138, acc: 0.593
+2025-08-26 01:25:10: Epoch: 016 New best TEST metrics
+2025-08-26 01:25:11: Epoch: 017 Train Loss: 0.620, ap: 0.609, auc: 0.682, f1: 0.574, acc: 0.638
+2025-08-26 01:25:11: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:25:12: Epoch: 017 Test results, ap: 0.206, auc: 0.380, f1: 0.148, acc: 0.626
+2025-08-26 01:25:12: Epoch: 017 New best TEST metrics
+2025-08-26 01:25:13: Epoch: 018 Train Loss: 0.618, ap: 0.593, auc: 0.680, f1: 0.586, acc: 0.624
+2025-08-26 01:25:13: Epoch: 018 Test results, ap: 0.208, auc: 0.424, f1: 0.169, acc: 0.602
+2025-08-26 01:25:13: Epoch: 018 New best TEST metrics
+2025-08-26 01:25:14: Epoch: 019 Train Loss: 0.616, ap: 0.596, auc: 0.680, f1: 0.593, acc: 0.630
+2025-08-26 01:25:14: Epoch: 019 Test results, ap: 0.187, auc: 0.358, f1: 0.066, acc: 0.537
+2025-08-26 01:25:15: Epoch: 020 Train Loss: 0.617, ap: 0.601, auc: 0.692, f1: 0.618, acc: 0.631
+2025-08-26 01:25:15: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:25:15: Epoch: 020 Test results, ap: 0.188, auc: 0.361, f1: 0.167, acc: 0.512
+2025-08-26 01:25:16: Epoch: 021 Train Loss: 0.600, ap: 0.603, auc: 0.697, f1: 0.625, acc: 0.623
+2025-08-26 01:25:16: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:25:17: Epoch: 021 Test results, ap: 0.190, auc: 0.365, f1: 0.167, acc: 0.512
+2025-08-26 01:25:18: Epoch: 022 Train Loss: 0.600, ap: 0.607, auc: 0.700, f1: 0.618, acc: 0.612
+2025-08-26 01:25:18: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:25:18: Epoch: 022 Test results, ap: 0.186, auc: 0.353, f1: 0.171, acc: 0.528
+2025-08-26 01:25:19: Epoch: 023 Train Loss: 0.595, ap: 0.599, auc: 0.684, f1: 0.588, acc: 0.612
+2025-08-26 01:25:19: Epoch: 023 Test results, ap: 0.197, auc: 0.377, f1: 0.000, acc: 0.707
+2025-08-26 01:25:20: Epoch: 024 Train Loss: 0.580, ap: 0.599, auc: 0.681, f1: 0.545, acc: 0.612
+2025-08-26 01:25:20: Epoch: 024 Test results, ap: 0.208, auc: 0.430, f1: 0.000, acc: 0.715
+2025-08-26 01:25:20: Epoch: 024 New best TEST metrics
+2025-08-26 01:25:22: Epoch: 025 Train Loss: 0.593, ap: 0.605, auc: 0.698, f1: 0.604, acc: 0.621
+2025-08-26 01:25:22: Epoch: 025 Test results, ap: 0.173, auc: 0.298, f1: 0.000, acc: 0.707
+2025-08-26 01:25:23: Epoch: 026 Train Loss: 0.585, ap: 0.614, auc: 0.697, f1: 0.576, acc: 0.631
+2025-08-26 01:25:23: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:25:23: Epoch: 026 Test results, ap: 0.217, auc: 0.421, f1: 0.000, acc: 0.715
+2025-08-26 01:25:23: Epoch: 026 New best TEST metrics
+2025-08-26 01:25:24: Epoch: 027 Train Loss: 0.576, ap: 0.635, auc: 0.714, f1: 0.600, acc: 0.647
+2025-08-26 01:25:24: Epoch: 027 New best VALIDATION metrics
+2025-08-26 01:25:24: Epoch: 027 Test results, ap: 0.202, auc: 0.395, f1: 0.000, acc: 0.707
+2025-08-26 01:25:25: Epoch: 028 Train Loss: 0.576, ap: 0.633, auc: 0.710, f1: 0.632, acc: 0.653
+2025-08-26 01:25:26: Epoch: 028 Test results, ap: 0.193, auc: 0.365, f1: 0.000, acc: 0.707
+2025-08-26 01:25:27: Epoch: 029 Train Loss: 0.575, ap: 0.650, auc: 0.708, f1: 0.597, acc: 0.653
+2025-08-26 01:25:27: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:25:27: Epoch: 029 Test results, ap: 0.216, auc: 0.431, f1: 0.000, acc: 0.707
+2025-08-26 01:25:27: Epoch: 029 New best TEST metrics
+2025-08-26 01:25:28: Epoch: 030 Train Loss: 0.565, ap: 0.654, auc: 0.712, f1: 0.610, acc: 0.646
+2025-08-26 01:25:28: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:25:28: Epoch: 030 Test results, ap: 0.215, auc: 0.439, f1: 0.000, acc: 0.707
+2025-08-26 01:25:28: Epoch: 030 New best TEST metrics
+2025-08-26 01:25:29: Epoch: 031 Train Loss: 0.551, ap: 0.676, auc: 0.734, f1: 0.638, acc: 0.663
+2025-08-26 01:25:29: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:25:29: Epoch: 031 Test results, ap: 0.211, auc: 0.427, f1: 0.000, acc: 0.707
+2025-08-26 01:25:30: Epoch: 032 Train Loss: 0.549, ap: 0.672, auc: 0.736, f1: 0.662, acc: 0.665
+2025-08-26 01:25:31: Epoch: 032 Test results, ap: 0.204, auc: 0.415, f1: 0.000, acc: 0.707
+2025-08-26 01:25:32: Epoch: 033 Train Loss: 0.537, ap: 0.695, auc: 0.742, f1: 0.645, acc: 0.670
+2025-08-26 01:25:32: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:25:32: Epoch: 033 Test results, ap: 0.209, auc: 0.435, f1: 0.000, acc: 0.707
+2025-08-26 01:25:33: Epoch: 034 Train Loss: 0.531, ap: 0.695, auc: 0.741, f1: 0.658, acc: 0.668
+2025-08-26 01:25:33: Epoch: 034 Test results, ap: 0.204, auc: 0.412, f1: 0.000, acc: 0.707
+2025-08-26 01:25:34: Epoch: 035 Train Loss: 0.530, ap: 0.703, auc: 0.749, f1: 0.654, acc: 0.672
+2025-08-26 01:25:34: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:25:35: Epoch: 035 Test results, ap: 0.213, auc: 0.437, f1: 0.000, acc: 0.707
+2025-08-26 01:25:36: Epoch: 036 Train Loss: 0.532, ap: 0.692, auc: 0.736, f1: 0.673, acc: 0.675
+2025-08-26 01:25:36: Epoch: 036 Test results, ap: 0.196, auc: 0.386, f1: 0.000, acc: 0.707
+2025-08-26 01:25:37: Epoch: 037 Train Loss: 0.526, ap: 0.699, auc: 0.742, f1: 0.652, acc: 0.674
+2025-08-26 01:25:37: Epoch: 037 Test results, ap: 0.213, auc: 0.436, f1: 0.000, acc: 0.715
+2025-08-26 01:25:38: Epoch: 038 Train Loss: 0.534, ap: 0.664, auc: 0.721, f1: 0.638, acc: 0.668
+2025-08-26 01:25:38: Epoch: 038 Test results, ap: 0.206, auc: 0.422, f1: 0.000, acc: 0.715
+2025-08-26 01:25:39: Epoch: 039 Train Loss: 0.527, ap: 0.682, auc: 0.743, f1: 0.650, acc: 0.675
+2025-08-26 01:25:40: Epoch: 039 Test results, ap: 0.202, auc: 0.403, f1: 0.000, acc: 0.715
+2025-08-26 01:25:41: Epoch: 040 Train Loss: 0.519, ap: 0.696, auc: 0.750, f1: 0.654, acc: 0.683
+2025-08-26 01:25:41: Epoch: 040 Test results, ap: 0.215, auc: 0.453, f1: 0.000, acc: 0.707
+2025-08-26 01:25:41: Epoch: 040 New best TEST metrics
+2025-08-26 01:25:42: Epoch: 041 Train Loss: 0.506, ap: 0.700, auc: 0.753, f1: 0.672, acc: 0.688
+2025-08-26 01:25:42: Epoch: 041 Test results, ap: 0.213, auc: 0.448, f1: 0.000, acc: 0.715
+2025-08-26 01:25:43: Epoch: 042 Train Loss: 0.502, ap: 0.699, auc: 0.754, f1: 0.669, acc: 0.679
+2025-08-26 01:25:43: Epoch: 042 New best VALIDATION metrics
+2025-08-26 01:25:43: Epoch: 042 Test results, ap: 0.209, auc: 0.436, f1: 0.000, acc: 0.707
+2025-08-26 01:25:44: Epoch: 043 Train Loss: 0.494, ap: 0.700, auc: 0.757, f1: 0.674, acc: 0.684
+2025-08-26 01:25:44: Epoch: 043 New best VALIDATION metrics
+2025-08-26 01:25:45: Epoch: 043 Test results, ap: 0.205, auc: 0.422, f1: 0.000, acc: 0.715
+2025-08-26 01:25:46: Epoch: 044 Train Loss: 0.505, ap: 0.702, auc: 0.761, f1: 0.678, acc: 0.686
+2025-08-26 01:25:46: Epoch: 044 New best VALIDATION metrics
+2025-08-26 01:25:46: Epoch: 044 Test results, ap: 0.207, auc: 0.429, f1: 0.000, acc: 0.707
+2025-08-26 01:25:47: Epoch: 045 Train Loss: 0.489, ap: 0.706, auc: 0.762, f1: 0.682, acc: 0.688
+2025-08-26 01:25:47: Epoch: 045 New best VALIDATION metrics
+2025-08-26 01:25:47: Epoch: 045 Test results, ap: 0.211, auc: 0.443, f1: 0.000, acc: 0.699
+2025-08-26 01:25:48: Epoch: 046 Train Loss: 0.492, ap: 0.703, auc: 0.760, f1: 0.679, acc: 0.690
+2025-08-26 01:25:48: Epoch: 046 Test results, ap: 0.214, auc: 0.446, f1: 0.000, acc: 0.699
+2025-08-26 01:25:49: Epoch: 047 Train Loss: 0.487, ap: 0.700, auc: 0.759, f1: 0.675, acc: 0.684
+2025-08-26 01:25:50: Epoch: 047 Test results, ap: 0.208, auc: 0.430, f1: 0.000, acc: 0.715
+2025-08-26 01:25:51: Epoch: 048 Train Loss: 0.489, ap: 0.713, auc: 0.764, f1: 0.674, acc: 0.683
+2025-08-26 01:25:51: Epoch: 048 New best VALIDATION metrics
+2025-08-26 01:25:51: Epoch: 048 Test results, ap: 0.211, auc: 0.440, f1: 0.000, acc: 0.715
+2025-08-26 01:25:52: Epoch: 049 Train Loss: 0.488, ap: 0.713, auc: 0.767, f1: 0.677, acc: 0.691
+2025-08-26 01:25:52: Epoch: 049 New best VALIDATION metrics
+2025-08-26 01:25:52: Epoch: 049 Test results, ap: 0.226, auc: 0.468, f1: 0.000, acc: 0.715
+2025-08-26 01:25:52: Epoch: 049 New best TEST metrics
+2025-08-26 01:25:53: Epoch: 050 Train Loss: 0.483, ap: 0.707, auc: 0.759, f1: 0.683, acc: 0.697
+2025-08-26 01:25:53: Epoch: 050 Test results, ap: 0.240, auc: 0.494, f1: 0.000, acc: 0.724
+2025-08-26 01:25:53: Epoch: 050 New best TEST metrics
+2025-08-26 01:25:53: used time 0.02h
+2025-08-26 01:25:53: Running Cross Validation 4
+2025-08-26 01:25:53: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:25:54: Epoch: 001 Train Loss: 0.694, ap: 0.530, auc: 0.603, f1: 0.540, acc: 0.570
+2025-08-26 01:25:54: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:25:55: Epoch: 001 Test results, ap: 0.257, auc: 0.419, f1: 0.392, acc: 0.244
+2025-08-26 01:25:56: Epoch: 002 Train Loss: 0.673, ap: 0.553, auc: 0.631, f1: 0.376, acc: 0.601
+2025-08-26 01:25:56: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:25:56: Epoch: 002 Test results, ap: 0.191, auc: 0.345, f1: 0.000, acc: 0.756
+2025-08-26 01:25:57: Epoch: 003 Train Loss: 0.660, ap: 0.558, auc: 0.637, f1: 0.545, acc: 0.591
+2025-08-26 01:25:57: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:25:57: Epoch: 003 Test results, ap: 0.230, auc: 0.480, f1: 0.000, acc: 0.707
+2025-08-26 01:25:58: Epoch: 004 Train Loss: 0.656, ap: 0.561, auc: 0.642, f1: 0.566, acc: 0.589
+2025-08-26 01:25:58: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:25:59: Epoch: 004 Test results, ap: 0.203, auc: 0.394, f1: 0.000, acc: 0.650
+2025-08-26 01:26:00: Epoch: 005 Train Loss: 0.652, ap: 0.557, auc: 0.627, f1: 0.500, acc: 0.626
+2025-08-26 01:26:00: Epoch: 005 Test results, ap: 0.225, auc: 0.437, f1: 0.000, acc: 0.715
+2025-08-26 01:26:01: Epoch: 006 Train Loss: 0.648, ap: 0.580, auc: 0.649, f1: 0.562, acc: 0.610
+2025-08-26 01:26:01: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:26:01: Epoch: 006 Test results, ap: 0.221, auc: 0.435, f1: 0.000, acc: 0.675
+2025-08-26 01:26:02: Epoch: 007 Train Loss: 0.640, ap: 0.569, auc: 0.647, f1: 0.550, acc: 0.600
+2025-08-26 01:26:02: Epoch: 007 Test results, ap: 0.211, auc: 0.414, f1: 0.000, acc: 0.683
+2025-08-26 01:26:03: Epoch: 008 Train Loss: 0.641, ap: 0.573, auc: 0.636, f1: 0.557, acc: 0.582
+2025-08-26 01:26:04: Epoch: 008 Test results, ap: 0.214, auc: 0.428, f1: 0.000, acc: 0.715
+2025-08-26 01:26:05: Epoch: 009 Train Loss: 0.645, ap: 0.552, auc: 0.634, f1: 0.532, acc: 0.612
+2025-08-26 01:26:05: Epoch: 009 Test results, ap: 0.255, auc: 0.546, f1: 0.000, acc: 0.707
+2025-08-26 01:26:06: Epoch: 010 Train Loss: 0.640, ap: 0.557, auc: 0.626, f1: 0.522, acc: 0.619
+2025-08-26 01:26:06: Epoch: 010 Test results, ap: 0.248, auc: 0.525, f1: 0.000, acc: 0.691
+2025-08-26 01:26:07: Epoch: 011 Train Loss: 0.633, ap: 0.550, auc: 0.640, f1: 0.513, acc: 0.616
+2025-08-26 01:26:07: Epoch: 011 Test results, ap: 0.257, auc: 0.539, f1: 0.000, acc: 0.699
+2025-08-26 01:26:07: Epoch: 011 New best TEST metrics
+2025-08-26 01:26:08: Epoch: 012 Train Loss: 0.628, ap: 0.582, auc: 0.646, f1: 0.555, acc: 0.621
+2025-08-26 01:26:09: Epoch: 012 Test results, ap: 0.245, auc: 0.523, f1: 0.000, acc: 0.642
+2025-08-26 01:26:10: Epoch: 013 Train Loss: 0.631, ap: 0.572, auc: 0.652, f1: 0.591, acc: 0.598
+2025-08-26 01:26:10: Epoch: 013 Test results, ap: 0.273, auc: 0.571, f1: 0.000, acc: 0.683
+2025-08-26 01:26:10: Epoch: 013 New best TEST metrics
+2025-08-26 01:26:11: Epoch: 014 Train Loss: 0.623, ap: 0.569, auc: 0.650, f1: 0.545, acc: 0.649
+2025-08-26 01:26:11: Epoch: 014 Test results, ap: 0.270, auc: 0.563, f1: 0.000, acc: 0.691
+2025-08-26 01:26:12: Epoch: 015 Train Loss: 0.626, ap: 0.574, auc: 0.650, f1: 0.547, acc: 0.640
+2025-08-26 01:26:13: Epoch: 015 Test results, ap: 0.280, auc: 0.593, f1: 0.000, acc: 0.691
+2025-08-26 01:26:13: Epoch: 015 New best TEST metrics
+2025-08-26 01:26:14: Epoch: 016 Train Loss: 0.620, ap: 0.602, auc: 0.660, f1: 0.594, acc: 0.640
+2025-08-26 01:26:14: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:26:14: Epoch: 016 Test results, ap: 0.281, auc: 0.585, f1: 0.000, acc: 0.691
+2025-08-26 01:26:15: Epoch: 017 Train Loss: 0.616, ap: 0.587, auc: 0.648, f1: 0.606, acc: 0.598
+2025-08-26 01:26:15: Epoch: 017 Test results, ap: 0.247, auc: 0.530, f1: 0.192, acc: 0.659
+2025-08-26 01:26:16: Epoch: 018 Train Loss: 0.623, ap: 0.571, auc: 0.642, f1: 0.580, acc: 0.600
+2025-08-26 01:26:16: Epoch: 018 Test results, ap: 0.284, auc: 0.556, f1: 0.240, acc: 0.691
+2025-08-26 01:26:17: Epoch: 019 Train Loss: 0.624, ap: 0.576, auc: 0.650, f1: 0.605, acc: 0.638
+2025-08-26 01:26:18: Epoch: 019 Test results, ap: 0.282, auc: 0.591, f1: 0.000, acc: 0.683
+2025-08-26 01:26:19: Epoch: 020 Train Loss: 0.614, ap: 0.573, auc: 0.645, f1: 0.588, acc: 0.624
+2025-08-26 01:26:19: Epoch: 020 Test results, ap: 0.286, auc: 0.586, f1: 0.000, acc: 0.699
+2025-08-26 01:26:20: Epoch: 021 Train Loss: 0.611, ap: 0.583, auc: 0.654, f1: 0.604, acc: 0.630
+2025-08-26 01:26:20: Epoch: 021 Test results, ap: 0.282, auc: 0.598, f1: 0.000, acc: 0.683
+2025-08-26 01:26:20: Epoch: 021 New best TEST metrics
+2025-08-26 01:26:21: Epoch: 022 Train Loss: 0.608, ap: 0.574, auc: 0.659, f1: 0.582, acc: 0.633
+2025-08-26 01:26:21: Epoch: 022 Test results, ap: 0.272, auc: 0.571, f1: 0.000, acc: 0.699
+2025-08-26 01:26:22: Epoch: 023 Train Loss: 0.603, ap: 0.583, auc: 0.659, f1: 0.584, acc: 0.646
+2025-08-26 01:26:23: Epoch: 023 Test results, ap: 0.275, auc: 0.572, f1: 0.000, acc: 0.715
+2025-08-26 01:26:24: Epoch: 024 Train Loss: 0.605, ap: 0.581, auc: 0.655, f1: 0.595, acc: 0.642
+2025-08-26 01:26:24: Epoch: 024 Test results, ap: 0.275, auc: 0.581, f1: 0.000, acc: 0.699
+2025-08-26 01:26:25: Epoch: 025 Train Loss: 0.604, ap: 0.591, auc: 0.654, f1: 0.613, acc: 0.642
+2025-08-26 01:26:25: Epoch: 025 Test results, ap: 0.275, auc: 0.578, f1: 0.000, acc: 0.699
+2025-08-26 01:26:26: Epoch: 026 Train Loss: 0.602, ap: 0.583, auc: 0.650, f1: 0.619, acc: 0.631
+2025-08-26 01:26:26: Epoch: 026 Test results, ap: 0.287, auc: 0.598, f1: 0.000, acc: 0.724
+2025-08-26 01:26:26: Epoch: 026 New best TEST metrics
+2025-08-26 01:26:27: Epoch: 027 Train Loss: 0.602, ap: 0.589, auc: 0.655, f1: 0.616, acc: 0.646
+2025-08-26 01:26:28: Epoch: 027 Test results, ap: 0.299, auc: 0.584, f1: 0.000, acc: 0.724
+2025-08-26 01:26:29: Epoch: 028 Train Loss: 0.595, ap: 0.586, auc: 0.659, f1: 0.626, acc: 0.619
+2025-08-26 01:26:29: Epoch: 028 Test results, ap: 0.243, auc: 0.494, f1: 0.000, acc: 0.691
+2025-08-26 01:26:30: Epoch: 029 Train Loss: 0.596, ap: 0.585, auc: 0.662, f1: 0.577, acc: 0.651
+2025-08-26 01:26:30: Epoch: 029 Test results, ap: 0.232, auc: 0.473, f1: 0.000, acc: 0.691
+2025-08-26 01:26:31: Epoch: 030 Train Loss: 0.598, ap: 0.601, auc: 0.659, f1: 0.613, acc: 0.637
+2025-08-26 01:26:32: Epoch: 030 Test results, ap: 0.229, auc: 0.465, f1: 0.000, acc: 0.691
+2025-08-26 01:26:33: Epoch: 031 Train Loss: 0.588, ap: 0.601, auc: 0.666, f1: 0.624, acc: 0.640
+2025-08-26 01:26:33: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:26:33: Epoch: 031 Test results, ap: 0.238, auc: 0.482, f1: 0.000, acc: 0.691
+2025-08-26 01:26:34: Epoch: 032 Train Loss: 0.587, ap: 0.597, auc: 0.669, f1: 0.626, acc: 0.642
+2025-08-26 01:26:34: Epoch: 032 Test results, ap: 0.224, auc: 0.459, f1: 0.000, acc: 0.691
+2025-08-26 01:26:35: Epoch: 033 Train Loss: 0.584, ap: 0.602, auc: 0.669, f1: 0.613, acc: 0.668
+2025-08-26 01:26:35: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:26:35: Epoch: 033 Test results, ap: 0.197, auc: 0.368, f1: 0.000, acc: 0.724
+2025-08-26 01:26:36: Epoch: 034 Train Loss: 0.587, ap: 0.625, auc: 0.675, f1: 0.631, acc: 0.619
+2025-08-26 01:26:36: Epoch: 034 New best VALIDATION metrics
+2025-08-26 01:26:37: Epoch: 034 Test results, ap: 0.209, auc: 0.383, f1: 0.000, acc: 0.699
+2025-08-26 01:26:38: Epoch: 035 Train Loss: 0.575, ap: 0.611, auc: 0.673, f1: 0.625, acc: 0.654
+2025-08-26 01:26:38: Epoch: 035 Test results, ap: 0.210, auc: 0.385, f1: 0.000, acc: 0.732
+2025-08-26 01:26:39: Epoch: 036 Train Loss: 0.570, ap: 0.608, auc: 0.672, f1: 0.619, acc: 0.654
+2025-08-26 01:26:39: Epoch: 036 Test results, ap: 0.215, auc: 0.396, f1: 0.000, acc: 0.748
+2025-08-26 01:26:40: Epoch: 037 Train Loss: 0.570, ap: 0.604, auc: 0.668, f1: 0.614, acc: 0.653
+2025-08-26 01:26:40: Epoch: 037 Test results, ap: 0.208, auc: 0.378, f1: 0.000, acc: 0.724
+2025-08-26 01:26:41: Epoch: 038 Train Loss: 0.574, ap: 0.619, auc: 0.675, f1: 0.622, acc: 0.668
+2025-08-26 01:26:42: Epoch: 038 Test results, ap: 0.210, auc: 0.388, f1: 0.000, acc: 0.732
+2025-08-26 01:26:43: Epoch: 039 Train Loss: 0.574, ap: 0.618, auc: 0.678, f1: 0.631, acc: 0.631
+2025-08-26 01:26:43: Epoch: 039 Test results, ap: 0.221, auc: 0.415, f1: 0.000, acc: 0.724
+2025-08-26 01:26:44: Epoch: 040 Train Loss: 0.569, ap: 0.598, auc: 0.668, f1: 0.612, acc: 0.617
+2025-08-26 01:26:44: Epoch: 040 Test results, ap: 0.201, auc: 0.353, f1: 0.000, acc: 0.707
+2025-08-26 01:26:45: Epoch: 041 Train Loss: 0.557, ap: 0.610, auc: 0.674, f1: 0.622, acc: 0.621
+2025-08-26 01:26:45: Epoch: 041 Test results, ap: 0.202, auc: 0.362, f1: 0.000, acc: 0.691
+2025-08-26 01:26:46: Epoch: 042 Train Loss: 0.557, ap: 0.620, auc: 0.676, f1: 0.621, acc: 0.651
+2025-08-26 01:26:47: Epoch: 042 Test results, ap: 0.190, auc: 0.348, f1: 0.000, acc: 0.707
+2025-08-26 01:26:48: Epoch: 043 Train Loss: 0.558, ap: 0.600, auc: 0.670, f1: 0.622, acc: 0.630
+2025-08-26 01:26:48: Epoch: 043 Test results, ap: 0.194, auc: 0.360, f1: 0.000, acc: 0.699
+2025-08-26 01:26:49: Epoch: 044 Train Loss: 0.550, ap: 0.618, auc: 0.679, f1: 0.619, acc: 0.660
+2025-08-26 01:26:49: Epoch: 044 Test results, ap: 0.191, auc: 0.355, f1: 0.000, acc: 0.699
+2025-08-26 01:26:50: Epoch: 045 Train Loss: 0.554, ap: 0.618, auc: 0.677, f1: 0.613, acc: 0.653
+2025-08-26 01:26:50: Epoch: 045 Test results, ap: 0.202, auc: 0.385, f1: 0.000, acc: 0.699
+2025-08-26 01:26:51: Epoch: 046 Train Loss: 0.551, ap: 0.622, auc: 0.679, f1: 0.631, acc: 0.646
+2025-08-26 01:26:51: Epoch: 046 New best VALIDATION metrics
+2025-08-26 01:26:52: Epoch: 046 Test results, ap: 0.193, auc: 0.358, f1: 0.000, acc: 0.691
+2025-08-26 01:26:53: Epoch: 047 Train Loss: 0.547, ap: 0.617, auc: 0.682, f1: 0.626, acc: 0.656
+2025-08-26 01:26:53: Epoch: 047 Test results, ap: 0.195, auc: 0.370, f1: 0.000, acc: 0.699
+2025-08-26 01:26:54: Epoch: 048 Train Loss: 0.552, ap: 0.606, auc: 0.680, f1: 0.626, acc: 0.654
+2025-08-26 01:26:54: Epoch: 048 Test results, ap: 0.194, auc: 0.367, f1: 0.000, acc: 0.699
+2025-08-26 01:26:55: Epoch: 049 Train Loss: 0.552, ap: 0.621, auc: 0.686, f1: 0.625, acc: 0.630
+2025-08-26 01:26:55: Epoch: 049 New best VALIDATION metrics
+2025-08-26 01:26:56: Epoch: 049 Test results, ap: 0.198, auc: 0.377, f1: 0.000, acc: 0.691
+2025-08-26 01:26:57: Epoch: 050 Train Loss: 0.551, ap: 0.617, auc: 0.682, f1: 0.630, acc: 0.653
+2025-08-26 01:26:57: Epoch: 050 Test results, ap: 0.214, auc: 0.422, f1: 0.000, acc: 0.699
+2025-08-26 01:26:57: used time 0.02h
+2025-08-26 01:26:57: Cross Validation Finished!
+2025-08-26 01:26:57: Best validation perform list
+[[0.72246039 0.71997273 0.66165411 0.68309861]
+ [0.76493204 0.82346272 0.72222221 0.75352114]
+ [0.74605531 0.79456151 0.72759855 0.73192239]
+ [0.71287584 0.76650763 0.67652494 0.69135803]
+ [0.62105989 0.68551111 0.625      0.62962961]]
+2025-08-26 01:26:57: mean: [0.713 0.758 0.683 0.698]
+2025-08-26 01:26:57: std: [0.05  0.05  0.038 0.043]
+2025-08-26 01:26:57: Best test perform list
+[[0.38038927 0.52365589 0.30232558 0.51219511]
+ [0.30533576 0.52831542 0.39215687 0.24390244]
+ [0.26400554 0.55017924 0.09302326 0.68292683]
+ [0.23951316 0.49354839 0.         0.72357726]
+ [0.28686613 0.59820789 0.         0.72357726]]
+2025-08-26 01:26:57: mean: [0.295 0.539 0.158 0.577]
+2025-08-26 01:26:57: std: [0.048 0.035 0.161 0.184]
diff --git a/run-cls/mha-diff-256-ce-32-0.001-50/config.json b/run-cls/mha-diff-256-ce-32-0.001-50/config.json
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@@ -0,0 +1 @@
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\ No newline at end of file
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diff --git a/run-cls/mha-diff-256-ce-32-0.001-50/result.txt b/run-cls/mha-diff-256-ce-32-0.001-50/result.txt
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@@ -0,0 +1,6 @@
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diff --git a/run-cls/mha-diff-256-ce-32-0.001-50/training.log b/run-cls/mha-diff-256-ce-32-0.001-50/training.log
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+2025-08-26 01:27:01: saving_dir: ./run-cls/mha-diff-256-ce-32-0.001-50
+2025-08-26 01:27:01: Loading Train Dataset
+2025-08-26 01:27:02: Loading Test Dataset
+2025-08-26 01:27:03: Running Cross Validation 0
+2025-08-26 01:27:03: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 01:27:04: Epoch: 001 Train Loss: 0.666, ap: 0.710, auc: 0.717, f1: 0.573, acc: 0.637
+2025-08-26 01:27:04: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:27:04: Epoch: 001 Test results, ap: 0.290, auc: 0.491, f1: 0.321, acc: 0.553
+2025-08-26 01:27:05: Epoch: 002 Train Loss: 0.633, ap: 0.744, auc: 0.764, f1: 0.665, acc: 0.685
+2025-08-26 01:27:05: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:27:05: Epoch: 002 Test results, ap: 0.339, auc: 0.530, f1: 0.355, acc: 0.675
+2025-08-26 01:27:06: Epoch: 003 Train Loss: 0.596, ap: 0.739, auc: 0.767, f1: 0.683, acc: 0.699
+2025-08-26 01:27:07: Epoch: 003 Test results, ap: 0.346, auc: 0.560, f1: 0.316, acc: 0.683
+2025-08-26 01:27:08: Epoch: 004 Train Loss: 0.572, ap: 0.785, auc: 0.797, f1: 0.701, acc: 0.722
+2025-08-26 01:27:08: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:27:08: Epoch: 004 Test results, ap: 0.341, auc: 0.561, f1: 0.321, acc: 0.691
+2025-08-26 01:27:09: Epoch: 005 Train Loss: 0.553, ap: 0.787, auc: 0.800, f1: 0.708, acc: 0.720
+2025-08-26 01:27:09: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:27:09: Epoch: 005 Test results, ap: 0.344, auc: 0.554, f1: 0.366, acc: 0.634
+2025-08-26 01:27:10: Epoch: 006 Train Loss: 0.534, ap: 0.814, auc: 0.835, f1: 0.746, acc: 0.750
+2025-08-26 01:27:10: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:27:10: Epoch: 006 Test results, ap: 0.311, auc: 0.548, f1: 0.290, acc: 0.602
+2025-08-26 01:27:11: Epoch: 007 Train Loss: 0.520, ap: 0.815, auc: 0.834, f1: 0.748, acc: 0.746
+2025-08-26 01:27:11: Epoch: 007 Test results, ap: 0.343, auc: 0.530, f1: 0.346, acc: 0.569
+2025-08-26 01:27:12: Epoch: 008 Train Loss: 0.506, ap: 0.808, auc: 0.816, f1: 0.727, acc: 0.729
+2025-08-26 01:27:13: Epoch: 008 Test results, ap: 0.346, auc: 0.579, f1: 0.346, acc: 0.569
+2025-08-26 01:27:14: Epoch: 009 Train Loss: 0.511, ap: 0.835, auc: 0.851, f1: 0.768, acc: 0.766
+2025-08-26 01:27:14: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:27:14: Epoch: 009 Test results, ap: 0.376, auc: 0.580, f1: 0.348, acc: 0.512
+2025-08-26 01:27:15: Epoch: 010 Train Loss: 0.500, ap: 0.857, auc: 0.869, f1: 0.774, acc: 0.780
+2025-08-26 01:27:15: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:27:15: Epoch: 010 Test results, ap: 0.344, auc: 0.543, f1: 0.366, acc: 0.520
+2025-08-26 01:27:16: Epoch: 011 Train Loss: 0.463, ap: 0.862, auc: 0.871, f1: 0.766, acc: 0.769
+2025-08-26 01:27:16: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:27:16: Epoch: 011 Test results, ap: 0.329, auc: 0.548, f1: 0.356, acc: 0.472
+2025-08-26 01:27:16: Epoch: 011 New best TEST metrics
+2025-08-26 01:27:17: Epoch: 012 Train Loss: 0.457, ap: 0.858, auc: 0.869, f1: 0.770, acc: 0.766
+2025-08-26 01:27:17: Epoch: 012 Test results, ap: 0.322, auc: 0.561, f1: 0.370, acc: 0.447
+2025-08-26 01:27:17: Epoch: 012 New best TEST metrics
+2025-08-26 01:27:18: Epoch: 013 Train Loss: 0.434, ap: 0.860, auc: 0.869, f1: 0.771, acc: 0.775
+2025-08-26 01:27:19: Epoch: 013 Test results, ap: 0.319, auc: 0.537, f1: 0.321, acc: 0.382
+2025-08-26 01:27:20: Epoch: 014 Train Loss: 0.434, ap: 0.868, auc: 0.873, f1: 0.755, acc: 0.773
+2025-08-26 01:27:20: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:27:20: Epoch: 014 Test results, ap: 0.387, auc: 0.560, f1: 0.360, acc: 0.480
+2025-08-26 01:27:20: Epoch: 014 New best TEST metrics
+2025-08-26 01:27:21: Epoch: 015 Train Loss: 0.432, ap: 0.879, auc: 0.882, f1: 0.788, acc: 0.792
+2025-08-26 01:27:21: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:27:21: Epoch: 015 Test results, ap: 0.344, auc: 0.563, f1: 0.356, acc: 0.472
+2025-08-26 01:27:22: Epoch: 016 Train Loss: 0.427, ap: 0.874, auc: 0.886, f1: 0.789, acc: 0.789
+2025-08-26 01:27:22: Epoch: 016 Test results, ap: 0.328, auc: 0.537, f1: 0.333, acc: 0.382
+2025-08-26 01:27:23: Epoch: 017 Train Loss: 0.412, ap: 0.882, auc: 0.888, f1: 0.771, acc: 0.778
+2025-08-26 01:27:23: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:27:23: Epoch: 017 Test results, ap: 0.346, auc: 0.561, f1: 0.360, acc: 0.423
+2025-08-26 01:27:24: Epoch: 018 Train Loss: 0.412, ap: 0.892, auc: 0.896, f1: 0.800, acc: 0.805
+2025-08-26 01:27:24: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:27:25: Epoch: 018 Test results, ap: 0.331, auc: 0.544, f1: 0.351, acc: 0.398
+2025-08-26 01:27:26: Epoch: 019 Train Loss: 0.412, ap: 0.878, auc: 0.882, f1: 0.779, acc: 0.775
+2025-08-26 01:27:26: Epoch: 019 Test results, ap: 0.323, auc: 0.544, f1: 0.348, acc: 0.390
+2025-08-26 01:27:27: Epoch: 020 Train Loss: 0.405, ap: 0.873, auc: 0.880, f1: 0.801, acc: 0.796
+2025-08-26 01:27:27: Epoch: 020 Test results, ap: 0.301, auc: 0.564, f1: 0.368, acc: 0.358
+2025-08-26 01:27:28: Epoch: 021 Train Loss: 0.382, ap: 0.891, auc: 0.896, f1: 0.775, acc: 0.782
+2025-08-26 01:27:28: Epoch: 021 Test results, ap: 0.293, auc: 0.543, f1: 0.336, acc: 0.390
+2025-08-26 01:27:29: Epoch: 022 Train Loss: 0.373, ap: 0.900, auc: 0.901, f1: 0.805, acc: 0.812
+2025-08-26 01:27:29: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:27:30: Epoch: 022 Test results, ap: 0.291, auc: 0.533, f1: 0.330, acc: 0.407
+2025-08-26 01:27:31: Epoch: 023 Train Loss: 0.365, ap: 0.907, auc: 0.907, f1: 0.809, acc: 0.815
+2025-08-26 01:27:31: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:27:31: Epoch: 023 Test results, ap: 0.269, auc: 0.522, f1: 0.330, acc: 0.407
+2025-08-26 01:27:32: Epoch: 024 Train Loss: 0.365, ap: 0.894, auc: 0.899, f1: 0.809, acc: 0.815
+2025-08-26 01:27:32: Epoch: 024 Test results, ap: 0.276, auc: 0.543, f1: 0.345, acc: 0.415
+2025-08-26 01:27:33: Epoch: 025 Train Loss: 0.356, ap: 0.900, auc: 0.901, f1: 0.806, acc: 0.808
+2025-08-26 01:27:33: Epoch: 025 Test results, ap: 0.279, auc: 0.540, f1: 0.336, acc: 0.390
+2025-08-26 01:27:34: Epoch: 026 Train Loss: 0.351, ap: 0.890, auc: 0.892, f1: 0.785, acc: 0.798
+2025-08-26 01:27:34: Epoch: 026 Test results, ap: 0.279, auc: 0.541, f1: 0.364, acc: 0.431
+2025-08-26 01:27:35: Epoch: 027 Train Loss: 0.348, ap: 0.903, auc: 0.905, f1: 0.805, acc: 0.812
+2025-08-26 01:27:36: Epoch: 027 Test results, ap: 0.266, auc: 0.530, f1: 0.336, acc: 0.390
+2025-08-26 01:27:36: Epoch: 028 Train Loss: 0.341, ap: 0.896, auc: 0.900, f1: 0.799, acc: 0.799
+2025-08-26 01:27:37: Epoch: 028 Test results, ap: 0.286, auc: 0.549, f1: 0.373, acc: 0.398
+2025-08-26 01:27:38: Epoch: 029 Train Loss: 0.344, ap: 0.908, auc: 0.910, f1: 0.819, acc: 0.822
+2025-08-26 01:27:38: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:27:38: Epoch: 029 Test results, ap: 0.277, auc: 0.536, f1: 0.330, acc: 0.407
+2025-08-26 01:27:39: Epoch: 030 Train Loss: 0.328, ap: 0.904, auc: 0.904, f1: 0.801, acc: 0.806
+2025-08-26 01:27:39: Epoch: 030 Test results, ap: 0.286, auc: 0.537, f1: 0.330, acc: 0.407
+2025-08-26 01:27:40: Epoch: 031 Train Loss: 0.318, ap: 0.908, auc: 0.908, f1: 0.822, acc: 0.827
+2025-08-26 01:27:40: Epoch: 031 Test results, ap: 0.281, auc: 0.539, f1: 0.336, acc: 0.390
+2025-08-26 01:27:41: Epoch: 032 Train Loss: 0.318, ap: 0.912, auc: 0.913, f1: 0.816, acc: 0.820
+2025-08-26 01:27:41: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:27:41: Epoch: 032 Test results, ap: 0.281, auc: 0.537, f1: 0.345, acc: 0.415
+2025-08-26 01:27:42: Epoch: 033 Train Loss: 0.314, ap: 0.910, auc: 0.909, f1: 0.815, acc: 0.820
+2025-08-26 01:27:43: Epoch: 033 Test results, ap: 0.284, auc: 0.541, f1: 0.330, acc: 0.407
+2025-08-26 01:27:44: Epoch: 034 Train Loss: 0.308, ap: 0.910, auc: 0.911, f1: 0.823, acc: 0.824
+2025-08-26 01:27:44: Epoch: 034 Test results, ap: 0.293, auc: 0.544, f1: 0.336, acc: 0.390
+2025-08-26 01:27:45: Epoch: 035 Train Loss: 0.308, ap: 0.908, auc: 0.906, f1: 0.810, acc: 0.817
+2025-08-26 01:27:45: Epoch: 035 Test results, ap: 0.295, auc: 0.540, f1: 0.330, acc: 0.407
+2025-08-26 01:27:46: Epoch: 036 Train Loss: 0.309, ap: 0.905, auc: 0.909, f1: 0.820, acc: 0.819
+2025-08-26 01:27:46: Epoch: 036 Test results, ap: 0.287, auc: 0.547, f1: 0.368, acc: 0.415
+2025-08-26 01:27:47: Epoch: 037 Train Loss: 0.305, ap: 0.913, auc: 0.914, f1: 0.817, acc: 0.820
+2025-08-26 01:27:47: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:27:47: Epoch: 037 Test results, ap: 0.304, auc: 0.545, f1: 0.330, acc: 0.407
+2025-08-26 01:27:48: Epoch: 038 Train Loss: 0.298, ap: 0.908, auc: 0.910, f1: 0.822, acc: 0.826
+2025-08-26 01:27:49: Epoch: 038 Test results, ap: 0.301, auc: 0.547, f1: 0.330, acc: 0.407
+2025-08-26 01:27:50: Epoch: 039 Train Loss: 0.298, ap: 0.913, auc: 0.913, f1: 0.817, acc: 0.820
+2025-08-26 01:27:50: Epoch: 039 Test results, ap: 0.293, auc: 0.537, f1: 0.330, acc: 0.407
+2025-08-26 01:27:51: Epoch: 040 Train Loss: 0.291, ap: 0.913, auc: 0.913, f1: 0.829, acc: 0.833
+2025-08-26 01:27:51: Epoch: 040 Test results, ap: 0.297, auc: 0.541, f1: 0.330, acc: 0.407
+2025-08-26 01:27:52: Epoch: 041 Train Loss: 0.283, ap: 0.913, auc: 0.913, f1: 0.826, acc: 0.829
+2025-08-26 01:27:52: Epoch: 041 Test results, ap: 0.294, auc: 0.537, f1: 0.330, acc: 0.407
+2025-08-26 01:27:53: Epoch: 042 Train Loss: 0.289, ap: 0.916, auc: 0.915, f1: 0.828, acc: 0.833
+2025-08-26 01:27:53: Epoch: 042 New best VALIDATION metrics
+2025-08-26 01:27:53: Epoch: 042 Test results, ap: 0.297, auc: 0.542, f1: 0.330, acc: 0.407
+2025-08-26 01:27:54: Epoch: 043 Train Loss: 0.285, ap: 0.916, auc: 0.916, f1: 0.835, acc: 0.838
+2025-08-26 01:27:54: Epoch: 043 New best VALIDATION metrics
+2025-08-26 01:27:55: Epoch: 043 Test results, ap: 0.297, auc: 0.541, f1: 0.330, acc: 0.407
+2025-08-26 01:27:56: Epoch: 044 Train Loss: 0.284, ap: 0.914, auc: 0.912, f1: 0.836, acc: 0.842
+2025-08-26 01:27:56: Epoch: 044 Test results, ap: 0.297, auc: 0.540, f1: 0.330, acc: 0.407
+2025-08-26 01:27:57: Epoch: 045 Train Loss: 0.281, ap: 0.914, auc: 0.914, f1: 0.831, acc: 0.836
+2025-08-26 01:27:57: Epoch: 045 Test results, ap: 0.298, auc: 0.541, f1: 0.330, acc: 0.407
+2025-08-26 01:27:58: Epoch: 046 Train Loss: 0.280, ap: 0.914, auc: 0.913, f1: 0.834, acc: 0.838
+2025-08-26 01:27:58: Epoch: 046 Test results, ap: 0.292, auc: 0.537, f1: 0.330, acc: 0.407
+2025-08-26 01:27:59: Epoch: 047 Train Loss: 0.274, ap: 0.915, auc: 0.917, f1: 0.829, acc: 0.833
+2025-08-26 01:27:59: Epoch: 047 Test results, ap: 0.296, auc: 0.541, f1: 0.345, acc: 0.415
+2025-08-26 01:28:00: Epoch: 048 Train Loss: 0.276, ap: 0.918, auc: 0.917, f1: 0.835, acc: 0.840
+2025-08-26 01:28:00: Epoch: 048 New best VALIDATION metrics
+2025-08-26 01:28:01: Epoch: 048 Test results, ap: 0.296, auc: 0.541, f1: 0.330, acc: 0.407
+2025-08-26 01:28:01: Epoch: 049 Train Loss: 0.277, ap: 0.916, auc: 0.916, f1: 0.824, acc: 0.829
+2025-08-26 01:28:02: Epoch: 049 Test results, ap: 0.291, auc: 0.537, f1: 0.330, acc: 0.407
+2025-08-26 01:28:03: Epoch: 050 Train Loss: 0.276, ap: 0.915, auc: 0.914, f1: 0.835, acc: 0.840
+2025-08-26 01:28:03: Epoch: 050 Test results, ap: 0.291, auc: 0.538, f1: 0.330, acc: 0.407
+2025-08-26 01:28:03: used time 0.02h
+2025-08-26 01:28:03: Running Cross Validation 1
+2025-08-26 01:28:03: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 01:28:04: Epoch: 001 Train Loss: 0.659, ap: 0.646, auc: 0.705, f1: 0.597, acc: 0.660
+2025-08-26 01:28:04: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:28:04: Epoch: 001 Test results, ap: 0.268, auc: 0.490, f1: 0.275, acc: 0.528
+2025-08-26 01:28:05: Epoch: 002 Train Loss: 0.598, ap: 0.660, auc: 0.726, f1: 0.609, acc: 0.658
+2025-08-26 01:28:05: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:28:05: Epoch: 002 Test results, ap: 0.314, auc: 0.497, f1: 0.282, acc: 0.545
+2025-08-26 01:28:06: Epoch: 003 Train Loss: 0.573, ap: 0.726, auc: 0.788, f1: 0.690, acc: 0.722
+2025-08-26 01:28:06: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:28:07: Epoch: 003 Test results, ap: 0.315, auc: 0.501, f1: 0.333, acc: 0.675
+2025-08-26 01:28:07: Epoch: 004 Train Loss: 0.545, ap: 0.757, auc: 0.812, f1: 0.696, acc: 0.729
+2025-08-26 01:28:07: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:28:08: Epoch: 004 Test results, ap: 0.296, auc: 0.476, f1: 0.344, acc: 0.659
+2025-08-26 01:28:09: Epoch: 005 Train Loss: 0.504, ap: 0.770, auc: 0.836, f1: 0.728, acc: 0.750
+2025-08-26 01:28:09: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:28:09: Epoch: 005 Test results, ap: 0.308, auc: 0.478, f1: 0.314, acc: 0.610
+2025-08-26 01:28:10: Epoch: 006 Train Loss: 0.488, ap: 0.799, auc: 0.831, f1: 0.718, acc: 0.746
+2025-08-26 01:28:10: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:28:10: Epoch: 006 Test results, ap: 0.267, auc: 0.506, f1: 0.179, acc: 0.626
+2025-08-26 01:28:11: Epoch: 007 Train Loss: 0.465, ap: 0.824, auc: 0.857, f1: 0.741, acc: 0.768
+2025-08-26 01:28:11: Epoch: 007 New best VALIDATION metrics
+2025-08-26 01:28:11: Epoch: 007 Test results, ap: 0.302, auc: 0.490, f1: 0.254, acc: 0.618
+2025-08-26 01:28:12: Epoch: 008 Train Loss: 0.455, ap: 0.815, auc: 0.858, f1: 0.744, acc: 0.762
+2025-08-26 01:28:13: Epoch: 008 Test results, ap: 0.365, auc: 0.519, f1: 0.310, acc: 0.602
+2025-08-26 01:28:13: Epoch: 009 Train Loss: 0.454, ap: 0.826, auc: 0.864, f1: 0.758, acc: 0.782
+2025-08-26 01:28:13: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:28:14: Epoch: 009 Test results, ap: 0.326, auc: 0.490, f1: 0.219, acc: 0.537
+2025-08-26 01:28:15: Epoch: 010 Train Loss: 0.436, ap: 0.844, auc: 0.872, f1: 0.742, acc: 0.782
+2025-08-26 01:28:15: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:28:15: Epoch: 010 Test results, ap: 0.347, auc: 0.505, f1: 0.250, acc: 0.659
+2025-08-26 01:28:16: Epoch: 011 Train Loss: 0.405, ap: 0.857, auc: 0.882, f1: 0.755, acc: 0.785
+2025-08-26 01:28:16: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:28:16: Epoch: 011 Test results, ap: 0.339, auc: 0.533, f1: 0.235, acc: 0.577
+2025-08-26 01:28:16: Epoch: 011 New best TEST metrics
+2025-08-26 01:28:17: Epoch: 012 Train Loss: 0.387, ap: 0.852, auc: 0.884, f1: 0.757, acc: 0.785
+2025-08-26 01:28:17: Epoch: 012 Test results, ap: 0.279, auc: 0.494, f1: 0.225, acc: 0.553
+2025-08-26 01:28:18: Epoch: 013 Train Loss: 0.378, ap: 0.854, auc: 0.888, f1: 0.768, acc: 0.794
+2025-08-26 01:28:18: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:28:18: Epoch: 013 Test results, ap: 0.290, auc: 0.533, f1: 0.265, acc: 0.593
+2025-08-26 01:28:19: Epoch: 014 Train Loss: 0.378, ap: 0.869, auc: 0.898, f1: 0.767, acc: 0.796
+2025-08-26 01:28:19: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:28:20: Epoch: 014 Test results, ap: 0.326, auc: 0.554, f1: 0.348, acc: 0.634
+2025-08-26 01:28:20: Epoch: 014 New best TEST metrics
+2025-08-26 01:28:21: Epoch: 015 Train Loss: 0.365, ap: 0.869, auc: 0.899, f1: 0.767, acc: 0.796
+2025-08-26 01:28:21: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:28:21: Epoch: 015 Test results, ap: 0.330, auc: 0.565, f1: 0.364, acc: 0.659
+2025-08-26 01:28:21: Epoch: 015 New best TEST metrics
+2025-08-26 01:28:22: Epoch: 016 Train Loss: 0.362, ap: 0.861, auc: 0.892, f1: 0.759, acc: 0.787
+2025-08-26 01:28:22: Epoch: 016 Test results, ap: 0.352, auc: 0.542, f1: 0.264, acc: 0.683
+2025-08-26 01:28:23: Epoch: 017 Train Loss: 0.355, ap: 0.875, auc: 0.898, f1: 0.770, acc: 0.796
+2025-08-26 01:28:23: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:28:23: Epoch: 017 Test results, ap: 0.337, auc: 0.561, f1: 0.361, acc: 0.683
+2025-08-26 01:28:23: Epoch: 017 New best TEST metrics
+2025-08-26 01:28:24: Epoch: 018 Train Loss: 0.359, ap: 0.875, auc: 0.901, f1: 0.773, acc: 0.803
+2025-08-26 01:28:24: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:28:24: Epoch: 018 Test results, ap: 0.307, auc: 0.554, f1: 0.345, acc: 0.691
+2025-08-26 01:28:25: Epoch: 019 Train Loss: 0.344, ap: 0.877, auc: 0.903, f1: 0.778, acc: 0.805
+2025-08-26 01:28:25: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:28:26: Epoch: 019 Test results, ap: 0.314, auc: 0.548, f1: 0.308, acc: 0.634
+2025-08-26 01:28:27: Epoch: 020 Train Loss: 0.338, ap: 0.881, auc: 0.909, f1: 0.808, acc: 0.831
+2025-08-26 01:28:27: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:28:27: Epoch: 020 Test results, ap: 0.314, auc: 0.575, f1: 0.339, acc: 0.683
+2025-08-26 01:28:28: Epoch: 021 Train Loss: 0.313, ap: 0.891, auc: 0.914, f1: 0.801, acc: 0.824
+2025-08-26 01:28:28: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:28:28: Epoch: 021 Test results, ap: 0.345, auc: 0.580, f1: 0.333, acc: 0.675
+2025-08-26 01:28:28: Epoch: 021 New best TEST metrics
+2025-08-26 01:28:29: Epoch: 022 Train Loss: 0.296, ap: 0.888, auc: 0.911, f1: 0.795, acc: 0.820
+2025-08-26 01:28:29: Epoch: 022 Test results, ap: 0.362, auc: 0.592, f1: 0.381, acc: 0.683
+2025-08-26 01:28:29: Epoch: 022 New best TEST metrics
+2025-08-26 01:28:30: Epoch: 023 Train Loss: 0.294, ap: 0.888, auc: 0.914, f1: 0.794, acc: 0.815
+2025-08-26 01:28:30: Epoch: 023 Test results, ap: 0.354, auc: 0.582, f1: 0.276, acc: 0.659
+2025-08-26 01:28:31: Epoch: 024 Train Loss: 0.294, ap: 0.894, auc: 0.917, f1: 0.796, acc: 0.820
+2025-08-26 01:28:31: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:28:32: Epoch: 024 Test results, ap: 0.342, auc: 0.570, f1: 0.296, acc: 0.691
+2025-08-26 01:28:33: Epoch: 025 Train Loss: 0.288, ap: 0.893, auc: 0.917, f1: 0.795, acc: 0.815
+2025-08-26 01:28:33: Epoch: 025 Test results, ap: 0.328, auc: 0.574, f1: 0.317, acc: 0.650
+2025-08-26 01:28:34: Epoch: 026 Train Loss: 0.284, ap: 0.881, auc: 0.907, f1: 0.787, acc: 0.810
+2025-08-26 01:28:34: Epoch: 026 Test results, ap: 0.340, auc: 0.580, f1: 0.316, acc: 0.683
+2025-08-26 01:28:35: Epoch: 027 Train Loss: 0.286, ap: 0.892, auc: 0.916, f1: 0.804, acc: 0.826
+2025-08-26 01:28:35: Epoch: 027 Test results, ap: 0.338, auc: 0.577, f1: 0.339, acc: 0.683
+2025-08-26 01:28:36: Epoch: 028 Train Loss: 0.280, ap: 0.898, auc: 0.920, f1: 0.815, acc: 0.838
+2025-08-26 01:28:36: Epoch: 028 New best VALIDATION metrics
+2025-08-26 01:28:36: Epoch: 028 Test results, ap: 0.353, auc: 0.577, f1: 0.296, acc: 0.691
+2025-08-26 01:28:37: Epoch: 029 Train Loss: 0.268, ap: 0.906, auc: 0.926, f1: 0.823, acc: 0.843
+2025-08-26 01:28:37: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:28:38: Epoch: 029 Test results, ap: 0.306, auc: 0.577, f1: 0.295, acc: 0.650
+2025-08-26 01:28:39: Epoch: 030 Train Loss: 0.267, ap: 0.898, auc: 0.923, f1: 0.816, acc: 0.838
+2025-08-26 01:28:39: Epoch: 030 Test results, ap: 0.295, auc: 0.562, f1: 0.281, acc: 0.626
+2025-08-26 01:28:40: Epoch: 031 Train Loss: 0.252, ap: 0.903, auc: 0.926, f1: 0.824, acc: 0.840
+2025-08-26 01:28:40: Epoch: 031 Test results, ap: 0.293, auc: 0.562, f1: 0.328, acc: 0.667
+2025-08-26 01:28:41: Epoch: 032 Train Loss: 0.245, ap: 0.902, auc: 0.922, f1: 0.812, acc: 0.833
+2025-08-26 01:28:41: Epoch: 032 Test results, ap: 0.352, auc: 0.588, f1: 0.296, acc: 0.691
+2025-08-26 01:28:42: Epoch: 033 Train Loss: 0.244, ap: 0.901, auc: 0.924, f1: 0.817, acc: 0.835
+2025-08-26 01:28:42: Epoch: 033 Test results, ap: 0.289, auc: 0.556, f1: 0.361, acc: 0.683
+2025-08-26 01:28:43: Epoch: 034 Train Loss: 0.245, ap: 0.905, auc: 0.926, f1: 0.810, acc: 0.831
+2025-08-26 01:28:44: Epoch: 034 Test results, ap: 0.327, auc: 0.582, f1: 0.327, acc: 0.699
+2025-08-26 01:28:45: Epoch: 035 Train Loss: 0.238, ap: 0.901, auc: 0.923, f1: 0.813, acc: 0.835
+2025-08-26 01:28:45: Epoch: 035 Test results, ap: 0.345, auc: 0.587, f1: 0.275, acc: 0.699
+2025-08-26 01:28:46: Epoch: 036 Train Loss: 0.243, ap: 0.904, auc: 0.925, f1: 0.815, acc: 0.835
+2025-08-26 01:28:46: Epoch: 036 Test results, ap: 0.321, auc: 0.580, f1: 0.310, acc: 0.675
+2025-08-26 01:28:47: Epoch: 037 Train Loss: 0.239, ap: 0.907, auc: 0.928, f1: 0.820, acc: 0.840
+2025-08-26 01:28:47: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:28:47: Epoch: 037 Test results, ap: 0.303, auc: 0.566, f1: 0.281, acc: 0.667
+2025-08-26 01:28:48: Epoch: 038 Train Loss: 0.236, ap: 0.899, auc: 0.924, f1: 0.818, acc: 0.838
+2025-08-26 01:28:48: Epoch: 038 Test results, ap: 0.340, auc: 0.578, f1: 0.222, acc: 0.659
+2025-08-26 01:28:49: Epoch: 039 Train Loss: 0.229, ap: 0.901, auc: 0.924, f1: 0.814, acc: 0.836
+2025-08-26 01:28:50: Epoch: 039 Test results, ap: 0.308, auc: 0.570, f1: 0.305, acc: 0.667
+2025-08-26 01:28:51: Epoch: 040 Train Loss: 0.229, ap: 0.891, auc: 0.918, f1: 0.807, acc: 0.826
+2025-08-26 01:28:51: Epoch: 040 Test results, ap: 0.288, auc: 0.563, f1: 0.235, acc: 0.683
+2025-08-26 01:28:52: Epoch: 041 Train Loss: 0.224, ap: 0.906, auc: 0.929, f1: 0.825, acc: 0.843
+2025-08-26 01:28:52: Epoch: 041 Test results, ap: 0.295, auc: 0.563, f1: 0.264, acc: 0.683
+2025-08-26 01:28:53: Epoch: 042 Train Loss: 0.214, ap: 0.906, auc: 0.929, f1: 0.821, acc: 0.840
+2025-08-26 01:28:53: Epoch: 042 Test results, ap: 0.297, auc: 0.563, f1: 0.231, acc: 0.675
+2025-08-26 01:28:54: Epoch: 043 Train Loss: 0.218, ap: 0.907, auc: 0.930, f1: 0.821, acc: 0.840
+2025-08-26 01:28:54: Epoch: 043 New best VALIDATION metrics
+2025-08-26 01:28:54: Epoch: 043 Test results, ap: 0.295, auc: 0.558, f1: 0.264, acc: 0.683
+2025-08-26 01:28:55: Epoch: 044 Train Loss: 0.220, ap: 0.902, auc: 0.927, f1: 0.826, acc: 0.843
+2025-08-26 01:28:56: Epoch: 044 Test results, ap: 0.289, auc: 0.554, f1: 0.269, acc: 0.691
+2025-08-26 01:28:57: Epoch: 045 Train Loss: 0.220, ap: 0.905, auc: 0.929, f1: 0.825, acc: 0.843
+2025-08-26 01:28:57: Epoch: 045 Test results, ap: 0.308, auc: 0.576, f1: 0.200, acc: 0.675
+2025-08-26 01:28:58: Epoch: 046 Train Loss: 0.216, ap: 0.907, auc: 0.930, f1: 0.824, acc: 0.843
+2025-08-26 01:28:58: Epoch: 046 Test results, ap: 0.301, auc: 0.566, f1: 0.240, acc: 0.691
+2025-08-26 01:28:59: Epoch: 047 Train Loss: 0.210, ap: 0.907, auc: 0.931, f1: 0.824, acc: 0.842
+2025-08-26 01:28:59: Epoch: 047 New best VALIDATION metrics
+2025-08-26 01:28:59: Epoch: 047 Test results, ap: 0.290, auc: 0.555, f1: 0.264, acc: 0.683
+2025-08-26 01:29:00: Epoch: 048 Train Loss: 0.215, ap: 0.911, auc: 0.933, f1: 0.822, acc: 0.842
+2025-08-26 01:29:00: Epoch: 048 New best VALIDATION metrics
+2025-08-26 01:29:00: Epoch: 048 Test results, ap: 0.297, auc: 0.562, f1: 0.269, acc: 0.691
+2025-08-26 01:29:01: Epoch: 049 Train Loss: 0.208, ap: 0.908, auc: 0.932, f1: 0.827, acc: 0.845
+2025-08-26 01:29:02: Epoch: 049 Test results, ap: 0.297, auc: 0.560, f1: 0.275, acc: 0.699
+2025-08-26 01:29:02: Epoch: 050 Train Loss: 0.205, ap: 0.910, auc: 0.933, f1: 0.819, acc: 0.838
+2025-08-26 01:29:03: Epoch: 050 Test results, ap: 0.285, auc: 0.554, f1: 0.259, acc: 0.675
+2025-08-26 01:29:03: used time 0.02h
+2025-08-26 01:29:03: Running Cross Validation 2
+2025-08-26 01:29:03: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:29:04: Epoch: 001 Train Loss: 0.680, ap: 0.591, auc: 0.668, f1: 0.546, acc: 0.619
+2025-08-26 01:29:04: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:29:04: Epoch: 001 Test results, ap: 0.238, auc: 0.495, f1: 0.337, acc: 0.488
+2025-08-26 01:29:05: Epoch: 002 Train Loss: 0.654, ap: 0.673, auc: 0.734, f1: 0.620, acc: 0.667
+2025-08-26 01:29:05: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:29:05: Epoch: 002 Test results, ap: 0.337, auc: 0.498, f1: 0.325, acc: 0.561
+2025-08-26 01:29:06: Epoch: 003 Train Loss: 0.578, ap: 0.725, auc: 0.764, f1: 0.668, acc: 0.690
+2025-08-26 01:29:06: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:29:06: Epoch: 003 Test results, ap: 0.337, auc: 0.510, f1: 0.250, acc: 0.561
+2025-08-26 01:29:07: Epoch: 004 Train Loss: 0.559, ap: 0.748, auc: 0.790, f1: 0.680, acc: 0.709
+2025-08-26 01:29:07: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:29:08: Epoch: 004 Test results, ap: 0.338, auc: 0.511, f1: 0.308, acc: 0.561
+2025-08-26 01:29:08: Epoch: 005 Train Loss: 0.521, ap: 0.766, auc: 0.803, f1: 0.724, acc: 0.737
+2025-08-26 01:29:08: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:29:09: Epoch: 005 Test results, ap: 0.323, auc: 0.494, f1: 0.293, acc: 0.528
+2025-08-26 01:29:10: Epoch: 006 Train Loss: 0.497, ap: 0.773, auc: 0.810, f1: 0.717, acc: 0.734
+2025-08-26 01:29:10: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:29:10: Epoch: 006 Test results, ap: 0.314, auc: 0.510, f1: 0.306, acc: 0.520
+2025-08-26 01:29:11: Epoch: 007 Train Loss: 0.473, ap: 0.777, auc: 0.805, f1: 0.717, acc: 0.735
+2025-08-26 01:29:11: Epoch: 007 Test results, ap: 0.354, auc: 0.540, f1: 0.261, acc: 0.585
+2025-08-26 01:29:12: Epoch: 008 Train Loss: 0.476, ap: 0.773, auc: 0.801, f1: 0.701, acc: 0.721
+2025-08-26 01:29:12: Epoch: 008 Test results, ap: 0.358, auc: 0.555, f1: 0.241, acc: 0.488
+2025-08-26 01:29:13: Epoch: 009 Train Loss: 0.472, ap: 0.781, auc: 0.811, f1: 0.715, acc: 0.732
+2025-08-26 01:29:13: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:29:14: Epoch: 009 Test results, ap: 0.347, auc: 0.547, f1: 0.308, acc: 0.488
+2025-08-26 01:29:15: Epoch: 010 Train Loss: 0.456, ap: 0.793, auc: 0.809, f1: 0.703, acc: 0.725
+2025-08-26 01:29:15: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:29:15: Epoch: 010 Test results, ap: 0.354, auc: 0.558, f1: 0.326, acc: 0.528
+2025-08-26 01:29:16: Epoch: 011 Train Loss: 0.434, ap: 0.788, auc: 0.817, f1: 0.707, acc: 0.728
+2025-08-26 01:29:16: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:29:16: Epoch: 011 Test results, ap: 0.367, auc: 0.577, f1: 0.384, acc: 0.504
+2025-08-26 01:29:16: Epoch: 011 New best TEST metrics
+2025-08-26 01:29:17: Epoch: 012 Train Loss: 0.435, ap: 0.794, auc: 0.814, f1: 0.715, acc: 0.732
+2025-08-26 01:29:17: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:29:17: Epoch: 012 Test results, ap: 0.354, auc: 0.571, f1: 0.246, acc: 0.602
+2025-08-26 01:29:18: Epoch: 013 Train Loss: 0.414, ap: 0.802, auc: 0.829, f1: 0.721, acc: 0.743
+2025-08-26 01:29:18: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:29:18: Epoch: 013 Test results, ap: 0.376, auc: 0.595, f1: 0.373, acc: 0.618
+2025-08-26 01:29:18: Epoch: 013 New best TEST metrics
+2025-08-26 01:29:19: Epoch: 014 Train Loss: 0.400, ap: 0.807, auc: 0.837, f1: 0.731, acc: 0.748
+2025-08-26 01:29:19: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:29:20: Epoch: 014 Test results, ap: 0.353, auc: 0.561, f1: 0.345, acc: 0.537
+2025-08-26 01:29:21: Epoch: 015 Train Loss: 0.405, ap: 0.809, auc: 0.840, f1: 0.749, acc: 0.758
+2025-08-26 01:29:21: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:29:21: Epoch: 015 Test results, ap: 0.378, auc: 0.596, f1: 0.324, acc: 0.593
+2025-08-26 01:29:21: Epoch: 015 New best TEST metrics
+2025-08-26 01:29:22: Epoch: 016 Train Loss: 0.398, ap: 0.815, auc: 0.843, f1: 0.726, acc: 0.739
+2025-08-26 01:29:22: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:29:22: Epoch: 016 Test results, ap: 0.316, auc: 0.546, f1: 0.304, acc: 0.553
+2025-08-26 01:29:23: Epoch: 017 Train Loss: 0.399, ap: 0.812, auc: 0.842, f1: 0.730, acc: 0.750
+2025-08-26 01:29:23: Epoch: 017 Test results, ap: 0.366, auc: 0.567, f1: 0.301, acc: 0.585
+2025-08-26 01:29:24: Epoch: 018 Train Loss: 0.389, ap: 0.821, auc: 0.850, f1: 0.746, acc: 0.758
+2025-08-26 01:29:24: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:29:24: Epoch: 018 Test results, ap: 0.377, auc: 0.588, f1: 0.329, acc: 0.602
+2025-08-26 01:29:25: Epoch: 019 Train Loss: 0.385, ap: 0.822, auc: 0.853, f1: 0.757, acc: 0.771
+2025-08-26 01:29:25: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:29:26: Epoch: 019 Test results, ap: 0.405, auc: 0.619, f1: 0.328, acc: 0.634
+2025-08-26 01:29:26: Epoch: 019 New best TEST metrics
+2025-08-26 01:29:27: Epoch: 020 Train Loss: 0.378, ap: 0.796, auc: 0.835, f1: 0.746, acc: 0.760
+2025-08-26 01:29:27: Epoch: 020 Test results, ap: 0.355, auc: 0.562, f1: 0.308, acc: 0.561
+2025-08-26 01:29:28: Epoch: 021 Train Loss: 0.370, ap: 0.821, auc: 0.852, f1: 0.757, acc: 0.772
+2025-08-26 01:29:28: Epoch: 021 Test results, ap: 0.386, auc: 0.606, f1: 0.328, acc: 0.634
+2025-08-26 01:29:29: Epoch: 022 Train Loss: 0.360, ap: 0.825, auc: 0.856, f1: 0.751, acc: 0.769
+2025-08-26 01:29:29: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:29:29: Epoch: 022 Test results, ap: 0.381, auc: 0.594, f1: 0.324, acc: 0.626
+2025-08-26 01:29:30: Epoch: 023 Train Loss: 0.351, ap: 0.826, auc: 0.859, f1: 0.758, acc: 0.774
+2025-08-26 01:29:30: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:29:30: Epoch: 023 Test results, ap: 0.361, auc: 0.593, f1: 0.333, acc: 0.610
+2025-08-26 01:29:31: Epoch: 024 Train Loss: 0.360, ap: 0.826, auc: 0.858, f1: 0.757, acc: 0.772
+2025-08-26 01:29:32: Epoch: 024 Test results, ap: 0.371, auc: 0.594, f1: 0.295, acc: 0.650
+2025-08-26 01:29:33: Epoch: 025 Train Loss: 0.349, ap: 0.827, auc: 0.860, f1: 0.763, acc: 0.776
+2025-08-26 01:29:33: Epoch: 025 New best VALIDATION metrics
+2025-08-26 01:29:33: Epoch: 025 Test results, ap: 0.410, auc: 0.614, f1: 0.333, acc: 0.610
+2025-08-26 01:29:33: Epoch: 025 New best TEST metrics
+2025-08-26 01:29:34: Epoch: 026 Train Loss: 0.344, ap: 0.829, auc: 0.863, f1: 0.769, acc: 0.781
+2025-08-26 01:29:34: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:29:34: Epoch: 026 Test results, ap: 0.397, auc: 0.596, f1: 0.324, acc: 0.626
+2025-08-26 01:29:35: Epoch: 027 Train Loss: 0.357, ap: 0.825, auc: 0.861, f1: 0.761, acc: 0.771
+2025-08-26 01:29:35: Epoch: 027 Test results, ap: 0.390, auc: 0.606, f1: 0.356, acc: 0.618
+2025-08-26 01:29:36: Epoch: 028 Train Loss: 0.345, ap: 0.820, auc: 0.858, f1: 0.769, acc: 0.781
+2025-08-26 01:29:36: Epoch: 028 Test results, ap: 0.411, auc: 0.602, f1: 0.373, acc: 0.618
+2025-08-26 01:29:37: Epoch: 029 Train Loss: 0.340, ap: 0.827, auc: 0.865, f1: 0.769, acc: 0.783
+2025-08-26 01:29:37: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:29:38: Epoch: 029 Test results, ap: 0.361, auc: 0.590, f1: 0.368, acc: 0.610
+2025-08-26 01:29:39: Epoch: 030 Train Loss: 0.347, ap: 0.830, auc: 0.867, f1: 0.771, acc: 0.785
+2025-08-26 01:29:39: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:29:39: Epoch: 030 Test results, ap: 0.384, auc: 0.606, f1: 0.338, acc: 0.618
+2025-08-26 01:29:40: Epoch: 031 Train Loss: 0.329, ap: 0.829, auc: 0.866, f1: 0.763, acc: 0.778
+2025-08-26 01:29:40: Epoch: 031 Test results, ap: 0.407, auc: 0.612, f1: 0.338, acc: 0.650
+2025-08-26 01:29:41: Epoch: 032 Train Loss: 0.329, ap: 0.833, auc: 0.869, f1: 0.762, acc: 0.778
+2025-08-26 01:29:41: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:29:41: Epoch: 032 Test results, ap: 0.374, auc: 0.605, f1: 0.319, acc: 0.618
+2025-08-26 01:29:42: Epoch: 033 Train Loss: 0.331, ap: 0.837, auc: 0.871, f1: 0.767, acc: 0.783
+2025-08-26 01:29:42: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:29:42: Epoch: 033 Test results, ap: 0.368, auc: 0.596, f1: 0.328, acc: 0.634
+2025-08-26 01:29:43: Epoch: 034 Train Loss: 0.331, ap: 0.834, auc: 0.870, f1: 0.772, acc: 0.785
+2025-08-26 01:29:44: Epoch: 034 Test results, ap: 0.371, auc: 0.601, f1: 0.333, acc: 0.610
+2025-08-26 01:29:45: Epoch: 035 Train Loss: 0.332, ap: 0.830, auc: 0.868, f1: 0.766, acc: 0.781
+2025-08-26 01:29:45: Epoch: 035 Test results, ap: 0.353, auc: 0.603, f1: 0.384, acc: 0.634
+2025-08-26 01:29:46: Epoch: 036 Train Loss: 0.328, ap: 0.835, auc: 0.871, f1: 0.772, acc: 0.785
+2025-08-26 01:29:46: Epoch: 036 Test results, ap: 0.365, auc: 0.600, f1: 0.395, acc: 0.626
+2025-08-26 01:29:47: Epoch: 037 Train Loss: 0.313, ap: 0.837, auc: 0.872, f1: 0.768, acc: 0.781
+2025-08-26 01:29:47: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:29:47: Epoch: 037 Test results, ap: 0.373, auc: 0.602, f1: 0.366, acc: 0.634
+2025-08-26 01:29:48: Epoch: 038 Train Loss: 0.323, ap: 0.837, auc: 0.871, f1: 0.778, acc: 0.792
+2025-08-26 01:29:48: Epoch: 038 Test results, ap: 0.364, auc: 0.597, f1: 0.356, acc: 0.618
+2025-08-26 01:29:49: Epoch: 039 Train Loss: 0.319, ap: 0.843, auc: 0.876, f1: 0.777, acc: 0.788
+2025-08-26 01:29:49: Epoch: 039 New best VALIDATION metrics
+2025-08-26 01:29:50: Epoch: 039 Test results, ap: 0.367, auc: 0.606, f1: 0.378, acc: 0.626
+2025-08-26 01:29:51: Epoch: 040 Train Loss: 0.319, ap: 0.834, auc: 0.867, f1: 0.769, acc: 0.783
+2025-08-26 01:29:51: Epoch: 040 Test results, ap: 0.377, auc: 0.603, f1: 0.378, acc: 0.626
+2025-08-26 01:29:52: Epoch: 041 Train Loss: 0.309, ap: 0.839, auc: 0.873, f1: 0.778, acc: 0.790
+2025-08-26 01:29:52: Epoch: 041 Test results, ap: 0.363, auc: 0.596, f1: 0.394, acc: 0.650
+2025-08-26 01:29:53: Epoch: 042 Train Loss: 0.310, ap: 0.841, auc: 0.875, f1: 0.779, acc: 0.792
+2025-08-26 01:29:53: Epoch: 042 Test results, ap: 0.353, auc: 0.595, f1: 0.366, acc: 0.634
+2025-08-26 01:29:54: Epoch: 043 Train Loss: 0.305, ap: 0.840, auc: 0.874, f1: 0.780, acc: 0.794
+2025-08-26 01:29:54: Epoch: 043 Test results, ap: 0.380, auc: 0.607, f1: 0.389, acc: 0.642
+2025-08-26 01:29:55: Epoch: 044 Train Loss: 0.307, ap: 0.842, auc: 0.876, f1: 0.775, acc: 0.788
+2025-08-26 01:29:56: Epoch: 044 Test results, ap: 0.353, auc: 0.600, f1: 0.371, acc: 0.642
+2025-08-26 01:29:56: Epoch: 045 Train Loss: 0.306, ap: 0.842, auc: 0.875, f1: 0.774, acc: 0.787
+2025-08-26 01:29:57: Epoch: 045 Test results, ap: 0.367, auc: 0.601, f1: 0.371, acc: 0.642
+2025-08-26 01:29:58: Epoch: 046 Train Loss: 0.303, ap: 0.841, auc: 0.875, f1: 0.780, acc: 0.794
+2025-08-26 01:29:58: Epoch: 046 Test results, ap: 0.382, auc: 0.604, f1: 0.394, acc: 0.650
+2025-08-26 01:29:59: Epoch: 047 Train Loss: 0.298, ap: 0.839, auc: 0.875, f1: 0.776, acc: 0.790
+2025-08-26 01:29:59: Epoch: 047 Test results, ap: 0.367, auc: 0.604, f1: 0.384, acc: 0.634
+2025-08-26 01:30:00: Epoch: 048 Train Loss: 0.308, ap: 0.832, auc: 0.871, f1: 0.774, acc: 0.788
+2025-08-26 01:30:00: Epoch: 048 Test results, ap: 0.368, auc: 0.602, f1: 0.378, acc: 0.626
+2025-08-26 01:30:01: Epoch: 049 Train Loss: 0.300, ap: 0.838, auc: 0.875, f1: 0.779, acc: 0.792
+2025-08-26 01:30:02: Epoch: 049 Test results, ap: 0.366, auc: 0.601, f1: 0.384, acc: 0.634
+2025-08-26 01:30:02: Epoch: 050 Train Loss: 0.300, ap: 0.843, auc: 0.877, f1: 0.786, acc: 0.799
+2025-08-26 01:30:02: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:30:03: Epoch: 050 Test results, ap: 0.385, auc: 0.604, f1: 0.384, acc: 0.634
+2025-08-26 01:30:03: used time 0.02h
+2025-08-26 01:30:03: Running Cross Validation 3
+2025-08-26 01:30:03: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:30:04: Epoch: 001 Train Loss: 0.670, ap: 0.663, auc: 0.707, f1: 0.563, acc: 0.658
+2025-08-26 01:30:04: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:30:04: Epoch: 001 Test results, ap: 0.305, auc: 0.551, f1: 0.371, acc: 0.504
+2025-08-26 01:30:05: Epoch: 002 Train Loss: 0.632, ap: 0.676, auc: 0.724, f1: 0.598, acc: 0.649
+2025-08-26 01:30:05: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:30:05: Epoch: 002 Test results, ap: 0.316, auc: 0.581, f1: 0.388, acc: 0.488
+2025-08-26 01:30:06: Epoch: 003 Train Loss: 0.612, ap: 0.720, auc: 0.776, f1: 0.623, acc: 0.704
+2025-08-26 01:30:06: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:30:06: Epoch: 003 Test results, ap: 0.326, auc: 0.592, f1: 0.388, acc: 0.667
+2025-08-26 01:30:07: Epoch: 004 Train Loss: 0.585, ap: 0.783, auc: 0.811, f1: 0.684, acc: 0.737
+2025-08-26 01:30:07: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:30:08: Epoch: 004 Test results, ap: 0.296, auc: 0.540, f1: 0.300, acc: 0.659
+2025-08-26 01:30:09: Epoch: 005 Train Loss: 0.549, ap: 0.837, auc: 0.855, f1: 0.728, acc: 0.771
+2025-08-26 01:30:09: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:30:09: Epoch: 005 Test results, ap: 0.296, auc: 0.547, f1: 0.377, acc: 0.650
+2025-08-26 01:30:10: Epoch: 006 Train Loss: 0.503, ap: 0.810, auc: 0.850, f1: 0.744, acc: 0.757
+2025-08-26 01:30:10: Epoch: 006 Test results, ap: 0.267, auc: 0.561, f1: 0.391, acc: 0.545
+2025-08-26 01:30:11: Epoch: 007 Train Loss: 0.515, ap: 0.824, auc: 0.859, f1: 0.747, acc: 0.760
+2025-08-26 01:30:11: Epoch: 007 Test results, ap: 0.289, auc: 0.578, f1: 0.380, acc: 0.496
+2025-08-26 01:30:12: Epoch: 008 Train Loss: 0.503, ap: 0.825, auc: 0.867, f1: 0.765, acc: 0.780
+2025-08-26 01:30:12: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:30:12: Epoch: 008 Test results, ap: 0.277, auc: 0.543, f1: 0.337, acc: 0.488
+2025-08-26 01:30:13: Epoch: 009 Train Loss: 0.489, ap: 0.827, auc: 0.866, f1: 0.764, acc: 0.785
+2025-08-26 01:30:13: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:30:14: Epoch: 009 Test results, ap: 0.285, auc: 0.550, f1: 0.347, acc: 0.602
+2025-08-26 01:30:15: Epoch: 010 Train Loss: 0.472, ap: 0.847, auc: 0.875, f1: 0.757, acc: 0.774
+2025-08-26 01:30:15: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:30:15: Epoch: 010 Test results, ap: 0.298, auc: 0.568, f1: 0.358, acc: 0.504
+2025-08-26 01:30:16: Epoch: 011 Train Loss: 0.456, ap: 0.860, auc: 0.883, f1: 0.768, acc: 0.787
+2025-08-26 01:30:16: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:30:16: Epoch: 011 Test results, ap: 0.302, auc: 0.582, f1: 0.395, acc: 0.577
+2025-08-26 01:30:16: Epoch: 011 New best TEST metrics
+2025-08-26 01:30:17: Epoch: 012 Train Loss: 0.442, ap: 0.849, auc: 0.883, f1: 0.774, acc: 0.792
+2025-08-26 01:30:17: Epoch: 012 Test results, ap: 0.307, auc: 0.582, f1: 0.338, acc: 0.585
+2025-08-26 01:30:17: Epoch: 012 New best TEST metrics
+2025-08-26 01:30:18: Epoch: 013 Train Loss: 0.441, ap: 0.853, auc: 0.884, f1: 0.783, acc: 0.801
+2025-08-26 01:30:18: Epoch: 013 Test results, ap: 0.276, auc: 0.548, f1: 0.322, acc: 0.520
+2025-08-26 01:30:19: Epoch: 014 Train Loss: 0.433, ap: 0.859, auc: 0.888, f1: 0.782, acc: 0.797
+2025-08-26 01:30:19: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:30:20: Epoch: 014 Test results, ap: 0.287, auc: 0.559, f1: 0.329, acc: 0.569
+2025-08-26 01:30:20: Epoch: 015 Train Loss: 0.431, ap: 0.864, auc: 0.890, f1: 0.778, acc: 0.804
+2025-08-26 01:30:20: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:30:21: Epoch: 015 Test results, ap: 0.283, auc: 0.553, f1: 0.351, acc: 0.610
+2025-08-26 01:30:22: Epoch: 016 Train Loss: 0.427, ap: 0.869, auc: 0.894, f1: 0.786, acc: 0.806
+2025-08-26 01:30:22: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:30:22: Epoch: 016 Test results, ap: 0.267, auc: 0.546, f1: 0.353, acc: 0.642
+2025-08-26 01:30:23: Epoch: 017 Train Loss: 0.420, ap: 0.870, auc: 0.898, f1: 0.802, acc: 0.825
+2025-08-26 01:30:23: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:30:23: Epoch: 017 Test results, ap: 0.272, auc: 0.547, f1: 0.366, acc: 0.634
+2025-08-26 01:30:24: Epoch: 018 Train Loss: 0.415, ap: 0.865, auc: 0.896, f1: 0.791, acc: 0.811
+2025-08-26 01:30:24: Epoch: 018 Test results, ap: 0.279, auc: 0.556, f1: 0.382, acc: 0.659
+2025-08-26 01:30:25: Epoch: 019 Train Loss: 0.407, ap: 0.874, auc: 0.897, f1: 0.790, acc: 0.815
+2025-08-26 01:30:25: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:30:26: Epoch: 019 Test results, ap: 0.268, auc: 0.547, f1: 0.328, acc: 0.667
+2025-08-26 01:30:26: Epoch: 020 Train Loss: 0.405, ap: 0.876, auc: 0.901, f1: 0.819, acc: 0.840
+2025-08-26 01:30:26: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:30:27: Epoch: 020 Test results, ap: 0.262, auc: 0.542, f1: 0.364, acc: 0.659
+2025-08-26 01:30:28: Epoch: 021 Train Loss: 0.397, ap: 0.884, auc: 0.905, f1: 0.799, acc: 0.822
+2025-08-26 01:30:28: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:30:28: Epoch: 021 Test results, ap: 0.267, auc: 0.551, f1: 0.369, acc: 0.667
+2025-08-26 01:30:29: Epoch: 022 Train Loss: 0.384, ap: 0.882, auc: 0.908, f1: 0.815, acc: 0.834
+2025-08-26 01:30:29: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:30:29: Epoch: 022 Test results, ap: 0.262, auc: 0.541, f1: 0.348, acc: 0.634
+2025-08-26 01:30:30: Epoch: 023 Train Loss: 0.386, ap: 0.875, auc: 0.901, f1: 0.797, acc: 0.822
+2025-08-26 01:30:30: Epoch: 023 Test results, ap: 0.267, auc: 0.549, f1: 0.381, acc: 0.683
+2025-08-26 01:30:31: Epoch: 024 Train Loss: 0.382, ap: 0.880, auc: 0.905, f1: 0.801, acc: 0.825
+2025-08-26 01:30:32: Epoch: 024 Test results, ap: 0.256, auc: 0.531, f1: 0.353, acc: 0.642
+2025-08-26 01:30:33: Epoch: 025 Train Loss: 0.377, ap: 0.883, auc: 0.907, f1: 0.806, acc: 0.829
+2025-08-26 01:30:33: Epoch: 025 Test results, ap: 0.257, auc: 0.532, f1: 0.358, acc: 0.650
+2025-08-26 01:30:34: Epoch: 026 Train Loss: 0.379, ap: 0.882, auc: 0.906, f1: 0.807, acc: 0.829
+2025-08-26 01:30:34: Epoch: 026 Test results, ap: 0.261, auc: 0.542, f1: 0.369, acc: 0.667
+2025-08-26 01:30:35: Epoch: 027 Train Loss: 0.377, ap: 0.884, auc: 0.907, f1: 0.806, acc: 0.829
+2025-08-26 01:30:35: Epoch: 027 New best VALIDATION metrics
+2025-08-26 01:30:35: Epoch: 027 Test results, ap: 0.255, auc: 0.529, f1: 0.358, acc: 0.650
+2025-08-26 01:30:36: Epoch: 028 Train Loss: 0.367, ap: 0.883, auc: 0.905, f1: 0.793, acc: 0.820
+2025-08-26 01:30:36: Epoch: 028 Test results, ap: 0.262, auc: 0.542, f1: 0.375, acc: 0.675
+2025-08-26 01:30:37: Epoch: 029 Train Loss: 0.370, ap: 0.888, auc: 0.914, f1: 0.818, acc: 0.840
+2025-08-26 01:30:37: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:30:38: Epoch: 029 Test results, ap: 0.256, auc: 0.533, f1: 0.358, acc: 0.650
+2025-08-26 01:30:39: Epoch: 030 Train Loss: 0.366, ap: 0.891, auc: 0.914, f1: 0.816, acc: 0.836
+2025-08-26 01:30:39: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:30:39: Epoch: 030 Test results, ap: 0.254, auc: 0.528, f1: 0.358, acc: 0.650
+2025-08-26 01:30:40: Epoch: 031 Train Loss: 0.359, ap: 0.889, auc: 0.911, f1: 0.804, acc: 0.827
+2025-08-26 01:30:40: Epoch: 031 Test results, ap: 0.256, auc: 0.532, f1: 0.358, acc: 0.650
+2025-08-26 01:30:41: Epoch: 032 Train Loss: 0.362, ap: 0.891, auc: 0.912, f1: 0.819, acc: 0.843
+2025-08-26 01:30:41: Epoch: 032 Test results, ap: 0.257, auc: 0.532, f1: 0.353, acc: 0.642
+2025-08-26 01:30:42: Epoch: 033 Train Loss: 0.357, ap: 0.893, auc: 0.915, f1: 0.817, acc: 0.838
+2025-08-26 01:30:42: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:30:42: Epoch: 033 Test results, ap: 0.252, auc: 0.525, f1: 0.353, acc: 0.642
+2025-08-26 01:30:43: Epoch: 034 Train Loss: 0.351, ap: 0.890, auc: 0.911, f1: 0.803, acc: 0.827
+2025-08-26 01:30:44: Epoch: 034 Test results, ap: 0.255, auc: 0.529, f1: 0.353, acc: 0.642
+2025-08-26 01:30:45: Epoch: 035 Train Loss: 0.353, ap: 0.886, auc: 0.909, f1: 0.811, acc: 0.834
+2025-08-26 01:30:45: Epoch: 035 Test results, ap: 0.252, auc: 0.525, f1: 0.295, acc: 0.650
+2025-08-26 01:30:46: Epoch: 036 Train Loss: 0.350, ap: 0.891, auc: 0.911, f1: 0.814, acc: 0.836
+2025-08-26 01:30:46: Epoch: 036 Test results, ap: 0.256, auc: 0.531, f1: 0.353, acc: 0.642
+2025-08-26 01:30:47: Epoch: 037 Train Loss: 0.346, ap: 0.893, auc: 0.913, f1: 0.813, acc: 0.834
+2025-08-26 01:30:47: Epoch: 037 Test results, ap: 0.258, auc: 0.534, f1: 0.353, acc: 0.642
+2025-08-26 01:30:48: Epoch: 038 Train Loss: 0.346, ap: 0.889, auc: 0.910, f1: 0.802, acc: 0.825
+2025-08-26 01:30:48: Epoch: 038 Test results, ap: 0.256, auc: 0.528, f1: 0.328, acc: 0.634
+2025-08-26 01:30:49: Epoch: 039 Train Loss: 0.346, ap: 0.891, auc: 0.911, f1: 0.800, acc: 0.827
+2025-08-26 01:30:50: Epoch: 039 Test results, ap: 0.254, auc: 0.524, f1: 0.333, acc: 0.642
+2025-08-26 01:30:50: Epoch: 040 Train Loss: 0.342, ap: 0.892, auc: 0.910, f1: 0.811, acc: 0.836
+2025-08-26 01:30:51: Epoch: 040 Test results, ap: 0.257, auc: 0.530, f1: 0.333, acc: 0.642
+2025-08-26 01:30:52: Epoch: 041 Train Loss: 0.333, ap: 0.892, auc: 0.911, f1: 0.802, acc: 0.827
+2025-08-26 01:30:52: Epoch: 041 Test results, ap: 0.255, auc: 0.527, f1: 0.328, acc: 0.634
+2025-08-26 01:30:53: Epoch: 042 Train Loss: 0.333, ap: 0.893, auc: 0.912, f1: 0.815, acc: 0.838
+2025-08-26 01:30:53: Epoch: 042 Test results, ap: 0.253, auc: 0.525, f1: 0.328, acc: 0.634
+2025-08-26 01:30:54: Epoch: 043 Train Loss: 0.331, ap: 0.892, auc: 0.911, f1: 0.804, acc: 0.829
+2025-08-26 01:30:54: Epoch: 043 Test results, ap: 0.251, auc: 0.519, f1: 0.328, acc: 0.634
+2025-08-26 01:30:55: Epoch: 044 Train Loss: 0.330, ap: 0.893, auc: 0.913, f1: 0.803, acc: 0.829
+2025-08-26 01:30:56: Epoch: 044 Test results, ap: 0.253, auc: 0.523, f1: 0.328, acc: 0.634
+2025-08-26 01:30:56: Epoch: 045 Train Loss: 0.329, ap: 0.892, auc: 0.912, f1: 0.798, acc: 0.824
+2025-08-26 01:30:57: Epoch: 045 Test results, ap: 0.251, auc: 0.520, f1: 0.328, acc: 0.634
+2025-08-26 01:30:58: Epoch: 046 Train Loss: 0.329, ap: 0.890, auc: 0.910, f1: 0.793, acc: 0.822
+2025-08-26 01:30:58: Epoch: 046 Test results, ap: 0.257, auc: 0.529, f1: 0.333, acc: 0.642
+2025-08-26 01:30:59: Epoch: 047 Train Loss: 0.329, ap: 0.893, auc: 0.914, f1: 0.802, acc: 0.827
+2025-08-26 01:30:59: Epoch: 047 Test results, ap: 0.251, auc: 0.521, f1: 0.328, acc: 0.634
+2025-08-26 01:31:00: Epoch: 048 Train Loss: 0.327, ap: 0.892, auc: 0.914, f1: 0.809, acc: 0.832
+2025-08-26 01:31:00: Epoch: 048 Test results, ap: 0.253, auc: 0.523, f1: 0.353, acc: 0.642
+2025-08-26 01:31:01: Epoch: 049 Train Loss: 0.319, ap: 0.892, auc: 0.914, f1: 0.814, acc: 0.836
+2025-08-26 01:31:02: Epoch: 049 Test results, ap: 0.252, auc: 0.523, f1: 0.353, acc: 0.642
+2025-08-26 01:31:03: Epoch: 050 Train Loss: 0.321, ap: 0.891, auc: 0.911, f1: 0.797, acc: 0.822
+2025-08-26 01:31:03: Epoch: 050 Test results, ap: 0.254, auc: 0.524, f1: 0.353, acc: 0.642
+2025-08-26 01:31:03: used time 0.02h
+2025-08-26 01:31:03: Running Cross Validation 4
+2025-08-26 01:31:03: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:31:04: Epoch: 001 Train Loss: 0.661, ap: 0.598, auc: 0.657, f1: 0.539, acc: 0.635
+2025-08-26 01:31:04: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:31:04: Epoch: 001 Test results, ap: 0.279, auc: 0.491, f1: 0.324, acc: 0.423
+2025-08-26 01:31:05: Epoch: 002 Train Loss: 0.611, ap: 0.638, auc: 0.694, f1: 0.604, acc: 0.658
+2025-08-26 01:31:05: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:31:05: Epoch: 002 Test results, ap: 0.298, auc: 0.465, f1: 0.317, acc: 0.545
+2025-08-26 01:31:06: Epoch: 003 Train Loss: 0.594, ap: 0.678, auc: 0.728, f1: 0.613, acc: 0.654
+2025-08-26 01:31:06: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:31:06: Epoch: 003 Test results, ap: 0.293, auc: 0.485, f1: 0.326, acc: 0.528
+2025-08-26 01:31:07: Epoch: 004 Train Loss: 0.568, ap: 0.708, auc: 0.756, f1: 0.671, acc: 0.702
+2025-08-26 01:31:07: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:31:08: Epoch: 004 Test results, ap: 0.270, auc: 0.418, f1: 0.237, acc: 0.528
+2025-08-26 01:31:09: Epoch: 005 Train Loss: 0.549, ap: 0.740, auc: 0.785, f1: 0.698, acc: 0.730
+2025-08-26 01:31:09: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:31:09: Epoch: 005 Test results, ap: 0.229, auc: 0.405, f1: 0.247, acc: 0.553
+2025-08-26 01:31:10: Epoch: 006 Train Loss: 0.532, ap: 0.774, auc: 0.802, f1: 0.699, acc: 0.741
+2025-08-26 01:31:10: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:31:10: Epoch: 006 Test results, ap: 0.298, auc: 0.487, f1: 0.344, acc: 0.659
+2025-08-26 01:31:11: Epoch: 007 Train Loss: 0.524, ap: 0.771, auc: 0.805, f1: 0.692, acc: 0.718
+2025-08-26 01:31:11: Epoch: 007 Test results, ap: 0.344, auc: 0.538, f1: 0.378, acc: 0.626
+2025-08-26 01:31:12: Epoch: 008 Train Loss: 0.508, ap: 0.759, auc: 0.810, f1: 0.728, acc: 0.744
+2025-08-26 01:31:12: Epoch: 008 Test results, ap: 0.253, auc: 0.488, f1: 0.276, acc: 0.488
+2025-08-26 01:31:13: Epoch: 009 Train Loss: 0.497, ap: 0.805, auc: 0.840, f1: 0.747, acc: 0.762
+2025-08-26 01:31:13: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:31:14: Epoch: 009 Test results, ap: 0.342, auc: 0.554, f1: 0.342, acc: 0.593
+2025-08-26 01:31:15: Epoch: 010 Train Loss: 0.477, ap: 0.809, auc: 0.828, f1: 0.712, acc: 0.744
+2025-08-26 01:31:15: Epoch: 010 Test results, ap: 0.317, auc: 0.574, f1: 0.375, acc: 0.593
+2025-08-26 01:31:16: Epoch: 011 Train Loss: 0.441, ap: 0.827, auc: 0.854, f1: 0.758, acc: 0.774
+2025-08-26 01:31:16: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:31:16: Epoch: 011 Test results, ap: 0.320, auc: 0.572, f1: 0.377, acc: 0.650
+2025-08-26 01:31:16: Epoch: 011 New best TEST metrics
+2025-08-26 01:31:17: Epoch: 012 Train Loss: 0.426, ap: 0.828, auc: 0.854, f1: 0.760, acc: 0.783
+2025-08-26 01:31:17: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:31:17: Epoch: 012 Test results, ap: 0.329, auc: 0.576, f1: 0.358, acc: 0.650
+2025-08-26 01:31:17: Epoch: 012 New best TEST metrics
+2025-08-26 01:31:18: Epoch: 013 Train Loss: 0.427, ap: 0.821, auc: 0.856, f1: 0.752, acc: 0.772
+2025-08-26 01:31:18: Epoch: 013 Test results, ap: 0.311, auc: 0.566, f1: 0.333, acc: 0.642
+2025-08-26 01:31:19: Epoch: 014 Train Loss: 0.402, ap: 0.852, auc: 0.875, f1: 0.771, acc: 0.792
+2025-08-26 01:31:19: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:31:20: Epoch: 014 Test results, ap: 0.324, auc: 0.577, f1: 0.328, acc: 0.667
+2025-08-26 01:31:21: Epoch: 015 Train Loss: 0.416, ap: 0.842, auc: 0.869, f1: 0.771, acc: 0.790
+2025-08-26 01:31:21: Epoch: 015 Test results, ap: 0.325, auc: 0.586, f1: 0.421, acc: 0.642
+2025-08-26 01:31:21: Epoch: 015 New best TEST metrics
+2025-08-26 01:31:22: Epoch: 016 Train Loss: 0.408, ap: 0.830, auc: 0.867, f1: 0.791, acc: 0.808
+2025-08-26 01:31:22: Epoch: 016 Test results, ap: 0.327, auc: 0.579, f1: 0.373, acc: 0.618
+2025-08-26 01:31:23: Epoch: 017 Train Loss: 0.397, ap: 0.854, auc: 0.882, f1: 0.790, acc: 0.806
+2025-08-26 01:31:23: Epoch: 017 New best VALIDATION metrics
+2025-08-26 01:31:23: Epoch: 017 Test results, ap: 0.366, auc: 0.596, f1: 0.385, acc: 0.610
+2025-08-26 01:31:23: Epoch: 017 New best TEST metrics
+2025-08-26 01:31:24: Epoch: 018 Train Loss: 0.391, ap: 0.846, auc: 0.875, f1: 0.775, acc: 0.797
+2025-08-26 01:31:25: Epoch: 018 Test results, ap: 0.338, auc: 0.589, f1: 0.361, acc: 0.626
+2025-08-26 01:31:26: Epoch: 019 Train Loss: 0.385, ap: 0.857, auc: 0.881, f1: 0.772, acc: 0.799
+2025-08-26 01:31:26: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:31:26: Epoch: 019 Test results, ap: 0.330, auc: 0.601, f1: 0.387, acc: 0.691
+2025-08-26 01:31:27: Epoch: 020 Train Loss: 0.385, ap: 0.849, auc: 0.878, f1: 0.768, acc: 0.794
+2025-08-26 01:31:27: Epoch: 020 Test results, ap: 0.321, auc: 0.570, f1: 0.290, acc: 0.642
+2025-08-26 01:31:28: Epoch: 021 Train Loss: 0.361, ap: 0.863, auc: 0.887, f1: 0.796, acc: 0.813
+2025-08-26 01:31:28: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:31:28: Epoch: 021 Test results, ap: 0.343, auc: 0.599, f1: 0.338, acc: 0.650
+2025-08-26 01:31:29: Epoch: 022 Train Loss: 0.357, ap: 0.868, auc: 0.893, f1: 0.809, acc: 0.827
+2025-08-26 01:31:29: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:31:29: Epoch: 022 Test results, ap: 0.354, auc: 0.625, f1: 0.381, acc: 0.683
+2025-08-26 01:31:29: Epoch: 022 New best TEST metrics
+2025-08-26 01:31:30: Epoch: 023 Train Loss: 0.346, ap: 0.873, auc: 0.894, f1: 0.802, acc: 0.822
+2025-08-26 01:31:30: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:31:31: Epoch: 023 Test results, ap: 0.344, auc: 0.606, f1: 0.381, acc: 0.683
+2025-08-26 01:31:32: Epoch: 024 Train Loss: 0.349, ap: 0.870, auc: 0.894, f1: 0.809, acc: 0.824
+2025-08-26 01:31:32: Epoch: 024 Test results, ap: 0.337, auc: 0.596, f1: 0.388, acc: 0.667
+2025-08-26 01:31:33: Epoch: 025 Train Loss: 0.350, ap: 0.873, auc: 0.896, f1: 0.797, acc: 0.817
+2025-08-26 01:31:33: Epoch: 025 New best VALIDATION metrics
+2025-08-26 01:31:33: Epoch: 025 Test results, ap: 0.342, auc: 0.607, f1: 0.400, acc: 0.707
+2025-08-26 01:31:34: Epoch: 026 Train Loss: 0.348, ap: 0.865, auc: 0.892, f1: 0.781, acc: 0.804
+2025-08-26 01:31:34: Epoch: 026 Test results, ap: 0.349, auc: 0.618, f1: 0.407, acc: 0.715
+2025-08-26 01:31:35: Epoch: 027 Train Loss: 0.341, ap: 0.871, auc: 0.895, f1: 0.795, acc: 0.820
+2025-08-26 01:31:35: Epoch: 027 Test results, ap: 0.327, auc: 0.589, f1: 0.379, acc: 0.707
+2025-08-26 01:31:36: Epoch: 028 Train Loss: 0.345, ap: 0.872, auc: 0.896, f1: 0.814, acc: 0.831
+2025-08-26 01:31:37: Epoch: 028 Test results, ap: 0.330, auc: 0.615, f1: 0.373, acc: 0.699
+2025-08-26 01:31:37: Epoch: 029 Train Loss: 0.343, ap: 0.868, auc: 0.892, f1: 0.788, acc: 0.811
+2025-08-26 01:31:38: Epoch: 029 Test results, ap: 0.323, auc: 0.593, f1: 0.393, acc: 0.724
+2025-08-26 01:31:39: Epoch: 030 Train Loss: 0.341, ap: 0.876, auc: 0.896, f1: 0.797, acc: 0.818
+2025-08-26 01:31:39: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:31:39: Epoch: 030 Test results, ap: 0.327, auc: 0.590, f1: 0.400, acc: 0.732
+2025-08-26 01:31:40: Epoch: 031 Train Loss: 0.329, ap: 0.876, auc: 0.897, f1: 0.801, acc: 0.820
+2025-08-26 01:31:40: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:31:40: Epoch: 031 Test results, ap: 0.334, auc: 0.608, f1: 0.400, acc: 0.707
+2025-08-26 01:31:41: Epoch: 032 Train Loss: 0.325, ap: 0.878, auc: 0.899, f1: 0.806, acc: 0.825
+2025-08-26 01:31:41: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:31:41: Epoch: 032 Test results, ap: 0.326, auc: 0.589, f1: 0.393, acc: 0.724
+2025-08-26 01:31:42: Epoch: 033 Train Loss: 0.324, ap: 0.878, auc: 0.897, f1: 0.805, acc: 0.824
+2025-08-26 01:31:43: Epoch: 033 Test results, ap: 0.325, auc: 0.595, f1: 0.386, acc: 0.715
+2025-08-26 01:31:44: Epoch: 034 Train Loss: 0.321, ap: 0.878, auc: 0.898, f1: 0.795, acc: 0.815
+2025-08-26 01:31:44: Epoch: 034 Test results, ap: 0.321, auc: 0.579, f1: 0.393, acc: 0.724
+2025-08-26 01:31:45: Epoch: 035 Train Loss: 0.322, ap: 0.880, auc: 0.900, f1: 0.809, acc: 0.827
+2025-08-26 01:31:45: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:31:45: Epoch: 035 Test results, ap: 0.334, auc: 0.590, f1: 0.400, acc: 0.707
+2025-08-26 01:31:46: Epoch: 036 Train Loss: 0.319, ap: 0.881, auc: 0.899, f1: 0.794, acc: 0.815
+2025-08-26 01:31:46: Epoch: 036 Test results, ap: 0.334, auc: 0.596, f1: 0.414, acc: 0.724
+2025-08-26 01:31:47: Epoch: 037 Train Loss: 0.314, ap: 0.879, auc: 0.898, f1: 0.803, acc: 0.824
+2025-08-26 01:31:47: Epoch: 037 Test results, ap: 0.329, auc: 0.587, f1: 0.407, acc: 0.715
+2025-08-26 01:31:48: Epoch: 038 Train Loss: 0.320, ap: 0.879, auc: 0.897, f1: 0.794, acc: 0.817
+2025-08-26 01:31:49: Epoch: 038 Test results, ap: 0.322, auc: 0.580, f1: 0.400, acc: 0.707
+2025-08-26 01:31:50: Epoch: 039 Train Loss: 0.319, ap: 0.878, auc: 0.898, f1: 0.812, acc: 0.829
+2025-08-26 01:31:50: Epoch: 039 Test results, ap: 0.329, auc: 0.591, f1: 0.400, acc: 0.707
+2025-08-26 01:31:51: Epoch: 040 Train Loss: 0.312, ap: 0.881, auc: 0.899, f1: 0.799, acc: 0.820
+2025-08-26 01:31:51: Epoch: 040 New best VALIDATION metrics
+2025-08-26 01:31:51: Epoch: 040 Test results, ap: 0.328, auc: 0.590, f1: 0.386, acc: 0.715
+2025-08-26 01:31:52: Epoch: 041 Train Loss: 0.307, ap: 0.880, auc: 0.900, f1: 0.802, acc: 0.822
+2025-08-26 01:31:52: Epoch: 041 Test results, ap: 0.329, auc: 0.584, f1: 0.393, acc: 0.699
+2025-08-26 01:31:53: Epoch: 042 Train Loss: 0.307, ap: 0.880, auc: 0.899, f1: 0.799, acc: 0.822
+2025-08-26 01:31:53: Epoch: 042 Test results, ap: 0.329, auc: 0.584, f1: 0.407, acc: 0.715
+2025-08-26 01:31:54: Epoch: 043 Train Loss: 0.307, ap: 0.879, auc: 0.898, f1: 0.786, acc: 0.811
+2025-08-26 01:31:55: Epoch: 043 Test results, ap: 0.330, auc: 0.582, f1: 0.386, acc: 0.715
+2025-08-26 01:31:56: Epoch: 044 Train Loss: 0.305, ap: 0.881, auc: 0.900, f1: 0.809, acc: 0.827
+2025-08-26 01:31:56: Epoch: 044 New best VALIDATION metrics
+2025-08-26 01:31:56: Epoch: 044 Test results, ap: 0.329, auc: 0.584, f1: 0.387, acc: 0.691
+2025-08-26 01:31:57: Epoch: 045 Train Loss: 0.303, ap: 0.882, auc: 0.900, f1: 0.800, acc: 0.822
+2025-08-26 01:31:57: Epoch: 045 Test results, ap: 0.327, auc: 0.583, f1: 0.400, acc: 0.707
+2025-08-26 01:31:58: Epoch: 046 Train Loss: 0.300, ap: 0.878, auc: 0.897, f1: 0.812, acc: 0.831
+2025-08-26 01:31:58: Epoch: 046 Test results, ap: 0.326, auc: 0.581, f1: 0.400, acc: 0.707
+2025-08-26 01:31:59: Epoch: 047 Train Loss: 0.295, ap: 0.882, auc: 0.901, f1: 0.806, acc: 0.825
+2025-08-26 01:31:59: Epoch: 047 New best VALIDATION metrics
+2025-08-26 01:31:59: Epoch: 047 Test results, ap: 0.331, auc: 0.582, f1: 0.387, acc: 0.691
+2025-08-26 01:32:00: Epoch: 048 Train Loss: 0.299, ap: 0.882, auc: 0.901, f1: 0.809, acc: 0.827
+2025-08-26 01:32:01: Epoch: 048 Test results, ap: 0.321, auc: 0.581, f1: 0.387, acc: 0.691
+2025-08-26 01:32:02: Epoch: 049 Train Loss: 0.298, ap: 0.880, auc: 0.898, f1: 0.801, acc: 0.822
+2025-08-26 01:32:02: Epoch: 049 Test results, ap: 0.326, auc: 0.582, f1: 0.387, acc: 0.691
+2025-08-26 01:32:03: Epoch: 050 Train Loss: 0.296, ap: 0.881, auc: 0.900, f1: 0.794, acc: 0.817
+2025-08-26 01:32:03: Epoch: 050 Test results, ap: 0.332, auc: 0.582, f1: 0.373, acc: 0.699
+2025-08-26 01:32:03: used time 0.02h
+2025-08-26 01:32:03: Cross Validation Finished!
+2025-08-26 01:32:03: Best validation perform list
+[[0.91753936 0.9169057  0.83544302 0.83978873]
+ [0.91074187 0.93331659 0.82213438 0.84154928]
+ [0.84320009 0.87741327 0.78571427 0.79894179]
+ [0.89322132 0.91477191 0.81673306 0.83774251]
+ [0.88227135 0.90115488 0.80550098 0.82539684]]
+2025-08-26 01:32:03: mean: [0.889 0.909 0.813 0.829]
+2025-08-26 01:32:03: std: [0.026 0.019 0.017 0.016]
+2025-08-26 01:32:03: Best test perform list
+[[0.38732296 0.56021506 0.36000001 0.47967479]
+ [0.36177319 0.59175628 0.38095239 0.68292683]
+ [0.4097313  0.61397851 0.33333334 0.60975611]
+ [0.30709988 0.58207887 0.33766234 0.58536583]
+ [0.35393876 0.62544805 0.38095239 0.68292683]]
+2025-08-26 01:32:03: mean: [0.364 0.595 0.359 0.608]
+2025-08-26 01:32:03: std: [0.035 0.023 0.02  0.075]
diff --git a/run-cls/mha-mlp-256-ce-32-0.001-50/config.json b/run-cls/mha-mlp-256-ce-32-0.001-50/config.json
new file mode 100644
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mha", "side_enc": null, "channels": 256, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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+2025-08-26 01:16:35: saving_dir: ./run-cls/mha-mlp-256-ce-32-0.001-50
+2025-08-26 01:16:35: Loading Train Dataset
+2025-08-26 01:16:35: Loading Test Dataset
+2025-08-26 01:16:36: Running Cross Validation 0
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+2025-08-26 01:16:37: Epoch: 001 New best VALIDATION metrics
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+2025-08-26 01:16:39: Epoch: 002 Train Loss: 0.636, ap: 0.730, auc: 0.733, f1: 0.653, acc: 0.665
+2025-08-26 01:16:39: Epoch: 002 New best VALIDATION metrics
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+2025-08-26 01:16:40: Epoch: 003 Train Loss: 0.597, ap: 0.744, auc: 0.746, f1: 0.652, acc: 0.667
+2025-08-26 01:16:40: Epoch: 003 New best VALIDATION metrics
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+2025-08-26 01:16:41: Epoch: 004 New best VALIDATION metrics
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+2025-08-26 01:16:42: Epoch: 005 New best VALIDATION metrics
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+2025-08-26 01:16:43: Epoch: 006 New best VALIDATION metrics
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+2025-08-26 01:16:44: Epoch: 007 New best VALIDATION metrics
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+2025-08-26 01:16:46: Epoch: 008 New best VALIDATION metrics
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+2025-08-26 01:16:49: Epoch: 011 New best VALIDATION metrics
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+2025-08-26 01:16:50: Epoch: 011 New best TEST metrics
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+2025-08-26 01:16:50: Epoch: 012 New best VALIDATION metrics
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+2025-08-26 01:16:55: Epoch: 016 New best VALIDATION metrics
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+2025-08-26 01:16:56: Epoch: 017 New best VALIDATION metrics
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+2025-08-26 01:16:59: Epoch: 019 New best VALIDATION metrics
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+2025-08-26 01:17:00: Epoch: 020 New best VALIDATION metrics
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+2025-08-26 01:17:03: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:17:03: Epoch: 022 Test results, ap: 0.210, auc: 0.393, f1: 0.125, acc: 0.545
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+2025-08-26 01:17:04: Epoch: 023 Test results, ap: 0.207, auc: 0.385, f1: 0.133, acc: 0.577
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+2025-08-26 01:17:05: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:17:05: Epoch: 024 Test results, ap: 0.208, auc: 0.389, f1: 0.151, acc: 0.634
+2025-08-26 01:17:06: Epoch: 025 Train Loss: 0.278, ap: 0.904, auc: 0.910, f1: 0.806, acc: 0.810
+2025-08-26 01:17:06: Epoch: 025 Test results, ap: 0.204, auc: 0.392, f1: 0.140, acc: 0.602
+2025-08-26 01:17:07: Epoch: 026 Train Loss: 0.271, ap: 0.911, auc: 0.917, f1: 0.812, acc: 0.820
+2025-08-26 01:17:07: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:17:08: Epoch: 026 Test results, ap: 0.206, auc: 0.394, f1: 0.138, acc: 0.593
+2025-08-26 01:17:09: Epoch: 027 Train Loss: 0.263, ap: 0.899, auc: 0.905, f1: 0.795, acc: 0.810
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+2025-08-26 01:17:10: Epoch: 028 Test results, ap: 0.208, auc: 0.398, f1: 0.140, acc: 0.602
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+2025-08-26 01:17:11: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:17:11: Epoch: 029 Test results, ap: 0.218, auc: 0.401, f1: 0.125, acc: 0.545
+2025-08-26 01:17:12: Epoch: 030 Train Loss: 0.253, ap: 0.918, auc: 0.921, f1: 0.827, acc: 0.835
+2025-08-26 01:17:12: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:17:12: Epoch: 030 Test results, ap: 0.213, auc: 0.414, f1: 0.136, acc: 0.585
+2025-08-26 01:17:13: Epoch: 031 Train Loss: 0.237, ap: 0.920, auc: 0.923, f1: 0.817, acc: 0.829
+2025-08-26 01:17:13: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:17:14: Epoch: 031 Test results, ap: 0.211, auc: 0.410, f1: 0.143, acc: 0.610
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+2025-08-26 01:17:14: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:17:15: Epoch: 032 Test results, ap: 0.211, auc: 0.407, f1: 0.140, acc: 0.602
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+2025-08-26 01:17:16: Epoch: 033 New best VALIDATION metrics
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+2025-08-26 01:17:17: Epoch: 034 New best VALIDATION metrics
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+2025-08-26 01:17:18: Epoch: 035 Test results, ap: 0.208, auc: 0.399, f1: 0.143, acc: 0.610
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+2025-08-26 01:17:19: Epoch: 036 Test results, ap: 0.207, auc: 0.404, f1: 0.082, acc: 0.634
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+2025-08-26 01:17:21: Epoch: 037 Test results, ap: 0.205, auc: 0.400, f1: 0.080, acc: 0.626
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+2025-08-26 01:17:25: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:17:25: Epoch: 041 Test results, ap: 0.208, auc: 0.404, f1: 0.154, acc: 0.642
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+2025-08-26 01:17:26: Epoch: 042 New best VALIDATION metrics
+2025-08-26 01:17:27: Epoch: 042 Test results, ap: 0.209, auc: 0.409, f1: 0.118, acc: 0.634
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+2025-08-26 01:17:32: Epoch: 047 New best VALIDATION metrics
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+2025-08-26 01:17:36: Epoch: 050 Test results, ap: 0.209, auc: 0.407, f1: 0.083, acc: 0.642
+2025-08-26 01:17:36: used time 0.02h
+2025-08-26 01:17:36: Running Cross Validation 1
+2025-08-26 01:17:36: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 01:17:37: Epoch: 001 Train Loss: 0.693, ap: 0.589, auc: 0.650, f1: 0.452, acc: 0.595
+2025-08-26 01:17:37: Epoch: 001 New best VALIDATION metrics
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+2025-08-26 01:17:39: Epoch: 003 New best VALIDATION metrics
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+2025-08-26 01:17:42: Epoch: 005 New best VALIDATION metrics
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+2025-08-26 01:17:43: Epoch: 006 Train Loss: 0.533, ap: 0.747, auc: 0.791, f1: 0.674, acc: 0.704
+2025-08-26 01:17:43: Epoch: 006 New best VALIDATION metrics
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+2025-08-26 01:17:44: Epoch: 007 Train Loss: 0.531, ap: 0.715, auc: 0.762, f1: 0.682, acc: 0.692
+2025-08-26 01:17:44: Epoch: 007 Test results, ap: 0.258, auc: 0.508, f1: 0.237, acc: 0.634
+2025-08-26 01:17:45: Epoch: 008 Train Loss: 0.529, ap: 0.685, auc: 0.741, f1: 0.643, acc: 0.665
+2025-08-26 01:17:45: Epoch: 008 Test results, ap: 0.286, auc: 0.514, f1: 0.140, acc: 0.699
+2025-08-26 01:17:46: Epoch: 009 Train Loss: 0.536, ap: 0.744, auc: 0.785, f1: 0.686, acc: 0.702
+2025-08-26 01:17:47: Epoch: 009 Test results, ap: 0.286, auc: 0.505, f1: 0.278, acc: 0.577
+2025-08-26 01:17:48: Epoch: 010 Train Loss: 0.504, ap: 0.778, auc: 0.801, f1: 0.679, acc: 0.713
+2025-08-26 01:17:48: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:17:48: Epoch: 010 Test results, ap: 0.308, auc: 0.524, f1: 0.130, acc: 0.675
+2025-08-26 01:17:49: Epoch: 011 Train Loss: 0.479, ap: 0.794, auc: 0.819, f1: 0.692, acc: 0.738
+2025-08-26 01:17:49: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:17:49: Epoch: 011 Test results, ap: 0.316, auc: 0.546, f1: 0.196, acc: 0.667
+2025-08-26 01:17:49: Epoch: 011 New best TEST metrics
+2025-08-26 01:17:50: Epoch: 012 Train Loss: 0.455, ap: 0.809, auc: 0.833, f1: 0.725, acc: 0.754
+2025-08-26 01:17:50: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:17:50: Epoch: 012 Test results, ap: 0.302, auc: 0.519, f1: 0.130, acc: 0.675
+2025-08-26 01:17:51: Epoch: 013 Train Loss: 0.445, ap: 0.790, auc: 0.828, f1: 0.690, acc: 0.748
+2025-08-26 01:17:51: Epoch: 013 Test results, ap: 0.288, auc: 0.478, f1: 0.154, acc: 0.732
+2025-08-26 01:17:52: Epoch: 014 Train Loss: 0.445, ap: 0.812, auc: 0.836, f1: 0.727, acc: 0.750
+2025-08-26 01:17:52: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:17:53: Epoch: 014 Test results, ap: 0.301, auc: 0.537, f1: 0.192, acc: 0.659
+2025-08-26 01:17:54: Epoch: 015 Train Loss: 0.430, ap: 0.815, auc: 0.833, f1: 0.713, acc: 0.750
+2025-08-26 01:17:54: Epoch: 015 New best VALIDATION metrics
+2025-08-26 01:17:54: Epoch: 015 Test results, ap: 0.282, auc: 0.506, f1: 0.136, acc: 0.691
+2025-08-26 01:17:55: Epoch: 016 Train Loss: 0.417, ap: 0.818, auc: 0.841, f1: 0.729, acc: 0.746
+2025-08-26 01:17:55: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:17:55: Epoch: 016 Test results, ap: 0.287, auc: 0.511, f1: 0.189, acc: 0.650
+2025-08-26 01:17:56: Epoch: 017 Train Loss: 0.422, ap: 0.804, auc: 0.842, f1: 0.744, acc: 0.773
+2025-08-26 01:17:56: Epoch: 017 Test results, ap: 0.252, auc: 0.449, f1: 0.133, acc: 0.577
+2025-08-26 01:17:57: Epoch: 018 Train Loss: 0.410, ap: 0.810, auc: 0.831, f1: 0.742, acc: 0.771
+2025-08-26 01:17:57: Epoch: 018 Test results, ap: 0.255, auc: 0.466, f1: 0.140, acc: 0.699
+2025-08-26 01:17:58: Epoch: 019 Train Loss: 0.399, ap: 0.820, auc: 0.840, f1: 0.741, acc: 0.750
+2025-08-26 01:17:58: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:17:59: Epoch: 019 Test results, ap: 0.288, auc: 0.524, f1: 0.185, acc: 0.642
+2025-08-26 01:17:59: Epoch: 020 Train Loss: 0.404, ap: 0.815, auc: 0.842, f1: 0.720, acc: 0.746
+2025-08-26 01:18:00: Epoch: 020 Test results, ap: 0.278, auc: 0.488, f1: 0.133, acc: 0.683
+2025-08-26 01:18:01: Epoch: 021 Train Loss: 0.374, ap: 0.824, auc: 0.850, f1: 0.749, acc: 0.769
+2025-08-26 01:18:01: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:18:01: Epoch: 021 Test results, ap: 0.246, auc: 0.468, f1: 0.113, acc: 0.618
+2025-08-26 01:18:02: Epoch: 022 Train Loss: 0.369, ap: 0.823, auc: 0.850, f1: 0.745, acc: 0.771
+2025-08-26 01:18:02: Epoch: 022 Test results, ap: 0.265, auc: 0.444, f1: 0.122, acc: 0.650
+2025-08-26 01:18:03: Epoch: 023 Train Loss: 0.377, ap: 0.820, auc: 0.848, f1: 0.750, acc: 0.778
+2025-08-26 01:18:03: Epoch: 023 Test results, ap: 0.273, auc: 0.472, f1: 0.133, acc: 0.683
+2025-08-26 01:18:04: Epoch: 024 Train Loss: 0.377, ap: 0.826, auc: 0.851, f1: 0.760, acc: 0.776
+2025-08-26 01:18:04: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:18:04: Epoch: 024 Test results, ap: 0.283, auc: 0.510, f1: 0.122, acc: 0.650
+2025-08-26 01:18:05: Epoch: 025 Train Loss: 0.364, ap: 0.821, auc: 0.847, f1: 0.733, acc: 0.771
+2025-08-26 01:18:06: Epoch: 025 Test results, ap: 0.286, auc: 0.496, f1: 0.140, acc: 0.699
+2025-08-26 01:18:07: Epoch: 026 Train Loss: 0.358, ap: 0.825, auc: 0.852, f1: 0.755, acc: 0.780
+2025-08-26 01:18:07: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:18:07: Epoch: 026 Test results, ap: 0.267, auc: 0.470, f1: 0.136, acc: 0.691
+2025-08-26 01:18:08: Epoch: 027 Train Loss: 0.357, ap: 0.825, auc: 0.850, f1: 0.766, acc: 0.787
+2025-08-26 01:18:08: Epoch: 027 Test results, ap: 0.289, auc: 0.499, f1: 0.128, acc: 0.667
+2025-08-26 01:18:09: Epoch: 028 Train Loss: 0.356, ap: 0.830, auc: 0.853, f1: 0.763, acc: 0.780
+2025-08-26 01:18:09: Epoch: 028 New best VALIDATION metrics
+2025-08-26 01:18:09: Epoch: 028 Test results, ap: 0.249, auc: 0.469, f1: 0.122, acc: 0.650
+2025-08-26 01:18:10: Epoch: 029 Train Loss: 0.355, ap: 0.828, auc: 0.851, f1: 0.744, acc: 0.768
+2025-08-26 01:18:10: Epoch: 029 Test results, ap: 0.283, auc: 0.498, f1: 0.128, acc: 0.667
+2025-08-26 01:18:11: Epoch: 030 Train Loss: 0.356, ap: 0.826, auc: 0.856, f1: 0.772, acc: 0.792
+2025-08-26 01:18:12: Epoch: 030 Test results, ap: 0.251, auc: 0.469, f1: 0.120, acc: 0.642
+2025-08-26 01:18:13: Epoch: 031 Train Loss: 0.345, ap: 0.827, auc: 0.857, f1: 0.761, acc: 0.785
+2025-08-26 01:18:13: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:18:13: Epoch: 031 Test results, ap: 0.276, auc: 0.481, f1: 0.109, acc: 0.602
+2025-08-26 01:18:14: Epoch: 032 Train Loss: 0.343, ap: 0.831, auc: 0.858, f1: 0.759, acc: 0.780
+2025-08-26 01:18:14: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:18:14: Epoch: 032 Test results, ap: 0.271, auc: 0.481, f1: 0.109, acc: 0.602
+2025-08-26 01:18:15: Epoch: 033 Train Loss: 0.334, ap: 0.834, auc: 0.859, f1: 0.757, acc: 0.782
+2025-08-26 01:18:15: Epoch: 033 New best VALIDATION metrics
+2025-08-26 01:18:15: Epoch: 033 Test results, ap: 0.287, auc: 0.504, f1: 0.125, acc: 0.659
+2025-08-26 01:18:16: Epoch: 034 Train Loss: 0.339, ap: 0.834, auc: 0.859, f1: 0.767, acc: 0.789
+2025-08-26 01:18:16: Epoch: 034 New best VALIDATION metrics
+2025-08-26 01:18:16: Epoch: 034 Test results, ap: 0.286, auc: 0.503, f1: 0.125, acc: 0.659
+2025-08-26 01:18:17: Epoch: 035 Train Loss: 0.343, ap: 0.823, auc: 0.855, f1: 0.771, acc: 0.790
+2025-08-26 01:18:18: Epoch: 035 Test results, ap: 0.277, auc: 0.520, f1: 0.122, acc: 0.650
+2025-08-26 01:18:18: Epoch: 036 Train Loss: 0.337, ap: 0.827, auc: 0.858, f1: 0.773, acc: 0.792
+2025-08-26 01:18:19: Epoch: 036 Test results, ap: 0.269, auc: 0.511, f1: 0.154, acc: 0.642
+2025-08-26 01:18:20: Epoch: 037 Train Loss: 0.339, ap: 0.833, auc: 0.861, f1: 0.782, acc: 0.799
+2025-08-26 01:18:20: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:18:20: Epoch: 037 Test results, ap: 0.302, auc: 0.499, f1: 0.125, acc: 0.659
+2025-08-26 01:18:21: Epoch: 038 Train Loss: 0.333, ap: 0.830, auc: 0.858, f1: 0.767, acc: 0.787
+2025-08-26 01:18:21: Epoch: 038 Test results, ap: 0.311, auc: 0.517, f1: 0.122, acc: 0.650
+2025-08-26 01:18:22: Epoch: 039 Train Loss: 0.329, ap: 0.830, auc: 0.858, f1: 0.750, acc: 0.780
+2025-08-26 01:18:22: Epoch: 039 Test results, ap: 0.307, auc: 0.511, f1: 0.125, acc: 0.659
+2025-08-26 01:18:23: Epoch: 040 Train Loss: 0.329, ap: 0.831, auc: 0.860, f1: 0.743, acc: 0.776
+2025-08-26 01:18:23: Epoch: 040 Test results, ap: 0.310, auc: 0.519, f1: 0.125, acc: 0.659
+2025-08-26 01:18:24: Epoch: 041 Train Loss: 0.328, ap: 0.832, auc: 0.860, f1: 0.772, acc: 0.796
+2025-08-26 01:18:25: Epoch: 041 Test results, ap: 0.308, auc: 0.514, f1: 0.125, acc: 0.659
+2025-08-26 01:18:26: Epoch: 042 Train Loss: 0.322, ap: 0.832, auc: 0.860, f1: 0.770, acc: 0.792
+2025-08-26 01:18:26: Epoch: 042 Test results, ap: 0.308, auc: 0.513, f1: 0.125, acc: 0.659
+2025-08-26 01:18:27: Epoch: 043 Train Loss: 0.316, ap: 0.831, auc: 0.860, f1: 0.758, acc: 0.787
+2025-08-26 01:18:27: Epoch: 043 Test results, ap: 0.305, auc: 0.508, f1: 0.125, acc: 0.659
+2025-08-26 01:18:28: Epoch: 044 Train Loss: 0.325, ap: 0.833, auc: 0.861, f1: 0.762, acc: 0.789
+2025-08-26 01:18:28: Epoch: 044 Test results, ap: 0.304, auc: 0.505, f1: 0.125, acc: 0.659
+2025-08-26 01:18:29: Epoch: 045 Train Loss: 0.323, ap: 0.831, auc: 0.860, f1: 0.771, acc: 0.796
+2025-08-26 01:18:29: Epoch: 045 Test results, ap: 0.304, auc: 0.504, f1: 0.125, acc: 0.659
+2025-08-26 01:18:30: Epoch: 046 Train Loss: 0.320, ap: 0.835, auc: 0.861, f1: 0.769, acc: 0.789
+2025-08-26 01:18:30: Epoch: 046 New best VALIDATION metrics
+2025-08-26 01:18:31: Epoch: 046 Test results, ap: 0.277, auc: 0.506, f1: 0.125, acc: 0.659
+2025-08-26 01:18:31: Epoch: 047 Train Loss: 0.319, ap: 0.835, auc: 0.862, f1: 0.770, acc: 0.794
+2025-08-26 01:18:31: Epoch: 047 New best VALIDATION metrics
+2025-08-26 01:18:32: Epoch: 047 Test results, ap: 0.293, auc: 0.504, f1: 0.128, acc: 0.667
+2025-08-26 01:18:33: Epoch: 048 Train Loss: 0.321, ap: 0.833, auc: 0.861, f1: 0.754, acc: 0.780
+2025-08-26 01:18:33: Epoch: 048 Test results, ap: 0.306, auc: 0.511, f1: 0.128, acc: 0.667
+2025-08-26 01:18:34: Epoch: 049 Train Loss: 0.313, ap: 0.835, auc: 0.862, f1: 0.762, acc: 0.787
+2025-08-26 01:18:34: Epoch: 049 Test results, ap: 0.283, auc: 0.516, f1: 0.125, acc: 0.659
+2025-08-26 01:18:35: Epoch: 050 Train Loss: 0.315, ap: 0.839, auc: 0.865, f1: 0.771, acc: 0.796
+2025-08-26 01:18:35: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:18:35: Epoch: 050 Test results, ap: 0.306, auc: 0.513, f1: 0.128, acc: 0.667
+2025-08-26 01:18:35: used time 0.02h
+2025-08-26 01:18:35: Running Cross Validation 2
+2025-08-26 01:18:35: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:18:36: Epoch: 001 Train Loss: 0.691, ap: 0.623, auc: 0.667, f1: 0.599, acc: 0.598
+2025-08-26 01:18:36: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:18:37: Epoch: 001 Test results, ap: 0.253, auc: 0.519, f1: 0.372, acc: 0.260
+2025-08-26 01:18:37: Epoch: 002 Train Loss: 0.652, ap: 0.682, auc: 0.720, f1: 0.557, acc: 0.635
+2025-08-26 01:18:37: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:18:38: Epoch: 002 Test results, ap: 0.234, auc: 0.491, f1: 0.302, acc: 0.512
+2025-08-26 01:18:39: Epoch: 003 Train Loss: 0.607, ap: 0.683, auc: 0.732, f1: 0.665, acc: 0.672
+2025-08-26 01:18:39: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:18:39: Epoch: 003 Test results, ap: 0.293, auc: 0.454, f1: 0.302, acc: 0.512
+2025-08-26 01:18:40: Epoch: 004 Train Loss: 0.589, ap: 0.717, auc: 0.778, f1: 0.699, acc: 0.725
+2025-08-26 01:18:40: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:18:40: Epoch: 004 Test results, ap: 0.241, auc: 0.468, f1: 0.333, acc: 0.577
+2025-08-26 01:18:41: Epoch: 005 Train Loss: 0.563, ap: 0.744, auc: 0.789, f1: 0.667, acc: 0.718
+2025-08-26 01:18:41: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:18:41: Epoch: 005 Test results, ap: 0.301, auc: 0.493, f1: 0.290, acc: 0.642
+2025-08-26 01:18:42: Epoch: 006 Train Loss: 0.533, ap: 0.768, auc: 0.807, f1: 0.647, acc: 0.721
+2025-08-26 01:18:42: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:18:42: Epoch: 006 Test results, ap: 0.325, auc: 0.510, f1: 0.314, acc: 0.610
+2025-08-26 01:18:43: Epoch: 007 Train Loss: 0.520, ap: 0.750, auc: 0.804, f1: 0.680, acc: 0.723
+2025-08-26 01:18:44: Epoch: 007 Test results, ap: 0.248, auc: 0.479, f1: 0.300, acc: 0.545
+2025-08-26 01:18:45: Epoch: 008 Train Loss: 0.511, ap: 0.784, auc: 0.827, f1: 0.734, acc: 0.764
+2025-08-26 01:18:45: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:18:45: Epoch: 008 Test results, ap: 0.292, auc: 0.489, f1: 0.314, acc: 0.610
+2025-08-26 01:18:46: Epoch: 009 Train Loss: 0.479, ap: 0.806, auc: 0.836, f1: 0.747, acc: 0.762
+2025-08-26 01:18:46: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:18:46: Epoch: 009 Test results, ap: 0.310, auc: 0.505, f1: 0.328, acc: 0.634
+2025-08-26 01:18:47: Epoch: 010 Train Loss: 0.470, ap: 0.801, auc: 0.835, f1: 0.735, acc: 0.732
+2025-08-26 01:18:47: Epoch: 010 Test results, ap: 0.274, auc: 0.492, f1: 0.329, acc: 0.537
+2025-08-26 01:18:48: Epoch: 011 Train Loss: 0.448, ap: 0.803, auc: 0.837, f1: 0.743, acc: 0.750
+2025-08-26 01:18:48: Epoch: 011 Test results, ap: 0.264, auc: 0.478, f1: 0.341, acc: 0.561
+2025-08-26 01:18:48: Epoch: 011 New best TEST metrics
+2025-08-26 01:18:49: Epoch: 012 Train Loss: 0.413, ap: 0.833, auc: 0.857, f1: 0.747, acc: 0.776
+2025-08-26 01:18:49: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:18:50: Epoch: 012 Test results, ap: 0.248, auc: 0.454, f1: 0.282, acc: 0.585
+2025-08-26 01:18:51: Epoch: 013 Train Loss: 0.401, ap: 0.820, auc: 0.854, f1: 0.749, acc: 0.771
+2025-08-26 01:18:51: Epoch: 013 Test results, ap: 0.237, auc: 0.468, f1: 0.282, acc: 0.545
+2025-08-26 01:18:52: Epoch: 014 Train Loss: 0.389, ap: 0.839, auc: 0.869, f1: 0.758, acc: 0.774
+2025-08-26 01:18:52: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:18:52: Epoch: 014 Test results, ap: 0.243, auc: 0.453, f1: 0.289, acc: 0.520
+2025-08-26 01:18:53: Epoch: 015 Train Loss: 0.377, ap: 0.829, auc: 0.863, f1: 0.750, acc: 0.772
+2025-08-26 01:18:53: Epoch: 015 Test results, ap: 0.254, auc: 0.482, f1: 0.289, acc: 0.561
+2025-08-26 01:18:54: Epoch: 016 Train Loss: 0.378, ap: 0.831, auc: 0.863, f1: 0.765, acc: 0.780
+2025-08-26 01:18:54: Epoch: 016 Test results, ap: 0.250, auc: 0.455, f1: 0.293, acc: 0.528
+2025-08-26 01:18:55: Epoch: 017 Train Loss: 0.381, ap: 0.838, auc: 0.864, f1: 0.744, acc: 0.765
+2025-08-26 01:18:55: Epoch: 017 Test results, ap: 0.291, auc: 0.510, f1: 0.301, acc: 0.585
+2025-08-26 01:18:55: Epoch: 017 New best TEST metrics
+2025-08-26 01:18:56: Epoch: 018 Train Loss: 0.368, ap: 0.840, auc: 0.870, f1: 0.765, acc: 0.780
+2025-08-26 01:18:56: Epoch: 018 New best VALIDATION metrics
+2025-08-26 01:18:57: Epoch: 018 Test results, ap: 0.279, auc: 0.462, f1: 0.282, acc: 0.545
+2025-08-26 01:18:58: Epoch: 019 Train Loss: 0.368, ap: 0.853, auc: 0.876, f1: 0.753, acc: 0.785
+2025-08-26 01:18:58: Epoch: 019 New best VALIDATION metrics
+2025-08-26 01:18:58: Epoch: 019 Test results, ap: 0.261, auc: 0.466, f1: 0.297, acc: 0.577
+2025-08-26 01:18:59: Epoch: 020 Train Loss: 0.342, ap: 0.846, auc: 0.876, f1: 0.756, acc: 0.781
+2025-08-26 01:18:59: Epoch: 020 Test results, ap: 0.245, auc: 0.471, f1: 0.306, acc: 0.593
+2025-08-26 01:19:00: Epoch: 021 Train Loss: 0.323, ap: 0.852, auc: 0.884, f1: 0.776, acc: 0.792
+2025-08-26 01:19:00: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:19:00: Epoch: 021 Test results, ap: 0.236, auc: 0.465, f1: 0.349, acc: 0.545
+2025-08-26 01:19:01: Epoch: 022 Train Loss: 0.318, ap: 0.859, auc: 0.883, f1: 0.776, acc: 0.792
+2025-08-26 01:19:01: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:19:01: Epoch: 022 Test results, ap: 0.242, auc: 0.453, f1: 0.329, acc: 0.537
+2025-08-26 01:19:02: Epoch: 023 Train Loss: 0.310, ap: 0.858, auc: 0.886, f1: 0.765, acc: 0.801
+2025-08-26 01:19:02: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:19:03: Epoch: 023 Test results, ap: 0.236, auc: 0.466, f1: 0.254, acc: 0.569
+2025-08-26 01:19:04: Epoch: 024 Train Loss: 0.307, ap: 0.864, auc: 0.892, f1: 0.787, acc: 0.804
+2025-08-26 01:19:04: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:19:04: Epoch: 024 Test results, ap: 0.272, auc: 0.485, f1: 0.301, acc: 0.585
+2025-08-26 01:19:05: Epoch: 025 Train Loss: 0.308, ap: 0.862, auc: 0.894, f1: 0.791, acc: 0.817
+2025-08-26 01:19:05: Epoch: 025 Test results, ap: 0.250, auc: 0.477, f1: 0.278, acc: 0.577
+2025-08-26 01:19:06: Epoch: 026 Train Loss: 0.290, ap: 0.864, auc: 0.893, f1: 0.785, acc: 0.806
+2025-08-26 01:19:06: Epoch: 026 New best VALIDATION metrics
+2025-08-26 01:19:06: Epoch: 026 Test results, ap: 0.265, auc: 0.474, f1: 0.354, acc: 0.585
+2025-08-26 01:19:07: Epoch: 027 Train Loss: 0.294, ap: 0.872, auc: 0.900, f1: 0.803, acc: 0.818
+2025-08-26 01:19:07: Epoch: 027 New best VALIDATION metrics
+2025-08-26 01:19:07: Epoch: 027 Test results, ap: 0.267, auc: 0.480, f1: 0.354, acc: 0.585
+2025-08-26 01:19:08: Epoch: 028 Train Loss: 0.296, ap: 0.863, auc: 0.894, f1: 0.786, acc: 0.811
+2025-08-26 01:19:09: Epoch: 028 Test results, ap: 0.265, auc: 0.478, f1: 0.239, acc: 0.585
+2025-08-26 01:19:10: Epoch: 029 Train Loss: 0.285, ap: 0.864, auc: 0.894, f1: 0.804, acc: 0.817
+2025-08-26 01:19:10: Epoch: 029 Test results, ap: 0.252, auc: 0.485, f1: 0.304, acc: 0.553
+2025-08-26 01:19:11: Epoch: 030 Train Loss: 0.281, ap: 0.876, auc: 0.900, f1: 0.805, acc: 0.824
+2025-08-26 01:19:11: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:19:11: Epoch: 030 Test results, ap: 0.257, auc: 0.466, f1: 0.337, acc: 0.553
+2025-08-26 01:19:12: Epoch: 031 Train Loss: 0.280, ap: 0.874, auc: 0.899, f1: 0.805, acc: 0.822
+2025-08-26 01:19:12: Epoch: 031 Test results, ap: 0.242, auc: 0.472, f1: 0.317, acc: 0.545
+2025-08-26 01:19:13: Epoch: 032 Train Loss: 0.259, ap: 0.877, auc: 0.902, f1: 0.807, acc: 0.820
+2025-08-26 01:19:13: Epoch: 032 New best VALIDATION metrics
+2025-08-26 01:19:13: Epoch: 032 Test results, ap: 0.261, auc: 0.474, f1: 0.326, acc: 0.528
+2025-08-26 01:19:14: Epoch: 033 Train Loss: 0.260, ap: 0.870, auc: 0.899, f1: 0.789, acc: 0.815
+2025-08-26 01:19:15: Epoch: 033 Test results, ap: 0.267, auc: 0.493, f1: 0.306, acc: 0.593
+2025-08-26 01:19:16: Epoch: 034 Train Loss: 0.264, ap: 0.872, auc: 0.900, f1: 0.805, acc: 0.824
+2025-08-26 01:19:16: Epoch: 034 Test results, ap: 0.264, auc: 0.484, f1: 0.329, acc: 0.569
+2025-08-26 01:19:17: Epoch: 035 Train Loss: 0.268, ap: 0.872, auc: 0.895, f1: 0.792, acc: 0.811
+2025-08-26 01:19:17: Epoch: 035 Test results, ap: 0.257, auc: 0.471, f1: 0.321, acc: 0.553
+2025-08-26 01:19:18: Epoch: 036 Train Loss: 0.256, ap: 0.879, auc: 0.903, f1: 0.809, acc: 0.825
+2025-08-26 01:19:18: Epoch: 036 New best VALIDATION metrics
+2025-08-26 01:19:18: Epoch: 036 Test results, ap: 0.248, auc: 0.481, f1: 0.304, acc: 0.553
+2025-08-26 01:19:19: Epoch: 037 Train Loss: 0.259, ap: 0.875, auc: 0.900, f1: 0.803, acc: 0.822
+2025-08-26 01:19:19: Epoch: 037 Test results, ap: 0.265, auc: 0.484, f1: 0.325, acc: 0.561
+2025-08-26 01:19:20: Epoch: 038 Train Loss: 0.250, ap: 0.877, auc: 0.902, f1: 0.812, acc: 0.829
+2025-08-26 01:19:21: Epoch: 038 Test results, ap: 0.250, auc: 0.489, f1: 0.289, acc: 0.561
+2025-08-26 01:19:21: Epoch: 039 Train Loss: 0.259, ap: 0.879, auc: 0.901, f1: 0.816, acc: 0.829
+2025-08-26 01:19:22: Epoch: 039 Test results, ap: 0.248, auc: 0.486, f1: 0.308, acc: 0.561
+2025-08-26 01:19:23: Epoch: 040 Train Loss: 0.252, ap: 0.880, auc: 0.902, f1: 0.809, acc: 0.824
+2025-08-26 01:19:23: Epoch: 040 New best VALIDATION metrics
+2025-08-26 01:19:23: Epoch: 040 Test results, ap: 0.248, auc: 0.480, f1: 0.313, acc: 0.537
+2025-08-26 01:19:24: Epoch: 041 Train Loss: 0.246, ap: 0.884, auc: 0.905, f1: 0.811, acc: 0.825
+2025-08-26 01:19:24: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:19:24: Epoch: 041 Test results, ap: 0.266, auc: 0.487, f1: 0.329, acc: 0.569
+2025-08-26 01:19:25: Epoch: 042 Train Loss: 0.245, ap: 0.882, auc: 0.904, f1: 0.811, acc: 0.825
+2025-08-26 01:19:25: Epoch: 042 Test results, ap: 0.264, auc: 0.482, f1: 0.329, acc: 0.537
+2025-08-26 01:19:26: Epoch: 043 Train Loss: 0.248, ap: 0.881, auc: 0.904, f1: 0.817, acc: 0.832
+2025-08-26 01:19:26: Epoch: 043 Test results, ap: 0.267, auc: 0.487, f1: 0.304, acc: 0.553
+2025-08-26 01:19:27: Epoch: 044 Train Loss: 0.244, ap: 0.879, auc: 0.904, f1: 0.813, acc: 0.829
+2025-08-26 01:19:28: Epoch: 044 Test results, ap: 0.267, auc: 0.490, f1: 0.316, acc: 0.577
+2025-08-26 01:19:29: Epoch: 045 Train Loss: 0.238, ap: 0.884, auc: 0.905, f1: 0.806, acc: 0.822
+2025-08-26 01:19:29: Epoch: 045 New best VALIDATION metrics
+2025-08-26 01:19:29: Epoch: 045 Test results, ap: 0.249, auc: 0.487, f1: 0.329, acc: 0.569
+2025-08-26 01:19:30: Epoch: 046 Train Loss: 0.243, ap: 0.883, auc: 0.905, f1: 0.806, acc: 0.824
+2025-08-26 01:19:30: Epoch: 046 Test results, ap: 0.247, auc: 0.484, f1: 0.308, acc: 0.561
+2025-08-26 01:19:31: Epoch: 047 Train Loss: 0.235, ap: 0.883, auc: 0.905, f1: 0.813, acc: 0.831
+2025-08-26 01:19:31: Epoch: 047 Test results, ap: 0.246, auc: 0.479, f1: 0.286, acc: 0.553
+2025-08-26 01:19:32: Epoch: 048 Train Loss: 0.244, ap: 0.885, auc: 0.906, f1: 0.810, acc: 0.827
+2025-08-26 01:19:32: Epoch: 048 New best VALIDATION metrics
+2025-08-26 01:19:32: Epoch: 048 Test results, ap: 0.247, auc: 0.481, f1: 0.267, acc: 0.553
+2025-08-26 01:19:33: Epoch: 049 Train Loss: 0.247, ap: 0.881, auc: 0.904, f1: 0.813, acc: 0.829
+2025-08-26 01:19:34: Epoch: 049 Test results, ap: 0.248, auc: 0.485, f1: 0.308, acc: 0.561
+2025-08-26 01:19:35: Epoch: 050 Train Loss: 0.235, ap: 0.885, auc: 0.907, f1: 0.813, acc: 0.829
+2025-08-26 01:19:35: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:19:35: Epoch: 050 Test results, ap: 0.250, auc: 0.487, f1: 0.286, acc: 0.553
+2025-08-26 01:19:35: used time 0.02h
+2025-08-26 01:19:35: Running Cross Validation 3
+2025-08-26 01:19:35: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:19:36: Epoch: 001 Train Loss: 0.687, ap: 0.617, auc: 0.656, f1: 0.500, acc: 0.644
+2025-08-26 01:19:36: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:19:36: Epoch: 001 Test results, ap: 0.229, auc: 0.453, f1: 0.317, acc: 0.333
+2025-08-26 01:19:37: Epoch: 002 Train Loss: 0.639, ap: 0.673, auc: 0.750, f1: 0.649, acc: 0.695
+2025-08-26 01:19:37: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:19:37: Epoch: 002 Test results, ap: 0.242, auc: 0.473, f1: 0.392, acc: 0.244
+2025-08-26 01:19:38: Epoch: 003 Train Loss: 0.597, ap: 0.744, auc: 0.794, f1: 0.699, acc: 0.702
+2025-08-26 01:19:38: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:19:38: Epoch: 003 Test results, ap: 0.279, auc: 0.548, f1: 0.360, acc: 0.537
+2025-08-26 01:19:39: Epoch: 004 Train Loss: 0.564, ap: 0.756, auc: 0.829, f1: 0.727, acc: 0.751
+2025-08-26 01:19:39: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:19:40: Epoch: 004 Test results, ap: 0.274, auc: 0.520, f1: 0.362, acc: 0.341
+2025-08-26 01:19:40: Epoch: 005 Train Loss: 0.545, ap: 0.814, auc: 0.849, f1: 0.748, acc: 0.757
+2025-08-26 01:19:40: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:19:41: Epoch: 005 Test results, ap: 0.373, auc: 0.558, f1: 0.353, acc: 0.553
+2025-08-26 01:19:42: Epoch: 006 Train Loss: 0.519, ap: 0.812, auc: 0.855, f1: 0.738, acc: 0.762
+2025-08-26 01:19:42: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:19:42: Epoch: 006 Test results, ap: 0.320, auc: 0.547, f1: 0.235, acc: 0.683
+2025-08-26 01:19:43: Epoch: 007 Train Loss: 0.500, ap: 0.820, auc: 0.868, f1: 0.764, acc: 0.792
+2025-08-26 01:19:43: Epoch: 007 New best VALIDATION metrics
+2025-08-26 01:19:43: Epoch: 007 Test results, ap: 0.256, auc: 0.524, f1: 0.321, acc: 0.553
+2025-08-26 01:19:44: Epoch: 008 Train Loss: 0.479, ap: 0.850, auc: 0.882, f1: 0.770, acc: 0.792
+2025-08-26 01:19:44: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:19:44: Epoch: 008 Test results, ap: 0.270, auc: 0.528, f1: 0.215, acc: 0.585
+2025-08-26 01:19:45: Epoch: 009 Train Loss: 0.469, ap: 0.828, auc: 0.862, f1: 0.730, acc: 0.781
+2025-08-26 01:19:45: Epoch: 009 Test results, ap: 0.313, auc: 0.566, f1: 0.347, acc: 0.602
+2025-08-26 01:19:46: Epoch: 010 Train Loss: 0.454, ap: 0.838, auc: 0.875, f1: 0.765, acc: 0.774
+2025-08-26 01:19:47: Epoch: 010 Test results, ap: 0.315, auc: 0.602, f1: 0.417, acc: 0.317
+2025-08-26 01:19:48: Epoch: 011 Train Loss: 0.416, ap: 0.848, auc: 0.879, f1: 0.772, acc: 0.781
+2025-08-26 01:19:48: Epoch: 011 Test results, ap: 0.344, auc: 0.620, f1: 0.417, acc: 0.317
+2025-08-26 01:19:48: Epoch: 011 New best TEST metrics
+2025-08-26 01:19:49: Epoch: 012 Train Loss: 0.401, ap: 0.867, auc: 0.897, f1: 0.783, acc: 0.806
+2025-08-26 01:19:49: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:19:49: Epoch: 012 Test results, ap: 0.319, auc: 0.570, f1: 0.431, acc: 0.398
+2025-08-26 01:19:50: Epoch: 013 Train Loss: 0.392, ap: 0.877, auc: 0.905, f1: 0.806, acc: 0.820
+2025-08-26 01:19:50: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:19:50: Epoch: 013 Test results, ap: 0.404, auc: 0.578, f1: 0.376, acc: 0.488
+2025-08-26 01:19:50: Epoch: 013 New best TEST metrics
+2025-08-26 01:19:51: Epoch: 014 Train Loss: 0.374, ap: 0.874, auc: 0.904, f1: 0.812, acc: 0.829
+2025-08-26 01:19:51: Epoch: 014 Test results, ap: 0.369, auc: 0.615, f1: 0.424, acc: 0.447
+2025-08-26 01:19:51: Epoch: 014 New best TEST metrics
+2025-08-26 01:19:52: Epoch: 015 Train Loss: 0.378, ap: 0.875, auc: 0.903, f1: 0.802, acc: 0.822
+2025-08-26 01:19:53: Epoch: 015 Test results, ap: 0.391, auc: 0.628, f1: 0.360, acc: 0.537
+2025-08-26 01:19:53: Epoch: 015 New best TEST metrics
+2025-08-26 01:19:54: Epoch: 016 Train Loss: 0.369, ap: 0.878, auc: 0.908, f1: 0.807, acc: 0.827
+2025-08-26 01:19:54: Epoch: 016 New best VALIDATION metrics
+2025-08-26 01:19:54: Epoch: 016 Test results, ap: 0.381, auc: 0.611, f1: 0.391, acc: 0.569
+2025-08-26 01:19:55: Epoch: 017 Train Loss: 0.368, ap: 0.863, auc: 0.898, f1: 0.785, acc: 0.815
+2025-08-26 01:19:55: Epoch: 017 Test results, ap: 0.374, auc: 0.580, f1: 0.395, acc: 0.602
+2025-08-26 01:19:56: Epoch: 018 Train Loss: 0.351, ap: 0.878, auc: 0.904, f1: 0.798, acc: 0.813
+2025-08-26 01:19:56: Epoch: 018 Test results, ap: 0.355, auc: 0.573, f1: 0.380, acc: 0.496
+2025-08-26 01:19:57: Epoch: 019 Train Loss: 0.348, ap: 0.878, auc: 0.906, f1: 0.807, acc: 0.827
+2025-08-26 01:19:57: Epoch: 019 Test results, ap: 0.371, auc: 0.571, f1: 0.404, acc: 0.569
+2025-08-26 01:19:58: Epoch: 020 Train Loss: 0.340, ap: 0.874, auc: 0.900, f1: 0.805, acc: 0.820
+2025-08-26 01:19:59: Epoch: 020 Test results, ap: 0.367, auc: 0.617, f1: 0.387, acc: 0.537
+2025-08-26 01:19:59: Epoch: 021 Train Loss: 0.321, ap: 0.885, auc: 0.911, f1: 0.818, acc: 0.836
+2025-08-26 01:19:59: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:20:00: Epoch: 021 Test results, ap: 0.385, auc: 0.609, f1: 0.409, acc: 0.553
+2025-08-26 01:20:01: Epoch: 022 Train Loss: 0.302, ap: 0.885, auc: 0.912, f1: 0.810, acc: 0.831
+2025-08-26 01:20:01: Epoch: 022 New best VALIDATION metrics
+2025-08-26 01:20:01: Epoch: 022 Test results, ap: 0.346, auc: 0.579, f1: 0.424, acc: 0.602
+2025-08-26 01:20:02: Epoch: 023 Train Loss: 0.298, ap: 0.885, auc: 0.913, f1: 0.822, acc: 0.841
+2025-08-26 01:20:02: Epoch: 023 New best VALIDATION metrics
+2025-08-26 01:20:02: Epoch: 023 Test results, ap: 0.388, auc: 0.585, f1: 0.420, acc: 0.618
+2025-08-26 01:20:03: Epoch: 024 Train Loss: 0.294, ap: 0.886, auc: 0.914, f1: 0.823, acc: 0.840
+2025-08-26 01:20:03: Epoch: 024 New best VALIDATION metrics
+2025-08-26 01:20:03: Epoch: 024 Test results, ap: 0.410, auc: 0.587, f1: 0.409, acc: 0.577
+2025-08-26 01:20:04: Epoch: 025 Train Loss: 0.284, ap: 0.888, auc: 0.915, f1: 0.817, acc: 0.838
+2025-08-26 01:20:04: Epoch: 025 New best VALIDATION metrics
+2025-08-26 01:20:05: Epoch: 025 Test results, ap: 0.409, auc: 0.577, f1: 0.405, acc: 0.593
+2025-08-26 01:20:05: Epoch: 026 Train Loss: 0.283, ap: 0.889, auc: 0.914, f1: 0.815, acc: 0.834
+2025-08-26 01:20:06: Epoch: 026 Test results, ap: 0.346, auc: 0.581, f1: 0.409, acc: 0.577
+2025-08-26 01:20:07: Epoch: 027 Train Loss: 0.283, ap: 0.888, auc: 0.912, f1: 0.810, acc: 0.832
+2025-08-26 01:20:07: Epoch: 027 Test results, ap: 0.329, auc: 0.570, f1: 0.415, acc: 0.610
+2025-08-26 01:20:08: Epoch: 028 Train Loss: 0.275, ap: 0.885, auc: 0.911, f1: 0.806, acc: 0.829
+2025-08-26 01:20:08: Epoch: 028 Test results, ap: 0.308, auc: 0.559, f1: 0.419, acc: 0.593
+2025-08-26 01:20:09: Epoch: 029 Train Loss: 0.273, ap: 0.891, auc: 0.914, f1: 0.816, acc: 0.834
+2025-08-26 01:20:09: Epoch: 029 New best VALIDATION metrics
+2025-08-26 01:20:09: Epoch: 029 Test results, ap: 0.388, auc: 0.562, f1: 0.350, acc: 0.577
+2025-08-26 01:20:10: Epoch: 030 Train Loss: 0.267, ap: 0.890, auc: 0.915, f1: 0.805, acc: 0.831
+2025-08-26 01:20:10: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:20:10: Epoch: 030 Test results, ap: 0.325, auc: 0.566, f1: 0.368, acc: 0.553
+2025-08-26 01:20:11: Epoch: 031 Train Loss: 0.259, ap: 0.896, auc: 0.918, f1: 0.826, acc: 0.847
+2025-08-26 01:20:11: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:20:12: Epoch: 031 Test results, ap: 0.324, auc: 0.561, f1: 0.386, acc: 0.561
+2025-08-26 01:20:13: Epoch: 032 Train Loss: 0.254, ap: 0.896, auc: 0.918, f1: 0.828, acc: 0.848
+2025-08-26 01:20:13: Epoch: 032 Test results, ap: 0.323, auc: 0.568, f1: 0.386, acc: 0.561
+2025-08-26 01:20:14: Epoch: 033 Train Loss: 0.250, ap: 0.892, auc: 0.917, f1: 0.826, acc: 0.847
+2025-08-26 01:20:14: Epoch: 033 Test results, ap: 0.339, auc: 0.572, f1: 0.382, acc: 0.553
+2025-08-26 01:20:15: Epoch: 034 Train Loss: 0.248, ap: 0.895, auc: 0.919, f1: 0.823, acc: 0.845
+2025-08-26 01:20:15: Epoch: 034 Test results, ap: 0.327, auc: 0.568, f1: 0.382, acc: 0.553
+2025-08-26 01:20:16: Epoch: 035 Train Loss: 0.251, ap: 0.896, auc: 0.919, f1: 0.824, acc: 0.845
+2025-08-26 01:20:16: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:20:16: Epoch: 035 Test results, ap: 0.306, auc: 0.555, f1: 0.337, acc: 0.520
+2025-08-26 01:20:17: Epoch: 036 Train Loss: 0.245, ap: 0.897, auc: 0.919, f1: 0.817, acc: 0.838
+2025-08-26 01:20:18: Epoch: 036 Test results, ap: 0.307, auc: 0.554, f1: 0.349, acc: 0.545
+2025-08-26 01:20:19: Epoch: 037 Train Loss: 0.245, ap: 0.892, auc: 0.917, f1: 0.817, acc: 0.838
+2025-08-26 01:20:19: Epoch: 037 Test results, ap: 0.297, auc: 0.550, f1: 0.341, acc: 0.528
+2025-08-26 01:20:20: Epoch: 038 Train Loss: 0.243, ap: 0.891, auc: 0.916, f1: 0.822, acc: 0.843
+2025-08-26 01:20:20: Epoch: 038 Test results, ap: 0.312, auc: 0.569, f1: 0.364, acc: 0.545
+2025-08-26 01:20:21: Epoch: 039 Train Loss: 0.243, ap: 0.895, auc: 0.919, f1: 0.819, acc: 0.841
+2025-08-26 01:20:21: Epoch: 039 Test results, ap: 0.296, auc: 0.549, f1: 0.360, acc: 0.537
+2025-08-26 01:20:22: Epoch: 040 Train Loss: 0.242, ap: 0.896, auc: 0.920, f1: 0.827, acc: 0.848
+2025-08-26 01:20:22: Epoch: 040 Test results, ap: 0.294, auc: 0.546, f1: 0.337, acc: 0.553
+2025-08-26 01:20:23: Epoch: 041 Train Loss: 0.228, ap: 0.897, auc: 0.920, f1: 0.827, acc: 0.848
+2025-08-26 01:20:23: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:20:24: Epoch: 041 Test results, ap: 0.302, auc: 0.548, f1: 0.353, acc: 0.553
+2025-08-26 01:20:25: Epoch: 042 Train Loss: 0.225, ap: 0.896, auc: 0.920, f1: 0.827, acc: 0.848
+2025-08-26 01:20:25: Epoch: 042 Test results, ap: 0.308, auc: 0.549, f1: 0.329, acc: 0.537
+2025-08-26 01:20:26: Epoch: 043 Train Loss: 0.228, ap: 0.895, auc: 0.920, f1: 0.832, acc: 0.852
+2025-08-26 01:20:26: Epoch: 043 Test results, ap: 0.308, auc: 0.552, f1: 0.360, acc: 0.537
+2025-08-26 01:20:27: Epoch: 044 Train Loss: 0.232, ap: 0.896, auc: 0.920, f1: 0.820, acc: 0.841
+2025-08-26 01:20:27: Epoch: 044 Test results, ap: 0.297, auc: 0.545, f1: 0.329, acc: 0.537
+2025-08-26 01:20:28: Epoch: 045 Train Loss: 0.227, ap: 0.895, auc: 0.919, f1: 0.823, acc: 0.845
+2025-08-26 01:20:28: Epoch: 045 Test results, ap: 0.301, auc: 0.548, f1: 0.345, acc: 0.537
+2025-08-26 01:20:29: Epoch: 046 Train Loss: 0.230, ap: 0.895, auc: 0.920, f1: 0.824, acc: 0.845
+2025-08-26 01:20:30: Epoch: 046 Test results, ap: 0.303, auc: 0.551, f1: 0.345, acc: 0.537
+2025-08-26 01:20:30: Epoch: 047 Train Loss: 0.224, ap: 0.896, auc: 0.920, f1: 0.827, acc: 0.847
+2025-08-26 01:20:31: Epoch: 047 Test results, ap: 0.299, auc: 0.548, f1: 0.345, acc: 0.537
+2025-08-26 01:20:32: Epoch: 048 Train Loss: 0.223, ap: 0.895, auc: 0.920, f1: 0.823, acc: 0.845
+2025-08-26 01:20:32: Epoch: 048 Test results, ap: 0.303, auc: 0.552, f1: 0.364, acc: 0.545
+2025-08-26 01:20:33: Epoch: 049 Train Loss: 0.219, ap: 0.893, auc: 0.918, f1: 0.821, acc: 0.841
+2025-08-26 01:20:33: Epoch: 049 Test results, ap: 0.301, auc: 0.547, f1: 0.345, acc: 0.537
+2025-08-26 01:20:34: Epoch: 050 Train Loss: 0.221, ap: 0.897, auc: 0.921, f1: 0.825, acc: 0.848
+2025-08-26 01:20:34: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:20:34: Epoch: 050 Test results, ap: 0.298, auc: 0.549, f1: 0.353, acc: 0.553
+2025-08-26 01:20:34: used time 0.02h
+2025-08-26 01:20:34: Running Cross Validation 4
+2025-08-26 01:20:34: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 01:20:35: Epoch: 001 Train Loss: 0.693, ap: 0.595, auc: 0.639, f1: 0.629, acc: 0.494
+2025-08-26 01:20:35: Epoch: 001 New best VALIDATION metrics
+2025-08-26 01:20:35: Epoch: 001 Test results, ap: 0.245, auc: 0.514, f1: 0.392, acc: 0.244
+2025-08-26 01:20:36: Epoch: 002 Train Loss: 0.643, ap: 0.584, auc: 0.650, f1: 0.581, acc: 0.593
+2025-08-26 01:20:36: Epoch: 002 New best VALIDATION metrics
+2025-08-26 01:20:37: Epoch: 002 Test results, ap: 0.251, auc: 0.448, f1: 0.293, acc: 0.569
+2025-08-26 01:20:38: Epoch: 003 Train Loss: 0.605, ap: 0.653, auc: 0.711, f1: 0.645, acc: 0.640
+2025-08-26 01:20:38: Epoch: 003 New best VALIDATION metrics
+2025-08-26 01:20:38: Epoch: 003 Test results, ap: 0.267, auc: 0.529, f1: 0.370, acc: 0.528
+2025-08-26 01:20:39: Epoch: 004 Train Loss: 0.567, ap: 0.666, auc: 0.719, f1: 0.631, acc: 0.695
+2025-08-26 01:20:39: Epoch: 004 New best VALIDATION metrics
+2025-08-26 01:20:39: Epoch: 004 Test results, ap: 0.280, auc: 0.553, f1: 0.333, acc: 0.577
+2025-08-26 01:20:40: Epoch: 005 Train Loss: 0.545, ap: 0.726, auc: 0.779, f1: 0.685, acc: 0.698
+2025-08-26 01:20:40: Epoch: 005 New best VALIDATION metrics
+2025-08-26 01:20:40: Epoch: 005 Test results, ap: 0.288, auc: 0.568, f1: 0.400, acc: 0.341
+2025-08-26 01:20:41: Epoch: 006 Train Loss: 0.518, ap: 0.734, auc: 0.780, f1: 0.689, acc: 0.720
+2025-08-26 01:20:41: Epoch: 006 New best VALIDATION metrics
+2025-08-26 01:20:41: Epoch: 006 Test results, ap: 0.294, auc: 0.597, f1: 0.413, acc: 0.561
+2025-08-26 01:20:42: Epoch: 007 Train Loss: 0.516, ap: 0.758, auc: 0.794, f1: 0.697, acc: 0.691
+2025-08-26 01:20:42: Epoch: 007 New best VALIDATION metrics
+2025-08-26 01:20:43: Epoch: 007 Test results, ap: 0.316, auc: 0.624, f1: 0.434, acc: 0.407
+2025-08-26 01:20:44: Epoch: 008 Train Loss: 0.481, ap: 0.778, auc: 0.811, f1: 0.708, acc: 0.748
+2025-08-26 01:20:44: Epoch: 008 New best VALIDATION metrics
+2025-08-26 01:20:44: Epoch: 008 Test results, ap: 0.326, auc: 0.607, f1: 0.400, acc: 0.463
+2025-08-26 01:20:45: Epoch: 009 Train Loss: 0.465, ap: 0.796, auc: 0.821, f1: 0.717, acc: 0.750
+2025-08-26 01:20:45: Epoch: 009 New best VALIDATION metrics
+2025-08-26 01:20:45: Epoch: 009 Test results, ap: 0.316, auc: 0.619, f1: 0.449, acc: 0.602
+2025-08-26 01:20:46: Epoch: 010 Train Loss: 0.439, ap: 0.817, auc: 0.843, f1: 0.749, acc: 0.776
+2025-08-26 01:20:46: Epoch: 010 New best VALIDATION metrics
+2025-08-26 01:20:46: Epoch: 010 Test results, ap: 0.353, auc: 0.645, f1: 0.448, acc: 0.480
+2025-08-26 01:20:47: Epoch: 011 Train Loss: 0.417, ap: 0.821, auc: 0.839, f1: 0.730, acc: 0.748
+2025-08-26 01:20:47: Epoch: 011 New best VALIDATION metrics
+2025-08-26 01:20:47: Epoch: 011 Test results, ap: 0.352, auc: 0.625, f1: 0.412, acc: 0.512
+2025-08-26 01:20:47: Epoch: 011 New best TEST metrics
+2025-08-26 01:20:48: Epoch: 012 Train Loss: 0.407, ap: 0.834, auc: 0.854, f1: 0.751, acc: 0.762
+2025-08-26 01:20:48: Epoch: 012 New best VALIDATION metrics
+2025-08-26 01:20:49: Epoch: 012 Test results, ap: 0.334, auc: 0.611, f1: 0.417, acc: 0.455
+2025-08-26 01:20:50: Epoch: 013 Train Loss: 0.405, ap: 0.835, auc: 0.859, f1: 0.756, acc: 0.771
+2025-08-26 01:20:50: Epoch: 013 New best VALIDATION metrics
+2025-08-26 01:20:50: Epoch: 013 Test results, ap: 0.349, auc: 0.641, f1: 0.456, acc: 0.496
+2025-08-26 01:20:50: Epoch: 013 New best TEST metrics
+2025-08-26 01:20:51: Epoch: 014 Train Loss: 0.396, ap: 0.845, auc: 0.862, f1: 0.760, acc: 0.780
+2025-08-26 01:20:51: Epoch: 014 New best VALIDATION metrics
+2025-08-26 01:20:51: Epoch: 014 Test results, ap: 0.327, auc: 0.584, f1: 0.326, acc: 0.496
+2025-08-26 01:20:52: Epoch: 015 Train Loss: 0.395, ap: 0.821, auc: 0.845, f1: 0.735, acc: 0.755
+2025-08-26 01:20:52: Epoch: 015 Test results, ap: 0.343, auc: 0.607, f1: 0.396, acc: 0.504
+2025-08-26 01:20:53: Epoch: 016 Train Loss: 0.386, ap: 0.834, auc: 0.856, f1: 0.752, acc: 0.769
+2025-08-26 01:20:53: Epoch: 016 Test results, ap: 0.347, auc: 0.610, f1: 0.393, acc: 0.447
+2025-08-26 01:20:54: Epoch: 017 Train Loss: 0.379, ap: 0.831, auc: 0.857, f1: 0.754, acc: 0.769
+2025-08-26 01:20:55: Epoch: 017 Test results, ap: 0.349, auc: 0.630, f1: 0.386, acc: 0.431
+2025-08-26 01:20:56: Epoch: 018 Train Loss: 0.372, ap: 0.841, auc: 0.861, f1: 0.741, acc: 0.769
+2025-08-26 01:20:56: Epoch: 018 Test results, ap: 0.310, auc: 0.594, f1: 0.349, acc: 0.545
+2025-08-26 01:20:57: Epoch: 019 Train Loss: 0.375, ap: 0.835, auc: 0.860, f1: 0.751, acc: 0.769
+2025-08-26 01:20:57: Epoch: 019 Test results, ap: 0.300, auc: 0.571, f1: 0.364, acc: 0.488
+2025-08-26 01:20:58: Epoch: 020 Train Loss: 0.375, ap: 0.856, auc: 0.876, f1: 0.774, acc: 0.783
+2025-08-26 01:20:58: Epoch: 020 New best VALIDATION metrics
+2025-08-26 01:20:58: Epoch: 020 Test results, ap: 0.309, auc: 0.576, f1: 0.404, acc: 0.447
+2025-08-26 01:20:59: Epoch: 021 Train Loss: 0.354, ap: 0.859, auc: 0.875, f1: 0.770, acc: 0.794
+2025-08-26 01:20:59: Epoch: 021 New best VALIDATION metrics
+2025-08-26 01:20:59: Epoch: 021 Test results, ap: 0.308, auc: 0.566, f1: 0.337, acc: 0.520
+2025-08-26 01:21:00: Epoch: 022 Train Loss: 0.351, ap: 0.856, auc: 0.873, f1: 0.770, acc: 0.787
+2025-08-26 01:21:01: Epoch: 022 Test results, ap: 0.290, auc: 0.557, f1: 0.391, acc: 0.545
+2025-08-26 01:21:01: Epoch: 023 Train Loss: 0.344, ap: 0.854, auc: 0.873, f1: 0.767, acc: 0.785
+2025-08-26 01:21:02: Epoch: 023 Test results, ap: 0.293, auc: 0.569, f1: 0.411, acc: 0.488
+2025-08-26 01:21:03: Epoch: 024 Train Loss: 0.347, ap: 0.851, auc: 0.870, f1: 0.754, acc: 0.774
+2025-08-26 01:21:03: Epoch: 024 Test results, ap: 0.273, auc: 0.534, f1: 0.404, acc: 0.520
+2025-08-26 01:21:04: Epoch: 025 Train Loss: 0.337, ap: 0.860, auc: 0.878, f1: 0.771, acc: 0.783
+2025-08-26 01:21:04: Epoch: 025 New best VALIDATION metrics
+2025-08-26 01:21:04: Epoch: 025 Test results, ap: 0.275, auc: 0.538, f1: 0.408, acc: 0.504
+2025-08-26 01:21:05: Epoch: 026 Train Loss: 0.335, ap: 0.857, auc: 0.876, f1: 0.768, acc: 0.780
+2025-08-26 01:21:05: Epoch: 026 Test results, ap: 0.261, auc: 0.525, f1: 0.400, acc: 0.488
+2025-08-26 01:21:06: Epoch: 027 Train Loss: 0.338, ap: 0.862, auc: 0.882, f1: 0.781, acc: 0.792
+2025-08-26 01:21:06: Epoch: 027 New best VALIDATION metrics
+2025-08-26 01:21:07: Epoch: 027 Test results, ap: 0.268, auc: 0.520, f1: 0.396, acc: 0.480
+2025-08-26 01:21:07: Epoch: 028 Train Loss: 0.330, ap: 0.862, auc: 0.877, f1: 0.774, acc: 0.788
+2025-08-26 01:21:08: Epoch: 028 Test results, ap: 0.279, auc: 0.542, f1: 0.380, acc: 0.496
+2025-08-26 01:21:09: Epoch: 029 Train Loss: 0.330, ap: 0.865, auc: 0.879, f1: 0.776, acc: 0.795
+2025-08-26 01:21:09: Epoch: 029 Test results, ap: 0.301, auc: 0.573, f1: 0.420, acc: 0.528
+2025-08-26 01:21:10: Epoch: 030 Train Loss: 0.331, ap: 0.866, auc: 0.880, f1: 0.788, acc: 0.810
+2025-08-26 01:21:10: Epoch: 030 New best VALIDATION metrics
+2025-08-26 01:21:10: Epoch: 030 Test results, ap: 0.281, auc: 0.560, f1: 0.368, acc: 0.553
+2025-08-26 01:21:11: Epoch: 031 Train Loss: 0.322, ap: 0.871, auc: 0.885, f1: 0.777, acc: 0.792
+2025-08-26 01:21:11: Epoch: 031 New best VALIDATION metrics
+2025-08-26 01:21:11: Epoch: 031 Test results, ap: 0.273, auc: 0.552, f1: 0.356, acc: 0.528
+2025-08-26 01:21:12: Epoch: 032 Train Loss: 0.328, ap: 0.869, auc: 0.884, f1: 0.783, acc: 0.797
+2025-08-26 01:21:12: Epoch: 032 Test results, ap: 0.267, auc: 0.541, f1: 0.360, acc: 0.480
+2025-08-26 01:21:13: Epoch: 033 Train Loss: 0.311, ap: 0.871, auc: 0.885, f1: 0.780, acc: 0.801
+2025-08-26 01:21:14: Epoch: 033 Test results, ap: 0.271, auc: 0.547, f1: 0.340, acc: 0.496
+2025-08-26 01:21:15: Epoch: 034 Train Loss: 0.314, ap: 0.870, auc: 0.885, f1: 0.781, acc: 0.795
+2025-08-26 01:21:15: Epoch: 034 Test results, ap: 0.267, auc: 0.533, f1: 0.356, acc: 0.472
+2025-08-26 01:21:16: Epoch: 035 Train Loss: 0.310, ap: 0.874, auc: 0.887, f1: 0.782, acc: 0.797
+2025-08-26 01:21:16: Epoch: 035 New best VALIDATION metrics
+2025-08-26 01:21:16: Epoch: 035 Test results, ap: 0.255, auc: 0.519, f1: 0.353, acc: 0.463
+2025-08-26 01:21:17: Epoch: 036 Train Loss: 0.309, ap: 0.874, auc: 0.886, f1: 0.781, acc: 0.795
+2025-08-26 01:21:17: Epoch: 036 Test results, ap: 0.251, auc: 0.517, f1: 0.404, acc: 0.496
+2025-08-26 01:21:18: Epoch: 037 Train Loss: 0.304, ap: 0.876, auc: 0.891, f1: 0.796, acc: 0.802
+2025-08-26 01:21:18: Epoch: 037 New best VALIDATION metrics
+2025-08-26 01:21:18: Epoch: 037 Test results, ap: 0.251, auc: 0.509, f1: 0.400, acc: 0.463
+2025-08-26 01:21:19: Epoch: 038 Train Loss: 0.306, ap: 0.878, auc: 0.892, f1: 0.789, acc: 0.802
+2025-08-26 01:21:19: Epoch: 038 New best VALIDATION metrics
+2025-08-26 01:21:20: Epoch: 038 Test results, ap: 0.265, auc: 0.511, f1: 0.314, acc: 0.431
+2025-08-26 01:21:21: Epoch: 039 Train Loss: 0.307, ap: 0.872, auc: 0.890, f1: 0.792, acc: 0.804
+2025-08-26 01:21:21: Epoch: 039 Test results, ap: 0.252, auc: 0.503, f1: 0.367, acc: 0.439
+2025-08-26 01:21:22: Epoch: 040 Train Loss: 0.298, ap: 0.878, auc: 0.891, f1: 0.782, acc: 0.799
+2025-08-26 01:21:22: Epoch: 040 Test results, ap: 0.247, auc: 0.499, f1: 0.295, acc: 0.455
+2025-08-26 01:21:23: Epoch: 041 Train Loss: 0.300, ap: 0.881, auc: 0.894, f1: 0.793, acc: 0.804
+2025-08-26 01:21:23: Epoch: 041 New best VALIDATION metrics
+2025-08-26 01:21:23: Epoch: 041 Test results, ap: 0.256, auc: 0.514, f1: 0.317, acc: 0.439
+2025-08-26 01:21:24: Epoch: 042 Train Loss: 0.292, ap: 0.880, auc: 0.893, f1: 0.792, acc: 0.802
+2025-08-26 01:21:24: Epoch: 042 Test results, ap: 0.260, auc: 0.506, f1: 0.365, acc: 0.463
+2025-08-26 01:21:25: Epoch: 043 Train Loss: 0.290, ap: 0.881, auc: 0.894, f1: 0.799, acc: 0.813
+2025-08-26 01:21:25: Epoch: 043 New best VALIDATION metrics
+2025-08-26 01:21:26: Epoch: 043 Test results, ap: 0.254, auc: 0.507, f1: 0.320, acc: 0.447
+2025-08-26 01:21:27: Epoch: 044 Train Loss: 0.296, ap: 0.880, auc: 0.894, f1: 0.795, acc: 0.808
+2025-08-26 01:21:27: Epoch: 044 Test results, ap: 0.260, auc: 0.506, f1: 0.333, acc: 0.447
+2025-08-26 01:21:28: Epoch: 045 Train Loss: 0.288, ap: 0.878, auc: 0.892, f1: 0.791, acc: 0.808
+2025-08-26 01:21:28: Epoch: 045 Test results, ap: 0.265, auc: 0.509, f1: 0.300, acc: 0.431
+2025-08-26 01:21:29: Epoch: 046 Train Loss: 0.289, ap: 0.883, auc: 0.896, f1: 0.797, acc: 0.810
+2025-08-26 01:21:29: Epoch: 046 New best VALIDATION metrics
+2025-08-26 01:21:29: Epoch: 046 Test results, ap: 0.264, auc: 0.510, f1: 0.333, acc: 0.447
+2025-08-26 01:21:30: Epoch: 047 Train Loss: 0.286, ap: 0.881, auc: 0.895, f1: 0.800, acc: 0.811
+2025-08-26 01:21:30: Epoch: 047 Test results, ap: 0.263, auc: 0.509, f1: 0.300, acc: 0.431
+2025-08-26 01:21:31: Epoch: 048 Train Loss: 0.291, ap: 0.882, auc: 0.894, f1: 0.788, acc: 0.801
+2025-08-26 01:21:32: Epoch: 048 Test results, ap: 0.268, auc: 0.514, f1: 0.300, acc: 0.431
+2025-08-26 01:21:33: Epoch: 049 Train Loss: 0.283, ap: 0.881, auc: 0.894, f1: 0.788, acc: 0.804
+2025-08-26 01:21:33: Epoch: 049 Test results, ap: 0.264, auc: 0.509, f1: 0.300, acc: 0.431
+2025-08-26 01:21:34: Epoch: 050 Train Loss: 0.284, ap: 0.883, auc: 0.896, f1: 0.793, acc: 0.808
+2025-08-26 01:21:34: Epoch: 050 New best VALIDATION metrics
+2025-08-26 01:21:34: Epoch: 050 Test results, ap: 0.264, auc: 0.510, f1: 0.303, acc: 0.439
+2025-08-26 01:21:34: used time 0.02h
+2025-08-26 01:21:34: Cross Validation Finished!
+2025-08-26 01:21:34: Best validation perform list
+[[0.92583573 0.92926055 0.82926828 0.83978873]
+ [0.83908373 0.86539817 0.770751   0.79577464]
+ [0.88541174 0.90668774 0.81310213 0.82892418]
+ [0.8968814  0.92057049 0.82520324 0.84832454]
+ [0.88315481 0.89582181 0.7931689  0.80776012]]
+2025-08-26 01:21:34: mean: [0.886 0.904 0.806 0.824]
+2025-08-26 01:21:34: std: [0.028 0.022 0.022 0.02 ]
+2025-08-26 01:21:34: Best test perform list
+[[0.23616478 0.45913976 0.13333334 0.57723576]
+ [0.3164497  0.54623652 0.19607843 0.66666669]
+ [0.29095644 0.50967741 0.30136988 0.58536583]
+ [0.39080733 0.62759858 0.35955057 0.53658539]
+ [0.34897995 0.6408602  0.45614034 0.49593496]]
+2025-08-26 01:21:34: mean: [0.317 0.557 0.289 0.572]
+2025-08-26 01:21:34: std: [0.052 0.069 0.115 0.057]
diff --git a/run-cls/non-siamese-rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/config.json b/run-cls/non-siamese-rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..15ea2d92cdca47a4370de51834060339a4b99053
--- /dev/null
+++ b/run-cls/non-siamese-rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": "mamba", "channels": 16, "fusion": "diff", "glob_feat": false, "non_siamese": true, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": true, "mix_pcs": false, "resize": [768], "gpu": 1, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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diff --git a/run-cls/non-siamese-rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/model_1.pth b/run-cls/non-siamese-rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/model_1.pth
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+2025-08-26 08:23:29: Epoch: 037 Test results, ap: 0.266, auc: 0.510, f1: 0.178, acc: 0.699
+2025-08-26 08:24:08: Epoch: 038 Train Loss: 0.165, ap: 0.859, auc: 0.895, f1: 0.834, acc: 0.840
+2025-08-26 08:24:12: Epoch: 038 Test results, ap: 0.275, auc: 0.513, f1: 0.271, acc: 0.650
+2025-08-26 08:24:51: Epoch: 039 Train Loss: 0.171, ap: 0.872, auc: 0.894, f1: 0.823, acc: 0.829
+2025-08-26 08:24:51: Epoch: 039 New best VALIDATION metrics
+2025-08-26 08:24:55: Epoch: 039 Test results, ap: 0.252, auc: 0.489, f1: 0.136, acc: 0.691
+2025-08-26 08:25:34: Epoch: 040 Train Loss: 0.156, ap: 0.865, auc: 0.896, f1: 0.826, acc: 0.836
+2025-08-26 08:25:38: Epoch: 040 Test results, ap: 0.266, auc: 0.499, f1: 0.174, acc: 0.691
+2025-08-26 08:26:18: Epoch: 041 Train Loss: 0.155, ap: 0.866, auc: 0.893, f1: 0.813, acc: 0.822
+2025-08-26 08:26:22: Epoch: 041 Test results, ap: 0.261, auc: 0.501, f1: 0.098, acc: 0.699
+2025-08-26 08:27:01: Epoch: 042 Train Loss: 0.149, ap: 0.864, auc: 0.895, f1: 0.828, acc: 0.836
+2025-08-26 08:27:05: Epoch: 042 Test results, ap: 0.268, auc: 0.497, f1: 0.235, acc: 0.683
+2025-08-26 08:27:44: Epoch: 043 Train Loss: 0.141, ap: 0.867, auc: 0.894, f1: 0.827, acc: 0.836
+2025-08-26 08:27:49: Epoch: 043 Test results, ap: 0.271, auc: 0.508, f1: 0.174, acc: 0.691
+2025-08-26 08:28:26: Epoch: 044 Train Loss: 0.137, ap: 0.861, auc: 0.892, f1: 0.827, acc: 0.835
+2025-08-26 08:28:31: Epoch: 044 Test results, ap: 0.273, auc: 0.512, f1: 0.213, acc: 0.699
+2025-08-26 08:29:10: Epoch: 045 Train Loss: 0.136, ap: 0.864, auc: 0.894, f1: 0.835, acc: 0.842
+2025-08-26 08:29:15: Epoch: 045 Test results, ap: 0.266, auc: 0.510, f1: 0.178, acc: 0.699
+2025-08-26 08:29:54: Epoch: 046 Train Loss: 0.139, ap: 0.865, auc: 0.895, f1: 0.828, acc: 0.836
+2025-08-26 08:29:58: Epoch: 046 Test results, ap: 0.253, auc: 0.488, f1: 0.140, acc: 0.699
+2025-08-26 08:30:37: Epoch: 047 Train Loss: 0.139, ap: 0.871, auc: 0.897, f1: 0.833, acc: 0.842
+2025-08-26 08:30:37: Epoch: 047 New best VALIDATION metrics
+2025-08-26 08:30:42: Epoch: 047 Test results, ap: 0.259, auc: 0.495, f1: 0.178, acc: 0.699
+2025-08-26 08:31:20: Epoch: 048 Train Loss: 0.136, ap: 0.865, auc: 0.894, f1: 0.828, acc: 0.838
+2025-08-26 08:31:24: Epoch: 048 Test results, ap: 0.254, auc: 0.493, f1: 0.098, acc: 0.699
+2025-08-26 08:32:03: Epoch: 049 Train Loss: 0.136, ap: 0.865, auc: 0.895, f1: 0.833, acc: 0.840
+2025-08-26 08:32:07: Epoch: 049 Test results, ap: 0.258, auc: 0.496, f1: 0.178, acc: 0.699
+2025-08-26 08:32:47: Epoch: 050 Train Loss: 0.141, ap: 0.862, auc: 0.895, f1: 0.831, acc: 0.836
+2025-08-26 08:32:51: Epoch: 050 Test results, ap: 0.273, auc: 0.518, f1: 0.250, acc: 0.659
+2025-08-26 08:32:51: used time 0.61h
+2025-08-26 08:32:52: Running Cross Validation 1
+2025-08-26 08:32:52: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 08:33:31: Epoch: 001 Train Loss: 0.673, ap: 0.511, auc: 0.555, f1: 0.420, acc: 0.606
+2025-08-26 08:33:31: Epoch: 001 New best VALIDATION metrics
+2025-08-26 08:33:35: Epoch: 001 Test results, ap: 0.284, auc: 0.445, f1: 0.000, acc: 0.756
+2025-08-26 08:34:13: Epoch: 002 Train Loss: 0.645, ap: 0.649, auc: 0.693, f1: 0.643, acc: 0.599
+2025-08-26 08:34:13: Epoch: 002 New best VALIDATION metrics
+2025-08-26 08:34:17: Epoch: 002 Test results, ap: 0.196, auc: 0.349, f1: 0.213, acc: 0.398
+2025-08-26 08:34:56: Epoch: 003 Train Loss: 0.606, ap: 0.619, auc: 0.694, f1: 0.000, acc: 0.562
+2025-08-26 08:35:00: Epoch: 003 Test results, ap: 0.205, auc: 0.427, f1: 0.000, acc: 0.756
+2025-08-26 08:35:39: Epoch: 004 Train Loss: 0.585, ap: 0.703, auc: 0.748, f1: 0.658, acc: 0.634
+2025-08-26 08:35:39: Epoch: 004 New best VALIDATION metrics
+2025-08-26 08:35:44: Epoch: 004 Test results, ap: 0.225, auc: 0.427, f1: 0.000, acc: 0.715
+2025-08-26 08:36:24: Epoch: 005 Train Loss: 0.571, ap: 0.562, auc: 0.661, f1: 0.636, acc: 0.565
+2025-08-26 08:36:28: Epoch: 005 Test results, ap: 0.301, auc: 0.552, f1: 0.395, acc: 0.252
+2025-08-26 08:37:08: Epoch: 006 Train Loss: 0.546, ap: 0.720, auc: 0.762, f1: 0.682, acc: 0.658
+2025-08-26 08:37:08: Epoch: 006 New best VALIDATION metrics
+2025-08-26 08:37:12: Epoch: 006 Test results, ap: 0.186, auc: 0.348, f1: 0.111, acc: 0.480
+2025-08-26 08:37:51: Epoch: 007 Train Loss: 0.532, ap: 0.707, auc: 0.750, f1: 0.638, acc: 0.708
+2025-08-26 08:37:55: Epoch: 007 Test results, ap: 0.226, auc: 0.418, f1: 0.000, acc: 0.748
+2025-08-26 08:38:35: Epoch: 008 Train Loss: 0.523, ap: 0.650, auc: 0.710, f1: 0.648, acc: 0.648
+2025-08-26 08:38:39: Epoch: 008 Test results, ap: 0.330, auc: 0.638, f1: 0.400, acc: 0.268
+2025-08-26 08:39:21: Epoch: 009 Train Loss: 0.504, ap: 0.784, auc: 0.808, f1: 0.691, acc: 0.695
+2025-08-26 08:39:21: Epoch: 009 New best VALIDATION metrics
+2025-08-26 08:39:26: Epoch: 009 Test results, ap: 0.268, auc: 0.572, f1: 0.053, acc: 0.707
+2025-08-26 08:40:05: Epoch: 010 Train Loss: 0.483, ap: 0.737, auc: 0.777, f1: 0.692, acc: 0.676
+2025-08-26 08:40:09: Epoch: 010 Test results, ap: 0.190, auc: 0.371, f1: 0.000, acc: 0.650
+2025-08-26 08:40:49: Epoch: 011 Train Loss: 0.442, ap: 0.789, auc: 0.810, f1: 0.664, acc: 0.746
+2025-08-26 08:40:49: Epoch: 011 New best VALIDATION metrics
+2025-08-26 08:40:53: Epoch: 011 Test results, ap: 0.239, auc: 0.503, f1: 0.000, acc: 0.756
+2025-08-26 08:40:53: Epoch: 011 New best TEST metrics
+2025-08-26 08:41:33: Epoch: 012 Train Loss: 0.412, ap: 0.737, auc: 0.771, f1: 0.661, acc: 0.713
+2025-08-26 08:41:37: Epoch: 012 Test results, ap: 0.335, auc: 0.666, f1: 0.000, acc: 0.756
+2025-08-26 08:41:37: Epoch: 012 New best TEST metrics
+2025-08-26 08:42:17: Epoch: 013 Train Loss: 0.394, ap: 0.779, auc: 0.816, f1: 0.733, acc: 0.757
+2025-08-26 08:42:21: Epoch: 013 Test results, ap: 0.284, auc: 0.587, f1: 0.000, acc: 0.756
+2025-08-26 08:43:00: Epoch: 014 Train Loss: 0.364, ap: 0.800, auc: 0.822, f1: 0.711, acc: 0.692
+2025-08-26 08:43:00: Epoch: 014 New best VALIDATION metrics
+2025-08-26 08:43:04: Epoch: 014 Test results, ap: 0.201, auc: 0.407, f1: 0.048, acc: 0.675
+2025-08-26 08:43:44: Epoch: 015 Train Loss: 0.362, ap: 0.847, auc: 0.875, f1: 0.764, acc: 0.766
+2025-08-26 08:43:44: Epoch: 015 New best VALIDATION metrics
+2025-08-26 08:43:49: Epoch: 015 Test results, ap: 0.250, auc: 0.524, f1: 0.049, acc: 0.683
+2025-08-26 08:44:27: Epoch: 016 Train Loss: 0.340, ap: 0.854, auc: 0.870, f1: 0.766, acc: 0.794
+2025-08-26 08:44:27: Epoch: 016 New best VALIDATION metrics
+2025-08-26 08:44:32: Epoch: 016 Test results, ap: 0.294, auc: 0.617, f1: 0.000, acc: 0.748
+2025-08-26 08:45:12: Epoch: 017 Train Loss: 0.342, ap: 0.837, auc: 0.869, f1: 0.754, acc: 0.783
+2025-08-26 08:45:16: Epoch: 017 Test results, ap: 0.191, auc: 0.373, f1: 0.000, acc: 0.715
+2025-08-26 08:45:55: Epoch: 018 Train Loss: 0.320, ap: 0.818, auc: 0.853, f1: 0.746, acc: 0.768
+2025-08-26 08:45:59: Epoch: 018 Test results, ap: 0.219, auc: 0.452, f1: 0.000, acc: 0.715
+2025-08-26 08:46:38: Epoch: 019 Train Loss: 0.311, ap: 0.834, auc: 0.865, f1: 0.771, acc: 0.780
+2025-08-26 08:46:42: Epoch: 019 Test results, ap: 0.252, auc: 0.524, f1: 0.196, acc: 0.667
+2025-08-26 08:47:22: Epoch: 020 Train Loss: 0.307, ap: 0.778, auc: 0.818, f1: 0.619, acc: 0.711
+2025-08-26 08:47:26: Epoch: 020 Test results, ap: 0.275, auc: 0.584, f1: 0.000, acc: 0.732
+2025-08-26 08:48:05: Epoch: 021 Train Loss: 0.273, ap: 0.862, auc: 0.887, f1: 0.793, acc: 0.805
+2025-08-26 08:48:05: Epoch: 021 New best VALIDATION metrics
+2025-08-26 08:48:10: Epoch: 021 Test results, ap: 0.294, auc: 0.587, f1: 0.100, acc: 0.707
+2025-08-26 08:48:50: Epoch: 022 Train Loss: 0.259, ap: 0.859, auc: 0.880, f1: 0.768, acc: 0.798
+2025-08-26 08:48:54: Epoch: 022 Test results, ap: 0.279, auc: 0.584, f1: 0.057, acc: 0.732
+2025-08-26 08:49:34: Epoch: 023 Train Loss: 0.250, ap: 0.865, auc: 0.889, f1: 0.776, acc: 0.806
+2025-08-26 08:49:34: Epoch: 023 New best VALIDATION metrics
+2025-08-26 08:49:39: Epoch: 023 Test results, ap: 0.298, auc: 0.595, f1: 0.057, acc: 0.732
+2025-08-26 08:50:18: Epoch: 024 Train Loss: 0.228, ap: 0.850, auc: 0.876, f1: 0.772, acc: 0.790
+2025-08-26 08:50:22: Epoch: 024 Test results, ap: 0.303, auc: 0.608, f1: 0.098, acc: 0.699
+2025-08-26 08:51:02: Epoch: 025 Train Loss: 0.231, ap: 0.865, auc: 0.889, f1: 0.793, acc: 0.815
+2025-08-26 08:51:06: Epoch: 025 Test results, ap: 0.298, auc: 0.595, f1: 0.103, acc: 0.715
+2025-08-26 08:51:46: Epoch: 026 Train Loss: 0.236, ap: 0.859, auc: 0.884, f1: 0.779, acc: 0.806
+2025-08-26 08:51:50: Epoch: 026 Test results, ap: 0.302, auc: 0.603, f1: 0.059, acc: 0.740
+2025-08-26 08:52:28: Epoch: 027 Train Loss: 0.219, ap: 0.873, auc: 0.896, f1: 0.812, acc: 0.829
+2025-08-26 08:52:28: Epoch: 027 New best VALIDATION metrics
+2025-08-26 08:52:33: Epoch: 027 Test results, ap: 0.306, auc: 0.605, f1: 0.061, acc: 0.748
+2025-08-26 08:53:12: Epoch: 028 Train Loss: 0.221, ap: 0.857, auc: 0.885, f1: 0.771, acc: 0.801
+2025-08-26 08:53:17: Epoch: 028 Test results, ap: 0.285, auc: 0.576, f1: 0.053, acc: 0.707
+2025-08-26 08:53:57: Epoch: 029 Train Loss: 0.223, ap: 0.863, auc: 0.890, f1: 0.798, acc: 0.815
+2025-08-26 08:54:02: Epoch: 029 Test results, ap: 0.328, auc: 0.624, f1: 0.111, acc: 0.740
+2025-08-26 08:54:42: Epoch: 030 Train Loss: 0.212, ap: 0.833, auc: 0.863, f1: 0.750, acc: 0.766
+2025-08-26 08:54:46: Epoch: 030 Test results, ap: 0.379, auc: 0.674, f1: 0.394, acc: 0.650
+2025-08-26 08:54:46: Epoch: 030 New best TEST metrics
+2025-08-26 08:55:26: Epoch: 031 Train Loss: 0.194, ap: 0.875, auc: 0.895, f1: 0.786, acc: 0.810
+2025-08-26 08:55:31: Epoch: 031 Test results, ap: 0.337, auc: 0.629, f1: 0.111, acc: 0.740
+2025-08-26 08:56:11: Epoch: 032 Train Loss: 0.181, ap: 0.871, auc: 0.895, f1: 0.793, acc: 0.812
+2025-08-26 08:56:15: Epoch: 032 Test results, ap: 0.337, auc: 0.635, f1: 0.154, acc: 0.732
+2025-08-26 08:56:55: Epoch: 033 Train Loss: 0.191, ap: 0.880, auc: 0.902, f1: 0.813, acc: 0.826
+2025-08-26 08:56:55: Epoch: 033 New best VALIDATION metrics
+2025-08-26 08:56:59: Epoch: 033 Test results, ap: 0.320, auc: 0.611, f1: 0.098, acc: 0.699
+2025-08-26 08:57:39: Epoch: 034 Train Loss: 0.171, ap: 0.876, auc: 0.897, f1: 0.815, acc: 0.831
+2025-08-26 08:57:43: Epoch: 034 Test results, ap: 0.336, auc: 0.632, f1: 0.111, acc: 0.740
+2025-08-26 08:58:22: Epoch: 035 Train Loss: 0.176, ap: 0.886, auc: 0.901, f1: 0.791, acc: 0.817
+2025-08-26 08:58:22: Epoch: 035 New best VALIDATION metrics
+2025-08-26 08:58:27: Epoch: 035 Test results, ap: 0.284, auc: 0.584, f1: 0.061, acc: 0.748
+2025-08-26 08:59:06: Epoch: 036 Train Loss: 0.176, ap: 0.879, auc: 0.899, f1: 0.792, acc: 0.815
+2025-08-26 08:59:10: Epoch: 036 Test results, ap: 0.320, auc: 0.616, f1: 0.118, acc: 0.756
+2025-08-26 08:59:51: Epoch: 037 Train Loss: 0.184, ap: 0.870, auc: 0.893, f1: 0.791, acc: 0.817
+2025-08-26 08:59:55: Epoch: 037 Test results, ap: 0.321, auc: 0.618, f1: 0.111, acc: 0.740
+2025-08-26 09:00:36: Epoch: 038 Train Loss: 0.172, ap: 0.882, auc: 0.901, f1: 0.802, acc: 0.820
+2025-08-26 09:00:40: Epoch: 038 Test results, ap: 0.342, auc: 0.632, f1: 0.178, acc: 0.699
+2025-08-26 09:01:20: Epoch: 039 Train Loss: 0.165, ap: 0.878, auc: 0.898, f1: 0.799, acc: 0.820
+2025-08-26 09:01:25: Epoch: 039 Test results, ap: 0.318, auc: 0.621, f1: 0.103, acc: 0.715
+2025-08-26 09:02:06: Epoch: 040 Train Loss: 0.157, ap: 0.878, auc: 0.898, f1: 0.809, acc: 0.826
+2025-08-26 09:02:11: Epoch: 040 Test results, ap: 0.329, auc: 0.630, f1: 0.103, acc: 0.715
+2025-08-26 09:02:50: Epoch: 041 Train Loss: 0.151, ap: 0.879, auc: 0.899, f1: 0.806, acc: 0.827
+2025-08-26 09:02:55: Epoch: 041 Test results, ap: 0.329, auc: 0.625, f1: 0.108, acc: 0.732
+2025-08-26 09:03:34: Epoch: 042 Train Loss: 0.143, ap: 0.877, auc: 0.898, f1: 0.790, acc: 0.815
+2025-08-26 09:03:38: Epoch: 042 Test results, ap: 0.328, auc: 0.623, f1: 0.111, acc: 0.740
+2025-08-26 09:04:18: Epoch: 043 Train Loss: 0.151, ap: 0.888, auc: 0.906, f1: 0.811, acc: 0.829
+2025-08-26 09:04:18: Epoch: 043 New best VALIDATION metrics
+2025-08-26 09:04:23: Epoch: 043 Test results, ap: 0.317, auc: 0.612, f1: 0.114, acc: 0.748
+2025-08-26 09:05:03: Epoch: 044 Train Loss: 0.149, ap: 0.888, auc: 0.906, f1: 0.816, acc: 0.836
+2025-08-26 09:05:08: Epoch: 044 Test results, ap: 0.324, auc: 0.614, f1: 0.100, acc: 0.707
+2025-08-26 09:05:49: Epoch: 045 Train Loss: 0.154, ap: 0.883, auc: 0.904, f1: 0.809, acc: 0.831
+2025-08-26 09:05:53: Epoch: 045 Test results, ap: 0.327, auc: 0.620, f1: 0.103, acc: 0.715
+2025-08-26 09:06:34: Epoch: 046 Train Loss: 0.142, ap: 0.882, auc: 0.905, f1: 0.802, acc: 0.826
+2025-08-26 09:06:38: Epoch: 046 Test results, ap: 0.331, auc: 0.624, f1: 0.103, acc: 0.715
+2025-08-26 09:07:18: Epoch: 047 Train Loss: 0.146, ap: 0.883, auc: 0.904, f1: 0.803, acc: 0.827
+2025-08-26 09:07:23: Epoch: 047 Test results, ap: 0.350, auc: 0.636, f1: 0.108, acc: 0.732
+2025-08-26 09:08:03: Epoch: 048 Train Loss: 0.143, ap: 0.884, auc: 0.907, f1: 0.807, acc: 0.826
+2025-08-26 09:08:08: Epoch: 048 Test results, ap: 0.333, auc: 0.630, f1: 0.143, acc: 0.707
+2025-08-26 09:08:47: Epoch: 049 Train Loss: 0.146, ap: 0.890, auc: 0.910, f1: 0.807, acc: 0.831
+2025-08-26 09:08:47: Epoch: 049 New best VALIDATION metrics
+2025-08-26 09:08:51: Epoch: 049 Test results, ap: 0.321, auc: 0.614, f1: 0.111, acc: 0.740
+2025-08-26 09:09:31: Epoch: 050 Train Loss: 0.135, ap: 0.885, auc: 0.906, f1: 0.809, acc: 0.827
+2025-08-26 09:09:35: Epoch: 050 Test results, ap: 0.344, auc: 0.637, f1: 0.108, acc: 0.732
+2025-08-26 09:09:35: used time 0.61h
+2025-08-26 09:09:36: Running Cross Validation 2
+2025-08-26 09:09:36: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 09:10:15: Epoch: 001 Train Loss: 0.675, ap: 0.622, auc: 0.624, f1: 0.031, acc: 0.563
+2025-08-26 09:10:15: Epoch: 001 New best VALIDATION metrics
+2025-08-26 09:10:20: Epoch: 001 Test results, ap: 0.167, auc: 0.262, f1: 0.000, acc: 0.756
+2025-08-26 09:11:00: Epoch: 002 Train Loss: 0.644, ap: 0.675, auc: 0.677, f1: 0.594, acc: 0.601
+2025-08-26 09:11:00: Epoch: 002 New best VALIDATION metrics
+2025-08-26 09:11:05: Epoch: 002 Test results, ap: 0.238, auc: 0.494, f1: 0.392, acc: 0.244
+2025-08-26 09:11:44: Epoch: 003 Train Loss: 0.632, ap: 0.693, auc: 0.704, f1: 0.615, acc: 0.501
+2025-08-26 09:11:44: Epoch: 003 New best VALIDATION metrics
+2025-08-26 09:11:49: Epoch: 003 Test results, ap: 0.227, auc: 0.452, f1: 0.336, acc: 0.390
+2025-08-26 09:12:28: Epoch: 004 Train Loss: 0.612, ap: 0.674, auc: 0.685, f1: 0.120, acc: 0.586
+2025-08-26 09:12:32: Epoch: 004 Test results, ap: 0.293, auc: 0.485, f1: 0.000, acc: 0.756
+2025-08-26 09:13:11: Epoch: 005 Train Loss: 0.606, ap: 0.726, auc: 0.751, f1: 0.492, acc: 0.679
+2025-08-26 09:13:11: Epoch: 005 New best VALIDATION metrics
+2025-08-26 09:13:16: Epoch: 005 Test results, ap: 0.349, auc: 0.571, f1: 0.000, acc: 0.756
+2025-08-26 09:13:56: Epoch: 006 Train Loss: 0.587, ap: 0.743, auc: 0.776, f1: 0.684, acc: 0.700
+2025-08-26 09:13:56: Epoch: 006 New best VALIDATION metrics
+2025-08-26 09:14:00: Epoch: 006 Test results, ap: 0.244, auc: 0.454, f1: 0.273, acc: 0.610
+2025-08-26 09:14:39: Epoch: 007 Train Loss: 0.548, ap: 0.747, auc: 0.776, f1: 0.683, acc: 0.649
+2025-08-26 09:14:39: Epoch: 007 New best VALIDATION metrics
+2025-08-26 09:14:43: Epoch: 007 Test results, ap: 0.317, auc: 0.542, f1: 0.358, acc: 0.650
+2025-08-26 09:15:24: Epoch: 008 Train Loss: 0.529, ap: 0.771, auc: 0.799, f1: 0.690, acc: 0.679
+2025-08-26 09:15:24: Epoch: 008 New best VALIDATION metrics
+2025-08-26 09:15:28: Epoch: 008 Test results, ap: 0.251, auc: 0.448, f1: 0.333, acc: 0.707
+2025-08-26 09:16:08: Epoch: 009 Train Loss: 0.517, ap: 0.769, auc: 0.802, f1: 0.707, acc: 0.718
+2025-08-26 09:16:08: Epoch: 009 New best VALIDATION metrics
+2025-08-26 09:16:13: Epoch: 009 Test results, ap: 0.231, auc: 0.422, f1: 0.222, acc: 0.659
+2025-08-26 09:16:52: Epoch: 010 Train Loss: 0.485, ap: 0.756, auc: 0.798, f1: 0.592, acc: 0.698
+2025-08-26 09:16:57: Epoch: 010 Test results, ap: 0.305, auc: 0.512, f1: 0.000, acc: 0.715
+2025-08-26 09:17:36: Epoch: 011 Train Loss: 0.440, ap: 0.834, auc: 0.856, f1: 0.732, acc: 0.735
+2025-08-26 09:17:36: Epoch: 011 New best VALIDATION metrics
+2025-08-26 09:17:41: Epoch: 011 Test results, ap: 0.292, auc: 0.524, f1: 0.338, acc: 0.650
+2025-08-26 09:17:41: Epoch: 011 New best TEST metrics
+2025-08-26 09:18:20: Epoch: 012 Train Loss: 0.405, ap: 0.744, auc: 0.785, f1: 0.476, acc: 0.674
+2025-08-26 09:18:25: Epoch: 012 Test results, ap: 0.351, auc: 0.543, f1: 0.121, acc: 0.764
+2025-08-26 09:18:25: Epoch: 012 New best TEST metrics
+2025-08-26 09:19:05: Epoch: 013 Train Loss: 0.386, ap: 0.816, auc: 0.852, f1: 0.582, acc: 0.714
+2025-08-26 09:19:09: Epoch: 013 Test results, ap: 0.307, auc: 0.538, f1: 0.114, acc: 0.748
+2025-08-26 09:19:48: Epoch: 014 Train Loss: 0.372, ap: 0.851, auc: 0.873, f1: 0.768, acc: 0.808
+2025-08-26 09:19:48: Epoch: 014 New best VALIDATION metrics
+2025-08-26 09:19:53: Epoch: 014 Test results, ap: 0.307, auc: 0.531, f1: 0.000, acc: 0.756
+2025-08-26 09:20:33: Epoch: 015 Train Loss: 0.339, ap: 0.863, auc: 0.885, f1: 0.776, acc: 0.788
+2025-08-26 09:20:33: Epoch: 015 New best VALIDATION metrics
+2025-08-26 09:20:38: Epoch: 015 Test results, ap: 0.296, auc: 0.537, f1: 0.328, acc: 0.667
+2025-08-26 09:21:16: Epoch: 016 Train Loss: 0.313, ap: 0.848, auc: 0.873, f1: 0.747, acc: 0.732
+2025-08-26 09:21:20: Epoch: 016 Test results, ap: 0.249, auc: 0.467, f1: 0.312, acc: 0.390
+2025-08-26 09:22:00: Epoch: 017 Train Loss: 0.299, ap: 0.883, auc: 0.903, f1: 0.767, acc: 0.813
+2025-08-26 09:22:00: Epoch: 017 New best VALIDATION metrics
+2025-08-26 09:22:04: Epoch: 017 Test results, ap: 0.361, auc: 0.568, f1: 0.211, acc: 0.756
+2025-08-26 09:22:04: Epoch: 017 New best TEST metrics
+2025-08-26 09:22:43: Epoch: 018 Train Loss: 0.291, ap: 0.883, auc: 0.906, f1: 0.802, acc: 0.825
+2025-08-26 09:22:43: Epoch: 018 New best VALIDATION metrics
+2025-08-26 09:22:48: Epoch: 018 Test results, ap: 0.314, auc: 0.544, f1: 0.333, acc: 0.642
+2025-08-26 09:23:27: Epoch: 019 Train Loss: 0.269, ap: 0.877, auc: 0.891, f1: 0.768, acc: 0.757
+2025-08-26 09:23:32: Epoch: 019 Test results, ap: 0.259, auc: 0.504, f1: 0.378, acc: 0.626
+2025-08-26 09:24:11: Epoch: 020 Train Loss: 0.268, ap: 0.861, auc: 0.893, f1: 0.778, acc: 0.804
+2025-08-26 09:24:15: Epoch: 020 Test results, ap: 0.329, auc: 0.547, f1: 0.382, acc: 0.553
+2025-08-26 09:24:54: Epoch: 021 Train Loss: 0.224, ap: 0.901, auc: 0.919, f1: 0.813, acc: 0.829
+2025-08-26 09:24:54: Epoch: 021 New best VALIDATION metrics
+2025-08-26 09:24:59: Epoch: 021 Test results, ap: 0.304, auc: 0.542, f1: 0.378, acc: 0.626
+2025-08-26 09:25:39: Epoch: 022 Train Loss: 0.189, ap: 0.898, auc: 0.921, f1: 0.826, acc: 0.843
+2025-08-26 09:25:43: Epoch: 022 Test results, ap: 0.332, auc: 0.561, f1: 0.370, acc: 0.528
+2025-08-26 09:26:23: Epoch: 023 Train Loss: 0.197, ap: 0.890, auc: 0.916, f1: 0.812, acc: 0.831
+2025-08-26 09:26:28: Epoch: 023 Test results, ap: 0.299, auc: 0.533, f1: 0.356, acc: 0.528
+2025-08-26 09:27:07: Epoch: 024 Train Loss: 0.184, ap: 0.893, auc: 0.918, f1: 0.811, acc: 0.841
+2025-08-26 09:27:11: Epoch: 024 Test results, ap: 0.342, auc: 0.553, f1: 0.393, acc: 0.699
+2025-08-26 09:27:50: Epoch: 025 Train Loss: 0.173, ap: 0.905, auc: 0.924, f1: 0.827, acc: 0.840
+2025-08-26 09:27:50: Epoch: 025 New best VALIDATION metrics
+2025-08-26 09:27:55: Epoch: 025 Test results, ap: 0.290, auc: 0.533, f1: 0.366, acc: 0.577
+2025-08-26 09:28:35: Epoch: 026 Train Loss: 0.175, ap: 0.903, auc: 0.924, f1: 0.830, acc: 0.848
+2025-08-26 09:28:39: Epoch: 026 Test results, ap: 0.330, auc: 0.546, f1: 0.386, acc: 0.585
+2025-08-26 09:29:19: Epoch: 027 Train Loss: 0.157, ap: 0.886, auc: 0.918, f1: 0.815, acc: 0.840
+2025-08-26 09:29:23: Epoch: 027 Test results, ap: 0.330, auc: 0.540, f1: 0.347, acc: 0.480
+2025-08-26 09:30:03: Epoch: 028 Train Loss: 0.162, ap: 0.898, auc: 0.918, f1: 0.809, acc: 0.831
+2025-08-26 09:30:07: Epoch: 028 Test results, ap: 0.322, auc: 0.547, f1: 0.345, acc: 0.691
+2025-08-26 09:30:45: Epoch: 029 Train Loss: 0.159, ap: 0.905, auc: 0.918, f1: 0.833, acc: 0.850
+2025-08-26 09:30:49: Epoch: 029 Test results, ap: 0.359, auc: 0.590, f1: 0.273, acc: 0.740
+2025-08-26 09:30:49: Epoch: 029 New best TEST metrics
+2025-08-26 09:31:29: Epoch: 030 Train Loss: 0.157, ap: 0.907, auc: 0.923, f1: 0.825, acc: 0.840
+2025-08-26 09:31:29: Epoch: 030 New best VALIDATION metrics
+2025-08-26 09:31:34: Epoch: 030 Test results, ap: 0.383, auc: 0.592, f1: 0.444, acc: 0.675
+2025-08-26 09:31:34: Epoch: 030 New best TEST metrics
+2025-08-26 09:32:13: Epoch: 031 Train Loss: 0.131, ap: 0.911, auc: 0.928, f1: 0.829, acc: 0.841
+2025-08-26 09:32:13: Epoch: 031 New best VALIDATION metrics
+2025-08-26 09:32:17: Epoch: 031 Test results, ap: 0.360, auc: 0.577, f1: 0.386, acc: 0.585
+2025-08-26 09:32:56: Epoch: 032 Train Loss: 0.121, ap: 0.910, auc: 0.930, f1: 0.840, acc: 0.852
+2025-08-26 09:32:56: Epoch: 032 New best VALIDATION metrics
+2025-08-26 09:33:01: Epoch: 032 Test results, ap: 0.344, auc: 0.581, f1: 0.416, acc: 0.634
+2025-08-26 09:33:41: Epoch: 033 Train Loss: 0.124, ap: 0.907, auc: 0.927, f1: 0.831, acc: 0.848
+2025-08-26 09:33:45: Epoch: 033 Test results, ap: 0.348, auc: 0.574, f1: 0.405, acc: 0.618
+2025-08-26 09:34:25: Epoch: 034 Train Loss: 0.121, ap: 0.909, auc: 0.928, f1: 0.834, acc: 0.850
+2025-08-26 09:34:29: Epoch: 034 Test results, ap: 0.349, auc: 0.578, f1: 0.400, acc: 0.610
+2025-08-26 09:35:08: Epoch: 035 Train Loss: 0.104, ap: 0.906, auc: 0.928, f1: 0.835, acc: 0.852
+2025-08-26 09:35:12: Epoch: 035 Test results, ap: 0.326, auc: 0.578, f1: 0.416, acc: 0.634
+2025-08-26 09:35:52: Epoch: 036 Train Loss: 0.109, ap: 0.908, auc: 0.929, f1: 0.836, acc: 0.859
+2025-08-26 09:35:57: Epoch: 036 Test results, ap: 0.356, auc: 0.586, f1: 0.435, acc: 0.683
+2025-08-26 09:36:35: Epoch: 037 Train Loss: 0.114, ap: 0.904, auc: 0.927, f1: 0.846, acc: 0.859
+2025-08-26 09:36:39: Epoch: 037 Test results, ap: 0.333, auc: 0.584, f1: 0.410, acc: 0.626
+2025-08-26 09:37:17: Epoch: 038 Train Loss: 0.112, ap: 0.905, auc: 0.927, f1: 0.848, acc: 0.862
+2025-08-26 09:37:21: Epoch: 038 Test results, ap: 0.328, auc: 0.580, f1: 0.432, acc: 0.659
+2025-08-26 09:38:01: Epoch: 039 Train Loss: 0.108, ap: 0.896, auc: 0.924, f1: 0.835, acc: 0.855
+2025-08-26 09:38:05: Epoch: 039 Test results, ap: 0.365, auc: 0.587, f1: 0.372, acc: 0.561
+2025-08-26 09:38:44: Epoch: 040 Train Loss: 0.104, ap: 0.904, auc: 0.928, f1: 0.844, acc: 0.859
+2025-08-26 09:38:48: Epoch: 040 Test results, ap: 0.344, auc: 0.584, f1: 0.405, acc: 0.618
+2025-08-26 09:39:28: Epoch: 041 Train Loss: 0.089, ap: 0.907, auc: 0.929, f1: 0.859, acc: 0.873
+2025-08-26 09:39:33: Epoch: 041 Test results, ap: 0.343, auc: 0.581, f1: 0.432, acc: 0.659
+2025-08-26 09:40:11: Epoch: 042 Train Loss: 0.091, ap: 0.909, auc: 0.928, f1: 0.841, acc: 0.857
+2025-08-26 09:40:16: Epoch: 042 Test results, ap: 0.367, auc: 0.583, f1: 0.432, acc: 0.659
+2025-08-26 09:40:54: Epoch: 043 Train Loss: 0.090, ap: 0.909, auc: 0.929, f1: 0.847, acc: 0.861
+2025-08-26 09:40:58: Epoch: 043 Test results, ap: 0.365, auc: 0.587, f1: 0.432, acc: 0.659
+2025-08-26 09:41:39: Epoch: 044 Train Loss: 0.091, ap: 0.909, auc: 0.930, f1: 0.848, acc: 0.866
+2025-08-26 09:41:43: Epoch: 044 Test results, ap: 0.366, auc: 0.582, f1: 0.421, acc: 0.642
+2025-08-26 09:42:22: Epoch: 045 Train Loss: 0.089, ap: 0.904, auc: 0.927, f1: 0.853, acc: 0.869
+2025-08-26 09:42:27: Epoch: 045 Test results, ap: 0.335, auc: 0.575, f1: 0.438, acc: 0.667
+2025-08-26 09:43:06: Epoch: 046 Train Loss: 0.085, ap: 0.908, auc: 0.931, f1: 0.846, acc: 0.864
+2025-08-26 09:43:10: Epoch: 046 Test results, ap: 0.367, auc: 0.580, f1: 0.410, acc: 0.626
+2025-08-26 09:43:50: Epoch: 047 Train Loss: 0.086, ap: 0.906, auc: 0.929, f1: 0.850, acc: 0.866
+2025-08-26 09:43:54: Epoch: 047 Test results, ap: 0.360, auc: 0.577, f1: 0.410, acc: 0.626
+2025-08-26 09:44:33: Epoch: 048 Train Loss: 0.088, ap: 0.910, auc: 0.931, f1: 0.841, acc: 0.854
+2025-08-26 09:44:33: Epoch: 048 New best VALIDATION metrics
+2025-08-26 09:44:38: Epoch: 048 Test results, ap: 0.360, auc: 0.582, f1: 0.400, acc: 0.610
+2025-08-26 09:45:17: Epoch: 049 Train Loss: 0.077, ap: 0.905, auc: 0.928, f1: 0.854, acc: 0.869
+2025-08-26 09:45:22: Epoch: 049 Test results, ap: 0.371, auc: 0.585, f1: 0.432, acc: 0.659
+2025-08-26 09:46:02: Epoch: 050 Train Loss: 0.079, ap: 0.907, auc: 0.928, f1: 0.840, acc: 0.855
+2025-08-26 09:46:06: Epoch: 050 Test results, ap: 0.373, auc: 0.589, f1: 0.405, acc: 0.618
+2025-08-26 09:46:06: used time 0.61h
+2025-08-26 09:46:06: Running Cross Validation 3
+2025-08-26 09:46:06: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 09:46:46: Epoch: 001 Train Loss: 0.666, ap: 0.601, auc: 0.656, f1: 0.000, acc: 0.570
+2025-08-26 09:46:46: Epoch: 001 New best VALIDATION metrics
+2025-08-26 09:46:50: Epoch: 001 Test results, ap: 0.205, auc: 0.415, f1: 0.000, acc: 0.756
+2025-08-26 09:47:29: Epoch: 002 Train Loss: 0.640, ap: 0.642, auc: 0.695, f1: 0.591, acc: 0.448
+2025-08-26 09:47:29: Epoch: 002 New best VALIDATION metrics
+2025-08-26 09:47:34: Epoch: 002 Test results, ap: 0.209, auc: 0.442, f1: 0.404, acc: 0.447
+2025-08-26 09:48:13: Epoch: 003 Train Loss: 0.613, ap: 0.638, auc: 0.703, f1: 0.558, acc: 0.647
+2025-08-26 09:48:13: Epoch: 003 New best VALIDATION metrics
+2025-08-26 09:48:18: Epoch: 003 Test results, ap: 0.207, auc: 0.428, f1: 0.038, acc: 0.593
+2025-08-26 09:48:57: Epoch: 004 Train Loss: 0.589, ap: 0.740, auc: 0.768, f1: 0.676, acc: 0.675
+2025-08-26 09:48:57: Epoch: 004 New best VALIDATION metrics
+2025-08-26 09:49:02: Epoch: 004 Test results, ap: 0.202, auc: 0.362, f1: 0.182, acc: 0.488
+2025-08-26 09:49:42: Epoch: 005 Train Loss: 0.556, ap: 0.731, auc: 0.756, f1: 0.620, acc: 0.713
+2025-08-26 09:49:46: Epoch: 005 Test results, ap: 0.246, auc: 0.447, f1: 0.000, acc: 0.756
+2025-08-26 09:50:27: Epoch: 006 Train Loss: 0.544, ap: 0.699, auc: 0.744, f1: 0.662, acc: 0.665
+2025-08-26 09:50:31: Epoch: 006 Test results, ap: 0.347, auc: 0.599, f1: 0.404, acc: 0.520
+2025-08-26 09:51:12: Epoch: 007 Train Loss: 0.520, ap: 0.722, auc: 0.767, f1: 0.649, acc: 0.670
+2025-08-26 09:51:16: Epoch: 007 Test results, ap: 0.258, auc: 0.421, f1: 0.232, acc: 0.569
+2025-08-26 09:51:56: Epoch: 008 Train Loss: 0.496, ap: 0.762, auc: 0.805, f1: 0.686, acc: 0.748
+2025-08-26 09:51:56: Epoch: 008 New best VALIDATION metrics
+2025-08-26 09:52:01: Epoch: 008 Test results, ap: 0.277, auc: 0.512, f1: 0.057, acc: 0.732
+2025-08-26 09:52:41: Epoch: 009 Train Loss: 0.489, ap: 0.674, auc: 0.743, f1: 0.343, acc: 0.642
+2025-08-26 09:52:46: Epoch: 009 Test results, ap: 0.272, auc: 0.527, f1: 0.000, acc: 0.756
+2025-08-26 09:53:26: Epoch: 010 Train Loss: 0.464, ap: 0.770, auc: 0.821, f1: 0.720, acc: 0.730
+2025-08-26 09:53:26: Epoch: 010 New best VALIDATION metrics
+2025-08-26 09:53:31: Epoch: 010 Test results, ap: 0.294, auc: 0.542, f1: 0.207, acc: 0.626
+2025-08-26 09:54:11: Epoch: 011 Train Loss: 0.405, ap: 0.798, auc: 0.838, f1: 0.721, acc: 0.755
+2025-08-26 09:54:11: Epoch: 011 New best VALIDATION metrics
+2025-08-26 09:54:16: Epoch: 011 Test results, ap: 0.274, auc: 0.565, f1: 0.200, acc: 0.675
+2025-08-26 09:54:16: Epoch: 011 New best TEST metrics
+2025-08-26 09:54:57: Epoch: 012 Train Loss: 0.387, ap: 0.829, auc: 0.844, f1: 0.726, acc: 0.732
+2025-08-26 09:54:57: Epoch: 012 New best VALIDATION metrics
+2025-08-26 09:55:01: Epoch: 012 Test results, ap: 0.268, auc: 0.543, f1: 0.222, acc: 0.602
+2025-08-26 09:55:41: Epoch: 013 Train Loss: 0.364, ap: 0.803, auc: 0.838, f1: 0.715, acc: 0.739
+2025-08-26 09:55:45: Epoch: 013 Test results, ap: 0.275, auc: 0.493, f1: 0.192, acc: 0.659
+2025-08-26 09:56:24: Epoch: 014 Train Loss: 0.353, ap: 0.817, auc: 0.858, f1: 0.763, acc: 0.764
+2025-08-26 09:56:24: Epoch: 014 New best VALIDATION metrics
+2025-08-26 09:56:29: Epoch: 014 Test results, ap: 0.301, auc: 0.608, f1: 0.273, acc: 0.610
+2025-08-26 09:56:29: Epoch: 014 New best TEST metrics
+2025-08-26 09:57:09: Epoch: 015 Train Loss: 0.330, ap: 0.812, auc: 0.854, f1: 0.757, acc: 0.780
+2025-08-26 09:57:13: Epoch: 015 Test results, ap: 0.311, auc: 0.541, f1: 0.098, acc: 0.699
+2025-08-26 09:57:53: Epoch: 016 Train Loss: 0.318, ap: 0.847, auc: 0.871, f1: 0.759, acc: 0.774
+2025-08-26 09:57:53: Epoch: 016 New best VALIDATION metrics
+2025-08-26 09:57:57: Epoch: 016 Test results, ap: 0.264, auc: 0.547, f1: 0.122, acc: 0.650
+2025-08-26 09:58:36: Epoch: 017 Train Loss: 0.316, ap: 0.818, auc: 0.850, f1: 0.729, acc: 0.769
+2025-08-26 09:58:40: Epoch: 017 Test results, ap: 0.335, auc: 0.621, f1: 0.217, acc: 0.707
+2025-08-26 09:58:40: Epoch: 017 New best TEST metrics
+2025-08-26 09:59:19: Epoch: 018 Train Loss: 0.302, ap: 0.818, auc: 0.862, f1: 0.766, acc: 0.792
+2025-08-26 09:59:23: Epoch: 018 Test results, ap: 0.325, auc: 0.588, f1: 0.204, acc: 0.683
+2025-08-26 10:00:03: Epoch: 019 Train Loss: 0.291, ap: 0.826, auc: 0.862, f1: 0.770, acc: 0.792
+2025-08-26 10:00:07: Epoch: 019 Test results, ap: 0.309, auc: 0.539, f1: 0.095, acc: 0.691
+2025-08-26 10:00:47: Epoch: 020 Train Loss: 0.280, ap: 0.825, auc: 0.861, f1: 0.764, acc: 0.774
+2025-08-26 10:00:51: Epoch: 020 Test results, ap: 0.295, auc: 0.562, f1: 0.093, acc: 0.683
+2025-08-26 10:01:29: Epoch: 021 Train Loss: 0.243, ap: 0.841, auc: 0.878, f1: 0.782, acc: 0.804
+2025-08-26 10:01:29: Epoch: 021 New best VALIDATION metrics
+2025-08-26 10:01:34: Epoch: 021 Test results, ap: 0.346, auc: 0.612, f1: 0.091, acc: 0.675
+2025-08-26 10:01:34: Epoch: 021 New best TEST metrics
+2025-08-26 10:02:13: Epoch: 022 Train Loss: 0.238, ap: 0.840, auc: 0.877, f1: 0.786, acc: 0.802
+2025-08-26 10:02:18: Epoch: 022 Test results, ap: 0.358, auc: 0.630, f1: 0.128, acc: 0.667
+2025-08-26 10:02:18: Epoch: 022 New best TEST metrics
+2025-08-26 10:02:58: Epoch: 023 Train Loss: 0.213, ap: 0.842, auc: 0.879, f1: 0.776, acc: 0.772
+2025-08-26 10:02:58: Epoch: 023 New best VALIDATION metrics
+2025-08-26 10:03:02: Epoch: 023 Test results, ap: 0.317, auc: 0.579, f1: 0.323, acc: 0.659
+2025-08-26 10:03:41: Epoch: 024 Train Loss: 0.228, ap: 0.842, auc: 0.878, f1: 0.778, acc: 0.797
+2025-08-26 10:03:45: Epoch: 024 Test results, ap: 0.327, auc: 0.569, f1: 0.226, acc: 0.667
+2025-08-26 10:04:25: Epoch: 025 Train Loss: 0.219, ap: 0.857, auc: 0.883, f1: 0.778, acc: 0.802
+2025-08-26 10:04:25: Epoch: 025 New best VALIDATION metrics
+2025-08-26 10:04:29: Epoch: 025 Test results, ap: 0.320, auc: 0.595, f1: 0.157, acc: 0.650
+2025-08-26 10:05:09: Epoch: 026 Train Loss: 0.215, ap: 0.841, auc: 0.882, f1: 0.778, acc: 0.792
+2025-08-26 10:05:13: Epoch: 026 Test results, ap: 0.332, auc: 0.585, f1: 0.226, acc: 0.667
+2025-08-26 10:05:53: Epoch: 027 Train Loss: 0.209, ap: 0.855, auc: 0.886, f1: 0.779, acc: 0.810
+2025-08-26 10:05:53: Epoch: 027 New best VALIDATION metrics
+2025-08-26 10:05:58: Epoch: 027 Test results, ap: 0.321, auc: 0.601, f1: 0.160, acc: 0.659
+2025-08-26 10:06:37: Epoch: 028 Train Loss: 0.209, ap: 0.844, auc: 0.872, f1: 0.763, acc: 0.806
+2025-08-26 10:06:41: Epoch: 028 Test results, ap: 0.281, auc: 0.576, f1: 0.000, acc: 0.732
+2025-08-26 10:07:21: Epoch: 029 Train Loss: 0.199, ap: 0.857, auc: 0.882, f1: 0.778, acc: 0.806
+2025-08-26 10:07:25: Epoch: 029 Test results, ap: 0.330, auc: 0.574, f1: 0.170, acc: 0.683
+2025-08-26 10:08:05: Epoch: 030 Train Loss: 0.206, ap: 0.853, auc: 0.886, f1: 0.778, acc: 0.787
+2025-08-26 10:08:09: Epoch: 030 Test results, ap: 0.314, auc: 0.550, f1: 0.169, acc: 0.602
+2025-08-26 10:08:48: Epoch: 031 Train Loss: 0.186, ap: 0.859, auc: 0.892, f1: 0.794, acc: 0.817
+2025-08-26 10:08:48: Epoch: 031 New best VALIDATION metrics
+2025-08-26 10:08:53: Epoch: 031 Test results, ap: 0.343, auc: 0.606, f1: 0.087, acc: 0.659
+2025-08-26 10:09:32: Epoch: 032 Train Loss: 0.172, ap: 0.858, auc: 0.896, f1: 0.806, acc: 0.824
+2025-08-26 10:09:32: Epoch: 032 New best VALIDATION metrics
+2025-08-26 10:09:37: Epoch: 032 Test results, ap: 0.329, auc: 0.581, f1: 0.120, acc: 0.642
+2025-08-26 10:10:16: Epoch: 033 Train Loss: 0.162, ap: 0.866, auc: 0.898, f1: 0.811, acc: 0.836
+2025-08-26 10:10:16: Epoch: 033 New best VALIDATION metrics
+2025-08-26 10:10:21: Epoch: 033 Test results, ap: 0.347, auc: 0.619, f1: 0.105, acc: 0.724
+2025-08-26 10:11:00: Epoch: 034 Train Loss: 0.161, ap: 0.863, auc: 0.894, f1: 0.796, acc: 0.822
+2025-08-26 10:11:05: Epoch: 034 Test results, ap: 0.324, auc: 0.587, f1: 0.140, acc: 0.699
+2025-08-26 10:11:45: Epoch: 035 Train Loss: 0.156, ap: 0.873, auc: 0.898, f1: 0.801, acc: 0.825
+2025-08-26 10:11:45: Epoch: 035 New best VALIDATION metrics
+2025-08-26 10:11:50: Epoch: 035 Test results, ap: 0.340, auc: 0.608, f1: 0.100, acc: 0.707
+2025-08-26 10:12:30: Epoch: 036 Train Loss: 0.162, ap: 0.867, auc: 0.897, f1: 0.791, acc: 0.811
+2025-08-26 10:12:34: Epoch: 036 Test results, ap: 0.341, auc: 0.600, f1: 0.204, acc: 0.683
+2025-08-26 10:13:15: Epoch: 037 Train Loss: 0.156, ap: 0.861, auc: 0.896, f1: 0.787, acc: 0.810
+2025-08-26 10:13:19: Epoch: 037 Test results, ap: 0.351, auc: 0.619, f1: 0.140, acc: 0.699
+2025-08-26 10:13:59: Epoch: 038 Train Loss: 0.154, ap: 0.863, auc: 0.898, f1: 0.810, acc: 0.834
+2025-08-26 10:14:04: Epoch: 038 Test results, ap: 0.352, auc: 0.621, f1: 0.143, acc: 0.707
+2025-08-26 10:14:44: Epoch: 039 Train Loss: 0.154, ap: 0.851, auc: 0.893, f1: 0.779, acc: 0.806
+2025-08-26 10:14:49: Epoch: 039 Test results, ap: 0.337, auc: 0.614, f1: 0.226, acc: 0.667
+2025-08-26 10:15:30: Epoch: 040 Train Loss: 0.147, ap: 0.865, auc: 0.903, f1: 0.809, acc: 0.829
+2025-08-26 10:15:34: Epoch: 040 Test results, ap: 0.341, auc: 0.607, f1: 0.130, acc: 0.675
+2025-08-26 10:16:14: Epoch: 041 Train Loss: 0.135, ap: 0.865, auc: 0.899, f1: 0.802, acc: 0.825
+2025-08-26 10:16:19: Epoch: 041 Test results, ap: 0.350, auc: 0.614, f1: 0.140, acc: 0.699
+2025-08-26 10:16:58: Epoch: 042 Train Loss: 0.142, ap: 0.866, auc: 0.900, f1: 0.809, acc: 0.832
+2025-08-26 10:17:03: Epoch: 042 Test results, ap: 0.352, auc: 0.615, f1: 0.143, acc: 0.707
+2025-08-26 10:17:42: Epoch: 043 Train Loss: 0.141, ap: 0.868, auc: 0.903, f1: 0.809, acc: 0.827
+2025-08-26 10:17:46: Epoch: 043 Test results, ap: 0.350, auc: 0.619, f1: 0.136, acc: 0.691
+2025-08-26 10:18:25: Epoch: 044 Train Loss: 0.140, ap: 0.869, auc: 0.903, f1: 0.807, acc: 0.829
+2025-08-26 10:18:25: Epoch: 044 New best VALIDATION metrics
+2025-08-26 10:18:30: Epoch: 044 Test results, ap: 0.352, auc: 0.623, f1: 0.140, acc: 0.699
+2025-08-26 10:19:09: Epoch: 045 Train Loss: 0.135, ap: 0.862, auc: 0.900, f1: 0.807, acc: 0.829
+2025-08-26 10:19:13: Epoch: 045 Test results, ap: 0.338, auc: 0.614, f1: 0.143, acc: 0.707
+2025-08-26 10:19:53: Epoch: 046 Train Loss: 0.129, ap: 0.866, auc: 0.903, f1: 0.810, acc: 0.831
+2025-08-26 10:19:57: Epoch: 046 Test results, ap: 0.352, auc: 0.630, f1: 0.108, acc: 0.732
+2025-08-26 10:20:37: Epoch: 047 Train Loss: 0.130, ap: 0.866, auc: 0.901, f1: 0.810, acc: 0.829
+2025-08-26 10:20:41: Epoch: 047 Test results, ap: 0.342, auc: 0.606, f1: 0.136, acc: 0.691
+2025-08-26 10:21:20: Epoch: 048 Train Loss: 0.135, ap: 0.861, auc: 0.897, f1: 0.798, acc: 0.825
+2025-08-26 10:21:24: Epoch: 048 Test results, ap: 0.322, auc: 0.610, f1: 0.146, acc: 0.715
+2025-08-26 10:22:03: Epoch: 049 Train Loss: 0.135, ap: 0.866, auc: 0.901, f1: 0.800, acc: 0.829
+2025-08-26 10:22:08: Epoch: 049 Test results, ap: 0.349, auc: 0.614, f1: 0.105, acc: 0.724
+2025-08-26 10:22:47: Epoch: 050 Train Loss: 0.132, ap: 0.869, auc: 0.903, f1: 0.811, acc: 0.840
+2025-08-26 10:22:51: Epoch: 050 Test results, ap: 0.352, auc: 0.620, f1: 0.103, acc: 0.715
+2025-08-26 10:22:51: used time 0.61h
+2025-08-26 10:22:51: Running Cross Validation 4
+2025-08-26 10:22:51: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 10:23:31: Epoch: 001 Train Loss: 0.666, ap: 0.579, auc: 0.570, f1: 0.589, acc: 0.439
+2025-08-26 10:23:31: Epoch: 001 New best VALIDATION metrics
+2025-08-26 10:23:35: Epoch: 001 Test results, ap: 0.251, auc: 0.420, f1: 0.392, acc: 0.244
+2025-08-26 10:24:15: Epoch: 002 Train Loss: 0.649, ap: 0.542, auc: 0.565, f1: 0.555, acc: 0.531
+2025-08-26 10:24:19: Epoch: 002 Test results, ap: 0.336, auc: 0.581, f1: 0.392, acc: 0.244
+2025-08-26 10:25:00: Epoch: 003 Train Loss: 0.639, ap: 0.574, auc: 0.604, f1: 0.398, acc: 0.616
+2025-08-26 10:25:00: Epoch: 003 New best VALIDATION metrics
+2025-08-26 10:25:04: Epoch: 003 Test results, ap: 0.238, auc: 0.529, f1: 0.000, acc: 0.756
+2025-08-26 10:25:44: Epoch: 004 Train Loss: 0.630, ap: 0.559, auc: 0.594, f1: 0.546, acc: 0.563
+2025-08-26 10:25:48: Epoch: 004 Test results, ap: 0.251, auc: 0.553, f1: 0.455, acc: 0.415
+2025-08-26 10:26:27: Epoch: 005 Train Loss: 0.617, ap: 0.608, auc: 0.653, f1: 0.517, acc: 0.614
+2025-08-26 10:26:27: Epoch: 005 New best VALIDATION metrics
+2025-08-26 10:26:32: Epoch: 005 Test results, ap: 0.169, auc: 0.275, f1: 0.138, acc: 0.390
+2025-08-26 10:27:11: Epoch: 006 Train Loss: 0.597, ap: 0.668, auc: 0.693, f1: 0.602, acc: 0.646
+2025-08-26 10:27:11: Epoch: 006 New best VALIDATION metrics
+2025-08-26 10:27:15: Epoch: 006 Test results, ap: 0.329, auc: 0.599, f1: 0.374, acc: 0.374
+2025-08-26 10:27:55: Epoch: 007 Train Loss: 0.557, ap: 0.692, auc: 0.729, f1: 0.652, acc: 0.603
+2025-08-26 10:27:55: Epoch: 007 New best VALIDATION metrics
+2025-08-26 10:28:00: Epoch: 007 Test results, ap: 0.214, auc: 0.429, f1: 0.386, acc: 0.276
+2025-08-26 10:28:40: Epoch: 008 Train Loss: 0.528, ap: 0.701, auc: 0.748, f1: 0.596, acc: 0.675
+2025-08-26 10:28:40: Epoch: 008 New best VALIDATION metrics
+2025-08-26 10:28:44: Epoch: 008 Test results, ap: 0.235, auc: 0.477, f1: 0.237, acc: 0.634
+2025-08-26 10:29:23: Epoch: 009 Train Loss: 0.477, ap: 0.751, auc: 0.791, f1: 0.691, acc: 0.644
+2025-08-26 10:29:23: Epoch: 009 New best VALIDATION metrics
+2025-08-26 10:29:28: Epoch: 009 Test results, ap: 0.362, auc: 0.515, f1: 0.394, acc: 0.325
+2025-08-26 10:30:07: Epoch: 010 Train Loss: 0.429, ap: 0.710, auc: 0.739, f1: 0.641, acc: 0.571
+2025-08-26 10:30:11: Epoch: 010 Test results, ap: 0.257, auc: 0.506, f1: 0.395, acc: 0.252
+2025-08-26 10:30:51: Epoch: 011 Train Loss: 0.385, ap: 0.797, auc: 0.832, f1: 0.743, acc: 0.741
+2025-08-26 10:30:51: Epoch: 011 New best VALIDATION metrics
+2025-08-26 10:30:55: Epoch: 011 Test results, ap: 0.243, auc: 0.501, f1: 0.410, acc: 0.415
+2025-08-26 10:30:55: Epoch: 011 New best TEST metrics
+2025-08-26 10:31:35: Epoch: 012 Train Loss: 0.362, ap: 0.823, auc: 0.862, f1: 0.747, acc: 0.781
+2025-08-26 10:31:35: Epoch: 012 New best VALIDATION metrics
+2025-08-26 10:31:39: Epoch: 012 Test results, ap: 0.232, auc: 0.488, f1: 0.222, acc: 0.545
+2025-08-26 10:32:18: Epoch: 013 Train Loss: 0.336, ap: 0.828, auc: 0.867, f1: 0.772, acc: 0.785
+2025-08-26 10:32:18: Epoch: 013 New best VALIDATION metrics
+2025-08-26 10:32:22: Epoch: 013 Test results, ap: 0.239, auc: 0.501, f1: 0.283, acc: 0.463
+2025-08-26 10:33:02: Epoch: 014 Train Loss: 0.324, ap: 0.828, auc: 0.864, f1: 0.702, acc: 0.769
+2025-08-26 10:33:06: Epoch: 014 Test results, ap: 0.226, auc: 0.478, f1: 0.131, acc: 0.569
+2025-08-26 10:33:46: Epoch: 015 Train Loss: 0.308, ap: 0.832, auc: 0.876, f1: 0.776, acc: 0.797
+2025-08-26 10:33:46: Epoch: 015 New best VALIDATION metrics
+2025-08-26 10:33:50: Epoch: 015 Test results, ap: 0.221, auc: 0.468, f1: 0.295, acc: 0.496
+2025-08-26 10:34:29: Epoch: 016 Train Loss: 0.300, ap: 0.839, auc: 0.875, f1: 0.774, acc: 0.783
+2025-08-26 10:34:29: Epoch: 016 New best VALIDATION metrics
+2025-08-26 10:34:34: Epoch: 016 Test results, ap: 0.237, auc: 0.496, f1: 0.340, acc: 0.463
+2025-08-26 10:35:13: Epoch: 017 Train Loss: 0.280, ap: 0.847, auc: 0.875, f1: 0.764, acc: 0.795
+2025-08-26 10:35:13: Epoch: 017 New best VALIDATION metrics
+2025-08-26 10:35:18: Epoch: 017 Test results, ap: 0.232, auc: 0.487, f1: 0.243, acc: 0.545
+2025-08-26 10:35:56: Epoch: 018 Train Loss: 0.264, ap: 0.847, auc: 0.885, f1: 0.715, acc: 0.778
+2025-08-26 10:35:56: Epoch: 018 New best VALIDATION metrics
+2025-08-26 10:36:00: Epoch: 018 Test results, ap: 0.229, auc: 0.481, f1: 0.257, acc: 0.577
+2025-08-26 10:36:39: Epoch: 019 Train Loss: 0.258, ap: 0.854, auc: 0.884, f1: 0.784, acc: 0.783
+2025-08-26 10:36:39: Epoch: 019 New best VALIDATION metrics
+2025-08-26 10:36:44: Epoch: 019 Test results, ap: 0.233, auc: 0.490, f1: 0.362, acc: 0.455
+2025-08-26 10:37:23: Epoch: 020 Train Loss: 0.238, ap: 0.851, auc: 0.895, f1: 0.805, acc: 0.822
+2025-08-26 10:37:23: Epoch: 020 New best VALIDATION metrics
+2025-08-26 10:37:27: Epoch: 020 Test results, ap: 0.218, auc: 0.466, f1: 0.286, acc: 0.512
+2025-08-26 10:38:07: Epoch: 021 Train Loss: 0.223, ap: 0.874, auc: 0.904, f1: 0.799, acc: 0.829
+2025-08-26 10:38:07: Epoch: 021 New best VALIDATION metrics
+2025-08-26 10:38:11: Epoch: 021 Test results, ap: 0.227, auc: 0.487, f1: 0.237, acc: 0.528
+2025-08-26 10:38:50: Epoch: 022 Train Loss: 0.202, ap: 0.866, auc: 0.899, f1: 0.805, acc: 0.817
+2025-08-26 10:38:54: Epoch: 022 Test results, ap: 0.218, auc: 0.467, f1: 0.337, acc: 0.520
+2025-08-26 10:39:33: Epoch: 023 Train Loss: 0.197, ap: 0.867, auc: 0.905, f1: 0.811, acc: 0.820
+2025-08-26 10:39:37: Epoch: 023 Test results, ap: 0.217, auc: 0.459, f1: 0.337, acc: 0.520
+2025-08-26 10:40:16: Epoch: 024 Train Loss: 0.191, ap: 0.874, auc: 0.906, f1: 0.811, acc: 0.820
+2025-08-26 10:40:16: Epoch: 024 New best VALIDATION metrics
+2025-08-26 10:40:20: Epoch: 024 Test results, ap: 0.218, auc: 0.467, f1: 0.362, acc: 0.512
+2025-08-26 10:40:59: Epoch: 025 Train Loss: 0.183, ap: 0.888, auc: 0.914, f1: 0.807, acc: 0.824
+2025-08-26 10:40:59: Epoch: 025 New best VALIDATION metrics
+2025-08-26 10:41:03: Epoch: 025 Test results, ap: 0.217, auc: 0.459, f1: 0.194, acc: 0.528
+2025-08-26 10:41:43: Epoch: 026 Train Loss: 0.177, ap: 0.870, auc: 0.892, f1: 0.798, acc: 0.818
+2025-08-26 10:41:47: Epoch: 026 Test results, ap: 0.211, auc: 0.446, f1: 0.211, acc: 0.512
+2025-08-26 10:42:26: Epoch: 027 Train Loss: 0.194, ap: 0.871, auc: 0.902, f1: 0.810, acc: 0.829
+2025-08-26 10:42:31: Epoch: 027 Test results, ap: 0.221, auc: 0.475, f1: 0.299, acc: 0.504
+2025-08-26 10:43:10: Epoch: 028 Train Loss: 0.173, ap: 0.875, auc: 0.912, f1: 0.821, acc: 0.838
+2025-08-26 10:43:14: Epoch: 028 Test results, ap: 0.213, auc: 0.451, f1: 0.189, acc: 0.512
+2025-08-26 10:43:53: Epoch: 029 Train Loss: 0.175, ap: 0.867, auc: 0.907, f1: 0.826, acc: 0.836
+2025-08-26 10:43:58: Epoch: 029 Test results, ap: 0.215, auc: 0.455, f1: 0.360, acc: 0.480
+2025-08-26 10:44:37: Epoch: 030 Train Loss: 0.154, ap: 0.886, auc: 0.916, f1: 0.840, acc: 0.852
+2025-08-26 10:44:37: Epoch: 030 New best VALIDATION metrics
+2025-08-26 10:44:41: Epoch: 030 Test results, ap: 0.215, auc: 0.458, f1: 0.296, acc: 0.537
+2025-08-26 10:45:21: Epoch: 031 Train Loss: 0.142, ap: 0.889, auc: 0.917, f1: 0.829, acc: 0.848
+2025-08-26 10:45:21: Epoch: 031 New best VALIDATION metrics
+2025-08-26 10:45:25: Epoch: 031 Test results, ap: 0.217, auc: 0.459, f1: 0.143, acc: 0.512
+2025-08-26 10:46:04: Epoch: 032 Train Loss: 0.138, ap: 0.889, auc: 0.916, f1: 0.833, acc: 0.847
+2025-08-26 10:46:09: Epoch: 032 Test results, ap: 0.214, auc: 0.453, f1: 0.216, acc: 0.528
+2025-08-26 10:46:48: Epoch: 033 Train Loss: 0.134, ap: 0.883, auc: 0.915, f1: 0.839, acc: 0.854
+2025-08-26 10:46:52: Epoch: 033 Test results, ap: 0.214, auc: 0.452, f1: 0.213, acc: 0.520
+2025-08-26 10:47:32: Epoch: 034 Train Loss: 0.125, ap: 0.889, auc: 0.917, f1: 0.828, acc: 0.847
+2025-08-26 10:47:32: Epoch: 034 New best VALIDATION metrics
+2025-08-26 10:47:37: Epoch: 034 Test results, ap: 0.217, auc: 0.457, f1: 0.116, acc: 0.504
+2025-08-26 10:48:16: Epoch: 035 Train Loss: 0.121, ap: 0.889, auc: 0.914, f1: 0.836, acc: 0.845
+2025-08-26 10:48:20: Epoch: 035 Test results, ap: 0.214, auc: 0.453, f1: 0.187, acc: 0.504
+2025-08-26 10:48:59: Epoch: 036 Train Loss: 0.118, ap: 0.882, auc: 0.912, f1: 0.823, acc: 0.831
+2025-08-26 10:49:04: Epoch: 036 Test results, ap: 0.210, auc: 0.441, f1: 0.160, acc: 0.488
+2025-08-26 10:49:43: Epoch: 037 Train Loss: 0.122, ap: 0.880, auc: 0.913, f1: 0.833, acc: 0.841
+2025-08-26 10:49:47: Epoch: 037 Test results, ap: 0.217, auc: 0.463, f1: 0.293, acc: 0.528
+2025-08-26 10:50:27: Epoch: 038 Train Loss: 0.123, ap: 0.891, auc: 0.918, f1: 0.833, acc: 0.854
+2025-08-26 10:50:27: Epoch: 038 New best VALIDATION metrics
+2025-08-26 10:50:32: Epoch: 038 Test results, ap: 0.217, auc: 0.460, f1: 0.194, acc: 0.528
+2025-08-26 10:51:12: Epoch: 039 Train Loss: 0.118, ap: 0.890, auc: 0.918, f1: 0.837, acc: 0.859
+2025-08-26 10:51:16: Epoch: 039 Test results, ap: 0.216, auc: 0.458, f1: 0.119, acc: 0.520
+2025-08-26 10:51:55: Epoch: 040 Train Loss: 0.113, ap: 0.891, auc: 0.922, f1: 0.842, acc: 0.850
+2025-08-26 10:51:55: Epoch: 040 New best VALIDATION metrics
+2025-08-26 10:52:00: Epoch: 040 Test results, ap: 0.216, auc: 0.457, f1: 0.263, acc: 0.545
+2025-08-26 10:52:38: Epoch: 041 Train Loss: 0.106, ap: 0.890, auc: 0.920, f1: 0.850, acc: 0.864
+2025-08-26 10:52:42: Epoch: 041 Test results, ap: 0.216, auc: 0.456, f1: 0.169, acc: 0.520
+2025-08-26 10:53:21: Epoch: 042 Train Loss: 0.098, ap: 0.891, auc: 0.919, f1: 0.844, acc: 0.861
+2025-08-26 10:53:25: Epoch: 042 Test results, ap: 0.216, auc: 0.457, f1: 0.192, acc: 0.520
+2025-08-26 10:54:05: Epoch: 043 Train Loss: 0.101, ap: 0.890, auc: 0.919, f1: 0.840, acc: 0.848
+2025-08-26 10:54:09: Epoch: 043 Test results, ap: 0.215, auc: 0.455, f1: 0.189, acc: 0.512
+2025-08-26 10:54:48: Epoch: 044 Train Loss: 0.091, ap: 0.892, auc: 0.921, f1: 0.851, acc: 0.868
+2025-08-26 10:54:53: Epoch: 044 Test results, ap: 0.214, auc: 0.453, f1: 0.194, acc: 0.528
+2025-08-26 10:55:33: Epoch: 045 Train Loss: 0.099, ap: 0.890, auc: 0.921, f1: 0.839, acc: 0.852
+2025-08-26 10:55:37: Epoch: 045 Test results, ap: 0.214, auc: 0.454, f1: 0.192, acc: 0.520
+2025-08-26 10:56:17: Epoch: 046 Train Loss: 0.091, ap: 0.894, auc: 0.922, f1: 0.845, acc: 0.861
+2025-08-26 10:56:17: Epoch: 046 New best VALIDATION metrics
+2025-08-26 10:56:21: Epoch: 046 Test results, ap: 0.218, auc: 0.461, f1: 0.145, acc: 0.520
+2025-08-26 10:57:01: Epoch: 047 Train Loss: 0.100, ap: 0.889, auc: 0.921, f1: 0.850, acc: 0.864
+2025-08-26 10:57:05: Epoch: 047 Test results, ap: 0.215, auc: 0.456, f1: 0.143, acc: 0.512
+2025-08-26 10:57:45: Epoch: 048 Train Loss: 0.088, ap: 0.891, auc: 0.920, f1: 0.844, acc: 0.861
+2025-08-26 10:57:49: Epoch: 048 Test results, ap: 0.214, auc: 0.453, f1: 0.143, acc: 0.512
+2025-08-26 10:58:29: Epoch: 049 Train Loss: 0.091, ap: 0.893, auc: 0.924, f1: 0.850, acc: 0.864
+2025-08-26 10:58:29: Epoch: 049 New best VALIDATION metrics
+2025-08-26 10:58:33: Epoch: 049 Test results, ap: 0.216, auc: 0.458, f1: 0.145, acc: 0.520
+2025-08-26 10:59:12: Epoch: 050 Train Loss: 0.083, ap: 0.894, auc: 0.923, f1: 0.858, acc: 0.871
+2025-08-26 10:59:16: Epoch: 050 Test results, ap: 0.214, auc: 0.452, f1: 0.216, acc: 0.528
+2025-08-26 10:59:16: used time 0.61h
+2025-08-26 10:59:16: Cross Validation Finished!
+2025-08-26 10:59:16: Best validation perform list
+[[0.87072873 0.89700133 0.83333331 0.84154928]
+ [0.88980389 0.90994704 0.80722892 0.8309859 ]
+ [0.91020417 0.93052995 0.84069097 0.85361552]
+ [0.86907303 0.90306044 0.80715704 0.82892418]
+ [0.89277267 0.92368215 0.84990251 0.86419755]]
+2025-08-26 10:59:16: mean: [0.887 0.913 0.828 0.844]
+2025-08-26 10:59:16: std: [0.015 0.013 0.018 0.013]
+2025-08-26 10:59:16: Best test perform list
+[[0.30148014 0.53189963 0.3116883  0.56910568]
+ [0.37904528 0.6741935  0.3943662  0.65040648]
+ [0.38260949 0.59247309 0.44444445 0.67479676]
+ [0.35811639 0.63046598 0.12765957 0.66666669]
+ [0.24341419 0.50071687 0.40983605 0.41463414]]
+2025-08-26 10:59:16: mean: [0.333 0.586 0.338 0.595]
+2025-08-26 10:59:16: std: [0.053 0.063 0.114 0.098]
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diff --git a/run-cls/rn18-att-16-768-ce-32-0.001-50/training.log b/run-cls/rn18-att-16-768-ce-32-0.001-50/training.log
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@@ -0,0 +1,586 @@
+2025-08-26 02:10:43: saving_dir: ./run-cls/rn18-att-16-768-ce-32-0.001-50
+2025-08-26 02:10:43: Loading Training Dataset
+2025-08-26 02:12:23: Loading Test Dataset
+2025-08-26 02:12:43: Running Cross Validation 0
+2025-08-26 02:12:43: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 02:13:23: Epoch: 001 Train Loss: 0.682, ap: 0.618, auc: 0.644, f1: 0.000, acc: 0.519
+2025-08-26 02:13:23: Epoch: 001 New best VALIDATION metrics
+2025-08-26 02:13:26: Epoch: 001 Test results, ap: 0.273, auc: 0.416, f1: 0.000, acc: 0.756
+2025-08-26 02:13:59: Epoch: 002 Train Loss: 0.665, ap: 0.647, auc: 0.677, f1: 0.597, acc: 0.641
+2025-08-26 02:13:59: Epoch: 002 New best VALIDATION metrics
+2025-08-26 02:14:02: Epoch: 002 Test results, ap: 0.412, auc: 0.634, f1: 0.000, acc: 0.756
+2025-08-26 02:14:36: Epoch: 003 Train Loss: 0.664, ap: 0.679, auc: 0.676, f1: 0.514, acc: 0.641
+2025-08-26 02:14:36: Epoch: 003 New best VALIDATION metrics
+2025-08-26 02:14:38: Epoch: 003 Test results, ap: 0.368, auc: 0.564, f1: 0.000, acc: 0.756
+2025-08-26 02:15:14: Epoch: 004 Train Loss: 0.665, ap: 0.660, auc: 0.677, f1: 0.621, acc: 0.646
+2025-08-26 02:15:17: Epoch: 004 Test results, ap: 0.362, auc: 0.558, f1: 0.216, acc: 0.764
+2025-08-26 02:15:51: Epoch: 005 Train Loss: 0.660, ap: 0.667, auc: 0.666, f1: 0.514, acc: 0.644
+2025-08-26 02:15:54: Epoch: 005 Test results, ap: 0.316, auc: 0.553, f1: 0.000, acc: 0.756
+2025-08-26 02:16:27: Epoch: 006 Train Loss: 0.661, ap: 0.677, auc: 0.679, f1: 0.496, acc: 0.639
+2025-08-26 02:16:27: Epoch: 006 New best VALIDATION metrics
+2025-08-26 02:16:30: Epoch: 006 Test results, ap: 0.196, auc: 0.400, f1: 0.000, acc: 0.756
+2025-08-26 02:17:04: Epoch: 007 Train Loss: 0.661, ap: 0.667, auc: 0.700, f1: 0.655, acc: 0.590
+2025-08-26 02:17:04: Epoch: 007 New best VALIDATION metrics
+2025-08-26 02:17:08: Epoch: 007 Test results, ap: 0.308, auc: 0.566, f1: 0.434, acc: 0.512
+2025-08-26 02:17:41: Epoch: 008 Train Loss: 0.659, ap: 0.668, auc: 0.694, f1: 0.641, acc: 0.650
+2025-08-26 02:17:45: Epoch: 008 Test results, ap: 0.489, auc: 0.651, f1: 0.400, acc: 0.439
+2025-08-26 02:18:19: Epoch: 009 Train Loss: 0.654, ap: 0.669, auc: 0.693, f1: 0.648, acc: 0.602
+2025-08-26 02:18:25: Epoch: 009 Test results, ap: 0.260, auc: 0.545, f1: 0.341, acc: 0.276
+2025-08-26 02:19:00: Epoch: 010 Train Loss: 0.657, ap: 0.658, auc: 0.689, f1: 0.620, acc: 0.646
+2025-08-26 02:19:04: Epoch: 010 Test results, ap: 0.297, auc: 0.600, f1: 0.000, acc: 0.756
+2025-08-26 02:19:38: Epoch: 011 Train Loss: 0.647, ap: 0.667, auc: 0.704, f1: 0.638, acc: 0.658
+2025-08-26 02:19:38: Epoch: 011 New best VALIDATION metrics
+2025-08-26 02:19:41: Epoch: 011 Test results, ap: 0.309, auc: 0.627, f1: 0.000, acc: 0.756
+2025-08-26 02:19:41: Epoch: 011 New best TEST metrics
+2025-08-26 02:20:16: Epoch: 012 Train Loss: 0.632, ap: 0.673, auc: 0.703, f1: 0.657, acc: 0.660
+2025-08-26 02:20:16: Epoch: 012 New best VALIDATION metrics
+2025-08-26 02:20:19: Epoch: 012 Test results, ap: 0.274, auc: 0.565, f1: 0.312, acc: 0.642
+2025-08-26 02:20:53: Epoch: 013 Train Loss: 0.633, ap: 0.678, auc: 0.698, f1: 0.648, acc: 0.667
+2025-08-26 02:20:57: Epoch: 013 Test results, ap: 0.255, auc: 0.515, f1: 0.160, acc: 0.659
+2025-08-26 02:21:31: Epoch: 014 Train Loss: 0.630, ap: 0.700, auc: 0.724, f1: 0.616, acc: 0.673
+2025-08-26 02:21:31: Epoch: 014 New best VALIDATION metrics
+2025-08-26 02:21:34: Epoch: 014 Test results, ap: 0.315, auc: 0.556, f1: 0.286, acc: 0.634
+2025-08-26 02:22:09: Epoch: 015 Train Loss: 0.615, ap: 0.686, auc: 0.719, f1: 0.638, acc: 0.674
+2025-08-26 02:22:12: Epoch: 015 Test results, ap: 0.426, auc: 0.640, f1: 0.431, acc: 0.699
+2025-08-26 02:22:12: Epoch: 015 New best TEST metrics
+2025-08-26 02:22:46: Epoch: 016 Train Loss: 0.607, ap: 0.656, auc: 0.708, f1: 0.440, acc: 0.606
+2025-08-26 02:22:49: Epoch: 016 Test results, ap: 0.409, auc: 0.625, f1: 0.000, acc: 0.756
+2025-08-26 02:23:23: Epoch: 017 Train Loss: 0.601, ap: 0.647, auc: 0.707, f1: 0.688, acc: 0.655
+2025-08-26 02:23:26: Epoch: 017 Test results, ap: 0.306, auc: 0.628, f1: 0.442, acc: 0.610
+2025-08-26 02:24:00: Epoch: 018 Train Loss: 0.588, ap: 0.649, auc: 0.697, f1: 0.673, acc: 0.646
+2025-08-26 02:24:03: Epoch: 018 Test results, ap: 0.379, auc: 0.552, f1: 0.430, acc: 0.504
+2025-08-26 02:24:37: Epoch: 019 Train Loss: 0.589, ap: 0.638, auc: 0.706, f1: 0.684, acc: 0.674
+2025-08-26 02:24:40: Epoch: 019 Test results, ap: 0.263, auc: 0.589, f1: 0.270, acc: 0.561
+2025-08-26 02:25:15: Epoch: 020 Train Loss: 0.581, ap: 0.644, auc: 0.689, f1: 0.670, acc: 0.650
+2025-08-26 02:25:18: Epoch: 020 Test results, ap: 0.343, auc: 0.657, f1: 0.452, acc: 0.626
+2025-08-26 02:25:52: Epoch: 021 Train Loss: 0.570, ap: 0.663, auc: 0.706, f1: 0.661, acc: 0.671
+2025-08-26 02:25:55: Epoch: 021 Test results, ap: 0.336, auc: 0.644, f1: 0.447, acc: 0.537
+2025-08-26 02:26:30: Epoch: 022 Train Loss: 0.562, ap: 0.630, auc: 0.687, f1: 0.628, acc: 0.646
+2025-08-26 02:26:33: Epoch: 022 Test results, ap: 0.326, auc: 0.655, f1: 0.245, acc: 0.699
+2025-08-26 02:27:07: Epoch: 023 Train Loss: 0.556, ap: 0.652, auc: 0.692, f1: 0.668, acc: 0.662
+2025-08-26 02:27:10: Epoch: 023 Test results, ap: 0.329, auc: 0.644, f1: 0.447, acc: 0.537
+2025-08-26 02:27:44: Epoch: 024 Train Loss: 0.550, ap: 0.645, auc: 0.697, f1: 0.674, acc: 0.673
+2025-08-26 02:27:47: Epoch: 024 Test results, ap: 0.335, auc: 0.649, f1: 0.451, acc: 0.545
+2025-08-26 02:28:21: Epoch: 025 Train Loss: 0.550, ap: 0.663, auc: 0.715, f1: 0.641, acc: 0.667
+2025-08-26 02:28:24: Epoch: 025 Test results, ap: 0.336, auc: 0.637, f1: 0.447, acc: 0.537
+2025-08-26 02:28:59: Epoch: 026 Train Loss: 0.543, ap: 0.646, auc: 0.696, f1: 0.672, acc: 0.664
+2025-08-26 02:29:02: Epoch: 026 Test results, ap: 0.304, auc: 0.633, f1: 0.434, acc: 0.512
+2025-08-26 02:29:37: Epoch: 027 Train Loss: 0.533, ap: 0.612, auc: 0.677, f1: 0.608, acc: 0.639
+2025-08-26 02:29:40: Epoch: 027 Test results, ap: 0.336, auc: 0.627, f1: 0.440, acc: 0.585
+2025-08-26 02:30:14: Epoch: 028 Train Loss: 0.528, ap: 0.645, auc: 0.700, f1: 0.688, acc: 0.678
+2025-08-26 02:30:17: Epoch: 028 Test results, ap: 0.280, auc: 0.581, f1: 0.442, acc: 0.528
+2025-08-26 02:30:51: Epoch: 029 Train Loss: 0.529, ap: 0.633, auc: 0.689, f1: 0.659, acc: 0.665
+2025-08-26 02:30:54: Epoch: 029 Test results, ap: 0.326, auc: 0.639, f1: 0.437, acc: 0.602
+2025-08-26 02:31:28: Epoch: 030 Train Loss: 0.531, ap: 0.644, auc: 0.683, f1: 0.596, acc: 0.636
+2025-08-26 02:31:31: Epoch: 030 Test results, ap: 0.336, auc: 0.635, f1: 0.447, acc: 0.537
+2025-08-26 02:32:06: Epoch: 031 Train Loss: 0.511, ap: 0.618, auc: 0.686, f1: 0.623, acc: 0.643
+2025-08-26 02:32:10: Epoch: 031 Test results, ap: 0.334, auc: 0.622, f1: 0.447, acc: 0.537
+2025-08-26 02:32:43: Epoch: 032 Train Loss: 0.508, ap: 0.632, auc: 0.677, f1: 0.606, acc: 0.636
+2025-08-26 02:32:46: Epoch: 032 Test results, ap: 0.284, auc: 0.605, f1: 0.447, acc: 0.537
+2025-08-26 02:33:20: Epoch: 033 Train Loss: 0.499, ap: 0.659, auc: 0.700, f1: 0.628, acc: 0.658
+2025-08-26 02:33:23: Epoch: 033 Test results, ap: 0.276, auc: 0.591, f1: 0.447, acc: 0.537
+2025-08-26 02:33:57: Epoch: 034 Train Loss: 0.486, ap: 0.634, auc: 0.667, f1: 0.594, acc: 0.632
+2025-08-26 02:34:00: Epoch: 034 Test results, ap: 0.283, auc: 0.602, f1: 0.447, acc: 0.537
+2025-08-26 02:34:34: Epoch: 035 Train Loss: 0.500, ap: 0.624, auc: 0.659, f1: 0.588, acc: 0.611
+2025-08-26 02:34:38: Epoch: 035 Test results, ap: 0.284, auc: 0.603, f1: 0.447, acc: 0.537
+2025-08-26 02:35:12: Epoch: 036 Train Loss: 0.490, ap: 0.613, auc: 0.651, f1: 0.557, acc: 0.625
+2025-08-26 02:35:15: Epoch: 036 Test results, ap: 0.284, auc: 0.601, f1: 0.447, acc: 0.618
+2025-08-26 02:35:49: Epoch: 037 Train Loss: 0.486, ap: 0.644, auc: 0.678, f1: 0.578, acc: 0.625
+2025-08-26 02:35:52: Epoch: 037 Test results, ap: 0.288, auc: 0.603, f1: 0.447, acc: 0.537
+2025-08-26 02:36:27: Epoch: 038 Train Loss: 0.476, ap: 0.654, auc: 0.696, f1: 0.650, acc: 0.671
+2025-08-26 02:36:30: Epoch: 038 Test results, ap: 0.288, auc: 0.608, f1: 0.447, acc: 0.537
+2025-08-26 02:37:04: Epoch: 039 Train Loss: 0.475, ap: 0.635, auc: 0.679, f1: 0.655, acc: 0.676
+2025-08-26 02:37:07: Epoch: 039 Test results, ap: 0.284, auc: 0.606, f1: 0.437, acc: 0.602
+2025-08-26 02:37:42: Epoch: 040 Train Loss: 0.473, ap: 0.624, auc: 0.646, f1: 0.572, acc: 0.623
+2025-08-26 02:37:45: Epoch: 040 Test results, ap: 0.289, auc: 0.614, f1: 0.463, acc: 0.585
+2025-08-26 02:38:19: Epoch: 041 Train Loss: 0.450, ap: 0.659, auc: 0.689, f1: 0.633, acc: 0.665
+2025-08-26 02:38:22: Epoch: 041 Test results, ap: 0.287, auc: 0.609, f1: 0.442, acc: 0.610
+2025-08-26 02:38:57: Epoch: 042 Train Loss: 0.454, ap: 0.639, auc: 0.665, f1: 0.559, acc: 0.636
+2025-08-26 02:39:00: Epoch: 042 Test results, ap: 0.295, auc: 0.610, f1: 0.375, acc: 0.593
+2025-08-26 02:39:34: Epoch: 043 Train Loss: 0.451, ap: 0.647, auc: 0.688, f1: 0.634, acc: 0.660
+2025-08-26 02:39:37: Epoch: 043 Test results, ap: 0.289, auc: 0.604, f1: 0.366, acc: 0.577
+2025-08-26 02:40:11: Epoch: 044 Train Loss: 0.445, ap: 0.655, auc: 0.697, f1: 0.635, acc: 0.674
+2025-08-26 02:40:14: Epoch: 044 Test results, ap: 0.292, auc: 0.605, f1: 0.442, acc: 0.610
+2025-08-26 02:40:48: Epoch: 045 Train Loss: 0.450, ap: 0.664, auc: 0.691, f1: 0.641, acc: 0.658
+2025-08-26 02:40:51: Epoch: 045 Test results, ap: 0.292, auc: 0.604, f1: 0.442, acc: 0.610
+2025-08-26 02:41:25: Epoch: 046 Train Loss: 0.448, ap: 0.659, auc: 0.687, f1: 0.640, acc: 0.669
+2025-08-26 02:41:28: Epoch: 046 Test results, ap: 0.291, auc: 0.604, f1: 0.437, acc: 0.602
+2025-08-26 02:42:03: Epoch: 047 Train Loss: 0.437, ap: 0.630, auc: 0.658, f1: 0.581, acc: 0.639
+2025-08-26 02:42:06: Epoch: 047 Test results, ap: 0.293, auc: 0.605, f1: 0.366, acc: 0.577
+2025-08-26 02:42:40: Epoch: 048 Train Loss: 0.447, ap: 0.652, auc: 0.673, f1: 0.607, acc: 0.658
+2025-08-26 02:42:43: Epoch: 048 Test results, ap: 0.294, auc: 0.610, f1: 0.442, acc: 0.610
+2025-08-26 02:43:17: Epoch: 049 Train Loss: 0.443, ap: 0.663, auc: 0.684, f1: 0.625, acc: 0.650
+2025-08-26 02:43:20: Epoch: 049 Test results, ap: 0.278, auc: 0.596, f1: 0.437, acc: 0.602
+2025-08-26 02:43:55: Epoch: 050 Train Loss: 0.435, ap: 0.638, auc: 0.669, f1: 0.601, acc: 0.650
+2025-08-26 02:43:58: Epoch: 050 Test results, ap: 0.285, auc: 0.601, f1: 0.366, acc: 0.577
+2025-08-26 02:43:58: used time 0.52h
+2025-08-26 02:43:58: Running Cross Validation 1
+2025-08-26 02:43:58: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 02:44:34: Epoch: 001 Train Loss: 0.672, ap: 0.566, auc: 0.638, f1: 0.462, acc: 0.611
+2025-08-26 02:44:34: Epoch: 001 New best VALIDATION metrics
+2025-08-26 02:44:39: Epoch: 001 Test results, ap: 0.169, auc: 0.265, f1: 0.000, acc: 0.756
+2025-08-26 02:45:13: Epoch: 002 Train Loss: 0.667, ap: 0.582, auc: 0.655, f1: 0.442, acc: 0.627
+2025-08-26 02:45:13: Epoch: 002 New best VALIDATION metrics
+2025-08-26 02:45:17: Epoch: 002 Test results, ap: 0.181, auc: 0.334, f1: 0.000, acc: 0.756
+2025-08-26 02:45:51: Epoch: 003 Train Loss: 0.664, ap: 0.585, auc: 0.667, f1: 0.593, acc: 0.630
+2025-08-26 02:45:51: Epoch: 003 New best VALIDATION metrics
+2025-08-26 02:45:55: Epoch: 003 Test results, ap: 0.226, auc: 0.482, f1: 0.000, acc: 0.756
+2025-08-26 02:46:29: Epoch: 004 Train Loss: 0.665, ap: 0.582, auc: 0.651, f1: 0.446, acc: 0.637
+2025-08-26 02:46:32: Epoch: 004 Test results, ap: 0.206, auc: 0.409, f1: 0.000, acc: 0.756
+2025-08-26 02:47:07: Epoch: 005 Train Loss: 0.662, ap: 0.560, auc: 0.641, f1: 0.426, acc: 0.639
+2025-08-26 02:47:10: Epoch: 005 Test results, ap: 0.252, auc: 0.555, f1: 0.000, acc: 0.756
+2025-08-26 02:47:44: Epoch: 006 Train Loss: 0.662, ap: 0.596, auc: 0.652, f1: 0.576, acc: 0.595
+2025-08-26 02:47:47: Epoch: 006 Test results, ap: 0.230, auc: 0.474, f1: 0.000, acc: 0.707
+2025-08-26 02:48:21: Epoch: 007 Train Loss: 0.655, ap: 0.587, auc: 0.661, f1: 0.605, acc: 0.632
+2025-08-26 02:48:25: Epoch: 007 Test results, ap: 0.241, auc: 0.537, f1: 0.048, acc: 0.675
+2025-08-26 02:48:59: Epoch: 008 Train Loss: 0.647, ap: 0.581, auc: 0.669, f1: 0.556, acc: 0.657
+2025-08-26 02:49:03: Epoch: 008 Test results, ap: 0.207, auc: 0.429, f1: 0.000, acc: 0.756
+2025-08-26 02:49:36: Epoch: 009 Train Loss: 0.642, ap: 0.527, auc: 0.595, f1: 0.585, acc: 0.458
+2025-08-26 02:49:40: Epoch: 009 Test results, ap: 0.201, auc: 0.393, f1: 0.235, acc: 0.472
+2025-08-26 02:50:14: Epoch: 010 Train Loss: 0.661, ap: 0.531, auc: 0.565, f1: 0.450, acc: 0.639
+2025-08-26 02:50:17: Epoch: 010 Test results, ap: 0.453, auc: 0.606, f1: 0.000, acc: 0.756
+2025-08-26 02:50:51: Epoch: 011 Train Loss: 0.663, ap: 0.565, auc: 0.647, f1: 0.498, acc: 0.630
+2025-08-26 02:50:54: Epoch: 011 Test results, ap: 0.323, auc: 0.622, f1: 0.000, acc: 0.756
+2025-08-26 02:50:54: Epoch: 011 New best TEST metrics
+2025-08-26 02:51:29: Epoch: 012 Train Loss: 0.660, ap: 0.583, auc: 0.666, f1: 0.602, acc: 0.646
+2025-08-26 02:51:32: Epoch: 012 Test results, ap: 0.193, auc: 0.380, f1: 0.000, acc: 0.756
+2025-08-26 02:52:06: Epoch: 013 Train Loss: 0.650, ap: 0.613, auc: 0.667, f1: 0.594, acc: 0.641
+2025-08-26 02:52:06: Epoch: 013 New best VALIDATION metrics
+2025-08-26 02:52:09: Epoch: 013 Test results, ap: 0.252, auc: 0.495, f1: 0.000, acc: 0.699
+2025-08-26 02:52:43: Epoch: 014 Train Loss: 0.649, ap: 0.581, auc: 0.663, f1: 0.605, acc: 0.618
+2025-08-26 02:52:46: Epoch: 014 Test results, ap: 0.187, auc: 0.362, f1: 0.000, acc: 0.667
+2025-08-26 02:53:20: Epoch: 015 Train Loss: 0.635, ap: 0.583, auc: 0.672, f1: 0.616, acc: 0.653
+2025-08-26 02:53:23: Epoch: 015 Test results, ap: 0.192, auc: 0.376, f1: 0.000, acc: 0.732
+2025-08-26 02:53:57: Epoch: 016 Train Loss: 0.636, ap: 0.592, auc: 0.649, f1: 0.561, acc: 0.655
+2025-08-26 02:54:00: Epoch: 016 Test results, ap: 0.206, auc: 0.424, f1: 0.000, acc: 0.748
+2025-08-26 02:54:33: Epoch: 017 Train Loss: 0.639, ap: 0.485, auc: 0.528, f1: 0.436, acc: 0.495
+2025-08-26 02:54:36: Epoch: 017 Test results, ap: 0.214, auc: 0.438, f1: 0.102, acc: 0.569
+2025-08-26 02:55:09: Epoch: 018 Train Loss: 0.630, ap: 0.612, auc: 0.674, f1: 0.620, acc: 0.644
+2025-08-26 02:55:09: Epoch: 018 New best VALIDATION metrics
+2025-08-26 02:55:13: Epoch: 018 Test results, ap: 0.181, auc: 0.332, f1: 0.000, acc: 0.707
+2025-08-26 02:55:45: Epoch: 019 Train Loss: 0.606, ap: 0.544, auc: 0.633, f1: 0.615, acc: 0.616
+2025-08-26 02:55:48: Epoch: 019 Test results, ap: 0.191, auc: 0.376, f1: 0.045, acc: 0.659
+2025-08-26 02:56:23: Epoch: 020 Train Loss: 0.603, ap: 0.645, auc: 0.688, f1: 0.628, acc: 0.650
+2025-08-26 02:56:23: Epoch: 020 New best VALIDATION metrics
+2025-08-26 02:56:26: Epoch: 020 Test results, ap: 0.246, auc: 0.541, f1: 0.000, acc: 0.707
+2025-08-26 02:57:01: Epoch: 021 Train Loss: 0.587, ap: 0.570, auc: 0.633, f1: 0.605, acc: 0.593
+2025-08-26 02:57:04: Epoch: 021 Test results, ap: 0.211, auc: 0.421, f1: 0.038, acc: 0.593
+2025-08-26 02:57:39: Epoch: 022 Train Loss: 0.584, ap: 0.664, auc: 0.689, f1: 0.603, acc: 0.655
+2025-08-26 02:57:39: Epoch: 022 New best VALIDATION metrics
+2025-08-26 02:57:42: Epoch: 022 Test results, ap: 0.269, auc: 0.516, f1: 0.000, acc: 0.691
+2025-08-26 02:58:17: Epoch: 023 Train Loss: 0.589, ap: 0.525, auc: 0.616, f1: 0.569, acc: 0.585
+2025-08-26 02:58:21: Epoch: 023 Test results, ap: 0.204, auc: 0.418, f1: 0.047, acc: 0.667
+2025-08-26 02:58:55: Epoch: 024 Train Loss: 0.568, ap: 0.638, auc: 0.682, f1: 0.605, acc: 0.653
+2025-08-26 02:58:58: Epoch: 024 Test results, ap: 0.236, auc: 0.495, f1: 0.000, acc: 0.724
+2025-08-26 02:59:32: Epoch: 025 Train Loss: 0.572, ap: 0.587, auc: 0.660, f1: 0.605, acc: 0.630
+2025-08-26 02:59:35: Epoch: 025 Test results, ap: 0.237, auc: 0.504, f1: 0.000, acc: 0.715
+2025-08-26 03:00:09: Epoch: 026 Train Loss: 0.575, ap: 0.563, auc: 0.637, f1: 0.522, acc: 0.585
+2025-08-26 03:00:12: Epoch: 026 Test results, ap: 0.207, auc: 0.430, f1: 0.000, acc: 0.715
+2025-08-26 03:00:46: Epoch: 027 Train Loss: 0.563, ap: 0.596, auc: 0.638, f1: 0.615, acc: 0.590
+2025-08-26 03:00:49: Epoch: 027 Test results, ap: 0.263, auc: 0.514, f1: 0.337, acc: 0.520
+2025-08-26 03:01:23: Epoch: 028 Train Loss: 0.558, ap: 0.608, auc: 0.664, f1: 0.615, acc: 0.636
+2025-08-26 03:01:26: Epoch: 028 Test results, ap: 0.272, auc: 0.499, f1: 0.441, acc: 0.691
+2025-08-26 03:02:01: Epoch: 029 Train Loss: 0.555, ap: 0.631, auc: 0.688, f1: 0.614, acc: 0.655
+2025-08-26 03:02:04: Epoch: 029 Test results, ap: 0.266, auc: 0.557, f1: 0.000, acc: 0.699
+2025-08-26 03:02:40: Epoch: 030 Train Loss: 0.554, ap: 0.651, auc: 0.687, f1: 0.613, acc: 0.660
+2025-08-26 03:02:44: Epoch: 030 Test results, ap: 0.256, auc: 0.499, f1: 0.353, acc: 0.553
+2025-08-26 03:03:20: Epoch: 031 Train Loss: 0.528, ap: 0.550, auc: 0.614, f1: 0.526, acc: 0.572
+2025-08-26 03:03:23: Epoch: 031 Test results, ap: 0.237, auc: 0.494, f1: 0.048, acc: 0.675
+2025-08-26 03:03:59: Epoch: 032 Train Loss: 0.533, ap: 0.681, auc: 0.717, f1: 0.612, acc: 0.651
+2025-08-26 03:03:59: Epoch: 032 New best VALIDATION metrics
+2025-08-26 03:04:02: Epoch: 032 Test results, ap: 0.275, auc: 0.589, f1: 0.000, acc: 0.691
+2025-08-26 03:04:38: Epoch: 033 Train Loss: 0.540, ap: 0.561, auc: 0.613, f1: 0.520, acc: 0.577
+2025-08-26 03:04:42: Epoch: 033 Test results, ap: 0.264, auc: 0.562, f1: 0.000, acc: 0.707
+2025-08-26 03:05:17: Epoch: 034 Train Loss: 0.534, ap: 0.597, auc: 0.663, f1: 0.537, acc: 0.614
+2025-08-26 03:05:20: Epoch: 034 Test results, ap: 0.260, auc: 0.562, f1: 0.000, acc: 0.707
+2025-08-26 03:06:03: Epoch: 035 Train Loss: 0.520, ap: 0.618, auc: 0.680, f1: 0.605, acc: 0.648
+2025-08-26 03:06:07: Epoch: 035 Test results, ap: 0.258, auc: 0.553, f1: 0.000, acc: 0.707
+2025-08-26 03:06:42: Epoch: 036 Train Loss: 0.523, ap: 0.614, auc: 0.659, f1: 0.595, acc: 0.627
+2025-08-26 03:06:45: Epoch: 036 Test results, ap: 0.314, auc: 0.610, f1: 0.240, acc: 0.691
+2025-08-26 03:07:19: Epoch: 037 Train Loss: 0.523, ap: 0.670, auc: 0.709, f1: 0.614, acc: 0.665
+2025-08-26 03:07:23: Epoch: 037 Test results, ap: 0.312, auc: 0.615, f1: 0.053, acc: 0.707
+2025-08-26 03:07:59: Epoch: 038 Train Loss: 0.518, ap: 0.669, auc: 0.694, f1: 0.619, acc: 0.637
+2025-08-26 03:08:02: Epoch: 038 Test results, ap: 0.274, auc: 0.537, f1: 0.250, acc: 0.659
+2025-08-26 03:08:37: Epoch: 039 Train Loss: 0.505, ap: 0.674, auc: 0.712, f1: 0.610, acc: 0.662
+2025-08-26 03:08:40: Epoch: 039 Test results, ap: 0.287, auc: 0.577, f1: 0.091, acc: 0.675
+2025-08-26 03:09:14: Epoch: 040 Train Loss: 0.495, ap: 0.643, auc: 0.685, f1: 0.588, acc: 0.639
+2025-08-26 03:09:17: Epoch: 040 Test results, ap: 0.295, auc: 0.558, f1: 0.275, acc: 0.699
+2025-08-26 03:09:51: Epoch: 041 Train Loss: 0.494, ap: 0.615, auc: 0.670, f1: 0.581, acc: 0.616
+2025-08-26 03:09:55: Epoch: 041 Test results, ap: 0.301, auc: 0.563, f1: 0.208, acc: 0.691
+2025-08-26 03:10:29: Epoch: 042 Train Loss: 0.495, ap: 0.662, auc: 0.694, f1: 0.617, acc: 0.632
+2025-08-26 03:10:32: Epoch: 042 Test results, ap: 0.327, auc: 0.579, f1: 0.462, acc: 0.715
+2025-08-26 03:11:06: Epoch: 043 Train Loss: 0.486, ap: 0.662, auc: 0.704, f1: 0.623, acc: 0.657
+2025-08-26 03:11:09: Epoch: 043 Test results, ap: 0.305, auc: 0.610, f1: 0.259, acc: 0.675
+2025-08-26 03:11:43: Epoch: 044 Train Loss: 0.475, ap: 0.633, auc: 0.682, f1: 0.610, acc: 0.630
+2025-08-26 03:11:46: Epoch: 044 Test results, ap: 0.309, auc: 0.627, f1: 0.174, acc: 0.691
+2025-08-26 03:12:20: Epoch: 045 Train Loss: 0.468, ap: 0.674, auc: 0.707, f1: 0.638, acc: 0.641
+2025-08-26 03:12:23: Epoch: 045 Test results, ap: 0.327, auc: 0.618, f1: 0.387, acc: 0.691
+2025-08-26 03:12:23: Epoch: 045 New best TEST metrics
+2025-08-26 03:12:57: Epoch: 046 Train Loss: 0.481, ap: 0.676, auc: 0.712, f1: 0.613, acc: 0.660
+2025-08-26 03:13:00: Epoch: 046 Test results, ap: 0.332, auc: 0.604, f1: 0.377, acc: 0.732
+2025-08-26 03:13:33: Epoch: 047 Train Loss: 0.479, ap: 0.690, auc: 0.711, f1: 0.618, acc: 0.660
+2025-08-26 03:13:33: Epoch: 047 New best VALIDATION metrics
+2025-08-26 03:13:36: Epoch: 047 Test results, ap: 0.322, auc: 0.635, f1: 0.208, acc: 0.691
+2025-08-26 03:13:36: Epoch: 047 New best TEST metrics
+2025-08-26 03:14:11: Epoch: 048 Train Loss: 0.466, ap: 0.671, auc: 0.718, f1: 0.637, acc: 0.665
+2025-08-26 03:14:14: Epoch: 048 Test results, ap: 0.323, auc: 0.605, f1: 0.486, acc: 0.691
+2025-08-26 03:14:47: Epoch: 049 Train Loss: 0.465, ap: 0.653, auc: 0.696, f1: 0.625, acc: 0.644
+2025-08-26 03:14:50: Epoch: 049 Test results, ap: 0.317, auc: 0.591, f1: 0.373, acc: 0.699
+2025-08-26 03:15:24: Epoch: 050 Train Loss: 0.458, ap: 0.668, auc: 0.710, f1: 0.627, acc: 0.648
+2025-08-26 03:15:27: Epoch: 050 Test results, ap: 0.317, auc: 0.588, f1: 0.406, acc: 0.691
+2025-08-26 03:15:27: used time 0.52h
+2025-08-26 03:15:27: Running Cross Validation 2
+2025-08-26 03:15:27: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 03:16:00: Epoch: 001 Train Loss: 0.673, ap: 0.619, auc: 0.670, f1: 0.620, acc: 0.600
+2025-08-26 03:16:00: Epoch: 001 New best VALIDATION metrics
+2025-08-26 03:16:03: Epoch: 001 Test results, ap: 0.295, auc: 0.402, f1: 0.397, acc: 0.285
+2025-08-26 03:16:37: Epoch: 002 Train Loss: 0.654, ap: 0.632, auc: 0.672, f1: 0.619, acc: 0.481
+2025-08-26 03:16:37: Epoch: 002 New best VALIDATION metrics
+2025-08-26 03:16:41: Epoch: 002 Test results, ap: 0.322, auc: 0.487, f1: 0.392, acc: 0.244
+2025-08-26 03:17:15: Epoch: 003 Train Loss: 0.652, ap: 0.638, auc: 0.683, f1: 0.601, acc: 0.598
+2025-08-26 03:17:15: Epoch: 003 New best VALIDATION metrics
+2025-08-26 03:17:18: Epoch: 003 Test results, ap: 0.399, auc: 0.569, f1: 0.322, acc: 0.350
+2025-08-26 03:17:52: Epoch: 004 Train Loss: 0.650, ap: 0.662, auc: 0.689, f1: 0.580, acc: 0.642
+2025-08-26 03:17:52: Epoch: 004 New best VALIDATION metrics
+2025-08-26 03:17:55: Epoch: 004 Test results, ap: 0.188, auc: 0.363, f1: 0.000, acc: 0.740
+2025-08-26 03:18:29: Epoch: 005 Train Loss: 0.636, ap: 0.624, auc: 0.671, f1: 0.603, acc: 0.580
+2025-08-26 03:18:32: Epoch: 005 Test results, ap: 0.380, auc: 0.626, f1: 0.406, acc: 0.309
+2025-08-26 03:19:05: Epoch: 006 Train Loss: 0.637, ap: 0.542, auc: 0.624, f1: 0.567, acc: 0.598
+2025-08-26 03:19:09: Epoch: 006 Test results, ap: 0.323, auc: 0.581, f1: 0.192, acc: 0.659
+2025-08-26 03:19:42: Epoch: 007 Train Loss: 0.626, ap: 0.568, auc: 0.629, f1: 0.484, acc: 0.578
+2025-08-26 03:19:45: Epoch: 007 Test results, ap: 0.208, auc: 0.429, f1: 0.000, acc: 0.691
+2025-08-26 03:20:19: Epoch: 008 Train Loss: 0.626, ap: 0.600, auc: 0.655, f1: 0.345, acc: 0.605
+2025-08-26 03:20:22: Epoch: 008 Test results, ap: 0.210, auc: 0.439, f1: 0.000, acc: 0.756
+2025-08-26 03:20:55: Epoch: 009 Train Loss: 0.625, ap: 0.608, auc: 0.648, f1: 0.585, acc: 0.571
+2025-08-26 03:20:58: Epoch: 009 Test results, ap: 0.262, auc: 0.551, f1: 0.189, acc: 0.650
+2025-08-26 03:21:33: Epoch: 010 Train Loss: 0.617, ap: 0.640, auc: 0.675, f1: 0.584, acc: 0.626
+2025-08-26 03:21:36: Epoch: 010 Test results, ap: 0.215, auc: 0.451, f1: 0.000, acc: 0.683
+2025-08-26 03:22:09: Epoch: 011 Train Loss: 0.595, ap: 0.597, auc: 0.659, f1: 0.591, acc: 0.612
+2025-08-26 03:22:13: Epoch: 011 Test results, ap: 0.219, auc: 0.466, f1: 0.000, acc: 0.667
+2025-08-26 03:22:13: Epoch: 011 New best TEST metrics
+2025-08-26 03:22:46: Epoch: 012 Train Loss: 0.584, ap: 0.626, auc: 0.676, f1: 0.452, acc: 0.646
+2025-08-26 03:22:49: Epoch: 012 Test results, ap: 0.217, auc: 0.458, f1: 0.000, acc: 0.756
+2025-08-26 03:23:22: Epoch: 013 Train Loss: 0.578, ap: 0.582, auc: 0.644, f1: 0.549, acc: 0.621
+2025-08-26 03:23:25: Epoch: 013 Test results, ap: 0.203, auc: 0.396, f1: 0.000, acc: 0.740
+2025-08-26 03:23:59: Epoch: 014 Train Loss: 0.576, ap: 0.586, auc: 0.651, f1: 0.586, acc: 0.616
+2025-08-26 03:24:03: Epoch: 014 Test results, ap: 0.198, auc: 0.387, f1: 0.000, acc: 0.667
+2025-08-26 03:24:36: Epoch: 015 Train Loss: 0.569, ap: 0.578, auc: 0.633, f1: 0.516, acc: 0.610
+2025-08-26 03:24:39: Epoch: 015 Test results, ap: 0.178, auc: 0.320, f1: 0.000, acc: 0.740
+2025-08-26 03:25:13: Epoch: 016 Train Loss: 0.563, ap: 0.625, auc: 0.668, f1: 0.545, acc: 0.612
+2025-08-26 03:25:16: Epoch: 016 Test results, ap: 0.163, auc: 0.240, f1: 0.000, acc: 0.740
+2025-08-26 03:25:50: Epoch: 017 Train Loss: 0.559, ap: 0.588, auc: 0.639, f1: 0.465, acc: 0.614
+2025-08-26 03:25:53: Epoch: 017 Test results, ap: 0.206, auc: 0.422, f1: 0.000, acc: 0.748
+2025-08-26 03:26:26: Epoch: 018 Train Loss: 0.540, ap: 0.506, auc: 0.591, f1: 0.518, acc: 0.584
+2025-08-26 03:26:30: Epoch: 018 Test results, ap: 0.201, auc: 0.366, f1: 0.095, acc: 0.691
+2025-08-26 03:27:04: Epoch: 019 Train Loss: 0.538, ap: 0.563, auc: 0.627, f1: 0.501, acc: 0.621
+2025-08-26 03:27:07: Epoch: 019 Test results, ap: 0.178, auc: 0.322, f1: 0.000, acc: 0.707
+2025-08-26 03:27:41: Epoch: 020 Train Loss: 0.538, ap: 0.536, auc: 0.601, f1: 0.467, acc: 0.582
+2025-08-26 03:27:44: Epoch: 020 Test results, ap: 0.193, auc: 0.334, f1: 0.000, acc: 0.707
+2025-08-26 03:28:17: Epoch: 021 Train Loss: 0.506, ap: 0.595, auc: 0.627, f1: 0.405, acc: 0.607
+2025-08-26 03:28:20: Epoch: 021 Test results, ap: 0.175, auc: 0.304, f1: 0.000, acc: 0.715
+2025-08-26 03:28:54: Epoch: 022 Train Loss: 0.511, ap: 0.575, auc: 0.604, f1: 0.409, acc: 0.612
+2025-08-26 03:28:57: Epoch: 022 Test results, ap: 0.170, auc: 0.282, f1: 0.000, acc: 0.732
+2025-08-26 03:29:31: Epoch: 023 Train Loss: 0.497, ap: 0.581, auc: 0.642, f1: 0.508, acc: 0.610
+2025-08-26 03:29:34: Epoch: 023 Test results, ap: 0.179, auc: 0.315, f1: 0.000, acc: 0.683
+2025-08-26 03:30:08: Epoch: 024 Train Loss: 0.498, ap: 0.570, auc: 0.643, f1: 0.515, acc: 0.601
+2025-08-26 03:30:11: Epoch: 024 Test results, ap: 0.186, auc: 0.317, f1: 0.048, acc: 0.675
+2025-08-26 03:30:44: Epoch: 025 Train Loss: 0.486, ap: 0.547, auc: 0.611, f1: 0.542, acc: 0.582
+2025-08-26 03:30:47: Epoch: 025 Test results, ap: 0.239, auc: 0.475, f1: 0.170, acc: 0.683
+2025-08-26 03:30:47: Epoch: 025 New best TEST metrics
+2025-08-26 03:31:21: Epoch: 026 Train Loss: 0.481, ap: 0.528, auc: 0.602, f1: 0.544, acc: 0.571
+2025-08-26 03:31:24: Epoch: 026 Test results, ap: 0.204, auc: 0.369, f1: 0.160, acc: 0.659
+2025-08-26 03:31:58: Epoch: 027 Train Loss: 0.492, ap: 0.600, auc: 0.649, f1: 0.502, acc: 0.601
+2025-08-26 03:32:01: Epoch: 027 Test results, ap: 0.194, auc: 0.338, f1: 0.098, acc: 0.699
+2025-08-26 03:32:35: Epoch: 028 Train Loss: 0.480, ap: 0.592, auc: 0.641, f1: 0.486, acc: 0.612
+2025-08-26 03:32:38: Epoch: 028 Test results, ap: 0.182, auc: 0.328, f1: 0.048, acc: 0.675
+2025-08-26 03:33:12: Epoch: 029 Train Loss: 0.468, ap: 0.546, auc: 0.616, f1: 0.588, acc: 0.577
+2025-08-26 03:33:15: Epoch: 029 Test results, ap: 0.263, auc: 0.561, f1: 0.175, acc: 0.618
+2025-08-26 03:33:15: Epoch: 029 New best TEST metrics
+2025-08-26 03:33:49: Epoch: 030 Train Loss: 0.463, ap: 0.620, auc: 0.661, f1: 0.545, acc: 0.638
+2025-08-26 03:33:52: Epoch: 030 Test results, ap: 0.179, auc: 0.314, f1: 0.000, acc: 0.683
+2025-08-26 03:34:26: Epoch: 031 Train Loss: 0.459, ap: 0.573, auc: 0.615, f1: 0.469, acc: 0.605
+2025-08-26 03:34:29: Epoch: 031 Test results, ap: 0.190, auc: 0.367, f1: 0.000, acc: 0.699
+2025-08-26 03:35:04: Epoch: 032 Train Loss: 0.457, ap: 0.618, auc: 0.691, f1: 0.614, acc: 0.638
+2025-08-26 03:35:07: Epoch: 032 Test results, ap: 0.226, auc: 0.465, f1: 0.145, acc: 0.618
+2025-08-26 03:35:41: Epoch: 033 Train Loss: 0.431, ap: 0.574, auc: 0.647, f1: 0.475, acc: 0.594
+2025-08-26 03:35:44: Epoch: 033 Test results, ap: 0.202, auc: 0.387, f1: 0.167, acc: 0.675
+2025-08-26 03:36:18: Epoch: 034 Train Loss: 0.431, ap: 0.547, auc: 0.618, f1: 0.536, acc: 0.578
+2025-08-26 03:36:21: Epoch: 034 Test results, ap: 0.216, auc: 0.428, f1: 0.145, acc: 0.618
+2025-08-26 03:36:55: Epoch: 035 Train Loss: 0.451, ap: 0.577, auc: 0.655, f1: 0.565, acc: 0.619
+2025-08-26 03:36:58: Epoch: 035 Test results, ap: 0.226, auc: 0.466, f1: 0.157, acc: 0.650
+2025-08-26 03:37:32: Epoch: 036 Train Loss: 0.434, ap: 0.617, auc: 0.682, f1: 0.570, acc: 0.644
+2025-08-26 03:37:35: Epoch: 036 Test results, ap: 0.203, auc: 0.385, f1: 0.163, acc: 0.667
+2025-08-26 03:38:08: Epoch: 037 Train Loss: 0.411, ap: 0.543, auc: 0.607, f1: 0.480, acc: 0.594
+2025-08-26 03:38:11: Epoch: 037 Test results, ap: 0.209, auc: 0.400, f1: 0.170, acc: 0.683
+2025-08-26 03:38:45: Epoch: 038 Train Loss: 0.413, ap: 0.607, auc: 0.674, f1: 0.570, acc: 0.608
+2025-08-26 03:38:48: Epoch: 038 Test results, ap: 0.216, auc: 0.430, f1: 0.182, acc: 0.634
+2025-08-26 03:39:21: Epoch: 039 Train Loss: 0.421, ap: 0.583, auc: 0.649, f1: 0.474, acc: 0.621
+2025-08-26 03:39:24: Epoch: 039 Test results, ap: 0.215, auc: 0.413, f1: 0.133, acc: 0.683
+2025-08-26 03:39:58: Epoch: 040 Train Loss: 0.405, ap: 0.606, auc: 0.674, f1: 0.571, acc: 0.651
+2025-08-26 03:40:01: Epoch: 040 Test results, ap: 0.211, auc: 0.409, f1: 0.192, acc: 0.659
+2025-08-26 03:40:35: Epoch: 041 Train Loss: 0.404, ap: 0.577, auc: 0.655, f1: 0.574, acc: 0.607
+2025-08-26 03:40:38: Epoch: 041 Test results, ap: 0.221, auc: 0.447, f1: 0.175, acc: 0.618
+2025-08-26 03:41:12: Epoch: 042 Train Loss: 0.392, ap: 0.591, auc: 0.665, f1: 0.557, acc: 0.633
+2025-08-26 03:41:15: Epoch: 042 Test results, ap: 0.216, auc: 0.426, f1: 0.189, acc: 0.650
+2025-08-26 03:41:49: Epoch: 043 Train Loss: 0.386, ap: 0.614, auc: 0.680, f1: 0.558, acc: 0.665
+2025-08-26 03:41:52: Epoch: 043 Test results, ap: 0.216, auc: 0.428, f1: 0.133, acc: 0.683
+2025-08-26 03:42:26: Epoch: 044 Train Loss: 0.391, ap: 0.574, auc: 0.642, f1: 0.529, acc: 0.598
+2025-08-26 03:42:29: Epoch: 044 Test results, ap: 0.217, auc: 0.437, f1: 0.157, acc: 0.650
+2025-08-26 03:43:03: Epoch: 045 Train Loss: 0.385, ap: 0.602, auc: 0.678, f1: 0.574, acc: 0.649
+2025-08-26 03:43:06: Epoch: 045 Test results, ap: 0.216, auc: 0.437, f1: 0.154, acc: 0.642
+2025-08-26 03:43:39: Epoch: 046 Train Loss: 0.380, ap: 0.570, auc: 0.649, f1: 0.564, acc: 0.621
+2025-08-26 03:43:43: Epoch: 046 Test results, ap: 0.222, auc: 0.452, f1: 0.172, acc: 0.610
+2025-08-26 03:44:16: Epoch: 047 Train Loss: 0.385, ap: 0.567, auc: 0.642, f1: 0.505, acc: 0.616
+2025-08-26 03:44:19: Epoch: 047 Test results, ap: 0.215, auc: 0.423, f1: 0.185, acc: 0.642
+2025-08-26 03:44:53: Epoch: 048 Train Loss: 0.372, ap: 0.585, auc: 0.676, f1: 0.554, acc: 0.623
+2025-08-26 03:44:56: Epoch: 048 Test results, ap: 0.234, auc: 0.478, f1: 0.172, acc: 0.610
+2025-08-26 03:45:29: Epoch: 049 Train Loss: 0.388, ap: 0.582, auc: 0.650, f1: 0.524, acc: 0.635
+2025-08-26 03:45:33: Epoch: 049 Test results, ap: 0.218, auc: 0.433, f1: 0.154, acc: 0.642
+2025-08-26 03:46:06: Epoch: 050 Train Loss: 0.374, ap: 0.609, auc: 0.694, f1: 0.586, acc: 0.633
+2025-08-26 03:46:09: Epoch: 050 Test results, ap: 0.230, auc: 0.480, f1: 0.172, acc: 0.610
+2025-08-26 03:46:09: used time 0.51h
+2025-08-26 03:46:09: Running Cross Validation 3
+2025-08-26 03:46:09: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 03:46:43: Epoch: 001 Train Loss: 0.679, ap: 0.574, auc: 0.641, f1: 0.567, acc: 0.628
+2025-08-26 03:46:43: Epoch: 001 New best VALIDATION metrics
+2025-08-26 03:46:46: Epoch: 001 Test results, ap: 0.308, auc: 0.473, f1: 0.222, acc: 0.772
+2025-08-26 03:47:20: Epoch: 002 Train Loss: 0.666, ap: 0.558, auc: 0.596, f1: 0.589, acc: 0.540
+2025-08-26 03:47:23: Epoch: 002 Test results, ap: 0.198, auc: 0.393, f1: 0.322, acc: 0.350
+2025-08-26 03:47:57: Epoch: 003 Train Loss: 0.660, ap: 0.573, auc: 0.634, f1: 0.580, acc: 0.614
+2025-08-26 03:48:00: Epoch: 003 Test results, ap: 0.285, auc: 0.544, f1: 0.000, acc: 0.756
+2025-08-26 03:48:34: Epoch: 004 Train Loss: 0.669, ap: 0.560, auc: 0.621, f1: 0.574, acc: 0.568
+2025-08-26 03:48:37: Epoch: 004 Test results, ap: 0.215, auc: 0.459, f1: 0.000, acc: 0.602
+2025-08-26 03:49:11: Epoch: 005 Train Loss: 0.658, ap: 0.558, auc: 0.640, f1: 0.579, acc: 0.628
+2025-08-26 03:49:14: Epoch: 005 Test results, ap: 0.211, auc: 0.439, f1: 0.000, acc: 0.756
+2025-08-26 03:49:48: Epoch: 006 Train Loss: 0.655, ap: 0.543, auc: 0.606, f1: 0.602, acc: 0.430
+2025-08-26 03:49:51: Epoch: 006 Test results, ap: 0.202, auc: 0.419, f1: 0.392, acc: 0.244
+2025-08-26 03:50:25: Epoch: 007 Train Loss: 0.655, ap: 0.560, auc: 0.649, f1: 0.581, acc: 0.631
+2025-08-26 03:50:28: Epoch: 007 Test results, ap: 0.207, auc: 0.429, f1: 0.000, acc: 0.756
+2025-08-26 03:51:02: Epoch: 008 Train Loss: 0.653, ap: 0.549, auc: 0.652, f1: 0.366, acc: 0.621
+2025-08-26 03:51:05: Epoch: 008 Test results, ap: 0.205, auc: 0.401, f1: 0.000, acc: 0.756
+2025-08-26 03:51:39: Epoch: 009 Train Loss: 0.646, ap: 0.547, auc: 0.655, f1: 0.456, acc: 0.621
+2025-08-26 03:51:43: Epoch: 009 Test results, ap: 0.203, auc: 0.411, f1: 0.000, acc: 0.756
+2025-08-26 03:52:17: Epoch: 010 Train Loss: 0.650, ap: 0.550, auc: 0.651, f1: 0.596, acc: 0.617
+2025-08-26 03:52:20: Epoch: 010 Test results, ap: 0.255, auc: 0.486, f1: 0.000, acc: 0.756
+2025-08-26 03:52:54: Epoch: 011 Train Loss: 0.632, ap: 0.522, auc: 0.628, f1: 0.547, acc: 0.577
+2025-08-26 03:52:57: Epoch: 011 Test results, ap: 0.171, auc: 0.283, f1: 0.395, acc: 0.252
+2025-08-26 03:52:57: Epoch: 011 New best TEST metrics
+2025-08-26 03:53:31: Epoch: 012 Train Loss: 0.636, ap: 0.546, auc: 0.646, f1: 0.507, acc: 0.601
+2025-08-26 03:53:34: Epoch: 012 Test results, ap: 0.240, auc: 0.477, f1: 0.121, acc: 0.528
+2025-08-26 03:53:34: Epoch: 012 New best TEST metrics
+2025-08-26 03:54:08: Epoch: 013 Train Loss: 0.626, ap: 0.541, auc: 0.656, f1: 0.538, acc: 0.642
+2025-08-26 03:54:12: Epoch: 013 Test results, ap: 0.218, auc: 0.421, f1: 0.170, acc: 0.683
+2025-08-26 03:54:45: Epoch: 014 Train Loss: 0.625, ap: 0.550, auc: 0.658, f1: 0.539, acc: 0.630
+2025-08-26 03:54:48: Epoch: 014 Test results, ap: 0.206, auc: 0.391, f1: 0.170, acc: 0.683
+2025-08-26 03:55:22: Epoch: 015 Train Loss: 0.619, ap: 0.552, auc: 0.662, f1: 0.622, acc: 0.621
+2025-08-26 03:55:26: Epoch: 015 Test results, ap: 0.193, auc: 0.379, f1: 0.000, acc: 0.602
+2025-08-26 03:55:59: Epoch: 016 Train Loss: 0.615, ap: 0.572, auc: 0.684, f1: 0.603, acc: 0.642
+2025-08-26 03:55:59: Epoch: 016 New best VALIDATION metrics
+2025-08-26 03:56:03: Epoch: 016 Test results, ap: 0.175, auc: 0.301, f1: 0.108, acc: 0.463
+2025-08-26 03:56:37: Epoch: 017 Train Loss: 0.610, ap: 0.566, auc: 0.662, f1: 0.599, acc: 0.623
+2025-08-26 03:56:40: Epoch: 017 Test results, ap: 0.209, auc: 0.399, f1: 0.177, acc: 0.472
+2025-08-26 03:57:13: Epoch: 018 Train Loss: 0.610, ap: 0.567, auc: 0.673, f1: 0.638, acc: 0.580
+2025-08-26 03:57:17: Epoch: 018 Test results, ap: 0.176, auc: 0.312, f1: 0.392, acc: 0.244
+2025-08-26 03:57:51: Epoch: 019 Train Loss: 0.607, ap: 0.577, auc: 0.679, f1: 0.608, acc: 0.621
+2025-08-26 03:57:51: Epoch: 019 New best VALIDATION metrics
+2025-08-26 03:57:54: Epoch: 019 Test results, ap: 0.214, auc: 0.406, f1: 0.341, acc: 0.341
+2025-08-26 03:58:28: Epoch: 020 Train Loss: 0.600, ap: 0.583, auc: 0.686, f1: 0.631, acc: 0.637
+2025-08-26 03:58:28: Epoch: 020 New best VALIDATION metrics
+2025-08-26 03:58:31: Epoch: 020 Test results, ap: 0.177, auc: 0.315, f1: 0.113, acc: 0.488
+2025-08-26 03:59:04: Epoch: 021 Train Loss: 0.586, ap: 0.583, auc: 0.695, f1: 0.636, acc: 0.665
+2025-08-26 03:59:04: Epoch: 021 New best VALIDATION metrics
+2025-08-26 03:59:08: Epoch: 021 Test results, ap: 0.190, auc: 0.345, f1: 0.278, acc: 0.325
+2025-08-26 03:59:41: Epoch: 022 Train Loss: 0.584, ap: 0.574, auc: 0.689, f1: 0.618, acc: 0.661
+2025-08-26 03:59:44: Epoch: 022 Test results, ap: 0.179, auc: 0.325, f1: 0.114, acc: 0.496
+2025-08-26 04:00:18: Epoch: 023 Train Loss: 0.577, ap: 0.574, auc: 0.687, f1: 0.641, acc: 0.651
+2025-08-26 04:00:22: Epoch: 023 Test results, ap: 0.196, auc: 0.335, f1: 0.114, acc: 0.496
+2025-08-26 04:00:56: Epoch: 024 Train Loss: 0.576, ap: 0.579, auc: 0.688, f1: 0.590, acc: 0.649
+2025-08-26 04:00:59: Epoch: 024 Test results, ap: 0.212, auc: 0.404, f1: 0.121, acc: 0.528
+2025-08-26 04:01:33: Epoch: 025 Train Loss: 0.572, ap: 0.586, auc: 0.695, f1: 0.615, acc: 0.656
+2025-08-26 04:01:33: Epoch: 025 New best VALIDATION metrics
+2025-08-26 04:01:36: Epoch: 025 Test results, ap: 0.201, auc: 0.360, f1: 0.116, acc: 0.504
+2025-08-26 04:02:10: Epoch: 026 Train Loss: 0.571, ap: 0.574, auc: 0.681, f1: 0.609, acc: 0.628
+2025-08-26 04:02:13: Epoch: 026 Test results, ap: 0.214, auc: 0.398, f1: 0.123, acc: 0.537
+2025-08-26 04:02:47: Epoch: 027 Train Loss: 0.564, ap: 0.580, auc: 0.693, f1: 0.616, acc: 0.668
+2025-08-26 04:02:50: Epoch: 027 Test results, ap: 0.213, auc: 0.341, f1: 0.121, acc: 0.528
+2025-08-26 04:03:24: Epoch: 028 Train Loss: 0.566, ap: 0.585, auc: 0.689, f1: 0.612, acc: 0.656
+2025-08-26 04:03:27: Epoch: 028 Test results, ap: 0.176, auc: 0.310, f1: 0.105, acc: 0.447
+2025-08-26 04:04:02: Epoch: 029 Train Loss: 0.561, ap: 0.569, auc: 0.688, f1: 0.576, acc: 0.649
+2025-08-26 04:04:06: Epoch: 029 Test results, ap: 0.208, auc: 0.341, f1: 0.114, acc: 0.496
+2025-08-26 04:04:40: Epoch: 030 Train Loss: 0.563, ap: 0.579, auc: 0.689, f1: 0.650, acc: 0.647
+2025-08-26 04:04:43: Epoch: 030 Test results, ap: 0.197, auc: 0.345, f1: 0.113, acc: 0.488
+2025-08-26 04:05:17: Epoch: 031 Train Loss: 0.552, ap: 0.584, auc: 0.694, f1: 0.617, acc: 0.653
+2025-08-26 04:05:20: Epoch: 031 Test results, ap: 0.197, auc: 0.340, f1: 0.121, acc: 0.528
+2025-08-26 04:05:54: Epoch: 032 Train Loss: 0.551, ap: 0.584, auc: 0.692, f1: 0.619, acc: 0.661
+2025-08-26 04:05:57: Epoch: 032 Test results, ap: 0.197, auc: 0.337, f1: 0.121, acc: 0.528
+2025-08-26 04:06:31: Epoch: 033 Train Loss: 0.552, ap: 0.580, auc: 0.690, f1: 0.635, acc: 0.661
+2025-08-26 04:06:34: Epoch: 033 Test results, ap: 0.195, auc: 0.328, f1: 0.113, acc: 0.488
+2025-08-26 04:07:08: Epoch: 034 Train Loss: 0.550, ap: 0.573, auc: 0.684, f1: 0.574, acc: 0.647
+2025-08-26 04:07:12: Epoch: 034 Test results, ap: 0.197, auc: 0.325, f1: 0.116, acc: 0.504
+2025-08-26 04:07:46: Epoch: 035 Train Loss: 0.544, ap: 0.583, auc: 0.691, f1: 0.626, acc: 0.651
+2025-08-26 04:07:50: Epoch: 035 Test results, ap: 0.200, auc: 0.335, f1: 0.121, acc: 0.528
+2025-08-26 04:08:24: Epoch: 036 Train Loss: 0.539, ap: 0.575, auc: 0.683, f1: 0.622, acc: 0.654
+2025-08-26 04:08:27: Epoch: 036 Test results, ap: 0.180, auc: 0.313, f1: 0.113, acc: 0.488
+2025-08-26 04:09:01: Epoch: 037 Train Loss: 0.544, ap: 0.579, auc: 0.688, f1: 0.654, acc: 0.668
+2025-08-26 04:09:04: Epoch: 037 Test results, ap: 0.192, auc: 0.324, f1: 0.114, acc: 0.496
+2025-08-26 04:09:39: Epoch: 038 Train Loss: 0.542, ap: 0.574, auc: 0.689, f1: 0.656, acc: 0.665
+2025-08-26 04:09:42: Epoch: 038 Test results, ap: 0.176, auc: 0.310, f1: 0.114, acc: 0.496
+2025-08-26 04:10:16: Epoch: 039 Train Loss: 0.535, ap: 0.583, auc: 0.694, f1: 0.640, acc: 0.646
+2025-08-26 04:10:19: Epoch: 039 Test results, ap: 0.196, auc: 0.325, f1: 0.118, acc: 0.512
+2025-08-26 04:10:53: Epoch: 040 Train Loss: 0.537, ap: 0.578, auc: 0.685, f1: 0.646, acc: 0.665
+2025-08-26 04:10:56: Epoch: 040 Test results, ap: 0.172, auc: 0.292, f1: 0.111, acc: 0.480
+2025-08-26 04:11:30: Epoch: 041 Train Loss: 0.519, ap: 0.580, auc: 0.691, f1: 0.636, acc: 0.661
+2025-08-26 04:11:34: Epoch: 041 Test results, ap: 0.172, auc: 0.290, f1: 0.113, acc: 0.488
+2025-08-26 04:12:08: Epoch: 042 Train Loss: 0.521, ap: 0.585, auc: 0.696, f1: 0.626, acc: 0.653
+2025-08-26 04:12:08: Epoch: 042 New best VALIDATION metrics
+2025-08-26 04:12:11: Epoch: 042 Test results, ap: 0.190, auc: 0.302, f1: 0.121, acc: 0.528
+2025-08-26 04:12:45: Epoch: 043 Train Loss: 0.519, ap: 0.584, auc: 0.698, f1: 0.637, acc: 0.660
+2025-08-26 04:12:45: Epoch: 043 New best VALIDATION metrics
+2025-08-26 04:12:48: Epoch: 043 Test results, ap: 0.192, auc: 0.312, f1: 0.121, acc: 0.528
+2025-08-26 04:13:23: Epoch: 044 Train Loss: 0.512, ap: 0.588, auc: 0.696, f1: 0.645, acc: 0.668
+2025-08-26 04:13:23: Epoch: 044 New best VALIDATION metrics
+2025-08-26 04:13:26: Epoch: 044 Test results, ap: 0.199, auc: 0.321, f1: 0.121, acc: 0.528
+2025-08-26 04:13:59: Epoch: 045 Train Loss: 0.509, ap: 0.580, auc: 0.691, f1: 0.607, acc: 0.649
+2025-08-26 04:14:03: Epoch: 045 Test results, ap: 0.198, auc: 0.316, f1: 0.125, acc: 0.545
+2025-08-26 04:14:37: Epoch: 046 Train Loss: 0.503, ap: 0.586, auc: 0.690, f1: 0.627, acc: 0.647
+2025-08-26 04:14:40: Epoch: 046 Test results, ap: 0.200, auc: 0.312, f1: 0.121, acc: 0.528
+2025-08-26 04:15:14: Epoch: 047 Train Loss: 0.506, ap: 0.589, auc: 0.697, f1: 0.651, acc: 0.668
+2025-08-26 04:15:14: Epoch: 047 New best VALIDATION metrics
+2025-08-26 04:15:17: Epoch: 047 Test results, ap: 0.200, auc: 0.302, f1: 0.118, acc: 0.512
+2025-08-26 04:15:51: Epoch: 048 Train Loss: 0.506, ap: 0.593, auc: 0.699, f1: 0.652, acc: 0.647
+2025-08-26 04:15:51: Epoch: 048 New best VALIDATION metrics
+2025-08-26 04:15:54: Epoch: 048 Test results, ap: 0.193, auc: 0.286, f1: 0.110, acc: 0.472
+2025-08-26 04:16:27: Epoch: 049 Train Loss: 0.502, ap: 0.585, auc: 0.693, f1: 0.653, acc: 0.651
+2025-08-26 04:16:30: Epoch: 049 Test results, ap: 0.192, auc: 0.292, f1: 0.111, acc: 0.480
+2025-08-26 04:17:05: Epoch: 050 Train Loss: 0.490, ap: 0.580, auc: 0.688, f1: 0.611, acc: 0.646
+2025-08-26 04:17:08: Epoch: 050 Test results, ap: 0.193, auc: 0.290, f1: 0.113, acc: 0.488
+2025-08-26 04:17:08: used time 0.52h
+2025-08-26 04:17:09: Running Cross Validation 4
+2025-08-26 04:17:09: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 04:17:42: Epoch: 001 Train Loss: 0.670, ap: 0.528, auc: 0.569, f1: 0.519, acc: 0.494
+2025-08-26 04:17:42: Epoch: 001 New best VALIDATION metrics
+2025-08-26 04:17:46: Epoch: 001 Test results, ap: 0.246, auc: 0.499, f1: 0.000, acc: 0.675
+2025-08-26 04:18:19: Epoch: 002 Train Loss: 0.659, ap: 0.540, auc: 0.582, f1: 0.535, acc: 0.540
+2025-08-26 04:18:19: Epoch: 002 New best VALIDATION metrics
+2025-08-26 04:18:23: Epoch: 002 Test results, ap: 0.220, auc: 0.440, f1: 0.333, acc: 0.512
+2025-08-26 04:18:56: Epoch: 003 Train Loss: 0.654, ap: 0.501, auc: 0.561, f1: 0.504, acc: 0.549
+2025-08-26 04:19:00: Epoch: 003 Test results, ap: 0.298, auc: 0.490, f1: 0.000, acc: 0.756
+2025-08-26 04:19:34: Epoch: 004 Train Loss: 0.657, ap: 0.550, auc: 0.606, f1: 0.523, acc: 0.575
+2025-08-26 04:19:34: Epoch: 004 New best VALIDATION metrics
+2025-08-26 04:19:37: Epoch: 004 Test results, ap: 0.320, auc: 0.530, f1: 0.000, acc: 0.756
+2025-08-26 04:20:12: Epoch: 005 Train Loss: 0.651, ap: 0.535, auc: 0.587, f1: 0.545, acc: 0.573
+2025-08-26 04:20:15: Epoch: 005 Test results, ap: 0.317, auc: 0.527, f1: 0.380, acc: 0.602
+2025-08-26 04:20:49: Epoch: 006 Train Loss: 0.651, ap: 0.535, auc: 0.579, f1: 0.477, acc: 0.571
+2025-08-26 04:20:52: Epoch: 006 Test results, ap: 0.343, auc: 0.546, f1: 0.000, acc: 0.756
+2025-08-26 04:21:26: Epoch: 007 Train Loss: 0.650, ap: 0.524, auc: 0.600, f1: 0.521, acc: 0.582
+2025-08-26 04:21:29: Epoch: 007 Test results, ap: 0.383, auc: 0.609, f1: 0.000, acc: 0.756
+2025-08-26 04:22:02: Epoch: 008 Train Loss: 0.644, ap: 0.552, auc: 0.622, f1: 0.531, acc: 0.577
+2025-08-26 04:22:02: Epoch: 008 New best VALIDATION metrics
+2025-08-26 04:22:06: Epoch: 008 Test results, ap: 0.330, auc: 0.540, f1: 0.370, acc: 0.585
+2025-08-26 04:22:40: Epoch: 009 Train Loss: 0.652, ap: 0.542, auc: 0.614, f1: 0.567, acc: 0.570
+2025-08-26 04:22:43: Epoch: 009 Test results, ap: 0.289, auc: 0.535, f1: 0.370, acc: 0.585
+2025-08-26 04:23:17: Epoch: 010 Train Loss: 0.644, ap: 0.545, auc: 0.608, f1: 0.562, acc: 0.566
+2025-08-26 04:23:20: Epoch: 010 Test results, ap: 0.260, auc: 0.480, f1: 0.341, acc: 0.276
+2025-08-26 04:23:54: Epoch: 011 Train Loss: 0.635, ap: 0.551, auc: 0.642, f1: 0.463, acc: 0.612
+2025-08-26 04:23:54: Epoch: 011 New best VALIDATION metrics
+2025-08-26 04:23:57: Epoch: 011 Test results, ap: 0.305, auc: 0.590, f1: 0.380, acc: 0.602
+2025-08-26 04:23:57: Epoch: 011 New best TEST metrics
+2025-08-26 04:24:31: Epoch: 012 Train Loss: 0.634, ap: 0.589, auc: 0.644, f1: 0.548, acc: 0.578
+2025-08-26 04:24:31: Epoch: 012 New best VALIDATION metrics
+2025-08-26 04:24:34: Epoch: 012 Test results, ap: 0.257, auc: 0.504, f1: 0.370, acc: 0.585
+2025-08-26 04:25:09: Epoch: 013 Train Loss: 0.633, ap: 0.568, auc: 0.644, f1: 0.553, acc: 0.575
+2025-08-26 04:25:12: Epoch: 013 Test results, ap: 0.248, auc: 0.491, f1: 0.000, acc: 0.756
+2025-08-26 04:25:46: Epoch: 014 Train Loss: 0.628, ap: 0.586, auc: 0.648, f1: 0.588, acc: 0.589
+2025-08-26 04:25:49: Epoch: 014 Test results, ap: 0.266, auc: 0.482, f1: 0.380, acc: 0.602
+2025-08-26 04:26:23: Epoch: 015 Train Loss: 0.625, ap: 0.583, auc: 0.651, f1: 0.542, acc: 0.603
+2025-08-26 04:26:23: Epoch: 015 New best VALIDATION metrics
+2025-08-26 04:26:27: Epoch: 015 Test results, ap: 0.238, auc: 0.461, f1: 0.357, acc: 0.561
+2025-08-26 04:27:01: Epoch: 016 Train Loss: 0.628, ap: 0.583, auc: 0.649, f1: 0.607, acc: 0.584
+2025-08-26 04:27:04: Epoch: 016 Test results, ap: 0.243, auc: 0.454, f1: 0.380, acc: 0.602
+2025-08-26 04:27:39: Epoch: 017 Train Loss: 0.625, ap: 0.549, auc: 0.630, f1: 0.594, acc: 0.586
+2025-08-26 04:27:42: Epoch: 017 Test results, ap: 0.250, auc: 0.515, f1: 0.380, acc: 0.602
+2025-08-26 04:28:15: Epoch: 018 Train Loss: 0.623, ap: 0.574, auc: 0.649, f1: 0.583, acc: 0.591
+2025-08-26 04:28:18: Epoch: 018 Test results, ap: 0.242, auc: 0.487, f1: 0.370, acc: 0.585
+2025-08-26 04:28:52: Epoch: 019 Train Loss: 0.615, ap: 0.568, auc: 0.650, f1: 0.571, acc: 0.587
+2025-08-26 04:28:56: Epoch: 019 Test results, ap: 0.280, auc: 0.511, f1: 0.361, acc: 0.569
+2025-08-26 04:29:30: Epoch: 020 Train Loss: 0.612, ap: 0.556, auc: 0.639, f1: 0.616, acc: 0.564
+2025-08-26 04:29:34: Epoch: 020 Test results, ap: 0.232, auc: 0.490, f1: 0.325, acc: 0.561
+2025-08-26 04:30:07: Epoch: 021 Train Loss: 0.610, ap: 0.573, auc: 0.657, f1: 0.570, acc: 0.624
+2025-08-26 04:30:10: Epoch: 021 Test results, ap: 0.247, auc: 0.487, f1: 0.000, acc: 0.756
+2025-08-26 04:30:44: Epoch: 022 Train Loss: 0.607, ap: 0.575, auc: 0.653, f1: 0.623, acc: 0.596
+2025-08-26 04:30:47: Epoch: 022 Test results, ap: 0.225, auc: 0.473, f1: 0.366, acc: 0.577
+2025-08-26 04:31:21: Epoch: 023 Train Loss: 0.592, ap: 0.548, auc: 0.651, f1: 0.525, acc: 0.607
+2025-08-26 04:31:24: Epoch: 023 Test results, ap: 0.283, auc: 0.508, f1: 0.000, acc: 0.756
+2025-08-26 04:31:58: Epoch: 024 Train Loss: 0.586, ap: 0.562, auc: 0.656, f1: 0.561, acc: 0.614
+2025-08-26 04:32:01: Epoch: 024 Test results, ap: 0.254, auc: 0.488, f1: 0.000, acc: 0.756
+2025-08-26 04:32:35: Epoch: 025 Train Loss: 0.590, ap: 0.563, auc: 0.654, f1: 0.549, acc: 0.591
+2025-08-26 04:32:39: Epoch: 025 Test results, ap: 0.273, auc: 0.488, f1: 0.312, acc: 0.463
+2025-08-26 04:33:13: Epoch: 026 Train Loss: 0.580, ap: 0.568, auc: 0.646, f1: 0.601, acc: 0.593
+2025-08-26 04:33:16: Epoch: 026 Test results, ap: 0.228, auc: 0.464, f1: 0.000, acc: 0.740
+2025-08-26 04:33:50: Epoch: 027 Train Loss: 0.566, ap: 0.579, auc: 0.644, f1: 0.573, acc: 0.614
+2025-08-26 04:33:53: Epoch: 027 Test results, ap: 0.293, auc: 0.504, f1: 0.349, acc: 0.545
+2025-08-26 04:34:27: Epoch: 028 Train Loss: 0.569, ap: 0.565, auc: 0.656, f1: 0.593, acc: 0.617
+2025-08-26 04:34:30: Epoch: 028 Test results, ap: 0.268, auc: 0.490, f1: 0.054, acc: 0.715
+2025-08-26 04:35:04: Epoch: 029 Train Loss: 0.569, ap: 0.567, auc: 0.653, f1: 0.583, acc: 0.619
+2025-08-26 04:35:08: Epoch: 029 Test results, ap: 0.303, auc: 0.518, f1: 0.385, acc: 0.610
+2025-08-26 04:35:41: Epoch: 030 Train Loss: 0.551, ap: 0.584, auc: 0.662, f1: 0.586, acc: 0.612
+2025-08-26 04:35:41: Epoch: 030 New best VALIDATION metrics
+2025-08-26 04:35:45: Epoch: 030 Test results, ap: 0.250, auc: 0.483, f1: 0.294, acc: 0.610
+2025-08-26 04:36:18: Epoch: 031 Train Loss: 0.544, ap: 0.574, auc: 0.655, f1: 0.595, acc: 0.594
+2025-08-26 04:36:22: Epoch: 031 Test results, ap: 0.221, auc: 0.446, f1: 0.043, acc: 0.642
+2025-08-26 04:36:55: Epoch: 032 Train Loss: 0.524, ap: 0.573, auc: 0.653, f1: 0.596, acc: 0.598
+2025-08-26 04:36:59: Epoch: 032 Test results, ap: 0.200, auc: 0.391, f1: 0.301, acc: 0.472
+2025-08-26 04:37:34: Epoch: 033 Train Loss: 0.521, ap: 0.566, auc: 0.639, f1: 0.597, acc: 0.586
+2025-08-26 04:37:37: Epoch: 033 Test results, ap: 0.212, auc: 0.415, f1: 0.185, acc: 0.569
+2025-08-26 04:38:12: Epoch: 034 Train Loss: 0.510, ap: 0.565, auc: 0.637, f1: 0.590, acc: 0.598
+2025-08-26 04:38:15: Epoch: 034 Test results, ap: 0.205, auc: 0.416, f1: 0.000, acc: 0.715
+2025-08-26 04:38:50: Epoch: 035 Train Loss: 0.513, ap: 0.555, auc: 0.640, f1: 0.631, acc: 0.578
+2025-08-26 04:38:53: Epoch: 035 Test results, ap: 0.244, auc: 0.442, f1: 0.286, acc: 0.390
+2025-08-26 04:39:27: Epoch: 036 Train Loss: 0.511, ap: 0.556, auc: 0.633, f1: 0.602, acc: 0.593
+2025-08-26 04:39:30: Epoch: 036 Test results, ap: 0.208, auc: 0.405, f1: 0.049, acc: 0.683
+2025-08-26 04:40:04: Epoch: 037 Train Loss: 0.499, ap: 0.570, auc: 0.649, f1: 0.559, acc: 0.616
+2025-08-26 04:40:08: Epoch: 037 Test results, ap: 0.198, auc: 0.391, f1: 0.000, acc: 0.691
+2025-08-26 04:40:42: Epoch: 038 Train Loss: 0.510, ap: 0.574, auc: 0.656, f1: 0.545, acc: 0.600
+2025-08-26 04:40:45: Epoch: 038 Test results, ap: 0.262, auc: 0.441, f1: 0.313, acc: 0.537
+2025-08-26 04:41:19: Epoch: 039 Train Loss: 0.499, ap: 0.563, auc: 0.647, f1: 0.591, acc: 0.598
+2025-08-26 04:41:22: Epoch: 039 Test results, ap: 0.194, auc: 0.370, f1: 0.000, acc: 0.659
+2025-08-26 04:41:57: Epoch: 040 Train Loss: 0.503, ap: 0.559, auc: 0.646, f1: 0.593, acc: 0.605
+2025-08-26 04:42:00: Epoch: 040 Test results, ap: 0.213, auc: 0.425, f1: 0.286, acc: 0.553
+2025-08-26 04:42:34: Epoch: 041 Train Loss: 0.480, ap: 0.534, auc: 0.615, f1: 0.603, acc: 0.582
+2025-08-26 04:42:37: Epoch: 041 Test results, ap: 0.211, auc: 0.421, f1: 0.286, acc: 0.553
+2025-08-26 04:43:11: Epoch: 042 Train Loss: 0.480, ap: 0.538, auc: 0.625, f1: 0.593, acc: 0.589
+2025-08-26 04:43:14: Epoch: 042 Test results, ap: 0.190, auc: 0.360, f1: 0.032, acc: 0.512
+2025-08-26 04:43:48: Epoch: 043 Train Loss: 0.475, ap: 0.551, auc: 0.636, f1: 0.566, acc: 0.589
+2025-08-26 04:43:51: Epoch: 043 Test results, ap: 0.199, auc: 0.392, f1: 0.098, acc: 0.553
+2025-08-26 04:44:25: Epoch: 044 Train Loss: 0.468, ap: 0.557, auc: 0.647, f1: 0.604, acc: 0.616
+2025-08-26 04:44:29: Epoch: 044 Test results, ap: 0.195, auc: 0.376, f1: 0.036, acc: 0.561
+2025-08-26 04:45:03: Epoch: 045 Train Loss: 0.474, ap: 0.528, auc: 0.627, f1: 0.618, acc: 0.596
+2025-08-26 04:45:06: Epoch: 045 Test results, ap: 0.196, auc: 0.380, f1: 0.169, acc: 0.520
+2025-08-26 04:45:40: Epoch: 046 Train Loss: 0.459, ap: 0.530, auc: 0.622, f1: 0.604, acc: 0.598
+2025-08-26 04:45:43: Epoch: 046 Test results, ap: 0.193, auc: 0.372, f1: 0.164, acc: 0.504
+2025-08-26 04:46:18: Epoch: 047 Train Loss: 0.466, ap: 0.527, auc: 0.607, f1: 0.568, acc: 0.582
+2025-08-26 04:46:21: Epoch: 047 Test results, ap: 0.195, auc: 0.380, f1: 0.000, acc: 0.683
+2025-08-26 04:46:55: Epoch: 048 Train Loss: 0.455, ap: 0.533, auc: 0.616, f1: 0.593, acc: 0.603
+2025-08-26 04:46:59: Epoch: 048 Test results, ap: 0.200, auc: 0.395, f1: 0.000, acc: 0.659
+2025-08-26 04:47:32: Epoch: 049 Train Loss: 0.454, ap: 0.536, auc: 0.618, f1: 0.580, acc: 0.601
+2025-08-26 04:47:35: Epoch: 049 Test results, ap: 0.208, auc: 0.405, f1: 0.197, acc: 0.602
+2025-08-26 04:48:09: Epoch: 050 Train Loss: 0.449, ap: 0.545, auc: 0.611, f1: 0.602, acc: 0.575
+2025-08-26 04:48:12: Epoch: 050 Test results, ap: 0.198, auc: 0.387, f1: 0.000, acc: 0.699
+2025-08-26 04:48:12: used time 0.52h
+2025-08-26 04:48:12: Cross Validation Finished!
+2025-08-26 04:48:12: Best validation perform list
+[[0.70000827 0.72362328 0.61570245 0.67253524]
+ [0.69016159 0.71137214 0.61782175 0.66021127]
+ [0.66246235 0.68941319 0.57971013 0.64197528]
+ [0.59339619 0.69900525 0.65156794 0.64726633]
+ [0.58355761 0.6618613  0.58646619 0.61199296]]
+2025-08-26 04:48:12: mean: [0.646 0.697 0.61  0.647]
+2025-08-26 04:48:12: std: [0.049 0.021 0.026 0.02 ]
+2025-08-26 04:48:12: Best test perform list
+[[0.42590195 0.64014339 0.43076923 0.69918698]
+ [0.32236597 0.63476706 0.20833333 0.69105691]
+ [0.26342815 0.56129032 0.1754386  0.61788619]
+ [0.24045268 0.47670251 0.12121212 0.52845526]
+ [0.30469018 0.59014332 0.37974682 0.60162604]]
+2025-08-26 04:48:12: mean: [0.311 0.581 0.263 0.628]
+2025-08-26 04:48:12: std: [0.064 0.06  0.12  0.063]
diff --git a/run-cls/rn18-att-16-mamba-pcs-768-ce-32-0.001-50/config.json b/run-cls/rn18-att-16-mamba-pcs-768-ce-32-0.001-50/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..17e4afe016e252b31422b437ac644091e0e4d15d
--- /dev/null
+++ b/run-cls/rn18-att-16-mamba-pcs-768-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": "mamba", "channels": 16, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": true, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
diff --git a/run-cls/rn18-att-16-mamba-pcs-768-ce-32-0.001-50/training.log b/run-cls/rn18-att-16-mamba-pcs-768-ce-32-0.001-50/training.log
new file mode 100644
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--- /dev/null
+++ b/run-cls/rn18-att-16-mamba-pcs-768-ce-32-0.001-50/training.log
@@ -0,0 +1,5 @@
+2025-08-26 01:55:26: saving_dir: ./run-cls/rn18-att-16-mamba-pcs-768-ce-32-0.001-50
+2025-08-26 01:55:26: Loading Training Dataset
+2025-08-26 01:56:12: Loading Test Dataset
+2025-08-26 01:56:20: Running Cross Validation 0
+2025-08-26 01:56:20: Fold 0  Train set:2269, Valid set:568, Test set: 123
diff --git a/run-cls/rn18-att-16-pcs-768-ce-32-0.001-50/config.json b/run-cls/rn18-att-16-pcs-768-ce-32-0.001-50/config.json
new file mode 100644
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--- /dev/null
+++ b/run-cls/rn18-att-16-pcs-768-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": null, "channels": 16, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": true, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
diff --git a/run-cls/rn18-att-16-pcs-768-ce-32-0.001-50/training.log b/run-cls/rn18-att-16-pcs-768-ce-32-0.001-50/training.log
new file mode 100644
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--- /dev/null
+++ b/run-cls/rn18-att-16-pcs-768-ce-32-0.001-50/training.log
@@ -0,0 +1,5 @@
+2025-08-26 01:54:20: saving_dir: ./run-cls/rn18-att-16-pcs-768-ce-32-0.001-50
+2025-08-26 01:54:20: Loading Training Dataset
+2025-08-26 01:55:07: Loading Test Dataset
+2025-08-26 01:55:15: Running Cross Validation 0
+2025-08-26 01:55:15: Fold 0  Train set:2269, Valid set:568, Test set: 123
diff --git a/run-cls/rn18-diff-16-768-ce-32-0.001-50/config.json b/run-cls/rn18-diff-16-768-ce-32-0.001-50/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..45a0630101951b3c207d202b946ce2d7ca81787e
--- /dev/null
+++ b/run-cls/rn18-diff-16-768-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": null, "channels": 16, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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diff --git a/run-cls/rn18-diff-16-768-ce-32-0.001-50/result.txt b/run-cls/rn18-diff-16-768-ce-32-0.001-50/result.txt
new file mode 100644
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+++ b/run-cls/rn18-diff-16-768-ce-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
+Valid
+0.9210119605064392,0.9407839298248291,0.8578473567962647,0.8734524607658386
+0.01546486835509762,0.010048726666369443,0.009597495211368212,0.009994377695042805
+Test
+0.28893017768859863,0.593405020236969,0.11824561357498169,0.7105690956115722
+0.025674720737079475,0.03268267897170541,0.07588735652066922,0.02094975070234342
diff --git a/run-cls/rn18-diff-16-768-ce-32-0.001-50/training.log b/run-cls/rn18-diff-16-768-ce-32-0.001-50/training.log
new file mode 100644
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+++ b/run-cls/rn18-diff-16-768-ce-32-0.001-50/training.log
@@ -0,0 +1,675 @@
+2025-08-26 04:48:44: saving_dir: ./run-cls/rn18-diff-16-768-ce-32-0.001-50
+2025-08-26 04:48:44: Loading Training Dataset
+2025-08-26 04:50:21: Loading Test Dataset
+2025-08-26 04:50:37: Running Cross Validation 0
+2025-08-26 04:50:37: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 04:51:29: Epoch: 001 Train Loss: 0.659, ap: 0.698, auc: 0.696, f1: 0.649, acc: 0.643
+2025-08-26 04:51:29: Epoch: 001 New best VALIDATION metrics
+2025-08-26 04:51:33: Epoch: 001 Test results, ap: 0.308, auc: 0.569, f1: 0.338, acc: 0.650
+2025-08-26 04:52:15: Epoch: 002 Train Loss: 0.621, ap: 0.770, auc: 0.783, f1: 0.712, acc: 0.722
+2025-08-26 04:52:15: Epoch: 002 New best VALIDATION metrics
+2025-08-26 04:52:19: Epoch: 002 Test results, ap: 0.283, auc: 0.582, f1: 0.319, acc: 0.618
+2025-08-26 04:52:58: Epoch: 003 Train Loss: 0.552, ap: 0.756, auc: 0.769, f1: 0.705, acc: 0.687
+2025-08-26 04:53:01: Epoch: 003 Test results, ap: 0.230, auc: 0.463, f1: 0.333, acc: 0.512
+2025-08-26 04:53:42: Epoch: 004 Train Loss: 0.506, ap: 0.822, auc: 0.839, f1: 0.772, acc: 0.750
+2025-08-26 04:53:42: Epoch: 004 New best VALIDATION metrics
+2025-08-26 04:53:46: Epoch: 004 Test results, ap: 0.211, auc: 0.441, f1: 0.354, acc: 0.407
+2025-08-26 04:54:25: Epoch: 005 Train Loss: 0.462, ap: 0.832, auc: 0.859, f1: 0.790, acc: 0.762
+2025-08-26 04:54:25: Epoch: 005 New best VALIDATION metrics
+2025-08-26 04:54:29: Epoch: 005 Test results, ap: 0.246, auc: 0.525, f1: 0.233, acc: 0.626
+2025-08-26 04:55:09: Epoch: 006 Train Loss: 0.439, ap: 0.854, auc: 0.860, f1: 0.767, acc: 0.752
+2025-08-26 04:55:09: Epoch: 006 New best VALIDATION metrics
+2025-08-26 04:55:13: Epoch: 006 Test results, ap: 0.227, auc: 0.484, f1: 0.381, acc: 0.472
+2025-08-26 04:55:52: Epoch: 007 Train Loss: 0.404, ap: 0.842, auc: 0.842, f1: 0.758, acc: 0.761
+2025-08-26 04:55:55: Epoch: 007 Test results, ap: 0.328, auc: 0.565, f1: 0.205, acc: 0.748
+2025-08-26 04:56:34: Epoch: 008 Train Loss: 0.380, ap: 0.882, auc: 0.886, f1: 0.789, acc: 0.796
+2025-08-26 04:56:34: Epoch: 008 New best VALIDATION metrics
+2025-08-26 04:56:37: Epoch: 008 Test results, ap: 0.250, auc: 0.516, f1: 0.317, acc: 0.650
+2025-08-26 04:57:17: Epoch: 009 Train Loss: 0.365, ap: 0.801, auc: 0.827, f1: 0.712, acc: 0.745
+2025-08-26 04:57:20: Epoch: 009 Test results, ap: 0.326, auc: 0.613, f1: 0.059, acc: 0.740
+2025-08-26 04:58:00: Epoch: 010 Train Loss: 0.334, ap: 0.860, auc: 0.882, f1: 0.785, acc: 0.799
+2025-08-26 04:58:03: Epoch: 010 Test results, ap: 0.292, auc: 0.601, f1: 0.054, acc: 0.715
+2025-08-26 04:58:42: Epoch: 011 Train Loss: 0.288, ap: 0.895, auc: 0.907, f1: 0.812, acc: 0.824
+2025-08-26 04:58:42: Epoch: 011 New best VALIDATION metrics
+2025-08-26 04:58:46: Epoch: 011 Test results, ap: 0.272, auc: 0.580, f1: 0.192, acc: 0.659
+2025-08-26 04:58:46: Epoch: 011 New best TEST metrics
+2025-08-26 04:59:26: Epoch: 012 Train Loss: 0.259, ap: 0.871, auc: 0.898, f1: 0.807, acc: 0.813
+2025-08-26 04:59:29: Epoch: 012 Test results, ap: 0.217, auc: 0.466, f1: 0.189, acc: 0.512
+2025-08-26 05:00:09: Epoch: 013 Train Loss: 0.255, ap: 0.897, auc: 0.905, f1: 0.798, acc: 0.813
+2025-08-26 05:00:09: Epoch: 013 New best VALIDATION metrics
+2025-08-26 05:00:13: Epoch: 013 Test results, ap: 0.282, auc: 0.588, f1: 0.226, acc: 0.667
+2025-08-26 05:00:13: Epoch: 013 New best TEST metrics
+2025-08-26 05:00:53: Epoch: 014 Train Loss: 0.241, ap: 0.902, auc: 0.911, f1: 0.811, acc: 0.822
+2025-08-26 05:00:53: Epoch: 014 New best VALIDATION metrics
+2025-08-26 05:00:56: Epoch: 014 Test results, ap: 0.216, auc: 0.460, f1: 0.147, acc: 0.528
+2025-08-26 05:01:35: Epoch: 015 Train Loss: 0.227, ap: 0.913, auc: 0.919, f1: 0.805, acc: 0.820
+2025-08-26 05:01:35: Epoch: 015 New best VALIDATION metrics
+2025-08-26 05:01:39: Epoch: 015 Test results, ap: 0.283, auc: 0.607, f1: 0.133, acc: 0.683
+2025-08-26 05:01:39: Epoch: 015 New best TEST metrics
+2025-08-26 05:02:18: Epoch: 016 Train Loss: 0.231, ap: 0.896, auc: 0.913, f1: 0.812, acc: 0.819
+2025-08-26 05:02:22: Epoch: 016 Test results, ap: 0.235, auc: 0.512, f1: 0.326, acc: 0.528
+2025-08-26 05:03:01: Epoch: 017 Train Loss: 0.227, ap: 0.909, auc: 0.918, f1: 0.827, acc: 0.838
+2025-08-26 05:03:05: Epoch: 017 Test results, ap: 0.290, auc: 0.611, f1: 0.222, acc: 0.659
+2025-08-26 05:03:05: Epoch: 017 New best TEST metrics
+2025-08-26 05:03:44: Epoch: 018 Train Loss: 0.224, ap: 0.895, auc: 0.908, f1: 0.820, acc: 0.833
+2025-08-26 05:03:47: Epoch: 018 Test results, ap: 0.305, auc: 0.629, f1: 0.103, acc: 0.715
+2025-08-26 05:03:47: Epoch: 018 New best TEST metrics
+2025-08-26 05:04:26: Epoch: 019 Train Loss: 0.234, ap: 0.912, auc: 0.921, f1: 0.853, acc: 0.857
+2025-08-26 05:04:26: Epoch: 019 New best VALIDATION metrics
+2025-08-26 05:04:30: Epoch: 019 Test results, ap: 0.256, auc: 0.555, f1: 0.172, acc: 0.610
+2025-08-26 05:05:08: Epoch: 020 Train Loss: 0.213, ap: 0.897, auc: 0.912, f1: 0.819, acc: 0.827
+2025-08-26 05:05:12: Epoch: 020 Test results, ap: 0.252, auc: 0.556, f1: 0.203, acc: 0.618
+2025-08-26 05:05:51: Epoch: 021 Train Loss: 0.179, ap: 0.913, auc: 0.926, f1: 0.847, acc: 0.852
+2025-08-26 05:05:51: Epoch: 021 New best VALIDATION metrics
+2025-08-26 05:05:54: Epoch: 021 Test results, ap: 0.247, auc: 0.541, f1: 0.169, acc: 0.602
+2025-08-26 05:06:33: Epoch: 022 Train Loss: 0.176, ap: 0.920, auc: 0.929, f1: 0.860, acc: 0.864
+2025-08-26 05:06:33: Epoch: 022 New best VALIDATION metrics
+2025-08-26 05:06:37: Epoch: 022 Test results, ap: 0.269, auc: 0.573, f1: 0.151, acc: 0.634
+2025-08-26 05:07:16: Epoch: 023 Train Loss: 0.155, ap: 0.924, auc: 0.931, f1: 0.843, acc: 0.852
+2025-08-26 05:07:16: Epoch: 023 New best VALIDATION metrics
+2025-08-26 05:07:20: Epoch: 023 Test results, ap: 0.264, auc: 0.571, f1: 0.120, acc: 0.642
+2025-08-26 05:07:59: Epoch: 024 Train Loss: 0.166, ap: 0.923, auc: 0.932, f1: 0.851, acc: 0.859
+2025-08-26 05:08:02: Epoch: 024 Test results, ap: 0.247, auc: 0.542, f1: 0.169, acc: 0.602
+2025-08-26 05:08:42: Epoch: 025 Train Loss: 0.156, ap: 0.914, auc: 0.922, f1: 0.830, acc: 0.840
+2025-08-26 05:08:45: Epoch: 025 Test results, ap: 0.272, auc: 0.587, f1: 0.115, acc: 0.626
+2025-08-26 05:09:25: Epoch: 026 Train Loss: 0.167, ap: 0.909, auc: 0.924, f1: 0.853, acc: 0.861
+2025-08-26 05:09:28: Epoch: 026 Test results, ap: 0.236, auc: 0.518, f1: 0.143, acc: 0.610
+2025-08-26 05:10:07: Epoch: 027 Train Loss: 0.156, ap: 0.923, auc: 0.933, f1: 0.852, acc: 0.859
+2025-08-26 05:10:07: Epoch: 027 New best VALIDATION metrics
+2025-08-26 05:10:11: Epoch: 027 Test results, ap: 0.246, auc: 0.538, f1: 0.145, acc: 0.618
+2025-08-26 05:10:50: Epoch: 028 Train Loss: 0.156, ap: 0.916, auc: 0.929, f1: 0.845, acc: 0.850
+2025-08-26 05:10:53: Epoch: 028 Test results, ap: 0.235, auc: 0.515, f1: 0.185, acc: 0.569
+2025-08-26 05:11:34: Epoch: 029 Train Loss: 0.149, ap: 0.914, auc: 0.932, f1: 0.853, acc: 0.859
+2025-08-26 05:11:37: Epoch: 029 Test results, ap: 0.237, auc: 0.522, f1: 0.206, acc: 0.561
+2025-08-26 05:12:16: Epoch: 030 Train Loss: 0.154, ap: 0.926, auc: 0.934, f1: 0.847, acc: 0.856
+2025-08-26 05:12:16: Epoch: 030 New best VALIDATION metrics
+2025-08-26 05:12:20: Epoch: 030 Test results, ap: 0.264, auc: 0.576, f1: 0.085, acc: 0.650
+2025-08-26 05:12:58: Epoch: 031 Train Loss: 0.134, ap: 0.924, auc: 0.934, f1: 0.854, acc: 0.863
+2025-08-26 05:13:02: Epoch: 031 Test results, ap: 0.263, auc: 0.569, f1: 0.122, acc: 0.650
+2025-08-26 05:13:42: Epoch: 032 Train Loss: 0.131, ap: 0.919, auc: 0.930, f1: 0.837, acc: 0.847
+2025-08-26 05:13:45: Epoch: 032 Test results, ap: 0.265, auc: 0.573, f1: 0.125, acc: 0.659
+2025-08-26 05:14:25: Epoch: 033 Train Loss: 0.127, ap: 0.927, auc: 0.938, f1: 0.865, acc: 0.871
+2025-08-26 05:14:25: Epoch: 033 New best VALIDATION metrics
+2025-08-26 05:14:29: Epoch: 033 Test results, ap: 0.246, auc: 0.539, f1: 0.172, acc: 0.610
+2025-08-26 05:15:08: Epoch: 034 Train Loss: 0.116, ap: 0.929, auc: 0.937, f1: 0.862, acc: 0.868
+2025-08-26 05:15:08: Epoch: 034 New best VALIDATION metrics
+2025-08-26 05:15:12: Epoch: 034 Test results, ap: 0.247, auc: 0.542, f1: 0.151, acc: 0.634
+2025-08-26 05:15:52: Epoch: 035 Train Loss: 0.122, ap: 0.926, auc: 0.936, f1: 0.862, acc: 0.868
+2025-08-26 05:15:56: Epoch: 035 Test results, ap: 0.243, auc: 0.532, f1: 0.197, acc: 0.602
+2025-08-26 05:16:35: Epoch: 036 Train Loss: 0.126, ap: 0.927, auc: 0.936, f1: 0.860, acc: 0.866
+2025-08-26 05:16:39: Epoch: 036 Test results, ap: 0.257, auc: 0.561, f1: 0.148, acc: 0.626
+2025-08-26 05:17:19: Epoch: 037 Train Loss: 0.115, ap: 0.926, auc: 0.937, f1: 0.855, acc: 0.861
+2025-08-26 05:17:22: Epoch: 037 Test results, ap: 0.246, auc: 0.538, f1: 0.207, acc: 0.626
+2025-08-26 05:18:02: Epoch: 038 Train Loss: 0.116, ap: 0.928, auc: 0.936, f1: 0.847, acc: 0.856
+2025-08-26 05:18:06: Epoch: 038 Test results, ap: 0.254, auc: 0.548, f1: 0.246, acc: 0.650
+2025-08-26 05:18:47: Epoch: 039 Train Loss: 0.120, ap: 0.930, auc: 0.938, f1: 0.856, acc: 0.863
+2025-08-26 05:18:47: Epoch: 039 New best VALIDATION metrics
+2025-08-26 05:18:50: Epoch: 039 Test results, ap: 0.249, auc: 0.546, f1: 0.185, acc: 0.642
+2025-08-26 05:19:30: Epoch: 040 Train Loss: 0.107, ap: 0.927, auc: 0.936, f1: 0.849, acc: 0.857
+2025-08-26 05:19:34: Epoch: 040 Test results, ap: 0.259, auc: 0.560, f1: 0.120, acc: 0.642
+2025-08-26 05:20:14: Epoch: 041 Train Loss: 0.104, ap: 0.927, auc: 0.940, f1: 0.868, acc: 0.873
+2025-08-26 05:20:18: Epoch: 041 Test results, ap: 0.256, auc: 0.551, f1: 0.237, acc: 0.634
+2025-08-26 05:20:57: Epoch: 042 Train Loss: 0.100, ap: 0.932, auc: 0.939, f1: 0.855, acc: 0.864
+2025-08-26 05:20:57: Epoch: 042 New best VALIDATION metrics
+2025-08-26 05:21:01: Epoch: 042 Test results, ap: 0.262, auc: 0.569, f1: 0.085, acc: 0.650
+2025-08-26 05:21:41: Epoch: 043 Train Loss: 0.102, ap: 0.933, auc: 0.942, f1: 0.862, acc: 0.868
+2025-08-26 05:21:41: Epoch: 043 New best VALIDATION metrics
+2025-08-26 05:21:45: Epoch: 043 Test results, ap: 0.261, auc: 0.562, f1: 0.122, acc: 0.650
+2025-08-26 05:22:25: Epoch: 044 Train Loss: 0.089, ap: 0.932, auc: 0.940, f1: 0.865, acc: 0.871
+2025-08-26 05:22:28: Epoch: 044 Test results, ap: 0.255, auc: 0.553, f1: 0.122, acc: 0.650
+2025-08-26 05:23:08: Epoch: 045 Train Loss: 0.094, ap: 0.932, auc: 0.940, f1: 0.859, acc: 0.866
+2025-08-26 05:23:11: Epoch: 045 Test results, ap: 0.257, auc: 0.556, f1: 0.122, acc: 0.650
+2025-08-26 05:23:51: Epoch: 046 Train Loss: 0.092, ap: 0.934, auc: 0.941, f1: 0.861, acc: 0.868
+2025-08-26 05:23:55: Epoch: 046 Test results, ap: 0.257, auc: 0.554, f1: 0.160, acc: 0.659
+2025-08-26 05:24:34: Epoch: 047 Train Loss: 0.098, ap: 0.935, auc: 0.943, f1: 0.863, acc: 0.870
+2025-08-26 05:24:34: Epoch: 047 New best VALIDATION metrics
+2025-08-26 05:24:38: Epoch: 047 Test results, ap: 0.258, auc: 0.561, f1: 0.218, acc: 0.650
+2025-08-26 05:25:17: Epoch: 048 Train Loss: 0.096, ap: 0.933, auc: 0.942, f1: 0.862, acc: 0.868
+2025-08-26 05:25:21: Epoch: 048 Test results, ap: 0.253, auc: 0.548, f1: 0.157, acc: 0.650
+2025-08-26 05:26:00: Epoch: 049 Train Loss: 0.093, ap: 0.935, auc: 0.943, f1: 0.863, acc: 0.870
+2025-08-26 05:26:00: Epoch: 049 New best VALIDATION metrics
+2025-08-26 05:26:04: Epoch: 049 Test results, ap: 0.256, auc: 0.552, f1: 0.192, acc: 0.659
+2025-08-26 05:26:44: Epoch: 050 Train Loss: 0.087, ap: 0.935, auc: 0.943, f1: 0.861, acc: 0.868
+2025-08-26 05:26:47: Epoch: 050 Test results, ap: 0.258, auc: 0.556, f1: 0.226, acc: 0.667
+2025-08-26 05:26:48: used time 0.60h
+2025-08-26 05:26:48: Running Cross Validation 1
+2025-08-26 05:26:48: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 05:27:28: Epoch: 001 Train Loss: 0.643, ap: 0.641, auc: 0.698, f1: 0.610, acc: 0.646
+2025-08-26 05:27:28: Epoch: 001 New best VALIDATION metrics
+2025-08-26 05:27:31: Epoch: 001 Test results, ap: 0.215, auc: 0.432, f1: 0.244, acc: 0.496
+2025-08-26 05:28:11: Epoch: 002 Train Loss: 0.602, ap: 0.682, auc: 0.738, f1: 0.659, acc: 0.655
+2025-08-26 05:28:11: Epoch: 002 New best VALIDATION metrics
+2025-08-26 05:28:15: Epoch: 002 Test results, ap: 0.256, auc: 0.542, f1: 0.382, acc: 0.553
+2025-08-26 05:28:54: Epoch: 003 Train Loss: 0.567, ap: 0.750, auc: 0.779, f1: 0.690, acc: 0.708
+2025-08-26 05:28:54: Epoch: 003 New best VALIDATION metrics
+2025-08-26 05:28:58: Epoch: 003 Test results, ap: 0.316, auc: 0.576, f1: 0.377, acc: 0.650
+2025-08-26 05:29:38: Epoch: 004 Train Loss: 0.536, ap: 0.750, auc: 0.791, f1: 0.724, acc: 0.732
+2025-08-26 05:29:38: Epoch: 004 New best VALIDATION metrics
+2025-08-26 05:29:41: Epoch: 004 Test results, ap: 0.279, auc: 0.530, f1: 0.348, acc: 0.512
+2025-08-26 05:30:22: Epoch: 005 Train Loss: 0.502, ap: 0.673, auc: 0.731, f1: 0.629, acc: 0.685
+2025-08-26 05:30:25: Epoch: 005 Test results, ap: 0.317, auc: 0.586, f1: 0.324, acc: 0.626
+2025-08-26 05:31:06: Epoch: 006 Train Loss: 0.460, ap: 0.791, auc: 0.838, f1: 0.739, acc: 0.745
+2025-08-26 05:31:06: Epoch: 006 New best VALIDATION metrics
+2025-08-26 05:31:09: Epoch: 006 Test results, ap: 0.270, auc: 0.510, f1: 0.298, acc: 0.463
+2025-08-26 05:31:50: Epoch: 007 Train Loss: 0.431, ap: 0.818, auc: 0.858, f1: 0.747, acc: 0.761
+2025-08-26 05:31:50: Epoch: 007 New best VALIDATION metrics
+2025-08-26 05:31:53: Epoch: 007 Test results, ap: 0.239, auc: 0.477, f1: 0.222, acc: 0.545
+2025-08-26 05:32:33: Epoch: 008 Train Loss: 0.418, ap: 0.789, auc: 0.836, f1: 0.730, acc: 0.764
+2025-08-26 05:32:36: Epoch: 008 Test results, ap: 0.292, auc: 0.575, f1: 0.277, acc: 0.618
+2025-08-26 05:33:15: Epoch: 009 Train Loss: 0.401, ap: 0.816, auc: 0.853, f1: 0.720, acc: 0.761
+2025-08-26 05:33:19: Epoch: 009 Test results, ap: 0.259, auc: 0.551, f1: 0.329, acc: 0.569
+2025-08-26 05:33:59: Epoch: 010 Train Loss: 0.362, ap: 0.831, auc: 0.859, f1: 0.757, acc: 0.761
+2025-08-26 05:33:59: Epoch: 010 New best VALIDATION metrics
+2025-08-26 05:34:03: Epoch: 010 Test results, ap: 0.266, auc: 0.537, f1: 0.353, acc: 0.553
+2025-08-26 05:34:43: Epoch: 011 Train Loss: 0.298, ap: 0.888, auc: 0.913, f1: 0.816, acc: 0.829
+2025-08-26 05:34:43: Epoch: 011 New best VALIDATION metrics
+2025-08-26 05:34:46: Epoch: 011 Test results, ap: 0.252, auc: 0.529, f1: 0.246, acc: 0.602
+2025-08-26 05:34:46: Epoch: 011 New best TEST metrics
+2025-08-26 05:35:28: Epoch: 012 Train Loss: 0.278, ap: 0.876, auc: 0.906, f1: 0.788, acc: 0.806
+2025-08-26 05:35:31: Epoch: 012 Test results, ap: 0.260, auc: 0.548, f1: 0.211, acc: 0.634
+2025-08-26 05:35:31: Epoch: 012 New best TEST metrics
+2025-08-26 05:36:11: Epoch: 013 Train Loss: 0.272, ap: 0.892, auc: 0.913, f1: 0.792, acc: 0.820
+2025-08-26 05:36:11: Epoch: 013 New best VALIDATION metrics
+2025-08-26 05:36:15: Epoch: 013 Test results, ap: 0.254, auc: 0.537, f1: 0.204, acc: 0.683
+2025-08-26 05:36:54: Epoch: 014 Train Loss: 0.249, ap: 0.889, auc: 0.908, f1: 0.796, acc: 0.819
+2025-08-26 05:36:58: Epoch: 014 Test results, ap: 0.252, auc: 0.519, f1: 0.271, acc: 0.650
+2025-08-26 05:37:38: Epoch: 015 Train Loss: 0.239, ap: 0.907, auc: 0.925, f1: 0.813, acc: 0.835
+2025-08-26 05:37:38: Epoch: 015 New best VALIDATION metrics
+2025-08-26 05:37:41: Epoch: 015 Test results, ap: 0.239, auc: 0.516, f1: 0.222, acc: 0.602
+2025-08-26 05:38:22: Epoch: 016 Train Loss: 0.223, ap: 0.903, auc: 0.926, f1: 0.814, acc: 0.833
+2025-08-26 05:38:26: Epoch: 016 Test results, ap: 0.275, auc: 0.568, f1: 0.286, acc: 0.634
+2025-08-26 05:38:26: Epoch: 016 New best TEST metrics
+2025-08-26 05:39:06: Epoch: 017 Train Loss: 0.213, ap: 0.898, auc: 0.916, f1: 0.791, acc: 0.817
+2025-08-26 05:39:09: Epoch: 017 Test results, ap: 0.243, auc: 0.524, f1: 0.258, acc: 0.626
+2025-08-26 05:39:50: Epoch: 018 Train Loss: 0.222, ap: 0.906, auc: 0.923, f1: 0.806, acc: 0.835
+2025-08-26 05:39:53: Epoch: 018 Test results, ap: 0.267, auc: 0.561, f1: 0.200, acc: 0.675
+2025-08-26 05:40:36: Epoch: 019 Train Loss: 0.204, ap: 0.903, auc: 0.924, f1: 0.797, acc: 0.824
+2025-08-26 05:40:42: Epoch: 019 Test results, ap: 0.259, auc: 0.554, f1: 0.163, acc: 0.667
+2025-08-26 05:41:25: Epoch: 020 Train Loss: 0.206, ap: 0.924, auc: 0.936, f1: 0.812, acc: 0.843
+2025-08-26 05:41:25: Epoch: 020 New best VALIDATION metrics
+2025-08-26 05:41:29: Epoch: 020 Test results, ap: 0.243, auc: 0.528, f1: 0.185, acc: 0.642
+2025-08-26 05:42:12: Epoch: 021 Train Loss: 0.172, ap: 0.913, auc: 0.939, f1: 0.841, acc: 0.857
+2025-08-26 05:42:15: Epoch: 021 Test results, ap: 0.266, auc: 0.561, f1: 0.250, acc: 0.659
+2025-08-26 05:42:55: Epoch: 022 Train Loss: 0.155, ap: 0.922, auc: 0.939, f1: 0.829, acc: 0.852
+2025-08-26 05:42:55: Epoch: 022 New best VALIDATION metrics
+2025-08-26 05:42:58: Epoch: 022 Test results, ap: 0.258, auc: 0.547, f1: 0.122, acc: 0.650
+2025-08-26 05:43:38: Epoch: 023 Train Loss: 0.156, ap: 0.928, auc: 0.942, f1: 0.839, acc: 0.861
+2025-08-26 05:43:38: Epoch: 023 New best VALIDATION metrics
+2025-08-26 05:43:41: Epoch: 023 Test results, ap: 0.272, auc: 0.573, f1: 0.192, acc: 0.659
+2025-08-26 05:43:41: Epoch: 023 New best TEST metrics
+2025-08-26 05:44:25: Epoch: 024 Train Loss: 0.151, ap: 0.926, auc: 0.945, f1: 0.838, acc: 0.859
+2025-08-26 05:44:25: Epoch: 024 New best VALIDATION metrics
+2025-08-26 05:44:30: Epoch: 024 Test results, ap: 0.260, auc: 0.554, f1: 0.211, acc: 0.634
+2025-08-26 05:45:11: Epoch: 025 Train Loss: 0.149, ap: 0.926, auc: 0.946, f1: 0.857, acc: 0.875
+2025-08-26 05:45:11: Epoch: 025 New best VALIDATION metrics
+2025-08-26 05:45:14: Epoch: 025 Test results, ap: 0.263, auc: 0.547, f1: 0.237, acc: 0.634
+2025-08-26 05:45:54: Epoch: 026 Train Loss: 0.147, ap: 0.932, auc: 0.947, f1: 0.840, acc: 0.864
+2025-08-26 05:45:54: Epoch: 026 New best VALIDATION metrics
+2025-08-26 05:45:57: Epoch: 026 Test results, ap: 0.261, auc: 0.560, f1: 0.120, acc: 0.642
+2025-08-26 05:46:37: Epoch: 027 Train Loss: 0.132, ap: 0.926, auc: 0.944, f1: 0.847, acc: 0.866
+2025-08-26 05:46:40: Epoch: 027 Test results, ap: 0.253, auc: 0.542, f1: 0.214, acc: 0.642
+2025-08-26 05:47:20: Epoch: 028 Train Loss: 0.137, ap: 0.928, auc: 0.947, f1: 0.843, acc: 0.864
+2025-08-26 05:47:23: Epoch: 028 Test results, ap: 0.262, auc: 0.552, f1: 0.218, acc: 0.650
+2025-08-26 05:48:04: Epoch: 029 Train Loss: 0.135, ap: 0.926, auc: 0.946, f1: 0.849, acc: 0.868
+2025-08-26 05:48:07: Epoch: 029 Test results, ap: 0.276, auc: 0.567, f1: 0.259, acc: 0.675
+2025-08-26 05:48:47: Epoch: 030 Train Loss: 0.130, ap: 0.922, auc: 0.936, f1: 0.814, acc: 0.847
+2025-08-26 05:48:51: Epoch: 030 Test results, ap: 0.283, auc: 0.584, f1: 0.136, acc: 0.691
+2025-08-26 05:48:51: Epoch: 030 New best TEST metrics
+2025-08-26 05:49:30: Epoch: 031 Train Loss: 0.121, ap: 0.934, auc: 0.951, f1: 0.857, acc: 0.877
+2025-08-26 05:49:30: Epoch: 031 New best VALIDATION metrics
+2025-08-26 05:49:34: Epoch: 031 Test results, ap: 0.267, auc: 0.568, f1: 0.214, acc: 0.642
+2025-08-26 05:50:14: Epoch: 032 Train Loss: 0.102, ap: 0.936, auc: 0.951, f1: 0.852, acc: 0.873
+2025-08-26 05:50:14: Epoch: 032 New best VALIDATION metrics
+2025-08-26 05:50:18: Epoch: 032 Test results, ap: 0.270, auc: 0.564, f1: 0.222, acc: 0.659
+2025-08-26 05:50:57: Epoch: 033 Train Loss: 0.099, ap: 0.935, auc: 0.952, f1: 0.861, acc: 0.880
+2025-08-26 05:50:57: Epoch: 033 New best VALIDATION metrics
+2025-08-26 05:51:01: Epoch: 033 Test results, ap: 0.281, auc: 0.575, f1: 0.255, acc: 0.667
+2025-08-26 05:51:41: Epoch: 034 Train Loss: 0.098, ap: 0.938, auc: 0.952, f1: 0.851, acc: 0.873
+2025-08-26 05:51:41: Epoch: 034 New best VALIDATION metrics
+2025-08-26 05:51:44: Epoch: 034 Test results, ap: 0.275, auc: 0.573, f1: 0.154, acc: 0.642
+2025-08-26 05:52:25: Epoch: 035 Train Loss: 0.102, ap: 0.940, auc: 0.953, f1: 0.851, acc: 0.873
+2025-08-26 05:52:25: Epoch: 035 New best VALIDATION metrics
+2025-08-26 05:52:28: Epoch: 035 Test results, ap: 0.248, auc: 0.538, f1: 0.138, acc: 0.593
+2025-08-26 05:53:08: Epoch: 036 Train Loss: 0.098, ap: 0.938, auc: 0.952, f1: 0.848, acc: 0.871
+2025-08-26 05:53:11: Epoch: 036 Test results, ap: 0.263, auc: 0.563, f1: 0.189, acc: 0.650
+2025-08-26 05:53:50: Epoch: 037 Train Loss: 0.098, ap: 0.939, auc: 0.954, f1: 0.865, acc: 0.886
+2025-08-26 05:53:50: Epoch: 037 New best VALIDATION metrics
+2025-08-26 05:53:54: Epoch: 037 Test results, ap: 0.285, auc: 0.587, f1: 0.231, acc: 0.675
+2025-08-26 05:53:54: Epoch: 037 New best TEST metrics
+2025-08-26 05:54:34: Epoch: 038 Train Loss: 0.096, ap: 0.938, auc: 0.954, f1: 0.852, acc: 0.875
+2025-08-26 05:54:40: Epoch: 038 Test results, ap: 0.269, auc: 0.570, f1: 0.192, acc: 0.659
+2025-08-26 05:55:22: Epoch: 039 Train Loss: 0.098, ap: 0.940, auc: 0.954, f1: 0.855, acc: 0.877
+2025-08-26 05:55:22: Epoch: 039 New best VALIDATION metrics
+2025-08-26 05:55:26: Epoch: 039 Test results, ap: 0.273, auc: 0.572, f1: 0.192, acc: 0.659
+2025-08-26 05:56:06: Epoch: 040 Train Loss: 0.095, ap: 0.943, auc: 0.957, f1: 0.860, acc: 0.880
+2025-08-26 05:56:06: Epoch: 040 New best VALIDATION metrics
+2025-08-26 05:56:11: Epoch: 040 Test results, ap: 0.262, auc: 0.558, f1: 0.200, acc: 0.610
+2025-08-26 05:56:53: Epoch: 041 Train Loss: 0.080, ap: 0.940, auc: 0.957, f1: 0.861, acc: 0.882
+2025-08-26 05:56:59: Epoch: 041 Test results, ap: 0.264, auc: 0.563, f1: 0.200, acc: 0.610
+2025-08-26 05:57:41: Epoch: 042 Train Loss: 0.083, ap: 0.937, auc: 0.956, f1: 0.851, acc: 0.871
+2025-08-26 05:57:45: Epoch: 042 Test results, ap: 0.267, auc: 0.569, f1: 0.203, acc: 0.618
+2025-08-26 05:58:28: Epoch: 043 Train Loss: 0.081, ap: 0.940, auc: 0.955, f1: 0.858, acc: 0.880
+2025-08-26 05:58:31: Epoch: 043 Test results, ap: 0.270, auc: 0.568, f1: 0.231, acc: 0.675
+2025-08-26 05:59:15: Epoch: 044 Train Loss: 0.078, ap: 0.938, auc: 0.957, f1: 0.867, acc: 0.886
+2025-08-26 05:59:19: Epoch: 044 Test results, ap: 0.278, auc: 0.581, f1: 0.255, acc: 0.667
+2025-08-26 05:59:57: Epoch: 045 Train Loss: 0.078, ap: 0.939, auc: 0.957, f1: 0.864, acc: 0.884
+2025-08-26 06:00:01: Epoch: 045 Test results, ap: 0.271, auc: 0.567, f1: 0.237, acc: 0.634
+2025-08-26 06:00:40: Epoch: 046 Train Loss: 0.076, ap: 0.941, auc: 0.959, f1: 0.874, acc: 0.893
+2025-08-26 06:00:40: Epoch: 046 New best VALIDATION metrics
+2025-08-26 06:00:43: Epoch: 046 Test results, ap: 0.268, auc: 0.569, f1: 0.203, acc: 0.618
+2025-08-26 06:01:23: Epoch: 047 Train Loss: 0.073, ap: 0.941, auc: 0.958, f1: 0.861, acc: 0.882
+2025-08-26 06:01:26: Epoch: 047 Test results, ap: 0.271, auc: 0.571, f1: 0.230, acc: 0.618
+2025-08-26 06:02:06: Epoch: 048 Train Loss: 0.081, ap: 0.940, auc: 0.958, f1: 0.870, acc: 0.887
+2025-08-26 06:02:10: Epoch: 048 Test results, ap: 0.274, auc: 0.571, f1: 0.203, acc: 0.618
+2025-08-26 06:02:49: Epoch: 049 Train Loss: 0.085, ap: 0.941, auc: 0.958, f1: 0.871, acc: 0.889
+2025-08-26 06:02:52: Epoch: 049 Test results, ap: 0.268, auc: 0.569, f1: 0.203, acc: 0.618
+2025-08-26 06:03:32: Epoch: 050 Train Loss: 0.070, ap: 0.941, auc: 0.958, f1: 0.865, acc: 0.886
+2025-08-26 06:03:35: Epoch: 050 Test results, ap: 0.270, auc: 0.568, f1: 0.203, acc: 0.618
+2025-08-26 06:03:36: used time 0.61h
+2025-08-26 06:03:36: Running Cross Validation 2
+2025-08-26 06:03:36: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 06:04:16: Epoch: 001 Train Loss: 0.671, ap: 0.636, auc: 0.653, f1: 0.603, acc: 0.536
+2025-08-26 06:04:16: Epoch: 001 New best VALIDATION metrics
+2025-08-26 06:04:20: Epoch: 001 Test results, ap: 0.206, auc: 0.429, f1: 0.392, acc: 0.244
+2025-08-26 06:05:01: Epoch: 002 Train Loss: 0.657, ap: 0.627, auc: 0.701, f1: 0.645, acc: 0.587
+2025-08-26 06:05:01: Epoch: 002 New best VALIDATION metrics
+2025-08-26 06:05:04: Epoch: 002 Test results, ap: 0.193, auc: 0.357, f1: 0.362, acc: 0.228
+2025-08-26 06:05:44: Epoch: 003 Train Loss: 0.645, ap: 0.654, auc: 0.703, f1: 0.561, acc: 0.647
+2025-08-26 06:05:44: Epoch: 003 New best VALIDATION metrics
+2025-08-26 06:05:47: Epoch: 003 Test results, ap: 0.304, auc: 0.540, f1: 0.305, acc: 0.667
+2025-08-26 06:06:27: Epoch: 004 Train Loss: 0.607, ap: 0.644, auc: 0.723, f1: 0.642, acc: 0.660
+2025-08-26 06:06:27: Epoch: 004 New best VALIDATION metrics
+2025-08-26 06:06:30: Epoch: 004 Test results, ap: 0.270, auc: 0.551, f1: 0.312, acc: 0.642
+2025-08-26 06:07:11: Epoch: 005 Train Loss: 0.559, ap: 0.755, auc: 0.784, f1: 0.689, acc: 0.679
+2025-08-26 06:07:11: Epoch: 005 New best VALIDATION metrics
+2025-08-26 06:07:14: Epoch: 005 Test results, ap: 0.268, auc: 0.568, f1: 0.391, acc: 0.545
+2025-08-26 06:07:54: Epoch: 006 Train Loss: 0.528, ap: 0.787, auc: 0.833, f1: 0.741, acc: 0.751
+2025-08-26 06:07:54: Epoch: 006 New best VALIDATION metrics
+2025-08-26 06:07:58: Epoch: 006 Test results, ap: 0.286, auc: 0.599, f1: 0.361, acc: 0.626
+2025-08-26 06:08:39: Epoch: 007 Train Loss: 0.467, ap: 0.768, auc: 0.820, f1: 0.719, acc: 0.720
+2025-08-26 06:08:43: Epoch: 007 Test results, ap: 0.315, auc: 0.648, f1: 0.456, acc: 0.650
+2025-08-26 06:09:23: Epoch: 008 Train Loss: 0.436, ap: 0.767, auc: 0.837, f1: 0.744, acc: 0.755
+2025-08-26 06:09:26: Epoch: 008 Test results, ap: 0.272, auc: 0.571, f1: 0.372, acc: 0.561
+2025-08-26 06:10:07: Epoch: 009 Train Loss: 0.410, ap: 0.826, auc: 0.857, f1: 0.754, acc: 0.778
+2025-08-26 06:10:07: Epoch: 009 New best VALIDATION metrics
+2025-08-26 06:10:10: Epoch: 009 Test results, ap: 0.332, auc: 0.644, f1: 0.143, acc: 0.707
+2025-08-26 06:10:49: Epoch: 010 Train Loss: 0.393, ap: 0.787, auc: 0.812, f1: 0.719, acc: 0.743
+2025-08-26 06:10:53: Epoch: 010 Test results, ap: 0.324, auc: 0.588, f1: 0.000, acc: 0.724
+2025-08-26 06:11:32: Epoch: 011 Train Loss: 0.336, ap: 0.827, auc: 0.873, f1: 0.770, acc: 0.778
+2025-08-26 06:11:32: Epoch: 011 New best VALIDATION metrics
+2025-08-26 06:11:36: Epoch: 011 Test results, ap: 0.263, auc: 0.564, f1: 0.342, acc: 0.593
+2025-08-26 06:11:36: Epoch: 011 New best TEST metrics
+2025-08-26 06:12:15: Epoch: 012 Train Loss: 0.296, ap: 0.853, auc: 0.894, f1: 0.793, acc: 0.811
+2025-08-26 06:12:15: Epoch: 012 New best VALIDATION metrics
+2025-08-26 06:12:19: Epoch: 012 Test results, ap: 0.267, auc: 0.559, f1: 0.231, acc: 0.675
+2025-08-26 06:12:59: Epoch: 013 Train Loss: 0.279, ap: 0.867, auc: 0.904, f1: 0.813, acc: 0.834
+2025-08-26 06:12:59: Epoch: 013 New best VALIDATION metrics
+2025-08-26 06:13:03: Epoch: 013 Test results, ap: 0.296, auc: 0.614, f1: 0.200, acc: 0.675
+2025-08-26 06:13:03: Epoch: 013 New best TEST metrics
+2025-08-26 06:13:43: Epoch: 014 Train Loss: 0.255, ap: 0.857, auc: 0.890, f1: 0.782, acc: 0.810
+2025-08-26 06:13:47: Epoch: 014 Test results, ap: 0.329, auc: 0.634, f1: 0.158, acc: 0.740
+2025-08-26 06:13:47: Epoch: 014 New best TEST metrics
+2025-08-26 06:14:28: Epoch: 015 Train Loss: 0.244, ap: 0.876, auc: 0.894, f1: 0.777, acc: 0.799
+2025-08-26 06:14:31: Epoch: 015 Test results, ap: 0.287, auc: 0.596, f1: 0.300, acc: 0.659
+2025-08-26 06:15:12: Epoch: 016 Train Loss: 0.255, ap: 0.878, auc: 0.906, f1: 0.800, acc: 0.822
+2025-08-26 06:15:12: Epoch: 016 New best VALIDATION metrics
+2025-08-26 06:15:15: Epoch: 016 Test results, ap: 0.263, auc: 0.560, f1: 0.250, acc: 0.610
+2025-08-26 06:15:55: Epoch: 017 Train Loss: 0.233, ap: 0.875, auc: 0.900, f1: 0.799, acc: 0.824
+2025-08-26 06:15:58: Epoch: 017 Test results, ap: 0.272, auc: 0.570, f1: 0.241, acc: 0.642
+2025-08-26 06:16:38: Epoch: 018 Train Loss: 0.228, ap: 0.862, auc: 0.898, f1: 0.798, acc: 0.818
+2025-08-26 06:16:41: Epoch: 018 Test results, ap: 0.253, auc: 0.562, f1: 0.341, acc: 0.561
+2025-08-26 06:17:21: Epoch: 019 Train Loss: 0.236, ap: 0.888, auc: 0.907, f1: 0.802, acc: 0.820
+2025-08-26 06:17:21: Epoch: 019 New best VALIDATION metrics
+2025-08-26 06:17:24: Epoch: 019 Test results, ap: 0.310, auc: 0.616, f1: 0.098, acc: 0.699
+2025-08-26 06:18:04: Epoch: 020 Train Loss: 0.216, ap: 0.872, auc: 0.903, f1: 0.811, acc: 0.822
+2025-08-26 06:18:08: Epoch: 020 Test results, ap: 0.265, auc: 0.552, f1: 0.273, acc: 0.610
+2025-08-26 06:18:47: Epoch: 021 Train Loss: 0.196, ap: 0.894, auc: 0.917, f1: 0.817, acc: 0.841
+2025-08-26 06:18:47: Epoch: 021 New best VALIDATION metrics
+2025-08-26 06:18:50: Epoch: 021 Test results, ap: 0.302, auc: 0.587, f1: 0.050, acc: 0.691
+2025-08-26 06:19:30: Epoch: 022 Train Loss: 0.165, ap: 0.900, auc: 0.923, f1: 0.820, acc: 0.840
+2025-08-26 06:19:30: Epoch: 022 New best VALIDATION metrics
+2025-08-26 06:19:33: Epoch: 022 Test results, ap: 0.265, auc: 0.563, f1: 0.160, acc: 0.659
+2025-08-26 06:20:13: Epoch: 023 Train Loss: 0.157, ap: 0.901, auc: 0.922, f1: 0.824, acc: 0.847
+2025-08-26 06:20:13: Epoch: 023 New best VALIDATION metrics
+2025-08-26 06:20:17: Epoch: 023 Test results, ap: 0.261, auc: 0.555, f1: 0.048, acc: 0.675
+2025-08-26 06:20:56: Epoch: 024 Train Loss: 0.169, ap: 0.899, auc: 0.922, f1: 0.822, acc: 0.841
+2025-08-26 06:20:59: Epoch: 024 Test results, ap: 0.268, auc: 0.564, f1: 0.093, acc: 0.683
+2025-08-26 06:21:38: Epoch: 025 Train Loss: 0.165, ap: 0.901, auc: 0.921, f1: 0.824, acc: 0.847
+2025-08-26 06:21:41: Epoch: 025 Test results, ap: 0.311, auc: 0.598, f1: 0.098, acc: 0.699
+2025-08-26 06:22:20: Epoch: 026 Train Loss: 0.162, ap: 0.905, auc: 0.922, f1: 0.824, acc: 0.847
+2025-08-26 06:22:20: Epoch: 026 New best VALIDATION metrics
+2025-08-26 06:22:24: Epoch: 026 Test results, ap: 0.325, auc: 0.610, f1: 0.108, acc: 0.732
+2025-08-26 06:23:07: Epoch: 027 Train Loss: 0.159, ap: 0.897, auc: 0.919, f1: 0.821, acc: 0.847
+2025-08-26 06:23:10: Epoch: 027 Test results, ap: 0.276, auc: 0.566, f1: 0.054, acc: 0.715
+2025-08-26 06:23:50: Epoch: 028 Train Loss: 0.156, ap: 0.905, auc: 0.929, f1: 0.842, acc: 0.861
+2025-08-26 06:23:50: Epoch: 028 New best VALIDATION metrics
+2025-08-26 06:23:54: Epoch: 028 Test results, ap: 0.271, auc: 0.569, f1: 0.095, acc: 0.691
+2025-08-26 06:24:34: Epoch: 029 Train Loss: 0.151, ap: 0.905, auc: 0.926, f1: 0.827, acc: 0.845
+2025-08-26 06:24:37: Epoch: 029 Test results, ap: 0.272, auc: 0.564, f1: 0.140, acc: 0.699
+2025-08-26 06:25:17: Epoch: 030 Train Loss: 0.151, ap: 0.897, auc: 0.921, f1: 0.812, acc: 0.838
+2025-08-26 06:25:20: Epoch: 030 Test results, ap: 0.299, auc: 0.560, f1: 0.103, acc: 0.715
+2025-08-26 06:26:00: Epoch: 031 Train Loss: 0.134, ap: 0.907, auc: 0.930, f1: 0.832, acc: 0.852
+2025-08-26 06:26:00: Epoch: 031 New best VALIDATION metrics
+2025-08-26 06:26:03: Epoch: 031 Test results, ap: 0.264, auc: 0.544, f1: 0.045, acc: 0.659
+2025-08-26 06:26:42: Epoch: 032 Train Loss: 0.122, ap: 0.912, auc: 0.932, f1: 0.830, acc: 0.850
+2025-08-26 06:26:42: Epoch: 032 New best VALIDATION metrics
+2025-08-26 06:26:46: Epoch: 032 Test results, ap: 0.282, auc: 0.575, f1: 0.100, acc: 0.707
+2025-08-26 06:27:25: Epoch: 033 Train Loss: 0.119, ap: 0.911, auc: 0.932, f1: 0.842, acc: 0.859
+2025-08-26 06:27:28: Epoch: 033 Test results, ap: 0.259, auc: 0.529, f1: 0.091, acc: 0.675
+2025-08-26 06:28:08: Epoch: 034 Train Loss: 0.128, ap: 0.907, auc: 0.929, f1: 0.835, acc: 0.855
+2025-08-26 06:28:11: Epoch: 034 Test results, ap: 0.263, auc: 0.555, f1: 0.049, acc: 0.683
+2025-08-26 06:28:50: Epoch: 035 Train Loss: 0.117, ap: 0.907, auc: 0.931, f1: 0.838, acc: 0.857
+2025-08-26 06:28:53: Epoch: 035 Test results, ap: 0.249, auc: 0.516, f1: 0.154, acc: 0.642
+2025-08-26 06:29:37: Epoch: 036 Train Loss: 0.127, ap: 0.911, auc: 0.931, f1: 0.835, acc: 0.852
+2025-08-26 06:29:40: Epoch: 036 Test results, ap: 0.257, auc: 0.534, f1: 0.089, acc: 0.667
+2025-08-26 06:30:22: Epoch: 037 Train Loss: 0.114, ap: 0.910, auc: 0.932, f1: 0.833, acc: 0.852
+2025-08-26 06:30:26: Epoch: 037 Test results, ap: 0.256, auc: 0.540, f1: 0.091, acc: 0.675
+2025-08-26 06:31:07: Epoch: 038 Train Loss: 0.114, ap: 0.913, auc: 0.932, f1: 0.840, acc: 0.859
+2025-08-26 06:31:07: Epoch: 038 New best VALIDATION metrics
+2025-08-26 06:31:10: Epoch: 038 Test results, ap: 0.287, auc: 0.568, f1: 0.049, acc: 0.683
+2025-08-26 06:31:51: Epoch: 039 Train Loss: 0.119, ap: 0.910, auc: 0.932, f1: 0.841, acc: 0.859
+2025-08-26 06:31:55: Epoch: 039 Test results, ap: 0.284, auc: 0.559, f1: 0.091, acc: 0.675
+2025-08-26 06:32:37: Epoch: 040 Train Loss: 0.111, ap: 0.915, auc: 0.936, f1: 0.843, acc: 0.861
+2025-08-26 06:32:37: Epoch: 040 New best VALIDATION metrics
+2025-08-26 06:32:40: Epoch: 040 Test results, ap: 0.256, auc: 0.549, f1: 0.160, acc: 0.659
+2025-08-26 06:33:20: Epoch: 041 Train Loss: 0.101, ap: 0.912, auc: 0.933, f1: 0.842, acc: 0.861
+2025-08-26 06:33:23: Epoch: 041 Test results, ap: 0.246, auc: 0.523, f1: 0.091, acc: 0.675
+2025-08-26 06:34:04: Epoch: 042 Train Loss: 0.098, ap: 0.911, auc: 0.933, f1: 0.844, acc: 0.862
+2025-08-26 06:34:07: Epoch: 042 Test results, ap: 0.253, auc: 0.537, f1: 0.087, acc: 0.659
+2025-08-26 06:34:47: Epoch: 043 Train Loss: 0.090, ap: 0.915, auc: 0.937, f1: 0.851, acc: 0.866
+2025-08-26 06:34:47: Epoch: 043 New best VALIDATION metrics
+2025-08-26 06:34:50: Epoch: 043 Test results, ap: 0.244, auc: 0.522, f1: 0.075, acc: 0.602
+2025-08-26 06:35:29: Epoch: 044 Train Loss: 0.091, ap: 0.913, auc: 0.934, f1: 0.847, acc: 0.866
+2025-08-26 06:35:32: Epoch: 044 Test results, ap: 0.273, auc: 0.537, f1: 0.093, acc: 0.683
+2025-08-26 06:36:12: Epoch: 045 Train Loss: 0.093, ap: 0.913, auc: 0.936, f1: 0.843, acc: 0.859
+2025-08-26 06:36:16: Epoch: 045 Test results, ap: 0.234, auc: 0.495, f1: 0.080, acc: 0.626
+2025-08-26 06:36:55: Epoch: 046 Train Loss: 0.089, ap: 0.909, auc: 0.935, f1: 0.837, acc: 0.855
+2025-08-26 06:36:59: Epoch: 046 Test results, ap: 0.229, auc: 0.482, f1: 0.078, acc: 0.618
+2025-08-26 06:37:40: Epoch: 047 Train Loss: 0.093, ap: 0.910, auc: 0.934, f1: 0.842, acc: 0.861
+2025-08-26 06:37:43: Epoch: 047 Test results, ap: 0.235, auc: 0.497, f1: 0.085, acc: 0.650
+2025-08-26 06:38:23: Epoch: 048 Train Loss: 0.097, ap: 0.919, auc: 0.938, f1: 0.851, acc: 0.868
+2025-08-26 06:38:23: Epoch: 048 New best VALIDATION metrics
+2025-08-26 06:38:26: Epoch: 048 Test results, ap: 0.271, auc: 0.532, f1: 0.087, acc: 0.659
+2025-08-26 06:39:06: Epoch: 049 Train Loss: 0.092, ap: 0.915, auc: 0.936, f1: 0.848, acc: 0.862
+2025-08-26 06:39:09: Epoch: 049 Test results, ap: 0.236, auc: 0.485, f1: 0.069, acc: 0.561
+2025-08-26 06:39:49: Epoch: 050 Train Loss: 0.080, ap: 0.915, auc: 0.935, f1: 0.847, acc: 0.866
+2025-08-26 06:39:53: Epoch: 050 Test results, ap: 0.254, auc: 0.529, f1: 0.087, acc: 0.659
+2025-08-26 06:39:53: used time 0.60h
+2025-08-26 06:39:53: Running Cross Validation 3
+2025-08-26 06:39:53: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 06:40:33: Epoch: 001 Train Loss: 0.657, ap: 0.615, auc: 0.659, f1: 0.602, acc: 0.564
+2025-08-26 06:40:33: Epoch: 001 New best VALIDATION metrics
+2025-08-26 06:40:37: Epoch: 001 Test results, ap: 0.248, auc: 0.510, f1: 0.400, acc: 0.317
+2025-08-26 06:41:16: Epoch: 002 Train Loss: 0.620, ap: 0.665, auc: 0.732, f1: 0.643, acc: 0.646
+2025-08-26 06:41:16: Epoch: 002 New best VALIDATION metrics
+2025-08-26 06:41:20: Epoch: 002 Test results, ap: 0.292, auc: 0.535, f1: 0.275, acc: 0.528
+2025-08-26 06:41:59: Epoch: 003 Train Loss: 0.557, ap: 0.695, auc: 0.743, f1: 0.657, acc: 0.698
+2025-08-26 06:41:59: Epoch: 003 New best VALIDATION metrics
+2025-08-26 06:42:02: Epoch: 003 Test results, ap: 0.382, auc: 0.689, f1: 0.340, acc: 0.748
+2025-08-26 06:42:42: Epoch: 004 Train Loss: 0.516, ap: 0.768, auc: 0.826, f1: 0.720, acc: 0.741
+2025-08-26 06:42:42: Epoch: 004 New best VALIDATION metrics
+2025-08-26 06:42:45: Epoch: 004 Test results, ap: 0.317, auc: 0.628, f1: 0.339, acc: 0.683
+2025-08-26 06:43:25: Epoch: 005 Train Loss: 0.448, ap: 0.788, auc: 0.836, f1: 0.727, acc: 0.743
+2025-08-26 06:43:25: Epoch: 005 New best VALIDATION metrics
+2025-08-26 06:43:28: Epoch: 005 Test results, ap: 0.319, auc: 0.637, f1: 0.384, acc: 0.634
+2025-08-26 06:44:08: Epoch: 006 Train Loss: 0.451, ap: 0.802, auc: 0.844, f1: 0.733, acc: 0.748
+2025-08-26 06:44:08: Epoch: 006 New best VALIDATION metrics
+2025-08-26 06:44:11: Epoch: 006 Test results, ap: 0.288, auc: 0.609, f1: 0.241, acc: 0.642
+2025-08-26 06:44:52: Epoch: 007 Train Loss: 0.406, ap: 0.799, auc: 0.862, f1: 0.762, acc: 0.783
+2025-08-26 06:44:52: Epoch: 007 New best VALIDATION metrics
+2025-08-26 06:44:58: Epoch: 007 Test results, ap: 0.251, auc: 0.532, f1: 0.299, acc: 0.618
+2025-08-26 06:45:37: Epoch: 008 Train Loss: 0.386, ap: 0.843, auc: 0.872, f1: 0.756, acc: 0.778
+2025-08-26 06:45:37: Epoch: 008 New best VALIDATION metrics
+2025-08-26 06:45:40: Epoch: 008 Test results, ap: 0.270, auc: 0.554, f1: 0.226, acc: 0.667
+2025-08-26 06:46:20: Epoch: 009 Train Loss: 0.353, ap: 0.860, auc: 0.873, f1: 0.753, acc: 0.785
+2025-08-26 06:46:20: Epoch: 009 New best VALIDATION metrics
+2025-08-26 06:46:23: Epoch: 009 Test results, ap: 0.270, auc: 0.585, f1: 0.050, acc: 0.691
+2025-08-26 06:47:03: Epoch: 010 Train Loss: 0.334, ap: 0.822, auc: 0.876, f1: 0.770, acc: 0.790
+2025-08-26 06:47:06: Epoch: 010 Test results, ap: 0.211, auc: 0.438, f1: 0.154, acc: 0.642
+2025-08-26 06:47:46: Epoch: 011 Train Loss: 0.286, ap: 0.866, auc: 0.896, f1: 0.782, acc: 0.811
+2025-08-26 06:47:46: Epoch: 011 New best VALIDATION metrics
+2025-08-26 06:47:49: Epoch: 011 Test results, ap: 0.228, auc: 0.495, f1: 0.043, acc: 0.642
+2025-08-26 06:47:49: Epoch: 011 New best TEST metrics
+2025-08-26 06:48:29: Epoch: 012 Train Loss: 0.266, ap: 0.868, auc: 0.898, f1: 0.784, acc: 0.813
+2025-08-26 06:48:29: Epoch: 012 New best VALIDATION metrics
+2025-08-26 06:48:33: Epoch: 012 Test results, ap: 0.260, auc: 0.539, f1: 0.093, acc: 0.683
+2025-08-26 06:48:33: Epoch: 012 New best TEST metrics
+2025-08-26 06:49:12: Epoch: 013 Train Loss: 0.248, ap: 0.879, auc: 0.892, f1: 0.775, acc: 0.813
+2025-08-26 06:49:12: Epoch: 013 New best VALIDATION metrics
+2025-08-26 06:49:15: Epoch: 013 Test results, ap: 0.268, auc: 0.561, f1: 0.000, acc: 0.715
+2025-08-26 06:49:15: Epoch: 013 New best TEST metrics
+2025-08-26 06:49:54: Epoch: 014 Train Loss: 0.245, ap: 0.872, auc: 0.897, f1: 0.776, acc: 0.804
+2025-08-26 06:49:57: Epoch: 014 Test results, ap: 0.228, auc: 0.485, f1: 0.091, acc: 0.675
+2025-08-26 06:50:37: Epoch: 015 Train Loss: 0.235, ap: 0.876, auc: 0.911, f1: 0.795, acc: 0.813
+2025-08-26 06:50:37: Epoch: 015 New best VALIDATION metrics
+2025-08-26 06:50:40: Epoch: 015 Test results, ap: 0.221, auc: 0.466, f1: 0.143, acc: 0.610
+2025-08-26 06:51:20: Epoch: 016 Train Loss: 0.224, ap: 0.880, auc: 0.909, f1: 0.798, acc: 0.818
+2025-08-26 06:51:20: Epoch: 016 New best VALIDATION metrics
+2025-08-26 06:51:23: Epoch: 016 Test results, ap: 0.247, auc: 0.529, f1: 0.100, acc: 0.707
+2025-08-26 06:52:06: Epoch: 017 Train Loss: 0.208, ap: 0.887, auc: 0.910, f1: 0.793, acc: 0.822
+2025-08-26 06:52:06: Epoch: 017 New best VALIDATION metrics
+2025-08-26 06:52:12: Epoch: 017 Test results, ap: 0.242, auc: 0.519, f1: 0.095, acc: 0.691
+2025-08-26 06:52:52: Epoch: 018 Train Loss: 0.215, ap: 0.891, auc: 0.913, f1: 0.794, acc: 0.824
+2025-08-26 06:52:52: Epoch: 018 New best VALIDATION metrics
+2025-08-26 06:52:56: Epoch: 018 Test results, ap: 0.263, auc: 0.553, f1: 0.105, acc: 0.724
+2025-08-26 06:53:37: Epoch: 019 Train Loss: 0.222, ap: 0.895, auc: 0.922, f1: 0.823, acc: 0.845
+2025-08-26 06:53:37: Epoch: 019 New best VALIDATION metrics
+2025-08-26 06:53:40: Epoch: 019 Test results, ap: 0.236, auc: 0.511, f1: 0.000, acc: 0.675
+2025-08-26 06:54:24: Epoch: 020 Train Loss: 0.198, ap: 0.881, auc: 0.911, f1: 0.808, acc: 0.831
+2025-08-26 06:54:27: Epoch: 020 Test results, ap: 0.223, auc: 0.464, f1: 0.100, acc: 0.707
+2025-08-26 06:55:09: Epoch: 021 Train Loss: 0.186, ap: 0.890, auc: 0.918, f1: 0.813, acc: 0.843
+2025-08-26 06:55:12: Epoch: 021 Test results, ap: 0.247, auc: 0.530, f1: 0.000, acc: 0.707
+2025-08-26 06:55:51: Epoch: 022 Train Loss: 0.164, ap: 0.892, auc: 0.923, f1: 0.823, acc: 0.845
+2025-08-26 06:55:54: Epoch: 022 Test results, ap: 0.226, auc: 0.484, f1: 0.000, acc: 0.675
+2025-08-26 06:56:35: Epoch: 023 Train Loss: 0.163, ap: 0.894, auc: 0.917, f1: 0.806, acc: 0.832
+2025-08-26 06:56:39: Epoch: 023 Test results, ap: 0.239, auc: 0.510, f1: 0.054, acc: 0.715
+2025-08-26 06:57:19: Epoch: 024 Train Loss: 0.150, ap: 0.890, auc: 0.922, f1: 0.820, acc: 0.845
+2025-08-26 06:57:22: Epoch: 024 Test results, ap: 0.209, auc: 0.439, f1: 0.045, acc: 0.659
+2025-08-26 06:58:02: Epoch: 025 Train Loss: 0.140, ap: 0.889, auc: 0.924, f1: 0.843, acc: 0.864
+2025-08-26 06:58:05: Epoch: 025 Test results, ap: 0.222, auc: 0.475, f1: 0.049, acc: 0.683
+2025-08-26 06:58:44: Epoch: 026 Train Loss: 0.148, ap: 0.902, auc: 0.928, f1: 0.843, acc: 0.862
+2025-08-26 06:58:44: Epoch: 026 New best VALIDATION metrics
+2025-08-26 06:58:47: Epoch: 026 Test results, ap: 0.235, auc: 0.503, f1: 0.000, acc: 0.691
+2025-08-26 06:59:27: Epoch: 027 Train Loss: 0.140, ap: 0.896, auc: 0.927, f1: 0.843, acc: 0.864
+2025-08-26 06:59:30: Epoch: 027 Test results, ap: 0.221, auc: 0.475, f1: 0.050, acc: 0.691
+2025-08-26 07:00:10: Epoch: 028 Train Loss: 0.140, ap: 0.896, auc: 0.924, f1: 0.841, acc: 0.857
+2025-08-26 07:00:13: Epoch: 028 Test results, ap: 0.246, auc: 0.523, f1: 0.000, acc: 0.691
+2025-08-26 07:00:53: Epoch: 029 Train Loss: 0.140, ap: 0.902, auc: 0.927, f1: 0.828, acc: 0.854
+2025-08-26 07:00:58: Epoch: 029 Test results, ap: 0.222, auc: 0.478, f1: 0.000, acc: 0.699
+2025-08-26 07:01:41: Epoch: 030 Train Loss: 0.135, ap: 0.906, auc: 0.927, f1: 0.817, acc: 0.845
+2025-08-26 07:01:41: Epoch: 030 New best VALIDATION metrics
+2025-08-26 07:01:44: Epoch: 030 Test results, ap: 0.237, auc: 0.520, f1: 0.000, acc: 0.707
+2025-08-26 07:02:25: Epoch: 031 Train Loss: 0.112, ap: 0.902, auc: 0.928, f1: 0.822, acc: 0.848
+2025-08-26 07:02:28: Epoch: 031 Test results, ap: 0.228, auc: 0.494, f1: 0.000, acc: 0.691
+2025-08-26 07:03:08: Epoch: 032 Train Loss: 0.106, ap: 0.908, auc: 0.931, f1: 0.826, acc: 0.852
+2025-08-26 07:03:08: Epoch: 032 New best VALIDATION metrics
+2025-08-26 07:03:11: Epoch: 032 Test results, ap: 0.232, auc: 0.500, f1: 0.000, acc: 0.699
+2025-08-26 07:03:50: Epoch: 033 Train Loss: 0.111, ap: 0.907, auc: 0.931, f1: 0.827, acc: 0.850
+2025-08-26 07:03:53: Epoch: 033 Test results, ap: 0.223, auc: 0.479, f1: 0.000, acc: 0.675
+2025-08-26 07:04:32: Epoch: 034 Train Loss: 0.104, ap: 0.908, auc: 0.931, f1: 0.834, acc: 0.857
+2025-08-26 07:04:35: Epoch: 034 Test results, ap: 0.230, auc: 0.497, f1: 0.000, acc: 0.675
+2025-08-26 07:05:15: Epoch: 035 Train Loss: 0.097, ap: 0.905, auc: 0.930, f1: 0.830, acc: 0.854
+2025-08-26 07:05:18: Epoch: 035 Test results, ap: 0.220, auc: 0.474, f1: 0.000, acc: 0.667
+2025-08-26 07:06:00: Epoch: 036 Train Loss: 0.106, ap: 0.907, auc: 0.932, f1: 0.824, acc: 0.848
+2025-08-26 07:06:00: Epoch: 036 New best VALIDATION metrics
+2025-08-26 07:06:04: Epoch: 036 Test results, ap: 0.225, auc: 0.486, f1: 0.000, acc: 0.691
+2025-08-26 07:06:44: Epoch: 037 Train Loss: 0.102, ap: 0.912, auc: 0.930, f1: 0.830, acc: 0.854
+2025-08-26 07:06:44: Epoch: 037 New best VALIDATION metrics
+2025-08-26 07:06:48: Epoch: 037 Test results, ap: 0.227, auc: 0.492, f1: 0.000, acc: 0.683
+2025-08-26 07:07:27: Epoch: 038 Train Loss: 0.105, ap: 0.906, auc: 0.930, f1: 0.834, acc: 0.857
+2025-08-26 07:07:30: Epoch: 038 Test results, ap: 0.221, auc: 0.478, f1: 0.000, acc: 0.683
+2025-08-26 07:08:09: Epoch: 039 Train Loss: 0.095, ap: 0.906, auc: 0.931, f1: 0.830, acc: 0.855
+2025-08-26 07:08:13: Epoch: 039 Test results, ap: 0.218, auc: 0.469, f1: 0.000, acc: 0.683
+2025-08-26 07:08:53: Epoch: 040 Train Loss: 0.091, ap: 0.910, auc: 0.931, f1: 0.834, acc: 0.859
+2025-08-26 07:08:57: Epoch: 040 Test results, ap: 0.227, auc: 0.491, f1: 0.000, acc: 0.699
+2025-08-26 07:09:36: Epoch: 041 Train Loss: 0.081, ap: 0.910, auc: 0.933, f1: 0.845, acc: 0.866
+2025-08-26 07:09:36: Epoch: 041 New best VALIDATION metrics
+2025-08-26 07:09:39: Epoch: 041 Test results, ap: 0.226, auc: 0.489, f1: 0.000, acc: 0.691
+2025-08-26 07:10:18: Epoch: 042 Train Loss: 0.078, ap: 0.908, auc: 0.932, f1: 0.837, acc: 0.859
+2025-08-26 07:10:21: Epoch: 042 Test results, ap: 0.223, auc: 0.484, f1: 0.000, acc: 0.667
+2025-08-26 07:11:01: Epoch: 043 Train Loss: 0.084, ap: 0.906, auc: 0.932, f1: 0.842, acc: 0.864
+2025-08-26 07:11:05: Epoch: 043 Test results, ap: 0.221, auc: 0.477, f1: 0.000, acc: 0.667
+2025-08-26 07:11:44: Epoch: 044 Train Loss: 0.082, ap: 0.909, auc: 0.932, f1: 0.831, acc: 0.855
+2025-08-26 07:11:47: Epoch: 044 Test results, ap: 0.222, auc: 0.480, f1: 0.000, acc: 0.683
+2025-08-26 07:12:27: Epoch: 045 Train Loss: 0.077, ap: 0.910, auc: 0.933, f1: 0.854, acc: 0.873
+2025-08-26 07:12:27: Epoch: 045 New best VALIDATION metrics
+2025-08-26 07:12:31: Epoch: 045 Test results, ap: 0.226, auc: 0.492, f1: 0.000, acc: 0.667
+2025-08-26 07:13:14: Epoch: 046 Train Loss: 0.072, ap: 0.902, auc: 0.931, f1: 0.854, acc: 0.871
+2025-08-26 07:13:17: Epoch: 046 Test results, ap: 0.214, auc: 0.457, f1: 0.047, acc: 0.667
+2025-08-26 07:13:56: Epoch: 047 Train Loss: 0.084, ap: 0.908, auc: 0.933, f1: 0.843, acc: 0.864
+2025-08-26 07:13:59: Epoch: 047 Test results, ap: 0.227, auc: 0.492, f1: 0.000, acc: 0.667
+2025-08-26 07:14:39: Epoch: 048 Train Loss: 0.076, ap: 0.908, auc: 0.933, f1: 0.842, acc: 0.864
+2025-08-26 07:14:42: Epoch: 048 Test results, ap: 0.229, auc: 0.496, f1: 0.000, acc: 0.675
+2025-08-26 07:15:21: Epoch: 049 Train Loss: 0.067, ap: 0.905, auc: 0.932, f1: 0.844, acc: 0.862
+2025-08-26 07:15:24: Epoch: 049 Test results, ap: 0.224, auc: 0.483, f1: 0.048, acc: 0.675
+2025-08-26 07:16:04: Epoch: 050 Train Loss: 0.077, ap: 0.909, auc: 0.933, f1: 0.847, acc: 0.868
+2025-08-26 07:16:07: Epoch: 050 Test results, ap: 0.230, auc: 0.499, f1: 0.000, acc: 0.675
+2025-08-26 07:16:07: used time 0.60h
+2025-08-26 07:16:08: Running Cross Validation 4
+2025-08-26 07:16:08: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 07:16:47: Epoch: 001 Train Loss: 0.657, ap: 0.616, auc: 0.701, f1: 0.538, acc: 0.640
+2025-08-26 07:16:47: Epoch: 001 New best VALIDATION metrics
+2025-08-26 07:16:50: Epoch: 001 Test results, ap: 0.317, auc: 0.585, f1: 0.105, acc: 0.724
+2025-08-26 07:17:30: Epoch: 002 Train Loss: 0.621, ap: 0.642, auc: 0.716, f1: 0.547, acc: 0.658
+2025-08-26 07:17:30: Epoch: 002 New best VALIDATION metrics
+2025-08-26 07:17:33: Epoch: 002 Test results, ap: 0.232, auc: 0.471, f1: 0.054, acc: 0.715
+2025-08-26 07:18:13: Epoch: 003 Train Loss: 0.584, ap: 0.670, auc: 0.717, f1: 0.616, acc: 0.651
+2025-08-26 07:18:13: Epoch: 003 New best VALIDATION metrics
+2025-08-26 07:18:16: Epoch: 003 Test results, ap: 0.368, auc: 0.671, f1: 0.261, acc: 0.724
+2025-08-26 07:18:56: Epoch: 004 Train Loss: 0.558, ap: 0.590, auc: 0.667, f1: 0.589, acc: 0.633
+2025-08-26 07:18:59: Epoch: 004 Test results, ap: 0.369, auc: 0.595, f1: 0.326, acc: 0.764
+2025-08-26 07:19:40: Epoch: 005 Train Loss: 0.528, ap: 0.691, auc: 0.721, f1: 0.642, acc: 0.658
+2025-08-26 07:19:40: Epoch: 005 New best VALIDATION metrics
+2025-08-26 07:19:44: Epoch: 005 Test results, ap: 0.380, auc: 0.719, f1: 0.495, acc: 0.618
+2025-08-26 07:20:24: Epoch: 006 Train Loss: 0.486, ap: 0.732, auc: 0.784, f1: 0.681, acc: 0.704
+2025-08-26 07:20:24: Epoch: 006 New best VALIDATION metrics
+2025-08-26 07:20:27: Epoch: 006 Test results, ap: 0.307, auc: 0.552, f1: 0.267, acc: 0.642
+2025-08-26 07:21:10: Epoch: 007 Train Loss: 0.447, ap: 0.739, auc: 0.802, f1: 0.710, acc: 0.705
+2025-08-26 07:21:10: Epoch: 007 New best VALIDATION metrics
+2025-08-26 07:21:13: Epoch: 007 Test results, ap: 0.226, auc: 0.486, f1: 0.340, acc: 0.496
+2025-08-26 07:21:56: Epoch: 008 Train Loss: 0.392, ap: 0.753, auc: 0.834, f1: 0.716, acc: 0.751
+2025-08-26 07:21:56: Epoch: 008 New best VALIDATION metrics
+2025-08-26 07:22:00: Epoch: 008 Test results, ap: 0.286, auc: 0.594, f1: 0.213, acc: 0.699
+2025-08-26 07:22:42: Epoch: 009 Train Loss: 0.368, ap: 0.797, auc: 0.857, f1: 0.743, acc: 0.764
+2025-08-26 07:22:42: Epoch: 009 New best VALIDATION metrics
+2025-08-26 07:22:45: Epoch: 009 Test results, ap: 0.272, auc: 0.547, f1: 0.231, acc: 0.675
+2025-08-26 07:23:31: Epoch: 010 Train Loss: 0.369, ap: 0.801, auc: 0.868, f1: 0.763, acc: 0.787
+2025-08-26 07:23:31: Epoch: 010 New best VALIDATION metrics
+2025-08-26 07:23:35: Epoch: 010 Test results, ap: 0.212, auc: 0.404, f1: 0.115, acc: 0.626
+2025-08-26 07:24:14: Epoch: 011 Train Loss: 0.305, ap: 0.834, auc: 0.874, f1: 0.764, acc: 0.797
+2025-08-26 07:24:14: Epoch: 011 New best VALIDATION metrics
+2025-08-26 07:24:17: Epoch: 011 Test results, ap: 0.243, auc: 0.522, f1: 0.091, acc: 0.675
+2025-08-26 07:24:17: Epoch: 011 New best TEST metrics
+2025-08-26 07:24:57: Epoch: 012 Train Loss: 0.279, ap: 0.831, auc: 0.897, f1: 0.795, acc: 0.811
+2025-08-26 07:24:57: Epoch: 012 New best VALIDATION metrics
+2025-08-26 07:25:01: Epoch: 012 Test results, ap: 0.237, auc: 0.510, f1: 0.148, acc: 0.626
+2025-08-26 07:25:40: Epoch: 013 Train Loss: 0.255, ap: 0.831, auc: 0.887, f1: 0.770, acc: 0.795
+2025-08-26 07:25:44: Epoch: 013 Test results, ap: 0.239, auc: 0.503, f1: 0.178, acc: 0.699
+2025-08-26 07:26:23: Epoch: 014 Train Loss: 0.242, ap: 0.867, auc: 0.914, f1: 0.821, acc: 0.838
+2025-08-26 07:26:23: Epoch: 014 New best VALIDATION metrics
+2025-08-26 07:26:27: Epoch: 014 Test results, ap: 0.235, auc: 0.490, f1: 0.118, acc: 0.634
+2025-08-26 07:27:06: Epoch: 015 Train Loss: 0.240, ap: 0.853, auc: 0.908, f1: 0.809, acc: 0.825
+2025-08-26 07:27:10: Epoch: 015 Test results, ap: 0.217, auc: 0.454, f1: 0.093, acc: 0.683
+2025-08-26 07:27:49: Epoch: 016 Train Loss: 0.230, ap: 0.854, auc: 0.897, f1: 0.792, acc: 0.820
+2025-08-26 07:27:52: Epoch: 016 Test results, ap: 0.254, auc: 0.516, f1: 0.136, acc: 0.691
+2025-08-26 07:27:52: Epoch: 016 New best TEST metrics
+2025-08-26 07:28:31: Epoch: 017 Train Loss: 0.222, ap: 0.863, auc: 0.901, f1: 0.791, acc: 0.813
+2025-08-26 07:28:35: Epoch: 017 Test results, ap: 0.251, auc: 0.516, f1: 0.133, acc: 0.683
+2025-08-26 07:29:15: Epoch: 018 Train Loss: 0.213, ap: 0.858, auc: 0.907, f1: 0.810, acc: 0.829
+2025-08-26 07:29:18: Epoch: 018 Test results, ap: 0.242, auc: 0.511, f1: 0.098, acc: 0.699
+2025-08-26 07:29:57: Epoch: 019 Train Loss: 0.211, ap: 0.882, auc: 0.916, f1: 0.805, acc: 0.827
+2025-08-26 07:29:57: Epoch: 019 New best VALIDATION metrics
+2025-08-26 07:30:00: Epoch: 019 Test results, ap: 0.244, auc: 0.508, f1: 0.098, acc: 0.699
+2025-08-26 07:30:40: Epoch: 020 Train Loss: 0.214, ap: 0.874, auc: 0.915, f1: 0.812, acc: 0.831
+2025-08-26 07:30:43: Epoch: 020 Test results, ap: 0.233, auc: 0.494, f1: 0.050, acc: 0.691
+2025-08-26 07:31:23: Epoch: 021 Train Loss: 0.180, ap: 0.891, auc: 0.927, f1: 0.828, acc: 0.847
+2025-08-26 07:31:23: Epoch: 021 New best VALIDATION metrics
+2025-08-26 07:31:26: Epoch: 021 Test results, ap: 0.241, auc: 0.515, f1: 0.093, acc: 0.683
+2025-08-26 07:32:06: Epoch: 022 Train Loss: 0.165, ap: 0.866, auc: 0.917, f1: 0.809, acc: 0.829
+2025-08-26 07:32:09: Epoch: 022 Test results, ap: 0.226, auc: 0.471, f1: 0.113, acc: 0.618
+2025-08-26 07:32:48: Epoch: 023 Train Loss: 0.170, ap: 0.885, auc: 0.923, f1: 0.820, acc: 0.836
+2025-08-26 07:32:51: Epoch: 023 Test results, ap: 0.242, auc: 0.523, f1: 0.047, acc: 0.667
+2025-08-26 07:33:32: Epoch: 024 Train Loss: 0.172, ap: 0.873, auc: 0.921, f1: 0.831, acc: 0.848
+2025-08-26 07:33:36: Epoch: 024 Test results, ap: 0.242, auc: 0.518, f1: 0.089, acc: 0.667
+2025-08-26 07:34:16: Epoch: 025 Train Loss: 0.155, ap: 0.863, auc: 0.915, f1: 0.815, acc: 0.836
+2025-08-26 07:34:21: Epoch: 025 Test results, ap: 0.239, auc: 0.505, f1: 0.136, acc: 0.691
+2025-08-26 07:35:07: Epoch: 026 Train Loss: 0.152, ap: 0.874, auc: 0.919, f1: 0.819, acc: 0.838
+2025-08-26 07:35:11: Epoch: 026 Test results, ap: 0.246, auc: 0.532, f1: 0.093, acc: 0.683
+2025-08-26 07:35:11: Epoch: 026 New best TEST metrics
+2025-08-26 07:35:50: Epoch: 027 Train Loss: 0.145, ap: 0.871, auc: 0.920, f1: 0.828, acc: 0.847
+2025-08-26 07:35:54: Epoch: 027 Test results, ap: 0.245, auc: 0.527, f1: 0.093, acc: 0.683
+2025-08-26 07:36:41: Epoch: 028 Train Loss: 0.154, ap: 0.872, auc: 0.917, f1: 0.828, acc: 0.847
+2025-08-26 07:36:46: Epoch: 028 Test results, ap: 0.246, auc: 0.526, f1: 0.133, acc: 0.683
+2025-08-26 07:37:28: Epoch: 029 Train Loss: 0.157, ap: 0.888, auc: 0.926, f1: 0.831, acc: 0.848
+2025-08-26 07:37:32: Epoch: 029 Test results, ap: 0.245, auc: 0.524, f1: 0.095, acc: 0.691
+2025-08-26 07:38:15: Epoch: 030 Train Loss: 0.149, ap: 0.865, auc: 0.913, f1: 0.817, acc: 0.836
+2025-08-26 07:38:20: Epoch: 030 Test results, ap: 0.249, auc: 0.540, f1: 0.093, acc: 0.683
+2025-08-26 07:38:20: Epoch: 030 New best TEST metrics
+2025-08-26 07:39:01: Epoch: 031 Train Loss: 0.129, ap: 0.879, auc: 0.922, f1: 0.831, acc: 0.848
+2025-08-26 07:39:06: Epoch: 031 Test results, ap: 0.250, auc: 0.539, f1: 0.050, acc: 0.691
+2025-08-26 07:39:06: Epoch: 031 New best TEST metrics
+2025-08-26 07:39:53: Epoch: 032 Train Loss: 0.123, ap: 0.875, auc: 0.920, f1: 0.829, acc: 0.848
+2025-08-26 07:39:58: Epoch: 032 Test results, ap: 0.249, auc: 0.533, f1: 0.140, acc: 0.699
+2025-08-26 07:40:43: Epoch: 033 Train Loss: 0.119, ap: 0.887, auc: 0.925, f1: 0.829, acc: 0.848
+2025-08-26 07:40:46: Epoch: 033 Test results, ap: 0.253, auc: 0.547, f1: 0.051, acc: 0.699
+2025-08-26 07:40:46: Epoch: 033 New best TEST metrics
+2025-08-26 07:41:26: Epoch: 034 Train Loss: 0.129, ap: 0.884, auc: 0.924, f1: 0.833, acc: 0.850
+2025-08-26 07:41:30: Epoch: 034 Test results, ap: 0.251, auc: 0.547, f1: 0.093, acc: 0.683
+2025-08-26 07:42:10: Epoch: 035 Train Loss: 0.120, ap: 0.894, auc: 0.926, f1: 0.832, acc: 0.848
+2025-08-26 07:42:10: Epoch: 035 New best VALIDATION metrics
+2025-08-26 07:42:13: Epoch: 035 Test results, ap: 0.256, auc: 0.548, f1: 0.093, acc: 0.683
+2025-08-26 07:42:13: Epoch: 035 New best TEST metrics
+2025-08-26 07:42:54: Epoch: 036 Train Loss: 0.121, ap: 0.880, auc: 0.924, f1: 0.823, acc: 0.843
+2025-08-26 07:42:57: Epoch: 036 Test results, ap: 0.248, auc: 0.536, f1: 0.133, acc: 0.683
+2025-08-26 07:43:37: Epoch: 037 Train Loss: 0.117, ap: 0.885, auc: 0.926, f1: 0.839, acc: 0.855
+2025-08-26 07:43:40: Epoch: 037 Test results, ap: 0.254, auc: 0.552, f1: 0.091, acc: 0.675
+2025-08-26 07:43:40: Epoch: 037 New best TEST metrics
+2025-08-26 07:44:22: Epoch: 038 Train Loss: 0.119, ap: 0.896, auc: 0.928, f1: 0.833, acc: 0.852
+2025-08-26 07:44:22: Epoch: 038 New best VALIDATION metrics
+2025-08-26 07:44:26: Epoch: 038 Test results, ap: 0.255, auc: 0.558, f1: 0.054, acc: 0.715
+2025-08-26 07:44:26: Epoch: 038 New best TEST metrics
+2025-08-26 07:45:09: Epoch: 039 Train Loss: 0.109, ap: 0.897, auc: 0.927, f1: 0.837, acc: 0.857
+2025-08-26 07:45:13: Epoch: 039 Test results, ap: 0.257, auc: 0.557, f1: 0.100, acc: 0.707
+2025-08-26 07:45:13: Epoch: 039 New best TEST metrics
+2025-08-26 07:45:57: Epoch: 040 Train Loss: 0.109, ap: 0.897, auc: 0.928, f1: 0.835, acc: 0.854
+2025-08-26 07:45:57: Epoch: 040 New best VALIDATION metrics
+2025-08-26 07:46:26: Epoch: 040 Test results, ap: 0.256, auc: 0.558, f1: 0.054, acc: 0.715
+2025-08-26 07:47:07: Epoch: 041 Train Loss: 0.105, ap: 0.891, auc: 0.930, f1: 0.853, acc: 0.868
+2025-08-26 07:47:11: Epoch: 041 Test results, ap: 0.255, auc: 0.551, f1: 0.053, acc: 0.707
+2025-08-26 07:47:51: Epoch: 042 Train Loss: 0.094, ap: 0.888, auc: 0.927, f1: 0.836, acc: 0.854
+2025-08-26 07:47:55: Epoch: 042 Test results, ap: 0.258, auc: 0.556, f1: 0.103, acc: 0.715
+2025-08-26 07:48:36: Epoch: 043 Train Loss: 0.099, ap: 0.899, auc: 0.930, f1: 0.847, acc: 0.864
+2025-08-26 07:48:36: Epoch: 043 New best VALIDATION metrics
+2025-08-26 07:48:39: Epoch: 043 Test results, ap: 0.255, auc: 0.549, f1: 0.150, acc: 0.724
+2025-08-26 07:49:20: Epoch: 044 Train Loss: 0.099, ap: 0.896, auc: 0.930, f1: 0.849, acc: 0.864
+2025-08-26 07:49:23: Epoch: 044 Test results, ap: 0.252, auc: 0.548, f1: 0.050, acc: 0.691
+2025-08-26 07:50:03: Epoch: 045 Train Loss: 0.092, ap: 0.893, auc: 0.927, f1: 0.845, acc: 0.862
+2025-08-26 07:50:07: Epoch: 045 Test results, ap: 0.254, auc: 0.552, f1: 0.053, acc: 0.707
+2025-08-26 07:50:47: Epoch: 046 Train Loss: 0.091, ap: 0.898, auc: 0.929, f1: 0.847, acc: 0.862
+2025-08-26 07:50:50: Epoch: 046 Test results, ap: 0.252, auc: 0.547, f1: 0.098, acc: 0.699
+2025-08-26 07:51:30: Epoch: 047 Train Loss: 0.088, ap: 0.890, auc: 0.927, f1: 0.848, acc: 0.862
+2025-08-26 07:51:33: Epoch: 047 Test results, ap: 0.251, auc: 0.550, f1: 0.136, acc: 0.691
+2025-08-26 07:52:14: Epoch: 048 Train Loss: 0.090, ap: 0.892, auc: 0.929, f1: 0.847, acc: 0.861
+2025-08-26 07:52:17: Epoch: 048 Test results, ap: 0.250, auc: 0.549, f1: 0.136, acc: 0.691
+2025-08-26 07:52:57: Epoch: 049 Train Loss: 0.089, ap: 0.888, auc: 0.928, f1: 0.845, acc: 0.859
+2025-08-26 07:53:00: Epoch: 049 Test results, ap: 0.252, auc: 0.547, f1: 0.136, acc: 0.691
+2025-08-26 07:53:40: Epoch: 050 Train Loss: 0.095, ap: 0.896, auc: 0.930, f1: 0.847, acc: 0.861
+2025-08-26 07:53:43: Epoch: 050 Test results, ap: 0.256, auc: 0.551, f1: 0.136, acc: 0.691
+2025-08-26 07:53:43: used time 0.63h
+2025-08-26 07:53:43: Cross Validation Finished!
+2025-08-26 07:53:43: Best validation perform list
+[[0.93524039 0.94304955 0.86346865 0.86971831]
+ [0.94116569 0.95890772 0.87370598 0.89260566]
+ [0.91896695 0.93835962 0.85148513 0.8677249 ]
+ [0.91032803 0.93322086 0.85365856 0.87301588]
+ [0.89935875 0.93038189 0.84691846 0.86419755]]
+2025-08-26 07:53:43: mean: [0.921 0.941 0.858 0.873]
+2025-08-26 07:53:43: std: [0.015 0.01  0.01  0.01 ]
+2025-08-26 07:53:43: Best test perform list
+[[0.30482379 0.62903225 0.1025641  0.71544713]
+ [0.28504932 0.58673835 0.23076923 0.67479676]
+ [0.32906821 0.63369179 0.15789473 0.73983741]
+ [0.26821083 0.56057346 0.         0.71544713]
+ [0.25749874 0.55698925 0.1        0.70731705]]
+2025-08-26 07:53:43: mean: [0.289 0.593 0.118 0.711]
+2025-08-26 07:53:43: std: [0.026 0.033 0.076 0.021]
diff --git a/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/config.json b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..19fc62ead3112786f076a900c8535abef949c63e
--- /dev/null
+++ b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": "mamba", "channels": 16, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": true, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
diff --git a/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/feature_preds.csv b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/feature_preds.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4bf909093fa0dfb09165f4cb9d2384117311389f
--- /dev/null
+++ b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/feature_preds.csv
@@ -0,0 +1,16384 @@
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diff --git a/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/features.pth b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/features.pth
new file mode 100644
index 0000000000000000000000000000000000000000..4fc285d9be278f68a4a1d63660d5cfb50d04ffa3
--- /dev/null
+++ b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/features.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93bdfd95f814d41b0b1476dbc1e1c6f10dadd89f4695764a89dfd2ead2577eb5
+size 8939517
diff --git a/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/inference_results.csv b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/inference_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d959825feb1003e1f69e5ad262acaa3551cb597f
--- /dev/null
+++ b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/inference_results.csv
@@ -0,0 +1,3 @@
+dataset,AUPRC,AUROC,F1,ACC
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+2025-08-26 07:59:49: Epoch: 005 New best VALIDATION metrics
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+2025-08-26 08:00:30: Epoch: 006 New best VALIDATION metrics
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+2025-08-26 08:01:13: Epoch: 007 Train Loss: 0.398, ap: 0.872, auc: 0.874, f1: 0.790, acc: 0.783
+2025-08-26 08:01:13: Epoch: 007 New best VALIDATION metrics
+2025-08-26 08:01:17: Epoch: 007 Test results, ap: 0.250, auc: 0.466, f1: 0.333, acc: 0.545
+2025-08-26 08:01:55: Epoch: 008 Train Loss: 0.368, ap: 0.884, auc: 0.890, f1: 0.799, acc: 0.787
+2025-08-26 08:01:55: Epoch: 008 New best VALIDATION metrics
+2025-08-26 08:01:59: Epoch: 008 Test results, ap: 0.200, auc: 0.353, f1: 0.255, acc: 0.333
+2025-08-26 08:02:38: Epoch: 009 Train Loss: 0.334, ap: 0.891, auc: 0.897, f1: 0.815, acc: 0.808
+2025-08-26 08:02:38: Epoch: 009 New best VALIDATION metrics
+2025-08-26 08:02:42: Epoch: 009 Test results, ap: 0.245, auc: 0.453, f1: 0.278, acc: 0.537
+2025-08-26 08:03:19: Epoch: 010 Train Loss: 0.313, ap: 0.899, auc: 0.905, f1: 0.825, acc: 0.813
+2025-08-26 08:03:19: Epoch: 010 New best VALIDATION metrics
+2025-08-26 08:03:23: Epoch: 010 Test results, ap: 0.209, auc: 0.373, f1: 0.245, acc: 0.398
+2025-08-26 08:04:01: Epoch: 011 Train Loss: 0.273, ap: 0.924, auc: 0.927, f1: 0.840, acc: 0.836
+2025-08-26 08:04:01: Epoch: 011 New best VALIDATION metrics
+2025-08-26 08:04:05: Epoch: 011 Test results, ap: 0.246, auc: 0.415, f1: 0.195, acc: 0.463
+2025-08-26 08:04:05: Epoch: 011 New best TEST metrics
+2025-08-26 08:04:42: Epoch: 012 Train Loss: 0.238, ap: 0.921, auc: 0.932, f1: 0.854, acc: 0.850
+2025-08-26 08:04:42: Epoch: 012 New best VALIDATION metrics
+2025-08-26 08:04:46: Epoch: 012 Test results, ap: 0.229, auc: 0.459, f1: 0.295, acc: 0.496
+2025-08-26 08:04:46: Epoch: 012 New best TEST metrics
+2025-08-26 08:05:25: Epoch: 013 Train Loss: 0.210, ap: 0.915, auc: 0.924, f1: 0.852, acc: 0.847
+2025-08-26 08:05:29: Epoch: 013 Test results, ap: 0.221, auc: 0.433, f1: 0.277, acc: 0.447
+2025-08-26 08:06:08: Epoch: 014 Train Loss: 0.218, ap: 0.926, auc: 0.930, f1: 0.842, acc: 0.840
+2025-08-26 08:06:08: Epoch: 014 New best VALIDATION metrics
+2025-08-26 08:06:12: Epoch: 014 Test results, ap: 0.241, auc: 0.458, f1: 0.286, acc: 0.472
+2025-08-26 08:06:12: Epoch: 014 New best TEST metrics
+2025-08-26 08:06:50: Epoch: 015 Train Loss: 0.193, ap: 0.916, auc: 0.930, f1: 0.851, acc: 0.845
+2025-08-26 08:06:54: Epoch: 015 Test results, ap: 0.231, auc: 0.449, f1: 0.298, acc: 0.463
+2025-08-26 08:07:33: Epoch: 016 Train Loss: 0.180, ap: 0.923, auc: 0.932, f1: 0.859, acc: 0.852
+2025-08-26 08:07:37: Epoch: 016 Test results, ap: 0.223, auc: 0.444, f1: 0.227, acc: 0.447
+2025-08-26 08:08:16: Epoch: 017 Train Loss: 0.188, ap: 0.925, auc: 0.925, f1: 0.845, acc: 0.847
+2025-08-26 08:08:20: Epoch: 017 Test results, ap: 0.235, auc: 0.449, f1: 0.213, acc: 0.520
+2025-08-26 08:09:00: Epoch: 018 Train Loss: 0.179, ap: 0.923, auc: 0.928, f1: 0.842, acc: 0.845
+2025-08-26 08:09:04: Epoch: 018 Test results, ap: 0.220, auc: 0.423, f1: 0.220, acc: 0.480
+2025-08-26 08:09:43: Epoch: 019 Train Loss: 0.163, ap: 0.909, auc: 0.923, f1: 0.859, acc: 0.854
+2025-08-26 08:09:47: Epoch: 019 Test results, ap: 0.239, auc: 0.486, f1: 0.300, acc: 0.545
+2025-08-26 08:09:47: Epoch: 019 New best TEST metrics
+2025-08-26 08:10:25: Epoch: 020 Train Loss: 0.154, ap: 0.934, auc: 0.939, f1: 0.854, acc: 0.854
+2025-08-26 08:10:25: Epoch: 020 New best VALIDATION metrics
+2025-08-26 08:10:30: Epoch: 020 Test results, ap: 0.228, auc: 0.463, f1: 0.253, acc: 0.472
+2025-08-26 08:11:08: Epoch: 021 Train Loss: 0.131, ap: 0.931, auc: 0.939, f1: 0.865, acc: 0.863
+2025-08-26 08:11:13: Epoch: 021 Test results, ap: 0.219, auc: 0.434, f1: 0.271, acc: 0.431
+2025-08-26 08:11:51: Epoch: 022 Train Loss: 0.114, ap: 0.934, auc: 0.938, f1: 0.860, acc: 0.861
+2025-08-26 08:11:55: Epoch: 022 Test results, ap: 0.239, auc: 0.461, f1: 0.241, acc: 0.488
+2025-08-26 08:12:33: Epoch: 023 Train Loss: 0.117, ap: 0.934, auc: 0.943, f1: 0.866, acc: 0.864
+2025-08-26 08:12:33: Epoch: 023 New best VALIDATION metrics
+2025-08-26 08:12:37: Epoch: 023 Test results, ap: 0.228, auc: 0.466, f1: 0.280, acc: 0.455
+2025-08-26 08:13:16: Epoch: 024 Train Loss: 0.113, ap: 0.934, auc: 0.941, f1: 0.867, acc: 0.866
+2025-08-26 08:13:20: Epoch: 024 Test results, ap: 0.233, auc: 0.468, f1: 0.282, acc: 0.504
+2025-08-26 08:13:59: Epoch: 025 Train Loss: 0.100, ap: 0.933, auc: 0.940, f1: 0.863, acc: 0.863
+2025-08-26 08:14:03: Epoch: 025 Test results, ap: 0.229, auc: 0.454, f1: 0.222, acc: 0.431
+2025-08-26 08:14:42: Epoch: 026 Train Loss: 0.102, ap: 0.939, auc: 0.945, f1: 0.858, acc: 0.857
+2025-08-26 08:14:42: Epoch: 026 New best VALIDATION metrics
+2025-08-26 08:14:47: Epoch: 026 Test results, ap: 0.218, auc: 0.430, f1: 0.209, acc: 0.447
+2025-08-26 08:15:25: Epoch: 027 Train Loss: 0.101, ap: 0.944, auc: 0.947, f1: 0.869, acc: 0.871
+2025-08-26 08:15:25: Epoch: 027 New best VALIDATION metrics
+2025-08-26 08:15:29: Epoch: 027 Test results, ap: 0.231, auc: 0.471, f1: 0.241, acc: 0.488
+2025-08-26 08:16:07: Epoch: 028 Train Loss: 0.097, ap: 0.937, auc: 0.946, f1: 0.865, acc: 0.866
+2025-08-26 08:16:11: Epoch: 028 Test results, ap: 0.219, auc: 0.445, f1: 0.253, acc: 0.423
+2025-08-26 08:16:48: Epoch: 029 Train Loss: 0.095, ap: 0.943, auc: 0.949, f1: 0.881, acc: 0.882
+2025-08-26 08:16:48: Epoch: 029 New best VALIDATION metrics
+2025-08-26 08:16:53: Epoch: 029 Test results, ap: 0.221, auc: 0.446, f1: 0.196, acc: 0.398
+2025-08-26 08:17:32: Epoch: 030 Train Loss: 0.092, ap: 0.946, auc: 0.950, f1: 0.870, acc: 0.871
+2025-08-26 08:17:32: Epoch: 030 New best VALIDATION metrics
+2025-08-26 08:17:36: Epoch: 030 Test results, ap: 0.231, auc: 0.466, f1: 0.217, acc: 0.472
+2025-08-26 08:18:15: Epoch: 031 Train Loss: 0.071, ap: 0.946, auc: 0.951, f1: 0.876, acc: 0.877
+2025-08-26 08:18:15: Epoch: 031 New best VALIDATION metrics
+2025-08-26 08:18:20: Epoch: 031 Test results, ap: 0.229, auc: 0.459, f1: 0.270, acc: 0.472
+2025-08-26 08:18:59: Epoch: 032 Train Loss: 0.067, ap: 0.948, auc: 0.950, f1: 0.876, acc: 0.879
+2025-08-26 08:18:59: Epoch: 032 New best VALIDATION metrics
+2025-08-26 08:19:03: Epoch: 032 Test results, ap: 0.233, auc: 0.471, f1: 0.217, acc: 0.472
+2025-08-26 08:19:41: Epoch: 033 Train Loss: 0.061, ap: 0.948, auc: 0.952, f1: 0.872, acc: 0.873
+2025-08-26 08:19:41: Epoch: 033 New best VALIDATION metrics
+2025-08-26 08:19:45: Epoch: 033 Test results, ap: 0.230, auc: 0.463, f1: 0.241, acc: 0.488
+2025-08-26 08:20:24: Epoch: 034 Train Loss: 0.066, ap: 0.949, auc: 0.953, f1: 0.874, acc: 0.877
+2025-08-26 08:20:24: Epoch: 034 New best VALIDATION metrics
+2025-08-26 08:20:28: Epoch: 034 Test results, ap: 0.232, auc: 0.470, f1: 0.289, acc: 0.520
+2025-08-26 08:21:07: Epoch: 035 Train Loss: 0.060, ap: 0.946, auc: 0.953, f1: 0.890, acc: 0.891
+2025-08-26 08:21:10: Epoch: 035 Test results, ap: 0.224, auc: 0.459, f1: 0.311, acc: 0.496
+2025-08-26 08:21:48: Epoch: 036 Train Loss: 0.059, ap: 0.947, auc: 0.951, f1: 0.883, acc: 0.884
+2025-08-26 08:21:52: Epoch: 036 Test results, ap: 0.226, auc: 0.459, f1: 0.265, acc: 0.504
+2025-08-26 08:22:30: Epoch: 037 Train Loss: 0.054, ap: 0.948, auc: 0.953, f1: 0.881, acc: 0.882
+2025-08-26 08:22:34: Epoch: 037 Test results, ap: 0.230, auc: 0.470, f1: 0.238, acc: 0.480
+2025-08-26 08:23:13: Epoch: 038 Train Loss: 0.051, ap: 0.950, auc: 0.954, f1: 0.886, acc: 0.887
+2025-08-26 08:23:13: Epoch: 038 New best VALIDATION metrics
+2025-08-26 08:23:17: Epoch: 038 Test results, ap: 0.225, auc: 0.456, f1: 0.235, acc: 0.472
+2025-08-26 08:23:55: Epoch: 039 Train Loss: 0.057, ap: 0.946, auc: 0.952, f1: 0.890, acc: 0.891
+2025-08-26 08:23:59: Epoch: 039 Test results, ap: 0.217, auc: 0.444, f1: 0.256, acc: 0.480
+2025-08-26 08:24:37: Epoch: 040 Train Loss: 0.055, ap: 0.945, auc: 0.952, f1: 0.875, acc: 0.877
+2025-08-26 08:24:41: Epoch: 040 Test results, ap: 0.222, auc: 0.453, f1: 0.295, acc: 0.496
+2025-08-26 08:25:20: Epoch: 041 Train Loss: 0.050, ap: 0.948, auc: 0.954, f1: 0.884, acc: 0.886
+2025-08-26 08:25:23: Epoch: 041 Test results, ap: 0.229, auc: 0.469, f1: 0.247, acc: 0.504
+2025-08-26 08:26:01: Epoch: 042 Train Loss: 0.043, ap: 0.950, auc: 0.956, f1: 0.885, acc: 0.887
+2025-08-26 08:26:01: Epoch: 042 New best VALIDATION metrics
+2025-08-26 08:26:05: Epoch: 042 Test results, ap: 0.226, auc: 0.462, f1: 0.259, acc: 0.488
+2025-08-26 08:26:44: Epoch: 043 Train Loss: 0.040, ap: 0.949, auc: 0.954, f1: 0.882, acc: 0.884
+2025-08-26 08:26:48: Epoch: 043 Test results, ap: 0.229, auc: 0.466, f1: 0.244, acc: 0.496
+2025-08-26 08:27:25: Epoch: 044 Train Loss: 0.040, ap: 0.951, auc: 0.955, f1: 0.885, acc: 0.887
+2025-08-26 08:27:25: Epoch: 044 New best VALIDATION metrics
+2025-08-26 08:27:29: Epoch: 044 Test results, ap: 0.233, auc: 0.473, f1: 0.241, acc: 0.488
+2025-08-26 08:28:07: Epoch: 045 Train Loss: 0.042, ap: 0.953, auc: 0.956, f1: 0.890, acc: 0.893
+2025-08-26 08:28:07: Epoch: 045 New best VALIDATION metrics
+2025-08-26 08:28:11: Epoch: 045 Test results, ap: 0.232, auc: 0.473, f1: 0.241, acc: 0.488
+2025-08-26 08:28:49: Epoch: 046 Train Loss: 0.043, ap: 0.947, auc: 0.955, f1: 0.886, acc: 0.887
+2025-08-26 08:28:53: Epoch: 046 Test results, ap: 0.236, auc: 0.482, f1: 0.244, acc: 0.496
+2025-08-26 08:29:32: Epoch: 047 Train Loss: 0.037, ap: 0.947, auc: 0.956, f1: 0.892, acc: 0.894
+2025-08-26 08:29:36: Epoch: 047 Test results, ap: 0.230, auc: 0.470, f1: 0.265, acc: 0.504
+2025-08-26 08:30:12: Epoch: 048 Train Loss: 0.036, ap: 0.948, auc: 0.957, f1: 0.900, acc: 0.901
+2025-08-26 08:30:16: Epoch: 048 Test results, ap: 0.229, auc: 0.465, f1: 0.241, acc: 0.488
+2025-08-26 08:30:55: Epoch: 049 Train Loss: 0.036, ap: 0.948, auc: 0.956, f1: 0.892, acc: 0.894
+2025-08-26 08:30:59: Epoch: 049 Test results, ap: 0.234, auc: 0.476, f1: 0.262, acc: 0.496
+2025-08-26 08:31:37: Epoch: 050 Train Loss: 0.042, ap: 0.943, auc: 0.954, f1: 0.898, acc: 0.900
+2025-08-26 08:31:41: Epoch: 050 Test results, ap: 0.229, auc: 0.467, f1: 0.265, acc: 0.504
+2025-08-26 08:31:41: used time 0.59h
+2025-08-26 08:31:41: Running Cross Validation 1
+2025-08-26 08:31:41: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 08:32:18: Epoch: 001 Train Loss: 0.644, ap: 0.746, auc: 0.784, f1: 0.664, acc: 0.722
+2025-08-26 08:32:18: Epoch: 001 New best VALIDATION metrics
+2025-08-26 08:32:22: Epoch: 001 Test results, ap: 0.258, auc: 0.556, f1: 0.133, acc: 0.577
+2025-08-26 08:33:00: Epoch: 002 Train Loss: 0.550, ap: 0.734, auc: 0.793, f1: 0.692, acc: 0.727
+2025-08-26 08:33:04: Epoch: 002 Test results, ap: 0.271, auc: 0.565, f1: 0.354, acc: 0.585
+2025-08-26 08:33:42: Epoch: 003 Train Loss: 0.523, ap: 0.799, auc: 0.839, f1: 0.730, acc: 0.775
+2025-08-26 08:33:42: Epoch: 003 New best VALIDATION metrics
+2025-08-26 08:33:46: Epoch: 003 Test results, ap: 0.291, auc: 0.601, f1: 0.231, acc: 0.675
+2025-08-26 08:34:25: Epoch: 004 Train Loss: 0.479, ap: 0.797, auc: 0.835, f1: 0.721, acc: 0.761
+2025-08-26 08:34:29: Epoch: 004 Test results, ap: 0.330, auc: 0.599, f1: 0.351, acc: 0.699
+2025-08-26 08:35:08: Epoch: 005 Train Loss: 0.442, ap: 0.834, auc: 0.868, f1: 0.772, acc: 0.796
+2025-08-26 08:35:08: Epoch: 005 New best VALIDATION metrics
+2025-08-26 08:35:12: Epoch: 005 Test results, ap: 0.258, auc: 0.524, f1: 0.324, acc: 0.593
+2025-08-26 08:35:51: Epoch: 006 Train Loss: 0.399, ap: 0.809, auc: 0.840, f1: 0.723, acc: 0.775
+2025-08-26 08:35:55: Epoch: 006 Test results, ap: 0.317, auc: 0.544, f1: 0.317, acc: 0.650
+2025-08-26 08:36:33: Epoch: 007 Train Loss: 0.373, ap: 0.823, auc: 0.847, f1: 0.709, acc: 0.761
+2025-08-26 08:36:37: Epoch: 007 Test results, ap: 0.325, auc: 0.553, f1: 0.295, acc: 0.650
+2025-08-26 08:37:16: Epoch: 008 Train Loss: 0.358, ap: 0.838, auc: 0.858, f1: 0.723, acc: 0.762
+2025-08-26 08:37:20: Epoch: 008 Test results, ap: 0.311, auc: 0.541, f1: 0.273, acc: 0.610
+2025-08-26 08:37:58: Epoch: 009 Train Loss: 0.344, ap: 0.850, auc: 0.882, f1: 0.776, acc: 0.806
+2025-08-26 08:37:58: Epoch: 009 New best VALIDATION metrics
+2025-08-26 08:38:02: Epoch: 009 Test results, ap: 0.256, auc: 0.534, f1: 0.337, acc: 0.553
+2025-08-26 08:38:41: Epoch: 010 Train Loss: 0.326, ap: 0.878, auc: 0.901, f1: 0.802, acc: 0.822
+2025-08-26 08:38:41: Epoch: 010 New best VALIDATION metrics
+2025-08-26 08:38:45: Epoch: 010 Test results, ap: 0.263, auc: 0.528, f1: 0.329, acc: 0.602
+2025-08-26 08:39:23: Epoch: 011 Train Loss: 0.278, ap: 0.891, auc: 0.909, f1: 0.802, acc: 0.831
+2025-08-26 08:39:23: Epoch: 011 New best VALIDATION metrics
+2025-08-26 08:39:28: Epoch: 011 Test results, ap: 0.289, auc: 0.531, f1: 0.306, acc: 0.593
+2025-08-26 08:39:28: Epoch: 011 New best TEST metrics
+2025-08-26 08:40:06: Epoch: 012 Train Loss: 0.256, ap: 0.896, auc: 0.920, f1: 0.817, acc: 0.838
+2025-08-26 08:40:06: Epoch: 012 New best VALIDATION metrics
+2025-08-26 08:40:10: Epoch: 012 Test results, ap: 0.260, auc: 0.529, f1: 0.325, acc: 0.561
+2025-08-26 08:40:49: Epoch: 013 Train Loss: 0.234, ap: 0.912, auc: 0.927, f1: 0.829, acc: 0.847
+2025-08-26 08:40:49: Epoch: 013 New best VALIDATION metrics
+2025-08-26 08:40:53: Epoch: 013 Test results, ap: 0.300, auc: 0.544, f1: 0.314, acc: 0.610
+2025-08-26 08:40:53: Epoch: 013 New best TEST metrics
+2025-08-26 08:41:32: Epoch: 014 Train Loss: 0.219, ap: 0.900, auc: 0.919, f1: 0.819, acc: 0.843
+2025-08-26 08:41:36: Epoch: 014 Test results, ap: 0.258, auc: 0.528, f1: 0.278, acc: 0.577
+2025-08-26 08:42:14: Epoch: 015 Train Loss: 0.204, ap: 0.918, auc: 0.931, f1: 0.818, acc: 0.842
+2025-08-26 08:42:14: Epoch: 015 New best VALIDATION metrics
+2025-08-26 08:42:18: Epoch: 015 Test results, ap: 0.258, auc: 0.539, f1: 0.308, acc: 0.561
+2025-08-26 08:42:58: Epoch: 016 Train Loss: 0.198, ap: 0.917, auc: 0.927, f1: 0.824, acc: 0.849
+2025-08-26 08:43:02: Epoch: 016 Test results, ap: 0.279, auc: 0.527, f1: 0.290, acc: 0.602
+2025-08-26 08:43:41: Epoch: 017 Train Loss: 0.209, ap: 0.904, auc: 0.917, f1: 0.826, acc: 0.847
+2025-08-26 08:43:44: Epoch: 017 Test results, ap: 0.304, auc: 0.534, f1: 0.310, acc: 0.602
+2025-08-26 08:44:22: Epoch: 018 Train Loss: 0.180, ap: 0.918, auc: 0.934, f1: 0.841, acc: 0.863
+2025-08-26 08:44:22: Epoch: 018 New best VALIDATION metrics
+2025-08-26 08:44:26: Epoch: 018 Test results, ap: 0.279, auc: 0.549, f1: 0.320, acc: 0.585
+2025-08-26 08:45:05: Epoch: 019 Train Loss: 0.193, ap: 0.912, auc: 0.931, f1: 0.836, acc: 0.857
+2025-08-26 08:45:09: Epoch: 019 Test results, ap: 0.312, auc: 0.535, f1: 0.312, acc: 0.569
+2025-08-26 08:45:09: Epoch: 019 New best TEST metrics
+2025-08-26 08:45:47: Epoch: 020 Train Loss: 0.173, ap: 0.917, auc: 0.932, f1: 0.839, acc: 0.861
+2025-08-26 08:45:51: Epoch: 020 Test results, ap: 0.313, auc: 0.534, f1: 0.308, acc: 0.561
+2025-08-26 08:45:51: Epoch: 020 New best TEST metrics
+2025-08-26 08:46:29: Epoch: 021 Train Loss: 0.144, ap: 0.921, auc: 0.937, f1: 0.846, acc: 0.866
+2025-08-26 08:46:29: Epoch: 021 New best VALIDATION metrics
+2025-08-26 08:46:33: Epoch: 021 Test results, ap: 0.297, auc: 0.548, f1: 0.338, acc: 0.585
+2025-08-26 08:47:12: Epoch: 022 Train Loss: 0.136, ap: 0.932, auc: 0.941, f1: 0.847, acc: 0.870
+2025-08-26 08:47:12: Epoch: 022 New best VALIDATION metrics
+2025-08-26 08:47:16: Epoch: 022 Test results, ap: 0.327, auc: 0.558, f1: 0.316, acc: 0.577
+2025-08-26 08:47:16: Epoch: 022 New best TEST metrics
+2025-08-26 08:47:54: Epoch: 023 Train Loss: 0.133, ap: 0.914, auc: 0.932, f1: 0.848, acc: 0.864
+2025-08-26 08:47:58: Epoch: 023 Test results, ap: 0.268, auc: 0.541, f1: 0.337, acc: 0.553
+2025-08-26 08:48:36: Epoch: 024 Train Loss: 0.127, ap: 0.922, auc: 0.938, f1: 0.851, acc: 0.870
+2025-08-26 08:48:40: Epoch: 024 Test results, ap: 0.309, auc: 0.548, f1: 0.351, acc: 0.610
+2025-08-26 08:49:19: Epoch: 025 Train Loss: 0.128, ap: 0.928, auc: 0.940, f1: 0.841, acc: 0.863
+2025-08-26 08:49:23: Epoch: 025 Test results, ap: 0.301, auc: 0.558, f1: 0.342, acc: 0.593
+2025-08-26 08:50:00: Epoch: 026 Train Loss: 0.120, ap: 0.930, auc: 0.942, f1: 0.851, acc: 0.870
+2025-08-26 08:50:04: Epoch: 026 Test results, ap: 0.322, auc: 0.548, f1: 0.301, acc: 0.585
+2025-08-26 08:50:42: Epoch: 027 Train Loss: 0.118, ap: 0.933, auc: 0.942, f1: 0.853, acc: 0.871
+2025-08-26 08:50:42: Epoch: 027 New best VALIDATION metrics
+2025-08-26 08:50:46: Epoch: 027 Test results, ap: 0.332, auc: 0.560, f1: 0.324, acc: 0.593
+2025-08-26 08:50:46: Epoch: 027 New best TEST metrics
+2025-08-26 08:51:25: Epoch: 028 Train Loss: 0.115, ap: 0.926, auc: 0.939, f1: 0.849, acc: 0.870
+2025-08-26 08:51:29: Epoch: 028 Test results, ap: 0.319, auc: 0.572, f1: 0.314, acc: 0.610
+2025-08-26 08:52:07: Epoch: 029 Train Loss: 0.129, ap: 0.934, auc: 0.945, f1: 0.857, acc: 0.879
+2025-08-26 08:52:07: Epoch: 029 New best VALIDATION metrics
+2025-08-26 08:52:11: Epoch: 029 Test results, ap: 0.314, auc: 0.552, f1: 0.373, acc: 0.618
+2025-08-26 08:52:50: Epoch: 030 Train Loss: 0.105, ap: 0.934, auc: 0.947, f1: 0.880, acc: 0.894
+2025-08-26 08:52:50: Epoch: 030 New best VALIDATION metrics
+2025-08-26 08:52:54: Epoch: 030 Test results, ap: 0.300, auc: 0.543, f1: 0.320, acc: 0.585
+2025-08-26 08:53:32: Epoch: 031 Train Loss: 0.094, ap: 0.931, auc: 0.945, f1: 0.866, acc: 0.882
+2025-08-26 08:53:36: Epoch: 031 Test results, ap: 0.310, auc: 0.560, f1: 0.359, acc: 0.593
+2025-08-26 08:54:15: Epoch: 032 Train Loss: 0.089, ap: 0.936, auc: 0.946, f1: 0.865, acc: 0.882
+2025-08-26 08:54:15: Epoch: 032 New best VALIDATION metrics
+2025-08-26 08:54:19: Epoch: 032 Test results, ap: 0.330, auc: 0.565, f1: 0.306, acc: 0.593
+2025-08-26 08:54:19: Epoch: 032 New best TEST metrics
+2025-08-26 08:54:58: Epoch: 033 Train Loss: 0.087, ap: 0.938, auc: 0.947, f1: 0.869, acc: 0.886
+2025-08-26 08:54:58: Epoch: 033 New best VALIDATION metrics
+2025-08-26 08:55:02: Epoch: 033 Test results, ap: 0.320, auc: 0.570, f1: 0.368, acc: 0.610
+2025-08-26 08:55:41: Epoch: 034 Train Loss: 0.087, ap: 0.938, auc: 0.948, f1: 0.871, acc: 0.887
+2025-08-26 08:55:41: Epoch: 034 New best VALIDATION metrics
+2025-08-26 08:55:46: Epoch: 034 Test results, ap: 0.330, auc: 0.561, f1: 0.347, acc: 0.602
+2025-08-26 08:56:24: Epoch: 035 Train Loss: 0.085, ap: 0.942, auc: 0.950, f1: 0.870, acc: 0.887
+2025-08-26 08:56:24: Epoch: 035 New best VALIDATION metrics
+2025-08-26 08:56:29: Epoch: 035 Test results, ap: 0.318, auc: 0.563, f1: 0.306, acc: 0.593
+2025-08-26 08:57:07: Epoch: 036 Train Loss: 0.080, ap: 0.938, auc: 0.948, f1: 0.867, acc: 0.886
+2025-08-26 08:57:11: Epoch: 036 Test results, ap: 0.311, auc: 0.558, f1: 0.324, acc: 0.593
+2025-08-26 08:57:49: Epoch: 037 Train Loss: 0.085, ap: 0.937, auc: 0.947, f1: 0.859, acc: 0.879
+2025-08-26 08:57:53: Epoch: 037 Test results, ap: 0.310, auc: 0.568, f1: 0.347, acc: 0.602
+2025-08-26 08:58:31: Epoch: 038 Train Loss: 0.080, ap: 0.939, auc: 0.947, f1: 0.865, acc: 0.884
+2025-08-26 08:58:35: Epoch: 038 Test results, ap: 0.332, auc: 0.568, f1: 0.351, acc: 0.610
+2025-08-26 08:58:35: Epoch: 038 New best TEST metrics
+2025-08-26 08:59:14: Epoch: 039 Train Loss: 0.083, ap: 0.938, auc: 0.949, f1: 0.867, acc: 0.884
+2025-08-26 08:59:18: Epoch: 039 Test results, ap: 0.305, auc: 0.558, f1: 0.342, acc: 0.593
+2025-08-26 08:59:56: Epoch: 040 Train Loss: 0.077, ap: 0.939, auc: 0.951, f1: 0.867, acc: 0.886
+2025-08-26 09:00:00: Epoch: 040 Test results, ap: 0.304, auc: 0.560, f1: 0.368, acc: 0.610
+2025-08-26 09:00:39: Epoch: 041 Train Loss: 0.071, ap: 0.942, auc: 0.952, f1: 0.879, acc: 0.894
+2025-08-26 09:00:39: Epoch: 041 New best VALIDATION metrics
+2025-08-26 09:00:43: Epoch: 041 Test results, ap: 0.305, auc: 0.558, f1: 0.395, acc: 0.626
+2025-08-26 09:01:23: Epoch: 042 Train Loss: 0.068, ap: 0.942, auc: 0.951, f1: 0.876, acc: 0.893
+2025-08-26 09:01:27: Epoch: 042 Test results, ap: 0.309, auc: 0.561, f1: 0.351, acc: 0.610
+2025-08-26 09:02:05: Epoch: 043 Train Loss: 0.074, ap: 0.943, auc: 0.952, f1: 0.878, acc: 0.894
+2025-08-26 09:02:05: Epoch: 043 New best VALIDATION metrics
+2025-08-26 09:02:09: Epoch: 043 Test results, ap: 0.314, auc: 0.563, f1: 0.373, acc: 0.618
+2025-08-26 09:02:48: Epoch: 044 Train Loss: 0.062, ap: 0.944, auc: 0.952, f1: 0.877, acc: 0.893
+2025-08-26 09:02:48: Epoch: 044 New best VALIDATION metrics
+2025-08-26 09:02:53: Epoch: 044 Test results, ap: 0.317, auc: 0.564, f1: 0.329, acc: 0.602
+2025-08-26 09:03:32: Epoch: 045 Train Loss: 0.070, ap: 0.942, auc: 0.950, f1: 0.869, acc: 0.887
+2025-08-26 09:03:36: Epoch: 045 Test results, ap: 0.314, auc: 0.561, f1: 0.329, acc: 0.602
+2025-08-26 09:04:15: Epoch: 046 Train Loss: 0.060, ap: 0.942, auc: 0.951, f1: 0.876, acc: 0.893
+2025-08-26 09:04:19: Epoch: 046 Test results, ap: 0.311, auc: 0.559, f1: 0.347, acc: 0.602
+2025-08-26 09:04:57: Epoch: 047 Train Loss: 0.064, ap: 0.941, auc: 0.950, f1: 0.876, acc: 0.893
+2025-08-26 09:05:01: Epoch: 047 Test results, ap: 0.321, auc: 0.562, f1: 0.368, acc: 0.610
+2025-08-26 09:05:40: Epoch: 048 Train Loss: 0.066, ap: 0.944, auc: 0.951, f1: 0.873, acc: 0.891
+2025-08-26 09:05:44: Epoch: 048 Test results, ap: 0.319, auc: 0.568, f1: 0.368, acc: 0.610
+2025-08-26 09:06:22: Epoch: 049 Train Loss: 0.062, ap: 0.941, auc: 0.951, f1: 0.876, acc: 0.891
+2025-08-26 09:06:26: Epoch: 049 Test results, ap: 0.296, auc: 0.566, f1: 0.368, acc: 0.610
+2025-08-26 09:07:05: Epoch: 050 Train Loss: 0.058, ap: 0.941, auc: 0.950, f1: 0.876, acc: 0.893
+2025-08-26 09:07:10: Epoch: 050 Test results, ap: 0.319, auc: 0.569, f1: 0.351, acc: 0.610
+2025-08-26 09:07:10: used time 0.59h
+2025-08-26 09:07:10: Running Cross Validation 2
+2025-08-26 09:07:10: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 09:07:49: Epoch: 001 Train Loss: 0.636, ap: 0.731, auc: 0.782, f1: 0.689, acc: 0.718
+2025-08-26 09:07:49: Epoch: 001 New best VALIDATION metrics
+2025-08-26 09:07:53: Epoch: 001 Test results, ap: 0.276, auc: 0.532, f1: 0.264, acc: 0.683
+2025-08-26 09:08:32: Epoch: 002 Train Loss: 0.551, ap: 0.764, auc: 0.819, f1: 0.745, acc: 0.744
+2025-08-26 09:08:32: Epoch: 002 New best VALIDATION metrics
+2025-08-26 09:08:36: Epoch: 002 Test results, ap: 0.266, auc: 0.573, f1: 0.370, acc: 0.585
+2025-08-26 09:09:15: Epoch: 003 Train Loss: 0.476, ap: 0.753, auc: 0.783, f1: 0.659, acc: 0.700
+2025-08-26 09:09:19: Epoch: 003 Test results, ap: 0.440, auc: 0.653, f1: 0.340, acc: 0.748
+2025-08-26 09:09:59: Epoch: 004 Train Loss: 0.438, ap: 0.801, auc: 0.837, f1: 0.727, acc: 0.721
+2025-08-26 09:09:59: Epoch: 004 New best VALIDATION metrics
+2025-08-26 09:10:03: Epoch: 004 Test results, ap: 0.343, auc: 0.641, f1: 0.400, acc: 0.561
+2025-08-26 09:10:42: Epoch: 005 Train Loss: 0.413, ap: 0.832, auc: 0.857, f1: 0.749, acc: 0.762
+2025-08-26 09:10:42: Epoch: 005 New best VALIDATION metrics
+2025-08-26 09:10:46: Epoch: 005 Test results, ap: 0.284, auc: 0.576, f1: 0.392, acc: 0.496
+2025-08-26 09:11:25: Epoch: 006 Train Loss: 0.366, ap: 0.848, auc: 0.861, f1: 0.745, acc: 0.772
+2025-08-26 09:11:25: Epoch: 006 New best VALIDATION metrics
+2025-08-26 09:11:29: Epoch: 006 Test results, ap: 0.425, auc: 0.638, f1: 0.360, acc: 0.740
+2025-08-26 09:12:08: Epoch: 007 Train Loss: 0.366, ap: 0.843, auc: 0.857, f1: 0.718, acc: 0.762
+2025-08-26 09:12:13: Epoch: 007 Test results, ap: 0.384, auc: 0.663, f1: 0.370, acc: 0.724
+2025-08-26 09:12:51: Epoch: 008 Train Loss: 0.342, ap: 0.857, auc: 0.876, f1: 0.766, acc: 0.787
+2025-08-26 09:12:51: Epoch: 008 New best VALIDATION metrics
+2025-08-26 09:12:56: Epoch: 008 Test results, ap: 0.322, auc: 0.614, f1: 0.400, acc: 0.561
+2025-08-26 09:13:33: Epoch: 009 Train Loss: 0.325, ap: 0.887, auc: 0.899, f1: 0.785, acc: 0.797
+2025-08-26 09:13:33: Epoch: 009 New best VALIDATION metrics
+2025-08-26 09:13:38: Epoch: 009 Test results, ap: 0.354, auc: 0.619, f1: 0.409, acc: 0.553
+2025-08-26 09:14:16: Epoch: 010 Train Loss: 0.307, ap: 0.898, auc: 0.903, f1: 0.787, acc: 0.811
+2025-08-26 09:14:16: Epoch: 010 New best VALIDATION metrics
+2025-08-26 09:14:20: Epoch: 010 Test results, ap: 0.300, auc: 0.543, f1: 0.203, acc: 0.618
+2025-08-26 09:14:58: Epoch: 011 Train Loss: 0.248, ap: 0.907, auc: 0.919, f1: 0.821, acc: 0.838
+2025-08-26 09:14:58: Epoch: 011 New best VALIDATION metrics
+2025-08-26 09:15:03: Epoch: 011 Test results, ap: 0.360, auc: 0.601, f1: 0.316, acc: 0.683
+2025-08-26 09:15:03: Epoch: 011 New best TEST metrics
+2025-08-26 09:15:41: Epoch: 012 Train Loss: 0.238, ap: 0.902, auc: 0.907, f1: 0.788, acc: 0.810
+2025-08-26 09:15:45: Epoch: 012 Test results, ap: 0.376, auc: 0.647, f1: 0.240, acc: 0.691
+2025-08-26 09:15:45: Epoch: 012 New best TEST metrics
+2025-08-26 09:16:24: Epoch: 013 Train Loss: 0.227, ap: 0.909, auc: 0.918, f1: 0.807, acc: 0.827
+2025-08-26 09:16:24: Epoch: 013 New best VALIDATION metrics
+2025-08-26 09:16:28: Epoch: 013 Test results, ap: 0.407, auc: 0.652, f1: 0.448, acc: 0.740
+2025-08-26 09:16:28: Epoch: 013 New best TEST metrics
+2025-08-26 09:17:08: Epoch: 014 Train Loss: 0.215, ap: 0.920, auc: 0.927, f1: 0.827, acc: 0.843
+2025-08-26 09:17:08: Epoch: 014 New best VALIDATION metrics
+2025-08-26 09:17:12: Epoch: 014 Test results, ap: 0.369, auc: 0.684, f1: 0.381, acc: 0.683
+2025-08-26 09:17:50: Epoch: 015 Train Loss: 0.209, ap: 0.921, auc: 0.930, f1: 0.838, acc: 0.852
+2025-08-26 09:17:50: Epoch: 015 New best VALIDATION metrics
+2025-08-26 09:17:55: Epoch: 015 Test results, ap: 0.320, auc: 0.623, f1: 0.349, acc: 0.667
+2025-08-26 09:18:34: Epoch: 016 Train Loss: 0.199, ap: 0.920, auc: 0.930, f1: 0.838, acc: 0.854
+2025-08-26 09:18:38: Epoch: 016 Test results, ap: 0.324, auc: 0.597, f1: 0.291, acc: 0.683
+2025-08-26 09:19:17: Epoch: 017 Train Loss: 0.193, ap: 0.920, auc: 0.930, f1: 0.828, acc: 0.845
+2025-08-26 09:19:21: Epoch: 017 Test results, ap: 0.323, auc: 0.596, f1: 0.327, acc: 0.699
+2025-08-26 09:19:59: Epoch: 018 Train Loss: 0.197, ap: 0.916, auc: 0.926, f1: 0.820, acc: 0.838
+2025-08-26 09:20:03: Epoch: 018 Test results, ap: 0.304, auc: 0.587, f1: 0.338, acc: 0.650
+2025-08-26 09:20:43: Epoch: 019 Train Loss: 0.179, ap: 0.900, auc: 0.912, f1: 0.809, acc: 0.836
+2025-08-26 09:20:47: Epoch: 019 Test results, ap: 0.294, auc: 0.604, f1: 0.204, acc: 0.683
+2025-08-26 09:21:26: Epoch: 020 Train Loss: 0.191, ap: 0.916, auc: 0.931, f1: 0.837, acc: 0.854
+2025-08-26 09:21:30: Epoch: 020 Test results, ap: 0.295, auc: 0.586, f1: 0.292, acc: 0.724
+2025-08-26 09:22:09: Epoch: 021 Train Loss: 0.154, ap: 0.925, auc: 0.938, f1: 0.855, acc: 0.869
+2025-08-26 09:22:09: Epoch: 021 New best VALIDATION metrics
+2025-08-26 09:22:13: Epoch: 021 Test results, ap: 0.307, auc: 0.600, f1: 0.245, acc: 0.699
+2025-08-26 09:22:51: Epoch: 022 Train Loss: 0.137, ap: 0.933, auc: 0.943, f1: 0.855, acc: 0.868
+2025-08-26 09:22:51: Epoch: 022 New best VALIDATION metrics
+2025-08-26 09:22:55: Epoch: 022 Test results, ap: 0.292, auc: 0.579, f1: 0.226, acc: 0.667
+2025-08-26 09:23:32: Epoch: 023 Train Loss: 0.130, ap: 0.929, auc: 0.941, f1: 0.858, acc: 0.871
+2025-08-26 09:23:36: Epoch: 023 Test results, ap: 0.277, auc: 0.563, f1: 0.226, acc: 0.667
+2025-08-26 09:24:14: Epoch: 024 Train Loss: 0.137, ap: 0.932, auc: 0.941, f1: 0.848, acc: 0.862
+2025-08-26 09:24:18: Epoch: 024 Test results, ap: 0.283, auc: 0.576, f1: 0.222, acc: 0.659
+2025-08-26 09:24:57: Epoch: 025 Train Loss: 0.125, ap: 0.932, auc: 0.944, f1: 0.857, acc: 0.868
+2025-08-26 09:25:01: Epoch: 025 Test results, ap: 0.288, auc: 0.580, f1: 0.295, acc: 0.650
+2025-08-26 09:25:40: Epoch: 026 Train Loss: 0.123, ap: 0.929, auc: 0.942, f1: 0.858, acc: 0.868
+2025-08-26 09:25:44: Epoch: 026 Test results, ap: 0.277, auc: 0.557, f1: 0.303, acc: 0.626
+2025-08-26 09:26:22: Epoch: 027 Train Loss: 0.124, ap: 0.928, auc: 0.942, f1: 0.859, acc: 0.871
+2025-08-26 09:26:26: Epoch: 027 Test results, ap: 0.282, auc: 0.564, f1: 0.328, acc: 0.634
+2025-08-26 09:27:05: Epoch: 028 Train Loss: 0.116, ap: 0.934, auc: 0.947, f1: 0.859, acc: 0.871
+2025-08-26 09:27:05: Epoch: 028 New best VALIDATION metrics
+2025-08-26 09:27:09: Epoch: 028 Test results, ap: 0.289, auc: 0.568, f1: 0.214, acc: 0.642
+2025-08-26 09:27:47: Epoch: 029 Train Loss: 0.122, ap: 0.933, auc: 0.944, f1: 0.855, acc: 0.866
+2025-08-26 09:27:51: Epoch: 029 Test results, ap: 0.289, auc: 0.565, f1: 0.328, acc: 0.634
+2025-08-26 09:28:29: Epoch: 030 Train Loss: 0.111, ap: 0.935, auc: 0.946, f1: 0.863, acc: 0.878
+2025-08-26 09:28:29: Epoch: 030 New best VALIDATION metrics
+2025-08-26 09:28:33: Epoch: 030 Test results, ap: 0.279, auc: 0.579, f1: 0.222, acc: 0.659
+2025-08-26 09:29:12: Epoch: 031 Train Loss: 0.103, ap: 0.939, auc: 0.951, f1: 0.872, acc: 0.884
+2025-08-26 09:29:12: Epoch: 031 New best VALIDATION metrics
+2025-08-26 09:29:16: Epoch: 031 Test results, ap: 0.281, auc: 0.568, f1: 0.269, acc: 0.691
+2025-08-26 09:29:54: Epoch: 032 Train Loss: 0.097, ap: 0.938, auc: 0.949, f1: 0.866, acc: 0.878
+2025-08-26 09:29:58: Epoch: 032 Test results, ap: 0.281, auc: 0.564, f1: 0.264, acc: 0.683
+2025-08-26 09:30:36: Epoch: 033 Train Loss: 0.098, ap: 0.938, auc: 0.949, f1: 0.861, acc: 0.873
+2025-08-26 09:30:40: Epoch: 033 Test results, ap: 0.273, auc: 0.551, f1: 0.250, acc: 0.659
+2025-08-26 09:31:18: Epoch: 034 Train Loss: 0.091, ap: 0.939, auc: 0.950, f1: 0.867, acc: 0.878
+2025-08-26 09:31:22: Epoch: 034 Test results, ap: 0.298, auc: 0.557, f1: 0.235, acc: 0.683
+2025-08-26 09:31:59: Epoch: 035 Train Loss: 0.091, ap: 0.936, auc: 0.949, f1: 0.870, acc: 0.884
+2025-08-26 09:32:03: Epoch: 035 Test results, ap: 0.280, auc: 0.557, f1: 0.250, acc: 0.659
+2025-08-26 09:32:42: Epoch: 036 Train Loss: 0.087, ap: 0.938, auc: 0.951, f1: 0.863, acc: 0.875
+2025-08-26 09:32:46: Epoch: 036 Test results, ap: 0.289, auc: 0.562, f1: 0.241, acc: 0.642
+2025-08-26 09:33:24: Epoch: 037 Train Loss: 0.088, ap: 0.936, auc: 0.950, f1: 0.872, acc: 0.884
+2025-08-26 09:33:28: Epoch: 037 Test results, ap: 0.275, auc: 0.545, f1: 0.280, acc: 0.707
+2025-08-26 09:34:05: Epoch: 038 Train Loss: 0.079, ap: 0.928, auc: 0.948, f1: 0.863, acc: 0.875
+2025-08-26 09:34:09: Epoch: 038 Test results, ap: 0.282, auc: 0.563, f1: 0.241, acc: 0.642
+2025-08-26 09:34:46: Epoch: 039 Train Loss: 0.082, ap: 0.934, auc: 0.949, f1: 0.865, acc: 0.877
+2025-08-26 09:34:50: Epoch: 039 Test results, ap: 0.278, auc: 0.565, f1: 0.259, acc: 0.675
+2025-08-26 09:35:27: Epoch: 040 Train Loss: 0.075, ap: 0.930, auc: 0.950, f1: 0.873, acc: 0.884
+2025-08-26 09:35:31: Epoch: 040 Test results, ap: 0.284, auc: 0.565, f1: 0.255, acc: 0.667
+2025-08-26 09:36:09: Epoch: 041 Train Loss: 0.068, ap: 0.935, auc: 0.950, f1: 0.872, acc: 0.885
+2025-08-26 09:36:13: Epoch: 041 Test results, ap: 0.281, auc: 0.566, f1: 0.226, acc: 0.667
+2025-08-26 09:36:52: Epoch: 042 Train Loss: 0.067, ap: 0.937, auc: 0.952, f1: 0.887, acc: 0.898
+2025-08-26 09:36:56: Epoch: 042 Test results, ap: 0.291, auc: 0.571, f1: 0.264, acc: 0.683
+2025-08-26 09:37:34: Epoch: 043 Train Loss: 0.063, ap: 0.935, auc: 0.950, f1: 0.871, acc: 0.884
+2025-08-26 09:37:38: Epoch: 043 Test results, ap: 0.282, auc: 0.558, f1: 0.231, acc: 0.675
+2025-08-26 09:38:16: Epoch: 044 Train Loss: 0.059, ap: 0.931, auc: 0.950, f1: 0.872, acc: 0.884
+2025-08-26 09:38:20: Epoch: 044 Test results, ap: 0.294, auc: 0.578, f1: 0.250, acc: 0.659
+2025-08-26 09:38:59: Epoch: 045 Train Loss: 0.066, ap: 0.932, auc: 0.951, f1: 0.875, acc: 0.887
+2025-08-26 09:39:02: Epoch: 045 Test results, ap: 0.283, auc: 0.561, f1: 0.250, acc: 0.659
+2025-08-26 09:39:42: Epoch: 046 Train Loss: 0.060, ap: 0.933, auc: 0.951, f1: 0.870, acc: 0.884
+2025-08-26 09:39:45: Epoch: 046 Test results, ap: 0.275, auc: 0.560, f1: 0.222, acc: 0.659
+2025-08-26 09:40:23: Epoch: 047 Train Loss: 0.062, ap: 0.936, auc: 0.954, f1: 0.882, acc: 0.892
+2025-08-26 09:40:27: Epoch: 047 Test results, ap: 0.281, auc: 0.563, f1: 0.276, acc: 0.659
+2025-08-26 09:41:06: Epoch: 048 Train Loss: 0.061, ap: 0.938, auc: 0.955, f1: 0.886, acc: 0.896
+2025-08-26 09:41:06: Epoch: 048 New best VALIDATION metrics
+2025-08-26 09:41:10: Epoch: 048 Test results, ap: 0.278, auc: 0.557, f1: 0.267, acc: 0.642
+2025-08-26 09:41:48: Epoch: 049 Train Loss: 0.055, ap: 0.936, auc: 0.953, f1: 0.878, acc: 0.889
+2025-08-26 09:41:52: Epoch: 049 Test results, ap: 0.281, auc: 0.564, f1: 0.267, acc: 0.642
+2025-08-26 09:42:30: Epoch: 050 Train Loss: 0.056, ap: 0.936, auc: 0.953, f1: 0.884, acc: 0.896
+2025-08-26 09:42:34: Epoch: 050 Test results, ap: 0.284, auc: 0.567, f1: 0.226, acc: 0.667
+2025-08-26 09:42:34: used time 0.59h
+2025-08-26 09:42:34: Running Cross Validation 3
+2025-08-26 09:42:34: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 09:43:12: Epoch: 001 Train Loss: 0.637, ap: 0.427, auc: 0.482, f1: 0.410, acc: 0.524
+2025-08-26 09:43:12: Epoch: 001 New best VALIDATION metrics
+2025-08-26 09:43:17: Epoch: 001 Test results, ap: 0.273, auc: 0.561, f1: 0.167, acc: 0.675
+2025-08-26 09:43:55: Epoch: 002 Train Loss: 0.593, ap: 0.746, auc: 0.801, f1: 0.687, acc: 0.684
+2025-08-26 09:43:55: Epoch: 002 New best VALIDATION metrics
+2025-08-26 09:43:59: Epoch: 002 Test results, ap: 0.221, auc: 0.452, f1: 0.164, acc: 0.585
+2025-08-26 09:44:38: Epoch: 003 Train Loss: 0.550, ap: 0.718, auc: 0.784, f1: 0.683, acc: 0.686
+2025-08-26 09:44:42: Epoch: 003 Test results, ap: 0.234, auc: 0.475, f1: 0.190, acc: 0.585
+2025-08-26 09:45:20: Epoch: 004 Train Loss: 0.514, ap: 0.797, auc: 0.840, f1: 0.747, acc: 0.762
+2025-08-26 09:45:20: Epoch: 004 New best VALIDATION metrics
+2025-08-26 09:45:24: Epoch: 004 Test results, ap: 0.254, auc: 0.504, f1: 0.146, acc: 0.715
+2025-08-26 09:46:03: Epoch: 005 Train Loss: 0.450, ap: 0.761, auc: 0.779, f1: 0.664, acc: 0.668
+2025-08-26 09:46:07: Epoch: 005 Test results, ap: 0.398, auc: 0.577, f1: 0.250, acc: 0.756
+2025-08-26 09:46:45: Epoch: 006 Train Loss: 0.431, ap: 0.820, auc: 0.861, f1: 0.756, acc: 0.760
+2025-08-26 09:46:45: Epoch: 006 New best VALIDATION metrics
+2025-08-26 09:46:49: Epoch: 006 Test results, ap: 0.282, auc: 0.553, f1: 0.353, acc: 0.642
+2025-08-26 09:47:28: Epoch: 007 Train Loss: 0.390, ap: 0.829, auc: 0.878, f1: 0.770, acc: 0.776
+2025-08-26 09:47:28: Epoch: 007 New best VALIDATION metrics
+2025-08-26 09:47:32: Epoch: 007 Test results, ap: 0.273, auc: 0.527, f1: 0.271, acc: 0.650
+2025-08-26 09:48:11: Epoch: 008 Train Loss: 0.361, ap: 0.845, auc: 0.888, f1: 0.788, acc: 0.802
+2025-08-26 09:48:11: Epoch: 008 New best VALIDATION metrics
+2025-08-26 09:48:14: Epoch: 008 Test results, ap: 0.258, auc: 0.524, f1: 0.200, acc: 0.610
+2025-08-26 09:48:52: Epoch: 009 Train Loss: 0.349, ap: 0.860, auc: 0.875, f1: 0.742, acc: 0.769
+2025-08-26 09:48:52: Epoch: 009 New best VALIDATION metrics
+2025-08-26 09:48:56: Epoch: 009 Test results, ap: 0.275, auc: 0.560, f1: 0.053, acc: 0.707
+2025-08-26 09:49:34: Epoch: 010 Train Loss: 0.333, ap: 0.873, auc: 0.895, f1: 0.789, acc: 0.804
+2025-08-26 09:49:34: Epoch: 010 New best VALIDATION metrics
+2025-08-26 09:49:38: Epoch: 010 Test results, ap: 0.307, auc: 0.536, f1: 0.222, acc: 0.715
+2025-08-26 09:50:17: Epoch: 011 Train Loss: 0.274, ap: 0.876, auc: 0.911, f1: 0.807, acc: 0.818
+2025-08-26 09:50:17: Epoch: 011 New best VALIDATION metrics
+2025-08-26 09:50:21: Epoch: 011 Test results, ap: 0.284, auc: 0.551, f1: 0.246, acc: 0.650
+2025-08-26 09:50:21: Epoch: 011 New best TEST metrics
+2025-08-26 09:51:00: Epoch: 012 Train Loss: 0.247, ap: 0.902, auc: 0.918, f1: 0.812, acc: 0.832
+2025-08-26 09:51:00: Epoch: 012 New best VALIDATION metrics
+2025-08-26 09:51:04: Epoch: 012 Test results, ap: 0.339, auc: 0.563, f1: 0.216, acc: 0.764
+2025-08-26 09:51:04: Epoch: 012 New best TEST metrics
+2025-08-26 09:51:42: Epoch: 013 Train Loss: 0.241, ap: 0.909, auc: 0.925, f1: 0.839, acc: 0.854
+2025-08-26 09:51:42: Epoch: 013 New best VALIDATION metrics
+2025-08-26 09:51:46: Epoch: 013 Test results, ap: 0.344, auc: 0.609, f1: 0.267, acc: 0.732
+2025-08-26 09:51:46: Epoch: 013 New best TEST metrics
+2025-08-26 09:52:25: Epoch: 014 Train Loss: 0.219, ap: 0.896, auc: 0.917, f1: 0.821, acc: 0.836
+2025-08-26 09:52:29: Epoch: 014 Test results, ap: 0.322, auc: 0.595, f1: 0.186, acc: 0.715
+2025-08-26 09:53:09: Epoch: 015 Train Loss: 0.220, ap: 0.893, auc: 0.926, f1: 0.842, acc: 0.855
+2025-08-26 09:53:12: Epoch: 015 Test results, ap: 0.296, auc: 0.568, f1: 0.271, acc: 0.650
+2025-08-26 09:53:52: Epoch: 016 Train Loss: 0.205, ap: 0.893, auc: 0.925, f1: 0.845, acc: 0.854
+2025-08-26 09:53:56: Epoch: 016 Test results, ap: 0.289, auc: 0.553, f1: 0.323, acc: 0.659
+2025-08-26 09:54:34: Epoch: 017 Train Loss: 0.206, ap: 0.899, auc: 0.927, f1: 0.826, acc: 0.838
+2025-08-26 09:54:38: Epoch: 017 Test results, ap: 0.280, auc: 0.550, f1: 0.222, acc: 0.659
+2025-08-26 09:55:16: Epoch: 018 Train Loss: 0.183, ap: 0.908, auc: 0.925, f1: 0.838, acc: 0.857
+2025-08-26 09:55:21: Epoch: 018 Test results, ap: 0.317, auc: 0.597, f1: 0.213, acc: 0.699
+2025-08-26 09:55:59: Epoch: 019 Train Loss: 0.183, ap: 0.922, auc: 0.938, f1: 0.857, acc: 0.871
+2025-08-26 09:55:59: Epoch: 019 New best VALIDATION metrics
+2025-08-26 09:56:03: Epoch: 019 Test results, ap: 0.317, auc: 0.576, f1: 0.302, acc: 0.699
+2025-08-26 09:56:42: Epoch: 020 Train Loss: 0.169, ap: 0.911, auc: 0.930, f1: 0.845, acc: 0.861
+2025-08-26 09:56:46: Epoch: 020 Test results, ap: 0.306, auc: 0.560, f1: 0.213, acc: 0.699
+2025-08-26 09:57:24: Epoch: 021 Train Loss: 0.147, ap: 0.923, auc: 0.942, f1: 0.858, acc: 0.871
+2025-08-26 09:57:24: Epoch: 021 New best VALIDATION metrics
+2025-08-26 09:57:28: Epoch: 021 Test results, ap: 0.294, auc: 0.552, f1: 0.246, acc: 0.650
+2025-08-26 09:58:06: Epoch: 022 Train Loss: 0.123, ap: 0.925, auc: 0.941, f1: 0.847, acc: 0.859
+2025-08-26 09:58:06: Epoch: 022 New best VALIDATION metrics
+2025-08-26 09:58:10: Epoch: 022 Test results, ap: 0.293, auc: 0.562, f1: 0.264, acc: 0.683
+2025-08-26 09:58:48: Epoch: 023 Train Loss: 0.112, ap: 0.922, auc: 0.941, f1: 0.863, acc: 0.875
+2025-08-26 09:58:52: Epoch: 023 Test results, ap: 0.285, auc: 0.555, f1: 0.231, acc: 0.675
+2025-08-26 09:59:31: Epoch: 024 Train Loss: 0.134, ap: 0.923, auc: 0.945, f1: 0.867, acc: 0.880
+2025-08-26 09:59:31: Epoch: 024 New best VALIDATION metrics
+2025-08-26 09:59:35: Epoch: 024 Test results, ap: 0.284, auc: 0.559, f1: 0.246, acc: 0.650
+2025-08-26 10:00:14: Epoch: 025 Train Loss: 0.122, ap: 0.922, auc: 0.943, f1: 0.855, acc: 0.869
+2025-08-26 10:00:18: Epoch: 025 Test results, ap: 0.278, auc: 0.559, f1: 0.267, acc: 0.642
+2025-08-26 10:00:56: Epoch: 026 Train Loss: 0.125, ap: 0.924, auc: 0.942, f1: 0.855, acc: 0.869
+2025-08-26 10:01:00: Epoch: 026 Test results, ap: 0.274, auc: 0.540, f1: 0.237, acc: 0.634
+2025-08-26 10:01:39: Epoch: 027 Train Loss: 0.114, ap: 0.924, auc: 0.943, f1: 0.852, acc: 0.868
+2025-08-26 10:01:43: Epoch: 027 Test results, ap: 0.276, auc: 0.558, f1: 0.271, acc: 0.650
+2025-08-26 10:02:20: Epoch: 028 Train Loss: 0.120, ap: 0.929, auc: 0.943, f1: 0.859, acc: 0.873
+2025-08-26 10:02:20: Epoch: 028 New best VALIDATION metrics
+2025-08-26 10:02:25: Epoch: 028 Test results, ap: 0.267, auc: 0.531, f1: 0.250, acc: 0.659
+2025-08-26 10:03:04: Epoch: 029 Train Loss: 0.099, ap: 0.928, auc: 0.945, f1: 0.864, acc: 0.878
+2025-08-26 10:03:04: Epoch: 029 New best VALIDATION metrics
+2025-08-26 10:03:08: Epoch: 029 Test results, ap: 0.272, auc: 0.545, f1: 0.237, acc: 0.634
+2025-08-26 10:03:45: Epoch: 030 Train Loss: 0.123, ap: 0.921, auc: 0.940, f1: 0.857, acc: 0.871
+2025-08-26 10:03:49: Epoch: 030 Test results, ap: 0.278, auc: 0.542, f1: 0.290, acc: 0.642
+2025-08-26 10:04:28: Epoch: 031 Train Loss: 0.092, ap: 0.928, auc: 0.946, f1: 0.863, acc: 0.878
+2025-08-26 10:04:28: Epoch: 031 New best VALIDATION metrics
+2025-08-26 10:04:32: Epoch: 031 Test results, ap: 0.273, auc: 0.544, f1: 0.317, acc: 0.650
+2025-08-26 10:05:11: Epoch: 032 Train Loss: 0.083, ap: 0.928, auc: 0.946, f1: 0.855, acc: 0.871
+2025-08-26 10:05:14: Epoch: 032 Test results, ap: 0.265, auc: 0.533, f1: 0.323, acc: 0.659
+2025-08-26 10:05:53: Epoch: 033 Train Loss: 0.077, ap: 0.922, auc: 0.945, f1: 0.863, acc: 0.877
+2025-08-26 10:05:57: Epoch: 033 Test results, ap: 0.264, auc: 0.535, f1: 0.286, acc: 0.634
+2025-08-26 10:06:35: Epoch: 034 Train Loss: 0.082, ap: 0.927, auc: 0.945, f1: 0.863, acc: 0.878
+2025-08-26 10:06:39: Epoch: 034 Test results, ap: 0.270, auc: 0.540, f1: 0.308, acc: 0.634
+2025-08-26 10:07:18: Epoch: 035 Train Loss: 0.078, ap: 0.926, auc: 0.946, f1: 0.871, acc: 0.884
+2025-08-26 10:07:22: Epoch: 035 Test results, ap: 0.259, auc: 0.525, f1: 0.333, acc: 0.642
+2025-08-26 10:08:00: Epoch: 036 Train Loss: 0.078, ap: 0.928, auc: 0.949, f1: 0.872, acc: 0.884
+2025-08-26 10:08:00: Epoch: 036 New best VALIDATION metrics
+2025-08-26 10:08:04: Epoch: 036 Test results, ap: 0.261, auc: 0.531, f1: 0.324, acc: 0.626
+2025-08-26 10:08:43: Epoch: 037 Train Loss: 0.075, ap: 0.929, auc: 0.947, f1: 0.859, acc: 0.875
+2025-08-26 10:08:47: Epoch: 037 Test results, ap: 0.265, auc: 0.537, f1: 0.262, acc: 0.634
+2025-08-26 10:09:26: Epoch: 038 Train Loss: 0.081, ap: 0.926, auc: 0.946, f1: 0.869, acc: 0.884
+2025-08-26 10:09:29: Epoch: 038 Test results, ap: 0.259, auc: 0.526, f1: 0.273, acc: 0.610
+2025-08-26 10:10:07: Epoch: 039 Train Loss: 0.073, ap: 0.927, auc: 0.944, f1: 0.861, acc: 0.875
+2025-08-26 10:10:11: Epoch: 039 Test results, ap: 0.268, auc: 0.537, f1: 0.233, acc: 0.626
+2025-08-26 10:10:49: Epoch: 040 Train Loss: 0.075, ap: 0.926, auc: 0.944, f1: 0.862, acc: 0.877
+2025-08-26 10:10:53: Epoch: 040 Test results, ap: 0.255, auc: 0.520, f1: 0.258, acc: 0.626
+2025-08-26 10:11:31: Epoch: 041 Train Loss: 0.061, ap: 0.922, auc: 0.947, f1: 0.867, acc: 0.880
+2025-08-26 10:11:35: Epoch: 041 Test results, ap: 0.252, auc: 0.519, f1: 0.254, acc: 0.618
+2025-08-26 10:12:14: Epoch: 042 Train Loss: 0.060, ap: 0.929, auc: 0.946, f1: 0.870, acc: 0.884
+2025-08-26 10:12:18: Epoch: 042 Test results, ap: 0.258, auc: 0.525, f1: 0.258, acc: 0.626
+2025-08-26 10:12:57: Epoch: 043 Train Loss: 0.059, ap: 0.923, auc: 0.947, f1: 0.875, acc: 0.889
+2025-08-26 10:13:00: Epoch: 043 Test results, ap: 0.258, auc: 0.528, f1: 0.254, acc: 0.618
+2025-08-26 10:13:39: Epoch: 044 Train Loss: 0.065, ap: 0.923, auc: 0.947, f1: 0.875, acc: 0.889
+2025-08-26 10:13:43: Epoch: 044 Test results, ap: 0.259, auc: 0.527, f1: 0.258, acc: 0.626
+2025-08-26 10:14:22: Epoch: 045 Train Loss: 0.056, ap: 0.923, auc: 0.947, f1: 0.877, acc: 0.891
+2025-08-26 10:14:26: Epoch: 045 Test results, ap: 0.258, auc: 0.530, f1: 0.254, acc: 0.618
+2025-08-26 10:15:05: Epoch: 046 Train Loss: 0.058, ap: 0.924, auc: 0.947, f1: 0.872, acc: 0.885
+2025-08-26 10:15:09: Epoch: 046 Test results, ap: 0.256, auc: 0.523, f1: 0.254, acc: 0.618
+2025-08-26 10:15:48: Epoch: 047 Train Loss: 0.058, ap: 0.925, auc: 0.947, f1: 0.875, acc: 0.889
+2025-08-26 10:15:52: Epoch: 047 Test results, ap: 0.256, auc: 0.520, f1: 0.258, acc: 0.626
+2025-08-26 10:16:31: Epoch: 048 Train Loss: 0.060, ap: 0.925, auc: 0.948, f1: 0.875, acc: 0.889
+2025-08-26 10:16:36: Epoch: 048 Test results, ap: 0.260, auc: 0.526, f1: 0.281, acc: 0.626
+2025-08-26 10:17:14: Epoch: 049 Train Loss: 0.058, ap: 0.923, auc: 0.947, f1: 0.874, acc: 0.887
+2025-08-26 10:17:18: Epoch: 049 Test results, ap: 0.255, auc: 0.523, f1: 0.312, acc: 0.642
+2025-08-26 10:17:57: Epoch: 050 Train Loss: 0.052, ap: 0.922, auc: 0.945, f1: 0.865, acc: 0.880
+2025-08-26 10:18:00: Epoch: 050 Test results, ap: 0.258, auc: 0.525, f1: 0.254, acc: 0.618
+2025-08-26 10:18:00: used time 0.59h
+2025-08-26 10:18:01: Running Cross Validation 4
+2025-08-26 10:18:01: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 10:18:40: Epoch: 001 Train Loss: 0.635, ap: 0.609, auc: 0.669, f1: 0.609, acc: 0.586
+2025-08-26 10:18:40: Epoch: 001 New best VALIDATION metrics
+2025-08-26 10:18:44: Epoch: 001 Test results, ap: 0.231, auc: 0.499, f1: 0.382, acc: 0.341
+2025-08-26 10:19:22: Epoch: 002 Train Loss: 0.549, ap: 0.635, auc: 0.687, f1: 0.552, acc: 0.651
+2025-08-26 10:19:22: Epoch: 002 New best VALIDATION metrics
+2025-08-26 10:19:26: Epoch: 002 Test results, ap: 0.273, auc: 0.579, f1: 0.115, acc: 0.626
+2025-08-26 10:20:05: Epoch: 003 Train Loss: 0.500, ap: 0.795, auc: 0.823, f1: 0.694, acc: 0.728
+2025-08-26 10:20:05: Epoch: 003 New best VALIDATION metrics
+2025-08-26 10:20:09: Epoch: 003 Test results, ap: 0.331, auc: 0.528, f1: 0.240, acc: 0.691
+2025-08-26 10:20:49: Epoch: 004 Train Loss: 0.446, ap: 0.770, auc: 0.823, f1: 0.709, acc: 0.720
+2025-08-26 10:20:52: Epoch: 004 Test results, ap: 0.239, auc: 0.441, f1: 0.361, acc: 0.569
+2025-08-26 10:21:31: Epoch: 005 Train Loss: 0.426, ap: 0.762, auc: 0.821, f1: 0.730, acc: 0.741
+2025-08-26 10:21:35: Epoch: 005 Test results, ap: 0.236, auc: 0.492, f1: 0.379, acc: 0.520
+2025-08-26 10:22:13: Epoch: 006 Train Loss: 0.393, ap: 0.841, auc: 0.867, f1: 0.758, acc: 0.774
+2025-08-26 10:22:13: Epoch: 006 New best VALIDATION metrics
+2025-08-26 10:22:17: Epoch: 006 Test results, ap: 0.342, auc: 0.593, f1: 0.292, acc: 0.724
+2025-08-26 10:22:56: Epoch: 007 Train Loss: 0.357, ap: 0.849, auc: 0.866, f1: 0.739, acc: 0.755
+2025-08-26 10:22:56: Epoch: 007 New best VALIDATION metrics
+2025-08-26 10:23:00: Epoch: 007 Test results, ap: 0.260, auc: 0.462, f1: 0.306, acc: 0.520
+2025-08-26 10:23:38: Epoch: 008 Train Loss: 0.334, ap: 0.834, auc: 0.872, f1: 0.762, acc: 0.774
+2025-08-26 10:23:42: Epoch: 008 Test results, ap: 0.262, auc: 0.470, f1: 0.315, acc: 0.504
+2025-08-26 10:24:21: Epoch: 009 Train Loss: 0.322, ap: 0.887, auc: 0.900, f1: 0.808, acc: 0.829
+2025-08-26 10:24:21: Epoch: 009 New best VALIDATION metrics
+2025-08-26 10:24:25: Epoch: 009 Test results, ap: 0.350, auc: 0.525, f1: 0.364, acc: 0.545
+2025-08-26 10:25:03: Epoch: 010 Train Loss: 0.301, ap: 0.875, auc: 0.895, f1: 0.782, acc: 0.795
+2025-08-26 10:25:06: Epoch: 010 Test results, ap: 0.297, auc: 0.522, f1: 0.333, acc: 0.512
+2025-08-26 10:25:45: Epoch: 011 Train Loss: 0.236, ap: 0.896, auc: 0.914, f1: 0.816, acc: 0.832
+2025-08-26 10:25:45: Epoch: 011 New best VALIDATION metrics
+2025-08-26 10:25:49: Epoch: 011 Test results, ap: 0.325, auc: 0.513, f1: 0.356, acc: 0.618
+2025-08-26 10:25:49: Epoch: 011 New best TEST metrics
+2025-08-26 10:26:27: Epoch: 012 Train Loss: 0.218, ap: 0.905, auc: 0.923, f1: 0.822, acc: 0.831
+2025-08-26 10:26:27: Epoch: 012 New best VALIDATION metrics
+2025-08-26 10:26:31: Epoch: 012 Test results, ap: 0.286, auc: 0.487, f1: 0.269, acc: 0.602
+2025-08-26 10:27:10: Epoch: 013 Train Loss: 0.203, ap: 0.903, auc: 0.924, f1: 0.822, acc: 0.838
+2025-08-26 10:27:14: Epoch: 013 Test results, ap: 0.323, auc: 0.493, f1: 0.262, acc: 0.634
+2025-08-26 10:27:53: Epoch: 014 Train Loss: 0.183, ap: 0.911, auc: 0.930, f1: 0.838, acc: 0.850
+2025-08-26 10:27:53: Epoch: 014 New best VALIDATION metrics
+2025-08-26 10:27:57: Epoch: 014 Test results, ap: 0.288, auc: 0.482, f1: 0.338, acc: 0.618
+2025-08-26 10:28:36: Epoch: 015 Train Loss: 0.179, ap: 0.908, auc: 0.933, f1: 0.835, acc: 0.845
+2025-08-26 10:28:40: Epoch: 015 Test results, ap: 0.335, auc: 0.514, f1: 0.356, acc: 0.618
+2025-08-26 10:28:40: Epoch: 015 New best TEST metrics
+2025-08-26 10:29:18: Epoch: 016 Train Loss: 0.166, ap: 0.883, auc: 0.904, f1: 0.802, acc: 0.822
+2025-08-26 10:29:22: Epoch: 016 Test results, ap: 0.408, auc: 0.557, f1: 0.358, acc: 0.650
+2025-08-26 10:29:22: Epoch: 016 New best TEST metrics
+2025-08-26 10:30:01: Epoch: 017 Train Loss: 0.185, ap: 0.904, auc: 0.924, f1: 0.827, acc: 0.836
+2025-08-26 10:30:05: Epoch: 017 Test results, ap: 0.261, auc: 0.494, f1: 0.295, acc: 0.455
+2025-08-26 10:30:43: Epoch: 018 Train Loss: 0.170, ap: 0.905, auc: 0.925, f1: 0.823, acc: 0.840
+2025-08-26 10:30:47: Epoch: 018 Test results, ap: 0.291, auc: 0.476, f1: 0.247, acc: 0.553
+2025-08-26 10:31:25: Epoch: 019 Train Loss: 0.159, ap: 0.920, auc: 0.934, f1: 0.842, acc: 0.855
+2025-08-26 10:31:25: Epoch: 019 New best VALIDATION metrics
+2025-08-26 10:31:29: Epoch: 019 Test results, ap: 0.283, auc: 0.474, f1: 0.306, acc: 0.593
+2025-08-26 10:32:07: Epoch: 020 Train Loss: 0.150, ap: 0.921, auc: 0.935, f1: 0.860, acc: 0.873
+2025-08-26 10:32:07: Epoch: 020 New best VALIDATION metrics
+2025-08-26 10:32:11: Epoch: 020 Test results, ap: 0.302, auc: 0.474, f1: 0.254, acc: 0.618
+2025-08-26 10:32:49: Epoch: 021 Train Loss: 0.129, ap: 0.922, auc: 0.938, f1: 0.848, acc: 0.859
+2025-08-26 10:32:49: Epoch: 021 New best VALIDATION metrics
+2025-08-26 10:32:53: Epoch: 021 Test results, ap: 0.245, auc: 0.462, f1: 0.265, acc: 0.593
+2025-08-26 10:33:32: Epoch: 022 Train Loss: 0.122, ap: 0.934, auc: 0.947, f1: 0.872, acc: 0.882
+2025-08-26 10:33:32: Epoch: 022 New best VALIDATION metrics
+2025-08-26 10:33:36: Epoch: 022 Test results, ap: 0.278, auc: 0.462, f1: 0.269, acc: 0.602
+2025-08-26 10:34:14: Epoch: 023 Train Loss: 0.111, ap: 0.936, auc: 0.948, f1: 0.859, acc: 0.869
+2025-08-26 10:34:14: Epoch: 023 New best VALIDATION metrics
+2025-08-26 10:34:17: Epoch: 023 Test results, ap: 0.268, auc: 0.464, f1: 0.294, acc: 0.610
+2025-08-26 10:34:54: Epoch: 024 Train Loss: 0.108, ap: 0.932, auc: 0.942, f1: 0.853, acc: 0.866
+2025-08-26 10:34:58: Epoch: 024 Test results, ap: 0.301, auc: 0.467, f1: 0.277, acc: 0.618
+2025-08-26 10:35:37: Epoch: 025 Train Loss: 0.114, ap: 0.933, auc: 0.945, f1: 0.857, acc: 0.869
+2025-08-26 10:35:40: Epoch: 025 Test results, ap: 0.273, auc: 0.475, f1: 0.281, acc: 0.626
+2025-08-26 10:36:18: Epoch: 026 Train Loss: 0.110, ap: 0.932, auc: 0.941, f1: 0.853, acc: 0.868
+2025-08-26 10:36:22: Epoch: 026 Test results, ap: 0.321, auc: 0.481, f1: 0.267, acc: 0.642
+2025-08-26 10:37:00: Epoch: 027 Train Loss: 0.111, ap: 0.930, auc: 0.944, f1: 0.853, acc: 0.862
+2025-08-26 10:37:04: Epoch: 027 Test results, ap: 0.303, auc: 0.491, f1: 0.324, acc: 0.626
+2025-08-26 10:37:42: Epoch: 028 Train Loss: 0.099, ap: 0.934, auc: 0.947, f1: 0.866, acc: 0.877
+2025-08-26 10:37:46: Epoch: 028 Test results, ap: 0.270, auc: 0.469, f1: 0.294, acc: 0.610
+2025-08-26 10:38:24: Epoch: 029 Train Loss: 0.107, ap: 0.936, auc: 0.947, f1: 0.862, acc: 0.873
+2025-08-26 10:38:28: Epoch: 029 Test results, ap: 0.283, auc: 0.482, f1: 0.258, acc: 0.626
+2025-08-26 10:39:06: Epoch: 030 Train Loss: 0.105, ap: 0.934, auc: 0.947, f1: 0.866, acc: 0.875
+2025-08-26 10:39:10: Epoch: 030 Test results, ap: 0.302, auc: 0.487, f1: 0.277, acc: 0.618
+2025-08-26 10:39:48: Epoch: 031 Train Loss: 0.083, ap: 0.935, auc: 0.947, f1: 0.864, acc: 0.873
+2025-08-26 10:39:52: Epoch: 031 Test results, ap: 0.276, auc: 0.483, f1: 0.328, acc: 0.634
+2025-08-26 10:40:31: Epoch: 032 Train Loss: 0.090, ap: 0.936, auc: 0.948, f1: 0.876, acc: 0.887
+2025-08-26 10:40:31: Epoch: 032 New best VALIDATION metrics
+2025-08-26 10:40:34: Epoch: 032 Test results, ap: 0.337, auc: 0.497, f1: 0.312, acc: 0.642
+2025-08-26 10:41:13: Epoch: 033 Train Loss: 0.082, ap: 0.935, auc: 0.948, f1: 0.864, acc: 0.875
+2025-08-26 10:41:17: Epoch: 033 Test results, ap: 0.330, auc: 0.488, f1: 0.312, acc: 0.642
+2025-08-26 10:41:54: Epoch: 034 Train Loss: 0.079, ap: 0.934, auc: 0.946, f1: 0.868, acc: 0.880
+2025-08-26 10:41:58: Epoch: 034 Test results, ap: 0.348, auc: 0.495, f1: 0.290, acc: 0.642
+2025-08-26 10:42:36: Epoch: 035 Train Loss: 0.080, ap: 0.935, auc: 0.948, f1: 0.861, acc: 0.873
+2025-08-26 10:42:40: Epoch: 035 Test results, ap: 0.337, auc: 0.496, f1: 0.317, acc: 0.650
+2025-08-26 10:43:19: Epoch: 036 Train Loss: 0.078, ap: 0.934, auc: 0.948, f1: 0.871, acc: 0.882
+2025-08-26 10:43:23: Epoch: 036 Test results, ap: 0.349, auc: 0.500, f1: 0.333, acc: 0.642
+2025-08-26 10:44:01: Epoch: 037 Train Loss: 0.083, ap: 0.933, auc: 0.946, f1: 0.870, acc: 0.880
+2025-08-26 10:44:05: Epoch: 037 Test results, ap: 0.362, auc: 0.513, f1: 0.349, acc: 0.667
+2025-08-26 10:44:43: Epoch: 038 Train Loss: 0.077, ap: 0.932, auc: 0.946, f1: 0.858, acc: 0.869
+2025-08-26 10:44:47: Epoch: 038 Test results, ap: 0.347, auc: 0.516, f1: 0.344, acc: 0.659
+2025-08-26 10:45:26: Epoch: 039 Train Loss: 0.080, ap: 0.935, auc: 0.948, f1: 0.867, acc: 0.880
+2025-08-26 10:45:29: Epoch: 039 Test results, ap: 0.322, auc: 0.494, f1: 0.323, acc: 0.659
+2025-08-26 10:46:08: Epoch: 040 Train Loss: 0.068, ap: 0.933, auc: 0.949, f1: 0.878, acc: 0.889
+2025-08-26 10:46:12: Epoch: 040 Test results, ap: 0.325, auc: 0.510, f1: 0.310, acc: 0.675
+2025-08-26 10:46:51: Epoch: 041 Train Loss: 0.064, ap: 0.933, auc: 0.949, f1: 0.876, acc: 0.887
+2025-08-26 10:46:55: Epoch: 041 Test results, ap: 0.364, auc: 0.514, f1: 0.333, acc: 0.675
+2025-08-26 10:47:33: Epoch: 042 Train Loss: 0.062, ap: 0.933, auc: 0.949, f1: 0.876, acc: 0.887
+2025-08-26 10:47:37: Epoch: 042 Test results, ap: 0.351, auc: 0.515, f1: 0.345, acc: 0.691
+2025-08-26 10:48:16: Epoch: 043 Train Loss: 0.060, ap: 0.933, auc: 0.949, f1: 0.867, acc: 0.878
+2025-08-26 10:48:20: Epoch: 043 Test results, ap: 0.364, auc: 0.508, f1: 0.323, acc: 0.659
+2025-08-26 10:48:59: Epoch: 044 Train Loss: 0.058, ap: 0.932, auc: 0.949, f1: 0.873, acc: 0.885
+2025-08-26 10:49:03: Epoch: 044 Test results, ap: 0.362, auc: 0.508, f1: 0.355, acc: 0.675
+2025-08-26 10:49:42: Epoch: 045 Train Loss: 0.062, ap: 0.934, auc: 0.948, f1: 0.878, acc: 0.889
+2025-08-26 10:49:46: Epoch: 045 Test results, ap: 0.367, auc: 0.517, f1: 0.333, acc: 0.675
+2025-08-26 10:50:25: Epoch: 046 Train Loss: 0.061, ap: 0.932, auc: 0.948, f1: 0.877, acc: 0.887
+2025-08-26 10:50:29: Epoch: 046 Test results, ap: 0.375, auc: 0.506, f1: 0.349, acc: 0.667
+2025-08-26 10:51:07: Epoch: 047 Train Loss: 0.060, ap: 0.934, auc: 0.949, f1: 0.866, acc: 0.878
+2025-08-26 10:51:11: Epoch: 047 Test results, ap: 0.361, auc: 0.514, f1: 0.323, acc: 0.659
+2025-08-26 10:51:49: Epoch: 048 Train Loss: 0.060, ap: 0.934, auc: 0.949, f1: 0.873, acc: 0.884
+2025-08-26 10:51:53: Epoch: 048 Test results, ap: 0.362, auc: 0.514, f1: 0.328, acc: 0.667
+2025-08-26 10:52:31: Epoch: 049 Train Loss: 0.058, ap: 0.935, auc: 0.949, f1: 0.879, acc: 0.891
+2025-08-26 10:52:35: Epoch: 049 Test results, ap: 0.359, auc: 0.515, f1: 0.310, acc: 0.675
+2025-08-26 10:53:14: Epoch: 050 Train Loss: 0.056, ap: 0.935, auc: 0.950, f1: 0.872, acc: 0.884
+2025-08-26 10:53:14: Epoch: 050 New best VALIDATION metrics
+2025-08-26 10:53:18: Epoch: 050 Test results, ap: 0.363, auc: 0.519, f1: 0.333, acc: 0.675
+2025-08-26 10:53:18: used time 0.59h
+2025-08-26 10:53:18: Cross Validation Finished!
+2025-08-26 10:53:18: Best validation perform list
+[[0.95255131 0.95634818 0.89048475 0.89260566]
+ [0.9444176  0.95232344 0.87676769 0.89260566]
+ [0.937572   0.95456147 0.88588005 0.89594358]
+ [0.92767191 0.94862461 0.87209302 0.88359791]
+ [0.9353888  0.95039272 0.87209302 0.88359791]]
+2025-08-26 10:53:18: mean: [0.94  0.952 0.879 0.89 ]
+2025-08-26 10:53:18: std: [0.008 0.003 0.007 0.005]
+2025-08-26 10:53:18: Best test perform list
+[[0.23920543 0.48602152 0.30000001 0.54471546]
+ [0.33201069 0.56774199 0.35135135 0.60975611]
+ [0.40679514 0.65161294 0.44827586 0.73983741]
+ [0.34399417 0.60896063 0.26666668 0.73170733]
+ [0.40777275 0.55698925 0.35820895 0.65040648]]
+2025-08-26 10:53:18: mean: [0.346 0.574 0.345 0.655]
+2025-08-26 10:53:18: std: [0.062 0.055 0.062 0.074]
diff --git a/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/uda_r2/config.json b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/uda_r2/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..0e8ab2d26f49ee707a7e81770849036878e0779f
--- /dev/null
+++ b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/uda_r2/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": "mamba", "channels": 16, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": true, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "case": "r2", "simple": false, "pt": false, "classes": 2}
\ No newline at end of file
diff --git a/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/uda_r2/feature_preds.csv b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/uda_r2/feature_preds.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f04246e429ddb7970771b4a2c76cf1f7f83d50d5
--- /dev/null
+++ b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/uda_r2/feature_preds.csv
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diff --git a/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/uda_r2/features.pth b/run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/uda_r2/features.pth
new file mode 100644
index 0000000000000000000000000000000000000000..e302aee02d9702428e7aa29fcc072380f9fc2418
--- /dev/null
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+2025-08-26 11:00:45: Loading Validation Dataset
+2025-08-26 11:00:49: Start UDA training
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+2025-08-26 11:36:24: Epoch 24 Train Loss: 0.1434 Val: ap: 0.5378 auc: 0.7588 f1: 0.0000 acc: 0.6939
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+2025-08-26 11:40:49: Epoch 27 Train Loss: 0.1443 Val: ap: 0.5185 auc: 0.7510 f1: 0.0000 acc: 0.6939
+2025-08-26 11:42:17: Epoch 28 Train Loss: 0.1307 Val: ap: 0.5125 auc: 0.7490 f1: 0.0000 acc: 0.6939
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+2025-08-26 11:48:11: Epoch 32 Train Loss: 0.1347 Val: ap: 0.4981 auc: 0.7431 f1: 0.0000 acc: 0.6939
+2025-08-26 11:49:40: Epoch 33 Train Loss: 0.1317 Val: ap: 0.4911 auc: 0.7412 f1: 0.0000 acc: 0.6939
+2025-08-26 11:51:09: Epoch 34 Train Loss: 0.1232 Val: ap: 0.4856 auc: 0.7333 f1: 0.0000 acc: 0.6939
+2025-08-26 11:52:37: Epoch 35 Train Loss: 0.1467 Val: ap: 0.5004 auc: 0.7412 f1: 0.0000 acc: 0.6939
+2025-08-26 11:54:05: Epoch 36 Train Loss: 0.1217 Val: ap: 0.5119 auc: 0.7431 f1: 0.0000 acc: 0.6939
+2025-08-26 11:55:34: Epoch 37 Train Loss: 0.1224 Val: ap: 0.5002 auc: 0.7314 f1: 0.0000 acc: 0.6939
+2025-08-26 11:57:01: Epoch 38 Train Loss: 0.1119 Val: ap: 0.4988 auc: 0.7353 f1: 0.0000 acc: 0.6939
+2025-08-26 11:58:30: Epoch 39 Train Loss: 0.1160 Val: ap: 0.4880 auc: 0.7314 f1: 0.0000 acc: 0.6939
+2025-08-26 11:59:59: Epoch 40 Train Loss: 0.1095 Val: ap: 0.5089 auc: 0.7353 f1: 0.0000 acc: 0.6939
+2025-08-26 12:01:28: Epoch 41 Train Loss: 0.1097 Val: ap: 0.4903 auc: 0.7275 f1: 0.0000 acc: 0.6939
+2025-08-26 12:02:57: Epoch 42 Train Loss: 0.1036 Val: ap: 0.4946 auc: 0.7353 f1: 0.0000 acc: 0.6939
+2025-08-26 12:04:26: Epoch 43 Train Loss: 0.1053 Val: ap: 0.5132 auc: 0.7471 f1: 0.0000 acc: 0.6939
+2025-08-26 12:05:54: Epoch 44 Train Loss: 0.1126 Val: ap: 0.4929 auc: 0.7392 f1: 0.0000 acc: 0.6939
+2025-08-26 12:07:23: Epoch 45 Train Loss: 0.1195 Val: ap: 0.5131 auc: 0.7412 f1: 0.0000 acc: 0.6939
+2025-08-26 12:08:51: Epoch 46 Train Loss: 0.1010 Val: ap: 0.4972 auc: 0.7392 f1: 0.0000 acc: 0.6939
+2025-08-26 12:10:20: Epoch 47 Train Loss: 0.1255 Val: ap: 0.5072 auc: 0.7431 f1: 0.0000 acc: 0.6939
+2025-08-26 12:11:49: Epoch 48 Train Loss: 0.1071 Val: ap: 0.5233 auc: 0.7471 f1: 0.0000 acc: 0.6939
+2025-08-26 12:13:17: Epoch 49 Train Loss: 0.0984 Val: ap: 0.4970 auc: 0.7353 f1: 0.0000 acc: 0.6939
+2025-08-26 12:14:45: Epoch 50 Train Loss: 0.0995 Val: ap: 0.5212 auc: 0.7431 f1: 0.0000 acc: 0.6939
+2025-08-26 12:14:45: UDA training finished
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+2025-08-26 04:51:28: Epoch: 001 New best VALIDATION metrics
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+2025-08-26 05:09:24: Epoch: 026 Train Loss: 0.159, ap: 0.919, auc: 0.933, f1: 0.835, acc: 0.849
+2025-08-26 05:09:28: Epoch: 026 Test results, ap: 0.239, auc: 0.484, f1: 0.148, acc: 0.626
+2025-08-26 05:10:08: Epoch: 027 Train Loss: 0.158, ap: 0.925, auc: 0.941, f1: 0.857, acc: 0.866
+2025-08-26 05:10:08: Epoch: 027 New best VALIDATION metrics
+2025-08-26 05:10:11: Epoch: 027 Test results, ap: 0.295, auc: 0.540, f1: 0.227, acc: 0.724
+2025-08-26 05:10:50: Epoch: 028 Train Loss: 0.151, ap: 0.927, auc: 0.940, f1: 0.858, acc: 0.870
+2025-08-26 05:10:50: Epoch: 028 New best VALIDATION metrics
+2025-08-26 05:10:53: Epoch: 028 Test results, ap: 0.296, auc: 0.562, f1: 0.261, acc: 0.724
+2025-08-26 05:11:34: Epoch: 029 Train Loss: 0.155, ap: 0.930, auc: 0.944, f1: 0.850, acc: 0.861
+2025-08-26 05:11:34: Epoch: 029 New best VALIDATION metrics
+2025-08-26 05:11:37: Epoch: 029 Test results, ap: 0.274, auc: 0.526, f1: 0.275, acc: 0.699
+2025-08-26 05:12:16: Epoch: 030 Train Loss: 0.150, ap: 0.927, auc: 0.941, f1: 0.854, acc: 0.864
+2025-08-26 05:12:20: Epoch: 030 Test results, ap: 0.272, auc: 0.515, f1: 0.204, acc: 0.683
+2025-08-26 05:12:59: Epoch: 031 Train Loss: 0.131, ap: 0.931, auc: 0.945, f1: 0.859, acc: 0.870
+2025-08-26 05:12:59: Epoch: 031 New best VALIDATION metrics
+2025-08-26 05:13:02: Epoch: 031 Test results, ap: 0.309, auc: 0.546, f1: 0.346, acc: 0.724
+2025-08-26 05:13:42: Epoch: 032 Train Loss: 0.125, ap: 0.927, auc: 0.944, f1: 0.851, acc: 0.863
+2025-08-26 05:13:46: Epoch: 032 Test results, ap: 0.286, auc: 0.541, f1: 0.269, acc: 0.691
+2025-08-26 05:14:26: Epoch: 033 Train Loss: 0.116, ap: 0.929, auc: 0.944, f1: 0.846, acc: 0.857
+2025-08-26 05:14:29: Epoch: 033 Test results, ap: 0.287, auc: 0.546, f1: 0.300, acc: 0.659
+2025-08-26 05:15:09: Epoch: 034 Train Loss: 0.115, ap: 0.928, auc: 0.944, f1: 0.856, acc: 0.866
+2025-08-26 05:15:13: Epoch: 034 Test results, ap: 0.265, auc: 0.520, f1: 0.250, acc: 0.659
+2025-08-26 05:15:53: Epoch: 035 Train Loss: 0.113, ap: 0.927, auc: 0.941, f1: 0.846, acc: 0.857
+2025-08-26 05:15:56: Epoch: 035 Test results, ap: 0.282, auc: 0.542, f1: 0.259, acc: 0.675
+2025-08-26 05:16:37: Epoch: 036 Train Loss: 0.113, ap: 0.934, auc: 0.944, f1: 0.842, acc: 0.857
+2025-08-26 05:16:37: Epoch: 036 New best VALIDATION metrics
+2025-08-26 05:16:40: Epoch: 036 Test results, ap: 0.293, auc: 0.537, f1: 0.310, acc: 0.675
+2025-08-26 05:17:20: Epoch: 037 Train Loss: 0.120, ap: 0.926, auc: 0.940, f1: 0.837, acc: 0.850
+2025-08-26 05:17:23: Epoch: 037 Test results, ap: 0.276, auc: 0.533, f1: 0.305, acc: 0.667
+2025-08-26 05:18:04: Epoch: 038 Train Loss: 0.118, ap: 0.927, auc: 0.940, f1: 0.840, acc: 0.852
+2025-08-26 05:18:07: Epoch: 038 Test results, ap: 0.259, auc: 0.526, f1: 0.290, acc: 0.642
+2025-08-26 05:18:48: Epoch: 039 Train Loss: 0.110, ap: 0.931, auc: 0.944, f1: 0.840, acc: 0.854
+2025-08-26 05:18:51: Epoch: 039 Test results, ap: 0.299, auc: 0.539, f1: 0.321, acc: 0.691
+2025-08-26 05:19:31: Epoch: 040 Train Loss: 0.103, ap: 0.932, auc: 0.943, f1: 0.853, acc: 0.864
+2025-08-26 05:19:34: Epoch: 040 Test results, ap: 0.264, auc: 0.507, f1: 0.286, acc: 0.634
+2025-08-26 05:20:14: Epoch: 041 Train Loss: 0.095, ap: 0.934, auc: 0.945, f1: 0.856, acc: 0.868
+2025-08-26 05:20:14: Epoch: 041 New best VALIDATION metrics
+2025-08-26 05:20:18: Epoch: 041 Test results, ap: 0.272, auc: 0.521, f1: 0.259, acc: 0.675
+2025-08-26 05:20:58: Epoch: 042 Train Loss: 0.094, ap: 0.935, auc: 0.944, f1: 0.860, acc: 0.871
+2025-08-26 05:21:01: Epoch: 042 Test results, ap: 0.290, auc: 0.528, f1: 0.269, acc: 0.691
+2025-08-26 05:21:40: Epoch: 043 Train Loss: 0.095, ap: 0.930, auc: 0.944, f1: 0.853, acc: 0.864
+2025-08-26 05:21:44: Epoch: 043 Test results, ap: 0.268, auc: 0.525, f1: 0.295, acc: 0.650
+2025-08-26 05:22:23: Epoch: 044 Train Loss: 0.084, ap: 0.933, auc: 0.943, f1: 0.852, acc: 0.864
+2025-08-26 05:22:26: Epoch: 044 Test results, ap: 0.269, auc: 0.519, f1: 0.246, acc: 0.650
+2025-08-26 05:23:07: Epoch: 045 Train Loss: 0.089, ap: 0.932, auc: 0.943, f1: 0.844, acc: 0.857
+2025-08-26 05:23:10: Epoch: 045 Test results, ap: 0.294, auc: 0.519, f1: 0.267, acc: 0.642
+2025-08-26 05:23:50: Epoch: 046 Train Loss: 0.089, ap: 0.935, auc: 0.945, f1: 0.857, acc: 0.868
+2025-08-26 05:23:50: Epoch: 046 New best VALIDATION metrics
+2025-08-26 05:23:54: Epoch: 046 Test results, ap: 0.265, auc: 0.528, f1: 0.312, acc: 0.642
+2025-08-26 05:24:34: Epoch: 047 Train Loss: 0.092, ap: 0.929, auc: 0.941, f1: 0.842, acc: 0.856
+2025-08-26 05:24:37: Epoch: 047 Test results, ap: 0.276, auc: 0.536, f1: 0.241, acc: 0.642
+2025-08-26 05:25:17: Epoch: 048 Train Loss: 0.087, ap: 0.928, auc: 0.943, f1: 0.849, acc: 0.859
+2025-08-26 05:25:21: Epoch: 048 Test results, ap: 0.259, auc: 0.515, f1: 0.281, acc: 0.626
+2025-08-26 05:25:59: Epoch: 049 Train Loss: 0.090, ap: 0.935, auc: 0.947, f1: 0.860, acc: 0.871
+2025-08-26 05:25:59: Epoch: 049 New best VALIDATION metrics
+2025-08-26 05:26:03: Epoch: 049 Test results, ap: 0.265, auc: 0.527, f1: 0.290, acc: 0.642
+2025-08-26 05:26:43: Epoch: 050 Train Loss: 0.085, ap: 0.935, auc: 0.947, f1: 0.857, acc: 0.868
+2025-08-26 05:26:43: Epoch: 050 New best VALIDATION metrics
+2025-08-26 05:26:47: Epoch: 050 Test results, ap: 0.258, auc: 0.522, f1: 0.262, acc: 0.634
+2025-08-26 05:26:47: used time 0.60h
+2025-08-26 05:26:47: Running Cross Validation 1
+2025-08-26 05:26:47: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 05:27:27: Epoch: 001 Train Loss: 0.646, ap: 0.653, auc: 0.682, f1: 0.597, acc: 0.648
+2025-08-26 05:27:27: Epoch: 001 New best VALIDATION metrics
+2025-08-26 05:27:31: Epoch: 001 Test results, ap: 0.303, auc: 0.460, f1: 0.275, acc: 0.528
+2025-08-26 05:28:12: Epoch: 002 Train Loss: 0.585, ap: 0.695, auc: 0.755, f1: 0.659, acc: 0.681
+2025-08-26 05:28:12: Epoch: 002 New best VALIDATION metrics
+2025-08-26 05:28:15: Epoch: 002 Test results, ap: 0.211, auc: 0.408, f1: 0.289, acc: 0.439
+2025-08-26 05:28:55: Epoch: 003 Train Loss: 0.531, ap: 0.719, auc: 0.780, f1: 0.690, acc: 0.704
+2025-08-26 05:28:55: Epoch: 003 New best VALIDATION metrics
+2025-08-26 05:28:59: Epoch: 003 Test results, ap: 0.207, auc: 0.383, f1: 0.260, acc: 0.398
+2025-08-26 05:29:39: Epoch: 004 Train Loss: 0.501, ap: 0.659, auc: 0.738, f1: 0.611, acc: 0.680
+2025-08-26 05:29:42: Epoch: 004 Test results, ap: 0.273, auc: 0.548, f1: 0.125, acc: 0.659
+2025-08-26 05:30:22: Epoch: 005 Train Loss: 0.468, ap: 0.759, auc: 0.819, f1: 0.689, acc: 0.734
+2025-08-26 05:30:22: Epoch: 005 New best VALIDATION metrics
+2025-08-26 05:30:25: Epoch: 005 Test results, ap: 0.292, auc: 0.530, f1: 0.167, acc: 0.675
+2025-08-26 05:31:04: Epoch: 006 Train Loss: 0.431, ap: 0.796, auc: 0.846, f1: 0.758, acc: 0.775
+2025-08-26 05:31:04: Epoch: 006 New best VALIDATION metrics
+2025-08-26 05:31:07: Epoch: 006 Test results, ap: 0.220, auc: 0.455, f1: 0.360, acc: 0.423
+2025-08-26 05:31:46: Epoch: 007 Train Loss: 0.414, ap: 0.817, auc: 0.871, f1: 0.758, acc: 0.782
+2025-08-26 05:31:46: Epoch: 007 New best VALIDATION metrics
+2025-08-26 05:31:50: Epoch: 007 Test results, ap: 0.213, auc: 0.415, f1: 0.289, acc: 0.439
+2025-08-26 05:32:29: Epoch: 008 Train Loss: 0.395, ap: 0.838, auc: 0.880, f1: 0.759, acc: 0.790
+2025-08-26 05:32:29: Epoch: 008 New best VALIDATION metrics
+2025-08-26 05:32:33: Epoch: 008 Test results, ap: 0.224, auc: 0.454, f1: 0.298, acc: 0.463
+2025-08-26 05:33:14: Epoch: 009 Train Loss: 0.378, ap: 0.849, auc: 0.889, f1: 0.770, acc: 0.805
+2025-08-26 05:33:14: Epoch: 009 New best VALIDATION metrics
+2025-08-26 05:33:17: Epoch: 009 Test results, ap: 0.276, auc: 0.530, f1: 0.259, acc: 0.675
+2025-08-26 05:33:58: Epoch: 010 Train Loss: 0.348, ap: 0.826, auc: 0.878, f1: 0.798, acc: 0.819
+2025-08-26 05:34:02: Epoch: 010 Test results, ap: 0.239, auc: 0.497, f1: 0.337, acc: 0.488
+2025-08-26 05:34:43: Epoch: 011 Train Loss: 0.303, ap: 0.887, auc: 0.913, f1: 0.801, acc: 0.824
+2025-08-26 05:34:43: Epoch: 011 New best VALIDATION metrics
+2025-08-26 05:34:46: Epoch: 011 Test results, ap: 0.240, auc: 0.460, f1: 0.250, acc: 0.561
+2025-08-26 05:34:46: Epoch: 011 New best TEST metrics
+2025-08-26 05:35:27: Epoch: 012 Train Loss: 0.297, ap: 0.858, auc: 0.903, f1: 0.821, acc: 0.838
+2025-08-26 05:35:31: Epoch: 012 Test results, ap: 0.265, auc: 0.479, f1: 0.286, acc: 0.553
+2025-08-26 05:35:31: Epoch: 012 New best TEST metrics
+2025-08-26 05:36:12: Epoch: 013 Train Loss: 0.284, ap: 0.898, auc: 0.922, f1: 0.805, acc: 0.831
+2025-08-26 05:36:12: Epoch: 013 New best VALIDATION metrics
+2025-08-26 05:36:15: Epoch: 013 Test results, ap: 0.272, auc: 0.461, f1: 0.286, acc: 0.553
+2025-08-26 05:36:56: Epoch: 014 Train Loss: 0.268, ap: 0.903, auc: 0.929, f1: 0.812, acc: 0.842
+2025-08-26 05:36:56: Epoch: 014 New best VALIDATION metrics
+2025-08-26 05:36:59: Epoch: 014 Test results, ap: 0.286, auc: 0.500, f1: 0.303, acc: 0.626
+2025-08-26 05:36:59: Epoch: 014 New best TEST metrics
+2025-08-26 05:37:39: Epoch: 015 Train Loss: 0.253, ap: 0.918, auc: 0.935, f1: 0.832, acc: 0.857
+2025-08-26 05:37:39: Epoch: 015 New best VALIDATION metrics
+2025-08-26 05:37:43: Epoch: 015 Test results, ap: 0.267, auc: 0.494, f1: 0.303, acc: 0.626
+2025-08-26 05:38:23: Epoch: 016 Train Loss: 0.257, ap: 0.889, auc: 0.922, f1: 0.813, acc: 0.836
+2025-08-26 05:38:27: Epoch: 016 Test results, ap: 0.208, auc: 0.416, f1: 0.301, acc: 0.472
+2025-08-26 05:39:08: Epoch: 017 Train Loss: 0.239, ap: 0.901, auc: 0.921, f1: 0.796, acc: 0.833
+2025-08-26 05:39:11: Epoch: 017 Test results, ap: 0.295, auc: 0.523, f1: 0.233, acc: 0.732
+2025-08-26 05:39:11: Epoch: 017 New best TEST metrics
+2025-08-26 05:39:51: Epoch: 018 Train Loss: 0.242, ap: 0.909, auc: 0.929, f1: 0.808, acc: 0.840
+2025-08-26 05:39:54: Epoch: 018 Test results, ap: 0.287, auc: 0.497, f1: 0.338, acc: 0.650
+2025-08-26 05:40:35: Epoch: 019 Train Loss: 0.228, ap: 0.890, auc: 0.922, f1: 0.811, acc: 0.836
+2025-08-26 05:40:39: Epoch: 019 Test results, ap: 0.224, auc: 0.429, f1: 0.267, acc: 0.463
+2025-08-26 05:41:20: Epoch: 020 Train Loss: 0.221, ap: 0.919, auc: 0.938, f1: 0.838, acc: 0.863
+2025-08-26 05:41:20: Epoch: 020 New best VALIDATION metrics
+2025-08-26 05:41:24: Epoch: 020 Test results, ap: 0.253, auc: 0.492, f1: 0.170, acc: 0.683
+2025-08-26 05:42:04: Epoch: 021 Train Loss: 0.209, ap: 0.911, auc: 0.935, f1: 0.828, acc: 0.850
+2025-08-26 05:42:08: Epoch: 021 Test results, ap: 0.235, auc: 0.441, f1: 0.267, acc: 0.553
+2025-08-26 05:42:47: Epoch: 022 Train Loss: 0.188, ap: 0.926, auc: 0.941, f1: 0.832, acc: 0.857
+2025-08-26 05:42:47: Epoch: 022 New best VALIDATION metrics
+2025-08-26 05:42:51: Epoch: 022 Test results, ap: 0.272, auc: 0.499, f1: 0.250, acc: 0.659
+2025-08-26 05:43:31: Epoch: 023 Train Loss: 0.181, ap: 0.922, auc: 0.940, f1: 0.831, acc: 0.856
+2025-08-26 05:43:35: Epoch: 023 Test results, ap: 0.277, auc: 0.490, f1: 0.314, acc: 0.610
+2025-08-26 05:44:15: Epoch: 024 Train Loss: 0.182, ap: 0.919, auc: 0.941, f1: 0.843, acc: 0.863
+2025-08-26 05:44:18: Epoch: 024 Test results, ap: 0.235, auc: 0.453, f1: 0.304, acc: 0.553
+2025-08-26 05:44:59: Epoch: 025 Train Loss: 0.182, ap: 0.919, auc: 0.942, f1: 0.840, acc: 0.863
+2025-08-26 05:45:02: Epoch: 025 Test results, ap: 0.258, auc: 0.458, f1: 0.308, acc: 0.561
+2025-08-26 05:45:41: Epoch: 026 Train Loss: 0.179, ap: 0.936, auc: 0.947, f1: 0.817, acc: 0.852
+2025-08-26 05:45:41: Epoch: 026 New best VALIDATION metrics
+2025-08-26 05:45:45: Epoch: 026 Test results, ap: 0.297, auc: 0.525, f1: 0.292, acc: 0.724
+2025-08-26 05:45:45: Epoch: 026 New best TEST metrics
+2025-08-26 05:46:25: Epoch: 027 Train Loss: 0.174, ap: 0.921, auc: 0.937, f1: 0.827, acc: 0.854
+2025-08-26 05:46:28: Epoch: 027 Test results, ap: 0.272, auc: 0.487, f1: 0.237, acc: 0.634
+2025-08-26 05:47:08: Epoch: 028 Train Loss: 0.170, ap: 0.905, auc: 0.934, f1: 0.818, acc: 0.840
+2025-08-26 05:47:11: Epoch: 028 Test results, ap: 0.266, auc: 0.476, f1: 0.326, acc: 0.528
+2025-08-26 05:47:50: Epoch: 029 Train Loss: 0.171, ap: 0.919, auc: 0.940, f1: 0.834, acc: 0.857
+2025-08-26 05:47:53: Epoch: 029 Test results, ap: 0.261, auc: 0.477, f1: 0.282, acc: 0.585
+2025-08-26 05:48:34: Epoch: 030 Train Loss: 0.173, ap: 0.922, auc: 0.938, f1: 0.812, acc: 0.843
+2025-08-26 05:48:37: Epoch: 030 Test results, ap: 0.281, auc: 0.499, f1: 0.271, acc: 0.650
+2025-08-26 05:49:18: Epoch: 031 Train Loss: 0.156, ap: 0.926, auc: 0.944, f1: 0.853, acc: 0.873
+2025-08-26 05:49:21: Epoch: 031 Test results, ap: 0.280, auc: 0.485, f1: 0.286, acc: 0.593
+2025-08-26 05:50:00: Epoch: 032 Train Loss: 0.147, ap: 0.926, auc: 0.945, f1: 0.835, acc: 0.859
+2025-08-26 05:50:04: Epoch: 032 Test results, ap: 0.275, auc: 0.487, f1: 0.316, acc: 0.577
+2025-08-26 05:50:45: Epoch: 033 Train Loss: 0.136, ap: 0.930, auc: 0.947, f1: 0.844, acc: 0.866
+2025-08-26 05:50:48: Epoch: 033 Test results, ap: 0.291, auc: 0.495, f1: 0.281, acc: 0.626
+2025-08-26 05:51:28: Epoch: 034 Train Loss: 0.136, ap: 0.930, auc: 0.946, f1: 0.842, acc: 0.866
+2025-08-26 05:51:31: Epoch: 034 Test results, ap: 0.294, auc: 0.503, f1: 0.323, acc: 0.659
+2025-08-26 05:52:11: Epoch: 035 Train Loss: 0.142, ap: 0.932, auc: 0.948, f1: 0.841, acc: 0.866
+2025-08-26 05:52:15: Epoch: 035 Test results, ap: 0.305, auc: 0.508, f1: 0.281, acc: 0.667
+2025-08-26 05:52:55: Epoch: 036 Train Loss: 0.132, ap: 0.924, auc: 0.945, f1: 0.843, acc: 0.868
+2025-08-26 05:52:58: Epoch: 036 Test results, ap: 0.265, auc: 0.467, f1: 0.293, acc: 0.569
+2025-08-26 05:53:39: Epoch: 037 Train Loss: 0.142, ap: 0.922, auc: 0.944, f1: 0.838, acc: 0.863
+2025-08-26 05:53:42: Epoch: 037 Test results, ap: 0.261, auc: 0.491, f1: 0.278, acc: 0.537
+2025-08-26 05:54:21: Epoch: 038 Train Loss: 0.135, ap: 0.930, auc: 0.947, f1: 0.830, acc: 0.857
+2025-08-26 05:54:24: Epoch: 038 Test results, ap: 0.289, auc: 0.500, f1: 0.323, acc: 0.659
+2025-08-26 05:55:05: Epoch: 039 Train Loss: 0.130, ap: 0.936, auc: 0.950, f1: 0.846, acc: 0.871
+2025-08-26 05:55:05: Epoch: 039 New best VALIDATION metrics
+2025-08-26 05:55:09: Epoch: 039 Test results, ap: 0.309, auc: 0.521, f1: 0.286, acc: 0.675
+2025-08-26 05:55:09: Epoch: 039 New best TEST metrics
+2025-08-26 05:55:49: Epoch: 040 Train Loss: 0.144, ap: 0.927, auc: 0.947, f1: 0.845, acc: 0.868
+2025-08-26 05:55:52: Epoch: 040 Test results, ap: 0.288, auc: 0.497, f1: 0.347, acc: 0.602
+2025-08-26 05:56:31: Epoch: 041 Train Loss: 0.127, ap: 0.931, auc: 0.949, f1: 0.843, acc: 0.868
+2025-08-26 05:56:35: Epoch: 041 Test results, ap: 0.295, auc: 0.506, f1: 0.333, acc: 0.642
+2025-08-26 05:57:14: Epoch: 042 Train Loss: 0.121, ap: 0.928, auc: 0.947, f1: 0.845, acc: 0.868
+2025-08-26 05:57:18: Epoch: 042 Test results, ap: 0.287, auc: 0.509, f1: 0.353, acc: 0.642
+2025-08-26 05:57:58: Epoch: 043 Train Loss: 0.121, ap: 0.928, auc: 0.947, f1: 0.851, acc: 0.873
+2025-08-26 05:58:02: Epoch: 043 Test results, ap: 0.294, auc: 0.504, f1: 0.373, acc: 0.618
+2025-08-26 05:58:42: Epoch: 044 Train Loss: 0.117, ap: 0.927, auc: 0.946, f1: 0.852, acc: 0.873
+2025-08-26 05:58:45: Epoch: 044 Test results, ap: 0.294, auc: 0.504, f1: 0.361, acc: 0.626
+2025-08-26 05:59:25: Epoch: 045 Train Loss: 0.117, ap: 0.923, auc: 0.944, f1: 0.846, acc: 0.870
+2025-08-26 05:59:29: Epoch: 045 Test results, ap: 0.295, auc: 0.504, f1: 0.361, acc: 0.626
+2025-08-26 06:00:09: Epoch: 046 Train Loss: 0.115, ap: 0.931, auc: 0.948, f1: 0.840, acc: 0.866
+2025-08-26 06:00:12: Epoch: 046 Test results, ap: 0.300, auc: 0.516, f1: 0.317, acc: 0.650
+2025-08-26 06:00:52: Epoch: 047 Train Loss: 0.112, ap: 0.925, auc: 0.945, f1: 0.841, acc: 0.864
+2025-08-26 06:00:56: Epoch: 047 Test results, ap: 0.309, auc: 0.517, f1: 0.400, acc: 0.683
+2025-08-26 06:01:36: Epoch: 048 Train Loss: 0.128, ap: 0.926, auc: 0.946, f1: 0.839, acc: 0.864
+2025-08-26 06:01:40: Epoch: 048 Test results, ap: 0.302, auc: 0.506, f1: 0.348, acc: 0.634
+2025-08-26 06:02:19: Epoch: 049 Train Loss: 0.124, ap: 0.930, auc: 0.947, f1: 0.845, acc: 0.870
+2025-08-26 06:02:23: Epoch: 049 Test results, ap: 0.301, auc: 0.511, f1: 0.375, acc: 0.675
+2025-08-26 06:03:03: Epoch: 050 Train Loss: 0.115, ap: 0.921, auc: 0.943, f1: 0.836, acc: 0.861
+2025-08-26 06:03:06: Epoch: 050 Test results, ap: 0.288, auc: 0.496, f1: 0.324, acc: 0.593
+2025-08-26 06:03:06: used time 0.61h
+2025-08-26 06:03:06: Running Cross Validation 2
+2025-08-26 06:03:06: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 06:03:46: Epoch: 001 Train Loss: 0.662, ap: 0.644, auc: 0.698, f1: 0.612, acc: 0.626
+2025-08-26 06:03:46: Epoch: 001 New best VALIDATION metrics
+2025-08-26 06:03:51: Epoch: 001 Test results, ap: 0.197, auc: 0.380, f1: 0.289, acc: 0.480
+2025-08-26 06:04:31: Epoch: 002 Train Loss: 0.621, ap: 0.712, auc: 0.738, f1: 0.656, acc: 0.630
+2025-08-26 06:04:31: Epoch: 002 New best VALIDATION metrics
+2025-08-26 06:04:34: Epoch: 002 Test results, ap: 0.257, auc: 0.447, f1: 0.308, acc: 0.341
+2025-08-26 06:05:14: Epoch: 003 Train Loss: 0.562, ap: 0.746, auc: 0.782, f1: 0.681, acc: 0.663
+2025-08-26 06:05:14: Epoch: 003 New best VALIDATION metrics
+2025-08-26 06:05:18: Epoch: 003 Test results, ap: 0.226, auc: 0.455, f1: 0.357, acc: 0.325
+2025-08-26 06:05:58: Epoch: 004 Train Loss: 0.532, ap: 0.735, auc: 0.770, f1: 0.661, acc: 0.654
+2025-08-26 06:06:02: Epoch: 004 Test results, ap: 0.236, auc: 0.508, f1: 0.292, acc: 0.488
+2025-08-26 06:06:42: Epoch: 005 Train Loss: 0.493, ap: 0.804, auc: 0.827, f1: 0.716, acc: 0.728
+2025-08-26 06:06:42: Epoch: 005 New best VALIDATION metrics
+2025-08-26 06:06:45: Epoch: 005 Test results, ap: 0.253, auc: 0.513, f1: 0.385, acc: 0.480
+2025-08-26 06:07:25: Epoch: 006 Train Loss: 0.487, ap: 0.809, auc: 0.838, f1: 0.724, acc: 0.728
+2025-08-26 06:07:25: Epoch: 006 New best VALIDATION metrics
+2025-08-26 06:07:29: Epoch: 006 Test results, ap: 0.207, auc: 0.418, f1: 0.342, acc: 0.374
+2025-08-26 06:08:10: Epoch: 007 Train Loss: 0.441, ap: 0.836, auc: 0.851, f1: 0.729, acc: 0.753
+2025-08-26 06:08:10: Epoch: 007 New best VALIDATION metrics
+2025-08-26 06:08:13: Epoch: 007 Test results, ap: 0.250, auc: 0.515, f1: 0.242, acc: 0.593
+2025-08-26 06:08:54: Epoch: 008 Train Loss: 0.421, ap: 0.798, auc: 0.842, f1: 0.745, acc: 0.762
+2025-08-26 06:08:57: Epoch: 008 Test results, ap: 0.225, auc: 0.461, f1: 0.274, acc: 0.439
+2025-08-26 06:09:38: Epoch: 009 Train Loss: 0.408, ap: 0.822, auc: 0.854, f1: 0.739, acc: 0.771
+2025-08-26 06:09:42: Epoch: 009 Test results, ap: 0.258, auc: 0.509, f1: 0.366, acc: 0.577
+2025-08-26 06:10:22: Epoch: 010 Train Loss: 0.376, ap: 0.833, auc: 0.861, f1: 0.744, acc: 0.764
+2025-08-26 06:10:22: Epoch: 010 New best VALIDATION metrics
+2025-08-26 06:10:25: Epoch: 010 Test results, ap: 0.317, auc: 0.585, f1: 0.364, acc: 0.602
+2025-08-26 06:11:05: Epoch: 011 Train Loss: 0.341, ap: 0.884, auc: 0.891, f1: 0.776, acc: 0.797
+2025-08-26 06:11:05: Epoch: 011 New best VALIDATION metrics
+2025-08-26 06:11:08: Epoch: 011 Test results, ap: 0.319, auc: 0.586, f1: 0.366, acc: 0.634
+2025-08-26 06:11:08: Epoch: 011 New best TEST metrics
+2025-08-26 06:11:48: Epoch: 012 Train Loss: 0.312, ap: 0.859, auc: 0.880, f1: 0.769, acc: 0.797
+2025-08-26 06:11:51: Epoch: 012 Test results, ap: 0.305, auc: 0.595, f1: 0.305, acc: 0.667
+2025-08-26 06:12:31: Epoch: 013 Train Loss: 0.298, ap: 0.887, auc: 0.898, f1: 0.785, acc: 0.813
+2025-08-26 06:12:31: Epoch: 013 New best VALIDATION metrics
+2025-08-26 06:12:35: Epoch: 013 Test results, ap: 0.369, auc: 0.623, f1: 0.316, acc: 0.683
+2025-08-26 06:12:35: Epoch: 013 New best TEST metrics
+2025-08-26 06:13:15: Epoch: 014 Train Loss: 0.280, ap: 0.886, auc: 0.902, f1: 0.793, acc: 0.817
+2025-08-26 06:13:15: Epoch: 014 New best VALIDATION metrics
+2025-08-26 06:13:19: Epoch: 014 Test results, ap: 0.259, auc: 0.549, f1: 0.380, acc: 0.602
+2025-08-26 06:13:59: Epoch: 015 Train Loss: 0.273, ap: 0.877, auc: 0.893, f1: 0.794, acc: 0.818
+2025-08-26 06:14:02: Epoch: 015 Test results, ap: 0.349, auc: 0.647, f1: 0.333, acc: 0.675
+2025-08-26 06:14:02: Epoch: 015 New best TEST metrics
+2025-08-26 06:14:43: Epoch: 016 Train Loss: 0.278, ap: 0.897, auc: 0.911, f1: 0.799, acc: 0.825
+2025-08-26 06:14:43: Epoch: 016 New best VALIDATION metrics
+2025-08-26 06:14:47: Epoch: 016 Test results, ap: 0.280, auc: 0.550, f1: 0.351, acc: 0.610
+2025-08-26 06:15:26: Epoch: 017 Train Loss: 0.252, ap: 0.891, auc: 0.905, f1: 0.804, acc: 0.831
+2025-08-26 06:15:30: Epoch: 017 Test results, ap: 0.304, auc: 0.584, f1: 0.344, acc: 0.659
+2025-08-26 06:16:10: Epoch: 018 Train Loss: 0.264, ap: 0.888, auc: 0.905, f1: 0.799, acc: 0.827
+2025-08-26 06:16:14: Epoch: 018 Test results, ap: 0.304, auc: 0.609, f1: 0.349, acc: 0.667
+2025-08-26 06:16:53: Epoch: 019 Train Loss: 0.272, ap: 0.873, auc: 0.891, f1: 0.789, acc: 0.817
+2025-08-26 06:16:57: Epoch: 019 Test results, ap: 0.333, auc: 0.614, f1: 0.344, acc: 0.659
+2025-08-26 06:17:37: Epoch: 020 Train Loss: 0.250, ap: 0.900, auc: 0.913, f1: 0.809, acc: 0.831
+2025-08-26 06:17:37: Epoch: 020 New best VALIDATION metrics
+2025-08-26 06:17:41: Epoch: 020 Test results, ap: 0.292, auc: 0.571, f1: 0.324, acc: 0.593
+2025-08-26 06:18:20: Epoch: 021 Train Loss: 0.214, ap: 0.914, auc: 0.923, f1: 0.819, acc: 0.845
+2025-08-26 06:18:20: Epoch: 021 New best VALIDATION metrics
+2025-08-26 06:18:24: Epoch: 021 Test results, ap: 0.350, auc: 0.625, f1: 0.302, acc: 0.699
+2025-08-26 06:19:04: Epoch: 022 Train Loss: 0.206, ap: 0.909, auc: 0.921, f1: 0.826, acc: 0.848
+2025-08-26 06:19:07: Epoch: 022 Test results, ap: 0.344, auc: 0.610, f1: 0.271, acc: 0.650
+2025-08-26 06:19:48: Epoch: 023 Train Loss: 0.197, ap: 0.911, auc: 0.921, f1: 0.814, acc: 0.836
+2025-08-26 06:19:52: Epoch: 023 Test results, ap: 0.354, auc: 0.628, f1: 0.305, acc: 0.667
+2025-08-26 06:20:32: Epoch: 024 Train Loss: 0.187, ap: 0.913, auc: 0.925, f1: 0.824, acc: 0.848
+2025-08-26 06:20:32: Epoch: 024 New best VALIDATION metrics
+2025-08-26 06:20:36: Epoch: 024 Test results, ap: 0.345, auc: 0.641, f1: 0.291, acc: 0.683
+2025-08-26 06:21:16: Epoch: 025 Train Loss: 0.191, ap: 0.914, auc: 0.926, f1: 0.821, acc: 0.843
+2025-08-26 06:21:16: Epoch: 025 New best VALIDATION metrics
+2025-08-26 06:21:19: Epoch: 025 Test results, ap: 0.322, auc: 0.614, f1: 0.254, acc: 0.618
+2025-08-26 06:21:59: Epoch: 026 Train Loss: 0.179, ap: 0.916, auc: 0.927, f1: 0.827, acc: 0.850
+2025-08-26 06:21:59: Epoch: 026 New best VALIDATION metrics
+2025-08-26 06:22:03: Epoch: 026 Test results, ap: 0.345, auc: 0.609, f1: 0.235, acc: 0.683
+2025-08-26 06:22:42: Epoch: 027 Train Loss: 0.185, ap: 0.912, auc: 0.926, f1: 0.817, acc: 0.845
+2025-08-26 06:22:45: Epoch: 027 Test results, ap: 0.332, auc: 0.626, f1: 0.233, acc: 0.732
+2025-08-26 06:23:25: Epoch: 028 Train Loss: 0.178, ap: 0.912, auc: 0.925, f1: 0.832, acc: 0.854
+2025-08-26 06:23:29: Epoch: 028 Test results, ap: 0.316, auc: 0.595, f1: 0.246, acc: 0.602
+2025-08-26 06:24:08: Epoch: 029 Train Loss: 0.183, ap: 0.908, auc: 0.925, f1: 0.827, acc: 0.848
+2025-08-26 06:24:12: Epoch: 029 Test results, ap: 0.304, auc: 0.585, f1: 0.319, acc: 0.618
+2025-08-26 06:24:52: Epoch: 030 Train Loss: 0.171, ap: 0.904, auc: 0.920, f1: 0.819, acc: 0.843
+2025-08-26 06:24:56: Epoch: 030 Test results, ap: 0.355, auc: 0.619, f1: 0.296, acc: 0.691
+2025-08-26 06:25:36: Epoch: 031 Train Loss: 0.154, ap: 0.919, auc: 0.933, f1: 0.821, acc: 0.841
+2025-08-26 06:25:36: Epoch: 031 New best VALIDATION metrics
+2025-08-26 06:25:40: Epoch: 031 Test results, ap: 0.325, auc: 0.600, f1: 0.294, acc: 0.610
+2025-08-26 06:26:19: Epoch: 032 Train Loss: 0.145, ap: 0.916, auc: 0.932, f1: 0.830, acc: 0.850
+2025-08-26 06:26:23: Epoch: 032 Test results, ap: 0.297, auc: 0.568, f1: 0.319, acc: 0.618
+2025-08-26 06:27:03: Epoch: 033 Train Loss: 0.145, ap: 0.918, auc: 0.932, f1: 0.833, acc: 0.852
+2025-08-26 06:27:07: Epoch: 033 Test results, ap: 0.320, auc: 0.592, f1: 0.338, acc: 0.650
+2025-08-26 06:27:46: Epoch: 034 Train Loss: 0.145, ap: 0.921, auc: 0.933, f1: 0.828, acc: 0.850
+2025-08-26 06:27:46: Epoch: 034 New best VALIDATION metrics
+2025-08-26 06:27:49: Epoch: 034 Test results, ap: 0.307, auc: 0.581, f1: 0.273, acc: 0.610
+2025-08-26 06:28:29: Epoch: 035 Train Loss: 0.147, ap: 0.923, auc: 0.935, f1: 0.850, acc: 0.868
+2025-08-26 06:28:29: Epoch: 035 New best VALIDATION metrics
+2025-08-26 06:28:33: Epoch: 035 Test results, ap: 0.324, auc: 0.600, f1: 0.271, acc: 0.650
+2025-08-26 06:29:13: Epoch: 036 Train Loss: 0.147, ap: 0.921, auc: 0.936, f1: 0.851, acc: 0.869
+2025-08-26 06:29:16: Epoch: 036 Test results, ap: 0.305, auc: 0.583, f1: 0.294, acc: 0.610
+2025-08-26 06:29:56: Epoch: 037 Train Loss: 0.150, ap: 0.921, auc: 0.933, f1: 0.833, acc: 0.852
+2025-08-26 06:30:00: Epoch: 037 Test results, ap: 0.304, auc: 0.583, f1: 0.319, acc: 0.618
+2025-08-26 06:30:40: Epoch: 038 Train Loss: 0.142, ap: 0.921, auc: 0.935, f1: 0.840, acc: 0.861
+2025-08-26 06:30:43: Epoch: 038 Test results, ap: 0.301, auc: 0.577, f1: 0.265, acc: 0.593
+2025-08-26 06:31:22: Epoch: 039 Train Loss: 0.143, ap: 0.922, auc: 0.936, f1: 0.842, acc: 0.861
+2025-08-26 06:31:26: Epoch: 039 Test results, ap: 0.320, auc: 0.579, f1: 0.299, acc: 0.618
+2025-08-26 06:32:06: Epoch: 040 Train Loss: 0.141, ap: 0.922, auc: 0.935, f1: 0.846, acc: 0.864
+2025-08-26 06:32:10: Epoch: 040 Test results, ap: 0.300, auc: 0.561, f1: 0.303, acc: 0.626
+2025-08-26 06:32:49: Epoch: 041 Train Loss: 0.132, ap: 0.925, auc: 0.938, f1: 0.847, acc: 0.864
+2025-08-26 06:32:49: Epoch: 041 New best VALIDATION metrics
+2025-08-26 06:32:52: Epoch: 041 Test results, ap: 0.309, auc: 0.569, f1: 0.273, acc: 0.610
+2025-08-26 06:33:32: Epoch: 042 Train Loss: 0.125, ap: 0.925, auc: 0.937, f1: 0.847, acc: 0.866
+2025-08-26 06:33:35: Epoch: 042 Test results, ap: 0.318, auc: 0.580, f1: 0.246, acc: 0.602
+2025-08-26 06:34:15: Epoch: 043 Train Loss: 0.123, ap: 0.925, auc: 0.938, f1: 0.846, acc: 0.864
+2025-08-26 06:34:15: Epoch: 043 New best VALIDATION metrics
+2025-08-26 06:34:19: Epoch: 043 Test results, ap: 0.309, auc: 0.566, f1: 0.265, acc: 0.593
+2025-08-26 06:34:59: Epoch: 044 Train Loss: 0.125, ap: 0.927, auc: 0.939, f1: 0.843, acc: 0.862
+2025-08-26 06:34:59: Epoch: 044 New best VALIDATION metrics
+2025-08-26 06:35:03: Epoch: 044 Test results, ap: 0.318, auc: 0.582, f1: 0.299, acc: 0.618
+2025-08-26 06:35:43: Epoch: 045 Train Loss: 0.122, ap: 0.926, auc: 0.938, f1: 0.847, acc: 0.866
+2025-08-26 06:35:46: Epoch: 045 Test results, ap: 0.312, auc: 0.572, f1: 0.239, acc: 0.585
+2025-08-26 06:36:26: Epoch: 046 Train Loss: 0.124, ap: 0.926, auc: 0.939, f1: 0.854, acc: 0.871
+2025-08-26 06:36:30: Epoch: 046 Test results, ap: 0.310, auc: 0.576, f1: 0.269, acc: 0.602
+2025-08-26 06:37:10: Epoch: 047 Train Loss: 0.117, ap: 0.927, auc: 0.939, f1: 0.848, acc: 0.868
+2025-08-26 06:37:14: Epoch: 047 Test results, ap: 0.323, auc: 0.586, f1: 0.250, acc: 0.610
+2025-08-26 06:37:54: Epoch: 048 Train Loss: 0.125, ap: 0.927, auc: 0.939, f1: 0.844, acc: 0.864
+2025-08-26 06:37:58: Epoch: 048 Test results, ap: 0.321, auc: 0.570, f1: 0.215, acc: 0.585
+2025-08-26 06:38:38: Epoch: 049 Train Loss: 0.122, ap: 0.926, auc: 0.939, f1: 0.850, acc: 0.868
+2025-08-26 06:38:41: Epoch: 049 Test results, ap: 0.318, auc: 0.580, f1: 0.250, acc: 0.610
+2025-08-26 06:39:22: Epoch: 050 Train Loss: 0.109, ap: 0.928, auc: 0.940, f1: 0.841, acc: 0.862
+2025-08-26 06:39:22: Epoch: 050 New best VALIDATION metrics
+2025-08-26 06:39:25: Epoch: 050 Test results, ap: 0.324, auc: 0.582, f1: 0.200, acc: 0.610
+2025-08-26 06:39:25: used time 0.61h
+2025-08-26 06:39:26: Running Cross Validation 3
+2025-08-26 06:39:26: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 06:40:06: Epoch: 001 Train Loss: 0.649, ap: 0.651, auc: 0.710, f1: 0.599, acc: 0.665
+2025-08-26 06:40:06: Epoch: 001 New best VALIDATION metrics
+2025-08-26 06:40:10: Epoch: 001 Test results, ap: 0.259, auc: 0.471, f1: 0.275, acc: 0.528
+2025-08-26 06:40:50: Epoch: 002 Train Loss: 0.597, ap: 0.719, auc: 0.767, f1: 0.635, acc: 0.688
+2025-08-26 06:40:50: Epoch: 002 New best VALIDATION metrics
+2025-08-26 06:40:53: Epoch: 002 Test results, ap: 0.193, auc: 0.368, f1: 0.203, acc: 0.488
+2025-08-26 06:41:32: Epoch: 003 Train Loss: 0.535, ap: 0.772, auc: 0.816, f1: 0.701, acc: 0.714
+2025-08-26 06:41:32: Epoch: 003 New best VALIDATION metrics
+2025-08-26 06:41:36: Epoch: 003 Test results, ap: 0.236, auc: 0.456, f1: 0.203, acc: 0.553
+2025-08-26 06:42:16: Epoch: 004 Train Loss: 0.513, ap: 0.778, auc: 0.813, f1: 0.691, acc: 0.711
+2025-08-26 06:42:16: Epoch: 004 New best VALIDATION metrics
+2025-08-26 06:42:20: Epoch: 004 Test results, ap: 0.243, auc: 0.514, f1: 0.286, acc: 0.593
+2025-08-26 06:43:00: Epoch: 005 Train Loss: 0.454, ap: 0.827, auc: 0.852, f1: 0.724, acc: 0.751
+2025-08-26 06:43:00: Epoch: 005 New best VALIDATION metrics
+2025-08-26 06:43:04: Epoch: 005 Test results, ap: 0.240, auc: 0.489, f1: 0.282, acc: 0.585
+2025-08-26 06:43:44: Epoch: 006 Train Loss: 0.445, ap: 0.809, auc: 0.833, f1: 0.690, acc: 0.748
+2025-08-26 06:43:48: Epoch: 006 Test results, ap: 0.279, auc: 0.533, f1: 0.321, acc: 0.691
+2025-08-26 06:44:27: Epoch: 007 Train Loss: 0.423, ap: 0.848, auc: 0.869, f1: 0.739, acc: 0.774
+2025-08-26 06:44:27: Epoch: 007 New best VALIDATION metrics
+2025-08-26 06:44:31: Epoch: 007 Test results, ap: 0.288, auc: 0.574, f1: 0.317, acc: 0.650
+2025-08-26 06:45:10: Epoch: 008 Train Loss: 0.402, ap: 0.837, auc: 0.873, f1: 0.773, acc: 0.792
+2025-08-26 06:45:14: Epoch: 008 Test results, ap: 0.271, auc: 0.541, f1: 0.344, acc: 0.659
+2025-08-26 06:45:54: Epoch: 009 Train Loss: 0.356, ap: 0.857, auc: 0.897, f1: 0.782, acc: 0.810
+2025-08-26 06:45:54: Epoch: 009 New best VALIDATION metrics
+2025-08-26 06:45:58: Epoch: 009 Test results, ap: 0.223, auc: 0.472, f1: 0.200, acc: 0.610
+2025-08-26 06:46:38: Epoch: 010 Train Loss: 0.342, ap: 0.847, auc: 0.871, f1: 0.750, acc: 0.776
+2025-08-26 06:46:41: Epoch: 010 Test results, ap: 0.232, auc: 0.489, f1: 0.286, acc: 0.593
+2025-08-26 06:47:22: Epoch: 011 Train Loss: 0.304, ap: 0.876, auc: 0.910, f1: 0.798, acc: 0.822
+2025-08-26 06:47:22: Epoch: 011 New best VALIDATION metrics
+2025-08-26 06:47:26: Epoch: 011 Test results, ap: 0.222, auc: 0.453, f1: 0.230, acc: 0.618
+2025-08-26 06:47:26: Epoch: 011 New best TEST metrics
+2025-08-26 06:48:06: Epoch: 012 Train Loss: 0.273, ap: 0.888, auc: 0.914, f1: 0.804, acc: 0.831
+2025-08-26 06:48:06: Epoch: 012 New best VALIDATION metrics
+2025-08-26 06:48:09: Epoch: 012 Test results, ap: 0.220, auc: 0.466, f1: 0.157, acc: 0.650
+2025-08-26 06:48:09: Epoch: 012 New best TEST metrics
+2025-08-26 06:48:49: Epoch: 013 Train Loss: 0.250, ap: 0.881, auc: 0.905, f1: 0.795, acc: 0.825
+2025-08-26 06:48:52: Epoch: 013 Test results, ap: 0.242, auc: 0.496, f1: 0.226, acc: 0.667
+2025-08-26 06:48:52: Epoch: 013 New best TEST metrics
+2025-08-26 06:49:31: Epoch: 014 Train Loss: 0.251, ap: 0.874, auc: 0.901, f1: 0.783, acc: 0.815
+2025-08-26 06:49:35: Epoch: 014 Test results, ap: 0.243, auc: 0.509, f1: 0.170, acc: 0.683
+2025-08-26 06:49:35: Epoch: 014 New best TEST metrics
+2025-08-26 06:50:14: Epoch: 015 Train Loss: 0.238, ap: 0.878, auc: 0.918, f1: 0.818, acc: 0.836
+2025-08-26 06:50:17: Epoch: 015 Test results, ap: 0.225, auc: 0.486, f1: 0.078, acc: 0.618
+2025-08-26 06:50:57: Epoch: 016 Train Loss: 0.231, ap: 0.891, auc: 0.928, f1: 0.830, acc: 0.847
+2025-08-26 06:50:57: Epoch: 016 New best VALIDATION metrics
+2025-08-26 06:51:01: Epoch: 016 Test results, ap: 0.234, auc: 0.490, f1: 0.185, acc: 0.642
+2025-08-26 06:51:39: Epoch: 017 Train Loss: 0.210, ap: 0.880, auc: 0.918, f1: 0.816, acc: 0.832
+2025-08-26 06:51:43: Epoch: 017 Test results, ap: 0.245, auc: 0.511, f1: 0.271, acc: 0.650
+2025-08-26 06:51:43: Epoch: 017 New best TEST metrics
+2025-08-26 06:52:22: Epoch: 018 Train Loss: 0.212, ap: 0.888, auc: 0.923, f1: 0.834, acc: 0.855
+2025-08-26 06:52:26: Epoch: 018 Test results, ap: 0.234, auc: 0.498, f1: 0.189, acc: 0.650
+2025-08-26 06:53:07: Epoch: 019 Train Loss: 0.212, ap: 0.895, auc: 0.922, f1: 0.799, acc: 0.829
+2025-08-26 06:53:11: Epoch: 019 Test results, ap: 0.220, auc: 0.471, f1: 0.115, acc: 0.626
+2025-08-26 06:53:51: Epoch: 020 Train Loss: 0.205, ap: 0.887, auc: 0.923, f1: 0.815, acc: 0.841
+2025-08-26 06:53:55: Epoch: 020 Test results, ap: 0.233, auc: 0.493, f1: 0.087, acc: 0.659
+2025-08-26 06:54:35: Epoch: 021 Train Loss: 0.182, ap: 0.898, auc: 0.930, f1: 0.833, acc: 0.859
+2025-08-26 06:54:35: Epoch: 021 New best VALIDATION metrics
+2025-08-26 06:54:39: Epoch: 021 Test results, ap: 0.238, auc: 0.499, f1: 0.204, acc: 0.683
+2025-08-26 06:55:18: Epoch: 022 Train Loss: 0.166, ap: 0.902, auc: 0.935, f1: 0.824, acc: 0.850
+2025-08-26 06:55:18: Epoch: 022 New best VALIDATION metrics
+2025-08-26 06:55:22: Epoch: 022 Test results, ap: 0.228, auc: 0.486, f1: 0.157, acc: 0.650
+2025-08-26 06:56:02: Epoch: 023 Train Loss: 0.172, ap: 0.893, auc: 0.927, f1: 0.830, acc: 0.854
+2025-08-26 06:56:05: Epoch: 023 Test results, ap: 0.245, auc: 0.523, f1: 0.163, acc: 0.667
+2025-08-26 06:56:05: Epoch: 023 New best TEST metrics
+2025-08-26 06:56:46: Epoch: 024 Train Loss: 0.150, ap: 0.892, auc: 0.933, f1: 0.841, acc: 0.862
+2025-08-26 06:56:49: Epoch: 024 Test results, ap: 0.221, auc: 0.470, f1: 0.115, acc: 0.626
+2025-08-26 06:57:31: Epoch: 025 Train Loss: 0.146, ap: 0.888, auc: 0.924, f1: 0.817, acc: 0.845
+2025-08-26 06:57:35: Epoch: 025 Test results, ap: 0.248, auc: 0.527, f1: 0.091, acc: 0.675
+2025-08-26 06:57:35: Epoch: 025 New best TEST metrics
+2025-08-26 06:58:16: Epoch: 026 Train Loss: 0.155, ap: 0.895, auc: 0.934, f1: 0.851, acc: 0.868
+2025-08-26 06:58:19: Epoch: 026 Test results, ap: 0.221, auc: 0.471, f1: 0.082, acc: 0.634
+2025-08-26 06:59:00: Epoch: 027 Train Loss: 0.146, ap: 0.899, auc: 0.936, f1: 0.851, acc: 0.873
+2025-08-26 06:59:04: Epoch: 027 Test results, ap: 0.234, auc: 0.497, f1: 0.085, acc: 0.650
+2025-08-26 06:59:45: Epoch: 028 Train Loss: 0.145, ap: 0.901, auc: 0.935, f1: 0.846, acc: 0.866
+2025-08-26 06:59:48: Epoch: 028 Test results, ap: 0.234, auc: 0.497, f1: 0.045, acc: 0.659
+2025-08-26 07:00:29: Epoch: 029 Train Loss: 0.145, ap: 0.905, auc: 0.935, f1: 0.840, acc: 0.862
+2025-08-26 07:00:29: Epoch: 029 New best VALIDATION metrics
+2025-08-26 07:00:32: Epoch: 029 Test results, ap: 0.221, auc: 0.467, f1: 0.043, acc: 0.634
+2025-08-26 07:01:13: Epoch: 030 Train Loss: 0.141, ap: 0.899, auc: 0.931, f1: 0.834, acc: 0.859
+2025-08-26 07:01:17: Epoch: 030 Test results, ap: 0.246, auc: 0.524, f1: 0.091, acc: 0.675
+2025-08-26 07:01:57: Epoch: 031 Train Loss: 0.117, ap: 0.898, auc: 0.935, f1: 0.843, acc: 0.862
+2025-08-26 07:02:00: Epoch: 031 Test results, ap: 0.225, auc: 0.480, f1: 0.087, acc: 0.659
+2025-08-26 07:02:40: Epoch: 032 Train Loss: 0.121, ap: 0.902, auc: 0.937, f1: 0.831, acc: 0.855
+2025-08-26 07:02:44: Epoch: 032 Test results, ap: 0.221, auc: 0.473, f1: 0.085, acc: 0.650
+2025-08-26 07:03:24: Epoch: 033 Train Loss: 0.116, ap: 0.898, auc: 0.938, f1: 0.850, acc: 0.869
+2025-08-26 07:03:27: Epoch: 033 Test results, ap: 0.224, auc: 0.481, f1: 0.082, acc: 0.634
+2025-08-26 07:04:08: Epoch: 034 Train Loss: 0.114, ap: 0.899, auc: 0.939, f1: 0.850, acc: 0.869
+2025-08-26 07:04:11: Epoch: 034 Test results, ap: 0.226, auc: 0.486, f1: 0.082, acc: 0.634
+2025-08-26 07:04:52: Epoch: 035 Train Loss: 0.102, ap: 0.896, auc: 0.936, f1: 0.848, acc: 0.869
+2025-08-26 07:04:55: Epoch: 035 Test results, ap: 0.226, auc: 0.485, f1: 0.082, acc: 0.634
+2025-08-26 07:05:35: Epoch: 036 Train Loss: 0.113, ap: 0.897, auc: 0.939, f1: 0.853, acc: 0.873
+2025-08-26 07:05:38: Epoch: 036 Test results, ap: 0.228, auc: 0.489, f1: 0.083, acc: 0.642
+2025-08-26 07:06:18: Epoch: 037 Train Loss: 0.109, ap: 0.897, auc: 0.936, f1: 0.850, acc: 0.871
+2025-08-26 07:06:21: Epoch: 037 Test results, ap: 0.228, auc: 0.489, f1: 0.082, acc: 0.634
+2025-08-26 07:07:00: Epoch: 038 Train Loss: 0.114, ap: 0.896, auc: 0.939, f1: 0.845, acc: 0.866
+2025-08-26 07:07:04: Epoch: 038 Test results, ap: 0.221, auc: 0.472, f1: 0.083, acc: 0.642
+2025-08-26 07:07:44: Epoch: 039 Train Loss: 0.103, ap: 0.894, auc: 0.937, f1: 0.848, acc: 0.868
+2025-08-26 07:07:48: Epoch: 039 Test results, ap: 0.224, auc: 0.481, f1: 0.080, acc: 0.626
+2025-08-26 07:08:27: Epoch: 040 Train Loss: 0.107, ap: 0.898, auc: 0.937, f1: 0.844, acc: 0.864
+2025-08-26 07:08:31: Epoch: 040 Test results, ap: 0.221, auc: 0.473, f1: 0.080, acc: 0.626
+2025-08-26 07:09:10: Epoch: 041 Train Loss: 0.087, ap: 0.895, auc: 0.939, f1: 0.855, acc: 0.873
+2025-08-26 07:09:14: Epoch: 041 Test results, ap: 0.225, auc: 0.484, f1: 0.082, acc: 0.634
+2025-08-26 07:09:54: Epoch: 042 Train Loss: 0.083, ap: 0.896, auc: 0.938, f1: 0.852, acc: 0.871
+2025-08-26 07:09:57: Epoch: 042 Test results, ap: 0.227, auc: 0.486, f1: 0.082, acc: 0.634
+2025-08-26 07:10:37: Epoch: 043 Train Loss: 0.090, ap: 0.893, auc: 0.940, f1: 0.857, acc: 0.875
+2025-08-26 07:10:40: Epoch: 043 Test results, ap: 0.225, auc: 0.483, f1: 0.083, acc: 0.642
+2025-08-26 07:11:19: Epoch: 044 Train Loss: 0.082, ap: 0.888, auc: 0.937, f1: 0.854, acc: 0.871
+2025-08-26 07:11:23: Epoch: 044 Test results, ap: 0.223, auc: 0.480, f1: 0.078, acc: 0.618
+2025-08-26 07:12:04: Epoch: 045 Train Loss: 0.078, ap: 0.894, auc: 0.939, f1: 0.862, acc: 0.880
+2025-08-26 07:12:08: Epoch: 045 Test results, ap: 0.228, auc: 0.488, f1: 0.082, acc: 0.634
+2025-08-26 07:12:48: Epoch: 046 Train Loss: 0.082, ap: 0.887, auc: 0.937, f1: 0.845, acc: 0.862
+2025-08-26 07:12:52: Epoch: 046 Test results, ap: 0.225, auc: 0.482, f1: 0.080, acc: 0.626
+2025-08-26 07:13:32: Epoch: 047 Train Loss: 0.083, ap: 0.896, auc: 0.937, f1: 0.855, acc: 0.875
+2025-08-26 07:13:35: Epoch: 047 Test results, ap: 0.229, auc: 0.489, f1: 0.085, acc: 0.650
+2025-08-26 07:14:15: Epoch: 048 Train Loss: 0.078, ap: 0.887, auc: 0.936, f1: 0.855, acc: 0.875
+2025-08-26 07:14:19: Epoch: 048 Test results, ap: 0.223, auc: 0.479, f1: 0.085, acc: 0.650
+2025-08-26 07:14:57: Epoch: 049 Train Loss: 0.072, ap: 0.892, auc: 0.939, f1: 0.866, acc: 0.884
+2025-08-26 07:15:01: Epoch: 049 Test results, ap: 0.225, auc: 0.483, f1: 0.082, acc: 0.634
+2025-08-26 07:15:41: Epoch: 050 Train Loss: 0.084, ap: 0.890, auc: 0.940, f1: 0.858, acc: 0.877
+2025-08-26 07:15:44: Epoch: 050 Test results, ap: 0.223, auc: 0.478, f1: 0.083, acc: 0.642
+2025-08-26 07:15:44: used time 0.61h
+2025-08-26 07:15:44: Running Cross Validation 4
+2025-08-26 07:15:44: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 07:16:25: Epoch: 001 Train Loss: 0.633, ap: 0.623, auc: 0.678, f1: 0.624, acc: 0.582
+2025-08-26 07:16:25: Epoch: 001 New best VALIDATION metrics
+2025-08-26 07:16:28: Epoch: 001 Test results, ap: 0.226, auc: 0.488, f1: 0.406, acc: 0.333
+2025-08-26 07:17:08: Epoch: 002 Train Loss: 0.584, ap: 0.714, auc: 0.774, f1: 0.684, acc: 0.684
+2025-08-26 07:17:08: Epoch: 002 New best VALIDATION metrics
+2025-08-26 07:17:12: Epoch: 002 Test results, ap: 0.263, auc: 0.551, f1: 0.350, acc: 0.577
+2025-08-26 07:17:51: Epoch: 003 Train Loss: 0.524, ap: 0.705, auc: 0.767, f1: 0.685, acc: 0.716
+2025-08-26 07:17:55: Epoch: 003 Test results, ap: 0.293, auc: 0.529, f1: 0.340, acc: 0.463
+2025-08-26 07:18:34: Epoch: 004 Train Loss: 0.489, ap: 0.744, auc: 0.784, f1: 0.649, acc: 0.697
+2025-08-26 07:18:34: Epoch: 004 New best VALIDATION metrics
+2025-08-26 07:18:37: Epoch: 004 Test results, ap: 0.433, auc: 0.647, f1: 0.364, acc: 0.772
+2025-08-26 07:19:18: Epoch: 005 Train Loss: 0.454, ap: 0.799, auc: 0.833, f1: 0.727, acc: 0.737
+2025-08-26 07:19:18: Epoch: 005 New best VALIDATION metrics
+2025-08-26 07:19:21: Epoch: 005 Test results, ap: 0.277, auc: 0.575, f1: 0.237, acc: 0.634
+2025-08-26 07:20:00: Epoch: 006 Train Loss: 0.411, ap: 0.759, auc: 0.788, f1: 0.656, acc: 0.723
+2025-08-26 07:20:04: Epoch: 006 Test results, ap: 0.346, auc: 0.647, f1: 0.000, acc: 0.756
+2025-08-26 07:20:44: Epoch: 007 Train Loss: 0.406, ap: 0.839, auc: 0.856, f1: 0.756, acc: 0.771
+2025-08-26 07:20:44: Epoch: 007 New best VALIDATION metrics
+2025-08-26 07:20:47: Epoch: 007 Test results, ap: 0.274, auc: 0.552, f1: 0.338, acc: 0.585
+2025-08-26 07:21:27: Epoch: 008 Train Loss: 0.355, ap: 0.781, auc: 0.841, f1: 0.710, acc: 0.758
+2025-08-26 07:21:31: Epoch: 008 Test results, ap: 0.367, auc: 0.630, f1: 0.233, acc: 0.732
+2025-08-26 07:22:11: Epoch: 009 Train Loss: 0.349, ap: 0.830, auc: 0.875, f1: 0.776, acc: 0.788
+2025-08-26 07:22:11: Epoch: 009 New best VALIDATION metrics
+2025-08-26 07:22:14: Epoch: 009 Test results, ap: 0.304, auc: 0.576, f1: 0.350, acc: 0.577
+2025-08-26 07:22:56: Epoch: 010 Train Loss: 0.340, ap: 0.844, auc: 0.871, f1: 0.746, acc: 0.774
+2025-08-26 07:22:56: Epoch: 010 New best VALIDATION metrics
+2025-08-26 07:23:00: Epoch: 010 Test results, ap: 0.356, auc: 0.643, f1: 0.346, acc: 0.724
+2025-08-26 07:23:40: Epoch: 011 Train Loss: 0.286, ap: 0.873, auc: 0.893, f1: 0.775, acc: 0.801
+2025-08-26 07:23:40: Epoch: 011 New best VALIDATION metrics
+2025-08-26 07:23:44: Epoch: 011 Test results, ap: 0.314, auc: 0.587, f1: 0.345, acc: 0.691
+2025-08-26 07:23:44: Epoch: 011 New best TEST metrics
+2025-08-26 07:24:25: Epoch: 012 Train Loss: 0.264, ap: 0.866, auc: 0.892, f1: 0.788, acc: 0.811
+2025-08-26 07:24:28: Epoch: 012 Test results, ap: 0.327, auc: 0.603, f1: 0.276, acc: 0.659
+2025-08-26 07:24:28: Epoch: 012 New best TEST metrics
+2025-08-26 07:25:09: Epoch: 013 Train Loss: 0.248, ap: 0.876, auc: 0.903, f1: 0.795, acc: 0.817
+2025-08-26 07:25:09: Epoch: 013 New best VALIDATION metrics
+2025-08-26 07:25:13: Epoch: 013 Test results, ap: 0.329, auc: 0.607, f1: 0.407, acc: 0.715
+2025-08-26 07:25:13: Epoch: 013 New best TEST metrics
+2025-08-26 07:25:52: Epoch: 014 Train Loss: 0.246, ap: 0.879, auc: 0.907, f1: 0.802, acc: 0.822
+2025-08-26 07:25:52: Epoch: 014 New best VALIDATION metrics
+2025-08-26 07:25:56: Epoch: 014 Test results, ap: 0.325, auc: 0.602, f1: 0.375, acc: 0.675
+2025-08-26 07:26:37: Epoch: 015 Train Loss: 0.224, ap: 0.889, auc: 0.914, f1: 0.823, acc: 0.847
+2025-08-26 07:26:37: Epoch: 015 New best VALIDATION metrics
+2025-08-26 07:26:41: Epoch: 015 Test results, ap: 0.323, auc: 0.610, f1: 0.308, acc: 0.707
+2025-08-26 07:27:22: Epoch: 016 Train Loss: 0.231, ap: 0.876, auc: 0.905, f1: 0.804, acc: 0.836
+2025-08-26 07:27:25: Epoch: 016 Test results, ap: 0.283, auc: 0.556, f1: 0.328, acc: 0.634
+2025-08-26 07:28:06: Epoch: 017 Train Loss: 0.222, ap: 0.898, auc: 0.914, f1: 0.819, acc: 0.840
+2025-08-26 07:28:06: Epoch: 017 New best VALIDATION metrics
+2025-08-26 07:28:09: Epoch: 017 Test results, ap: 0.343, auc: 0.624, f1: 0.387, acc: 0.691
+2025-08-26 07:28:09: Epoch: 017 New best TEST metrics
+2025-08-26 07:28:51: Epoch: 018 Train Loss: 0.203, ap: 0.894, auc: 0.913, f1: 0.819, acc: 0.840
+2025-08-26 07:28:54: Epoch: 018 Test results, ap: 0.302, auc: 0.595, f1: 0.333, acc: 0.675
+2025-08-26 07:29:35: Epoch: 019 Train Loss: 0.208, ap: 0.901, auc: 0.920, f1: 0.815, acc: 0.838
+2025-08-26 07:29:35: Epoch: 019 New best VALIDATION metrics
+2025-08-26 07:29:39: Epoch: 019 Test results, ap: 0.330, auc: 0.627, f1: 0.414, acc: 0.724
+2025-08-26 07:30:19: Epoch: 020 Train Loss: 0.203, ap: 0.882, auc: 0.912, f1: 0.811, acc: 0.831
+2025-08-26 07:30:23: Epoch: 020 Test results, ap: 0.271, auc: 0.562, f1: 0.430, acc: 0.634
+2025-08-26 07:31:03: Epoch: 021 Train Loss: 0.171, ap: 0.902, auc: 0.924, f1: 0.830, acc: 0.850
+2025-08-26 07:31:03: Epoch: 021 New best VALIDATION metrics
+2025-08-26 07:31:07: Epoch: 021 Test results, ap: 0.342, auc: 0.631, f1: 0.431, acc: 0.699
+2025-08-26 07:31:07: Epoch: 021 New best TEST metrics
+2025-08-26 07:31:48: Epoch: 022 Train Loss: 0.162, ap: 0.914, auc: 0.930, f1: 0.834, acc: 0.854
+2025-08-26 07:31:48: Epoch: 022 New best VALIDATION metrics
+2025-08-26 07:31:51: Epoch: 022 Test results, ap: 0.361, auc: 0.644, f1: 0.429, acc: 0.740
+2025-08-26 07:31:51: Epoch: 022 New best TEST metrics
+2025-08-26 07:32:33: Epoch: 023 Train Loss: 0.153, ap: 0.911, auc: 0.930, f1: 0.841, acc: 0.859
+2025-08-26 07:32:37: Epoch: 023 Test results, ap: 0.346, auc: 0.651, f1: 0.393, acc: 0.699
+2025-08-26 07:33:18: Epoch: 024 Train Loss: 0.154, ap: 0.914, auc: 0.928, f1: 0.822, acc: 0.843
+2025-08-26 07:33:21: Epoch: 024 Test results, ap: 0.332, auc: 0.621, f1: 0.441, acc: 0.732
+2025-08-26 07:34:02: Epoch: 025 Train Loss: 0.145, ap: 0.909, auc: 0.928, f1: 0.820, acc: 0.840
+2025-08-26 07:34:06: Epoch: 025 Test results, ap: 0.336, auc: 0.624, f1: 0.407, acc: 0.715
+2025-08-26 07:34:47: Epoch: 026 Train Loss: 0.144, ap: 0.915, auc: 0.927, f1: 0.821, acc: 0.845
+2025-08-26 07:34:50: Epoch: 026 Test results, ap: 0.370, auc: 0.648, f1: 0.467, acc: 0.740
+2025-08-26 07:34:50: Epoch: 026 New best TEST metrics
+2025-08-26 07:35:30: Epoch: 027 Train Loss: 0.140, ap: 0.902, auc: 0.923, f1: 0.832, acc: 0.850
+2025-08-26 07:35:33: Epoch: 027 Test results, ap: 0.319, auc: 0.617, f1: 0.373, acc: 0.699
+2025-08-26 07:36:14: Epoch: 028 Train Loss: 0.141, ap: 0.911, auc: 0.927, f1: 0.832, acc: 0.854
+2025-08-26 07:36:17: Epoch: 028 Test results, ap: 0.362, auc: 0.641, f1: 0.419, acc: 0.707
+2025-08-26 07:36:58: Epoch: 029 Train Loss: 0.141, ap: 0.910, auc: 0.928, f1: 0.836, acc: 0.855
+2025-08-26 07:37:01: Epoch: 029 Test results, ap: 0.346, auc: 0.641, f1: 0.435, acc: 0.683
+2025-08-26 07:37:42: Epoch: 030 Train Loss: 0.140, ap: 0.904, auc: 0.925, f1: 0.833, acc: 0.850
+2025-08-26 07:37:45: Epoch: 030 Test results, ap: 0.334, auc: 0.632, f1: 0.441, acc: 0.691
+2025-08-26 07:38:26: Epoch: 031 Train Loss: 0.122, ap: 0.916, auc: 0.932, f1: 0.840, acc: 0.857
+2025-08-26 07:38:26: Epoch: 031 New best VALIDATION metrics
+2025-08-26 07:38:30: Epoch: 031 Test results, ap: 0.345, auc: 0.632, f1: 0.414, acc: 0.724
+2025-08-26 07:39:09: Epoch: 032 Train Loss: 0.121, ap: 0.913, auc: 0.928, f1: 0.830, acc: 0.850
+2025-08-26 07:39:13: Epoch: 032 Test results, ap: 0.360, auc: 0.637, f1: 0.459, acc: 0.732
+2025-08-26 07:39:54: Epoch: 033 Train Loss: 0.111, ap: 0.912, auc: 0.930, f1: 0.837, acc: 0.855
+2025-08-26 07:39:57: Epoch: 033 Test results, ap: 0.332, auc: 0.621, f1: 0.452, acc: 0.724
+2025-08-26 07:40:38: Epoch: 034 Train Loss: 0.120, ap: 0.921, auc: 0.934, f1: 0.842, acc: 0.862
+2025-08-26 07:40:38: Epoch: 034 New best VALIDATION metrics
+2025-08-26 07:40:42: Epoch: 034 Test results, ap: 0.372, auc: 0.639, f1: 0.500, acc: 0.756
+2025-08-26 07:41:22: Epoch: 035 Train Loss: 0.109, ap: 0.918, auc: 0.933, f1: 0.831, acc: 0.848
+2025-08-26 07:41:25: Epoch: 035 Test results, ap: 0.344, auc: 0.632, f1: 0.448, acc: 0.740
+2025-08-26 07:42:05: Epoch: 036 Train Loss: 0.106, ap: 0.917, auc: 0.931, f1: 0.832, acc: 0.850
+2025-08-26 07:42:08: Epoch: 036 Test results, ap: 0.371, auc: 0.652, f1: 0.483, acc: 0.756
+2025-08-26 07:42:08: Epoch: 036 New best TEST metrics
+2025-08-26 07:42:48: Epoch: 037 Train Loss: 0.107, ap: 0.921, auc: 0.933, f1: 0.832, acc: 0.852
+2025-08-26 07:42:51: Epoch: 037 Test results, ap: 0.363, auc: 0.650, f1: 0.467, acc: 0.740
+2025-08-26 07:43:31: Epoch: 038 Train Loss: 0.108, ap: 0.918, auc: 0.932, f1: 0.827, acc: 0.847
+2025-08-26 07:43:35: Epoch: 038 Test results, ap: 0.359, auc: 0.643, f1: 0.475, acc: 0.748
+2025-08-26 07:44:15: Epoch: 039 Train Loss: 0.100, ap: 0.917, auc: 0.931, f1: 0.836, acc: 0.855
+2025-08-26 07:44:19: Epoch: 039 Test results, ap: 0.355, auc: 0.636, f1: 0.407, acc: 0.715
+2025-08-26 07:45:00: Epoch: 040 Train Loss: 0.112, ap: 0.914, auc: 0.928, f1: 0.838, acc: 0.857
+2025-08-26 07:45:03: Epoch: 040 Test results, ap: 0.348, auc: 0.627, f1: 0.467, acc: 0.740
+2025-08-26 07:45:45: Epoch: 041 Train Loss: 0.101, ap: 0.917, auc: 0.932, f1: 0.834, acc: 0.852
+2025-08-26 07:45:49: Epoch: 041 Test results, ap: 0.350, auc: 0.636, f1: 0.459, acc: 0.732
+2025-08-26 07:46:37: Epoch: 042 Train Loss: 0.095, ap: 0.918, auc: 0.933, f1: 0.836, acc: 0.854
+2025-08-26 07:46:40: Epoch: 042 Test results, ap: 0.351, auc: 0.637, f1: 0.441, acc: 0.732
+2025-08-26 07:47:21: Epoch: 043 Train Loss: 0.090, ap: 0.920, auc: 0.934, f1: 0.834, acc: 0.854
+2025-08-26 07:47:25: Epoch: 043 Test results, ap: 0.357, auc: 0.636, f1: 0.441, acc: 0.732
+2025-08-26 07:48:06: Epoch: 044 Train Loss: 0.087, ap: 0.923, auc: 0.937, f1: 0.831, acc: 0.850
+2025-08-26 07:48:06: Epoch: 044 New best VALIDATION metrics
+2025-08-26 07:48:10: Epoch: 044 Test results, ap: 0.366, auc: 0.643, f1: 0.441, acc: 0.732
+2025-08-26 07:48:50: Epoch: 045 Train Loss: 0.090, ap: 0.917, auc: 0.933, f1: 0.841, acc: 0.859
+2025-08-26 07:48:53: Epoch: 045 Test results, ap: 0.346, auc: 0.633, f1: 0.484, acc: 0.740
+2025-08-26 07:49:34: Epoch: 046 Train Loss: 0.092, ap: 0.919, auc: 0.934, f1: 0.831, acc: 0.850
+2025-08-26 07:49:37: Epoch: 046 Test results, ap: 0.361, auc: 0.642, f1: 0.467, acc: 0.740
+2025-08-26 07:50:19: Epoch: 047 Train Loss: 0.086, ap: 0.920, auc: 0.933, f1: 0.835, acc: 0.854
+2025-08-26 07:50:22: Epoch: 047 Test results, ap: 0.368, auc: 0.644, f1: 0.441, acc: 0.732
+2025-08-26 07:51:04: Epoch: 048 Train Loss: 0.089, ap: 0.922, auc: 0.935, f1: 0.840, acc: 0.861
+2025-08-26 07:51:07: Epoch: 048 Test results, ap: 0.384, auc: 0.649, f1: 0.441, acc: 0.732
+2025-08-26 07:51:07: Epoch: 048 New best TEST metrics
+2025-08-26 07:51:47: Epoch: 049 Train Loss: 0.089, ap: 0.918, auc: 0.934, f1: 0.828, acc: 0.847
+2025-08-26 07:51:51: Epoch: 049 Test results, ap: 0.352, auc: 0.636, f1: 0.467, acc: 0.740
+2025-08-26 07:52:32: Epoch: 050 Train Loss: 0.095, ap: 0.916, auc: 0.933, f1: 0.836, acc: 0.854
+2025-08-26 07:52:36: Epoch: 050 Test results, ap: 0.352, auc: 0.637, f1: 0.484, acc: 0.740
+2025-08-26 07:52:36: used time 0.61h
+2025-08-26 07:52:36: Cross Validation Finished!
+2025-08-26 07:52:36: Best validation perform list
+[[0.93450081 0.94699192 0.85659653 0.86795777]
+ [0.9363991  0.94989359 0.8463158  0.87147886]
+ [0.92794484 0.93983597 0.84146339 0.86243385]
+ [0.90453821 0.93509877 0.83950615 0.86243385]
+ [0.92319965 0.93663967 0.83101392 0.85008818]]
+2025-08-26 07:52:36: mean: [0.925 0.942 0.843 0.863]
+2025-08-26 07:52:36: std: [0.011 0.006 0.008 0.007]
+2025-08-26 07:52:36: Best test perform list
+[[0.29179832 0.60107529 0.22222222 0.71544713]
+ [0.30884862 0.52078855 0.2857143  0.67479676]
+ [0.34884569 0.646595   0.33333334 0.67479676]
+ [0.24755499 0.52688169 0.09090909 0.67479676]
+ [0.38412094 0.64910394 0.44067797 0.73170733]]
+2025-08-26 07:52:36: mean: [0.316 0.589 0.275 0.694]
+2025-08-26 07:52:36: std: [0.047 0.056 0.116 0.024]
diff --git a/run-cls/rn18-mlp-16-768-ce-32-0.001-50/config.json b/run-cls/rn18-mlp-16-768-ce-32-0.001-50/config.json
new file mode 100644
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+++ b/run-cls/rn18-mlp-16-768-ce-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": null, "channels": 16, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "cls", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "ce", "dir": false, "classes": 2}
\ No newline at end of file
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diff --git a/run-cls/rn18-mlp-16-768-ce-32-0.001-50/result.txt b/run-cls/rn18-mlp-16-768-ce-32-0.001-50/result.txt
new file mode 100644
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--- /dev/null
+++ b/run-cls/rn18-mlp-16-768-ce-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
+Valid
+0.9084785103797912,0.932526957988739,0.8489877700805664,0.8681670308113099
+0.015430930776636341,0.009198613140211004,0.019769161077257045,0.01277110172055867
+Test
+0.3029677987098694,0.5729032397270203,0.09055890589952469,0.7170731663703919
+0.05110854215949202,0.054773117816275946,0.10517586152484307,0.044943985173625405
diff --git a/run-cls/rn18-mlp-16-768-ce-32-0.001-50/training.log b/run-cls/rn18-mlp-16-768-ce-32-0.001-50/training.log
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+++ b/run-cls/rn18-mlp-16-768-ce-32-0.001-50/training.log
@@ -0,0 +1,673 @@
+2025-08-26 02:10:43: saving_dir: ./run-cls/rn18-mlp-16-768-ce-32-0.001-50
+2025-08-26 02:10:43: Loading Training Dataset
+2025-08-26 02:12:20: Loading Test Dataset
+2025-08-26 02:12:48: Running Cross Validation 0
+2025-08-26 02:12:48: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 02:13:26: Epoch: 001 Train Loss: 0.671, ap: 0.646, auc: 0.663, f1: 0.617, acc: 0.595
+2025-08-26 02:13:26: Epoch: 001 New best VALIDATION metrics
+2025-08-26 02:13:29: Epoch: 001 Test results, ap: 0.271, auc: 0.424, f1: 0.322, acc: 0.350
+2025-08-26 02:14:03: Epoch: 002 Train Loss: 0.666, ap: 0.663, auc: 0.668, f1: 0.623, acc: 0.636
+2025-08-26 02:14:03: Epoch: 002 New best VALIDATION metrics
+2025-08-26 02:14:06: Epoch: 002 Test results, ap: 0.260, auc: 0.368, f1: 0.125, acc: 0.545
+2025-08-26 02:14:39: Epoch: 003 Train Loss: 0.663, ap: 0.662, auc: 0.680, f1: 0.615, acc: 0.643
+2025-08-26 02:14:39: Epoch: 003 New best VALIDATION metrics
+2025-08-26 02:14:42: Epoch: 003 Test results, ap: 0.276, auc: 0.515, f1: 0.442, acc: 0.610
+2025-08-26 02:15:15: Epoch: 004 Train Loss: 0.650, ap: 0.705, auc: 0.702, f1: 0.533, acc: 0.651
+2025-08-26 02:15:15: Epoch: 004 New best VALIDATION metrics
+2025-08-26 02:15:18: Epoch: 004 Test results, ap: 0.231, auc: 0.466, f1: 0.000, acc: 0.748
+2025-08-26 02:15:51: Epoch: 005 Train Loss: 0.650, ap: 0.714, auc: 0.710, f1: 0.585, acc: 0.658
+2025-08-26 02:15:51: Epoch: 005 New best VALIDATION metrics
+2025-08-26 02:15:55: Epoch: 005 Test results, ap: 0.209, auc: 0.427, f1: 0.000, acc: 0.748
+2025-08-26 02:16:29: Epoch: 006 Train Loss: 0.639, ap: 0.692, auc: 0.708, f1: 0.658, acc: 0.665
+2025-08-26 02:16:32: Epoch: 006 Test results, ap: 0.195, auc: 0.381, f1: 0.330, acc: 0.374
+2025-08-26 02:17:05: Epoch: 007 Train Loss: 0.617, ap: 0.767, auc: 0.766, f1: 0.683, acc: 0.678
+2025-08-26 02:17:05: Epoch: 007 New best VALIDATION metrics
+2025-08-26 02:17:08: Epoch: 007 Test results, ap: 0.215, auc: 0.430, f1: 0.000, acc: 0.724
+2025-08-26 02:17:42: Epoch: 008 Train Loss: 0.579, ap: 0.665, auc: 0.715, f1: 0.129, acc: 0.548
+2025-08-26 02:17:45: Epoch: 008 Test results, ap: 0.246, auc: 0.536, f1: 0.000, acc: 0.756
+2025-08-26 02:18:18: Epoch: 009 Train Loss: 0.540, ap: 0.796, auc: 0.805, f1: 0.645, acc: 0.713
+2025-08-26 02:18:22: Epoch: 009 New best VALIDATION metrics
+2025-08-26 02:18:26: Epoch: 009 Test results, ap: 0.257, auc: 0.559, f1: 0.000, acc: 0.756
+2025-08-26 02:19:00: Epoch: 010 Train Loss: 0.508, ap: 0.838, auc: 0.847, f1: 0.713, acc: 0.759
+2025-08-26 02:19:00: Epoch: 010 New best VALIDATION metrics
+2025-08-26 02:19:03: Epoch: 010 Test results, ap: 0.272, auc: 0.574, f1: 0.000, acc: 0.740
+2025-08-26 02:19:38: Epoch: 011 Train Loss: 0.422, ap: 0.824, auc: 0.839, f1: 0.728, acc: 0.750
+2025-08-26 02:19:41: Epoch: 011 Test results, ap: 0.277, auc: 0.593, f1: 0.000, acc: 0.724
+2025-08-26 02:19:41: Epoch: 011 New best TEST metrics
+2025-08-26 02:20:15: Epoch: 012 Train Loss: 0.390, ap: 0.856, auc: 0.873, f1: 0.786, acc: 0.801
+2025-08-26 02:20:15: Epoch: 012 New best VALIDATION metrics
+2025-08-26 02:20:19: Epoch: 012 Test results, ap: 0.227, auc: 0.485, f1: 0.000, acc: 0.715
+2025-08-26 02:20:53: Epoch: 013 Train Loss: 0.340, ap: 0.863, auc: 0.878, f1: 0.784, acc: 0.792
+2025-08-26 02:20:53: Epoch: 013 New best VALIDATION metrics
+2025-08-26 02:20:57: Epoch: 013 Test results, ap: 0.221, auc: 0.471, f1: 0.000, acc: 0.691
+2025-08-26 02:21:31: Epoch: 014 Train Loss: 0.320, ap: 0.881, auc: 0.889, f1: 0.786, acc: 0.799
+2025-08-26 02:21:31: Epoch: 014 New best VALIDATION metrics
+2025-08-26 02:21:34: Epoch: 014 Test results, ap: 0.232, auc: 0.500, f1: 0.000, acc: 0.732
+2025-08-26 02:22:09: Epoch: 015 Train Loss: 0.296, ap: 0.877, auc: 0.882, f1: 0.729, acc: 0.769
+2025-08-26 02:22:12: Epoch: 015 Test results, ap: 0.245, auc: 0.539, f1: 0.000, acc: 0.756
+2025-08-26 02:22:46: Epoch: 016 Train Loss: 0.276, ap: 0.882, auc: 0.888, f1: 0.759, acc: 0.789
+2025-08-26 02:22:49: Epoch: 016 Test results, ap: 0.254, auc: 0.557, f1: 0.000, acc: 0.748
+2025-08-26 02:23:23: Epoch: 017 Train Loss: 0.262, ap: 0.892, auc: 0.902, f1: 0.790, acc: 0.812
+2025-08-26 02:23:23: Epoch: 017 New best VALIDATION metrics
+2025-08-26 02:23:26: Epoch: 017 Test results, ap: 0.247, auc: 0.537, f1: 0.000, acc: 0.715
+2025-08-26 02:24:00: Epoch: 018 Train Loss: 0.240, ap: 0.889, auc: 0.893, f1: 0.737, acc: 0.783
+2025-08-26 02:24:03: Epoch: 018 Test results, ap: 0.268, auc: 0.575, f1: 0.000, acc: 0.748
+2025-08-26 02:24:37: Epoch: 019 Train Loss: 0.234, ap: 0.898, auc: 0.909, f1: 0.834, acc: 0.843
+2025-08-26 02:24:37: Epoch: 019 New best VALIDATION metrics
+2025-08-26 02:24:41: Epoch: 019 Test results, ap: 0.244, auc: 0.530, f1: 0.000, acc: 0.724
+2025-08-26 02:25:14: Epoch: 020 Train Loss: 0.219, ap: 0.864, auc: 0.897, f1: 0.836, acc: 0.829
+2025-08-26 02:25:18: Epoch: 020 Test results, ap: 0.250, auc: 0.543, f1: 0.089, acc: 0.667
+2025-08-26 02:25:52: Epoch: 021 Train Loss: 0.199, ap: 0.900, auc: 0.915, f1: 0.776, acc: 0.806
+2025-08-26 02:25:52: Epoch: 021 New best VALIDATION metrics
+2025-08-26 02:25:55: Epoch: 021 Test results, ap: 0.258, auc: 0.566, f1: 0.000, acc: 0.715
+2025-08-26 02:26:29: Epoch: 022 Train Loss: 0.181, ap: 0.901, auc: 0.919, f1: 0.854, acc: 0.854
+2025-08-26 02:26:29: Epoch: 022 New best VALIDATION metrics
+2025-08-26 02:26:32: Epoch: 022 Test results, ap: 0.238, auc: 0.525, f1: 0.000, acc: 0.667
+2025-08-26 02:27:06: Epoch: 023 Train Loss: 0.171, ap: 0.913, auc: 0.916, f1: 0.830, acc: 0.838
+2025-08-26 02:27:06: Epoch: 023 New best VALIDATION metrics
+2025-08-26 02:27:10: Epoch: 023 Test results, ap: 0.262, auc: 0.583, f1: 0.000, acc: 0.715
+2025-08-26 02:27:44: Epoch: 024 Train Loss: 0.171, ap: 0.908, auc: 0.915, f1: 0.832, acc: 0.847
+2025-08-26 02:27:47: Epoch: 024 Test results, ap: 0.245, auc: 0.543, f1: 0.000, acc: 0.699
+2025-08-26 02:28:21: Epoch: 025 Train Loss: 0.164, ap: 0.919, auc: 0.924, f1: 0.842, acc: 0.854
+2025-08-26 02:28:21: Epoch: 025 New best VALIDATION metrics
+2025-08-26 02:28:24: Epoch: 025 Test results, ap: 0.248, auc: 0.546, f1: 0.000, acc: 0.707
+2025-08-26 02:28:58: Epoch: 026 Train Loss: 0.165, ap: 0.915, auc: 0.918, f1: 0.844, acc: 0.852
+2025-08-26 02:29:01: Epoch: 026 Test results, ap: 0.255, auc: 0.569, f1: 0.000, acc: 0.715
+2025-08-26 02:29:35: Epoch: 027 Train Loss: 0.164, ap: 0.925, auc: 0.931, f1: 0.847, acc: 0.856
+2025-08-26 02:29:35: Epoch: 027 New best VALIDATION metrics
+2025-08-26 02:29:38: Epoch: 027 Test results, ap: 0.262, auc: 0.581, f1: 0.000, acc: 0.715
+2025-08-26 02:30:13: Epoch: 028 Train Loss: 0.148, ap: 0.919, auc: 0.925, f1: 0.854, acc: 0.864
+2025-08-26 02:30:16: Epoch: 028 Test results, ap: 0.239, auc: 0.525, f1: 0.000, acc: 0.715
+2025-08-26 02:30:50: Epoch: 029 Train Loss: 0.152, ap: 0.918, auc: 0.914, f1: 0.799, acc: 0.824
+2025-08-26 02:30:53: Epoch: 029 Test results, ap: 0.264, auc: 0.584, f1: 0.000, acc: 0.756
+2025-08-26 02:31:26: Epoch: 030 Train Loss: 0.149, ap: 0.925, auc: 0.932, f1: 0.867, acc: 0.868
+2025-08-26 02:31:26: Epoch: 030 New best VALIDATION metrics
+2025-08-26 02:31:30: Epoch: 030 Test results, ap: 0.247, auc: 0.550, f1: 0.000, acc: 0.667
+2025-08-26 02:32:03: Epoch: 031 Train Loss: 0.127, ap: 0.923, auc: 0.928, f1: 0.829, acc: 0.847
+2025-08-26 02:32:07: Epoch: 031 Test results, ap: 0.248, auc: 0.549, f1: 0.000, acc: 0.724
+2025-08-26 02:32:40: Epoch: 032 Train Loss: 0.124, ap: 0.924, auc: 0.932, f1: 0.853, acc: 0.863
+2025-08-26 02:32:40: Epoch: 032 New best VALIDATION metrics
+2025-08-26 02:32:43: Epoch: 032 Test results, ap: 0.241, auc: 0.529, f1: 0.000, acc: 0.715
+2025-08-26 02:33:16: Epoch: 033 Train Loss: 0.118, ap: 0.929, auc: 0.932, f1: 0.854, acc: 0.868
+2025-08-26 02:33:16: Epoch: 033 New best VALIDATION metrics
+2025-08-26 02:33:19: Epoch: 033 Test results, ap: 0.246, auc: 0.541, f1: 0.000, acc: 0.715
+2025-08-26 02:33:54: Epoch: 034 Train Loss: 0.119, ap: 0.926, auc: 0.932, f1: 0.857, acc: 0.866
+2025-08-26 02:33:57: Epoch: 034 Test results, ap: 0.239, auc: 0.528, f1: 0.000, acc: 0.715
+2025-08-26 02:34:31: Epoch: 035 Train Loss: 0.116, ap: 0.926, auc: 0.932, f1: 0.863, acc: 0.868
+2025-08-26 02:34:35: Epoch: 035 Test results, ap: 0.258, auc: 0.574, f1: 0.000, acc: 0.699
+2025-08-26 02:35:08: Epoch: 036 Train Loss: 0.117, ap: 0.925, auc: 0.929, f1: 0.857, acc: 0.864
+2025-08-26 02:35:12: Epoch: 036 Test results, ap: 0.254, auc: 0.551, f1: 0.000, acc: 0.724
+2025-08-26 02:35:45: Epoch: 037 Train Loss: 0.110, ap: 0.926, auc: 0.929, f1: 0.840, acc: 0.852
+2025-08-26 02:35:49: Epoch: 037 Test results, ap: 0.271, auc: 0.597, f1: 0.000, acc: 0.724
+2025-08-26 02:36:22: Epoch: 038 Train Loss: 0.122, ap: 0.921, auc: 0.932, f1: 0.854, acc: 0.863
+2025-08-26 02:36:25: Epoch: 038 Test results, ap: 0.242, auc: 0.533, f1: 0.000, acc: 0.707
+2025-08-26 02:37:00: Epoch: 039 Train Loss: 0.115, ap: 0.921, auc: 0.932, f1: 0.856, acc: 0.861
+2025-08-26 02:37:03: Epoch: 039 Test results, ap: 0.240, auc: 0.533, f1: 0.000, acc: 0.699
+2025-08-26 02:37:36: Epoch: 040 Train Loss: 0.106, ap: 0.925, auc: 0.934, f1: 0.857, acc: 0.866
+2025-08-26 02:37:40: Epoch: 040 Test results, ap: 0.245, auc: 0.541, f1: 0.000, acc: 0.715
+2025-08-26 02:38:13: Epoch: 041 Train Loss: 0.106, ap: 0.928, auc: 0.932, f1: 0.859, acc: 0.868
+2025-08-26 02:38:16: Epoch: 041 Test results, ap: 0.258, auc: 0.568, f1: 0.000, acc: 0.724
+2025-08-26 02:38:51: Epoch: 042 Train Loss: 0.100, ap: 0.925, auc: 0.932, f1: 0.853, acc: 0.863
+2025-08-26 02:38:54: Epoch: 042 Test results, ap: 0.248, auc: 0.547, f1: 0.000, acc: 0.715
+2025-08-26 02:39:27: Epoch: 043 Train Loss: 0.099, ap: 0.926, auc: 0.933, f1: 0.867, acc: 0.873
+2025-08-26 02:39:30: Epoch: 043 Test results, ap: 0.250, auc: 0.553, f1: 0.000, acc: 0.707
+2025-08-26 02:40:04: Epoch: 044 Train Loss: 0.099, ap: 0.928, auc: 0.935, f1: 0.857, acc: 0.866
+2025-08-26 02:40:04: Epoch: 044 New best VALIDATION metrics
+2025-08-26 02:40:07: Epoch: 044 Test results, ap: 0.250, auc: 0.551, f1: 0.000, acc: 0.715
+2025-08-26 02:40:41: Epoch: 045 Train Loss: 0.093, ap: 0.927, auc: 0.935, f1: 0.868, acc: 0.877
+2025-08-26 02:40:45: Epoch: 045 Test results, ap: 0.247, auc: 0.543, f1: 0.000, acc: 0.715
+2025-08-26 02:41:19: Epoch: 046 Train Loss: 0.088, ap: 0.927, auc: 0.935, f1: 0.856, acc: 0.864
+2025-08-26 02:41:22: Epoch: 046 Test results, ap: 0.244, auc: 0.532, f1: 0.000, acc: 0.715
+2025-08-26 02:41:56: Epoch: 047 Train Loss: 0.107, ap: 0.925, auc: 0.935, f1: 0.864, acc: 0.870
+2025-08-26 02:42:00: Epoch: 047 Test results, ap: 0.248, auc: 0.543, f1: 0.000, acc: 0.707
+2025-08-26 02:42:34: Epoch: 048 Train Loss: 0.100, ap: 0.928, auc: 0.937, f1: 0.868, acc: 0.875
+2025-08-26 02:42:34: Epoch: 048 New best VALIDATION metrics
+2025-08-26 02:42:38: Epoch: 048 Test results, ap: 0.245, auc: 0.538, f1: 0.000, acc: 0.707
+2025-08-26 02:43:12: Epoch: 049 Train Loss: 0.099, ap: 0.930, auc: 0.937, f1: 0.869, acc: 0.879
+2025-08-26 02:43:12: Epoch: 049 New best VALIDATION metrics
+2025-08-26 02:43:15: Epoch: 049 Test results, ap: 0.244, auc: 0.530, f1: 0.000, acc: 0.715
+2025-08-26 02:43:49: Epoch: 050 Train Loss: 0.094, ap: 0.928, auc: 0.937, f1: 0.863, acc: 0.873
+2025-08-26 02:43:53: Epoch: 050 Test results, ap: 0.239, auc: 0.522, f1: 0.000, acc: 0.715
+2025-08-26 02:43:53: used time 0.52h
+2025-08-26 02:43:53: Running Cross Validation 1
+2025-08-26 02:43:53: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-26 02:44:27: Epoch: 001 Train Loss: 0.671, ap: 0.537, auc: 0.598, f1: 0.593, acc: 0.447
+2025-08-26 02:44:27: Epoch: 001 New best VALIDATION metrics
+2025-08-26 02:44:30: Epoch: 001 Test results, ap: 0.349, auc: 0.549, f1: 0.392, acc: 0.244
+2025-08-26 02:45:05: Epoch: 002 Train Loss: 0.662, ap: 0.576, auc: 0.625, f1: 0.427, acc: 0.646
+2025-08-26 02:45:05: Epoch: 002 New best VALIDATION metrics
+2025-08-26 02:45:08: Epoch: 002 Test results, ap: 0.261, auc: 0.501, f1: 0.000, acc: 0.740
+2025-08-26 02:45:43: Epoch: 003 Train Loss: 0.664, ap: 0.590, auc: 0.637, f1: 0.430, acc: 0.636
+2025-08-26 02:45:43: Epoch: 003 New best VALIDATION metrics
+2025-08-26 02:45:46: Epoch: 003 Test results, ap: 0.230, auc: 0.449, f1: 0.000, acc: 0.691
+2025-08-26 02:46:20: Epoch: 004 Train Loss: 0.656, ap: 0.565, auc: 0.632, f1: 0.578, acc: 0.636
+2025-08-26 02:46:23: Epoch: 004 Test results, ap: 0.270, auc: 0.512, f1: 0.000, acc: 0.691
+2025-08-26 02:46:57: Epoch: 005 Train Loss: 0.658, ap: 0.566, auc: 0.640, f1: 0.594, acc: 0.621
+2025-08-26 02:47:00: Epoch: 005 Test results, ap: 0.258, auc: 0.461, f1: 0.345, acc: 0.537
+2025-08-26 02:47:34: Epoch: 006 Train Loss: 0.650, ap: 0.617, auc: 0.648, f1: 0.596, acc: 0.618
+2025-08-26 02:47:34: Epoch: 006 New best VALIDATION metrics
+2025-08-26 02:47:37: Epoch: 006 Test results, ap: 0.188, auc: 0.363, f1: 0.265, acc: 0.325
+2025-08-26 02:48:11: Epoch: 007 Train Loss: 0.639, ap: 0.633, auc: 0.662, f1: 0.624, acc: 0.569
+2025-08-26 02:48:11: Epoch: 007 New best VALIDATION metrics
+2025-08-26 02:48:14: Epoch: 007 Test results, ap: 0.203, auc: 0.421, f1: 0.207, acc: 0.439
+2025-08-26 02:48:48: Epoch: 008 Train Loss: 0.620, ap: 0.645, auc: 0.689, f1: 0.515, acc: 0.629
+2025-08-26 02:48:48: Epoch: 008 New best VALIDATION metrics
+2025-08-26 02:48:51: Epoch: 008 Test results, ap: 0.211, auc: 0.404, f1: 0.268, acc: 0.333
+2025-08-26 02:49:24: Epoch: 009 Train Loss: 0.597, ap: 0.611, auc: 0.668, f1: 0.599, acc: 0.588
+2025-08-26 02:49:28: Epoch: 009 Test results, ap: 0.174, auc: 0.297, f1: 0.122, acc: 0.415
+2025-08-26 02:50:02: Epoch: 010 Train Loss: 0.569, ap: 0.714, auc: 0.743, f1: 0.600, acc: 0.664
+2025-08-26 02:50:02: Epoch: 010 New best VALIDATION metrics
+2025-08-26 02:50:05: Epoch: 010 Test results, ap: 0.201, auc: 0.401, f1: 0.000, acc: 0.699
+2025-08-26 02:50:39: Epoch: 011 Train Loss: 0.513, ap: 0.753, auc: 0.790, f1: 0.643, acc: 0.717
+2025-08-26 02:50:39: Epoch: 011 New best VALIDATION metrics
+2025-08-26 02:50:42: Epoch: 011 Test results, ap: 0.253, auc: 0.484, f1: 0.286, acc: 0.634
+2025-08-26 02:50:42: Epoch: 011 New best TEST metrics
+2025-08-26 02:51:16: Epoch: 012 Train Loss: 0.480, ap: 0.802, auc: 0.824, f1: 0.728, acc: 0.741
+2025-08-26 02:51:16: Epoch: 012 New best VALIDATION metrics
+2025-08-26 02:51:19: Epoch: 012 Test results, ap: 0.201, auc: 0.397, f1: 0.304, acc: 0.366
+2025-08-26 02:51:53: Epoch: 013 Train Loss: 0.449, ap: 0.793, auc: 0.844, f1: 0.762, acc: 0.764
+2025-08-26 02:51:53: Epoch: 013 New best VALIDATION metrics
+2025-08-26 02:51:56: Epoch: 013 Test results, ap: 0.184, auc: 0.340, f1: 0.290, acc: 0.244
+2025-08-26 02:52:31: Epoch: 014 Train Loss: 0.426, ap: 0.796, auc: 0.851, f1: 0.741, acc: 0.738
+2025-08-26 02:52:31: Epoch: 014 New best VALIDATION metrics
+2025-08-26 02:52:34: Epoch: 014 Test results, ap: 0.212, auc: 0.416, f1: 0.286, acc: 0.472
+2025-08-26 02:53:07: Epoch: 015 Train Loss: 0.385, ap: 0.848, auc: 0.890, f1: 0.784, acc: 0.798
+2025-08-26 02:53:07: Epoch: 015 New best VALIDATION metrics
+2025-08-26 02:53:11: Epoch: 015 Test results, ap: 0.220, auc: 0.414, f1: 0.300, acc: 0.431
+2025-08-26 02:53:45: Epoch: 016 Train Loss: 0.369, ap: 0.855, auc: 0.873, f1: 0.718, acc: 0.782
+2025-08-26 02:53:48: Epoch: 016 Test results, ap: 0.213, auc: 0.420, f1: 0.051, acc: 0.699
+2025-08-26 02:54:22: Epoch: 017 Train Loss: 0.330, ap: 0.856, auc: 0.880, f1: 0.755, acc: 0.778
+2025-08-26 02:54:25: Epoch: 017 Test results, ap: 0.185, auc: 0.352, f1: 0.125, acc: 0.431
+2025-08-26 02:54:59: Epoch: 018 Train Loss: 0.298, ap: 0.865, auc: 0.903, f1: 0.813, acc: 0.824
+2025-08-26 02:54:59: Epoch: 018 New best VALIDATION metrics
+2025-08-26 02:55:03: Epoch: 018 Test results, ap: 0.186, auc: 0.339, f1: 0.294, acc: 0.415
+2025-08-26 02:55:37: Epoch: 019 Train Loss: 0.289, ap: 0.891, auc: 0.907, f1: 0.801, acc: 0.829
+2025-08-26 02:55:37: Epoch: 019 New best VALIDATION metrics
+2025-08-26 02:55:40: Epoch: 019 Test results, ap: 0.216, auc: 0.454, f1: 0.045, acc: 0.659
+2025-08-26 02:56:14: Epoch: 020 Train Loss: 0.270, ap: 0.886, auc: 0.902, f1: 0.777, acc: 0.810
+2025-08-26 02:56:18: Epoch: 020 Test results, ap: 0.213, auc: 0.444, f1: 0.040, acc: 0.610
+2025-08-26 02:56:52: Epoch: 021 Train Loss: 0.227, ap: 0.893, auc: 0.923, f1: 0.827, acc: 0.843
+2025-08-26 02:56:52: Epoch: 021 New best VALIDATION metrics
+2025-08-26 02:56:56: Epoch: 021 Test results, ap: 0.225, auc: 0.424, f1: 0.312, acc: 0.463
+2025-08-26 02:57:30: Epoch: 022 Train Loss: 0.207, ap: 0.893, auc: 0.925, f1: 0.841, acc: 0.854
+2025-08-26 02:57:30: Epoch: 022 New best VALIDATION metrics
+2025-08-26 02:57:34: Epoch: 022 Test results, ap: 0.213, auc: 0.408, f1: 0.333, acc: 0.415
+2025-08-26 02:58:07: Epoch: 023 Train Loss: 0.209, ap: 0.907, auc: 0.928, f1: 0.828, acc: 0.847
+2025-08-26 02:58:07: Epoch: 023 New best VALIDATION metrics
+2025-08-26 02:58:10: Epoch: 023 Test results, ap: 0.227, auc: 0.446, f1: 0.278, acc: 0.537
+2025-08-26 02:58:44: Epoch: 024 Train Loss: 0.195, ap: 0.903, auc: 0.931, f1: 0.832, acc: 0.850
+2025-08-26 02:58:47: Epoch: 024 Test results, ap: 0.225, auc: 0.431, f1: 0.323, acc: 0.488
+2025-08-26 02:59:22: Epoch: 025 Train Loss: 0.198, ap: 0.888, auc: 0.924, f1: 0.828, acc: 0.840
+2025-08-26 02:59:25: Epoch: 025 Test results, ap: 0.209, auc: 0.391, f1: 0.283, acc: 0.382
+2025-08-26 02:59:59: Epoch: 026 Train Loss: 0.192, ap: 0.898, auc: 0.929, f1: 0.835, acc: 0.849
+2025-08-26 03:00:03: Epoch: 026 Test results, ap: 0.218, auc: 0.419, f1: 0.308, acc: 0.488
+2025-08-26 03:00:37: Epoch: 027 Train Loss: 0.178, ap: 0.883, auc: 0.925, f1: 0.844, acc: 0.856
+2025-08-26 03:00:41: Epoch: 027 Test results, ap: 0.199, auc: 0.381, f1: 0.321, acc: 0.415
+2025-08-26 03:01:14: Epoch: 028 Train Loss: 0.175, ap: 0.906, auc: 0.932, f1: 0.847, acc: 0.863
+2025-08-26 03:01:14: Epoch: 028 New best VALIDATION metrics
+2025-08-26 03:01:18: Epoch: 028 Test results, ap: 0.208, auc: 0.406, f1: 0.304, acc: 0.480
+2025-08-26 03:01:52: Epoch: 029 Train Loss: 0.173, ap: 0.915, auc: 0.937, f1: 0.852, acc: 0.866
+2025-08-26 03:01:52: Epoch: 029 New best VALIDATION metrics
+2025-08-26 03:01:55: Epoch: 029 Test results, ap: 0.214, auc: 0.422, f1: 0.293, acc: 0.528
+2025-08-26 03:02:29: Epoch: 030 Train Loss: 0.162, ap: 0.908, auc: 0.933, f1: 0.847, acc: 0.866
+2025-08-26 03:02:32: Epoch: 030 Test results, ap: 0.240, auc: 0.472, f1: 0.337, acc: 0.520
+2025-08-26 03:03:08: Epoch: 031 Train Loss: 0.159, ap: 0.900, auc: 0.935, f1: 0.855, acc: 0.868
+2025-08-26 03:03:12: Epoch: 031 Test results, ap: 0.215, auc: 0.422, f1: 0.318, acc: 0.407
+2025-08-26 03:03:47: Epoch: 032 Train Loss: 0.140, ap: 0.897, auc: 0.931, f1: 0.846, acc: 0.859
+2025-08-26 03:03:50: Epoch: 032 Test results, ap: 0.217, auc: 0.424, f1: 0.333, acc: 0.415
+2025-08-26 03:04:23: Epoch: 033 Train Loss: 0.140, ap: 0.898, auc: 0.933, f1: 0.844, acc: 0.861
+2025-08-26 03:04:27: Epoch: 033 Test results, ap: 0.236, auc: 0.459, f1: 0.340, acc: 0.431
+2025-08-26 03:05:01: Epoch: 034 Train Loss: 0.133, ap: 0.900, auc: 0.935, f1: 0.859, acc: 0.873
+2025-08-26 03:05:04: Epoch: 034 Test results, ap: 0.207, auc: 0.409, f1: 0.356, acc: 0.472
+2025-08-26 03:05:41: Epoch: 035 Train Loss: 0.132, ap: 0.911, auc: 0.938, f1: 0.853, acc: 0.870
+2025-08-26 03:05:47: Epoch: 035 Test results, ap: 0.218, auc: 0.435, f1: 0.322, acc: 0.520
+2025-08-26 03:06:24: Epoch: 036 Train Loss: 0.135, ap: 0.911, auc: 0.939, f1: 0.845, acc: 0.863
+2025-08-26 03:06:27: Epoch: 036 Test results, ap: 0.214, auc: 0.427, f1: 0.325, acc: 0.561
+2025-08-26 03:07:01: Epoch: 037 Train Loss: 0.132, ap: 0.902, auc: 0.933, f1: 0.843, acc: 0.859
+2025-08-26 03:07:05: Epoch: 037 Test results, ap: 0.214, auc: 0.432, f1: 0.341, acc: 0.528
+2025-08-26 03:07:39: Epoch: 038 Train Loss: 0.128, ap: 0.907, auc: 0.935, f1: 0.857, acc: 0.871
+2025-08-26 03:07:42: Epoch: 038 Test results, ap: 0.222, auc: 0.445, f1: 0.344, acc: 0.504
+2025-08-26 03:08:16: Epoch: 039 Train Loss: 0.132, ap: 0.917, auc: 0.939, f1: 0.852, acc: 0.871
+2025-08-26 03:08:16: Epoch: 039 New best VALIDATION metrics
+2025-08-26 03:08:19: Epoch: 039 Test results, ap: 0.241, auc: 0.479, f1: 0.235, acc: 0.577
+2025-08-26 03:08:53: Epoch: 040 Train Loss: 0.128, ap: 0.916, auc: 0.940, f1: 0.850, acc: 0.868
+2025-08-26 03:08:57: Epoch: 040 Test results, ap: 0.230, auc: 0.469, f1: 0.282, acc: 0.585
+2025-08-26 03:09:31: Epoch: 041 Train Loss: 0.116, ap: 0.916, auc: 0.942, f1: 0.841, acc: 0.864
+2025-08-26 03:09:31: Epoch: 041 New best VALIDATION metrics
+2025-08-26 03:09:35: Epoch: 041 Test results, ap: 0.225, auc: 0.459, f1: 0.297, acc: 0.577
+2025-08-26 03:10:08: Epoch: 042 Train Loss: 0.117, ap: 0.914, auc: 0.937, f1: 0.847, acc: 0.868
+2025-08-26 03:10:11: Epoch: 042 Test results, ap: 0.231, auc: 0.470, f1: 0.306, acc: 0.593
+2025-08-26 03:10:45: Epoch: 043 Train Loss: 0.119, ap: 0.910, auc: 0.939, f1: 0.853, acc: 0.870
+2025-08-26 03:10:48: Epoch: 043 Test results, ap: 0.225, auc: 0.456, f1: 0.353, acc: 0.553
+2025-08-26 03:11:23: Epoch: 044 Train Loss: 0.111, ap: 0.918, auc: 0.942, f1: 0.850, acc: 0.868
+2025-08-26 03:11:23: Epoch: 044 New best VALIDATION metrics
+2025-08-26 03:11:26: Epoch: 044 Test results, ap: 0.229, auc: 0.463, f1: 0.286, acc: 0.593
+2025-08-26 03:11:59: Epoch: 045 Train Loss: 0.111, ap: 0.917, auc: 0.941, f1: 0.853, acc: 0.870
+2025-08-26 03:12:03: Epoch: 045 Test results, ap: 0.233, auc: 0.463, f1: 0.286, acc: 0.593
+2025-08-26 03:12:37: Epoch: 046 Train Loss: 0.111, ap: 0.907, auc: 0.938, f1: 0.858, acc: 0.875
+2025-08-26 03:12:40: Epoch: 046 Test results, ap: 0.237, auc: 0.468, f1: 0.329, acc: 0.537
+2025-08-26 03:13:13: Epoch: 047 Train Loss: 0.119, ap: 0.912, auc: 0.940, f1: 0.852, acc: 0.870
+2025-08-26 03:13:16: Epoch: 047 Test results, ap: 0.230, auc: 0.465, f1: 0.357, acc: 0.561
+2025-08-26 03:13:50: Epoch: 048 Train Loss: 0.113, ap: 0.909, auc: 0.940, f1: 0.855, acc: 0.873
+2025-08-26 03:13:54: Epoch: 048 Test results, ap: 0.226, auc: 0.455, f1: 0.313, acc: 0.537
+2025-08-26 03:14:27: Epoch: 049 Train Loss: 0.106, ap: 0.913, auc: 0.941, f1: 0.846, acc: 0.870
+2025-08-26 03:14:31: Epoch: 049 Test results, ap: 0.233, auc: 0.471, f1: 0.316, acc: 0.577
+2025-08-26 03:15:05: Epoch: 050 Train Loss: 0.099, ap: 0.911, auc: 0.940, f1: 0.846, acc: 0.864
+2025-08-26 03:15:08: Epoch: 050 Test results, ap: 0.233, auc: 0.470, f1: 0.346, acc: 0.569
+2025-08-26 03:15:08: used time 0.52h
+2025-08-26 03:15:08: Running Cross Validation 2
+2025-08-26 03:15:08: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 03:15:42: Epoch: 001 Train Loss: 0.685, ap: 0.593, auc: 0.643, f1: 0.474, acc: 0.656
+2025-08-26 03:15:42: Epoch: 001 New best VALIDATION metrics
+2025-08-26 03:15:46: Epoch: 001 Test results, ap: 0.172, auc: 0.291, f1: 0.000, acc: 0.756
+2025-08-26 03:16:20: Epoch: 002 Train Loss: 0.669, ap: 0.599, auc: 0.654, f1: 0.607, acc: 0.554
+2025-08-26 03:16:20: Epoch: 002 New best VALIDATION metrics
+2025-08-26 03:16:23: Epoch: 002 Test results, ap: 0.175, auc: 0.292, f1: 0.048, acc: 0.675
+2025-08-26 03:16:57: Epoch: 003 Train Loss: 0.650, ap: 0.630, auc: 0.670, f1: 0.620, acc: 0.487
+2025-08-26 03:16:57: Epoch: 003 New best VALIDATION metrics
+2025-08-26 03:17:00: Epoch: 003 Test results, ap: 0.232, auc: 0.463, f1: 0.392, acc: 0.244
+2025-08-26 03:17:34: Epoch: 004 Train Loss: 0.650, ap: 0.632, auc: 0.670, f1: 0.499, acc: 0.642
+2025-08-26 03:17:34: Epoch: 004 New best VALIDATION metrics
+2025-08-26 03:17:37: Epoch: 004 Test results, ap: 0.176, auc: 0.308, f1: 0.000, acc: 0.740
+2025-08-26 03:18:11: Epoch: 005 Train Loss: 0.645, ap: 0.639, auc: 0.681, f1: 0.540, acc: 0.667
+2025-08-26 03:18:11: Epoch: 005 New best VALIDATION metrics
+2025-08-26 03:18:15: Epoch: 005 Test results, ap: 0.172, auc: 0.285, f1: 0.000, acc: 0.756
+2025-08-26 03:18:48: Epoch: 006 Train Loss: 0.640, ap: 0.646, auc: 0.681, f1: 0.594, acc: 0.640
+2025-08-26 03:18:48: Epoch: 006 New best VALIDATION metrics
+2025-08-26 03:18:51: Epoch: 006 Test results, ap: 0.168, auc: 0.266, f1: 0.125, acc: 0.317
+2025-08-26 03:19:26: Epoch: 007 Train Loss: 0.634, ap: 0.620, auc: 0.678, f1: 0.546, acc: 0.663
+2025-08-26 03:19:29: Epoch: 007 Test results, ap: 0.174, auc: 0.297, f1: 0.000, acc: 0.756
+2025-08-26 03:20:02: Epoch: 008 Train Loss: 0.635, ap: 0.676, auc: 0.687, f1: 0.593, acc: 0.642
+2025-08-26 03:20:02: Epoch: 008 New best VALIDATION metrics
+2025-08-26 03:20:05: Epoch: 008 Test results, ap: 0.180, auc: 0.325, f1: 0.000, acc: 0.545
+2025-08-26 03:20:39: Epoch: 009 Train Loss: 0.623, ap: 0.616, auc: 0.668, f1: 0.605, acc: 0.554
+2025-08-26 03:20:42: Epoch: 009 Test results, ap: 0.213, auc: 0.401, f1: 0.172, acc: 0.610
+2025-08-26 03:21:16: Epoch: 010 Train Loss: 0.602, ap: 0.614, auc: 0.683, f1: 0.632, acc: 0.559
+2025-08-26 03:21:19: Epoch: 010 Test results, ap: 0.171, auc: 0.276, f1: 0.025, acc: 0.358
+2025-08-26 03:21:53: Epoch: 011 Train Loss: 0.572, ap: 0.733, auc: 0.761, f1: 0.650, acc: 0.697
+2025-08-26 03:21:53: Epoch: 011 New best VALIDATION metrics
+2025-08-26 03:21:56: Epoch: 011 Test results, ap: 0.184, auc: 0.344, f1: 0.000, acc: 0.748
+2025-08-26 03:21:56: Epoch: 011 New best TEST metrics
+2025-08-26 03:22:31: Epoch: 012 Train Loss: 0.540, ap: 0.784, auc: 0.814, f1: 0.696, acc: 0.755
+2025-08-26 03:22:31: Epoch: 012 New best VALIDATION metrics
+2025-08-26 03:22:34: Epoch: 012 Test results, ap: 0.193, auc: 0.369, f1: 0.000, acc: 0.748
+2025-08-26 03:22:34: Epoch: 012 New best TEST metrics
+2025-08-26 03:23:08: Epoch: 013 Train Loss: 0.505, ap: 0.781, auc: 0.813, f1: 0.646, acc: 0.734
+2025-08-26 03:23:11: Epoch: 013 Test results, ap: 0.201, auc: 0.401, f1: 0.000, acc: 0.756
+2025-08-26 03:23:11: Epoch: 013 New best TEST metrics
+2025-08-26 03:23:45: Epoch: 014 Train Loss: 0.482, ap: 0.765, auc: 0.821, f1: 0.728, acc: 0.743
+2025-08-26 03:23:48: Epoch: 014 Test results, ap: 0.197, auc: 0.386, f1: 0.104, acc: 0.439
+2025-08-26 03:24:22: Epoch: 015 Train Loss: 0.474, ap: 0.754, auc: 0.813, f1: 0.682, acc: 0.737
+2025-08-26 03:24:25: Epoch: 015 Test results, ap: 0.275, auc: 0.542, f1: 0.000, acc: 0.740
+2025-08-26 03:24:25: Epoch: 015 New best TEST metrics
+2025-08-26 03:24:59: Epoch: 016 Train Loss: 0.440, ap: 0.817, auc: 0.838, f1: 0.683, acc: 0.751
+2025-08-26 03:24:59: Epoch: 016 New best VALIDATION metrics
+2025-08-26 03:25:02: Epoch: 016 Test results, ap: 0.292, auc: 0.540, f1: 0.000, acc: 0.756
+2025-08-26 03:25:02: Epoch: 016 New best TEST metrics
+2025-08-26 03:25:36: Epoch: 017 Train Loss: 0.410, ap: 0.804, auc: 0.837, f1: 0.694, acc: 0.760
+2025-08-26 03:25:40: Epoch: 017 Test results, ap: 0.326, auc: 0.575, f1: 0.000, acc: 0.756
+2025-08-26 03:25:40: Epoch: 017 New best TEST metrics
+2025-08-26 03:26:14: Epoch: 018 Train Loss: 0.392, ap: 0.813, auc: 0.850, f1: 0.733, acc: 0.771
+2025-08-26 03:26:14: Epoch: 018 New best VALIDATION metrics
+2025-08-26 03:26:17: Epoch: 018 Test results, ap: 0.187, auc: 0.353, f1: 0.000, acc: 0.691
+2025-08-26 03:26:51: Epoch: 019 Train Loss: 0.382, ap: 0.863, auc: 0.885, f1: 0.760, acc: 0.810
+2025-08-26 03:26:51: Epoch: 019 New best VALIDATION metrics
+2025-08-26 03:26:54: Epoch: 019 Test results, ap: 0.326, auc: 0.608, f1: 0.000, acc: 0.748
+2025-08-26 03:26:54: Epoch: 019 New best TEST metrics
+2025-08-26 03:27:28: Epoch: 020 Train Loss: 0.335, ap: 0.822, auc: 0.856, f1: 0.713, acc: 0.774
+2025-08-26 03:27:31: Epoch: 020 Test results, ap: 0.272, auc: 0.558, f1: 0.000, acc: 0.748
+2025-08-26 03:28:05: Epoch: 021 Train Loss: 0.312, ap: 0.869, auc: 0.895, f1: 0.798, acc: 0.824
+2025-08-26 03:28:05: Epoch: 021 New best VALIDATION metrics
+2025-08-26 03:28:08: Epoch: 021 Test results, ap: 0.332, auc: 0.585, f1: 0.059, acc: 0.740
+2025-08-26 03:28:42: Epoch: 022 Train Loss: 0.294, ap: 0.829, auc: 0.878, f1: 0.769, acc: 0.795
+2025-08-26 03:28:45: Epoch: 022 Test results, ap: 0.238, auc: 0.477, f1: 0.000, acc: 0.691
+2025-08-26 03:29:19: Epoch: 023 Train Loss: 0.265, ap: 0.863, auc: 0.895, f1: 0.782, acc: 0.817
+2025-08-26 03:29:22: Epoch: 023 Test results, ap: 0.327, auc: 0.599, f1: 0.000, acc: 0.740
+2025-08-26 03:29:57: Epoch: 024 Train Loss: 0.253, ap: 0.877, auc: 0.902, f1: 0.769, acc: 0.817
+2025-08-26 03:29:57: Epoch: 024 New best VALIDATION metrics
+2025-08-26 03:30:00: Epoch: 024 Test results, ap: 0.255, auc: 0.505, f1: 0.000, acc: 0.748
+2025-08-26 03:30:34: Epoch: 025 Train Loss: 0.250, ap: 0.877, auc: 0.900, f1: 0.781, acc: 0.818
+2025-08-26 03:30:37: Epoch: 025 Test results, ap: 0.307, auc: 0.558, f1: 0.000, acc: 0.740
+2025-08-26 03:31:11: Epoch: 026 Train Loss: 0.239, ap: 0.868, auc: 0.900, f1: 0.793, acc: 0.824
+2025-08-26 03:31:14: Epoch: 026 Test results, ap: 0.242, auc: 0.488, f1: 0.056, acc: 0.724
+2025-08-26 03:31:48: Epoch: 027 Train Loss: 0.253, ap: 0.883, auc: 0.905, f1: 0.792, acc: 0.831
+2025-08-26 03:31:48: Epoch: 027 New best VALIDATION metrics
+2025-08-26 03:31:51: Epoch: 027 Test results, ap: 0.278, auc: 0.545, f1: 0.000, acc: 0.732
+2025-08-26 03:32:26: Epoch: 028 Train Loss: 0.219, ap: 0.885, auc: 0.906, f1: 0.817, acc: 0.838
+2025-08-26 03:32:26: Epoch: 028 New best VALIDATION metrics
+2025-08-26 03:32:29: Epoch: 028 Test results, ap: 0.255, auc: 0.525, f1: 0.299, acc: 0.618
+2025-08-26 03:33:02: Epoch: 029 Train Loss: 0.221, ap: 0.880, auc: 0.906, f1: 0.795, acc: 0.829
+2025-08-26 03:33:06: Epoch: 029 Test results, ap: 0.234, auc: 0.484, f1: 0.047, acc: 0.667
+2025-08-26 03:33:40: Epoch: 030 Train Loss: 0.213, ap: 0.890, auc: 0.909, f1: 0.802, acc: 0.834
+2025-08-26 03:33:40: Epoch: 030 New best VALIDATION metrics
+2025-08-26 03:33:44: Epoch: 030 Test results, ap: 0.230, auc: 0.474, f1: 0.050, acc: 0.691
+2025-08-26 03:34:18: Epoch: 031 Train Loss: 0.201, ap: 0.885, auc: 0.912, f1: 0.785, acc: 0.824
+2025-08-26 03:34:21: Epoch: 031 Test results, ap: 0.243, auc: 0.503, f1: 0.048, acc: 0.675
+2025-08-26 03:34:55: Epoch: 032 Train Loss: 0.188, ap: 0.891, auc: 0.915, f1: 0.789, acc: 0.829
+2025-08-26 03:34:55: Epoch: 032 New best VALIDATION metrics
+2025-08-26 03:34:58: Epoch: 032 Test results, ap: 0.238, auc: 0.482, f1: 0.054, acc: 0.715
+2025-08-26 03:35:32: Epoch: 033 Train Loss: 0.179, ap: 0.894, auc: 0.914, f1: 0.788, acc: 0.827
+2025-08-26 03:35:32: Epoch: 033 New best VALIDATION metrics
+2025-08-26 03:35:36: Epoch: 033 Test results, ap: 0.238, auc: 0.484, f1: 0.056, acc: 0.724
+2025-08-26 03:36:09: Epoch: 034 Train Loss: 0.186, ap: 0.888, auc: 0.910, f1: 0.801, acc: 0.836
+2025-08-26 03:36:12: Epoch: 034 Test results, ap: 0.244, auc: 0.503, f1: 0.054, acc: 0.715
+2025-08-26 03:36:46: Epoch: 035 Train Loss: 0.176, ap: 0.885, auc: 0.912, f1: 0.808, acc: 0.838
+2025-08-26 03:36:49: Epoch: 035 Test results, ap: 0.244, auc: 0.507, f1: 0.043, acc: 0.634
+2025-08-26 03:37:23: Epoch: 036 Train Loss: 0.171, ap: 0.895, auc: 0.918, f1: 0.808, acc: 0.843
+2025-08-26 03:37:23: Epoch: 036 New best VALIDATION metrics
+2025-08-26 03:37:26: Epoch: 036 Test results, ap: 0.261, auc: 0.531, f1: 0.056, acc: 0.724
+2025-08-26 03:38:01: Epoch: 037 Train Loss: 0.170, ap: 0.897, auc: 0.913, f1: 0.799, acc: 0.836
+2025-08-26 03:38:04: Epoch: 037 Test results, ap: 0.240, auc: 0.489, f1: 0.057, acc: 0.732
+2025-08-26 03:38:38: Epoch: 038 Train Loss: 0.162, ap: 0.877, auc: 0.910, f1: 0.809, acc: 0.832
+2025-08-26 03:38:41: Epoch: 038 Test results, ap: 0.244, auc: 0.513, f1: 0.203, acc: 0.618
+2025-08-26 03:39:15: Epoch: 039 Train Loss: 0.168, ap: 0.889, auc: 0.915, f1: 0.813, acc: 0.843
+2025-08-26 03:39:19: Epoch: 039 Test results, ap: 0.254, auc: 0.534, f1: 0.042, acc: 0.626
+2025-08-26 03:39:53: Epoch: 040 Train Loss: 0.152, ap: 0.869, auc: 0.906, f1: 0.815, acc: 0.838
+2025-08-26 03:39:56: Epoch: 040 Test results, ap: 0.244, auc: 0.510, f1: 0.343, acc: 0.626
+2025-08-26 03:40:31: Epoch: 041 Train Loss: 0.152, ap: 0.899, auc: 0.920, f1: 0.808, acc: 0.847
+2025-08-26 03:40:31: Epoch: 041 New best VALIDATION metrics
+2025-08-26 03:40:34: Epoch: 041 Test results, ap: 0.253, auc: 0.531, f1: 0.049, acc: 0.683
+2025-08-26 03:41:08: Epoch: 042 Train Loss: 0.138, ap: 0.897, auc: 0.921, f1: 0.829, acc: 0.857
+2025-08-26 03:41:11: Epoch: 042 Test results, ap: 0.246, auc: 0.515, f1: 0.148, acc: 0.626
+2025-08-26 03:41:45: Epoch: 043 Train Loss: 0.147, ap: 0.890, auc: 0.918, f1: 0.808, acc: 0.845
+2025-08-26 03:41:48: Epoch: 043 Test results, ap: 0.244, auc: 0.511, f1: 0.049, acc: 0.683
+2025-08-26 03:42:22: Epoch: 044 Train Loss: 0.137, ap: 0.897, auc: 0.920, f1: 0.820, acc: 0.854
+2025-08-26 03:42:25: Epoch: 044 Test results, ap: 0.246, auc: 0.520, f1: 0.047, acc: 0.667
+2025-08-26 03:42:59: Epoch: 045 Train Loss: 0.140, ap: 0.903, auc: 0.920, f1: 0.815, acc: 0.850
+2025-08-26 03:42:59: Epoch: 045 New best VALIDATION metrics
+2025-08-26 03:43:02: Epoch: 045 Test results, ap: 0.239, auc: 0.504, f1: 0.054, acc: 0.715
+2025-08-26 03:43:36: Epoch: 046 Train Loss: 0.136, ap: 0.896, auc: 0.920, f1: 0.822, acc: 0.850
+2025-08-26 03:43:39: Epoch: 046 Test results, ap: 0.247, auc: 0.517, f1: 0.154, acc: 0.642
+2025-08-26 03:44:12: Epoch: 047 Train Loss: 0.137, ap: 0.898, auc: 0.921, f1: 0.834, acc: 0.862
+2025-08-26 03:44:16: Epoch: 047 Test results, ap: 0.245, auc: 0.514, f1: 0.049, acc: 0.683
+2025-08-26 03:44:49: Epoch: 048 Train Loss: 0.130, ap: 0.887, auc: 0.917, f1: 0.800, acc: 0.834
+2025-08-26 03:44:52: Epoch: 048 Test results, ap: 0.241, auc: 0.505, f1: 0.045, acc: 0.659
+2025-08-26 03:45:27: Epoch: 049 Train Loss: 0.131, ap: 0.900, auc: 0.920, f1: 0.813, acc: 0.848
+2025-08-26 03:45:30: Epoch: 049 Test results, ap: 0.246, auc: 0.519, f1: 0.047, acc: 0.667
+2025-08-26 03:46:04: Epoch: 050 Train Loss: 0.137, ap: 0.889, auc: 0.919, f1: 0.802, acc: 0.841
+2025-08-26 03:46:07: Epoch: 050 Test results, ap: 0.238, auc: 0.498, f1: 0.047, acc: 0.667
+2025-08-26 03:46:08: used time 0.52h
+2025-08-26 03:46:08: Running Cross Validation 3
+2025-08-26 03:46:08: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 03:46:42: Epoch: 001 Train Loss: 0.679, ap: 0.487, auc: 0.523, f1: 0.602, acc: 0.430
+2025-08-26 03:46:42: Epoch: 001 New best VALIDATION metrics
+2025-08-26 03:46:45: Epoch: 001 Test results, ap: 0.408, auc: 0.581, f1: 0.392, acc: 0.244
+2025-08-26 03:47:19: Epoch: 002 Train Loss: 0.656, ap: 0.627, auc: 0.670, f1: 0.550, acc: 0.628
+2025-08-26 03:47:19: Epoch: 002 New best VALIDATION metrics
+2025-08-26 03:47:22: Epoch: 002 Test results, ap: 0.228, auc: 0.477, f1: 0.000, acc: 0.748
+2025-08-26 03:47:56: Epoch: 003 Train Loss: 0.641, ap: 0.636, auc: 0.690, f1: 0.621, acc: 0.637
+2025-08-26 03:47:56: Epoch: 003 New best VALIDATION metrics
+2025-08-26 03:48:00: Epoch: 003 Test results, ap: 0.190, auc: 0.339, f1: 0.135, acc: 0.480
+2025-08-26 03:48:34: Epoch: 004 Train Loss: 0.627, ap: 0.596, auc: 0.690, f1: 0.649, acc: 0.621
+2025-08-26 03:48:37: Epoch: 004 Test results, ap: 0.213, auc: 0.382, f1: 0.397, acc: 0.260
+2025-08-26 03:49:11: Epoch: 005 Train Loss: 0.604, ap: 0.707, auc: 0.755, f1: 0.655, acc: 0.663
+2025-08-26 03:49:11: Epoch: 005 New best VALIDATION metrics
+2025-08-26 03:49:14: Epoch: 005 Test results, ap: 0.205, auc: 0.418, f1: 0.000, acc: 0.699
+2025-08-26 03:49:48: Epoch: 006 Train Loss: 0.559, ap: 0.721, auc: 0.776, f1: 0.665, acc: 0.608
+2025-08-26 03:49:48: Epoch: 006 New best VALIDATION metrics
+2025-08-26 03:49:52: Epoch: 006 Test results, ap: 0.209, auc: 0.419, f1: 0.336, acc: 0.390
+2025-08-26 03:50:26: Epoch: 007 Train Loss: 0.522, ap: 0.755, auc: 0.806, f1: 0.706, acc: 0.743
+2025-08-26 03:50:26: Epoch: 007 New best VALIDATION metrics
+2025-08-26 03:50:29: Epoch: 007 Test results, ap: 0.214, auc: 0.418, f1: 0.000, acc: 0.756
+2025-08-26 03:51:03: Epoch: 008 Train Loss: 0.485, ap: 0.736, auc: 0.806, f1: 0.675, acc: 0.732
+2025-08-26 03:51:07: Epoch: 008 Test results, ap: 0.318, auc: 0.609, f1: 0.000, acc: 0.756
+2025-08-26 03:51:40: Epoch: 009 Train Loss: 0.450, ap: 0.808, auc: 0.837, f1: 0.724, acc: 0.764
+2025-08-26 03:51:40: Epoch: 009 New best VALIDATION metrics
+2025-08-26 03:51:43: Epoch: 009 Test results, ap: 0.250, auc: 0.526, f1: 0.000, acc: 0.748
+2025-08-26 03:52:17: Epoch: 010 Train Loss: 0.435, ap: 0.812, auc: 0.851, f1: 0.738, acc: 0.737
+2025-08-26 03:52:17: Epoch: 010 New best VALIDATION metrics
+2025-08-26 03:52:20: Epoch: 010 Test results, ap: 0.246, auc: 0.513, f1: 0.269, acc: 0.602
+2025-08-26 03:52:54: Epoch: 011 Train Loss: 0.360, ap: 0.862, auc: 0.884, f1: 0.768, acc: 0.787
+2025-08-26 03:52:54: Epoch: 011 New best VALIDATION metrics
+2025-08-26 03:52:57: Epoch: 011 Test results, ap: 0.272, auc: 0.539, f1: 0.000, acc: 0.732
+2025-08-26 03:52:57: Epoch: 011 New best TEST metrics
+2025-08-26 03:53:31: Epoch: 012 Train Loss: 0.327, ap: 0.857, auc: 0.884, f1: 0.760, acc: 0.790
+2025-08-26 03:53:34: Epoch: 012 Test results, ap: 0.266, auc: 0.553, f1: 0.000, acc: 0.715
+2025-08-26 03:53:34: Epoch: 012 New best TEST metrics
+2025-08-26 03:54:08: Epoch: 013 Train Loss: 0.298, ap: 0.873, auc: 0.894, f1: 0.766, acc: 0.776
+2025-08-26 03:54:08: Epoch: 013 New best VALIDATION metrics
+2025-08-26 03:54:12: Epoch: 013 Test results, ap: 0.252, auc: 0.513, f1: 0.133, acc: 0.683
+2025-08-26 03:54:46: Epoch: 014 Train Loss: 0.295, ap: 0.871, auc: 0.897, f1: 0.779, acc: 0.804
+2025-08-26 03:54:46: Epoch: 014 New best VALIDATION metrics
+2025-08-26 03:54:49: Epoch: 014 Test results, ap: 0.258, auc: 0.528, f1: 0.095, acc: 0.691
+2025-08-26 03:55:23: Epoch: 015 Train Loss: 0.283, ap: 0.874, auc: 0.891, f1: 0.770, acc: 0.797
+2025-08-26 03:55:26: Epoch: 015 Test results, ap: 0.304, auc: 0.586, f1: 0.000, acc: 0.748
+2025-08-26 03:55:26: Epoch: 015 New best TEST metrics
+2025-08-26 03:56:00: Epoch: 016 Train Loss: 0.258, ap: 0.874, auc: 0.909, f1: 0.809, acc: 0.825
+2025-08-26 03:56:00: Epoch: 016 New best VALIDATION metrics
+2025-08-26 03:56:03: Epoch: 016 Test results, ap: 0.267, auc: 0.560, f1: 0.051, acc: 0.699
+2025-08-26 03:56:37: Epoch: 017 Train Loss: 0.254, ap: 0.892, auc: 0.913, f1: 0.808, acc: 0.829
+2025-08-26 03:56:37: Epoch: 017 New best VALIDATION metrics
+2025-08-26 03:56:40: Epoch: 017 Test results, ap: 0.262, auc: 0.554, f1: 0.000, acc: 0.715
+2025-08-26 03:57:14: Epoch: 018 Train Loss: 0.238, ap: 0.880, auc: 0.914, f1: 0.813, acc: 0.838
+2025-08-26 03:57:17: Epoch: 018 Test results, ap: 0.266, auc: 0.557, f1: 0.054, acc: 0.715
+2025-08-26 03:57:51: Epoch: 019 Train Loss: 0.229, ap: 0.890, auc: 0.912, f1: 0.804, acc: 0.829
+2025-08-26 03:57:54: Epoch: 019 Test results, ap: 0.331, auc: 0.594, f1: 0.000, acc: 0.756
+2025-08-26 03:57:54: Epoch: 019 New best TEST metrics
+2025-08-26 03:58:27: Epoch: 020 Train Loss: 0.231, ap: 0.897, auc: 0.922, f1: 0.820, acc: 0.850
+2025-08-26 03:58:27: Epoch: 020 New best VALIDATION metrics
+2025-08-26 03:58:31: Epoch: 020 Test results, ap: 0.286, auc: 0.541, f1: 0.000, acc: 0.748
+2025-08-26 03:59:05: Epoch: 021 Train Loss: 0.199, ap: 0.899, auc: 0.915, f1: 0.804, acc: 0.838
+2025-08-26 03:59:08: Epoch: 021 Test results, ap: 0.348, auc: 0.594, f1: 0.065, acc: 0.764
+2025-08-26 03:59:08: Epoch: 021 New best TEST metrics
+2025-08-26 03:59:42: Epoch: 022 Train Loss: 0.194, ap: 0.892, auc: 0.924, f1: 0.819, acc: 0.847
+2025-08-26 03:59:45: Epoch: 022 Test results, ap: 0.332, auc: 0.594, f1: 0.062, acc: 0.756
+2025-08-26 04:00:19: Epoch: 023 Train Loss: 0.172, ap: 0.896, auc: 0.926, f1: 0.815, acc: 0.840
+2025-08-26 04:00:19: Epoch: 023 New best VALIDATION metrics
+2025-08-26 04:00:22: Epoch: 023 Test results, ap: 0.323, auc: 0.563, f1: 0.061, acc: 0.748
+2025-08-26 04:00:56: Epoch: 024 Train Loss: 0.176, ap: 0.883, auc: 0.927, f1: 0.828, acc: 0.850
+2025-08-26 04:00:59: Epoch: 024 Test results, ap: 0.289, auc: 0.589, f1: 0.095, acc: 0.691
+2025-08-26 04:01:33: Epoch: 025 Train Loss: 0.175, ap: 0.900, auc: 0.924, f1: 0.824, acc: 0.843
+2025-08-26 04:01:33: Epoch: 025 New best VALIDATION metrics
+2025-08-26 04:01:36: Epoch: 025 Test results, ap: 0.330, auc: 0.598, f1: 0.065, acc: 0.764
+2025-08-26 04:02:10: Epoch: 026 Train Loss: 0.173, ap: 0.894, auc: 0.928, f1: 0.829, acc: 0.850
+2025-08-26 04:02:13: Epoch: 026 Test results, ap: 0.330, auc: 0.613, f1: 0.059, acc: 0.740
+2025-08-26 04:02:13: Epoch: 026 New best TEST metrics
+2025-08-26 04:02:47: Epoch: 027 Train Loss: 0.178, ap: 0.890, auc: 0.930, f1: 0.837, acc: 0.859
+2025-08-26 04:02:51: Epoch: 027 Test results, ap: 0.343, auc: 0.612, f1: 0.061, acc: 0.748
+2025-08-26 04:02:51: Epoch: 027 New best TEST metrics
+2025-08-26 04:03:24: Epoch: 028 Train Loss: 0.167, ap: 0.902, auc: 0.933, f1: 0.835, acc: 0.859
+2025-08-26 04:03:24: Epoch: 028 New best VALIDATION metrics
+2025-08-26 04:03:28: Epoch: 028 Test results, ap: 0.393, auc: 0.639, f1: 0.065, acc: 0.764
+2025-08-26 04:03:28: Epoch: 028 New best TEST metrics
+2025-08-26 04:04:01: Epoch: 029 Train Loss: 0.157, ap: 0.907, auc: 0.935, f1: 0.848, acc: 0.864
+2025-08-26 04:04:01: Epoch: 029 New best VALIDATION metrics
+2025-08-26 04:04:05: Epoch: 029 Test results, ap: 0.381, auc: 0.648, f1: 0.059, acc: 0.740
+2025-08-26 04:04:38: Epoch: 030 Train Loss: 0.148, ap: 0.893, auc: 0.930, f1: 0.829, acc: 0.852
+2025-08-26 04:04:42: Epoch: 030 Test results, ap: 0.315, auc: 0.616, f1: 0.059, acc: 0.740
+2025-08-26 04:05:16: Epoch: 031 Train Loss: 0.156, ap: 0.889, auc: 0.933, f1: 0.840, acc: 0.862
+2025-08-26 04:05:20: Epoch: 031 Test results, ap: 0.329, auc: 0.614, f1: 0.108, acc: 0.732
+2025-08-26 04:05:54: Epoch: 032 Train Loss: 0.133, ap: 0.903, auc: 0.934, f1: 0.849, acc: 0.873
+2025-08-26 04:05:57: Epoch: 032 Test results, ap: 0.371, auc: 0.629, f1: 0.062, acc: 0.756
+2025-08-26 04:06:31: Epoch: 033 Train Loss: 0.129, ap: 0.897, auc: 0.935, f1: 0.840, acc: 0.859
+2025-08-26 04:06:34: Epoch: 033 Test results, ap: 0.356, auc: 0.620, f1: 0.059, acc: 0.740
+2025-08-26 04:07:09: Epoch: 034 Train Loss: 0.127, ap: 0.883, auc: 0.933, f1: 0.842, acc: 0.855
+2025-08-26 04:07:12: Epoch: 034 Test results, ap: 0.299, auc: 0.581, f1: 0.174, acc: 0.691
+2025-08-26 04:07:47: Epoch: 035 Train Loss: 0.133, ap: 0.899, auc: 0.936, f1: 0.844, acc: 0.866
+2025-08-26 04:07:50: Epoch: 035 Test results, ap: 0.365, auc: 0.609, f1: 0.061, acc: 0.748
+2025-08-26 04:08:24: Epoch: 036 Train Loss: 0.122, ap: 0.899, auc: 0.937, f1: 0.851, acc: 0.866
+2025-08-26 04:08:27: Epoch: 036 Test results, ap: 0.354, auc: 0.614, f1: 0.059, acc: 0.740
+2025-08-26 04:09:01: Epoch: 037 Train Loss: 0.131, ap: 0.895, auc: 0.933, f1: 0.837, acc: 0.855
+2025-08-26 04:09:04: Epoch: 037 Test results, ap: 0.314, auc: 0.603, f1: 0.051, acc: 0.699
+2025-08-26 04:09:37: Epoch: 038 Train Loss: 0.125, ap: 0.887, auc: 0.938, f1: 0.854, acc: 0.875
+2025-08-26 04:09:41: Epoch: 038 Test results, ap: 0.325, auc: 0.599, f1: 0.108, acc: 0.732
+2025-08-26 04:10:15: Epoch: 039 Train Loss: 0.116, ap: 0.893, auc: 0.935, f1: 0.847, acc: 0.864
+2025-08-26 04:10:18: Epoch: 039 Test results, ap: 0.311, auc: 0.606, f1: 0.054, acc: 0.715
+2025-08-26 04:10:53: Epoch: 040 Train Loss: 0.115, ap: 0.892, auc: 0.935, f1: 0.849, acc: 0.866
+2025-08-26 04:10:57: Epoch: 040 Test results, ap: 0.324, auc: 0.611, f1: 0.105, acc: 0.724
+2025-08-26 04:11:31: Epoch: 041 Train Loss: 0.108, ap: 0.897, auc: 0.937, f1: 0.851, acc: 0.868
+2025-08-26 04:11:34: Epoch: 041 Test results, ap: 0.353, auc: 0.619, f1: 0.059, acc: 0.740
+2025-08-26 04:12:08: Epoch: 042 Train Loss: 0.107, ap: 0.901, auc: 0.940, f1: 0.851, acc: 0.871
+2025-08-26 04:12:11: Epoch: 042 Test results, ap: 0.354, auc: 0.619, f1: 0.059, acc: 0.740
+2025-08-26 04:12:45: Epoch: 043 Train Loss: 0.111, ap: 0.903, auc: 0.940, f1: 0.850, acc: 0.868
+2025-08-26 04:12:45: Epoch: 043 New best VALIDATION metrics
+2025-08-26 04:12:48: Epoch: 043 Test results, ap: 0.343, auc: 0.615, f1: 0.061, acc: 0.748
+2025-08-26 04:13:22: Epoch: 044 Train Loss: 0.103, ap: 0.901, auc: 0.940, f1: 0.848, acc: 0.869
+2025-08-26 04:13:26: Epoch: 044 Test results, ap: 0.370, auc: 0.630, f1: 0.061, acc: 0.748
+2025-08-26 04:13:59: Epoch: 045 Train Loss: 0.106, ap: 0.896, auc: 0.941, f1: 0.856, acc: 0.873
+2025-08-26 04:14:02: Epoch: 045 Test results, ap: 0.323, auc: 0.614, f1: 0.056, acc: 0.724
+2025-08-26 04:14:36: Epoch: 046 Train Loss: 0.108, ap: 0.903, auc: 0.941, f1: 0.854, acc: 0.875
+2025-08-26 04:14:36: Epoch: 046 New best VALIDATION metrics
+2025-08-26 04:14:40: Epoch: 046 Test results, ap: 0.379, auc: 0.629, f1: 0.061, acc: 0.748
+2025-08-26 04:15:13: Epoch: 047 Train Loss: 0.102, ap: 0.900, auc: 0.939, f1: 0.847, acc: 0.868
+2025-08-26 04:15:16: Epoch: 047 Test results, ap: 0.362, auc: 0.629, f1: 0.111, acc: 0.740
+2025-08-26 04:15:50: Epoch: 048 Train Loss: 0.100, ap: 0.894, auc: 0.941, f1: 0.855, acc: 0.875
+2025-08-26 04:15:53: Epoch: 048 Test results, ap: 0.333, auc: 0.609, f1: 0.056, acc: 0.724
+2025-08-26 04:16:27: Epoch: 049 Train Loss: 0.097, ap: 0.903, auc: 0.940, f1: 0.859, acc: 0.878
+2025-08-26 04:16:30: Epoch: 049 Test results, ap: 0.326, auc: 0.594, f1: 0.061, acc: 0.748
+2025-08-26 04:17:04: Epoch: 050 Train Loss: 0.096, ap: 0.906, auc: 0.941, f1: 0.868, acc: 0.885
+2025-08-26 04:17:04: Epoch: 050 New best VALIDATION metrics
+2025-08-26 04:17:08: Epoch: 050 Test results, ap: 0.385, auc: 0.627, f1: 0.062, acc: 0.756
+2025-08-26 04:17:08: used time 0.52h
+2025-08-26 04:17:08: Running Cross Validation 4
+2025-08-26 04:17:08: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 04:17:42: Epoch: 001 Train Loss: 0.667, ap: 0.520, auc: 0.553, f1: 0.540, acc: 0.568
+2025-08-26 04:17:42: Epoch: 001 New best VALIDATION metrics
+2025-08-26 04:17:45: Epoch: 001 Test results, ap: 0.334, auc: 0.503, f1: 0.341, acc: 0.276
+2025-08-26 04:18:19: Epoch: 002 Train Loss: 0.656, ap: 0.548, auc: 0.612, f1: 0.516, acc: 0.570
+2025-08-26 04:18:19: Epoch: 002 New best VALIDATION metrics
+2025-08-26 04:18:23: Epoch: 002 Test results, ap: 0.299, auc: 0.514, f1: 0.000, acc: 0.756
+2025-08-26 04:18:56: Epoch: 003 Train Loss: 0.656, ap: 0.528, auc: 0.594, f1: 0.513, acc: 0.575
+2025-08-26 04:19:00: Epoch: 003 Test results, ap: 0.392, auc: 0.575, f1: 0.000, acc: 0.756
+2025-08-26 04:19:34: Epoch: 004 Train Loss: 0.654, ap: 0.544, auc: 0.588, f1: 0.475, acc: 0.607
+2025-08-26 04:19:37: Epoch: 004 Test results, ap: 0.232, auc: 0.470, f1: 0.000, acc: 0.724
+2025-08-26 04:20:11: Epoch: 005 Train Loss: 0.647, ap: 0.540, auc: 0.596, f1: 0.556, acc: 0.571
+2025-08-26 04:20:14: Epoch: 005 Test results, ap: 0.207, auc: 0.371, f1: 0.116, acc: 0.504
+2025-08-26 04:20:48: Epoch: 006 Train Loss: 0.638, ap: 0.601, auc: 0.627, f1: 0.565, acc: 0.527
+2025-08-26 04:20:48: Epoch: 006 New best VALIDATION metrics
+2025-08-26 04:20:52: Epoch: 006 Test results, ap: 0.182, auc: 0.328, f1: 0.392, acc: 0.244
+2025-08-26 04:21:25: Epoch: 007 Train Loss: 0.618, ap: 0.628, auc: 0.657, f1: 0.606, acc: 0.593
+2025-08-26 04:21:25: Epoch: 007 New best VALIDATION metrics
+2025-08-26 04:21:29: Epoch: 007 Test results, ap: 0.174, auc: 0.301, f1: 0.000, acc: 0.528
+2025-08-26 04:22:03: Epoch: 008 Train Loss: 0.605, ap: 0.610, auc: 0.670, f1: 0.573, acc: 0.626
+2025-08-26 04:22:06: Epoch: 008 Test results, ap: 0.179, auc: 0.324, f1: 0.041, acc: 0.618
+2025-08-26 04:22:40: Epoch: 009 Train Loss: 0.540, ap: 0.578, auc: 0.646, f1: 0.628, acc: 0.550
+2025-08-26 04:22:43: Epoch: 009 Test results, ap: 0.183, auc: 0.345, f1: 0.395, acc: 0.252
+2025-08-26 04:23:17: Epoch: 010 Train Loss: 0.511, ap: 0.718, auc: 0.766, f1: 0.685, acc: 0.718
+2025-08-26 04:23:17: Epoch: 010 New best VALIDATION metrics
+2025-08-26 04:23:20: Epoch: 010 Test results, ap: 0.216, auc: 0.433, f1: 0.000, acc: 0.740
+2025-08-26 04:23:54: Epoch: 011 Train Loss: 0.451, ap: 0.767, auc: 0.824, f1: 0.718, acc: 0.716
+2025-08-26 04:23:54: Epoch: 011 New best VALIDATION metrics
+2025-08-26 04:23:57: Epoch: 011 Test results, ap: 0.180, auc: 0.330, f1: 0.000, acc: 0.593
+2025-08-26 04:23:57: Epoch: 011 New best TEST metrics
+2025-08-26 04:24:31: Epoch: 012 Train Loss: 0.401, ap: 0.757, auc: 0.832, f1: 0.733, acc: 0.721
+2025-08-26 04:24:34: Epoch: 012 Test results, ap: 0.194, auc: 0.387, f1: 0.162, acc: 0.496
+2025-08-26 04:24:34: Epoch: 012 New best TEST metrics
+2025-08-26 04:25:09: Epoch: 013 Train Loss: 0.390, ap: 0.799, auc: 0.853, f1: 0.748, acc: 0.751
+2025-08-26 04:25:09: Epoch: 013 New best VALIDATION metrics
+2025-08-26 04:25:12: Epoch: 013 Test results, ap: 0.189, auc: 0.368, f1: 0.034, acc: 0.537
+2025-08-26 04:25:46: Epoch: 014 Train Loss: 0.350, ap: 0.791, auc: 0.853, f1: 0.731, acc: 0.765
+2025-08-26 04:25:49: Epoch: 014 Test results, ap: 0.205, auc: 0.426, f1: 0.000, acc: 0.675
+2025-08-26 04:25:49: Epoch: 014 New best TEST metrics
+2025-08-26 04:26:23: Epoch: 015 Train Loss: 0.336, ap: 0.812, auc: 0.870, f1: 0.763, acc: 0.767
+2025-08-26 04:26:23: Epoch: 015 New best VALIDATION metrics
+2025-08-26 04:26:27: Epoch: 015 Test results, ap: 0.204, auc: 0.423, f1: 0.043, acc: 0.642
+2025-08-26 04:27:01: Epoch: 016 Train Loss: 0.324, ap: 0.834, auc: 0.874, f1: 0.750, acc: 0.769
+2025-08-26 04:27:01: Epoch: 016 New best VALIDATION metrics
+2025-08-26 04:27:04: Epoch: 016 Test results, ap: 0.247, auc: 0.510, f1: 0.000, acc: 0.699
+2025-08-26 04:27:04: Epoch: 016 New best TEST metrics
+2025-08-26 04:27:38: Epoch: 017 Train Loss: 0.296, ap: 0.833, auc: 0.881, f1: 0.789, acc: 0.795
+2025-08-26 04:27:38: Epoch: 017 New best VALIDATION metrics
+2025-08-26 04:27:41: Epoch: 017 Test results, ap: 0.235, auc: 0.477, f1: 0.100, acc: 0.707
+2025-08-26 04:28:15: Epoch: 018 Train Loss: 0.281, ap: 0.849, auc: 0.880, f1: 0.740, acc: 0.785
+2025-08-26 04:28:15: Epoch: 018 New best VALIDATION metrics
+2025-08-26 04:28:18: Epoch: 018 Test results, ap: 0.253, auc: 0.534, f1: 0.000, acc: 0.715
+2025-08-26 04:28:18: Epoch: 018 New best TEST metrics
+2025-08-26 04:28:52: Epoch: 019 Train Loss: 0.283, ap: 0.850, auc: 0.889, f1: 0.796, acc: 0.806
+2025-08-26 04:28:52: Epoch: 019 New best VALIDATION metrics
+2025-08-26 04:28:55: Epoch: 019 Test results, ap: 0.221, auc: 0.471, f1: 0.000, acc: 0.675
+2025-08-26 04:29:29: Epoch: 020 Train Loss: 0.259, ap: 0.845, auc: 0.895, f1: 0.795, acc: 0.810
+2025-08-26 04:29:29: Epoch: 020 New best VALIDATION metrics
+2025-08-26 04:29:32: Epoch: 020 Test results, ap: 0.216, auc: 0.442, f1: 0.043, acc: 0.642
+2025-08-26 04:30:06: Epoch: 021 Train Loss: 0.228, ap: 0.853, auc: 0.898, f1: 0.805, acc: 0.818
+2025-08-26 04:30:06: Epoch: 021 New best VALIDATION metrics
+2025-08-26 04:30:10: Epoch: 021 Test results, ap: 0.233, auc: 0.488, f1: 0.130, acc: 0.675
+2025-08-26 04:30:44: Epoch: 022 Train Loss: 0.215, ap: 0.855, auc: 0.900, f1: 0.806, acc: 0.822
+2025-08-26 04:30:44: Epoch: 022 New best VALIDATION metrics
+2025-08-26 04:30:47: Epoch: 022 Test results, ap: 0.236, auc: 0.489, f1: 0.130, acc: 0.675
+2025-08-26 04:31:21: Epoch: 023 Train Loss: 0.224, ap: 0.867, auc: 0.902, f1: 0.789, acc: 0.817
+2025-08-26 04:31:21: Epoch: 023 New best VALIDATION metrics
+2025-08-26 04:31:25: Epoch: 023 Test results, ap: 0.231, auc: 0.486, f1: 0.000, acc: 0.707
+2025-08-26 04:31:57: Epoch: 024 Train Loss: 0.199, ap: 0.866, auc: 0.906, f1: 0.815, acc: 0.825
+2025-08-26 04:31:57: Epoch: 024 New best VALIDATION metrics
+2025-08-26 04:32:01: Epoch: 024 Test results, ap: 0.246, auc: 0.516, f1: 0.167, acc: 0.675
+2025-08-26 04:32:35: Epoch: 025 Train Loss: 0.200, ap: 0.868, auc: 0.903, f1: 0.790, acc: 0.811
+2025-08-26 04:32:39: Epoch: 025 Test results, ap: 0.257, auc: 0.533, f1: 0.049, acc: 0.683
+2025-08-26 04:32:39: Epoch: 025 New best TEST metrics
+2025-08-26 04:33:13: Epoch: 026 Train Loss: 0.190, ap: 0.850, auc: 0.898, f1: 0.808, acc: 0.817
+2025-08-26 04:33:16: Epoch: 026 Test results, ap: 0.233, auc: 0.470, f1: 0.185, acc: 0.642
+2025-08-26 04:33:50: Epoch: 027 Train Loss: 0.182, ap: 0.836, auc: 0.898, f1: 0.811, acc: 0.825
+2025-08-26 04:33:53: Epoch: 027 Test results, ap: 0.230, auc: 0.481, f1: 0.130, acc: 0.675
+2025-08-26 04:34:26: Epoch: 028 Train Loss: 0.196, ap: 0.867, auc: 0.915, f1: 0.819, acc: 0.834
+2025-08-26 04:34:26: Epoch: 028 New best VALIDATION metrics
+2025-08-26 04:34:30: Epoch: 028 Test results, ap: 0.233, auc: 0.489, f1: 0.098, acc: 0.699
+2025-08-26 04:35:04: Epoch: 029 Train Loss: 0.174, ap: 0.860, auc: 0.910, f1: 0.827, acc: 0.843
+2025-08-26 04:35:07: Epoch: 029 Test results, ap: 0.246, auc: 0.506, f1: 0.136, acc: 0.691
+2025-08-26 04:35:41: Epoch: 030 Train Loss: 0.176, ap: 0.862, auc: 0.908, f1: 0.819, acc: 0.840
+2025-08-26 04:35:44: Epoch: 030 Test results, ap: 0.237, auc: 0.495, f1: 0.000, acc: 0.675
+2025-08-26 04:36:18: Epoch: 031 Train Loss: 0.155, ap: 0.863, auc: 0.912, f1: 0.812, acc: 0.831
+2025-08-26 04:36:22: Epoch: 031 Test results, ap: 0.237, auc: 0.500, f1: 0.000, acc: 0.675
+2025-08-26 04:36:55: Epoch: 032 Train Loss: 0.159, ap: 0.879, auc: 0.916, f1: 0.819, acc: 0.834
+2025-08-26 04:36:55: Epoch: 032 New best VALIDATION metrics
+2025-08-26 04:36:59: Epoch: 032 Test results, ap: 0.250, auc: 0.525, f1: 0.050, acc: 0.691
+2025-08-26 04:37:33: Epoch: 033 Train Loss: 0.155, ap: 0.874, auc: 0.915, f1: 0.815, acc: 0.840
+2025-08-26 04:37:37: Epoch: 033 Test results, ap: 0.247, auc: 0.515, f1: 0.049, acc: 0.683
+2025-08-26 04:38:11: Epoch: 034 Train Loss: 0.149, ap: 0.865, auc: 0.913, f1: 0.821, acc: 0.832
+2025-08-26 04:38:14: Epoch: 034 Test results, ap: 0.242, auc: 0.509, f1: 0.089, acc: 0.667
+2025-08-26 04:38:49: Epoch: 035 Train Loss: 0.138, ap: 0.876, auc: 0.915, f1: 0.831, acc: 0.847
+2025-08-26 04:38:52: Epoch: 035 Test results, ap: 0.248, auc: 0.518, f1: 0.051, acc: 0.699
+2025-08-26 04:39:27: Epoch: 036 Train Loss: 0.158, ap: 0.871, auc: 0.915, f1: 0.832, acc: 0.843
+2025-08-26 04:39:30: Epoch: 036 Test results, ap: 0.250, auc: 0.523, f1: 0.095, acc: 0.691
+2025-08-26 04:40:05: Epoch: 037 Train Loss: 0.153, ap: 0.879, auc: 0.913, f1: 0.812, acc: 0.829
+2025-08-26 04:40:08: Epoch: 037 Test results, ap: 0.260, auc: 0.524, f1: 0.103, acc: 0.715
+2025-08-26 04:40:42: Epoch: 038 Train Loss: 0.144, ap: 0.874, auc: 0.918, f1: 0.837, acc: 0.850
+2025-08-26 04:40:45: Epoch: 038 Test results, ap: 0.245, auc: 0.511, f1: 0.093, acc: 0.683
+2025-08-26 04:41:19: Epoch: 039 Train Loss: 0.145, ap: 0.864, auc: 0.915, f1: 0.834, acc: 0.841
+2025-08-26 04:41:23: Epoch: 039 Test results, ap: 0.246, auc: 0.504, f1: 0.204, acc: 0.683
+2025-08-26 04:41:56: Epoch: 040 Train Loss: 0.138, ap: 0.873, auc: 0.917, f1: 0.832, acc: 0.847
+2025-08-26 04:41:59: Epoch: 040 Test results, ap: 0.255, auc: 0.515, f1: 0.103, acc: 0.715
+2025-08-26 04:42:33: Epoch: 041 Train Loss: 0.126, ap: 0.877, auc: 0.918, f1: 0.812, acc: 0.832
+2025-08-26 04:42:37: Epoch: 041 Test results, ap: 0.259, auc: 0.529, f1: 0.103, acc: 0.715
+2025-08-26 04:43:11: Epoch: 042 Train Loss: 0.131, ap: 0.877, auc: 0.919, f1: 0.831, acc: 0.848
+2025-08-26 04:43:11: Epoch: 042 New best VALIDATION metrics
+2025-08-26 04:43:14: Epoch: 042 Test results, ap: 0.258, auc: 0.523, f1: 0.103, acc: 0.715
+2025-08-26 04:43:48: Epoch: 043 Train Loss: 0.127, ap: 0.878, auc: 0.919, f1: 0.829, acc: 0.848
+2025-08-26 04:43:48: Epoch: 043 New best VALIDATION metrics
+2025-08-26 04:43:51: Epoch: 043 Test results, ap: 0.254, auc: 0.527, f1: 0.053, acc: 0.707
+2025-08-26 04:44:25: Epoch: 044 Train Loss: 0.125, ap: 0.882, auc: 0.921, f1: 0.829, acc: 0.848
+2025-08-26 04:44:25: Epoch: 044 New best VALIDATION metrics
+2025-08-26 04:44:29: Epoch: 044 Test results, ap: 0.259, auc: 0.534, f1: 0.000, acc: 0.707
+2025-08-26 04:44:29: Epoch: 044 New best TEST metrics
+2025-08-26 04:45:04: Epoch: 045 Train Loss: 0.121, ap: 0.878, auc: 0.919, f1: 0.840, acc: 0.857
+2025-08-26 04:45:07: Epoch: 045 Test results, ap: 0.262, auc: 0.538, f1: 0.150, acc: 0.724
+2025-08-26 04:45:07: Epoch: 045 New best TEST metrics
+2025-08-26 04:45:41: Epoch: 046 Train Loss: 0.126, ap: 0.878, auc: 0.921, f1: 0.844, acc: 0.859
+2025-08-26 04:45:44: Epoch: 046 Test results, ap: 0.256, auc: 0.527, f1: 0.143, acc: 0.707
+2025-08-26 04:46:18: Epoch: 047 Train Loss: 0.113, ap: 0.875, auc: 0.918, f1: 0.832, acc: 0.850
+2025-08-26 04:46:21: Epoch: 047 Test results, ap: 0.266, auc: 0.541, f1: 0.103, acc: 0.715
+2025-08-26 04:46:21: Epoch: 047 New best TEST metrics
+2025-08-26 04:46:55: Epoch: 048 Train Loss: 0.123, ap: 0.879, auc: 0.919, f1: 0.830, acc: 0.847
+2025-08-26 04:46:58: Epoch: 048 Test results, ap: 0.266, auc: 0.539, f1: 0.103, acc: 0.715
+2025-08-26 04:47:32: Epoch: 049 Train Loss: 0.111, ap: 0.884, auc: 0.922, f1: 0.843, acc: 0.859
+2025-08-26 04:47:32: Epoch: 049 New best VALIDATION metrics
+2025-08-26 04:47:35: Epoch: 049 Test results, ap: 0.263, auc: 0.538, f1: 0.053, acc: 0.707
+2025-08-26 04:48:09: Epoch: 050 Train Loss: 0.114, ap: 0.879, auc: 0.921, f1: 0.848, acc: 0.862
+2025-08-26 04:48:13: Epoch: 050 Test results, ap: 0.263, auc: 0.530, f1: 0.195, acc: 0.732
+2025-08-26 04:48:13: used time 0.52h
+2025-08-26 04:48:13: Cross Validation Finished!
+2025-08-26 04:48:13: Best validation perform list
+[[0.93041396 0.93726951 0.86907023 0.87852114]
+ [0.91840047 0.94193697 0.85029942 0.86795777]
+ [0.90325212 0.92047954 0.81481481 0.85008818]
+ [0.90586436 0.94054842 0.86761713 0.88536155]
+ [0.88446164 0.92240036 0.84313726 0.85890651]]
+2025-08-26 04:48:13: mean: [0.908 0.933 0.849 0.868]
+2025-08-26 04:48:13: std: [0.015 0.009 0.02  0.013]
+2025-08-26 04:48:13: Best test perform list
+[[0.27714825 0.59283155 0.         0.72357726]
+ [0.25297916 0.48387098 0.2857143  0.63414633]
+ [0.32588559 0.6082437  0.         0.74796748]
+ [0.39254373 0.63906813 0.06451613 0.76422763]
+ [0.26628226 0.54050183 0.1025641  0.71544713]]
+2025-08-26 04:48:13: mean: [0.303 0.573 0.091 0.717]
+2025-08-26 04:48:13: std: [0.051 0.055 0.105 0.045]
diff --git a/run-reg/_rn18-diff-16-mamba-pcs-768-mse-32-0.001-48/uda_r2/config.json b/run-reg/_rn18-diff-16-mamba-pcs-768-mse-32-0.001-48/uda_r2/config.json
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+2025-08-07 16:14:29: Loading Training Dataset
+2025-08-07 16:14:55: Loading Validation Dataset
+2025-08-07 16:14:57: Start UDA training
+2025-08-07 16:16:36: Epoch 1 Train Loss: 2.3675 Val: mae: 0.7017 rse: 1.4502 pcc: -0.5765 kcc: -0.4686
+2025-08-07 16:16:36: Epoch: 1 New best VAL metrics
+2025-08-07 16:18:11: Epoch 2 Train Loss: 1.2621 Val: mae: 0.6928 rse: 1.3971 pcc: -0.4918 kcc: -0.3788
+2025-08-07 16:18:11: Epoch: 2 New best VAL metrics
+2025-08-07 16:19:49: Epoch 3 Train Loss: 0.8557 Val: mae: 0.7120 rse: 1.4755 pcc: -0.5499 kcc: -0.4219
+2025-08-07 16:21:26: Epoch 4 Train Loss: 0.6144 Val: mae: 0.7103 rse: 1.4657 pcc: -0.5312 kcc: -0.4219
+2025-08-07 16:23:02: Epoch 5 Train Loss: 0.4756 Val: mae: 0.6927 rse: 1.3854 pcc: -0.4131 kcc: -0.2998
+2025-08-07 16:23:02: Epoch: 5 New best VAL metrics
+2025-08-07 16:24:39: Epoch 6 Train Loss: 0.3836 Val: mae: 0.6812 rse: 1.3342 pcc: -0.1555 kcc: -0.1023
+2025-08-07 16:24:39: Epoch: 6 New best VAL metrics
+2025-08-07 16:26:15: Epoch 7 Train Loss: 0.3386 Val: mae: 0.6741 rse: 1.3052 pcc: 0.2082 kcc: 0.1885
+2025-08-07 16:26:15: Epoch: 7 New best VAL metrics
+2025-08-07 16:27:51: Epoch 8 Train Loss: 0.3064 Val: mae: 0.6705 rse: 1.2918 pcc: 0.3733 kcc: 0.3034
+2025-08-07 16:27:51: Epoch: 8 New best VAL metrics
+2025-08-07 16:29:28: Epoch 9 Train Loss: 0.2489 Val: mae: 0.6699 rse: 1.2915 pcc: 0.3955 kcc: 0.2675
+2025-08-07 16:31:04: Epoch 10 Train Loss: 0.2276 Val: mae: 0.6654 rse: 1.2752 pcc: 0.4662 kcc: 0.3429
+2025-08-07 16:31:04: Epoch: 10 New best VAL metrics
+2025-08-07 16:32:41: Epoch 11 Train Loss: 0.2153 Val: mae: 0.6616 rse: 1.2626 pcc: 0.5515 kcc: 0.4363
+2025-08-07 16:32:41: Epoch: 11 New best VAL metrics
+2025-08-07 16:34:17: Epoch 12 Train Loss: 0.2106 Val: mae: 0.6616 rse: 1.2614 pcc: 0.5758 kcc: 0.4255
+2025-08-07 16:34:17: Epoch: 12 New best VAL metrics
+2025-08-07 16:35:54: Epoch 13 Train Loss: 0.2130 Val: mae: 0.6591 rse: 1.2508 pcc: 0.5981 kcc: 0.4435
+2025-08-07 16:35:54: Epoch: 13 New best VAL metrics
+2025-08-07 16:37:31: Epoch 14 Train Loss: 0.2056 Val: mae: 0.6570 rse: 1.2427 pcc: 0.6358 kcc: 0.4686
+2025-08-07 16:37:31: Epoch: 14 New best VAL metrics
+2025-08-07 16:39:08: Epoch 15 Train Loss: 0.1799 Val: mae: 0.6582 rse: 1.2493 pcc: 0.6298 kcc: 0.4722
+2025-08-07 16:40:45: Epoch 16 Train Loss: 0.1766 Val: mae: 0.6584 rse: 1.2493 pcc: 0.6363 kcc: 0.4542
+2025-08-07 16:42:21: Epoch 17 Train Loss: 0.1790 Val: mae: 0.6557 rse: 1.2388 pcc: 0.6411 kcc: 0.4937
+2025-08-07 16:42:21: Epoch: 17 New best VAL metrics
+2025-08-07 16:43:58: Epoch 18 Train Loss: 0.1653 Val: mae: 0.6571 rse: 1.2439 pcc: 0.6440 kcc: 0.4758
+2025-08-07 16:45:33: Epoch 19 Train Loss: 0.1553 Val: mae: 0.6624 rse: 1.2658 pcc: 0.6478 kcc: 0.4614
+2025-08-07 16:47:10: Epoch 20 Train Loss: 0.1461 Val: mae: 0.6626 rse: 1.2676 pcc: 0.6325 kcc: 0.4614
diff --git a/run-reg/_rn18-diff-16-mamba-pcs-768-mse-32-0.001-49/uda_r2/config.json b/run-reg/_rn18-diff-16-mamba-pcs-768-mse-32-0.001-49/uda_r2/config.json
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+2025-08-07 16:13:35: Loading Training Dataset
+2025-08-07 16:14:00: Loading Validation Dataset
+2025-08-07 16:14:01: Start UDA training
+2025-08-07 16:15:26: Epoch 1 Train Loss: 2.3285 Val: mae: 0.6975 rse: 1.4201 pcc: -0.5895 kcc: -0.4686
+2025-08-07 16:15:26: Epoch: 1 New best VAL metrics
+2025-08-07 16:16:49: Epoch 2 Train Loss: 1.4009 Val: mae: 0.6866 rse: 1.3657 pcc: -0.4611 kcc: -0.3752
+2025-08-07 16:16:49: Epoch: 2 New best VAL metrics
+2025-08-07 16:18:11: Epoch 3 Train Loss: 1.0487 Val: mae: 0.6921 rse: 1.3839 pcc: -0.3618 kcc: -0.2998
+2025-08-07 16:18:11: Epoch: 3 New best VAL metrics
+2025-08-07 16:19:35: Epoch 4 Train Loss: 0.7559 Val: mae: 0.6970 rse: 1.4042 pcc: -0.3816 kcc: -0.2891
+2025-08-07 16:20:58: Epoch 5 Train Loss: 0.5433 Val: mae: 0.6898 rse: 1.3711 pcc: -0.2763 kcc: -0.2172
+2025-08-07 16:20:58: Epoch: 5 New best VAL metrics
+2025-08-07 16:22:20: Epoch 6 Train Loss: 0.4040 Val: mae: 0.6712 rse: 1.2899 pcc: 0.2761 kcc: 0.2101
+2025-08-07 16:22:20: Epoch: 6 New best VAL metrics
+2025-08-07 16:23:42: Epoch 7 Train Loss: 0.3483 Val: mae: 0.6671 rse: 1.2706 pcc: 0.3563 kcc: 0.2603
+2025-08-07 16:23:42: Epoch: 7 New best VAL metrics
+2025-08-07 16:25:04: Epoch 8 Train Loss: 0.3136 Val: mae: 0.6590 rse: 1.2393 pcc: 0.5300 kcc: 0.3716
+2025-08-07 16:25:04: Epoch: 8 New best VAL metrics
+2025-08-07 16:26:27: Epoch 9 Train Loss: 0.2619 Val: mae: 0.6565 rse: 1.2269 pcc: 0.5825 kcc: 0.4076
+2025-08-07 16:26:27: Epoch: 9 New best VAL metrics
+2025-08-07 16:27:51: Epoch 10 Train Loss: 0.2576 Val: mae: 0.6531 rse: 1.2113 pcc: 0.6365 kcc: 0.4470
+2025-08-07 16:27:51: Epoch: 10 New best VAL metrics
+2025-08-07 16:29:15: Epoch 11 Train Loss: 0.2439 Val: mae: 0.6502 rse: 1.1971 pcc: 0.6799 kcc: 0.4973
+2025-08-07 16:29:15: Epoch: 11 New best VAL metrics
+2025-08-07 16:30:38: Epoch 12 Train Loss: 0.1998 Val: mae: 0.6511 rse: 1.1981 pcc: 0.6893 kcc: 0.5117
+2025-08-07 16:30:38: Epoch: 12 New best VAL metrics
+2025-08-07 16:32:00: Epoch 13 Train Loss: 0.2019 Val: mae: 0.6550 rse: 1.2119 pcc: 0.6472 kcc: 0.4435
+2025-08-07 16:33:23: Epoch 14 Train Loss: 0.2010 Val: mae: 0.6556 rse: 1.2120 pcc: 0.6372 kcc: 0.4219
+2025-08-07 16:34:45: Epoch 15 Train Loss: 0.1801 Val: mae: 0.6559 rse: 1.2108 pcc: 0.6130 kcc: 0.4040
+2025-08-07 16:36:08: Epoch 16 Train Loss: 0.1759 Val: mae: 0.6564 rse: 1.2136 pcc: 0.6223 kcc: 0.4111
+2025-08-07 16:37:31: Epoch 17 Train Loss: 0.1644 Val: mae: 0.6596 rse: 1.2281 pcc: 0.6078 kcc: 0.3788
+2025-08-07 16:38:55: Epoch 18 Train Loss: 0.1638 Val: mae: 0.6585 rse: 1.2233 pcc: 0.6298 kcc: 0.4076
+2025-08-07 16:40:18: Epoch 19 Train Loss: 0.1527 Val: mae: 0.6603 rse: 1.2311 pcc: 0.6183 kcc: 0.4040
+2025-08-07 16:41:41: Epoch 20 Train Loss: 0.1585 Val: mae: 0.6621 rse: 1.2395 pcc: 0.6151 kcc: 0.3788
+2025-08-07 16:43:03: Epoch 21 Train Loss: 0.1483 Val: mae: 0.6649 rse: 1.2536 pcc: 0.6040 kcc: 0.3681
+2025-08-07 16:44:26: Epoch 22 Train Loss: 0.1318 Val: mae: 0.6639 rse: 1.2513 pcc: 0.6346 kcc: 0.3824
+2025-08-07 16:45:49: Epoch 23 Train Loss: 0.1279 Val: mae: 0.6683 rse: 1.2708 pcc: 0.5806 kcc: 0.3537
+2025-08-07 16:47:13: Epoch 24 Train Loss: 0.1237 Val: mae: 0.6676 rse: 1.2699 pcc: 0.6043 kcc: 0.3788
diff --git a/run-reg/cnn-att-16-768-oneway-smoothl1-32-0.001-35/config.json b/run-reg/cnn-att-16-768-oneway-smoothl1-32-0.001-35/config.json
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@@ -0,0 +1 @@
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diff --git a/run-reg/cnn-att-16-768-oneway-smoothl1-32-0.001-35/training.log b/run-reg/cnn-att-16-768-oneway-smoothl1-32-0.001-35/training.log
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+2025-07-09 15:42:34: saving_dir: ./run-reg/cnn-att-16-768-oneway-smoothl1-32-0.001-35
+2025-07-09 15:42:34: Loading Training Dataset
+2025-07-09 15:43:23: Loading Test Dataset
+2025-07-09 15:43:30: Running Cross Validation 0
+2025-07-09 15:43:30: Fold 0  Train set:405, Valid set:102, Test set: 108
diff --git a/run-reg/gru-att-256-mse-32-0.001-50/config.json b/run-reg/gru-att-256-mse-32-0.001-50/config.json
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@@ -0,0 +1 @@
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diff --git a/run-reg/gru-att-256-mse-32-0.001-50/training.log b/run-reg/gru-att-256-mse-32-0.001-50/training.log
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+2025-07-10 15:22:28: saving_dir: ./run-reg/gru-att-256-mse-32-0.001-50
+2025-07-10 15:22:28: Loading Train Dataset
+2025-07-10 15:22:28: Loading Test Dataset
+2025-07-10 15:22:29: Running Cross Validation 0
+2025-07-10 15:22:29: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 15:22:30: Epoch: 001 Train Loss: 2.937, mae: 1.198, rse: 1.002, pcc: -0.009, kcc: -0.025
+2025-07-10 15:22:30: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:22:30: Epoch: 001 Test results, ap: mae: 0.647, rse: 1.268, pcc: -0.196, kcc: -0.172
+2025-07-10 15:22:31: Epoch: 002 Train Loss: 2.916, mae: 1.203, rse: 1.000, pcc: 0.079, kcc: 0.073
+2025-07-10 15:22:31: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:22:31: Epoch: 002 Test results, ap: mae: 0.633, rse: 1.216, pcc: 0.361, kcc: 0.234
+2025-07-10 15:22:32: Epoch: 003 Train Loss: 2.904, mae: 1.203, rse: 1.000, pcc: 0.190, kcc: 0.102
+2025-07-10 15:22:32: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:22:32: Epoch: 003 Test results, ap: mae: 0.633, rse: 1.218, pcc: 0.274, kcc: 0.109
+2025-07-10 15:22:33: Epoch: 004 Train Loss: 2.926, mae: 1.208, rse: 1.000, pcc: 0.115, kcc: 0.102
+2025-07-10 15:22:33: Epoch: 004 Test results, ap: mae: 0.617, rse: 1.168, pcc: 0.262, kcc: 0.159
+2025-07-10 15:22:34: Epoch: 005 Train Loss: 2.929, mae: 1.200, rse: 1.001, pcc: 0.287, kcc: 0.169
+2025-07-10 15:22:34: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:22:34: Epoch: 005 Test results, ap: mae: 0.640, rse: 1.242, pcc: -0.137, kcc: -0.097
+2025-07-10 15:22:35: Epoch: 006 Train Loss: 2.913, mae: 1.207, rse: 0.997, pcc: 0.265, kcc: 0.110
+2025-07-10 15:22:35: Epoch: 006 Test results, ap: mae: 0.617, rse: 1.167, pcc: 0.183, kcc: 0.020
+2025-07-10 15:22:36: Epoch: 007 Train Loss: 2.910, mae: 1.197, rse: 1.003, pcc: 0.014, kcc: 0.087
+2025-07-10 15:22:36: Epoch: 007 Test results, ap: mae: 0.651, rse: 1.283, pcc: 0.395, kcc: 0.252
+2025-07-10 15:22:37: Epoch: 008 Train Loss: 2.910, mae: 1.206, rse: 1.000, pcc: -0.084, kcc: -0.034
+2025-07-10 15:22:37: Epoch: 008 Test results, ap: mae: 0.624, rse: 1.189, pcc: -0.231, kcc: -0.140
+2025-07-10 15:22:38: Epoch: 009 Train Loss: 2.923, mae: 1.204, rse: 1.000, pcc: 0.018, kcc: -0.039
+2025-07-10 15:22:38: Epoch: 009 Test results, ap: mae: 0.628, rse: 1.202, pcc: 0.082, kcc: 0.053
+2025-07-10 15:22:38: Epoch: 010 Train Loss: 2.897, mae: 1.247, rse: 1.016, pcc: 0.040, kcc: -0.014
+2025-07-10 15:22:39: Epoch: 010 Test results, ap: mae: 0.583, rse: 1.019, pcc: 0.217, kcc: 0.116
+2025-07-10 15:22:39: Epoch: 011 Train Loss: 2.920, mae: 1.211, rse: 1.000, pcc: 0.052, kcc: 0.012
+2025-07-10 15:22:40: Epoch: 011 Test results, ap: mae: 0.611, rse: 1.145, pcc: 0.215, kcc: 0.092
+2025-07-10 15:22:40: Epoch: 011 New best TEST metrics
+2025-07-10 15:22:40: Epoch: 012 Train Loss: 2.909, mae: 1.218, rse: 1.002, pcc: 0.041, kcc: -0.002
+2025-07-10 15:22:41: Epoch: 012 Test results, ap: mae: 0.601, rse: 1.103, pcc: 0.212, kcc: 0.105
+2025-07-10 15:22:41: Epoch: 012 New best TEST metrics
+2025-07-10 15:22:41: Epoch: 013 Train Loss: 2.887, mae: 1.215, rse: 1.001, pcc: -0.025, kcc: 0.021
+2025-07-10 15:22:41: Epoch: 013 Test results, ap: mae: 0.604, rse: 1.115, pcc: -0.209, kcc: -0.122
+2025-07-10 15:22:42: Epoch: 014 Train Loss: 2.891, mae: 1.216, rse: 1.001, pcc: 0.057, kcc: 0.023
+2025-07-10 15:22:42: Epoch: 014 Test results, ap: mae: 0.603, rse: 1.112, pcc: 0.218, kcc: 0.149
+2025-07-10 15:22:42: Epoch: 014 New best TEST metrics
+2025-07-10 15:22:43: Epoch: 015 Train Loss: 2.901, mae: 1.214, rse: 1.001, pcc: 0.060, kcc: 0.028
+2025-07-10 15:22:43: Epoch: 015 Test results, ap: mae: 0.605, rse: 1.121, pcc: 0.216, kcc: 0.172
+2025-07-10 15:22:43: Epoch: 015 New best TEST metrics
+2025-07-10 15:22:44: Epoch: 016 Train Loss: 2.895, mae: 1.212, rse: 1.000, pcc: 0.086, kcc: 0.055
+2025-07-10 15:22:44: Epoch: 016 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.198, kcc: 0.172
+2025-07-10 15:22:45: Epoch: 017 Train Loss: 2.889, mae: 1.191, rse: 0.991, pcc: 0.264, kcc: 0.142
+2025-07-10 15:22:45: Epoch: 017 Test results, ap: mae: 0.654, rse: 1.295, pcc: 0.197, kcc: 0.100
+2025-07-10 15:22:46: Epoch: 018 Train Loss: 2.845, mae: 1.233, rse: 0.991, pcc: 0.317, kcc: 0.172
+2025-07-10 15:22:46: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:22:46: Epoch: 018 Test results, ap: mae: 0.581, rse: 1.001, pcc: 0.028, kcc: -0.178
+2025-07-10 15:22:47: Epoch: 019 Train Loss: 2.883, mae: 1.202, rse: 1.001, pcc: -0.032, kcc: 0.014
+2025-07-10 15:22:47: Epoch: 019 Test results, ap: mae: 0.634, rse: 1.220, pcc: 0.171, kcc: -0.002
+2025-07-10 15:22:48: Epoch: 020 Train Loss: 2.921, mae: 1.233, rse: 1.008, pcc: 0.039, kcc: 0.031
+2025-07-10 15:22:48: Epoch: 020 Test results, ap: mae: 0.588, rse: 1.048, pcc: 0.178, kcc: 0.113
+2025-07-10 15:22:49: Epoch: 021 Train Loss: 2.899, mae: 1.210, rse: 1.000, pcc: 0.045, kcc: -0.033
+2025-07-10 15:22:49: Epoch: 021 Test results, ap: mae: 0.612, rse: 1.149, pcc: -0.247, kcc: -0.270
+2025-07-10 15:22:50: Epoch: 022 Train Loss: 2.909, mae: 1.206, rse: 1.000, pcc: 0.043, kcc: -0.040
+2025-07-10 15:22:50: Epoch: 022 Test results, ap: mae: 0.622, rse: 1.183, pcc: -0.247, kcc: -0.251
+2025-07-10 15:22:51: Epoch: 023 Train Loss: 2.903, mae: 1.212, rse: 1.000, pcc: 0.046, kcc: 0.024
+2025-07-10 15:22:51: Epoch: 023 Test results, ap: mae: 0.608, rse: 1.132, pcc: -0.227, kcc: -0.242
+2025-07-10 15:22:51: Epoch: 024 Train Loss: 2.899, mae: 1.208, rse: 1.000, pcc: 0.048, kcc: 0.036
+2025-07-10 15:22:52: Epoch: 024 Test results, ap: mae: 0.616, rse: 1.161, pcc: -0.205, kcc: -0.167
+2025-07-10 15:22:52: Epoch: 025 Train Loss: 2.918, mae: 1.210, rse: 1.000, pcc: 0.053, kcc: 0.033
+2025-07-10 15:22:53: Epoch: 025 Test results, ap: mae: 0.612, rse: 1.149, pcc: -0.190, kcc: -0.079
+2025-07-10 15:22:53: Epoch: 026 Train Loss: 2.906, mae: 1.221, rse: 1.003, pcc: 0.050, kcc: 0.041
+2025-07-10 15:22:53: Epoch: 026 Test results, ap: mae: 0.596, rse: 1.083, pcc: -0.163, kcc: -0.077
+2025-07-10 15:22:54: Epoch: 027 Train Loss: 2.912, mae: 1.217, rse: 1.002, pcc: 0.066, kcc: 0.039
+2025-07-10 15:22:54: Epoch: 027 Test results, ap: mae: 0.601, rse: 1.105, pcc: -0.127, kcc: -0.086
+2025-07-10 15:22:55: Epoch: 028 Train Loss: 2.896, mae: 1.219, rse: 1.002, pcc: 0.054, kcc: 0.047
+2025-07-10 15:22:55: Epoch: 028 Test results, ap: mae: 0.598, rse: 1.091, pcc: -0.142, kcc: -0.071
+2025-07-10 15:22:56: Epoch: 029 Train Loss: 2.898, mae: 1.212, rse: 1.000, pcc: 0.068, kcc: 0.049
+2025-07-10 15:22:56: Epoch: 029 Test results, ap: mae: 0.607, rse: 1.126, pcc: -0.148, kcc: -0.064
+2025-07-10 15:22:57: Epoch: 030 Train Loss: 2.902, mae: 1.217, rse: 1.001, pcc: 0.062, kcc: 0.096
+2025-07-10 15:22:57: Epoch: 030 Test results, ap: mae: 0.597, rse: 1.086, pcc: -0.161, kcc: -0.079
+2025-07-10 15:22:58: Epoch: 031 Train Loss: 2.881, mae: 1.197, rse: 0.992, pcc: 0.119, kcc: 0.054
+2025-07-10 15:22:58: Epoch: 031 Test results, ap: mae: 0.633, rse: 1.218, pcc: -0.180, kcc: -0.088
+2025-07-10 15:22:59: Epoch: 032 Train Loss: 2.879, mae: 1.198, rse: 0.981, pcc: 0.160, kcc: 0.084
+2025-07-10 15:22:59: Epoch: 032 Test results, ap: mae: 0.614, rse: 1.154, pcc: -0.187, kcc: -0.129
+2025-07-10 15:23:00: Epoch: 033 Train Loss: 2.822, mae: 1.176, rse: 0.940, pcc: 0.277, kcc: 0.134
+2025-07-10 15:23:00: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:23:00: Epoch: 033 Test results, ap: mae: 0.600, rse: 1.098, pcc: -0.072, kcc: -0.039
+2025-07-10 15:23:01: Epoch: 034 Train Loss: 2.715, mae: 1.140, rse: 0.897, pcc: 0.325, kcc: 0.142
+2025-07-10 15:23:01: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:23:01: Epoch: 034 Test results, ap: mae: 0.724, rse: 1.550, pcc: -0.006, kcc: -0.053
+2025-07-10 15:23:02: Epoch: 035 Train Loss: 2.688, mae: 1.146, rse: 0.897, pcc: 0.321, kcc: 0.141
+2025-07-10 15:23:02: Epoch: 035 Test results, ap: mae: 0.684, rse: 1.415, pcc: -0.121, kcc: -0.063
+2025-07-10 15:23:02: Epoch: 036 Train Loss: 2.662, mae: 1.143, rse: 0.888, pcc: 0.343, kcc: 0.232
+2025-07-10 15:23:02: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:23:03: Epoch: 036 Test results, ap: mae: 0.655, rse: 1.302, pcc: -0.132, kcc: -0.082
+2025-07-10 15:23:03: Epoch: 037 Train Loss: 2.654, mae: 1.125, rse: 0.881, pcc: 0.349, kcc: 0.237
+2025-07-10 15:23:03: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:23:04: Epoch: 037 Test results, ap: mae: 0.649, rse: 1.286, pcc: -0.108, kcc: -0.070
+2025-07-10 15:23:04: Epoch: 038 Train Loss: 2.636, mae: 1.113, rse: 0.866, pcc: 0.382, kcc: 0.250
+2025-07-10 15:23:04: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:23:04: Epoch: 038 Test results, ap: mae: 0.706, rse: 1.475, pcc: 0.158, kcc: 0.091
+2025-07-10 15:23:05: Epoch: 039 Train Loss: 2.606, mae: 1.108, rse: 0.863, pcc: 0.386, kcc: 0.230
+2025-07-10 15:23:05: Epoch: 039 Test results, ap: mae: 0.755, rse: 1.668, pcc: -0.005, kcc: -0.117
+2025-07-10 15:23:06: Epoch: 040 Train Loss: 2.640, mae: 1.112, rse: 0.864, pcc: 0.377, kcc: 0.242
+2025-07-10 15:23:06: Epoch: 040 Test results, ap: mae: 0.675, rse: 1.393, pcc: -0.182, kcc: -0.117
+2025-07-10 15:23:07: Epoch: 041 Train Loss: 2.593, mae: 1.104, rse: 0.845, pcc: 0.400, kcc: 0.270
+2025-07-10 15:23:07: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:23:07: Epoch: 041 Test results, ap: mae: 0.662, rse: 1.320, pcc: 0.185, kcc: 0.108
+2025-07-10 15:23:08: Epoch: 042 Train Loss: 2.580, mae: 1.107, rse: 0.853, pcc: 0.388, kcc: 0.257
+2025-07-10 15:23:08: Epoch: 042 Test results, ap: mae: 0.638, rse: 1.230, pcc: -0.047, kcc: -0.102
+2025-07-10 15:23:09: Epoch: 043 Train Loss: 2.583, mae: 1.101, rse: 0.842, pcc: 0.402, kcc: 0.261
+2025-07-10 15:23:09: Epoch: 043 Test results, ap: mae: 0.706, rse: 1.464, pcc: 0.206, kcc: 0.119
+2025-07-10 15:23:10: Epoch: 044 Train Loss: 2.561, mae: 1.102, rse: 0.845, pcc: 0.402, kcc: 0.263
+2025-07-10 15:23:10: Epoch: 044 Test results, ap: mae: 0.640, rse: 1.240, pcc: 0.171, kcc: 0.099
+2025-07-10 15:23:11: Epoch: 045 Train Loss: 2.583, mae: 1.099, rse: 0.843, pcc: 0.404, kcc: 0.273
+2025-07-10 15:23:11: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:23:11: Epoch: 045 Test results, ap: mae: 0.637, rse: 1.223, pcc: 0.095, kcc: 0.020
+2025-07-10 15:23:12: Epoch: 046 Train Loss: 2.583, mae: 1.098, rse: 0.841, pcc: 0.405, kcc: 0.272
+2025-07-10 15:23:12: Epoch: 046 New best VALIDATION metrics
+2025-07-10 15:23:12: Epoch: 046 Test results, ap: mae: 0.656, rse: 1.297, pcc: 0.179, kcc: 0.099
+2025-07-10 15:23:12: Epoch: 047 Train Loss: 2.590, mae: 1.099, rse: 0.843, pcc: 0.404, kcc: 0.268
+2025-07-10 15:23:13: Epoch: 047 Test results, ap: mae: 0.630, rse: 1.203, pcc: 0.132, kcc: 0.110
+2025-07-10 15:23:13: Epoch: 048 Train Loss: 2.578, mae: 1.098, rse: 0.838, pcc: 0.404, kcc: 0.266
+2025-07-10 15:23:14: Epoch: 048 Test results, ap: mae: 0.697, rse: 1.423, pcc: 0.161, kcc: 0.103
+2025-07-10 15:23:14: Epoch: 049 Train Loss: 2.567, mae: 1.098, rse: 0.841, pcc: 0.406, kcc: 0.274
+2025-07-10 15:23:14: Epoch: 049 New best VALIDATION metrics
+2025-07-10 15:23:15: Epoch: 049 Test results, ap: mae: 0.686, rse: 1.385, pcc: 0.166, kcc: 0.057
+2025-07-10 15:23:15: Epoch: 050 Train Loss: 2.582, mae: 1.113, rse: 0.844, pcc: 0.404, kcc: 0.276
+2025-07-10 15:23:15: Epoch: 050 Test results, ap: mae: 0.601, rse: 1.068, pcc: -0.050, kcc: -0.091
+2025-07-10 15:23:15: used time 0.01h
+2025-07-10 15:23:15: Running Cross Validation 1
+2025-07-10 15:23:15: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 15:23:16: Epoch: 001 Train Loss: 2.866, mae: 1.255, rse: 1.001, pcc: 0.088, kcc: 0.136
+2025-07-10 15:23:16: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:23:16: Epoch: 001 Test results, ap: mae: 0.611, rse: 1.145, pcc: 0.258, kcc: 0.110
+2025-07-10 15:23:17: Epoch: 002 Train Loss: 2.867, mae: 1.254, rse: 1.001, pcc: 0.029, kcc: -0.117
+2025-07-10 15:23:17: Epoch: 002 Test results, ap: mae: 0.614, rse: 1.156, pcc: -0.183, kcc: -0.012
+2025-07-10 15:23:18: Epoch: 003 Train Loss: 2.854, mae: 1.252, rse: 1.003, pcc: -0.032, kcc: -0.062
+2025-07-10 15:23:18: Epoch: 003 Test results, ap: mae: 0.624, rse: 1.188, pcc: -0.295, kcc: -0.157
+2025-07-10 15:23:19: Epoch: 004 Train Loss: 2.848, mae: 1.253, rse: 1.002, pcc: 0.007, kcc: -0.011
+2025-07-10 15:23:19: Epoch: 004 Test results, ap: mae: 0.617, rse: 1.166, pcc: -0.224, kcc: -0.143
+2025-07-10 15:23:20: Epoch: 005 Train Loss: 2.852, mae: 1.253, rse: 1.002, pcc: 0.013, kcc: 0.092
+2025-07-10 15:23:20: Epoch: 005 Test results, ap: mae: 0.620, rse: 1.177, pcc: 0.018, kcc: -0.048
+2025-07-10 15:23:21: Epoch: 006 Train Loss: 2.848, mae: 1.259, rse: 1.000, pcc: 0.053, kcc: 0.002
+2025-07-10 15:23:21: Epoch: 006 Test results, ap: mae: 0.602, rse: 1.106, pcc: -0.094, kcc: -0.157
+2025-07-10 15:23:22: Epoch: 007 Train Loss: 2.852, mae: 1.253, rse: 1.002, pcc: 0.036, kcc: 0.048
+2025-07-10 15:23:22: Epoch: 007 Test results, ap: mae: 0.617, rse: 1.168, pcc: -0.160, kcc: -0.091
+2025-07-10 15:23:23: Epoch: 008 Train Loss: 2.819, mae: 1.252, rse: 1.003, pcc: 0.058, kcc: 0.126
+2025-07-10 15:23:23: Epoch: 008 Test results, ap: mae: 0.622, rse: 1.184, pcc: 0.130, kcc: 0.072
+2025-07-10 15:23:23: Epoch: 009 Train Loss: 2.856, mae: 1.251, rse: 1.005, pcc: 0.064, kcc: 0.098
+2025-07-10 15:23:24: Epoch: 009 Test results, ap: mae: 0.633, rse: 1.220, pcc: 0.039, kcc: 0.060
+2025-07-10 15:23:24: Epoch: 010 Train Loss: 2.847, mae: 1.257, rse: 1.000, pcc: 0.030, kcc: 0.025
+2025-07-10 15:23:25: Epoch: 010 Test results, ap: mae: 0.607, rse: 1.127, pcc: -0.008, kcc: -0.010
+2025-07-10 15:23:25: Epoch: 011 Train Loss: 2.860, mae: 1.253, rse: 1.002, pcc: 0.016, kcc: 0.019
+2025-07-10 15:23:25: Epoch: 011 Test results, ap: mae: 0.617, rse: 1.166, pcc: -0.076, kcc: -0.100
+2025-07-10 15:23:25: Epoch: 011 New best TEST metrics
+2025-07-10 15:23:26: Epoch: 012 Train Loss: 2.850, mae: 1.255, rse: 1.001, pcc: -0.019, kcc: -0.018
+2025-07-10 15:23:26: Epoch: 012 Test results, ap: mae: 0.611, rse: 1.145, pcc: 0.091, kcc: 0.078
+2025-07-10 15:23:26: Epoch: 012 New best TEST metrics
+2025-07-10 15:23:27: Epoch: 013 Train Loss: 2.867, mae: 1.253, rse: 1.002, pcc: -0.020, kcc: 0.046
+2025-07-10 15:23:27: Epoch: 013 Test results, ap: mae: 0.619, rse: 1.172, pcc: 0.012, kcc: -0.052
+2025-07-10 15:23:28: Epoch: 014 Train Loss: 2.851, mae: 1.256, rse: 1.000, pcc: 0.022, kcc: -0.002
+2025-07-10 15:23:28: Epoch: 014 Test results, ap: mae: 0.607, rse: 1.129, pcc: -0.051, kcc: -0.011
+2025-07-10 15:23:29: Epoch: 015 Train Loss: 2.847, mae: 1.257, rse: 1.000, pcc: 0.000, kcc: 0.008
+2025-07-10 15:23:29: Epoch: 015 Test results, ap: mae: 0.606, rse: 1.124, pcc: -0.048, kcc: -0.018
+2025-07-10 15:23:30: Epoch: 016 Train Loss: 2.857, mae: 1.251, rse: 1.004, pcc: 0.023, kcc: -0.010
+2025-07-10 15:23:30: Epoch: 016 Test results, ap: mae: 0.632, rse: 1.215, pcc: -0.052, kcc: -0.048
+2025-07-10 15:23:31: Epoch: 017 Train Loss: 2.853, mae: 1.252, rse: 1.003, pcc: 0.029, kcc: -0.011
+2025-07-10 15:23:31: Epoch: 017 Test results, ap: mae: 0.625, rse: 1.191, pcc: -0.045, kcc: -0.039
+2025-07-10 15:23:32: Epoch: 018 Train Loss: 2.847, mae: 1.257, rse: 1.000, pcc: -0.014, kcc: -0.014
+2025-07-10 15:23:32: Epoch: 018 Test results, ap: mae: 0.606, rse: 1.122, pcc: 0.107, kcc: 0.110
+2025-07-10 15:23:32: Epoch: 018 New best TEST metrics
+2025-07-10 15:23:33: Epoch: 019 Train Loss: 2.863, mae: 1.251, rse: 1.004, pcc: 0.034, kcc: 0.027
+2025-07-10 15:23:33: Epoch: 019 Test results, ap: mae: 0.631, rse: 1.212, pcc: -0.102, kcc: -0.107
+2025-07-10 15:23:34: Epoch: 020 Train Loss: 2.846, mae: 1.256, rse: 1.001, pcc: 0.031, kcc: 0.016
+2025-07-10 15:23:34: Epoch: 020 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.076, kcc: -0.117
+2025-07-10 15:23:34: Epoch: 021 Train Loss: 2.853, mae: 1.254, rse: 1.001, pcc: 0.034, kcc: 0.024
+2025-07-10 15:23:35: Epoch: 021 Test results, ap: mae: 0.614, rse: 1.155, pcc: -0.065, kcc: -0.115
+2025-07-10 15:23:35: Epoch: 022 Train Loss: 2.857, mae: 1.257, rse: 1.000, pcc: -0.031, kcc: -0.025
+2025-07-10 15:23:36: Epoch: 022 Test results, ap: mae: 0.606, rse: 1.123, pcc: 0.068, kcc: 0.102
+2025-07-10 15:23:36: Epoch: 023 Train Loss: 2.869, mae: 1.255, rse: 1.001, pcc: 0.041, kcc: 0.052
+2025-07-10 15:23:36: Epoch: 023 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.078, kcc: -0.120
+2025-07-10 15:23:37: Epoch: 024 Train Loss: 2.843, mae: 1.254, rse: 1.001, pcc: 0.030, kcc: 0.013
+2025-07-10 15:23:37: Epoch: 024 Test results, ap: mae: 0.615, rse: 1.158, pcc: -0.060, kcc: -0.111
+2025-07-10 15:23:38: Epoch: 025 Train Loss: 2.847, mae: 1.255, rse: 1.001, pcc: 0.061, kcc: 0.037
+2025-07-10 15:23:38: Epoch: 025 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.119, kcc: 0.101
+2025-07-10 15:23:39: Epoch: 026 Train Loss: 2.835, mae: 1.254, rse: 1.001, pcc: 0.041, kcc: 0.058
+2025-07-10 15:23:39: Epoch: 026 Test results, ap: mae: 0.613, rse: 1.152, pcc: -0.077, kcc: -0.114
+2025-07-10 15:23:39: Epoch: 027 Train Loss: 2.831, mae: 1.254, rse: 1.001, pcc: 0.042, kcc: 0.076
+2025-07-10 15:23:40: Epoch: 027 Test results, ap: mae: 0.615, rse: 1.158, pcc: -0.069, kcc: -0.120
+2025-07-10 15:23:40: Epoch: 028 Train Loss: 2.807, mae: 1.254, rse: 1.001, pcc: 0.044, kcc: 0.072
+2025-07-10 15:23:40: Epoch: 028 Test results, ap: mae: 0.613, rse: 1.153, pcc: -0.063, kcc: -0.127
+2025-07-10 15:23:41: Epoch: 029 Train Loss: 2.851, mae: 1.252, rse: 1.003, pcc: 0.043, kcc: 0.071
+2025-07-10 15:23:41: Epoch: 029 Test results, ap: mae: 0.624, rse: 1.187, pcc: -0.065, kcc: -0.119
+2025-07-10 15:23:42: Epoch: 030 Train Loss: 2.839, mae: 1.255, rse: 1.001, pcc: 0.049, kcc: 0.071
+2025-07-10 15:23:42: Epoch: 030 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.048, kcc: -0.114
+2025-07-10 15:23:43: Epoch: 031 Train Loss: 2.816, mae: 1.254, rse: 1.001, pcc: 0.048, kcc: 0.077
+2025-07-10 15:23:43: Epoch: 031 Test results, ap: mae: 0.613, rse: 1.152, pcc: -0.055, kcc: -0.114
+2025-07-10 15:23:44: Epoch: 032 Train Loss: 2.841, mae: 1.255, rse: 1.001, pcc: 0.049, kcc: 0.076
+2025-07-10 15:23:44: Epoch: 032 Test results, ap: mae: 0.612, rse: 1.148, pcc: -0.053, kcc: -0.112
+2025-07-10 15:23:44: Epoch: 033 Train Loss: 2.827, mae: 1.254, rse: 1.002, pcc: 0.047, kcc: 0.077
+2025-07-10 15:23:45: Epoch: 033 Test results, ap: mae: 0.615, rse: 1.160, pcc: -0.066, kcc: -0.108
+2025-07-10 15:23:45: Epoch: 034 Train Loss: 2.840, mae: 1.255, rse: 1.001, pcc: 0.050, kcc: 0.079
+2025-07-10 15:23:45: Epoch: 034 Test results, ap: mae: 0.613, rse: 1.150, pcc: -0.025, kcc: -0.100
+2025-07-10 15:23:46: Epoch: 035 Train Loss: 2.847, mae: 1.254, rse: 1.002, pcc: 0.051, kcc: 0.075
+2025-07-10 15:23:46: Epoch: 035 Test results, ap: mae: 0.615, rse: 1.161, pcc: -0.054, kcc: -0.108
+2025-07-10 15:23:47: Epoch: 036 Train Loss: 2.854, mae: 1.254, rse: 1.001, pcc: 0.051, kcc: 0.077
+2025-07-10 15:23:47: Epoch: 036 Test results, ap: mae: 0.614, rse: 1.155, pcc: -0.040, kcc: -0.111
+2025-07-10 15:23:48: Epoch: 037 Train Loss: 2.825, mae: 1.255, rse: 1.001, pcc: 0.052, kcc: 0.078
+2025-07-10 15:23:48: Epoch: 037 Test results, ap: mae: 0.612, rse: 1.149, pcc: -0.045, kcc: -0.108
+2025-07-10 15:23:49: Epoch: 038 Train Loss: 2.832, mae: 1.254, rse: 1.001, pcc: 0.053, kcc: 0.078
+2025-07-10 15:23:49: Epoch: 038 Test results, ap: mae: 0.613, rse: 1.153, pcc: -0.040, kcc: -0.106
+2025-07-10 15:23:50: Epoch: 039 Train Loss: 2.828, mae: 1.254, rse: 1.001, pcc: 0.055, kcc: 0.082
+2025-07-10 15:23:50: Epoch: 039 Test results, ap: mae: 0.613, rse: 1.153, pcc: -0.032, kcc: -0.077
+2025-07-10 15:23:50: Epoch: 040 Train Loss: 2.845, mae: 1.254, rse: 1.001, pcc: 0.058, kcc: 0.089
+2025-07-10 15:23:51: Epoch: 040 Test results, ap: mae: 0.615, rse: 1.157, pcc: -0.013, kcc: -0.018
+2025-07-10 15:23:51: Epoch: 041 Train Loss: 2.856, mae: 1.254, rse: 1.001, pcc: 0.059, kcc: 0.092
+2025-07-10 15:23:51: Epoch: 041 Test results, ap: mae: 0.614, rse: 1.154, pcc: -0.005, kcc: -0.009
+2025-07-10 15:23:52: Epoch: 042 Train Loss: 2.861, mae: 1.254, rse: 1.001, pcc: 0.060, kcc: 0.095
+2025-07-10 15:23:52: Epoch: 042 Test results, ap: mae: 0.614, rse: 1.156, pcc: -0.000, kcc: -0.007
+2025-07-10 15:23:53: Epoch: 043 Train Loss: 2.836, mae: 1.254, rse: 1.001, pcc: 0.062, kcc: 0.096
+2025-07-10 15:23:53: Epoch: 043 Test results, ap: mae: 0.613, rse: 1.150, pcc: 0.004, kcc: -0.003
+2025-07-10 15:23:54: Epoch: 044 Train Loss: 2.824, mae: 1.254, rse: 1.001, pcc: 0.063, kcc: 0.099
+2025-07-10 15:23:54: Epoch: 044 Test results, ap: mae: 0.614, rse: 1.157, pcc: 0.011, kcc: -0.005
+2025-07-10 15:23:55: Epoch: 045 Train Loss: 2.835, mae: 1.254, rse: 1.001, pcc: 0.064, kcc: 0.107
+2025-07-10 15:23:55: Epoch: 045 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.015, kcc: 0.002
+2025-07-10 15:23:55: Epoch: 046 Train Loss: 2.862, mae: 1.253, rse: 1.002, pcc: 0.069, kcc: 0.124
+2025-07-10 15:23:56: Epoch: 046 Test results, ap: mae: 0.617, rse: 1.167, pcc: 0.019, kcc: 0.010
+2025-07-10 15:23:56: Epoch: 047 Train Loss: 2.853, mae: 1.254, rse: 1.001, pcc: 0.070, kcc: 0.166
+2025-07-10 15:23:56: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:23:56: Epoch: 047 Test results, ap: mae: 0.614, rse: 1.157, pcc: 0.034, kcc: 0.007
+2025-07-10 15:23:57: Epoch: 048 Train Loss: 2.839, mae: 1.254, rse: 1.001, pcc: 0.076, kcc: 0.178
+2025-07-10 15:23:57: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:23:57: Epoch: 048 Test results, ap: mae: 0.615, rse: 1.159, pcc: 0.044, kcc: 0.028
+2025-07-10 15:23:58: Epoch: 049 Train Loss: 2.816, mae: 1.254, rse: 1.001, pcc: 0.081, kcc: 0.184
+2025-07-10 15:23:58: Epoch: 049 New best VALIDATION metrics
+2025-07-10 15:23:58: Epoch: 049 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.050, kcc: 0.035
+2025-07-10 15:23:58: Epoch: 050 Train Loss: 2.840, mae: 1.254, rse: 1.001, pcc: 0.087, kcc: 0.192
+2025-07-10 15:23:58: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:23:58: Epoch: 050 Test results, ap: mae: 0.615, rse: 1.158, pcc: 0.055, kcc: 0.042
+2025-07-10 15:23:58: used time 0.01h
+2025-07-10 15:23:58: Running Cross Validation 2
+2025-07-10 15:23:58: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:23:59: Epoch: 001 Train Loss: 2.900, mae: 1.253, rse: 1.000, pcc: -0.059, kcc: 0.038
+2025-07-10 15:23:59: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:23:59: Epoch: 001 Test results, ap: mae: 0.620, rse: 1.176, pcc: 0.108, kcc: 0.031
+2025-07-10 15:24:00: Epoch: 002 Train Loss: 2.927, mae: 1.256, rse: 1.000, pcc: 0.210, kcc: 0.125
+2025-07-10 15:24:00: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:24:00: Epoch: 002 Test results, ap: mae: 0.606, rse: 1.123, pcc: 0.014, kcc: -0.072
+2025-07-10 15:24:01: Epoch: 003 Train Loss: 2.957, mae: 1.255, rse: 1.021, pcc: -0.255, kcc: -0.132
+2025-07-10 15:24:01: Epoch: 003 Test results, ap: mae: 0.700, rse: 1.458, pcc: -0.221, kcc: -0.098
+2025-07-10 15:24:02: Epoch: 004 Train Loss: 2.886, mae: 1.252, rse: 1.013, pcc: -0.022, kcc: 0.043
+2025-07-10 15:24:02: Epoch: 004 Test results, ap: mae: 0.686, rse: 1.408, pcc: 0.007, kcc: 0.070
+2025-07-10 15:24:03: Epoch: 005 Train Loss: 2.896, mae: 1.262, rse: 1.003, pcc: 0.013, kcc: 0.104
+2025-07-10 15:24:03: Epoch: 005 Test results, ap: mae: 0.594, rse: 1.072, pcc: 0.255, kcc: 0.188
+2025-07-10 15:24:04: Epoch: 006 Train Loss: 2.846, mae: 1.260, rse: 1.002, pcc: 0.004, kcc: -0.034
+2025-07-10 15:24:04: Epoch: 006 Test results, ap: mae: 0.596, rse: 1.084, pcc: -0.065, kcc: 0.064
+2025-07-10 15:24:05: Epoch: 007 Train Loss: 2.899, mae: 1.253, rse: 1.000, pcc: 0.011, kcc: 0.206
+2025-07-10 15:24:05: Epoch: 007 Test results, ap: mae: 0.619, rse: 1.173, pcc: 0.204, kcc: 0.137
+2025-07-10 15:24:06: Epoch: 008 Train Loss: 2.880, mae: 1.261, rse: 1.002, pcc: -0.007, kcc: -0.129
+2025-07-10 15:24:06: Epoch: 008 Test results, ap: mae: 0.596, rse: 1.081, pcc: 0.002, kcc: 0.090
+2025-07-10 15:24:07: Epoch: 009 Train Loss: 2.892, mae: 1.255, rse: 1.000, pcc: 0.022, kcc: 0.095
+2025-07-10 15:24:08: Epoch: 009 Test results, ap: mae: 0.611, rse: 1.145, pcc: 0.154, kcc: 0.163
+2025-07-10 15:24:08: Epoch: 010 Train Loss: 2.917, mae: 1.255, rse: 1.000, pcc: 0.005, kcc: 0.041
+2025-07-10 15:24:09: Epoch: 010 Test results, ap: mae: 0.608, rse: 1.133, pcc: 0.103, kcc: -0.021
+2025-07-10 15:24:10: Epoch: 011 Train Loss: 2.875, mae: 1.252, rse: 1.001, pcc: 0.025, kcc: 0.077
+2025-07-10 15:24:10: Epoch: 011 Test results, ap: mae: 0.627, rse: 1.199, pcc: 0.057, kcc: -0.041
+2025-07-10 15:24:10: Epoch: 011 New best TEST metrics
+2025-07-10 15:24:11: Epoch: 012 Train Loss: 2.899, mae: 1.251, rse: 1.002, pcc: 0.045, kcc: 0.097
+2025-07-10 15:24:11: Epoch: 012 Test results, ap: mae: 0.636, rse: 1.228, pcc: 0.073, kcc: -0.027
+2025-07-10 15:24:12: Epoch: 013 Train Loss: 2.889, mae: 1.255, rse: 1.000, pcc: 0.028, kcc: 0.100
+2025-07-10 15:24:12: Epoch: 013 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.075, kcc: -0.039
+2025-07-10 15:24:12: Epoch: 013 New best TEST metrics
+2025-07-10 15:24:13: Epoch: 014 Train Loss: 2.912, mae: 1.253, rse: 1.000, pcc: 0.008, kcc: 0.082
+2025-07-10 15:24:13: Epoch: 014 Test results, ap: mae: 0.620, rse: 1.175, pcc: -0.018, kcc: -0.100
+2025-07-10 15:24:14: Epoch: 015 Train Loss: 2.879, mae: 1.256, rse: 1.000, pcc: 0.042, kcc: 0.088
+2025-07-10 15:24:14: Epoch: 015 Test results, ap: mae: 0.605, rse: 1.121, pcc: 0.085, kcc: -0.024
+2025-07-10 15:24:14: Epoch: 015 New best TEST metrics
+2025-07-10 15:24:15: Epoch: 016 Train Loss: 2.906, mae: 1.255, rse: 1.000, pcc: 0.025, kcc: 0.112
+2025-07-10 15:24:15: Epoch: 016 Test results, ap: mae: 0.610, rse: 1.138, pcc: 0.137, kcc: -0.018
+2025-07-10 15:24:15: Epoch: 016 New best TEST metrics
+2025-07-10 15:24:16: Epoch: 017 Train Loss: 2.891, mae: 1.255, rse: 1.000, pcc: 0.013, kcc: 0.108
+2025-07-10 15:24:16: Epoch: 017 Test results, ap: mae: 0.609, rse: 1.136, pcc: 0.127, kcc: 0.015
+2025-07-10 15:24:16: Epoch: 017 New best TEST metrics
+2025-07-10 15:24:17: Epoch: 018 Train Loss: 2.899, mae: 1.256, rse: 1.000, pcc: 0.012, kcc: 0.127
+2025-07-10 15:24:18: Epoch: 018 Test results, ap: mae: 0.605, rse: 1.120, pcc: 0.102, kcc: -0.024
+2025-07-10 15:24:18: Epoch: 019 Train Loss: 2.892, mae: 1.254, rse: 1.000, pcc: 0.011, kcc: 0.109
+2025-07-10 15:24:19: Epoch: 019 Test results, ap: mae: 0.614, rse: 1.157, pcc: 0.110, kcc: 0.004
+2025-07-10 15:24:19: Epoch: 020 Train Loss: 2.895, mae: 1.256, rse: 1.000, pcc: 0.011, kcc: 0.118
+2025-07-10 15:24:20: Epoch: 020 Test results, ap: mae: 0.605, rse: 1.120, pcc: 0.094, kcc: -0.037
+2025-07-10 15:24:21: Epoch: 021 Train Loss: 2.888, mae: 1.255, rse: 1.000, pcc: 0.012, kcc: 0.113
+2025-07-10 15:24:21: Epoch: 021 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.086, kcc: -0.038
+2025-07-10 15:24:22: Epoch: 022 Train Loss: 2.906, mae: 1.254, rse: 1.000, pcc: 0.014, kcc: 0.114
+2025-07-10 15:24:22: Epoch: 022 Test results, ap: mae: 0.612, rse: 1.147, pcc: 0.074, kcc: -0.035
+2025-07-10 15:24:23: Epoch: 023 Train Loss: 2.873, mae: 1.255, rse: 1.000, pcc: 0.016, kcc: 0.119
+2025-07-10 15:24:23: Epoch: 023 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.082, kcc: -0.034
+2025-07-10 15:24:24: Epoch: 024 Train Loss: 2.882, mae: 1.257, rse: 1.000, pcc: 0.018, kcc: 0.120
+2025-07-10 15:24:24: Epoch: 024 Test results, ap: mae: 0.603, rse: 1.110, pcc: 0.088, kcc: -0.035
+2025-07-10 15:24:25: Epoch: 025 Train Loss: 2.882, mae: 1.254, rse: 1.000, pcc: 0.029, kcc: 0.120
+2025-07-10 15:24:25: Epoch: 025 Test results, ap: mae: 0.615, rse: 1.159, pcc: 0.062, kcc: -0.044
+2025-07-10 15:24:26: Epoch: 026 Train Loss: 2.892, mae: 1.253, rse: 0.998, pcc: 0.068, kcc: 0.125
+2025-07-10 15:24:26: Epoch: 026 Test results, ap: mae: 0.615, rse: 1.159, pcc: 0.067, kcc: -0.038
+2025-07-10 15:24:27: Epoch: 027 Train Loss: 2.851, mae: 1.238, rse: 0.977, pcc: 0.230, kcc: 0.162
+2025-07-10 15:24:27: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:24:27: Epoch: 027 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.003, kcc: -0.066
+2025-07-10 15:24:28: Epoch: 028 Train Loss: 2.731, mae: 1.212, rse: 0.938, pcc: 0.263, kcc: 0.232
+2025-07-10 15:24:28: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:24:28: Epoch: 028 Test results, ap: mae: 0.632, rse: 1.215, pcc: -0.075, kcc: -0.122
+2025-07-10 15:24:29: Epoch: 029 Train Loss: 2.676, mae: 1.214, rse: 0.942, pcc: 0.297, kcc: 0.236
+2025-07-10 15:24:29: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:24:29: Epoch: 029 Test results, ap: mae: 0.731, rse: 1.588, pcc: -0.029, kcc: -0.085
+2025-07-10 15:24:30: Epoch: 030 Train Loss: 2.631, mae: 1.177, rse: 0.896, pcc: 0.328, kcc: 0.234
+2025-07-10 15:24:30: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:24:30: Epoch: 030 Test results, ap: mae: 0.604, rse: 1.102, pcc: -0.031, kcc: -0.080
+2025-07-10 15:24:31: Epoch: 031 Train Loss: 2.585, mae: 1.176, rse: 0.897, pcc: 0.322, kcc: 0.242
+2025-07-10 15:24:31: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:24:31: Epoch: 031 Test results, ap: mae: 0.661, rse: 1.326, pcc: -0.082, kcc: -0.122
+2025-07-10 15:24:32: Epoch: 032 Train Loss: 2.584, mae: 1.178, rse: 0.896, pcc: 0.329, kcc: 0.248
+2025-07-10 15:24:32: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:24:33: Epoch: 032 Test results, ap: mae: 0.688, rse: 1.430, pcc: -0.101, kcc: -0.127
+2025-07-10 15:24:33: Epoch: 033 Train Loss: 2.564, mae: 1.167, rse: 0.890, pcc: 0.334, kcc: 0.244
+2025-07-10 15:24:33: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:24:33: Epoch: 033 Test results, ap: mae: 0.626, rse: 1.191, pcc: -0.095, kcc: -0.126
+2025-07-10 15:24:34: Epoch: 034 Train Loss: 2.541, mae: 1.168, rse: 0.891, pcc: 0.335, kcc: 0.247
+2025-07-10 15:24:34: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:24:34: Epoch: 034 Test results, ap: mae: 0.646, rse: 1.273, pcc: -0.113, kcc: -0.134
+2025-07-10 15:24:35: Epoch: 035 Train Loss: 2.521, mae: 1.158, rse: 0.878, pcc: 0.352, kcc: 0.252
+2025-07-10 15:24:35: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:24:35: Epoch: 035 Test results, ap: mae: 0.633, rse: 1.209, pcc: -0.135, kcc: -0.137
+2025-07-10 15:24:36: Epoch: 036 Train Loss: 2.549, mae: 1.179, rse: 0.888, pcc: 0.345, kcc: 0.249
+2025-07-10 15:24:36: Epoch: 036 Test results, ap: mae: 0.702, rse: 1.495, pcc: -0.138, kcc: -0.136
+2025-07-10 15:24:37: Epoch: 037 Train Loss: 2.499, mae: 1.163, rse: 0.872, pcc: 0.362, kcc: 0.254
+2025-07-10 15:24:37: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:24:37: Epoch: 037 Test results, ap: mae: 0.699, rse: 1.478, pcc: -0.154, kcc: -0.142
+2025-07-10 15:24:38: Epoch: 038 Train Loss: 2.498, mae: 1.162, rse: 0.883, pcc: 0.356, kcc: 0.255
+2025-07-10 15:24:38: Epoch: 038 Test results, ap: mae: 0.624, rse: 1.164, pcc: -0.153, kcc: -0.142
+2025-07-10 15:24:39: Epoch: 039 Train Loss: 2.520, mae: 1.150, rse: 0.869, pcc: 0.366, kcc: 0.256
+2025-07-10 15:24:39: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:24:39: Epoch: 039 Test results, ap: mae: 0.629, rse: 1.184, pcc: -0.163, kcc: -0.143
+2025-07-10 15:24:40: Epoch: 040 Train Loss: 2.456, mae: 1.144, rse: 0.851, pcc: 0.387, kcc: 0.266
+2025-07-10 15:24:40: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:24:40: Epoch: 040 Test results, ap: mae: 0.671, rse: 1.376, pcc: -0.171, kcc: -0.143
+2025-07-10 15:24:41: Epoch: 041 Train Loss: 2.415, mae: 1.134, rse: 0.843, pcc: 0.399, kcc: 0.276
+2025-07-10 15:24:41: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:24:41: Epoch: 041 Test results, ap: mae: 0.681, rse: 1.408, pcc: -0.174, kcc: -0.127
+2025-07-10 15:24:42: Epoch: 042 Train Loss: 2.373, mae: 1.117, rse: 0.824, pcc: 0.420, kcc: 0.282
+2025-07-10 15:24:42: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:24:43: Epoch: 042 Test results, ap: mae: 0.663, rse: 1.340, pcc: -0.169, kcc: -0.126
+2025-07-10 15:24:43: Epoch: 043 Train Loss: 2.333, mae: 1.119, rse: 0.831, pcc: 0.413, kcc: 0.285
+2025-07-10 15:24:43: Epoch: 043 Test results, ap: mae: 0.691, rse: 1.445, pcc: -0.172, kcc: -0.122
+2025-07-10 15:24:44: Epoch: 044 Train Loss: 2.358, mae: 1.114, rse: 0.832, pcc: 0.410, kcc: 0.289
+2025-07-10 15:24:44: Epoch: 044 Test results, ap: mae: 0.636, rse: 1.229, pcc: -0.174, kcc: -0.124
+2025-07-10 15:24:45: Epoch: 045 Train Loss: 2.344, mae: 1.106, rse: 0.827, pcc: 0.421, kcc: 0.292
+2025-07-10 15:24:45: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:24:45: Epoch: 045 Test results, ap: mae: 0.627, rse: 1.178, pcc: -0.163, kcc: -0.120
+2025-07-10 15:24:46: Epoch: 046 Train Loss: 2.303, mae: 1.091, rse: 0.804, pcc: 0.447, kcc: 0.304
+2025-07-10 15:24:46: Epoch: 046 New best VALIDATION metrics
+2025-07-10 15:24:46: Epoch: 046 Test results, ap: mae: 0.638, rse: 1.231, pcc: -0.167, kcc: -0.111
+2025-07-10 15:24:47: Epoch: 047 Train Loss: 2.223, mae: 1.084, rse: 0.800, pcc: 0.452, kcc: 0.311
+2025-07-10 15:24:47: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:24:47: Epoch: 047 Test results, ap: mae: 0.669, rse: 1.359, pcc: -0.174, kcc: -0.103
+2025-07-10 15:24:48: Epoch: 048 Train Loss: 2.282, mae: 1.082, rse: 0.801, pcc: 0.452, kcc: 0.311
+2025-07-10 15:24:48: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:24:48: Epoch: 048 Test results, ap: mae: 0.640, rse: 1.247, pcc: -0.177, kcc: -0.092
+2025-07-10 15:24:49: Epoch: 049 Train Loss: 2.305, mae: 1.105, rse: 0.831, pcc: 0.413, kcc: 0.296
+2025-07-10 15:24:49: Epoch: 049 Test results, ap: mae: 0.637, rse: 1.226, pcc: -0.172, kcc: -0.094
+2025-07-10 15:24:50: Epoch: 050 Train Loss: 2.286, mae: 1.082, rse: 0.797, pcc: 0.454, kcc: 0.310
+2025-07-10 15:24:50: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:24:50: Epoch: 050 Test results, ap: mae: 0.647, rse: 1.269, pcc: -0.168, kcc: -0.093
+2025-07-10 15:24:50: used time 0.01h
+2025-07-10 15:24:50: Running Cross Validation 3
+2025-07-10 15:24:50: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:24:51: Epoch: 001 Train Loss: 2.829, mae: 1.292, rse: 1.001, pcc: -0.055, kcc: 0.006
+2025-07-10 15:24:51: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:24:51: Epoch: 001 Test results, ap: mae: 0.623, rse: 1.187, pcc: -0.358, kcc: -0.227
+2025-07-10 15:24:52: Epoch: 002 Train Loss: 2.818, mae: 1.323, rse: 1.013, pcc: 0.048, kcc: 0.054
+2025-07-10 15:24:52: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:24:52: Epoch: 002 Test results, ap: mae: 0.581, rse: 1.007, pcc: 0.210, kcc: 0.160
+2025-07-10 15:24:53: Epoch: 003 Train Loss: 2.794, mae: 1.292, rse: 1.001, pcc: -0.078, kcc: -0.153
+2025-07-10 15:24:53: Epoch: 003 Test results, ap: mae: 0.624, rse: 1.188, pcc: -0.757, kcc: -0.250
+2025-07-10 15:24:54: Epoch: 004 Train Loss: 2.792, mae: 1.304, rse: 1.003, pcc: 0.149, kcc: 0.150
+2025-07-10 15:24:54: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:24:54: Epoch: 004 Test results, ap: mae: 0.589, rse: 1.052, pcc: -0.062, kcc: 0.097
+2025-07-10 15:24:55: Epoch: 005 Train Loss: 2.828, mae: 1.300, rse: 1.001, pcc: 0.087, kcc: -0.029
+2025-07-10 15:24:56: Epoch: 005 Test results, ap: mae: 0.595, rse: 1.076, pcc: 0.211, kcc: 0.186
+2025-07-10 15:24:56: Epoch: 006 Train Loss: 2.815, mae: 1.292, rse: 1.001, pcc: 0.050, kcc: 0.056
+2025-07-10 15:24:57: Epoch: 006 Test results, ap: mae: 0.619, rse: 1.174, pcc: 0.121, kcc: -0.191
+2025-07-10 15:24:57: Epoch: 007 Train Loss: 2.799, mae: 1.292, rse: 1.001, pcc: 0.108, kcc: 0.098
+2025-07-10 15:24:58: Epoch: 007 Test results, ap: mae: 0.618, rse: 1.171, pcc: 0.616, kcc: -0.055
+2025-07-10 15:24:58: Epoch: 008 Train Loss: 2.816, mae: 1.292, rse: 1.001, pcc: 0.051, kcc: 0.070
+2025-07-10 15:24:59: Epoch: 008 Test results, ap: mae: 0.621, rse: 1.178, pcc: 0.419, kcc: -0.017
+2025-07-10 15:24:59: Epoch: 009 Train Loss: 2.799, mae: 1.299, rse: 1.001, pcc: 0.042, kcc: 0.046
+2025-07-10 15:24:59: Epoch: 009 Test results, ap: mae: 0.595, rse: 1.078, pcc: -0.193, kcc: 0.141
+2025-07-10 15:25:00: Epoch: 010 Train Loss: 2.821, mae: 1.295, rse: 1.000, pcc: -0.104, kcc: -0.056
+2025-07-10 15:25:01: Epoch: 010 Test results, ap: mae: 0.607, rse: 1.128, pcc: 0.057, kcc: 0.034
+2025-07-10 15:25:01: Epoch: 011 Train Loss: 2.804, mae: 1.294, rse: 1.000, pcc: 0.063, kcc: 0.059
+2025-07-10 15:25:01: Epoch: 011 Test results, ap: mae: 0.609, rse: 1.136, pcc: 0.870, kcc: -0.045
+2025-07-10 15:25:01: Epoch: 011 New best TEST metrics
+2025-07-10 15:25:02: Epoch: 012 Train Loss: 2.784, mae: 1.294, rse: 1.000, pcc: 0.038, kcc: -0.081
+2025-07-10 15:25:02: Epoch: 012 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.596, kcc: 0.068
+2025-07-10 15:25:03: Epoch: 013 Train Loss: 2.795, mae: 1.293, rse: 1.000, pcc: 0.092, kcc: 0.032
+2025-07-10 15:25:03: Epoch: 013 Test results, ap: mae: 0.612, rse: 1.149, pcc: 0.192, kcc: 0.012
+2025-07-10 15:25:04: Epoch: 014 Train Loss: 2.791, mae: 1.293, rse: 1.000, pcc: 0.099, kcc: 0.000
+2025-07-10 15:25:04: Epoch: 014 Test results, ap: mae: 0.612, rse: 1.148, pcc: -0.171, kcc: 0.080
+2025-07-10 15:25:05: Epoch: 015 Train Loss: 2.798, mae: 1.294, rse: 1.000, pcc: 0.025, kcc: 0.029
+2025-07-10 15:25:05: Epoch: 015 Test results, ap: mae: 0.608, rse: 1.131, pcc: -0.324, kcc: 0.186
+2025-07-10 15:25:06: Epoch: 016 Train Loss: 2.820, mae: 1.292, rse: 1.002, pcc: 0.070, kcc: 0.042
+2025-07-10 15:25:06: Epoch: 016 Test results, ap: mae: 0.629, rse: 1.204, pcc: 0.264, kcc: 0.001
+2025-07-10 15:25:07: Epoch: 017 Train Loss: 2.792, mae: 1.293, rse: 1.000, pcc: 0.104, kcc: 0.139
+2025-07-10 15:25:07: Epoch: 017 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.209, kcc: 0.108
+2025-07-10 15:25:08: Epoch: 018 Train Loss: 2.793, mae: 1.294, rse: 1.000, pcc: 0.100, kcc: 0.081
+2025-07-10 15:25:08: Epoch: 018 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.256, kcc: 0.103
+2025-07-10 15:25:09: Epoch: 019 Train Loss: 2.798, mae: 1.293, rse: 1.000, pcc: 0.012, kcc: -0.089
+2025-07-10 15:25:09: Epoch: 019 Test results, ap: mae: 0.614, rse: 1.155, pcc: -0.205, kcc: 0.089
+2025-07-10 15:25:10: Epoch: 020 Train Loss: 2.814, mae: 1.295, rse: 1.000, pcc: -0.105, kcc: -0.024
+2025-07-10 15:25:10: Epoch: 020 Test results, ap: mae: 0.606, rse: 1.124, pcc: 0.196, kcc: -0.038
+2025-07-10 15:25:11: Epoch: 021 Train Loss: 2.801, mae: 1.294, rse: 1.000, pcc: -0.104, kcc: 0.018
+2025-07-10 15:25:11: Epoch: 021 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.893, kcc: -0.059
+2025-07-10 15:25:12: Epoch: 022 Train Loss: 2.762, mae: 1.293, rse: 1.000, pcc: -0.110, kcc: -0.025
+2025-07-10 15:25:12: Epoch: 022 Test results, ap: mae: 0.612, rse: 1.148, pcc: -0.536, kcc: -0.088
+2025-07-10 15:25:13: Epoch: 023 Train Loss: 2.815, mae: 1.294, rse: 1.000, pcc: -0.110, kcc: -0.045
+2025-07-10 15:25:13: Epoch: 023 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.415, kcc: -0.018
+2025-07-10 15:25:14: Epoch: 024 Train Loss: 2.805, mae: 1.294, rse: 1.000, pcc: 0.098, kcc: -0.030
+2025-07-10 15:25:14: Epoch: 024 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.262, kcc: -0.020
+2025-07-10 15:25:15: Epoch: 025 Train Loss: 2.790, mae: 1.294, rse: 1.000, pcc: 0.094, kcc: 0.033
+2025-07-10 15:25:15: Epoch: 025 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.619, kcc: -0.024
+2025-07-10 15:25:16: Epoch: 026 Train Loss: 2.800, mae: 1.294, rse: 1.000, pcc: 0.082, kcc: 0.078
+2025-07-10 15:25:16: Epoch: 026 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.114, kcc: -0.126
+2025-07-10 15:25:17: Epoch: 027 Train Loss: 2.799, mae: 1.294, rse: 1.000, pcc: 0.072, kcc: 0.114
+2025-07-10 15:25:17: Epoch: 027 Test results, ap: mae: 0.609, rse: 1.135, pcc: nan, kcc: nan
+2025-07-10 15:25:18: Epoch: 028 Train Loss: 2.787, mae: 1.294, rse: 1.000, pcc: 0.085, kcc: 0.087
+2025-07-10 15:25:18: Epoch: 028 Test results, ap: mae: 0.609, rse: 1.137, pcc: 1.000, kcc: 0.048
+2025-07-10 15:25:18: Epoch: 028 New best TEST metrics
+2025-07-10 15:25:19: Epoch: 029 Train Loss: 2.814, mae: 1.294, rse: 1.000, pcc: -0.095, kcc: -0.086
+2025-07-10 15:25:19: Epoch: 029 Test results, ap: mae: 0.612, rse: 1.147, pcc: -0.505, kcc: -0.067
+2025-07-10 15:25:20: Epoch: 030 Train Loss: 2.778, mae: 1.294, rse: 1.000, pcc: 0.089, kcc: 0.022
+2025-07-10 15:25:20: Epoch: 030 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.554, kcc: -0.092
+2025-07-10 15:25:21: Epoch: 031 Train Loss: 2.798, mae: 1.294, rse: 1.000, pcc: -0.092, kcc: -0.097
+2025-07-10 15:25:21: Epoch: 031 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.180, kcc: 0.072
+2025-07-10 15:25:22: Epoch: 032 Train Loss: 2.798, mae: 1.294, rse: 1.000, pcc: -0.097, kcc: -0.137
+2025-07-10 15:25:22: Epoch: 032 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.512, kcc: -0.106
+2025-07-10 15:25:23: Epoch: 033 Train Loss: 2.796, mae: 1.294, rse: 1.000, pcc: -0.096, kcc: -0.142
+2025-07-10 15:25:23: Epoch: 033 Test results, ap: mae: 0.612, rse: 1.146, pcc: 0.367, kcc: 0.091
+2025-07-10 15:25:24: Epoch: 034 Train Loss: 2.785, mae: 1.294, rse: 1.000, pcc: 0.078, kcc: -0.010
+2025-07-10 15:25:24: Epoch: 034 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.267, kcc: -0.029
+2025-07-10 15:25:25: Epoch: 035 Train Loss: 2.798, mae: 1.294, rse: 1.000, pcc: 0.104, kcc: 0.024
+2025-07-10 15:25:25: Epoch: 035 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.427, kcc: 0.013
+2025-07-10 15:25:26: Epoch: 036 Train Loss: 2.791, mae: 1.294, rse: 1.000, pcc: 0.103, kcc: 0.080
+2025-07-10 15:25:26: Epoch: 036 Test results, ap: mae: 0.611, rse: 1.143, pcc: -1.000, kcc: 0.077
+2025-07-10 15:25:27: Epoch: 037 Train Loss: 2.777, mae: 1.294, rse: 1.000, pcc: 0.100, kcc: 0.044
+2025-07-10 15:25:27: Epoch: 037 Test results, ap: mae: 0.612, rse: 1.147, pcc: 0.800, kcc: 0.184
+2025-07-10 15:25:28: Epoch: 038 Train Loss: 2.808, mae: 1.294, rse: 1.000, pcc: 0.102, kcc: 0.077
+2025-07-10 15:25:29: Epoch: 038 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.590, kcc: -0.011
+2025-07-10 15:25:29: Epoch: 039 Train Loss: 2.789, mae: 1.294, rse: 1.000, pcc: 0.097, kcc: 0.092
+2025-07-10 15:25:30: Epoch: 039 Test results, ap: mae: 0.611, rse: 1.145, pcc: 0.484, kcc: 0.062
+2025-07-10 15:25:30: Epoch: 040 Train Loss: 2.803, mae: 1.294, rse: 1.000, pcc: 0.097, kcc: 0.123
+2025-07-10 15:25:31: Epoch: 040 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.447, kcc: 0.035
+2025-07-10 15:25:31: Epoch: 041 Train Loss: 2.779, mae: 1.294, rse: 1.000, pcc: 0.098, kcc: 0.095
+2025-07-10 15:25:32: Epoch: 041 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.091, kcc: 0.040
+2025-07-10 15:25:32: Epoch: 042 Train Loss: 2.775, mae: 1.294, rse: 1.000, pcc: 0.097, kcc: 0.060
+2025-07-10 15:25:33: Epoch: 042 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.120, kcc: 0.039
+2025-07-10 15:25:33: Epoch: 043 Train Loss: 2.805, mae: 1.294, rse: 1.000, pcc: 0.096, kcc: 0.077
+2025-07-10 15:25:34: Epoch: 043 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.166, kcc: 0.134
+2025-07-10 15:25:34: Epoch: 044 Train Loss: 2.791, mae: 1.294, rse: 1.000, pcc: 0.095, kcc: 0.094
+2025-07-10 15:25:35: Epoch: 044 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.195, kcc: 0.083
+2025-07-10 15:25:35: Epoch: 045 Train Loss: 2.798, mae: 1.294, rse: 1.000, pcc: 0.095, kcc: 0.085
+2025-07-10 15:25:36: Epoch: 045 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.111, kcc: 0.080
+2025-07-10 15:25:36: Epoch: 046 Train Loss: 2.789, mae: 1.294, rse: 1.000, pcc: 0.095, kcc: 0.100
+2025-07-10 15:25:37: Epoch: 046 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.204, kcc: 0.069
+2025-07-10 15:25:38: Epoch: 047 Train Loss: 2.794, mae: 1.294, rse: 1.000, pcc: 0.096, kcc: 0.118
+2025-07-10 15:25:38: Epoch: 047 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.020, kcc: 0.039
+2025-07-10 15:25:39: Epoch: 048 Train Loss: 2.799, mae: 1.294, rse: 1.000, pcc: 0.096, kcc: 0.074
+2025-07-10 15:25:39: Epoch: 048 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.132, kcc: 0.117
+2025-07-10 15:25:39: Epoch: 049 Train Loss: 2.788, mae: 1.294, rse: 1.000, pcc: 0.096, kcc: 0.093
+2025-07-10 15:25:40: Epoch: 049 Test results, ap: mae: 0.611, rse: 1.143, pcc: 0.102, kcc: 0.044
+2025-07-10 15:25:40: Epoch: 050 Train Loss: 2.750, mae: 1.294, rse: 1.000, pcc: 0.096, kcc: 0.095
+2025-07-10 15:25:41: Epoch: 050 Test results, ap: mae: 0.612, rse: 1.146, pcc: 0.059, kcc: 0.083
+2025-07-10 15:25:41: used time 0.01h
+2025-07-10 15:25:41: Running Cross Validation 4
+2025-07-10 15:25:41: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:25:41: Epoch: 001 Train Loss: 2.882, mae: 1.209, rse: 1.001, pcc: -0.033, kcc: 0.103
+2025-07-10 15:25:41: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:25:42: Epoch: 001 Test results, ap: mae: 0.628, rse: 1.201, pcc: 0.097, kcc: 0.025
+2025-07-10 15:25:43: Epoch: 002 Train Loss: 2.905, mae: 1.209, rse: 1.008, pcc: -0.003, kcc: 0.102
+2025-07-10 15:25:43: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:25:43: Epoch: 002 Test results, ap: mae: 0.669, rse: 1.351, pcc: 0.335, kcc: 0.161
+2025-07-10 15:25:44: Epoch: 003 Train Loss: 2.906, mae: 1.208, rse: 1.005, pcc: 0.005, kcc: 0.054
+2025-07-10 15:25:44: Epoch: 003 Test results, ap: mae: 0.658, rse: 1.311, pcc: 0.104, kcc: 0.082
+2025-07-10 15:25:45: Epoch: 004 Train Loss: 2.875, mae: 1.208, rse: 1.002, pcc: 0.030, kcc: 0.128
+2025-07-10 15:25:45: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:25:45: Epoch: 004 Test results, ap: mae: 0.643, rse: 1.253, pcc: 0.154, kcc: 0.070
+2025-07-10 15:25:46: Epoch: 005 Train Loss: 2.861, mae: 1.210, rse: 1.000, pcc: 0.084, kcc: 0.098
+2025-07-10 15:25:46: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:25:46: Epoch: 005 Test results, ap: mae: 0.621, rse: 1.180, pcc: 0.214, kcc: 0.083
+2025-07-10 15:25:47: Epoch: 006 Train Loss: 2.869, mae: 1.208, rse: 1.002, pcc: 0.210, kcc: 0.120
+2025-07-10 15:25:47: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:25:47: Epoch: 006 Test results, ap: mae: 0.642, rse: 1.249, pcc: 0.198, kcc: 0.054
+2025-07-10 15:25:48: Epoch: 007 Train Loss: 2.810, mae: 1.191, rse: 0.967, pcc: 0.187, kcc: 0.164
+2025-07-10 15:25:48: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:25:48: Epoch: 007 Test results, ap: mae: 0.723, rse: 1.547, pcc: -0.035, kcc: -0.127
+2025-07-10 15:25:49: Epoch: 008 Train Loss: 2.648, mae: 1.169, rse: 0.918, pcc: 0.289, kcc: 0.204
+2025-07-10 15:25:49: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:25:49: Epoch: 008 Test results, ap: mae: 0.685, rse: 1.361, pcc: -0.477, kcc: -0.323
+2025-07-10 15:25:50: Epoch: 009 Train Loss: 2.584, mae: 1.199, rse: 0.945, pcc: 0.302, kcc: 0.203
+2025-07-10 15:25:50: Epoch: 009 Test results, ap: mae: 0.746, rse: 1.588, pcc: 0.155, kcc: 0.134
+2025-07-10 15:25:51: Epoch: 010 Train Loss: 2.511, mae: 1.175, rse: 0.918, pcc: 0.299, kcc: 0.213
+2025-07-10 15:25:51: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:25:51: Epoch: 010 Test results, ap: mae: 0.755, rse: 1.592, pcc: -0.042, kcc: 0.000
+2025-07-10 15:25:52: Epoch: 011 Train Loss: 2.466, mae: 1.166, rse: 0.908, pcc: 0.307, kcc: 0.208
+2025-07-10 15:25:52: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:25:52: Epoch: 011 Test results, ap: mae: 0.653, rse: 1.281, pcc: -0.169, kcc: -0.226
+2025-07-10 15:25:52: Epoch: 011 New best TEST metrics
+2025-07-10 15:25:53: Epoch: 012 Train Loss: 2.463, mae: 1.165, rse: 0.897, pcc: 0.329, kcc: 0.227
+2025-07-10 15:25:53: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:25:53: Epoch: 012 Test results, ap: mae: 0.744, rse: 1.660, pcc: -0.181, kcc: -0.224
+2025-07-10 15:25:54: Epoch: 013 Train Loss: 2.443, mae: 1.159, rse: 0.888, pcc: 0.341, kcc: 0.246
+2025-07-10 15:25:54: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:25:54: Epoch: 013 Test results, ap: mae: 0.729, rse: 1.661, pcc: -0.209, kcc: -0.191
+2025-07-10 15:25:55: Epoch: 014 Train Loss: 2.416, mae: 1.153, rse: 0.894, pcc: 0.328, kcc: 0.242
+2025-07-10 15:25:55: Epoch: 014 Test results, ap: mae: 0.731, rse: 1.580, pcc: -0.134, kcc: -0.111
+2025-07-10 15:25:56: Epoch: 015 Train Loss: 2.410, mae: 1.159, rse: 0.901, pcc: 0.320, kcc: 0.231
+2025-07-10 15:25:56: Epoch: 015 Test results, ap: mae: 0.757, rse: 1.579, pcc: 0.031, kcc: 0.049
+2025-07-10 15:25:56: Epoch: 015 New best TEST metrics
+2025-07-10 15:25:57: Epoch: 016 Train Loss: 2.396, mae: 1.143, rse: 0.877, pcc: 0.357, kcc: 0.251
+2025-07-10 15:25:57: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:25:57: Epoch: 016 Test results, ap: mae: 0.826, rse: 1.911, pcc: -0.161, kcc: -0.058
+2025-07-10 15:25:58: Epoch: 017 Train Loss: 2.375, mae: 1.178, rse: 0.898, pcc: 0.345, kcc: 0.251
+2025-07-10 15:25:58: Epoch: 017 Test results, ap: mae: 0.913, rse: 2.236, pcc: -0.138, kcc: -0.040
+2025-07-10 15:25:59: Epoch: 018 Train Loss: 2.362, mae: 1.162, rse: 0.906, pcc: 0.332, kcc: 0.249
+2025-07-10 15:25:59: Epoch: 018 Test results, ap: mae: 0.747, rse: 1.605, pcc: -0.050, kcc: -0.038
+2025-07-10 15:26:00: Epoch: 019 Train Loss: 2.373, mae: 1.154, rse: 0.885, pcc: 0.349, kcc: 0.259
+2025-07-10 15:26:00: Epoch: 019 Test results, ap: mae: 0.863, rse: 1.976, pcc: 0.012, kcc: 0.036
+2025-07-10 15:26:01: Epoch: 020 Train Loss: 2.373, mae: 1.152, rse: 0.891, pcc: 0.331, kcc: 0.213
+2025-07-10 15:26:01: Epoch: 020 Test results, ap: mae: 0.705, rse: 1.449, pcc: 0.405, kcc: 0.176
+2025-07-10 15:26:01: Epoch: 020 New best TEST metrics
+2025-07-10 15:26:02: Epoch: 021 Train Loss: 2.338, mae: 1.122, rse: 0.859, pcc: 0.380, kcc: 0.262
+2025-07-10 15:26:02: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:26:02: Epoch: 021 Test results, ap: mae: 0.682, rse: 1.348, pcc: 0.093, kcc: 0.044
+2025-07-10 15:26:03: Epoch: 022 Train Loss: 2.268, mae: 1.114, rse: 0.832, pcc: 0.415, kcc: 0.282
+2025-07-10 15:26:03: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:26:03: Epoch: 022 Test results, ap: mae: 0.793, rse: 1.707, pcc: 0.127, kcc: 0.066
+2025-07-10 15:26:04: Epoch: 023 Train Loss: 2.214, mae: 1.125, rse: 0.830, pcc: 0.437, kcc: 0.294
+2025-07-10 15:26:04: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:26:05: Epoch: 023 Test results, ap: mae: 0.911, rse: 2.149, pcc: 0.095, kcc: 0.071
+2025-07-10 15:26:05: Epoch: 024 Train Loss: 2.187, mae: 1.098, rse: 0.824, pcc: 0.421, kcc: 0.291
+2025-07-10 15:26:05: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:26:06: Epoch: 024 Test results, ap: mae: 0.692, rse: 1.402, pcc: 0.053, kcc: -0.002
+2025-07-10 15:26:06: Epoch: 025 Train Loss: 2.134, mae: 1.085, rse: 0.813, pcc: 0.449, kcc: 0.305
+2025-07-10 15:26:06: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:26:06: Epoch: 025 Test results, ap: mae: 0.625, rse: 1.131, pcc: 0.205, kcc: 0.091
+2025-07-10 15:26:06: Epoch: 025 New best TEST metrics
+2025-07-10 15:26:07: Epoch: 026 Train Loss: 2.074, mae: 1.070, rse: 0.801, pcc: 0.449, kcc: 0.325
+2025-07-10 15:26:07: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:26:07: Epoch: 026 Test results, ap: mae: 0.752, rse: 1.594, pcc: 0.123, kcc: 0.059
+2025-07-10 15:26:08: Epoch: 027 Train Loss: 2.020, mae: 1.053, rse: 0.772, pcc: 0.490, kcc: 0.342
+2025-07-10 15:26:08: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:26:08: Epoch: 027 Test results, ap: mae: 0.723, rse: 1.411, pcc: 0.183, kcc: 0.096
+2025-07-10 15:26:09: Epoch: 028 Train Loss: 1.966, mae: 1.040, rse: 0.735, pcc: 0.520, kcc: 0.349
+2025-07-10 15:26:09: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:26:09: Epoch: 028 Test results, ap: mae: 0.766, rse: 1.578, pcc: 0.235, kcc: 0.122
+2025-07-10 15:26:10: Epoch: 029 Train Loss: 1.992, mae: 1.044, rse: 0.735, pcc: 0.519, kcc: 0.342
+2025-07-10 15:26:10: Epoch: 029 Test results, ap: mae: 0.791, rse: 1.658, pcc: 0.221, kcc: 0.095
+2025-07-10 15:26:11: Epoch: 030 Train Loss: 1.937, mae: 1.050, rse: 0.752, pcc: 0.498, kcc: 0.362
+2025-07-10 15:26:11: Epoch: 030 Test results, ap: mae: 0.740, rse: 1.469, pcc: 0.234, kcc: 0.115
+2025-07-10 15:26:12: Epoch: 031 Train Loss: 1.798, mae: 1.021, rse: 0.733, pcc: 0.527, kcc: 0.371
+2025-07-10 15:26:12: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:26:12: Epoch: 031 Test results, ap: mae: 0.796, rse: 1.779, pcc: 0.225, kcc: 0.116
+2025-07-10 15:26:13: Epoch: 032 Train Loss: 1.731, mae: 1.004, rse: 0.680, pcc: 0.568, kcc: 0.394
+2025-07-10 15:26:13: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:26:13: Epoch: 032 Test results, ap: mae: 0.836, rse: 1.820, pcc: 0.214, kcc: 0.112
+2025-07-10 15:26:14: Epoch: 033 Train Loss: 1.706, mae: 0.992, rse: 0.665, pcc: 0.580, kcc: 0.396
+2025-07-10 15:26:14: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:26:14: Epoch: 033 Test results, ap: mae: 0.839, rse: 1.854, pcc: 0.219, kcc: 0.124
+2025-07-10 15:26:15: Epoch: 034 Train Loss: 1.705, mae: 1.024, rse: 0.693, pcc: 0.560, kcc: 0.387
+2025-07-10 15:26:15: Epoch: 034 Test results, ap: mae: 0.888, rse: 2.042, pcc: 0.223, kcc: 0.121
+2025-07-10 15:26:16: Epoch: 035 Train Loss: 1.756, mae: 1.013, rse: 0.682, pcc: 0.565, kcc: 0.379
+2025-07-10 15:26:16: Epoch: 035 Test results, ap: mae: 0.759, rse: 1.537, pcc: 0.244, kcc: 0.132
+2025-07-10 15:26:17: Epoch: 036 Train Loss: 1.676, mae: 1.004, rse: 0.689, pcc: 0.560, kcc: 0.394
+2025-07-10 15:26:17: Epoch: 036 Test results, ap: mae: 0.821, rse: 1.841, pcc: 0.204, kcc: 0.094
+2025-07-10 15:26:17: Epoch: 037 Train Loss: 1.612, mae: 0.995, rse: 0.685, pcc: 0.565, kcc: 0.399
+2025-07-10 15:26:18: Epoch: 037 Test results, ap: mae: 0.887, rse: 2.128, pcc: 0.218, kcc: 0.111
+2025-07-10 15:26:18: Epoch: 038 Train Loss: 1.615, mae: 0.999, rse: 0.681, pcc: 0.569, kcc: 0.396
+2025-07-10 15:26:19: Epoch: 038 Test results, ap: mae: 0.798, rse: 1.742, pcc: 0.225, kcc: 0.118
+2025-07-10 15:26:19: Epoch: 039 Train Loss: 1.596, mae: 1.016, rse: 0.699, pcc: 0.567, kcc: 0.401
+2025-07-10 15:26:20: Epoch: 039 Test results, ap: mae: 0.885, rse: 2.161, pcc: 0.211, kcc: 0.115
+2025-07-10 15:26:20: Epoch: 040 Train Loss: 1.570, mae: 0.996, rse: 0.682, pcc: 0.565, kcc: 0.390
+2025-07-10 15:26:20: Epoch: 040 Test results, ap: mae: 0.814, rse: 1.823, pcc: 0.208, kcc: 0.122
+2025-07-10 15:26:21: Epoch: 041 Train Loss: 1.498, mae: 0.988, rse: 0.674, pcc: 0.575, kcc: 0.392
+2025-07-10 15:26:21: Epoch: 041 Test results, ap: mae: 0.802, rse: 1.822, pcc: 0.221, kcc: 0.124
+2025-07-10 15:26:22: Epoch: 042 Train Loss: 1.488, mae: 0.986, rse: 0.665, pcc: 0.581, kcc: 0.401
+2025-07-10 15:26:22: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:26:22: Epoch: 042 Test results, ap: mae: 0.827, rse: 1.900, pcc: 0.216, kcc: 0.126
+2025-07-10 15:26:23: Epoch: 043 Train Loss: 1.481, mae: 0.979, rse: 0.661, pcc: 0.587, kcc: 0.404
+2025-07-10 15:26:23: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:26:23: Epoch: 043 Test results, ap: mae: 0.836, rse: 1.950, pcc: 0.228, kcc: 0.139
+2025-07-10 15:26:24: Epoch: 044 Train Loss: 1.423, mae: 0.980, rse: 0.663, pcc: 0.583, kcc: 0.404
+2025-07-10 15:26:24: Epoch: 044 Test results, ap: mae: 0.798, rse: 1.788, pcc: 0.220, kcc: 0.129
+2025-07-10 15:26:25: Epoch: 045 Train Loss: 1.425, mae: 0.984, rse: 0.671, pcc: 0.575, kcc: 0.390
+2025-07-10 15:26:25: Epoch: 045 Test results, ap: mae: 0.758, rse: 1.621, pcc: 0.224, kcc: 0.116
+2025-07-10 15:26:26: Epoch: 046 Train Loss: 1.446, mae: 0.995, rse: 0.690, pcc: 0.576, kcc: 0.402
+2025-07-10 15:26:26: Epoch: 046 Test results, ap: mae: 0.881, rse: 2.176, pcc: 0.206, kcc: 0.119
+2025-07-10 15:26:27: Epoch: 047 Train Loss: 1.406, mae: 1.002, rse: 0.697, pcc: 0.576, kcc: 0.410
+2025-07-10 15:26:27: Epoch: 047 Test results, ap: mae: 0.926, rse: 2.344, pcc: 0.228, kcc: 0.137
+2025-07-10 15:26:28: Epoch: 048 Train Loss: 1.431, mae: 0.972, rse: 0.650, pcc: 0.595, kcc: 0.409
+2025-07-10 15:26:28: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:26:28: Epoch: 048 Test results, ap: mae: 0.836, rse: 1.950, pcc: 0.229, kcc: 0.146
+2025-07-10 15:26:29: Epoch: 049 Train Loss: 1.395, mae: 0.983, rse: 0.673, pcc: 0.578, kcc: 0.397
+2025-07-10 15:26:29: Epoch: 049 Test results, ap: mae: 0.812, rse: 1.845, pcc: 0.205, kcc: 0.094
+2025-07-10 15:26:29: Epoch: 050 Train Loss: 1.376, mae: 0.977, rse: 0.662, pcc: 0.588, kcc: 0.403
+2025-07-10 15:26:30: Epoch: 050 Test results, ap: mae: 0.806, rse: 1.849, pcc: 0.215, kcc: 0.096
+2025-07-10 15:26:30: used time 0.01h
+2025-07-10 15:26:30: Cross Validation Finished!
+2025-07-10 15:26:30: Best validation perform list
+[[1.09803295 0.84136748 0.40554556 0.27412945]
+ [1.25383472 1.00137281 0.08683053 0.19196014]
+ [1.08201396 0.79673654 0.45400098 0.31039989]
+ [1.30438006 1.00273216 0.14908585 0.14973344]
+ [0.97226816 0.65049469 0.59481841 0.40940866]]
+2025-07-10 15:26:30: mean: [1.142 0.859 0.338 0.267]
+2025-07-10 15:26:30: std: [0.121 0.133 0.191 0.091]
+2025-07-10 15:26:30: Best test perform list
+[[0.60527295 1.12090456 0.21566236 0.17247991]
+ [0.60560536 1.12214613 0.10715421 0.11036519]
+ [0.60907263 1.13607621 0.12702592 0.01475824]
+ [0.6092357  1.13670313 1.         0.04827779]
+ [0.62483877 1.13077927 0.20460171 0.09054663]]
+2025-07-10 15:26:30: mean: [0.611 1.129 0.331 0.087]
+2025-07-10 15:26:30: std: [0.007 0.007 0.337 0.054]
diff --git a/run-reg/gru-diff-256-mse-32-0.001-50/config.json b/run-reg/gru-diff-256-mse-32-0.001-50/config.json
new file mode 100644
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "gru", "side_enc": null, "channels": 256, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
\ No newline at end of file
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diff --git a/run-reg/gru-diff-256-mse-32-0.001-50/result.txt b/run-reg/gru-diff-256-mse-32-0.001-50/result.txt
new file mode 100644
index 0000000000000000000000000000000000000000..00997147f7d4cca332e105b89404f67626810d5a
--- /dev/null
+++ b/run-reg/gru-diff-256-mse-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
+Valid
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+Test
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diff --git a/run-reg/gru-diff-256-mse-32-0.001-50/training.log b/run-reg/gru-diff-256-mse-32-0.001-50/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..a65a0fc14c76f58354e010e008c595d56a506b67
--- /dev/null
+++ b/run-reg/gru-diff-256-mse-32-0.001-50/training.log
@@ -0,0 +1,720 @@
+2025-07-10 15:26:33: saving_dir: ./run-reg/gru-diff-256-mse-32-0.001-50
+2025-07-10 15:26:33: Loading Train Dataset
+2025-07-10 15:26:33: Loading Test Dataset
+2025-07-10 15:26:34: Running Cross Validation 0
+2025-07-10 15:26:34: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 15:26:35: Epoch: 001 Train Loss: 2.896, mae: 1.210, rse: 1.000, pcc: 0.006, kcc: 0.101
+2025-07-10 15:26:35: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:26:35: Epoch: 001 Test results, ap: mae: 0.613, rse: 1.151, pcc: -0.161, kcc: -0.170
+2025-07-10 15:26:35: Epoch: 002 Train Loss: 2.893, mae: 1.211, rse: 1.000, pcc: -0.001, kcc: 0.153
+2025-07-10 15:26:35: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:26:36: Epoch: 002 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.127, kcc: -0.196
+2025-07-10 15:26:36: Epoch: 003 Train Loss: 2.879, mae: 1.173, rse: 0.959, pcc: 0.297, kcc: 0.214
+2025-07-10 15:26:36: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:26:36: Epoch: 003 Test results, ap: mae: 0.645, rse: 1.269, pcc: -0.231, kcc: -0.191
+2025-07-10 15:26:37: Epoch: 004 Train Loss: 2.680, mae: 1.125, rse: 0.890, pcc: 0.334, kcc: 0.248
+2025-07-10 15:26:37: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:26:37: Epoch: 004 Test results, ap: mae: 0.717, rse: 1.586, pcc: -0.214, kcc: -0.115
+2025-07-10 15:26:38: Epoch: 005 Train Loss: 2.611, mae: 1.108, rse: 0.874, pcc: 0.369, kcc: 0.266
+2025-07-10 15:26:38: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:26:38: Epoch: 005 Test results, ap: mae: 0.633, rse: 1.213, pcc: -0.198, kcc: -0.101
+2025-07-10 15:26:39: Epoch: 006 Train Loss: 2.519, mae: 1.114, rse: 0.886, pcc: 0.375, kcc: 0.239
+2025-07-10 15:26:39: Epoch: 006 Test results, ap: mae: 0.620, rse: 1.163, pcc: -0.191, kcc: -0.093
+2025-07-10 15:26:40: Epoch: 007 Train Loss: 2.495, mae: 1.161, rse: 0.923, pcc: 0.299, kcc: 0.223
+2025-07-10 15:26:40: Epoch: 007 Test results, ap: mae: 0.662, rse: 1.370, pcc: -0.043, kcc: -0.113
+2025-07-10 15:26:41: Epoch: 008 Train Loss: 2.596, mae: 1.107, rse: 0.864, pcc: 0.369, kcc: 0.265
+2025-07-10 15:26:41: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:26:41: Epoch: 008 Test results, ap: mae: 0.615, rse: 1.153, pcc: -0.176, kcc: -0.094
+2025-07-10 15:26:42: Epoch: 009 Train Loss: 2.523, mae: 1.119, rse: 0.872, pcc: 0.365, kcc: 0.252
+2025-07-10 15:26:42: Epoch: 009 Test results, ap: mae: 0.605, rse: 1.118, pcc: -0.006, kcc: -0.074
+2025-07-10 15:26:42: Epoch: 010 Train Loss: 2.446, mae: 1.113, rse: 0.869, pcc: 0.365, kcc: 0.288
+2025-07-10 15:26:42: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:26:43: Epoch: 010 Test results, ap: mae: 0.596, rse: 1.081, pcc: 0.410, kcc: 0.292
+2025-07-10 15:26:43: Epoch: 011 Train Loss: 2.454, mae: 1.107, rse: 0.860, pcc: 0.378, kcc: 0.299
+2025-07-10 15:26:43: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:26:43: Epoch: 011 Test results, ap: mae: 0.620, rse: 1.173, pcc: -0.006, kcc: -0.073
+2025-07-10 15:26:43: Epoch: 011 New best TEST metrics
+2025-07-10 15:26:44: Epoch: 012 Train Loss: 2.364, mae: 1.081, rse: 0.832, pcc: 0.415, kcc: 0.321
+2025-07-10 15:26:44: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:26:44: Epoch: 012 Test results, ap: mae: 0.637, rse: 1.305, pcc: -0.012, kcc: -0.075
+2025-07-10 15:26:45: Epoch: 013 Train Loss: 2.273, mae: 1.034, rse: 0.758, pcc: 0.498, kcc: 0.359
+2025-07-10 15:26:45: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:26:45: Epoch: 013 Test results, ap: mae: 0.774, rse: 1.648, pcc: -0.184, kcc: -0.118
+2025-07-10 15:26:46: Epoch: 014 Train Loss: 2.048, mae: 0.920, rse: 0.609, pcc: 0.629, kcc: 0.453
+2025-07-10 15:26:46: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:26:46: Epoch: 014 Test results, ap: mae: 0.799, rse: 1.769, pcc: -0.053, kcc: -0.044
+2025-07-10 15:26:47: Epoch: 015 Train Loss: 1.629, mae: 0.879, rse: 0.566, pcc: 0.659, kcc: 0.485
+2025-07-10 15:26:47: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:26:47: Epoch: 015 Test results, ap: mae: 0.844, rse: 1.822, pcc: 0.053, kcc: 0.016
+2025-07-10 15:26:48: Epoch: 016 Train Loss: 1.392, mae: 0.806, rse: 0.484, pcc: 0.719, kcc: 0.531
+2025-07-10 15:26:48: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:26:48: Epoch: 016 Test results, ap: mae: 0.899, rse: 2.222, pcc: 0.003, kcc: 0.006
+2025-07-10 15:26:49: Epoch: 017 Train Loss: 1.226, mae: 0.747, rse: 0.402, pcc: 0.775, kcc: 0.564
+2025-07-10 15:26:49: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:26:49: Epoch: 017 Test results, ap: mae: 0.883, rse: 2.145, pcc: 0.212, kcc: 0.133
+2025-07-10 15:26:50: Epoch: 018 Train Loss: 1.059, mae: 0.706, rse: 0.367, pcc: 0.800, kcc: 0.602
+2025-07-10 15:26:50: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:26:50: Epoch: 018 Test results, ap: mae: 0.778, rse: 1.643, pcc: 0.184, kcc: 0.125
+2025-07-10 15:26:50: Epoch: 019 Train Loss: 0.999, mae: 0.630, rse: 0.287, pcc: 0.847, kcc: 0.644
+2025-07-10 15:26:50: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:26:51: Epoch: 019 Test results, ap: mae: 0.800, rse: 1.736, pcc: 0.183, kcc: 0.116
+2025-07-10 15:26:51: Epoch: 020 Train Loss: 0.779, mae: 0.561, rse: 0.250, pcc: 0.867, kcc: 0.674
+2025-07-10 15:26:51: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:26:51: Epoch: 020 Test results, ap: mae: 0.723, rse: 1.446, pcc: 0.254, kcc: 0.155
+2025-07-10 15:26:51: Epoch: 020 New best TEST metrics
+2025-07-10 15:26:52: Epoch: 021 Train Loss: 0.601, mae: 0.497, rse: 0.214, pcc: 0.887, kcc: 0.707
+2025-07-10 15:26:52: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:26:52: Epoch: 021 Test results, ap: mae: 0.693, rse: 1.335, pcc: 0.319, kcc: 0.193
+2025-07-10 15:26:52: Epoch: 021 New best TEST metrics
+2025-07-10 15:26:53: Epoch: 022 Train Loss: 0.524, mae: 0.487, rse: 0.209, pcc: 0.892, kcc: 0.711
+2025-07-10 15:26:53: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:26:53: Epoch: 022 Test results, ap: mae: 0.674, rse: 1.289, pcc: 0.363, kcc: 0.221
+2025-07-10 15:26:53: Epoch: 022 New best TEST metrics
+2025-07-10 15:26:54: Epoch: 023 Train Loss: 0.485, mae: 0.432, rse: 0.190, pcc: 0.900, kcc: 0.739
+2025-07-10 15:26:54: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:26:54: Epoch: 023 Test results, ap: mae: 0.731, rse: 1.524, pcc: 0.305, kcc: 0.190
+2025-07-10 15:26:55: Epoch: 024 Train Loss: 0.441, mae: 0.416, rse: 0.180, pcc: 0.906, kcc: 0.750
+2025-07-10 15:26:55: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:26:55: Epoch: 024 Test results, ap: mae: 0.728, rse: 1.526, pcc: 0.295, kcc: 0.181
+2025-07-10 15:26:56: Epoch: 025 Train Loss: 0.423, mae: 0.436, rse: 0.175, pcc: 0.909, kcc: 0.745
+2025-07-10 15:26:56: Epoch: 025 Test results, ap: mae: 0.738, rse: 1.512, pcc: 0.318, kcc: 0.185
+2025-07-10 15:26:57: Epoch: 026 Train Loss: 0.374, mae: 0.395, rse: 0.169, pcc: 0.913, kcc: 0.766
+2025-07-10 15:26:57: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:26:57: Epoch: 026 Test results, ap: mae: 0.735, rse: 1.567, pcc: 0.298, kcc: 0.185
+2025-07-10 15:26:57: Epoch: 027 Train Loss: 0.349, mae: 0.379, rse: 0.154, pcc: 0.921, kcc: 0.778
+2025-07-10 15:26:57: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:26:58: Epoch: 027 Test results, ap: mae: 0.749, rse: 1.622, pcc: 0.286, kcc: 0.177
+2025-07-10 15:26:58: Epoch: 028 Train Loss: 0.311, mae: 0.382, rse: 0.159, pcc: 0.917, kcc: 0.769
+2025-07-10 15:26:58: Epoch: 028 Test results, ap: mae: 0.786, rse: 1.818, pcc: 0.311, kcc: 0.205
+2025-07-10 15:26:59: Epoch: 029 Train Loss: 0.279, mae: 0.341, rse: 0.138, pcc: 0.929, kcc: 0.788
+2025-07-10 15:26:59: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:26:59: Epoch: 029 Test results, ap: mae: 0.781, rse: 1.793, pcc: 0.315, kcc: 0.198
+2025-07-10 15:27:00: Epoch: 030 Train Loss: 0.250, mae: 0.352, rse: 0.142, pcc: 0.927, kcc: 0.788
+2025-07-10 15:27:00: Epoch: 030 Test results, ap: mae: 0.806, rse: 1.908, pcc: 0.311, kcc: 0.198
+2025-07-10 15:27:01: Epoch: 031 Train Loss: 0.228, mae: 0.316, rse: 0.136, pcc: 0.930, kcc: 0.813
+2025-07-10 15:27:01: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:27:01: Epoch: 031 Test results, ap: mae: 0.795, rse: 1.830, pcc: 0.306, kcc: 0.198
+2025-07-10 15:27:02: Epoch: 032 Train Loss: 0.214, mae: 0.302, rse: 0.127, pcc: 0.934, kcc: 0.811
+2025-07-10 15:27:02: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:27:02: Epoch: 032 Test results, ap: mae: 0.815, rse: 1.960, pcc: 0.311, kcc: 0.192
+2025-07-10 15:27:03: Epoch: 033 Train Loss: 0.202, mae: 0.288, rse: 0.123, pcc: 0.937, kcc: 0.820
+2025-07-10 15:27:03: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:27:03: Epoch: 033 Test results, ap: mae: 0.830, rse: 2.000, pcc: 0.303, kcc: 0.193
+2025-07-10 15:27:03: Epoch: 034 Train Loss: 0.201, mae: 0.290, rse: 0.129, pcc: 0.935, kcc: 0.823
+2025-07-10 15:27:04: Epoch: 034 Test results, ap: mae: 0.842, rse: 2.106, pcc: 0.276, kcc: 0.178
+2025-07-10 15:27:04: Epoch: 035 Train Loss: 0.188, mae: 0.289, rse: 0.127, pcc: 0.934, kcc: 0.819
+2025-07-10 15:27:04: Epoch: 035 Test results, ap: mae: 0.830, rse: 2.040, pcc: 0.291, kcc: 0.186
+2025-07-10 15:27:05: Epoch: 036 Train Loss: 0.188, mae: 0.266, rse: 0.117, pcc: 0.941, kcc: 0.830
+2025-07-10 15:27:05: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:27:05: Epoch: 036 Test results, ap: mae: 0.836, rse: 2.018, pcc: 0.286, kcc: 0.187
+2025-07-10 15:27:06: Epoch: 037 Train Loss: 0.171, mae: 0.269, rse: 0.118, pcc: 0.940, kcc: 0.829
+2025-07-10 15:27:06: Epoch: 037 Test results, ap: mae: 0.860, rse: 2.182, pcc: 0.293, kcc: 0.188
+2025-07-10 15:27:07: Epoch: 038 Train Loss: 0.167, mae: 0.287, rse: 0.114, pcc: 0.941, kcc: 0.826
+2025-07-10 15:27:07: Epoch: 038 Test results, ap: mae: 0.831, rse: 2.031, pcc: 0.294, kcc: 0.186
+2025-07-10 15:27:08: Epoch: 039 Train Loss: 0.172, mae: 0.263, rse: 0.119, pcc: 0.940, kcc: 0.832
+2025-07-10 15:27:08: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:27:08: Epoch: 039 Test results, ap: mae: 0.851, rse: 2.158, pcc: 0.273, kcc: 0.179
+2025-07-10 15:27:08: Epoch: 040 Train Loss: 0.144, mae: 0.262, rse: 0.116, pcc: 0.941, kcc: 0.832
+2025-07-10 15:27:08: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:27:08: Epoch: 040 Test results, ap: mae: 0.852, rse: 2.213, pcc: 0.287, kcc: 0.192
+2025-07-10 15:27:09: Epoch: 041 Train Loss: 0.147, mae: 0.244, rse: 0.109, pcc: 0.944, kcc: 0.844
+2025-07-10 15:27:09: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:27:09: Epoch: 041 Test results, ap: mae: 0.825, rse: 2.084, pcc: 0.283, kcc: 0.189
+2025-07-10 15:27:10: Epoch: 042 Train Loss: 0.140, mae: 0.240, rse: 0.105, pcc: 0.946, kcc: 0.845
+2025-07-10 15:27:10: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:27:10: Epoch: 042 Test results, ap: mae: 0.837, rse: 2.128, pcc: 0.291, kcc: 0.191
+2025-07-10 15:27:11: Epoch: 043 Train Loss: 0.136, mae: 0.237, rse: 0.109, pcc: 0.945, kcc: 0.849
+2025-07-10 15:27:11: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:27:11: Epoch: 043 Test results, ap: mae: 0.846, rse: 2.209, pcc: 0.271, kcc: 0.182
+2025-07-10 15:27:12: Epoch: 044 Train Loss: 0.136, mae: 0.234, rse: 0.105, pcc: 0.946, kcc: 0.849
+2025-07-10 15:27:12: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:27:12: Epoch: 044 Test results, ap: mae: 0.837, rse: 2.158, pcc: 0.275, kcc: 0.186
+2025-07-10 15:27:12: Epoch: 045 Train Loss: 0.137, mae: 0.223, rse: 0.103, pcc: 0.948, kcc: 0.853
+2025-07-10 15:27:12: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:27:13: Epoch: 045 Test results, ap: mae: 0.837, rse: 2.161, pcc: 0.276, kcc: 0.188
+2025-07-10 15:27:13: Epoch: 046 Train Loss: 0.130, mae: 0.235, rse: 0.107, pcc: 0.946, kcc: 0.850
+2025-07-10 15:27:13: Epoch: 046 Test results, ap: mae: 0.840, rse: 2.168, pcc: 0.264, kcc: 0.179
+2025-07-10 15:27:14: Epoch: 047 Train Loss: 0.132, mae: 0.222, rse: 0.101, pcc: 0.949, kcc: 0.856
+2025-07-10 15:27:14: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:27:14: Epoch: 047 Test results, ap: mae: 0.833, rse: 2.132, pcc: 0.276, kcc: 0.185
+2025-07-10 15:27:15: Epoch: 048 Train Loss: 0.129, mae: 0.219, rse: 0.100, pcc: 0.949, kcc: 0.856
+2025-07-10 15:27:15: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:27:15: Epoch: 048 Test results, ap: mae: 0.820, rse: 2.088, pcc: 0.268, kcc: 0.178
+2025-07-10 15:27:15: Epoch: 049 Train Loss: 0.123, mae: 0.210, rse: 0.098, pcc: 0.950, kcc: 0.861
+2025-07-10 15:27:15: Epoch: 049 New best VALIDATION metrics
+2025-07-10 15:27:16: Epoch: 049 Test results, ap: mae: 0.831, rse: 2.125, pcc: 0.269, kcc: 0.180
+2025-07-10 15:27:16: Epoch: 050 Train Loss: 0.123, mae: 0.219, rse: 0.103, pcc: 0.948, kcc: 0.852
+2025-07-10 15:27:16: Epoch: 050 Test results, ap: mae: 0.845, rse: 2.206, pcc: 0.274, kcc: 0.186
+2025-07-10 15:27:16: used time 0.01h
+2025-07-10 15:27:16: Running Cross Validation 1
+2025-07-10 15:27:16: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 15:27:17: Epoch: 001 Train Loss: 2.815, mae: 1.253, rse: 1.002, pcc: -0.031, kcc: 0.061
+2025-07-10 15:27:17: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:27:17: Epoch: 001 Test results, ap: mae: 0.619, rse: 1.172, pcc: 0.039, kcc: 0.090
+2025-07-10 15:27:18: Epoch: 002 Train Loss: 2.821, mae: 1.252, rse: 1.000, pcc: 0.174, kcc: 0.086
+2025-07-10 15:27:18: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:27:18: Epoch: 002 Test results, ap: mae: 0.619, rse: 1.172, pcc: -0.205, kcc: -0.199
+2025-07-10 15:27:19: Epoch: 003 Train Loss: 2.588, mae: 1.137, rse: 0.838, pcc: 0.411, kcc: 0.274
+2025-07-10 15:27:19: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:27:19: Epoch: 003 Test results, ap: mae: 0.697, rse: 1.442, pcc: -0.068, kcc: -0.011
+2025-07-10 15:27:19: Epoch: 004 Train Loss: 2.030, mae: 0.964, rse: 0.622, pcc: 0.629, kcc: 0.415
+2025-07-10 15:27:19: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:27:20: Epoch: 004 Test results, ap: mae: 0.608, rse: 1.121, pcc: 0.129, kcc: 0.037
+2025-07-10 15:27:20: Epoch: 005 Train Loss: 1.766, mae: 0.941, rse: 0.599, pcc: 0.640, kcc: 0.420
+2025-07-10 15:27:20: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:27:20: Epoch: 005 Test results, ap: mae: 0.632, rse: 1.186, pcc: 0.116, kcc: 0.064
+2025-07-10 15:27:21: Epoch: 006 Train Loss: 1.527, mae: 0.895, rse: 0.545, pcc: 0.682, kcc: 0.455
+2025-07-10 15:27:21: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:27:21: Epoch: 006 Test results, ap: mae: 0.608, rse: 1.111, pcc: 0.168, kcc: 0.091
+2025-07-10 15:27:22: Epoch: 007 Train Loss: 1.428, mae: 0.840, rse: 0.481, pcc: 0.725, kcc: 0.495
+2025-07-10 15:27:22: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:27:22: Epoch: 007 Test results, ap: mae: 0.645, rse: 1.164, pcc: 0.283, kcc: 0.165
+2025-07-10 15:27:23: Epoch: 008 Train Loss: 1.281, mae: 0.824, rse: 0.476, pcc: 0.734, kcc: 0.536
+2025-07-10 15:27:23: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:27:23: Epoch: 008 Test results, ap: mae: 0.601, rse: 1.070, pcc: 0.282, kcc: 0.133
+2025-07-10 15:27:23: Epoch: 009 Train Loss: 1.163, mae: 0.812, rse: 0.455, pcc: 0.748, kcc: 0.540
+2025-07-10 15:27:23: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:27:24: Epoch: 009 Test results, ap: mae: 0.731, rse: 1.417, pcc: 0.294, kcc: 0.168
+2025-07-10 15:27:24: Epoch: 010 Train Loss: 1.007, mae: 0.734, rse: 0.388, pcc: 0.784, kcc: 0.577
+2025-07-10 15:27:24: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:27:25: Epoch: 010 Test results, ap: mae: 0.697, rse: 1.298, pcc: 0.314, kcc: 0.162
+2025-07-10 15:27:25: Epoch: 011 Train Loss: 0.869, mae: 0.680, rse: 0.343, pcc: 0.812, kcc: 0.625
+2025-07-10 15:27:25: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:27:25: Epoch: 011 Test results, ap: mae: 0.700, rse: 1.322, pcc: 0.325, kcc: 0.176
+2025-07-10 15:27:25: Epoch: 011 New best TEST metrics
+2025-07-10 15:27:26: Epoch: 012 Train Loss: 0.736, mae: 0.634, rse: 0.326, pcc: 0.826, kcc: 0.652
+2025-07-10 15:27:26: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:27:26: Epoch: 012 Test results, ap: mae: 0.723, rse: 1.425, pcc: 0.329, kcc: 0.174
+2025-07-10 15:27:26: Epoch: 013 Train Loss: 0.664, mae: 0.602, rse: 0.297, pcc: 0.847, kcc: 0.679
+2025-07-10 15:27:26: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:27:27: Epoch: 013 Test results, ap: mae: 0.745, rse: 1.523, pcc: 0.326, kcc: 0.165
+2025-07-10 15:27:27: Epoch: 014 Train Loss: 0.628, mae: 0.545, rse: 0.258, pcc: 0.862, kcc: 0.699
+2025-07-10 15:27:27: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:27:27: Epoch: 014 Test results, ap: mae: 0.712, rse: 1.376, pcc: 0.308, kcc: 0.152
+2025-07-10 15:27:28: Epoch: 015 Train Loss: 0.556, mae: 0.521, rse: 0.238, pcc: 0.874, kcc: 0.709
+2025-07-10 15:27:28: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:27:28: Epoch: 015 Test results, ap: mae: 0.715, rse: 1.401, pcc: 0.302, kcc: 0.134
+2025-07-10 15:27:28: Epoch: 016 Train Loss: 0.466, mae: 0.499, rse: 0.224, pcc: 0.882, kcc: 0.724
+2025-07-10 15:27:28: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:27:29: Epoch: 016 Test results, ap: mae: 0.730, rse: 1.459, pcc: 0.295, kcc: 0.127
+2025-07-10 15:27:29: Epoch: 017 Train Loss: 0.454, mae: 0.448, rse: 0.200, pcc: 0.895, kcc: 0.748
+2025-07-10 15:27:29: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:27:29: Epoch: 017 Test results, ap: mae: 0.748, rse: 1.544, pcc: 0.309, kcc: 0.141
+2025-07-10 15:27:30: Epoch: 018 Train Loss: 0.411, mae: 0.471, rse: 0.208, pcc: 0.892, kcc: 0.740
+2025-07-10 15:27:30: Epoch: 018 Test results, ap: mae: 0.724, rse: 1.453, pcc: 0.302, kcc: 0.134
+2025-07-10 15:27:31: Epoch: 019 Train Loss: 0.348, mae: 0.413, rse: 0.166, pcc: 0.916, kcc: 0.774
+2025-07-10 15:27:31: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:27:31: Epoch: 019 Test results, ap: mae: 0.785, rse: 1.686, pcc: 0.318, kcc: 0.166
+2025-07-10 15:27:32: Epoch: 020 Train Loss: 0.285, mae: 0.365, rse: 0.142, pcc: 0.927, kcc: 0.791
+2025-07-10 15:27:32: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:27:32: Epoch: 020 Test results, ap: mae: 0.745, rse: 1.558, pcc: 0.309, kcc: 0.167
+2025-07-10 15:27:33: Epoch: 021 Train Loss: 0.198, mae: 0.321, rse: 0.121, pcc: 0.938, kcc: 0.803
+2025-07-10 15:27:33: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:27:33: Epoch: 021 Test results, ap: mae: 0.783, rse: 1.703, pcc: 0.307, kcc: 0.155
+2025-07-10 15:27:34: Epoch: 022 Train Loss: 0.169, mae: 0.290, rse: 0.111, pcc: 0.943, kcc: 0.818
+2025-07-10 15:27:34: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:27:34: Epoch: 022 Test results, ap: mae: 0.756, rse: 1.593, pcc: 0.306, kcc: 0.156
+2025-07-10 15:27:35: Epoch: 023 Train Loss: 0.161, mae: 0.299, rse: 0.114, pcc: 0.942, kcc: 0.816
+2025-07-10 15:27:35: Epoch: 023 Test results, ap: mae: 0.787, rse: 1.745, pcc: 0.296, kcc: 0.143
+2025-07-10 15:27:36: Epoch: 024 Train Loss: 0.142, mae: 0.282, rse: 0.108, pcc: 0.946, kcc: 0.828
+2025-07-10 15:27:36: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:27:36: Epoch: 024 Test results, ap: mae: 0.795, rse: 1.763, pcc: 0.290, kcc: 0.151
+2025-07-10 15:27:37: Epoch: 025 Train Loss: 0.145, mae: 0.271, rse: 0.103, pcc: 0.947, kcc: 0.829
+2025-07-10 15:27:37: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:27:37: Epoch: 025 Test results, ap: mae: 0.747, rse: 1.583, pcc: 0.302, kcc: 0.152
+2025-07-10 15:27:38: Epoch: 026 Train Loss: 0.123, mae: 0.272, rse: 0.098, pcc: 0.950, kcc: 0.832
+2025-07-10 15:27:38: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:27:39: Epoch: 026 Test results, ap: mae: 0.745, rse: 1.607, pcc: 0.292, kcc: 0.147
+2025-07-10 15:27:39: Epoch: 027 Train Loss: 0.114, mae: 0.251, rse: 0.093, pcc: 0.953, kcc: 0.840
+2025-07-10 15:27:39: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:27:40: Epoch: 027 Test results, ap: mae: 0.756, rse: 1.635, pcc: 0.294, kcc: 0.148
+2025-07-10 15:27:40: Epoch: 028 Train Loss: 0.106, mae: 0.247, rse: 0.093, pcc: 0.953, kcc: 0.841
+2025-07-10 15:27:40: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:27:41: Epoch: 028 Test results, ap: mae: 0.751, rse: 1.600, pcc: 0.297, kcc: 0.149
+2025-07-10 15:27:41: Epoch: 029 Train Loss: 0.111, mae: 0.251, rse: 0.096, pcc: 0.951, kcc: 0.842
+2025-07-10 15:27:42: Epoch: 029 Test results, ap: mae: 0.733, rse: 1.552, pcc: 0.306, kcc: 0.165
+2025-07-10 15:27:43: Epoch: 030 Train Loss: 0.114, mae: 0.237, rse: 0.089, pcc: 0.955, kcc: 0.849
+2025-07-10 15:27:43: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:27:43: Epoch: 030 Test results, ap: mae: 0.763, rse: 1.671, pcc: 0.297, kcc: 0.156
+2025-07-10 15:27:43: Epoch: 031 Train Loss: 0.095, mae: 0.220, rse: 0.088, pcc: 0.955, kcc: 0.854
+2025-07-10 15:27:43: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:27:44: Epoch: 031 Test results, ap: mae: 0.755, rse: 1.656, pcc: 0.303, kcc: 0.157
+2025-07-10 15:27:45: Epoch: 032 Train Loss: 0.089, mae: 0.216, rse: 0.090, pcc: 0.954, kcc: 0.854
+2025-07-10 15:27:45: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:27:45: Epoch: 032 Test results, ap: mae: 0.759, rse: 1.665, pcc: 0.301, kcc: 0.152
+2025-07-10 15:27:46: Epoch: 033 Train Loss: 0.080, mae: 0.201, rse: 0.087, pcc: 0.956, kcc: 0.864
+2025-07-10 15:27:46: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:27:46: Epoch: 033 Test results, ap: mae: 0.754, rse: 1.648, pcc: 0.302, kcc: 0.151
+2025-07-10 15:27:47: Epoch: 034 Train Loss: 0.082, mae: 0.203, rse: 0.084, pcc: 0.957, kcc: 0.864
+2025-07-10 15:27:47: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:27:47: Epoch: 034 Test results, ap: mae: 0.760, rse: 1.655, pcc: 0.304, kcc: 0.162
+2025-07-10 15:27:48: Epoch: 035 Train Loss: 0.080, mae: 0.206, rse: 0.087, pcc: 0.955, kcc: 0.865
+2025-07-10 15:27:48: Epoch: 035 Test results, ap: mae: 0.735, rse: 1.555, pcc: 0.297, kcc: 0.156
+2025-07-10 15:27:49: Epoch: 036 Train Loss: 0.073, mae: 0.203, rse: 0.084, pcc: 0.957, kcc: 0.866
+2025-07-10 15:27:49: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:27:49: Epoch: 036 Test results, ap: mae: 0.730, rse: 1.540, pcc: 0.311, kcc: 0.161
+2025-07-10 15:27:50: Epoch: 037 Train Loss: 0.086, mae: 0.202, rse: 0.082, pcc: 0.958, kcc: 0.866
+2025-07-10 15:27:50: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:27:50: Epoch: 037 Test results, ap: mae: 0.757, rse: 1.673, pcc: 0.312, kcc: 0.167
+2025-07-10 15:27:51: Epoch: 038 Train Loss: 0.076, mae: 0.199, rse: 0.084, pcc: 0.957, kcc: 0.865
+2025-07-10 15:27:51: Epoch: 038 Test results, ap: mae: 0.753, rse: 1.656, pcc: 0.300, kcc: 0.154
+2025-07-10 15:27:52: Epoch: 039 Train Loss: 0.073, mae: 0.195, rse: 0.085, pcc: 0.957, kcc: 0.870
+2025-07-10 15:27:52: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:27:52: Epoch: 039 Test results, ap: mae: 0.739, rse: 1.606, pcc: 0.309, kcc: 0.157
+2025-07-10 15:27:53: Epoch: 040 Train Loss: 0.069, mae: 0.200, rse: 0.086, pcc: 0.956, kcc: 0.868
+2025-07-10 15:27:53: Epoch: 040 Test results, ap: mae: 0.758, rse: 1.695, pcc: 0.307, kcc: 0.163
+2025-07-10 15:27:54: Epoch: 041 Train Loss: 0.064, mae: 0.186, rse: 0.084, pcc: 0.957, kcc: 0.875
+2025-07-10 15:27:54: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:27:54: Epoch: 041 Test results, ap: mae: 0.750, rse: 1.673, pcc: 0.308, kcc: 0.163
+2025-07-10 15:27:55: Epoch: 042 Train Loss: 0.066, mae: 0.185, rse: 0.082, pcc: 0.958, kcc: 0.876
+2025-07-10 15:27:55: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:27:55: Epoch: 042 Test results, ap: mae: 0.747, rse: 1.653, pcc: 0.297, kcc: 0.149
+2025-07-10 15:27:56: Epoch: 043 Train Loss: 0.068, mae: 0.184, rse: 0.082, pcc: 0.958, kcc: 0.876
+2025-07-10 15:27:56: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:27:56: Epoch: 043 Test results, ap: mae: 0.734, rse: 1.609, pcc: 0.301, kcc: 0.155
+2025-07-10 15:27:57: Epoch: 044 Train Loss: 0.070, mae: 0.191, rse: 0.084, pcc: 0.957, kcc: 0.873
+2025-07-10 15:27:57: Epoch: 044 Test results, ap: mae: 0.749, rse: 1.658, pcc: 0.306, kcc: 0.159
+2025-07-10 15:27:58: Epoch: 045 Train Loss: 0.066, mae: 0.181, rse: 0.084, pcc: 0.957, kcc: 0.878
+2025-07-10 15:27:58: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:27:58: Epoch: 045 Test results, ap: mae: 0.738, rse: 1.616, pcc: 0.294, kcc: 0.147
+2025-07-10 15:27:59: Epoch: 046 Train Loss: 0.061, mae: 0.176, rse: 0.082, pcc: 0.959, kcc: 0.879
+2025-07-10 15:27:59: Epoch: 046 New best VALIDATION metrics
+2025-07-10 15:27:59: Epoch: 046 Test results, ap: mae: 0.754, rse: 1.668, pcc: 0.304, kcc: 0.157
+2025-07-10 15:28:00: Epoch: 047 Train Loss: 0.066, mae: 0.179, rse: 0.082, pcc: 0.958, kcc: 0.881
+2025-07-10 15:28:00: Epoch: 047 Test results, ap: mae: 0.740, rse: 1.624, pcc: 0.304, kcc: 0.159
+2025-07-10 15:28:01: Epoch: 048 Train Loss: 0.068, mae: 0.174, rse: 0.080, pcc: 0.959, kcc: 0.880
+2025-07-10 15:28:01: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:28:01: Epoch: 048 Test results, ap: mae: 0.744, rse: 1.646, pcc: 0.307, kcc: 0.159
+2025-07-10 15:28:02: Epoch: 049 Train Loss: 0.062, mae: 0.183, rse: 0.082, pcc: 0.958, kcc: 0.878
+2025-07-10 15:28:02: Epoch: 049 Test results, ap: mae: 0.742, rse: 1.632, pcc: 0.299, kcc: 0.157
+2025-07-10 15:28:03: Epoch: 050 Train Loss: 0.064, mae: 0.167, rse: 0.080, pcc: 0.959, kcc: 0.884
+2025-07-10 15:28:03: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:28:03: Epoch: 050 Test results, ap: mae: 0.756, rse: 1.692, pcc: 0.298, kcc: 0.155
+2025-07-10 15:28:03: used time 0.01h
+2025-07-10 15:28:03: Running Cross Validation 2
+2025-07-10 15:28:03: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:28:04: Epoch: 001 Train Loss: 2.879, mae: 1.255, rse: 1.000, pcc: 0.005, kcc: 0.171
+2025-07-10 15:28:04: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:28:04: Epoch: 001 Test results, ap: mae: 0.614, rse: 1.155, pcc: -0.282, kcc: -0.152
+2025-07-10 15:28:05: Epoch: 002 Train Loss: 2.865, mae: 1.174, rse: 0.877, pcc: 0.413, kcc: 0.271
+2025-07-10 15:28:05: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:28:05: Epoch: 002 Test results, ap: mae: 0.696, rse: 1.462, pcc: -0.208, kcc: -0.167
+2025-07-10 15:28:06: Epoch: 003 Train Loss: 2.818, mae: 1.160, rse: 0.867, pcc: 0.388, kcc: 0.235
+2025-07-10 15:28:06: Epoch: 003 Test results, ap: mae: 0.769, rse: 1.842, pcc: -0.215, kcc: -0.161
+2025-07-10 15:28:07: Epoch: 004 Train Loss: 2.731, mae: 1.183, rse: 0.897, pcc: 0.322, kcc: 0.240
+2025-07-10 15:28:07: Epoch: 004 Test results, ap: mae: 0.700, rse: 1.561, pcc: -0.255, kcc: -0.196
+2025-07-10 15:28:08: Epoch: 005 Train Loss: 2.822, mae: 1.192, rse: 0.904, pcc: 0.344, kcc: 0.219
+2025-07-10 15:28:08: Epoch: 005 Test results, ap: mae: 0.680, rse: 1.426, pcc: -0.200, kcc: -0.213
+2025-07-10 15:28:09: Epoch: 006 Train Loss: 2.629, mae: 1.181, rse: 0.882, pcc: 0.349, kcc: 0.223
+2025-07-10 15:28:09: Epoch: 006 Test results, ap: mae: 0.710, rse: 1.569, pcc: -0.187, kcc: -0.209
+2025-07-10 15:28:10: Epoch: 007 Train Loss: 2.646, mae: 1.171, rse: 0.866, pcc: 0.368, kcc: 0.224
+2025-07-10 15:28:10: Epoch: 007 Test results, ap: mae: 0.707, rse: 1.556, pcc: -0.186, kcc: -0.193
+2025-07-10 15:28:11: Epoch: 008 Train Loss: 2.601, mae: 1.177, rse: 0.869, pcc: 0.365, kcc: 0.187
+2025-07-10 15:28:11: Epoch: 008 Test results, ap: mae: 0.718, rse: 1.608, pcc: -0.187, kcc: -0.194
+2025-07-10 15:28:12: Epoch: 009 Train Loss: 2.641, mae: 1.176, rse: 0.868, pcc: 0.364, kcc: 0.184
+2025-07-10 15:28:12: Epoch: 009 Test results, ap: mae: 0.721, rse: 1.626, pcc: -0.186, kcc: -0.163
+2025-07-10 15:28:13: Epoch: 010 Train Loss: 2.611, mae: 1.174, rse: 0.877, pcc: 0.362, kcc: 0.207
+2025-07-10 15:28:13: Epoch: 010 Test results, ap: mae: 0.675, rse: 1.413, pcc: -0.187, kcc: -0.199
+2025-07-10 15:28:13: Epoch: 011 Train Loss: 2.632, mae: 1.195, rse: 0.901, pcc: 0.330, kcc: 0.144
+2025-07-10 15:28:14: Epoch: 011 Test results, ap: mae: 0.672, rse: 1.400, pcc: -0.186, kcc: -0.192
+2025-07-10 15:28:14: Epoch: 011 New best TEST metrics
+2025-07-10 15:28:14: Epoch: 012 Train Loss: 2.734, mae: 1.200, rse: 0.915, pcc: 0.304, kcc: 0.180
+2025-07-10 15:28:15: Epoch: 012 Test results, ap: mae: 0.674, rse: 1.409, pcc: -0.187, kcc: -0.199
+2025-07-10 15:28:15: Epoch: 013 Train Loss: 2.723, mae: 1.185, rse: 0.896, pcc: 0.329, kcc: 0.208
+2025-07-10 15:28:15: Epoch: 013 Test results, ap: mae: 0.682, rse: 1.447, pcc: -0.187, kcc: -0.190
+2025-07-10 15:28:16: Epoch: 014 Train Loss: 2.617, mae: 1.166, rse: 0.874, pcc: 0.365, kcc: 0.189
+2025-07-10 15:28:16: Epoch: 014 Test results, ap: mae: 0.704, rse: 1.540, pcc: -0.199, kcc: -0.189
+2025-07-10 15:28:17: Epoch: 015 Train Loss: 2.626, mae: 1.152, rse: 0.856, pcc: 0.382, kcc: 0.200
+2025-07-10 15:28:17: Epoch: 015 Test results, ap: mae: 0.742, rse: 1.714, pcc: -0.198, kcc: -0.182
+2025-07-10 15:28:18: Epoch: 016 Train Loss: 2.594, mae: 1.154, rse: 0.859, pcc: 0.385, kcc: 0.211
+2025-07-10 15:28:18: Epoch: 016 Test results, ap: mae: 0.732, rse: 1.672, pcc: -0.199, kcc: -0.181
+2025-07-10 15:28:19: Epoch: 017 Train Loss: 2.582, mae: 1.151, rse: 0.854, pcc: 0.385, kcc: 0.208
+2025-07-10 15:28:19: Epoch: 017 Test results, ap: mae: 0.749, rse: 1.748, pcc: -0.199, kcc: -0.144
+2025-07-10 15:28:20: Epoch: 018 Train Loss: 2.595, mae: 1.150, rse: 0.852, pcc: 0.385, kcc: 0.205
+2025-07-10 15:28:20: Epoch: 018 Test results, ap: mae: 0.768, rse: 1.836, pcc: -0.199, kcc: -0.124
+2025-07-10 15:28:21: Epoch: 019 Train Loss: 2.603, mae: 1.141, rse: 0.842, pcc: 0.399, kcc: 0.207
+2025-07-10 15:28:21: Epoch: 019 Test results, ap: mae: 0.769, rse: 1.844, pcc: -0.194, kcc: -0.108
+2025-07-10 15:28:22: Epoch: 020 Train Loss: 2.543, mae: 1.154, rse: 0.869, pcc: 0.373, kcc: 0.179
+2025-07-10 15:28:22: Epoch: 020 Test results, ap: mae: 0.748, rse: 1.747, pcc: -0.204, kcc: -0.114
+2025-07-10 15:28:23: Epoch: 021 Train Loss: 2.604, mae: 1.158, rse: 0.873, pcc: 0.363, kcc: 0.246
+2025-07-10 15:28:23: Epoch: 021 Test results, ap: mae: 0.763, rse: 1.816, pcc: -0.204, kcc: -0.117
+2025-07-10 15:28:24: Epoch: 022 Train Loss: 2.603, mae: 1.149, rse: 0.861, pcc: 0.382, kcc: 0.261
+2025-07-10 15:28:24: Epoch: 022 Test results, ap: mae: 0.768, rse: 1.835, pcc: -0.204, kcc: -0.127
+2025-07-10 15:28:24: Epoch: 023 Train Loss: 2.649, mae: 1.169, rse: 0.875, pcc: 0.357, kcc: 0.259
+2025-07-10 15:28:25: Epoch: 023 Test results, ap: mae: 0.742, rse: 1.709, pcc: -0.199, kcc: -0.116
+2025-07-10 15:28:25: Epoch: 024 Train Loss: 2.620, mae: 1.150, rse: 0.851, pcc: 0.390, kcc: 0.265
+2025-07-10 15:28:25: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:28:26: Epoch: 024 Test results, ap: mae: 0.748, rse: 1.737, pcc: -0.199, kcc: -0.116
+2025-07-10 15:28:26: Epoch: 025 Train Loss: 2.571, mae: 1.135, rse: 0.837, pcc: 0.415, kcc: 0.284
+2025-07-10 15:28:26: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:28:27: Epoch: 025 Test results, ap: mae: 0.741, rse: 1.705, pcc: -0.199, kcc: -0.130
+2025-07-10 15:28:27: Epoch: 026 Train Loss: 2.498, mae: 1.133, rse: 0.834, pcc: 0.415, kcc: 0.295
+2025-07-10 15:28:27: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:28:28: Epoch: 026 Test results, ap: mae: 0.763, rse: 1.820, pcc: -0.219, kcc: -0.128
+2025-07-10 15:28:28: Epoch: 027 Train Loss: 2.526, mae: 1.117, rse: 0.817, pcc: 0.437, kcc: 0.316
+2025-07-10 15:28:28: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:28:28: Epoch: 027 Test results, ap: mae: 0.778, rse: 1.886, pcc: -0.195, kcc: -0.103
+2025-07-10 15:28:29: Epoch: 028 Train Loss: 2.566, mae: 1.125, rse: 0.824, pcc: 0.431, kcc: 0.319
+2025-07-10 15:28:29: Epoch: 028 Test results, ap: mae: 0.756, rse: 1.776, pcc: -0.198, kcc: -0.088
+2025-07-10 15:28:30: Epoch: 029 Train Loss: 2.625, mae: 1.153, rse: 0.855, pcc: 0.418, kcc: 0.310
+2025-07-10 15:28:30: Epoch: 029 Test results, ap: mae: 0.717, rse: 1.589, pcc: -0.198, kcc: -0.059
+2025-07-10 15:28:31: Epoch: 030 Train Loss: 2.550, mae: 1.131, rse: 0.826, pcc: 0.424, kcc: 0.329
+2025-07-10 15:28:31: Epoch: 030 Test results, ap: mae: 0.760, rse: 1.793, pcc: -0.198, kcc: -0.070
+2025-07-10 15:28:32: Epoch: 031 Train Loss: 2.515, mae: 1.134, rse: 0.831, pcc: 0.428, kcc: 0.334
+2025-07-10 15:28:32: Epoch: 031 Test results, ap: mae: 0.735, rse: 1.678, pcc: -0.196, kcc: -0.039
+2025-07-10 15:28:33: Epoch: 032 Train Loss: 2.495, mae: 1.126, rse: 0.810, pcc: 0.467, kcc: 0.342
+2025-07-10 15:28:33: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:28:33: Epoch: 032 Test results, ap: mae: 0.694, rse: 1.505, pcc: -0.180, kcc: -0.036
+2025-07-10 15:28:33: Epoch: 032 New best TEST metrics
+2025-07-10 15:28:34: Epoch: 033 Train Loss: 2.383, mae: 1.152, rse: 0.846, pcc: 0.400, kcc: 0.358
+2025-07-10 15:28:34: Epoch: 033 Test results, ap: mae: 0.789, rse: 1.958, pcc: -0.188, kcc: -0.055
+2025-07-10 15:28:35: Epoch: 034 Train Loss: 2.442, mae: 1.165, rse: 0.834, pcc: 0.438, kcc: 0.302
+2025-07-10 15:28:35: Epoch: 034 Test results, ap: mae: 0.674, rse: 1.305, pcc: -0.035, kcc: 0.005
+2025-07-10 15:28:35: Epoch: 034 New best TEST metrics
+2025-07-10 15:28:36: Epoch: 035 Train Loss: 2.343, mae: 1.068, rse: 0.740, pcc: 0.517, kcc: 0.387
+2025-07-10 15:28:36: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:28:36: Epoch: 035 Test results, ap: mae: 0.745, rse: 1.745, pcc: -0.178, kcc: -0.031
+2025-07-10 15:28:37: Epoch: 036 Train Loss: 2.270, mae: 1.045, rse: 0.711, pcc: 0.543, kcc: 0.379
+2025-07-10 15:28:37: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:28:37: Epoch: 036 Test results, ap: mae: 0.750, rse: 1.700, pcc: -0.144, kcc: 0.001
+2025-07-10 15:28:38: Epoch: 037 Train Loss: 2.166, mae: 1.050, rse: 0.715, pcc: 0.536, kcc: 0.398
+2025-07-10 15:28:38: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:28:38: Epoch: 037 Test results, ap: mae: 0.789, rse: 1.954, pcc: -0.187, kcc: -0.070
+2025-07-10 15:28:39: Epoch: 038 Train Loss: 2.135, mae: 1.058, rse: 0.719, pcc: 0.533, kcc: 0.366
+2025-07-10 15:28:39: Epoch: 038 Test results, ap: mae: 0.812, rse: 2.007, pcc: -0.093, kcc: -0.000
+2025-07-10 15:28:40: Epoch: 039 Train Loss: 2.134, mae: 1.011, rse: 0.677, pcc: 0.573, kcc: 0.411
+2025-07-10 15:28:40: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:28:40: Epoch: 039 Test results, ap: mae: 0.792, rse: 1.754, pcc: -0.109, kcc: -0.058
+2025-07-10 15:28:41: Epoch: 040 Train Loss: 2.089, mae: 1.003, rse: 0.662, pcc: 0.585, kcc: 0.411
+2025-07-10 15:28:41: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:28:41: Epoch: 040 Test results, ap: mae: 0.769, rse: 1.677, pcc: -0.098, kcc: -0.058
+2025-07-10 15:28:41: Epoch: 041 Train Loss: 2.058, mae: 1.000, rse: 0.658, pcc: 0.588, kcc: 0.410
+2025-07-10 15:28:41: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:28:42: Epoch: 041 Test results, ap: mae: 0.792, rse: 1.751, pcc: -0.092, kcc: -0.053
+2025-07-10 15:28:42: Epoch: 042 Train Loss: 2.042, mae: 1.003, rse: 0.656, pcc: 0.591, kcc: 0.409
+2025-07-10 15:28:42: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:28:43: Epoch: 042 Test results, ap: mae: 0.779, rse: 1.694, pcc: -0.078, kcc: -0.059
+2025-07-10 15:28:43: Epoch: 043 Train Loss: 2.039, mae: 0.997, rse: 0.649, pcc: 0.596, kcc: 0.411
+2025-07-10 15:28:43: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:28:44: Epoch: 043 Test results, ap: mae: 0.789, rse: 1.723, pcc: -0.073, kcc: -0.061
+2025-07-10 15:28:44: Epoch: 044 Train Loss: 2.005, mae: 0.994, rse: 0.646, pcc: 0.597, kcc: 0.410
+2025-07-10 15:28:44: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:28:45: Epoch: 044 Test results, ap: mae: 0.800, rse: 1.777, pcc: -0.087, kcc: -0.045
+2025-07-10 15:28:45: Epoch: 045 Train Loss: 1.998, mae: 0.990, rse: 0.641, pcc: 0.601, kcc: 0.413
+2025-07-10 15:28:45: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:28:45: Epoch: 045 Test results, ap: mae: 0.790, rse: 1.739, pcc: -0.084, kcc: -0.039
+2025-07-10 15:28:46: Epoch: 046 Train Loss: 1.997, mae: 0.989, rse: 0.639, pcc: 0.603, kcc: 0.410
+2025-07-10 15:28:46: Epoch: 046 New best VALIDATION metrics
+2025-07-10 15:28:46: Epoch: 046 Test results, ap: mae: 0.793, rse: 1.733, pcc: -0.073, kcc: -0.044
+2025-07-10 15:28:47: Epoch: 047 Train Loss: 1.977, mae: 0.987, rse: 0.638, pcc: 0.602, kcc: 0.415
+2025-07-10 15:28:47: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:28:48: Epoch: 047 Test results, ap: mae: 0.814, rse: 1.861, pcc: -0.103, kcc: -0.021
+2025-07-10 15:28:48: Epoch: 048 Train Loss: 1.961, mae: 0.988, rse: 0.641, pcc: 0.602, kcc: 0.413
+2025-07-10 15:28:48: Epoch: 048 Test results, ap: mae: 0.781, rse: 1.690, pcc: -0.075, kcc: -0.048
+2025-07-10 15:28:49: Epoch: 049 Train Loss: 1.949, mae: 0.987, rse: 0.640, pcc: 0.601, kcc: 0.412
+2025-07-10 15:28:49: Epoch: 049 Test results, ap: mae: 0.807, rse: 1.805, pcc: -0.094, kcc: -0.044
+2025-07-10 15:28:50: Epoch: 050 Train Loss: 1.929, mae: 0.979, rse: 0.633, pcc: 0.608, kcc: 0.411
+2025-07-10 15:28:50: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:28:50: Epoch: 050 Test results, ap: mae: 0.822, rse: 1.825, pcc: -0.072, kcc: -0.059
+2025-07-10 15:28:50: used time 0.01h
+2025-07-10 15:28:50: Running Cross Validation 3
+2025-07-10 15:28:50: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:28:51: Epoch: 001 Train Loss: 2.821, mae: 1.294, rse: 1.000, pcc: 0.104, kcc: 0.007
+2025-07-10 15:28:51: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:28:51: Epoch: 001 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.075, kcc: -0.162
+2025-07-10 15:28:52: Epoch: 002 Train Loss: 2.543, mae: 1.235, rse: 0.889, pcc: 0.379, kcc: 0.249
+2025-07-10 15:28:52: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:28:52: Epoch: 002 Test results, ap: mae: 0.593, rse: 1.037, pcc: 0.139, kcc: 0.074
+2025-07-10 15:28:53: Epoch: 003 Train Loss: 2.143, mae: 1.114, rse: 0.731, pcc: 0.529, kcc: 0.338
+2025-07-10 15:28:53: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:28:53: Epoch: 003 Test results, ap: mae: 0.961, rse: 2.528, pcc: 0.041, kcc: 0.017
+2025-07-10 15:28:54: Epoch: 004 Train Loss: 1.821, mae: 0.960, rse: 0.593, pcc: 0.638, kcc: 0.415
+2025-07-10 15:28:54: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:28:54: Epoch: 004 Test results, ap: mae: 0.702, rse: 1.457, pcc: -0.031, kcc: -0.023
+2025-07-10 15:28:55: Epoch: 005 Train Loss: 1.649, mae: 0.979, rse: 0.573, pcc: 0.656, kcc: 0.423
+2025-07-10 15:28:55: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:28:55: Epoch: 005 Test results, ap: mae: 0.662, rse: 1.333, pcc: -0.003, kcc: 0.059
+2025-07-10 15:28:56: Epoch: 006 Train Loss: 1.494, mae: 0.900, rse: 0.520, pcc: 0.704, kcc: 0.476
+2025-07-10 15:28:56: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:28:56: Epoch: 006 Test results, ap: mae: 0.806, rse: 1.720, pcc: 0.073, kcc: 0.074
+2025-07-10 15:28:57: Epoch: 007 Train Loss: 1.376, mae: 0.905, rse: 0.503, pcc: 0.710, kcc: 0.492
+2025-07-10 15:28:57: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:28:57: Epoch: 007 Test results, ap: mae: 0.666, rse: 1.267, pcc: 0.117, kcc: 0.092
+2025-07-10 15:28:58: Epoch: 008 Train Loss: 1.342, mae: 0.899, rse: 0.503, pcc: 0.717, kcc: 0.494
+2025-07-10 15:28:58: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:28:58: Epoch: 008 Test results, ap: mae: 0.715, rse: 1.477, pcc: 0.066, kcc: 0.041
+2025-07-10 15:28:59: Epoch: 009 Train Loss: 1.199, mae: 0.877, rse: 0.488, pcc: 0.724, kcc: 0.508
+2025-07-10 15:28:59: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:28:59: Epoch: 009 Test results, ap: mae: 0.776, rse: 1.645, pcc: 0.058, kcc: 0.058
+2025-07-10 15:29:00: Epoch: 010 Train Loss: 1.119, mae: 0.777, rse: 0.417, pcc: 0.765, kcc: 0.557
+2025-07-10 15:29:00: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:29:00: Epoch: 010 Test results, ap: mae: 0.989, rse: 2.452, pcc: 0.010, kcc: 0.006
+2025-07-10 15:29:01: Epoch: 011 Train Loss: 0.925, mae: 0.710, rse: 0.376, pcc: 0.790, kcc: 0.600
+2025-07-10 15:29:01: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:29:01: Epoch: 011 Test results, ap: mae: 1.056, rse: 2.877, pcc: 0.045, kcc: 0.033
+2025-07-10 15:29:01: Epoch: 011 New best TEST metrics
+2025-07-10 15:29:02: Epoch: 012 Train Loss: 0.889, mae: 0.696, rse: 0.357, pcc: 0.803, kcc: 0.618
+2025-07-10 15:29:02: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:29:02: Epoch: 012 Test results, ap: mae: 0.901, rse: 1.980, pcc: 0.157, kcc: 0.092
+2025-07-10 15:29:02: Epoch: 012 New best TEST metrics
+2025-07-10 15:29:03: Epoch: 013 Train Loss: 0.773, mae: 0.733, rse: 0.376, pcc: 0.801, kcc: 0.627
+2025-07-10 15:29:03: Epoch: 013 Test results, ap: mae: 0.899, rse: 2.042, pcc: 0.172, kcc: 0.106
+2025-07-10 15:29:04: Epoch: 014 Train Loss: 0.684, mae: 0.640, rse: 0.330, pcc: 0.820, kcc: 0.657
+2025-07-10 15:29:04: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:29:04: Epoch: 014 Test results, ap: mae: 0.839, rse: 1.784, pcc: 0.211, kcc: 0.123
+2025-07-10 15:29:04: Epoch: 014 New best TEST metrics
+2025-07-10 15:29:05: Epoch: 015 Train Loss: 0.626, mae: 0.634, rse: 0.330, pcc: 0.820, kcc: 0.653
+2025-07-10 15:29:05: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:29:05: Epoch: 015 Test results, ap: mae: 0.957, rse: 2.444, pcc: 0.120, kcc: 0.076
+2025-07-10 15:29:06: Epoch: 016 Train Loss: 0.576, mae: 0.601, rse: 0.310, pcc: 0.835, kcc: 0.681
+2025-07-10 15:29:06: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:29:06: Epoch: 016 Test results, ap: mae: 0.763, rse: 1.543, pcc: 0.204, kcc: 0.144
+2025-07-10 15:29:06: Epoch: 016 New best TEST metrics
+2025-07-10 15:29:07: Epoch: 017 Train Loss: 0.521, mae: 0.526, rse: 0.274, pcc: 0.854, kcc: 0.712
+2025-07-10 15:29:07: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:29:07: Epoch: 017 Test results, ap: mae: 1.036, rse: 2.830, pcc: 0.127, kcc: 0.099
+2025-07-10 15:29:08: Epoch: 018 Train Loss: 0.459, mae: 0.571, rse: 0.277, pcc: 0.856, kcc: 0.705
+2025-07-10 15:29:08: Epoch: 018 Test results, ap: mae: 1.033, rse: 2.874, pcc: 0.168, kcc: 0.125
+2025-07-10 15:29:09: Epoch: 019 Train Loss: 0.407, mae: 0.508, rse: 0.240, pcc: 0.883, kcc: 0.741
+2025-07-10 15:29:09: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:29:09: Epoch: 019 Test results, ap: mae: 0.936, rse: 2.590, pcc: 0.248, kcc: 0.181
+2025-07-10 15:29:10: Epoch: 020 Train Loss: 0.395, mae: 0.512, rse: 0.240, pcc: 0.879, kcc: 0.729
+2025-07-10 15:29:10: Epoch: 020 Test results, ap: mae: 0.840, rse: 1.936, pcc: 0.172, kcc: 0.091
+2025-07-10 15:29:11: Epoch: 021 Train Loss: 0.311, mae: 0.421, rse: 0.209, pcc: 0.890, kcc: 0.763
+2025-07-10 15:29:11: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:29:11: Epoch: 021 Test results, ap: mae: 0.884, rse: 2.202, pcc: 0.254, kcc: 0.172
+2025-07-10 15:29:12: Epoch: 022 Train Loss: 0.250, mae: 0.397, rse: 0.202, pcc: 0.895, kcc: 0.779
+2025-07-10 15:29:12: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:29:12: Epoch: 022 Test results, ap: mae: 1.026, rse: 2.858, pcc: 0.233, kcc: 0.160
+2025-07-10 15:29:13: Epoch: 023 Train Loss: 0.248, mae: 0.395, rse: 0.201, pcc: 0.898, kcc: 0.786
+2025-07-10 15:29:13: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:29:13: Epoch: 023 Test results, ap: mae: 0.989, rse: 2.587, pcc: 0.222, kcc: 0.151
+2025-07-10 15:29:14: Epoch: 024 Train Loss: 0.241, mae: 0.378, rse: 0.183, pcc: 0.905, kcc: 0.791
+2025-07-10 15:29:14: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:29:14: Epoch: 024 Test results, ap: mae: 0.950, rse: 2.471, pcc: 0.241, kcc: 0.160
+2025-07-10 15:29:14: Epoch: 025 Train Loss: 0.228, mae: 0.382, rse: 0.190, pcc: 0.901, kcc: 0.792
+2025-07-10 15:29:15: Epoch: 025 Test results, ap: mae: 1.022, rse: 2.847, pcc: 0.242, kcc: 0.164
+2025-07-10 15:29:15: Epoch: 026 Train Loss: 0.223, mae: 0.377, rse: 0.185, pcc: 0.904, kcc: 0.798
+2025-07-10 15:29:15: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:29:16: Epoch: 026 Test results, ap: mae: 1.030, rse: 2.847, pcc: 0.229, kcc: 0.154
+2025-07-10 15:29:16: Epoch: 027 Train Loss: 0.204, mae: 0.343, rse: 0.176, pcc: 0.909, kcc: 0.810
+2025-07-10 15:29:16: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:29:17: Epoch: 027 Test results, ap: mae: 0.935, rse: 2.389, pcc: 0.239, kcc: 0.158
+2025-07-10 15:29:17: Epoch: 028 Train Loss: 0.192, mae: 0.358, rse: 0.180, pcc: 0.907, kcc: 0.803
+2025-07-10 15:29:18: Epoch: 028 Test results, ap: mae: 1.073, rse: 3.123, pcc: 0.235, kcc: 0.156
+2025-07-10 15:29:18: Epoch: 029 Train Loss: 0.189, mae: 0.340, rse: 0.172, pcc: 0.912, kcc: 0.818
+2025-07-10 15:29:18: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:29:19: Epoch: 029 Test results, ap: mae: 1.029, rse: 2.794, pcc: 0.232, kcc: 0.155
+2025-07-10 15:29:19: Epoch: 030 Train Loss: 0.199, mae: 0.348, rse: 0.167, pcc: 0.914, kcc: 0.817
+2025-07-10 15:29:19: Epoch: 030 Test results, ap: mae: 0.949, rse: 2.440, pcc: 0.225, kcc: 0.158
+2025-07-10 15:29:20: Epoch: 031 Train Loss: 0.172, mae: 0.327, rse: 0.163, pcc: 0.916, kcc: 0.824
+2025-07-10 15:29:20: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:29:20: Epoch: 031 Test results, ap: mae: 1.037, rse: 2.836, pcc: 0.230, kcc: 0.157
+2025-07-10 15:29:21: Epoch: 032 Train Loss: 0.159, mae: 0.313, rse: 0.154, pcc: 0.921, kcc: 0.831
+2025-07-10 15:29:21: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:29:21: Epoch: 032 Test results, ap: mae: 0.989, rse: 2.608, pcc: 0.240, kcc: 0.161
+2025-07-10 15:29:22: Epoch: 033 Train Loss: 0.148, mae: 0.311, rse: 0.155, pcc: 0.920, kcc: 0.832
+2025-07-10 15:29:23: Epoch: 033 Test results, ap: mae: 1.028, rse: 2.833, pcc: 0.237, kcc: 0.159
+2025-07-10 15:29:23: Epoch: 034 Train Loss: 0.153, mae: 0.307, rse: 0.158, pcc: 0.919, kcc: 0.832
+2025-07-10 15:29:23: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:29:23: Epoch: 034 Test results, ap: mae: 1.052, rse: 3.029, pcc: 0.240, kcc: 0.166
+2025-07-10 15:29:24: Epoch: 035 Train Loss: 0.149, mae: 0.299, rse: 0.155, pcc: 0.922, kcc: 0.836
+2025-07-10 15:29:24: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:29:24: Epoch: 035 Test results, ap: mae: 1.044, rse: 2.946, pcc: 0.242, kcc: 0.162
+2025-07-10 15:29:25: Epoch: 036 Train Loss: 0.145, mae: 0.292, rse: 0.150, pcc: 0.923, kcc: 0.841
+2025-07-10 15:29:25: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:29:25: Epoch: 036 Test results, ap: mae: 1.036, rse: 2.862, pcc: 0.230, kcc: 0.155
+2025-07-10 15:29:26: Epoch: 037 Train Loss: 0.135, mae: 0.299, rse: 0.146, pcc: 0.924, kcc: 0.840
+2025-07-10 15:29:26: Epoch: 037 Test results, ap: mae: 1.033, rse: 2.840, pcc: 0.244, kcc: 0.166
+2025-07-10 15:29:27: Epoch: 038 Train Loss: 0.136, mae: 0.284, rse: 0.144, pcc: 0.927, kcc: 0.847
+2025-07-10 15:29:27: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:29:27: Epoch: 038 Test results, ap: mae: 1.012, rse: 2.706, pcc: 0.244, kcc: 0.160
+2025-07-10 15:29:28: Epoch: 039 Train Loss: 0.139, mae: 0.312, rse: 0.147, pcc: 0.924, kcc: 0.835
+2025-07-10 15:29:28: Epoch: 039 Test results, ap: mae: 1.137, rse: 3.477, pcc: 0.239, kcc: 0.167
+2025-07-10 15:29:29: Epoch: 040 Train Loss: 0.130, mae: 0.284, rse: 0.147, pcc: 0.925, kcc: 0.845
+2025-07-10 15:29:29: Epoch: 040 Test results, ap: mae: 1.089, rse: 3.159, pcc: 0.257, kcc: 0.173
+2025-07-10 15:29:30: Epoch: 041 Train Loss: 0.124, mae: 0.270, rse: 0.139, pcc: 0.929, kcc: 0.851
+2025-07-10 15:29:30: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:29:30: Epoch: 041 Test results, ap: mae: 1.042, rse: 2.892, pcc: 0.249, kcc: 0.164
+2025-07-10 15:29:31: Epoch: 042 Train Loss: 0.125, mae: 0.271, rse: 0.138, pcc: 0.930, kcc: 0.853
+2025-07-10 15:29:31: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:29:31: Epoch: 042 Test results, ap: mae: 1.060, rse: 2.969, pcc: 0.246, kcc: 0.163
+2025-07-10 15:29:32: Epoch: 043 Train Loss: 0.121, mae: 0.275, rse: 0.139, pcc: 0.929, kcc: 0.850
+2025-07-10 15:29:32: Epoch: 043 Test results, ap: mae: 1.033, rse: 2.850, pcc: 0.256, kcc: 0.169
+2025-07-10 15:29:33: Epoch: 044 Train Loss: 0.124, mae: 0.271, rse: 0.139, pcc: 0.929, kcc: 0.851
+2025-07-10 15:29:33: Epoch: 044 Test results, ap: mae: 1.119, rse: 3.309, pcc: 0.253, kcc: 0.168
+2025-07-10 15:29:34: Epoch: 045 Train Loss: 0.122, mae: 0.274, rse: 0.138, pcc: 0.929, kcc: 0.851
+2025-07-10 15:29:34: Epoch: 045 Test results, ap: mae: 1.123, rse: 3.334, pcc: 0.248, kcc: 0.168
+2025-07-10 15:29:35: Epoch: 046 Train Loss: 0.118, mae: 0.268, rse: 0.137, pcc: 0.930, kcc: 0.855
+2025-07-10 15:29:35: Epoch: 046 New best VALIDATION metrics
+2025-07-10 15:29:35: Epoch: 046 Test results, ap: mae: 1.104, rse: 3.221, pcc: 0.245, kcc: 0.166
+2025-07-10 15:29:36: Epoch: 047 Train Loss: 0.121, mae: 0.266, rse: 0.137, pcc: 0.930, kcc: 0.855
+2025-07-10 15:29:36: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:29:36: Epoch: 047 Test results, ap: mae: 1.077, rse: 3.117, pcc: 0.253, kcc: 0.167
+2025-07-10 15:29:37: Epoch: 048 Train Loss: 0.114, mae: 0.263, rse: 0.135, pcc: 0.931, kcc: 0.857
+2025-07-10 15:29:37: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:29:37: Epoch: 048 Test results, ap: mae: 1.084, rse: 3.124, pcc: 0.247, kcc: 0.162
+2025-07-10 15:29:37: Epoch: 049 Train Loss: 0.113, mae: 0.258, rse: 0.135, pcc: 0.931, kcc: 0.857
+2025-07-10 15:29:37: Epoch: 049 New best VALIDATION metrics
+2025-07-10 15:29:38: Epoch: 049 Test results, ap: mae: 1.096, rse: 3.143, pcc: 0.255, kcc: 0.166
+2025-07-10 15:29:38: Epoch: 050 Train Loss: 0.110, mae: 0.266, rse: 0.136, pcc: 0.930, kcc: 0.856
+2025-07-10 15:29:39: Epoch: 050 Test results, ap: mae: 1.085, rse: 3.134, pcc: 0.239, kcc: 0.164
+2025-07-10 15:29:39: used time 0.01h
+2025-07-10 15:29:39: Running Cross Validation 4
+2025-07-10 15:29:39: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:29:39: Epoch: 001 Train Loss: 2.857, mae: 1.210, rse: 1.000, pcc: -0.027, kcc: 0.040
+2025-07-10 15:29:39: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:29:39: Epoch: 001 Test results, ap: mae: 0.621, rse: 1.180, pcc: -0.073, kcc: -0.135
+2025-07-10 15:29:40: Epoch: 002 Train Loss: 2.794, mae: 1.212, rse: 0.993, pcc: 0.116, kcc: 0.113
+2025-07-10 15:29:40: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:29:40: Epoch: 002 Test results, ap: mae: 0.596, rse: 1.079, pcc: -0.015, kcc: -0.043
+2025-07-10 15:29:41: Epoch: 003 Train Loss: 2.661, mae: 1.181, rse: 0.960, pcc: 0.247, kcc: 0.181
+2025-07-10 15:29:41: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:29:41: Epoch: 003 Test results, ap: mae: 0.648, rse: 1.275, pcc: -0.192, kcc: -0.135
+2025-07-10 15:29:42: Epoch: 004 Train Loss: 2.409, mae: 1.092, rse: 0.784, pcc: 0.476, kcc: 0.295
+2025-07-10 15:29:42: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:29:42: Epoch: 004 Test results, ap: mae: 0.648, rse: 1.260, pcc: 0.106, kcc: 0.053
+2025-07-10 15:29:43: Epoch: 005 Train Loss: 1.836, mae: 1.031, rse: 0.709, pcc: 0.540, kcc: 0.379
+2025-07-10 15:29:43: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:29:43: Epoch: 005 Test results, ap: mae: 0.643, rse: 1.229, pcc: 0.295, kcc: 0.098
+2025-07-10 15:29:44: Epoch: 006 Train Loss: 1.764, mae: 0.999, rse: 0.665, pcc: 0.601, kcc: 0.362
+2025-07-10 15:29:44: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:29:44: Epoch: 006 Test results, ap: mae: 0.643, rse: 1.230, pcc: 0.203, kcc: 0.142
+2025-07-10 15:29:45: Epoch: 007 Train Loss: 1.745, mae: 1.010, rse: 0.665, pcc: 0.584, kcc: 0.363
+2025-07-10 15:29:45: Epoch: 007 Test results, ap: mae: 0.696, rse: 1.395, pcc: 0.180, kcc: 0.107
+2025-07-10 15:29:45: Epoch: 008 Train Loss: 1.812, mae: 1.045, rse: 0.726, pcc: 0.528, kcc: 0.357
+2025-07-10 15:29:46: Epoch: 008 Test results, ap: mae: 0.672, rse: 1.362, pcc: -0.009, kcc: -0.017
+2025-07-10 15:29:46: Epoch: 009 Train Loss: 1.585, mae: 0.900, rse: 0.552, pcc: 0.670, kcc: 0.424
+2025-07-10 15:29:46: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:29:46: Epoch: 009 Test results, ap: mae: 0.796, rse: 1.803, pcc: 0.070, kcc: -0.027
+2025-07-10 15:29:47: Epoch: 010 Train Loss: 1.344, mae: 0.859, rse: 0.514, pcc: 0.699, kcc: 0.473
+2025-07-10 15:29:47: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:29:47: Epoch: 010 Test results, ap: mae: 0.744, rse: 1.602, pcc: -0.054, kcc: -0.095
+2025-07-10 15:29:48: Epoch: 011 Train Loss: 1.239, mae: 0.811, rse: 0.459, pcc: 0.737, kcc: 0.507
+2025-07-10 15:29:48: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:29:48: Epoch: 011 Test results, ap: mae: 0.829, rse: 1.893, pcc: 0.023, kcc: -0.088
+2025-07-10 15:29:48: Epoch: 011 New best TEST metrics
+2025-07-10 15:29:49: Epoch: 012 Train Loss: 1.127, mae: 0.793, rse: 0.442, pcc: 0.748, kcc: 0.535
+2025-07-10 15:29:49: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:29:49: Epoch: 012 Test results, ap: mae: 0.957, rse: 2.323, pcc: 0.086, kcc: 0.013
+2025-07-10 15:29:50: Epoch: 013 Train Loss: 1.098, mae: 0.769, rse: 0.435, pcc: 0.765, kcc: 0.561
+2025-07-10 15:29:50: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:29:50: Epoch: 013 Test results, ap: mae: 0.950, rse: 2.502, pcc: -0.103, kcc: -0.120
+2025-07-10 15:29:51: Epoch: 014 Train Loss: 1.008, mae: 0.700, rse: 0.379, pcc: 0.791, kcc: 0.601
+2025-07-10 15:29:51: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:29:51: Epoch: 014 Test results, ap: mae: 1.024, rse: 2.732, pcc: -0.033, kcc: -0.078
+2025-07-10 15:29:52: Epoch: 015 Train Loss: 0.939, mae: 0.660, rse: 0.345, pcc: 0.810, kcc: 0.611
+2025-07-10 15:29:52: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:29:52: Epoch: 015 Test results, ap: mae: 1.014, rse: 2.587, pcc: -0.026, kcc: -0.079
+2025-07-10 15:29:53: Epoch: 016 Train Loss: 1.026, mae: 0.656, rse: 0.342, pcc: 0.811, kcc: 0.633
+2025-07-10 15:29:53: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:29:53: Epoch: 016 Test results, ap: mae: 0.840, rse: 2.036, pcc: 0.022, kcc: -0.088
+2025-07-10 15:29:53: Epoch: 017 Train Loss: 0.858, mae: 0.661, rse: 0.345, pcc: 0.811, kcc: 0.636
+2025-07-10 15:29:54: Epoch: 017 Test results, ap: mae: 0.781, rse: 1.807, pcc: 0.167, kcc: 0.031
+2025-07-10 15:29:54: Epoch: 017 New best TEST metrics
+2025-07-10 15:29:54: Epoch: 018 Train Loss: 0.785, mae: 0.613, rse: 0.316, pcc: 0.830, kcc: 0.665
+2025-07-10 15:29:54: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:29:55: Epoch: 018 Test results, ap: mae: 0.880, rse: 2.224, pcc: 0.028, kcc: -0.034
+2025-07-10 15:29:55: Epoch: 019 Train Loss: 0.745, mae: 0.619, rse: 0.320, pcc: 0.828, kcc: 0.657
+2025-07-10 15:29:55: Epoch: 019 Test results, ap: mae: 0.938, rse: 2.411, pcc: 0.030, kcc: -0.078
+2025-07-10 15:29:56: Epoch: 020 Train Loss: 0.675, mae: 0.606, rse: 0.309, pcc: 0.840, kcc: 0.673
+2025-07-10 15:29:56: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:29:56: Epoch: 020 Test results, ap: mae: 0.891, rse: 2.208, pcc: 0.073, kcc: 0.008
+2025-07-10 15:29:57: Epoch: 021 Train Loss: 0.607, mae: 0.520, rse: 0.248, pcc: 0.869, kcc: 0.705
+2025-07-10 15:29:57: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:29:57: Epoch: 021 Test results, ap: mae: 0.916, rse: 2.371, pcc: 0.084, kcc: -0.027
+2025-07-10 15:29:58: Epoch: 022 Train Loss: 0.492, mae: 0.476, rse: 0.225, pcc: 0.882, kcc: 0.730
+2025-07-10 15:29:58: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:29:58: Epoch: 022 Test results, ap: mae: 0.942, rse: 2.466, pcc: 0.090, kcc: -0.040
+2025-07-10 15:29:59: Epoch: 023 Train Loss: 0.459, mae: 0.472, rse: 0.231, pcc: 0.877, kcc: 0.725
+2025-07-10 15:29:59: Epoch: 023 Test results, ap: mae: 0.885, rse: 2.240, pcc: 0.114, kcc: -0.000
+2025-07-10 15:30:00: Epoch: 024 Train Loss: 0.430, mae: 0.440, rse: 0.214, pcc: 0.887, kcc: 0.740
+2025-07-10 15:30:00: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:30:00: Epoch: 024 Test results, ap: mae: 0.909, rse: 2.418, pcc: 0.093, kcc: -0.014
+2025-07-10 15:30:01: Epoch: 025 Train Loss: 0.375, mae: 0.420, rse: 0.200, pcc: 0.895, kcc: 0.756
+2025-07-10 15:30:01: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:30:01: Epoch: 025 Test results, ap: mae: 0.911, rse: 2.407, pcc: 0.110, kcc: -0.007
+2025-07-10 15:30:02: Epoch: 026 Train Loss: 0.357, mae: 0.397, rse: 0.200, pcc: 0.895, kcc: 0.770
+2025-07-10 15:30:02: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:30:02: Epoch: 026 Test results, ap: mae: 0.927, rse: 2.399, pcc: 0.151, kcc: 0.021
+2025-07-10 15:30:02: Epoch: 027 Train Loss: 0.310, mae: 0.370, rse: 0.182, pcc: 0.905, kcc: 0.786
+2025-07-10 15:30:02: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:30:03: Epoch: 027 Test results, ap: mae: 0.848, rse: 2.205, pcc: 0.100, kcc: -0.027
+2025-07-10 15:30:03: Epoch: 028 Train Loss: 0.274, mae: 0.375, rse: 0.175, pcc: 0.909, kcc: 0.785
+2025-07-10 15:30:03: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:30:04: Epoch: 028 Test results, ap: mae: 0.825, rse: 2.067, pcc: 0.145, kcc: 0.036
+2025-07-10 15:30:04: Epoch: 029 Train Loss: 0.277, mae: 0.366, rse: 0.180, pcc: 0.906, kcc: 0.796
+2025-07-10 15:30:04: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:30:04: Epoch: 029 Test results, ap: mae: 0.849, rse: 2.252, pcc: 0.136, kcc: 0.016
+2025-07-10 15:30:05: Epoch: 030 Train Loss: 0.297, mae: 0.405, rse: 0.200, pcc: 0.895, kcc: 0.774
+2025-07-10 15:30:05: Epoch: 030 Test results, ap: mae: 1.007, rse: 2.859, pcc: 0.104, kcc: 0.024
+2025-07-10 15:30:06: Epoch: 031 Train Loss: 0.246, mae: 0.317, rse: 0.165, pcc: 0.914, kcc: 0.818
+2025-07-10 15:30:06: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:30:06: Epoch: 031 Test results, ap: mae: 0.887, rse: 2.407, pcc: 0.155, kcc: 0.043
+2025-07-10 15:30:07: Epoch: 032 Train Loss: 0.219, mae: 0.335, rse: 0.167, pcc: 0.913, kcc: 0.805
+2025-07-10 15:30:07: Epoch: 032 Test results, ap: mae: 0.881, rse: 2.411, pcc: 0.149, kcc: 0.040
+2025-07-10 15:30:08: Epoch: 033 Train Loss: 0.207, mae: 0.320, rse: 0.169, pcc: 0.912, kcc: 0.822
+2025-07-10 15:30:08: Epoch: 033 Test results, ap: mae: 0.878, rse: 2.458, pcc: 0.117, kcc: 0.039
+2025-07-10 15:30:09: Epoch: 034 Train Loss: 0.202, mae: 0.308, rse: 0.162, pcc: 0.916, kcc: 0.826
+2025-07-10 15:30:09: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:30:09: Epoch: 034 Test results, ap: mae: 0.891, rse: 2.499, pcc: 0.155, kcc: 0.053
+2025-07-10 15:30:10: Epoch: 035 Train Loss: 0.191, mae: 0.284, rse: 0.159, pcc: 0.917, kcc: 0.838
+2025-07-10 15:30:10: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:30:10: Epoch: 035 Test results, ap: mae: 0.852, rse: 2.296, pcc: 0.176, kcc: 0.057
+2025-07-10 15:30:10: Epoch: 036 Train Loss: 0.187, mae: 0.289, rse: 0.150, pcc: 0.923, kcc: 0.842
+2025-07-10 15:30:10: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:30:11: Epoch: 036 Test results, ap: mae: 0.869, rse: 2.403, pcc: 0.137, kcc: 0.050
+2025-07-10 15:30:11: Epoch: 037 Train Loss: 0.176, mae: 0.279, rse: 0.153, pcc: 0.921, kcc: 0.836
+2025-07-10 15:30:11: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:30:12: Epoch: 037 Test results, ap: mae: 0.919, rse: 2.552, pcc: 0.170, kcc: 0.070
+2025-07-10 15:30:12: Epoch: 038 Train Loss: 0.178, mae: 0.271, rse: 0.148, pcc: 0.924, kcc: 0.846
+2025-07-10 15:30:12: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:30:12: Epoch: 038 Test results, ap: mae: 0.902, rse: 2.506, pcc: 0.150, kcc: 0.050
+2025-07-10 15:30:13: Epoch: 039 Train Loss: 0.164, mae: 0.279, rse: 0.158, pcc: 0.919, kcc: 0.841
+2025-07-10 15:30:13: Epoch: 039 Test results, ap: mae: 0.924, rse: 2.540, pcc: 0.155, kcc: 0.051
+2025-07-10 15:30:14: Epoch: 040 Train Loss: 0.152, mae: 0.276, rse: 0.155, pcc: 0.921, kcc: 0.843
+2025-07-10 15:30:14: Epoch: 040 Test results, ap: mae: 0.912, rse: 2.503, pcc: 0.148, kcc: 0.054
+2025-07-10 15:30:15: Epoch: 041 Train Loss: 0.147, mae: 0.248, rse: 0.146, pcc: 0.925, kcc: 0.856
+2025-07-10 15:30:15: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:30:15: Epoch: 041 Test results, ap: mae: 0.928, rse: 2.578, pcc: 0.145, kcc: 0.058
+2025-07-10 15:30:16: Epoch: 042 Train Loss: 0.141, mae: 0.255, rse: 0.147, pcc: 0.924, kcc: 0.856
+2025-07-10 15:30:16: Epoch: 042 Test results, ap: mae: 0.912, rse: 2.535, pcc: 0.153, kcc: 0.055
+2025-07-10 15:30:17: Epoch: 043 Train Loss: 0.142, mae: 0.247, rse: 0.143, pcc: 0.927, kcc: 0.857
+2025-07-10 15:30:17: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:30:17: Epoch: 043 Test results, ap: mae: 0.914, rse: 2.562, pcc: 0.158, kcc: 0.062
+2025-07-10 15:30:18: Epoch: 044 Train Loss: 0.132, mae: 0.250, rse: 0.136, pcc: 0.930, kcc: 0.856
+2025-07-10 15:30:18: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:30:18: Epoch: 044 Test results, ap: mae: 0.906, rse: 2.487, pcc: 0.158, kcc: 0.057
+2025-07-10 15:30:19: Epoch: 045 Train Loss: 0.140, mae: 0.237, rse: 0.138, pcc: 0.929, kcc: 0.862
+2025-07-10 15:30:19: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:30:19: Epoch: 045 Test results, ap: mae: 0.943, rse: 2.692, pcc: 0.150, kcc: 0.065
+2025-07-10 15:30:19: Epoch: 046 Train Loss: 0.133, mae: 0.242, rse: 0.134, pcc: 0.931, kcc: 0.859
+2025-07-10 15:30:20: Epoch: 046 Test results, ap: mae: 0.938, rse: 2.650, pcc: 0.161, kcc: 0.065
+2025-07-10 15:30:20: Epoch: 047 Train Loss: 0.125, mae: 0.239, rse: 0.137, pcc: 0.929, kcc: 0.860
+2025-07-10 15:30:21: Epoch: 047 Test results, ap: mae: 0.919, rse: 2.527, pcc: 0.175, kcc: 0.072
+2025-07-10 15:30:21: Epoch: 048 Train Loss: 0.125, mae: 0.234, rse: 0.137, pcc: 0.930, kcc: 0.862
+2025-07-10 15:30:21: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:30:21: Epoch: 048 Test results, ap: mae: 0.930, rse: 2.681, pcc: 0.139, kcc: 0.058
+2025-07-10 15:30:22: Epoch: 049 Train Loss: 0.123, mae: 0.225, rse: 0.131, pcc: 0.933, kcc: 0.866
+2025-07-10 15:30:22: Epoch: 049 New best VALIDATION metrics
+2025-07-10 15:30:22: Epoch: 049 Test results, ap: mae: 0.938, rse: 2.645, pcc: 0.161, kcc: 0.068
+2025-07-10 15:30:23: Epoch: 050 Train Loss: 0.125, mae: 0.229, rse: 0.138, pcc: 0.929, kcc: 0.866
+2025-07-10 15:30:23: Epoch: 050 Test results, ap: mae: 0.950, rse: 2.710, pcc: 0.169, kcc: 0.071
+2025-07-10 15:30:23: used time 0.01h
+2025-07-10 15:30:23: Cross Validation Finished!
+2025-07-10 15:30:23: Best validation perform list
+[[0.20976557 0.09759991 0.95049369 0.86080915]
+ [0.16655277 0.07990903 0.95948762 0.88443214]
+ [0.97916174 0.63270158 0.608069   0.41102764]
+ [0.25795969 0.13478361 0.9312551  0.85728008]
+ [0.22549213 0.13095115 0.93260366 0.8655346 ]]
+2025-07-10 15:30:23: mean: [0.368 0.215 0.876 0.776]
+2025-07-10 15:30:23: std: [0.307 0.21  0.135 0.183]
+2025-07-10 15:30:23: Best test perform list
+[[ 0.67379135  1.28948522  0.3628042   0.22098929]
+ [ 0.70007771  1.32162595  0.32488963  0.17588943]
+ [ 0.67390901  1.30489779 -0.03467477  0.00450999]
+ [ 0.76329112  1.54286897  0.20370078  0.14362569]
+ [ 0.78097028  1.80673587  0.16746022  0.03052913]]
+2025-07-10 15:30:23: mean: [0.718 1.453 0.205 0.115]
+2025-07-10 15:30:23: std: [0.045 0.2   0.14  0.084]
diff --git a/run-reg/gru-mlp-256-mse-32-0.001-50/config.json b/run-reg/gru-mlp-256-mse-32-0.001-50/config.json
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "gru", "side_enc": null, "channels": 256, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
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@@ -0,0 +1,6 @@
+Valid
+0.36499759256839753,0.1891046106815338,0.8988825559616089,0.7901013016700744
+0.23210862645531743,0.12330967449671666,0.07104731001985312,0.11934340643581116
+Test
+0.6408398866653442,1.1936347246170045,0.2810789614915848,0.18484002202749253
+0.0627600628917929,0.20048519458184802,0.08473062469083883,0.058806866049634796
diff --git a/run-reg/gru-mlp-256-mse-32-0.001-50/training.log b/run-reg/gru-mlp-256-mse-32-0.001-50/training.log
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@@ -0,0 +1,741 @@
+2025-07-10 15:18:28: saving_dir: ./run-reg/gru-mlp-256-mse-32-0.001-50
+2025-07-10 15:18:29: Loading Train Dataset
+2025-07-10 15:18:29: Loading Test Dataset
+2025-07-10 15:18:30: Running Cross Validation 0
+2025-07-10 15:18:30: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 15:18:31: Epoch: 001 Train Loss: 2.912, mae: 1.210, rse: 1.000, pcc: 0.126, kcc: 0.093
+2025-07-10 15:18:31: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:18:31: Epoch: 001 Test results, ap: mae: 0.614, rse: 1.154, pcc: -0.025, kcc: -0.068
+2025-07-10 15:18:32: Epoch: 002 Train Loss: 2.862, mae: 1.209, rse: 1.000, pcc: 0.280, kcc: 0.127
+2025-07-10 15:18:32: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:18:32: Epoch: 002 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.204, kcc: 0.127
+2025-07-10 15:18:32: Epoch: 003 Train Loss: 2.908, mae: 1.202, rse: 0.990, pcc: 0.323, kcc: 0.158
+2025-07-10 15:18:32: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:18:33: Epoch: 003 Test results, ap: mae: 0.620, rse: 1.177, pcc: -0.293, kcc: -0.177
+2025-07-10 15:18:33: Epoch: 004 Train Loss: 2.792, mae: 1.160, rse: 0.920, pcc: 0.294, kcc: 0.228
+2025-07-10 15:18:33: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:18:34: Epoch: 004 Test results, ap: mae: 0.804, rse: 1.858, pcc: -0.084, kcc: -0.072
+2025-07-10 15:18:34: Epoch: 005 Train Loss: 2.672, mae: 1.134, rse: 0.898, pcc: 0.339, kcc: 0.234
+2025-07-10 15:18:34: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:18:35: Epoch: 005 Test results, ap: mae: 0.749, rse: 1.639, pcc: -0.062, kcc: -0.173
+2025-07-10 15:18:35: Epoch: 006 Train Loss: 2.651, mae: 1.130, rse: 0.890, pcc: 0.338, kcc: 0.227
+2025-07-10 15:18:35: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:18:36: Epoch: 006 Test results, ap: mae: 0.750, rse: 1.645, pcc: -0.103, kcc: -0.179
+2025-07-10 15:18:36: Epoch: 007 Train Loss: 2.632, mae: 1.150, rse: 0.923, pcc: 0.289, kcc: 0.209
+2025-07-10 15:18:37: Epoch: 007 Test results, ap: mae: 1.139, rse: 3.140, pcc: 0.154, kcc: 0.170
+2025-07-10 15:18:37: Epoch: 008 Train Loss: 2.648, mae: 1.109, rse: 0.865, pcc: 0.374, kcc: 0.258
+2025-07-10 15:18:37: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:18:38: Epoch: 008 Test results, ap: mae: 0.840, rse: 1.981, pcc: 0.071, kcc: 0.090
+2025-07-10 15:18:38: Epoch: 009 Train Loss: 2.577, mae: 1.114, rse: 0.857, pcc: 0.379, kcc: 0.279
+2025-07-10 15:18:38: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:18:39: Epoch: 009 Test results, ap: mae: 0.803, rse: 1.868, pcc: -0.210, kcc: -0.111
+2025-07-10 15:18:39: Epoch: 010 Train Loss: 2.365, mae: 1.111, rse: 0.832, pcc: 0.434, kcc: 0.265
+2025-07-10 15:18:39: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:18:40: Epoch: 010 Test results, ap: mae: 0.843, rse: 2.061, pcc: -0.147, kcc: -0.108
+2025-07-10 15:18:40: Epoch: 011 Train Loss: 2.224, mae: 1.045, rse: 0.754, pcc: 0.521, kcc: 0.347
+2025-07-10 15:18:40: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:18:41: Epoch: 011 Test results, ap: mae: 0.861, rse: 2.166, pcc: -0.245, kcc: -0.219
+2025-07-10 15:18:41: Epoch: 011 New best TEST metrics
+2025-07-10 15:18:41: Epoch: 012 Train Loss: 2.113, mae: 1.001, rse: 0.705, pcc: 0.544, kcc: 0.375
+2025-07-10 15:18:41: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:18:42: Epoch: 012 Test results, ap: mae: 0.791, rse: 1.882, pcc: -0.360, kcc: -0.282
+2025-07-10 15:18:42: Epoch: 012 New best TEST metrics
+2025-07-10 15:18:42: Epoch: 013 Train Loss: 2.064, mae: 1.017, rse: 0.713, pcc: 0.538, kcc: 0.365
+2025-07-10 15:18:43: Epoch: 013 Test results, ap: mae: 0.808, rse: 1.904, pcc: -0.227, kcc: -0.212
+2025-07-10 15:18:43: Epoch: 013 New best TEST metrics
+2025-07-10 15:18:43: Epoch: 014 Train Loss: 1.955, mae: 0.951, rse: 0.621, pcc: 0.621, kcc: 0.441
+2025-07-10 15:18:43: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:18:43: Epoch: 014 Test results, ap: mae: 0.841, rse: 2.063, pcc: -0.214, kcc: -0.171
+2025-07-10 15:18:44: Epoch: 015 Train Loss: 1.627, mae: 0.823, rse: 0.484, pcc: 0.720, kcc: 0.498
+2025-07-10 15:18:44: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:18:44: Epoch: 015 Test results, ap: mae: 0.799, rse: 1.841, pcc: -0.292, kcc: -0.235
+2025-07-10 15:18:45: Epoch: 016 Train Loss: 1.352, mae: 0.713, rse: 0.381, pcc: 0.794, kcc: 0.581
+2025-07-10 15:18:45: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:18:45: Epoch: 016 Test results, ap: mae: 0.818, rse: 1.818, pcc: -0.181, kcc: -0.146
+2025-07-10 15:18:45: Epoch: 016 New best TEST metrics
+2025-07-10 15:18:46: Epoch: 017 Train Loss: 1.092, mae: 0.751, rse: 0.390, pcc: 0.782, kcc: 0.565
+2025-07-10 15:18:46: Epoch: 017 Test results, ap: mae: 0.960, rse: 2.207, pcc: -0.022, kcc: 0.032
+2025-07-10 15:18:47: Epoch: 018 Train Loss: 0.980, mae: 0.604, rse: 0.288, pcc: 0.846, kcc: 0.652
+2025-07-10 15:18:47: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:18:47: Epoch: 018 Test results, ap: mae: 0.744, rse: 1.565, pcc: 0.043, kcc: 0.036
+2025-07-10 15:18:47: Epoch: 018 New best TEST metrics
+2025-07-10 15:18:48: Epoch: 019 Train Loss: 0.788, mae: 0.565, rse: 0.254, pcc: 0.864, kcc: 0.671
+2025-07-10 15:18:48: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:18:48: Epoch: 019 Test results, ap: mae: 0.734, rse: 1.555, pcc: -0.022, kcc: 0.011
+2025-07-10 15:18:49: Epoch: 020 Train Loss: 0.643, mae: 0.578, rse: 0.249, pcc: 0.871, kcc: 0.685
+2025-07-10 15:18:49: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:18:49: Epoch: 020 Test results, ap: mae: 0.798, rse: 1.701, pcc: -0.025, kcc: 0.006
+2025-07-10 15:18:50: Epoch: 021 Train Loss: 0.520, mae: 0.451, rse: 0.180, pcc: 0.906, kcc: 0.743
+2025-07-10 15:18:50: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:18:50: Epoch: 021 Test results, ap: mae: 0.690, rse: 1.346, pcc: 0.057, kcc: 0.055
+2025-07-10 15:18:50: Epoch: 021 New best TEST metrics
+2025-07-10 15:18:51: Epoch: 022 Train Loss: 0.412, mae: 0.458, rse: 0.185, pcc: 0.903, kcc: 0.743
+2025-07-10 15:18:51: Epoch: 022 Test results, ap: mae: 0.688, rse: 1.344, pcc: 0.102, kcc: 0.077
+2025-07-10 15:18:51: Epoch: 022 New best TEST metrics
+2025-07-10 15:18:52: Epoch: 023 Train Loss: 0.363, mae: 0.451, rse: 0.175, pcc: 0.910, kcc: 0.750
+2025-07-10 15:18:52: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:18:52: Epoch: 023 Test results, ap: mae: 0.712, rse: 1.427, pcc: 0.109, kcc: 0.080
+2025-07-10 15:18:53: Epoch: 024 Train Loss: 0.331, mae: 0.370, rse: 0.141, pcc: 0.927, kcc: 0.791
+2025-07-10 15:18:53: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:18:53: Epoch: 024 Test results, ap: mae: 0.710, rse: 1.372, pcc: 0.177, kcc: 0.104
+2025-07-10 15:18:53: Epoch: 024 New best TEST metrics
+2025-07-10 15:18:54: Epoch: 025 Train Loss: 0.294, mae: 0.421, rse: 0.158, pcc: 0.920, kcc: 0.769
+2025-07-10 15:18:54: Epoch: 025 Test results, ap: mae: 0.793, rse: 1.632, pcc: 0.188, kcc: 0.107
+2025-07-10 15:18:55: Epoch: 026 Train Loss: 0.244, mae: 0.337, rse: 0.127, pcc: 0.935, kcc: 0.806
+2025-07-10 15:18:55: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:18:55: Epoch: 026 Test results, ap: mae: 0.741, rse: 1.494, pcc: 0.186, kcc: 0.116
+2025-07-10 15:18:56: Epoch: 027 Train Loss: 0.232, mae: 0.337, rse: 0.126, pcc: 0.936, kcc: 0.803
+2025-07-10 15:18:56: Epoch: 027 Test results, ap: mae: 0.785, rse: 1.648, pcc: 0.169, kcc: 0.109
+2025-07-10 15:18:57: Epoch: 028 Train Loss: 0.229, mae: 0.321, rse: 0.119, pcc: 0.940, kcc: 0.811
+2025-07-10 15:18:57: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:18:57: Epoch: 028 Test results, ap: mae: 0.803, rse: 1.708, pcc: 0.162, kcc: 0.104
+2025-07-10 15:18:58: Epoch: 029 Train Loss: 0.202, mae: 0.309, rse: 0.117, pcc: 0.941, kcc: 0.824
+2025-07-10 15:18:58: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:18:58: Epoch: 029 Test results, ap: mae: 0.904, rse: 2.090, pcc: 0.139, kcc: 0.074
+2025-07-10 15:18:59: Epoch: 030 Train Loss: 0.177, mae: 0.294, rse: 0.111, pcc: 0.944, kcc: 0.825
+2025-07-10 15:18:59: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:18:59: Epoch: 030 Test results, ap: mae: 0.745, rse: 1.510, pcc: 0.208, kcc: 0.108
+2025-07-10 15:19:00: Epoch: 031 Train Loss: 0.158, mae: 0.267, rse: 0.101, pcc: 0.949, kcc: 0.841
+2025-07-10 15:19:00: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:19:00: Epoch: 031 Test results, ap: mae: 0.853, rse: 1.907, pcc: 0.173, kcc: 0.086
+2025-07-10 15:19:01: Epoch: 032 Train Loss: 0.158, mae: 0.277, rse: 0.108, pcc: 0.945, kcc: 0.837
+2025-07-10 15:19:01: Epoch: 032 Test results, ap: mae: 0.873, rse: 1.987, pcc: 0.158, kcc: 0.069
+2025-07-10 15:19:02: Epoch: 033 Train Loss: 0.146, mae: 0.254, rse: 0.099, pcc: 0.951, kcc: 0.847
+2025-07-10 15:19:02: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:19:02: Epoch: 033 Test results, ap: mae: 0.892, rse: 2.067, pcc: 0.181, kcc: 0.085
+2025-07-10 15:19:03: Epoch: 034 Train Loss: 0.130, mae: 0.262, rse: 0.097, pcc: 0.951, kcc: 0.847
+2025-07-10 15:19:03: Epoch: 034 Test results, ap: mae: 0.964, rse: 2.334, pcc: 0.187, kcc: 0.085
+2025-07-10 15:19:04: Epoch: 035 Train Loss: 0.130, mae: 0.244, rse: 0.095, pcc: 0.952, kcc: 0.852
+2025-07-10 15:19:04: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:19:04: Epoch: 035 Test results, ap: mae: 0.885, rse: 2.039, pcc: 0.179, kcc: 0.080
+2025-07-10 15:19:05: Epoch: 036 Train Loss: 0.126, mae: 0.251, rse: 0.094, pcc: 0.953, kcc: 0.855
+2025-07-10 15:19:05: Epoch: 036 Test results, ap: mae: 0.959, rse: 2.323, pcc: 0.175, kcc: 0.086
+2025-07-10 15:19:06: Epoch: 037 Train Loss: 0.124, mae: 0.249, rse: 0.092, pcc: 0.953, kcc: 0.854
+2025-07-10 15:19:06: Epoch: 037 Test results, ap: mae: 0.950, rse: 2.291, pcc: 0.194, kcc: 0.094
+2025-07-10 15:19:07: Epoch: 038 Train Loss: 0.126, mae: 0.248, rse: 0.092, pcc: 0.953, kcc: 0.855
+2025-07-10 15:19:07: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:19:07: Epoch: 038 Test results, ap: mae: 0.983, rse: 2.440, pcc: 0.170, kcc: 0.068
+2025-07-10 15:19:07: Epoch: 039 Train Loss: 0.120, mae: 0.240, rse: 0.092, pcc: 0.953, kcc: 0.856
+2025-07-10 15:19:07: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:19:08: Epoch: 039 Test results, ap: mae: 0.913, rse: 2.159, pcc: 0.196, kcc: 0.082
+2025-07-10 15:19:08: Epoch: 040 Train Loss: 0.119, mae: 0.248, rse: 0.093, pcc: 0.953, kcc: 0.852
+2025-07-10 15:19:09: Epoch: 040 Test results, ap: mae: 0.949, rse: 2.310, pcc: 0.200, kcc: 0.086
+2025-07-10 15:19:09: Epoch: 041 Train Loss: 0.111, mae: 0.220, rse: 0.086, pcc: 0.956, kcc: 0.864
+2025-07-10 15:19:09: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:19:09: Epoch: 041 Test results, ap: mae: 0.909, rse: 2.142, pcc: 0.212, kcc: 0.094
+2025-07-10 15:19:10: Epoch: 042 Train Loss: 0.097, mae: 0.221, rse: 0.087, pcc: 0.956, kcc: 0.864
+2025-07-10 15:19:10: Epoch: 042 Test results, ap: mae: 0.956, rse: 2.338, pcc: 0.202, kcc: 0.084
+2025-07-10 15:19:11: Epoch: 043 Train Loss: 0.107, mae: 0.230, rse: 0.092, pcc: 0.955, kcc: 0.864
+2025-07-10 15:19:11: Epoch: 043 Test results, ap: mae: 0.957, rse: 2.348, pcc: 0.184, kcc: 0.077
+2025-07-10 15:19:12: Epoch: 044 Train Loss: 0.098, mae: 0.211, rse: 0.086, pcc: 0.956, kcc: 0.870
+2025-07-10 15:19:12: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:19:12: Epoch: 044 Test results, ap: mae: 0.958, rse: 2.329, pcc: 0.196, kcc: 0.082
+2025-07-10 15:19:13: Epoch: 045 Train Loss: 0.098, mae: 0.212, rse: 0.087, pcc: 0.956, kcc: 0.872
+2025-07-10 15:19:13: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:19:13: Epoch: 045 Test results, ap: mae: 0.990, rse: 2.467, pcc: 0.189, kcc: 0.085
+2025-07-10 15:19:14: Epoch: 046 Train Loss: 0.099, mae: 0.211, rse: 0.083, pcc: 0.958, kcc: 0.874
+2025-07-10 15:19:14: Epoch: 046 New best VALIDATION metrics
+2025-07-10 15:19:14: Epoch: 046 Test results, ap: mae: 0.989, rse: 2.487, pcc: 0.182, kcc: 0.078
+2025-07-10 15:19:15: Epoch: 047 Train Loss: 0.090, mae: 0.211, rse: 0.085, pcc: 0.957, kcc: 0.871
+2025-07-10 15:19:15: Epoch: 047 Test results, ap: mae: 0.999, rse: 2.494, pcc: 0.194, kcc: 0.088
+2025-07-10 15:19:15: Epoch: 048 Train Loss: 0.097, mae: 0.210, rse: 0.085, pcc: 0.958, kcc: 0.870
+2025-07-10 15:19:16: Epoch: 048 Test results, ap: mae: 0.977, rse: 2.419, pcc: 0.216, kcc: 0.088
+2025-07-10 15:19:16: Epoch: 049 Train Loss: 0.095, mae: 0.208, rse: 0.084, pcc: 0.958, kcc: 0.871
+2025-07-10 15:19:17: Epoch: 049 Test results, ap: mae: 1.028, rse: 2.644, pcc: 0.206, kcc: 0.093
+2025-07-10 15:19:17: Epoch: 050 Train Loss: 0.094, mae: 0.199, rse: 0.081, pcc: 0.959, kcc: 0.876
+2025-07-10 15:19:17: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:19:17: Epoch: 050 Test results, ap: mae: 1.008, rse: 2.555, pcc: 0.203, kcc: 0.091
+2025-07-10 15:19:17: used time 0.01h
+2025-07-10 15:19:17: Running Cross Validation 1
+2025-07-10 15:19:17: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 15:19:18: Epoch: 001 Train Loss: 2.837, mae: 1.259, rse: 1.000, pcc: -0.025, kcc: 0.038
+2025-07-10 15:19:18: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:19:18: Epoch: 001 Test results, ap: mae: 0.602, rse: 1.107, pcc: -0.050, kcc: -0.047
+2025-07-10 15:19:19: Epoch: 002 Train Loss: 2.853, mae: 1.278, rse: 1.004, pcc: 0.038, kcc: 0.040
+2025-07-10 15:19:19: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:19:19: Epoch: 002 Test results, ap: mae: 0.584, rse: 1.028, pcc: -0.105, kcc: -0.066
+2025-07-10 15:19:20: Epoch: 003 Train Loss: 2.853, mae: 1.260, rse: 1.040, pcc: -0.051, kcc: -0.014
+2025-07-10 15:19:20: Epoch: 003 Test results, ap: mae: 0.723, rse: 1.546, pcc: -0.144, kcc: -0.037
+2025-07-10 15:19:21: Epoch: 004 Train Loss: 2.851, mae: 1.253, rse: 1.000, pcc: 0.090, kcc: 0.071
+2025-07-10 15:19:21: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:19:21: Epoch: 004 Test results, ap: mae: 0.627, rse: 1.198, pcc: -0.186, kcc: -0.072
+2025-07-10 15:19:22: Epoch: 005 Train Loss: 2.769, mae: 1.304, rse: 1.094, pcc: -0.111, kcc: -0.096
+2025-07-10 15:19:22: Epoch: 005 Test results, ap: mae: 0.644, rse: 1.249, pcc: -0.172, kcc: -0.047
+2025-07-10 15:19:22: Epoch: 006 Train Loss: 2.878, mae: 1.269, rse: 0.994, pcc: 0.172, kcc: 0.171
+2025-07-10 15:19:22: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:19:23: Epoch: 006 Test results, ap: mae: 0.586, rse: 1.037, pcc: 0.025, kcc: 0.022
+2025-07-10 15:19:23: Epoch: 007 Train Loss: 2.827, mae: 1.257, rse: 1.000, pcc: 0.027, kcc: -0.070
+2025-07-10 15:19:23: Epoch: 007 Test results, ap: mae: 0.605, rse: 1.120, pcc: -0.035, kcc: -0.062
+2025-07-10 15:19:24: Epoch: 008 Train Loss: 2.831, mae: 1.264, rse: 0.998, pcc: 0.045, kcc: 0.092
+2025-07-10 15:19:24: Epoch: 008 Test results, ap: mae: 0.594, rse: 1.074, pcc: 0.231, kcc: 0.187
+2025-07-10 15:19:25: Epoch: 009 Train Loss: 2.791, mae: 1.254, rse: 0.999, pcc: 0.058, kcc: -0.063
+2025-07-10 15:19:25: Epoch: 009 Test results, ap: mae: 0.613, rse: 1.151, pcc: 0.098, kcc: 0.071
+2025-07-10 15:19:26: Epoch: 010 Train Loss: 2.856, mae: 1.254, rse: 1.000, pcc: 0.050, kcc: -0.014
+2025-07-10 15:19:26: Epoch: 010 Test results, ap: mae: 0.612, rse: 1.148, pcc: 0.149, kcc: 0.130
+2025-07-10 15:19:27: Epoch: 011 Train Loss: 2.842, mae: 1.253, rse: 1.000, pcc: 0.045, kcc: -0.004
+2025-07-10 15:19:27: Epoch: 011 Test results, ap: mae: 0.615, rse: 1.160, pcc: 0.222, kcc: 0.159
+2025-07-10 15:19:27: Epoch: 011 New best TEST metrics
+2025-07-10 15:19:28: Epoch: 012 Train Loss: 2.844, mae: 1.254, rse: 0.999, pcc: 0.052, kcc: 0.044
+2025-07-10 15:19:28: Epoch: 012 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.025, kcc: 0.017
+2025-07-10 15:19:29: Epoch: 013 Train Loss: 2.839, mae: 1.249, rse: 1.004, pcc: 0.054, kcc: -0.036
+2025-07-10 15:19:29: Epoch: 013 Test results, ap: mae: 0.637, rse: 1.231, pcc: 0.058, kcc: 0.039
+2025-07-10 15:19:29: Epoch: 014 Train Loss: 2.836, mae: 1.251, rse: 1.001, pcc: 0.050, kcc: -0.015
+2025-07-10 15:19:30: Epoch: 014 Test results, ap: mae: 0.622, rse: 1.183, pcc: 0.148, kcc: 0.081
+2025-07-10 15:19:30: Epoch: 015 Train Loss: 2.820, mae: 1.261, rse: 0.998, pcc: 0.049, kcc: 0.029
+2025-07-10 15:19:30: Epoch: 015 Test results, ap: mae: 0.596, rse: 1.084, pcc: -0.127, kcc: -0.086
+2025-07-10 15:19:31: Epoch: 016 Train Loss: 2.834, mae: 1.255, rse: 0.999, pcc: 0.053, kcc: 0.143
+2025-07-10 15:19:31: Epoch: 016 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.030, kcc: -0.054
+2025-07-10 15:19:32: Epoch: 017 Train Loss: 2.841, mae: 1.255, rse: 0.998, pcc: 0.057, kcc: 0.190
+2025-07-10 15:19:32: Epoch: 017 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.038, kcc: 0.031
+2025-07-10 15:19:33: Epoch: 018 Train Loss: 2.822, mae: 1.256, rse: 0.998, pcc: 0.054, kcc: 0.178
+2025-07-10 15:19:33: Epoch: 018 Test results, ap: mae: 0.606, rse: 1.124, pcc: 0.046, kcc: 0.020
+2025-07-10 15:19:34: Epoch: 019 Train Loss: 2.837, mae: 1.254, rse: 0.999, pcc: 0.051, kcc: 0.203
+2025-07-10 15:19:34: Epoch: 019 Test results, ap: mae: 0.613, rse: 1.153, pcc: -0.173, kcc: -0.082
+2025-07-10 15:19:35: Epoch: 020 Train Loss: 2.833, mae: 1.251, rse: 1.001, pcc: 0.038, kcc: 0.183
+2025-07-10 15:19:35: Epoch: 020 Test results, ap: mae: 0.623, rse: 1.187, pcc: -0.182, kcc: -0.150
+2025-07-10 15:19:36: Epoch: 021 Train Loss: 2.818, mae: 1.251, rse: 1.000, pcc: 0.051, kcc: 0.221
+2025-07-10 15:19:36: Epoch: 021 Test results, ap: mae: 0.621, rse: 1.180, pcc: -0.170, kcc: -0.133
+2025-07-10 15:19:36: Epoch: 022 Train Loss: 2.821, mae: 1.250, rse: 0.998, pcc: 0.074, kcc: 0.220
+2025-07-10 15:19:37: Epoch: 022 Test results, ap: mae: 0.623, rse: 1.184, pcc: -0.145, kcc: -0.128
+2025-07-10 15:19:37: Epoch: 023 Train Loss: 2.801, mae: 1.243, rse: 0.990, pcc: 0.151, kcc: 0.259
+2025-07-10 15:19:37: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:19:38: Epoch: 023 Test results, ap: mae: 0.629, rse: 1.201, pcc: -0.103, kcc: -0.117
+2025-07-10 15:19:38: Epoch: 024 Train Loss: 2.800, mae: 1.247, rse: 0.998, pcc: 0.110, kcc: 0.261
+2025-07-10 15:19:38: Epoch: 024 Test results, ap: mae: 0.644, rse: 1.256, pcc: -0.122, kcc: -0.122
+2025-07-10 15:19:39: Epoch: 025 Train Loss: 2.747, mae: 1.277, rse: 1.001, pcc: 0.047, kcc: 0.110
+2025-07-10 15:19:39: Epoch: 025 Test results, ap: mae: 0.589, rse: 1.053, pcc: -0.194, kcc: -0.136
+2025-07-10 15:19:40: Epoch: 026 Train Loss: 2.829, mae: 1.241, rse: 0.977, pcc: 0.263, kcc: 0.279
+2025-07-10 15:19:40: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:19:40: Epoch: 026 Test results, ap: mae: 0.617, rse: 1.165, pcc: -0.080, kcc: -0.094
+2025-07-10 15:19:41: Epoch: 027 Train Loss: 2.649, mae: 1.138, rse: 0.828, pcc: 0.429, kcc: 0.320
+2025-07-10 15:19:41: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:19:41: Epoch: 027 Test results, ap: mae: 0.694, rse: 1.409, pcc: -0.015, kcc: -0.044
+2025-07-10 15:19:42: Epoch: 028 Train Loss: 2.705, mae: 1.207, rse: 0.919, pcc: 0.331, kcc: 0.231
+2025-07-10 15:19:42: Epoch: 028 Test results, ap: mae: 0.613, rse: 1.109, pcc: 0.032, kcc: 0.032
+2025-07-10 15:19:43: Epoch: 029 Train Loss: 2.485, mae: 1.147, rse: 0.823, pcc: 0.421, kcc: 0.268
+2025-07-10 15:19:43: Epoch: 029 Test results, ap: mae: 0.684, rse: 1.336, pcc: -0.004, kcc: -0.085
+2025-07-10 15:19:44: Epoch: 030 Train Loss: 2.390, mae: 1.140, rse: 0.813, pcc: 0.435, kcc: 0.271
+2025-07-10 15:19:44: Epoch: 030 Test results, ap: mae: 0.629, rse: 1.197, pcc: -0.073, kcc: -0.092
+2025-07-10 15:19:44: Epoch: 031 Train Loss: 2.266, mae: 1.139, rse: 0.806, pcc: 0.459, kcc: 0.302
+2025-07-10 15:19:44: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:19:45: Epoch: 031 Test results, ap: mae: 0.740, rse: 1.642, pcc: -0.123, kcc: -0.085
+2025-07-10 15:19:45: Epoch: 032 Train Loss: 2.277, mae: 1.121, rse: 0.785, pcc: 0.465, kcc: 0.301
+2025-07-10 15:19:45: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:19:46: Epoch: 032 Test results, ap: mae: 0.632, rse: 1.181, pcc: -0.161, kcc: -0.193
+2025-07-10 15:19:46: Epoch: 033 Train Loss: 2.214, mae: 1.103, rse: 0.778, pcc: 0.476, kcc: 0.335
+2025-07-10 15:19:46: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:19:46: Epoch: 033 Test results, ap: mae: 0.690, rse: 1.422, pcc: -0.359, kcc: -0.284
+2025-07-10 15:19:47: Epoch: 034 Train Loss: 2.168, mae: 1.109, rse: 0.777, pcc: 0.490, kcc: 0.321
+2025-07-10 15:19:47: Epoch: 034 Test results, ap: mae: 0.750, rse: 1.675, pcc: -0.110, kcc: -0.089
+2025-07-10 15:19:48: Epoch: 035 Train Loss: 2.134, mae: 1.077, rse: 0.738, pcc: 0.525, kcc: 0.350
+2025-07-10 15:19:48: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:19:48: Epoch: 035 Test results, ap: mae: 0.766, rse: 1.684, pcc: -0.210, kcc: -0.162
+2025-07-10 15:19:49: Epoch: 036 Train Loss: 2.155, mae: 1.104, rse: 0.777, pcc: 0.487, kcc: 0.338
+2025-07-10 15:19:49: Epoch: 036 Test results, ap: mae: 0.649, rse: 1.188, pcc: -0.081, kcc: -0.135
+2025-07-10 15:19:50: Epoch: 037 Train Loss: 2.114, mae: 1.077, rse: 0.739, pcc: 0.512, kcc: 0.356
+2025-07-10 15:19:50: Epoch: 037 Test results, ap: mae: 0.613, rse: 1.103, pcc: 0.230, kcc: 0.147
+2025-07-10 15:19:50: Epoch: 037 New best TEST metrics
+2025-07-10 15:19:51: Epoch: 038 Train Loss: 1.970, mae: 1.038, rse: 0.689, pcc: 0.560, kcc: 0.389
+2025-07-10 15:19:51: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:19:51: Epoch: 038 Test results, ap: mae: 0.626, rse: 1.082, pcc: 0.043, kcc: 0.050
+2025-07-10 15:19:51: Epoch: 039 Train Loss: 1.956, mae: 1.025, rse: 0.671, pcc: 0.580, kcc: 0.400
+2025-07-10 15:19:51: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:19:52: Epoch: 039 Test results, ap: mae: 0.642, rse: 1.145, pcc: 0.010, kcc: 0.024
+2025-07-10 15:19:52: Epoch: 040 Train Loss: 1.913, mae: 1.020, rse: 0.660, pcc: 0.584, kcc: 0.408
+2025-07-10 15:19:52: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:19:53: Epoch: 040 Test results, ap: mae: 0.546, rse: 0.876, pcc: 0.367, kcc: 0.225
+2025-07-10 15:19:53: Epoch: 040 New best TEST metrics
+2025-07-10 15:19:53: Epoch: 041 Train Loss: 1.741, mae: 0.968, rse: 0.622, pcc: 0.616, kcc: 0.430
+2025-07-10 15:19:53: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:19:53: Epoch: 041 Test results, ap: mae: 0.622, rse: 1.035, pcc: 0.196, kcc: 0.094
+2025-07-10 15:19:54: Epoch: 042 Train Loss: 1.635, mae: 0.956, rse: 0.595, pcc: 0.639, kcc: 0.449
+2025-07-10 15:19:54: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:19:54: Epoch: 042 Test results, ap: mae: 0.601, rse: 1.041, pcc: 0.033, kcc: 0.054
+2025-07-10 15:19:55: Epoch: 043 Train Loss: 1.531, mae: 0.917, rse: 0.533, pcc: 0.685, kcc: 0.488
+2025-07-10 15:19:55: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:19:55: Epoch: 043 Test results, ap: mae: 0.788, rse: 1.654, pcc: 0.257, kcc: 0.168
+2025-07-10 15:19:56: Epoch: 044 Train Loss: 1.444, mae: 0.961, rse: 0.591, pcc: 0.644, kcc: 0.469
+2025-07-10 15:19:56: Epoch: 044 Test results, ap: mae: 0.708, rse: 1.481, pcc: 0.175, kcc: 0.092
+2025-07-10 15:19:57: Epoch: 045 Train Loss: 1.408, mae: 0.858, rse: 0.475, pcc: 0.730, kcc: 0.530
+2025-07-10 15:19:57: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:19:57: Epoch: 045 Test results, ap: mae: 0.622, rse: 1.096, pcc: 0.247, kcc: 0.187
+2025-07-10 15:19:58: Epoch: 046 Train Loss: 1.255, mae: 0.872, rse: 0.502, pcc: 0.716, kcc: 0.521
+2025-07-10 15:19:58: Epoch: 046 Test results, ap: mae: 0.608, rse: 1.224, pcc: 0.090, kcc: 0.071
+2025-07-10 15:19:59: Epoch: 047 Train Loss: 1.238, mae: 0.853, rse: 0.455, pcc: 0.738, kcc: 0.530
+2025-07-10 15:19:59: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:19:59: Epoch: 047 Test results, ap: mae: 0.738, rse: 1.568, pcc: -0.042, kcc: -0.000
+2025-07-10 15:19:59: Epoch: 048 Train Loss: 1.208, mae: 0.822, rse: 0.431, pcc: 0.755, kcc: 0.550
+2025-07-10 15:19:59: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:20:00: Epoch: 048 Test results, ap: mae: 0.652, rse: 1.274, pcc: 0.211, kcc: 0.152
+2025-07-10 15:20:00: Epoch: 049 Train Loss: 1.193, mae: 0.830, rse: 0.429, pcc: 0.757, kcc: 0.543
+2025-07-10 15:20:00: Epoch: 049 Test results, ap: mae: 0.817, rse: 1.885, pcc: 0.051, kcc: 0.057
+2025-07-10 15:20:01: Epoch: 050 Train Loss: 1.154, mae: 0.819, rse: 0.417, pcc: 0.766, kcc: 0.558
+2025-07-10 15:20:01: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:20:01: Epoch: 050 Test results, ap: mae: 0.762, rse: 1.646, pcc: 0.110, kcc: 0.110
+2025-07-10 15:20:01: used time 0.01h
+2025-07-10 15:20:01: Running Cross Validation 2
+2025-07-10 15:20:01: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:20:02: Epoch: 001 Train Loss: 2.893, mae: 1.256, rse: 1.000, pcc: 0.130, kcc: 0.115
+2025-07-10 15:20:02: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:20:02: Epoch: 001 Test results, ap: mae: 0.605, rse: 1.118, pcc: 0.136, kcc: 0.009
+2025-07-10 15:20:03: Epoch: 002 Train Loss: 2.905, mae: 1.254, rse: 0.998, pcc: 0.195, kcc: 0.102
+2025-07-10 15:20:03: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:20:03: Epoch: 002 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.225, kcc: 0.115
+2025-07-10 15:20:04: Epoch: 003 Train Loss: 2.899, mae: 1.265, rse: 1.004, pcc: -0.021, kcc: -0.107
+2025-07-10 15:20:04: Epoch: 003 Test results, ap: mae: 0.593, rse: 1.066, pcc: 0.054, kcc: 0.096
+2025-07-10 15:20:05: Epoch: 004 Train Loss: 2.917, mae: 1.245, rse: 0.991, pcc: 0.145, kcc: 0.110
+2025-07-10 15:20:05: Epoch: 004 Test results, ap: mae: 0.642, rse: 1.258, pcc: -0.025, kcc: -0.156
+2025-07-10 15:20:06: Epoch: 005 Train Loss: 2.893, mae: 1.242, rse: 0.988, pcc: 0.214, kcc: 0.108
+2025-07-10 15:20:06: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:20:06: Epoch: 005 Test results, ap: mae: 0.701, rse: 1.466, pcc: 0.222, kcc: 0.103
+2025-07-10 15:20:07: Epoch: 006 Train Loss: 2.694, mae: 1.185, rse: 0.902, pcc: 0.320, kcc: 0.237
+2025-07-10 15:20:07: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:20:07: Epoch: 006 Test results, ap: mae: 0.742, rse: 1.617, pcc: -0.008, kcc: -0.118
+2025-07-10 15:20:07: Epoch: 007 Train Loss: 2.565, mae: 1.186, rse: 0.894, pcc: 0.336, kcc: 0.241
+2025-07-10 15:20:07: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:20:08: Epoch: 007 Test results, ap: mae: 0.669, rse: 1.405, pcc: -0.054, kcc: -0.034
+2025-07-10 15:20:08: Epoch: 008 Train Loss: 2.561, mae: 1.192, rse: 0.885, pcc: 0.364, kcc: 0.268
+2025-07-10 15:20:08: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:20:08: Epoch: 008 Test results, ap: mae: 1.173, rse: 3.338, pcc: 0.201, kcc: 0.089
+2025-07-10 15:20:09: Epoch: 009 Train Loss: 2.521, mae: 1.171, rse: 0.882, pcc: 0.353, kcc: 0.252
+2025-07-10 15:20:09: Epoch: 009 Test results, ap: mae: 0.685, rse: 1.417, pcc: -0.149, kcc: -0.082
+2025-07-10 15:20:10: Epoch: 010 Train Loss: 2.484, mae: 1.170, rse: 0.893, pcc: 0.384, kcc: 0.290
+2025-07-10 15:20:10: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:20:10: Epoch: 010 Test results, ap: mae: 0.668, rse: 1.352, pcc: -0.165, kcc: -0.117
+2025-07-10 15:20:11: Epoch: 011 Train Loss: 2.365, mae: 1.150, rse: 0.849, pcc: 0.394, kcc: 0.302
+2025-07-10 15:20:11: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:20:11: Epoch: 011 Test results, ap: mae: 0.762, rse: 1.695, pcc: -0.192, kcc: -0.123
+2025-07-10 15:20:11: Epoch: 011 New best TEST metrics
+2025-07-10 15:20:11: Epoch: 012 Train Loss: 2.328, mae: 1.108, rse: 0.768, pcc: 0.483, kcc: 0.326
+2025-07-10 15:20:11: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:20:12: Epoch: 012 Test results, ap: mae: 0.857, rse: 2.068, pcc: -0.152, kcc: -0.122
+2025-07-10 15:20:12: Epoch: 013 Train Loss: 2.154, mae: 1.078, rse: 0.749, pcc: 0.516, kcc: 0.350
+2025-07-10 15:20:12: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:20:12: Epoch: 013 Test results, ap: mae: 0.698, rse: 1.437, pcc: -0.122, kcc: -0.132
+2025-07-10 15:20:12: Epoch: 013 New best TEST metrics
+2025-07-10 15:20:13: Epoch: 014 Train Loss: 1.930, mae: 0.930, rse: 0.577, pcc: 0.652, kcc: 0.440
+2025-07-10 15:20:13: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:20:13: Epoch: 014 Test results, ap: mae: 0.824, rse: 1.886, pcc: 0.144, kcc: 0.162
+2025-07-10 15:20:14: Epoch: 015 Train Loss: 1.630, mae: 0.882, rse: 0.527, pcc: 0.693, kcc: 0.467
+2025-07-10 15:20:14: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:20:14: Epoch: 015 Test results, ap: mae: 0.709, rse: 1.386, pcc: 0.309, kcc: 0.211
+2025-07-10 15:20:14: Epoch: 015 New best TEST metrics
+2025-07-10 15:20:15: Epoch: 016 Train Loss: 1.438, mae: 0.829, rse: 0.455, pcc: 0.739, kcc: 0.524
+2025-07-10 15:20:15: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:20:15: Epoch: 016 Test results, ap: mae: 0.731, rse: 1.435, pcc: 0.296, kcc: 0.190
+2025-07-10 15:20:15: Epoch: 017 Train Loss: 1.211, mae: 0.782, rse: 0.421, pcc: 0.763, kcc: 0.537
+2025-07-10 15:20:15: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:20:16: Epoch: 017 Test results, ap: mae: 0.688, rse: 1.274, pcc: 0.395, kcc: 0.262
+2025-07-10 15:20:16: Epoch: 017 New best TEST metrics
+2025-07-10 15:20:16: Epoch: 018 Train Loss: 1.073, mae: 0.831, rse: 0.494, pcc: 0.718, kcc: 0.513
+2025-07-10 15:20:16: Epoch: 018 Test results, ap: mae: 0.802, rse: 1.717, pcc: 0.435, kcc: 0.290
+2025-07-10 15:20:17: Epoch: 019 Train Loss: 0.979, mae: 0.730, rse: 0.386, pcc: 0.785, kcc: 0.575
+2025-07-10 15:20:17: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:20:17: Epoch: 019 Test results, ap: mae: 0.794, rse: 1.632, pcc: 0.382, kcc: 0.257
+2025-07-10 15:20:18: Epoch: 020 Train Loss: 0.833, mae: 0.710, rse: 0.380, pcc: 0.791, kcc: 0.584
+2025-07-10 15:20:18: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:20:18: Epoch: 020 Test results, ap: mae: 0.766, rse: 1.592, pcc: 0.358, kcc: 0.228
+2025-07-10 15:20:19: Epoch: 021 Train Loss: 0.718, mae: 0.646, rse: 0.324, pcc: 0.822, kcc: 0.618
+2025-07-10 15:20:19: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:20:19: Epoch: 021 Test results, ap: mae: 0.768, rse: 1.594, pcc: 0.283, kcc: 0.199
+2025-07-10 15:20:20: Epoch: 022 Train Loss: 0.635, mae: 0.633, rse: 0.324, pcc: 0.822, kcc: 0.620
+2025-07-10 15:20:20: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:20:20: Epoch: 022 Test results, ap: mae: 0.820, rse: 1.783, pcc: 0.301, kcc: 0.207
+2025-07-10 15:20:20: Epoch: 023 Train Loss: 0.607, mae: 0.632, rse: 0.327, pcc: 0.824, kcc: 0.631
+2025-07-10 15:20:20: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:20:21: Epoch: 023 Test results, ap: mae: 0.656, rse: 1.287, pcc: 0.292, kcc: 0.224
+2025-07-10 15:20:21: Epoch: 024 Train Loss: 0.555, mae: 0.644, rse: 0.316, pcc: 0.828, kcc: 0.631
+2025-07-10 15:20:21: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:20:21: Epoch: 024 Test results, ap: mae: 0.829, rse: 1.872, pcc: 0.204, kcc: 0.163
+2025-07-10 15:20:22: Epoch: 025 Train Loss: 0.551, mae: 0.610, rse: 0.308, pcc: 0.838, kcc: 0.649
+2025-07-10 15:20:22: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:20:22: Epoch: 025 Test results, ap: mae: 0.835, rse: 1.977, pcc: 0.101, kcc: 0.086
+2025-07-10 15:20:23: Epoch: 026 Train Loss: 0.461, mae: 0.545, rse: 0.267, pcc: 0.857, kcc: 0.685
+2025-07-10 15:20:23: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:20:23: Epoch: 026 Test results, ap: mae: 0.966, rse: 2.593, pcc: -0.000, kcc: 0.020
+2025-07-10 15:20:23: Epoch: 027 Train Loss: 0.410, mae: 0.531, rse: 0.260, pcc: 0.860, kcc: 0.685
+2025-07-10 15:20:23: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:20:24: Epoch: 027 Test results, ap: mae: 0.990, rse: 2.660, pcc: 0.016, kcc: 0.043
+2025-07-10 15:20:24: Epoch: 028 Train Loss: 0.382, mae: 0.497, rse: 0.254, pcc: 0.867, kcc: 0.705
+2025-07-10 15:20:24: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:20:24: Epoch: 028 Test results, ap: mae: 1.027, rse: 2.894, pcc: -0.001, kcc: 0.026
+2025-07-10 15:20:25: Epoch: 029 Train Loss: 0.327, mae: 0.514, rse: 0.276, pcc: 0.854, kcc: 0.700
+2025-07-10 15:20:25: Epoch: 029 Test results, ap: mae: 1.088, rse: 3.217, pcc: -0.034, kcc: -0.017
+2025-07-10 15:20:26: Epoch: 030 Train Loss: 0.316, mae: 0.470, rse: 0.253, pcc: 0.866, kcc: 0.719
+2025-07-10 15:20:26: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:20:26: Epoch: 030 Test results, ap: mae: 0.965, rse: 2.592, pcc: -0.062, kcc: -0.023
+2025-07-10 15:20:26: Epoch: 031 Train Loss: 0.277, mae: 0.442, rse: 0.237, pcc: 0.875, kcc: 0.736
+2025-07-10 15:20:26: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:20:26: Epoch: 031 Test results, ap: mae: 0.982, rse: 2.660, pcc: -0.061, kcc: -0.015
+2025-07-10 15:20:27: Epoch: 032 Train Loss: 0.248, mae: 0.435, rse: 0.243, pcc: 0.873, kcc: 0.740
+2025-07-10 15:20:27: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:20:27: Epoch: 032 Test results, ap: mae: 1.031, rse: 2.955, pcc: -0.063, kcc: -0.031
+2025-07-10 15:20:28: Epoch: 033 Train Loss: 0.238, mae: 0.421, rse: 0.231, pcc: 0.879, kcc: 0.748
+2025-07-10 15:20:28: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:20:28: Epoch: 033 Test results, ap: mae: 1.006, rse: 2.794, pcc: -0.075, kcc: -0.026
+2025-07-10 15:20:28: Epoch: 034 Train Loss: 0.219, mae: 0.417, rse: 0.244, pcc: 0.873, kcc: 0.747
+2025-07-10 15:20:28: Epoch: 034 Test results, ap: mae: 1.070, rse: 3.171, pcc: -0.071, kcc: -0.031
+2025-07-10 15:20:29: Epoch: 035 Train Loss: 0.202, mae: 0.416, rse: 0.249, pcc: 0.874, kcc: 0.753
+2025-07-10 15:20:29: Epoch: 035 Test results, ap: mae: 1.050, rse: 3.061, pcc: -0.082, kcc: -0.038
+2025-07-10 15:20:30: Epoch: 036 Train Loss: 0.213, mae: 0.411, rse: 0.248, pcc: 0.868, kcc: 0.754
+2025-07-10 15:20:30: Epoch: 036 Test results, ap: mae: 1.097, rse: 3.235, pcc: -0.134, kcc: -0.075
+2025-07-10 15:20:31: Epoch: 037 Train Loss: 0.181, mae: 0.399, rse: 0.242, pcc: 0.873, kcc: 0.758
+2025-07-10 15:20:31: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:20:31: Epoch: 037 Test results, ap: mae: 1.037, rse: 3.031, pcc: -0.111, kcc: -0.048
+2025-07-10 15:20:32: Epoch: 038 Train Loss: 0.188, mae: 0.374, rse: 0.228, pcc: 0.881, kcc: 0.769
+2025-07-10 15:20:32: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:20:32: Epoch: 038 Test results, ap: mae: 1.075, rse: 3.159, pcc: -0.111, kcc: -0.067
+2025-07-10 15:20:33: Epoch: 039 Train Loss: 0.182, mae: 0.406, rse: 0.240, pcc: 0.875, kcc: 0.761
+2025-07-10 15:20:33: Epoch: 039 Test results, ap: mae: 0.994, rse: 2.813, pcc: -0.085, kcc: -0.032
+2025-07-10 15:20:34: Epoch: 040 Train Loss: 0.171, mae: 0.362, rse: 0.221, pcc: 0.887, kcc: 0.778
+2025-07-10 15:20:34: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:20:34: Epoch: 040 Test results, ap: mae: 1.031, rse: 2.974, pcc: -0.076, kcc: -0.037
+2025-07-10 15:20:35: Epoch: 041 Train Loss: 0.154, mae: 0.363, rse: 0.227, pcc: 0.881, kcc: 0.778
+2025-07-10 15:20:35: Epoch: 041 Test results, ap: mae: 1.014, rse: 2.901, pcc: -0.080, kcc: -0.036
+2025-07-10 15:20:36: Epoch: 042 Train Loss: 0.145, mae: 0.355, rse: 0.227, pcc: 0.881, kcc: 0.782
+2025-07-10 15:20:36: Epoch: 042 Test results, ap: mae: 1.020, rse: 2.940, pcc: -0.086, kcc: -0.039
+2025-07-10 15:20:37: Epoch: 043 Train Loss: 0.143, mae: 0.353, rse: 0.224, pcc: 0.885, kcc: 0.783
+2025-07-10 15:20:37: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:20:37: Epoch: 043 Test results, ap: mae: 1.008, rse: 2.870, pcc: -0.090, kcc: -0.041
+2025-07-10 15:20:38: Epoch: 044 Train Loss: 0.143, mae: 0.349, rse: 0.225, pcc: 0.883, kcc: 0.783
+2025-07-10 15:20:38: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:20:38: Epoch: 044 Test results, ap: mae: 0.990, rse: 2.808, pcc: -0.086, kcc: -0.040
+2025-07-10 15:20:39: Epoch: 045 Train Loss: 0.144, mae: 0.341, rse: 0.230, pcc: 0.881, kcc: 0.787
+2025-07-10 15:20:39: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:20:40: Epoch: 045 Test results, ap: mae: 1.049, rse: 3.088, pcc: -0.088, kcc: -0.039
+2025-07-10 15:20:40: Epoch: 046 Train Loss: 0.146, mae: 0.350, rse: 0.222, pcc: 0.885, kcc: 0.785
+2025-07-10 15:20:40: Epoch: 046 New best VALIDATION metrics
+2025-07-10 15:20:41: Epoch: 046 Test results, ap: mae: 1.144, rse: 3.556, pcc: -0.123, kcc: -0.069
+2025-07-10 15:20:41: Epoch: 047 Train Loss: 0.135, mae: 0.344, rse: 0.232, pcc: 0.882, kcc: 0.790
+2025-07-10 15:20:42: Epoch: 047 Test results, ap: mae: 1.074, rse: 3.210, pcc: -0.095, kcc: -0.044
+2025-07-10 15:20:42: Epoch: 048 Train Loss: 0.137, mae: 0.332, rse: 0.227, pcc: 0.883, kcc: 0.793
+2025-07-10 15:20:42: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:20:43: Epoch: 048 Test results, ap: mae: 1.114, rse: 3.406, pcc: -0.105, kcc: -0.051
+2025-07-10 15:20:44: Epoch: 049 Train Loss: 0.124, mae: 0.336, rse: 0.221, pcc: 0.885, kcc: 0.790
+2025-07-10 15:20:44: Epoch: 049 New best VALIDATION metrics
+2025-07-10 15:20:44: Epoch: 049 Test results, ap: mae: 1.033, rse: 2.974, pcc: -0.098, kcc: -0.043
+2025-07-10 15:20:45: Epoch: 050 Train Loss: 0.131, mae: 0.324, rse: 0.221, pcc: 0.886, kcc: 0.795
+2025-07-10 15:20:45: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:20:45: Epoch: 050 Test results, ap: mae: 1.104, rse: 3.322, pcc: -0.100, kcc: -0.046
+2025-07-10 15:20:45: used time 0.01h
+2025-07-10 15:20:45: Running Cross Validation 3
+2025-07-10 15:20:45: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:20:46: Epoch: 001 Train Loss: 2.805, mae: 1.296, rse: 1.000, pcc: 0.137, kcc: 0.084
+2025-07-10 15:20:46: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:20:46: Epoch: 001 Test results, ap: mae: 0.604, rse: 1.114, pcc: 0.284, kcc: 0.120
+2025-07-10 15:20:47: Epoch: 002 Train Loss: 2.803, mae: 1.298, rse: 0.993, pcc: 0.296, kcc: 0.213
+2025-07-10 15:20:47: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:20:47: Epoch: 002 Test results, ap: mae: 0.589, rse: 1.052, pcc: 0.427, kcc: 0.238
+2025-07-10 15:20:48: Epoch: 003 Train Loss: 2.741, mae: 1.261, rse: 0.943, pcc: 0.290, kcc: 0.222
+2025-07-10 15:20:48: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:20:48: Epoch: 003 Test results, ap: mae: 0.596, rse: 1.090, pcc: 0.214, kcc: 0.185
+2025-07-10 15:20:49: Epoch: 004 Train Loss: 2.544, mae: 1.271, rse: 0.947, pcc: 0.270, kcc: 0.236
+2025-07-10 15:20:49: Epoch: 004 Test results, ap: mae: 0.868, rse: 2.097, pcc: 0.250, kcc: 0.127
+2025-07-10 15:20:50: Epoch: 005 Train Loss: 2.459, mae: 1.252, rse: 0.929, pcc: 0.295, kcc: 0.227
+2025-07-10 15:20:50: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:20:50: Epoch: 005 Test results, ap: mae: 0.799, rse: 1.822, pcc: 0.009, kcc: 0.042
+2025-07-10 15:20:51: Epoch: 006 Train Loss: 2.444, mae: 1.234, rse: 0.908, pcc: 0.306, kcc: 0.244
+2025-07-10 15:20:51: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:20:51: Epoch: 006 Test results, ap: mae: 0.743, rse: 1.611, pcc: 0.079, kcc: 0.087
+2025-07-10 15:20:52: Epoch: 007 Train Loss: 2.426, mae: 1.265, rse: 0.948, pcc: 0.290, kcc: 0.245
+2025-07-10 15:20:52: Epoch: 007 Test results, ap: mae: 0.943, rse: 2.365, pcc: 0.103, kcc: 0.079
+2025-07-10 15:20:53: Epoch: 008 Train Loss: 2.399, mae: 1.215, rse: 0.877, pcc: 0.375, kcc: 0.254
+2025-07-10 15:20:53: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:20:54: Epoch: 008 Test results, ap: mae: 0.885, rse: 2.196, pcc: -0.271, kcc: -0.185
+2025-07-10 15:20:54: Epoch: 009 Train Loss: 2.245, mae: 1.116, rse: 0.763, pcc: 0.506, kcc: 0.363
+2025-07-10 15:20:54: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:20:55: Epoch: 009 Test results, ap: mae: 0.716, rse: 1.544, pcc: -0.150, kcc: -0.122
+2025-07-10 15:20:56: Epoch: 010 Train Loss: 2.142, mae: 1.093, rse: 0.711, pcc: 0.550, kcc: 0.398
+2025-07-10 15:20:56: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:20:56: Epoch: 010 Test results, ap: mae: 0.891, rse: 2.212, pcc: -0.106, kcc: -0.087
+2025-07-10 15:20:57: Epoch: 011 Train Loss: 1.940, mae: 1.056, rse: 0.660, pcc: 0.592, kcc: 0.395
+2025-07-10 15:20:57: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:20:57: Epoch: 011 Test results, ap: mae: 0.690, rse: 1.417, pcc: 0.044, kcc: 0.038
+2025-07-10 15:20:57: Epoch: 011 New best TEST metrics
+2025-07-10 15:20:58: Epoch: 012 Train Loss: 1.789, mae: 0.978, rse: 0.574, pcc: 0.658, kcc: 0.475
+2025-07-10 15:20:58: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:20:58: Epoch: 012 Test results, ap: mae: 0.803, rse: 1.884, pcc: -0.046, kcc: -0.063
+2025-07-10 15:20:59: Epoch: 013 Train Loss: 1.635, mae: 0.913, rse: 0.506, pcc: 0.710, kcc: 0.519
+2025-07-10 15:20:59: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:20:59: Epoch: 013 Test results, ap: mae: 0.724, rse: 1.513, pcc: 0.147, kcc: 0.097
+2025-07-10 15:20:59: Epoch: 013 New best TEST metrics
+2025-07-10 15:21:00: Epoch: 014 Train Loss: 1.464, mae: 0.859, rse: 0.455, pcc: 0.740, kcc: 0.542
+2025-07-10 15:21:00: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:21:00: Epoch: 014 Test results, ap: mae: 0.649, rse: 1.237, pcc: 0.173, kcc: 0.094
+2025-07-10 15:21:00: Epoch: 014 New best TEST metrics
+2025-07-10 15:21:01: Epoch: 015 Train Loss: 1.273, mae: 0.841, rse: 0.450, pcc: 0.742, kcc: 0.544
+2025-07-10 15:21:01: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:21:01: Epoch: 015 Test results, ap: mae: 0.628, rse: 1.167, pcc: 0.238, kcc: 0.147
+2025-07-10 15:21:01: Epoch: 015 New best TEST metrics
+2025-07-10 15:21:02: Epoch: 016 Train Loss: 1.131, mae: 0.807, rse: 0.421, pcc: 0.770, kcc: 0.582
+2025-07-10 15:21:02: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:21:02: Epoch: 016 Test results, ap: mae: 0.675, rse: 1.391, pcc: 0.038, kcc: 0.018
+2025-07-10 15:21:03: Epoch: 017 Train Loss: 1.061, mae: 0.778, rse: 0.402, pcc: 0.775, kcc: 0.582
+2025-07-10 15:21:03: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:21:03: Epoch: 017 Test results, ap: mae: 0.588, rse: 1.050, pcc: 0.224, kcc: 0.131
+2025-07-10 15:21:03: Epoch: 017 New best TEST metrics
+2025-07-10 15:21:04: Epoch: 018 Train Loss: 0.969, mae: 0.756, rse: 0.388, pcc: 0.784, kcc: 0.593
+2025-07-10 15:21:04: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:21:04: Epoch: 018 Test results, ap: mae: 0.608, rse: 1.144, pcc: 0.054, kcc: 0.025
+2025-07-10 15:21:05: Epoch: 019 Train Loss: 0.869, mae: 0.700, rse: 0.334, pcc: 0.818, kcc: 0.637
+2025-07-10 15:21:05: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:21:05: Epoch: 019 Test results, ap: mae: 0.667, rse: 1.410, pcc: 0.033, kcc: 0.019
+2025-07-10 15:21:06: Epoch: 020 Train Loss: 0.824, mae: 0.721, rse: 0.348, pcc: 0.824, kcc: 0.643
+2025-07-10 15:21:06: Epoch: 020 Test results, ap: mae: 0.734, rse: 1.705, pcc: 0.107, kcc: 0.063
+2025-07-10 15:21:07: Epoch: 021 Train Loss: 0.639, mae: 0.609, rse: 0.283, pcc: 0.847, kcc: 0.668
+2025-07-10 15:21:07: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:21:07: Epoch: 021 Test results, ap: mae: 0.637, rse: 1.388, pcc: 0.045, kcc: 0.044
+2025-07-10 15:21:07: Epoch: 022 Train Loss: 0.596, mae: 0.575, rse: 0.266, pcc: 0.858, kcc: 0.684
+2025-07-10 15:21:07: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:21:08: Epoch: 022 Test results, ap: mae: 0.669, rse: 1.503, pcc: 0.006, kcc: 0.013
+2025-07-10 15:21:08: Epoch: 023 Train Loss: 0.519, mae: 0.557, rse: 0.261, pcc: 0.861, kcc: 0.696
+2025-07-10 15:21:08: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:21:09: Epoch: 023 Test results, ap: mae: 0.756, rse: 1.919, pcc: -0.042, kcc: -0.019
+2025-07-10 15:21:09: Epoch: 024 Train Loss: 0.499, mae: 0.556, rse: 0.247, pcc: 0.869, kcc: 0.701
+2025-07-10 15:21:09: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:21:10: Epoch: 024 Test results, ap: mae: 0.763, rse: 1.842, pcc: 0.024, kcc: -0.009
+2025-07-10 15:21:10: Epoch: 025 Train Loss: 0.455, mae: 0.525, rse: 0.232, pcc: 0.877, kcc: 0.709
+2025-07-10 15:21:10: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:21:11: Epoch: 025 Test results, ap: mae: 0.813, rse: 2.189, pcc: -0.024, kcc: -0.033
+2025-07-10 15:21:11: Epoch: 026 Train Loss: 0.436, mae: 0.519, rse: 0.228, pcc: 0.886, kcc: 0.736
+2025-07-10 15:21:11: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:21:12: Epoch: 026 Test results, ap: mae: 0.967, rse: 3.190, pcc: -0.047, kcc: -0.032
+2025-07-10 15:21:12: Epoch: 027 Train Loss: 0.380, mae: 0.486, rse: 0.212, pcc: 0.892, kcc: 0.745
+2025-07-10 15:21:12: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:21:12: Epoch: 027 Test results, ap: mae: 0.940, rse: 3.074, pcc: -0.041, kcc: -0.033
+2025-07-10 15:21:13: Epoch: 028 Train Loss: 0.363, mae: 0.468, rse: 0.197, pcc: 0.897, kcc: 0.750
+2025-07-10 15:21:13: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:21:13: Epoch: 028 Test results, ap: mae: 0.874, rse: 2.674, pcc: -0.083, kcc: -0.045
+2025-07-10 15:21:14: Epoch: 029 Train Loss: 0.326, mae: 0.419, rse: 0.174, pcc: 0.910, kcc: 0.772
+2025-07-10 15:21:14: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:21:14: Epoch: 029 Test results, ap: mae: 0.933, rse: 3.009, pcc: -0.095, kcc: -0.029
+2025-07-10 15:21:15: Epoch: 030 Train Loss: 0.287, mae: 0.398, rse: 0.166, pcc: 0.914, kcc: 0.787
+2025-07-10 15:21:15: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:21:15: Epoch: 030 Test results, ap: mae: 0.913, rse: 2.850, pcc: -0.086, kcc: -0.028
+2025-07-10 15:21:16: Epoch: 031 Train Loss: 0.250, mae: 0.370, rse: 0.158, pcc: 0.919, kcc: 0.797
+2025-07-10 15:21:16: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:21:16: Epoch: 031 Test results, ap: mae: 0.964, rse: 3.225, pcc: -0.094, kcc: -0.034
+2025-07-10 15:21:17: Epoch: 032 Train Loss: 0.233, mae: 0.365, rse: 0.155, pcc: 0.919, kcc: 0.798
+2025-07-10 15:21:17: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:21:17: Epoch: 032 Test results, ap: mae: 0.977, rse: 3.291, pcc: -0.087, kcc: -0.033
+2025-07-10 15:21:18: Epoch: 033 Train Loss: 0.229, mae: 0.354, rse: 0.150, pcc: 0.922, kcc: 0.802
+2025-07-10 15:21:18: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:21:18: Epoch: 033 Test results, ap: mae: 1.020, rse: 3.513, pcc: -0.066, kcc: -0.029
+2025-07-10 15:21:19: Epoch: 034 Train Loss: 0.216, mae: 0.358, rse: 0.147, pcc: 0.924, kcc: 0.805
+2025-07-10 15:21:19: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:21:19: Epoch: 034 Test results, ap: mae: 0.993, rse: 3.411, pcc: -0.102, kcc: -0.035
+2025-07-10 15:21:20: Epoch: 035 Train Loss: 0.218, mae: 0.343, rse: 0.147, pcc: 0.925, kcc: 0.812
+2025-07-10 15:21:20: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:21:20: Epoch: 035 Test results, ap: mae: 1.062, rse: 3.846, pcc: -0.068, kcc: -0.022
+2025-07-10 15:21:21: Epoch: 036 Train Loss: 0.214, mae: 0.331, rse: 0.142, pcc: 0.927, kcc: 0.815
+2025-07-10 15:21:21: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:21:21: Epoch: 036 Test results, ap: mae: 1.086, rse: 4.143, pcc: -0.074, kcc: -0.008
+2025-07-10 15:21:22: Epoch: 037 Train Loss: 0.197, mae: 0.332, rse: 0.137, pcc: 0.929, kcc: 0.819
+2025-07-10 15:21:22: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:21:22: Epoch: 037 Test results, ap: mae: 1.058, rse: 3.869, pcc: -0.064, kcc: -0.012
+2025-07-10 15:21:23: Epoch: 038 Train Loss: 0.184, mae: 0.327, rse: 0.132, pcc: 0.932, kcc: 0.821
+2025-07-10 15:21:23: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:21:23: Epoch: 038 Test results, ap: mae: 1.071, rse: 3.954, pcc: -0.071, kcc: -0.019
+2025-07-10 15:21:24: Epoch: 039 Train Loss: 0.187, mae: 0.322, rse: 0.132, pcc: 0.932, kcc: 0.825
+2025-07-10 15:21:24: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:21:24: Epoch: 039 Test results, ap: mae: 1.117, rse: 4.341, pcc: -0.065, kcc: -0.007
+2025-07-10 15:21:25: Epoch: 040 Train Loss: 0.176, mae: 0.315, rse: 0.136, pcc: 0.930, kcc: 0.823
+2025-07-10 15:21:25: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:21:25: Epoch: 040 Test results, ap: mae: 1.138, rse: 4.533, pcc: -0.069, kcc: -0.010
+2025-07-10 15:21:26: Epoch: 041 Train Loss: 0.168, mae: 0.307, rse: 0.130, pcc: 0.933, kcc: 0.829
+2025-07-10 15:21:26: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:21:26: Epoch: 041 Test results, ap: mae: 1.085, rse: 4.164, pcc: -0.075, kcc: -0.013
+2025-07-10 15:21:27: Epoch: 042 Train Loss: 0.156, mae: 0.304, rse: 0.129, pcc: 0.934, kcc: 0.831
+2025-07-10 15:21:27: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:21:27: Epoch: 042 Test results, ap: mae: 1.098, rse: 4.221, pcc: -0.074, kcc: -0.014
+2025-07-10 15:21:28: Epoch: 043 Train Loss: 0.155, mae: 0.297, rse: 0.126, pcc: 0.935, kcc: 0.836
+2025-07-10 15:21:28: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:21:28: Epoch: 043 Test results, ap: mae: 1.068, rse: 3.970, pcc: -0.057, kcc: -0.018
+2025-07-10 15:21:29: Epoch: 044 Train Loss: 0.145, mae: 0.289, rse: 0.122, pcc: 0.937, kcc: 0.840
+2025-07-10 15:21:29: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:21:29: Epoch: 044 Test results, ap: mae: 1.108, rse: 4.320, pcc: -0.062, kcc: -0.007
+2025-07-10 15:21:30: Epoch: 045 Train Loss: 0.159, mae: 0.289, rse: 0.122, pcc: 0.937, kcc: 0.839
+2025-07-10 15:21:30: Epoch: 045 Test results, ap: mae: 1.099, rse: 4.266, pcc: -0.074, kcc: -0.012
+2025-07-10 15:21:31: Epoch: 046 Train Loss: 0.146, mae: 0.302, rse: 0.122, pcc: 0.937, kcc: 0.841
+2025-07-10 15:21:31: Epoch: 046 Test results, ap: mae: 1.124, rse: 4.437, pcc: -0.055, kcc: -0.007
+2025-07-10 15:21:32: Epoch: 047 Train Loss: 0.138, mae: 0.290, rse: 0.121, pcc: 0.938, kcc: 0.840
+2025-07-10 15:21:32: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:21:32: Epoch: 047 Test results, ap: mae: 1.113, rse: 4.286, pcc: -0.062, kcc: -0.012
+2025-07-10 15:21:32: Epoch: 048 Train Loss: 0.143, mae: 0.273, rse: 0.120, pcc: 0.938, kcc: 0.847
+2025-07-10 15:21:32: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:21:33: Epoch: 048 Test results, ap: mae: 1.136, rse: 4.533, pcc: -0.062, kcc: -0.011
+2025-07-10 15:21:33: Epoch: 049 Train Loss: 0.136, mae: 0.274, rse: 0.119, pcc: 0.939, kcc: 0.846
+2025-07-10 15:21:33: Epoch: 049 New best VALIDATION metrics
+2025-07-10 15:21:34: Epoch: 049 Test results, ap: mae: 1.143, rse: 4.661, pcc: -0.063, kcc: -0.012
+2025-07-10 15:21:34: Epoch: 050 Train Loss: 0.125, mae: 0.279, rse: 0.116, pcc: 0.941, kcc: 0.851
+2025-07-10 15:21:34: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:21:35: Epoch: 050 Test results, ap: mae: 1.105, rse: 4.336, pcc: -0.067, kcc: -0.007
+2025-07-10 15:21:35: used time 0.01h
+2025-07-10 15:21:35: Running Cross Validation 4
+2025-07-10 15:21:35: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:21:35: Epoch: 001 Train Loss: 2.871, mae: 1.210, rse: 1.000, pcc: 0.007, kcc: 0.106
+2025-07-10 15:21:35: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:21:36: Epoch: 001 Test results, ap: mae: 0.619, rse: 1.172, pcc: 0.053, kcc: 0.022
+2025-07-10 15:21:36: Epoch: 002 Train Loss: 2.835, mae: 1.210, rse: 1.000, pcc: 0.130, kcc: 0.090
+2025-07-10 15:21:36: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:21:37: Epoch: 002 Test results, ap: mae: 0.618, rse: 1.169, pcc: 0.002, kcc: 0.006
+2025-07-10 15:21:37: Epoch: 003 Train Loss: 2.847, mae: 1.200, rse: 0.972, pcc: 0.190, kcc: 0.084
+2025-07-10 15:21:37: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:21:37: Epoch: 003 Test results, ap: mae: 0.629, rse: 1.219, pcc: 0.199, kcc: 0.072
+2025-07-10 15:21:38: Epoch: 004 Train Loss: 2.623, mae: 1.207, rse: 0.938, pcc: 0.299, kcc: 0.192
+2025-07-10 15:21:38: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:21:38: Epoch: 004 Test results, ap: mae: 1.075, rse: 2.934, pcc: -0.080, kcc: -0.077
+2025-07-10 15:21:39: Epoch: 005 Train Loss: 2.519, mae: 1.186, rse: 0.927, pcc: 0.315, kcc: 0.224
+2025-07-10 15:21:39: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:21:39: Epoch: 005 Test results, ap: mae: 0.715, rse: 1.620, pcc: -0.008, kcc: -0.044
+2025-07-10 15:21:40: Epoch: 006 Train Loss: 2.479, mae: 1.140, rse: 0.863, pcc: 0.371, kcc: 0.259
+2025-07-10 15:21:40: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:21:40: Epoch: 006 Test results, ap: mae: 0.841, rse: 2.083, pcc: -0.206, kcc: -0.116
+2025-07-10 15:21:41: Epoch: 007 Train Loss: 2.375, mae: 1.143, rse: 0.874, pcc: 0.374, kcc: 0.269
+2025-07-10 15:21:41: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:21:41: Epoch: 007 Test results, ap: mae: 0.771, rse: 1.793, pcc: -0.146, kcc: -0.107
+2025-07-10 15:21:42: Epoch: 008 Train Loss: 2.272, mae: 1.111, rse: 0.834, pcc: 0.416, kcc: 0.295
+2025-07-10 15:21:42: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:21:42: Epoch: 008 Test results, ap: mae: 0.791, rse: 1.917, pcc: -0.154, kcc: -0.066
+2025-07-10 15:21:43: Epoch: 009 Train Loss: 2.094, mae: 1.117, rse: 0.844, pcc: 0.417, kcc: 0.283
+2025-07-10 15:21:43: Epoch: 009 Test results, ap: mae: 0.736, rse: 1.611, pcc: -0.155, kcc: -0.032
+2025-07-10 15:21:44: Epoch: 010 Train Loss: 2.090, mae: 1.088, rse: 0.800, pcc: 0.460, kcc: 0.323
+2025-07-10 15:21:44: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:21:44: Epoch: 010 Test results, ap: mae: 0.732, rse: 1.626, pcc: -0.085, kcc: -0.029
+2025-07-10 15:21:45: Epoch: 011 Train Loss: 1.732, mae: 0.974, rse: 0.672, pcc: 0.576, kcc: 0.407
+2025-07-10 15:21:45: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:21:45: Epoch: 011 Test results, ap: mae: 0.838, rse: 1.852, pcc: -0.041, kcc: -0.006
+2025-07-10 15:21:45: Epoch: 011 New best TEST metrics
+2025-07-10 15:21:46: Epoch: 012 Train Loss: 1.405, mae: 0.900, rse: 0.561, pcc: 0.666, kcc: 0.478
+2025-07-10 15:21:46: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:21:46: Epoch: 012 Test results, ap: mae: 0.732, rse: 1.562, pcc: 0.010, kcc: 0.005
+2025-07-10 15:21:46: Epoch: 012 New best TEST metrics
+2025-07-10 15:21:47: Epoch: 013 Train Loss: 1.216, mae: 0.812, rse: 0.459, pcc: 0.737, kcc: 0.533
+2025-07-10 15:21:47: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:21:47: Epoch: 013 Test results, ap: mae: 0.744, rse: 1.560, pcc: 0.116, kcc: 0.073
+2025-07-10 15:21:47: Epoch: 013 New best TEST metrics
+2025-07-10 15:21:48: Epoch: 014 Train Loss: 0.986, mae: 0.744, rse: 0.405, pcc: 0.772, kcc: 0.579
+2025-07-10 15:21:48: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:21:48: Epoch: 014 Test results, ap: mae: 0.736, rse: 1.569, pcc: 0.128, kcc: 0.079
+2025-07-10 15:21:48: Epoch: 014 New best TEST metrics
+2025-07-10 15:21:49: Epoch: 015 Train Loss: 0.866, mae: 0.635, rse: 0.335, pcc: 0.816, kcc: 0.626
+2025-07-10 15:21:49: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:21:49: Epoch: 015 Test results, ap: mae: 0.719, rse: 1.570, pcc: 0.137, kcc: 0.104
+2025-07-10 15:21:49: Epoch: 015 New best TEST metrics
+2025-07-10 15:21:50: Epoch: 016 Train Loss: 0.760, mae: 0.590, rse: 0.292, pcc: 0.842, kcc: 0.669
+2025-07-10 15:21:50: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:21:50: Epoch: 016 Test results, ap: mae: 0.701, rse: 1.531, pcc: 0.250, kcc: 0.161
+2025-07-10 15:21:50: Epoch: 016 New best TEST metrics
+2025-07-10 15:21:51: Epoch: 017 Train Loss: 0.637, mae: 0.545, rse: 0.273, pcc: 0.856, kcc: 0.696
+2025-07-10 15:21:51: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:21:51: Epoch: 017 Test results, ap: mae: 0.708, rse: 1.529, pcc: 0.150, kcc: 0.115
+2025-07-10 15:21:52: Epoch: 018 Train Loss: 0.554, mae: 0.557, rse: 0.254, pcc: 0.864, kcc: 0.689
+2025-07-10 15:21:52: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:21:52: Epoch: 018 Test results, ap: mae: 0.783, rse: 1.761, pcc: 0.049, kcc: 0.022
+2025-07-10 15:21:53: Epoch: 019 Train Loss: 0.494, mae: 0.500, rse: 0.230, pcc: 0.878, kcc: 0.707
+2025-07-10 15:21:53: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:21:53: Epoch: 019 Test results, ap: mae: 0.700, rse: 1.488, pcc: 0.093, kcc: 0.087
+2025-07-10 15:21:54: Epoch: 020 Train Loss: 0.423, mae: 0.416, rse: 0.189, pcc: 0.901, kcc: 0.756
+2025-07-10 15:21:54: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:21:54: Epoch: 020 Test results, ap: mae: 0.734, rse: 1.625, pcc: 0.077, kcc: 0.075
+2025-07-10 15:21:55: Epoch: 021 Train Loss: 0.296, mae: 0.382, rse: 0.174, pcc: 0.909, kcc: 0.771
+2025-07-10 15:21:55: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:21:55: Epoch: 021 Test results, ap: mae: 0.727, rse: 1.668, pcc: 0.121, kcc: 0.116
+2025-07-10 15:21:56: Epoch: 022 Train Loss: 0.278, mae: 0.362, rse: 0.175, pcc: 0.909, kcc: 0.778
+2025-07-10 15:21:56: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:21:56: Epoch: 022 Test results, ap: mae: 0.764, rse: 1.882, pcc: 0.105, kcc: 0.108
+2025-07-10 15:21:57: Epoch: 023 Train Loss: 0.271, mae: 0.361, rse: 0.168, pcc: 0.914, kcc: 0.786
+2025-07-10 15:21:57: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:21:57: Epoch: 023 Test results, ap: mae: 0.775, rse: 1.757, pcc: 0.113, kcc: 0.107
+2025-07-10 15:21:58: Epoch: 024 Train Loss: 0.241, mae: 0.356, rse: 0.157, pcc: 0.920, kcc: 0.792
+2025-07-10 15:21:58: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:21:58: Epoch: 024 Test results, ap: mae: 0.701, rse: 1.515, pcc: 0.196, kcc: 0.162
+2025-07-10 15:21:59: Epoch: 025 Train Loss: 0.219, mae: 0.320, rse: 0.152, pcc: 0.921, kcc: 0.805
+2025-07-10 15:21:59: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:21:59: Epoch: 025 Test results, ap: mae: 0.744, rse: 1.659, pcc: 0.154, kcc: 0.133
+2025-07-10 15:22:00: Epoch: 026 Train Loss: 0.209, mae: 0.322, rse: 0.148, pcc: 0.924, kcc: 0.808
+2025-07-10 15:22:00: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:22:00: Epoch: 026 Test results, ap: mae: 0.743, rse: 1.646, pcc: 0.154, kcc: 0.142
+2025-07-10 15:22:01: Epoch: 027 Train Loss: 0.205, mae: 0.325, rse: 0.152, pcc: 0.922, kcc: 0.808
+2025-07-10 15:22:01: Epoch: 027 Test results, ap: mae: 0.723, rse: 1.607, pcc: 0.137, kcc: 0.124
+2025-07-10 15:22:02: Epoch: 028 Train Loss: 0.189, mae: 0.305, rse: 0.139, pcc: 0.929, kcc: 0.818
+2025-07-10 15:22:02: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:22:02: Epoch: 028 Test results, ap: mae: 0.692, rse: 1.514, pcc: 0.192, kcc: 0.180
+2025-07-10 15:22:03: Epoch: 029 Train Loss: 0.183, mae: 0.289, rse: 0.137, pcc: 0.929, kcc: 0.823
+2025-07-10 15:22:03: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:22:03: Epoch: 029 Test results, ap: mae: 0.710, rse: 1.500, pcc: 0.219, kcc: 0.164
+2025-07-10 15:22:04: Epoch: 030 Train Loss: 0.167, mae: 0.274, rse: 0.125, pcc: 0.936, kcc: 0.834
+2025-07-10 15:22:04: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:22:04: Epoch: 030 Test results, ap: mae: 0.670, rse: 1.447, pcc: 0.228, kcc: 0.191
+2025-07-10 15:22:04: Epoch: 030 New best TEST metrics
+2025-07-10 15:22:05: Epoch: 031 Train Loss: 0.143, mae: 0.250, rse: 0.123, pcc: 0.937, kcc: 0.843
+2025-07-10 15:22:05: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:22:05: Epoch: 031 Test results, ap: mae: 0.707, rse: 1.522, pcc: 0.218, kcc: 0.189
+2025-07-10 15:22:06: Epoch: 032 Train Loss: 0.134, mae: 0.254, rse: 0.125, pcc: 0.936, kcc: 0.840
+2025-07-10 15:22:06: Epoch: 032 Test results, ap: mae: 0.704, rse: 1.475, pcc: 0.233, kcc: 0.175
+2025-07-10 15:22:07: Epoch: 033 Train Loss: 0.137, mae: 0.276, rse: 0.129, pcc: 0.934, kcc: 0.839
+2025-07-10 15:22:07: Epoch: 033 Test results, ap: mae: 0.739, rse: 1.620, pcc: 0.213, kcc: 0.167
+2025-07-10 15:22:08: Epoch: 034 Train Loss: 0.124, mae: 0.260, rse: 0.123, pcc: 0.937, kcc: 0.834
+2025-07-10 15:22:08: Epoch: 034 Test results, ap: mae: 0.672, rse: 1.396, pcc: 0.242, kcc: 0.202
+2025-07-10 15:22:08: Epoch: 034 New best TEST metrics
+2025-07-10 15:22:09: Epoch: 035 Train Loss: 0.120, mae: 0.240, rse: 0.122, pcc: 0.937, kcc: 0.844
+2025-07-10 15:22:09: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:22:09: Epoch: 035 Test results, ap: mae: 0.724, rse: 1.611, pcc: 0.200, kcc: 0.189
+2025-07-10 15:22:10: Epoch: 036 Train Loss: 0.113, mae: 0.241, rse: 0.121, pcc: 0.938, kcc: 0.846
+2025-07-10 15:22:10: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:22:10: Epoch: 036 Test results, ap: mae: 0.698, rse: 1.488, pcc: 0.230, kcc: 0.179
+2025-07-10 15:22:11: Epoch: 037 Train Loss: 0.113, mae: 0.232, rse: 0.121, pcc: 0.938, kcc: 0.849
+2025-07-10 15:22:11: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:22:11: Epoch: 037 Test results, ap: mae: 0.706, rse: 1.510, pcc: 0.216, kcc: 0.189
+2025-07-10 15:22:12: Epoch: 038 Train Loss: 0.108, mae: 0.243, rse: 0.120, pcc: 0.939, kcc: 0.849
+2025-07-10 15:22:12: Epoch: 038 Test results, ap: mae: 0.743, rse: 1.633, pcc: 0.199, kcc: 0.173
+2025-07-10 15:22:13: Epoch: 039 Train Loss: 0.104, mae: 0.231, rse: 0.118, pcc: 0.939, kcc: 0.853
+2025-07-10 15:22:13: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:22:13: Epoch: 039 Test results, ap: mae: 0.706, rse: 1.539, pcc: 0.213, kcc: 0.195
+2025-07-10 15:22:14: Epoch: 040 Train Loss: 0.100, mae: 0.238, rse: 0.120, pcc: 0.938, kcc: 0.851
+2025-07-10 15:22:14: Epoch: 040 Test results, ap: mae: 0.713, rse: 1.566, pcc: 0.215, kcc: 0.201
+2025-07-10 15:22:15: Epoch: 041 Train Loss: 0.091, mae: 0.215, rse: 0.117, pcc: 0.940, kcc: 0.858
+2025-07-10 15:22:15: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:22:15: Epoch: 041 Test results, ap: mae: 0.695, rse: 1.512, pcc: 0.223, kcc: 0.198
+2025-07-10 15:22:16: Epoch: 042 Train Loss: 0.093, mae: 0.218, rse: 0.114, pcc: 0.942, kcc: 0.860
+2025-07-10 15:22:16: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:22:16: Epoch: 042 Test results, ap: mae: 0.697, rse: 1.481, pcc: 0.234, kcc: 0.191
+2025-07-10 15:22:17: Epoch: 043 Train Loss: 0.091, mae: 0.217, rse: 0.116, pcc: 0.941, kcc: 0.862
+2025-07-10 15:22:17: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:22:17: Epoch: 043 Test results, ap: mae: 0.716, rse: 1.548, pcc: 0.222, kcc: 0.184
+2025-07-10 15:22:18: Epoch: 044 Train Loss: 0.086, mae: 0.210, rse: 0.114, pcc: 0.941, kcc: 0.863
+2025-07-10 15:22:18: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:22:18: Epoch: 044 Test results, ap: mae: 0.712, rse: 1.546, pcc: 0.215, kcc: 0.180
+2025-07-10 15:22:19: Epoch: 045 Train Loss: 0.087, mae: 0.212, rse: 0.112, pcc: 0.942, kcc: 0.861
+2025-07-10 15:22:19: Epoch: 045 Test results, ap: mae: 0.709, rse: 1.532, pcc: 0.218, kcc: 0.195
+2025-07-10 15:22:20: Epoch: 046 Train Loss: 0.084, mae: 0.213, rse: 0.113, pcc: 0.942, kcc: 0.861
+2025-07-10 15:22:20: Epoch: 046 Test results, ap: mae: 0.702, rse: 1.505, pcc: 0.227, kcc: 0.189
+2025-07-10 15:22:21: Epoch: 047 Train Loss: 0.084, mae: 0.210, rse: 0.113, pcc: 0.942, kcc: 0.866
+2025-07-10 15:22:21: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:22:21: Epoch: 047 Test results, ap: mae: 0.716, rse: 1.565, pcc: 0.216, kcc: 0.187
+2025-07-10 15:22:22: Epoch: 048 Train Loss: 0.083, mae: 0.207, rse: 0.110, pcc: 0.944, kcc: 0.866
+2025-07-10 15:22:22: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:22:22: Epoch: 048 Test results, ap: mae: 0.708, rse: 1.544, pcc: 0.223, kcc: 0.186
+2025-07-10 15:22:23: Epoch: 049 Train Loss: 0.087, mae: 0.212, rse: 0.113, pcc: 0.942, kcc: 0.865
+2025-07-10 15:22:23: Epoch: 049 Test results, ap: mae: 0.704, rse: 1.518, pcc: 0.226, kcc: 0.190
+2025-07-10 15:22:24: Epoch: 050 Train Loss: 0.083, mae: 0.203, rse: 0.110, pcc: 0.943, kcc: 0.869
+2025-07-10 15:22:24: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:22:24: Epoch: 050 Test results, ap: mae: 0.698, rse: 1.512, pcc: 0.222, kcc: 0.186
+2025-07-10 15:22:24: used time 0.01h
+2025-07-10 15:22:24: Cross Validation Finished!
+2025-07-10 15:22:24: Best validation perform list
+[[0.19886135 0.08131002 0.95893389 0.87649113]
+ [0.8194828  0.41684762 0.76571149 0.55833954]
+ [0.32419032 0.22090217 0.88573039 0.79515761]
+ [0.27937341 0.11599951 0.94069481 0.85110158]
+ [0.20308009 0.11046373 0.94334221 0.86941665]]
+2025-07-10 15:22:24: mean: [0.365 0.189 0.899 0.79 ]
+2025-07-10 15:22:24: std: [0.232 0.123 0.071 0.119]
+2025-07-10 15:22:24: Best test perform list
+[[0.71049076 1.37222993 0.17713149 0.10407659]
+ [0.54628086 0.87559676 0.36661699 0.22480543]
+ [0.6876626  1.27395666 0.39523095 0.26227301]
+ [0.58823538 1.05038321 0.22424756 0.13113651]
+ [0.67152983 1.39600706 0.24216782 0.20190857]]
+2025-07-10 15:22:24: mean: [0.641 1.194 0.281 0.185]
+2025-07-10 15:22:24: std: [0.063 0.2   0.085 0.059]
diff --git a/run-reg/lstm-att-256-bmc-32-0.001-35/config.json b/run-reg/lstm-att-256-bmc-32-0.001-35/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..cf2f141589711bacfe892fb966599f97e5265bff
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+2025-07-09 15:10:13: saving_dir: ./run-reg/lstm-att-256-bmc-32-0.001-35
+2025-07-09 15:10:13: Loading Train Dataset
+2025-07-09 15:10:14: Loading Test Dataset
+2025-07-09 15:10:15: Running Cross Validation 0
+2025-07-09 15:10:15: Fold 0  Train set:1888, Valid set:473, Test set: 108
+2025-07-09 15:10:16: Epoch: 001 Train Loss: 8.935, mae: 1.431, rse: 1.041, pcc: 0.040, kcc: 0.133
+2025-07-09 15:10:16: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:10:16: Epoch: 001 Test results, ap: mae: 0.614, rse: 1.051, pcc: 0.290, kcc: 0.181
+2025-07-09 15:10:17: Epoch: 002 Train Loss: 8.952, mae: 1.364, rse: 1.000, pcc: -0.030, kcc: 0.003
+2025-07-09 15:10:18: Epoch: 002 Test results, ap: mae: 0.589, rse: 1.054, pcc: 0.231, kcc: 0.065
+2025-07-09 15:10:19: Epoch: 003 Train Loss: 8.924, mae: 1.391, rse: 1.012, pcc: 0.019, kcc: -0.088
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+2025-07-09 15:10:20: Epoch: 004 Train Loss: 8.947, mae: 1.364, rse: 1.000, pcc: 0.001, kcc: -0.020
+2025-07-09 15:10:21: Epoch: 004 Test results, ap: mae: 0.589, rse: 1.054, pcc: 0.676, kcc: 0.142
+2025-07-09 15:10:22: Epoch: 005 Train Loss: 8.928, mae: 1.364, rse: 1.000, pcc: 0.046, kcc: 0.018
+2025-07-09 15:10:22: Epoch: 005 Test results, ap: mae: 0.590, rse: 1.055, pcc: -0.468, kcc: -0.150
+2025-07-09 15:10:23: Epoch: 006 Train Loss: 8.936, mae: 1.354, rse: 1.004, pcc: -0.037, kcc: 0.082
+2025-07-09 15:10:24: Epoch: 006 Test results, ap: mae: 0.615, rse: 1.161, pcc: 0.177, kcc: 0.047
+2025-07-09 15:10:25: Epoch: 007 Train Loss: 8.935, mae: 1.360, rse: 1.000, pcc: 0.038, kcc: -0.064
+2025-07-09 15:10:25: Epoch: 007 Test results, ap: mae: 0.596, rse: 1.082, pcc: -0.675, kcc: -0.073
+2025-07-09 15:10:26: Epoch: 008 Train Loss: 8.935, mae: 1.363, rse: 1.000, pcc: 0.047, kcc: 0.137
+2025-07-09 15:10:26: Epoch: 008 New best VALIDATION metrics
+2025-07-09 15:10:26: Epoch: 008 Test results, ap: mae: 0.590, rse: 1.058, pcc: 0.084, kcc: -0.027
+2025-07-09 15:10:28: Epoch: 009 Train Loss: 8.934, mae: 1.366, rse: 1.000, pcc: 0.000, kcc: -0.001
+2025-07-09 15:10:28: Epoch: 009 Test results, ap: mae: 0.587, rse: 1.045, pcc: nan, kcc: nan
+2025-07-09 15:10:29: Epoch: 010 Train Loss: 8.924, mae: 1.367, rse: 1.001, pcc: 0.048, kcc: 0.080
+2025-07-09 15:10:29: Epoch: 010 Test results, ap: mae: 0.587, rse: 1.040, pcc: -1.000, kcc: -0.029
+2025-07-09 15:10:31: Epoch: 011 Train Loss: 8.930, mae: 1.367, rse: 1.000, pcc: 0.037, kcc: 0.103
+2025-07-09 15:10:31: Epoch: 011 Test results, ap: mae: 0.587, rse: 1.041, pcc: 1.000, kcc: -0.009
+2025-07-09 15:10:31: Epoch: 011 New best TEST metrics
+2025-07-09 15:10:32: Epoch: 012 Train Loss: 8.927, mae: 1.369, rse: 1.001, pcc: 0.046, kcc: 0.079
+2025-07-09 15:10:32: Epoch: 012 Test results, ap: mae: 0.585, rse: 1.034, pcc: -0.315, kcc: 0.200
+2025-07-09 15:10:33: Epoch: 013 Train Loss: 8.929, mae: 1.370, rse: 1.001, pcc: 0.042, kcc: 0.029
+2025-07-09 15:10:34: Epoch: 013 Test results, ap: mae: 0.584, rse: 1.028, pcc: 0.964, kcc: 0.042
+2025-07-09 15:10:34: Epoch: 013 New best TEST metrics
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+2025-07-09 15:10:38: Epoch: 016 Train Loss: 8.936, mae: 1.369, rse: 1.001, pcc: 0.042, kcc: 0.100
+2025-07-09 15:10:38: Epoch: 016 Test results, ap: mae: 0.585, rse: 1.034, pcc: 0.102, kcc: 0.037
+2025-07-09 15:10:39: Epoch: 017 Train Loss: 8.923, mae: 1.369, rse: 1.001, pcc: 0.037, kcc: 0.099
+2025-07-09 15:10:40: Epoch: 017 Test results, ap: mae: 0.585, rse: 1.034, pcc: -0.907, kcc: -0.172
+2025-07-09 15:10:41: Epoch: 018 Train Loss: 8.930, mae: 1.371, rse: 1.002, pcc: 0.036, kcc: 0.034
+2025-07-09 15:10:41: Epoch: 018 Test results, ap: mae: 0.584, rse: 1.025, pcc: -1.000, kcc: nan
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+2025-07-09 15:10:47: Epoch: 022 Train Loss: 8.924, mae: 1.372, rse: 1.002, pcc: 0.044, kcc: 0.120
+2025-07-09 15:10:47: Epoch: 022 Test results, ap: mae: 0.584, rse: 1.024, pcc: 1.000, kcc: 0.060
+2025-07-09 15:10:47: Epoch: 022 New best TEST metrics
+2025-07-09 15:10:48: Epoch: 023 Train Loss: 8.924, mae: 1.369, rse: 1.001, pcc: -0.025, kcc: -0.013
+2025-07-09 15:10:48: Epoch: 023 Test results, ap: mae: 0.585, rse: 1.033, pcc: 1.000, kcc: 0.007
+2025-07-09 15:10:49: Epoch: 024 Train Loss: 8.932, mae: 1.373, rse: 1.002, pcc: 0.043, kcc: 0.131
+2025-07-09 15:10:50: Epoch: 024 Test results, ap: mae: 0.583, rse: 1.020, pcc: 0.121, kcc: -0.098
+2025-07-09 15:10:51: Epoch: 025 Train Loss: 8.931, mae: 1.370, rse: 1.001, pcc: 0.040, kcc: 0.102
+2025-07-09 15:10:51: Epoch: 025 Test results, ap: mae: 0.584, rse: 1.029, pcc: 0.140, kcc: 0.075
+2025-07-09 15:10:52: Epoch: 026 Train Loss: 8.923, mae: 1.370, rse: 1.001, pcc: 0.042, kcc: 0.076
+2025-07-09 15:10:53: Epoch: 026 Test results, ap: mae: 0.584, rse: 1.028, pcc: -0.517, kcc: 0.114
+2025-07-09 15:10:54: Epoch: 027 Train Loss: 8.926, mae: 1.372, rse: 1.002, pcc: 0.042, kcc: 0.044
+2025-07-09 15:10:54: Epoch: 027 Test results, ap: mae: 0.584, rse: 1.024, pcc: 0.118, kcc: 0.005
+2025-07-09 15:10:55: Epoch: 028 Train Loss: 8.925, mae: 1.372, rse: 1.002, pcc: 0.050, kcc: 0.098
+2025-07-09 15:10:56: Epoch: 028 Test results, ap: mae: 0.584, rse: 1.023, pcc: 0.384, kcc: 0.046
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+2025-07-09 15:11:04: Epoch: 034 Test results, ap: mae: 0.584, rse: 1.028, pcc: -0.227, kcc: -0.082
+2025-07-09 15:11:05: Epoch: 035 Train Loss: 8.922, mae: 1.371, rse: 1.001, pcc: 0.042, kcc: 0.007
+2025-07-09 15:11:06: Epoch: 035 Test results, ap: mae: 0.584, rse: 1.028, pcc: -0.592, kcc: 0.035
+2025-07-09 15:11:06: used time 0.01h
+2025-07-09 15:11:06: Running Cross Validation 1
+2025-07-09 15:11:06: Fold 1  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:11:07: Epoch: 001 Train Loss: 8.946, mae: 1.336, rse: 1.002, pcc: 0.077, kcc: -0.060
+2025-07-09 15:11:07: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:11:07: Epoch: 001 Test results, ap: mae: 0.652, rse: 1.288, pcc: -0.320, kcc: -0.170
+2025-07-09 15:11:08: Epoch: 002 Train Loss: 8.943, mae: 1.423, rse: 1.075, pcc: 0.169, kcc: 0.183
+2025-07-09 15:11:08: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:11:09: Epoch: 002 Test results, ap: mae: 0.608, rse: 1.041, pcc: 0.328, kcc: 0.146
+2025-07-09 15:11:10: Epoch: 003 Train Loss: 8.948, mae: 1.376, rse: 1.026, pcc: 0.047, kcc: -0.107
+2025-07-09 15:11:10: Epoch: 003 Test results, ap: mae: 0.581, rse: 1.004, pcc: -0.128, kcc: -0.121
+2025-07-09 15:11:12: Epoch: 004 Train Loss: 8.931, mae: 1.377, rse: 1.026, pcc: 0.146, kcc: 0.177
+2025-07-09 15:11:12: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:11:12: Epoch: 004 Test results, ap: mae: 0.581, rse: 1.004, pcc: 0.294, kcc: 0.156
+2025-07-09 15:11:13: Epoch: 005 Train Loss: 8.930, mae: 1.388, rse: 1.037, pcc: -0.151, kcc: -0.189
+2025-07-09 15:11:13: Epoch: 005 Test results, ap: mae: 0.583, rse: 1.000, pcc: -0.162, kcc: -0.198
+2025-07-09 15:11:15: Epoch: 006 Train Loss: 8.939, mae: 1.348, rse: 1.004, pcc: 0.144, kcc: -0.035
+2025-07-09 15:11:15: Epoch: 006 Test results, ap: mae: 0.597, rse: 1.088, pcc: -0.331, kcc: -0.132
+2025-07-09 15:11:16: Epoch: 007 Train Loss: 8.918, mae: 1.340, rse: 1.000, pcc: 0.094, kcc: -0.044
+2025-07-09 15:11:17: Epoch: 007 Test results, ap: mae: 0.619, rse: 1.172, pcc: -0.260, kcc: -0.141
+2025-07-09 15:11:18: Epoch: 008 Train Loss: 8.939, mae: 1.371, rse: 1.021, pcc: -0.151, kcc: -0.154
+2025-07-09 15:11:18: Epoch: 008 Test results, ap: mae: 0.582, rse: 1.011, pcc: -0.048, kcc: -0.113
+2025-07-09 15:11:19: Epoch: 009 Train Loss: 8.932, mae: 1.373, rse: 1.022, pcc: 0.143, kcc: 0.181
+2025-07-09 15:11:19: Epoch: 009 New best VALIDATION metrics
+2025-07-09 15:11:20: Epoch: 009 Test results, ap: mae: 0.582, rse: 1.008, pcc: 0.328, kcc: 0.192
+2025-07-09 15:11:21: Epoch: 010 Train Loss: 8.933, mae: 1.370, rse: 1.020, pcc: 0.156, kcc: 0.134
+2025-07-09 15:11:21: Epoch: 010 Test results, ap: mae: 0.582, rse: 1.012, pcc: -0.033, kcc: -0.069
+2025-07-09 15:11:23: Epoch: 011 Train Loss: 8.920, mae: 1.378, rse: 1.027, pcc: 0.154, kcc: 0.121
+2025-07-09 15:11:23: Epoch: 011 Test results, ap: mae: 0.581, rse: 1.003, pcc: 1.000, kcc: 0.018
+2025-07-09 15:11:23: Epoch: 011 New best TEST metrics
+2025-07-09 15:11:24: Epoch: 012 Train Loss: 8.920, mae: 1.375, rse: 1.024, pcc: 0.158, kcc: 0.070
+2025-07-09 15:11:24: Epoch: 012 Test results, ap: mae: 0.581, rse: 1.006, pcc: 0.326, kcc: 0.113
+2025-07-09 15:11:26: Epoch: 013 Train Loss: 8.922, mae: 1.377, rse: 1.027, pcc: 0.153, kcc: 0.112
+2025-07-09 15:11:26: Epoch: 013 Test results, ap: mae: 0.581, rse: 1.003, pcc: 0.319, kcc: 0.178
+2025-07-09 15:11:27: Epoch: 014 Train Loss: 8.924, mae: 1.366, rse: 1.016, pcc: 0.155, kcc: 0.091
+2025-07-09 15:11:28: Epoch: 014 Test results, ap: mae: 0.583, rse: 1.022, pcc: -1.000, kcc: 0.107
+2025-07-09 15:11:29: Epoch: 015 Train Loss: 8.928, mae: 1.383, rse: 1.033, pcc: 0.148, kcc: 0.129
+2025-07-09 15:11:29: Epoch: 015 Test results, ap: mae: 0.582, rse: 1.000, pcc: -0.464, kcc: 0.180
+2025-07-09 15:11:30: Epoch: 016 Train Loss: 8.921, mae: 1.375, rse: 1.025, pcc: 0.151, kcc: 0.123
+2025-07-09 15:11:31: Epoch: 016 Test results, ap: mae: 0.581, rse: 1.005, pcc: -0.065, kcc: 0.081
+2025-07-09 15:11:32: Epoch: 017 Train Loss: 8.928, mae: 1.377, rse: 1.026, pcc: 0.150, kcc: 0.112
+2025-07-09 15:11:32: Epoch: 017 Test results, ap: mae: 0.581, rse: 1.004, pcc: 0.017, kcc: 0.100
+2025-07-09 15:11:34: Epoch: 018 Train Loss: 8.921, mae: 1.380, rse: 1.029, pcc: 0.148, kcc: 0.113
+2025-07-09 15:11:34: Epoch: 018 Test results, ap: mae: 0.581, rse: 1.002, pcc: 0.062, kcc: 0.115
+2025-07-09 15:11:35: Epoch: 019 Train Loss: 8.924, mae: 1.377, rse: 1.026, pcc: 0.078, kcc: -0.003
+2025-07-09 15:11:35: Epoch: 019 Test results, ap: mae: 0.581, rse: 1.004, pcc: -0.028, kcc: -0.057
+2025-07-09 15:11:37: Epoch: 020 Train Loss: 8.927, mae: 1.378, rse: 1.028, pcc: -0.068, kcc: 0.002
+2025-07-09 15:11:37: Epoch: 020 Test results, ap: mae: 0.581, rse: 1.003, pcc: -0.018, kcc: -0.130
+2025-07-09 15:11:38: Epoch: 021 Train Loss: 8.920, mae: 1.377, rse: 1.026, pcc: -0.067, kcc: -0.058
+2025-07-09 15:11:39: Epoch: 021 Test results, ap: mae: 0.581, rse: 1.004, pcc: 0.686, kcc: -0.054
+2025-07-09 15:11:40: Epoch: 022 Train Loss: 8.914, mae: 1.380, rse: 1.030, pcc: 0.075, kcc: 0.031
+2025-07-09 15:11:40: Epoch: 022 Test results, ap: mae: 0.581, rse: 1.002, pcc: -0.377, kcc: -0.041
+2025-07-09 15:11:41: Epoch: 023 Train Loss: 8.925, mae: 1.377, rse: 1.027, pcc: 0.022, kcc: -0.062
+2025-07-09 15:11:42: Epoch: 023 Test results, ap: mae: 0.581, rse: 1.004, pcc: -0.658, kcc: -0.109
+2025-07-09 15:11:43: Epoch: 024 Train Loss: 8.917, mae: 1.378, rse: 1.027, pcc: -0.058, kcc: -0.022
+2025-07-09 15:11:43: Epoch: 024 Test results, ap: mae: 0.581, rse: 1.003, pcc: 0.299, kcc: 0.073
+2025-07-09 15:11:45: Epoch: 025 Train Loss: 8.928, mae: 1.380, rse: 1.029, pcc: -0.018, kcc: -0.013
+2025-07-09 15:11:45: Epoch: 025 Test results, ap: mae: 0.581, rse: 1.002, pcc: -0.149, kcc: -0.014
+2025-07-09 15:11:46: Epoch: 026 Train Loss: 8.924, mae: 1.374, rse: 1.023, pcc: -0.034, kcc: -0.050
+2025-07-09 15:11:46: Epoch: 026 Test results, ap: mae: 0.581, rse: 1.007, pcc: 0.064, kcc: -0.042
+2025-07-09 15:11:48: Epoch: 027 Train Loss: 8.931, mae: 1.375, rse: 1.024, pcc: -0.043, kcc: -0.015
+2025-07-09 15:11:48: Epoch: 027 Test results, ap: mae: 0.581, rse: 1.006, pcc: 0.331, kcc: 0.095
+2025-07-09 15:11:49: Epoch: 028 Train Loss: 8.920, mae: 1.378, rse: 1.027, pcc: -0.038, kcc: -0.050
+2025-07-09 15:11:50: Epoch: 028 Test results, ap: mae: 0.581, rse: 1.003, pcc: -0.526, kcc: -0.056
+2025-07-09 15:11:51: Epoch: 029 Train Loss: 8.910, mae: 1.380, rse: 1.029, pcc: -0.038, kcc: -0.016
+2025-07-09 15:11:51: Epoch: 029 Test results, ap: mae: 0.581, rse: 1.002, pcc: 0.183, kcc: -0.082
+2025-07-09 15:11:52: Epoch: 030 Train Loss: 8.926, mae: 1.377, rse: 1.027, pcc: -0.019, kcc: -0.009
+2025-07-09 15:11:53: Epoch: 030 Test results, ap: mae: 0.581, rse: 1.003, pcc: 0.734, kcc: 0.155
+2025-07-09 15:11:54: Epoch: 031 Train Loss: 8.915, mae: 1.377, rse: 1.026, pcc: -0.031, kcc: -0.034
+2025-07-09 15:11:54: Epoch: 031 Test results, ap: mae: 0.581, rse: 1.004, pcc: -1.000, kcc: nan
+2025-07-09 15:11:55: Epoch: 032 Train Loss: 8.923, mae: 1.377, rse: 1.026, pcc: -0.020, kcc: -0.017
+2025-07-09 15:11:56: Epoch: 032 Test results, ap: mae: 0.581, rse: 1.004, pcc: 1.000, kcc: -0.042
+2025-07-09 15:11:57: Epoch: 033 Train Loss: 8.931, mae: 1.375, rse: 1.025, pcc: -0.017, kcc: -0.038
+2025-07-09 15:11:57: Epoch: 033 Test results, ap: mae: 0.581, rse: 1.005, pcc: 0.599, kcc: 0.136
+2025-07-09 15:11:59: Epoch: 034 Train Loss: 8.919, mae: 1.376, rse: 1.026, pcc: -0.013, kcc: -0.042
+2025-07-09 15:11:59: Epoch: 034 Test results, ap: mae: 0.581, rse: 1.004, pcc: -0.049, kcc: 0.027
+2025-07-09 15:12:00: Epoch: 035 Train Loss: 8.920, mae: 1.378, rse: 1.028, pcc: -0.014, kcc: -0.020
+2025-07-09 15:12:00: Epoch: 035 Test results, ap: mae: 0.581, rse: 1.003, pcc: 0.143, kcc: -0.008
+2025-07-09 15:12:00: used time 0.02h
+2025-07-09 15:12:00: Running Cross Validation 2
+2025-07-09 15:12:00: Fold 2  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:12:02: Epoch: 001 Train Loss: 8.851, mae: 1.483, rse: 1.000, pcc: 0.056, kcc: 0.090
+2025-07-09 15:12:02: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:12:02: Epoch: 001 Test results, ap: mae: 0.595, rse: 1.079, pcc: 0.245, kcc: 0.269
+2025-07-09 15:12:03: Epoch: 002 Train Loss: 8.806, mae: 1.484, rse: 1.001, pcc: 0.063, kcc: 0.088
+2025-07-09 15:12:03: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:12:04: Epoch: 002 Test results, ap: mae: 0.594, rse: 1.075, pcc: 0.249, kcc: 0.236
+2025-07-09 15:12:05: Epoch: 003 Train Loss: 8.791, mae: 1.528, rse: 1.024, pcc: 0.082, kcc: 0.097
+2025-07-09 15:12:05: Epoch: 003 Test results, ap: mae: 0.588, rse: 1.004, pcc: 0.193, kcc: 0.225
+2025-07-09 15:12:07: Epoch: 004 Train Loss: 8.809, mae: 1.480, rse: 1.000, pcc: -0.030, kcc: -0.054
+2025-07-09 15:12:07: Epoch: 004 Test results, ap: mae: 0.600, rse: 1.098, pcc: -0.075, kcc: -0.259
+2025-07-09 15:12:08: Epoch: 005 Train Loss: 8.810, mae: 1.499, rse: 1.006, pcc: -0.041, kcc: -0.054
+2025-07-09 15:12:08: Epoch: 005 Test results, ap: mae: 0.583, rse: 1.020, pcc: -0.138, kcc: -0.216
+2025-07-09 15:12:10: Epoch: 006 Train Loss: 8.810, mae: 1.485, rse: 1.001, pcc: -0.069, kcc: -0.103
+2025-07-09 15:12:10: Epoch: 006 Test results, ap: mae: 0.593, rse: 1.070, pcc: -0.127, kcc: -0.149
+2025-07-09 15:12:11: Epoch: 007 Train Loss: 8.808, mae: 1.504, rse: 1.008, pcc: 0.069, kcc: 0.111
+2025-07-09 15:12:11: Epoch: 007 New best VALIDATION metrics
+2025-07-09 15:12:11: Epoch: 007 Test results, ap: mae: 0.582, rse: 1.010, pcc: 0.014, kcc: 0.072
+2025-07-09 15:12:13: Epoch: 008 Train Loss: 8.876, mae: 1.515, rse: 1.015, pcc: 0.062, kcc: 0.101
+2025-07-09 15:12:13: Epoch: 008 Test results, ap: mae: 0.582, rse: 1.000, pcc: -0.060, kcc: 0.100
+2025-07-09 15:12:14: Epoch: 009 Train Loss: 8.817, mae: 1.487, rse: 1.001, pcc: -0.063, kcc: 0.007
+2025-07-09 15:12:15: Epoch: 009 Test results, ap: mae: 0.590, rse: 1.058, pcc: 0.021, kcc: 0.000
+2025-07-09 15:12:16: Epoch: 010 Train Loss: 8.797, mae: 1.503, rse: 1.008, pcc: 0.064, kcc: 0.080
+2025-07-09 15:12:16: Epoch: 010 Test results, ap: mae: 0.582, rse: 1.012, pcc: 0.062, kcc: 0.056
+2025-07-09 15:12:17: Epoch: 011 Train Loss: 8.786, mae: 1.502, rse: 1.007, pcc: 0.065, kcc: 0.055
+2025-07-09 15:12:18: Epoch: 011 Test results, ap: mae: 0.582, rse: 1.014, pcc: 0.285, kcc: 0.084
+2025-07-09 15:12:18: Epoch: 011 New best TEST metrics
+2025-07-09 15:12:19: Epoch: 012 Train Loss: 8.794, mae: 1.503, rse: 1.008, pcc: 0.060, kcc: 0.060
+2025-07-09 15:12:19: Epoch: 012 Test results, ap: mae: 0.582, rse: 1.012, pcc: -0.138, kcc: 0.261
+2025-07-09 15:12:21: Epoch: 013 Train Loss: 8.796, mae: 1.505, rse: 1.009, pcc: -0.066, kcc: -0.053
+2025-07-09 15:12:21: Epoch: 013 Test results, ap: mae: 0.582, rse: 1.009, pcc: -0.329, kcc: 0.073
+2025-07-09 15:12:22: Epoch: 014 Train Loss: 8.788, mae: 1.510, rse: 1.012, pcc: 0.063, kcc: -0.011
+2025-07-09 15:12:22: Epoch: 014 Test results, ap: mae: 0.581, rse: 1.003, pcc: -0.260, kcc: -0.144
+2025-07-09 15:12:24: Epoch: 015 Train Loss: 8.788, mae: 1.541, rse: 1.035, pcc: -0.067, kcc: -0.121
+2025-07-09 15:12:24: Epoch: 015 Test results, ap: mae: 0.599, rse: 1.022, pcc: -0.115, kcc: -0.114
+2025-07-09 15:12:25: Epoch: 016 Train Loss: 8.804, mae: 1.500, rse: 1.006, pcc: -0.062, kcc: -0.050
+2025-07-09 15:12:25: Epoch: 016 Test results, ap: mae: 0.583, rse: 1.018, pcc: -0.057, kcc: -0.144
+2025-07-09 15:12:27: Epoch: 017 Train Loss: 8.780, mae: 1.510, rse: 1.011, pcc: 0.063, kcc: 0.073
+2025-07-09 15:12:27: Epoch: 017 Test results, ap: mae: 0.581, rse: 1.004, pcc: 0.254, kcc: 0.067
+2025-07-09 15:12:28: Epoch: 018 Train Loss: 8.791, mae: 1.515, rse: 1.015, pcc: 0.064, kcc: 0.106
+2025-07-09 15:12:29: Epoch: 018 Test results, ap: mae: 0.582, rse: 1.001, pcc: -0.058, kcc: 0.016
+2025-07-09 15:12:30: Epoch: 019 Train Loss: 8.790, mae: 1.507, rse: 1.010, pcc: 0.064, kcc: 0.006
+2025-07-09 15:12:30: Epoch: 019 Test results, ap: mae: 0.581, rse: 1.007, pcc: 0.264, kcc: 0.053
+2025-07-09 15:12:32: Epoch: 020 Train Loss: 8.790, mae: 1.511, rse: 1.012, pcc: -0.062, kcc: -0.098
+2025-07-09 15:12:32: Epoch: 020 Test results, ap: mae: 0.581, rse: 1.002, pcc: 0.295, kcc: -0.117
+2025-07-09 15:12:33: Epoch: 021 Train Loss: 8.782, mae: 1.505, rse: 1.009, pcc: -0.064, kcc: -0.053
+2025-07-09 15:12:33: Epoch: 021 Test results, ap: mae: 0.582, rse: 1.010, pcc: -0.031, kcc: -0.028
+2025-07-09 15:12:35: Epoch: 022 Train Loss: 8.781, mae: 1.507, rse: 1.010, pcc: -0.064, kcc: -0.044
+2025-07-09 15:12:35: Epoch: 022 Test results, ap: mae: 0.581, rse: 1.007, pcc: 0.380, kcc: -0.042
+2025-07-09 15:12:36: Epoch: 023 Train Loss: 8.794, mae: 1.506, rse: 1.009, pcc: -0.062, kcc: -0.033
+2025-07-09 15:12:36: Epoch: 023 Test results, ap: mae: 0.582, rse: 1.008, pcc: 0.124, kcc: -0.084
+2025-07-09 15:12:38: Epoch: 024 Train Loss: 8.798, mae: 1.508, rse: 1.010, pcc: 0.065, kcc: -0.001
+2025-07-09 15:12:38: Epoch: 024 Test results, ap: mae: 0.581, rse: 1.005, pcc: 0.197, kcc: 0.013
+2025-07-09 15:12:39: Epoch: 025 Train Loss: 8.808, mae: 1.507, rse: 1.010, pcc: 0.065, kcc: 0.034
+2025-07-09 15:12:40: Epoch: 025 Test results, ap: mae: 0.581, rse: 1.007, pcc: 0.243, kcc: 0.017
+2025-07-09 15:12:41: Epoch: 026 Train Loss: 8.782, mae: 1.506, rse: 1.010, pcc: -0.061, kcc: -0.055
+2025-07-09 15:12:41: Epoch: 026 Test results, ap: mae: 0.581, rse: 1.007, pcc: -0.401, kcc: -0.081
+2025-07-09 15:12:42: Epoch: 027 Train Loss: 8.788, mae: 1.506, rse: 1.009, pcc: -0.054, kcc: -0.065
+2025-07-09 15:12:43: Epoch: 027 Test results, ap: mae: 0.582, rse: 1.008, pcc: 0.097, kcc: -0.009
+2025-07-09 15:12:44: Epoch: 028 Train Loss: 8.794, mae: 1.505, rse: 1.009, pcc: -0.063, kcc: -0.093
+2025-07-09 15:12:44: Epoch: 028 Test results, ap: mae: 0.582, rse: 1.009, pcc: 0.099, kcc: -0.041
+2025-07-09 15:12:45: Epoch: 029 Train Loss: 8.785, mae: 1.501, rse: 1.007, pcc: -0.063, kcc: -0.096
+2025-07-09 15:12:46: Epoch: 029 Test results, ap: mae: 0.583, rse: 1.016, pcc: -0.010, kcc: -0.053
+2025-07-09 15:12:47: Epoch: 030 Train Loss: 8.783, mae: 1.504, rse: 1.008, pcc: -0.063, kcc: -0.067
+2025-07-09 15:12:47: Epoch: 030 Test results, ap: mae: 0.582, rse: 1.010, pcc: 0.057, kcc: -0.079
+2025-07-09 15:12:49: Epoch: 031 Train Loss: 8.784, mae: 1.502, rse: 1.007, pcc: -0.063, kcc: -0.055
+2025-07-09 15:12:49: Epoch: 031 Test results, ap: mae: 0.582, rse: 1.013, pcc: -0.076, kcc: -0.045
+2025-07-09 15:12:50: Epoch: 032 Train Loss: 8.786, mae: 1.502, rse: 1.007, pcc: -0.063, kcc: -0.086
+2025-07-09 15:12:50: Epoch: 032 Test results, ap: mae: 0.582, rse: 1.013, pcc: 0.119, kcc: -0.079
+2025-07-09 15:12:52: Epoch: 033 Train Loss: 8.774, mae: 1.505, rse: 1.009, pcc: -0.063, kcc: -0.118
+2025-07-09 15:12:52: Epoch: 033 Test results, ap: mae: 0.582, rse: 1.009, pcc: 0.142, kcc: -0.043
+2025-07-09 15:12:53: Epoch: 034 Train Loss: 8.793, mae: 1.505, rse: 1.009, pcc: -0.062, kcc: -0.051
+2025-07-09 15:12:54: Epoch: 034 Test results, ap: mae: 0.582, rse: 1.009, pcc: -0.239, kcc: -0.045
+2025-07-09 15:12:55: Epoch: 035 Train Loss: 8.777, mae: 1.504, rse: 1.008, pcc: -0.060, kcc: -0.084
+2025-07-09 15:12:55: Epoch: 035 Test results, ap: mae: 0.582, rse: 1.010, pcc: -0.322, kcc: 0.047
+2025-07-09 15:12:55: used time 0.02h
+2025-07-09 15:12:55: Running Cross Validation 3
+2025-07-09 15:12:55: Fold 3  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:12:56: Epoch: 001 Train Loss: 8.927, mae: 1.321, rse: 1.023, pcc: 0.067, kcc: 0.130
+2025-07-09 15:12:56: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:12:57: Epoch: 001 Test results, ap: mae: 0.624, rse: 1.190, pcc: 0.144, kcc: 0.150
+2025-07-09 15:12:58: Epoch: 002 Train Loss: 8.917, mae: 1.337, rse: 1.000, pcc: 0.070, kcc: 0.143
+2025-07-09 15:12:58: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:12:58: Epoch: 002 Test results, ap: mae: 0.581, rse: 1.001, pcc: 0.139, kcc: 0.144
+2025-07-09 15:13:00: Epoch: 003 Train Loss: 8.905, mae: 1.322, rse: 1.028, pcc: 0.056, kcc: 0.138
+2025-07-09 15:13:00: Epoch: 003 Test results, ap: mae: 0.636, rse: 1.228, pcc: 0.121, kcc: 0.111
+2025-07-09 15:13:01: Epoch: 004 Train Loss: 8.915, mae: 1.322, rse: 1.004, pcc: 0.164, kcc: 0.184
+2025-07-09 15:13:01: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:13:01: Epoch: 004 Test results, ap: mae: 0.590, rse: 1.057, pcc: 0.196, kcc: 0.198
+2025-07-09 15:13:03: Epoch: 005 Train Loss: 8.926, mae: 1.352, rse: 1.004, pcc: -0.019, kcc: -0.099
+2025-07-09 15:13:03: Epoch: 005 Test results, ap: mae: 0.589, rse: 1.006, pcc: -0.605, kcc: 0.061
+2025-07-09 15:13:04: Epoch: 006 Train Loss: 8.916, mae: 1.321, rse: 1.020, pcc: -0.001, kcc: -0.026
+2025-07-09 15:13:05: Epoch: 006 Test results, ap: mae: 0.617, rse: 1.167, pcc: 1.000, kcc: nan
+2025-07-09 15:13:06: Epoch: 007 Train Loss: 8.922, mae: 1.323, rse: 1.005, pcc: 0.019, kcc: -0.054
+2025-07-09 15:13:06: Epoch: 007 Test results, ap: mae: 0.591, rse: 1.059, pcc: -0.581, kcc: 0.163
+2025-07-09 15:13:07: Epoch: 008 Train Loss: 8.891, mae: 1.331, rse: 1.000, pcc: -0.045, kcc: -0.094
+2025-07-09 15:13:08: Epoch: 008 Test results, ap: mae: 0.582, rse: 1.011, pcc: -0.248, kcc: -0.234
+2025-07-09 15:13:09: Epoch: 009 Train Loss: 8.905, mae: 1.321, rse: 1.009, pcc: 0.005, kcc: -0.011
+2025-07-09 15:13:09: Epoch: 009 Test results, ap: mae: 0.597, rse: 1.088, pcc: -1.000, kcc: nan
+2025-07-09 15:13:10: Epoch: 010 Train Loss: 8.914, mae: 1.326, rse: 1.002, pcc: -0.036, kcc: 0.018
+2025-07-09 15:13:11: Epoch: 010 Test results, ap: mae: 0.586, rse: 1.036, pcc: -1.000, kcc: -0.232
+2025-07-09 15:13:12: Epoch: 011 Train Loss: 8.901, mae: 1.322, rse: 1.005, pcc: 0.000, kcc: 0.011
+2025-07-09 15:13:12: Epoch: 011 Test results, ap: mae: 0.591, rse: 1.062, pcc: -1.000, kcc: nan
+2025-07-09 15:13:13: Epoch: 012 Train Loss: 8.898, mae: 1.327, rse: 1.002, pcc: -0.090, kcc: 0.094
+2025-07-09 15:13:14: Epoch: 012 Test results, ap: mae: 0.585, rse: 1.030, pcc: -0.370, kcc: -0.136
+2025-07-09 15:13:14: Epoch: 012 New best TEST metrics
+2025-07-09 15:13:15: Epoch: 013 Train Loss: 8.904, mae: 1.323, rse: 1.004, pcc: 0.046, kcc: 0.074
+2025-07-09 15:13:15: Epoch: 013 Test results, ap: mae: 0.589, rse: 1.054, pcc: 0.098, kcc: -0.014
+2025-07-09 15:13:15: Epoch: 013 New best TEST metrics
+2025-07-09 15:13:17: Epoch: 014 Train Loss: 8.902, mae: 1.325, rse: 1.002, pcc: 0.172, kcc: 0.189
+2025-07-09 15:13:17: Epoch: 014 New best VALIDATION metrics
+2025-07-09 15:13:17: Epoch: 014 Test results, ap: mae: 0.586, rse: 1.037, pcc: nan, kcc: nan
+2025-07-09 15:13:18: Epoch: 015 Train Loss: 8.904, mae: 1.323, rse: 1.004, pcc: -0.068, kcc: -0.050
+2025-07-09 15:13:19: Epoch: 015 Test results, ap: mae: 0.590, rse: 1.055, pcc: -0.087, kcc: 0.020
+2025-07-09 15:13:20: Epoch: 016 Train Loss: 8.904, mae: 1.324, rse: 1.004, pcc: -0.116, kcc: -0.001
+2025-07-09 15:13:20: Epoch: 016 Test results, ap: mae: 0.588, rse: 1.050, pcc: -0.554, kcc: -0.169
+2025-07-09 15:13:21: Epoch: 017 Train Loss: 8.910, mae: 1.324, rse: 1.003, pcc: 0.000, kcc: 0.011
+2025-07-09 15:13:22: Epoch: 017 Test results, ap: mae: 0.588, rse: 1.047, pcc: nan, kcc: nan
+2025-07-09 15:13:23: Epoch: 018 Train Loss: 8.904, mae: 1.323, rse: 1.004, pcc: 0.000, kcc: 0.011
+2025-07-09 15:13:23: Epoch: 018 Test results, ap: mae: 0.590, rse: 1.055, pcc: -1.000, kcc: nan
+2025-07-09 15:13:25: Epoch: 019 Train Loss: 8.909, mae: 1.326, rse: 1.002, pcc: 0.164, kcc: 0.037
+2025-07-09 15:13:25: Epoch: 019 Test results, ap: mae: 0.586, rse: 1.037, pcc: 1.000, kcc: nan
+2025-07-09 15:13:26: Epoch: 020 Train Loss: 8.908, mae: 1.324, rse: 1.003, pcc: -0.002, kcc: 0.021
+2025-07-09 15:13:26: Epoch: 020 Test results, ap: mae: 0.588, rse: 1.047, pcc: -0.172, kcc: -0.068
+2025-07-09 15:13:28: Epoch: 021 Train Loss: 8.903, mae: 1.323, rse: 1.004, pcc: -0.011, kcc: -0.081
+2025-07-09 15:13:28: Epoch: 021 Test results, ap: mae: 0.590, rse: 1.055, pcc: 0.021, kcc: 0.085
+2025-07-09 15:13:28: Epoch: 021 New best TEST metrics
+2025-07-09 15:13:29: Epoch: 022 Train Loss: 8.899, mae: 1.325, rse: 1.003, pcc: 0.048, kcc: -0.033
+2025-07-09 15:13:30: Epoch: 022 Test results, ap: mae: 0.586, rse: 1.040, pcc: -1.000, kcc: 0.042
+2025-07-09 15:13:31: Epoch: 023 Train Loss: 8.900, mae: 1.325, rse: 1.003, pcc: -0.274, kcc: 0.046
+2025-07-09 15:13:31: Epoch: 023 Test results, ap: mae: 0.587, rse: 1.040, pcc: nan, kcc: nan
+2025-07-09 15:13:32: Epoch: 024 Train Loss: 8.900, mae: 1.324, rse: 1.003, pcc: 0.000, kcc: 0.011
+2025-07-09 15:13:33: Epoch: 024 Test results, ap: mae: 0.588, rse: 1.047, pcc: 1.000, kcc: nan
+2025-07-09 15:13:34: Epoch: 025 Train Loss: 8.910, mae: 1.324, rse: 1.003, pcc: -0.146, kcc: -0.142
+2025-07-09 15:13:34: Epoch: 025 Test results, ap: mae: 0.588, rse: 1.046, pcc: nan, kcc: nan
+2025-07-09 15:13:35: Epoch: 026 Train Loss: 8.894, mae: 1.324, rse: 1.003, pcc: 0.000, kcc: 0.011
+2025-07-09 15:13:36: Epoch: 026 Test results, ap: mae: 0.587, rse: 1.045, pcc: -1.000, kcc: nan
+2025-07-09 15:13:37: Epoch: 027 Train Loss: 8.897, mae: 1.325, rse: 1.003, pcc: -0.005, kcc: 0.226
+2025-07-09 15:13:37: Epoch: 027 Test results, ap: mae: 0.586, rse: 1.039, pcc: 0.254, kcc: 0.250
+2025-07-09 15:13:37: Epoch: 027 New best TEST metrics
+2025-07-09 15:13:39: Epoch: 028 Train Loss: 8.903, mae: 1.326, rse: 1.002, pcc: 0.221, kcc: 0.057
+2025-07-09 15:13:39: Epoch: 028 Test results, ap: mae: 0.585, rse: 1.033, pcc: 0.960, kcc: 0.072
+2025-07-09 15:13:39: Epoch: 028 New best TEST metrics
+2025-07-09 15:13:40: Epoch: 029 Train Loss: 8.905, mae: 1.324, rse: 1.003, pcc: -0.019, kcc: 0.050
+2025-07-09 15:13:40: Epoch: 029 Test results, ap: mae: 0.587, rse: 1.044, pcc: -0.293, kcc: -0.156
+2025-07-09 15:13:42: Epoch: 030 Train Loss: 8.895, mae: 1.326, rse: 1.002, pcc: -0.048, kcc: -0.013
+2025-07-09 15:13:42: Epoch: 030 Test results, ap: mae: 0.586, rse: 1.037, pcc: 1.000, kcc: -0.088
+2025-07-09 15:13:43: Epoch: 031 Train Loss: 8.887, mae: 1.326, rse: 1.002, pcc: 0.013, kcc: 0.061
+2025-07-09 15:13:44: Epoch: 031 Test results, ap: mae: 0.585, rse: 1.033, pcc: nan, kcc: nan
+2025-07-09 15:13:45: Epoch: 032 Train Loss: 8.897, mae: 1.326, rse: 1.002, pcc: 0.327, kcc: -0.013
+2025-07-09 15:13:45: Epoch: 032 Test results, ap: mae: 0.585, rse: 1.033, pcc: nan, kcc: nan
+2025-07-09 15:13:46: Epoch: 033 Train Loss: 8.895, mae: 1.326, rse: 1.002, pcc: 0.539, kcc: -0.013
+2025-07-09 15:13:46: Epoch: 033 New best VALIDATION metrics
+2025-07-09 15:13:47: Epoch: 033 Test results, ap: mae: 0.586, rse: 1.036, pcc: nan, kcc: nan
+2025-07-09 15:13:48: Epoch: 034 Train Loss: 8.894, mae: 1.326, rse: 1.002, pcc: -0.294, kcc: -0.038
+2025-07-09 15:13:48: Epoch: 034 Test results, ap: mae: 0.585, rse: 1.033, pcc: 1.000, kcc: 0.254
+2025-07-09 15:13:48: Epoch: 034 New best TEST metrics
+2025-07-09 15:13:50: Epoch: 035 Train Loss: 8.905, mae: 1.325, rse: 1.003, pcc: 1.000, kcc: 0.011
+2025-07-09 15:13:50: Epoch: 035 New best VALIDATION metrics
+2025-07-09 15:13:50: Epoch: 035 Test results, ap: mae: 0.586, rse: 1.038, pcc: 0.118, kcc: 0.037
+2025-07-09 15:13:50: used time 0.02h
+2025-07-09 15:13:50: Running Cross Validation 4
+2025-07-09 15:13:50: Fold 4  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:13:51: Epoch: 001 Train Loss: 8.927, mae: 1.398, rse: 1.019, pcc: 0.078, kcc: 0.092
+2025-07-09 15:13:51: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:13:52: Epoch: 001 Test results, ap: mae: 0.582, rse: 1.013, pcc: 0.070, kcc: 0.072
+2025-07-09 15:13:53: Epoch: 002 Train Loss: 8.898, mae: 1.453, rse: 1.065, pcc: 0.075, kcc: 0.106
+2025-07-09 15:13:53: Epoch: 002 Test results, ap: mae: 0.602, rse: 1.029, pcc: 0.076, kcc: 0.075
+2025-07-09 15:13:54: Epoch: 003 Train Loss: 8.938, mae: 1.397, rse: 1.018, pcc: -0.071, kcc: -0.114
+2025-07-09 15:13:55: Epoch: 003 Test results, ap: mae: 0.582, rse: 1.014, pcc: -0.109, kcc: -0.144
+2025-07-09 15:13:56: Epoch: 004 Train Loss: 8.904, mae: 1.363, rse: 1.001, pcc: -0.093, kcc: -0.120
+2025-07-09 15:13:56: Epoch: 004 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.106, kcc: -0.086
+2025-07-09 15:13:58: Epoch: 005 Train Loss: 8.895, mae: 1.482, rse: 1.093, pcc: 0.056, kcc: 0.100
+2025-07-09 15:13:58: Epoch: 005 Test results, ap: mae: 0.628, rse: 1.084, pcc: 0.033, kcc: 0.066
+2025-07-09 15:13:59: Epoch: 006 Train Loss: 8.922, mae: 1.511, rse: 1.122, pcc: 0.046, kcc: 0.062
+2025-07-09 15:13:59: Epoch: 006 Test results, ap: mae: 0.658, rse: 1.160, pcc: -0.154, kcc: -0.107
+2025-07-09 15:14:01: Epoch: 007 Train Loss: 8.900, mae: 1.408, rse: 1.026, pcc: 0.042, kcc: 0.013
+2025-07-09 15:14:01: Epoch: 007 Test results, ap: mae: 0.581, rse: 1.003, pcc: -0.017, kcc: -0.006
+2025-07-09 15:14:02: Epoch: 008 Train Loss: 8.895, mae: 1.402, rse: 1.022, pcc: 0.052, kcc: 0.145
+2025-07-09 15:14:02: Epoch: 008 New best VALIDATION metrics
+2025-07-09 15:14:02: Epoch: 008 Test results, ap: mae: 0.581, rse: 1.008, pcc: 0.264, kcc: 0.092
+2025-07-09 15:14:04: Epoch: 009 Train Loss: 8.891, mae: 1.466, rse: 1.077, pcc: 0.050, kcc: 0.098
+2025-07-09 15:14:04: Epoch: 009 Test results, ap: mae: 0.613, rse: 1.050, pcc: -0.104, kcc: -0.196
+2025-07-09 15:14:05: Epoch: 010 Train Loss: 8.876, mae: 1.393, rse: 1.016, pcc: 0.055, kcc: 0.081
+2025-07-09 15:14:06: Epoch: 010 Test results, ap: mae: 0.583, rse: 1.020, pcc: 1.000, kcc: -0.094
+2025-07-09 15:14:07: Epoch: 011 Train Loss: 8.888, mae: 1.396, rse: 1.018, pcc: -0.055, kcc: 0.044
+2025-07-09 15:14:07: Epoch: 011 Test results, ap: mae: 0.583, rse: 1.016, pcc: -0.426, kcc: -0.052
+2025-07-09 15:14:07: Epoch: 011 New best TEST metrics
+2025-07-09 15:14:08: Epoch: 012 Train Loss: 8.911, mae: 1.391, rse: 1.014, pcc: 0.052, kcc: 0.051
+2025-07-09 15:14:09: Epoch: 012 Test results, ap: mae: 0.584, rse: 1.025, pcc: 0.353, kcc: 0.082
+2025-07-09 15:14:09: Epoch: 012 New best TEST metrics
+2025-07-09 15:14:10: Epoch: 013 Train Loss: 8.882, mae: 1.404, rse: 1.023, pcc: 0.058, kcc: 0.149
+2025-07-09 15:14:10: Epoch: 013 New best VALIDATION metrics
+2025-07-09 15:14:10: Epoch: 013 Test results, ap: mae: 0.581, rse: 1.006, pcc: -1.000, kcc: 0.058
+2025-07-09 15:14:12: Epoch: 014 Train Loss: 8.891, mae: 1.420, rse: 1.036, pcc: 0.058, kcc: 0.104
+2025-07-09 15:14:12: Epoch: 014 Test results, ap: mae: 0.583, rse: 1.000, pcc: nan, kcc: nan
+2025-07-09 15:14:13: Epoch: 015 Train Loss: 8.888, mae: 1.407, rse: 1.026, pcc: 0.058, kcc: 0.130
+2025-07-09 15:14:14: Epoch: 015 Test results, ap: mae: 0.581, rse: 1.003, pcc: 0.276, kcc: -0.096
+2025-07-09 15:14:15: Epoch: 016 Train Loss: 8.874, mae: 1.400, rse: 1.021, pcc: 0.057, kcc: 0.048
+2025-07-09 15:14:15: Epoch: 016 Test results, ap: mae: 0.582, rse: 1.010, pcc: 0.189, kcc: 0.123
+2025-07-09 15:14:16: Epoch: 017 Train Loss: 8.899, mae: 1.398, rse: 1.019, pcc: -0.056, kcc: -0.107
+2025-07-09 15:14:17: Epoch: 017 Test results, ap: mae: 0.582, rse: 1.013, pcc: 0.493, kcc: -0.044
+2025-07-09 15:14:17: Epoch: 017 New best TEST metrics
+2025-07-09 15:14:18: Epoch: 018 Train Loss: 8.900, mae: 1.375, rse: 1.006, pcc: 0.055, kcc: 0.143
+2025-07-09 15:14:18: Epoch: 018 New best VALIDATION metrics
+2025-07-09 15:14:18: Epoch: 018 Test results, ap: mae: 0.593, rse: 1.067, pcc: -0.032, kcc: 0.010
+2025-07-09 15:14:19: Epoch: 019 Train Loss: 8.886, mae: 1.406, rse: 1.025, pcc: 0.057, kcc: 0.148
+2025-07-09 15:14:20: Epoch: 019 Test results, ap: mae: 0.581, rse: 1.004, pcc: -0.323, kcc: 0.002
+2025-07-09 15:14:21: Epoch: 020 Train Loss: 8.895, mae: 1.398, rse: 1.019, pcc: 0.057, kcc: 0.096
+2025-07-09 15:14:21: Epoch: 020 Test results, ap: mae: 0.582, rse: 1.012, pcc: 0.392, kcc: 0.089
+2025-07-09 15:14:21: Epoch: 020 New best TEST metrics
+2025-07-09 15:14:23: Epoch: 021 Train Loss: 8.885, mae: 1.400, rse: 1.020, pcc: 0.058, kcc: 0.119
+2025-07-09 15:14:23: Epoch: 021 Test results, ap: mae: 0.582, rse: 1.010, pcc: -0.345, kcc: 0.028
+2025-07-09 15:14:24: Epoch: 022 Train Loss: 8.895, mae: 1.402, rse: 1.022, pcc: 0.058, kcc: 0.117
+2025-07-09 15:14:24: Epoch: 022 Test results, ap: mae: 0.581, rse: 1.008, pcc: 0.228, kcc: -0.105
+2025-07-09 15:14:26: Epoch: 023 Train Loss: 8.893, mae: 1.397, rse: 1.018, pcc: 0.059, kcc: 0.101
+2025-07-09 15:14:26: Epoch: 023 Test results, ap: mae: 0.582, rse: 1.014, pcc: -0.027, kcc: 0.078
+2025-07-09 15:14:27: Epoch: 024 Train Loss: 8.889, mae: 1.402, rse: 1.022, pcc: -0.058, kcc: -0.004
+2025-07-09 15:14:28: Epoch: 024 Test results, ap: mae: 0.582, rse: 1.008, pcc: -0.116, kcc: 0.013
+2025-07-09 15:14:29: Epoch: 025 Train Loss: 8.882, mae: 1.400, rse: 1.021, pcc: -0.059, kcc: -0.124
+2025-07-09 15:14:29: Epoch: 025 Test results, ap: mae: 0.582, rse: 1.010, pcc: -1.000, kcc: -0.014
+2025-07-09 15:14:30: Epoch: 026 Train Loss: 8.880, mae: 1.402, rse: 1.022, pcc: -0.058, kcc: 0.019
+2025-07-09 15:14:31: Epoch: 026 Test results, ap: mae: 0.582, rse: 1.008, pcc: 0.001, kcc: -0.009
+2025-07-09 15:14:32: Epoch: 027 Train Loss: 8.881, mae: 1.403, rse: 1.023, pcc: 0.058, kcc: 0.037
+2025-07-09 15:14:32: Epoch: 027 Test results, ap: mae: 0.581, rse: 1.006, pcc: 0.265, kcc: -0.014
+2025-07-09 15:14:34: Epoch: 028 Train Loss: 8.893, mae: 1.396, rse: 1.017, pcc: 0.059, kcc: 0.141
+2025-07-09 15:14:34: Epoch: 028 Test results, ap: mae: 0.583, rse: 1.016, pcc: -0.451, kcc: 0.057
+2025-07-09 15:14:35: Epoch: 029 Train Loss: 8.886, mae: 1.397, rse: 1.018, pcc: 0.059, kcc: 0.120
+2025-07-09 15:14:35: Epoch: 029 Test results, ap: mae: 0.582, rse: 1.014, pcc: 0.322, kcc: 0.055
+2025-07-09 15:14:37: Epoch: 030 Train Loss: 8.882, mae: 1.402, rse: 1.022, pcc: 0.058, kcc: 0.023
+2025-07-09 15:14:37: Epoch: 030 Test results, ap: mae: 0.582, rse: 1.008, pcc: 1.000, kcc: 0.006
+2025-07-09 15:14:37: Epoch: 030 New best TEST metrics
+2025-07-09 15:14:38: Epoch: 031 Train Loss: 8.898, mae: 1.401, rse: 1.021, pcc: 0.060, kcc: 0.001
+2025-07-09 15:14:39: Epoch: 031 Test results, ap: mae: 0.582, rse: 1.008, pcc: -0.291, kcc: 0.008
+2025-07-09 15:14:40: Epoch: 032 Train Loss: 8.899, mae: 1.400, rse: 1.020, pcc: 0.060, kcc: 0.081
+2025-07-09 15:14:40: Epoch: 032 Test results, ap: mae: 0.582, rse: 1.010, pcc: -0.358, kcc: -0.047
+2025-07-09 15:14:41: Epoch: 033 Train Loss: 8.896, mae: 1.402, rse: 1.022, pcc: 0.058, kcc: 0.029
+2025-07-09 15:14:42: Epoch: 033 Test results, ap: mae: 0.582, rse: 1.008, pcc: -0.522, kcc: -0.097
+2025-07-09 15:14:43: Epoch: 034 Train Loss: 8.886, mae: 1.400, rse: 1.020, pcc: 0.060, kcc: 0.119
+2025-07-09 15:14:43: Epoch: 034 Test results, ap: mae: 0.582, rse: 1.010, pcc: -0.661, kcc: 0.118
+2025-07-09 15:14:45: Epoch: 035 Train Loss: 8.898, mae: 1.400, rse: 1.021, pcc: 0.060, kcc: 0.126
+2025-07-09 15:14:45: Epoch: 035 Test results, ap: mae: 0.582, rse: 1.009, pcc: 0.019, kcc: 0.060
+2025-07-09 15:14:45: used time 0.02h
+2025-07-09 15:14:45: Cross Validation Finished!
+2025-07-09 15:14:45: Best validation perform list
+[[1.36347997 1.00003719 0.04698431 0.13658682]
+ [1.37286067 1.02239466 0.14315383 0.18052977]
+ [1.50418377 1.00831723 0.06946141 0.11135479]
+ [1.32528687 1.00250185 1.         0.01123285]
+ [1.37540603 1.00592256 0.05545446 0.14258204]]
+2025-07-09 15:14:45: mean: [1.388 1.008 0.263 0.116]
+2025-07-09 15:14:45: std: [0.061 0.008 0.37  0.057]
+2025-07-09 15:14:45: Best test perform list
+[[0.58372492 1.02441084 1.         0.06004199]
+ [0.58108222 1.00289237 1.         0.01784542]
+ [0.58239472 1.0144012  0.28474334 0.08428963]
+ [0.58535612 1.03348923 1.         0.25394046]
+ [0.58157623 1.00814068 1.         0.00583903]]
+2025-07-09 15:14:45: mean: [0.583 1.017 0.857 0.084]
+2025-07-09 15:14:45: std: [0.002 0.011 0.286 0.089]
diff --git a/run-reg/lstm-att-256-mse-32-0.001-50/config.json b/run-reg/lstm-att-256-mse-32-0.001-50/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..abe4a292cfc818838fbb8855ba34139bff49c4c9
--- /dev/null
+++ b/run-reg/lstm-att-256-mse-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
\ No newline at end of file
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@@ -0,0 +1,6 @@
+Valid
+1.233748698234558,0.9798805117607117,0.15882132947444916,0.12372604012489319
+0.03596779564775909,0.040735533620826755,0.10483193017484893,0.02743861922115147
+Test
+0.6097168445587158,1.1384960174560548,0.7835628390312195,0.0964507669210434
+0.0032724053900586247,0.012934232776240001,0.2727266628902652,0.08239535705782922
diff --git a/run-reg/lstm-att-256-mse-32-0.001-50/training.log b/run-reg/lstm-att-256-mse-32-0.001-50/training.log
new file mode 100644
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+++ b/run-reg/lstm-att-256-mse-32-0.001-50/training.log
@@ -0,0 +1,601 @@
+2025-07-10 14:45:25: saving_dir: ./run-reg/lstm-att-256-mse-32-0.001-50
+2025-07-10 14:45:25: Loading Train Dataset
+2025-07-10 14:45:25: Loading Test Dataset
+2025-07-10 14:45:26: Running Cross Validation 0
+2025-07-10 14:45:26: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 14:45:27: Epoch: 001 Train Loss: 2.900, mae: 1.202, rse: 1.001, pcc: -0.127, kcc: -0.113
+2025-07-10 14:45:27: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:45:27: Epoch: 001 Test results, ap: mae: 0.637, rse: 1.230, pcc: 0.014, kcc: 0.015
+2025-07-10 14:45:28: Epoch: 002 Train Loss: 2.928, mae: 1.217, rse: 1.002, pcc: 0.065, kcc: -0.023
+2025-07-10 14:45:28: Epoch: 002 New best VALIDATION metrics
+2025-07-10 14:45:28: Epoch: 002 Test results, ap: mae: 0.602, rse: 1.107, pcc: 0.117, kcc: 0.089
+2025-07-10 14:45:28: Epoch: 003 Train Loss: 2.946, mae: 1.201, rse: 1.001, pcc: -0.056, kcc: -0.024
+2025-07-10 14:45:28: Epoch: 003 Test results, ap: mae: 0.639, rse: 1.240, pcc: -0.253, kcc: -0.156
+2025-07-10 14:45:29: Epoch: 004 Train Loss: 2.928, mae: 1.205, rse: 1.000, pcc: 0.046, kcc: 0.093
+2025-07-10 14:45:29: Epoch: 004 New best VALIDATION metrics
+2025-07-10 14:45:29: Epoch: 004 Test results, ap: mae: 0.625, rse: 1.191, pcc: 0.241, kcc: 0.139
+2025-07-10 14:45:30: Epoch: 005 Train Loss: 2.869, mae: 1.214, rse: 1.001, pcc: 0.059, kcc: 0.122
+2025-07-10 14:45:30: Epoch: 005 New best VALIDATION metrics
+2025-07-10 14:45:30: Epoch: 005 Test results, ap: mae: 0.605, rse: 1.120, pcc: 0.210, kcc: 0.081
+2025-07-10 14:45:30: Epoch: 006 Train Loss: 2.896, mae: 1.203, rse: 1.000, pcc: 0.089, kcc: 0.134
+2025-07-10 14:45:30: Epoch: 006 New best VALIDATION metrics
+2025-07-10 14:45:31: Epoch: 006 Test results, ap: mae: 0.632, rse: 1.216, pcc: 0.212, kcc: 0.107
+2025-07-10 14:45:31: Epoch: 007 Train Loss: 2.910, mae: 1.210, rse: 1.000, pcc: 0.094, kcc: 0.067
+2025-07-10 14:45:31: Epoch: 007 Test results, ap: mae: 0.613, rse: 1.152, pcc: 0.144, kcc: 0.055
+2025-07-10 14:45:32: Epoch: 008 Train Loss: 2.906, mae: 1.212, rse: 1.001, pcc: 0.057, kcc: -0.103
+2025-07-10 14:45:32: Epoch: 008 Test results, ap: mae: 0.608, rse: 1.133, pcc: -0.207, kcc: -0.098
+2025-07-10 14:45:32: Epoch: 009 Train Loss: 2.916, mae: 1.211, rse: 1.000, pcc: 0.100, kcc: 0.080
+2025-07-10 14:45:33: Epoch: 009 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.102, kcc: 0.064
+2025-07-10 14:45:34: Epoch: 010 Train Loss: 2.893, mae: 1.211, rse: 1.000, pcc: -0.098, kcc: -0.080
+2025-07-10 14:45:34: Epoch: 010 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.133, kcc: -0.051
+2025-07-10 14:45:35: Epoch: 011 Train Loss: 2.888, mae: 1.211, rse: 1.000, pcc: -0.102, kcc: -0.087
+2025-07-10 14:45:35: Epoch: 011 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.155, kcc: -0.057
+2025-07-10 14:45:35: Epoch: 011 New best TEST metrics
+2025-07-10 14:45:36: Epoch: 012 Train Loss: 2.896, mae: 1.212, rse: 1.001, pcc: 0.103, kcc: 0.087
+2025-07-10 14:45:36: Epoch: 012 Test results, ap: mae: 0.608, rse: 1.134, pcc: -0.167, kcc: 0.013
+2025-07-10 14:45:36: Epoch: 012 New best TEST metrics
+2025-07-10 14:45:37: Epoch: 013 Train Loss: 2.890, mae: 1.212, rse: 1.000, pcc: -0.050, kcc: -0.095
+2025-07-10 14:45:37: Epoch: 013 Test results, ap: mae: 0.610, rse: 1.138, pcc: 0.322, kcc: -0.018
+2025-07-10 14:45:37: Epoch: 013 New best TEST metrics
+2025-07-10 14:45:38: Epoch: 014 Train Loss: 2.899, mae: 1.216, rse: 1.002, pcc: 0.102, kcc: 0.104
+2025-07-10 14:45:38: Epoch: 014 Test results, ap: mae: 0.602, rse: 1.109, pcc: 0.120, kcc: 0.034
+2025-07-10 14:45:39: Epoch: 015 Train Loss: 2.897, mae: 1.212, rse: 1.001, pcc: -0.107, kcc: -0.099
+2025-07-10 14:45:39: Epoch: 015 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.150, kcc: -0.041
+2025-07-10 14:45:40: Epoch: 016 Train Loss: 2.918, mae: 1.212, rse: 1.001, pcc: 0.104, kcc: 0.103
+2025-07-10 14:45:40: Epoch: 016 Test results, ap: mae: 0.609, rse: 1.136, pcc: 0.560, kcc: 0.044
+2025-07-10 14:45:40: Epoch: 016 New best TEST metrics
+2025-07-10 14:45:41: Epoch: 017 Train Loss: 2.900, mae: 1.212, rse: 1.001, pcc: 0.105, kcc: 0.096
+2025-07-10 14:45:41: Epoch: 017 Test results, ap: mae: 0.609, rse: 1.135, pcc: 0.016, kcc: 0.010
+2025-07-10 14:45:42: Epoch: 018 Train Loss: 2.904, mae: 1.212, rse: 1.001, pcc: 0.106, kcc: 0.100
+2025-07-10 14:45:43: Epoch: 018 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.032, kcc: 0.000
+2025-07-10 14:45:43: Epoch: 019 Train Loss: 2.892, mae: 1.213, rse: 1.001, pcc: 0.108, kcc: 0.105
+2025-07-10 14:45:44: Epoch: 019 Test results, ap: mae: 0.608, rse: 1.131, pcc: 0.055, kcc: 0.018
+2025-07-10 14:45:44: Epoch: 020 Train Loss: 2.916, mae: 1.214, rse: 1.001, pcc: -0.110, kcc: -0.116
+2025-07-10 14:45:45: Epoch: 020 Test results, ap: mae: 0.606, rse: 1.122, pcc: -0.063, kcc: -0.093
+2025-07-10 14:45:46: Epoch: 021 Train Loss: 2.902, mae: 1.212, rse: 1.001, pcc: 0.080, kcc: -0.010
+2025-07-10 14:45:46: Epoch: 021 Test results, ap: mae: 0.609, rse: 1.134, pcc: -0.265, kcc: -0.142
+2025-07-10 14:45:47: Epoch: 022 Train Loss: 2.895, mae: 1.212, rse: 1.000, pcc: 0.091, kcc: 0.034
+2025-07-10 14:45:47: Epoch: 022 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.506, kcc: -0.074
+2025-07-10 14:45:48: Epoch: 023 Train Loss: 2.890, mae: 1.212, rse: 1.000, pcc: 0.106, kcc: 0.075
+2025-07-10 14:45:48: Epoch: 023 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.096, kcc: -0.046
+2025-07-10 14:45:49: Epoch: 024 Train Loss: 2.880, mae: 1.214, rse: 1.001, pcc: -0.114, kcc: -0.124
+2025-07-10 14:45:49: Epoch: 024 Test results, ap: mae: 0.606, rse: 1.122, pcc: -0.176, kcc: -0.121
+2025-07-10 14:45:50: Epoch: 025 Train Loss: 2.901, mae: 1.210, rse: 1.000, pcc: 0.110, kcc: 0.109
+2025-07-10 14:45:50: Epoch: 025 Test results, ap: mae: 0.612, rse: 1.148, pcc: 0.099, kcc: 0.041
+2025-07-10 14:45:51: Epoch: 026 Train Loss: 2.903, mae: 1.213, rse: 1.001, pcc: 0.085, kcc: -0.018
+2025-07-10 14:45:51: Epoch: 026 Test results, ap: mae: 0.608, rse: 1.131, pcc: -0.444, kcc: -0.129
+2025-07-10 14:45:52: Epoch: 027 Train Loss: 2.906, mae: 1.211, rse: 1.000, pcc: 0.109, kcc: 0.112
+2025-07-10 14:45:52: Epoch: 027 Test results, ap: mae: 0.611, rse: 1.146, pcc: 0.097, kcc: 0.085
+2025-07-10 14:45:53: Epoch: 028 Train Loss: 2.906, mae: 1.210, rse: 1.000, pcc: 0.111, kcc: 0.115
+2025-07-10 14:45:54: Epoch: 028 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.110, kcc: 0.101
+2025-07-10 14:45:54: Epoch: 029 Train Loss: 2.900, mae: 1.213, rse: 1.001, pcc: 0.110, kcc: 0.113
+2025-07-10 14:45:55: Epoch: 029 Test results, ap: mae: 0.607, rse: 1.129, pcc: 0.101, kcc: 0.047
+2025-07-10 14:45:56: Epoch: 030 Train Loss: 2.905, mae: 1.212, rse: 1.001, pcc: 0.110, kcc: 0.114
+2025-07-10 14:45:56: Epoch: 030 Test results, ap: mae: 0.608, rse: 1.133, pcc: 0.042, kcc: 0.026
+2025-07-10 14:45:57: Epoch: 031 Train Loss: 2.866, mae: 1.213, rse: 1.001, pcc: 0.110, kcc: 0.113
+2025-07-10 14:45:57: Epoch: 031 Test results, ap: mae: 0.608, rse: 1.131, pcc: -0.038, kcc: 0.021
+2025-07-10 14:45:58: Epoch: 032 Train Loss: 2.892, mae: 1.211, rse: 1.000, pcc: 0.111, kcc: 0.116
+2025-07-10 14:45:58: Epoch: 032 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.004, kcc: 0.012
+2025-07-10 14:45:59: Epoch: 033 Train Loss: 2.905, mae: 1.213, rse: 1.001, pcc: 0.110, kcc: 0.115
+2025-07-10 14:45:59: Epoch: 033 Test results, ap: mae: 0.607, rse: 1.128, pcc: -0.031, kcc: -0.008
+2025-07-10 14:46:00: Epoch: 034 Train Loss: 2.888, mae: 1.213, rse: 1.001, pcc: 0.110, kcc: 0.116
+2025-07-10 14:46:00: Epoch: 034 Test results, ap: mae: 0.607, rse: 1.129, pcc: -0.051, kcc: -0.011
+2025-07-10 14:46:01: Epoch: 035 Train Loss: 2.889, mae: 1.214, rse: 1.001, pcc: 0.109, kcc: 0.115
+2025-07-10 14:46:01: Epoch: 035 Test results, ap: mae: 0.606, rse: 1.122, pcc: -0.089, kcc: -0.021
+2025-07-10 14:46:02: Epoch: 036 Train Loss: 2.912, mae: 1.212, rse: 1.001, pcc: 0.110, kcc: 0.121
+2025-07-10 14:46:02: Epoch: 036 Test results, ap: mae: 0.608, rse: 1.132, pcc: -0.025, kcc: -0.013
+2025-07-10 14:46:03: Epoch: 037 Train Loss: 2.906, mae: 1.213, rse: 1.001, pcc: 0.111, kcc: 0.123
+2025-07-10 14:46:03: Epoch: 037 New best VALIDATION metrics
+2025-07-10 14:46:03: Epoch: 037 Test results, ap: mae: 0.607, rse: 1.129, pcc: -0.070, kcc: -0.027
+2025-07-10 14:46:04: Epoch: 038 Train Loss: 2.895, mae: 1.213, rse: 1.001, pcc: 0.111, kcc: 0.124
+2025-07-10 14:46:04: Epoch: 038 New best VALIDATION metrics
+2025-07-10 14:46:04: Epoch: 038 Test results, ap: mae: 0.607, rse: 1.128, pcc: -0.105, kcc: -0.033
+2025-07-10 14:46:05: Epoch: 039 Train Loss: 2.901, mae: 1.214, rse: 1.001, pcc: 0.113, kcc: 0.127
+2025-07-10 14:46:05: Epoch: 039 New best VALIDATION metrics
+2025-07-10 14:46:05: Epoch: 039 Test results, ap: mae: 0.607, rse: 1.126, pcc: -0.030, kcc: -0.022
+2025-07-10 14:46:06: Epoch: 040 Train Loss: 2.929, mae: 1.213, rse: 1.001, pcc: 0.115, kcc: 0.131
+2025-07-10 14:46:06: Epoch: 040 New best VALIDATION metrics
+2025-07-10 14:46:06: Epoch: 040 Test results, ap: mae: 0.608, rse: 1.130, pcc: -0.038, kcc: -0.014
+2025-07-10 14:46:07: Epoch: 041 Train Loss: 2.919, mae: 1.213, rse: 1.001, pcc: 0.116, kcc: 0.133
+2025-07-10 14:46:07: Epoch: 041 New best VALIDATION metrics
+2025-07-10 14:46:07: Epoch: 041 Test results, ap: mae: 0.607, rse: 1.127, pcc: -0.056, kcc: -0.019
+2025-07-10 14:46:08: Epoch: 042 Train Loss: 2.902, mae: 1.213, rse: 1.001, pcc: 0.117, kcc: 0.136
+2025-07-10 14:46:08: Epoch: 042 New best VALIDATION metrics
+2025-07-10 14:46:08: Epoch: 042 Test results, ap: mae: 0.607, rse: 1.129, pcc: -0.046, kcc: -0.024
+2025-07-10 14:46:09: Epoch: 043 Train Loss: 2.900, mae: 1.213, rse: 1.001, pcc: 0.118, kcc: 0.139
+2025-07-10 14:46:09: Epoch: 043 New best VALIDATION metrics
+2025-07-10 14:46:09: Epoch: 043 Test results, ap: mae: 0.607, rse: 1.128, pcc: -0.056, kcc: -0.030
+2025-07-10 14:46:10: Epoch: 044 Train Loss: 2.911, mae: 1.213, rse: 1.001, pcc: 0.119, kcc: 0.142
+2025-07-10 14:46:10: Epoch: 044 New best VALIDATION metrics
+2025-07-10 14:46:10: Epoch: 044 Test results, ap: mae: 0.608, rse: 1.130, pcc: -0.063, kcc: -0.034
+2025-07-10 14:46:11: Epoch: 045 Train Loss: 2.917, mae: 1.213, rse: 1.001, pcc: 0.120, kcc: 0.144
+2025-07-10 14:46:11: Epoch: 045 New best VALIDATION metrics
+2025-07-10 14:46:11: Epoch: 045 Test results, ap: mae: 0.607, rse: 1.127, pcc: -0.074, kcc: -0.032
+2025-07-10 14:46:12: Epoch: 046 Train Loss: 2.904, mae: 1.212, rse: 1.001, pcc: 0.122, kcc: 0.147
+2025-07-10 14:46:12: Epoch: 046 New best VALIDATION metrics
+2025-07-10 14:46:12: Epoch: 046 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.088, kcc: -0.042
+2025-07-10 14:46:13: Epoch: 047 Train Loss: 2.911, mae: 1.213, rse: 1.001, pcc: 0.125, kcc: 0.149
+2025-07-10 14:46:13: Epoch: 047 New best VALIDATION metrics
+2025-07-10 14:46:13: Epoch: 047 Test results, ap: mae: 0.608, rse: 1.131, pcc: -0.097, kcc: -0.051
+2025-07-10 14:46:14: Epoch: 048 Train Loss: 2.894, mae: 1.213, rse: 1.001, pcc: 0.129, kcc: 0.152
+2025-07-10 14:46:14: Epoch: 048 New best VALIDATION metrics
+2025-07-10 14:46:14: Epoch: 048 Test results, ap: mae: 0.608, rse: 1.132, pcc: -0.100, kcc: -0.060
+2025-07-10 14:46:15: Epoch: 049 Train Loss: 2.887, mae: 1.213, rse: 1.001, pcc: 0.135, kcc: 0.151
+2025-07-10 14:46:15: Epoch: 049 New best VALIDATION metrics
+2025-07-10 14:46:15: Epoch: 049 Test results, ap: mae: 0.607, rse: 1.129, pcc: -0.126, kcc: -0.070
+2025-07-10 14:46:16: Epoch: 050 Train Loss: 2.878, mae: 1.213, rse: 1.001, pcc: 0.147, kcc: 0.147
+2025-07-10 14:46:16: Epoch: 050 New best VALIDATION metrics
+2025-07-10 14:46:16: Epoch: 050 Test results, ap: mae: 0.607, rse: 1.128, pcc: -0.137, kcc: -0.077
+2025-07-10 14:46:16: used time 0.01h
+2025-07-10 14:46:16: Running Cross Validation 1
+2025-07-10 14:46:16: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 14:46:17: Epoch: 001 Train Loss: 2.873, mae: 1.252, rse: 1.003, pcc: 0.089, kcc: 0.111
+2025-07-10 14:46:17: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:46:17: Epoch: 001 Test results, ap: mae: 0.625, rse: 1.192, pcc: 0.057, kcc: -0.044
+2025-07-10 14:46:18: Epoch: 002 Train Loss: 2.852, mae: 1.292, rse: 1.011, pcc: 0.140, kcc: 0.112
+2025-07-10 14:46:18: Epoch: 002 New best VALIDATION metrics
+2025-07-10 14:46:18: Epoch: 002 Test results, ap: mae: 0.581, rse: 1.005, pcc: 0.170, kcc: 0.135
+2025-07-10 14:46:19: Epoch: 003 Train Loss: 2.866, mae: 1.251, rse: 1.004, pcc: 0.109, kcc: 0.113
+2025-07-10 14:46:19: Epoch: 003 New best VALIDATION metrics
+2025-07-10 14:46:19: Epoch: 003 Test results, ap: mae: 0.630, rse: 1.209, pcc: 0.189, kcc: 0.189
+2025-07-10 14:46:20: Epoch: 004 Train Loss: 2.857, mae: 1.251, rse: 1.007, pcc: -0.117, kcc: -0.120
+2025-07-10 14:46:20: Epoch: 004 Test results, ap: mae: 0.642, rse: 1.250, pcc: -0.254, kcc: -0.196
+2025-07-10 14:46:21: Epoch: 005 Train Loss: 2.855, mae: 1.253, rse: 1.002, pcc: -0.011, kcc: -0.073
+2025-07-10 14:46:21: Epoch: 005 Test results, ap: mae: 0.619, rse: 1.173, pcc: 0.029, kcc: 0.009
+2025-07-10 14:46:22: Epoch: 006 Train Loss: 2.851, mae: 1.257, rse: 1.000, pcc: -0.062, kcc: -0.084
+2025-07-10 14:46:22: Epoch: 006 Test results, ap: mae: 0.607, rse: 1.128, pcc: -0.067, kcc: -0.032
+2025-07-10 14:46:23: Epoch: 007 Train Loss: 2.843, mae: 1.253, rse: 1.002, pcc: 0.062, kcc: 0.105
+2025-07-10 14:46:23: Epoch: 007 Test results, ap: mae: 0.617, rse: 1.166, pcc: -0.015, kcc: 0.054
+2025-07-10 14:46:24: Epoch: 008 Train Loss: 2.847, mae: 1.256, rse: 1.001, pcc: 0.061, kcc: 0.128
+2025-07-10 14:46:24: Epoch: 008 Test results, ap: mae: 0.608, rse: 1.132, pcc: -0.431, kcc: 0.025
+2025-07-10 14:46:25: Epoch: 009 Train Loss: 2.848, mae: 1.251, rse: 1.004, pcc: -0.058, kcc: -0.097
+2025-07-10 14:46:25: Epoch: 009 Test results, ap: mae: 0.630, rse: 1.208, pcc: -0.196, kcc: -0.008
+2025-07-10 14:46:26: Epoch: 010 Train Loss: 2.817, mae: 1.253, rse: 1.002, pcc: 0.055, kcc: 0.096
+2025-07-10 14:46:26: Epoch: 010 Test results, ap: mae: 0.616, rse: 1.163, pcc: 0.133, kcc: 0.179
+2025-07-10 14:46:27: Epoch: 011 Train Loss: 2.850, mae: 1.255, rse: 1.001, pcc: 0.063, kcc: 0.110
+2025-07-10 14:46:27: Epoch: 011 Test results, ap: mae: 0.612, rse: 1.148, pcc: 0.181, kcc: 0.156
+2025-07-10 14:46:27: Epoch: 011 New best TEST metrics
+2025-07-10 14:46:28: Epoch: 012 Train Loss: 2.850, mae: 1.254, rse: 1.002, pcc: 0.054, kcc: 0.111
+2025-07-10 14:46:28: Epoch: 012 Test results, ap: mae: 0.615, rse: 1.160, pcc: 0.050, kcc: 0.098
+2025-07-10 14:46:29: Epoch: 013 Train Loss: 2.833, mae: 1.255, rse: 1.001, pcc: 0.060, kcc: 0.109
+2025-07-10 14:46:29: Epoch: 013 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.231, kcc: 0.089
+2025-07-10 14:46:30: Epoch: 014 Train Loss: 2.841, mae: 1.253, rse: 1.002, pcc: 0.066, kcc: 0.111
+2025-07-10 14:46:30: Epoch: 014 Test results, ap: mae: 0.618, rse: 1.170, pcc: 0.207, kcc: 0.158
+2025-07-10 14:46:31: Epoch: 015 Train Loss: 2.864, mae: 1.252, rse: 1.003, pcc: 0.055, kcc: 0.105
+2025-07-10 14:46:31: Epoch: 015 Test results, ap: mae: 0.626, rse: 1.195, pcc: 0.210, kcc: 0.157
+2025-07-10 14:46:32: Epoch: 016 Train Loss: 2.826, mae: 1.252, rse: 1.003, pcc: 0.062, kcc: 0.082
+2025-07-10 14:46:32: Epoch: 016 Test results, ap: mae: 0.625, rse: 1.191, pcc: 0.130, kcc: 0.064
+2025-07-10 14:46:33: Epoch: 017 Train Loss: 2.840, mae: 1.256, rse: 1.001, pcc: 0.061, kcc: 0.104
+2025-07-10 14:46:33: Epoch: 017 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.320, kcc: 0.070
+2025-07-10 14:46:34: Epoch: 018 Train Loss: 2.825, mae: 1.257, rse: 1.000, pcc: 0.052, kcc: 0.080
+2025-07-10 14:46:34: Epoch: 018 Test results, ap: mae: 0.605, rse: 1.120, pcc: -0.467, kcc: 0.087
+2025-07-10 14:46:35: Epoch: 019 Train Loss: 2.836, mae: 1.255, rse: 1.001, pcc: 0.066, kcc: 0.147
+2025-07-10 14:46:35: Epoch: 019 Test results, ap: mae: 0.613, rse: 1.150, pcc: 0.207, kcc: 0.100
+2025-07-10 14:46:36: Epoch: 020 Train Loss: 2.845, mae: 1.252, rse: 1.003, pcc: -0.048, kcc: -0.083
+2025-07-10 14:46:36: Epoch: 020 Test results, ap: mae: 0.622, rse: 1.182, pcc: -0.146, kcc: -0.033
+2025-07-10 14:46:37: Epoch: 021 Train Loss: 2.856, mae: 1.253, rse: 1.002, pcc: 0.065, kcc: 0.115
+2025-07-10 14:46:37: Epoch: 021 Test results, ap: mae: 0.619, rse: 1.173, pcc: -0.367, kcc: 0.119
+2025-07-10 14:46:38: Epoch: 022 Train Loss: 2.856, mae: 1.255, rse: 1.001, pcc: 0.063, kcc: 0.100
+2025-07-10 14:46:38: Epoch: 022 Test results, ap: mae: 0.613, rse: 1.150, pcc: 0.217, kcc: 0.142
+2025-07-10 14:46:38: Epoch: 022 New best TEST metrics
+2025-07-10 14:46:39: Epoch: 023 Train Loss: 2.841, mae: 1.256, rse: 1.001, pcc: 0.058, kcc: 0.060
+2025-07-10 14:46:39: Epoch: 023 Test results, ap: mae: 0.608, rse: 1.132, pcc: -0.124, kcc: -0.025
+2025-07-10 14:46:40: Epoch: 024 Train Loss: 2.844, mae: 1.252, rse: 1.003, pcc: 0.063, kcc: 0.127
+2025-07-10 14:46:40: Epoch: 024 Test results, ap: mae: 0.623, rse: 1.186, pcc: -0.060, kcc: 0.088
+2025-07-10 14:46:41: Epoch: 025 Train Loss: 2.828, mae: 1.256, rse: 1.001, pcc: 0.063, kcc: 0.125
+2025-07-10 14:46:41: Epoch: 025 Test results, ap: mae: 0.609, rse: 1.138, pcc: 0.186, kcc: 0.251
+2025-07-10 14:46:41: Epoch: 025 New best TEST metrics
+2025-07-10 14:46:42: Epoch: 026 Train Loss: 2.835, mae: 1.254, rse: 1.002, pcc: 0.062, kcc: 0.097
+2025-07-10 14:46:42: Epoch: 026 Test results, ap: mae: 0.616, rse: 1.161, pcc: -0.134, kcc: 0.006
+2025-07-10 14:46:43: Epoch: 027 Train Loss: 2.856, mae: 1.254, rse: 1.002, pcc: 0.063, kcc: 0.097
+2025-07-10 14:46:43: Epoch: 027 Test results, ap: mae: 0.616, rse: 1.162, pcc: 0.151, kcc: 0.081
+2025-07-10 14:46:44: Epoch: 028 Train Loss: 2.833, mae: 1.254, rse: 1.001, pcc: 0.061, kcc: 0.111
+2025-07-10 14:46:44: Epoch: 028 Test results, ap: mae: 0.614, rse: 1.157, pcc: -0.022, kcc: 0.135
+2025-07-10 14:46:45: Epoch: 029 Train Loss: 2.842, mae: 1.254, rse: 1.001, pcc: 0.062, kcc: 0.104
+2025-07-10 14:46:45: Epoch: 029 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.173, kcc: 0.169
+2025-07-10 14:46:46: Epoch: 030 Train Loss: 2.837, mae: 1.257, rse: 1.000, pcc: 0.068, kcc: 0.115
+2025-07-10 14:46:46: Epoch: 030 Test results, ap: mae: 0.605, rse: 1.121, pcc: 0.139, kcc: 0.134
+2025-07-10 14:46:47: Epoch: 031 Train Loss: 2.836, mae: 1.256, rse: 1.001, pcc: 0.057, kcc: 0.106
+2025-07-10 14:46:47: Epoch: 031 Test results, ap: mae: 0.610, rse: 1.138, pcc: 0.260, kcc: 0.192
+2025-07-10 14:46:47: Epoch: 031 New best TEST metrics
+2025-07-10 14:46:48: Epoch: 032 Train Loss: 2.839, mae: 1.255, rse: 1.001, pcc: 0.060, kcc: 0.108
+2025-07-10 14:46:48: Epoch: 032 Test results, ap: mae: 0.612, rse: 1.149, pcc: 0.227, kcc: 0.140
+2025-07-10 14:46:49: Epoch: 033 Train Loss: 2.851, mae: 1.254, rse: 1.001, pcc: -0.043, kcc: -0.161
+2025-07-10 14:46:49: Epoch: 033 Test results, ap: mae: 0.613, rse: 1.153, pcc: -0.188, kcc: -0.185
+2025-07-10 14:46:50: Epoch: 034 Train Loss: 2.859, mae: 1.255, rse: 1.001, pcc: 0.061, kcc: 0.112
+2025-07-10 14:46:50: Epoch: 034 Test results, ap: mae: 0.612, rse: 1.148, pcc: 0.238, kcc: 0.169
+2025-07-10 14:46:51: Epoch: 035 Train Loss: 2.833, mae: 1.254, rse: 1.001, pcc: 0.060, kcc: 0.103
+2025-07-10 14:46:51: Epoch: 035 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.137, kcc: 0.132
+2025-07-10 14:46:52: Epoch: 036 Train Loss: 2.847, mae: 1.254, rse: 1.001, pcc: 0.056, kcc: 0.114
+2025-07-10 14:46:52: Epoch: 036 Test results, ap: mae: 0.613, rse: 1.152, pcc: 0.036, kcc: 0.049
+2025-07-10 14:46:53: Epoch: 037 Train Loss: 2.830, mae: 1.254, rse: 1.001, pcc: 0.054, kcc: 0.104
+2025-07-10 14:46:53: Epoch: 037 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.190, kcc: 0.137
+2025-07-10 14:46:54: Epoch: 038 Train Loss: 2.841, mae: 1.254, rse: 1.001, pcc: 0.057, kcc: 0.111
+2025-07-10 14:46:54: Epoch: 038 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.157, kcc: 0.155
+2025-07-10 14:46:55: Epoch: 039 Train Loss: 2.840, mae: 1.254, rse: 1.001, pcc: 0.056, kcc: 0.112
+2025-07-10 14:46:55: Epoch: 039 Test results, ap: mae: 0.615, rse: 1.159, pcc: 0.196, kcc: 0.185
+2025-07-10 14:46:56: Epoch: 040 Train Loss: 2.845, mae: 1.254, rse: 1.001, pcc: 0.062, kcc: 0.097
+2025-07-10 14:46:56: Epoch: 040 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.138, kcc: 0.174
+2025-07-10 14:46:57: Epoch: 041 Train Loss: 2.830, mae: 1.255, rse: 1.001, pcc: 0.054, kcc: 0.103
+2025-07-10 14:46:57: Epoch: 041 Test results, ap: mae: 0.612, rse: 1.149, pcc: 0.220, kcc: 0.184
+2025-07-10 14:46:58: Epoch: 042 Train Loss: 2.820, mae: 1.254, rse: 1.001, pcc: 0.055, kcc: 0.111
+2025-07-10 14:46:58: Epoch: 042 Test results, ap: mae: 0.613, rse: 1.153, pcc: 0.253, kcc: 0.199
+2025-07-10 14:46:59: Epoch: 043 Train Loss: 2.837, mae: 1.254, rse: 1.001, pcc: 0.054, kcc: 0.100
+2025-07-10 14:46:59: Epoch: 043 Test results, ap: mae: 0.615, rse: 1.160, pcc: 0.271, kcc: 0.202
+2025-07-10 14:47:00: Epoch: 044 Train Loss: 2.848, mae: 1.253, rse: 1.001, pcc: 0.055, kcc: 0.105
+2025-07-10 14:47:00: Epoch: 044 Test results, ap: mae: 0.615, rse: 1.161, pcc: 0.270, kcc: 0.201
+2025-07-10 14:47:01: Epoch: 045 Train Loss: 2.856, mae: 1.255, rse: 1.001, pcc: 0.145, kcc: 0.118
+2025-07-10 14:47:01: Epoch: 045 New best VALIDATION metrics
+2025-07-10 14:47:01: Epoch: 045 Test results, ap: mae: 0.613, rse: 1.150, pcc: 0.100, kcc: 0.109
+2025-07-10 14:47:02: Epoch: 046 Train Loss: 2.831, mae: 1.254, rse: 1.000, pcc: 0.212, kcc: 0.106
+2025-07-10 14:47:02: Epoch: 046 New best VALIDATION metrics
+2025-07-10 14:47:02: Epoch: 046 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.358, kcc: 0.253
+2025-07-10 14:47:02: Epoch: 046 New best TEST metrics
+2025-07-10 14:47:03: Epoch: 047 Train Loss: 2.823, mae: 1.228, rse: 0.975, pcc: 0.324, kcc: 0.115
+2025-07-10 14:47:03: Epoch: 047 New best VALIDATION metrics
+2025-07-10 14:47:03: Epoch: 047 Test results, ap: mae: 0.712, rse: 1.502, pcc: 0.239, kcc: 0.196
+2025-07-10 14:47:04: Epoch: 048 Train Loss: 2.667, mae: 1.228, rse: 0.920, pcc: 0.334, kcc: 0.113
+2025-07-10 14:47:04: Epoch: 048 New best VALIDATION metrics
+2025-07-10 14:47:04: Epoch: 048 Test results, ap: mae: 0.980, rse: 2.534, pcc: 0.203, kcc: 0.187
+2025-07-10 14:47:05: Epoch: 049 Train Loss: 2.646, mae: 1.202, rse: 0.904, pcc: 0.315, kcc: 0.126
+2025-07-10 14:47:05: Epoch: 049 New best VALIDATION metrics
+2025-07-10 14:47:05: Epoch: 049 Test results, ap: mae: 0.719, rse: 1.528, pcc: 0.185, kcc: 0.110
+2025-07-10 14:47:06: Epoch: 050 Train Loss: 2.655, mae: 1.195, rse: 0.898, pcc: 0.342, kcc: 0.118
+2025-07-10 14:47:06: Epoch: 050 New best VALIDATION metrics
+2025-07-10 14:47:06: Epoch: 050 Test results, ap: mae: 0.778, rse: 1.761, pcc: 0.185, kcc: 0.166
+2025-07-10 14:47:06: used time 0.01h
+2025-07-10 14:47:06: Running Cross Validation 2
+2025-07-10 14:47:06: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:47:07: Epoch: 001 Train Loss: 2.891, mae: 1.251, rse: 1.004, pcc: 0.039, kcc: -0.036
+2025-07-10 14:47:07: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:47:07: Epoch: 001 Test results, ap: mae: 0.648, rse: 1.271, pcc: 0.055, kcc: -0.063
+2025-07-10 14:47:08: Epoch: 002 Train Loss: 2.901, mae: 1.253, rse: 1.000, pcc: -0.079, kcc: -0.113
+2025-07-10 14:47:09: Epoch: 002 Test results, ap: mae: 0.620, rse: 1.177, pcc: -0.237, kcc: -0.087
+2025-07-10 14:47:09: Epoch: 003 Train Loss: 2.895, mae: 1.258, rse: 1.001, pcc: 0.051, kcc: -0.072
+2025-07-10 14:47:10: Epoch: 003 Test results, ap: mae: 0.602, rse: 1.105, pcc: -0.052, kcc: -0.039
+2025-07-10 14:47:10: Epoch: 004 Train Loss: 2.870, mae: 1.253, rse: 1.000, pcc: 0.080, kcc: -0.105
+2025-07-10 14:47:11: Epoch: 004 Test results, ap: mae: 0.619, rse: 1.175, pcc: 0.193, kcc: 0.008
+2025-07-10 14:47:12: Epoch: 005 Train Loss: 2.897, mae: 1.252, rse: 1.001, pcc: -0.057, kcc: 0.041
+2025-07-10 14:47:12: Epoch: 005 Test results, ap: mae: 0.625, rse: 1.193, pcc: 0.106, kcc: 0.178
+2025-07-10 14:47:13: Epoch: 006 Train Loss: 2.887, mae: 1.255, rse: 1.000, pcc: 0.046, kcc: 0.111
+2025-07-10 14:47:13: Epoch: 006 New best VALIDATION metrics
+2025-07-10 14:47:13: Epoch: 006 Test results, ap: mae: 0.608, rse: 1.132, pcc: 0.248, kcc: 0.066
+2025-07-10 14:47:14: Epoch: 007 Train Loss: 2.886, mae: 1.254, rse: 1.000, pcc: -0.062, kcc: -0.115
+2025-07-10 14:47:14: Epoch: 007 Test results, ap: mae: 0.613, rse: 1.151, pcc: -0.659, kcc: -0.142
+2025-07-10 14:47:15: Epoch: 008 Train Loss: 2.913, mae: 1.257, rse: 1.001, pcc: 0.083, kcc: -0.076
+2025-07-10 14:47:15: Epoch: 008 Test results, ap: mae: 0.603, rse: 1.112, pcc: 0.157, kcc: -0.199
+2025-07-10 14:47:16: Epoch: 009 Train Loss: 2.882, mae: 1.252, rse: 1.001, pcc: 0.029, kcc: 0.016
+2025-07-10 14:47:16: Epoch: 009 Test results, ap: mae: 0.628, rse: 1.202, pcc: -0.360, kcc: -0.120
+2025-07-10 14:47:17: Epoch: 010 Train Loss: 2.875, mae: 1.256, rse: 1.000, pcc: 0.071, kcc: 0.108
+2025-07-10 14:47:17: Epoch: 010 New best VALIDATION metrics
+2025-07-10 14:47:17: Epoch: 010 Test results, ap: mae: 0.606, rse: 1.125, pcc: 0.125, kcc: 0.045
+2025-07-10 14:47:18: Epoch: 011 Train Loss: 2.892, mae: 1.254, rse: 1.000, pcc: 0.083, kcc: 0.094
+2025-07-10 14:47:18: Epoch: 011 New best VALIDATION metrics
+2025-07-10 14:47:18: Epoch: 011 Test results, ap: mae: 0.615, rse: 1.159, pcc: -1.000, kcc: 0.002
+2025-07-10 14:47:18: Epoch: 011 New best TEST metrics
+2025-07-10 14:47:19: Epoch: 012 Train Loss: 2.864, mae: 1.255, rse: 1.000, pcc: -0.001, kcc: -0.041
+2025-07-10 14:47:19: Epoch: 012 Test results, ap: mae: 0.611, rse: 1.143, pcc: -1.000, kcc: -0.102
+2025-07-10 14:47:20: Epoch: 013 Train Loss: 2.894, mae: 1.254, rse: 1.000, pcc: 0.084, kcc: 0.026
+2025-07-10 14:47:20: Epoch: 013 Test results, ap: mae: 0.613, rse: 1.151, pcc: -0.068, kcc: 0.011
+2025-07-10 14:47:20: Epoch: 013 New best TEST metrics
+2025-07-10 14:47:21: Epoch: 014 Train Loss: 2.874, mae: 1.255, rse: 1.000, pcc: -0.038, kcc: -0.043
+2025-07-10 14:47:21: Epoch: 014 Test results, ap: mae: 0.611, rse: 1.146, pcc: nan, kcc: nan
+2025-07-10 14:47:22: Epoch: 015 Train Loss: 2.857, mae: 1.257, rse: 1.001, pcc: -0.060, kcc: -0.060
+2025-07-10 14:47:22: Epoch: 015 Test results, ap: mae: 0.603, rse: 1.110, pcc: -0.734, kcc: 0.001
+2025-07-10 14:47:23: Epoch: 016 Train Loss: 2.899, mae: 1.254, rse: 1.000, pcc: -0.080, kcc: -0.041
+2025-07-10 14:47:23: Epoch: 016 Test results, ap: mae: 0.614, rse: 1.157, pcc: 1.000, kcc: -0.038
+2025-07-10 14:47:23: Epoch: 016 New best TEST metrics
+2025-07-10 14:47:24: Epoch: 017 Train Loss: 2.882, mae: 1.255, rse: 1.000, pcc: 0.060, kcc: 0.022
+2025-07-10 14:47:24: Epoch: 017 Test results, ap: mae: 0.609, rse: 1.135, pcc: 0.875, kcc: 0.097
+2025-07-10 14:47:24: Epoch: 017 New best TEST metrics
+2025-07-10 14:47:25: Epoch: 018 Train Loss: 2.889, mae: 1.257, rse: 1.001, pcc: -0.067, kcc: -0.132
+2025-07-10 14:47:25: Epoch: 018 Test results, ap: mae: 0.604, rse: 1.114, pcc: -0.059, kcc: 0.100
+2025-07-10 14:47:26: Epoch: 019 Train Loss: 2.879, mae: 1.255, rse: 1.000, pcc: 0.042, kcc: 0.004
+2025-07-10 14:47:26: Epoch: 019 Test results, ap: mae: 0.612, rse: 1.146, pcc: 0.246, kcc: 0.048
+2025-07-10 14:47:27: Epoch: 020 Train Loss: 2.881, mae: 1.257, rse: 1.000, pcc: -0.012, kcc: -0.062
+2025-07-10 14:47:27: Epoch: 020 Test results, ap: mae: 0.604, rse: 1.116, pcc: 1.000, kcc: 0.049
+2025-07-10 14:47:27: Epoch: 020 New best TEST metrics
+2025-07-10 14:47:28: Epoch: 021 Train Loss: 2.879, mae: 1.255, rse: 1.000, pcc: 0.058, kcc: 0.051
+2025-07-10 14:47:28: Epoch: 021 Test results, ap: mae: 0.608, rse: 1.132, pcc: 0.869, kcc: -0.044
+2025-07-10 14:47:29: Epoch: 022 Train Loss: 2.895, mae: 1.255, rse: 1.000, pcc: 0.026, kcc: -0.083
+2025-07-10 14:47:29: Epoch: 022 Test results, ap: mae: 0.612, rse: 1.146, pcc: 0.531, kcc: 0.116
+2025-07-10 14:47:30: Epoch: 023 Train Loss: 2.898, mae: 1.255, rse: 1.000, pcc: 0.023, kcc: -0.002
+2025-07-10 14:47:30: Epoch: 023 Test results, ap: mae: 0.611, rse: 1.145, pcc: -1.000, kcc: -0.178
+2025-07-10 14:47:31: Epoch: 024 Train Loss: 2.887, mae: 1.255, rse: 1.000, pcc: -0.078, kcc: -0.110
+2025-07-10 14:47:31: Epoch: 024 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.296, kcc: -0.026
+2025-07-10 14:47:32: Epoch: 025 Train Loss: 2.906, mae: 1.255, rse: 1.000, pcc: 0.055, kcc: -0.026
+2025-07-10 14:47:32: Epoch: 025 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.233, kcc: 0.179
+2025-07-10 14:47:33: Epoch: 026 Train Loss: 2.878, mae: 1.255, rse: 1.000, pcc: 0.064, kcc: 0.072
+2025-07-10 14:47:33: Epoch: 026 Test results, ap: mae: 0.609, rse: 1.137, pcc: -1.000, kcc: -0.000
+2025-07-10 14:47:34: Epoch: 027 Train Loss: 2.888, mae: 1.255, rse: 1.000, pcc: 0.083, kcc: 0.124
+2025-07-10 14:47:34: Epoch: 027 New best VALIDATION metrics
+2025-07-10 14:47:34: Epoch: 027 Test results, ap: mae: 0.608, rse: 1.132, pcc: 0.544, kcc: -0.024
+2025-07-10 14:47:35: Epoch: 028 Train Loss: 2.866, mae: 1.255, rse: 1.000, pcc: 0.078, kcc: 0.049
+2025-07-10 14:47:35: Epoch: 028 Test results, ap: mae: 0.609, rse: 1.136, pcc: nan, kcc: nan
+2025-07-10 14:47:36: Epoch: 029 Train Loss: 2.884, mae: 1.255, rse: 1.000, pcc: 0.087, kcc: 0.074
+2025-07-10 14:47:36: Epoch: 029 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.013, kcc: -0.018
+2025-07-10 14:47:37: Epoch: 030 Train Loss: 2.900, mae: 1.255, rse: 1.000, pcc: 0.016, kcc: 0.032
+2025-07-10 14:47:37: Epoch: 030 Test results, ap: mae: 0.609, rse: 1.134, pcc: nan, kcc: nan
+2025-07-10 14:47:38: Epoch: 031 Train Loss: 2.901, mae: 1.255, rse: 1.000, pcc: 0.072, kcc: 0.039
+2025-07-10 14:47:38: Epoch: 031 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.589, kcc: 0.143
+2025-07-10 14:47:39: Epoch: 032 Train Loss: 2.891, mae: 1.255, rse: 1.000, pcc: 0.071, kcc: 0.030
+2025-07-10 14:47:39: Epoch: 032 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.036, kcc: 0.070
+2025-07-10 14:47:40: Epoch: 033 Train Loss: 2.888, mae: 1.255, rse: 1.000, pcc: 0.074, kcc: 0.012
+2025-07-10 14:47:40: Epoch: 033 Test results, ap: mae: 0.609, rse: 1.137, pcc: -1.000, kcc: -0.059
+2025-07-10 14:47:41: Epoch: 034 Train Loss: 2.873, mae: 1.255, rse: 1.000, pcc: 0.057, kcc: 0.047
+2025-07-10 14:47:41: Epoch: 034 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.222, kcc: 0.071
+2025-07-10 14:47:42: Epoch: 035 Train Loss: 2.886, mae: 1.255, rse: 1.000, pcc: 0.033, kcc: 0.035
+2025-07-10 14:47:42: Epoch: 035 Test results, ap: mae: 0.609, rse: 1.136, pcc: nan, kcc: nan
+2025-07-10 14:47:43: Epoch: 036 Train Loss: 2.876, mae: 1.255, rse: 1.000, pcc: 0.073, kcc: 0.042
+2025-07-10 14:47:43: Epoch: 036 Test results, ap: mae: 0.610, rse: 1.141, pcc: nan, kcc: nan
+2025-07-10 14:47:44: Epoch: 037 Train Loss: 2.896, mae: 1.255, rse: 1.000, pcc: 0.094, kcc: 0.011
+2025-07-10 14:47:44: Epoch: 037 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.805, kcc: 0.021
+2025-07-10 14:47:45: Epoch: 038 Train Loss: 2.894, mae: 1.255, rse: 1.000, pcc: 0.081, kcc: 0.094
+2025-07-10 14:47:45: Epoch: 038 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.487, kcc: 0.135
+2025-07-10 14:47:45: Epoch: 039 Train Loss: 2.893, mae: 1.255, rse: 1.000, pcc: 0.062, kcc: 0.044
+2025-07-10 14:47:46: Epoch: 039 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.068, kcc: -0.116
+2025-07-10 14:47:46: Epoch: 040 Train Loss: 2.900, mae: 1.255, rse: 1.000, pcc: 0.005, kcc: 0.003
+2025-07-10 14:47:47: Epoch: 040 Test results, ap: mae: 0.611, rse: 1.143, pcc: 0.686, kcc: 0.121
+2025-07-10 14:47:47: Epoch: 041 Train Loss: 2.860, mae: 1.255, rse: 1.000, pcc: 0.045, kcc: -0.095
+2025-07-10 14:47:48: Epoch: 041 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.005, kcc: 0.060
+2025-07-10 14:47:48: Epoch: 042 Train Loss: 2.896, mae: 1.255, rse: 1.000, pcc: 0.067, kcc: 0.018
+2025-07-10 14:47:48: Epoch: 042 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.125, kcc: 0.074
+2025-07-10 14:47:49: Epoch: 043 Train Loss: 2.897, mae: 1.255, rse: 1.000, pcc: 0.070, kcc: 0.071
+2025-07-10 14:47:49: Epoch: 043 Test results, ap: mae: 0.610, rse: 1.139, pcc: -1.000, kcc: 0.098
+2025-07-10 14:47:50: Epoch: 044 Train Loss: 2.896, mae: 1.255, rse: 1.000, pcc: 0.072, kcc: 0.013
+2025-07-10 14:47:50: Epoch: 044 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.035, kcc: 0.045
+2025-07-10 14:47:51: Epoch: 045 Train Loss: 2.893, mae: 1.255, rse: 1.000, pcc: 0.086, kcc: -0.043
+2025-07-10 14:47:51: Epoch: 045 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.532, kcc: -0.064
+2025-07-10 14:47:52: Epoch: 046 Train Loss: 2.891, mae: 1.255, rse: 1.000, pcc: 0.088, kcc: 0.120
+2025-07-10 14:47:52: Epoch: 046 New best VALIDATION metrics
+2025-07-10 14:47:52: Epoch: 046 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.616, kcc: 0.188
+2025-07-10 14:47:53: Epoch: 047 Train Loss: 2.895, mae: 1.255, rse: 1.000, pcc: 0.089, kcc: 0.048
+2025-07-10 14:47:53: Epoch: 047 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.475, kcc: 0.049
+2025-07-10 14:47:54: Epoch: 048 Train Loss: 2.878, mae: 1.255, rse: 1.000, pcc: 0.081, kcc: -0.076
+2025-07-10 14:47:54: Epoch: 048 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.661, kcc: -0.007
+2025-07-10 14:47:55: Epoch: 049 Train Loss: 2.889, mae: 1.255, rse: 1.000, pcc: 0.090, kcc: 0.047
+2025-07-10 14:47:55: Epoch: 049 Test results, ap: mae: 0.610, rse: 1.138, pcc: -1.000, kcc: nan
+2025-07-10 14:47:56: Epoch: 050 Train Loss: 2.901, mae: 1.255, rse: 1.000, pcc: 0.085, kcc: 0.050
+2025-07-10 14:47:56: Epoch: 050 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.264, kcc: -0.019
+2025-07-10 14:47:56: used time 0.01h
+2025-07-10 14:47:56: Running Cross Validation 3
+2025-07-10 14:47:56: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:47:57: Epoch: 001 Train Loss: 2.823, mae: 1.319, rse: 1.010, pcc: 0.170, kcc: 0.102
+2025-07-10 14:47:57: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:47:57: Epoch: 001 Test results, ap: mae: 0.582, rse: 1.012, pcc: 0.053, kcc: 0.072
+2025-07-10 14:47:58: Epoch: 002 Train Loss: 2.850, mae: 1.292, rse: 1.001, pcc: -0.105, kcc: -0.106
+2025-07-10 14:47:58: Epoch: 002 Test results, ap: mae: 0.624, rse: 1.188, pcc: -0.093, kcc: -0.113
+2025-07-10 14:47:59: Epoch: 003 Train Loss: 2.803, mae: 1.291, rse: 1.006, pcc: 0.130, kcc: 0.113
+2025-07-10 14:47:59: Epoch: 003 New best VALIDATION metrics
+2025-07-10 14:47:59: Epoch: 003 Test results, ap: mae: 0.650, rse: 1.279, pcc: 0.131, kcc: 0.078
+2025-07-10 14:48:00: Epoch: 004 Train Loss: 2.814, mae: 1.298, rse: 1.001, pcc: 0.125, kcc: 0.104
+2025-07-10 14:48:00: Epoch: 004 Test results, ap: mae: 0.597, rse: 1.086, pcc: 0.227, kcc: 0.096
+2025-07-10 14:48:01: Epoch: 005 Train Loss: 2.801, mae: 1.293, rse: 1.000, pcc: 0.123, kcc: 0.102
+2025-07-10 14:48:01: Epoch: 005 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.162, kcc: 0.089
+2025-07-10 14:48:01: Epoch: 006 Train Loss: 2.806, mae: 1.481, rse: 1.238, pcc: -0.152, kcc: -0.114
+2025-07-10 14:48:02: Epoch: 006 Test results, ap: mae: 1.154, rse: 3.265, pcc: -0.203, kcc: -0.058
+2025-07-10 14:48:02: Epoch: 007 Train Loss: 2.825, mae: 1.293, rse: 1.001, pcc: -0.116, kcc: -0.115
+2025-07-10 14:48:03: Epoch: 007 Test results, ap: mae: 0.616, rse: 1.165, pcc: -0.135, kcc: -0.059
+2025-07-10 14:48:03: Epoch: 008 Train Loss: 2.776, mae: 1.291, rse: 1.003, pcc: 0.100, kcc: 0.101
+2025-07-10 14:48:03: Epoch: 008 Test results, ap: mae: 0.636, rse: 1.227, pcc: 0.075, kcc: -0.009
+2025-07-10 14:48:04: Epoch: 009 Train Loss: 2.801, mae: 1.298, rse: 1.000, pcc: 0.077, kcc: -0.037
+2025-07-10 14:48:04: Epoch: 009 Test results, ap: mae: 0.598, rse: 1.092, pcc: 0.039, kcc: 0.167
+2025-07-10 14:48:05: Epoch: 010 Train Loss: 2.804, mae: 1.298, rse: 1.000, pcc: 0.105, kcc: 0.117
+2025-07-10 14:48:05: Epoch: 010 Test results, ap: mae: 0.598, rse: 1.092, pcc: -0.235, kcc: -0.044
+2025-07-10 14:48:06: Epoch: 011 Train Loss: 2.815, mae: 1.292, rse: 1.001, pcc: 0.103, kcc: 0.103
+2025-07-10 14:48:06: Epoch: 011 Test results, ap: mae: 0.623, rse: 1.185, pcc: 0.100, kcc: 0.075
+2025-07-10 14:48:06: Epoch: 011 New best TEST metrics
+2025-07-10 14:48:07: Epoch: 012 Train Loss: 2.804, mae: 1.293, rse: 1.001, pcc: 0.105, kcc: 0.111
+2025-07-10 14:48:07: Epoch: 012 Test results, ap: mae: 0.618, rse: 1.169, pcc: 0.033, kcc: 0.025
+2025-07-10 14:48:08: Epoch: 013 Train Loss: 2.801, mae: 1.294, rse: 1.000, pcc: 0.104, kcc: 0.127
+2025-07-10 14:48:08: Epoch: 013 Test results, ap: mae: 0.612, rse: 1.147, pcc: 0.090, kcc: 0.104
+2025-07-10 14:48:08: Epoch: 013 New best TEST metrics
+2025-07-10 14:48:09: Epoch: 014 Train Loss: 2.792, mae: 1.293, rse: 1.000, pcc: 0.103, kcc: 0.115
+2025-07-10 14:48:09: Epoch: 014 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.126, kcc: 0.031
+2025-07-10 14:48:10: Epoch: 015 Train Loss: 2.794, mae: 1.293, rse: 1.000, pcc: 0.101, kcc: -0.010
+2025-07-10 14:48:10: Epoch: 015 Test results, ap: mae: 0.612, rse: 1.148, pcc: -0.094, kcc: -0.006
+2025-07-10 14:48:11: Epoch: 016 Train Loss: 2.785, mae: 1.294, rse: 1.000, pcc: 0.101, kcc: 0.117
+2025-07-10 14:48:11: Epoch: 016 Test results, ap: mae: 0.609, rse: 1.134, pcc: -0.262, kcc: 0.042
+2025-07-10 14:48:12: Epoch: 017 Train Loss: 2.815, mae: 1.292, rse: 1.001, pcc: 0.104, kcc: 0.107
+2025-07-10 14:48:12: Epoch: 017 Test results, ap: mae: 0.620, rse: 1.176, pcc: 0.068, kcc: 0.119
+2025-07-10 14:48:13: Epoch: 018 Train Loss: 2.806, mae: 1.293, rse: 1.000, pcc: 0.105, kcc: 0.110
+2025-07-10 14:48:13: Epoch: 018 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.254, kcc: 0.104
+2025-07-10 14:48:13: Epoch: 018 New best TEST metrics
+2025-07-10 14:48:14: Epoch: 019 Train Loss: 2.827, mae: 1.295, rse: 1.000, pcc: 0.103, kcc: 0.083
+2025-07-10 14:48:14: Epoch: 019 Test results, ap: mae: 0.605, rse: 1.121, pcc: -0.009, kcc: -0.016
+2025-07-10 14:48:14: Epoch: 020 Train Loss: 2.790, mae: 1.293, rse: 1.001, pcc: 0.109, kcc: 0.089
+2025-07-10 14:48:15: Epoch: 020 Test results, ap: mae: 0.617, rse: 1.167, pcc: 0.071, kcc: 0.104
+2025-07-10 14:48:15: Epoch: 021 Train Loss: 2.781, mae: 1.293, rse: 1.000, pcc: 0.144, kcc: 0.098
+2025-07-10 14:48:15: Epoch: 021 New best VALIDATION metrics
+2025-07-10 14:48:16: Epoch: 021 Test results, ap: mae: 0.613, rse: 1.152, pcc: 0.093, kcc: 0.116
+2025-07-10 14:48:16: Epoch: 022 Train Loss: 2.779, mae: 1.296, rse: 1.000, pcc: 0.129, kcc: 0.074
+2025-07-10 14:48:17: Epoch: 022 Test results, ap: mae: 0.602, rse: 1.108, pcc: 0.129, kcc: 0.094
+2025-07-10 14:48:17: Epoch: 023 Train Loss: 2.768, mae: 1.293, rse: 1.000, pcc: 0.132, kcc: 0.089
+2025-07-10 14:48:17: Epoch: 023 Test results, ap: mae: 0.615, rse: 1.158, pcc: 0.110, kcc: 0.055
+2025-07-10 14:48:18: Epoch: 024 Train Loss: 2.798, mae: 1.295, rse: 1.000, pcc: -0.116, kcc: -0.132
+2025-07-10 14:48:18: Epoch: 024 Test results, ap: mae: 0.604, rse: 1.117, pcc: -0.084, kcc: -0.073
+2025-07-10 14:48:19: Epoch: 025 Train Loss: 2.816, mae: 1.294, rse: 1.000, pcc: 0.184, kcc: 0.156
+2025-07-10 14:48:19: Epoch: 025 New best VALIDATION metrics
+2025-07-10 14:48:19: Epoch: 025 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.066, kcc: -0.046
+2025-07-10 14:48:20: Epoch: 026 Train Loss: 2.792, mae: 1.293, rse: 1.000, pcc: 0.067, kcc: -0.029
+2025-07-10 14:48:20: Epoch: 026 Test results, ap: mae: 0.613, rse: 1.152, pcc: 0.285, kcc: -0.105
+2025-07-10 14:48:21: Epoch: 027 Train Loss: 2.816, mae: 1.294, rse: 1.000, pcc: 0.095, kcc: 0.014
+2025-07-10 14:48:21: Epoch: 027 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.127, kcc: -0.112
+2025-07-10 14:48:22: Epoch: 028 Train Loss: 2.795, mae: 1.293, rse: 1.000, pcc: 0.045, kcc: 0.004
+2025-07-10 14:48:22: Epoch: 028 Test results, ap: mae: 0.612, rse: 1.149, pcc: 0.243, kcc: -0.050
+2025-07-10 14:48:23: Epoch: 029 Train Loss: 2.767, mae: 1.294, rse: 1.000, pcc: 0.030, kcc: 0.041
+2025-07-10 14:48:23: Epoch: 029 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.301, kcc: 0.064
+2025-07-10 14:48:23: Epoch: 029 New best TEST metrics
+2025-07-10 14:48:24: Epoch: 030 Train Loss: 2.819, mae: 1.294, rse: 1.000, pcc: 0.171, kcc: 0.122
+2025-07-10 14:48:24: Epoch: 030 Test results, ap: mae: 0.612, rse: 1.146, pcc: 0.809, kcc: 0.058
+2025-07-10 14:48:24: Epoch: 030 New best TEST metrics
+2025-07-10 14:48:25: Epoch: 031 Train Loss: 2.809, mae: 1.294, rse: 1.000, pcc: 0.008, kcc: 0.001
+2025-07-10 14:48:25: Epoch: 031 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.186, kcc: -0.131
+2025-07-10 14:48:26: Epoch: 032 Train Loss: 2.800, mae: 1.294, rse: 1.000, pcc: 0.055, kcc: 0.063
+2025-07-10 14:48:26: Epoch: 032 Test results, ap: mae: 0.608, rse: 1.134, pcc: 0.027, kcc: -0.051
+2025-07-10 14:48:27: Epoch: 033 Train Loss: 2.799, mae: 1.293, rse: 1.000, pcc: -0.051, kcc: -0.039
+2025-07-10 14:48:27: Epoch: 033 Test results, ap: mae: 0.614, rse: 1.156, pcc: -0.423, kcc: -0.057
+2025-07-10 14:48:28: Epoch: 034 Train Loss: 2.790, mae: 1.293, rse: 1.000, pcc: -0.052, kcc: -0.004
+2025-07-10 14:48:28: Epoch: 034 Test results, ap: mae: 0.613, rse: 1.151, pcc: 1.000, kcc: -0.020
+2025-07-10 14:48:28: Epoch: 034 New best TEST metrics
+2025-07-10 14:48:28: Epoch: 035 Train Loss: 2.804, mae: 1.294, rse: 1.000, pcc: 0.053, kcc: 0.030
+2025-07-10 14:48:29: Epoch: 035 Test results, ap: mae: 0.612, rse: 1.147, pcc: -0.651, kcc: -0.014
+2025-07-10 14:48:29: Epoch: 036 Train Loss: 2.780, mae: 1.293, rse: 1.000, pcc: 0.020, kcc: -0.016
+2025-07-10 14:48:30: Epoch: 036 Test results, ap: mae: 0.613, rse: 1.150, pcc: -0.015, kcc: -0.114
+2025-07-10 14:48:30: Epoch: 037 Train Loss: 2.791, mae: 1.294, rse: 1.000, pcc: 0.076, kcc: 0.062
+2025-07-10 14:48:30: Epoch: 037 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.030, kcc: 0.063
+2025-07-10 14:48:31: Epoch: 038 Train Loss: 2.787, mae: 1.294, rse: 1.000, pcc: 0.075, kcc: 0.063
+2025-07-10 14:48:31: Epoch: 038 Test results, ap: mae: 0.612, rse: 1.147, pcc: 0.570, kcc: 0.002
+2025-07-10 14:48:32: Epoch: 039 Train Loss: 2.810, mae: 1.293, rse: 1.001, pcc: -0.002, kcc: -0.089
+2025-07-10 14:48:32: Epoch: 039 Test results, ap: mae: 0.617, rse: 1.166, pcc: -0.005, kcc: 0.021
+2025-07-10 14:48:33: Epoch: 040 Train Loss: 2.796, mae: 1.293, rse: 1.000, pcc: 0.073, kcc: 0.022
+2025-07-10 14:48:33: Epoch: 040 Test results, ap: mae: 0.612, rse: 1.148, pcc: -0.197, kcc: -0.042
+2025-07-10 14:48:34: Epoch: 041 Train Loss: 2.813, mae: 1.293, rse: 1.000, pcc: 0.129, kcc: 0.116
+2025-07-10 14:48:34: Epoch: 041 Test results, ap: mae: 0.612, rse: 1.149, pcc: 0.135, kcc: 0.058
+2025-07-10 14:48:35: Epoch: 042 Train Loss: 2.804, mae: 1.294, rse: 1.000, pcc: -0.036, kcc: 0.008
+2025-07-10 14:48:35: Epoch: 042 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.044, kcc: 0.077
+2025-07-10 14:48:36: Epoch: 043 Train Loss: 2.794, mae: 1.294, rse: 1.000, pcc: -0.002, kcc: 0.030
+2025-07-10 14:48:36: Epoch: 043 Test results, ap: mae: 0.611, rse: 1.145, pcc: 1.000, kcc: 0.031
+2025-07-10 14:48:36: Epoch: 043 New best TEST metrics
+2025-07-10 14:48:37: Epoch: 044 Train Loss: 2.776, mae: 1.293, rse: 1.000, pcc: 0.098, kcc: 0.039
+2025-07-10 14:48:37: Epoch: 044 Test results, ap: mae: 0.612, rse: 1.148, pcc: 0.061, kcc: -0.044
+2025-07-10 14:48:38: Epoch: 045 Train Loss: 2.755, mae: 1.294, rse: 1.000, pcc: 0.024, kcc: 0.047
+2025-07-10 14:48:38: Epoch: 045 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.287, kcc: 0.006
+2025-07-10 14:48:39: Epoch: 046 Train Loss: 2.808, mae: 1.294, rse: 1.000, pcc: 0.013, kcc: -0.002
+2025-07-10 14:48:39: Epoch: 046 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.528, kcc: 0.105
+2025-07-10 14:48:39: Epoch: 047 Train Loss: 2.808, mae: 1.293, rse: 1.000, pcc: 0.012, kcc: 0.014
+2025-07-10 14:48:40: Epoch: 047 Test results, ap: mae: 0.612, rse: 1.149, pcc: 1.000, kcc: 0.008
+2025-07-10 14:48:40: Epoch: 048 Train Loss: 2.794, mae: 1.293, rse: 1.000, pcc: 0.056, kcc: 0.053
+2025-07-10 14:48:40: Epoch: 048 Test results, ap: mae: 0.612, rse: 1.149, pcc: 0.169, kcc: 0.103
+2025-07-10 14:48:41: Epoch: 049 Train Loss: 2.792, mae: 1.294, rse: 1.000, pcc: 0.110, kcc: 0.104
+2025-07-10 14:48:41: Epoch: 049 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.372, kcc: 0.044
+2025-07-10 14:48:42: Epoch: 050 Train Loss: 2.796, mae: 1.294, rse: 1.000, pcc: 0.048, kcc: 0.036
+2025-07-10 14:48:42: Epoch: 050 Test results, ap: mae: 0.612, rse: 1.146, pcc: 0.899, kcc: -0.063
+2025-07-10 14:48:42: used time 0.01h
+2025-07-10 14:48:42: Running Cross Validation 4
+2025-07-10 14:48:42: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:48:43: Epoch: 001 Train Loss: 2.841, mae: 1.211, rse: 1.000, pcc: 0.033, kcc: 0.078
+2025-07-10 14:48:43: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:48:43: Epoch: 001 Test results, ap: mae: 0.612, rse: 1.149, pcc: 0.080, kcc: 0.049
+2025-07-10 14:48:44: Epoch: 002 Train Loss: 2.848, mae: 1.213, rse: 1.000, pcc: -0.008, kcc: 0.087
+2025-07-10 14:48:44: Epoch: 002 Test results, ap: mae: 0.606, rse: 1.125, pcc: 0.045, kcc: 0.028
+2025-07-10 14:48:45: Epoch: 003 Train Loss: 2.876, mae: 1.210, rse: 1.000, pcc: 0.002, kcc: 0.055
+2025-07-10 14:48:45: Epoch: 003 Test results, ap: mae: 0.621, rse: 1.178, pcc: -0.064, kcc: 0.095
+2025-07-10 14:48:45: Epoch: 004 Train Loss: 2.842, mae: 1.213, rse: 1.000, pcc: -0.026, kcc: -0.094
+2025-07-10 14:48:46: Epoch: 004 Test results, ap: mae: 0.608, rse: 1.131, pcc: -0.107, kcc: -0.049
+2025-07-10 14:48:46: Epoch: 005 Train Loss: 2.861, mae: 1.209, rse: 1.002, pcc: -0.023, kcc: -0.086
+2025-07-10 14:48:46: Epoch: 005 Test results, ap: mae: 0.640, rse: 1.243, pcc: -0.101, kcc: -0.006
+2025-07-10 14:48:47: Epoch: 006 Train Loss: 2.869, mae: 1.210, rse: 1.000, pcc: 0.002, kcc: 0.078
+2025-07-10 14:48:47: Epoch: 006 Test results, ap: mae: 0.617, rse: 1.166, pcc: 0.076, kcc: 0.076
+2025-07-10 14:48:48: Epoch: 007 Train Loss: 2.847, mae: 1.215, rse: 1.001, pcc: -0.015, kcc: -0.095
+2025-07-10 14:48:48: Epoch: 007 Test results, ap: mae: 0.602, rse: 1.109, pcc: -0.089, kcc: -0.116
+2025-07-10 14:48:49: Epoch: 008 Train Loss: 2.835, mae: 1.209, rse: 1.001, pcc: -0.003, kcc: -0.059
+2025-07-10 14:48:49: Epoch: 008 Test results, ap: mae: 0.632, rse: 1.215, pcc: -0.102, kcc: -0.040
+2025-07-10 14:48:50: Epoch: 009 Train Loss: 2.875, mae: 1.211, rse: 1.000, pcc: 0.014, kcc: -0.067
+2025-07-10 14:48:50: Epoch: 009 Test results, ap: mae: 0.614, rse: 1.155, pcc: -1.000, kcc: 0.016
+2025-07-10 14:48:50: Epoch: 010 Train Loss: 2.853, mae: 1.212, rse: 1.000, pcc: -0.003, kcc: -0.064
+2025-07-10 14:48:51: Epoch: 010 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.429, kcc: 0.131
+2025-07-10 14:48:51: Epoch: 011 Train Loss: 2.841, mae: 1.212, rse: 1.000, pcc: 0.007, kcc: -0.036
+2025-07-10 14:48:52: Epoch: 011 Test results, ap: mae: 0.608, rse: 1.133, pcc: -0.154, kcc: -0.036
+2025-07-10 14:48:52: Epoch: 011 New best TEST metrics
+2025-07-10 14:48:52: Epoch: 012 Train Loss: 2.846, mae: 1.216, rse: 1.001, pcc: 0.004, kcc: -0.079
+2025-07-10 14:48:52: Epoch: 012 Test results, ap: mae: 0.600, rse: 1.100, pcc: -0.021, kcc: 0.100
+2025-07-10 14:48:52: Epoch: 012 New best TEST metrics
+2025-07-10 14:48:53: Epoch: 013 Train Loss: 2.856, mae: 1.210, rse: 1.000, pcc: -0.016, kcc: -0.111
+2025-07-10 14:48:53: Epoch: 013 Test results, ap: mae: 0.616, rse: 1.163, pcc: 0.543, kcc: -0.092
+2025-07-10 14:48:53: Epoch: 013 New best TEST metrics
+2025-07-10 14:48:54: Epoch: 014 Train Loss: 2.868, mae: 1.213, rse: 1.000, pcc: 0.004, kcc: -0.051
+2025-07-10 14:48:54: Epoch: 014 Test results, ap: mae: 0.605, rse: 1.120, pcc: -0.564, kcc: -0.088
+2025-07-10 14:48:55: Epoch: 015 Train Loss: 2.824, mae: 1.213, rse: 1.000, pcc: -0.007, kcc: -0.045
+2025-07-10 14:48:55: Epoch: 015 Test results, ap: mae: 0.608, rse: 1.130, pcc: -0.413, kcc: 0.000
+2025-07-10 14:48:56: Epoch: 016 Train Loss: 2.856, mae: 1.212, rse: 1.000, pcc: -0.036, kcc: -0.059
+2025-07-10 14:48:56: Epoch: 016 Test results, ap: mae: 0.611, rse: 1.143, pcc: 0.812, kcc: -0.070
+2025-07-10 14:48:56: Epoch: 016 New best TEST metrics
+2025-07-10 14:48:57: Epoch: 017 Train Loss: 2.850, mae: 1.211, rse: 1.000, pcc: -0.010, kcc: -0.048
+2025-07-10 14:48:57: Epoch: 017 Test results, ap: mae: 0.613, rse: 1.151, pcc: -1.000, kcc: nan
+2025-07-10 14:48:57: Epoch: 018 Train Loss: 2.859, mae: 1.212, rse: 1.000, pcc: -0.041, kcc: -0.009
+2025-07-10 14:48:57: Epoch: 018 Test results, ap: mae: 0.610, rse: 1.140, pcc: nan, kcc: nan
+2025-07-10 14:48:58: Epoch: 019 Train Loss: 2.841, mae: 1.213, rse: 1.000, pcc: 0.008, kcc: 0.067
+2025-07-10 14:48:58: Epoch: 019 Test results, ap: mae: 0.606, rse: 1.124, pcc: -0.302, kcc: -0.034
+2025-07-10 14:48:59: Epoch: 020 Train Loss: 2.847, mae: 1.212, rse: 1.000, pcc: -0.007, kcc: -0.016
+2025-07-10 14:48:59: Epoch: 020 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.252, kcc: -0.024
+2025-07-10 14:49:00: Epoch: 021 Train Loss: 2.871, mae: 1.212, rse: 1.000, pcc: 0.005, kcc: 0.005
+2025-07-10 14:49:00: Epoch: 021 Test results, ap: mae: 0.610, rse: 1.141, pcc: nan, kcc: nan
+2025-07-10 14:49:00: Epoch: 022 Train Loss: 2.855, mae: 1.211, rse: 1.000, pcc: 0.004, kcc: -0.057
+2025-07-10 14:49:00: Epoch: 022 Test results, ap: mae: 0.612, rse: 1.146, pcc: 0.432, kcc: -0.050
+2025-07-10 14:49:01: Epoch: 023 Train Loss: 2.845, mae: 1.212, rse: 1.000, pcc: 0.006, kcc: 0.046
+2025-07-10 14:49:01: Epoch: 023 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.010, kcc: 0.062
+2025-07-10 14:49:02: Epoch: 024 Train Loss: 2.862, mae: 1.212, rse: 1.000, pcc: 0.013, kcc: 0.073
+2025-07-10 14:49:02: Epoch: 024 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.070, kcc: 0.092
+2025-07-10 14:49:03: Epoch: 025 Train Loss: 2.846, mae: 1.211, rse: 1.000, pcc: -0.002, kcc: -0.005
+2025-07-10 14:49:03: Epoch: 025 Test results, ap: mae: 0.613, rse: 1.151, pcc: 0.625, kcc: 0.110
+2025-07-10 14:49:04: Epoch: 026 Train Loss: 2.847, mae: 1.212, rse: 1.000, pcc: 0.009, kcc: -0.025
+2025-07-10 14:49:04: Epoch: 026 Test results, ap: mae: 0.608, rse: 1.132, pcc: 1.000, kcc: -0.063
+2025-07-10 14:49:04: Epoch: 026 New best TEST metrics
+2025-07-10 14:49:05: Epoch: 027 Train Loss: 2.844, mae: 1.211, rse: 1.000, pcc: -0.022, kcc: 0.028
+2025-07-10 14:49:05: Epoch: 027 Test results, ap: mae: 0.613, rse: 1.150, pcc: -0.133, kcc: -0.057
+2025-07-10 14:49:06: Epoch: 028 Train Loss: 2.827, mae: 1.211, rse: 1.000, pcc: -0.016, kcc: 0.005
+2025-07-10 14:49:06: Epoch: 028 Test results, ap: mae: 0.612, rse: 1.149, pcc: nan, kcc: nan
+2025-07-10 14:49:07: Epoch: 029 Train Loss: 2.858, mae: 1.212, rse: 1.000, pcc: -0.010, kcc: 0.029
+2025-07-10 14:49:08: Epoch: 029 Test results, ap: mae: 0.611, rse: 1.143, pcc: 0.031, kcc: 0.085
+2025-07-10 14:49:08: Epoch: 030 Train Loss: 2.843, mae: 1.212, rse: 1.000, pcc: -0.020, kcc: -0.010
+2025-07-10 14:49:09: Epoch: 030 Test results, ap: mae: 0.610, rse: 1.141, pcc: 1.000, kcc: 0.105
+2025-07-10 14:49:09: Epoch: 030 New best TEST metrics
+2025-07-10 14:49:10: Epoch: 031 Train Loss: 2.853, mae: 1.212, rse: 1.000, pcc: -0.027, kcc: 0.001
+2025-07-10 14:49:10: Epoch: 031 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.027, kcc: 0.091
+2025-07-10 14:49:11: Epoch: 032 Train Loss: 2.848, mae: 1.212, rse: 1.000, pcc: -0.038, kcc: -0.024
+2025-07-10 14:49:11: Epoch: 032 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.562, kcc: -0.069
+2025-07-10 14:49:12: Epoch: 033 Train Loss: 2.828, mae: 1.212, rse: 1.000, pcc: -0.027, kcc: -0.039
+2025-07-10 14:49:12: Epoch: 033 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.955, kcc: -0.203
+2025-07-10 14:49:13: Epoch: 034 Train Loss: 2.863, mae: 1.212, rse: 1.000, pcc: -0.021, kcc: 0.095
+2025-07-10 14:49:13: Epoch: 034 Test results, ap: mae: 0.609, rse: 1.136, pcc: 0.511, kcc: 0.084
+2025-07-10 14:49:14: Epoch: 035 Train Loss: 2.845, mae: 1.212, rse: 1.000, pcc: -0.023, kcc: -0.026
+2025-07-10 14:49:14: Epoch: 035 Test results, ap: mae: 0.609, rse: 1.135, pcc: 0.055, kcc: -0.014
+2025-07-10 14:49:15: Epoch: 036 Train Loss: 2.851, mae: 1.212, rse: 1.000, pcc: -0.026, kcc: -0.018
+2025-07-10 14:49:16: Epoch: 036 Test results, ap: mae: 0.610, rse: 1.140, pcc: nan, kcc: nan
+2025-07-10 14:49:16: Epoch: 037 Train Loss: 2.860, mae: 1.212, rse: 1.000, pcc: -0.026, kcc: -0.003
+2025-07-10 14:49:17: Epoch: 037 Test results, ap: mae: 0.610, rse: 1.141, pcc: nan, kcc: nan
+2025-07-10 14:49:18: Epoch: 038 Train Loss: 2.842, mae: 1.212, rse: 1.000, pcc: -0.035, kcc: -0.072
+2025-07-10 14:49:18: Epoch: 038 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.551, kcc: 0.099
+2025-07-10 14:49:19: Epoch: 039 Train Loss: 2.846, mae: 1.212, rse: 1.000, pcc: -0.039, kcc: -0.052
+2025-07-10 14:49:19: Epoch: 039 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.543, kcc: -0.110
+2025-07-10 14:49:20: Epoch: 040 Train Loss: 2.846, mae: 1.212, rse: 1.000, pcc: -0.036, kcc: -0.005
+2025-07-10 14:49:20: Epoch: 040 Test results, ap: mae: 0.610, rse: 1.138, pcc: 1.000, kcc: nan
+2025-07-10 14:49:21: Epoch: 041 Train Loss: 2.827, mae: 1.212, rse: 1.000, pcc: -0.030, kcc: -0.018
+2025-07-10 14:49:21: Epoch: 041 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.128, kcc: -0.017
+2025-07-10 14:49:22: Epoch: 042 Train Loss: 2.848, mae: 1.212, rse: 1.000, pcc: -0.035, kcc: 0.023
+2025-07-10 14:49:22: Epoch: 042 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.885, kcc: 0.040
+2025-07-10 14:49:23: Epoch: 043 Train Loss: 2.844, mae: 1.212, rse: 1.000, pcc: -0.034, kcc: 0.001
+2025-07-10 14:49:23: Epoch: 043 Test results, ap: mae: 0.610, rse: 1.140, pcc: nan, kcc: nan
+2025-07-10 14:49:24: Epoch: 044 Train Loss: 2.839, mae: 1.212, rse: 1.000, pcc: -0.038, kcc: 0.028
+2025-07-10 14:49:25: Epoch: 044 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.700, kcc: 0.040
+2025-07-10 14:49:26: Epoch: 045 Train Loss: 2.835, mae: 1.212, rse: 1.000, pcc: -0.038, kcc: 0.017
+2025-07-10 14:49:26: Epoch: 045 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.728, kcc: 0.032
+2025-07-10 14:49:27: Epoch: 046 Train Loss: 2.852, mae: 1.212, rse: 1.000, pcc: -0.035, kcc: -0.031
+2025-07-10 14:49:27: Epoch: 046 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.484, kcc: -0.013
+2025-07-10 14:49:28: Epoch: 047 Train Loss: 2.859, mae: 1.212, rse: 1.000, pcc: -0.033, kcc: -0.025
+2025-07-10 14:49:28: Epoch: 047 Test results, ap: mae: 0.610, rse: 1.142, pcc: nan, kcc: nan
+2025-07-10 14:49:29: Epoch: 048 Train Loss: 2.830, mae: 1.212, rse: 1.000, pcc: -0.026, kcc: 0.018
+2025-07-10 14:49:29: Epoch: 048 Test results, ap: mae: 0.610, rse: 1.139, pcc: 1.000, kcc: nan
+2025-07-10 14:49:30: Epoch: 049 Train Loss: 2.865, mae: 1.212, rse: 1.000, pcc: -0.029, kcc: -0.000
+2025-07-10 14:49:30: Epoch: 049 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.027, kcc: -0.056
+2025-07-10 14:49:31: Epoch: 050 Train Loss: 2.857, mae: 1.212, rse: 1.000, pcc: -0.034, kcc: -0.014
+2025-07-10 14:49:31: Epoch: 050 Test results, ap: mae: 0.610, rse: 1.140, pcc: nan, kcc: nan
+2025-07-10 14:49:31: used time 0.01h
+2025-07-10 14:49:31: Cross Validation Finished!
+2025-07-10 14:49:31: Best validation perform list
+[[1.21318138 1.00079811 0.14666288 0.147194  ]
+ [1.19539177 0.89841145 0.34159103 0.11823802]
+ [1.25516188 1.00007164 0.08796822 0.11950422]
+ [1.29380846 1.00011134 0.18444084 0.15603282]
+ [1.2112     1.00001001 0.03344368 0.07766115]]
+2025-07-10 14:49:31: mean: [1.234 0.98  0.159 0.124]
+2025-07-10 14:49:31: std: [0.036 0.041 0.105 0.027]
+2025-07-10 14:49:31: Best test perform list
+[[0.60895807 1.1356163  0.56030071 0.04388955]
+ [0.61388522 1.15489507 0.35751349 0.25325304]
+ [0.6040343  1.11596668 1.         0.04884756]
+ [0.61145234 1.1453557  1.         0.0312174 ]
+ [0.61025429 1.14064634 1.         0.10504627]]
+2025-07-10 14:49:31: mean: [0.61  1.138 0.784 0.096]
+2025-07-10 14:49:31: std: [0.003 0.013 0.273 0.082]
diff --git a/run-reg/lstm-att-256-oneway-bmc-32-0.001-35/config.json b/run-reg/lstm-att-256-oneway-bmc-32-0.001-35/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..656c1089ec52227a976205ac82356a2a3c3cf089
--- /dev/null
+++ b/run-reg/lstm-att-256-oneway-bmc-32-0.001-35/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 35, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "bmc", "dir": false, "classes": 1}
\ No newline at end of file
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diff --git a/run-reg/lstm-att-256-oneway-bmc-32-0.001-35/result.txt b/run-reg/lstm-att-256-oneway-bmc-32-0.001-35/result.txt
new file mode 100644
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@@ -0,0 +1,6 @@
+Valid
+1.2557482719421387,1.004944348335266,-0.009653364494442939,0.036468886956572535
+0.06739746679525188,0.0033085541442129662,0.14471536487178432,0.1116530723974056
+Test
+0.6129845261573792,1.0507606744766236,0.7529963403940201,0.032167321816086766
+0.006149405377400864,0.013291849262632714,0.4607198783327786,0.07854915835528369
diff --git a/run-reg/lstm-att-256-oneway-bmc-32-0.001-35/training.log b/run-reg/lstm-att-256-oneway-bmc-32-0.001-35/training.log
new file mode 100644
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+++ b/run-reg/lstm-att-256-oneway-bmc-32-0.001-35/training.log
@@ -0,0 +1,412 @@
+2025-07-09 14:46:11: saving_dir: ./run-reg/lstm-att-256-oneway-bmc-32-0.001-35
+2025-07-09 14:46:11: Loading Train Dataset
+2025-07-09 14:46:12: Loading Test Dataset
+2025-07-09 14:46:13: Running Cross Validation 0
+2025-07-09 14:46:13: Fold 0  Train set:405, Valid set:102, Test set: 108
+2025-07-09 14:46:14: Epoch: 001 Train Loss: 8.772, mae: 1.168, rse: 1.000, pcc: 0.064, kcc: 0.153
+2025-07-09 14:46:14: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:46:14: Epoch: 001 Test results, ap: mae: 0.586, rse: 1.036, pcc: -0.142, kcc: -0.115
+2025-07-09 14:46:14: Epoch: 002 Train Loss: 8.726, mae: 1.246, rse: 1.031, pcc: -0.084, kcc: 0.047
+2025-07-09 14:46:15: Epoch: 002 Test results, ap: mae: 0.604, rse: 1.032, pcc: -0.091, kcc: -0.068
+2025-07-09 14:46:15: Epoch: 003 Train Loss: 8.685, mae: 1.256, rse: 1.038, pcc: -0.098, kcc: 0.013
+2025-07-09 14:46:16: Epoch: 003 Test results, ap: mae: 0.612, rse: 1.047, pcc: -0.115, kcc: -0.067
+2025-07-09 14:46:16: Epoch: 004 Train Loss: 8.678, mae: 1.265, rse: 1.045, pcc: -0.115, kcc: 0.028
+2025-07-09 14:46:16: Epoch: 004 Test results, ap: mae: 0.619, rse: 1.064, pcc: -0.141, kcc: -0.107
+2025-07-09 14:46:17: Epoch: 005 Train Loss: 8.678, mae: 1.234, rse: 1.024, pcc: -0.119, kcc: -0.032
+2025-07-09 14:46:17: Epoch: 005 Test results, ap: mae: 0.596, rse: 1.018, pcc: -0.110, kcc: -0.116
+2025-07-09 14:46:18: Epoch: 006 Train Loss: 8.674, mae: 1.249, rse: 1.033, pcc: -0.122, kcc: -0.040
+2025-07-09 14:46:18: Epoch: 006 Test results, ap: mae: 0.607, rse: 1.037, pcc: -0.053, kcc: -0.056
+2025-07-09 14:46:19: Epoch: 007 Train Loss: 8.683, mae: 1.297, rse: 1.071, pcc: -0.125, kcc: -0.025
+2025-07-09 14:46:19: Epoch: 007 Test results, ap: mae: 0.649, rse: 1.136, pcc: 0.024, kcc: -0.083
+2025-07-09 14:46:19: Epoch: 008 Train Loss: 8.613, mae: 1.265, rse: 1.045, pcc: -0.117, kcc: -0.063
+2025-07-09 14:46:20: Epoch: 008 Test results, ap: mae: 0.619, rse: 1.064, pcc: 0.571, kcc: -0.100
+2025-07-09 14:46:20: Epoch: 009 Train Loss: 8.578, mae: 1.252, rse: 1.035, pcc: -0.077, kcc: -0.085
+2025-07-09 14:46:21: Epoch: 009 Test results, ap: mae: 0.608, rse: 1.041, pcc: 0.070, kcc: -0.045
+2025-07-09 14:46:21: Epoch: 010 Train Loss: 8.642, mae: 1.281, rse: 1.057, pcc: -0.099, kcc: 0.002
+2025-07-09 14:46:21: Epoch: 010 Test results, ap: mae: 0.633, rse: 1.096, pcc: -0.656, kcc: -0.043
+2025-07-09 14:46:22: Epoch: 011 Train Loss: 8.674, mae: 1.267, rse: 1.046, pcc: -0.119, kcc: -0.060
+2025-07-09 14:46:22: Epoch: 011 Test results, ap: mae: 0.621, rse: 1.068, pcc: 1.000, kcc: 0.025
+2025-07-09 14:46:22: Epoch: 011 New best TEST metrics
+2025-07-09 14:46:23: Epoch: 012 Train Loss: 8.705, mae: 1.269, rse: 1.048, pcc: -0.122, kcc: -0.065
+2025-07-09 14:46:23: Epoch: 012 Test results, ap: mae: 0.622, rse: 1.071, pcc: -0.460, kcc: -0.273
+2025-07-09 14:46:24: Epoch: 013 Train Loss: 8.636, mae: 1.270, rse: 1.048, pcc: -0.124, kcc: -0.101
+2025-07-09 14:46:24: Epoch: 013 Test results, ap: mae: 0.623, rse: 1.073, pcc: 0.336, kcc: 0.030
+2025-07-09 14:46:24: Epoch: 014 Train Loss: 8.663, mae: 1.262, rse: 1.042, pcc: -0.092, kcc: 0.010
+2025-07-09 14:46:25: Epoch: 014 Test results, ap: mae: 0.616, rse: 1.057, pcc: 0.586, kcc: 0.088
+2025-07-09 14:46:25: Epoch: 015 Train Loss: 8.476, mae: 1.244, rse: 1.030, pcc: -0.055, kcc: -0.160
+2025-07-09 14:46:26: Epoch: 015 Test results, ap: mae: 0.603, rse: 1.030, pcc: -0.283, kcc: -0.106
+2025-07-09 14:46:26: Epoch: 016 Train Loss: 8.621, mae: 1.237, rse: 1.025, pcc: -0.034, kcc: -0.001
+2025-07-09 14:46:26: Epoch: 016 Test results, ap: mae: 0.598, rse: 1.021, pcc: 0.805, kcc: 0.018
+2025-07-09 14:46:27: Epoch: 017 Train Loss: 8.414, mae: 1.263, rse: 1.043, pcc: -0.091, kcc: -0.105
+2025-07-09 14:46:27: Epoch: 017 Test results, ap: mae: 0.617, rse: 1.060, pcc: -0.135, kcc: 0.098
+2025-07-09 14:46:28: Epoch: 018 Train Loss: 8.716, mae: 1.290, rse: 1.065, pcc: -0.103, kcc: -0.072
+2025-07-09 14:46:28: Epoch: 018 Test results, ap: mae: 0.642, rse: 1.119, pcc: nan, kcc: nan
+2025-07-09 14:46:29: Epoch: 019 Train Loss: 8.692, mae: 1.251, rse: 1.035, pcc: -0.033, kcc: -0.083
+2025-07-09 14:46:29: Epoch: 019 Test results, ap: mae: 0.608, rse: 1.040, pcc: 0.639, kcc: -0.083
+2025-07-09 14:46:29: Epoch: 020 Train Loss: 8.677, mae: 1.260, rse: 1.041, pcc: -0.077, kcc: -0.016
+2025-07-09 14:46:30: Epoch: 020 Test results, ap: mae: 0.615, rse: 1.054, pcc: -0.207, kcc: -0.013
+2025-07-09 14:46:30: Epoch: 021 Train Loss: 8.560, mae: 1.254, rse: 1.036, pcc: -0.104, kcc: -0.037
+2025-07-09 14:46:31: Epoch: 021 Test results, ap: mae: 0.610, rse: 1.044, pcc: -1.000, kcc: nan
+2025-07-09 14:46:31: Epoch: 022 Train Loss: 8.716, mae: 1.267, rse: 1.046, pcc: -0.116, kcc: -0.131
+2025-07-09 14:46:31: Epoch: 022 Test results, ap: mae: 0.620, rse: 1.068, pcc: -0.020, kcc: -0.028
+2025-07-09 14:46:32: Epoch: 023 Train Loss: 8.702, mae: 1.270, rse: 1.049, pcc: -0.141, kcc: -0.130
+2025-07-09 14:46:32: Epoch: 023 Test results, ap: mae: 0.623, rse: 1.074, pcc: -0.913, kcc: -0.084
+2025-07-09 14:46:33: Epoch: 024 Train Loss: 8.656, mae: 1.269, rse: 1.048, pcc: -0.108, kcc: -0.165
+2025-07-09 14:46:33: Epoch: 024 Test results, ap: mae: 0.622, rse: 1.071, pcc: -1.000, kcc: nan
+2025-07-09 14:46:34: Epoch: 025 Train Loss: 8.473, mae: 1.255, rse: 1.037, pcc: -0.009, kcc: -0.128
+2025-07-09 14:46:34: Epoch: 025 Test results, ap: mae: 0.611, rse: 1.045, pcc: nan, kcc: nan
+2025-07-09 14:46:34: Epoch: 026 Train Loss: 8.448, mae: 1.250, rse: 1.034, pcc: 0.050, kcc: -0.018
+2025-07-09 14:46:35: Epoch: 026 Test results, ap: mae: 0.607, rse: 1.039, pcc: 0.119, kcc: -0.140
+2025-07-09 14:46:35: Epoch: 027 Train Loss: 8.718, mae: 1.238, rse: 1.026, pcc: 0.067, kcc: 0.106
+2025-07-09 14:46:36: Epoch: 027 Test results, ap: mae: 0.599, rse: 1.022, pcc: -1.000, kcc: nan
+2025-07-09 14:46:36: Epoch: 028 Train Loss: 8.670, mae: 1.253, rse: 1.036, pcc: -0.037, kcc: 0.028
+2025-07-09 14:46:36: Epoch: 028 Test results, ap: mae: 0.609, rse: 1.043, pcc: -0.526, kcc: -0.080
+2025-07-09 14:46:37: Epoch: 029 Train Loss: 8.426, mae: 1.266, rse: 1.045, pcc: -0.090, kcc: -0.127
+2025-07-09 14:46:37: Epoch: 029 Test results, ap: mae: 0.619, rse: 1.065, pcc: -1.000, kcc: nan
+2025-07-09 14:46:38: Epoch: 030 Train Loss: 8.661, mae: 1.255, rse: 1.037, pcc: -0.070, kcc: -0.103
+2025-07-09 14:46:38: Epoch: 030 Test results, ap: mae: 0.611, rse: 1.046, pcc: 0.021, kcc: 0.237
+2025-07-09 14:46:39: Epoch: 031 Train Loss: 8.631, mae: 1.263, rse: 1.043, pcc: -0.044, kcc: -0.052
+2025-07-09 14:46:39: Epoch: 031 Test results, ap: mae: 0.617, rse: 1.060, pcc: nan, kcc: nan
+2025-07-09 14:46:39: Epoch: 032 Train Loss: 8.682, mae: 1.260, rse: 1.041, pcc: 0.000, kcc: 0.051
+2025-07-09 14:46:40: Epoch: 032 Test results, ap: mae: 0.615, rse: 1.054, pcc: -0.501, kcc: -0.040
+2025-07-09 14:46:40: Epoch: 033 Train Loss: 8.679, mae: 1.270, rse: 1.048, pcc: -0.122, kcc: -0.137
+2025-07-09 14:46:41: Epoch: 033 Test results, ap: mae: 0.623, rse: 1.073, pcc: -0.221, kcc: -0.085
+2025-07-09 14:46:41: Epoch: 034 Train Loss: 8.596, mae: 1.273, rse: 1.051, pcc: -0.136, kcc: -0.160
+2025-07-09 14:46:41: Epoch: 034 Test results, ap: mae: 0.626, rse: 1.081, pcc: nan, kcc: nan
+2025-07-09 14:46:42: Epoch: 035 Train Loss: 8.657, mae: 1.274, rse: 1.052, pcc: -0.114, kcc: -0.166
+2025-07-09 14:46:42: Epoch: 035 Test results, ap: mae: 0.627, rse: 1.082, pcc: nan, kcc: nan
+2025-07-09 14:46:42: used time 0.01h
+2025-07-09 14:46:42: Running Cross Validation 1
+2025-07-09 14:46:42: Fold 1  Train set:405, Valid set:102, Test set: 108
+2025-07-09 14:46:43: Epoch: 001 Train Loss: 8.545, mae: 1.285, rse: 1.003, pcc: -0.041, kcc: -0.104
+2025-07-09 14:46:43: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:46:43: Epoch: 001 Test results, ap: mae: 0.583, rse: 1.000, pcc: 0.205, kcc: 0.164
+2025-07-09 14:46:44: Epoch: 002 Train Loss: 8.576, mae: 1.327, rse: 1.017, pcc: -0.095, kcc: -0.169
+2025-07-09 14:46:44: Epoch: 002 Test results, ap: mae: 0.605, rse: 1.033, pcc: 0.097, kcc: 0.095
+2025-07-09 14:46:45: Epoch: 003 Train Loss: 8.544, mae: 1.319, rse: 1.013, pcc: -0.117, kcc: -0.146
+2025-07-09 14:46:45: Epoch: 003 Test results, ap: mae: 0.599, rse: 1.022, pcc: -0.042, kcc: 0.094
+2025-07-09 14:46:45: Epoch: 004 Train Loss: 8.527, mae: 1.323, rse: 1.015, pcc: -0.101, kcc: -0.156
+2025-07-09 14:46:46: Epoch: 004 Test results, ap: mae: 0.602, rse: 1.028, pcc: 0.144, kcc: 0.062
+2025-07-09 14:46:46: Epoch: 005 Train Loss: 8.473, mae: 1.318, rse: 1.013, pcc: -0.099, kcc: -0.114
+2025-07-09 14:46:46: Epoch: 005 Test results, ap: mae: 0.599, rse: 1.022, pcc: 0.086, kcc: 0.070
+2025-07-09 14:46:47: Epoch: 006 Train Loss: 8.508, mae: 1.327, rse: 1.017, pcc: -0.097, kcc: -0.104
+2025-07-09 14:46:47: Epoch: 006 Test results, ap: mae: 0.605, rse: 1.034, pcc: 0.071, kcc: 0.083
+2025-07-09 14:46:48: Epoch: 007 Train Loss: 8.543, mae: 1.321, rse: 1.014, pcc: -0.098, kcc: -0.096
+2025-07-09 14:46:48: Epoch: 007 Test results, ap: mae: 0.601, rse: 1.025, pcc: 0.105, kcc: 0.048
+2025-07-09 14:46:49: Epoch: 008 Train Loss: 8.500, mae: 1.299, rse: 1.006, pcc: -0.100, kcc: -0.093
+2025-07-09 14:46:49: Epoch: 008 Test results, ap: mae: 0.588, rse: 1.005, pcc: 0.095, kcc: -0.041
+2025-07-09 14:46:50: Epoch: 009 Train Loss: 8.486, mae: 1.312, rse: 1.011, pcc: -0.098, kcc: -0.090
+2025-07-09 14:46:50: Epoch: 009 Test results, ap: mae: 0.595, rse: 1.015, pcc: -0.139, kcc: 0.041
+2025-07-09 14:46:50: Epoch: 010 Train Loss: 8.496, mae: 1.358, rse: 1.032, pcc: -0.105, kcc: -0.101
+2025-07-09 14:46:51: Epoch: 010 Test results, ap: mae: 0.629, rse: 1.088, pcc: -0.289, kcc: 0.049
+2025-07-09 14:46:51: Epoch: 011 Train Loss: 8.411, mae: 1.304, rse: 1.008, pcc: -0.096, kcc: -0.131
+2025-07-09 14:46:51: Epoch: 011 Test results, ap: mae: 0.591, rse: 1.008, pcc: -0.770, kcc: -0.110
+2025-07-09 14:46:51: Epoch: 011 New best TEST metrics
+2025-07-09 14:46:52: Epoch: 012 Train Loss: 8.570, mae: 1.323, rse: 1.015, pcc: -0.107, kcc: -0.096
+2025-07-09 14:46:52: Epoch: 012 Test results, ap: mae: 0.602, rse: 1.028, pcc: -0.201, kcc: 0.004
+2025-07-09 14:46:52: Epoch: 012 New best TEST metrics
+2025-07-09 14:46:53: Epoch: 013 Train Loss: 8.309, mae: 1.302, rse: 1.007, pcc: 0.092, kcc: 0.062
+2025-07-09 14:46:53: Epoch: 013 New best VALIDATION metrics
+2025-07-09 14:46:53: Epoch: 013 Test results, ap: mae: 0.590, rse: 1.007, pcc: -0.167, kcc: -0.013
+2025-07-09 14:46:53: Epoch: 013 New best TEST metrics
+2025-07-09 14:46:54: Epoch: 014 Train Loss: 8.521, mae: 1.345, rse: 1.026, pcc: -0.116, kcc: -0.109
+2025-07-09 14:46:54: Epoch: 014 Test results, ap: mae: 0.619, rse: 1.064, pcc: -0.288, kcc: 0.000
+2025-07-09 14:46:55: Epoch: 015 Train Loss: 8.525, mae: 1.325, rse: 1.016, pcc: -0.101, kcc: -0.121
+2025-07-09 14:46:55: Epoch: 015 Test results, ap: mae: 0.604, rse: 1.031, pcc: -0.221, kcc: -0.022
+2025-07-09 14:46:55: Epoch: 016 Train Loss: 8.572, mae: 1.300, rse: 1.007, pcc: -0.037, kcc: -0.130
+2025-07-09 14:46:56: Epoch: 016 Test results, ap: mae: 0.588, rse: 1.005, pcc: -0.209, kcc: -0.030
+2025-07-09 14:46:56: Epoch: 017 Train Loss: 8.493, mae: 1.336, rse: 1.021, pcc: -0.106, kcc: -0.093
+2025-07-09 14:46:57: Epoch: 017 Test results, ap: mae: 0.612, rse: 1.047, pcc: 0.136, kcc: 0.126
+2025-07-09 14:46:57: Epoch: 017 New best TEST metrics
+2025-07-09 14:46:57: Epoch: 018 Train Loss: 8.519, mae: 1.319, rse: 1.013, pcc: -0.116, kcc: -0.180
+2025-07-09 14:46:57: Epoch: 018 Test results, ap: mae: 0.599, rse: 1.022, pcc: -0.256, kcc: 0.036
+2025-07-09 14:46:58: Epoch: 019 Train Loss: 8.467, mae: 1.318, rse: 1.013, pcc: -0.105, kcc: -0.135
+2025-07-09 14:46:58: Epoch: 019 Test results, ap: mae: 0.598, rse: 1.021, pcc: -0.321, kcc: 0.195
+2025-07-09 14:46:59: Epoch: 020 Train Loss: 8.550, mae: 1.346, rse: 1.026, pcc: -0.114, kcc: -0.086
+2025-07-09 14:46:59: Epoch: 020 Test results, ap: mae: 0.619, rse: 1.065, pcc: -0.303, kcc: 0.259
+2025-07-09 14:47:00: Epoch: 021 Train Loss: 8.535, mae: 1.327, rse: 1.017, pcc: -0.106, kcc: -0.103
+2025-07-09 14:47:00: Epoch: 021 Test results, ap: mae: 0.605, rse: 1.034, pcc: 1.000, kcc: 0.091
+2025-07-09 14:47:00: Epoch: 021 New best TEST metrics
+2025-07-09 14:47:00: Epoch: 022 Train Loss: 8.252, mae: 1.324, rse: 1.016, pcc: -0.134, kcc: -0.122
+2025-07-09 14:47:01: Epoch: 022 Test results, ap: mae: 0.603, rse: 1.030, pcc: -0.713, kcc: 0.218
+2025-07-09 14:47:01: Epoch: 023 Train Loss: 8.552, mae: 1.317, rse: 1.013, pcc: -0.095, kcc: -0.100
+2025-07-09 14:47:02: Epoch: 023 Test results, ap: mae: 0.598, rse: 1.021, pcc: 0.034, kcc: 0.063
+2025-07-09 14:47:02: Epoch: 024 Train Loss: 8.539, mae: 1.330, rse: 1.018, pcc: -0.119, kcc: -0.145
+2025-07-09 14:47:02: Epoch: 024 Test results, ap: mae: 0.608, rse: 1.039, pcc: 0.666, kcc: 0.121
+2025-07-09 14:47:03: Epoch: 025 Train Loss: 8.519, mae: 1.329, rse: 1.018, pcc: -0.113, kcc: -0.162
+2025-07-09 14:47:03: Epoch: 025 Test results, ap: mae: 0.606, rse: 1.036, pcc: 0.178, kcc: 0.088
+2025-07-09 14:47:04: Epoch: 026 Train Loss: 8.466, mae: 1.343, rse: 1.024, pcc: -0.105, kcc: -0.185
+2025-07-09 14:47:04: Epoch: 026 Test results, ap: mae: 0.617, rse: 1.059, pcc: 1.000, kcc: nan
+2025-07-09 14:47:05: Epoch: 027 Train Loss: 8.553, mae: 1.332, rse: 1.019, pcc: -0.106, kcc: -0.195
+2025-07-09 14:47:05: Epoch: 027 Test results, ap: mae: 0.609, rse: 1.041, pcc: -0.410, kcc: 0.096
+2025-07-09 14:47:06: Epoch: 028 Train Loss: 8.506, mae: 1.336, rse: 1.021, pcc: -0.127, kcc: -0.190
+2025-07-09 14:47:06: Epoch: 028 Test results, ap: mae: 0.612, rse: 1.048, pcc: 0.013, kcc: -0.007
+2025-07-09 14:47:06: Epoch: 029 Train Loss: 8.558, mae: 1.322, rse: 1.015, pcc: -0.134, kcc: -0.247
+2025-07-09 14:47:07: Epoch: 029 Test results, ap: mae: 0.601, rse: 1.027, pcc: -0.396, kcc: 0.239
+2025-07-09 14:47:07: Epoch: 030 Train Loss: 8.503, mae: 1.322, rse: 1.015, pcc: -0.133, kcc: -0.127
+2025-07-09 14:47:07: Epoch: 030 Test results, ap: mae: 0.601, rse: 1.027, pcc: -0.448, kcc: 0.055
+2025-07-09 14:47:08: Epoch: 031 Train Loss: 8.538, mae: 1.321, rse: 1.014, pcc: -0.093, kcc: -0.125
+2025-07-09 14:47:08: Epoch: 031 Test results, ap: mae: 0.601, rse: 1.025, pcc: -0.068, kcc: 0.011
+2025-07-09 14:47:09: Epoch: 032 Train Loss: 8.544, mae: 1.325, rse: 1.016, pcc: -0.085, kcc: -0.170
+2025-07-09 14:47:09: Epoch: 032 Test results, ap: mae: 0.604, rse: 1.031, pcc: nan, kcc: nan
+2025-07-09 14:47:10: Epoch: 033 Train Loss: 8.556, mae: 1.325, rse: 1.016, pcc: -0.039, kcc: -0.052
+2025-07-09 14:47:10: Epoch: 033 Test results, ap: mae: 0.603, rse: 1.031, pcc: -0.427, kcc: 0.092
+2025-07-09 14:47:11: Epoch: 034 Train Loss: 8.547, mae: 1.321, rse: 1.015, pcc: 0.044, kcc: -0.033
+2025-07-09 14:47:11: Epoch: 034 Test results, ap: mae: 0.601, rse: 1.026, pcc: -0.366, kcc: 0.066
+2025-07-09 14:47:11: Epoch: 035 Train Loss: 8.447, mae: 1.322, rse: 1.015, pcc: -0.010, kcc: -0.060
+2025-07-09 14:47:12: Epoch: 035 Test results, ap: mae: 0.602, rse: 1.027, pcc: -0.010, kcc: -0.018
+2025-07-09 14:47:12: used time 0.01h
+2025-07-09 14:47:12: Running Cross Validation 2
+2025-07-09 14:47:12: Fold 2  Train set:406, Valid set:101, Test set: 108
+2025-07-09 14:47:12: Epoch: 001 Train Loss: 8.663, mae: 1.219, rse: 1.009, pcc: -0.016, kcc: 0.112
+2025-07-09 14:47:12: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:47:13: Epoch: 001 Test results, ap: mae: 0.590, rse: 1.055, pcc: 0.174, kcc: 0.044
+2025-07-09 14:47:13: Epoch: 002 Train Loss: 8.567, mae: 1.235, rse: 1.000, pcc: -0.077, kcc: 0.125
+2025-07-09 14:47:13: Epoch: 002 Test results, ap: mae: 0.581, rse: 1.002, pcc: 0.162, kcc: 0.104
+2025-07-09 14:47:14: Epoch: 003 Train Loss: 8.493, mae: 1.288, rse: 1.014, pcc: -0.079, kcc: 0.107
+2025-07-09 14:47:14: Epoch: 003 Test results, ap: mae: 0.622, rse: 1.071, pcc: 0.146, kcc: 0.096
+2025-07-09 14:47:15: Epoch: 004 Train Loss: 8.490, mae: 1.249, rse: 1.001, pcc: -0.081, kcc: 0.095
+2025-07-09 14:47:15: Epoch: 004 Test results, ap: mae: 0.588, rse: 1.005, pcc: 0.182, kcc: 0.132
+2025-07-09 14:47:16: Epoch: 005 Train Loss: 8.430, mae: 1.289, rse: 1.014, pcc: -0.085, kcc: 0.104
+2025-07-09 14:47:16: Epoch: 005 Test results, ap: mae: 0.623, rse: 1.074, pcc: 0.177, kcc: 0.143
+2025-07-09 14:47:16: Epoch: 006 Train Loss: 8.428, mae: 1.253, rse: 1.002, pcc: -0.093, kcc: 0.092
+2025-07-09 14:47:17: Epoch: 006 Test results, ap: mae: 0.591, rse: 1.009, pcc: 0.219, kcc: 0.124
+2025-07-09 14:47:17: Epoch: 007 Train Loss: 8.403, mae: 1.303, rse: 1.022, pcc: -0.093, kcc: 0.096
+2025-07-09 14:47:18: Epoch: 007 Test results, ap: mae: 0.641, rse: 1.115, pcc: 0.258, kcc: 0.085
+2025-07-09 14:47:18: Epoch: 008 Train Loss: 8.541, mae: 1.251, rse: 1.002, pcc: -0.097, kcc: 0.090
+2025-07-09 14:47:18: Epoch: 008 Test results, ap: mae: 0.590, rse: 1.007, pcc: -0.246, kcc: 0.115
+2025-07-09 14:47:19: Epoch: 009 Train Loss: 8.510, mae: 1.258, rse: 1.003, pcc: -0.101, kcc: 0.086
+2025-07-09 14:47:19: Epoch: 009 Test results, ap: mae: 0.594, rse: 1.015, pcc: 0.203, kcc: 0.051
+2025-07-09 14:47:20: Epoch: 010 Train Loss: 8.480, mae: 1.276, rse: 1.009, pcc: 0.115, kcc: -0.098
+2025-07-09 14:47:20: Epoch: 010 Test results, ap: mae: 0.611, rse: 1.046, pcc: -0.095, kcc: -0.181
+2025-07-09 14:47:21: Epoch: 011 Train Loss: 8.487, mae: 1.284, rse: 1.012, pcc: -0.110, kcc: -0.076
+2025-07-09 14:47:21: Epoch: 011 Test results, ap: mae: 0.618, rse: 1.062, pcc: 0.727, kcc: 0.009
+2025-07-09 14:47:21: Epoch: 011 New best TEST metrics
+2025-07-09 14:47:22: Epoch: 012 Train Loss: 8.517, mae: 1.280, rse: 1.011, pcc: -0.112, kcc: 0.041
+2025-07-09 14:47:22: Epoch: 012 Test results, ap: mae: 0.615, rse: 1.053, pcc: -0.300, kcc: 0.054
+2025-07-09 14:47:22: Epoch: 013 Train Loss: 8.548, mae: 1.275, rse: 1.009, pcc: -0.119, kcc: 0.033
+2025-07-09 14:47:23: Epoch: 013 Test results, ap: mae: 0.610, rse: 1.043, pcc: -0.446, kcc: 0.030
+2025-07-09 14:47:23: Epoch: 014 Train Loss: 8.492, mae: 1.269, rse: 1.006, pcc: -0.124, kcc: 0.000
+2025-07-09 14:47:23: Epoch: 014 Test results, ap: mae: 0.604, rse: 1.031, pcc: -1.000, kcc: 0.084
+2025-07-09 14:47:24: Epoch: 015 Train Loss: 8.453, mae: 1.272, rse: 1.007, pcc: -0.104, kcc: 0.036
+2025-07-09 14:47:24: Epoch: 015 Test results, ap: mae: 0.607, rse: 1.037, pcc: -0.033, kcc: 0.127
+2025-07-09 14:47:25: Epoch: 016 Train Loss: 8.491, mae: 1.278, rse: 1.010, pcc: -0.122, kcc: -0.003
+2025-07-09 14:47:25: Epoch: 016 Test results, ap: mae: 0.613, rse: 1.050, pcc: -0.214, kcc: 0.212
+2025-07-09 14:47:26: Epoch: 017 Train Loss: 8.447, mae: 1.285, rse: 1.013, pcc: -0.097, kcc: 0.002
+2025-07-09 14:47:26: Epoch: 017 Test results, ap: mae: 0.619, rse: 1.065, pcc: -1.000, kcc: 0.005
+2025-07-09 14:47:27: Epoch: 018 Train Loss: 8.453, mae: 1.264, rse: 1.005, pcc: 0.026, kcc: 0.035
+2025-07-09 14:47:27: Epoch: 018 Test results, ap: mae: 0.600, rse: 1.023, pcc: nan, kcc: nan
+2025-07-09 14:47:27: Epoch: 019 Train Loss: 8.464, mae: 1.288, rse: 1.014, pcc: -0.090, kcc: -0.002
+2025-07-09 14:47:28: Epoch: 019 Test results, ap: mae: 0.622, rse: 1.071, pcc: 0.462, kcc: -0.056
+2025-07-09 14:47:28: Epoch: 020 Train Loss: 8.543, mae: 1.291, rse: 1.015, pcc: -0.106, kcc: 0.062
+2025-07-09 14:47:28: Epoch: 020 Test results, ap: mae: 0.626, rse: 1.080, pcc: 0.402, kcc: -0.026
+2025-07-09 14:47:29: Epoch: 021 Train Loss: 8.512, mae: 1.283, rse: 1.012, pcc: -0.116, kcc: -0.039
+2025-07-09 14:47:29: Epoch: 021 Test results, ap: mae: 0.617, rse: 1.059, pcc: 1.000, kcc: 0.107
+2025-07-09 14:47:29: Epoch: 021 New best TEST metrics
+2025-07-09 14:47:30: Epoch: 022 Train Loss: 8.403, mae: 1.279, rse: 1.010, pcc: -0.109, kcc: -0.065
+2025-07-09 14:47:30: Epoch: 022 Test results, ap: mae: 0.613, rse: 1.051, pcc: 0.289, kcc: -0.088
+2025-07-09 14:47:31: Epoch: 023 Train Loss: 8.452, mae: 1.283, rse: 1.012, pcc: -0.114, kcc: -0.083
+2025-07-09 14:47:31: Epoch: 023 Test results, ap: mae: 0.617, rse: 1.060, pcc: nan, kcc: nan
+2025-07-09 14:47:32: Epoch: 024 Train Loss: 8.497, mae: 1.276, rse: 1.009, pcc: -0.116, kcc: -0.059
+2025-07-09 14:47:32: Epoch: 024 Test results, ap: mae: 0.611, rse: 1.046, pcc: -0.918, kcc: -0.060
+2025-07-09 14:47:32: Epoch: 025 Train Loss: 8.516, mae: 1.286, rse: 1.013, pcc: -0.113, kcc: -0.019
+2025-07-09 14:47:33: Epoch: 025 Test results, ap: mae: 0.620, rse: 1.067, pcc: -0.377, kcc: 0.092
+2025-07-09 14:47:33: Epoch: 026 Train Loss: 8.430, mae: 1.272, rse: 1.007, pcc: -0.127, kcc: 0.001
+2025-07-09 14:47:34: Epoch: 026 Test results, ap: mae: 0.606, rse: 1.036, pcc: 1.000, kcc: 0.049
+2025-07-09 14:47:34: Epoch: 027 Train Loss: 8.509, mae: 1.258, rse: 1.003, pcc: 0.048, kcc: -0.039
+2025-07-09 14:47:34: Epoch: 027 Test results, ap: mae: 0.595, rse: 1.015, pcc: -0.548, kcc: 0.173
+2025-07-09 14:47:35: Epoch: 028 Train Loss: 8.454, mae: 1.275, rse: 1.009, pcc: -0.059, kcc: -0.046
+2025-07-09 14:47:35: Epoch: 028 Test results, ap: mae: 0.610, rse: 1.043, pcc: nan, kcc: nan
+2025-07-09 14:47:36: Epoch: 029 Train Loss: 8.374, mae: 1.277, rse: 1.009, pcc: -0.093, kcc: -0.024
+2025-07-09 14:47:36: Epoch: 029 Test results, ap: mae: 0.612, rse: 1.047, pcc: -0.539, kcc: 0.067
+2025-07-09 14:47:37: Epoch: 030 Train Loss: 8.489, mae: 1.282, rse: 1.011, pcc: -0.129, kcc: -0.092
+2025-07-09 14:47:37: Epoch: 030 Test results, ap: mae: 0.616, rse: 1.057, pcc: -0.850, kcc: -0.044
+2025-07-09 14:47:38: Epoch: 031 Train Loss: 8.445, mae: 1.283, rse: 1.012, pcc: -0.100, kcc: -0.038
+2025-07-09 14:47:38: Epoch: 031 Test results, ap: mae: 0.617, rse: 1.059, pcc: 0.029, kcc: 0.023
+2025-07-09 14:47:38: Epoch: 032 Train Loss: 8.486, mae: 1.278, rse: 1.010, pcc: -0.105, kcc: -0.053
+2025-07-09 14:47:39: Epoch: 032 Test results, ap: mae: 0.612, rse: 1.048, pcc: -0.633, kcc: -0.062
+2025-07-09 14:47:39: Epoch: 033 Train Loss: 8.465, mae: 1.277, rse: 1.010, pcc: -0.106, kcc: -0.123
+2025-07-09 14:47:39: Epoch: 033 Test results, ap: mae: 0.612, rse: 1.048, pcc: 1.000, kcc: -0.232
+2025-07-09 14:47:40: Epoch: 034 Train Loss: 8.469, mae: 1.278, rse: 1.010, pcc: -0.080, kcc: -0.116
+2025-07-09 14:47:40: Epoch: 034 Test results, ap: mae: 0.613, rse: 1.050, pcc: 0.265, kcc: 0.014
+2025-07-09 14:47:41: Epoch: 035 Train Loss: 8.398, mae: 1.286, rse: 1.013, pcc: -0.119, kcc: -0.061
+2025-07-09 14:47:41: Epoch: 035 Test results, ap: mae: 0.620, rse: 1.066, pcc: -0.640, kcc: -0.225
+2025-07-09 14:47:41: used time 0.01h
+2025-07-09 14:47:41: Running Cross Validation 3
+2025-07-09 14:47:41: Fold 3  Train set:406, Valid set:101, Test set: 108
+2025-07-09 14:47:42: Epoch: 001 Train Loss: 8.510, mae: 1.321, rse: 1.014, pcc: 0.056, kcc: 0.016
+2025-07-09 14:47:42: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:47:42: Epoch: 001 Test results, ap: mae: 0.596, rse: 1.082, pcc: -0.198, kcc: -0.113
+2025-07-09 14:47:43: Epoch: 002 Train Loss: 8.494, mae: 1.391, rse: 1.022, pcc: -0.025, kcc: -0.042
+2025-07-09 14:47:43: Epoch: 002 Test results, ap: mae: 0.646, rse: 1.128, pcc: -0.024, kcc: 0.016
+2025-07-09 14:47:44: Epoch: 003 Train Loss: 8.548, mae: 1.363, rse: 1.008, pcc: -0.026, kcc: -0.030
+2025-07-09 14:47:44: Epoch: 003 Test results, ap: mae: 0.611, rse: 1.045, pcc: 0.035, kcc: 0.046
+2025-07-09 14:47:44: Epoch: 004 Train Loss: 8.509, mae: 1.372, rse: 1.012, pcc: -0.041, kcc: -0.035
+2025-07-09 14:47:45: Epoch: 004 Test results, ap: mae: 0.621, rse: 1.069, pcc: 0.060, kcc: 0.024
+2025-07-09 14:47:45: Epoch: 005 Train Loss: 8.517, mae: 1.342, rse: 1.001, pcc: -0.040, kcc: -0.025
+2025-07-09 14:47:46: Epoch: 005 Test results, ap: mae: 0.590, rse: 1.007, pcc: 0.039, kcc: -0.001
+2025-07-09 14:47:46: Epoch: 006 Train Loss: 8.499, mae: 1.352, rse: 1.004, pcc: -0.047, kcc: -0.033
+2025-07-09 14:47:46: Epoch: 006 Test results, ap: mae: 0.599, rse: 1.021, pcc: 0.061, kcc: 0.070
+2025-07-09 14:47:47: Epoch: 007 Train Loss: 8.515, mae: 1.361, rse: 1.007, pcc: -0.044, kcc: 0.000
+2025-07-09 14:47:47: Epoch: 007 Test results, ap: mae: 0.608, rse: 1.040, pcc: 0.059, kcc: 0.071
+2025-07-09 14:47:48: Epoch: 008 Train Loss: 8.468, mae: 1.350, rse: 1.003, pcc: -0.047, kcc: -0.032
+2025-07-09 14:47:48: Epoch: 008 Test results, ap: mae: 0.597, rse: 1.018, pcc: 0.045, kcc: 0.057
+2025-07-09 14:47:49: Epoch: 009 Train Loss: 8.453, mae: 1.365, rse: 1.009, pcc: -0.048, kcc: -0.018
+2025-07-09 14:47:49: Epoch: 009 Test results, ap: mae: 0.612, rse: 1.049, pcc: 0.031, kcc: 0.115
+2025-07-09 14:47:50: Epoch: 010 Train Loss: 8.510, mae: 1.340, rse: 1.001, pcc: -0.049, kcc: -0.055
+2025-07-09 14:47:50: Epoch: 010 Test results, ap: mae: 0.587, rse: 1.004, pcc: 0.076, kcc: 0.188
+2025-07-09 14:47:50: Epoch: 011 Train Loss: 8.474, mae: 1.363, rse: 1.008, pcc: -0.047, kcc: 0.000
+2025-07-09 14:47:51: Epoch: 011 Test results, ap: mae: 0.610, rse: 1.044, pcc: -0.163, kcc: 0.115
+2025-07-09 14:47:51: Epoch: 011 New best TEST metrics
+2025-07-09 14:47:51: Epoch: 012 Train Loss: 8.509, mae: 1.371, rse: 1.011, pcc: -0.047, kcc: -0.020
+2025-07-09 14:47:52: Epoch: 012 Test results, ap: mae: 0.620, rse: 1.066, pcc: -0.022, kcc: 0.067
+2025-07-09 14:47:52: Epoch: 012 New best TEST metrics
+2025-07-09 14:47:52: Epoch: 013 Train Loss: 8.502, mae: 1.381, rse: 1.017, pcc: -0.049, kcc: -0.022
+2025-07-09 14:47:52: Epoch: 013 Test results, ap: mae: 0.633, rse: 1.096, pcc: 0.015, kcc: 0.044
+2025-07-09 14:47:53: Epoch: 014 Train Loss: 8.416, mae: 1.360, rse: 1.006, pcc: -0.047, kcc: -0.034
+2025-07-09 14:47:53: Epoch: 014 Test results, ap: mae: 0.606, rse: 1.037, pcc: 0.612, kcc: 0.103
+2025-07-09 14:47:53: Epoch: 014 New best TEST metrics
+2025-07-09 14:47:54: Epoch: 015 Train Loss: 8.532, mae: 1.357, rse: 1.005, pcc: -0.045, kcc: -0.103
+2025-07-09 14:47:54: Epoch: 015 Test results, ap: mae: 0.603, rse: 1.031, pcc: -0.981, kcc: 0.050
+2025-07-09 14:47:55: Epoch: 016 Train Loss: 8.500, mae: 1.370, rse: 1.011, pcc: -0.049, kcc: -0.040
+2025-07-09 14:47:55: Epoch: 016 Test results, ap: mae: 0.618, rse: 1.062, pcc: -0.400, kcc: 0.031
+2025-07-09 14:47:56: Epoch: 017 Train Loss: 8.488, mae: 1.369, rse: 1.010, pcc: -0.048, kcc: -0.137
+2025-07-09 14:47:56: Epoch: 017 Test results, ap: mae: 0.617, rse: 1.060, pcc: -0.116, kcc: 0.172
+2025-07-09 14:47:57: Epoch: 018 Train Loss: 8.523, mae: 1.372, rse: 1.012, pcc: -0.051, kcc: -0.106
+2025-07-09 14:47:57: Epoch: 018 Test results, ap: mae: 0.620, rse: 1.068, pcc: -0.544, kcc: 0.049
+2025-07-09 14:47:58: Epoch: 019 Train Loss: 8.461, mae: 1.374, rse: 1.013, pcc: -0.050, kcc: -0.087
+2025-07-09 14:47:58: Epoch: 019 Test results, ap: mae: 0.623, rse: 1.074, pcc: -0.999, kcc: 0.107
+2025-07-09 14:47:58: Epoch: 020 Train Loss: 8.518, mae: 1.372, rse: 1.012, pcc: -0.049, kcc: -0.090
+2025-07-09 14:47:59: Epoch: 020 Test results, ap: mae: 0.621, rse: 1.069, pcc: 0.824, kcc: -0.016
+2025-07-09 14:47:59: Epoch: 020 New best TEST metrics
+2025-07-09 14:47:59: Epoch: 021 Train Loss: 8.538, mae: 1.355, rse: 1.005, pcc: 0.060, kcc: -0.033
+2025-07-09 14:48:00: Epoch: 021 Test results, ap: mae: 0.602, rse: 1.027, pcc: 0.084, kcc: 0.071
+2025-07-09 14:48:00: Epoch: 022 Train Loss: 8.506, mae: 1.360, rse: 1.006, pcc: 0.099, kcc: 0.024
+2025-07-09 14:48:00: Epoch: 022 New best VALIDATION metrics
+2025-07-09 14:48:00: Epoch: 022 Test results, ap: mae: 0.606, rse: 1.037, pcc: 0.932, kcc: 0.052
+2025-07-09 14:48:00: Epoch: 022 New best TEST metrics
+2025-07-09 14:48:01: Epoch: 023 Train Loss: 8.510, mae: 1.365, rse: 1.008, pcc: -0.003, kcc: -0.100
+2025-07-09 14:48:01: Epoch: 023 Test results, ap: mae: 0.612, rse: 1.049, pcc: -0.292, kcc: 0.005
+2025-07-09 14:48:02: Epoch: 024 Train Loss: 8.522, mae: 1.358, rse: 1.006, pcc: 0.057, kcc: 0.060
+2025-07-09 14:48:02: Epoch: 024 Test results, ap: mae: 0.605, rse: 1.033, pcc: 0.231, kcc: -0.054
+2025-07-09 14:48:03: Epoch: 025 Train Loss: 8.500, mae: 1.364, rse: 1.008, pcc: 0.054, kcc: 0.044
+2025-07-09 14:48:03: Epoch: 025 Test results, ap: mae: 0.611, rse: 1.046, pcc: -1.000, kcc: -0.047
+2025-07-09 14:48:04: Epoch: 026 Train Loss: 8.525, mae: 1.352, rse: 1.004, pcc: 0.052, kcc: 0.028
+2025-07-09 14:48:04: Epoch: 026 Test results, ap: mae: 0.598, rse: 1.021, pcc: 0.948, kcc: -0.171
+2025-07-09 14:48:05: Epoch: 027 Train Loss: 8.432, mae: 1.347, rse: 1.002, pcc: 0.056, kcc: -0.002
+2025-07-09 14:48:05: Epoch: 027 Test results, ap: mae: 0.594, rse: 1.014, pcc: -0.315, kcc: -0.213
+2025-07-09 14:48:05: Epoch: 028 Train Loss: 8.442, mae: 1.371, rse: 1.011, pcc: -0.036, kcc: 0.084
+2025-07-09 14:48:06: Epoch: 028 Test results, ap: mae: 0.619, rse: 1.065, pcc: -0.247, kcc: 0.047
+2025-07-09 14:48:06: Epoch: 029 Train Loss: 8.469, mae: 1.377, rse: 1.015, pcc: -0.049, kcc: -0.037
+2025-07-09 14:48:06: Epoch: 029 Test results, ap: mae: 0.628, rse: 1.085, pcc: -0.290, kcc: -0.052
+2025-07-09 14:48:07: Epoch: 030 Train Loss: 8.535, mae: 1.383, rse: 1.018, pcc: -0.046, kcc: -0.091
+2025-07-09 14:48:07: Epoch: 030 Test results, ap: mae: 0.635, rse: 1.102, pcc: -0.883, kcc: 0.094
+2025-07-09 14:48:08: Epoch: 031 Train Loss: 8.528, mae: 1.365, rse: 1.009, pcc: -0.040, kcc: -0.039
+2025-07-09 14:48:08: Epoch: 031 Test results, ap: mae: 0.613, rse: 1.051, pcc: nan, kcc: nan
+2025-07-09 14:48:09: Epoch: 032 Train Loss: 8.450, mae: 1.361, rse: 1.007, pcc: -0.040, kcc: -0.055
+2025-07-09 14:48:09: Epoch: 032 Test results, ap: mae: 0.608, rse: 1.041, pcc: -0.063, kcc: 0.012
+2025-07-09 14:48:10: Epoch: 033 Train Loss: 8.494, mae: 1.357, rse: 1.006, pcc: -0.033, kcc: -0.071
+2025-07-09 14:48:10: Epoch: 033 Test results, ap: mae: 0.604, rse: 1.032, pcc: 1.000, kcc: nan
+2025-07-09 14:48:11: Epoch: 034 Train Loss: 8.461, mae: 1.357, rse: 1.006, pcc: -0.012, kcc: 0.005
+2025-07-09 14:48:11: Epoch: 034 Test results, ap: mae: 0.604, rse: 1.032, pcc: 0.320, kcc: -0.038
+2025-07-09 14:48:11: Epoch: 035 Train Loss: 8.528, mae: 1.358, rse: 1.006, pcc: -0.020, kcc: -0.099
+2025-07-09 14:48:12: Epoch: 035 Test results, ap: mae: 0.604, rse: 1.032, pcc: -1.000, kcc: 0.110
+2025-07-09 14:48:12: used time 0.01h
+2025-07-09 14:48:12: Running Cross Validation 4
+2025-07-09 14:48:12: Fold 4  Train set:406, Valid set:101, Test set: 108
+2025-07-09 14:48:12: Epoch: 001 Train Loss: 8.835, mae: 1.384, rse: 1.206, pcc: -0.196, kcc: -0.202
+2025-07-09 14:48:12: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:48:13: Epoch: 001 Test results, ap: mae: 0.774, rse: 1.530, pcc: 0.028, kcc: 0.074
+2025-07-09 14:48:13: Epoch: 002 Train Loss: 8.817, mae: 1.230, rse: 1.002, pcc: -0.287, kcc: -0.169
+2025-07-09 14:48:13: Epoch: 002 New best VALIDATION metrics
+2025-07-09 14:48:13: Epoch: 002 Test results, ap: mae: 0.582, rse: 1.014, pcc: -0.139, kcc: -0.164
+2025-07-09 14:48:14: Epoch: 003 Train Loss: 8.667, mae: 1.285, rse: 1.050, pcc: -0.331, kcc: -0.177
+2025-07-09 14:48:14: Epoch: 003 Test results, ap: mae: 0.620, rse: 1.066, pcc: -0.188, kcc: -0.149
+2025-07-09 14:48:15: Epoch: 004 Train Loss: 8.711, mae: 1.284, rse: 1.050, pcc: -0.331, kcc: -0.174
+2025-07-09 14:48:15: Epoch: 004 Test results, ap: mae: 0.619, rse: 1.065, pcc: -0.255, kcc: -0.190
+2025-07-09 14:48:16: Epoch: 005 Train Loss: 8.714, mae: 1.317, rse: 1.093, pcc: -0.333, kcc: -0.176
+2025-07-09 14:48:16: Epoch: 005 Test results, ap: mae: 0.665, rse: 1.179, pcc: -0.264, kcc: -0.206
+2025-07-09 14:48:17: Epoch: 006 Train Loss: 8.682, mae: 1.301, rse: 1.070, pcc: -0.332, kcc: -0.174
+2025-07-09 14:48:17: Epoch: 006 Test results, ap: mae: 0.641, rse: 1.115, pcc: -0.267, kcc: -0.211
+2025-07-09 14:48:17: Epoch: 007 Train Loss: 8.682, mae: 1.276, rse: 1.040, pcc: -0.328, kcc: -0.166
+2025-07-09 14:48:18: Epoch: 007 Test results, ap: mae: 0.609, rse: 1.042, pcc: -0.246, kcc: -0.179
+2025-07-09 14:48:18: Epoch: 008 Train Loss: 8.663, mae: 1.317, rse: 1.092, pcc: -0.331, kcc: -0.160
+2025-07-09 14:48:19: Epoch: 008 Test results, ap: mae: 0.664, rse: 1.175, pcc: -0.140, kcc: -0.150
+2025-07-09 14:48:19: Epoch: 009 Train Loss: 8.650, mae: 1.278, rse: 1.042, pcc: -0.334, kcc: -0.162
+2025-07-09 14:48:19: Epoch: 009 Test results, ap: mae: 0.612, rse: 1.047, pcc: -0.150, kcc: -0.137
+2025-07-09 14:48:20: Epoch: 010 Train Loss: 8.666, mae: 1.293, rse: 1.060, pcc: -0.338, kcc: -0.164
+2025-07-09 14:48:20: Epoch: 010 Test results, ap: mae: 0.630, rse: 1.090, pcc: -0.215, kcc: -0.130
+2025-07-09 14:48:21: Epoch: 011 Train Loss: 8.760, mae: 1.318, rse: 1.095, pcc: -0.343, kcc: -0.162
+2025-07-09 14:48:21: Epoch: 011 Test results, ap: mae: 0.666, rse: 1.184, pcc: -0.218, kcc: -0.131
+2025-07-09 14:48:21: Epoch: 011 New best TEST metrics
+2025-07-09 14:48:22: Epoch: 012 Train Loss: 8.815, mae: 1.320, rse: 1.098, pcc: -0.348, kcc: -0.169
+2025-07-09 14:48:22: Epoch: 012 Test results, ap: mae: 0.669, rse: 1.191, pcc: -0.222, kcc: -0.163
+2025-07-09 14:48:23: Epoch: 013 Train Loss: 8.537, mae: 1.304, rse: 1.074, pcc: -0.343, kcc: -0.167
+2025-07-09 14:48:23: Epoch: 013 Test results, ap: mae: 0.645, rse: 1.125, pcc: -0.251, kcc: -0.169
+2025-07-09 14:48:23: Epoch: 013 New best TEST metrics
+2025-07-09 14:48:24: Epoch: 014 Train Loss: 8.613, mae: 1.294, rse: 1.062, pcc: -0.341, kcc: -0.170
+2025-07-09 14:48:24: Epoch: 014 Test results, ap: mae: 0.632, rse: 1.093, pcc: -0.261, kcc: -0.177
+2025-07-09 14:48:24: Epoch: 014 New best TEST metrics
+2025-07-09 14:48:25: Epoch: 015 Train Loss: 8.656, mae: 1.297, rse: 1.065, pcc: -0.339, kcc: -0.170
+2025-07-09 14:48:25: Epoch: 015 Test results, ap: mae: 0.635, rse: 1.102, pcc: -0.245, kcc: -0.157
+2025-07-09 14:48:25: Epoch: 015 New best TEST metrics
+2025-07-09 14:48:26: Epoch: 016 Train Loss: 8.751, mae: 1.273, rse: 1.036, pcc: -0.335, kcc: -0.167
+2025-07-09 14:48:26: Epoch: 016 Test results, ap: mae: 0.605, rse: 1.035, pcc: -0.213, kcc: -0.158
+2025-07-09 14:48:26: Epoch: 016 New best TEST metrics
+2025-07-09 14:48:26: Epoch: 017 Train Loss: 8.665, mae: 1.286, rse: 1.052, pcc: -0.337, kcc: -0.167
+2025-07-09 14:48:27: Epoch: 017 Test results, ap: mae: 0.621, rse: 1.070, pcc: -0.230, kcc: -0.152
+2025-07-09 14:48:27: Epoch: 018 Train Loss: 8.720, mae: 1.302, rse: 1.072, pcc: -0.333, kcc: -0.163
+2025-07-09 14:48:28: Epoch: 018 Test results, ap: mae: 0.643, rse: 1.120, pcc: -0.173, kcc: -0.139
+2025-07-09 14:48:28: Epoch: 019 Train Loss: 8.719, mae: 1.290, rse: 1.056, pcc: -0.330, kcc: -0.164
+2025-07-09 14:48:28: Epoch: 019 Test results, ap: mae: 0.626, rse: 1.080, pcc: -0.236, kcc: -0.156
+2025-07-09 14:48:29: Epoch: 020 Train Loss: 8.692, mae: 1.308, rse: 1.080, pcc: -0.326, kcc: -0.173
+2025-07-09 14:48:29: Epoch: 020 Test results, ap: mae: 0.652, rse: 1.142, pcc: -0.179, kcc: -0.145
+2025-07-09 14:48:30: Epoch: 021 Train Loss: 8.734, mae: 1.301, rse: 1.070, pcc: -0.326, kcc: -0.168
+2025-07-09 14:48:30: Epoch: 021 Test results, ap: mae: 0.641, rse: 1.116, pcc: -0.289, kcc: -0.135
+2025-07-09 14:48:31: Epoch: 022 Train Loss: 8.710, mae: 1.307, rse: 1.078, pcc: -0.329, kcc: -0.170
+2025-07-09 14:48:31: Epoch: 022 Test results, ap: mae: 0.649, rse: 1.136, pcc: -0.153, kcc: -0.134
+2025-07-09 14:48:32: Epoch: 023 Train Loss: 8.542, mae: 1.304, rse: 1.074, pcc: -0.330, kcc: -0.166
+2025-07-09 14:48:32: Epoch: 023 Test results, ap: mae: 0.645, rse: 1.125, pcc: -0.197, kcc: -0.139
+2025-07-09 14:48:32: Epoch: 024 Train Loss: 8.645, mae: 1.301, rse: 1.071, pcc: -0.329, kcc: -0.167
+2025-07-09 14:48:33: Epoch: 024 Test results, ap: mae: 0.642, rse: 1.117, pcc: -0.162, kcc: -0.142
+2025-07-09 14:48:33: Epoch: 025 Train Loss: 8.725, mae: 1.301, rse: 1.071, pcc: -0.328, kcc: -0.168
+2025-07-09 14:48:33: Epoch: 025 Test results, ap: mae: 0.642, rse: 1.117, pcc: -0.223, kcc: -0.146
+2025-07-09 14:48:34: Epoch: 026 Train Loss: 8.722, mae: 1.298, rse: 1.067, pcc: -0.328, kcc: -0.165
+2025-07-09 14:48:34: Epoch: 026 Test results, ap: mae: 0.638, rse: 1.107, pcc: -0.213, kcc: -0.142
+2025-07-09 14:48:35: Epoch: 027 Train Loss: 8.704, mae: 1.281, rse: 1.046, pcc: -0.329, kcc: -0.169
+2025-07-09 14:48:35: Epoch: 027 Test results, ap: mae: 0.616, rse: 1.056, pcc: -0.167, kcc: -0.114
+2025-07-09 14:48:35: Epoch: 027 New best TEST metrics
+2025-07-09 14:48:36: Epoch: 028 Train Loss: 8.658, mae: 1.289, rse: 1.056, pcc: -0.330, kcc: -0.164
+2025-07-09 14:48:36: Epoch: 028 Test results, ap: mae: 0.625, rse: 1.079, pcc: -0.191, kcc: -0.162
+2025-07-09 14:48:37: Epoch: 029 Train Loss: 8.682, mae: 1.284, rse: 1.050, pcc: -0.328, kcc: -0.168
+2025-07-09 14:48:37: Epoch: 029 Test results, ap: mae: 0.619, rse: 1.064, pcc: -0.177, kcc: -0.111
+2025-07-09 14:48:38: Epoch: 030 Train Loss: 8.640, mae: 1.299, rse: 1.068, pcc: -0.330, kcc: -0.165
+2025-07-09 14:48:38: Epoch: 030 Test results, ap: mae: 0.638, rse: 1.109, pcc: -0.170, kcc: -0.133
+2025-07-09 14:48:38: Epoch: 031 Train Loss: 8.753, mae: 1.295, rse: 1.063, pcc: -0.329, kcc: -0.170
+2025-07-09 14:48:39: Epoch: 031 Test results, ap: mae: 0.634, rse: 1.098, pcc: -0.232, kcc: -0.167
+2025-07-09 14:48:39: Epoch: 032 Train Loss: 8.701, mae: 1.290, rse: 1.057, pcc: -0.327, kcc: -0.168
+2025-07-09 14:48:39: Epoch: 032 Test results, ap: mae: 0.627, rse: 1.082, pcc: -0.112, kcc: -0.140
+2025-07-09 14:48:40: Epoch: 033 Train Loss: 8.593, mae: 1.290, rse: 1.057, pcc: -0.327, kcc: -0.167
+2025-07-09 14:48:40: Epoch: 033 Test results, ap: mae: 0.626, rse: 1.081, pcc: -0.139, kcc: -0.137
+2025-07-09 14:48:41: Epoch: 034 Train Loss: 8.608, mae: 1.297, rse: 1.065, pcc: -0.328, kcc: -0.169
+2025-07-09 14:48:41: Epoch: 034 Test results, ap: mae: 0.636, rse: 1.102, pcc: -0.161, kcc: -0.131
+2025-07-09 14:48:42: Epoch: 035 Train Loss: 8.715, mae: 1.295, rse: 1.063, pcc: -0.325, kcc: -0.166
+2025-07-09 14:48:42: Epoch: 035 Test results, ap: mae: 0.633, rse: 1.096, pcc: -0.270, kcc: -0.135
+2025-07-09 14:48:42: used time 0.01h
+2025-07-09 14:48:42: Cross Validation Finished!
+2025-07-09 14:48:42: Best validation perform list
+[[ 1.16779459  0.99999213  0.06392121  0.15275076]
+ [ 1.30235457  1.00743639  0.09182414  0.06244198]
+ [ 1.21855688  1.00872481 -0.01599212  0.11230694]
+ [ 1.35958052  1.00638592  0.09928962  0.02386478]
+ [ 1.2304548   1.00218248 -0.28730968 -0.16902003]]
+2025-07-09 14:48:42: mean: [ 1.256  1.005 -0.01   0.036]
+2025-07-09 14:48:42: std: [0.067 0.003 0.145 0.112]
+2025-07-09 14:48:42: Best test perform list
+[[ 0.62065798  1.06800437  1.          0.02480716]
+ [ 0.60507154  1.03394175  1.          0.09117118]
+ [ 0.61718446  1.05949962  1.          0.10699822]
+ [ 0.60641646  1.03656662  0.93191463  0.05174502]
+ [ 0.61559218  1.05579102 -0.16693293 -0.11388498]]
+2025-07-09 14:48:42: mean: [0.613 1.051 0.753 0.032]
+2025-07-09 14:48:42: std: [0.006 0.013 0.461 0.079]
diff --git a/run-reg/lstm-att-256-oneway-huber-32-0.001-35/config.json b/run-reg/lstm-att-256-oneway-huber-32-0.001-35/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..2327d7100c17b30ca62ee2f5d3d4a61c7a5b3af7
--- /dev/null
+++ b/run-reg/lstm-att-256-oneway-huber-32-0.001-35/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 35, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "huber", "dir": false, "classes": 1}
\ No newline at end of file
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diff --git a/run-reg/lstm-att-256-oneway-huber-32-0.001-35/result.txt b/run-reg/lstm-att-256-oneway-huber-32-0.001-35/result.txt
new file mode 100644
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@@ -0,0 +1,6 @@
+Valid
+1.2195348262786865,1.0143987655639648,0.5522955119609833,0.08072424717247487
+0.05636189504232379,0.008485297610945394,0.42375641175849965,0.040850996766563015
+Test
+0.6018625020980835,1.1067482709884644,0.5091117322444916,0.1449792802333832
+0.0036349888290255834,0.01519701101707068,0.41698953422272916,0.19330570697784424
diff --git a/run-reg/lstm-att-256-oneway-huber-32-0.001-35/training.log b/run-reg/lstm-att-256-oneway-huber-32-0.001-35/training.log
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@@ -0,0 +1,414 @@
+2025-07-09 14:41:28: saving_dir: ./run-reg/lstm-att-256-oneway-huber-32-0.001-35
+2025-07-09 14:41:28: Loading Train Dataset
+2025-07-09 14:41:29: Loading Test Dataset
+2025-07-09 14:41:30: Running Cross Validation 0
+2025-07-09 14:41:30: Fold 0  Train set:405, Valid set:102, Test set: 108
+2025-07-09 14:41:30: Epoch: 001 Train Loss: 0.854, mae: 1.160, rse: 1.001, pcc: -0.034, kcc: -0.040
+2025-07-09 14:41:30: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:41:31: Epoch: 001 Test results, ap: mae: 0.591, rse: 1.061, pcc: -0.421, kcc: 0.044
+2025-07-09 14:41:31: Epoch: 002 Train Loss: 0.849, mae: 1.147, rse: 1.014, pcc: 0.029, kcc: 0.071
+2025-07-09 14:41:31: Epoch: 002 New best VALIDATION metrics
+2025-07-09 14:41:32: Epoch: 002 Test results, ap: mae: 0.622, rse: 1.184, pcc: 0.314, kcc: -0.056
+2025-07-09 14:41:32: Epoch: 003 Train Loss: 0.866, mae: 1.147, rse: 1.013, pcc: 0.032, kcc: -0.163
+2025-07-09 14:41:32: Epoch: 003 Test results, ap: mae: 0.621, rse: 1.179, pcc: -0.732, kcc: -0.074
+2025-07-09 14:41:33: Epoch: 004 Train Loss: 0.837, mae: 1.150, rse: 1.006, pcc: -0.130, kcc: 0.073
+2025-07-09 14:41:33: Epoch: 004 Test results, ap: mae: 0.606, rse: 1.125, pcc: -0.157, kcc: -0.051
+2025-07-09 14:41:34: Epoch: 005 Train Loss: 0.846, mae: 1.150, rse: 1.007, pcc: -0.036, kcc: 0.025
+2025-07-09 14:41:34: Epoch: 005 Test results, ap: mae: 0.608, rse: 1.133, pcc: nan, kcc: nan
+2025-07-09 14:41:35: Epoch: 006 Train Loss: 0.849, mae: 1.150, rse: 1.007, pcc: 0.066, kcc: 0.021
+2025-07-09 14:41:35: Epoch: 006 Test results, ap: mae: 0.607, rse: 1.129, pcc: 0.299, kcc: 0.048
+2025-07-09 14:41:35: Epoch: 007 Train Loss: 0.854, mae: 1.150, rse: 1.007, pcc: -0.062, kcc: -0.131
+2025-07-09 14:41:36: Epoch: 007 Test results, ap: mae: 0.607, rse: 1.128, pcc: 0.431, kcc: 0.081
+2025-07-09 14:41:36: Epoch: 008 Train Loss: 0.843, mae: 1.153, rse: 1.004, pcc: -0.279, kcc: -0.028
+2025-07-09 14:41:36: Epoch: 008 Test results, ap: mae: 0.602, rse: 1.105, pcc: 0.141, kcc: 0.152
+2025-07-09 14:41:37: Epoch: 009 Train Loss: 0.826, mae: 1.151, rse: 1.005, pcc: -1.000, kcc: nan
+2025-07-09 14:41:37: Epoch: 009 Test results, ap: mae: 0.604, rse: 1.115, pcc: -1.000, kcc: nan
+2025-07-09 14:41:38: Epoch: 010 Train Loss: 0.841, mae: 1.151, rse: 1.006, pcc: -0.262, kcc: -0.067
+2025-07-09 14:41:38: Epoch: 010 Test results, ap: mae: 0.606, rse: 1.123, pcc: nan, kcc: nan
+2025-07-09 14:41:39: Epoch: 011 Train Loss: 0.858, mae: 1.150, rse: 1.007, pcc: 0.075, kcc: 0.113
+2025-07-09 14:41:39: Epoch: 011 New best VALIDATION metrics
+2025-07-09 14:41:39: Epoch: 011 Test results, ap: mae: 0.607, rse: 1.129, pcc: nan, kcc: nan
+2025-07-09 14:41:40: Epoch: 012 Train Loss: 0.862, mae: 1.150, rse: 1.007, pcc: 0.004, kcc: 0.015
+2025-07-09 14:41:40: Epoch: 012 Test results, ap: mae: 0.607, rse: 1.128, pcc: nan, kcc: nan
+2025-07-09 14:41:40: Epoch: 013 Train Loss: 0.848, mae: 1.151, rse: 1.006, pcc: -0.131, kcc: -0.006
+2025-07-09 14:41:41: Epoch: 013 Test results, ap: mae: 0.606, rse: 1.124, pcc: -0.688, kcc: -0.242
+2025-07-09 14:41:41: Epoch: 013 New best TEST metrics
+2025-07-09 14:41:41: Epoch: 014 Train Loss: 0.855, mae: 1.151, rse: 1.006, pcc: nan, kcc: nan
+2025-07-09 14:41:41: Epoch: 014 Test results, ap: mae: 0.605, rse: 1.120, pcc: 1.000, kcc: nan
+2025-07-09 14:41:42: Epoch: 015 Train Loss: 0.824, mae: 1.151, rse: 1.006, pcc: -1.000, kcc: nan
+2025-07-09 14:41:42: Epoch: 015 Test results, ap: mae: 0.605, rse: 1.120, pcc: -1.000, kcc: nan
+2025-07-09 14:41:43: Epoch: 016 Train Loss: 0.833, mae: 1.151, rse: 1.006, pcc: -1.000, kcc: nan
+2025-07-09 14:41:43: Epoch: 016 Test results, ap: mae: 0.604, rse: 1.116, pcc: nan, kcc: nan
+2025-07-09 14:41:44: Epoch: 017 Train Loss: 0.824, mae: 1.151, rse: 1.006, pcc: nan, kcc: nan
+2025-07-09 14:41:44: Epoch: 017 Test results, ap: mae: 0.606, rse: 1.122, pcc: nan, kcc: nan
+2025-07-09 14:41:44: Epoch: 018 Train Loss: 0.862, mae: 1.152, rse: 1.005, pcc: -1.000, kcc: nan
+2025-07-09 14:41:45: Epoch: 018 Test results, ap: mae: 0.602, rse: 1.108, pcc: nan, kcc: nan
+2025-07-09 14:41:45: Epoch: 019 Train Loss: 0.848, mae: 1.152, rse: 1.005, pcc: -1.000, kcc: nan
+2025-07-09 14:41:46: Epoch: 019 Test results, ap: mae: 0.602, rse: 1.109, pcc: nan, kcc: nan
+2025-07-09 14:41:46: Epoch: 020 Train Loss: 0.839, mae: 1.152, rse: 1.005, pcc: -1.000, kcc: 0.046
+2025-07-09 14:41:46: Epoch: 020 Test results, ap: mae: 0.603, rse: 1.111, pcc: -1.000, kcc: nan
+2025-07-09 14:41:47: Epoch: 021 Train Loss: 0.824, mae: 1.152, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:41:47: Epoch: 021 Test results, ap: mae: 0.603, rse: 1.111, pcc: -1.000, kcc: nan
+2025-07-09 14:41:48: Epoch: 022 Train Loss: 0.860, mae: 1.152, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:41:48: Epoch: 022 Test results, ap: mae: 0.603, rse: 1.110, pcc: nan, kcc: nan
+2025-07-09 14:41:49: Epoch: 023 Train Loss: 0.856, mae: 1.152, rse: 1.005, pcc: -1.000, kcc: nan
+2025-07-09 14:41:49: Epoch: 023 Test results, ap: mae: 0.603, rse: 1.110, pcc: nan, kcc: nan
+2025-07-09 14:41:49: Epoch: 024 Train Loss: 0.838, mae: 1.152, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:41:50: Epoch: 024 Test results, ap: mae: 0.603, rse: 1.110, pcc: nan, kcc: nan
+2025-07-09 14:41:50: Epoch: 025 Train Loss: 0.818, mae: 1.152, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:41:51: Epoch: 025 Test results, ap: mae: 0.602, rse: 1.109, pcc: -0.688, kcc: -0.242
+2025-07-09 14:41:51: Epoch: 025 New best TEST metrics
+2025-07-09 14:41:51: Epoch: 026 Train Loss: 0.822, mae: 1.152, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:41:51: Epoch: 026 Test results, ap: mae: 0.603, rse: 1.110, pcc: -1.000, kcc: nan
+2025-07-09 14:41:52: Epoch: 027 Train Loss: 0.848, mae: 1.152, rse: 1.005, pcc: -0.071, kcc: -0.165
+2025-07-09 14:41:52: Epoch: 027 Test results, ap: mae: 0.602, rse: 1.109, pcc: -0.235, kcc: -0.242
+2025-07-09 14:41:52: Epoch: 027 New best TEST metrics
+2025-07-09 14:41:53: Epoch: 028 Train Loss: 0.848, mae: 1.152, rse: 1.005, pcc: -0.256, kcc: 0.165
+2025-07-09 14:41:53: Epoch: 028 Test results, ap: mae: 0.603, rse: 1.110, pcc: -0.733, kcc: 0.242
+2025-07-09 14:41:54: Epoch: 029 Train Loss: 0.834, mae: 1.152, rse: 1.005, pcc: -1.000, kcc: -0.165
+2025-07-09 14:41:54: Epoch: 029 Test results, ap: mae: 0.603, rse: 1.111, pcc: -1.000, kcc: -0.242
+2025-07-09 14:41:54: Epoch: 030 Train Loss: 0.850, mae: 1.152, rse: 1.005, pcc: -1.000, kcc: nan
+2025-07-09 14:41:55: Epoch: 030 Test results, ap: mae: 0.603, rse: 1.111, pcc: nan, kcc: nan
+2025-07-09 14:41:55: Epoch: 031 Train Loss: 0.831, mae: 1.152, rse: 1.005, pcc: -1.000, kcc: nan
+2025-07-09 14:41:56: Epoch: 031 Test results, ap: mae: 0.603, rse: 1.109, pcc: nan, kcc: nan
+2025-07-09 14:41:56: Epoch: 032 Train Loss: 0.861, mae: 1.152, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:41:56: Epoch: 032 Test results, ap: mae: 0.602, rse: 1.109, pcc: nan, kcc: nan
+2025-07-09 14:41:57: Epoch: 033 Train Loss: 0.846, mae: 1.152, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:41:57: Epoch: 033 Test results, ap: mae: 0.602, rse: 1.109, pcc: nan, kcc: nan
+2025-07-09 14:41:58: Epoch: 034 Train Loss: 0.850, mae: 1.152, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:41:58: Epoch: 034 Test results, ap: mae: 0.602, rse: 1.109, pcc: nan, kcc: nan
+2025-07-09 14:41:59: Epoch: 035 Train Loss: 0.851, mae: 1.152, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:41:59: Epoch: 035 Test results, ap: mae: 0.602, rse: 1.108, pcc: nan, kcc: nan
+2025-07-09 14:41:59: used time 0.01h
+2025-07-09 14:41:59: Running Cross Validation 1
+2025-07-09 14:41:59: Fold 1  Train set:405, Valid set:102, Test set: 108
+2025-07-09 14:41:59: Epoch: 001 Train Loss: 0.815, mae: 1.208, rse: 1.010, pcc: 0.066, kcc: 0.047
+2025-07-09 14:41:59: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:42:00: Epoch: 001 Test results, ap: mae: 0.605, rse: 1.120, pcc: -0.134, kcc: 0.018
+2025-07-09 14:42:00: Epoch: 002 Train Loss: 0.858, mae: 1.217, rse: 1.006, pcc: 0.028, kcc: -0.037
+2025-07-09 14:42:01: Epoch: 002 Test results, ap: mae: 0.598, rse: 1.091, pcc: 0.494, kcc: 0.070
+2025-07-09 14:42:01: Epoch: 003 Train Loss: 0.836, mae: 1.223, rse: 1.004, pcc: -0.045, kcc: -0.056
+2025-07-09 14:42:01: Epoch: 003 Test results, ap: mae: 0.594, rse: 1.074, pcc: 0.107, kcc: 0.029
+2025-07-09 14:42:02: Epoch: 004 Train Loss: 0.842, mae: 1.200, rse: 1.013, pcc: -0.013, kcc: 0.096
+2025-07-09 14:42:02: Epoch: 004 Test results, ap: mae: 0.613, rse: 1.150, pcc: 0.485, kcc: -0.041
+2025-07-09 14:42:03: Epoch: 005 Train Loss: 0.817, mae: 1.226, rse: 1.004, pcc: 0.039, kcc: -0.064
+2025-07-09 14:42:03: Epoch: 005 Test results, ap: mae: 0.592, rse: 1.066, pcc: 0.622, kcc: 0.227
+2025-07-09 14:42:04: Epoch: 006 Train Loss: 0.845, mae: 1.202, rse: 1.012, pcc: 0.042, kcc: 0.077
+2025-07-09 14:42:04: Epoch: 006 New best VALIDATION metrics
+2025-07-09 14:42:04: Epoch: 006 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.378, kcc: -0.100
+2025-07-09 14:42:05: Epoch: 007 Train Loss: 0.847, mae: 1.217, rse: 1.006, pcc: -0.280, kcc: 0.087
+2025-07-09 14:42:05: Epoch: 007 Test results, ap: mae: 0.598, rse: 1.091, pcc: 0.391, kcc: 0.093
+2025-07-09 14:42:05: Epoch: 008 Train Loss: 0.826, mae: 1.209, rse: 1.009, pcc: -0.561, kcc: 0.051
+2025-07-09 14:42:06: Epoch: 008 Test results, ap: mae: 0.604, rse: 1.116, pcc: nan, kcc: nan
+2025-07-09 14:42:06: Epoch: 009 Train Loss: 0.819, mae: 1.207, rse: 1.010, pcc: nan, kcc: nan
+2025-07-09 14:42:06: Epoch: 009 Test results, ap: mae: 0.605, rse: 1.121, pcc: nan, kcc: nan
+2025-07-09 14:42:07: Epoch: 010 Train Loss: 0.835, mae: 1.233, rse: 1.002, pcc: -0.313, kcc: -0.003
+2025-07-09 14:42:07: Epoch: 010 Test results, ap: mae: 0.589, rse: 1.051, pcc: nan, kcc: nan
+2025-07-09 14:42:08: Epoch: 011 Train Loss: 0.814, mae: 1.217, rse: 1.006, pcc: nan, kcc: nan
+2025-07-09 14:42:08: Epoch: 011 Test results, ap: mae: 0.598, rse: 1.090, pcc: nan, kcc: nan
+2025-07-09 14:42:09: Epoch: 012 Train Loss: 0.853, mae: 1.216, rse: 1.006, pcc: nan, kcc: nan
+2025-07-09 14:42:09: Epoch: 012 Test results, ap: mae: 0.598, rse: 1.092, pcc: nan, kcc: nan
+2025-07-09 14:42:10: Epoch: 013 Train Loss: 0.805, mae: 1.222, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:42:10: Epoch: 013 Test results, ap: mae: 0.594, rse: 1.076, pcc: nan, kcc: nan
+2025-07-09 14:42:10: Epoch: 014 Train Loss: 0.840, mae: 1.231, rse: 1.003, pcc: nan, kcc: nan
+2025-07-09 14:42:11: Epoch: 014 Test results, ap: mae: 0.590, rse: 1.057, pcc: nan, kcc: nan
+2025-07-09 14:42:11: Epoch: 015 Train Loss: 0.831, mae: 1.218, rse: 1.006, pcc: -1.000, kcc: nan
+2025-07-09 14:42:11: Epoch: 015 Test results, ap: mae: 0.597, rse: 1.087, pcc: nan, kcc: nan
+2025-07-09 14:42:12: Epoch: 016 Train Loss: 0.849, mae: 1.210, rse: 1.009, pcc: nan, kcc: nan
+2025-07-09 14:42:12: Epoch: 016 Test results, ap: mae: 0.603, rse: 1.113, pcc: nan, kcc: nan
+2025-07-09 14:42:13: Epoch: 017 Train Loss: 0.838, mae: 1.213, rse: 1.007, pcc: nan, kcc: nan
+2025-07-09 14:42:13: Epoch: 017 Test results, ap: mae: 0.600, rse: 1.101, pcc: nan, kcc: nan
+2025-07-09 14:42:14: Epoch: 018 Train Loss: 0.828, mae: 1.214, rse: 1.007, pcc: -1.000, kcc: nan
+2025-07-09 14:42:14: Epoch: 018 Test results, ap: mae: 0.600, rse: 1.098, pcc: nan, kcc: nan
+2025-07-09 14:42:15: Epoch: 019 Train Loss: 0.811, mae: 1.223, rse: 1.004, pcc: -1.000, kcc: nan
+2025-07-09 14:42:15: Epoch: 019 Test results, ap: mae: 0.594, rse: 1.073, pcc: -1.000, kcc: nan
+2025-07-09 14:42:15: Epoch: 020 Train Loss: 0.852, mae: 1.230, rse: 1.003, pcc: -0.053, kcc: 0.025
+2025-07-09 14:42:16: Epoch: 020 Test results, ap: mae: 0.590, rse: 1.057, pcc: nan, kcc: nan
+2025-07-09 14:42:16: Epoch: 021 Train Loss: 0.834, mae: 1.227, rse: 1.004, pcc: nan, kcc: nan
+2025-07-09 14:42:16: Epoch: 021 Test results, ap: mae: 0.592, rse: 1.066, pcc: 1.000, kcc: nan
+2025-07-09 14:42:17: Epoch: 022 Train Loss: 0.807, mae: 1.222, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:42:17: Epoch: 022 Test results, ap: mae: 0.595, rse: 1.077, pcc: nan, kcc: nan
+2025-07-09 14:42:18: Epoch: 023 Train Loss: 0.847, mae: 1.221, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:42:18: Epoch: 023 Test results, ap: mae: 0.596, rse: 1.080, pcc: nan, kcc: nan
+2025-07-09 14:42:19: Epoch: 024 Train Loss: 0.851, mae: 1.222, rse: 1.005, pcc: -0.324, kcc: 0.049
+2025-07-09 14:42:19: Epoch: 024 Test results, ap: mae: 0.595, rse: 1.078, pcc: nan, kcc: nan
+2025-07-09 14:42:19: Epoch: 025 Train Loss: 0.830, mae: 1.222, rse: 1.005, pcc: -1.000, kcc: -0.074
+2025-07-09 14:42:20: Epoch: 025 Test results, ap: mae: 0.595, rse: 1.078, pcc: 0.688, kcc: 0.242
+2025-07-09 14:42:20: Epoch: 025 New best TEST metrics
+2025-07-09 14:42:20: Epoch: 026 Train Loss: 0.836, mae: 1.222, rse: 1.005, pcc: -1.000, kcc: nan
+2025-07-09 14:42:21: Epoch: 026 Test results, ap: mae: 0.594, rse: 1.076, pcc: nan, kcc: nan
+2025-07-09 14:42:21: Epoch: 027 Train Loss: 0.840, mae: 1.223, rse: 1.005, pcc: nan, kcc: nan
+2025-07-09 14:42:21: Epoch: 027 Test results, ap: mae: 0.594, rse: 1.075, pcc: nan, kcc: nan
+2025-07-09 14:42:22: Epoch: 028 Train Loss: 0.838, mae: 1.222, rse: 1.005, pcc: 0.004, kcc: 0.074
+2025-07-09 14:42:22: Epoch: 028 Test results, ap: mae: 0.595, rse: 1.078, pcc: nan, kcc: nan
+2025-07-09 14:42:23: Epoch: 029 Train Loss: 0.831, mae: 1.217, rse: 1.006, pcc: nan, kcc: nan
+2025-07-09 14:42:23: Epoch: 029 Test results, ap: mae: 0.598, rse: 1.091, pcc: 1.000, kcc: nan
+2025-07-09 14:42:24: Epoch: 030 Train Loss: 0.825, mae: 1.217, rse: 1.006, pcc: nan, kcc: nan
+2025-07-09 14:42:24: Epoch: 030 Test results, ap: mae: 0.598, rse: 1.091, pcc: nan, kcc: nan
+2025-07-09 14:42:25: Epoch: 031 Train Loss: 0.831, mae: 1.216, rse: 1.006, pcc: nan, kcc: nan
+2025-07-09 14:42:25: Epoch: 031 Test results, ap: mae: 0.599, rse: 1.093, pcc: nan, kcc: nan
+2025-07-09 14:42:25: Epoch: 032 Train Loss: 0.854, mae: 1.217, rse: 1.006, pcc: nan, kcc: nan
+2025-07-09 14:42:26: Epoch: 032 Test results, ap: mae: 0.598, rse: 1.090, pcc: nan, kcc: nan
+2025-07-09 14:42:26: Epoch: 033 Train Loss: 0.838, mae: 1.217, rse: 1.006, pcc: -1.000, kcc: nan
+2025-07-09 14:42:26: Epoch: 033 Test results, ap: mae: 0.598, rse: 1.090, pcc: nan, kcc: nan
+2025-07-09 14:42:27: Epoch: 034 Train Loss: 0.843, mae: 1.217, rse: 1.006, pcc: nan, kcc: nan
+2025-07-09 14:42:27: Epoch: 034 Test results, ap: mae: 0.598, rse: 1.091, pcc: nan, kcc: nan
+2025-07-09 14:42:28: Epoch: 035 Train Loss: 0.817, mae: 1.216, rse: 1.006, pcc: -1.000, kcc: nan
+2025-07-09 14:42:28: Epoch: 035 Test results, ap: mae: 0.598, rse: 1.091, pcc: nan, kcc: nan
+2025-07-09 14:42:28: used time 0.01h
+2025-07-09 14:42:28: Running Cross Validation 2
+2025-07-09 14:42:28: Fold 2  Train set:406, Valid set:101, Test set: 108
+2025-07-09 14:42:29: Epoch: 001 Train Loss: 0.871, mae: 1.220, rse: 1.043, pcc: -0.022, kcc: 0.146
+2025-07-09 14:42:29: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:42:29: Epoch: 001 Test results, ap: mae: 0.643, rse: 1.252, pcc: 0.171, kcc: 0.073
+2025-07-09 14:42:30: Epoch: 002 Train Loss: 0.831, mae: 1.218, rse: 1.010, pcc: 0.014, kcc: 0.151
+2025-07-09 14:42:30: Epoch: 002 New best VALIDATION metrics
+2025-07-09 14:42:30: Epoch: 002 Test results, ap: mae: 0.592, rse: 1.065, pcc: -0.611, kcc: -0.047
+2025-07-09 14:42:30: Epoch: 003 Train Loss: 0.836, mae: 1.224, rse: 1.051, pcc: -0.075, kcc: -0.130
+2025-07-09 14:42:31: Epoch: 003 Test results, ap: mae: 0.656, rse: 1.302, pcc: 0.261, kcc: 0.143
+2025-07-09 14:42:31: Epoch: 004 Train Loss: 0.845, mae: 1.217, rse: 1.030, pcc: -0.114, kcc: -0.103
+2025-07-09 14:42:31: Epoch: 004 Test results, ap: mae: 0.621, rse: 1.180, pcc: -0.037, kcc: -0.062
+2025-07-09 14:42:32: Epoch: 005 Train Loss: 0.824, mae: 1.217, rse: 1.016, pcc: 0.095, kcc: 0.212
+2025-07-09 14:42:32: Epoch: 005 New best VALIDATION metrics
+2025-07-09 14:42:32: Epoch: 005 Test results, ap: mae: 0.599, rse: 1.097, pcc: 0.635, kcc: -0.012
+2025-07-09 14:42:33: Epoch: 006 Train Loss: 0.817, mae: 1.219, rse: 1.040, pcc: 0.013, kcc: 0.077
+2025-07-09 14:42:33: Epoch: 006 Test results, ap: mae: 0.638, rse: 1.236, pcc: -0.456, kcc: -0.140
+2025-07-09 14:42:34: Epoch: 007 Train Loss: 0.819, mae: 1.217, rse: 1.016, pcc: 0.125, kcc: 0.099
+2025-07-09 14:42:34: Epoch: 007 Test results, ap: mae: 0.600, rse: 1.100, pcc: 0.088, kcc: 0.072
+2025-07-09 14:42:35: Epoch: 008 Train Loss: 0.845, mae: 1.217, rse: 1.028, pcc: 0.070, kcc: -0.112
+2025-07-09 14:42:35: Epoch: 008 Test results, ap: mae: 0.618, rse: 1.171, pcc: 0.843, kcc: -0.101
+2025-07-09 14:42:35: Epoch: 009 Train Loss: 0.840, mae: 1.217, rse: 1.023, pcc: -0.183, kcc: -0.060
+2025-07-09 14:42:36: Epoch: 009 Test results, ap: mae: 0.610, rse: 1.141, pcc: -1.000, kcc: 0.027
+2025-07-09 14:42:36: Epoch: 010 Train Loss: 0.840, mae: 1.217, rse: 1.021, pcc: 0.020, kcc: -0.068
+2025-07-09 14:42:37: Epoch: 010 Test results, ap: mae: 0.607, rse: 1.128, pcc: -0.275, kcc: 0.062
+2025-07-09 14:42:37: Epoch: 011 Train Loss: 0.847, mae: 1.217, rse: 1.028, pcc: 0.085, kcc: -0.036
+2025-07-09 14:42:37: Epoch: 011 Test results, ap: mae: 0.618, rse: 1.170, pcc: 1.000, kcc: -0.242
+2025-07-09 14:42:37: Epoch: 011 New best TEST metrics
+2025-07-09 14:42:38: Epoch: 012 Train Loss: 0.843, mae: 1.217, rse: 1.021, pcc: 0.212, kcc: 0.006
+2025-07-09 14:42:38: Epoch: 012 Test results, ap: mae: 0.607, rse: 1.126, pcc: nan, kcc: nan
+2025-07-09 14:42:39: Epoch: 013 Train Loss: 0.848, mae: 1.217, rse: 1.023, pcc: 0.200, kcc: 0.092
+2025-07-09 14:42:39: Epoch: 013 Test results, ap: mae: 0.610, rse: 1.139, pcc: nan, kcc: nan
+2025-07-09 14:42:40: Epoch: 014 Train Loss: 0.834, mae: 1.217, rse: 1.026, pcc: -0.098, kcc: 0.043
+2025-07-09 14:42:40: Epoch: 014 Test results, ap: mae: 0.614, rse: 1.155, pcc: 1.000, kcc: nan
+2025-07-09 14:42:41: Epoch: 015 Train Loss: 0.831, mae: 1.217, rse: 1.022, pcc: 0.286, kcc: 0.061
+2025-07-09 14:42:41: Epoch: 015 New best VALIDATION metrics
+2025-07-09 14:42:41: Epoch: 015 Test results, ap: mae: 0.608, rse: 1.133, pcc: nan, kcc: nan
+2025-07-09 14:42:42: Epoch: 016 Train Loss: 0.842, mae: 1.217, rse: 1.021, pcc: 0.109, kcc: 0.036
+2025-07-09 14:42:42: Epoch: 016 Test results, ap: mae: 0.607, rse: 1.129, pcc: -0.562, kcc: -0.242
+2025-07-09 14:42:42: Epoch: 017 Train Loss: 0.831, mae: 1.216, rse: 1.018, pcc: 0.229, kcc: -0.024
+2025-07-09 14:42:43: Epoch: 017 Test results, ap: mae: 0.603, rse: 1.112, pcc: nan, kcc: nan
+2025-07-09 14:42:43: Epoch: 018 Train Loss: 0.826, mae: 1.217, rse: 1.022, pcc: -0.012, kcc: -0.050
+2025-07-09 14:42:44: Epoch: 018 Test results, ap: mae: 0.608, rse: 1.131, pcc: nan, kcc: nan
+2025-07-09 14:42:44: Epoch: 019 Train Loss: 0.838, mae: 1.216, rse: 1.019, pcc: 0.002, kcc: -0.101
+2025-07-09 14:42:44: Epoch: 019 Test results, ap: mae: 0.603, rse: 1.113, pcc: nan, kcc: nan
+2025-07-09 14:42:45: Epoch: 020 Train Loss: 0.855, mae: 1.217, rse: 1.027, pcc: -0.053, kcc: -0.063
+2025-07-09 14:42:45: Epoch: 020 Test results, ap: mae: 0.615, rse: 1.160, pcc: -0.235, kcc: -0.242
+2025-07-09 14:42:46: Epoch: 021 Train Loss: 0.843, mae: 1.217, rse: 1.025, pcc: 0.054, kcc: 0.105
+2025-07-09 14:42:46: Epoch: 021 Test results, ap: mae: 0.612, rse: 1.149, pcc: nan, kcc: nan
+2025-07-09 14:42:47: Epoch: 022 Train Loss: 0.817, mae: 1.217, rse: 1.025, pcc: 0.186, kcc: -0.069
+2025-07-09 14:42:47: Epoch: 022 Test results, ap: mae: 0.613, rse: 1.150, pcc: nan, kcc: nan
+2025-07-09 14:42:47: Epoch: 023 Train Loss: 0.830, mae: 1.216, rse: 1.019, pcc: 0.156, kcc: 0.141
+2025-07-09 14:42:48: Epoch: 023 Test results, ap: mae: 0.604, rse: 1.116, pcc: 1.000, kcc: nan
+2025-07-09 14:42:48: Epoch: 024 Train Loss: 0.832, mae: 1.216, rse: 1.018, pcc: 0.054, kcc: 0.105
+2025-07-09 14:42:49: Epoch: 024 Test results, ap: mae: 0.603, rse: 1.111, pcc: nan, kcc: nan
+2025-07-09 14:42:49: Epoch: 025 Train Loss: 0.842, mae: 1.216, rse: 1.018, pcc: -0.075, kcc: -0.033
+2025-07-09 14:42:49: Epoch: 025 Test results, ap: mae: 0.602, rse: 1.107, pcc: nan, kcc: nan
+2025-07-09 14:42:50: Epoch: 026 Train Loss: 0.814, mae: 1.217, rse: 1.025, pcc: -0.026, kcc: -0.135
+2025-07-09 14:42:50: Epoch: 026 Test results, ap: mae: 0.613, rse: 1.151, pcc: 0.235, kcc: 0.242
+2025-07-09 14:42:51: Epoch: 027 Train Loss: 0.844, mae: 1.217, rse: 1.027, pcc: 0.398, kcc: 0.049
+2025-07-09 14:42:51: Epoch: 027 New best VALIDATION metrics
+2025-07-09 14:42:51: Epoch: 027 Test results, ap: mae: 0.615, rse: 1.161, pcc: 0.235, kcc: 0.242
+2025-07-09 14:42:52: Epoch: 028 Train Loss: 0.823, mae: 1.217, rse: 1.026, pcc: 0.645, kcc: 0.136
+2025-07-09 14:42:52: Epoch: 028 New best VALIDATION metrics
+2025-07-09 14:42:52: Epoch: 028 Test results, ap: mae: 0.614, rse: 1.157, pcc: nan, kcc: nan
+2025-07-09 14:42:53: Epoch: 029 Train Loss: 0.819, mae: 1.217, rse: 1.023, pcc: -0.114, kcc: -0.024
+2025-07-09 14:42:53: Epoch: 029 Test results, ap: mae: 0.610, rse: 1.140, pcc: nan, kcc: nan
+2025-07-09 14:42:53: Epoch: 030 Train Loss: 0.829, mae: 1.217, rse: 1.021, pcc: nan, kcc: nan
+2025-07-09 14:42:54: Epoch: 030 Test results, ap: mae: 0.607, rse: 1.127, pcc: nan, kcc: nan
+2025-07-09 14:42:54: Epoch: 031 Train Loss: 0.820, mae: 1.216, rse: 1.020, pcc: -0.788, kcc: -0.199
+2025-07-09 14:42:54: Epoch: 031 Test results, ap: mae: 0.606, rse: 1.124, pcc: 0.562, kcc: 0.242
+2025-07-09 14:42:54: Epoch: 031 New best TEST metrics
+2025-07-09 14:42:55: Epoch: 032 Train Loss: 0.846, mae: 1.217, rse: 1.022, pcc: 1.000, kcc: nan
+2025-07-09 14:42:55: Epoch: 032 Test results, ap: mae: 0.608, rse: 1.133, pcc: nan, kcc: nan
+2025-07-09 14:42:56: Epoch: 033 Train Loss: 0.833, mae: 1.216, rse: 1.020, pcc: 0.313, kcc: 0.048
+2025-07-09 14:42:56: Epoch: 033 Test results, ap: mae: 0.606, rse: 1.124, pcc: nan, kcc: nan
+2025-07-09 14:42:57: Epoch: 034 Train Loss: 0.835, mae: 1.216, rse: 1.020, pcc: 0.541, kcc: 0.081
+2025-07-09 14:42:57: Epoch: 034 Test results, ap: mae: 0.605, rse: 1.121, pcc: 1.000, kcc: 0.242
+2025-07-09 14:42:57: Epoch: 034 New best TEST metrics
+2025-07-09 14:42:58: Epoch: 035 Train Loss: 0.821, mae: 1.216, rse: 1.020, pcc: 0.376, kcc: -0.028
+2025-07-09 14:42:58: Epoch: 035 Test results, ap: mae: 0.606, rse: 1.122, pcc: nan, kcc: nan
+2025-07-09 14:42:58: used time 0.01h
+2025-07-09 14:42:58: Running Cross Validation 3
+2025-07-09 14:42:58: Fold 3  Train set:406, Valid set:101, Test set: 108
+2025-07-09 14:42:58: Epoch: 001 Train Loss: 0.797, mae: 1.336, rse: 1.055, pcc: 0.020, kcc: -0.016
+2025-07-09 14:42:58: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:42:59: Epoch: 001 Test results, ap: mae: 0.661, rse: 1.320, pcc: -0.130, kcc: -0.076
+2025-07-09 14:42:59: Epoch: 002 Train Loss: 0.803, mae: 1.323, rse: 1.024, pcc: 0.050, kcc: 0.066
+2025-07-09 14:42:59: Epoch: 002 New best VALIDATION metrics
+2025-07-09 14:43:00: Epoch: 002 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.270, kcc: 0.021
+2025-07-09 14:43:00: Epoch: 003 Train Loss: 0.830, mae: 1.328, rse: 1.039, pcc: 0.034, kcc: -0.036
+2025-07-09 14:43:00: Epoch: 003 Test results, ap: mae: 0.635, rse: 1.225, pcc: 0.447, kcc: -0.005
+2025-07-09 14:43:01: Epoch: 004 Train Loss: 0.807, mae: 1.321, rse: 1.018, pcc: 0.057, kcc: -0.069
+2025-07-09 14:43:01: Epoch: 004 Test results, ap: mae: 0.601, rse: 1.103, pcc: nan, kcc: nan
+2025-07-09 14:43:02: Epoch: 005 Train Loss: 0.810, mae: 1.325, rse: 1.031, pcc: 0.055, kcc: -0.113
+2025-07-09 14:43:02: Epoch: 005 Test results, ap: mae: 0.620, rse: 1.176, pcc: -1.000, kcc: -0.123
+2025-07-09 14:43:03: Epoch: 006 Train Loss: 0.816, mae: 1.325, rse: 1.031, pcc: 0.013, kcc: -0.096
+2025-07-09 14:43:03: Epoch: 006 Test results, ap: mae: 0.621, rse: 1.179, pcc: -0.221, kcc: -0.059
+2025-07-09 14:43:04: Epoch: 007 Train Loss: 0.815, mae: 1.324, rse: 1.028, pcc: 0.044, kcc: 0.054
+2025-07-09 14:43:04: Epoch: 007 Test results, ap: mae: 0.616, rse: 1.165, pcc: nan, kcc: nan
+2025-07-09 14:43:04: Epoch: 008 Train Loss: 0.800, mae: 1.322, rse: 1.019, pcc: 0.753, kcc: 0.139
+2025-07-09 14:43:04: Epoch: 008 New best VALIDATION metrics
+2025-07-09 14:43:05: Epoch: 008 Test results, ap: mae: 0.602, rse: 1.108, pcc: 0.153, kcc: 0.172
+2025-07-09 14:43:05: Epoch: 009 Train Loss: 0.804, mae: 1.322, rse: 1.023, pcc: -0.018, kcc: -0.056
+2025-07-09 14:43:06: Epoch: 009 Test results, ap: mae: 0.609, rse: 1.135, pcc: nan, kcc: nan
+2025-07-09 14:43:06: Epoch: 010 Train Loss: 0.793, mae: 1.326, rse: 1.034, pcc: -0.027, kcc: 0.067
+2025-07-09 14:43:06: Epoch: 010 Test results, ap: mae: 0.627, rse: 1.197, pcc: nan, kcc: nan
+2025-07-09 14:43:07: Epoch: 011 Train Loss: 0.806, mae: 1.325, rse: 1.033, pcc: 0.015, kcc: 0.058
+2025-07-09 14:43:07: Epoch: 011 Test results, ap: mae: 0.624, rse: 1.189, pcc: nan, kcc: nan
+2025-07-09 14:43:08: Epoch: 012 Train Loss: 0.806, mae: 1.324, rse: 1.028, pcc: 0.345, kcc: 0.084
+2025-07-09 14:43:08: Epoch: 012 Test results, ap: mae: 0.616, rse: 1.163, pcc: -1.000, kcc: nan
+2025-07-09 14:43:09: Epoch: 013 Train Loss: 0.807, mae: 1.323, rse: 1.027, pcc: 0.013, kcc: 0.058
+2025-07-09 14:43:09: Epoch: 013 Test results, ap: mae: 0.615, rse: 1.158, pcc: nan, kcc: nan
+2025-07-09 14:43:09: Epoch: 014 Train Loss: 0.791, mae: 1.323, rse: 1.024, pcc: -0.466, kcc: 0.019
+2025-07-09 14:43:10: Epoch: 014 Test results, ap: mae: 0.610, rse: 1.141, pcc: -1.000, kcc: nan
+2025-07-09 14:43:10: Epoch: 015 Train Loss: 0.820, mae: 1.322, rse: 1.020, pcc: 0.042, kcc: -0.038
+2025-07-09 14:43:11: Epoch: 015 Test results, ap: mae: 0.604, rse: 1.116, pcc: 0.688, kcc: 0.242
+2025-07-09 14:43:11: Epoch: 015 New best TEST metrics
+2025-07-09 14:43:11: Epoch: 016 Train Loss: 0.812, mae: 1.323, rse: 1.024, pcc: 0.020, kcc: 0.148
+2025-07-09 14:43:11: Epoch: 016 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.235, kcc: -0.242
+2025-07-09 14:43:12: Epoch: 017 Train Loss: 0.807, mae: 1.323, rse: 1.025, pcc: -0.056, kcc: -0.031
+2025-07-09 14:43:12: Epoch: 017 Test results, ap: mae: 0.611, rse: 1.142, pcc: nan, kcc: nan
+2025-07-09 14:43:13: Epoch: 018 Train Loss: 0.818, mae: 1.322, rse: 1.023, pcc: -0.335, kcc: -0.011
+2025-07-09 14:43:13: Epoch: 018 Test results, ap: mae: 0.608, rse: 1.131, pcc: -0.733, kcc: 0.242
+2025-07-09 14:43:14: Epoch: 019 Train Loss: 0.811, mae: 1.322, rse: 1.021, pcc: 0.386, kcc: 0.058
+2025-07-09 14:43:14: Epoch: 019 Test results, ap: mae: 0.606, rse: 1.124, pcc: nan, kcc: nan
+2025-07-09 14:43:14: Epoch: 020 Train Loss: 0.819, mae: 1.322, rse: 1.021, pcc: nan, kcc: nan
+2025-07-09 14:43:15: Epoch: 020 Test results, ap: mae: 0.606, rse: 1.123, pcc: nan, kcc: nan
+2025-07-09 14:43:15: Epoch: 021 Train Loss: 0.823, mae: 1.322, rse: 1.023, pcc: 0.012, kcc: 0.020
+2025-07-09 14:43:16: Epoch: 021 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.235, kcc: 0.242
+2025-07-09 14:43:16: Epoch: 022 Train Loss: 0.813, mae: 1.322, rse: 1.022, pcc: -0.004, kcc: -0.068
+2025-07-09 14:43:16: Epoch: 022 Test results, ap: mae: 0.607, rse: 1.129, pcc: nan, kcc: nan
+2025-07-09 14:43:17: Epoch: 023 Train Loss: 0.804, mae: 1.322, rse: 1.022, pcc: 1.000, kcc: 0.058
+2025-07-09 14:43:17: Epoch: 023 New best VALIDATION metrics
+2025-07-09 14:43:17: Epoch: 023 Test results, ap: mae: 0.607, rse: 1.129, pcc: 1.000, kcc: nan
+2025-07-09 14:43:18: Epoch: 024 Train Loss: 0.805, mae: 1.323, rse: 1.025, pcc: -0.018, kcc: 0.004
+2025-07-09 14:43:18: Epoch: 024 Test results, ap: mae: 0.611, rse: 1.143, pcc: 0.733, kcc: -0.242
+2025-07-09 14:43:19: Epoch: 025 Train Loss: 0.805, mae: 1.322, rse: 1.023, pcc: nan, kcc: nan
+2025-07-09 14:43:19: Epoch: 025 Test results, ap: mae: 0.608, rse: 1.134, pcc: -1.000, kcc: nan
+2025-07-09 14:43:20: Epoch: 026 Train Loss: 0.803, mae: 1.323, rse: 1.024, pcc: nan, kcc: nan
+2025-07-09 14:43:20: Epoch: 026 Test results, ap: mae: 0.610, rse: 1.140, pcc: nan, kcc: nan
+2025-07-09 14:43:20: Epoch: 027 Train Loss: 0.793, mae: 1.323, rse: 1.025, pcc: -0.002, kcc: -0.017
+2025-07-09 14:43:21: Epoch: 027 Test results, ap: mae: 0.612, rse: 1.146, pcc: -1.000, kcc: nan
+2025-07-09 14:43:21: Epoch: 028 Train Loss: 0.796, mae: 1.322, rse: 1.023, pcc: 1.000, kcc: nan
+2025-07-09 14:43:22: Epoch: 028 Test results, ap: mae: 0.609, rse: 1.134, pcc: nan, kcc: nan
+2025-07-09 14:43:22: Epoch: 029 Train Loss: 0.799, mae: 1.322, rse: 1.023, pcc: 1.000, kcc: nan
+2025-07-09 14:43:22: Epoch: 029 Test results, ap: mae: 0.608, rse: 1.131, pcc: 1.000, kcc: nan
+2025-07-09 14:43:23: Epoch: 030 Train Loss: 0.807, mae: 1.322, rse: 1.020, pcc: nan, kcc: nan
+2025-07-09 14:43:23: Epoch: 030 Test results, ap: mae: 0.604, rse: 1.117, pcc: 0.688, kcc: 0.242
+2025-07-09 14:43:24: Epoch: 031 Train Loss: 0.806, mae: 1.322, rse: 1.021, pcc: 1.000, kcc: nan
+2025-07-09 14:43:24: Epoch: 031 Test results, ap: mae: 0.606, rse: 1.123, pcc: nan, kcc: nan
+2025-07-09 14:43:25: Epoch: 032 Train Loss: 0.800, mae: 1.322, rse: 1.022, pcc: -1.000, kcc: nan
+2025-07-09 14:43:25: Epoch: 032 Test results, ap: mae: 0.607, rse: 1.128, pcc: 1.000, kcc: nan
+2025-07-09 14:43:25: Epoch: 033 Train Loss: 0.806, mae: 1.322, rse: 1.023, pcc: -1.000, kcc: nan
+2025-07-09 14:43:26: Epoch: 033 Test results, ap: mae: 0.608, rse: 1.133, pcc: 1.000, kcc: nan
+2025-07-09 14:43:26: Epoch: 034 Train Loss: 0.797, mae: 1.322, rse: 1.023, pcc: nan, kcc: nan
+2025-07-09 14:43:27: Epoch: 034 Test results, ap: mae: 0.608, rse: 1.131, pcc: -1.000, kcc: nan
+2025-07-09 14:43:27: Epoch: 035 Train Loss: 0.820, mae: 1.322, rse: 1.022, pcc: nan, kcc: nan
+2025-07-09 14:43:27: Epoch: 035 Test results, ap: mae: 0.607, rse: 1.126, pcc: nan, kcc: nan
+2025-07-09 14:43:27: used time 0.01h
+2025-07-09 14:43:27: Running Cross Validation 4
+2025-07-09 14:43:27: Fold 4  Train set:406, Valid set:101, Test set: 108
+2025-07-09 14:43:28: Epoch: 001 Train Loss: 0.854, mae: 1.225, rse: 1.000, pcc: -0.152, kcc: -0.103
+2025-07-09 14:43:28: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:43:28: Epoch: 001 Test results, ap: mae: 0.584, rse: 1.027, pcc: 0.025, kcc: 0.113
+2025-07-09 14:43:29: Epoch: 002 Train Loss: 0.865, mae: 1.199, rse: 1.021, pcc: -0.215, kcc: -0.203
+2025-07-09 14:43:29: Epoch: 002 Test results, ap: mae: 0.643, rse: 1.253, pcc: 0.043, kcc: 0.124
+2025-07-09 14:43:30: Epoch: 003 Train Loss: 0.838, mae: 1.206, rse: 1.005, pcc: -0.198, kcc: -0.150
+2025-07-09 14:43:30: Epoch: 003 Test results, ap: mae: 0.605, rse: 1.119, pcc: -0.131, kcc: 0.087
+2025-07-09 14:43:31: Epoch: 004 Train Loss: 0.845, mae: 1.203, rse: 1.009, pcc: -0.256, kcc: -0.178
+2025-07-09 14:43:31: Epoch: 004 Test results, ap: mae: 0.615, rse: 1.160, pcc: 0.019, kcc: 0.018
+2025-07-09 14:43:31: Epoch: 005 Train Loss: 0.850, mae: 1.200, rse: 1.017, pcc: -0.305, kcc: -0.160
+2025-07-09 14:43:32: Epoch: 005 Test results, ap: mae: 0.634, rse: 1.223, pcc: -0.157, kcc: -0.083
+2025-07-09 14:43:32: Epoch: 006 Train Loss: 0.832, mae: 1.205, rse: 1.006, pcc: -0.286, kcc: -0.116
+2025-07-09 14:43:33: Epoch: 006 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.552, kcc: -0.044
+2025-07-09 14:43:33: Epoch: 007 Train Loss: 0.840, mae: 1.202, rse: 1.010, pcc: -0.196, kcc: -0.216
+2025-07-09 14:43:33: Epoch: 007 Test results, ap: mae: 0.618, rse: 1.171, pcc: -0.121, kcc: -0.083
+2025-07-09 14:43:34: Epoch: 008 Train Loss: 0.851, mae: 1.202, rse: 1.010, pcc: -0.308, kcc: -0.152
+2025-07-09 14:43:34: Epoch: 008 Test results, ap: mae: 0.618, rse: 1.169, pcc: nan, kcc: nan
+2025-07-09 14:43:35: Epoch: 009 Train Loss: 0.813, mae: 1.212, rse: 1.001, pcc: -0.310, kcc: -0.137
+2025-07-09 14:43:35: Epoch: 009 Test results, ap: mae: 0.595, rse: 1.080, pcc: nan, kcc: nan
+2025-07-09 14:43:36: Epoch: 010 Train Loss: 0.832, mae: 1.202, rse: 1.010, pcc: -0.246, kcc: -0.116
+2025-07-09 14:43:36: Epoch: 010 Test results, ap: mae: 0.618, rse: 1.170, pcc: 0.334, kcc: -0.048
+2025-07-09 14:43:37: Epoch: 011 Train Loss: 0.859, mae: 1.199, rse: 1.024, pcc: 0.249, kcc: 0.200
+2025-07-09 14:43:37: Epoch: 011 New best VALIDATION metrics
+2025-07-09 14:43:37: Epoch: 011 Test results, ap: mae: 0.648, rse: 1.274, pcc: 0.514, kcc: -0.029
+2025-07-09 14:43:37: Epoch: 011 New best TEST metrics
+2025-07-09 14:43:38: Epoch: 012 Train Loss: 0.862, mae: 1.207, rse: 1.004, pcc: -0.258, kcc: -0.211
+2025-07-09 14:43:38: Epoch: 012 Test results, ap: mae: 0.604, rse: 1.116, pcc: nan, kcc: nan
+2025-07-09 14:43:38: Epoch: 013 Train Loss: 0.823, mae: 1.210, rse: 1.002, pcc: -0.246, kcc: -0.187
+2025-07-09 14:43:39: Epoch: 013 Test results, ap: mae: 0.598, rse: 1.091, pcc: nan, kcc: nan
+2025-07-09 14:43:39: Epoch: 014 Train Loss: 0.829, mae: 1.208, rse: 1.003, pcc: -0.234, kcc: -0.244
+2025-07-09 14:43:40: Epoch: 014 Test results, ap: mae: 0.601, rse: 1.103, pcc: nan, kcc: nan
+2025-07-09 14:43:40: Epoch: 015 Train Loss: 0.835, mae: 1.207, rse: 1.004, pcc: -0.065, kcc: -0.012
+2025-07-09 14:43:40: Epoch: 015 Test results, ap: mae: 0.603, rse: 1.112, pcc: -1.000, kcc: nan
+2025-07-09 14:43:41: Epoch: 016 Train Loss: 0.844, mae: 1.206, rse: 1.005, pcc: 0.270, kcc: 0.230
+2025-07-09 14:43:41: Epoch: 016 New best VALIDATION metrics
+2025-07-09 14:43:41: Epoch: 016 Test results, ap: mae: 0.606, rse: 1.126, pcc: -0.404, kcc: -0.242
+2025-07-09 14:43:42: Epoch: 017 Train Loss: 0.837, mae: 1.204, rse: 1.008, pcc: -0.051, kcc: -0.009
+2025-07-09 14:43:42: Epoch: 017 Test results, ap: mae: 0.612, rse: 1.149, pcc: -1.000, kcc: nan
+2025-07-09 14:43:43: Epoch: 018 Train Loss: 0.837, mae: 1.207, rse: 1.004, pcc: -0.358, kcc: -0.255
+2025-07-09 14:43:43: Epoch: 018 Test results, ap: mae: 0.604, rse: 1.117, pcc: -1.000, kcc: nan
+2025-07-09 14:43:44: Epoch: 019 Train Loss: 0.842, mae: 1.215, rse: 1.001, pcc: 0.298, kcc: 0.202
+2025-07-09 14:43:44: Epoch: 019 Test results, ap: mae: 0.592, rse: 1.066, pcc: 1.000, kcc: nan
+2025-07-09 14:43:44: Epoch: 020 Train Loss: 0.850, mae: 1.210, rse: 1.002, pcc: -0.130, kcc: -0.234
+2025-07-09 14:43:45: Epoch: 020 Test results, ap: mae: 0.598, rse: 1.090, pcc: nan, kcc: nan
+2025-07-09 14:43:45: Epoch: 021 Train Loss: 0.854, mae: 1.205, rse: 1.007, pcc: -0.180, kcc: -0.134
+2025-07-09 14:43:46: Epoch: 021 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.047, kcc: -0.070
+2025-07-09 14:43:46: Epoch: 022 Train Loss: 0.842, mae: 1.204, rse: 1.008, pcc: -0.179, kcc: -0.194
+2025-07-09 14:43:46: Epoch: 022 Test results, ap: mae: 0.613, rse: 1.150, pcc: nan, kcc: nan
+2025-07-09 14:43:47: Epoch: 023 Train Loss: 0.824, mae: 1.205, rse: 1.006, pcc: -0.271, kcc: -0.225
+2025-07-09 14:43:47: Epoch: 023 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.567, kcc: -0.227
+2025-07-09 14:43:48: Epoch: 024 Train Loss: 0.838, mae: 1.206, rse: 1.005, pcc: 0.030, kcc: -0.015
+2025-07-09 14:43:48: Epoch: 024 Test results, ap: mae: 0.605, rse: 1.121, pcc: -0.562, kcc: -0.242
+2025-07-09 14:43:49: Epoch: 025 Train Loss: 0.859, mae: 1.208, rse: 1.004, pcc: 0.074, kcc: -0.115
+2025-07-09 14:43:49: Epoch: 025 Test results, ap: mae: 0.602, rse: 1.108, pcc: -0.127, kcc: -0.145
+2025-07-09 14:43:50: Epoch: 026 Train Loss: 0.856, mae: 1.207, rse: 1.004, pcc: nan, kcc: nan
+2025-07-09 14:43:50: Epoch: 026 Test results, ap: mae: 0.603, rse: 1.111, pcc: -1.000, kcc: nan
+2025-07-09 14:43:51: Epoch: 027 Train Loss: 0.842, mae: 1.205, rse: 1.007, pcc: 0.224, kcc: 0.193
+2025-07-09 14:43:51: Epoch: 027 Test results, ap: mae: 0.610, rse: 1.140, pcc: nan, kcc: nan
+2025-07-09 14:43:51: Epoch: 028 Train Loss: 0.828, mae: 1.205, rse: 1.006, pcc: 0.434, kcc: 0.006
+2025-07-09 14:43:52: Epoch: 028 Test results, ap: mae: 0.608, rse: 1.133, pcc: nan, kcc: nan
+2025-07-09 14:43:52: Epoch: 029 Train Loss: 0.824, mae: 1.204, rse: 1.008, pcc: nan, kcc: nan
+2025-07-09 14:43:53: Epoch: 029 Test results, ap: mae: 0.613, rse: 1.150, pcc: nan, kcc: nan
+2025-07-09 14:43:53: Epoch: 030 Train Loss: 0.851, mae: 1.205, rse: 1.006, pcc: 0.028, kcc: -0.010
+2025-07-09 14:43:53: Epoch: 030 Test results, ap: mae: 0.608, rse: 1.130, pcc: -0.595, kcc: 0.242
+2025-07-09 14:43:54: Epoch: 031 Train Loss: 0.850, mae: 1.207, rse: 1.004, pcc: 0.085, kcc: 0.010
+2025-07-09 14:43:54: Epoch: 031 Test results, ap: mae: 0.603, rse: 1.110, pcc: 0.404, kcc: 0.242
+2025-07-09 14:43:54: Epoch: 031 New best TEST metrics
+2025-07-09 14:43:55: Epoch: 032 Train Loss: 0.841, mae: 1.207, rse: 1.004, pcc: 1.000, kcc: 0.020
+2025-07-09 14:43:55: Epoch: 032 New best VALIDATION metrics
+2025-07-09 14:43:55: Epoch: 032 Test results, ap: mae: 0.604, rse: 1.117, pcc: -1.000, kcc: nan
+2025-07-09 14:43:56: Epoch: 033 Train Loss: 0.834, mae: 1.205, rse: 1.006, pcc: 1.000, kcc: nan
+2025-07-09 14:43:56: Epoch: 033 Test results, ap: mae: 0.608, rse: 1.132, pcc: nan, kcc: nan
+2025-07-09 14:43:57: Epoch: 034 Train Loss: 0.825, mae: 1.206, rse: 1.005, pcc: 1.000, kcc: nan
+2025-07-09 14:43:57: Epoch: 034 Test results, ap: mae: 0.605, rse: 1.122, pcc: nan, kcc: nan
+2025-07-09 14:43:57: Epoch: 035 Train Loss: 0.846, mae: 1.207, rse: 1.005, pcc: 0.019, kcc: -0.035
+2025-07-09 14:43:58: Epoch: 035 Test results, ap: mae: 0.605, rse: 1.118, pcc: -1.000, kcc: nan
+2025-07-09 14:43:58: used time 0.01h
+2025-07-09 14:43:58: Cross Validation Finished!
+2025-07-09 14:43:58: Best validation perform list
+[[1.14998841 1.00690877 0.07464657 0.11259572]
+ [1.20187283 1.01219869 0.04211944 0.07717969]
+ [1.21680641 1.02610278 0.64471155 0.13619991]
+ [1.32230854 1.02233744 1.         0.05788444]
+ [1.20669794 1.00444615 1.         0.01976148]]
+2025-07-09 14:43:58: mean: [1.22  1.014 0.552 0.081]
+2025-07-09 14:43:58: std: [0.056 0.008 0.424 0.041]
+2025-07-09 14:43:58: Best test perform list
+[[ 0.60229945  1.10857546 -0.23479605 -0.24163213]
+ [ 0.59491688  1.07767129  0.68804866  0.24163213]
+ [ 0.60537201  1.12123501  1.          0.24163213]
+ [ 0.604002    1.11583376  0.68804866  0.24163213]
+ [ 0.60272217  1.11042583  0.40425739  0.24163213]]
+2025-07-09 14:43:58: mean: [0.602 1.107 0.509 0.145]
+2025-07-09 14:43:58: std: [0.004 0.015 0.417 0.193]
diff --git a/run-reg/lstm-att-256-oneway-mse-32-0.001-35/config.json b/run-reg/lstm-att-256-oneway-mse-32-0.001-35/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..4e38da4c644966f94bae9554911ea314c118f9de
--- /dev/null
+++ b/run-reg/lstm-att-256-oneway-mse-32-0.001-35/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 35, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
\ No newline at end of file
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diff --git a/run-reg/lstm-att-256-oneway-mse-32-0.001-35/result.txt b/run-reg/lstm-att-256-oneway-mse-32-0.001-35/result.txt
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@@ -0,0 +1,6 @@
+Valid
+1.2412442684173584,1.0036046981811524,0.1221136573702097,0.11949358209967613
+0.05751901434739186,0.002534954302824238,0.12595841875470454,0.037466960315849174
+Test
+0.5831132292747497,1.019420337677002,1.0,-0.0349266042932868
+0.0012710402392088333,0.008911810731053123,0.0,0.05898230028718993
diff --git a/run-reg/lstm-att-256-oneway-mse-32-0.001-35/training.log b/run-reg/lstm-att-256-oneway-mse-32-0.001-35/training.log
new file mode 100644
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@@ -0,0 +1,419 @@
+2025-07-09 14:18:05: saving_dir: ./run-reg/lstm-att-256-oneway-mse-32-0.001-35
+2025-07-09 14:18:05: Loading Train Dataset
+2025-07-09 14:18:05: Loading Test Dataset
+2025-07-09 14:18:06: Running Cross Validation 0
+2025-07-09 14:18:06: Fold 0  Train set:403, Valid set:101, Test set: 108
+2025-07-09 14:18:07: Epoch: 001 Train Loss: 2.868, mae: 1.238, rse: 1.005, pcc: -0.013, kcc: 0.131
+2025-07-09 14:18:07: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:18:07: Epoch: 001 Test results, ap: mae: 0.603, rse: 1.112, pcc: -0.170, kcc: -0.144
+2025-07-09 14:18:08: Epoch: 002 Train Loss: 2.774, mae: 1.254, rse: 1.000, pcc: -0.030, kcc: 0.117
+2025-07-09 14:18:08: Epoch: 002 Test results, ap: mae: 0.586, rse: 1.037, pcc: -0.087, kcc: -0.047
+2025-07-09 14:18:09: Epoch: 003 Train Loss: 2.562, mae: 1.273, rse: 1.002, pcc: -0.081, kcc: 0.127
+2025-07-09 14:18:09: Epoch: 003 Test results, ap: mae: 0.581, rse: 1.007, pcc: -0.046, kcc: -0.006
+2025-07-09 14:18:10: Epoch: 004 Train Loss: 2.881, mae: 1.252, rse: 1.000, pcc: -0.080, kcc: 0.126
+2025-07-09 14:18:10: Epoch: 004 Test results, ap: mae: 0.587, rse: 1.042, pcc: -0.031, kcc: -0.021
+2025-07-09 14:18:10: Epoch: 005 Train Loss: 2.732, mae: 1.256, rse: 1.000, pcc: -0.095, kcc: 0.017
+2025-07-09 14:18:11: Epoch: 005 Test results, ap: mae: 0.585, rse: 1.034, pcc: 0.358, kcc: -0.083
+2025-07-09 14:18:11: Epoch: 006 Train Loss: 2.843, mae: 1.263, rse: 1.000, pcc: -0.064, kcc: 0.011
+2025-07-09 14:18:11: Epoch: 006 Test results, ap: mae: 0.583, rse: 1.020, pcc: -1.000, kcc: 0.009
+2025-07-09 14:18:12: Epoch: 007 Train Loss: 2.782, mae: 1.261, rse: 1.000, pcc: -0.025, kcc: -0.119
+2025-07-09 14:18:12: Epoch: 007 Test results, ap: mae: 0.583, rse: 1.022, pcc: 0.112, kcc: 0.050
+2025-07-09 14:18:13: Epoch: 008 Train Loss: 2.866, mae: 1.246, rse: 1.001, pcc: -0.058, kcc: -0.019
+2025-07-09 14:18:13: Epoch: 008 Test results, ap: mae: 0.591, rse: 1.060, pcc: -0.112, kcc: 0.004
+2025-07-09 14:18:14: Epoch: 009 Train Loss: 2.778, mae: 1.261, rse: 1.000, pcc: 0.071, kcc: 0.157
+2025-07-09 14:18:14: Epoch: 009 New best VALIDATION metrics
+2025-07-09 14:18:14: Epoch: 009 Test results, ap: mae: 0.584, rse: 1.023, pcc: 0.964, kcc: -0.068
+2025-07-09 14:18:15: Epoch: 010 Train Loss: 2.853, mae: 1.260, rse: 1.000, pcc: 0.003, kcc: 0.090
+2025-07-09 14:18:15: Epoch: 010 Test results, ap: mae: 0.584, rse: 1.025, pcc: 0.269, kcc: -0.016
+2025-07-09 14:18:15: Epoch: 011 Train Loss: 2.638, mae: 1.270, rse: 1.001, pcc: -0.019, kcc: 0.113
+2025-07-09 14:18:16: Epoch: 011 Test results, ap: mae: 0.582, rse: 1.010, pcc: 0.062, kcc: 0.086
+2025-07-09 14:18:16: Epoch: 011 New best TEST metrics
+2025-07-09 14:18:16: Epoch: 012 Train Loss: 2.822, mae: 1.278, rse: 1.003, pcc: -0.056, kcc: 0.108
+2025-07-09 14:18:16: Epoch: 012 Test results, ap: mae: 0.581, rse: 1.003, pcc: -0.819, kcc: -0.105
+2025-07-09 14:18:17: Epoch: 013 Train Loss: 2.831, mae: 1.253, rse: 1.000, pcc: -0.093, kcc: -0.027
+2025-07-09 14:18:17: Epoch: 013 Test results, ap: mae: 0.587, rse: 1.040, pcc: 0.020, kcc: 0.020
+2025-07-09 14:18:18: Epoch: 014 Train Loss: 2.640, mae: 1.261, rse: 1.000, pcc: -0.120, kcc: -0.026
+2025-07-09 14:18:18: Epoch: 014 Test results, ap: mae: 0.583, rse: 1.023, pcc: 0.506, kcc: 0.193
+2025-07-09 14:18:18: Epoch: 014 New best TEST metrics
+2025-07-09 14:18:19: Epoch: 015 Train Loss: 2.846, mae: 1.276, rse: 1.002, pcc: -0.145, kcc: -0.001
+2025-07-09 14:18:19: Epoch: 015 Test results, ap: mae: 0.581, rse: 1.005, pcc: 0.038, kcc: 0.062
+2025-07-09 14:18:20: Epoch: 016 Train Loss: 2.816, mae: 1.263, rse: 1.000, pcc: -0.123, kcc: -0.021
+2025-07-09 14:18:20: Epoch: 016 Test results, ap: mae: 0.583, rse: 1.020, pcc: 0.324, kcc: 0.130
+2025-07-09 14:18:20: Epoch: 017 Train Loss: 2.841, mae: 1.268, rse: 1.001, pcc: -0.114, kcc: -0.007
+2025-07-09 14:18:21: Epoch: 017 Test results, ap: mae: 0.582, rse: 1.012, pcc: -0.922, kcc: -0.045
+2025-07-09 14:18:21: Epoch: 018 Train Loss: 2.712, mae: 1.269, rse: 1.001, pcc: -0.121, kcc: 0.000
+2025-07-09 14:18:21: Epoch: 018 Test results, ap: mae: 0.582, rse: 1.010, pcc: 0.046, kcc: 0.169
+2025-07-09 14:18:22: Epoch: 019 Train Loss: 2.823, mae: 1.276, rse: 1.002, pcc: -0.118, kcc: -0.022
+2025-07-09 14:18:22: Epoch: 019 Test results, ap: mae: 0.581, rse: 1.004, pcc: -1.000, kcc: 0.105
+2025-07-09 14:18:23: Epoch: 020 Train Loss: 2.846, mae: 1.272, rse: 1.002, pcc: -0.103, kcc: -0.076
+2025-07-09 14:18:23: Epoch: 020 Test results, ap: mae: 0.582, rse: 1.008, pcc: -0.255, kcc: 0.036
+2025-07-09 14:18:24: Epoch: 021 Train Loss: 2.830, mae: 1.268, rse: 1.001, pcc: -0.053, kcc: 0.002
+2025-07-09 14:18:24: Epoch: 021 Test results, ap: mae: 0.582, rse: 1.012, pcc: -0.335, kcc: 0.080
+2025-07-09 14:18:24: Epoch: 022 Train Loss: 2.692, mae: 1.268, rse: 1.001, pcc: -0.119, kcc: -0.072
+2025-07-09 14:18:25: Epoch: 022 Test results, ap: mae: 0.582, rse: 1.012, pcc: -0.032, kcc: 0.002
+2025-07-09 14:18:25: Epoch: 023 Train Loss: 2.853, mae: 1.265, rse: 1.001, pcc: 0.036, kcc: -0.081
+2025-07-09 14:18:26: Epoch: 023 Test results, ap: mae: 0.583, rse: 1.016, pcc: 1.000, kcc: nan
+2025-07-09 14:18:26: Epoch: 024 Train Loss: 2.832, mae: 1.273, rse: 1.002, pcc: -0.111, kcc: -0.116
+2025-07-09 14:18:26: Epoch: 024 Test results, ap: mae: 0.581, rse: 1.007, pcc: 0.640, kcc: -0.012
+2025-07-09 14:18:27: Epoch: 025 Train Loss: 2.688, mae: 1.269, rse: 1.001, pcc: -0.090, kcc: -0.066
+2025-07-09 14:18:27: Epoch: 025 Test results, ap: mae: 0.582, rse: 1.011, pcc: 1.000, kcc: 0.041
+2025-07-09 14:18:27: Epoch: 025 New best TEST metrics
+2025-07-09 14:18:28: Epoch: 026 Train Loss: 2.749, mae: 1.268, rse: 1.001, pcc: -0.031, kcc: -0.025
+2025-07-09 14:18:28: Epoch: 026 Test results, ap: mae: 0.582, rse: 1.012, pcc: 0.491, kcc: 0.047
+2025-07-09 14:18:29: Epoch: 027 Train Loss: 2.799, mae: 1.266, rse: 1.001, pcc: 0.019, kcc: -0.067
+2025-07-09 14:18:29: Epoch: 027 Test results, ap: mae: 0.582, rse: 1.015, pcc: nan, kcc: nan
+2025-07-09 14:18:29: Epoch: 028 Train Loss: 2.803, mae: 1.263, rse: 1.000, pcc: -0.004, kcc: 0.009
+2025-07-09 14:18:30: Epoch: 028 Test results, ap: mae: 0.583, rse: 1.019, pcc: -0.039, kcc: -0.054
+2025-07-09 14:18:30: Epoch: 029 Train Loss: 2.848, mae: 1.263, rse: 1.000, pcc: -0.040, kcc: 0.009
+2025-07-09 14:18:30: Epoch: 029 Test results, ap: mae: 0.583, rse: 1.019, pcc: nan, kcc: nan
+2025-07-09 14:18:31: Epoch: 030 Train Loss: 2.812, mae: 1.265, rse: 1.000, pcc: -0.075, kcc: -0.145
+2025-07-09 14:18:31: Epoch: 030 Test results, ap: mae: 0.583, rse: 1.016, pcc: nan, kcc: nan
+2025-07-09 14:18:32: Epoch: 031 Train Loss: 2.849, mae: 1.264, rse: 1.000, pcc: -0.091, kcc: -0.158
+2025-07-09 14:18:32: Epoch: 031 Test results, ap: mae: 0.583, rse: 1.017, pcc: -1.000, kcc: -0.128
+2025-07-09 14:18:33: Epoch: 032 Train Loss: 2.755, mae: 1.265, rse: 1.000, pcc: -0.072, kcc: -0.106
+2025-07-09 14:18:33: Epoch: 032 Test results, ap: mae: 0.583, rse: 1.017, pcc: nan, kcc: nan
+2025-07-09 14:18:34: Epoch: 033 Train Loss: 2.892, mae: 1.265, rse: 1.000, pcc: -0.090, kcc: -0.149
+2025-07-09 14:18:34: Epoch: 033 Test results, ap: mae: 0.583, rse: 1.016, pcc: -0.762, kcc: -0.338
+2025-07-09 14:18:34: Epoch: 034 Train Loss: 2.715, mae: 1.268, rse: 1.001, pcc: -0.112, kcc: -0.158
+2025-07-09 14:18:35: Epoch: 034 Test results, ap: mae: 0.582, rse: 1.013, pcc: 0.594, kcc: 0.201
+2025-07-09 14:18:35: Epoch: 035 Train Loss: 2.784, mae: 1.268, rse: 1.001, pcc: -0.095, kcc: -0.137
+2025-07-09 14:18:35: Epoch: 035 Test results, ap: mae: 0.582, rse: 1.012, pcc: -0.296, kcc: -0.109
+2025-07-09 14:18:35: used time 0.01h
+2025-07-09 14:18:35: Running Cross Validation 1
+2025-07-09 14:18:35: Fold 1  Train set:403, Valid set:101, Test set: 108
+2025-07-09 14:18:36: Epoch: 001 Train Loss: 2.931, mae: 1.202, rse: 1.000, pcc: -0.035, kcc: -0.009
+2025-07-09 14:18:36: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:18:36: Epoch: 001 Test results, ap: mae: 0.581, rse: 1.006, pcc: -0.022, kcc: 0.002
+2025-07-09 14:18:37: Epoch: 002 Train Loss: 2.901, mae: 1.172, rse: 1.010, pcc: -0.050, kcc: -0.110
+2025-07-09 14:18:37: Epoch: 002 Test results, ap: mae: 0.591, rse: 1.059, pcc: 0.212, kcc: 0.156
+2025-07-09 14:18:38: Epoch: 003 Train Loss: 2.839, mae: 1.187, rse: 1.004, pcc: -0.099, kcc: -0.124
+2025-07-09 14:18:38: Epoch: 003 Test results, ap: mae: 0.584, rse: 1.026, pcc: 0.138, kcc: 0.077
+2025-07-09 14:18:39: Epoch: 004 Train Loss: 2.894, mae: 1.214, rse: 1.000, pcc: -0.114, kcc: -0.123
+2025-07-09 14:18:39: Epoch: 004 Test results, ap: mae: 0.582, rse: 1.000, pcc: 0.043, kcc: 0.052
+2025-07-09 14:18:39: Epoch: 005 Train Loss: 2.907, mae: 1.172, rse: 1.010, pcc: -0.115, kcc: -0.106
+2025-07-09 14:18:40: Epoch: 005 Test results, ap: mae: 0.591, rse: 1.061, pcc: 0.172, kcc: 0.043
+2025-07-09 14:18:40: Epoch: 006 Train Loss: 2.906, mae: 1.202, rse: 1.001, pcc: -0.089, kcc: -0.122
+2025-07-09 14:18:40: Epoch: 006 Test results, ap: mae: 0.581, rse: 1.007, pcc: 0.572, kcc: 0.048
+2025-07-09 14:18:41: Epoch: 007 Train Loss: 2.883, mae: 1.179, rse: 1.006, pcc: -0.105, kcc: -0.174
+2025-07-09 14:18:41: Epoch: 007 Test results, ap: mae: 0.587, rse: 1.041, pcc: 0.120, kcc: 0.078
+2025-07-09 14:18:42: Epoch: 008 Train Loss: 2.968, mae: 1.160, rse: 1.018, pcc: -0.094, kcc: -0.181
+2025-07-09 14:18:42: Epoch: 008 Test results, ap: mae: 0.601, rse: 1.103, pcc: 0.259, kcc: 0.197
+2025-07-09 14:18:43: Epoch: 009 Train Loss: 2.859, mae: 1.193, rse: 1.002, pcc: -0.101, kcc: -0.120
+2025-07-09 14:18:43: Epoch: 009 Test results, ap: mae: 0.583, rse: 1.017, pcc: 0.207, kcc: 0.060
+2025-07-09 14:18:43: Epoch: 010 Train Loss: 2.941, mae: 1.204, rse: 1.000, pcc: -0.102, kcc: -0.152
+2025-07-09 14:18:44: Epoch: 010 Test results, ap: mae: 0.581, rse: 1.004, pcc: 0.088, kcc: 0.068
+2025-07-09 14:18:44: Epoch: 011 Train Loss: 2.796, mae: 1.174, rse: 1.009, pcc: -0.105, kcc: -0.200
+2025-07-09 14:18:45: Epoch: 011 Test results, ap: mae: 0.589, rse: 1.054, pcc: 0.568, kcc: 0.096
+2025-07-09 14:18:45: Epoch: 011 New best TEST metrics
+2025-07-09 14:18:45: Epoch: 012 Train Loss: 2.865, mae: 1.182, rse: 1.005, pcc: -0.110, kcc: -0.165
+2025-07-09 14:18:45: Epoch: 012 Test results, ap: mae: 0.586, rse: 1.035, pcc: 0.132, kcc: -0.034
+2025-07-09 14:18:46: Epoch: 013 Train Loss: 2.801, mae: 1.201, rse: 1.001, pcc: -0.095, kcc: -0.091
+2025-07-09 14:18:46: Epoch: 013 Test results, ap: mae: 0.581, rse: 1.007, pcc: 0.306, kcc: 0.077
+2025-07-09 14:18:47: Epoch: 014 Train Loss: 2.943, mae: 1.194, rse: 1.002, pcc: -0.025, kcc: -0.122
+2025-07-09 14:18:47: Epoch: 014 Test results, ap: mae: 0.582, rse: 1.014, pcc: -0.351, kcc: -0.057
+2025-07-09 14:18:48: Epoch: 015 Train Loss: 2.939, mae: 1.182, rse: 1.005, pcc: 0.123, kcc: 0.021
+2025-07-09 14:18:48: Epoch: 015 New best VALIDATION metrics
+2025-07-09 14:18:48: Epoch: 015 Test results, ap: mae: 0.586, rse: 1.035, pcc: -0.053, kcc: -0.163
+2025-07-09 14:18:49: Epoch: 016 Train Loss: 2.934, mae: 1.201, rse: 1.001, pcc: -0.078, kcc: -0.193
+2025-07-09 14:18:49: Epoch: 016 Test results, ap: mae: 0.581, rse: 1.007, pcc: 0.647, kcc: 0.077
+2025-07-09 14:18:49: Epoch: 016 New best TEST metrics
+2025-07-09 14:18:49: Epoch: 017 Train Loss: 2.882, mae: 1.194, rse: 1.002, pcc: -0.075, kcc: -0.211
+2025-07-09 14:18:50: Epoch: 017 Test results, ap: mae: 0.583, rse: 1.016, pcc: nan, kcc: nan
+2025-07-09 14:18:50: Epoch: 018 Train Loss: 2.858, mae: 1.193, rse: 1.002, pcc: -0.074, kcc: -0.062
+2025-07-09 14:18:50: Epoch: 018 Test results, ap: mae: 0.583, rse: 1.017, pcc: 0.436, kcc: 0.051
+2025-07-09 14:18:51: Epoch: 019 Train Loss: 2.935, mae: 1.184, rse: 1.005, pcc: -0.057, kcc: -0.050
+2025-07-09 14:18:51: Epoch: 019 Test results, ap: mae: 0.585, rse: 1.032, pcc: 1.000, kcc: -0.069
+2025-07-09 14:18:51: Epoch: 019 New best TEST metrics
+2025-07-09 14:18:52: Epoch: 020 Train Loss: 2.901, mae: 1.178, rse: 1.007, pcc: 0.009, kcc: -0.048
+2025-07-09 14:18:52: Epoch: 020 Test results, ap: mae: 0.587, rse: 1.044, pcc: 0.093, kcc: 0.066
+2025-07-09 14:18:53: Epoch: 021 Train Loss: 2.836, mae: 1.176, rse: 1.007, pcc: 0.032, kcc: 0.114
+2025-07-09 14:18:53: Epoch: 021 New best VALIDATION metrics
+2025-07-09 14:18:53: Epoch: 021 Test results, ap: mae: 0.588, rse: 1.047, pcc: -0.001, kcc: -0.154
+2025-07-09 14:18:54: Epoch: 022 Train Loss: 2.893, mae: 1.188, rse: 1.003, pcc: -0.086, kcc: -0.197
+2025-07-09 14:18:54: Epoch: 022 Test results, ap: mae: 0.584, rse: 1.024, pcc: -0.056, kcc: 0.057
+2025-07-09 14:18:54: Epoch: 023 Train Loss: 2.818, mae: 1.187, rse: 1.004, pcc: -0.049, kcc: -0.124
+2025-07-09 14:18:55: Epoch: 023 Test results, ap: mae: 0.584, rse: 1.025, pcc: 0.096, kcc: 0.028
+2025-07-09 14:18:55: Epoch: 024 Train Loss: 2.858, mae: 1.195, rse: 1.002, pcc: -0.055, kcc: -0.157
+2025-07-09 14:18:56: Epoch: 024 Test results, ap: mae: 0.582, rse: 1.013, pcc: 0.757, kcc: -0.205
+2025-07-09 14:18:56: Epoch: 025 Train Loss: 2.892, mae: 1.190, rse: 1.003, pcc: -0.056, kcc: -0.130
+2025-07-09 14:18:56: Epoch: 025 Test results, ap: mae: 0.583, rse: 1.020, pcc: 0.917, kcc: -0.069
+2025-07-09 14:18:57: Epoch: 026 Train Loss: 2.794, mae: 1.192, rse: 1.002, pcc: -0.073, kcc: -0.190
+2025-07-09 14:18:57: Epoch: 026 Test results, ap: mae: 0.583, rse: 1.018, pcc: 1.000, kcc: -0.120
+2025-07-09 14:18:58: Epoch: 027 Train Loss: 2.863, mae: 1.203, rse: 1.000, pcc: -0.091, kcc: -0.306
+2025-07-09 14:18:58: Epoch: 027 Test results, ap: mae: 0.581, rse: 1.006, pcc: 0.231, kcc: 0.129
+2025-07-09 14:18:59: Epoch: 028 Train Loss: 2.913, mae: 1.190, rse: 1.003, pcc: -0.085, kcc: -0.047
+2025-07-09 14:18:59: Epoch: 028 Test results, ap: mae: 0.583, rse: 1.021, pcc: 0.184, kcc: 0.047
+2025-07-09 14:18:59: Epoch: 029 Train Loss: 2.901, mae: 1.198, rse: 1.001, pcc: -0.077, kcc: -0.220
+2025-07-09 14:19:00: Epoch: 029 Test results, ap: mae: 0.582, rse: 1.010, pcc: 0.437, kcc: 0.114
+2025-07-09 14:19:00: Epoch: 030 Train Loss: 2.870, mae: 1.191, rse: 1.003, pcc: -0.049, kcc: -0.235
+2025-07-09 14:19:00: Epoch: 030 Test results, ap: mae: 0.583, rse: 1.020, pcc: 0.411, kcc: -0.028
+2025-07-09 14:19:01: Epoch: 031 Train Loss: 2.943, mae: 1.190, rse: 1.003, pcc: -0.059, kcc: -0.214
+2025-07-09 14:19:01: Epoch: 031 Test results, ap: mae: 0.583, rse: 1.021, pcc: -0.016, kcc: 0.037
+2025-07-09 14:19:02: Epoch: 032 Train Loss: 2.950, mae: 1.194, rse: 1.002, pcc: -0.079, kcc: -0.200
+2025-07-09 14:19:02: Epoch: 032 Test results, ap: mae: 0.583, rse: 1.015, pcc: 0.235, kcc: -0.113
+2025-07-09 14:19:03: Epoch: 033 Train Loss: 2.917, mae: 1.191, rse: 1.003, pcc: -0.053, kcc: -0.063
+2025-07-09 14:19:03: Epoch: 033 Test results, ap: mae: 0.583, rse: 1.020, pcc: -0.329, kcc: -0.203
+2025-07-09 14:19:04: Epoch: 034 Train Loss: 2.904, mae: 1.192, rse: 1.002, pcc: -0.093, kcc: -0.196
+2025-07-09 14:19:04: Epoch: 034 Test results, ap: mae: 0.583, rse: 1.017, pcc: -1.000, kcc: 0.065
+2025-07-09 14:19:04: Epoch: 035 Train Loss: 2.894, mae: 1.189, rse: 1.003, pcc: 0.078, kcc: -0.051
+2025-07-09 14:19:05: Epoch: 035 Test results, ap: mae: 0.583, rse: 1.023, pcc: nan, kcc: nan
+2025-07-09 14:19:05: used time 0.01h
+2025-07-09 14:19:05: Running Cross Validation 2
+2025-07-09 14:19:05: Fold 2  Train set:403, Valid set:101, Test set: 108
+2025-07-09 14:19:05: Epoch: 001 Train Loss: 2.796, mae: 1.224, rse: 1.016, pcc: 0.005, kcc: 0.044
+2025-07-09 14:19:05: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:19:06: Epoch: 001 Test results, ap: mae: 0.604, rse: 1.117, pcc: 0.129, kcc: 0.096
+2025-07-09 14:19:06: Epoch: 002 Train Loss: 2.705, mae: 1.227, rse: 1.007, pcc: -0.029, kcc: -0.022
+2025-07-09 14:19:06: Epoch: 002 Test results, ap: mae: 0.590, rse: 1.055, pcc: 0.156, kcc: 0.071
+2025-07-09 14:19:07: Epoch: 003 Train Loss: 2.635, mae: 1.232, rse: 1.002, pcc: -0.064, kcc: -0.045
+2025-07-09 14:19:07: Epoch: 003 Test results, ap: mae: 0.583, rse: 1.022, pcc: 0.146, kcc: 0.099
+2025-07-09 14:19:08: Epoch: 004 Train Loss: 2.653, mae: 1.225, rse: 1.009, pcc: -0.080, kcc: -0.070
+2025-07-09 14:19:08: Epoch: 004 Test results, ap: mae: 0.593, rse: 1.071, pcc: 0.124, kcc: 0.077
+2025-07-09 14:19:09: Epoch: 005 Train Loss: 2.732, mae: 1.237, rse: 1.001, pcc: -0.068, kcc: -0.015
+2025-07-09 14:19:09: Epoch: 005 Test results, ap: mae: 0.582, rse: 1.008, pcc: -0.224, kcc: 0.071
+2025-07-09 14:19:09: Epoch: 006 Train Loss: 2.701, mae: 1.224, rse: 1.017, pcc: -0.073, kcc: -0.010
+2025-07-09 14:19:10: Epoch: 006 Test results, ap: mae: 0.605, rse: 1.120, pcc: 0.224, kcc: 0.135
+2025-07-09 14:19:10: Epoch: 007 Train Loss: 2.841, mae: 1.227, rse: 1.006, pcc: -0.084, kcc: -0.000
+2025-07-09 14:19:11: Epoch: 007 Test results, ap: mae: 0.589, rse: 1.052, pcc: -0.173, kcc: 0.042
+2025-07-09 14:19:11: Epoch: 008 Train Loss: 2.742, mae: 1.234, rse: 1.001, pcc: -0.096, kcc: 0.029
+2025-07-09 14:19:11: Epoch: 008 Test results, ap: mae: 0.583, rse: 1.016, pcc: -0.221, kcc: 0.124
+2025-07-09 14:19:12: Epoch: 009 Train Loss: 2.786, mae: 1.231, rse: 1.003, pcc: -0.110, kcc: -0.014
+2025-07-09 14:19:12: Epoch: 009 Test results, ap: mae: 0.584, rse: 1.025, pcc: -1.000, kcc: -0.076
+2025-07-09 14:19:13: Epoch: 010 Train Loss: 2.766, mae: 1.225, rse: 1.009, pcc: -0.054, kcc: 0.063
+2025-07-09 14:19:13: Epoch: 010 Test results, ap: mae: 0.593, rse: 1.070, pcc: 0.546, kcc: 0.068
+2025-07-09 14:19:14: Epoch: 011 Train Loss: 2.767, mae: 1.240, rse: 1.000, pcc: -0.057, kcc: -0.001
+2025-07-09 14:19:14: Epoch: 011 Test results, ap: mae: 0.581, rse: 1.003, pcc: 0.337, kcc: -0.026
+2025-07-09 14:19:14: Epoch: 011 New best TEST metrics
+2025-07-09 14:19:14: Epoch: 012 Train Loss: 2.668, mae: 1.227, rse: 1.007, pcc: -0.080, kcc: -0.051
+2025-07-09 14:19:15: Epoch: 012 Test results, ap: mae: 0.590, rse: 1.055, pcc: 0.143, kcc: 0.004
+2025-07-09 14:19:15: Epoch: 013 Train Loss: 2.783, mae: 1.228, rse: 1.005, pcc: -0.090, kcc: -0.132
+2025-07-09 14:19:16: Epoch: 013 Test results, ap: mae: 0.587, rse: 1.040, pcc: -0.753, kcc: -0.156
+2025-07-09 14:19:16: Epoch: 014 Train Loss: 2.685, mae: 1.233, rse: 1.002, pcc: -0.080, kcc: -0.034
+2025-07-09 14:19:16: Epoch: 014 Test results, ap: mae: 0.583, rse: 1.018, pcc: 1.000, kcc: -0.055
+2025-07-09 14:19:16: Epoch: 014 New best TEST metrics
+2025-07-09 14:19:17: Epoch: 015 Train Loss: 2.738, mae: 1.230, rse: 1.003, pcc: -0.067, kcc: -0.052
+2025-07-09 14:19:17: Epoch: 015 Test results, ap: mae: 0.585, rse: 1.029, pcc: -1.000, kcc: -0.027
+2025-07-09 14:19:18: Epoch: 016 Train Loss: 2.729, mae: 1.235, rse: 1.001, pcc: -0.082, kcc: -0.084
+2025-07-09 14:19:18: Epoch: 016 Test results, ap: mae: 0.582, rse: 1.013, pcc: 0.315, kcc: 0.071
+2025-07-09 14:19:19: Epoch: 017 Train Loss: 2.704, mae: 1.235, rse: 1.001, pcc: -0.149, kcc: -0.197
+2025-07-09 14:19:19: Epoch: 017 Test results, ap: mae: 0.582, rse: 1.012, pcc: -0.021, kcc: 0.084
+2025-07-09 14:19:19: Epoch: 018 Train Loss: 2.769, mae: 1.229, rse: 1.004, pcc: 0.001, kcc: -0.021
+2025-07-09 14:19:20: Epoch: 018 Test results, ap: mae: 0.586, rse: 1.036, pcc: -1.000, kcc: 0.020
+2025-07-09 14:19:20: Epoch: 019 Train Loss: 2.695, mae: 1.236, rse: 1.001, pcc: -0.045, kcc: -0.060
+2025-07-09 14:19:21: Epoch: 019 Test results, ap: mae: 0.582, rse: 1.011, pcc: 0.288, kcc: -0.001
+2025-07-09 14:19:21: Epoch: 020 Train Loss: 2.731, mae: 1.231, rse: 1.003, pcc: -0.036, kcc: 0.022
+2025-07-09 14:19:21: Epoch: 020 Test results, ap: mae: 0.584, rse: 1.028, pcc: -0.210, kcc: -0.169
+2025-07-09 14:19:22: Epoch: 021 Train Loss: 2.811, mae: 1.230, rse: 1.003, pcc: -0.144, kcc: -0.070
+2025-07-09 14:19:22: Epoch: 021 Test results, ap: mae: 0.584, rse: 1.029, pcc: 0.239, kcc: 0.070
+2025-07-09 14:19:23: Epoch: 022 Train Loss: 2.644, mae: 1.231, rse: 1.003, pcc: 0.009, kcc: 0.008
+2025-07-09 14:19:23: Epoch: 022 Test results, ap: mae: 0.584, rse: 1.025, pcc: -0.431, kcc: -0.043
+2025-07-09 14:19:24: Epoch: 023 Train Loss: 2.709, mae: 1.231, rse: 1.003, pcc: 0.015, kcc: 0.019
+2025-07-09 14:19:24: Epoch: 023 Test results, ap: mae: 0.584, rse: 1.027, pcc: -0.636, kcc: -0.261
+2025-07-09 14:19:24: Epoch: 024 Train Loss: 2.734, mae: 1.232, rse: 1.002, pcc: -0.010, kcc: 0.105
+2025-07-09 14:19:24: Epoch: 024 New best VALIDATION metrics
+2025-07-09 14:19:25: Epoch: 024 Test results, ap: mae: 0.584, rse: 1.023, pcc: nan, kcc: nan
+2025-07-09 14:19:25: Epoch: 025 Train Loss: 2.656, mae: 1.233, rse: 1.002, pcc: -0.037, kcc: -0.001
+2025-07-09 14:19:26: Epoch: 025 Test results, ap: mae: 0.583, rse: 1.019, pcc: 0.082, kcc: 0.149
+2025-07-09 14:19:26: Epoch: 026 Train Loss: 2.782, mae: 1.232, rse: 1.002, pcc: -0.055, kcc: 0.046
+2025-07-09 14:19:26: Epoch: 026 Test results, ap: mae: 0.583, rse: 1.022, pcc: -0.295, kcc: -0.050
+2025-07-09 14:19:27: Epoch: 027 Train Loss: 2.757, mae: 1.233, rse: 1.002, pcc: -0.052, kcc: -0.011
+2025-07-09 14:19:27: Epoch: 027 Test results, ap: mae: 0.583, rse: 1.017, pcc: -0.182, kcc: -0.003
+2025-07-09 14:19:28: Epoch: 028 Train Loss: 2.730, mae: 1.232, rse: 1.002, pcc: -0.081, kcc: -0.133
+2025-07-09 14:19:28: Epoch: 028 Test results, ap: mae: 0.583, rse: 1.023, pcc: 0.555, kcc: -0.093
+2025-07-09 14:19:29: Epoch: 029 Train Loss: 2.711, mae: 1.231, rse: 1.003, pcc: -0.091, kcc: -0.056
+2025-07-09 14:19:29: Epoch: 029 Test results, ap: mae: 0.584, rse: 1.024, pcc: 0.817, kcc: 0.118
+2025-07-09 14:19:30: Epoch: 030 Train Loss: 2.691, mae: 1.235, rse: 1.001, pcc: -0.125, kcc: -0.042
+2025-07-09 14:19:30: Epoch: 030 Test results, ap: mae: 0.582, rse: 1.013, pcc: -0.420, kcc: 0.065
+2025-07-09 14:19:30: Epoch: 031 Train Loss: 2.682, mae: 1.233, rse: 1.002, pcc: -0.147, kcc: -0.088
+2025-07-09 14:19:31: Epoch: 031 Test results, ap: mae: 0.583, rse: 1.018, pcc: -1.000, kcc: 0.060
+2025-07-09 14:19:31: Epoch: 032 Train Loss: 2.750, mae: 1.232, rse: 1.002, pcc: -0.095, kcc: -0.039
+2025-07-09 14:19:31: Epoch: 032 Test results, ap: mae: 0.583, rse: 1.021, pcc: 1.000, kcc: -0.023
+2025-07-09 14:19:31: Epoch: 032 New best TEST metrics
+2025-07-09 14:19:32: Epoch: 033 Train Loss: 2.770, mae: 1.233, rse: 1.002, pcc: -0.107, kcc: -0.122
+2025-07-09 14:19:32: Epoch: 033 Test results, ap: mae: 0.583, rse: 1.018, pcc: -0.896, kcc: -0.147
+2025-07-09 14:19:33: Epoch: 034 Train Loss: 2.718, mae: 1.231, rse: 1.003, pcc: -0.110, kcc: -0.071
+2025-07-09 14:19:33: Epoch: 034 Test results, ap: mae: 0.584, rse: 1.025, pcc: 0.252, kcc: 0.089
+2025-07-09 14:19:34: Epoch: 035 Train Loss: 2.775, mae: 1.230, rse: 1.003, pcc: -0.106, kcc: -0.153
+2025-07-09 14:19:34: Epoch: 035 Test results, ap: mae: 0.585, rse: 1.030, pcc: 0.572, kcc: -0.026
+2025-07-09 14:19:34: used time 0.01h
+2025-07-09 14:19:34: Running Cross Validation 3
+2025-07-09 14:19:34: Fold 3  Train set:403, Valid set:101, Test set: 108
+2025-07-09 14:19:35: Epoch: 001 Train Loss: 2.751, mae: 1.337, rse: 1.006, pcc: 0.007, kcc: -0.072
+2025-07-09 14:19:35: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:19:35: Epoch: 001 Test results, ap: mae: 0.588, rse: 1.047, pcc: -0.163, kcc: -0.053
+2025-07-09 14:19:36: Epoch: 002 Train Loss: 2.718, mae: 1.346, rse: 1.000, pcc: 0.034, kcc: -0.007
+2025-07-09 14:19:36: Epoch: 002 New best VALIDATION metrics
+2025-07-09 14:19:36: Epoch: 002 Test results, ap: mae: 0.581, rse: 1.006, pcc: -0.230, kcc: -0.050
+2025-07-09 14:19:36: Epoch: 003 Train Loss: 2.731, mae: 1.335, rse: 1.021, pcc: -0.036, kcc: -0.023
+2025-07-09 14:19:37: Epoch: 003 Test results, ap: mae: 0.611, rse: 1.145, pcc: 0.013, kcc: 0.007
+2025-07-09 14:19:37: Epoch: 004 Train Loss: 2.775, mae: 1.342, rse: 1.002, pcc: -0.016, kcc: -0.057
+2025-07-09 14:19:37: Epoch: 004 Test results, ap: mae: 0.583, rse: 1.019, pcc: -0.285, kcc: -0.063
+2025-07-09 14:19:38: Epoch: 005 Train Loss: 2.829, mae: 1.336, rse: 1.011, pcc: -0.025, kcc: -0.081
+2025-07-09 14:19:38: Epoch: 005 Test results, ap: mae: 0.595, rse: 1.080, pcc: 0.148, kcc: 0.175
+2025-07-09 14:19:39: Epoch: 006 Train Loss: 2.782, mae: 1.362, rse: 1.002, pcc: -0.042, kcc: -0.112
+2025-07-09 14:19:39: Epoch: 006 Test results, ap: mae: 0.589, rse: 1.006, pcc: -0.092, kcc: -0.013
+2025-07-09 14:19:40: Epoch: 007 Train Loss: 2.598, mae: 1.335, rse: 1.020, pcc: 0.038, kcc: 0.060
+2025-07-09 14:19:40: Epoch: 007 New best VALIDATION metrics
+2025-07-09 14:19:40: Epoch: 007 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.391, kcc: -0.240
+2025-07-09 14:19:41: Epoch: 008 Train Loss: 2.703, mae: 1.338, rse: 1.005, pcc: -0.061, kcc: -0.039
+2025-07-09 14:19:41: Epoch: 008 Test results, ap: mae: 0.586, rse: 1.040, pcc: 0.181, kcc: 0.179
+2025-07-09 14:19:41: Epoch: 009 Train Loss: 2.787, mae: 1.344, rse: 1.001, pcc: -0.078, kcc: -0.055
+2025-07-09 14:19:42: Epoch: 009 Test results, ap: mae: 0.582, rse: 1.012, pcc: 0.150, kcc: 0.165
+2025-07-09 14:19:42: Epoch: 010 Train Loss: 2.711, mae: 1.340, rse: 1.003, pcc: -0.079, kcc: -0.060
+2025-07-09 14:19:43: Epoch: 010 Test results, ap: mae: 0.584, rse: 1.028, pcc: 0.153, kcc: 0.121
+2025-07-09 14:19:43: Epoch: 011 Train Loss: 2.774, mae: 1.343, rse: 1.001, pcc: -0.080, kcc: -0.058
+2025-07-09 14:19:43: Epoch: 011 Test results, ap: mae: 0.582, rse: 1.014, pcc: 0.140, kcc: 0.139
+2025-07-09 14:19:43: Epoch: 011 New best TEST metrics
+2025-07-09 14:19:44: Epoch: 012 Train Loss: 2.744, mae: 1.341, rse: 1.002, pcc: -0.076, kcc: -0.053
+2025-07-09 14:19:44: Epoch: 012 Test results, ap: mae: 0.583, rse: 1.023, pcc: 0.225, kcc: 0.149
+2025-07-09 14:19:44: Epoch: 012 New best TEST metrics
+2025-07-09 14:19:45: Epoch: 013 Train Loss: 2.733, mae: 1.341, rse: 1.002, pcc: -0.073, kcc: -0.052
+2025-07-09 14:19:45: Epoch: 013 Test results, ap: mae: 0.584, rse: 1.024, pcc: 0.199, kcc: 0.131
+2025-07-09 14:19:46: Epoch: 014 Train Loss: 2.742, mae: 1.343, rse: 1.002, pcc: -0.068, kcc: -0.045
+2025-07-09 14:19:46: Epoch: 014 Test results, ap: mae: 0.583, rse: 1.016, pcc: 0.209, kcc: 0.217
+2025-07-09 14:19:46: Epoch: 014 New best TEST metrics
+2025-07-09 14:19:47: Epoch: 015 Train Loss: 2.711, mae: 1.342, rse: 1.002, pcc: -0.065, kcc: -0.041
+2025-07-09 14:19:47: Epoch: 015 Test results, ap: mae: 0.583, rse: 1.019, pcc: -0.105, kcc: 0.086
+2025-07-09 14:19:47: Epoch: 016 Train Loss: 2.685, mae: 1.341, rse: 1.003, pcc: -0.065, kcc: -0.049
+2025-07-09 14:19:48: Epoch: 016 Test results, ap: mae: 0.584, rse: 1.024, pcc: 0.153, kcc: 0.015
+2025-07-09 14:19:48: Epoch: 017 Train Loss: 2.788, mae: 1.341, rse: 1.002, pcc: -0.057, kcc: -0.055
+2025-07-09 14:19:49: Epoch: 017 Test results, ap: mae: 0.583, rse: 1.022, pcc: -0.314, kcc: -0.035
+2025-07-09 14:19:49: Epoch: 018 Train Loss: 2.755, mae: 1.344, rse: 1.001, pcc: -0.059, kcc: -0.056
+2025-07-09 14:19:49: Epoch: 018 Test results, ap: mae: 0.582, rse: 1.011, pcc: -0.888, kcc: 0.084
+2025-07-09 14:19:50: Epoch: 019 Train Loss: 2.795, mae: 1.336, rse: 1.008, pcc: -0.035, kcc: -0.090
+2025-07-09 14:19:50: Epoch: 019 Test results, ap: mae: 0.590, rse: 1.059, pcc: -0.391, kcc: 0.037
+2025-07-09 14:19:51: Epoch: 020 Train Loss: 2.683, mae: 1.338, rse: 1.005, pcc: -0.049, kcc: -0.079
+2025-07-09 14:19:51: Epoch: 020 Test results, ap: mae: 0.586, rse: 1.039, pcc: 0.236, kcc: -0.005
+2025-07-09 14:19:52: Epoch: 021 Train Loss: 2.753, mae: 1.341, rse: 1.002, pcc: -0.055, kcc: -0.057
+2025-07-09 14:19:52: Epoch: 021 Test results, ap: mae: 0.583, rse: 1.023, pcc: -0.316, kcc: 0.148
+2025-07-09 14:19:53: Epoch: 022 Train Loss: 2.773, mae: 1.342, rse: 1.002, pcc: -0.062, kcc: -0.081
+2025-07-09 14:19:53: Epoch: 022 Test results, ap: mae: 0.583, rse: 1.019, pcc: 0.188, kcc: 0.060
+2025-07-09 14:19:53: Epoch: 023 Train Loss: 2.715, mae: 1.341, rse: 1.002, pcc: -0.029, kcc: -0.060
+2025-07-09 14:19:54: Epoch: 023 Test results, ap: mae: 0.583, rse: 1.022, pcc: 0.145, kcc: 0.013
+2025-07-09 14:19:54: Epoch: 024 Train Loss: 2.745, mae: 1.340, rse: 1.003, pcc: -0.072, kcc: -0.063
+2025-07-09 14:19:55: Epoch: 024 Test results, ap: mae: 0.585, rse: 1.029, pcc: -0.274, kcc: 0.062
+2025-07-09 14:19:55: Epoch: 025 Train Loss: 2.724, mae: 1.340, rse: 1.003, pcc: -0.058, kcc: -0.066
+2025-07-09 14:19:56: Epoch: 025 Test results, ap: mae: 0.585, rse: 1.030, pcc: nan, kcc: nan
+2025-07-09 14:19:56: Epoch: 026 Train Loss: 2.806, mae: 1.340, rse: 1.003, pcc: 0.003, kcc: -0.035
+2025-07-09 14:19:56: Epoch: 026 Test results, ap: mae: 0.585, rse: 1.029, pcc: 0.310, kcc: -0.005
+2025-07-09 14:19:57: Epoch: 027 Train Loss: 2.757, mae: 1.341, rse: 1.002, pcc: 0.001, kcc: -0.003
+2025-07-09 14:19:57: Epoch: 027 Test results, ap: mae: 0.583, rse: 1.023, pcc: -1.000, kcc: -0.092
+2025-07-09 14:19:58: Epoch: 028 Train Loss: 2.781, mae: 1.343, rse: 1.002, pcc: -0.022, kcc: -0.017
+2025-07-09 14:19:58: Epoch: 028 Test results, ap: mae: 0.583, rse: 1.017, pcc: -0.044, kcc: -0.042
+2025-07-09 14:19:59: Epoch: 029 Train Loss: 2.749, mae: 1.342, rse: 1.002, pcc: 0.138, kcc: 0.056
+2025-07-09 14:19:59: Epoch: 029 New best VALIDATION metrics
+2025-07-09 14:19:59: Epoch: 029 Test results, ap: mae: 0.583, rse: 1.022, pcc: -0.196, kcc: 0.031
+2025-07-09 14:20:00: Epoch: 030 Train Loss: 2.695, mae: 1.339, rse: 1.004, pcc: 0.083, kcc: 0.076
+2025-07-09 14:20:00: Epoch: 030 Test results, ap: mae: 0.585, rse: 1.034, pcc: nan, kcc: nan
+2025-07-09 14:20:00: Epoch: 031 Train Loss: 2.785, mae: 1.340, rse: 1.003, pcc: 0.047, kcc: 0.055
+2025-07-09 14:20:01: Epoch: 031 Test results, ap: mae: 0.585, rse: 1.029, pcc: nan, kcc: nan
+2025-07-09 14:20:01: Epoch: 032 Train Loss: 2.777, mae: 1.341, rse: 1.003, pcc: 0.175, kcc: 0.060
+2025-07-09 14:20:01: Epoch: 032 New best VALIDATION metrics
+2025-07-09 14:20:02: Epoch: 032 Test results, ap: mae: 0.584, rse: 1.026, pcc: -0.705, kcc: 0.222
+2025-07-09 14:20:02: Epoch: 033 Train Loss: 2.753, mae: 1.342, rse: 1.002, pcc: -0.016, kcc: -0.108
+2025-07-09 14:20:03: Epoch: 033 Test results, ap: mae: 0.583, rse: 1.020, pcc: 0.017, kcc: 0.085
+2025-07-09 14:20:03: Epoch: 034 Train Loss: 2.687, mae: 1.342, rse: 1.002, pcc: 0.009, kcc: 0.030
+2025-07-09 14:20:03: Epoch: 034 Test results, ap: mae: 0.583, rse: 1.022, pcc: 0.512, kcc: -0.023
+2025-07-09 14:20:03: Epoch: 034 New best TEST metrics
+2025-07-09 14:20:04: Epoch: 035 Train Loss: 2.741, mae: 1.341, rse: 1.003, pcc: 0.232, kcc: -0.000
+2025-07-09 14:20:04: Epoch: 035 Test results, ap: mae: 0.584, rse: 1.025, pcc: 1.000, kcc: 0.005
+2025-07-09 14:20:04: Epoch: 035 New best TEST metrics
+2025-07-09 14:20:04: used time 0.01h
+2025-07-09 14:20:04: Running Cross Validation 4
+2025-07-09 14:20:04: Fold 4  Train set:404, Valid set:100, Test set: 108
+2025-07-09 14:20:05: Epoch: 001 Train Loss: 3.149, mae: 1.159, rse: 1.002, pcc: -0.162, kcc: -0.142
+2025-07-09 14:20:05: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:20:05: Epoch: 001 Test results, ap: mae: 0.607, rse: 1.127, pcc: -0.007, kcc: 0.014
+2025-07-09 14:20:06: Epoch: 002 Train Loss: 2.967, mae: 1.258, rse: 1.051, pcc: -0.261, kcc: -0.168
+2025-07-09 14:20:06: Epoch: 002 Test results, ap: mae: 0.607, rse: 1.038, pcc: -0.113, kcc: -0.030
+2025-07-09 14:20:07: Epoch: 003 Train Loss: 3.053, mae: 1.153, rse: 1.005, pcc: -0.336, kcc: -0.170
+2025-07-09 14:20:07: Epoch: 003 Test results, ap: mae: 0.618, rse: 1.169, pcc: -0.179, kcc: -0.087
+2025-07-09 14:20:08: Epoch: 004 Train Loss: 2.973, mae: 1.190, rse: 1.003, pcc: -0.297, kcc: -0.179
+2025-07-09 14:20:08: Epoch: 004 Test results, ap: mae: 0.583, rse: 1.023, pcc: -0.202, kcc: -0.053
+2025-07-09 14:20:09: Epoch: 005 Train Loss: 2.959, mae: 1.213, rse: 1.014, pcc: -0.303, kcc: -0.180
+2025-07-09 14:20:09: Epoch: 005 Test results, ap: mae: 0.582, rse: 1.001, pcc: -0.112, kcc: -0.022
+2025-07-09 14:20:09: Epoch: 006 Train Loss: 2.993, mae: 1.201, rse: 1.007, pcc: -0.294, kcc: -0.170
+2025-07-09 14:20:10: Epoch: 006 Test results, ap: mae: 0.582, rse: 1.008, pcc: -0.171, kcc: -0.079
+2025-07-09 14:20:10: Epoch: 007 Train Loss: 2.988, mae: 1.184, rse: 1.001, pcc: -0.304, kcc: -0.152
+2025-07-09 14:20:11: Epoch: 007 Test results, ap: mae: 0.586, rse: 1.037, pcc: -0.090, kcc: -0.006
+2025-07-09 14:20:11: Epoch: 008 Train Loss: 2.994, mae: 1.217, rse: 1.017, pcc: 0.348, kcc: 0.169
+2025-07-09 14:20:11: Epoch: 008 New best VALIDATION metrics
+2025-07-09 14:20:11: Epoch: 008 Test results, ap: mae: 0.583, rse: 1.000, pcc: 0.675, kcc: 0.105
+2025-07-09 14:20:12: Epoch: 009 Train Loss: 3.067, mae: 1.197, rse: 1.005, pcc: 0.342, kcc: 0.162
+2025-07-09 14:20:12: Epoch: 009 New best VALIDATION metrics
+2025-07-09 14:20:12: Epoch: 009 Test results, ap: mae: 0.582, rse: 1.013, pcc: 0.279, kcc: 0.159
+2025-07-09 14:20:13: Epoch: 010 Train Loss: 2.992, mae: 1.215, rse: 1.015, pcc: -0.343, kcc: -0.163
+2025-07-09 14:20:13: Epoch: 010 Test results, ap: mae: 0.582, rse: 1.000, pcc: -0.313, kcc: -0.131
+2025-07-09 14:20:14: Epoch: 011 Train Loss: 2.867, mae: 1.204, rse: 1.009, pcc: -0.321, kcc: -0.189
+2025-07-09 14:20:14: Epoch: 011 Test results, ap: mae: 0.581, rse: 1.006, pcc: -0.208, kcc: -0.139
+2025-07-09 14:20:14: Epoch: 011 New best TEST metrics
+2025-07-09 14:20:15: Epoch: 012 Train Loss: 3.022, mae: 1.212, rse: 1.014, pcc: -0.332, kcc: -0.172
+2025-07-09 14:20:15: Epoch: 012 Test results, ap: mae: 0.582, rse: 1.001, pcc: -0.297, kcc: -0.152
+2025-07-09 14:20:15: Epoch: 013 Train Loss: 2.866, mae: 1.200, rse: 1.007, pcc: -0.334, kcc: -0.190
+2025-07-09 14:20:16: Epoch: 013 Test results, ap: mae: 0.582, rse: 1.010, pcc: -0.419, kcc: -0.174
+2025-07-09 14:20:16: Epoch: 014 Train Loss: 2.897, mae: 1.207, rse: 1.010, pcc: -0.333, kcc: -0.161
+2025-07-09 14:20:17: Epoch: 014 Test results, ap: mae: 0.581, rse: 1.003, pcc: -0.208, kcc: -0.141
+2025-07-09 14:20:17: Epoch: 014 New best TEST metrics
+2025-07-09 14:20:17: Epoch: 015 Train Loss: 2.984, mae: 1.201, rse: 1.007, pcc: -0.330, kcc: -0.177
+2025-07-09 14:20:17: Epoch: 015 Test results, ap: mae: 0.582, rse: 1.008, pcc: -0.200, kcc: -0.153
+2025-07-09 14:20:18: Epoch: 016 Train Loss: 2.976, mae: 1.202, rse: 1.007, pcc: -0.328, kcc: -0.158
+2025-07-09 14:20:18: Epoch: 016 Test results, ap: mae: 0.581, rse: 1.008, pcc: -0.064, kcc: -0.125
+2025-07-09 14:20:18: Epoch: 016 New best TEST metrics
+2025-07-09 14:20:19: Epoch: 017 Train Loss: 2.997, mae: 1.209, rse: 1.011, pcc: -0.325, kcc: -0.183
+2025-07-09 14:20:19: Epoch: 017 Test results, ap: mae: 0.581, rse: 1.002, pcc: -0.225, kcc: -0.181
+2025-07-09 14:20:20: Epoch: 018 Train Loss: 2.859, mae: 1.211, rse: 1.013, pcc: -0.320, kcc: -0.168
+2025-07-09 14:20:20: Epoch: 018 Test results, ap: mae: 0.581, rse: 1.001, pcc: 0.203, kcc: -0.141
+2025-07-09 14:20:20: Epoch: 018 New best TEST metrics
+2025-07-09 14:20:21: Epoch: 019 Train Loss: 2.967, mae: 1.212, rse: 1.013, pcc: -0.311, kcc: -0.154
+2025-07-09 14:20:21: Epoch: 019 Test results, ap: mae: 0.581, rse: 1.001, pcc: -0.071, kcc: -0.075
+2025-07-09 14:20:22: Epoch: 020 Train Loss: 2.948, mae: 1.207, rse: 1.010, pcc: 0.319, kcc: 0.126
+2025-07-09 14:20:22: Epoch: 020 Test results, ap: mae: 0.581, rse: 1.003, pcc: 1.000, kcc: nan
+2025-07-09 14:20:22: Epoch: 021 Train Loss: 2.966, mae: 1.203, rse: 1.008, pcc: -0.307, kcc: -0.143
+2025-07-09 14:20:23: Epoch: 021 Test results, ap: mae: 0.581, rse: 1.006, pcc: -0.924, kcc: -0.121
+2025-07-09 14:20:23: Epoch: 022 Train Loss: 2.956, mae: 1.203, rse: 1.008, pcc: -0.319, kcc: -0.190
+2025-07-09 14:20:24: Epoch: 022 Test results, ap: mae: 0.581, rse: 1.006, pcc: -0.453, kcc: -0.130
+2025-07-09 14:20:24: Epoch: 023 Train Loss: 3.018, mae: 1.203, rse: 1.008, pcc: -0.324, kcc: -0.188
+2025-07-09 14:20:24: Epoch: 023 Test results, ap: mae: 0.581, rse: 1.006, pcc: -1.000, kcc: nan
+2025-07-09 14:20:25: Epoch: 024 Train Loss: 2.897, mae: 1.201, rse: 1.007, pcc: -0.315, kcc: -0.167
+2025-07-09 14:20:25: Epoch: 024 Test results, ap: mae: 0.582, rse: 1.009, pcc: -0.137, kcc: -0.244
+2025-07-09 14:20:26: Epoch: 025 Train Loss: 2.947, mae: 1.192, rse: 1.004, pcc: -0.308, kcc: -0.173
+2025-07-09 14:20:26: Epoch: 025 Test results, ap: mae: 0.583, rse: 1.020, pcc: -0.807, kcc: -0.139
+2025-07-09 14:20:27: Epoch: 026 Train Loss: 2.889, mae: 1.200, rse: 1.007, pcc: -0.313, kcc: -0.190
+2025-07-09 14:20:27: Epoch: 026 Test results, ap: mae: 0.582, rse: 1.009, pcc: -0.497, kcc: -0.095
+2025-07-09 14:20:28: Epoch: 027 Train Loss: 2.935, mae: 1.206, rse: 1.010, pcc: -0.321, kcc: -0.184
+2025-07-09 14:20:28: Epoch: 027 Test results, ap: mae: 0.581, rse: 1.004, pcc: nan, kcc: nan
+2025-07-09 14:20:28: Epoch: 028 Train Loss: 2.954, mae: 1.201, rse: 1.007, pcc: -0.315, kcc: -0.135
+2025-07-09 14:20:29: Epoch: 028 Test results, ap: mae: 0.582, rse: 1.008, pcc: 1.000, kcc: -0.129
+2025-07-09 14:20:29: Epoch: 028 New best TEST metrics
+2025-07-09 14:20:29: Epoch: 029 Train Loss: 2.982, mae: 1.200, rse: 1.007, pcc: -0.313, kcc: -0.163
+2025-07-09 14:20:30: Epoch: 029 Test results, ap: mae: 0.582, rse: 1.010, pcc: -1.000, kcc: -0.011
+2025-07-09 14:20:30: Epoch: 030 Train Loss: 2.897, mae: 1.199, rse: 1.006, pcc: -0.307, kcc: -0.158
+2025-07-09 14:20:30: Epoch: 030 Test results, ap: mae: 0.582, rse: 1.010, pcc: -0.076, kcc: 0.016
+2025-07-09 14:20:31: Epoch: 031 Train Loss: 2.948, mae: 1.200, rse: 1.007, pcc: -0.322, kcc: -0.195
+2025-07-09 14:20:31: Epoch: 031 Test results, ap: mae: 0.582, rse: 1.009, pcc: nan, kcc: nan
+2025-07-09 14:20:32: Epoch: 032 Train Loss: 3.011, mae: 1.200, rse: 1.006, pcc: -0.298, kcc: -0.125
+2025-07-09 14:20:32: Epoch: 032 Test results, ap: mae: 0.582, rse: 1.010, pcc: -0.048, kcc: 0.244
+2025-07-09 14:20:33: Epoch: 033 Train Loss: 2.912, mae: 1.202, rse: 1.008, pcc: -0.307, kcc: -0.152
+2025-07-09 14:20:33: Epoch: 033 Test results, ap: mae: 0.581, rse: 1.007, pcc: nan, kcc: nan
+2025-07-09 14:20:34: Epoch: 034 Train Loss: 2.915, mae: 1.200, rse: 1.007, pcc: -0.289, kcc: -0.171
+2025-07-09 14:20:34: Epoch: 034 Test results, ap: mae: 0.582, rse: 1.009, pcc: -1.000, kcc: -0.145
+2025-07-09 14:20:34: Epoch: 035 Train Loss: 2.968, mae: 1.201, rse: 1.007, pcc: -0.267, kcc: -0.137
+2025-07-09 14:20:35: Epoch: 035 Test results, ap: mae: 0.582, rse: 1.008, pcc: -0.184, kcc: 0.017
+2025-07-09 14:20:35: used time 0.01h
+2025-07-09 14:20:35: Cross Validation Finished!
+2025-07-09 14:20:35: Best validation perform list
+[[ 1.26081169  1.00011837  0.0707258   0.15697844]
+ [ 1.17648649  1.00748193  0.03233116  0.11361226]
+ [ 1.23158348  1.00239646 -0.00950413  0.10527106]
+ [ 1.34067011  1.00275862  0.17455512  0.05968639]
+ [ 1.19666958  1.0052681   0.34246033  0.16191976]]
+2025-07-09 14:20:35: mean: [1.241 1.004 0.122 0.119]
+2025-07-09 14:20:35: std: [0.058 0.003 0.126 0.037]
+2025-07-09 14:20:35: Best test perform list
+[[ 0.58195847  1.0108434   1.          0.04060024]
+ [ 0.58503699  1.03181088  1.         -0.06882342]
+ [ 0.58317381  1.02137828  1.         -0.02330423]
+ [ 0.58385938  1.02518106  1.          0.00548551]
+ [ 0.58153749  1.00788808  1.         -0.12859112]]
+2025-07-09 14:20:35: mean: [ 0.583  1.019  1.    -0.035]
+2025-07-09 14:20:35: std: [0.001 0.009 0.    0.059]
diff --git a/run-reg/lstm-att-256-oneway-smoothl1-32-0.001-35/config.json b/run-reg/lstm-att-256-oneway-smoothl1-32-0.001-35/config.json
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 35, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "smoothl1", "dir": false, "classes": 1}
\ No newline at end of file
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diff --git a/run-reg/lstm-att-256-oneway-smoothl1-32-0.001-35/result.txt b/run-reg/lstm-att-256-oneway-smoothl1-32-0.001-35/result.txt
new file mode 100644
index 0000000000000000000000000000000000000000..22044c76ad72ab35b9e2533df11aa1e775cd0bf8
--- /dev/null
+++ b/run-reg/lstm-att-256-oneway-smoothl1-32-0.001-35/result.txt
@@ -0,0 +1,6 @@
+Valid
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+0.05647024072740785,0.006276762016564407,0.027006480901964103,0.038265940247943706
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diff --git a/run-reg/lstm-att-256-oneway-smoothl1-32-0.001-35/training.log b/run-reg/lstm-att-256-oneway-smoothl1-32-0.001-35/training.log
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--- /dev/null
+++ b/run-reg/lstm-att-256-oneway-smoothl1-32-0.001-35/training.log
@@ -0,0 +1,424 @@
+2025-07-09 15:36:41: saving_dir: ./run-reg/lstm-att-256-oneway-smoothl1-32-0.001-35
+2025-07-09 15:36:41: Loading Train Dataset
+2025-07-09 15:36:42: Loading Test Dataset
+2025-07-09 15:36:43: Running Cross Validation 0
+2025-07-09 15:36:43: Fold 0  Train set:405, Valid set:102, Test set: 108
+2025-07-09 15:36:44: Epoch: 001 Train Loss: 0.855, mae: 1.160, rse: 1.001, pcc: -0.061, kcc: 0.097
+2025-07-09 15:36:44: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:36:44: Epoch: 001 Test results, ap: mae: 0.590, rse: 1.058, pcc: -0.233, kcc: -0.152
+2025-07-09 15:36:45: Epoch: 002 Train Loss: 0.848, mae: 1.144, rse: 1.029, pcc: 0.014, kcc: -0.101
+2025-07-09 15:36:45: Epoch: 002 Test results, ap: mae: 0.654, rse: 1.293, pcc: 0.188, kcc: 0.137
+2025-07-09 15:36:46: Epoch: 003 Train Loss: 0.868, mae: 1.149, rse: 1.009, pcc: -0.085, kcc: -0.023
+2025-07-09 15:36:46: Epoch: 003 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.142, kcc: -0.108
+2025-07-09 15:36:46: Epoch: 004 Train Loss: 0.839, mae: 1.147, rse: 1.013, pcc: -0.006, kcc: 0.083
+2025-07-09 15:36:46: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:36:47: Epoch: 004 Test results, ap: mae: 0.621, rse: 1.180, pcc: -0.074, kcc: -0.077
+2025-07-09 15:36:47: Epoch: 005 Train Loss: 0.847, mae: 1.150, rse: 1.007, pcc: -0.014, kcc: 0.082
+2025-07-09 15:36:48: Epoch: 005 Test results, ap: mae: 0.608, rse: 1.131, pcc: -0.136, kcc: -0.090
+2025-07-09 15:36:48: Epoch: 006 Train Loss: 0.849, mae: 1.148, rse: 1.011, pcc: -0.022, kcc: 0.092
+2025-07-09 15:36:48: Epoch: 006 Test results, ap: mae: 0.615, rse: 1.160, pcc: -0.088, kcc: -0.091
+2025-07-09 15:36:49: Epoch: 007 Train Loss: 0.855, mae: 1.153, rse: 1.004, pcc: -0.046, kcc: -0.039
+2025-07-09 15:36:49: Epoch: 007 Test results, ap: mae: 0.600, rse: 1.099, pcc: -0.384, kcc: 0.086
+2025-07-09 15:36:50: Epoch: 008 Train Loss: 0.843, mae: 1.157, rse: 1.002, pcc: -0.030, kcc: -0.070
+2025-07-09 15:36:50: Epoch: 008 Test results, ap: mae: 0.594, rse: 1.072, pcc: -0.390, kcc: -0.017
+2025-07-09 15:36:51: Epoch: 009 Train Loss: 0.830, mae: 1.147, rse: 1.013, pcc: -0.079, kcc: 0.015
+2025-07-09 15:36:51: Epoch: 009 Test results, ap: mae: 0.620, rse: 1.176, pcc: -0.048, kcc: -0.128
+2025-07-09 15:36:52: Epoch: 010 Train Loss: 0.844, mae: 1.149, rse: 1.009, pcc: -0.021, kcc: 0.086
+2025-07-09 15:36:52: Epoch: 010 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.686, kcc: 0.061
+2025-07-09 15:36:53: Epoch: 011 Train Loss: 0.861, mae: 1.148, rse: 1.011, pcc: 0.016, kcc: 0.102
+2025-07-09 15:36:53: Epoch: 011 New best VALIDATION metrics
+2025-07-09 15:36:53: Epoch: 011 Test results, ap: mae: 0.615, rse: 1.160, pcc: -0.452, kcc: -0.014
+2025-07-09 15:36:53: Epoch: 011 New best TEST metrics
+2025-07-09 15:36:53: Epoch: 012 Train Loss: 0.861, mae: 1.151, rse: 1.006, pcc: 0.109, kcc: 0.141
+2025-07-09 15:36:53: Epoch: 012 New best VALIDATION metrics
+2025-07-09 15:36:54: Epoch: 012 Test results, ap: mae: 0.604, rse: 1.117, pcc: -0.119, kcc: -0.005
+2025-07-09 15:36:54: Epoch: 012 New best TEST metrics
+2025-07-09 15:36:54: Epoch: 013 Train Loss: 0.848, mae: 1.154, rse: 1.003, pcc: 0.119, kcc: 0.107
+2025-07-09 15:36:55: Epoch: 013 Test results, ap: mae: 0.598, rse: 1.089, pcc: -0.117, kcc: 0.003
+2025-07-09 15:36:55: Epoch: 013 New best TEST metrics
+2025-07-09 15:36:55: Epoch: 014 Train Loss: 0.853, mae: 1.155, rse: 1.003, pcc: 0.136, kcc: 0.038
+2025-07-09 15:36:56: Epoch: 014 Test results, ap: mae: 0.597, rse: 1.088, pcc: -0.087, kcc: -0.052
+2025-07-09 15:36:56: Epoch: 015 Train Loss: 0.826, mae: 1.149, rse: 1.008, pcc: 0.004, kcc: 0.082
+2025-07-09 15:36:56: Epoch: 015 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.023, kcc: 0.019
+2025-07-09 15:36:56: Epoch: 015 New best TEST metrics
+2025-07-09 15:36:57: Epoch: 016 Train Loss: 0.834, mae: 1.150, rse: 1.007, pcc: 0.112, kcc: 0.177
+2025-07-09 15:36:57: Epoch: 016 New best VALIDATION metrics
+2025-07-09 15:36:57: Epoch: 016 Test results, ap: mae: 0.607, rse: 1.127, pcc: -0.256, kcc: -0.023
+2025-07-09 15:36:58: Epoch: 017 Train Loss: 0.827, mae: 1.149, rse: 1.009, pcc: 0.052, kcc: 0.116
+2025-07-09 15:36:58: Epoch: 017 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.010, kcc: -0.004
+2025-07-09 15:36:59: Epoch: 018 Train Loss: 0.864, mae: 1.154, rse: 1.003, pcc: -0.012, kcc: -0.015
+2025-07-09 15:36:59: Epoch: 018 Test results, ap: mae: 0.599, rse: 1.096, pcc: -0.095, kcc: -0.045
+2025-07-09 15:37:00: Epoch: 019 Train Loss: 0.848, mae: 1.151, rse: 1.005, pcc: -0.022, kcc: 0.014
+2025-07-09 15:37:00: Epoch: 019 Test results, ap: mae: 0.604, rse: 1.116, pcc: -0.225, kcc: 0.007
+2025-07-09 15:37:01: Epoch: 020 Train Loss: 0.840, mae: 1.150, rse: 1.007, pcc: -0.023, kcc: 0.015
+2025-07-09 15:37:01: Epoch: 020 Test results, ap: mae: 0.607, rse: 1.127, pcc: -0.053, kcc: 0.004
+2025-07-09 15:37:01: Epoch: 021 Train Loss: 0.825, mae: 1.150, rse: 1.007, pcc: -0.023, kcc: -0.008
+2025-07-09 15:37:02: Epoch: 021 Test results, ap: mae: 0.608, rse: 1.130, pcc: 0.145, kcc: -0.027
+2025-07-09 15:37:02: Epoch: 021 New best TEST metrics
+2025-07-09 15:37:02: Epoch: 022 Train Loss: 0.861, mae: 1.152, rse: 1.005, pcc: -0.018, kcc: -0.021
+2025-07-09 15:37:03: Epoch: 022 Test results, ap: mae: 0.604, rse: 1.114, pcc: -0.033, kcc: -0.018
+2025-07-09 15:37:03: Epoch: 023 Train Loss: 0.856, mae: 1.152, rse: 1.005, pcc: -0.018, kcc: -0.023
+2025-07-09 15:37:03: Epoch: 023 Test results, ap: mae: 0.604, rse: 1.115, pcc: -0.046, kcc: -0.010
+2025-07-09 15:37:04: Epoch: 024 Train Loss: 0.839, mae: 1.151, rse: 1.005, pcc: -0.013, kcc: -0.001
+2025-07-09 15:37:04: Epoch: 024 Test results, ap: mae: 0.604, rse: 1.115, pcc: 0.195, kcc: -0.012
+2025-07-09 15:37:04: Epoch: 024 New best TEST metrics
+2025-07-09 15:37:05: Epoch: 025 Train Loss: 0.818, mae: 1.152, rse: 1.005, pcc: -0.012, kcc: -0.008
+2025-07-09 15:37:05: Epoch: 025 Test results, ap: mae: 0.603, rse: 1.110, pcc: -0.046, kcc: -0.017
+2025-07-09 15:37:06: Epoch: 026 Train Loss: 0.823, mae: 1.152, rse: 1.005, pcc: -0.016, kcc: 0.002
+2025-07-09 15:37:06: Epoch: 026 Test results, ap: mae: 0.603, rse: 1.114, pcc: -0.128, kcc: -0.043
+2025-07-09 15:37:07: Epoch: 027 Train Loss: 0.848, mae: 1.152, rse: 1.005, pcc: -0.019, kcc: 0.005
+2025-07-09 15:37:07: Epoch: 027 Test results, ap: mae: 0.602, rse: 1.108, pcc: -0.174, kcc: -0.020
+2025-07-09 15:37:08: Epoch: 028 Train Loss: 0.848, mae: 1.152, rse: 1.005, pcc: -0.022, kcc: -0.006
+2025-07-09 15:37:08: Epoch: 028 Test results, ap: mae: 0.603, rse: 1.111, pcc: -0.238, kcc: -0.036
+2025-07-09 15:37:08: Epoch: 029 Train Loss: 0.833, mae: 1.152, rse: 1.005, pcc: -0.019, kcc: 0.014
+2025-07-09 15:37:09: Epoch: 029 Test results, ap: mae: 0.603, rse: 1.113, pcc: -0.145, kcc: -0.011
+2025-07-09 15:37:09: Epoch: 030 Train Loss: 0.850, mae: 1.152, rse: 1.005, pcc: -0.016, kcc: -0.007
+2025-07-09 15:37:10: Epoch: 030 Test results, ap: mae: 0.603, rse: 1.111, pcc: -0.018, kcc: -0.039
+2025-07-09 15:37:10: Epoch: 031 Train Loss: 0.831, mae: 1.153, rse: 1.004, pcc: 0.009, kcc: 0.000
+2025-07-09 15:37:10: Epoch: 031 Test results, ap: mae: 0.601, rse: 1.103, pcc: 0.034, kcc: -0.036
+2025-07-09 15:37:11: Epoch: 032 Train Loss: 0.862, mae: 1.153, rse: 1.004, pcc: 0.014, kcc: 0.014
+2025-07-09 15:37:11: Epoch: 032 Test results, ap: mae: 0.601, rse: 1.101, pcc: -0.067, kcc: 0.005
+2025-07-09 15:37:12: Epoch: 033 Train Loss: 0.846, mae: 1.153, rse: 1.004, pcc: 0.015, kcc: -0.008
+2025-07-09 15:37:12: Epoch: 033 Test results, ap: mae: 0.601, rse: 1.102, pcc: 0.004, kcc: -0.006
+2025-07-09 15:37:13: Epoch: 034 Train Loss: 0.850, mae: 1.153, rse: 1.004, pcc: 0.006, kcc: -0.016
+2025-07-09 15:37:13: Epoch: 034 Test results, ap: mae: 0.601, rse: 1.102, pcc: 0.089, kcc: -0.052
+2025-07-09 15:37:14: Epoch: 035 Train Loss: 0.852, mae: 1.153, rse: 1.004, pcc: 0.002, kcc: -0.004
+2025-07-09 15:37:14: Epoch: 035 Test results, ap: mae: 0.601, rse: 1.102, pcc: -0.076, kcc: -0.026
+2025-07-09 15:37:14: used time 0.01h
+2025-07-09 15:37:14: Running Cross Validation 1
+2025-07-09 15:37:14: Fold 1  Train set:405, Valid set:102, Test set: 108
+2025-07-09 15:37:15: Epoch: 001 Train Loss: 0.818, mae: 1.198, rse: 1.014, pcc: 0.103, kcc: 0.088
+2025-07-09 15:37:15: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:37:15: Epoch: 001 Test results, ap: mae: 0.614, rse: 1.157, pcc: 0.052, kcc: 0.088
+2025-07-09 15:37:15: Epoch: 002 Train Loss: 0.858, mae: 1.232, rse: 1.002, pcc: 0.065, kcc: -0.014
+2025-07-09 15:37:16: Epoch: 002 Test results, ap: mae: 0.589, rse: 1.054, pcc: 0.101, kcc: 0.149
+2025-07-09 15:37:16: Epoch: 003 Train Loss: 0.836, mae: 1.222, rse: 1.005, pcc: 0.084, kcc: 0.037
+2025-07-09 15:37:17: Epoch: 003 Test results, ap: mae: 0.595, rse: 1.077, pcc: 0.140, kcc: 0.159
+2025-07-09 15:37:17: Epoch: 004 Train Loss: 0.843, mae: 1.203, rse: 1.012, pcc: 0.072, kcc: 0.077
+2025-07-09 15:37:17: Epoch: 004 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.173, kcc: 0.164
+2025-07-09 15:37:18: Epoch: 005 Train Loss: 0.817, mae: 1.227, rse: 1.003, pcc: 0.068, kcc: 0.071
+2025-07-09 15:37:18: Epoch: 005 Test results, ap: mae: 0.592, rse: 1.064, pcc: 0.192, kcc: 0.163
+2025-07-09 15:37:19: Epoch: 006 Train Loss: 0.845, mae: 1.216, rse: 1.006, pcc: 0.072, kcc: 0.073
+2025-07-09 15:37:19: Epoch: 006 Test results, ap: mae: 0.598, rse: 1.092, pcc: 0.148, kcc: 0.146
+2025-07-09 15:37:20: Epoch: 007 Train Loss: 0.849, mae: 1.212, rse: 1.008, pcc: 0.053, kcc: 0.072
+2025-07-09 15:37:20: Epoch: 007 Test results, ap: mae: 0.602, rse: 1.106, pcc: 0.164, kcc: 0.145
+2025-07-09 15:37:21: Epoch: 008 Train Loss: 0.827, mae: 1.207, rse: 1.010, pcc: 0.056, kcc: 0.062
+2025-07-09 15:37:21: Epoch: 008 Test results, ap: mae: 0.606, rse: 1.123, pcc: 0.161, kcc: 0.143
+2025-07-09 15:37:22: Epoch: 009 Train Loss: 0.818, mae: 1.214, rse: 1.007, pcc: -0.025, kcc: -0.171
+2025-07-09 15:37:22: Epoch: 009 Test results, ap: mae: 0.600, rse: 1.100, pcc: 0.140, kcc: -0.174
+2025-07-09 15:37:22: Epoch: 010 Train Loss: 0.834, mae: 1.244, rse: 1.001, pcc: 0.063, kcc: 0.023
+2025-07-09 15:37:23: Epoch: 010 Test results, ap: mae: 0.585, rse: 1.032, pcc: 0.141, kcc: 0.080
+2025-07-09 15:37:23: Epoch: 011 Train Loss: 0.816, mae: 1.206, rse: 1.010, pcc: 0.062, kcc: 0.035
+2025-07-09 15:37:24: Epoch: 011 Test results, ap: mae: 0.607, rse: 1.127, pcc: 0.109, kcc: 0.102
+2025-07-09 15:37:24: Epoch: 011 New best TEST metrics
+2025-07-09 15:37:24: Epoch: 012 Train Loss: 0.854, mae: 1.216, rse: 1.007, pcc: 0.074, kcc: 0.021
+2025-07-09 15:37:25: Epoch: 012 Test results, ap: mae: 0.599, rse: 1.094, pcc: 0.154, kcc: 0.100
+2025-07-09 15:37:25: Epoch: 012 New best TEST metrics
+2025-07-09 15:37:25: Epoch: 013 Train Loss: 0.807, mae: 1.228, rse: 1.003, pcc: 0.072, kcc: 0.033
+2025-07-09 15:37:25: Epoch: 013 Test results, ap: mae: 0.592, rse: 1.063, pcc: 0.181, kcc: 0.145
+2025-07-09 15:37:25: Epoch: 013 New best TEST metrics
+2025-07-09 15:37:26: Epoch: 014 Train Loss: 0.840, mae: 1.234, rse: 1.002, pcc: 0.061, kcc: 0.023
+2025-07-09 15:37:26: Epoch: 014 Test results, ap: mae: 0.588, rse: 1.049, pcc: 0.162, kcc: 0.176
+2025-07-09 15:37:26: Epoch: 014 New best TEST metrics
+2025-07-09 15:37:27: Epoch: 015 Train Loss: 0.832, mae: 1.212, rse: 1.008, pcc: 0.063, kcc: 0.014
+2025-07-09 15:37:27: Epoch: 015 Test results, ap: mae: 0.602, rse: 1.106, pcc: -0.102, kcc: -0.023
+2025-07-09 15:37:28: Epoch: 016 Train Loss: 0.854, mae: 1.213, rse: 1.008, pcc: 0.043, kcc: 0.010
+2025-07-09 15:37:28: Epoch: 016 Test results, ap: mae: 0.601, rse: 1.103, pcc: 0.170, kcc: 0.169
+2025-07-09 15:37:29: Epoch: 017 Train Loss: 0.838, mae: 1.218, rse: 1.006, pcc: 0.038, kcc: -0.013
+2025-07-09 15:37:29: Epoch: 017 Test results, ap: mae: 0.597, rse: 1.087, pcc: 0.424, kcc: 0.253
+2025-07-09 15:37:29: Epoch: 017 New best TEST metrics
+2025-07-09 15:37:30: Epoch: 018 Train Loss: 0.831, mae: 1.215, rse: 1.007, pcc: 0.045, kcc: -0.006
+2025-07-09 15:37:30: Epoch: 018 Test results, ap: mae: 0.599, rse: 1.096, pcc: -0.030, kcc: 0.174
+2025-07-09 15:37:31: Epoch: 019 Train Loss: 0.811, mae: 1.238, rse: 1.001, pcc: 0.046, kcc: 0.076
+2025-07-09 15:37:31: Epoch: 019 Test results, ap: mae: 0.587, rse: 1.042, pcc: 0.116, kcc: 0.133
+2025-07-09 15:37:31: Epoch: 020 Train Loss: 0.853, mae: 1.235, rse: 1.002, pcc: 0.053, kcc: 0.070
+2025-07-09 15:37:32: Epoch: 020 Test results, ap: mae: 0.588, rse: 1.048, pcc: 0.119, kcc: 0.143
+2025-07-09 15:37:32: Epoch: 021 Train Loss: 0.835, mae: 1.221, rse: 1.005, pcc: 0.056, kcc: 0.069
+2025-07-09 15:37:33: Epoch: 021 Test results, ap: mae: 0.595, rse: 1.079, pcc: -0.040, kcc: 0.139
+2025-07-09 15:37:33: Epoch: 022 Train Loss: 0.806, mae: 1.215, rse: 1.007, pcc: 0.059, kcc: 0.066
+2025-07-09 15:37:34: Epoch: 022 Test results, ap: mae: 0.599, rse: 1.095, pcc: -0.040, kcc: 0.136
+2025-07-09 15:37:34: Epoch: 023 Train Loss: 0.845, mae: 1.217, rse: 1.006, pcc: 0.061, kcc: 0.065
+2025-07-09 15:37:35: Epoch: 023 Test results, ap: mae: 0.598, rse: 1.090, pcc: 0.071, kcc: 0.095
+2025-07-09 15:37:35: Epoch: 024 Train Loss: 0.850, mae: 1.224, rse: 1.004, pcc: 0.058, kcc: 0.067
+2025-07-09 15:37:35: Epoch: 024 Test results, ap: mae: 0.594, rse: 1.072, pcc: 0.205, kcc: 0.139
+2025-07-09 15:37:36: Epoch: 025 Train Loss: 0.830, mae: 1.225, rse: 1.004, pcc: 0.058, kcc: 0.082
+2025-07-09 15:37:36: Epoch: 025 Test results, ap: mae: 0.593, rse: 1.068, pcc: 0.021, kcc: 0.130
+2025-07-09 15:37:37: Epoch: 026 Train Loss: 0.835, mae: 1.227, rse: 1.003, pcc: 0.057, kcc: 0.078
+2025-07-09 15:37:37: Epoch: 026 Test results, ap: mae: 0.592, rse: 1.065, pcc: 0.069, kcc: 0.153
+2025-07-09 15:37:38: Epoch: 027 Train Loss: 0.840, mae: 1.226, rse: 1.004, pcc: 0.059, kcc: 0.088
+2025-07-09 15:37:38: Epoch: 027 Test results, ap: mae: 0.593, rse: 1.068, pcc: 0.113, kcc: 0.142
+2025-07-09 15:37:39: Epoch: 028 Train Loss: 0.837, mae: 1.222, rse: 1.005, pcc: 0.061, kcc: 0.034
+2025-07-09 15:37:39: Epoch: 028 Test results, ap: mae: 0.595, rse: 1.077, pcc: 0.056, kcc: 0.130
+2025-07-09 15:37:40: Epoch: 029 Train Loss: 0.832, mae: 1.212, rse: 1.008, pcc: 0.070, kcc: -0.041
+2025-07-09 15:37:40: Epoch: 029 Test results, ap: mae: 0.602, rse: 1.106, pcc: -0.251, kcc: 0.130
+2025-07-09 15:37:41: Epoch: 030 Train Loss: 0.824, mae: 1.218, rse: 1.006, pcc: 0.060, kcc: 0.034
+2025-07-09 15:37:41: Epoch: 030 Test results, ap: mae: 0.597, rse: 1.087, pcc: 0.252, kcc: 0.160
+2025-07-09 15:37:42: Epoch: 031 Train Loss: 0.834, mae: 1.218, rse: 1.006, pcc: 0.060, kcc: 0.035
+2025-07-09 15:37:42: Epoch: 031 Test results, ap: mae: 0.597, rse: 1.088, pcc: 0.238, kcc: 0.136
+2025-07-09 15:37:43: Epoch: 032 Train Loss: 0.857, mae: 1.221, rse: 1.005, pcc: 0.058, kcc: 0.063
+2025-07-09 15:37:43: Epoch: 032 Test results, ap: mae: 0.595, rse: 1.078, pcc: 0.117, kcc: 0.137
+2025-07-09 15:37:44: Epoch: 033 Train Loss: 0.840, mae: 1.220, rse: 1.005, pcc: 0.057, kcc: 0.052
+2025-07-09 15:37:44: Epoch: 033 Test results, ap: mae: 0.596, rse: 1.081, pcc: 0.093, kcc: 0.131
+2025-07-09 15:37:45: Epoch: 034 Train Loss: 0.843, mae: 1.219, rse: 1.006, pcc: 0.057, kcc: 0.037
+2025-07-09 15:37:45: Epoch: 034 Test results, ap: mae: 0.597, rse: 1.086, pcc: 0.176, kcc: 0.157
+2025-07-09 15:37:45: Epoch: 035 Train Loss: 0.815, mae: 1.219, rse: 1.006, pcc: 0.056, kcc: 0.040
+2025-07-09 15:37:46: Epoch: 035 Test results, ap: mae: 0.597, rse: 1.085, pcc: 0.205, kcc: 0.145
+2025-07-09 15:37:46: used time 0.01h
+2025-07-09 15:37:46: Running Cross Validation 2
+2025-07-09 15:37:46: Fold 2  Train set:406, Valid set:101, Test set: 108
+2025-07-09 15:37:46: Epoch: 001 Train Loss: 0.867, mae: 1.230, rse: 1.070, pcc: -0.013, kcc: 0.090
+2025-07-09 15:37:46: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:37:47: Epoch: 001 Test results, ap: mae: 0.686, rse: 1.409, pcc: 0.260, kcc: 0.097
+2025-07-09 15:37:47: Epoch: 002 Train Loss: 0.833, mae: 1.218, rse: 1.011, pcc: -0.106, kcc: -0.055
+2025-07-09 15:37:48: Epoch: 002 Test results, ap: mae: 0.592, rse: 1.066, pcc: -0.101, kcc: -0.076
+2025-07-09 15:37:48: Epoch: 003 Train Loss: 0.837, mae: 1.224, rse: 1.051, pcc: 0.066, kcc: 0.046
+2025-07-09 15:37:48: Epoch: 003 New best VALIDATION metrics
+2025-07-09 15:37:48: Epoch: 003 Test results, ap: mae: 0.656, rse: 1.302, pcc: 0.091, kcc: 0.084
+2025-07-09 15:37:49: Epoch: 004 Train Loss: 0.843, mae: 1.217, rse: 1.021, pcc: -0.013, kcc: 0.042
+2025-07-09 15:37:49: Epoch: 004 Test results, ap: mae: 0.608, rse: 1.130, pcc: 0.074, kcc: 0.071
+2025-07-09 15:37:50: Epoch: 005 Train Loss: 0.822, mae: 1.216, rse: 1.019, pcc: 0.094, kcc: 0.073
+2025-07-09 15:37:50: Epoch: 005 New best VALIDATION metrics
+2025-07-09 15:37:50: Epoch: 005 Test results, ap: mae: 0.604, rse: 1.116, pcc: 0.056, kcc: 0.067
+2025-07-09 15:37:51: Epoch: 006 Train Loss: 0.818, mae: 1.220, rse: 1.041, pcc: -0.016, kcc: -0.044
+2025-07-09 15:37:51: Epoch: 006 Test results, ap: mae: 0.640, rse: 1.242, pcc: -0.295, kcc: -0.130
+2025-07-09 15:37:52: Epoch: 007 Train Loss: 0.822, mae: 1.216, rse: 1.017, pcc: -0.012, kcc: 0.035
+2025-07-09 15:37:52: Epoch: 007 Test results, ap: mae: 0.601, rse: 1.102, pcc: 0.107, kcc: 0.099
+2025-07-09 15:37:53: Epoch: 008 Train Loss: 0.847, mae: 1.216, rse: 1.019, pcc: -0.041, kcc: 0.032
+2025-07-09 15:37:53: Epoch: 008 Test results, ap: mae: 0.604, rse: 1.115, pcc: 0.072, kcc: 0.132
+2025-07-09 15:37:54: Epoch: 009 Train Loss: 0.842, mae: 1.217, rse: 1.024, pcc: -0.068, kcc: -0.066
+2025-07-09 15:37:54: Epoch: 009 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.218, kcc: 0.148
+2025-07-09 15:37:55: Epoch: 010 Train Loss: 0.840, mae: 1.217, rse: 1.021, pcc: -0.053, kcc: -0.002
+2025-07-09 15:37:55: Epoch: 010 Test results, ap: mae: 0.607, rse: 1.126, pcc: -0.041, kcc: 0.079
+2025-07-09 15:37:56: Epoch: 011 Train Loss: 0.844, mae: 1.217, rse: 1.025, pcc: -0.058, kcc: 0.006
+2025-07-09 15:37:56: Epoch: 011 Test results, ap: mae: 0.612, rse: 1.148, pcc: 0.207, kcc: 0.085
+2025-07-09 15:37:56: Epoch: 011 New best TEST metrics
+2025-07-09 15:37:56: Epoch: 012 Train Loss: 0.844, mae: 1.217, rse: 1.016, pcc: -0.045, kcc: -0.024
+2025-07-09 15:37:57: Epoch: 012 Test results, ap: mae: 0.600, rse: 1.100, pcc: 0.063, kcc: 0.135
+2025-07-09 15:37:57: Epoch: 013 Train Loss: 0.849, mae: 1.217, rse: 1.023, pcc: -0.055, kcc: 0.035
+2025-07-09 15:37:58: Epoch: 013 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.114, kcc: 0.136
+2025-07-09 15:37:58: Epoch: 014 Train Loss: 0.836, mae: 1.217, rse: 1.025, pcc: -0.052, kcc: -0.000
+2025-07-09 15:37:59: Epoch: 014 Test results, ap: mae: 0.613, rse: 1.153, pcc: -0.201, kcc: 0.058
+2025-07-09 15:37:59: Epoch: 015 Train Loss: 0.832, mae: 1.216, rse: 1.018, pcc: -0.038, kcc: 0.014
+2025-07-09 15:37:59: Epoch: 015 Test results, ap: mae: 0.603, rse: 1.112, pcc: -0.047, kcc: 0.078
+2025-07-09 15:38:00: Epoch: 016 Train Loss: 0.843, mae: 1.216, rse: 1.020, pcc: -0.047, kcc: -0.014
+2025-07-09 15:38:00: Epoch: 016 Test results, ap: mae: 0.605, rse: 1.121, pcc: 0.016, kcc: 0.031
+2025-07-09 15:38:01: Epoch: 017 Train Loss: 0.831, mae: 1.216, rse: 1.018, pcc: -0.048, kcc: -0.047
+2025-07-09 15:38:01: Epoch: 017 Test results, ap: mae: 0.603, rse: 1.111, pcc: 0.057, kcc: 0.080
+2025-07-09 15:38:02: Epoch: 018 Train Loss: 0.827, mae: 1.216, rse: 1.021, pcc: -0.049, kcc: -0.002
+2025-07-09 15:38:02: Epoch: 018 Test results, ap: mae: 0.606, rse: 1.125, pcc: 0.028, kcc: 0.170
+2025-07-09 15:38:03: Epoch: 019 Train Loss: 0.837, mae: 1.217, rse: 1.016, pcc: -0.039, kcc: 0.021
+2025-07-09 15:38:03: Epoch: 019 Test results, ap: mae: 0.600, rse: 1.100, pcc: 0.136, kcc: 0.097
+2025-07-09 15:38:04: Epoch: 020 Train Loss: 0.856, mae: 1.217, rse: 1.027, pcc: -0.051, kcc: -0.012
+2025-07-09 15:38:04: Epoch: 020 Test results, ap: mae: 0.616, rse: 1.163, pcc: 0.308, kcc: 0.145
+2025-07-09 15:38:04: Epoch: 020 New best TEST metrics
+2025-07-09 15:38:05: Epoch: 021 Train Loss: 0.842, mae: 1.217, rse: 1.023, pcc: -0.048, kcc: 0.006
+2025-07-09 15:38:05: Epoch: 021 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.035, kcc: 0.129
+2025-07-09 15:38:05: Epoch: 022 Train Loss: 0.819, mae: 1.217, rse: 1.024, pcc: -0.048, kcc: 0.004
+2025-07-09 15:38:06: Epoch: 022 Test results, ap: mae: 0.611, rse: 1.143, pcc: 0.074, kcc: 0.097
+2025-07-09 15:38:06: Epoch: 023 Train Loss: 0.829, mae: 1.216, rse: 1.017, pcc: -0.046, kcc: 0.019
+2025-07-09 15:38:07: Epoch: 023 Test results, ap: mae: 0.601, rse: 1.105, pcc: 0.138, kcc: 0.068
+2025-07-09 15:38:07: Epoch: 024 Train Loss: 0.832, mae: 1.216, rse: 1.017, pcc: -0.039, kcc: 0.010
+2025-07-09 15:38:08: Epoch: 024 Test results, ap: mae: 0.601, rse: 1.102, pcc: 0.106, kcc: 0.120
+2025-07-09 15:38:08: Epoch: 025 Train Loss: 0.842, mae: 1.217, rse: 1.017, pcc: -0.039, kcc: 0.023
+2025-07-09 15:38:08: Epoch: 025 Test results, ap: mae: 0.601, rse: 1.101, pcc: -0.111, kcc: 0.124
+2025-07-09 15:38:09: Epoch: 026 Train Loss: 0.816, mae: 1.217, rse: 1.025, pcc: -0.050, kcc: 0.039
+2025-07-09 15:38:09: Epoch: 026 Test results, ap: mae: 0.613, rse: 1.153, pcc: 0.389, kcc: 0.126
+2025-07-09 15:38:09: Epoch: 026 New best TEST metrics
+2025-07-09 15:38:10: Epoch: 027 Train Loss: 0.846, mae: 1.217, rse: 1.027, pcc: -0.051, kcc: -0.003
+2025-07-09 15:38:10: Epoch: 027 Test results, ap: mae: 0.615, rse: 1.161, pcc: 0.064, kcc: 0.161
+2025-07-09 15:38:11: Epoch: 028 Train Loss: 0.822, mae: 1.217, rse: 1.026, pcc: -0.052, kcc: -0.040
+2025-07-09 15:38:11: Epoch: 028 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.427, kcc: 0.158
+2025-07-09 15:38:11: Epoch: 028 New best TEST metrics
+2025-07-09 15:38:12: Epoch: 029 Train Loss: 0.819, mae: 1.217, rse: 1.022, pcc: -0.045, kcc: 0.027
+2025-07-09 15:38:12: Epoch: 029 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.363, kcc: 0.187
+2025-07-09 15:38:13: Epoch: 030 Train Loss: 0.828, mae: 1.216, rse: 1.020, pcc: -0.039, kcc: 0.019
+2025-07-09 15:38:13: Epoch: 030 Test results, ap: mae: 0.605, rse: 1.119, pcc: 0.032, kcc: 0.143
+2025-07-09 15:38:14: Epoch: 031 Train Loss: 0.821, mae: 1.216, rse: 1.019, pcc: -0.038, kcc: 0.006
+2025-07-09 15:38:14: Epoch: 031 Test results, ap: mae: 0.604, rse: 1.115, pcc: 0.056, kcc: 0.134
+2025-07-09 15:38:15: Epoch: 032 Train Loss: 0.846, mae: 1.216, rse: 1.020, pcc: -0.039, kcc: 0.031
+2025-07-09 15:38:15: Epoch: 032 Test results, ap: mae: 0.606, rse: 1.124, pcc: 0.090, kcc: 0.118
+2025-07-09 15:38:15: Epoch: 033 Train Loss: 0.836, mae: 1.216, rse: 1.020, pcc: -0.043, kcc: 0.034
+2025-07-09 15:38:16: Epoch: 033 Test results, ap: mae: 0.605, rse: 1.121, pcc: 0.079, kcc: 0.130
+2025-07-09 15:38:16: Epoch: 034 Train Loss: 0.836, mae: 1.217, rse: 1.021, pcc: -0.049, kcc: -0.005
+2025-07-09 15:38:17: Epoch: 034 Test results, ap: mae: 0.607, rse: 1.128, pcc: 0.146, kcc: 0.163
+2025-07-09 15:38:17: Epoch: 035 Train Loss: 0.822, mae: 1.217, rse: 1.022, pcc: -0.052, kcc: -0.082
+2025-07-09 15:38:17: Epoch: 035 Test results, ap: mae: 0.609, rse: 1.135, pcc: 0.259, kcc: 0.192
+2025-07-09 15:38:18: used time 0.01h
+2025-07-09 15:38:18: Running Cross Validation 3
+2025-07-09 15:38:18: Fold 3  Train set:406, Valid set:101, Test set: 108
+2025-07-09 15:38:18: Epoch: 001 Train Loss: 0.796, mae: 1.338, rse: 1.059, pcc: 0.109, kcc: 0.026
+2025-07-09 15:38:18: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:38:18: Epoch: 001 Test results, ap: mae: 0.666, rse: 1.341, pcc: -0.050, kcc: 0.001
+2025-07-09 15:38:19: Epoch: 002 Train Loss: 0.804, mae: 1.320, rse: 1.011, pcc: 0.101, kcc: 0.073
+2025-07-09 15:38:19: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:38:19: Epoch: 002 Test results, ap: mae: 0.592, rse: 1.065, pcc: 0.047, kcc: 0.067
+2025-07-09 15:38:20: Epoch: 003 Train Loss: 0.831, mae: 1.327, rse: 1.038, pcc: 0.067, kcc: 0.036
+2025-07-09 15:38:20: Epoch: 003 Test results, ap: mae: 0.633, rse: 1.218, pcc: -0.119, kcc: -0.084
+2025-07-09 15:38:21: Epoch: 004 Train Loss: 0.806, mae: 1.320, rse: 1.013, pcc: 0.085, kcc: 0.091
+2025-07-09 15:38:21: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:38:21: Epoch: 004 Test results, ap: mae: 0.595, rse: 1.076, pcc: 0.074, kcc: 0.020
+2025-07-09 15:38:22: Epoch: 005 Train Loss: 0.811, mae: 1.327, rse: 1.037, pcc: 0.075, kcc: 0.077
+2025-07-09 15:38:22: Epoch: 005 Test results, ap: mae: 0.631, rse: 1.212, pcc: 0.105, kcc: 0.012
+2025-07-09 15:38:23: Epoch: 006 Train Loss: 0.818, mae: 1.324, rse: 1.027, pcc: 0.076, kcc: 0.095
+2025-07-09 15:38:23: Epoch: 006 Test results, ap: mae: 0.615, rse: 1.158, pcc: 0.082, kcc: 0.020
+2025-07-09 15:38:24: Epoch: 007 Train Loss: 0.815, mae: 1.323, rse: 1.024, pcc: 0.074, kcc: 0.090
+2025-07-09 15:38:24: Epoch: 007 Test results, ap: mae: 0.610, rse: 1.138, pcc: 0.123, kcc: 0.022
+2025-07-09 15:38:24: Epoch: 008 Train Loss: 0.801, mae: 1.322, rse: 1.022, pcc: 0.076, kcc: 0.096
+2025-07-09 15:38:25: Epoch: 008 Test results, ap: mae: 0.607, rse: 1.127, pcc: 0.110, kcc: 0.036
+2025-07-09 15:38:25: Epoch: 009 Train Loss: 0.805, mae: 1.322, rse: 1.023, pcc: 0.074, kcc: 0.101
+2025-07-09 15:38:26: Epoch: 009 Test results, ap: mae: 0.608, rse: 1.131, pcc: 0.127, kcc: 0.030
+2025-07-09 15:38:26: Epoch: 010 Train Loss: 0.794, mae: 1.326, rse: 1.034, pcc: 0.067, kcc: 0.083
+2025-07-09 15:38:27: Epoch: 010 Test results, ap: mae: 0.626, rse: 1.196, pcc: -0.054, kcc: 0.010
+2025-07-09 15:38:27: Epoch: 011 Train Loss: 0.807, mae: 1.324, rse: 1.030, pcc: 0.069, kcc: 0.101
+2025-07-09 15:38:27: Epoch: 011 Test results, ap: mae: 0.619, rse: 1.172, pcc: 0.009, kcc: 0.023
+2025-07-09 15:38:27: Epoch: 011 New best TEST metrics
+2025-07-09 15:38:28: Epoch: 012 Train Loss: 0.808, mae: 1.323, rse: 1.025, pcc: 0.071, kcc: 0.114
+2025-07-09 15:38:28: Epoch: 012 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.096, kcc: 0.010
+2025-07-09 15:38:28: Epoch: 012 New best TEST metrics
+2025-07-09 15:38:29: Epoch: 013 Train Loss: 0.807, mae: 1.324, rse: 1.029, pcc: 0.066, kcc: 0.111
+2025-07-09 15:38:29: Epoch: 013 Test results, ap: mae: 0.617, rse: 1.165, pcc: 0.049, kcc: 0.016
+2025-07-09 15:38:30: Epoch: 014 Train Loss: 0.791, mae: 1.323, rse: 1.024, pcc: 0.066, kcc: 0.110
+2025-07-09 15:38:30: Epoch: 014 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.144, kcc: 0.031
+2025-07-09 15:38:30: Epoch: 014 New best TEST metrics
+2025-07-09 15:38:31: Epoch: 015 Train Loss: 0.820, mae: 1.321, rse: 1.018, pcc: 0.068, kcc: 0.097
+2025-07-09 15:38:31: Epoch: 015 Test results, ap: mae: 0.601, rse: 1.102, pcc: 0.116, kcc: 0.008
+2025-07-09 15:38:32: Epoch: 016 Train Loss: 0.814, mae: 1.323, rse: 1.027, pcc: 0.067, kcc: 0.104
+2025-07-09 15:38:32: Epoch: 016 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.108, kcc: 0.018
+2025-07-09 15:38:33: Epoch: 017 Train Loss: 0.806, mae: 1.323, rse: 1.024, pcc: 0.069, kcc: 0.086
+2025-07-09 15:38:33: Epoch: 017 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.126, kcc: 0.010
+2025-07-09 15:38:34: Epoch: 018 Train Loss: 0.819, mae: 1.322, rse: 1.021, pcc: 0.069, kcc: 0.082
+2025-07-09 15:38:34: Epoch: 018 Test results, ap: mae: 0.606, rse: 1.124, pcc: 0.073, kcc: 0.014
+2025-07-09 15:38:35: Epoch: 019 Train Loss: 0.811, mae: 1.322, rse: 1.021, pcc: 0.069, kcc: 0.090
+2025-07-09 15:38:35: Epoch: 019 Test results, ap: mae: 0.605, rse: 1.119, pcc: 0.066, kcc: 0.016
+2025-07-09 15:38:35: Epoch: 020 Train Loss: 0.819, mae: 1.322, rse: 1.020, pcc: 0.069, kcc: 0.096
+2025-07-09 15:38:36: Epoch: 020 Test results, ap: mae: 0.605, rse: 1.118, pcc: 0.088, kcc: 0.003
+2025-07-09 15:38:36: Epoch: 021 Train Loss: 0.824, mae: 1.323, rse: 1.024, pcc: 0.067, kcc: 0.093
+2025-07-09 15:38:37: Epoch: 021 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.024, kcc: 0.012
+2025-07-09 15:38:37: Epoch: 022 Train Loss: 0.814, mae: 1.322, rse: 1.023, pcc: 0.068, kcc: 0.112
+2025-07-09 15:38:37: Epoch: 022 Test results, ap: mae: 0.608, rse: 1.131, pcc: 0.135, kcc: 0.017
+2025-07-09 15:38:38: Epoch: 023 Train Loss: 0.804, mae: 1.322, rse: 1.022, pcc: 0.067, kcc: 0.102
+2025-07-09 15:38:38: Epoch: 023 Test results, ap: mae: 0.607, rse: 1.129, pcc: 0.111, kcc: 0.009
+2025-07-09 15:38:39: Epoch: 024 Train Loss: 0.805, mae: 1.324, rse: 1.027, pcc: 0.065, kcc: 0.121
+2025-07-09 15:38:39: Epoch: 024 Test results, ap: mae: 0.615, rse: 1.158, pcc: 0.030, kcc: 0.026
+2025-07-09 15:38:40: Epoch: 025 Train Loss: 0.805, mae: 1.322, rse: 1.022, pcc: 0.067, kcc: 0.106
+2025-07-09 15:38:40: Epoch: 025 Test results, ap: mae: 0.607, rse: 1.126, pcc: 0.081, kcc: 0.027
+2025-07-09 15:38:41: Epoch: 026 Train Loss: 0.803, mae: 1.323, rse: 1.024, pcc: 0.066, kcc: 0.115
+2025-07-09 15:38:41: Epoch: 026 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.086, kcc: 0.014
+2025-07-09 15:38:42: Epoch: 027 Train Loss: 0.793, mae: 1.323, rse: 1.026, pcc: 0.066, kcc: 0.111
+2025-07-09 15:38:42: Epoch: 027 Test results, ap: mae: 0.613, rse: 1.151, pcc: 0.112, kcc: 0.013
+2025-07-09 15:38:43: Epoch: 028 Train Loss: 0.796, mae: 1.322, rse: 1.020, pcc: 0.068, kcc: 0.103
+2025-07-09 15:38:43: Epoch: 028 Test results, ap: mae: 0.605, rse: 1.119, pcc: 0.069, kcc: 0.008
+2025-07-09 15:38:43: Epoch: 029 Train Loss: 0.799, mae: 1.322, rse: 1.021, pcc: 0.068, kcc: 0.115
+2025-07-09 15:38:44: Epoch: 029 Test results, ap: mae: 0.605, rse: 1.120, pcc: 0.071, kcc: 0.014
+2025-07-09 15:38:44: Epoch: 030 Train Loss: 0.807, mae: 1.321, rse: 1.017, pcc: 0.069, kcc: 0.116
+2025-07-09 15:38:44: Epoch: 030 New best VALIDATION metrics
+2025-07-09 15:38:45: Epoch: 030 Test results, ap: mae: 0.600, rse: 1.098, pcc: 0.092, kcc: 0.008
+2025-07-09 15:38:45: Epoch: 031 Train Loss: 0.806, mae: 1.322, rse: 1.021, pcc: 0.068, kcc: 0.129
+2025-07-09 15:38:45: Epoch: 031 New best VALIDATION metrics
+2025-07-09 15:38:46: Epoch: 031 Test results, ap: mae: 0.605, rse: 1.119, pcc: 0.069, kcc: -0.004
+2025-07-09 15:38:46: Epoch: 032 Train Loss: 0.801, mae: 1.322, rse: 1.023, pcc: 0.067, kcc: 0.103
+2025-07-09 15:38:47: Epoch: 032 Test results, ap: mae: 0.608, rse: 1.134, pcc: 0.017, kcc: 0.014
+2025-07-09 15:38:47: Epoch: 033 Train Loss: 0.807, mae: 1.323, rse: 1.025, pcc: 0.067, kcc: 0.127
+2025-07-09 15:38:47: Epoch: 033 Test results, ap: mae: 0.611, rse: 1.143, pcc: 0.005, kcc: 0.005
+2025-07-09 15:38:48: Epoch: 034 Train Loss: 0.797, mae: 1.323, rse: 1.023, pcc: 0.067, kcc: 0.111
+2025-07-09 15:38:48: Epoch: 034 Test results, ap: mae: 0.609, rse: 1.136, pcc: 0.151, kcc: 0.016
+2025-07-09 15:38:49: Epoch: 035 Train Loss: 0.820, mae: 1.322, rse: 1.021, pcc: 0.068, kcc: 0.119
+2025-07-09 15:38:49: Epoch: 035 Test results, ap: mae: 0.606, rse: 1.122, pcc: -0.012, kcc: -0.013
+2025-07-09 15:38:49: used time 0.01h
+2025-07-09 15:38:49: Running Cross Validation 4
+2025-07-09 15:38:49: Fold 4  Train set:406, Valid set:101, Test set: 108
+2025-07-09 15:38:50: Epoch: 001 Train Loss: 0.856, mae: 1.231, rse: 1.002, pcc: -0.291, kcc: -0.163
+2025-07-09 15:38:50: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:38:50: Epoch: 001 Test results, ap: mae: 0.582, rse: 1.013, pcc: -0.008, kcc: 0.041
+2025-07-09 15:38:51: Epoch: 002 Train Loss: 0.866, mae: 1.199, rse: 1.022, pcc: -0.331, kcc: -0.157
+2025-07-09 15:38:51: Epoch: 002 Test results, ap: mae: 0.645, rse: 1.260, pcc: -0.101, kcc: -0.082
+2025-07-09 15:38:52: Epoch: 003 Train Loss: 0.838, mae: 1.209, rse: 1.003, pcc: -0.339, kcc: -0.166
+2025-07-09 15:38:52: Epoch: 003 Test results, ap: mae: 0.600, rse: 1.097, pcc: -0.138, kcc: -0.102
+2025-07-09 15:38:53: Epoch: 004 Train Loss: 0.846, mae: 1.199, rse: 1.019, pcc: -0.317, kcc: -0.156
+2025-07-09 15:38:53: Epoch: 004 Test results, ap: mae: 0.639, rse: 1.239, pcc: -0.156, kcc: -0.111
+2025-07-09 15:38:53: Epoch: 005 Train Loss: 0.851, mae: 1.206, rse: 1.005, pcc: -0.331, kcc: -0.159
+2025-07-09 15:38:54: Epoch: 005 Test results, ap: mae: 0.606, rse: 1.123, pcc: -0.156, kcc: -0.110
+2025-07-09 15:38:54: Epoch: 006 Train Loss: 0.828, mae: 1.206, rse: 1.006, pcc: -0.335, kcc: -0.157
+2025-07-09 15:38:55: Epoch: 006 Test results, ap: mae: 0.607, rse: 1.129, pcc: -0.153, kcc: -0.115
+2025-07-09 15:38:55: Epoch: 007 Train Loss: 0.841, mae: 1.203, rse: 1.010, pcc: -0.335, kcc: -0.159
+2025-07-09 15:38:56: Epoch: 007 Test results, ap: mae: 0.617, rse: 1.166, pcc: -0.150, kcc: -0.123
+2025-07-09 15:38:56: Epoch: 008 Train Loss: 0.851, mae: 1.205, rse: 1.007, pcc: -0.340, kcc: -0.153
+2025-07-09 15:38:56: Epoch: 008 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.149, kcc: -0.114
+2025-07-09 15:38:57: Epoch: 009 Train Loss: 0.813, mae: 1.212, rse: 1.002, pcc: -0.340, kcc: -0.148
+2025-07-09 15:38:57: Epoch: 009 Test results, ap: mae: 0.596, rse: 1.083, pcc: -0.147, kcc: -0.107
+2025-07-09 15:38:58: Epoch: 010 Train Loss: 0.832, mae: 1.200, rse: 1.017, pcc: -0.331, kcc: -0.144
+2025-07-09 15:38:58: Epoch: 010 Test results, ap: mae: 0.634, rse: 1.221, pcc: -0.148, kcc: -0.150
+2025-07-09 15:38:59: Epoch: 011 Train Loss: 0.853, mae: 1.209, rse: 1.003, pcc: -0.337, kcc: -0.144
+2025-07-09 15:38:59: Epoch: 011 Test results, ap: mae: 0.599, rse: 1.096, pcc: -0.129, kcc: -0.125
+2025-07-09 15:38:59: Epoch: 011 New best TEST metrics
+2025-07-09 15:39:00: Epoch: 012 Train Loss: 0.865, mae: 1.212, rse: 1.001, pcc: -0.298, kcc: -0.112
+2025-07-09 15:39:00: Epoch: 012 New best VALIDATION metrics
+2025-07-09 15:39:00: Epoch: 012 Test results, ap: mae: 0.595, rse: 1.079, pcc: -0.057, kcc: -0.094
+2025-07-09 15:39:00: Epoch: 012 New best TEST metrics
+2025-07-09 15:39:01: Epoch: 013 Train Loss: 0.820, mae: 1.206, rse: 1.005, pcc: 0.151, kcc: 0.149
+2025-07-09 15:39:01: Epoch: 013 New best VALIDATION metrics
+2025-07-09 15:39:01: Epoch: 013 Test results, ap: mae: 0.606, rse: 1.125, pcc: 0.142, kcc: 0.075
+2025-07-09 15:39:01: Epoch: 013 New best TEST metrics
+2025-07-09 15:39:02: Epoch: 014 Train Loss: 0.829, mae: 1.209, rse: 1.003, pcc: -0.329, kcc: -0.091
+2025-07-09 15:39:02: Epoch: 014 Test results, ap: mae: 0.600, rse: 1.101, pcc: -0.046, kcc: -0.025
+2025-07-09 15:39:03: Epoch: 015 Train Loss: 0.837, mae: 1.211, rse: 1.002, pcc: -0.333, kcc: -0.093
+2025-07-09 15:39:03: Epoch: 015 Test results, ap: mae: 0.597, rse: 1.088, pcc: -0.050, kcc: -0.040
+2025-07-09 15:39:03: Epoch: 016 Train Loss: 0.845, mae: 1.206, rse: 1.005, pcc: -0.331, kcc: -0.072
+2025-07-09 15:39:04: Epoch: 016 Test results, ap: mae: 0.605, rse: 1.122, pcc: 0.016, kcc: -0.027
+2025-07-09 15:39:04: Epoch: 017 Train Loss: 0.839, mae: 1.203, rse: 1.009, pcc: -0.329, kcc: -0.135
+2025-07-09 15:39:05: Epoch: 017 Test results, ap: mae: 0.615, rse: 1.159, pcc: 0.051, kcc: 0.033
+2025-07-09 15:39:05: Epoch: 018 Train Loss: 0.837, mae: 1.208, rse: 1.005, pcc: -0.294, kcc: -0.037
+2025-07-09 15:39:05: Epoch: 018 Test results, ap: mae: 0.602, rse: 1.109, pcc: 0.108, kcc: 0.078
+2025-07-09 15:39:06: Epoch: 019 Train Loss: 0.842, mae: 1.212, rse: 1.001, pcc: 0.031, kcc: 0.157
+2025-07-09 15:39:06: Epoch: 019 Test results, ap: mae: 0.596, rse: 1.082, pcc: 0.227, kcc: -0.006
+2025-07-09 15:39:06: Epoch: 019 New best TEST metrics
+2025-07-09 15:39:07: Epoch: 020 Train Loss: 0.853, mae: 1.217, rse: 1.000, pcc: -0.022, kcc: -0.250
+2025-07-09 15:39:07: Epoch: 020 Test results, ap: mae: 0.590, rse: 1.058, pcc: 0.028, kcc: 0.069
+2025-07-09 15:39:08: Epoch: 021 Train Loss: 0.860, mae: 1.200, rse: 1.018, pcc: -0.014, kcc: -0.235
+2025-07-09 15:39:08: Epoch: 021 Test results, ap: mae: 0.637, rse: 1.232, pcc: -0.127, kcc: -0.107
+2025-07-09 15:39:09: Epoch: 022 Train Loss: 0.843, mae: 1.203, rse: 1.009, pcc: -0.358, kcc: -0.156
+2025-07-09 15:39:09: Epoch: 022 Test results, ap: mae: 0.616, rse: 1.161, pcc: -0.159, kcc: -0.071
+2025-07-09 15:39:10: Epoch: 023 Train Loss: 0.824, mae: 1.207, rse: 1.004, pcc: -0.351, kcc: -0.144
+2025-07-09 15:39:10: Epoch: 023 Test results, ap: mae: 0.603, rse: 1.113, pcc: -0.130, kcc: -0.082
+2025-07-09 15:39:11: Epoch: 024 Train Loss: 0.839, mae: 1.209, rse: 1.003, pcc: -0.352, kcc: -0.151
+2025-07-09 15:39:11: Epoch: 024 Test results, ap: mae: 0.600, rse: 1.098, pcc: -0.099, kcc: -0.100
+2025-07-09 15:39:12: Epoch: 025 Train Loss: 0.860, mae: 1.208, rse: 1.003, pcc: -0.355, kcc: -0.152
+2025-07-09 15:39:12: Epoch: 025 Test results, ap: mae: 0.602, rse: 1.106, pcc: -0.189, kcc: -0.089
+2025-07-09 15:39:12: Epoch: 026 Train Loss: 0.856, mae: 1.207, rse: 1.004, pcc: -0.349, kcc: -0.141
+2025-07-09 15:39:13: Epoch: 026 Test results, ap: mae: 0.604, rse: 1.116, pcc: -0.184, kcc: -0.076
+2025-07-09 15:39:13: Epoch: 027 Train Loss: 0.842, mae: 1.204, rse: 1.008, pcc: -0.351, kcc: -0.149
+2025-07-09 15:39:14: Epoch: 027 Test results, ap: mae: 0.612, rse: 1.147, pcc: -0.113, kcc: -0.043
+2025-07-09 15:39:14: Epoch: 028 Train Loss: 0.829, mae: 1.205, rse: 1.006, pcc: -0.349, kcc: -0.144
+2025-07-09 15:39:15: Epoch: 028 Test results, ap: mae: 0.608, rse: 1.132, pcc: 0.015, kcc: -0.083
+2025-07-09 15:39:15: Epoch: 029 Train Loss: 0.825, mae: 1.204, rse: 1.008, pcc: -0.352, kcc: -0.149
+2025-07-09 15:39:15: Epoch: 029 Test results, ap: mae: 0.612, rse: 1.149, pcc: -0.090, kcc: -0.104
+2025-07-09 15:39:16: Epoch: 030 Train Loss: 0.850, mae: 1.206, rse: 1.005, pcc: -0.352, kcc: -0.142
+2025-07-09 15:39:16: Epoch: 030 Test results, ap: mae: 0.605, rse: 1.120, pcc: -0.111, kcc: -0.067
+2025-07-09 15:39:17: Epoch: 031 Train Loss: 0.850, mae: 1.209, rse: 1.003, pcc: -0.353, kcc: -0.138
+2025-07-09 15:39:17: Epoch: 031 Test results, ap: mae: 0.600, rse: 1.100, pcc: -0.217, kcc: -0.093
+2025-07-09 15:39:18: Epoch: 032 Train Loss: 0.842, mae: 1.207, rse: 1.004, pcc: -0.354, kcc: -0.151
+2025-07-09 15:39:18: Epoch: 032 Test results, ap: mae: 0.604, rse: 1.115, pcc: -0.052, kcc: -0.073
+2025-07-09 15:39:19: Epoch: 033 Train Loss: 0.835, mae: 1.205, rse: 1.006, pcc: -0.359, kcc: -0.155
+2025-07-09 15:39:19: Epoch: 033 Test results, ap: mae: 0.609, rse: 1.135, pcc: -0.143, kcc: -0.049
+2025-07-09 15:39:20: Epoch: 034 Train Loss: 0.825, mae: 1.207, rse: 1.005, pcc: -0.360, kcc: -0.155
+2025-07-09 15:39:20: Epoch: 034 Test results, ap: mae: 0.605, rse: 1.118, pcc: -0.094, kcc: -0.077
+2025-07-09 15:39:20: Epoch: 035 Train Loss: 0.846, mae: 1.207, rse: 1.004, pcc: -0.363, kcc: -0.163
+2025-07-09 15:39:21: Epoch: 035 Test results, ap: mae: 0.604, rse: 1.115, pcc: -0.134, kcc: -0.070
+2025-07-09 15:39:21: used time 0.01h
+2025-07-09 15:39:21: Cross Validation Finished!
+2025-07-09 15:39:21: Best validation perform list
+[[1.15021753 1.00665903 0.11215667 0.17681707]
+ [1.19826531 1.01428795 0.10262267 0.08809494]
+ [1.21639657 1.01906097 0.09424975 0.0732675 ]
+ [1.32199097 1.02060378 0.06782796 0.12937777]
+ [1.20594144 1.00520873 0.15075938 0.14933416]]
+2025-07-09 15:39:21: mean: [1.219 1.013 0.106 0.123]
+2025-07-09 15:39:21: std: [0.056 0.006 0.027 0.038]
+2025-07-09 15:39:21: Best test perform list
+[[ 0.60381055  1.11504459  0.19493501 -0.01229222]
+ [ 0.59713262  1.08721793  0.42400333  0.2531811 ]
+ [ 0.61390114  1.15488172  0.4270975   0.15752919]
+ [ 0.60929984  1.13694966  0.14373295  0.03065973]
+ [ 0.59589809  1.08215332  0.22700758 -0.00625489]]
+2025-07-09 15:39:21: mean: [0.604 1.115 0.283 0.085]
+2025-07-09 15:39:21: std: [0.007 0.028 0.119 0.104]
diff --git a/run-reg/lstm-diff-256-mse-32-0.001-50/config.json b/run-reg/lstm-diff-256-mse-32-0.001-50/config.json
new file mode 100644
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--- /dev/null
+++ b/run-reg/lstm-diff-256-mse-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
\ No newline at end of file
diff --git a/run-reg/lstm-diff-256-mse-32-0.001-50/inference_results.csv b/run-reg/lstm-diff-256-mse-32-0.001-50/inference_results.csv
new file mode 100644
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+++ b/run-reg/lstm-diff-256-mse-32-0.001-50/inference_results.csv
@@ -0,0 +1,2 @@
+dataset,MAE,RSE,PCC,KCC
+r2_case,52.83,110.27,-13.61,-17.39
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+2025-07-10 14:49:35: saving_dir: ./run-reg/lstm-diff-256-mse-32-0.001-50
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+2025-07-10 14:49:36: Loading Test Dataset
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+2025-07-10 14:49:36: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 14:49:37: Epoch: 001 Train Loss: 2.893, mae: 1.209, rse: 1.000, pcc: 0.000, kcc: 0.057
+2025-07-10 14:49:37: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:49:37: Epoch: 001 Test results, ap: mae: 0.615, rse: 1.160, pcc: 0.091, kcc: 0.013
+2025-07-10 14:49:38: Epoch: 002 Train Loss: 2.902, mae: 1.209, rse: 1.000, pcc: -0.002, kcc: 0.086
+2025-07-10 14:49:38: Epoch: 002 New best VALIDATION metrics
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+2025-07-10 14:49:39: Epoch: 003 Train Loss: 2.911, mae: 1.208, rse: 1.000, pcc: -0.010, kcc: -0.004
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+2025-07-10 14:49:41: Epoch: 005 New best VALIDATION metrics
+2025-07-10 14:49:41: Epoch: 005 Test results, ap: mae: 0.605, rse: 1.120, pcc: -0.100, kcc: -0.016
+2025-07-10 14:49:42: Epoch: 006 Train Loss: 2.905, mae: 1.212, rse: 1.001, pcc: 0.012, kcc: 0.084
+2025-07-10 14:49:42: Epoch: 006 Test results, ap: mae: 0.609, rse: 1.135, pcc: -0.033, kcc: 0.007
+2025-07-10 14:49:43: Epoch: 007 Train Loss: 2.914, mae: 1.215, rse: 1.001, pcc: 0.014, kcc: 0.086
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+2025-07-10 14:49:44: Epoch: 008 Train Loss: 2.900, mae: 1.213, rse: 1.001, pcc: 0.023, kcc: 0.095
+2025-07-10 14:49:44: Epoch: 008 New best VALIDATION metrics
+2025-07-10 14:49:44: Epoch: 008 Test results, ap: mae: 0.608, rse: 1.130, pcc: -0.056, kcc: -0.013
+2025-07-10 14:49:44: Epoch: 009 Train Loss: 2.905, mae: 1.212, rse: 1.001, pcc: 0.056, kcc: 0.097
+2025-07-10 14:49:44: Epoch: 009 New best VALIDATION metrics
+2025-07-10 14:49:45: Epoch: 009 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.128, kcc: -0.069
+2025-07-10 14:49:45: Epoch: 010 Train Loss: 2.884, mae: 1.262, rse: 1.050, pcc: -0.233, kcc: -0.193
+2025-07-10 14:49:46: Epoch: 010 Test results, ap: mae: 0.582, rse: 1.041, pcc: 0.178, kcc: 0.156
+2025-07-10 14:49:46: Epoch: 011 Train Loss: 2.853, mae: 1.188, rse: 0.976, pcc: 0.207, kcc: 0.181
+2025-07-10 14:49:46: Epoch: 011 New best VALIDATION metrics
+2025-07-10 14:49:47: Epoch: 011 Test results, ap: mae: 0.624, rse: 1.170, pcc: -0.090, kcc: -0.117
+2025-07-10 14:49:47: Epoch: 011 New best TEST metrics
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+2025-07-10 14:49:47: Epoch: 012 New best VALIDATION metrics
+2025-07-10 14:49:47: Epoch: 012 Test results, ap: mae: 0.693, rse: 1.436, pcc: -0.117, kcc: -0.023
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+2025-07-10 14:49:49: Epoch: 014 New best VALIDATION metrics
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+2025-07-10 14:49:50: Epoch: 015 New best VALIDATION metrics
+2025-07-10 14:49:50: Epoch: 015 Test results, ap: mae: 0.729, rse: 1.640, pcc: -0.171, kcc: -0.025
+2025-07-10 14:49:51: Epoch: 016 Train Loss: 2.861, mae: 1.154, rse: 0.932, pcc: 0.362, kcc: 0.237
+2025-07-10 14:49:51: Epoch: 016 Test results, ap: mae: 0.665, rse: 1.350, pcc: -0.187, kcc: -0.076
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+2025-07-10 14:49:53: Epoch: 018 New best VALIDATION metrics
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+2025-07-10 14:49:54: Epoch: 019 New best VALIDATION metrics
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+2025-07-10 14:49:55: Epoch: 020 Test results, ap: mae: 0.716, rse: 1.522, pcc: -0.116, kcc: -0.079
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+2025-07-10 14:49:56: Epoch: 021 New best VALIDATION metrics
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+2025-07-10 14:49:58: Epoch: 023 New best VALIDATION metrics
+2025-07-10 14:49:58: Epoch: 023 Test results, ap: mae: 0.762, rse: 1.773, pcc: -0.116, kcc: -0.046
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+2025-07-10 14:49:59: Epoch: 024 Test results, ap: mae: 0.819, rse: 2.064, pcc: -0.189, kcc: -0.083
+2025-07-10 14:50:00: Epoch: 025 Train Loss: 2.704, mae: 1.148, rse: 0.939, pcc: 0.268, kcc: 0.194
+2025-07-10 14:50:00: Epoch: 025 Test results, ap: mae: 0.639, rse: 1.211, pcc: -0.101, kcc: -0.165
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+2025-07-10 14:50:07: Epoch: 033 Test results, ap: mae: 0.675, rse: 1.344, pcc: -0.068, kcc: -0.133
+2025-07-10 14:50:08: Epoch: 034 Train Loss: 2.414, mae: 1.091, rse: 0.843, pcc: 0.401, kcc: 0.239
+2025-07-10 14:50:08: Epoch: 034 Test results, ap: mae: 0.698, rse: 1.495, pcc: -0.147, kcc: -0.138
+2025-07-10 14:50:09: Epoch: 035 Train Loss: 2.395, mae: 1.089, rse: 0.838, pcc: 0.405, kcc: 0.230
+2025-07-10 14:50:09: Epoch: 035 Test results, ap: mae: 0.674, rse: 1.343, pcc: -0.073, kcc: -0.133
+2025-07-10 14:50:10: Epoch: 036 Train Loss: 2.422, mae: 1.091, rse: 0.838, pcc: 0.404, kcc: 0.244
+2025-07-10 14:50:10: Epoch: 036 Test results, ap: mae: 0.667, rse: 1.298, pcc: -0.039, kcc: -0.133
+2025-07-10 14:50:11: Epoch: 037 Train Loss: 2.389, mae: 1.088, rse: 0.836, pcc: 0.407, kcc: 0.236
+2025-07-10 14:50:11: Epoch: 037 Test results, ap: mae: 0.678, rse: 1.372, pcc: -0.094, kcc: -0.136
+2025-07-10 14:50:12: Epoch: 038 Train Loss: 2.367, mae: 1.090, rse: 0.838, pcc: 0.405, kcc: 0.248
+2025-07-10 14:50:12: Epoch: 038 Test results, ap: mae: 0.673, rse: 1.332, pcc: -0.061, kcc: -0.131
+2025-07-10 14:50:13: Epoch: 039 Train Loss: 2.362, mae: 1.091, rse: 0.838, pcc: 0.404, kcc: 0.249
+2025-07-10 14:50:13: Epoch: 039 Test results, ap: mae: 0.669, rse: 1.308, pcc: -0.044, kcc: -0.122
+2025-07-10 14:50:14: Epoch: 040 Train Loss: 2.376, mae: 1.086, rse: 0.838, pcc: 0.406, kcc: 0.251
+2025-07-10 14:50:14: Epoch: 040 Test results, ap: mae: 0.692, rse: 1.458, pcc: -0.136, kcc: -0.153
+2025-07-10 14:50:15: Epoch: 041 Train Loss: 2.388, mae: 1.089, rse: 0.838, pcc: 0.406, kcc: 0.250
+2025-07-10 14:50:15: Epoch: 041 Test results, ap: mae: 0.678, rse: 1.368, pcc: -0.086, kcc: -0.142
+2025-07-10 14:50:16: Epoch: 042 Train Loss: 2.364, mae: 1.088, rse: 0.836, pcc: 0.408, kcc: 0.253
+2025-07-10 14:50:16: Epoch: 042 Test results, ap: mae: 0.676, rse: 1.358, pcc: -0.082, kcc: -0.139
+2025-07-10 14:50:17: Epoch: 043 Train Loss: 2.364, mae: 1.089, rse: 0.836, pcc: 0.407, kcc: 0.256
+2025-07-10 14:50:17: Epoch: 043 Test results, ap: mae: 0.673, rse: 1.335, pcc: -0.066, kcc: -0.137
+2025-07-10 14:50:18: Epoch: 044 Train Loss: 2.359, mae: 1.086, rse: 0.836, pcc: 0.410, kcc: 0.259
+2025-07-10 14:50:18: Epoch: 044 Test results, ap: mae: 0.686, rse: 1.417, pcc: -0.110, kcc: -0.152
+2025-07-10 14:50:19: Epoch: 045 Train Loss: 2.362, mae: 1.085, rse: 0.838, pcc: 0.409, kcc: 0.263
+2025-07-10 14:50:19: Epoch: 045 Test results, ap: mae: 0.690, rse: 1.443, pcc: -0.124, kcc: -0.159
+2025-07-10 14:50:19: Epoch: 046 Train Loss: 2.348, mae: 1.087, rse: 0.836, pcc: 0.408, kcc: 0.265
+2025-07-10 14:50:20: Epoch: 046 Test results, ap: mae: 0.676, rse: 1.355, pcc: -0.083, kcc: -0.139
+2025-07-10 14:50:20: Epoch: 047 Train Loss: 2.339, mae: 1.089, rse: 0.836, pcc: 0.409, kcc: 0.271
+2025-07-10 14:50:21: Epoch: 047 Test results, ap: mae: 0.674, rse: 1.340, pcc: -0.067, kcc: -0.128
+2025-07-10 14:50:21: Epoch: 048 Train Loss: 2.365, mae: 1.083, rse: 0.837, pcc: 0.411, kcc: 0.271
+2025-07-10 14:50:22: Epoch: 048 Test results, ap: mae: 0.689, rse: 1.432, pcc: -0.122, kcc: -0.153
+2025-07-10 14:50:22: Epoch: 049 Train Loss: 2.360, mae: 1.085, rse: 0.831, pcc: 0.413, kcc: 0.276
+2025-07-10 14:50:22: Epoch: 049 New best VALIDATION metrics
+2025-07-10 14:50:22: Epoch: 049 Test results, ap: mae: 0.668, rse: 1.306, pcc: -0.047, kcc: -0.082
+2025-07-10 14:50:23: Epoch: 050 Train Loss: 2.346, mae: 1.084, rse: 0.831, pcc: 0.414, kcc: 0.277
+2025-07-10 14:50:23: Epoch: 050 New best VALIDATION metrics
+2025-07-10 14:50:23: Epoch: 050 Test results, ap: mae: 0.675, rse: 1.344, pcc: -0.070, kcc: -0.109
+2025-07-10 14:50:23: used time 0.01h
+2025-07-10 14:50:23: Running Cross Validation 1
+2025-07-10 14:50:23: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 14:50:24: Epoch: 001 Train Loss: 2.863, mae: 1.255, rse: 1.001, pcc: -0.028, kcc: 0.129
+2025-07-10 14:50:24: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:50:24: Epoch: 001 Test results, ap: mae: 0.612, rse: 1.148, pcc: 0.058, kcc: 0.027
+2025-07-10 14:50:25: Epoch: 002 Train Loss: 2.858, mae: 1.253, rse: 1.002, pcc: 0.001, kcc: 0.147
+2025-07-10 14:50:25: Epoch: 002 New best VALIDATION metrics
+2025-07-10 14:50:25: Epoch: 002 Test results, ap: mae: 0.620, rse: 1.175, pcc: -0.032, kcc: -0.045
+2025-07-10 14:50:26: Epoch: 003 Train Loss: 2.843, mae: 1.253, rse: 1.002, pcc: 0.011, kcc: 0.217
+2025-07-10 14:50:26: Epoch: 003 New best VALIDATION metrics
+2025-07-10 14:50:26: Epoch: 003 Test results, ap: mae: 0.619, rse: 1.174, pcc: -0.181, kcc: -0.192
+2025-07-10 14:50:27: Epoch: 004 Train Loss: 2.823, mae: 1.251, rse: 1.005, pcc: 0.049, kcc: 0.083
+2025-07-10 14:50:27: Epoch: 004 Test results, ap: mae: 0.635, rse: 1.225, pcc: -0.115, kcc: -0.154
+2025-07-10 14:50:28: Epoch: 005 Train Loss: 2.828, mae: 1.252, rse: 1.003, pcc: -0.101, kcc: -0.137
+2025-07-10 14:50:28: Epoch: 005 Test results, ap: mae: 0.623, rse: 1.185, pcc: 0.071, kcc: 0.046
+2025-07-10 14:50:29: Epoch: 006 Train Loss: 2.819, mae: 1.256, rse: 1.001, pcc: 0.063, kcc: 0.052
+2025-07-10 14:50:29: Epoch: 006 Test results, ap: mae: 0.609, rse: 1.136, pcc: 0.031, kcc: 0.057
+2025-07-10 14:50:30: Epoch: 007 Train Loss: 2.851, mae: 1.258, rse: 1.000, pcc: 0.002, kcc: 0.023
+2025-07-10 14:50:30: Epoch: 007 Test results, ap: mae: 0.604, rse: 1.116, pcc: 0.042, kcc: 0.032
+2025-07-10 14:50:31: Epoch: 008 Train Loss: 2.844, mae: 1.255, rse: 1.001, pcc: -0.014, kcc: 0.046
+2025-07-10 14:50:31: Epoch: 008 Test results, ap: mae: 0.612, rse: 1.148, pcc: 0.005, kcc: 0.004
+2025-07-10 14:50:32: Epoch: 009 Train Loss: 2.844, mae: 1.255, rse: 1.001, pcc: -0.008, kcc: 0.077
+2025-07-10 14:50:32: Epoch: 009 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.063, kcc: -0.012
+2025-07-10 14:50:33: Epoch: 010 Train Loss: 2.840, mae: 1.254, rse: 1.001, pcc: 0.005, kcc: 0.080
+2025-07-10 14:50:33: Epoch: 010 Test results, ap: mae: 0.615, rse: 1.159, pcc: -0.070, kcc: -0.051
+2025-07-10 14:50:34: Epoch: 011 Train Loss: 2.860, mae: 1.254, rse: 1.001, pcc: 0.016, kcc: 0.107
+2025-07-10 14:50:34: Epoch: 011 Test results, ap: mae: 0.614, rse: 1.154, pcc: -0.056, kcc: -0.025
+2025-07-10 14:50:34: Epoch: 011 New best TEST metrics
+2025-07-10 14:50:35: Epoch: 012 Train Loss: 2.826, mae: 1.253, rse: 1.002, pcc: 0.034, kcc: 0.115
+2025-07-10 14:50:35: Epoch: 012 Test results, ap: mae: 0.618, rse: 1.171, pcc: -0.091, kcc: -0.055
+2025-07-10 14:50:36: Epoch: 013 Train Loss: 2.840, mae: 1.254, rse: 1.001, pcc: 0.043, kcc: 0.127
+2025-07-10 14:50:36: Epoch: 013 Test results, ap: mae: 0.614, rse: 1.157, pcc: -0.093, kcc: -0.062
+2025-07-10 14:50:37: Epoch: 014 Train Loss: 2.798, mae: 1.252, rse: 1.002, pcc: 0.075, kcc: 0.132
+2025-07-10 14:50:37: Epoch: 014 Test results, ap: mae: 0.622, rse: 1.184, pcc: -0.125, kcc: -0.085
+2025-07-10 14:50:38: Epoch: 015 Train Loss: 2.768, mae: 1.283, rse: 1.018, pcc: 0.081, kcc: 0.047
+2025-07-10 14:50:38: Epoch: 015 Test results, ap: mae: 0.619, rse: 1.128, pcc: 0.071, kcc: 0.094
+2025-07-10 14:50:38: Epoch: 015 New best TEST metrics
+2025-07-10 14:50:39: Epoch: 016 Train Loss: 2.605, mae: 1.196, rse: 0.895, pcc: 0.326, kcc: 0.246
+2025-07-10 14:50:39: Epoch: 016 New best VALIDATION metrics
+2025-07-10 14:50:39: Epoch: 016 Test results, ap: mae: 0.612, rse: 1.141, pcc: -0.175, kcc: -0.127
+2025-07-10 14:50:40: Epoch: 017 Train Loss: 2.473, mae: 1.155, rse: 0.848, pcc: 0.391, kcc: 0.275
+2025-07-10 14:50:40: Epoch: 017 New best VALIDATION metrics
+2025-07-10 14:50:40: Epoch: 017 Test results, ap: mae: 0.651, rse: 1.303, pcc: -0.235, kcc: -0.147
+2025-07-10 14:50:41: Epoch: 018 Train Loss: 2.447, mae: 1.128, rse: 0.817, pcc: 0.429, kcc: 0.303
+2025-07-10 14:50:41: Epoch: 018 New best VALIDATION metrics
+2025-07-10 14:50:41: Epoch: 018 Test results, ap: mae: 0.711, rse: 1.567, pcc: -0.209, kcc: -0.136
+2025-07-10 14:50:42: Epoch: 019 Train Loss: 2.314, mae: 1.071, rse: 0.736, pcc: 0.524, kcc: 0.369
+2025-07-10 14:50:42: Epoch: 019 New best VALIDATION metrics
+2025-07-10 14:50:42: Epoch: 019 Test results, ap: mae: 0.689, rse: 1.477, pcc: -0.237, kcc: -0.113
+2025-07-10 14:50:43: Epoch: 020 Train Loss: 2.094, mae: 1.039, rse: 0.684, pcc: 0.573, kcc: 0.376
+2025-07-10 14:50:43: Epoch: 020 New best VALIDATION metrics
+2025-07-10 14:50:43: Epoch: 020 Test results, ap: mae: 0.742, rse: 1.633, pcc: -0.169, kcc: -0.076
+2025-07-10 14:50:43: Epoch: 021 Train Loss: 1.923, mae: 1.007, rse: 0.655, pcc: 0.588, kcc: 0.401
+2025-07-10 14:50:43: Epoch: 021 New best VALIDATION metrics
+2025-07-10 14:50:44: Epoch: 021 Test results, ap: mae: 0.669, rse: 1.477, pcc: -0.223, kcc: -0.061
+2025-07-10 14:50:44: Epoch: 022 Train Loss: 1.760, mae: 0.966, rse: 0.593, pcc: 0.641, kcc: 0.435
+2025-07-10 14:50:44: Epoch: 022 New best VALIDATION metrics
+2025-07-10 14:50:45: Epoch: 022 Test results, ap: mae: 0.712, rse: 1.663, pcc: -0.231, kcc: -0.086
+2025-07-10 14:50:45: Epoch: 023 Train Loss: 1.682, mae: 0.924, rse: 0.549, pcc: 0.682, kcc: 0.463
+2025-07-10 14:50:45: Epoch: 023 New best VALIDATION metrics
+2025-07-10 14:50:46: Epoch: 023 Test results, ap: mae: 0.711, rse: 1.657, pcc: -0.178, kcc: -0.064
+2025-07-10 14:50:46: Epoch: 024 Train Loss: 1.626, mae: 0.888, rse: 0.538, pcc: 0.686, kcc: 0.451
+2025-07-10 14:50:46: Epoch: 024 New best VALIDATION metrics
+2025-07-10 14:50:47: Epoch: 024 Test results, ap: mae: 0.723, rse: 1.714, pcc: -0.161, kcc: -0.079
+2025-07-10 14:50:47: Epoch: 025 Train Loss: 1.608, mae: 0.952, rse: 0.582, pcc: 0.648, kcc: 0.417
+2025-07-10 14:50:48: Epoch: 025 Test results, ap: mae: 0.776, rse: 1.882, pcc: -0.170, kcc: -0.096
+2025-07-10 14:50:48: Epoch: 026 Train Loss: 1.554, mae: 0.869, rse: 0.513, pcc: 0.700, kcc: 0.455
+2025-07-10 14:50:48: Epoch: 026 New best VALIDATION metrics
+2025-07-10 14:50:48: Epoch: 026 Test results, ap: mae: 0.791, rse: 1.980, pcc: -0.162, kcc: -0.087
+2025-07-10 14:50:49: Epoch: 027 Train Loss: 1.473, mae: 0.856, rse: 0.500, pcc: 0.707, kcc: 0.470
+2025-07-10 14:50:49: Epoch: 027 New best VALIDATION metrics
+2025-07-10 14:50:50: Epoch: 027 Test results, ap: mae: 0.769, rse: 1.915, pcc: -0.152, kcc: -0.091
+2025-07-10 14:50:50: Epoch: 028 Train Loss: 1.470, mae: 0.855, rse: 0.503, pcc: 0.705, kcc: 0.470
+2025-07-10 14:50:51: Epoch: 028 Test results, ap: mae: 0.766, rse: 1.918, pcc: -0.152, kcc: -0.099
+2025-07-10 14:50:51: Epoch: 029 Train Loss: 1.505, mae: 0.871, rse: 0.511, pcc: 0.700, kcc: 0.464
+2025-07-10 14:50:52: Epoch: 029 Test results, ap: mae: 0.753, rse: 1.819, pcc: -0.142, kcc: -0.101
+2025-07-10 14:50:52: Epoch: 030 Train Loss: 1.479, mae: 0.872, rse: 0.509, pcc: 0.703, kcc: 0.473
+2025-07-10 14:50:52: Epoch: 030 Test results, ap: mae: 0.763, rse: 1.871, pcc: -0.155, kcc: -0.125
+2025-07-10 14:50:53: Epoch: 031 Train Loss: 1.423, mae: 0.872, rse: 0.505, pcc: 0.704, kcc: 0.473
+2025-07-10 14:50:53: Epoch: 031 Test results, ap: mae: 0.756, rse: 1.827, pcc: -0.148, kcc: -0.096
+2025-07-10 14:50:54: Epoch: 032 Train Loss: 1.347, mae: 0.859, rse: 0.494, pcc: 0.712, kcc: 0.483
+2025-07-10 14:50:54: Epoch: 032 New best VALIDATION metrics
+2025-07-10 14:50:54: Epoch: 032 Test results, ap: mae: 0.783, rse: 1.964, pcc: -0.147, kcc: -0.122
+2025-07-10 14:50:55: Epoch: 033 Train Loss: 1.348, mae: 0.847, rse: 0.483, pcc: 0.719, kcc: 0.489
+2025-07-10 14:50:55: Epoch: 033 New best VALIDATION metrics
+2025-07-10 14:50:55: Epoch: 033 Test results, ap: mae: 0.775, rse: 1.959, pcc: -0.164, kcc: -0.086
+2025-07-10 14:50:56: Epoch: 034 Train Loss: 1.354, mae: 0.844, rse: 0.482, pcc: 0.720, kcc: 0.490
+2025-07-10 14:50:56: Epoch: 034 New best VALIDATION metrics
+2025-07-10 14:50:56: Epoch: 034 Test results, ap: mae: 0.762, rse: 1.869, pcc: -0.148, kcc: -0.077
+2025-07-10 14:50:57: Epoch: 035 Train Loss: 1.339, mae: 0.883, rse: 0.513, pcc: 0.698, kcc: 0.467
+2025-07-10 14:50:57: Epoch: 035 Test results, ap: mae: 0.833, rse: 2.201, pcc: -0.178, kcc: -0.120
+2025-07-10 14:50:58: Epoch: 036 Train Loss: 1.326, mae: 0.834, rse: 0.470, pcc: 0.729, kcc: 0.495
+2025-07-10 14:50:58: Epoch: 036 New best VALIDATION metrics
+2025-07-10 14:50:58: Epoch: 036 Test results, ap: mae: 0.810, rse: 2.144, pcc: -0.175, kcc: -0.096
+2025-07-10 14:50:59: Epoch: 037 Train Loss: 1.308, mae: 0.839, rse: 0.481, pcc: 0.721, kcc: 0.495
+2025-07-10 14:50:59: Epoch: 037 Test results, ap: mae: 0.783, rse: 1.982, pcc: -0.155, kcc: -0.108
+2025-07-10 14:51:00: Epoch: 038 Train Loss: 1.286, mae: 0.834, rse: 0.467, pcc: 0.730, kcc: 0.499
+2025-07-10 14:51:00: Epoch: 038 New best VALIDATION metrics
+2025-07-10 14:51:00: Epoch: 038 Test results, ap: mae: 0.785, rse: 2.015, pcc: -0.163, kcc: -0.058
+2025-07-10 14:51:01: Epoch: 039 Train Loss: 1.334, mae: 0.829, rse: 0.474, pcc: 0.726, kcc: 0.505
+2025-07-10 14:51:01: Epoch: 039 Test results, ap: mae: 0.767, rse: 1.887, pcc: -0.143, kcc: -0.049
+2025-07-10 14:51:02: Epoch: 040 Train Loss: 1.297, mae: 0.841, rse: 0.479, pcc: 0.723, kcc: 0.495
+2025-07-10 14:51:02: Epoch: 040 Test results, ap: mae: 0.758, rse: 1.859, pcc: -0.146, kcc: -0.045
+2025-07-10 14:51:03: Epoch: 041 Train Loss: 1.313, mae: 0.850, rse: 0.476, pcc: 0.724, kcc: 0.488
+2025-07-10 14:51:03: Epoch: 041 Test results, ap: mae: 0.798, rse: 2.040, pcc: -0.155, kcc: -0.072
+2025-07-10 14:51:04: Epoch: 042 Train Loss: 1.262, mae: 0.823, rse: 0.453, pcc: 0.740, kcc: 0.507
+2025-07-10 14:51:04: Epoch: 042 New best VALIDATION metrics
+2025-07-10 14:51:04: Epoch: 042 Test results, ap: mae: 0.789, rse: 2.022, pcc: -0.158, kcc: -0.066
+2025-07-10 14:51:05: Epoch: 043 Train Loss: 1.247, mae: 0.830, rse: 0.468, pcc: 0.730, kcc: 0.501
+2025-07-10 14:51:05: Epoch: 043 Test results, ap: mae: 0.791, rse: 2.036, pcc: -0.163, kcc: -0.064
+2025-07-10 14:51:06: Epoch: 044 Train Loss: 1.253, mae: 0.826, rse: 0.466, pcc: 0.731, kcc: 0.499
+2025-07-10 14:51:06: Epoch: 044 Test results, ap: mae: 0.792, rse: 2.044, pcc: -0.159, kcc: -0.076
+2025-07-10 14:51:07: Epoch: 045 Train Loss: 1.225, mae: 0.815, rse: 0.454, pcc: 0.739, kcc: 0.510
+2025-07-10 14:51:07: Epoch: 045 New best VALIDATION metrics
+2025-07-10 14:51:07: Epoch: 045 Test results, ap: mae: 0.783, rse: 1.996, pcc: -0.151, kcc: -0.065
+2025-07-10 14:51:08: Epoch: 046 Train Loss: 1.236, mae: 0.837, rse: 0.470, pcc: 0.728, kcc: 0.493
+2025-07-10 14:51:08: Epoch: 046 Test results, ap: mae: 0.798, rse: 2.087, pcc: -0.166, kcc: -0.063
+2025-07-10 14:51:09: Epoch: 047 Train Loss: 1.239, mae: 0.821, rse: 0.454, pcc: 0.739, kcc: 0.505
+2025-07-10 14:51:09: Epoch: 047 Test results, ap: mae: 0.795, rse: 2.068, pcc: -0.161, kcc: -0.061
+2025-07-10 14:51:10: Epoch: 048 Train Loss: 1.226, mae: 0.827, rse: 0.462, pcc: 0.734, kcc: 0.505
+2025-07-10 14:51:10: Epoch: 048 Test results, ap: mae: 0.793, rse: 2.025, pcc: -0.153, kcc: -0.065
+2025-07-10 14:51:10: Epoch: 049 Train Loss: 1.228, mae: 0.834, rse: 0.467, pcc: 0.731, kcc: 0.496
+2025-07-10 14:51:11: Epoch: 049 Test results, ap: mae: 0.803, rse: 2.126, pcc: -0.164, kcc: -0.042
+2025-07-10 14:51:11: Epoch: 050 Train Loss: 1.226, mae: 0.823, rse: 0.458, pcc: 0.736, kcc: 0.505
+2025-07-10 14:51:12: Epoch: 050 Test results, ap: mae: 0.785, rse: 2.008, pcc: -0.153, kcc: -0.051
+2025-07-10 14:51:12: used time 0.01h
+2025-07-10 14:51:12: Running Cross Validation 2
+2025-07-10 14:51:12: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:51:12: Epoch: 001 Train Loss: 2.898, mae: 1.257, rse: 1.001, pcc: 0.058, kcc: 0.040
+2025-07-10 14:51:12: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:51:13: Epoch: 001 Test results, ap: mae: 0.603, rse: 1.113, pcc: 0.051, kcc: 0.016
+2025-07-10 14:51:13: Epoch: 002 Train Loss: 2.891, mae: 1.258, rse: 1.001, pcc: -0.204, kcc: -0.172
+2025-07-10 14:51:14: Epoch: 002 Test results, ap: mae: 0.602, rse: 1.105, pcc: -0.123, kcc: -0.067
+2025-07-10 14:51:14: Epoch: 003 Train Loss: 2.883, mae: 1.255, rse: 1.000, pcc: -0.105, kcc: -0.099
+2025-07-10 14:51:14: Epoch: 003 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.113, kcc: -0.065
+2025-07-10 14:51:15: Epoch: 004 Train Loss: 2.895, mae: 1.256, rse: 1.000, pcc: -0.009, kcc: 0.014
+2025-07-10 14:51:15: Epoch: 004 Test results, ap: mae: 0.608, rse: 1.131, pcc: -0.056, kcc: 0.124
+2025-07-10 14:51:16: Epoch: 005 Train Loss: 2.909, mae: 1.255, rse: 1.000, pcc: 0.059, kcc: -0.090
+2025-07-10 14:51:16: Epoch: 005 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.033, kcc: -0.052
+2025-07-10 14:51:17: Epoch: 006 Train Loss: 2.893, mae: 1.255, rse: 1.000, pcc: -0.167, kcc: -0.124
+2025-07-10 14:51:17: Epoch: 006 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.076, kcc: -0.068
+2025-07-10 14:51:18: Epoch: 007 Train Loss: 2.890, mae: 1.255, rse: 1.000, pcc: -0.122, kcc: -0.103
+2025-07-10 14:51:18: Epoch: 007 Test results, ap: mae: 0.609, rse: 1.136, pcc: 0.144, kcc: 0.152
+2025-07-10 14:51:19: Epoch: 008 Train Loss: 2.894, mae: 1.254, rse: 1.000, pcc: -0.022, kcc: -0.063
+2025-07-10 14:51:19: Epoch: 008 Test results, ap: mae: 0.616, rse: 1.162, pcc: -0.355, kcc: -0.130
+2025-07-10 14:51:20: Epoch: 009 Train Loss: 2.877, mae: 1.257, rse: 1.001, pcc: -0.014, kcc: 0.033
+2025-07-10 14:51:20: Epoch: 009 Test results, ap: mae: 0.603, rse: 1.110, pcc: -0.083, kcc: -0.053
+2025-07-10 14:51:20: Epoch: 010 Train Loss: 2.897, mae: 1.255, rse: 1.000, pcc: 0.047, kcc: 0.173
+2025-07-10 14:51:20: Epoch: 010 New best VALIDATION metrics
+2025-07-10 14:51:21: Epoch: 010 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.088, kcc: -0.087
+2025-07-10 14:51:21: Epoch: 011 Train Loss: 2.903, mae: 1.255, rse: 1.000, pcc: -0.215, kcc: -0.112
+2025-07-10 14:51:22: Epoch: 011 Test results, ap: mae: 0.608, rse: 1.133, pcc: 0.099, kcc: 0.099
+2025-07-10 14:51:22: Epoch: 011 New best TEST metrics
+2025-07-10 14:51:22: Epoch: 012 Train Loss: 2.885, mae: 1.255, rse: 1.000, pcc: -0.089, kcc: -0.009
+2025-07-10 14:51:22: Epoch: 012 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.055, kcc: 0.093
+2025-07-10 14:51:23: Epoch: 013 Train Loss: 2.873, mae: 1.255, rse: 1.000, pcc: 0.089, kcc: 0.145
+2025-07-10 14:51:23: Epoch: 013 New best VALIDATION metrics
+2025-07-10 14:51:23: Epoch: 013 Test results, ap: mae: 0.611, rse: 1.142, pcc: 1.000, kcc: nan
+2025-07-10 14:51:24: Epoch: 014 Train Loss: 2.891, mae: 1.255, rse: 1.000, pcc: -0.024, kcc: 0.052
+2025-07-10 14:51:24: Epoch: 014 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.022, kcc: -0.033
+2025-07-10 14:51:25: Epoch: 015 Train Loss: 2.893, mae: 1.254, rse: 1.000, pcc: -0.022, kcc: 0.102
+2025-07-10 14:51:25: Epoch: 015 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.071, kcc: -0.009
+2025-07-10 14:51:26: Epoch: 016 Train Loss: 2.880, mae: 1.254, rse: 1.000, pcc: -0.013, kcc: 0.003
+2025-07-10 14:51:26: Epoch: 016 Test results, ap: mae: 0.614, rse: 1.155, pcc: -0.059, kcc: -0.196
+2025-07-10 14:51:27: Epoch: 017 Train Loss: 2.895, mae: 1.255, rse: 1.000, pcc: 0.097, kcc: 0.119
+2025-07-10 14:51:27: Epoch: 017 Test results, ap: mae: 0.609, rse: 1.135, pcc: 0.111, kcc: 0.199
+2025-07-10 14:51:27: Epoch: 017 New best TEST metrics
+2025-07-10 14:51:28: Epoch: 018 Train Loss: 2.890, mae: 1.257, rse: 1.000, pcc: 0.052, kcc: 0.175
+2025-07-10 14:51:28: Epoch: 018 Test results, ap: mae: 0.604, rse: 1.118, pcc: -0.335, kcc: -0.306
+2025-07-10 14:51:28: Epoch: 019 Train Loss: 2.858, mae: 1.255, rse: 1.000, pcc: -0.016, kcc: 0.067
+2025-07-10 14:51:29: Epoch: 019 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.412, kcc: 0.234
+2025-07-10 14:51:29: Epoch: 019 New best TEST metrics
+2025-07-10 14:51:29: Epoch: 020 Train Loss: 2.879, mae: 1.255, rse: 1.000, pcc: 0.051, kcc: 0.060
+2025-07-10 14:51:29: Epoch: 020 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.249, kcc: -0.107
+2025-07-10 14:51:30: Epoch: 021 Train Loss: 2.880, mae: 1.255, rse: 1.000, pcc: -0.021, kcc: -0.050
+2025-07-10 14:51:30: Epoch: 021 Test results, ap: mae: 0.609, rse: 1.134, pcc: -0.192, kcc: -0.181
+2025-07-10 14:51:31: Epoch: 022 Train Loss: 2.903, mae: 1.255, rse: 1.000, pcc: 0.065, kcc: -0.002
+2025-07-10 14:51:31: Epoch: 022 Test results, ap: mae: 0.610, rse: 1.140, pcc: 1.000, kcc: 0.251
+2025-07-10 14:51:31: Epoch: 022 New best TEST metrics
+2025-07-10 14:51:32: Epoch: 023 Train Loss: 2.889, mae: 1.256, rse: 1.000, pcc: -0.055, kcc: -0.040
+2025-07-10 14:51:32: Epoch: 023 Test results, ap: mae: 0.608, rse: 1.130, pcc: 0.059, kcc: -0.033
+2025-07-10 14:51:33: Epoch: 024 Train Loss: 2.862, mae: 1.255, rse: 1.000, pcc: 0.030, kcc: 0.076
+2025-07-10 14:51:33: Epoch: 024 Test results, ap: mae: 0.611, rse: 1.143, pcc: 0.123, kcc: 0.204
+2025-07-10 14:51:34: Epoch: 025 Train Loss: 2.884, mae: 1.255, rse: 1.000, pcc: 0.019, kcc: 0.072
+2025-07-10 14:51:34: Epoch: 025 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.094, kcc: -0.010
+2025-07-10 14:51:35: Epoch: 026 Train Loss: 2.901, mae: 1.255, rse: 1.000, pcc: -0.021, kcc: 0.011
+2025-07-10 14:51:35: Epoch: 026 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.135, kcc: 0.047
+2025-07-10 14:51:35: Epoch: 027 Train Loss: 2.888, mae: 1.255, rse: 1.000, pcc: -0.063, kcc: -0.105
+2025-07-10 14:51:36: Epoch: 027 Test results, ap: mae: 0.611, rse: 1.145, pcc: 0.122, kcc: 0.010
+2025-07-10 14:51:36: Epoch: 028 Train Loss: 2.882, mae: 1.255, rse: 1.000, pcc: -0.068, kcc: -0.070
+2025-07-10 14:51:36: Epoch: 028 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.385, kcc: -0.074
+2025-07-10 14:51:37: Epoch: 029 Train Loss: 2.870, mae: 1.255, rse: 1.000, pcc: -0.053, kcc: -0.058
+2025-07-10 14:51:37: Epoch: 029 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.053, kcc: 0.000
+2025-07-10 14:51:38: Epoch: 030 Train Loss: 2.908, mae: 1.255, rse: 1.000, pcc: -0.127, kcc: -0.236
+2025-07-10 14:51:38: Epoch: 030 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.072, kcc: 0.043
+2025-07-10 14:51:39: Epoch: 031 Train Loss: 2.895, mae: 1.255, rse: 1.000, pcc: -0.068, kcc: -0.053
+2025-07-10 14:51:39: Epoch: 031 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.145, kcc: -0.140
+2025-07-10 14:51:40: Epoch: 032 Train Loss: 2.881, mae: 1.255, rse: 1.000, pcc: -0.049, kcc: -0.061
+2025-07-10 14:51:40: Epoch: 032 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.101, kcc: 0.135
+2025-07-10 14:51:41: Epoch: 033 Train Loss: 2.876, mae: 1.255, rse: 1.000, pcc: -0.122, kcc: -0.035
+2025-07-10 14:51:41: Epoch: 033 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.021, kcc: -0.089
+2025-07-10 14:51:42: Epoch: 034 Train Loss: 2.896, mae: 1.255, rse: 1.000, pcc: 0.049, kcc: 0.036
+2025-07-10 14:51:42: Epoch: 034 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.019, kcc: 0.005
+2025-07-10 14:51:42: Epoch: 035 Train Loss: 2.883, mae: 1.255, rse: 1.000, pcc: -0.056, kcc: -0.009
+2025-07-10 14:51:43: Epoch: 035 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.272, kcc: -0.242
+2025-07-10 14:51:43: Epoch: 036 Train Loss: 2.891, mae: 1.255, rse: 1.000, pcc: 0.008, kcc: 0.017
+2025-07-10 14:51:43: Epoch: 036 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.020, kcc: 0.022
+2025-07-10 14:51:44: Epoch: 037 Train Loss: 2.886, mae: 1.255, rse: 1.000, pcc: -0.027, kcc: -0.031
+2025-07-10 14:51:44: Epoch: 037 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.268, kcc: 0.183
+2025-07-10 14:51:45: Epoch: 038 Train Loss: 2.902, mae: 1.255, rse: 1.000, pcc: -0.016, kcc: -0.028
+2025-07-10 14:51:45: Epoch: 038 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.149, kcc: 0.111
+2025-07-10 14:51:46: Epoch: 039 Train Loss: 2.899, mae: 1.255, rse: 1.000, pcc: 0.043, kcc: 0.051
+2025-07-10 14:51:46: Epoch: 039 Test results, ap: mae: 0.610, rse: 1.138, pcc: 0.036, kcc: 0.075
+2025-07-10 14:51:47: Epoch: 040 Train Loss: 2.880, mae: 1.255, rse: 1.000, pcc: 0.011, kcc: 0.051
+2025-07-10 14:51:47: Epoch: 040 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.321, kcc: 0.072
+2025-07-10 14:51:48: Epoch: 041 Train Loss: 2.889, mae: 1.255, rse: 1.000, pcc: 0.019, kcc: -0.000
+2025-07-10 14:51:48: Epoch: 041 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.052, kcc: -0.070
+2025-07-10 14:51:49: Epoch: 042 Train Loss: 2.893, mae: 1.255, rse: 1.000, pcc: -0.117, kcc: -0.040
+2025-07-10 14:51:49: Epoch: 042 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.123, kcc: -0.005
+2025-07-10 14:51:49: Epoch: 043 Train Loss: 2.885, mae: 1.255, rse: 1.000, pcc: 0.022, kcc: 0.165
+2025-07-10 14:51:50: Epoch: 043 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.165, kcc: -0.059
+2025-07-10 14:51:50: Epoch: 044 Train Loss: 2.889, mae: 1.255, rse: 1.000, pcc: -0.004, kcc: -0.023
+2025-07-10 14:51:50: Epoch: 044 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.320, kcc: -0.319
+2025-07-10 14:51:51: Epoch: 045 Train Loss: 2.891, mae: 1.255, rse: 1.000, pcc: 0.005, kcc: -0.002
+2025-07-10 14:51:51: Epoch: 045 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.123, kcc: 0.068
+2025-07-10 14:51:52: Epoch: 046 Train Loss: 2.893, mae: 1.255, rse: 1.000, pcc: 0.067, kcc: 0.115
+2025-07-10 14:51:52: Epoch: 046 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.339, kcc: -0.251
+2025-07-10 14:51:53: Epoch: 047 Train Loss: 2.878, mae: 1.255, rse: 1.000, pcc: -0.027, kcc: -0.051
+2025-07-10 14:51:53: Epoch: 047 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.032, kcc: -0.163
+2025-07-10 14:51:54: Epoch: 048 Train Loss: 2.900, mae: 1.255, rse: 1.000, pcc: -0.006, kcc: 0.057
+2025-07-10 14:51:54: Epoch: 048 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.162, kcc: -0.148
+2025-07-10 14:51:55: Epoch: 049 Train Loss: 2.885, mae: 1.255, rse: 1.000, pcc: -0.140, kcc: -0.071
+2025-07-10 14:51:55: Epoch: 049 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.296, kcc: 0.148
+2025-07-10 14:51:55: Epoch: 050 Train Loss: 2.869, mae: 1.255, rse: 1.000, pcc: 0.078, kcc: 0.089
+2025-07-10 14:51:56: Epoch: 050 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.033, kcc: -0.011
+2025-07-10 14:51:56: used time 0.01h
+2025-07-10 14:51:56: Running Cross Validation 3
+2025-07-10 14:51:56: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:51:56: Epoch: 001 Train Loss: 2.802, mae: 1.291, rse: 0.998, pcc: 0.153, kcc: -0.027
+2025-07-10 14:51:56: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:51:57: Epoch: 001 Test results, ap: mae: 0.613, rse: 1.151, pcc: -0.045, kcc: -0.134
+2025-07-10 14:51:57: Epoch: 002 Train Loss: 2.818, mae: 1.293, rse: 1.000, pcc: 0.022, kcc: 0.031
+2025-07-10 14:51:57: Epoch: 002 Test results, ap: mae: 0.613, rse: 1.152, pcc: -0.074, kcc: -0.054
+2025-07-10 14:51:58: Epoch: 003 Train Loss: 2.809, mae: 1.293, rse: 1.000, pcc: -0.041, kcc: -0.063
+2025-07-10 14:51:58: Epoch: 003 Test results, ap: mae: 0.613, rse: 1.153, pcc: 0.262, kcc: 0.164
+2025-07-10 14:51:59: Epoch: 004 Train Loss: 2.809, mae: 1.293, rse: 1.000, pcc: 0.104, kcc: 0.153
+2025-07-10 14:51:59: Epoch: 004 New best VALIDATION metrics
+2025-07-10 14:51:59: Epoch: 004 Test results, ap: mae: 0.615, rse: 1.161, pcc: -0.089, kcc: -0.037
+2025-07-10 14:52:00: Epoch: 005 Train Loss: 2.783, mae: 1.293, rse: 1.000, pcc: 0.003, kcc: 0.266
+2025-07-10 14:52:00: Epoch: 005 New best VALIDATION metrics
+2025-07-10 14:52:00: Epoch: 005 Test results, ap: mae: 0.613, rse: 1.151, pcc: 0.347, kcc: -0.111
+2025-07-10 14:52:01: Epoch: 006 Train Loss: 2.799, mae: 1.295, rse: 1.000, pcc: 0.185, kcc: 0.202
+2025-07-10 14:52:01: Epoch: 006 New best VALIDATION metrics
+2025-07-10 14:52:01: Epoch: 006 Test results, ap: mae: 0.605, rse: 1.119, pcc: -0.157, kcc: -0.143
+2025-07-10 14:52:02: Epoch: 007 Train Loss: 2.799, mae: 1.293, rse: 1.000, pcc: 0.249, kcc: 0.222
+2025-07-10 14:52:02: Epoch: 007 New best VALIDATION metrics
+2025-07-10 14:52:02: Epoch: 007 Test results, ap: mae: 0.614, rse: 1.155, pcc: -0.056, kcc: -0.014
+2025-07-10 14:52:03: Epoch: 008 Train Loss: 2.807, mae: 1.294, rse: 1.000, pcc: -0.203, kcc: -0.245
+2025-07-10 14:52:03: Epoch: 008 Test results, ap: mae: 0.608, rse: 1.133, pcc: 0.205, kcc: 0.178
+2025-07-10 14:52:03: Epoch: 009 Train Loss: 2.794, mae: 1.293, rse: 1.000, pcc: -0.238, kcc: -0.143
+2025-07-10 14:52:04: Epoch: 009 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.129, kcc: 0.172
+2025-07-10 14:52:04: Epoch: 010 Train Loss: 2.792, mae: 1.250, rse: 0.948, pcc: 0.332, kcc: 0.252
+2025-07-10 14:52:04: Epoch: 010 New best VALIDATION metrics
+2025-07-10 14:52:04: Epoch: 010 Test results, ap: mae: 0.674, rse: 1.400, pcc: -0.233, kcc: -0.164
+2025-07-10 14:52:05: Epoch: 011 Train Loss: 2.555, mae: 1.216, rse: 0.903, pcc: 0.387, kcc: 0.283
+2025-07-10 14:52:05: Epoch: 011 New best VALIDATION metrics
+2025-07-10 14:52:05: Epoch: 011 Test results, ap: mae: 0.637, rse: 1.226, pcc: -0.210, kcc: -0.119
+2025-07-10 14:52:05: Epoch: 011 New best TEST metrics
+2025-07-10 14:52:06: Epoch: 012 Train Loss: 2.620, mae: 1.189, rse: 0.874, pcc: 0.369, kcc: 0.237
+2025-07-10 14:52:06: Epoch: 012 Test results, ap: mae: 0.796, rse: 1.942, pcc: -0.254, kcc: -0.139
+2025-07-10 14:52:07: Epoch: 013 Train Loss: 2.538, mae: 1.169, rse: 0.853, pcc: 0.390, kcc: 0.233
+2025-07-10 14:52:07: Epoch: 013 New best VALIDATION metrics
+2025-07-10 14:52:07: Epoch: 013 Test results, ap: mae: 0.841, rse: 2.155, pcc: -0.258, kcc: -0.194
+2025-07-10 14:52:08: Epoch: 014 Train Loss: 2.539, mae: 1.174, rse: 0.853, pcc: 0.408, kcc: 0.244
+2025-07-10 14:52:08: Epoch: 014 New best VALIDATION metrics
+2025-07-10 14:52:08: Epoch: 014 Test results, ap: mae: 0.762, rse: 1.811, pcc: -0.201, kcc: -0.070
+2025-07-10 14:52:09: Epoch: 015 Train Loss: 2.491, mae: 1.160, rse: 0.840, pcc: 0.413, kcc: 0.241
+2025-07-10 14:52:09: Epoch: 015 New best VALIDATION metrics
+2025-07-10 14:52:09: Epoch: 015 Test results, ap: mae: 0.783, rse: 1.906, pcc: -0.201, kcc: -0.205
+2025-07-10 14:52:09: Epoch: 016 Train Loss: 2.487, mae: 1.166, rse: 0.845, pcc: 0.415, kcc: 0.260
+2025-07-10 14:52:09: Epoch: 016 New best VALIDATION metrics
+2025-07-10 14:52:10: Epoch: 016 Test results, ap: mae: 0.762, rse: 1.819, pcc: -0.195, kcc: -0.160
+2025-07-10 14:52:10: Epoch: 017 Train Loss: 2.485, mae: 1.166, rse: 0.845, pcc: 0.411, kcc: 0.245
+2025-07-10 14:52:10: Epoch: 017 Test results, ap: mae: 0.772, rse: 1.862, pcc: -0.195, kcc: -0.156
+2025-07-10 14:52:11: Epoch: 018 Train Loss: 2.453, mae: 1.147, rse: 0.832, pcc: 0.410, kcc: 0.277
+2025-07-10 14:52:11: Epoch: 018 New best VALIDATION metrics
+2025-07-10 14:52:11: Epoch: 018 Test results, ap: mae: 0.831, rse: 2.145, pcc: -0.194, kcc: -0.091
+2025-07-10 14:52:12: Epoch: 019 Train Loss: 2.448, mae: 1.150, rse: 0.835, pcc: 0.408, kcc: 0.266
+2025-07-10 14:52:12: Epoch: 019 Test results, ap: mae: 0.819, rse: 2.086, pcc: -0.194, kcc: -0.101
+2025-07-10 14:52:13: Epoch: 020 Train Loss: 2.431, mae: 1.149, rse: 0.832, pcc: 0.422, kcc: 0.277
+2025-07-10 14:52:13: Epoch: 020 New best VALIDATION metrics
+2025-07-10 14:52:13: Epoch: 020 Test results, ap: mae: 0.782, rse: 1.909, pcc: -0.194, kcc: -0.142
+2025-07-10 14:52:13: Epoch: 021 Train Loss: 2.419, mae: 1.147, rse: 0.831, pcc: 0.420, kcc: 0.284
+2025-07-10 14:52:13: Epoch: 021 New best VALIDATION metrics
+2025-07-10 14:52:14: Epoch: 021 Test results, ap: mae: 0.803, rse: 2.005, pcc: -0.194, kcc: -0.102
+2025-07-10 14:52:14: Epoch: 022 Train Loss: 2.446, mae: 1.141, rse: 0.826, pcc: 0.424, kcc: 0.309
+2025-07-10 14:52:14: Epoch: 022 New best VALIDATION metrics
+2025-07-10 14:52:14: Epoch: 022 Test results, ap: mae: 0.809, rse: 2.034, pcc: -0.194, kcc: -0.117
+2025-07-10 14:52:15: Epoch: 023 Train Loss: 2.425, mae: 1.136, rse: 0.823, pcc: 0.424, kcc: 0.311
+2025-07-10 14:52:15: Epoch: 023 New best VALIDATION metrics
+2025-07-10 14:52:15: Epoch: 023 Test results, ap: mae: 0.815, rse: 2.061, pcc: -0.194, kcc: -0.092
+2025-07-10 14:52:16: Epoch: 024 Train Loss: 2.413, mae: 1.136, rse: 0.824, pcc: 0.425, kcc: 0.299
+2025-07-10 14:52:16: Epoch: 024 Test results, ap: mae: 0.810, rse: 2.037, pcc: -0.194, kcc: -0.071
+2025-07-10 14:52:17: Epoch: 025 Train Loss: 2.430, mae: 1.140, rse: 0.833, pcc: 0.417, kcc: 0.303
+2025-07-10 14:52:17: Epoch: 025 Test results, ap: mae: 0.807, rse: 2.022, pcc: -0.194, kcc: -0.079
+2025-07-10 14:52:18: Epoch: 026 Train Loss: 2.415, mae: 1.132, rse: 0.819, pcc: 0.431, kcc: 0.305
+2025-07-10 14:52:18: Epoch: 026 New best VALIDATION metrics
+2025-07-10 14:52:18: Epoch: 026 Test results, ap: mae: 0.814, rse: 2.058, pcc: -0.194, kcc: -0.063
+2025-07-10 14:52:18: Epoch: 027 Train Loss: 2.399, mae: 1.132, rse: 0.820, pcc: 0.427, kcc: 0.293
+2025-07-10 14:52:19: Epoch: 027 Test results, ap: mae: 0.822, rse: 2.100, pcc: -0.194, kcc: -0.056
+2025-07-10 14:52:19: Epoch: 028 Train Loss: 2.397, mae: 1.128, rse: 0.817, pcc: 0.430, kcc: 0.286
+2025-07-10 14:52:19: Epoch: 028 Test results, ap: mae: 0.829, rse: 2.135, pcc: -0.194, kcc: 0.014
+2025-07-10 14:52:20: Epoch: 029 Train Loss: 2.394, mae: 1.142, rse: 0.831, pcc: 0.414, kcc: 0.280
+2025-07-10 14:52:20: Epoch: 029 Test results, ap: mae: 0.813, rse: 2.050, pcc: -0.194, kcc: -0.074
+2025-07-10 14:52:21: Epoch: 030 Train Loss: 2.412, mae: 1.133, rse: 0.824, pcc: 0.429, kcc: 0.292
+2025-07-10 14:52:21: Epoch: 030 Test results, ap: mae: 0.805, rse: 2.012, pcc: -0.193, kcc: -0.039
+2025-07-10 14:52:22: Epoch: 031 Train Loss: 2.398, mae: 1.144, rse: 0.828, pcc: 0.422, kcc: 0.276
+2025-07-10 14:52:22: Epoch: 031 Test results, ap: mae: 0.806, rse: 2.018, pcc: -0.194, kcc: -0.052
+2025-07-10 14:52:22: Epoch: 032 Train Loss: 2.418, mae: 1.142, rse: 0.826, pcc: 0.422, kcc: 0.273
+2025-07-10 14:52:23: Epoch: 032 Test results, ap: mae: 0.812, rse: 2.048, pcc: -0.194, kcc: -0.047
+2025-07-10 14:52:23: Epoch: 033 Train Loss: 2.424, mae: 1.142, rse: 0.827, pcc: 0.422, kcc: 0.272
+2025-07-10 14:52:23: Epoch: 033 Test results, ap: mae: 0.811, rse: 2.042, pcc: -0.193, kcc: -0.033
+2025-07-10 14:52:24: Epoch: 034 Train Loss: 2.417, mae: 1.139, rse: 0.824, pcc: 0.424, kcc: 0.284
+2025-07-10 14:52:24: Epoch: 034 Test results, ap: mae: 0.815, rse: 2.066, pcc: -0.193, kcc: -0.016
+2025-07-10 14:52:25: Epoch: 035 Train Loss: 2.431, mae: 1.140, rse: 0.826, pcc: 0.424, kcc: 0.302
+2025-07-10 14:52:25: Epoch: 035 Test results, ap: mae: 0.809, rse: 2.032, pcc: -0.193, kcc: 0.010
+2025-07-10 14:52:26: Epoch: 036 Train Loss: 2.424, mae: 1.140, rse: 0.826, pcc: 0.425, kcc: 0.279
+2025-07-10 14:52:26: Epoch: 036 Test results, ap: mae: 0.811, rse: 2.042, pcc: -0.193, kcc: -0.022
+2025-07-10 14:52:27: Epoch: 037 Train Loss: 2.406, mae: 1.140, rse: 0.825, pcc: 0.422, kcc: 0.257
+2025-07-10 14:52:27: Epoch: 037 Test results, ap: mae: 0.822, rse: 2.098, pcc: -0.193, kcc: 0.009
+2025-07-10 14:52:27: Epoch: 038 Train Loss: 2.423, mae: 1.141, rse: 0.826, pcc: 0.422, kcc: 0.271
+2025-07-10 14:52:27: Epoch: 038 Test results, ap: mae: 0.813, rse: 2.054, pcc: -0.193, kcc: -0.004
+2025-07-10 14:52:28: Epoch: 039 Train Loss: 2.409, mae: 1.140, rse: 0.826, pcc: 0.422, kcc: 0.259
+2025-07-10 14:52:28: Epoch: 039 Test results, ap: mae: 0.814, rse: 2.061, pcc: -0.193, kcc: -0.013
+2025-07-10 14:52:29: Epoch: 040 Train Loss: 2.425, mae: 1.149, rse: 0.832, pcc: 0.417, kcc: 0.248
+2025-07-10 14:52:29: Epoch: 040 Test results, ap: mae: 0.804, rse: 2.007, pcc: -0.193, kcc: -0.002
+2025-07-10 14:52:29: Epoch: 041 Train Loss: 2.418, mae: 1.142, rse: 0.827, pcc: 0.423, kcc: 0.259
+2025-07-10 14:52:30: Epoch: 041 Test results, ap: mae: 0.805, rse: 2.012, pcc: -0.193, kcc: 0.003
+2025-07-10 14:52:30: Epoch: 042 Train Loss: 2.427, mae: 1.149, rse: 0.831, pcc: 0.417, kcc: 0.252
+2025-07-10 14:52:30: Epoch: 042 Test results, ap: mae: 0.804, rse: 2.011, pcc: -0.193, kcc: -0.042
+2025-07-10 14:52:31: Epoch: 043 Train Loss: 2.423, mae: 1.157, rse: 0.835, pcc: 0.410, kcc: 0.245
+2025-07-10 14:52:31: Epoch: 043 Test results, ap: mae: 0.806, rse: 2.021, pcc: -0.193, kcc: -0.052
+2025-07-10 14:52:32: Epoch: 044 Train Loss: 2.423, mae: 1.158, rse: 0.836, pcc: 0.410, kcc: 0.253
+2025-07-10 14:52:32: Epoch: 044 Test results, ap: mae: 0.801, rse: 1.992, pcc: -0.193, kcc: -0.009
+2025-07-10 14:52:33: Epoch: 045 Train Loss: 2.438, mae: 1.163, rse: 0.841, pcc: 0.411, kcc: 0.257
+2025-07-10 14:52:33: Epoch: 045 Test results, ap: mae: 0.778, rse: 1.885, pcc: -0.193, kcc: -0.150
+2025-07-10 14:52:34: Epoch: 046 Train Loss: 2.419, mae: 1.143, rse: 0.828, pcc: 0.423, kcc: 0.274
+2025-07-10 14:52:34: Epoch: 046 Test results, ap: mae: 0.797, rse: 1.976, pcc: -0.193, kcc: -0.145
+2025-07-10 14:52:35: Epoch: 047 Train Loss: 2.422, mae: 1.142, rse: 0.827, pcc: 0.423, kcc: 0.276
+2025-07-10 14:52:35: Epoch: 047 Test results, ap: mae: 0.799, rse: 1.985, pcc: -0.193, kcc: -0.139
+2025-07-10 14:52:36: Epoch: 048 Train Loss: 2.420, mae: 1.147, rse: 0.830, pcc: 0.418, kcc: 0.264
+2025-07-10 14:52:36: Epoch: 048 Test results, ap: mae: 0.803, rse: 2.005, pcc: -0.193, kcc: -0.135
+2025-07-10 14:52:37: Epoch: 049 Train Loss: 2.413, mae: 1.156, rse: 0.849, pcc: 0.394, kcc: 0.262
+2025-07-10 14:52:37: Epoch: 049 Test results, ap: mae: 0.806, rse: 2.016, pcc: -0.193, kcc: -0.141
+2025-07-10 14:52:38: Epoch: 050 Train Loss: 2.371, mae: 1.138, rse: 0.824, pcc: 0.426, kcc: 0.281
+2025-07-10 14:52:38: Epoch: 050 Test results, ap: mae: 0.809, rse: 2.032, pcc: -0.193, kcc: -0.153
+2025-07-10 14:52:38: used time 0.01h
+2025-07-10 14:52:38: Running Cross Validation 4
+2025-07-10 14:52:38: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:52:39: Epoch: 001 Train Loss: 2.851, mae: 1.214, rse: 1.000, pcc: 0.070, kcc: 0.031
+2025-07-10 14:52:39: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:52:39: Epoch: 001 Test results, ap: mae: 0.605, rse: 1.119, pcc: -0.008, kcc: -0.039
+2025-07-10 14:52:40: Epoch: 002 Train Loss: 2.852, mae: 1.219, rse: 1.002, pcc: 0.071, kcc: 0.015
+2025-07-10 14:52:40: Epoch: 002 Test results, ap: mae: 0.595, rse: 1.080, pcc: 0.002, kcc: -0.019
+2025-07-10 14:52:41: Epoch: 003 Train Loss: 2.861, mae: 1.212, rse: 1.000, pcc: 0.025, kcc: -0.036
+2025-07-10 14:52:41: Epoch: 003 Test results, ap: mae: 0.611, rse: 1.145, pcc: 0.005, kcc: 0.029
+2025-07-10 14:52:42: Epoch: 004 Train Loss: 2.849, mae: 1.210, rse: 1.000, pcc: -0.028, kcc: -0.088
+2025-07-10 14:52:43: Epoch: 004 Test results, ap: mae: 0.620, rse: 1.177, pcc: -0.004, kcc: 0.007
+2025-07-10 14:52:43: Epoch: 005 Train Loss: 2.852, mae: 1.213, rse: 1.000, pcc: -0.036, kcc: -0.089
+2025-07-10 14:52:44: Epoch: 005 Test results, ap: mae: 0.605, rse: 1.120, pcc: -0.061, kcc: -0.031
+2025-07-10 14:52:44: Epoch: 006 Train Loss: 2.854, mae: 1.212, rse: 1.000, pcc: -0.052, kcc: -0.115
+2025-07-10 14:52:45: Epoch: 006 Test results, ap: mae: 0.609, rse: 1.136, pcc: 0.180, kcc: 0.199
+2025-07-10 14:52:46: Epoch: 007 Train Loss: 2.841, mae: 1.214, rse: 1.001, pcc: -0.023, kcc: -0.001
+2025-07-10 14:52:46: Epoch: 007 Test results, ap: mae: 0.604, rse: 1.116, pcc: 0.167, kcc: 0.182
+2025-07-10 14:52:47: Epoch: 008 Train Loss: 2.853, mae: 1.211, rse: 1.000, pcc: -0.016, kcc: 0.062
+2025-07-10 14:52:47: Epoch: 008 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.091, kcc: 0.012
+2025-07-10 14:52:48: Epoch: 009 Train Loss: 2.831, mae: 1.212, rse: 1.000, pcc: -0.035, kcc: -0.019
+2025-07-10 14:52:48: Epoch: 009 Test results, ap: mae: 0.609, rse: 1.136, pcc: 0.155, kcc: 0.074
+2025-07-10 14:52:49: Epoch: 010 Train Loss: 2.848, mae: 1.212, rse: 1.000, pcc: 0.068, kcc: 0.141
+2025-07-10 14:52:49: Epoch: 010 New best VALIDATION metrics
+2025-07-10 14:52:49: Epoch: 010 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.266, kcc: -0.013
+2025-07-10 14:52:50: Epoch: 011 Train Loss: 2.862, mae: 1.211, rse: 1.000, pcc: -0.092, kcc: -0.070
+2025-07-10 14:52:50: Epoch: 011 Test results, ap: mae: 0.615, rse: 1.161, pcc: -0.120, kcc: 0.018
+2025-07-10 14:52:50: Epoch: 011 New best TEST metrics
+2025-07-10 14:52:51: Epoch: 012 Train Loss: 2.855, mae: 1.212, rse: 1.000, pcc: 0.056, kcc: 0.142
+2025-07-10 14:52:51: Epoch: 012 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.160, kcc: -0.194
+2025-07-10 14:52:52: Epoch: 013 Train Loss: 2.852, mae: 1.212, rse: 1.000, pcc: -0.015, kcc: 0.056
+2025-07-10 14:52:52: Epoch: 013 Test results, ap: mae: 0.611, rse: 1.143, pcc: 0.381, kcc: 0.146
+2025-07-10 14:52:52: Epoch: 013 New best TEST metrics
+2025-07-10 14:52:53: Epoch: 014 Train Loss: 2.854, mae: 1.212, rse: 1.000, pcc: -0.082, kcc: -0.056
+2025-07-10 14:52:53: Epoch: 014 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.285, kcc: 0.202
+2025-07-10 14:52:54: Epoch: 015 Train Loss: 2.855, mae: 1.212, rse: 1.000, pcc: -0.016, kcc: 0.101
+2025-07-10 14:52:54: Epoch: 015 Test results, ap: mae: 0.609, rse: 1.135, pcc: 0.004, kcc: -0.041
+2025-07-10 14:52:55: Epoch: 016 Train Loss: 2.857, mae: 1.212, rse: 1.000, pcc: -0.068, kcc: -0.022
+2025-07-10 14:52:55: Epoch: 016 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.116, kcc: -0.137
+2025-07-10 14:52:56: Epoch: 017 Train Loss: 2.861, mae: 1.212, rse: 1.000, pcc: -0.036, kcc: -0.037
+2025-07-10 14:52:56: Epoch: 017 Test results, ap: mae: 0.608, rse: 1.132, pcc: -0.227, kcc: -0.060
+2025-07-10 14:52:57: Epoch: 018 Train Loss: 2.859, mae: 1.212, rse: 1.000, pcc: -0.027, kcc: 0.026
+2025-07-10 14:52:57: Epoch: 018 Test results, ap: mae: 0.609, rse: 1.135, pcc: 0.176, kcc: -0.061
+2025-07-10 14:52:58: Epoch: 019 Train Loss: 2.850, mae: 1.211, rse: 1.000, pcc: 0.006, kcc: 0.073
+2025-07-10 14:52:59: Epoch: 019 Test results, ap: mae: 0.612, rse: 1.147, pcc: 0.183, kcc: 0.195
+2025-07-10 14:52:59: Epoch: 020 Train Loss: 2.851, mae: 1.212, rse: 1.000, pcc: -0.084, kcc: -0.103
+2025-07-10 14:53:00: Epoch: 020 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.036, kcc: 0.060
+2025-07-10 14:53:00: Epoch: 021 Train Loss: 2.843, mae: 1.211, rse: 1.000, pcc: 0.002, kcc: 0.028
+2025-07-10 14:53:00: Epoch: 021 Test results, ap: mae: 0.613, rse: 1.151, pcc: -0.283, kcc: -0.226
+2025-07-10 14:53:01: Epoch: 022 Train Loss: 2.839, mae: 1.211, rse: 1.000, pcc: -0.117, kcc: -0.015
+2025-07-10 14:53:02: Epoch: 022 Test results, ap: mae: 0.612, rse: 1.148, pcc: -0.115, kcc: -0.083
+2025-07-10 14:53:02: Epoch: 023 Train Loss: 2.845, mae: 1.211, rse: 1.000, pcc: -0.112, kcc: -0.067
+2025-07-10 14:53:03: Epoch: 023 Test results, ap: mae: 0.613, rse: 1.150, pcc: 0.417, kcc: 0.200
+2025-07-10 14:53:03: Epoch: 023 New best TEST metrics
+2025-07-10 14:53:03: Epoch: 024 Train Loss: 2.843, mae: 1.212, rse: 1.000, pcc: -0.072, kcc: 0.041
+2025-07-10 14:53:04: Epoch: 024 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.056, kcc: -0.028
+2025-07-10 14:53:04: Epoch: 025 Train Loss: 2.856, mae: 1.212, rse: 1.000, pcc: -0.085, kcc: -0.029
+2025-07-10 14:53:04: Epoch: 025 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.303, kcc: 0.100
+2025-07-10 14:53:05: Epoch: 026 Train Loss: 2.839, mae: 1.212, rse: 1.000, pcc: -0.129, kcc: -0.131
+2025-07-10 14:53:05: Epoch: 026 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.043, kcc: 0.081
+2025-07-10 14:53:06: Epoch: 027 Train Loss: 2.853, mae: 1.212, rse: 1.000, pcc: -0.070, kcc: 0.027
+2025-07-10 14:53:06: Epoch: 027 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.138, kcc: -0.155
+2025-07-10 14:53:07: Epoch: 028 Train Loss: 2.837, mae: 1.212, rse: 1.000, pcc: -0.041, kcc: 0.064
+2025-07-10 14:53:07: Epoch: 028 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.152, kcc: -0.165
+2025-07-10 14:53:08: Epoch: 029 Train Loss: 2.866, mae: 1.212, rse: 1.000, pcc: 0.065, kcc: 0.149
+2025-07-10 14:53:08: Epoch: 029 New best VALIDATION metrics
+2025-07-10 14:53:08: Epoch: 029 Test results, ap: mae: 0.609, rse: 1.135, pcc: -0.239, kcc: -0.147
+2025-07-10 14:53:09: Epoch: 030 Train Loss: 2.858, mae: 1.212, rse: 1.000, pcc: -0.088, kcc: -0.077
+2025-07-10 14:53:09: Epoch: 030 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.129, kcc: 0.168
+2025-07-10 14:53:10: Epoch: 031 Train Loss: 2.869, mae: 1.212, rse: 1.000, pcc: -0.049, kcc: 0.086
+2025-07-10 14:53:10: Epoch: 031 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.009, kcc: -0.066
+2025-07-10 14:53:11: Epoch: 032 Train Loss: 2.863, mae: 1.212, rse: 1.000, pcc: -0.013, kcc: 0.103
+2025-07-10 14:53:11: Epoch: 032 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.200, kcc: -0.168
+2025-07-10 14:53:12: Epoch: 033 Train Loss: 2.844, mae: 1.212, rse: 1.000, pcc: -0.036, kcc: 0.038
+2025-07-10 14:53:12: Epoch: 033 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.183, kcc: -0.052
+2025-07-10 14:53:13: Epoch: 034 Train Loss: 2.851, mae: 1.212, rse: 1.000, pcc: -0.065, kcc: 0.000
+2025-07-10 14:53:13: Epoch: 034 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.048, kcc: 0.066
+2025-07-10 14:53:14: Epoch: 035 Train Loss: 2.844, mae: 1.212, rse: 1.000, pcc: -0.080, kcc: -0.129
+2025-07-10 14:53:14: Epoch: 035 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.264, kcc: 0.191
+2025-07-10 14:53:15: Epoch: 036 Train Loss: 2.879, mae: 1.212, rse: 1.000, pcc: -0.035, kcc: 0.065
+2025-07-10 14:53:15: Epoch: 036 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.020, kcc: 0.000
+2025-07-10 14:53:16: Epoch: 037 Train Loss: 2.833, mae: 1.212, rse: 1.000, pcc: -0.021, kcc: 0.066
+2025-07-10 14:53:16: Epoch: 037 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.200, kcc: -0.165
+2025-07-10 14:53:17: Epoch: 038 Train Loss: 2.866, mae: 1.212, rse: 1.000, pcc: -0.049, kcc: 0.186
+2025-07-10 14:53:17: Epoch: 038 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.057, kcc: -0.181
+2025-07-10 14:53:18: Epoch: 039 Train Loss: 2.845, mae: 1.212, rse: 1.000, pcc: -0.008, kcc: 0.102
+2025-07-10 14:53:18: Epoch: 039 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.223, kcc: -0.103
+2025-07-10 14:53:19: Epoch: 040 Train Loss: 2.827, mae: 1.212, rse: 1.000, pcc: -0.070, kcc: 0.035
+2025-07-10 14:53:19: Epoch: 040 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.040, kcc: -0.057
+2025-07-10 14:53:20: Epoch: 041 Train Loss: 2.853, mae: 1.212, rse: 1.000, pcc: -0.033, kcc: 0.088
+2025-07-10 14:53:20: Epoch: 041 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.120, kcc: -0.200
+2025-07-10 14:53:21: Epoch: 042 Train Loss: 2.853, mae: 1.212, rse: 1.000, pcc: -0.036, kcc: 0.081
+2025-07-10 14:53:21: Epoch: 042 Test results, ap: mae: 0.610, rse: 1.142, pcc: -0.092, kcc: -0.103
+2025-07-10 14:53:22: Epoch: 043 Train Loss: 2.856, mae: 1.212, rse: 1.000, pcc: -0.041, kcc: 0.062
+2025-07-10 14:53:22: Epoch: 043 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.170, kcc: -0.131
+2025-07-10 14:53:23: Epoch: 044 Train Loss: 2.862, mae: 1.212, rse: 1.000, pcc: 0.010, kcc: 0.124
+2025-07-10 14:53:23: Epoch: 044 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.228, kcc: -0.181
+2025-07-10 14:53:24: Epoch: 045 Train Loss: 2.833, mae: 1.212, rse: 1.000, pcc: 0.027, kcc: 0.108
+2025-07-10 14:53:24: Epoch: 045 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.212, kcc: -0.159
+2025-07-10 14:53:25: Epoch: 046 Train Loss: 2.852, mae: 1.212, rse: 1.000, pcc: -0.028, kcc: 0.091
+2025-07-10 14:53:25: Epoch: 046 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.081, kcc: -0.153
+2025-07-10 14:53:25: Epoch: 047 Train Loss: 2.845, mae: 1.212, rse: 1.000, pcc: -0.002, kcc: 0.131
+2025-07-10 14:53:26: Epoch: 047 Test results, ap: mae: 0.610, rse: 1.142, pcc: -0.067, kcc: -0.200
+2025-07-10 14:53:27: Epoch: 048 Train Loss: 2.825, mae: 1.212, rse: 1.000, pcc: 0.024, kcc: 0.105
+2025-07-10 14:53:27: Epoch: 048 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.047, kcc: -0.174
+2025-07-10 14:53:27: Epoch: 049 Train Loss: 2.839, mae: 1.212, rse: 1.000, pcc: 0.035, kcc: 0.111
+2025-07-10 14:53:28: Epoch: 049 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.065, kcc: -0.184
+2025-07-10 14:53:28: Epoch: 050 Train Loss: 2.856, mae: 1.212, rse: 1.000, pcc: 0.035, kcc: 0.087
+2025-07-10 14:53:29: Epoch: 050 Test results, ap: mae: 0.609, rse: 1.136, pcc: 0.001, kcc: -0.133
+2025-07-10 14:53:29: used time 0.01h
+2025-07-10 14:53:29: Cross Validation Finished!
+2025-07-10 14:53:29: Best validation perform list
+[[1.08364844 0.83137828 0.4136453  0.27671823]
+ [0.81485951 0.45423013 0.73889595 0.51023138]
+ [1.25482154 1.00002813 0.08883833 0.14457947]
+ [1.13177741 0.81926966 0.4307085  0.30461094]
+ [1.21219623 1.00012875 0.06517226 0.14892392]]
+2025-07-10 14:53:29: mean: [1.099 0.821 0.347 0.277]
+2025-07-10 14:53:29: std: [0.154 0.199 0.249 0.133]
+2025-07-10 14:53:29: Best test perform list
+[[ 0.62409979  1.17049742 -0.08985285 -0.1169127 ]
+ [ 0.61881471  1.12775636  0.07053012  0.09366893]
+ [ 0.61021036  1.14047492  1.          0.25122237]
+ [ 0.63660157  1.22625029 -0.2098556  -0.11903544]
+ [ 0.61267132  1.15022111  0.41707653  0.1998973 ]]
+2025-07-10 14:53:29: mean: [0.62  1.163 0.238 0.062]
+2025-07-10 14:53:29: std: [0.009 0.035 0.436 0.155]
diff --git a/run-reg/lstm-diff-256-oneway-smoothl1-32-0.001-35/config.json b/run-reg/lstm-diff-256-oneway-smoothl1-32-0.001-35/config.json
new file mode 100644
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+++ b/run-reg/lstm-diff-256-oneway-smoothl1-32-0.001-35/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": true, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 35, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "smoothl1", "dir": false, "classes": 1}
\ No newline at end of file
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@@ -0,0 +1,6 @@
+Valid
+1.1987690687179566,0.9788788557052612,0.23085934221744536,0.16010830253362657
+0.03987551153831574,0.024038206231474863,0.03976089684684769,0.040319078465643976
+Test
+0.6059341430664062,1.0787009716033935,0.11137357726693153,0.03253195732831955
+0.014375988946893854,0.02479899824710972,0.12647658760146968,0.1437368104504358
diff --git a/run-reg/lstm-diff-256-oneway-smoothl1-32-0.001-35/training.log b/run-reg/lstm-diff-256-oneway-smoothl1-32-0.001-35/training.log
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@@ -0,0 +1,451 @@
+2025-07-09 15:23:35: saving_dir: ./run-reg/lstm-diff-256-oneway-smoothl1-32-0.001-35
+2025-07-09 15:23:35: Loading Train Dataset
+2025-07-09 15:23:36: Loading Test Dataset
+2025-07-09 15:23:37: Running Cross Validation 0
+2025-07-09 15:23:37: Fold 0  Train set:405, Valid set:102, Test set: 108
+2025-07-09 15:23:38: Epoch: 001 Train Loss: 0.865, mae: 1.144, rse: 1.030, pcc: -0.004, kcc: -0.153
+2025-07-09 15:23:38: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:23:38: Epoch: 001 Test results, ap: mae: 0.656, rse: 1.302, pcc: 0.026, kcc: 0.022
+2025-07-09 15:23:39: Epoch: 002 Train Loss: 0.848, mae: 1.146, rse: 1.016, pcc: -0.015, kcc: -0.161
+2025-07-09 15:23:39: Epoch: 002 Test results, ap: mae: 0.628, rse: 1.200, pcc: 0.019, kcc: 0.049
+2025-07-09 15:23:40: Epoch: 003 Train Loss: 0.840, mae: 1.149, rse: 1.009, pcc: 0.004, kcc: -0.007
+2025-07-09 15:23:40: Epoch: 003 New best VALIDATION metrics
+2025-07-09 15:23:40: Epoch: 003 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.038, kcc: 0.010
+2025-07-09 15:23:41: Epoch: 004 Train Loss: 0.847, mae: 1.151, rse: 1.006, pcc: 0.025, kcc: 0.138
+2025-07-09 15:23:41: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:23:41: Epoch: 004 Test results, ap: mae: 0.604, rse: 1.117, pcc: 0.008, kcc: -0.013
+2025-07-09 15:23:41: Epoch: 005 Train Loss: 0.844, mae: 1.156, rse: 1.002, pcc: -0.044, kcc: -0.164
+2025-07-09 15:23:42: Epoch: 005 Test results, ap: mae: 0.595, rse: 1.080, pcc: 0.038, kcc: 0.054
+2025-07-09 15:23:42: Epoch: 006 Train Loss: 0.836, mae: 1.156, rse: 1.002, pcc: -0.020, kcc: -0.045
+2025-07-09 15:23:43: Epoch: 006 Test results, ap: mae: 0.595, rse: 1.077, pcc: 0.010, kcc: -0.017
+2025-07-09 15:23:43: Epoch: 007 Train Loss: 0.838, mae: 1.157, rse: 1.002, pcc: -0.001, kcc: 0.137
+2025-07-09 15:23:43: Epoch: 007 Test results, ap: mae: 0.594, rse: 1.072, pcc: -0.102, kcc: -0.110
+2025-07-09 15:23:44: Epoch: 008 Train Loss: 0.844, mae: 1.156, rse: 1.002, pcc: 0.011, kcc: 0.138
+2025-07-09 15:23:44: Epoch: 008 Test results, ap: mae: 0.595, rse: 1.080, pcc: -0.021, kcc: -0.078
+2025-07-09 15:23:45: Epoch: 009 Train Loss: 0.859, mae: 1.153, rse: 1.004, pcc: 0.042, kcc: 0.170
+2025-07-09 15:23:45: Epoch: 009 New best VALIDATION metrics
+2025-07-09 15:23:45: Epoch: 009 Test results, ap: mae: 0.600, rse: 1.099, pcc: -0.037, kcc: -0.097
+2025-07-09 15:23:46: Epoch: 010 Train Loss: 0.849, mae: 1.154, rse: 1.003, pcc: 0.049, kcc: 0.166
+2025-07-09 15:23:46: Epoch: 010 New best VALIDATION metrics
+2025-07-09 15:23:46: Epoch: 010 Test results, ap: mae: 0.599, rse: 1.096, pcc: -0.088, kcc: -0.138
+2025-07-09 15:23:47: Epoch: 011 Train Loss: 0.852, mae: 1.153, rse: 1.004, pcc: 0.088, kcc: 0.173
+2025-07-09 15:23:47: Epoch: 011 New best VALIDATION metrics
+2025-07-09 15:23:47: Epoch: 011 Test results, ap: mae: 0.600, rse: 1.097, pcc: -0.067, kcc: -0.136
+2025-07-09 15:23:47: Epoch: 011 New best TEST metrics
+2025-07-09 15:23:48: Epoch: 012 Train Loss: 0.850, mae: 1.153, rse: 1.004, pcc: 0.039, kcc: 0.202
+2025-07-09 15:23:48: Epoch: 012 Test results, ap: mae: 0.602, rse: 1.105, pcc: -0.067, kcc: -0.120
+2025-07-09 15:23:48: Epoch: 012 New best TEST metrics
+2025-07-09 15:23:49: Epoch: 013 Train Loss: 0.835, mae: 1.154, rse: 1.003, pcc: 0.044, kcc: 0.190
+2025-07-09 15:23:49: Epoch: 013 Test results, ap: mae: 0.599, rse: 1.094, pcc: -0.060, kcc: -0.120
+2025-07-09 15:23:49: Epoch: 013 New best TEST metrics
+2025-07-09 15:23:50: Epoch: 014 Train Loss: 0.849, mae: 1.154, rse: 1.002, pcc: 0.165, kcc: 0.161
+2025-07-09 15:23:50: Epoch: 014 New best VALIDATION metrics
+2025-07-09 15:23:50: Epoch: 014 Test results, ap: mae: 0.598, rse: 1.089, pcc: -0.090, kcc: -0.149
+2025-07-09 15:23:50: Epoch: 015 Train Loss: 0.838, mae: 1.156, rse: 1.002, pcc: -0.005, kcc: 0.177
+2025-07-09 15:23:51: Epoch: 015 Test results, ap: mae: 0.596, rse: 1.080, pcc: 0.003, kcc: -0.061
+2025-07-09 15:23:51: Epoch: 015 New best TEST metrics
+2025-07-09 15:23:51: Epoch: 016 Train Loss: 0.824, mae: 1.155, rse: 1.002, pcc: -0.115, kcc: -0.181
+2025-07-09 15:23:52: Epoch: 016 Test results, ap: mae: 0.596, rse: 1.082, pcc: 0.051, kcc: 0.123
+2025-07-09 15:23:52: Epoch: 016 New best TEST metrics
+2025-07-09 15:23:52: Epoch: 017 Train Loss: 0.859, mae: 1.156, rse: 1.002, pcc: -0.082, kcc: -0.194
+2025-07-09 15:23:52: Epoch: 017 Test results, ap: mae: 0.595, rse: 1.076, pcc: 0.032, kcc: 0.093
+2025-07-09 15:23:53: Epoch: 018 Train Loss: 0.853, mae: 1.156, rse: 1.002, pcc: -0.081, kcc: -0.189
+2025-07-09 15:23:53: Epoch: 018 Test results, ap: mae: 0.595, rse: 1.079, pcc: 0.055, kcc: 0.115
+2025-07-09 15:23:54: Epoch: 019 Train Loss: 0.845, mae: 1.154, rse: 1.003, pcc: -0.098, kcc: -0.203
+2025-07-09 15:23:54: Epoch: 019 Test results, ap: mae: 0.598, rse: 1.090, pcc: -0.127, kcc: -0.118
+2025-07-09 15:23:55: Epoch: 020 Train Loss: 0.834, mae: 1.154, rse: 1.003, pcc: -0.143, kcc: -0.167
+2025-07-09 15:23:55: Epoch: 020 Test results, ap: mae: 0.598, rse: 1.090, pcc: -0.029, kcc: 0.009
+2025-07-09 15:23:56: Epoch: 021 Train Loss: 0.822, mae: 1.154, rse: 1.003, pcc: 0.047, kcc: 0.142
+2025-07-09 15:23:56: Epoch: 021 Test results, ap: mae: 0.598, rse: 1.091, pcc: -0.037, kcc: -0.101
+2025-07-09 15:23:57: Epoch: 022 Train Loss: 0.824, mae: 1.154, rse: 1.003, pcc: 0.057, kcc: 0.166
+2025-07-09 15:23:57: Epoch: 022 Test results, ap: mae: 0.598, rse: 1.093, pcc: -0.043, kcc: -0.107
+2025-07-09 15:23:58: Epoch: 023 Train Loss: 0.841, mae: 1.154, rse: 1.003, pcc: 0.112, kcc: 0.174
+2025-07-09 15:23:58: Epoch: 023 Test results, ap: mae: 0.599, rse: 1.093, pcc: -0.063, kcc: -0.134
+2025-07-09 15:23:58: Epoch: 024 Train Loss: 0.829, mae: 1.154, rse: 1.003, pcc: 0.172, kcc: 0.163
+2025-07-09 15:23:58: Epoch: 024 New best VALIDATION metrics
+2025-07-09 15:23:59: Epoch: 024 Test results, ap: mae: 0.598, rse: 1.091, pcc: -0.071, kcc: -0.152
+2025-07-09 15:23:59: Epoch: 025 Train Loss: 0.833, mae: 1.154, rse: 1.003, pcc: 0.083, kcc: 0.183
+2025-07-09 15:24:00: Epoch: 025 Test results, ap: mae: 0.599, rse: 1.093, pcc: -0.020, kcc: -0.142
+2025-07-09 15:24:00: Epoch: 026 Train Loss: 0.831, mae: 1.154, rse: 1.003, pcc: 0.057, kcc: 0.163
+2025-07-09 15:24:00: Epoch: 026 Test results, ap: mae: 0.599, rse: 1.093, pcc: -0.021, kcc: -0.073
+2025-07-09 15:24:01: Epoch: 027 Train Loss: 0.822, mae: 1.154, rse: 1.003, pcc: 0.053, kcc: 0.125
+2025-07-09 15:24:01: Epoch: 027 Test results, ap: mae: 0.598, rse: 1.092, pcc: -0.040, kcc: -0.092
+2025-07-09 15:24:02: Epoch: 028 Train Loss: 0.834, mae: 1.154, rse: 1.003, pcc: 0.055, kcc: 0.174
+2025-07-09 15:24:02: Epoch: 028 Test results, ap: mae: 0.599, rse: 1.093, pcc: -0.023, kcc: -0.078
+2025-07-09 15:24:03: Epoch: 029 Train Loss: 0.852, mae: 1.154, rse: 1.004, pcc: 0.064, kcc: 0.168
+2025-07-09 15:24:03: Epoch: 029 Test results, ap: mae: 0.599, rse: 1.097, pcc: -0.027, kcc: -0.066
+2025-07-09 15:24:04: Epoch: 030 Train Loss: 0.850, mae: 1.154, rse: 1.003, pcc: 0.082, kcc: 0.178
+2025-07-09 15:24:04: Epoch: 030 Test results, ap: mae: 0.599, rse: 1.096, pcc: -0.047, kcc: -0.115
+2025-07-09 15:24:05: Epoch: 031 Train Loss: 0.851, mae: 1.153, rse: 1.004, pcc: 0.099, kcc: 0.178
+2025-07-09 15:24:05: Epoch: 031 Test results, ap: mae: 0.600, rse: 1.098, pcc: -0.057, kcc: -0.123
+2025-07-09 15:24:06: Epoch: 032 Train Loss: 0.849, mae: 1.154, rse: 1.003, pcc: 0.138, kcc: 0.181
+2025-07-09 15:24:06: Epoch: 032 Test results, ap: mae: 0.599, rse: 1.095, pcc: -0.077, kcc: -0.137
+2025-07-09 15:24:06: Epoch: 033 Train Loss: 0.832, mae: 1.153, rse: 1.003, pcc: 0.196, kcc: 0.170
+2025-07-09 15:24:06: Epoch: 033 New best VALIDATION metrics
+2025-07-09 15:24:07: Epoch: 033 Test results, ap: mae: 0.599, rse: 1.095, pcc: -0.092, kcc: -0.163
+2025-07-09 15:24:07: Epoch: 034 Train Loss: 0.853, mae: 1.152, rse: 1.002, pcc: 0.242, kcc: 0.164
+2025-07-09 15:24:07: Epoch: 034 New best VALIDATION metrics
+2025-07-09 15:24:08: Epoch: 034 Test results, ap: mae: 0.600, rse: 1.096, pcc: -0.057, kcc: -0.167
+2025-07-09 15:24:08: Epoch: 035 Train Loss: 0.841, mae: 1.149, rse: 1.002, pcc: 0.164, kcc: 0.150
+2025-07-09 15:24:09: Epoch: 035 Test results, ap: mae: 0.604, rse: 1.111, pcc: -0.256, kcc: -0.162
+2025-07-09 15:24:09: used time 0.01h
+2025-07-09 15:24:09: Running Cross Validation 1
+2025-07-09 15:24:09: Fold 1  Train set:405, Valid set:102, Test set: 108
+2025-07-09 15:24:09: Epoch: 001 Train Loss: 0.888, mae: 1.172, rse: 1.063, pcc: -0.013, kcc: -0.156
+2025-07-09 15:24:09: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:24:09: Epoch: 001 Test results, ap: mae: 0.709, rse: 1.490, pcc: 0.003, kcc: 0.022
+2025-07-09 15:24:10: Epoch: 002 Train Loss: 0.853, mae: 1.182, rse: 1.035, pcc: -0.020, kcc: -0.149
+2025-07-09 15:24:10: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:24:10: Epoch: 002 Test results, ap: mae: 0.658, rse: 1.309, pcc: 0.028, kcc: 0.064
+2025-07-09 15:24:11: Epoch: 003 Train Loss: 0.841, mae: 1.199, rse: 1.014, pcc: -0.015, kcc: -0.126
+2025-07-09 15:24:11: Epoch: 003 New best VALIDATION metrics
+2025-07-09 15:24:11: Epoch: 003 Test results, ap: mae: 0.615, rse: 1.160, pcc: 0.016, kcc: 0.041
+2025-07-09 15:24:12: Epoch: 004 Train Loss: 0.829, mae: 1.218, rse: 1.006, pcc: 0.002, kcc: -0.045
+2025-07-09 15:24:12: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:24:12: Epoch: 004 Test results, ap: mae: 0.598, rse: 1.089, pcc: -0.005, kcc: -0.019
+2025-07-09 15:24:13: Epoch: 005 Train Loss: 0.821, mae: 1.222, rse: 1.005, pcc: 0.004, kcc: -0.005
+2025-07-09 15:24:13: Epoch: 005 New best VALIDATION metrics
+2025-07-09 15:24:13: Epoch: 005 Test results, ap: mae: 0.595, rse: 1.076, pcc: 0.006, kcc: 0.029
+2025-07-09 15:24:14: Epoch: 006 Train Loss: 0.825, mae: 1.224, rse: 1.004, pcc: -0.004, kcc: -0.127
+2025-07-09 15:24:14: Epoch: 006 Test results, ap: mae: 0.594, rse: 1.072, pcc: 0.001, kcc: 0.013
+2025-07-09 15:24:15: Epoch: 007 Train Loss: 0.833, mae: 1.220, rse: 1.005, pcc: -0.007, kcc: -0.109
+2025-07-09 15:24:15: Epoch: 007 Test results, ap: mae: 0.596, rse: 1.082, pcc: 0.041, kcc: 0.036
+2025-07-09 15:24:16: Epoch: 008 Train Loss: 0.828, mae: 1.221, rse: 1.005, pcc: -0.001, kcc: 0.059
+2025-07-09 15:24:16: Epoch: 008 New best VALIDATION metrics
+2025-07-09 15:24:16: Epoch: 008 Test results, ap: mae: 0.595, rse: 1.080, pcc: -0.016, kcc: -0.045
+2025-07-09 15:24:16: Epoch: 009 Train Loss: 0.821, mae: 1.219, rse: 1.006, pcc: -0.004, kcc: 0.030
+2025-07-09 15:24:17: Epoch: 009 Test results, ap: mae: 0.597, rse: 1.087, pcc: 0.017, kcc: 0.014
+2025-07-09 15:24:17: Epoch: 010 Train Loss: 0.819, mae: 1.208, rse: 1.008, pcc: 0.039, kcc: 0.080
+2025-07-09 15:24:17: Epoch: 010 New best VALIDATION metrics
+2025-07-09 15:24:18: Epoch: 010 Test results, ap: mae: 0.605, rse: 1.119, pcc: -0.138, kcc: -0.164
+2025-07-09 15:24:18: Epoch: 011 Train Loss: 0.834, mae: 1.209, rse: 1.009, pcc: 0.017, kcc: 0.032
+2025-07-09 15:24:19: Epoch: 011 Test results, ap: mae: 0.604, rse: 1.115, pcc: -0.044, kcc: -0.091
+2025-07-09 15:24:19: Epoch: 011 New best TEST metrics
+2025-07-09 15:24:19: Epoch: 012 Train Loss: 0.841, mae: 1.213, rse: 1.007, pcc: 0.021, kcc: 0.044
+2025-07-09 15:24:19: Epoch: 012 Test results, ap: mae: 0.601, rse: 1.104, pcc: 0.040, kcc: 0.021
+2025-07-09 15:24:19: Epoch: 012 New best TEST metrics
+2025-07-09 15:24:20: Epoch: 013 Train Loss: 0.836, mae: 1.219, rse: 1.005, pcc: 0.025, kcc: 0.047
+2025-07-09 15:24:20: Epoch: 013 Test results, ap: mae: 0.597, rse: 1.085, pcc: 0.034, kcc: -0.001
+2025-07-09 15:24:21: Epoch: 014 Train Loss: 0.839, mae: 1.217, rse: 1.005, pcc: 0.051, kcc: 0.045
+2025-07-09 15:24:21: Epoch: 014 Test results, ap: mae: 0.598, rse: 1.090, pcc: -0.019, kcc: -0.074
+2025-07-09 15:24:22: Epoch: 015 Train Loss: 0.839, mae: 1.193, rse: 0.998, pcc: 0.167, kcc: 0.079
+2025-07-09 15:24:22: Epoch: 015 New best VALIDATION metrics
+2025-07-09 15:24:22: Epoch: 015 Test results, ap: mae: 0.609, rse: 1.123, pcc: -0.176, kcc: -0.177
+2025-07-09 15:24:23: Epoch: 016 Train Loss: 0.834, mae: 1.226, rse: 1.001, pcc: 0.053, kcc: 0.013
+2025-07-09 15:24:23: Epoch: 016 Test results, ap: mae: 0.590, rse: 1.053, pcc: 0.162, kcc: 0.095
+2025-07-09 15:24:23: Epoch: 016 New best TEST metrics
+2025-07-09 15:24:24: Epoch: 017 Train Loss: 0.827, mae: 1.236, rse: 1.001, pcc: 0.013, kcc: 0.017
+2025-07-09 15:24:24: Epoch: 017 Test results, ap: mae: 0.589, rse: 1.049, pcc: -0.016, kcc: -0.093
+2025-07-09 15:24:25: Epoch: 018 Train Loss: 0.829, mae: 1.226, rse: 1.001, pcc: 0.046, kcc: 0.041
+2025-07-09 15:24:25: Epoch: 018 Test results, ap: mae: 0.593, rse: 1.067, pcc: -0.038, kcc: -0.117
+2025-07-09 15:24:25: Epoch: 019 Train Loss: 0.825, mae: 1.197, rse: 0.994, pcc: 0.120, kcc: 0.091
+2025-07-09 15:24:26: Epoch: 019 Test results, ap: mae: 0.614, rse: 1.124, pcc: -0.124, kcc: -0.148
+2025-07-09 15:24:26: Epoch: 020 Train Loss: 0.794, mae: 1.234, rse: 0.996, pcc: 0.170, kcc: 0.059
+2025-07-09 15:24:27: Epoch: 020 Test results, ap: mae: 0.586, rse: 1.035, pcc: 0.033, kcc: -0.014
+2025-07-09 15:24:27: Epoch: 021 Train Loss: 0.827, mae: 1.236, rse: 0.996, pcc: 0.166, kcc: 0.051
+2025-07-09 15:24:27: Epoch: 021 Test results, ap: mae: 0.586, rse: 1.034, pcc: -0.023, kcc: -0.063
+2025-07-09 15:24:28: Epoch: 022 Train Loss: 0.821, mae: 1.197, rse: 0.981, pcc: 0.216, kcc: 0.104
+2025-07-09 15:24:28: Epoch: 022 New best VALIDATION metrics
+2025-07-09 15:24:28: Epoch: 022 Test results, ap: mae: 0.604, rse: 1.087, pcc: -0.160, kcc: -0.172
+2025-07-09 15:24:29: Epoch: 023 Train Loss: 0.796, mae: 1.271, rse: 1.030, pcc: 0.055, kcc: 0.110
+2025-07-09 15:24:29: Epoch: 023 Test results, ap: mae: 0.692, rse: 1.361, pcc: -0.219, kcc: -0.138
+2025-07-09 15:24:30: Epoch: 024 Train Loss: 0.769, mae: 1.202, rse: 0.982, pcc: 0.159, kcc: 0.087
+2025-07-09 15:24:30: Epoch: 024 Test results, ap: mae: 0.635, rse: 1.175, pcc: -0.141, kcc: -0.155
+2025-07-09 15:24:31: Epoch: 025 Train Loss: 0.777, mae: 1.194, rse: 0.985, pcc: 0.191, kcc: 0.116
+2025-07-09 15:24:31: Epoch: 025 Test results, ap: mae: 0.695, rse: 1.465, pcc: -0.234, kcc: -0.143
+2025-07-09 15:24:32: Epoch: 026 Train Loss: 0.767, mae: 1.235, rse: 1.005, pcc: 0.134, kcc: 0.099
+2025-07-09 15:24:32: Epoch: 026 Test results, ap: mae: 0.695, rse: 1.462, pcc: -0.243, kcc: -0.126
+2025-07-09 15:24:33: Epoch: 027 Train Loss: 0.748, mae: 1.234, rse: 0.994, pcc: 0.167, kcc: 0.099
+2025-07-09 15:24:33: Epoch: 027 Test results, ap: mae: 0.681, rse: 1.389, pcc: -0.221, kcc: -0.114
+2025-07-09 15:24:34: Epoch: 028 Train Loss: 0.721, mae: 1.173, rse: 0.954, pcc: 0.269, kcc: 0.129
+2025-07-09 15:24:34: Epoch: 028 New best VALIDATION metrics
+2025-07-09 15:24:34: Epoch: 028 Test results, ap: mae: 0.655, rse: 1.226, pcc: -0.095, kcc: -0.135
+2025-07-09 15:24:35: Epoch: 029 Train Loss: 0.793, mae: 1.180, rse: 0.949, pcc: 0.281, kcc: 0.150
+2025-07-09 15:24:35: Epoch: 029 New best VALIDATION metrics
+2025-07-09 15:24:35: Epoch: 029 Test results, ap: mae: 0.662, rse: 1.251, pcc: -0.088, kcc: -0.118
+2025-07-09 15:24:35: Epoch: 030 Train Loss: 0.781, mae: 1.178, rse: 0.943, pcc: 0.280, kcc: 0.158
+2025-07-09 15:24:35: Epoch: 030 New best VALIDATION metrics
+2025-07-09 15:24:36: Epoch: 030 Test results, ap: mae: 0.656, rse: 1.223, pcc: -0.096, kcc: -0.127
+2025-07-09 15:24:36: Epoch: 031 Train Loss: 0.755, mae: 1.189, rse: 0.949, pcc: 0.275, kcc: 0.144
+2025-07-09 15:24:37: Epoch: 031 Test results, ap: mae: 0.676, rse: 1.324, pcc: -0.135, kcc: -0.153
+2025-07-09 15:24:37: Epoch: 032 Train Loss: 0.776, mae: 1.204, rse: 0.957, pcc: 0.257, kcc: 0.153
+2025-07-09 15:24:37: Epoch: 032 Test results, ap: mae: 0.690, rse: 1.397, pcc: -0.168, kcc: -0.142
+2025-07-09 15:24:38: Epoch: 033 Train Loss: 0.739, mae: 1.217, rse: 0.958, pcc: 0.226, kcc: 0.113
+2025-07-09 15:24:38: Epoch: 033 Test results, ap: mae: 0.669, rse: 1.278, pcc: -0.148, kcc: -0.121
+2025-07-09 15:24:39: Epoch: 034 Train Loss: 0.736, mae: 1.255, rse: 0.992, pcc: 0.219, kcc: 0.124
+2025-07-09 15:24:39: Epoch: 034 Test results, ap: mae: 0.717, rse: 1.526, pcc: -0.216, kcc: -0.115
+2025-07-09 15:24:40: Epoch: 035 Train Loss: 0.738, mae: 1.210, rse: 0.954, pcc: 0.237, kcc: 0.107
+2025-07-09 15:24:40: Epoch: 035 Test results, ap: mae: 0.679, rse: 1.336, pcc: -0.174, kcc: -0.125
+2025-07-09 15:24:40: used time 0.01h
+2025-07-09 15:24:40: Running Cross Validation 2
+2025-07-09 15:24:40: Fold 2  Train set:406, Valid set:101, Test set: 108
+2025-07-09 15:24:41: Epoch: 001 Train Loss: 0.872, mae: 1.236, rse: 1.081, pcc: -0.016, kcc: -0.019
+2025-07-09 15:24:41: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:24:41: Epoch: 001 Test results, ap: mae: 0.704, rse: 1.474, pcc: 0.018, kcc: 0.071
+2025-07-09 15:24:42: Epoch: 002 Train Loss: 0.849, mae: 1.223, rse: 1.050, pcc: 0.001, kcc: -0.018
+2025-07-09 15:24:42: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:24:42: Epoch: 002 Test results, ap: mae: 0.655, rse: 1.297, pcc: 0.023, kcc: 0.065
+2025-07-09 15:24:43: Epoch: 003 Train Loss: 0.840, mae: 1.217, rse: 1.028, pcc: -0.375, kcc: -0.089
+2025-07-09 15:24:43: Epoch: 003 Test results, ap: mae: 0.618, rse: 1.169, pcc: 0.027, kcc: 0.047
+2025-07-09 15:24:44: Epoch: 004 Train Loss: 0.843, mae: 1.217, rse: 1.022, pcc: -0.099, kcc: 0.076
+2025-07-09 15:24:44: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:24:44: Epoch: 004 Test results, ap: mae: 0.608, rse: 1.132, pcc: 0.155, kcc: 0.092
+2025-07-09 15:24:44: Epoch: 005 Train Loss: 0.828, mae: 1.217, rse: 1.017, pcc: -0.115, kcc: -0.016
+2025-07-09 15:24:45: Epoch: 005 Test results, ap: mae: 0.601, rse: 1.103, pcc: -0.026, kcc: -0.075
+2025-07-09 15:24:45: Epoch: 006 Train Loss: 0.848, mae: 1.217, rse: 1.019, pcc: -0.094, kcc: -0.002
+2025-07-09 15:24:46: Epoch: 006 Test results, ap: mae: 0.603, rse: 1.112, pcc: -0.038, kcc: -0.037
+2025-07-09 15:24:46: Epoch: 007 Train Loss: 0.856, mae: 1.218, rse: 1.013, pcc: -0.092, kcc: 0.003
+2025-07-09 15:24:47: Epoch: 007 Test results, ap: mae: 0.595, rse: 1.077, pcc: 0.002, kcc: 0.007
+2025-07-09 15:24:47: Epoch: 008 Train Loss: 0.829, mae: 1.217, rse: 1.021, pcc: -0.130, kcc: 0.067
+2025-07-09 15:24:47: Epoch: 008 Test results, ap: mae: 0.606, rse: 1.123, pcc: -0.023, kcc: -0.062
+2025-07-09 15:24:48: Epoch: 009 Train Loss: 0.838, mae: 1.216, rse: 1.019, pcc: -0.120, kcc: 0.064
+2025-07-09 15:24:48: Epoch: 009 Test results, ap: mae: 0.603, rse: 1.112, pcc: -0.037, kcc: -0.095
+2025-07-09 15:24:49: Epoch: 010 Train Loss: 0.842, mae: 1.207, rse: 1.038, pcc: 0.124, kcc: 0.138
+2025-07-09 15:24:49: Epoch: 010 New best VALIDATION metrics
+2025-07-09 15:24:49: Epoch: 010 Test results, ap: mae: 0.633, rse: 1.192, pcc: -0.046, kcc: -0.160
+2025-07-09 15:24:50: Epoch: 011 Train Loss: 0.816, mae: 1.217, rse: 1.022, pcc: 0.211, kcc: 0.186
+2025-07-09 15:24:50: Epoch: 011 New best VALIDATION metrics
+2025-07-09 15:24:50: Epoch: 011 Test results, ap: mae: 0.643, rse: 1.209, pcc: -0.039, kcc: -0.144
+2025-07-09 15:24:50: Epoch: 011 New best TEST metrics
+2025-07-09 15:24:51: Epoch: 012 Train Loss: 0.804, mae: 1.188, rse: 1.000, pcc: 0.183, kcc: 0.234
+2025-07-09 15:24:51: Epoch: 012 New best VALIDATION metrics
+2025-07-09 15:24:51: Epoch: 012 Test results, ap: mae: 0.616, rse: 1.115, pcc: -0.059, kcc: -0.155
+2025-07-09 15:24:51: Epoch: 012 New best TEST metrics
+2025-07-09 15:24:52: Epoch: 013 Train Loss: 0.787, mae: 1.201, rse: 1.022, pcc: 0.151, kcc: 0.145
+2025-07-09 15:24:52: Epoch: 013 Test results, ap: mae: 0.633, rse: 1.152, pcc: -0.018, kcc: -0.116
+2025-07-09 15:24:52: Epoch: 013 New best TEST metrics
+2025-07-09 15:24:53: Epoch: 014 Train Loss: 0.800, mae: 1.188, rse: 0.992, pcc: 0.244, kcc: 0.233
+2025-07-09 15:24:53: Epoch: 014 New best VALIDATION metrics
+2025-07-09 15:24:53: Epoch: 014 Test results, ap: mae: 0.678, rse: 1.427, pcc: -0.201, kcc: -0.132
+2025-07-09 15:24:53: Epoch: 015 Train Loss: 0.785, mae: 1.210, rse: 1.011, pcc: 0.148, kcc: 0.112
+2025-07-09 15:24:54: Epoch: 015 Test results, ap: mae: 0.681, rse: 1.403, pcc: -0.207, kcc: -0.193
+2025-07-09 15:24:54: Epoch: 016 Train Loss: 0.789, mae: 1.177, rse: 0.987, pcc: 0.183, kcc: 0.172
+2025-07-09 15:24:55: Epoch: 016 Test results, ap: mae: 0.659, rse: 1.297, pcc: -0.207, kcc: -0.077
+2025-07-09 15:24:55: Epoch: 017 Train Loss: 0.798, mae: 1.179, rse: 0.982, pcc: 0.210, kcc: 0.158
+2025-07-09 15:24:56: Epoch: 017 Test results, ap: mae: 0.671, rse: 1.357, pcc: -0.198, kcc: -0.159
+2025-07-09 15:24:56: Epoch: 018 Train Loss: 0.764, mae: 1.207, rse: 1.014, pcc: 0.124, kcc: 0.102
+2025-07-09 15:24:56: Epoch: 018 Test results, ap: mae: 0.695, rse: 1.424, pcc: -0.186, kcc: -0.142
+2025-07-09 15:24:57: Epoch: 019 Train Loss: 0.804, mae: 1.212, rse: 1.017, pcc: 0.099, kcc: 0.078
+2025-07-09 15:24:57: Epoch: 019 Test results, ap: mae: 0.691, rse: 1.430, pcc: -0.204, kcc: -0.219
+2025-07-09 15:24:58: Epoch: 020 Train Loss: 0.829, mae: 1.214, rse: 1.022, pcc: 0.085, kcc: 0.060
+2025-07-09 15:24:58: Epoch: 020 Test results, ap: mae: 0.693, rse: 1.445, pcc: -0.204, kcc: -0.114
+2025-07-09 15:24:59: Epoch: 021 Train Loss: 0.787, mae: 1.232, rse: 1.044, pcc: 0.085, kcc: 0.044
+2025-07-09 15:24:59: Epoch: 021 Test results, ap: mae: 0.727, rse: 1.607, pcc: -0.204, kcc: -0.144
+2025-07-09 15:25:00: Epoch: 022 Train Loss: 0.784, mae: 1.226, rse: 1.035, pcc: 0.085, kcc: 0.039
+2025-07-09 15:25:00: Epoch: 022 Test results, ap: mae: 0.712, rse: 1.542, pcc: -0.204, kcc: -0.187
+2025-07-09 15:25:01: Epoch: 023 Train Loss: 0.798, mae: 1.223, rse: 1.033, pcc: 0.085, kcc: 0.047
+2025-07-09 15:25:01: Epoch: 023 Test results, ap: mae: 0.707, rse: 1.520, pcc: -0.204, kcc: -0.142
+2025-07-09 15:25:02: Epoch: 024 Train Loss: 0.798, mae: 1.216, rse: 1.027, pcc: 0.063, kcc: 0.037
+2025-07-09 15:25:02: Epoch: 024 Test results, ap: mae: 0.684, rse: 1.404, pcc: -0.204, kcc: -0.155
+2025-07-09 15:25:02: Epoch: 025 Train Loss: 0.816, mae: 1.186, rse: 0.977, pcc: 0.191, kcc: 0.153
+2025-07-09 15:25:03: Epoch: 025 Test results, ap: mae: 0.658, rse: 1.293, pcc: -0.263, kcc: -0.190
+2025-07-09 15:25:03: Epoch: 026 Train Loss: 0.768, mae: 1.185, rse: 0.991, pcc: 0.164, kcc: 0.150
+2025-07-09 15:25:04: Epoch: 026 Test results, ap: mae: 0.647, rse: 1.188, pcc: -0.129, kcc: -0.143
+2025-07-09 15:25:04: Epoch: 027 Train Loss: 0.783, mae: 1.182, rse: 0.993, pcc: 0.161, kcc: 0.202
+2025-07-09 15:25:04: Epoch: 027 Test results, ap: mae: 0.629, rse: 1.098, pcc: -0.019, kcc: -0.126
+2025-07-09 15:25:04: Epoch: 027 New best TEST metrics
+2025-07-09 15:25:05: Epoch: 028 Train Loss: 0.802, mae: 1.187, rse: 1.002, pcc: 0.161, kcc: 0.179
+2025-07-09 15:25:05: Epoch: 028 Test results, ap: mae: 0.637, rse: 1.124, pcc: -0.019, kcc: -0.163
+2025-07-09 15:25:06: Epoch: 029 Train Loss: 0.794, mae: 1.195, rse: 1.004, pcc: 0.152, kcc: 0.164
+2025-07-09 15:25:06: Epoch: 029 Test results, ap: mae: 0.636, rse: 1.122, pcc: -0.019, kcc: -0.128
+2025-07-09 15:25:07: Epoch: 030 Train Loss: 0.781, mae: 1.195, rse: 1.003, pcc: 0.151, kcc: 0.195
+2025-07-09 15:25:07: Epoch: 030 Test results, ap: mae: 0.635, rse: 1.119, pcc: -0.019, kcc: -0.107
+2025-07-09 15:25:08: Epoch: 031 Train Loss: 0.780, mae: 1.193, rse: 1.000, pcc: 0.151, kcc: 0.197
+2025-07-09 15:25:08: Epoch: 031 Test results, ap: mae: 0.632, rse: 1.109, pcc: -0.019, kcc: -0.113
+2025-07-09 15:25:09: Epoch: 032 Train Loss: 0.771, mae: 1.194, rse: 1.003, pcc: 0.151, kcc: 0.173
+2025-07-09 15:25:09: Epoch: 032 Test results, ap: mae: 0.633, rse: 1.115, pcc: -0.019, kcc: -0.105
+2025-07-09 15:25:09: Epoch: 032 New best TEST metrics
+2025-07-09 15:25:10: Epoch: 033 Train Loss: 0.783, mae: 1.192, rse: 1.000, pcc: 0.151, kcc: 0.178
+2025-07-09 15:25:10: Epoch: 033 Test results, ap: mae: 0.631, rse: 1.107, pcc: -0.019, kcc: -0.102
+2025-07-09 15:25:10: Epoch: 033 New best TEST metrics
+2025-07-09 15:25:10: Epoch: 034 Train Loss: 0.789, mae: 1.192, rse: 1.001, pcc: 0.151, kcc: 0.213
+2025-07-09 15:25:11: Epoch: 034 Test results, ap: mae: 0.631, rse: 1.108, pcc: -0.019, kcc: -0.128
+2025-07-09 15:25:11: Epoch: 035 Train Loss: 0.787, mae: 1.193, rse: 1.002, pcc: 0.151, kcc: 0.198
+2025-07-09 15:25:12: Epoch: 035 Test results, ap: mae: 0.632, rse: 1.113, pcc: -0.019, kcc: -0.126
+2025-07-09 15:25:12: used time 0.01h
+2025-07-09 15:25:12: Running Cross Validation 3
+2025-07-09 15:25:12: Fold 3  Train set:406, Valid set:101, Test set: 108
+2025-07-09 15:25:12: Epoch: 001 Train Loss: 0.811, mae: 1.342, rse: 1.067, pcc: 0.045, kcc: -0.050
+2025-07-09 15:25:12: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:25:13: Epoch: 001 Test results, ap: mae: 0.680, rse: 1.390, pcc: -0.061, kcc: -0.055
+2025-07-09 15:25:13: Epoch: 002 Train Loss: 0.803, mae: 1.333, rse: 1.050, pcc: 0.044, kcc: 0.009
+2025-07-09 15:25:13: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:25:13: Epoch: 002 Test results, ap: mae: 0.653, rse: 1.292, pcc: -0.055, kcc: -0.086
+2025-07-09 15:25:14: Epoch: 003 Train Loss: 0.831, mae: 1.327, rse: 1.038, pcc: 0.046, kcc: 0.029
+2025-07-09 15:25:14: Epoch: 003 New best VALIDATION metrics
+2025-07-09 15:25:14: Epoch: 003 Test results, ap: mae: 0.634, rse: 1.223, pcc: -0.051, kcc: -0.098
+2025-07-09 15:25:15: Epoch: 004 Train Loss: 0.808, mae: 1.323, rse: 1.027, pcc: 0.046, kcc: 0.036
+2025-07-09 15:25:15: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:25:15: Epoch: 004 Test results, ap: mae: 0.614, rse: 1.156, pcc: -0.017, kcc: -0.044
+2025-07-09 15:25:16: Epoch: 005 Train Loss: 0.812, mae: 1.323, rse: 1.025, pcc: -0.057, kcc: -0.023
+2025-07-09 15:25:16: Epoch: 005 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.014, kcc: 0.020
+2025-07-09 15:25:17: Epoch: 006 Train Loss: 0.799, mae: 1.323, rse: 1.027, pcc: -0.027, kcc: 0.032
+2025-07-09 15:25:17: Epoch: 006 Test results, ap: mae: 0.614, rse: 1.154, pcc: -0.000, kcc: -0.005
+2025-07-09 15:25:18: Epoch: 007 Train Loss: 0.802, mae: 1.323, rse: 1.027, pcc: 0.103, kcc: 0.062
+2025-07-09 15:25:18: Epoch: 007 New best VALIDATION metrics
+2025-07-09 15:25:18: Epoch: 007 Test results, ap: mae: 0.613, rse: 1.153, pcc: -0.104, kcc: -0.129
+2025-07-09 15:25:19: Epoch: 008 Train Loss: 0.816, mae: 1.323, rse: 1.024, pcc: 0.050, kcc: 0.066
+2025-07-09 15:25:19: Epoch: 008 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.111, kcc: -0.188
+2025-07-09 15:25:20: Epoch: 009 Train Loss: 0.767, mae: 1.322, rse: 1.020, pcc: 0.059, kcc: 0.070
+2025-07-09 15:25:20: Epoch: 009 Test results, ap: mae: 0.605, rse: 1.118, pcc: -0.098, kcc: -0.133
+2025-07-09 15:25:20: Epoch: 010 Train Loss: 0.812, mae: 1.322, rse: 1.022, pcc: 0.075, kcc: 0.061
+2025-07-09 15:25:21: Epoch: 010 Test results, ap: mae: 0.608, rse: 1.130, pcc: -0.094, kcc: -0.170
+2025-07-09 15:25:21: Epoch: 011 Train Loss: 0.803, mae: 1.323, rse: 1.024, pcc: 0.024, kcc: 0.065
+2025-07-09 15:25:22: Epoch: 011 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.073, kcc: -0.139
+2025-07-09 15:25:22: Epoch: 011 New best TEST metrics
+2025-07-09 15:25:22: Epoch: 012 Train Loss: 0.785, mae: 1.322, rse: 1.022, pcc: 0.120, kcc: 0.048
+2025-07-09 15:25:22: Epoch: 012 New best VALIDATION metrics
+2025-07-09 15:25:22: Epoch: 012 Test results, ap: mae: 0.607, rse: 1.126, pcc: -0.061, kcc: -0.249
+2025-07-09 15:25:23: Epoch: 013 Train Loss: 0.795, mae: 1.303, rse: 1.017, pcc: 0.169, kcc: 0.068
+2025-07-09 15:25:23: Epoch: 013 New best VALIDATION metrics
+2025-07-09 15:25:23: Epoch: 013 Test results, ap: mae: 0.619, rse: 1.146, pcc: -0.009, kcc: -0.229
+2025-07-09 15:25:24: Epoch: 014 Train Loss: 0.788, mae: 1.309, rse: 1.025, pcc: 0.169, kcc: 0.105
+2025-07-09 15:25:24: Epoch: 014 New best VALIDATION metrics
+2025-07-09 15:25:24: Epoch: 014 Test results, ap: mae: 0.652, rse: 1.260, pcc: -0.122, kcc: -0.159
+2025-07-09 15:25:25: Epoch: 015 Train Loss: 0.762, mae: 1.278, rse: 0.987, pcc: 0.210, kcc: 0.131
+2025-07-09 15:25:25: Epoch: 015 New best VALIDATION metrics
+2025-07-09 15:25:25: Epoch: 015 Test results, ap: mae: 0.622, rse: 1.113, pcc: -0.021, kcc: -0.169
+2025-07-09 15:25:25: Epoch: 015 New best TEST metrics
+2025-07-09 15:25:26: Epoch: 016 Train Loss: 0.779, mae: 1.281, rse: 0.975, pcc: 0.222, kcc: 0.129
+2025-07-09 15:25:26: Epoch: 016 New best VALIDATION metrics
+2025-07-09 15:25:26: Epoch: 016 Test results, ap: mae: 0.612, rse: 1.073, pcc: 0.006, kcc: -0.156
+2025-07-09 15:25:26: Epoch: 016 New best TEST metrics
+2025-07-09 15:25:27: Epoch: 017 Train Loss: 0.744, mae: 1.295, rse: 1.000, pcc: 0.178, kcc: 0.112
+2025-07-09 15:25:27: Epoch: 017 Test results, ap: mae: 0.637, rse: 1.142, pcc: -0.022, kcc: -0.154
+2025-07-09 15:25:28: Epoch: 018 Train Loss: 0.780, mae: 1.293, rse: 0.984, pcc: 0.168, kcc: 0.102
+2025-07-09 15:25:28: Epoch: 018 Test results, ap: mae: 0.621, rse: 1.102, pcc: -0.092, kcc: -0.171
+2025-07-09 15:25:29: Epoch: 019 Train Loss: 0.761, mae: 1.309, rse: 1.021, pcc: 0.096, kcc: 0.084
+2025-07-09 15:25:29: Epoch: 019 Test results, ap: mae: 0.649, rse: 1.212, pcc: -0.180, kcc: -0.174
+2025-07-09 15:25:29: Epoch: 020 Train Loss: 0.736, mae: 1.323, rse: 1.038, pcc: 0.100, kcc: 0.103
+2025-07-09 15:25:30: Epoch: 020 Test results, ap: mae: 0.662, rse: 1.263, pcc: -0.179, kcc: -0.170
+2025-07-09 15:25:30: Epoch: 021 Train Loss: 0.749, mae: 1.322, rse: 1.035, pcc: 0.100, kcc: 0.129
+2025-07-09 15:25:31: Epoch: 021 Test results, ap: mae: 0.676, rse: 1.382, pcc: -0.263, kcc: -0.172
+2025-07-09 15:25:31: Epoch: 022 Train Loss: 0.768, mae: 1.325, rse: 1.034, pcc: 0.065, kcc: 0.128
+2025-07-09 15:25:31: Epoch: 022 Test results, ap: mae: 0.672, rse: 1.356, pcc: -0.267, kcc: -0.169
+2025-07-09 15:25:32: Epoch: 023 Train Loss: 0.783, mae: 1.321, rse: 1.041, pcc: 0.035, kcc: 0.140
+2025-07-09 15:25:32: Epoch: 023 Test results, ap: mae: 0.678, rse: 1.351, pcc: -0.287, kcc: -0.173
+2025-07-09 15:25:33: Epoch: 024 Train Loss: 0.763, mae: 1.326, rse: 1.034, pcc: 0.058, kcc: 0.126
+2025-07-09 15:25:33: Epoch: 024 Test results, ap: mae: 0.647, rse: 1.258, pcc: -0.185, kcc: -0.149
+2025-07-09 15:25:34: Epoch: 025 Train Loss: 0.771, mae: 1.318, rse: 1.020, pcc: 0.087, kcc: 0.123
+2025-07-09 15:25:34: Epoch: 025 Test results, ap: mae: 0.654, rse: 1.244, pcc: -0.234, kcc: -0.173
+2025-07-09 15:25:35: Epoch: 026 Train Loss: 0.766, mae: 1.309, rse: 0.999, pcc: 0.152, kcc: 0.076
+2025-07-09 15:25:35: Epoch: 026 Test results, ap: mae: 0.649, rse: 1.190, pcc: -0.150, kcc: -0.173
+2025-07-09 15:25:36: Epoch: 027 Train Loss: 0.761, mae: 1.281, rse: 0.977, pcc: 0.180, kcc: 0.124
+2025-07-09 15:25:36: Epoch: 027 Test results, ap: mae: 0.627, rse: 1.088, pcc: -0.019, kcc: -0.190
+2025-07-09 15:25:37: Epoch: 028 Train Loss: 0.762, mae: 1.271, rse: 0.957, pcc: 0.230, kcc: 0.120
+2025-07-09 15:25:37: Epoch: 028 New best VALIDATION metrics
+2025-07-09 15:25:37: Epoch: 028 Test results, ap: mae: 0.622, rse: 1.074, pcc: 0.012, kcc: -0.163
+2025-07-09 15:25:37: Epoch: 029 Train Loss: 0.769, mae: 1.283, rse: 0.960, pcc: 0.215, kcc: 0.117
+2025-07-09 15:25:38: Epoch: 029 Test results, ap: mae: 0.614, rse: 1.051, pcc: 0.022, kcc: -0.166
+2025-07-09 15:25:38: Epoch: 029 New best TEST metrics
+2025-07-09 15:25:38: Epoch: 030 Train Loss: 0.739, mae: 1.285, rse: 0.962, pcc: 0.215, kcc: 0.116
+2025-07-09 15:25:39: Epoch: 030 Test results, ap: mae: 0.616, rse: 1.059, pcc: 0.020, kcc: -0.179
+2025-07-09 15:25:39: Epoch: 031 Train Loss: 0.763, mae: 1.285, rse: 0.962, pcc: 0.215, kcc: 0.115
+2025-07-09 15:25:40: Epoch: 031 Test results, ap: mae: 0.616, rse: 1.058, pcc: 0.019, kcc: -0.167
+2025-07-09 15:25:40: Epoch: 032 Train Loss: 0.769, mae: 1.287, rse: 0.963, pcc: 0.212, kcc: 0.114
+2025-07-09 15:25:40: Epoch: 032 Test results, ap: mae: 0.612, rse: 1.047, pcc: 0.028, kcc: -0.168
+2025-07-09 15:25:40: Epoch: 032 New best TEST metrics
+2025-07-09 15:25:41: Epoch: 033 Train Loss: 0.764, mae: 1.283, rse: 0.961, pcc: 0.215, kcc: 0.115
+2025-07-09 15:25:41: Epoch: 033 Test results, ap: mae: 0.613, rse: 1.053, pcc: 0.018, kcc: -0.173
+2025-07-09 15:25:42: Epoch: 034 Train Loss: 0.748, mae: 1.271, rse: 0.957, pcc: 0.228, kcc: 0.122
+2025-07-09 15:25:42: Epoch: 034 Test results, ap: mae: 0.618, rse: 1.064, pcc: 0.011, kcc: -0.173
+2025-07-09 15:25:43: Epoch: 035 Train Loss: 0.741, mae: 1.271, rse: 0.959, pcc: 0.226, kcc: 0.124
+2025-07-09 15:25:43: Epoch: 035 Test results, ap: mae: 0.624, rse: 1.083, pcc: -0.009, kcc: -0.170
+2025-07-09 15:25:43: used time 0.01h
+2025-07-09 15:25:43: Running Cross Validation 4
+2025-07-09 15:25:43: Fold 4  Train set:406, Valid set:101, Test set: 108
+2025-07-09 15:25:44: Epoch: 001 Train Loss: 0.850, mae: 1.199, rse: 1.029, pcc: 0.008, kcc: 0.011
+2025-07-09 15:25:44: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:25:44: Epoch: 001 Test results, ap: mae: 0.657, rse: 1.306, pcc: -0.030, kcc: -0.078
+2025-07-09 15:25:45: Epoch: 002 Train Loss: 0.863, mae: 1.200, rse: 1.016, pcc: -0.018, kcc: -0.079
+2025-07-09 15:25:45: Epoch: 002 Test results, ap: mae: 0.633, rse: 1.218, pcc: 0.009, kcc: -0.072
+2025-07-09 15:25:46: Epoch: 003 Train Loss: 0.860, mae: 1.204, rse: 1.009, pcc: -0.010, kcc: 0.053
+2025-07-09 15:25:46: Epoch: 003 New best VALIDATION metrics
+2025-07-09 15:25:46: Epoch: 003 Test results, ap: mae: 0.614, rse: 1.156, pcc: -0.020, kcc: -0.034
+2025-07-09 15:25:46: Epoch: 004 Train Loss: 0.834, mae: 1.207, rse: 1.005, pcc: 0.030, kcc: 0.093
+2025-07-09 15:25:46: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:25:47: Epoch: 004 Test results, ap: mae: 0.606, rse: 1.123, pcc: -0.063, kcc: -0.122
+2025-07-09 15:25:47: Epoch: 005 Train Loss: 0.819, mae: 1.205, rse: 1.000, pcc: 0.159, kcc: 0.125
+2025-07-09 15:25:47: Epoch: 005 New best VALIDATION metrics
+2025-07-09 15:25:48: Epoch: 005 Test results, ap: mae: 0.604, rse: 1.113, pcc: -0.085, kcc: -0.157
+2025-07-09 15:25:48: Epoch: 006 Train Loss: 0.848, mae: 1.212, rse: 1.002, pcc: -0.275, kcc: -0.163
+2025-07-09 15:25:49: Epoch: 006 Test results, ap: mae: 0.595, rse: 1.080, pcc: 0.027, kcc: 0.121
+2025-07-09 15:25:49: Epoch: 007 Train Loss: 0.841, mae: 1.210, rse: 1.003, pcc: -0.031, kcc: -0.207
+2025-07-09 15:25:49: Epoch: 007 Test results, ap: mae: 0.599, rse: 1.095, pcc: 0.121, kcc: 0.175
+2025-07-09 15:25:50: Epoch: 008 Train Loss: 0.835, mae: 1.208, rse: 1.004, pcc: -0.021, kcc: -0.297
+2025-07-09 15:25:50: Epoch: 008 Test results, ap: mae: 0.603, rse: 1.109, pcc: 0.238, kcc: 0.164
+2025-07-09 15:25:51: Epoch: 009 Train Loss: 0.852, mae: 1.210, rse: 1.002, pcc: -0.008, kcc: -0.110
+2025-07-09 15:25:51: Epoch: 009 Test results, ap: mae: 0.598, rse: 1.092, pcc: 0.320, kcc: 0.169
+2025-07-09 15:25:52: Epoch: 010 Train Loss: 0.850, mae: 1.207, rse: 1.005, pcc: -0.016, kcc: -0.215
+2025-07-09 15:25:52: Epoch: 010 Test results, ap: mae: 0.605, rse: 1.118, pcc: 0.102, kcc: 0.189
+2025-07-09 15:25:53: Epoch: 011 Train Loss: 0.807, mae: 1.207, rse: 1.004, pcc: -0.014, kcc: -0.211
+2025-07-09 15:25:53: Epoch: 011 Test results, ap: mae: 0.604, rse: 1.114, pcc: 0.133, kcc: 0.137
+2025-07-09 15:25:53: Epoch: 011 New best TEST metrics
+2025-07-09 15:25:54: Epoch: 012 Train Loss: 0.819, mae: 1.207, rse: 1.004, pcc: -0.026, kcc: -0.270
+2025-07-09 15:25:54: Epoch: 012 Test results, ap: mae: 0.604, rse: 1.116, pcc: 0.191, kcc: 0.108
+2025-07-09 15:25:54: Epoch: 012 New best TEST metrics
+2025-07-09 15:25:55: Epoch: 013 Train Loss: 0.853, mae: 1.207, rse: 1.004, pcc: -0.009, kcc: -0.169
+2025-07-09 15:25:55: Epoch: 013 Test results, ap: mae: 0.604, rse: 1.116, pcc: -0.083, kcc: -0.031
+2025-07-09 15:25:55: Epoch: 014 Train Loss: 0.846, mae: 1.207, rse: 1.004, pcc: -0.033, kcc: -0.307
+2025-07-09 15:25:56: Epoch: 014 Test results, ap: mae: 0.603, rse: 1.111, pcc: 0.067, kcc: 0.057
+2025-07-09 15:25:56: Epoch: 015 Train Loss: 0.855, mae: 1.207, rse: 1.004, pcc: -0.004, kcc: -0.103
+2025-07-09 15:25:57: Epoch: 015 Test results, ap: mae: 0.603, rse: 1.111, pcc: 0.183, kcc: 0.096
+2025-07-09 15:25:57: Epoch: 016 Train Loss: 0.838, mae: 1.208, rse: 1.003, pcc: -0.003, kcc: -0.083
+2025-07-09 15:25:58: Epoch: 016 Test results, ap: mae: 0.601, rse: 1.104, pcc: 0.335, kcc: 0.215
+2025-07-09 15:25:58: Epoch: 016 New best TEST metrics
+2025-07-09 15:25:58: Epoch: 017 Train Loss: 0.806, mae: 1.209, rse: 1.003, pcc: 0.001, kcc: -0.060
+2025-07-09 15:25:58: Epoch: 017 Test results, ap: mae: 0.600, rse: 1.098, pcc: 0.185, kcc: 0.083
+2025-07-09 15:25:59: Epoch: 018 Train Loss: 0.830, mae: 1.209, rse: 1.003, pcc: -0.026, kcc: -0.143
+2025-07-09 15:25:59: Epoch: 018 Test results, ap: mae: 0.601, rse: 1.101, pcc: 0.074, kcc: 0.088
+2025-07-09 15:26:00: Epoch: 019 Train Loss: 0.851, mae: 1.209, rse: 1.003, pcc: 0.019, kcc: 0.070
+2025-07-09 15:26:00: Epoch: 019 Test results, ap: mae: 0.601, rse: 1.102, pcc: 0.044, kcc: 0.062
+2025-07-09 15:26:01: Epoch: 020 Train Loss: 0.848, mae: 1.208, rse: 1.003, pcc: 0.004, kcc: -0.032
+2025-07-09 15:26:01: Epoch: 020 Test results, ap: mae: 0.601, rse: 1.105, pcc: 0.168, kcc: 0.109
+2025-07-09 15:26:02: Epoch: 021 Train Loss: 0.808, mae: 1.208, rse: 1.003, pcc: -0.028, kcc: -0.108
+2025-07-09 15:26:02: Epoch: 021 Test results, ap: mae: 0.601, rse: 1.104, pcc: 0.054, kcc: -0.026
+2025-07-09 15:26:03: Epoch: 022 Train Loss: 0.849, mae: 1.208, rse: 1.003, pcc: -0.013, kcc: -0.160
+2025-07-09 15:26:03: Epoch: 022 Test results, ap: mae: 0.601, rse: 1.104, pcc: -0.081, kcc: -0.079
+2025-07-09 15:26:04: Epoch: 023 Train Loss: 0.831, mae: 1.208, rse: 1.003, pcc: -0.023, kcc: -0.279
+2025-07-09 15:26:04: Epoch: 023 Test results, ap: mae: 0.601, rse: 1.102, pcc: 0.266, kcc: 0.229
+2025-07-09 15:26:04: Epoch: 024 Train Loss: 0.856, mae: 1.209, rse: 1.003, pcc: 0.001, kcc: 0.007
+2025-07-09 15:26:05: Epoch: 024 Test results, ap: mae: 0.600, rse: 1.100, pcc: 0.067, kcc: 0.060
+2025-07-09 15:26:05: Epoch: 025 Train Loss: 0.834, mae: 1.209, rse: 1.003, pcc: -0.010, kcc: -0.277
+2025-07-09 15:26:06: Epoch: 025 Test results, ap: mae: 0.600, rse: 1.099, pcc: 0.170, kcc: 0.175
+2025-07-09 15:26:06: Epoch: 026 Train Loss: 0.840, mae: 1.208, rse: 1.003, pcc: -0.024, kcc: -0.265
+2025-07-09 15:26:07: Epoch: 026 Test results, ap: mae: 0.601, rse: 1.103, pcc: 0.263, kcc: 0.076
+2025-07-09 15:26:07: Epoch: 027 Train Loss: 0.854, mae: 1.208, rse: 1.003, pcc: -0.021, kcc: -0.240
+2025-07-09 15:26:07: Epoch: 027 Test results, ap: mae: 0.601, rse: 1.104, pcc: 0.251, kcc: 0.119
+2025-07-09 15:26:08: Epoch: 028 Train Loss: 0.836, mae: 1.208, rse: 1.003, pcc: 0.001, kcc: 0.024
+2025-07-09 15:26:08: Epoch: 028 Test results, ap: mae: 0.601, rse: 1.103, pcc: -0.182, kcc: -0.133
+2025-07-09 15:26:09: Epoch: 029 Train Loss: 0.835, mae: 1.208, rse: 1.003, pcc: -0.010, kcc: -0.236
+2025-07-09 15:26:09: Epoch: 029 Test results, ap: mae: 0.601, rse: 1.103, pcc: 0.225, kcc: 0.130
+2025-07-09 15:26:10: Epoch: 030 Train Loss: 0.830, mae: 1.208, rse: 1.003, pcc: -0.025, kcc: -0.292
+2025-07-09 15:26:10: Epoch: 030 Test results, ap: mae: 0.601, rse: 1.103, pcc: 0.123, kcc: 0.165
+2025-07-09 15:26:11: Epoch: 031 Train Loss: 0.831, mae: 1.208, rse: 1.003, pcc: -0.004, kcc: -0.141
+2025-07-09 15:26:11: Epoch: 031 Test results, ap: mae: 0.601, rse: 1.104, pcc: 0.251, kcc: 0.084
+2025-07-09 15:26:12: Epoch: 032 Train Loss: 0.840, mae: 1.208, rse: 1.003, pcc: -0.006, kcc: -0.239
+2025-07-09 15:26:12: Epoch: 032 Test results, ap: mae: 0.602, rse: 1.106, pcc: -0.149, kcc: -0.104
+2025-07-09 15:26:12: Epoch: 033 Train Loss: 0.862, mae: 1.208, rse: 1.004, pcc: -0.002, kcc: -0.083
+2025-07-09 15:26:13: Epoch: 033 Test results, ap: mae: 0.602, rse: 1.108, pcc: -0.133, kcc: -0.098
+2025-07-09 15:26:13: Epoch: 034 Train Loss: 0.847, mae: 1.208, rse: 1.004, pcc: 0.001, kcc: -0.056
+2025-07-09 15:26:14: Epoch: 034 Test results, ap: mae: 0.602, rse: 1.109, pcc: -0.382, kcc: -0.237
+2025-07-09 15:26:14: Epoch: 035 Train Loss: 0.842, mae: 1.208, rse: 1.004, pcc: -0.014, kcc: -0.237
+2025-07-09 15:26:15: Epoch: 035 Test results, ap: mae: 0.602, rse: 1.109, pcc: 0.191, kcc: 0.095
+2025-07-09 15:26:15: used time 0.01h
+2025-07-09 15:26:15: Cross Validation Finished!
+2025-07-09 15:26:15: Best validation perform list
+[[1.15207791 1.00194013 0.24194613 0.16413185]
+ [1.17778528 0.94311339 0.28014019 0.15758331]
+ [1.18844223 0.99228239 0.24374972 0.23306078]
+ [1.27079356 0.95733619 0.22961108 0.12042979]
+ [1.20474637 0.99972218 0.15884958 0.12533578]]
+2025-07-09 15:26:15: mean: [1.199 0.979 0.231 0.16 ]
+2025-07-09 15:26:15: std: [0.04  0.024 0.04  0.04 ]
+2025-07-09 15:26:15: Best test perform list
+[[ 0.59581274  1.08176363  0.05082143  0.12250586]
+ [ 0.58984691  1.053262    0.16225724  0.09470969]
+ [ 0.6306445   1.10722423 -0.01860482 -0.10151861]
+ [ 0.61217779  1.04746759  0.02776802 -0.16783847]
+ [ 0.60118878  1.10378742  0.33462602  0.21480131]]
+2025-07-09 15:26:15: mean: [0.606 1.079 0.111 0.033]
+2025-07-09 15:26:15: std: [0.014 0.025 0.126 0.144]
diff --git a/run-reg/lstm-diff-256-smoothl1-32-0.001-35/config.json b/run-reg/lstm-diff-256-smoothl1-32-0.001-35/config.json
new file mode 100644
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 35, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "smoothl1", "dir": false, "classes": 1}
\ No newline at end of file
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@@ -0,0 +1,6 @@
+Valid
+0.861349594593048,0.5357752829790116,0.6676396369934082,0.5461733520030976
+0.3065863639274036,0.28265962739379397,0.20193131620841098,0.1677884965922561
+Test
+0.6279038071632386,1.1805023908615113,0.1270310588181019,0.08400026559829712
+0.033941222355018286,0.09344660135777452,0.07754947410673832,0.031155071714419398
diff --git a/run-reg/lstm-diff-256-smoothl1-32-0.001-35/training.log b/run-reg/lstm-diff-256-smoothl1-32-0.001-35/training.log
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@@ -0,0 +1,486 @@
+2025-07-09 15:15:49: saving_dir: ./run-reg/lstm-diff-256-smoothl1-32-0.001-35
+2025-07-09 15:15:49: Loading Train Dataset
+2025-07-09 15:15:49: Loading Test Dataset
+2025-07-09 15:15:50: Running Cross Validation 0
+2025-07-09 15:15:50: Fold 0  Train set:1888, Valid set:473, Test set: 108
+2025-07-09 15:15:52: Epoch: 001 Train Loss: 0.972, mae: 1.353, rse: 1.008, pcc: 0.040, kcc: 0.012
+2025-07-09 15:15:52: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:15:52: Epoch: 001 Test results, ap: mae: 0.634, rse: 1.223, pcc: -0.085, kcc: -0.163
+2025-07-09 15:15:53: Epoch: 002 Train Loss: 0.969, mae: 1.354, rse: 1.005, pcc: 0.151, kcc: 0.104
+2025-07-09 15:15:53: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:15:54: Epoch: 002 Test results, ap: mae: 0.622, rse: 1.182, pcc: -0.236, kcc: -0.204
+2025-07-09 15:15:55: Epoch: 003 Train Loss: 0.969, mae: 1.354, rse: 1.005, pcc: 0.085, kcc: 0.095
+2025-07-09 15:15:55: Epoch: 003 Test results, ap: mae: 0.622, rse: 1.183, pcc: -0.277, kcc: -0.236
+2025-07-09 15:15:56: Epoch: 004 Train Loss: 0.970, mae: 1.354, rse: 1.005, pcc: 0.197, kcc: 0.190
+2025-07-09 15:15:56: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:15:56: Epoch: 004 Test results, ap: mae: 0.619, rse: 1.174, pcc: 0.040, kcc: 0.021
+2025-07-09 15:15:58: Epoch: 005 Train Loss: 0.970, mae: 1.353, rse: 1.007, pcc: 0.238, kcc: 0.188
+2025-07-09 15:15:58: Epoch: 005 New best VALIDATION metrics
+2025-07-09 15:15:58: Epoch: 005 Test results, ap: mae: 0.629, rse: 1.205, pcc: -0.155, kcc: -0.108
+2025-07-09 15:15:59: Epoch: 006 Train Loss: 0.970, mae: 1.354, rse: 1.005, pcc: 0.192, kcc: 0.146
+2025-07-09 15:15:59: Epoch: 006 Test results, ap: mae: 0.621, rse: 1.180, pcc: -0.035, kcc: -0.061
+2025-07-09 15:16:01: Epoch: 007 Train Loss: 0.969, mae: 1.354, rse: 1.004, pcc: 0.149, kcc: 0.194
+2025-07-09 15:16:01: Epoch: 007 Test results, ap: mae: 0.618, rse: 1.170, pcc: -0.043, kcc: -0.143
+2025-07-09 15:16:02: Epoch: 008 Train Loss: 0.970, mae: 1.355, rse: 1.003, pcc: 0.038, kcc: 0.013
+2025-07-09 15:16:02: Epoch: 008 Test results, ap: mae: 0.614, rse: 1.156, pcc: -0.090, kcc: -0.048
+2025-07-09 15:16:04: Epoch: 009 Train Loss: 0.970, mae: 1.354, rse: 1.005, pcc: 0.125, kcc: 0.148
+2025-07-09 15:16:04: Epoch: 009 Test results, ap: mae: 0.618, rse: 1.171, pcc: -0.156, kcc: -0.098
+2025-07-09 15:16:05: Epoch: 010 Train Loss: 0.970, mae: 1.354, rse: 1.005, pcc: 0.016, kcc: 0.040
+2025-07-09 15:16:05: Epoch: 010 Test results, ap: mae: 0.622, rse: 1.182, pcc: 0.049, kcc: 0.078
+2025-07-09 15:16:07: Epoch: 011 Train Loss: 0.969, mae: 1.354, rse: 1.005, pcc: -0.095, kcc: -0.073
+2025-07-09 15:16:07: Epoch: 011 Test results, ap: mae: 0.621, rse: 1.180, pcc: 0.256, kcc: 0.130
+2025-07-09 15:16:07: Epoch: 011 New best TEST metrics
+2025-07-09 15:16:08: Epoch: 012 Train Loss: 0.970, mae: 1.354, rse: 1.006, pcc: 0.142, kcc: 0.165
+2025-07-09 15:16:08: Epoch: 012 Test results, ap: mae: 0.625, rse: 1.190, pcc: -0.153, kcc: -0.172
+2025-07-09 15:16:10: Epoch: 013 Train Loss: 0.970, mae: 1.354, rse: 1.005, pcc: 0.216, kcc: 0.190
+2025-07-09 15:16:10: Epoch: 013 Test results, ap: mae: 0.622, rse: 1.181, pcc: -0.062, kcc: -0.072
+2025-07-09 15:16:11: Epoch: 014 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.175, kcc: 0.161
+2025-07-09 15:16:11: Epoch: 014 Test results, ap: mae: 0.624, rse: 1.189, pcc: -0.162, kcc: -0.188
+2025-07-09 15:16:13: Epoch: 015 Train Loss: 0.969, mae: 1.354, rse: 1.005, pcc: 0.212, kcc: 0.149
+2025-07-09 15:16:13: Epoch: 015 Test results, ap: mae: 0.619, rse: 1.174, pcc: -0.223, kcc: -0.210
+2025-07-09 15:16:14: Epoch: 016 Train Loss: 0.970, mae: 1.353, rse: 1.005, pcc: 0.247, kcc: 0.181
+2025-07-09 15:16:14: Epoch: 016 New best VALIDATION metrics
+2025-07-09 15:16:14: Epoch: 016 Test results, ap: mae: 0.625, rse: 1.191, pcc: -0.312, kcc: -0.189
+2025-07-09 15:16:16: Epoch: 017 Train Loss: 0.967, mae: 1.352, rse: 1.001, pcc: 0.072, kcc: 0.225
+2025-07-09 15:16:16: Epoch: 017 Test results, ap: mae: 0.627, rse: 1.183, pcc: 0.046, kcc: -0.171
+2025-07-09 15:16:17: Epoch: 018 Train Loss: 0.964, mae: 1.355, rse: 1.003, pcc: 0.226, kcc: 0.227
+2025-07-09 15:16:17: Epoch: 018 New best VALIDATION metrics
+2025-07-09 15:16:17: Epoch: 018 Test results, ap: mae: 0.612, rse: 1.149, pcc: -0.149, kcc: -0.127
+2025-07-09 15:16:18: Epoch: 019 Train Loss: 0.970, mae: 1.354, rse: 1.004, pcc: 0.278, kcc: 0.244
+2025-07-09 15:16:18: Epoch: 019 New best VALIDATION metrics
+2025-07-09 15:16:19: Epoch: 019 Test results, ap: mae: 0.616, rse: 1.164, pcc: -0.233, kcc: -0.183
+2025-07-09 15:16:20: Epoch: 020 Train Loss: 0.974, mae: 1.353, rse: 1.006, pcc: 0.121, kcc: 0.206
+2025-07-09 15:16:20: Epoch: 020 Test results, ap: mae: 0.626, rse: 1.196, pcc: -0.224, kcc: -0.129
+2025-07-09 15:16:21: Epoch: 021 Train Loss: 0.968, mae: 1.348, rse: 1.001, pcc: 0.331, kcc: 0.261
+2025-07-09 15:16:21: Epoch: 021 New best VALIDATION metrics
+2025-07-09 15:16:22: Epoch: 021 Test results, ap: mae: 0.628, rse: 1.202, pcc: -0.193, kcc: -0.182
+2025-07-09 15:16:23: Epoch: 022 Train Loss: 0.950, mae: 1.292, rse: 0.945, pcc: 0.335, kcc: 0.242
+2025-07-09 15:16:23: Epoch: 022 New best VALIDATION metrics
+2025-07-09 15:16:23: Epoch: 022 Test results, ap: mae: 0.646, rse: 1.245, pcc: -0.095, kcc: -0.180
+2025-07-09 15:16:24: Epoch: 023 Train Loss: 0.921, mae: 1.242, rse: 0.899, pcc: 0.332, kcc: 0.259
+2025-07-09 15:16:24: Epoch: 023 New best VALIDATION metrics
+2025-07-09 15:16:25: Epoch: 023 Test results, ap: mae: 0.758, rse: 1.792, pcc: -0.190, kcc: -0.173
+2025-07-09 15:16:26: Epoch: 024 Train Loss: 0.893, mae: 1.260, rse: 0.909, pcc: 0.302, kcc: 0.260
+2025-07-09 15:16:26: Epoch: 024 Test results, ap: mae: 0.733, rse: 1.668, pcc: -0.188, kcc: -0.177
+2025-07-09 15:16:27: Epoch: 025 Train Loss: 0.891, mae: 1.224, rse: 0.893, pcc: 0.341, kcc: 0.265
+2025-07-09 15:16:27: Epoch: 025 New best VALIDATION metrics
+2025-07-09 15:16:28: Epoch: 025 Test results, ap: mae: 0.810, rse: 2.065, pcc: -0.206, kcc: -0.180
+2025-07-09 15:16:29: Epoch: 026 Train Loss: 0.891, mae: 1.221, rse: 0.878, pcc: 0.351, kcc: 0.262
+2025-07-09 15:16:29: Epoch: 026 New best VALIDATION metrics
+2025-07-09 15:16:29: Epoch: 026 Test results, ap: mae: 0.742, rse: 1.706, pcc: -0.198, kcc: -0.144
+2025-07-09 15:16:30: Epoch: 027 Train Loss: 0.874, mae: 1.207, rse: 0.876, pcc: 0.359, kcc: 0.262
+2025-07-09 15:16:30: Epoch: 027 New best VALIDATION metrics
+2025-07-09 15:16:31: Epoch: 027 Test results, ap: mae: 0.780, rse: 1.903, pcc: -0.207, kcc: -0.148
+2025-07-09 15:16:32: Epoch: 028 Train Loss: 0.882, mae: 1.229, rse: 0.898, pcc: 0.344, kcc: 0.264
+2025-07-09 15:16:32: Epoch: 028 Test results, ap: mae: 0.780, rse: 1.891, pcc: -0.198, kcc: -0.143
+2025-07-09 15:16:33: Epoch: 029 Train Loss: 0.888, mae: 1.275, rse: 0.934, pcc: 0.273, kcc: 0.229
+2025-07-09 15:16:34: Epoch: 029 Test results, ap: mae: 0.734, rse: 1.677, pcc: -0.190, kcc: -0.163
+2025-07-09 15:16:35: Epoch: 030 Train Loss: 0.882, mae: 1.259, rse: 0.937, pcc: 0.281, kcc: 0.277
+2025-07-09 15:16:35: Epoch: 030 Test results, ap: mae: 0.773, rse: 1.863, pcc: -0.195, kcc: -0.154
+2025-07-09 15:16:36: Epoch: 031 Train Loss: 0.881, mae: 1.217, rse: 0.876, pcc: 0.353, kcc: 0.284
+2025-07-09 15:16:36: Epoch: 031 New best VALIDATION metrics
+2025-07-09 15:16:36: Epoch: 031 Test results, ap: mae: 0.758, rse: 1.796, pcc: -0.213, kcc: -0.155
+2025-07-09 15:16:38: Epoch: 032 Train Loss: 0.868, mae: 1.222, rse: 0.892, pcc: 0.343, kcc: 0.287
+2025-07-09 15:16:38: Epoch: 032 Test results, ap: mae: 0.803, rse: 2.003, pcc: -0.199, kcc: -0.168
+2025-07-09 15:16:39: Epoch: 033 Train Loss: 0.860, mae: 1.206, rse: 0.875, pcc: 0.362, kcc: 0.290
+2025-07-09 15:16:39: Epoch: 033 New best VALIDATION metrics
+2025-07-09 15:16:39: Epoch: 033 Test results, ap: mae: 0.789, rse: 1.948, pcc: -0.199, kcc: -0.172
+2025-07-09 15:16:41: Epoch: 034 Train Loss: 0.855, mae: 1.197, rse: 0.861, pcc: 0.377, kcc: 0.312
+2025-07-09 15:16:41: Epoch: 034 New best VALIDATION metrics
+2025-07-09 15:16:41: Epoch: 034 Test results, ap: mae: 0.789, rse: 1.958, pcc: -0.211, kcc: -0.165
+2025-07-09 15:16:42: Epoch: 035 Train Loss: 0.842, mae: 1.177, rse: 0.833, pcc: 0.412, kcc: 0.365
+2025-07-09 15:16:42: Epoch: 035 New best VALIDATION metrics
+2025-07-09 15:16:42: Epoch: 035 Test results, ap: mae: 0.808, rse: 2.055, pcc: -0.204, kcc: -0.164
+2025-07-09 15:16:42: used time 0.01h
+2025-07-09 15:16:42: Running Cross Validation 1
+2025-07-09 15:16:42: Fold 1  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:16:44: Epoch: 001 Train Loss: 0.980, mae: 1.338, rse: 1.000, pcc: 0.039, kcc: 0.025
+2025-07-09 15:16:44: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:16:44: Epoch: 001 Test results, ap: mae: 0.630, rse: 1.208, pcc: -0.117, kcc: -0.237
+2025-07-09 15:16:45: Epoch: 002 Train Loss: 0.973, mae: 1.336, rse: 0.998, pcc: 0.071, kcc: -0.026
+2025-07-09 15:16:45: Epoch: 002 Test results, ap: mae: 0.629, rse: 1.203, pcc: -0.141, kcc: -0.148
+2025-07-09 15:16:47: Epoch: 003 Train Loss: 0.955, mae: 1.337, rse: 0.995, pcc: 0.200, kcc: 0.176
+2025-07-09 15:16:47: Epoch: 003 New best VALIDATION metrics
+2025-07-09 15:16:47: Epoch: 003 Test results, ap: mae: 0.611, rse: 1.136, pcc: -0.192, kcc: -0.171
+2025-07-09 15:16:48: Epoch: 004 Train Loss: 0.907, mae: 1.264, rse: 0.915, pcc: 0.312, kcc: 0.223
+2025-07-09 15:16:48: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:16:48: Epoch: 004 Test results, ap: mae: 0.731, rse: 1.639, pcc: -0.259, kcc: -0.166
+2025-07-09 15:16:50: Epoch: 005 Train Loss: 0.850, mae: 1.261, rse: 0.915, pcc: 0.311, kcc: 0.240
+2025-07-09 15:16:50: Epoch: 005 New best VALIDATION metrics
+2025-07-09 15:16:50: Epoch: 005 Test results, ap: mae: 0.641, rse: 1.257, pcc: -0.253, kcc: -0.153
+2025-07-09 15:16:51: Epoch: 006 Train Loss: 0.836, mae: 1.302, rse: 0.956, pcc: 0.273, kcc: 0.219
+2025-07-09 15:16:51: Epoch: 006 Test results, ap: mae: 0.743, rse: 1.725, pcc: -0.247, kcc: -0.145
+2025-07-09 15:16:53: Epoch: 007 Train Loss: 0.967, mae: 1.278, rse: 0.934, pcc: 0.303, kcc: 0.227
+2025-07-09 15:16:53: Epoch: 007 Test results, ap: mae: 0.654, rse: 1.325, pcc: -0.244, kcc: -0.167
+2025-07-09 15:16:54: Epoch: 008 Train Loss: 0.850, mae: 1.234, rse: 0.862, pcc: 0.412, kcc: 0.297
+2025-07-09 15:16:54: Epoch: 008 New best VALIDATION metrics
+2025-07-09 15:16:54: Epoch: 008 Test results, ap: mae: 0.787, rse: 1.866, pcc: -0.238, kcc: -0.142
+2025-07-09 15:16:56: Epoch: 009 Train Loss: 0.917, mae: 1.339, rse: 1.000, pcc: 0.155, kcc: 0.110
+2025-07-09 15:16:56: Epoch: 009 Test results, ap: mae: 0.621, rse: 1.178, pcc: -0.191, kcc: -0.175
+2025-07-09 15:16:57: Epoch: 010 Train Loss: 0.937, mae: 1.323, rse: 0.983, pcc: 0.173, kcc: 0.106
+2025-07-09 15:16:57: Epoch: 010 Test results, ap: mae: 0.603, rse: 1.112, pcc: -0.043, kcc: -0.172
+2025-07-09 15:16:59: Epoch: 011 Train Loss: 0.930, mae: 1.256, rse: 0.918, pcc: 0.288, kcc: 0.195
+2025-07-09 15:16:59: Epoch: 011 Test results, ap: mae: 0.678, rse: 1.395, pcc: -0.211, kcc: -0.156
+2025-07-09 15:16:59: Epoch: 011 New best TEST metrics
+2025-07-09 15:17:00: Epoch: 012 Train Loss: 0.869, mae: 1.235, rse: 0.890, pcc: 0.336, kcc: 0.231
+2025-07-09 15:17:00: Epoch: 012 Test results, ap: mae: 0.613, rse: 1.124, pcc: -0.140, kcc: -0.165
+2025-07-09 15:17:00: Epoch: 012 New best TEST metrics
+2025-07-09 15:17:01: Epoch: 013 Train Loss: 0.835, mae: 1.259, rse: 0.881, pcc: 0.371, kcc: 0.284
+2025-07-09 15:17:02: Epoch: 013 Test results, ap: mae: 0.579, rse: 1.015, pcc: -0.016, kcc: -0.037
+2025-07-09 15:17:02: Epoch: 013 New best TEST metrics
+2025-07-09 15:17:03: Epoch: 014 Train Loss: 0.764, mae: 1.266, rse: 0.887, pcc: 0.349, kcc: 0.244
+2025-07-09 15:17:03: Epoch: 014 Test results, ap: mae: 0.581, rse: 1.022, pcc: 0.128, kcc: 0.075
+2025-07-09 15:17:03: Epoch: 014 New best TEST metrics
+2025-07-09 15:17:04: Epoch: 015 Train Loss: 0.710, mae: 1.085, rse: 0.687, pcc: 0.571, kcc: 0.408
+2025-07-09 15:17:04: Epoch: 015 New best VALIDATION metrics
+2025-07-09 15:17:05: Epoch: 015 Test results, ap: mae: 0.589, rse: 1.067, pcc: 0.094, kcc: 0.083
+2025-07-09 15:17:06: Epoch: 016 Train Loss: 0.667, mae: 0.980, rse: 0.591, pcc: 0.643, kcc: 0.462
+2025-07-09 15:17:06: Epoch: 016 New best VALIDATION metrics
+2025-07-09 15:17:06: Epoch: 016 Test results, ap: mae: 0.769, rse: 1.573, pcc: -0.135, kcc: -0.082
+2025-07-09 15:17:07: Epoch: 017 Train Loss: 0.596, mae: 0.967, rse: 0.572, pcc: 0.657, kcc: 0.466
+2025-07-09 15:17:07: Epoch: 017 New best VALIDATION metrics
+2025-07-09 15:17:08: Epoch: 017 Test results, ap: mae: 0.741, rse: 1.559, pcc: -0.071, kcc: -0.009
+2025-07-09 15:17:09: Epoch: 018 Train Loss: 0.572, mae: 0.935, rse: 0.539, pcc: 0.685, kcc: 0.497
+2025-07-09 15:17:09: Epoch: 018 New best VALIDATION metrics
+2025-07-09 15:17:09: Epoch: 018 Test results, ap: mae: 0.960, rse: 2.700, pcc: -0.023, kcc: 0.028
+2025-07-09 15:17:10: Epoch: 019 Train Loss: 0.543, mae: 0.870, rse: 0.488, pcc: 0.718, kcc: 0.521
+2025-07-09 15:17:10: Epoch: 019 New best VALIDATION metrics
+2025-07-09 15:17:11: Epoch: 019 Test results, ap: mae: 0.822, rse: 1.888, pcc: -0.045, kcc: -0.019
+2025-07-09 15:17:12: Epoch: 020 Train Loss: 0.479, mae: 0.799, rse: 0.443, pcc: 0.748, kcc: 0.563
+2025-07-09 15:17:12: Epoch: 020 New best VALIDATION metrics
+2025-07-09 15:17:12: Epoch: 020 Test results, ap: mae: 0.822, rse: 1.924, pcc: -0.028, kcc: -0.014
+2025-07-09 15:17:13: Epoch: 021 Train Loss: 0.451, mae: 0.781, rse: 0.419, pcc: 0.764, kcc: 0.586
+2025-07-09 15:17:13: Epoch: 021 New best VALIDATION metrics
+2025-07-09 15:17:14: Epoch: 021 Test results, ap: mae: 0.812, rse: 1.944, pcc: 0.039, kcc: 0.036
+2025-07-09 15:17:15: Epoch: 022 Train Loss: 0.427, mae: 0.761, rse: 0.408, pcc: 0.771, kcc: 0.599
+2025-07-09 15:17:15: Epoch: 022 New best VALIDATION metrics
+2025-07-09 15:17:15: Epoch: 022 Test results, ap: mae: 0.857, rse: 2.139, pcc: 0.004, kcc: 0.020
+2025-07-09 15:17:17: Epoch: 023 Train Loss: 0.469, mae: 0.810, rse: 0.442, pcc: 0.748, kcc: 0.561
+2025-07-09 15:17:17: Epoch: 023 Test results, ap: mae: 0.672, rse: 1.307, pcc: -0.015, kcc: 0.002
+2025-07-09 15:17:18: Epoch: 024 Train Loss: 0.434, mae: 0.760, rse: 0.406, pcc: 0.772, kcc: 0.594
+2025-07-09 15:17:18: Epoch: 024 Test results, ap: mae: 0.758, rse: 1.640, pcc: -0.100, kcc: -0.053
+2025-07-09 15:17:19: Epoch: 025 Train Loss: 0.413, mae: 0.741, rse: 0.376, pcc: 0.792, kcc: 0.604
+2025-07-09 15:17:19: Epoch: 025 New best VALIDATION metrics
+2025-07-09 15:17:20: Epoch: 025 Test results, ap: mae: 0.787, rse: 1.764, pcc: -0.075, kcc: -0.050
+2025-07-09 15:17:21: Epoch: 026 Train Loss: 0.391, mae: 0.727, rse: 0.371, pcc: 0.795, kcc: 0.608
+2025-07-09 15:17:21: Epoch: 026 New best VALIDATION metrics
+2025-07-09 15:17:21: Epoch: 026 Test results, ap: mae: 0.766, rse: 1.680, pcc: -0.065, kcc: -0.040
+2025-07-09 15:17:23: Epoch: 027 Train Loss: 0.377, mae: 0.697, rse: 0.361, pcc: 0.802, kcc: 0.628
+2025-07-09 15:17:23: Epoch: 027 New best VALIDATION metrics
+2025-07-09 15:17:23: Epoch: 027 Test results, ap: mae: 0.828, rse: 1.923, pcc: -0.054, kcc: -0.021
+2025-07-09 15:17:24: Epoch: 028 Train Loss: 0.359, mae: 0.704, rse: 0.359, pcc: 0.801, kcc: 0.626
+2025-07-09 15:17:24: Epoch: 028 Test results, ap: mae: 0.813, rse: 1.819, pcc: -0.040, kcc: -0.002
+2025-07-09 15:17:25: Epoch: 029 Train Loss: 0.350, mae: 0.683, rse: 0.356, pcc: 0.803, kcc: 0.630
+2025-07-09 15:17:25: Epoch: 029 New best VALIDATION metrics
+2025-07-09 15:17:26: Epoch: 029 Test results, ap: mae: 0.811, rse: 1.855, pcc: -0.075, kcc: -0.029
+2025-07-09 15:17:27: Epoch: 030 Train Loss: 0.342, mae: 0.647, rse: 0.327, pcc: 0.821, kcc: 0.652
+2025-07-09 15:17:27: Epoch: 030 New best VALIDATION metrics
+2025-07-09 15:17:27: Epoch: 030 Test results, ap: mae: 0.819, rse: 1.879, pcc: -0.041, kcc: -0.019
+2025-07-09 15:17:28: Epoch: 031 Train Loss: 0.321, mae: 0.621, rse: 0.315, pcc: 0.828, kcc: 0.668
+2025-07-09 15:17:28: Epoch: 031 New best VALIDATION metrics
+2025-07-09 15:17:29: Epoch: 031 Test results, ap: mae: 0.856, rse: 1.999, pcc: -0.038, kcc: -0.009
+2025-07-09 15:17:30: Epoch: 032 Train Loss: 0.310, mae: 0.619, rse: 0.314, pcc: 0.829, kcc: 0.670
+2025-07-09 15:17:30: Epoch: 032 New best VALIDATION metrics
+2025-07-09 15:17:30: Epoch: 032 Test results, ap: mae: 0.787, rse: 1.793, pcc: -0.019, kcc: 0.016
+2025-07-09 15:17:31: Epoch: 033 Train Loss: 0.301, mae: 0.599, rse: 0.305, pcc: 0.834, kcc: 0.678
+2025-07-09 15:17:31: Epoch: 033 New best VALIDATION metrics
+2025-07-09 15:17:32: Epoch: 033 Test results, ap: mae: 0.895, rse: 2.172, pcc: -0.043, kcc: -0.015
+2025-07-09 15:17:33: Epoch: 034 Train Loss: 0.297, mae: 0.588, rse: 0.304, pcc: 0.834, kcc: 0.684
+2025-07-09 15:17:33: Epoch: 034 New best VALIDATION metrics
+2025-07-09 15:17:33: Epoch: 034 Test results, ap: mae: 0.953, rse: 2.478, pcc: -0.049, kcc: -0.020
+2025-07-09 15:17:35: Epoch: 035 Train Loss: 0.291, mae: 0.577, rse: 0.301, pcc: 0.837, kcc: 0.688
+2025-07-09 15:17:35: Epoch: 035 New best VALIDATION metrics
+2025-07-09 15:17:35: Epoch: 035 Test results, ap: mae: 0.952, rse: 2.416, pcc: -0.051, kcc: -0.022
+2025-07-09 15:17:35: used time 0.01h
+2025-07-09 15:17:35: Running Cross Validation 2
+2025-07-09 15:17:35: Fold 2  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:17:36: Epoch: 001 Train Loss: 0.943, mae: 1.476, rse: 1.000, pcc: 0.018, kcc: 0.013
+2025-07-09 15:17:36: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:17:36: Epoch: 001 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.051, kcc: 0.111
+2025-07-09 15:17:37: Epoch: 002 Train Loss: 0.942, mae: 1.470, rse: 1.005, pcc: 0.036, kcc: 0.095
+2025-07-09 15:17:37: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:17:38: Epoch: 002 Test results, ap: mae: 0.649, rse: 1.277, pcc: -0.008, kcc: 0.029
+2025-07-09 15:17:39: Epoch: 003 Train Loss: 0.940, mae: 1.474, rse: 1.001, pcc: -0.018, kcc: 0.032
+2025-07-09 15:17:39: Epoch: 003 Test results, ap: mae: 0.617, rse: 1.167, pcc: -0.123, kcc: -0.192
+2025-07-09 15:17:40: Epoch: 004 Train Loss: 0.940, mae: 1.472, rse: 1.000, pcc: 0.166, kcc: 0.045
+2025-07-09 15:17:40: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:17:41: Epoch: 004 Test results, ap: mae: 0.625, rse: 1.190, pcc: -0.052, kcc: -0.138
+2025-07-09 15:17:42: Epoch: 005 Train Loss: 0.943, mae: 1.474, rse: 1.001, pcc: 0.072, kcc: 0.088
+2025-07-09 15:17:42: Epoch: 005 Test results, ap: mae: 0.620, rse: 1.176, pcc: -0.185, kcc: -0.108
+2025-07-09 15:17:43: Epoch: 006 Train Loss: 0.941, mae: 1.473, rse: 1.001, pcc: -0.010, kcc: -0.089
+2025-07-09 15:17:44: Epoch: 006 Test results, ap: mae: 0.624, rse: 1.189, pcc: 0.030, kcc: 0.032
+2025-07-09 15:17:45: Epoch: 007 Train Loss: 0.941, mae: 1.473, rse: 1.002, pcc: 0.006, kcc: -0.029
+2025-07-09 15:17:45: Epoch: 007 Test results, ap: mae: 0.630, rse: 1.208, pcc: 0.040, kcc: 0.048
+2025-07-09 15:17:46: Epoch: 008 Train Loss: 0.941, mae: 1.474, rse: 1.001, pcc: 0.007, kcc: -0.002
+2025-07-09 15:17:47: Epoch: 008 Test results, ap: mae: 0.622, rse: 1.183, pcc: 0.039, kcc: 0.041
+2025-07-09 15:17:48: Epoch: 009 Train Loss: 0.941, mae: 1.474, rse: 1.001, pcc: 0.009, kcc: 0.023
+2025-07-09 15:17:48: Epoch: 009 Test results, ap: mae: 0.621, rse: 1.178, pcc: 0.006, kcc: 0.038
+2025-07-09 15:17:49: Epoch: 010 Train Loss: 0.941, mae: 1.473, rse: 1.001, pcc: 0.010, kcc: 0.036
+2025-07-09 15:17:50: Epoch: 010 Test results, ap: mae: 0.624, rse: 1.187, pcc: -0.077, kcc: -0.014
+2025-07-09 15:17:51: Epoch: 011 Train Loss: 0.941, mae: 1.474, rse: 1.001, pcc: 0.010, kcc: 0.058
+2025-07-09 15:17:51: Epoch: 011 Test results, ap: mae: 0.622, rse: 1.183, pcc: -0.001, kcc: 0.021
+2025-07-09 15:17:51: Epoch: 011 New best TEST metrics
+2025-07-09 15:17:52: Epoch: 012 Train Loss: 0.939, mae: 1.474, rse: 1.001, pcc: 0.011, kcc: 0.048
+2025-07-09 15:17:52: Epoch: 012 Test results, ap: mae: 0.623, rse: 1.187, pcc: -0.029, kcc: 0.003
+2025-07-09 15:17:54: Epoch: 013 Train Loss: 0.942, mae: 1.473, rse: 1.001, pcc: 0.012, kcc: 0.063
+2025-07-09 15:17:54: Epoch: 013 Test results, ap: mae: 0.624, rse: 1.189, pcc: 0.007, kcc: 0.009
+2025-07-09 15:17:55: Epoch: 014 Train Loss: 0.941, mae: 1.473, rse: 1.001, pcc: 0.013, kcc: 0.041
+2025-07-09 15:17:55: Epoch: 014 Test results, ap: mae: 0.625, rse: 1.192, pcc: -0.024, kcc: -0.006
+2025-07-09 15:17:57: Epoch: 015 Train Loss: 0.940, mae: 1.473, rse: 1.001, pcc: 0.013, kcc: 0.054
+2025-07-09 15:17:57: Epoch: 015 Test results, ap: mae: 0.625, rse: 1.191, pcc: -0.010, kcc: 0.030
+2025-07-09 15:17:58: Epoch: 016 Train Loss: 0.941, mae: 1.473, rse: 1.002, pcc: 0.015, kcc: 0.069
+2025-07-09 15:17:58: Epoch: 016 Test results, ap: mae: 0.626, rse: 1.196, pcc: -0.091, kcc: -0.017
+2025-07-09 15:18:00: Epoch: 017 Train Loss: 0.941, mae: 1.474, rse: 1.001, pcc: 0.016, kcc: 0.088
+2025-07-09 15:18:00: Epoch: 017 Test results, ap: mae: 0.621, rse: 1.178, pcc: 0.020, kcc: 0.010
+2025-07-09 15:18:00: Epoch: 017 New best TEST metrics
+2025-07-09 15:18:01: Epoch: 018 Train Loss: 0.941, mae: 1.474, rse: 1.001, pcc: 0.018, kcc: 0.109
+2025-07-09 15:18:01: Epoch: 018 Test results, ap: mae: 0.619, rse: 1.172, pcc: 0.035, kcc: 0.070
+2025-07-09 15:18:01: Epoch: 018 New best TEST metrics
+2025-07-09 15:18:03: Epoch: 019 Train Loss: 0.940, mae: 1.473, rse: 1.001, pcc: 0.159, kcc: 0.093
+2025-07-09 15:18:03: Epoch: 019 New best VALIDATION metrics
+2025-07-09 15:18:03: Epoch: 019 Test results, ap: mae: 0.623, rse: 1.184, pcc: -0.185, kcc: -0.224
+2025-07-09 15:18:04: Epoch: 020 Train Loss: 0.937, mae: 1.448, rse: 0.974, pcc: 0.318, kcc: 0.246
+2025-07-09 15:18:04: Epoch: 020 New best VALIDATION metrics
+2025-07-09 15:18:04: Epoch: 020 Test results, ap: mae: 0.641, rse: 1.260, pcc: -0.294, kcc: -0.133
+2025-07-09 15:18:05: Epoch: 021 Train Loss: 0.856, mae: 1.363, rse: 0.865, pcc: 0.421, kcc: 0.309
+2025-07-09 15:18:05: Epoch: 021 New best VALIDATION metrics
+2025-07-09 15:18:06: Epoch: 021 Test results, ap: mae: 0.675, rse: 1.385, pcc: -0.195, kcc: -0.125
+2025-07-09 15:18:07: Epoch: 022 Train Loss: 0.722, mae: 1.149, rse: 0.665, pcc: 0.585, kcc: 0.437
+2025-07-09 15:18:07: Epoch: 022 New best VALIDATION metrics
+2025-07-09 15:18:07: Epoch: 022 Test results, ap: mae: 0.833, rse: 2.059, pcc: -0.056, kcc: -0.054
+2025-07-09 15:18:08: Epoch: 023 Train Loss: 0.752, mae: 1.238, rse: 0.752, pcc: 0.522, kcc: 0.413
+2025-07-09 15:18:09: Epoch: 023 Test results, ap: mae: 0.658, rse: 1.330, pcc: -0.025, kcc: 0.039
+2025-07-09 15:18:10: Epoch: 024 Train Loss: 0.698, mae: 1.130, rse: 0.625, pcc: 0.615, kcc: 0.448
+2025-07-09 15:18:10: Epoch: 024 New best VALIDATION metrics
+2025-07-09 15:18:10: Epoch: 024 Test results, ap: mae: 0.956, rse: 2.460, pcc: 0.059, kcc: 0.026
+2025-07-09 15:18:11: Epoch: 025 Train Loss: 0.587, mae: 1.019, rse: 0.537, pcc: 0.687, kcc: 0.515
+2025-07-09 15:18:11: Epoch: 025 New best VALIDATION metrics
+2025-07-09 15:18:12: Epoch: 025 Test results, ap: mae: 0.731, rse: 1.600, pcc: 0.059, kcc: 0.002
+2025-07-09 15:18:13: Epoch: 026 Train Loss: 0.539, mae: 1.003, rse: 0.516, pcc: 0.706, kcc: 0.532
+2025-07-09 15:18:13: Epoch: 026 New best VALIDATION metrics
+2025-07-09 15:18:13: Epoch: 026 Test results, ap: mae: 0.802, rse: 1.865, pcc: 0.028, kcc: 0.002
+2025-07-09 15:18:14: Epoch: 027 Train Loss: 0.510, mae: 0.993, rse: 0.512, pcc: 0.698, kcc: 0.528
+2025-07-09 15:18:14: Epoch: 027 New best VALIDATION metrics
+2025-07-09 15:18:15: Epoch: 027 Test results, ap: mae: 0.902, rse: 2.224, pcc: 0.066, kcc: 0.028
+2025-07-09 15:18:16: Epoch: 028 Train Loss: 0.515, mae: 0.947, rse: 0.470, pcc: 0.730, kcc: 0.558
+2025-07-09 15:18:16: Epoch: 028 New best VALIDATION metrics
+2025-07-09 15:18:16: Epoch: 028 Test results, ap: mae: 0.876, rse: 2.173, pcc: 0.047, kcc: 0.014
+2025-07-09 15:18:17: Epoch: 029 Train Loss: 0.490, mae: 0.968, rse: 0.493, pcc: 0.713, kcc: 0.548
+2025-07-09 15:18:17: Epoch: 029 Test results, ap: mae: 0.926, rse: 2.366, pcc: 0.043, kcc: 0.014
+2025-07-09 15:18:19: Epoch: 030 Train Loss: 0.477, mae: 0.951, rse: 0.467, pcc: 0.733, kcc: 0.549
+2025-07-09 15:18:19: Epoch: 030 Test results, ap: mae: 0.855, rse: 2.115, pcc: 0.046, kcc: 0.007
+2025-07-09 15:18:20: Epoch: 031 Train Loss: 0.452, mae: 0.895, rse: 0.433, pcc: 0.762, kcc: 0.583
+2025-07-09 15:18:20: Epoch: 031 New best VALIDATION metrics
+2025-07-09 15:18:20: Epoch: 031 Test results, ap: mae: 0.808, rse: 1.846, pcc: 0.082, kcc: 0.023
+2025-07-09 15:18:21: Epoch: 032 Train Loss: 0.441, mae: 0.895, rse: 0.442, pcc: 0.747, kcc: 0.573
+2025-07-09 15:18:22: Epoch: 032 Test results, ap: mae: 0.901, rse: 2.273, pcc: 0.069, kcc: 0.024
+2025-07-09 15:18:23: Epoch: 033 Train Loss: 0.437, mae: 0.892, rse: 0.431, pcc: 0.756, kcc: 0.577
+2025-07-09 15:18:23: Epoch: 033 Test results, ap: mae: 0.874, rse: 2.081, pcc: 0.082, kcc: 0.025
+2025-07-09 15:18:24: Epoch: 034 Train Loss: 0.420, mae: 0.859, rse: 0.411, pcc: 0.768, kcc: 0.594
+2025-07-09 15:18:24: Epoch: 034 New best VALIDATION metrics
+2025-07-09 15:18:25: Epoch: 034 Test results, ap: mae: 0.895, rse: 2.251, pcc: 0.071, kcc: 0.021
+2025-07-09 15:18:26: Epoch: 035 Train Loss: 0.410, mae: 0.851, rse: 0.407, pcc: 0.771, kcc: 0.596
+2025-07-09 15:18:26: Epoch: 035 New best VALIDATION metrics
+2025-07-09 15:18:26: Epoch: 035 Test results, ap: mae: 0.900, rse: 2.243, pcc: 0.079, kcc: 0.022
+2025-07-09 15:18:26: used time 0.01h
+2025-07-09 15:18:26: Running Cross Validation 3
+2025-07-09 15:18:26: Fold 3  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:18:27: Epoch: 001 Train Loss: 0.976, mae: 1.324, rse: 1.034, pcc: 0.028, kcc: -0.038
+2025-07-09 15:18:27: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:18:28: Epoch: 001 Test results, ap: mae: 0.648, rse: 1.272, pcc: -0.029, kcc: -0.102
+2025-07-09 15:18:29: Epoch: 002 Train Loss: 0.974, mae: 1.322, rse: 1.036, pcc: 0.079, kcc: -0.023
+2025-07-09 15:18:29: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:18:29: Epoch: 002 Test results, ap: mae: 0.656, rse: 1.301, pcc: -0.215, kcc: -0.203
+2025-07-09 15:18:30: Epoch: 003 Train Loss: 0.961, mae: 1.232, rse: 0.914, pcc: 0.437, kcc: 0.335
+2025-07-09 15:18:30: Epoch: 003 New best VALIDATION metrics
+2025-07-09 15:18:30: Epoch: 003 Test results, ap: mae: 0.668, rse: 1.404, pcc: -0.260, kcc: -0.144
+2025-07-09 15:18:32: Epoch: 004 Train Loss: 0.902, mae: 1.322, rse: 1.026, pcc: -0.101, kcc: -0.156
+2025-07-09 15:18:32: Epoch: 004 Test results, ap: mae: 0.629, rse: 1.203, pcc: 0.142, kcc: 0.081
+2025-07-09 15:18:33: Epoch: 005 Train Loss: 0.973, mae: 1.323, rse: 1.032, pcc: -0.063, kcc: -0.136
+2025-07-09 15:18:33: Epoch: 005 Test results, ap: mae: 0.642, rse: 1.251, pcc: 0.093, kcc: 0.051
+2025-07-09 15:18:35: Epoch: 006 Train Loss: 0.972, mae: 1.323, rse: 1.031, pcc: -0.061, kcc: -0.134
+2025-07-09 15:18:35: Epoch: 006 Test results, ap: mae: 0.640, rse: 1.243, pcc: 0.090, kcc: 0.052
+2025-07-09 15:18:36: Epoch: 007 Train Loss: 0.971, mae: 1.323, rse: 1.031, pcc: -0.058, kcc: -0.133
+2025-07-09 15:18:36: Epoch: 007 Test results, ap: mae: 0.640, rse: 1.242, pcc: 0.084, kcc: 0.055
+2025-07-09 15:18:37: Epoch: 008 Train Loss: 0.973, mae: 1.323, rse: 1.031, pcc: -0.056, kcc: -0.131
+2025-07-09 15:18:38: Epoch: 008 Test results, ap: mae: 0.641, rse: 1.247, pcc: 0.078, kcc: 0.055
+2025-07-09 15:18:39: Epoch: 009 Train Loss: 0.975, mae: 1.324, rse: 1.035, pcc: -0.053, kcc: -0.128
+2025-07-09 15:18:39: Epoch: 009 Test results, ap: mae: 0.647, rse: 1.269, pcc: 0.071, kcc: 0.049
+2025-07-09 15:18:40: Epoch: 010 Train Loss: 0.973, mae: 1.322, rse: 1.029, pcc: -0.050, kcc: -0.120
+2025-07-09 15:18:41: Epoch: 010 Test results, ap: mae: 0.637, rse: 1.232, pcc: 0.073, kcc: 0.049
+2025-07-09 15:18:42: Epoch: 011 Train Loss: 0.973, mae: 1.323, rse: 1.030, pcc: -0.048, kcc: -0.117
+2025-07-09 15:18:42: Epoch: 011 Test results, ap: mae: 0.639, rse: 1.240, pcc: 0.066, kcc: 0.037
+2025-07-09 15:18:42: Epoch: 011 New best TEST metrics
+2025-07-09 15:18:43: Epoch: 012 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: -0.047, kcc: -0.117
+2025-07-09 15:18:44: Epoch: 012 Test results, ap: mae: 0.636, rse: 1.229, pcc: 0.061, kcc: 0.038
+2025-07-09 15:18:44: Epoch: 012 New best TEST metrics
+2025-07-09 15:18:45: Epoch: 013 Train Loss: 0.973, mae: 1.322, rse: 1.027, pcc: -0.045, kcc: -0.113
+2025-07-09 15:18:45: Epoch: 013 Test results, ap: mae: 0.633, rse: 1.218, pcc: 0.062, kcc: 0.040
+2025-07-09 15:18:45: Epoch: 013 New best TEST metrics
+2025-07-09 15:18:46: Epoch: 014 Train Loss: 0.973, mae: 1.323, rse: 1.030, pcc: -0.043, kcc: -0.112
+2025-07-09 15:18:46: Epoch: 014 Test results, ap: mae: 0.640, rse: 1.241, pcc: 0.061, kcc: 0.033
+2025-07-09 15:18:48: Epoch: 015 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: -0.042, kcc: -0.110
+2025-07-09 15:18:48: Epoch: 015 Test results, ap: mae: 0.637, rse: 1.233, pcc: 0.056, kcc: 0.026
+2025-07-09 15:18:49: Epoch: 016 Train Loss: 0.973, mae: 1.323, rse: 1.030, pcc: -0.039, kcc: -0.106
+2025-07-09 15:18:49: Epoch: 016 Test results, ap: mae: 0.639, rse: 1.240, pcc: 0.050, kcc: 0.028
+2025-07-09 15:18:51: Epoch: 017 Train Loss: 0.972, mae: 1.323, rse: 1.030, pcc: -0.037, kcc: -0.105
+2025-07-09 15:18:51: Epoch: 017 Test results, ap: mae: 0.639, rse: 1.240, pcc: 0.048, kcc: 0.017
+2025-07-09 15:18:52: Epoch: 018 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: -0.034, kcc: -0.102
+2025-07-09 15:18:52: Epoch: 018 Test results, ap: mae: 0.636, rse: 1.229, pcc: 0.049, kcc: 0.018
+2025-07-09 15:18:53: Epoch: 019 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: -0.031, kcc: -0.095
+2025-07-09 15:18:54: Epoch: 019 Test results, ap: mae: 0.637, rse: 1.233, pcc: 0.034, kcc: 0.005
+2025-07-09 15:18:55: Epoch: 020 Train Loss: 0.973, mae: 1.322, rse: 1.029, pcc: -0.027, kcc: -0.092
+2025-07-09 15:18:55: Epoch: 020 Test results, ap: mae: 0.637, rse: 1.233, pcc: 0.024, kcc: -0.015
+2025-07-09 15:18:56: Epoch: 021 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: -0.025, kcc: -0.086
+2025-07-09 15:18:57: Epoch: 021 Test results, ap: mae: 0.637, rse: 1.230, pcc: 0.010, kcc: -0.039
+2025-07-09 15:18:58: Epoch: 022 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: -0.021, kcc: -0.073
+2025-07-09 15:18:58: Epoch: 022 Test results, ap: mae: 0.637, rse: 1.232, pcc: -0.008, kcc: -0.045
+2025-07-09 15:18:59: Epoch: 023 Train Loss: 0.972, mae: 1.322, rse: 1.030, pcc: -0.016, kcc: -0.048
+2025-07-09 15:19:00: Epoch: 023 Test results, ap: mae: 0.638, rse: 1.236, pcc: -0.033, kcc: -0.057
+2025-07-09 15:19:01: Epoch: 024 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: 0.021, kcc: 0.032
+2025-07-09 15:19:01: Epoch: 024 Test results, ap: mae: 0.637, rse: 1.233, pcc: -0.125, kcc: -0.128
+2025-07-09 15:19:02: Epoch: 025 Train Loss: 0.973, mae: 1.323, rse: 1.030, pcc: -0.017, kcc: -0.085
+2025-07-09 15:19:03: Epoch: 025 Test results, ap: mae: 0.638, rse: 1.237, pcc: 0.030, kcc: -0.011
+2025-07-09 15:19:04: Epoch: 026 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: -0.015, kcc: -0.083
+2025-07-09 15:19:04: Epoch: 026 Test results, ap: mae: 0.636, rse: 1.229, pcc: 0.012, kcc: -0.020
+2025-07-09 15:19:05: Epoch: 027 Train Loss: 0.972, mae: 1.322, rse: 1.027, pcc: -0.010, kcc: -0.032
+2025-07-09 15:19:05: Epoch: 027 Test results, ap: mae: 0.633, rse: 1.216, pcc: 0.023, kcc: -0.004
+2025-07-09 15:19:07: Epoch: 028 Train Loss: 0.972, mae: 1.322, rse: 1.028, pcc: -0.007, kcc: -0.034
+2025-07-09 15:19:07: Epoch: 028 Test results, ap: mae: 0.635, rse: 1.224, pcc: 0.001, kcc: -0.014
+2025-07-09 15:19:08: Epoch: 029 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: -0.003, kcc: -0.037
+2025-07-09 15:19:08: Epoch: 029 Test results, ap: mae: 0.637, rse: 1.231, pcc: 0.001, kcc: -0.036
+2025-07-09 15:19:09: Epoch: 030 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: 0.001, kcc: -0.018
+2025-07-09 15:19:10: Epoch: 030 Test results, ap: mae: 0.637, rse: 1.230, pcc: -0.003, kcc: -0.015
+2025-07-09 15:19:11: Epoch: 031 Train Loss: 0.973, mae: 1.322, rse: 1.029, pcc: 0.003, kcc: -0.016
+2025-07-09 15:19:11: Epoch: 031 Test results, ap: mae: 0.637, rse: 1.231, pcc: -0.008, kcc: -0.052
+2025-07-09 15:19:12: Epoch: 032 Train Loss: 0.973, mae: 1.322, rse: 1.029, pcc: 0.005, kcc: -0.013
+2025-07-09 15:19:13: Epoch: 032 Test results, ap: mae: 0.637, rse: 1.232, pcc: -0.007, kcc: -0.011
+2025-07-09 15:19:14: Epoch: 033 Train Loss: 0.973, mae: 1.322, rse: 1.029, pcc: 0.007, kcc: -0.007
+2025-07-09 15:19:14: Epoch: 033 Test results, ap: mae: 0.637, rse: 1.233, pcc: -0.007, kcc: -0.026
+2025-07-09 15:19:15: Epoch: 034 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: 0.004, kcc: -0.014
+2025-07-09 15:19:15: Epoch: 034 Test results, ap: mae: 0.636, rse: 1.229, pcc: 0.029, kcc: 0.013
+2025-07-09 15:19:17: Epoch: 035 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: 0.008, kcc: 0.012
+2025-07-09 15:19:17: Epoch: 035 Test results, ap: mae: 0.638, rse: 1.235, pcc: 0.008, kcc: 0.003
+2025-07-09 15:19:17: used time 0.01h
+2025-07-09 15:19:17: Running Cross Validation 4
+2025-07-09 15:19:17: Fold 4  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:19:18: Epoch: 001 Train Loss: 0.971, mae: 1.360, rse: 1.000, pcc: -0.125, kcc: -0.048
+2025-07-09 15:19:18: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:19:18: Epoch: 001 Test results, ap: mae: 0.622, rse: 1.184, pcc: -0.030, kcc: -0.105
+2025-07-09 15:19:20: Epoch: 002 Train Loss: 0.966, mae: 1.362, rse: 1.000, pcc: 0.028, kcc: 0.129
+2025-07-09 15:19:20: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:19:20: Epoch: 002 Test results, ap: mae: 0.614, rse: 1.155, pcc: -0.057, kcc: 0.046
+2025-07-09 15:19:21: Epoch: 003 Train Loss: 0.966, mae: 1.362, rse: 1.001, pcc: 0.058, kcc: 0.053
+2025-07-09 15:19:21: Epoch: 003 Test results, ap: mae: 0.613, rse: 1.152, pcc: -0.158, kcc: -0.088
+2025-07-09 15:19:22: Epoch: 004 Train Loss: 0.966, mae: 1.365, rse: 1.001, pcc: 0.076, kcc: 0.121
+2025-07-09 15:19:22: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:19:23: Epoch: 004 Test results, ap: mae: 0.607, rse: 1.128, pcc: -0.167, kcc: -0.213
+2025-07-09 15:19:24: Epoch: 005 Train Loss: 0.967, mae: 1.360, rse: 0.999, pcc: 0.255, kcc: 0.199
+2025-07-09 15:19:24: Epoch: 005 New best VALIDATION metrics
+2025-07-09 15:19:24: Epoch: 005 Test results, ap: mae: 0.622, rse: 1.182, pcc: -0.222, kcc: -0.161
+2025-07-09 15:19:25: Epoch: 006 Train Loss: 0.961, mae: 1.322, rse: 0.955, pcc: 0.314, kcc: 0.227
+2025-07-09 15:19:25: Epoch: 006 New best VALIDATION metrics
+2025-07-09 15:19:26: Epoch: 006 Test results, ap: mae: 0.655, rse: 1.311, pcc: -0.243, kcc: -0.161
+2025-07-09 15:19:27: Epoch: 007 Train Loss: 0.878, mae: 1.366, rse: 0.990, pcc: 0.215, kcc: 0.164
+2025-07-09 15:19:27: Epoch: 007 Test results, ap: mae: 0.823, rse: 1.805, pcc: -0.137, kcc: -0.081
+2025-07-09 15:19:28: Epoch: 008 Train Loss: 0.747, mae: 1.128, rse: 0.717, pcc: 0.536, kcc: 0.341
+2025-07-09 15:19:28: Epoch: 008 New best VALIDATION metrics
+2025-07-09 15:19:29: Epoch: 008 Test results, ap: mae: 0.860, rse: 2.320, pcc: -0.139, kcc: -0.088
+2025-07-09 15:19:30: Epoch: 009 Train Loss: 0.632, mae: 0.919, rse: 0.518, pcc: 0.697, kcc: 0.506
+2025-07-09 15:19:30: Epoch: 009 New best VALIDATION metrics
+2025-07-09 15:19:30: Epoch: 009 Test results, ap: mae: 0.768, rse: 1.613, pcc: -0.005, kcc: 0.005
+2025-07-09 15:19:31: Epoch: 010 Train Loss: 0.560, mae: 0.919, rse: 0.512, pcc: 0.699, kcc: 0.512
+2025-07-09 15:19:31: Epoch: 010 New best VALIDATION metrics
+2025-07-09 15:19:32: Epoch: 010 Test results, ap: mae: 0.857, rse: 2.073, pcc: -0.137, kcc: -0.076
+2025-07-09 15:19:33: Epoch: 011 Train Loss: 0.502, mae: 0.877, rse: 0.466, pcc: 0.737, kcc: 0.531
+2025-07-09 15:19:33: Epoch: 011 New best VALIDATION metrics
+2025-07-09 15:19:33: Epoch: 011 Test results, ap: mae: 0.876, rse: 2.094, pcc: -0.060, kcc: -0.027
+2025-07-09 15:19:33: Epoch: 011 New best TEST metrics
+2025-07-09 15:19:34: Epoch: 012 Train Loss: 0.481, mae: 0.838, rse: 0.443, pcc: 0.749, kcc: 0.559
+2025-07-09 15:19:34: Epoch: 012 New best VALIDATION metrics
+2025-07-09 15:19:35: Epoch: 012 Test results, ap: mae: 0.814, rse: 1.854, pcc: -0.100, kcc: -0.057
+2025-07-09 15:19:35: Epoch: 012 New best TEST metrics
+2025-07-09 15:19:36: Epoch: 013 Train Loss: 0.457, mae: 0.795, rse: 0.413, pcc: 0.774, kcc: 0.591
+2025-07-09 15:19:36: Epoch: 013 New best VALIDATION metrics
+2025-07-09 15:19:36: Epoch: 013 Test results, ap: mae: 0.785, rse: 1.661, pcc: -0.010, kcc: 0.004
+2025-07-09 15:19:36: Epoch: 013 New best TEST metrics
+2025-07-09 15:19:37: Epoch: 014 Train Loss: 0.426, mae: 0.777, rse: 0.393, pcc: 0.785, kcc: 0.598
+2025-07-09 15:19:37: Epoch: 014 New best VALIDATION metrics
+2025-07-09 15:19:37: Epoch: 014 Test results, ap: mae: 0.814, rse: 1.769, pcc: 0.040, kcc: 0.032
+2025-07-09 15:19:39: Epoch: 015 Train Loss: 0.419, mae: 0.769, rse: 0.387, pcc: 0.791, kcc: 0.604
+2025-07-09 15:19:39: Epoch: 015 New best VALIDATION metrics
+2025-07-09 15:19:39: Epoch: 015 Test results, ap: mae: 0.749, rse: 1.509, pcc: 0.032, kcc: 0.034
+2025-07-09 15:19:39: Epoch: 015 New best TEST metrics
+2025-07-09 15:19:40: Epoch: 016 Train Loss: 0.407, mae: 0.747, rse: 0.371, pcc: 0.798, kcc: 0.614
+2025-07-09 15:19:40: Epoch: 016 New best VALIDATION metrics
+2025-07-09 15:19:41: Epoch: 016 Test results, ap: mae: 0.774, rse: 1.557, pcc: 0.038, kcc: 0.027
+2025-07-09 15:19:42: Epoch: 017 Train Loss: 0.370, mae: 0.718, rse: 0.354, pcc: 0.810, kcc: 0.632
+2025-07-09 15:19:42: Epoch: 017 New best VALIDATION metrics
+2025-07-09 15:19:42: Epoch: 017 Test results, ap: mae: 0.759, rse: 1.516, pcc: 0.046, kcc: 0.037
+2025-07-09 15:19:43: Epoch: 018 Train Loss: 0.360, mae: 0.719, rse: 0.360, pcc: 0.804, kcc: 0.626
+2025-07-09 15:19:44: Epoch: 018 Test results, ap: mae: 0.717, rse: 1.347, pcc: 0.175, kcc: 0.133
+2025-07-09 15:19:44: Epoch: 018 New best TEST metrics
+2025-07-09 15:19:45: Epoch: 019 Train Loss: 0.336, mae: 0.670, rse: 0.327, pcc: 0.823, kcc: 0.650
+2025-07-09 15:19:45: Epoch: 019 New best VALIDATION metrics
+2025-07-09 15:19:45: Epoch: 019 Test results, ap: mae: 0.763, rse: 1.492, pcc: 0.155, kcc: 0.110
+2025-07-09 15:19:46: Epoch: 020 Train Loss: 0.329, mae: 0.677, rse: 0.329, pcc: 0.824, kcc: 0.646
+2025-07-09 15:19:47: Epoch: 020 Test results, ap: mae: 0.811, rse: 1.683, pcc: 0.039, kcc: 0.029
+2025-07-09 15:19:48: Epoch: 021 Train Loss: 0.298, mae: 0.624, rse: 0.310, pcc: 0.832, kcc: 0.670
+2025-07-09 15:19:48: Epoch: 021 New best VALIDATION metrics
+2025-07-09 15:19:48: Epoch: 021 Test results, ap: mae: 0.739, rse: 1.460, pcc: 0.111, kcc: 0.080
+2025-07-09 15:19:49: Epoch: 022 Train Loss: 0.286, mae: 0.601, rse: 0.294, pcc: 0.842, kcc: 0.684
+2025-07-09 15:19:49: Epoch: 022 New best VALIDATION metrics
+2025-07-09 15:19:50: Epoch: 022 Test results, ap: mae: 0.693, rse: 1.335, pcc: 0.122, kcc: 0.096
+2025-07-09 15:19:51: Epoch: 023 Train Loss: 0.267, mae: 0.601, rse: 0.292, pcc: 0.842, kcc: 0.684
+2025-07-09 15:19:51: Epoch: 023 New best VALIDATION metrics
+2025-07-09 15:19:51: Epoch: 023 Test results, ap: mae: 0.767, rse: 1.565, pcc: 0.089, kcc: 0.068
+2025-07-09 15:19:52: Epoch: 024 Train Loss: 0.260, mae: 0.604, rse: 0.295, pcc: 0.840, kcc: 0.679
+2025-07-09 15:19:53: Epoch: 024 Test results, ap: mae: 0.686, rse: 1.311, pcc: 0.155, kcc: 0.106
+2025-07-09 15:19:53: Epoch: 024 New best TEST metrics
+2025-07-09 15:19:54: Epoch: 025 Train Loss: 0.256, mae: 0.565, rse: 0.277, pcc: 0.853, kcc: 0.700
+2025-07-09 15:19:54: Epoch: 025 New best VALIDATION metrics
+2025-07-09 15:19:54: Epoch: 025 Test results, ap: mae: 0.716, rse: 1.464, pcc: 0.131, kcc: 0.101
+2025-07-09 15:19:55: Epoch: 026 Train Loss: 0.249, mae: 0.536, rse: 0.264, pcc: 0.860, kcc: 0.712
+2025-07-09 15:19:55: Epoch: 026 New best VALIDATION metrics
+2025-07-09 15:19:56: Epoch: 026 Test results, ap: mae: 0.703, rse: 1.415, pcc: 0.115, kcc: 0.081
+2025-07-09 15:19:57: Epoch: 027 Train Loss: 0.246, mae: 0.562, rse: 0.271, pcc: 0.855, kcc: 0.700
+2025-07-09 15:19:57: Epoch: 027 Test results, ap: mae: 0.728, rse: 1.495, pcc: 0.085, kcc: 0.058
+2025-07-09 15:19:58: Epoch: 028 Train Loss: 0.243, mae: 0.518, rse: 0.256, pcc: 0.864, kcc: 0.721
+2025-07-09 15:19:58: Epoch: 028 New best VALIDATION metrics
+2025-07-09 15:19:59: Epoch: 028 Test results, ap: mae: 0.712, rse: 1.481, pcc: 0.123, kcc: 0.095
+2025-07-09 15:20:00: Epoch: 029 Train Loss: 0.230, mae: 0.521, rse: 0.258, pcc: 0.862, kcc: 0.720
+2025-07-09 15:20:00: Epoch: 029 Test results, ap: mae: 0.724, rse: 1.524, pcc: 0.107, kcc: 0.078
+2025-07-09 15:20:01: Epoch: 030 Train Loss: 0.236, mae: 0.528, rse: 0.250, pcc: 0.866, kcc: 0.720
+2025-07-09 15:20:02: Epoch: 030 Test results, ap: mae: 0.758, rse: 1.688, pcc: 0.087, kcc: 0.065
+2025-07-09 15:20:03: Epoch: 031 Train Loss: 0.213, mae: 0.505, rse: 0.251, pcc: 0.867, kcc: 0.727
+2025-07-09 15:20:03: Epoch: 031 New best VALIDATION metrics
+2025-07-09 15:20:03: Epoch: 031 Test results, ap: mae: 0.777, rse: 1.754, pcc: 0.070, kcc: 0.055
+2025-07-09 15:20:05: Epoch: 032 Train Loss: 0.203, mae: 0.489, rse: 0.234, pcc: 0.876, kcc: 0.738
+2025-07-09 15:20:05: Epoch: 032 New best VALIDATION metrics
+2025-07-09 15:20:05: Epoch: 032 Test results, ap: mae: 0.775, rse: 1.761, pcc: 0.098, kcc: 0.068
+2025-07-09 15:20:06: Epoch: 033 Train Loss: 0.201, mae: 0.490, rse: 0.239, pcc: 0.873, kcc: 0.736
+2025-07-09 15:20:06: Epoch: 033 Test results, ap: mae: 0.796, rse: 1.852, pcc: 0.074, kcc: 0.054
+2025-07-09 15:20:08: Epoch: 034 Train Loss: 0.196, mae: 0.473, rse: 0.226, pcc: 0.880, kcc: 0.745
+2025-07-09 15:20:08: Epoch: 034 New best VALIDATION metrics
+2025-07-09 15:20:08: Epoch: 034 Test results, ap: mae: 0.815, rse: 1.945, pcc: 0.069, kcc: 0.053
+2025-07-09 15:20:09: Epoch: 035 Train Loss: 0.186, mae: 0.471, rse: 0.225, pcc: 0.881, kcc: 0.747
+2025-07-09 15:20:09: Epoch: 035 New best VALIDATION metrics
+2025-07-09 15:20:09: Epoch: 035 Test results, ap: mae: 0.812, rse: 1.949, pcc: 0.067, kcc: 0.057
+2025-07-09 15:20:09: used time 0.01h
+2025-07-09 15:20:09: Cross Validation Finished!
+2025-07-09 15:20:09: Best validation perform list
+[[1.17682338 0.83270389 0.41188517 0.36459386]
+ [0.57673734 0.30101562 0.83709753 0.6880945 ]
+ [0.85054702 0.40704897 0.77146333 0.59617913]
+ [1.23153734 0.91359586 0.43659022 0.33472547]
+ [0.47110289 0.22451209 0.88116193 0.7472738 ]]
+2025-07-09 15:20:09: mean: [0.861 0.536 0.668 0.546]
+2025-07-09 15:20:09: std: [0.307 0.283 0.202 0.168]
+2025-07-09 15:20:09: Best test perform list
+[[0.62123275 1.17984748 0.25552356 0.13002065]
+ [0.58065999 1.02181184 0.1278944  0.07458822]
+ [0.61871761 1.17217076 0.03473068 0.07003216]
+ [0.6329186  1.21769869 0.06185238 0.0395491 ]
+ [0.6859901  1.31098318 0.15515427 0.10581119]]
+2025-07-09 15:20:09: mean: [0.628 1.181 0.127 0.084]
+2025-07-09 15:20:09: std: [0.034 0.093 0.078 0.031]
diff --git a/run-reg/lstm-mlp-256-mse-32-0.001-50/config.json b/run-reg/lstm-mlp-256-mse-32-0.001-50/config.json
new file mode 100644
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "lstm", "side_enc": null, "channels": 256, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
\ No newline at end of file
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+2025-07-10 14:41:34: saving_dir: ./run-reg/lstm-mlp-256-mse-32-0.001-50
+2025-07-10 14:41:34: Loading Train Dataset
+2025-07-10 14:41:34: Loading Test Dataset
+2025-07-10 14:41:35: Running Cross Validation 0
+2025-07-10 14:41:35: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 14:41:36: Epoch: 001 Train Loss: 2.934, mae: 1.210, rse: 1.000, pcc: 0.100, kcc: 0.054
+2025-07-10 14:41:36: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:41:36: Epoch: 001 Test results, ap: mae: 0.612, rse: 1.148, pcc: 0.115, kcc: 0.124
+2025-07-10 14:41:37: Epoch: 002 Train Loss: 2.906, mae: 1.203, rse: 1.000, pcc: 0.111, kcc: 0.075
+2025-07-10 14:41:37: Epoch: 002 New best VALIDATION metrics
+2025-07-10 14:41:37: Epoch: 002 Test results, ap: mae: 0.632, rse: 1.214, pcc: 0.152, kcc: 0.120
+2025-07-10 14:41:38: Epoch: 003 Train Loss: 2.906, mae: 1.205, rse: 1.000, pcc: 0.118, kcc: 0.083
+2025-07-10 14:41:38: Epoch: 003 New best VALIDATION metrics
+2025-07-10 14:41:38: Epoch: 003 Test results, ap: mae: 0.626, rse: 1.193, pcc: 0.178, kcc: 0.155
+2025-07-10 14:41:38: Epoch: 004 Train Loss: 2.892, mae: 1.208, rse: 1.000, pcc: 0.135, kcc: 0.088
+2025-07-10 14:41:38: Epoch: 004 New best VALIDATION metrics
+2025-07-10 14:41:39: Epoch: 004 Test results, ap: mae: 0.616, rse: 1.163, pcc: 0.165, kcc: 0.175
+2025-07-10 14:41:39: Epoch: 005 Train Loss: 2.903, mae: 1.218, rse: 1.002, pcc: 0.110, kcc: 0.081
+2025-07-10 14:41:39: Epoch: 005 Test results, ap: mae: 0.600, rse: 1.099, pcc: 0.174, kcc: 0.186
+2025-07-10 14:41:40: Epoch: 006 Train Loss: 2.917, mae: 1.224, rse: 1.005, pcc: 0.132, kcc: 0.106
+2025-07-10 14:41:40: Epoch: 006 Test results, ap: mae: 0.594, rse: 1.074, pcc: 0.157, kcc: 0.056
+2025-07-10 14:41:41: Epoch: 007 Train Loss: 2.918, mae: 1.214, rse: 1.001, pcc: 0.165, kcc: 0.100
+2025-07-10 14:41:41: Epoch: 007 New best VALIDATION metrics
+2025-07-10 14:41:41: Epoch: 007 Test results, ap: mae: 0.605, rse: 1.120, pcc: 0.223, kcc: 0.145
+2025-07-10 14:41:42: Epoch: 008 Train Loss: 2.920, mae: 1.203, rse: 1.000, pcc: 0.167, kcc: 0.089
+2025-07-10 14:41:42: Epoch: 008 New best VALIDATION metrics
+2025-07-10 14:41:42: Epoch: 008 Test results, ap: mae: 0.632, rse: 1.213, pcc: 0.018, kcc: 0.020
+2025-07-10 14:41:42: Epoch: 009 Train Loss: 2.922, mae: 1.211, rse: 1.000, pcc: 0.145, kcc: 0.109
+2025-07-10 14:41:43: Epoch: 009 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.091, kcc: 0.084
+2025-07-10 14:41:43: Epoch: 010 Train Loss: 2.911, mae: 1.214, rse: 1.001, pcc: 0.082, kcc: 0.131
+2025-07-10 14:41:43: Epoch: 010 Test results, ap: mae: 0.605, rse: 1.120, pcc: -0.595, kcc: 0.242
+2025-07-10 14:41:44: Epoch: 011 Train Loss: 2.915, mae: 1.213, rse: 1.001, pcc: -0.002, kcc: 0.034
+2025-07-10 14:41:44: Epoch: 011 Test results, ap: mae: 0.607, rse: 1.128, pcc: nan, kcc: nan
+2025-07-10 14:41:45: Epoch: 012 Train Loss: 2.903, mae: 1.204, rse: 1.000, pcc: 0.022, kcc: -0.016
+2025-07-10 14:41:45: Epoch: 012 Test results, ap: mae: 0.630, rse: 1.208, pcc: -0.235, kcc: -0.242
+2025-07-10 14:41:45: Epoch: 012 New best TEST metrics
+2025-07-10 14:41:46: Epoch: 013 Train Loss: 2.879, mae: 1.207, rse: 1.000, pcc: 0.029, kcc: -0.009
+2025-07-10 14:41:46: Epoch: 013 Test results, ap: mae: 0.621, rse: 1.179, pcc: -0.733, kcc: 0.242
+2025-07-10 14:41:46: Epoch: 013 New best TEST metrics
+2025-07-10 14:41:46: Epoch: 014 Train Loss: 2.931, mae: 1.220, rse: 1.003, pcc: 0.006, kcc: 0.048
+2025-07-10 14:41:47: Epoch: 014 Test results, ap: mae: 0.598, rse: 1.091, pcc: -0.118, kcc: -0.037
+2025-07-10 14:41:47: Epoch: 014 New best TEST metrics
+2025-07-10 14:41:47: Epoch: 015 Train Loss: 2.894, mae: 1.219, rse: 1.003, pcc: 0.075, kcc: 0.022
+2025-07-10 14:41:47: Epoch: 015 Test results, ap: mae: 0.599, rse: 1.095, pcc: nan, kcc: nan
+2025-07-10 14:41:48: Epoch: 016 Train Loss: 2.918, mae: 1.209, rse: 1.000, pcc: 0.020, kcc: 0.048
+2025-07-10 14:41:48: Epoch: 016 Test results, ap: mae: 0.614, rse: 1.155, pcc: nan, kcc: nan
+2025-07-10 14:41:49: Epoch: 017 Train Loss: 2.905, mae: 1.208, rse: 1.000, pcc: 0.088, kcc: -0.007
+2025-07-10 14:41:49: Epoch: 017 Test results, ap: mae: 0.616, rse: 1.162, pcc: nan, kcc: nan
+2025-07-10 14:41:50: Epoch: 018 Train Loss: 2.913, mae: 1.209, rse: 1.000, pcc: -0.087, kcc: 0.014
+2025-07-10 14:41:50: Epoch: 018 Test results, ap: mae: 0.615, rse: 1.159, pcc: -1.000, kcc: -0.202
+2025-07-10 14:41:50: Epoch: 019 Train Loss: 2.879, mae: 1.211, rse: 1.000, pcc: -0.008, kcc: -0.012
+2025-07-10 14:41:50: Epoch: 019 Test results, ap: mae: 0.610, rse: 1.140, pcc: nan, kcc: nan
+2025-07-10 14:41:51: Epoch: 020 Train Loss: 2.925, mae: 1.214, rse: 1.001, pcc: 0.167, kcc: -0.040
+2025-07-10 14:41:51: Epoch: 020 Test results, ap: mae: 0.606, rse: 1.123, pcc: -0.688, kcc: -0.242
+2025-07-10 14:41:52: Epoch: 021 Train Loss: 2.908, mae: 1.210, rse: 1.000, pcc: 0.067, kcc: -0.034
+2025-07-10 14:41:52: Epoch: 021 Test results, ap: mae: 0.612, rse: 1.149, pcc: nan, kcc: nan
+2025-07-10 14:41:53: Epoch: 022 Train Loss: 2.903, mae: 1.213, rse: 1.001, pcc: 0.059, kcc: -0.072
+2025-07-10 14:41:53: Epoch: 022 Test results, ap: mae: 0.608, rse: 1.132, pcc: -0.745, kcc: -0.245
+2025-07-10 14:41:54: Epoch: 023 Train Loss: 2.889, mae: 1.211, rse: 1.000, pcc: -0.023, kcc: -0.047
+2025-07-10 14:41:54: Epoch: 023 Test results, ap: mae: 0.610, rse: 1.140, pcc: nan, kcc: nan
+2025-07-10 14:41:54: Epoch: 024 Train Loss: 2.891, mae: 1.216, rse: 1.002, pcc: -0.008, kcc: -0.012
+2025-07-10 14:41:54: Epoch: 024 Test results, ap: mae: 0.602, rse: 1.109, pcc: nan, kcc: nan
+2025-07-10 14:41:55: Epoch: 025 Train Loss: 2.902, mae: 1.216, rse: 1.002, pcc: -0.008, kcc: -0.012
+2025-07-10 14:41:55: Epoch: 025 Test results, ap: mae: 0.603, rse: 1.110, pcc: nan, kcc: nan
+2025-07-10 14:41:56: Epoch: 026 Train Loss: 2.903, mae: 1.212, rse: 1.001, pcc: 0.017, kcc: 0.147
+2025-07-10 14:41:56: Epoch: 026 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.451, kcc: 0.082
+2025-07-10 14:41:57: Epoch: 027 Train Loss: 2.897, mae: 1.213, rse: 1.001, pcc: 0.050, kcc: 0.017
+2025-07-10 14:41:57: Epoch: 027 Test results, ap: mae: 0.607, rse: 1.128, pcc: 0.871, kcc: -0.027
+2025-07-10 14:41:57: Epoch: 027 New best TEST metrics
+2025-07-10 14:41:58: Epoch: 028 Train Loss: 2.916, mae: 1.214, rse: 1.001, pcc: 0.031, kcc: -0.038
+2025-07-10 14:41:58: Epoch: 028 Test results, ap: mae: 0.606, rse: 1.124, pcc: 0.733, kcc: -0.242
+2025-07-10 14:41:58: Epoch: 029 Train Loss: 2.902, mae: 1.211, rse: 1.000, pcc: 0.057, kcc: 0.043
+2025-07-10 14:41:58: Epoch: 029 Test results, ap: mae: 0.611, rse: 1.143, pcc: -1.000, kcc: nan
+2025-07-10 14:41:59: Epoch: 030 Train Loss: 2.922, mae: 1.211, rse: 1.000, pcc: 0.043, kcc: 0.134
+2025-07-10 14:41:59: Epoch: 030 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.733, kcc: 0.242
+2025-07-10 14:42:00: Epoch: 031 Train Loss: 2.901, mae: 1.211, rse: 1.000, pcc: 0.035, kcc: -0.003
+2025-07-10 14:42:00: Epoch: 031 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.188, kcc: 0.075
+2025-07-10 14:42:01: Epoch: 032 Train Loss: 2.887, mae: 1.214, rse: 1.001, pcc: 0.046, kcc: 0.029
+2025-07-10 14:42:01: Epoch: 032 Test results, ap: mae: 0.606, rse: 1.123, pcc: -1.000, kcc: nan
+2025-07-10 14:42:01: Epoch: 033 Train Loss: 2.905, mae: 1.213, rse: 1.001, pcc: 0.100, kcc: 0.050
+2025-07-10 14:42:01: Epoch: 033 Test results, ap: mae: 0.607, rse: 1.129, pcc: nan, kcc: nan
+2025-07-10 14:42:02: Epoch: 034 Train Loss: 2.900, mae: 1.212, rse: 1.001, pcc: 0.026, kcc: 0.025
+2025-07-10 14:42:02: Epoch: 034 Test results, ap: mae: 0.609, rse: 1.137, pcc: 1.000, kcc: 0.242
+2025-07-10 14:42:02: Epoch: 034 New best TEST metrics
+2025-07-10 14:42:03: Epoch: 035 Train Loss: 2.919, mae: 1.213, rse: 1.001, pcc: 0.010, kcc: 0.017
+2025-07-10 14:42:03: Epoch: 035 Test results, ap: mae: 0.608, rse: 1.130, pcc: 0.155, kcc: 0.157
+2025-07-10 14:42:03: Epoch: 036 Train Loss: 2.908, mae: 1.212, rse: 1.001, pcc: -0.007, kcc: 0.041
+2025-07-10 14:42:04: Epoch: 036 Test results, ap: mae: 0.608, rse: 1.133, pcc: -1.000, kcc: nan
+2025-07-10 14:42:04: Epoch: 037 Train Loss: 2.913, mae: 1.212, rse: 1.001, pcc: 0.017, kcc: -0.012
+2025-07-10 14:42:05: Epoch: 037 Test results, ap: mae: 0.609, rse: 1.136, pcc: nan, kcc: nan
+2025-07-10 14:42:05: Epoch: 038 Train Loss: 2.919, mae: 1.213, rse: 1.001, pcc: -0.004, kcc: 0.013
+2025-07-10 14:42:06: Epoch: 038 Test results, ap: mae: 0.607, rse: 1.128, pcc: -0.733, kcc: 0.242
+2025-07-10 14:42:07: Epoch: 039 Train Loss: 2.909, mae: 1.212, rse: 1.001, pcc: -0.006, kcc: -0.003
+2025-07-10 14:42:07: Epoch: 039 Test results, ap: mae: 0.609, rse: 1.134, pcc: -1.000, kcc: nan
+2025-07-10 14:42:08: Epoch: 040 Train Loss: 2.874, mae: 1.213, rse: 1.001, pcc: 0.021, kcc: 0.017
+2025-07-10 14:42:08: Epoch: 040 Test results, ap: mae: 0.608, rse: 1.130, pcc: nan, kcc: nan
+2025-07-10 14:42:09: Epoch: 041 Train Loss: 2.909, mae: 1.214, rse: 1.001, pcc: -0.008, kcc: -0.012
+2025-07-10 14:42:09: Epoch: 041 Test results, ap: mae: 0.606, rse: 1.125, pcc: nan, kcc: nan
+2025-07-10 14:42:10: Epoch: 042 Train Loss: 2.901, mae: 1.213, rse: 1.001, pcc: 0.004, kcc: 0.070
+2025-07-10 14:42:10: Epoch: 042 Test results, ap: mae: 0.607, rse: 1.129, pcc: -0.733, kcc: 0.242
+2025-07-10 14:42:11: Epoch: 043 Train Loss: 2.901, mae: 1.212, rse: 1.001, pcc: -0.002, kcc: 0.063
+2025-07-10 14:42:11: Epoch: 043 Test results, ap: mae: 0.608, rse: 1.133, pcc: 0.733, kcc: -0.242
+2025-07-10 14:42:12: Epoch: 044 Train Loss: 2.905, mae: 1.212, rse: 1.001, pcc: 0.031, kcc: -0.012
+2025-07-10 14:42:12: Epoch: 044 Test results, ap: mae: 0.609, rse: 1.134, pcc: nan, kcc: nan
+2025-07-10 14:42:13: Epoch: 045 Train Loss: 2.903, mae: 1.212, rse: 1.001, pcc: -0.015, kcc: -0.039
+2025-07-10 14:42:13: Epoch: 045 Test results, ap: mae: 0.609, rse: 1.135, pcc: -0.649, kcc: -0.246
+2025-07-10 14:42:14: Epoch: 046 Train Loss: 2.899, mae: 1.213, rse: 1.001, pcc: -0.008, kcc: -0.012
+2025-07-10 14:42:14: Epoch: 046 Test results, ap: mae: 0.608, rse: 1.132, pcc: nan, kcc: nan
+2025-07-10 14:42:15: Epoch: 047 Train Loss: 2.897, mae: 1.212, rse: 1.001, pcc: 0.029, kcc: -0.004
+2025-07-10 14:42:15: Epoch: 047 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.562, kcc: 0.242
+2025-07-10 14:42:16: Epoch: 048 Train Loss: 2.895, mae: 1.212, rse: 1.001, pcc: -0.009, kcc: -0.016
+2025-07-10 14:42:16: Epoch: 048 Test results, ap: mae: 0.609, rse: 1.135, pcc: nan, kcc: nan
+2025-07-10 14:42:17: Epoch: 049 Train Loss: 2.913, mae: 1.213, rse: 1.001, pcc: -0.008, kcc: 0.016
+2025-07-10 14:42:17: Epoch: 049 Test results, ap: mae: 0.608, rse: 1.131, pcc: nan, kcc: nan
+2025-07-10 14:42:18: Epoch: 050 Train Loss: 2.892, mae: 1.213, rse: 1.001, pcc: 0.026, kcc: 0.002
+2025-07-10 14:42:18: Epoch: 050 Test results, ap: mae: 0.607, rse: 1.129, pcc: nan, kcc: nan
+2025-07-10 14:42:18: used time 0.01h
+2025-07-10 14:42:18: Running Cross Validation 1
+2025-07-10 14:42:18: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 14:42:19: Epoch: 001 Train Loss: 2.846, mae: 1.251, rse: 1.021, pcc: 0.017, kcc: -0.116
+2025-07-10 14:42:19: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:42:19: Epoch: 001 Test results, ap: mae: 0.689, rse: 1.418, pcc: 0.038, kcc: 0.092
+2025-07-10 14:42:20: Epoch: 002 Train Loss: 2.855, mae: 1.252, rse: 1.002, pcc: 0.072, kcc: 0.167
+2025-07-10 14:42:20: Epoch: 002 New best VALIDATION metrics
+2025-07-10 14:42:20: Epoch: 002 Test results, ap: mae: 0.621, rse: 1.180, pcc: 0.153, kcc: -0.088
+2025-07-10 14:42:21: Epoch: 003 Train Loss: 2.844, mae: 1.254, rse: 1.001, pcc: 0.067, kcc: 0.184
+2025-07-10 14:42:21: Epoch: 003 New best VALIDATION metrics
+2025-07-10 14:42:21: Epoch: 003 Test results, ap: mae: 0.613, rse: 1.152, pcc: -0.385, kcc: -0.032
+2025-07-10 14:42:22: Epoch: 004 Train Loss: 2.824, mae: 1.251, rse: 1.004, pcc: 0.064, kcc: 0.189
+2025-07-10 14:42:22: Epoch: 004 New best VALIDATION metrics
+2025-07-10 14:42:22: Epoch: 004 Test results, ap: mae: 0.628, rse: 1.202, pcc: -0.260, kcc: -0.052
+2025-07-10 14:42:23: Epoch: 005 Train Loss: 2.854, mae: 1.251, rse: 1.008, pcc: 0.061, kcc: 0.151
+2025-07-10 14:42:23: Epoch: 005 Test results, ap: mae: 0.645, rse: 1.262, pcc: 0.094, kcc: 0.056
+2025-07-10 14:42:24: Epoch: 006 Train Loss: 2.854, mae: 1.251, rse: 1.006, pcc: 0.060, kcc: 0.184
+2025-07-10 14:42:24: Epoch: 006 Test results, ap: mae: 0.637, rse: 1.233, pcc: -0.149, kcc: -0.205
+2025-07-10 14:42:25: Epoch: 007 Train Loss: 2.853, mae: 1.267, rse: 1.001, pcc: 0.064, kcc: 0.157
+2025-07-10 14:42:25: Epoch: 007 Test results, ap: mae: 0.592, rse: 1.063, pcc: -0.420, kcc: -0.091
+2025-07-10 14:42:26: Epoch: 008 Train Loss: 2.850, mae: 1.251, rse: 1.005, pcc: 0.066, kcc: 0.213
+2025-07-10 14:42:26: Epoch: 008 New best VALIDATION metrics
+2025-07-10 14:42:27: Epoch: 008 Test results, ap: mae: 0.633, rse: 1.218, pcc: 0.358, kcc: 0.011
+2025-07-10 14:42:27: Epoch: 009 Train Loss: 2.855, mae: 1.255, rse: 1.001, pcc: 0.065, kcc: 0.175
+2025-07-10 14:42:28: Epoch: 009 Test results, ap: mae: 0.612, rse: 1.147, pcc: -0.125, kcc: -0.151
+2025-07-10 14:42:28: Epoch: 010 Train Loss: 2.839, mae: 1.261, rse: 1.000, pcc: 0.060, kcc: 0.177
+2025-07-10 14:42:29: Epoch: 010 Test results, ap: mae: 0.599, rse: 1.095, pcc: -0.352, kcc: -0.185
+2025-07-10 14:42:29: Epoch: 011 Train Loss: 2.834, mae: 1.260, rse: 1.000, pcc: 0.060, kcc: 0.193
+2025-07-10 14:42:30: Epoch: 011 Test results, ap: mae: 0.601, rse: 1.102, pcc: -0.661, kcc: -0.063
+2025-07-10 14:42:30: Epoch: 011 New best TEST metrics
+2025-07-10 14:42:31: Epoch: 012 Train Loss: 2.818, mae: 1.253, rse: 1.002, pcc: 0.064, kcc: 0.165
+2025-07-10 14:42:31: Epoch: 012 Test results, ap: mae: 0.621, rse: 1.178, pcc: 0.276, kcc: 0.082
+2025-07-10 14:42:31: Epoch: 012 New best TEST metrics
+2025-07-10 14:42:32: Epoch: 013 Train Loss: 2.851, mae: 1.253, rse: 1.002, pcc: 0.062, kcc: 0.150
+2025-07-10 14:42:32: Epoch: 013 Test results, ap: mae: 0.619, rse: 1.173, pcc: -0.801, kcc: 0.023
+2025-07-10 14:42:33: Epoch: 014 Train Loss: 2.811, mae: 1.253, rse: 1.002, pcc: 0.062, kcc: 0.167
+2025-07-10 14:42:33: Epoch: 014 Test results, ap: mae: 0.617, rse: 1.166, pcc: -0.160, kcc: -0.164
+2025-07-10 14:42:34: Epoch: 015 Train Loss: 2.861, mae: 1.256, rse: 1.000, pcc: 0.062, kcc: 0.172
+2025-07-10 14:42:34: Epoch: 015 Test results, ap: mae: 0.608, rse: 1.131, pcc: 0.117, kcc: -0.047
+2025-07-10 14:42:34: Epoch: 016 Train Loss: 2.864, mae: 1.255, rse: 1.001, pcc: 0.062, kcc: 0.144
+2025-07-10 14:42:35: Epoch: 016 Test results, ap: mae: 0.612, rse: 1.149, pcc: -0.220, kcc: -0.040
+2025-07-10 14:42:35: Epoch: 017 Train Loss: 2.855, mae: 1.256, rse: 1.000, pcc: 0.063, kcc: 0.200
+2025-07-10 14:42:36: Epoch: 017 Test results, ap: mae: 0.607, rse: 1.129, pcc: -0.378, kcc: 0.017
+2025-07-10 14:42:36: Epoch: 018 Train Loss: 2.839, mae: 1.258, rse: 1.000, pcc: 0.062, kcc: 0.141
+2025-07-10 14:42:37: Epoch: 018 Test results, ap: mae: 0.605, rse: 1.118, pcc: 0.430, kcc: 0.062
+2025-07-10 14:42:37: Epoch: 018 New best TEST metrics
+2025-07-10 14:42:37: Epoch: 019 Train Loss: 2.825, mae: 1.254, rse: 1.001, pcc: 0.062, kcc: 0.167
+2025-07-10 14:42:38: Epoch: 019 Test results, ap: mae: 0.614, rse: 1.157, pcc: -0.735, kcc: 0.036
+2025-07-10 14:42:38: Epoch: 020 Train Loss: 2.832, mae: 1.253, rse: 1.002, pcc: 0.063, kcc: 0.160
+2025-07-10 14:42:38: Epoch: 020 Test results, ap: mae: 0.619, rse: 1.174, pcc: 0.240, kcc: 0.056
+2025-07-10 14:42:39: Epoch: 021 Train Loss: 2.835, mae: 1.254, rse: 1.001, pcc: 0.063, kcc: 0.142
+2025-07-10 14:42:39: Epoch: 021 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.789, kcc: 0.048
+2025-07-10 14:42:39: Epoch: 021 New best TEST metrics
+2025-07-10 14:42:40: Epoch: 022 Train Loss: 2.857, mae: 1.253, rse: 1.002, pcc: 0.063, kcc: 0.131
+2025-07-10 14:42:40: Epoch: 022 Test results, ap: mae: 0.618, rse: 1.171, pcc: -0.144, kcc: -0.073
+2025-07-10 14:42:41: Epoch: 023 Train Loss: 2.859, mae: 1.255, rse: 1.001, pcc: 0.063, kcc: 0.145
+2025-07-10 14:42:41: Epoch: 023 Test results, ap: mae: 0.611, rse: 1.145, pcc: 0.021, kcc: -0.003
+2025-07-10 14:42:42: Epoch: 024 Train Loss: 2.859, mae: 1.254, rse: 1.002, pcc: 0.063, kcc: 0.128
+2025-07-10 14:42:42: Epoch: 024 Test results, ap: mae: 0.615, rse: 1.160, pcc: -0.170, kcc: 0.000
+2025-07-10 14:42:43: Epoch: 025 Train Loss: 2.846, mae: 1.254, rse: 1.002, pcc: 0.062, kcc: 0.106
+2025-07-10 14:42:43: Epoch: 025 Test results, ap: mae: 0.615, rse: 1.161, pcc: -0.019, kcc: -0.011
+2025-07-10 14:42:44: Epoch: 026 Train Loss: 2.805, mae: 1.253, rse: 1.002, pcc: 0.063, kcc: 0.112
+2025-07-10 14:42:44: Epoch: 026 Test results, ap: mae: 0.616, rse: 1.164, pcc: -0.004, kcc: -0.021
+2025-07-10 14:42:45: Epoch: 027 Train Loss: 2.843, mae: 1.254, rse: 1.002, pcc: 0.063, kcc: 0.102
+2025-07-10 14:42:45: Epoch: 027 Test results, ap: mae: 0.615, rse: 1.160, pcc: 0.019, kcc: 0.001
+2025-07-10 14:42:46: Epoch: 028 Train Loss: 2.804, mae: 1.253, rse: 1.002, pcc: 0.063, kcc: 0.096
+2025-07-10 14:42:46: Epoch: 028 Test results, ap: mae: 0.618, rse: 1.171, pcc: 0.019, kcc: 0.016
+2025-07-10 14:42:47: Epoch: 029 Train Loss: 2.850, mae: 1.255, rse: 1.001, pcc: 0.067, kcc: 0.098
+2025-07-10 14:42:47: Epoch: 029 Test results, ap: mae: 0.612, rse: 1.149, pcc: 0.033, kcc: 0.045
+2025-07-10 14:42:48: Epoch: 030 Train Loss: 2.821, mae: 1.257, rse: 1.000, pcc: -0.094, kcc: -0.171
+2025-07-10 14:42:48: Epoch: 030 Test results, ap: mae: 0.606, rse: 1.122, pcc: -0.251, kcc: -0.135
+2025-07-10 14:42:48: Epoch: 031 Train Loss: 2.843, mae: 1.256, rse: 1.001, pcc: -0.002, kcc: -0.110
+2025-07-10 14:42:49: Epoch: 031 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.036, kcc: 0.046
+2025-07-10 14:42:49: Epoch: 032 Train Loss: 2.857, mae: 1.254, rse: 1.002, pcc: 0.105, kcc: 0.168
+2025-07-10 14:42:50: Epoch: 032 Test results, ap: mae: 0.615, rse: 1.160, pcc: 0.051, kcc: 0.000
+2025-07-10 14:42:50: Epoch: 033 Train Loss: 2.854, mae: 1.254, rse: 1.001, pcc: 0.089, kcc: 0.167
+2025-07-10 14:42:50: Epoch: 033 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.064, kcc: -0.015
+2025-07-10 14:42:51: Epoch: 034 Train Loss: 2.861, mae: 1.254, rse: 1.002, pcc: 0.090, kcc: 0.164
+2025-07-10 14:42:52: Epoch: 034 Test results, ap: mae: 0.615, rse: 1.160, pcc: -0.093, kcc: 0.002
+2025-07-10 14:42:52: Epoch: 035 Train Loss: 2.830, mae: 1.254, rse: 1.001, pcc: 0.084, kcc: 0.155
+2025-07-10 14:42:52: Epoch: 035 Test results, ap: mae: 0.614, rse: 1.157, pcc: 0.117, kcc: 0.007
+2025-07-10 14:42:53: Epoch: 036 Train Loss: 2.835, mae: 1.254, rse: 1.002, pcc: 0.094, kcc: 0.168
+2025-07-10 14:42:53: Epoch: 036 Test results, ap: mae: 0.616, rse: 1.162, pcc: -0.039, kcc: -0.099
+2025-07-10 14:42:54: Epoch: 037 Train Loss: 2.843, mae: 1.254, rse: 1.001, pcc: 0.100, kcc: 0.168
+2025-07-10 14:42:54: Epoch: 037 Test results, ap: mae: 0.614, rse: 1.154, pcc: -0.021, kcc: 0.100
+2025-07-10 14:42:55: Epoch: 038 Train Loss: 2.815, mae: 1.255, rse: 1.001, pcc: 0.094, kcc: 0.161
+2025-07-10 14:42:55: Epoch: 038 Test results, ap: mae: 0.613, rse: 1.150, pcc: 0.034, kcc: 0.059
+2025-07-10 14:42:56: Epoch: 039 Train Loss: 2.845, mae: 1.254, rse: 1.001, pcc: 0.097, kcc: 0.165
+2025-07-10 14:42:56: Epoch: 039 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.171, kcc: 0.085
+2025-07-10 14:42:57: Epoch: 040 Train Loss: 2.845, mae: 1.254, rse: 1.002, pcc: 0.095, kcc: 0.176
+2025-07-10 14:42:57: Epoch: 040 Test results, ap: mae: 0.616, rse: 1.161, pcc: 0.063, kcc: 0.009
+2025-07-10 14:42:58: Epoch: 041 Train Loss: 2.845, mae: 1.254, rse: 1.001, pcc: 0.095, kcc: 0.162
+2025-07-10 14:42:58: Epoch: 041 Test results, ap: mae: 0.615, rse: 1.159, pcc: 0.129, kcc: 0.012
+2025-07-10 14:42:59: Epoch: 042 Train Loss: 2.854, mae: 1.254, rse: 1.001, pcc: 0.098, kcc: 0.162
+2025-07-10 14:42:59: Epoch: 042 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.113, kcc: 0.097
+2025-07-10 14:43:00: Epoch: 043 Train Loss: 2.850, mae: 1.254, rse: 1.001, pcc: 0.093, kcc: 0.166
+2025-07-10 14:43:00: Epoch: 043 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.112, kcc: 0.028
+2025-07-10 14:43:01: Epoch: 044 Train Loss: 2.828, mae: 1.254, rse: 1.001, pcc: 0.100, kcc: 0.167
+2025-07-10 14:43:01: Epoch: 044 Test results, ap: mae: 0.615, rse: 1.157, pcc: 0.197, kcc: 0.106
+2025-07-10 14:43:02: Epoch: 045 Train Loss: 2.853, mae: 1.254, rse: 1.001, pcc: 0.094, kcc: 0.164
+2025-07-10 14:43:02: Epoch: 045 Test results, ap: mae: 0.614, rse: 1.156, pcc: -0.022, kcc: 0.102
+2025-07-10 14:43:03: Epoch: 046 Train Loss: 2.829, mae: 1.254, rse: 1.002, pcc: 0.097, kcc: 0.170
+2025-07-10 14:43:03: Epoch: 046 Test results, ap: mae: 0.616, rse: 1.161, pcc: 0.208, kcc: 0.054
+2025-07-10 14:43:04: Epoch: 047 Train Loss: 2.840, mae: 1.254, rse: 1.002, pcc: 0.100, kcc: 0.167
+2025-07-10 14:43:04: Epoch: 047 Test results, ap: mae: 0.616, rse: 1.161, pcc: 0.021, kcc: 0.108
+2025-07-10 14:43:05: Epoch: 048 Train Loss: 2.835, mae: 1.254, rse: 1.001, pcc: 0.098, kcc: 0.171
+2025-07-10 14:43:05: Epoch: 048 Test results, ap: mae: 0.615, rse: 1.158, pcc: 0.038, kcc: 0.050
+2025-07-10 14:43:06: Epoch: 049 Train Loss: 2.812, mae: 1.254, rse: 1.002, pcc: 0.100, kcc: 0.159
+2025-07-10 14:43:06: Epoch: 049 Test results, ap: mae: 0.615, rse: 1.160, pcc: 0.176, kcc: 0.000
+2025-07-10 14:43:07: Epoch: 050 Train Loss: 2.844, mae: 1.254, rse: 1.001, pcc: 0.102, kcc: 0.170
+2025-07-10 14:43:07: Epoch: 050 Test results, ap: mae: 0.613, rse: 1.151, pcc: 0.125, kcc: 0.028
+2025-07-10 14:43:07: used time 0.01h
+2025-07-10 14:43:07: Running Cross Validation 2
+2025-07-10 14:43:07: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:43:08: Epoch: 001 Train Loss: 2.895, mae: 1.258, rse: 1.001, pcc: -0.074, kcc: 0.107
+2025-07-10 14:43:08: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:43:08: Epoch: 001 Test results, ap: mae: 0.601, rse: 1.104, pcc: -0.751, kcc: -0.136
+2025-07-10 14:43:09: Epoch: 002 Train Loss: 2.900, mae: 1.252, rse: 1.000, pcc: 0.030, kcc: 0.088
+2025-07-10 14:43:09: Epoch: 002 New best VALIDATION metrics
+2025-07-10 14:43:09: Epoch: 002 Test results, ap: mae: 0.624, rse: 1.189, pcc: -0.252, kcc: 0.042
+2025-07-10 14:43:10: Epoch: 003 Train Loss: 2.895, mae: 1.261, rse: 1.002, pcc: 0.051, kcc: 0.063
+2025-07-10 14:43:10: Epoch: 003 Test results, ap: mae: 0.596, rse: 1.081, pcc: -0.382, kcc: 0.006
+2025-07-10 14:43:11: Epoch: 004 Train Loss: 2.888, mae: 1.258, rse: 1.001, pcc: 0.062, kcc: 0.033
+2025-07-10 14:43:11: Epoch: 004 Test results, ap: mae: 0.600, rse: 1.101, pcc: -0.329, kcc: 0.119
+2025-07-10 14:43:11: Epoch: 005 Train Loss: 2.852, mae: 1.251, rse: 1.004, pcc: 0.058, kcc: 0.015
+2025-07-10 14:43:12: Epoch: 005 Test results, ap: mae: 0.648, rse: 1.272, pcc: -0.158, kcc: -0.026
+2025-07-10 14:43:12: Epoch: 006 Train Loss: 2.897, mae: 1.252, rse: 1.001, pcc: 0.082, kcc: -0.093
+2025-07-10 14:43:13: Epoch: 006 Test results, ap: mae: 0.631, rse: 1.212, pcc: -0.484, kcc: -0.063
+2025-07-10 14:43:13: Epoch: 007 Train Loss: 2.865, mae: 1.257, rse: 1.001, pcc: 0.075, kcc: 0.018
+2025-07-10 14:43:14: Epoch: 007 Test results, ap: mae: 0.602, rse: 1.108, pcc: -0.045, kcc: 0.010
+2025-07-10 14:43:14: Epoch: 008 Train Loss: 2.867, mae: 1.260, rse: 1.002, pcc: 0.080, kcc: 0.093
+2025-07-10 14:43:14: Epoch: 008 New best VALIDATION metrics
+2025-07-10 14:43:14: Epoch: 008 Test results, ap: mae: 0.597, rse: 1.086, pcc: 0.010, kcc: 0.006
+2025-07-10 14:43:15: Epoch: 009 Train Loss: 2.883, mae: 1.253, rse: 1.000, pcc: 0.083, kcc: 0.120
+2025-07-10 14:43:15: Epoch: 009 New best VALIDATION metrics
+2025-07-10 14:43:15: Epoch: 009 Test results, ap: mae: 0.620, rse: 1.176, pcc: -0.260, kcc: 0.081
+2025-07-10 14:43:16: Epoch: 010 Train Loss: 2.903, mae: 1.253, rse: 1.000, pcc: 0.079, kcc: 0.106
+2025-07-10 14:43:16: Epoch: 010 Test results, ap: mae: 0.619, rse: 1.172, pcc: -0.601, kcc: 0.087
+2025-07-10 14:43:17: Epoch: 011 Train Loss: 2.901, mae: 1.254, rse: 1.000, pcc: 0.075, kcc: 0.079
+2025-07-10 14:43:17: Epoch: 011 Test results, ap: mae: 0.616, rse: 1.164, pcc: -0.434, kcc: -0.023
+2025-07-10 14:43:17: Epoch: 011 New best TEST metrics
+2025-07-10 14:43:18: Epoch: 012 Train Loss: 2.918, mae: 1.253, rse: 1.000, pcc: 0.079, kcc: 0.129
+2025-07-10 14:43:18: Epoch: 012 New best VALIDATION metrics
+2025-07-10 14:43:18: Epoch: 012 Test results, ap: mae: 0.619, rse: 1.173, pcc: 0.104, kcc: 0.021
+2025-07-10 14:43:18: Epoch: 012 New best TEST metrics
+2025-07-10 14:43:19: Epoch: 013 Train Loss: 2.836, mae: 1.253, rse: 1.000, pcc: 0.089, kcc: 0.154
+2025-07-10 14:43:19: Epoch: 013 New best VALIDATION metrics
+2025-07-10 14:43:19: Epoch: 013 Test results, ap: mae: 0.622, rse: 1.182, pcc: 0.004, kcc: 0.010
+2025-07-10 14:43:20: Epoch: 014 Train Loss: 2.897, mae: 1.255, rse: 1.000, pcc: 0.088, kcc: 0.158
+2025-07-10 14:43:20: Epoch: 014 New best VALIDATION metrics
+2025-07-10 14:43:20: Epoch: 014 Test results, ap: mae: 0.608, rse: 1.133, pcc: 0.040, kcc: 0.032
+2025-07-10 14:43:21: Epoch: 015 Train Loss: 2.888, mae: 1.255, rse: 1.000, pcc: 0.077, kcc: 0.084
+2025-07-10 14:43:21: Epoch: 015 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.004, kcc: -0.014
+2025-07-10 14:43:22: Epoch: 016 Train Loss: 2.877, mae: 1.255, rse: 1.000, pcc: 0.031, kcc: 0.035
+2025-07-10 14:43:22: Epoch: 016 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.282, kcc: -0.052
+2025-07-10 14:43:23: Epoch: 017 Train Loss: 2.870, mae: 1.254, rse: 1.000, pcc: 0.029, kcc: 0.073
+2025-07-10 14:43:23: Epoch: 017 Test results, ap: mae: 0.614, rse: 1.157, pcc: 0.059, kcc: 0.092
+2025-07-10 14:43:23: Epoch: 017 New best TEST metrics
+2025-07-10 14:43:24: Epoch: 018 Train Loss: 2.899, mae: 1.254, rse: 1.000, pcc: 0.057, kcc: 0.083
+2025-07-10 14:43:24: Epoch: 018 Test results, ap: mae: 0.612, rse: 1.148, pcc: 0.066, kcc: -0.051
+2025-07-10 14:43:25: Epoch: 019 Train Loss: 2.885, mae: 1.256, rse: 1.000, pcc: 0.039, kcc: 0.066
+2025-07-10 14:43:25: Epoch: 019 Test results, ap: mae: 0.606, rse: 1.124, pcc: 0.952, kcc: 0.095
+2025-07-10 14:43:25: Epoch: 019 New best TEST metrics
+2025-07-10 14:43:26: Epoch: 020 Train Loss: 2.848, mae: 1.253, rse: 1.000, pcc: 0.037, kcc: 0.032
+2025-07-10 14:43:26: Epoch: 020 Test results, ap: mae: 0.620, rse: 1.175, pcc: 0.249, kcc: 0.114
+2025-07-10 14:43:27: Epoch: 021 Train Loss: 2.867, mae: 1.255, rse: 1.000, pcc: 0.045, kcc: 0.049
+2025-07-10 14:43:27: Epoch: 021 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.081, kcc: 0.003
+2025-07-10 14:43:28: Epoch: 022 Train Loss: 2.884, mae: 1.255, rse: 1.000, pcc: 0.048, kcc: 0.032
+2025-07-10 14:43:28: Epoch: 022 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.093, kcc: -0.073
+2025-07-10 14:43:29: Epoch: 023 Train Loss: 2.878, mae: 1.255, rse: 1.000, pcc: 0.045, kcc: -0.066
+2025-07-10 14:43:29: Epoch: 023 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.278, kcc: 0.063
+2025-07-10 14:43:30: Epoch: 024 Train Loss: 2.892, mae: 1.255, rse: 1.000, pcc: 0.047, kcc: 0.047
+2025-07-10 14:43:30: Epoch: 024 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.310, kcc: 0.050
+2025-07-10 14:43:31: Epoch: 025 Train Loss: 2.896, mae: 1.256, rse: 1.000, pcc: 0.057, kcc: 0.087
+2025-07-10 14:43:31: Epoch: 025 Test results, ap: mae: 0.607, rse: 1.128, pcc: 0.554, kcc: 0.016
+2025-07-10 14:43:32: Epoch: 026 Train Loss: 2.864, mae: 1.255, rse: 1.000, pcc: 0.040, kcc: 0.028
+2025-07-10 14:43:32: Epoch: 026 Test results, ap: mae: 0.612, rse: 1.147, pcc: -0.013, kcc: 0.091
+2025-07-10 14:43:33: Epoch: 027 Train Loss: 2.877, mae: 1.255, rse: 1.000, pcc: 0.045, kcc: 0.024
+2025-07-10 14:43:33: Epoch: 027 Test results, ap: mae: 0.612, rse: 1.146, pcc: 0.072, kcc: 0.123
+2025-07-10 14:43:34: Epoch: 028 Train Loss: 2.897, mae: 1.255, rse: 1.000, pcc: 0.047, kcc: 0.063
+2025-07-10 14:43:34: Epoch: 028 Test results, ap: mae: 0.609, rse: 1.135, pcc: 0.374, kcc: 0.059
+2025-07-10 14:43:35: Epoch: 029 Train Loss: 2.871, mae: 1.255, rse: 1.000, pcc: 0.042, kcc: -0.047
+2025-07-10 14:43:35: Epoch: 029 Test results, ap: mae: 0.610, rse: 1.142, pcc: 0.022, kcc: -0.037
+2025-07-10 14:43:36: Epoch: 030 Train Loss: 2.871, mae: 1.254, rse: 1.000, pcc: 0.059, kcc: 0.068
+2025-07-10 14:43:36: Epoch: 030 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.296, kcc: 0.092
+2025-07-10 14:43:37: Epoch: 031 Train Loss: 2.858, mae: 1.255, rse: 1.000, pcc: 0.057, kcc: 0.085
+2025-07-10 14:43:37: Epoch: 031 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.188, kcc: 0.007
+2025-07-10 14:43:38: Epoch: 032 Train Loss: 2.885, mae: 1.255, rse: 1.000, pcc: 0.057, kcc: 0.052
+2025-07-10 14:43:38: Epoch: 032 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.351, kcc: 0.009
+2025-07-10 14:43:38: Epoch: 033 Train Loss: 2.896, mae: 1.255, rse: 1.000, pcc: 0.058, kcc: 0.093
+2025-07-10 14:43:39: Epoch: 033 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.191, kcc: 0.067
+2025-07-10 14:43:39: Epoch: 034 Train Loss: 2.888, mae: 1.255, rse: 1.000, pcc: 0.063, kcc: 0.103
+2025-07-10 14:43:40: Epoch: 034 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.740, kcc: -0.052
+2025-07-10 14:43:40: Epoch: 035 Train Loss: 2.876, mae: 1.255, rse: 1.000, pcc: 0.060, kcc: 0.036
+2025-07-10 14:43:41: Epoch: 035 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.778, kcc: 0.089
+2025-07-10 14:43:41: Epoch: 036 Train Loss: 2.867, mae: 1.255, rse: 1.000, pcc: 0.065, kcc: 0.106
+2025-07-10 14:43:42: Epoch: 036 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.399, kcc: 0.066
+2025-07-10 14:43:42: Epoch: 037 Train Loss: 2.885, mae: 1.254, rse: 1.000, pcc: 0.071, kcc: 0.093
+2025-07-10 14:43:42: Epoch: 037 Test results, ap: mae: 0.612, rse: 1.149, pcc: -0.342, kcc: -0.042
+2025-07-10 14:43:43: Epoch: 038 Train Loss: 2.900, mae: 1.255, rse: 1.000, pcc: 0.060, kcc: 0.043
+2025-07-10 14:43:43: Epoch: 038 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.309, kcc: 0.080
+2025-07-10 14:43:44: Epoch: 039 Train Loss: 2.900, mae: 1.255, rse: 1.000, pcc: 0.060, kcc: 0.032
+2025-07-10 14:43:44: Epoch: 039 Test results, ap: mae: 0.609, rse: 1.138, pcc: -0.349, kcc: 0.028
+2025-07-10 14:43:45: Epoch: 040 Train Loss: 2.906, mae: 1.255, rse: 1.000, pcc: 0.068, kcc: 0.063
+2025-07-10 14:43:45: Epoch: 040 Test results, ap: mae: 0.612, rse: 1.146, pcc: 0.368, kcc: 0.027
+2025-07-10 14:43:46: Epoch: 041 Train Loss: 2.873, mae: 1.255, rse: 1.000, pcc: 0.073, kcc: 0.112
+2025-07-10 14:43:46: Epoch: 041 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.324, kcc: -0.054
+2025-07-10 14:43:47: Epoch: 042 Train Loss: 2.907, mae: 1.255, rse: 1.000, pcc: 0.065, kcc: 0.088
+2025-07-10 14:43:47: Epoch: 042 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.012, kcc: 0.138
+2025-07-10 14:43:48: Epoch: 043 Train Loss: 2.887, mae: 1.255, rse: 1.000, pcc: 0.071, kcc: 0.069
+2025-07-10 14:43:48: Epoch: 043 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.349, kcc: -0.080
+2025-07-10 14:43:49: Epoch: 044 Train Loss: 2.886, mae: 1.255, rse: 1.000, pcc: 0.069, kcc: 0.093
+2025-07-10 14:43:49: Epoch: 044 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.547, kcc: -0.077
+2025-07-10 14:43:50: Epoch: 045 Train Loss: 2.883, mae: 1.255, rse: 1.000, pcc: 0.071, kcc: 0.071
+2025-07-10 14:43:50: Epoch: 045 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.015, kcc: -0.018
+2025-07-10 14:43:51: Epoch: 046 Train Loss: 2.873, mae: 1.255, rse: 1.000, pcc: 0.070, kcc: 0.043
+2025-07-10 14:43:51: Epoch: 046 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.030, kcc: -0.032
+2025-07-10 14:43:52: Epoch: 047 Train Loss: 2.887, mae: 1.255, rse: 1.000, pcc: 0.066, kcc: 0.091
+2025-07-10 14:43:52: Epoch: 047 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.132, kcc: 0.103
+2025-07-10 14:43:53: Epoch: 048 Train Loss: 2.865, mae: 1.255, rse: 1.000, pcc: 0.082, kcc: 0.104
+2025-07-10 14:43:53: Epoch: 048 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.293, kcc: -0.045
+2025-07-10 14:43:54: Epoch: 049 Train Loss: 2.885, mae: 1.255, rse: 1.000, pcc: 0.052, kcc: -0.008
+2025-07-10 14:43:54: Epoch: 049 Test results, ap: mae: 0.609, rse: 1.137, pcc: -0.869, kcc: 0.142
+2025-07-10 14:43:55: Epoch: 050 Train Loss: 2.890, mae: 1.255, rse: 1.000, pcc: 0.066, kcc: 0.046
+2025-07-10 14:43:55: Epoch: 050 Test results, ap: mae: 0.609, rse: 1.135, pcc: -0.388, kcc: 0.062
+2025-07-10 14:43:55: used time 0.01h
+2025-07-10 14:43:55: Running Cross Validation 3
+2025-07-10 14:43:55: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:43:56: Epoch: 001 Train Loss: 2.824, mae: 1.313, rse: 1.007, pcc: 0.112, kcc: 0.116
+2025-07-10 14:43:56: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:43:56: Epoch: 001 Test results, ap: mae: 0.584, rse: 1.025, pcc: 0.165, kcc: 0.074
+2025-07-10 14:43:57: Epoch: 002 Train Loss: 2.788, mae: 1.308, rse: 1.004, pcc: -0.017, kcc: -0.045
+2025-07-10 14:43:57: Epoch: 002 Test results, ap: mae: 0.586, rse: 1.038, pcc: -0.226, kcc: -0.074
+2025-07-10 14:43:57: Epoch: 003 Train Loss: 2.824, mae: 1.296, rse: 1.000, pcc: 0.099, kcc: 0.104
+2025-07-10 14:43:58: Epoch: 003 Test results, ap: mae: 0.603, rse: 1.110, pcc: 0.122, kcc: 0.015
+2025-07-10 14:43:58: Epoch: 004 Train Loss: 2.805, mae: 1.293, rse: 1.000, pcc: 0.103, kcc: 0.105
+2025-07-10 14:43:58: Epoch: 004 New best VALIDATION metrics
+2025-07-10 14:43:59: Epoch: 004 Test results, ap: mae: 0.615, rse: 1.158, pcc: 0.179, kcc: 0.079
+2025-07-10 14:43:59: Epoch: 005 Train Loss: 2.796, mae: 1.300, rse: 1.001, pcc: 0.102, kcc: 0.104
+2025-07-10 14:44:00: Epoch: 005 Test results, ap: mae: 0.594, rse: 1.072, pcc: 0.207, kcc: 0.059
+2025-07-10 14:44:00: Epoch: 006 Train Loss: 2.809, mae: 1.296, rse: 1.000, pcc: 0.103, kcc: 0.101
+2025-07-10 14:44:01: Epoch: 006 Test results, ap: mae: 0.603, rse: 1.110, pcc: 0.176, kcc: 0.048
+2025-07-10 14:44:01: Epoch: 007 Train Loss: 2.798, mae: 1.294, rse: 1.000, pcc: 0.112, kcc: 0.097
+2025-07-10 14:44:01: Epoch: 007 New best VALIDATION metrics
+2025-07-10 14:44:02: Epoch: 007 Test results, ap: mae: 0.609, rse: 1.138, pcc: 0.151, kcc: 0.041
+2025-07-10 14:44:02: Epoch: 008 Train Loss: 2.824, mae: 1.296, rse: 1.000, pcc: 0.097, kcc: 0.091
+2025-07-10 14:44:03: Epoch: 008 Test results, ap: mae: 0.602, rse: 1.106, pcc: 0.283, kcc: 0.200
+2025-07-10 14:44:03: Epoch: 009 Train Loss: 2.798, mae: 1.297, rse: 1.000, pcc: 0.093, kcc: 0.084
+2025-07-10 14:44:04: Epoch: 009 Test results, ap: mae: 0.600, rse: 1.097, pcc: 0.031, kcc: 0.042
+2025-07-10 14:44:04: Epoch: 010 Train Loss: 2.824, mae: 1.292, rse: 1.001, pcc: 0.125, kcc: 0.075
+2025-07-10 14:44:05: Epoch: 010 Test results, ap: mae: 0.622, rse: 1.182, pcc: 0.225, kcc: 0.132
+2025-07-10 14:44:05: Epoch: 011 Train Loss: 2.801, mae: 1.294, rse: 1.000, pcc: 0.083, kcc: 0.118
+2025-07-10 14:44:06: Epoch: 011 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.001, kcc: -0.098
+2025-07-10 14:44:06: Epoch: 011 New best TEST metrics
+2025-07-10 14:44:06: Epoch: 012 Train Loss: 2.796, mae: 1.293, rse: 1.001, pcc: 0.085, kcc: 0.060
+2025-07-10 14:44:07: Epoch: 012 Test results, ap: mae: 0.617, rse: 1.166, pcc: 0.059, kcc: -0.040
+2025-07-10 14:44:07: Epoch: 012 New best TEST metrics
+2025-07-10 14:44:07: Epoch: 013 Train Loss: 2.800, mae: 1.295, rse: 1.000, pcc: 0.085, kcc: 0.136
+2025-07-10 14:44:07: Epoch: 013 New best VALIDATION metrics
+2025-07-10 14:44:08: Epoch: 013 Test results, ap: mae: 0.607, rse: 1.129, pcc: 0.173, kcc: 0.045
+2025-07-10 14:44:08: Epoch: 013 New best TEST metrics
+2025-07-10 14:44:08: Epoch: 014 Train Loss: 2.793, mae: 1.291, rse: 1.003, pcc: 0.083, kcc: -0.021
+2025-07-10 14:44:08: Epoch: 014 Test results, ap: mae: 0.637, rse: 1.231, pcc: 0.073, kcc: 0.025
+2025-07-10 14:44:09: Epoch: 015 Train Loss: 2.807, mae: 1.292, rse: 1.001, pcc: 0.081, kcc: -0.015
+2025-07-10 14:44:09: Epoch: 015 Test results, ap: mae: 0.620, rse: 1.177, pcc: 0.094, kcc: 0.026
+2025-07-10 14:44:10: Epoch: 016 Train Loss: 2.798, mae: 1.294, rse: 1.000, pcc: 0.079, kcc: -0.007
+2025-07-10 14:44:10: Epoch: 016 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.117, kcc: 0.035
+2025-07-10 14:44:11: Epoch: 017 Train Loss: 2.807, mae: 1.293, rse: 1.000, pcc: 0.080, kcc: 0.009
+2025-07-10 14:44:11: Epoch: 017 Test results, ap: mae: 0.615, rse: 1.159, pcc: 0.129, kcc: 0.057
+2025-07-10 14:44:12: Epoch: 018 Train Loss: 2.809, mae: 1.297, rse: 1.000, pcc: 0.077, kcc: 0.091
+2025-07-10 14:44:12: Epoch: 018 Test results, ap: mae: 0.601, rse: 1.103, pcc: 0.200, kcc: 0.037
+2025-07-10 14:44:12: Epoch: 018 New best TEST metrics
+2025-07-10 14:44:13: Epoch: 019 Train Loss: 2.798, mae: 1.293, rse: 1.000, pcc: 0.076, kcc: 0.100
+2025-07-10 14:44:13: Epoch: 019 Test results, ap: mae: 0.616, rse: 1.162, pcc: 0.240, kcc: 0.079
+2025-07-10 14:44:13: Epoch: 019 New best TEST metrics
+2025-07-10 14:44:13: Epoch: 020 Train Loss: 2.738, mae: 1.265, rse: 0.950, pcc: 0.229, kcc: 0.106
+2025-07-10 14:44:13: Epoch: 020 New best VALIDATION metrics
+2025-07-10 14:44:14: Epoch: 020 Test results, ap: mae: 0.697, rse: 1.454, pcc: 0.385, kcc: 0.104
+2025-07-10 14:44:14: Epoch: 021 Train Loss: 2.578, mae: 1.248, rse: 0.945, pcc: 0.236, kcc: 0.161
+2025-07-10 14:44:14: Epoch: 021 New best VALIDATION metrics
+2025-07-10 14:44:14: Epoch: 021 Test results, ap: mae: 0.747, rse: 1.637, pcc: -0.442, kcc: -0.252
+2025-07-10 14:44:15: Epoch: 022 Train Loss: 2.564, mae: 1.254, rse: 0.952, pcc: 0.219, kcc: 0.130
+2025-07-10 14:44:15: Epoch: 022 Test results, ap: mae: 0.708, rse: 1.489, pcc: -0.458, kcc: -0.242
+2025-07-10 14:44:16: Epoch: 023 Train Loss: 2.768, mae: 1.300, rse: 1.001, pcc: -0.161, kcc: -0.072
+2025-07-10 14:44:16: Epoch: 023 Test results, ap: mae: 0.594, rse: 1.072, pcc: -0.057, kcc: 0.026
+2025-07-10 14:44:17: Epoch: 024 Train Loss: 2.791, mae: 1.295, rse: 1.000, pcc: -0.164, kcc: -0.078
+2025-07-10 14:44:17: Epoch: 024 Test results, ap: mae: 0.605, rse: 1.122, pcc: -0.138, kcc: -0.043
+2025-07-10 14:44:18: Epoch: 025 Train Loss: 2.807, mae: 1.294, rse: 1.000, pcc: -0.163, kcc: -0.070
+2025-07-10 14:44:18: Epoch: 025 Test results, ap: mae: 0.612, rse: 1.147, pcc: 0.024, kcc: 0.020
+2025-07-10 14:44:19: Epoch: 026 Train Loss: 2.787, mae: 1.295, rse: 1.000, pcc: -0.176, kcc: -0.062
+2025-07-10 14:44:19: Epoch: 026 Test results, ap: mae: 0.607, rse: 1.128, pcc: -0.204, kcc: -0.113
+2025-07-10 14:44:19: Epoch: 027 Train Loss: 2.810, mae: 1.294, rse: 1.000, pcc: -0.174, kcc: -0.067
+2025-07-10 14:44:20: Epoch: 027 Test results, ap: mae: 0.608, rse: 1.132, pcc: -0.099, kcc: -0.056
+2025-07-10 14:44:20: Epoch: 028 Train Loss: 2.800, mae: 1.295, rse: 1.000, pcc: -0.168, kcc: -0.052
+2025-07-10 14:44:20: Epoch: 028 Test results, ap: mae: 0.607, rse: 1.129, pcc: -0.044, kcc: -0.028
+2025-07-10 14:44:21: Epoch: 029 Train Loss: 2.809, mae: 1.293, rse: 1.000, pcc: -0.181, kcc: -0.076
+2025-07-10 14:44:21: Epoch: 029 Test results, ap: mae: 0.613, rse: 1.152, pcc: -0.016, kcc: 0.031
+2025-07-10 14:44:22: Epoch: 030 Train Loss: 2.799, mae: 1.294, rse: 1.000, pcc: -0.170, kcc: -0.061
+2025-07-10 14:44:22: Epoch: 030 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.143, kcc: -0.049
+2025-07-10 14:44:23: Epoch: 031 Train Loss: 2.793, mae: 1.293, rse: 1.000, pcc: -0.160, kcc: -0.053
+2025-07-10 14:44:23: Epoch: 031 Test results, ap: mae: 0.613, rse: 1.153, pcc: -0.087, kcc: 0.006
+2025-07-10 14:44:24: Epoch: 032 Train Loss: 2.794, mae: 1.293, rse: 1.000, pcc: -0.158, kcc: -0.056
+2025-07-10 14:44:24: Epoch: 032 Test results, ap: mae: 0.613, rse: 1.150, pcc: -0.181, kcc: -0.050
+2025-07-10 14:44:25: Epoch: 033 Train Loss: 2.791, mae: 1.294, rse: 1.000, pcc: -0.160, kcc: -0.063
+2025-07-10 14:44:25: Epoch: 033 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.254, kcc: -0.088
+2025-07-10 14:44:25: Epoch: 034 Train Loss: 2.797, mae: 1.294, rse: 1.000, pcc: -0.154, kcc: -0.066
+2025-07-10 14:44:26: Epoch: 034 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.063, kcc: -0.071
+2025-07-10 14:44:26: Epoch: 035 Train Loss: 2.806, mae: 1.293, rse: 1.000, pcc: -0.160, kcc: -0.056
+2025-07-10 14:44:27: Epoch: 035 Test results, ap: mae: 0.614, rse: 1.154, pcc: -0.181, kcc: -0.090
+2025-07-10 14:44:27: Epoch: 036 Train Loss: 2.796, mae: 1.293, rse: 1.000, pcc: -0.158, kcc: -0.063
+2025-07-10 14:44:27: Epoch: 036 Test results, ap: mae: 0.614, rse: 1.155, pcc: -0.158, kcc: -0.030
+2025-07-10 14:44:28: Epoch: 037 Train Loss: 2.789, mae: 1.294, rse: 1.000, pcc: -0.143, kcc: -0.057
+2025-07-10 14:44:28: Epoch: 037 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.158, kcc: -0.091
+2025-07-10 14:44:29: Epoch: 038 Train Loss: 2.792, mae: 1.293, rse: 1.000, pcc: -0.145, kcc: -0.059
+2025-07-10 14:44:29: Epoch: 038 Test results, ap: mae: 0.612, rse: 1.149, pcc: -0.088, kcc: -0.010
+2025-07-10 14:44:30: Epoch: 039 Train Loss: 2.806, mae: 1.294, rse: 1.000, pcc: -0.140, kcc: -0.053
+2025-07-10 14:44:30: Epoch: 039 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.081, kcc: -0.028
+2025-07-10 14:44:31: Epoch: 040 Train Loss: 2.803, mae: 1.293, rse: 1.000, pcc: -0.144, kcc: -0.061
+2025-07-10 14:44:31: Epoch: 040 Test results, ap: mae: 0.615, rse: 1.158, pcc: -0.126, kcc: -0.042
+2025-07-10 14:44:32: Epoch: 041 Train Loss: 2.825, mae: 1.293, rse: 1.000, pcc: -0.142, kcc: -0.059
+2025-07-10 14:44:32: Epoch: 041 Test results, ap: mae: 0.614, rse: 1.157, pcc: -0.233, kcc: -0.126
+2025-07-10 14:44:33: Epoch: 042 Train Loss: 2.796, mae: 1.293, rse: 1.000, pcc: -0.145, kcc: -0.060
+2025-07-10 14:44:33: Epoch: 042 Test results, ap: mae: 0.614, rse: 1.154, pcc: -0.183, kcc: -0.061
+2025-07-10 14:44:33: Epoch: 043 Train Loss: 2.813, mae: 1.293, rse: 1.000, pcc: -0.149, kcc: -0.060
+2025-07-10 14:44:34: Epoch: 043 Test results, ap: mae: 0.613, rse: 1.150, pcc: -0.090, kcc: -0.043
+2025-07-10 14:44:34: Epoch: 044 Train Loss: 2.777, mae: 1.293, rse: 1.000, pcc: -0.135, kcc: -0.067
+2025-07-10 14:44:34: Epoch: 044 Test results, ap: mae: 0.614, rse: 1.154, pcc: -0.238, kcc: -0.057
+2025-07-10 14:44:35: Epoch: 045 Train Loss: 2.804, mae: 1.294, rse: 1.000, pcc: -0.139, kcc: -0.064
+2025-07-10 14:44:35: Epoch: 045 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.116, kcc: -0.016
+2025-07-10 14:44:36: Epoch: 046 Train Loss: 2.808, mae: 1.294, rse: 1.000, pcc: -0.150, kcc: -0.061
+2025-07-10 14:44:36: Epoch: 046 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.170, kcc: 0.015
+2025-07-10 14:44:37: Epoch: 047 Train Loss: 2.799, mae: 1.294, rse: 1.000, pcc: -0.148, kcc: -0.059
+2025-07-10 14:44:37: Epoch: 047 Test results, ap: mae: 0.612, rse: 1.147, pcc: -0.053, kcc: 0.046
+2025-07-10 14:44:38: Epoch: 048 Train Loss: 2.803, mae: 1.294, rse: 1.000, pcc: -0.145, kcc: -0.062
+2025-07-10 14:44:38: Epoch: 048 Test results, ap: mae: 0.609, rse: 1.135, pcc: 0.048, kcc: 0.112
+2025-07-10 14:44:39: Epoch: 049 Train Loss: 2.811, mae: 1.293, rse: 1.000, pcc: -0.146, kcc: -0.064
+2025-07-10 14:44:39: Epoch: 049 Test results, ap: mae: 0.612, rse: 1.148, pcc: -0.116, kcc: -0.082
+2025-07-10 14:44:40: Epoch: 050 Train Loss: 2.816, mae: 1.294, rse: 1.000, pcc: -0.141, kcc: -0.054
+2025-07-10 14:44:40: Epoch: 050 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.013, kcc: -0.010
+2025-07-10 14:44:40: used time 0.01h
+2025-07-10 14:44:40: Running Cross Validation 4
+2025-07-10 14:44:40: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:44:40: Epoch: 001 Train Loss: 2.856, mae: 1.209, rse: 1.002, pcc: -0.007, kcc: 0.129
+2025-07-10 14:44:40: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:44:41: Epoch: 001 Test results, ap: mae: 0.639, rse: 1.238, pcc: 0.107, kcc: 0.003
+2025-07-10 14:44:41: Epoch: 002 Train Loss: 2.866, mae: 1.210, rse: 1.001, pcc: -0.047, kcc: 0.095
+2025-07-10 14:44:41: Epoch: 002 Test results, ap: mae: 0.620, rse: 1.177, pcc: 0.279, kcc: 0.134
+2025-07-10 14:44:42: Epoch: 003 Train Loss: 2.874, mae: 1.220, rse: 1.003, pcc: 0.003, kcc: -0.030
+2025-07-10 14:44:42: Epoch: 003 Test results, ap: mae: 0.595, rse: 1.077, pcc: -0.138, kcc: -0.020
+2025-07-10 14:44:43: Epoch: 004 Train Loss: 2.862, mae: 1.220, rse: 1.003, pcc: 0.076, kcc: 0.109
+2025-07-10 14:44:43: Epoch: 004 New best VALIDATION metrics
+2025-07-10 14:44:43: Epoch: 004 Test results, ap: mae: 0.594, rse: 1.073, pcc: 0.448, kcc: -0.006
+2025-07-10 14:44:44: Epoch: 005 Train Loss: 2.829, mae: 1.212, rse: 1.000, pcc: -0.007, kcc: -0.061
+2025-07-10 14:44:44: Epoch: 005 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.047, kcc: 0.032
+2025-07-10 14:44:45: Epoch: 006 Train Loss: 2.845, mae: 1.209, rse: 1.002, pcc: 0.009, kcc: 0.001
+2025-07-10 14:44:45: Epoch: 006 Test results, ap: mae: 0.641, rse: 1.247, pcc: -0.529, kcc: -0.041
+2025-07-10 14:44:46: Epoch: 007 Train Loss: 2.872, mae: 1.212, rse: 1.000, pcc: 0.000, kcc: -0.037
+2025-07-10 14:44:46: Epoch: 007 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.653, kcc: 0.159
+2025-07-10 14:44:46: Epoch: 008 Train Loss: 2.826, mae: 1.220, rse: 1.003, pcc: 0.012, kcc: -0.016
+2025-07-10 14:44:47: Epoch: 008 Test results, ap: mae: 0.594, rse: 1.073, pcc: 0.417, kcc: -0.166
+2025-07-10 14:44:47: Epoch: 009 Train Loss: 2.847, mae: 1.215, rse: 1.001, pcc: 0.066, kcc: 0.054
+2025-07-10 14:44:48: Epoch: 009 Test results, ap: mae: 0.601, rse: 1.103, pcc: -0.027, kcc: -0.019
+2025-07-10 14:44:48: Epoch: 010 Train Loss: 2.866, mae: 1.213, rse: 1.000, pcc: -0.012, kcc: -0.051
+2025-07-10 14:44:48: Epoch: 010 Test results, ap: mae: 0.606, rse: 1.125, pcc: -1.000, kcc: 0.112
+2025-07-10 14:44:49: Epoch: 011 Train Loss: 2.854, mae: 1.210, rse: 1.000, pcc: -0.018, kcc: -0.059
+2025-07-10 14:44:49: Epoch: 011 Test results, ap: mae: 0.616, rse: 1.162, pcc: 0.634, kcc: 0.124
+2025-07-10 14:44:49: Epoch: 011 New best TEST metrics
+2025-07-10 14:44:50: Epoch: 012 Train Loss: 2.846, mae: 1.210, rse: 1.000, pcc: -0.001, kcc: -0.001
+2025-07-10 14:44:50: Epoch: 012 Test results, ap: mae: 0.618, rse: 1.169, pcc: 0.022, kcc: 0.131
+2025-07-10 14:44:51: Epoch: 013 Train Loss: 2.835, mae: 1.214, rse: 1.000, pcc: 0.007, kcc: -0.031
+2025-07-10 14:44:51: Epoch: 013 Test results, ap: mae: 0.604, rse: 1.118, pcc: -0.819, kcc: -0.096
+2025-07-10 14:44:52: Epoch: 014 Train Loss: 2.853, mae: 1.210, rse: 1.000, pcc: -0.014, kcc: 0.002
+2025-07-10 14:44:52: Epoch: 014 Test results, ap: mae: 0.620, rse: 1.178, pcc: -0.564, kcc: -0.058
+2025-07-10 14:44:53: Epoch: 015 Train Loss: 2.845, mae: 1.212, rse: 1.000, pcc: 0.024, kcc: 0.077
+2025-07-10 14:44:53: Epoch: 015 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.736, kcc: 0.047
+2025-07-10 14:44:53: Epoch: 016 Train Loss: 2.844, mae: 1.212, rse: 1.000, pcc: 0.001, kcc: 0.050
+2025-07-10 14:44:54: Epoch: 016 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.200, kcc: -0.003
+2025-07-10 14:44:54: Epoch: 017 Train Loss: 2.842, mae: 1.213, rse: 1.000, pcc: -0.004, kcc: 0.058
+2025-07-10 14:44:55: Epoch: 017 Test results, ap: mae: 0.606, rse: 1.125, pcc: -0.035, kcc: 0.027
+2025-07-10 14:44:55: Epoch: 018 Train Loss: 2.860, mae: 1.212, rse: 1.000, pcc: 0.002, kcc: -0.020
+2025-07-10 14:44:55: Epoch: 018 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.209, kcc: 0.144
+2025-07-10 14:44:56: Epoch: 019 Train Loss: 2.828, mae: 1.210, rse: 1.000, pcc: -0.009, kcc: -0.078
+2025-07-10 14:44:56: Epoch: 019 Test results, ap: mae: 0.623, rse: 1.185, pcc: -0.081, kcc: -0.057
+2025-07-10 14:44:57: Epoch: 020 Train Loss: 2.869, mae: 1.214, rse: 1.001, pcc: -0.000, kcc: -0.071
+2025-07-10 14:44:57: Epoch: 020 Test results, ap: mae: 0.603, rse: 1.112, pcc: 0.193, kcc: 0.117
+2025-07-10 14:44:58: Epoch: 021 Train Loss: 2.851, mae: 1.212, rse: 1.000, pcc: -0.004, kcc: -0.007
+2025-07-10 14:44:58: Epoch: 021 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.061, kcc: 0.109
+2025-07-10 14:44:59: Epoch: 022 Train Loss: 2.842, mae: 1.211, rse: 1.000, pcc: 0.001, kcc: -0.044
+2025-07-10 14:44:59: Epoch: 022 Test results, ap: mae: 0.612, rse: 1.147, pcc: 1.000, kcc: 0.073
+2025-07-10 14:44:59: Epoch: 022 New best TEST metrics
+2025-07-10 14:45:00: Epoch: 023 Train Loss: 2.836, mae: 1.212, rse: 1.000, pcc: -0.004, kcc: -0.062
+2025-07-10 14:45:00: Epoch: 023 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.261, kcc: 0.117
+2025-07-10 14:45:00: Epoch: 024 Train Loss: 2.862, mae: 1.212, rse: 1.000, pcc: -0.002, kcc: -0.038
+2025-07-10 14:45:01: Epoch: 024 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.567, kcc: -0.196
+2025-07-10 14:45:01: Epoch: 025 Train Loss: 2.841, mae: 1.212, rse: 1.000, pcc: -0.015, kcc: -0.044
+2025-07-10 14:45:01: Epoch: 025 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.383, kcc: 0.012
+2025-07-10 14:45:02: Epoch: 026 Train Loss: 2.844, mae: 1.213, rse: 1.000, pcc: 0.017, kcc: -0.001
+2025-07-10 14:45:02: Epoch: 026 Test results, ap: mae: 0.606, rse: 1.125, pcc: 0.246, kcc: -0.091
+2025-07-10 14:45:03: Epoch: 027 Train Loss: 2.874, mae: 1.213, rse: 1.000, pcc: -0.003, kcc: -0.029
+2025-07-10 14:45:03: Epoch: 027 Test results, ap: mae: 0.608, rse: 1.130, pcc: 0.552, kcc: 0.075
+2025-07-10 14:45:04: Epoch: 028 Train Loss: 2.858, mae: 1.211, rse: 1.000, pcc: 0.014, kcc: 0.092
+2025-07-10 14:45:04: Epoch: 028 Test results, ap: mae: 0.613, rse: 1.150, pcc: 0.087, kcc: -0.016
+2025-07-10 14:45:05: Epoch: 029 Train Loss: 2.845, mae: 1.212, rse: 1.000, pcc: 0.010, kcc: 0.008
+2025-07-10 14:45:05: Epoch: 029 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.404, kcc: -0.029
+2025-07-10 14:45:06: Epoch: 030 Train Loss: 2.859, mae: 1.212, rse: 1.000, pcc: -0.019, kcc: -0.132
+2025-07-10 14:45:06: Epoch: 030 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.297, kcc: 0.037
+2025-07-10 14:45:06: Epoch: 031 Train Loss: 2.847, mae: 1.212, rse: 1.000, pcc: -0.009, kcc: -0.027
+2025-07-10 14:45:07: Epoch: 031 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.206, kcc: -0.122
+2025-07-10 14:45:07: Epoch: 032 Train Loss: 2.815, mae: 1.212, rse: 1.000, pcc: 0.015, kcc: 0.051
+2025-07-10 14:45:07: Epoch: 032 Test results, ap: mae: 0.611, rse: 1.145, pcc: 0.327, kcc: -0.095
+2025-07-10 14:45:08: Epoch: 033 Train Loss: 2.850, mae: 1.212, rse: 1.000, pcc: -0.012, kcc: 0.004
+2025-07-10 14:45:08: Epoch: 033 Test results, ap: mae: 0.608, rse: 1.132, pcc: -0.691, kcc: -0.098
+2025-07-10 14:45:09: Epoch: 034 Train Loss: 2.862, mae: 1.212, rse: 1.000, pcc: -0.007, kcc: -0.129
+2025-07-10 14:45:09: Epoch: 034 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.585, kcc: 0.095
+2025-07-10 14:45:10: Epoch: 035 Train Loss: 2.858, mae: 1.212, rse: 1.000, pcc: 0.000, kcc: -0.005
+2025-07-10 14:45:10: Epoch: 035 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.271, kcc: 0.036
+2025-07-10 14:45:11: Epoch: 036 Train Loss: 2.843, mae: 1.211, rse: 1.000, pcc: 0.001, kcc: -0.000
+2025-07-10 14:45:11: Epoch: 036 Test results, ap: mae: 0.612, rse: 1.149, pcc: -0.231, kcc: 0.113
+2025-07-10 14:45:11: Epoch: 037 Train Loss: 2.844, mae: 1.211, rse: 1.000, pcc: 0.000, kcc: -0.045
+2025-07-10 14:45:11: Epoch: 037 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.006, kcc: 0.006
+2025-07-10 14:45:12: Epoch: 038 Train Loss: 2.851, mae: 1.212, rse: 1.000, pcc: -0.009, kcc: 0.017
+2025-07-10 14:45:12: Epoch: 038 Test results, ap: mae: 0.609, rse: 1.136, pcc: -0.677, kcc: -0.034
+2025-07-10 14:45:13: Epoch: 039 Train Loss: 2.844, mae: 1.212, rse: 1.000, pcc: 0.002, kcc: -0.008
+2025-07-10 14:45:13: Epoch: 039 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.346, kcc: -0.050
+2025-07-10 14:45:14: Epoch: 040 Train Loss: 2.859, mae: 1.212, rse: 1.000, pcc: 0.001, kcc: 0.038
+2025-07-10 14:45:14: Epoch: 040 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.466, kcc: 0.074
+2025-07-10 14:45:14: Epoch: 041 Train Loss: 2.827, mae: 1.212, rse: 1.000, pcc: -0.000, kcc: -0.059
+2025-07-10 14:45:15: Epoch: 041 Test results, ap: mae: 0.608, rse: 1.134, pcc: -0.384, kcc: 0.136
+2025-07-10 14:45:15: Epoch: 042 Train Loss: 2.853, mae: 1.212, rse: 1.000, pcc: -0.008, kcc: -0.075
+2025-07-10 14:45:15: Epoch: 042 Test results, ap: mae: 0.609, rse: 1.135, pcc: -0.175, kcc: 0.043
+2025-07-10 14:45:16: Epoch: 043 Train Loss: 2.835, mae: 1.212, rse: 1.000, pcc: -0.004, kcc: -0.010
+2025-07-10 14:45:16: Epoch: 043 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.438, kcc: 0.108
+2025-07-10 14:45:17: Epoch: 044 Train Loss: 2.865, mae: 1.212, rse: 1.000, pcc: 0.004, kcc: 0.050
+2025-07-10 14:45:17: Epoch: 044 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.554, kcc: -0.077
+2025-07-10 14:45:18: Epoch: 045 Train Loss: 2.851, mae: 1.212, rse: 1.000, pcc: -0.012, kcc: -0.046
+2025-07-10 14:45:18: Epoch: 045 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.062, kcc: 0.073
+2025-07-10 14:45:18: Epoch: 046 Train Loss: 2.849, mae: 1.212, rse: 1.000, pcc: -0.004, kcc: -0.038
+2025-07-10 14:45:18: Epoch: 046 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.533, kcc: 0.032
+2025-07-10 14:45:19: Epoch: 047 Train Loss: 2.860, mae: 1.212, rse: 1.000, pcc: -0.002, kcc: -0.101
+2025-07-10 14:45:19: Epoch: 047 Test results, ap: mae: 0.609, rse: 1.137, pcc: 1.000, kcc: -0.098
+2025-07-10 14:45:20: Epoch: 048 Train Loss: 2.847, mae: 1.212, rse: 1.000, pcc: -0.004, kcc: -0.015
+2025-07-10 14:45:20: Epoch: 048 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.356, kcc: 0.053
+2025-07-10 14:45:21: Epoch: 049 Train Loss: 2.858, mae: 1.212, rse: 1.000, pcc: -0.007, kcc: -0.016
+2025-07-10 14:45:21: Epoch: 049 Test results, ap: mae: 0.611, rse: 1.144, pcc: -1.000, kcc: -0.135
+2025-07-10 14:45:21: Epoch: 050 Train Loss: 2.864, mae: 1.212, rse: 1.000, pcc: 0.000, kcc: -0.018
+2025-07-10 14:45:22: Epoch: 050 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.579, kcc: 0.171
+2025-07-10 14:45:22: used time 0.01h
+2025-07-10 14:45:22: Cross Validation Finished!
+2025-07-10 14:45:22: Best validation perform list
+[[1.20306337 1.00028241 0.16697328 0.08890122]
+ [1.25095439 1.00465322 0.06554058 0.21338643]
+ [1.2554009  1.00011146 0.08849797 0.15823331]
+ [1.24846721 0.94476593 0.23575406 0.16093539]
+ [1.22024155 1.00290918 0.07609069 0.10852996]]
+2025-07-10 14:45:22: mean: [1.236 0.991 0.127 0.146]
+2025-07-10 14:45:22: std: [0.02  0.023 0.065 0.044]
+2025-07-10 14:45:22: Best test perform list
+[[0.60920197 1.13657367 1.         0.24163213]
+ [0.61379099 1.15446138 0.78924841 0.04808137]
+ [0.60617119 1.12437022 0.95208246 0.09461359]
+ [0.61582255 1.16230071 0.23985647 0.07945888]
+ [0.61193413 1.14727163 1.         0.07305036]]
+2025-07-10 14:45:22: mean: [0.611 1.145 0.796 0.107]
+2025-07-10 14:45:22: std: [0.003 0.013 0.289 0.069]
diff --git a/run-reg/mamba-att-256-mse-32-0.001-50/config.json b/run-reg/mamba-att-256-mse-32-0.001-50/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f134683001256388f81245b95ca0a7a13a3aa6a5
--- /dev/null
+++ b/run-reg/mamba-att-256-mse-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mamba", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
\ No newline at end of file
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+2025-07-10 14:58:23: saving_dir: ./run-reg/mamba-att-256-mse-32-0.001-50
+2025-07-10 14:58:23: Loading Train Dataset
+2025-07-10 14:58:23: Loading Test Dataset
+2025-07-10 14:58:24: Running Cross Validation 0
+2025-07-10 14:58:24: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 14:58:25: Epoch: 001 Train Loss: 2.942, mae: 1.205, rse: 1.000, pcc: 0.173, kcc: 0.026
+2025-07-10 14:58:25: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:58:25: Epoch: 001 Test results, ap: mae: 0.627, rse: 1.198, pcc: -0.297, kcc: -0.096
+2025-07-10 14:58:26: Epoch: 002 Train Loss: 2.915, mae: 1.197, rse: 1.003, pcc: -0.191, kcc: -0.114
+2025-07-10 14:58:26: Epoch: 002 Test results, ap: mae: 0.654, rse: 1.294, pcc: -0.315, kcc: -0.092
+2025-07-10 14:58:27: Epoch: 003 Train Loss: 2.900, mae: 1.220, rse: 1.003, pcc: 0.184, kcc: 0.093
+2025-07-10 14:58:27: Epoch: 003 New best VALIDATION metrics
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+2025-07-10 14:58:28: Epoch: 004 Train Loss: 2.921, mae: 1.210, rse: 1.000, pcc: 0.244, kcc: 0.163
+2025-07-10 14:58:28: Epoch: 004 New best VALIDATION metrics
+2025-07-10 14:58:28: Epoch: 004 Test results, ap: mae: 0.613, rse: 1.151, pcc: -0.261, kcc: -0.155
+2025-07-10 14:58:29: Epoch: 005 Train Loss: 2.918, mae: 1.202, rse: 1.001, pcc: -0.235, kcc: -0.100
+2025-07-10 14:58:30: Epoch: 005 Test results, ap: mae: 0.634, rse: 1.221, pcc: -0.094, kcc: -0.077
+2025-07-10 14:58:31: Epoch: 006 Train Loss: 2.925, mae: 1.202, rse: 1.001, pcc: 0.120, kcc: 0.079
+2025-07-10 14:58:31: Epoch: 006 Test results, ap: mae: 0.635, rse: 1.224, pcc: -0.142, kcc: -0.108
+2025-07-10 14:58:32: Epoch: 007 Train Loss: 2.918, mae: 1.198, rse: 1.002, pcc: -0.147, kcc: -0.122
+2025-07-10 14:58:32: Epoch: 007 Test results, ap: mae: 0.647, rse: 1.269, pcc: 0.099, kcc: 0.069
+2025-07-10 14:58:33: Epoch: 008 Train Loss: 2.918, mae: 1.207, rse: 1.000, pcc: 0.113, kcc: 0.116
+2025-07-10 14:58:33: Epoch: 008 Test results, ap: mae: 0.621, rse: 1.179, pcc: -0.155, kcc: -0.124
+2025-07-10 14:58:34: Epoch: 009 Train Loss: 2.890, mae: 1.203, rse: 1.000, pcc: -0.052, kcc: -0.019
+2025-07-10 14:58:34: Epoch: 009 Test results, ap: mae: 0.632, rse: 1.215, pcc: -0.159, kcc: -0.146
+2025-07-10 14:58:35: Epoch: 010 Train Loss: 2.915, mae: 1.209, rse: 1.000, pcc: 0.311, kcc: 0.225
+2025-07-10 14:58:35: Epoch: 010 New best VALIDATION metrics
+2025-07-10 14:58:35: Epoch: 010 Test results, ap: mae: 0.614, rse: 1.155, pcc: -0.023, kcc: -0.068
+2025-07-10 14:58:36: Epoch: 011 Train Loss: 2.896, mae: 1.213, rse: 1.001, pcc: 0.302, kcc: 0.228
+2025-07-10 14:58:36: Epoch: 011 Test results, ap: mae: 0.607, rse: 1.128, pcc: -0.064, kcc: -0.066
+2025-07-10 14:58:36: Epoch: 011 New best TEST metrics
+2025-07-10 14:58:37: Epoch: 012 Train Loss: 2.900, mae: 1.214, rse: 1.001, pcc: 0.304, kcc: 0.221
+2025-07-10 14:58:38: Epoch: 012 Test results, ap: mae: 0.606, rse: 1.125, pcc: -0.094, kcc: -0.065
+2025-07-10 14:58:38: Epoch: 013 Train Loss: 2.905, mae: 1.212, rse: 1.000, pcc: 0.320, kcc: 0.213
+2025-07-10 14:58:39: Epoch: 013 Test results, ap: mae: 0.610, rse: 1.138, pcc: -0.042, kcc: -0.045
+2025-07-10 14:58:39: Epoch: 013 New best TEST metrics
+2025-07-10 14:58:40: Epoch: 014 Train Loss: 2.903, mae: 1.210, rse: 1.000, pcc: 0.305, kcc: 0.223
+2025-07-10 14:58:40: Epoch: 014 Test results, ap: mae: 0.613, rse: 1.150, pcc: -0.080, kcc: -0.067
+2025-07-10 14:58:41: Epoch: 015 Train Loss: 2.918, mae: 1.210, rse: 1.000, pcc: 0.301, kcc: 0.200
+2025-07-10 14:58:41: Epoch: 015 Test results, ap: mae: 0.612, rse: 1.147, pcc: -0.096, kcc: -0.067
+2025-07-10 14:58:42: Epoch: 016 Train Loss: 2.914, mae: 1.213, rse: 1.001, pcc: 0.305, kcc: 0.191
+2025-07-10 14:58:42: Epoch: 016 Test results, ap: mae: 0.608, rse: 1.132, pcc: -0.079, kcc: -0.053
+2025-07-10 14:58:43: Epoch: 017 Train Loss: 2.908, mae: 1.214, rse: 1.001, pcc: 0.303, kcc: 0.175
+2025-07-10 14:58:43: Epoch: 017 Test results, ap: mae: 0.605, rse: 1.120, pcc: -0.054, kcc: -0.026
+2025-07-10 14:58:43: Epoch: 017 New best TEST metrics
+2025-07-10 14:58:44: Epoch: 018 Train Loss: 2.875, mae: 1.211, rse: 1.000, pcc: -0.284, kcc: -0.170
+2025-07-10 14:58:44: Epoch: 018 Test results, ap: mae: 0.610, rse: 1.142, pcc: 0.074, kcc: 0.030
+2025-07-10 14:58:44: Epoch: 018 New best TEST metrics
+2025-07-10 14:58:45: Epoch: 019 Train Loss: 2.907, mae: 1.212, rse: 1.001, pcc: -0.286, kcc: -0.173
+2025-07-10 14:58:46: Epoch: 019 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.094, kcc: 0.061
+2025-07-10 14:58:46: Epoch: 019 New best TEST metrics
+2025-07-10 14:58:47: Epoch: 020 Train Loss: 2.908, mae: 1.211, rse: 1.000, pcc: 0.267, kcc: 0.131
+2025-07-10 14:58:47: Epoch: 020 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.223, kcc: -0.254
+2025-07-10 14:58:48: Epoch: 021 Train Loss: 2.917, mae: 1.211, rse: 1.000, pcc: 0.295, kcc: 0.173
+2025-07-10 14:58:48: Epoch: 021 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.168, kcc: -0.130
+2025-07-10 14:58:49: Epoch: 022 Train Loss: 2.890, mae: 1.211, rse: 1.000, pcc: 0.299, kcc: 0.173
+2025-07-10 14:58:49: Epoch: 022 Test results, ap: mae: 0.610, rse: 1.140, pcc: -0.109, kcc: -0.070
+2025-07-10 14:58:50: Epoch: 023 Train Loss: 2.907, mae: 1.211, rse: 1.000, pcc: 0.302, kcc: 0.177
+2025-07-10 14:58:50: Epoch: 023 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.088, kcc: -0.046
+2025-07-10 14:58:51: Epoch: 024 Train Loss: 2.878, mae: 1.211, rse: 1.000, pcc: 0.301, kcc: 0.183
+2025-07-10 14:58:51: Epoch: 024 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.073, kcc: -0.052
+2025-07-10 14:58:52: Epoch: 025 Train Loss: 2.909, mae: 1.212, rse: 1.001, pcc: 0.302, kcc: 0.183
+2025-07-10 14:58:52: Epoch: 025 Test results, ap: mae: 0.608, rse: 1.133, pcc: -0.075, kcc: -0.041
+2025-07-10 14:58:53: Epoch: 026 Train Loss: 2.907, mae: 1.213, rse: 1.001, pcc: 0.304, kcc: 0.182
+2025-07-10 14:58:54: Epoch: 026 Test results, ap: mae: 0.607, rse: 1.130, pcc: -0.060, kcc: -0.030
+2025-07-10 14:58:54: Epoch: 027 Train Loss: 2.913, mae: 1.213, rse: 1.001, pcc: 0.301, kcc: 0.187
+2025-07-10 14:58:55: Epoch: 027 Test results, ap: mae: 0.608, rse: 1.132, pcc: -0.062, kcc: -0.027
+2025-07-10 14:58:56: Epoch: 028 Train Loss: 2.906, mae: 1.212, rse: 1.001, pcc: 0.299, kcc: 0.180
+2025-07-10 14:58:56: Epoch: 028 Test results, ap: mae: 0.608, rse: 1.133, pcc: -0.051, kcc: -0.016
+2025-07-10 14:58:57: Epoch: 029 Train Loss: 2.912, mae: 1.212, rse: 1.001, pcc: 0.296, kcc: 0.173
+2025-07-10 14:58:57: Epoch: 029 Test results, ap: mae: 0.608, rse: 1.133, pcc: -0.043, kcc: -0.011
+2025-07-10 14:58:58: Epoch: 030 Train Loss: 2.925, mae: 1.212, rse: 1.001, pcc: 0.295, kcc: 0.167
+2025-07-10 14:58:58: Epoch: 030 Test results, ap: mae: 0.608, rse: 1.133, pcc: -0.042, kcc: -0.004
+2025-07-10 14:58:59: Epoch: 031 Train Loss: 2.872, mae: 1.214, rse: 1.001, pcc: 0.295, kcc: 0.165
+2025-07-10 14:58:59: Epoch: 031 Test results, ap: mae: 0.607, rse: 1.126, pcc: -0.038, kcc: 0.002
+2025-07-10 14:59:00: Epoch: 032 Train Loss: 2.905, mae: 1.213, rse: 1.001, pcc: 0.291, kcc: 0.162
+2025-07-10 14:59:00: Epoch: 032 Test results, ap: mae: 0.608, rse: 1.131, pcc: -0.050, kcc: 0.003
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+2025-07-10 14:59:05: Epoch: 036 Train Loss: 2.899, mae: 1.213, rse: 1.001, pcc: 0.287, kcc: 0.159
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+2025-07-10 14:59:06: Epoch: 037 Train Loss: 2.908, mae: 1.213, rse: 1.001, pcc: 0.283, kcc: 0.156
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+2025-07-10 14:59:07: Epoch: 038 Train Loss: 2.901, mae: 1.213, rse: 1.001, pcc: 0.280, kcc: 0.153
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+2025-07-10 14:59:13: Epoch: 044 Train Loss: 2.904, mae: 1.213, rse: 1.001, pcc: 0.267, kcc: 0.143
+2025-07-10 14:59:13: Epoch: 044 Test results, ap: mae: 0.608, rse: 1.131, pcc: 0.035, kcc: 0.070
+2025-07-10 14:59:14: Epoch: 045 Train Loss: 2.906, mae: 1.213, rse: 1.001, pcc: 0.268, kcc: 0.142
+2025-07-10 14:59:14: Epoch: 045 Test results, ap: mae: 0.607, rse: 1.128, pcc: 0.044, kcc: 0.074
+2025-07-10 14:59:15: Epoch: 046 Train Loss: 2.907, mae: 1.213, rse: 1.001, pcc: 0.264, kcc: 0.139
+2025-07-10 14:59:16: Epoch: 046 Test results, ap: mae: 0.608, rse: 1.130, pcc: 0.054, kcc: 0.078
+2025-07-10 14:59:16: Epoch: 047 Train Loss: 2.910, mae: 1.213, rse: 1.001, pcc: 0.262, kcc: 0.138
+2025-07-10 14:59:17: Epoch: 047 Test results, ap: mae: 0.608, rse: 1.131, pcc: 0.066, kcc: 0.077
+2025-07-10 14:59:17: Epoch: 048 Train Loss: 2.899, mae: 1.213, rse: 1.001, pcc: 0.260, kcc: 0.137
+2025-07-10 14:59:18: Epoch: 048 Test results, ap: mae: 0.608, rse: 1.132, pcc: 0.077, kcc: 0.079
+2025-07-10 14:59:18: Epoch: 048 New best TEST metrics
+2025-07-10 14:59:19: Epoch: 049 Train Loss: 2.900, mae: 1.212, rse: 1.000, pcc: 0.259, kcc: 0.135
+2025-07-10 14:59:19: Epoch: 049 Test results, ap: mae: 0.608, rse: 1.133, pcc: 0.095, kcc: 0.081
+2025-07-10 14:59:19: Epoch: 049 New best TEST metrics
+2025-07-10 14:59:20: Epoch: 050 Train Loss: 2.884, mae: 1.211, rse: 1.000, pcc: 0.270, kcc: 0.152
+2025-07-10 14:59:20: Epoch: 050 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.071, kcc: 0.064
+2025-07-10 14:59:20: used time 0.02h
+2025-07-10 14:59:20: Running Cross Validation 1
+2025-07-10 14:59:20: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 14:59:21: Epoch: 001 Train Loss: 2.870, mae: 1.255, rse: 1.028, pcc: 0.244, kcc: 0.150
+2025-07-10 14:59:21: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:59:21: Epoch: 001 Test results, ap: mae: 0.708, rse: 1.487, pcc: 0.217, kcc: 0.112
+2025-07-10 14:59:22: Epoch: 002 Train Loss: 2.865, mae: 1.254, rse: 1.001, pcc: 0.245, kcc: 0.165
+2025-07-10 14:59:22: Epoch: 002 New best VALIDATION metrics
+2025-07-10 14:59:22: Epoch: 002 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.135, kcc: 0.063
+2025-07-10 14:59:23: Epoch: 003 Train Loss: 2.842, mae: 1.256, rse: 1.001, pcc: 0.266, kcc: 0.161
+2025-07-10 14:59:23: Epoch: 003 New best VALIDATION metrics
+2025-07-10 14:59:23: Epoch: 003 Test results, ap: mae: 0.609, rse: 1.134, pcc: 0.165, kcc: 0.093
+2025-07-10 14:59:24: Epoch: 004 Train Loss: 2.855, mae: 1.251, rse: 1.014, pcc: 0.283, kcc: 0.183
+2025-07-10 14:59:24: Epoch: 004 New best VALIDATION metrics
+2025-07-10 14:59:24: Epoch: 004 Test results, ap: mae: 0.665, rse: 1.337, pcc: 0.049, kcc: 0.029
+2025-07-10 14:59:25: Epoch: 005 Train Loss: 2.871, mae: 1.263, rse: 1.000, pcc: -0.273, kcc: -0.216
+2025-07-10 14:59:25: Epoch: 005 Test results, ap: mae: 0.596, rse: 1.084, pcc: -0.074, kcc: -0.028
+2025-07-10 14:59:26: Epoch: 006 Train Loss: 2.876, mae: 1.257, rse: 1.000, pcc: -0.011, kcc: 0.026
+2025-07-10 14:59:26: Epoch: 006 Test results, ap: mae: 0.606, rse: 1.123, pcc: -0.189, kcc: -0.127
+2025-07-10 14:59:27: Epoch: 007 Train Loss: 2.849, mae: 1.252, rse: 1.002, pcc: 0.098, kcc: 0.097
+2025-07-10 14:59:27: Epoch: 007 Test results, ap: mae: 0.622, rse: 1.181, pcc: 0.251, kcc: 0.175
+2025-07-10 14:59:28: Epoch: 008 Train Loss: 2.842, mae: 1.253, rse: 1.002, pcc: -0.201, kcc: -0.151
+2025-07-10 14:59:28: Epoch: 008 Test results, ap: mae: 0.619, rse: 1.173, pcc: -0.329, kcc: -0.187
+2025-07-10 14:59:29: Epoch: 009 Train Loss: 2.855, mae: 1.253, rse: 1.002, pcc: 0.019, kcc: 0.027
+2025-07-10 14:59:29: Epoch: 009 Test results, ap: mae: 0.620, rse: 1.175, pcc: -0.184, kcc: -0.128
+2025-07-10 14:59:30: Epoch: 010 Train Loss: 2.803, mae: 1.259, rse: 1.000, pcc: -0.217, kcc: -0.111
+2025-07-10 14:59:30: Epoch: 010 Test results, ap: mae: 0.602, rse: 1.108, pcc: -0.007, kcc: -0.017
+2025-07-10 14:59:31: Epoch: 011 Train Loss: 2.843, mae: 1.256, rse: 1.001, pcc: -0.060, kcc: -0.088
+2025-07-10 14:59:31: Epoch: 011 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.156, kcc: 0.032
+2025-07-10 14:59:31: Epoch: 011 New best TEST metrics
+2025-07-10 14:59:32: Epoch: 012 Train Loss: 2.835, mae: 1.255, rse: 1.001, pcc: 0.156, kcc: 0.131
+2025-07-10 14:59:33: Epoch: 012 Test results, ap: mae: 0.610, rse: 1.140, pcc: 0.264, kcc: 0.106
+2025-07-10 14:59:33: Epoch: 012 New best TEST metrics
+2025-07-10 14:59:34: Epoch: 013 Train Loss: 2.847, mae: 1.253, rse: 1.002, pcc: -0.081, kcc: -0.072
+2025-07-10 14:59:34: Epoch: 013 Test results, ap: mae: 0.619, rse: 1.173, pcc: 0.164, kcc: 0.112
+2025-07-10 14:59:35: Epoch: 014 Train Loss: 2.868, mae: 1.254, rse: 1.001, pcc: 0.141, kcc: 0.139
+2025-07-10 14:59:35: Epoch: 014 Test results, ap: mae: 0.613, rse: 1.151, pcc: -0.012, kcc: -0.002
+2025-07-10 14:59:36: Epoch: 015 Train Loss: 2.848, mae: 1.253, rse: 1.002, pcc: 0.118, kcc: 0.057
+2025-07-10 14:59:36: Epoch: 015 Test results, ap: mae: 0.617, rse: 1.167, pcc: 0.279, kcc: 0.176
+2025-07-10 14:59:36: Epoch: 015 New best TEST metrics
+2025-07-10 14:59:37: Epoch: 016 Train Loss: 2.856, mae: 1.252, rse: 1.003, pcc: -0.051, kcc: -0.059
+2025-07-10 14:59:37: Epoch: 016 Test results, ap: mae: 0.624, rse: 1.187, pcc: 0.169, kcc: 0.112
+2025-07-10 14:59:39: Epoch: 017 Train Loss: 2.838, mae: 1.253, rse: 1.002, pcc: 0.065, kcc: 0.009
+2025-07-10 14:59:39: Epoch: 017 Test results, ap: mae: 0.617, rse: 1.166, pcc: 0.261, kcc: 0.165
+2025-07-10 14:59:40: Epoch: 018 Train Loss: 2.854, mae: 1.256, rse: 1.001, pcc: 0.142, kcc: 0.074
+2025-07-10 14:59:40: Epoch: 018 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.282, kcc: 0.176
+2025-07-10 14:59:40: Epoch: 018 New best TEST metrics
+2025-07-10 14:59:41: Epoch: 019 Train Loss: 2.841, mae: 1.256, rse: 1.000, pcc: 0.154, kcc: 0.077
+2025-07-10 14:59:41: Epoch: 019 Test results, ap: mae: 0.608, rse: 1.130, pcc: 0.294, kcc: 0.178
+2025-07-10 14:59:41: Epoch: 019 New best TEST metrics
+2025-07-10 14:59:42: Epoch: 020 Train Loss: 2.850, mae: 1.253, rse: 1.002, pcc: 0.181, kcc: 0.108
+2025-07-10 14:59:43: Epoch: 020 Test results, ap: mae: 0.616, rse: 1.163, pcc: 0.297, kcc: 0.164
+2025-07-10 14:59:44: Epoch: 021 Train Loss: 2.849, mae: 1.254, rse: 1.001, pcc: 0.215, kcc: 0.142
+2025-07-10 14:59:44: Epoch: 021 Test results, ap: mae: 0.615, rse: 1.159, pcc: 0.272, kcc: 0.155
+2025-07-10 14:59:45: Epoch: 022 Train Loss: 2.843, mae: 1.255, rse: 1.001, pcc: 0.219, kcc: 0.168
+2025-07-10 14:59:45: Epoch: 022 Test results, ap: mae: 0.610, rse: 1.139, pcc: 0.195, kcc: 0.122
+2025-07-10 14:59:46: Epoch: 023 Train Loss: 2.852, mae: 1.256, rse: 1.001, pcc: 0.222, kcc: 0.157
+2025-07-10 14:59:46: Epoch: 023 Test results, ap: mae: 0.609, rse: 1.137, pcc: 0.255, kcc: 0.143
+2025-07-10 14:59:47: Epoch: 024 Train Loss: 2.840, mae: 1.254, rse: 1.001, pcc: 0.223, kcc: 0.136
+2025-07-10 14:59:48: Epoch: 024 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.274, kcc: 0.146
+2025-07-10 14:59:49: Epoch: 025 Train Loss: 2.856, mae: 1.254, rse: 1.001, pcc: 0.224, kcc: 0.139
+2025-07-10 14:59:49: Epoch: 025 Test results, ap: mae: 0.613, rse: 1.153, pcc: 0.272, kcc: 0.152
+2025-07-10 14:59:50: Epoch: 026 Train Loss: 2.843, mae: 1.253, rse: 1.002, pcc: 0.234, kcc: 0.212
+2025-07-10 14:59:50: Epoch: 026 Test results, ap: mae: 0.619, rse: 1.172, pcc: 0.098, kcc: 0.036
+2025-07-10 14:59:51: Epoch: 027 Train Loss: 2.856, mae: 1.254, rse: 1.001, pcc: 0.207, kcc: 0.205
+2025-07-10 14:59:52: Epoch: 027 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.099, kcc: 0.028
+2025-07-10 14:59:53: Epoch: 028 Train Loss: 2.840, mae: 1.253, rse: 1.002, pcc: 0.217, kcc: 0.137
+2025-07-10 14:59:53: Epoch: 028 Test results, ap: mae: 0.617, rse: 1.168, pcc: 0.254, kcc: 0.126
+2025-07-10 14:59:54: Epoch: 029 Train Loss: 2.856, mae: 1.254, rse: 1.001, pcc: 0.200, kcc: 0.126
+2025-07-10 14:59:54: Epoch: 029 Test results, ap: mae: 0.615, rse: 1.158, pcc: 0.274, kcc: 0.116
+2025-07-10 14:59:55: Epoch: 030 Train Loss: 2.847, mae: 1.255, rse: 1.001, pcc: 0.197, kcc: 0.122
+2025-07-10 14:59:55: Epoch: 030 Test results, ap: mae: 0.612, rse: 1.147, pcc: 0.284, kcc: 0.122
+2025-07-10 14:59:56: Epoch: 031 Train Loss: 2.854, mae: 1.254, rse: 1.001, pcc: 0.197, kcc: 0.122
+2025-07-10 14:59:57: Epoch: 031 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.281, kcc: 0.121
+2025-07-10 14:59:58: Epoch: 032 Train Loss: 2.851, mae: 1.254, rse: 1.001, pcc: 0.197, kcc: 0.123
+2025-07-10 14:59:58: Epoch: 032 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.281, kcc: 0.117
+2025-07-10 14:59:59: Epoch: 033 Train Loss: 2.849, mae: 1.254, rse: 1.001, pcc: 0.202, kcc: 0.126
+2025-07-10 14:59:59: Epoch: 033 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.279, kcc: 0.114
+2025-07-10 15:00:00: Epoch: 034 Train Loss: 2.821, mae: 1.254, rse: 1.001, pcc: 0.204, kcc: 0.127
+2025-07-10 15:00:00: Epoch: 034 Test results, ap: mae: 0.613, rse: 1.152, pcc: 0.279, kcc: 0.117
+2025-07-10 15:00:01: Epoch: 035 Train Loss: 2.869, mae: 1.254, rse: 1.001, pcc: 0.208, kcc: 0.132
+2025-07-10 15:00:01: Epoch: 035 Test results, ap: mae: 0.613, rse: 1.150, pcc: 0.278, kcc: 0.111
+2025-07-10 15:00:02: Epoch: 036 Train Loss: 2.846, mae: 1.254, rse: 1.001, pcc: 0.204, kcc: 0.127
+2025-07-10 15:00:03: Epoch: 036 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.278, kcc: 0.111
+2025-07-10 15:00:04: Epoch: 037 Train Loss: 2.824, mae: 1.254, rse: 1.002, pcc: 0.202, kcc: 0.124
+2025-07-10 15:00:04: Epoch: 037 Test results, ap: mae: 0.616, rse: 1.161, pcc: 0.273, kcc: 0.112
+2025-07-10 15:00:05: Epoch: 038 Train Loss: 2.842, mae: 1.255, rse: 1.001, pcc: 0.213, kcc: 0.134
+2025-07-10 15:00:05: Epoch: 038 Test results, ap: mae: 0.613, rse: 1.150, pcc: 0.268, kcc: 0.111
+2025-07-10 15:00:06: Epoch: 039 Train Loss: 2.831, mae: 1.254, rse: 1.001, pcc: 0.209, kcc: 0.131
+2025-07-10 15:00:06: Epoch: 039 Test results, ap: mae: 0.615, rse: 1.158, pcc: 0.269, kcc: 0.105
+2025-07-10 15:00:07: Epoch: 040 Train Loss: 2.836, mae: 1.254, rse: 1.001, pcc: 0.214, kcc: 0.135
+2025-07-10 15:00:07: Epoch: 040 Test results, ap: mae: 0.614, rse: 1.157, pcc: 0.266, kcc: 0.114
+2025-07-10 15:00:08: Epoch: 041 Train Loss: 2.834, mae: 1.254, rse: 1.001, pcc: 0.212, kcc: 0.134
+2025-07-10 15:00:08: Epoch: 041 Test results, ap: mae: 0.613, rse: 1.151, pcc: 0.271, kcc: 0.112
+2025-07-10 15:00:09: Epoch: 042 Train Loss: 2.851, mae: 1.254, rse: 1.001, pcc: 0.212, kcc: 0.133
+2025-07-10 15:00:10: Epoch: 042 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.268, kcc: 0.110
+2025-07-10 15:00:11: Epoch: 043 Train Loss: 2.796, mae: 1.254, rse: 1.001, pcc: 0.213, kcc: 0.134
+2025-07-10 15:00:11: Epoch: 043 Test results, ap: mae: 0.614, rse: 1.157, pcc: 0.269, kcc: 0.113
+2025-07-10 15:00:12: Epoch: 044 Train Loss: 2.843, mae: 1.254, rse: 1.001, pcc: 0.214, kcc: 0.135
+2025-07-10 15:00:12: Epoch: 044 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.267, kcc: 0.115
+2025-07-10 15:00:13: Epoch: 045 Train Loss: 2.848, mae: 1.254, rse: 1.001, pcc: 0.212, kcc: 0.134
+2025-07-10 15:00:13: Epoch: 045 Test results, ap: mae: 0.613, rse: 1.152, pcc: 0.266, kcc: 0.105
+2025-07-10 15:00:14: Epoch: 046 Train Loss: 2.828, mae: 1.254, rse: 1.001, pcc: 0.212, kcc: 0.134
+2025-07-10 15:00:14: Epoch: 046 Test results, ap: mae: 0.613, rse: 1.153, pcc: 0.263, kcc: 0.106
+2025-07-10 15:00:15: Epoch: 047 Train Loss: 2.830, mae: 1.254, rse: 1.001, pcc: 0.211, kcc: 0.134
+2025-07-10 15:00:16: Epoch: 047 Test results, ap: mae: 0.613, rse: 1.151, pcc: 0.265, kcc: 0.101
+2025-07-10 15:00:17: Epoch: 048 Train Loss: 2.835, mae: 1.255, rse: 1.001, pcc: 0.211, kcc: 0.134
+2025-07-10 15:00:17: Epoch: 048 Test results, ap: mae: 0.612, rse: 1.149, pcc: 0.264, kcc: 0.100
+2025-07-10 15:00:18: Epoch: 049 Train Loss: 2.859, mae: 1.254, rse: 1.001, pcc: 0.214, kcc: 0.134
+2025-07-10 15:00:18: Epoch: 049 Test results, ap: mae: 0.614, rse: 1.156, pcc: 0.261, kcc: 0.100
+2025-07-10 15:00:19: Epoch: 050 Train Loss: 2.854, mae: 1.254, rse: 1.001, pcc: 0.215, kcc: 0.134
+2025-07-10 15:00:19: Epoch: 050 Test results, ap: mae: 0.614, rse: 1.154, pcc: 0.261, kcc: 0.098
+2025-07-10 15:00:19: used time 0.02h
+2025-07-10 15:00:19: Running Cross Validation 2
+2025-07-10 15:00:19: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:00:20: Epoch: 001 Train Loss: 2.907, mae: 1.251, rse: 1.001, pcc: 0.186, kcc: 0.116
+2025-07-10 15:00:20: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:00:20: Epoch: 001 Test results, ap: mae: 0.634, rse: 1.220, pcc: 0.189, kcc: 0.078
+2025-07-10 15:00:21: Epoch: 002 Train Loss: 2.875, mae: 1.256, rse: 1.000, pcc: 0.142, kcc: 0.120
+2025-07-10 15:00:22: Epoch: 002 Test results, ap: mae: 0.606, rse: 1.124, pcc: -0.033, kcc: -0.086
+2025-07-10 15:00:23: Epoch: 003 Train Loss: 2.903, mae: 1.258, rse: 1.001, pcc: 0.241, kcc: 0.144
+2025-07-10 15:00:23: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:00:23: Epoch: 003 Test results, ap: mae: 0.602, rse: 1.105, pcc: 0.075, kcc: -0.020
+2025-07-10 15:00:24: Epoch: 004 Train Loss: 2.913, mae: 1.252, rse: 1.001, pcc: -0.297, kcc: -0.207
+2025-07-10 15:00:24: Epoch: 004 Test results, ap: mae: 0.632, rse: 1.216, pcc: -0.060, kcc: 0.071
+2025-07-10 15:00:25: Epoch: 005 Train Loss: 2.908, mae: 1.260, rse: 1.002, pcc: 0.290, kcc: 0.201
+2025-07-10 15:00:25: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:00:25: Epoch: 005 Test results, ap: mae: 0.597, rse: 1.086, pcc: 0.199, kcc: -0.019
+2025-07-10 15:00:26: Epoch: 006 Train Loss: 2.859, mae: 1.205, rse: 0.930, pcc: 0.285, kcc: 0.215
+2025-07-10 15:00:26: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:00:26: Epoch: 006 Test results, ap: mae: 0.723, rse: 1.489, pcc: 0.193, kcc: 0.140
+2025-07-10 15:00:27: Epoch: 007 Train Loss: 2.699, mae: 1.229, rse: 0.961, pcc: 0.248, kcc: 0.225
+2025-07-10 15:00:28: Epoch: 007 Test results, ap: mae: 0.787, rse: 1.793, pcc: 0.248, kcc: 0.164
+2025-07-10 15:00:29: Epoch: 008 Train Loss: 2.645, mae: 1.184, rse: 0.899, pcc: 0.342, kcc: 0.242
+2025-07-10 15:00:29: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:00:29: Epoch: 008 Test results, ap: mae: 0.623, rse: 1.170, pcc: 0.066, kcc: 0.052
+2025-07-10 15:00:30: Epoch: 009 Train Loss: 2.572, mae: 1.185, rse: 0.884, pcc: 0.349, kcc: 0.247
+2025-07-10 15:00:30: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:00:30: Epoch: 009 Test results, ap: mae: 0.860, rse: 2.015, pcc: 0.013, kcc: 0.088
+2025-07-10 15:00:31: Epoch: 010 Train Loss: 2.550, mae: 1.194, rse: 0.906, pcc: 0.337, kcc: 0.243
+2025-07-10 15:00:31: Epoch: 010 Test results, ap: mae: 0.765, rse: 1.676, pcc: -0.054, kcc: 0.061
+2025-07-10 15:00:32: Epoch: 011 Train Loss: 2.495, mae: 1.185, rse: 0.905, pcc: 0.341, kcc: 0.250
+2025-07-10 15:00:32: Epoch: 011 Test results, ap: mae: 0.687, rse: 1.380, pcc: 0.060, kcc: 0.056
+2025-07-10 15:00:32: Epoch: 011 New best TEST metrics
+2025-07-10 15:00:33: Epoch: 012 Train Loss: 2.512, mae: 1.174, rse: 0.883, pcc: 0.344, kcc: 0.251
+2025-07-10 15:00:33: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:00:34: Epoch: 012 Test results, ap: mae: 0.678, rse: 1.351, pcc: 0.123, kcc: 0.097
+2025-07-10 15:00:34: Epoch: 012 New best TEST metrics
+2025-07-10 15:00:35: Epoch: 013 Train Loss: 2.471, mae: 1.175, rse: 0.885, pcc: 0.342, kcc: 0.258
+2025-07-10 15:00:35: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:00:35: Epoch: 013 Test results, ap: mae: 0.690, rse: 1.405, pcc: 0.107, kcc: 0.084
+2025-07-10 15:00:36: Epoch: 014 Train Loss: 2.465, mae: 1.176, rse: 0.875, pcc: 0.355, kcc: 0.257
+2025-07-10 15:00:36: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:00:36: Epoch: 014 Test results, ap: mae: 0.731, rse: 1.543, pcc: 0.134, kcc: 0.084
+2025-07-10 15:00:37: Epoch: 015 Train Loss: 2.450, mae: 1.197, rse: 0.907, pcc: 0.339, kcc: 0.273
+2025-07-10 15:00:37: Epoch: 015 Test results, ap: mae: 0.785, rse: 1.772, pcc: 0.150, kcc: 0.098
+2025-07-10 15:00:38: Epoch: 016 Train Loss: 2.405, mae: 1.175, rse: 0.871, pcc: 0.361, kcc: 0.263
+2025-07-10 15:00:38: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:00:38: Epoch: 016 Test results, ap: mae: 0.732, rse: 1.573, pcc: -0.095, kcc: -0.073
+2025-07-10 15:00:39: Epoch: 017 Train Loss: 2.440, mae: 1.168, rse: 0.873, pcc: 0.362, kcc: 0.269
+2025-07-10 15:00:39: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:00:40: Epoch: 017 Test results, ap: mae: 0.658, rse: 1.298, pcc: -0.109, kcc: -0.079
+2025-07-10 15:00:41: Epoch: 018 Train Loss: 2.411, mae: 1.184, rse: 0.881, pcc: 0.352, kcc: 0.263
+2025-07-10 15:00:41: Epoch: 018 Test results, ap: mae: 0.796, rse: 1.844, pcc: -0.117, kcc: -0.044
+2025-07-10 15:00:42: Epoch: 019 Train Loss: 2.399, mae: 1.193, rse: 0.896, pcc: 0.346, kcc: 0.244
+2025-07-10 15:00:42: Epoch: 019 Test results, ap: mae: 0.869, rse: 2.078, pcc: 0.140, kcc: 0.087
+2025-07-10 15:00:43: Epoch: 020 Train Loss: 2.384, mae: 1.172, rse: 0.874, pcc: 0.372, kcc: 0.270
+2025-07-10 15:00:43: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:00:43: Epoch: 020 Test results, ap: mae: 0.677, rse: 1.370, pcc: -0.137, kcc: -0.048
+2025-07-10 15:00:44: Epoch: 021 Train Loss: 2.339, mae: 1.174, rse: 0.865, pcc: 0.370, kcc: 0.273
+2025-07-10 15:00:44: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:00:44: Epoch: 021 Test results, ap: mae: 0.756, rse: 1.685, pcc: -0.118, kcc: -0.054
+2025-07-10 15:00:45: Epoch: 022 Train Loss: 2.317, mae: 1.173, rse: 0.875, pcc: 0.382, kcc: 0.273
+2025-07-10 15:00:45: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:00:46: Epoch: 022 Test results, ap: mae: 0.697, rse: 1.371, pcc: -0.074, kcc: -0.044
+2025-07-10 15:00:47: Epoch: 023 Train Loss: 2.299, mae: 1.174, rse: 0.866, pcc: 0.375, kcc: 0.259
+2025-07-10 15:00:47: Epoch: 023 Test results, ap: mae: 0.679, rse: 1.293, pcc: -0.067, kcc: -0.049
+2025-07-10 15:00:48: Epoch: 024 Train Loss: 2.265, mae: 1.176, rse: 0.889, pcc: 0.370, kcc: 0.270
+2025-07-10 15:00:48: Epoch: 024 Test results, ap: mae: 0.641, rse: 1.177, pcc: 0.072, kcc: 0.015
+2025-07-10 15:00:48: Epoch: 024 New best TEST metrics
+2025-07-10 15:00:49: Epoch: 025 Train Loss: 2.186, mae: 1.193, rse: 0.883, pcc: 0.377, kcc: 0.259
+2025-07-10 15:00:49: Epoch: 025 Test results, ap: mae: 0.726, rse: 1.448, pcc: 0.014, kcc: -0.010
+2025-07-10 15:00:50: Epoch: 026 Train Loss: 2.195, mae: 1.172, rse: 0.853, pcc: 0.387, kcc: 0.258
+2025-07-10 15:00:50: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:00:50: Epoch: 026 Test results, ap: mae: 0.687, rse: 1.338, pcc: -0.021, kcc: -0.036
+2025-07-10 15:00:51: Epoch: 027 Train Loss: 2.102, mae: 1.152, rse: 0.846, pcc: 0.414, kcc: 0.281
+2025-07-10 15:00:51: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:00:52: Epoch: 027 Test results, ap: mae: 0.641, rse: 1.157, pcc: 0.043, kcc: -0.003
+2025-07-10 15:00:53: Epoch: 028 Train Loss: 2.049, mae: 1.142, rse: 0.820, pcc: 0.440, kcc: 0.273
+2025-07-10 15:00:53: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:00:53: Epoch: 028 Test results, ap: mae: 0.744, rse: 1.581, pcc: 0.026, kcc: 0.001
+2025-07-10 15:00:54: Epoch: 029 Train Loss: 1.969, mae: 1.134, rse: 0.813, pcc: 0.433, kcc: 0.276
+2025-07-10 15:00:54: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:00:54: Epoch: 029 Test results, ap: mae: 0.685, rse: 1.333, pcc: -0.011, kcc: 0.002
+2025-07-10 15:00:55: Epoch: 030 Train Loss: 1.980, mae: 1.115, rse: 0.793, pcc: 0.457, kcc: 0.284
+2025-07-10 15:00:55: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:00:55: Epoch: 030 Test results, ap: mae: 0.665, rse: 1.255, pcc: 0.135, kcc: 0.027
+2025-07-10 15:00:56: Epoch: 031 Train Loss: 1.844, mae: 1.108, rse: 0.804, pcc: 0.460, kcc: 0.289
+2025-07-10 15:00:56: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:00:57: Epoch: 031 Test results, ap: mae: 0.676, rse: 1.261, pcc: 0.093, kcc: 0.047
+2025-07-10 15:00:57: Epoch: 032 Train Loss: 1.765, mae: 1.087, rse: 0.778, pcc: 0.483, kcc: 0.297
+2025-07-10 15:00:58: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:00:58: Epoch: 032 Test results, ap: mae: 0.696, rse: 1.365, pcc: 0.101, kcc: 0.052
+2025-07-10 15:00:59: Epoch: 033 Train Loss: 1.759, mae: 1.086, rse: 0.780, pcc: 0.486, kcc: 0.311
+2025-07-10 15:00:59: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:00:59: Epoch: 033 Test results, ap: mae: 0.679, rse: 1.309, pcc: 0.088, kcc: 0.026
+2025-07-10 15:01:00: Epoch: 034 Train Loss: 1.716, mae: 1.073, rse: 0.785, pcc: 0.501, kcc: 0.319
+2025-07-10 15:01:00: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:01:00: Epoch: 034 Test results, ap: mae: 0.690, rse: 1.347, pcc: 0.089, kcc: 0.038
+2025-07-10 15:01:01: Epoch: 035 Train Loss: 1.723, mae: 1.088, rse: 0.765, pcc: 0.487, kcc: 0.316
+2025-07-10 15:01:01: Epoch: 035 Test results, ap: mae: 0.684, rse: 1.314, pcc: 0.072, kcc: 0.008
+2025-07-10 15:01:02: Epoch: 036 Train Loss: 1.690, mae: 1.083, rse: 0.796, pcc: 0.486, kcc: 0.305
+2025-07-10 15:01:03: Epoch: 036 Test results, ap: mae: 0.723, rse: 1.461, pcc: 0.087, kcc: 0.044
+2025-07-10 15:01:04: Epoch: 037 Train Loss: 1.699, mae: 1.083, rse: 0.767, pcc: 0.496, kcc: 0.333
+2025-07-10 15:01:04: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:01:04: Epoch: 037 Test results, ap: mae: 0.740, rse: 1.558, pcc: 0.097, kcc: 0.037
+2025-07-10 15:01:05: Epoch: 038 Train Loss: 1.665, mae: 1.077, rse: 0.778, pcc: 0.494, kcc: 0.320
+2025-07-10 15:01:05: Epoch: 038 Test results, ap: mae: 0.739, rse: 1.531, pcc: 0.030, kcc: 0.022
+2025-07-10 15:01:06: Epoch: 039 Train Loss: 1.653, mae: 1.072, rse: 0.760, pcc: 0.498, kcc: 0.324
+2025-07-10 15:01:06: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:01:06: Epoch: 039 Test results, ap: mae: 0.728, rse: 1.501, pcc: 0.046, kcc: 0.037
+2025-07-10 15:01:07: Epoch: 040 Train Loss: 1.627, mae: 1.087, rse: 0.771, pcc: 0.485, kcc: 0.317
+2025-07-10 15:01:07: Epoch: 040 Test results, ap: mae: 0.716, rse: 1.452, pcc: 0.028, kcc: 0.028
+2025-07-10 15:01:08: Epoch: 041 Train Loss: 1.597, mae: 1.062, rse: 0.757, pcc: 0.506, kcc: 0.329
+2025-07-10 15:01:08: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:01:09: Epoch: 041 Test results, ap: mae: 0.742, rse: 1.564, pcc: 0.014, kcc: 0.020
+2025-07-10 15:01:10: Epoch: 042 Train Loss: 1.584, mae: 1.070, rse: 0.760, pcc: 0.503, kcc: 0.326
+2025-07-10 15:01:10: Epoch: 042 Test results, ap: mae: 0.760, rse: 1.646, pcc: -0.003, kcc: 0.013
+2025-07-10 15:01:11: Epoch: 043 Train Loss: 1.592, mae: 1.063, rse: 0.755, pcc: 0.509, kcc: 0.336
+2025-07-10 15:01:11: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:01:11: Epoch: 043 Test results, ap: mae: 0.754, rse: 1.631, pcc: 0.008, kcc: 0.012
+2025-07-10 15:01:12: Epoch: 044 Train Loss: 1.589, mae: 1.064, rse: 0.753, pcc: 0.509, kcc: 0.337
+2025-07-10 15:01:12: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:01:12: Epoch: 044 Test results, ap: mae: 0.757, rse: 1.643, pcc: 0.008, kcc: 0.006
+2025-07-10 15:01:13: Epoch: 045 Train Loss: 1.583, mae: 1.062, rse: 0.759, pcc: 0.507, kcc: 0.333
+2025-07-10 15:01:13: Epoch: 045 Test results, ap: mae: 0.745, rse: 1.591, pcc: -0.003, kcc: 0.002
+2025-07-10 15:01:14: Epoch: 046 Train Loss: 1.561, mae: 1.056, rse: 0.760, pcc: 0.515, kcc: 0.341
+2025-07-10 15:01:14: Epoch: 046 New best VALIDATION metrics
+2025-07-10 15:01:15: Epoch: 046 Test results, ap: mae: 0.749, rse: 1.603, pcc: 0.009, kcc: 0.003
+2025-07-10 15:01:16: Epoch: 047 Train Loss: 1.561, mae: 1.060, rse: 0.749, pcc: 0.514, kcc: 0.336
+2025-07-10 15:01:16: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:01:16: Epoch: 047 Test results, ap: mae: 0.756, rse: 1.658, pcc: -0.013, kcc: -0.000
+2025-07-10 15:01:17: Epoch: 048 Train Loss: 1.561, mae: 1.062, rse: 0.754, pcc: 0.511, kcc: 0.339
+2025-07-10 15:01:17: Epoch: 048 Test results, ap: mae: 0.759, rse: 1.627, pcc: -0.010, kcc: -0.006
+2025-07-10 15:01:18: Epoch: 049 Train Loss: 1.549, mae: 1.064, rse: 0.763, pcc: 0.509, kcc: 0.339
+2025-07-10 15:01:18: Epoch: 049 Test results, ap: mae: 0.778, rse: 1.716, pcc: -0.020, kcc: -0.016
+2025-07-10 15:01:19: Epoch: 050 Train Loss: 1.540, mae: 1.065, rse: 0.757, pcc: 0.509, kcc: 0.332
+2025-07-10 15:01:20: Epoch: 050 Test results, ap: mae: 0.792, rse: 1.765, pcc: -0.020, kcc: -0.010
+2025-07-10 15:01:20: used time 0.02h
+2025-07-10 15:01:20: Running Cross Validation 3
+2025-07-10 15:01:20: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:01:21: Epoch: 001 Train Loss: 2.822, mae: 1.291, rse: 1.004, pcc: 0.156, kcc: 0.098
+2025-07-10 15:01:21: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:01:21: Epoch: 001 Test results, ap: mae: 0.643, rse: 1.254, pcc: 0.006, kcc: 0.044
+2025-07-10 15:01:22: Epoch: 002 Train Loss: 2.815, mae: 1.291, rse: 1.009, pcc: 0.150, kcc: 0.088
+2025-07-10 15:01:22: Epoch: 002 Test results, ap: mae: 0.662, rse: 1.327, pcc: -0.013, kcc: 0.001
+2025-07-10 15:01:23: Epoch: 003 Train Loss: 2.805, mae: 1.301, rse: 1.001, pcc: -0.010, kcc: 0.054
+2025-07-10 15:01:23: Epoch: 003 Test results, ap: mae: 0.593, rse: 1.068, pcc: 0.262, kcc: 0.230
+2025-07-10 15:01:24: Epoch: 004 Train Loss: 2.787, mae: 1.291, rse: 1.005, pcc: -0.040, kcc: -0.078
+2025-07-10 15:01:24: Epoch: 004 Test results, ap: mae: 0.646, rse: 1.266, pcc: -0.154, kcc: -0.023
+2025-07-10 15:01:25: Epoch: 005 Train Loss: 2.813, mae: 1.296, rse: 1.000, pcc: 0.024, kcc: -0.022
+2025-07-10 15:01:26: Epoch: 005 Test results, ap: mae: 0.602, rse: 1.106, pcc: -0.151, kcc: 0.009
+2025-07-10 15:01:27: Epoch: 006 Train Loss: 2.807, mae: 1.292, rse: 1.001, pcc: 0.095, kcc: 0.088
+2025-07-10 15:01:27: Epoch: 006 Test results, ap: mae: 0.618, rse: 1.171, pcc: 0.038, kcc: -0.039
+2025-07-10 15:01:28: Epoch: 007 Train Loss: 2.800, mae: 1.292, rse: 1.002, pcc: 0.031, kcc: -0.051
+2025-07-10 15:01:28: Epoch: 007 Test results, ap: mae: 0.629, rse: 1.206, pcc: 0.373, kcc: -0.059
+2025-07-10 15:01:29: Epoch: 008 Train Loss: 2.812, mae: 1.293, rse: 1.000, pcc: 0.124, kcc: 0.084
+2025-07-10 15:01:29: Epoch: 008 Test results, ap: mae: 0.616, rse: 1.161, pcc: -0.003, kcc: -0.169
+2025-07-10 15:01:30: Epoch: 009 Train Loss: 2.807, mae: 1.292, rse: 1.001, pcc: -0.040, kcc: -0.052
+2025-07-10 15:01:30: Epoch: 009 Test results, ap: mae: 0.619, rse: 1.172, pcc: 0.412, kcc: 0.306
+2025-07-10 15:01:31: Epoch: 010 Train Loss: 2.817, mae: 1.293, rse: 1.001, pcc: 0.076, kcc: -0.077
+2025-07-10 15:01:32: Epoch: 010 Test results, ap: mae: 0.616, rse: 1.165, pcc: -0.134, kcc: -0.026
+2025-07-10 15:01:33: Epoch: 011 Train Loss: 2.802, mae: 1.292, rse: 1.001, pcc: 0.114, kcc: 0.103
+2025-07-10 15:01:33: Epoch: 011 Test results, ap: mae: 0.618, rse: 1.171, pcc: -0.140, kcc: -0.146
+2025-07-10 15:01:33: Epoch: 011 New best TEST metrics
+2025-07-10 15:01:34: Epoch: 012 Train Loss: 2.808, mae: 1.293, rse: 1.001, pcc: 0.086, kcc: -0.044
+2025-07-10 15:01:34: Epoch: 012 Test results, ap: mae: 0.617, rse: 1.166, pcc: 0.096, kcc: 0.214
+2025-07-10 15:01:34: Epoch: 012 New best TEST metrics
+2025-07-10 15:01:35: Epoch: 013 Train Loss: 2.820, mae: 1.293, rse: 1.000, pcc: -0.085, kcc: 0.023
+2025-07-10 15:01:35: Epoch: 013 Test results, ap: mae: 0.615, rse: 1.160, pcc: -0.045, kcc: -0.167
+2025-07-10 15:01:36: Epoch: 014 Train Loss: 2.799, mae: 1.294, rse: 1.000, pcc: 0.096, kcc: 0.104
+2025-07-10 15:01:36: Epoch: 014 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.041, kcc: -0.096
+2025-07-10 15:01:37: Epoch: 015 Train Loss: 2.806, mae: 1.293, rse: 1.000, pcc: -0.098, kcc: 0.025
+2025-07-10 15:01:38: Epoch: 015 Test results, ap: mae: 0.614, rse: 1.156, pcc: -0.118, kcc: -0.174
+2025-07-10 15:01:39: Epoch: 016 Train Loss: 2.795, mae: 1.293, rse: 1.000, pcc: -0.102, kcc: 0.046
+2025-07-10 15:01:39: Epoch: 016 Test results, ap: mae: 0.613, rse: 1.153, pcc: -0.047, kcc: -0.126
+2025-07-10 15:01:40: Epoch: 017 Train Loss: 2.787, mae: 1.294, rse: 1.000, pcc: -0.097, kcc: -0.121
+2025-07-10 15:01:40: Epoch: 017 Test results, ap: mae: 0.612, rse: 1.147, pcc: -0.219, kcc: -0.020
+2025-07-10 15:01:41: Epoch: 018 Train Loss: 2.803, mae: 1.293, rse: 1.000, pcc: -0.105, kcc: 0.035
+2025-07-10 15:01:41: Epoch: 018 Test results, ap: mae: 0.613, rse: 1.150, pcc: -1.000, kcc: -0.060
+2025-07-10 15:01:42: Epoch: 019 Train Loss: 2.817, mae: 1.293, rse: 1.000, pcc: -0.109, kcc: -0.073
+2025-07-10 15:01:42: Epoch: 019 Test results, ap: mae: 0.615, rse: 1.160, pcc: -0.016, kcc: 0.040
+2025-07-10 15:01:43: Epoch: 020 Train Loss: 2.812, mae: 1.293, rse: 1.000, pcc: -0.100, kcc: 0.031
+2025-07-10 15:01:43: Epoch: 020 Test results, ap: mae: 0.613, rse: 1.151, pcc: -0.236, kcc: -0.282
+2025-07-10 15:01:44: Epoch: 021 Train Loss: 2.758, mae: 1.294, rse: 1.000, pcc: -0.097, kcc: 0.038
+2025-07-10 15:01:45: Epoch: 021 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.124, kcc: -0.278
+2025-07-10 15:01:46: Epoch: 022 Train Loss: 2.782, mae: 1.294, rse: 1.000, pcc: -0.098, kcc: -0.033
+2025-07-10 15:01:46: Epoch: 022 Test results, ap: mae: 0.612, rse: 1.147, pcc: -0.490, kcc: -0.130
+2025-07-10 15:01:47: Epoch: 023 Train Loss: 2.784, mae: 1.294, rse: 1.000, pcc: -0.095, kcc: 0.024
+2025-07-10 15:01:47: Epoch: 023 Test results, ap: mae: 0.612, rse: 1.146, pcc: 0.064, kcc: -0.262
+2025-07-10 15:01:48: Epoch: 024 Train Loss: 2.793, mae: 1.294, rse: 1.000, pcc: -0.099, kcc: -0.137
+2025-07-10 15:01:48: Epoch: 024 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.165, kcc: 0.077
+2025-07-10 15:01:49: Epoch: 025 Train Loss: 2.797, mae: 1.294, rse: 1.000, pcc: -0.056, kcc: -0.126
+2025-07-10 15:01:49: Epoch: 025 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.052, kcc: 0.059
+2025-07-10 15:01:50: Epoch: 026 Train Loss: 2.802, mae: 1.294, rse: 1.000, pcc: 0.090, kcc: -0.024
+2025-07-10 15:01:50: Epoch: 026 Test results, ap: mae: 0.612, rse: 1.146, pcc: 0.146, kcc: 0.117
+2025-07-10 15:01:51: Epoch: 027 Train Loss: 2.794, mae: 1.294, rse: 1.000, pcc: 0.102, kcc: 0.063
+2025-07-10 15:01:51: Epoch: 027 Test results, ap: mae: 0.612, rse: 1.146, pcc: -1.000, kcc: 0.152
+2025-07-10 15:01:52: Epoch: 028 Train Loss: 2.801, mae: 1.293, rse: 1.000, pcc: -0.104, kcc: 0.009
+2025-07-10 15:01:53: Epoch: 028 Test results, ap: mae: 0.613, rse: 1.151, pcc: -0.655, kcc: -0.178
+2025-07-10 15:01:53: Epoch: 029 Train Loss: 2.783, mae: 1.293, rse: 1.000, pcc: 0.102, kcc: -0.012
+2025-07-10 15:01:54: Epoch: 029 Test results, ap: mae: 0.613, rse: 1.152, pcc: -0.169, kcc: 0.190
+2025-07-10 15:01:55: Epoch: 030 Train Loss: 2.793, mae: 1.294, rse: 1.000, pcc: 0.106, kcc: -0.026
+2025-07-10 15:01:55: Epoch: 030 Test results, ap: mae: 0.611, rse: 1.145, pcc: 0.126, kcc: 0.159
+2025-07-10 15:01:56: Epoch: 031 Train Loss: 2.796, mae: 1.294, rse: 1.000, pcc: 0.108, kcc: -0.024
+2025-07-10 15:01:56: Epoch: 031 Test results, ap: mae: 0.611, rse: 1.146, pcc: 0.121, kcc: 0.182
+2025-07-10 15:01:56: Epoch: 031 New best TEST metrics
+2025-07-10 15:01:57: Epoch: 032 Train Loss: 2.779, mae: 1.294, rse: 1.000, pcc: 0.109, kcc: -0.026
+2025-07-10 15:01:57: Epoch: 032 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.386, kcc: 0.231
+2025-07-10 15:01:57: Epoch: 032 New best TEST metrics
+2025-07-10 15:01:58: Epoch: 033 Train Loss: 2.800, mae: 1.294, rse: 1.000, pcc: 0.109, kcc: 0.007
+2025-07-10 15:01:58: Epoch: 033 Test results, ap: mae: 0.611, rse: 1.142, pcc: 0.256, kcc: 0.240
+2025-07-10 15:01:59: Epoch: 034 Train Loss: 2.798, mae: 1.294, rse: 1.000, pcc: 0.109, kcc: 0.010
+2025-07-10 15:01:59: Epoch: 034 Test results, ap: mae: 0.610, rse: 1.141, pcc: 0.316, kcc: 0.225
+2025-07-10 15:02:00: Epoch: 035 Train Loss: 2.793, mae: 1.293, rse: 1.000, pcc: 0.100, kcc: 0.152
+2025-07-10 15:02:00: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:02:01: Epoch: 035 Test results, ap: mae: 0.613, rse: 1.150, pcc: -0.260, kcc: 0.052
+2025-07-10 15:02:01: Epoch: 036 Train Loss: 2.778, mae: 1.293, rse: 1.000, pcc: 0.093, kcc: 0.090
+2025-07-10 15:02:02: Epoch: 036 Test results, ap: mae: 0.612, rse: 1.149, pcc: -0.189, kcc: -0.053
+2025-07-10 15:02:03: Epoch: 037 Train Loss: 2.807, mae: 1.294, rse: 1.000, pcc: 0.110, kcc: 0.088
+2025-07-10 15:02:03: Epoch: 037 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.138, kcc: -0.079
+2025-07-10 15:02:04: Epoch: 038 Train Loss: 2.801, mae: 1.294, rse: 1.000, pcc: 0.111, kcc: 0.083
+2025-07-10 15:02:04: Epoch: 038 Test results, ap: mae: 0.611, rse: 1.143, pcc: 0.297, kcc: -0.061
+2025-07-10 15:02:05: Epoch: 039 Train Loss: 2.792, mae: 1.294, rse: 1.000, pcc: 0.110, kcc: 0.091
+2025-07-10 15:02:05: Epoch: 039 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.127, kcc: -0.050
+2025-07-10 15:02:06: Epoch: 040 Train Loss: 2.814, mae: 1.294, rse: 1.000, pcc: 0.108, kcc: 0.093
+2025-07-10 15:02:06: Epoch: 040 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.134, kcc: -0.047
+2025-07-10 15:02:07: Epoch: 041 Train Loss: 2.788, mae: 1.294, rse: 1.000, pcc: 0.109, kcc: 0.088
+2025-07-10 15:02:07: Epoch: 041 Test results, ap: mae: 0.611, rse: 1.142, pcc: -0.147, kcc: -0.089
+2025-07-10 15:02:08: Epoch: 042 Train Loss: 2.803, mae: 1.294, rse: 1.000, pcc: 0.107, kcc: 0.090
+2025-07-10 15:02:09: Epoch: 042 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.177, kcc: -0.077
+2025-07-10 15:02:09: Epoch: 043 Train Loss: 2.813, mae: 1.294, rse: 1.000, pcc: 0.107, kcc: 0.163
+2025-07-10 15:02:09: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:02:10: Epoch: 043 Test results, ap: mae: 0.610, rse: 1.139, pcc: -0.186, kcc: -0.036
+2025-07-10 15:02:11: Epoch: 044 Train Loss: 2.793, mae: 1.294, rse: 1.000, pcc: 0.107, kcc: 0.146
+2025-07-10 15:02:11: Epoch: 044 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.161, kcc: -0.042
+2025-07-10 15:02:12: Epoch: 045 Train Loss: 2.781, mae: 1.294, rse: 1.000, pcc: 0.108, kcc: 0.084
+2025-07-10 15:02:12: Epoch: 045 Test results, ap: mae: 0.611, rse: 1.144, pcc: 0.058, kcc: -0.068
+2025-07-10 15:02:13: Epoch: 046 Train Loss: 2.810, mae: 1.294, rse: 1.000, pcc: 0.108, kcc: 0.089
+2025-07-10 15:02:13: Epoch: 046 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.058, kcc: -0.068
+2025-07-10 15:02:14: Epoch: 047 Train Loss: 2.793, mae: 1.294, rse: 1.000, pcc: 0.109, kcc: 0.086
+2025-07-10 15:02:14: Epoch: 047 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.137, kcc: -0.052
+2025-07-10 15:02:15: Epoch: 048 Train Loss: 2.793, mae: 1.294, rse: 1.000, pcc: 0.110, kcc: 0.093
+2025-07-10 15:02:15: Epoch: 048 Test results, ap: mae: 0.611, rse: 1.144, pcc: -0.270, kcc: -0.078
+2025-07-10 15:02:16: Epoch: 049 Train Loss: 2.801, mae: 1.294, rse: 1.000, pcc: 0.109, kcc: 0.082
+2025-07-10 15:02:17: Epoch: 049 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.280, kcc: -0.073
+2025-07-10 15:02:18: Epoch: 050 Train Loss: 2.794, mae: 1.294, rse: 1.000, pcc: 0.109, kcc: 0.096
+2025-07-10 15:02:18: Epoch: 050 Test results, ap: mae: 0.611, rse: 1.143, pcc: -0.083, kcc: -0.069
+2025-07-10 15:02:18: used time 0.02h
+2025-07-10 15:02:18: Running Cross Validation 4
+2025-07-10 15:02:18: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:02:19: Epoch: 001 Train Loss: 2.860, mae: 1.208, rse: 1.003, pcc: 0.227, kcc: 0.136
+2025-07-10 15:02:19: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:02:19: Epoch: 001 Test results, ap: mae: 0.646, rse: 1.263, pcc: 0.123, kcc: 0.070
+2025-07-10 15:02:20: Epoch: 002 Train Loss: 2.863, mae: 1.208, rse: 1.005, pcc: 0.004, kcc: -0.026
+2025-07-10 15:02:20: Epoch: 002 Test results, ap: mae: 0.659, rse: 1.315, pcc: 0.252, kcc: 0.161
+2025-07-10 15:02:21: Epoch: 003 Train Loss: 2.877, mae: 1.208, rse: 1.003, pcc: 0.168, kcc: 0.069
+2025-07-10 15:02:21: Epoch: 003 Test results, ap: mae: 0.647, rse: 1.267, pcc: 0.377, kcc: 0.268
+2025-07-10 15:02:22: Epoch: 004 Train Loss: 2.857, mae: 1.210, rse: 1.000, pcc: 0.246, kcc: 0.137
+2025-07-10 15:02:22: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:02:22: Epoch: 004 Test results, ap: mae: 0.625, rse: 1.191, pcc: 0.003, kcc: -0.097
+2025-07-10 15:02:23: Epoch: 005 Train Loss: 2.853, mae: 1.216, rse: 1.001, pcc: 0.256, kcc: 0.129
+2025-07-10 15:02:23: Epoch: 005 Test results, ap: mae: 0.601, rse: 1.102, pcc: 0.078, kcc: -0.047
+2025-07-10 15:02:24: Epoch: 006 Train Loss: 2.870, mae: 1.216, rse: 1.000, pcc: 0.229, kcc: 0.124
+2025-07-10 15:02:25: Epoch: 006 Test results, ap: mae: 0.599, rse: 1.093, pcc: 0.077, kcc: -0.056
+2025-07-10 15:02:26: Epoch: 007 Train Loss: 2.814, mae: 1.471, rse: 1.234, pcc: 0.108, kcc: 0.084
+2025-07-10 15:02:26: Epoch: 007 Test results, ap: mae: 0.757, rse: 1.469, pcc: -0.017, kcc: -0.035
+2025-07-10 15:02:27: Epoch: 008 Train Loss: 2.874, mae: 1.246, rse: 1.019, pcc: 0.255, kcc: 0.141
+2025-07-10 15:02:27: Epoch: 008 Test results, ap: mae: 0.581, rse: 1.006, pcc: 0.112, kcc: -0.060
+2025-07-10 15:02:28: Epoch: 009 Train Loss: 2.779, mae: 1.161, rse: 0.887, pcc: 0.338, kcc: 0.136
+2025-07-10 15:02:28: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:02:28: Epoch: 009 Test results, ap: mae: 0.809, rse: 1.879, pcc: -0.028, kcc: -0.107
+2025-07-10 15:02:29: Epoch: 010 Train Loss: 2.620, mae: 1.164, rse: 0.917, pcc: 0.308, kcc: 0.210
+2025-07-10 15:02:29: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:02:29: Epoch: 010 Test results, ap: mae: 0.797, rse: 1.855, pcc: -0.119, kcc: -0.043
+2025-07-10 15:02:30: Epoch: 011 Train Loss: 2.530, mae: 1.202, rse: 0.944, pcc: 0.306, kcc: 0.218
+2025-07-10 15:02:31: Epoch: 011 Test results, ap: mae: 0.837, rse: 2.001, pcc: -0.095, kcc: -0.054
+2025-07-10 15:02:31: Epoch: 011 New best TEST metrics
+2025-07-10 15:02:31: Epoch: 012 Train Loss: 2.524, mae: 1.170, rse: 0.900, pcc: 0.324, kcc: 0.228
+2025-07-10 15:02:31: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:02:32: Epoch: 012 Test results, ap: mae: 0.786, rse: 1.802, pcc: 0.032, kcc: 0.012
+2025-07-10 15:02:32: Epoch: 012 New best TEST metrics
+2025-07-10 15:02:33: Epoch: 013 Train Loss: 2.495, mae: 1.173, rse: 0.917, pcc: 0.302, kcc: 0.219
+2025-07-10 15:02:33: Epoch: 013 Test results, ap: mae: 0.651, rse: 1.305, pcc: -0.057, kcc: -0.053
+2025-07-10 15:02:33: Epoch: 013 New best TEST metrics
+2025-07-10 15:02:34: Epoch: 014 Train Loss: 2.492, mae: 1.177, rse: 0.911, pcc: 0.314, kcc: 0.229
+2025-07-10 15:02:34: Epoch: 014 Test results, ap: mae: 0.806, rse: 1.883, pcc: 0.008, kcc: -0.004
+2025-07-10 15:02:35: Epoch: 015 Train Loss: 2.457, mae: 1.160, rse: 0.900, pcc: 0.316, kcc: 0.233
+2025-07-10 15:02:35: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:02:35: Epoch: 015 Test results, ap: mae: 0.650, rse: 1.299, pcc: -0.041, kcc: -0.028
+2025-07-10 15:02:35: Epoch: 015 New best TEST metrics
+2025-07-10 15:02:36: Epoch: 016 Train Loss: 2.425, mae: 1.162, rse: 0.896, pcc: 0.327, kcc: 0.240
+2025-07-10 15:02:36: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:02:36: Epoch: 016 Test results, ap: mae: 0.811, rse: 1.900, pcc: -0.158, kcc: -0.124
+2025-07-10 15:02:37: Epoch: 017 Train Loss: 2.437, mae: 1.153, rse: 0.899, pcc: 0.320, kcc: 0.234
+2025-07-10 15:02:37: Epoch: 017 Test results, ap: mae: 0.688, rse: 1.446, pcc: -0.099, kcc: -0.122
+2025-07-10 15:02:38: Epoch: 018 Train Loss: 2.417, mae: 1.153, rse: 0.883, pcc: 0.347, kcc: 0.255
+2025-07-10 15:02:38: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:02:38: Epoch: 018 Test results, ap: mae: 0.740, rse: 1.630, pcc: 0.006, kcc: 0.019
+2025-07-10 15:02:39: Epoch: 019 Train Loss: 2.431, mae: 1.142, rse: 0.879, pcc: 0.349, kcc: 0.248
+2025-07-10 15:02:39: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:02:40: Epoch: 019 Test results, ap: mae: 0.696, rse: 1.460, pcc: 0.066, kcc: -0.027
+2025-07-10 15:02:40: Epoch: 020 Train Loss: 2.446, mae: 1.146, rse: 0.881, pcc: 0.346, kcc: 0.247
+2025-07-10 15:02:41: Epoch: 020 Test results, ap: mae: 0.702, rse: 1.489, pcc: -0.026, kcc: -0.043
+2025-07-10 15:02:42: Epoch: 021 Train Loss: 2.389, mae: 1.147, rse: 0.886, pcc: 0.346, kcc: 0.254
+2025-07-10 15:02:42: Epoch: 021 Test results, ap: mae: 0.661, rse: 1.364, pcc: -0.004, kcc: 0.009
+2025-07-10 15:02:43: Epoch: 022 Train Loss: 2.382, mae: 1.147, rse: 0.885, pcc: 0.353, kcc: 0.259
+2025-07-10 15:02:43: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:02:43: Epoch: 022 Test results, ap: mae: 0.720, rse: 1.580, pcc: 0.000, kcc: 0.006
+2025-07-10 15:02:44: Epoch: 023 Train Loss: 2.368, mae: 1.139, rse: 0.879, pcc: 0.352, kcc: 0.259
+2025-07-10 15:02:44: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:02:44: Epoch: 023 Test results, ap: mae: 0.715, rse: 1.574, pcc: -0.011, kcc: 0.010
+2025-07-10 15:02:45: Epoch: 024 Train Loss: 2.368, mae: 1.128, rse: 0.866, pcc: 0.366, kcc: 0.262
+2025-07-10 15:02:45: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:02:45: Epoch: 024 Test results, ap: mae: 0.696, rse: 1.484, pcc: 0.004, kcc: -0.007
+2025-07-10 15:02:46: Epoch: 025 Train Loss: 2.330, mae: 1.132, rse: 0.875, pcc: 0.359, kcc: 0.261
+2025-07-10 15:02:46: Epoch: 025 Test results, ap: mae: 0.699, rse: 1.510, pcc: 0.045, kcc: 0.036
+2025-07-10 15:02:47: Epoch: 026 Train Loss: 2.336, mae: 1.137, rse: 0.879, pcc: 0.361, kcc: 0.263
+2025-07-10 15:02:47: Epoch: 026 Test results, ap: mae: 0.709, rse: 1.556, pcc: 0.085, kcc: 0.044
+2025-07-10 15:02:48: Epoch: 027 Train Loss: 2.334, mae: 1.138, rse: 0.890, pcc: 0.356, kcc: 0.262
+2025-07-10 15:02:48: Epoch: 027 Test results, ap: mae: 0.643, rse: 1.323, pcc: 0.118, kcc: 0.051
+2025-07-10 15:02:48: Epoch: 027 New best TEST metrics
+2025-07-10 15:02:49: Epoch: 028 Train Loss: 2.314, mae: 1.128, rse: 0.867, pcc: 0.370, kcc: 0.264
+2025-07-10 15:02:49: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:02:49: Epoch: 028 Test results, ap: mae: 0.684, rse: 1.470, pcc: 0.128, kcc: 0.190
+2025-07-10 15:02:50: Epoch: 029 Train Loss: 2.280, mae: 1.127, rse: 0.870, pcc: 0.369, kcc: 0.271
+2025-07-10 15:02:50: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:02:50: Epoch: 029 Test results, ap: mae: 0.670, rse: 1.423, pcc: 0.111, kcc: 0.094
+2025-07-10 15:02:51: Epoch: 030 Train Loss: 2.311, mae: 1.138, rse: 0.870, pcc: 0.380, kcc: 0.264
+2025-07-10 15:02:51: Epoch: 030 Test results, ap: mae: 0.721, rse: 1.581, pcc: 0.098, kcc: 0.222
+2025-07-10 15:02:52: Epoch: 031 Train Loss: 2.295, mae: 1.121, rse: 0.857, pcc: 0.378, kcc: 0.275
+2025-07-10 15:02:52: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:02:53: Epoch: 031 Test results, ap: mae: 0.642, rse: 1.337, pcc: 0.112, kcc: 0.151
+2025-07-10 15:02:53: Epoch: 031 New best TEST metrics
+2025-07-10 15:02:54: Epoch: 032 Train Loss: 2.238, mae: 1.123, rse: 0.857, pcc: 0.380, kcc: 0.276
+2025-07-10 15:02:54: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:02:54: Epoch: 032 Test results, ap: mae: 0.655, rse: 1.381, pcc: 0.117, kcc: 0.173
+2025-07-10 15:02:55: Epoch: 033 Train Loss: 2.218, mae: 1.123, rse: 0.860, pcc: 0.379, kcc: 0.278
+2025-07-10 15:02:55: Epoch: 033 Test results, ap: mae: 0.650, rse: 1.348, pcc: 0.108, kcc: 0.169
+2025-07-10 15:02:56: Epoch: 034 Train Loss: 2.227, mae: 1.130, rse: 0.862, pcc: 0.381, kcc: 0.276
+2025-07-10 15:02:56: Epoch: 034 Test results, ap: mae: 0.663, rse: 1.411, pcc: 0.105, kcc: 0.175
+2025-07-10 15:02:57: Epoch: 035 Train Loss: 2.192, mae: 1.126, rse: 0.861, pcc: 0.382, kcc: 0.276
+2025-07-10 15:02:57: Epoch: 035 Test results, ap: mae: 0.621, rse: 1.223, pcc: 0.110, kcc: 0.154
+2025-07-10 15:02:57: Epoch: 035 New best TEST metrics
+2025-07-10 15:02:58: Epoch: 036 Train Loss: 2.176, mae: 1.129, rse: 0.878, pcc: 0.366, kcc: 0.277
+2025-07-10 15:02:58: Epoch: 036 Test results, ap: mae: 0.657, rse: 1.351, pcc: 0.099, kcc: 0.157
+2025-07-10 15:02:58: Epoch: 037 Train Loss: 2.183, mae: 1.136, rse: 0.871, pcc: 0.375, kcc: 0.275
+2025-07-10 15:02:59: Epoch: 037 Test results, ap: mae: 0.654, rse: 1.319, pcc: 0.083, kcc: 0.092
+2025-07-10 15:03:00: Epoch: 038 Train Loss: 2.176, mae: 1.119, rse: 0.863, pcc: 0.374, kcc: 0.278
+2025-07-10 15:03:00: Epoch: 038 Test results, ap: mae: 0.656, rse: 1.363, pcc: 0.096, kcc: 0.144
+2025-07-10 15:03:01: Epoch: 039 Train Loss: 2.162, mae: 1.126, rse: 0.860, pcc: 0.388, kcc: 0.280
+2025-07-10 15:03:01: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:03:01: Epoch: 039 Test results, ap: mae: 0.710, rse: 1.547, pcc: 0.095, kcc: 0.154
+2025-07-10 15:03:02: Epoch: 040 Train Loss: 2.116, mae: 1.123, rse: 0.876, pcc: 0.365, kcc: 0.272
+2025-07-10 15:03:02: Epoch: 040 Test results, ap: mae: 0.654, rse: 1.359, pcc: 0.083, kcc: 0.118
+2025-07-10 15:03:03: Epoch: 041 Train Loss: 2.110, mae: 1.120, rse: 0.866, pcc: 0.382, kcc: 0.281
+2025-07-10 15:03:03: Epoch: 041 Test results, ap: mae: 0.666, rse: 1.407, pcc: 0.091, kcc: 0.124
+2025-07-10 15:03:05: Epoch: 042 Train Loss: 2.118, mae: 1.116, rse: 0.857, pcc: 0.387, kcc: 0.281
+2025-07-10 15:03:05: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:03:05: Epoch: 042 Test results, ap: mae: 0.664, rse: 1.416, pcc: 0.094, kcc: 0.148
+2025-07-10 15:03:06: Epoch: 043 Train Loss: 2.103, mae: 1.111, rse: 0.852, pcc: 0.392, kcc: 0.288
+2025-07-10 15:03:06: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:03:06: Epoch: 043 Test results, ap: mae: 0.672, rse: 1.449, pcc: 0.091, kcc: 0.129
+2025-07-10 15:03:07: Epoch: 044 Train Loss: 2.113, mae: 1.115, rse: 0.865, pcc: 0.378, kcc: 0.281
+2025-07-10 15:03:07: Epoch: 044 Test results, ap: mae: 0.669, rse: 1.443, pcc: 0.092, kcc: 0.135
+2025-07-10 15:03:08: Epoch: 045 Train Loss: 2.101, mae: 1.110, rse: 0.855, pcc: 0.389, kcc: 0.285
+2025-07-10 15:03:09: Epoch: 045 Test results, ap: mae: 0.679, rse: 1.482, pcc: 0.095, kcc: 0.145
+2025-07-10 15:03:10: Epoch: 046 Train Loss: 2.074, mae: 1.114, rse: 0.860, pcc: 0.388, kcc: 0.285
+2025-07-10 15:03:10: Epoch: 046 Test results, ap: mae: 0.681, rse: 1.486, pcc: 0.099, kcc: 0.143
+2025-07-10 15:03:11: Epoch: 047 Train Loss: 2.069, mae: 1.110, rse: 0.853, pcc: 0.396, kcc: 0.289
+2025-07-10 15:03:11: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:03:11: Epoch: 047 Test results, ap: mae: 0.683, rse: 1.509, pcc: 0.103, kcc: 0.162
+2025-07-10 15:03:12: Epoch: 048 Train Loss: 2.063, mae: 1.111, rse: 0.848, pcc: 0.398, kcc: 0.287
+2025-07-10 15:03:12: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:03:12: Epoch: 048 Test results, ap: mae: 0.693, rse: 1.531, pcc: 0.091, kcc: 0.162
+2025-07-10 15:03:13: Epoch: 049 Train Loss: 2.090, mae: 1.111, rse: 0.849, pcc: 0.400, kcc: 0.288
+2025-07-10 15:03:13: Epoch: 049 New best VALIDATION metrics
+2025-07-10 15:03:13: Epoch: 049 Test results, ap: mae: 0.692, rse: 1.538, pcc: 0.107, kcc: 0.174
+2025-07-10 15:03:14: Epoch: 050 Train Loss: 2.079, mae: 1.108, rse: 0.850, pcc: 0.399, kcc: 0.288
+2025-07-10 15:03:14: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:03:15: Epoch: 050 Test results, ap: mae: 0.683, rse: 1.508, pcc: 0.109, kcc: 0.161
+2025-07-10 15:03:15: used time 0.02h
+2025-07-10 15:03:15: Cross Validation Finished!
+2025-07-10 15:03:15: Best validation perform list
+[[1.20938194 1.0001446  0.31124902 0.22503898]
+ [1.25065482 1.01368558 0.28266528 0.183391  ]
+ [1.06004715 0.74912304 0.51368999 0.33599359]
+ [1.29393899 1.00008416 0.10695691 0.16276938]
+ [1.10776198 0.85018516 0.39909571 0.28835642]]
+2025-07-10 15:03:15: mean: [1.184 0.923 0.323 0.239]
+2025-07-10 15:03:15: std: [0.088 0.106 0.135 0.065]
+2025-07-10 15:03:15: Best test perform list
+[[0.60839051 1.13326228 0.09464387 0.08101329]
+ [0.60765427 1.13029397 0.294126   0.17808697]
+ [0.64084625 1.17660511 0.07195061 0.01491764]
+ [0.61106843 1.14383852 0.38574123 0.23145686]
+ [0.62101609 1.22319472 0.10981089 0.15438028]]
+2025-07-10 15:03:15: mean: [0.618 1.161 0.191 0.132]
+2025-07-10 15:03:15: std: [0.012 0.035 0.125 0.076]
diff --git a/run-reg/mamba-att-256-smoothl1-32-0.001-35/config.json b/run-reg/mamba-att-256-smoothl1-32-0.001-35/config.json
new file mode 100644
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mamba", "side_enc": null, "channels": 256, "fusion": "att", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 35, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "smoothl1", "dir": false, "classes": 1}
\ No newline at end of file
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diff --git a/run-reg/mamba-att-256-smoothl1-32-0.001-35/result.txt b/run-reg/mamba-att-256-smoothl1-32-0.001-35/result.txt
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@@ -0,0 +1,6 @@
+Valid
+1.2945796012878419,0.9056097507476807,0.33518263697624207,0.24723032414913176
+0.10794476175972864,0.08375891151736639,0.10413622885565034,0.057321937456024444
+Test
+0.6140984773635865,1.150085997581482,0.23133993670344352,0.1585920572280884
+0.012798074337887978,0.05057357677994779,0.13689703680374588,0.11266817362806701
diff --git a/run-reg/mamba-att-256-smoothl1-32-0.001-35/training.log b/run-reg/mamba-att-256-smoothl1-32-0.001-35/training.log
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@@ -0,0 +1,488 @@
+2025-07-09 14:59:19: saving_dir: ./run-reg/mamba-att-256-smoothl1-32-0.001-35
+2025-07-09 14:59:19: Loading Train Dataset
+2025-07-09 14:59:20: Loading Test Dataset
+2025-07-09 14:59:21: Running Cross Validation 0
+2025-07-09 14:59:21: Fold 0  Train set:1888, Valid set:473, Test set: 108
+2025-07-09 14:59:24: Epoch: 001 Train Loss: 0.971, mae: 1.365, rse: 1.000, pcc: 0.113, kcc: 0.059
+2025-07-09 14:59:24: Epoch: 001 New best VALIDATION metrics
+2025-07-09 14:59:24: Epoch: 001 Test results, ap: mae: 0.588, rse: 1.048, pcc: 0.242, kcc: 0.209
+2025-07-09 14:59:27: Epoch: 002 Train Loss: 0.973, mae: 1.359, rse: 1.000, pcc: 0.040, kcc: 0.081
+2025-07-09 14:59:27: Epoch: 002 Test results, ap: mae: 0.599, rse: 1.094, pcc: -0.272, kcc: -0.220
+2025-07-09 14:59:29: Epoch: 003 Train Loss: 0.975, mae: 1.353, rse: 1.008, pcc: -0.087, kcc: -0.147
+2025-07-09 14:59:30: Epoch: 003 Test results, ap: mae: 0.634, rse: 1.222, pcc: -0.016, kcc: -0.013
+2025-07-09 14:59:32: Epoch: 004 Train Loss: 0.972, mae: 1.353, rse: 1.010, pcc: 0.070, kcc: 0.126
+2025-07-09 14:59:32: Epoch: 004 New best VALIDATION metrics
+2025-07-09 14:59:33: Epoch: 004 Test results, ap: mae: 0.640, rse: 1.242, pcc: -0.238, kcc: -0.230
+2025-07-09 14:59:35: Epoch: 005 Train Loss: 0.971, mae: 1.353, rse: 1.009, pcc: -0.071, kcc: -0.130
+2025-07-09 14:59:35: Epoch: 005 Test results, ap: mae: 0.636, rse: 1.227, pcc: 0.158, kcc: 0.138
+2025-07-09 14:59:38: Epoch: 006 Train Loss: 0.971, mae: 1.354, rse: 1.005, pcc: -0.144, kcc: -0.202
+2025-07-09 14:59:38: Epoch: 006 Test results, ap: mae: 0.620, rse: 1.176, pcc: 0.140, kcc: 0.119
+2025-07-09 14:59:41: Epoch: 007 Train Loss: 0.971, mae: 1.353, rse: 1.008, pcc: -0.149, kcc: -0.155
+2025-07-09 14:59:41: Epoch: 007 Test results, ap: mae: 0.635, rse: 1.223, pcc: 0.336, kcc: 0.197
+2025-07-09 14:59:44: Epoch: 008 Train Loss: 0.971, mae: 1.354, rse: 1.006, pcc: 0.146, kcc: 0.125
+2025-07-09 14:59:44: Epoch: 008 New best VALIDATION metrics
+2025-07-09 14:59:44: Epoch: 008 Test results, ap: mae: 0.624, rse: 1.190, pcc: -0.025, kcc: -0.020
+2025-07-09 14:59:46: Epoch: 009 Train Loss: 0.969, mae: 1.353, rse: 1.009, pcc: 0.222, kcc: 0.167
+2025-07-09 14:59:46: Epoch: 009 New best VALIDATION metrics
+2025-07-09 14:59:47: Epoch: 009 Test results, ap: mae: 0.637, rse: 1.232, pcc: -0.047, kcc: -0.085
+2025-07-09 14:59:49: Epoch: 010 Train Loss: 0.970, mae: 1.354, rse: 1.007, pcc: 0.169, kcc: 0.122
+2025-07-09 14:59:50: Epoch: 010 Test results, ap: mae: 0.628, rse: 1.200, pcc: 0.248, kcc: 0.141
+2025-07-09 14:59:52: Epoch: 011 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.215, kcc: 0.158
+2025-07-09 14:59:52: Epoch: 011 Test results, ap: mae: 0.625, rse: 1.193, pcc: 0.034, kcc: 0.032
+2025-07-09 14:59:52: Epoch: 011 New best TEST metrics
+2025-07-09 14:59:55: Epoch: 012 Train Loss: 0.970, mae: 1.354, rse: 1.006, pcc: 0.222, kcc: 0.158
+2025-07-09 14:59:55: Epoch: 012 Test results, ap: mae: 0.626, rse: 1.196, pcc: 0.093, kcc: 0.040
+2025-07-09 14:59:55: Epoch: 012 New best TEST metrics
+2025-07-09 14:59:58: Epoch: 013 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.215, kcc: 0.205
+2025-07-09 14:59:58: Epoch: 013 New best VALIDATION metrics
+2025-07-09 14:59:58: Epoch: 013 Test results, ap: mae: 0.623, rse: 1.186, pcc: 0.077, kcc: -0.019
+2025-07-09 15:00:01: Epoch: 014 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.224, kcc: 0.140
+2025-07-09 15:00:01: Epoch: 014 Test results, ap: mae: 0.625, rse: 1.191, pcc: 0.342, kcc: 0.233
+2025-07-09 15:00:01: Epoch: 014 New best TEST metrics
+2025-07-09 15:00:04: Epoch: 015 Train Loss: 0.969, mae: 1.353, rse: 1.007, pcc: 0.222, kcc: 0.198
+2025-07-09 15:00:04: Epoch: 015 Test results, ap: mae: 0.628, rse: 1.203, pcc: 0.195, kcc: 0.090
+2025-07-09 15:00:06: Epoch: 016 Train Loss: 0.970, mae: 1.354, rse: 1.005, pcc: 0.213, kcc: 0.201
+2025-07-09 15:00:07: Epoch: 016 Test results, ap: mae: 0.622, rse: 1.181, pcc: 0.155, kcc: 0.040
+2025-07-09 15:00:09: Epoch: 017 Train Loss: 0.970, mae: 1.354, rse: 1.006, pcc: 0.172, kcc: 0.191
+2025-07-09 15:00:09: Epoch: 017 Test results, ap: mae: 0.627, rse: 1.197, pcc: 0.053, kcc: -0.014
+2025-07-09 15:00:12: Epoch: 018 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.216, kcc: 0.205
+2025-07-09 15:00:12: Epoch: 018 New best VALIDATION metrics
+2025-07-09 15:00:12: Epoch: 018 Test results, ap: mae: 0.624, rse: 1.189, pcc: 0.145, kcc: 0.058
+2025-07-09 15:00:15: Epoch: 019 Train Loss: 0.970, mae: 1.354, rse: 1.006, pcc: -0.228, kcc: -0.165
+2025-07-09 15:00:15: Epoch: 019 Test results, ap: mae: 0.627, rse: 1.198, pcc: -0.284, kcc: -0.231
+2025-07-09 15:00:18: Epoch: 020 Train Loss: 0.969, mae: 1.354, rse: 1.005, pcc: 0.141, kcc: 0.117
+2025-07-09 15:00:18: Epoch: 020 Test results, ap: mae: 0.620, rse: 1.176, pcc: 0.403, kcc: 0.266
+2025-07-09 15:00:18: Epoch: 020 New best TEST metrics
+2025-07-09 15:00:21: Epoch: 021 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: -0.118, kcc: -0.143
+2025-07-09 15:00:21: Epoch: 021 Test results, ap: mae: 0.626, rse: 1.194, pcc: 0.147, kcc: 0.107
+2025-07-09 15:00:23: Epoch: 022 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.121, kcc: 0.073
+2025-07-09 15:00:24: Epoch: 022 Test results, ap: mae: 0.625, rse: 1.191, pcc: 0.375, kcc: 0.271
+2025-07-09 15:00:26: Epoch: 023 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.153, kcc: 0.116
+2025-07-09 15:00:27: Epoch: 023 Test results, ap: mae: 0.625, rse: 1.193, pcc: 0.366, kcc: 0.273
+2025-07-09 15:00:29: Epoch: 024 Train Loss: 0.969, mae: 1.354, rse: 1.005, pcc: 0.231, kcc: 0.162
+2025-07-09 15:00:29: Epoch: 024 Test results, ap: mae: 0.622, rse: 1.181, pcc: 0.360, kcc: 0.210
+2025-07-09 15:00:32: Epoch: 025 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.232, kcc: 0.177
+2025-07-09 15:00:32: Epoch: 025 Test results, ap: mae: 0.625, rse: 1.192, pcc: 0.299, kcc: 0.169
+2025-07-09 15:00:35: Epoch: 026 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.235, kcc: 0.184
+2025-07-09 15:00:35: Epoch: 026 Test results, ap: mae: 0.624, rse: 1.188, pcc: 0.294, kcc: 0.168
+2025-07-09 15:00:38: Epoch: 027 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.235, kcc: 0.178
+2025-07-09 15:00:38: Epoch: 027 Test results, ap: mae: 0.623, rse: 1.186, pcc: 0.290, kcc: 0.168
+2025-07-09 15:00:40: Epoch: 028 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.234, kcc: 0.176
+2025-07-09 15:00:41: Epoch: 028 Test results, ap: mae: 0.623, rse: 1.186, pcc: 0.272, kcc: 0.159
+2025-07-09 15:00:43: Epoch: 029 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.236, kcc: 0.190
+2025-07-09 15:00:43: Epoch: 029 New best VALIDATION metrics
+2025-07-09 15:00:44: Epoch: 029 Test results, ap: mae: 0.623, rse: 1.187, pcc: 0.263, kcc: 0.163
+2025-07-09 15:00:46: Epoch: 030 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.239, kcc: 0.200
+2025-07-09 15:00:46: Epoch: 030 New best VALIDATION metrics
+2025-07-09 15:00:46: Epoch: 030 Test results, ap: mae: 0.624, rse: 1.188, pcc: 0.251, kcc: 0.161
+2025-07-09 15:00:49: Epoch: 031 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.241, kcc: 0.200
+2025-07-09 15:00:49: Epoch: 031 New best VALIDATION metrics
+2025-07-09 15:00:49: Epoch: 031 Test results, ap: mae: 0.623, rse: 1.186, pcc: 0.253, kcc: 0.159
+2025-07-09 15:00:52: Epoch: 032 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.243, kcc: 0.204
+2025-07-09 15:00:52: Epoch: 032 New best VALIDATION metrics
+2025-07-09 15:00:52: Epoch: 032 Test results, ap: mae: 0.625, rse: 1.190, pcc: 0.258, kcc: 0.159
+2025-07-09 15:00:55: Epoch: 033 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.247, kcc: 0.207
+2025-07-09 15:00:55: Epoch: 033 New best VALIDATION metrics
+2025-07-09 15:00:55: Epoch: 033 Test results, ap: mae: 0.624, rse: 1.190, pcc: 0.250, kcc: 0.156
+2025-07-09 15:00:58: Epoch: 034 Train Loss: 0.969, mae: 1.354, rse: 1.006, pcc: 0.250, kcc: 0.208
+2025-07-09 15:00:58: Epoch: 034 New best VALIDATION metrics
+2025-07-09 15:00:58: Epoch: 034 Test results, ap: mae: 0.623, rse: 1.187, pcc: 0.253, kcc: 0.154
+2025-07-09 15:01:01: Epoch: 035 Train Loss: 0.969, mae: 1.354, rse: 1.004, pcc: 0.255, kcc: 0.209
+2025-07-09 15:01:01: Epoch: 035 New best VALIDATION metrics
+2025-07-09 15:01:01: Epoch: 035 Test results, ap: mae: 0.619, rse: 1.172, pcc: 0.260, kcc: 0.156
+2025-07-09 15:01:01: used time 0.03h
+2025-07-09 15:01:01: Running Cross Validation 1
+2025-07-09 15:01:01: Fold 1  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:01:03: Epoch: 001 Train Loss: 0.976, mae: 1.339, rse: 1.000, pcc: 0.158, kcc: 0.098
+2025-07-09 15:01:03: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:01:04: Epoch: 001 Test results, ap: mae: 0.622, rse: 1.182, pcc: -0.149, kcc: -0.253
+2025-07-09 15:01:06: Epoch: 002 Train Loss: 0.978, mae: 1.337, rse: 1.000, pcc: 0.168, kcc: 0.102
+2025-07-09 15:01:06: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:01:07: Epoch: 002 Test results, ap: mae: 0.641, rse: 1.244, pcc: 0.302, kcc: 0.113
+2025-07-09 15:01:09: Epoch: 003 Train Loss: 0.974, mae: 1.339, rse: 1.000, pcc: 0.273, kcc: 0.199
+2025-07-09 15:01:09: Epoch: 003 New best VALIDATION metrics
+2025-07-09 15:01:09: Epoch: 003 Test results, ap: mae: 0.626, rse: 1.195, pcc: 0.077, kcc: 0.079
+2025-07-09 15:01:12: Epoch: 004 Train Loss: 0.973, mae: 1.338, rse: 1.000, pcc: 0.080, kcc: 0.042
+2025-07-09 15:01:12: Epoch: 004 Test results, ap: mae: 0.634, rse: 1.221, pcc: 0.210, kcc: 0.114
+2025-07-09 15:01:15: Epoch: 005 Train Loss: 0.974, mae: 1.337, rse: 1.001, pcc: -0.084, kcc: -0.104
+2025-07-09 15:01:15: Epoch: 005 Test results, ap: mae: 0.644, rse: 1.256, pcc: 0.026, kcc: 0.180
+2025-07-09 15:01:18: Epoch: 006 Train Loss: 0.974, mae: 1.337, rse: 1.000, pcc: 0.014, kcc: 0.099
+2025-07-09 15:01:18: Epoch: 006 Test results, ap: mae: 0.636, rse: 1.230, pcc: 0.076, kcc: -0.013
+2025-07-09 15:01:21: Epoch: 007 Train Loss: 0.973, mae: 1.337, rse: 1.000, pcc: 0.223, kcc: 0.180
+2025-07-09 15:01:21: Epoch: 007 Test results, ap: mae: 0.638, rse: 1.235, pcc: 0.071, kcc: 0.061
+2025-07-09 15:01:23: Epoch: 008 Train Loss: 0.973, mae: 1.338, rse: 1.000, pcc: -0.069, kcc: -0.110
+2025-07-09 15:01:24: Epoch: 008 Test results, ap: mae: 0.636, rse: 1.227, pcc: 0.152, kcc: 0.151
+2025-07-09 15:01:26: Epoch: 009 Train Loss: 0.972, mae: 1.340, rse: 1.000, pcc: -0.262, kcc: -0.179
+2025-07-09 15:01:27: Epoch: 009 Test results, ap: mae: 0.620, rse: 1.177, pcc: -0.235, kcc: -0.089
+2025-07-09 15:01:29: Epoch: 010 Train Loss: 0.972, mae: 1.338, rse: 1.000, pcc: 0.130, kcc: 0.077
+2025-07-09 15:01:29: Epoch: 010 Test results, ap: mae: 0.636, rse: 1.228, pcc: 0.169, kcc: 0.234
+2025-07-09 15:01:32: Epoch: 011 Train Loss: 0.972, mae: 1.339, rse: 1.000, pcc: 0.281, kcc: 0.195
+2025-07-09 15:01:32: Epoch: 011 New best VALIDATION metrics
+2025-07-09 15:01:32: Epoch: 011 Test results, ap: mae: 0.624, rse: 1.187, pcc: 0.234, kcc: 0.103
+2025-07-09 15:01:32: Epoch: 011 New best TEST metrics
+2025-07-09 15:01:35: Epoch: 012 Train Loss: 0.972, mae: 1.340, rse: 1.000, pcc: 0.254, kcc: 0.178
+2025-07-09 15:01:35: Epoch: 012 Test results, ap: mae: 0.621, rse: 1.180, pcc: 0.198, kcc: 0.069
+2025-07-09 15:01:38: Epoch: 013 Train Loss: 0.971, mae: 1.338, rse: 1.000, pcc: -0.255, kcc: -0.158
+2025-07-09 15:01:38: Epoch: 013 Test results, ap: mae: 0.628, rse: 1.203, pcc: -0.251, kcc: -0.059
+2025-07-09 15:01:41: Epoch: 014 Train Loss: 0.972, mae: 1.339, rse: 1.000, pcc: 0.043, kcc: 0.079
+2025-07-09 15:01:41: Epoch: 014 Test results, ap: mae: 0.624, rse: 1.190, pcc: -0.028, kcc: -0.050
+2025-07-09 15:01:43: Epoch: 015 Train Loss: 0.972, mae: 1.339, rse: 1.000, pcc: 0.305, kcc: 0.210
+2025-07-09 15:01:43: Epoch: 015 New best VALIDATION metrics
+2025-07-09 15:01:44: Epoch: 015 Test results, ap: mae: 0.621, rse: 1.180, pcc: 0.194, kcc: 0.148
+2025-07-09 15:01:44: Epoch: 015 New best TEST metrics
+2025-07-09 15:01:46: Epoch: 016 Train Loss: 0.972, mae: 1.340, rse: 1.000, pcc: 0.301, kcc: 0.210
+2025-07-09 15:01:47: Epoch: 016 Test results, ap: mae: 0.619, rse: 1.172, pcc: 0.176, kcc: 0.132
+2025-07-09 15:01:49: Epoch: 017 Train Loss: 0.972, mae: 1.339, rse: 1.000, pcc: 0.306, kcc: 0.210
+2025-07-09 15:01:49: Epoch: 017 New best VALIDATION metrics
+2025-07-09 15:01:49: Epoch: 017 Test results, ap: mae: 0.623, rse: 1.184, pcc: 0.156, kcc: 0.113
+2025-07-09 15:01:52: Epoch: 018 Train Loss: 0.972, mae: 1.339, rse: 1.000, pcc: 0.298, kcc: 0.201
+2025-07-09 15:01:52: Epoch: 018 Test results, ap: mae: 0.623, rse: 1.184, pcc: 0.230, kcc: 0.103
+2025-07-09 15:01:55: Epoch: 019 Train Loss: 0.972, mae: 1.340, rse: 1.000, pcc: 0.303, kcc: 0.214
+2025-07-09 15:01:55: Epoch: 019 New best VALIDATION metrics
+2025-07-09 15:01:55: Epoch: 019 Test results, ap: mae: 0.621, rse: 1.180, pcc: 0.230, kcc: 0.149
+2025-07-09 15:01:55: Epoch: 019 New best TEST metrics
+2025-07-09 15:01:58: Epoch: 020 Train Loss: 0.972, mae: 1.340, rse: 1.000, pcc: 0.310, kcc: 0.217
+2025-07-09 15:01:58: Epoch: 020 New best VALIDATION metrics
+2025-07-09 15:01:58: Epoch: 020 Test results, ap: mae: 0.620, rse: 1.177, pcc: 0.185, kcc: 0.145
+2025-07-09 15:02:01: Epoch: 021 Train Loss: 0.972, mae: 1.339, rse: 1.000, pcc: 0.311, kcc: 0.218
+2025-07-09 15:02:01: Epoch: 021 New best VALIDATION metrics
+2025-07-09 15:02:01: Epoch: 021 Test results, ap: mae: 0.623, rse: 1.185, pcc: 0.182, kcc: 0.145
+2025-07-09 15:02:03: Epoch: 022 Train Loss: 0.972, mae: 1.340, rse: 1.000, pcc: 0.309, kcc: 0.214
+2025-07-09 15:02:04: Epoch: 022 Test results, ap: mae: 0.620, rse: 1.177, pcc: 0.197, kcc: 0.146
+2025-07-09 15:02:06: Epoch: 023 Train Loss: 0.972, mae: 1.339, rse: 1.000, pcc: 0.311, kcc: 0.213
+2025-07-09 15:02:07: Epoch: 023 Test results, ap: mae: 0.621, rse: 1.179, pcc: 0.218, kcc: 0.151
+2025-07-09 15:02:09: Epoch: 024 Train Loss: 0.969, mae: 1.293, rse: 0.946, pcc: 0.258, kcc: 0.204
+2025-07-09 15:02:09: Epoch: 024 New best VALIDATION metrics
+2025-07-09 15:02:10: Epoch: 024 Test results, ap: mae: 0.764, rse: 1.706, pcc: 0.178, kcc: 0.087
+2025-07-09 15:02:12: Epoch: 025 Train Loss: 0.939, mae: 1.281, rse: 0.925, pcc: 0.319, kcc: 0.234
+2025-07-09 15:02:12: Epoch: 025 New best VALIDATION metrics
+2025-07-09 15:02:12: Epoch: 025 Test results, ap: mae: 0.725, rse: 1.553, pcc: -0.049, kcc: -0.050
+2025-07-09 15:02:15: Epoch: 026 Train Loss: 0.912, mae: 1.265, rse: 0.897, pcc: 0.328, kcc: 0.242
+2025-07-09 15:02:15: Epoch: 026 New best VALIDATION metrics
+2025-07-09 15:02:15: Epoch: 026 Test results, ap: mae: 0.842, rse: 2.009, pcc: -0.132, kcc: -0.072
+2025-07-09 15:02:18: Epoch: 027 Train Loss: 0.892, mae: 1.266, rse: 0.903, pcc: 0.338, kcc: 0.251
+2025-07-09 15:02:18: Epoch: 027 New best VALIDATION metrics
+2025-07-09 15:02:18: Epoch: 027 Test results, ap: mae: 1.017, rse: 2.688, pcc: -0.117, kcc: -0.080
+2025-07-09 15:02:21: Epoch: 028 Train Loss: 0.890, mae: 1.256, rse: 0.897, pcc: 0.340, kcc: 0.253
+2025-07-09 15:02:21: Epoch: 028 New best VALIDATION metrics
+2025-07-09 15:02:21: Epoch: 028 Test results, ap: mae: 0.763, rse: 1.706, pcc: -0.102, kcc: -0.029
+2025-07-09 15:02:24: Epoch: 029 Train Loss: 0.882, mae: 1.290, rse: 0.917, pcc: 0.343, kcc: 0.261
+2025-07-09 15:02:24: Epoch: 029 Test results, ap: mae: 1.119, rse: 3.116, pcc: -0.235, kcc: -0.163
+2025-07-09 15:02:26: Epoch: 030 Train Loss: 0.875, mae: 1.245, rse: 0.888, pcc: 0.351, kcc: 0.259
+2025-07-09 15:02:26: Epoch: 030 New best VALIDATION metrics
+2025-07-09 15:02:27: Epoch: 030 Test results, ap: mae: 0.846, rse: 2.035, pcc: -0.168, kcc: -0.086
+2025-07-09 15:02:29: Epoch: 031 Train Loss: 0.863, mae: 1.244, rse: 0.883, pcc: 0.355, kcc: 0.260
+2025-07-09 15:02:29: Epoch: 031 New best VALIDATION metrics
+2025-07-09 15:02:30: Epoch: 031 Test results, ap: mae: 0.825, rse: 1.954, pcc: -0.152, kcc: -0.107
+2025-07-09 15:02:32: Epoch: 032 Train Loss: 0.853, mae: 1.236, rse: 0.873, pcc: 0.359, kcc: 0.262
+2025-07-09 15:02:32: Epoch: 032 New best VALIDATION metrics
+2025-07-09 15:02:32: Epoch: 032 Test results, ap: mae: 0.727, rse: 1.573, pcc: -0.089, kcc: -0.047
+2025-07-09 15:02:35: Epoch: 033 Train Loss: 0.855, mae: 1.238, rse: 0.876, pcc: 0.362, kcc: 0.258
+2025-07-09 15:02:35: Epoch: 033 Test results, ap: mae: 0.666, rse: 1.355, pcc: -0.029, kcc: 0.007
+2025-07-09 15:02:38: Epoch: 034 Train Loss: 0.849, mae: 1.244, rse: 0.873, pcc: 0.359, kcc: 0.262
+2025-07-09 15:02:38: Epoch: 034 Test results, ap: mae: 0.725, rse: 1.563, pcc: 0.047, kcc: 0.032
+2025-07-09 15:02:41: Epoch: 035 Train Loss: 0.839, mae: 1.242, rse: 0.881, pcc: 0.374, kcc: 0.264
+2025-07-09 15:02:41: Epoch: 035 New best VALIDATION metrics
+2025-07-09 15:02:41: Epoch: 035 Test results, ap: mae: 0.669, rse: 1.363, pcc: 0.054, kcc: 0.037
+2025-07-09 15:02:41: used time 0.03h
+2025-07-09 15:02:41: Running Cross Validation 2
+2025-07-09 15:02:41: Fold 2  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:02:44: Epoch: 001 Train Loss: 0.944, mae: 1.475, rse: 1.001, pcc: 0.004, kcc: 0.017
+2025-07-09 15:02:44: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:02:44: Epoch: 001 Test results, ap: mae: 0.615, rse: 1.159, pcc: 0.237, kcc: 0.246
+2025-07-09 15:02:47: Epoch: 002 Train Loss: 0.943, mae: 1.475, rse: 1.000, pcc: 0.178, kcc: 0.160
+2025-07-09 15:02:47: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:02:47: Epoch: 002 Test results, ap: mae: 0.614, rse: 1.155, pcc: 0.143, kcc: 0.127
+2025-07-09 15:02:49: Epoch: 003 Train Loss: 0.944, mae: 1.470, rse: 1.006, pcc: 0.055, kcc: 0.096
+2025-07-09 15:02:50: Epoch: 003 Test results, ap: mae: 0.655, rse: 1.297, pcc: -0.000, kcc: -0.020
+2025-07-09 15:02:52: Epoch: 004 Train Loss: 0.943, mae: 1.472, rse: 1.003, pcc: -0.211, kcc: -0.158
+2025-07-09 15:02:53: Epoch: 004 Test results, ap: mae: 0.637, rse: 1.233, pcc: -0.001, kcc: 0.116
+2025-07-09 15:02:55: Epoch: 005 Train Loss: 0.946, mae: 1.470, rse: 1.006, pcc: -0.053, kcc: -0.025
+2025-07-09 15:02:55: Epoch: 005 Test results, ap: mae: 0.655, rse: 1.297, pcc: -0.190, kcc: -0.136
+2025-07-09 15:02:58: Epoch: 006 Train Loss: 0.942, mae: 1.473, rse: 1.001, pcc: -0.036, kcc: -0.008
+2025-07-09 15:02:58: Epoch: 006 Test results, ap: mae: 0.624, rse: 1.189, pcc: 0.048, kcc: -0.012
+2025-07-09 15:03:01: Epoch: 007 Train Loss: 0.944, mae: 1.471, rse: 1.005, pcc: 0.039, kcc: 0.104
+2025-07-09 15:03:01: Epoch: 007 Test results, ap: mae: 0.647, rse: 1.268, pcc: 0.083, kcc: 0.014
+2025-07-09 15:03:04: Epoch: 008 Train Loss: 0.942, mae: 1.474, rse: 1.001, pcc: 0.068, kcc: 0.068
+2025-07-09 15:03:04: Epoch: 008 Test results, ap: mae: 0.618, rse: 1.170, pcc: 0.070, kcc: 0.019
+2025-07-09 15:03:07: Epoch: 009 Train Loss: 0.942, mae: 1.474, rse: 1.001, pcc: -0.169, kcc: -0.191
+2025-07-09 15:03:07: Epoch: 009 Test results, ap: mae: 0.621, rse: 1.180, pcc: -0.160, kcc: -0.066
+2025-07-09 15:03:09: Epoch: 010 Train Loss: 0.943, mae: 1.474, rse: 1.001, pcc: 0.137, kcc: 0.174
+2025-07-09 15:03:10: Epoch: 010 Test results, ap: mae: 0.618, rse: 1.169, pcc: 0.128, kcc: 0.069
+2025-07-09 15:03:12: Epoch: 011 Train Loss: 0.939, mae: 1.470, rse: 1.006, pcc: 0.084, kcc: 0.130
+2025-07-09 15:03:13: Epoch: 011 Test results, ap: mae: 0.654, rse: 1.294, pcc: 0.011, kcc: -0.023
+2025-07-09 15:03:13: Epoch: 011 New best TEST metrics
+2025-07-09 15:03:15: Epoch: 012 Train Loss: 0.941, mae: 1.473, rse: 1.001, pcc: -0.084, kcc: -0.143
+2025-07-09 15:03:15: Epoch: 012 Test results, ap: mae: 0.624, rse: 1.188, pcc: -0.111, kcc: -0.123
+2025-07-09 15:03:18: Epoch: 013 Train Loss: 0.940, mae: 1.473, rse: 1.002, pcc: 0.089, kcc: 0.117
+2025-07-09 15:03:18: Epoch: 013 Test results, ap: mae: 0.628, rse: 1.202, pcc: 0.094, kcc: 0.111
+2025-07-09 15:03:18: Epoch: 013 New best TEST metrics
+2025-07-09 15:03:21: Epoch: 014 Train Loss: 0.942, mae: 1.473, rse: 1.002, pcc: 0.104, kcc: 0.173
+2025-07-09 15:03:21: Epoch: 014 Test results, ap: mae: 0.627, rse: 1.197, pcc: 0.197, kcc: 0.133
+2025-07-09 15:03:21: Epoch: 014 New best TEST metrics
+2025-07-09 15:03:24: Epoch: 015 Train Loss: 0.941, mae: 1.473, rse: 1.002, pcc: -0.109, kcc: -0.171
+2025-07-09 15:03:24: Epoch: 015 Test results, ap: mae: 0.629, rse: 1.204, pcc: -0.179, kcc: -0.132
+2025-07-09 15:03:27: Epoch: 016 Train Loss: 0.941, mae: 1.474, rse: 1.001, pcc: 0.098, kcc: 0.174
+2025-07-09 15:03:27: Epoch: 016 Test results, ap: mae: 0.622, rse: 1.183, pcc: 0.218, kcc: 0.144
+2025-07-09 15:03:27: Epoch: 016 New best TEST metrics
+2025-07-09 15:03:30: Epoch: 017 Train Loss: 0.941, mae: 1.473, rse: 1.001, pcc: 0.101, kcc: 0.173
+2025-07-09 15:03:30: Epoch: 017 Test results, ap: mae: 0.626, rse: 1.194, pcc: 0.232, kcc: 0.149
+2025-07-09 15:03:30: Epoch: 017 New best TEST metrics
+2025-07-09 15:03:32: Epoch: 018 Train Loss: 0.941, mae: 1.475, rse: 1.000, pcc: 0.107, kcc: 0.171
+2025-07-09 15:03:33: Epoch: 018 Test results, ap: mae: 0.615, rse: 1.158, pcc: 0.193, kcc: 0.142
+2025-07-09 15:03:33: Epoch: 018 New best TEST metrics
+2025-07-09 15:03:35: Epoch: 019 Train Loss: 0.940, mae: 1.473, rse: 1.002, pcc: 0.095, kcc: 0.165
+2025-07-09 15:03:36: Epoch: 019 Test results, ap: mae: 0.629, rse: 1.205, pcc: 0.272, kcc: 0.161
+2025-07-09 15:03:36: Epoch: 019 New best TEST metrics
+2025-07-09 15:03:38: Epoch: 020 Train Loss: 0.941, mae: 1.473, rse: 1.002, pcc: 0.091, kcc: 0.155
+2025-07-09 15:03:39: Epoch: 020 Test results, ap: mae: 0.626, rse: 1.195, pcc: 0.270, kcc: 0.178
+2025-07-09 15:03:39: Epoch: 020 New best TEST metrics
+2025-07-09 15:03:41: Epoch: 021 Train Loss: 0.940, mae: 1.474, rse: 1.001, pcc: 0.094, kcc: 0.158
+2025-07-09 15:03:41: Epoch: 021 Test results, ap: mae: 0.623, rse: 1.184, pcc: 0.233, kcc: 0.173
+2025-07-09 15:03:44: Epoch: 022 Train Loss: 0.941, mae: 1.473, rse: 1.001, pcc: 0.092, kcc: 0.151
+2025-07-09 15:03:44: Epoch: 022 Test results, ap: mae: 0.625, rse: 1.192, pcc: 0.243, kcc: 0.183
+2025-07-09 15:03:47: Epoch: 023 Train Loss: 0.940, mae: 1.473, rse: 1.001, pcc: 0.092, kcc: 0.151
+2025-07-09 15:03:47: Epoch: 023 Test results, ap: mae: 0.624, rse: 1.189, pcc: 0.244, kcc: 0.190
+2025-07-09 15:03:50: Epoch: 024 Train Loss: 0.941, mae: 1.474, rse: 1.001, pcc: 0.081, kcc: -0.011
+2025-07-09 15:03:50: Epoch: 024 Test results, ap: mae: 0.622, rse: 1.182, pcc: -0.116, kcc: -0.139
+2025-07-09 15:03:53: Epoch: 025 Train Loss: 0.939, mae: 1.474, rse: 1.001, pcc: 0.086, kcc: 0.180
+2025-07-09 15:03:53: Epoch: 025 Test results, ap: mae: 0.623, rse: 1.184, pcc: 0.109, kcc: 0.147
+2025-07-09 15:03:56: Epoch: 026 Train Loss: 0.939, mae: 1.473, rse: 1.001, pcc: 0.090, kcc: 0.183
+2025-07-09 15:03:56: Epoch: 026 Test results, ap: mae: 0.625, rse: 1.191, pcc: 0.193, kcc: 0.169
+2025-07-09 15:03:58: Epoch: 027 Train Loss: 0.941, mae: 1.473, rse: 1.001, pcc: 0.093, kcc: 0.183
+2025-07-09 15:03:59: Epoch: 027 Test results, ap: mae: 0.624, rse: 1.189, pcc: 0.243, kcc: 0.165
+2025-07-09 15:04:01: Epoch: 028 Train Loss: 0.938, mae: 1.473, rse: 1.001, pcc: 0.091, kcc: 0.193
+2025-07-09 15:04:02: Epoch: 028 Test results, ap: mae: 0.626, rse: 1.194, pcc: 0.318, kcc: 0.177
+2025-07-09 15:04:02: Epoch: 028 New best TEST metrics
+2025-07-09 15:04:04: Epoch: 029 Train Loss: 0.940, mae: 1.473, rse: 1.002, pcc: 0.085, kcc: 0.065
+2025-07-09 15:04:04: Epoch: 029 Test results, ap: mae: 0.627, rse: 1.197, pcc: -0.035, kcc: -0.012
+2025-07-09 15:04:07: Epoch: 030 Train Loss: 0.941, mae: 1.473, rse: 1.002, pcc: 0.087, kcc: 0.110
+2025-07-09 15:04:07: Epoch: 030 Test results, ap: mae: 0.627, rse: 1.197, pcc: 0.143, kcc: 0.098
+2025-07-09 15:04:10: Epoch: 031 Train Loss: 0.941, mae: 1.473, rse: 1.001, pcc: 0.089, kcc: 0.186
+2025-07-09 15:04:10: Epoch: 031 Test results, ap: mae: 0.625, rse: 1.193, pcc: 0.308, kcc: 0.270
+2025-07-09 15:04:10: Epoch: 031 New best TEST metrics
+2025-07-09 15:04:13: Epoch: 032 Train Loss: 0.940, mae: 1.473, rse: 1.001, pcc: 0.089, kcc: 0.195
+2025-07-09 15:04:13: Epoch: 032 Test results, ap: mae: 0.626, rse: 1.194, pcc: 0.274, kcc: 0.229
+2025-07-09 15:04:16: Epoch: 033 Train Loss: 0.940, mae: 1.473, rse: 1.001, pcc: 0.089, kcc: 0.189
+2025-07-09 15:04:16: Epoch: 033 Test results, ap: mae: 0.626, rse: 1.194, pcc: 0.350, kcc: 0.195
+2025-07-09 15:04:18: Epoch: 034 Train Loss: 0.940, mae: 1.473, rse: 1.001, pcc: 0.091, kcc: 0.173
+2025-07-09 15:04:19: Epoch: 034 Test results, ap: mae: 0.625, rse: 1.191, pcc: 0.293, kcc: 0.191
+2025-07-09 15:04:21: Epoch: 035 Train Loss: 0.940, mae: 1.473, rse: 1.001, pcc: 0.091, kcc: 0.166
+2025-07-09 15:04:22: Epoch: 035 Test results, ap: mae: 0.624, rse: 1.187, pcc: 0.295, kcc: 0.189
+2025-07-09 15:04:22: used time 0.03h
+2025-07-09 15:04:22: Running Cross Validation 3
+2025-07-09 15:04:22: Fold 3  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:04:24: Epoch: 001 Train Loss: 0.974, mae: 1.322, rse: 1.026, pcc: -0.158, kcc: -0.117
+2025-07-09 15:04:24: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:04:25: Epoch: 001 Test results, ap: mae: 0.630, rse: 1.208, pcc: -0.170, kcc: -0.210
+2025-07-09 15:04:27: Epoch: 002 Train Loss: 0.975, mae: 1.325, rse: 1.039, pcc: 0.052, kcc: 0.036
+2025-07-09 15:04:27: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:04:27: Epoch: 002 Test results, ap: mae: 0.656, rse: 1.301, pcc: -0.239, kcc: -0.338
+2025-07-09 15:04:30: Epoch: 003 Train Loss: 0.974, mae: 1.325, rse: 1.038, pcc: 0.237, kcc: 0.201
+2025-07-09 15:04:30: Epoch: 003 New best VALIDATION metrics
+2025-07-09 15:04:30: Epoch: 003 Test results, ap: mae: 0.654, rse: 1.295, pcc: -0.130, kcc: -0.086
+2025-07-09 15:04:33: Epoch: 004 Train Loss: 0.975, mae: 1.325, rse: 1.041, pcc: 0.218, kcc: 0.174
+2025-07-09 15:04:33: Epoch: 004 Test results, ap: mae: 0.658, rse: 1.312, pcc: -0.110, kcc: -0.097
+2025-07-09 15:04:36: Epoch: 005 Train Loss: 0.975, mae: 1.324, rse: 1.034, pcc: 0.186, kcc: 0.136
+2025-07-09 15:04:36: Epoch: 005 Test results, ap: mae: 0.647, rse: 1.267, pcc: -0.140, kcc: -0.126
+2025-07-09 15:04:39: Epoch: 006 Train Loss: 0.973, mae: 1.323, rse: 1.031, pcc: 0.293, kcc: 0.197
+2025-07-09 15:04:39: Epoch: 006 New best VALIDATION metrics
+2025-07-09 15:04:39: Epoch: 006 Test results, ap: mae: 0.641, rse: 1.246, pcc: -0.102, kcc: -0.115
+2025-07-09 15:04:42: Epoch: 007 Train Loss: 0.973, mae: 1.322, rse: 1.029, pcc: 0.289, kcc: 0.208
+2025-07-09 15:04:42: Epoch: 007 New best VALIDATION metrics
+2025-07-09 15:04:42: Epoch: 007 Test results, ap: mae: 0.637, rse: 1.232, pcc: -0.096, kcc: -0.101
+2025-07-09 15:04:45: Epoch: 008 Train Loss: 0.973, mae: 1.322, rse: 1.027, pcc: -0.112, kcc: -0.060
+2025-07-09 15:04:45: Epoch: 008 Test results, ap: mae: 0.632, rse: 1.215, pcc: 0.103, kcc: 0.164
+2025-07-09 15:04:47: Epoch: 009 Train Loss: 0.975, mae: 1.324, rse: 1.034, pcc: -0.289, kcc: -0.221
+2025-07-09 15:04:48: Epoch: 009 Test results, ap: mae: 0.647, rse: 1.268, pcc: 0.067, kcc: 0.052
+2025-07-09 15:04:50: Epoch: 010 Train Loss: 0.973, mae: 1.323, rse: 1.031, pcc: 0.270, kcc: 0.197
+2025-07-09 15:04:51: Epoch: 010 Test results, ap: mae: 0.641, rse: 1.246, pcc: -0.042, kcc: -0.045
+2025-07-09 15:04:53: Epoch: 011 Train Loss: 0.972, mae: 1.322, rse: 1.026, pcc: 0.281, kcc: 0.207
+2025-07-09 15:04:53: Epoch: 011 Test results, ap: mae: 0.632, rse: 1.214, pcc: -0.030, kcc: -0.030
+2025-07-09 15:04:53: Epoch: 011 New best TEST metrics
+2025-07-09 15:04:56: Epoch: 012 Train Loss: 0.972, mae: 1.322, rse: 1.029, pcc: 0.299, kcc: 0.198
+2025-07-09 15:04:56: Epoch: 012 New best VALIDATION metrics
+2025-07-09 15:04:56: Epoch: 012 Test results, ap: mae: 0.636, rse: 1.230, pcc: 0.329, kcc: 0.237
+2025-07-09 15:04:56: Epoch: 012 New best TEST metrics
+2025-07-09 15:04:59: Epoch: 013 Train Loss: 0.973, mae: 1.322, rse: 1.029, pcc: 0.316, kcc: 0.217
+2025-07-09 15:04:59: Epoch: 013 New best VALIDATION metrics
+2025-07-09 15:04:59: Epoch: 013 Test results, ap: mae: 0.637, rse: 1.233, pcc: 0.054, kcc: 0.045
+2025-07-09 15:05:02: Epoch: 014 Train Loss: 0.973, mae: 1.323, rse: 1.033, pcc: 0.262, kcc: 0.203
+2025-07-09 15:05:02: Epoch: 014 Test results, ap: mae: 0.645, rse: 1.260, pcc: -0.025, kcc: 0.022
+2025-07-09 15:05:05: Epoch: 015 Train Loss: 0.973, mae: 1.323, rse: 1.031, pcc: 0.315, kcc: 0.224
+2025-07-09 15:05:05: Epoch: 015 New best VALIDATION metrics
+2025-07-09 15:05:05: Epoch: 015 Test results, ap: mae: 0.640, rse: 1.243, pcc: 0.038, kcc: 0.038
+2025-07-09 15:05:08: Epoch: 016 Train Loss: 0.973, mae: 1.322, rse: 1.026, pcc: 0.323, kcc: 0.233
+2025-07-09 15:05:08: Epoch: 016 New best VALIDATION metrics
+2025-07-09 15:05:08: Epoch: 016 Test results, ap: mae: 0.632, rse: 1.214, pcc: 0.049, kcc: 0.052
+2025-07-09 15:05:11: Epoch: 017 Train Loss: 0.974, mae: 1.322, rse: 1.027, pcc: -0.335, kcc: -0.223
+2025-07-09 15:05:11: Epoch: 017 Test results, ap: mae: 0.633, rse: 1.218, pcc: -0.090, kcc: -0.090
+2025-07-09 15:05:14: Epoch: 018 Train Loss: 0.973, mae: 1.322, rse: 1.030, pcc: 0.336, kcc: 0.228
+2025-07-09 15:05:14: Epoch: 018 New best VALIDATION metrics
+2025-07-09 15:05:14: Epoch: 018 Test results, ap: mae: 0.638, rse: 1.237, pcc: 0.108, kcc: 0.074
+2025-07-09 15:05:17: Epoch: 019 Train Loss: 0.973, mae: 1.322, rse: 1.026, pcc: 0.342, kcc: 0.239
+2025-07-09 15:05:17: Epoch: 019 New best VALIDATION metrics
+2025-07-09 15:05:17: Epoch: 019 Test results, ap: mae: 0.632, rse: 1.213, pcc: 0.128, kcc: 0.092
+2025-07-09 15:05:20: Epoch: 020 Train Loss: 0.973, mae: 1.323, rse: 1.033, pcc: 0.351, kcc: 0.249
+2025-07-09 15:05:20: Epoch: 020 New best VALIDATION metrics
+2025-07-09 15:05:20: Epoch: 020 Test results, ap: mae: 0.645, rse: 1.260, pcc: 0.166, kcc: 0.123
+2025-07-09 15:05:23: Epoch: 021 Train Loss: 0.973, mae: 1.322, rse: 1.031, pcc: 0.354, kcc: 0.254
+2025-07-09 15:05:23: Epoch: 021 New best VALIDATION metrics
+2025-07-09 15:05:23: Epoch: 021 Test results, ap: mae: 0.646, rse: 1.263, pcc: 0.183, kcc: 0.128
+2025-07-09 15:05:26: Epoch: 022 Train Loss: 0.950, mae: 1.256, rse: 0.892, pcc: 0.369, kcc: 0.270
+2025-07-09 15:05:26: Epoch: 022 New best VALIDATION metrics
+2025-07-09 15:05:26: Epoch: 022 Test results, ap: mae: 0.589, rse: 1.053, pcc: 0.227, kcc: 0.148
+2025-07-09 15:05:26: Epoch: 022 New best TEST metrics
+2025-07-09 15:05:29: Epoch: 023 Train Loss: 0.912, mae: 1.253, rse: 0.908, pcc: 0.369, kcc: 0.261
+2025-07-09 15:05:29: Epoch: 023 Test results, ap: mae: 0.769, rse: 1.730, pcc: 0.122, kcc: 0.017
+2025-07-09 15:05:32: Epoch: 024 Train Loss: 0.895, mae: 1.253, rse: 0.886, pcc: 0.363, kcc: 0.260
+2025-07-09 15:05:32: Epoch: 024 Test results, ap: mae: 1.033, rse: 2.788, pcc: -0.429, kcc: -0.236
+2025-07-09 15:05:34: Epoch: 025 Train Loss: 0.888, mae: 1.253, rse: 0.872, pcc: 0.373, kcc: 0.268
+2025-07-09 15:05:34: Epoch: 025 New best VALIDATION metrics
+2025-07-09 15:05:35: Epoch: 025 Test results, ap: mae: 0.930, rse: 2.334, pcc: 0.083, kcc: -0.007
+2025-07-09 15:05:37: Epoch: 026 Train Loss: 0.872, mae: 1.247, rse: 0.873, pcc: 0.380, kcc: 0.271
+2025-07-09 15:05:37: Epoch: 026 New best VALIDATION metrics
+2025-07-09 15:05:38: Epoch: 026 Test results, ap: mae: 0.979, rse: 2.525, pcc: 0.073, kcc: -0.001
+2025-07-09 15:05:40: Epoch: 027 Train Loss: 0.872, mae: 1.266, rse: 0.877, pcc: 0.385, kcc: 0.276
+2025-07-09 15:05:40: Epoch: 027 Test results, ap: mae: 1.154, rse: 3.262, pcc: -0.034, kcc: -0.077
+2025-07-09 15:05:43: Epoch: 028 Train Loss: 0.870, mae: 1.241, rse: 0.859, pcc: 0.383, kcc: 0.278
+2025-07-09 15:05:43: Epoch: 028 New best VALIDATION metrics
+2025-07-09 15:05:43: Epoch: 028 Test results, ap: mae: 1.097, rse: 3.008, pcc: 0.083, kcc: -0.063
+2025-07-09 15:05:46: Epoch: 029 Train Loss: 0.866, mae: 1.238, rse: 0.858, pcc: 0.388, kcc: 0.277
+2025-07-09 15:05:46: Epoch: 029 New best VALIDATION metrics
+2025-07-09 15:05:46: Epoch: 029 Test results, ap: mae: 1.062, rse: 2.862, pcc: 0.024, kcc: -0.051
+2025-07-09 15:05:49: Epoch: 030 Train Loss: 0.870, mae: 1.242, rse: 0.871, pcc: 0.383, kcc: 0.277
+2025-07-09 15:05:49: Epoch: 030 Test results, ap: mae: 1.047, rse: 2.790, pcc: 0.114, kcc: 0.011
+2025-07-09 15:05:52: Epoch: 031 Train Loss: 0.860, mae: 1.239, rse: 0.855, pcc: 0.387, kcc: 0.277
+2025-07-09 15:05:52: Epoch: 031 New best VALIDATION metrics
+2025-07-09 15:05:52: Epoch: 031 Test results, ap: mae: 1.005, rse: 2.627, pcc: 0.067, kcc: 0.033
+2025-07-09 15:05:55: Epoch: 032 Train Loss: 0.859, mae: 1.251, rse: 0.866, pcc: 0.393, kcc: 0.274
+2025-07-09 15:05:55: Epoch: 032 Test results, ap: mae: 1.091, rse: 2.984, pcc: 0.053, kcc: 0.025
+2025-07-09 15:05:57: Epoch: 033 Train Loss: 0.851, mae: 1.252, rse: 0.872, pcc: 0.395, kcc: 0.270
+2025-07-09 15:05:58: Epoch: 033 Test results, ap: mae: 1.078, rse: 2.930, pcc: 0.042, kcc: 0.013
+2025-07-09 15:06:00: Epoch: 034 Train Loss: 0.849, mae: 1.240, rse: 0.870, pcc: 0.395, kcc: 0.271
+2025-07-09 15:06:01: Epoch: 034 Test results, ap: mae: 1.058, rse: 2.838, pcc: 0.090, kcc: 0.018
+2025-07-09 15:06:03: Epoch: 035 Train Loss: 0.848, mae: 1.231, rse: 0.847, pcc: 0.402, kcc: 0.276
+2025-07-09 15:06:03: Epoch: 035 New best VALIDATION metrics
+2025-07-09 15:06:04: Epoch: 035 Test results, ap: mae: 1.062, rse: 2.861, pcc: 0.038, kcc: 0.026
+2025-07-09 15:06:04: used time 0.03h
+2025-07-09 15:06:04: Running Cross Validation 4
+2025-07-09 15:06:04: Fold 4  Train set:1889, Valid set:472, Test set: 108
+2025-07-09 15:06:06: Epoch: 001 Train Loss: 0.973, mae: 1.366, rse: 1.002, pcc: -0.180, kcc: -0.103
+2025-07-09 15:06:06: Epoch: 001 New best VALIDATION metrics
+2025-07-09 15:06:07: Epoch: 001 Test results, ap: mae: 0.605, rse: 1.120, pcc: 0.169, kcc: 0.094
+2025-07-09 15:06:09: Epoch: 002 Train Loss: 0.966, mae: 1.352, rse: 1.005, pcc: 0.196, kcc: 0.164
+2025-07-09 15:06:09: Epoch: 002 New best VALIDATION metrics
+2025-07-09 15:06:09: Epoch: 002 Test results, ap: mae: 0.681, rse: 1.392, pcc: 0.068, kcc: 0.045
+2025-07-09 15:06:12: Epoch: 003 Train Loss: 0.970, mae: 1.373, rse: 1.005, pcc: -0.273, kcc: -0.196
+2025-07-09 15:06:12: Epoch: 003 Test results, ap: mae: 0.595, rse: 1.077, pcc: -0.089, kcc: -0.053
+2025-07-09 15:06:15: Epoch: 004 Train Loss: 0.974, mae: 1.382, rse: 1.009, pcc: 0.291, kcc: 0.183
+2025-07-09 15:06:15: Epoch: 004 New best VALIDATION metrics
+2025-07-09 15:06:15: Epoch: 004 Test results, ap: mae: 0.587, rse: 1.045, pcc: 0.368, kcc: 0.252
+2025-07-09 15:06:18: Epoch: 005 Train Loss: 0.967, mae: 1.361, rse: 1.000, pcc: 0.295, kcc: 0.190
+2025-07-09 15:06:18: Epoch: 005 New best VALIDATION metrics
+2025-07-09 15:06:18: Epoch: 005 Test results, ap: mae: 0.619, rse: 1.173, pcc: 0.319, kcc: 0.168
+2025-07-09 15:06:21: Epoch: 006 Train Loss: 0.975, mae: 1.371, rse: 0.996, pcc: 0.206, kcc: 0.123
+2025-07-09 15:06:21: Epoch: 006 Test results, ap: mae: 0.587, rse: 1.039, pcc: -0.300, kcc: -0.220
+2025-07-09 15:06:24: Epoch: 007 Train Loss: 0.958, mae: 1.358, rse: 0.995, pcc: 0.288, kcc: 0.195
+2025-07-09 15:06:24: Epoch: 007 New best VALIDATION metrics
+2025-07-09 15:06:24: Epoch: 007 Test results, ap: mae: 0.623, rse: 1.184, pcc: 0.116, kcc: 0.077
+2025-07-09 15:06:26: Epoch: 008 Train Loss: 0.931, mae: 1.312, rse: 0.933, pcc: 0.319, kcc: 0.213
+2025-07-09 15:06:26: Epoch: 008 New best VALIDATION metrics
+2025-07-09 15:06:27: Epoch: 008 Test results, ap: mae: 0.844, rse: 2.014, pcc: 0.008, kcc: 0.037
+2025-07-09 15:06:29: Epoch: 009 Train Loss: 0.899, mae: 1.306, rse: 0.922, pcc: 0.326, kcc: 0.223
+2025-07-09 15:06:29: Epoch: 009 New best VALIDATION metrics
+2025-07-09 15:06:30: Epoch: 009 Test results, ap: mae: 0.753, rse: 1.667, pcc: 0.174, kcc: 0.108
+2025-07-09 15:06:32: Epoch: 010 Train Loss: 0.894, mae: 1.298, rse: 0.894, pcc: 0.332, kcc: 0.231
+2025-07-09 15:06:32: Epoch: 010 New best VALIDATION metrics
+2025-07-09 15:06:33: Epoch: 010 Test results, ap: mae: 0.901, rse: 2.234, pcc: 0.067, kcc: 0.037
+2025-07-09 15:06:35: Epoch: 011 Train Loss: 0.870, mae: 1.290, rse: 0.888, pcc: 0.338, kcc: 0.230
+2025-07-09 15:06:35: Epoch: 011 New best VALIDATION metrics
+2025-07-09 15:06:35: Epoch: 011 Test results, ap: mae: 0.965, rse: 2.478, pcc: 0.026, kcc: -0.006
+2025-07-09 15:06:35: Epoch: 011 New best TEST metrics
+2025-07-09 15:06:38: Epoch: 012 Train Loss: 0.877, mae: 1.292, rse: 0.891, pcc: 0.334, kcc: 0.232
+2025-07-09 15:06:38: Epoch: 012 Test results, ap: mae: 0.829, rse: 1.971, pcc: -0.004, kcc: -0.018
+2025-07-09 15:06:38: Epoch: 012 New best TEST metrics
+2025-07-09 15:06:41: Epoch: 013 Train Loss: 0.863, mae: 1.287, rse: 0.889, pcc: 0.335, kcc: 0.233
+2025-07-09 15:06:41: Epoch: 013 New best VALIDATION metrics
+2025-07-09 15:06:41: Epoch: 013 Test results, ap: mae: 0.758, rse: 1.703, pcc: 0.010, kcc: -0.008
+2025-07-09 15:06:41: Epoch: 013 New best TEST metrics
+2025-07-09 15:06:44: Epoch: 014 Train Loss: 0.868, mae: 1.290, rse: 0.888, pcc: 0.336, kcc: 0.234
+2025-07-09 15:06:44: Epoch: 014 Test results, ap: mae: 0.771, rse: 1.759, pcc: 0.012, kcc: -0.002
+2025-07-09 15:06:47: Epoch: 015 Train Loss: 0.859, mae: 1.282, rse: 0.887, pcc: 0.337, kcc: 0.230
+2025-07-09 15:06:47: Epoch: 015 New best VALIDATION metrics
+2025-07-09 15:06:47: Epoch: 015 Test results, ap: mae: 0.776, rse: 1.771, pcc: -0.036, kcc: -0.062
+2025-07-09 15:06:50: Epoch: 016 Train Loss: 0.859, mae: 1.292, rse: 0.894, pcc: 0.333, kcc: 0.231
+2025-07-09 15:06:50: Epoch: 016 Test results, ap: mae: 0.707, rse: 1.521, pcc: -0.048, kcc: -0.086
+2025-07-09 15:06:50: Epoch: 016 New best TEST metrics
+2025-07-09 15:06:53: Epoch: 017 Train Loss: 0.858, mae: 1.288, rse: 0.900, pcc: 0.345, kcc: 0.238
+2025-07-09 15:06:53: Epoch: 017 Test results, ap: mae: 0.701, rse: 1.499, pcc: -0.043, kcc: -0.055
+2025-07-09 15:06:53: Epoch: 017 New best TEST metrics
+2025-07-09 15:06:55: Epoch: 018 Train Loss: 0.846, mae: 1.289, rse: 0.904, pcc: 0.330, kcc: 0.238
+2025-07-09 15:06:56: Epoch: 018 Test results, ap: mae: 0.646, rse: 1.259, pcc: -0.088, kcc: -0.116
+2025-07-09 15:06:56: Epoch: 018 New best TEST metrics
+2025-07-09 15:06:58: Epoch: 019 Train Loss: 0.852, mae: 1.280, rse: 0.888, pcc: 0.353, kcc: 0.239
+2025-07-09 15:06:58: Epoch: 019 New best VALIDATION metrics
+2025-07-09 15:06:59: Epoch: 019 Test results, ap: mae: 0.788, rse: 1.843, pcc: -0.004, kcc: -0.011
+2025-07-09 15:07:01: Epoch: 020 Train Loss: 0.847, mae: 1.278, rse: 0.893, pcc: 0.334, kcc: 0.238
+2025-07-09 15:07:02: Epoch: 020 Test results, ap: mae: 0.656, rse: 1.306, pcc: -0.099, kcc: -0.096
+2025-07-09 15:07:04: Epoch: 021 Train Loss: 0.833, mae: 1.287, rse: 0.905, pcc: 0.341, kcc: 0.245
+2025-07-09 15:07:05: Epoch: 021 Test results, ap: mae: 0.670, rse: 1.365, pcc: -0.083, kcc: -0.079
+2025-07-09 15:07:07: Epoch: 022 Train Loss: 0.834, mae: 1.281, rse: 0.890, pcc: 0.346, kcc: 0.247
+2025-07-09 15:07:07: Epoch: 022 Test results, ap: mae: 0.702, rse: 1.530, pcc: -0.090, kcc: -0.090
+2025-07-09 15:07:10: Epoch: 023 Train Loss: 0.830, mae: 1.266, rse: 0.876, pcc: 0.355, kcc: 0.251
+2025-07-09 15:07:10: Epoch: 023 New best VALIDATION metrics
+2025-07-09 15:07:10: Epoch: 023 Test results, ap: mae: 0.667, rse: 1.376, pcc: -0.063, kcc: -0.070
+2025-07-09 15:07:13: Epoch: 024 Train Loss: 0.822, mae: 1.297, rse: 0.909, pcc: 0.351, kcc: 0.259
+2025-07-09 15:07:13: Epoch: 024 Test results, ap: mae: 0.655, rse: 1.293, pcc: -0.054, kcc: -0.081
+2025-07-09 15:07:13: Epoch: 024 New best TEST metrics
+2025-07-09 15:07:16: Epoch: 025 Train Loss: 0.816, mae: 1.266, rse: 0.875, pcc: 0.365, kcc: 0.261
+2025-07-09 15:07:16: Epoch: 025 New best VALIDATION metrics
+2025-07-09 15:07:16: Epoch: 025 Test results, ap: mae: 0.670, rse: 1.388, pcc: -0.057, kcc: -0.084
+2025-07-09 15:07:19: Epoch: 026 Train Loss: 0.816, mae: 1.277, rse: 0.895, pcc: 0.355, kcc: 0.260
+2025-07-09 15:07:19: Epoch: 026 Test results, ap: mae: 0.611, rse: 1.114, pcc: -0.059, kcc: -0.062
+2025-07-09 15:07:19: Epoch: 026 New best TEST metrics
+2025-07-09 15:07:22: Epoch: 027 Train Loss: 0.810, mae: 1.273, rse: 0.882, pcc: 0.368, kcc: 0.264
+2025-07-09 15:07:22: Epoch: 027 Test results, ap: mae: 0.650, rse: 1.297, pcc: -0.013, kcc: -0.023
+2025-07-09 15:07:25: Epoch: 028 Train Loss: 0.807, mae: 1.280, rse: 0.891, pcc: 0.382, kcc: 0.271
+2025-07-09 15:07:25: Epoch: 028 Test results, ap: mae: 0.664, rse: 1.336, pcc: -0.005, kcc: -0.040
+2025-07-09 15:07:28: Epoch: 029 Train Loss: 0.796, mae: 1.278, rse: 0.883, pcc: 0.384, kcc: 0.268
+2025-07-09 15:07:28: Epoch: 029 New best VALIDATION metrics
+2025-07-09 15:07:28: Epoch: 029 Test results, ap: mae: 0.613, rse: 1.122, pcc: -0.027, kcc: -0.066
+2025-07-09 15:07:28: Epoch: 029 New best TEST metrics
+2025-07-09 15:07:31: Epoch: 030 Train Loss: 0.781, mae: 1.252, rse: 0.868, pcc: 0.400, kcc: 0.279
+2025-07-09 15:07:31: Epoch: 030 New best VALIDATION metrics
+2025-07-09 15:07:31: Epoch: 030 Test results, ap: mae: 0.615, rse: 1.149, pcc: -0.011, kcc: -0.040
+2025-07-09 15:07:31: Epoch: 030 New best TEST metrics
+2025-07-09 15:07:33: Epoch: 031 Train Loss: 0.740, mae: 1.216, rse: 0.838, pcc: 0.407, kcc: 0.283
+2025-07-09 15:07:33: Epoch: 031 New best VALIDATION metrics
+2025-07-09 15:07:34: Epoch: 031 Test results, ap: mae: 0.618, rse: 1.162, pcc: -0.020, kcc: -0.052
+2025-07-09 15:07:36: Epoch: 032 Train Loss: 0.732, mae: 1.214, rse: 0.827, pcc: 0.434, kcc: 0.303
+2025-07-09 15:07:36: Epoch: 032 New best VALIDATION metrics
+2025-07-09 15:07:37: Epoch: 032 Test results, ap: mae: 0.619, rse: 1.178, pcc: -0.019, kcc: -0.041
+2025-07-09 15:07:39: Epoch: 033 Train Loss: 0.723, mae: 1.201, rse: 0.818, pcc: 0.445, kcc: 0.311
+2025-07-09 15:07:39: Epoch: 033 New best VALIDATION metrics
+2025-07-09 15:07:40: Epoch: 033 Test results, ap: mae: 0.635, rse: 1.239, pcc: -0.053, kcc: -0.064
+2025-07-09 15:07:42: Epoch: 034 Train Loss: 0.710, mae: 1.171, rse: 0.795, pcc: 0.466, kcc: 0.326
+2025-07-09 15:07:42: Epoch: 034 New best VALIDATION metrics
+2025-07-09 15:07:43: Epoch: 034 Test results, ap: mae: 0.631, rse: 1.216, pcc: -0.060, kcc: -0.067
+2025-07-09 15:07:45: Epoch: 035 Train Loss: 0.697, mae: 1.188, rse: 0.805, pcc: 0.449, kcc: 0.317
+2025-07-09 15:07:45: Epoch: 035 Test results, ap: mae: 0.646, rse: 1.233, pcc: -0.079, kcc: -0.067
+2025-07-09 15:07:45: used time 0.03h
+2025-07-09 15:07:45: Cross Validation Finished!
+2025-07-09 15:07:45: Best validation perform list
+[[1.35404801 1.00448489 0.25543636 0.20862713]
+ [1.24172127 0.88124448 0.37369126 0.26416996]
+ [1.47511554 1.00038135 0.17814383 0.16048665]
+ [1.23121274 0.84740812 0.40221617 0.27642056]
+ [1.17080045 0.79452991 0.46642557 0.32644731]]
+2025-07-09 15:07:45: mean: [1.295 0.906 0.335 0.247]
+2025-07-09 15:07:45: std: [0.108 0.084 0.104 0.057]
+2025-07-09 15:07:45: Best test perform list
+[[ 0.61986834  1.17566133  0.40267083  0.26601684]
+ [ 0.62120163  1.17975116  0.23035583  0.14940935]
+ [ 0.62531698  1.19267249  0.30756599  0.26964137]
+ [ 0.58942038  1.05292499  0.22666898  0.14778875]
+ [ 0.61468506  1.14942002 -0.01056195 -0.03989603]]
+2025-07-09 15:07:45: mean: [0.614 1.15  0.231 0.159]
+2025-07-09 15:07:45: std: [0.013 0.051 0.137 0.113]
diff --git a/run-reg/mamba-diff-256-mse-32-0.001-50/config.json b/run-reg/mamba-diff-256-mse-32-0.001-50/config.json
new file mode 100644
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--- /dev/null
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mamba", "side_enc": null, "channels": 256, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 231, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
\ No newline at end of file
diff --git a/run-reg/mamba-diff-256-mse-32-0.001-50/inference_results.csv b/run-reg/mamba-diff-256-mse-32-0.001-50/inference_results.csv
new file mode 100644
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@@ -0,0 +1,3 @@
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diff --git a/run-reg/mamba-diff-256-mse-32-0.001-50/preds_test.csv b/run-reg/mamba-diff-256-mse-32-0.001-50/preds_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9bb8b51707ff14bf763a8654322966a2a5e767f7
--- /dev/null
+++ b/run-reg/mamba-diff-256-mse-32-0.001-50/preds_test.csv
@@ -0,0 +1,8 @@
+model_0_test,model_1_test,model_2_test,model_3_test,model_4_test,fusion,gt
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diff --git a/run-reg/mamba-diff-256-mse-32-0.001-50/result.txt b/run-reg/mamba-diff-256-mse-32-0.001-50/result.txt
new file mode 100644
index 0000000000000000000000000000000000000000..6f117a09a4c525875e8d81d98f9fa59983504f9d
--- /dev/null
+++ b/run-reg/mamba-diff-256-mse-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
+Valid
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diff --git a/run-reg/mamba-diff-256-mse-32-0.001-50/training.log b/run-reg/mamba-diff-256-mse-32-0.001-50/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..42d10798ff16b366095b90b838dc278cc6d02f0a
--- /dev/null
+++ b/run-reg/mamba-diff-256-mse-32-0.001-50/training.log
@@ -0,0 +1,716 @@
+2025-08-27 10:23:39: saving_dir: ./run-reg/mamba-diff-256-mse-32-0.001-50
+2025-08-27 10:23:39: Loading Train Dataset
+2025-08-27 10:23:39: Loading Test Dataset
+2025-08-27 10:23:40: Running Cross Validation 0
+2025-08-27 10:23:40: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-27 10:23:44: Epoch: 001 Train Loss: 2.758, mae: 1.049, rse: 0.758, pcc: 0.492, kcc: 0.349
+2025-08-27 10:23:44: Epoch: 001 New best VALIDATION metrics
+2025-08-27 10:23:44: Epoch: 001 Test results, ap: mae: 0.757, rse: 1.810, pcc: -0.015, kcc: -0.020
+2025-08-27 10:23:47: Epoch: 002 Train Loss: 2.044, mae: 0.970, rse: 0.660, pcc: 0.589, kcc: 0.423
+2025-08-27 10:23:47: Epoch: 002 New best VALIDATION metrics
+2025-08-27 10:23:47: Epoch: 002 Test results, ap: mae: 0.741, rse: 1.762, pcc: 0.073, kcc: 0.037
+2025-08-27 10:23:51: Epoch: 003 Train Loss: 1.722, mae: 0.869, rse: 0.533, pcc: 0.697, kcc: 0.481
+2025-08-27 10:23:51: Epoch: 003 New best VALIDATION metrics
+2025-08-27 10:23:51: Epoch: 003 Test results, ap: mae: 0.768, rse: 1.770, pcc: 0.013, kcc: 0.013
+2025-08-27 10:23:54: Epoch: 004 Train Loss: 1.494, mae: 0.803, rse: 0.480, pcc: 0.722, kcc: 0.526
+2025-08-27 10:23:54: Epoch: 004 New best VALIDATION metrics
+2025-08-27 10:23:55: Epoch: 004 Test results, ap: mae: 0.772, rse: 1.753, pcc: -0.014, kcc: -0.026
+2025-08-27 10:23:58: Epoch: 005 Train Loss: 1.325, mae: 0.747, rse: 0.404, pcc: 0.772, kcc: 0.555
+2025-08-27 10:23:58: Epoch: 005 New best VALIDATION metrics
+2025-08-27 10:23:58: Epoch: 005 Test results, ap: mae: 0.943, rse: 2.753, pcc: 0.037, kcc: 0.033
+2025-08-27 10:24:01: Epoch: 006 Train Loss: 1.228, mae: 0.776, rse: 0.436, pcc: 0.752, kcc: 0.554
+2025-08-27 10:24:02: Epoch: 006 Test results, ap: mae: 0.842, rse: 2.054, pcc: 0.031, kcc: 0.014
+2025-08-27 10:24:05: Epoch: 007 Train Loss: 1.099, mae: 0.674, rse: 0.355, pcc: 0.807, kcc: 0.603
+2025-08-27 10:24:05: Epoch: 007 New best VALIDATION metrics
+2025-08-27 10:24:05: Epoch: 007 Test results, ap: mae: 0.891, rse: 2.492, pcc: -0.043, kcc: -0.024
+2025-08-27 10:24:09: Epoch: 008 Train Loss: 0.906, mae: 0.654, rse: 0.323, pcc: 0.825, kcc: 0.615
+2025-08-27 10:24:09: Epoch: 008 New best VALIDATION metrics
+2025-08-27 10:24:09: Epoch: 008 Test results, ap: mae: 0.935, rse: 2.666, pcc: -0.062, kcc: -0.039
+2025-08-27 10:24:12: Epoch: 009 Train Loss: 0.760, mae: 0.586, rse: 0.293, pcc: 0.844, kcc: 0.658
+2025-08-27 10:24:12: Epoch: 009 New best VALIDATION metrics
+2025-08-27 10:24:12: Epoch: 009 Test results, ap: mae: 1.187, rse: 4.379, pcc: -0.074, kcc: -0.038
+2025-08-27 10:24:16: Epoch: 010 Train Loss: 0.655, mae: 0.584, rse: 0.257, pcc: 0.863, kcc: 0.655
+2025-08-27 10:24:16: Epoch: 010 New best VALIDATION metrics
+2025-08-27 10:24:16: Epoch: 010 Test results, ap: mae: 1.044, rse: 3.249, pcc: -0.141, kcc: -0.090
+2025-08-27 10:24:19: Epoch: 011 Train Loss: 0.503, mae: 0.482, rse: 0.210, pcc: 0.889, kcc: 0.705
+2025-08-27 10:24:19: Epoch: 011 New best VALIDATION metrics
+2025-08-27 10:24:19: Epoch: 011 Test results, ap: mae: 1.145, rse: 4.067, pcc: -0.166, kcc: -0.102
+2025-08-27 10:24:19: Epoch: 011 New best TEST metrics
+2025-08-27 10:24:23: Epoch: 012 Train Loss: 0.403, mae: 0.452, rse: 0.205, pcc: 0.897, kcc: 0.717
+2025-08-27 10:24:23: Epoch: 012 New best VALIDATION metrics
+2025-08-27 10:24:23: Epoch: 012 Test results, ap: mae: 1.275, rse: 4.935, pcc: -0.201, kcc: -0.122
+2025-08-27 10:24:26: Epoch: 013 Train Loss: 0.359, mae: 0.469, rse: 0.210, pcc: 0.898, kcc: 0.718
+2025-08-27 10:24:26: Epoch: 013 Test results, ap: mae: 1.384, rse: 5.715, pcc: -0.175, kcc: -0.100
+2025-08-27 10:24:29: Epoch: 014 Train Loss: 0.352, mae: 0.445, rse: 0.192, pcc: 0.901, kcc: 0.724
+2025-08-27 10:24:29: Epoch: 014 New best VALIDATION metrics
+2025-08-27 10:24:30: Epoch: 014 Test results, ap: mae: 1.163, rse: 4.087, pcc: -0.208, kcc: -0.129
+2025-08-27 10:24:33: Epoch: 015 Train Loss: 0.301, mae: 0.438, rse: 0.176, pcc: 0.913, kcc: 0.745
+2025-08-27 10:24:33: Epoch: 015 New best VALIDATION metrics
+2025-08-27 10:24:33: Epoch: 015 Test results, ap: mae: 1.284, rse: 5.106, pcc: -0.213, kcc: -0.125
+2025-08-27 10:24:36: Epoch: 016 Train Loss: 0.293, mae: 0.431, rse: 0.173, pcc: 0.910, kcc: 0.731
+2025-08-27 10:24:37: Epoch: 016 Test results, ap: mae: 1.227, rse: 4.608, pcc: -0.208, kcc: -0.127
+2025-08-27 10:24:40: Epoch: 017 Train Loss: 0.281, mae: 0.402, rse: 0.165, pcc: 0.922, kcc: 0.761
+2025-08-27 10:24:40: Epoch: 017 New best VALIDATION metrics
+2025-08-27 10:24:40: Epoch: 017 Test results, ap: mae: 1.403, rse: 6.023, pcc: -0.237, kcc: -0.146
+2025-08-27 10:24:43: Epoch: 018 Train Loss: 0.238, mae: 0.376, rse: 0.142, pcc: 0.929, kcc: 0.776
+2025-08-27 10:24:43: Epoch: 018 New best VALIDATION metrics
+2025-08-27 10:24:43: Epoch: 018 Test results, ap: mae: 1.303, rse: 5.419, pcc: -0.206, kcc: -0.133
+2025-08-27 10:24:47: Epoch: 019 Train Loss: 0.231, mae: 0.380, rse: 0.143, pcc: 0.927, kcc: 0.766
+2025-08-27 10:24:47: Epoch: 019 Test results, ap: mae: 1.413, rse: 5.893, pcc: -0.230, kcc: -0.138
+2025-08-27 10:24:50: Epoch: 020 Train Loss: 0.230, mae: 0.348, rse: 0.132, pcc: 0.933, kcc: 0.782
+2025-08-27 10:24:50: Epoch: 020 New best VALIDATION metrics
+2025-08-27 10:24:50: Epoch: 020 Test results, ap: mae: 1.345, rse: 5.597, pcc: -0.220, kcc: -0.138
+2025-08-27 10:24:53: Epoch: 021 Train Loss: 0.166, mae: 0.318, rse: 0.127, pcc: 0.935, kcc: 0.802
+2025-08-27 10:24:53: Epoch: 021 New best VALIDATION metrics
+2025-08-27 10:24:54: Epoch: 021 Test results, ap: mae: 1.376, rse: 5.751, pcc: -0.238, kcc: -0.143
+2025-08-27 10:24:57: Epoch: 022 Train Loss: 0.152, mae: 0.312, rse: 0.121, pcc: 0.939, kcc: 0.805
+2025-08-27 10:24:57: Epoch: 022 New best VALIDATION metrics
+2025-08-27 10:24:57: Epoch: 022 Test results, ap: mae: 1.321, rse: 5.558, pcc: -0.241, kcc: -0.154
+2025-08-27 10:25:00: Epoch: 023 Train Loss: 0.161, mae: 0.299, rse: 0.122, pcc: 0.939, kcc: 0.811
+2025-08-27 10:25:00: Epoch: 023 New best VALIDATION metrics
+2025-08-27 10:25:01: Epoch: 023 Test results, ap: mae: 1.376, rse: 5.786, pcc: -0.245, kcc: -0.158
+2025-08-27 10:25:04: Epoch: 024 Train Loss: 0.126, mae: 0.285, rse: 0.118, pcc: 0.940, kcc: 0.818
+2025-08-27 10:25:04: Epoch: 024 New best VALIDATION metrics
+2025-08-27 10:25:04: Epoch: 024 Test results, ap: mae: 1.361, rse: 5.684, pcc: -0.232, kcc: -0.158
+2025-08-27 10:25:07: Epoch: 025 Train Loss: 0.121, mae: 0.281, rse: 0.110, pcc: 0.945, kcc: 0.821
+2025-08-27 10:25:07: Epoch: 025 New best VALIDATION metrics
+2025-08-27 10:25:07: Epoch: 025 Test results, ap: mae: 1.376, rse: 5.711, pcc: -0.233, kcc: -0.159
+2025-08-27 10:25:10: Epoch: 026 Train Loss: 0.136, mae: 0.293, rse: 0.110, pcc: 0.943, kcc: 0.816
+2025-08-27 10:25:11: Epoch: 026 Test results, ap: mae: 1.342, rse: 5.343, pcc: -0.209, kcc: -0.156
+2025-08-27 10:25:14: Epoch: 027 Train Loss: 0.133, mae: 0.279, rse: 0.110, pcc: 0.945, kcc: 0.823
+2025-08-27 10:25:14: Epoch: 027 New best VALIDATION metrics
+2025-08-27 10:25:14: Epoch: 027 Test results, ap: mae: 1.353, rse: 5.562, pcc: -0.210, kcc: -0.161
+2025-08-27 10:25:17: Epoch: 028 Train Loss: 0.107, mae: 0.269, rse: 0.108, pcc: 0.945, kcc: 0.827
+2025-08-27 10:25:17: Epoch: 028 New best VALIDATION metrics
+2025-08-27 10:25:17: Epoch: 028 Test results, ap: mae: 1.350, rse: 5.491, pcc: -0.216, kcc: -0.158
+2025-08-27 10:25:21: Epoch: 029 Train Loss: 0.112, mae: 0.280, rse: 0.109, pcc: 0.945, kcc: 0.821
+2025-08-27 10:25:21: Epoch: 029 Test results, ap: mae: 1.345, rse: 5.458, pcc: -0.223, kcc: -0.174
+2025-08-27 10:25:24: Epoch: 030 Train Loss: 0.119, mae: 0.291, rse: 0.110, pcc: 0.946, kcc: 0.822
+2025-08-27 10:25:24: Epoch: 030 Test results, ap: mae: 1.264, rse: 4.922, pcc: -0.194, kcc: -0.151
+2025-08-27 10:25:27: Epoch: 031 Train Loss: 0.100, mae: 0.250, rse: 0.104, pcc: 0.948, kcc: 0.837
+2025-08-27 10:25:27: Epoch: 031 New best VALIDATION metrics
+2025-08-27 10:25:28: Epoch: 031 Test results, ap: mae: 1.321, rse: 5.223, pcc: -0.201, kcc: -0.155
+2025-08-27 10:25:31: Epoch: 032 Train Loss: 0.086, mae: 0.247, rse: 0.103, pcc: 0.948, kcc: 0.839
+2025-08-27 10:25:31: Epoch: 032 New best VALIDATION metrics
+2025-08-27 10:25:31: Epoch: 032 Test results, ap: mae: 1.361, rse: 5.479, pcc: -0.200, kcc: -0.157
+2025-08-27 10:25:34: Epoch: 033 Train Loss: 0.086, mae: 0.249, rse: 0.107, pcc: 0.947, kcc: 0.837
+2025-08-27 10:25:34: Epoch: 033 Test results, ap: mae: 1.388, rse: 5.656, pcc: -0.198, kcc: -0.155
+2025-08-27 10:25:37: Epoch: 034 Train Loss: 0.087, mae: 0.249, rse: 0.106, pcc: 0.948, kcc: 0.840
+2025-08-27 10:25:38: Epoch: 034 Test results, ap: mae: 1.399, rse: 5.764, pcc: -0.188, kcc: -0.143
+2025-08-27 10:25:41: Epoch: 035 Train Loss: 0.082, mae: 0.243, rse: 0.102, pcc: 0.949, kcc: 0.840
+2025-08-27 10:25:41: Epoch: 035 New best VALIDATION metrics
+2025-08-27 10:25:41: Epoch: 035 Test results, ap: mae: 1.334, rse: 5.280, pcc: -0.179, kcc: -0.141
+2025-08-27 10:25:44: Epoch: 036 Train Loss: 0.086, mae: 0.239, rse: 0.103, pcc: 0.948, kcc: 0.843
+2025-08-27 10:25:44: Epoch: 036 New best VALIDATION metrics
+2025-08-27 10:25:45: Epoch: 036 Test results, ap: mae: 1.356, rse: 5.416, pcc: -0.180, kcc: -0.139
+2025-08-27 10:25:48: Epoch: 037 Train Loss: 0.084, mae: 0.243, rse: 0.104, pcc: 0.948, kcc: 0.840
+2025-08-27 10:25:48: Epoch: 037 Test results, ap: mae: 1.359, rse: 5.459, pcc: -0.185, kcc: -0.151
+2025-08-27 10:25:51: Epoch: 038 Train Loss: 0.082, mae: 0.240, rse: 0.101, pcc: 0.949, kcc: 0.844
+2025-08-27 10:25:51: Epoch: 038 New best VALIDATION metrics
+2025-08-27 10:25:52: Epoch: 038 Test results, ap: mae: 1.359, rse: 5.429, pcc: -0.172, kcc: -0.135
+2025-08-27 10:25:55: Epoch: 039 Train Loss: 0.083, mae: 0.241, rse: 0.104, pcc: 0.948, kcc: 0.846
+2025-08-27 10:25:55: Epoch: 039 Test results, ap: mae: 1.373, rse: 5.556, pcc: -0.179, kcc: -0.141
+2025-08-27 10:25:58: Epoch: 040 Train Loss: 0.083, mae: 0.241, rse: 0.099, pcc: 0.950, kcc: 0.841
+2025-08-27 10:25:58: Epoch: 040 Test results, ap: mae: 1.303, rse: 5.008, pcc: -0.171, kcc: -0.132
+2025-08-27 10:26:01: Epoch: 041 Train Loss: 0.078, mae: 0.220, rse: 0.098, pcc: 0.951, kcc: 0.850
+2025-08-27 10:26:01: Epoch: 041 New best VALIDATION metrics
+2025-08-27 10:26:02: Epoch: 041 Test results, ap: mae: 1.328, rse: 5.205, pcc: -0.175, kcc: -0.143
+2025-08-27 10:26:05: Epoch: 042 Train Loss: 0.073, mae: 0.224, rse: 0.098, pcc: 0.951, kcc: 0.851
+2025-08-27 10:26:05: Epoch: 042 Test results, ap: mae: 1.322, rse: 5.127, pcc: -0.174, kcc: -0.139
+2025-08-27 10:26:08: Epoch: 043 Train Loss: 0.072, mae: 0.216, rse: 0.098, pcc: 0.951, kcc: 0.854
+2025-08-27 10:26:08: Epoch: 043 New best VALIDATION metrics
+2025-08-27 10:26:09: Epoch: 043 Test results, ap: mae: 1.346, rse: 5.277, pcc: -0.175, kcc: -0.135
+2025-08-27 10:26:12: Epoch: 044 Train Loss: 0.075, mae: 0.219, rse: 0.098, pcc: 0.951, kcc: 0.853
+2025-08-27 10:26:12: Epoch: 044 Test results, ap: mae: 1.339, rse: 5.234, pcc: -0.173, kcc: -0.138
+2025-08-27 10:26:15: Epoch: 045 Train Loss: 0.074, mae: 0.218, rse: 0.098, pcc: 0.951, kcc: 0.853
+2025-08-27 10:26:15: Epoch: 045 Test results, ap: mae: 1.328, rse: 5.153, pcc: -0.167, kcc: -0.130
+2025-08-27 10:26:18: Epoch: 046 Train Loss: 0.072, mae: 0.224, rse: 0.099, pcc: 0.950, kcc: 0.851
+2025-08-27 10:26:19: Epoch: 046 Test results, ap: mae: 1.299, rse: 4.933, pcc: -0.159, kcc: -0.125
+2025-08-27 10:26:22: Epoch: 047 Train Loss: 0.070, mae: 0.219, rse: 0.100, pcc: 0.950, kcc: 0.853
+2025-08-27 10:26:22: Epoch: 047 Test results, ap: mae: 1.326, rse: 5.156, pcc: -0.166, kcc: -0.135
+2025-08-27 10:26:25: Epoch: 048 Train Loss: 0.066, mae: 0.222, rse: 0.102, pcc: 0.949, kcc: 0.852
+2025-08-27 10:26:26: Epoch: 048 Test results, ap: mae: 1.349, rse: 5.366, pcc: -0.179, kcc: -0.145
+2025-08-27 10:26:29: Epoch: 049 Train Loss: 0.069, mae: 0.218, rse: 0.100, pcc: 0.950, kcc: 0.855
+2025-08-27 10:26:29: Epoch: 049 Test results, ap: mae: 1.308, rse: 5.082, pcc: -0.173, kcc: -0.140
+2025-08-27 10:26:32: Epoch: 050 Train Loss: 0.068, mae: 0.220, rse: 0.099, pcc: 0.951, kcc: 0.855
+2025-08-27 10:26:32: Epoch: 050 Test results, ap: mae: 1.311, rse: 5.083, pcc: -0.171, kcc: -0.134
+2025-08-27 10:26:32: used time 0.05h
+2025-08-27 10:26:32: Running Cross Validation 1
+2025-08-27 10:26:32: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-27 10:26:35: Epoch: 001 Train Loss: 2.748, mae: 1.219, rse: 0.941, pcc: 0.396, kcc: 0.266
+2025-08-27 10:26:35: Epoch: 001 New best VALIDATION metrics
+2025-08-27 10:26:36: Epoch: 001 Test results, ap: mae: 0.567, rse: 1.115, pcc: -0.122, kcc: -0.078
+2025-08-27 10:26:39: Epoch: 002 Train Loss: 2.436, mae: 1.157, rse: 0.851, pcc: 0.426, kcc: 0.273
+2025-08-27 10:26:39: Epoch: 002 New best VALIDATION metrics
+2025-08-27 10:26:39: Epoch: 002 Test results, ap: mae: 0.571, rse: 1.085, pcc: 0.155, kcc: 0.067
+2025-08-27 10:26:42: Epoch: 003 Train Loss: 2.092, mae: 1.062, rse: 0.700, pcc: 0.570, kcc: 0.385
+2025-08-27 10:26:42: Epoch: 003 New best VALIDATION metrics
+2025-08-27 10:26:43: Epoch: 003 Test results, ap: mae: 0.633, rse: 1.219, pcc: 0.159, kcc: 0.114
+2025-08-27 10:26:46: Epoch: 004 Train Loss: 1.756, mae: 0.941, rse: 0.573, pcc: 0.654, kcc: 0.461
+2025-08-27 10:26:46: Epoch: 004 New best VALIDATION metrics
+2025-08-27 10:26:46: Epoch: 004 Test results, ap: mae: 0.964, rse: 2.748, pcc: 0.115, kcc: 0.093
+2025-08-27 10:26:49: Epoch: 005 Train Loss: 1.387, mae: 0.843, rse: 0.472, pcc: 0.730, kcc: 0.519
+2025-08-27 10:26:49: Epoch: 005 New best VALIDATION metrics
+2025-08-27 10:26:49: Epoch: 005 Test results, ap: mae: 1.037, rse: 3.404, pcc: 0.249, kcc: 0.169
+2025-08-27 10:26:52: Epoch: 006 Train Loss: 1.203, mae: 0.774, rse: 0.416, pcc: 0.766, kcc: 0.569
+2025-08-27 10:26:52: Epoch: 006 New best VALIDATION metrics
+2025-08-27 10:26:53: Epoch: 006 Test results, ap: mae: 0.950, rse: 2.978, pcc: 0.279, kcc: 0.191
+2025-08-27 10:26:56: Epoch: 007 Train Loss: 1.010, mae: 0.712, rse: 0.366, pcc: 0.797, kcc: 0.608
+2025-08-27 10:26:56: Epoch: 007 New best VALIDATION metrics
+2025-08-27 10:26:56: Epoch: 007 Test results, ap: mae: 0.847, rse: 2.397, pcc: 0.229, kcc: 0.168
+2025-08-27 10:26:59: Epoch: 008 Train Loss: 0.892, mae: 0.698, rse: 0.354, pcc: 0.811, kcc: 0.624
+2025-08-27 10:26:59: Epoch: 008 New best VALIDATION metrics
+2025-08-27 10:27:00: Epoch: 008 Test results, ap: mae: 0.824, rse: 2.263, pcc: 0.290, kcc: 0.202
+2025-08-27 10:27:03: Epoch: 009 Train Loss: 0.708, mae: 0.635, rse: 0.299, pcc: 0.838, kcc: 0.656
+2025-08-27 10:27:03: Epoch: 009 New best VALIDATION metrics
+2025-08-27 10:27:03: Epoch: 009 Test results, ap: mae: 0.908, rse: 2.604, pcc: 0.296, kcc: 0.187
+2025-08-27 10:27:06: Epoch: 010 Train Loss: 0.657, mae: 0.586, rse: 0.274, pcc: 0.856, kcc: 0.679
+2025-08-27 10:27:06: Epoch: 010 New best VALIDATION metrics
+2025-08-27 10:27:06: Epoch: 010 Test results, ap: mae: 1.083, rse: 3.885, pcc: 0.291, kcc: 0.214
+2025-08-27 10:27:09: Epoch: 011 Train Loss: 0.489, mae: 0.482, rse: 0.200, pcc: 0.895, kcc: 0.737
+2025-08-27 10:27:09: Epoch: 011 New best VALIDATION metrics
+2025-08-27 10:27:10: Epoch: 011 Test results, ap: mae: 1.021, rse: 3.342, pcc: 0.187, kcc: 0.116
+2025-08-27 10:27:10: Epoch: 011 New best TEST metrics
+2025-08-27 10:27:13: Epoch: 012 Train Loss: 0.404, mae: 0.455, rse: 0.182, pcc: 0.904, kcc: 0.749
+2025-08-27 10:27:13: Epoch: 012 New best VALIDATION metrics
+2025-08-27 10:27:13: Epoch: 012 Test results, ap: mae: 1.016, rse: 3.491, pcc: 0.192, kcc: 0.131
+2025-08-27 10:27:16: Epoch: 013 Train Loss: 0.337, mae: 0.416, rse: 0.172, pcc: 0.911, kcc: 0.763
+2025-08-27 10:27:16: Epoch: 013 New best VALIDATION metrics
+2025-08-27 10:27:17: Epoch: 013 Test results, ap: mae: 1.102, rse: 3.883, pcc: 0.192, kcc: 0.121
+2025-08-27 10:27:20: Epoch: 014 Train Loss: 0.308, mae: 0.401, rse: 0.156, pcc: 0.920, kcc: 0.775
+2025-08-27 10:27:20: Epoch: 014 New best VALIDATION metrics
+2025-08-27 10:27:20: Epoch: 014 Test results, ap: mae: 1.072, rse: 3.634, pcc: 0.173, kcc: 0.094
+2025-08-27 10:27:23: Epoch: 015 Train Loss: 0.276, mae: 0.389, rse: 0.156, pcc: 0.920, kcc: 0.783
+2025-08-27 10:27:23: Epoch: 015 New best VALIDATION metrics
+2025-08-27 10:27:23: Epoch: 015 Test results, ap: mae: 1.045, rse: 3.507, pcc: 0.240, kcc: 0.147
+2025-08-27 10:27:27: Epoch: 016 Train Loss: 0.261, mae: 0.383, rse: 0.144, pcc: 0.926, kcc: 0.792
+2025-08-27 10:27:27: Epoch: 016 New best VALIDATION metrics
+2025-08-27 10:27:27: Epoch: 016 Test results, ap: mae: 0.985, rse: 3.023, pcc: 0.176, kcc: 0.092
+2025-08-27 10:27:27: Epoch: 016 New best TEST metrics
+2025-08-27 10:27:30: Epoch: 017 Train Loss: 0.237, mae: 0.376, rse: 0.139, pcc: 0.928, kcc: 0.787
+2025-08-27 10:27:30: Epoch: 017 New best VALIDATION metrics
+2025-08-27 10:27:30: Epoch: 017 Test results, ap: mae: 1.024, rse: 3.297, pcc: 0.173, kcc: 0.076
+2025-08-27 10:27:33: Epoch: 018 Train Loss: 0.221, mae: 0.366, rse: 0.135, pcc: 0.931, kcc: 0.789
+2025-08-27 10:27:33: Epoch: 018 New best VALIDATION metrics
+2025-08-27 10:27:34: Epoch: 018 Test results, ap: mae: 0.946, rse: 2.839, pcc: 0.256, kcc: 0.144
+2025-08-27 10:27:34: Epoch: 018 New best TEST metrics
+2025-08-27 10:27:37: Epoch: 019 Train Loss: 0.193, mae: 0.372, rse: 0.132, pcc: 0.932, kcc: 0.783
+2025-08-27 10:27:37: Epoch: 019 Test results, ap: mae: 1.113, rse: 4.072, pcc: 0.205, kcc: 0.094
+2025-08-27 10:27:40: Epoch: 020 Train Loss: 0.182, mae: 0.314, rse: 0.112, pcc: 0.943, kcc: 0.815
+2025-08-27 10:27:40: Epoch: 020 New best VALIDATION metrics
+2025-08-27 10:27:41: Epoch: 020 Test results, ap: mae: 0.995, rse: 3.139, pcc: 0.212, kcc: 0.111
+2025-08-27 10:27:44: Epoch: 021 Train Loss: 0.140, mae: 0.267, rse: 0.101, pcc: 0.949, kcc: 0.836
+2025-08-27 10:27:44: Epoch: 021 New best VALIDATION metrics
+2025-08-27 10:27:44: Epoch: 021 Test results, ap: mae: 1.018, rse: 3.384, pcc: 0.207, kcc: 0.090
+2025-08-27 10:27:47: Epoch: 022 Train Loss: 0.124, mae: 0.277, rse: 0.101, pcc: 0.948, kcc: 0.832
+2025-08-27 10:27:47: Epoch: 022 Test results, ap: mae: 1.005, rse: 3.350, pcc: 0.205, kcc: 0.089
+2025-08-27 10:27:50: Epoch: 023 Train Loss: 0.127, mae: 0.257, rse: 0.100, pcc: 0.950, kcc: 0.841
+2025-08-27 10:27:50: Epoch: 023 New best VALIDATION metrics
+2025-08-27 10:27:51: Epoch: 023 Test results, ap: mae: 1.082, rse: 3.818, pcc: 0.212, kcc: 0.095
+2025-08-27 10:27:54: Epoch: 024 Train Loss: 0.116, mae: 0.250, rse: 0.093, pcc: 0.952, kcc: 0.846
+2025-08-27 10:27:54: Epoch: 024 New best VALIDATION metrics
+2025-08-27 10:27:54: Epoch: 024 Test results, ap: mae: 0.980, rse: 3.157, pcc: 0.212, kcc: 0.086
+2025-08-27 10:27:57: Epoch: 025 Train Loss: 0.115, mae: 0.261, rse: 0.096, pcc: 0.952, kcc: 0.845
+2025-08-27 10:27:58: Epoch: 025 Test results, ap: mae: 1.035, rse: 3.501, pcc: 0.205, kcc: 0.085
+2025-08-27 10:28:01: Epoch: 026 Train Loss: 0.115, mae: 0.273, rse: 0.098, pcc: 0.951, kcc: 0.835
+2025-08-27 10:28:01: Epoch: 026 Test results, ap: mae: 0.987, rse: 3.212, pcc: 0.234, kcc: 0.093
+2025-08-27 10:28:04: Epoch: 027 Train Loss: 0.104, mae: 0.259, rse: 0.097, pcc: 0.951, kcc: 0.841
+2025-08-27 10:28:04: Epoch: 027 Test results, ap: mae: 0.999, rse: 3.210, pcc: 0.236, kcc: 0.108
+2025-08-27 10:28:07: Epoch: 028 Train Loss: 0.098, mae: 0.246, rse: 0.094, pcc: 0.952, kcc: 0.844
+2025-08-27 10:28:07: Epoch: 028 New best VALIDATION metrics
+2025-08-27 10:28:08: Epoch: 028 Test results, ap: mae: 0.991, rse: 3.179, pcc: 0.228, kcc: 0.093
+2025-08-27 10:28:11: Epoch: 029 Train Loss: 0.096, mae: 0.253, rse: 0.098, pcc: 0.951, kcc: 0.841
+2025-08-27 10:28:11: Epoch: 029 Test results, ap: mae: 0.974, rse: 3.076, pcc: 0.234, kcc: 0.096
+2025-08-27 10:28:14: Epoch: 030 Train Loss: 0.100, mae: 0.243, rse: 0.092, pcc: 0.953, kcc: 0.850
+2025-08-27 10:28:14: Epoch: 030 New best VALIDATION metrics
+2025-08-27 10:28:15: Epoch: 030 Test results, ap: mae: 0.960, rse: 2.987, pcc: 0.221, kcc: 0.075
+2025-08-27 10:28:18: Epoch: 031 Train Loss: 0.074, mae: 0.210, rse: 0.087, pcc: 0.956, kcc: 0.864
+2025-08-27 10:28:18: Epoch: 031 New best VALIDATION metrics
+2025-08-27 10:28:18: Epoch: 031 Test results, ap: mae: 1.005, rse: 3.308, pcc: 0.216, kcc: 0.079
+2025-08-27 10:28:21: Epoch: 032 Train Loss: 0.077, mae: 0.221, rse: 0.085, pcc: 0.957, kcc: 0.861
+2025-08-27 10:28:21: Epoch: 032 Test results, ap: mae: 0.958, rse: 3.017, pcc: 0.212, kcc: 0.071
+2025-08-27 10:28:25: Epoch: 033 Train Loss: 0.074, mae: 0.202, rse: 0.084, pcc: 0.957, kcc: 0.867
+2025-08-27 10:28:25: Epoch: 033 New best VALIDATION metrics
+2025-08-27 10:28:25: Epoch: 033 Test results, ap: mae: 0.980, rse: 3.136, pcc: 0.202, kcc: 0.068
+2025-08-27 10:28:28: Epoch: 034 Train Loss: 0.073, mae: 0.217, rse: 0.087, pcc: 0.956, kcc: 0.860
+2025-08-27 10:28:28: Epoch: 034 Test results, ap: mae: 0.975, rse: 3.103, pcc: 0.219, kcc: 0.071
+2025-08-27 10:28:31: Epoch: 035 Train Loss: 0.074, mae: 0.214, rse: 0.088, pcc: 0.956, kcc: 0.863
+2025-08-27 10:28:32: Epoch: 035 Test results, ap: mae: 1.013, rse: 3.328, pcc: 0.209, kcc: 0.073
+2025-08-27 10:28:35: Epoch: 036 Train Loss: 0.069, mae: 0.197, rse: 0.085, pcc: 0.957, kcc: 0.869
+2025-08-27 10:28:35: Epoch: 036 New best VALIDATION metrics
+2025-08-27 10:28:35: Epoch: 036 Test results, ap: mae: 1.001, rse: 3.276, pcc: 0.212, kcc: 0.073
+2025-08-27 10:28:38: Epoch: 037 Train Loss: 0.067, mae: 0.204, rse: 0.086, pcc: 0.956, kcc: 0.867
+2025-08-27 10:28:39: Epoch: 037 Test results, ap: mae: 1.005, rse: 3.280, pcc: 0.214, kcc: 0.083
+2025-08-27 10:28:42: Epoch: 038 Train Loss: 0.068, mae: 0.190, rse: 0.084, pcc: 0.958, kcc: 0.871
+2025-08-27 10:28:42: Epoch: 038 New best VALIDATION metrics
+2025-08-27 10:28:42: Epoch: 038 Test results, ap: mae: 1.010, rse: 3.323, pcc: 0.210, kcc: 0.080
+2025-08-27 10:28:45: Epoch: 039 Train Loss: 0.063, mae: 0.197, rse: 0.082, pcc: 0.958, kcc: 0.872
+2025-08-27 10:28:45: Epoch: 039 Test results, ap: mae: 0.999, rse: 3.282, pcc: 0.207, kcc: 0.072
+2025-08-27 10:28:48: Epoch: 040 Train Loss: 0.064, mae: 0.205, rse: 0.084, pcc: 0.957, kcc: 0.873
+2025-08-27 10:28:49: Epoch: 040 Test results, ap: mae: 0.992, rse: 3.208, pcc: 0.202, kcc: 0.077
+2025-08-27 10:28:52: Epoch: 041 Train Loss: 0.059, mae: 0.183, rse: 0.082, pcc: 0.959, kcc: 0.878
+2025-08-27 10:28:52: Epoch: 041 New best VALIDATION metrics
+2025-08-27 10:28:52: Epoch: 041 Test results, ap: mae: 1.011, rse: 3.327, pcc: 0.203, kcc: 0.068
+2025-08-27 10:28:55: Epoch: 042 Train Loss: 0.055, mae: 0.182, rse: 0.082, pcc: 0.958, kcc: 0.879
+2025-08-27 10:28:55: Epoch: 042 New best VALIDATION metrics
+2025-08-27 10:28:55: Epoch: 042 Test results, ap: mae: 0.991, rse: 3.200, pcc: 0.204, kcc: 0.074
+2025-08-27 10:28:58: Epoch: 043 Train Loss: 0.055, mae: 0.184, rse: 0.081, pcc: 0.959, kcc: 0.879
+2025-08-27 10:28:59: Epoch: 043 Test results, ap: mae: 1.011, rse: 3.356, pcc: 0.198, kcc: 0.068
+2025-08-27 10:29:02: Epoch: 044 Train Loss: 0.060, mae: 0.192, rse: 0.084, pcc: 0.957, kcc: 0.876
+2025-08-27 10:29:02: Epoch: 044 Test results, ap: mae: 1.003, rse: 3.303, pcc: 0.200, kcc: 0.067
+2025-08-27 10:29:05: Epoch: 045 Train Loss: 0.049, mae: 0.173, rse: 0.081, pcc: 0.959, kcc: 0.883
+2025-08-27 10:29:05: Epoch: 045 New best VALIDATION metrics
+2025-08-27 10:29:06: Epoch: 045 Test results, ap: mae: 0.997, rse: 3.286, pcc: 0.197, kcc: 0.066
+2025-08-27 10:29:09: Epoch: 046 Train Loss: 0.055, mae: 0.183, rse: 0.083, pcc: 0.958, kcc: 0.879
+2025-08-27 10:29:09: Epoch: 046 Test results, ap: mae: 1.011, rse: 3.368, pcc: 0.202, kcc: 0.070
+2025-08-27 10:29:12: Epoch: 047 Train Loss: 0.053, mae: 0.178, rse: 0.081, pcc: 0.959, kcc: 0.882
+2025-08-27 10:29:12: Epoch: 047 Test results, ap: mae: 1.007, rse: 3.315, pcc: 0.203, kcc: 0.063
+2025-08-27 10:29:15: Epoch: 048 Train Loss: 0.055, mae: 0.181, rse: 0.083, pcc: 0.959, kcc: 0.880
+2025-08-27 10:29:16: Epoch: 048 Test results, ap: mae: 1.023, rse: 3.431, pcc: 0.207, kcc: 0.071
+2025-08-27 10:29:19: Epoch: 049 Train Loss: 0.053, mae: 0.173, rse: 0.080, pcc: 0.959, kcc: 0.884
+2025-08-27 10:29:19: Epoch: 049 New best VALIDATION metrics
+2025-08-27 10:29:19: Epoch: 049 Test results, ap: mae: 0.998, rse: 3.281, pcc: 0.195, kcc: 0.061
+2025-08-27 10:29:22: Epoch: 050 Train Loss: 0.053, mae: 0.178, rse: 0.080, pcc: 0.959, kcc: 0.881
+2025-08-27 10:29:23: Epoch: 050 Test results, ap: mae: 0.985, rse: 3.196, pcc: 0.200, kcc: 0.063
+2025-08-27 10:29:23: used time 0.05h
+2025-08-27 10:29:23: Running Cross Validation 2
+2025-08-27 10:29:23: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-27 10:29:26: Epoch: 001 Train Loss: 2.803, mae: 1.169, rse: 0.876, pcc: 0.425, kcc: 0.281
+2025-08-27 10:29:26: Epoch: 001 New best VALIDATION metrics
+2025-08-27 10:29:26: Epoch: 001 Test results, ap: mae: 0.623, rse: 1.341, pcc: -0.136, kcc: -0.085
+2025-08-27 10:29:29: Epoch: 002 Train Loss: 2.401, mae: 1.044, rse: 0.701, pcc: 0.560, kcc: 0.395
+2025-08-27 10:29:29: Epoch: 002 New best VALIDATION metrics
+2025-08-27 10:29:30: Epoch: 002 Test results, ap: mae: 0.794, rse: 2.004, pcc: -0.099, kcc: -0.031
+2025-08-27 10:29:33: Epoch: 003 Train Loss: 2.102, mae: 0.968, rse: 0.626, pcc: 0.622, kcc: 0.435
+2025-08-27 10:29:33: Epoch: 003 New best VALIDATION metrics
+2025-08-27 10:29:33: Epoch: 003 Test results, ap: mae: 1.050, rse: 3.471, pcc: 0.089, kcc: 0.086
+2025-08-27 10:29:36: Epoch: 004 Train Loss: 1.585, mae: 0.884, rse: 0.508, pcc: 0.704, kcc: 0.495
+2025-08-27 10:29:36: Epoch: 004 New best VALIDATION metrics
+2025-08-27 10:29:36: Epoch: 004 Test results, ap: mae: 0.971, rse: 3.189, pcc: 0.139, kcc: 0.119
+2025-08-27 10:29:39: Epoch: 005 Train Loss: 1.297, mae: 0.771, rse: 0.409, pcc: 0.770, kcc: 0.554
+2025-08-27 10:29:39: Epoch: 005 New best VALIDATION metrics
+2025-08-27 10:29:40: Epoch: 005 Test results, ap: mae: 1.174, rse: 4.728, pcc: 0.041, kcc: 0.060
+2025-08-27 10:29:43: Epoch: 006 Train Loss: 1.069, mae: 0.733, rse: 0.392, pcc: 0.781, kcc: 0.588
+2025-08-27 10:29:43: Epoch: 006 New best VALIDATION metrics
+2025-08-27 10:29:43: Epoch: 006 Test results, ap: mae: 1.125, rse: 4.256, pcc: 0.091, kcc: 0.070
+2025-08-27 10:29:46: Epoch: 007 Train Loss: 0.920, mae: 0.680, rse: 0.339, pcc: 0.816, kcc: 0.623
+2025-08-27 10:29:46: Epoch: 007 New best VALIDATION metrics
+2025-08-27 10:29:47: Epoch: 007 Test results, ap: mae: 0.989, rse: 3.039, pcc: 0.066, kcc: 0.053
+2025-08-27 10:29:50: Epoch: 008 Train Loss: 0.746, mae: 0.614, rse: 0.297, pcc: 0.839, kcc: 0.658
+2025-08-27 10:29:50: Epoch: 008 New best VALIDATION metrics
+2025-08-27 10:29:50: Epoch: 008 Test results, ap: mae: 1.026, rse: 3.351, pcc: 0.107, kcc: 0.087
+2025-08-27 10:29:53: Epoch: 009 Train Loss: 0.627, mae: 0.554, rse: 0.257, pcc: 0.862, kcc: 0.698
+2025-08-27 10:29:53: Epoch: 009 New best VALIDATION metrics
+2025-08-27 10:29:53: Epoch: 009 Test results, ap: mae: 0.993, rse: 3.055, pcc: 0.194, kcc: 0.103
+2025-08-27 10:29:56: Epoch: 010 Train Loss: 0.504, mae: 0.584, rse: 0.268, pcc: 0.858, kcc: 0.669
+2025-08-27 10:29:57: Epoch: 010 Test results, ap: mae: 1.008, rse: 3.177, pcc: 0.149, kcc: 0.077
+2025-08-27 10:30:00: Epoch: 011 Train Loss: 0.399, mae: 0.433, rse: 0.207, pcc: 0.893, kcc: 0.750
+2025-08-27 10:30:00: Epoch: 011 New best VALIDATION metrics
+2025-08-27 10:30:00: Epoch: 011 Test results, ap: mae: 1.045, rse: 3.216, pcc: 0.160, kcc: 0.087
+2025-08-27 10:30:00: Epoch: 011 New best TEST metrics
+2025-08-27 10:30:03: Epoch: 012 Train Loss: 0.311, mae: 0.403, rse: 0.182, pcc: 0.905, kcc: 0.773
+2025-08-27 10:30:03: Epoch: 012 New best VALIDATION metrics
+2025-08-27 10:30:04: Epoch: 012 Test results, ap: mae: 1.022, rse: 3.181, pcc: 0.157, kcc: 0.087
+2025-08-27 10:30:04: Epoch: 012 New best TEST metrics
+2025-08-27 10:30:07: Epoch: 013 Train Loss: 0.256, mae: 0.403, rse: 0.188, pcc: 0.902, kcc: 0.778
+2025-08-27 10:30:07: Epoch: 013 Test results, ap: mae: 1.025, rse: 3.208, pcc: 0.153, kcc: 0.071
+2025-08-27 10:30:10: Epoch: 014 Train Loss: 0.224, mae: 0.390, rse: 0.187, pcc: 0.907, kcc: 0.776
+2025-08-27 10:30:10: Epoch: 014 New best VALIDATION metrics
+2025-08-27 10:30:11: Epoch: 014 Test results, ap: mae: 1.099, rse: 3.603, pcc: 0.162, kcc: 0.067
+2025-08-27 10:30:14: Epoch: 015 Train Loss: 0.235, mae: 0.344, rse: 0.162, pcc: 0.916, kcc: 0.799
+2025-08-27 10:30:14: Epoch: 015 New best VALIDATION metrics
+2025-08-27 10:30:14: Epoch: 015 Test results, ap: mae: 0.999, rse: 3.028, pcc: 0.203, kcc: 0.102
+2025-08-27 10:30:14: Epoch: 015 New best TEST metrics
+2025-08-27 10:30:17: Epoch: 016 Train Loss: 0.188, mae: 0.329, rse: 0.159, pcc: 0.919, kcc: 0.808
+2025-08-27 10:30:17: Epoch: 016 New best VALIDATION metrics
+2025-08-27 10:30:17: Epoch: 016 Test results, ap: mae: 1.035, rse: 3.253, pcc: 0.163, kcc: 0.077
+2025-08-27 10:30:20: Epoch: 017 Train Loss: 0.184, mae: 0.351, rse: 0.158, pcc: 0.918, kcc: 0.805
+2025-08-27 10:30:21: Epoch: 017 Test results, ap: mae: 0.980, rse: 2.966, pcc: 0.199, kcc: 0.097
+2025-08-27 10:30:21: Epoch: 017 New best TEST metrics
+2025-08-27 10:30:24: Epoch: 018 Train Loss: 0.183, mae: 0.351, rse: 0.170, pcc: 0.915, kcc: 0.804
+2025-08-27 10:30:24: Epoch: 018 Test results, ap: mae: 1.016, rse: 3.087, pcc: 0.186, kcc: 0.086
+2025-08-27 10:30:27: Epoch: 019 Train Loss: 0.157, mae: 0.354, rse: 0.179, pcc: 0.908, kcc: 0.794
+2025-08-27 10:30:28: Epoch: 019 Test results, ap: mae: 1.011, rse: 3.111, pcc: 0.162, kcc: 0.075
+2025-08-27 10:30:31: Epoch: 020 Train Loss: 0.167, mae: 0.349, rse: 0.161, pcc: 0.917, kcc: 0.792
+2025-08-27 10:30:31: Epoch: 020 Test results, ap: mae: 1.000, rse: 3.001, pcc: 0.170, kcc: 0.072
+2025-08-27 10:30:34: Epoch: 021 Train Loss: 0.125, mae: 0.290, rse: 0.161, pcc: 0.917, kcc: 0.824
+2025-08-27 10:30:34: Epoch: 021 New best VALIDATION metrics
+2025-08-27 10:30:34: Epoch: 021 Test results, ap: mae: 1.093, rse: 3.620, pcc: 0.192, kcc: 0.090
+2025-08-27 10:30:37: Epoch: 022 Train Loss: 0.120, mae: 0.289, rse: 0.162, pcc: 0.917, kcc: 0.826
+2025-08-27 10:30:37: Epoch: 022 New best VALIDATION metrics
+2025-08-27 10:30:38: Epoch: 022 Test results, ap: mae: 1.125, rse: 3.957, pcc: 0.187, kcc: 0.100
+2025-08-27 10:30:41: Epoch: 023 Train Loss: 0.104, mae: 0.295, rse: 0.162, pcc: 0.916, kcc: 0.825
+2025-08-27 10:30:41: Epoch: 023 Test results, ap: mae: 1.064, rse: 3.408, pcc: 0.189, kcc: 0.090
+2025-08-27 10:30:44: Epoch: 024 Train Loss: 0.102, mae: 0.288, rse: 0.157, pcc: 0.919, kcc: 0.831
+2025-08-27 10:30:44: Epoch: 024 New best VALIDATION metrics
+2025-08-27 10:30:45: Epoch: 024 Test results, ap: mae: 1.098, rse: 3.650, pcc: 0.180, kcc: 0.089
+2025-08-27 10:30:48: Epoch: 025 Train Loss: 0.101, mae: 0.293, rse: 0.161, pcc: 0.920, kcc: 0.827
+2025-08-27 10:30:48: Epoch: 025 Test results, ap: mae: 1.228, rse: 4.519, pcc: 0.188, kcc: 0.092
+2025-08-27 10:30:51: Epoch: 026 Train Loss: 0.102, mae: 0.277, rse: 0.161, pcc: 0.918, kcc: 0.830
+2025-08-27 10:30:51: Epoch: 026 New best VALIDATION metrics
+2025-08-27 10:30:51: Epoch: 026 Test results, ap: mae: 1.191, rse: 4.378, pcc: 0.185, kcc: 0.095
+2025-08-27 10:30:54: Epoch: 027 Train Loss: 0.089, mae: 0.281, rse: 0.158, pcc: 0.919, kcc: 0.833
+2025-08-27 10:30:54: Epoch: 027 New best VALIDATION metrics
+2025-08-27 10:30:55: Epoch: 027 Test results, ap: mae: 1.225, rse: 4.555, pcc: 0.182, kcc: 0.099
+2025-08-27 10:30:58: Epoch: 028 Train Loss: 0.092, mae: 0.281, rse: 0.166, pcc: 0.915, kcc: 0.836
+2025-08-27 10:30:58: Epoch: 028 Test results, ap: mae: 1.180, rse: 4.183, pcc: 0.187, kcc: 0.099
+2025-08-27 10:31:01: Epoch: 029 Train Loss: 0.084, mae: 0.277, rse: 0.157, pcc: 0.919, kcc: 0.837
+2025-08-27 10:31:01: Epoch: 029 New best VALIDATION metrics
+2025-08-27 10:31:02: Epoch: 029 Test results, ap: mae: 1.162, rse: 4.104, pcc: 0.190, kcc: 0.101
+2025-08-27 10:31:05: Epoch: 030 Train Loss: 0.094, mae: 0.273, rse: 0.157, pcc: 0.920, kcc: 0.839
+2025-08-27 10:31:05: Epoch: 030 New best VALIDATION metrics
+2025-08-27 10:31:05: Epoch: 030 Test results, ap: mae: 1.167, rse: 4.019, pcc: 0.194, kcc: 0.099
+2025-08-27 10:31:08: Epoch: 031 Train Loss: 0.082, mae: 0.252, rse: 0.157, pcc: 0.921, kcc: 0.847
+2025-08-27 10:31:08: Epoch: 031 New best VALIDATION metrics
+2025-08-27 10:31:08: Epoch: 031 Test results, ap: mae: 1.230, rse: 4.528, pcc: 0.179, kcc: 0.095
+2025-08-27 10:31:11: Epoch: 032 Train Loss: 0.073, mae: 0.247, rse: 0.153, pcc: 0.921, kcc: 0.849
+2025-08-27 10:31:11: Epoch: 032 New best VALIDATION metrics
+2025-08-27 10:31:12: Epoch: 032 Test results, ap: mae: 1.172, rse: 4.125, pcc: 0.187, kcc: 0.098
+2025-08-27 10:31:15: Epoch: 033 Train Loss: 0.070, mae: 0.252, rse: 0.155, pcc: 0.921, kcc: 0.846
+2025-08-27 10:31:15: Epoch: 033 Test results, ap: mae: 1.254, rse: 4.710, pcc: 0.179, kcc: 0.096
+2025-08-27 10:31:18: Epoch: 034 Train Loss: 0.073, mae: 0.255, rse: 0.155, pcc: 0.921, kcc: 0.848
+2025-08-27 10:31:19: Epoch: 034 Test results, ap: mae: 1.203, rse: 4.388, pcc: 0.182, kcc: 0.096
+2025-08-27 10:31:22: Epoch: 035 Train Loss: 0.074, mae: 0.256, rse: 0.156, pcc: 0.920, kcc: 0.847
+2025-08-27 10:31:22: Epoch: 035 Test results, ap: mae: 1.241, rse: 4.673, pcc: 0.178, kcc: 0.098
+2025-08-27 10:31:25: Epoch: 036 Train Loss: 0.067, mae: 0.248, rse: 0.156, pcc: 0.920, kcc: 0.850
+2025-08-27 10:31:25: Epoch: 036 Test results, ap: mae: 1.210, rse: 4.441, pcc: 0.178, kcc: 0.095
+2025-08-27 10:31:29: Epoch: 037 Train Loss: 0.073, mae: 0.250, rse: 0.158, pcc: 0.920, kcc: 0.852
+2025-08-27 10:31:29: Epoch: 037 Test results, ap: mae: 1.288, rse: 5.050, pcc: 0.187, kcc: 0.105
+2025-08-27 10:31:32: Epoch: 038 Train Loss: 0.068, mae: 0.244, rse: 0.154, pcc: 0.921, kcc: 0.852
+2025-08-27 10:31:32: Epoch: 038 New best VALIDATION metrics
+2025-08-27 10:31:32: Epoch: 038 Test results, ap: mae: 1.239, rse: 4.585, pcc: 0.188, kcc: 0.100
+2025-08-27 10:31:35: Epoch: 039 Train Loss: 0.075, mae: 0.246, rse: 0.153, pcc: 0.922, kcc: 0.854
+2025-08-27 10:31:35: Epoch: 039 New best VALIDATION metrics
+2025-08-27 10:31:36: Epoch: 039 Test results, ap: mae: 1.239, rse: 4.631, pcc: 0.180, kcc: 0.096
+2025-08-27 10:31:39: Epoch: 040 Train Loss: 0.068, mae: 0.245, rse: 0.159, pcc: 0.919, kcc: 0.853
+2025-08-27 10:31:39: Epoch: 040 Test results, ap: mae: 1.267, rse: 4.799, pcc: 0.186, kcc: 0.099
+2025-08-27 10:31:42: Epoch: 041 Train Loss: 0.063, mae: 0.236, rse: 0.156, pcc: 0.921, kcc: 0.856
+2025-08-27 10:31:42: Epoch: 041 New best VALIDATION metrics
+2025-08-27 10:31:43: Epoch: 041 Test results, ap: mae: 1.261, rse: 4.786, pcc: 0.185, kcc: 0.104
+2025-08-27 10:31:46: Epoch: 042 Train Loss: 0.065, mae: 0.237, rse: 0.159, pcc: 0.919, kcc: 0.858
+2025-08-27 10:31:46: Epoch: 042 Test results, ap: mae: 1.244, rse: 4.645, pcc: 0.178, kcc: 0.095
+2025-08-27 10:31:49: Epoch: 043 Train Loss: 0.057, mae: 0.231, rse: 0.156, pcc: 0.921, kcc: 0.861
+2025-08-27 10:31:49: Epoch: 043 New best VALIDATION metrics
+2025-08-27 10:31:49: Epoch: 043 Test results, ap: mae: 1.279, rse: 4.886, pcc: 0.179, kcc: 0.096
+2025-08-27 10:31:52: Epoch: 044 Train Loss: 0.064, mae: 0.229, rse: 0.154, pcc: 0.922, kcc: 0.861
+2025-08-27 10:31:52: Epoch: 044 New best VALIDATION metrics
+2025-08-27 10:31:53: Epoch: 044 Test results, ap: mae: 1.272, rse: 4.840, pcc: 0.177, kcc: 0.091
+2025-08-27 10:31:56: Epoch: 045 Train Loss: 0.060, mae: 0.229, rse: 0.154, pcc: 0.922, kcc: 0.861
+2025-08-27 10:31:56: Epoch: 045 Test results, ap: mae: 1.291, rse: 4.927, pcc: 0.188, kcc: 0.095
+2025-08-27 10:31:59: Epoch: 046 Train Loss: 0.059, mae: 0.233, rse: 0.156, pcc: 0.921, kcc: 0.860
+2025-08-27 10:32:00: Epoch: 046 Test results, ap: mae: 1.281, rse: 4.882, pcc: 0.183, kcc: 0.095
+2025-08-27 10:32:03: Epoch: 047 Train Loss: 0.060, mae: 0.227, rse: 0.154, pcc: 0.922, kcc: 0.860
+2025-08-27 10:32:03: Epoch: 047 New best VALIDATION metrics
+2025-08-27 10:32:03: Epoch: 047 Test results, ap: mae: 1.281, rse: 4.904, pcc: 0.182, kcc: 0.094
+2025-08-27 10:32:06: Epoch: 048 Train Loss: 0.065, mae: 0.231, rse: 0.156, pcc: 0.920, kcc: 0.859
+2025-08-27 10:32:06: Epoch: 048 Test results, ap: mae: 1.259, rse: 4.732, pcc: 0.186, kcc: 0.098
+2025-08-27 10:32:09: Epoch: 049 Train Loss: 0.061, mae: 0.231, rse: 0.155, pcc: 0.920, kcc: 0.859
+2025-08-27 10:32:10: Epoch: 049 Test results, ap: mae: 1.263, rse: 4.752, pcc: 0.179, kcc: 0.093
+2025-08-27 10:32:13: Epoch: 050 Train Loss: 0.060, mae: 0.233, rse: 0.160, pcc: 0.920, kcc: 0.860
+2025-08-27 10:32:13: Epoch: 050 Test results, ap: mae: 1.323, rse: 5.185, pcc: 0.185, kcc: 0.099
+2025-08-27 10:32:13: used time 0.05h
+2025-08-27 10:32:13: Running Cross Validation 3
+2025-08-27 10:32:13: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-27 10:32:16: Epoch: 001 Train Loss: 2.730, mae: 1.193, rse: 0.851, pcc: 0.471, kcc: 0.326
+2025-08-27 10:32:16: Epoch: 001 New best VALIDATION metrics
+2025-08-27 10:32:17: Epoch: 001 Test results, ap: mae: 0.564, rse: 1.071, pcc: 0.100, kcc: 0.047
+2025-08-27 10:32:20: Epoch: 002 Train Loss: 2.074, mae: 1.044, rse: 0.673, pcc: 0.610, kcc: 0.440
+2025-08-27 10:32:20: Epoch: 002 New best VALIDATION metrics
+2025-08-27 10:32:20: Epoch: 002 Test results, ap: mae: 0.631, rse: 1.253, pcc: 0.127, kcc: 0.063
+2025-08-27 10:32:23: Epoch: 003 Train Loss: 1.696, mae: 0.934, rse: 0.572, pcc: 0.657, kcc: 0.465
+2025-08-27 10:32:23: Epoch: 003 New best VALIDATION metrics
+2025-08-27 10:32:23: Epoch: 003 Test results, ap: mae: 0.933, rse: 2.579, pcc: 0.174, kcc: 0.129
+2025-08-27 10:32:26: Epoch: 004 Train Loss: 1.354, mae: 0.920, rse: 0.546, pcc: 0.679, kcc: 0.487
+2025-08-27 10:32:26: Epoch: 004 New best VALIDATION metrics
+2025-08-27 10:32:27: Epoch: 004 Test results, ap: mae: 0.754, rse: 1.821, pcc: -0.020, kcc: 0.016
+2025-08-27 10:32:30: Epoch: 005 Train Loss: 1.210, mae: 0.791, rse: 0.455, pcc: 0.738, kcc: 0.534
+2025-08-27 10:32:30: Epoch: 005 New best VALIDATION metrics
+2025-08-27 10:32:30: Epoch: 005 Test results, ap: mae: 0.855, rse: 2.260, pcc: -0.015, kcc: 0.013
+2025-08-27 10:32:33: Epoch: 006 Train Loss: 1.034, mae: 0.747, rse: 0.419, pcc: 0.776, kcc: 0.575
+2025-08-27 10:32:33: Epoch: 006 New best VALIDATION metrics
+2025-08-27 10:32:34: Epoch: 006 Test results, ap: mae: 1.006, rse: 3.099, pcc: -0.043, kcc: -0.000
+2025-08-27 10:32:37: Epoch: 007 Train Loss: 0.889, mae: 0.680, rse: 0.365, pcc: 0.808, kcc: 0.610
+2025-08-27 10:32:37: Epoch: 007 New best VALIDATION metrics
+2025-08-27 10:32:37: Epoch: 007 Test results, ap: mae: 0.991, rse: 2.931, pcc: 0.008, kcc: 0.018
+2025-08-27 10:32:40: Epoch: 008 Train Loss: 0.759, mae: 0.622, rse: 0.308, pcc: 0.835, kcc: 0.639
+2025-08-27 10:32:40: Epoch: 008 New best VALIDATION metrics
+2025-08-27 10:32:40: Epoch: 008 Test results, ap: mae: 1.014, rse: 3.031, pcc: 0.075, kcc: 0.058
+2025-08-27 10:32:44: Epoch: 009 Train Loss: 0.650, mae: 0.572, rse: 0.278, pcc: 0.851, kcc: 0.670
+2025-08-27 10:32:44: Epoch: 009 New best VALIDATION metrics
+2025-08-27 10:32:44: Epoch: 009 Test results, ap: mae: 0.934, rse: 2.521, pcc: 0.125, kcc: 0.090
+2025-08-27 10:32:47: Epoch: 010 Train Loss: 0.567, mae: 0.515, rse: 0.224, pcc: 0.882, kcc: 0.707
+2025-08-27 10:32:47: Epoch: 010 New best VALIDATION metrics
+2025-08-27 10:32:47: Epoch: 010 Test results, ap: mae: 0.903, rse: 2.356, pcc: 0.138, kcc: 0.105
+2025-08-27 10:32:50: Epoch: 011 Train Loss: 0.405, mae: 0.454, rse: 0.201, pcc: 0.894, kcc: 0.741
+2025-08-27 10:32:50: Epoch: 011 New best VALIDATION metrics
+2025-08-27 10:32:51: Epoch: 011 Test results, ap: mae: 1.061, rse: 3.141, pcc: 0.152, kcc: 0.110
+2025-08-27 10:32:51: Epoch: 011 New best TEST metrics
+2025-08-27 10:32:54: Epoch: 012 Train Loss: 0.334, mae: 0.429, rse: 0.194, pcc: 0.899, kcc: 0.762
+2025-08-27 10:32:54: Epoch: 012 New best VALIDATION metrics
+2025-08-27 10:32:54: Epoch: 012 Test results, ap: mae: 1.136, rse: 3.790, pcc: 0.167, kcc: 0.124
+2025-08-27 10:32:57: Epoch: 013 Train Loss: 0.296, mae: 0.394, rse: 0.180, pcc: 0.907, kcc: 0.780
+2025-08-27 10:32:57: Epoch: 013 New best VALIDATION metrics
+2025-08-27 10:32:58: Epoch: 013 Test results, ap: mae: 1.226, rse: 4.232, pcc: 0.163, kcc: 0.111
+2025-08-27 10:33:01: Epoch: 014 Train Loss: 0.247, mae: 0.378, rse: 0.168, pcc: 0.913, kcc: 0.790
+2025-08-27 10:33:01: Epoch: 014 New best VALIDATION metrics
+2025-08-27 10:33:01: Epoch: 014 Test results, ap: mae: 1.200, rse: 4.079, pcc: 0.195, kcc: 0.121
+2025-08-27 10:33:04: Epoch: 015 Train Loss: 0.241, mae: 0.396, rse: 0.174, pcc: 0.909, kcc: 0.780
+2025-08-27 10:33:04: Epoch: 015 Test results, ap: mae: 1.376, rse: 5.297, pcc: 0.183, kcc: 0.121
+2025-08-27 10:33:07: Epoch: 016 Train Loss: 0.213, mae: 0.376, rse: 0.163, pcc: 0.916, kcc: 0.795
+2025-08-27 10:33:07: Epoch: 016 New best VALIDATION metrics
+2025-08-27 10:33:08: Epoch: 016 Test results, ap: mae: 1.313, rse: 4.879, pcc: 0.198, kcc: 0.124
+2025-08-27 10:33:11: Epoch: 017 Train Loss: 0.196, mae: 0.358, rse: 0.144, pcc: 0.927, kcc: 0.806
+2025-08-27 10:33:11: Epoch: 017 New best VALIDATION metrics
+2025-08-27 10:33:11: Epoch: 017 Test results, ap: mae: 1.359, rse: 5.143, pcc: 0.216, kcc: 0.142
+2025-08-27 10:33:14: Epoch: 018 Train Loss: 0.166, mae: 0.317, rse: 0.141, pcc: 0.927, kcc: 0.817
+2025-08-27 10:33:14: Epoch: 018 New best VALIDATION metrics
+2025-08-27 10:33:15: Epoch: 018 Test results, ap: mae: 1.398, rse: 5.442, pcc: 0.224, kcc: 0.143
+2025-08-27 10:33:18: Epoch: 019 Train Loss: 0.172, mae: 0.329, rse: 0.129, pcc: 0.934, kcc: 0.816
+2025-08-27 10:33:18: Epoch: 019 New best VALIDATION metrics
+2025-08-27 10:33:18: Epoch: 019 Test results, ap: mae: 1.374, rse: 5.202, pcc: 0.241, kcc: 0.154
+2025-08-27 10:33:21: Epoch: 020 Train Loss: 0.215, mae: 0.374, rse: 0.145, pcc: 0.926, kcc: 0.802
+2025-08-27 10:33:21: Epoch: 020 Test results, ap: mae: 1.284, rse: 4.613, pcc: 0.254, kcc: 0.172
+2025-08-27 10:33:25: Epoch: 021 Train Loss: 0.138, mae: 0.298, rse: 0.122, pcc: 0.937, kcc: 0.832
+2025-08-27 10:33:25: Epoch: 021 New best VALIDATION metrics
+2025-08-27 10:33:25: Epoch: 021 Test results, ap: mae: 1.389, rse: 5.288, pcc: 0.244, kcc: 0.167
+2025-08-27 10:33:28: Epoch: 022 Train Loss: 0.116, mae: 0.273, rse: 0.116, pcc: 0.940, kcc: 0.844
+2025-08-27 10:33:28: Epoch: 022 New best VALIDATION metrics
+2025-08-27 10:33:28: Epoch: 022 Test results, ap: mae: 1.415, rse: 5.465, pcc: 0.244, kcc: 0.163
+2025-08-27 10:33:31: Epoch: 023 Train Loss: 0.113, mae: 0.260, rse: 0.115, pcc: 0.941, kcc: 0.849
+2025-08-27 10:33:31: Epoch: 023 New best VALIDATION metrics
+2025-08-27 10:33:32: Epoch: 023 Test results, ap: mae: 1.358, rse: 5.003, pcc: 0.250, kcc: 0.163
+2025-08-27 10:33:35: Epoch: 024 Train Loss: 0.098, mae: 0.251, rse: 0.108, pcc: 0.944, kcc: 0.855
+2025-08-27 10:33:35: Epoch: 024 New best VALIDATION metrics
+2025-08-27 10:33:35: Epoch: 024 Test results, ap: mae: 1.501, rse: 6.151, pcc: 0.257, kcc: 0.170
+2025-08-27 10:33:38: Epoch: 025 Train Loss: 0.100, mae: 0.268, rse: 0.109, pcc: 0.944, kcc: 0.848
+2025-08-27 10:33:39: Epoch: 025 Test results, ap: mae: 1.464, rse: 5.792, pcc: 0.248, kcc: 0.162
+2025-08-27 10:33:42: Epoch: 026 Train Loss: 0.096, mae: 0.276, rse: 0.107, pcc: 0.945, kcc: 0.848
+2025-08-27 10:33:42: Epoch: 026 Test results, ap: mae: 1.424, rse: 5.499, pcc: 0.261, kcc: 0.168
+2025-08-27 10:33:45: Epoch: 027 Train Loss: 0.101, mae: 0.254, rse: 0.104, pcc: 0.947, kcc: 0.859
+2025-08-27 10:33:45: Epoch: 027 New best VALIDATION metrics
+2025-08-27 10:33:45: Epoch: 027 Test results, ap: mae: 1.480, rse: 5.974, pcc: 0.252, kcc: 0.165
+2025-08-27 10:33:48: Epoch: 028 Train Loss: 0.090, mae: 0.253, rse: 0.102, pcc: 0.948, kcc: 0.858
+2025-08-27 10:33:48: Epoch: 028 New best VALIDATION metrics
+2025-08-27 10:33:49: Epoch: 028 Test results, ap: mae: 1.433, rse: 5.574, pcc: 0.251, kcc: 0.164
+2025-08-27 10:33:52: Epoch: 029 Train Loss: 0.089, mae: 0.232, rse: 0.097, pcc: 0.951, kcc: 0.870
+2025-08-27 10:33:52: Epoch: 029 New best VALIDATION metrics
+2025-08-27 10:33:52: Epoch: 029 Test results, ap: mae: 1.464, rse: 5.836, pcc: 0.270, kcc: 0.176
+2025-08-27 10:33:55: Epoch: 030 Train Loss: 0.084, mae: 0.256, rse: 0.102, pcc: 0.948, kcc: 0.859
+2025-08-27 10:33:56: Epoch: 030 Test results, ap: mae: 1.464, rse: 5.781, pcc: 0.273, kcc: 0.177
+2025-08-27 10:33:59: Epoch: 031 Train Loss: 0.077, mae: 0.213, rse: 0.096, pcc: 0.951, kcc: 0.878
+2025-08-27 10:33:59: Epoch: 031 New best VALIDATION metrics
+2025-08-27 10:33:59: Epoch: 031 Test results, ap: mae: 1.538, rse: 6.499, pcc: 0.276, kcc: 0.183
+2025-08-27 10:34:02: Epoch: 032 Train Loss: 0.072, mae: 0.212, rse: 0.095, pcc: 0.952, kcc: 0.877
+2025-08-27 10:34:02: Epoch: 032 New best VALIDATION metrics
+2025-08-27 10:34:02: Epoch: 032 Test results, ap: mae: 1.528, rse: 6.416, pcc: 0.279, kcc: 0.184
+2025-08-27 10:34:05: Epoch: 033 Train Loss: 0.074, mae: 0.217, rse: 0.092, pcc: 0.953, kcc: 0.877
+2025-08-27 10:34:06: Epoch: 033 Test results, ap: mae: 1.505, rse: 6.163, pcc: 0.280, kcc: 0.182
+2025-08-27 10:34:09: Epoch: 034 Train Loss: 0.073, mae: 0.218, rse: 0.092, pcc: 0.953, kcc: 0.882
+2025-08-27 10:34:09: Epoch: 034 New best VALIDATION metrics
+2025-08-27 10:34:09: Epoch: 034 Test results, ap: mae: 1.469, rse: 5.900, pcc: 0.288, kcc: 0.192
+2025-08-27 10:34:12: Epoch: 035 Train Loss: 0.071, mae: 0.209, rse: 0.091, pcc: 0.954, kcc: 0.884
+2025-08-27 10:34:12: Epoch: 035 New best VALIDATION metrics
+2025-08-27 10:34:13: Epoch: 035 Test results, ap: mae: 1.477, rse: 6.004, pcc: 0.285, kcc: 0.191
+2025-08-27 10:34:16: Epoch: 036 Train Loss: 0.066, mae: 0.221, rse: 0.091, pcc: 0.953, kcc: 0.875
+2025-08-27 10:34:16: Epoch: 036 Test results, ap: mae: 1.535, rse: 6.563, pcc: 0.288, kcc: 0.194
+2025-08-27 10:34:19: Epoch: 037 Train Loss: 0.064, mae: 0.210, rse: 0.087, pcc: 0.956, kcc: 0.882
+2025-08-27 10:34:19: Epoch: 037 New best VALIDATION metrics
+2025-08-27 10:34:20: Epoch: 037 Test results, ap: mae: 1.508, rse: 6.318, pcc: 0.294, kcc: 0.201
+2025-08-27 10:34:23: Epoch: 038 Train Loss: 0.065, mae: 0.202, rse: 0.088, pcc: 0.955, kcc: 0.886
+2025-08-27 10:34:23: Epoch: 038 New best VALIDATION metrics
+2025-08-27 10:34:23: Epoch: 038 Test results, ap: mae: 1.506, rse: 6.377, pcc: 0.291, kcc: 0.195
+2025-08-27 10:34:26: Epoch: 039 Train Loss: 0.060, mae: 0.204, rse: 0.089, pcc: 0.955, kcc: 0.887
+2025-08-27 10:34:26: Epoch: 039 Test results, ap: mae: 1.530, rse: 6.624, pcc: 0.286, kcc: 0.189
+2025-08-27 10:34:30: Epoch: 040 Train Loss: 0.063, mae: 0.200, rse: 0.087, pcc: 0.956, kcc: 0.886
+2025-08-27 10:34:30: Epoch: 040 New best VALIDATION metrics
+2025-08-27 10:34:30: Epoch: 040 Test results, ap: mae: 1.496, rse: 6.347, pcc: 0.296, kcc: 0.196
+2025-08-27 10:34:33: Epoch: 041 Train Loss: 0.059, mae: 0.189, rse: 0.084, pcc: 0.957, kcc: 0.892
+2025-08-27 10:34:33: Epoch: 041 New best VALIDATION metrics
+2025-08-27 10:34:33: Epoch: 041 Test results, ap: mae: 1.521, rse: 6.524, pcc: 0.294, kcc: 0.197
+2025-08-27 10:34:36: Epoch: 042 Train Loss: 0.057, mae: 0.188, rse: 0.085, pcc: 0.957, kcc: 0.892
+2025-08-27 10:34:37: Epoch: 042 Test results, ap: mae: 1.535, rse: 6.676, pcc: 0.292, kcc: 0.195
+2025-08-27 10:34:40: Epoch: 043 Train Loss: 0.055, mae: 0.186, rse: 0.086, pcc: 0.957, kcc: 0.894
+2025-08-27 10:34:40: Epoch: 043 New best VALIDATION metrics
+2025-08-27 10:34:40: Epoch: 043 Test results, ap: mae: 1.541, rse: 6.818, pcc: 0.296, kcc: 0.199
+2025-08-27 10:34:43: Epoch: 044 Train Loss: 0.057, mae: 0.192, rse: 0.085, pcc: 0.957, kcc: 0.890
+2025-08-27 10:34:44: Epoch: 044 Test results, ap: mae: 1.512, rse: 6.492, pcc: 0.298, kcc: 0.200
+2025-08-27 10:34:47: Epoch: 045 Train Loss: 0.057, mae: 0.197, rse: 0.086, pcc: 0.956, kcc: 0.888
+2025-08-27 10:34:47: Epoch: 045 Test results, ap: mae: 1.532, rse: 6.674, pcc: 0.294, kcc: 0.197
+2025-08-27 10:34:50: Epoch: 046 Train Loss: 0.056, mae: 0.190, rse: 0.084, pcc: 0.957, kcc: 0.892
+2025-08-27 10:34:50: Epoch: 046 Test results, ap: mae: 1.519, rse: 6.583, pcc: 0.300, kcc: 0.201
+2025-08-27 10:34:53: Epoch: 047 Train Loss: 0.058, mae: 0.190, rse: 0.086, pcc: 0.956, kcc: 0.893
+2025-08-27 10:34:54: Epoch: 047 Test results, ap: mae: 1.526, rse: 6.621, pcc: 0.299, kcc: 0.200
+2025-08-27 10:34:57: Epoch: 048 Train Loss: 0.049, mae: 0.183, rse: 0.086, pcc: 0.957, kcc: 0.894
+2025-08-27 10:34:57: Epoch: 048 New best VALIDATION metrics
+2025-08-27 10:34:57: Epoch: 048 Test results, ap: mae: 1.542, rse: 6.801, pcc: 0.299, kcc: 0.201
+2025-08-27 10:35:00: Epoch: 049 Train Loss: 0.053, mae: 0.180, rse: 0.084, pcc: 0.957, kcc: 0.897
+2025-08-27 10:35:00: Epoch: 049 New best VALIDATION metrics
+2025-08-27 10:35:01: Epoch: 049 Test results, ap: mae: 1.539, rse: 6.767, pcc: 0.302, kcc: 0.203
+2025-08-27 10:35:04: Epoch: 050 Train Loss: 0.054, mae: 0.189, rse: 0.084, pcc: 0.957, kcc: 0.891
+2025-08-27 10:35:04: Epoch: 050 Test results, ap: mae: 1.516, rse: 6.636, pcc: 0.295, kcc: 0.197
+2025-08-27 10:35:04: used time 0.05h
+2025-08-27 10:35:04: Running Cross Validation 4
+2025-08-27 10:35:04: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-27 10:35:07: Epoch: 001 Train Loss: 2.640, mae: 1.177, rse: 0.881, pcc: 0.391, kcc: 0.265
+2025-08-27 10:35:07: Epoch: 001 New best VALIDATION metrics
+2025-08-27 10:35:07: Epoch: 001 Test results, ap: mae: 0.746, rse: 1.802, pcc: -0.022, kcc: 0.009
+2025-08-27 10:35:11: Epoch: 002 Train Loss: 2.117, mae: 1.063, rse: 0.755, pcc: 0.538, kcc: 0.358
+2025-08-27 10:35:11: Epoch: 002 New best VALIDATION metrics
+2025-08-27 10:35:11: Epoch: 002 Test results, ap: mae: 0.647, rse: 1.296, pcc: -0.075, kcc: -0.042
+2025-08-27 10:35:14: Epoch: 003 Train Loss: 1.932, mae: 1.027, rse: 0.710, pcc: 0.545, kcc: 0.377
+2025-08-27 10:35:14: Epoch: 003 New best VALIDATION metrics
+2025-08-27 10:35:14: Epoch: 003 Test results, ap: mae: 0.966, rse: 2.803, pcc: -0.102, kcc: -0.048
+2025-08-27 10:35:17: Epoch: 004 Train Loss: 1.637, mae: 0.949, rse: 0.623, pcc: 0.616, kcc: 0.431
+2025-08-27 10:35:17: Epoch: 004 New best VALIDATION metrics
+2025-08-27 10:35:18: Epoch: 004 Test results, ap: mae: 0.931, rse: 2.668, pcc: -0.061, kcc: -0.013
+2025-08-27 10:35:21: Epoch: 005 Train Loss: 1.461, mae: 0.915, rse: 0.578, pcc: 0.656, kcc: 0.463
+2025-08-27 10:35:21: Epoch: 005 New best VALIDATION metrics
+2025-08-27 10:35:21: Epoch: 005 Test results, ap: mae: 0.785, rse: 1.859, pcc: -0.075, kcc: -0.013
+2025-08-27 10:35:24: Epoch: 006 Train Loss: 1.264, mae: 0.827, rse: 0.491, pcc: 0.716, kcc: 0.509
+2025-08-27 10:35:24: Epoch: 006 New best VALIDATION metrics
+2025-08-27 10:35:25: Epoch: 006 Test results, ap: mae: 1.045, rse: 3.677, pcc: -0.049, kcc: 0.004
+2025-08-27 10:35:28: Epoch: 007 Train Loss: 1.113, mae: 0.785, rse: 0.460, pcc: 0.742, kcc: 0.546
+2025-08-27 10:35:28: Epoch: 007 New best VALIDATION metrics
+2025-08-27 10:35:28: Epoch: 007 Test results, ap: mae: 1.098, rse: 4.323, pcc: -0.035, kcc: 0.020
+2025-08-27 10:35:31: Epoch: 008 Train Loss: 0.962, mae: 0.712, rse: 0.389, pcc: 0.786, kcc: 0.586
+2025-08-27 10:35:31: Epoch: 008 New best VALIDATION metrics
+2025-08-27 10:35:31: Epoch: 008 Test results, ap: mae: 0.792, rse: 2.057, pcc: 0.088, kcc: 0.069
+2025-08-27 10:35:34: Epoch: 009 Train Loss: 0.820, mae: 0.671, rse: 0.352, pcc: 0.806, kcc: 0.613
+2025-08-27 10:35:34: Epoch: 009 New best VALIDATION metrics
+2025-08-27 10:35:35: Epoch: 009 Test results, ap: mae: 1.128, rse: 4.400, pcc: 0.071, kcc: 0.056
+2025-08-27 10:35:38: Epoch: 010 Train Loss: 0.647, mae: 0.560, rse: 0.271, pcc: 0.854, kcc: 0.670
+2025-08-27 10:35:38: Epoch: 010 New best VALIDATION metrics
+2025-08-27 10:35:38: Epoch: 010 Test results, ap: mae: 1.113, rse: 4.070, pcc: 0.113, kcc: 0.098
+2025-08-27 10:35:41: Epoch: 011 Train Loss: 0.505, mae: 0.523, rse: 0.246, pcc: 0.869, kcc: 0.692
+2025-08-27 10:35:41: Epoch: 011 New best VALIDATION metrics
+2025-08-27 10:35:42: Epoch: 011 Test results, ap: mae: 1.288, rse: 5.295, pcc: 0.079, kcc: 0.069
+2025-08-27 10:35:42: Epoch: 011 New best TEST metrics
+2025-08-27 10:35:45: Epoch: 012 Train Loss: 0.424, mae: 0.501, rse: 0.238, pcc: 0.874, kcc: 0.698
+2025-08-27 10:35:45: Epoch: 012 New best VALIDATION metrics
+2025-08-27 10:35:45: Epoch: 012 Test results, ap: mae: 1.198, rse: 4.633, pcc: 0.084, kcc: 0.063
+2025-08-27 10:35:45: Epoch: 012 New best TEST metrics
+2025-08-27 10:35:48: Epoch: 013 Train Loss: 0.371, mae: 0.450, rse: 0.208, pcc: 0.890, kcc: 0.727
+2025-08-27 10:35:48: Epoch: 013 New best VALIDATION metrics
+2025-08-27 10:35:49: Epoch: 013 Test results, ap: mae: 1.183, rse: 4.584, pcc: 0.092, kcc: 0.061
+2025-08-27 10:35:49: Epoch: 013 New best TEST metrics
+2025-08-27 10:35:52: Epoch: 014 Train Loss: 0.328, mae: 0.449, rse: 0.208, pcc: 0.891, kcc: 0.726
+2025-08-27 10:35:52: Epoch: 014 Test results, ap: mae: 1.106, rse: 4.067, pcc: 0.110, kcc: 0.071
+2025-08-27 10:35:52: Epoch: 014 New best TEST metrics
+2025-08-27 10:35:55: Epoch: 015 Train Loss: 0.309, mae: 0.444, rse: 0.210, pcc: 0.890, kcc: 0.733
+2025-08-27 10:35:55: Epoch: 015 New best VALIDATION metrics
+2025-08-27 10:35:55: Epoch: 015 Test results, ap: mae: 1.071, rse: 3.899, pcc: 0.105, kcc: 0.067
+2025-08-27 10:35:55: Epoch: 015 New best TEST metrics
+2025-08-27 10:35:58: Epoch: 016 Train Loss: 0.275, mae: 0.409, rse: 0.189, pcc: 0.902, kcc: 0.750
+2025-08-27 10:35:58: Epoch: 016 New best VALIDATION metrics
+2025-08-27 10:35:59: Epoch: 016 Test results, ap: mae: 1.103, rse: 4.008, pcc: 0.122, kcc: 0.074
+2025-08-27 10:36:02: Epoch: 017 Train Loss: 0.248, mae: 0.401, rse: 0.185, pcc: 0.904, kcc: 0.759
+2025-08-27 10:36:02: Epoch: 017 New best VALIDATION metrics
+2025-08-27 10:36:02: Epoch: 017 Test results, ap: mae: 1.138, rse: 4.146, pcc: 0.131, kcc: 0.069
+2025-08-27 10:36:05: Epoch: 018 Train Loss: 0.210, mae: 0.338, rse: 0.159, pcc: 0.917, kcc: 0.785
+2025-08-27 10:36:05: Epoch: 018 New best VALIDATION metrics
+2025-08-27 10:36:06: Epoch: 018 Test results, ap: mae: 1.153, rse: 4.165, pcc: 0.187, kcc: 0.104
+2025-08-27 10:36:09: Epoch: 019 Train Loss: 0.199, mae: 0.362, rse: 0.165, pcc: 0.914, kcc: 0.777
+2025-08-27 10:36:09: Epoch: 019 Test results, ap: mae: 0.946, rse: 2.871, pcc: 0.159, kcc: 0.089
+2025-08-27 10:36:09: Epoch: 019 New best TEST metrics
+2025-08-27 10:36:12: Epoch: 020 Train Loss: 0.187, mae: 0.328, rse: 0.158, pcc: 0.919, kcc: 0.793
+2025-08-27 10:36:12: Epoch: 020 New best VALIDATION metrics
+2025-08-27 10:36:13: Epoch: 020 Test results, ap: mae: 1.003, rse: 3.206, pcc: 0.151, kcc: 0.083
+2025-08-27 10:36:16: Epoch: 021 Train Loss: 0.141, mae: 0.301, rse: 0.155, pcc: 0.919, kcc: 0.805
+2025-08-27 10:36:16: Epoch: 021 New best VALIDATION metrics
+2025-08-27 10:36:16: Epoch: 021 Test results, ap: mae: 1.003, rse: 3.182, pcc: 0.167, kcc: 0.099
+2025-08-27 10:36:19: Epoch: 022 Train Loss: 0.133, mae: 0.297, rse: 0.148, pcc: 0.924, kcc: 0.806
+2025-08-27 10:36:19: Epoch: 022 New best VALIDATION metrics
+2025-08-27 10:36:19: Epoch: 022 Test results, ap: mae: 1.031, rse: 3.405, pcc: 0.155, kcc: 0.083
+2025-08-27 10:36:22: Epoch: 023 Train Loss: 0.123, mae: 0.284, rse: 0.145, pcc: 0.925, kcc: 0.817
+2025-08-27 10:36:22: Epoch: 023 New best VALIDATION metrics
+2025-08-27 10:36:23: Epoch: 023 Test results, ap: mae: 0.977, rse: 3.006, pcc: 0.179, kcc: 0.104
+2025-08-27 10:36:26: Epoch: 024 Train Loss: 0.112, mae: 0.298, rse: 0.141, pcc: 0.927, kcc: 0.812
+2025-08-27 10:36:26: Epoch: 024 Test results, ap: mae: 1.004, rse: 3.155, pcc: 0.164, kcc: 0.092
+2025-08-27 10:36:29: Epoch: 025 Train Loss: 0.115, mae: 0.267, rse: 0.140, pcc: 0.927, kcc: 0.825
+2025-08-27 10:36:29: Epoch: 025 New best VALIDATION metrics
+2025-08-27 10:36:30: Epoch: 025 Test results, ap: mae: 0.990, rse: 3.092, pcc: 0.160, kcc: 0.089
+2025-08-27 10:36:33: Epoch: 026 Train Loss: 0.111, mae: 0.274, rse: 0.142, pcc: 0.926, kcc: 0.824
+2025-08-27 10:36:33: Epoch: 026 Test results, ap: mae: 1.019, rse: 3.284, pcc: 0.166, kcc: 0.092
+2025-08-27 10:36:36: Epoch: 027 Train Loss: 0.101, mae: 0.270, rse: 0.144, pcc: 0.926, kcc: 0.827
+2025-08-27 10:36:36: Epoch: 027 Test results, ap: mae: 0.997, rse: 3.186, pcc: 0.165, kcc: 0.089
+2025-08-27 10:36:39: Epoch: 028 Train Loss: 0.101, mae: 0.263, rse: 0.141, pcc: 0.927, kcc: 0.827
+2025-08-27 10:36:39: Epoch: 028 New best VALIDATION metrics
+2025-08-27 10:36:40: Epoch: 028 Test results, ap: mae: 0.969, rse: 3.014, pcc: 0.183, kcc: 0.100
+2025-08-27 10:36:43: Epoch: 029 Train Loss: 0.098, mae: 0.259, rse: 0.135, pcc: 0.930, kcc: 0.833
+2025-08-27 10:36:43: Epoch: 029 New best VALIDATION metrics
+2025-08-27 10:36:43: Epoch: 029 Test results, ap: mae: 0.996, rse: 3.123, pcc: 0.171, kcc: 0.094
+2025-08-27 10:36:46: Epoch: 030 Train Loss: 0.089, mae: 0.251, rse: 0.142, pcc: 0.926, kcc: 0.837
+2025-08-27 10:36:46: Epoch: 030 New best VALIDATION metrics
+2025-08-27 10:36:47: Epoch: 030 Test results, ap: mae: 0.989, rse: 3.150, pcc: 0.207, kcc: 0.123
+2025-08-27 10:36:50: Epoch: 031 Train Loss: 0.086, mae: 0.239, rse: 0.140, pcc: 0.928, kcc: 0.841
+2025-08-27 10:36:50: Epoch: 031 New best VALIDATION metrics
+2025-08-27 10:36:50: Epoch: 031 Test results, ap: mae: 0.956, rse: 2.891, pcc: 0.194, kcc: 0.114
+2025-08-27 10:36:50: Epoch: 031 New best TEST metrics
+2025-08-27 10:36:53: Epoch: 032 Train Loss: 0.078, mae: 0.234, rse: 0.138, pcc: 0.929, kcc: 0.843
+2025-08-27 10:36:53: Epoch: 032 New best VALIDATION metrics
+2025-08-27 10:36:53: Epoch: 032 Test results, ap: mae: 0.975, rse: 3.027, pcc: 0.210, kcc: 0.125
+2025-08-27 10:36:57: Epoch: 033 Train Loss: 0.076, mae: 0.236, rse: 0.134, pcc: 0.931, kcc: 0.845
+2025-08-27 10:36:57: Epoch: 033 New best VALIDATION metrics
+2025-08-27 10:36:57: Epoch: 033 Test results, ap: mae: 0.949, rse: 2.877, pcc: 0.205, kcc: 0.111
+2025-08-27 10:36:57: Epoch: 033 New best TEST metrics
+2025-08-27 10:37:00: Epoch: 034 Train Loss: 0.076, mae: 0.224, rse: 0.132, pcc: 0.932, kcc: 0.849
+2025-08-27 10:37:00: Epoch: 034 New best VALIDATION metrics
+2025-08-27 10:37:00: Epoch: 034 Test results, ap: mae: 0.977, rse: 3.045, pcc: 0.190, kcc: 0.107
+2025-08-27 10:37:03: Epoch: 035 Train Loss: 0.071, mae: 0.230, rse: 0.134, pcc: 0.931, kcc: 0.849
+2025-08-27 10:37:04: Epoch: 035 Test results, ap: mae: 0.956, rse: 2.913, pcc: 0.200, kcc: 0.107
+2025-08-27 10:37:07: Epoch: 036 Train Loss: 0.069, mae: 0.248, rse: 0.135, pcc: 0.930, kcc: 0.843
+2025-08-27 10:37:07: Epoch: 036 Test results, ap: mae: 0.957, rse: 2.909, pcc: 0.199, kcc: 0.102
+2025-08-27 10:37:10: Epoch: 037 Train Loss: 0.073, mae: 0.240, rse: 0.132, pcc: 0.932, kcc: 0.841
+2025-08-27 10:37:11: Epoch: 037 Test results, ap: mae: 0.951, rse: 2.857, pcc: 0.187, kcc: 0.097
+2025-08-27 10:37:14: Epoch: 038 Train Loss: 0.073, mae: 0.223, rse: 0.133, pcc: 0.931, kcc: 0.850
+2025-08-27 10:37:14: Epoch: 038 New best VALIDATION metrics
+2025-08-27 10:37:14: Epoch: 038 Test results, ap: mae: 0.973, rse: 3.018, pcc: 0.192, kcc: 0.104
+2025-08-27 10:37:17: Epoch: 039 Train Loss: 0.073, mae: 0.225, rse: 0.132, pcc: 0.932, kcc: 0.850
+2025-08-27 10:37:17: Epoch: 039 New best VALIDATION metrics
+2025-08-27 10:37:17: Epoch: 039 Test results, ap: mae: 0.971, rse: 3.019, pcc: 0.188, kcc: 0.098
+2025-08-27 10:37:20: Epoch: 040 Train Loss: 0.065, mae: 0.223, rse: 0.133, pcc: 0.931, kcc: 0.852
+2025-08-27 10:37:20: Epoch: 040 New best VALIDATION metrics
+2025-08-27 10:37:21: Epoch: 040 Test results, ap: mae: 0.936, rse: 2.812, pcc: 0.202, kcc: 0.102
+2025-08-27 10:37:21: Epoch: 040 New best TEST metrics
+2025-08-27 10:37:24: Epoch: 041 Train Loss: 0.062, mae: 0.224, rse: 0.134, pcc: 0.931, kcc: 0.851
+2025-08-27 10:37:24: Epoch: 041 Test results, ap: mae: 0.953, rse: 2.899, pcc: 0.197, kcc: 0.100
+2025-08-27 10:37:27: Epoch: 042 Train Loss: 0.063, mae: 0.212, rse: 0.133, pcc: 0.931, kcc: 0.856
+2025-08-27 10:37:27: Epoch: 042 New best VALIDATION metrics
+2025-08-27 10:37:28: Epoch: 042 Test results, ap: mae: 0.959, rse: 2.956, pcc: 0.206, kcc: 0.105
+2025-08-27 10:37:31: Epoch: 043 Train Loss: 0.060, mae: 0.214, rse: 0.130, pcc: 0.933, kcc: 0.856
+2025-08-27 10:37:31: Epoch: 043 New best VALIDATION metrics
+2025-08-27 10:37:31: Epoch: 043 Test results, ap: mae: 0.965, rse: 2.990, pcc: 0.207, kcc: 0.103
+2025-08-27 10:37:34: Epoch: 044 Train Loss: 0.061, mae: 0.218, rse: 0.137, pcc: 0.929, kcc: 0.855
+2025-08-27 10:37:35: Epoch: 044 Test results, ap: mae: 0.969, rse: 3.000, pcc: 0.212, kcc: 0.111
+2025-08-27 10:37:38: Epoch: 045 Train Loss: 0.068, mae: 0.216, rse: 0.135, pcc: 0.931, kcc: 0.852
+2025-08-27 10:37:38: Epoch: 045 Test results, ap: mae: 0.966, rse: 3.013, pcc: 0.207, kcc: 0.108
+2025-08-27 10:37:41: Epoch: 046 Train Loss: 0.061, mae: 0.209, rse: 0.129, pcc: 0.933, kcc: 0.856
+2025-08-27 10:37:41: Epoch: 046 New best VALIDATION metrics
+2025-08-27 10:37:41: Epoch: 046 Test results, ap: mae: 0.962, rse: 2.985, pcc: 0.203, kcc: 0.104
+2025-08-27 10:37:44: Epoch: 047 Train Loss: 0.061, mae: 0.212, rse: 0.133, pcc: 0.932, kcc: 0.854
+2025-08-27 10:37:45: Epoch: 047 Test results, ap: mae: 0.967, rse: 3.006, pcc: 0.208, kcc: 0.104
+2025-08-27 10:37:48: Epoch: 048 Train Loss: 0.060, mae: 0.208, rse: 0.134, pcc: 0.931, kcc: 0.856
+2025-08-27 10:37:48: Epoch: 048 Test results, ap: mae: 0.961, rse: 2.956, pcc: 0.207, kcc: 0.106
+2025-08-27 10:37:51: Epoch: 049 Train Loss: 0.061, mae: 0.213, rse: 0.136, pcc: 0.931, kcc: 0.856
+2025-08-27 10:37:52: Epoch: 049 Test results, ap: mae: 0.974, rse: 3.017, pcc: 0.213, kcc: 0.111
+2025-08-27 10:37:55: Epoch: 050 Train Loss: 0.058, mae: 0.206, rse: 0.131, pcc: 0.933, kcc: 0.858
+2025-08-27 10:37:55: Epoch: 050 New best VALIDATION metrics
+2025-08-27 10:37:55: Epoch: 050 Test results, ap: mae: 0.978, rse: 3.052, pcc: 0.213, kcc: 0.110
+2025-08-27 10:37:55: used time 0.05h
+2025-08-27 10:37:55: Cross Validation Finished!
+2025-08-27 10:37:55: Best validation perform list
+[[0.21645416 0.09821794 0.95085686 0.85400116]
+ [0.17259322 0.08020909 0.95937645 0.88380998]
+ [0.22717349 0.15361911 0.92191589 0.86028361]
+ [0.17987001 0.08428867 0.95747864 0.89664793]
+ [0.20593545 0.13077629 0.93272144 0.85767072]]
+2025-08-27 10:37:55: mean: [0.2   0.109 0.944 0.87 ]
+2025-08-27 10:37:55: std: [0.021 0.028 0.015 0.017]
+2025-08-27 10:37:55: Best test perform list
+[[ 1.14470124  4.0673151  -0.16620539 -0.10233145]
+ [ 0.94616967  2.8391211   0.25633579  0.14427933]
+ [ 0.98030573  2.96628714  0.19898324  0.09725496]
+ [ 1.06082904  3.14112377  0.15157922  0.11007979]
+ [ 0.93627232  2.81228948  0.20231098  0.10206427]]
+2025-08-27 10:37:55: mean: [1.014 3.165 0.129 0.07 ]
+2025-08-27 10:37:55: std: [0.079 0.466 0.151 0.088]
diff --git a/run-reg/mamba-diff-256-mse-32-0.001-50/uda_r2/config.json b/run-reg/mamba-diff-256-mse-32-0.001-50/uda_r2/config.json
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diff --git a/run-reg/mamba-diff-256-mse-32-0.001-50/uda_r2/training.log b/run-reg/mamba-diff-256-mse-32-0.001-50/uda_r2/training.log
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+2025-11-15 16:26:09: saving_dir: ./run-reg/mamba-diff-256-mse-32-0.001-50/uda_r2
+2025-11-15 16:26:09: Loading Training Dataset
+2025-11-15 16:26:10: Loading Validation Dataset
+2025-11-15 16:26:11: Start UDA training
+2025-11-15 16:26:18: Epoch 1 Train Loss: 2.8788 Val: mae: 0.6101 rse: 1.5823 pcc: -0.3458 kcc: -0.2390
+2025-11-15 16:26:18: Epoch: 1 New best VAL metrics
+2025-11-15 16:26:26: Epoch 2 Train Loss: 2.5627 Val: mae: 0.6572 rse: 1.8441 pcc: -0.1172 kcc: -0.0128
+2025-11-15 16:26:26: Epoch: 2 New best VAL metrics
+2025-11-15 16:26:33: Epoch 3 Train Loss: 2.1007 Val: mae: 0.6594 rse: 1.7282 pcc: -0.0330 kcc: 0.0026
+2025-11-15 16:26:33: Epoch: 3 New best VAL metrics
+2025-11-15 16:26:41: Epoch 4 Train Loss: 1.8692 Val: mae: 0.5751 rse: 1.2354 pcc: 0.1257 kcc: 0.1191
+2025-11-15 16:26:41: Epoch: 4 New best VAL metrics
+2025-11-15 16:26:48: Epoch 5 Train Loss: 1.7790 Val: mae: 0.5000 rse: 0.9705 pcc: 0.2569 kcc: 0.2030
+2025-11-15 16:26:48: Epoch: 5 New best VAL metrics
+2025-11-15 16:26:56: Epoch 6 Train Loss: 1.6514 Val: mae: 0.4722 rse: 0.8639 pcc: 0.3719 kcc: 0.2732
+2025-11-15 16:26:56: Epoch: 6 New best VAL metrics
+2025-11-15 16:27:03: Epoch 7 Train Loss: 1.5924 Val: mae: 0.4636 rse: 0.8352 pcc: 0.4784 kcc: 0.3212
+2025-11-15 16:27:03: Epoch: 7 New best VAL metrics
+2025-11-15 16:27:11: Epoch 8 Train Loss: 1.4696 Val: mae: 0.4735 rse: 0.8528 pcc: 0.5868 kcc: 0.3520
+2025-11-15 16:27:11: Epoch: 8 New best VAL metrics
+2025-11-15 16:27:18: Epoch 9 Train Loss: 1.4133 Val: mae: 0.4897 rse: 0.9119 pcc: 0.6105 kcc: 0.3469
+2025-11-15 16:27:26: Epoch 10 Train Loss: 1.3762 Val: mae: 0.5023 rse: 0.9615 pcc: 0.6121 kcc: 0.3366
+2025-11-15 16:27:33: Epoch 11 Train Loss: 1.2717 Val: mae: 0.5105 rse: 1.0045 pcc: 0.6119 kcc: 0.3366
+2025-11-15 16:27:41: Epoch 12 Train Loss: 1.1770 Val: mae: 0.5205 rse: 1.0560 pcc: 0.5942 kcc: 0.3246
+2025-11-15 16:27:48: Epoch 13 Train Loss: 1.1010 Val: mae: 0.5317 rse: 1.1120 pcc: 0.5620 kcc: 0.2835
+2025-11-15 16:27:56: Epoch 14 Train Loss: 1.0041 Val: mae: 0.5424 rse: 1.1770 pcc: 0.5508 kcc: 0.2938
+2025-11-15 16:28:03: Epoch 15 Train Loss: 0.9791 Val: mae: 0.5501 rse: 1.2136 pcc: 0.5403 kcc: 0.2852
+2025-11-15 16:28:11: Epoch 16 Train Loss: 0.8925 Val: mae: 0.5584 rse: 1.2602 pcc: 0.5154 kcc: 0.2784
+2025-11-15 16:28:18: Epoch 17 Train Loss: 0.8059 Val: mae: 0.5654 rse: 1.2948 pcc: 0.4918 kcc: 0.2732
+2025-11-15 16:28:26: Epoch 18 Train Loss: 0.7575 Val: mae: 0.5752 rse: 1.3487 pcc: 0.4402 kcc: 0.2321
+2025-11-15 16:28:33: Epoch 19 Train Loss: 0.7263 Val: mae: 0.5830 rse: 1.3892 pcc: 0.3456 kcc: 0.1636
+2025-11-15 16:28:41: Epoch 20 Train Loss: 0.6682 Val: mae: 0.5917 rse: 1.4400 pcc: 0.2698 kcc: 0.1259
+2025-11-15 16:28:48: Epoch 21 Train Loss: 0.6329 Val: mae: 0.6014 rse: 1.4960 pcc: 0.1840 kcc: 0.0831
+2025-11-15 16:28:56: Epoch 22 Train Loss: 0.5785 Val: mae: 0.6124 rse: 1.5724 pcc: 0.0330 kcc: 0.0060
+2025-11-15 16:29:04: Epoch 23 Train Loss: 0.5584 Val: mae: 0.6155 rse: 1.5961 pcc: -0.0004 kcc: -0.0060
+2025-11-15 16:29:11: Epoch 24 Train Loss: 0.5215 Val: mae: 0.6128 rse: 1.5924 pcc: -0.0467 kcc: -0.0454
+2025-11-15 16:29:19: Epoch 25 Train Loss: 0.4750 Val: mae: 0.6154 rse: 1.6141 pcc: -0.0680 kcc: -0.0505
+2025-11-15 16:29:26: Epoch 26 Train Loss: 0.4657 Val: mae: 0.6086 rse: 1.5900 pcc: -0.0798 kcc: -0.0694
+2025-11-15 16:29:34: Epoch 27 Train Loss: 0.3898 Val: mae: 0.6111 rse: 1.6042 pcc: -0.1135 kcc: -0.0899
+2025-11-15 16:29:41: Epoch 28 Train Loss: 0.3601 Val: mae: 0.6078 rse: 1.5983 pcc: -0.1124 kcc: -0.1054
+2025-11-15 16:29:49: Epoch 29 Train Loss: 0.3339 Val: mae: 0.6067 rse: 1.5987 pcc: -0.1290 kcc: -0.1105
+2025-11-15 16:29:56: Epoch 30 Train Loss: 0.3216 Val: mae: 0.6069 rse: 1.5988 pcc: -0.1471 kcc: -0.1054
+2025-11-15 16:30:04: Epoch 31 Train Loss: 0.2871 Val: mae: 0.6028 rse: 1.5805 pcc: -0.1345 kcc: -0.0882
+2025-11-15 16:30:11: Epoch 32 Train Loss: 0.3010 Val: mae: 0.5914 rse: 1.5239 pcc: -0.0984 kcc: -0.0660
+2025-11-15 16:30:19: Epoch 33 Train Loss: 0.2657 Val: mae: 0.5899 rse: 1.5142 pcc: -0.0847 kcc: -0.0677
+2025-11-15 16:30:26: Epoch 34 Train Loss: 0.2437 Val: mae: 0.5876 rse: 1.4945 pcc: -0.0404 kcc: -0.0368
+2025-11-15 16:30:34: Epoch 35 Train Loss: 0.2372 Val: mae: 0.5830 rse: 1.4695 pcc: -0.0238 kcc: -0.0128
+2025-11-15 16:30:42: Epoch 36 Train Loss: 0.2485 Val: mae: 0.5824 rse: 1.4656 pcc: -0.0111 kcc: -0.0180
+2025-11-15 16:30:49: Epoch 37 Train Loss: 0.2299 Val: mae: 0.5799 rse: 1.4517 pcc: -0.0120 kcc: -0.0060
+2025-11-15 16:30:57: Epoch 38 Train Loss: 0.2075 Val: mae: 0.5790 rse: 1.4462 pcc: -0.0158 kcc: -0.0248
+2025-11-15 16:31:04: Epoch 39 Train Loss: 0.2102 Val: mae: 0.5784 rse: 1.4462 pcc: 0.0119 kcc: -0.0077
+2025-11-15 16:31:12: Epoch 40 Train Loss: 0.1867 Val: mae: 0.5761 rse: 1.4390 pcc: 0.0284 kcc: 0.0128
+2025-11-15 16:31:19: Epoch 41 Train Loss: 0.1827 Val: mae: 0.5754 rse: 1.4392 pcc: 0.0211 kcc: 0.0060
+2025-11-15 16:31:27: Epoch 42 Train Loss: 0.1732 Val: mae: 0.5744 rse: 1.4302 pcc: 0.0613 kcc: 0.0368
+2025-11-15 16:31:34: Epoch 43 Train Loss: 0.1759 Val: mae: 0.5692 rse: 1.4018 pcc: 0.1169 kcc: 0.0762
+2025-11-15 16:31:42: Epoch 44 Train Loss: 0.1572 Val: mae: 0.5668 rse: 1.3886 pcc: 0.1495 kcc: 0.0831
+2025-11-15 16:31:49: Epoch 45 Train Loss: 0.1850 Val: mae: 0.5658 rse: 1.3789 pcc: 0.1961 kcc: 0.1054
+2025-11-15 16:31:57: Epoch 46 Train Loss: 0.1867 Val: mae: 0.5601 rse: 1.3501 pcc: 0.2425 kcc: 0.1482
+2025-11-15 16:32:04: Epoch 47 Train Loss: 0.1761 Val: mae: 0.5600 rse: 1.3450 pcc: 0.3135 kcc: 0.2116
+2025-11-15 16:32:12: Epoch 48 Train Loss: 0.1426 Val: mae: 0.5569 rse: 1.3295 pcc: 0.3402 kcc: 0.2424
+2025-11-15 16:32:19: Epoch 49 Train Loss: 0.1966 Val: mae: 0.5541 rse: 1.3187 pcc: 0.3502 kcc: 0.2493
+2025-11-15 16:32:27: Epoch 50 Train Loss: 0.1531 Val: mae: 0.5547 rse: 1.3264 pcc: 0.3170 kcc: 0.2373
+2025-11-15 16:32:27: UDA training finished
diff --git a/run-reg/mamba-mlp-256-mse-32-0.001-50/config.json b/run-reg/mamba-mlp-256-mse-32-0.001-50/config.json
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diff --git a/run-reg/mamba-mlp-256-mse-32-0.001-50/training.log b/run-reg/mamba-mlp-256-mse-32-0.001-50/training.log
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+2025-07-10 14:53:32: saving_dir: ./run-reg/mamba-mlp-256-mse-32-0.001-50
+2025-07-10 14:53:32: Loading Train Dataset
+2025-07-10 14:53:33: Loading Test Dataset
+2025-07-10 14:53:33: Running Cross Validation 0
+2025-07-10 14:53:33: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 14:53:34: Epoch: 001 Train Loss: 2.921, mae: 1.206, rse: 1.000, pcc: 0.250, kcc: 0.114
+2025-07-10 14:53:34: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:53:35: Epoch: 001 Test results, ap: mae: 0.622, rse: 1.181, pcc: 0.114, kcc: 0.109
+2025-07-10 14:53:36: Epoch: 002 Train Loss: 2.921, mae: 1.208, rse: 0.995, pcc: 0.257, kcc: 0.117
+2025-07-10 14:53:36: Epoch: 002 New best VALIDATION metrics
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+2025-07-10 14:53:37: Epoch: 003 Train Loss: 2.828, mae: 1.210, rse: 1.003, pcc: -0.210, kcc: -0.061
+2025-07-10 14:53:37: Epoch: 003 Test results, ap: mae: 0.614, rse: 1.156, pcc: -0.354, kcc: -0.210
+2025-07-10 14:53:38: Epoch: 004 Train Loss: 2.903, mae: 1.194, rse: 1.005, pcc: 0.268, kcc: 0.187
+2025-07-10 14:53:38: Epoch: 004 New best VALIDATION metrics
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+2025-07-10 14:53:39: Epoch: 005 Train Loss: 2.909, mae: 1.207, rse: 0.997, pcc: 0.288, kcc: 0.168
+2025-07-10 14:53:39: Epoch: 005 Test results, ap: mae: 0.618, rse: 1.170, pcc: -0.017, kcc: 0.032
+2025-07-10 14:53:40: Epoch: 006 Train Loss: 2.803, mae: 1.148, rse: 0.907, pcc: 0.338, kcc: 0.241
+2025-07-10 14:53:40: Epoch: 006 New best VALIDATION metrics
+2025-07-10 14:53:40: Epoch: 006 Test results, ap: mae: 0.688, rse: 1.418, pcc: -0.070, kcc: -0.110
+2025-07-10 14:53:41: Epoch: 007 Train Loss: 2.671, mae: 1.176, rse: 0.913, pcc: 0.360, kcc: 0.240
+2025-07-10 14:53:42: Epoch: 007 Test results, ap: mae: 0.646, rse: 1.275, pcc: -0.129, kcc: -0.108
+2025-07-10 14:53:42: Epoch: 008 Train Loss: 2.687, mae: 1.160, rse: 0.908, pcc: 0.373, kcc: 0.255
+2025-07-10 14:53:42: Epoch: 008 New best VALIDATION metrics
+2025-07-10 14:53:43: Epoch: 008 Test results, ap: mae: 0.624, rse: 1.193, pcc: -0.121, kcc: -0.129
+2025-07-10 14:53:44: Epoch: 009 Train Loss: 2.598, mae: 1.103, rse: 0.849, pcc: 0.396, kcc: 0.263
+2025-07-10 14:53:44: Epoch: 009 New best VALIDATION metrics
+2025-07-10 14:53:44: Epoch: 009 Test results, ap: mae: 0.677, rse: 1.372, pcc: -0.042, kcc: -0.170
+2025-07-10 14:53:45: Epoch: 010 Train Loss: 2.578, mae: 1.085, rse: 0.836, pcc: 0.416, kcc: 0.300
+2025-07-10 14:53:45: Epoch: 010 New best VALIDATION metrics
+2025-07-10 14:53:45: Epoch: 010 Test results, ap: mae: 0.689, rse: 1.418, pcc: -0.115, kcc: -0.174
+2025-07-10 14:53:46: Epoch: 011 Train Loss: 2.513, mae: 1.084, rse: 0.823, pcc: 0.434, kcc: 0.300
+2025-07-10 14:53:46: Epoch: 011 New best VALIDATION metrics
+2025-07-10 14:53:46: Epoch: 011 Test results, ap: mae: 0.661, rse: 1.309, pcc: -0.082, kcc: -0.168
+2025-07-10 14:53:46: Epoch: 011 New best TEST metrics
+2025-07-10 14:53:47: Epoch: 012 Train Loss: 2.465, mae: 1.066, rse: 0.785, pcc: 0.466, kcc: 0.316
+2025-07-10 14:53:47: Epoch: 012 New best VALIDATION metrics
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+2025-07-10 14:53:47: Epoch: 012 New best TEST metrics
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+2025-07-10 14:53:48: Epoch: 013 New best VALIDATION metrics
+2025-07-10 14:53:48: Epoch: 013 Test results, ap: mae: 0.704, rse: 1.394, pcc: 0.175, kcc: 0.016
+2025-07-10 14:53:48: Epoch: 013 New best TEST metrics
+2025-07-10 14:53:49: Epoch: 014 Train Loss: 2.201, mae: 1.009, rse: 0.680, pcc: 0.566, kcc: 0.391
+2025-07-10 14:53:49: Epoch: 014 New best VALIDATION metrics
+2025-07-10 14:53:50: Epoch: 014 Test results, ap: mae: 0.698, rse: 1.365, pcc: 0.120, kcc: -0.023
+2025-07-10 14:53:51: Epoch: 015 Train Loss: 1.820, mae: 0.955, rse: 0.592, pcc: 0.647, kcc: 0.443
+2025-07-10 14:53:51: Epoch: 015 New best VALIDATION metrics
+2025-07-10 14:53:51: Epoch: 015 Test results, ap: mae: 0.604, rse: 1.055, pcc: 0.174, kcc: 0.012
+2025-07-10 14:53:51: Epoch: 015 New best TEST metrics
+2025-07-10 14:53:52: Epoch: 016 Train Loss: 1.482, mae: 0.875, rse: 0.504, pcc: 0.706, kcc: 0.487
+2025-07-10 14:53:52: Epoch: 016 New best VALIDATION metrics
+2025-07-10 14:53:52: Epoch: 016 Test results, ap: mae: 0.788, rse: 1.842, pcc: 0.079, kcc: 0.068
+2025-07-10 14:53:53: Epoch: 017 Train Loss: 1.306, mae: 0.819, rse: 0.456, pcc: 0.741, kcc: 0.524
+2025-07-10 14:53:53: Epoch: 017 New best VALIDATION metrics
+2025-07-10 14:53:53: Epoch: 017 Test results, ap: mae: 0.742, rse: 1.601, pcc: -0.041, kcc: -0.019
+2025-07-10 14:53:54: Epoch: 018 Train Loss: 1.176, mae: 0.727, rse: 0.381, pcc: 0.792, kcc: 0.569
+2025-07-10 14:53:54: Epoch: 018 New best VALIDATION metrics
+2025-07-10 14:53:54: Epoch: 018 Test results, ap: mae: 1.219, rse: 4.749, pcc: 0.141, kcc: 0.100
+2025-07-10 14:53:55: Epoch: 019 Train Loss: 0.941, mae: 0.680, rse: 0.326, pcc: 0.822, kcc: 0.590
+2025-07-10 14:53:55: Epoch: 019 New best VALIDATION metrics
+2025-07-10 14:53:55: Epoch: 019 Test results, ap: mae: 1.276, rse: 4.930, pcc: 0.132, kcc: 0.095
+2025-07-10 14:53:56: Epoch: 020 Train Loss: 0.800, mae: 0.606, rse: 0.285, pcc: 0.847, kcc: 0.636
+2025-07-10 14:53:56: Epoch: 020 New best VALIDATION metrics
+2025-07-10 14:53:57: Epoch: 020 Test results, ap: mae: 1.398, rse: 5.878, pcc: 0.163, kcc: 0.122
+2025-07-10 14:53:58: Epoch: 021 Train Loss: 0.625, mae: 0.547, rse: 0.244, pcc: 0.871, kcc: 0.666
+2025-07-10 14:53:58: Epoch: 021 New best VALIDATION metrics
+2025-07-10 14:53:58: Epoch: 021 Test results, ap: mae: 1.332, rse: 5.508, pcc: 0.178, kcc: 0.150
+2025-07-10 14:53:59: Epoch: 022 Train Loss: 0.585, mae: 0.527, rse: 0.232, pcc: 0.878, kcc: 0.679
+2025-07-10 14:53:59: Epoch: 022 New best VALIDATION metrics
+2025-07-10 14:53:59: Epoch: 022 Test results, ap: mae: 1.417, rse: 6.086, pcc: 0.184, kcc: 0.145
+2025-07-10 14:54:00: Epoch: 023 Train Loss: 0.521, mae: 0.492, rse: 0.207, pcc: 0.891, kcc: 0.696
+2025-07-10 14:54:00: Epoch: 023 New best VALIDATION metrics
+2025-07-10 14:54:00: Epoch: 023 Test results, ap: mae: 1.438, rse: 6.319, pcc: 0.183, kcc: 0.135
+2025-07-10 14:54:01: Epoch: 024 Train Loss: 0.431, mae: 0.461, rse: 0.184, pcc: 0.904, kcc: 0.711
+2025-07-10 14:54:01: Epoch: 024 New best VALIDATION metrics
+2025-07-10 14:54:01: Epoch: 024 Test results, ap: mae: 1.406, rse: 6.225, pcc: 0.178, kcc: 0.138
+2025-07-10 14:54:02: Epoch: 025 Train Loss: 0.384, mae: 0.434, rse: 0.174, pcc: 0.910, kcc: 0.726
+2025-07-10 14:54:02: Epoch: 025 New best VALIDATION metrics
+2025-07-10 14:54:02: Epoch: 025 Test results, ap: mae: 1.454, rse: 6.647, pcc: 0.186, kcc: 0.141
+2025-07-10 14:54:03: Epoch: 026 Train Loss: 0.337, mae: 0.453, rse: 0.181, pcc: 0.905, kcc: 0.712
+2025-07-10 14:54:04: Epoch: 026 Test results, ap: mae: 1.500, rse: 6.789, pcc: 0.177, kcc: 0.131
+2025-07-10 14:54:04: Epoch: 027 Train Loss: 0.327, mae: 0.416, rse: 0.166, pcc: 0.915, kcc: 0.732
+2025-07-10 14:54:04: Epoch: 027 New best VALIDATION metrics
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+2025-07-10 14:54:06: Epoch: 028 Train Loss: 0.298, mae: 0.430, rse: 0.171, pcc: 0.914, kcc: 0.737
+2025-07-10 14:54:06: Epoch: 028 Test results, ap: mae: 1.604, rse: 7.739, pcc: 0.160, kcc: 0.116
+2025-07-10 14:54:07: Epoch: 029 Train Loss: 0.273, mae: 0.383, rse: 0.154, pcc: 0.921, kcc: 0.751
+2025-07-10 14:54:07: Epoch: 029 New best VALIDATION metrics
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+2025-07-10 14:54:08: Epoch: 030 New best VALIDATION metrics
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+2025-07-10 14:54:09: Epoch: 031 Train Loss: 0.224, mae: 0.351, rse: 0.147, pcc: 0.924, kcc: 0.772
+2025-07-10 14:54:09: Epoch: 031 New best VALIDATION metrics
+2025-07-10 14:54:09: Epoch: 031 Test results, ap: mae: 1.684, rse: 7.961, pcc: 0.169, kcc: 0.107
+2025-07-10 14:54:10: Epoch: 032 Train Loss: 0.216, mae: 0.346, rse: 0.148, pcc: 0.924, kcc: 0.773
+2025-07-10 14:54:10: Epoch: 032 New best VALIDATION metrics
+2025-07-10 14:54:11: Epoch: 032 Test results, ap: mae: 1.663, rse: 7.902, pcc: 0.167, kcc: 0.105
+2025-07-10 14:54:12: Epoch: 033 Train Loss: 0.210, mae: 0.337, rse: 0.145, pcc: 0.926, kcc: 0.776
+2025-07-10 14:54:12: Epoch: 033 New best VALIDATION metrics
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+2025-07-10 14:54:13: Epoch: 034 Train Loss: 0.179, mae: 0.333, rse: 0.141, pcc: 0.928, kcc: 0.783
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+2025-07-10 14:54:13: Epoch: 034 Test results, ap: mae: 1.791, rse: 9.153, pcc: 0.163, kcc: 0.102
+2025-07-10 14:54:14: Epoch: 035 Train Loss: 0.176, mae: 0.325, rse: 0.142, pcc: 0.927, kcc: 0.785
+2025-07-10 14:54:14: Epoch: 035 New best VALIDATION metrics
+2025-07-10 14:54:14: Epoch: 035 Test results, ap: mae: 1.791, rse: 9.068, pcc: 0.167, kcc: 0.107
+2025-07-10 14:54:15: Epoch: 036 Train Loss: 0.182, mae: 0.319, rse: 0.142, pcc: 0.927, kcc: 0.789
+2025-07-10 14:54:15: Epoch: 036 New best VALIDATION metrics
+2025-07-10 14:54:15: Epoch: 036 Test results, ap: mae: 1.831, rse: 9.455, pcc: 0.166, kcc: 0.103
+2025-07-10 14:54:16: Epoch: 037 Train Loss: 0.170, mae: 0.322, rse: 0.142, pcc: 0.927, kcc: 0.788
+2025-07-10 14:54:17: Epoch: 037 Test results, ap: mae: 1.814, rse: 9.347, pcc: 0.166, kcc: 0.103
+2025-07-10 14:54:17: Epoch: 038 Train Loss: 0.167, mae: 0.317, rse: 0.143, pcc: 0.927, kcc: 0.785
+2025-07-10 14:54:18: Epoch: 038 Test results, ap: mae: 1.823, rse: 9.339, pcc: 0.161, kcc: 0.101
+2025-07-10 14:54:19: Epoch: 039 Train Loss: 0.163, mae: 0.312, rse: 0.138, pcc: 0.929, kcc: 0.792
+2025-07-10 14:54:19: Epoch: 039 New best VALIDATION metrics
+2025-07-10 14:54:19: Epoch: 039 Test results, ap: mae: 1.785, rse: 9.033, pcc: 0.163, kcc: 0.097
+2025-07-10 14:54:20: Epoch: 040 Train Loss: 0.157, mae: 0.311, rse: 0.138, pcc: 0.929, kcc: 0.794
+2025-07-10 14:54:20: Epoch: 040 New best VALIDATION metrics
+2025-07-10 14:54:20: Epoch: 040 Test results, ap: mae: 1.798, rse: 9.133, pcc: 0.155, kcc: 0.098
+2025-07-10 14:54:21: Epoch: 041 Train Loss: 0.153, mae: 0.302, rse: 0.137, pcc: 0.929, kcc: 0.796
+2025-07-10 14:54:21: Epoch: 041 New best VALIDATION metrics
+2025-07-10 14:54:21: Epoch: 041 Test results, ap: mae: 1.804, rse: 9.201, pcc: 0.159, kcc: 0.103
+2025-07-10 14:54:22: Epoch: 042 Train Loss: 0.137, mae: 0.299, rse: 0.139, pcc: 0.928, kcc: 0.800
+2025-07-10 14:54:22: Epoch: 042 New best VALIDATION metrics
+2025-07-10 14:54:22: Epoch: 042 Test results, ap: mae: 1.793, rse: 9.074, pcc: 0.163, kcc: 0.100
+2025-07-10 14:54:23: Epoch: 043 Train Loss: 0.137, mae: 0.294, rse: 0.137, pcc: 0.930, kcc: 0.802
+2025-07-10 14:54:23: Epoch: 043 New best VALIDATION metrics
+2025-07-10 14:54:23: Epoch: 043 Test results, ap: mae: 1.788, rse: 9.057, pcc: 0.162, kcc: 0.104
+2025-07-10 14:54:24: Epoch: 044 Train Loss: 0.129, mae: 0.291, rse: 0.139, pcc: 0.929, kcc: 0.801
+2025-07-10 14:54:25: Epoch: 044 Test results, ap: mae: 1.793, rse: 9.088, pcc: 0.162, kcc: 0.100
+2025-07-10 14:54:26: Epoch: 045 Train Loss: 0.125, mae: 0.289, rse: 0.138, pcc: 0.930, kcc: 0.803
+2025-07-10 14:54:26: Epoch: 045 New best VALIDATION metrics
+2025-07-10 14:54:26: Epoch: 045 Test results, ap: mae: 1.813, rse: 9.310, pcc: 0.160, kcc: 0.100
+2025-07-10 14:54:27: Epoch: 046 Train Loss: 0.133, mae: 0.287, rse: 0.140, pcc: 0.929, kcc: 0.803
+2025-07-10 14:54:27: Epoch: 046 Test results, ap: mae: 1.832, rse: 9.532, pcc: 0.166, kcc: 0.104
+2025-07-10 14:54:28: Epoch: 047 Train Loss: 0.128, mae: 0.291, rse: 0.139, pcc: 0.929, kcc: 0.803
+2025-07-10 14:54:28: Epoch: 047 Test results, ap: mae: 1.839, rse: 9.569, pcc: 0.161, kcc: 0.099
+2025-07-10 14:54:29: Epoch: 048 Train Loss: 0.125, mae: 0.283, rse: 0.137, pcc: 0.930, kcc: 0.805
+2025-07-10 14:54:29: Epoch: 048 New best VALIDATION metrics
+2025-07-10 14:54:29: Epoch: 048 Test results, ap: mae: 1.806, rse: 9.265, pcc: 0.163, kcc: 0.099
+2025-07-10 14:54:30: Epoch: 049 Train Loss: 0.128, mae: 0.284, rse: 0.139, pcc: 0.929, kcc: 0.804
+2025-07-10 14:54:30: Epoch: 049 Test results, ap: mae: 1.818, rse: 9.403, pcc: 0.159, kcc: 0.101
+2025-07-10 14:54:31: Epoch: 050 Train Loss: 0.125, mae: 0.278, rse: 0.138, pcc: 0.931, kcc: 0.809
+2025-07-10 14:54:31: Epoch: 050 New best VALIDATION metrics
+2025-07-10 14:54:32: Epoch: 050 Test results, ap: mae: 1.853, rse: 9.814, pcc: 0.161, kcc: 0.104
+2025-07-10 14:54:32: used time 0.02h
+2025-07-10 14:54:32: Running Cross Validation 1
+2025-07-10 14:54:32: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 14:54:33: Epoch: 001 Train Loss: 2.855, mae: 1.259, rse: 1.000, pcc: 0.273, kcc: 0.165
+2025-07-10 14:54:33: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:54:33: Epoch: 001 Test results, ap: mae: 0.602, rse: 1.106, pcc: 0.059, kcc: 0.045
+2025-07-10 14:54:34: Epoch: 002 Train Loss: 2.850, mae: 1.251, rse: 1.008, pcc: 0.305, kcc: 0.228
+2025-07-10 14:54:34: Epoch: 002 New best VALIDATION metrics
+2025-07-10 14:54:34: Epoch: 002 Test results, ap: mae: 0.648, rse: 1.271, pcc: -0.147, kcc: -0.117
+2025-07-10 14:54:35: Epoch: 003 Train Loss: 2.852, mae: 1.251, rse: 1.013, pcc: 0.037, kcc: -0.001
+2025-07-10 14:54:35: Epoch: 003 Test results, ap: mae: 0.664, rse: 1.332, pcc: -0.105, kcc: -0.128
+2025-07-10 14:54:36: Epoch: 004 Train Loss: 2.825, mae: 1.264, rse: 1.001, pcc: -0.289, kcc: -0.160
+2025-07-10 14:54:36: Epoch: 004 Test results, ap: mae: 0.595, rse: 1.076, pcc: -0.198, kcc: -0.041
+2025-07-10 14:54:37: Epoch: 005 Train Loss: 2.854, mae: 1.255, rse: 1.001, pcc: -0.054, kcc: -0.063
+2025-07-10 14:54:37: Epoch: 005 Test results, ap: mae: 0.610, rse: 1.141, pcc: -0.150, kcc: -0.116
+2025-07-10 14:54:38: Epoch: 006 Train Loss: 2.851, mae: 1.251, rse: 1.009, pcc: 0.135, kcc: 0.115
+2025-07-10 14:54:38: Epoch: 006 Test results, ap: mae: 0.652, rse: 1.286, pcc: 0.038, kcc: -0.084
+2025-07-10 14:54:39: Epoch: 007 Train Loss: 2.861, mae: 1.258, rse: 1.000, pcc: 0.134, kcc: 0.132
+2025-07-10 14:54:40: Epoch: 007 Test results, ap: mae: 0.604, rse: 1.115, pcc: 0.148, kcc: 0.091
+2025-07-10 14:54:40: Epoch: 008 Train Loss: 2.850, mae: 1.255, rse: 1.001, pcc: -0.101, kcc: -0.147
+2025-07-10 14:54:41: Epoch: 008 Test results, ap: mae: 0.612, rse: 1.146, pcc: -0.129, kcc: -0.071
+2025-07-10 14:54:42: Epoch: 009 Train Loss: 2.833, mae: 1.255, rse: 1.001, pcc: 0.055, kcc: 0.059
+2025-07-10 14:54:42: Epoch: 009 Test results, ap: mae: 0.611, rse: 1.145, pcc: -0.126, kcc: 0.003
+2025-07-10 14:54:43: Epoch: 010 Train Loss: 2.824, mae: 1.263, rse: 1.000, pcc: -0.049, kcc: -0.028
+2025-07-10 14:54:43: Epoch: 010 Test results, ap: mae: 0.596, rse: 1.084, pcc: -0.080, kcc: 0.010
+2025-07-10 14:54:44: Epoch: 011 Train Loss: 2.838, mae: 1.253, rse: 1.002, pcc: 0.128, kcc: 0.117
+2025-07-10 14:54:44: Epoch: 011 Test results, ap: mae: 0.616, rse: 1.163, pcc: -0.095, kcc: -0.012
+2025-07-10 14:54:44: Epoch: 011 New best TEST metrics
+2025-07-10 14:54:45: Epoch: 012 Train Loss: 2.816, mae: 1.254, rse: 1.001, pcc: 0.146, kcc: 0.145
+2025-07-10 14:54:45: Epoch: 012 Test results, ap: mae: 0.613, rse: 1.151, pcc: -0.037, kcc: -0.045
+2025-07-10 14:54:45: Epoch: 012 New best TEST metrics
+2025-07-10 14:54:46: Epoch: 013 Train Loss: 2.834, mae: 1.253, rse: 1.002, pcc: 0.164, kcc: 0.147
+2025-07-10 14:54:46: Epoch: 013 Test results, ap: mae: 0.616, rse: 1.165, pcc: 0.016, kcc: -0.086
+2025-07-10 14:54:47: Epoch: 014 Train Loss: 2.853, mae: 1.254, rse: 1.001, pcc: 0.172, kcc: 0.136
+2025-07-10 14:54:47: Epoch: 014 Test results, ap: mae: 0.615, rse: 1.159, pcc: 0.053, kcc: -0.082
+2025-07-10 14:54:47: Epoch: 014 New best TEST metrics
+2025-07-10 14:54:48: Epoch: 015 Train Loss: 2.832, mae: 1.253, rse: 1.002, pcc: 0.201, kcc: 0.133
+2025-07-10 14:54:48: Epoch: 015 Test results, ap: mae: 0.617, rse: 1.165, pcc: 0.105, kcc: -0.041
+2025-07-10 14:54:48: Epoch: 015 New best TEST metrics
+2025-07-10 14:54:49: Epoch: 016 Train Loss: 2.835, mae: 1.253, rse: 1.002, pcc: 0.233, kcc: 0.144
+2025-07-10 14:54:50: Epoch: 016 Test results, ap: mae: 0.620, rse: 1.175, pcc: 0.128, kcc: -0.039
+2025-07-10 14:54:50: Epoch: 016 New best TEST metrics
+2025-07-10 14:54:51: Epoch: 017 Train Loss: 2.849, mae: 1.252, rse: 1.002, pcc: 0.247, kcc: 0.155
+2025-07-10 14:54:51: Epoch: 017 Test results, ap: mae: 0.621, rse: 1.179, pcc: 0.121, kcc: -0.037
+2025-07-10 14:54:52: Epoch: 018 Train Loss: 2.844, mae: 1.253, rse: 1.001, pcc: 0.285, kcc: 0.186
+2025-07-10 14:54:52: Epoch: 018 Test results, ap: mae: 0.617, rse: 1.167, pcc: 0.096, kcc: -0.011
+2025-07-10 14:54:52: Epoch: 018 New best TEST metrics
+2025-07-10 14:54:53: Epoch: 019 Train Loss: 2.772, mae: 1.199, rse: 0.902, pcc: 0.325, kcc: 0.268
+2025-07-10 14:54:53: Epoch: 019 New best VALIDATION metrics
+2025-07-10 14:54:53: Epoch: 019 Test results, ap: mae: 0.661, rse: 1.359, pcc: -0.015, kcc: -0.056
+2025-07-10 14:54:54: Epoch: 020 Train Loss: 2.618, mae: 1.181, rse: 0.872, pcc: 0.377, kcc: 0.267
+2025-07-10 14:54:54: Epoch: 020 New best VALIDATION metrics
+2025-07-10 14:54:54: Epoch: 020 Test results, ap: mae: 0.620, rse: 1.160, pcc: -0.096, kcc: -0.137
+2025-07-10 14:54:55: Epoch: 021 Train Loss: 2.535, mae: 1.157, rse: 0.850, pcc: 0.397, kcc: 0.285
+2025-07-10 14:54:55: Epoch: 021 New best VALIDATION metrics
+2025-07-10 14:54:55: Epoch: 021 Test results, ap: mae: 0.656, rse: 1.317, pcc: -0.060, kcc: -0.127
+2025-07-10 14:54:56: Epoch: 022 Train Loss: 2.424, mae: 1.125, rse: 0.810, pcc: 0.437, kcc: 0.304
+2025-07-10 14:54:56: Epoch: 022 New best VALIDATION metrics
+2025-07-10 14:54:56: Epoch: 022 Test results, ap: mae: 0.657, rse: 1.328, pcc: -0.128, kcc: -0.118
+2025-07-10 14:54:57: Epoch: 023 Train Loss: 2.281, mae: 1.074, rse: 0.758, pcc: 0.496, kcc: 0.318
+2025-07-10 14:54:57: Epoch: 023 New best VALIDATION metrics
+2025-07-10 14:54:57: Epoch: 023 Test results, ap: mae: 0.674, rse: 1.395, pcc: -0.136, kcc: -0.171
+2025-07-10 14:54:58: Epoch: 024 Train Loss: 1.918, mae: 1.049, rse: 0.724, pcc: 0.528, kcc: 0.328
+2025-07-10 14:54:58: Epoch: 024 New best VALIDATION metrics
+2025-07-10 14:54:58: Epoch: 024 Test results, ap: mae: 0.677, rse: 1.458, pcc: -0.146, kcc: -0.141
+2025-07-10 14:54:59: Epoch: 025 Train Loss: 1.826, mae: 1.028, rse: 0.695, pcc: 0.562, kcc: 0.362
+2025-07-10 14:54:59: Epoch: 025 New best VALIDATION metrics
+2025-07-10 14:55:00: Epoch: 025 Test results, ap: mae: 0.992, rse: 2.602, pcc: -0.094, kcc: -0.029
+2025-07-10 14:55:01: Epoch: 026 Train Loss: 1.654, mae: 0.979, rse: 0.664, pcc: 0.582, kcc: 0.379
+2025-07-10 14:55:01: Epoch: 026 New best VALIDATION metrics
+2025-07-10 14:55:01: Epoch: 026 Test results, ap: mae: 0.641, rse: 1.231, pcc: 0.061, kcc: -0.004
+2025-07-10 14:55:02: Epoch: 027 Train Loss: 1.539, mae: 0.964, rse: 0.600, pcc: 0.636, kcc: 0.407
+2025-07-10 14:55:02: Epoch: 027 New best VALIDATION metrics
+2025-07-10 14:55:02: Epoch: 027 Test results, ap: mae: 0.973, rse: 2.633, pcc: -0.120, kcc: -0.093
+2025-07-10 14:55:03: Epoch: 028 Train Loss: 1.429, mae: 0.930, rse: 0.570, pcc: 0.660, kcc: 0.447
+2025-07-10 14:55:03: Epoch: 028 New best VALIDATION metrics
+2025-07-10 14:55:03: Epoch: 028 Test results, ap: mae: 0.902, rse: 2.411, pcc: -0.142, kcc: -0.082
+2025-07-10 14:55:04: Epoch: 029 Train Loss: 1.275, mae: 0.880, rse: 0.533, pcc: 0.687, kcc: 0.464
+2025-07-10 14:55:04: Epoch: 029 New best VALIDATION metrics
+2025-07-10 14:55:04: Epoch: 029 Test results, ap: mae: 0.797, rse: 1.901, pcc: -0.142, kcc: -0.098
+2025-07-10 14:55:05: Epoch: 030 Train Loss: 1.175, mae: 0.875, rse: 0.522, pcc: 0.694, kcc: 0.492
+2025-07-10 14:55:05: Epoch: 030 New best VALIDATION metrics
+2025-07-10 14:55:05: Epoch: 030 Test results, ap: mae: 0.874, rse: 2.279, pcc: -0.162, kcc: -0.119
+2025-07-10 14:55:06: Epoch: 031 Train Loss: 1.058, mae: 0.828, rse: 0.482, pcc: 0.722, kcc: 0.510
+2025-07-10 14:55:06: Epoch: 031 New best VALIDATION metrics
+2025-07-10 14:55:06: Epoch: 031 Test results, ap: mae: 0.882, rse: 2.195, pcc: -0.116, kcc: -0.069
+2025-07-10 14:55:07: Epoch: 032 Train Loss: 1.029, mae: 0.828, rse: 0.482, pcc: 0.721, kcc: 0.511
+2025-07-10 14:55:07: Epoch: 032 New best VALIDATION metrics
+2025-07-10 14:55:07: Epoch: 032 Test results, ap: mae: 0.908, rse: 2.322, pcc: -0.120, kcc: -0.080
+2025-07-10 14:55:08: Epoch: 033 Train Loss: 0.989, mae: 0.812, rse: 0.469, pcc: 0.730, kcc: 0.525
+2025-07-10 14:55:08: Epoch: 033 New best VALIDATION metrics
+2025-07-10 14:55:09: Epoch: 033 Test results, ap: mae: 0.881, rse: 2.232, pcc: -0.115, kcc: -0.075
+2025-07-10 14:55:09: Epoch: 034 Train Loss: 0.959, mae: 0.779, rse: 0.447, pcc: 0.746, kcc: 0.542
+2025-07-10 14:55:09: Epoch: 034 New best VALIDATION metrics
+2025-07-10 14:55:10: Epoch: 034 Test results, ap: mae: 0.857, rse: 2.147, pcc: -0.098, kcc: -0.049
+2025-07-10 14:55:11: Epoch: 035 Train Loss: 0.915, mae: 0.792, rse: 0.447, pcc: 0.746, kcc: 0.543
+2025-07-10 14:55:11: Epoch: 035 Test results, ap: mae: 0.982, rse: 2.596, pcc: -0.094, kcc: -0.058
+2025-07-10 14:55:12: Epoch: 036 Train Loss: 0.887, mae: 0.761, rse: 0.432, pcc: 0.755, kcc: 0.562
+2025-07-10 14:55:12: Epoch: 036 New best VALIDATION metrics
+2025-07-10 14:55:12: Epoch: 036 Test results, ap: mae: 0.923, rse: 2.470, pcc: -0.110, kcc: -0.056
+2025-07-10 14:55:13: Epoch: 037 Train Loss: 0.860, mae: 0.743, rse: 0.416, pcc: 0.765, kcc: 0.568
+2025-07-10 14:55:13: Epoch: 037 New best VALIDATION metrics
+2025-07-10 14:55:13: Epoch: 037 Test results, ap: mae: 1.000, rse: 2.806, pcc: -0.105, kcc: -0.064
+2025-07-10 14:55:14: Epoch: 038 Train Loss: 0.830, mae: 0.718, rse: 0.398, pcc: 0.777, kcc: 0.586
+2025-07-10 14:55:14: Epoch: 038 New best VALIDATION metrics
+2025-07-10 14:55:14: Epoch: 038 Test results, ap: mae: 0.929, rse: 2.518, pcc: -0.062, kcc: -0.036
+2025-07-10 14:55:15: Epoch: 039 Train Loss: 0.790, mae: 0.720, rse: 0.397, pcc: 0.779, kcc: 0.585
+2025-07-10 14:55:15: Epoch: 039 New best VALIDATION metrics
+2025-07-10 14:55:15: Epoch: 039 Test results, ap: mae: 1.072, rse: 3.098, pcc: -0.090, kcc: -0.062
+2025-07-10 14:55:16: Epoch: 040 Train Loss: 0.766, mae: 0.699, rse: 0.384, pcc: 0.786, kcc: 0.598
+2025-07-10 14:55:16: Epoch: 040 New best VALIDATION metrics
+2025-07-10 14:55:16: Epoch: 040 Test results, ap: mae: 1.080, rse: 3.133, pcc: -0.080, kcc: -0.059
+2025-07-10 14:55:17: Epoch: 041 Train Loss: 0.709, mae: 0.675, rse: 0.364, pcc: 0.798, kcc: 0.612
+2025-07-10 14:55:17: Epoch: 041 New best VALIDATION metrics
+2025-07-10 14:55:17: Epoch: 041 Test results, ap: mae: 1.007, rse: 2.796, pcc: -0.049, kcc: -0.033
+2025-07-10 14:55:18: Epoch: 042 Train Loss: 0.699, mae: 0.670, rse: 0.360, pcc: 0.800, kcc: 0.614
+2025-07-10 14:55:18: Epoch: 042 New best VALIDATION metrics
+2025-07-10 14:55:19: Epoch: 042 Test results, ap: mae: 1.045, rse: 2.981, pcc: -0.040, kcc: -0.018
+2025-07-10 14:55:19: Epoch: 043 Train Loss: 0.695, mae: 0.657, rse: 0.350, pcc: 0.806, kcc: 0.624
+2025-07-10 14:55:19: Epoch: 043 New best VALIDATION metrics
+2025-07-10 14:55:20: Epoch: 043 Test results, ap: mae: 1.054, rse: 3.043, pcc: -0.064, kcc: -0.041
+2025-07-10 14:55:21: Epoch: 044 Train Loss: 0.658, mae: 0.652, rse: 0.345, pcc: 0.810, kcc: 0.628
+2025-07-10 14:55:21: Epoch: 044 New best VALIDATION metrics
+2025-07-10 14:55:21: Epoch: 044 Test results, ap: mae: 1.073, rse: 3.128, pcc: -0.083, kcc: -0.052
+2025-07-10 14:55:22: Epoch: 045 Train Loss: 0.659, mae: 0.638, rse: 0.336, pcc: 0.815, kcc: 0.635
+2025-07-10 14:55:22: Epoch: 045 New best VALIDATION metrics
+2025-07-10 14:55:22: Epoch: 045 Test results, ap: mae: 1.063, rse: 3.076, pcc: -0.081, kcc: -0.048
+2025-07-10 14:55:23: Epoch: 046 Train Loss: 0.640, mae: 0.646, rse: 0.336, pcc: 0.816, kcc: 0.635
+2025-07-10 14:55:23: Epoch: 046 Test results, ap: mae: 1.090, rse: 3.250, pcc: -0.104, kcc: -0.066
+2025-07-10 14:55:24: Epoch: 047 Train Loss: 0.624, mae: 0.621, rse: 0.323, pcc: 0.823, kcc: 0.645
+2025-07-10 14:55:24: Epoch: 047 New best VALIDATION metrics
+2025-07-10 14:55:24: Epoch: 047 Test results, ap: mae: 1.066, rse: 3.186, pcc: -0.095, kcc: -0.054
+2025-07-10 14:55:25: Epoch: 048 Train Loss: 0.607, mae: 0.612, rse: 0.316, pcc: 0.827, kcc: 0.651
+2025-07-10 14:55:25: Epoch: 048 New best VALIDATION metrics
+2025-07-10 14:55:25: Epoch: 048 Test results, ap: mae: 1.034, rse: 3.033, pcc: -0.080, kcc: -0.039
+2025-07-10 14:55:26: Epoch: 049 Train Loss: 0.599, mae: 0.608, rse: 0.312, pcc: 0.830, kcc: 0.653
+2025-07-10 14:55:26: Epoch: 049 New best VALIDATION metrics
+2025-07-10 14:55:26: Epoch: 049 Test results, ap: mae: 1.114, rse: 3.452, pcc: -0.116, kcc: -0.065
+2025-07-10 14:55:27: Epoch: 050 Train Loss: 0.579, mae: 0.596, rse: 0.302, pcc: 0.836, kcc: 0.659
+2025-07-10 14:55:27: Epoch: 050 New best VALIDATION metrics
+2025-07-10 14:55:27: Epoch: 050 Test results, ap: mae: 1.116, rse: 3.451, pcc: -0.135, kcc: -0.081
+2025-07-10 14:55:27: used time 0.02h
+2025-07-10 14:55:27: Running Cross Validation 2
+2025-07-10 14:55:27: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:55:28: Epoch: 001 Train Loss: 2.908, mae: 1.256, rse: 1.000, pcc: 0.263, kcc: 0.172
+2025-07-10 14:55:28: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:55:29: Epoch: 001 Test results, ap: mae: 0.607, rse: 1.129, pcc: -0.103, kcc: -0.078
+2025-07-10 14:55:30: Epoch: 002 Train Loss: 2.919, mae: 1.253, rse: 1.000, pcc: 0.268, kcc: 0.171
+2025-07-10 14:55:30: Epoch: 002 New best VALIDATION metrics
+2025-07-10 14:55:30: Epoch: 002 Test results, ap: mae: 0.619, rse: 1.173, pcc: -0.070, kcc: -0.075
+2025-07-10 14:55:31: Epoch: 003 Train Loss: 2.900, mae: 1.254, rse: 1.000, pcc: 0.265, kcc: 0.179
+2025-07-10 14:55:31: Epoch: 003 New best VALIDATION metrics
+2025-07-10 14:55:31: Epoch: 003 Test results, ap: mae: 0.613, rse: 1.151, pcc: -0.051, kcc: -0.175
+2025-07-10 14:55:32: Epoch: 004 Train Loss: 2.767, mae: 1.214, rse: 0.935, pcc: 0.270, kcc: 0.217
+2025-07-10 14:55:32: Epoch: 004 New best VALIDATION metrics
+2025-07-10 14:55:32: Epoch: 004 Test results, ap: mae: 0.749, rse: 1.641, pcc: -0.056, kcc: -0.189
+2025-07-10 14:55:33: Epoch: 005 Train Loss: 2.654, mae: 1.213, rse: 0.926, pcc: 0.298, kcc: 0.237
+2025-07-10 14:55:33: Epoch: 005 New best VALIDATION metrics
+2025-07-10 14:55:33: Epoch: 005 Test results, ap: mae: 0.833, rse: 1.962, pcc: 0.391, kcc: 0.201
+2025-07-10 14:55:34: Epoch: 006 Train Loss: 2.558, mae: 1.116, rse: 0.819, pcc: 0.494, kcc: 0.320
+2025-07-10 14:55:34: Epoch: 006 New best VALIDATION metrics
+2025-07-10 14:55:34: Epoch: 006 Test results, ap: mae: 0.708, rse: 1.464, pcc: 0.083, kcc: 0.007
+2025-07-10 14:55:35: Epoch: 007 Train Loss: 2.283, mae: 1.024, rse: 0.687, pcc: 0.568, kcc: 0.395
+2025-07-10 14:55:35: Epoch: 007 New best VALIDATION metrics
+2025-07-10 14:55:35: Epoch: 007 Test results, ap: mae: 0.809, rse: 1.836, pcc: 0.162, kcc: 0.163
+2025-07-10 14:55:36: Epoch: 008 Train Loss: 1.922, mae: 0.999, rse: 0.645, pcc: 0.637, kcc: 0.420
+2025-07-10 14:55:36: Epoch: 008 New best VALIDATION metrics
+2025-07-10 14:55:36: Epoch: 008 Test results, ap: mae: 0.619, rse: 1.140, pcc: 0.270, kcc: 0.164
+2025-07-10 14:55:37: Epoch: 009 Train Loss: 1.628, mae: 0.858, rse: 0.469, pcc: 0.731, kcc: 0.529
+2025-07-10 14:55:37: Epoch: 009 New best VALIDATION metrics
+2025-07-10 14:55:37: Epoch: 009 Test results, ap: mae: 0.705, rse: 1.437, pcc: 0.155, kcc: 0.083
+2025-07-10 14:55:38: Epoch: 010 Train Loss: 1.205, mae: 0.789, rse: 0.415, pcc: 0.768, kcc: 0.565
+2025-07-10 14:55:38: Epoch: 010 New best VALIDATION metrics
+2025-07-10 14:55:39: Epoch: 010 Test results, ap: mae: 0.675, rse: 1.355, pcc: 0.218, kcc: 0.147
+2025-07-10 14:55:39: Epoch: 011 Train Loss: 0.948, mae: 0.665, rse: 0.316, pcc: 0.830, kcc: 0.632
+2025-07-10 14:55:39: Epoch: 011 New best VALIDATION metrics
+2025-07-10 14:55:40: Epoch: 011 Test results, ap: mae: 0.639, rse: 1.225, pcc: 0.269, kcc: 0.125
+2025-07-10 14:55:40: Epoch: 011 New best TEST metrics
+2025-07-10 14:55:40: Epoch: 012 Train Loss: 0.853, mae: 0.634, rse: 0.292, pcc: 0.846, kcc: 0.651
+2025-07-10 14:55:40: Epoch: 012 New best VALIDATION metrics
+2025-07-10 14:55:41: Epoch: 012 Test results, ap: mae: 0.758, rse: 1.685, pcc: 0.207, kcc: 0.086
+2025-07-10 14:55:41: Epoch: 013 Train Loss: 0.746, mae: 0.575, rse: 0.258, pcc: 0.862, kcc: 0.679
+2025-07-10 14:55:41: Epoch: 013 New best VALIDATION metrics
+2025-07-10 14:55:42: Epoch: 013 Test results, ap: mae: 0.703, rse: 1.489, pcc: 0.220, kcc: 0.111
+2025-07-10 14:55:43: Epoch: 014 Train Loss: 0.687, mae: 0.563, rse: 0.238, pcc: 0.873, kcc: 0.689
+2025-07-10 14:55:43: Epoch: 014 New best VALIDATION metrics
+2025-07-10 14:55:43: Epoch: 014 Test results, ap: mae: 0.702, rse: 1.449, pcc: 0.235, kcc: 0.113
+2025-07-10 14:55:44: Epoch: 015 Train Loss: 0.632, mae: 0.523, rse: 0.227, pcc: 0.881, kcc: 0.704
+2025-07-10 14:55:44: Epoch: 015 New best VALIDATION metrics
+2025-07-10 14:55:44: Epoch: 015 Test results, ap: mae: 0.744, rse: 1.595, pcc: 0.195, kcc: 0.108
+2025-07-10 14:55:45: Epoch: 016 Train Loss: 0.541, mae: 0.485, rse: 0.193, pcc: 0.899, kcc: 0.732
+2025-07-10 14:55:45: Epoch: 016 New best VALIDATION metrics
+2025-07-10 14:55:45: Epoch: 016 Test results, ap: mae: 0.809, rse: 1.834, pcc: 0.160, kcc: 0.095
+2025-07-10 14:55:46: Epoch: 017 Train Loss: 0.466, mae: 0.432, rse: 0.165, pcc: 0.915, kcc: 0.759
+2025-07-10 14:55:46: Epoch: 017 New best VALIDATION metrics
+2025-07-10 14:55:46: Epoch: 017 Test results, ap: mae: 0.827, rse: 1.880, pcc: 0.152, kcc: 0.095
+2025-07-10 14:55:47: Epoch: 018 Train Loss: 0.397, mae: 0.421, rse: 0.166, pcc: 0.913, kcc: 0.752
+2025-07-10 14:55:47: Epoch: 018 New best VALIDATION metrics
+2025-07-10 14:55:47: Epoch: 018 Test results, ap: mae: 0.821, rse: 1.874, pcc: 0.138, kcc: 0.078
+2025-07-10 14:55:48: Epoch: 019 Train Loss: 0.319, mae: 0.411, rse: 0.155, pcc: 0.920, kcc: 0.762
+2025-07-10 14:55:48: Epoch: 019 New best VALIDATION metrics
+2025-07-10 14:55:48: Epoch: 019 Test results, ap: mae: 0.872, rse: 2.185, pcc: 0.106, kcc: 0.079
+2025-07-10 14:55:49: Epoch: 020 Train Loss: 0.291, mae: 0.398, rse: 0.152, pcc: 0.921, kcc: 0.775
+2025-07-10 14:55:49: Epoch: 020 New best VALIDATION metrics
+2025-07-10 14:55:49: Epoch: 020 Test results, ap: mae: 0.917, rse: 2.331, pcc: 0.076, kcc: 0.044
+2025-07-10 14:55:50: Epoch: 021 Train Loss: 0.218, mae: 0.322, rse: 0.120, pcc: 0.938, kcc: 0.811
+2025-07-10 14:55:50: Epoch: 021 New best VALIDATION metrics
+2025-07-10 14:55:50: Epoch: 021 Test results, ap: mae: 0.965, rse: 2.597, pcc: 0.065, kcc: 0.044
+2025-07-10 14:55:51: Epoch: 022 Train Loss: 0.193, mae: 0.295, rse: 0.117, pcc: 0.940, kcc: 0.823
+2025-07-10 14:55:51: Epoch: 022 New best VALIDATION metrics
+2025-07-10 14:55:51: Epoch: 022 Test results, ap: mae: 0.991, rse: 2.751, pcc: 0.064, kcc: 0.044
+2025-07-10 14:55:52: Epoch: 023 Train Loss: 0.190, mae: 0.319, rse: 0.122, pcc: 0.937, kcc: 0.814
+2025-07-10 14:55:52: Epoch: 023 Test results, ap: mae: 1.037, rse: 2.976, pcc: 0.072, kcc: 0.037
+2025-07-10 14:55:53: Epoch: 024 Train Loss: 0.178, mae: 0.306, rse: 0.119, pcc: 0.940, kcc: 0.827
+2025-07-10 14:55:53: Epoch: 024 Test results, ap: mae: 1.008, rse: 2.842, pcc: 0.037, kcc: 0.022
+2025-07-10 14:55:54: Epoch: 025 Train Loss: 0.168, mae: 0.312, rse: 0.119, pcc: 0.939, kcc: 0.820
+2025-07-10 14:55:54: Epoch: 025 Test results, ap: mae: 1.024, rse: 2.858, pcc: 0.028, kcc: 0.004
+2025-07-10 14:55:55: Epoch: 026 Train Loss: 0.162, mae: 0.321, rse: 0.117, pcc: 0.940, kcc: 0.811
+2025-07-10 14:55:55: Epoch: 026 Test results, ap: mae: 1.020, rse: 2.900, pcc: 0.067, kcc: 0.044
+2025-07-10 14:55:56: Epoch: 027 Train Loss: 0.146, mae: 0.281, rse: 0.108, pcc: 0.945, kcc: 0.833
+2025-07-10 14:55:56: Epoch: 027 New best VALIDATION metrics
+2025-07-10 14:55:56: Epoch: 027 Test results, ap: mae: 1.027, rse: 2.951, pcc: 0.074, kcc: 0.045
+2025-07-10 14:55:57: Epoch: 028 Train Loss: 0.131, mae: 0.290, rse: 0.108, pcc: 0.945, kcc: 0.830
+2025-07-10 14:55:57: Epoch: 028 Test results, ap: mae: 1.077, rse: 3.238, pcc: 0.059, kcc: 0.020
+2025-07-10 14:55:58: Epoch: 029 Train Loss: 0.124, mae: 0.292, rse: 0.114, pcc: 0.941, kcc: 0.827
+2025-07-10 14:55:58: Epoch: 029 Test results, ap: mae: 1.044, rse: 3.014, pcc: 0.052, kcc: 0.027
+2025-07-10 14:55:59: Epoch: 030 Train Loss: 0.127, mae: 0.292, rse: 0.110, pcc: 0.944, kcc: 0.835
+2025-07-10 14:55:59: Epoch: 030 Test results, ap: mae: 1.064, rse: 3.098, pcc: 0.074, kcc: 0.033
+2025-07-10 14:56:00: Epoch: 031 Train Loss: 0.117, mae: 0.264, rse: 0.104, pcc: 0.947, kcc: 0.839
+2025-07-10 14:56:00: Epoch: 031 New best VALIDATION metrics
+2025-07-10 14:56:00: Epoch: 031 Test results, ap: mae: 1.078, rse: 3.235, pcc: 0.045, kcc: 0.024
+2025-07-10 14:56:01: Epoch: 032 Train Loss: 0.105, mae: 0.258, rse: 0.102, pcc: 0.948, kcc: 0.846
+2025-07-10 14:56:01: Epoch: 032 New best VALIDATION metrics
+2025-07-10 14:56:01: Epoch: 032 Test results, ap: mae: 1.075, rse: 3.200, pcc: 0.054, kcc: 0.029
+2025-07-10 14:56:02: Epoch: 033 Train Loss: 0.104, mae: 0.255, rse: 0.099, pcc: 0.949, kcc: 0.849
+2025-07-10 14:56:02: Epoch: 033 New best VALIDATION metrics
+2025-07-10 14:56:02: Epoch: 033 Test results, ap: mae: 1.087, rse: 3.286, pcc: 0.064, kcc: 0.027
+2025-07-10 14:56:03: Epoch: 034 Train Loss: 0.101, mae: 0.251, rse: 0.100, pcc: 0.949, kcc: 0.852
+2025-07-10 14:56:03: Epoch: 034 New best VALIDATION metrics
+2025-07-10 14:56:03: Epoch: 034 Test results, ap: mae: 1.092, rse: 3.317, pcc: 0.058, kcc: 0.032
+2025-07-10 14:56:04: Epoch: 035 Train Loss: 0.091, mae: 0.245, rse: 0.098, pcc: 0.950, kcc: 0.854
+2025-07-10 14:56:04: Epoch: 035 New best VALIDATION metrics
+2025-07-10 14:56:05: Epoch: 035 Test results, ap: mae: 1.101, rse: 3.415, pcc: 0.064, kcc: 0.031
+2025-07-10 14:56:06: Epoch: 036 Train Loss: 0.098, mae: 0.249, rse: 0.098, pcc: 0.950, kcc: 0.853
+2025-07-10 14:56:06: Epoch: 036 Test results, ap: mae: 1.117, rse: 3.505, pcc: 0.050, kcc: 0.027
+2025-07-10 14:56:07: Epoch: 037 Train Loss: 0.091, mae: 0.250, rse: 0.098, pcc: 0.950, kcc: 0.853
+2025-07-10 14:56:07: Epoch: 037 Test results, ap: mae: 1.097, rse: 3.353, pcc: 0.071, kcc: 0.036
+2025-07-10 14:56:08: Epoch: 038 Train Loss: 0.094, mae: 0.256, rse: 0.100, pcc: 0.949, kcc: 0.846
+2025-07-10 14:56:08: Epoch: 038 Test results, ap: mae: 1.100, rse: 3.411, pcc: 0.061, kcc: 0.031
+2025-07-10 14:56:09: Epoch: 039 Train Loss: 0.092, mae: 0.239, rse: 0.098, pcc: 0.950, kcc: 0.856
+2025-07-10 14:56:09: Epoch: 039 New best VALIDATION metrics
+2025-07-10 14:56:10: Epoch: 039 Test results, ap: mae: 1.081, rse: 3.281, pcc: 0.062, kcc: 0.031
+2025-07-10 14:56:11: Epoch: 040 Train Loss: 0.094, mae: 0.248, rse: 0.105, pcc: 0.947, kcc: 0.854
+2025-07-10 14:56:11: Epoch: 040 Test results, ap: mae: 1.109, rse: 3.455, pcc: 0.072, kcc: 0.035
+2025-07-10 14:56:12: Epoch: 041 Train Loss: 0.093, mae: 0.232, rse: 0.095, pcc: 0.951, kcc: 0.858
+2025-07-10 14:56:12: Epoch: 041 New best VALIDATION metrics
+2025-07-10 14:56:12: Epoch: 041 Test results, ap: mae: 1.119, rse: 3.470, pcc: 0.065, kcc: 0.026
+2025-07-10 14:56:13: Epoch: 042 Train Loss: 0.083, mae: 0.233, rse: 0.096, pcc: 0.951, kcc: 0.858
+2025-07-10 14:56:13: Epoch: 042 Test results, ap: mae: 1.114, rse: 3.467, pcc: 0.068, kcc: 0.028
+2025-07-10 14:56:14: Epoch: 043 Train Loss: 0.079, mae: 0.233, rse: 0.096, pcc: 0.951, kcc: 0.856
+2025-07-10 14:56:14: Epoch: 043 Test results, ap: mae: 1.103, rse: 3.403, pcc: 0.066, kcc: 0.027
+2025-07-10 14:56:15: Epoch: 044 Train Loss: 0.079, mae: 0.231, rse: 0.097, pcc: 0.951, kcc: 0.858
+2025-07-10 14:56:16: Epoch: 044 Test results, ap: mae: 1.105, rse: 3.410, pcc: 0.065, kcc: 0.029
+2025-07-10 14:56:17: Epoch: 045 Train Loss: 0.080, mae: 0.234, rse: 0.095, pcc: 0.951, kcc: 0.859
+2025-07-10 14:56:17: Epoch: 045 Test results, ap: mae: 1.116, rse: 3.493, pcc: 0.073, kcc: 0.029
+2025-07-10 14:56:18: Epoch: 046 Train Loss: 0.077, mae: 0.229, rse: 0.096, pcc: 0.951, kcc: 0.860
+2025-07-10 14:56:18: Epoch: 046 New best VALIDATION metrics
+2025-07-10 14:56:18: Epoch: 046 Test results, ap: mae: 1.116, rse: 3.483, pcc: 0.067, kcc: 0.032
+2025-07-10 14:56:19: Epoch: 047 Train Loss: 0.075, mae: 0.231, rse: 0.095, pcc: 0.951, kcc: 0.861
+2025-07-10 14:56:19: Epoch: 047 New best VALIDATION metrics
+2025-07-10 14:56:19: Epoch: 047 Test results, ap: mae: 1.119, rse: 3.493, pcc: 0.066, kcc: 0.024
+2025-07-10 14:56:20: Epoch: 048 Train Loss: 0.077, mae: 0.228, rse: 0.095, pcc: 0.951, kcc: 0.862
+2025-07-10 14:56:20: Epoch: 048 New best VALIDATION metrics
+2025-07-10 14:56:20: Epoch: 048 Test results, ap: mae: 1.130, rse: 3.595, pcc: 0.074, kcc: 0.033
+2025-07-10 14:56:21: Epoch: 049 Train Loss: 0.075, mae: 0.234, rse: 0.096, pcc: 0.951, kcc: 0.859
+2025-07-10 14:56:22: Epoch: 049 Test results, ap: mae: 1.133, rse: 3.604, pcc: 0.069, kcc: 0.032
+2025-07-10 14:56:23: Epoch: 050 Train Loss: 0.078, mae: 0.232, rse: 0.096, pcc: 0.951, kcc: 0.859
+2025-07-10 14:56:23: Epoch: 050 Test results, ap: mae: 1.135, rse: 3.630, pcc: 0.073, kcc: 0.032
+2025-07-10 14:56:23: used time 0.02h
+2025-07-10 14:56:23: Running Cross Validation 3
+2025-07-10 14:56:23: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:56:24: Epoch: 001 Train Loss: 2.812, mae: 1.303, rse: 1.002, pcc: 0.214, kcc: 0.130
+2025-07-10 14:56:24: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:56:24: Epoch: 001 Test results, ap: mae: 0.591, rse: 1.060, pcc: 0.012, kcc: -0.020
+2025-07-10 14:56:25: Epoch: 002 Train Loss: 2.866, mae: 1.293, rse: 1.000, pcc: 0.188, kcc: 0.108
+2025-07-10 14:56:25: Epoch: 002 Test results, ap: mae: 0.613, rse: 1.150, pcc: 0.163, kcc: 0.029
+2025-07-10 14:56:26: Epoch: 003 Train Loss: 2.800, mae: 1.291, rse: 1.006, pcc: 0.199, kcc: 0.125
+2025-07-10 14:56:27: Epoch: 003 Test results, ap: mae: 0.654, rse: 1.294, pcc: 0.267, kcc: 0.062
+2025-07-10 14:56:28: Epoch: 004 Train Loss: 2.798, mae: 1.274, rse: 0.967, pcc: 0.229, kcc: 0.117
+2025-07-10 14:56:28: Epoch: 004 New best VALIDATION metrics
+2025-07-10 14:56:28: Epoch: 004 Test results, ap: mae: 0.641, rse: 1.246, pcc: 0.337, kcc: 0.157
+2025-07-10 14:56:29: Epoch: 005 Train Loss: 2.612, mae: 1.260, rse: 0.939, pcc: 0.260, kcc: 0.220
+2025-07-10 14:56:29: Epoch: 005 New best VALIDATION metrics
+2025-07-10 14:56:29: Epoch: 005 Test results, ap: mae: 0.828, rse: 1.952, pcc: 0.204, kcc: 0.120
+2025-07-10 14:56:30: Epoch: 006 Train Loss: 2.547, mae: 1.239, rse: 0.924, pcc: 0.294, kcc: 0.224
+2025-07-10 14:56:30: Epoch: 006 New best VALIDATION metrics
+2025-07-10 14:56:30: Epoch: 006 Test results, ap: mae: 0.725, rse: 1.552, pcc: 0.287, kcc: 0.160
+2025-07-10 14:56:31: Epoch: 007 Train Loss: 2.533, mae: 1.247, rse: 0.922, pcc: 0.290, kcc: 0.230
+2025-07-10 14:56:31: Epoch: 007 Test results, ap: mae: 0.968, rse: 2.478, pcc: 0.339, kcc: 0.223
+2025-07-10 14:56:32: Epoch: 008 Train Loss: 2.445, mae: 1.234, rse: 0.913, pcc: 0.307, kcc: 0.227
+2025-07-10 14:56:32: Epoch: 008 New best VALIDATION metrics
+2025-07-10 14:56:33: Epoch: 008 Test results, ap: mae: 0.936, rse: 2.334, pcc: 0.320, kcc: 0.176
+2025-07-10 14:56:34: Epoch: 009 Train Loss: 2.433, mae: 1.221, rse: 0.887, pcc: 0.339, kcc: 0.218
+2025-07-10 14:56:34: Epoch: 009 New best VALIDATION metrics
+2025-07-10 14:56:34: Epoch: 009 Test results, ap: mae: 0.954, rse: 2.401, pcc: 0.259, kcc: 0.137
+2025-07-10 14:56:35: Epoch: 010 Train Loss: 2.208, mae: 1.093, rse: 0.717, pcc: 0.533, kcc: 0.358
+2025-07-10 14:56:35: Epoch: 010 New best VALIDATION metrics
+2025-07-10 14:56:35: Epoch: 010 Test results, ap: mae: 0.728, rse: 1.639, pcc: 0.017, kcc: -0.032
+2025-07-10 14:56:36: Epoch: 011 Train Loss: 1.848, mae: 0.991, rse: 0.597, pcc: 0.637, kcc: 0.437
+2025-07-10 14:56:36: Epoch: 011 New best VALIDATION metrics
+2025-07-10 14:56:36: Epoch: 011 Test results, ap: mae: 0.802, rse: 1.912, pcc: -0.067, kcc: -0.035
+2025-07-10 14:56:36: Epoch: 011 New best TEST metrics
+2025-07-10 14:56:37: Epoch: 012 Train Loss: 1.560, mae: 0.950, rse: 0.572, pcc: 0.670, kcc: 0.481
+2025-07-10 14:56:37: Epoch: 012 New best VALIDATION metrics
+2025-07-10 14:56:37: Epoch: 012 Test results, ap: mae: 0.665, rse: 1.356, pcc: 0.072, kcc: 0.037
+2025-07-10 14:56:37: Epoch: 012 New best TEST metrics
+2025-07-10 14:56:38: Epoch: 013 Train Loss: 1.337, mae: 0.832, rse: 0.446, pcc: 0.748, kcc: 0.563
+2025-07-10 14:56:38: Epoch: 013 New best VALIDATION metrics
+2025-07-10 14:56:38: Epoch: 013 Test results, ap: mae: 0.796, rse: 1.754, pcc: 0.071, kcc: 0.052
+2025-07-10 14:56:39: Epoch: 014 Train Loss: 1.113, mae: 0.802, rse: 0.412, pcc: 0.769, kcc: 0.580
+2025-07-10 14:56:39: Epoch: 014 New best VALIDATION metrics
+2025-07-10 14:56:39: Epoch: 014 Test results, ap: mae: 0.832, rse: 2.026, pcc: 0.078, kcc: 0.035
+2025-07-10 14:56:40: Epoch: 015 Train Loss: 0.955, mae: 0.665, rse: 0.318, pcc: 0.830, kcc: 0.651
+2025-07-10 14:56:40: Epoch: 015 New best VALIDATION metrics
+2025-07-10 14:56:41: Epoch: 015 Test results, ap: mae: 0.831, rse: 2.028, pcc: 0.186, kcc: 0.121
+2025-07-10 14:56:42: Epoch: 016 Train Loss: 0.723, mae: 0.668, rse: 0.315, pcc: 0.837, kcc: 0.663
+2025-07-10 14:56:42: Epoch: 016 New best VALIDATION metrics
+2025-07-10 14:56:42: Epoch: 016 Test results, ap: mae: 1.043, rse: 3.094, pcc: 0.163, kcc: 0.088
+2025-07-10 14:56:43: Epoch: 017 Train Loss: 0.582, mae: 0.549, rse: 0.247, pcc: 0.870, kcc: 0.698
+2025-07-10 14:56:43: Epoch: 017 New best VALIDATION metrics
+2025-07-10 14:56:43: Epoch: 017 Test results, ap: mae: 1.099, rse: 3.324, pcc: 0.195, kcc: 0.122
+2025-07-10 14:56:44: Epoch: 018 Train Loss: 0.473, mae: 0.509, rse: 0.225, pcc: 0.881, kcc: 0.729
+2025-07-10 14:56:44: Epoch: 018 New best VALIDATION metrics
+2025-07-10 14:56:44: Epoch: 018 Test results, ap: mae: 0.967, rse: 2.569, pcc: 0.247, kcc: 0.142
+2025-07-10 14:56:45: Epoch: 019 Train Loss: 0.395, mae: 0.493, rse: 0.216, pcc: 0.887, kcc: 0.739
+2025-07-10 14:56:45: Epoch: 019 New best VALIDATION metrics
+2025-07-10 14:56:45: Epoch: 019 Test results, ap: mae: 0.975, rse: 2.650, pcc: 0.233, kcc: 0.139
+2025-07-10 14:56:46: Epoch: 020 Train Loss: 0.346, mae: 0.487, rse: 0.205, pcc: 0.892, kcc: 0.732
+2025-07-10 14:56:46: Epoch: 020 New best VALIDATION metrics
+2025-07-10 14:56:46: Epoch: 020 Test results, ap: mae: 0.881, rse: 2.082, pcc: 0.296, kcc: 0.163
+2025-07-10 14:56:47: Epoch: 021 Train Loss: 0.266, mae: 0.398, rse: 0.175, pcc: 0.910, kcc: 0.783
+2025-07-10 14:56:47: Epoch: 021 New best VALIDATION metrics
+2025-07-10 14:56:48: Epoch: 021 Test results, ap: mae: 0.929, rse: 2.477, pcc: 0.258, kcc: 0.153
+2025-07-10 14:56:49: Epoch: 022 Train Loss: 0.232, mae: 0.395, rse: 0.180, pcc: 0.908, kcc: 0.783
+2025-07-10 14:56:49: Epoch: 022 Test results, ap: mae: 0.918, rse: 2.425, pcc: 0.283, kcc: 0.163
+2025-07-10 14:56:50: Epoch: 023 Train Loss: 0.208, mae: 0.365, rse: 0.167, pcc: 0.914, kcc: 0.796
+2025-07-10 14:56:50: Epoch: 023 New best VALIDATION metrics
+2025-07-10 14:56:50: Epoch: 023 Test results, ap: mae: 0.967, rse: 2.629, pcc: 0.259, kcc: 0.148
+2025-07-10 14:56:51: Epoch: 024 Train Loss: 0.195, mae: 0.378, rse: 0.176, pcc: 0.908, kcc: 0.794
+2025-07-10 14:56:51: Epoch: 024 Test results, ap: mae: 0.938, rse: 2.554, pcc: 0.270, kcc: 0.156
+2025-07-10 14:56:52: Epoch: 025 Train Loss: 0.193, mae: 0.342, rse: 0.161, pcc: 0.918, kcc: 0.807
+2025-07-10 14:56:52: Epoch: 025 New best VALIDATION metrics
+2025-07-10 14:56:52: Epoch: 025 Test results, ap: mae: 0.978, rse: 2.719, pcc: 0.275, kcc: 0.168
+2025-07-10 14:56:53: Epoch: 026 Train Loss: 0.165, mae: 0.345, rse: 0.159, pcc: 0.917, kcc: 0.808
+2025-07-10 14:56:53: Epoch: 026 Test results, ap: mae: 0.908, rse: 2.407, pcc: 0.284, kcc: 0.162
+2025-07-10 14:56:54: Epoch: 027 Train Loss: 0.164, mae: 0.337, rse: 0.159, pcc: 0.919, kcc: 0.810
+2025-07-10 14:56:54: Epoch: 027 New best VALIDATION metrics
+2025-07-10 14:56:55: Epoch: 027 Test results, ap: mae: 0.943, rse: 2.557, pcc: 0.263, kcc: 0.149
+2025-07-10 14:56:56: Epoch: 028 Train Loss: 0.162, mae: 0.328, rse: 0.153, pcc: 0.921, kcc: 0.816
+2025-07-10 14:56:56: Epoch: 028 New best VALIDATION metrics
+2025-07-10 14:56:56: Epoch: 028 Test results, ap: mae: 0.946, rse: 2.534, pcc: 0.269, kcc: 0.146
+2025-07-10 14:56:57: Epoch: 029 Train Loss: 0.152, mae: 0.321, rse: 0.141, pcc: 0.927, kcc: 0.820
+2025-07-10 14:56:57: Epoch: 029 New best VALIDATION metrics
+2025-07-10 14:56:57: Epoch: 029 Test results, ap: mae: 1.016, rse: 2.913, pcc: 0.259, kcc: 0.144
+2025-07-10 14:56:58: Epoch: 030 Train Loss: 0.140, mae: 0.315, rse: 0.148, pcc: 0.923, kcc: 0.824
+2025-07-10 14:56:58: Epoch: 030 Test results, ap: mae: 0.979, rse: 2.751, pcc: 0.266, kcc: 0.149
+2025-07-10 14:56:59: Epoch: 031 Train Loss: 0.110, mae: 0.304, rse: 0.147, pcc: 0.924, kcc: 0.826
+2025-07-10 14:56:59: Epoch: 031 New best VALIDATION metrics
+2025-07-10 14:56:59: Epoch: 031 Test results, ap: mae: 0.991, rse: 2.827, pcc: 0.277, kcc: 0.160
+2025-07-10 14:57:00: Epoch: 032 Train Loss: 0.113, mae: 0.286, rse: 0.140, pcc: 0.929, kcc: 0.836
+2025-07-10 14:57:00: Epoch: 032 New best VALIDATION metrics
+2025-07-10 14:57:00: Epoch: 032 Test results, ap: mae: 1.007, rse: 2.879, pcc: 0.277, kcc: 0.156
+2025-07-10 14:57:01: Epoch: 033 Train Loss: 0.105, mae: 0.285, rse: 0.139, pcc: 0.929, kcc: 0.837
+2025-07-10 14:57:01: Epoch: 033 New best VALIDATION metrics
+2025-07-10 14:57:01: Epoch: 033 Test results, ap: mae: 1.015, rse: 2.890, pcc: 0.285, kcc: 0.169
+2025-07-10 14:57:03: Epoch: 034 Train Loss: 0.106, mae: 0.284, rse: 0.142, pcc: 0.928, kcc: 0.837
+2025-07-10 14:57:03: Epoch: 034 Test results, ap: mae: 1.019, rse: 2.949, pcc: 0.269, kcc: 0.158
+2025-07-10 14:57:04: Epoch: 035 Train Loss: 0.099, mae: 0.276, rse: 0.135, pcc: 0.931, kcc: 0.841
+2025-07-10 14:57:04: Epoch: 035 New best VALIDATION metrics
+2025-07-10 14:57:04: Epoch: 035 Test results, ap: mae: 1.041, rse: 3.033, pcc: 0.268, kcc: 0.156
+2025-07-10 14:57:05: Epoch: 036 Train Loss: 0.098, mae: 0.276, rse: 0.136, pcc: 0.930, kcc: 0.842
+2025-07-10 14:57:05: Epoch: 036 Test results, ap: mae: 1.038, rse: 3.023, pcc: 0.274, kcc: 0.155
+2025-07-10 14:57:06: Epoch: 037 Train Loss: 0.095, mae: 0.277, rse: 0.134, pcc: 0.931, kcc: 0.840
+2025-07-10 14:57:06: Epoch: 037 Test results, ap: mae: 1.008, rse: 2.864, pcc: 0.284, kcc: 0.161
+2025-07-10 14:57:07: Epoch: 038 Train Loss: 0.097, mae: 0.259, rse: 0.125, pcc: 0.936, kcc: 0.848
+2025-07-10 14:57:07: Epoch: 038 New best VALIDATION metrics
+2025-07-10 14:57:07: Epoch: 038 Test results, ap: mae: 1.022, rse: 2.955, pcc: 0.272, kcc: 0.162
+2025-07-10 14:57:08: Epoch: 039 Train Loss: 0.100, mae: 0.277, rse: 0.128, pcc: 0.934, kcc: 0.842
+2025-07-10 14:57:08: Epoch: 039 Test results, ap: mae: 1.056, rse: 3.130, pcc: 0.261, kcc: 0.148
+2025-07-10 14:57:09: Epoch: 040 Train Loss: 0.088, mae: 0.267, rse: 0.129, pcc: 0.934, kcc: 0.844
+2025-07-10 14:57:10: Epoch: 040 Test results, ap: mae: 1.043, rse: 3.082, pcc: 0.261, kcc: 0.153
+2025-07-10 14:57:11: Epoch: 041 Train Loss: 0.078, mae: 0.251, rse: 0.127, pcc: 0.935, kcc: 0.851
+2025-07-10 14:57:11: Epoch: 041 New best VALIDATION metrics
+2025-07-10 14:57:11: Epoch: 041 Test results, ap: mae: 1.058, rse: 3.158, pcc: 0.272, kcc: 0.160
+2025-07-10 14:57:12: Epoch: 042 Train Loss: 0.071, mae: 0.248, rse: 0.125, pcc: 0.936, kcc: 0.853
+2025-07-10 14:57:12: Epoch: 042 New best VALIDATION metrics
+2025-07-10 14:57:12: Epoch: 042 Test results, ap: mae: 1.077, rse: 3.287, pcc: 0.266, kcc: 0.152
+2025-07-10 14:57:13: Epoch: 043 Train Loss: 0.074, mae: 0.254, rse: 0.125, pcc: 0.936, kcc: 0.851
+2025-07-10 14:57:13: Epoch: 043 Test results, ap: mae: 1.071, rse: 3.229, pcc: 0.259, kcc: 0.150
+2025-07-10 14:57:14: Epoch: 044 Train Loss: 0.084, mae: 0.246, rse: 0.120, pcc: 0.938, kcc: 0.854
+2025-07-10 14:57:14: Epoch: 044 New best VALIDATION metrics
+2025-07-10 14:57:14: Epoch: 044 Test results, ap: mae: 1.067, rse: 3.222, pcc: 0.264, kcc: 0.151
+2025-07-10 14:57:15: Epoch: 045 Train Loss: 0.077, mae: 0.251, rse: 0.125, pcc: 0.936, kcc: 0.850
+2025-07-10 14:57:15: Epoch: 045 Test results, ap: mae: 1.073, rse: 3.278, pcc: 0.267, kcc: 0.153
+2025-07-10 14:57:16: Epoch: 046 Train Loss: 0.071, mae: 0.245, rse: 0.119, pcc: 0.939, kcc: 0.856
+2025-07-10 14:57:16: Epoch: 046 New best VALIDATION metrics
+2025-07-10 14:57:17: Epoch: 046 Test results, ap: mae: 1.080, rse: 3.294, pcc: 0.266, kcc: 0.151
+2025-07-10 14:57:18: Epoch: 047 Train Loss: 0.072, mae: 0.244, rse: 0.123, pcc: 0.937, kcc: 0.856
+2025-07-10 14:57:18: Epoch: 047 Test results, ap: mae: 1.085, rse: 3.313, pcc: 0.266, kcc: 0.147
+2025-07-10 14:57:19: Epoch: 048 Train Loss: 0.077, mae: 0.251, rse: 0.122, pcc: 0.937, kcc: 0.853
+2025-07-10 14:57:19: Epoch: 048 Test results, ap: mae: 1.052, rse: 3.115, pcc: 0.268, kcc: 0.154
+2025-07-10 14:57:20: Epoch: 049 Train Loss: 0.071, mae: 0.244, rse: 0.121, pcc: 0.939, kcc: 0.856
+2025-07-10 14:57:20: Epoch: 049 Test results, ap: mae: 1.103, rse: 3.425, pcc: 0.258, kcc: 0.146
+2025-07-10 14:57:21: Epoch: 050 Train Loss: 0.068, mae: 0.248, rse: 0.119, pcc: 0.939, kcc: 0.856
+2025-07-10 14:57:21: Epoch: 050 Test results, ap: mae: 1.080, rse: 3.260, pcc: 0.262, kcc: 0.147
+2025-07-10 14:57:21: used time 0.02h
+2025-07-10 14:57:21: Running Cross Validation 4
+2025-07-10 14:57:21: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 14:57:22: Epoch: 001 Train Loss: 2.878, mae: 1.214, rse: 1.000, pcc: 0.214, kcc: 0.122
+2025-07-10 14:57:22: Epoch: 001 New best VALIDATION metrics
+2025-07-10 14:57:22: Epoch: 001 Test results, ap: mae: 0.603, rse: 1.113, pcc: 0.162, kcc: 0.063
+2025-07-10 14:57:23: Epoch: 002 Train Loss: 2.823, mae: 1.185, rse: 0.952, pcc: 0.267, kcc: 0.143
+2025-07-10 14:57:23: Epoch: 002 New best VALIDATION metrics
+2025-07-10 14:57:24: Epoch: 002 Test results, ap: mae: 0.770, rse: 1.724, pcc: 0.060, kcc: -0.076
+2025-07-10 14:57:25: Epoch: 003 Train Loss: 2.613, mae: 1.191, rse: 0.933, pcc: 0.275, kcc: 0.209
+2025-07-10 14:57:25: Epoch: 003 New best VALIDATION metrics
+2025-07-10 14:57:25: Epoch: 003 Test results, ap: mae: 0.797, rse: 1.838, pcc: 0.154, kcc: 0.072
+2025-07-10 14:57:26: Epoch: 004 Train Loss: 2.562, mae: 1.200, rse: 0.921, pcc: 0.309, kcc: 0.214
+2025-07-10 14:57:26: Epoch: 004 New best VALIDATION metrics
+2025-07-10 14:57:26: Epoch: 004 Test results, ap: mae: 1.036, rse: 2.731, pcc: 0.195, kcc: 0.111
+2025-07-10 14:57:27: Epoch: 005 Train Loss: 2.469, mae: 1.153, rse: 0.883, pcc: 0.366, kcc: 0.229
+2025-07-10 14:57:27: Epoch: 005 New best VALIDATION metrics
+2025-07-10 14:57:27: Epoch: 005 Test results, ap: mae: 0.694, rse: 1.434, pcc: 0.189, kcc: 0.085
+2025-07-10 14:57:28: Epoch: 006 Train Loss: 2.170, mae: 1.005, rse: 0.704, pcc: 0.549, kcc: 0.374
+2025-07-10 14:57:28: Epoch: 006 New best VALIDATION metrics
+2025-07-10 14:57:28: Epoch: 006 Test results, ap: mae: 0.783, rse: 1.678, pcc: -0.132, kcc: -0.079
+2025-07-10 14:57:29: Epoch: 007 Train Loss: 1.589, mae: 0.882, rse: 0.545, pcc: 0.677, kcc: 0.498
+2025-07-10 14:57:29: Epoch: 007 New best VALIDATION metrics
+2025-07-10 14:57:29: Epoch: 007 Test results, ap: mae: 0.907, rse: 2.265, pcc: -0.081, kcc: -0.029
+2025-07-10 14:57:30: Epoch: 008 Train Loss: 1.212, mae: 0.739, rse: 0.417, pcc: 0.765, kcc: 0.572
+2025-07-10 14:57:30: Epoch: 008 New best VALIDATION metrics
+2025-07-10 14:57:31: Epoch: 008 Test results, ap: mae: 0.857, rse: 1.956, pcc: -0.042, kcc: -0.010
+2025-07-10 14:57:32: Epoch: 009 Train Loss: 0.954, mae: 0.716, rse: 0.378, pcc: 0.790, kcc: 0.585
+2025-07-10 14:57:32: Epoch: 009 New best VALIDATION metrics
+2025-07-10 14:57:32: Epoch: 009 Test results, ap: mae: 0.978, rse: 2.460, pcc: 0.038, kcc: 0.048
+2025-07-10 14:57:33: Epoch: 010 Train Loss: 0.793, mae: 0.598, rse: 0.279, pcc: 0.850, kcc: 0.642
+2025-07-10 14:57:33: Epoch: 010 New best VALIDATION metrics
+2025-07-10 14:57:33: Epoch: 010 Test results, ap: mae: 1.357, rse: 4.502, pcc: -0.061, kcc: -0.025
+2025-07-10 14:57:34: Epoch: 011 Train Loss: 0.614, mae: 0.562, rse: 0.257, pcc: 0.865, kcc: 0.666
+2025-07-10 14:57:34: Epoch: 011 New best VALIDATION metrics
+2025-07-10 14:57:34: Epoch: 011 Test results, ap: mae: 1.271, rse: 4.043, pcc: 0.022, kcc: 0.029
+2025-07-10 14:57:34: Epoch: 011 New best TEST metrics
+2025-07-10 14:57:35: Epoch: 012 Train Loss: 0.537, mae: 0.493, rse: 0.222, pcc: 0.882, kcc: 0.702
+2025-07-10 14:57:35: Epoch: 012 New best VALIDATION metrics
+2025-07-10 14:57:35: Epoch: 012 Test results, ap: mae: 1.394, rse: 5.070, pcc: -0.012, kcc: 0.005
+2025-07-10 14:57:36: Epoch: 013 Train Loss: 0.440, mae: 0.522, rse: 0.232, pcc: 0.878, kcc: 0.697
+2025-07-10 14:57:36: Epoch: 013 Test results, ap: mae: 1.331, rse: 4.428, pcc: -0.049, kcc: 0.002
+2025-07-10 14:57:37: Epoch: 014 Train Loss: 0.409, mae: 0.487, rse: 0.225, pcc: 0.881, kcc: 0.717
+2025-07-10 14:57:37: Epoch: 014 New best VALIDATION metrics
+2025-07-10 14:57:38: Epoch: 014 Test results, ap: mae: 1.236, rse: 3.946, pcc: -0.009, kcc: 0.010
+2025-07-10 14:57:38: Epoch: 014 New best TEST metrics
+2025-07-10 14:57:39: Epoch: 015 Train Loss: 0.362, mae: 0.472, rse: 0.215, pcc: 0.886, kcc: 0.725
+2025-07-10 14:57:39: Epoch: 015 New best VALIDATION metrics
+2025-07-10 14:57:39: Epoch: 015 Test results, ap: mae: 1.362, rse: 4.992, pcc: -0.038, kcc: -0.016
+2025-07-10 14:57:40: Epoch: 016 Train Loss: 0.305, mae: 0.446, rse: 0.203, pcc: 0.893, kcc: 0.738
+2025-07-10 14:57:40: Epoch: 016 New best VALIDATION metrics
+2025-07-10 14:57:40: Epoch: 016 Test results, ap: mae: 1.415, rse: 5.539, pcc: -0.009, kcc: 0.003
+2025-07-10 14:57:41: Epoch: 017 Train Loss: 0.277, mae: 0.461, rse: 0.226, pcc: 0.881, kcc: 0.740
+2025-07-10 14:57:41: Epoch: 017 Test results, ap: mae: 1.312, rse: 5.094, pcc: -0.054, kcc: -0.006
+2025-07-10 14:57:42: Epoch: 018 Train Loss: 0.247, mae: 0.388, rse: 0.187, pcc: 0.902, kcc: 0.769
+2025-07-10 14:57:42: Epoch: 018 New best VALIDATION metrics
+2025-07-10 14:57:42: Epoch: 018 Test results, ap: mae: 1.416, rse: 5.742, pcc: -0.048, kcc: -0.003
+2025-07-10 14:57:43: Epoch: 019 Train Loss: 0.197, mae: 0.390, rse: 0.195, pcc: 0.899, kcc: 0.769
+2025-07-10 14:57:44: Epoch: 019 Test results, ap: mae: 1.218, rse: 4.242, pcc: -0.005, kcc: 0.016
+2025-07-10 14:57:44: Epoch: 020 Train Loss: 0.204, mae: 0.397, rse: 0.188, pcc: 0.903, kcc: 0.777
+2025-07-10 14:57:45: Epoch: 020 Test results, ap: mae: 1.304, rse: 4.922, pcc: -0.030, kcc: 0.005
+2025-07-10 14:57:46: Epoch: 021 Train Loss: 0.157, mae: 0.334, rse: 0.162, pcc: 0.916, kcc: 0.802
+2025-07-10 14:57:46: Epoch: 021 New best VALIDATION metrics
+2025-07-10 14:57:46: Epoch: 021 Test results, ap: mae: 1.239, rse: 4.438, pcc: -0.009, kcc: 0.032
+2025-07-10 14:57:47: Epoch: 022 Train Loss: 0.128, mae: 0.319, rse: 0.166, pcc: 0.914, kcc: 0.809
+2025-07-10 14:57:47: Epoch: 022 New best VALIDATION metrics
+2025-07-10 14:57:47: Epoch: 022 Test results, ap: mae: 1.280, rse: 4.760, pcc: -0.011, kcc: 0.023
+2025-07-10 14:57:48: Epoch: 023 Train Loss: 0.128, mae: 0.309, rse: 0.152, pcc: 0.921, kcc: 0.813
+2025-07-10 14:57:48: Epoch: 023 New best VALIDATION metrics
+2025-07-10 14:57:48: Epoch: 023 Test results, ap: mae: 1.248, rse: 4.589, pcc: -0.009, kcc: 0.032
+2025-07-10 14:57:49: Epoch: 024 Train Loss: 0.117, mae: 0.323, rse: 0.160, pcc: 0.916, kcc: 0.810
+2025-07-10 14:57:49: Epoch: 024 Test results, ap: mae: 1.200, rse: 4.252, pcc: -0.007, kcc: 0.039
+2025-07-10 14:57:50: Epoch: 025 Train Loss: 0.112, mae: 0.313, rse: 0.163, pcc: 0.918, kcc: 0.820
+2025-07-10 14:57:51: Epoch: 025 Test results, ap: mae: 1.191, rse: 4.334, pcc: 0.011, kcc: 0.061
+2025-07-10 14:57:52: Epoch: 026 Train Loss: 0.109, mae: 0.299, rse: 0.155, pcc: 0.922, kcc: 0.819
+2025-07-10 14:57:52: Epoch: 026 New best VALIDATION metrics
+2025-07-10 14:57:52: Epoch: 026 Test results, ap: mae: 1.202, rse: 4.497, pcc: 0.001, kcc: 0.051
+2025-07-10 14:57:53: Epoch: 027 Train Loss: 0.110, mae: 0.312, rse: 0.151, pcc: 0.922, kcc: 0.816
+2025-07-10 14:57:53: Epoch: 027 Test results, ap: mae: 1.109, rse: 3.757, pcc: 0.045, kcc: 0.082
+2025-07-10 14:57:53: Epoch: 027 New best TEST metrics
+2025-07-10 14:57:54: Epoch: 028 Train Loss: 0.107, mae: 0.286, rse: 0.145, pcc: 0.925, kcc: 0.825
+2025-07-10 14:57:54: Epoch: 028 New best VALIDATION metrics
+2025-07-10 14:57:54: Epoch: 028 Test results, ap: mae: 1.177, rse: 3.985, pcc: 0.049, kcc: 0.077
+2025-07-10 14:57:55: Epoch: 029 Train Loss: 0.097, mae: 0.286, rse: 0.148, pcc: 0.924, kcc: 0.827
+2025-07-10 14:57:55: Epoch: 029 Test results, ap: mae: 1.135, rse: 3.956, pcc: 0.026, kcc: 0.070
+2025-07-10 14:57:56: Epoch: 030 Train Loss: 0.102, mae: 0.287, rse: 0.152, pcc: 0.921, kcc: 0.827
+2025-07-10 14:57:57: Epoch: 030 Test results, ap: mae: 1.051, rse: 3.544, pcc: 0.062, kcc: 0.095
+2025-07-10 14:57:57: Epoch: 030 New best TEST metrics
+2025-07-10 14:57:58: Epoch: 031 Train Loss: 0.082, mae: 0.273, rse: 0.148, pcc: 0.924, kcc: 0.837
+2025-07-10 14:57:58: Epoch: 031 New best VALIDATION metrics
+2025-07-10 14:57:58: Epoch: 031 Test results, ap: mae: 1.086, rse: 3.675, pcc: 0.049, kcc: 0.096
+2025-07-10 14:57:59: Epoch: 032 Train Loss: 0.081, mae: 0.271, rse: 0.148, pcc: 0.924, kcc: 0.836
+2025-07-10 14:57:59: Epoch: 032 New best VALIDATION metrics
+2025-07-10 14:57:59: Epoch: 032 Test results, ap: mae: 1.086, rse: 3.608, pcc: 0.050, kcc: 0.092
+2025-07-10 14:58:00: Epoch: 033 Train Loss: 0.080, mae: 0.262, rse: 0.142, pcc: 0.927, kcc: 0.840
+2025-07-10 14:58:00: Epoch: 033 New best VALIDATION metrics
+2025-07-10 14:58:00: Epoch: 033 Test results, ap: mae: 1.093, rse: 3.671, pcc: 0.059, kcc: 0.092
+2025-07-10 14:58:01: Epoch: 034 Train Loss: 0.080, mae: 0.266, rse: 0.145, pcc: 0.926, kcc: 0.837
+2025-07-10 14:58:01: Epoch: 034 Test results, ap: mae: 1.081, rse: 3.634, pcc: 0.064, kcc: 0.103
+2025-07-10 14:58:02: Epoch: 035 Train Loss: 0.073, mae: 0.265, rse: 0.147, pcc: 0.925, kcc: 0.836
+2025-07-10 14:58:02: Epoch: 035 Test results, ap: mae: 1.116, rse: 3.752, pcc: 0.073, kcc: 0.106
+2025-07-10 14:58:03: Epoch: 036 Train Loss: 0.075, mae: 0.255, rse: 0.144, pcc: 0.926, kcc: 0.844
+2025-07-10 14:58:03: Epoch: 036 New best VALIDATION metrics
+2025-07-10 14:58:04: Epoch: 036 Test results, ap: mae: 1.094, rse: 3.794, pcc: 0.062, kcc: 0.108
+2025-07-10 14:58:05: Epoch: 037 Train Loss: 0.068, mae: 0.252, rse: 0.140, pcc: 0.928, kcc: 0.843
+2025-07-10 14:58:05: Epoch: 037 New best VALIDATION metrics
+2025-07-10 14:58:05: Epoch: 037 Test results, ap: mae: 1.081, rse: 3.678, pcc: 0.066, kcc: 0.103
+2025-07-10 14:58:06: Epoch: 038 Train Loss: 0.075, mae: 0.257, rse: 0.144, pcc: 0.926, kcc: 0.843
+2025-07-10 14:58:06: Epoch: 038 Test results, ap: mae: 1.098, rse: 3.733, pcc: 0.077, kcc: 0.109
+2025-07-10 14:58:07: Epoch: 039 Train Loss: 0.070, mae: 0.249, rse: 0.140, pcc: 0.928, kcc: 0.848
+2025-07-10 14:58:07: Epoch: 039 New best VALIDATION metrics
+2025-07-10 14:58:07: Epoch: 039 Test results, ap: mae: 1.113, rse: 3.873, pcc: 0.079, kcc: 0.107
+2025-07-10 14:58:08: Epoch: 040 Train Loss: 0.068, mae: 0.249, rse: 0.142, pcc: 0.927, kcc: 0.845
+2025-07-10 14:58:08: Epoch: 040 Test results, ap: mae: 1.092, rse: 3.718, pcc: 0.093, kcc: 0.117
+2025-07-10 14:58:09: Epoch: 041 Train Loss: 0.072, mae: 0.242, rse: 0.143, pcc: 0.927, kcc: 0.848
+2025-07-10 14:58:09: Epoch: 041 New best VALIDATION metrics
+2025-07-10 14:58:09: Epoch: 041 Test results, ap: mae: 1.108, rse: 3.838, pcc: 0.078, kcc: 0.107
+2025-07-10 14:58:10: Epoch: 042 Train Loss: 0.062, mae: 0.244, rse: 0.145, pcc: 0.926, kcc: 0.849
+2025-07-10 14:58:10: Epoch: 042 Test results, ap: mae: 1.089, rse: 3.678, pcc: 0.085, kcc: 0.112
+2025-07-10 14:58:11: Epoch: 043 Train Loss: 0.061, mae: 0.235, rse: 0.141, pcc: 0.928, kcc: 0.852
+2025-07-10 14:58:11: Epoch: 043 New best VALIDATION metrics
+2025-07-10 14:58:11: Epoch: 043 Test results, ap: mae: 1.108, rse: 3.835, pcc: 0.082, kcc: 0.118
+2025-07-10 14:58:12: Epoch: 044 Train Loss: 0.064, mae: 0.235, rse: 0.141, pcc: 0.928, kcc: 0.854
+2025-07-10 14:58:12: Epoch: 044 New best VALIDATION metrics
+2025-07-10 14:58:13: Epoch: 044 Test results, ap: mae: 1.104, rse: 3.799, pcc: 0.083, kcc: 0.110
+2025-07-10 14:58:13: Epoch: 045 Train Loss: 0.061, mae: 0.234, rse: 0.143, pcc: 0.927, kcc: 0.855
+2025-07-10 14:58:14: Epoch: 045 Test results, ap: mae: 1.074, rse: 3.623, pcc: 0.099, kcc: 0.124
+2025-07-10 14:58:15: Epoch: 046 Train Loss: 0.061, mae: 0.234, rse: 0.141, pcc: 0.928, kcc: 0.854
+2025-07-10 14:58:15: Epoch: 046 New best VALIDATION metrics
+2025-07-10 14:58:15: Epoch: 046 Test results, ap: mae: 1.101, rse: 3.794, pcc: 0.089, kcc: 0.120
+2025-07-10 14:58:16: Epoch: 047 Train Loss: 0.063, mae: 0.236, rse: 0.141, pcc: 0.927, kcc: 0.854
+2025-07-10 14:58:16: Epoch: 047 Test results, ap: mae: 1.088, rse: 3.704, pcc: 0.097, kcc: 0.126
+2025-07-10 14:58:17: Epoch: 048 Train Loss: 0.059, mae: 0.230, rse: 0.138, pcc: 0.929, kcc: 0.855
+2025-07-10 14:58:17: Epoch: 048 New best VALIDATION metrics
+2025-07-10 14:58:17: Epoch: 048 Test results, ap: mae: 1.117, rse: 3.891, pcc: 0.080, kcc: 0.113
+2025-07-10 14:58:18: Epoch: 049 Train Loss: 0.056, mae: 0.231, rse: 0.141, pcc: 0.928, kcc: 0.855
+2025-07-10 14:58:18: Epoch: 049 Test results, ap: mae: 1.113, rse: 3.857, pcc: 0.097, kcc: 0.117
+2025-07-10 14:58:19: Epoch: 050 Train Loss: 0.063, mae: 0.237, rse: 0.141, pcc: 0.927, kcc: 0.853
+2025-07-10 14:58:19: Epoch: 050 Test results, ap: mae: 1.068, rse: 3.554, pcc: 0.095, kcc: 0.118
+2025-07-10 14:58:19: Epoch: 050 New best TEST metrics
+2025-07-10 14:58:19: used time 0.02h
+2025-07-10 14:58:19: Cross Validation Finished!
+2025-07-10 14:58:19: Best validation perform list
+[[0.27837899 0.13773869 0.93067634 0.80912089]
+ [0.59626687 0.30158713 0.83578759 0.65939653]
+ [0.22833879 0.09489031 0.95149726 0.86165625]
+ [0.24471642 0.11925614 0.93891883 0.85633147]
+ [0.23008136 0.13796736 0.92889577 0.85504943]]
+2025-07-10 14:58:19: mean: [0.316 0.158 0.917 0.808]
+2025-07-10 14:58:19: std: [0.142 0.073 0.041 0.077]
+2025-07-10 14:58:19: Best test perform list
+[[ 0.60403055  1.05525005  0.17403738  0.01179535]
+ [ 0.61698633  1.16668093  0.09647775 -0.01144842]
+ [ 0.63898003  1.22462845  0.26900008  0.12489191]
+ [ 0.66516846  1.35624492  0.07232875  0.03712065]
+ [ 1.06830382  3.55438495  0.09526599  0.11760654]]
+2025-07-10 14:58:19: mean: [0.719 1.671 0.141 0.056]
+2025-07-10 14:58:19: std: [0.176 0.946 0.072 0.055]
diff --git a/run-reg/mha-att-256-mse-32-0.001-50/config.json b/run-reg/mha-att-256-mse-32-0.001-50/config.json
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@@ -0,0 +1 @@
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+Valid
+0.9451729655265808,0.6071897089481354,0.6194915056228638,0.40189799666404724
+0.14583904481051804,0.1448030166949319,0.11358593341126523,0.0881247534985349
+Test
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diff --git a/run-reg/mha-att-256-mse-32-0.001-50/training.log b/run-reg/mha-att-256-mse-32-0.001-50/training.log
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+2025-07-10 15:11:31: saving_dir: ./run-reg/mha-att-256-mse-32-0.001-50
+2025-07-10 15:11:31: Loading Train Dataset
+2025-07-10 15:11:32: Loading Test Dataset
+2025-07-10 15:11:32: Running Cross Validation 0
+2025-07-10 15:11:32: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 15:11:33: Epoch: 001 Train Loss: 2.924, mae: 1.211, rse: 0.996, pcc: 0.342, kcc: 0.227
+2025-07-10 15:11:33: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:11:34: Epoch: 001 Test results, ap: mae: 0.608, rse: 1.133, pcc: -0.251, kcc: -0.151
+2025-07-10 15:11:34: Epoch: 002 Train Loss: 2.826, mae: 1.173, rse: 0.945, pcc: 0.382, kcc: 0.245
+2025-07-10 15:11:34: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:11:35: Epoch: 002 Test results, ap: mae: 0.629, rse: 1.200, pcc: -0.257, kcc: -0.077
+2025-07-10 15:11:35: Epoch: 003 Train Loss: 2.674, mae: 1.120, rse: 0.879, pcc: 0.377, kcc: 0.248
+2025-07-10 15:11:35: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:11:36: Epoch: 003 Test results, ap: mae: 0.804, rse: 1.853, pcc: -0.388, kcc: -0.232
+2025-07-10 15:11:36: Epoch: 004 Train Loss: 2.655, mae: 1.134, rse: 0.856, pcc: 0.407, kcc: 0.255
+2025-07-10 15:11:36: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:11:36: Epoch: 004 Test results, ap: mae: 0.715, rse: 1.520, pcc: -0.361, kcc: -0.284
+2025-07-10 15:11:37: Epoch: 005 Train Loss: 2.614, mae: 1.132, rse: 0.895, pcc: 0.376, kcc: 0.255
+2025-07-10 15:11:37: Epoch: 005 Test results, ap: mae: 0.845, rse: 2.015, pcc: -0.163, kcc: -0.171
+2025-07-10 15:11:38: Epoch: 006 Train Loss: 2.625, mae: 1.130, rse: 0.862, pcc: 0.389, kcc: 0.265
+2025-07-10 15:11:38: Epoch: 006 Test results, ap: mae: 0.690, rse: 1.408, pcc: -0.005, kcc: 0.022
+2025-07-10 15:11:39: Epoch: 007 Train Loss: 2.555, mae: 1.125, rse: 0.852, pcc: 0.402, kcc: 0.274
+2025-07-10 15:11:39: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:11:39: Epoch: 007 Test results, ap: mae: 0.615, rse: 1.132, pcc: -0.101, kcc: -0.210
+2025-07-10 15:11:40: Epoch: 008 Train Loss: 2.625, mae: 1.159, rse: 0.897, pcc: 0.396, kcc: 0.267
+2025-07-10 15:11:40: Epoch: 008 Test results, ap: mae: 0.582, rse: 1.032, pcc: 0.163, kcc: 0.090
+2025-07-10 15:11:40: Epoch: 009 Train Loss: 2.598, mae: 1.122, rse: 0.868, pcc: 0.399, kcc: 0.270
+2025-07-10 15:11:41: Epoch: 009 Test results, ap: mae: 0.723, rse: 1.550, pcc: -0.005, kcc: 0.019
+2025-07-10 15:11:41: Epoch: 010 Train Loss: 2.555, mae: 1.130, rse: 0.865, pcc: 0.391, kcc: 0.282
+2025-07-10 15:11:42: Epoch: 010 Test results, ap: mae: 0.918, rse: 2.286, pcc: 0.154, kcc: 0.084
+2025-07-10 15:11:42: Epoch: 011 Train Loss: 2.506, mae: 1.117, rse: 0.868, pcc: 0.412, kcc: 0.283
+2025-07-10 15:11:42: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:11:42: Epoch: 011 Test results, ap: mae: 0.785, rse: 1.789, pcc: 0.080, kcc: 0.028
+2025-07-10 15:11:42: Epoch: 011 New best TEST metrics
+2025-07-10 15:11:43: Epoch: 012 Train Loss: 2.465, mae: 1.126, rse: 0.854, pcc: 0.412, kcc: 0.274
+2025-07-10 15:11:43: Epoch: 012 Test results, ap: mae: 0.667, rse: 1.346, pcc: 0.046, kcc: 0.040
+2025-07-10 15:11:43: Epoch: 012 New best TEST metrics
+2025-07-10 15:11:44: Epoch: 013 Train Loss: 2.466, mae: 1.108, rse: 0.834, pcc: 0.410, kcc: 0.278
+2025-07-10 15:11:44: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:11:44: Epoch: 013 Test results, ap: mae: 0.659, rse: 1.315, pcc: 0.079, kcc: 0.077
+2025-07-10 15:11:44: Epoch: 013 New best TEST metrics
+2025-07-10 15:11:45: Epoch: 014 Train Loss: 2.368, mae: 1.109, rse: 0.839, pcc: 0.419, kcc: 0.279
+2025-07-10 15:11:45: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:11:45: Epoch: 014 Test results, ap: mae: 0.670, rse: 1.305, pcc: 0.178, kcc: 0.093
+2025-07-10 15:11:45: Epoch: 014 New best TEST metrics
+2025-07-10 15:11:46: Epoch: 015 Train Loss: 2.394, mae: 1.150, rse: 0.866, pcc: 0.410, kcc: 0.279
+2025-07-10 15:11:46: Epoch: 015 Test results, ap: mae: 0.579, rse: 0.994, pcc: 0.212, kcc: 0.106
+2025-07-10 15:11:46: Epoch: 015 New best TEST metrics
+2025-07-10 15:11:47: Epoch: 016 Train Loss: 2.360, mae: 1.101, rse: 0.820, pcc: 0.425, kcc: 0.282
+2025-07-10 15:11:47: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:11:47: Epoch: 016 Test results, ap: mae: 0.636, rse: 1.186, pcc: 0.081, kcc: 0.057
+2025-07-10 15:11:47: Epoch: 017 Train Loss: 2.313, mae: 1.089, rse: 0.804, pcc: 0.444, kcc: 0.299
+2025-07-10 15:11:47: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:11:48: Epoch: 017 Test results, ap: mae: 0.680, rse: 1.342, pcc: -0.146, kcc: -0.152
+2025-07-10 15:11:48: Epoch: 018 Train Loss: 2.302, mae: 1.100, rse: 0.818, pcc: 0.444, kcc: 0.303
+2025-07-10 15:11:48: Epoch: 018 Test results, ap: mae: 0.741, rse: 1.602, pcc: -0.080, kcc: -0.014
+2025-07-10 15:11:49: Epoch: 019 Train Loss: 2.311, mae: 1.125, rse: 0.822, pcc: 0.434, kcc: 0.283
+2025-07-10 15:11:49: Epoch: 019 Test results, ap: mae: 0.658, rse: 1.225, pcc: -0.061, kcc: -0.042
+2025-07-10 15:11:50: Epoch: 020 Train Loss: 2.301, mae: 1.106, rse: 0.835, pcc: 0.415, kcc: 0.288
+2025-07-10 15:11:50: Epoch: 020 Test results, ap: mae: 0.750, rse: 1.608, pcc: 0.022, kcc: 0.011
+2025-07-10 15:11:51: Epoch: 021 Train Loss: 2.192, mae: 1.098, rse: 0.807, pcc: 0.457, kcc: 0.295
+2025-07-10 15:11:51: Epoch: 021 Test results, ap: mae: 0.711, rse: 1.471, pcc: 0.042, kcc: -0.018
+2025-07-10 15:11:52: Epoch: 022 Train Loss: 2.147, mae: 1.066, rse: 0.767, pcc: 0.486, kcc: 0.295
+2025-07-10 15:11:52: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:11:52: Epoch: 022 Test results, ap: mae: 0.643, rse: 1.227, pcc: 0.050, kcc: -0.022
+2025-07-10 15:11:53: Epoch: 023 Train Loss: 2.083, mae: 1.066, rse: 0.758, pcc: 0.494, kcc: 0.295
+2025-07-10 15:11:53: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:11:53: Epoch: 023 Test results, ap: mae: 0.737, rse: 1.652, pcc: -0.010, kcc: -0.049
+2025-07-10 15:11:54: Epoch: 024 Train Loss: 2.057, mae: 1.046, rse: 0.751, pcc: 0.512, kcc: 0.310
+2025-07-10 15:11:54: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:11:54: Epoch: 024 Test results, ap: mae: 0.743, rse: 1.644, pcc: 0.027, kcc: -0.047
+2025-07-10 15:11:55: Epoch: 025 Train Loss: 1.992, mae: 1.046, rse: 0.734, pcc: 0.518, kcc: 0.306
+2025-07-10 15:11:55: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:11:55: Epoch: 025 Test results, ap: mae: 0.664, rse: 1.340, pcc: 0.012, kcc: -0.040
+2025-07-10 15:11:56: Epoch: 026 Train Loss: 1.949, mae: 1.053, rse: 0.744, pcc: 0.521, kcc: 0.318
+2025-07-10 15:11:56: Epoch: 026 Test results, ap: mae: 0.743, rse: 1.674, pcc: 0.045, kcc: 0.000
+2025-07-10 15:11:56: Epoch: 027 Train Loss: 1.844, mae: 1.044, rse: 0.740, pcc: 0.517, kcc: 0.311
+2025-07-10 15:11:57: Epoch: 027 Test results, ap: mae: 0.664, rse: 1.279, pcc: 0.035, kcc: -0.001
+2025-07-10 15:11:57: Epoch: 028 Train Loss: 1.809, mae: 1.018, rse: 0.693, pcc: 0.555, kcc: 0.328
+2025-07-10 15:11:57: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:11:57: Epoch: 028 Test results, ap: mae: 0.680, rse: 1.346, pcc: 0.026, kcc: -0.039
+2025-07-10 15:11:58: Epoch: 029 Train Loss: 1.747, mae: 1.022, rse: 0.691, pcc: 0.566, kcc: 0.322
+2025-07-10 15:11:58: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:11:58: Epoch: 029 Test results, ap: mae: 0.664, rse: 1.213, pcc: -0.027, kcc: -0.065
+2025-07-10 15:11:59: Epoch: 030 Train Loss: 1.731, mae: 1.012, rse: 0.664, pcc: 0.582, kcc: 0.327
+2025-07-10 15:11:59: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:11:59: Epoch: 030 Test results, ap: mae: 0.666, rse: 1.272, pcc: 0.034, kcc: -0.048
+2025-07-10 15:12:00: Epoch: 031 Train Loss: 1.683, mae: 1.003, rse: 0.669, pcc: 0.575, kcc: 0.336
+2025-07-10 15:12:00: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:12:00: Epoch: 031 Test results, ap: mae: 0.667, rse: 1.238, pcc: 0.050, kcc: -0.042
+2025-07-10 15:12:01: Epoch: 032 Train Loss: 1.627, mae: 1.008, rse: 0.673, pcc: 0.573, kcc: 0.329
+2025-07-10 15:12:01: Epoch: 032 Test results, ap: mae: 0.689, rse: 1.266, pcc: 0.029, kcc: -0.043
+2025-07-10 15:12:02: Epoch: 033 Train Loss: 1.641, mae: 0.993, rse: 0.665, pcc: 0.581, kcc: 0.338
+2025-07-10 15:12:02: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:12:02: Epoch: 033 Test results, ap: mae: 0.667, rse: 1.258, pcc: 0.066, kcc: -0.036
+2025-07-10 15:12:03: Epoch: 034 Train Loss: 1.608, mae: 0.991, rse: 0.662, pcc: 0.582, kcc: 0.341
+2025-07-10 15:12:03: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:12:03: Epoch: 034 Test results, ap: mae: 0.648, rse: 1.195, pcc: 0.026, kcc: -0.051
+2025-07-10 15:12:03: Epoch: 035 Train Loss: 1.584, mae: 0.980, rse: 0.649, pcc: 0.593, kcc: 0.347
+2025-07-10 15:12:03: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:12:04: Epoch: 035 Test results, ap: mae: 0.652, rse: 1.259, pcc: 0.027, kcc: -0.035
+2025-07-10 15:12:04: Epoch: 036 Train Loss: 1.584, mae: 0.987, rse: 0.663, pcc: 0.584, kcc: 0.351
+2025-07-10 15:12:04: Epoch: 036 Test results, ap: mae: 0.669, rse: 1.241, pcc: 0.032, kcc: -0.043
+2025-07-10 15:12:05: Epoch: 037 Train Loss: 1.564, mae: 0.980, rse: 0.663, pcc: 0.587, kcc: 0.354
+2025-07-10 15:12:05: Epoch: 037 Test results, ap: mae: 0.647, rse: 1.234, pcc: 0.032, kcc: -0.025
+2025-07-10 15:12:06: Epoch: 038 Train Loss: 1.549, mae: 1.027, rse: 0.696, pcc: 0.571, kcc: 0.339
+2025-07-10 15:12:06: Epoch: 038 Test results, ap: mae: 0.743, rse: 1.441, pcc: 0.036, kcc: -0.034
+2025-07-10 15:12:07: Epoch: 039 Train Loss: 1.540, mae: 1.014, rse: 0.695, pcc: 0.559, kcc: 0.337
+2025-07-10 15:12:07: Epoch: 039 Test results, ap: mae: 0.682, rse: 1.250, pcc: 0.027, kcc: -0.037
+2025-07-10 15:12:07: Epoch: 040 Train Loss: 1.543, mae: 0.983, rse: 0.656, pcc: 0.590, kcc: 0.354
+2025-07-10 15:12:08: Epoch: 040 Test results, ap: mae: 0.656, rse: 1.218, pcc: 0.046, kcc: -0.054
+2025-07-10 15:12:08: Epoch: 041 Train Loss: 1.515, mae: 0.978, rse: 0.655, pcc: 0.589, kcc: 0.353
+2025-07-10 15:12:08: Epoch: 041 Test results, ap: mae: 0.663, rse: 1.251, pcc: 0.048, kcc: -0.054
+2025-07-10 15:12:09: Epoch: 042 Train Loss: 1.486, mae: 0.977, rse: 0.658, pcc: 0.590, kcc: 0.350
+2025-07-10 15:12:09: Epoch: 042 Test results, ap: mae: 0.681, rse: 1.322, pcc: 0.052, kcc: -0.056
+2025-07-10 15:12:10: Epoch: 043 Train Loss: 1.472, mae: 0.991, rse: 0.662, pcc: 0.586, kcc: 0.348
+2025-07-10 15:12:10: Epoch: 043 Test results, ap: mae: 0.709, rse: 1.354, pcc: 0.068, kcc: -0.036
+2025-07-10 15:12:11: Epoch: 044 Train Loss: 1.486, mae: 0.970, rse: 0.648, pcc: 0.597, kcc: 0.357
+2025-07-10 15:12:11: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:12:11: Epoch: 044 Test results, ap: mae: 0.658, rse: 1.263, pcc: 0.052, kcc: -0.046
+2025-07-10 15:12:11: Epoch: 045 Train Loss: 1.487, mae: 0.983, rse: 0.653, pcc: 0.593, kcc: 0.354
+2025-07-10 15:12:12: Epoch: 045 Test results, ap: mae: 0.683, rse: 1.286, pcc: 0.057, kcc: -0.031
+2025-07-10 15:12:12: Epoch: 046 Train Loss: 1.457, mae: 0.969, rse: 0.657, pcc: 0.593, kcc: 0.352
+2025-07-10 15:12:12: Epoch: 046 Test results, ap: mae: 0.651, rse: 1.249, pcc: 0.087, kcc: -0.004
+2025-07-10 15:12:13: Epoch: 047 Train Loss: 1.418, mae: 0.970, rse: 0.642, pcc: 0.603, kcc: 0.358
+2025-07-10 15:12:13: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:12:13: Epoch: 047 Test results, ap: mae: 0.666, rse: 1.244, pcc: 0.075, kcc: -0.020
+2025-07-10 15:12:14: Epoch: 048 Train Loss: 1.459, mae: 0.966, rse: 0.648, pcc: 0.597, kcc: 0.354
+2025-07-10 15:12:14: Epoch: 048 Test results, ap: mae: 0.643, rse: 1.201, pcc: 0.101, kcc: 0.011
+2025-07-10 15:12:15: Epoch: 049 Train Loss: 1.439, mae: 0.989, rse: 0.665, pcc: 0.586, kcc: 0.350
+2025-07-10 15:12:15: Epoch: 049 Test results, ap: mae: 0.680, rse: 1.289, pcc: 0.085, kcc: -0.018
+2025-07-10 15:12:15: Epoch: 050 Train Loss: 1.457, mae: 0.986, rse: 0.664, pcc: 0.587, kcc: 0.347
+2025-07-10 15:12:16: Epoch: 050 Test results, ap: mae: 0.679, rse: 1.319, pcc: 0.083, kcc: 0.003
+2025-07-10 15:12:16: used time 0.01h
+2025-07-10 15:12:16: Running Cross Validation 1
+2025-07-10 15:12:16: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 15:12:16: Epoch: 001 Train Loss: 2.845, mae: 1.243, rse: 0.991, pcc: 0.311, kcc: 0.202
+2025-07-10 15:12:16: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:12:16: Epoch: 001 Test results, ap: mae: 0.638, rse: 1.235, pcc: -0.014, kcc: 0.024
+2025-07-10 15:12:17: Epoch: 002 Train Loss: 2.764, mae: 1.218, rse: 0.934, pcc: 0.345, kcc: 0.245
+2025-07-10 15:12:17: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:12:17: Epoch: 002 Test results, ap: mae: 0.782, rse: 1.773, pcc: -0.034, kcc: -0.102
+2025-07-10 15:12:18: Epoch: 003 Train Loss: 2.594, mae: 1.191, rse: 0.890, pcc: 0.378, kcc: 0.247
+2025-07-10 15:12:18: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:12:18: Epoch: 003 Test results, ap: mae: 0.683, rse: 1.400, pcc: -0.002, kcc: -0.039
+2025-07-10 15:12:19: Epoch: 004 Train Loss: 2.547, mae: 1.171, rse: 0.865, pcc: 0.374, kcc: 0.246
+2025-07-10 15:12:19: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:12:19: Epoch: 004 Test results, ap: mae: 0.628, rse: 1.208, pcc: -0.033, kcc: -0.159
+2025-07-10 15:12:19: Epoch: 005 Train Loss: 2.573, mae: 1.184, rse: 0.884, pcc: 0.380, kcc: 0.262
+2025-07-10 15:12:19: Epoch: 005 Test results, ap: mae: 0.647, rse: 1.290, pcc: -0.011, kcc: -0.053
+2025-07-10 15:12:20: Epoch: 006 Train Loss: 2.543, mae: 1.227, rse: 0.927, pcc: 0.348, kcc: 0.266
+2025-07-10 15:12:20: Epoch: 006 Test results, ap: mae: 0.827, rse: 1.938, pcc: 0.009, kcc: -0.022
+2025-07-10 15:12:21: Epoch: 007 Train Loss: 2.563, mae: 1.179, rse: 0.860, pcc: 0.383, kcc: 0.259
+2025-07-10 15:12:21: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:12:21: Epoch: 007 Test results, ap: mae: 0.686, rse: 1.411, pcc: -0.002, kcc: 0.008
+2025-07-10 15:12:22: Epoch: 008 Train Loss: 2.474, mae: 1.173, rse: 0.852, pcc: 0.399, kcc: 0.256
+2025-07-10 15:12:22: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:12:22: Epoch: 008 Test results, ap: mae: 0.609, rse: 1.091, pcc: -0.035, kcc: -0.070
+2025-07-10 15:12:23: Epoch: 009 Train Loss: 2.470, mae: 1.158, rse: 0.847, pcc: 0.414, kcc: 0.266
+2025-07-10 15:12:23: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:12:23: Epoch: 009 Test results, ap: mae: 0.721, rse: 1.534, pcc: 0.046, kcc: 0.007
+2025-07-10 15:12:23: Epoch: 010 Train Loss: 2.447, mae: 1.218, rse: 0.889, pcc: 0.479, kcc: 0.302
+2025-07-10 15:12:23: Epoch: 010 Test results, ap: mae: 0.699, rse: 1.256, pcc: 0.005, kcc: -0.084
+2025-07-10 15:12:24: Epoch: 011 Train Loss: 2.395, mae: 1.103, rse: 0.784, pcc: 0.477, kcc: 0.321
+2025-07-10 15:12:24: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:12:24: Epoch: 011 Test results, ap: mae: 0.666, rse: 1.314, pcc: 0.052, kcc: -0.046
+2025-07-10 15:12:24: Epoch: 011 New best TEST metrics
+2025-07-10 15:12:25: Epoch: 012 Train Loss: 2.262, mae: 1.103, rse: 0.765, pcc: 0.505, kcc: 0.321
+2025-07-10 15:12:25: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:12:25: Epoch: 012 Test results, ap: mae: 0.740, rse: 1.649, pcc: -0.020, kcc: -0.053
+2025-07-10 15:12:26: Epoch: 013 Train Loss: 2.162, mae: 1.041, rse: 0.707, pcc: 0.542, kcc: 0.349
+2025-07-10 15:12:26: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:12:26: Epoch: 013 Test results, ap: mae: 0.763, rse: 1.687, pcc: 0.053, kcc: -0.035
+2025-07-10 15:12:26: Epoch: 014 Train Loss: 2.097, mae: 1.036, rse: 0.705, pcc: 0.545, kcc: 0.352
+2025-07-10 15:12:26: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:12:27: Epoch: 014 Test results, ap: mae: 0.700, rse: 1.465, pcc: 0.044, kcc: 0.042
+2025-07-10 15:12:27: Epoch: 015 Train Loss: 1.968, mae: 0.998, rse: 0.663, pcc: 0.584, kcc: 0.371
+2025-07-10 15:12:27: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:12:28: Epoch: 015 Test results, ap: mae: 0.657, rse: 1.294, pcc: 0.031, kcc: -0.088
+2025-07-10 15:12:28: Epoch: 016 Train Loss: 1.851, mae: 1.004, rse: 0.672, pcc: 0.573, kcc: 0.377
+2025-07-10 15:12:28: Epoch: 016 Test results, ap: mae: 0.694, rse: 1.476, pcc: -0.068, kcc: -0.111
+2025-07-10 15:12:29: Epoch: 017 Train Loss: 1.805, mae: 0.976, rse: 0.603, pcc: 0.655, kcc: 0.422
+2025-07-10 15:12:29: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:12:29: Epoch: 017 Test results, ap: mae: 0.955, rse: 2.463, pcc: 0.060, kcc: -0.026
+2025-07-10 15:12:30: Epoch: 018 Train Loss: 1.712, mae: 0.942, rse: 0.561, pcc: 0.671, kcc: 0.441
+2025-07-10 15:12:30: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:12:30: Epoch: 018 Test results, ap: mae: 1.011, rse: 2.688, pcc: -0.065, kcc: -0.062
+2025-07-10 15:12:30: Epoch: 019 Train Loss: 1.643, mae: 0.857, rse: 0.495, pcc: 0.721, kcc: 0.477
+2025-07-10 15:12:30: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:12:31: Epoch: 019 Test results, ap: mae: 0.743, rse: 1.616, pcc: -0.069, kcc: -0.050
+2025-07-10 15:12:31: Epoch: 020 Train Loss: 1.510, mae: 0.899, rse: 0.535, pcc: 0.692, kcc: 0.462
+2025-07-10 15:12:31: Epoch: 020 Test results, ap: mae: 0.715, rse: 1.531, pcc: -0.057, kcc: -0.062
+2025-07-10 15:12:32: Epoch: 021 Train Loss: 1.462, mae: 0.846, rse: 0.475, pcc: 0.725, kcc: 0.493
+2025-07-10 15:12:32: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:12:32: Epoch: 021 Test results, ap: mae: 0.857, rse: 2.049, pcc: -0.047, kcc: -0.048
+2025-07-10 15:12:33: Epoch: 022 Train Loss: 1.333, mae: 0.831, rse: 0.468, pcc: 0.730, kcc: 0.490
+2025-07-10 15:12:33: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:12:33: Epoch: 022 Test results, ap: mae: 0.810, rse: 1.880, pcc: -0.033, kcc: -0.059
+2025-07-10 15:12:34: Epoch: 023 Train Loss: 1.364, mae: 0.845, rse: 0.473, pcc: 0.729, kcc: 0.484
+2025-07-10 15:12:34: Epoch: 023 Test results, ap: mae: 0.828, rse: 1.949, pcc: -0.036, kcc: -0.036
+2025-07-10 15:12:34: Epoch: 024 Train Loss: 1.300, mae: 0.804, rse: 0.445, pcc: 0.745, kcc: 0.492
+2025-07-10 15:12:34: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:12:35: Epoch: 024 Test results, ap: mae: 0.790, rse: 1.794, pcc: -0.013, kcc: -0.025
+2025-07-10 15:12:35: Epoch: 025 Train Loss: 1.283, mae: 0.824, rse: 0.455, pcc: 0.739, kcc: 0.478
+2025-07-10 15:12:35: Epoch: 025 Test results, ap: mae: 0.848, rse: 2.026, pcc: 0.018, kcc: 0.031
+2025-07-10 15:12:36: Epoch: 026 Train Loss: 1.266, mae: 0.837, rse: 0.458, pcc: 0.743, kcc: 0.481
+2025-07-10 15:12:36: Epoch: 026 Test results, ap: mae: 0.695, rse: 1.426, pcc: 0.029, kcc: 0.040
+2025-07-10 15:12:37: Epoch: 027 Train Loss: 1.208, mae: 0.785, rse: 0.428, pcc: 0.757, kcc: 0.501
+2025-07-10 15:12:37: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:12:37: Epoch: 027 Test results, ap: mae: 0.705, rse: 1.471, pcc: 0.051, kcc: 0.083
+2025-07-10 15:12:38: Epoch: 028 Train Loss: 1.204, mae: 0.770, rse: 0.425, pcc: 0.761, kcc: 0.516
+2025-07-10 15:12:38: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:12:38: Epoch: 028 Test results, ap: mae: 0.663, rse: 1.328, pcc: 0.028, kcc: 0.056
+2025-07-10 15:12:38: Epoch: 028 New best TEST metrics
+2025-07-10 15:12:38: Epoch: 029 Train Loss: 1.181, mae: 0.786, rse: 0.423, pcc: 0.761, kcc: 0.524
+2025-07-10 15:12:38: Epoch: 029 Test results, ap: mae: 0.647, rse: 1.252, pcc: 0.027, kcc: 0.059
+2025-07-10 15:12:38: Epoch: 029 New best TEST metrics
+2025-07-10 15:12:39: Epoch: 030 Train Loss: 1.163, mae: 0.803, rse: 0.433, pcc: 0.755, kcc: 0.511
+2025-07-10 15:12:39: Epoch: 030 Test results, ap: mae: 0.651, rse: 1.287, pcc: 0.061, kcc: 0.093
+2025-07-10 15:12:39: Epoch: 030 New best TEST metrics
+2025-07-10 15:12:40: Epoch: 031 Train Loss: 1.102, mae: 0.772, rse: 0.407, pcc: 0.773, kcc: 0.523
+2025-07-10 15:12:40: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:12:40: Epoch: 031 Test results, ap: mae: 0.685, rse: 1.400, pcc: 0.068, kcc: 0.062
+2025-07-10 15:12:41: Epoch: 032 Train Loss: 1.081, mae: 0.764, rse: 0.400, pcc: 0.777, kcc: 0.539
+2025-07-10 15:12:41: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:12:41: Epoch: 032 Test results, ap: mae: 0.653, rse: 1.283, pcc: 0.067, kcc: 0.065
+2025-07-10 15:12:41: Epoch: 033 Train Loss: 1.079, mae: 0.754, rse: 0.398, pcc: 0.777, kcc: 0.535
+2025-07-10 15:12:41: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:12:42: Epoch: 033 Test results, ap: mae: 0.634, rse: 1.196, pcc: 0.096, kcc: 0.080
+2025-07-10 15:12:42: Epoch: 033 New best TEST metrics
+2025-07-10 15:12:42: Epoch: 034 Train Loss: 1.078, mae: 0.780, rse: 0.412, pcc: 0.772, kcc: 0.523
+2025-07-10 15:12:42: Epoch: 034 Test results, ap: mae: 0.690, rse: 1.408, pcc: 0.107, kcc: 0.075
+2025-07-10 15:12:43: Epoch: 035 Train Loss: 1.077, mae: 0.753, rse: 0.399, pcc: 0.777, kcc: 0.546
+2025-07-10 15:12:43: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:12:43: Epoch: 035 Test results, ap: mae: 0.630, rse: 1.143, pcc: 0.088, kcc: 0.074
+2025-07-10 15:12:43: Epoch: 035 New best TEST metrics
+2025-07-10 15:12:44: Epoch: 036 Train Loss: 1.077, mae: 0.751, rse: 0.388, pcc: 0.784, kcc: 0.542
+2025-07-10 15:12:44: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:12:44: Epoch: 036 Test results, ap: mae: 0.650, rse: 1.262, pcc: 0.095, kcc: 0.081
+2025-07-10 15:12:45: Epoch: 037 Train Loss: 1.053, mae: 0.749, rse: 0.385, pcc: 0.784, kcc: 0.538
+2025-07-10 15:12:45: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:12:45: Epoch: 037 Test results, ap: mae: 0.665, rse: 1.306, pcc: 0.112, kcc: 0.079
+2025-07-10 15:12:46: Epoch: 038 Train Loss: 1.035, mae: 0.737, rse: 0.376, pcc: 0.790, kcc: 0.551
+2025-07-10 15:12:46: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:12:46: Epoch: 038 Test results, ap: mae: 0.651, rse: 1.223, pcc: 0.082, kcc: 0.073
+2025-07-10 15:12:46: Epoch: 039 Train Loss: 1.032, mae: 0.727, rse: 0.372, pcc: 0.795, kcc: 0.557
+2025-07-10 15:12:46: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:12:46: Epoch: 039 Test results, ap: mae: 0.651, rse: 1.181, pcc: 0.073, kcc: 0.068
+2025-07-10 15:12:47: Epoch: 040 Train Loss: 1.030, mae: 0.743, rse: 0.381, pcc: 0.788, kcc: 0.535
+2025-07-10 15:12:47: Epoch: 040 Test results, ap: mae: 0.692, rse: 1.398, pcc: 0.116, kcc: 0.076
+2025-07-10 15:12:48: Epoch: 041 Train Loss: 1.008, mae: 0.727, rse: 0.373, pcc: 0.792, kcc: 0.552
+2025-07-10 15:12:48: Epoch: 041 Test results, ap: mae: 0.663, rse: 1.280, pcc: 0.085, kcc: 0.068
+2025-07-10 15:12:49: Epoch: 042 Train Loss: 0.980, mae: 0.718, rse: 0.365, pcc: 0.797, kcc: 0.556
+2025-07-10 15:12:49: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:12:49: Epoch: 042 Test results, ap: mae: 0.659, rse: 1.264, pcc: 0.087, kcc: 0.070
+2025-07-10 15:12:49: Epoch: 043 Train Loss: 0.972, mae: 0.729, rse: 0.373, pcc: 0.793, kcc: 0.549
+2025-07-10 15:12:50: Epoch: 043 Test results, ap: mae: 0.664, rse: 1.302, pcc: 0.087, kcc: 0.066
+2025-07-10 15:12:50: Epoch: 044 Train Loss: 0.969, mae: 0.739, rse: 0.373, pcc: 0.794, kcc: 0.552
+2025-07-10 15:12:50: Epoch: 044 Test results, ap: mae: 0.677, rse: 1.351, pcc: 0.102, kcc: 0.074
+2025-07-10 15:12:51: Epoch: 045 Train Loss: 0.961, mae: 0.727, rse: 0.366, pcc: 0.797, kcc: 0.558
+2025-07-10 15:12:51: Epoch: 045 Test results, ap: mae: 0.692, rse: 1.381, pcc: 0.075, kcc: 0.064
+2025-07-10 15:12:52: Epoch: 046 Train Loss: 0.962, mae: 0.715, rse: 0.360, pcc: 0.800, kcc: 0.560
+2025-07-10 15:12:52: Epoch: 046 New best VALIDATION metrics
+2025-07-10 15:12:52: Epoch: 046 Test results, ap: mae: 0.685, rse: 1.364, pcc: 0.082, kcc: 0.071
+2025-07-10 15:12:52: Epoch: 047 Train Loss: 0.962, mae: 0.716, rse: 0.360, pcc: 0.800, kcc: 0.556
+2025-07-10 15:12:53: Epoch: 047 Test results, ap: mae: 0.688, rse: 1.378, pcc: 0.075, kcc: 0.064
+2025-07-10 15:12:53: Epoch: 048 Train Loss: 0.947, mae: 0.713, rse: 0.358, pcc: 0.801, kcc: 0.556
+2025-07-10 15:12:53: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:12:53: Epoch: 048 Test results, ap: mae: 0.694, rse: 1.393, pcc: 0.070, kcc: 0.062
+2025-07-10 15:12:54: Epoch: 049 Train Loss: 0.953, mae: 0.721, rse: 0.359, pcc: 0.801, kcc: 0.556
+2025-07-10 15:12:54: Epoch: 049 Test results, ap: mae: 0.721, rse: 1.462, pcc: 0.053, kcc: 0.061
+2025-07-10 15:12:55: Epoch: 050 Train Loss: 0.942, mae: 0.719, rse: 0.359, pcc: 0.801, kcc: 0.560
+2025-07-10 15:12:55: Epoch: 050 Test results, ap: mae: 0.684, rse: 1.336, pcc: 0.065, kcc: 0.054
+2025-07-10 15:12:55: used time 0.01h
+2025-07-10 15:12:55: Running Cross Validation 2
+2025-07-10 15:12:55: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:12:56: Epoch: 001 Train Loss: 2.883, mae: 1.268, rse: 1.006, pcc: 0.262, kcc: 0.173
+2025-07-10 15:12:56: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:12:56: Epoch: 001 Test results, ap: mae: 0.587, rse: 1.042, pcc: 0.315, kcc: 0.163
+2025-07-10 15:12:57: Epoch: 002 Train Loss: 2.775, mae: 1.181, rse: 0.918, pcc: 0.326, kcc: 0.250
+2025-07-10 15:12:57: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:12:57: Epoch: 002 Test results, ap: mae: 0.567, rse: 0.962, pcc: 0.242, kcc: -0.070
+2025-07-10 15:12:58: Epoch: 003 Train Loss: 2.688, mae: 1.203, rse: 0.920, pcc: 0.338, kcc: 0.233
+2025-07-10 15:12:58: Epoch: 003 Test results, ap: mae: 0.577, rse: 0.976, pcc: 0.168, kcc: -0.212
+2025-07-10 15:12:58: Epoch: 004 Train Loss: 2.609, mae: 1.175, rse: 0.891, pcc: 0.336, kcc: 0.244
+2025-07-10 15:12:58: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:12:59: Epoch: 004 Test results, ap: mae: 0.703, rse: 1.479, pcc: -0.084, kcc: -0.197
+2025-07-10 15:12:59: Epoch: 005 Train Loss: 2.589, mae: 1.186, rse: 0.890, pcc: 0.336, kcc: 0.245
+2025-07-10 15:12:59: Epoch: 005 Test results, ap: mae: 0.686, rse: 1.382, pcc: -0.046, kcc: -0.206
+2025-07-10 15:13:00: Epoch: 006 Train Loss: 2.539, mae: 1.210, rse: 0.943, pcc: 0.322, kcc: 0.233
+2025-07-10 15:13:00: Epoch: 006 Test results, ap: mae: 0.806, rse: 1.885, pcc: -0.114, kcc: -0.172
+2025-07-10 15:13:01: Epoch: 007 Train Loss: 2.529, mae: 1.223, rse: 0.931, pcc: 0.339, kcc: 0.235
+2025-07-10 15:13:01: Epoch: 007 Test results, ap: mae: 0.662, rse: 1.204, pcc: -0.186, kcc: -0.169
+2025-07-10 15:13:01: Epoch: 008 Train Loss: 2.505, mae: 1.197, rse: 0.908, pcc: 0.309, kcc: 0.253
+2025-07-10 15:13:02: Epoch: 008 Test results, ap: mae: 0.844, rse: 2.023, pcc: -0.186, kcc: -0.171
+2025-07-10 15:13:02: Epoch: 009 Train Loss: 2.488, mae: 1.190, rse: 0.918, pcc: 0.363, kcc: 0.263
+2025-07-10 15:13:02: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:13:02: Epoch: 009 Test results, ap: mae: 0.758, rse: 1.564, pcc: -0.330, kcc: -0.278
+2025-07-10 15:13:03: Epoch: 010 Train Loss: 2.478, mae: 1.136, rse: 0.831, pcc: 0.424, kcc: 0.275
+2025-07-10 15:13:03: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:13:03: Epoch: 010 Test results, ap: mae: 0.720, rse: 1.513, pcc: -0.239, kcc: -0.227
+2025-07-10 15:13:04: Epoch: 011 Train Loss: 2.333, mae: 1.114, rse: 0.801, pcc: 0.457, kcc: 0.293
+2025-07-10 15:13:04: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:13:04: Epoch: 011 Test results, ap: mae: 0.745, rse: 1.591, pcc: -0.024, kcc: -0.112
+2025-07-10 15:13:04: Epoch: 011 New best TEST metrics
+2025-07-10 15:13:05: Epoch: 012 Train Loss: 2.274, mae: 1.131, rse: 0.835, pcc: 0.459, kcc: 0.300
+2025-07-10 15:13:05: Epoch: 012 Test results, ap: mae: 0.814, rse: 1.827, pcc: 0.061, kcc: -0.020
+2025-07-10 15:13:05: Epoch: 013 Train Loss: 2.185, mae: 1.090, rse: 0.773, pcc: 0.477, kcc: 0.299
+2025-07-10 15:13:05: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:13:05: Epoch: 013 Test results, ap: mae: 0.786, rse: 1.755, pcc: -0.078, kcc: -0.105
+2025-07-10 15:13:06: Epoch: 014 Train Loss: 2.126, mae: 1.090, rse: 0.767, pcc: 0.486, kcc: 0.301
+2025-07-10 15:13:06: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:13:06: Epoch: 014 Test results, ap: mae: 0.896, rse: 2.216, pcc: -0.115, kcc: -0.094
+2025-07-10 15:13:07: Epoch: 015 Train Loss: 2.117, mae: 1.080, rse: 0.761, pcc: 0.497, kcc: 0.314
+2025-07-10 15:13:07: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:13:07: Epoch: 015 Test results, ap: mae: 0.681, rse: 1.294, pcc: -0.115, kcc: -0.123
+2025-07-10 15:13:07: Epoch: 015 New best TEST metrics
+2025-07-10 15:13:07: Epoch: 016 Train Loss: 2.038, mae: 1.087, rse: 0.743, pcc: 0.519, kcc: 0.324
+2025-07-10 15:13:07: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:13:08: Epoch: 016 Test results, ap: mae: 0.691, rse: 1.356, pcc: 0.191, kcc: 0.062
+2025-07-10 15:13:08: Epoch: 016 New best TEST metrics
+2025-07-10 15:13:08: Epoch: 017 Train Loss: 1.964, mae: 1.066, rse: 0.722, pcc: 0.530, kcc: 0.339
+2025-07-10 15:13:08: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:13:08: Epoch: 017 Test results, ap: mae: 0.769, rse: 1.594, pcc: 0.054, kcc: -0.055
+2025-07-10 15:13:09: Epoch: 018 Train Loss: 1.920, mae: 1.059, rse: 0.719, pcc: 0.535, kcc: 0.325
+2025-07-10 15:13:09: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:13:09: Epoch: 018 Test results, ap: mae: 0.716, rse: 1.413, pcc: 0.089, kcc: -0.078
+2025-07-10 15:13:10: Epoch: 019 Train Loss: 1.861, mae: 1.051, rse: 0.719, pcc: 0.532, kcc: 0.335
+2025-07-10 15:13:10: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:13:10: Epoch: 019 Test results, ap: mae: 0.688, rse: 1.298, pcc: 0.119, kcc: 0.070
+2025-07-10 15:13:10: Epoch: 020 Train Loss: 1.800, mae: 1.021, rse: 0.661, pcc: 0.585, kcc: 0.361
+2025-07-10 15:13:10: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:13:11: Epoch: 020 Test results, ap: mae: 0.644, rse: 1.175, pcc: 0.080, kcc: -0.020
+2025-07-10 15:13:11: Epoch: 020 New best TEST metrics
+2025-07-10 15:13:11: Epoch: 021 Train Loss: 1.649, mae: 1.005, rse: 0.659, pcc: 0.584, kcc: 0.367
+2025-07-10 15:13:11: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:13:11: Epoch: 021 Test results, ap: mae: 0.700, rse: 1.346, pcc: 0.070, kcc: -0.001
+2025-07-10 15:13:12: Epoch: 022 Train Loss: 1.618, mae: 1.028, rse: 0.675, pcc: 0.573, kcc: 0.355
+2025-07-10 15:13:12: Epoch: 022 Test results, ap: mae: 0.753, rse: 1.515, pcc: 0.043, kcc: 0.035
+2025-07-10 15:13:13: Epoch: 023 Train Loss: 1.596, mae: 1.034, rse: 0.683, pcc: 0.566, kcc: 0.352
+2025-07-10 15:13:13: Epoch: 023 Test results, ap: mae: 0.704, rse: 1.415, pcc: 0.166, kcc: 0.118
+2025-07-10 15:13:13: Epoch: 024 Train Loss: 1.582, mae: 0.995, rse: 0.638, pcc: 0.614, kcc: 0.384
+2025-07-10 15:13:13: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:13:13: Epoch: 024 Test results, ap: mae: 0.713, rse: 1.429, pcc: -0.018, kcc: -0.031
+2025-07-10 15:13:14: Epoch: 025 Train Loss: 1.499, mae: 0.974, rse: 0.624, pcc: 0.624, kcc: 0.399
+2025-07-10 15:13:14: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:13:14: Epoch: 025 Test results, ap: mae: 0.820, rse: 1.763, pcc: -0.102, kcc: -0.044
+2025-07-10 15:13:15: Epoch: 026 Train Loss: 1.531, mae: 0.957, rse: 0.602, pcc: 0.633, kcc: 0.406
+2025-07-10 15:13:15: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:13:15: Epoch: 026 Test results, ap: mae: 0.810, rse: 1.717, pcc: -0.071, kcc: 0.003
+2025-07-10 15:13:15: Epoch: 027 Train Loss: 1.462, mae: 0.972, rse: 0.615, pcc: 0.626, kcc: 0.405
+2025-07-10 15:13:16: Epoch: 027 Test results, ap: mae: 0.809, rse: 1.776, pcc: -0.060, kcc: 0.001
+2025-07-10 15:13:16: Epoch: 028 Train Loss: 1.422, mae: 0.931, rse: 0.578, pcc: 0.649, kcc: 0.423
+2025-07-10 15:13:16: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:13:16: Epoch: 028 Test results, ap: mae: 0.763, rse: 1.589, pcc: -0.104, kcc: -0.023
+2025-07-10 15:13:17: Epoch: 029 Train Loss: 1.402, mae: 0.944, rse: 0.600, pcc: 0.637, kcc: 0.416
+2025-07-10 15:13:17: Epoch: 029 Test results, ap: mae: 0.763, rse: 1.684, pcc: -0.088, kcc: 0.000
+2025-07-10 15:13:18: Epoch: 030 Train Loss: 1.387, mae: 0.941, rse: 0.589, pcc: 0.647, kcc: 0.420
+2025-07-10 15:13:18: Epoch: 030 Test results, ap: mae: 0.753, rse: 1.571, pcc: -0.106, kcc: -0.012
+2025-07-10 15:13:18: Epoch: 031 Train Loss: 1.325, mae: 0.936, rse: 0.605, pcc: 0.646, kcc: 0.422
+2025-07-10 15:13:18: Epoch: 031 Test results, ap: mae: 0.800, rse: 1.797, pcc: -0.080, kcc: 0.002
+2025-07-10 15:13:19: Epoch: 032 Train Loss: 1.299, mae: 0.917, rse: 0.573, pcc: 0.658, kcc: 0.431
+2025-07-10 15:13:19: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:13:19: Epoch: 032 Test results, ap: mae: 0.718, rse: 1.562, pcc: -0.074, kcc: 0.031
+2025-07-10 15:13:20: Epoch: 033 Train Loss: 1.283, mae: 0.932, rse: 0.587, pcc: 0.646, kcc: 0.418
+2025-07-10 15:13:20: Epoch: 033 Test results, ap: mae: 0.759, rse: 1.656, pcc: -0.085, kcc: 0.006
+2025-07-10 15:13:20: Epoch: 034 Train Loss: 1.311, mae: 0.906, rse: 0.569, pcc: 0.659, kcc: 0.434
+2025-07-10 15:13:20: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:13:21: Epoch: 034 Test results, ap: mae: 0.761, rse: 1.658, pcc: -0.106, kcc: -0.018
+2025-07-10 15:13:21: Epoch: 035 Train Loss: 1.249, mae: 0.914, rse: 0.578, pcc: 0.653, kcc: 0.425
+2025-07-10 15:13:21: Epoch: 035 Test results, ap: mae: 0.776, rse: 1.704, pcc: -0.071, kcc: 0.028
+2025-07-10 15:13:22: Epoch: 036 Train Loss: 1.253, mae: 0.916, rse: 0.577, pcc: 0.655, kcc: 0.424
+2025-07-10 15:13:22: Epoch: 036 Test results, ap: mae: 0.750, rse: 1.628, pcc: -0.085, kcc: 0.007
+2025-07-10 15:13:23: Epoch: 037 Train Loss: 1.225, mae: 0.925, rse: 0.586, pcc: 0.649, kcc: 0.425
+2025-07-10 15:13:23: Epoch: 037 Test results, ap: mae: 0.772, rse: 1.724, pcc: -0.077, kcc: 0.001
+2025-07-10 15:13:23: Epoch: 038 Train Loss: 1.234, mae: 0.906, rse: 0.566, pcc: 0.662, kcc: 0.437
+2025-07-10 15:13:23: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:13:24: Epoch: 038 Test results, ap: mae: 0.791, rse: 1.781, pcc: -0.105, kcc: 0.009
+2025-07-10 15:13:24: Epoch: 039 Train Loss: 1.200, mae: 0.912, rse: 0.575, pcc: 0.663, kcc: 0.432
+2025-07-10 15:13:24: Epoch: 039 Test results, ap: mae: 0.797, rse: 1.861, pcc: -0.015, kcc: 0.055
+2025-07-10 15:13:25: Epoch: 040 Train Loss: 1.226, mae: 0.929, rse: 0.589, pcc: 0.653, kcc: 0.422
+2025-07-10 15:13:25: Epoch: 040 Test results, ap: mae: 0.795, rse: 1.822, pcc: -0.046, kcc: 0.042
+2025-07-10 15:13:25: Epoch: 041 Train Loss: 1.190, mae: 0.909, rse: 0.577, pcc: 0.660, kcc: 0.431
+2025-07-10 15:13:25: Epoch: 041 Test results, ap: mae: 0.793, rse: 1.817, pcc: -0.063, kcc: 0.032
+2025-07-10 15:13:26: Epoch: 042 Train Loss: 1.161, mae: 0.910, rse: 0.589, pcc: 0.651, kcc: 0.430
+2025-07-10 15:13:26: Epoch: 042 Test results, ap: mae: 0.818, rse: 1.900, pcc: -0.088, kcc: 0.010
+2025-07-10 15:13:27: Epoch: 043 Train Loss: 1.161, mae: 0.901, rse: 0.568, pcc: 0.662, kcc: 0.433
+2025-07-10 15:13:27: Epoch: 043 Test results, ap: mae: 0.793, rse: 1.830, pcc: -0.067, kcc: 0.036
+2025-07-10 15:13:27: Epoch: 044 Train Loss: 1.136, mae: 0.900, rse: 0.574, pcc: 0.664, kcc: 0.437
+2025-07-10 15:13:27: Epoch: 044 Test results, ap: mae: 0.817, rse: 1.922, pcc: -0.042, kcc: 0.041
+2025-07-10 15:13:28: Epoch: 045 Train Loss: 1.181, mae: 0.902, rse: 0.569, pcc: 0.662, kcc: 0.436
+2025-07-10 15:13:28: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:13:28: Epoch: 045 Test results, ap: mae: 0.785, rse: 1.835, pcc: -0.010, kcc: 0.063
+2025-07-10 15:13:28: Epoch: 046 Train Loss: 1.175, mae: 0.899, rse: 0.573, pcc: 0.659, kcc: 0.435
+2025-07-10 15:13:28: Epoch: 046 Test results, ap: mae: 0.780, rse: 1.755, pcc: -0.038, kcc: 0.046
+2025-07-10 15:13:29: Epoch: 047 Train Loss: 1.148, mae: 0.904, rse: 0.574, pcc: 0.661, kcc: 0.431
+2025-07-10 15:13:29: Epoch: 047 Test results, ap: mae: 0.777, rse: 1.783, pcc: -0.026, kcc: 0.059
+2025-07-10 15:13:30: Epoch: 048 Train Loss: 1.155, mae: 0.899, rse: 0.568, pcc: 0.663, kcc: 0.435
+2025-07-10 15:13:30: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:13:30: Epoch: 048 Test results, ap: mae: 0.783, rse: 1.768, pcc: -0.017, kcc: 0.059
+2025-07-10 15:13:31: Epoch: 049 Train Loss: 1.142, mae: 0.901, rse: 0.577, pcc: 0.658, kcc: 0.428
+2025-07-10 15:13:31: Epoch: 049 Test results, ap: mae: 0.788, rse: 1.775, pcc: -0.013, kcc: 0.063
+2025-07-10 15:13:31: Epoch: 050 Train Loss: 1.145, mae: 0.897, rse: 0.577, pcc: 0.658, kcc: 0.438
+2025-07-10 15:13:32: Epoch: 050 Test results, ap: mae: 0.805, rse: 1.858, pcc: -0.042, kcc: 0.056
+2025-07-10 15:13:32: used time 0.01h
+2025-07-10 15:13:32: Running Cross Validation 3
+2025-07-10 15:13:32: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:13:32: Epoch: 001 Train Loss: 2.828, mae: 1.298, rse: 1.000, pcc: 0.187, kcc: 0.140
+2025-07-10 15:13:32: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:13:33: Epoch: 001 Test results, ap: mae: 0.598, rse: 1.091, pcc: -0.114, kcc: -0.047
+2025-07-10 15:13:33: Epoch: 002 Train Loss: 2.797, mae: 1.291, rse: 0.998, pcc: 0.257, kcc: 0.211
+2025-07-10 15:13:33: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:13:33: Epoch: 002 Test results, ap: mae: 0.619, rse: 1.174, pcc: 0.141, kcc: -0.000
+2025-07-10 15:13:34: Epoch: 003 Train Loss: 2.718, mae: 1.246, rse: 0.927, pcc: 0.284, kcc: 0.217
+2025-07-10 15:13:34: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:13:35: Epoch: 003 Test results, ap: mae: 0.789, rse: 1.795, pcc: 0.257, kcc: -0.064
+2025-07-10 15:13:35: Epoch: 004 Train Loss: 2.552, mae: 1.245, rse: 0.924, pcc: 0.292, kcc: 0.223
+2025-07-10 15:13:35: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:13:36: Epoch: 004 Test results, ap: mae: 0.609, rse: 1.125, pcc: -0.082, kcc: -0.163
+2025-07-10 15:13:36: Epoch: 005 Train Loss: 2.523, mae: 1.282, rse: 0.953, pcc: 0.288, kcc: 0.237
+2025-07-10 15:13:37: Epoch: 005 Test results, ap: mae: 0.983, rse: 2.549, pcc: -0.046, kcc: -0.179
+2025-07-10 15:13:37: Epoch: 006 Train Loss: 2.497, mae: 1.234, rse: 0.911, pcc: 0.302, kcc: 0.227
+2025-07-10 15:13:37: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:13:38: Epoch: 006 Test results, ap: mae: 0.791, rse: 1.804, pcc: -0.072, kcc: -0.210
+2025-07-10 15:13:38: Epoch: 007 Train Loss: 2.455, mae: 1.237, rse: 0.920, pcc: 0.309, kcc: 0.234
+2025-07-10 15:13:38: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:13:39: Epoch: 007 Test results, ap: mae: 0.685, rse: 1.399, pcc: -0.041, kcc: -0.149
+2025-07-10 15:13:40: Epoch: 008 Train Loss: 2.450, mae: 1.229, rse: 0.911, pcc: 0.315, kcc: 0.240
+2025-07-10 15:13:40: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:13:40: Epoch: 008 Test results, ap: mae: 0.778, rse: 1.746, pcc: -0.060, kcc: -0.154
+2025-07-10 15:13:41: Epoch: 009 Train Loss: 2.423, mae: 1.220, rse: 0.892, pcc: 0.331, kcc: 0.241
+2025-07-10 15:13:41: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:13:41: Epoch: 009 Test results, ap: mae: 0.761, rse: 1.686, pcc: -0.004, kcc: -0.105
+2025-07-10 15:13:41: Epoch: 010 Train Loss: 2.459, mae: 1.207, rse: 0.887, pcc: 0.339, kcc: 0.260
+2025-07-10 15:13:41: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:13:42: Epoch: 010 Test results, ap: mae: 0.656, rse: 1.297, pcc: -0.041, kcc: -0.139
+2025-07-10 15:13:42: Epoch: 011 Train Loss: 2.359, mae: 1.222, rse: 0.893, pcc: 0.339, kcc: 0.253
+2025-07-10 15:13:43: Epoch: 011 Test results, ap: mae: 0.745, rse: 1.657, pcc: -0.040, kcc: -0.138
+2025-07-10 15:13:43: Epoch: 011 New best TEST metrics
+2025-07-10 15:13:43: Epoch: 012 Train Loss: 2.373, mae: 1.213, rse: 0.884, pcc: 0.342, kcc: 0.258
+2025-07-10 15:13:44: Epoch: 012 Test results, ap: mae: 0.710, rse: 1.511, pcc: -0.028, kcc: -0.135
+2025-07-10 15:13:44: Epoch: 012 New best TEST metrics
+2025-07-10 15:13:44: Epoch: 013 Train Loss: 2.374, mae: 1.217, rse: 0.894, pcc: 0.339, kcc: 0.268
+2025-07-10 15:13:45: Epoch: 013 Test results, ap: mae: 0.652, rse: 1.284, pcc: -0.029, kcc: -0.130
+2025-07-10 15:13:45: Epoch: 013 New best TEST metrics
+2025-07-10 15:13:46: Epoch: 014 Train Loss: 2.348, mae: 1.223, rse: 0.883, pcc: 0.356, kcc: 0.266
+2025-07-10 15:13:46: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:13:46: Epoch: 014 Test results, ap: mae: 0.789, rse: 1.818, pcc: -0.013, kcc: -0.021
+2025-07-10 15:13:46: Epoch: 015 Train Loss: 2.340, mae: 1.220, rse: 0.884, pcc: 0.347, kcc: 0.261
+2025-07-10 15:13:47: Epoch: 015 Test results, ap: mae: 0.788, rse: 1.800, pcc: -0.015, kcc: -0.007
+2025-07-10 15:13:47: Epoch: 016 Train Loss: 2.338, mae: 1.233, rse: 0.886, pcc: 0.355, kcc: 0.268
+2025-07-10 15:13:48: Epoch: 016 Test results, ap: mae: 0.826, rse: 1.962, pcc: -0.030, kcc: -0.075
+2025-07-10 15:13:48: Epoch: 017 Train Loss: 2.295, mae: 1.229, rse: 0.896, pcc: 0.339, kcc: 0.254
+2025-07-10 15:13:49: Epoch: 017 Test results, ap: mae: 0.654, rse: 1.288, pcc: -0.008, kcc: -0.060
+2025-07-10 15:13:49: Epoch: 017 New best TEST metrics
+2025-07-10 15:13:50: Epoch: 018 Train Loss: 2.298, mae: 1.213, rse: 0.874, pcc: 0.358, kcc: 0.269
+2025-07-10 15:13:50: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:13:50: Epoch: 018 Test results, ap: mae: 0.695, rse: 1.439, pcc: -0.041, kcc: -0.101
+2025-07-10 15:13:51: Epoch: 019 Train Loss: 2.303, mae: 1.220, rse: 0.883, pcc: 0.355, kcc: 0.265
+2025-07-10 15:13:51: Epoch: 019 Test results, ap: mae: 0.683, rse: 1.414, pcc: -0.058, kcc: -0.100
+2025-07-10 15:13:51: Epoch: 020 Train Loss: 2.331, mae: 1.215, rse: 0.877, pcc: 0.352, kcc: 0.253
+2025-07-10 15:13:52: Epoch: 020 Test results, ap: mae: 0.693, rse: 1.432, pcc: -0.031, kcc: -0.108
+2025-07-10 15:13:52: Epoch: 021 Train Loss: 2.261, mae: 1.229, rse: 0.883, pcc: 0.348, kcc: 0.261
+2025-07-10 15:13:53: Epoch: 021 Test results, ap: mae: 0.683, rse: 1.415, pcc: -0.069, kcc: -0.097
+2025-07-10 15:13:53: Epoch: 022 Train Loss: 2.254, mae: 1.223, rse: 0.872, pcc: 0.364, kcc: 0.272
+2025-07-10 15:13:53: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:13:53: Epoch: 022 Test results, ap: mae: 0.721, rse: 1.531, pcc: -0.044, kcc: -0.107
+2025-07-10 15:13:54: Epoch: 023 Train Loss: 2.236, mae: 1.231, rse: 0.877, pcc: 0.355, kcc: 0.265
+2025-07-10 15:13:54: Epoch: 023 Test results, ap: mae: 0.672, rse: 1.352, pcc: -0.068, kcc: -0.091
+2025-07-10 15:13:55: Epoch: 024 Train Loss: 2.213, mae: 1.223, rse: 0.873, pcc: 0.356, kcc: 0.260
+2025-07-10 15:13:56: Epoch: 024 Test results, ap: mae: 0.654, rse: 1.259, pcc: -0.066, kcc: -0.069
+2025-07-10 15:13:56: Epoch: 025 Train Loss: 2.184, mae: 1.233, rse: 0.878, pcc: 0.361, kcc: 0.269
+2025-07-10 15:13:57: Epoch: 025 Test results, ap: mae: 0.682, rse: 1.357, pcc: -0.064, kcc: -0.036
+2025-07-10 15:13:57: Epoch: 026 Train Loss: 2.228, mae: 1.234, rse: 0.886, pcc: 0.348, kcc: 0.259
+2025-07-10 15:13:57: Epoch: 026 Test results, ap: mae: 0.676, rse: 1.230, pcc: -0.057, kcc: -0.082
+2025-07-10 15:13:58: Epoch: 027 Train Loss: 2.227, mae: 1.223, rse: 0.868, pcc: 0.368, kcc: 0.277
+2025-07-10 15:13:58: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:13:58: Epoch: 027 Test results, ap: mae: 0.672, rse: 1.317, pcc: -0.062, kcc: -0.062
+2025-07-10 15:13:59: Epoch: 028 Train Loss: 2.182, mae: 1.212, rse: 0.861, pcc: 0.373, kcc: 0.278
+2025-07-10 15:13:59: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:13:59: Epoch: 028 Test results, ap: mae: 0.661, rse: 1.242, pcc: -0.066, kcc: -0.070
+2025-07-10 15:14:00: Epoch: 029 Train Loss: 2.201, mae: 1.217, rse: 0.864, pcc: 0.373, kcc: 0.279
+2025-07-10 15:14:00: Epoch: 029 Test results, ap: mae: 0.673, rse: 1.343, pcc: -0.070, kcc: -0.071
+2025-07-10 15:14:01: Epoch: 030 Train Loss: 2.200, mae: 1.226, rse: 0.868, pcc: 0.370, kcc: 0.274
+2025-07-10 15:14:01: Epoch: 030 Test results, ap: mae: 0.684, rse: 1.360, pcc: -0.066, kcc: -0.052
+2025-07-10 15:14:02: Epoch: 031 Train Loss: 2.150, mae: 1.224, rse: 0.864, pcc: 0.378, kcc: 0.282
+2025-07-10 15:14:02: Epoch: 031 Test results, ap: mae: 0.682, rse: 1.372, pcc: -0.067, kcc: -0.069
+2025-07-10 15:14:03: Epoch: 032 Train Loss: 2.127, mae: 1.222, rse: 0.861, pcc: 0.383, kcc: 0.288
+2025-07-10 15:14:03: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:14:03: Epoch: 032 Test results, ap: mae: 0.682, rse: 1.352, pcc: -0.071, kcc: -0.046
+2025-07-10 15:14:04: Epoch: 033 Train Loss: 2.142, mae: 1.221, rse: 0.859, pcc: 0.385, kcc: 0.282
+2025-07-10 15:14:04: Epoch: 033 Test results, ap: mae: 0.711, rse: 1.385, pcc: -0.067, kcc: -0.074
+2025-07-10 15:14:04: Epoch: 034 Train Loss: 2.119, mae: 1.216, rse: 0.854, pcc: 0.392, kcc: 0.288
+2025-07-10 15:14:04: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:14:05: Epoch: 034 Test results, ap: mae: 0.683, rse: 1.356, pcc: -0.063, kcc: -0.068
+2025-07-10 15:14:05: Epoch: 035 Train Loss: 2.104, mae: 1.208, rse: 0.846, pcc: 0.400, kcc: 0.293
+2025-07-10 15:14:05: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:14:06: Epoch: 035 Test results, ap: mae: 0.693, rse: 1.320, pcc: -0.068, kcc: -0.059
+2025-07-10 15:14:06: Epoch: 036 Train Loss: 2.098, mae: 1.203, rse: 0.837, pcc: 0.411, kcc: 0.296
+2025-07-10 15:14:06: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:14:06: Epoch: 036 Test results, ap: mae: 0.673, rse: 1.334, pcc: -0.070, kcc: -0.048
+2025-07-10 15:14:07: Epoch: 037 Train Loss: 2.115, mae: 1.200, rse: 0.836, pcc: 0.415, kcc: 0.295
+2025-07-10 15:14:07: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:14:07: Epoch: 037 Test results, ap: mae: 0.697, rse: 1.337, pcc: -0.072, kcc: -0.047
+2025-07-10 15:14:08: Epoch: 038 Train Loss: 2.105, mae: 1.192, rse: 0.827, pcc: 0.419, kcc: 0.298
+2025-07-10 15:14:08: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:14:08: Epoch: 038 Test results, ap: mae: 0.668, rse: 1.308, pcc: -0.072, kcc: -0.073
+2025-07-10 15:14:09: Epoch: 039 Train Loss: 2.088, mae: 1.193, rse: 0.820, pcc: 0.428, kcc: 0.303
+2025-07-10 15:14:09: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:14:09: Epoch: 039 Test results, ap: mae: 0.668, rse: 1.319, pcc: -0.077, kcc: -0.047
+2025-07-10 15:14:10: Epoch: 040 Train Loss: 2.043, mae: 1.196, rse: 0.822, pcc: 0.429, kcc: 0.299
+2025-07-10 15:14:10: Epoch: 040 Test results, ap: mae: 0.677, rse: 1.353, pcc: -0.075, kcc: -0.046
+2025-07-10 15:14:10: Epoch: 041 Train Loss: 2.051, mae: 1.186, rse: 0.817, pcc: 0.431, kcc: 0.297
+2025-07-10 15:14:10: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:14:11: Epoch: 041 Test results, ap: mae: 0.672, rse: 1.294, pcc: -0.075, kcc: -0.056
+2025-07-10 15:14:11: Epoch: 042 Train Loss: 2.024, mae: 1.185, rse: 0.820, pcc: 0.432, kcc: 0.296
+2025-07-10 15:14:11: Epoch: 042 Test results, ap: mae: 0.687, rse: 1.313, pcc: -0.076, kcc: -0.056
+2025-07-10 15:14:12: Epoch: 043 Train Loss: 2.022, mae: 1.184, rse: 0.819, pcc: 0.434, kcc: 0.294
+2025-07-10 15:14:12: Epoch: 043 Test results, ap: mae: 0.682, rse: 1.292, pcc: -0.078, kcc: -0.073
+2025-07-10 15:14:13: Epoch: 044 Train Loss: 2.020, mae: 1.181, rse: 0.808, pcc: 0.442, kcc: 0.299
+2025-07-10 15:14:13: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:14:13: Epoch: 044 Test results, ap: mae: 0.678, rse: 1.359, pcc: -0.079, kcc: -0.054
+2025-07-10 15:14:14: Epoch: 045 Train Loss: 1.989, mae: 1.172, rse: 0.803, pcc: 0.446, kcc: 0.299
+2025-07-10 15:14:14: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:14:14: Epoch: 045 Test results, ap: mae: 0.672, rse: 1.302, pcc: -0.079, kcc: -0.087
+2025-07-10 15:14:15: Epoch: 046 Train Loss: 1.994, mae: 1.170, rse: 0.804, pcc: 0.446, kcc: 0.298
+2025-07-10 15:14:15: Epoch: 046 Test results, ap: mae: 0.663, rse: 1.253, pcc: -0.079, kcc: -0.058
+2025-07-10 15:14:16: Epoch: 047 Train Loss: 2.003, mae: 1.173, rse: 0.807, pcc: 0.445, kcc: 0.295
+2025-07-10 15:14:16: Epoch: 047 Test results, ap: mae: 0.667, rse: 1.262, pcc: -0.075, kcc: -0.058
+2025-07-10 15:14:16: Epoch: 048 Train Loss: 1.992, mae: 1.173, rse: 0.816, pcc: 0.444, kcc: 0.298
+2025-07-10 15:14:17: Epoch: 048 Test results, ap: mae: 0.693, rse: 1.262, pcc: -0.079, kcc: -0.084
+2025-07-10 15:14:17: Epoch: 049 Train Loss: 1.974, mae: 1.165, rse: 0.798, pcc: 0.453, kcc: 0.301
+2025-07-10 15:14:17: Epoch: 049 New best VALIDATION metrics
+2025-07-10 15:14:17: Epoch: 049 Test results, ap: mae: 0.680, rse: 1.254, pcc: -0.076, kcc: -0.060
+2025-07-10 15:14:18: Epoch: 050 Train Loss: 1.945, mae: 1.169, rse: 0.801, pcc: 0.457, kcc: 0.295
+2025-07-10 15:14:18: Epoch: 050 Test results, ap: mae: 0.666, rse: 1.292, pcc: -0.081, kcc: -0.085
+2025-07-10 15:14:18: used time 0.01h
+2025-07-10 15:14:18: Running Cross Validation 4
+2025-07-10 15:14:18: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:14:19: Epoch: 001 Train Loss: 2.838, mae: 1.254, rse: 1.025, pcc: 0.287, kcc: 0.192
+2025-07-10 15:14:19: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:14:19: Epoch: 001 Test results, ap: mae: 0.582, rse: 0.999, pcc: 0.373, kcc: 0.220
+2025-07-10 15:14:20: Epoch: 002 Train Loss: 2.722, mae: 1.167, rse: 0.926, pcc: 0.334, kcc: 0.189
+2025-07-10 15:14:20: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:14:20: Epoch: 002 Test results, ap: mae: 0.683, rse: 1.392, pcc: 0.310, kcc: 0.058
+2025-07-10 15:14:21: Epoch: 003 Train Loss: 2.597, mae: 1.249, rse: 0.997, pcc: 0.254, kcc: 0.199
+2025-07-10 15:14:21: Epoch: 003 Test results, ap: mae: 0.541, rse: 0.861, pcc: 0.424, kcc: 0.241
+2025-07-10 15:14:22: Epoch: 004 Train Loss: 2.606, mae: 1.201, rse: 0.949, pcc: 0.274, kcc: 0.190
+2025-07-10 15:14:22: Epoch: 004 Test results, ap: mae: 0.564, rse: 0.959, pcc: 0.332, kcc: 0.104
+2025-07-10 15:14:22: Epoch: 005 Train Loss: 2.567, mae: 1.188, rse: 0.926, pcc: 0.301, kcc: 0.194
+2025-07-10 15:14:23: Epoch: 005 Test results, ap: mae: 0.700, rse: 1.406, pcc: 0.457, kcc: 0.233
+2025-07-10 15:14:23: Epoch: 006 Train Loss: 2.509, mae: 1.180, rse: 0.924, pcc: 0.289, kcc: 0.195
+2025-07-10 15:14:24: Epoch: 006 Test results, ap: mae: 0.648, rse: 1.191, pcc: 0.185, kcc: 0.051
+2025-07-10 15:14:24: Epoch: 007 Train Loss: 2.500, mae: 1.153, rse: 0.891, pcc: 0.345, kcc: 0.230
+2025-07-10 15:14:24: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:14:24: Epoch: 007 Test results, ap: mae: 0.716, rse: 1.505, pcc: 0.220, kcc: 0.084
+2025-07-10 15:14:25: Epoch: 008 Train Loss: 2.461, mae: 1.160, rse: 0.904, pcc: 0.317, kcc: 0.240
+2025-07-10 15:14:25: Epoch: 008 Test results, ap: mae: 0.684, rse: 1.322, pcc: 0.340, kcc: 0.202
+2025-07-10 15:14:26: Epoch: 009 Train Loss: 2.434, mae: 1.207, rse: 0.927, pcc: 0.331, kcc: 0.245
+2025-07-10 15:14:26: Epoch: 009 Test results, ap: mae: 0.802, rse: 1.841, pcc: 0.158, kcc: 0.052
+2025-07-10 15:14:27: Epoch: 010 Train Loss: 2.423, mae: 1.145, rse: 0.878, pcc: 0.350, kcc: 0.244
+2025-07-10 15:14:27: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:14:27: Epoch: 010 Test results, ap: mae: 0.698, rse: 1.481, pcc: 0.099, kcc: 0.011
+2025-07-10 15:14:28: Epoch: 011 Train Loss: 2.405, mae: 1.143, rse: 0.879, pcc: 0.352, kcc: 0.250
+2025-07-10 15:14:28: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:14:28: Epoch: 011 Test results, ap: mae: 0.667, rse: 1.346, pcc: -0.004, kcc: -0.017
+2025-07-10 15:14:28: Epoch: 011 New best TEST metrics
+2025-07-10 15:14:29: Epoch: 012 Train Loss: 2.381, mae: 1.160, rse: 0.890, pcc: 0.344, kcc: 0.260
+2025-07-10 15:14:29: Epoch: 012 Test results, ap: mae: 0.711, rse: 1.568, pcc: 0.031, kcc: 0.018
+2025-07-10 15:14:30: Epoch: 013 Train Loss: 2.348, mae: 1.130, rse: 0.851, pcc: 0.387, kcc: 0.271
+2025-07-10 15:14:30: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:14:30: Epoch: 013 Test results, ap: mae: 0.724, rse: 1.567, pcc: 0.061, kcc: 0.047
+2025-07-10 15:14:31: Epoch: 014 Train Loss: 2.346, mae: 1.156, rse: 0.883, pcc: 0.357, kcc: 0.255
+2025-07-10 15:14:31: Epoch: 014 Test results, ap: mae: 0.705, rse: 1.519, pcc: 0.120, kcc: 0.070
+2025-07-10 15:14:31: Epoch: 014 New best TEST metrics
+2025-07-10 15:14:31: Epoch: 015 Train Loss: 2.340, mae: 1.134, rse: 0.863, pcc: 0.370, kcc: 0.262
+2025-07-10 15:14:32: Epoch: 015 Test results, ap: mae: 0.680, rse: 1.433, pcc: 0.093, kcc: 0.045
+2025-07-10 15:14:32: Epoch: 015 New best TEST metrics
+2025-07-10 15:14:32: Epoch: 016 Train Loss: 2.310, mae: 1.143, rse: 0.855, pcc: 0.388, kcc: 0.268
+2025-07-10 15:14:32: Epoch: 016 Test results, ap: mae: 0.777, rse: 1.806, pcc: 0.035, kcc: 0.017
+2025-07-10 15:14:33: Epoch: 017 Train Loss: 2.297, mae: 1.127, rse: 0.851, pcc: 0.386, kcc: 0.269
+2025-07-10 15:14:33: Epoch: 017 Test results, ap: mae: 0.705, rse: 1.518, pcc: 0.092, kcc: 0.051
+2025-07-10 15:14:34: Epoch: 018 Train Loss: 2.254, mae: 1.143, rse: 0.876, pcc: 0.363, kcc: 0.269
+2025-07-10 15:14:34: Epoch: 018 Test results, ap: mae: 0.833, rse: 1.957, pcc: 0.096, kcc: 0.056
+2025-07-10 15:14:35: Epoch: 019 Train Loss: 2.251, mae: 1.115, rse: 0.824, pcc: 0.423, kcc: 0.303
+2025-07-10 15:14:35: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:14:35: Epoch: 019 Test results, ap: mae: 0.748, rse: 1.639, pcc: 0.030, kcc: 0.026
+2025-07-10 15:14:36: Epoch: 020 Train Loss: 2.198, mae: 1.144, rse: 0.862, pcc: 0.386, kcc: 0.283
+2025-07-10 15:14:36: Epoch: 020 Test results, ap: mae: 0.774, rse: 1.713, pcc: 0.027, kcc: 0.019
+2025-07-10 15:14:37: Epoch: 021 Train Loss: 2.171, mae: 1.139, rse: 0.871, pcc: 0.382, kcc: 0.282
+2025-07-10 15:14:37: Epoch: 021 Test results, ap: mae: 0.832, rse: 1.968, pcc: 0.065, kcc: 0.060
+2025-07-10 15:14:38: Epoch: 022 Train Loss: 2.119, mae: 1.146, rse: 0.829, pcc: 0.439, kcc: 0.314
+2025-07-10 15:14:38: Epoch: 022 Test results, ap: mae: 0.955, rse: 2.515, pcc: 0.040, kcc: 0.042
+2025-07-10 15:14:38: Epoch: 023 Train Loss: 2.094, mae: 1.118, rse: 0.805, pcc: 0.452, kcc: 0.321
+2025-07-10 15:14:38: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:14:39: Epoch: 023 Test results, ap: mae: 0.894, rse: 2.254, pcc: 0.060, kcc: 0.057
+2025-07-10 15:14:39: Epoch: 024 Train Loss: 2.057, mae: 1.091, rse: 0.788, pcc: 0.461, kcc: 0.323
+2025-07-10 15:14:39: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:14:40: Epoch: 024 Test results, ap: mae: 0.822, rse: 1.954, pcc: 0.016, kcc: 0.034
+2025-07-10 15:14:40: Epoch: 025 Train Loss: 2.001, mae: 1.078, rse: 0.756, pcc: 0.506, kcc: 0.348
+2025-07-10 15:14:40: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:14:40: Epoch: 025 Test results, ap: mae: 0.926, rse: 2.419, pcc: 0.014, kcc: 0.025
+2025-07-10 15:14:41: Epoch: 026 Train Loss: 1.926, mae: 1.086, rse: 0.767, pcc: 0.487, kcc: 0.328
+2025-07-10 15:14:41: Epoch: 026 Test results, ap: mae: 0.914, rse: 2.246, pcc: 0.064, kcc: 0.058
+2025-07-10 15:14:42: Epoch: 027 Train Loss: 1.967, mae: 1.075, rse: 0.784, pcc: 0.466, kcc: 0.328
+2025-07-10 15:14:42: Epoch: 027 Test results, ap: mae: 0.801, rse: 1.791, pcc: 0.065, kcc: 0.050
+2025-07-10 15:14:43: Epoch: 028 Train Loss: 1.860, mae: 1.071, rse: 0.787, pcc: 0.494, kcc: 0.338
+2025-07-10 15:14:43: Epoch: 028 Test results, ap: mae: 0.959, rse: 2.569, pcc: 0.042, kcc: 0.045
+2025-07-10 15:14:44: Epoch: 029 Train Loss: 1.876, mae: 1.052, rse: 0.757, pcc: 0.494, kcc: 0.335
+2025-07-10 15:14:44: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:14:44: Epoch: 029 Test results, ap: mae: 0.779, rse: 1.788, pcc: 0.018, kcc: 0.030
+2025-07-10 15:14:45: Epoch: 030 Train Loss: 1.896, mae: 1.069, rse: 0.776, pcc: 0.482, kcc: 0.328
+2025-07-10 15:14:45: Epoch: 030 Test results, ap: mae: 0.883, rse: 2.214, pcc: 0.010, kcc: 0.018
+2025-07-10 15:14:45: Epoch: 031 Train Loss: 1.812, mae: 1.049, rse: 0.755, pcc: 0.505, kcc: 0.336
+2025-07-10 15:14:45: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:14:46: Epoch: 031 Test results, ap: mae: 0.886, rse: 2.230, pcc: 0.038, kcc: 0.032
+2025-07-10 15:14:46: Epoch: 032 Train Loss: 1.782, mae: 1.048, rse: 0.760, pcc: 0.497, kcc: 0.333
+2025-07-10 15:14:46: Epoch: 032 Test results, ap: mae: 0.857, rse: 2.061, pcc: 0.055, kcc: 0.044
+2025-07-10 15:14:47: Epoch: 033 Train Loss: 1.780, mae: 1.043, rse: 0.734, pcc: 0.528, kcc: 0.337
+2025-07-10 15:14:47: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:14:47: Epoch: 033 Test results, ap: mae: 0.926, rse: 2.445, pcc: 0.033, kcc: 0.024
+2025-07-10 15:14:48: Epoch: 034 Train Loss: 1.789, mae: 1.038, rse: 0.738, pcc: 0.520, kcc: 0.336
+2025-07-10 15:14:48: Epoch: 034 Test results, ap: mae: 0.902, rse: 2.309, pcc: 0.044, kcc: 0.032
+2025-07-10 15:14:49: Epoch: 035 Train Loss: 1.732, mae: 1.021, rse: 0.723, pcc: 0.537, kcc: 0.346
+2025-07-10 15:14:49: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:14:49: Epoch: 035 Test results, ap: mae: 0.919, rse: 2.414, pcc: 0.034, kcc: 0.018
+2025-07-10 15:14:50: Epoch: 036 Train Loss: 1.760, mae: 1.022, rse: 0.731, pcc: 0.525, kcc: 0.341
+2025-07-10 15:14:50: Epoch: 036 Test results, ap: mae: 0.895, rse: 2.250, pcc: 0.055, kcc: 0.031
+2025-07-10 15:14:51: Epoch: 037 Train Loss: 1.713, mae: 1.013, rse: 0.710, pcc: 0.546, kcc: 0.345
+2025-07-10 15:14:51: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:14:51: Epoch: 037 Test results, ap: mae: 0.914, rse: 2.391, pcc: 0.025, kcc: 0.018
+2025-07-10 15:14:52: Epoch: 038 Train Loss: 1.715, mae: 1.017, rse: 0.712, pcc: 0.541, kcc: 0.342
+2025-07-10 15:14:52: Epoch: 038 Test results, ap: mae: 0.833, rse: 1.974, pcc: 0.061, kcc: 0.036
+2025-07-10 15:14:53: Epoch: 039 Train Loss: 1.680, mae: 1.012, rse: 0.714, pcc: 0.545, kcc: 0.348
+2025-07-10 15:14:53: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:14:53: Epoch: 039 Test results, ap: mae: 0.862, rse: 2.114, pcc: 0.050, kcc: 0.027
+2025-07-10 15:14:53: Epoch: 040 Train Loss: 1.698, mae: 1.005, rse: 0.710, pcc: 0.539, kcc: 0.341
+2025-07-10 15:14:54: Epoch: 040 Test results, ap: mae: 0.851, rse: 2.024, pcc: 0.011, kcc: 0.008
+2025-07-10 15:14:54: Epoch: 041 Train Loss: 1.658, mae: 0.998, rse: 0.687, pcc: 0.562, kcc: 0.349
+2025-07-10 15:14:54: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:14:55: Epoch: 041 Test results, ap: mae: 0.879, rse: 2.254, pcc: -0.016, kcc: -0.013
+2025-07-10 15:14:55: Epoch: 042 Train Loss: 1.631, mae: 1.002, rse: 0.694, pcc: 0.558, kcc: 0.352
+2025-07-10 15:14:55: Epoch: 042 Test results, ap: mae: 0.922, rse: 2.414, pcc: -0.003, kcc: -0.008
+2025-07-10 15:14:56: Epoch: 043 Train Loss: 1.639, mae: 0.998, rse: 0.692, pcc: 0.558, kcc: 0.350
+2025-07-10 15:14:56: Epoch: 043 Test results, ap: mae: 0.870, rse: 2.179, pcc: -0.007, kcc: -0.009
+2025-07-10 15:14:57: Epoch: 044 Train Loss: 1.616, mae: 0.988, rse: 0.677, pcc: 0.570, kcc: 0.356
+2025-07-10 15:14:57: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:14:57: Epoch: 044 Test results, ap: mae: 0.879, rse: 2.225, pcc: -0.016, kcc: -0.014
+2025-07-10 15:14:58: Epoch: 045 Train Loss: 1.610, mae: 0.987, rse: 0.676, pcc: 0.569, kcc: 0.359
+2025-07-10 15:14:58: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:14:58: Epoch: 045 Test results, ap: mae: 0.862, rse: 2.118, pcc: -0.012, kcc: -0.012
+2025-07-10 15:14:59: Epoch: 046 Train Loss: 1.578, mae: 0.998, rse: 0.691, pcc: 0.565, kcc: 0.356
+2025-07-10 15:14:59: Epoch: 046 Test results, ap: mae: 0.947, rse: 2.552, pcc: -0.013, kcc: -0.015
+2025-07-10 15:15:00: Epoch: 047 Train Loss: 1.581, mae: 0.990, rse: 0.684, pcc: 0.565, kcc: 0.355
+2025-07-10 15:15:00: Epoch: 047 Test results, ap: mae: 0.897, rse: 2.307, pcc: -0.013, kcc: -0.017
+2025-07-10 15:15:01: Epoch: 048 Train Loss: 1.554, mae: 0.995, rse: 0.689, pcc: 0.567, kcc: 0.356
+2025-07-10 15:15:01: Epoch: 048 Test results, ap: mae: 0.957, rse: 2.588, pcc: 0.001, kcc: -0.005
+2025-07-10 15:15:01: Epoch: 049 Train Loss: 1.547, mae: 0.987, rse: 0.679, pcc: 0.573, kcc: 0.359
+2025-07-10 15:15:02: Epoch: 049 Test results, ap: mae: 0.947, rse: 2.544, pcc: -0.009, kcc: -0.013
+2025-07-10 15:15:02: Epoch: 050 Train Loss: 1.524, mae: 0.979, rse: 0.669, pcc: 0.578, kcc: 0.359
+2025-07-10 15:15:02: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:15:02: Epoch: 050 Test results, ap: mae: 0.920, rse: 2.417, pcc: -0.034, kcc: -0.028
+2025-07-10 15:15:02: used time 0.01h
+2025-07-10 15:15:02: Cross Validation Finished!
+2025-07-10 15:15:02: Best validation perform list
+[[0.97010523 0.64176178 0.60258269 0.35800976]
+ [0.71276063 0.35847422 0.80103225 0.55639827]
+ [0.89907408 0.56845796 0.6626389  0.43476203]
+ [1.16532135 0.79809386 0.45307964 0.30134493]
+ [0.97860354 0.66916072 0.57812405 0.35897499]]
+2025-07-10 15:15:02: mean: [0.945 0.607 0.619 0.402]
+2025-07-10 15:15:02: std: [0.146 0.145 0.114 0.088]
+2025-07-10 15:15:02: Best test perform list
+[[ 0.57881653  0.99387544  0.21210277  0.10634079]
+ [ 0.62975883  1.1433332   0.08762272  0.07389437]
+ [ 0.64410597  1.17506719  0.0798921  -0.01977455]
+ [ 0.65393597  1.28797698 -0.00786248 -0.06036442]
+ [ 0.6804629   1.43293023  0.09309202  0.04475293]]
+2025-07-10 15:15:02: mean: [0.637 1.207 0.093 0.029]
+2025-07-10 15:15:02: std: [0.034 0.147 0.07  0.061]
diff --git a/run-reg/mha-diff-256-mse-32-0.001-50/config.json b/run-reg/mha-diff-256-mse-32-0.001-50/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..b2602f2252b577330494200f0504ca6c933e70ff
--- /dev/null
+++ b/run-reg/mha-diff-256-mse-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mha", "side_enc": null, "channels": 256, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
\ No newline at end of file
diff --git a/run-reg/mha-diff-256-mse-32-0.001-50/inference_results.csv b/run-reg/mha-diff-256-mse-32-0.001-50/inference_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..eb1054044ae5fa7e1718b5e4258724af93a86119
--- /dev/null
+++ b/run-reg/mha-diff-256-mse-32-0.001-50/inference_results.csv
@@ -0,0 +1,2 @@
+dataset,MAE,RSE,PCC,KCC
+r2_case,61.88,153.16,17.56,10.88
diff --git a/run-reg/mha-diff-256-mse-32-0.001-50/model_0.pth b/run-reg/mha-diff-256-mse-32-0.001-50/model_0.pth
new file mode 100644
index 0000000000000000000000000000000000000000..8413ff157b514c339feda6eea313ec82f7b867a1
--- /dev/null
+++ b/run-reg/mha-diff-256-mse-32-0.001-50/model_0.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b9e1a09f492a6e89626fcc7ed5c9f233bb6acfa7da4432633222b287c656bdb8
+size 1407215
diff --git a/run-reg/mha-diff-256-mse-32-0.001-50/model_0_last.pth b/run-reg/mha-diff-256-mse-32-0.001-50/model_0_last.pth
new file mode 100644
index 0000000000000000000000000000000000000000..3e7ee28aaaf393957536e26ad7c144cde77623cc
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+2025-07-10 15:15:11: Epoch: 005 New best VALIDATION metrics
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+2025-07-10 15:15:13: Epoch: 007 Train Loss: 1.332, mae: 0.796, rse: 0.433, pcc: 0.767, kcc: 0.532
+2025-07-10 15:15:13: Epoch: 007 New best VALIDATION metrics
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+2025-07-10 15:15:14: Epoch: 008 Train Loss: 1.215, mae: 0.741, rse: 0.375, pcc: 0.791, kcc: 0.548
+2025-07-10 15:15:14: Epoch: 008 New best VALIDATION metrics
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+2025-07-10 15:15:14: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:15:15: Epoch: 009 Test results, ap: mae: 0.991, rse: 2.765, pcc: -0.147, kcc: -0.092
+2025-07-10 15:15:15: Epoch: 010 Train Loss: 1.107, mae: 0.731, rse: 0.376, pcc: 0.791, kcc: 0.569
+2025-07-10 15:15:15: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:15:15: Epoch: 010 Test results, ap: mae: 0.925, rse: 2.530, pcc: -0.081, kcc: -0.058
+2025-07-10 15:15:16: Epoch: 011 Train Loss: 0.994, mae: 0.674, rse: 0.331, pcc: 0.819, kcc: 0.592
+2025-07-10 15:15:16: Epoch: 011 New best VALIDATION metrics
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+2025-07-10 15:15:16: Epoch: 011 New best TEST metrics
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+2025-07-10 15:15:18: Epoch: 013 New best VALIDATION metrics
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+2025-07-10 15:15:19: Epoch: 015 Train Loss: 0.888, mae: 0.656, rse: 0.298, pcc: 0.842, kcc: 0.612
+2025-07-10 15:15:20: Epoch: 015 Test results, ap: mae: 1.061, rse: 3.395, pcc: -0.343, kcc: -0.196
+2025-07-10 15:15:20: Epoch: 016 Train Loss: 0.867, mae: 0.635, rse: 0.297, pcc: 0.839, kcc: 0.622
+2025-07-10 15:15:20: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:15:20: Epoch: 016 Test results, ap: mae: 0.884, rse: 2.404, pcc: -0.120, kcc: -0.044
+2025-07-10 15:15:20: Epoch: 016 New best TEST metrics
+2025-07-10 15:15:21: Epoch: 017 Train Loss: 0.866, mae: 0.650, rse: 0.297, pcc: 0.839, kcc: 0.617
+2025-07-10 15:15:21: Epoch: 017 Test results, ap: mae: 0.987, rse: 2.999, pcc: -0.102, kcc: -0.035
+2025-07-10 15:15:22: Epoch: 018 Train Loss: 0.848, mae: 0.632, rse: 0.276, pcc: 0.856, kcc: 0.632
+2025-07-10 15:15:22: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:15:22: Epoch: 018 Test results, ap: mae: 0.923, rse: 2.414, pcc: -0.191, kcc: -0.110
+2025-07-10 15:15:23: Epoch: 019 Train Loss: 0.799, mae: 0.610, rse: 0.262, pcc: 0.859, kcc: 0.641
+2025-07-10 15:15:23: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:15:23: Epoch: 019 Test results, ap: mae: 1.020, rse: 3.080, pcc: -0.203, kcc: -0.104
+2025-07-10 15:15:24: Epoch: 020 Train Loss: 0.758, mae: 0.636, rse: 0.291, pcc: 0.843, kcc: 0.634
+2025-07-10 15:15:24: Epoch: 020 Test results, ap: mae: 1.063, rse: 3.371, pcc: -0.313, kcc: -0.176
+2025-07-10 15:15:24: Epoch: 021 Train Loss: 0.750, mae: 0.612, rse: 0.272, pcc: 0.855, kcc: 0.637
+2025-07-10 15:15:25: Epoch: 021 Test results, ap: mae: 1.050, rse: 3.472, pcc: -0.139, kcc: -0.063
+2025-07-10 15:15:25: Epoch: 022 Train Loss: 0.733, mae: 0.601, rse: 0.263, pcc: 0.860, kcc: 0.646
+2025-07-10 15:15:25: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:15:25: Epoch: 022 Test results, ap: mae: 1.070, rse: 3.544, pcc: -0.135, kcc: -0.062
+2025-07-10 15:15:26: Epoch: 023 Train Loss: 0.712, mae: 0.592, rse: 0.256, pcc: 0.864, kcc: 0.649
+2025-07-10 15:15:26: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:15:26: Epoch: 023 Test results, ap: mae: 1.117, rse: 3.463, pcc: -0.185, kcc: -0.093
+2025-07-10 15:15:27: Epoch: 024 Train Loss: 0.696, mae: 0.605, rse: 0.266, pcc: 0.860, kcc: 0.643
+2025-07-10 15:15:27: Epoch: 024 Test results, ap: mae: 1.035, rse: 3.250, pcc: -0.052, kcc: -0.007
+2025-07-10 15:15:28: Epoch: 025 Train Loss: 0.692, mae: 0.595, rse: 0.258, pcc: 0.863, kcc: 0.638
+2025-07-10 15:15:28: Epoch: 025 Test results, ap: mae: 1.033, rse: 3.172, pcc: -0.008, kcc: 0.011
+2025-07-10 15:15:29: Epoch: 026 Train Loss: 0.686, mae: 0.594, rse: 0.255, pcc: 0.865, kcc: 0.642
+2025-07-10 15:15:29: Epoch: 026 Test results, ap: mae: 1.131, rse: 3.614, pcc: -0.081, kcc: -0.029
+2025-07-10 15:15:29: Epoch: 027 Train Loss: 0.672, mae: 0.589, rse: 0.253, pcc: 0.867, kcc: 0.644
+2025-07-10 15:15:29: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:15:30: Epoch: 027 Test results, ap: mae: 1.082, rse: 3.368, pcc: -0.076, kcc: -0.020
+2025-07-10 15:15:30: Epoch: 028 Train Loss: 0.666, mae: 0.573, rse: 0.242, pcc: 0.871, kcc: 0.653
+2025-07-10 15:15:30: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:15:31: Epoch: 028 Test results, ap: mae: 1.105, rse: 3.591, pcc: -0.034, kcc: -0.007
+2025-07-10 15:15:31: Epoch: 029 Train Loss: 0.668, mae: 0.600, rse: 0.255, pcc: 0.863, kcc: 0.637
+2025-07-10 15:15:31: Epoch: 029 Test results, ap: mae: 0.989, rse: 2.855, pcc: -0.028, kcc: -0.007
+2025-07-10 15:15:32: Epoch: 030 Train Loss: 0.650, mae: 0.581, rse: 0.244, pcc: 0.870, kcc: 0.653
+2025-07-10 15:15:32: Epoch: 030 Test results, ap: mae: 1.057, rse: 3.195, pcc: -0.019, kcc: 0.004
+2025-07-10 15:15:33: Epoch: 031 Train Loss: 0.623, mae: 0.569, rse: 0.238, pcc: 0.873, kcc: 0.663
+2025-07-10 15:15:33: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:15:33: Epoch: 031 Test results, ap: mae: 1.052, rse: 3.181, pcc: 0.004, kcc: 0.014
+2025-07-10 15:15:34: Epoch: 032 Train Loss: 0.614, mae: 0.566, rse: 0.239, pcc: 0.874, kcc: 0.660
+2025-07-10 15:15:34: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:15:34: Epoch: 032 Test results, ap: mae: 1.090, rse: 3.418, pcc: -0.009, kcc: 0.003
+2025-07-10 15:15:34: Epoch: 033 Train Loss: 0.615, mae: 0.565, rse: 0.240, pcc: 0.873, kcc: 0.659
+2025-07-10 15:15:35: Epoch: 033 Test results, ap: mae: 1.088, rse: 3.347, pcc: -0.021, kcc: -0.004
+2025-07-10 15:15:35: Epoch: 034 Train Loss: 0.624, mae: 0.562, rse: 0.237, pcc: 0.874, kcc: 0.667
+2025-07-10 15:15:35: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:15:35: Epoch: 034 Test results, ap: mae: 1.139, rse: 3.614, pcc: -0.022, kcc: 0.001
+2025-07-10 15:15:36: Epoch: 035 Train Loss: 0.599, mae: 0.555, rse: 0.232, pcc: 0.877, kcc: 0.670
+2025-07-10 15:15:36: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:15:36: Epoch: 035 Test results, ap: mae: 1.128, rse: 3.542, pcc: -0.021, kcc: -0.005
+2025-07-10 15:15:37: Epoch: 036 Train Loss: 0.598, mae: 0.567, rse: 0.238, pcc: 0.873, kcc: 0.658
+2025-07-10 15:15:37: Epoch: 036 Test results, ap: mae: 1.145, rse: 3.642, pcc: -0.014, kcc: -0.001
+2025-07-10 15:15:37: Epoch: 037 Train Loss: 0.597, mae: 0.553, rse: 0.232, pcc: 0.877, kcc: 0.672
+2025-07-10 15:15:37: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:15:38: Epoch: 037 Test results, ap: mae: 1.137, rse: 3.515, pcc: -0.038, kcc: -0.012
+2025-07-10 15:15:38: Epoch: 038 Train Loss: 0.569, mae: 0.572, rse: 0.245, pcc: 0.872, kcc: 0.656
+2025-07-10 15:15:38: Epoch: 038 Test results, ap: mae: 1.151, rse: 3.708, pcc: -0.001, kcc: 0.004
+2025-07-10 15:15:39: Epoch: 039 Train Loss: 0.582, mae: 0.562, rse: 0.235, pcc: 0.875, kcc: 0.666
+2025-07-10 15:15:39: Epoch: 039 Test results, ap: mae: 1.167, rse: 3.778, pcc: -0.018, kcc: -0.009
+2025-07-10 15:15:40: Epoch: 040 Train Loss: 0.596, mae: 0.553, rse: 0.232, pcc: 0.877, kcc: 0.671
+2025-07-10 15:15:40: Epoch: 040 Test results, ap: mae: 1.176, rse: 3.768, pcc: -0.043, kcc: -0.022
+2025-07-10 15:15:41: Epoch: 041 Train Loss: 0.557, mae: 0.555, rse: 0.230, pcc: 0.878, kcc: 0.671
+2025-07-10 15:15:41: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:15:41: Epoch: 041 Test results, ap: mae: 1.166, rse: 3.735, pcc: -0.030, kcc: -0.013
+2025-07-10 15:15:41: Epoch: 042 Train Loss: 0.541, mae: 0.546, rse: 0.227, pcc: 0.880, kcc: 0.672
+2025-07-10 15:15:41: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:15:41: Epoch: 042 Test results, ap: mae: 1.183, rse: 3.838, pcc: -0.027, kcc: -0.012
+2025-07-10 15:15:42: Epoch: 043 Train Loss: 0.553, mae: 0.549, rse: 0.229, pcc: 0.878, kcc: 0.672
+2025-07-10 15:15:42: Epoch: 043 Test results, ap: mae: 1.198, rse: 3.915, pcc: -0.045, kcc: -0.024
+2025-07-10 15:15:43: Epoch: 044 Train Loss: 0.542, mae: 0.547, rse: 0.229, pcc: 0.881, kcc: 0.672
+2025-07-10 15:15:43: Epoch: 044 Test results, ap: mae: 1.247, rse: 4.299, pcc: -0.032, kcc: -0.014
+2025-07-10 15:15:43: Epoch: 045 Train Loss: 0.527, mae: 0.540, rse: 0.225, pcc: 0.882, kcc: 0.677
+2025-07-10 15:15:43: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:15:44: Epoch: 045 Test results, ap: mae: 1.226, rse: 4.090, pcc: -0.035, kcc: -0.013
+2025-07-10 15:15:44: Epoch: 046 Train Loss: 0.532, mae: 0.543, rse: 0.228, pcc: 0.880, kcc: 0.677
+2025-07-10 15:15:44: Epoch: 046 Test results, ap: mae: 1.272, rse: 4.340, pcc: -0.055, kcc: -0.029
+2025-07-10 15:15:45: Epoch: 047 Train Loss: 0.538, mae: 0.545, rse: 0.224, pcc: 0.882, kcc: 0.676
+2025-07-10 15:15:45: Epoch: 047 Test results, ap: mae: 1.249, rse: 4.222, pcc: -0.054, kcc: -0.033
+2025-07-10 15:15:46: Epoch: 048 Train Loss: 0.534, mae: 0.546, rse: 0.226, pcc: 0.881, kcc: 0.676
+2025-07-10 15:15:46: Epoch: 048 Test results, ap: mae: 1.252, rse: 4.228, pcc: -0.044, kcc: -0.024
+2025-07-10 15:15:46: Epoch: 049 Train Loss: 0.518, mae: 0.543, rse: 0.224, pcc: 0.882, kcc: 0.675
+2025-07-10 15:15:47: Epoch: 049 Test results, ap: mae: 1.249, rse: 4.251, pcc: -0.043, kcc: -0.025
+2025-07-10 15:15:47: Epoch: 050 Train Loss: 0.530, mae: 0.547, rse: 0.230, pcc: 0.880, kcc: 0.675
+2025-07-10 15:15:47: Epoch: 050 Test results, ap: mae: 1.308, rse: 4.672, pcc: -0.039, kcc: -0.021
+2025-07-10 15:15:47: used time 0.01h
+2025-07-10 15:15:47: Running Cross Validation 1
+2025-07-10 15:15:47: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 15:15:48: Epoch: 001 Train Loss: 2.536, mae: 1.142, rse: 0.842, pcc: 0.505, kcc: 0.331
+2025-07-10 15:15:48: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:15:48: Epoch: 001 Test results, ap: mae: 0.672, rse: 1.347, pcc: -0.128, kcc: -0.080
+2025-07-10 15:15:49: Epoch: 002 Train Loss: 1.901, mae: 1.022, rse: 0.660, pcc: 0.609, kcc: 0.429
+2025-07-10 15:15:49: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:15:49: Epoch: 002 Test results, ap: mae: 0.687, rse: 1.388, pcc: -0.043, kcc: -0.039
+2025-07-10 15:15:49: Epoch: 003 Train Loss: 1.633, mae: 0.868, rse: 0.496, pcc: 0.712, kcc: 0.499
+2025-07-10 15:15:49: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:15:50: Epoch: 003 Test results, ap: mae: 0.904, rse: 2.367, pcc: -0.049, kcc: -0.038
+2025-07-10 15:15:50: Epoch: 004 Train Loss: 1.412, mae: 0.836, rse: 0.470, pcc: 0.734, kcc: 0.513
+2025-07-10 15:15:50: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:15:50: Epoch: 004 Test results, ap: mae: 0.947, rse: 2.498, pcc: -0.044, kcc: -0.018
+2025-07-10 15:15:51: Epoch: 005 Train Loss: 1.251, mae: 0.841, rse: 0.461, pcc: 0.748, kcc: 0.524
+2025-07-10 15:15:51: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:15:51: Epoch: 005 Test results, ap: mae: 1.417, rse: 5.149, pcc: -0.078, kcc: -0.062
+2025-07-10 15:15:52: Epoch: 006 Train Loss: 1.248, mae: 0.760, rse: 0.385, pcc: 0.787, kcc: 0.578
+2025-07-10 15:15:52: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:15:52: Epoch: 006 Test results, ap: mae: 1.107, rse: 3.383, pcc: -0.045, kcc: -0.030
+2025-07-10 15:15:52: Epoch: 007 Train Loss: 1.115, mae: 0.751, rse: 0.381, pcc: 0.789, kcc: 0.580
+2025-07-10 15:15:52: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:15:53: Epoch: 007 Test results, ap: mae: 1.239, rse: 4.776, pcc: -0.046, kcc: -0.002
+2025-07-10 15:15:53: Epoch: 008 Train Loss: 1.056, mae: 0.743, rse: 0.364, pcc: 0.811, kcc: 0.583
+2025-07-10 15:15:53: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:15:53: Epoch: 008 Test results, ap: mae: 1.108, rse: 3.425, pcc: -0.097, kcc: -0.061
+2025-07-10 15:15:54: Epoch: 009 Train Loss: 0.976, mae: 0.741, rse: 0.384, pcc: 0.785, kcc: 0.577
+2025-07-10 15:15:54: Epoch: 009 Test results, ap: mae: 1.088, rse: 3.335, pcc: -0.160, kcc: -0.105
+2025-07-10 15:15:55: Epoch: 010 Train Loss: 0.977, mae: 0.728, rse: 0.339, pcc: 0.817, kcc: 0.598
+2025-07-10 15:15:55: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:15:55: Epoch: 010 Test results, ap: mae: 1.351, rse: 5.374, pcc: -0.176, kcc: -0.115
+2025-07-10 15:15:55: Epoch: 011 Train Loss: 0.848, mae: 0.667, rse: 0.292, pcc: 0.845, kcc: 0.626
+2025-07-10 15:15:55: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:15:56: Epoch: 011 Test results, ap: mae: 1.113, rse: 3.481, pcc: -0.180, kcc: -0.120
+2025-07-10 15:15:56: Epoch: 011 New best TEST metrics
+2025-07-10 15:15:56: Epoch: 012 Train Loss: 0.785, mae: 0.620, rse: 0.261, pcc: 0.861, kcc: 0.652
+2025-07-10 15:15:56: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:15:56: Epoch: 012 Test results, ap: mae: 1.195, rse: 3.868, pcc: -0.189, kcc: -0.127
+2025-07-10 15:15:57: Epoch: 013 Train Loss: 0.753, mae: 0.605, rse: 0.252, pcc: 0.868, kcc: 0.661
+2025-07-10 15:15:57: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:15:57: Epoch: 013 Test results, ap: mae: 1.124, rse: 3.307, pcc: -0.166, kcc: -0.124
+2025-07-10 15:15:57: Epoch: 013 New best TEST metrics
+2025-07-10 15:15:58: Epoch: 014 Train Loss: 0.775, mae: 0.599, rse: 0.247, pcc: 0.870, kcc: 0.666
+2025-07-10 15:15:58: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:15:58: Epoch: 014 Test results, ap: mae: 1.170, rse: 3.646, pcc: -0.206, kcc: -0.135
+2025-07-10 15:15:58: Epoch: 015 Train Loss: 0.702, mae: 0.601, rse: 0.250, pcc: 0.866, kcc: 0.663
+2025-07-10 15:15:59: Epoch: 015 Test results, ap: mae: 1.107, rse: 3.241, pcc: -0.117, kcc: -0.071
+2025-07-10 15:15:59: Epoch: 015 New best TEST metrics
+2025-07-10 15:15:59: Epoch: 016 Train Loss: 0.682, mae: 0.612, rse: 0.253, pcc: 0.866, kcc: 0.652
+2025-07-10 15:15:59: Epoch: 016 Test results, ap: mae: 1.035, rse: 3.095, pcc: -0.084, kcc: -0.037
+2025-07-10 15:15:59: Epoch: 016 New best TEST metrics
+2025-07-10 15:16:00: Epoch: 017 Train Loss: 0.689, mae: 0.599, rse: 0.248, pcc: 0.868, kcc: 0.657
+2025-07-10 15:16:00: Epoch: 017 Test results, ap: mae: 1.137, rse: 3.759, pcc: -0.069, kcc: -0.038
+2025-07-10 15:16:01: Epoch: 018 Train Loss: 0.626, mae: 0.657, rse: 0.301, pcc: 0.846, kcc: 0.645
+2025-07-10 15:16:01: Epoch: 018 Test results, ap: mae: 0.961, rse: 2.642, pcc: -0.112, kcc: -0.052
+2025-07-10 15:16:01: Epoch: 018 New best TEST metrics
+2025-07-10 15:16:01: Epoch: 019 Train Loss: 0.618, mae: 0.572, rse: 0.226, pcc: 0.885, kcc: 0.685
+2025-07-10 15:16:01: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:16:02: Epoch: 019 Test results, ap: mae: 1.249, rse: 4.333, pcc: -0.139, kcc: -0.094
+2025-07-10 15:16:02: Epoch: 020 Train Loss: 0.548, mae: 0.562, rse: 0.218, pcc: 0.886, kcc: 0.683
+2025-07-10 15:16:02: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:16:02: Epoch: 020 Test results, ap: mae: 1.194, rse: 3.966, pcc: -0.158, kcc: -0.097
+2025-07-10 15:16:03: Epoch: 021 Train Loss: 0.512, mae: 0.529, rse: 0.204, pcc: 0.892, kcc: 0.695
+2025-07-10 15:16:03: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:16:03: Epoch: 021 Test results, ap: mae: 1.115, rse: 3.499, pcc: -0.146, kcc: -0.092
+2025-07-10 15:16:04: Epoch: 022 Train Loss: 0.491, mae: 0.524, rse: 0.197, pcc: 0.896, kcc: 0.703
+2025-07-10 15:16:04: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:16:04: Epoch: 022 Test results, ap: mae: 1.142, rse: 3.752, pcc: -0.154, kcc: -0.087
+2025-07-10 15:16:04: Epoch: 023 Train Loss: 0.468, mae: 0.524, rse: 0.196, pcc: 0.897, kcc: 0.703
+2025-07-10 15:16:04: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:16:05: Epoch: 023 Test results, ap: mae: 1.092, rse: 3.399, pcc: -0.128, kcc: -0.066
+2025-07-10 15:16:05: Epoch: 024 Train Loss: 0.454, mae: 0.522, rse: 0.192, pcc: 0.899, kcc: 0.707
+2025-07-10 15:16:05: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:16:05: Epoch: 024 Test results, ap: mae: 1.156, rse: 3.762, pcc: -0.135, kcc: -0.076
+2025-07-10 15:16:06: Epoch: 025 Train Loss: 0.445, mae: 0.541, rse: 0.205, pcc: 0.892, kcc: 0.690
+2025-07-10 15:16:06: Epoch: 025 Test results, ap: mae: 1.067, rse: 3.329, pcc: -0.082, kcc: -0.039
+2025-07-10 15:16:07: Epoch: 026 Train Loss: 0.458, mae: 0.519, rse: 0.193, pcc: 0.899, kcc: 0.708
+2025-07-10 15:16:07: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:16:07: Epoch: 026 Test results, ap: mae: 1.175, rse: 3.882, pcc: -0.097, kcc: -0.062
+2025-07-10 15:16:07: Epoch: 027 Train Loss: 0.415, mae: 0.506, rse: 0.187, pcc: 0.903, kcc: 0.715
+2025-07-10 15:16:07: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:16:08: Epoch: 027 Test results, ap: mae: 1.115, rse: 3.581, pcc: -0.122, kcc: -0.068
+2025-07-10 15:16:08: Epoch: 028 Train Loss: 0.446, mae: 0.536, rse: 0.203, pcc: 0.895, kcc: 0.695
+2025-07-10 15:16:08: Epoch: 028 Test results, ap: mae: 1.066, rse: 3.320, pcc: -0.099, kcc: -0.053
+2025-07-10 15:16:09: Epoch: 029 Train Loss: 0.426, mae: 0.529, rse: 0.207, pcc: 0.893, kcc: 0.701
+2025-07-10 15:16:09: Epoch: 029 Test results, ap: mae: 1.176, rse: 4.018, pcc: -0.079, kcc: -0.045
+2025-07-10 15:16:10: Epoch: 030 Train Loss: 0.404, mae: 0.496, rse: 0.185, pcc: 0.904, kcc: 0.717
+2025-07-10 15:16:10: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:16:10: Epoch: 030 Test results, ap: mae: 1.158, rse: 4.015, pcc: -0.074, kcc: -0.038
+2025-07-10 15:16:11: Epoch: 031 Train Loss: 0.403, mae: 0.486, rse: 0.178, pcc: 0.907, kcc: 0.719
+2025-07-10 15:16:11: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:16:11: Epoch: 031 Test results, ap: mae: 1.158, rse: 3.927, pcc: -0.117, kcc: -0.073
+2025-07-10 15:16:11: Epoch: 032 Train Loss: 0.399, mae: 0.485, rse: 0.178, pcc: 0.907, kcc: 0.719
+2025-07-10 15:16:11: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:16:11: Epoch: 032 Test results, ap: mae: 1.159, rse: 3.935, pcc: -0.123, kcc: -0.075
+2025-07-10 15:16:12: Epoch: 033 Train Loss: 0.379, mae: 0.492, rse: 0.182, pcc: 0.905, kcc: 0.715
+2025-07-10 15:16:12: Epoch: 033 Test results, ap: mae: 1.169, rse: 4.052, pcc: -0.097, kcc: -0.049
+2025-07-10 15:16:13: Epoch: 034 Train Loss: 0.378, mae: 0.473, rse: 0.171, pcc: 0.911, kcc: 0.727
+2025-07-10 15:16:13: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:16:13: Epoch: 034 Test results, ap: mae: 1.169, rse: 4.051, pcc: -0.104, kcc: -0.059
+2025-07-10 15:16:14: Epoch: 035 Train Loss: 0.371, mae: 0.468, rse: 0.172, pcc: 0.910, kcc: 0.725
+2025-07-10 15:16:14: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:16:14: Epoch: 035 Test results, ap: mae: 1.160, rse: 3.990, pcc: -0.116, kcc: -0.070
+2025-07-10 15:16:14: Epoch: 036 Train Loss: 0.378, mae: 0.476, rse: 0.177, pcc: 0.908, kcc: 0.721
+2025-07-10 15:16:14: Epoch: 036 Test results, ap: mae: 1.227, rse: 4.394, pcc: -0.104, kcc: -0.056
+2025-07-10 15:16:15: Epoch: 037 Train Loss: 0.359, mae: 0.480, rse: 0.177, pcc: 0.907, kcc: 0.724
+2025-07-10 15:16:15: Epoch: 037 Test results, ap: mae: 1.190, rse: 4.140, pcc: -0.133, kcc: -0.079
+2025-07-10 15:16:16: Epoch: 038 Train Loss: 0.372, mae: 0.476, rse: 0.174, pcc: 0.909, kcc: 0.723
+2025-07-10 15:16:16: Epoch: 038 Test results, ap: mae: 1.163, rse: 4.029, pcc: -0.112, kcc: -0.063
+2025-07-10 15:16:17: Epoch: 039 Train Loss: 0.350, mae: 0.468, rse: 0.173, pcc: 0.910, kcc: 0.726
+2025-07-10 15:16:17: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:16:17: Epoch: 039 Test results, ap: mae: 1.157, rse: 3.906, pcc: -0.113, kcc: -0.069
+2025-07-10 15:16:17: Epoch: 040 Train Loss: 0.351, mae: 0.472, rse: 0.169, pcc: 0.912, kcc: 0.726
+2025-07-10 15:16:17: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:16:18: Epoch: 040 Test results, ap: mae: 1.153, rse: 3.969, pcc: -0.118, kcc: -0.068
+2025-07-10 15:16:18: Epoch: 041 Train Loss: 0.353, mae: 0.463, rse: 0.168, pcc: 0.913, kcc: 0.730
+2025-07-10 15:16:18: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:16:18: Epoch: 041 Test results, ap: mae: 1.169, rse: 4.030, pcc: -0.126, kcc: -0.076
+2025-07-10 15:16:19: Epoch: 042 Train Loss: 0.350, mae: 0.463, rse: 0.169, pcc: 0.912, kcc: 0.728
+2025-07-10 15:16:19: Epoch: 042 Test results, ap: mae: 1.161, rse: 3.991, pcc: -0.123, kcc: -0.075
+2025-07-10 15:16:20: Epoch: 043 Train Loss: 0.343, mae: 0.457, rse: 0.165, pcc: 0.914, kcc: 0.729
+2025-07-10 15:16:20: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:16:20: Epoch: 043 Test results, ap: mae: 1.158, rse: 3.979, pcc: -0.115, kcc: -0.070
+2025-07-10 15:16:20: Epoch: 044 Train Loss: 0.352, mae: 0.460, rse: 0.168, pcc: 0.913, kcc: 0.730
+2025-07-10 15:16:21: Epoch: 044 Test results, ap: mae: 1.161, rse: 3.987, pcc: -0.122, kcc: -0.073
+2025-07-10 15:16:21: Epoch: 045 Train Loss: 0.331, mae: 0.461, rse: 0.168, pcc: 0.913, kcc: 0.730
+2025-07-10 15:16:21: Epoch: 045 Test results, ap: mae: 1.171, rse: 4.094, pcc: -0.117, kcc: -0.064
+2025-07-10 15:16:22: Epoch: 046 Train Loss: 0.330, mae: 0.458, rse: 0.167, pcc: 0.913, kcc: 0.729
+2025-07-10 15:16:22: Epoch: 046 Test results, ap: mae: 1.180, rse: 4.150, pcc: -0.127, kcc: -0.070
+2025-07-10 15:16:23: Epoch: 047 Train Loss: 0.335, mae: 0.457, rse: 0.165, pcc: 0.914, kcc: 0.731
+2025-07-10 15:16:23: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:16:23: Epoch: 047 Test results, ap: mae: 1.178, rse: 4.179, pcc: -0.124, kcc: -0.067
+2025-07-10 15:16:23: Epoch: 048 Train Loss: 0.334, mae: 0.465, rse: 0.168, pcc: 0.913, kcc: 0.726
+2025-07-10 15:16:23: Epoch: 048 Test results, ap: mae: 1.155, rse: 4.026, pcc: -0.123, kcc: -0.061
+2025-07-10 15:16:24: Epoch: 049 Train Loss: 0.320, mae: 0.457, rse: 0.164, pcc: 0.914, kcc: 0.732
+2025-07-10 15:16:24: Epoch: 049 New best VALIDATION metrics
+2025-07-10 15:16:24: Epoch: 049 Test results, ap: mae: 1.188, rse: 4.253, pcc: -0.135, kcc: -0.073
+2025-07-10 15:16:25: Epoch: 050 Train Loss: 0.324, mae: 0.462, rse: 0.168, pcc: 0.913, kcc: 0.731
+2025-07-10 15:16:25: Epoch: 050 Test results, ap: mae: 1.163, rse: 3.983, pcc: -0.136, kcc: -0.076
+2025-07-10 15:16:25: used time 0.01h
+2025-07-10 15:16:25: Running Cross Validation 2
+2025-07-10 15:16:25: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:16:26: Epoch: 001 Train Loss: 2.738, mae: 1.119, rse: 0.812, pcc: 0.441, kcc: 0.273
+2025-07-10 15:16:26: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:16:26: Epoch: 001 Test results, ap: mae: 0.679, rse: 1.367, pcc: -0.107, kcc: -0.140
+2025-07-10 15:16:27: Epoch: 002 Train Loss: 2.473, mae: 1.123, rse: 0.812, pcc: 0.435, kcc: 0.299
+2025-07-10 15:16:27: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:16:27: Epoch: 002 Test results, ap: mae: 0.700, rse: 1.443, pcc: -0.025, kcc: -0.061
+2025-07-10 15:16:27: Epoch: 003 Train Loss: 2.406, mae: 1.136, rse: 0.835, pcc: 0.412, kcc: 0.265
+2025-07-10 15:16:27: Epoch: 003 Test results, ap: mae: 0.656, rse: 1.272, pcc: -0.042, kcc: -0.076
+2025-07-10 15:16:28: Epoch: 004 Train Loss: 2.300, mae: 1.033, rse: 0.696, pcc: 0.552, kcc: 0.386
+2025-07-10 15:16:28: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:16:28: Epoch: 004 Test results, ap: mae: 0.775, rse: 1.822, pcc: -0.141, kcc: -0.104
+2025-07-10 15:16:29: Epoch: 005 Train Loss: 1.905, mae: 0.989, rse: 0.645, pcc: 0.598, kcc: 0.412
+2025-07-10 15:16:29: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:16:29: Epoch: 005 Test results, ap: mae: 0.825, rse: 2.100, pcc: -0.190, kcc: -0.138
+2025-07-10 15:16:30: Epoch: 006 Train Loss: 1.699, mae: 0.905, rse: 0.543, pcc: 0.678, kcc: 0.472
+2025-07-10 15:16:30: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:16:30: Epoch: 006 Test results, ap: mae: 0.833, rse: 2.098, pcc: -0.095, kcc: -0.071
+2025-07-10 15:16:30: Epoch: 007 Train Loss: 1.559, mae: 0.958, rse: 0.604, pcc: 0.643, kcc: 0.441
+2025-07-10 15:16:31: Epoch: 007 Test results, ap: mae: 0.821, rse: 1.953, pcc: -0.049, kcc: -0.077
+2025-07-10 15:16:31: Epoch: 008 Train Loss: 1.495, mae: 0.896, rse: 0.524, pcc: 0.691, kcc: 0.485
+2025-07-10 15:16:31: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:16:31: Epoch: 008 Test results, ap: mae: 0.749, rse: 1.683, pcc: 0.024, kcc: 0.000
+2025-07-10 15:16:32: Epoch: 009 Train Loss: 1.373, mae: 0.883, rse: 0.499, pcc: 0.714, kcc: 0.503
+2025-07-10 15:16:32: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:16:32: Epoch: 009 Test results, ap: mae: 0.731, rse: 1.570, pcc: 0.006, kcc: -0.018
+2025-07-10 15:16:33: Epoch: 010 Train Loss: 1.306, mae: 0.872, rse: 0.499, pcc: 0.710, kcc: 0.509
+2025-07-10 15:16:33: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:16:33: Epoch: 010 Test results, ap: mae: 0.727, rse: 1.550, pcc: 0.024, kcc: -0.004
+2025-07-10 15:16:33: Epoch: 011 Train Loss: 1.207, mae: 0.839, rse: 0.460, pcc: 0.737, kcc: 0.528
+2025-07-10 15:16:33: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:16:34: Epoch: 011 Test results, ap: mae: 0.691, rse: 1.421, pcc: 0.097, kcc: 0.039
+2025-07-10 15:16:34: Epoch: 011 New best TEST metrics
+2025-07-10 15:16:34: Epoch: 012 Train Loss: 1.150, mae: 0.831, rse: 0.451, pcc: 0.744, kcc: 0.537
+2025-07-10 15:16:34: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:16:34: Epoch: 012 Test results, ap: mae: 0.670, rse: 1.341, pcc: 0.161, kcc: 0.045
+2025-07-10 15:16:34: Epoch: 012 New best TEST metrics
+2025-07-10 15:16:35: Epoch: 013 Train Loss: 1.131, mae: 0.832, rse: 0.457, pcc: 0.739, kcc: 0.533
+2025-07-10 15:16:35: Epoch: 013 Test results, ap: mae: 0.669, rse: 1.356, pcc: 0.189, kcc: 0.017
+2025-07-10 15:16:36: Epoch: 014 Train Loss: 1.119, mae: 0.832, rse: 0.454, pcc: 0.744, kcc: 0.540
+2025-07-10 15:16:36: Epoch: 014 Test results, ap: mae: 0.715, rse: 1.525, pcc: 0.144, kcc: 0.035
+2025-07-10 15:16:36: Epoch: 015 Train Loss: 1.088, mae: 0.804, rse: 0.434, pcc: 0.754, kcc: 0.551
+2025-07-10 15:16:36: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:16:36: Epoch: 015 Test results, ap: mae: 0.702, rse: 1.442, pcc: 0.128, kcc: 0.011
+2025-07-10 15:16:37: Epoch: 016 Train Loss: 1.033, mae: 0.816, rse: 0.457, pcc: 0.740, kcc: 0.546
+2025-07-10 15:16:37: Epoch: 016 Test results, ap: mae: 0.698, rse: 1.409, pcc: 0.162, kcc: 0.050
+2025-07-10 15:16:38: Epoch: 017 Train Loss: 1.035, mae: 0.830, rse: 0.472, pcc: 0.736, kcc: 0.546
+2025-07-10 15:16:38: Epoch: 017 Test results, ap: mae: 0.725, rse: 1.486, pcc: 0.155, kcc: 0.039
+2025-07-10 15:16:38: Epoch: 018 Train Loss: 1.032, mae: 0.807, rse: 0.460, pcc: 0.740, kcc: 0.542
+2025-07-10 15:16:38: Epoch: 018 Test results, ap: mae: 0.726, rse: 1.485, pcc: 0.192, kcc: 0.063
+2025-07-10 15:16:39: Epoch: 019 Train Loss: 0.989, mae: 0.807, rse: 0.451, pcc: 0.751, kcc: 0.555
+2025-07-10 15:16:39: Epoch: 019 Test results, ap: mae: 0.733, rse: 1.536, pcc: 0.163, kcc: 0.045
+2025-07-10 15:16:39: Epoch: 020 Train Loss: 0.972, mae: 0.793, rse: 0.440, pcc: 0.756, kcc: 0.563
+2025-07-10 15:16:39: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:16:40: Epoch: 020 Test results, ap: mae: 0.750, rse: 1.613, pcc: 0.194, kcc: 0.060
+2025-07-10 15:16:40: Epoch: 021 Train Loss: 0.922, mae: 0.770, rse: 0.423, pcc: 0.761, kcc: 0.574
+2025-07-10 15:16:40: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:16:40: Epoch: 021 Test results, ap: mae: 0.717, rse: 1.553, pcc: 0.154, kcc: 0.044
+2025-07-10 15:16:41: Epoch: 022 Train Loss: 0.920, mae: 0.775, rse: 0.431, pcc: 0.762, kcc: 0.573
+2025-07-10 15:16:41: Epoch: 022 Test results, ap: mae: 0.774, rse: 1.725, pcc: 0.172, kcc: 0.061
+2025-07-10 15:16:41: Epoch: 023 Train Loss: 0.897, mae: 0.761, rse: 0.416, pcc: 0.770, kcc: 0.578
+2025-07-10 15:16:41: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:16:42: Epoch: 023 Test results, ap: mae: 0.784, rse: 1.783, pcc: 0.215, kcc: 0.063
+2025-07-10 15:16:42: Epoch: 024 Train Loss: 0.887, mae: 0.759, rse: 0.420, pcc: 0.765, kcc: 0.577
+2025-07-10 15:16:42: Epoch: 024 Test results, ap: mae: 0.801, rse: 1.883, pcc: 0.226, kcc: 0.069
+2025-07-10 15:16:43: Epoch: 025 Train Loss: 0.879, mae: 0.766, rse: 0.425, pcc: 0.763, kcc: 0.578
+2025-07-10 15:16:43: Epoch: 025 Test results, ap: mae: 0.790, rse: 1.815, pcc: 0.218, kcc: 0.067
+2025-07-10 15:16:44: Epoch: 026 Train Loss: 0.879, mae: 0.744, rse: 0.405, pcc: 0.774, kcc: 0.584
+2025-07-10 15:16:44: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:16:44: Epoch: 026 Test results, ap: mae: 0.821, rse: 1.923, pcc: 0.220, kcc: 0.067
+2025-07-10 15:16:44: Epoch: 027 Train Loss: 0.845, mae: 0.756, rse: 0.414, pcc: 0.769, kcc: 0.581
+2025-07-10 15:16:44: Epoch: 027 Test results, ap: mae: 0.824, rse: 1.985, pcc: 0.236, kcc: 0.071
+2025-07-10 15:16:45: Epoch: 028 Train Loss: 0.844, mae: 0.745, rse: 0.408, pcc: 0.775, kcc: 0.585
+2025-07-10 15:16:45: Epoch: 028 Test results, ap: mae: 0.823, rse: 1.930, pcc: 0.250, kcc: 0.077
+2025-07-10 15:16:46: Epoch: 029 Train Loss: 0.837, mae: 0.752, rse: 0.424, pcc: 0.770, kcc: 0.588
+2025-07-10 15:16:46: Epoch: 029 Test results, ap: mae: 0.824, rse: 1.953, pcc: 0.243, kcc: 0.077
+2025-07-10 15:16:46: Epoch: 030 Train Loss: 0.812, mae: 0.759, rse: 0.426, pcc: 0.770, kcc: 0.583
+2025-07-10 15:16:46: Epoch: 030 Test results, ap: mae: 0.874, rse: 2.251, pcc: 0.246, kcc: 0.094
+2025-07-10 15:16:47: Epoch: 031 Train Loss: 0.789, mae: 0.753, rse: 0.421, pcc: 0.764, kcc: 0.583
+2025-07-10 15:16:47: Epoch: 031 Test results, ap: mae: 0.844, rse: 2.096, pcc: 0.245, kcc: 0.105
+2025-07-10 15:16:48: Epoch: 032 Train Loss: 0.781, mae: 0.729, rse: 0.404, pcc: 0.777, kcc: 0.594
+2025-07-10 15:16:48: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:16:48: Epoch: 032 Test results, ap: mae: 0.844, rse: 2.078, pcc: 0.253, kcc: 0.086
+2025-07-10 15:16:48: Epoch: 033 Train Loss: 0.796, mae: 0.730, rse: 0.401, pcc: 0.777, kcc: 0.596
+2025-07-10 15:16:48: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:16:49: Epoch: 033 Test results, ap: mae: 0.839, rse: 2.049, pcc: 0.252, kcc: 0.090
+2025-07-10 15:16:49: Epoch: 034 Train Loss: 0.773, mae: 0.734, rse: 0.403, pcc: 0.778, kcc: 0.597
+2025-07-10 15:16:49: Epoch: 034 Test results, ap: mae: 0.831, rse: 1.972, pcc: 0.275, kcc: 0.098
+2025-07-10 15:16:50: Epoch: 035 Train Loss: 0.764, mae: 0.720, rse: 0.394, pcc: 0.781, kcc: 0.601
+2025-07-10 15:16:50: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:16:50: Epoch: 035 Test results, ap: mae: 0.839, rse: 2.034, pcc: 0.256, kcc: 0.095
+2025-07-10 15:16:50: Epoch: 036 Train Loss: 0.754, mae: 0.719, rse: 0.397, pcc: 0.781, kcc: 0.600
+2025-07-10 15:16:51: Epoch: 036 Test results, ap: mae: 0.853, rse: 2.089, pcc: 0.258, kcc: 0.103
+2025-07-10 15:16:51: Epoch: 037 Train Loss: 0.757, mae: 0.735, rse: 0.410, pcc: 0.776, kcc: 0.597
+2025-07-10 15:16:51: Epoch: 037 Test results, ap: mae: 0.857, rse: 2.089, pcc: 0.258, kcc: 0.101
+2025-07-10 15:16:52: Epoch: 038 Train Loss: 0.732, mae: 0.711, rse: 0.394, pcc: 0.782, kcc: 0.605
+2025-07-10 15:16:52: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:16:52: Epoch: 038 Test results, ap: mae: 0.858, rse: 2.120, pcc: 0.263, kcc: 0.110
+2025-07-10 15:16:53: Epoch: 039 Train Loss: 0.745, mae: 0.745, rse: 0.422, pcc: 0.763, kcc: 0.597
+2025-07-10 15:16:53: Epoch: 039 Test results, ap: mae: 0.821, rse: 1.931, pcc: 0.292, kcc: 0.107
+2025-07-10 15:16:53: Epoch: 040 Train Loss: 0.739, mae: 0.720, rse: 0.409, pcc: 0.777, kcc: 0.603
+2025-07-10 15:16:53: Epoch: 040 Test results, ap: mae: 0.930, rse: 2.504, pcc: 0.259, kcc: 0.099
+2025-07-10 15:16:54: Epoch: 041 Train Loss: 0.708, mae: 0.711, rse: 0.395, pcc: 0.783, kcc: 0.608
+2025-07-10 15:16:54: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:16:54: Epoch: 041 Test results, ap: mae: 0.918, rse: 2.466, pcc: 0.284, kcc: 0.108
+2025-07-10 15:16:55: Epoch: 042 Train Loss: 0.701, mae: 0.712, rse: 0.402, pcc: 0.780, kcc: 0.607
+2025-07-10 15:16:55: Epoch: 042 Test results, ap: mae: 0.908, rse: 2.394, pcc: 0.282, kcc: 0.115
+2025-07-10 15:16:55: Epoch: 043 Train Loss: 0.690, mae: 0.706, rse: 0.392, pcc: 0.784, kcc: 0.611
+2025-07-10 15:16:55: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:16:55: Epoch: 043 Test results, ap: mae: 0.903, rse: 2.376, pcc: 0.281, kcc: 0.112
+2025-07-10 15:16:56: Epoch: 044 Train Loss: 0.692, mae: 0.714, rse: 0.400, pcc: 0.781, kcc: 0.610
+2025-07-10 15:16:56: Epoch: 044 Test results, ap: mae: 0.952, rse: 2.656, pcc: 0.287, kcc: 0.104
+2025-07-10 15:16:56: Epoch: 045 Train Loss: 0.682, mae: 0.700, rse: 0.394, pcc: 0.786, kcc: 0.613
+2025-07-10 15:16:56: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:16:56: Epoch: 045 Test results, ap: mae: 0.931, rse: 2.530, pcc: 0.279, kcc: 0.110
+2025-07-10 15:16:57: Epoch: 046 Train Loss: 0.706, mae: 0.706, rse: 0.392, pcc: 0.787, kcc: 0.613
+2025-07-10 15:16:57: Epoch: 046 Test results, ap: mae: 0.933, rse: 2.557, pcc: 0.285, kcc: 0.105
+2025-07-10 15:16:57: Epoch: 047 Train Loss: 0.694, mae: 0.708, rse: 0.399, pcc: 0.781, kcc: 0.611
+2025-07-10 15:16:58: Epoch: 047 Test results, ap: mae: 0.933, rse: 2.534, pcc: 0.272, kcc: 0.101
+2025-07-10 15:16:58: Epoch: 048 Train Loss: 0.689, mae: 0.702, rse: 0.394, pcc: 0.785, kcc: 0.614
+2025-07-10 15:16:58: Epoch: 048 Test results, ap: mae: 0.959, rse: 2.716, pcc: 0.284, kcc: 0.105
+2025-07-10 15:16:59: Epoch: 049 Train Loss: 0.670, mae: 0.706, rse: 0.402, pcc: 0.782, kcc: 0.614
+2025-07-10 15:16:59: Epoch: 049 Test results, ap: mae: 0.989, rse: 2.953, pcc: 0.295, kcc: 0.107
+2025-07-10 15:16:59: Epoch: 050 Train Loss: 0.690, mae: 0.695, rse: 0.390, pcc: 0.787, kcc: 0.618
+2025-07-10 15:16:59: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:16:59: Epoch: 050 Test results, ap: mae: 0.960, rse: 2.742, pcc: 0.289, kcc: 0.111
+2025-07-10 15:16:59: used time 0.01h
+2025-07-10 15:16:59: Running Cross Validation 3
+2025-07-10 15:16:59: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:17:00: Epoch: 001 Train Loss: 2.625, mae: 1.136, rse: 0.794, pcc: 0.457, kcc: 0.342
+2025-07-10 15:17:00: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:17:00: Epoch: 001 Test results, ap: mae: 0.943, rse: 2.696, pcc: -0.214, kcc: -0.130
+2025-07-10 15:17:01: Epoch: 002 Train Loss: 2.165, mae: 1.030, rse: 0.631, pcc: 0.615, kcc: 0.439
+2025-07-10 15:17:01: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:17:01: Epoch: 002 Test results, ap: mae: 0.922, rse: 2.293, pcc: -0.186, kcc: -0.123
+2025-07-10 15:17:02: Epoch: 003 Train Loss: 1.732, mae: 0.956, rse: 0.533, pcc: 0.689, kcc: 0.491
+2025-07-10 15:17:02: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:17:02: Epoch: 003 Test results, ap: mae: 1.030, rse: 2.985, pcc: -0.265, kcc: -0.161
+2025-07-10 15:17:03: Epoch: 004 Train Loss: 1.479, mae: 0.905, rse: 0.491, pcc: 0.737, kcc: 0.527
+2025-07-10 15:17:03: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:17:03: Epoch: 004 Test results, ap: mae: 0.915, rse: 2.536, pcc: -0.252, kcc: -0.168
+2025-07-10 15:17:04: Epoch: 005 Train Loss: 1.320, mae: 0.849, rse: 0.422, pcc: 0.763, kcc: 0.562
+2025-07-10 15:17:04: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:17:04: Epoch: 005 Test results, ap: mae: 1.039, rse: 3.115, pcc: -0.198, kcc: -0.120
+2025-07-10 15:17:05: Epoch: 006 Train Loss: 1.257, mae: 0.844, rse: 0.425, pcc: 0.777, kcc: 0.570
+2025-07-10 15:17:05: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:17:05: Epoch: 006 Test results, ap: mae: 0.819, rse: 1.926, pcc: -0.125, kcc: -0.086
+2025-07-10 15:17:05: Epoch: 007 Train Loss: 1.115, mae: 0.774, rse: 0.375, pcc: 0.794, kcc: 0.592
+2025-07-10 15:17:05: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:17:06: Epoch: 007 Test results, ap: mae: 0.914, rse: 2.317, pcc: 0.079, kcc: 0.045
+2025-07-10 15:17:06: Epoch: 008 Train Loss: 1.092, mae: 0.770, rse: 0.383, pcc: 0.790, kcc: 0.584
+2025-07-10 15:17:06: Epoch: 008 Test results, ap: mae: 0.902, rse: 2.296, pcc: 0.059, kcc: 0.018
+2025-07-10 15:17:07: Epoch: 009 Train Loss: 1.019, mae: 0.740, rse: 0.348, pcc: 0.808, kcc: 0.609
+2025-07-10 15:17:07: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:17:07: Epoch: 009 Test results, ap: mae: 0.778, rse: 1.908, pcc: 0.180, kcc: 0.104
+2025-07-10 15:17:08: Epoch: 010 Train Loss: 0.990, mae: 0.770, rse: 0.368, pcc: 0.796, kcc: 0.589
+2025-07-10 15:17:08: Epoch: 010 Test results, ap: mae: 0.916, rse: 2.488, pcc: 0.051, kcc: 0.011
+2025-07-10 15:17:09: Epoch: 011 Train Loss: 0.898, mae: 0.728, rse: 0.342, pcc: 0.812, kcc: 0.612
+2025-07-10 15:17:09: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:17:09: Epoch: 011 Test results, ap: mae: 0.821, rse: 2.175, pcc: 0.124, kcc: 0.112
+2025-07-10 15:17:09: Epoch: 011 New best TEST metrics
+2025-07-10 15:17:10: Epoch: 012 Train Loss: 0.874, mae: 0.709, rse: 0.326, pcc: 0.821, kcc: 0.624
+2025-07-10 15:17:10: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:17:10: Epoch: 012 Test results, ap: mae: 0.825, rse: 2.351, pcc: 0.179, kcc: 0.148
+2025-07-10 15:17:11: Epoch: 013 Train Loss: 0.848, mae: 0.694, rse: 0.323, pcc: 0.823, kcc: 0.624
+2025-07-10 15:17:11: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:17:11: Epoch: 013 Test results, ap: mae: 0.829, rse: 2.302, pcc: 0.164, kcc: 0.124
+2025-07-10 15:17:12: Epoch: 014 Train Loss: 0.831, mae: 0.718, rse: 0.332, pcc: 0.818, kcc: 0.614
+2025-07-10 15:17:12: Epoch: 014 Test results, ap: mae: 0.831, rse: 2.349, pcc: 0.130, kcc: 0.125
+2025-07-10 15:17:13: Epoch: 015 Train Loss: 0.842, mae: 0.708, rse: 0.322, pcc: 0.824, kcc: 0.626
+2025-07-10 15:17:13: Epoch: 015 Test results, ap: mae: 0.808, rse: 2.173, pcc: 0.163, kcc: 0.145
+2025-07-10 15:17:13: Epoch: 015 New best TEST metrics
+2025-07-10 15:17:14: Epoch: 016 Train Loss: 0.827, mae: 0.740, rse: 0.349, pcc: 0.815, kcc: 0.613
+2025-07-10 15:17:14: Epoch: 016 Test results, ap: mae: 0.749, rse: 1.865, pcc: 0.193, kcc: 0.145
+2025-07-10 15:17:14: Epoch: 016 New best TEST metrics
+2025-07-10 15:17:15: Epoch: 017 Train Loss: 0.843, mae: 0.708, rse: 0.327, pcc: 0.825, kcc: 0.628
+2025-07-10 15:17:15: Epoch: 017 Test results, ap: mae: 0.770, rse: 1.969, pcc: 0.170, kcc: 0.138
+2025-07-10 15:17:15: Epoch: 018 Train Loss: 0.805, mae: 0.732, rse: 0.342, pcc: 0.819, kcc: 0.618
+2025-07-10 15:17:16: Epoch: 018 Test results, ap: mae: 0.747, rse: 1.820, pcc: 0.183, kcc: 0.133
+2025-07-10 15:17:16: Epoch: 018 New best TEST metrics
+2025-07-10 15:17:16: Epoch: 019 Train Loss: 0.774, mae: 0.690, rse: 0.316, pcc: 0.828, kcc: 0.631
+2025-07-10 15:17:16: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:17:16: Epoch: 019 Test results, ap: mae: 0.814, rse: 2.239, pcc: 0.177, kcc: 0.159
+2025-07-10 15:17:17: Epoch: 020 Train Loss: 0.775, mae: 0.739, rse: 0.343, pcc: 0.822, kcc: 0.624
+2025-07-10 15:17:17: Epoch: 020 Test results, ap: mae: 0.727, rse: 1.776, pcc: 0.170, kcc: 0.139
+2025-07-10 15:17:17: Epoch: 020 New best TEST metrics
+2025-07-10 15:17:18: Epoch: 021 Train Loss: 0.742, mae: 0.696, rse: 0.318, pcc: 0.827, kcc: 0.629
+2025-07-10 15:17:18: Epoch: 021 Test results, ap: mae: 0.786, rse: 2.112, pcc: 0.186, kcc: 0.147
+2025-07-10 15:17:19: Epoch: 022 Train Loss: 0.735, mae: 0.671, rse: 0.313, pcc: 0.829, kcc: 0.636
+2025-07-10 15:17:19: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:17:19: Epoch: 022 Test results, ap: mae: 0.814, rse: 2.262, pcc: 0.173, kcc: 0.133
+2025-07-10 15:17:20: Epoch: 023 Train Loss: 0.696, mae: 0.681, rse: 0.315, pcc: 0.829, kcc: 0.636
+2025-07-10 15:17:20: Epoch: 023 Test results, ap: mae: 0.788, rse: 2.082, pcc: 0.178, kcc: 0.141
+2025-07-10 15:17:21: Epoch: 024 Train Loss: 0.685, mae: 0.668, rse: 0.310, pcc: 0.835, kcc: 0.642
+2025-07-10 15:17:21: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:17:21: Epoch: 024 Test results, ap: mae: 0.861, rse: 2.499, pcc: 0.168, kcc: 0.137
+2025-07-10 15:17:22: Epoch: 025 Train Loss: 0.698, mae: 0.662, rse: 0.307, pcc: 0.834, kcc: 0.643
+2025-07-10 15:17:22: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:17:22: Epoch: 025 Test results, ap: mae: 0.843, rse: 2.378, pcc: 0.151, kcc: 0.135
+2025-07-10 15:17:23: Epoch: 026 Train Loss: 0.687, mae: 0.664, rse: 0.298, pcc: 0.838, kcc: 0.647
+2025-07-10 15:17:23: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:17:23: Epoch: 026 Test results, ap: mae: 0.857, rse: 2.437, pcc: 0.164, kcc: 0.129
+2025-07-10 15:17:24: Epoch: 027 Train Loss: 0.686, mae: 0.659, rse: 0.300, pcc: 0.838, kcc: 0.644
+2025-07-10 15:17:24: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:17:24: Epoch: 027 Test results, ap: mae: 0.835, rse: 2.331, pcc: 0.180, kcc: 0.168
+2025-07-10 15:17:25: Epoch: 028 Train Loss: 0.668, mae: 0.658, rse: 0.298, pcc: 0.838, kcc: 0.646
+2025-07-10 15:17:25: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:17:25: Epoch: 028 Test results, ap: mae: 0.859, rse: 2.401, pcc: 0.133, kcc: 0.126
+2025-07-10 15:17:26: Epoch: 029 Train Loss: 0.651, mae: 0.660, rse: 0.296, pcc: 0.840, kcc: 0.647
+2025-07-10 15:17:26: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:17:26: Epoch: 029 Test results, ap: mae: 0.850, rse: 2.339, pcc: 0.174, kcc: 0.141
+2025-07-10 15:17:26: Epoch: 030 Train Loss: 0.654, mae: 0.653, rse: 0.289, pcc: 0.845, kcc: 0.649
+2025-07-10 15:17:26: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:17:27: Epoch: 030 Test results, ap: mae: 0.885, rse: 2.506, pcc: 0.160, kcc: 0.148
+2025-07-10 15:17:27: Epoch: 031 Train Loss: 0.590, mae: 0.639, rse: 0.282, pcc: 0.847, kcc: 0.654
+2025-07-10 15:17:27: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:17:28: Epoch: 031 Test results, ap: mae: 0.873, rse: 2.442, pcc: 0.143, kcc: 0.134
+2025-07-10 15:17:28: Epoch: 032 Train Loss: 0.586, mae: 0.645, rse: 0.285, pcc: 0.845, kcc: 0.649
+2025-07-10 15:17:29: Epoch: 032 Test results, ap: mae: 0.856, rse: 2.357, pcc: 0.137, kcc: 0.122
+2025-07-10 15:17:29: Epoch: 033 Train Loss: 0.584, mae: 0.629, rse: 0.277, pcc: 0.851, kcc: 0.660
+2025-07-10 15:17:29: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:17:29: Epoch: 033 Test results, ap: mae: 0.885, rse: 2.559, pcc: 0.151, kcc: 0.134
+2025-07-10 15:17:30: Epoch: 034 Train Loss: 0.568, mae: 0.629, rse: 0.275, pcc: 0.853, kcc: 0.659
+2025-07-10 15:17:30: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:17:30: Epoch: 034 Test results, ap: mae: 0.902, rse: 2.651, pcc: 0.143, kcc: 0.132
+2025-07-10 15:17:31: Epoch: 035 Train Loss: 0.560, mae: 0.629, rse: 0.272, pcc: 0.853, kcc: 0.660
+2025-07-10 15:17:31: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:17:31: Epoch: 035 Test results, ap: mae: 0.882, rse: 2.497, pcc: 0.148, kcc: 0.138
+2025-07-10 15:17:32: Epoch: 036 Train Loss: 0.562, mae: 0.630, rse: 0.267, pcc: 0.856, kcc: 0.662
+2025-07-10 15:17:32: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:17:32: Epoch: 036 Test results, ap: mae: 0.900, rse: 2.611, pcc: 0.154, kcc: 0.149
+2025-07-10 15:17:33: Epoch: 037 Train Loss: 0.557, mae: 0.640, rse: 0.273, pcc: 0.853, kcc: 0.658
+2025-07-10 15:17:33: Epoch: 037 Test results, ap: mae: 0.889, rse: 2.547, pcc: 0.179, kcc: 0.161
+2025-07-10 15:17:33: Epoch: 038 Train Loss: 0.542, mae: 0.617, rse: 0.264, pcc: 0.858, kcc: 0.663
+2025-07-10 15:17:33: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:17:34: Epoch: 038 Test results, ap: mae: 0.899, rse: 2.554, pcc: 0.133, kcc: 0.131
+2025-07-10 15:17:34: Epoch: 039 Train Loss: 0.532, mae: 0.631, rse: 0.264, pcc: 0.858, kcc: 0.664
+2025-07-10 15:17:34: Epoch: 039 Test results, ap: mae: 0.873, rse: 2.491, pcc: 0.162, kcc: 0.155
+2025-07-10 15:17:35: Epoch: 040 Train Loss: 0.527, mae: 0.614, rse: 0.257, pcc: 0.862, kcc: 0.670
+2025-07-10 15:17:35: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:17:35: Epoch: 040 Test results, ap: mae: 0.905, rse: 2.598, pcc: 0.143, kcc: 0.135
+2025-07-10 15:17:36: Epoch: 041 Train Loss: 0.505, mae: 0.612, rse: 0.254, pcc: 0.864, kcc: 0.669
+2025-07-10 15:17:36: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:17:36: Epoch: 041 Test results, ap: mae: 0.908, rse: 2.605, pcc: 0.156, kcc: 0.146
+2025-07-10 15:17:37: Epoch: 042 Train Loss: 0.502, mae: 0.594, rse: 0.249, pcc: 0.867, kcc: 0.676
+2025-07-10 15:17:37: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:17:37: Epoch: 042 Test results, ap: mae: 0.938, rse: 2.756, pcc: 0.135, kcc: 0.131
+2025-07-10 15:17:38: Epoch: 043 Train Loss: 0.490, mae: 0.594, rse: 0.247, pcc: 0.868, kcc: 0.678
+2025-07-10 15:17:38: Epoch: 043 New best VALIDATION metrics
+2025-07-10 15:17:38: Epoch: 043 Test results, ap: mae: 0.925, rse: 2.725, pcc: 0.139, kcc: 0.131
+2025-07-10 15:17:38: Epoch: 044 Train Loss: 0.481, mae: 0.592, rse: 0.247, pcc: 0.868, kcc: 0.679
+2025-07-10 15:17:38: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:17:38: Epoch: 044 Test results, ap: mae: 0.954, rse: 2.815, pcc: 0.130, kcc: 0.131
+2025-07-10 15:17:39: Epoch: 045 Train Loss: 0.469, mae: 0.595, rse: 0.250, pcc: 0.866, kcc: 0.675
+2025-07-10 15:17:39: Epoch: 045 Test results, ap: mae: 0.935, rse: 2.749, pcc: 0.114, kcc: 0.123
+2025-07-10 15:17:40: Epoch: 046 Train Loss: 0.481, mae: 0.594, rse: 0.246, pcc: 0.869, kcc: 0.678
+2025-07-10 15:17:40: Epoch: 046 Test results, ap: mae: 0.892, rse: 2.548, pcc: 0.141, kcc: 0.134
+2025-07-10 15:17:41: Epoch: 047 Train Loss: 0.486, mae: 0.592, rse: 0.243, pcc: 0.870, kcc: 0.681
+2025-07-10 15:17:41: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:17:41: Epoch: 047 Test results, ap: mae: 0.916, rse: 2.686, pcc: 0.144, kcc: 0.137
+2025-07-10 15:17:42: Epoch: 048 Train Loss: 0.482, mae: 0.586, rse: 0.243, pcc: 0.870, kcc: 0.680
+2025-07-10 15:17:42: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:17:42: Epoch: 048 Test results, ap: mae: 0.912, rse: 2.687, pcc: 0.139, kcc: 0.133
+2025-07-10 15:17:43: Epoch: 049 Train Loss: 0.465, mae: 0.581, rse: 0.238, pcc: 0.874, kcc: 0.684
+2025-07-10 15:17:43: Epoch: 049 New best VALIDATION metrics
+2025-07-10 15:17:43: Epoch: 049 Test results, ap: mae: 0.954, rse: 2.883, pcc: 0.131, kcc: 0.124
+2025-07-10 15:17:43: Epoch: 050 Train Loss: 0.480, mae: 0.589, rse: 0.240, pcc: 0.872, kcc: 0.682
+2025-07-10 15:17:44: Epoch: 050 Test results, ap: mae: 0.921, rse: 2.760, pcc: 0.137, kcc: 0.125
+2025-07-10 15:17:44: used time 0.01h
+2025-07-10 15:17:44: Running Cross Validation 4
+2025-07-10 15:17:44: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:17:44: Epoch: 001 Train Loss: 2.658, mae: 1.147, rse: 0.886, pcc: 0.369, kcc: 0.225
+2025-07-10 15:17:44: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:17:44: Epoch: 001 Test results, ap: mae: 1.021, rse: 3.102, pcc: -0.237, kcc: -0.154
+2025-07-10 15:17:45: Epoch: 002 Train Loss: 2.332, mae: 1.084, rse: 0.762, pcc: 0.491, kcc: 0.333
+2025-07-10 15:17:45: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:17:45: Epoch: 002 Test results, ap: mae: 0.877, rse: 2.243, pcc: -0.180, kcc: -0.111
+2025-07-10 15:17:46: Epoch: 003 Train Loss: 1.933, mae: 0.974, rse: 0.638, pcc: 0.603, kcc: 0.414
+2025-07-10 15:17:46: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:17:46: Epoch: 003 Test results, ap: mae: 0.998, rse: 2.802, pcc: -0.149, kcc: -0.109
+2025-07-10 15:17:47: Epoch: 004 Train Loss: 1.567, mae: 0.900, rse: 0.543, pcc: 0.678, kcc: 0.479
+2025-07-10 15:17:47: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:17:47: Epoch: 004 Test results, ap: mae: 1.096, rse: 3.148, pcc: -0.107, kcc: -0.075
+2025-07-10 15:17:47: Epoch: 005 Train Loss: 1.432, mae: 0.888, rse: 0.541, pcc: 0.685, kcc: 0.494
+2025-07-10 15:17:47: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:17:48: Epoch: 005 Test results, ap: mae: 1.202, rse: 3.739, pcc: -0.200, kcc: -0.129
+2025-07-10 15:17:48: Epoch: 006 Train Loss: 1.305, mae: 0.892, rse: 0.535, pcc: 0.707, kcc: 0.501
+2025-07-10 15:17:48: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:17:48: Epoch: 006 Test results, ap: mae: 0.937, rse: 2.273, pcc: -0.107, kcc: -0.068
+2025-07-10 15:17:49: Epoch: 007 Train Loss: 1.304, mae: 0.890, rse: 0.523, pcc: 0.713, kcc: 0.513
+2025-07-10 15:17:49: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:17:49: Epoch: 007 Test results, ap: mae: 1.413, rse: 5.133, pcc: -0.219, kcc: -0.146
+2025-07-10 15:17:50: Epoch: 008 Train Loss: 1.178, mae: 0.813, rse: 0.481, pcc: 0.738, kcc: 0.540
+2025-07-10 15:17:50: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:17:50: Epoch: 008 Test results, ap: mae: 0.986, rse: 2.633, pcc: -0.148, kcc: -0.078
+2025-07-10 15:17:51: Epoch: 009 Train Loss: 1.147, mae: 0.828, rse: 0.451, pcc: 0.742, kcc: 0.547
+2025-07-10 15:17:51: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:17:51: Epoch: 009 Test results, ap: mae: 1.022, rse: 2.697, pcc: -0.063, kcc: -0.037
+2025-07-10 15:17:51: Epoch: 010 Train Loss: 1.125, mae: 0.780, rse: 0.421, pcc: 0.764, kcc: 0.564
+2025-07-10 15:17:51: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:17:52: Epoch: 010 Test results, ap: mae: 1.037, rse: 2.776, pcc: -0.145, kcc: -0.103
+2025-07-10 15:17:52: Epoch: 011 Train Loss: 1.008, mae: 0.759, rse: 0.428, pcc: 0.758, kcc: 0.575
+2025-07-10 15:17:52: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:17:52: Epoch: 011 Test results, ap: mae: 1.048, rse: 3.053, pcc: -0.169, kcc: -0.097
+2025-07-10 15:17:52: Epoch: 011 New best TEST metrics
+2025-07-10 15:17:53: Epoch: 012 Train Loss: 0.988, mae: 0.745, rse: 0.404, pcc: 0.775, kcc: 0.579
+2025-07-10 15:17:53: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:17:53: Epoch: 012 Test results, ap: mae: 1.046, rse: 3.096, pcc: -0.108, kcc: -0.039
+2025-07-10 15:17:53: Epoch: 012 New best TEST metrics
+2025-07-10 15:17:54: Epoch: 013 Train Loss: 0.924, mae: 0.737, rse: 0.389, pcc: 0.785, kcc: 0.589
+2025-07-10 15:17:54: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:17:54: Epoch: 013 Test results, ap: mae: 1.076, rse: 3.324, pcc: -0.134, kcc: -0.074
+2025-07-10 15:17:55: Epoch: 014 Train Loss: 0.943, mae: 0.735, rse: 0.421, pcc: 0.767, kcc: 0.589
+2025-07-10 15:17:55: Epoch: 014 Test results, ap: mae: 1.035, rse: 3.003, pcc: -0.161, kcc: -0.090
+2025-07-10 15:17:55: Epoch: 014 New best TEST metrics
+2025-07-10 15:17:56: Epoch: 015 Train Loss: 0.900, mae: 0.734, rse: 0.387, pcc: 0.785, kcc: 0.573
+2025-07-10 15:17:56: Epoch: 015 Test results, ap: mae: 1.126, rse: 3.882, pcc: -0.197, kcc: -0.087
+2025-07-10 15:17:56: Epoch: 016 Train Loss: 0.892, mae: 0.707, rse: 0.372, pcc: 0.793, kcc: 0.598
+2025-07-10 15:17:56: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:17:57: Epoch: 016 Test results, ap: mae: 1.106, rse: 3.646, pcc: -0.150, kcc: -0.075
+2025-07-10 15:17:57: Epoch: 017 Train Loss: 0.854, mae: 0.700, rse: 0.370, pcc: 0.796, kcc: 0.598
+2025-07-10 15:17:57: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:17:57: Epoch: 017 Test results, ap: mae: 1.087, rse: 3.317, pcc: -0.176, kcc: -0.101
+2025-07-10 15:17:58: Epoch: 018 Train Loss: 0.800, mae: 0.677, rse: 0.355, pcc: 0.808, kcc: 0.610
+2025-07-10 15:17:58: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:17:58: Epoch: 018 Test results, ap: mae: 1.105, rse: 3.613, pcc: -0.267, kcc: -0.154
+2025-07-10 15:17:59: Epoch: 019 Train Loss: 0.778, mae: 0.685, rse: 0.368, pcc: 0.797, kcc: 0.598
+2025-07-10 15:17:59: Epoch: 019 Test results, ap: mae: 1.067, rse: 3.140, pcc: -0.130, kcc: -0.067
+2025-07-10 15:18:00: Epoch: 020 Train Loss: 0.779, mae: 0.679, rse: 0.351, pcc: 0.811, kcc: 0.604
+2025-07-10 15:18:00: Epoch: 020 Test results, ap: mae: 0.976, rse: 2.540, pcc: -0.097, kcc: -0.065
+2025-07-10 15:18:00: Epoch: 020 New best TEST metrics
+2025-07-10 15:18:01: Epoch: 021 Train Loss: 0.683, mae: 0.650, rse: 0.335, pcc: 0.817, kcc: 0.616
+2025-07-10 15:18:01: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:18:01: Epoch: 021 Test results, ap: mae: 1.019, rse: 2.754, pcc: -0.101, kcc: -0.046
+2025-07-10 15:18:01: Epoch: 022 Train Loss: 0.643, mae: 0.644, rse: 0.330, pcc: 0.821, kcc: 0.623
+2025-07-10 15:18:01: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:18:02: Epoch: 022 Test results, ap: mae: 1.054, rse: 2.821, pcc: -0.072, kcc: -0.036
+2025-07-10 15:18:02: Epoch: 023 Train Loss: 0.636, mae: 0.636, rse: 0.323, pcc: 0.825, kcc: 0.623
+2025-07-10 15:18:02: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:18:02: Epoch: 023 Test results, ap: mae: 1.027, rse: 2.711, pcc: -0.068, kcc: -0.042
+2025-07-10 15:18:03: Epoch: 024 Train Loss: 0.627, mae: 0.605, rse: 0.316, pcc: 0.828, kcc: 0.631
+2025-07-10 15:18:03: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:18:03: Epoch: 024 Test results, ap: mae: 1.094, rse: 3.220, pcc: -0.107, kcc: -0.060
+2025-07-10 15:18:04: Epoch: 025 Train Loss: 0.604, mae: 0.631, rse: 0.315, pcc: 0.830, kcc: 0.625
+2025-07-10 15:18:04: Epoch: 025 Test results, ap: mae: 1.061, rse: 3.027, pcc: -0.088, kcc: -0.042
+2025-07-10 15:18:05: Epoch: 026 Train Loss: 0.589, mae: 0.595, rse: 0.305, pcc: 0.836, kcc: 0.645
+2025-07-10 15:18:05: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:18:05: Epoch: 026 Test results, ap: mae: 1.098, rse: 3.105, pcc: -0.080, kcc: -0.061
+2025-07-10 15:18:06: Epoch: 027 Train Loss: 0.585, mae: 0.593, rse: 0.308, pcc: 0.834, kcc: 0.641
+2025-07-10 15:18:06: Epoch: 027 Test results, ap: mae: 1.096, rse: 3.098, pcc: -0.063, kcc: -0.044
+2025-07-10 15:18:06: Epoch: 028 Train Loss: 0.577, mae: 0.599, rse: 0.300, pcc: 0.840, kcc: 0.643
+2025-07-10 15:18:06: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:18:07: Epoch: 028 Test results, ap: mae: 1.076, rse: 3.047, pcc: -0.079, kcc: -0.045
+2025-07-10 15:18:07: Epoch: 029 Train Loss: 0.549, mae: 0.611, rse: 0.311, pcc: 0.838, kcc: 0.645
+2025-07-10 15:18:07: Epoch: 029 Test results, ap: mae: 1.249, rse: 4.504, pcc: -0.093, kcc: -0.069
+2025-07-10 15:18:08: Epoch: 030 Train Loss: 0.552, mae: 0.588, rse: 0.299, pcc: 0.840, kcc: 0.658
+2025-07-10 15:18:08: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:18:08: Epoch: 030 Test results, ap: mae: 1.117, rse: 3.514, pcc: -0.031, kcc: -0.037
+2025-07-10 15:18:09: Epoch: 031 Train Loss: 0.526, mae: 0.560, rse: 0.282, pcc: 0.848, kcc: 0.659
+2025-07-10 15:18:09: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:18:09: Epoch: 031 Test results, ap: mae: 1.174, rse: 3.955, pcc: -0.049, kcc: -0.052
+2025-07-10 15:18:10: Epoch: 032 Train Loss: 0.505, mae: 0.561, rse: 0.282, pcc: 0.849, kcc: 0.661
+2025-07-10 15:18:10: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:18:10: Epoch: 032 Test results, ap: mae: 1.192, rse: 3.945, pcc: -0.075, kcc: -0.070
+2025-07-10 15:18:11: Epoch: 033 Train Loss: 0.513, mae: 0.569, rse: 0.285, pcc: 0.848, kcc: 0.662
+2025-07-10 15:18:11: Epoch: 033 Test results, ap: mae: 1.155, rse: 3.688, pcc: -0.064, kcc: -0.058
+2025-07-10 15:18:11: Epoch: 034 Train Loss: 0.502, mae: 0.560, rse: 0.279, pcc: 0.850, kcc: 0.658
+2025-07-10 15:18:11: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:18:12: Epoch: 034 Test results, ap: mae: 1.164, rse: 3.800, pcc: -0.060, kcc: -0.068
+2025-07-10 15:18:12: Epoch: 035 Train Loss: 0.505, mae: 0.574, rse: 0.286, pcc: 0.849, kcc: 0.657
+2025-07-10 15:18:12: Epoch: 035 Test results, ap: mae: 1.194, rse: 4.044, pcc: -0.037, kcc: -0.035
+2025-07-10 15:18:13: Epoch: 036 Train Loss: 0.494, mae: 0.572, rse: 0.280, pcc: 0.850, kcc: 0.654
+2025-07-10 15:18:13: Epoch: 036 Test results, ap: mae: 1.163, rse: 3.847, pcc: -0.044, kcc: -0.052
+2025-07-10 15:18:14: Epoch: 037 Train Loss: 0.495, mae: 0.550, rse: 0.275, pcc: 0.853, kcc: 0.670
+2025-07-10 15:18:14: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:18:14: Epoch: 037 Test results, ap: mae: 1.203, rse: 4.089, pcc: -0.054, kcc: -0.044
+2025-07-10 15:18:15: Epoch: 038 Train Loss: 0.480, mae: 0.559, rse: 0.276, pcc: 0.854, kcc: 0.663
+2025-07-10 15:18:15: Epoch: 038 Test results, ap: mae: 1.180, rse: 3.902, pcc: -0.011, kcc: -0.030
+2025-07-10 15:18:16: Epoch: 039 Train Loss: 0.491, mae: 0.564, rse: 0.279, pcc: 0.854, kcc: 0.665
+2025-07-10 15:18:16: Epoch: 039 Test results, ap: mae: 1.229, rse: 4.302, pcc: -0.028, kcc: -0.045
+2025-07-10 15:18:17: Epoch: 040 Train Loss: 0.480, mae: 0.540, rse: 0.270, pcc: 0.856, kcc: 0.671
+2025-07-10 15:18:17: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:18:17: Epoch: 040 Test results, ap: mae: 1.200, rse: 4.044, pcc: -0.059, kcc: -0.060
+2025-07-10 15:18:17: Epoch: 041 Train Loss: 0.467, mae: 0.543, rse: 0.272, pcc: 0.855, kcc: 0.674
+2025-07-10 15:18:18: Epoch: 041 Test results, ap: mae: 1.197, rse: 3.981, pcc: -0.033, kcc: -0.039
+2025-07-10 15:18:18: Epoch: 042 Train Loss: 0.461, mae: 0.541, rse: 0.271, pcc: 0.856, kcc: 0.675
+2025-07-10 15:18:18: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:18:18: Epoch: 042 Test results, ap: mae: 1.185, rse: 3.888, pcc: -0.041, kcc: -0.046
+2025-07-10 15:18:19: Epoch: 043 Train Loss: 0.466, mae: 0.542, rse: 0.272, pcc: 0.856, kcc: 0.674
+2025-07-10 15:18:19: Epoch: 043 Test results, ap: mae: 1.212, rse: 4.105, pcc: -0.043, kcc: -0.052
+2025-07-10 15:18:20: Epoch: 044 Train Loss: 0.455, mae: 0.557, rse: 0.273, pcc: 0.854, kcc: 0.667
+2025-07-10 15:18:20: Epoch: 044 Test results, ap: mae: 1.171, rse: 3.918, pcc: -0.029, kcc: -0.037
+2025-07-10 15:18:21: Epoch: 045 Train Loss: 0.456, mae: 0.530, rse: 0.265, pcc: 0.859, kcc: 0.678
+2025-07-10 15:18:21: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:18:21: Epoch: 045 Test results, ap: mae: 1.226, rse: 4.211, pcc: -0.046, kcc: -0.054
+2025-07-10 15:18:22: Epoch: 046 Train Loss: 0.451, mae: 0.535, rse: 0.268, pcc: 0.857, kcc: 0.676
+2025-07-10 15:18:22: Epoch: 046 Test results, ap: mae: 1.202, rse: 4.014, pcc: -0.054, kcc: -0.054
+2025-07-10 15:18:23: Epoch: 047 Train Loss: 0.454, mae: 0.533, rse: 0.267, pcc: 0.858, kcc: 0.679
+2025-07-10 15:18:23: Epoch: 047 Test results, ap: mae: 1.206, rse: 4.022, pcc: -0.070, kcc: -0.062
+2025-07-10 15:18:23: Epoch: 048 Train Loss: 0.454, mae: 0.535, rse: 0.267, pcc: 0.858, kcc: 0.674
+2025-07-10 15:18:24: Epoch: 048 Test results, ap: mae: 1.225, rse: 4.183, pcc: -0.053, kcc: -0.056
+2025-07-10 15:18:24: Epoch: 049 Train Loss: 0.438, mae: 0.544, rse: 0.270, pcc: 0.856, kcc: 0.677
+2025-07-10 15:18:25: Epoch: 049 Test results, ap: mae: 1.211, rse: 4.145, pcc: -0.032, kcc: -0.045
+2025-07-10 15:18:25: Epoch: 050 Train Loss: 0.440, mae: 0.526, rse: 0.263, pcc: 0.860, kcc: 0.680
+2025-07-10 15:18:25: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:18:25: Epoch: 050 Test results, ap: mae: 1.219, rse: 4.124, pcc: -0.063, kcc: -0.059
+2025-07-10 15:18:25: used time 0.01h
+2025-07-10 15:18:25: Cross Validation Finished!
+2025-07-10 15:18:25: Best validation perform list
+[[0.54047638 0.22459121 0.88156855 0.676907  ]
+ [0.45736286 0.16379844 0.91448545 0.73201919]
+ [0.69520152 0.3897939  0.78672487 0.61804825]
+ [0.58052617 0.23789485 0.87390876 0.68448102]
+ [0.52552354 0.26252246 0.86043316 0.67985916]]
+2025-07-10 15:18:25: mean: [0.56  0.256 0.863 0.678]
+2025-07-10 15:18:25: std: [0.078 0.075 0.042 0.036]
+2025-07-10 15:18:25: Best test perform list
+[[ 0.88406205  2.4042635  -0.11974634 -0.04440601]
+ [ 0.96065831  2.64217854 -0.11156548 -0.05169137]
+ [ 0.66956723  1.34055293  0.16068555  0.04544678]
+ [ 0.72682923  1.77631569  0.17004098  0.13946262]
+ [ 0.9756214   2.54044151 -0.09717689 -0.0648744 ]]
+2025-07-10 15:18:25: mean: [0.843 2.141 0.    0.005]
+2025-07-10 15:18:25: std: [0.124 0.501 0.135 0.078]
diff --git a/run-reg/mha-mlp-256-mse-32-0.001-50/config.json b/run-reg/mha-mlp-256-mse-32-0.001-50/config.json
new file mode 100644
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "mha", "side_enc": null, "channels": 256, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
\ No newline at end of file
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+2025-07-10 15:08:10: saving_dir: ./run-reg/mha-mlp-256-mse-32-0.001-50
+2025-07-10 15:08:10: Loading Train Dataset
+2025-07-10 15:08:11: Loading Test Dataset
+2025-07-10 15:08:11: Running Cross Validation 0
+2025-07-10 15:08:11: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 15:08:12: Epoch: 001 Train Loss: 2.904, mae: 1.217, rse: 0.993, pcc: 0.307, kcc: 0.227
+2025-07-10 15:08:12: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:08:12: Epoch: 001 Test results, ap: mae: 0.594, rse: 1.075, pcc: -0.147, kcc: -0.098
+2025-07-10 15:08:13: Epoch: 002 Train Loss: 2.739, mae: 1.169, rse: 0.912, pcc: 0.352, kcc: 0.260
+2025-07-10 15:08:13: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:08:13: Epoch: 002 Test results, ap: mae: 0.598, rse: 1.078, pcc: -0.079, kcc: -0.011
+2025-07-10 15:08:14: Epoch: 003 Train Loss: 2.602, mae: 1.079, rse: 0.804, pcc: 0.473, kcc: 0.312
+2025-07-10 15:08:14: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:08:14: Epoch: 003 Test results, ap: mae: 0.845, rse: 2.066, pcc: -0.160, kcc: -0.121
+2025-07-10 15:08:15: Epoch: 004 Train Loss: 2.244, mae: 1.119, rse: 0.814, pcc: 0.491, kcc: 0.312
+2025-07-10 15:08:15: Epoch: 004 Test results, ap: mae: 0.657, rse: 1.173, pcc: -0.144, kcc: -0.076
+2025-07-10 15:08:16: Epoch: 005 Train Loss: 2.105, mae: 1.007, rse: 0.698, pcc: 0.553, kcc: 0.378
+2025-07-10 15:08:16: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:08:16: Epoch: 005 Test results, ap: mae: 0.745, rse: 1.492, pcc: -0.109, kcc: -0.114
+2025-07-10 15:08:16: Epoch: 006 Train Loss: 1.984, mae: 0.973, rse: 0.670, pcc: 0.577, kcc: 0.393
+2025-07-10 15:08:16: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:08:17: Epoch: 006 Test results, ap: mae: 0.700, rse: 1.334, pcc: -0.134, kcc: -0.104
+2025-07-10 15:08:17: Epoch: 007 Train Loss: 1.891, mae: 0.997, rse: 0.678, pcc: 0.578, kcc: 0.388
+2025-07-10 15:08:17: Epoch: 007 Test results, ap: mae: 0.711, rse: 1.478, pcc: -0.123, kcc: -0.080
+2025-07-10 15:08:18: Epoch: 008 Train Loss: 1.770, mae: 0.951, rse: 0.630, pcc: 0.609, kcc: 0.416
+2025-07-10 15:08:18: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:08:18: Epoch: 008 Test results, ap: mae: 0.695, rse: 1.381, pcc: -0.044, kcc: -0.088
+2025-07-10 15:08:19: Epoch: 009 Train Loss: 1.685, mae: 0.994, rse: 0.672, pcc: 0.603, kcc: 0.430
+2025-07-10 15:08:19: Epoch: 009 Test results, ap: mae: 0.662, rse: 1.316, pcc: -0.113, kcc: -0.086
+2025-07-10 15:08:20: Epoch: 010 Train Loss: 1.661, mae: 0.963, rse: 0.639, pcc: 0.605, kcc: 0.433
+2025-07-10 15:08:20: Epoch: 010 Test results, ap: mae: 0.646, rse: 1.228, pcc: -0.054, kcc: -0.025
+2025-07-10 15:08:21: Epoch: 011 Train Loss: 1.522, mae: 0.926, rse: 0.624, pcc: 0.627, kcc: 0.453
+2025-07-10 15:08:21: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:08:21: Epoch: 011 Test results, ap: mae: 0.642, rse: 1.170, pcc: -0.062, kcc: -0.033
+2025-07-10 15:08:21: Epoch: 011 New best TEST metrics
+2025-07-10 15:08:21: Epoch: 012 Train Loss: 1.502, mae: 0.883, rse: 0.557, pcc: 0.666, kcc: 0.469
+2025-07-10 15:08:21: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:08:22: Epoch: 012 Test results, ap: mae: 0.625, rse: 1.166, pcc: 0.122, kcc: 0.058
+2025-07-10 15:08:22: Epoch: 012 New best TEST metrics
+2025-07-10 15:08:22: Epoch: 013 Train Loss: 1.385, mae: 0.930, rse: 0.622, pcc: 0.619, kcc: 0.433
+2025-07-10 15:08:22: Epoch: 013 Test results, ap: mae: 0.633, rse: 1.162, pcc: 0.181, kcc: 0.050
+2025-07-10 15:08:22: Epoch: 013 New best TEST metrics
+2025-07-10 15:08:23: Epoch: 014 Train Loss: 1.486, mae: 0.915, rse: 0.587, pcc: 0.650, kcc: 0.462
+2025-07-10 15:08:23: Epoch: 014 Test results, ap: mae: 0.641, rse: 1.217, pcc: 0.158, kcc: 0.067
+2025-07-10 15:08:24: Epoch: 015 Train Loss: 1.367, mae: 0.879, rse: 0.557, pcc: 0.667, kcc: 0.480
+2025-07-10 15:08:24: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:08:24: Epoch: 015 Test results, ap: mae: 0.614, rse: 1.105, pcc: 0.248, kcc: 0.131
+2025-07-10 15:08:24: Epoch: 015 New best TEST metrics
+2025-07-10 15:08:25: Epoch: 016 Train Loss: 1.289, mae: 0.889, rse: 0.575, pcc: 0.663, kcc: 0.483
+2025-07-10 15:08:25: Epoch: 016 Test results, ap: mae: 0.605, rse: 1.060, pcc: 0.294, kcc: 0.131
+2025-07-10 15:08:25: Epoch: 016 New best TEST metrics
+2025-07-10 15:08:26: Epoch: 017 Train Loss: 1.327, mae: 0.879, rse: 0.554, pcc: 0.673, kcc: 0.478
+2025-07-10 15:08:26: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:08:26: Epoch: 017 Test results, ap: mae: 0.679, rse: 1.315, pcc: 0.283, kcc: 0.098
+2025-07-10 15:08:26: Epoch: 018 Train Loss: 1.289, mae: 0.844, rse: 0.520, pcc: 0.697, kcc: 0.500
+2025-07-10 15:08:26: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:08:27: Epoch: 018 Test results, ap: mae: 0.624, rse: 1.101, pcc: 0.286, kcc: 0.137
+2025-07-10 15:08:27: Epoch: 019 Train Loss: 1.233, mae: 0.853, rse: 0.528, pcc: 0.693, kcc: 0.497
+2025-07-10 15:08:27: Epoch: 019 Test results, ap: mae: 0.692, rse: 1.339, pcc: 0.197, kcc: 0.102
+2025-07-10 15:08:28: Epoch: 020 Train Loss: 1.264, mae: 0.830, rse: 0.506, pcc: 0.709, kcc: 0.510
+2025-07-10 15:08:28: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:08:28: Epoch: 020 Test results, ap: mae: 0.886, rse: 2.328, pcc: 0.228, kcc: 0.091
+2025-07-10 15:08:29: Epoch: 021 Train Loss: 1.159, mae: 0.816, rse: 0.495, pcc: 0.715, kcc: 0.521
+2025-07-10 15:08:29: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:08:29: Epoch: 021 Test results, ap: mae: 0.739, rse: 1.599, pcc: 0.244, kcc: 0.108
+2025-07-10 15:08:30: Epoch: 022 Train Loss: 1.144, mae: 0.811, rse: 0.485, pcc: 0.718, kcc: 0.526
+2025-07-10 15:08:30: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:08:30: Epoch: 022 Test results, ap: mae: 1.077, rse: 3.144, pcc: 0.124, kcc: 0.070
+2025-07-10 15:08:31: Epoch: 023 Train Loss: 1.143, mae: 0.820, rse: 0.499, pcc: 0.711, kcc: 0.520
+2025-07-10 15:08:31: Epoch: 023 Test results, ap: mae: 1.479, rse: 6.196, pcc: 0.026, kcc: 0.013
+2025-07-10 15:08:32: Epoch: 024 Train Loss: 1.150, mae: 0.810, rse: 0.481, pcc: 0.720, kcc: 0.529
+2025-07-10 15:08:32: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:08:32: Epoch: 024 Test results, ap: mae: 1.526, rse: 6.033, pcc: 0.079, kcc: 0.036
+2025-07-10 15:08:32: Epoch: 025 Train Loss: 1.121, mae: 0.815, rse: 0.490, pcc: 0.717, kcc: 0.524
+2025-07-10 15:08:33: Epoch: 025 Test results, ap: mae: 1.749, rse: 8.482, pcc: 0.041, kcc: 0.025
+2025-07-10 15:08:33: Epoch: 026 Train Loss: 1.095, mae: 0.800, rse: 0.473, pcc: 0.726, kcc: 0.530
+2025-07-10 15:08:33: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:08:34: Epoch: 026 Test results, ap: mae: 1.163, rse: 3.752, pcc: 0.119, kcc: 0.086
+2025-07-10 15:08:34: Epoch: 027 Train Loss: 1.087, mae: 0.805, rse: 0.475, pcc: 0.725, kcc: 0.529
+2025-07-10 15:08:34: Epoch: 027 Test results, ap: mae: 1.882, rse: 9.316, pcc: 0.067, kcc: 0.034
+2025-07-10 15:08:35: Epoch: 028 Train Loss: 1.093, mae: 0.806, rse: 0.481, pcc: 0.722, kcc: 0.535
+2025-07-10 15:08:35: Epoch: 028 Test results, ap: mae: 1.966, rse: 10.375, pcc: 0.022, kcc: 0.019
+2025-07-10 15:08:36: Epoch: 029 Train Loss: 1.085, mae: 0.779, rse: 0.459, pcc: 0.741, kcc: 0.548
+2025-07-10 15:08:36: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:08:36: Epoch: 029 Test results, ap: mae: 2.624, rse: 16.339, pcc: 0.069, kcc: 0.019
+2025-07-10 15:08:36: Epoch: 030 Train Loss: 1.063, mae: 0.793, rse: 0.461, pcc: 0.740, kcc: 0.543
+2025-07-10 15:08:37: Epoch: 030 Test results, ap: mae: 1.707, rse: 8.403, pcc: 0.043, kcc: 0.044
+2025-07-10 15:08:37: Epoch: 031 Train Loss: 0.983, mae: 0.769, rse: 0.448, pcc: 0.747, kcc: 0.555
+2025-07-10 15:08:37: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:08:37: Epoch: 031 Test results, ap: mae: 2.487, rse: 15.516, pcc: 0.019, kcc: -0.000
+2025-07-10 15:08:38: Epoch: 032 Train Loss: 0.977, mae: 0.781, rse: 0.460, pcc: 0.746, kcc: 0.553
+2025-07-10 15:08:38: Epoch: 032 Test results, ap: mae: 2.159, rse: 12.970, pcc: -0.006, kcc: -0.012
+2025-07-10 15:08:39: Epoch: 033 Train Loss: 0.971, mae: 0.765, rse: 0.441, pcc: 0.753, kcc: 0.559
+2025-07-10 15:08:39: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:08:39: Epoch: 033 Test results, ap: mae: 2.451, rse: 15.625, pcc: 0.010, kcc: -0.016
+2025-07-10 15:08:39: Epoch: 034 Train Loss: 0.952, mae: 0.768, rse: 0.435, pcc: 0.755, kcc: 0.558
+2025-07-10 15:08:39: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:08:40: Epoch: 034 Test results, ap: mae: 2.257, rse: 14.086, pcc: 0.006, kcc: -0.028
+2025-07-10 15:08:40: Epoch: 035 Train Loss: 0.960, mae: 0.757, rse: 0.424, pcc: 0.763, kcc: 0.565
+2025-07-10 15:08:40: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:08:40: Epoch: 035 Test results, ap: mae: 2.440, rse: 16.044, pcc: 0.005, kcc: -0.016
+2025-07-10 15:08:41: Epoch: 036 Train Loss: 0.932, mae: 0.755, rse: 0.417, pcc: 0.764, kcc: 0.566
+2025-07-10 15:08:41: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:08:41: Epoch: 036 Test results, ap: mae: 2.241, rse: 14.101, pcc: 0.009, kcc: 0.005
+2025-07-10 15:08:42: Epoch: 037 Train Loss: 0.933, mae: 0.745, rse: 0.410, pcc: 0.772, kcc: 0.572
+2025-07-10 15:08:42: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:08:42: Epoch: 037 Test results, ap: mae: 2.254, rse: 14.298, pcc: 0.013, kcc: 0.004
+2025-07-10 15:08:42: Epoch: 038 Train Loss: 0.906, mae: 0.744, rse: 0.419, pcc: 0.769, kcc: 0.571
+2025-07-10 15:08:43: Epoch: 038 Test results, ap: mae: 2.430, rse: 16.151, pcc: -0.004, kcc: -0.019
+2025-07-10 15:08:43: Epoch: 039 Train Loss: 0.913, mae: 0.758, rse: 0.420, pcc: 0.771, kcc: 0.566
+2025-07-10 15:08:43: Epoch: 039 Test results, ap: mae: 1.758, rse: 9.884, pcc: 0.002, kcc: 0.026
+2025-07-10 15:08:44: Epoch: 040 Train Loss: 0.928, mae: 0.742, rse: 0.408, pcc: 0.773, kcc: 0.569
+2025-07-10 15:08:44: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:08:44: Epoch: 040 Test results, ap: mae: 2.046, rse: 12.051, pcc: 0.021, kcc: 0.015
+2025-07-10 15:08:45: Epoch: 041 Train Loss: 0.880, mae: 0.747, rse: 0.410, pcc: 0.770, kcc: 0.566
+2025-07-10 15:08:45: Epoch: 041 Test results, ap: mae: 2.207, rse: 13.208, pcc: 0.009, kcc: 0.006
+2025-07-10 15:08:45: Epoch: 042 Train Loss: 0.879, mae: 0.730, rse: 0.396, pcc: 0.779, kcc: 0.578
+2025-07-10 15:08:45: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:08:45: Epoch: 042 Test results, ap: mae: 2.255, rse: 14.513, pcc: -0.006, kcc: -0.013
+2025-07-10 15:08:46: Epoch: 043 Train Loss: 0.867, mae: 0.739, rse: 0.401, pcc: 0.776, kcc: 0.572
+2025-07-10 15:08:46: Epoch: 043 Test results, ap: mae: 2.240, rse: 13.977, pcc: 0.017, kcc: 0.007
+2025-07-10 15:08:47: Epoch: 044 Train Loss: 0.866, mae: 0.746, rse: 0.401, pcc: 0.775, kcc: 0.569
+2025-07-10 15:08:47: Epoch: 044 Test results, ap: mae: 2.119, rse: 12.674, pcc: 0.024, kcc: 0.013
+2025-07-10 15:08:48: Epoch: 045 Train Loss: 0.821, mae: 0.727, rse: 0.397, pcc: 0.781, kcc: 0.579
+2025-07-10 15:08:48: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:08:48: Epoch: 045 Test results, ap: mae: 2.166, rse: 13.523, pcc: 0.024, kcc: 0.008
+2025-07-10 15:08:48: Epoch: 046 Train Loss: 0.857, mae: 0.727, rse: 0.396, pcc: 0.780, kcc: 0.578
+2025-07-10 15:08:48: Epoch: 046 Test results, ap: mae: 2.084, rse: 12.411, pcc: 0.035, kcc: 0.020
+2025-07-10 15:08:49: Epoch: 047 Train Loss: 0.832, mae: 0.725, rse: 0.392, pcc: 0.783, kcc: 0.579
+2025-07-10 15:08:49: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:08:49: Epoch: 047 Test results, ap: mae: 2.071, rse: 12.403, pcc: 0.042, kcc: 0.021
+2025-07-10 15:08:50: Epoch: 048 Train Loss: 0.844, mae: 0.727, rse: 0.389, pcc: 0.784, kcc: 0.578
+2025-07-10 15:08:50: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:08:50: Epoch: 048 Test results, ap: mae: 2.007, rse: 11.414, pcc: 0.052, kcc: 0.027
+2025-07-10 15:08:50: Epoch: 049 Train Loss: 0.832, mae: 0.737, rse: 0.397, pcc: 0.782, kcc: 0.576
+2025-07-10 15:08:51: Epoch: 049 Test results, ap: mae: 2.094, rse: 12.444, pcc: 0.045, kcc: 0.019
+2025-07-10 15:08:51: Epoch: 050 Train Loss: 0.825, mae: 0.725, rse: 0.388, pcc: 0.784, kcc: 0.579
+2025-07-10 15:08:51: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:08:51: Epoch: 050 Test results, ap: mae: 2.027, rse: 12.169, pcc: 0.030, kcc: 0.015
+2025-07-10 15:08:51: used time 0.01h
+2025-07-10 15:08:51: Running Cross Validation 1
+2025-07-10 15:08:51: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-10 15:08:52: Epoch: 001 Train Loss: 2.868, mae: 1.251, rse: 1.004, pcc: 0.297, kcc: 0.164
+2025-07-10 15:08:52: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:08:52: Epoch: 001 Test results, ap: mae: 0.631, rse: 1.213, pcc: 0.335, kcc: 0.174
+2025-07-10 15:08:53: Epoch: 002 Train Loss: 2.796, mae: 1.202, rse: 0.915, pcc: 0.329, kcc: 0.210
+2025-07-10 15:08:53: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:08:53: Epoch: 002 Test results, ap: mae: 0.615, rse: 1.160, pcc: 0.457, kcc: 0.381
+2025-07-10 15:08:53: Epoch: 003 Train Loss: 2.627, mae: 1.196, rse: 0.890, pcc: 0.358, kcc: 0.231
+2025-07-10 15:08:53: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:08:54: Epoch: 003 Test results, ap: mae: 0.558, rse: 0.949, pcc: 0.455, kcc: 0.335
+2025-07-10 15:08:54: Epoch: 004 Train Loss: 2.577, mae: 1.165, rse: 0.865, pcc: 0.368, kcc: 0.248
+2025-07-10 15:08:54: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:08:54: Epoch: 004 Test results, ap: mae: 0.597, rse: 1.093, pcc: 0.371, kcc: 0.275
+2025-07-10 15:08:55: Epoch: 005 Train Loss: 2.533, mae: 1.172, rse: 0.868, pcc: 0.388, kcc: 0.258
+2025-07-10 15:08:55: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:08:55: Epoch: 005 Test results, ap: mae: 0.711, rse: 1.446, pcc: 0.203, kcc: 0.144
+2025-07-10 15:08:56: Epoch: 006 Train Loss: 2.396, mae: 1.142, rse: 0.822, pcc: 0.441, kcc: 0.298
+2025-07-10 15:08:56: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:08:56: Epoch: 006 Test results, ap: mae: 1.026, rse: 2.764, pcc: -0.036, kcc: -0.011
+2025-07-10 15:08:56: Epoch: 007 Train Loss: 2.198, mae: 1.076, rse: 0.729, pcc: 0.539, kcc: 0.357
+2025-07-10 15:08:56: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:08:57: Epoch: 007 Test results, ap: mae: 1.018, rse: 2.745, pcc: -0.174, kcc: -0.163
+2025-07-10 15:08:57: Epoch: 008 Train Loss: 2.113, mae: 1.063, rse: 0.722, pcc: 0.541, kcc: 0.342
+2025-07-10 15:08:57: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:08:57: Epoch: 008 Test results, ap: mae: 0.921, rse: 2.478, pcc: -0.112, kcc: -0.141
+2025-07-10 15:08:58: Epoch: 009 Train Loss: 1.805, mae: 1.029, rse: 0.681, pcc: 0.567, kcc: 0.368
+2025-07-10 15:08:58: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:08:58: Epoch: 009 Test results, ap: mae: 0.672, rse: 1.486, pcc: 0.271, kcc: 0.225
+2025-07-10 15:08:59: Epoch: 010 Train Loss: 1.726, mae: 0.985, rse: 0.647, pcc: 0.599, kcc: 0.399
+2025-07-10 15:08:59: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:08:59: Epoch: 010 Test results, ap: mae: 0.667, rse: 1.491, pcc: -0.001, kcc: 0.103
+2025-07-10 15:08:59: Epoch: 011 Train Loss: 1.626, mae: 0.960, rse: 0.614, pcc: 0.622, kcc: 0.425
+2025-07-10 15:08:59: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:09:00: Epoch: 011 Test results, ap: mae: 0.662, rse: 1.396, pcc: -0.008, kcc: 0.071
+2025-07-10 15:09:00: Epoch: 011 New best TEST metrics
+2025-07-10 15:09:00: Epoch: 012 Train Loss: 1.540, mae: 0.941, rse: 0.600, pcc: 0.642, kcc: 0.448
+2025-07-10 15:09:00: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:09:00: Epoch: 012 Test results, ap: mae: 0.718, rse: 1.747, pcc: 0.029, kcc: 0.057
+2025-07-10 15:09:01: Epoch: 013 Train Loss: 1.491, mae: 0.927, rse: 0.582, pcc: 0.652, kcc: 0.458
+2025-07-10 15:09:01: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:09:01: Epoch: 013 Test results, ap: mae: 0.723, rse: 1.737, pcc: 0.019, kcc: 0.053
+2025-07-10 15:09:02: Epoch: 014 Train Loss: 1.456, mae: 0.922, rse: 0.576, pcc: 0.652, kcc: 0.452
+2025-07-10 15:09:02: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:09:02: Epoch: 014 Test results, ap: mae: 0.701, rse: 1.633, pcc: -0.009, kcc: -0.010
+2025-07-10 15:09:02: Epoch: 015 Train Loss: 1.423, mae: 0.921, rse: 0.577, pcc: 0.662, kcc: 0.467
+2025-07-10 15:09:02: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:09:03: Epoch: 015 Test results, ap: mae: 0.832, rse: 2.337, pcc: 0.095, kcc: 0.034
+2025-07-10 15:09:03: Epoch: 016 Train Loss: 1.412, mae: 0.908, rse: 0.561, pcc: 0.666, kcc: 0.473
+2025-07-10 15:09:03: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:09:03: Epoch: 016 Test results, ap: mae: 0.772, rse: 2.008, pcc: 0.105, kcc: 0.035
+2025-07-10 15:09:04: Epoch: 017 Train Loss: 1.375, mae: 0.900, rse: 0.559, pcc: 0.668, kcc: 0.474
+2025-07-10 15:09:04: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:09:04: Epoch: 017 Test results, ap: mae: 0.687, rse: 1.470, pcc: 0.105, kcc: 0.067
+2025-07-10 15:09:04: Epoch: 017 New best TEST metrics
+2025-07-10 15:09:05: Epoch: 018 Train Loss: 1.347, mae: 0.919, rse: 0.570, pcc: 0.665, kcc: 0.475
+2025-07-10 15:09:05: Epoch: 018 Test results, ap: mae: 0.816, rse: 2.114, pcc: -0.045, kcc: -0.020
+2025-07-10 15:09:05: Epoch: 019 Train Loss: 1.342, mae: 0.903, rse: 0.563, pcc: 0.665, kcc: 0.473
+2025-07-10 15:09:06: Epoch: 019 Test results, ap: mae: 0.727, rse: 1.647, pcc: 0.123, kcc: 0.058
+2025-07-10 15:09:06: Epoch: 020 Train Loss: 1.304, mae: 0.898, rse: 0.553, pcc: 0.671, kcc: 0.480
+2025-07-10 15:09:06: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:09:06: Epoch: 020 Test results, ap: mae: 0.781, rse: 1.850, pcc: 0.139, kcc: 0.033
+2025-07-10 15:09:07: Epoch: 021 Train Loss: 1.271, mae: 0.885, rse: 0.548, pcc: 0.675, kcc: 0.484
+2025-07-10 15:09:07: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:09:07: Epoch: 021 Test results, ap: mae: 0.684, rse: 1.470, pcc: 0.087, kcc: 0.009
+2025-07-10 15:09:08: Epoch: 022 Train Loss: 1.241, mae: 0.887, rse: 0.538, pcc: 0.681, kcc: 0.490
+2025-07-10 15:09:08: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:09:08: Epoch: 022 Test results, ap: mae: 0.750, rse: 1.769, pcc: 0.086, kcc: 0.003
+2025-07-10 15:09:08: Epoch: 023 Train Loss: 1.231, mae: 0.878, rse: 0.540, pcc: 0.680, kcc: 0.490
+2025-07-10 15:09:08: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:09:09: Epoch: 023 Test results, ap: mae: 0.715, rse: 1.582, pcc: 0.069, kcc: 0.006
+2025-07-10 15:09:09: Epoch: 024 Train Loss: 1.245, mae: 0.871, rse: 0.534, pcc: 0.684, kcc: 0.491
+2025-07-10 15:09:09: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:09:09: Epoch: 024 Test results, ap: mae: 0.762, rse: 1.922, pcc: 0.002, kcc: -0.016
+2025-07-10 15:09:10: Epoch: 025 Train Loss: 1.219, mae: 0.885, rse: 0.543, pcc: 0.683, kcc: 0.495
+2025-07-10 15:09:10: Epoch: 025 Test results, ap: mae: 0.813, rse: 2.095, pcc: -0.008, kcc: -0.028
+2025-07-10 15:09:11: Epoch: 026 Train Loss: 1.205, mae: 0.888, rse: 0.540, pcc: 0.680, kcc: 0.489
+2025-07-10 15:09:11: Epoch: 026 Test results, ap: mae: 0.773, rse: 1.999, pcc: 0.049, kcc: 0.008
+2025-07-10 15:09:11: Epoch: 027 Train Loss: 1.220, mae: 0.875, rse: 0.532, pcc: 0.687, kcc: 0.495
+2025-07-10 15:09:11: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:09:12: Epoch: 027 Test results, ap: mae: 0.779, rse: 1.943, pcc: 0.003, kcc: -0.007
+2025-07-10 15:09:12: Epoch: 028 Train Loss: 1.196, mae: 0.883, rse: 0.536, pcc: 0.686, kcc: 0.499
+2025-07-10 15:09:12: Epoch: 028 Test results, ap: mae: 0.795, rse: 2.023, pcc: 0.029, kcc: -0.012
+2025-07-10 15:09:13: Epoch: 029 Train Loss: 1.203, mae: 0.870, rse: 0.526, pcc: 0.689, kcc: 0.504
+2025-07-10 15:09:13: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:09:13: Epoch: 029 Test results, ap: mae: 0.738, rse: 1.761, pcc: 0.042, kcc: -0.014
+2025-07-10 15:09:14: Epoch: 030 Train Loss: 1.195, mae: 0.888, rse: 0.540, pcc: 0.685, kcc: 0.501
+2025-07-10 15:09:14: Epoch: 030 Test results, ap: mae: 0.831, rse: 2.139, pcc: 0.068, kcc: 0.019
+2025-07-10 15:09:14: Epoch: 031 Train Loss: 1.187, mae: 0.861, rse: 0.518, pcc: 0.695, kcc: 0.510
+2025-07-10 15:09:14: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:09:15: Epoch: 031 Test results, ap: mae: 0.761, rse: 1.850, pcc: 0.112, kcc: 0.043
+2025-07-10 15:09:15: Epoch: 032 Train Loss: 1.159, mae: 0.864, rse: 0.521, pcc: 0.694, kcc: 0.508
+2025-07-10 15:09:15: Epoch: 032 Test results, ap: mae: 0.750, rse: 1.776, pcc: 0.115, kcc: 0.042
+2025-07-10 15:09:16: Epoch: 033 Train Loss: 1.149, mae: 0.855, rse: 0.522, pcc: 0.692, kcc: 0.508
+2025-07-10 15:09:16: Epoch: 033 Test results, ap: mae: 0.749, rse: 1.771, pcc: 0.087, kcc: 0.032
+2025-07-10 15:09:17: Epoch: 034 Train Loss: 1.139, mae: 0.859, rse: 0.525, pcc: 0.693, kcc: 0.509
+2025-07-10 15:09:17: Epoch: 034 Test results, ap: mae: 0.795, rse: 1.996, pcc: 0.120, kcc: 0.036
+2025-07-10 15:09:17: Epoch: 035 Train Loss: 1.167, mae: 0.856, rse: 0.520, pcc: 0.694, kcc: 0.511
+2025-07-10 15:09:17: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:09:18: Epoch: 035 Test results, ap: mae: 0.771, rse: 1.898, pcc: 0.108, kcc: 0.040
+2025-07-10 15:09:18: Epoch: 036 Train Loss: 1.148, mae: 0.852, rse: 0.518, pcc: 0.697, kcc: 0.510
+2025-07-10 15:09:18: Epoch: 036 New best VALIDATION metrics
+2025-07-10 15:09:18: Epoch: 036 Test results, ap: mae: 0.776, rse: 1.939, pcc: 0.109, kcc: 0.036
+2025-07-10 15:09:19: Epoch: 037 Train Loss: 1.127, mae: 0.858, rse: 0.520, pcc: 0.697, kcc: 0.513
+2025-07-10 15:09:19: Epoch: 037 Test results, ap: mae: 0.800, rse: 2.041, pcc: 0.087, kcc: 0.042
+2025-07-10 15:09:20: Epoch: 038 Train Loss: 1.130, mae: 0.842, rse: 0.513, pcc: 0.701, kcc: 0.516
+2025-07-10 15:09:20: Epoch: 038 New best VALIDATION metrics
+2025-07-10 15:09:20: Epoch: 038 Test results, ap: mae: 0.745, rse: 1.827, pcc: 0.094, kcc: 0.027
+2025-07-10 15:09:20: Epoch: 039 Train Loss: 1.113, mae: 0.851, rse: 0.513, pcc: 0.699, kcc: 0.512
+2025-07-10 15:09:20: Epoch: 039 Test results, ap: mae: 0.781, rse: 1.936, pcc: 0.103, kcc: 0.040
+2025-07-10 15:09:21: Epoch: 040 Train Loss: 1.127, mae: 0.839, rse: 0.508, pcc: 0.703, kcc: 0.519
+2025-07-10 15:09:21: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:09:21: Epoch: 040 Test results, ap: mae: 0.782, rse: 1.977, pcc: 0.084, kcc: 0.034
+2025-07-10 15:09:22: Epoch: 041 Train Loss: 1.114, mae: 0.838, rse: 0.508, pcc: 0.703, kcc: 0.518
+2025-07-10 15:09:22: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:09:22: Epoch: 041 Test results, ap: mae: 0.777, rse: 1.961, pcc: 0.071, kcc: 0.043
+2025-07-10 15:09:22: Epoch: 042 Train Loss: 1.084, mae: 0.836, rse: 0.502, pcc: 0.707, kcc: 0.520
+2025-07-10 15:09:22: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:09:23: Epoch: 042 Test results, ap: mae: 0.764, rse: 1.916, pcc: 0.046, kcc: 0.016
+2025-07-10 15:09:23: Epoch: 043 Train Loss: 1.096, mae: 0.835, rse: 0.504, pcc: 0.706, kcc: 0.519
+2025-07-10 15:09:23: Epoch: 043 Test results, ap: mae: 0.758, rse: 1.918, pcc: 0.026, kcc: 0.005
+2025-07-10 15:09:24: Epoch: 044 Train Loss: 1.109, mae: 0.831, rse: 0.499, pcc: 0.709, kcc: 0.521
+2025-07-10 15:09:24: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:09:24: Epoch: 044 Test results, ap: mae: 0.760, rse: 1.904, pcc: 0.056, kcc: 0.033
+2025-07-10 15:09:25: Epoch: 045 Train Loss: 1.102, mae: 0.832, rse: 0.500, pcc: 0.708, kcc: 0.522
+2025-07-10 15:09:25: Epoch: 045 Test results, ap: mae: 0.799, rse: 2.056, pcc: -0.005, kcc: -0.015
+2025-07-10 15:09:25: Epoch: 046 Train Loss: 1.101, mae: 0.833, rse: 0.498, pcc: 0.709, kcc: 0.521
+2025-07-10 15:09:26: Epoch: 046 Test results, ap: mae: 0.786, rse: 2.022, pcc: 0.007, kcc: 0.007
+2025-07-10 15:09:26: Epoch: 047 Train Loss: 1.074, mae: 0.832, rse: 0.500, pcc: 0.709, kcc: 0.522
+2025-07-10 15:09:26: Epoch: 047 Test results, ap: mae: 0.794, rse: 2.067, pcc: 0.019, kcc: 0.016
+2025-07-10 15:09:27: Epoch: 048 Train Loss: 1.083, mae: 0.834, rse: 0.500, pcc: 0.709, kcc: 0.520
+2025-07-10 15:09:27: Epoch: 048 Test results, ap: mae: 0.781, rse: 2.002, pcc: 0.014, kcc: 0.006
+2025-07-10 15:09:28: Epoch: 049 Train Loss: 1.103, mae: 0.839, rse: 0.501, pcc: 0.708, kcc: 0.518
+2025-07-10 15:09:28: Epoch: 049 Test results, ap: mae: 0.807, rse: 2.092, pcc: 0.025, kcc: 0.018
+2025-07-10 15:09:28: Epoch: 050 Train Loss: 1.092, mae: 0.831, rse: 0.497, pcc: 0.710, kcc: 0.524
+2025-07-10 15:09:28: Epoch: 050 New best VALIDATION metrics
+2025-07-10 15:09:29: Epoch: 050 Test results, ap: mae: 0.782, rse: 1.986, pcc: 0.013, kcc: 0.006
+2025-07-10 15:09:29: used time 0.01h
+2025-07-10 15:09:29: Running Cross Validation 2
+2025-07-10 15:09:29: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:09:29: Epoch: 001 Train Loss: 2.909, mae: 1.255, rse: 0.998, pcc: 0.287, kcc: 0.213
+2025-07-10 15:09:29: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:09:29: Epoch: 001 Test results, ap: mae: 0.603, rse: 1.111, pcc: -0.057, kcc: -0.111
+2025-07-10 15:09:30: Epoch: 002 Train Loss: 2.690, mae: 1.195, rse: 0.922, pcc: 0.280, kcc: 0.232
+2025-07-10 15:09:30: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:09:30: Epoch: 002 Test results, ap: mae: 0.649, rse: 1.273, pcc: 0.072, kcc: -0.027
+2025-07-10 15:09:31: Epoch: 003 Train Loss: 2.587, mae: 1.176, rse: 0.906, pcc: 0.312, kcc: 0.232
+2025-07-10 15:09:31: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:09:31: Epoch: 003 Test results, ap: mae: 0.617, rse: 1.152, pcc: 0.133, kcc: 0.058
+2025-07-10 15:09:32: Epoch: 004 Train Loss: 2.549, mae: 1.152, rse: 0.867, pcc: 0.366, kcc: 0.265
+2025-07-10 15:09:32: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:09:32: Epoch: 004 Test results, ap: mae: 0.741, rse: 1.584, pcc: -0.021, kcc: -0.069
+2025-07-10 15:09:32: Epoch: 005 Train Loss: 2.361, mae: 1.089, rse: 0.776, pcc: 0.489, kcc: 0.345
+2025-07-10 15:09:32: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:09:32: Epoch: 005 Test results, ap: mae: 0.654, rse: 1.221, pcc: -0.044, kcc: -0.104
+2025-07-10 15:09:33: Epoch: 006 Train Loss: 2.234, mae: 1.057, rse: 0.749, pcc: 0.511, kcc: 0.353
+2025-07-10 15:09:33: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:09:33: Epoch: 006 Test results, ap: mae: 0.718, rse: 1.496, pcc: -0.024, kcc: -0.042
+2025-07-10 15:09:34: Epoch: 007 Train Loss: 2.150, mae: 1.073, rse: 0.753, pcc: 0.507, kcc: 0.347
+2025-07-10 15:09:34: Epoch: 007 Test results, ap: mae: 0.598, rse: 1.008, pcc: 0.363, kcc: 0.215
+2025-07-10 15:09:35: Epoch: 008 Train Loss: 2.149, mae: 1.051, rse: 0.721, pcc: 0.540, kcc: 0.362
+2025-07-10 15:09:35: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:09:35: Epoch: 008 Test results, ap: mae: 0.715, rse: 1.462, pcc: 0.074, kcc: 0.077
+2025-07-10 15:09:35: Epoch: 009 Train Loss: 2.107, mae: 1.028, rse: 0.708, pcc: 0.544, kcc: 0.371
+2025-07-10 15:09:35: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:09:35: Epoch: 009 Test results, ap: mae: 0.605, rse: 1.060, pcc: 0.248, kcc: 0.151
+2025-07-10 15:09:36: Epoch: 010 Train Loss: 2.037, mae: 1.018, rse: 0.700, pcc: 0.550, kcc: 0.371
+2025-07-10 15:09:36: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:09:36: Epoch: 010 Test results, ap: mae: 0.625, rse: 1.216, pcc: 0.264, kcc: 0.154
+2025-07-10 15:09:37: Epoch: 011 Train Loss: 1.929, mae: 1.022, rse: 0.688, pcc: 0.573, kcc: 0.394
+2025-07-10 15:09:37: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:09:37: Epoch: 011 Test results, ap: mae: 0.683, rse: 1.365, pcc: 0.101, kcc: 0.060
+2025-07-10 15:09:37: Epoch: 011 New best TEST metrics
+2025-07-10 15:09:37: Epoch: 012 Train Loss: 1.867, mae: 0.974, rse: 0.650, pcc: 0.604, kcc: 0.417
+2025-07-10 15:09:37: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:09:37: Epoch: 012 Test results, ap: mae: 0.742, rse: 1.724, pcc: 0.263, kcc: 0.155
+2025-07-10 15:09:38: Epoch: 013 Train Loss: 1.864, mae: 0.957, rse: 0.625, pcc: 0.613, kcc: 0.428
+2025-07-10 15:09:38: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:09:38: Epoch: 013 Test results, ap: mae: 0.670, rse: 1.509, pcc: 0.185, kcc: 0.115
+2025-07-10 15:09:38: Epoch: 013 New best TEST metrics
+2025-07-10 15:09:39: Epoch: 014 Train Loss: 1.774, mae: 0.949, rse: 0.618, pcc: 0.621, kcc: 0.428
+2025-07-10 15:09:39: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:09:39: Epoch: 014 Test results, ap: mae: 0.746, rse: 1.559, pcc: 0.032, kcc: 0.043
+2025-07-10 15:09:39: Epoch: 015 Train Loss: 1.725, mae: 0.949, rse: 0.611, pcc: 0.626, kcc: 0.435
+2025-07-10 15:09:39: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:09:39: Epoch: 015 Test results, ap: mae: 0.696, rse: 1.381, pcc: 0.037, kcc: 0.022
+2025-07-10 15:09:40: Epoch: 016 Train Loss: 1.700, mae: 0.922, rse: 0.579, pcc: 0.651, kcc: 0.447
+2025-07-10 15:09:40: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:09:40: Epoch: 016 Test results, ap: mae: 0.739, rse: 1.618, pcc: -0.085, kcc: -0.008
+2025-07-10 15:09:40: Epoch: 017 Train Loss: 1.576, mae: 0.928, rse: 0.584, pcc: 0.647, kcc: 0.444
+2025-07-10 15:09:41: Epoch: 017 Test results, ap: mae: 0.802, rse: 1.960, pcc: -0.049, kcc: -0.005
+2025-07-10 15:09:41: Epoch: 018 Train Loss: 1.570, mae: 0.899, rse: 0.558, pcc: 0.666, kcc: 0.461
+2025-07-10 15:09:41: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:09:41: Epoch: 018 Test results, ap: mae: 0.753, rse: 1.722, pcc: 0.059, kcc: 0.049
+2025-07-10 15:09:42: Epoch: 019 Train Loss: 1.507, mae: 0.895, rse: 0.544, pcc: 0.675, kcc: 0.468
+2025-07-10 15:09:42: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:09:42: Epoch: 019 Test results, ap: mae: 0.776, rse: 1.858, pcc: 0.071, kcc: 0.069
+2025-07-10 15:09:43: Epoch: 020 Train Loss: 1.522, mae: 0.924, rse: 0.560, pcc: 0.670, kcc: 0.446
+2025-07-10 15:09:43: Epoch: 020 Test results, ap: mae: 0.781, rse: 1.924, pcc: 0.009, kcc: -0.006
+2025-07-10 15:09:43: Epoch: 021 Train Loss: 1.450, mae: 0.877, rse: 0.528, pcc: 0.687, kcc: 0.472
+2025-07-10 15:09:43: Epoch: 021 New best VALIDATION metrics
+2025-07-10 15:09:43: Epoch: 021 Test results, ap: mae: 0.813, rse: 2.086, pcc: 0.027, kcc: 0.027
+2025-07-10 15:09:44: Epoch: 022 Train Loss: 1.418, mae: 0.876, rse: 0.526, pcc: 0.688, kcc: 0.480
+2025-07-10 15:09:44: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:09:44: Epoch: 022 Test results, ap: mae: 0.826, rse: 2.124, pcc: 0.017, kcc: 0.037
+2025-07-10 15:09:45: Epoch: 023 Train Loss: 1.354, mae: 0.868, rse: 0.512, pcc: 0.698, kcc: 0.483
+2025-07-10 15:09:45: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:09:45: Epoch: 023 Test results, ap: mae: 0.823, rse: 2.114, pcc: -0.032, kcc: 0.007
+2025-07-10 15:09:45: Epoch: 024 Train Loss: 1.362, mae: 0.868, rse: 0.515, pcc: 0.697, kcc: 0.482
+2025-07-10 15:09:45: Epoch: 024 Test results, ap: mae: 0.851, rse: 2.214, pcc: -0.076, kcc: -0.002
+2025-07-10 15:09:46: Epoch: 025 Train Loss: 1.347, mae: 0.860, rse: 0.502, pcc: 0.708, kcc: 0.491
+2025-07-10 15:09:46: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:09:46: Epoch: 025 Test results, ap: mae: 0.955, rse: 2.785, pcc: -0.148, kcc: -0.059
+2025-07-10 15:09:46: Epoch: 026 Train Loss: 1.329, mae: 0.871, rse: 0.507, pcc: 0.703, kcc: 0.490
+2025-07-10 15:09:47: Epoch: 026 Test results, ap: mae: 0.989, rse: 2.880, pcc: -0.211, kcc: -0.160
+2025-07-10 15:09:47: Epoch: 027 Train Loss: 1.328, mae: 0.858, rse: 0.500, pcc: 0.709, kcc: 0.498
+2025-07-10 15:09:47: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:09:47: Epoch: 027 Test results, ap: mae: 1.031, rse: 3.052, pcc: -0.185, kcc: -0.131
+2025-07-10 15:09:48: Epoch: 028 Train Loss: 1.318, mae: 0.859, rse: 0.514, pcc: 0.698, kcc: 0.486
+2025-07-10 15:09:48: Epoch: 028 Test results, ap: mae: 1.002, rse: 2.998, pcc: -0.151, kcc: -0.066
+2025-07-10 15:09:48: Epoch: 029 Train Loss: 1.318, mae: 0.856, rse: 0.497, pcc: 0.710, kcc: 0.492
+2025-07-10 15:09:48: Epoch: 029 New best VALIDATION metrics
+2025-07-10 15:09:49: Epoch: 029 Test results, ap: mae: 1.059, rse: 3.290, pcc: -0.204, kcc: -0.135
+2025-07-10 15:09:49: Epoch: 030 Train Loss: 1.285, mae: 0.851, rse: 0.491, pcc: 0.716, kcc: 0.506
+2025-07-10 15:09:49: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:09:49: Epoch: 030 Test results, ap: mae: 1.095, rse: 3.462, pcc: -0.243, kcc: -0.183
+2025-07-10 15:09:50: Epoch: 031 Train Loss: 1.252, mae: 0.843, rse: 0.486, pcc: 0.718, kcc: 0.507
+2025-07-10 15:09:50: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:09:50: Epoch: 031 Test results, ap: mae: 1.078, rse: 3.397, pcc: -0.258, kcc: -0.181
+2025-07-10 15:09:51: Epoch: 032 Train Loss: 1.234, mae: 0.841, rse: 0.484, pcc: 0.720, kcc: 0.506
+2025-07-10 15:09:51: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:09:51: Epoch: 032 Test results, ap: mae: 1.083, rse: 3.401, pcc: -0.253, kcc: -0.181
+2025-07-10 15:09:51: Epoch: 033 Train Loss: 1.227, mae: 0.851, rse: 0.495, pcc: 0.713, kcc: 0.498
+2025-07-10 15:09:51: Epoch: 033 Test results, ap: mae: 1.103, rse: 3.410, pcc: -0.242, kcc: -0.191
+2025-07-10 15:09:52: Epoch: 034 Train Loss: 1.216, mae: 0.841, rse: 0.481, pcc: 0.721, kcc: 0.508
+2025-07-10 15:09:52: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:09:52: Epoch: 034 Test results, ap: mae: 1.094, rse: 3.405, pcc: -0.252, kcc: -0.196
+2025-07-10 15:09:53: Epoch: 035 Train Loss: 1.206, mae: 0.839, rse: 0.481, pcc: 0.721, kcc: 0.508
+2025-07-10 15:09:53: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:09:53: Epoch: 035 Test results, ap: mae: 1.071, rse: 3.226, pcc: -0.248, kcc: -0.195
+2025-07-10 15:09:53: Epoch: 036 Train Loss: 1.196, mae: 0.848, rse: 0.487, pcc: 0.719, kcc: 0.507
+2025-07-10 15:09:53: Epoch: 036 Test results, ap: mae: 1.129, rse: 3.658, pcc: -0.227, kcc: -0.175
+2025-07-10 15:09:54: Epoch: 037 Train Loss: 1.207, mae: 0.833, rse: 0.477, pcc: 0.724, kcc: 0.515
+2025-07-10 15:09:54: Epoch: 037 New best VALIDATION metrics
+2025-07-10 15:09:54: Epoch: 037 Test results, ap: mae: 1.106, rse: 3.529, pcc: -0.252, kcc: -0.186
+2025-07-10 15:09:55: Epoch: 038 Train Loss: 1.196, mae: 0.839, rse: 0.482, pcc: 0.721, kcc: 0.510
+2025-07-10 15:09:55: Epoch: 038 Test results, ap: mae: 1.143, rse: 3.721, pcc: -0.245, kcc: -0.197
+2025-07-10 15:09:55: Epoch: 039 Train Loss: 1.185, mae: 0.845, rse: 0.482, pcc: 0.722, kcc: 0.509
+2025-07-10 15:09:55: Epoch: 039 Test results, ap: mae: 1.143, rse: 3.765, pcc: -0.251, kcc: -0.196
+2025-07-10 15:09:56: Epoch: 040 Train Loss: 1.189, mae: 0.840, rse: 0.476, pcc: 0.726, kcc: 0.515
+2025-07-10 15:09:56: Epoch: 040 Test results, ap: mae: 1.134, rse: 3.691, pcc: -0.235, kcc: -0.189
+2025-07-10 15:09:56: Epoch: 041 Train Loss: 1.181, mae: 0.832, rse: 0.476, pcc: 0.726, kcc: 0.514
+2025-07-10 15:09:56: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:09:56: Epoch: 041 Test results, ap: mae: 1.145, rse: 3.813, pcc: -0.252, kcc: -0.205
+2025-07-10 15:09:57: Epoch: 042 Train Loss: 1.109, mae: 0.833, rse: 0.476, pcc: 0.726, kcc: 0.517
+2025-07-10 15:09:57: Epoch: 042 New best VALIDATION metrics
+2025-07-10 15:09:57: Epoch: 042 Test results, ap: mae: 1.163, rse: 3.871, pcc: -0.245, kcc: -0.211
+2025-07-10 15:09:57: Epoch: 043 Train Loss: 1.154, mae: 0.833, rse: 0.477, pcc: 0.725, kcc: 0.518
+2025-07-10 15:09:58: Epoch: 043 Test results, ap: mae: 1.167, rse: 3.907, pcc: -0.252, kcc: -0.220
+2025-07-10 15:09:58: Epoch: 044 Train Loss: 1.165, mae: 0.830, rse: 0.473, pcc: 0.727, kcc: 0.518
+2025-07-10 15:09:58: Epoch: 044 New best VALIDATION metrics
+2025-07-10 15:09:58: Epoch: 044 Test results, ap: mae: 1.154, rse: 3.809, pcc: -0.256, kcc: -0.215
+2025-07-10 15:09:59: Epoch: 045 Train Loss: 1.172, mae: 0.827, rse: 0.472, pcc: 0.728, kcc: 0.521
+2025-07-10 15:09:59: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:09:59: Epoch: 045 Test results, ap: mae: 1.151, rse: 3.715, pcc: -0.259, kcc: -0.217
+2025-07-10 15:09:59: Epoch: 046 Train Loss: 1.146, mae: 0.826, rse: 0.469, pcc: 0.730, kcc: 0.520
+2025-07-10 15:09:59: Epoch: 046 New best VALIDATION metrics
+2025-07-10 15:09:59: Epoch: 046 Test results, ap: mae: 1.154, rse: 3.773, pcc: -0.260, kcc: -0.215
+2025-07-10 15:10:00: Epoch: 047 Train Loss: 1.156, mae: 0.823, rse: 0.469, pcc: 0.730, kcc: 0.523
+2025-07-10 15:10:00: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:10:00: Epoch: 047 Test results, ap: mae: 1.164, rse: 3.824, pcc: -0.263, kcc: -0.225
+2025-07-10 15:10:01: Epoch: 048 Train Loss: 1.136, mae: 0.826, rse: 0.472, pcc: 0.728, kcc: 0.520
+2025-07-10 15:10:01: Epoch: 048 Test results, ap: mae: 1.142, rse: 3.663, pcc: -0.262, kcc: -0.228
+2025-07-10 15:10:02: Epoch: 049 Train Loss: 1.149, mae: 0.831, rse: 0.475, pcc: 0.726, kcc: 0.522
+2025-07-10 15:10:02: Epoch: 049 Test results, ap: mae: 1.170, rse: 3.853, pcc: -0.259, kcc: -0.241
+2025-07-10 15:10:03: Epoch: 050 Train Loss: 1.123, mae: 0.835, rse: 0.478, pcc: 0.726, kcc: 0.518
+2025-07-10 15:10:03: Epoch: 050 Test results, ap: mae: 1.169, rse: 3.788, pcc: -0.257, kcc: -0.229
+2025-07-10 15:10:03: used time 0.01h
+2025-07-10 15:10:03: Running Cross Validation 3
+2025-07-10 15:10:03: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:10:04: Epoch: 001 Train Loss: 2.777, mae: 1.286, rse: 0.968, pcc: 0.202, kcc: 0.208
+2025-07-10 15:10:04: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:10:04: Epoch: 001 Test results, ap: mae: 0.612, rse: 1.121, pcc: -0.145, kcc: -0.214
+2025-07-10 15:10:05: Epoch: 002 Train Loss: 2.562, mae: 1.265, rse: 0.953, pcc: 0.303, kcc: 0.211
+2025-07-10 15:10:05: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:10:05: Epoch: 002 Test results, ap: mae: 0.772, rse: 1.728, pcc: -0.123, kcc: -0.064
+2025-07-10 15:10:06: Epoch: 003 Train Loss: 2.463, mae: 1.263, rse: 0.925, pcc: 0.305, kcc: 0.208
+2025-07-10 15:10:06: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:10:06: Epoch: 003 Test results, ap: mae: 0.935, rse: 2.362, pcc: -0.116, kcc: -0.163
+2025-07-10 15:10:07: Epoch: 004 Train Loss: 2.457, mae: 1.224, rse: 0.892, pcc: 0.334, kcc: 0.256
+2025-07-10 15:10:07: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:10:07: Epoch: 004 Test results, ap: mae: 0.638, rse: 1.231, pcc: -0.070, kcc: -0.092
+2025-07-10 15:10:08: Epoch: 005 Train Loss: 2.352, mae: 1.189, rse: 0.827, pcc: 0.427, kcc: 0.280
+2025-07-10 15:10:08: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:10:08: Epoch: 005 Test results, ap: mae: 0.906, rse: 2.411, pcc: -0.271, kcc: -0.154
+2025-07-10 15:10:08: Epoch: 006 Train Loss: 2.201, mae: 1.168, rse: 0.828, pcc: 0.441, kcc: 0.298
+2025-07-10 15:10:08: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:10:09: Epoch: 006 Test results, ap: mae: 0.691, rse: 1.435, pcc: -0.048, kcc: -0.104
+2025-07-10 15:10:09: Epoch: 007 Train Loss: 2.120, mae: 1.062, rse: 0.710, pcc: 0.553, kcc: 0.401
+2025-07-10 15:10:09: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:10:10: Epoch: 007 Test results, ap: mae: 0.655, rse: 1.278, pcc: -0.038, kcc: -0.114
+2025-07-10 15:10:10: Epoch: 008 Train Loss: 1.956, mae: 1.008, rse: 0.656, pcc: 0.603, kcc: 0.436
+2025-07-10 15:10:10: Epoch: 008 New best VALIDATION metrics
+2025-07-10 15:10:11: Epoch: 008 Test results, ap: mae: 0.738, rse: 1.634, pcc: -0.009, kcc: -0.137
+2025-07-10 15:10:11: Epoch: 009 Train Loss: 1.852, mae: 0.978, rse: 0.636, pcc: 0.605, kcc: 0.435
+2025-07-10 15:10:11: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:10:11: Epoch: 009 Test results, ap: mae: 0.720, rse: 1.574, pcc: -0.069, kcc: -0.128
+2025-07-10 15:10:12: Epoch: 010 Train Loss: 1.799, mae: 0.940, rse: 0.578, pcc: 0.654, kcc: 0.479
+2025-07-10 15:10:12: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:10:12: Epoch: 010 Test results, ap: mae: 0.916, rse: 2.373, pcc: -0.074, kcc: -0.066
+2025-07-10 15:10:13: Epoch: 011 Train Loss: 1.683, mae: 0.898, rse: 0.536, pcc: 0.683, kcc: 0.495
+2025-07-10 15:10:13: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:10:13: Epoch: 011 Test results, ap: mae: 0.862, rse: 2.150, pcc: -0.085, kcc: -0.050
+2025-07-10 15:10:13: Epoch: 011 New best TEST metrics
+2025-07-10 15:10:14: Epoch: 012 Train Loss: 1.554, mae: 0.885, rse: 0.512, pcc: 0.700, kcc: 0.520
+2025-07-10 15:10:14: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:10:14: Epoch: 012 Test results, ap: mae: 0.867, rse: 2.236, pcc: -0.008, kcc: -0.006
+2025-07-10 15:10:14: Epoch: 012 New best TEST metrics
+2025-07-10 15:10:15: Epoch: 013 Train Loss: 1.551, mae: 0.880, rse: 0.483, pcc: 0.727, kcc: 0.535
+2025-07-10 15:10:15: Epoch: 013 New best VALIDATION metrics
+2025-07-10 15:10:15: Epoch: 013 Test results, ap: mae: 0.848, rse: 2.145, pcc: -0.103, kcc: -0.057
+2025-07-10 15:10:16: Epoch: 014 Train Loss: 1.442, mae: 0.839, rse: 0.452, pcc: 0.742, kcc: 0.551
+2025-07-10 15:10:16: Epoch: 014 New best VALIDATION metrics
+2025-07-10 15:10:16: Epoch: 014 Test results, ap: mae: 0.876, rse: 2.132, pcc: 0.028, kcc: 0.006
+2025-07-10 15:10:16: Epoch: 014 New best TEST metrics
+2025-07-10 15:10:17: Epoch: 015 Train Loss: 1.461, mae: 0.793, rse: 0.421, pcc: 0.762, kcc: 0.568
+2025-07-10 15:10:17: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:10:17: Epoch: 015 Test results, ap: mae: 0.853, rse: 2.122, pcc: 0.083, kcc: 0.069
+2025-07-10 15:10:17: Epoch: 015 New best TEST metrics
+2025-07-10 15:10:18: Epoch: 016 Train Loss: 1.382, mae: 0.867, rse: 0.479, pcc: 0.724, kcc: 0.529
+2025-07-10 15:10:18: Epoch: 016 Test results, ap: mae: 0.770, rse: 1.800, pcc: 0.018, kcc: 0.021
+2025-07-10 15:10:18: Epoch: 016 New best TEST metrics
+2025-07-10 15:10:19: Epoch: 017 Train Loss: 1.357, mae: 0.822, rse: 0.435, pcc: 0.752, kcc: 0.558
+2025-07-10 15:10:19: Epoch: 017 Test results, ap: mae: 0.948, rse: 2.532, pcc: 0.027, kcc: 0.042
+2025-07-10 15:10:19: Epoch: 018 Train Loss: 1.247, mae: 0.782, rse: 0.398, pcc: 0.781, kcc: 0.581
+2025-07-10 15:10:19: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:10:20: Epoch: 018 Test results, ap: mae: 0.828, rse: 2.088, pcc: 0.058, kcc: 0.053
+2025-07-10 15:10:20: Epoch: 019 Train Loss: 1.210, mae: 0.776, rse: 0.386, pcc: 0.788, kcc: 0.586
+2025-07-10 15:10:20: Epoch: 019 New best VALIDATION metrics
+2025-07-10 15:10:21: Epoch: 019 Test results, ap: mae: 0.805, rse: 1.915, pcc: 0.043, kcc: 0.025
+2025-07-10 15:10:21: Epoch: 020 Train Loss: 1.105, mae: 0.764, rse: 0.374, pcc: 0.801, kcc: 0.595
+2025-07-10 15:10:21: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:10:21: Epoch: 020 Test results, ap: mae: 0.838, rse: 2.148, pcc: 0.049, kcc: 0.021
+2025-07-10 15:10:22: Epoch: 021 Train Loss: 1.033, mae: 0.767, rse: 0.372, pcc: 0.800, kcc: 0.590
+2025-07-10 15:10:22: Epoch: 021 Test results, ap: mae: 0.944, rse: 2.671, pcc: 0.056, kcc: 0.041
+2025-07-10 15:10:23: Epoch: 022 Train Loss: 0.990, mae: 0.748, rse: 0.357, pcc: 0.804, kcc: 0.594
+2025-07-10 15:10:23: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:10:23: Epoch: 022 Test results, ap: mae: 0.904, rse: 2.488, pcc: 0.155, kcc: 0.113
+2025-07-10 15:10:24: Epoch: 023 Train Loss: 0.961, mae: 0.767, rse: 0.366, pcc: 0.802, kcc: 0.591
+2025-07-10 15:10:24: Epoch: 023 Test results, ap: mae: 0.912, rse: 2.554, pcc: 0.159, kcc: 0.116
+2025-07-10 15:10:25: Epoch: 024 Train Loss: 0.950, mae: 0.726, rse: 0.337, pcc: 0.817, kcc: 0.610
+2025-07-10 15:10:25: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:10:25: Epoch: 024 Test results, ap: mae: 0.912, rse: 2.546, pcc: 0.216, kcc: 0.131
+2025-07-10 15:10:26: Epoch: 025 Train Loss: 0.904, mae: 0.709, rse: 0.327, pcc: 0.821, kcc: 0.618
+2025-07-10 15:10:26: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:10:26: Epoch: 025 Test results, ap: mae: 0.897, rse: 2.456, pcc: 0.194, kcc: 0.120
+2025-07-10 15:10:27: Epoch: 026 Train Loss: 0.878, mae: 0.702, rse: 0.317, pcc: 0.827, kcc: 0.626
+2025-07-10 15:10:27: Epoch: 026 New best VALIDATION metrics
+2025-07-10 15:10:27: Epoch: 026 Test results, ap: mae: 0.899, rse: 2.239, pcc: 0.227, kcc: 0.136
+2025-07-10 15:10:28: Epoch: 027 Train Loss: 0.853, mae: 0.735, rse: 0.338, pcc: 0.816, kcc: 0.618
+2025-07-10 15:10:28: Epoch: 027 Test results, ap: mae: 1.027, rse: 3.346, pcc: 0.126, kcc: 0.097
+2025-07-10 15:10:28: Epoch: 028 Train Loss: 0.855, mae: 0.693, rse: 0.310, pcc: 0.833, kcc: 0.633
+2025-07-10 15:10:28: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:10:29: Epoch: 028 Test results, ap: mae: 0.946, rse: 2.802, pcc: 0.175, kcc: 0.125
+2025-07-10 15:10:29: Epoch: 029 Train Loss: 0.812, mae: 0.695, rse: 0.312, pcc: 0.830, kcc: 0.639
+2025-07-10 15:10:29: Epoch: 029 Test results, ap: mae: 0.966, rse: 2.991, pcc: 0.113, kcc: 0.084
+2025-07-10 15:10:30: Epoch: 030 Train Loss: 0.818, mae: 0.683, rse: 0.302, pcc: 0.836, kcc: 0.633
+2025-07-10 15:10:30: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:10:30: Epoch: 030 Test results, ap: mae: 1.046, rse: 3.417, pcc: 0.155, kcc: 0.092
+2025-07-10 15:10:31: Epoch: 031 Train Loss: 0.754, mae: 0.664, rse: 0.293, pcc: 0.842, kcc: 0.647
+2025-07-10 15:10:31: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:10:31: Epoch: 031 Test results, ap: mae: 0.989, rse: 3.102, pcc: 0.173, kcc: 0.109
+2025-07-10 15:10:32: Epoch: 032 Train Loss: 0.738, mae: 0.646, rse: 0.279, pcc: 0.850, kcc: 0.653
+2025-07-10 15:10:32: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:10:32: Epoch: 032 Test results, ap: mae: 1.005, rse: 3.233, pcc: 0.182, kcc: 0.117
+2025-07-10 15:10:32: Epoch: 033 Train Loss: 0.749, mae: 0.648, rse: 0.279, pcc: 0.850, kcc: 0.654
+2025-07-10 15:10:32: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:10:33: Epoch: 033 Test results, ap: mae: 0.996, rse: 3.099, pcc: 0.174, kcc: 0.106
+2025-07-10 15:10:33: Epoch: 034 Train Loss: 0.735, mae: 0.634, rse: 0.270, pcc: 0.855, kcc: 0.660
+2025-07-10 15:10:33: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:10:33: Epoch: 034 Test results, ap: mae: 1.016, rse: 3.175, pcc: 0.183, kcc: 0.112
+2025-07-10 15:10:34: Epoch: 035 Train Loss: 0.735, mae: 0.626, rse: 0.262, pcc: 0.860, kcc: 0.667
+2025-07-10 15:10:34: Epoch: 035 New best VALIDATION metrics
+2025-07-10 15:10:34: Epoch: 035 Test results, ap: mae: 1.033, rse: 3.221, pcc: 0.199, kcc: 0.113
+2025-07-10 15:10:35: Epoch: 036 Train Loss: 0.710, mae: 0.639, rse: 0.272, pcc: 0.854, kcc: 0.665
+2025-07-10 15:10:35: Epoch: 036 Test results, ap: mae: 1.032, rse: 3.186, pcc: 0.206, kcc: 0.119
+2025-07-10 15:10:36: Epoch: 037 Train Loss: 0.683, mae: 0.635, rse: 0.267, pcc: 0.856, kcc: 0.665
+2025-07-10 15:10:36: Epoch: 037 Test results, ap: mae: 1.044, rse: 3.247, pcc: 0.190, kcc: 0.099
+2025-07-10 15:10:37: Epoch: 038 Train Loss: 0.686, mae: 0.643, rse: 0.269, pcc: 0.857, kcc: 0.664
+2025-07-10 15:10:37: Epoch: 038 Test results, ap: mae: 1.078, rse: 3.446, pcc: 0.178, kcc: 0.100
+2025-07-10 15:10:37: Epoch: 039 Train Loss: 0.700, mae: 0.628, rse: 0.260, pcc: 0.860, kcc: 0.668
+2025-07-10 15:10:38: Epoch: 039 Test results, ap: mae: 1.060, rse: 3.240, pcc: 0.201, kcc: 0.107
+2025-07-10 15:10:38: Epoch: 040 Train Loss: 0.674, mae: 0.638, rse: 0.263, pcc: 0.861, kcc: 0.672
+2025-07-10 15:10:38: Epoch: 040 Test results, ap: mae: 1.066, rse: 3.463, pcc: 0.187, kcc: 0.104
+2025-07-10 15:10:39: Epoch: 041 Train Loss: 0.669, mae: 0.607, rse: 0.250, pcc: 0.866, kcc: 0.677
+2025-07-10 15:10:39: Epoch: 041 New best VALIDATION metrics
+2025-07-10 15:10:39: Epoch: 041 Test results, ap: mae: 1.059, rse: 3.259, pcc: 0.221, kcc: 0.116
+2025-07-10 15:10:40: Epoch: 042 Train Loss: 0.650, mae: 0.617, rse: 0.252, pcc: 0.865, kcc: 0.674
+2025-07-10 15:10:40: Epoch: 042 Test results, ap: mae: 1.085, rse: 3.452, pcc: 0.207, kcc: 0.112
+2025-07-10 15:10:40: Epoch: 043 Train Loss: 0.643, mae: 0.614, rse: 0.250, pcc: 0.866, kcc: 0.676
+2025-07-10 15:10:41: Epoch: 043 Test results, ap: mae: 1.068, rse: 3.345, pcc: 0.205, kcc: 0.112
+2025-07-10 15:10:41: Epoch: 044 Train Loss: 0.666, mae: 0.617, rse: 0.252, pcc: 0.866, kcc: 0.674
+2025-07-10 15:10:41: Epoch: 044 Test results, ap: mae: 1.088, rse: 3.502, pcc: 0.211, kcc: 0.112
+2025-07-10 15:10:42: Epoch: 045 Train Loss: 0.629, mae: 0.609, rse: 0.246, pcc: 0.869, kcc: 0.679
+2025-07-10 15:10:42: Epoch: 045 New best VALIDATION metrics
+2025-07-10 15:10:42: Epoch: 045 Test results, ap: mae: 1.092, rse: 3.482, pcc: 0.211, kcc: 0.112
+2025-07-10 15:10:43: Epoch: 046 Train Loss: 0.629, mae: 0.619, rse: 0.250, pcc: 0.867, kcc: 0.677
+2025-07-10 15:10:43: Epoch: 046 Test results, ap: mae: 1.077, rse: 3.409, pcc: 0.206, kcc: 0.106
+2025-07-10 15:10:44: Epoch: 047 Train Loss: 0.632, mae: 0.609, rse: 0.245, pcc: 0.869, kcc: 0.680
+2025-07-10 15:10:44: Epoch: 047 New best VALIDATION metrics
+2025-07-10 15:10:44: Epoch: 047 Test results, ap: mae: 1.062, rse: 3.313, pcc: 0.201, kcc: 0.105
+2025-07-10 15:10:45: Epoch: 048 Train Loss: 0.632, mae: 0.596, rse: 0.239, pcc: 0.873, kcc: 0.685
+2025-07-10 15:10:45: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:10:45: Epoch: 048 Test results, ap: mae: 1.062, rse: 3.279, pcc: 0.207, kcc: 0.109
+2025-07-10 15:10:45: Epoch: 049 Train Loss: 0.630, mae: 0.605, rse: 0.242, pcc: 0.871, kcc: 0.683
+2025-07-10 15:10:46: Epoch: 049 Test results, ap: mae: 1.065, rse: 3.322, pcc: 0.201, kcc: 0.109
+2025-07-10 15:10:46: Epoch: 050 Train Loss: 0.630, mae: 0.603, rse: 0.241, pcc: 0.872, kcc: 0.680
+2025-07-10 15:10:46: Epoch: 050 Test results, ap: mae: 1.084, rse: 3.421, pcc: 0.211, kcc: 0.112
+2025-07-10 15:10:46: used time 0.01h
+2025-07-10 15:10:46: Running Cross Validation 4
+2025-07-10 15:10:46: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-10 15:10:47: Epoch: 001 Train Loss: 2.837, mae: 1.187, rse: 0.971, pcc: 0.264, kcc: 0.189
+2025-07-10 15:10:47: Epoch: 001 New best VALIDATION metrics
+2025-07-10 15:10:47: Epoch: 001 Test results, ap: mae: 0.674, rse: 1.360, pcc: 0.326, kcc: 0.072
+2025-07-10 15:10:48: Epoch: 002 Train Loss: 2.640, mae: 1.196, rse: 0.934, pcc: 0.273, kcc: 0.187
+2025-07-10 15:10:48: Epoch: 002 New best VALIDATION metrics
+2025-07-10 15:10:48: Epoch: 002 Test results, ap: mae: 0.568, rse: 0.979, pcc: 0.377, kcc: 0.205
+2025-07-10 15:10:49: Epoch: 003 Train Loss: 2.534, mae: 1.164, rse: 0.916, pcc: 0.321, kcc: 0.223
+2025-07-10 15:10:49: Epoch: 003 New best VALIDATION metrics
+2025-07-10 15:10:49: Epoch: 003 Test results, ap: mae: 0.583, rse: 1.020, pcc: 0.295, kcc: 0.157
+2025-07-10 15:10:50: Epoch: 004 Train Loss: 2.343, mae: 1.119, rse: 0.838, pcc: 0.414, kcc: 0.266
+2025-07-10 15:10:50: Epoch: 004 New best VALIDATION metrics
+2025-07-10 15:10:50: Epoch: 004 Test results, ap: mae: 0.622, rse: 1.153, pcc: -0.141, kcc: -0.067
+2025-07-10 15:10:50: Epoch: 005 Train Loss: 1.943, mae: 1.027, rse: 0.708, pcc: 0.552, kcc: 0.370
+2025-07-10 15:10:50: Epoch: 005 New best VALIDATION metrics
+2025-07-10 15:10:51: Epoch: 005 Test results, ap: mae: 0.825, rse: 2.037, pcc: -0.134, kcc: -0.065
+2025-07-10 15:10:51: Epoch: 006 Train Loss: 1.814, mae: 0.982, rse: 0.665, pcc: 0.588, kcc: 0.404
+2025-07-10 15:10:51: Epoch: 006 New best VALIDATION metrics
+2025-07-10 15:10:51: Epoch: 006 Test results, ap: mae: 0.762, rse: 1.797, pcc: -0.120, kcc: -0.060
+2025-07-10 15:10:52: Epoch: 007 Train Loss: 1.774, mae: 0.928, rse: 0.618, pcc: 0.625, kcc: 0.442
+2025-07-10 15:10:52: Epoch: 007 New best VALIDATION metrics
+2025-07-10 15:10:52: Epoch: 007 Test results, ap: mae: 0.780, rse: 1.748, pcc: -0.088, kcc: -0.039
+2025-07-10 15:10:53: Epoch: 008 Train Loss: 1.657, mae: 0.969, rse: 0.667, pcc: 0.595, kcc: 0.418
+2025-07-10 15:10:53: Epoch: 008 Test results, ap: mae: 0.805, rse: 1.881, pcc: -0.097, kcc: -0.067
+2025-07-10 15:10:54: Epoch: 009 Train Loss: 1.526, mae: 0.911, rse: 0.610, pcc: 0.632, kcc: 0.454
+2025-07-10 15:10:54: Epoch: 009 New best VALIDATION metrics
+2025-07-10 15:10:54: Epoch: 009 Test results, ap: mae: 0.693, rse: 1.297, pcc: 0.017, kcc: 0.040
+2025-07-10 15:10:55: Epoch: 010 Train Loss: 1.371, mae: 0.860, rse: 0.551, pcc: 0.671, kcc: 0.497
+2025-07-10 15:10:55: Epoch: 010 New best VALIDATION metrics
+2025-07-10 15:10:55: Epoch: 010 Test results, ap: mae: 0.783, rse: 1.770, pcc: -0.037, kcc: 0.012
+2025-07-10 15:10:55: Epoch: 011 Train Loss: 1.262, mae: 0.816, rse: 0.525, pcc: 0.690, kcc: 0.508
+2025-07-10 15:10:55: Epoch: 011 New best VALIDATION metrics
+2025-07-10 15:10:56: Epoch: 011 Test results, ap: mae: 0.752, rse: 1.539, pcc: -0.064, kcc: -0.005
+2025-07-10 15:10:56: Epoch: 011 New best TEST metrics
+2025-07-10 15:10:56: Epoch: 012 Train Loss: 1.170, mae: 0.795, rse: 0.503, pcc: 0.706, kcc: 0.523
+2025-07-10 15:10:56: Epoch: 012 New best VALIDATION metrics
+2025-07-10 15:10:56: Epoch: 012 Test results, ap: mae: 0.807, rse: 1.712, pcc: -0.107, kcc: -0.039
+2025-07-10 15:10:57: Epoch: 013 Train Loss: 1.150, mae: 0.803, rse: 0.504, pcc: 0.706, kcc: 0.520
+2025-07-10 15:10:57: Epoch: 013 Test results, ap: mae: 0.852, rse: 1.894, pcc: -0.104, kcc: -0.042
+2025-07-10 15:10:58: Epoch: 014 Train Loss: 1.112, mae: 0.804, rse: 0.506, pcc: 0.705, kcc: 0.525
+2025-07-10 15:10:58: Epoch: 014 Test results, ap: mae: 0.887, rse: 2.039, pcc: -0.104, kcc: -0.066
+2025-07-10 15:10:59: Epoch: 015 Train Loss: 1.098, mae: 0.771, rse: 0.481, pcc: 0.721, kcc: 0.541
+2025-07-10 15:10:59: Epoch: 015 New best VALIDATION metrics
+2025-07-10 15:10:59: Epoch: 015 Test results, ap: mae: 0.829, rse: 1.825, pcc: -0.127, kcc: -0.097
+2025-07-10 15:11:00: Epoch: 016 Train Loss: 1.047, mae: 0.768, rse: 0.473, pcc: 0.727, kcc: 0.543
+2025-07-10 15:11:00: Epoch: 016 New best VALIDATION metrics
+2025-07-10 15:11:00: Epoch: 016 Test results, ap: mae: 0.833, rse: 1.829, pcc: -0.092, kcc: -0.108
+2025-07-10 15:11:00: Epoch: 017 Train Loss: 1.039, mae: 0.769, rse: 0.475, pcc: 0.727, kcc: 0.549
+2025-07-10 15:11:00: Epoch: 017 New best VALIDATION metrics
+2025-07-10 15:11:01: Epoch: 017 Test results, ap: mae: 0.776, rse: 1.621, pcc: -0.105, kcc: -0.096
+2025-07-10 15:11:01: Epoch: 018 Train Loss: 1.056, mae: 0.764, rse: 0.473, pcc: 0.726, kcc: 0.543
+2025-07-10 15:11:01: Epoch: 018 New best VALIDATION metrics
+2025-07-10 15:11:01: Epoch: 018 Test results, ap: mae: 0.769, rse: 1.594, pcc: -0.053, kcc: -0.044
+2025-07-10 15:11:02: Epoch: 019 Train Loss: 0.989, mae: 0.775, rse: 0.476, pcc: 0.725, kcc: 0.542
+2025-07-10 15:11:02: Epoch: 019 Test results, ap: mae: 0.860, rse: 1.996, pcc: -0.076, kcc: -0.100
+2025-07-10 15:11:03: Epoch: 020 Train Loss: 1.023, mae: 0.758, rse: 0.473, pcc: 0.727, kcc: 0.551
+2025-07-10 15:11:03: Epoch: 020 New best VALIDATION metrics
+2025-07-10 15:11:03: Epoch: 020 Test results, ap: mae: 0.838, rse: 1.935, pcc: -0.133, kcc: -0.131
+2025-07-10 15:11:04: Epoch: 021 Train Loss: 1.000, mae: 0.763, rse: 0.472, pcc: 0.728, kcc: 0.552
+2025-07-10 15:11:04: Epoch: 021 Test results, ap: mae: 0.881, rse: 2.041, pcc: -0.101, kcc: -0.118
+2025-07-10 15:11:04: Epoch: 022 Train Loss: 0.945, mae: 0.763, rse: 0.463, pcc: 0.733, kcc: 0.558
+2025-07-10 15:11:04: Epoch: 022 New best VALIDATION metrics
+2025-07-10 15:11:05: Epoch: 022 Test results, ap: mae: 0.951, rse: 2.365, pcc: -0.061, kcc: -0.064
+2025-07-10 15:11:05: Epoch: 023 Train Loss: 0.933, mae: 0.748, rse: 0.454, pcc: 0.741, kcc: 0.563
+2025-07-10 15:11:05: Epoch: 023 New best VALIDATION metrics
+2025-07-10 15:11:05: Epoch: 023 Test results, ap: mae: 0.995, rse: 2.653, pcc: -0.015, kcc: -0.033
+2025-07-10 15:11:06: Epoch: 024 Train Loss: 0.923, mae: 0.740, rse: 0.449, pcc: 0.743, kcc: 0.570
+2025-07-10 15:11:06: Epoch: 024 New best VALIDATION metrics
+2025-07-10 15:11:06: Epoch: 024 Test results, ap: mae: 0.999, rse: 2.632, pcc: 0.022, kcc: -0.021
+2025-07-10 15:11:07: Epoch: 025 Train Loss: 0.924, mae: 0.727, rse: 0.440, pcc: 0.750, kcc: 0.577
+2025-07-10 15:11:07: Epoch: 025 New best VALIDATION metrics
+2025-07-10 15:11:07: Epoch: 025 Test results, ap: mae: 0.924, rse: 2.300, pcc: 0.007, kcc: -0.020
+2025-07-10 15:11:08: Epoch: 026 Train Loss: 0.909, mae: 0.731, rse: 0.439, pcc: 0.750, kcc: 0.576
+2025-07-10 15:11:08: Epoch: 026 Test results, ap: mae: 0.936, rse: 2.334, pcc: 0.029, kcc: -0.006
+2025-07-10 15:11:09: Epoch: 027 Train Loss: 0.883, mae: 0.723, rse: 0.434, pcc: 0.753, kcc: 0.577
+2025-07-10 15:11:09: Epoch: 027 New best VALIDATION metrics
+2025-07-10 15:11:09: Epoch: 027 Test results, ap: mae: 0.975, rse: 2.596, pcc: 0.032, kcc: -0.006
+2025-07-10 15:11:09: Epoch: 028 Train Loss: 0.887, mae: 0.723, rse: 0.431, pcc: 0.755, kcc: 0.580
+2025-07-10 15:11:09: Epoch: 028 New best VALIDATION metrics
+2025-07-10 15:11:10: Epoch: 028 Test results, ap: mae: 0.950, rse: 2.493, pcc: 0.033, kcc: -0.004
+2025-07-10 15:11:10: Epoch: 029 Train Loss: 0.869, mae: 0.726, rse: 0.430, pcc: 0.755, kcc: 0.577
+2025-07-10 15:11:10: Epoch: 029 Test results, ap: mae: 1.036, rse: 3.062, pcc: 0.045, kcc: 0.000
+2025-07-10 15:11:11: Epoch: 030 Train Loss: 0.861, mae: 0.721, rse: 0.425, pcc: 0.759, kcc: 0.585
+2025-07-10 15:11:11: Epoch: 030 New best VALIDATION metrics
+2025-07-10 15:11:11: Epoch: 030 Test results, ap: mae: 0.932, rse: 2.495, pcc: 0.074, kcc: 0.027
+2025-07-10 15:11:12: Epoch: 031 Train Loss: 0.823, mae: 0.716, rse: 0.422, pcc: 0.761, kcc: 0.587
+2025-07-10 15:11:12: Epoch: 031 New best VALIDATION metrics
+2025-07-10 15:11:12: Epoch: 031 Test results, ap: mae: 1.073, rse: 3.270, pcc: 0.073, kcc: 0.028
+2025-07-10 15:11:13: Epoch: 032 Train Loss: 0.852, mae: 0.715, rse: 0.420, pcc: 0.761, kcc: 0.588
+2025-07-10 15:11:13: Epoch: 032 New best VALIDATION metrics
+2025-07-10 15:11:13: Epoch: 032 Test results, ap: mae: 0.998, rse: 2.876, pcc: 0.078, kcc: 0.019
+2025-07-10 15:11:14: Epoch: 033 Train Loss: 0.837, mae: 0.705, rse: 0.419, pcc: 0.763, kcc: 0.592
+2025-07-10 15:11:14: Epoch: 033 New best VALIDATION metrics
+2025-07-10 15:11:14: Epoch: 033 Test results, ap: mae: 0.976, rse: 2.762, pcc: 0.083, kcc: 0.024
+2025-07-10 15:11:14: Epoch: 034 Train Loss: 0.829, mae: 0.703, rse: 0.415, pcc: 0.765, kcc: 0.596
+2025-07-10 15:11:14: Epoch: 034 New best VALIDATION metrics
+2025-07-10 15:11:15: Epoch: 034 Test results, ap: mae: 1.045, rse: 3.195, pcc: 0.086, kcc: 0.024
+2025-07-10 15:11:15: Epoch: 035 Train Loss: 0.817, mae: 0.707, rse: 0.418, pcc: 0.763, kcc: 0.589
+2025-07-10 15:11:16: Epoch: 035 Test results, ap: mae: 1.085, rse: 3.409, pcc: 0.107, kcc: 0.032
+2025-07-10 15:11:16: Epoch: 036 Train Loss: 0.823, mae: 0.708, rse: 0.417, pcc: 0.764, kcc: 0.592
+2025-07-10 15:11:16: Epoch: 036 Test results, ap: mae: 1.081, rse: 3.400, pcc: 0.111, kcc: 0.032
+2025-07-10 15:11:17: Epoch: 037 Train Loss: 0.815, mae: 0.705, rse: 0.416, pcc: 0.764, kcc: 0.594
+2025-07-10 15:11:17: Epoch: 037 Test results, ap: mae: 1.013, rse: 3.019, pcc: 0.096, kcc: 0.031
+2025-07-10 15:11:18: Epoch: 038 Train Loss: 0.823, mae: 0.704, rse: 0.415, pcc: 0.765, kcc: 0.595
+2025-07-10 15:11:18: Epoch: 038 Test results, ap: mae: 1.017, rse: 3.105, pcc: 0.095, kcc: 0.025
+2025-07-10 15:11:19: Epoch: 039 Train Loss: 0.821, mae: 0.700, rse: 0.415, pcc: 0.765, kcc: 0.595
+2025-07-10 15:11:19: Epoch: 039 New best VALIDATION metrics
+2025-07-10 15:11:19: Epoch: 039 Test results, ap: mae: 1.172, rse: 3.932, pcc: 0.109, kcc: 0.040
+2025-07-10 15:11:19: Epoch: 040 Train Loss: 0.802, mae: 0.698, rse: 0.413, pcc: 0.766, kcc: 0.598
+2025-07-10 15:11:19: Epoch: 040 New best VALIDATION metrics
+2025-07-10 15:11:20: Epoch: 040 Test results, ap: mae: 1.106, rse: 3.578, pcc: 0.103, kcc: 0.034
+2025-07-10 15:11:20: Epoch: 041 Train Loss: 0.808, mae: 0.702, rse: 0.414, pcc: 0.766, kcc: 0.595
+2025-07-10 15:11:20: Epoch: 041 Test results, ap: mae: 1.127, rse: 3.676, pcc: 0.105, kcc: 0.036
+2025-07-10 15:11:21: Epoch: 042 Train Loss: 0.796, mae: 0.702, rse: 0.415, pcc: 0.765, kcc: 0.595
+2025-07-10 15:11:21: Epoch: 042 Test results, ap: mae: 1.125, rse: 3.678, pcc: 0.111, kcc: 0.034
+2025-07-10 15:11:22: Epoch: 043 Train Loss: 0.798, mae: 0.701, rse: 0.413, pcc: 0.766, kcc: 0.596
+2025-07-10 15:11:22: Epoch: 043 Test results, ap: mae: 1.118, rse: 3.645, pcc: 0.110, kcc: 0.033
+2025-07-10 15:11:23: Epoch: 044 Train Loss: 0.793, mae: 0.703, rse: 0.415, pcc: 0.766, kcc: 0.594
+2025-07-10 15:11:23: Epoch: 044 Test results, ap: mae: 1.155, rse: 3.839, pcc: 0.105, kcc: 0.033
+2025-07-10 15:11:24: Epoch: 045 Train Loss: 0.805, mae: 0.699, rse: 0.413, pcc: 0.767, kcc: 0.596
+2025-07-10 15:11:24: Epoch: 045 Test results, ap: mae: 1.144, rse: 3.783, pcc: 0.107, kcc: 0.034
+2025-07-10 15:11:24: Epoch: 046 Train Loss: 0.789, mae: 0.696, rse: 0.411, pcc: 0.767, kcc: 0.597
+2025-07-10 15:11:24: Epoch: 046 New best VALIDATION metrics
+2025-07-10 15:11:25: Epoch: 046 Test results, ap: mae: 1.145, rse: 3.836, pcc: 0.110, kcc: 0.034
+2025-07-10 15:11:25: Epoch: 047 Train Loss: 0.788, mae: 0.698, rse: 0.412, pcc: 0.767, kcc: 0.598
+2025-07-10 15:11:25: Epoch: 047 Test results, ap: mae: 1.117, rse: 3.651, pcc: 0.106, kcc: 0.033
+2025-07-10 15:11:26: Epoch: 048 Train Loss: 0.787, mae: 0.696, rse: 0.411, pcc: 0.768, kcc: 0.599
+2025-07-10 15:11:26: Epoch: 048 New best VALIDATION metrics
+2025-07-10 15:11:26: Epoch: 048 Test results, ap: mae: 1.104, rse: 3.613, pcc: 0.099, kcc: 0.031
+2025-07-10 15:11:27: Epoch: 049 Train Loss: 0.796, mae: 0.698, rse: 0.412, pcc: 0.767, kcc: 0.597
+2025-07-10 15:11:27: Epoch: 049 Test results, ap: mae: 1.090, rse: 3.522, pcc: 0.106, kcc: 0.035
+2025-07-10 15:11:28: Epoch: 050 Train Loss: 0.768, mae: 0.696, rse: 0.412, pcc: 0.767, kcc: 0.596
+2025-07-10 15:11:28: Epoch: 050 Test results, ap: mae: 1.122, rse: 3.721, pcc: 0.098, kcc: 0.032
+2025-07-10 15:11:28: used time 0.01h
+2025-07-10 15:11:28: Cross Validation Finished!
+2025-07-10 15:11:28: Best validation perform list
+[[0.72535801 0.38833493 0.78412294 0.5785836 ]
+ [0.83113241 0.49696597 0.70992452 0.52378279]
+ [0.82291895 0.46909893 0.7300328  0.52295887]
+ [0.59571636 0.23934488 0.87263811 0.68458086]
+ [0.69564599 0.41082388 0.76761478 0.5992105 ]]
+2025-07-10 15:11:28: mean: [0.734 0.401 0.773 0.582]
+2025-07-10 15:11:28: std: [0.087 0.09  0.056 0.06 ]
+2025-07-10 15:11:28: Best test perform list
+[[ 0.60531873  1.06012595  0.29388592  0.13078958]
+ [ 0.68706387  1.47015882  0.10518017  0.06695594]
+ [ 0.67008978  1.50942826  0.18467049  0.11517809]
+ [ 0.77031428  1.80041909  0.0180029   0.02081532]
+ [ 0.75190043  1.53941    -0.06396599 -0.00520383]]
+2025-07-10 15:11:28: mean: [0.697 1.476 0.108 0.066]
+2025-07-10 15:11:28: std: [0.059 0.238 0.125 0.052]
diff --git a/run-reg/non-siamese-rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/config.json b/run-reg/non-siamese-rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/config.json
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": "mamba", "channels": 16, "fusion": "diff", "glob_feat": false, "non_siamese": true, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": true, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
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+0.023712078301765745,0.051783088257316416,0.02754600796079869,0.00856954890048307
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diff --git a/run-reg/non-siamese-rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/training.log b/run-reg/non-siamese-rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/training.log
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+2025-08-25 22:23:55: saving_dir: ./run-reg/rn18-non-siamese-diff-16-mamba-pcs-768-mse-32-0.001-50
+2025-08-25 22:23:55: Loading Training Dataset
+2025-08-25 22:24:20: Loading Test Dataset
+2025-08-25 22:24:24: Running Cross Validation 0
+2025-08-25 22:24:24: Fold 0  Train set:2269, Valid set:568, Test set: 123
+2025-08-25 22:24:55: Epoch: 001 Train Loss: 2.630, mae: 1.846, rse: 1.755, pcc: 0.137, kcc: 0.164
+2025-08-25 22:24:55: Epoch: 001 New best VALIDATION metrics
+2025-08-25 22:24:56: Epoch: 001 Test results, ap: mae: 1.256, rse: 3.941, pcc: -0.176, kcc: -0.179
+2025-08-25 22:25:26: Epoch: 002 Train Loss: 2.379, mae: 1.134, rse: 0.858, pcc: 0.423, kcc: 0.312
+2025-08-25 22:25:26: Epoch: 002 New best VALIDATION metrics
+2025-08-25 22:25:28: Epoch: 002 Test results, ap: mae: 0.766, rse: 1.640, pcc: -0.042, kcc: -0.018
+2025-08-25 22:25:58: Epoch: 003 Train Loss: 2.257, mae: 1.104, rse: 0.852, pcc: 0.495, kcc: 0.338
+2025-08-25 22:25:58: Epoch: 003 New best VALIDATION metrics
+2025-08-25 22:25:59: Epoch: 003 Test results, ap: mae: 0.638, rse: 1.200, pcc: -0.111, kcc: -0.132
+2025-08-25 22:26:29: Epoch: 004 Train Loss: 2.159, mae: 1.109, rse: 0.853, pcc: 0.506, kcc: 0.352
+2025-08-25 22:26:29: Epoch: 004 New best VALIDATION metrics
+2025-08-25 22:26:31: Epoch: 004 Test results, ap: mae: 0.693, rse: 1.584, pcc: -0.111, kcc: 0.032
+2025-08-25 22:27:01: Epoch: 005 Train Loss: 2.082, mae: 0.998, rse: 0.697, pcc: 0.561, kcc: 0.392
+2025-08-25 22:27:01: Epoch: 005 New best VALIDATION metrics
+2025-08-25 22:27:02: Epoch: 005 Test results, ap: mae: 0.727, rse: 1.430, pcc: -0.065, kcc: -0.010
+2025-08-25 22:27:33: Epoch: 006 Train Loss: 1.899, mae: 1.009, rse: 0.704, pcc: 0.568, kcc: 0.386
+2025-08-25 22:27:34: Epoch: 006 Test results, ap: mae: 0.783, rse: 1.935, pcc: 0.215, kcc: 0.153
+2025-08-25 22:28:04: Epoch: 007 Train Loss: 1.862, mae: 0.967, rse: 0.658, pcc: 0.622, kcc: 0.428
+2025-08-25 22:28:04: Epoch: 007 New best VALIDATION metrics
+2025-08-25 22:28:05: Epoch: 007 Test results, ap: mae: 0.676, rse: 1.383, pcc: -0.061, kcc: 0.010
+2025-08-25 22:28:36: Epoch: 008 Train Loss: 1.637, mae: 1.020, rse: 0.693, pcc: 0.585, kcc: 0.398
+2025-08-25 22:28:37: Epoch: 008 Test results, ap: mae: 0.822, rse: 1.895, pcc: 0.019, kcc: 0.023
+2025-08-25 22:29:07: Epoch: 009 Train Loss: 1.565, mae: 1.054, rse: 0.701, pcc: 0.557, kcc: 0.376
+2025-08-25 22:29:08: Epoch: 009 Test results, ap: mae: 1.123, rse: 3.213, pcc: -0.074, kcc: -0.096
+2025-08-25 22:29:39: Epoch: 010 Train Loss: 1.485, mae: 0.883, rse: 0.564, pcc: 0.704, kcc: 0.507
+2025-08-25 22:29:39: Epoch: 010 New best VALIDATION metrics
+2025-08-25 22:29:40: Epoch: 010 Test results, ap: mae: 1.257, rse: 4.186, pcc: -0.202, kcc: -0.095
+2025-08-25 22:30:10: Epoch: 011 Train Loss: 1.234, mae: 0.795, rse: 0.451, pcc: 0.745, kcc: 0.544
+2025-08-25 22:30:10: Epoch: 011 New best VALIDATION metrics
+2025-08-25 22:30:12: Epoch: 011 Test results, ap: mae: 1.433, rse: 5.288, pcc: -0.073, kcc: 0.003
+2025-08-25 22:30:12: Epoch: 011 New best TEST metrics
+2025-08-25 22:30:42: Epoch: 012 Train Loss: 1.076, mae: 0.783, rse: 0.443, pcc: 0.751, kcc: 0.547
+2025-08-25 22:30:42: Epoch: 012 New best VALIDATION metrics
+2025-08-25 22:30:43: Epoch: 012 Test results, ap: mae: 1.078, rse: 3.051, pcc: -0.317, kcc: -0.179
+2025-08-25 22:30:43: Epoch: 012 New best TEST metrics
+2025-08-25 22:31:13: Epoch: 013 Train Loss: 1.046, mae: 0.746, rse: 0.408, pcc: 0.771, kcc: 0.563
+2025-08-25 22:31:13: Epoch: 013 New best VALIDATION metrics
+2025-08-25 22:31:15: Epoch: 013 Test results, ap: mae: 0.977, rse: 2.589, pcc: -0.024, kcc: 0.034
+2025-08-25 22:31:15: Epoch: 013 New best TEST metrics
+2025-08-25 22:31:45: Epoch: 014 Train Loss: 0.977, mae: 0.926, rse: 0.558, pcc: 0.770, kcc: 0.557
+2025-08-25 22:31:46: Epoch: 014 Test results, ap: mae: 0.676, rse: 1.585, pcc: -0.046, kcc: 0.004
+2025-08-25 22:31:46: Epoch: 014 New best TEST metrics
+2025-08-25 22:32:16: Epoch: 015 Train Loss: 0.840, mae: 0.755, rse: 0.391, pcc: 0.792, kcc: 0.567
+2025-08-25 22:32:16: Epoch: 015 New best VALIDATION metrics
+2025-08-25 22:32:18: Epoch: 015 Test results, ap: mae: 1.002, rse: 2.674, pcc: -0.085, kcc: -0.002
+2025-08-25 22:32:48: Epoch: 016 Train Loss: 0.744, mae: 0.706, rse: 0.334, pcc: 0.826, kcc: 0.592
+2025-08-25 22:32:48: Epoch: 016 New best VALIDATION metrics
+2025-08-25 22:32:49: Epoch: 016 Test results, ap: mae: 1.187, rse: 4.036, pcc: 0.008, kcc: 0.044
+2025-08-25 22:33:19: Epoch: 017 Train Loss: 0.686, mae: 0.802, rse: 0.423, pcc: 0.821, kcc: 0.593
+2025-08-25 22:33:21: Epoch: 017 Test results, ap: mae: 1.399, rse: 5.250, pcc: -0.071, kcc: 0.002
+2025-08-25 22:33:51: Epoch: 018 Train Loss: 0.634, mae: 0.744, rse: 0.355, pcc: 0.823, kcc: 0.597
+2025-08-25 22:33:52: Epoch: 018 Test results, ap: mae: 1.302, rse: 4.436, pcc: 0.006, kcc: 0.065
+2025-08-25 22:34:22: Epoch: 019 Train Loss: 0.618, mae: 0.591, rse: 0.251, pcc: 0.872, kcc: 0.668
+2025-08-25 22:34:22: Epoch: 019 New best VALIDATION metrics
+2025-08-25 22:34:24: Epoch: 019 Test results, ap: mae: 1.233, rse: 4.233, pcc: 0.007, kcc: 0.052
+2025-08-25 22:34:54: Epoch: 020 Train Loss: 0.568, mae: 0.758, rse: 0.355, pcc: 0.869, kcc: 0.650
+2025-08-25 22:34:55: Epoch: 020 Test results, ap: mae: 1.284, rse: 5.010, pcc: 0.060, kcc: 0.068
+2025-08-25 22:35:25: Epoch: 021 Train Loss: 0.467, mae: 0.649, rse: 0.252, pcc: 0.885, kcc: 0.678
+2025-08-25 22:35:27: Epoch: 021 Test results, ap: mae: 1.026, rse: 3.142, pcc: 0.083, kcc: 0.094
+2025-08-25 22:35:57: Epoch: 022 Train Loss: 0.405, mae: 0.534, rse: 0.207, pcc: 0.892, kcc: 0.681
+2025-08-25 22:35:57: Epoch: 022 New best VALIDATION metrics
+2025-08-25 22:35:58: Epoch: 022 Test results, ap: mae: 1.185, rse: 4.092, pcc: -0.029, kcc: 0.020
+2025-08-25 22:36:28: Epoch: 023 Train Loss: 0.393, mae: 0.513, rse: 0.191, pcc: 0.902, kcc: 0.699
+2025-08-25 22:36:28: Epoch: 023 New best VALIDATION metrics
+2025-08-25 22:36:30: Epoch: 023 Test results, ap: mae: 1.088, rse: 3.440, pcc: -0.019, kcc: 0.039
+2025-08-25 22:37:00: Epoch: 024 Train Loss: 0.370, mae: 0.499, rse: 0.201, pcc: 0.906, kcc: 0.717
+2025-08-25 22:37:00: Epoch: 024 New best VALIDATION metrics
+2025-08-25 22:37:01: Epoch: 024 Test results, ap: mae: 1.398, rse: 5.429, pcc: -0.002, kcc: 0.046
+2025-08-25 22:37:32: Epoch: 025 Train Loss: 0.359, mae: 0.536, rse: 0.200, pcc: 0.901, kcc: 0.699
+2025-08-25 22:37:33: Epoch: 025 Test results, ap: mae: 1.224, rse: 4.309, pcc: -0.029, kcc: 0.019
+2025-08-25 22:38:03: Epoch: 026 Train Loss: 0.333, mae: 0.469, rse: 0.168, pcc: 0.916, kcc: 0.725
+2025-08-25 22:38:03: Epoch: 026 New best VALIDATION metrics
+2025-08-25 22:38:05: Epoch: 026 Test results, ap: mae: 1.077, rse: 3.373, pcc: -0.008, kcc: 0.040
+2025-08-25 22:38:35: Epoch: 027 Train Loss: 0.331, mae: 0.477, rse: 0.164, pcc: 0.918, kcc: 0.733
+2025-08-25 22:38:35: Epoch: 027 New best VALIDATION metrics
+2025-08-25 22:38:36: Epoch: 027 Test results, ap: mae: 1.140, rse: 3.835, pcc: -0.061, kcc: 0.018
+2025-08-25 22:39:07: Epoch: 028 Train Loss: 0.315, mae: 0.446, rse: 0.163, pcc: 0.916, kcc: 0.734
+2025-08-25 22:39:07: Epoch: 028 New best VALIDATION metrics
+2025-08-25 22:39:08: Epoch: 028 Test results, ap: mae: 1.171, rse: 4.180, pcc: -0.068, kcc: 0.009
+2025-08-25 22:39:39: Epoch: 029 Train Loss: 0.323, mae: 0.486, rse: 0.168, pcc: 0.915, kcc: 0.722
+2025-08-25 22:39:40: Epoch: 029 Test results, ap: mae: 1.132, rse: 3.914, pcc: -0.038, kcc: 0.024
+2025-08-25 22:40:10: Epoch: 030 Train Loss: 0.296, mae: 0.445, rse: 0.162, pcc: 0.921, kcc: 0.737
+2025-08-25 22:40:10: Epoch: 030 New best VALIDATION metrics
+2025-08-25 22:40:11: Epoch: 030 Test results, ap: mae: 1.265, rse: 4.755, pcc: -0.060, kcc: -0.001
+2025-08-25 22:40:42: Epoch: 031 Train Loss: 0.258, mae: 0.443, rse: 0.157, pcc: 0.925, kcc: 0.743
+2025-08-25 22:40:42: Epoch: 031 New best VALIDATION metrics
+2025-08-25 22:40:43: Epoch: 031 Test results, ap: mae: 1.243, rse: 4.488, pcc: -0.084, kcc: -0.021
+2025-08-25 22:41:13: Epoch: 032 Train Loss: 0.275, mae: 0.436, rse: 0.149, pcc: 0.925, kcc: 0.751
+2025-08-25 22:41:13: Epoch: 032 New best VALIDATION metrics
+2025-08-25 22:41:15: Epoch: 032 Test results, ap: mae: 1.026, rse: 3.235, pcc: -0.006, kcc: 0.057
+2025-08-25 22:41:45: Epoch: 033 Train Loss: 0.256, mae: 0.403, rse: 0.136, pcc: 0.930, kcc: 0.755
+2025-08-25 22:41:45: Epoch: 033 New best VALIDATION metrics
+2025-08-25 22:41:47: Epoch: 033 Test results, ap: mae: 1.164, rse: 4.089, pcc: -0.022, kcc: 0.037
+2025-08-25 22:42:17: Epoch: 034 Train Loss: 0.246, mae: 0.409, rse: 0.142, pcc: 0.930, kcc: 0.751
+2025-08-25 22:42:18: Epoch: 034 Test results, ap: mae: 1.148, rse: 3.954, pcc: -0.012, kcc: 0.043
+2025-08-25 22:42:48: Epoch: 035 Train Loss: 0.233, mae: 0.439, rse: 0.160, pcc: 0.929, kcc: 0.755
+2025-08-25 22:42:49: Epoch: 035 Test results, ap: mae: 1.267, rse: 4.539, pcc: -0.001, kcc: 0.048
+2025-08-25 22:43:20: Epoch: 036 Train Loss: 0.232, mae: 0.450, rse: 0.165, pcc: 0.926, kcc: 0.747
+2025-08-25 22:43:21: Epoch: 036 Test results, ap: mae: 1.218, rse: 4.281, pcc: -0.044, kcc: 0.021
+2025-08-25 22:43:51: Epoch: 037 Train Loss: 0.247, mae: 0.389, rse: 0.134, pcc: 0.931, kcc: 0.753
+2025-08-25 22:43:51: Epoch: 037 New best VALIDATION metrics
+2025-08-25 22:43:53: Epoch: 037 Test results, ap: mae: 1.134, rse: 3.857, pcc: -0.004, kcc: 0.046
+2025-08-25 22:44:23: Epoch: 038 Train Loss: 0.243, mae: 0.416, rse: 0.158, pcc: 0.929, kcc: 0.760
+2025-08-25 22:44:24: Epoch: 038 Test results, ap: mae: 1.241, rse: 4.467, pcc: -0.021, kcc: 0.042
+2025-08-25 22:44:54: Epoch: 039 Train Loss: 0.222, mae: 0.379, rse: 0.130, pcc: 0.933, kcc: 0.761
+2025-08-25 22:44:54: Epoch: 039 New best VALIDATION metrics
+2025-08-25 22:44:56: Epoch: 039 Test results, ap: mae: 1.075, rse: 3.605, pcc: -0.013, kcc: 0.049
+2025-08-25 22:45:26: Epoch: 040 Train Loss: 0.233, mae: 0.419, rse: 0.140, pcc: 0.928, kcc: 0.747
+2025-08-25 22:45:27: Epoch: 040 Test results, ap: mae: 1.045, rse: 3.327, pcc: 0.001, kcc: 0.053
+2025-08-25 22:45:57: Epoch: 041 Train Loss: 0.216, mae: 0.376, rse: 0.137, pcc: 0.934, kcc: 0.766
+2025-08-25 22:45:57: Epoch: 041 New best VALIDATION metrics
+2025-08-25 22:45:58: Epoch: 041 Test results, ap: mae: 1.176, rse: 4.182, pcc: -0.001, kcc: 0.048
+2025-08-25 22:46:28: Epoch: 042 Train Loss: 0.185, mae: 0.368, rse: 0.132, pcc: 0.932, kcc: 0.768
+2025-08-25 22:46:28: Epoch: 042 New best VALIDATION metrics
+2025-08-25 22:46:30: Epoch: 042 Test results, ap: mae: 1.119, rse: 3.842, pcc: -0.007, kcc: 0.049
+2025-08-25 22:47:00: Epoch: 043 Train Loss: 0.210, mae: 0.388, rse: 0.130, pcc: 0.933, kcc: 0.769
+2025-08-25 22:47:01: Epoch: 043 Test results, ap: mae: 1.128, rse: 3.897, pcc: -0.014, kcc: 0.046
+2025-08-25 22:47:32: Epoch: 044 Train Loss: 0.206, mae: 0.355, rse: 0.128, pcc: 0.937, kcc: 0.774
+2025-08-25 22:47:32: Epoch: 044 New best VALIDATION metrics
+2025-08-25 22:47:33: Epoch: 044 Test results, ap: mae: 1.172, rse: 4.144, pcc: -0.014, kcc: 0.048
+2025-08-25 22:48:03: Epoch: 045 Train Loss: 0.202, mae: 0.369, rse: 0.123, pcc: 0.936, kcc: 0.768
+2025-08-25 22:48:05: Epoch: 045 Test results, ap: mae: 1.120, rse: 3.850, pcc: -0.016, kcc: 0.037
+2025-08-25 22:48:35: Epoch: 046 Train Loss: 0.206, mae: 0.357, rse: 0.125, pcc: 0.936, kcc: 0.771
+2025-08-25 22:48:37: Epoch: 046 Test results, ap: mae: 1.131, rse: 3.886, pcc: -0.006, kcc: 0.045
+2025-08-25 22:49:06: Epoch: 047 Train Loss: 0.204, mae: 0.377, rse: 0.132, pcc: 0.937, kcc: 0.770
+2025-08-25 22:49:08: Epoch: 047 Test results, ap: mae: 1.134, rse: 3.921, pcc: 0.005, kcc: 0.062
+2025-08-25 22:49:38: Epoch: 048 Train Loss: 0.199, mae: 0.346, rse: 0.126, pcc: 0.936, kcc: 0.777
+2025-08-25 22:49:38: Epoch: 048 New best VALIDATION metrics
+2025-08-25 22:49:39: Epoch: 048 Test results, ap: mae: 1.122, rse: 3.894, pcc: 0.017, kcc: 0.054
+2025-08-25 22:50:10: Epoch: 049 Train Loss: 0.213, mae: 0.379, rse: 0.135, pcc: 0.936, kcc: 0.773
+2025-08-25 22:50:11: Epoch: 049 Test results, ap: mae: 1.118, rse: 3.831, pcc: 0.015, kcc: 0.056
+2025-08-25 22:50:41: Epoch: 050 Train Loss: 0.211, mae: 0.389, rse: 0.139, pcc: 0.940, kcc: 0.782
+2025-08-25 22:50:43: Epoch: 050 Test results, ap: mae: 1.175, rse: 4.156, pcc: 0.014, kcc: 0.051
+2025-08-25 22:50:43: used time 0.44h
+2025-08-25 22:50:43: Running Cross Validation 1
+2025-08-25 22:50:43: Fold 1  Train set:2269, Valid set:568, Test set: 123
+2025-08-25 22:51:13: Epoch: 001 Train Loss: 2.582, mae: 1.472, rse: 1.247, pcc: 0.246, kcc: 0.224
+2025-08-25 22:51:13: Epoch: 001 New best VALIDATION metrics
+2025-08-25 22:51:15: Epoch: 001 Test results, ap: mae: 1.425, rse: 5.026, pcc: -0.426, kcc: -0.291
+2025-08-25 22:51:45: Epoch: 002 Train Loss: 2.328, mae: 1.143, rse: 0.822, pcc: 0.432, kcc: 0.286
+2025-08-25 22:51:45: Epoch: 002 New best VALIDATION metrics
+2025-08-25 22:51:46: Epoch: 002 Test results, ap: mae: 0.614, rse: 1.254, pcc: -0.030, kcc: -0.062
+2025-08-25 22:52:16: Epoch: 003 Train Loss: 2.196, mae: 1.338, rse: 1.049, pcc: 0.282, kcc: 0.174
+2025-08-25 22:52:17: Epoch: 003 Test results, ap: mae: 0.643, rse: 1.377, pcc: 0.029, kcc: -0.018
+2025-08-25 22:52:47: Epoch: 004 Train Loss: 2.135, mae: 1.096, rse: 0.777, pcc: 0.476, kcc: 0.332
+2025-08-25 22:52:47: Epoch: 004 New best VALIDATION metrics
+2025-08-25 22:52:49: Epoch: 004 Test results, ap: mae: 0.680, rse: 1.321, pcc: 0.061, kcc: 0.043
+2025-08-25 22:53:19: Epoch: 005 Train Loss: 1.939, mae: 1.134, rse: 0.810, pcc: 0.447, kcc: 0.316
+2025-08-25 22:53:21: Epoch: 005 Test results, ap: mae: 1.156, rse: 3.408, pcc: -0.011, kcc: -0.024
+2025-08-25 22:53:51: Epoch: 006 Train Loss: 1.809, mae: 1.285, rse: 1.021, pcc: 0.454, kcc: 0.330
+2025-08-25 22:53:52: Epoch: 006 Test results, ap: mae: 0.978, rse: 3.011, pcc: -0.139, kcc: -0.149
+2025-08-25 22:54:22: Epoch: 007 Train Loss: 1.708, mae: 1.410, rse: 1.167, pcc: 0.312, kcc: 0.197
+2025-08-25 22:54:23: Epoch: 007 Test results, ap: mae: 1.545, rse: 5.711, pcc: 0.135, kcc: 0.075
+2025-08-25 22:54:54: Epoch: 008 Train Loss: 1.580, mae: 1.811, rse: 1.576, pcc: 0.448, kcc: 0.351
+2025-08-25 22:54:55: Epoch: 008 Test results, ap: mae: 2.226, rse: 11.299, pcc: -0.051, kcc: -0.054
+2025-08-25 22:55:25: Epoch: 009 Train Loss: 1.475, mae: 1.456, rse: 1.232, pcc: 0.259, kcc: 0.161
+2025-08-25 22:55:26: Epoch: 009 Test results, ap: mae: 2.082, rse: 9.684, pcc: 0.242, kcc: 0.297
+2025-08-25 22:55:56: Epoch: 010 Train Loss: 1.354, mae: 1.141, rse: 0.772, pcc: 0.650, kcc: 0.480
+2025-08-25 22:55:56: Epoch: 010 New best VALIDATION metrics
+2025-08-25 22:55:58: Epoch: 010 Test results, ap: mae: 0.784, rse: 1.879, pcc: 0.283, kcc: 0.198
+2025-08-25 22:56:28: Epoch: 011 Train Loss: 1.051, mae: 0.816, rse: 0.472, pcc: 0.732, kcc: 0.541
+2025-08-25 22:56:28: Epoch: 011 New best VALIDATION metrics
+2025-08-25 22:56:30: Epoch: 011 Test results, ap: mae: 0.630, rse: 1.374, pcc: 0.356, kcc: 0.323
+2025-08-25 22:56:30: Epoch: 011 New best TEST metrics
+2025-08-25 22:57:00: Epoch: 012 Train Loss: 0.919, mae: 1.086, rse: 0.683, pcc: 0.734, kcc: 0.550
+2025-08-25 22:57:01: Epoch: 012 Test results, ap: mae: 1.107, rse: 3.599, pcc: 0.428, kcc: 0.310
+2025-08-25 22:57:31: Epoch: 013 Train Loss: 0.852, mae: 0.752, rse: 0.425, pcc: 0.761, kcc: 0.571
+2025-08-25 22:57:31: Epoch: 013 New best VALIDATION metrics
+2025-08-25 22:57:33: Epoch: 013 Test results, ap: mae: 0.702, rse: 1.612, pcc: 0.378, kcc: 0.310
+2025-08-25 22:58:03: Epoch: 014 Train Loss: 0.764, mae: 1.038, rse: 0.625, pcc: 0.747, kcc: 0.548
+2025-08-25 22:58:04: Epoch: 014 Test results, ap: mae: 0.702, rse: 1.562, pcc: 0.299, kcc: 0.225
+2025-08-25 22:58:34: Epoch: 015 Train Loss: 0.705, mae: 0.705, rse: 0.367, pcc: 0.807, kcc: 0.601
+2025-08-25 22:58:34: Epoch: 015 New best VALIDATION metrics
+2025-08-25 22:58:35: Epoch: 015 Test results, ap: mae: 0.795, rse: 2.016, pcc: 0.280, kcc: 0.197
+2025-08-25 22:59:06: Epoch: 016 Train Loss: 0.636, mae: 0.672, rse: 0.354, pcc: 0.810, kcc: 0.604
+2025-08-25 22:59:06: Epoch: 016 New best VALIDATION metrics
+2025-08-25 22:59:07: Epoch: 016 Test results, ap: mae: 0.738, rse: 2.142, pcc: 0.314, kcc: 0.279
+2025-08-25 22:59:37: Epoch: 017 Train Loss: 0.662, mae: 0.951, rse: 0.545, pcc: 0.766, kcc: 0.573
+2025-08-25 22:59:39: Epoch: 017 Test results, ap: mae: 1.428, rse: 5.320, pcc: 0.233, kcc: 0.157
+2025-08-25 23:00:09: Epoch: 018 Train Loss: 0.619, mae: 0.821, rse: 0.461, pcc: 0.763, kcc: 0.558
+2025-08-25 23:00:10: Epoch: 018 Test results, ap: mae: 1.623, rse: 7.053, pcc: 0.244, kcc: 0.173
+2025-08-25 23:00:40: Epoch: 019 Train Loss: 0.578, mae: 0.857, rse: 0.476, pcc: 0.732, kcc: 0.526
+2025-08-25 23:00:42: Epoch: 019 Test results, ap: mae: 1.393, rse: 5.220, pcc: 0.360, kcc: 0.249
+2025-08-25 23:01:12: Epoch: 020 Train Loss: 0.488, mae: 0.560, rse: 0.266, pcc: 0.858, kcc: 0.671
+2025-08-25 23:01:12: Epoch: 020 New best VALIDATION metrics
+2025-08-25 23:01:13: Epoch: 020 Test results, ap: mae: 0.859, rse: 2.398, pcc: 0.299, kcc: 0.254
+2025-08-25 23:01:43: Epoch: 021 Train Loss: 0.433, mae: 0.609, rse: 0.283, pcc: 0.858, kcc: 0.664
+2025-08-25 23:01:44: Epoch: 021 Test results, ap: mae: 0.954, rse: 3.013, pcc: 0.359, kcc: 0.281
+2025-08-25 23:02:14: Epoch: 022 Train Loss: 0.408, mae: 0.537, rse: 0.253, pcc: 0.870, kcc: 0.688
+2025-08-25 23:02:14: Epoch: 022 New best VALIDATION metrics
+2025-08-25 23:02:16: Epoch: 022 Test results, ap: mae: 0.746, rse: 2.060, pcc: 0.334, kcc: 0.294
+2025-08-25 23:02:46: Epoch: 023 Train Loss: 0.367, mae: 0.568, rse: 0.252, pcc: 0.879, kcc: 0.701
+2025-08-25 23:02:48: Epoch: 023 Test results, ap: mae: 0.739, rse: 1.960, pcc: 0.336, kcc: 0.295
+2025-08-25 23:03:18: Epoch: 024 Train Loss: 0.384, mae: 0.565, rse: 0.251, pcc: 0.876, kcc: 0.692
+2025-08-25 23:03:19: Epoch: 024 Test results, ap: mae: 0.752, rse: 1.847, pcc: 0.357, kcc: 0.273
+2025-08-25 23:03:49: Epoch: 025 Train Loss: 0.328, mae: 0.590, rse: 0.267, pcc: 0.871, kcc: 0.693
+2025-08-25 23:03:51: Epoch: 025 Test results, ap: mae: 0.833, rse: 2.274, pcc: 0.377, kcc: 0.298
+2025-08-25 23:04:21: Epoch: 026 Train Loss: 0.395, mae: 0.514, rse: 0.230, pcc: 0.879, kcc: 0.701
+2025-08-25 23:04:21: Epoch: 026 New best VALIDATION metrics
+2025-08-25 23:04:22: Epoch: 026 Test results, ap: mae: 0.789, rse: 2.092, pcc: 0.333, kcc: 0.281
+2025-08-25 23:04:52: Epoch: 027 Train Loss: 0.322, mae: 0.504, rse: 0.222, pcc: 0.884, kcc: 0.707
+2025-08-25 23:04:52: Epoch: 027 New best VALIDATION metrics
+2025-08-25 23:04:54: Epoch: 027 Test results, ap: mae: 0.834, rse: 2.438, pcc: 0.300, kcc: 0.241
+2025-08-25 23:05:24: Epoch: 028 Train Loss: 0.315, mae: 0.476, rse: 0.219, pcc: 0.886, kcc: 0.719
+2025-08-25 23:05:24: Epoch: 028 New best VALIDATION metrics
+2025-08-25 23:05:25: Epoch: 028 Test results, ap: mae: 0.865, rse: 2.734, pcc: 0.355, kcc: 0.297
+2025-08-25 23:05:56: Epoch: 029 Train Loss: 0.324, mae: 0.739, rse: 0.345, pcc: 0.873, kcc: 0.706
+2025-08-25 23:05:57: Epoch: 029 Test results, ap: mae: 0.796, rse: 2.396, pcc: 0.358, kcc: 0.286
+2025-08-25 23:06:27: Epoch: 030 Train Loss: 0.303, mae: 0.491, rse: 0.215, pcc: 0.888, kcc: 0.725
+2025-08-25 23:06:29: Epoch: 030 Test results, ap: mae: 0.779, rse: 2.279, pcc: 0.367, kcc: 0.292
+2025-08-25 23:06:59: Epoch: 031 Train Loss: 0.273, mae: 0.472, rse: 0.208, pcc: 0.893, kcc: 0.730
+2025-08-25 23:06:59: Epoch: 031 New best VALIDATION metrics
+2025-08-25 23:07:00: Epoch: 031 Test results, ap: mae: 0.792, rse: 2.234, pcc: 0.345, kcc: 0.284
+2025-08-25 23:07:30: Epoch: 032 Train Loss: 0.269, mae: 0.460, rse: 0.208, pcc: 0.895, kcc: 0.736
+2025-08-25 23:07:30: Epoch: 032 New best VALIDATION metrics
+2025-08-25 23:07:32: Epoch: 032 Test results, ap: mae: 0.777, rse: 2.249, pcc: 0.368, kcc: 0.305
+2025-08-25 23:08:02: Epoch: 033 Train Loss: 0.257, mae: 0.437, rse: 0.195, pcc: 0.899, kcc: 0.736
+2025-08-25 23:08:02: Epoch: 033 New best VALIDATION metrics
+2025-08-25 23:08:04: Epoch: 033 Test results, ap: mae: 0.813, rse: 2.466, pcc: 0.347, kcc: 0.289
+2025-08-25 23:08:34: Epoch: 034 Train Loss: 0.246, mae: 0.456, rse: 0.205, pcc: 0.893, kcc: 0.722
+2025-08-25 23:08:35: Epoch: 034 Test results, ap: mae: 0.838, rse: 2.474, pcc: 0.327, kcc: 0.269
+2025-08-25 23:09:05: Epoch: 035 Train Loss: 0.249, mae: 0.429, rse: 0.197, pcc: 0.900, kcc: 0.745
+2025-08-25 23:09:05: Epoch: 035 New best VALIDATION metrics
+2025-08-25 23:09:07: Epoch: 035 Test results, ap: mae: 0.823, rse: 2.519, pcc: 0.331, kcc: 0.277
+2025-08-25 23:09:37: Epoch: 036 Train Loss: 0.253, mae: 0.442, rse: 0.199, pcc: 0.896, kcc: 0.732
+2025-08-25 23:09:39: Epoch: 036 Test results, ap: mae: 0.814, rse: 2.486, pcc: 0.360, kcc: 0.305
+2025-08-25 23:10:09: Epoch: 037 Train Loss: 0.242, mae: 0.447, rse: 0.204, pcc: 0.901, kcc: 0.737
+2025-08-25 23:10:10: Epoch: 037 Test results, ap: mae: 0.869, rse: 2.576, pcc: 0.332, kcc: 0.280
+2025-08-25 23:10:41: Epoch: 038 Train Loss: 0.237, mae: 0.437, rse: 0.193, pcc: 0.900, kcc: 0.741
+2025-08-25 23:10:42: Epoch: 038 Test results, ap: mae: 0.816, rse: 2.317, pcc: 0.344, kcc: 0.286
+2025-08-25 23:11:12: Epoch: 039 Train Loss: 0.235, mae: 0.420, rse: 0.192, pcc: 0.900, kcc: 0.750
+2025-08-25 23:11:12: Epoch: 039 New best VALIDATION metrics
+2025-08-25 23:11:14: Epoch: 039 Test results, ap: mae: 0.826, rse: 2.621, pcc: 0.348, kcc: 0.304
+2025-08-25 23:11:44: Epoch: 040 Train Loss: 0.232, mae: 0.434, rse: 0.196, pcc: 0.901, kcc: 0.745
+2025-08-25 23:11:45: Epoch: 040 Test results, ap: mae: 0.878, rse: 2.648, pcc: 0.318, kcc: 0.282
+2025-08-25 23:12:15: Epoch: 041 Train Loss: 0.236, mae: 0.414, rse: 0.189, pcc: 0.902, kcc: 0.752
+2025-08-25 23:12:15: Epoch: 041 New best VALIDATION metrics
+2025-08-25 23:12:17: Epoch: 041 Test results, ap: mae: 0.788, rse: 2.361, pcc: 0.354, kcc: 0.305
+2025-08-25 23:12:47: Epoch: 042 Train Loss: 0.210, mae: 0.410, rse: 0.191, pcc: 0.902, kcc: 0.751
+2025-08-25 23:12:47: Epoch: 042 New best VALIDATION metrics
+2025-08-25 23:12:48: Epoch: 042 Test results, ap: mae: 0.832, rse: 2.620, pcc: 0.353, kcc: 0.302
+2025-08-25 23:13:19: Epoch: 043 Train Loss: 0.213, mae: 0.410, rse: 0.188, pcc: 0.903, kcc: 0.750
+2025-08-25 23:13:19: Epoch: 043 New best VALIDATION metrics
+2025-08-25 23:13:20: Epoch: 043 Test results, ap: mae: 0.824, rse: 2.496, pcc: 0.337, kcc: 0.294
+2025-08-25 23:13:50: Epoch: 044 Train Loss: 0.212, mae: 0.418, rse: 0.188, pcc: 0.902, kcc: 0.750
+2025-08-25 23:13:52: Epoch: 044 Test results, ap: mae: 0.830, rse: 2.611, pcc: 0.352, kcc: 0.298
+2025-08-25 23:14:22: Epoch: 045 Train Loss: 0.226, mae: 0.404, rse: 0.184, pcc: 0.904, kcc: 0.753
+2025-08-25 23:14:22: Epoch: 045 New best VALIDATION metrics
+2025-08-25 23:14:23: Epoch: 045 Test results, ap: mae: 0.835, rse: 2.583, pcc: 0.341, kcc: 0.288
+2025-08-25 23:14:54: Epoch: 046 Train Loss: 0.200, mae: 0.422, rse: 0.193, pcc: 0.901, kcc: 0.742
+2025-08-25 23:14:55: Epoch: 046 Test results, ap: mae: 0.861, rse: 2.740, pcc: 0.346, kcc: 0.289
+2025-08-25 23:15:25: Epoch: 047 Train Loss: 0.213, mae: 0.401, rse: 0.187, pcc: 0.904, kcc: 0.753
+2025-08-25 23:15:25: Epoch: 047 New best VALIDATION metrics
+2025-08-25 23:15:26: Epoch: 047 Test results, ap: mae: 0.851, rse: 2.679, pcc: 0.345, kcc: 0.290
+2025-08-25 23:15:57: Epoch: 048 Train Loss: 0.195, mae: 0.397, rse: 0.183, pcc: 0.905, kcc: 0.751
+2025-08-25 23:15:57: Epoch: 048 New best VALIDATION metrics
+2025-08-25 23:15:58: Epoch: 048 Test results, ap: mae: 0.831, rse: 2.542, pcc: 0.334, kcc: 0.287
+2025-08-25 23:16:28: Epoch: 049 Train Loss: 0.204, mae: 0.418, rse: 0.188, pcc: 0.903, kcc: 0.752
+2025-08-25 23:16:30: Epoch: 049 Test results, ap: mae: 0.852, rse: 2.646, pcc: 0.339, kcc: 0.281
+2025-08-25 23:17:00: Epoch: 050 Train Loss: 0.200, mae: 0.399, rse: 0.181, pcc: 0.906, kcc: 0.763
+2025-08-25 23:17:00: Epoch: 050 New best VALIDATION metrics
+2025-08-25 23:17:01: Epoch: 050 Test results, ap: mae: 0.873, rse: 2.798, pcc: 0.319, kcc: 0.275
+2025-08-25 23:17:01: used time 0.44h
+2025-08-25 23:17:01: Running Cross Validation 2
+2025-08-25 23:17:01: Fold 2  Train set:2270, Valid set:567, Test set: 123
+2025-08-25 23:17:32: Epoch: 001 Train Loss: 2.642, mae: 1.361, rse: 1.127, pcc: -0.005, kcc: 0.010
+2025-08-25 23:17:32: Epoch: 001 New best VALIDATION metrics
+2025-08-25 23:17:33: Epoch: 001 Test results, ap: mae: 1.063, rse: 3.078, pcc: 0.164, kcc: 0.060
+2025-08-25 23:18:03: Epoch: 002 Train Loss: 2.478, mae: 1.440, rse: 1.115, pcc: 0.430, kcc: 0.296
+2025-08-25 23:18:03: Epoch: 002 New best VALIDATION metrics
+2025-08-25 23:18:05: Epoch: 002 Test results, ap: mae: 1.099, rse: 3.493, pcc: -0.083, kcc: -0.065
+2025-08-25 23:18:35: Epoch: 003 Train Loss: 2.276, mae: 1.167, rse: 0.863, pcc: 0.428, kcc: 0.304
+2025-08-25 23:18:35: Epoch: 003 New best VALIDATION metrics
+2025-08-25 23:18:36: Epoch: 003 Test results, ap: mae: 0.856, rse: 2.111, pcc: 0.050, kcc: -0.030
+2025-08-25 23:19:07: Epoch: 004 Train Loss: 2.125, mae: 1.177, rse: 0.891, pcc: 0.348, kcc: 0.251
+2025-08-25 23:19:08: Epoch: 004 Test results, ap: mae: 0.758, rse: 1.715, pcc: 0.041, kcc: -0.002
+2025-08-25 23:19:38: Epoch: 005 Train Loss: 1.990, mae: 1.030, rse: 0.708, pcc: 0.551, kcc: 0.400
+2025-08-25 23:19:38: Epoch: 005 New best VALIDATION metrics
+2025-08-25 23:19:40: Epoch: 005 Test results, ap: mae: 0.662, rse: 1.429, pcc: -0.018, kcc: -0.031
+2025-08-25 23:20:10: Epoch: 006 Train Loss: 1.838, mae: 1.013, rse: 0.666, pcc: 0.625, kcc: 0.465
+2025-08-25 23:20:10: Epoch: 006 New best VALIDATION metrics
+2025-08-25 23:20:11: Epoch: 006 Test results, ap: mae: 0.632, rse: 1.315, pcc: 0.156, kcc: 0.116
+2025-08-25 23:20:41: Epoch: 007 Train Loss: 1.808, mae: 1.066, rse: 0.720, pcc: 0.629, kcc: 0.476
+2025-08-25 23:20:43: Epoch: 007 Test results, ap: mae: 0.844, rse: 1.962, pcc: -0.293, kcc: -0.180
+2025-08-25 23:21:13: Epoch: 008 Train Loss: 1.634, mae: 1.175, rse: 0.848, pcc: 0.483, kcc: 0.333
+2025-08-25 23:21:15: Epoch: 008 Test results, ap: mae: 1.347, rse: 4.503, pcc: 0.161, kcc: 0.141
+2025-08-25 23:21:45: Epoch: 009 Train Loss: 1.498, mae: 0.999, rse: 0.645, pcc: 0.653, kcc: 0.474
+2025-08-25 23:21:45: Epoch: 009 New best VALIDATION metrics
+2025-08-25 23:21:46: Epoch: 009 Test results, ap: mae: 1.099, rse: 3.334, pcc: -0.024, kcc: -0.035
+2025-08-25 23:22:17: Epoch: 010 Train Loss: 1.370, mae: 0.946, rse: 0.638, pcc: 0.604, kcc: 0.454
+2025-08-25 23:22:18: Epoch: 010 Test results, ap: mae: 0.722, rse: 1.571, pcc: 0.236, kcc: 0.145
+2025-08-25 23:22:49: Epoch: 011 Train Loss: 1.042, mae: 1.298, rse: 0.892, pcc: 0.711, kcc: 0.533
+2025-08-25 23:22:50: Epoch: 011 Test results, ap: mae: 0.878, rse: 2.667, pcc: 0.279, kcc: 0.200
+2025-08-25 23:22:50: Epoch: 011 New best TEST metrics
+2025-08-25 23:23:20: Epoch: 012 Train Loss: 0.910, mae: 0.801, rse: 0.432, pcc: 0.781, kcc: 0.596
+2025-08-25 23:23:20: Epoch: 012 New best VALIDATION metrics
+2025-08-25 23:23:22: Epoch: 012 Test results, ap: mae: 1.046, rse: 2.992, pcc: 0.271, kcc: 0.194
+2025-08-25 23:23:52: Epoch: 013 Train Loss: 0.785, mae: 0.738, rse: 0.404, pcc: 0.782, kcc: 0.584
+2025-08-25 23:23:52: Epoch: 013 New best VALIDATION metrics
+2025-08-25 23:23:53: Epoch: 013 Test results, ap: mae: 1.040, rse: 3.005, pcc: 0.269, kcc: 0.176
+2025-08-25 23:24:23: Epoch: 014 Train Loss: 0.700, mae: 0.726, rse: 0.360, pcc: 0.809, kcc: 0.625
+2025-08-25 23:24:23: Epoch: 014 New best VALIDATION metrics
+2025-08-25 23:24:25: Epoch: 014 Test results, ap: mae: 0.872, rse: 2.281, pcc: 0.269, kcc: 0.176
+2025-08-25 23:24:25: Epoch: 014 New best TEST metrics
+2025-08-25 23:24:55: Epoch: 015 Train Loss: 0.634, mae: 0.646, rse: 0.317, pcc: 0.842, kcc: 0.660
+2025-08-25 23:24:55: Epoch: 015 New best VALIDATION metrics
+2025-08-25 23:24:57: Epoch: 015 Test results, ap: mae: 1.038, rse: 3.071, pcc: 0.300, kcc: 0.182
+2025-08-25 23:25:27: Epoch: 016 Train Loss: 0.563, mae: 0.790, rse: 0.407, pcc: 0.839, kcc: 0.657
+2025-08-25 23:25:28: Epoch: 016 Test results, ap: mae: 1.353, rse: 5.044, pcc: 0.185, kcc: 0.095
+2025-08-25 23:25:58: Epoch: 017 Train Loss: 0.530, mae: 0.711, rse: 0.369, pcc: 0.818, kcc: 0.633
+2025-08-25 23:26:00: Epoch: 017 Test results, ap: mae: 0.842, rse: 2.194, pcc: 0.322, kcc: 0.209
+2025-08-25 23:26:00: Epoch: 017 New best TEST metrics
+2025-08-25 23:26:30: Epoch: 018 Train Loss: 0.532, mae: 0.729, rse: 0.399, pcc: 0.816, kcc: 0.622
+2025-08-25 23:26:31: Epoch: 018 Test results, ap: mae: 0.965, rse: 2.982, pcc: 0.327, kcc: 0.232
+2025-08-25 23:27:02: Epoch: 019 Train Loss: 0.447, mae: 0.772, rse: 0.392, pcc: 0.835, kcc: 0.666
+2025-08-25 23:27:03: Epoch: 019 Test results, ap: mae: 0.852, rse: 2.225, pcc: 0.368, kcc: 0.264
+2025-08-25 23:27:03: Epoch: 019 New best TEST metrics
+2025-08-25 23:27:33: Epoch: 020 Train Loss: 0.436, mae: 0.645, rse: 0.317, pcc: 0.861, kcc: 0.690
+2025-08-25 23:27:33: Epoch: 020 New best VALIDATION metrics
+2025-08-25 23:27:35: Epoch: 020 Test results, ap: mae: 0.891, rse: 2.737, pcc: 0.378, kcc: 0.243
+2025-08-25 23:28:05: Epoch: 021 Train Loss: 0.366, mae: 0.553, rse: 0.279, pcc: 0.866, kcc: 0.710
+2025-08-25 23:28:05: Epoch: 021 New best VALIDATION metrics
+2025-08-25 23:28:07: Epoch: 021 Test results, ap: mae: 0.916, rse: 2.455, pcc: 0.368, kcc: 0.232
+2025-08-25 23:28:37: Epoch: 022 Train Loss: 0.312, mae: 0.516, rse: 0.258, pcc: 0.867, kcc: 0.714
+2025-08-25 23:28:37: Epoch: 022 New best VALIDATION metrics
+2025-08-25 23:28:38: Epoch: 022 Test results, ap: mae: 0.918, rse: 2.466, pcc: 0.369, kcc: 0.229
+2025-08-25 23:29:08: Epoch: 023 Train Loss: 0.307, mae: 0.500, rse: 0.230, pcc: 0.880, kcc: 0.721
+2025-08-25 23:29:08: Epoch: 023 New best VALIDATION metrics
+2025-08-25 23:29:10: Epoch: 023 Test results, ap: mae: 0.887, rse: 2.289, pcc: 0.360, kcc: 0.207
+2025-08-25 23:29:40: Epoch: 024 Train Loss: 0.307, mae: 0.511, rse: 0.254, pcc: 0.868, kcc: 0.710
+2025-08-25 23:29:41: Epoch: 024 Test results, ap: mae: 1.091, rse: 3.446, pcc: 0.345, kcc: 0.216
+2025-08-25 23:30:12: Epoch: 025 Train Loss: 0.315, mae: 0.479, rse: 0.239, pcc: 0.875, kcc: 0.731
+2025-08-25 23:30:12: Epoch: 025 New best VALIDATION metrics
+2025-08-25 23:30:13: Epoch: 025 Test results, ap: mae: 0.837, rse: 2.046, pcc: 0.377, kcc: 0.230
+2025-08-25 23:30:13: Epoch: 025 New best TEST metrics
+2025-08-25 23:30:43: Epoch: 026 Train Loss: 0.300, mae: 0.479, rse: 0.244, pcc: 0.871, kcc: 0.732
+2025-08-25 23:30:45: Epoch: 026 Test results, ap: mae: 0.899, rse: 2.374, pcc: 0.305, kcc: 0.200
+2025-08-25 23:31:15: Epoch: 027 Train Loss: 0.262, mae: 0.512, rse: 0.268, pcc: 0.863, kcc: 0.710
+2025-08-25 23:31:16: Epoch: 027 Test results, ap: mae: 1.105, rse: 3.469, pcc: 0.280, kcc: 0.176
+2025-08-25 23:31:46: Epoch: 028 Train Loss: 0.262, mae: 0.511, rse: 0.243, pcc: 0.882, kcc: 0.739
+2025-08-25 23:31:48: Epoch: 028 Test results, ap: mae: 1.133, rse: 3.536, pcc: 0.332, kcc: 0.193
+2025-08-25 23:32:18: Epoch: 029 Train Loss: 0.256, mae: 0.509, rse: 0.242, pcc: 0.877, kcc: 0.718
+2025-08-25 23:32:19: Epoch: 029 Test results, ap: mae: 0.960, rse: 2.654, pcc: 0.382, kcc: 0.220
+2025-08-25 23:32:50: Epoch: 030 Train Loss: 0.241, mae: 0.484, rse: 0.246, pcc: 0.874, kcc: 0.738
+2025-08-25 23:32:51: Epoch: 030 Test results, ap: mae: 0.895, rse: 2.293, pcc: 0.364, kcc: 0.232
+2025-08-25 23:33:21: Epoch: 031 Train Loss: 0.230, mae: 0.450, rse: 0.224, pcc: 0.883, kcc: 0.744
+2025-08-25 23:33:21: Epoch: 031 New best VALIDATION metrics
+2025-08-25 23:33:22: Epoch: 031 Test results, ap: mae: 0.819, rse: 1.988, pcc: 0.358, kcc: 0.217
+2025-08-25 23:33:22: Epoch: 031 New best TEST metrics
+2025-08-25 23:33:53: Epoch: 032 Train Loss: 0.223, mae: 0.469, rse: 0.233, pcc: 0.882, kcc: 0.744
+2025-08-25 23:33:54: Epoch: 032 Test results, ap: mae: 0.963, rse: 2.620, pcc: 0.335, kcc: 0.193
+2025-08-25 23:34:24: Epoch: 033 Train Loss: 0.208, mae: 0.439, rse: 0.240, pcc: 0.877, kcc: 0.745
+2025-08-25 23:34:26: Epoch: 033 Test results, ap: mae: 0.989, rse: 2.749, pcc: 0.353, kcc: 0.212
+2025-08-25 23:34:56: Epoch: 034 Train Loss: 0.221, mae: 0.442, rse: 0.235, pcc: 0.879, kcc: 0.747
+2025-08-25 23:34:57: Epoch: 034 Test results, ap: mae: 0.906, rse: 2.372, pcc: 0.356, kcc: 0.221
+2025-08-25 23:35:28: Epoch: 035 Train Loss: 0.206, mae: 0.430, rse: 0.224, pcc: 0.883, kcc: 0.753
+2025-08-25 23:35:28: Epoch: 035 New best VALIDATION metrics
+2025-08-25 23:35:29: Epoch: 035 Test results, ap: mae: 0.941, rse: 2.493, pcc: 0.342, kcc: 0.203
+2025-08-25 23:35:59: Epoch: 036 Train Loss: 0.204, mae: 0.450, rse: 0.242, pcc: 0.880, kcc: 0.749
+2025-08-25 23:36:00: Epoch: 036 Test results, ap: mae: 0.986, rse: 2.714, pcc: 0.342, kcc: 0.205
+2025-08-25 23:36:31: Epoch: 037 Train Loss: 0.197, mae: 0.433, rse: 0.237, pcc: 0.878, kcc: 0.749
+2025-08-25 23:36:32: Epoch: 037 Test results, ap: mae: 0.984, rse: 2.761, pcc: 0.346, kcc: 0.221
+2025-08-25 23:37:02: Epoch: 038 Train Loss: 0.199, mae: 0.485, rse: 0.245, pcc: 0.874, kcc: 0.738
+2025-08-25 23:37:03: Epoch: 038 Test results, ap: mae: 0.948, rse: 2.511, pcc: 0.348, kcc: 0.224
+2025-08-25 23:37:34: Epoch: 039 Train Loss: 0.202, mae: 0.435, rse: 0.229, pcc: 0.882, kcc: 0.749
+2025-08-25 23:37:35: Epoch: 039 Test results, ap: mae: 0.904, rse: 2.372, pcc: 0.331, kcc: 0.205
+2025-08-25 23:38:05: Epoch: 040 Train Loss: 0.204, mae: 0.460, rse: 0.247, pcc: 0.875, kcc: 0.740
+2025-08-25 23:38:07: Epoch: 040 Test results, ap: mae: 1.116, rse: 3.510, pcc: 0.333, kcc: 0.194
+2025-08-25 23:38:37: Epoch: 041 Train Loss: 0.174, mae: 0.439, rse: 0.231, pcc: 0.879, kcc: 0.748
+2025-08-25 23:38:38: Epoch: 041 Test results, ap: mae: 0.939, rse: 2.528, pcc: 0.326, kcc: 0.198
+2025-08-25 23:39:08: Epoch: 042 Train Loss: 0.177, mae: 0.426, rse: 0.239, pcc: 0.877, kcc: 0.752
+2025-08-25 23:39:10: Epoch: 042 Test results, ap: mae: 1.046, rse: 3.047, pcc: 0.324, kcc: 0.205
+2025-08-25 23:39:40: Epoch: 043 Train Loss: 0.174, mae: 0.436, rse: 0.234, pcc: 0.879, kcc: 0.751
+2025-08-25 23:39:41: Epoch: 043 Test results, ap: mae: 0.945, rse: 2.572, pcc: 0.325, kcc: 0.203
+2025-08-25 23:40:11: Epoch: 044 Train Loss: 0.161, mae: 0.416, rse: 0.233, pcc: 0.880, kcc: 0.756
+2025-08-25 23:40:11: Epoch: 044 New best VALIDATION metrics
+2025-08-25 23:40:13: Epoch: 044 Test results, ap: mae: 0.970, rse: 2.638, pcc: 0.322, kcc: 0.202
+2025-08-25 23:40:43: Epoch: 045 Train Loss: 0.164, mae: 0.424, rse: 0.230, pcc: 0.881, kcc: 0.756
+2025-08-25 23:40:44: Epoch: 045 Test results, ap: mae: 0.948, rse: 2.550, pcc: 0.322, kcc: 0.199
+2025-08-25 23:41:14: Epoch: 046 Train Loss: 0.173, mae: 0.420, rse: 0.227, pcc: 0.882, kcc: 0.755
+2025-08-25 23:41:14: Epoch: 046 New best VALIDATION metrics
+2025-08-25 23:41:16: Epoch: 046 Test results, ap: mae: 0.974, rse: 2.661, pcc: 0.314, kcc: 0.191
+2025-08-25 23:41:46: Epoch: 047 Train Loss: 0.171, mae: 0.424, rse: 0.224, pcc: 0.885, kcc: 0.758
+2025-08-25 23:41:46: Epoch: 047 New best VALIDATION metrics
+2025-08-25 23:41:47: Epoch: 047 Test results, ap: mae: 0.963, rse: 2.602, pcc: 0.326, kcc: 0.196
+2025-08-25 23:42:18: Epoch: 048 Train Loss: 0.175, mae: 0.424, rse: 0.236, pcc: 0.877, kcc: 0.754
+2025-08-25 23:42:19: Epoch: 048 Test results, ap: mae: 0.974, rse: 2.705, pcc: 0.318, kcc: 0.200
+2025-08-25 23:42:49: Epoch: 049 Train Loss: 0.173, mae: 0.414, rse: 0.228, pcc: 0.882, kcc: 0.761
+2025-08-25 23:42:49: Epoch: 049 New best VALIDATION metrics
+2025-08-25 23:42:50: Epoch: 049 Test results, ap: mae: 0.886, rse: 2.270, pcc: 0.325, kcc: 0.198
+2025-08-25 23:43:20: Epoch: 050 Train Loss: 0.153, mae: 0.424, rse: 0.239, pcc: 0.877, kcc: 0.756
+2025-08-25 23:43:21: Epoch: 050 Test results, ap: mae: 0.952, rse: 2.541, pcc: 0.337, kcc: 0.214
+2025-08-25 23:43:21: used time 0.44h
+2025-08-25 23:43:22: Running Cross Validation 3
+2025-08-25 23:43:22: Fold 3  Train set:2270, Valid set:567, Test set: 123
+2025-08-25 23:43:52: Epoch: 001 Train Loss: 2.614, mae: 1.320, rse: 1.008, pcc: 0.030, kcc: 0.104
+2025-08-25 23:43:52: Epoch: 001 New best VALIDATION metrics
+2025-08-25 23:43:53: Epoch: 001 Test results, ap: mae: 0.542, rse: 0.999, pcc: 0.358, kcc: 0.222
+2025-08-25 23:44:23: Epoch: 002 Train Loss: 2.346, mae: 1.298, rse: 0.967, pcc: 0.418, kcc: 0.294
+2025-08-25 23:44:23: Epoch: 002 New best VALIDATION metrics
+2025-08-25 23:44:25: Epoch: 002 Test results, ap: mae: 0.859, rse: 2.338, pcc: -0.113, kcc: -0.075
+2025-08-25 23:44:55: Epoch: 003 Train Loss: 2.141, mae: 1.577, rse: 1.298, pcc: 0.365, kcc: 0.324
+2025-08-25 23:44:56: Epoch: 003 Test results, ap: mae: 1.304, rse: 4.406, pcc: -0.229, kcc: -0.189
+2025-08-25 23:45:27: Epoch: 004 Train Loss: 2.089, mae: 1.324, rse: 0.995, pcc: 0.342, kcc: 0.276
+2025-08-25 23:45:28: Epoch: 004 Test results, ap: mae: 1.085, rse: 3.211, pcc: -0.157, kcc: -0.083
+2025-08-25 23:45:58: Epoch: 005 Train Loss: 1.911, mae: 1.291, rse: 0.880, pcc: 0.529, kcc: 0.363
+2025-08-25 23:45:58: Epoch: 005 New best VALIDATION metrics
+2025-08-25 23:46:00: Epoch: 005 Test results, ap: mae: 0.891, rse: 2.417, pcc: -0.078, kcc: -0.040
+2025-08-25 23:46:30: Epoch: 006 Train Loss: 1.740, mae: 1.519, rse: 1.208, pcc: 0.522, kcc: 0.347
+2025-08-25 23:46:31: Epoch: 006 Test results, ap: mae: 1.037, rse: 2.847, pcc: -0.174, kcc: -0.041
+2025-08-25 23:47:01: Epoch: 007 Train Loss: 1.627, mae: 1.349, rse: 0.938, pcc: 0.508, kcc: 0.296
+2025-08-25 23:47:03: Epoch: 007 Test results, ap: mae: 0.804, rse: 2.047, pcc: -0.005, kcc: 0.040
+2025-08-25 23:47:33: Epoch: 008 Train Loss: 1.508, mae: 1.713, rse: 1.486, pcc: 0.473, kcc: 0.302
+2025-08-25 23:47:34: Epoch: 008 Test results, ap: mae: 1.240, rse: 4.337, pcc: 0.027, kcc: 0.055
+2025-08-25 23:48:04: Epoch: 009 Train Loss: 1.323, mae: 1.399, rse: 0.960, pcc: 0.569, kcc: 0.421
+2025-08-25 23:48:06: Epoch: 009 Test results, ap: mae: 1.350, rse: 5.050, pcc: -0.348, kcc: -0.216
+2025-08-25 23:48:36: Epoch: 010 Train Loss: 1.268, mae: 0.988, rse: 0.594, pcc: 0.667, kcc: 0.490
+2025-08-25 23:48:36: Epoch: 010 New best VALIDATION metrics
+2025-08-25 23:48:37: Epoch: 010 Test results, ap: mae: 0.815, rse: 2.211, pcc: -0.163, kcc: -0.071
+2025-08-25 23:49:08: Epoch: 011 Train Loss: 1.058, mae: 0.853, rse: 0.496, pcc: 0.725, kcc: 0.553
+2025-08-25 23:49:08: Epoch: 011 New best VALIDATION metrics
+2025-08-25 23:49:09: Epoch: 011 Test results, ap: mae: 1.086, rse: 3.772, pcc: -0.256, kcc: -0.133
+2025-08-25 23:49:09: Epoch: 011 New best TEST metrics
+2025-08-25 23:49:39: Epoch: 012 Train Loss: 0.893, mae: 0.863, rse: 0.494, pcc: 0.735, kcc: 0.563
+2025-08-25 23:49:39: Epoch: 012 New best VALIDATION metrics
+2025-08-25 23:49:41: Epoch: 012 Test results, ap: mae: 0.893, rse: 2.704, pcc: -0.188, kcc: -0.060
+2025-08-25 23:49:41: Epoch: 012 New best TEST metrics
+2025-08-25 23:50:11: Epoch: 013 Train Loss: 0.828, mae: 0.855, rse: 0.495, pcc: 0.747, kcc: 0.573
+2025-08-25 23:50:11: Epoch: 013 New best VALIDATION metrics
+2025-08-25 23:50:12: Epoch: 013 Test results, ap: mae: 0.818, rse: 2.208, pcc: -0.064, kcc: 0.009
+2025-08-25 23:50:12: Epoch: 013 New best TEST metrics
+2025-08-25 23:50:42: Epoch: 014 Train Loss: 0.717, mae: 1.126, rse: 0.728, pcc: 0.749, kcc: 0.572
+2025-08-25 23:50:43: Epoch: 014 Test results, ap: mae: 1.452, rse: 5.491, pcc: -0.020, kcc: 0.057
+2025-08-25 23:51:14: Epoch: 015 Train Loss: 0.632, mae: 0.773, rse: 0.409, pcc: 0.793, kcc: 0.624
+2025-08-25 23:51:14: Epoch: 015 New best VALIDATION metrics
+2025-08-25 23:51:15: Epoch: 015 Test results, ap: mae: 0.849, rse: 2.272, pcc: -0.056, kcc: -0.009
+2025-08-25 23:51:45: Epoch: 016 Train Loss: 0.592, mae: 0.715, rse: 0.403, pcc: 0.787, kcc: 0.629
+2025-08-25 23:51:45: Epoch: 016 New best VALIDATION metrics
+2025-08-25 23:51:47: Epoch: 016 Test results, ap: mae: 0.926, rse: 2.972, pcc: -0.007, kcc: 0.051
+2025-08-25 23:52:17: Epoch: 017 Train Loss: 0.496, mae: 0.644, rse: 0.373, pcc: 0.803, kcc: 0.647
+2025-08-25 23:52:17: Epoch: 017 New best VALIDATION metrics
+2025-08-25 23:52:18: Epoch: 017 Test results, ap: mae: 0.877, rse: 2.659, pcc: -0.062, kcc: 0.010
+2025-08-25 23:52:49: Epoch: 018 Train Loss: 0.471, mae: 0.698, rse: 0.381, pcc: 0.814, kcc: 0.638
+2025-08-25 23:52:50: Epoch: 018 Test results, ap: mae: 1.237, rse: 4.617, pcc: -0.014, kcc: 0.015
+2025-08-25 23:53:20: Epoch: 019 Train Loss: 0.450, mae: 0.685, rse: 0.360, pcc: 0.820, kcc: 0.651
+2025-08-25 23:53:21: Epoch: 019 Test results, ap: mae: 1.136, rse: 3.877, pcc: 0.050, kcc: 0.050
+2025-08-25 23:53:52: Epoch: 020 Train Loss: 0.433, mae: 0.830, rse: 0.407, pcc: 0.839, kcc: 0.662
+2025-08-25 23:53:53: Epoch: 020 Test results, ap: mae: 1.156, rse: 4.121, pcc: -0.049, kcc: 0.002
+2025-08-25 23:54:23: Epoch: 021 Train Loss: 0.328, mae: 0.534, rse: 0.298, pcc: 0.843, kcc: 0.706
+2025-08-25 23:54:23: Epoch: 021 New best VALIDATION metrics
+2025-08-25 23:54:25: Epoch: 021 Test results, ap: mae: 1.097, rse: 3.772, pcc: -0.070, kcc: -0.025
+2025-08-25 23:54:55: Epoch: 022 Train Loss: 0.308, mae: 0.510, rse: 0.292, pcc: 0.845, kcc: 0.716
+2025-08-25 23:54:55: Epoch: 022 New best VALIDATION metrics
+2025-08-25 23:54:56: Epoch: 022 Test results, ap: mae: 0.966, rse: 3.047, pcc: -0.072, kcc: -0.002
+2025-08-25 23:55:26: Epoch: 023 Train Loss: 0.280, mae: 0.517, rse: 0.293, pcc: 0.841, kcc: 0.722
+2025-08-25 23:55:28: Epoch: 023 Test results, ap: mae: 0.953, rse: 2.974, pcc: -0.042, kcc: 0.006
+2025-08-25 23:55:58: Epoch: 024 Train Loss: 0.285, mae: 0.595, rse: 0.322, pcc: 0.851, kcc: 0.718
+2025-08-25 23:55:59: Epoch: 024 Test results, ap: mae: 1.389, rse: 5.630, pcc: -0.106, kcc: -0.059
+2025-08-25 23:56:29: Epoch: 025 Train Loss: 0.263, mae: 0.494, rse: 0.311, pcc: 0.840, kcc: 0.728
+2025-08-25 23:56:29: Epoch: 025 New best VALIDATION metrics
+2025-08-25 23:56:31: Epoch: 025 Test results, ap: mae: 1.084, rse: 3.634, pcc: -0.064, kcc: -0.023
+2025-08-25 23:57:01: Epoch: 026 Train Loss: 0.246, mae: 0.489, rse: 0.291, pcc: 0.847, kcc: 0.738
+2025-08-25 23:57:01: Epoch: 026 New best VALIDATION metrics
+2025-08-25 23:57:02: Epoch: 026 Test results, ap: mae: 0.966, rse: 2.984, pcc: -0.085, kcc: -0.023
+2025-08-25 23:57:32: Epoch: 027 Train Loss: 0.238, mae: 0.544, rse: 0.326, pcc: 0.830, kcc: 0.711
+2025-08-25 23:57:33: Epoch: 027 Test results, ap: mae: 0.986, rse: 3.086, pcc: -0.082, kcc: -0.026
+2025-08-25 23:58:03: Epoch: 028 Train Loss: 0.266, mae: 0.508, rse: 0.296, pcc: 0.847, kcc: 0.739
+2025-08-25 23:58:05: Epoch: 028 Test results, ap: mae: 0.815, rse: 2.234, pcc: -0.090, kcc: -0.026
+2025-08-25 23:58:35: Epoch: 029 Train Loss: 0.242, mae: 0.545, rse: 0.328, pcc: 0.839, kcc: 0.718
+2025-08-25 23:58:36: Epoch: 029 Test results, ap: mae: 0.929, rse: 2.926, pcc: 0.014, kcc: 0.063
+2025-08-25 23:59:06: Epoch: 030 Train Loss: 0.252, mae: 0.473, rse: 0.295, pcc: 0.845, kcc: 0.738
+2025-08-25 23:59:06: Epoch: 030 New best VALIDATION metrics
+2025-08-25 23:59:08: Epoch: 030 Test results, ap: mae: 0.902, rse: 2.634, pcc: -0.052, kcc: -0.005
+2025-08-25 23:59:38: Epoch: 031 Train Loss: 0.207, mae: 0.461, rse: 0.293, pcc: 0.849, kcc: 0.752
+2025-08-25 23:59:38: Epoch: 031 New best VALIDATION metrics
+2025-08-25 23:59:40: Epoch: 031 Test results, ap: mae: 0.980, rse: 3.067, pcc: -0.066, kcc: -0.023
+2025-08-26 00:00:10: Epoch: 032 Train Loss: 0.201, mae: 0.456, rse: 0.288, pcc: 0.849, kcc: 0.746
+2025-08-26 00:00:10: Epoch: 032 New best VALIDATION metrics
+2025-08-26 00:00:11: Epoch: 032 Test results, ap: mae: 0.887, rse: 2.631, pcc: -0.028, kcc: 0.027
+2025-08-26 00:00:41: Epoch: 033 Train Loss: 0.209, mae: 0.455, rse: 0.284, pcc: 0.849, kcc: 0.749
+2025-08-26 00:00:41: Epoch: 033 New best VALIDATION metrics
+2025-08-26 00:00:43: Epoch: 033 Test results, ap: mae: 0.965, rse: 3.009, pcc: -0.050, kcc: 0.003
+2025-08-26 00:01:13: Epoch: 034 Train Loss: 0.181, mae: 0.445, rse: 0.291, pcc: 0.850, kcc: 0.761
+2025-08-26 00:01:13: Epoch: 034 New best VALIDATION metrics
+2025-08-26 00:01:14: Epoch: 034 Test results, ap: mae: 0.945, rse: 2.922, pcc: -0.043, kcc: 0.003
+2025-08-26 00:01:45: Epoch: 035 Train Loss: 0.203, mae: 0.441, rse: 0.289, pcc: 0.848, kcc: 0.758
+2025-08-26 00:01:45: Epoch: 035 New best VALIDATION metrics
+2025-08-26 00:01:46: Epoch: 035 Test results, ap: mae: 0.962, rse: 2.991, pcc: -0.072, kcc: -0.018
+2025-08-26 00:02:16: Epoch: 036 Train Loss: 0.193, mae: 0.450, rse: 0.290, pcc: 0.852, kcc: 0.757
+2025-08-26 00:02:18: Epoch: 036 Test results, ap: mae: 1.015, rse: 3.289, pcc: -0.051, kcc: -0.010
+2025-08-26 00:02:48: Epoch: 037 Train Loss: 0.199, mae: 0.456, rse: 0.292, pcc: 0.849, kcc: 0.748
+2025-08-26 00:02:49: Epoch: 037 Test results, ap: mae: 0.965, rse: 2.979, pcc: -0.039, kcc: 0.003
+2025-08-26 00:03:20: Epoch: 038 Train Loss: 0.181, mae: 0.438, rse: 0.289, pcc: 0.850, kcc: 0.753
+2025-08-26 00:03:20: Epoch: 038 New best VALIDATION metrics
+2025-08-26 00:03:21: Epoch: 038 Test results, ap: mae: 0.967, rse: 2.956, pcc: -0.049, kcc: 0.000
+2025-08-26 00:03:51: Epoch: 039 Train Loss: 0.190, mae: 0.421, rse: 0.275, pcc: 0.856, kcc: 0.764
+2025-08-26 00:03:51: Epoch: 039 New best VALIDATION metrics
+2025-08-26 00:03:53: Epoch: 039 Test results, ap: mae: 0.915, rse: 2.701, pcc: -0.090, kcc: -0.019
+2025-08-26 00:04:23: Epoch: 040 Train Loss: 0.196, mae: 0.432, rse: 0.290, pcc: 0.848, kcc: 0.760
+2025-08-26 00:04:24: Epoch: 040 Test results, ap: mae: 1.017, rse: 3.154, pcc: -0.048, kcc: -0.007
+2025-08-26 00:04:54: Epoch: 041 Train Loss: 0.171, mae: 0.419, rse: 0.290, pcc: 0.851, kcc: 0.770
+2025-08-26 00:04:56: Epoch: 041 Test results, ap: mae: 0.951, rse: 2.857, pcc: -0.037, kcc: 0.010
+2025-08-26 00:05:26: Epoch: 042 Train Loss: 0.165, mae: 0.421, rse: 0.298, pcc: 0.848, kcc: 0.768
+2025-08-26 00:05:28: Epoch: 042 Test results, ap: mae: 1.028, rse: 3.262, pcc: -0.051, kcc: -0.008
+2025-08-26 00:05:58: Epoch: 043 Train Loss: 0.162, mae: 0.436, rse: 0.292, pcc: 0.847, kcc: 0.765
+2025-08-26 00:05:59: Epoch: 043 Test results, ap: mae: 1.000, rse: 3.033, pcc: -0.057, kcc: -0.016
+2025-08-26 00:06:29: Epoch: 044 Train Loss: 0.166, mae: 0.426, rse: 0.293, pcc: 0.848, kcc: 0.766
+2025-08-26 00:06:31: Epoch: 044 Test results, ap: mae: 0.991, rse: 3.047, pcc: -0.052, kcc: -0.013
+2025-08-26 00:07:01: Epoch: 045 Train Loss: 0.159, mae: 0.420, rse: 0.295, pcc: 0.850, kcc: 0.772
+2025-08-26 00:07:02: Epoch: 045 Test results, ap: mae: 0.999, rse: 3.060, pcc: -0.050, kcc: -0.013
+2025-08-26 00:07:32: Epoch: 046 Train Loss: 0.165, mae: 0.420, rse: 0.299, pcc: 0.849, kcc: 0.775
+2025-08-26 00:07:33: Epoch: 046 Test results, ap: mae: 0.966, rse: 2.968, pcc: -0.062, kcc: -0.010
+2025-08-26 00:08:03: Epoch: 047 Train Loss: 0.178, mae: 0.421, rse: 0.281, pcc: 0.856, kcc: 0.776
+2025-08-26 00:08:03: Epoch: 047 New best VALIDATION metrics
+2025-08-26 00:08:05: Epoch: 047 Test results, ap: mae: 0.942, rse: 2.733, pcc: -0.058, kcc: -0.018
+2025-08-26 00:08:35: Epoch: 048 Train Loss: 0.162, mae: 0.417, rse: 0.283, pcc: 0.851, kcc: 0.769
+2025-08-26 00:08:36: Epoch: 048 Test results, ap: mae: 0.978, rse: 2.969, pcc: -0.034, kcc: 0.002
+2025-08-26 00:09:07: Epoch: 049 Train Loss: 0.156, mae: 0.411, rse: 0.290, pcc: 0.853, kcc: 0.777
+2025-08-26 00:09:08: Epoch: 049 Test results, ap: mae: 1.000, rse: 3.101, pcc: -0.046, kcc: -0.011
+2025-08-26 00:09:39: Epoch: 050 Train Loss: 0.155, mae: 0.395, rse: 0.283, pcc: 0.853, kcc: 0.780
+2025-08-26 00:09:39: Epoch: 050 New best VALIDATION metrics
+2025-08-26 00:09:40: Epoch: 050 Test results, ap: mae: 1.009, rse: 3.092, pcc: -0.067, kcc: -0.019
+2025-08-26 00:09:40: used time 0.44h
+2025-08-26 00:09:40: Running Cross Validation 4
+2025-08-26 00:09:40: Fold 4  Train set:2270, Valid set:567, Test set: 123
+2025-08-26 00:10:10: Epoch: 001 Train Loss: 2.549, mae: 1.693, rse: 1.574, pcc: 0.229, kcc: 0.155
+2025-08-26 00:10:10: Epoch: 001 New best VALIDATION metrics
+2025-08-26 00:10:12: Epoch: 001 Test results, ap: mae: 1.935, rse: 8.500, pcc: -0.154, kcc: -0.134
+2025-08-26 00:10:42: Epoch: 002 Train Loss: 2.245, mae: 1.113, rse: 0.840, pcc: 0.444, kcc: 0.309
+2025-08-26 00:10:42: Epoch: 002 New best VALIDATION metrics
+2025-08-26 00:10:43: Epoch: 002 Test results, ap: mae: 0.700, rse: 1.610, pcc: -0.203, kcc: -0.186
+2025-08-26 00:11:14: Epoch: 003 Train Loss: 2.017, mae: 1.190, rse: 0.942, pcc: 0.274, kcc: 0.228
+2025-08-26 00:11:15: Epoch: 003 Test results, ap: mae: 1.029, rse: 3.154, pcc: -0.026, kcc: -0.036
+2025-08-26 00:11:45: Epoch: 004 Train Loss: 1.815, mae: 1.176, rse: 0.824, pcc: 0.562, kcc: 0.394
+2025-08-26 00:11:45: Epoch: 004 New best VALIDATION metrics
+2025-08-26 00:11:46: Epoch: 004 Test results, ap: mae: 0.692, rse: 1.548, pcc: 0.171, kcc: 0.171
+2025-08-26 00:12:16: Epoch: 005 Train Loss: 1.614, mae: 1.171, rse: 0.916, pcc: 0.367, kcc: 0.307
+2025-08-26 00:12:18: Epoch: 005 Test results, ap: mae: 1.163, rse: 3.794, pcc: -0.024, kcc: -0.010
+2025-08-26 00:12:48: Epoch: 006 Train Loss: 1.440, mae: 0.905, rse: 0.559, pcc: 0.672, kcc: 0.498
+2025-08-26 00:12:48: Epoch: 006 New best VALIDATION metrics
+2025-08-26 00:12:50: Epoch: 006 Test results, ap: mae: 0.836, rse: 2.062, pcc: 0.048, kcc: 0.006
+2025-08-26 00:13:20: Epoch: 007 Train Loss: 1.310, mae: 1.113, rse: 0.719, pcc: 0.623, kcc: 0.450
+2025-08-26 00:13:21: Epoch: 007 Test results, ap: mae: 0.800, rse: 2.056, pcc: 0.253, kcc: 0.177
+2025-08-26 00:13:52: Epoch: 008 Train Loss: 1.144, mae: 1.056, rse: 0.675, pcc: 0.706, kcc: 0.494
+2025-08-26 00:13:53: Epoch: 008 Test results, ap: mae: 1.228, rse: 4.216, pcc: 0.202, kcc: 0.122
+2025-08-26 00:14:23: Epoch: 009 Train Loss: 0.966, mae: 1.075, rse: 0.721, pcc: 0.587, kcc: 0.422
+2025-08-26 00:14:25: Epoch: 009 Test results, ap: mae: 1.006, rse: 2.941, pcc: 0.035, kcc: 0.000
+2025-08-26 00:14:55: Epoch: 010 Train Loss: 0.922, mae: 0.735, rse: 0.417, pcc: 0.766, kcc: 0.578
+2025-08-26 00:14:55: Epoch: 010 New best VALIDATION metrics
+2025-08-26 00:14:57: Epoch: 010 Test results, ap: mae: 0.823, rse: 2.073, pcc: 0.073, kcc: 0.050
+2025-08-26 00:15:27: Epoch: 011 Train Loss: 0.713, mae: 0.646, rse: 0.353, pcc: 0.811, kcc: 0.629
+2025-08-26 00:15:27: Epoch: 011 New best VALIDATION metrics
+2025-08-26 00:15:28: Epoch: 011 Test results, ap: mae: 0.853, rse: 2.417, pcc: 0.222, kcc: 0.143
+2025-08-26 00:15:28: Epoch: 011 New best TEST metrics
+2025-08-26 00:15:58: Epoch: 012 Train Loss: 0.626, mae: 0.694, rse: 0.369, pcc: 0.795, kcc: 0.616
+2025-08-26 00:15:59: Epoch: 012 Test results, ap: mae: 0.954, rse: 2.870, pcc: 0.222, kcc: 0.139
+2025-08-26 00:16:30: Epoch: 013 Train Loss: 0.512, mae: 0.626, rse: 0.338, pcc: 0.828, kcc: 0.654
+2025-08-26 00:16:30: Epoch: 013 New best VALIDATION metrics
+2025-08-26 00:16:31: Epoch: 013 Test results, ap: mae: 0.894, rse: 2.500, pcc: 0.233, kcc: 0.145
+2025-08-26 00:17:01: Epoch: 014 Train Loss: 0.485, mae: 0.604, rse: 0.309, pcc: 0.839, kcc: 0.661
+2025-08-26 00:17:01: Epoch: 014 New best VALIDATION metrics
+2025-08-26 00:17:03: Epoch: 014 Test results, ap: mae: 0.819, rse: 2.050, pcc: 0.215, kcc: 0.126
+2025-08-26 00:17:03: Epoch: 014 New best TEST metrics
+2025-08-26 00:17:33: Epoch: 015 Train Loss: 0.474, mae: 0.575, rse: 0.302, pcc: 0.838, kcc: 0.653
+2025-08-26 00:17:33: Epoch: 015 New best VALIDATION metrics
+2025-08-26 00:17:34: Epoch: 015 Test results, ap: mae: 0.815, rse: 2.147, pcc: 0.248, kcc: 0.158
+2025-08-26 00:18:04: Epoch: 016 Train Loss: 0.414, mae: 0.548, rse: 0.282, pcc: 0.851, kcc: 0.682
+2025-08-26 00:18:04: Epoch: 016 New best VALIDATION metrics
+2025-08-26 00:18:06: Epoch: 016 Test results, ap: mae: 0.732, rse: 1.806, pcc: 0.235, kcc: 0.146
+2025-08-26 00:18:06: Epoch: 016 New best TEST metrics
+2025-08-26 00:18:36: Epoch: 017 Train Loss: 0.402, mae: 0.646, rse: 0.335, pcc: 0.831, kcc: 0.633
+2025-08-26 00:18:37: Epoch: 017 Test results, ap: mae: 0.803, rse: 1.942, pcc: 0.282, kcc: 0.193
+2025-08-26 00:19:07: Epoch: 018 Train Loss: 0.370, mae: 0.623, rse: 0.317, pcc: 0.848, kcc: 0.666
+2025-08-26 00:19:09: Epoch: 018 Test results, ap: mae: 1.029, rse: 3.215, pcc: 0.271, kcc: 0.173
+2025-08-26 00:19:39: Epoch: 019 Train Loss: 0.356, mae: 0.536, rse: 0.259, pcc: 0.865, kcc: 0.696
+2025-08-26 00:19:39: Epoch: 019 New best VALIDATION metrics
+2025-08-26 00:19:40: Epoch: 019 Test results, ap: mae: 0.700, rse: 1.679, pcc: 0.157, kcc: 0.108
+2025-08-26 00:19:40: Epoch: 019 New best TEST metrics
+2025-08-26 00:20:10: Epoch: 020 Train Loss: 0.367, mae: 0.515, rse: 0.262, pcc: 0.862, kcc: 0.676
+2025-08-26 00:20:12: Epoch: 020 Test results, ap: mae: 0.787, rse: 2.034, pcc: 0.328, kcc: 0.205
+2025-08-26 00:20:42: Epoch: 021 Train Loss: 0.318, mae: 0.469, rse: 0.232, pcc: 0.877, kcc: 0.715
+2025-08-26 00:20:42: Epoch: 021 New best VALIDATION metrics
+2025-08-26 00:20:44: Epoch: 021 Test results, ap: mae: 0.796, rse: 2.028, pcc: 0.216, kcc: 0.133
+2025-08-26 00:21:14: Epoch: 022 Train Loss: 0.270, mae: 0.475, rse: 0.240, pcc: 0.874, kcc: 0.714
+2025-08-26 00:21:15: Epoch: 022 Test results, ap: mae: 0.785, rse: 2.067, pcc: 0.284, kcc: 0.195
+2025-08-26 00:21:46: Epoch: 023 Train Loss: 0.270, mae: 0.466, rse: 0.239, pcc: 0.874, kcc: 0.715
+2025-08-26 00:21:47: Epoch: 023 Test results, ap: mae: 0.713, rse: 1.708, pcc: 0.214, kcc: 0.139
+2025-08-26 00:21:47: Epoch: 023 New best TEST metrics
+2025-08-26 00:22:17: Epoch: 024 Train Loss: 0.264, mae: 0.504, rse: 0.246, pcc: 0.881, kcc: 0.725
+2025-08-26 00:22:19: Epoch: 024 Test results, ap: mae: 0.722, rse: 1.716, pcc: 0.213, kcc: 0.134
+2025-08-26 00:22:49: Epoch: 025 Train Loss: 0.268, mae: 0.488, rse: 0.240, pcc: 0.877, kcc: 0.718
+2025-08-26 00:22:50: Epoch: 025 Test results, ap: mae: 0.744, rse: 1.771, pcc: 0.216, kcc: 0.141
+2025-08-26 00:23:21: Epoch: 026 Train Loss: 0.235, mae: 0.481, rse: 0.232, pcc: 0.883, kcc: 0.727
+2025-08-26 00:23:21: Epoch: 026 New best VALIDATION metrics
+2025-08-26 00:23:22: Epoch: 026 Test results, ap: mae: 0.714, rse: 1.696, pcc: 0.253, kcc: 0.161
+2025-08-26 00:23:22: Epoch: 026 New best TEST metrics
+2025-08-26 00:23:53: Epoch: 027 Train Loss: 0.262, mae: 0.452, rse: 0.226, pcc: 0.882, kcc: 0.723
+2025-08-26 00:23:53: Epoch: 027 New best VALIDATION metrics
+2025-08-26 00:23:54: Epoch: 027 Test results, ap: mae: 0.729, rse: 1.701, pcc: 0.220, kcc: 0.149
+2025-08-26 00:24:24: Epoch: 028 Train Loss: 0.235, mae: 0.463, rse: 0.231, pcc: 0.882, kcc: 0.729
+2025-08-26 00:24:26: Epoch: 028 Test results, ap: mae: 0.728, rse: 1.798, pcc: 0.212, kcc: 0.123
+2025-08-26 00:24:56: Epoch: 029 Train Loss: 0.218, mae: 0.440, rse: 0.222, pcc: 0.882, kcc: 0.736
+2025-08-26 00:24:56: Epoch: 029 New best VALIDATION metrics
+2025-08-26 00:24:57: Epoch: 029 Test results, ap: mae: 0.744, rse: 1.900, pcc: 0.220, kcc: 0.152
+2025-08-26 00:25:27: Epoch: 030 Train Loss: 0.240, mae: 0.440, rse: 0.226, pcc: 0.881, kcc: 0.723
+2025-08-26 00:25:29: Epoch: 030 Test results, ap: mae: 0.753, rse: 1.932, pcc: 0.264, kcc: 0.186
+2025-08-26 00:25:59: Epoch: 031 Train Loss: 0.218, mae: 0.427, rse: 0.217, pcc: 0.887, kcc: 0.731
+2025-08-26 00:25:59: Epoch: 031 New best VALIDATION metrics
+2025-08-26 00:26:00: Epoch: 031 Test results, ap: mae: 0.721, rse: 1.768, pcc: 0.266, kcc: 0.185
+2025-08-26 00:26:31: Epoch: 032 Train Loss: 0.204, mae: 0.418, rse: 0.213, pcc: 0.888, kcc: 0.741
+2025-08-26 00:26:31: Epoch: 032 New best VALIDATION metrics
+2025-08-26 00:26:32: Epoch: 032 Test results, ap: mae: 0.743, rse: 1.832, pcc: 0.274, kcc: 0.189
+2025-08-26 00:27:02: Epoch: 033 Train Loss: 0.196, mae: 0.420, rse: 0.217, pcc: 0.889, kcc: 0.744
+2025-08-26 00:27:04: Epoch: 033 Test results, ap: mae: 0.770, rse: 2.006, pcc: 0.221, kcc: 0.154
+2025-08-26 00:27:34: Epoch: 034 Train Loss: 0.212, mae: 0.393, rse: 0.211, pcc: 0.890, kcc: 0.749
+2025-08-26 00:27:34: Epoch: 034 New best VALIDATION metrics
+2025-08-26 00:27:35: Epoch: 034 Test results, ap: mae: 0.729, rse: 1.778, pcc: 0.264, kcc: 0.173
+2025-08-26 00:28:06: Epoch: 035 Train Loss: 0.182, mae: 0.470, rse: 0.235, pcc: 0.888, kcc: 0.743
+2025-08-26 00:28:07: Epoch: 035 Test results, ap: mae: 0.705, rse: 1.616, pcc: 0.281, kcc: 0.189
+2025-08-26 00:28:07: Epoch: 035 New best TEST metrics
+2025-08-26 00:28:37: Epoch: 036 Train Loss: 0.180, mae: 0.423, rse: 0.221, pcc: 0.887, kcc: 0.741
+2025-08-26 00:28:39: Epoch: 036 Test results, ap: mae: 0.713, rse: 1.688, pcc: 0.303, kcc: 0.206
+2025-08-26 00:29:09: Epoch: 037 Train Loss: 0.189, mae: 0.388, rse: 0.203, pcc: 0.893, kcc: 0.752
+2025-08-26 00:29:09: Epoch: 037 New best VALIDATION metrics
+2025-08-26 00:29:10: Epoch: 037 Test results, ap: mae: 0.790, rse: 2.004, pcc: 0.239, kcc: 0.169
+2025-08-26 00:29:40: Epoch: 038 Train Loss: 0.189, mae: 0.420, rse: 0.219, pcc: 0.887, kcc: 0.743
+2025-08-26 00:29:42: Epoch: 038 Test results, ap: mae: 0.708, rse: 1.663, pcc: 0.283, kcc: 0.194
+2025-08-26 00:30:12: Epoch: 039 Train Loss: 0.177, mae: 0.390, rse: 0.211, pcc: 0.892, kcc: 0.752
+2025-08-26 00:30:14: Epoch: 039 Test results, ap: mae: 0.778, rse: 1.961, pcc: 0.268, kcc: 0.189
+2025-08-26 00:30:44: Epoch: 040 Train Loss: 0.187, mae: 0.406, rse: 0.213, pcc: 0.889, kcc: 0.742
+2025-08-26 00:30:45: Epoch: 040 Test results, ap: mae: 0.779, rse: 1.958, pcc: 0.268, kcc: 0.188
+2025-08-26 00:31:16: Epoch: 041 Train Loss: 0.172, mae: 0.388, rse: 0.206, pcc: 0.891, kcc: 0.753
+2025-08-26 00:31:17: Epoch: 041 Test results, ap: mae: 0.756, rse: 1.864, pcc: 0.274, kcc: 0.191
+2025-08-26 00:31:47: Epoch: 042 Train Loss: 0.163, mae: 0.388, rse: 0.208, pcc: 0.892, kcc: 0.754
+2025-08-26 00:31:49: Epoch: 042 Test results, ap: mae: 0.715, rse: 1.693, pcc: 0.296, kcc: 0.197
+2025-08-26 00:32:19: Epoch: 043 Train Loss: 0.164, mae: 0.380, rse: 0.205, pcc: 0.892, kcc: 0.752
+2025-08-26 00:32:19: Epoch: 043 New best VALIDATION metrics
+2025-08-26 00:32:20: Epoch: 043 Test results, ap: mae: 0.750, rse: 1.854, pcc: 0.276, kcc: 0.189
+2025-08-26 00:32:51: Epoch: 044 Train Loss: 0.164, mae: 0.393, rse: 0.207, pcc: 0.892, kcc: 0.754
+2025-08-26 00:32:52: Epoch: 044 Test results, ap: mae: 0.756, rse: 1.869, pcc: 0.284, kcc: 0.203
+2025-08-26 00:33:22: Epoch: 045 Train Loss: 0.162, mae: 0.381, rse: 0.206, pcc: 0.892, kcc: 0.757
+2025-08-26 00:33:22: Epoch: 045 New best VALIDATION metrics
+2025-08-26 00:33:23: Epoch: 045 Test results, ap: mae: 0.720, rse: 1.726, pcc: 0.307, kcc: 0.207
+2025-08-26 00:33:54: Epoch: 046 Train Loss: 0.160, mae: 0.375, rse: 0.206, pcc: 0.892, kcc: 0.755
+2025-08-26 00:33:54: Epoch: 046 New best VALIDATION metrics
+2025-08-26 00:33:55: Epoch: 046 Test results, ap: mae: 0.789, rse: 2.010, pcc: 0.301, kcc: 0.211
+2025-08-26 00:34:26: Epoch: 047 Train Loss: 0.154, mae: 0.400, rse: 0.211, pcc: 0.892, kcc: 0.754
+2025-08-26 00:34:27: Epoch: 047 Test results, ap: mae: 0.768, rse: 1.898, pcc: 0.276, kcc: 0.195
+2025-08-26 00:34:57: Epoch: 048 Train Loss: 0.156, mae: 0.379, rse: 0.209, pcc: 0.892, kcc: 0.758
+2025-08-26 00:34:58: Epoch: 048 Test results, ap: mae: 0.709, rse: 1.674, pcc: 0.314, kcc: 0.210
+2025-08-26 00:35:29: Epoch: 049 Train Loss: 0.157, mae: 0.374, rse: 0.205, pcc: 0.893, kcc: 0.756
+2025-08-26 00:35:29: Epoch: 049 New best VALIDATION metrics
+2025-08-26 00:35:30: Epoch: 049 Test results, ap: mae: 0.794, rse: 2.025, pcc: 0.266, kcc: 0.189
+2025-08-26 00:36:00: Epoch: 050 Train Loss: 0.159, mae: 0.374, rse: 0.201, pcc: 0.895, kcc: 0.760
+2025-08-26 00:36:00: Epoch: 050 New best VALIDATION metrics
+2025-08-26 00:36:02: Epoch: 050 Test results, ap: mae: 0.770, rse: 1.912, pcc: 0.292, kcc: 0.205
+2025-08-26 00:36:02: used time 0.44h
+2025-08-26 00:36:02: Cross Validation Finished!
+2025-08-26 00:36:02: Best validation perform list
+[[0.34571844 0.12585984 0.93617362 0.77681106]
+ [0.39945301 0.18121381 0.90648603 0.76269341]
+ [0.414251   0.22780757 0.88188231 0.76096612]
+ [0.39458844 0.28277779 0.8526141  0.78030437]
+ [0.37432086 0.20082751 0.89464444 0.76020825]]
+2025-08-26 00:36:02: mean: [0.386 0.204 0.894 0.768]
+2025-08-26 00:36:02: std: [0.024 0.052 0.028 0.009]
+2025-08-26 00:36:02: Best test perform list
+[[ 0.67580432  1.58463275 -0.04585078  0.00427494]
+ [ 0.63014281  1.37382817  0.35647956  0.32302541]
+ [ 0.81860375  1.98787081  0.35804623  0.21695337]
+ [ 0.8184945   2.20767736 -0.06421589  0.00935144]
+ [ 0.7053926   1.61644924  0.2812092   0.18889906]]
+2025-08-26 00:36:02: mean: [0.73  1.754 0.177 0.149]
+2025-08-26 00:36:02: std: [0.076 0.301 0.192 0.124]
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+Valid
+0.9904417851849645,0.6555920676489474,0.592325503910902,0.39393817977278067
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+2025-07-19 01:00:39: Loading Training Dataset
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+2025-07-19 01:01:07: Running Cross Validation 0
+2025-07-19 01:01:07: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-19 01:01:43: Epoch: 001 Train Loss: 2.727, mae: 1.363, rse: 1.080, pcc: 0.363, kcc: 0.275
+2025-07-19 01:01:43: Epoch: 001 New best VALIDATION metrics
+2025-07-19 01:01:45: Epoch: 001 Test results, ap: mae: 0.976, rse: 2.319, pcc: -0.282, kcc: -0.372
+2025-07-19 01:02:20: Epoch: 002 Train Loss: 2.751, mae: 1.318, rse: 1.199, pcc: 0.262, kcc: 0.239
+2025-07-19 01:02:21: Epoch: 002 Test results, ap: mae: 1.126, rse: 3.140, pcc: -0.229, kcc: -0.044
+2025-07-19 01:02:56: Epoch: 003 Train Loss: 2.703, mae: 1.176, rse: 0.981, pcc: 0.338, kcc: 0.250
+2025-07-19 01:02:56: Epoch: 003 New best VALIDATION metrics
+2025-07-19 01:02:57: Epoch: 003 Test results, ap: mae: 0.812, rse: 1.894, pcc: -0.345, kcc: -0.201
+2025-07-19 01:03:32: Epoch: 004 Train Loss: 2.645, mae: 1.117, rse: 0.849, pcc: 0.416, kcc: 0.279
+2025-07-19 01:03:32: Epoch: 004 New best VALIDATION metrics
+2025-07-19 01:03:34: Epoch: 004 Test results, ap: mae: 0.688, rse: 1.319, pcc: -0.414, kcc: -0.328
+2025-07-19 01:04:09: Epoch: 005 Train Loss: 2.627, mae: 1.119, rse: 0.839, pcc: 0.405, kcc: 0.291
+2025-07-19 01:04:09: Epoch: 005 New best VALIDATION metrics
+2025-07-19 01:04:10: Epoch: 005 Test results, ap: mae: 0.936, rse: 2.254, pcc: -0.466, kcc: -0.391
+2025-07-19 01:04:45: Epoch: 006 Train Loss: 2.621, mae: 1.153, rse: 0.893, pcc: 0.403, kcc: 0.258
+2025-07-19 01:04:46: Epoch: 006 Test results, ap: mae: 0.679, rse: 1.290, pcc: -0.487, kcc: -0.360
+2025-07-19 01:05:21: Epoch: 007 Train Loss: 2.550, mae: 1.148, rse: 0.881, pcc: 0.357, kcc: 0.237
+2025-07-19 01:05:23: Epoch: 007 Test results, ap: mae: 0.887, rse: 2.057, pcc: -0.398, kcc: -0.337
+2025-07-19 01:05:57: Epoch: 008 Train Loss: 2.557, mae: 1.175, rse: 0.927, pcc: 0.285, kcc: 0.172
+2025-07-19 01:05:59: Epoch: 008 Test results, ap: mae: 0.731, rse: 1.647, pcc: -0.273, kcc: -0.111
+2025-07-19 01:06:34: Epoch: 009 Train Loss: 2.452, mae: 1.146, rse: 0.879, pcc: 0.382, kcc: 0.264
+2025-07-19 01:06:35: Epoch: 009 Test results, ap: mae: 0.780, rse: 1.593, pcc: -0.048, kcc: -0.190
+2025-07-19 01:07:10: Epoch: 010 Train Loss: 2.425, mae: 1.139, rse: 0.850, pcc: 0.413, kcc: 0.288
+2025-07-19 01:07:12: Epoch: 010 Test results, ap: mae: 0.973, rse: 2.406, pcc: -0.245, kcc: -0.313
+2025-07-19 01:07:46: Epoch: 011 Train Loss: 2.265, mae: 1.193, rse: 0.937, pcc: 0.280, kcc: 0.215
+2025-07-19 01:07:48: Epoch: 011 Test results, ap: mae: 1.009, rse: 2.593, pcc: -0.333, kcc: -0.286
+2025-07-19 01:07:48: Epoch: 011 New best TEST metrics
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+2025-07-19 01:08:24: Epoch: 012 Test results, ap: mae: 0.924, rse: 2.118, pcc: -0.104, kcc: -0.180
+2025-07-19 01:08:24: Epoch: 012 New best TEST metrics
+2025-07-19 01:08:59: Epoch: 013 Train Loss: 2.155, mae: 1.141, rse: 0.852, pcc: 0.397, kcc: 0.266
+2025-07-19 01:09:01: Epoch: 013 Test results, ap: mae: 0.923, rse: 2.130, pcc: -0.172, kcc: -0.238
+2025-07-19 01:09:35: Epoch: 014 Train Loss: 2.025, mae: 1.138, rse: 0.856, pcc: 0.394, kcc: 0.263
+2025-07-19 01:09:37: Epoch: 014 Test results, ap: mae: 0.906, rse: 2.080, pcc: -0.220, kcc: -0.236
+2025-07-19 01:10:11: Epoch: 015 Train Loss: 2.027, mae: 1.176, rse: 0.879, pcc: 0.430, kcc: 0.290
+2025-07-19 01:10:13: Epoch: 015 Test results, ap: mae: 1.149, rse: 3.232, pcc: -0.158, kcc: -0.297
+2025-07-19 01:10:47: Epoch: 016 Train Loss: 2.056, mae: 1.138, rse: 0.834, pcc: 0.432, kcc: 0.288
+2025-07-19 01:10:47: Epoch: 016 New best VALIDATION metrics
+2025-07-19 01:10:49: Epoch: 016 Test results, ap: mae: 0.954, rse: 2.260, pcc: -0.169, kcc: -0.220
+2025-07-19 01:11:24: Epoch: 017 Train Loss: 1.913, mae: 1.194, rse: 0.950, pcc: 0.306, kcc: 0.192
+2025-07-19 01:11:25: Epoch: 017 Test results, ap: mae: 0.902, rse: 2.169, pcc: -0.260, kcc: -0.238
+2025-07-19 01:12:00: Epoch: 018 Train Loss: 1.858, mae: 1.145, rse: 0.865, pcc: 0.399, kcc: 0.260
+2025-07-19 01:12:02: Epoch: 018 Test results, ap: mae: 0.887, rse: 2.001, pcc: -0.248, kcc: -0.260
+2025-07-19 01:12:36: Epoch: 019 Train Loss: 1.895, mae: 1.170, rse: 0.867, pcc: 0.412, kcc: 0.271
+2025-07-19 01:12:38: Epoch: 019 Test results, ap: mae: 0.958, rse: 2.283, pcc: -0.115, kcc: -0.226
+2025-07-19 01:13:13: Epoch: 020 Train Loss: 1.769, mae: 1.262, rse: 1.071, pcc: 0.182, kcc: 0.103
+2025-07-19 01:13:14: Epoch: 020 Test results, ap: mae: 0.962, rse: 2.388, pcc: -0.397, kcc: -0.332
+2025-07-19 01:13:49: Epoch: 021 Train Loss: 1.633, mae: 1.209, rse: 0.931, pcc: 0.379, kcc: 0.241
+2025-07-19 01:13:51: Epoch: 021 Test results, ap: mae: 1.151, rse: 3.362, pcc: -0.206, kcc: -0.237
+2025-07-19 01:14:25: Epoch: 022 Train Loss: 1.640, mae: 1.121, rse: 0.826, pcc: 0.450, kcc: 0.294
+2025-07-19 01:14:25: Epoch: 022 New best VALIDATION metrics
+2025-07-19 01:14:27: Epoch: 022 Test results, ap: mae: 0.995, rse: 2.497, pcc: -0.144, kcc: -0.211
+2025-07-19 01:15:02: Epoch: 023 Train Loss: 1.589, mae: 1.176, rse: 0.903, pcc: 0.383, kcc: 0.229
+2025-07-19 01:15:03: Epoch: 023 Test results, ap: mae: 1.016, rse: 2.631, pcc: -0.174, kcc: -0.231
+2025-07-19 01:15:38: Epoch: 024 Train Loss: 1.657, mae: 1.124, rse: 0.835, pcc: 0.429, kcc: 0.278
+2025-07-19 01:15:39: Epoch: 024 Test results, ap: mae: 0.840, rse: 1.835, pcc: -0.083, kcc: -0.087
+2025-07-19 01:15:39: Epoch: 024 New best TEST metrics
+2025-07-19 01:16:14: Epoch: 025 Train Loss: 1.619, mae: 1.138, rse: 0.847, pcc: 0.451, kcc: 0.290
+2025-07-19 01:16:16: Epoch: 025 Test results, ap: mae: 0.938, rse: 2.317, pcc: -0.154, kcc: -0.143
+2025-07-19 01:16:51: Epoch: 026 Train Loss: 1.528, mae: 1.162, rse: 0.883, pcc: 0.439, kcc: 0.286
+2025-07-19 01:16:52: Epoch: 026 Test results, ap: mae: 1.027, rse: 2.741, pcc: -0.116, kcc: -0.158
+2025-07-19 01:17:27: Epoch: 027 Train Loss: 1.488, mae: 1.177, rse: 0.891, pcc: 0.393, kcc: 0.226
+2025-07-19 01:17:28: Epoch: 027 Test results, ap: mae: 0.948, rse: 2.440, pcc: -0.257, kcc: -0.213
+2025-07-19 01:18:03: Epoch: 028 Train Loss: 1.437, mae: 1.170, rse: 0.937, pcc: 0.362, kcc: 0.234
+2025-07-19 01:18:05: Epoch: 028 Test results, ap: mae: 1.006, rse: 2.681, pcc: -0.230, kcc: -0.222
+2025-07-19 01:18:39: Epoch: 029 Train Loss: 1.434, mae: 1.370, rse: 1.166, pcc: 0.253, kcc: 0.143
+2025-07-19 01:18:41: Epoch: 029 Test results, ap: mae: 1.342, rse: 4.555, pcc: -0.264, kcc: -0.230
+2025-07-19 01:19:16: Epoch: 030 Train Loss: 1.414, mae: 1.222, rse: 0.979, pcc: 0.344, kcc: 0.240
+2025-07-19 01:19:17: Epoch: 030 Test results, ap: mae: 1.331, rse: 4.469, pcc: -0.276, kcc: -0.261
+2025-07-19 01:19:52: Epoch: 031 Train Loss: 1.389, mae: 1.181, rse: 0.908, pcc: 0.394, kcc: 0.254
+2025-07-19 01:19:53: Epoch: 031 Test results, ap: mae: 1.153, rse: 3.427, pcc: -0.219, kcc: -0.206
+2025-07-19 01:20:28: Epoch: 032 Train Loss: 1.303, mae: 1.180, rse: 0.894, pcc: 0.443, kcc: 0.284
+2025-07-19 01:20:30: Epoch: 032 Test results, ap: mae: 1.346, rse: 4.615, pcc: -0.221, kcc: -0.227
+2025-07-19 01:21:04: Epoch: 033 Train Loss: 1.347, mae: 1.196, rse: 0.999, pcc: 0.310, kcc: 0.211
+2025-07-19 01:21:06: Epoch: 033 Test results, ap: mae: 1.082, rse: 3.214, pcc: -0.259, kcc: -0.196
+2025-07-19 01:21:40: Epoch: 034 Train Loss: 1.310, mae: 1.174, rse: 0.924, pcc: 0.383, kcc: 0.244
+2025-07-19 01:21:42: Epoch: 034 Test results, ap: mae: 1.080, rse: 3.063, pcc: -0.160, kcc: -0.171
+2025-07-19 01:22:17: Epoch: 035 Train Loss: 1.298, mae: 1.204, rse: 0.913, pcc: 0.427, kcc: 0.276
+2025-07-19 01:22:18: Epoch: 035 Test results, ap: mae: 1.328, rse: 4.609, pcc: -0.221, kcc: -0.200
+2025-07-19 01:22:53: Epoch: 036 Train Loss: 1.229, mae: 1.230, rse: 0.980, pcc: 0.366, kcc: 0.223
+2025-07-19 01:22:54: Epoch: 036 Test results, ap: mae: 1.331, rse: 4.738, pcc: -0.173, kcc: -0.145
+2025-07-19 01:23:29: Epoch: 037 Train Loss: 1.211, mae: 1.204, rse: 0.988, pcc: 0.311, kcc: 0.185
+2025-07-19 01:23:31: Epoch: 037 Test results, ap: mae: 1.052, rse: 3.191, pcc: -0.168, kcc: -0.111
+2025-07-19 01:24:06: Epoch: 038 Train Loss: 1.255, mae: 1.226, rse: 0.994, pcc: 0.365, kcc: 0.228
+2025-07-19 01:24:07: Epoch: 038 Test results, ap: mae: 1.250, rse: 4.212, pcc: -0.184, kcc: -0.152
+2025-07-19 01:24:42: Epoch: 039 Train Loss: 1.171, mae: 1.206, rse: 0.927, pcc: 0.372, kcc: 0.223
+2025-07-19 01:24:43: Epoch: 039 Test results, ap: mae: 1.180, rse: 3.755, pcc: -0.168, kcc: -0.133
+2025-07-19 01:25:18: Epoch: 040 Train Loss: 1.207, mae: 1.250, rse: 1.085, pcc: 0.233, kcc: 0.149
+2025-07-19 01:25:19: Epoch: 040 Test results, ap: mae: 1.154, rse: 3.560, pcc: -0.159, kcc: -0.111
+2025-07-19 01:25:54: Epoch: 041 Train Loss: 1.117, mae: 1.219, rse: 0.968, pcc: 0.363, kcc: 0.239
+2025-07-19 01:25:56: Epoch: 041 Test results, ap: mae: 1.277, rse: 4.412, pcc: -0.169, kcc: -0.132
+2025-07-19 01:26:31: Epoch: 042 Train Loss: 1.072, mae: 1.203, rse: 0.955, pcc: 0.437, kcc: 0.301
+2025-07-19 01:26:32: Epoch: 042 Test results, ap: mae: 1.409, rse: 5.421, pcc: -0.174, kcc: -0.144
+2025-07-19 01:27:07: Epoch: 043 Train Loss: 1.082, mae: 1.174, rse: 0.917, pcc: 0.441, kcc: 0.304
+2025-07-19 01:27:08: Epoch: 043 Test results, ap: mae: 1.280, rse: 4.578, pcc: -0.137, kcc: -0.113
+2025-07-19 01:27:43: Epoch: 044 Train Loss: 1.013, mae: 1.176, rse: 0.917, pcc: 0.426, kcc: 0.290
+2025-07-19 01:27:45: Epoch: 044 Test results, ap: mae: 1.373, rse: 5.232, pcc: -0.173, kcc: -0.129
+2025-07-19 01:28:19: Epoch: 045 Train Loss: 1.003, mae: 1.168, rse: 0.909, pcc: 0.421, kcc: 0.280
+2025-07-19 01:28:21: Epoch: 045 Test results, ap: mae: 1.193, rse: 4.020, pcc: -0.127, kcc: -0.102
+2025-07-19 01:28:55: Epoch: 046 Train Loss: 0.976, mae: 1.216, rse: 0.961, pcc: 0.373, kcc: 0.229
+2025-07-19 01:28:57: Epoch: 046 Test results, ap: mae: 1.274, rse: 4.675, pcc: -0.144, kcc: -0.100
+2025-07-19 01:29:32: Epoch: 047 Train Loss: 0.910, mae: 1.209, rse: 0.956, pcc: 0.417, kcc: 0.282
+2025-07-19 01:29:33: Epoch: 047 Test results, ap: mae: 1.381, rse: 5.411, pcc: -0.149, kcc: -0.111
+2025-07-19 01:30:08: Epoch: 048 Train Loss: 0.993, mae: 1.278, rse: 1.044, pcc: 0.279, kcc: 0.150
+2025-07-19 01:30:10: Epoch: 048 Test results, ap: mae: 1.318, rse: 5.007, pcc: -0.154, kcc: -0.103
+2025-07-19 01:30:44: Epoch: 049 Train Loss: 0.930, mae: 1.188, rse: 0.925, pcc: 0.390, kcc: 0.260
+2025-07-19 01:30:46: Epoch: 049 Test results, ap: mae: 1.284, rse: 4.843, pcc: -0.163, kcc: -0.105
+2025-07-19 01:31:20: Epoch: 050 Train Loss: 0.937, mae: 1.237, rse: 0.968, pcc: 0.354, kcc: 0.213
+2025-07-19 01:31:22: Epoch: 050 Test results, ap: mae: 1.355, rse: 5.316, pcc: -0.140, kcc: -0.086
+2025-07-19 01:31:22: used time 0.50h
+2025-07-19 01:31:22: Running Cross Validation 1
+2025-07-19 01:31:22: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-19 01:31:57: Epoch: 001 Train Loss: 2.665, mae: 1.199, rse: 0.923, pcc: 0.348, kcc: 0.230
+2025-07-19 01:31:57: Epoch: 001 New best VALIDATION metrics
+2025-07-19 01:31:58: Epoch: 001 Test results, ap: mae: 0.703, rse: 1.487, pcc: -0.480, kcc: -0.334
+2025-07-19 01:32:33: Epoch: 002 Train Loss: 2.577, mae: 1.209, rse: 0.908, pcc: 0.324, kcc: 0.223
+2025-07-19 01:32:35: Epoch: 002 Test results, ap: mae: 0.686, rse: 1.336, pcc: -0.298, kcc: -0.215
+2025-07-19 01:33:09: Epoch: 003 Train Loss: 2.562, mae: 1.207, rse: 0.900, pcc: 0.376, kcc: 0.228
+2025-07-19 01:33:09: Epoch: 003 New best VALIDATION metrics
+2025-07-19 01:33:11: Epoch: 003 Test results, ap: mae: 0.845, rse: 2.155, pcc: -0.401, kcc: -0.324
+2025-07-19 01:33:46: Epoch: 004 Train Loss: 2.512, mae: 1.231, rse: 0.906, pcc: 0.331, kcc: 0.242
+2025-07-19 01:33:47: Epoch: 004 Test results, ap: mae: 0.786, rse: 1.615, pcc: -0.214, kcc: -0.144
+2025-07-19 01:34:22: Epoch: 005 Train Loss: 2.485, mae: 1.179, rse: 0.888, pcc: 0.355, kcc: 0.243
+2025-07-19 01:34:22: Epoch: 005 New best VALIDATION metrics
+2025-07-19 01:34:23: Epoch: 005 Test results, ap: mae: 0.727, rse: 1.536, pcc: -0.474, kcc: -0.310
+2025-07-19 01:34:58: Epoch: 006 Train Loss: 2.465, mae: 1.181, rse: 0.879, pcc: 0.360, kcc: 0.263
+2025-07-19 01:34:58: Epoch: 006 New best VALIDATION metrics
+2025-07-19 01:35:00: Epoch: 006 Test results, ap: mae: 0.771, rse: 1.567, pcc: -0.317, kcc: -0.197
+2025-07-19 01:35:34: Epoch: 007 Train Loss: 2.481, mae: 1.149, rse: 0.855, pcc: 0.382, kcc: 0.263
+2025-07-19 01:35:34: Epoch: 007 New best VALIDATION metrics
+2025-07-19 01:35:36: Epoch: 007 Test results, ap: mae: 0.794, rse: 1.692, pcc: -0.507, kcc: -0.342
+2025-07-19 01:36:11: Epoch: 008 Train Loss: 2.453, mae: 1.174, rse: 0.889, pcc: 0.345, kcc: 0.243
+2025-07-19 01:36:12: Epoch: 008 Test results, ap: mae: 0.697, rse: 1.366, pcc: -0.447, kcc: -0.323
+2025-07-19 01:36:47: Epoch: 009 Train Loss: 2.381, mae: 1.236, rse: 0.946, pcc: 0.241, kcc: 0.162
+2025-07-19 01:36:49: Epoch: 009 Test results, ap: mae: 0.611, rse: 1.127, pcc: 0.047, kcc: 0.117
+2025-07-19 01:37:23: Epoch: 010 Train Loss: 2.380, mae: 1.221, rse: 0.928, pcc: 0.317, kcc: 0.215
+2025-07-19 01:37:25: Epoch: 010 Test results, ap: mae: 0.711, rse: 1.547, pcc: -0.108, kcc: 0.032
+2025-07-19 01:38:00: Epoch: 011 Train Loss: 2.180, mae: 1.203, rse: 0.857, pcc: 0.409, kcc: 0.285
+2025-07-19 01:38:01: Epoch: 011 Test results, ap: mae: 0.860, rse: 1.876, pcc: -0.122, kcc: -0.090
+2025-07-19 01:38:01: Epoch: 011 New best TEST metrics
+2025-07-19 01:38:36: Epoch: 012 Train Loss: 2.043, mae: 1.303, rse: 1.003, pcc: 0.187, kcc: 0.102
+2025-07-19 01:38:37: Epoch: 012 Test results, ap: mae: 0.664, rse: 1.263, pcc: -0.121, kcc: -0.068
+2025-07-19 01:38:37: Epoch: 012 New best TEST metrics
+2025-07-19 01:39:12: Epoch: 013 Train Loss: 2.035, mae: 1.212, rse: 0.892, pcc: 0.372, kcc: 0.263
+2025-07-19 01:39:14: Epoch: 013 Test results, ap: mae: 0.883, rse: 1.982, pcc: -0.234, kcc: -0.114
+2025-07-19 01:39:48: Epoch: 014 Train Loss: 1.945, mae: 1.237, rse: 0.921, pcc: 0.345, kcc: 0.216
+2025-07-19 01:39:50: Epoch: 014 Test results, ap: mae: 0.758, rse: 1.658, pcc: -0.178, kcc: -0.087
+2025-07-19 01:40:25: Epoch: 015 Train Loss: 1.942, mae: 1.250, rse: 0.942, pcc: 0.298, kcc: 0.194
+2025-07-19 01:40:26: Epoch: 015 Test results, ap: mae: 0.837, rse: 1.783, pcc: -0.096, kcc: -0.036
+2025-07-19 01:41:01: Epoch: 016 Train Loss: 1.941, mae: 1.182, rse: 0.854, pcc: 0.395, kcc: 0.265
+2025-07-19 01:41:03: Epoch: 016 Test results, ap: mae: 0.875, rse: 2.005, pcc: -0.261, kcc: -0.104
+2025-07-19 01:41:37: Epoch: 017 Train Loss: 1.729, mae: 1.204, rse: 0.893, pcc: 0.375, kcc: 0.241
+2025-07-19 01:41:39: Epoch: 017 Test results, ap: mae: 0.844, rse: 2.051, pcc: -0.118, kcc: -0.035
+2025-07-19 01:42:13: Epoch: 018 Train Loss: 1.840, mae: 1.357, rse: 1.124, pcc: 0.156, kcc: 0.096
+2025-07-19 01:42:15: Epoch: 018 Test results, ap: mae: 0.884, rse: 2.373, pcc: -0.199, kcc: -0.077
+2025-07-19 01:42:49: Epoch: 019 Train Loss: 1.674, mae: 1.311, rse: 1.042, pcc: 0.166, kcc: 0.104
+2025-07-19 01:42:51: Epoch: 019 Test results, ap: mae: 0.878, rse: 2.125, pcc: -0.093, kcc: 0.001
+2025-07-19 01:43:26: Epoch: 020 Train Loss: 1.675, mae: 1.307, rse: 1.015, pcc: 0.339, kcc: 0.221
+2025-07-19 01:43:27: Epoch: 020 Test results, ap: mae: 1.292, rse: 4.051, pcc: -0.307, kcc: -0.167
+2025-07-19 01:44:02: Epoch: 021 Train Loss: 1.579, mae: 1.267, rse: 0.971, pcc: 0.292, kcc: 0.188
+2025-07-19 01:44:03: Epoch: 021 Test results, ap: mae: 0.957, rse: 2.549, pcc: -0.327, kcc: -0.170
+2025-07-19 01:44:38: Epoch: 022 Train Loss: 1.485, mae: 1.255, rse: 0.961, pcc: 0.374, kcc: 0.253
+2025-07-19 01:44:40: Epoch: 022 Test results, ap: mae: 1.138, rse: 3.351, pcc: -0.256, kcc: -0.129
+2025-07-19 01:45:15: Epoch: 023 Train Loss: 1.484, mae: 1.240, rse: 0.942, pcc: 0.353, kcc: 0.233
+2025-07-19 01:45:16: Epoch: 023 Test results, ap: mae: 0.932, rse: 2.500, pcc: -0.212, kcc: -0.088
+2025-07-19 01:45:51: Epoch: 024 Train Loss: 1.435, mae: 1.259, rse: 0.983, pcc: 0.318, kcc: 0.207
+2025-07-19 01:45:52: Epoch: 024 Test results, ap: mae: 0.977, rse: 2.666, pcc: -0.262, kcc: -0.139
+2025-07-19 01:46:27: Epoch: 025 Train Loss: 1.468, mae: 1.198, rse: 0.864, pcc: 0.438, kcc: 0.291
+2025-07-19 01:46:27: Epoch: 025 New best VALIDATION metrics
+2025-07-19 01:46:29: Epoch: 025 Test results, ap: mae: 0.927, rse: 2.520, pcc: -0.240, kcc: -0.111
+2025-07-19 01:47:03: Epoch: 026 Train Loss: 1.460, mae: 1.230, rse: 0.911, pcc: 0.411, kcc: 0.264
+2025-07-19 01:47:05: Epoch: 026 Test results, ap: mae: 1.000, rse: 2.837, pcc: -0.241, kcc: -0.110
+2025-07-19 01:47:40: Epoch: 027 Train Loss: 1.429, mae: 1.308, rse: 1.040, pcc: 0.262, kcc: 0.163
+2025-07-19 01:47:41: Epoch: 027 Test results, ap: mae: 0.959, rse: 2.643, pcc: -0.189, kcc: -0.076
+2025-07-19 01:48:16: Epoch: 028 Train Loss: 1.425, mae: 1.247, rse: 0.984, pcc: 0.349, kcc: 0.226
+2025-07-19 01:48:18: Epoch: 028 Test results, ap: mae: 1.085, rse: 3.225, pcc: -0.240, kcc: -0.127
+2025-07-19 01:48:52: Epoch: 029 Train Loss: 1.385, mae: 1.299, rse: 1.026, pcc: 0.294, kcc: 0.166
+2025-07-19 01:48:54: Epoch: 029 Test results, ap: mae: 1.011, rse: 2.894, pcc: -0.138, kcc: -0.070
+2025-07-19 01:49:28: Epoch: 030 Train Loss: 1.417, mae: 1.332, rse: 1.090, pcc: 0.239, kcc: 0.169
+2025-07-19 01:49:30: Epoch: 030 Test results, ap: mae: 1.054, rse: 3.172, pcc: -0.208, kcc: -0.071
+2025-07-19 01:50:05: Epoch: 031 Train Loss: 1.300, mae: 1.301, rse: 1.068, pcc: 0.275, kcc: 0.174
+2025-07-19 01:50:06: Epoch: 031 Test results, ap: mae: 1.054, rse: 3.228, pcc: -0.116, kcc: -0.058
+2025-07-19 01:50:41: Epoch: 032 Train Loss: 1.262, mae: 1.234, rse: 0.937, pcc: 0.385, kcc: 0.267
+2025-07-19 01:50:43: Epoch: 032 Test results, ap: mae: 0.932, rse: 2.594, pcc: -0.154, kcc: -0.036
+2025-07-19 01:51:17: Epoch: 033 Train Loss: 1.283, mae: 1.281, rse: 1.011, pcc: 0.312, kcc: 0.215
+2025-07-19 01:51:19: Epoch: 033 Test results, ap: mae: 1.008, rse: 3.004, pcc: -0.148, kcc: -0.036
+2025-07-19 01:51:53: Epoch: 034 Train Loss: 1.268, mae: 1.266, rse: 0.986, pcc: 0.301, kcc: 0.175
+2025-07-19 01:51:55: Epoch: 034 Test results, ap: mae: 0.932, rse: 2.481, pcc: -0.105, kcc: -0.048
+2025-07-19 01:52:29: Epoch: 035 Train Loss: 1.273, mae: 1.274, rse: 1.003, pcc: 0.301, kcc: 0.194
+2025-07-19 01:52:31: Epoch: 035 Test results, ap: mae: 0.969, rse: 2.732, pcc: -0.104, kcc: -0.022
+2025-07-19 01:53:06: Epoch: 036 Train Loss: 1.237, mae: 1.270, rse: 0.993, pcc: 0.337, kcc: 0.215
+2025-07-19 01:53:07: Epoch: 036 Test results, ap: mae: 0.998, rse: 2.887, pcc: -0.092, kcc: -0.014
+2025-07-19 01:53:41: Epoch: 037 Train Loss: 1.224, mae: 1.243, rse: 0.956, pcc: 0.352, kcc: 0.241
+2025-07-19 01:53:43: Epoch: 037 Test results, ap: mae: 0.939, rse: 2.620, pcc: -0.070, kcc: -0.005
+2025-07-19 01:54:18: Epoch: 038 Train Loss: 1.228, mae: 1.221, rse: 0.929, pcc: 0.387, kcc: 0.258
+2025-07-19 01:54:19: Epoch: 038 Test results, ap: mae: 0.935, rse: 2.562, pcc: -0.065, kcc: 0.005
+2025-07-19 01:54:54: Epoch: 039 Train Loss: 1.194, mae: 1.214, rse: 0.917, pcc: 0.373, kcc: 0.256
+2025-07-19 01:54:56: Epoch: 039 Test results, ap: mae: 0.937, rse: 2.622, pcc: -0.060, kcc: 0.018
+2025-07-19 01:55:30: Epoch: 040 Train Loss: 1.185, mae: 1.300, rse: 1.056, pcc: 0.259, kcc: 0.163
+2025-07-19 01:55:32: Epoch: 040 Test results, ap: mae: 1.036, rse: 3.198, pcc: -0.042, kcc: 0.007
+2025-07-19 01:56:06: Epoch: 041 Train Loss: 1.150, mae: 1.264, rse: 0.976, pcc: 0.339, kcc: 0.231
+2025-07-19 01:56:08: Epoch: 041 Test results, ap: mae: 0.991, rse: 2.909, pcc: -0.046, kcc: 0.012
+2025-07-19 01:56:42: Epoch: 042 Train Loss: 1.164, mae: 1.281, rse: 1.011, pcc: 0.320, kcc: 0.209
+2025-07-19 01:56:44: Epoch: 042 Test results, ap: mae: 0.985, rse: 2.917, pcc: -0.023, kcc: 0.015
+2025-07-19 01:57:19: Epoch: 043 Train Loss: 1.147, mae: 1.305, rse: 1.060, pcc: 0.277, kcc: 0.179
+2025-07-19 01:57:20: Epoch: 043 Test results, ap: mae: 1.063, rse: 3.363, pcc: -0.035, kcc: 0.008
+2025-07-19 01:57:55: Epoch: 044 Train Loss: 1.153, mae: 1.285, rse: 1.030, pcc: 0.279, kcc: 0.182
+2025-07-19 01:57:57: Epoch: 044 Test results, ap: mae: 1.053, rse: 3.327, pcc: -0.032, kcc: 0.005
+2025-07-19 01:58:31: Epoch: 045 Train Loss: 1.145, mae: 1.264, rse: 0.999, pcc: 0.286, kcc: 0.184
+2025-07-19 01:58:33: Epoch: 045 Test results, ap: mae: 0.937, rse: 2.659, pcc: -0.034, kcc: 0.022
+2025-07-19 01:59:07: Epoch: 046 Train Loss: 1.138, mae: 1.277, rse: 1.037, pcc: 0.295, kcc: 0.220
+2025-07-19 01:59:09: Epoch: 046 Test results, ap: mae: 0.991, rse: 2.949, pcc: -0.030, kcc: 0.016
+2025-07-19 01:59:43: Epoch: 047 Train Loss: 1.131, mae: 1.284, rse: 1.008, pcc: 0.342, kcc: 0.239
+2025-07-19 01:59:45: Epoch: 047 Test results, ap: mae: 0.991, rse: 3.017, pcc: -0.052, kcc: 0.015
+2025-07-19 02:00:20: Epoch: 048 Train Loss: 1.125, mae: 1.286, rse: 1.027, pcc: 0.302, kcc: 0.187
+2025-07-19 02:00:21: Epoch: 048 Test results, ap: mae: 1.037, rse: 3.333, pcc: -0.015, kcc: 0.022
+2025-07-19 02:00:56: Epoch: 049 Train Loss: 1.106, mae: 1.260, rse: 0.996, pcc: 0.328, kcc: 0.227
+2025-07-19 02:00:57: Epoch: 049 Test results, ap: mae: 1.001, rse: 3.045, pcc: -0.050, kcc: 0.018
+2025-07-19 02:01:32: Epoch: 050 Train Loss: 1.135, mae: 1.239, rse: 0.973, pcc: 0.346, kcc: 0.238
+2025-07-19 02:01:34: Epoch: 050 Test results, ap: mae: 0.977, rse: 2.819, pcc: -0.021, kcc: 0.021
+2025-07-19 02:01:34: used time 0.50h
+2025-07-19 02:01:34: Running Cross Validation 2
+2025-07-19 02:01:34: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 02:02:09: Epoch: 001 Train Loss: 2.735, mae: 1.176, rse: 0.914, pcc: 0.337, kcc: 0.251
+2025-07-19 02:02:09: Epoch: 001 New best VALIDATION metrics
+2025-07-19 02:02:10: Epoch: 001 Test results, ap: mae: 0.719, rse: 1.412, pcc: -0.427, kcc: -0.282
+2025-07-19 02:02:45: Epoch: 002 Train Loss: 2.634, mae: 1.225, rse: 0.945, pcc: 0.261, kcc: 0.172
+2025-07-19 02:02:46: Epoch: 002 Test results, ap: mae: 0.637, rse: 1.113, pcc: 0.092, kcc: 0.004
+2025-07-19 02:03:21: Epoch: 003 Train Loss: 2.604, mae: 1.175, rse: 0.887, pcc: 0.373, kcc: 0.260
+2025-07-19 02:03:21: Epoch: 003 New best VALIDATION metrics
+2025-07-19 02:03:23: Epoch: 003 Test results, ap: mae: 0.605, rse: 1.093, pcc: -0.260, kcc: -0.180
+2025-07-19 02:03:58: Epoch: 004 Train Loss: 2.518, mae: 1.277, rse: 1.010, pcc: 0.279, kcc: 0.199
+2025-07-19 02:03:59: Epoch: 004 Test results, ap: mae: 0.715, rse: 1.412, pcc: -0.411, kcc: -0.259
+2025-07-19 02:04:34: Epoch: 005 Train Loss: 2.505, mae: 1.180, rse: 0.898, pcc: 0.322, kcc: 0.242
+2025-07-19 02:04:36: Epoch: 005 Test results, ap: mae: 0.677, rse: 1.390, pcc: -0.399, kcc: -0.197
+2025-07-19 02:05:10: Epoch: 006 Train Loss: 2.507, mae: 1.179, rse: 0.886, pcc: 0.342, kcc: 0.233
+2025-07-19 02:05:12: Epoch: 006 Test results, ap: mae: 0.756, rse: 1.655, pcc: -0.192, kcc: 0.047
+2025-07-19 02:05:46: Epoch: 007 Train Loss: 2.466, mae: 1.181, rse: 0.926, pcc: 0.339, kcc: 0.253
+2025-07-19 02:05:48: Epoch: 007 Test results, ap: mae: 0.693, rse: 1.332, pcc: -0.137, kcc: -0.079
+2025-07-19 02:06:23: Epoch: 008 Train Loss: 2.436, mae: 1.197, rse: 0.909, pcc: 0.358, kcc: 0.239
+2025-07-19 02:06:24: Epoch: 008 Test results, ap: mae: 0.780, rse: 1.632, pcc: -0.421, kcc: -0.320
+2025-07-19 02:06:59: Epoch: 009 Train Loss: 2.408, mae: 1.184, rse: 0.903, pcc: 0.351, kcc: 0.250
+2025-07-19 02:07:00: Epoch: 009 Test results, ap: mae: 0.779, rse: 1.752, pcc: -0.301, kcc: -0.149
+2025-07-19 02:07:35: Epoch: 010 Train Loss: 2.374, mae: 1.166, rse: 0.874, pcc: 0.363, kcc: 0.255
+2025-07-19 02:07:35: Epoch: 010 New best VALIDATION metrics
+2025-07-19 02:07:37: Epoch: 010 Test results, ap: mae: 0.639, rse: 1.233, pcc: -0.240, kcc: -0.142
+2025-07-19 02:08:11: Epoch: 011 Train Loss: 2.363, mae: 1.152, rse: 0.855, pcc: 0.383, kcc: 0.261
+2025-07-19 02:08:11: Epoch: 011 New best VALIDATION metrics
+2025-07-19 02:08:13: Epoch: 011 Test results, ap: mae: 0.720, rse: 1.518, pcc: -0.239, kcc: -0.109
+2025-07-19 02:08:13: Epoch: 011 New best TEST metrics
+2025-07-19 02:08:47: Epoch: 012 Train Loss: 2.357, mae: 1.149, rse: 0.857, pcc: 0.380, kcc: 0.274
+2025-07-19 02:08:47: Epoch: 012 New best VALIDATION metrics
+2025-07-19 02:08:49: Epoch: 012 Test results, ap: mae: 0.701, rse: 1.449, pcc: -0.242, kcc: -0.119
+2025-07-19 02:08:49: Epoch: 012 New best TEST metrics
+2025-07-19 02:09:24: Epoch: 013 Train Loss: 2.315, mae: 1.150, rse: 0.847, pcc: 0.396, kcc: 0.256
+2025-07-19 02:09:24: Epoch: 013 New best VALIDATION metrics
+2025-07-19 02:09:25: Epoch: 013 Test results, ap: mae: 0.751, rse: 1.611, pcc: -0.248, kcc: -0.185
+2025-07-19 02:10:00: Epoch: 014 Train Loss: 2.292, mae: 1.134, rse: 0.854, pcc: 0.388, kcc: 0.280
+2025-07-19 02:10:00: Epoch: 014 New best VALIDATION metrics
+2025-07-19 02:10:02: Epoch: 014 Test results, ap: mae: 0.651, rse: 1.193, pcc: -0.137, kcc: -0.107
+2025-07-19 02:10:02: Epoch: 014 New best TEST metrics
+2025-07-19 02:10:36: Epoch: 015 Train Loss: 2.297, mae: 1.220, rse: 0.986, pcc: 0.368, kcc: 0.250
+2025-07-19 02:10:38: Epoch: 015 Test results, ap: mae: 0.836, rse: 1.852, pcc: -0.049, kcc: 0.006
+2025-07-19 02:11:12: Epoch: 016 Train Loss: 2.263, mae: 1.166, rse: 0.881, pcc: 0.374, kcc: 0.235
+2025-07-19 02:11:14: Epoch: 016 Test results, ap: mae: 0.880, rse: 2.236, pcc: -0.428, kcc: -0.280
+2025-07-19 02:11:49: Epoch: 017 Train Loss: 2.230, mae: 1.134, rse: 0.836, pcc: 0.422, kcc: 0.296
+2025-07-19 02:11:49: Epoch: 017 New best VALIDATION metrics
+2025-07-19 02:11:50: Epoch: 017 Test results, ap: mae: 0.727, rse: 1.622, pcc: -0.231, kcc: -0.111
+2025-07-19 02:12:25: Epoch: 018 Train Loss: 2.182, mae: 1.147, rse: 0.872, pcc: 0.397, kcc: 0.282
+2025-07-19 02:12:27: Epoch: 018 Test results, ap: mae: 0.680, rse: 1.327, pcc: -0.371, kcc: -0.221
+2025-07-19 02:13:01: Epoch: 019 Train Loss: 2.145, mae: 1.157, rse: 0.913, pcc: 0.410, kcc: 0.307
+2025-07-19 02:13:03: Epoch: 019 Test results, ap: mae: 0.891, rse: 1.973, pcc: -0.137, kcc: -0.069
+2025-07-19 02:13:37: Epoch: 020 Train Loss: 2.168, mae: 1.116, rse: 0.812, pcc: 0.437, kcc: 0.306
+2025-07-19 02:13:37: Epoch: 020 New best VALIDATION metrics
+2025-07-19 02:13:39: Epoch: 020 Test results, ap: mae: 0.691, rse: 1.482, pcc: -0.023, kcc: 0.020
+2025-07-19 02:14:14: Epoch: 021 Train Loss: 2.068, mae: 1.130, rse: 0.855, pcc: 0.436, kcc: 0.307
+2025-07-19 02:14:15: Epoch: 021 Test results, ap: mae: 0.756, rse: 1.601, pcc: -0.303, kcc: -0.212
+2025-07-19 02:14:50: Epoch: 022 Train Loss: 2.044, mae: 1.111, rse: 0.811, pcc: 0.449, kcc: 0.320
+2025-07-19 02:14:50: Epoch: 022 New best VALIDATION metrics
+2025-07-19 02:14:52: Epoch: 022 Test results, ap: mae: 0.756, rse: 1.696, pcc: -0.296, kcc: -0.201
+2025-07-19 02:15:26: Epoch: 023 Train Loss: 2.011, mae: 1.132, rse: 0.832, pcc: 0.428, kcc: 0.287
+2025-07-19 02:15:28: Epoch: 023 Test results, ap: mae: 0.665, rse: 1.356, pcc: -0.312, kcc: -0.173
+2025-07-19 02:16:03: Epoch: 024 Train Loss: 2.000, mae: 1.107, rse: 0.821, pcc: 0.450, kcc: 0.319
+2025-07-19 02:16:04: Epoch: 024 Test results, ap: mae: 0.735, rse: 1.578, pcc: -0.282, kcc: -0.130
+2025-07-19 02:16:39: Epoch: 025 Train Loss: 1.953, mae: 1.125, rse: 0.859, pcc: 0.436, kcc: 0.312
+2025-07-19 02:16:40: Epoch: 025 Test results, ap: mae: 0.700, rse: 1.417, pcc: -0.318, kcc: -0.209
+2025-07-19 02:17:15: Epoch: 026 Train Loss: 1.934, mae: 1.095, rse: 0.803, pcc: 0.450, kcc: 0.328
+2025-07-19 02:17:15: Epoch: 026 New best VALIDATION metrics
+2025-07-19 02:17:17: Epoch: 026 Test results, ap: mae: 0.686, rse: 1.424, pcc: -0.337, kcc: -0.172
+2025-07-19 02:17:51: Epoch: 027 Train Loss: 1.906, mae: 1.149, rse: 0.866, pcc: 0.428, kcc: 0.284
+2025-07-19 02:17:53: Epoch: 027 Test results, ap: mae: 0.743, rse: 1.580, pcc: -0.295, kcc: -0.158
+2025-07-19 02:18:28: Epoch: 028 Train Loss: 1.920, mae: 1.100, rse: 0.800, pcc: 0.461, kcc: 0.317
+2025-07-19 02:18:29: Epoch: 028 Test results, ap: mae: 0.722, rse: 1.517, pcc: -0.332, kcc: -0.212
+2025-07-19 02:19:04: Epoch: 029 Train Loss: 1.869, mae: 1.105, rse: 0.828, pcc: 0.443, kcc: 0.325
+2025-07-19 02:19:06: Epoch: 029 Test results, ap: mae: 0.663, rse: 1.263, pcc: -0.102, kcc: -0.042
+2025-07-19 02:19:06: Epoch: 029 New best TEST metrics
+2025-07-19 02:19:40: Epoch: 030 Train Loss: 1.869, mae: 1.127, rse: 0.818, pcc: 0.456, kcc: 0.325
+2025-07-19 02:19:42: Epoch: 030 Test results, ap: mae: 0.721, rse: 1.595, pcc: -0.245, kcc: -0.101
+2025-07-19 02:20:17: Epoch: 031 Train Loss: 1.776, mae: 1.112, rse: 0.819, pcc: 0.453, kcc: 0.320
+2025-07-19 02:20:18: Epoch: 031 Test results, ap: mae: 0.674, rse: 1.387, pcc: -0.173, kcc: -0.102
+2025-07-19 02:20:53: Epoch: 032 Train Loss: 1.722, mae: 1.126, rse: 0.839, pcc: 0.447, kcc: 0.311
+2025-07-19 02:20:55: Epoch: 032 Test results, ap: mae: 0.748, rse: 1.808, pcc: -0.245, kcc: -0.108
+2025-07-19 02:21:29: Epoch: 033 Train Loss: 1.635, mae: 1.136, rse: 0.860, pcc: 0.431, kcc: 0.297
+2025-07-19 02:21:31: Epoch: 033 Test results, ap: mae: 0.671, rse: 1.358, pcc: -0.198, kcc: -0.126
+2025-07-19 02:22:05: Epoch: 034 Train Loss: 1.722, mae: 1.092, rse: 0.811, pcc: 0.472, kcc: 0.332
+2025-07-19 02:22:05: Epoch: 034 New best VALIDATION metrics
+2025-07-19 02:22:07: Epoch: 034 Test results, ap: mae: 0.733, rse: 1.583, pcc: -0.255, kcc: -0.138
+2025-07-19 02:22:42: Epoch: 035 Train Loss: 1.675, mae: 1.100, rse: 0.804, pcc: 0.458, kcc: 0.332
+2025-07-19 02:22:43: Epoch: 035 Test results, ap: mae: 0.646, rse: 1.278, pcc: -0.118, kcc: -0.048
+2025-07-19 02:23:18: Epoch: 036 Train Loss: 1.558, mae: 1.080, rse: 0.764, pcc: 0.501, kcc: 0.358
+2025-07-19 02:23:18: Epoch: 036 New best VALIDATION metrics
+2025-07-19 02:23:20: Epoch: 036 Test results, ap: mae: 0.684, rse: 1.448, pcc: -0.258, kcc: -0.144
+2025-07-19 02:23:55: Epoch: 037 Train Loss: 1.591, mae: 1.090, rse: 0.790, pcc: 0.482, kcc: 0.335
+2025-07-19 02:23:56: Epoch: 037 Test results, ap: mae: 0.722, rse: 1.639, pcc: -0.342, kcc: -0.188
+2025-07-19 02:24:31: Epoch: 038 Train Loss: 1.479, mae: 1.097, rse: 0.794, pcc: 0.499, kcc: 0.339
+2025-07-19 02:24:33: Epoch: 038 Test results, ap: mae: 0.730, rse: 1.538, pcc: -0.177, kcc: -0.061
+2025-07-19 02:25:07: Epoch: 039 Train Loss: 1.499, mae: 1.122, rse: 0.819, pcc: 0.472, kcc: 0.302
+2025-07-19 02:25:09: Epoch: 039 Test results, ap: mae: 0.689, rse: 1.418, pcc: -0.123, kcc: -0.050
+2025-07-19 02:25:43: Epoch: 040 Train Loss: 1.486, mae: 1.105, rse: 0.799, pcc: 0.479, kcc: 0.318
+2025-07-19 02:25:45: Epoch: 040 Test results, ap: mae: 0.670, rse: 1.383, pcc: -0.145, kcc: -0.062
+2025-07-19 02:26:20: Epoch: 041 Train Loss: 1.389, mae: 1.087, rse: 0.787, pcc: 0.485, kcc: 0.325
+2025-07-19 02:26:21: Epoch: 041 Test results, ap: mae: 0.714, rse: 1.652, pcc: -0.256, kcc: -0.141
+2025-07-19 02:26:56: Epoch: 042 Train Loss: 1.379, mae: 1.109, rse: 0.813, pcc: 0.476, kcc: 0.318
+2025-07-19 02:26:58: Epoch: 042 Test results, ap: mae: 0.713, rse: 1.573, pcc: -0.235, kcc: -0.138
+2025-07-19 02:27:32: Epoch: 043 Train Loss: 1.343, mae: 1.131, rse: 0.841, pcc: 0.453, kcc: 0.310
+2025-07-19 02:27:34: Epoch: 043 Test results, ap: mae: 0.703, rse: 1.570, pcc: -0.295, kcc: -0.172
+2025-07-19 02:28:09: Epoch: 044 Train Loss: 1.283, mae: 1.113, rse: 0.816, pcc: 0.466, kcc: 0.308
+2025-07-19 02:28:10: Epoch: 044 Test results, ap: mae: 0.716, rse: 1.632, pcc: -0.283, kcc: -0.171
+2025-07-19 02:28:45: Epoch: 045 Train Loss: 1.302, mae: 1.110, rse: 0.815, pcc: 0.475, kcc: 0.314
+2025-07-19 02:28:46: Epoch: 045 Test results, ap: mae: 0.696, rse: 1.566, pcc: -0.277, kcc: -0.157
+2025-07-19 02:29:21: Epoch: 046 Train Loss: 1.326, mae: 1.094, rse: 0.804, pcc: 0.468, kcc: 0.318
+2025-07-19 02:29:23: Epoch: 046 Test results, ap: mae: 0.686, rse: 1.571, pcc: -0.263, kcc: -0.119
+2025-07-19 02:29:57: Epoch: 047 Train Loss: 1.310, mae: 1.137, rse: 0.854, pcc: 0.462, kcc: 0.308
+2025-07-19 02:29:59: Epoch: 047 Test results, ap: mae: 0.738, rse: 1.758, pcc: -0.266, kcc: -0.151
+2025-07-19 02:30:33: Epoch: 048 Train Loss: 1.312, mae: 1.105, rse: 0.811, pcc: 0.478, kcc: 0.323
+2025-07-19 02:30:35: Epoch: 048 Test results, ap: mae: 0.682, rse: 1.510, pcc: -0.251, kcc: -0.151
+2025-07-19 02:31:09: Epoch: 049 Train Loss: 1.312, mae: 1.147, rse: 0.874, pcc: 0.433, kcc: 0.296
+2025-07-19 02:31:11: Epoch: 049 Test results, ap: mae: 0.729, rse: 1.827, pcc: -0.303, kcc: -0.161
+2025-07-19 02:31:46: Epoch: 050 Train Loss: 1.282, mae: 1.108, rse: 0.829, pcc: 0.456, kcc: 0.318
+2025-07-19 02:31:47: Epoch: 050 Test results, ap: mae: 0.768, rse: 2.021, pcc: -0.250, kcc: -0.100
+2025-07-19 02:31:47: used time 0.50h
+2025-07-19 02:31:47: Running Cross Validation 3
+2025-07-19 02:31:47: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 02:32:22: Epoch: 001 Train Loss: 2.657, mae: 1.299, rse: 1.000, pcc: 0.259, kcc: 0.162
+2025-07-19 02:32:22: Epoch: 001 New best VALIDATION metrics
+2025-07-19 02:32:23: Epoch: 001 Test results, ap: mae: 0.837, rse: 1.987, pcc: 0.011, kcc: 0.010
+2025-07-19 02:32:58: Epoch: 002 Train Loss: 2.536, mae: 1.323, rse: 0.992, pcc: 0.326, kcc: 0.240
+2025-07-19 02:32:58: Epoch: 002 New best VALIDATION metrics
+2025-07-19 02:33:00: Epoch: 002 Test results, ap: mae: 0.591, rse: 1.011, pcc: 0.090, kcc: -0.002
+2025-07-19 02:33:35: Epoch: 003 Train Loss: 2.427, mae: 1.249, rse: 0.920, pcc: 0.306, kcc: 0.220
+2025-07-19 02:33:35: Epoch: 003 New best VALIDATION metrics
+2025-07-19 02:33:36: Epoch: 003 Test results, ap: mae: 0.910, rse: 2.079, pcc: -0.352, kcc: -0.200
+2025-07-19 02:34:11: Epoch: 004 Train Loss: 2.445, mae: 1.283, rse: 0.967, pcc: 0.286, kcc: 0.214
+2025-07-19 02:34:12: Epoch: 004 Test results, ap: mae: 0.893, rse: 2.022, pcc: -0.248, kcc: -0.291
+2025-07-19 02:34:47: Epoch: 005 Train Loss: 2.429, mae: 1.259, rse: 0.931, pcc: 0.308, kcc: 0.227
+2025-07-19 02:34:49: Epoch: 005 Test results, ap: mae: 0.832, rse: 1.763, pcc: -0.282, kcc: -0.281
+2025-07-19 02:35:23: Epoch: 006 Train Loss: 2.444, mae: 1.290, rse: 0.955, pcc: 0.336, kcc: 0.236
+2025-07-19 02:35:25: Epoch: 006 Test results, ap: mae: 1.055, rse: 2.736, pcc: -0.178, kcc: -0.290
+2025-07-19 02:36:00: Epoch: 007 Train Loss: 2.378, mae: 1.242, rse: 0.895, pcc: 0.340, kcc: 0.241
+2025-07-19 02:36:00: Epoch: 007 New best VALIDATION metrics
+2025-07-19 02:36:01: Epoch: 007 Test results, ap: mae: 0.818, rse: 1.731, pcc: -0.248, kcc: -0.327
+2025-07-19 02:36:36: Epoch: 008 Train Loss: 2.354, mae: 1.231, rse: 0.893, pcc: 0.333, kcc: 0.236
+2025-07-19 02:36:36: Epoch: 008 New best VALIDATION metrics
+2025-07-19 02:36:37: Epoch: 008 Test results, ap: mae: 0.683, rse: 1.305, pcc: -0.364, kcc: -0.296
+2025-07-19 02:37:12: Epoch: 009 Train Loss: 2.339, mae: 1.245, rse: 0.907, pcc: 0.330, kcc: 0.238
+2025-07-19 02:37:14: Epoch: 009 Test results, ap: mae: 0.904, rse: 2.060, pcc: -0.164, kcc: -0.277
+2025-07-19 02:37:49: Epoch: 010 Train Loss: 2.314, mae: 1.271, rse: 0.924, pcc: 0.337, kcc: 0.250
+2025-07-19 02:37:50: Epoch: 010 Test results, ap: mae: 0.705, rse: 1.336, pcc: -0.267, kcc: -0.284
+2025-07-19 02:38:25: Epoch: 011 Train Loss: 2.265, mae: 1.212, rse: 0.869, pcc: 0.362, kcc: 0.267
+2025-07-19 02:38:25: Epoch: 011 New best VALIDATION metrics
+2025-07-19 02:38:26: Epoch: 011 Test results, ap: mae: 0.759, rse: 1.506, pcc: -0.304, kcc: -0.320
+2025-07-19 02:38:26: Epoch: 011 New best TEST metrics
+2025-07-19 02:39:01: Epoch: 012 Train Loss: 2.214, mae: 1.309, rse: 0.945, pcc: 0.360, kcc: 0.265
+2025-07-19 02:39:03: Epoch: 012 Test results, ap: mae: 0.800, rse: 1.693, pcc: -0.150, kcc: -0.193
+2025-07-19 02:39:03: Epoch: 012 New best TEST metrics
+2025-07-19 02:39:37: Epoch: 013 Train Loss: 2.138, mae: 1.232, rse: 0.887, pcc: 0.383, kcc: 0.282
+2025-07-19 02:39:39: Epoch: 013 Test results, ap: mae: 0.818, rse: 1.738, pcc: -0.290, kcc: -0.250
+2025-07-19 02:40:13: Epoch: 014 Train Loss: 2.098, mae: 1.239, rse: 0.961, pcc: 0.311, kcc: 0.258
+2025-07-19 02:40:15: Epoch: 014 Test results, ap: mae: 0.758, rse: 1.595, pcc: -0.322, kcc: -0.260
+2025-07-19 02:40:49: Epoch: 015 Train Loss: 2.073, mae: 1.198, rse: 0.871, pcc: 0.365, kcc: 0.282
+2025-07-19 02:40:49: Epoch: 015 New best VALIDATION metrics
+2025-07-19 02:40:51: Epoch: 015 Test results, ap: mae: 0.807, rse: 1.746, pcc: -0.184, kcc: -0.170
+2025-07-19 02:41:26: Epoch: 016 Train Loss: 2.023, mae: 1.212, rse: 0.912, pcc: 0.357, kcc: 0.280
+2025-07-19 02:41:27: Epoch: 016 Test results, ap: mae: 0.897, rse: 2.085, pcc: -0.342, kcc: -0.274
+2025-07-19 02:42:02: Epoch: 017 Train Loss: 1.915, mae: 1.205, rse: 0.882, pcc: 0.383, kcc: 0.286
+2025-07-19 02:42:02: Epoch: 017 New best VALIDATION metrics
+2025-07-19 02:42:03: Epoch: 017 Test results, ap: mae: 0.969, rse: 2.360, pcc: -0.172, kcc: -0.258
+2025-07-19 02:42:38: Epoch: 018 Train Loss: 1.905, mae: 1.438, rse: 1.149, pcc: 0.320, kcc: 0.230
+2025-07-19 02:42:40: Epoch: 018 Test results, ap: mae: 1.053, rse: 3.064, pcc: -0.061, kcc: -0.134
+2025-07-19 02:43:14: Epoch: 019 Train Loss: 1.798, mae: 1.211, rse: 0.884, pcc: 0.381, kcc: 0.292
+2025-07-19 02:43:16: Epoch: 019 Test results, ap: mae: 1.164, rse: 3.442, pcc: -0.118, kcc: -0.094
+2025-07-19 02:43:51: Epoch: 020 Train Loss: 1.729, mae: 1.262, rse: 0.938, pcc: 0.373, kcc: 0.273
+2025-07-19 02:43:52: Epoch: 020 Test results, ap: mae: 1.126, rse: 3.102, pcc: 0.023, kcc: -0.024
+2025-07-19 02:44:27: Epoch: 021 Train Loss: 1.560, mae: 1.135, rse: 0.806, pcc: 0.442, kcc: 0.343
+2025-07-19 02:44:27: Epoch: 021 New best VALIDATION metrics
+2025-07-19 02:44:29: Epoch: 021 Test results, ap: mae: 1.042, rse: 2.827, pcc: -0.176, kcc: -0.142
+2025-07-19 02:45:03: Epoch: 022 Train Loss: 1.438, mae: 1.112, rse: 0.780, pcc: 0.480, kcc: 0.354
+2025-07-19 02:45:03: Epoch: 022 New best VALIDATION metrics
+2025-07-19 02:45:05: Epoch: 022 Test results, ap: mae: 0.955, rse: 2.450, pcc: -0.056, kcc: -0.066
+2025-07-19 02:45:40: Epoch: 023 Train Loss: 1.329, mae: 1.129, rse: 0.819, pcc: 0.451, kcc: 0.334
+2025-07-19 02:45:41: Epoch: 023 Test results, ap: mae: 1.027, rse: 2.814, pcc: -0.165, kcc: -0.141
+2025-07-19 02:46:16: Epoch: 024 Train Loss: 1.302, mae: 1.147, rse: 0.834, pcc: 0.438, kcc: 0.349
+2025-07-19 02:46:17: Epoch: 024 Test results, ap: mae: 0.850, rse: 1.981, pcc: -0.132, kcc: -0.124
+2025-07-19 02:46:52: Epoch: 025 Train Loss: 1.246, mae: 1.132, rse: 0.837, pcc: 0.450, kcc: 0.336
+2025-07-19 02:46:54: Epoch: 025 Test results, ap: mae: 1.270, rse: 4.113, pcc: -0.170, kcc: -0.138
+2025-07-19 02:47:29: Epoch: 026 Train Loss: 1.150, mae: 1.105, rse: 0.816, pcc: 0.455, kcc: 0.356
+2025-07-19 02:47:30: Epoch: 026 Test results, ap: mae: 1.008, rse: 2.691, pcc: -0.173, kcc: -0.133
+2025-07-19 02:48:05: Epoch: 027 Train Loss: 1.071, mae: 1.144, rse: 0.865, pcc: 0.420, kcc: 0.328
+2025-07-19 02:48:06: Epoch: 027 Test results, ap: mae: 1.040, rse: 2.834, pcc: -0.199, kcc: -0.160
+2025-07-19 02:48:41: Epoch: 028 Train Loss: 1.065, mae: 1.140, rse: 0.883, pcc: 0.390, kcc: 0.335
+2025-07-19 02:48:43: Epoch: 028 Test results, ap: mae: 0.933, rse: 2.333, pcc: -0.143, kcc: -0.125
+2025-07-19 02:49:17: Epoch: 029 Train Loss: 1.032, mae: 1.185, rse: 0.879, pcc: 0.454, kcc: 0.342
+2025-07-19 02:49:19: Epoch: 029 Test results, ap: mae: 0.843, rse: 1.884, pcc: -0.151, kcc: -0.108
+2025-07-19 02:49:53: Epoch: 030 Train Loss: 0.935, mae: 1.127, rse: 0.867, pcc: 0.414, kcc: 0.314
+2025-07-19 02:49:55: Epoch: 030 Test results, ap: mae: 0.823, rse: 1.891, pcc: -0.073, kcc: -0.061
+2025-07-19 02:50:29: Epoch: 031 Train Loss: 0.836, mae: 1.140, rse: 0.846, pcc: 0.480, kcc: 0.350
+2025-07-19 02:50:31: Epoch: 031 Test results, ap: mae: 0.858, rse: 1.961, pcc: -0.153, kcc: -0.116
+2025-07-19 02:51:06: Epoch: 032 Train Loss: 0.837, mae: 1.072, rse: 0.787, pcc: 0.492, kcc: 0.351
+2025-07-19 02:51:06: Epoch: 032 New best VALIDATION metrics
+2025-07-19 02:51:07: Epoch: 032 Test results, ap: mae: 0.948, rse: 2.392, pcc: -0.106, kcc: -0.092
+2025-07-19 02:51:42: Epoch: 033 Train Loss: 0.806, mae: 1.070, rse: 0.770, pcc: 0.505, kcc: 0.338
+2025-07-19 02:51:42: Epoch: 033 New best VALIDATION metrics
+2025-07-19 02:51:44: Epoch: 033 Test results, ap: mae: 0.994, rse: 2.666, pcc: -0.036, kcc: -0.060
+2025-07-19 02:52:18: Epoch: 034 Train Loss: 0.796, mae: 1.083, rse: 0.810, pcc: 0.477, kcc: 0.350
+2025-07-19 02:52:20: Epoch: 034 Test results, ap: mae: 0.887, rse: 2.132, pcc: -0.124, kcc: -0.087
+2025-07-19 02:52:55: Epoch: 035 Train Loss: 0.711, mae: 1.143, rse: 0.854, pcc: 0.504, kcc: 0.357
+2025-07-19 02:52:56: Epoch: 035 Test results, ap: mae: 0.868, rse: 1.955, pcc: -0.106, kcc: -0.076
+2025-07-19 02:53:31: Epoch: 036 Train Loss: 0.718, mae: 1.082, rse: 0.827, pcc: 0.485, kcc: 0.371
+2025-07-19 02:53:32: Epoch: 036 Test results, ap: mae: 0.885, rse: 2.117, pcc: -0.125, kcc: -0.084
+2025-07-19 02:54:07: Epoch: 037 Train Loss: 0.704, mae: 1.081, rse: 0.841, pcc: 0.469, kcc: 0.363
+2025-07-19 02:54:09: Epoch: 037 Test results, ap: mae: 0.849, rse: 1.945, pcc: -0.085, kcc: -0.066
+2025-07-19 02:54:43: Epoch: 038 Train Loss: 0.661, mae: 1.076, rse: 0.841, pcc: 0.482, kcc: 0.370
+2025-07-19 02:54:45: Epoch: 038 Test results, ap: mae: 0.925, rse: 2.320, pcc: -0.097, kcc: -0.073
+2025-07-19 02:55:19: Epoch: 039 Train Loss: 0.653, mae: 1.082, rse: 0.803, pcc: 0.502, kcc: 0.389
+2025-07-19 02:55:19: Epoch: 039 New best VALIDATION metrics
+2025-07-19 02:55:21: Epoch: 039 Test results, ap: mae: 0.997, rse: 2.668, pcc: -0.160, kcc: -0.109
+2025-07-19 02:55:56: Epoch: 040 Train Loss: 0.641, mae: 1.067, rse: 0.790, pcc: 0.501, kcc: 0.390
+2025-07-19 02:55:56: Epoch: 040 New best VALIDATION metrics
+2025-07-19 02:55:57: Epoch: 040 Test results, ap: mae: 0.874, rse: 2.102, pcc: -0.071, kcc: -0.046
+2025-07-19 02:56:32: Epoch: 041 Train Loss: 0.573, mae: 1.061, rse: 0.787, pcc: 0.497, kcc: 0.373
+2025-07-19 02:56:34: Epoch: 041 Test results, ap: mae: 0.902, rse: 2.179, pcc: -0.098, kcc: -0.067
+2025-07-19 02:57:08: Epoch: 042 Train Loss: 0.571, mae: 1.057, rse: 0.793, pcc: 0.489, kcc: 0.366
+2025-07-19 02:57:10: Epoch: 042 Test results, ap: mae: 0.872, rse: 2.022, pcc: -0.096, kcc: -0.065
+2025-07-19 02:57:44: Epoch: 043 Train Loss: 0.586, mae: 1.096, rse: 0.850, pcc: 0.474, kcc: 0.370
+2025-07-19 02:57:46: Epoch: 043 Test results, ap: mae: 0.893, rse: 2.187, pcc: -0.081, kcc: -0.049
+2025-07-19 02:58:21: Epoch: 044 Train Loss: 0.553, mae: 1.077, rse: 0.845, pcc: 0.459, kcc: 0.351
+2025-07-19 02:58:22: Epoch: 044 Test results, ap: mae: 0.863, rse: 1.999, pcc: -0.071, kcc: -0.060
+2025-07-19 02:58:57: Epoch: 045 Train Loss: 0.509, mae: 1.077, rse: 0.832, pcc: 0.469, kcc: 0.363
+2025-07-19 02:58:59: Epoch: 045 Test results, ap: mae: 0.869, rse: 2.013, pcc: -0.093, kcc: -0.060
+2025-07-19 02:59:33: Epoch: 046 Train Loss: 0.504, mae: 1.080, rse: 0.811, pcc: 0.501, kcc: 0.368
+2025-07-19 02:59:35: Epoch: 046 Test results, ap: mae: 0.861, rse: 1.963, pcc: -0.118, kcc: -0.083
+2025-07-19 03:00:09: Epoch: 047 Train Loss: 0.505, mae: 1.117, rse: 0.827, pcc: 0.509, kcc: 0.374
+2025-07-19 03:00:11: Epoch: 047 Test results, ap: mae: 0.861, rse: 1.947, pcc: -0.149, kcc: -0.091
+2025-07-19 03:00:46: Epoch: 048 Train Loss: 0.510, mae: 1.082, rse: 0.840, pcc: 0.479, kcc: 0.366
+2025-07-19 03:00:47: Epoch: 048 Test results, ap: mae: 0.907, rse: 2.226, pcc: -0.104, kcc: -0.071
+2025-07-19 03:01:22: Epoch: 049 Train Loss: 0.500, mae: 1.101, rse: 0.828, pcc: 0.489, kcc: 0.376
+2025-07-19 03:01:24: Epoch: 049 Test results, ap: mae: 0.834, rse: 1.895, pcc: -0.099, kcc: -0.061
+2025-07-19 03:01:58: Epoch: 050 Train Loss: 0.502, mae: 1.068, rse: 0.783, pcc: 0.516, kcc: 0.386
+2025-07-19 03:01:58: Epoch: 050 New best VALIDATION metrics
+2025-07-19 03:02:00: Epoch: 050 Test results, ap: mae: 0.880, rse: 2.086, pcc: -0.111, kcc: -0.074
+2025-07-19 03:02:00: used time 0.50h
+2025-07-19 03:02:00: Running Cross Validation 4
+2025-07-19 03:02:00: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 03:02:35: Epoch: 001 Train Loss: 2.735, mae: 1.246, rse: 0.999, pcc: 0.182, kcc: 0.134
+2025-07-19 03:02:35: Epoch: 001 New best VALIDATION metrics
+2025-07-19 03:02:36: Epoch: 001 Test results, ap: mae: 0.591, rse: 1.009, pcc: 0.084, kcc: 0.070
+2025-07-19 03:03:11: Epoch: 002 Train Loss: 2.579, mae: 1.184, rse: 0.908, pcc: 0.313, kcc: 0.188
+2025-07-19 03:03:11: Epoch: 002 New best VALIDATION metrics
+2025-07-19 03:03:12: Epoch: 002 Test results, ap: mae: 0.596, rse: 1.022, pcc: -0.090, kcc: -0.061
+2025-07-19 03:03:47: Epoch: 003 Train Loss: 2.596, mae: 1.186, rse: 0.915, pcc: 0.293, kcc: 0.209
+2025-07-19 03:03:49: Epoch: 003 Test results, ap: mae: 0.759, rse: 1.480, pcc: -0.111, kcc: -0.027
+2025-07-19 03:04:24: Epoch: 004 Train Loss: 2.560, mae: 1.157, rse: 0.880, pcc: 0.348, kcc: 0.211
+2025-07-19 03:04:24: Epoch: 004 New best VALIDATION metrics
+2025-07-19 03:04:25: Epoch: 004 Test results, ap: mae: 0.656, rse: 1.203, pcc: -0.473, kcc: -0.303
+2025-07-19 03:05:00: Epoch: 005 Train Loss: 2.532, mae: 1.159, rse: 0.924, pcc: 0.329, kcc: 0.233
+2025-07-19 03:05:01: Epoch: 005 Test results, ap: mae: 0.639, rse: 1.228, pcc: -0.318, kcc: -0.220
+2025-07-19 03:05:36: Epoch: 006 Train Loss: 2.467, mae: 1.162, rse: 0.906, pcc: 0.320, kcc: 0.212
+2025-07-19 03:05:38: Epoch: 006 Test results, ap: mae: 0.603, rse: 1.042, pcc: -0.142, kcc: -0.071
+2025-07-19 03:06:12: Epoch: 007 Train Loss: 2.449, mae: 1.223, rse: 0.937, pcc: 0.311, kcc: 0.205
+2025-07-19 03:06:14: Epoch: 007 Test results, ap: mae: 0.667, rse: 1.269, pcc: -0.544, kcc: -0.383
+2025-07-19 03:06:49: Epoch: 008 Train Loss: 2.442, mae: 1.188, rse: 0.919, pcc: 0.292, kcc: 0.191
+2025-07-19 03:06:50: Epoch: 008 Test results, ap: mae: 0.752, rse: 1.505, pcc: -0.423, kcc: -0.314
+2025-07-19 03:07:25: Epoch: 009 Train Loss: 2.392, mae: 1.154, rse: 0.916, pcc: 0.301, kcc: 0.212
+2025-07-19 03:07:27: Epoch: 009 Test results, ap: mae: 0.880, rse: 1.953, pcc: -0.420, kcc: -0.400
+2025-07-19 03:08:01: Epoch: 010 Train Loss: 2.382, mae: 1.170, rse: 0.906, pcc: 0.328, kcc: 0.240
+2025-07-19 03:08:03: Epoch: 010 Test results, ap: mae: 0.770, rse: 1.610, pcc: -0.396, kcc: -0.324
+2025-07-19 03:08:38: Epoch: 011 Train Loss: 2.340, mae: 1.138, rse: 0.877, pcc: 0.374, kcc: 0.257
+2025-07-19 03:08:38: Epoch: 011 New best VALIDATION metrics
+2025-07-19 03:08:40: Epoch: 011 Test results, ap: mae: 0.970, rse: 2.371, pcc: -0.413, kcc: -0.364
+2025-07-19 03:08:40: Epoch: 011 New best TEST metrics
+2025-07-19 03:09:14: Epoch: 012 Train Loss: 2.276, mae: 1.124, rse: 0.855, pcc: 0.382, kcc: 0.261
+2025-07-19 03:09:14: Epoch: 012 New best VALIDATION metrics
+2025-07-19 03:09:16: Epoch: 012 Test results, ap: mae: 0.895, rse: 1.974, pcc: -0.332, kcc: -0.360
+2025-07-19 03:09:16: Epoch: 012 New best TEST metrics
+2025-07-19 03:09:51: Epoch: 013 Train Loss: 2.214, mae: 1.103, rse: 0.848, pcc: 0.398, kcc: 0.279
+2025-07-19 03:09:51: Epoch: 013 New best VALIDATION metrics
+2025-07-19 03:09:53: Epoch: 013 Test results, ap: mae: 1.055, rse: 2.748, pcc: -0.531, kcc: -0.435
+2025-07-19 03:10:27: Epoch: 014 Train Loss: 2.148, mae: 1.111, rse: 0.860, pcc: 0.387, kcc: 0.281
+2025-07-19 03:10:29: Epoch: 014 Test results, ap: mae: 0.922, rse: 2.037, pcc: -0.292, kcc: -0.291
+2025-07-19 03:10:29: Epoch: 014 New best TEST metrics
+2025-07-19 03:11:04: Epoch: 015 Train Loss: 2.201, mae: 1.083, rse: 0.803, pcc: 0.446, kcc: 0.290
+2025-07-19 03:11:04: Epoch: 015 New best VALIDATION metrics
+2025-07-19 03:11:06: Epoch: 015 Test results, ap: mae: 0.918, rse: 2.097, pcc: -0.432, kcc: -0.372
+2025-07-19 03:11:40: Epoch: 016 Train Loss: 2.078, mae: 1.114, rse: 0.825, pcc: 0.433, kcc: 0.293
+2025-07-19 03:11:42: Epoch: 016 Test results, ap: mae: 0.837, rse: 1.745, pcc: -0.262, kcc: -0.284
+2025-07-19 03:11:42: Epoch: 016 New best TEST metrics
+2025-07-19 03:12:17: Epoch: 017 Train Loss: 2.072, mae: 1.115, rse: 0.844, pcc: 0.419, kcc: 0.264
+2025-07-19 03:12:18: Epoch: 017 Test results, ap: mae: 1.114, rse: 2.945, pcc: -0.380, kcc: -0.394
+2025-07-19 03:12:53: Epoch: 018 Train Loss: 2.050, mae: 1.191, rse: 0.891, pcc: 0.417, kcc: 0.278
+2025-07-19 03:12:55: Epoch: 018 Test results, ap: mae: 0.937, rse: 2.174, pcc: -0.228, kcc: -0.396
+2025-07-19 03:13:29: Epoch: 019 Train Loss: 1.997, mae: 1.212, rse: 0.927, pcc: 0.407, kcc: 0.283
+2025-07-19 03:13:31: Epoch: 019 Test results, ap: mae: 0.917, rse: 2.100, pcc: -0.243, kcc: -0.266
+2025-07-19 03:14:06: Epoch: 020 Train Loss: 1.926, mae: 1.099, rse: 0.830, pcc: 0.424, kcc: 0.290
+2025-07-19 03:14:08: Epoch: 020 Test results, ap: mae: 0.857, rse: 1.847, pcc: -0.292, kcc: -0.278
+2025-07-19 03:14:42: Epoch: 021 Train Loss: 1.883, mae: 1.118, rse: 0.818, pcc: 0.454, kcc: 0.320
+2025-07-19 03:14:44: Epoch: 021 Test results, ap: mae: 0.936, rse: 2.129, pcc: -0.284, kcc: -0.248
+2025-07-19 03:15:19: Epoch: 022 Train Loss: 1.880, mae: 1.239, rse: 0.934, pcc: 0.454, kcc: 0.319
+2025-07-19 03:15:20: Epoch: 022 Test results, ap: mae: 1.113, rse: 2.889, pcc: -0.292, kcc: -0.256
+2025-07-19 03:15:55: Epoch: 023 Train Loss: 1.741, mae: 1.075, rse: 0.783, pcc: 0.478, kcc: 0.329
+2025-07-19 03:15:55: Epoch: 023 New best VALIDATION metrics
+2025-07-19 03:15:57: Epoch: 023 Test results, ap: mae: 0.937, rse: 2.120, pcc: -0.308, kcc: -0.255
+2025-07-19 03:16:32: Epoch: 024 Train Loss: 1.678, mae: 1.070, rse: 0.783, pcc: 0.467, kcc: 0.320
+2025-07-19 03:16:33: Epoch: 024 Test results, ap: mae: 1.040, rse: 2.556, pcc: -0.282, kcc: -0.308
+2025-07-19 03:17:08: Epoch: 025 Train Loss: 1.652, mae: 1.144, rse: 0.833, pcc: 0.491, kcc: 0.330
+2025-07-19 03:17:10: Epoch: 025 Test results, ap: mae: 1.100, rse: 2.835, pcc: -0.348, kcc: -0.348
+2025-07-19 03:17:45: Epoch: 026 Train Loss: 1.639, mae: 1.103, rse: 0.788, pcc: 0.495, kcc: 0.332
+2025-07-19 03:17:46: Epoch: 026 Test results, ap: mae: 1.213, rse: 3.473, pcc: -0.261, kcc: -0.328
+2025-07-19 03:18:21: Epoch: 027 Train Loss: 1.651, mae: 1.058, rse: 0.739, pcc: 0.518, kcc: 0.339
+2025-07-19 03:18:21: Epoch: 027 New best VALIDATION metrics
+2025-07-19 03:18:23: Epoch: 027 Test results, ap: mae: 1.072, rse: 2.728, pcc: -0.338, kcc: -0.283
+2025-07-19 03:18:57: Epoch: 028 Train Loss: 1.562, mae: 1.087, rse: 0.776, pcc: 0.512, kcc: 0.341
+2025-07-19 03:18:59: Epoch: 028 Test results, ap: mae: 1.133, rse: 3.024, pcc: -0.358, kcc: -0.328
+2025-07-19 03:19:34: Epoch: 029 Train Loss: 1.485, mae: 1.167, rse: 0.920, pcc: 0.396, kcc: 0.279
+2025-07-19 03:19:35: Epoch: 029 Test results, ap: mae: 0.997, rse: 2.460, pcc: -0.299, kcc: -0.271
+2025-07-19 03:20:10: Epoch: 030 Train Loss: 1.531, mae: 1.050, rse: 0.741, pcc: 0.519, kcc: 0.345
+2025-07-19 03:20:10: Epoch: 030 New best VALIDATION metrics
+2025-07-19 03:20:12: Epoch: 030 Test results, ap: mae: 0.985, rse: 2.286, pcc: -0.312, kcc: -0.307
+2025-07-19 03:20:46: Epoch: 031 Train Loss: 1.412, mae: 1.061, rse: 0.753, pcc: 0.544, kcc: 0.360
+2025-07-19 03:20:46: Epoch: 031 New best VALIDATION metrics
+2025-07-19 03:20:48: Epoch: 031 Test results, ap: mae: 1.168, rse: 3.220, pcc: -0.338, kcc: -0.272
+2025-07-19 03:21:22: Epoch: 032 Train Loss: 1.369, mae: 1.109, rse: 0.784, pcc: 0.534, kcc: 0.346
+2025-07-19 03:21:24: Epoch: 032 Test results, ap: mae: 1.273, rse: 3.676, pcc: -0.327, kcc: -0.303
+2025-07-19 03:21:59: Epoch: 033 Train Loss: 1.282, mae: 1.119, rse: 0.807, pcc: 0.484, kcc: 0.334
+2025-07-19 03:22:00: Epoch: 033 Test results, ap: mae: 1.245, rse: 3.617, pcc: -0.389, kcc: -0.314
+2025-07-19 03:22:35: Epoch: 034 Train Loss: 1.256, mae: 1.079, rse: 0.756, pcc: 0.566, kcc: 0.370
+2025-07-19 03:22:35: Epoch: 034 New best VALIDATION metrics
+2025-07-19 03:22:37: Epoch: 034 Test results, ap: mae: 1.249, rse: 3.680, pcc: -0.356, kcc: -0.307
+2025-07-19 03:23:12: Epoch: 035 Train Loss: 1.234, mae: 1.100, rse: 0.753, pcc: 0.543, kcc: 0.347
+2025-07-19 03:23:13: Epoch: 035 Test results, ap: mae: 1.169, rse: 3.476, pcc: -0.360, kcc: -0.281
+2025-07-19 03:23:48: Epoch: 036 Train Loss: 1.214, mae: 1.089, rse: 0.786, pcc: 0.489, kcc: 0.325
+2025-07-19 03:23:50: Epoch: 036 Test results, ap: mae: 1.210, rse: 3.573, pcc: -0.349, kcc: -0.291
+2025-07-19 03:24:24: Epoch: 037 Train Loss: 1.202, mae: 1.032, rse: 0.698, pcc: 0.572, kcc: 0.374
+2025-07-19 03:24:24: Epoch: 037 New best VALIDATION metrics
+2025-07-19 03:24:26: Epoch: 037 Test results, ap: mae: 1.100, rse: 3.100, pcc: -0.382, kcc: -0.293
+2025-07-19 03:25:01: Epoch: 038 Train Loss: 1.194, mae: 1.054, rse: 0.723, pcc: 0.559, kcc: 0.360
+2025-07-19 03:25:02: Epoch: 038 Test results, ap: mae: 1.143, rse: 3.196, pcc: -0.368, kcc: -0.312
+2025-07-19 03:25:37: Epoch: 039 Train Loss: 1.164, mae: 1.033, rse: 0.690, pcc: 0.566, kcc: 0.368
+2025-07-19 03:25:38: Epoch: 039 Test results, ap: mae: 0.976, rse: 2.371, pcc: -0.345, kcc: -0.319
+2025-07-19 03:26:13: Epoch: 040 Train Loss: 1.157, mae: 1.012, rse: 0.675, pcc: 0.584, kcc: 0.383
+2025-07-19 03:26:13: Epoch: 040 New best VALIDATION metrics
+2025-07-19 03:26:15: Epoch: 040 Test results, ap: mae: 1.090, rse: 2.951, pcc: -0.360, kcc: -0.293
+2025-07-19 03:26:50: Epoch: 041 Train Loss: 1.074, mae: 1.093, rse: 0.762, pcc: 0.530, kcc: 0.348
+2025-07-19 03:26:51: Epoch: 041 Test results, ap: mae: 1.118, rse: 3.135, pcc: -0.340, kcc: -0.294
+2025-07-19 03:27:26: Epoch: 042 Train Loss: 1.039, mae: 1.024, rse: 0.698, pcc: 0.593, kcc: 0.383
+2025-07-19 03:27:28: Epoch: 042 Test results, ap: mae: 1.024, rse: 2.605, pcc: -0.327, kcc: -0.291
+2025-07-19 03:28:02: Epoch: 043 Train Loss: 1.037, mae: 1.022, rse: 0.694, pcc: 0.599, kcc: 0.385
+2025-07-19 03:28:04: Epoch: 043 Test results, ap: mae: 1.016, rse: 2.817, pcc: -0.349, kcc: -0.301
+2025-07-19 03:28:39: Epoch: 044 Train Loss: 1.044, mae: 1.064, rse: 0.716, pcc: 0.563, kcc: 0.371
+2025-07-19 03:28:40: Epoch: 044 Test results, ap: mae: 1.048, rse: 2.793, pcc: -0.331, kcc: -0.279
+2025-07-19 03:29:15: Epoch: 045 Train Loss: 1.007, mae: 1.042, rse: 0.695, pcc: 0.579, kcc: 0.374
+2025-07-19 03:29:17: Epoch: 045 Test results, ap: mae: 1.088, rse: 2.978, pcc: -0.298, kcc: -0.288
+2025-07-19 03:29:51: Epoch: 046 Train Loss: 1.006, mae: 1.094, rse: 0.807, pcc: 0.484, kcc: 0.295
+2025-07-19 03:29:53: Epoch: 046 Test results, ap: mae: 1.072, rse: 2.920, pcc: -0.306, kcc: -0.291
+2025-07-19 03:30:28: Epoch: 047 Train Loss: 0.977, mae: 1.025, rse: 0.685, pcc: 0.592, kcc: 0.386
+2025-07-19 03:30:29: Epoch: 047 Test results, ap: mae: 1.082, rse: 3.047, pcc: -0.343, kcc: -0.281
+2025-07-19 03:31:04: Epoch: 048 Train Loss: 1.006, mae: 1.160, rse: 0.926, pcc: 0.404, kcc: 0.233
+2025-07-19 03:31:06: Epoch: 048 Test results, ap: mae: 1.046, rse: 2.934, pcc: -0.330, kcc: -0.299
+2025-07-19 03:31:41: Epoch: 049 Train Loss: 1.013, mae: 1.058, rse: 0.718, pcc: 0.553, kcc: 0.358
+2025-07-19 03:31:42: Epoch: 049 Test results, ap: mae: 1.051, rse: 3.021, pcc: -0.349, kcc: -0.274
+2025-07-19 03:32:17: Epoch: 050 Train Loss: 0.965, mae: 1.168, rse: 0.933, pcc: 0.418, kcc: 0.251
+2025-07-19 03:32:19: Epoch: 050 Test results, ap: mae: 1.079, rse: 3.153, pcc: -0.345, kcc: -0.304
+2025-07-19 03:32:19: used time 0.51h
+2025-07-19 03:32:19: Cross Validation Finished!
+2025-07-19 03:32:19: Best validation perform list
+[[1.12059772 0.82574457 0.44965464 0.29447865]
+ [1.19769001 0.86402327 0.43797734 0.29079548]
+ [1.07973027 0.76405692 0.50084877 0.35773006]
+ [1.06769693 0.78309417 0.51600319 0.38590211]
+ [1.01242924 0.67548472 0.5838449  0.38282841]]
+2025-07-19 03:32:19: mean: [1.096 0.782 0.498 0.342]
+2025-07-19 03:32:19: std: [0.062 0.064 0.052 0.042]
+2025-07-19 03:32:19: Best test perform list
+[[ 0.83961564  1.83470333 -0.08275906 -0.08742433]
+ [ 0.66382498  1.26282096 -0.12129004 -0.0679967 ]
+ [ 0.6628232   1.26271176 -0.10154214 -0.04163063]
+ [ 0.80004501  1.69261503 -0.14990109 -0.19254169]
+ [ 0.83659363  1.74487901 -0.26226959 -0.28412908]]
+2025-07-19 03:32:19: mean: [ 0.761  1.56  -0.144 -0.135]
+2025-07-19 03:32:19: std: [0.081 0.247 0.063 0.091]
diff --git a/run-reg/rn18-diff-16-768-mse-32-0.001-50/config.json b/run-reg/rn18-diff-16-768-mse-32-0.001-50/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..40a3a727715e48801219334edb93d413d641ff62
--- /dev/null
+++ b/run-reg/rn18-diff-16-768-mse-32-0.001-50/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": null, "channels": 16, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
\ No newline at end of file
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@@ -0,0 +1,6 @@
+Valid
+0.28970997631549833,0.10784570425748825,0.9476770281791687,0.8432228684425354
+0.03742355125957915,0.03284034940287654,0.016927475628411303,0.01999797502185032
+Test
+0.8199699521064758,1.9594690799713135,0.17654628604650496,0.12385113835334778
+0.0694550855994478,0.3224962098539642,0.05094632033890756,0.03617116705823035
diff --git a/run-reg/rn18-diff-16-768-mse-32-0.001-50/training.log b/run-reg/rn18-diff-16-768-mse-32-0.001-50/training.log
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@@ -0,0 +1,692 @@
+2025-07-19 03:32:22: saving_dir: ./run-reg/rn18-diff-16-768-mse-32-0.001-50
+2025-07-19 03:32:22: Loading Training Dataset
+2025-07-19 03:32:46: Loading Test Dataset
+2025-07-19 03:32:49: Running Cross Validation 0
+2025-07-19 03:32:49: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-19 03:33:25: Epoch: 001 Train Loss: 2.457, mae: 1.192, rse: 0.968, pcc: 0.386, kcc: 0.274
+2025-07-19 03:33:25: Epoch: 001 New best VALIDATION metrics
+2025-07-19 03:33:27: Epoch: 001 Test results, ap: mae: 0.930, rse: 2.741, pcc: -0.068, kcc: -0.043
+2025-07-19 03:34:01: Epoch: 002 Train Loss: 1.788, mae: 1.059, rse: 0.786, pcc: 0.490, kcc: 0.382
+2025-07-19 03:34:01: Epoch: 002 New best VALIDATION metrics
+2025-07-19 03:34:02: Epoch: 002 Test results, ap: mae: 0.739, rse: 1.612, pcc: -0.068, kcc: -0.022
+2025-07-19 03:34:37: Epoch: 003 Train Loss: 1.385, mae: 0.890, rse: 0.520, pcc: 0.722, kcc: 0.479
+2025-07-19 03:34:37: Epoch: 003 New best VALIDATION metrics
+2025-07-19 03:34:38: Epoch: 003 Test results, ap: mae: 0.924, rse: 2.459, pcc: 0.069, kcc: 0.089
+2025-07-19 03:35:13: Epoch: 004 Train Loss: 1.066, mae: 0.871, rse: 0.525, pcc: 0.751, kcc: 0.501
+2025-07-19 03:35:13: Epoch: 004 New best VALIDATION metrics
+2025-07-19 03:35:14: Epoch: 004 Test results, ap: mae: 1.349, rse: 4.853, pcc: 0.154, kcc: 0.122
+2025-07-19 03:35:48: Epoch: 005 Train Loss: 0.844, mae: 0.792, rse: 0.449, pcc: 0.754, kcc: 0.563
+2025-07-19 03:35:48: Epoch: 005 New best VALIDATION metrics
+2025-07-19 03:35:50: Epoch: 005 Test results, ap: mae: 0.811, rse: 1.930, pcc: 0.074, kcc: 0.055
+2025-07-19 03:36:24: Epoch: 006 Train Loss: 0.714, mae: 0.611, rse: 0.253, pcc: 0.865, kcc: 0.661
+2025-07-19 03:36:24: Epoch: 006 New best VALIDATION metrics
+2025-07-19 03:36:25: Epoch: 006 Test results, ap: mae: 1.002, rse: 2.902, pcc: 0.152, kcc: 0.102
+2025-07-19 03:37:00: Epoch: 007 Train Loss: 0.590, mae: 0.545, rse: 0.207, pcc: 0.892, kcc: 0.714
+2025-07-19 03:37:00: Epoch: 007 New best VALIDATION metrics
+2025-07-19 03:37:01: Epoch: 007 Test results, ap: mae: 0.989, rse: 2.845, pcc: 0.067, kcc: 0.049
+2025-07-19 03:37:35: Epoch: 008 Train Loss: 0.474, mae: 0.482, rse: 0.173, pcc: 0.912, kcc: 0.729
+2025-07-19 03:37:35: Epoch: 008 New best VALIDATION metrics
+2025-07-19 03:37:37: Epoch: 008 Test results, ap: mae: 1.075, rse: 3.259, pcc: 0.112, kcc: 0.062
+2025-07-19 03:38:11: Epoch: 009 Train Loss: 0.444, mae: 0.480, rse: 0.170, pcc: 0.916, kcc: 0.731
+2025-07-19 03:38:11: Epoch: 009 New best VALIDATION metrics
+2025-07-19 03:38:13: Epoch: 009 Test results, ap: mae: 0.845, rse: 2.153, pcc: 0.090, kcc: 0.079
+2025-07-19 03:38:47: Epoch: 010 Train Loss: 0.409, mae: 0.592, rse: 0.249, pcc: 0.880, kcc: 0.691
+2025-07-19 03:38:48: Epoch: 010 Test results, ap: mae: 0.725, rse: 1.539, pcc: 0.027, kcc: 0.037
+2025-07-19 03:39:23: Epoch: 011 Train Loss: 0.288, mae: 0.396, rse: 0.119, pcc: 0.945, kcc: 0.794
+2025-07-19 03:39:23: Epoch: 011 New best VALIDATION metrics
+2025-07-19 03:39:24: Epoch: 011 Test results, ap: mae: 0.987, rse: 2.860, pcc: 0.072, kcc: 0.073
+2025-07-19 03:39:24: Epoch: 011 New best TEST metrics
+2025-07-19 03:39:59: Epoch: 012 Train Loss: 0.231, mae: 0.381, rse: 0.111, pcc: 0.952, kcc: 0.801
+2025-07-19 03:39:59: Epoch: 012 New best VALIDATION metrics
+2025-07-19 03:40:00: Epoch: 012 Test results, ap: mae: 1.169, rse: 3.730, pcc: 0.126, kcc: 0.098
+2025-07-19 03:40:35: Epoch: 013 Train Loss: 0.215, mae: 0.400, rse: 0.124, pcc: 0.944, kcc: 0.794
+2025-07-19 03:40:36: Epoch: 013 Test results, ap: mae: 1.151, rse: 3.718, pcc: 0.109, kcc: 0.092
+2025-07-19 03:41:10: Epoch: 014 Train Loss: 0.215, mae: 0.326, rse: 0.094, pcc: 0.954, kcc: 0.818
+2025-07-19 03:41:10: Epoch: 014 New best VALIDATION metrics
+2025-07-19 03:41:12: Epoch: 014 Test results, ap: mae: 1.057, rse: 3.197, pcc: 0.122, kcc: 0.104
+2025-07-19 03:41:46: Epoch: 015 Train Loss: 0.200, mae: 0.350, rse: 0.096, pcc: 0.953, kcc: 0.809
+2025-07-19 03:41:47: Epoch: 015 Test results, ap: mae: 0.978, rse: 2.797, pcc: 0.117, kcc: 0.093
+2025-07-19 03:41:47: Epoch: 015 New best TEST metrics
+2025-07-19 03:42:22: Epoch: 016 Train Loss: 0.175, mae: 0.335, rse: 0.095, pcc: 0.954, kcc: 0.817
+2025-07-19 03:42:23: Epoch: 016 Test results, ap: mae: 0.845, rse: 2.181, pcc: 0.115, kcc: 0.102
+2025-07-19 03:42:23: Epoch: 016 New best TEST metrics
+2025-07-19 03:42:57: Epoch: 017 Train Loss: 0.181, mae: 0.320, rse: 0.089, pcc: 0.959, kcc: 0.825
+2025-07-19 03:42:57: Epoch: 017 New best VALIDATION metrics
+2025-07-19 03:42:59: Epoch: 017 Test results, ap: mae: 1.005, rse: 2.909, pcc: 0.143, kcc: 0.107
+2025-07-19 03:43:33: Epoch: 018 Train Loss: 0.172, mae: 0.321, rse: 0.084, pcc: 0.960, kcc: 0.824
+2025-07-19 03:43:33: Epoch: 018 New best VALIDATION metrics
+2025-07-19 03:43:35: Epoch: 018 Test results, ap: mae: 1.014, rse: 3.110, pcc: 0.082, kcc: 0.078
+2025-07-19 03:44:09: Epoch: 019 Train Loss: 0.169, mae: 0.330, rse: 0.093, pcc: 0.960, kcc: 0.835
+2025-07-19 03:44:10: Epoch: 019 Test results, ap: mae: 1.145, rse: 3.788, pcc: 0.125, kcc: 0.103
+2025-07-19 03:44:45: Epoch: 020 Train Loss: 0.176, mae: 0.305, rse: 0.081, pcc: 0.960, kcc: 0.829
+2025-07-19 03:44:45: Epoch: 020 New best VALIDATION metrics
+2025-07-19 03:44:46: Epoch: 020 Test results, ap: mae: 1.074, rse: 3.235, pcc: 0.148, kcc: 0.100
+2025-07-19 03:45:21: Epoch: 021 Train Loss: 0.145, mae: 0.281, rse: 0.072, pcc: 0.965, kcc: 0.847
+2025-07-19 03:45:21: Epoch: 021 New best VALIDATION metrics
+2025-07-19 03:45:22: Epoch: 021 Test results, ap: mae: 0.936, rse: 2.605, pcc: 0.146, kcc: 0.118
+2025-07-19 03:45:56: Epoch: 022 Train Loss: 0.127, mae: 0.276, rse: 0.074, pcc: 0.965, kcc: 0.844
+2025-07-19 03:45:56: Epoch: 022 New best VALIDATION metrics
+2025-07-19 03:45:58: Epoch: 022 Test results, ap: mae: 1.030, rse: 3.129, pcc: 0.139, kcc: 0.106
+2025-07-19 03:46:32: Epoch: 023 Train Loss: 0.128, mae: 0.281, rse: 0.075, pcc: 0.966, kcc: 0.849
+2025-07-19 03:46:33: Epoch: 023 Test results, ap: mae: 1.044, rse: 3.086, pcc: 0.137, kcc: 0.099
+2025-07-19 03:47:08: Epoch: 024 Train Loss: 0.134, mae: 0.278, rse: 0.071, pcc: 0.967, kcc: 0.853
+2025-07-19 03:47:08: Epoch: 024 New best VALIDATION metrics
+2025-07-19 03:47:09: Epoch: 024 Test results, ap: mae: 1.062, rse: 3.269, pcc: 0.104, kcc: 0.080
+2025-07-19 03:47:44: Epoch: 025 Train Loss: 0.116, mae: 0.277, rse: 0.071, pcc: 0.967, kcc: 0.854
+2025-07-19 03:47:44: Epoch: 025 New best VALIDATION metrics
+2025-07-19 03:47:45: Epoch: 025 Test results, ap: mae: 1.034, rse: 3.109, pcc: 0.139, kcc: 0.108
+2025-07-19 03:48:19: Epoch: 026 Train Loss: 0.114, mae: 0.291, rse: 0.076, pcc: 0.967, kcc: 0.858
+2025-07-19 03:48:21: Epoch: 026 Test results, ap: mae: 1.080, rse: 3.256, pcc: 0.165, kcc: 0.113
+2025-07-19 03:48:55: Epoch: 027 Train Loss: 0.117, mae: 0.262, rse: 0.068, pcc: 0.968, kcc: 0.858
+2025-07-19 03:48:55: Epoch: 027 New best VALIDATION metrics
+2025-07-19 03:48:56: Epoch: 027 Test results, ap: mae: 1.014, rse: 2.958, pcc: 0.187, kcc: 0.119
+2025-07-19 03:49:30: Epoch: 028 Train Loss: 0.112, mae: 0.261, rse: 0.068, pcc: 0.966, kcc: 0.850
+2025-07-19 03:49:32: Epoch: 028 Test results, ap: mae: 0.996, rse: 2.895, pcc: 0.155, kcc: 0.113
+2025-07-19 03:50:06: Epoch: 029 Train Loss: 0.127, mae: 0.267, rse: 0.067, pcc: 0.968, kcc: 0.848
+2025-07-19 03:50:08: Epoch: 029 Test results, ap: mae: 0.961, rse: 2.790, pcc: 0.180, kcc: 0.125
+2025-07-19 03:50:42: Epoch: 030 Train Loss: 0.114, mae: 0.297, rse: 0.080, pcc: 0.965, kcc: 0.857
+2025-07-19 03:50:43: Epoch: 030 Test results, ap: mae: 1.078, rse: 3.215, pcc: 0.165, kcc: 0.119
+2025-07-19 03:51:17: Epoch: 031 Train Loss: 0.105, mae: 0.272, rse: 0.071, pcc: 0.970, kcc: 0.867
+2025-07-19 03:51:19: Epoch: 031 Test results, ap: mae: 1.077, rse: 3.273, pcc: 0.179, kcc: 0.122
+2025-07-19 03:51:53: Epoch: 032 Train Loss: 0.106, mae: 0.249, rse: 0.063, pcc: 0.970, kcc: 0.865
+2025-07-19 03:51:53: Epoch: 032 New best VALIDATION metrics
+2025-07-19 03:51:54: Epoch: 032 Test results, ap: mae: 0.985, rse: 2.866, pcc: 0.184, kcc: 0.129
+2025-07-19 03:52:29: Epoch: 033 Train Loss: 0.103, mae: 0.267, rse: 0.071, pcc: 0.969, kcc: 0.866
+2025-07-19 03:52:30: Epoch: 033 Test results, ap: mae: 1.127, rse: 3.532, pcc: 0.180, kcc: 0.127
+2025-07-19 03:53:04: Epoch: 034 Train Loss: 0.101, mae: 0.246, rse: 0.063, pcc: 0.970, kcc: 0.866
+2025-07-19 03:53:04: Epoch: 034 New best VALIDATION metrics
+2025-07-19 03:53:06: Epoch: 034 Test results, ap: mae: 1.012, rse: 2.984, pcc: 0.196, kcc: 0.134
+2025-07-19 03:53:40: Epoch: 035 Train Loss: 0.106, mae: 0.242, rse: 0.062, pcc: 0.971, kcc: 0.868
+2025-07-19 03:53:40: Epoch: 035 New best VALIDATION metrics
+2025-07-19 03:53:41: Epoch: 035 Test results, ap: mae: 1.063, rse: 3.249, pcc: 0.189, kcc: 0.136
+2025-07-19 03:54:15: Epoch: 036 Train Loss: 0.094, mae: 0.245, rse: 0.065, pcc: 0.970, kcc: 0.869
+2025-07-19 03:54:17: Epoch: 036 Test results, ap: mae: 1.080, rse: 3.337, pcc: 0.174, kcc: 0.119
+2025-07-19 03:54:51: Epoch: 037 Train Loss: 0.098, mae: 0.252, rse: 0.067, pcc: 0.971, kcc: 0.873
+2025-07-19 03:54:52: Epoch: 037 Test results, ap: mae: 1.079, rse: 3.295, pcc: 0.183, kcc: 0.125
+2025-07-19 03:55:27: Epoch: 038 Train Loss: 0.101, mae: 0.240, rse: 0.061, pcc: 0.971, kcc: 0.870
+2025-07-19 03:55:27: Epoch: 038 New best VALIDATION metrics
+2025-07-19 03:55:28: Epoch: 038 Test results, ap: mae: 0.978, rse: 2.789, pcc: 0.192, kcc: 0.131
+2025-07-19 03:56:02: Epoch: 039 Train Loss: 0.093, mae: 0.242, rse: 0.063, pcc: 0.970, kcc: 0.864
+2025-07-19 03:56:04: Epoch: 039 Test results, ap: mae: 0.988, rse: 2.875, pcc: 0.181, kcc: 0.121
+2025-07-19 03:56:38: Epoch: 040 Train Loss: 0.097, mae: 0.237, rse: 0.061, pcc: 0.970, kcc: 0.868
+2025-07-19 03:56:38: Epoch: 040 New best VALIDATION metrics
+2025-07-19 03:56:40: Epoch: 040 Test results, ap: mae: 1.016, rse: 2.990, pcc: 0.179, kcc: 0.113
+2025-07-19 03:57:14: Epoch: 041 Train Loss: 0.088, mae: 0.243, rse: 0.064, pcc: 0.971, kcc: 0.872
+2025-07-19 03:57:15: Epoch: 041 Test results, ap: mae: 1.094, rse: 3.460, pcc: 0.182, kcc: 0.122
+2025-07-19 03:57:50: Epoch: 042 Train Loss: 0.086, mae: 0.239, rse: 0.061, pcc: 0.972, kcc: 0.875
+2025-07-19 03:57:50: Epoch: 042 New best VALIDATION metrics
+2025-07-19 03:57:51: Epoch: 042 Test results, ap: mae: 1.034, rse: 3.110, pcc: 0.186, kcc: 0.125
+2025-07-19 03:58:25: Epoch: 043 Train Loss: 0.091, mae: 0.229, rse: 0.059, pcc: 0.972, kcc: 0.875
+2025-07-19 03:58:25: Epoch: 043 New best VALIDATION metrics
+2025-07-19 03:58:27: Epoch: 043 Test results, ap: mae: 1.020, rse: 3.055, pcc: 0.185, kcc: 0.130
+2025-07-19 03:59:01: Epoch: 044 Train Loss: 0.096, mae: 0.232, rse: 0.060, pcc: 0.972, kcc: 0.878
+2025-07-19 03:59:02: Epoch: 044 Test results, ap: mae: 1.020, rse: 3.044, pcc: 0.188, kcc: 0.130
+2025-07-19 03:59:37: Epoch: 045 Train Loss: 0.085, mae: 0.242, rse: 0.065, pcc: 0.971, kcc: 0.875
+2025-07-19 03:59:38: Epoch: 045 Test results, ap: mae: 1.085, rse: 3.365, pcc: 0.199, kcc: 0.141
+2025-07-19 04:00:13: Epoch: 046 Train Loss: 0.084, mae: 0.242, rse: 0.063, pcc: 0.972, kcc: 0.876
+2025-07-19 04:00:14: Epoch: 046 Test results, ap: mae: 1.071, rse: 3.269, pcc: 0.201, kcc: 0.142
+2025-07-19 04:00:48: Epoch: 047 Train Loss: 0.083, mae: 0.226, rse: 0.060, pcc: 0.972, kcc: 0.878
+2025-07-19 04:00:48: Epoch: 047 New best VALIDATION metrics
+2025-07-19 04:00:50: Epoch: 047 Test results, ap: mae: 1.030, rse: 3.050, pcc: 0.200, kcc: 0.138
+2025-07-19 04:01:24: Epoch: 048 Train Loss: 0.082, mae: 0.241, rse: 0.063, pcc: 0.972, kcc: 0.876
+2025-07-19 04:01:26: Epoch: 048 Test results, ap: mae: 1.027, rse: 3.061, pcc: 0.196, kcc: 0.139
+2025-07-19 04:02:00: Epoch: 049 Train Loss: 0.085, mae: 0.228, rse: 0.059, pcc: 0.973, kcc: 0.880
+2025-07-19 04:02:00: Epoch: 049 New best VALIDATION metrics
+2025-07-19 04:02:01: Epoch: 049 Test results, ap: mae: 1.048, rse: 3.216, pcc: 0.195, kcc: 0.126
+2025-07-19 04:02:36: Epoch: 050 Train Loss: 0.083, mae: 0.239, rse: 0.063, pcc: 0.972, kcc: 0.875
+2025-07-19 04:02:37: Epoch: 050 Test results, ap: mae: 1.064, rse: 3.251, pcc: 0.207, kcc: 0.137
+2025-07-19 04:02:37: used time 0.50h
+2025-07-19 04:02:37: Running Cross Validation 1
+2025-07-19 04:02:37: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-19 04:03:11: Epoch: 001 Train Loss: 2.521, mae: 1.305, rse: 1.071, pcc: -0.129, kcc: -0.024
+2025-07-19 04:03:11: Epoch: 001 New best VALIDATION metrics
+2025-07-19 04:03:13: Epoch: 001 Test results, ap: mae: 0.612, rse: 1.151, pcc: -0.054, kcc: -0.142
+2025-07-19 04:03:47: Epoch: 002 Train Loss: 1.996, mae: 0.945, rse: 0.604, pcc: 0.636, kcc: 0.460
+2025-07-19 04:03:47: Epoch: 002 New best VALIDATION metrics
+2025-07-19 04:03:49: Epoch: 002 Test results, ap: mae: 0.611, rse: 1.045, pcc: 0.339, kcc: 0.219
+2025-07-19 04:04:23: Epoch: 003 Train Loss: 1.592, mae: 0.885, rse: 0.539, pcc: 0.685, kcc: 0.509
+2025-07-19 04:04:23: Epoch: 003 New best VALIDATION metrics
+2025-07-19 04:04:24: Epoch: 003 Test results, ap: mae: 0.713, rse: 1.344, pcc: 0.303, kcc: 0.222
+2025-07-19 04:04:59: Epoch: 004 Train Loss: 1.262, mae: 0.789, rse: 0.428, pcc: 0.760, kcc: 0.582
+2025-07-19 04:04:59: Epoch: 004 New best VALIDATION metrics
+2025-07-19 04:05:00: Epoch: 004 Test results, ap: mae: 0.697, rse: 1.343, pcc: 0.404, kcc: 0.247
+2025-07-19 04:05:34: Epoch: 005 Train Loss: 1.077, mae: 0.753, rse: 0.384, pcc: 0.790, kcc: 0.604
+2025-07-19 04:05:34: Epoch: 005 New best VALIDATION metrics
+2025-07-19 04:05:36: Epoch: 005 Test results, ap: mae: 0.791, rse: 1.693, pcc: 0.406, kcc: 0.259
+2025-07-19 04:06:10: Epoch: 006 Train Loss: 0.845, mae: 0.768, rse: 0.403, pcc: 0.788, kcc: 0.611
+2025-07-19 04:06:11: Epoch: 006 Test results, ap: mae: 0.969, rse: 2.658, pcc: 0.407, kcc: 0.259
+2025-07-19 04:06:45: Epoch: 007 Train Loss: 0.764, mae: 0.688, rse: 0.335, pcc: 0.827, kcc: 0.654
+2025-07-19 04:06:45: Epoch: 007 New best VALIDATION metrics
+2025-07-19 04:06:47: Epoch: 007 Test results, ap: mae: 0.913, rse: 2.637, pcc: 0.394, kcc: 0.281
+2025-07-19 04:07:21: Epoch: 008 Train Loss: 0.632, mae: 0.641, rse: 0.297, pcc: 0.842, kcc: 0.669
+2025-07-19 04:07:21: Epoch: 008 New best VALIDATION metrics
+2025-07-19 04:07:22: Epoch: 008 Test results, ap: mae: 0.903, rse: 2.261, pcc: 0.440, kcc: 0.306
+2025-07-19 04:07:57: Epoch: 009 Train Loss: 0.610, mae: 0.590, rse: 0.266, pcc: 0.858, kcc: 0.686
+2025-07-19 04:07:57: Epoch: 009 New best VALIDATION metrics
+2025-07-19 04:07:58: Epoch: 009 Test results, ap: mae: 0.886, rse: 2.247, pcc: 0.340, kcc: 0.255
+2025-07-19 04:08:32: Epoch: 010 Train Loss: 0.549, mae: 0.566, rse: 0.249, pcc: 0.874, kcc: 0.706
+2025-07-19 04:08:32: Epoch: 010 New best VALIDATION metrics
+2025-07-19 04:08:34: Epoch: 010 Test results, ap: mae: 1.005, rse: 2.767, pcc: 0.351, kcc: 0.238
+2025-07-19 04:09:08: Epoch: 011 Train Loss: 0.450, mae: 0.552, rse: 0.230, pcc: 0.880, kcc: 0.703
+2025-07-19 04:09:08: Epoch: 011 New best VALIDATION metrics
+2025-07-19 04:09:09: Epoch: 011 Test results, ap: mae: 0.873, rse: 2.096, pcc: 0.335, kcc: 0.240
+2025-07-19 04:09:09: Epoch: 011 New best TEST metrics
+2025-07-19 04:09:44: Epoch: 012 Train Loss: 0.357, mae: 0.479, rse: 0.184, pcc: 0.905, kcc: 0.738
+2025-07-19 04:09:44: Epoch: 012 New best VALIDATION metrics
+2025-07-19 04:09:45: Epoch: 012 Test results, ap: mae: 0.914, rse: 2.269, pcc: 0.319, kcc: 0.215
+2025-07-19 04:10:20: Epoch: 013 Train Loss: 0.355, mae: 0.518, rse: 0.210, pcc: 0.889, kcc: 0.725
+2025-07-19 04:10:21: Epoch: 013 Test results, ap: mae: 0.830, rse: 1.888, pcc: 0.267, kcc: 0.190
+2025-07-19 04:10:21: Epoch: 013 New best TEST metrics
+2025-07-19 04:10:55: Epoch: 014 Train Loss: 0.329, mae: 0.466, rse: 0.185, pcc: 0.908, kcc: 0.747
+2025-07-19 04:10:55: Epoch: 014 New best VALIDATION metrics
+2025-07-19 04:10:57: Epoch: 014 Test results, ap: mae: 1.096, rse: 3.228, pcc: 0.319, kcc: 0.196
+2025-07-19 04:11:31: Epoch: 015 Train Loss: 0.287, mae: 0.452, rse: 0.172, pcc: 0.912, kcc: 0.748
+2025-07-19 04:11:31: Epoch: 015 New best VALIDATION metrics
+2025-07-19 04:11:33: Epoch: 015 Test results, ap: mae: 0.991, rse: 2.674, pcc: 0.347, kcc: 0.213
+2025-07-19 04:12:07: Epoch: 016 Train Loss: 0.307, mae: 0.485, rse: 0.180, pcc: 0.906, kcc: 0.735
+2025-07-19 04:12:08: Epoch: 016 Test results, ap: mae: 0.880, rse: 2.214, pcc: 0.364, kcc: 0.248
+2025-07-19 04:12:43: Epoch: 017 Train Loss: 0.284, mae: 0.459, rse: 0.162, pcc: 0.919, kcc: 0.754
+2025-07-19 04:12:43: Epoch: 017 New best VALIDATION metrics
+2025-07-19 04:12:44: Epoch: 017 Test results, ap: mae: 1.032, rse: 2.965, pcc: 0.340, kcc: 0.209
+2025-07-19 04:13:19: Epoch: 018 Train Loss: 0.276, mae: 0.411, rse: 0.136, pcc: 0.933, kcc: 0.776
+2025-07-19 04:13:19: Epoch: 018 New best VALIDATION metrics
+2025-07-19 04:13:20: Epoch: 018 Test results, ap: mae: 1.045, rse: 2.994, pcc: 0.313, kcc: 0.198
+2025-07-19 04:13:54: Epoch: 019 Train Loss: 0.233, mae: 0.440, rse: 0.157, pcc: 0.918, kcc: 0.757
+2025-07-19 04:13:56: Epoch: 019 Test results, ap: mae: 0.900, rse: 2.240, pcc: 0.250, kcc: 0.174
+2025-07-19 04:14:30: Epoch: 020 Train Loss: 0.264, mae: 0.428, rse: 0.148, pcc: 0.927, kcc: 0.768
+2025-07-19 04:14:31: Epoch: 020 Test results, ap: mae: 0.947, rse: 2.460, pcc: 0.284, kcc: 0.187
+2025-07-19 04:15:06: Epoch: 021 Train Loss: 0.203, mae: 0.404, rse: 0.139, pcc: 0.933, kcc: 0.782
+2025-07-19 04:15:06: Epoch: 021 New best VALIDATION metrics
+2025-07-19 04:15:07: Epoch: 021 Test results, ap: mae: 1.086, rse: 3.285, pcc: 0.276, kcc: 0.182
+2025-07-19 04:15:41: Epoch: 022 Train Loss: 0.189, mae: 0.404, rse: 0.135, pcc: 0.938, kcc: 0.792
+2025-07-19 04:15:41: Epoch: 022 New best VALIDATION metrics
+2025-07-19 04:15:43: Epoch: 022 Test results, ap: mae: 1.114, rse: 3.317, pcc: 0.317, kcc: 0.205
+2025-07-19 04:16:17: Epoch: 023 Train Loss: 0.186, mae: 0.373, rse: 0.122, pcc: 0.941, kcc: 0.796
+2025-07-19 04:16:17: Epoch: 023 New best VALIDATION metrics
+2025-07-19 04:16:19: Epoch: 023 Test results, ap: mae: 1.070, rse: 3.191, pcc: 0.283, kcc: 0.172
+2025-07-19 04:16:53: Epoch: 024 Train Loss: 0.174, mae: 0.390, rse: 0.131, pcc: 0.938, kcc: 0.795
+2025-07-19 04:16:55: Epoch: 024 Test results, ap: mae: 1.183, rse: 3.924, pcc: 0.282, kcc: 0.179
+2025-07-19 04:17:29: Epoch: 025 Train Loss: 0.168, mae: 0.365, rse: 0.118, pcc: 0.943, kcc: 0.799
+2025-07-19 04:17:29: Epoch: 025 New best VALIDATION metrics
+2025-07-19 04:17:30: Epoch: 025 Test results, ap: mae: 1.128, rse: 3.490, pcc: 0.291, kcc: 0.180
+2025-07-19 04:18:05: Epoch: 026 Train Loss: 0.157, mae: 0.364, rse: 0.114, pcc: 0.943, kcc: 0.800
+2025-07-19 04:18:05: Epoch: 026 New best VALIDATION metrics
+2025-07-19 04:18:06: Epoch: 026 Test results, ap: mae: 1.056, rse: 3.134, pcc: 0.271, kcc: 0.175
+2025-07-19 04:18:41: Epoch: 027 Train Loss: 0.166, mae: 0.359, rse: 0.117, pcc: 0.942, kcc: 0.800
+2025-07-19 04:18:41: Epoch: 027 New best VALIDATION metrics
+2025-07-19 04:18:42: Epoch: 027 Test results, ap: mae: 0.994, rse: 2.790, pcc: 0.246, kcc: 0.161
+2025-07-19 04:19:16: Epoch: 028 Train Loss: 0.155, mae: 0.372, rse: 0.116, pcc: 0.944, kcc: 0.802
+2025-07-19 04:19:18: Epoch: 028 Test results, ap: mae: 1.076, rse: 3.180, pcc: 0.244, kcc: 0.164
+2025-07-19 04:19:52: Epoch: 029 Train Loss: 0.162, mae: 0.362, rse: 0.113, pcc: 0.944, kcc: 0.801
+2025-07-19 04:19:52: Epoch: 029 New best VALIDATION metrics
+2025-07-19 04:19:54: Epoch: 029 Test results, ap: mae: 1.040, rse: 2.998, pcc: 0.277, kcc: 0.179
+2025-07-19 04:20:28: Epoch: 030 Train Loss: 0.166, mae: 0.356, rse: 0.115, pcc: 0.945, kcc: 0.808
+2025-07-19 04:20:28: Epoch: 030 New best VALIDATION metrics
+2025-07-19 04:20:29: Epoch: 030 Test results, ap: mae: 1.118, rse: 3.435, pcc: 0.307, kcc: 0.204
+2025-07-19 04:21:04: Epoch: 031 Train Loss: 0.134, mae: 0.312, rse: 0.099, pcc: 0.951, kcc: 0.823
+2025-07-19 04:21:04: Epoch: 031 New best VALIDATION metrics
+2025-07-19 04:21:05: Epoch: 031 Test results, ap: mae: 1.009, rse: 2.874, pcc: 0.271, kcc: 0.179
+2025-07-19 04:21:39: Epoch: 032 Train Loss: 0.121, mae: 0.324, rse: 0.104, pcc: 0.951, kcc: 0.826
+2025-07-19 04:21:41: Epoch: 032 Test results, ap: mae: 1.048, rse: 3.113, pcc: 0.271, kcc: 0.186
+2025-07-19 04:22:15: Epoch: 033 Train Loss: 0.124, mae: 0.305, rse: 0.098, pcc: 0.952, kcc: 0.826
+2025-07-19 04:22:15: Epoch: 033 New best VALIDATION metrics
+2025-07-19 04:22:16: Epoch: 033 Test results, ap: mae: 1.025, rse: 2.920, pcc: 0.271, kcc: 0.189
+2025-07-19 04:22:51: Epoch: 034 Train Loss: 0.129, mae: 0.305, rse: 0.096, pcc: 0.953, kcc: 0.829
+2025-07-19 04:22:51: Epoch: 034 New best VALIDATION metrics
+2025-07-19 04:22:52: Epoch: 034 Test results, ap: mae: 1.002, rse: 2.796, pcc: 0.269, kcc: 0.175
+2025-07-19 04:23:27: Epoch: 035 Train Loss: 0.122, mae: 0.322, rse: 0.100, pcc: 0.952, kcc: 0.822
+2025-07-19 04:23:28: Epoch: 035 Test results, ap: mae: 1.021, rse: 2.922, pcc: 0.273, kcc: 0.179
+2025-07-19 04:24:02: Epoch: 036 Train Loss: 0.116, mae: 0.303, rse: 0.096, pcc: 0.954, kcc: 0.829
+2025-07-19 04:24:02: Epoch: 036 New best VALIDATION metrics
+2025-07-19 04:24:04: Epoch: 036 Test results, ap: mae: 0.992, rse: 2.811, pcc: 0.246, kcc: 0.156
+2025-07-19 04:24:38: Epoch: 037 Train Loss: 0.115, mae: 0.300, rse: 0.095, pcc: 0.955, kcc: 0.833
+2025-07-19 04:24:38: Epoch: 037 New best VALIDATION metrics
+2025-07-19 04:24:40: Epoch: 037 Test results, ap: mae: 1.020, rse: 2.927, pcc: 0.254, kcc: 0.159
+2025-07-19 04:25:14: Epoch: 038 Train Loss: 0.108, mae: 0.317, rse: 0.105, pcc: 0.952, kcc: 0.831
+2025-07-19 04:25:15: Epoch: 038 Test results, ap: mae: 1.044, rse: 3.116, pcc: 0.249, kcc: 0.159
+2025-07-19 04:25:50: Epoch: 039 Train Loss: 0.120, mae: 0.299, rse: 0.096, pcc: 0.954, kcc: 0.834
+2025-07-19 04:25:50: Epoch: 039 New best VALIDATION metrics
+2025-07-19 04:25:51: Epoch: 039 Test results, ap: mae: 1.049, rse: 3.083, pcc: 0.236, kcc: 0.162
+2025-07-19 04:26:26: Epoch: 040 Train Loss: 0.121, mae: 0.308, rse: 0.098, pcc: 0.953, kcc: 0.829
+2025-07-19 04:26:27: Epoch: 040 Test results, ap: mae: 1.023, rse: 2.971, pcc: 0.276, kcc: 0.176
+2025-07-19 04:27:01: Epoch: 041 Train Loss: 0.119, mae: 0.294, rse: 0.093, pcc: 0.956, kcc: 0.841
+2025-07-19 04:27:01: Epoch: 041 New best VALIDATION metrics
+2025-07-19 04:27:03: Epoch: 041 Test results, ap: mae: 1.030, rse: 2.983, pcc: 0.247, kcc: 0.157
+2025-07-19 04:27:37: Epoch: 042 Train Loss: 0.105, mae: 0.290, rse: 0.095, pcc: 0.954, kcc: 0.840
+2025-07-19 04:27:38: Epoch: 042 Test results, ap: mae: 1.019, rse: 2.916, pcc: 0.238, kcc: 0.150
+2025-07-19 04:28:13: Epoch: 043 Train Loss: 0.106, mae: 0.291, rse: 0.094, pcc: 0.956, kcc: 0.843
+2025-07-19 04:28:13: Epoch: 043 New best VALIDATION metrics
+2025-07-19 04:28:14: Epoch: 043 Test results, ap: mae: 1.034, rse: 3.022, pcc: 0.250, kcc: 0.163
+2025-07-19 04:28:48: Epoch: 044 Train Loss: 0.106, mae: 0.275, rse: 0.089, pcc: 0.957, kcc: 0.845
+2025-07-19 04:28:48: Epoch: 044 New best VALIDATION metrics
+2025-07-19 04:28:50: Epoch: 044 Test results, ap: mae: 1.039, rse: 3.064, pcc: 0.230, kcc: 0.155
+2025-07-19 04:29:24: Epoch: 045 Train Loss: 0.093, mae: 0.268, rse: 0.088, pcc: 0.958, kcc: 0.848
+2025-07-19 04:29:24: Epoch: 045 New best VALIDATION metrics
+2025-07-19 04:29:26: Epoch: 045 Test results, ap: mae: 1.042, rse: 3.068, pcc: 0.230, kcc: 0.156
+2025-07-19 04:30:00: Epoch: 046 Train Loss: 0.102, mae: 0.285, rse: 0.092, pcc: 0.956, kcc: 0.841
+2025-07-19 04:30:01: Epoch: 046 Test results, ap: mae: 0.978, rse: 2.712, pcc: 0.236, kcc: 0.151
+2025-07-19 04:30:36: Epoch: 047 Train Loss: 0.102, mae: 0.276, rse: 0.089, pcc: 0.957, kcc: 0.842
+2025-07-19 04:30:37: Epoch: 047 Test results, ap: mae: 0.997, rse: 2.825, pcc: 0.223, kcc: 0.156
+2025-07-19 04:31:11: Epoch: 048 Train Loss: 0.103, mae: 0.283, rse: 0.090, pcc: 0.956, kcc: 0.843
+2025-07-19 04:31:13: Epoch: 048 Test results, ap: mae: 1.007, rse: 2.897, pcc: 0.232, kcc: 0.154
+2025-07-19 04:31:47: Epoch: 049 Train Loss: 0.103, mae: 0.280, rse: 0.092, pcc: 0.956, kcc: 0.843
+2025-07-19 04:31:49: Epoch: 049 Test results, ap: mae: 1.012, rse: 2.910, pcc: 0.244, kcc: 0.164
+2025-07-19 04:32:23: Epoch: 050 Train Loss: 0.103, mae: 0.274, rse: 0.088, pcc: 0.956, kcc: 0.841
+2025-07-19 04:32:25: Epoch: 050 Test results, ap: mae: 0.986, rse: 2.750, pcc: 0.237, kcc: 0.166
+2025-07-19 04:32:25: used time 0.50h
+2025-07-19 04:32:25: Running Cross Validation 2
+2025-07-19 04:32:25: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 04:32:59: Epoch: 001 Train Loss: 2.580, mae: 1.121, rse: 0.811, pcc: 0.443, kcc: 0.277
+2025-07-19 04:32:59: Epoch: 001 New best VALIDATION metrics
+2025-07-19 04:33:00: Epoch: 001 Test results, ap: mae: 0.714, rse: 1.518, pcc: -0.053, kcc: -0.037
+2025-07-19 04:33:35: Epoch: 002 Train Loss: 2.137, mae: 1.036, rse: 0.694, pcc: 0.554, kcc: 0.380
+2025-07-19 04:33:35: Epoch: 002 New best VALIDATION metrics
+2025-07-19 04:33:36: Epoch: 002 Test results, ap: mae: 0.640, rse: 1.171, pcc: 0.115, kcc: 0.074
+2025-07-19 04:34:10: Epoch: 003 Train Loss: 1.805, mae: 0.951, rse: 0.586, pcc: 0.645, kcc: 0.474
+2025-07-19 04:34:10: Epoch: 003 New best VALIDATION metrics
+2025-07-19 04:34:12: Epoch: 003 Test results, ap: mae: 0.625, rse: 1.159, pcc: 0.111, kcc: 0.067
+2025-07-19 04:34:46: Epoch: 004 Train Loss: 1.491, mae: 0.864, rse: 0.480, pcc: 0.729, kcc: 0.519
+2025-07-19 04:34:46: Epoch: 004 New best VALIDATION metrics
+2025-07-19 04:34:48: Epoch: 004 Test results, ap: mae: 0.656, rse: 1.242, pcc: 0.211, kcc: 0.131
+2025-07-19 04:35:22: Epoch: 005 Train Loss: 1.171, mae: 1.001, rse: 0.666, pcc: 0.623, kcc: 0.429
+2025-07-19 04:35:23: Epoch: 005 Test results, ap: mae: 0.746, rse: 1.760, pcc: 0.151, kcc: 0.121
+2025-07-19 04:35:58: Epoch: 006 Train Loss: 1.043, mae: 1.038, rse: 0.726, pcc: 0.621, kcc: 0.443
+2025-07-19 04:35:59: Epoch: 006 Test results, ap: mae: 1.576, rse: 5.915, pcc: 0.277, kcc: 0.211
+2025-07-19 04:36:34: Epoch: 007 Train Loss: 0.868, mae: 0.706, rse: 0.362, pcc: 0.811, kcc: 0.627
+2025-07-19 04:36:34: Epoch: 007 New best VALIDATION metrics
+2025-07-19 04:36:35: Epoch: 007 Test results, ap: mae: 1.070, rse: 3.279, pcc: 0.204, kcc: 0.127
+2025-07-19 04:37:09: Epoch: 008 Train Loss: 0.721, mae: 0.676, rse: 0.317, pcc: 0.827, kcc: 0.627
+2025-07-19 04:37:09: Epoch: 008 New best VALIDATION metrics
+2025-07-19 04:37:11: Epoch: 008 Test results, ap: mae: 0.968, rse: 2.771, pcc: 0.142, kcc: 0.110
+2025-07-19 04:37:45: Epoch: 009 Train Loss: 0.673, mae: 0.631, rse: 0.263, pcc: 0.860, kcc: 0.660
+2025-07-19 04:37:45: Epoch: 009 New best VALIDATION metrics
+2025-07-19 04:37:46: Epoch: 009 Test results, ap: mae: 0.756, rse: 1.649, pcc: 0.111, kcc: 0.059
+2025-07-19 04:38:21: Epoch: 010 Train Loss: 0.621, mae: 0.644, rse: 0.320, pcc: 0.846, kcc: 0.664
+2025-07-19 04:38:22: Epoch: 010 Test results, ap: mae: 0.989, rse: 2.746, pcc: 0.231, kcc: 0.181
+2025-07-19 04:38:56: Epoch: 011 Train Loss: 0.468, mae: 0.551, rse: 0.257, pcc: 0.874, kcc: 0.699
+2025-07-19 04:38:56: Epoch: 011 New best VALIDATION metrics
+2025-07-19 04:38:58: Epoch: 011 Test results, ap: mae: 1.266, rse: 4.413, pcc: 0.252, kcc: 0.186
+2025-07-19 04:38:58: Epoch: 011 New best TEST metrics
+2025-07-19 04:39:32: Epoch: 012 Train Loss: 0.408, mae: 0.537, rse: 0.219, pcc: 0.886, kcc: 0.703
+2025-07-19 04:39:32: Epoch: 012 New best VALIDATION metrics
+2025-07-19 04:39:34: Epoch: 012 Test results, ap: mae: 0.838, rse: 2.023, pcc: 0.188, kcc: 0.135
+2025-07-19 04:39:34: Epoch: 012 New best TEST metrics
+2025-07-19 04:40:08: Epoch: 013 Train Loss: 0.400, mae: 0.514, rse: 0.223, pcc: 0.883, kcc: 0.717
+2025-07-19 04:40:08: Epoch: 013 New best VALIDATION metrics
+2025-07-19 04:40:10: Epoch: 013 Test results, ap: mae: 0.895, rse: 2.204, pcc: 0.152, kcc: 0.087
+2025-07-19 04:40:44: Epoch: 014 Train Loss: 0.339, mae: 0.490, rse: 0.206, pcc: 0.893, kcc: 0.721
+2025-07-19 04:40:44: Epoch: 014 New best VALIDATION metrics
+2025-07-19 04:40:45: Epoch: 014 Test results, ap: mae: 0.904, rse: 2.328, pcc: 0.197, kcc: 0.145
+2025-07-19 04:41:19: Epoch: 015 Train Loss: 0.338, mae: 0.473, rse: 0.208, pcc: 0.897, kcc: 0.734
+2025-07-19 04:41:19: Epoch: 015 New best VALIDATION metrics
+2025-07-19 04:41:21: Epoch: 015 Test results, ap: mae: 0.921, rse: 2.495, pcc: 0.231, kcc: 0.178
+2025-07-19 04:41:55: Epoch: 016 Train Loss: 0.294, mae: 0.468, rse: 0.198, pcc: 0.896, kcc: 0.742
+2025-07-19 04:41:55: Epoch: 016 New best VALIDATION metrics
+2025-07-19 04:41:57: Epoch: 016 Test results, ap: mae: 1.065, rse: 3.069, pcc: 0.195, kcc: 0.138
+2025-07-19 04:42:31: Epoch: 017 Train Loss: 0.314, mae: 0.512, rse: 0.225, pcc: 0.884, kcc: 0.723
+2025-07-19 04:42:32: Epoch: 017 Test results, ap: mae: 0.997, rse: 2.611, pcc: 0.200, kcc: 0.128
+2025-07-19 04:43:07: Epoch: 018 Train Loss: 0.285, mae: 0.512, rse: 0.223, pcc: 0.901, kcc: 0.733
+2025-07-19 04:43:08: Epoch: 018 Test results, ap: mae: 1.370, rse: 5.085, pcc: 0.220, kcc: 0.161
+2025-07-19 04:43:42: Epoch: 019 Train Loss: 0.261, mae: 0.447, rse: 0.191, pcc: 0.908, kcc: 0.762
+2025-07-19 04:43:42: Epoch: 019 New best VALIDATION metrics
+2025-07-19 04:43:44: Epoch: 019 Test results, ap: mae: 1.107, rse: 3.557, pcc: 0.161, kcc: 0.136
+2025-07-19 04:44:18: Epoch: 020 Train Loss: 0.240, mae: 0.501, rse: 0.219, pcc: 0.899, kcc: 0.744
+2025-07-19 04:44:19: Epoch: 020 Test results, ap: mae: 1.097, rse: 3.448, pcc: 0.218, kcc: 0.173
+2025-07-19 04:44:54: Epoch: 021 Train Loss: 0.226, mae: 0.470, rse: 0.200, pcc: 0.906, kcc: 0.758
+2025-07-19 04:44:55: Epoch: 021 Test results, ap: mae: 1.060, rse: 3.265, pcc: 0.182, kcc: 0.151
+2025-07-19 04:45:29: Epoch: 022 Train Loss: 0.198, mae: 0.405, rse: 0.171, pcc: 0.920, kcc: 0.786
+2025-07-19 04:45:29: Epoch: 022 New best VALIDATION metrics
+2025-07-19 04:45:31: Epoch: 022 Test results, ap: mae: 1.087, rse: 3.450, pcc: 0.203, kcc: 0.163
+2025-07-19 04:46:05: Epoch: 023 Train Loss: 0.188, mae: 0.400, rse: 0.176, pcc: 0.916, kcc: 0.786
+2025-07-19 04:46:07: Epoch: 023 Test results, ap: mae: 1.047, rse: 3.153, pcc: 0.195, kcc: 0.152
+2025-07-19 04:46:41: Epoch: 024 Train Loss: 0.185, mae: 0.399, rse: 0.160, pcc: 0.921, kcc: 0.789
+2025-07-19 04:46:41: Epoch: 024 New best VALIDATION metrics
+2025-07-19 04:46:42: Epoch: 024 Test results, ap: mae: 1.046, rse: 3.112, pcc: 0.164, kcc: 0.131
+2025-07-19 04:47:17: Epoch: 025 Train Loss: 0.178, mae: 0.524, rse: 0.236, pcc: 0.899, kcc: 0.752
+2025-07-19 04:47:18: Epoch: 025 Test results, ap: mae: 1.244, rse: 4.337, pcc: 0.226, kcc: 0.173
+2025-07-19 04:47:52: Epoch: 026 Train Loss: 0.174, mae: 0.388, rse: 0.149, pcc: 0.926, kcc: 0.793
+2025-07-19 04:47:52: Epoch: 026 New best VALIDATION metrics
+2025-07-19 04:47:54: Epoch: 026 Test results, ap: mae: 1.024, rse: 2.939, pcc: 0.157, kcc: 0.129
+2025-07-19 04:48:28: Epoch: 027 Train Loss: 0.161, mae: 0.383, rse: 0.148, pcc: 0.929, kcc: 0.796
+2025-07-19 04:48:28: Epoch: 027 New best VALIDATION metrics
+2025-07-19 04:48:30: Epoch: 027 Test results, ap: mae: 1.021, rse: 2.970, pcc: 0.174, kcc: 0.139
+2025-07-19 04:49:04: Epoch: 028 Train Loss: 0.169, mae: 0.377, rse: 0.152, pcc: 0.927, kcc: 0.801
+2025-07-19 04:49:04: Epoch: 028 New best VALIDATION metrics
+2025-07-19 04:49:05: Epoch: 028 Test results, ap: mae: 1.070, rse: 3.300, pcc: 0.130, kcc: 0.110
+2025-07-19 04:49:40: Epoch: 029 Train Loss: 0.176, mae: 0.409, rse: 0.167, pcc: 0.923, kcc: 0.796
+2025-07-19 04:49:41: Epoch: 029 Test results, ap: mae: 1.112, rse: 3.558, pcc: 0.156, kcc: 0.138
+2025-07-19 04:50:15: Epoch: 030 Train Loss: 0.159, mae: 0.359, rse: 0.145, pcc: 0.928, kcc: 0.805
+2025-07-19 04:50:15: Epoch: 030 New best VALIDATION metrics
+2025-07-19 04:50:17: Epoch: 030 Test results, ap: mae: 1.065, rse: 3.214, pcc: 0.167, kcc: 0.136
+2025-07-19 04:50:51: Epoch: 031 Train Loss: 0.138, mae: 0.368, rse: 0.148, pcc: 0.930, kcc: 0.812
+2025-07-19 04:50:53: Epoch: 031 Test results, ap: mae: 1.101, rse: 3.530, pcc: 0.147, kcc: 0.131
+2025-07-19 04:51:27: Epoch: 032 Train Loss: 0.146, mae: 0.358, rse: 0.147, pcc: 0.930, kcc: 0.814
+2025-07-19 04:51:27: Epoch: 032 New best VALIDATION metrics
+2025-07-19 04:51:28: Epoch: 032 Test results, ap: mae: 1.096, rse: 3.460, pcc: 0.165, kcc: 0.138
+2025-07-19 04:52:03: Epoch: 033 Train Loss: 0.129, mae: 0.358, rse: 0.142, pcc: 0.933, kcc: 0.818
+2025-07-19 04:52:03: Epoch: 033 New best VALIDATION metrics
+2025-07-19 04:52:04: Epoch: 033 Test results, ap: mae: 1.063, rse: 3.284, pcc: 0.151, kcc: 0.129
+2025-07-19 04:52:38: Epoch: 034 Train Loss: 0.133, mae: 0.376, rse: 0.149, pcc: 0.930, kcc: 0.817
+2025-07-19 04:52:40: Epoch: 034 Test results, ap: mae: 1.065, rse: 3.287, pcc: 0.151, kcc: 0.129
+2025-07-19 04:53:14: Epoch: 035 Train Loss: 0.134, mae: 0.369, rse: 0.147, pcc: 0.933, kcc: 0.819
+2025-07-19 04:53:15: Epoch: 035 Test results, ap: mae: 1.099, rse: 3.553, pcc: 0.158, kcc: 0.133
+2025-07-19 04:53:50: Epoch: 036 Train Loss: 0.125, mae: 0.374, rse: 0.153, pcc: 0.930, kcc: 0.814
+2025-07-19 04:53:51: Epoch: 036 Test results, ap: mae: 1.169, rse: 4.030, pcc: 0.165, kcc: 0.142
+2025-07-19 04:54:25: Epoch: 037 Train Loss: 0.114, mae: 0.375, rse: 0.147, pcc: 0.932, kcc: 0.819
+2025-07-19 04:54:26: Epoch: 037 Test results, ap: mae: 1.079, rse: 3.377, pcc: 0.149, kcc: 0.133
+2025-07-19 04:55:01: Epoch: 038 Train Loss: 0.125, mae: 0.351, rse: 0.141, pcc: 0.934, kcc: 0.819
+2025-07-19 04:55:01: Epoch: 038 New best VALIDATION metrics
+2025-07-19 04:55:02: Epoch: 038 Test results, ap: mae: 1.095, rse: 3.519, pcc: 0.170, kcc: 0.149
+2025-07-19 04:55:37: Epoch: 039 Train Loss: 0.120, mae: 0.359, rse: 0.146, pcc: 0.933, kcc: 0.821
+2025-07-19 04:55:38: Epoch: 039 Test results, ap: mae: 1.099, rse: 3.539, pcc: 0.165, kcc: 0.136
+2025-07-19 04:56:12: Epoch: 040 Train Loss: 0.118, mae: 0.343, rse: 0.136, pcc: 0.936, kcc: 0.824
+2025-07-19 04:56:12: Epoch: 040 New best VALIDATION metrics
+2025-07-19 04:56:14: Epoch: 040 Test results, ap: mae: 1.114, rse: 3.584, pcc: 0.156, kcc: 0.125
+2025-07-19 04:56:48: Epoch: 041 Train Loss: 0.107, mae: 0.354, rse: 0.140, pcc: 0.935, kcc: 0.828
+2025-07-19 04:56:49: Epoch: 041 Test results, ap: mae: 1.087, rse: 3.418, pcc: 0.153, kcc: 0.128
+2025-07-19 04:57:24: Epoch: 042 Train Loss: 0.105, mae: 0.336, rse: 0.137, pcc: 0.937, kcc: 0.831
+2025-07-19 04:57:24: Epoch: 042 New best VALIDATION metrics
+2025-07-19 04:57:25: Epoch: 042 Test results, ap: mae: 1.104, rse: 3.552, pcc: 0.157, kcc: 0.131
+2025-07-19 04:57:59: Epoch: 043 Train Loss: 0.093, mae: 0.346, rse: 0.138, pcc: 0.937, kcc: 0.835
+2025-07-19 04:58:01: Epoch: 043 Test results, ap: mae: 1.080, rse: 3.421, pcc: 0.152, kcc: 0.130
+2025-07-19 04:58:35: Epoch: 044 Train Loss: 0.104, mae: 0.363, rse: 0.143, pcc: 0.936, kcc: 0.833
+2025-07-19 04:58:37: Epoch: 044 Test results, ap: mae: 1.126, rse: 3.712, pcc: 0.158, kcc: 0.135
+2025-07-19 04:59:11: Epoch: 045 Train Loss: 0.102, mae: 0.354, rse: 0.141, pcc: 0.936, kcc: 0.835
+2025-07-19 04:59:12: Epoch: 045 Test results, ap: mae: 1.098, rse: 3.542, pcc: 0.155, kcc: 0.131
+2025-07-19 04:59:46: Epoch: 046 Train Loss: 0.098, mae: 0.355, rse: 0.140, pcc: 0.937, kcc: 0.832
+2025-07-19 04:59:48: Epoch: 046 Test results, ap: mae: 1.114, rse: 3.611, pcc: 0.133, kcc: 0.118
+2025-07-19 05:00:22: Epoch: 047 Train Loss: 0.098, mae: 0.349, rse: 0.142, pcc: 0.936, kcc: 0.836
+2025-07-19 05:00:23: Epoch: 047 Test results, ap: mae: 1.112, rse: 3.601, pcc: 0.131, kcc: 0.116
+2025-07-19 05:00:58: Epoch: 048 Train Loss: 0.105, mae: 0.344, rse: 0.138, pcc: 0.937, kcc: 0.835
+2025-07-19 05:00:59: Epoch: 048 Test results, ap: mae: 1.109, rse: 3.618, pcc: 0.139, kcc: 0.122
+2025-07-19 05:01:33: Epoch: 049 Train Loss: 0.093, mae: 0.352, rse: 0.141, pcc: 0.936, kcc: 0.833
+2025-07-19 05:01:35: Epoch: 049 Test results, ap: mae: 1.137, rse: 3.793, pcc: 0.125, kcc: 0.116
+2025-07-19 05:02:09: Epoch: 050 Train Loss: 0.103, mae: 0.354, rse: 0.142, pcc: 0.936, kcc: 0.835
+2025-07-19 05:02:10: Epoch: 050 Test results, ap: mae: 1.145, rse: 3.840, pcc: 0.140, kcc: 0.124
+2025-07-19 05:02:11: used time 0.50h
+2025-07-19 05:02:11: Running Cross Validation 3
+2025-07-19 05:02:11: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 05:02:45: Epoch: 001 Train Loss: 2.629, mae: 1.197, rse: 0.846, pcc: 0.413, kcc: 0.278
+2025-07-19 05:02:45: Epoch: 001 New best VALIDATION metrics
+2025-07-19 05:02:46: Epoch: 001 Test results, ap: mae: 0.703, rse: 1.410, pcc: 0.050, kcc: 0.053
+2025-07-19 05:03:20: Epoch: 002 Train Loss: 2.170, mae: 1.053, rse: 0.661, pcc: 0.625, kcc: 0.433
+2025-07-19 05:03:20: Epoch: 002 New best VALIDATION metrics
+2025-07-19 05:03:22: Epoch: 002 Test results, ap: mae: 0.610, rse: 1.030, pcc: 0.212, kcc: 0.118
+2025-07-19 05:03:56: Epoch: 003 Train Loss: 1.737, mae: 1.201, rse: 0.864, pcc: 0.446, kcc: 0.252
+2025-07-19 05:03:58: Epoch: 003 Test results, ap: mae: 1.039, rse: 3.270, pcc: 0.046, kcc: 0.057
+2025-07-19 05:04:32: Epoch: 004 Train Loss: 1.379, mae: 0.938, rse: 0.532, pcc: 0.689, kcc: 0.483
+2025-07-19 05:04:32: Epoch: 004 New best VALIDATION metrics
+2025-07-19 05:04:33: Epoch: 004 Test results, ap: mae: 0.609, rse: 1.187, pcc: 0.012, kcc: 0.080
+2025-07-19 05:05:08: Epoch: 005 Train Loss: 1.107, mae: 0.740, rse: 0.352, pcc: 0.812, kcc: 0.603
+2025-07-19 05:05:08: Epoch: 005 New best VALIDATION metrics
+2025-07-19 05:05:09: Epoch: 005 Test results, ap: mae: 0.775, rse: 1.783, pcc: 0.100, kcc: 0.032
+2025-07-19 05:05:44: Epoch: 006 Train Loss: 0.917, mae: 0.727, rse: 0.356, pcc: 0.803, kcc: 0.601
+2025-07-19 05:05:45: Epoch: 006 Test results, ap: mae: 0.787, rse: 1.843, pcc: -0.038, kcc: -0.039
+2025-07-19 05:06:19: Epoch: 007 Train Loss: 0.834, mae: 0.675, rse: 0.311, pcc: 0.835, kcc: 0.644
+2025-07-19 05:06:19: Epoch: 007 New best VALIDATION metrics
+2025-07-19 05:06:21: Epoch: 007 Test results, ap: mae: 0.855, rse: 2.381, pcc: -0.024, kcc: -0.022
+2025-07-19 05:06:55: Epoch: 008 Train Loss: 0.655, mae: 0.702, rse: 0.319, pcc: 0.832, kcc: 0.638
+2025-07-19 05:06:57: Epoch: 008 Test results, ap: mae: 0.897, rse: 2.344, pcc: 0.131, kcc: 0.079
+2025-07-19 05:07:31: Epoch: 009 Train Loss: 0.655, mae: 0.643, rse: 0.281, pcc: 0.848, kcc: 0.649
+2025-07-19 05:07:31: Epoch: 009 New best VALIDATION metrics
+2025-07-19 05:07:32: Epoch: 009 Test results, ap: mae: 1.015, rse: 3.207, pcc: 0.043, kcc: -0.011
+2025-07-19 05:08:07: Epoch: 010 Train Loss: 0.625, mae: 0.588, rse: 0.259, pcc: 0.865, kcc: 0.685
+2025-07-19 05:08:07: Epoch: 010 New best VALIDATION metrics
+2025-07-19 05:08:08: Epoch: 010 Test results, ap: mae: 0.863, rse: 2.193, pcc: 0.044, kcc: -0.003
+2025-07-19 05:08:43: Epoch: 011 Train Loss: 0.501, mae: 0.508, rse: 0.219, pcc: 0.884, kcc: 0.722
+2025-07-19 05:08:43: Epoch: 011 New best VALIDATION metrics
+2025-07-19 05:08:44: Epoch: 011 Test results, ap: mae: 0.896, rse: 2.412, pcc: 0.082, kcc: 0.030
+2025-07-19 05:08:44: Epoch: 011 New best TEST metrics
+2025-07-19 05:09:19: Epoch: 012 Train Loss: 0.441, mae: 0.533, rse: 0.236, pcc: 0.877, kcc: 0.716
+2025-07-19 05:09:20: Epoch: 012 Test results, ap: mae: 0.978, rse: 2.884, pcc: 0.097, kcc: 0.043
+2025-07-19 05:09:54: Epoch: 013 Train Loss: 0.463, mae: 0.527, rse: 0.226, pcc: 0.880, kcc: 0.718
+2025-07-19 05:09:56: Epoch: 013 Test results, ap: mae: 1.031, rse: 3.181, pcc: 0.115, kcc: 0.058
+2025-07-19 05:10:30: Epoch: 014 Train Loss: 0.423, mae: 0.519, rse: 0.225, pcc: 0.883, kcc: 0.723
+2025-07-19 05:10:32: Epoch: 014 Test results, ap: mae: 1.006, rse: 3.021, pcc: 0.107, kcc: 0.059
+2025-07-19 05:11:06: Epoch: 015 Train Loss: 0.415, mae: 0.517, rse: 0.221, pcc: 0.884, kcc: 0.730
+2025-07-19 05:11:07: Epoch: 015 Test results, ap: mae: 0.918, rse: 2.573, pcc: 0.069, kcc: 0.023
+2025-07-19 05:11:42: Epoch: 016 Train Loss: 0.396, mae: 0.509, rse: 0.219, pcc: 0.884, kcc: 0.719
+2025-07-19 05:11:43: Epoch: 016 Test results, ap: mae: 0.984, rse: 2.849, pcc: 0.124, kcc: 0.054
+2025-07-19 05:12:17: Epoch: 017 Train Loss: 0.374, mae: 0.512, rse: 0.212, pcc: 0.889, kcc: 0.727
+2025-07-19 05:12:17: Epoch: 017 New best VALIDATION metrics
+2025-07-19 05:12:19: Epoch: 017 Test results, ap: mae: 0.975, rse: 2.807, pcc: 0.077, kcc: 0.011
+2025-07-19 05:12:53: Epoch: 018 Train Loss: 0.373, mae: 0.490, rse: 0.196, pcc: 0.897, kcc: 0.733
+2025-07-19 05:12:53: Epoch: 018 New best VALIDATION metrics
+2025-07-19 05:12:55: Epoch: 018 Test results, ap: mae: 0.871, rse: 2.222, pcc: 0.087, kcc: 0.025
+2025-07-19 05:12:55: Epoch: 018 New best TEST metrics
+2025-07-19 05:13:29: Epoch: 019 Train Loss: 0.374, mae: 0.499, rse: 0.208, pcc: 0.892, kcc: 0.736
+2025-07-19 05:13:30: Epoch: 019 Test results, ap: mae: 1.062, rse: 3.284, pcc: 0.110, kcc: 0.045
+2025-07-19 05:14:05: Epoch: 020 Train Loss: 0.340, mae: 0.475, rse: 0.187, pcc: 0.902, kcc: 0.739
+2025-07-19 05:14:05: Epoch: 020 New best VALIDATION metrics
+2025-07-19 05:14:06: Epoch: 020 Test results, ap: mae: 0.902, rse: 2.383, pcc: 0.111, kcc: 0.049
+2025-07-19 05:14:40: Epoch: 021 Train Loss: 0.307, mae: 0.452, rse: 0.183, pcc: 0.906, kcc: 0.754
+2025-07-19 05:14:40: Epoch: 021 New best VALIDATION metrics
+2025-07-19 05:14:41: Epoch: 021 Test results, ap: mae: 1.009, rse: 2.990, pcc: 0.125, kcc: 0.053
+2025-07-19 05:15:16: Epoch: 022 Train Loss: 0.275, mae: 0.456, rse: 0.186, pcc: 0.905, kcc: 0.748
+2025-07-19 05:15:17: Epoch: 022 Test results, ap: mae: 0.981, rse: 2.877, pcc: 0.159, kcc: 0.091
+2025-07-19 05:15:51: Epoch: 023 Train Loss: 0.270, mae: 0.444, rse: 0.175, pcc: 0.909, kcc: 0.752
+2025-07-19 05:15:51: Epoch: 023 New best VALIDATION metrics
+2025-07-19 05:15:53: Epoch: 023 Test results, ap: mae: 0.939, rse: 2.658, pcc: 0.166, kcc: 0.104
+2025-07-19 05:16:27: Epoch: 024 Train Loss: 0.246, mae: 0.417, rse: 0.161, pcc: 0.916, kcc: 0.765
+2025-07-19 05:16:27: Epoch: 024 New best VALIDATION metrics
+2025-07-19 05:16:29: Epoch: 024 Test results, ap: mae: 0.989, rse: 2.916, pcc: 0.136, kcc: 0.081
+2025-07-19 05:17:03: Epoch: 025 Train Loss: 0.220, mae: 0.415, rse: 0.149, pcc: 0.923, kcc: 0.766
+2025-07-19 05:17:03: Epoch: 025 New best VALIDATION metrics
+2025-07-19 05:17:04: Epoch: 025 Test results, ap: mae: 0.898, rse: 2.321, pcc: 0.154, kcc: 0.094
+2025-07-19 05:17:04: Epoch: 025 New best TEST metrics
+2025-07-19 05:17:39: Epoch: 026 Train Loss: 0.238, mae: 0.418, rse: 0.153, pcc: 0.921, kcc: 0.766
+2025-07-19 05:17:40: Epoch: 026 Test results, ap: mae: 0.896, rse: 2.332, pcc: 0.138, kcc: 0.094
+2025-07-19 05:18:15: Epoch: 027 Train Loss: 0.223, mae: 0.409, rse: 0.150, pcc: 0.923, kcc: 0.773
+2025-07-19 05:18:15: Epoch: 027 New best VALIDATION metrics
+2025-07-19 05:18:16: Epoch: 027 Test results, ap: mae: 1.072, rse: 3.283, pcc: 0.100, kcc: 0.061
+2025-07-19 05:18:50: Epoch: 028 Train Loss: 0.194, mae: 0.392, rse: 0.138, pcc: 0.928, kcc: 0.779
+2025-07-19 05:18:50: Epoch: 028 New best VALIDATION metrics
+2025-07-19 05:18:52: Epoch: 028 Test results, ap: mae: 0.923, rse: 2.485, pcc: 0.171, kcc: 0.109
+2025-07-19 05:19:26: Epoch: 029 Train Loss: 0.196, mae: 0.407, rse: 0.149, pcc: 0.924, kcc: 0.778
+2025-07-19 05:19:28: Epoch: 029 Test results, ap: mae: 0.970, rse: 2.770, pcc: 0.155, kcc: 0.101
+2025-07-19 05:20:02: Epoch: 030 Train Loss: 0.204, mae: 0.394, rse: 0.141, pcc: 0.928, kcc: 0.788
+2025-07-19 05:20:02: Epoch: 030 New best VALIDATION metrics
+2025-07-19 05:20:03: Epoch: 030 Test results, ap: mae: 0.942, rse: 2.626, pcc: 0.130, kcc: 0.084
+2025-07-19 05:20:38: Epoch: 031 Train Loss: 0.165, mae: 0.365, rse: 0.133, pcc: 0.932, kcc: 0.796
+2025-07-19 05:20:38: Epoch: 031 New best VALIDATION metrics
+2025-07-19 05:20:39: Epoch: 031 Test results, ap: mae: 0.931, rse: 2.540, pcc: 0.151, kcc: 0.102
+2025-07-19 05:21:13: Epoch: 032 Train Loss: 0.149, mae: 0.363, rse: 0.129, pcc: 0.934, kcc: 0.798
+2025-07-19 05:21:13: Epoch: 032 New best VALIDATION metrics
+2025-07-19 05:21:15: Epoch: 032 Test results, ap: mae: 1.021, rse: 3.046, pcc: 0.143, kcc: 0.097
+2025-07-19 05:21:49: Epoch: 033 Train Loss: 0.156, mae: 0.356, rse: 0.128, pcc: 0.935, kcc: 0.801
+2025-07-19 05:21:49: Epoch: 033 New best VALIDATION metrics
+2025-07-19 05:21:51: Epoch: 033 Test results, ap: mae: 0.911, rse: 2.473, pcc: 0.174, kcc: 0.119
+2025-07-19 05:22:25: Epoch: 034 Train Loss: 0.151, mae: 0.343, rse: 0.122, pcc: 0.938, kcc: 0.809
+2025-07-19 05:22:25: Epoch: 034 New best VALIDATION metrics
+2025-07-19 05:22:26: Epoch: 034 Test results, ap: mae: 0.970, rse: 2.756, pcc: 0.167, kcc: 0.104
+2025-07-19 05:23:00: Epoch: 035 Train Loss: 0.154, mae: 0.374, rse: 0.132, pcc: 0.934, kcc: 0.798
+2025-07-19 05:23:02: Epoch: 035 Test results, ap: mae: 0.989, rse: 2.869, pcc: 0.124, kcc: 0.079
+2025-07-19 05:23:36: Epoch: 036 Train Loss: 0.144, mae: 0.353, rse: 0.125, pcc: 0.937, kcc: 0.807
+2025-07-19 05:23:37: Epoch: 036 Test results, ap: mae: 0.931, rse: 2.544, pcc: 0.137, kcc: 0.089
+2025-07-19 05:24:12: Epoch: 037 Train Loss: 0.144, mae: 0.348, rse: 0.122, pcc: 0.939, kcc: 0.813
+2025-07-19 05:24:12: Epoch: 037 New best VALIDATION metrics
+2025-07-19 05:24:13: Epoch: 037 Test results, ap: mae: 0.957, rse: 2.742, pcc: 0.153, kcc: 0.102
+2025-07-19 05:24:47: Epoch: 038 Train Loss: 0.144, mae: 0.339, rse: 0.120, pcc: 0.940, kcc: 0.814
+2025-07-19 05:24:47: Epoch: 038 New best VALIDATION metrics
+2025-07-19 05:24:49: Epoch: 038 Test results, ap: mae: 0.936, rse: 2.572, pcc: 0.160, kcc: 0.107
+2025-07-19 05:25:23: Epoch: 039 Train Loss: 0.132, mae: 0.331, rse: 0.115, pcc: 0.942, kcc: 0.815
+2025-07-19 05:25:23: Epoch: 039 New best VALIDATION metrics
+2025-07-19 05:25:24: Epoch: 039 Test results, ap: mae: 0.950, rse: 2.672, pcc: 0.157, kcc: 0.103
+2025-07-19 05:25:59: Epoch: 040 Train Loss: 0.134, mae: 0.353, rse: 0.120, pcc: 0.940, kcc: 0.811
+2025-07-19 05:26:00: Epoch: 040 Test results, ap: mae: 0.901, rse: 2.398, pcc: 0.150, kcc: 0.096
+2025-07-19 05:26:34: Epoch: 041 Train Loss: 0.129, mae: 0.321, rse: 0.112, pcc: 0.944, kcc: 0.826
+2025-07-19 05:26:34: Epoch: 041 New best VALIDATION metrics
+2025-07-19 05:26:36: Epoch: 041 Test results, ap: mae: 0.982, rse: 2.872, pcc: 0.138, kcc: 0.094
+2025-07-19 05:27:10: Epoch: 042 Train Loss: 0.123, mae: 0.320, rse: 0.110, pcc: 0.945, kcc: 0.827
+2025-07-19 05:27:10: Epoch: 042 New best VALIDATION metrics
+2025-07-19 05:27:12: Epoch: 042 Test results, ap: mae: 0.963, rse: 2.766, pcc: 0.144, kcc: 0.099
+2025-07-19 05:27:46: Epoch: 043 Train Loss: 0.116, mae: 0.316, rse: 0.111, pcc: 0.945, kcc: 0.824
+2025-07-19 05:27:48: Epoch: 043 Test results, ap: mae: 0.978, rse: 2.830, pcc: 0.161, kcc: 0.108
+2025-07-19 05:28:22: Epoch: 044 Train Loss: 0.130, mae: 0.324, rse: 0.112, pcc: 0.945, kcc: 0.827
+2025-07-19 05:28:23: Epoch: 044 Test results, ap: mae: 0.985, rse: 2.876, pcc: 0.165, kcc: 0.111
+2025-07-19 05:28:58: Epoch: 045 Train Loss: 0.110, mae: 0.314, rse: 0.110, pcc: 0.945, kcc: 0.829
+2025-07-19 05:28:58: Epoch: 045 New best VALIDATION metrics
+2025-07-19 05:28:59: Epoch: 045 Test results, ap: mae: 0.945, rse: 2.700, pcc: 0.146, kcc: 0.105
+2025-07-19 05:29:33: Epoch: 046 Train Loss: 0.112, mae: 0.308, rse: 0.107, pcc: 0.947, kcc: 0.831
+2025-07-19 05:29:33: Epoch: 046 New best VALIDATION metrics
+2025-07-19 05:29:35: Epoch: 046 Test results, ap: mae: 0.980, rse: 2.839, pcc: 0.131, kcc: 0.091
+2025-07-19 05:30:09: Epoch: 047 Train Loss: 0.114, mae: 0.305, rse: 0.110, pcc: 0.946, kcc: 0.829
+2025-07-19 05:30:11: Epoch: 047 Test results, ap: mae: 0.950, rse: 2.678, pcc: 0.156, kcc: 0.104
+2025-07-19 05:30:45: Epoch: 048 Train Loss: 0.110, mae: 0.304, rse: 0.109, pcc: 0.946, kcc: 0.833
+2025-07-19 05:30:45: Epoch: 048 New best VALIDATION metrics
+2025-07-19 05:30:46: Epoch: 048 Test results, ap: mae: 0.932, rse: 2.601, pcc: 0.160, kcc: 0.110
+2025-07-19 05:31:21: Epoch: 049 Train Loss: 0.113, mae: 0.314, rse: 0.109, pcc: 0.945, kcc: 0.827
+2025-07-19 05:31:22: Epoch: 049 Test results, ap: mae: 0.942, rse: 2.640, pcc: 0.157, kcc: 0.106
+2025-07-19 05:31:56: Epoch: 050 Train Loss: 0.109, mae: 0.308, rse: 0.105, pcc: 0.948, kcc: 0.833
+2025-07-19 05:31:56: Epoch: 050 New best VALIDATION metrics
+2025-07-19 05:31:58: Epoch: 050 Test results, ap: mae: 0.903, rse: 2.461, pcc: 0.144, kcc: 0.100
+2025-07-19 05:31:58: used time 0.50h
+2025-07-19 05:31:58: Running Cross Validation 4
+2025-07-19 05:31:58: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 05:32:32: Epoch: 001 Train Loss: 2.458, mae: 1.141, rse: 0.900, pcc: 0.341, kcc: 0.204
+2025-07-19 05:32:32: Epoch: 001 New best VALIDATION metrics
+2025-07-19 05:32:34: Epoch: 001 Test results, ap: mae: 0.602, rse: 1.109, pcc: 0.112, kcc: 0.039
+2025-07-19 05:33:08: Epoch: 002 Train Loss: 1.959, mae: 1.141, rse: 0.872, pcc: 0.415, kcc: 0.296
+2025-07-19 05:33:08: Epoch: 002 New best VALIDATION metrics
+2025-07-19 05:33:10: Epoch: 002 Test results, ap: mae: 0.585, rse: 1.024, pcc: 0.099, kcc: 0.084
+2025-07-19 05:33:44: Epoch: 003 Train Loss: 1.491, mae: 0.917, rse: 0.562, pcc: 0.670, kcc: 0.470
+2025-07-19 05:33:44: Epoch: 003 New best VALIDATION metrics
+2025-07-19 05:33:45: Epoch: 003 Test results, ap: mae: 0.878, rse: 2.252, pcc: 0.131, kcc: 0.079
+2025-07-19 05:34:20: Epoch: 004 Train Loss: 1.222, mae: 0.824, rse: 0.478, pcc: 0.734, kcc: 0.535
+2025-07-19 05:34:20: Epoch: 004 New best VALIDATION metrics
+2025-07-19 05:34:21: Epoch: 004 Test results, ap: mae: 0.759, rse: 1.749, pcc: 0.168, kcc: 0.131
+2025-07-19 05:34:56: Epoch: 005 Train Loss: 1.040, mae: 0.877, rse: 0.499, pcc: 0.724, kcc: 0.483
+2025-07-19 05:34:57: Epoch: 005 Test results, ap: mae: 0.875, rse: 2.134, pcc: 0.204, kcc: 0.152
+2025-07-19 05:35:31: Epoch: 006 Train Loss: 0.943, mae: 0.831, rse: 0.489, pcc: 0.724, kcc: 0.533
+2025-07-19 05:35:33: Epoch: 006 Test results, ap: mae: 0.783, rse: 1.788, pcc: 0.110, kcc: 0.045
+2025-07-19 05:36:07: Epoch: 007 Train Loss: 0.794, mae: 0.716, rse: 0.373, pcc: 0.793, kcc: 0.590
+2025-07-19 05:36:07: Epoch: 007 New best VALIDATION metrics
+2025-07-19 05:36:09: Epoch: 007 Test results, ap: mae: 0.830, rse: 2.148, pcc: 0.008, kcc: -0.014
+2025-07-19 05:36:43: Epoch: 008 Train Loss: 0.685, mae: 0.743, rse: 0.407, pcc: 0.773, kcc: 0.579
+2025-07-19 05:36:44: Epoch: 008 Test results, ap: mae: 0.889, rse: 2.176, pcc: 0.135, kcc: 0.046
+2025-07-19 05:37:19: Epoch: 009 Train Loss: 0.616, mae: 0.609, rse: 0.307, pcc: 0.834, kcc: 0.634
+2025-07-19 05:37:19: Epoch: 009 New best VALIDATION metrics
+2025-07-19 05:37:20: Epoch: 009 Test results, ap: mae: 0.810, rse: 1.948, pcc: 0.091, kcc: 0.068
+2025-07-19 05:37:55: Epoch: 010 Train Loss: 0.600, mae: 0.663, rse: 0.353, pcc: 0.806, kcc: 0.617
+2025-07-19 05:37:56: Epoch: 010 Test results, ap: mae: 0.767, rse: 1.739, pcc: 0.067, kcc: 0.033
+2025-07-19 05:38:30: Epoch: 011 Train Loss: 0.469, mae: 0.539, rse: 0.262, pcc: 0.862, kcc: 0.686
+2025-07-19 05:38:30: Epoch: 011 New best VALIDATION metrics
+2025-07-19 05:38:32: Epoch: 011 Test results, ap: mae: 0.850, rse: 2.159, pcc: 0.178, kcc: 0.116
+2025-07-19 05:38:32: Epoch: 011 New best TEST metrics
+2025-07-19 05:39:06: Epoch: 012 Train Loss: 0.450, mae: 0.545, rse: 0.266, pcc: 0.861, kcc: 0.677
+2025-07-19 05:39:08: Epoch: 012 Test results, ap: mae: 0.980, rse: 2.800, pcc: 0.195, kcc: 0.122
+2025-07-19 05:39:42: Epoch: 013 Train Loss: 0.410, mae: 0.502, rse: 0.241, pcc: 0.874, kcc: 0.697
+2025-07-19 05:39:42: Epoch: 013 New best VALIDATION metrics
+2025-07-19 05:39:43: Epoch: 013 Test results, ap: mae: 0.779, rse: 1.761, pcc: 0.184, kcc: 0.120
+2025-07-19 05:39:43: Epoch: 013 New best TEST metrics
+2025-07-19 05:40:18: Epoch: 014 Train Loss: 0.364, mae: 0.512, rse: 0.250, pcc: 0.868, kcc: 0.691
+2025-07-19 05:40:19: Epoch: 014 Test results, ap: mae: 0.841, rse: 2.100, pcc: 0.157, kcc: 0.096
+2025-07-19 05:40:54: Epoch: 015 Train Loss: 0.350, mae: 0.570, rse: 0.270, pcc: 0.861, kcc: 0.654
+2025-07-19 05:40:55: Epoch: 015 Test results, ap: mae: 0.928, rse: 2.567, pcc: 0.174, kcc: 0.122
+2025-07-19 05:41:30: Epoch: 016 Train Loss: 0.347, mae: 0.486, rse: 0.228, pcc: 0.882, kcc: 0.707
+2025-07-19 05:41:30: Epoch: 016 New best VALIDATION metrics
+2025-07-19 05:41:31: Epoch: 016 Test results, ap: mae: 0.863, rse: 2.276, pcc: 0.182, kcc: 0.131
+2025-07-19 05:42:05: Epoch: 017 Train Loss: 0.338, mae: 0.485, rse: 0.236, pcc: 0.875, kcc: 0.716
+2025-07-19 05:42:07: Epoch: 017 Test results, ap: mae: 0.779, rse: 1.806, pcc: 0.137, kcc: 0.083
+2025-07-19 05:42:41: Epoch: 018 Train Loss: 0.308, mae: 0.502, rse: 0.250, pcc: 0.866, kcc: 0.705
+2025-07-19 05:42:42: Epoch: 018 Test results, ap: mae: 0.780, rse: 1.682, pcc: 0.109, kcc: 0.046
+2025-07-19 05:43:17: Epoch: 019 Train Loss: 0.304, mae: 0.464, rse: 0.214, pcc: 0.887, kcc: 0.716
+2025-07-19 05:43:17: Epoch: 019 New best VALIDATION metrics
+2025-07-19 05:43:18: Epoch: 019 Test results, ap: mae: 0.690, rse: 1.384, pcc: 0.159, kcc: 0.099
+2025-07-19 05:43:18: Epoch: 019 New best TEST metrics
+2025-07-19 05:43:53: Epoch: 020 Train Loss: 0.280, mae: 0.433, rse: 0.213, pcc: 0.889, kcc: 0.732
+2025-07-19 05:43:53: Epoch: 020 New best VALIDATION metrics
+2025-07-19 05:43:54: Epoch: 020 Test results, ap: mae: 0.755, rse: 1.628, pcc: 0.178, kcc: 0.102
+2025-07-19 05:44:29: Epoch: 021 Train Loss: 0.253, mae: 0.422, rse: 0.199, pcc: 0.899, kcc: 0.745
+2025-07-19 05:44:29: Epoch: 021 New best VALIDATION metrics
+2025-07-19 05:44:30: Epoch: 021 Test results, ap: mae: 0.781, rse: 1.867, pcc: 0.147, kcc: 0.095
+2025-07-19 05:45:04: Epoch: 022 Train Loss: 0.232, mae: 0.411, rse: 0.203, pcc: 0.896, kcc: 0.752
+2025-07-19 05:45:04: Epoch: 022 New best VALIDATION metrics
+2025-07-19 05:45:06: Epoch: 022 Test results, ap: mae: 0.820, rse: 2.029, pcc: 0.159, kcc: 0.108
+2025-07-19 05:45:40: Epoch: 023 Train Loss: 0.225, mae: 0.416, rse: 0.198, pcc: 0.898, kcc: 0.745
+2025-07-19 05:45:41: Epoch: 023 Test results, ap: mae: 0.787, rse: 1.845, pcc: 0.169, kcc: 0.105
+2025-07-19 05:46:16: Epoch: 024 Train Loss: 0.215, mae: 0.417, rse: 0.202, pcc: 0.896, kcc: 0.745
+2025-07-19 05:46:17: Epoch: 024 Test results, ap: mae: 0.746, rse: 1.584, pcc: 0.116, kcc: 0.066
+2025-07-19 05:46:51: Epoch: 025 Train Loss: 0.194, mae: 0.394, rse: 0.186, pcc: 0.903, kcc: 0.759
+2025-07-19 05:46:51: Epoch: 025 New best VALIDATION metrics
+2025-07-19 05:46:53: Epoch: 025 Test results, ap: mae: 0.752, rse: 1.617, pcc: 0.110, kcc: 0.062
+2025-07-19 05:47:27: Epoch: 026 Train Loss: 0.198, mae: 0.376, rse: 0.182, pcc: 0.905, kcc: 0.770
+2025-07-19 05:47:27: Epoch: 026 New best VALIDATION metrics
+2025-07-19 05:47:29: Epoch: 026 Test results, ap: mae: 0.734, rse: 1.565, pcc: 0.123, kcc: 0.073
+2025-07-19 05:48:03: Epoch: 027 Train Loss: 0.198, mae: 0.389, rse: 0.189, pcc: 0.901, kcc: 0.764
+2025-07-19 05:48:04: Epoch: 027 Test results, ap: mae: 0.731, rse: 1.561, pcc: 0.128, kcc: 0.087
+2025-07-19 05:48:39: Epoch: 028 Train Loss: 0.200, mae: 0.421, rse: 0.188, pcc: 0.903, kcc: 0.761
+2025-07-19 05:48:40: Epoch: 028 Test results, ap: mae: 0.709, rse: 1.478, pcc: 0.146, kcc: 0.093
+2025-07-19 05:49:14: Epoch: 029 Train Loss: 0.184, mae: 0.372, rse: 0.177, pcc: 0.909, kcc: 0.774
+2025-07-19 05:49:14: Epoch: 029 New best VALIDATION metrics
+2025-07-19 05:49:16: Epoch: 029 Test results, ap: mae: 0.759, rse: 1.679, pcc: 0.094, kcc: 0.063
+2025-07-19 05:49:50: Epoch: 030 Train Loss: 0.173, mae: 0.369, rse: 0.174, pcc: 0.910, kcc: 0.779
+2025-07-19 05:49:50: Epoch: 030 New best VALIDATION metrics
+2025-07-19 05:49:52: Epoch: 030 Test results, ap: mae: 0.713, rse: 1.514, pcc: 0.131, kcc: 0.085
+2025-07-19 05:50:26: Epoch: 031 Train Loss: 0.156, mae: 0.347, rse: 0.165, pcc: 0.915, kcc: 0.796
+2025-07-19 05:50:26: Epoch: 031 New best VALIDATION metrics
+2025-07-19 05:50:27: Epoch: 031 Test results, ap: mae: 0.731, rse: 1.584, pcc: 0.134, kcc: 0.087
+2025-07-19 05:51:01: Epoch: 032 Train Loss: 0.145, mae: 0.356, rse: 0.168, pcc: 0.915, kcc: 0.795
+2025-07-19 05:51:03: Epoch: 032 Test results, ap: mae: 0.746, rse: 1.658, pcc: 0.130, kcc: 0.089
+2025-07-19 05:51:37: Epoch: 033 Train Loss: 0.154, mae: 0.349, rse: 0.171, pcc: 0.913, kcc: 0.797
+2025-07-19 05:51:39: Epoch: 033 Test results, ap: mae: 0.773, rse: 1.732, pcc: 0.105, kcc: 0.069
+2025-07-19 05:52:13: Epoch: 034 Train Loss: 0.143, mae: 0.349, rse: 0.166, pcc: 0.916, kcc: 0.798
+2025-07-19 05:52:14: Epoch: 034 Test results, ap: mae: 0.764, rse: 1.728, pcc: 0.144, kcc: 0.101
+2025-07-19 05:52:49: Epoch: 035 Train Loss: 0.144, mae: 0.346, rse: 0.159, pcc: 0.919, kcc: 0.805
+2025-07-19 05:52:49: Epoch: 035 New best VALIDATION metrics
+2025-07-19 05:52:50: Epoch: 035 Test results, ap: mae: 0.730, rse: 1.615, pcc: 0.140, kcc: 0.099
+2025-07-19 05:53:24: Epoch: 036 Train Loss: 0.145, mae: 0.341, rse: 0.163, pcc: 0.916, kcc: 0.800
+2025-07-19 05:53:26: Epoch: 036 Test results, ap: mae: 0.723, rse: 1.555, pcc: 0.112, kcc: 0.082
+2025-07-19 05:54:00: Epoch: 037 Train Loss: 0.141, mae: 0.336, rse: 0.160, pcc: 0.919, kcc: 0.804
+2025-07-19 05:54:00: Epoch: 037 New best VALIDATION metrics
+2025-07-19 05:54:02: Epoch: 037 Test results, ap: mae: 0.730, rse: 1.608, pcc: 0.120, kcc: 0.083
+2025-07-19 05:54:36: Epoch: 038 Train Loss: 0.134, mae: 0.342, rse: 0.165, pcc: 0.917, kcc: 0.801
+2025-07-19 05:54:37: Epoch: 038 Test results, ap: mae: 0.762, rse: 1.747, pcc: 0.096, kcc: 0.069
+2025-07-19 05:55:12: Epoch: 039 Train Loss: 0.136, mae: 0.333, rse: 0.164, pcc: 0.917, kcc: 0.802
+2025-07-19 05:55:13: Epoch: 039 Test results, ap: mae: 0.750, rse: 1.648, pcc: 0.073, kcc: 0.056
+2025-07-19 05:55:47: Epoch: 040 Train Loss: 0.138, mae: 0.333, rse: 0.165, pcc: 0.916, kcc: 0.806
+2025-07-19 05:55:49: Epoch: 040 Test results, ap: mae: 0.764, rse: 1.717, pcc: 0.127, kcc: 0.082
+2025-07-19 05:56:23: Epoch: 041 Train Loss: 0.128, mae: 0.333, rse: 0.158, pcc: 0.920, kcc: 0.813
+2025-07-19 05:56:23: Epoch: 041 New best VALIDATION metrics
+2025-07-19 05:56:25: Epoch: 041 Test results, ap: mae: 0.747, rse: 1.688, pcc: 0.132, kcc: 0.094
+2025-07-19 05:56:59: Epoch: 042 Train Loss: 0.114, mae: 0.323, rse: 0.157, pcc: 0.920, kcc: 0.816
+2025-07-19 05:56:59: Epoch: 042 New best VALIDATION metrics
+2025-07-19 05:57:00: Epoch: 042 Test results, ap: mae: 0.746, rse: 1.672, pcc: 0.126, kcc: 0.089
+2025-07-19 05:57:35: Epoch: 043 Train Loss: 0.118, mae: 0.326, rse: 0.156, pcc: 0.922, kcc: 0.819
+2025-07-19 05:57:35: Epoch: 043 New best VALIDATION metrics
+2025-07-19 05:57:36: Epoch: 043 Test results, ap: mae: 0.746, rse: 1.674, pcc: 0.128, kcc: 0.093
+2025-07-19 05:58:10: Epoch: 044 Train Loss: 0.116, mae: 0.324, rse: 0.158, pcc: 0.920, kcc: 0.818
+2025-07-19 05:58:12: Epoch: 044 Test results, ap: mae: 0.782, rse: 1.810, pcc: 0.127, kcc: 0.093
+2025-07-19 05:58:46: Epoch: 045 Train Loss: 0.119, mae: 0.346, rse: 0.156, pcc: 0.923, kcc: 0.817
+2025-07-19 05:58:48: Epoch: 045 Test results, ap: mae: 0.758, rse: 1.716, pcc: 0.136, kcc: 0.102
+2025-07-19 05:59:22: Epoch: 046 Train Loss: 0.110, mae: 0.314, rse: 0.149, pcc: 0.924, kcc: 0.823
+2025-07-19 05:59:22: Epoch: 046 New best VALIDATION metrics
+2025-07-19 05:59:23: Epoch: 046 Test results, ap: mae: 0.719, rse: 1.536, pcc: 0.109, kcc: 0.076
+2025-07-19 05:59:58: Epoch: 047 Train Loss: 0.119, mae: 0.318, rse: 0.152, pcc: 0.922, kcc: 0.820
+2025-07-19 05:59:59: Epoch: 047 Test results, ap: mae: 0.718, rse: 1.533, pcc: 0.100, kcc: 0.070
+2025-07-19 06:00:34: Epoch: 048 Train Loss: 0.105, mae: 0.309, rse: 0.152, pcc: 0.924, kcc: 0.824
+2025-07-19 06:00:34: Epoch: 048 New best VALIDATION metrics
+2025-07-19 06:00:35: Epoch: 048 Test results, ap: mae: 0.736, rse: 1.623, pcc: 0.116, kcc: 0.082
+2025-07-19 06:01:09: Epoch: 049 Train Loss: 0.119, mae: 0.308, rse: 0.150, pcc: 0.923, kcc: 0.823
+2025-07-19 06:01:09: Epoch: 049 New best VALIDATION metrics
+2025-07-19 06:01:11: Epoch: 049 Test results, ap: mae: 0.729, rse: 1.605, pcc: 0.116, kcc: 0.088
+2025-07-19 06:01:45: Epoch: 050 Train Loss: 0.111, mae: 0.316, rse: 0.149, pcc: 0.925, kcc: 0.823
+2025-07-19 06:01:47: Epoch: 050 Test results, ap: mae: 0.727, rse: 1.593, pcc: 0.120, kcc: 0.091
+2025-07-19 06:01:47: used time 0.50h
+2025-07-19 06:01:47: Cross Validation Finished!
+2025-07-19 06:01:47: Best validation perform list
+[[0.22832306 0.05934171 0.97275233 0.87990129]
+ [0.26845753 0.08809258 0.95757961 0.84810841]
+ [0.33562213 0.13701877 0.93668753 0.83112103]
+ [0.30774131 0.10464162 0.94787657 0.83348966]
+ [0.30840585 0.15013383 0.92348909 0.82349396]]
+2025-07-19 06:01:47: mean: [0.29  0.108 0.948 0.843]
+2025-07-19 06:01:47: std: [0.037 0.033 0.017 0.02 ]
+2025-07-19 06:01:47: Best test perform list
+[[0.84455246 2.18097925 0.11451439 0.10164814]
+ [0.82953858 1.88849604 0.26696688 0.19011323]
+ [0.83778828 2.02342296 0.18829076 0.13495263]
+ [0.89828354 2.32063675 0.15418521 0.09366893]
+ [0.68968689 1.3838104  0.1587742  0.09887276]]
+2025-07-19 06:01:47: mean: [0.82  1.959 0.177 0.124]
+2025-07-19 06:01:47: std: [0.069 0.322 0.051 0.036]
diff --git a/run-reg/rn18-diff-16-mamba-768-mse-32-0.001-50/uda_Indolicidin/config.json b/run-reg/rn18-diff-16-mamba-768-mse-32-0.001-50/uda_Indolicidin/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..112c65a580aa3635b16846d3e97b60ff07ab07c3
--- /dev/null
+++ b/run-reg/rn18-diff-16-mamba-768-mse-32-0.001-50/uda_Indolicidin/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": "mamba", "channels": 16, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "case": "Indolicidin", "classes": 1}
\ No newline at end of file
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diff --git a/run-reg/rn18-diff-16-mamba-768-mse-32-0.001-50/uda_Indolicidin/training.log b/run-reg/rn18-diff-16-mamba-768-mse-32-0.001-50/uda_Indolicidin/training.log
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+2025-08-08 01:05:11: saving_dir: ./run-reg/rn18-diff-16-mamba-768-mse-32-0.001-50/uda_Indolicidin
+2025-08-08 01:05:11: Loading Training Dataset
+2025-08-08 01:05:35: Start UDA training
+2025-08-08 01:06:58: Epoch 1 Train Loss: 2.429
+2025-08-08 01:08:20: Epoch 2 Train Loss: 1.423
+2025-08-08 01:09:41: Epoch 3 Train Loss: 1.029
+2025-08-08 01:11:03: Epoch 4 Train Loss: 0.6754
+2025-08-08 01:12:24: Epoch 5 Train Loss: 0.5041
+2025-08-08 01:13:45: Epoch 6 Train Loss: 0.4409
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diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/preds_case_sfda.csv b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/preds_case_sfda.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a31025efeadee625e7ea727fad16a1814dc2756e
--- /dev/null
+++ b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/preds_case_sfda.csv
@@ -0,0 +1,16384 @@
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diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/preds_r2_case.csv b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/preds_r2_case.csv
new file mode 100644
index 0000000000000000000000000000000000000000..920c6a903153eca7a1455c06769f5cb905811279
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diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/preds_test.csv b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/preds_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..8b40fd76b3bf53741774a0ac016d49f50e867eab
--- /dev/null
+++ b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/preds_test.csv
@@ -0,0 +1,8 @@
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diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/result.txt b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/result.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4a5a209369b4249ac53e9aab8d8e4219fab62af4
--- /dev/null
+++ b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
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diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/sfda_tuning.log b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/sfda_tuning.log
new file mode 100644
index 0000000000000000000000000000000000000000..7b10372814461ffd30463df1d55cda4e024947f5
--- /dev/null
+++ b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/sfda_tuning.log
@@ -0,0 +1,574 @@
+2025-07-25 22:28:06: Fold 0
+2025-07-25 22:28:18: Iteration 10, Train Loss: 0.6318, Val: mae: 1.0367 rse: 3.4193 pcc: -0.1800 kcc: -0.1023
+2025-07-25 22:28:18: NEW best validation iteration
+2025-07-25 22:28:26: Iteration 20, Train Loss: 0.2900, Val: mae: 0.9929 rse: 3.2313 pcc: -0.1951 kcc: -0.1131
+2025-07-25 22:28:26: NEW best validation iteration
+2025-07-25 22:28:33: Iteration 30, Train Loss: 0.6267, Val: mae: 0.9445 rse: 2.9760 pcc: -0.2289 kcc: -0.1203
+2025-07-25 22:28:33: NEW best validation iteration
+2025-07-25 22:28:40: Iteration 40, Train Loss: 0.1362, Val: mae: 0.8353 rse: 2.3432 pcc: -0.1242 kcc: -0.0628
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+2025-07-25 22:28:48: Iteration 50, Train Loss: 0.2375, Val: mae: 0.7857 rse: 2.0739 pcc: -0.0844 kcc: -0.0341
+2025-07-25 22:28:48: NEW best validation iteration
+2025-07-25 22:28:55: Iteration 60, Train Loss: 0.0697, Val: mae: 0.7499 rse: 1.9164 pcc: -0.0111 kcc: 0.0341
+2025-07-25 22:28:55: NEW best validation iteration
+2025-07-25 22:29:03: Iteration 70, Train Loss: 0.1366, Val: mae: 0.7129 rse: 1.6810 pcc: 0.0541 kcc: 0.0664
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+2025-07-25 22:29:10: NEW best validation iteration
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+2025-07-25 22:29:17: NEW best validation iteration
+2025-07-25 22:29:25: Iteration 100, Train Loss: 0.0709, Val: mae: 0.7023 rse: 1.5395 pcc: 0.1719 kcc: 0.1598
+2025-07-25 22:29:32: Iteration 110, Train Loss: 0.0902, Val: mae: 0.7052 rse: 1.5655 pcc: 0.1932 kcc: 0.1634
+2025-07-25 22:29:39: Iteration 120, Train Loss: 0.0234, Val: mae: 0.7190 rse: 1.5988 pcc: 0.1455 kcc: 0.1167
+2025-07-25 22:29:47: Iteration 130, Train Loss: 0.0349, Val: mae: 0.7148 rse: 1.6171 pcc: 0.0737 kcc: 0.0521
+2025-07-25 22:29:54: Iteration 140, Train Loss: 0.0824, Val: mae: 0.7103 rse: 1.6316 pcc: 0.0228 kcc: 0.0449
+2025-07-25 22:30:01: Iteration 150, Train Loss: 0.0334, Val: mae: 0.7249 rse: 1.6970 pcc: -0.1033 kcc: -0.0054
+2025-07-25 22:30:09: Iteration 160, Train Loss: 0.0376, Val: mae: 0.6980 rse: 1.5983 pcc: -0.0346 kcc: 0.0269
+2025-07-25 22:30:16: Iteration 170, Train Loss: 0.0224, Val: mae: 0.6996 rse: 1.5756 pcc: -0.0528 kcc: 0.0126
+2025-07-25 22:30:24: Iteration 180, Train Loss: 0.0268, Val: mae: 0.7089 rse: 1.5702 pcc: -0.0703 kcc: -0.0305
+2025-07-25 22:30:31: Iteration 190, Train Loss: 0.0273, Val: mae: 0.7149 rse: 1.5864 pcc: -0.0496 kcc: -0.0413
+2025-07-25 22:30:38: Iteration 200, Train Loss: 0.0217, Val: mae: 0.7184 rse: 1.5990 pcc: -0.1059 kcc: -0.0592
+2025-07-25 22:30:46: Iteration 210, Train Loss: 0.0091, Val: mae: 0.6986 rse: 1.5324 pcc: 0.0186 kcc: 0.0449
+2025-07-25 22:30:53: Iteration 220, Train Loss: 0.0213, Val: mae: 0.6914 rse: 1.4906 pcc: 0.0856 kcc: 0.0808
+2025-07-25 22:31:00: Iteration 230, Train Loss: 0.0107, Val: mae: 0.6931 rse: 1.4626 pcc: 0.1693 kcc: 0.1275
+2025-07-25 22:31:00: NEW best validation iteration
+2025-07-25 22:31:08: Iteration 240, Train Loss: 0.0050, Val: mae: 0.6872 rse: 1.4215 pcc: 0.2744 kcc: 0.2065
+2025-07-25 22:31:08: NEW best validation iteration
+2025-07-25 22:31:15: Iteration 250, Train Loss: 0.0068, Val: mae: 0.6931 rse: 1.4315 pcc: 0.2844 kcc: 0.2208
+2025-07-25 22:31:15: NEW best validation iteration
+2025-07-25 22:31:23: Iteration 260, Train Loss: 0.0064, Val: mae: 0.6965 rse: 1.4585 pcc: 0.2335 kcc: 0.1849
+2025-07-25 22:31:30: Iteration 270, Train Loss: 0.0045, Val: mae: 0.6984 rse: 1.4515 pcc: 0.2344 kcc: 0.1598
+2025-07-25 22:31:37: Iteration 280, Train Loss: 0.0091, Val: mae: 0.6955 rse: 1.4379 pcc: 0.2225 kcc: 0.1742
+2025-07-25 22:31:45: Iteration 290, Train Loss: 0.0047, Val: mae: 0.7026 rse: 1.4715 pcc: 0.1833 kcc: 0.1167
+2025-07-25 22:31:52: Iteration 300, Train Loss: 0.0089, Val: mae: 0.7000 rse: 1.4646 pcc: 0.2100 kcc: 0.1418
+2025-07-25 22:31:59: Iteration 310, Train Loss: 0.0031, Val: mae: 0.7007 rse: 1.4524 pcc: 0.2614 kcc: 0.1454
+2025-07-25 22:32:07: Iteration 320, Train Loss: 0.0056, Val: mae: 0.7055 rse: 1.4678 pcc: 0.2100 kcc: 0.1634
+2025-07-25 22:32:14: Iteration 330, Train Loss: 0.0069, Val: mae: 0.6981 rse: 1.4533 pcc: 0.2195 kcc: 0.1382
+2025-07-25 22:32:21: Iteration 340, Train Loss: 0.0070, Val: mae: 0.7045 rse: 1.4588 pcc: 0.1985 kcc: 0.1275
+2025-07-25 22:32:29: Iteration 350, Train Loss: 0.0042, Val: mae: 0.7037 rse: 1.4528 pcc: 0.2125 kcc: 0.1347
+2025-07-25 22:32:36: Iteration 360, Train Loss: 0.0031, Val: mae: 0.6969 rse: 1.4314 pcc: 0.3243 kcc: 0.2172
+2025-07-25 22:32:36: NEW best validation iteration
+2025-07-25 22:32:44: Iteration 370, Train Loss: 0.0031, Val: mae: 0.6970 rse: 1.4247 pcc: 0.3112 kcc: 0.2065
+2025-07-25 22:32:51: Iteration 380, Train Loss: 0.0021, Val: mae: 0.6946 rse: 1.4347 pcc: 0.2286 kcc: 0.1670
+2025-07-25 22:32:58: Iteration 390, Train Loss: 0.0018, Val: mae: 0.6920 rse: 1.4228 pcc: 0.2562 kcc: 0.1634
+2025-07-25 22:33:06: Iteration 400, Train Loss: 0.0027, Val: mae: 0.7012 rse: 1.4722 pcc: 0.0368 kcc: 0.0197
+2025-07-25 22:33:13: Iteration 410, Train Loss: 0.0017, Val: mae: 0.7061 rse: 1.4852 pcc: 0.0309 kcc: 0.0054
+2025-07-25 22:33:21: Iteration 420, Train Loss: 0.0061, Val: mae: 0.7149 rse: 1.5140 pcc: 0.0547 kcc: 0.0413
+2025-07-25 22:33:28: Iteration 430, Train Loss: 0.0032, Val: mae: 0.7156 rse: 1.5266 pcc: -0.0199 kcc: -0.0341
+2025-07-25 22:33:35: Iteration 440, Train Loss: 0.0029, Val: mae: 0.7126 rse: 1.5149 pcc: 0.0040 kcc: -0.0269
+2025-07-25 22:33:43: Iteration 450, Train Loss: 0.0008, Val: mae: 0.7127 rse: 1.5196 pcc: -0.0929 kcc: -0.0628
+2025-07-25 22:33:50: Iteration 460, Train Loss: 0.0030, Val: mae: 0.7067 rse: 1.5036 pcc: 0.0253 kcc: 0.0054
+2025-07-25 22:33:58: Iteration 470, Train Loss: 0.0029, Val: mae: 0.6982 rse: 1.4801 pcc: 0.0377 kcc: -0.0126
+2025-07-25 22:34:05: Iteration 480, Train Loss: 0.0018, Val: mae: 0.6932 rse: 1.4456 pcc: 0.1470 kcc: 0.0485
+2025-07-25 22:34:12: Iteration 490, Train Loss: 0.0025, Val: mae: 0.6989 rse: 1.4672 pcc: 0.0256 kcc: 0.0018
+2025-07-25 22:34:20: Iteration 500, Train Loss: 0.0022, Val: mae: 0.6977 rse: 1.4577 pcc: 0.0707 kcc: 0.0521
+2025-07-25 22:34:27: Iteration 510, Train Loss: 0.0025, Val: mae: 0.6995 rse: 1.4608 pcc: 0.1707 kcc: 0.0916
+2025-07-25 22:34:34: Iteration 520, Train Loss: 0.0021, Val: mae: 0.7073 rse: 1.4938 pcc: -0.0285 kcc: -0.0305
+2025-07-25 22:34:42: Iteration 530, Train Loss: 0.0009, Val: mae: 0.7080 rse: 1.5048 pcc: -0.0526 kcc: -0.0628
+2025-07-25 22:34:49: Iteration 540, Train Loss: 0.0041, Val: mae: 0.7078 rse: 1.5119 pcc: -0.1382 kcc: -0.0772
+2025-07-25 22:34:57: Iteration 550, Train Loss: 0.0017, Val: mae: 0.7143 rse: 1.5220 pcc: -0.1750 kcc: -0.1095
+2025-07-25 22:35:04: Iteration 560, Train Loss: 0.0047, Val: mae: 0.7136 rse: 1.5251 pcc: -0.2116 kcc: -0.1526
+2025-07-25 22:35:11: Iteration 570, Train Loss: 0.0012, Val: mae: 0.7069 rse: 1.5100 pcc: -0.0940 kcc: -0.0700
+2025-07-25 22:35:19: Iteration 580, Train Loss: 0.0016, Val: mae: 0.7133 rse: 1.5199 pcc: -0.1975 kcc: -0.1526
+2025-07-25 22:35:26: Iteration 590, Train Loss: 0.0016, Val: mae: 0.7074 rse: 1.4812 pcc: -0.0458 kcc: -0.0592
+2025-07-25 22:35:33: Iteration 600, Train Loss: 0.0015, Val: mae: 0.7084 rse: 1.4921 pcc: -0.0509 kcc: -0.0952
+2025-07-25 22:35:41: Iteration 610, Train Loss: 0.0008, Val: mae: 0.7118 rse: 1.5134 pcc: -0.2360 kcc: -0.2244
+2025-07-25 22:35:48: Iteration 620, Train Loss: 0.0011, Val: mae: 0.7161 rse: 1.5244 pcc: -0.3224 kcc: -0.2496
+2025-07-25 22:35:56: Iteration 630, Train Loss: 0.0015, Val: mae: 0.7167 rse: 1.5184 pcc: -0.2797 kcc: -0.2137
+2025-07-25 22:36:03: Iteration 640, Train Loss: 0.0007, Val: mae: 0.7076 rse: 1.4961 pcc: -0.1748 kcc: -0.1454
+2025-07-25 22:36:10: Iteration 650, Train Loss: 0.0014, Val: mae: 0.7069 rse: 1.4840 pcc: -0.2617 kcc: -0.2567
+2025-07-25 22:36:18: Iteration 660, Train Loss: 0.0016, Val: mae: 0.7001 rse: 1.4638 pcc: -0.0877 kcc: -0.0952
+2025-07-25 22:36:25: Iteration 670, Train Loss: 0.0007, Val: mae: 0.7118 rse: 1.5249 pcc: -0.2372 kcc: -0.2352
+2025-07-25 22:36:33: Iteration 680, Train Loss: 0.0012, Val: mae: 0.7139 rse: 1.5399 pcc: -0.2774 kcc: -0.2137
+2025-07-25 22:36:40: Iteration 690, Train Loss: 0.0009, Val: mae: 0.7143 rse: 1.5344 pcc: -0.2981 kcc: -0.2388
+2025-07-25 22:36:47: Iteration 700, Train Loss: 0.0012, Val: mae: 0.7043 rse: 1.4871 pcc: -0.2203 kcc: -0.1921
+2025-07-25 22:36:55: Iteration 710, Train Loss: 0.0013, Val: mae: 0.7053 rse: 1.4982 pcc: -0.2418 kcc: -0.1885
+2025-07-25 22:37:02: Iteration 720, Train Loss: 0.0013, Val: mae: 0.7099 rse: 1.5223 pcc: -0.2678 kcc: -0.1957
+2025-07-25 22:37:09: Iteration 730, Train Loss: 0.0010, Val: mae: 0.7090 rse: 1.5140 pcc: -0.2387 kcc: -0.2675
+2025-07-25 22:37:17: Iteration 740, Train Loss: 0.0008, Val: mae: 0.7123 rse: 1.5199 pcc: -0.2038 kcc: -0.1777
+2025-07-25 22:37:24: Iteration 750, Train Loss: 0.0006, Val: mae: 0.7039 rse: 1.5116 pcc: -0.1005 kcc: -0.0987
+2025-07-25 22:37:31: Iteration 760, Train Loss: 0.0015, Val: mae: 0.7094 rse: 1.4965 pcc: -0.1298 kcc: -0.1311
+2025-07-25 22:37:39: Iteration 770, Train Loss: 0.0011, Val: mae: 0.7088 rse: 1.4857 pcc: -0.1284 kcc: -0.0952
+2025-07-25 22:37:46: Iteration 780, Train Loss: 0.0030, Val: mae: 0.7099 rse: 1.5077 pcc: -0.2339 kcc: -0.2244
+2025-07-25 22:37:54: Iteration 790, Train Loss: 0.0013, Val: mae: 0.7142 rse: 1.5097 pcc: -0.3126 kcc: -0.2783
+2025-07-25 22:38:01: Iteration 800, Train Loss: 0.0009, Val: mae: 0.7129 rse: 1.5069 pcc: -0.3061 kcc: -0.2280
+2025-07-25 22:38:08: Iteration 810, Train Loss: 0.0017, Val: mae: 0.7143 rse: 1.5315 pcc: -0.2916 kcc: -0.1921
+2025-07-25 22:38:16: Iteration 820, Train Loss: 0.0007, Val: mae: 0.7133 rse: 1.5246 pcc: -0.3454 kcc: -0.2531
+2025-07-25 22:38:23: Iteration 830, Train Loss: 0.0009, Val: mae: 0.7182 rse: 1.5440 pcc: -0.3681 kcc: -0.2639
+2025-07-25 22:38:30: Iteration 840, Train Loss: 0.0006, Val: mae: 0.7194 rse: 1.5512 pcc: -0.3490 kcc: -0.2711
+2025-07-25 22:38:38: Iteration 850, Train Loss: 0.0014, Val: mae: 0.7151 rse: 1.5416 pcc: -0.3400 kcc: -0.2424
+2025-07-25 22:38:45: Iteration 860, Train Loss: 0.0008, Val: mae: 0.7160 rse: 1.5398 pcc: -0.4087 kcc: -0.2926
+2025-07-25 22:38:53: Iteration 870, Train Loss: 0.0011, Val: mae: 0.7121 rse: 1.5181 pcc: -0.3496 kcc: -0.2460
+2025-07-25 22:39:00: Iteration 880, Train Loss: 0.0008, Val: mae: 0.7051 rse: 1.4838 pcc: -0.1300 kcc: -0.1131
+2025-07-25 22:39:07: Iteration 890, Train Loss: 0.0007, Val: mae: 0.7025 rse: 1.4827 pcc: -0.1219 kcc: -0.0592
+2025-07-25 22:39:15: Iteration 900, Train Loss: 0.0010, Val: mae: 0.7044 rse: 1.4784 pcc: -0.1283 kcc: -0.1311
+2025-07-25 22:39:22: Iteration 910, Train Loss: 0.0011, Val: mae: 0.7095 rse: 1.5042 pcc: -0.3357 kcc: -0.2352
+2025-07-25 22:39:30: Iteration 920, Train Loss: 0.0009, Val: mae: 0.7114 rse: 1.5120 pcc: -0.3276 kcc: -0.2639
+2025-07-25 22:39:37: Iteration 930, Train Loss: 0.0008, Val: mae: 0.7134 rse: 1.5311 pcc: -0.3049 kcc: -0.2460
+2025-07-25 22:39:44: Iteration 940, Train Loss: 0.0006, Val: mae: 0.7143 rse: 1.5303 pcc: -0.3882 kcc: -0.2998
+2025-07-25 22:39:52: Iteration 950, Train Loss: 0.0007, Val: mae: 0.7082 rse: 1.5027 pcc: -0.2530 kcc: -0.1885
+2025-07-25 22:39:59: Iteration 960, Train Loss: 0.0007, Val: mae: 0.7032 rse: 1.4678 pcc: -0.0138 kcc: -0.0628
+2025-07-25 22:40:07: Iteration 970, Train Loss: 0.0009, Val: mae: 0.7113 rse: 1.4901 pcc: -0.2318 kcc: -0.2172
+2025-07-25 22:40:14: Iteration 980, Train Loss: 0.0009, Val: mae: 0.7051 rse: 1.4718 pcc: -0.1082 kcc: -0.1849
+2025-07-25 22:40:21: Iteration 990, Train Loss: 0.0012, Val: mae: 0.7097 rse: 1.4745 pcc: -0.0815 kcc: -0.1275
+2025-07-25 22:40:29: Iteration 1000, Train Loss: 0.0002, Val: mae: 0.7086 rse: 1.5044 pcc: -0.3362 kcc: -0.2208
+2025-07-25 22:40:29: Fold 1
+2025-07-25 22:40:38: Iteration 10, Train Loss: 1.0591, Val: mae: 0.9434 rse: 2.3467 pcc: 0.3090 kcc: 0.2172
+2025-07-25 22:40:38: NEW best validation iteration
+2025-07-25 22:40:45: Iteration 20, Train Loss: 0.5290, Val: mae: 0.8484 rse: 1.9917 pcc: 0.2585 kcc: 0.1526
+2025-07-25 22:40:45: NEW best validation iteration
+2025-07-25 22:40:52: Iteration 30, Train Loss: 0.4540, Val: mae: 0.8632 rse: 2.2440 pcc: 0.2786 kcc: 0.1670
+2025-07-25 22:41:00: Iteration 40, Train Loss: 0.3057, Val: mae: 0.9247 rse: 2.4707 pcc: 0.2247 kcc: 0.1526
+2025-07-25 22:41:07: Iteration 50, Train Loss: 0.2070, Val: mae: 1.0197 rse: 2.9876 pcc: 0.1915 kcc: 0.1167
+2025-07-25 22:41:14: Iteration 60, Train Loss: 0.2143, Val: mae: 0.9900 rse: 2.9270 pcc: 0.0794 kcc: 0.0557
+2025-07-25 22:41:21: Iteration 70, Train Loss: 0.1312, Val: mae: 0.8656 rse: 2.1552 pcc: -0.0294 kcc: -0.0090
+2025-07-25 22:41:29: Iteration 80, Train Loss: 0.1304, Val: mae: 0.8937 rse: 2.3589 pcc: -0.0512 kcc: -0.0377
+2025-07-25 22:41:36: Iteration 90, Train Loss: 0.0733, Val: mae: 0.8720 rse: 2.2759 pcc: -0.0325 kcc: -0.0269
+2025-07-25 22:41:43: Iteration 100, Train Loss: 0.0403, Val: mae: 0.8774 rse: 2.3386 pcc: -0.0903 kcc: -0.0628
+2025-07-25 22:41:51: Iteration 110, Train Loss: 0.0893, Val: mae: 0.8724 rse: 2.2853 pcc: -0.1523 kcc: -0.1023
+2025-07-25 22:41:58: Iteration 120, Train Loss: 0.0886, Val: mae: 0.9007 rse: 2.4605 pcc: -0.2251 kcc: -0.1490
+2025-07-25 22:42:05: Iteration 130, Train Loss: 0.0267, Val: mae: 0.8965 rse: 2.4796 pcc: -0.2231 kcc: -0.1598
+2025-07-25 22:42:12: Iteration 140, Train Loss: 0.0300, Val: mae: 0.8634 rse: 2.2873 pcc: -0.1996 kcc: -0.1526
+2025-07-25 22:42:20: Iteration 150, Train Loss: 0.0491, Val: mae: 0.8469 rse: 2.2289 pcc: -0.1845 kcc: -0.1275
+2025-07-25 22:42:27: Iteration 160, Train Loss: 0.0123, Val: mae: 0.8503 rse: 2.2598 pcc: -0.1143 kcc: -0.0952
+2025-07-25 22:42:34: Iteration 170, Train Loss: 0.0198, Val: mae: 0.8164 rse: 2.0399 pcc: -0.0888 kcc: -0.0628
+2025-07-25 22:42:42: Iteration 180, Train Loss: 0.0189, Val: mae: 0.8307 rse: 2.0974 pcc: 0.0577 kcc: 0.0233
+2025-07-25 22:42:49: Iteration 190, Train Loss: 0.0281, Val: mae: 0.7731 rse: 1.7813 pcc: 0.1355 kcc: 0.1059
+2025-07-25 22:42:49: NEW best validation iteration
+2025-07-25 22:42:56: Iteration 200, Train Loss: 0.0203, Val: mae: 0.7521 rse: 1.6213 pcc: 0.0863 kcc: 0.1167
+2025-07-25 22:42:56: NEW best validation iteration
+2025-07-25 22:43:04: Iteration 210, Train Loss: 0.0274, Val: mae: 0.7427 rse: 1.6029 pcc: 0.1108 kcc: 0.1382
+2025-07-25 22:43:04: NEW best validation iteration
+2025-07-25 22:43:11: Iteration 220, Train Loss: 0.0192, Val: mae: 0.7710 rse: 1.7456 pcc: 0.1799 kcc: 0.1347
+2025-07-25 22:43:18: Iteration 230, Train Loss: 0.0197, Val: mae: 0.7905 rse: 1.8451 pcc: 0.2897 kcc: 0.2316
+2025-07-25 22:43:26: Iteration 240, Train Loss: 0.0170, Val: mae: 0.7617 rse: 1.6991 pcc: 0.3378 kcc: 0.2172
+2025-07-25 22:43:26: NEW best validation iteration
+2025-07-25 22:43:33: Iteration 250, Train Loss: 0.0222, Val: mae: 0.7403 rse: 1.5810 pcc: 0.2617 kcc: 0.2316
+2025-07-25 22:43:33: NEW best validation iteration
+2025-07-25 22:43:40: Iteration 260, Train Loss: 0.0190, Val: mae: 0.7330 rse: 1.5376 pcc: 0.2827 kcc: 0.2065
+2025-07-25 22:43:40: NEW best validation iteration
+2025-07-25 22:43:48: Iteration 270, Train Loss: 0.0303, Val: mae: 0.7369 rse: 1.5748 pcc: 0.1790 kcc: 0.1418
+2025-07-25 22:43:55: Iteration 280, Train Loss: 0.0117, Val: mae: 0.7402 rse: 1.5736 pcc: 0.0657 kcc: 0.0664
+2025-07-25 22:44:02: Iteration 290, Train Loss: 0.0233, Val: mae: 0.7198 rse: 1.4860 pcc: 0.1844 kcc: 0.1813
+2025-07-25 22:44:09: Iteration 300, Train Loss: 0.0149, Val: mae: 0.7232 rse: 1.4879 pcc: 0.1247 kcc: 0.0952
+2025-07-25 22:44:17: Iteration 310, Train Loss: 0.0084, Val: mae: 0.7358 rse: 1.5534 pcc: 0.0192 kcc: 0.0413
+2025-07-25 22:44:24: Iteration 320, Train Loss: 0.0077, Val: mae: 0.7561 rse: 1.6975 pcc: 0.0750 kcc: 0.0916
+2025-07-25 22:44:31: Iteration 330, Train Loss: 0.0122, Val: mae: 0.7554 rse: 1.7149 pcc: -0.0251 kcc: 0.0054
+2025-07-25 22:44:39: Iteration 340, Train Loss: 0.0078, Val: mae: 0.7396 rse: 1.6026 pcc: -0.0231 kcc: -0.0377
+2025-07-25 22:44:46: Iteration 350, Train Loss: 0.0066, Val: mae: 0.7305 rse: 1.5497 pcc: 0.0531 kcc: 0.0305
+2025-07-25 22:44:53: Iteration 360, Train Loss: 0.0146, Val: mae: 0.7354 rse: 1.5672 pcc: -0.0262 kcc: -0.0018
+2025-07-25 22:45:00: Iteration 370, Train Loss: 0.0059, Val: mae: 0.7390 rse: 1.5966 pcc: 0.0259 kcc: 0.0377
+2025-07-25 22:45:08: Iteration 380, Train Loss: 0.0074, Val: mae: 0.7218 rse: 1.5532 pcc: 0.1133 kcc: 0.1095
+2025-07-25 22:45:15: Iteration 390, Train Loss: 0.0047, Val: mae: 0.7205 rse: 1.5269 pcc: 0.0776 kcc: 0.0772
+2025-07-25 22:45:22: Iteration 400, Train Loss: 0.0093, Val: mae: 0.7223 rse: 1.5381 pcc: 0.1982 kcc: 0.1813
+2025-07-25 22:45:29: Iteration 410, Train Loss: 0.0061, Val: mae: 0.7244 rse: 1.5427 pcc: 0.3656 kcc: 0.2388
+2025-07-25 22:45:29: NEW best validation iteration
+2025-07-25 22:45:37: Iteration 420, Train Loss: 0.0055, Val: mae: 0.7344 rse: 1.6021 pcc: 0.2289 kcc: 0.1526
+2025-07-25 22:45:44: Iteration 430, Train Loss: 0.0051, Val: mae: 0.7238 rse: 1.5689 pcc: 0.3729 kcc: 0.2460
+2025-07-25 22:45:51: Iteration 440, Train Loss: 0.0035, Val: mae: 0.7255 rse: 1.5816 pcc: 0.2444 kcc: 0.2208
+2025-07-25 22:45:59: Iteration 450, Train Loss: 0.0043, Val: mae: 0.7259 rse: 1.5678 pcc: 0.1832 kcc: 0.1670
+2025-07-25 22:46:06: Iteration 460, Train Loss: 0.0058, Val: mae: 0.7222 rse: 1.5424 pcc: -0.0484 kcc: -0.0233
+2025-07-25 22:46:13: Iteration 470, Train Loss: 0.0042, Val: mae: 0.7205 rse: 1.5533 pcc: -0.0643 kcc: -0.0269
+2025-07-25 22:46:21: Iteration 480, Train Loss: 0.0033, Val: mae: 0.7185 rse: 1.5402 pcc: 0.0121 kcc: 0.0736
+2025-07-25 22:46:28: Iteration 490, Train Loss: 0.0018, Val: mae: 0.7183 rse: 1.5475 pcc: 0.1610 kcc: 0.1562
+2025-07-25 22:46:35: Iteration 500, Train Loss: 0.0038, Val: mae: 0.7293 rse: 1.6057 pcc: 0.1451 kcc: 0.1885
+2025-07-25 22:46:42: Iteration 510, Train Loss: 0.0027, Val: mae: 0.7226 rse: 1.5904 pcc: 0.2457 kcc: 0.2388
+2025-07-25 22:46:50: Iteration 520, Train Loss: 0.0017, Val: mae: 0.7200 rse: 1.5770 pcc: 0.1872 kcc: 0.2316
+2025-07-25 22:46:57: Iteration 530, Train Loss: 0.0019, Val: mae: 0.7246 rse: 1.5886 pcc: 0.0893 kcc: 0.0700
+2025-07-25 22:47:04: Iteration 540, Train Loss: 0.0040, Val: mae: 0.7192 rse: 1.5695 pcc: 0.2814 kcc: 0.2244
+2025-07-25 22:47:12: Iteration 550, Train Loss: 0.0016, Val: mae: 0.7091 rse: 1.5212 pcc: 0.1406 kcc: 0.1347
+2025-07-25 22:47:19: Iteration 560, Train Loss: 0.0052, Val: mae: 0.7134 rse: 1.5345 pcc: 0.1610 kcc: 0.1382
+2025-07-25 22:47:26: Iteration 570, Train Loss: 0.0006, Val: mae: 0.7178 rse: 1.5585 pcc: 0.3942 kcc: 0.3142
+2025-07-25 22:47:26: NEW best validation iteration
+2025-07-25 22:47:33: Iteration 580, Train Loss: 0.0021, Val: mae: 0.7187 rse: 1.5616 pcc: 0.3520 kcc: 0.2424
+2025-07-25 22:47:41: Iteration 590, Train Loss: 0.0017, Val: mae: 0.7166 rse: 1.5680 pcc: 0.3138 kcc: 0.2675
+2025-07-25 22:47:48: Iteration 600, Train Loss: 0.0008, Val: mae: 0.7209 rse: 1.5838 pcc: 0.3118 kcc: 0.2603
+2025-07-25 22:47:55: Iteration 610, Train Loss: 0.0010, Val: mae: 0.7179 rse: 1.5722 pcc: 0.3856 kcc: 0.3070
+2025-07-25 22:48:03: Iteration 620, Train Loss: 0.0013, Val: mae: 0.7213 rse: 1.5708 pcc: 0.2262 kcc: 0.2603
+2025-07-25 22:48:10: Iteration 630, Train Loss: 0.0017, Val: mae: 0.7121 rse: 1.5458 pcc: 0.2580 kcc: 0.3286
+2025-07-25 22:48:17: Iteration 640, Train Loss: 0.0014, Val: mae: 0.7189 rse: 1.5559 pcc: 0.0353 kcc: 0.0736
+2025-07-25 22:48:25: Iteration 650, Train Loss: 0.0021, Val: mae: 0.7206 rse: 1.5712 pcc: -0.0491 kcc: -0.0233
+2025-07-25 22:48:32: Iteration 660, Train Loss: 0.0013, Val: mae: 0.7171 rse: 1.5625 pcc: -0.0275 kcc: 0.0233
+2025-07-25 22:48:39: Iteration 670, Train Loss: 0.0023, Val: mae: 0.7148 rse: 1.5669 pcc: 0.0860 kcc: 0.1239
+2025-07-25 22:48:47: Iteration 680, Train Loss: 0.0019, Val: mae: 0.7223 rse: 1.6062 pcc: 0.0391 kcc: 0.0700
+2025-07-25 22:48:54: Iteration 690, Train Loss: 0.0011, Val: mae: 0.7197 rse: 1.5867 pcc: 0.2423 kcc: 0.2316
+2025-07-25 22:49:01: Iteration 700, Train Loss: 0.0037, Val: mae: 0.7214 rse: 1.5769 pcc: 0.2892 kcc: 0.2065
+2025-07-25 22:49:08: Iteration 710, Train Loss: 0.0011, Val: mae: 0.7234 rse: 1.5917 pcc: 0.3158 kcc: 0.2460
+2025-07-25 22:49:16: Iteration 720, Train Loss: 0.0009, Val: mae: 0.7171 rse: 1.5817 pcc: 0.1548 kcc: 0.1347
+2025-07-25 22:49:23: Iteration 730, Train Loss: 0.0011, Val: mae: 0.7229 rse: 1.5814 pcc: 0.0151 kcc: 0.0521
+2025-07-25 22:49:30: Iteration 740, Train Loss: 0.0007, Val: mae: 0.7213 rse: 1.5780 pcc: 0.0934 kcc: 0.1239
+2025-07-25 22:49:38: Iteration 750, Train Loss: 0.0012, Val: mae: 0.7214 rse: 1.5616 pcc: 0.2002 kcc: 0.1849
+2025-07-25 22:49:45: Iteration 760, Train Loss: 0.0018, Val: mae: 0.7211 rse: 1.5815 pcc: -0.0475 kcc: 0.0664
+2025-07-25 22:49:52: Iteration 770, Train Loss: 0.0007, Val: mae: 0.7176 rse: 1.5492 pcc: 0.1784 kcc: 0.2101
+2025-07-25 22:50:00: Iteration 780, Train Loss: 0.0007, Val: mae: 0.7111 rse: 1.5389 pcc: 0.1039 kcc: 0.1634
+2025-07-25 22:50:07: Iteration 790, Train Loss: 0.0009, Val: mae: 0.7112 rse: 1.5481 pcc: 0.2058 kcc: 0.2101
+2025-07-25 22:50:14: Iteration 800, Train Loss: 0.0012, Val: mae: 0.7188 rse: 1.5811 pcc: 0.0689 kcc: 0.1167
+2025-07-25 22:50:21: Iteration 810, Train Loss: 0.0012, Val: mae: 0.7199 rse: 1.5853 pcc: -0.0146 kcc: -0.0090
+2025-07-25 22:50:29: Iteration 820, Train Loss: 0.0009, Val: mae: 0.7204 rse: 1.5839 pcc: -0.1060 kcc: 0.0018
+2025-07-25 22:50:36: Iteration 830, Train Loss: 0.0018, Val: mae: 0.7242 rse: 1.5826 pcc: -0.0975 kcc: -0.0377
+2025-07-25 22:50:43: Iteration 840, Train Loss: 0.0015, Val: mae: 0.7150 rse: 1.5562 pcc: 0.1892 kcc: 0.2101
+2025-07-25 22:50:51: Iteration 850, Train Loss: 0.0010, Val: mae: 0.7113 rse: 1.5268 pcc: 0.3494 kcc: 0.2101
+2025-07-25 22:50:58: Iteration 860, Train Loss: 0.0011, Val: mae: 0.7179 rse: 1.5658 pcc: 0.2636 kcc: 0.1849
+2025-07-25 22:51:05: Iteration 870, Train Loss: 0.0016, Val: mae: 0.7190 rse: 1.5721 pcc: 0.3038 kcc: 0.1311
+2025-07-25 22:51:13: Iteration 880, Train Loss: 0.0010, Val: mae: 0.7141 rse: 1.5690 pcc: 0.3953 kcc: 0.2783
+2025-07-25 22:51:20: Iteration 890, Train Loss: 0.0015, Val: mae: 0.7210 rse: 1.5867 pcc: 0.2597 kcc: 0.1957
+2025-07-25 22:51:27: Iteration 900, Train Loss: 0.0012, Val: mae: 0.7203 rse: 1.5792 pcc: 0.0914 kcc: 0.0916
+2025-07-25 22:51:34: Iteration 910, Train Loss: 0.0008, Val: mae: 0.7233 rse: 1.5838 pcc: 0.1131 kcc: 0.1742
+2025-07-25 22:51:42: Iteration 920, Train Loss: 0.0006, Val: mae: 0.7164 rse: 1.5740 pcc: 0.2179 kcc: 0.2208
+2025-07-25 22:51:49: Iteration 930, Train Loss: 0.0009, Val: mae: 0.7193 rse: 1.5748 pcc: 0.2084 kcc: 0.1849
+2025-07-25 22:51:56: Iteration 940, Train Loss: 0.0009, Val: mae: 0.7199 rse: 1.5682 pcc: 0.2399 kcc: 0.2352
+2025-07-25 22:52:04: Iteration 950, Train Loss: 0.0007, Val: mae: 0.7232 rse: 1.5822 pcc: 0.0201 kcc: 0.0880
+2025-07-25 22:52:11: Iteration 960, Train Loss: 0.0011, Val: mae: 0.7218 rse: 1.5860 pcc: 0.0729 kcc: 0.0736
+2025-07-25 22:52:18: Iteration 970, Train Loss: 0.0008, Val: mae: 0.7256 rse: 1.6026 pcc: -0.0593 kcc: -0.0054
+2025-07-25 22:52:25: Iteration 980, Train Loss: 0.0014, Val: mae: 0.7217 rse: 1.5751 pcc: -0.0282 kcc: 0.0341
+2025-07-25 22:52:33: Iteration 990, Train Loss: 0.0006, Val: mae: 0.7221 rse: 1.5979 pcc: -0.1649 kcc: -0.1095
+2025-07-25 22:52:40: Iteration 1000, Train Loss: 0.0009, Val: mae: 0.7208 rse: 1.5718 pcc: 0.0302 kcc: 0.0664
+2025-07-25 22:52:40: Fold 2
+2025-07-25 22:52:49: Iteration 10, Train Loss: 1.7232, Val: mae: 0.7259 rse: 1.3910 pcc: 0.4424 kcc: 0.2388
+2025-07-25 22:52:49: NEW best validation iteration
+2025-07-25 22:52:56: Iteration 20, Train Loss: 0.6162, Val: mae: 0.7560 rse: 1.4413 pcc: 0.5151 kcc: 0.2998
+2025-07-25 22:52:56: NEW best validation iteration
+2025-07-25 22:53:03: Iteration 30, Train Loss: 0.2742, Val: mae: 0.7574 rse: 1.5505 pcc: 0.5729 kcc: 0.3393
+2025-07-25 22:53:11: Iteration 40, Train Loss: 0.2461, Val: mae: 0.8638 rse: 1.9429 pcc: 0.5255 kcc: 0.3214
+2025-07-25 22:53:18: Iteration 50, Train Loss: 0.2281, Val: mae: 0.8982 rse: 2.1523 pcc: 0.4299 kcc: 0.2711
+2025-07-25 22:53:25: Iteration 60, Train Loss: 0.1068, Val: mae: 0.9101 rse: 2.2378 pcc: 0.4265 kcc: 0.2567
+2025-07-25 22:53:33: Iteration 70, Train Loss: 0.1536, Val: mae: 0.9383 rse: 2.4361 pcc: 0.3719 kcc: 0.2424
+2025-07-25 22:53:40: Iteration 80, Train Loss: 0.0628, Val: mae: 0.9138 rse: 2.3310 pcc: 0.4040 kcc: 0.3034
+2025-07-25 22:53:47: Iteration 90, Train Loss: 0.0573, Val: mae: 0.8654 rse: 2.2520 pcc: 0.3003 kcc: 0.2137
+2025-07-25 22:53:55: Iteration 100, Train Loss: 0.0491, Val: mae: 0.8302 rse: 2.1371 pcc: 0.2347 kcc: 0.1777
+2025-07-25 22:54:02: Iteration 110, Train Loss: 0.1610, Val: mae: 0.8299 rse: 2.1186 pcc: 0.2387 kcc: 0.1490
+2025-07-25 22:54:09: Iteration 120, Train Loss: 0.0624, Val: mae: 0.7808 rse: 1.9057 pcc: 0.3196 kcc: 0.2029
+2025-07-25 22:54:16: Iteration 130, Train Loss: 0.0323, Val: mae: 0.7548 rse: 1.7637 pcc: 0.4201 kcc: 0.3034
+2025-07-25 22:54:24: Iteration 140, Train Loss: 0.0456, Val: mae: 0.7354 rse: 1.6854 pcc: 0.4633 kcc: 0.3250
+2025-07-25 22:54:31: Iteration 150, Train Loss: 0.0572, Val: mae: 0.7419 rse: 1.6813 pcc: 0.5287 kcc: 0.3645
+2025-07-25 22:54:38: Iteration 160, Train Loss: 0.0953, Val: mae: 0.7394 rse: 1.6752 pcc: 0.5038 kcc: 0.3250
+2025-07-25 22:54:46: Iteration 170, Train Loss: 0.0320, Val: mae: 0.7165 rse: 1.5512 pcc: 0.5687 kcc: 0.3896
+2025-07-25 22:54:46: NEW best validation iteration
+2025-07-25 22:54:53: Iteration 180, Train Loss: 0.0420, Val: mae: 0.7143 rse: 1.5265 pcc: 0.5832 kcc: 0.4076
+2025-07-25 22:54:53: NEW best validation iteration
+2025-07-25 22:55:00: Iteration 190, Train Loss: 0.0171, Val: mae: 0.7348 rse: 1.6317 pcc: 0.5804 kcc: 0.3716
+2025-07-25 22:55:08: Iteration 200, Train Loss: 0.0199, Val: mae: 0.7192 rse: 1.5785 pcc: 0.5642 kcc: 0.4004
+2025-07-25 22:55:15: Iteration 210, Train Loss: 0.0161, Val: mae: 0.7127 rse: 1.5802 pcc: 0.5504 kcc: 0.3968
+2025-07-25 22:55:22: Iteration 220, Train Loss: 0.0090, Val: mae: 0.6792 rse: 1.4245 pcc: 0.6086 kcc: 0.4327
+2025-07-25 22:55:22: NEW best validation iteration
+2025-07-25 22:55:29: Iteration 230, Train Loss: 0.0148, Val: mae: 0.6795 rse: 1.4466 pcc: 0.5861 kcc: 0.4542
+2025-07-25 22:55:37: Iteration 240, Train Loss: 0.0261, Val: mae: 0.7013 rse: 1.4966 pcc: 0.6205 kcc: 0.4865
+2025-07-25 22:55:44: Iteration 250, Train Loss: 0.0085, Val: mae: 0.7043 rse: 1.5283 pcc: 0.6139 kcc: 0.4722
+2025-07-25 22:55:51: Iteration 260, Train Loss: 0.0216, Val: mae: 0.6963 rse: 1.5314 pcc: 0.6051 kcc: 0.4291
+2025-07-25 22:55:59: Iteration 270, Train Loss: 0.0118, Val: mae: 0.6988 rse: 1.5371 pcc: 0.5639 kcc: 0.4183
+2025-07-25 22:56:06: Iteration 280, Train Loss: 0.0205, Val: mae: 0.6998 rse: 1.5607 pcc: 0.4160 kcc: 0.3250
+2025-07-25 22:56:13: Iteration 290, Train Loss: 0.0175, Val: mae: 0.6961 rse: 1.5210 pcc: 0.3991 kcc: 0.2998
+2025-07-25 22:56:21: Iteration 300, Train Loss: 0.0117, Val: mae: 0.6909 rse: 1.4789 pcc: 0.4747 kcc: 0.3321
+2025-07-25 22:56:28: Iteration 310, Train Loss: 0.0083, Val: mae: 0.6895 rse: 1.4456 pcc: 0.4756 kcc: 0.3429
+2025-07-25 22:56:35: Iteration 320, Train Loss: 0.0040, Val: mae: 0.6800 rse: 1.4165 pcc: 0.4968 kcc: 0.3429
+2025-07-25 22:56:42: Iteration 330, Train Loss: 0.0105, Val: mae: 0.6937 rse: 1.4976 pcc: 0.4472 kcc: 0.3142
+2025-07-25 22:56:50: Iteration 340, Train Loss: 0.0113, Val: mae: 0.6894 rse: 1.4944 pcc: 0.5414 kcc: 0.3752
+2025-07-25 22:56:57: Iteration 350, Train Loss: 0.0201, Val: mae: 0.6783 rse: 1.4593 pcc: 0.5414 kcc: 0.3932
+2025-07-25 22:57:04: Iteration 360, Train Loss: 0.0087, Val: mae: 0.6802 rse: 1.4432 pcc: 0.5520 kcc: 0.4076
+2025-07-25 22:57:12: Iteration 370, Train Loss: 0.0077, Val: mae: 0.6763 rse: 1.4491 pcc: 0.4868 kcc: 0.3896
+2025-07-25 22:57:19: Iteration 380, Train Loss: 0.0095, Val: mae: 0.6573 rse: 1.3504 pcc: 0.5151 kcc: 0.4111
+2025-07-25 22:57:26: Iteration 390, Train Loss: 0.0033, Val: mae: 0.6679 rse: 1.3823 pcc: 0.5454 kcc: 0.4435
+2025-07-25 22:57:26: NEW best validation iteration
+2025-07-25 22:57:34: Iteration 400, Train Loss: 0.0087, Val: mae: 0.6827 rse: 1.4170 pcc: 0.5132 kcc: 0.3968
+2025-07-25 22:57:41: Iteration 410, Train Loss: 0.0060, Val: mae: 0.7042 rse: 1.5154 pcc: 0.4220 kcc: 0.3357
+2025-07-25 22:57:48: Iteration 420, Train Loss: 0.0071, Val: mae: 0.6972 rse: 1.4927 pcc: 0.3809 kcc: 0.3070
+2025-07-25 22:57:55: Iteration 430, Train Loss: 0.0050, Val: mae: 0.6917 rse: 1.4669 pcc: 0.4159 kcc: 0.3250
+2025-07-25 22:58:03: Iteration 440, Train Loss: 0.0075, Val: mae: 0.6854 rse: 1.4211 pcc: 0.4847 kcc: 0.3968
+2025-07-25 22:58:10: Iteration 450, Train Loss: 0.0071, Val: mae: 0.6858 rse: 1.4329 pcc: 0.4644 kcc: 0.3824
+2025-07-25 22:58:17: Iteration 460, Train Loss: 0.0221, Val: mae: 0.6829 rse: 1.4072 pcc: 0.5205 kcc: 0.4076
+2025-07-25 22:58:25: Iteration 470, Train Loss: 0.0038, Val: mae: 0.6653 rse: 1.3545 pcc: 0.4666 kcc: 0.3932
+2025-07-25 22:58:32: Iteration 480, Train Loss: 0.0053, Val: mae: 0.6795 rse: 1.3717 pcc: 0.5114 kcc: 0.4111
+2025-07-25 22:58:39: Iteration 490, Train Loss: 0.0043, Val: mae: 0.6878 rse: 1.4011 pcc: 0.4103 kcc: 0.3321
+2025-07-25 22:58:46: Iteration 500, Train Loss: 0.0043, Val: mae: 0.6894 rse: 1.4088 pcc: 0.4100 kcc: 0.3106
+2025-07-25 22:58:54: Iteration 510, Train Loss: 0.0037, Val: mae: 0.6996 rse: 1.4660 pcc: 0.3097 kcc: 0.2639
+2025-07-25 22:59:01: Iteration 520, Train Loss: 0.0030, Val: mae: 0.6983 rse: 1.4975 pcc: 0.2459 kcc: 0.1993
+2025-07-25 22:59:08: Iteration 530, Train Loss: 0.0031, Val: mae: 0.6902 rse: 1.4523 pcc: 0.3115 kcc: 0.2603
+2025-07-25 22:59:16: Iteration 540, Train Loss: 0.0029, Val: mae: 0.6936 rse: 1.4745 pcc: 0.3145 kcc: 0.2496
+2025-07-25 22:59:23: Iteration 550, Train Loss: 0.0027, Val: mae: 0.6969 rse: 1.4697 pcc: 0.3020 kcc: 0.2783
+2025-07-25 22:59:30: Iteration 560, Train Loss: 0.0033, Val: mae: 0.6887 rse: 1.4340 pcc: 0.3790 kcc: 0.2891
+2025-07-25 22:59:38: Iteration 570, Train Loss: 0.0035, Val: mae: 0.6865 rse: 1.4207 pcc: 0.4502 kcc: 0.3609
+2025-07-25 22:59:45: Iteration 580, Train Loss: 0.0015, Val: mae: 0.6812 rse: 1.3822 pcc: 0.5047 kcc: 0.3645
+2025-07-25 22:59:52: Iteration 590, Train Loss: 0.0040, Val: mae: 0.6875 rse: 1.3941 pcc: 0.4689 kcc: 0.3537
+2025-07-25 22:59:59: Iteration 600, Train Loss: 0.0024, Val: mae: 0.6940 rse: 1.4026 pcc: 0.4057 kcc: 0.2926
+2025-07-25 23:00:07: Iteration 610, Train Loss: 0.0007, Val: mae: 0.6858 rse: 1.3923 pcc: 0.3842 kcc: 0.2855
+2025-07-25 23:00:14: Iteration 620, Train Loss: 0.0017, Val: mae: 0.7000 rse: 1.4326 pcc: 0.3986 kcc: 0.2819
+2025-07-25 23:00:21: Iteration 630, Train Loss: 0.0037, Val: mae: 0.7008 rse: 1.4509 pcc: 0.3769 kcc: 0.2603
+2025-07-25 23:00:29: Iteration 640, Train Loss: 0.0042, Val: mae: 0.6970 rse: 1.4397 pcc: 0.4153 kcc: 0.2962
+2025-07-25 23:00:36: Iteration 650, Train Loss: 0.0017, Val: mae: 0.6979 rse: 1.4438 pcc: 0.4508 kcc: 0.3393
+2025-07-25 23:00:43: Iteration 660, Train Loss: 0.0046, Val: mae: 0.7088 rse: 1.4754 pcc: 0.3212 kcc: 0.2675
+2025-07-25 23:00:51: Iteration 670, Train Loss: 0.0023, Val: mae: 0.7151 rse: 1.5138 pcc: 0.2491 kcc: 0.2280
+2025-07-25 23:00:58: Iteration 680, Train Loss: 0.0022, Val: mae: 0.7149 rse: 1.5291 pcc: 0.2385 kcc: 0.2029
+2025-07-25 23:01:05: Iteration 690, Train Loss: 0.0006, Val: mae: 0.7115 rse: 1.4994 pcc: 0.2244 kcc: 0.2244
+2025-07-25 23:01:13: Iteration 700, Train Loss: 0.0022, Val: mae: 0.6989 rse: 1.4390 pcc: 0.3123 kcc: 0.2711
+2025-07-25 23:01:20: Iteration 710, Train Loss: 0.0018, Val: mae: 0.7109 rse: 1.4974 pcc: 0.2626 kcc: 0.2137
+2025-07-25 23:01:27: Iteration 720, Train Loss: 0.0007, Val: mae: 0.7140 rse: 1.5114 pcc: 0.2791 kcc: 0.1993
+2025-07-25 23:01:34: Iteration 730, Train Loss: 0.0013, Val: mae: 0.7128 rse: 1.5282 pcc: 0.2278 kcc: 0.1598
+2025-07-25 23:01:42: Iteration 740, Train Loss: 0.0017, Val: mae: 0.7197 rse: 1.5431 pcc: 0.1447 kcc: 0.1203
+2025-07-25 23:01:49: Iteration 750, Train Loss: 0.0010, Val: mae: 0.7219 rse: 1.5227 pcc: 0.1992 kcc: 0.1598
+2025-07-25 23:01:56: Iteration 760, Train Loss: 0.0014, Val: mae: 0.7079 rse: 1.4894 pcc: 0.3589 kcc: 0.3070
+2025-07-25 23:02:04: Iteration 770, Train Loss: 0.0013, Val: mae: 0.7189 rse: 1.5532 pcc: 0.2007 kcc: 0.1957
+2025-07-25 23:02:11: Iteration 780, Train Loss: 0.0020, Val: mae: 0.7151 rse: 1.5255 pcc: 0.2625 kcc: 0.2244
+2025-07-25 23:02:18: Iteration 790, Train Loss: 0.0010, Val: mae: 0.7026 rse: 1.4780 pcc: 0.4006 kcc: 0.2531
+2025-07-25 23:02:26: Iteration 800, Train Loss: 0.0012, Val: mae: 0.7056 rse: 1.4884 pcc: 0.2973 kcc: 0.2065
+2025-07-25 23:02:33: Iteration 810, Train Loss: 0.0018, Val: mae: 0.7051 rse: 1.4778 pcc: 0.2650 kcc: 0.2101
+2025-07-25 23:02:40: Iteration 820, Train Loss: 0.0016, Val: mae: 0.7013 rse: 1.4526 pcc: 0.3040 kcc: 0.1993
+2025-07-25 23:02:47: Iteration 830, Train Loss: 0.0008, Val: mae: 0.7013 rse: 1.4617 pcc: 0.3904 kcc: 0.2962
+2025-07-25 23:02:55: Iteration 840, Train Loss: 0.0009, Val: mae: 0.6998 rse: 1.4741 pcc: 0.3808 kcc: 0.2891
+2025-07-25 23:03:02: Iteration 850, Train Loss: 0.0013, Val: mae: 0.7074 rse: 1.5006 pcc: 0.3073 kcc: 0.2316
+2025-07-25 23:03:09: Iteration 860, Train Loss: 0.0009, Val: mae: 0.7155 rse: 1.5246 pcc: 0.2067 kcc: 0.1526
+2025-07-25 23:03:17: Iteration 870, Train Loss: 0.0006, Val: mae: 0.7167 rse: 1.5385 pcc: 0.1468 kcc: 0.1275
+2025-07-25 23:03:24: Iteration 880, Train Loss: 0.0019, Val: mae: 0.7232 rse: 1.5685 pcc: 0.0144 kcc: 0.0018
+2025-07-25 23:03:31: Iteration 890, Train Loss: 0.0022, Val: mae: 0.7184 rse: 1.5412 pcc: 0.1417 kcc: 0.1167
+2025-07-25 23:03:38: Iteration 900, Train Loss: 0.0014, Val: mae: 0.7123 rse: 1.5256 pcc: 0.1548 kcc: 0.0987
+2025-07-25 23:03:46: Iteration 910, Train Loss: 0.0023, Val: mae: 0.7147 rse: 1.5198 pcc: 0.2583 kcc: 0.1706
+2025-07-25 23:03:53: Iteration 920, Train Loss: 0.0015, Val: mae: 0.7199 rse: 1.5431 pcc: 0.1683 kcc: 0.1167
+2025-07-25 23:04:00: Iteration 930, Train Loss: 0.0016, Val: mae: 0.7142 rse: 1.5197 pcc: 0.2127 kcc: 0.1921
+2025-07-25 23:04:08: Iteration 940, Train Loss: 0.0010, Val: mae: 0.7117 rse: 1.5164 pcc: 0.2697 kcc: 0.1742
+2025-07-25 23:04:15: Iteration 950, Train Loss: 0.0013, Val: mae: 0.7114 rse: 1.5169 pcc: 0.2731 kcc: 0.2208
+2025-07-25 23:04:22: Iteration 960, Train Loss: 0.0011, Val: mae: 0.7130 rse: 1.5228 pcc: 0.2864 kcc: 0.1921
+2025-07-25 23:04:30: Iteration 970, Train Loss: 0.0014, Val: mae: 0.7203 rse: 1.5453 pcc: 0.1449 kcc: 0.1203
+2025-07-25 23:04:37: Iteration 980, Train Loss: 0.0017, Val: mae: 0.7125 rse: 1.5169 pcc: 0.1797 kcc: 0.1418
+2025-07-25 23:04:44: Iteration 990, Train Loss: 0.0014, Val: mae: 0.7147 rse: 1.5318 pcc: 0.1781 kcc: 0.1347
+2025-07-25 23:04:51: Iteration 1000, Train Loss: 0.0011, Val: mae: 0.7126 rse: 1.5252 pcc: 0.2106 kcc: 0.1239
+2025-07-25 23:04:51: Fold 3
+2025-07-25 23:05:00: Iteration 10, Train Loss: 3.2193, Val: mae: 0.7265 rse: 1.2760 pcc: 0.5873 kcc: 0.3501
+2025-07-25 23:05:00: NEW best validation iteration
+2025-07-25 23:05:08: Iteration 20, Train Loss: 2.0378, Val: mae: 0.6731 rse: 1.4406 pcc: 0.5705 kcc: 0.3142
+2025-07-25 23:05:15: Iteration 30, Train Loss: 0.5821, Val: mae: 0.8164 rse: 1.8856 pcc: 0.5955 kcc: 0.3681
+2025-07-25 23:05:22: Iteration 40, Train Loss: 0.6055, Val: mae: 0.8981 rse: 2.1696 pcc: 0.5738 kcc: 0.3716
+2025-07-25 23:05:29: Iteration 50, Train Loss: 0.5518, Val: mae: 1.0057 rse: 2.7186 pcc: 0.5172 kcc: 0.3178
+2025-07-25 23:05:37: Iteration 60, Train Loss: 0.3696, Val: mae: 1.0489 rse: 2.8792 pcc: 0.4768 kcc: 0.2603
+2025-07-25 23:05:44: Iteration 70, Train Loss: 0.5271, Val: mae: 1.0183 rse: 2.7113 pcc: 0.4841 kcc: 0.2711
+2025-07-25 23:05:51: Iteration 80, Train Loss: 0.5576, Val: mae: 1.0037 rse: 2.6485 pcc: 0.4961 kcc: 0.2639
+2025-07-25 23:05:59: Iteration 90, Train Loss: 0.1832, Val: mae: 0.9692 rse: 2.5103 pcc: 0.4868 kcc: 0.2496
+2025-07-25 23:06:06: Iteration 100, Train Loss: 0.5169, Val: mae: 0.8713 rse: 2.0895 pcc: 0.5243 kcc: 0.2819
+2025-07-25 23:06:13: Iteration 110, Train Loss: 0.1328, Val: mae: 0.9377 rse: 2.3222 pcc: 0.5397 kcc: 0.2998
+2025-07-25 23:06:21: Iteration 120, Train Loss: 0.2048, Val: mae: 1.0139 rse: 2.6653 pcc: 0.5264 kcc: 0.2711
+2025-07-25 23:06:28: Iteration 130, Train Loss: 0.2401, Val: mae: 1.0254 rse: 2.7552 pcc: 0.4828 kcc: 0.2531
+2025-07-25 23:06:35: Iteration 140, Train Loss: 0.1312, Val: mae: 1.0020 rse: 2.5907 pcc: 0.5460 kcc: 0.3034
+2025-07-25 23:06:42: Iteration 150, Train Loss: 0.2018, Val: mae: 0.9350 rse: 2.3731 pcc: 0.4721 kcc: 0.2783
+2025-07-25 23:06:50: Iteration 160, Train Loss: 0.1144, Val: mae: 0.9026 rse: 2.1596 pcc: 0.5006 kcc: 0.2639
+2025-07-25 23:06:57: Iteration 170, Train Loss: 0.1211, Val: mae: 0.8699 rse: 2.0318 pcc: 0.5386 kcc: 0.3142
+2025-07-25 23:07:04: Iteration 180, Train Loss: 0.1449, Val: mae: 0.8970 rse: 2.1471 pcc: 0.5255 kcc: 0.2998
+2025-07-25 23:07:12: Iteration 190, Train Loss: 0.0602, Val: mae: 0.9374 rse: 2.3212 pcc: 0.4845 kcc: 0.2926
+2025-07-25 23:07:19: Iteration 200, Train Loss: 0.0860, Val: mae: 0.9534 rse: 2.4623 pcc: 0.4188 kcc: 0.2603
+2025-07-25 23:07:26: Iteration 210, Train Loss: 0.1689, Val: mae: 0.9264 rse: 2.3664 pcc: 0.3639 kcc: 0.1885
+2025-07-25 23:07:33: Iteration 220, Train Loss: 0.1744, Val: mae: 0.9003 rse: 2.2758 pcc: 0.4216 kcc: 0.2029
+2025-07-25 23:07:41: Iteration 230, Train Loss: 0.1020, Val: mae: 0.9010 rse: 2.2553 pcc: 0.4393 kcc: 0.2316
+2025-07-25 23:07:48: Iteration 240, Train Loss: 0.0909, Val: mae: 0.8797 rse: 2.1393 pcc: 0.4039 kcc: 0.2208
+2025-07-25 23:07:55: Iteration 250, Train Loss: 0.0456, Val: mae: 0.8237 rse: 1.9391 pcc: 0.4190 kcc: 0.2998
+2025-07-25 23:08:03: Iteration 260, Train Loss: 0.0372, Val: mae: 0.8174 rse: 1.9386 pcc: 0.4281 kcc: 0.2962
+2025-07-25 23:08:10: Iteration 270, Train Loss: 0.0714, Val: mae: 0.8036 rse: 1.8789 pcc: 0.4109 kcc: 0.3034
+2025-07-25 23:08:17: Iteration 280, Train Loss: 0.0835, Val: mae: 0.7814 rse: 1.8093 pcc: 0.4202 kcc: 0.3357
+2025-07-25 23:08:24: Iteration 290, Train Loss: 0.0483, Val: mae: 0.7907 rse: 1.8088 pcc: 0.4565 kcc: 0.2926
+2025-07-25 23:08:32: Iteration 300, Train Loss: 0.0566, Val: mae: 0.8160 rse: 1.9206 pcc: 0.3923 kcc: 0.3321
+2025-07-25 23:08:39: Iteration 310, Train Loss: 0.0730, Val: mae: 0.8198 rse: 1.9645 pcc: 0.3822 kcc: 0.3106
+2025-07-25 23:08:46: Iteration 320, Train Loss: 0.0730, Val: mae: 0.8149 rse: 2.0130 pcc: 0.2909 kcc: 0.2603
+2025-07-25 23:08:54: Iteration 330, Train Loss: 0.0488, Val: mae: 0.8327 rse: 2.0377 pcc: 0.3673 kcc: 0.2567
+2025-07-25 23:09:01: Iteration 340, Train Loss: 0.0662, Val: mae: 0.8156 rse: 1.9285 pcc: 0.4819 kcc: 0.3034
+2025-07-25 23:09:08: Iteration 350, Train Loss: 0.0492, Val: mae: 0.8263 rse: 1.9409 pcc: 0.5269 kcc: 0.3752
+2025-07-25 23:09:15: Iteration 360, Train Loss: 0.0455, Val: mae: 0.8645 rse: 2.1060 pcc: 0.4562 kcc: 0.2891
+2025-07-25 23:09:23: Iteration 370, Train Loss: 0.0386, Val: mae: 0.8288 rse: 1.9151 pcc: 0.5098 kcc: 0.3429
+2025-07-25 23:09:30: Iteration 380, Train Loss: 0.0552, Val: mae: 0.8198 rse: 1.8966 pcc: 0.5074 kcc: 0.3286
+2025-07-25 23:09:37: Iteration 390, Train Loss: 0.0473, Val: mae: 0.8179 rse: 1.8798 pcc: 0.5259 kcc: 0.3070
+2025-07-25 23:09:45: Iteration 400, Train Loss: 0.0420, Val: mae: 0.7898 rse: 1.7735 pcc: 0.5633 kcc: 0.3752
+2025-07-25 23:09:52: Iteration 410, Train Loss: 0.1141, Val: mae: 0.7810 rse: 1.7967 pcc: 0.5036 kcc: 0.3393
+2025-07-25 23:09:59: Iteration 420, Train Loss: 0.0619, Val: mae: 0.7741 rse: 1.7713 pcc: 0.4100 kcc: 0.3142
+2025-07-25 23:10:06: Iteration 430, Train Loss: 0.0422, Val: mae: 0.7521 rse: 1.7446 pcc: 0.4143 kcc: 0.3537
+2025-07-25 23:10:14: Iteration 440, Train Loss: 0.0338, Val: mae: 0.7544 rse: 1.7588 pcc: 0.3990 kcc: 0.3034
+2025-07-25 23:10:21: Iteration 450, Train Loss: 0.0326, Val: mae: 0.7808 rse: 1.8467 pcc: 0.3657 kcc: 0.2460
+2025-07-25 23:10:28: Iteration 460, Train Loss: 0.0567, Val: mae: 0.8133 rse: 1.9317 pcc: 0.3576 kcc: 0.2675
+2025-07-25 23:10:36: Iteration 470, Train Loss: 0.0145, Val: mae: 0.7778 rse: 1.8203 pcc: 0.3616 kcc: 0.2783
+2025-07-25 23:10:43: Iteration 480, Train Loss: 0.0216, Val: mae: 0.7568 rse: 1.7478 pcc: 0.4306 kcc: 0.2855
+2025-07-25 23:10:50: Iteration 490, Train Loss: 0.0659, Val: mae: 0.7729 rse: 1.7844 pcc: 0.4773 kcc: 0.3393
+2025-07-25 23:10:57: Iteration 500, Train Loss: 0.0205, Val: mae: 0.7818 rse: 1.8317 pcc: 0.4282 kcc: 0.3393
+2025-07-25 23:11:05: Iteration 510, Train Loss: 0.0469, Val: mae: 0.7580 rse: 1.7573 pcc: 0.3780 kcc: 0.3214
+2025-07-25 23:11:12: Iteration 520, Train Loss: 0.0285, Val: mae: 0.7870 rse: 1.8643 pcc: 0.3702 kcc: 0.2855
+2025-07-25 23:11:19: Iteration 530, Train Loss: 0.0399, Val: mae: 0.7519 rse: 1.7594 pcc: 0.4280 kcc: 0.2783
+2025-07-25 23:11:27: Iteration 540, Train Loss: 0.0259, Val: mae: 0.7509 rse: 1.7469 pcc: 0.3974 kcc: 0.2567
+2025-07-25 23:11:34: Iteration 550, Train Loss: 0.0178, Val: mae: 0.7666 rse: 1.7882 pcc: 0.3531 kcc: 0.2352
+2025-07-25 23:11:41: Iteration 560, Train Loss: 0.0209, Val: mae: 0.7813 rse: 1.8548 pcc: 0.3644 kcc: 0.2531
+2025-07-25 23:11:48: Iteration 570, Train Loss: 0.0189, Val: mae: 0.7841 rse: 1.8503 pcc: 0.4156 kcc: 0.2352
+2025-07-25 23:11:56: Iteration 580, Train Loss: 0.0139, Val: mae: 0.7687 rse: 1.7836 pcc: 0.4332 kcc: 0.2639
+2025-07-25 23:12:03: Iteration 590, Train Loss: 0.0298, Val: mae: 0.7487 rse: 1.7191 pcc: 0.4018 kcc: 0.2316
+2025-07-25 23:12:10: Iteration 600, Train Loss: 0.0392, Val: mae: 0.7189 rse: 1.6195 pcc: 0.4699 kcc: 0.2891
+2025-07-25 23:12:18: Iteration 610, Train Loss: 0.0102, Val: mae: 0.7215 rse: 1.6236 pcc: 0.4254 kcc: 0.2208
+2025-07-25 23:12:25: Iteration 620, Train Loss: 0.0280, Val: mae: 0.7168 rse: 1.6044 pcc: 0.4726 kcc: 0.2675
+2025-07-25 23:12:32: Iteration 630, Train Loss: 0.0119, Val: mae: 0.7225 rse: 1.5991 pcc: 0.4424 kcc: 0.2639
+2025-07-25 23:12:39: Iteration 640, Train Loss: 0.0206, Val: mae: 0.7012 rse: 1.5366 pcc: 0.4463 kcc: 0.2208
+2025-07-25 23:12:47: Iteration 650, Train Loss: 0.0081, Val: mae: 0.6956 rse: 1.5096 pcc: 0.4640 kcc: 0.2531
+2025-07-25 23:12:54: Iteration 660, Train Loss: 0.0109, Val: mae: 0.6660 rse: 1.3917 pcc: 0.5085 kcc: 0.2998
+2025-07-25 23:13:01: Iteration 670, Train Loss: 0.0074, Val: mae: 0.6556 rse: 1.3572 pcc: 0.5061 kcc: 0.3034
+2025-07-25 23:13:09: Iteration 680, Train Loss: 0.0095, Val: mae: 0.6534 rse: 1.3176 pcc: 0.5110 kcc: 0.3034
+2025-07-25 23:13:16: Iteration 690, Train Loss: 0.0035, Val: mae: 0.6420 rse: 1.2904 pcc: 0.4885 kcc: 0.2998
+2025-07-25 23:13:23: Iteration 700, Train Loss: 0.0109, Val: mae: 0.6296 rse: 1.2240 pcc: 0.5281 kcc: 0.2711
+2025-07-25 23:13:23: NEW best validation iteration
+2025-07-25 23:13:31: Iteration 710, Train Loss: 0.0040, Val: mae: 0.6447 rse: 1.2843 pcc: 0.4967 kcc: 0.2819
+2025-07-25 23:13:38: Iteration 720, Train Loss: 0.0115, Val: mae: 0.6374 rse: 1.2515 pcc: 0.5076 kcc: 0.2316
+2025-07-25 23:13:45: Iteration 730, Train Loss: 0.0193, Val: mae: 0.6284 rse: 1.2385 pcc: 0.4440 kcc: 0.2567
+2025-07-25 23:13:53: Iteration 740, Train Loss: 0.0081, Val: mae: 0.6376 rse: 1.2546 pcc: 0.4886 kcc: 0.2603
+2025-07-25 23:14:00: Iteration 750, Train Loss: 0.0065, Val: mae: 0.6467 rse: 1.3042 pcc: 0.4622 kcc: 0.2352
+2025-07-25 23:14:07: Iteration 760, Train Loss: 0.0113, Val: mae: 0.6427 rse: 1.2767 pcc: 0.4863 kcc: 0.2208
+2025-07-25 23:14:15: Iteration 770, Train Loss: 0.0068, Val: mae: 0.6474 rse: 1.2847 pcc: 0.5038 kcc: 0.2675
+2025-07-25 23:14:22: Iteration 780, Train Loss: 0.0107, Val: mae: 0.6409 rse: 1.2476 pcc: 0.5008 kcc: 0.2855
+2025-07-25 23:14:29: Iteration 790, Train Loss: 0.0074, Val: mae: 0.6344 rse: 1.2208 pcc: 0.4358 kcc: 0.2567
+2025-07-25 23:14:36: Iteration 800, Train Loss: 0.0031, Val: mae: 0.6213 rse: 1.1590 pcc: 0.4964 kcc: 0.3106
+2025-07-25 23:14:36: NEW best validation iteration
+2025-07-25 23:14:44: Iteration 810, Train Loss: 0.0090, Val: mae: 0.6237 rse: 1.1532 pcc: 0.4935 kcc: 0.2962
+2025-07-25 23:14:51: Iteration 820, Train Loss: 0.0135, Val: mae: 0.6184 rse: 1.1523 pcc: 0.5065 kcc: 0.3250
+2025-07-25 23:14:51: NEW best validation iteration
+2025-07-25 23:14:58: Iteration 830, Train Loss: 0.0066, Val: mae: 0.6346 rse: 1.2087 pcc: 0.4722 kcc: 0.2747
+2025-07-25 23:15:06: Iteration 840, Train Loss: 0.0036, Val: mae: 0.6571 rse: 1.3124 pcc: 0.4212 kcc: 0.2137
+2025-07-25 23:15:13: Iteration 850, Train Loss: 0.0098, Val: mae: 0.6496 rse: 1.2943 pcc: 0.4897 kcc: 0.2388
+2025-07-25 23:15:20: Iteration 860, Train Loss: 0.0031, Val: mae: 0.6459 rse: 1.2656 pcc: 0.4730 kcc: 0.2316
+2025-07-25 23:15:28: Iteration 870, Train Loss: 0.0052, Val: mae: 0.6434 rse: 1.2706 pcc: 0.4406 kcc: 0.2675
+2025-07-25 23:15:35: Iteration 880, Train Loss: 0.0061, Val: mae: 0.6286 rse: 1.2113 pcc: 0.4188 kcc: 0.2388
+2025-07-25 23:15:42: Iteration 890, Train Loss: 0.0043, Val: mae: 0.6326 rse: 1.2050 pcc: 0.4426 kcc: 0.2496
+2025-07-25 23:15:49: Iteration 900, Train Loss: 0.0089, Val: mae: 0.6369 rse: 1.2331 pcc: 0.4735 kcc: 0.2460
+2025-07-25 23:15:57: Iteration 910, Train Loss: 0.0031, Val: mae: 0.6412 rse: 1.2548 pcc: 0.4449 kcc: 0.2280
+2025-07-25 23:16:04: Iteration 920, Train Loss: 0.0067, Val: mae: 0.6388 rse: 1.2474 pcc: 0.4281 kcc: 0.2244
+2025-07-25 23:16:11: Iteration 930, Train Loss: 0.0040, Val: mae: 0.6344 rse: 1.2248 pcc: 0.4809 kcc: 0.2567
+2025-07-25 23:16:19: Iteration 940, Train Loss: 0.0081, Val: mae: 0.6330 rse: 1.2037 pcc: 0.4467 kcc: 0.2388
+2025-07-25 23:16:26: Iteration 950, Train Loss: 0.0035, Val: mae: 0.6398 rse: 1.2551 pcc: 0.4651 kcc: 0.2675
+2025-07-25 23:16:33: Iteration 960, Train Loss: 0.0023, Val: mae: 0.6498 rse: 1.2868 pcc: 0.4983 kcc: 0.2962
+2025-07-25 23:16:40: Iteration 970, Train Loss: 0.0043, Val: mae: 0.6525 rse: 1.3194 pcc: 0.4032 kcc: 0.2352
+2025-07-25 23:16:48: Iteration 980, Train Loss: 0.0027, Val: mae: 0.6527 rse: 1.3100 pcc: 0.3728 kcc: 0.2819
+2025-07-25 23:16:55: Iteration 990, Train Loss: 0.0031, Val: mae: 0.6615 rse: 1.3397 pcc: 0.4257 kcc: 0.2855
+2025-07-25 23:17:02: Iteration 1000, Train Loss: 0.0036, Val: mae: 0.6627 rse: 1.3424 pcc: 0.4073 kcc: 0.2460
+2025-07-25 23:17:02: Fold 4
+2025-07-25 23:17:12: Iteration 10, Train Loss: 1.4555, Val: mae: 1.3521 rse: 4.4963 pcc: 0.4997 kcc: 0.3501
+2025-07-25 23:17:12: NEW best validation iteration
+2025-07-25 23:17:19: Iteration 20, Train Loss: 0.7723, Val: mae: 1.6769 rse: 6.2286 pcc: 0.5924 kcc: 0.4865
+2025-07-25 23:17:26: Iteration 30, Train Loss: 0.8579, Val: mae: 1.8640 rse: 7.5368 pcc: 0.6243 kcc: 0.5009
+2025-07-25 23:17:33: Iteration 40, Train Loss: 0.5523, Val: mae: 1.5369 rse: 5.2999 pcc: 0.5730 kcc: 0.4650
+2025-07-25 23:17:41: Iteration 50, Train Loss: 0.3648, Val: mae: 1.5552 rse: 5.3225 pcc: 0.5717 kcc: 0.4901
+2025-07-25 23:17:48: Iteration 60, Train Loss: 0.2342, Val: mae: 1.5743 rse: 5.4556 pcc: 0.5565 kcc: 0.4399
+2025-07-25 23:17:55: Iteration 70, Train Loss: 0.1728, Val: mae: 1.6608 rse: 6.0242 pcc: 0.5392 kcc: 0.4470
+2025-07-25 23:18:03: Iteration 80, Train Loss: 0.2015, Val: mae: 1.5117 rse: 5.1527 pcc: 0.4715 kcc: 0.3573
+2025-07-25 23:18:10: Iteration 90, Train Loss: 0.1079, Val: mae: 1.4979 rse: 5.0330 pcc: 0.5319 kcc: 0.3716
+2025-07-25 23:18:17: Iteration 100, Train Loss: 0.0616, Val: mae: 1.4361 rse: 4.6925 pcc: 0.5202 kcc: 0.3716
+2025-07-25 23:18:25: Iteration 110, Train Loss: 0.0817, Val: mae: 1.2849 rse: 3.9727 pcc: 0.4084 kcc: 0.2926
+2025-07-25 23:18:25: NEW best validation iteration
+2025-07-25 23:18:32: Iteration 120, Train Loss: 0.0874, Val: mae: 1.2032 rse: 3.5768 pcc: 0.4052 kcc: 0.2567
+2025-07-25 23:18:32: NEW best validation iteration
+2025-07-25 23:18:39: Iteration 130, Train Loss: 0.0659, Val: mae: 1.1438 rse: 3.2938 pcc: 0.4102 kcc: 0.2998
+2025-07-25 23:18:39: NEW best validation iteration
+2025-07-25 23:18:47: Iteration 140, Train Loss: 0.0638, Val: mae: 1.1352 rse: 3.2075 pcc: 0.4620 kcc: 0.3178
+2025-07-25 23:18:47: NEW best validation iteration
+2025-07-25 23:18:54: Iteration 150, Train Loss: 0.1231, Val: mae: 1.1604 rse: 3.2966 pcc: 0.4974 kcc: 0.3609
+2025-07-25 23:19:01: Iteration 160, Train Loss: 0.0859, Val: mae: 1.0194 rse: 2.6728 pcc: 0.5286 kcc: 0.4004
+2025-07-25 23:19:01: NEW best validation iteration
+2025-07-25 23:19:09: Iteration 170, Train Loss: 0.0587, Val: mae: 0.9644 rse: 2.5103 pcc: 0.4098 kcc: 0.2675
+2025-07-25 23:19:16: Iteration 180, Train Loss: 0.0282, Val: mae: 0.9274 rse: 2.3378 pcc: 0.3761 kcc: 0.2244
+2025-07-25 23:19:16: NEW best validation iteration
+2025-07-25 23:19:23: Iteration 190, Train Loss: 0.0514, Val: mae: 0.9634 rse: 2.4707 pcc: 0.4267 kcc: 0.2711
+2025-07-25 23:19:30: Iteration 200, Train Loss: 0.0961, Val: mae: 0.9602 rse: 2.4474 pcc: 0.4547 kcc: 0.3250
+2025-07-25 23:19:30: NEW best validation iteration
+2025-07-25 23:19:38: Iteration 210, Train Loss: 0.0276, Val: mae: 0.9219 rse: 2.3257 pcc: 0.4368 kcc: 0.3178
+2025-07-25 23:19:38: NEW best validation iteration
+2025-07-25 23:19:45: Iteration 220, Train Loss: 0.0183, Val: mae: 0.8496 rse: 2.0292 pcc: 0.4506 kcc: 0.3034
+2025-07-25 23:19:45: NEW best validation iteration
+2025-07-25 23:19:53: Iteration 230, Train Loss: 0.0435, Val: mae: 0.8135 rse: 1.9044 pcc: 0.4275 kcc: 0.2531
+2025-07-25 23:19:53: NEW best validation iteration
+2025-07-25 23:20:00: Iteration 240, Train Loss: 0.0193, Val: mae: 0.7954 rse: 1.8192 pcc: 0.4047 kcc: 0.2567
+2025-07-25 23:20:00: NEW best validation iteration
+2025-07-25 23:20:07: Iteration 250, Train Loss: 0.0170, Val: mae: 0.7782 rse: 1.7608 pcc: 0.5034 kcc: 0.3429
+2025-07-25 23:20:07: NEW best validation iteration
+2025-07-25 23:20:15: Iteration 260, Train Loss: 0.0185, Val: mae: 0.7743 rse: 1.7434 pcc: 0.4912 kcc: 0.3357
+2025-07-25 23:20:15: NEW best validation iteration
+2025-07-25 23:20:22: Iteration 270, Train Loss: 0.0256, Val: mae: 0.7498 rse: 1.6493 pcc: 0.4925 kcc: 0.3178
+2025-07-25 23:20:22: NEW best validation iteration
+2025-07-25 23:20:29: Iteration 280, Train Loss: 0.0093, Val: mae: 0.7462 rse: 1.6474 pcc: 0.4387 kcc: 0.2603
+2025-07-25 23:20:37: Iteration 290, Train Loss: 0.0122, Val: mae: 0.7273 rse: 1.5503 pcc: 0.4868 kcc: 0.3357
+2025-07-25 23:20:37: NEW best validation iteration
+2025-07-25 23:20:44: Iteration 300, Train Loss: 0.0063, Val: mae: 0.7174 rse: 1.5255 pcc: 0.4742 kcc: 0.2998
+2025-07-25 23:20:51: Iteration 310, Train Loss: 0.0096, Val: mae: 0.7114 rse: 1.5134 pcc: 0.4299 kcc: 0.2747
+2025-07-25 23:20:59: Iteration 320, Train Loss: 0.0046, Val: mae: 0.7038 rse: 1.4881 pcc: 0.4822 kcc: 0.3178
+2025-07-25 23:20:59: NEW best validation iteration
+2025-07-25 23:21:06: Iteration 330, Train Loss: 0.0081, Val: mae: 0.7009 rse: 1.4793 pcc: 0.4797 kcc: 0.3106
+2025-07-25 23:21:06: NEW best validation iteration
+2025-07-25 23:21:13: Iteration 340, Train Loss: 0.0039, Val: mae: 0.7008 rse: 1.4813 pcc: 0.4967 kcc: 0.3357
+2025-07-25 23:21:13: NEW best validation iteration
+2025-07-25 23:21:21: Iteration 350, Train Loss: 0.0089, Val: mae: 0.6961 rse: 1.4538 pcc: 0.4968 kcc: 0.2962
+2025-07-25 23:21:28: Iteration 360, Train Loss: 0.0033, Val: mae: 0.6963 rse: 1.4399 pcc: 0.5135 kcc: 0.3034
+2025-07-25 23:21:28: NEW best validation iteration
+2025-07-25 23:21:35: Iteration 370, Train Loss: 0.0082, Val: mae: 0.6898 rse: 1.4376 pcc: 0.4962 kcc: 0.3214
+2025-07-25 23:21:35: NEW best validation iteration
+2025-07-25 23:21:43: Iteration 380, Train Loss: 0.0044, Val: mae: 0.6904 rse: 1.4336 pcc: 0.4940 kcc: 0.2926
+2025-07-25 23:21:50: Iteration 390, Train Loss: 0.0023, Val: mae: 0.6880 rse: 1.4130 pcc: 0.5135 kcc: 0.3106
+2025-07-25 23:21:50: NEW best validation iteration
+2025-07-25 23:21:57: Iteration 400, Train Loss: 0.0070, Val: mae: 0.6852 rse: 1.4039 pcc: 0.5215 kcc: 0.3178
+2025-07-25 23:21:57: NEW best validation iteration
+2025-07-25 23:22:05: Iteration 410, Train Loss: 0.0029, Val: mae: 0.6865 rse: 1.4026 pcc: 0.5116 kcc: 0.3070
+2025-07-25 23:22:12: Iteration 420, Train Loss: 0.0030, Val: mae: 0.6838 rse: 1.3743 pcc: 0.5141 kcc: 0.3106
+2025-07-25 23:22:12: NEW best validation iteration
+2025-07-25 23:22:19: Iteration 430, Train Loss: 0.0025, Val: mae: 0.6779 rse: 1.3833 pcc: 0.5669 kcc: 0.3537
+2025-07-25 23:22:19: NEW best validation iteration
+2025-07-25 23:22:27: Iteration 440, Train Loss: 0.0062, Val: mae: 0.6789 rse: 1.3778 pcc: 0.5326 kcc: 0.3609
+2025-07-25 23:22:34: Iteration 450, Train Loss: 0.0028, Val: mae: 0.6773 rse: 1.3653 pcc: 0.5303 kcc: 0.3429
+2025-07-25 23:22:41: Iteration 460, Train Loss: 0.0021, Val: mae: 0.6680 rse: 1.3501 pcc: 0.5627 kcc: 0.3716
+2025-07-25 23:22:41: NEW best validation iteration
+2025-07-25 23:22:49: Iteration 470, Train Loss: 0.0023, Val: mae: 0.6764 rse: 1.3574 pcc: 0.5659 kcc: 0.3716
+2025-07-25 23:22:56: Iteration 480, Train Loss: 0.0033, Val: mae: 0.6865 rse: 1.4055 pcc: 0.5195 kcc: 0.3286
+2025-07-25 23:23:03: Iteration 490, Train Loss: 0.0013, Val: mae: 0.6857 rse: 1.4161 pcc: 0.4551 kcc: 0.2783
+2025-07-25 23:23:10: Iteration 500, Train Loss: 0.0028, Val: mae: 0.6826 rse: 1.4035 pcc: 0.5140 kcc: 0.3465
+2025-07-25 23:23:18: Iteration 510, Train Loss: 0.0019, Val: mae: 0.6834 rse: 1.4035 pcc: 0.5037 kcc: 0.2926
+2025-07-25 23:23:25: Iteration 520, Train Loss: 0.0023, Val: mae: 0.6814 rse: 1.4078 pcc: 0.4206 kcc: 0.2891
+2025-07-25 23:23:32: Iteration 530, Train Loss: 0.0022, Val: mae: 0.6784 rse: 1.3844 pcc: 0.5493 kcc: 0.3393
+2025-07-25 23:23:40: Iteration 540, Train Loss: 0.0012, Val: mae: 0.6805 rse: 1.3805 pcc: 0.5140 kcc: 0.3250
+2025-07-25 23:23:47: Iteration 550, Train Loss: 0.0016, Val: mae: 0.6743 rse: 1.3533 pcc: 0.6216 kcc: 0.4040
+2025-07-25 23:23:47: NEW best validation iteration
+2025-07-25 23:23:54: Iteration 560, Train Loss: 0.0012, Val: mae: 0.6830 rse: 1.3965 pcc: 0.5152 kcc: 0.3537
+2025-07-25 23:24:02: Iteration 570, Train Loss: 0.0013, Val: mae: 0.6752 rse: 1.3802 pcc: 0.5319 kcc: 0.3681
+2025-07-25 23:24:09: Iteration 580, Train Loss: 0.0020, Val: mae: 0.6791 rse: 1.3966 pcc: 0.5193 kcc: 0.3537
+2025-07-25 23:24:16: Iteration 590, Train Loss: 0.0015, Val: mae: 0.6831 rse: 1.4032 pcc: 0.5075 kcc: 0.2998
+2025-07-25 23:24:24: Iteration 600, Train Loss: 0.0013, Val: mae: 0.6749 rse: 1.3809 pcc: 0.6015 kcc: 0.4183
+2025-07-25 23:24:31: Iteration 610, Train Loss: 0.0014, Val: mae: 0.6817 rse: 1.4049 pcc: 0.4601 kcc: 0.2783
+2025-07-25 23:24:38: Iteration 620, Train Loss: 0.0011, Val: mae: 0.6865 rse: 1.4143 pcc: 0.4385 kcc: 0.2891
+2025-07-25 23:24:45: Iteration 630, Train Loss: 0.0018, Val: mae: 0.6843 rse: 1.4002 pcc: 0.5475 kcc: 0.3752
+2025-07-25 23:24:53: Iteration 640, Train Loss: 0.0015, Val: mae: 0.6834 rse: 1.4140 pcc: 0.4831 kcc: 0.3178
+2025-07-25 23:25:00: Iteration 650, Train Loss: 0.0019, Val: mae: 0.6865 rse: 1.4221 pcc: 0.4484 kcc: 0.3106
+2025-07-25 23:25:07: Iteration 660, Train Loss: 0.0008, Val: mae: 0.6834 rse: 1.4045 pcc: 0.4884 kcc: 0.3178
+2025-07-25 23:25:15: Iteration 670, Train Loss: 0.0012, Val: mae: 0.6839 rse: 1.4005 pcc: 0.5005 kcc: 0.3214
+2025-07-25 23:25:22: Iteration 680, Train Loss: 0.0023, Val: mae: 0.6809 rse: 1.4008 pcc: 0.5159 kcc: 0.3393
+2025-07-25 23:25:29: Iteration 690, Train Loss: 0.0015, Val: mae: 0.6869 rse: 1.4224 pcc: 0.4370 kcc: 0.2747
+2025-07-25 23:25:37: Iteration 700, Train Loss: 0.0011, Val: mae: 0.6865 rse: 1.4097 pcc: 0.4528 kcc: 0.2783
+2025-07-25 23:25:44: Iteration 710, Train Loss: 0.0005, Val: mae: 0.6879 rse: 1.4097 pcc: 0.4817 kcc: 0.2962
+2025-07-25 23:25:51: Iteration 720, Train Loss: 0.0018, Val: mae: 0.6816 rse: 1.3988 pcc: 0.5314 kcc: 0.3357
+2025-07-25 23:25:59: Iteration 730, Train Loss: 0.0009, Val: mae: 0.6848 rse: 1.4063 pcc: 0.4013 kcc: 0.2388
+2025-07-25 23:26:06: Iteration 740, Train Loss: 0.0006, Val: mae: 0.6827 rse: 1.3988 pcc: 0.4006 kcc: 0.2567
+2025-07-25 23:26:13: Iteration 750, Train Loss: 0.0006, Val: mae: 0.6835 rse: 1.4065 pcc: 0.3593 kcc: 0.1885
+2025-07-25 23:26:21: Iteration 760, Train Loss: 0.0011, Val: mae: 0.6815 rse: 1.3927 pcc: 0.4185 kcc: 0.2747
+2025-07-25 23:26:28: Iteration 770, Train Loss: 0.0005, Val: mae: 0.6868 rse: 1.4114 pcc: 0.3412 kcc: 0.2029
+2025-07-25 23:26:35: Iteration 780, Train Loss: 0.0011, Val: mae: 0.6817 rse: 1.3949 pcc: 0.4860 kcc: 0.3142
+2025-07-25 23:26:43: Iteration 790, Train Loss: 0.0012, Val: mae: 0.6837 rse: 1.4104 pcc: 0.4161 kcc: 0.2531
+2025-07-25 23:26:50: Iteration 800, Train Loss: 0.0017, Val: mae: 0.6848 rse: 1.4059 pcc: 0.4577 kcc: 0.2747
+2025-07-25 23:26:57: Iteration 810, Train Loss: 0.0009, Val: mae: 0.6890 rse: 1.4162 pcc: 0.3639 kcc: 0.2460
+2025-07-25 23:27:04: Iteration 820, Train Loss: 0.0011, Val: mae: 0.6860 rse: 1.4062 pcc: 0.3650 kcc: 0.2101
+2025-07-25 23:27:12: Iteration 830, Train Loss: 0.0009, Val: mae: 0.6777 rse: 1.3804 pcc: 0.5595 kcc: 0.3681
+2025-07-25 23:27:19: Iteration 840, Train Loss: 0.0008, Val: mae: 0.6817 rse: 1.3958 pcc: 0.4382 kcc: 0.2639
+2025-07-25 23:27:26: Iteration 850, Train Loss: 0.0009, Val: mae: 0.6874 rse: 1.4167 pcc: 0.4222 kcc: 0.2926
+2025-07-25 23:27:34: Iteration 860, Train Loss: 0.0005, Val: mae: 0.6916 rse: 1.4333 pcc: 0.2415 kcc: 0.0952
+2025-07-25 23:27:41: Iteration 870, Train Loss: 0.0010, Val: mae: 0.6935 rse: 1.4263 pcc: 0.2720 kcc: 0.1526
+2025-07-25 23:27:48: Iteration 880, Train Loss: 0.0008, Val: mae: 0.6890 rse: 1.4173 pcc: 0.4339 kcc: 0.2460
+2025-07-25 23:27:56: Iteration 890, Train Loss: 0.0009, Val: mae: 0.6809 rse: 1.3885 pcc: 0.5311 kcc: 0.4040
+2025-07-25 23:28:03: Iteration 900, Train Loss: 0.0009, Val: mae: 0.6813 rse: 1.3960 pcc: 0.4778 kcc: 0.3501
+2025-07-25 23:28:10: Iteration 910, Train Loss: 0.0014, Val: mae: 0.6793 rse: 1.3937 pcc: 0.5756 kcc: 0.4004
+2025-07-25 23:28:17: Iteration 920, Train Loss: 0.0008, Val: mae: 0.6871 rse: 1.4017 pcc: 0.4568 kcc: 0.2926
+2025-07-25 23:28:25: Iteration 930, Train Loss: 0.0008, Val: mae: 0.6825 rse: 1.4015 pcc: 0.4579 kcc: 0.2891
+2025-07-25 23:28:32: Iteration 940, Train Loss: 0.0006, Val: mae: 0.6853 rse: 1.4081 pcc: 0.3996 kcc: 0.2424
+2025-07-25 23:28:39: Iteration 950, Train Loss: 0.0006, Val: mae: 0.6826 rse: 1.4002 pcc: 0.4764 kcc: 0.3286
+2025-07-25 23:28:47: Iteration 960, Train Loss: 0.0007, Val: mae: 0.6826 rse: 1.3927 pcc: 0.4614 kcc: 0.3034
+2025-07-25 23:28:54: Iteration 970, Train Loss: 0.0006, Val: mae: 0.6846 rse: 1.4027 pcc: 0.3895 kcc: 0.2531
+2025-07-25 23:29:01: Iteration 980, Train Loss: 0.0010, Val: mae: 0.6854 rse: 1.4043 pcc: 0.4407 kcc: 0.3034
+2025-07-25 23:29:08: Iteration 990, Train Loss: 0.0009, Val: mae: 0.6900 rse: 1.4226 pcc: 0.2307 kcc: 0.1634
+2025-07-25 23:29:16: Iteration 1000, Train Loss: 0.0013, Val: mae: 0.6891 rse: 1.4219 pcc: 0.2672 kcc: 0.1706
+2025-07-25 23:29:16: SFDA Tuning Finished!
+2025-07-25 23:29:16: Best validation perform list
+[[0.69688427 1.4314419  0.32431367 0.21724078]
+ [0.71784693 1.55850708 0.39415872 0.31419122]
+ [0.66787606 1.38231206 0.54537761 0.44345847]
+ [0.61836267 1.15233195 0.50647682 0.32496348]
+ [0.67433196 1.35328197 0.62155199 0.40396014]]
+2025-07-25 23:29:16: mean: [0.6751 1.3756 0.4784 0.3408]
+2025-07-25 23:29:16: std: [0.0334 0.1319 0.1064 0.0784]
diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/training.log b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..b90396b64f0fe2331bab26a0eb0564eea80ba566
--- /dev/null
+++ b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/training.log
@@ -0,0 +1,693 @@
+2025-07-19 17:38:24: saving_dir: ./run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50
+2025-07-19 17:38:24: Loading Training Dataset
+2025-07-19 17:38:49: Loading Test Dataset
+2025-07-19 17:38:52: Running Cross Validation 0
+2025-07-19 17:38:52: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-19 17:39:25: Epoch: 001 Train Loss: 2.311, mae: 0.978, rse: 0.655, pcc: 0.601, kcc: 0.441
+2025-07-19 17:39:25: Epoch: 001 New best VALIDATION metrics
+2025-07-19 17:39:26: Epoch: 001 Test results, ap: mae: 0.752, rse: 1.586, pcc: 0.018, kcc: 0.007
+2025-07-19 17:39:56: Epoch: 002 Train Loss: 1.734, mae: 0.902, rse: 0.559, pcc: 0.672, kcc: 0.466
+2025-07-19 17:39:56: Epoch: 002 New best VALIDATION metrics
+2025-07-19 17:39:57: Epoch: 002 Test results, ap: mae: 0.696, rse: 1.409, pcc: 0.091, kcc: 0.083
+2025-07-19 17:40:27: Epoch: 003 Train Loss: 1.345, mae: 0.747, rse: 0.414, pcc: 0.778, kcc: 0.573
+2025-07-19 17:40:27: Epoch: 003 New best VALIDATION metrics
+2025-07-19 17:40:28: Epoch: 003 Test results, ap: mae: 0.789, rse: 1.803, pcc: -0.143, kcc: -0.123
+2025-07-19 17:40:58: Epoch: 004 Train Loss: 1.119, mae: 0.785, rse: 0.433, pcc: 0.757, kcc: 0.582
+2025-07-19 17:40:59: Epoch: 004 Test results, ap: mae: 0.872, rse: 2.182, pcc: -0.075, kcc: -0.118
+2025-07-19 17:41:29: Epoch: 005 Train Loss: 0.941, mae: 0.732, rse: 0.364, pcc: 0.803, kcc: 0.608
+2025-07-19 17:41:29: Epoch: 005 New best VALIDATION metrics
+2025-07-19 17:41:31: Epoch: 005 Test results, ap: mae: 0.868, rse: 2.148, pcc: -0.075, kcc: -0.080
+2025-07-19 17:42:01: Epoch: 006 Train Loss: 0.761, mae: 0.595, rse: 0.254, pcc: 0.865, kcc: 0.647
+2025-07-19 17:42:01: Epoch: 006 New best VALIDATION metrics
+2025-07-19 17:42:02: Epoch: 006 Test results, ap: mae: 0.886, rse: 2.269, pcc: -0.071, kcc: -0.046
+2025-07-19 17:42:31: Epoch: 007 Train Loss: 0.615, mae: 0.534, rse: 0.214, pcc: 0.888, kcc: 0.702
+2025-07-19 17:42:31: Epoch: 007 New best VALIDATION metrics
+2025-07-19 17:42:33: Epoch: 007 Test results, ap: mae: 1.037, rse: 3.175, pcc: -0.150, kcc: -0.082
+2025-07-19 17:43:03: Epoch: 008 Train Loss: 0.554, mae: 0.492, rse: 0.207, pcc: 0.892, kcc: 0.718
+2025-07-19 17:43:03: Epoch: 008 New best VALIDATION metrics
+2025-07-19 17:43:04: Epoch: 008 Test results, ap: mae: 0.923, rse: 2.470, pcc: -0.121, kcc: -0.071
+2025-07-19 17:43:34: Epoch: 009 Train Loss: 0.442, mae: 0.506, rse: 0.201, pcc: 0.901, kcc: 0.714
+2025-07-19 17:43:35: Epoch: 009 Test results, ap: mae: 0.893, rse: 2.267, pcc: 0.018, kcc: -0.014
+2025-07-19 17:44:05: Epoch: 010 Train Loss: 0.451, mae: 0.434, rse: 0.166, pcc: 0.915, kcc: 0.737
+2025-07-19 17:44:05: Epoch: 010 New best VALIDATION metrics
+2025-07-19 17:44:07: Epoch: 010 Test results, ap: mae: 0.926, rse: 2.408, pcc: 0.091, kcc: 0.049
+2025-07-19 17:44:37: Epoch: 011 Train Loss: 0.304, mae: 0.361, rse: 0.128, pcc: 0.934, kcc: 0.774
+2025-07-19 17:44:37: Epoch: 011 New best VALIDATION metrics
+2025-07-19 17:44:38: Epoch: 011 Test results, ap: mae: 0.933, rse: 2.422, pcc: -0.026, kcc: -0.026
+2025-07-19 17:44:38: Epoch: 011 New best TEST metrics
+2025-07-19 17:45:09: Epoch: 012 Train Loss: 0.214, mae: 0.355, rse: 0.115, pcc: 0.941, kcc: 0.780
+2025-07-19 17:45:09: Epoch: 012 New best VALIDATION metrics
+2025-07-19 17:45:10: Epoch: 012 Test results, ap: mae: 0.914, rse: 2.361, pcc: -0.002, kcc: -0.012
+2025-07-19 17:45:10: Epoch: 012 New best TEST metrics
+2025-07-19 17:45:40: Epoch: 013 Train Loss: 0.189, mae: 0.309, rse: 0.103, pcc: 0.948, kcc: 0.804
+2025-07-19 17:45:40: Epoch: 013 New best VALIDATION metrics
+2025-07-19 17:45:41: Epoch: 013 Test results, ap: mae: 0.957, rse: 2.575, pcc: 0.017, kcc: -0.010
+2025-07-19 17:46:11: Epoch: 014 Train Loss: 0.182, mae: 0.305, rse: 0.106, pcc: 0.946, kcc: 0.808
+2025-07-19 17:46:11: Epoch: 014 New best VALIDATION metrics
+2025-07-19 17:46:12: Epoch: 014 Test results, ap: mae: 0.948, rse: 2.536, pcc: 0.001, kcc: -0.017
+2025-07-19 17:46:42: Epoch: 015 Train Loss: 0.158, mae: 0.279, rse: 0.095, pcc: 0.952, kcc: 0.823
+2025-07-19 17:46:42: Epoch: 015 New best VALIDATION metrics
+2025-07-19 17:46:43: Epoch: 015 Test results, ap: mae: 0.936, rse: 2.497, pcc: 0.046, kcc: 0.010
+2025-07-19 17:47:13: Epoch: 016 Train Loss: 0.158, mae: 0.315, rse: 0.101, pcc: 0.949, kcc: 0.814
+2025-07-19 17:47:14: Epoch: 016 Test results, ap: mae: 0.965, rse: 2.666, pcc: -0.061, kcc: -0.056
+2025-07-19 17:47:45: Epoch: 017 Train Loss: 0.139, mae: 0.298, rse: 0.097, pcc: 0.952, kcc: 0.822
+2025-07-19 17:47:46: Epoch: 017 Test results, ap: mae: 0.893, rse: 2.211, pcc: 0.033, kcc: 0.006
+2025-07-19 17:47:46: Epoch: 017 New best TEST metrics
+2025-07-19 17:48:16: Epoch: 018 Train Loss: 0.144, mae: 0.280, rse: 0.093, pcc: 0.954, kcc: 0.822
+2025-07-19 17:48:16: Epoch: 018 New best VALIDATION metrics
+2025-07-19 17:48:18: Epoch: 018 Test results, ap: mae: 0.978, rse: 2.656, pcc: 0.008, kcc: -0.012
+2025-07-19 17:48:47: Epoch: 019 Train Loss: 0.133, mae: 0.279, rse: 0.102, pcc: 0.952, kcc: 0.824
+2025-07-19 17:48:49: Epoch: 019 Test results, ap: mae: 0.995, rse: 2.659, pcc: 0.033, kcc: 0.005
+2025-07-19 17:49:19: Epoch: 020 Train Loss: 0.134, mae: 0.264, rse: 0.090, pcc: 0.954, kcc: 0.835
+2025-07-19 17:49:19: Epoch: 020 New best VALIDATION metrics
+2025-07-19 17:49:20: Epoch: 020 Test results, ap: mae: 0.911, rse: 2.348, pcc: 0.039, kcc: 0.009
+2025-07-19 17:49:50: Epoch: 021 Train Loss: 0.104, mae: 0.227, rse: 0.084, pcc: 0.958, kcc: 0.853
+2025-07-19 17:49:50: Epoch: 021 New best VALIDATION metrics
+2025-07-19 17:49:52: Epoch: 021 Test results, ap: mae: 0.947, rse: 2.450, pcc: 0.044, kcc: 0.012
+2025-07-19 17:50:22: Epoch: 022 Train Loss: 0.084, mae: 0.212, rse: 0.078, pcc: 0.961, kcc: 0.857
+2025-07-19 17:50:22: Epoch: 022 New best VALIDATION metrics
+2025-07-19 17:50:23: Epoch: 022 Test results, ap: mae: 0.931, rse: 2.338, pcc: 0.053, kcc: 0.011
+2025-07-19 17:50:54: Epoch: 023 Train Loss: 0.085, mae: 0.220, rse: 0.079, pcc: 0.960, kcc: 0.857
+2025-07-19 17:50:55: Epoch: 023 Test results, ap: mae: 0.903, rse: 2.237, pcc: 0.061, kcc: 0.020
+2025-07-19 17:50:55: Epoch: 023 New best TEST metrics
+2025-07-19 17:51:25: Epoch: 024 Train Loss: 0.077, mae: 0.205, rse: 0.075, pcc: 0.962, kcc: 0.867
+2025-07-19 17:51:25: Epoch: 024 New best VALIDATION metrics
+2025-07-19 17:51:27: Epoch: 024 Test results, ap: mae: 0.950, rse: 2.458, pcc: 0.040, kcc: 0.007
+2025-07-19 17:51:57: Epoch: 025 Train Loss: 0.076, mae: 0.223, rse: 0.078, pcc: 0.960, kcc: 0.856
+2025-07-19 17:51:58: Epoch: 025 Test results, ap: mae: 0.916, rse: 2.291, pcc: 0.071, kcc: 0.041
+2025-07-19 17:52:28: Epoch: 026 Train Loss: 0.088, mae: 0.212, rse: 0.077, pcc: 0.962, kcc: 0.860
+2025-07-19 17:52:29: Epoch: 026 Test results, ap: mae: 0.949, rse: 2.473, pcc: 0.026, kcc: -0.001
+2025-07-19 17:52:59: Epoch: 027 Train Loss: 0.080, mae: 0.203, rse: 0.077, pcc: 0.961, kcc: 0.864
+2025-07-19 17:53:00: Epoch: 027 Test results, ap: mae: 0.946, rse: 2.439, pcc: 0.042, kcc: 0.018
+2025-07-19 17:53:30: Epoch: 028 Train Loss: 0.072, mae: 0.197, rse: 0.075, pcc: 0.962, kcc: 0.868
+2025-07-19 17:53:30: Epoch: 028 New best VALIDATION metrics
+2025-07-19 17:53:31: Epoch: 028 Test results, ap: mae: 0.944, rse: 2.417, pcc: 0.047, kcc: 0.016
+2025-07-19 17:54:01: Epoch: 029 Train Loss: 0.081, mae: 0.189, rse: 0.070, pcc: 0.964, kcc: 0.870
+2025-07-19 17:54:01: Epoch: 029 New best VALIDATION metrics
+2025-07-19 17:54:03: Epoch: 029 Test results, ap: mae: 0.934, rse: 2.400, pcc: 0.039, kcc: 0.013
+2025-07-19 17:54:33: Epoch: 030 Train Loss: 0.072, mae: 0.210, rse: 0.075, pcc: 0.962, kcc: 0.863
+2025-07-19 17:54:34: Epoch: 030 Test results, ap: mae: 0.871, rse: 2.130, pcc: 0.077, kcc: 0.039
+2025-07-19 17:54:34: Epoch: 030 New best TEST metrics
+2025-07-19 17:55:05: Epoch: 031 Train Loss: 0.070, mae: 0.180, rse: 0.071, pcc: 0.964, kcc: 0.876
+2025-07-19 17:55:05: Epoch: 031 New best VALIDATION metrics
+2025-07-19 17:55:06: Epoch: 031 Test results, ap: mae: 0.904, rse: 2.281, pcc: 0.077, kcc: 0.034
+2025-07-19 17:55:36: Epoch: 032 Train Loss: 0.067, mae: 0.176, rse: 0.075, pcc: 0.962, kcc: 0.877
+2025-07-19 17:55:37: Epoch: 032 Test results, ap: mae: 0.929, rse: 2.397, pcc: 0.059, kcc: 0.025
+2025-07-19 17:56:07: Epoch: 033 Train Loss: 0.069, mae: 0.190, rse: 0.074, pcc: 0.962, kcc: 0.873
+2025-07-19 17:56:08: Epoch: 033 Test results, ap: mae: 0.919, rse: 2.330, pcc: 0.055, kcc: 0.028
+2025-07-19 17:56:38: Epoch: 034 Train Loss: 0.072, mae: 0.175, rse: 0.075, pcc: 0.963, kcc: 0.876
+2025-07-19 17:56:39: Epoch: 034 Test results, ap: mae: 0.932, rse: 2.421, pcc: 0.070, kcc: 0.029
+2025-07-19 17:57:09: Epoch: 035 Train Loss: 0.063, mae: 0.186, rse: 0.073, pcc: 0.964, kcc: 0.874
+2025-07-19 17:57:11: Epoch: 035 Test results, ap: mae: 0.942, rse: 2.391, pcc: 0.056, kcc: 0.029
+2025-07-19 17:57:41: Epoch: 036 Train Loss: 0.062, mae: 0.180, rse: 0.070, pcc: 0.964, kcc: 0.876
+2025-07-19 17:57:41: Epoch: 036 New best VALIDATION metrics
+2025-07-19 17:57:42: Epoch: 036 Test results, ap: mae: 0.924, rse: 2.341, pcc: 0.074, kcc: 0.042
+2025-07-19 17:58:12: Epoch: 037 Train Loss: 0.069, mae: 0.177, rse: 0.073, pcc: 0.963, kcc: 0.877
+2025-07-19 17:58:13: Epoch: 037 Test results, ap: mae: 0.922, rse: 2.355, pcc: 0.076, kcc: 0.040
+2025-07-19 17:58:43: Epoch: 038 Train Loss: 0.061, mae: 0.173, rse: 0.072, pcc: 0.965, kcc: 0.880
+2025-07-19 17:58:43: Epoch: 038 New best VALIDATION metrics
+2025-07-19 17:58:44: Epoch: 038 Test results, ap: mae: 0.934, rse: 2.448, pcc: 0.074, kcc: 0.041
+2025-07-19 17:59:14: Epoch: 039 Train Loss: 0.057, mae: 0.187, rse: 0.074, pcc: 0.963, kcc: 0.873
+2025-07-19 17:59:16: Epoch: 039 Test results, ap: mae: 0.910, rse: 2.281, pcc: 0.079, kcc: 0.049
+2025-07-19 17:59:46: Epoch: 040 Train Loss: 0.059, mae: 0.181, rse: 0.075, pcc: 0.963, kcc: 0.875
+2025-07-19 17:59:47: Epoch: 040 Test results, ap: mae: 0.923, rse: 2.356, pcc: 0.072, kcc: 0.034
+2025-07-19 18:00:17: Epoch: 041 Train Loss: 0.057, mae: 0.163, rse: 0.069, pcc: 0.965, kcc: 0.884
+2025-07-19 18:00:17: Epoch: 041 New best VALIDATION metrics
+2025-07-19 18:00:18: Epoch: 041 Test results, ap: mae: 0.922, rse: 2.346, pcc: 0.067, kcc: 0.033
+2025-07-19 18:00:48: Epoch: 042 Train Loss: 0.056, mae: 0.164, rse: 0.071, pcc: 0.964, kcc: 0.884
+2025-07-19 18:00:49: Epoch: 042 Test results, ap: mae: 0.934, rse: 2.388, pcc: 0.068, kcc: 0.039
+2025-07-19 18:01:19: Epoch: 043 Train Loss: 0.050, mae: 0.160, rse: 0.070, pcc: 0.965, kcc: 0.887
+2025-07-19 18:01:19: Epoch: 043 New best VALIDATION metrics
+2025-07-19 18:01:20: Epoch: 043 Test results, ap: mae: 0.919, rse: 2.311, pcc: 0.087, kcc: 0.052
+2025-07-19 18:01:51: Epoch: 044 Train Loss: 0.054, mae: 0.156, rse: 0.069, pcc: 0.965, kcc: 0.888
+2025-07-19 18:01:51: Epoch: 044 New best VALIDATION metrics
+2025-07-19 18:01:52: Epoch: 044 Test results, ap: mae: 0.934, rse: 2.357, pcc: 0.078, kcc: 0.050
+2025-07-19 18:02:22: Epoch: 045 Train Loss: 0.053, mae: 0.155, rse: 0.068, pcc: 0.966, kcc: 0.887
+2025-07-19 18:02:22: Epoch: 045 New best VALIDATION metrics
+2025-07-19 18:02:23: Epoch: 045 Test results, ap: mae: 0.929, rse: 2.368, pcc: 0.062, kcc: 0.041
+2025-07-19 18:02:53: Epoch: 046 Train Loss: 0.055, mae: 0.160, rse: 0.068, pcc: 0.966, kcc: 0.887
+2025-07-19 18:02:54: Epoch: 046 Test results, ap: mae: 0.931, rse: 2.359, pcc: 0.063, kcc: 0.041
+2025-07-19 18:03:25: Epoch: 047 Train Loss: 0.054, mae: 0.159, rse: 0.069, pcc: 0.965, kcc: 0.887
+2025-07-19 18:03:26: Epoch: 047 Test results, ap: mae: 0.932, rse: 2.406, pcc: 0.068, kcc: 0.038
+2025-07-19 18:03:56: Epoch: 048 Train Loss: 0.053, mae: 0.160, rse: 0.071, pcc: 0.964, kcc: 0.885
+2025-07-19 18:03:58: Epoch: 048 Test results, ap: mae: 0.934, rse: 2.428, pcc: 0.063, kcc: 0.034
+2025-07-19 18:04:27: Epoch: 049 Train Loss: 0.047, mae: 0.160, rse: 0.071, pcc: 0.965, kcc: 0.887
+2025-07-19 18:04:29: Epoch: 049 Test results, ap: mae: 0.946, rse: 2.466, pcc: 0.069, kcc: 0.041
+2025-07-19 18:04:59: Epoch: 050 Train Loss: 0.054, mae: 0.164, rse: 0.070, pcc: 0.965, kcc: 0.886
+2025-07-19 18:05:00: Epoch: 050 Test results, ap: mae: 0.934, rse: 2.415, pcc: 0.085, kcc: 0.050
+2025-07-19 18:05:00: used time 0.44h
+2025-07-19 18:05:00: Running Cross Validation 1
+2025-07-19 18:05:00: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-19 18:05:30: Epoch: 001 Train Loss: 2.460, mae: 1.267, rse: 1.015, pcc: 0.160, kcc: 0.165
+2025-07-19 18:05:30: Epoch: 001 New best VALIDATION metrics
+2025-07-19 18:05:31: Epoch: 001 Test results, ap: mae: 0.639, rse: 1.281, pcc: 0.028, kcc: 0.127
+2025-07-19 18:06:01: Epoch: 002 Train Loss: 1.861, mae: 1.069, rse: 0.706, pcc: 0.584, kcc: 0.429
+2025-07-19 18:06:01: Epoch: 002 New best VALIDATION metrics
+2025-07-19 18:06:03: Epoch: 002 Test results, ap: mae: 0.635, rse: 1.213, pcc: 0.116, kcc: 0.042
+2025-07-19 18:06:32: Epoch: 003 Train Loss: 1.417, mae: 0.862, rse: 0.501, pcc: 0.718, kcc: 0.526
+2025-07-19 18:06:32: Epoch: 003 New best VALIDATION metrics
+2025-07-19 18:06:34: Epoch: 003 Test results, ap: mae: 0.737, rse: 1.532, pcc: 0.265, kcc: 0.134
+2025-07-19 18:07:04: Epoch: 004 Train Loss: 1.155, mae: 0.882, rse: 0.478, pcc: 0.741, kcc: 0.550
+2025-07-19 18:07:04: Epoch: 004 New best VALIDATION metrics
+2025-07-19 18:07:06: Epoch: 004 Test results, ap: mae: 0.795, rse: 1.772, pcc: 0.023, kcc: 0.002
+2025-07-19 18:07:35: Epoch: 005 Train Loss: 0.848, mae: 0.677, rse: 0.338, pcc: 0.816, kcc: 0.624
+2025-07-19 18:07:35: Epoch: 005 New best VALIDATION metrics
+2025-07-19 18:07:37: Epoch: 005 Test results, ap: mae: 0.711, rse: 1.460, pcc: 0.169, kcc: 0.118
+2025-07-19 18:08:07: Epoch: 006 Train Loss: 0.764, mae: 0.658, rse: 0.326, pcc: 0.823, kcc: 0.645
+2025-07-19 18:08:07: Epoch: 006 New best VALIDATION metrics
+2025-07-19 18:08:08: Epoch: 006 Test results, ap: mae: 0.636, rse: 1.150, pcc: 0.394, kcc: 0.233
+2025-07-19 18:08:38: Epoch: 007 Train Loss: 0.646, mae: 0.609, rse: 0.275, pcc: 0.853, kcc: 0.677
+2025-07-19 18:08:38: Epoch: 007 New best VALIDATION metrics
+2025-07-19 18:08:39: Epoch: 007 Test results, ap: mae: 0.652, rse: 1.238, pcc: 0.203, kcc: 0.065
+2025-07-19 18:09:09: Epoch: 008 Train Loss: 0.562, mae: 0.629, rse: 0.302, pcc: 0.843, kcc: 0.676
+2025-07-19 18:09:10: Epoch: 008 Test results, ap: mae: 0.768, rse: 1.685, pcc: 0.428, kcc: 0.261
+2025-07-19 18:09:40: Epoch: 009 Train Loss: 0.538, mae: 0.601, rse: 0.263, pcc: 0.858, kcc: 0.687
+2025-07-19 18:09:40: Epoch: 009 New best VALIDATION metrics
+2025-07-19 18:09:41: Epoch: 009 Test results, ap: mae: 0.651, rse: 1.181, pcc: 0.249, kcc: 0.113
+2025-07-19 18:10:11: Epoch: 010 Train Loss: 0.448, mae: 0.516, rse: 0.227, pcc: 0.884, kcc: 0.731
+2025-07-19 18:10:11: Epoch: 010 New best VALIDATION metrics
+2025-07-19 18:10:13: Epoch: 010 Test results, ap: mae: 0.720, rse: 1.471, pcc: 0.311, kcc: 0.176
+2025-07-19 18:10:43: Epoch: 011 Train Loss: 0.369, mae: 0.446, rse: 0.194, pcc: 0.899, kcc: 0.758
+2025-07-19 18:10:43: Epoch: 011 New best VALIDATION metrics
+2025-07-19 18:10:44: Epoch: 011 Test results, ap: mae: 0.740, rse: 1.430, pcc: 0.319, kcc: 0.175
+2025-07-19 18:10:44: Epoch: 011 New best TEST metrics
+2025-07-19 18:11:14: Epoch: 012 Train Loss: 0.290, mae: 0.426, rse: 0.180, pcc: 0.908, kcc: 0.772
+2025-07-19 18:11:14: Epoch: 012 New best VALIDATION metrics
+2025-07-19 18:11:16: Epoch: 012 Test results, ap: mae: 0.684, rse: 1.377, pcc: 0.374, kcc: 0.227
+2025-07-19 18:11:16: Epoch: 012 New best TEST metrics
+2025-07-19 18:11:46: Epoch: 013 Train Loss: 0.319, mae: 0.435, rse: 0.166, pcc: 0.915, kcc: 0.768
+2025-07-19 18:11:46: Epoch: 013 New best VALIDATION metrics
+2025-07-19 18:11:47: Epoch: 013 Test results, ap: mae: 0.746, rse: 1.470, pcc: 0.273, kcc: 0.148
+2025-07-19 18:12:17: Epoch: 014 Train Loss: 0.256, mae: 0.387, rse: 0.160, pcc: 0.920, kcc: 0.792
+2025-07-19 18:12:17: Epoch: 014 New best VALIDATION metrics
+2025-07-19 18:12:19: Epoch: 014 Test results, ap: mae: 0.784, rse: 1.781, pcc: 0.355, kcc: 0.212
+2025-07-19 18:12:49: Epoch: 015 Train Loss: 0.238, mae: 0.433, rse: 0.170, pcc: 0.918, kcc: 0.770
+2025-07-19 18:12:50: Epoch: 015 Test results, ap: mae: 0.798, rse: 1.890, pcc: 0.311, kcc: 0.169
+2025-07-19 18:13:20: Epoch: 016 Train Loss: 0.203, mae: 0.378, rse: 0.142, pcc: 0.927, kcc: 0.787
+2025-07-19 18:13:20: Epoch: 016 New best VALIDATION metrics
+2025-07-19 18:13:22: Epoch: 016 Test results, ap: mae: 0.746, rse: 1.652, pcc: 0.329, kcc: 0.201
+2025-07-19 18:13:52: Epoch: 017 Train Loss: 0.213, mae: 0.376, rse: 0.141, pcc: 0.929, kcc: 0.793
+2025-07-19 18:13:52: Epoch: 017 New best VALIDATION metrics
+2025-07-19 18:13:53: Epoch: 017 Test results, ap: mae: 0.741, rse: 1.485, pcc: 0.341, kcc: 0.179
+2025-07-19 18:14:23: Epoch: 018 Train Loss: 0.192, mae: 0.334, rse: 0.109, pcc: 0.946, kcc: 0.816
+2025-07-19 18:14:23: Epoch: 018 New best VALIDATION metrics
+2025-07-19 18:14:25: Epoch: 018 Test results, ap: mae: 0.770, rse: 1.646, pcc: 0.286, kcc: 0.147
+2025-07-19 18:14:55: Epoch: 019 Train Loss: 0.188, mae: 0.364, rse: 0.133, pcc: 0.933, kcc: 0.795
+2025-07-19 18:14:56: Epoch: 019 Test results, ap: mae: 0.760, rse: 1.627, pcc: 0.324, kcc: 0.174
+2025-07-19 18:15:26: Epoch: 020 Train Loss: 0.174, mae: 0.361, rse: 0.130, pcc: 0.938, kcc: 0.806
+2025-07-19 18:15:27: Epoch: 020 Test results, ap: mae: 0.734, rse: 1.532, pcc: 0.348, kcc: 0.188
+2025-07-19 18:15:57: Epoch: 021 Train Loss: 0.134, mae: 0.298, rse: 0.104, pcc: 0.949, kcc: 0.836
+2025-07-19 18:15:57: Epoch: 021 New best VALIDATION metrics
+2025-07-19 18:15:59: Epoch: 021 Test results, ap: mae: 0.757, rse: 1.701, pcc: 0.296, kcc: 0.162
+2025-07-19 18:16:29: Epoch: 022 Train Loss: 0.119, mae: 0.272, rse: 0.096, pcc: 0.953, kcc: 0.840
+2025-07-19 18:16:29: Epoch: 022 New best VALIDATION metrics
+2025-07-19 18:16:30: Epoch: 022 Test results, ap: mae: 0.770, rse: 1.727, pcc: 0.291, kcc: 0.150
+2025-07-19 18:17:00: Epoch: 023 Train Loss: 0.111, mae: 0.277, rse: 0.098, pcc: 0.950, kcc: 0.842
+2025-07-19 18:17:02: Epoch: 023 Test results, ap: mae: 0.754, rse: 1.619, pcc: 0.294, kcc: 0.155
+2025-07-19 18:17:32: Epoch: 024 Train Loss: 0.108, mae: 0.282, rse: 0.097, pcc: 0.952, kcc: 0.843
+2025-07-19 18:17:33: Epoch: 024 Test results, ap: mae: 0.761, rse: 1.645, pcc: 0.298, kcc: 0.146
+2025-07-19 18:18:03: Epoch: 025 Train Loss: 0.107, mae: 0.264, rse: 0.095, pcc: 0.954, kcc: 0.848
+2025-07-19 18:18:03: Epoch: 025 New best VALIDATION metrics
+2025-07-19 18:18:04: Epoch: 025 Test results, ap: mae: 0.804, rse: 1.834, pcc: 0.296, kcc: 0.146
+2025-07-19 18:18:34: Epoch: 026 Train Loss: 0.102, mae: 0.273, rse: 0.097, pcc: 0.951, kcc: 0.846
+2025-07-19 18:18:35: Epoch: 026 Test results, ap: mae: 0.757, rse: 1.695, pcc: 0.284, kcc: 0.145
+2025-07-19 18:19:05: Epoch: 027 Train Loss: 0.094, mae: 0.258, rse: 0.093, pcc: 0.955, kcc: 0.853
+2025-07-19 18:19:05: Epoch: 027 New best VALIDATION metrics
+2025-07-19 18:19:06: Epoch: 027 Test results, ap: mae: 0.835, rse: 1.968, pcc: 0.211, kcc: 0.079
+2025-07-19 18:19:36: Epoch: 028 Train Loss: 0.091, mae: 0.255, rse: 0.093, pcc: 0.954, kcc: 0.855
+2025-07-19 18:19:36: Epoch: 028 New best VALIDATION metrics
+2025-07-19 18:19:38: Epoch: 028 Test results, ap: mae: 0.811, rse: 1.904, pcc: 0.265, kcc: 0.129
+2025-07-19 18:20:08: Epoch: 029 Train Loss: 0.097, mae: 0.243, rse: 0.088, pcc: 0.957, kcc: 0.856
+2025-07-19 18:20:08: Epoch: 029 New best VALIDATION metrics
+2025-07-19 18:20:09: Epoch: 029 Test results, ap: mae: 0.808, rse: 1.845, pcc: 0.257, kcc: 0.128
+2025-07-19 18:20:39: Epoch: 030 Train Loss: 0.091, mae: 0.241, rse: 0.086, pcc: 0.956, kcc: 0.857
+2025-07-19 18:20:39: Epoch: 030 New best VALIDATION metrics
+2025-07-19 18:20:40: Epoch: 030 Test results, ap: mae: 0.802, rse: 1.816, pcc: 0.214, kcc: 0.093
+2025-07-19 18:21:10: Epoch: 031 Train Loss: 0.088, mae: 0.226, rse: 0.088, pcc: 0.957, kcc: 0.868
+2025-07-19 18:21:10: Epoch: 031 New best VALIDATION metrics
+2025-07-19 18:21:12: Epoch: 031 Test results, ap: mae: 0.819, rse: 1.914, pcc: 0.249, kcc: 0.107
+2025-07-19 18:21:42: Epoch: 032 Train Loss: 0.074, mae: 0.232, rse: 0.086, pcc: 0.959, kcc: 0.870
+2025-07-19 18:21:43: Epoch: 032 Test results, ap: mae: 0.823, rse: 1.937, pcc: 0.233, kcc: 0.103
+2025-07-19 18:22:13: Epoch: 033 Train Loss: 0.073, mae: 0.225, rse: 0.084, pcc: 0.959, kcc: 0.873
+2025-07-19 18:22:13: Epoch: 033 New best VALIDATION metrics
+2025-07-19 18:22:15: Epoch: 033 Test results, ap: mae: 0.828, rse: 1.950, pcc: 0.221, kcc: 0.086
+2025-07-19 18:22:45: Epoch: 034 Train Loss: 0.069, mae: 0.244, rse: 0.092, pcc: 0.956, kcc: 0.865
+2025-07-19 18:22:46: Epoch: 034 Test results, ap: mae: 0.841, rse: 2.030, pcc: 0.243, kcc: 0.115
+2025-07-19 18:23:16: Epoch: 035 Train Loss: 0.070, mae: 0.215, rse: 0.080, pcc: 0.961, kcc: 0.873
+2025-07-19 18:23:16: Epoch: 035 New best VALIDATION metrics
+2025-07-19 18:23:18: Epoch: 035 Test results, ap: mae: 0.838, rse: 1.987, pcc: 0.231, kcc: 0.112
+2025-07-19 18:23:48: Epoch: 036 Train Loss: 0.069, mae: 0.220, rse: 0.081, pcc: 0.960, kcc: 0.868
+2025-07-19 18:23:49: Epoch: 036 Test results, ap: mae: 0.849, rse: 2.048, pcc: 0.223, kcc: 0.102
+2025-07-19 18:24:19: Epoch: 037 Train Loss: 0.064, mae: 0.218, rse: 0.081, pcc: 0.960, kcc: 0.871
+2025-07-19 18:24:20: Epoch: 037 Test results, ap: mae: 0.842, rse: 2.023, pcc: 0.207, kcc: 0.092
+2025-07-19 18:24:50: Epoch: 038 Train Loss: 0.065, mae: 0.209, rse: 0.079, pcc: 0.961, kcc: 0.876
+2025-07-19 18:24:50: Epoch: 038 New best VALIDATION metrics
+2025-07-19 18:24:52: Epoch: 038 Test results, ap: mae: 0.862, rse: 2.093, pcc: 0.197, kcc: 0.079
+2025-07-19 18:25:22: Epoch: 039 Train Loss: 0.068, mae: 0.260, rse: 0.092, pcc: 0.959, kcc: 0.869
+2025-07-19 18:25:23: Epoch: 039 Test results, ap: mae: 0.909, rse: 2.307, pcc: 0.196, kcc: 0.077
+2025-07-19 18:25:53: Epoch: 040 Train Loss: 0.069, mae: 0.224, rse: 0.082, pcc: 0.960, kcc: 0.872
+2025-07-19 18:25:54: Epoch: 040 Test results, ap: mae: 0.841, rse: 1.993, pcc: 0.200, kcc: 0.080
+2025-07-19 18:26:24: Epoch: 041 Train Loss: 0.059, mae: 0.205, rse: 0.080, pcc: 0.961, kcc: 0.882
+2025-07-19 18:26:24: Epoch: 041 New best VALIDATION metrics
+2025-07-19 18:26:26: Epoch: 041 Test results, ap: mae: 0.870, rse: 2.131, pcc: 0.208, kcc: 0.085
+2025-07-19 18:26:56: Epoch: 042 Train Loss: 0.055, mae: 0.195, rse: 0.078, pcc: 0.961, kcc: 0.884
+2025-07-19 18:26:56: Epoch: 042 New best VALIDATION metrics
+2025-07-19 18:26:57: Epoch: 042 Test results, ap: mae: 0.867, rse: 2.121, pcc: 0.204, kcc: 0.079
+2025-07-19 18:27:27: Epoch: 043 Train Loss: 0.058, mae: 0.207, rse: 0.079, pcc: 0.962, kcc: 0.883
+2025-07-19 18:27:29: Epoch: 043 Test results, ap: mae: 0.890, rse: 2.225, pcc: 0.180, kcc: 0.063
+2025-07-19 18:27:59: Epoch: 044 Train Loss: 0.059, mae: 0.197, rse: 0.077, pcc: 0.962, kcc: 0.887
+2025-07-19 18:27:59: Epoch: 044 New best VALIDATION metrics
+2025-07-19 18:28:00: Epoch: 044 Test results, ap: mae: 0.865, rse: 2.096, pcc: 0.179, kcc: 0.065
+2025-07-19 18:28:30: Epoch: 045 Train Loss: 0.061, mae: 0.193, rse: 0.076, pcc: 0.962, kcc: 0.887
+2025-07-19 18:28:30: Epoch: 045 New best VALIDATION metrics
+2025-07-19 18:28:31: Epoch: 045 Test results, ap: mae: 0.875, rse: 2.142, pcc: 0.175, kcc: 0.061
+2025-07-19 18:29:01: Epoch: 046 Train Loss: 0.057, mae: 0.204, rse: 0.079, pcc: 0.961, kcc: 0.883
+2025-07-19 18:29:03: Epoch: 046 Test results, ap: mae: 0.893, rse: 2.215, pcc: 0.173, kcc: 0.061
+2025-07-19 18:29:33: Epoch: 047 Train Loss: 0.054, mae: 0.189, rse: 0.077, pcc: 0.962, kcc: 0.890
+2025-07-19 18:29:33: Epoch: 047 New best VALIDATION metrics
+2025-07-19 18:29:34: Epoch: 047 Test results, ap: mae: 0.893, rse: 2.231, pcc: 0.168, kcc: 0.057
+2025-07-19 18:30:05: Epoch: 048 Train Loss: 0.052, mae: 0.192, rse: 0.076, pcc: 0.963, kcc: 0.891
+2025-07-19 18:30:05: Epoch: 048 New best VALIDATION metrics
+2025-07-19 18:30:06: Epoch: 048 Test results, ap: mae: 0.886, rse: 2.179, pcc: 0.161, kcc: 0.049
+2025-07-19 18:30:36: Epoch: 049 Train Loss: 0.051, mae: 0.194, rse: 0.074, pcc: 0.963, kcc: 0.890
+2025-07-19 18:30:37: Epoch: 049 Test results, ap: mae: 0.873, rse: 2.118, pcc: 0.156, kcc: 0.048
+2025-07-19 18:31:07: Epoch: 050 Train Loss: 0.053, mae: 0.194, rse: 0.075, pcc: 0.963, kcc: 0.889
+2025-07-19 18:31:09: Epoch: 050 Test results, ap: mae: 0.901, rse: 2.263, pcc: 0.150, kcc: 0.045
+2025-07-19 18:31:09: used time 0.44h
+2025-07-19 18:31:09: Running Cross Validation 2
+2025-07-19 18:31:09: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 18:31:39: Epoch: 001 Train Loss: 2.508, mae: 1.096, rse: 0.740, pcc: 0.560, kcc: 0.386
+2025-07-19 18:31:39: Epoch: 001 New best VALIDATION metrics
+2025-07-19 18:31:40: Epoch: 001 Test results, ap: mae: 0.934, rse: 2.676, pcc: -0.163, kcc: -0.108
+2025-07-19 18:32:10: Epoch: 002 Train Loss: 1.840, mae: 0.864, rse: 0.511, pcc: 0.716, kcc: 0.528
+2025-07-19 18:32:10: Epoch: 002 New best VALIDATION metrics
+2025-07-19 18:32:12: Epoch: 002 Test results, ap: mae: 0.734, rse: 1.502, pcc: -0.062, kcc: -0.036
+2025-07-19 18:32:41: Epoch: 003 Train Loss: 1.488, mae: 0.905, rse: 0.511, pcc: 0.712, kcc: 0.488
+2025-07-19 18:32:43: Epoch: 003 Test results, ap: mae: 0.780, rse: 1.815, pcc: 0.055, kcc: 0.040
+2025-07-19 18:33:13: Epoch: 004 Train Loss: 1.227, mae: 0.832, rse: 0.434, pcc: 0.754, kcc: 0.540
+2025-07-19 18:33:13: Epoch: 004 New best VALIDATION metrics
+2025-07-19 18:33:14: Epoch: 004 Test results, ap: mae: 0.773, rse: 1.668, pcc: 0.053, kcc: 0.029
+2025-07-19 18:33:44: Epoch: 005 Train Loss: 1.031, mae: 0.690, rse: 0.320, pcc: 0.827, kcc: 0.621
+2025-07-19 18:33:44: Epoch: 005 New best VALIDATION metrics
+2025-07-19 18:33:45: Epoch: 005 Test results, ap: mae: 0.815, rse: 1.890, pcc: 0.132, kcc: 0.093
+2025-07-19 18:34:16: Epoch: 006 Train Loss: 0.865, mae: 0.631, rse: 0.293, pcc: 0.843, kcc: 0.651
+2025-07-19 18:34:16: Epoch: 006 New best VALIDATION metrics
+2025-07-19 18:34:17: Epoch: 006 Test results, ap: mae: 0.782, rse: 1.848, pcc: 0.242, kcc: 0.172
+2025-07-19 18:34:47: Epoch: 007 Train Loss: 0.759, mae: 0.583, rse: 0.254, pcc: 0.864, kcc: 0.682
+2025-07-19 18:34:47: Epoch: 007 New best VALIDATION metrics
+2025-07-19 18:34:48: Epoch: 007 Test results, ap: mae: 0.816, rse: 2.045, pcc: 0.282, kcc: 0.167
+2025-07-19 18:35:18: Epoch: 008 Train Loss: 0.528, mae: 0.537, rse: 0.208, pcc: 0.890, kcc: 0.702
+2025-07-19 18:35:18: Epoch: 008 New best VALIDATION metrics
+2025-07-19 18:35:20: Epoch: 008 Test results, ap: mae: 0.973, rse: 2.623, pcc: 0.213, kcc: 0.120
+2025-07-19 18:35:50: Epoch: 009 Train Loss: 0.510, mae: 0.544, rse: 0.221, pcc: 0.884, kcc: 0.717
+2025-07-19 18:35:51: Epoch: 009 Test results, ap: mae: 0.785, rse: 1.858, pcc: 0.265, kcc: 0.151
+2025-07-19 18:36:21: Epoch: 010 Train Loss: 0.363, mae: 0.475, rse: 0.189, pcc: 0.901, kcc: 0.735
+2025-07-19 18:36:21: Epoch: 010 New best VALIDATION metrics
+2025-07-19 18:36:23: Epoch: 010 Test results, ap: mae: 0.891, rse: 2.367, pcc: 0.167, kcc: 0.076
+2025-07-19 18:36:53: Epoch: 011 Train Loss: 0.304, mae: 0.394, rse: 0.149, pcc: 0.923, kcc: 0.773
+2025-07-19 18:36:53: Epoch: 011 New best VALIDATION metrics
+2025-07-19 18:36:54: Epoch: 011 Test results, ap: mae: 0.918, rse: 2.475, pcc: 0.265, kcc: 0.125
+2025-07-19 18:36:54: Epoch: 011 New best TEST metrics
+2025-07-19 18:37:24: Epoch: 012 Train Loss: 0.225, mae: 0.403, rse: 0.155, pcc: 0.919, kcc: 0.774
+2025-07-19 18:37:25: Epoch: 012 Test results, ap: mae: 0.964, rse: 2.715, pcc: 0.192, kcc: 0.093
+2025-07-19 18:37:55: Epoch: 013 Train Loss: 0.206, mae: 0.388, rse: 0.161, pcc: 0.920, kcc: 0.779
+2025-07-19 18:37:56: Epoch: 013 Test results, ap: mae: 1.114, rse: 3.536, pcc: 0.264, kcc: 0.139
+2025-07-19 18:38:27: Epoch: 014 Train Loss: 0.203, mae: 0.369, rse: 0.144, pcc: 0.926, kcc: 0.789
+2025-07-19 18:38:27: Epoch: 014 New best VALIDATION metrics
+2025-07-19 18:38:28: Epoch: 014 Test results, ap: mae: 0.990, rse: 2.738, pcc: 0.278, kcc: 0.160
+2025-07-19 18:38:58: Epoch: 015 Train Loss: 0.210, mae: 0.384, rse: 0.147, pcc: 0.925, kcc: 0.794
+2025-07-19 18:38:59: Epoch: 015 Test results, ap: mae: 0.972, rse: 2.631, pcc: 0.262, kcc: 0.124
+2025-07-19 18:39:30: Epoch: 016 Train Loss: 0.171, mae: 0.365, rse: 0.145, pcc: 0.926, kcc: 0.792
+2025-07-19 18:39:30: Epoch: 016 New best VALIDATION metrics
+2025-07-19 18:39:31: Epoch: 016 Test results, ap: mae: 0.937, rse: 2.497, pcc: 0.273, kcc: 0.132
+2025-07-19 18:40:01: Epoch: 017 Train Loss: 0.172, mae: 0.357, rse: 0.135, pcc: 0.930, kcc: 0.797
+2025-07-19 18:40:01: Epoch: 017 New best VALIDATION metrics
+2025-07-19 18:40:02: Epoch: 017 Test results, ap: mae: 0.852, rse: 2.103, pcc: 0.283, kcc: 0.139
+2025-07-19 18:40:02: Epoch: 017 New best TEST metrics
+2025-07-19 18:40:33: Epoch: 018 Train Loss: 0.150, mae: 0.338, rse: 0.133, pcc: 0.934, kcc: 0.809
+2025-07-19 18:40:33: Epoch: 018 New best VALIDATION metrics
+2025-07-19 18:40:34: Epoch: 018 Test results, ap: mae: 1.008, rse: 2.934, pcc: 0.242, kcc: 0.120
+2025-07-19 18:41:04: Epoch: 019 Train Loss: 0.152, mae: 0.327, rse: 0.135, pcc: 0.934, kcc: 0.817
+2025-07-19 18:41:04: Epoch: 019 New best VALIDATION metrics
+2025-07-19 18:41:06: Epoch: 019 Test results, ap: mae: 1.066, rse: 3.202, pcc: 0.219, kcc: 0.115
+2025-07-19 18:41:36: Epoch: 020 Train Loss: 0.143, mae: 0.318, rse: 0.127, pcc: 0.937, kcc: 0.824
+2025-07-19 18:41:36: Epoch: 020 New best VALIDATION metrics
+2025-07-19 18:41:37: Epoch: 020 Test results, ap: mae: 1.003, rse: 2.823, pcc: 0.275, kcc: 0.138
+2025-07-19 18:42:07: Epoch: 021 Train Loss: 0.109, mae: 0.266, rse: 0.108, pcc: 0.946, kcc: 0.844
+2025-07-19 18:42:07: Epoch: 021 New best VALIDATION metrics
+2025-07-19 18:42:09: Epoch: 021 Test results, ap: mae: 0.941, rse: 2.492, pcc: 0.255, kcc: 0.136
+2025-07-19 18:42:38: Epoch: 022 Train Loss: 0.095, mae: 0.265, rse: 0.108, pcc: 0.946, kcc: 0.845
+2025-07-19 18:42:38: Epoch: 022 New best VALIDATION metrics
+2025-07-19 18:42:40: Epoch: 022 Test results, ap: mae: 0.952, rse: 2.570, pcc: 0.232, kcc: 0.106
+2025-07-19 18:43:10: Epoch: 023 Train Loss: 0.096, mae: 0.248, rse: 0.106, pcc: 0.947, kcc: 0.852
+2025-07-19 18:43:10: Epoch: 023 New best VALIDATION metrics
+2025-07-19 18:43:12: Epoch: 023 Test results, ap: mae: 0.940, rse: 2.493, pcc: 0.254, kcc: 0.126
+2025-07-19 18:43:42: Epoch: 024 Train Loss: 0.080, mae: 0.260, rse: 0.106, pcc: 0.947, kcc: 0.850
+2025-07-19 18:43:43: Epoch: 024 Test results, ap: mae: 0.932, rse: 2.467, pcc: 0.244, kcc: 0.124
+2025-07-19 18:44:13: Epoch: 025 Train Loss: 0.086, mae: 0.265, rse: 0.110, pcc: 0.947, kcc: 0.850
+2025-07-19 18:44:14: Epoch: 025 Test results, ap: mae: 1.045, rse: 3.016, pcc: 0.245, kcc: 0.115
+2025-07-19 18:44:44: Epoch: 026 Train Loss: 0.091, mae: 0.269, rse: 0.112, pcc: 0.947, kcc: 0.854
+2025-07-19 18:44:45: Epoch: 026 Test results, ap: mae: 0.965, rse: 2.609, pcc: 0.250, kcc: 0.136
+2025-07-19 18:45:15: Epoch: 027 Train Loss: 0.088, mae: 0.228, rse: 0.097, pcc: 0.951, kcc: 0.864
+2025-07-19 18:45:15: Epoch: 027 New best VALIDATION metrics
+2025-07-19 18:45:17: Epoch: 027 Test results, ap: mae: 0.925, rse: 2.426, pcc: 0.261, kcc: 0.138
+2025-07-19 18:45:47: Epoch: 028 Train Loss: 0.083, mae: 0.258, rse: 0.106, pcc: 0.948, kcc: 0.860
+2025-07-19 18:45:48: Epoch: 028 Test results, ap: mae: 0.929, rse: 2.434, pcc: 0.253, kcc: 0.135
+2025-07-19 18:46:18: Epoch: 029 Train Loss: 0.088, mae: 0.248, rse: 0.101, pcc: 0.951, kcc: 0.861
+2025-07-19 18:46:20: Epoch: 029 Test results, ap: mae: 0.926, rse: 2.363, pcc: 0.248, kcc: 0.127
+2025-07-19 18:46:49: Epoch: 030 Train Loss: 0.088, mae: 0.266, rse: 0.106, pcc: 0.948, kcc: 0.858
+2025-07-19 18:46:51: Epoch: 030 Test results, ap: mae: 0.960, rse: 2.606, pcc: 0.268, kcc: 0.145
+2025-07-19 18:47:21: Epoch: 031 Train Loss: 0.082, mae: 0.215, rse: 0.096, pcc: 0.952, kcc: 0.870
+2025-07-19 18:47:21: Epoch: 031 New best VALIDATION metrics
+2025-07-19 18:47:22: Epoch: 031 Test results, ap: mae: 0.959, rse: 2.544, pcc: 0.246, kcc: 0.121
+2025-07-19 18:47:52: Epoch: 032 Train Loss: 0.066, mae: 0.225, rse: 0.099, pcc: 0.951, kcc: 0.866
+2025-07-19 18:47:53: Epoch: 032 Test results, ap: mae: 0.973, rse: 2.607, pcc: 0.241, kcc: 0.126
+2025-07-19 18:48:23: Epoch: 033 Train Loss: 0.065, mae: 0.225, rse: 0.099, pcc: 0.951, kcc: 0.869
+2025-07-19 18:48:24: Epoch: 033 Test results, ap: mae: 0.968, rse: 2.621, pcc: 0.249, kcc: 0.129
+2025-07-19 18:48:55: Epoch: 034 Train Loss: 0.068, mae: 0.221, rse: 0.098, pcc: 0.952, kcc: 0.872
+2025-07-19 18:48:56: Epoch: 034 Test results, ap: mae: 0.998, rse: 2.733, pcc: 0.266, kcc: 0.130
+2025-07-19 18:49:26: Epoch: 035 Train Loss: 0.065, mae: 0.207, rse: 0.096, pcc: 0.952, kcc: 0.872
+2025-07-19 18:49:26: Epoch: 035 New best VALIDATION metrics
+2025-07-19 18:49:27: Epoch: 035 Test results, ap: mae: 0.973, rse: 2.626, pcc: 0.256, kcc: 0.136
+2025-07-19 18:49:57: Epoch: 036 Train Loss: 0.064, mae: 0.225, rse: 0.097, pcc: 0.952, kcc: 0.870
+2025-07-19 18:49:58: Epoch: 036 Test results, ap: mae: 1.029, rse: 2.965, pcc: 0.252, kcc: 0.130
+2025-07-19 18:50:29: Epoch: 037 Train Loss: 0.070, mae: 0.218, rse: 0.097, pcc: 0.952, kcc: 0.871
+2025-07-19 18:50:30: Epoch: 037 Test results, ap: mae: 0.993, rse: 2.750, pcc: 0.226, kcc: 0.118
+2025-07-19 18:51:00: Epoch: 038 Train Loss: 0.058, mae: 0.221, rse: 0.098, pcc: 0.952, kcc: 0.870
+2025-07-19 18:51:01: Epoch: 038 Test results, ap: mae: 1.021, rse: 2.944, pcc: 0.239, kcc: 0.135
+2025-07-19 18:51:31: Epoch: 039 Train Loss: 0.062, mae: 0.221, rse: 0.097, pcc: 0.952, kcc: 0.874
+2025-07-19 18:51:32: Epoch: 039 Test results, ap: mae: 0.974, rse: 2.670, pcc: 0.248, kcc: 0.134
+2025-07-19 18:52:02: Epoch: 040 Train Loss: 0.056, mae: 0.216, rse: 0.094, pcc: 0.953, kcc: 0.873
+2025-07-19 18:52:04: Epoch: 040 Test results, ap: mae: 1.006, rse: 2.840, pcc: 0.251, kcc: 0.132
+2025-07-19 18:52:33: Epoch: 041 Train Loss: 0.054, mae: 0.208, rse: 0.094, pcc: 0.953, kcc: 0.879
+2025-07-19 18:52:33: Epoch: 041 New best VALIDATION metrics
+2025-07-19 18:52:35: Epoch: 041 Test results, ap: mae: 0.978, rse: 2.633, pcc: 0.251, kcc: 0.130
+2025-07-19 18:53:05: Epoch: 042 Train Loss: 0.055, mae: 0.199, rse: 0.092, pcc: 0.954, kcc: 0.881
+2025-07-19 18:53:05: Epoch: 042 New best VALIDATION metrics
+2025-07-19 18:53:06: Epoch: 042 Test results, ap: mae: 0.973, rse: 2.602, pcc: 0.259, kcc: 0.130
+2025-07-19 18:53:36: Epoch: 043 Train Loss: 0.049, mae: 0.200, rse: 0.093, pcc: 0.954, kcc: 0.878
+2025-07-19 18:53:37: Epoch: 043 Test results, ap: mae: 0.973, rse: 2.619, pcc: 0.258, kcc: 0.134
+2025-07-19 18:54:07: Epoch: 044 Train Loss: 0.054, mae: 0.199, rse: 0.093, pcc: 0.954, kcc: 0.882
+2025-07-19 18:54:09: Epoch: 044 Test results, ap: mae: 0.993, rse: 2.725, pcc: 0.267, kcc: 0.136
+2025-07-19 18:54:38: Epoch: 045 Train Loss: 0.052, mae: 0.191, rse: 0.092, pcc: 0.954, kcc: 0.882
+2025-07-19 18:54:38: Epoch: 045 New best VALIDATION metrics
+2025-07-19 18:54:40: Epoch: 045 Test results, ap: mae: 0.989, rse: 2.717, pcc: 0.257, kcc: 0.136
+2025-07-19 18:55:10: Epoch: 046 Train Loss: 0.052, mae: 0.201, rse: 0.093, pcc: 0.954, kcc: 0.881
+2025-07-19 18:55:12: Epoch: 046 Test results, ap: mae: 0.975, rse: 2.625, pcc: 0.263, kcc: 0.141
+2025-07-19 18:55:42: Epoch: 047 Train Loss: 0.051, mae: 0.199, rse: 0.092, pcc: 0.954, kcc: 0.881
+2025-07-19 18:55:43: Epoch: 047 Test results, ap: mae: 0.954, rse: 2.491, pcc: 0.267, kcc: 0.138
+2025-07-19 18:56:13: Epoch: 048 Train Loss: 0.054, mae: 0.198, rse: 0.092, pcc: 0.954, kcc: 0.883
+2025-07-19 18:56:14: Epoch: 048 Test results, ap: mae: 0.981, rse: 2.638, pcc: 0.261, kcc: 0.133
+2025-07-19 18:56:44: Epoch: 049 Train Loss: 0.047, mae: 0.193, rse: 0.090, pcc: 0.955, kcc: 0.883
+2025-07-19 18:56:44: Epoch: 049 New best VALIDATION metrics
+2025-07-19 18:56:45: Epoch: 049 Test results, ap: mae: 0.951, rse: 2.491, pcc: 0.262, kcc: 0.142
+2025-07-19 18:57:15: Epoch: 050 Train Loss: 0.057, mae: 0.208, rse: 0.092, pcc: 0.954, kcc: 0.880
+2025-07-19 18:57:16: Epoch: 050 Test results, ap: mae: 0.967, rse: 2.559, pcc: 0.253, kcc: 0.133
+2025-07-19 18:57:16: used time 0.44h
+2025-07-19 18:57:16: Running Cross Validation 3
+2025-07-19 18:57:16: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 18:57:46: Epoch: 001 Train Loss: 2.661, mae: 1.202, rse: 0.869, pcc: 0.423, kcc: 0.273
+2025-07-19 18:57:46: Epoch: 001 New best VALIDATION metrics
+2025-07-19 18:57:47: Epoch: 001 Test results, ap: mae: 0.609, rse: 1.099, pcc: 0.121, kcc: 0.104
+2025-07-19 18:58:17: Epoch: 002 Train Loss: 2.095, mae: 1.041, rse: 0.669, pcc: 0.580, kcc: 0.385
+2025-07-19 18:58:17: Epoch: 002 New best VALIDATION metrics
+2025-07-19 18:58:19: Epoch: 002 Test results, ap: mae: 0.847, rse: 1.929, pcc: -0.052, kcc: -0.012
+2025-07-19 18:58:49: Epoch: 003 Train Loss: 1.741, mae: 0.939, rse: 0.529, pcc: 0.693, kcc: 0.470
+2025-07-19 18:58:49: Epoch: 003 New best VALIDATION metrics
+2025-07-19 18:58:50: Epoch: 003 Test results, ap: mae: 0.753, rse: 1.531, pcc: 0.160, kcc: 0.057
+2025-07-19 18:59:20: Epoch: 004 Train Loss: 1.345, mae: 0.849, rse: 0.463, pcc: 0.735, kcc: 0.519
+2025-07-19 18:59:20: Epoch: 004 New best VALIDATION metrics
+2025-07-19 18:59:21: Epoch: 004 Test results, ap: mae: 0.849, rse: 2.014, pcc: 0.053, kcc: -0.004
+2025-07-19 18:59:51: Epoch: 005 Train Loss: 1.068, mae: 0.750, rse: 0.373, pcc: 0.802, kcc: 0.587
+2025-07-19 18:59:51: Epoch: 005 New best VALIDATION metrics
+2025-07-19 18:59:53: Epoch: 005 Test results, ap: mae: 0.860, rse: 2.012, pcc: -0.056, kcc: -0.034
+2025-07-19 19:00:23: Epoch: 006 Train Loss: 0.858, mae: 0.766, rse: 0.391, pcc: 0.794, kcc: 0.579
+2025-07-19 19:00:24: Epoch: 006 Test results, ap: mae: 0.972, rse: 2.556, pcc: 0.082, kcc: 0.009
+2025-07-19 19:00:54: Epoch: 007 Train Loss: 0.703, mae: 0.583, rse: 0.280, pcc: 0.851, kcc: 0.664
+2025-07-19 19:00:54: Epoch: 007 New best VALIDATION metrics
+2025-07-19 19:00:55: Epoch: 007 Test results, ap: mae: 0.808, rse: 1.782, pcc: 0.123, kcc: 0.046
+2025-07-19 19:01:25: Epoch: 008 Train Loss: 0.567, mae: 0.559, rse: 0.238, pcc: 0.875, kcc: 0.706
+2025-07-19 19:01:25: Epoch: 008 New best VALIDATION metrics
+2025-07-19 19:01:26: Epoch: 008 Test results, ap: mae: 0.988, rse: 2.843, pcc: 0.009, kcc: -0.002
+2025-07-19 19:01:56: Epoch: 009 Train Loss: 0.470, mae: 0.487, rse: 0.201, pcc: 0.894, kcc: 0.742
+2025-07-19 19:01:56: Epoch: 009 New best VALIDATION metrics
+2025-07-19 19:01:57: Epoch: 009 Test results, ap: mae: 0.952, rse: 2.424, pcc: 0.069, kcc: -0.001
+2025-07-19 19:02:28: Epoch: 010 Train Loss: 0.362, mae: 0.479, rse: 0.187, pcc: 0.903, kcc: 0.739
+2025-07-19 19:02:28: Epoch: 010 New best VALIDATION metrics
+2025-07-19 19:02:29: Epoch: 010 Test results, ap: mae: 0.979, rse: 2.561, pcc: 0.136, kcc: 0.043
+2025-07-19 19:02:59: Epoch: 011 Train Loss: 0.257, mae: 0.365, rse: 0.151, pcc: 0.922, kcc: 0.801
+2025-07-19 19:02:59: Epoch: 011 New best VALIDATION metrics
+2025-07-19 19:03:01: Epoch: 011 Test results, ap: mae: 0.947, rse: 2.442, pcc: 0.081, kcc: 0.016
+2025-07-19 19:03:01: Epoch: 011 New best TEST metrics
+2025-07-19 19:03:31: Epoch: 012 Train Loss: 0.208, mae: 0.370, rse: 0.144, pcc: 0.927, kcc: 0.806
+2025-07-19 19:03:31: Epoch: 012 New best VALIDATION metrics
+2025-07-19 19:03:32: Epoch: 012 Test results, ap: mae: 1.034, rse: 2.893, pcc: 0.142, kcc: 0.052
+2025-07-19 19:04:02: Epoch: 013 Train Loss: 0.182, mae: 0.400, rse: 0.158, pcc: 0.924, kcc: 0.802
+2025-07-19 19:04:03: Epoch: 013 Test results, ap: mae: 1.053, rse: 3.112, pcc: 0.083, kcc: 0.024
+2025-07-19 19:04:33: Epoch: 014 Train Loss: 0.174, mae: 0.347, rse: 0.131, pcc: 0.932, kcc: 0.817
+2025-07-19 19:04:33: Epoch: 014 New best VALIDATION metrics
+2025-07-19 19:04:34: Epoch: 014 Test results, ap: mae: 1.009, rse: 2.727, pcc: 0.131, kcc: 0.049
+2025-07-19 19:05:05: Epoch: 015 Train Loss: 0.164, mae: 0.388, rse: 0.142, pcc: 0.929, kcc: 0.808
+2025-07-19 19:05:06: Epoch: 015 Test results, ap: mae: 0.994, rse: 2.726, pcc: 0.130, kcc: 0.045
+2025-07-19 19:05:36: Epoch: 016 Train Loss: 0.164, mae: 0.319, rse: 0.120, pcc: 0.939, kcc: 0.832
+2025-07-19 19:05:36: Epoch: 016 New best VALIDATION metrics
+2025-07-19 19:05:37: Epoch: 016 Test results, ap: mae: 0.996, rse: 2.755, pcc: 0.142, kcc: 0.057
+2025-07-19 19:06:07: Epoch: 017 Train Loss: 0.131, mae: 0.298, rse: 0.116, pcc: 0.941, kcc: 0.841
+2025-07-19 19:06:07: Epoch: 017 New best VALIDATION metrics
+2025-07-19 19:06:08: Epoch: 017 Test results, ap: mae: 1.118, rse: 3.361, pcc: 0.119, kcc: 0.037
+2025-07-19 19:06:38: Epoch: 018 Train Loss: 0.123, mae: 0.306, rse: 0.115, pcc: 0.944, kcc: 0.849
+2025-07-19 19:06:38: Epoch: 018 New best VALIDATION metrics
+2025-07-19 19:06:40: Epoch: 018 Test results, ap: mae: 1.178, rse: 3.724, pcc: 0.150, kcc: 0.057
+2025-07-19 19:07:09: Epoch: 019 Train Loss: 0.120, mae: 0.301, rse: 0.117, pcc: 0.940, kcc: 0.843
+2025-07-19 19:07:11: Epoch: 019 Test results, ap: mae: 1.130, rse: 3.437, pcc: 0.187, kcc: 0.072
+2025-07-19 19:07:41: Epoch: 020 Train Loss: 0.126, mae: 0.287, rse: 0.113, pcc: 0.944, kcc: 0.858
+2025-07-19 19:07:41: Epoch: 020 New best VALIDATION metrics
+2025-07-19 19:07:42: Epoch: 020 Test results, ap: mae: 1.123, rse: 3.406, pcc: 0.135, kcc: 0.050
+2025-07-19 19:08:12: Epoch: 021 Train Loss: 0.094, mae: 0.282, rse: 0.112, pcc: 0.945, kcc: 0.865
+2025-07-19 19:08:12: Epoch: 021 New best VALIDATION metrics
+2025-07-19 19:08:13: Epoch: 021 Test results, ap: mae: 1.211, rse: 3.908, pcc: 0.173, kcc: 0.078
+2025-07-19 19:08:43: Epoch: 022 Train Loss: 0.087, mae: 0.247, rse: 0.101, pcc: 0.949, kcc: 0.870
+2025-07-19 19:08:43: Epoch: 022 New best VALIDATION metrics
+2025-07-19 19:08:44: Epoch: 022 Test results, ap: mae: 1.177, rse: 3.715, pcc: 0.171, kcc: 0.065
+2025-07-19 19:09:14: Epoch: 023 Train Loss: 0.078, mae: 0.238, rse: 0.103, pcc: 0.948, kcc: 0.876
+2025-07-19 19:09:14: Epoch: 023 New best VALIDATION metrics
+2025-07-19 19:09:15: Epoch: 023 Test results, ap: mae: 1.237, rse: 4.043, pcc: 0.160, kcc: 0.075
+2025-07-19 19:09:46: Epoch: 024 Train Loss: 0.078, mae: 0.242, rse: 0.101, pcc: 0.949, kcc: 0.871
+2025-07-19 19:09:47: Epoch: 024 Test results, ap: mae: 1.232, rse: 4.040, pcc: 0.205, kcc: 0.098
+2025-07-19 19:10:17: Epoch: 025 Train Loss: 0.072, mae: 0.239, rse: 0.103, pcc: 0.948, kcc: 0.876
+2025-07-19 19:10:18: Epoch: 025 Test results, ap: mae: 1.151, rse: 3.574, pcc: 0.190, kcc: 0.084
+2025-07-19 19:10:48: Epoch: 026 Train Loss: 0.080, mae: 0.241, rse: 0.102, pcc: 0.948, kcc: 0.871
+2025-07-19 19:10:49: Epoch: 026 Test results, ap: mae: 1.188, rse: 3.793, pcc: 0.195, kcc: 0.089
+2025-07-19 19:11:19: Epoch: 027 Train Loss: 0.075, mae: 0.229, rse: 0.099, pcc: 0.951, kcc: 0.879
+2025-07-19 19:11:19: Epoch: 027 New best VALIDATION metrics
+2025-07-19 19:11:20: Epoch: 027 Test results, ap: mae: 1.243, rse: 4.117, pcc: 0.179, kcc: 0.090
+2025-07-19 19:11:50: Epoch: 028 Train Loss: 0.071, mae: 0.222, rse: 0.097, pcc: 0.951, kcc: 0.883
+2025-07-19 19:11:50: Epoch: 028 New best VALIDATION metrics
+2025-07-19 19:11:52: Epoch: 028 Test results, ap: mae: 1.177, rse: 3.699, pcc: 0.184, kcc: 0.085
+2025-07-19 19:12:21: Epoch: 029 Train Loss: 0.073, mae: 0.239, rse: 0.098, pcc: 0.951, kcc: 0.875
+2025-07-19 19:12:23: Epoch: 029 Test results, ap: mae: 1.217, rse: 3.982, pcc: 0.208, kcc: 0.102
+2025-07-19 19:12:53: Epoch: 030 Train Loss: 0.074, mae: 0.245, rse: 0.101, pcc: 0.950, kcc: 0.872
+2025-07-19 19:12:54: Epoch: 030 Test results, ap: mae: 1.244, rse: 4.167, pcc: 0.187, kcc: 0.083
+2025-07-19 19:13:24: Epoch: 031 Train Loss: 0.062, mae: 0.218, rse: 0.096, pcc: 0.952, kcc: 0.887
+2025-07-19 19:13:24: Epoch: 031 New best VALIDATION metrics
+2025-07-19 19:13:26: Epoch: 031 Test results, ap: mae: 1.226, rse: 4.060, pcc: 0.189, kcc: 0.091
+2025-07-19 19:13:56: Epoch: 032 Train Loss: 0.060, mae: 0.210, rse: 0.098, pcc: 0.952, kcc: 0.888
+2025-07-19 19:13:56: Epoch: 032 New best VALIDATION metrics
+2025-07-19 19:13:57: Epoch: 032 Test results, ap: mae: 1.287, rse: 4.442, pcc: 0.190, kcc: 0.093
+2025-07-19 19:14:27: Epoch: 033 Train Loss: 0.058, mae: 0.206, rse: 0.093, pcc: 0.953, kcc: 0.892
+2025-07-19 19:14:27: Epoch: 033 New best VALIDATION metrics
+2025-07-19 19:14:28: Epoch: 033 Test results, ap: mae: 1.186, rse: 3.830, pcc: 0.196, kcc: 0.096
+2025-07-19 19:14:58: Epoch: 034 Train Loss: 0.059, mae: 0.217, rse: 0.100, pcc: 0.950, kcc: 0.884
+2025-07-19 19:15:00: Epoch: 034 Test results, ap: mae: 1.272, rse: 4.348, pcc: 0.196, kcc: 0.097
+2025-07-19 19:15:30: Epoch: 035 Train Loss: 0.062, mae: 0.223, rse: 0.097, pcc: 0.952, kcc: 0.888
+2025-07-19 19:15:31: Epoch: 035 Test results, ap: mae: 1.253, rse: 4.223, pcc: 0.200, kcc: 0.095
+2025-07-19 19:16:01: Epoch: 036 Train Loss: 0.057, mae: 0.212, rse: 0.094, pcc: 0.952, kcc: 0.892
+2025-07-19 19:16:02: Epoch: 036 Test results, ap: mae: 1.176, rse: 3.702, pcc: 0.190, kcc: 0.097
+2025-07-19 19:16:32: Epoch: 037 Train Loss: 0.060, mae: 0.210, rse: 0.092, pcc: 0.953, kcc: 0.888
+2025-07-19 19:16:33: Epoch: 037 Test results, ap: mae: 1.193, rse: 3.867, pcc: 0.207, kcc: 0.104
+2025-07-19 19:17:04: Epoch: 038 Train Loss: 0.056, mae: 0.206, rse: 0.093, pcc: 0.953, kcc: 0.889
+2025-07-19 19:17:05: Epoch: 038 Test results, ap: mae: 1.225, rse: 4.038, pcc: 0.212, kcc: 0.103
+2025-07-19 19:17:35: Epoch: 039 Train Loss: 0.058, mae: 0.219, rse: 0.096, pcc: 0.952, kcc: 0.887
+2025-07-19 19:17:36: Epoch: 039 Test results, ap: mae: 1.200, rse: 3.906, pcc: 0.196, kcc: 0.098
+2025-07-19 19:18:06: Epoch: 040 Train Loss: 0.053, mae: 0.221, rse: 0.094, pcc: 0.953, kcc: 0.886
+2025-07-19 19:18:07: Epoch: 040 Test results, ap: mae: 1.216, rse: 3.990, pcc: 0.194, kcc: 0.097
+2025-07-19 19:18:37: Epoch: 041 Train Loss: 0.058, mae: 0.192, rse: 0.093, pcc: 0.953, kcc: 0.899
+2025-07-19 19:18:37: Epoch: 041 New best VALIDATION metrics
+2025-07-19 19:18:39: Epoch: 041 Test results, ap: mae: 1.251, rse: 4.246, pcc: 0.195, kcc: 0.095
+2025-07-19 19:19:09: Epoch: 042 Train Loss: 0.047, mae: 0.191, rse: 0.093, pcc: 0.953, kcc: 0.898
+2025-07-19 19:19:10: Epoch: 042 Test results, ap: mae: 1.236, rse: 4.132, pcc: 0.203, kcc: 0.101
+2025-07-19 19:19:40: Epoch: 043 Train Loss: 0.054, mae: 0.198, rse: 0.095, pcc: 0.952, kcc: 0.895
+2025-07-19 19:19:41: Epoch: 043 Test results, ap: mae: 1.221, rse: 4.026, pcc: 0.201, kcc: 0.099
+2025-07-19 19:20:11: Epoch: 044 Train Loss: 0.053, mae: 0.196, rse: 0.093, pcc: 0.953, kcc: 0.897
+2025-07-19 19:20:12: Epoch: 044 Test results, ap: mae: 1.240, rse: 4.173, pcc: 0.202, kcc: 0.100
+2025-07-19 19:20:43: Epoch: 045 Train Loss: 0.056, mae: 0.187, rse: 0.094, pcc: 0.952, kcc: 0.898
+2025-07-19 19:20:43: Epoch: 045 New best VALIDATION metrics
+2025-07-19 19:20:44: Epoch: 045 Test results, ap: mae: 1.224, rse: 4.049, pcc: 0.199, kcc: 0.099
+2025-07-19 19:21:14: Epoch: 046 Train Loss: 0.052, mae: 0.196, rse: 0.092, pcc: 0.954, kcc: 0.897
+2025-07-19 19:21:15: Epoch: 046 Test results, ap: mae: 1.227, rse: 4.071, pcc: 0.197, kcc: 0.095
+2025-07-19 19:21:45: Epoch: 047 Train Loss: 0.049, mae: 0.189, rse: 0.093, pcc: 0.954, kcc: 0.900
+2025-07-19 19:21:45: Epoch: 047 New best VALIDATION metrics
+2025-07-19 19:21:46: Epoch: 047 Test results, ap: mae: 1.256, rse: 4.254, pcc: 0.207, kcc: 0.103
+2025-07-19 19:22:16: Epoch: 048 Train Loss: 0.049, mae: 0.194, rse: 0.093, pcc: 0.953, kcc: 0.896
+2025-07-19 19:22:18: Epoch: 048 Test results, ap: mae: 1.250, rse: 4.211, pcc: 0.210, kcc: 0.106
+2025-07-19 19:22:48: Epoch: 049 Train Loss: 0.048, mae: 0.189, rse: 0.092, pcc: 0.953, kcc: 0.898
+2025-07-19 19:22:49: Epoch: 049 Test results, ap: mae: 1.223, rse: 4.032, pcc: 0.199, kcc: 0.104
+2025-07-19 19:23:20: Epoch: 050 Train Loss: 0.049, mae: 0.200, rse: 0.094, pcc: 0.953, kcc: 0.896
+2025-07-19 19:23:21: Epoch: 050 Test results, ap: mae: 1.257, rse: 4.258, pcc: 0.203, kcc: 0.102
+2025-07-19 19:23:21: used time 0.43h
+2025-07-19 19:23:21: Running Cross Validation 4
+2025-07-19 19:23:21: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 19:23:51: Epoch: 001 Train Loss: 2.344, mae: 1.129, rse: 0.838, pcc: 0.434, kcc: 0.297
+2025-07-19 19:23:51: Epoch: 001 New best VALIDATION metrics
+2025-07-19 19:23:53: Epoch: 001 Test results, ap: mae: 0.823, rse: 2.135, pcc: -0.173, kcc: -0.088
+2025-07-19 19:24:23: Epoch: 002 Train Loss: 1.799, mae: 0.928, rse: 0.568, pcc: 0.668, kcc: 0.448
+2025-07-19 19:24:23: Epoch: 002 New best VALIDATION metrics
+2025-07-19 19:24:24: Epoch: 002 Test results, ap: mae: 0.782, rse: 1.747, pcc: -0.038, kcc: -0.038
+2025-07-19 19:24:54: Epoch: 003 Train Loss: 1.360, mae: 0.994, rse: 0.655, pcc: 0.622, kcc: 0.462
+2025-07-19 19:24:55: Epoch: 003 Test results, ap: mae: 0.814, rse: 1.866, pcc: -0.062, kcc: -0.080
+2025-07-19 19:25:25: Epoch: 004 Train Loss: 1.154, mae: 0.811, rse: 0.452, pcc: 0.753, kcc: 0.547
+2025-07-19 19:25:25: Epoch: 004 New best VALIDATION metrics
+2025-07-19 19:25:26: Epoch: 004 Test results, ap: mae: 0.865, rse: 2.022, pcc: 0.166, kcc: 0.059
+2025-07-19 19:25:56: Epoch: 005 Train Loss: 0.904, mae: 0.673, rse: 0.364, pcc: 0.805, kcc: 0.600
+2025-07-19 19:25:56: Epoch: 005 New best VALIDATION metrics
+2025-07-19 19:25:58: Epoch: 005 Test results, ap: mae: 0.888, rse: 2.113, pcc: 0.328, kcc: 0.177
+2025-07-19 19:26:28: Epoch: 006 Train Loss: 0.718, mae: 0.662, rse: 0.348, pcc: 0.817, kcc: 0.620
+2025-07-19 19:26:28: Epoch: 006 New best VALIDATION metrics
+2025-07-19 19:26:29: Epoch: 006 Test results, ap: mae: 0.901, rse: 2.435, pcc: 0.031, kcc: 0.028
+2025-07-19 19:26:59: Epoch: 007 Train Loss: 0.556, mae: 0.555, rse: 0.267, pcc: 0.859, kcc: 0.673
+2025-07-19 19:26:59: Epoch: 007 New best VALIDATION metrics
+2025-07-19 19:27:00: Epoch: 007 Test results, ap: mae: 0.884, rse: 2.355, pcc: 0.115, kcc: 0.082
+2025-07-19 19:27:31: Epoch: 008 Train Loss: 0.450, mae: 0.543, rse: 0.238, pcc: 0.877, kcc: 0.676
+2025-07-19 19:27:31: Epoch: 008 New best VALIDATION metrics
+2025-07-19 19:27:32: Epoch: 008 Test results, ap: mae: 0.965, rse: 2.481, pcc: 0.271, kcc: 0.168
+2025-07-19 19:28:02: Epoch: 009 Train Loss: 0.419, mae: 0.513, rse: 0.232, pcc: 0.878, kcc: 0.690
+2025-07-19 19:28:02: Epoch: 009 New best VALIDATION metrics
+2025-07-19 19:28:03: Epoch: 009 Test results, ap: mae: 0.875, rse: 2.305, pcc: 0.129, kcc: 0.103
+2025-07-19 19:28:33: Epoch: 010 Train Loss: 0.327, mae: 0.452, rse: 0.197, pcc: 0.897, kcc: 0.735
+2025-07-19 19:28:33: Epoch: 010 New best VALIDATION metrics
+2025-07-19 19:28:35: Epoch: 010 Test results, ap: mae: 0.888, rse: 2.566, pcc: 0.103, kcc: 0.103
+2025-07-19 19:29:04: Epoch: 011 Train Loss: 0.253, mae: 0.352, rse: 0.175, pcc: 0.908, kcc: 0.791
+2025-07-19 19:29:04: Epoch: 011 New best VALIDATION metrics
+2025-07-19 19:29:06: Epoch: 011 Test results, ap: mae: 0.933, rse: 2.641, pcc: 0.115, kcc: 0.112
+2025-07-19 19:29:06: Epoch: 011 New best TEST metrics
+2025-07-19 19:29:36: Epoch: 012 Train Loss: 0.195, mae: 0.329, rse: 0.164, pcc: 0.916, kcc: 0.798
+2025-07-19 19:29:36: Epoch: 012 New best VALIDATION metrics
+2025-07-19 19:29:37: Epoch: 012 Test results, ap: mae: 0.913, rse: 2.582, pcc: 0.071, kcc: 0.081
+2025-07-19 19:29:37: Epoch: 012 New best TEST metrics
+2025-07-19 19:30:08: Epoch: 013 Train Loss: 0.176, mae: 0.320, rse: 0.150, pcc: 0.922, kcc: 0.807
+2025-07-19 19:30:08: Epoch: 013 New best VALIDATION metrics
+2025-07-19 19:30:09: Epoch: 013 Test results, ap: mae: 0.890, rse: 2.446, pcc: 0.123, kcc: 0.112
+2025-07-19 19:30:09: Epoch: 013 New best TEST metrics
+2025-07-19 19:30:39: Epoch: 014 Train Loss: 0.145, mae: 0.312, rse: 0.146, pcc: 0.924, kcc: 0.811
+2025-07-19 19:30:39: Epoch: 014 New best VALIDATION metrics
+2025-07-19 19:30:41: Epoch: 014 Test results, ap: mae: 0.867, rse: 2.369, pcc: 0.107, kcc: 0.104
+2025-07-19 19:30:41: Epoch: 014 New best TEST metrics
+2025-07-19 19:31:11: Epoch: 015 Train Loss: 0.160, mae: 0.347, rse: 0.152, pcc: 0.922, kcc: 0.806
+2025-07-19 19:31:12: Epoch: 015 Test results, ap: mae: 0.816, rse: 2.111, pcc: 0.116, kcc: 0.100
+2025-07-19 19:31:12: Epoch: 015 New best TEST metrics
+2025-07-19 19:31:42: Epoch: 016 Train Loss: 0.162, mae: 0.325, rse: 0.152, pcc: 0.925, kcc: 0.812
+2025-07-19 19:31:43: Epoch: 016 Test results, ap: mae: 0.899, rse: 2.542, pcc: 0.092, kcc: 0.080
+2025-07-19 19:32:13: Epoch: 017 Train Loss: 0.135, mae: 0.292, rse: 0.141, pcc: 0.927, kcc: 0.823
+2025-07-19 19:32:13: Epoch: 017 New best VALIDATION metrics
+2025-07-19 19:32:15: Epoch: 017 Test results, ap: mae: 0.852, rse: 2.228, pcc: 0.155, kcc: 0.130
+2025-07-19 19:32:44: Epoch: 018 Train Loss: 0.133, mae: 0.308, rse: 0.141, pcc: 0.927, kcc: 0.825
+2025-07-19 19:32:46: Epoch: 018 Test results, ap: mae: 0.878, rse: 2.399, pcc: 0.130, kcc: 0.120
+2025-07-19 19:33:16: Epoch: 019 Train Loss: 0.121, mae: 0.302, rse: 0.140, pcc: 0.929, kcc: 0.829
+2025-07-19 19:33:17: Epoch: 019 Test results, ap: mae: 0.870, rse: 2.304, pcc: 0.189, kcc: 0.141
+2025-07-19 19:33:47: Epoch: 020 Train Loss: 0.127, mae: 0.284, rse: 0.141, pcc: 0.928, kcc: 0.834
+2025-07-19 19:33:47: Epoch: 020 New best VALIDATION metrics
+2025-07-19 19:33:49: Epoch: 020 Test results, ap: mae: 0.895, rse: 2.447, pcc: 0.169, kcc: 0.133
+2025-07-19 19:34:19: Epoch: 021 Train Loss: 0.113, mae: 0.259, rse: 0.134, pcc: 0.931, kcc: 0.845
+2025-07-19 19:34:19: Epoch: 021 New best VALIDATION metrics
+2025-07-19 19:34:20: Epoch: 021 Test results, ap: mae: 0.858, rse: 2.305, pcc: 0.124, kcc: 0.111
+2025-07-19 19:34:51: Epoch: 022 Train Loss: 0.087, mae: 0.261, rse: 0.131, pcc: 0.932, kcc: 0.845
+2025-07-19 19:34:51: Epoch: 022 New best VALIDATION metrics
+2025-07-19 19:34:52: Epoch: 022 Test results, ap: mae: 0.874, rse: 2.312, pcc: 0.146, kcc: 0.127
+2025-07-19 19:35:22: Epoch: 023 Train Loss: 0.083, mae: 0.266, rse: 0.129, pcc: 0.934, kcc: 0.850
+2025-07-19 19:35:22: Epoch: 023 New best VALIDATION metrics
+2025-07-19 19:35:23: Epoch: 023 Test results, ap: mae: 0.846, rse: 2.209, pcc: 0.181, kcc: 0.133
+2025-07-19 19:35:53: Epoch: 024 Train Loss: 0.102, mae: 0.252, rse: 0.131, pcc: 0.932, kcc: 0.849
+2025-07-19 19:35:53: Epoch: 024 New best VALIDATION metrics
+2025-07-19 19:35:55: Epoch: 024 Test results, ap: mae: 0.900, rse: 2.481, pcc: 0.118, kcc: 0.103
+2025-07-19 19:36:25: Epoch: 025 Train Loss: 0.091, mae: 0.250, rse: 0.129, pcc: 0.934, kcc: 0.856
+2025-07-19 19:36:25: Epoch: 025 New best VALIDATION metrics
+2025-07-19 19:36:26: Epoch: 025 Test results, ap: mae: 0.864, rse: 2.287, pcc: 0.156, kcc: 0.124
+2025-07-19 19:36:56: Epoch: 026 Train Loss: 0.083, mae: 0.252, rse: 0.124, pcc: 0.937, kcc: 0.856
+2025-07-19 19:36:56: Epoch: 026 New best VALIDATION metrics
+2025-07-19 19:36:58: Epoch: 026 Test results, ap: mae: 0.840, rse: 2.163, pcc: 0.182, kcc: 0.135
+2025-07-19 19:36:58: Epoch: 026 New best TEST metrics
+2025-07-19 19:37:28: Epoch: 027 Train Loss: 0.084, mae: 0.259, rse: 0.131, pcc: 0.932, kcc: 0.857
+2025-07-19 19:37:29: Epoch: 027 Test results, ap: mae: 0.882, rse: 2.349, pcc: 0.146, kcc: 0.118
+2025-07-19 19:37:59: Epoch: 028 Train Loss: 0.082, mae: 0.253, rse: 0.129, pcc: 0.934, kcc: 0.852
+2025-07-19 19:38:00: Epoch: 028 Test results, ap: mae: 0.946, rse: 2.685, pcc: 0.113, kcc: 0.100
+2025-07-19 19:38:31: Epoch: 029 Train Loss: 0.084, mae: 0.253, rse: 0.135, pcc: 0.930, kcc: 0.853
+2025-07-19 19:38:32: Epoch: 029 Test results, ap: mae: 0.929, rse: 2.574, pcc: 0.126, kcc: 0.099
+2025-07-19 19:39:02: Epoch: 030 Train Loss: 0.083, mae: 0.239, rse: 0.127, pcc: 0.935, kcc: 0.859
+2025-07-19 19:39:02: Epoch: 030 New best VALIDATION metrics
+2025-07-19 19:39:03: Epoch: 030 Test results, ap: mae: 0.903, rse: 2.495, pcc: 0.103, kcc: 0.091
+2025-07-19 19:39:33: Epoch: 031 Train Loss: 0.075, mae: 0.228, rse: 0.123, pcc: 0.936, kcc: 0.868
+2025-07-19 19:39:33: Epoch: 031 New best VALIDATION metrics
+2025-07-19 19:39:34: Epoch: 031 Test results, ap: mae: 0.868, rse: 2.280, pcc: 0.149, kcc: 0.117
+2025-07-19 19:40:04: Epoch: 032 Train Loss: 0.065, mae: 0.230, rse: 0.122, pcc: 0.937, kcc: 0.871
+2025-07-19 19:40:04: Epoch: 032 New best VALIDATION metrics
+2025-07-19 19:40:05: Epoch: 032 Test results, ap: mae: 0.865, rse: 2.240, pcc: 0.160, kcc: 0.126
+2025-07-19 19:40:35: Epoch: 033 Train Loss: 0.064, mae: 0.218, rse: 0.125, pcc: 0.937, kcc: 0.871
+2025-07-19 19:40:35: Epoch: 033 New best VALIDATION metrics
+2025-07-19 19:40:36: Epoch: 033 Test results, ap: mae: 0.895, rse: 2.408, pcc: 0.138, kcc: 0.114
+2025-07-19 19:41:07: Epoch: 034 Train Loss: 0.069, mae: 0.220, rse: 0.122, pcc: 0.938, kcc: 0.871
+2025-07-19 19:41:07: Epoch: 034 New best VALIDATION metrics
+2025-07-19 19:41:08: Epoch: 034 Test results, ap: mae: 0.871, rse: 2.282, pcc: 0.164, kcc: 0.125
+2025-07-19 19:41:38: Epoch: 035 Train Loss: 0.068, mae: 0.222, rse: 0.121, pcc: 0.938, kcc: 0.872
+2025-07-19 19:41:40: Epoch: 035 Test results, ap: mae: 0.902, rse: 2.425, pcc: 0.148, kcc: 0.117
+2025-07-19 19:42:10: Epoch: 036 Train Loss: 0.062, mae: 0.232, rse: 0.124, pcc: 0.937, kcc: 0.870
+2025-07-19 19:42:11: Epoch: 036 Test results, ap: mae: 0.890, rse: 2.385, pcc: 0.138, kcc: 0.110
+2025-07-19 19:42:41: Epoch: 037 Train Loss: 0.067, mae: 0.222, rse: 0.124, pcc: 0.936, kcc: 0.873
+2025-07-19 19:42:42: Epoch: 037 Test results, ap: mae: 0.907, rse: 2.458, pcc: 0.140, kcc: 0.108
+2025-07-19 19:43:12: Epoch: 038 Train Loss: 0.066, mae: 0.209, rse: 0.119, pcc: 0.939, kcc: 0.876
+2025-07-19 19:43:12: Epoch: 038 New best VALIDATION metrics
+2025-07-19 19:43:14: Epoch: 038 Test results, ap: mae: 0.897, rse: 2.443, pcc: 0.136, kcc: 0.110
+2025-07-19 19:43:44: Epoch: 039 Train Loss: 0.070, mae: 0.234, rse: 0.123, pcc: 0.937, kcc: 0.867
+2025-07-19 19:43:45: Epoch: 039 Test results, ap: mae: 0.877, rse: 2.327, pcc: 0.141, kcc: 0.116
+2025-07-19 19:44:15: Epoch: 040 Train Loss: 0.069, mae: 0.231, rse: 0.121, pcc: 0.938, kcc: 0.871
+2025-07-19 19:44:16: Epoch: 040 Test results, ap: mae: 0.860, rse: 2.258, pcc: 0.143, kcc: 0.114
+2025-07-19 19:44:46: Epoch: 041 Train Loss: 0.062, mae: 0.215, rse: 0.121, pcc: 0.939, kcc: 0.878
+2025-07-19 19:44:48: Epoch: 041 Test results, ap: mae: 0.891, rse: 2.382, pcc: 0.147, kcc: 0.113
+2025-07-19 19:45:18: Epoch: 042 Train Loss: 0.056, mae: 0.204, rse: 0.120, pcc: 0.939, kcc: 0.881
+2025-07-19 19:45:18: Epoch: 042 New best VALIDATION metrics
+2025-07-19 19:45:19: Epoch: 042 Test results, ap: mae: 0.902, rse: 2.464, pcc: 0.136, kcc: 0.106
+2025-07-19 19:45:49: Epoch: 043 Train Loss: 0.058, mae: 0.212, rse: 0.122, pcc: 0.938, kcc: 0.877
+2025-07-19 19:45:50: Epoch: 043 Test results, ap: mae: 0.898, rse: 2.400, pcc: 0.139, kcc: 0.110
+2025-07-19 19:46:21: Epoch: 044 Train Loss: 0.060, mae: 0.217, rse: 0.122, pcc: 0.938, kcc: 0.879
+2025-07-19 19:46:22: Epoch: 044 Test results, ap: mae: 0.919, rse: 2.521, pcc: 0.134, kcc: 0.103
+2025-07-19 19:46:52: Epoch: 045 Train Loss: 0.059, mae: 0.211, rse: 0.119, pcc: 0.939, kcc: 0.880
+2025-07-19 19:46:53: Epoch: 045 Test results, ap: mae: 0.892, rse: 2.379, pcc: 0.138, kcc: 0.110
+2025-07-19 19:47:24: Epoch: 046 Train Loss: 0.055, mae: 0.220, rse: 0.118, pcc: 0.940, kcc: 0.879
+2025-07-19 19:47:25: Epoch: 046 Test results, ap: mae: 0.884, rse: 2.348, pcc: 0.144, kcc: 0.114
+2025-07-19 19:47:55: Epoch: 047 Train Loss: 0.058, mae: 0.208, rse: 0.117, pcc: 0.940, kcc: 0.882
+2025-07-19 19:47:55: Epoch: 047 New best VALIDATION metrics
+2025-07-19 19:47:57: Epoch: 047 Test results, ap: mae: 0.913, rse: 2.486, pcc: 0.137, kcc: 0.104
+2025-07-19 19:48:27: Epoch: 048 Train Loss: 0.054, mae: 0.212, rse: 0.120, pcc: 0.939, kcc: 0.879
+2025-07-19 19:48:28: Epoch: 048 Test results, ap: mae: 0.901, rse: 2.451, pcc: 0.133, kcc: 0.114
+2025-07-19 19:48:58: Epoch: 049 Train Loss: 0.052, mae: 0.213, rse: 0.118, pcc: 0.940, kcc: 0.879
+2025-07-19 19:49:00: Epoch: 049 Test results, ap: mae: 0.894, rse: 2.397, pcc: 0.137, kcc: 0.110
+2025-07-19 19:49:30: Epoch: 050 Train Loss: 0.056, mae: 0.207, rse: 0.118, pcc: 0.940, kcc: 0.883
+2025-07-19 19:49:30: Epoch: 050 New best VALIDATION metrics
+2025-07-19 19:49:31: Epoch: 050 Test results, ap: mae: 0.893, rse: 2.423, pcc: 0.133, kcc: 0.110
+2025-07-19 19:49:31: used time 0.44h
+2025-07-19 19:49:31: Cross Validation Finished!
+2025-07-19 19:49:31: Best validation perform list
+[[0.15451472 0.06834846 0.96568006 0.88690841]
+ [0.19203474 0.07551187 0.96265066 0.89131361]
+ [0.19318339 0.08979634 0.95486528 0.88303214]
+ [0.18943752 0.09288565 0.95350653 0.89985579]
+ [0.20661853 0.11785129 0.93976271 0.88330954]]
+2025-07-19 19:49:31: mean: [0.187 0.089 0.955 0.889]
+2025-07-19 19:49:31: std: [0.017 0.017 0.009 0.006]
+2025-07-19 19:49:31: Best test perform list
+[[0.87117219 2.12957168 0.07718965 0.03850834]
+ [0.68376589 1.3765496  0.37397563 0.22654004]
+ [0.85231709 2.10298252 0.28332445 0.13911571]
+ [0.94678849 2.44216228 0.08085858 0.01561149]
+ [0.8400948  2.16296887 0.1817822  0.13460572]]
+2025-07-19 19:49:31: mean: [0.839 2.043 0.199 0.111]
+2025-07-19 19:49:31: std: [0.086 0.355 0.116 0.076]
diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_Anoplin/config.json b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_Anoplin/config.json
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diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_Anoplin/training.log b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_Anoplin/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..9766f3c036969c44090fb0f2c8fd0c4f399c1666
--- /dev/null
+++ b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_Anoplin/training.log
@@ -0,0 +1,18 @@
+2025-08-09 13:42:49: saving_dir: ./run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_Anoplin
+2025-08-09 13:42:49: Loading Training Dataset
+2025-08-09 13:43:18: Start UDA training
+2025-08-09 13:44:50: Epoch 1 Train Loss: 2.398
+2025-08-09 13:46:19: Epoch 2 Train Loss: 1.36
+2025-08-09 13:47:49: Epoch 3 Train Loss: 0.9227
+2025-08-09 13:49:18: Epoch 4 Train Loss: 0.5936
+2025-08-09 13:50:47: Epoch 5 Train Loss: 0.4856
+2025-08-09 13:52:17: Epoch 6 Train Loss: 0.4
+2025-08-09 13:53:45: Epoch 7 Train Loss: 0.3511
+2025-08-09 13:55:14: Epoch 8 Train Loss: 0.3008
+2025-08-09 13:56:44: Epoch 9 Train Loss: 0.2728
+2025-08-09 13:58:14: Epoch 10 Train Loss: 0.2357
+2025-08-09 13:59:41: Epoch 11 Train Loss: 0.2332
+2025-08-09 14:01:10: Epoch 12 Train Loss: 0.2042
+2025-08-09 14:02:40: Epoch 13 Train Loss: 0.2128
+2025-08-09 14:04:09: Epoch 14 Train Loss: 0.1914
+2025-08-09 14:04:09: UDA training finished
diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_Indolicidin/config.json b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_Indolicidin/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..fffa560b63c2a9c55ef7aa20a9323820f309f525
--- /dev/null
+++ b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_Indolicidin/config.json
@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": "mamba", "channels": 16, "fusion": "diff", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 42, "pcs": true, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "case": "Indolicidin", "simple": false, "pt": false, "classes": 1}
\ No newline at end of file
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+2025-08-09 13:26:27: Start UDA training
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+2025-08-09 13:30:34: Epoch 3 Train Loss: 0.866
+2025-08-09 13:31:55: Epoch 4 Train Loss: 0.6028
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+2025-08-09 13:35:59: Epoch 7 Train Loss: 0.2899
+2025-08-09 13:37:20: Epoch 8 Train Loss: 0.2635
+2025-08-09 13:38:41: Epoch 9 Train Loss: 0.2287
+2025-08-09 13:40:02: Epoch 10 Train Loss: 0.2276
+2025-08-09 13:41:23: Epoch 11 Train Loss: 0.2131
+2025-08-09 13:42:45: Epoch 12 Train Loss: 0.183
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diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/preds_case.csv b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/preds_case.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b0203a6b0a10d19b1252dddf1afd857c83175fc2
--- /dev/null
+++ b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/preds_case.csv
@@ -0,0 +1,16384 @@
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diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/preds_case_last.csv b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/preds_case_last.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f5ceb1fdd9636d4cb11d10b2d0e9cefbf2b72277
--- /dev/null
+++ b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/preds_case_last.csv
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diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/preds_r2_case.csv b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/preds_r2_case.csv
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+++ b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/preds_r2_case.csv
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diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/preds_test.csv b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/preds_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..44025f84c705735301e11931d64e201dda9d586a
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diff --git a/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/training.log b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..32859e4caa4357399af0d44ecb59b1da90d282e6
--- /dev/null
+++ b/run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2/training.log
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+2025-08-24 21:31:46: saving_dir: ./run-reg/rn18-diff-16-mamba-pcs-768-mse-32-0.001-50/uda_r2
+2025-08-24 21:31:46: Loading Training Dataset
+2025-08-24 21:32:11: Loading Validation Dataset
+2025-08-24 21:32:13: Start UDA training
+2025-08-24 21:33:37: Epoch 1 Train Loss: 2.4002 Val: mae: 0.5689 rse: 1.3516 pcc: -0.5081 kcc: -0.3418
+2025-08-24 21:33:37: Epoch: 1 New best VAL metrics
+2025-08-24 21:34:58: Epoch 2 Train Loss: 1.3213 Val: mae: 0.5524 rse: 1.2695 pcc: 0.3027 kcc: 0.2150
+2025-08-24 21:34:58: Epoch: 2 New best VAL metrics
+2025-08-24 21:36:20: Epoch 3 Train Loss: 0.8927 Val: mae: 0.5494 rse: 1.2514 pcc: 0.6508 kcc: 0.4634
+2025-08-24 21:36:20: Epoch: 3 New best VAL metrics
+2025-08-24 21:37:41: Epoch 4 Train Loss: 0.6445 Val: mae: 0.5461 rse: 1.2327 pcc: 0.2403 kcc: 0.1979
+2025-08-24 21:39:03: Epoch 5 Train Loss: 0.4883 Val: mae: 0.5480 rse: 1.2408 pcc: 0.1537 kcc: 0.1208
+2025-08-24 21:40:24: Epoch 6 Train Loss: 0.3688 Val: mae: 0.5419 rse: 1.2084 pcc: 0.3390 kcc: 0.2270
+2025-08-24 21:41:46: Epoch 7 Train Loss: 0.3327 Val: mae: 0.5386 rse: 1.1903 pcc: 0.4377 kcc: 0.2989
+2025-08-24 21:43:07: Epoch 8 Train Loss: 0.2822 Val: mae: 0.5293 rse: 1.1437 pcc: 0.6460 kcc: 0.4685
+2025-08-24 21:43:07: Epoch: 8 New best VAL metrics
+2025-08-24 21:44:29: Epoch 9 Train Loss: 0.2385 Val: mae: 0.5273 rse: 1.1329 pcc: 0.7042 kcc: 0.5199
+2025-08-24 21:44:29: Epoch: 9 New best VAL metrics
+2025-08-24 21:45:50: Epoch 10 Train Loss: 0.2208 Val: mae: 0.5240 rse: 1.1151 pcc: 0.7316 kcc: 0.5234
+2025-08-24 21:45:50: Epoch: 10 New best VAL metrics
+2025-08-24 21:47:12: Epoch 11 Train Loss: 0.2023 Val: mae: 0.5225 rse: 1.1047 pcc: 0.7347 kcc: 0.5199
+2025-08-24 21:47:12: Epoch: 11 New best VAL metrics
+2025-08-24 21:48:33: Epoch 12 Train Loss: 0.2066 Val: mae: 0.5206 rse: 1.0945 pcc: 0.7475 kcc: 0.5456
+2025-08-24 21:48:33: Epoch: 12 New best VAL metrics
+2025-08-24 21:49:54: Epoch 13 Train Loss: 0.2075 Val: mae: 0.5212 rse: 1.0970 pcc: 0.7353 kcc: 0.5234
+2025-08-24 21:51:15: Epoch 14 Train Loss: 0.1983 Val: mae: 0.5237 rse: 1.1085 pcc: 0.7011 kcc: 0.4754
+2025-08-24 21:52:36: Epoch 15 Train Loss: 0.1708 Val: mae: 0.5256 rse: 1.1197 pcc: 0.6958 kcc: 0.4651
+2025-08-24 21:53:58: Epoch 16 Train Loss: 0.1636 Val: mae: 0.5240 rse: 1.1115 pcc: 0.6903 kcc: 0.4634
+2025-08-24 21:55:19: Epoch 17 Train Loss: 0.1736 Val: mae: 0.5227 rse: 1.1055 pcc: 0.7055 kcc: 0.4771
+2025-08-24 21:56:41: Epoch 18 Train Loss: 0.1563 Val: mae: 0.5215 rse: 1.1002 pcc: 0.6954 kcc: 0.4548
+2025-08-24 21:58:02: Epoch 19 Train Loss: 0.1420 Val: mae: 0.5209 rse: 1.0978 pcc: 0.6513 kcc: 0.4069
+2025-08-24 21:59:24: Epoch 20 Train Loss: 0.1355 Val: mae: 0.5184 rse: 1.0882 pcc: 0.6203 kcc: 0.3829
+2025-08-24 22:00:45: Epoch 21 Train Loss: 0.1430 Val: mae: 0.5169 rse: 1.0816 pcc: 0.5759 kcc: 0.3520
+2025-08-24 22:02:07: Epoch 22 Train Loss: 0.1416 Val: mae: 0.5170 rse: 1.0853 pcc: 0.5663 kcc: 0.3555
+2025-08-24 22:03:28: Epoch 23 Train Loss: 0.1367 Val: mae: 0.5146 rse: 1.0735 pcc: 0.5486 kcc: 0.3486
+2025-08-24 22:04:49: Epoch 24 Train Loss: 0.1443 Val: mae: 0.5163 rse: 1.0839 pcc: 0.5339 kcc: 0.3418
+2025-08-24 22:06:10: Epoch 25 Train Loss: 0.1483 Val: mae: 0.5184 rse: 1.0952 pcc: 0.5248 kcc: 0.3366
+2025-08-24 22:07:32: Epoch 26 Train Loss: 0.1251 Val: mae: 0.5174 rse: 1.0905 pcc: 0.5360 kcc: 0.3486
+2025-08-24 22:08:53: Epoch 27 Train Loss: 0.1204 Val: mae: 0.5170 rse: 1.0911 pcc: 0.5435 kcc: 0.3777
+2025-08-24 22:10:14: Epoch 28 Train Loss: 0.1194 Val: mae: 0.5209 rse: 1.1075 pcc: 0.5489 kcc: 0.3743
+2025-08-24 22:11:36: Epoch 29 Train Loss: 0.1067 Val: mae: 0.5269 rse: 1.1346 pcc: 0.5310 kcc: 0.3486
+2025-08-24 22:12:57: Epoch 30 Train Loss: 0.0939 Val: mae: 0.5281 rse: 1.1405 pcc: 0.5750 kcc: 0.3846
+2025-08-24 22:14:18: Epoch 31 Train Loss: 0.1099 Val: mae: 0.5291 rse: 1.1442 pcc: 0.5906 kcc: 0.3932
+2025-08-24 22:15:39: Epoch 32 Train Loss: 0.0991 Val: mae: 0.5309 rse: 1.1513 pcc: 0.6020 kcc: 0.4000
+2025-08-24 22:17:00: Epoch 33 Train Loss: 0.0924 Val: mae: 0.5337 rse: 1.1656 pcc: 0.6004 kcc: 0.3949
+2025-08-24 22:18:22: Epoch 34 Train Loss: 0.0975 Val: mae: 0.5369 rse: 1.1851 pcc: 0.5740 kcc: 0.3760
+2025-08-24 22:19:43: Epoch 35 Train Loss: 0.0858 Val: mae: 0.5383 rse: 1.1922 pcc: 0.6030 kcc: 0.4051
+2025-08-24 22:21:05: Epoch 36 Train Loss: 0.0800 Val: mae: 0.5406 rse: 1.2035 pcc: 0.6007 kcc: 0.4034
+2025-08-24 22:22:26: Epoch 37 Train Loss: 0.0787 Val: mae: 0.5424 rse: 1.2130 pcc: 0.5911 kcc: 0.4069
+2025-08-24 22:23:47: Epoch 38 Train Loss: 0.0969 Val: mae: 0.5418 rse: 1.2109 pcc: 0.5828 kcc: 0.4223
+2025-08-24 22:25:09: Epoch 39 Train Loss: 0.0839 Val: mae: 0.5418 rse: 1.2081 pcc: 0.6214 kcc: 0.4137
+2025-08-24 22:26:30: Epoch 40 Train Loss: 0.0802 Val: mae: 0.5416 rse: 1.2092 pcc: 0.6158 kcc: 0.4206
+2025-08-24 22:27:51: Epoch 41 Train Loss: 0.0823 Val: mae: 0.5416 rse: 1.2105 pcc: 0.6079 kcc: 0.4274
+2025-08-24 22:29:12: Epoch 42 Train Loss: 0.0817 Val: mae: 0.5435 rse: 1.2196 pcc: 0.6280 kcc: 0.4223
+2025-08-24 22:30:33: Epoch 43 Train Loss: 0.0789 Val: mae: 0.5453 rse: 1.2255 pcc: 0.6298 kcc: 0.4223
+2025-08-24 22:31:55: Epoch 44 Train Loss: 0.0815 Val: mae: 0.5422 rse: 1.2150 pcc: 0.6060 kcc: 0.4463
+2025-08-24 22:33:16: Epoch 45 Train Loss: 0.0755 Val: mae: 0.5451 rse: 1.2282 pcc: 0.6111 kcc: 0.4377
+2025-08-24 22:34:38: Epoch 46 Train Loss: 0.0812 Val: mae: 0.5456 rse: 1.2331 pcc: 0.6238 kcc: 0.4428
+2025-08-24 22:35:59: Epoch 47 Train Loss: 0.0737 Val: mae: 0.5477 rse: 1.2412 pcc: 0.6275 kcc: 0.4120
+2025-08-24 22:37:20: Epoch 48 Train Loss: 0.0685 Val: mae: 0.5478 rse: 1.2426 pcc: 0.6086 kcc: 0.4086
+2025-08-24 22:38:41: Epoch 49 Train Loss: 0.0701 Val: mae: 0.5493 rse: 1.2517 pcc: 0.6085 kcc: 0.4240
+2025-08-24 22:40:03: Epoch 50 Train Loss: 0.0728 Val: mae: 0.5523 rse: 1.2671 pcc: 0.6216 kcc: 0.4377
+2025-08-24 22:40:03: UDA training finished
\ No newline at end of file
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@@ -0,0 +1,8 @@
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+0.7094451,1.8543679,0.81342477,0.14509232,-0.2811723,0.64823157,4.5338497
+0.52945316,1.1735715,0.87084574,-0.019454142,0.74599534,0.6600823,0.1681648
+0.03722284,2.5762765,0.64089257,0.41750723,-0.21468376,0.6914431,4.1952624
+0.3120494,2.348806,0.71347636,0.15942587,0.71591955,0.84993535,0.51158845
+2.323228,1.7793983,2.61202,1.216462,1.8086604,1.9479538,1.6666666
+0.70074004,0.81206137,0.17428428,0.05918475,-0.08992197,0.3312697,0.2
+0.20794843,1.4257663,0.8075228,0.8910285,0.15297052,0.69704735,-0.16666667
diff --git a/run-reg/rn18-diff-16-pcs-768-mse-32-0.001-50/result.txt b/run-reg/rn18-diff-16-pcs-768-mse-32-0.001-50/result.txt
new file mode 100644
index 0000000000000000000000000000000000000000..455d42fcd4cdb0aa3036fb88ea6acfa9e74ade09
--- /dev/null
+++ b/run-reg/rn18-diff-16-pcs-768-mse-32-0.001-50/result.txt
@@ -0,0 +1,6 @@
+Valid
+0.27019732594490053,0.09723109751939774,0.9523546576499939,0.8477108359336853
+0.018509423670048587,0.022955041050943616,0.012835531041722057,0.010934086746135812
+Test
+0.6820284724235535,1.3657851219177246,0.25061834454536436,0.16013917326927185
+0.04557725420995127,0.20043021354838755,0.06409163830739946,0.023705305386351
diff --git a/run-reg/rn18-diff-16-pcs-768-mse-32-0.001-50/training.log b/run-reg/rn18-diff-16-pcs-768-mse-32-0.001-50/training.log
new file mode 100644
index 0000000000000000000000000000000000000000..ebe3eeb808fa42b0e5091b8b353fe25208f846c4
--- /dev/null
+++ b/run-reg/rn18-diff-16-pcs-768-mse-32-0.001-50/training.log
@@ -0,0 +1,694 @@
+2025-07-19 17:40:09: saving_dir: ./run-reg/rn18-diff-16-pcs-768-mse-32-0.001-50
+2025-07-19 17:40:09: Loading Training Dataset
+2025-07-19 17:40:35: Loading Test Dataset
+2025-07-19 17:40:39: Running Cross Validation 0
+2025-07-19 17:40:39: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-19 17:41:16: Epoch: 001 Train Loss: 2.416, mae: 1.092, rse: 0.871, pcc: 0.419, kcc: 0.321
+2025-07-19 17:41:16: Epoch: 001 New best VALIDATION metrics
+2025-07-19 17:41:17: Epoch: 001 Test results, ap: mae: 1.018, rse: 3.315, pcc: -0.228, kcc: -0.107
+2025-07-19 17:41:52: Epoch: 002 Train Loss: 1.892, mae: 1.028, rse: 0.733, pcc: 0.573, kcc: 0.362
+2025-07-19 17:41:52: Epoch: 002 New best VALIDATION metrics
+2025-07-19 17:41:53: Epoch: 002 Test results, ap: mae: 0.597, rse: 1.092, pcc: 0.125, kcc: 0.075
+2025-07-19 17:42:27: Epoch: 003 Train Loss: 1.575, mae: 0.917, rse: 0.566, pcc: 0.674, kcc: 0.438
+2025-07-19 17:42:27: Epoch: 003 New best VALIDATION metrics
+2025-07-19 17:42:29: Epoch: 003 Test results, ap: mae: 0.586, rse: 1.052, pcc: 0.175, kcc: 0.082
+2025-07-19 17:43:04: Epoch: 004 Train Loss: 1.290, mae: 0.840, rse: 0.482, pcc: 0.726, kcc: 0.496
+2025-07-19 17:43:04: Epoch: 004 New best VALIDATION metrics
+2025-07-19 17:43:06: Epoch: 004 Test results, ap: mae: 0.689, rse: 1.441, pcc: 0.034, kcc: 0.018
+2025-07-19 17:43:40: Epoch: 005 Train Loss: 1.007, mae: 0.742, rse: 0.380, pcc: 0.800, kcc: 0.568
+2025-07-19 17:43:40: Epoch: 005 New best VALIDATION metrics
+2025-07-19 17:43:42: Epoch: 005 Test results, ap: mae: 0.585, rse: 1.086, pcc: 0.176, kcc: 0.132
+2025-07-19 17:44:16: Epoch: 006 Train Loss: 0.893, mae: 0.643, rse: 0.307, pcc: 0.840, kcc: 0.643
+2025-07-19 17:44:16: Epoch: 006 New best VALIDATION metrics
+2025-07-19 17:44:18: Epoch: 006 Test results, ap: mae: 0.671, rse: 1.417, pcc: 0.252, kcc: 0.162
+2025-07-19 17:44:52: Epoch: 007 Train Loss: 0.775, mae: 0.664, rse: 0.317, pcc: 0.851, kcc: 0.648
+2025-07-19 17:44:54: Epoch: 007 Test results, ap: mae: 0.603, rse: 1.146, pcc: 0.152, kcc: 0.181
+2025-07-19 17:45:28: Epoch: 008 Train Loss: 0.631, mae: 0.603, rse: 0.271, pcc: 0.857, kcc: 0.668
+2025-07-19 17:45:28: Epoch: 008 New best VALIDATION metrics
+2025-07-19 17:45:30: Epoch: 008 Test results, ap: mae: 0.587, rse: 1.107, pcc: 0.287, kcc: 0.228
+2025-07-19 17:46:04: Epoch: 009 Train Loss: 0.604, mae: 0.555, rse: 0.250, pcc: 0.876, kcc: 0.700
+2025-07-19 17:46:04: Epoch: 009 New best VALIDATION metrics
+2025-07-19 17:46:06: Epoch: 009 Test results, ap: mae: 0.583, rse: 1.057, pcc: 0.313, kcc: 0.226
+2025-07-19 17:46:40: Epoch: 010 Train Loss: 0.510, mae: 0.577, rse: 0.257, pcc: 0.864, kcc: 0.663
+2025-07-19 17:46:42: Epoch: 010 Test results, ap: mae: 0.687, rse: 1.498, pcc: 0.229, kcc: 0.195
+2025-07-19 17:47:16: Epoch: 011 Train Loss: 0.402, mae: 0.432, rse: 0.168, pcc: 0.914, kcc: 0.748
+2025-07-19 17:47:16: Epoch: 011 New best VALIDATION metrics
+2025-07-19 17:47:18: Epoch: 011 Test results, ap: mae: 0.733, rse: 1.655, pcc: 0.315, kcc: 0.220
+2025-07-19 17:47:18: Epoch: 011 New best TEST metrics
+2025-07-19 17:47:52: Epoch: 012 Train Loss: 0.347, mae: 0.427, rse: 0.167, pcc: 0.916, kcc: 0.749
+2025-07-19 17:47:52: Epoch: 012 New best VALIDATION metrics
+2025-07-19 17:47:54: Epoch: 012 Test results, ap: mae: 0.828, rse: 2.040, pcc: 0.294, kcc: 0.199
+2025-07-19 17:48:29: Epoch: 013 Train Loss: 0.288, mae: 0.440, rse: 0.174, pcc: 0.914, kcc: 0.748
+2025-07-19 17:48:30: Epoch: 013 Test results, ap: mae: 0.805, rse: 1.986, pcc: 0.271, kcc: 0.173
+2025-07-19 17:49:05: Epoch: 014 Train Loss: 0.269, mae: 0.402, rse: 0.154, pcc: 0.923, kcc: 0.768
+2025-07-19 17:49:05: Epoch: 014 New best VALIDATION metrics
+2025-07-19 17:49:06: Epoch: 014 Test results, ap: mae: 0.820, rse: 2.000, pcc: 0.241, kcc: 0.162
+2025-07-19 17:49:40: Epoch: 015 Train Loss: 0.250, mae: 0.408, rse: 0.160, pcc: 0.918, kcc: 0.756
+2025-07-19 17:49:42: Epoch: 015 Test results, ap: mae: 0.755, rse: 1.728, pcc: 0.272, kcc: 0.192
+2025-07-19 17:50:16: Epoch: 016 Train Loss: 0.239, mae: 0.405, rse: 0.160, pcc: 0.919, kcc: 0.769
+2025-07-19 17:50:18: Epoch: 016 Test results, ap: mae: 0.819, rse: 2.016, pcc: 0.248, kcc: 0.172
+2025-07-19 17:50:53: Epoch: 017 Train Loss: 0.231, mae: 0.379, rse: 0.142, pcc: 0.931, kcc: 0.787
+2025-07-19 17:50:53: Epoch: 017 New best VALIDATION metrics
+2025-07-19 17:50:55: Epoch: 017 Test results, ap: mae: 0.779, rse: 1.876, pcc: 0.227, kcc: 0.175
+2025-07-19 17:51:30: Epoch: 018 Train Loss: 0.213, mae: 0.368, rse: 0.139, pcc: 0.929, kcc: 0.775
+2025-07-19 17:51:30: Epoch: 018 New best VALIDATION metrics
+2025-07-19 17:51:31: Epoch: 018 Test results, ap: mae: 0.781, rse: 1.741, pcc: 0.242, kcc: 0.168
+2025-07-19 17:52:06: Epoch: 019 Train Loss: 0.214, mae: 0.403, rse: 0.152, pcc: 0.927, kcc: 0.774
+2025-07-19 17:52:07: Epoch: 019 Test results, ap: mae: 0.741, rse: 1.657, pcc: 0.264, kcc: 0.190
+2025-07-19 17:52:41: Epoch: 020 Train Loss: 0.233, mae: 0.383, rse: 0.135, pcc: 0.931, kcc: 0.784
+2025-07-19 17:52:41: Epoch: 020 New best VALIDATION metrics
+2025-07-19 17:52:43: Epoch: 020 Test results, ap: mae: 0.786, rse: 1.786, pcc: 0.214, kcc: 0.156
+2025-07-19 17:53:17: Epoch: 021 Train Loss: 0.189, mae: 0.319, rse: 0.117, pcc: 0.940, kcc: 0.807
+2025-07-19 17:53:17: Epoch: 021 New best VALIDATION metrics
+2025-07-19 17:53:19: Epoch: 021 Test results, ap: mae: 0.767, rse: 1.746, pcc: 0.218, kcc: 0.179
+2025-07-19 17:53:54: Epoch: 022 Train Loss: 0.168, mae: 0.338, rse: 0.129, pcc: 0.935, kcc: 0.797
+2025-07-19 17:53:55: Epoch: 022 Test results, ap: mae: 0.804, rse: 1.934, pcc: 0.242, kcc: 0.176
+2025-07-19 17:54:29: Epoch: 023 Train Loss: 0.159, mae: 0.337, rse: 0.117, pcc: 0.941, kcc: 0.801
+2025-07-19 17:54:31: Epoch: 023 Test results, ap: mae: 0.784, rse: 1.851, pcc: 0.215, kcc: 0.161
+2025-07-19 17:55:06: Epoch: 024 Train Loss: 0.182, mae: 0.336, rse: 0.121, pcc: 0.938, kcc: 0.803
+2025-07-19 17:55:07: Epoch: 024 Test results, ap: mae: 0.789, rse: 1.870, pcc: 0.221, kcc: 0.168
+2025-07-19 17:55:42: Epoch: 025 Train Loss: 0.154, mae: 0.339, rse: 0.125, pcc: 0.939, kcc: 0.804
+2025-07-19 17:55:44: Epoch: 025 Test results, ap: mae: 0.804, rse: 1.911, pcc: 0.237, kcc: 0.190
+2025-07-19 17:56:18: Epoch: 026 Train Loss: 0.151, mae: 0.328, rse: 0.122, pcc: 0.939, kcc: 0.811
+2025-07-19 17:56:20: Epoch: 026 Test results, ap: mae: 0.801, rse: 1.861, pcc: 0.272, kcc: 0.202
+2025-07-19 17:56:54: Epoch: 027 Train Loss: 0.153, mae: 0.325, rse: 0.117, pcc: 0.942, kcc: 0.808
+2025-07-19 17:56:56: Epoch: 027 Test results, ap: mae: 0.754, rse: 1.709, pcc: 0.217, kcc: 0.180
+2025-07-19 17:57:30: Epoch: 028 Train Loss: 0.152, mae: 0.316, rse: 0.115, pcc: 0.942, kcc: 0.814
+2025-07-19 17:57:30: Epoch: 028 New best VALIDATION metrics
+2025-07-19 17:57:32: Epoch: 028 Test results, ap: mae: 0.834, rse: 2.132, pcc: 0.200, kcc: 0.177
+2025-07-19 17:58:06: Epoch: 029 Train Loss: 0.161, mae: 0.331, rse: 0.116, pcc: 0.942, kcc: 0.809
+2025-07-19 17:58:08: Epoch: 029 Test results, ap: mae: 0.703, rse: 1.466, pcc: 0.220, kcc: 0.176
+2025-07-19 17:58:08: Epoch: 029 New best TEST metrics
+2025-07-19 17:58:42: Epoch: 030 Train Loss: 0.150, mae: 0.326, rse: 0.115, pcc: 0.943, kcc: 0.815
+2025-07-19 17:58:44: Epoch: 030 Test results, ap: mae: 0.785, rse: 1.826, pcc: 0.236, kcc: 0.191
+2025-07-19 17:59:18: Epoch: 031 Train Loss: 0.136, mae: 0.316, rse: 0.114, pcc: 0.945, kcc: 0.821
+2025-07-19 17:59:18: Epoch: 031 New best VALIDATION metrics
+2025-07-19 17:59:19: Epoch: 031 Test results, ap: mae: 0.802, rse: 1.977, pcc: 0.213, kcc: 0.181
+2025-07-19 17:59:54: Epoch: 032 Train Loss: 0.135, mae: 0.293, rse: 0.110, pcc: 0.946, kcc: 0.825
+2025-07-19 17:59:54: Epoch: 032 New best VALIDATION metrics
+2025-07-19 17:59:55: Epoch: 032 Test results, ap: mae: 0.806, rse: 1.976, pcc: 0.225, kcc: 0.181
+2025-07-19 18:00:30: Epoch: 033 Train Loss: 0.134, mae: 0.314, rse: 0.111, pcc: 0.945, kcc: 0.822
+2025-07-19 18:00:31: Epoch: 033 Test results, ap: mae: 0.764, rse: 1.799, pcc: 0.218, kcc: 0.183
+2025-07-19 18:01:05: Epoch: 034 Train Loss: 0.123, mae: 0.301, rse: 0.109, pcc: 0.946, kcc: 0.824
+2025-07-19 18:01:07: Epoch: 034 Test results, ap: mae: 0.792, rse: 1.997, pcc: 0.215, kcc: 0.183
+2025-07-19 18:01:41: Epoch: 035 Train Loss: 0.132, mae: 0.300, rse: 0.107, pcc: 0.947, kcc: 0.821
+2025-07-19 18:01:43: Epoch: 035 Test results, ap: mae: 0.729, rse: 1.646, pcc: 0.229, kcc: 0.192
+2025-07-19 18:02:17: Epoch: 036 Train Loss: 0.124, mae: 0.285, rse: 0.106, pcc: 0.948, kcc: 0.828
+2025-07-19 18:02:17: Epoch: 036 New best VALIDATION metrics
+2025-07-19 18:02:19: Epoch: 036 Test results, ap: mae: 0.787, rse: 1.934, pcc: 0.214, kcc: 0.187
+2025-07-19 18:02:53: Epoch: 037 Train Loss: 0.124, mae: 0.299, rse: 0.112, pcc: 0.946, kcc: 0.826
+2025-07-19 18:02:55: Epoch: 037 Test results, ap: mae: 0.780, rse: 1.876, pcc: 0.218, kcc: 0.185
+2025-07-19 18:03:29: Epoch: 038 Train Loss: 0.129, mae: 0.294, rse: 0.107, pcc: 0.947, kcc: 0.827
+2025-07-19 18:03:31: Epoch: 038 Test results, ap: mae: 0.806, rse: 1.966, pcc: 0.233, kcc: 0.192
+2025-07-19 18:04:05: Epoch: 039 Train Loss: 0.120, mae: 0.291, rse: 0.107, pcc: 0.947, kcc: 0.827
+2025-07-19 18:04:07: Epoch: 039 Test results, ap: mae: 0.777, rse: 1.881, pcc: 0.236, kcc: 0.197
+2025-07-19 18:04:41: Epoch: 040 Train Loss: 0.120, mae: 0.296, rse: 0.107, pcc: 0.947, kcc: 0.830
+2025-07-19 18:04:43: Epoch: 040 Test results, ap: mae: 0.789, rse: 1.858, pcc: 0.257, kcc: 0.203
+2025-07-19 18:05:17: Epoch: 041 Train Loss: 0.109, mae: 0.284, rse: 0.104, pcc: 0.950, kcc: 0.834
+2025-07-19 18:05:17: Epoch: 041 New best VALIDATION metrics
+2025-07-19 18:05:18: Epoch: 041 Test results, ap: mae: 0.784, rse: 1.901, pcc: 0.233, kcc: 0.197
+2025-07-19 18:05:53: Epoch: 042 Train Loss: 0.108, mae: 0.282, rse: 0.100, pcc: 0.951, kcc: 0.838
+2025-07-19 18:05:53: Epoch: 042 New best VALIDATION metrics
+2025-07-19 18:05:54: Epoch: 042 Test results, ap: mae: 0.742, rse: 1.683, pcc: 0.225, kcc: 0.206
+2025-07-19 18:06:28: Epoch: 043 Train Loss: 0.110, mae: 0.290, rse: 0.103, pcc: 0.950, kcc: 0.834
+2025-07-19 18:06:30: Epoch: 043 Test results, ap: mae: 0.739, rse: 1.714, pcc: 0.222, kcc: 0.188
+2025-07-19 18:07:05: Epoch: 044 Train Loss: 0.109, mae: 0.280, rse: 0.099, pcc: 0.951, kcc: 0.839
+2025-07-19 18:07:05: Epoch: 044 New best VALIDATION metrics
+2025-07-19 18:07:07: Epoch: 044 Test results, ap: mae: 0.751, rse: 1.729, pcc: 0.229, kcc: 0.188
+2025-07-19 18:07:41: Epoch: 045 Train Loss: 0.098, mae: 0.287, rse: 0.106, pcc: 0.950, kcc: 0.835
+2025-07-19 18:07:43: Epoch: 045 Test results, ap: mae: 0.786, rse: 1.912, pcc: 0.230, kcc: 0.188
+2025-07-19 18:08:17: Epoch: 046 Train Loss: 0.103, mae: 0.285, rse: 0.100, pcc: 0.952, kcc: 0.840
+2025-07-19 18:08:19: Epoch: 046 Test results, ap: mae: 0.757, rse: 1.767, pcc: 0.234, kcc: 0.198
+2025-07-19 18:08:53: Epoch: 047 Train Loss: 0.099, mae: 0.270, rse: 0.097, pcc: 0.952, kcc: 0.841
+2025-07-19 18:08:53: Epoch: 047 New best VALIDATION metrics
+2025-07-19 18:08:55: Epoch: 047 Test results, ap: mae: 0.763, rse: 1.815, pcc: 0.231, kcc: 0.190
+2025-07-19 18:09:30: Epoch: 048 Train Loss: 0.098, mae: 0.279, rse: 0.100, pcc: 0.952, kcc: 0.840
+2025-07-19 18:09:31: Epoch: 048 Test results, ap: mae: 0.790, rse: 1.934, pcc: 0.227, kcc: 0.190
+2025-07-19 18:10:06: Epoch: 049 Train Loss: 0.101, mae: 0.275, rse: 0.098, pcc: 0.953, kcc: 0.844
+2025-07-19 18:10:07: Epoch: 049 Test results, ap: mae: 0.770, rse: 1.839, pcc: 0.229, kcc: 0.194
+2025-07-19 18:10:42: Epoch: 050 Train Loss: 0.093, mae: 0.283, rse: 0.103, pcc: 0.952, kcc: 0.841
+2025-07-19 18:10:43: Epoch: 050 Test results, ap: mae: 0.823, rse: 2.125, pcc: 0.221, kcc: 0.188
+2025-07-19 18:10:43: used time 0.50h
+2025-07-19 18:10:43: Running Cross Validation 1
+2025-07-19 18:10:43: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-19 18:11:18: Epoch: 001 Train Loss: 2.373, mae: 1.191, rse: 0.905, pcc: 0.316, kcc: 0.214
+2025-07-19 18:11:18: Epoch: 001 New best VALIDATION metrics
+2025-07-19 18:11:19: Epoch: 001 Test results, ap: mae: 0.562, rse: 1.041, pcc: 0.181, kcc: 0.068
+2025-07-19 18:11:54: Epoch: 002 Train Loss: 1.860, mae: 1.303, rse: 1.042, pcc: 0.270, kcc: 0.173
+2025-07-19 18:11:56: Epoch: 002 Test results, ap: mae: 0.949, rse: 3.007, pcc: -0.195, kcc: -0.111
+2025-07-19 18:12:30: Epoch: 003 Train Loss: 1.521, mae: 0.938, rse: 0.571, pcc: 0.660, kcc: 0.449
+2025-07-19 18:12:30: Epoch: 003 New best VALIDATION metrics
+2025-07-19 18:12:32: Epoch: 003 Test results, ap: mae: 0.641, rse: 1.208, pcc: 0.218, kcc: 0.137
+2025-07-19 18:13:06: Epoch: 004 Train Loss: 1.289, mae: 0.862, rse: 0.518, pcc: 0.697, kcc: 0.538
+2025-07-19 18:13:06: Epoch: 004 New best VALIDATION metrics
+2025-07-19 18:13:08: Epoch: 004 Test results, ap: mae: 0.865, rse: 2.543, pcc: -0.141, kcc: -0.019
+2025-07-19 18:13:42: Epoch: 005 Train Loss: 1.088, mae: 0.862, rse: 0.489, pcc: 0.718, kcc: 0.548
+2025-07-19 18:13:42: Epoch: 005 New best VALIDATION metrics
+2025-07-19 18:13:44: Epoch: 005 Test results, ap: mae: 0.686, rse: 1.313, pcc: 0.047, kcc: 0.028
+2025-07-19 18:14:18: Epoch: 006 Train Loss: 0.905, mae: 0.764, rse: 0.390, pcc: 0.784, kcc: 0.573
+2025-07-19 18:14:18: Epoch: 006 New best VALIDATION metrics
+2025-07-19 18:14:20: Epoch: 006 Test results, ap: mae: 0.718, rse: 1.565, pcc: 0.114, kcc: 0.129
+2025-07-19 18:14:55: Epoch: 007 Train Loss: 0.775, mae: 0.669, rse: 0.333, pcc: 0.825, kcc: 0.653
+2025-07-19 18:14:55: Epoch: 007 New best VALIDATION metrics
+2025-07-19 18:14:56: Epoch: 007 Test results, ap: mae: 0.673, rse: 1.390, pcc: 0.294, kcc: 0.174
+2025-07-19 18:15:31: Epoch: 008 Train Loss: 0.625, mae: 0.599, rse: 0.280, pcc: 0.851, kcc: 0.672
+2025-07-19 18:15:31: Epoch: 008 New best VALIDATION metrics
+2025-07-19 18:15:32: Epoch: 008 Test results, ap: mae: 0.715, rse: 1.404, pcc: 0.071, kcc: 0.038
+2025-07-19 18:16:07: Epoch: 009 Train Loss: 0.606, mae: 0.616, rse: 0.287, pcc: 0.856, kcc: 0.689
+2025-07-19 18:16:08: Epoch: 009 Test results, ap: mae: 0.734, rse: 1.622, pcc: -0.033, kcc: 0.015
+2025-07-19 18:16:43: Epoch: 010 Train Loss: 0.550, mae: 0.595, rse: 0.281, pcc: 0.871, kcc: 0.701
+2025-07-19 18:16:43: Epoch: 010 New best VALIDATION metrics
+2025-07-19 18:16:44: Epoch: 010 Test results, ap: mae: 0.707, rse: 1.454, pcc: 0.227, kcc: 0.134
+2025-07-19 18:17:19: Epoch: 011 Train Loss: 0.454, mae: 0.549, rse: 0.230, pcc: 0.880, kcc: 0.701
+2025-07-19 18:17:19: Epoch: 011 New best VALIDATION metrics
+2025-07-19 18:17:20: Epoch: 011 Test results, ap: mae: 0.714, rse: 1.423, pcc: 0.051, kcc: 0.018
+2025-07-19 18:17:20: Epoch: 011 New best TEST metrics
+2025-07-19 18:17:55: Epoch: 012 Train Loss: 0.371, mae: 0.499, rse: 0.193, pcc: 0.909, kcc: 0.741
+2025-07-19 18:17:55: Epoch: 012 New best VALIDATION metrics
+2025-07-19 18:17:56: Epoch: 012 Test results, ap: mae: 0.709, rse: 1.423, pcc: 0.001, kcc: -0.009
+2025-07-19 18:18:31: Epoch: 013 Train Loss: 0.365, mae: 0.461, rse: 0.174, pcc: 0.911, kcc: 0.743
+2025-07-19 18:18:31: Epoch: 013 New best VALIDATION metrics
+2025-07-19 18:18:33: Epoch: 013 Test results, ap: mae: 0.671, rse: 1.281, pcc: 0.050, kcc: 0.036
+2025-07-19 18:18:33: Epoch: 013 New best TEST metrics
+2025-07-19 18:19:07: Epoch: 014 Train Loss: 0.329, mae: 0.508, rse: 0.190, pcc: 0.911, kcc: 0.734
+2025-07-19 18:19:09: Epoch: 014 Test results, ap: mae: 0.718, rse: 1.472, pcc: 0.092, kcc: 0.076
+2025-07-19 18:19:43: Epoch: 015 Train Loss: 0.275, mae: 0.469, rse: 0.160, pcc: 0.922, kcc: 0.748
+2025-07-19 18:19:43: Epoch: 015 New best VALIDATION metrics
+2025-07-19 18:19:45: Epoch: 015 Test results, ap: mae: 0.636, rse: 1.168, pcc: 0.154, kcc: 0.117
+2025-07-19 18:19:45: Epoch: 015 New best TEST metrics
+2025-07-19 18:20:20: Epoch: 016 Train Loss: 0.295, mae: 0.439, rse: 0.152, pcc: 0.925, kcc: 0.763
+2025-07-19 18:20:20: Epoch: 016 New best VALIDATION metrics
+2025-07-19 18:20:21: Epoch: 016 Test results, ap: mae: 0.656, rse: 1.243, pcc: 0.062, kcc: 0.038
+2025-07-19 18:20:56: Epoch: 017 Train Loss: 0.277, mae: 0.418, rse: 0.130, pcc: 0.936, kcc: 0.776
+2025-07-19 18:20:56: Epoch: 017 New best VALIDATION metrics
+2025-07-19 18:20:57: Epoch: 017 Test results, ap: mae: 0.710, rse: 1.382, pcc: 0.148, kcc: 0.112
+2025-07-19 18:21:32: Epoch: 018 Train Loss: 0.242, mae: 0.405, rse: 0.132, pcc: 0.939, kcc: 0.793
+2025-07-19 18:21:32: Epoch: 018 New best VALIDATION metrics
+2025-07-19 18:21:33: Epoch: 018 Test results, ap: mae: 0.686, rse: 1.315, pcc: 0.096, kcc: 0.066
+2025-07-19 18:22:08: Epoch: 019 Train Loss: 0.236, mae: 0.451, rse: 0.145, pcc: 0.925, kcc: 0.755
+2025-07-19 18:22:09: Epoch: 019 Test results, ap: mae: 0.733, rse: 1.647, pcc: -0.091, kcc: -0.077
+2025-07-19 18:22:44: Epoch: 020 Train Loss: 0.240, mae: 0.426, rse: 0.140, pcc: 0.935, kcc: 0.778
+2025-07-19 18:22:45: Epoch: 020 Test results, ap: mae: 0.638, rse: 1.185, pcc: 0.000, kcc: 0.015
+2025-07-19 18:23:20: Epoch: 021 Train Loss: 0.193, mae: 0.382, rse: 0.121, pcc: 0.944, kcc: 0.798
+2025-07-19 18:23:20: Epoch: 021 New best VALIDATION metrics
+2025-07-19 18:23:22: Epoch: 021 Test results, ap: mae: 0.666, rse: 1.237, pcc: 0.125, kcc: 0.096
+2025-07-19 18:23:57: Epoch: 022 Train Loss: 0.180, mae: 0.385, rse: 0.121, pcc: 0.945, kcc: 0.802
+2025-07-19 18:23:57: Epoch: 022 New best VALIDATION metrics
+2025-07-19 18:23:58: Epoch: 022 Test results, ap: mae: 0.654, rse: 1.179, pcc: 0.114, kcc: 0.083
+2025-07-19 18:24:33: Epoch: 023 Train Loss: 0.170, mae: 0.375, rse: 0.115, pcc: 0.949, kcc: 0.809
+2025-07-19 18:24:33: Epoch: 023 New best VALIDATION metrics
+2025-07-19 18:24:34: Epoch: 023 Test results, ap: mae: 0.663, rse: 1.243, pcc: 0.059, kcc: 0.059
+2025-07-19 18:25:09: Epoch: 024 Train Loss: 0.169, mae: 0.370, rse: 0.110, pcc: 0.948, kcc: 0.813
+2025-07-19 18:25:09: Epoch: 024 New best VALIDATION metrics
+2025-07-19 18:25:11: Epoch: 024 Test results, ap: mae: 0.646, rse: 1.189, pcc: 0.127, kcc: 0.086
+2025-07-19 18:25:45: Epoch: 025 Train Loss: 0.156, mae: 0.360, rse: 0.110, pcc: 0.949, kcc: 0.813
+2025-07-19 18:25:45: Epoch: 025 New best VALIDATION metrics
+2025-07-19 18:25:46: Epoch: 025 Test results, ap: mae: 0.682, rse: 1.278, pcc: 0.070, kcc: 0.045
+2025-07-19 18:26:20: Epoch: 026 Train Loss: 0.153, mae: 0.351, rse: 0.111, pcc: 0.950, kcc: 0.815
+2025-07-19 18:26:20: Epoch: 026 New best VALIDATION metrics
+2025-07-19 18:26:22: Epoch: 026 Test results, ap: mae: 0.672, rse: 1.250, pcc: 0.098, kcc: 0.075
+2025-07-19 18:26:58: Epoch: 027 Train Loss: 0.167, mae: 0.334, rse: 0.099, pcc: 0.952, kcc: 0.816
+2025-07-19 18:26:58: Epoch: 027 New best VALIDATION metrics
+2025-07-19 18:26:59: Epoch: 027 Test results, ap: mae: 0.672, rse: 1.270, pcc: 0.046, kcc: 0.033
+2025-07-19 18:27:34: Epoch: 028 Train Loss: 0.143, mae: 0.375, rse: 0.117, pcc: 0.948, kcc: 0.816
+2025-07-19 18:27:36: Epoch: 028 Test results, ap: mae: 0.741, rse: 1.504, pcc: 0.111, kcc: 0.082
+2025-07-19 18:28:10: Epoch: 029 Train Loss: 0.148, mae: 0.377, rse: 0.114, pcc: 0.948, kcc: 0.803
+2025-07-19 18:28:11: Epoch: 029 Test results, ap: mae: 0.631, rse: 1.130, pcc: 0.118, kcc: 0.077
+2025-07-19 18:28:46: Epoch: 030 Train Loss: 0.153, mae: 0.389, rse: 0.122, pcc: 0.950, kcc: 0.814
+2025-07-19 18:28:47: Epoch: 030 Test results, ap: mae: 0.673, rse: 1.265, pcc: 0.136, kcc: 0.099
+2025-07-19 18:29:22: Epoch: 031 Train Loss: 0.136, mae: 0.328, rse: 0.096, pcc: 0.955, kcc: 0.829
+2025-07-19 18:29:22: Epoch: 031 New best VALIDATION metrics
+2025-07-19 18:29:23: Epoch: 031 Test results, ap: mae: 0.651, rse: 1.201, pcc: 0.117, kcc: 0.066
+2025-07-19 18:29:58: Epoch: 032 Train Loss: 0.127, mae: 0.338, rse: 0.096, pcc: 0.958, kcc: 0.835
+2025-07-19 18:29:59: Epoch: 032 Test results, ap: mae: 0.650, rse: 1.191, pcc: 0.110, kcc: 0.071
+2025-07-19 18:30:33: Epoch: 033 Train Loss: 0.122, mae: 0.314, rse: 0.094, pcc: 0.954, kcc: 0.827
+2025-07-19 18:30:33: Epoch: 033 New best VALIDATION metrics
+2025-07-19 18:30:35: Epoch: 033 Test results, ap: mae: 0.651, rse: 1.200, pcc: 0.103, kcc: 0.066
+2025-07-19 18:31:10: Epoch: 034 Train Loss: 0.129, mae: 0.345, rse: 0.104, pcc: 0.956, kcc: 0.834
+2025-07-19 18:31:11: Epoch: 034 Test results, ap: mae: 0.672, rse: 1.272, pcc: 0.141, kcc: 0.090
+2025-07-19 18:31:46: Epoch: 035 Train Loss: 0.115, mae: 0.334, rse: 0.102, pcc: 0.955, kcc: 0.827
+2025-07-19 18:31:47: Epoch: 035 Test results, ap: mae: 0.646, rse: 1.179, pcc: 0.155, kcc: 0.092
+2025-07-19 18:32:22: Epoch: 036 Train Loss: 0.116, mae: 0.348, rse: 0.101, pcc: 0.956, kcc: 0.831
+2025-07-19 18:32:23: Epoch: 036 Test results, ap: mae: 0.666, rse: 1.257, pcc: 0.136, kcc: 0.083
+2025-07-19 18:32:58: Epoch: 037 Train Loss: 0.110, mae: 0.323, rse: 0.097, pcc: 0.957, kcc: 0.836
+2025-07-19 18:32:58: Epoch: 037 New best VALIDATION metrics
+2025-07-19 18:32:59: Epoch: 037 Test results, ap: mae: 0.657, rse: 1.216, pcc: 0.116, kcc: 0.073
+2025-07-19 18:33:34: Epoch: 038 Train Loss: 0.115, mae: 0.339, rse: 0.101, pcc: 0.957, kcc: 0.833
+2025-07-19 18:33:36: Epoch: 038 Test results, ap: mae: 0.657, rse: 1.210, pcc: 0.107, kcc: 0.071
+2025-07-19 18:34:10: Epoch: 039 Train Loss: 0.115, mae: 0.319, rse: 0.098, pcc: 0.956, kcc: 0.834
+2025-07-19 18:34:11: Epoch: 039 Test results, ap: mae: 0.675, rse: 1.273, pcc: 0.137, kcc: 0.078
+2025-07-19 18:34:46: Epoch: 040 Train Loss: 0.123, mae: 0.328, rse: 0.100, pcc: 0.956, kcc: 0.838
+2025-07-19 18:34:47: Epoch: 040 Test results, ap: mae: 0.663, rse: 1.220, pcc: 0.151, kcc: 0.094
+2025-07-19 18:35:22: Epoch: 041 Train Loss: 0.122, mae: 0.318, rse: 0.091, pcc: 0.959, kcc: 0.844
+2025-07-19 18:35:22: Epoch: 041 New best VALIDATION metrics
+2025-07-19 18:35:23: Epoch: 041 Test results, ap: mae: 0.655, rse: 1.198, pcc: 0.121, kcc: 0.075
+2025-07-19 18:35:58: Epoch: 042 Train Loss: 0.111, mae: 0.314, rse: 0.093, pcc: 0.957, kcc: 0.838
+2025-07-19 18:35:59: Epoch: 042 Test results, ap: mae: 0.649, rse: 1.182, pcc: 0.127, kcc: 0.075
+2025-07-19 18:36:34: Epoch: 043 Train Loss: 0.107, mae: 0.327, rse: 0.101, pcc: 0.958, kcc: 0.847
+2025-07-19 18:36:35: Epoch: 043 Test results, ap: mae: 0.680, rse: 1.290, pcc: 0.134, kcc: 0.086
+2025-07-19 18:37:10: Epoch: 044 Train Loss: 0.114, mae: 0.295, rse: 0.089, pcc: 0.959, kcc: 0.845
+2025-07-19 18:37:10: Epoch: 044 New best VALIDATION metrics
+2025-07-19 18:37:11: Epoch: 044 Test results, ap: mae: 0.667, rse: 1.248, pcc: 0.142, kcc: 0.085
+2025-07-19 18:37:46: Epoch: 045 Train Loss: 0.098, mae: 0.304, rse: 0.092, pcc: 0.959, kcc: 0.848
+2025-07-19 18:37:47: Epoch: 045 Test results, ap: mae: 0.660, rse: 1.227, pcc: 0.149, kcc: 0.096
+2025-07-19 18:38:22: Epoch: 046 Train Loss: 0.106, mae: 0.313, rse: 0.090, pcc: 0.959, kcc: 0.842
+2025-07-19 18:38:23: Epoch: 046 Test results, ap: mae: 0.648, rse: 1.197, pcc: 0.127, kcc: 0.071
+2025-07-19 18:38:58: Epoch: 047 Train Loss: 0.102, mae: 0.296, rse: 0.088, pcc: 0.961, kcc: 0.852
+2025-07-19 18:38:58: Epoch: 047 New best VALIDATION metrics
+2025-07-19 18:38:59: Epoch: 047 Test results, ap: mae: 0.668, rse: 1.257, pcc: 0.087, kcc: 0.057
+2025-07-19 18:39:34: Epoch: 048 Train Loss: 0.106, mae: 0.303, rse: 0.088, pcc: 0.959, kcc: 0.844
+2025-07-19 18:39:36: Epoch: 048 Test results, ap: mae: 0.658, rse: 1.220, pcc: 0.131, kcc: 0.073
+2025-07-19 18:40:11: Epoch: 049 Train Loss: 0.103, mae: 0.298, rse: 0.088, pcc: 0.959, kcc: 0.842
+2025-07-19 18:40:12: Epoch: 049 Test results, ap: mae: 0.651, rse: 1.202, pcc: 0.129, kcc: 0.075
+2025-07-19 18:40:47: Epoch: 050 Train Loss: 0.104, mae: 0.294, rse: 0.090, pcc: 0.958, kcc: 0.846
+2025-07-19 18:40:48: Epoch: 050 Test results, ap: mae: 0.649, rse: 1.187, pcc: 0.148, kcc: 0.090
+2025-07-19 18:40:48: used time 0.50h
+2025-07-19 18:40:48: Running Cross Validation 2
+2025-07-19 18:40:48: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 18:41:23: Epoch: 001 Train Loss: 2.358, mae: 0.934, rse: 0.579, pcc: 0.669, kcc: 0.485
+2025-07-19 18:41:23: Epoch: 001 New best VALIDATION metrics
+2025-07-19 18:41:24: Epoch: 001 Test results, ap: mae: 0.573, rse: 0.962, pcc: 0.302, kcc: 0.118
+2025-07-19 18:41:59: Epoch: 002 Train Loss: 1.595, mae: 0.957, rse: 0.583, pcc: 0.655, kcc: 0.469
+2025-07-19 18:42:00: Epoch: 002 Test results, ap: mae: 0.740, rse: 1.470, pcc: 0.223, kcc: 0.125
+2025-07-19 18:42:34: Epoch: 003 Train Loss: 1.258, mae: 0.774, rse: 0.400, pcc: 0.785, kcc: 0.587
+2025-07-19 18:42:34: Epoch: 003 New best VALIDATION metrics
+2025-07-19 18:42:36: Epoch: 003 Test results, ap: mae: 0.853, rse: 2.156, pcc: 0.326, kcc: 0.159
+2025-07-19 18:43:12: Epoch: 004 Train Loss: 1.048, mae: 0.735, rse: 0.371, pcc: 0.795, kcc: 0.586
+2025-07-19 18:43:12: Epoch: 004 New best VALIDATION metrics
+2025-07-19 18:43:13: Epoch: 004 Test results, ap: mae: 0.780, rse: 1.728, pcc: 0.329, kcc: 0.113
+2025-07-19 18:43:48: Epoch: 005 Train Loss: 0.873, mae: 0.667, rse: 0.310, pcc: 0.833, kcc: 0.638
+2025-07-19 18:43:48: Epoch: 005 New best VALIDATION metrics
+2025-07-19 18:43:50: Epoch: 005 Test results, ap: mae: 0.892, rse: 2.446, pcc: 0.186, kcc: 0.070
+2025-07-19 18:44:24: Epoch: 006 Train Loss: 0.837, mae: 0.653, rse: 0.288, pcc: 0.844, kcc: 0.642
+2025-07-19 18:44:24: Epoch: 006 New best VALIDATION metrics
+2025-07-19 18:44:26: Epoch: 006 Test results, ap: mae: 0.632, rse: 1.251, pcc: 0.316, kcc: 0.166
+2025-07-19 18:45:00: Epoch: 007 Train Loss: 0.652, mae: 0.553, rse: 0.239, pcc: 0.873, kcc: 0.692
+2025-07-19 18:45:00: Epoch: 007 New best VALIDATION metrics
+2025-07-19 18:45:02: Epoch: 007 Test results, ap: mae: 0.718, rse: 1.577, pcc: 0.343, kcc: 0.156
+2025-07-19 18:45:36: Epoch: 008 Train Loss: 0.613, mae: 0.549, rse: 0.233, pcc: 0.879, kcc: 0.698
+2025-07-19 18:45:36: Epoch: 008 New best VALIDATION metrics
+2025-07-19 18:45:38: Epoch: 008 Test results, ap: mae: 0.763, rse: 1.743, pcc: 0.241, kcc: 0.107
+2025-07-19 18:46:12: Epoch: 009 Train Loss: 0.573, mae: 0.583, rse: 0.256, pcc: 0.877, kcc: 0.698
+2025-07-19 18:46:14: Epoch: 009 Test results, ap: mae: 0.745, rse: 1.672, pcc: 0.353, kcc: 0.155
+2025-07-19 18:46:48: Epoch: 010 Train Loss: 0.533, mae: 0.540, rse: 0.229, pcc: 0.880, kcc: 0.700
+2025-07-19 18:46:48: Epoch: 010 New best VALIDATION metrics
+2025-07-19 18:46:50: Epoch: 010 Test results, ap: mae: 0.670, rse: 1.367, pcc: 0.302, kcc: 0.172
+2025-07-19 18:47:24: Epoch: 011 Train Loss: 0.400, mae: 0.479, rse: 0.196, pcc: 0.899, kcc: 0.733
+2025-07-19 18:47:24: Epoch: 011 New best VALIDATION metrics
+2025-07-19 18:47:26: Epoch: 011 Test results, ap: mae: 0.714, rse: 1.594, pcc: 0.318, kcc: 0.152
+2025-07-19 18:47:26: Epoch: 011 New best TEST metrics
+2025-07-19 18:48:00: Epoch: 012 Train Loss: 0.363, mae: 0.467, rse: 0.188, pcc: 0.901, kcc: 0.738
+2025-07-19 18:48:00: Epoch: 012 New best VALIDATION metrics
+2025-07-19 18:48:02: Epoch: 012 Test results, ap: mae: 0.698, rse: 1.515, pcc: 0.316, kcc: 0.165
+2025-07-19 18:48:02: Epoch: 012 New best TEST metrics
+2025-07-19 18:48:36: Epoch: 013 Train Loss: 0.357, mae: 0.442, rse: 0.174, pcc: 0.912, kcc: 0.758
+2025-07-19 18:48:36: Epoch: 013 New best VALIDATION metrics
+2025-07-19 18:48:38: Epoch: 013 Test results, ap: mae: 0.756, rse: 1.774, pcc: 0.314, kcc: 0.171
+2025-07-19 18:49:12: Epoch: 014 Train Loss: 0.295, mae: 0.430, rse: 0.162, pcc: 0.917, kcc: 0.758
+2025-07-19 18:49:12: Epoch: 014 New best VALIDATION metrics
+2025-07-19 18:49:14: Epoch: 014 Test results, ap: mae: 0.737, rse: 1.713, pcc: 0.338, kcc: 0.182
+2025-07-19 18:49:48: Epoch: 015 Train Loss: 0.312, mae: 0.480, rse: 0.192, pcc: 0.914, kcc: 0.758
+2025-07-19 18:49:50: Epoch: 015 Test results, ap: mae: 0.867, rse: 2.256, pcc: 0.283, kcc: 0.138
+2025-07-19 18:50:24: Epoch: 016 Train Loss: 0.242, mae: 0.411, rse: 0.157, pcc: 0.920, kcc: 0.766
+2025-07-19 18:50:24: Epoch: 016 New best VALIDATION metrics
+2025-07-19 18:50:26: Epoch: 016 Test results, ap: mae: 0.647, rse: 1.284, pcc: 0.248, kcc: 0.134
+2025-07-19 18:50:26: Epoch: 016 New best TEST metrics
+2025-07-19 18:51:01: Epoch: 017 Train Loss: 0.252, mae: 0.408, rse: 0.144, pcc: 0.925, kcc: 0.770
+2025-07-19 18:51:01: Epoch: 017 New best VALIDATION metrics
+2025-07-19 18:51:02: Epoch: 017 Test results, ap: mae: 0.650, rse: 1.288, pcc: 0.315, kcc: 0.169
+2025-07-19 18:51:02: Epoch: 017 New best TEST metrics
+2025-07-19 18:51:37: Epoch: 018 Train Loss: 0.244, mae: 0.427, rse: 0.163, pcc: 0.922, kcc: 0.763
+2025-07-19 18:51:39: Epoch: 018 Test results, ap: mae: 1.031, rse: 3.195, pcc: 0.255, kcc: 0.116
+2025-07-19 18:52:13: Epoch: 019 Train Loss: 0.219, mae: 0.430, rse: 0.156, pcc: 0.927, kcc: 0.782
+2025-07-19 18:52:15: Epoch: 019 Test results, ap: mae: 0.682, rse: 1.461, pcc: 0.280, kcc: 0.144
+2025-07-19 18:52:49: Epoch: 020 Train Loss: 0.206, mae: 0.409, rse: 0.145, pcc: 0.931, kcc: 0.789
+2025-07-19 18:52:49: Epoch: 020 New best VALIDATION metrics
+2025-07-19 18:52:51: Epoch: 020 Test results, ap: mae: 0.799, rse: 1.974, pcc: 0.292, kcc: 0.164
+2025-07-19 18:53:25: Epoch: 021 Train Loss: 0.176, mae: 0.371, rse: 0.129, pcc: 0.938, kcc: 0.805
+2025-07-19 18:53:25: Epoch: 021 New best VALIDATION metrics
+2025-07-19 18:53:27: Epoch: 021 Test results, ap: mae: 0.822, rse: 2.052, pcc: 0.306, kcc: 0.167
+2025-07-19 18:54:01: Epoch: 022 Train Loss: 0.160, mae: 0.348, rse: 0.127, pcc: 0.938, kcc: 0.810
+2025-07-19 18:54:01: Epoch: 022 New best VALIDATION metrics
+2025-07-19 18:54:03: Epoch: 022 Test results, ap: mae: 0.835, rse: 2.130, pcc: 0.301, kcc: 0.172
+2025-07-19 18:54:37: Epoch: 023 Train Loss: 0.150, mae: 0.423, rse: 0.162, pcc: 0.930, kcc: 0.792
+2025-07-19 18:54:39: Epoch: 023 Test results, ap: mae: 1.050, rse: 3.212, pcc: 0.279, kcc: 0.153
+2025-07-19 18:55:14: Epoch: 024 Train Loss: 0.152, mae: 0.331, rse: 0.113, pcc: 0.943, kcc: 0.816
+2025-07-19 18:55:14: Epoch: 024 New best VALIDATION metrics
+2025-07-19 18:55:16: Epoch: 024 Test results, ap: mae: 0.800, rse: 1.953, pcc: 0.276, kcc: 0.155
+2025-07-19 18:55:50: Epoch: 025 Train Loss: 0.139, mae: 0.374, rse: 0.136, pcc: 0.938, kcc: 0.809
+2025-07-19 18:55:51: Epoch: 025 Test results, ap: mae: 1.016, rse: 3.024, pcc: 0.276, kcc: 0.149
+2025-07-19 18:56:26: Epoch: 026 Train Loss: 0.133, mae: 0.323, rse: 0.111, pcc: 0.945, kcc: 0.816
+2025-07-19 18:56:26: Epoch: 026 New best VALIDATION metrics
+2025-07-19 18:56:27: Epoch: 026 Test results, ap: mae: 0.854, rse: 2.214, pcc: 0.233, kcc: 0.128
+2025-07-19 18:57:02: Epoch: 027 Train Loss: 0.128, mae: 0.329, rse: 0.112, pcc: 0.944, kcc: 0.817
+2025-07-19 18:57:03: Epoch: 027 Test results, ap: mae: 0.717, rse: 1.593, pcc: 0.305, kcc: 0.162
+2025-07-19 18:57:38: Epoch: 028 Train Loss: 0.139, mae: 0.320, rse: 0.107, pcc: 0.946, kcc: 0.820
+2025-07-19 18:57:38: Epoch: 028 New best VALIDATION metrics
+2025-07-19 18:57:39: Epoch: 028 Test results, ap: mae: 0.800, rse: 1.934, pcc: 0.234, kcc: 0.132
+2025-07-19 18:58:14: Epoch: 029 Train Loss: 0.148, mae: 0.358, rse: 0.130, pcc: 0.938, kcc: 0.811
+2025-07-19 18:58:15: Epoch: 029 Test results, ap: mae: 1.056, rse: 3.218, pcc: 0.274, kcc: 0.156
+2025-07-19 18:58:51: Epoch: 030 Train Loss: 0.128, mae: 0.318, rse: 0.109, pcc: 0.946, kcc: 0.824
+2025-07-19 18:58:51: Epoch: 030 New best VALIDATION metrics
+2025-07-19 18:58:52: Epoch: 030 Test results, ap: mae: 0.844, rse: 2.098, pcc: 0.259, kcc: 0.139
+2025-07-19 18:59:27: Epoch: 031 Train Loss: 0.116, mae: 0.299, rse: 0.104, pcc: 0.949, kcc: 0.835
+2025-07-19 18:59:27: Epoch: 031 New best VALIDATION metrics
+2025-07-19 18:59:28: Epoch: 031 Test results, ap: mae: 0.789, rse: 1.892, pcc: 0.268, kcc: 0.152
+2025-07-19 19:00:03: Epoch: 032 Train Loss: 0.114, mae: 0.303, rse: 0.108, pcc: 0.948, kcc: 0.835
+2025-07-19 19:00:04: Epoch: 032 Test results, ap: mae: 0.918, rse: 2.505, pcc: 0.255, kcc: 0.142
+2025-07-19 19:00:39: Epoch: 033 Train Loss: 0.104, mae: 0.295, rse: 0.102, pcc: 0.949, kcc: 0.835
+2025-07-19 19:00:39: Epoch: 033 New best VALIDATION metrics
+2025-07-19 19:00:40: Epoch: 033 Test results, ap: mae: 0.833, rse: 2.111, pcc: 0.272, kcc: 0.150
+2025-07-19 19:01:15: Epoch: 034 Train Loss: 0.104, mae: 0.298, rse: 0.106, pcc: 0.948, kcc: 0.838
+2025-07-19 19:01:16: Epoch: 034 Test results, ap: mae: 0.858, rse: 2.231, pcc: 0.284, kcc: 0.161
+2025-07-19 19:01:51: Epoch: 035 Train Loss: 0.106, mae: 0.290, rse: 0.103, pcc: 0.950, kcc: 0.842
+2025-07-19 19:01:51: Epoch: 035 New best VALIDATION metrics
+2025-07-19 19:01:52: Epoch: 035 Test results, ap: mae: 0.880, rse: 2.353, pcc: 0.271, kcc: 0.150
+2025-07-19 19:02:27: Epoch: 036 Train Loss: 0.095, mae: 0.300, rse: 0.106, pcc: 0.949, kcc: 0.838
+2025-07-19 19:02:28: Epoch: 036 Test results, ap: mae: 0.940, rse: 2.596, pcc: 0.288, kcc: 0.160
+2025-07-19 19:03:03: Epoch: 037 Train Loss: 0.097, mae: 0.291, rse: 0.100, pcc: 0.950, kcc: 0.838
+2025-07-19 19:03:04: Epoch: 037 Test results, ap: mae: 0.848, rse: 2.181, pcc: 0.257, kcc: 0.147
+2025-07-19 19:03:39: Epoch: 038 Train Loss: 0.105, mae: 0.293, rse: 0.102, pcc: 0.951, kcc: 0.842
+2025-07-19 19:03:40: Epoch: 038 Test results, ap: mae: 0.888, rse: 2.344, pcc: 0.270, kcc: 0.144
+2025-07-19 19:04:14: Epoch: 039 Train Loss: 0.095, mae: 0.289, rse: 0.103, pcc: 0.951, kcc: 0.843
+2025-07-19 19:04:14: Epoch: 039 New best VALIDATION metrics
+2025-07-19 19:04:16: Epoch: 039 Test results, ap: mae: 0.931, rse: 2.554, pcc: 0.264, kcc: 0.141
+2025-07-19 19:04:51: Epoch: 040 Train Loss: 0.097, mae: 0.279, rse: 0.098, pcc: 0.951, kcc: 0.840
+2025-07-19 19:04:51: Epoch: 040 New best VALIDATION metrics
+2025-07-19 19:04:52: Epoch: 040 Test results, ap: mae: 0.796, rse: 1.948, pcc: 0.280, kcc: 0.154
+2025-07-19 19:05:27: Epoch: 041 Train Loss: 0.094, mae: 0.286, rse: 0.102, pcc: 0.951, kcc: 0.845
+2025-07-19 19:05:28: Epoch: 041 Test results, ap: mae: 0.849, rse: 2.156, pcc: 0.286, kcc: 0.154
+2025-07-19 19:06:03: Epoch: 042 Train Loss: 0.090, mae: 0.276, rse: 0.099, pcc: 0.952, kcc: 0.847
+2025-07-19 19:06:03: Epoch: 042 New best VALIDATION metrics
+2025-07-19 19:06:05: Epoch: 042 Test results, ap: mae: 0.885, rse: 2.327, pcc: 0.268, kcc: 0.138
+2025-07-19 19:06:39: Epoch: 043 Train Loss: 0.077, mae: 0.276, rse: 0.098, pcc: 0.952, kcc: 0.849
+2025-07-19 19:06:39: Epoch: 043 New best VALIDATION metrics
+2025-07-19 19:06:41: Epoch: 043 Test results, ap: mae: 0.867, rse: 2.264, pcc: 0.272, kcc: 0.149
+2025-07-19 19:07:15: Epoch: 044 Train Loss: 0.089, mae: 0.285, rse: 0.101, pcc: 0.952, kcc: 0.848
+2025-07-19 19:07:17: Epoch: 044 Test results, ap: mae: 0.908, rse: 2.445, pcc: 0.261, kcc: 0.139
+2025-07-19 19:07:52: Epoch: 045 Train Loss: 0.085, mae: 0.271, rse: 0.096, pcc: 0.953, kcc: 0.850
+2025-07-19 19:07:52: Epoch: 045 New best VALIDATION metrics
+2025-07-19 19:07:53: Epoch: 045 Test results, ap: mae: 0.881, rse: 2.324, pcc: 0.253, kcc: 0.129
+2025-07-19 19:08:27: Epoch: 046 Train Loss: 0.083, mae: 0.284, rse: 0.103, pcc: 0.950, kcc: 0.844
+2025-07-19 19:08:29: Epoch: 046 Test results, ap: mae: 0.894, rse: 2.392, pcc: 0.255, kcc: 0.131
+2025-07-19 19:09:04: Epoch: 047 Train Loss: 0.083, mae: 0.276, rse: 0.101, pcc: 0.951, kcc: 0.848
+2025-07-19 19:09:05: Epoch: 047 Test results, ap: mae: 0.899, rse: 2.434, pcc: 0.255, kcc: 0.135
+2025-07-19 19:09:39: Epoch: 048 Train Loss: 0.082, mae: 0.275, rse: 0.101, pcc: 0.951, kcc: 0.849
+2025-07-19 19:09:41: Epoch: 048 Test results, ap: mae: 0.890, rse: 2.360, pcc: 0.265, kcc: 0.133
+2025-07-19 19:10:15: Epoch: 049 Train Loss: 0.080, mae: 0.276, rse: 0.097, pcc: 0.952, kcc: 0.848
+2025-07-19 19:10:17: Epoch: 049 Test results, ap: mae: 0.870, rse: 2.271, pcc: 0.261, kcc: 0.136
+2025-07-19 19:10:51: Epoch: 050 Train Loss: 0.090, mae: 0.289, rse: 0.103, pcc: 0.951, kcc: 0.848
+2025-07-19 19:10:53: Epoch: 050 Test results, ap: mae: 0.897, rse: 2.397, pcc: 0.279, kcc: 0.156
+2025-07-19 19:10:53: used time 0.50h
+2025-07-19 19:10:53: Running Cross Validation 3
+2025-07-19 19:10:53: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 19:11:28: Epoch: 001 Train Loss: 2.508, mae: 1.149, rse: 0.755, pcc: 0.496, kcc: 0.304
+2025-07-19 19:11:28: Epoch: 001 New best VALIDATION metrics
+2025-07-19 19:11:29: Epoch: 001 Test results, ap: mae: 0.605, rse: 1.053, pcc: 0.146, kcc: 0.100
+2025-07-19 19:12:04: Epoch: 002 Train Loss: 1.928, mae: 1.023, rse: 0.633, pcc: 0.607, kcc: 0.420
+2025-07-19 19:12:04: Epoch: 002 New best VALIDATION metrics
+2025-07-19 19:12:05: Epoch: 002 Test results, ap: mae: 0.628, rse: 1.206, pcc: 0.121, kcc: 0.070
+2025-07-19 19:12:40: Epoch: 003 Train Loss: 1.468, mae: 0.856, rse: 0.468, pcc: 0.732, kcc: 0.522
+2025-07-19 19:12:40: Epoch: 003 New best VALIDATION metrics
+2025-07-19 19:12:41: Epoch: 003 Test results, ap: mae: 0.797, rse: 1.826, pcc: 0.030, kcc: -0.017
+2025-07-19 19:13:16: Epoch: 004 Train Loss: 1.223, mae: 0.844, rse: 0.449, pcc: 0.747, kcc: 0.548
+2025-07-19 19:13:16: Epoch: 004 New best VALIDATION metrics
+2025-07-19 19:13:17: Epoch: 004 Test results, ap: mae: 0.661, rse: 1.362, pcc: 0.263, kcc: 0.172
+2025-07-19 19:13:52: Epoch: 005 Train Loss: 0.964, mae: 0.742, rse: 0.370, pcc: 0.797, kcc: 0.606
+2025-07-19 19:13:52: Epoch: 005 New best VALIDATION metrics
+2025-07-19 19:13:53: Epoch: 005 Test results, ap: mae: 0.733, rse: 1.554, pcc: 0.111, kcc: 0.071
+2025-07-19 19:14:28: Epoch: 006 Train Loss: 0.853, mae: 0.720, rse: 0.352, pcc: 0.806, kcc: 0.623
+2025-07-19 19:14:28: Epoch: 006 New best VALIDATION metrics
+2025-07-19 19:14:30: Epoch: 006 Test results, ap: mae: 0.652, rse: 1.208, pcc: 0.272, kcc: 0.150
+2025-07-19 19:15:04: Epoch: 007 Train Loss: 0.699, mae: 0.608, rse: 0.248, pcc: 0.870, kcc: 0.678
+2025-07-19 19:15:04: Epoch: 007 New best VALIDATION metrics
+2025-07-19 19:15:06: Epoch: 007 Test results, ap: mae: 0.718, rse: 1.500, pcc: 0.067, kcc: 0.012
+2025-07-19 19:15:40: Epoch: 008 Train Loss: 0.629, mae: 0.579, rse: 0.224, pcc: 0.881, kcc: 0.681
+2025-07-19 19:15:40: Epoch: 008 New best VALIDATION metrics
+2025-07-19 19:15:42: Epoch: 008 Test results, ap: mae: 0.754, rse: 1.604, pcc: 0.130, kcc: 0.087
+2025-07-19 19:16:16: Epoch: 009 Train Loss: 0.553, mae: 0.580, rse: 0.262, pcc: 0.860, kcc: 0.680
+2025-07-19 19:16:18: Epoch: 009 Test results, ap: mae: 0.764, rse: 1.644, pcc: 0.094, kcc: 0.043
+2025-07-19 19:16:53: Epoch: 010 Train Loss: 0.498, mae: 0.569, rse: 0.221, pcc: 0.886, kcc: 0.698
+2025-07-19 19:16:53: Epoch: 010 New best VALIDATION metrics
+2025-07-19 19:16:54: Epoch: 010 Test results, ap: mae: 0.784, rse: 1.655, pcc: 0.316, kcc: 0.194
+2025-07-19 19:17:29: Epoch: 011 Train Loss: 0.366, mae: 0.452, rse: 0.150, pcc: 0.924, kcc: 0.755
+2025-07-19 19:17:29: Epoch: 011 New best VALIDATION metrics
+2025-07-19 19:17:30: Epoch: 011 Test results, ap: mae: 0.711, rse: 1.339, pcc: 0.157, kcc: 0.091
+2025-07-19 19:17:30: Epoch: 011 New best TEST metrics
+2025-07-19 19:18:05: Epoch: 012 Train Loss: 0.288, mae: 0.419, rse: 0.137, pcc: 0.931, kcc: 0.773
+2025-07-19 19:18:05: Epoch: 012 New best VALIDATION metrics
+2025-07-19 19:18:06: Epoch: 012 Test results, ap: mae: 0.719, rse: 1.428, pcc: 0.231, kcc: 0.144
+2025-07-19 19:18:06: Epoch: 012 New best TEST metrics
+2025-07-19 19:18:41: Epoch: 013 Train Loss: 0.291, mae: 0.414, rse: 0.132, pcc: 0.932, kcc: 0.775
+2025-07-19 19:18:41: Epoch: 013 New best VALIDATION metrics
+2025-07-19 19:18:42: Epoch: 013 Test results, ap: mae: 0.694, rse: 1.329, pcc: 0.237, kcc: 0.147
+2025-07-19 19:18:42: Epoch: 013 New best TEST metrics
+2025-07-19 19:19:17: Epoch: 014 Train Loss: 0.256, mae: 0.436, rse: 0.148, pcc: 0.925, kcc: 0.769
+2025-07-19 19:19:18: Epoch: 014 Test results, ap: mae: 0.709, rse: 1.400, pcc: 0.244, kcc: 0.154
+2025-07-19 19:19:53: Epoch: 015 Train Loss: 0.257, mae: 0.441, rse: 0.136, pcc: 0.933, kcc: 0.772
+2025-07-19 19:19:54: Epoch: 015 Test results, ap: mae: 0.691, rse: 1.293, pcc: 0.210, kcc: 0.126
+2025-07-19 19:20:29: Epoch: 016 Train Loss: 0.222, mae: 0.405, rse: 0.123, pcc: 0.941, kcc: 0.795
+2025-07-19 19:20:29: Epoch: 016 New best VALIDATION metrics
+2025-07-19 19:20:30: Epoch: 016 Test results, ap: mae: 0.660, rse: 1.198, pcc: 0.236, kcc: 0.155
+2025-07-19 19:20:30: Epoch: 016 New best TEST metrics
+2025-07-19 19:21:05: Epoch: 017 Train Loss: 0.218, mae: 0.403, rse: 0.122, pcc: 0.939, kcc: 0.789
+2025-07-19 19:21:06: Epoch: 017 Test results, ap: mae: 0.732, rse: 1.468, pcc: 0.231, kcc: 0.146
+2025-07-19 19:21:41: Epoch: 018 Train Loss: 0.219, mae: 0.392, rse: 0.114, pcc: 0.943, kcc: 0.795
+2025-07-19 19:21:41: Epoch: 018 New best VALIDATION metrics
+2025-07-19 19:21:42: Epoch: 018 Test results, ap: mae: 0.749, rse: 1.531, pcc: 0.204, kcc: 0.119
+2025-07-19 19:22:17: Epoch: 019 Train Loss: 0.225, mae: 0.351, rse: 0.103, pcc: 0.950, kcc: 0.817
+2025-07-19 19:22:17: Epoch: 019 New best VALIDATION metrics
+2025-07-19 19:22:19: Epoch: 019 Test results, ap: mae: 0.732, rse: 1.513, pcc: 0.203, kcc: 0.136
+2025-07-19 19:22:54: Epoch: 020 Train Loss: 0.213, mae: 0.335, rse: 0.099, pcc: 0.950, kcc: 0.816
+2025-07-19 19:22:54: Epoch: 020 New best VALIDATION metrics
+2025-07-19 19:22:55: Epoch: 020 Test results, ap: mae: 0.788, rse: 1.722, pcc: 0.213, kcc: 0.131
+2025-07-19 19:23:30: Epoch: 021 Train Loss: 0.167, mae: 0.329, rse: 0.091, pcc: 0.955, kcc: 0.826
+2025-07-19 19:23:30: Epoch: 021 New best VALIDATION metrics
+2025-07-19 19:23:31: Epoch: 021 Test results, ap: mae: 0.774, rse: 1.651, pcc: 0.166, kcc: 0.119
+2025-07-19 19:24:06: Epoch: 022 Train Loss: 0.146, mae: 0.307, rse: 0.086, pcc: 0.958, kcc: 0.835
+2025-07-19 19:24:06: Epoch: 022 New best VALIDATION metrics
+2025-07-19 19:24:08: Epoch: 022 Test results, ap: mae: 0.777, rse: 1.671, pcc: 0.167, kcc: 0.106
+2025-07-19 19:24:42: Epoch: 023 Train Loss: 0.134, mae: 0.297, rse: 0.085, pcc: 0.957, kcc: 0.834
+2025-07-19 19:24:42: Epoch: 023 New best VALIDATION metrics
+2025-07-19 19:24:43: Epoch: 023 Test results, ap: mae: 0.746, rse: 1.536, pcc: 0.226, kcc: 0.138
+2025-07-19 19:25:18: Epoch: 024 Train Loss: 0.134, mae: 0.290, rse: 0.079, pcc: 0.961, kcc: 0.841
+2025-07-19 19:25:18: Epoch: 024 New best VALIDATION metrics
+2025-07-19 19:25:19: Epoch: 024 Test results, ap: mae: 0.746, rse: 1.536, pcc: 0.199, kcc: 0.128
+2025-07-19 19:25:54: Epoch: 025 Train Loss: 0.126, mae: 0.294, rse: 0.084, pcc: 0.959, kcc: 0.839
+2025-07-19 19:25:55: Epoch: 025 Test results, ap: mae: 0.770, rse: 1.631, pcc: 0.206, kcc: 0.129
+2025-07-19 19:26:30: Epoch: 026 Train Loss: 0.132, mae: 0.282, rse: 0.082, pcc: 0.959, kcc: 0.843
+2025-07-19 19:26:30: Epoch: 026 New best VALIDATION metrics
+2025-07-19 19:26:32: Epoch: 026 Test results, ap: mae: 0.734, rse: 1.489, pcc: 0.194, kcc: 0.121
+2025-07-19 19:27:06: Epoch: 027 Train Loss: 0.136, mae: 0.282, rse: 0.076, pcc: 0.962, kcc: 0.842
+2025-07-19 19:27:06: Epoch: 027 New best VALIDATION metrics
+2025-07-19 19:27:08: Epoch: 027 Test results, ap: mae: 0.745, rse: 1.531, pcc: 0.226, kcc: 0.142
+2025-07-19 19:27:42: Epoch: 028 Train Loss: 0.134, mae: 0.300, rse: 0.084, pcc: 0.958, kcc: 0.838
+2025-07-19 19:27:44: Epoch: 028 Test results, ap: mae: 0.774, rse: 1.659, pcc: 0.192, kcc: 0.124
+2025-07-19 19:28:18: Epoch: 029 Train Loss: 0.126, mae: 0.299, rse: 0.082, pcc: 0.960, kcc: 0.845
+2025-07-19 19:28:20: Epoch: 029 Test results, ap: mae: 0.734, rse: 1.478, pcc: 0.177, kcc: 0.109
+2025-07-19 19:28:55: Epoch: 030 Train Loss: 0.141, mae: 0.289, rse: 0.088, pcc: 0.958, kcc: 0.846
+2025-07-19 19:28:56: Epoch: 030 Test results, ap: mae: 0.780, rse: 1.662, pcc: 0.220, kcc: 0.136
+2025-07-19 19:29:31: Epoch: 031 Train Loss: 0.119, mae: 0.271, rse: 0.077, pcc: 0.962, kcc: 0.852
+2025-07-19 19:29:31: Epoch: 031 New best VALIDATION metrics
+2025-07-19 19:29:32: Epoch: 031 Test results, ap: mae: 0.746, rse: 1.525, pcc: 0.196, kcc: 0.127
+2025-07-19 19:30:07: Epoch: 032 Train Loss: 0.111, mae: 0.273, rse: 0.078, pcc: 0.962, kcc: 0.852
+2025-07-19 19:30:08: Epoch: 032 Test results, ap: mae: 0.748, rse: 1.546, pcc: 0.232, kcc: 0.148
+2025-07-19 19:30:43: Epoch: 033 Train Loss: 0.118, mae: 0.274, rse: 0.075, pcc: 0.963, kcc: 0.849
+2025-07-19 19:30:44: Epoch: 033 Test results, ap: mae: 0.736, rse: 1.485, pcc: 0.214, kcc: 0.142
+2025-07-19 19:31:19: Epoch: 034 Train Loss: 0.112, mae: 0.265, rse: 0.073, pcc: 0.964, kcc: 0.854
+2025-07-19 19:31:19: Epoch: 034 New best VALIDATION metrics
+2025-07-19 19:31:20: Epoch: 034 Test results, ap: mae: 0.726, rse: 1.448, pcc: 0.207, kcc: 0.139
+2025-07-19 19:31:55: Epoch: 035 Train Loss: 0.112, mae: 0.272, rse: 0.075, pcc: 0.963, kcc: 0.854
+2025-07-19 19:31:56: Epoch: 035 Test results, ap: mae: 0.724, rse: 1.435, pcc: 0.221, kcc: 0.141
+2025-07-19 19:32:31: Epoch: 036 Train Loss: 0.104, mae: 0.276, rse: 0.078, pcc: 0.962, kcc: 0.854
+2025-07-19 19:32:32: Epoch: 036 Test results, ap: mae: 0.750, rse: 1.545, pcc: 0.222, kcc: 0.144
+2025-07-19 19:33:07: Epoch: 037 Train Loss: 0.107, mae: 0.263, rse: 0.074, pcc: 0.963, kcc: 0.857
+2025-07-19 19:33:07: Epoch: 037 New best VALIDATION metrics
+2025-07-19 19:33:08: Epoch: 037 Test results, ap: mae: 0.735, rse: 1.491, pcc: 0.221, kcc: 0.137
+2025-07-19 19:33:43: Epoch: 038 Train Loss: 0.104, mae: 0.262, rse: 0.071, pcc: 0.965, kcc: 0.857
+2025-07-19 19:33:43: Epoch: 038 New best VALIDATION metrics
+2025-07-19 19:33:44: Epoch: 038 Test results, ap: mae: 0.765, rse: 1.618, pcc: 0.191, kcc: 0.119
+2025-07-19 19:34:20: Epoch: 039 Train Loss: 0.107, mae: 0.266, rse: 0.076, pcc: 0.964, kcc: 0.855
+2025-07-19 19:34:22: Epoch: 039 Test results, ap: mae: 0.787, rse: 1.725, pcc: 0.194, kcc: 0.125
+2025-07-19 19:34:57: Epoch: 040 Train Loss: 0.104, mae: 0.269, rse: 0.075, pcc: 0.964, kcc: 0.859
+2025-07-19 19:34:58: Epoch: 040 Test results, ap: mae: 0.752, rse: 1.545, pcc: 0.212, kcc: 0.139
+2025-07-19 19:35:32: Epoch: 041 Train Loss: 0.098, mae: 0.248, rse: 0.071, pcc: 0.965, kcc: 0.861
+2025-07-19 19:35:32: Epoch: 041 New best VALIDATION metrics
+2025-07-19 19:35:34: Epoch: 041 Test results, ap: mae: 0.739, rse: 1.489, pcc: 0.203, kcc: 0.131
+2025-07-19 19:36:09: Epoch: 042 Train Loss: 0.092, mae: 0.242, rse: 0.069, pcc: 0.966, kcc: 0.864
+2025-07-19 19:36:09: Epoch: 042 New best VALIDATION metrics
+2025-07-19 19:36:10: Epoch: 042 Test results, ap: mae: 0.760, rse: 1.579, pcc: 0.208, kcc: 0.132
+2025-07-19 19:36:45: Epoch: 043 Train Loss: 0.101, mae: 0.249, rse: 0.071, pcc: 0.965, kcc: 0.862
+2025-07-19 19:36:46: Epoch: 043 Test results, ap: mae: 0.743, rse: 1.519, pcc: 0.212, kcc: 0.135
+2025-07-19 19:37:21: Epoch: 044 Train Loss: 0.104, mae: 0.246, rse: 0.071, pcc: 0.965, kcc: 0.864
+2025-07-19 19:37:22: Epoch: 044 Test results, ap: mae: 0.756, rse: 1.574, pcc: 0.212, kcc: 0.135
+2025-07-19 19:37:56: Epoch: 045 Train Loss: 0.093, mae: 0.243, rse: 0.070, pcc: 0.965, kcc: 0.863
+2025-07-19 19:37:58: Epoch: 045 Test results, ap: mae: 0.742, rse: 1.513, pcc: 0.213, kcc: 0.139
+2025-07-19 19:38:33: Epoch: 046 Train Loss: 0.092, mae: 0.253, rse: 0.071, pcc: 0.965, kcc: 0.864
+2025-07-19 19:38:34: Epoch: 046 Test results, ap: mae: 0.754, rse: 1.553, pcc: 0.226, kcc: 0.148
+2025-07-19 19:39:09: Epoch: 047 Train Loss: 0.093, mae: 0.238, rse: 0.070, pcc: 0.966, kcc: 0.864
+2025-07-19 19:39:09: Epoch: 047 New best VALIDATION metrics
+2025-07-19 19:39:10: Epoch: 047 Test results, ap: mae: 0.766, rse: 1.610, pcc: 0.220, kcc: 0.141
+2025-07-19 19:39:45: Epoch: 048 Train Loss: 0.094, mae: 0.238, rse: 0.067, pcc: 0.967, kcc: 0.864
+2025-07-19 19:39:45: Epoch: 048 New best VALIDATION metrics
+2025-07-19 19:39:46: Epoch: 048 Test results, ap: mae: 0.767, rse: 1.615, pcc: 0.210, kcc: 0.138
+2025-07-19 19:40:21: Epoch: 049 Train Loss: 0.096, mae: 0.263, rse: 0.074, pcc: 0.964, kcc: 0.859
+2025-07-19 19:40:22: Epoch: 049 Test results, ap: mae: 0.748, rse: 1.546, pcc: 0.210, kcc: 0.133
+2025-07-19 19:40:57: Epoch: 050 Train Loss: 0.089, mae: 0.244, rse: 0.068, pcc: 0.967, kcc: 0.866
+2025-07-19 19:40:58: Epoch: 050 Test results, ap: mae: 0.731, rse: 1.464, pcc: 0.226, kcc: 0.145
+2025-07-19 19:40:58: used time 0.50h
+2025-07-19 19:40:58: Running Cross Validation 4
+2025-07-19 19:40:58: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 19:41:33: Epoch: 001 Train Loss: 2.284, mae: 1.082, rse: 0.785, pcc: 0.473, kcc: 0.299
+2025-07-19 19:41:33: Epoch: 001 New best VALIDATION metrics
+2025-07-19 19:41:34: Epoch: 001 Test results, ap: mae: 0.592, rse: 1.105, pcc: 0.117, kcc: 0.116
+2025-07-19 19:42:09: Epoch: 002 Train Loss: 1.514, mae: 1.095, rse: 0.784, pcc: 0.510, kcc: 0.365
+2025-07-19 19:42:09: Epoch: 002 New best VALIDATION metrics
+2025-07-19 19:42:10: Epoch: 002 Test results, ap: mae: 1.053, rse: 3.019, pcc: 0.017, kcc: -0.010
+2025-07-19 19:42:45: Epoch: 003 Train Loss: 1.225, mae: 0.840, rse: 0.489, pcc: 0.720, kcc: 0.500
+2025-07-19 19:42:45: Epoch: 003 New best VALIDATION metrics
+2025-07-19 19:42:46: Epoch: 003 Test results, ap: mae: 1.110, rse: 3.396, pcc: 0.208, kcc: 0.151
+2025-07-19 19:43:21: Epoch: 004 Train Loss: 1.039, mae: 0.769, rse: 0.459, pcc: 0.744, kcc: 0.563
+2025-07-19 19:43:21: Epoch: 004 New best VALIDATION metrics
+2025-07-19 19:43:22: Epoch: 004 Test results, ap: mae: 1.084, rse: 3.233, pcc: 0.257, kcc: 0.155
+2025-07-19 19:43:57: Epoch: 005 Train Loss: 0.896, mae: 0.694, rse: 0.350, pcc: 0.810, kcc: 0.586
+2025-07-19 19:43:57: Epoch: 005 New best VALIDATION metrics
+2025-07-19 19:43:59: Epoch: 005 Test results, ap: mae: 1.065, rse: 3.098, pcc: 0.250, kcc: 0.152
+2025-07-19 19:44:33: Epoch: 006 Train Loss: 0.801, mae: 0.721, rse: 0.385, pcc: 0.785, kcc: 0.575
+2025-07-19 19:44:34: Epoch: 006 Test results, ap: mae: 0.930, rse: 2.568, pcc: 0.345, kcc: 0.205
+2025-07-19 19:45:09: Epoch: 007 Train Loss: 0.702, mae: 0.651, rse: 0.348, pcc: 0.812, kcc: 0.628
+2025-07-19 19:45:09: Epoch: 007 New best VALIDATION metrics
+2025-07-19 19:45:10: Epoch: 007 Test results, ap: mae: 0.716, rse: 1.565, pcc: 0.365, kcc: 0.190
+2025-07-19 19:45:45: Epoch: 008 Train Loss: 0.646, mae: 0.597, rse: 0.312, pcc: 0.829, kcc: 0.643
+2025-07-19 19:45:45: Epoch: 008 New best VALIDATION metrics
+2025-07-19 19:45:47: Epoch: 008 Test results, ap: mae: 0.803, rse: 1.968, pcc: 0.356, kcc: 0.184
+2025-07-19 19:46:21: Epoch: 009 Train Loss: 0.607, mae: 0.633, rse: 0.327, pcc: 0.823, kcc: 0.629
+2025-07-19 19:46:23: Epoch: 009 Test results, ap: mae: 0.678, rse: 1.249, pcc: 0.342, kcc: 0.173
+2025-07-19 19:46:58: Epoch: 010 Train Loss: 0.584, mae: 0.624, rse: 0.294, pcc: 0.841, kcc: 0.633
+2025-07-19 19:46:59: Epoch: 010 Test results, ap: mae: 0.828, rse: 2.086, pcc: 0.353, kcc: 0.242
+2025-07-19 19:47:34: Epoch: 011 Train Loss: 0.476, mae: 0.500, rse: 0.242, pcc: 0.871, kcc: 0.698
+2025-07-19 19:47:34: Epoch: 011 New best VALIDATION metrics
+2025-07-19 19:47:35: Epoch: 011 Test results, ap: mae: 1.018, rse: 2.903, pcc: 0.330, kcc: 0.177
+2025-07-19 19:47:35: Epoch: 011 New best TEST metrics
+2025-07-19 19:48:10: Epoch: 012 Train Loss: 0.429, mae: 0.525, rse: 0.251, pcc: 0.868, kcc: 0.691
+2025-07-19 19:48:11: Epoch: 012 Test results, ap: mae: 1.000, rse: 2.731, pcc: 0.301, kcc: 0.168
+2025-07-19 19:48:11: Epoch: 012 New best TEST metrics
+2025-07-19 19:48:46: Epoch: 013 Train Loss: 0.411, mae: 0.487, rse: 0.227, pcc: 0.880, kcc: 0.711
+2025-07-19 19:48:46: Epoch: 013 New best VALIDATION metrics
+2025-07-19 19:48:48: Epoch: 013 Test results, ap: mae: 1.030, rse: 2.958, pcc: 0.304, kcc: 0.171
+2025-07-19 19:49:22: Epoch: 014 Train Loss: 0.369, mae: 0.500, rse: 0.236, pcc: 0.879, kcc: 0.703
+2025-07-19 19:49:24: Epoch: 014 Test results, ap: mae: 1.247, rse: 4.071, pcc: 0.329, kcc: 0.188
+2025-07-19 19:49:58: Epoch: 015 Train Loss: 0.342, mae: 0.466, rse: 0.212, pcc: 0.888, kcc: 0.720
+2025-07-19 19:49:58: Epoch: 015 New best VALIDATION metrics
+2025-07-19 19:50:00: Epoch: 015 Test results, ap: mae: 0.983, rse: 2.693, pcc: 0.312, kcc: 0.170
+2025-07-19 19:50:00: Epoch: 015 New best TEST metrics
+2025-07-19 19:50:34: Epoch: 016 Train Loss: 0.320, mae: 0.427, rse: 0.201, pcc: 0.898, kcc: 0.741
+2025-07-19 19:50:34: Epoch: 016 New best VALIDATION metrics
+2025-07-19 19:50:36: Epoch: 016 Test results, ap: mae: 0.938, rse: 2.526, pcc: 0.329, kcc: 0.180
+2025-07-19 19:50:36: Epoch: 016 New best TEST metrics
+2025-07-19 19:51:11: Epoch: 017 Train Loss: 0.289, mae: 0.455, rse: 0.216, pcc: 0.886, kcc: 0.733
+2025-07-19 19:51:12: Epoch: 017 Test results, ap: mae: 1.069, rse: 3.189, pcc: 0.269, kcc: 0.158
+2025-07-19 19:51:47: Epoch: 018 Train Loss: 0.265, mae: 0.417, rse: 0.187, pcc: 0.902, kcc: 0.753
+2025-07-19 19:51:47: Epoch: 018 New best VALIDATION metrics
+2025-07-19 19:51:48: Epoch: 018 Test results, ap: mae: 0.913, rse: 2.347, pcc: 0.313, kcc: 0.183
+2025-07-19 19:51:48: Epoch: 018 New best TEST metrics
+2025-07-19 19:52:23: Epoch: 019 Train Loss: 0.257, mae: 0.426, rse: 0.205, pcc: 0.897, kcc: 0.747
+2025-07-19 19:52:24: Epoch: 019 Test results, ap: mae: 0.828, rse: 1.988, pcc: 0.292, kcc: 0.160
+2025-07-19 19:52:24: Epoch: 019 New best TEST metrics
+2025-07-19 19:52:59: Epoch: 020 Train Loss: 0.249, mae: 0.409, rse: 0.184, pcc: 0.906, kcc: 0.757
+2025-07-19 19:52:59: Epoch: 020 New best VALIDATION metrics
+2025-07-19 19:53:00: Epoch: 020 Test results, ap: mae: 0.910, rse: 2.375, pcc: 0.269, kcc: 0.150
+2025-07-19 19:53:34: Epoch: 021 Train Loss: 0.211, mae: 0.353, rse: 0.164, pcc: 0.915, kcc: 0.776
+2025-07-19 19:53:34: Epoch: 021 New best VALIDATION metrics
+2025-07-19 19:53:36: Epoch: 021 Test results, ap: mae: 0.888, rse: 2.278, pcc: 0.322, kcc: 0.187
+2025-07-19 19:54:11: Epoch: 022 Train Loss: 0.186, mae: 0.349, rse: 0.164, pcc: 0.917, kcc: 0.782
+2025-07-19 19:54:11: Epoch: 022 New best VALIDATION metrics
+2025-07-19 19:54:12: Epoch: 022 Test results, ap: mae: 0.921, rse: 2.441, pcc: 0.324, kcc: 0.189
+2025-07-19 19:54:47: Epoch: 023 Train Loss: 0.180, mae: 0.351, rse: 0.164, pcc: 0.915, kcc: 0.778
+2025-07-19 19:54:48: Epoch: 023 Test results, ap: mae: 0.920, rse: 2.391, pcc: 0.319, kcc: 0.194
+2025-07-19 19:55:23: Epoch: 024 Train Loss: 0.171, mae: 0.344, rse: 0.159, pcc: 0.918, kcc: 0.789
+2025-07-19 19:55:23: Epoch: 024 New best VALIDATION metrics
+2025-07-19 19:55:24: Epoch: 024 Test results, ap: mae: 0.867, rse: 2.145, pcc: 0.332, kcc: 0.201
+2025-07-19 19:55:59: Epoch: 025 Train Loss: 0.160, mae: 0.352, rse: 0.157, pcc: 0.918, kcc: 0.786
+2025-07-19 19:56:00: Epoch: 025 Test results, ap: mae: 0.861, rse: 2.112, pcc: 0.297, kcc: 0.174
+2025-07-19 19:56:35: Epoch: 026 Train Loss: 0.157, mae: 0.330, rse: 0.158, pcc: 0.918, kcc: 0.794
+2025-07-19 19:56:35: Epoch: 026 New best VALIDATION metrics
+2025-07-19 19:56:36: Epoch: 026 Test results, ap: mae: 0.859, rse: 2.106, pcc: 0.331, kcc: 0.179
+2025-07-19 19:57:11: Epoch: 027 Train Loss: 0.153, mae: 0.338, rse: 0.156, pcc: 0.919, kcc: 0.797
+2025-07-19 19:57:12: Epoch: 027 Test results, ap: mae: 0.867, rse: 2.141, pcc: 0.319, kcc: 0.192
+2025-07-19 19:57:47: Epoch: 028 Train Loss: 0.160, mae: 0.350, rse: 0.160, pcc: 0.917, kcc: 0.789
+2025-07-19 19:57:48: Epoch: 028 Test results, ap: mae: 0.854, rse: 2.111, pcc: 0.314, kcc: 0.187
+2025-07-19 19:58:23: Epoch: 029 Train Loss: 0.144, mae: 0.333, rse: 0.162, pcc: 0.920, kcc: 0.800
+2025-07-19 19:58:23: Epoch: 029 New best VALIDATION metrics
+2025-07-19 19:58:24: Epoch: 029 Test results, ap: mae: 0.916, rse: 2.488, pcc: 0.302, kcc: 0.185
+2025-07-19 19:58:59: Epoch: 030 Train Loss: 0.140, mae: 0.332, rse: 0.156, pcc: 0.919, kcc: 0.801
+2025-07-19 19:58:59: Epoch: 030 New best VALIDATION metrics
+2025-07-19 19:59:01: Epoch: 030 Test results, ap: mae: 0.761, rse: 1.709, pcc: 0.328, kcc: 0.184
+2025-07-19 19:59:01: Epoch: 030 New best TEST metrics
+2025-07-19 19:59:35: Epoch: 031 Train Loss: 0.127, mae: 0.305, rse: 0.148, pcc: 0.924, kcc: 0.812
+2025-07-19 19:59:35: Epoch: 031 New best VALIDATION metrics
+2025-07-19 19:59:37: Epoch: 031 Test results, ap: mae: 0.873, rse: 2.216, pcc: 0.322, kcc: 0.190
+2025-07-19 20:00:11: Epoch: 032 Train Loss: 0.113, mae: 0.303, rse: 0.149, pcc: 0.923, kcc: 0.814
+2025-07-19 20:00:11: Epoch: 032 New best VALIDATION metrics
+2025-07-19 20:00:13: Epoch: 032 Test results, ap: mae: 0.904, rse: 2.346, pcc: 0.322, kcc: 0.191
+2025-07-19 20:00:47: Epoch: 033 Train Loss: 0.121, mae: 0.310, rse: 0.150, pcc: 0.923, kcc: 0.814
+2025-07-19 20:00:49: Epoch: 033 Test results, ap: mae: 0.942, rse: 2.519, pcc: 0.309, kcc: 0.183
+2025-07-19 20:01:23: Epoch: 034 Train Loss: 0.120, mae: 0.314, rse: 0.150, pcc: 0.923, kcc: 0.808
+2025-07-19 20:01:25: Epoch: 034 Test results, ap: mae: 0.929, rse: 2.449, pcc: 0.303, kcc: 0.177
+2025-07-19 20:01:59: Epoch: 035 Train Loss: 0.110, mae: 0.293, rse: 0.143, pcc: 0.926, kcc: 0.818
+2025-07-19 20:01:59: Epoch: 035 New best VALIDATION metrics
+2025-07-19 20:02:01: Epoch: 035 Test results, ap: mae: 0.882, rse: 2.239, pcc: 0.312, kcc: 0.187
+2025-07-19 20:02:35: Epoch: 036 Train Loss: 0.117, mae: 0.296, rse: 0.143, pcc: 0.926, kcc: 0.818
+2025-07-19 20:02:37: Epoch: 036 Test results, ap: mae: 0.898, rse: 2.332, pcc: 0.314, kcc: 0.184
+2025-07-19 20:03:11: Epoch: 037 Train Loss: 0.119, mae: 0.298, rse: 0.145, pcc: 0.925, kcc: 0.816
+2025-07-19 20:03:13: Epoch: 037 Test results, ap: mae: 0.918, rse: 2.362, pcc: 0.322, kcc: 0.190
+2025-07-19 20:03:47: Epoch: 038 Train Loss: 0.113, mae: 0.305, rse: 0.148, pcc: 0.923, kcc: 0.815
+2025-07-19 20:03:49: Epoch: 038 Test results, ap: mae: 0.786, rse: 1.847, pcc: 0.314, kcc: 0.186
+2025-07-19 20:04:24: Epoch: 039 Train Loss: 0.111, mae: 0.291, rse: 0.146, pcc: 0.925, kcc: 0.820
+2025-07-19 20:04:24: Epoch: 039 New best VALIDATION metrics
+2025-07-19 20:04:25: Epoch: 039 Test results, ap: mae: 0.894, rse: 2.264, pcc: 0.319, kcc: 0.189
+2025-07-19 20:05:00: Epoch: 040 Train Loss: 0.115, mae: 0.298, rse: 0.147, pcc: 0.924, kcc: 0.821
+2025-07-19 20:05:01: Epoch: 040 Test results, ap: mae: 0.852, rse: 2.061, pcc: 0.306, kcc: 0.177
+2025-07-19 20:05:35: Epoch: 041 Train Loss: 0.108, mae: 0.287, rse: 0.144, pcc: 0.926, kcc: 0.824
+2025-07-19 20:05:35: Epoch: 041 New best VALIDATION metrics
+2025-07-19 20:05:37: Epoch: 041 Test results, ap: mae: 0.901, rse: 2.325, pcc: 0.318, kcc: 0.188
+2025-07-19 20:06:12: Epoch: 042 Train Loss: 0.100, mae: 0.282, rse: 0.142, pcc: 0.926, kcc: 0.823
+2025-07-19 20:06:12: Epoch: 042 New best VALIDATION metrics
+2025-07-19 20:06:13: Epoch: 042 Test results, ap: mae: 0.878, rse: 2.198, pcc: 0.314, kcc: 0.181
+2025-07-19 20:06:48: Epoch: 043 Train Loss: 0.100, mae: 0.282, rse: 0.140, pcc: 0.928, kcc: 0.827
+2025-07-19 20:06:48: Epoch: 043 New best VALIDATION metrics
+2025-07-19 20:06:49: Epoch: 043 Test results, ap: mae: 0.915, rse: 2.387, pcc: 0.308, kcc: 0.178
+2025-07-19 20:07:24: Epoch: 044 Train Loss: 0.100, mae: 0.284, rse: 0.142, pcc: 0.927, kcc: 0.826
+2025-07-19 20:07:25: Epoch: 044 Test results, ap: mae: 0.945, rse: 2.515, pcc: 0.313, kcc: 0.188
+2025-07-19 20:08:00: Epoch: 045 Train Loss: 0.103, mae: 0.289, rse: 0.139, pcc: 0.928, kcc: 0.827
+2025-07-19 20:08:01: Epoch: 045 Test results, ap: mae: 0.919, rse: 2.389, pcc: 0.316, kcc: 0.187
+2025-07-19 20:08:36: Epoch: 046 Train Loss: 0.097, mae: 0.280, rse: 0.139, pcc: 0.928, kcc: 0.830
+2025-07-19 20:08:36: Epoch: 046 New best VALIDATION metrics
+2025-07-19 20:08:37: Epoch: 046 Test results, ap: mae: 0.859, rse: 2.137, pcc: 0.314, kcc: 0.188
+2025-07-19 20:09:12: Epoch: 047 Train Loss: 0.103, mae: 0.289, rse: 0.142, pcc: 0.927, kcc: 0.828
+2025-07-19 20:09:13: Epoch: 047 Test results, ap: mae: 0.853, rse: 2.091, pcc: 0.320, kcc: 0.192
+2025-07-19 20:09:48: Epoch: 048 Train Loss: 0.091, mae: 0.280, rse: 0.139, pcc: 0.929, kcc: 0.832
+2025-07-19 20:09:48: Epoch: 048 New best VALIDATION metrics
+2025-07-19 20:09:49: Epoch: 048 Test results, ap: mae: 0.919, rse: 2.422, pcc: 0.306, kcc: 0.182
+2025-07-19 20:10:24: Epoch: 049 Train Loss: 0.102, mae: 0.280, rse: 0.141, pcc: 0.927, kcc: 0.828
+2025-07-19 20:10:25: Epoch: 049 Test results, ap: mae: 0.885, rse: 2.247, pcc: 0.313, kcc: 0.188
+2025-07-19 20:11:00: Epoch: 050 Train Loss: 0.094, mae: 0.276, rse: 0.138, pcc: 0.929, kcc: 0.832
+2025-07-19 20:11:00: Epoch: 050 New best VALIDATION metrics
+2025-07-19 20:11:01: Epoch: 050 Test results, ap: mae: 0.921, rse: 2.408, pcc: 0.305, kcc: 0.187
+2025-07-19 20:11:01: used time 0.50h
+2025-07-19 20:11:01: Cross Validation Finished!
+2025-07-19 20:11:01: Best validation perform list
+[[0.27008021 0.09716063 0.95244491 0.84059685]
+ [0.29582396 0.0881184  0.96055251 0.85189134]
+ [0.27072796 0.09631826 0.95267159 0.84955198]
+ [0.23824409 0.066925   0.96701813 0.86446965]
+ [0.27611041 0.1376332  0.92908615 0.83204436]]
+2025-07-19 20:11:01: mean: [0.27  0.097 0.952 0.848]
+2025-07-19 20:11:01: std: [0.019 0.023 0.013 0.011]
+2025-07-19 20:11:01: Best test perform list
+[[0.70299482 1.46575272 0.21975383 0.17588943]
+ [0.63593799 1.16836452 0.15439402 0.11656578]
+ [0.6497705  1.2876606  0.31500858 0.16895099]
+ [0.66004616 1.1982547  0.23605855 0.15542103]
+ [0.76139289 1.70889306 0.32787675 0.18386863]]
+2025-07-19 20:11:01: mean: [0.682 1.366 0.251 0.16 ]
+2025-07-19 20:11:01: std: [0.046 0.2   0.064 0.024]
diff --git a/run-reg/rn18-mlp-16-768-mse-32-0.001-50/config.json b/run-reg/rn18-mlp-16-768-mse-32-0.001-50/config.json
new file mode 100644
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@@ -0,0 +1 @@
+{"model": "resnet34", "q_encoder": "rn18", "side_enc": null, "channels": 16, "fusion": "mlp", "glob_feat": false, "non_siamese": false, "task": "reg", "one_way": false, "max_length": 30, "split": 5, "seed": 1, "pcs": false, "mix_pcs": false, "resize": [768], "gpu": 0, "batch_size": 32, "epochs": 50, "lr": 0.001, "decay": 0.0005, "pretrain": "", "metric_avg": "macro", "loss": "mse", "dir": false, "classes": 1}
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@@ -0,0 +1,6 @@
+Valid
+0.32651410698890687,0.12956162989139558,0.9340258121490479,0.8147412896156311
+0.029976958584722636,0.019648186767177986,0.010837673724242478,0.02329224506898742
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diff --git a/run-reg/rn18-mlp-16-768-mse-32-0.001-50/training.log b/run-reg/rn18-mlp-16-768-mse-32-0.001-50/training.log
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@@ -0,0 +1,703 @@
+2025-07-18 22:29:12: saving_dir: ./run-reg/rn18-mlp-16-768-mse-32-0.001-50
+2025-07-18 22:29:12: Loading Training Dataset
+2025-07-18 22:29:37: Loading Test Dataset
+2025-07-18 22:29:40: Running Cross Validation 0
+2025-07-18 22:29:40: Fold 0  Train set:2269, Valid set:568, Test set: 108
+2025-07-18 22:30:17: Epoch: 001 Train Loss: 2.751, mae: 1.121, rse: 0.849, pcc: 0.399, kcc: 0.270
+2025-07-18 22:30:17: Epoch: 001 New best VALIDATION metrics
+2025-07-18 22:30:19: Epoch: 001 Test results, ap: mae: 0.675, rse: 1.360, pcc: -0.204, kcc: -0.188
+2025-07-18 22:30:53: Epoch: 002 Train Loss: 2.656, mae: 1.093, rse: 0.832, pcc: 0.420, kcc: 0.287
+2025-07-18 22:30:53: Epoch: 002 New best VALIDATION metrics
+2025-07-18 22:30:55: Epoch: 002 Test results, ap: mae: 0.693, rse: 1.425, pcc: -0.283, kcc: -0.217
+2025-07-18 22:31:29: Epoch: 003 Train Loss: 2.621, mae: 1.124, rse: 0.852, pcc: 0.402, kcc: 0.282
+2025-07-18 22:31:31: Epoch: 003 Test results, ap: mae: 0.607, rse: 1.120, pcc: -0.170, kcc: -0.076
+2025-07-18 22:32:06: Epoch: 004 Train Loss: 2.599, mae: 1.153, rse: 0.896, pcc: 0.424, kcc: 0.284
+2025-07-18 22:32:07: Epoch: 004 Test results, ap: mae: 1.075, rse: 2.958, pcc: -0.412, kcc: -0.233
+2025-07-18 22:32:42: Epoch: 005 Train Loss: 2.553, mae: 1.121, rse: 0.835, pcc: 0.424, kcc: 0.290
+2025-07-18 22:32:44: Epoch: 005 Test results, ap: mae: 0.576, rse: 0.997, pcc: 0.181, kcc: 0.117
+2025-07-18 22:33:18: Epoch: 006 Train Loss: 2.549, mae: 1.080, rse: 0.800, pcc: 0.448, kcc: 0.308
+2025-07-18 22:33:18: Epoch: 006 New best VALIDATION metrics
+2025-07-18 22:33:20: Epoch: 006 Test results, ap: mae: 0.631, rse: 1.231, pcc: 0.155, kcc: 0.111
+2025-07-18 22:33:54: Epoch: 007 Train Loss: 2.492, mae: 1.078, rse: 0.793, pcc: 0.460, kcc: 0.323
+2025-07-18 22:33:54: Epoch: 007 New best VALIDATION metrics
+2025-07-18 22:33:56: Epoch: 007 Test results, ap: mae: 0.703, rse: 1.525, pcc: -0.036, kcc: -0.021
+2025-07-18 22:34:31: Epoch: 008 Train Loss: 2.440, mae: 1.096, rse: 0.811, pcc: 0.463, kcc: 0.327
+2025-07-18 22:34:32: Epoch: 008 Test results, ap: mae: 0.890, rse: 2.317, pcc: -0.137, kcc: -0.107
+2025-07-18 22:35:07: Epoch: 009 Train Loss: 2.312, mae: 1.078, rse: 0.779, pcc: 0.485, kcc: 0.336
+2025-07-18 22:35:07: Epoch: 009 New best VALIDATION metrics
+2025-07-18 22:35:09: Epoch: 009 Test results, ap: mae: 0.732, rse: 1.603, pcc: -0.126, kcc: -0.095
+2025-07-18 22:35:43: Epoch: 010 Train Loss: 2.250, mae: 1.046, rse: 0.740, pcc: 0.520, kcc: 0.357
+2025-07-18 22:35:43: Epoch: 010 New best VALIDATION metrics
+2025-07-18 22:35:45: Epoch: 010 Test results, ap: mae: 0.786, rse: 1.739, pcc: 0.136, kcc: 0.067
+2025-07-18 22:36:19: Epoch: 011 Train Loss: 2.144, mae: 1.042, rse: 0.724, pcc: 0.526, kcc: 0.368
+2025-07-18 22:36:19: Epoch: 011 New best VALIDATION metrics
+2025-07-18 22:36:21: Epoch: 011 Test results, ap: mae: 0.609, rse: 1.148, pcc: 0.063, kcc: 0.048
+2025-07-18 22:36:21: Epoch: 011 New best TEST metrics
+2025-07-18 22:36:55: Epoch: 012 Train Loss: 1.935, mae: 1.059, rse: 0.736, pcc: 0.516, kcc: 0.371
+2025-07-18 22:36:57: Epoch: 012 Test results, ap: mae: 0.644, rse: 1.216, pcc: 0.157, kcc: 0.131
+2025-07-18 22:36:57: Epoch: 012 New best TEST metrics
+2025-07-18 22:37:32: Epoch: 013 Train Loss: 1.834, mae: 0.933, rse: 0.582, pcc: 0.648, kcc: 0.430
+2025-07-18 22:37:32: Epoch: 013 New best VALIDATION metrics
+2025-07-18 22:37:33: Epoch: 013 Test results, ap: mae: 0.690, rse: 1.344, pcc: -0.001, kcc: 0.001
+2025-07-18 22:38:08: Epoch: 014 Train Loss: 1.592, mae: 0.928, rse: 0.559, pcc: 0.675, kcc: 0.469
+2025-07-18 22:38:08: Epoch: 014 New best VALIDATION metrics
+2025-07-18 22:38:09: Epoch: 014 Test results, ap: mae: 0.704, rse: 1.550, pcc: -0.105, kcc: -0.017
+2025-07-18 22:38:44: Epoch: 015 Train Loss: 1.376, mae: 0.845, rse: 0.486, pcc: 0.727, kcc: 0.517
+2025-07-18 22:38:44: Epoch: 015 New best VALIDATION metrics
+2025-07-18 22:38:46: Epoch: 015 Test results, ap: mae: 0.748, rse: 1.811, pcc: -0.030, kcc: 0.008
+2025-07-18 22:39:20: Epoch: 016 Train Loss: 1.201, mae: 0.831, rse: 0.481, pcc: 0.739, kcc: 0.537
+2025-07-18 22:39:20: Epoch: 016 New best VALIDATION metrics
+2025-07-18 22:39:22: Epoch: 016 Test results, ap: mae: 0.683, rse: 1.490, pcc: -0.011, kcc: -0.025
+2025-07-18 22:39:57: Epoch: 017 Train Loss: 1.014, mae: 0.779, rse: 0.417, pcc: 0.769, kcc: 0.543
+2025-07-18 22:39:57: Epoch: 017 New best VALIDATION metrics
+2025-07-18 22:39:58: Epoch: 017 Test results, ap: mae: 0.673, rse: 1.525, pcc: 0.024, kcc: 0.045
+2025-07-18 22:40:33: Epoch: 018 Train Loss: 0.971, mae: 0.733, rse: 0.366, pcc: 0.806, kcc: 0.588
+2025-07-18 22:40:33: Epoch: 018 New best VALIDATION metrics
+2025-07-18 22:40:34: Epoch: 018 Test results, ap: mae: 0.695, rse: 1.415, pcc: 0.028, kcc: 0.025
+2025-07-18 22:41:09: Epoch: 019 Train Loss: 0.833, mae: 0.685, rse: 0.357, pcc: 0.816, kcc: 0.606
+2025-07-18 22:41:09: Epoch: 019 New best VALIDATION metrics
+2025-07-18 22:41:11: Epoch: 019 Test results, ap: mae: 0.857, rse: 2.136, pcc: 0.011, kcc: 0.025
+2025-07-18 22:41:45: Epoch: 020 Train Loss: 0.743, mae: 0.736, rse: 0.425, pcc: 0.766, kcc: 0.597
+2025-07-18 22:41:47: Epoch: 020 Test results, ap: mae: 0.863, rse: 2.422, pcc: 0.033, kcc: 0.042
+2025-07-18 22:42:21: Epoch: 021 Train Loss: 0.600, mae: 0.631, rse: 0.279, pcc: 0.868, kcc: 0.654
+2025-07-18 22:42:21: Epoch: 021 New best VALIDATION metrics
+2025-07-18 22:42:23: Epoch: 021 Test results, ap: mae: 0.748, rse: 1.614, pcc: -0.003, kcc: -0.027
+2025-07-18 22:42:58: Epoch: 022 Train Loss: 0.513, mae: 0.545, rse: 0.231, pcc: 0.878, kcc: 0.678
+2025-07-18 22:42:58: Epoch: 022 New best VALIDATION metrics
+2025-07-18 22:42:59: Epoch: 022 Test results, ap: mae: 0.769, rse: 1.681, pcc: 0.042, kcc: 0.011
+2025-07-18 22:43:34: Epoch: 023 Train Loss: 0.483, mae: 0.512, rse: 0.211, pcc: 0.891, kcc: 0.700
+2025-07-18 22:43:34: Epoch: 023 New best VALIDATION metrics
+2025-07-18 22:43:36: Epoch: 023 Test results, ap: mae: 0.778, rse: 1.768, pcc: 0.026, kcc: 0.015
+2025-07-18 22:44:10: Epoch: 024 Train Loss: 0.449, mae: 0.528, rse: 0.229, pcc: 0.879, kcc: 0.697
+2025-07-18 22:44:12: Epoch: 024 Test results, ap: mae: 0.796, rse: 1.855, pcc: 0.025, kcc: -0.000
+2025-07-18 22:44:47: Epoch: 025 Train Loss: 0.427, mae: 0.550, rse: 0.224, pcc: 0.895, kcc: 0.705
+2025-07-18 22:44:48: Epoch: 025 Test results, ap: mae: 0.729, rse: 1.546, pcc: 0.036, kcc: 0.003
+2025-07-18 22:45:23: Epoch: 026 Train Loss: 0.404, mae: 0.497, rse: 0.215, pcc: 0.888, kcc: 0.714
+2025-07-18 22:45:23: Epoch: 026 New best VALIDATION metrics
+2025-07-18 22:45:24: Epoch: 026 Test results, ap: mae: 0.777, rse: 1.789, pcc: 0.059, kcc: 0.056
+2025-07-18 22:45:59: Epoch: 027 Train Loss: 0.381, mae: 0.489, rse: 0.196, pcc: 0.898, kcc: 0.719
+2025-07-18 22:45:59: Epoch: 027 New best VALIDATION metrics
+2025-07-18 22:46:01: Epoch: 027 Test results, ap: mae: 0.764, rse: 1.704, pcc: 0.077, kcc: 0.052
+2025-07-18 22:46:35: Epoch: 028 Train Loss: 0.357, mae: 0.460, rse: 0.179, pcc: 0.908, kcc: 0.733
+2025-07-18 22:46:35: Epoch: 028 New best VALIDATION metrics
+2025-07-18 22:46:37: Epoch: 028 Test results, ap: mae: 0.779, rse: 1.847, pcc: 0.107, kcc: 0.083
+2025-07-18 22:47:12: Epoch: 029 Train Loss: 0.319, mae: 0.480, rse: 0.182, pcc: 0.915, kcc: 0.734
+2025-07-18 22:47:13: Epoch: 029 Test results, ap: mae: 0.720, rse: 1.544, pcc: 0.100, kcc: 0.064
+2025-07-18 22:47:48: Epoch: 030 Train Loss: 0.342, mae: 0.450, rse: 0.175, pcc: 0.911, kcc: 0.745
+2025-07-18 22:47:48: Epoch: 030 New best VALIDATION metrics
+2025-07-18 22:47:50: Epoch: 030 Test results, ap: mae: 0.778, rse: 1.814, pcc: 0.067, kcc: 0.043
+2025-07-18 22:48:24: Epoch: 031 Train Loss: 0.269, mae: 0.408, rse: 0.152, pcc: 0.924, kcc: 0.764
+2025-07-18 22:48:24: Epoch: 031 New best VALIDATION metrics
+2025-07-18 22:48:26: Epoch: 031 Test results, ap: mae: 0.766, rse: 1.709, pcc: 0.019, kcc: -0.009
+2025-07-18 22:49:00: Epoch: 032 Train Loss: 0.249, mae: 0.443, rse: 0.164, pcc: 0.922, kcc: 0.757
+2025-07-18 22:49:02: Epoch: 032 Test results, ap: mae: 0.781, rse: 1.737, pcc: 0.055, kcc: 0.022
+2025-07-18 22:49:37: Epoch: 033 Train Loss: 0.231, mae: 0.422, rse: 0.164, pcc: 0.920, kcc: 0.762
+2025-07-18 22:49:38: Epoch: 033 Test results, ap: mae: 0.797, rse: 1.835, pcc: 0.073, kcc: 0.042
+2025-07-18 22:50:13: Epoch: 034 Train Loss: 0.221, mae: 0.405, rse: 0.151, pcc: 0.927, kcc: 0.773
+2025-07-18 22:50:13: Epoch: 034 New best VALIDATION metrics
+2025-07-18 22:50:15: Epoch: 034 Test results, ap: mae: 0.765, rse: 1.707, pcc: 0.043, kcc: 0.018
+2025-07-18 22:50:49: Epoch: 035 Train Loss: 0.215, mae: 0.397, rse: 0.144, pcc: 0.929, kcc: 0.775
+2025-07-18 22:50:49: Epoch: 035 New best VALIDATION metrics
+2025-07-18 22:50:51: Epoch: 035 Test results, ap: mae: 0.779, rse: 1.775, pcc: 0.067, kcc: 0.059
+2025-07-18 22:51:25: Epoch: 036 Train Loss: 0.212, mae: 0.411, rse: 0.153, pcc: 0.925, kcc: 0.774
+2025-07-18 22:51:27: Epoch: 036 Test results, ap: mae: 0.773, rse: 1.735, pcc: 0.061, kcc: 0.038
+2025-07-18 22:52:01: Epoch: 037 Train Loss: 0.211, mae: 0.380, rse: 0.139, pcc: 0.929, kcc: 0.778
+2025-07-18 22:52:01: Epoch: 037 New best VALIDATION metrics
+2025-07-18 22:52:03: Epoch: 037 Test results, ap: mae: 0.775, rse: 1.745, pcc: 0.037, kcc: 0.011
+2025-07-18 22:52:37: Epoch: 038 Train Loss: 0.205, mae: 0.384, rse: 0.145, pcc: 0.929, kcc: 0.779
+2025-07-18 22:52:39: Epoch: 038 Test results, ap: mae: 0.806, rse: 1.902, pcc: 0.035, kcc: -0.004
+2025-07-18 22:53:14: Epoch: 039 Train Loss: 0.214, mae: 0.362, rse: 0.134, pcc: 0.933, kcc: 0.788
+2025-07-18 22:53:14: Epoch: 039 New best VALIDATION metrics
+2025-07-18 22:53:15: Epoch: 039 Test results, ap: mae: 0.773, rse: 1.792, pcc: 0.071, kcc: 0.044
+2025-07-18 22:53:50: Epoch: 040 Train Loss: 0.192, mae: 0.391, rse: 0.143, pcc: 0.933, kcc: 0.789
+2025-07-18 22:53:52: Epoch: 040 Test results, ap: mae: 0.780, rse: 1.837, pcc: 0.063, kcc: 0.038
+2025-07-18 22:54:26: Epoch: 041 Train Loss: 0.189, mae: 0.351, rse: 0.131, pcc: 0.933, kcc: 0.792
+2025-07-18 22:54:26: Epoch: 041 New best VALIDATION metrics
+2025-07-18 22:54:28: Epoch: 041 Test results, ap: mae: 0.794, rse: 1.858, pcc: 0.055, kcc: 0.028
+2025-07-18 22:55:03: Epoch: 042 Train Loss: 0.175, mae: 0.386, rse: 0.141, pcc: 0.933, kcc: 0.793
+2025-07-18 22:55:04: Epoch: 042 Test results, ap: mae: 0.800, rse: 1.907, pcc: 0.048, kcc: 0.028
+2025-07-18 22:55:39: Epoch: 043 Train Loss: 0.177, mae: 0.370, rse: 0.139, pcc: 0.934, kcc: 0.791
+2025-07-18 22:55:40: Epoch: 043 Test results, ap: mae: 0.813, rse: 1.944, pcc: 0.043, kcc: 0.012
+2025-07-18 22:56:15: Epoch: 044 Train Loss: 0.163, mae: 0.360, rse: 0.131, pcc: 0.936, kcc: 0.796
+2025-07-18 22:56:17: Epoch: 044 Test results, ap: mae: 0.781, rse: 1.829, pcc: 0.043, kcc: 0.029
+2025-07-18 22:56:51: Epoch: 045 Train Loss: 0.169, mae: 0.390, rse: 0.148, pcc: 0.934, kcc: 0.798
+2025-07-18 22:56:53: Epoch: 045 Test results, ap: mae: 0.836, rse: 2.103, pcc: 0.038, kcc: 0.012
+2025-07-18 22:57:27: Epoch: 046 Train Loss: 0.157, mae: 0.406, rse: 0.153, pcc: 0.931, kcc: 0.793
+2025-07-18 22:57:29: Epoch: 046 Test results, ap: mae: 0.829, rse: 2.068, pcc: 0.048, kcc: 0.034
+2025-07-18 22:58:03: Epoch: 047 Train Loss: 0.157, mae: 0.348, rse: 0.134, pcc: 0.933, kcc: 0.796
+2025-07-18 22:58:03: Epoch: 047 New best VALIDATION metrics
+2025-07-18 22:58:05: Epoch: 047 Test results, ap: mae: 0.815, rse: 2.007, pcc: 0.025, kcc: 0.007
+2025-07-18 22:58:40: Epoch: 048 Train Loss: 0.163, mae: 0.381, rse: 0.142, pcc: 0.933, kcc: 0.797
+2025-07-18 22:58:41: Epoch: 048 Test results, ap: mae: 0.819, rse: 2.036, pcc: 0.041, kcc: 0.026
+2025-07-18 22:59:16: Epoch: 049 Train Loss: 0.157, mae: 0.347, rse: 0.130, pcc: 0.935, kcc: 0.797
+2025-07-18 22:59:16: Epoch: 049 New best VALIDATION metrics
+2025-07-18 22:59:17: Epoch: 049 Test results, ap: mae: 0.810, rse: 1.944, pcc: 0.024, kcc: 0.007
+2025-07-18 22:59:52: Epoch: 050 Train Loss: 0.168, mae: 0.353, rse: 0.136, pcc: 0.934, kcc: 0.802
+2025-07-18 22:59:53: Epoch: 050 Test results, ap: mae: 0.827, rse: 2.030, pcc: 0.032, kcc: 0.020
+2025-07-18 22:59:53: used time 0.50h
+2025-07-18 22:59:53: Running Cross Validation 1
+2025-07-18 22:59:53: Fold 1  Train set:2269, Valid set:568, Test set: 108
+2025-07-18 23:00:28: Epoch: 001 Train Loss: 2.658, mae: 1.329, rse: 1.057, pcc: -0.199, kcc: -0.067
+2025-07-18 23:00:28: Epoch: 001 New best VALIDATION metrics
+2025-07-18 23:00:30: Epoch: 001 Test results, ap: mae: 0.582, rse: 1.017, pcc: 0.443, kcc: 0.268
+2025-07-18 23:01:04: Epoch: 002 Train Loss: 2.626, mae: 1.248, rse: 0.943, pcc: 0.288, kcc: 0.180
+2025-07-18 23:01:04: Epoch: 002 New best VALIDATION metrics
+2025-07-18 23:01:06: Epoch: 002 Test results, ap: mae: 0.575, rse: 0.978, pcc: 0.329, kcc: 0.262
+2025-07-18 23:01:41: Epoch: 003 Train Loss: 2.568, mae: 1.183, rse: 0.900, pcc: 0.365, kcc: 0.250
+2025-07-18 23:01:41: Epoch: 003 New best VALIDATION metrics
+2025-07-18 23:01:42: Epoch: 003 Test results, ap: mae: 0.640, rse: 1.239, pcc: 0.093, kcc: 0.024
+2025-07-18 23:02:17: Epoch: 004 Train Loss: 2.530, mae: 1.164, rse: 0.870, pcc: 0.366, kcc: 0.259
+2025-07-18 23:02:17: Epoch: 004 New best VALIDATION metrics
+2025-07-18 23:02:18: Epoch: 004 Test results, ap: mae: 0.576, rse: 0.977, pcc: 0.152, kcc: 0.051
+2025-07-18 23:02:53: Epoch: 005 Train Loss: 2.517, mae: 1.170, rse: 0.879, pcc: 0.371, kcc: 0.259
+2025-07-18 23:02:55: Epoch: 005 Test results, ap: mae: 0.607, rse: 1.116, pcc: -0.258, kcc: -0.195
+2025-07-18 23:03:29: Epoch: 006 Train Loss: 2.493, mae: 1.195, rse: 0.911, pcc: 0.311, kcc: 0.230
+2025-07-18 23:03:31: Epoch: 006 Test results, ap: mae: 0.650, rse: 1.226, pcc: -0.428, kcc: -0.289
+2025-07-18 23:04:06: Epoch: 007 Train Loss: 2.454, mae: 1.160, rse: 0.862, pcc: 0.379, kcc: 0.265
+2025-07-18 23:04:06: Epoch: 007 New best VALIDATION metrics
+2025-07-18 23:04:07: Epoch: 007 Test results, ap: mae: 0.642, rse: 1.266, pcc: -0.078, kcc: -0.097
+2025-07-18 23:04:42: Epoch: 008 Train Loss: 2.339, mae: 1.142, rse: 0.855, pcc: 0.398, kcc: 0.286
+2025-07-18 23:04:42: Epoch: 008 New best VALIDATION metrics
+2025-07-18 23:04:43: Epoch: 008 Test results, ap: mae: 0.649, rse: 1.282, pcc: -0.175, kcc: -0.027
+2025-07-18 23:05:18: Epoch: 009 Train Loss: 2.229, mae: 1.124, rse: 0.843, pcc: 0.472, kcc: 0.304
+2025-07-18 23:05:18: Epoch: 009 New best VALIDATION metrics
+2025-07-18 23:05:20: Epoch: 009 Test results, ap: mae: 0.595, rse: 1.067, pcc: 0.003, kcc: -0.040
+2025-07-18 23:05:54: Epoch: 010 Train Loss: 2.128, mae: 1.119, rse: 0.812, pcc: 0.491, kcc: 0.304
+2025-07-18 23:05:54: Epoch: 010 New best VALIDATION metrics
+2025-07-18 23:05:56: Epoch: 010 Test results, ap: mae: 0.784, rse: 1.835, pcc: -0.275, kcc: -0.248
+2025-07-18 23:06:31: Epoch: 011 Train Loss: 1.951, mae: 0.993, rse: 0.685, pcc: 0.564, kcc: 0.348
+2025-07-18 23:06:31: Epoch: 011 New best VALIDATION metrics
+2025-07-18 23:06:32: Epoch: 011 Test results, ap: mae: 0.618, rse: 1.180, pcc: -0.069, kcc: -0.000
+2025-07-18 23:06:32: Epoch: 011 New best TEST metrics
+2025-07-18 23:07:07: Epoch: 012 Train Loss: 1.810, mae: 0.988, rse: 0.678, pcc: 0.582, kcc: 0.365
+2025-07-18 23:07:07: Epoch: 012 New best VALIDATION metrics
+2025-07-18 23:07:08: Epoch: 012 Test results, ap: mae: 0.653, rse: 1.274, pcc: -0.219, kcc: -0.127
+2025-07-18 23:07:43: Epoch: 013 Train Loss: 1.704, mae: 0.968, rse: 0.653, pcc: 0.593, kcc: 0.391
+2025-07-18 23:07:43: Epoch: 013 New best VALIDATION metrics
+2025-07-18 23:07:44: Epoch: 013 Test results, ap: mae: 0.621, rse: 1.189, pcc: -0.094, kcc: -0.014
+2025-07-18 23:08:19: Epoch: 014 Train Loss: 1.644, mae: 0.950, rse: 0.609, pcc: 0.629, kcc: 0.387
+2025-07-18 23:08:19: Epoch: 014 New best VALIDATION metrics
+2025-07-18 23:08:21: Epoch: 014 Test results, ap: mae: 0.667, rse: 1.384, pcc: -0.289, kcc: -0.257
+2025-07-18 23:08:55: Epoch: 015 Train Loss: 1.567, mae: 0.923, rse: 0.578, pcc: 0.653, kcc: 0.419
+2025-07-18 23:08:55: Epoch: 015 New best VALIDATION metrics
+2025-07-18 23:08:57: Epoch: 015 Test results, ap: mae: 0.683, rse: 1.448, pcc: -0.293, kcc: -0.246
+2025-07-18 23:09:32: Epoch: 016 Train Loss: 1.448, mae: 0.908, rse: 0.559, pcc: 0.677, kcc: 0.448
+2025-07-18 23:09:32: Epoch: 016 New best VALIDATION metrics
+2025-07-18 23:09:33: Epoch: 016 Test results, ap: mae: 0.716, rse: 1.647, pcc: -0.218, kcc: -0.113
+2025-07-18 23:10:08: Epoch: 017 Train Loss: 1.372, mae: 0.861, rse: 0.494, pcc: 0.715, kcc: 0.491
+2025-07-18 23:10:08: Epoch: 017 New best VALIDATION metrics
+2025-07-18 23:10:10: Epoch: 017 Test results, ap: mae: 0.615, rse: 1.174, pcc: -0.066, kcc: -0.102
+2025-07-18 23:10:44: Epoch: 018 Train Loss: 1.301, mae: 0.870, rse: 0.504, pcc: 0.705, kcc: 0.484
+2025-07-18 23:10:46: Epoch: 018 Test results, ap: mae: 0.664, rse: 1.360, pcc: -0.099, kcc: -0.094
+2025-07-18 23:11:20: Epoch: 019 Train Loss: 1.220, mae: 0.840, rse: 0.487, pcc: 0.724, kcc: 0.494
+2025-07-18 23:11:20: Epoch: 019 New best VALIDATION metrics
+2025-07-18 23:11:22: Epoch: 019 Test results, ap: mae: 0.703, rse: 1.501, pcc: -0.141, kcc: -0.132
+2025-07-18 23:11:57: Epoch: 020 Train Loss: 1.076, mae: 0.826, rse: 0.453, pcc: 0.750, kcc: 0.526
+2025-07-18 23:11:57: Epoch: 020 New best VALIDATION metrics
+2025-07-18 23:11:58: Epoch: 020 Test results, ap: mae: 0.652, rse: 1.303, pcc: 0.057, kcc: 0.003
+2025-07-18 23:12:33: Epoch: 021 Train Loss: 0.889, mae: 0.769, rse: 0.407, pcc: 0.780, kcc: 0.585
+2025-07-18 23:12:33: Epoch: 021 New best VALIDATION metrics
+2025-07-18 23:12:34: Epoch: 021 Test results, ap: mae: 0.679, rse: 1.404, pcc: 0.075, kcc: 0.097
+2025-07-18 23:13:09: Epoch: 022 Train Loss: 0.822, mae: 0.697, rse: 0.358, pcc: 0.805, kcc: 0.608
+2025-07-18 23:13:09: Epoch: 022 New best VALIDATION metrics
+2025-07-18 23:13:11: Epoch: 022 Test results, ap: mae: 0.673, rse: 1.313, pcc: -0.003, kcc: -0.066
+2025-07-18 23:13:45: Epoch: 023 Train Loss: 0.726, mae: 0.671, rse: 0.358, pcc: 0.802, kcc: 0.621
+2025-07-18 23:13:45: Epoch: 023 New best VALIDATION metrics
+2025-07-18 23:13:47: Epoch: 023 Test results, ap: mae: 0.693, rse: 1.398, pcc: 0.104, kcc: 0.094
+2025-07-18 23:14:21: Epoch: 024 Train Loss: 0.670, mae: 0.638, rse: 0.309, pcc: 0.832, kcc: 0.636
+2025-07-18 23:14:21: Epoch: 024 New best VALIDATION metrics
+2025-07-18 23:14:23: Epoch: 024 Test results, ap: mae: 0.662, rse: 1.252, pcc: 0.066, kcc: -0.003
+2025-07-18 23:14:23: Epoch: 024 New best TEST metrics
+2025-07-18 23:14:57: Epoch: 025 Train Loss: 0.639, mae: 0.654, rse: 0.316, pcc: 0.833, kcc: 0.637
+2025-07-18 23:14:59: Epoch: 025 Test results, ap: mae: 0.748, rse: 1.593, pcc: -0.068, kcc: -0.107
+2025-07-18 23:15:33: Epoch: 026 Train Loss: 0.575, mae: 0.596, rse: 0.284, pcc: 0.847, kcc: 0.661
+2025-07-18 23:15:33: Epoch: 026 New best VALIDATION metrics
+2025-07-18 23:15:35: Epoch: 026 Test results, ap: mae: 0.734, rse: 1.541, pcc: 0.071, kcc: -0.015
+2025-07-18 23:16:10: Epoch: 027 Train Loss: 0.589, mae: 0.592, rse: 0.272, pcc: 0.855, kcc: 0.667
+2025-07-18 23:16:10: Epoch: 027 New best VALIDATION metrics
+2025-07-18 23:16:11: Epoch: 027 Test results, ap: mae: 0.730, rse: 1.481, pcc: 0.019, kcc: -0.027
+2025-07-18 23:16:46: Epoch: 028 Train Loss: 0.499, mae: 0.565, rse: 0.256, pcc: 0.865, kcc: 0.683
+2025-07-18 23:16:46: Epoch: 028 New best VALIDATION metrics
+2025-07-18 23:16:47: Epoch: 028 Test results, ap: mae: 0.725, rse: 1.513, pcc: 0.109, kcc: 0.069
+2025-07-18 23:17:22: Epoch: 029 Train Loss: 0.478, mae: 0.589, rse: 0.275, pcc: 0.857, kcc: 0.676
+2025-07-18 23:17:23: Epoch: 029 Test results, ap: mae: 0.781, rse: 1.742, pcc: 0.107, kcc: 0.049
+2025-07-18 23:17:58: Epoch: 030 Train Loss: 0.453, mae: 0.546, rse: 0.237, pcc: 0.875, kcc: 0.678
+2025-07-18 23:17:58: Epoch: 030 New best VALIDATION metrics
+2025-07-18 23:18:00: Epoch: 030 Test results, ap: mae: 0.723, rse: 1.473, pcc: 0.046, kcc: -0.018
+2025-07-18 23:18:34: Epoch: 031 Train Loss: 0.389, mae: 0.518, rse: 0.216, pcc: 0.887, kcc: 0.704
+2025-07-18 23:18:34: Epoch: 031 New best VALIDATION metrics
+2025-07-18 23:18:36: Epoch: 031 Test results, ap: mae: 0.751, rse: 1.580, pcc: 0.061, kcc: 0.008
+2025-07-18 23:19:10: Epoch: 032 Train Loss: 0.341, mae: 0.509, rse: 0.207, pcc: 0.891, kcc: 0.699
+2025-07-18 23:19:10: Epoch: 032 New best VALIDATION metrics
+2025-07-18 23:19:12: Epoch: 032 Test results, ap: mae: 0.734, rse: 1.484, pcc: 0.100, kcc: 0.028
+2025-07-18 23:19:46: Epoch: 033 Train Loss: 0.321, mae: 0.492, rse: 0.201, pcc: 0.895, kcc: 0.715
+2025-07-18 23:19:46: Epoch: 033 New best VALIDATION metrics
+2025-07-18 23:19:48: Epoch: 033 Test results, ap: mae: 0.750, rse: 1.500, pcc: 0.086, kcc: 0.031
+2025-07-18 23:20:22: Epoch: 034 Train Loss: 0.305, mae: 0.496, rse: 0.202, pcc: 0.897, kcc: 0.722
+2025-07-18 23:20:22: Epoch: 034 New best VALIDATION metrics
+2025-07-18 23:20:24: Epoch: 034 Test results, ap: mae: 0.748, rse: 1.560, pcc: 0.098, kcc: 0.034
+2025-07-18 23:20:59: Epoch: 035 Train Loss: 0.286, mae: 0.474, rse: 0.193, pcc: 0.900, kcc: 0.724
+2025-07-18 23:20:59: Epoch: 035 New best VALIDATION metrics
+2025-07-18 23:21:00: Epoch: 035 Test results, ap: mae: 0.747, rse: 1.511, pcc: 0.118, kcc: 0.040
+2025-07-18 23:21:35: Epoch: 036 Train Loss: 0.280, mae: 0.461, rse: 0.186, pcc: 0.905, kcc: 0.735
+2025-07-18 23:21:35: Epoch: 036 New best VALIDATION metrics
+2025-07-18 23:21:37: Epoch: 036 Test results, ap: mae: 0.739, rse: 1.474, pcc: 0.132, kcc: 0.056
+2025-07-18 23:22:11: Epoch: 037 Train Loss: 0.279, mae: 0.458, rse: 0.183, pcc: 0.906, kcc: 0.738
+2025-07-18 23:22:11: Epoch: 037 New best VALIDATION metrics
+2025-07-18 23:22:13: Epoch: 037 Test results, ap: mae: 0.735, rse: 1.484, pcc: 0.142, kcc: 0.065
+2025-07-18 23:22:48: Epoch: 038 Train Loss: 0.255, mae: 0.466, rse: 0.187, pcc: 0.905, kcc: 0.741
+2025-07-18 23:22:49: Epoch: 038 Test results, ap: mae: 0.742, rse: 1.522, pcc: 0.166, kcc: 0.075
+2025-07-18 23:23:24: Epoch: 039 Train Loss: 0.252, mae: 0.443, rse: 0.174, pcc: 0.910, kcc: 0.744
+2025-07-18 23:23:24: Epoch: 039 New best VALIDATION metrics
+2025-07-18 23:23:25: Epoch: 039 Test results, ap: mae: 0.717, rse: 1.405, pcc: 0.149, kcc: 0.049
+2025-07-18 23:24:00: Epoch: 040 Train Loss: 0.263, mae: 0.424, rse: 0.178, pcc: 0.908, kcc: 0.754
+2025-07-18 23:24:00: Epoch: 040 New best VALIDATION metrics
+2025-07-18 23:24:02: Epoch: 040 Test results, ap: mae: 0.751, rse: 1.548, pcc: 0.144, kcc: 0.077
+2025-07-18 23:24:36: Epoch: 041 Train Loss: 0.206, mae: 0.415, rse: 0.164, pcc: 0.917, kcc: 0.760
+2025-07-18 23:24:36: Epoch: 041 New best VALIDATION metrics
+2025-07-18 23:24:38: Epoch: 041 Test results, ap: mae: 0.745, rse: 1.523, pcc: 0.148, kcc: 0.052
+2025-07-18 23:25:12: Epoch: 042 Train Loss: 0.200, mae: 0.404, rse: 0.159, pcc: 0.918, kcc: 0.764
+2025-07-18 23:25:12: Epoch: 042 New best VALIDATION metrics
+2025-07-18 23:25:14: Epoch: 042 Test results, ap: mae: 0.732, rse: 1.457, pcc: 0.156, kcc: 0.050
+2025-07-18 23:25:49: Epoch: 043 Train Loss: 0.214, mae: 0.402, rse: 0.159, pcc: 0.919, kcc: 0.765
+2025-07-18 23:25:49: Epoch: 043 New best VALIDATION metrics
+2025-07-18 23:25:50: Epoch: 043 Test results, ap: mae: 0.739, rse: 1.473, pcc: 0.156, kcc: 0.057
+2025-07-18 23:26:25: Epoch: 044 Train Loss: 0.198, mae: 0.403, rse: 0.163, pcc: 0.916, kcc: 0.766
+2025-07-18 23:26:26: Epoch: 044 Test results, ap: mae: 0.726, rse: 1.448, pcc: 0.170, kcc: 0.071
+2025-07-18 23:27:01: Epoch: 045 Train Loss: 0.193, mae: 0.389, rse: 0.152, pcc: 0.921, kcc: 0.769
+2025-07-18 23:27:01: Epoch: 045 New best VALIDATION metrics
+2025-07-18 23:27:03: Epoch: 045 Test results, ap: mae: 0.740, rse: 1.450, pcc: 0.154, kcc: 0.049
+2025-07-18 23:27:37: Epoch: 046 Train Loss: 0.192, mae: 0.395, rse: 0.156, pcc: 0.920, kcc: 0.767
+2025-07-18 23:27:39: Epoch: 046 Test results, ap: mae: 0.742, rse: 1.506, pcc: 0.150, kcc: 0.045
+2025-07-18 23:28:13: Epoch: 047 Train Loss: 0.186, mae: 0.385, rse: 0.153, pcc: 0.923, kcc: 0.778
+2025-07-18 23:28:13: Epoch: 047 New best VALIDATION metrics
+2025-07-18 23:28:15: Epoch: 047 Test results, ap: mae: 0.752, rse: 1.514, pcc: 0.203, kcc: 0.083
+2025-07-18 23:28:50: Epoch: 048 Train Loss: 0.193, mae: 0.382, rse: 0.153, pcc: 0.922, kcc: 0.776
+2025-07-18 23:28:50: Epoch: 048 New best VALIDATION metrics
+2025-07-18 23:28:51: Epoch: 048 Test results, ap: mae: 0.744, rse: 1.497, pcc: 0.191, kcc: 0.073
+2025-07-18 23:29:26: Epoch: 049 Train Loss: 0.171, mae: 0.375, rse: 0.152, pcc: 0.922, kcc: 0.780
+2025-07-18 23:29:26: Epoch: 049 New best VALIDATION metrics
+2025-07-18 23:29:28: Epoch: 049 Test results, ap: mae: 0.747, rse: 1.527, pcc: 0.203, kcc: 0.086
+2025-07-18 23:30:02: Epoch: 050 Train Loss: 0.185, mae: 0.372, rse: 0.151, pcc: 0.921, kcc: 0.781
+2025-07-18 23:30:02: Epoch: 050 New best VALIDATION metrics
+2025-07-18 23:30:04: Epoch: 050 Test results, ap: mae: 0.739, rse: 1.491, pcc: 0.187, kcc: 0.080
+2025-07-18 23:30:04: used time 0.50h
+2025-07-18 23:30:04: Running Cross Validation 2
+2025-07-18 23:30:04: Fold 2  Train set:2270, Valid set:567, Test set: 108
+2025-07-18 23:30:39: Epoch: 001 Train Loss: 2.678, mae: 1.267, rse: 1.006, pcc: 0.309, kcc: 0.209
+2025-07-18 23:30:39: Epoch: 001 New best VALIDATION metrics
+2025-07-18 23:30:40: Epoch: 001 Test results, ap: mae: 0.724, rse: 1.542, pcc: 0.344, kcc: 0.228
+2025-07-18 23:31:15: Epoch: 002 Train Loss: 2.613, mae: 1.190, rse: 0.907, pcc: 0.326, kcc: 0.232
+2025-07-18 23:31:15: Epoch: 002 New best VALIDATION metrics
+2025-07-18 23:31:16: Epoch: 002 Test results, ap: mae: 0.620, rse: 1.099, pcc: -0.263, kcc: -0.249
+2025-07-18 23:31:51: Epoch: 003 Train Loss: 2.571, mae: 1.171, rse: 0.884, pcc: 0.346, kcc: 0.254
+2025-07-18 23:31:51: Epoch: 003 New best VALIDATION metrics
+2025-07-18 23:31:52: Epoch: 003 Test results, ap: mae: 0.612, rse: 1.061, pcc: -0.106, kcc: -0.065
+2025-07-18 23:32:27: Epoch: 004 Train Loss: 2.516, mae: 1.165, rse: 0.882, pcc: 0.354, kcc: 0.266
+2025-07-18 23:32:27: Epoch: 004 New best VALIDATION metrics
+2025-07-18 23:32:29: Epoch: 004 Test results, ap: mae: 0.672, rse: 1.327, pcc: -0.110, kcc: -0.063
+2025-07-18 23:33:03: Epoch: 005 Train Loss: 2.428, mae: 1.190, rse: 0.911, pcc: 0.370, kcc: 0.275
+2025-07-18 23:33:05: Epoch: 005 Test results, ap: mae: 0.715, rse: 1.540, pcc: -0.153, kcc: -0.136
+2025-07-18 23:33:40: Epoch: 006 Train Loss: 2.383, mae: 1.142, rse: 0.834, pcc: 0.433, kcc: 0.309
+2025-07-18 23:33:40: Epoch: 006 New best VALIDATION metrics
+2025-07-18 23:33:41: Epoch: 006 Test results, ap: mae: 0.734, rse: 1.545, pcc: -0.213, kcc: -0.210
+2025-07-18 23:34:16: Epoch: 007 Train Loss: 2.315, mae: 1.192, rse: 0.894, pcc: 0.362, kcc: 0.272
+2025-07-18 23:34:18: Epoch: 007 Test results, ap: mae: 0.756, rse: 1.649, pcc: 0.118, kcc: 0.071
+2025-07-18 23:34:52: Epoch: 008 Train Loss: 2.011, mae: 0.991, rse: 0.660, pcc: 0.588, kcc: 0.417
+2025-07-18 23:34:52: Epoch: 008 New best VALIDATION metrics
+2025-07-18 23:34:54: Epoch: 008 Test results, ap: mae: 0.794, rse: 1.891, pcc: -0.364, kcc: -0.292
+2025-07-18 23:35:28: Epoch: 009 Train Loss: 1.706, mae: 0.882, rse: 0.511, pcc: 0.714, kcc: 0.508
+2025-07-18 23:35:28: Epoch: 009 New best VALIDATION metrics
+2025-07-18 23:35:30: Epoch: 009 Test results, ap: mae: 1.136, rse: 3.341, pcc: -0.164, kcc: -0.155
+2025-07-18 23:36:05: Epoch: 010 Train Loss: 1.489, mae: 0.805, rse: 0.427, pcc: 0.758, kcc: 0.545
+2025-07-18 23:36:05: Epoch: 010 New best VALIDATION metrics
+2025-07-18 23:36:06: Epoch: 010 Test results, ap: mae: 0.841, rse: 1.915, pcc: 0.161, kcc: 0.079
+2025-07-18 23:36:41: Epoch: 011 Train Loss: 1.020, mae: 0.679, rse: 0.321, pcc: 0.825, kcc: 0.627
+2025-07-18 23:36:41: Epoch: 011 New best VALIDATION metrics
+2025-07-18 23:36:42: Epoch: 011 Test results, ap: mae: 0.817, rse: 1.926, pcc: 0.066, kcc: 0.045
+2025-07-18 23:36:42: Epoch: 011 New best TEST metrics
+2025-07-18 23:37:17: Epoch: 012 Train Loss: 0.851, mae: 0.698, rse: 0.335, pcc: 0.820, kcc: 0.618
+2025-07-18 23:37:18: Epoch: 012 Test results, ap: mae: 0.904, rse: 2.366, pcc: 0.066, kcc: 0.018
+2025-07-18 23:37:53: Epoch: 013 Train Loss: 0.734, mae: 0.680, rse: 0.308, pcc: 0.833, kcc: 0.622
+2025-07-18 23:37:53: Epoch: 013 New best VALIDATION metrics
+2025-07-18 23:37:54: Epoch: 013 Test results, ap: mae: 0.977, rse: 2.723, pcc: 0.100, kcc: 0.057
+2025-07-18 23:38:29: Epoch: 014 Train Loss: 0.663, mae: 0.704, rse: 0.350, pcc: 0.834, kcc: 0.636
+2025-07-18 23:38:31: Epoch: 014 Test results, ap: mae: 0.913, rse: 2.561, pcc: 0.156, kcc: 0.114
+2025-07-18 23:39:05: Epoch: 015 Train Loss: 0.563, mae: 0.641, rse: 0.291, pcc: 0.852, kcc: 0.662
+2025-07-18 23:39:05: Epoch: 015 New best VALIDATION metrics
+2025-07-18 23:39:07: Epoch: 015 Test results, ap: mae: 1.066, rse: 3.118, pcc: 0.129, kcc: 0.075
+2025-07-18 23:39:41: Epoch: 016 Train Loss: 0.536, mae: 0.655, rse: 0.318, pcc: 0.863, kcc: 0.672
+2025-07-18 23:39:43: Epoch: 016 Test results, ap: mae: 1.069, rse: 3.499, pcc: 0.247, kcc: 0.165
+2025-07-18 23:40:18: Epoch: 017 Train Loss: 0.510, mae: 0.586, rse: 0.252, pcc: 0.867, kcc: 0.672
+2025-07-18 23:40:18: Epoch: 017 New best VALIDATION metrics
+2025-07-18 23:40:19: Epoch: 017 Test results, ap: mae: 0.887, rse: 2.456, pcc: 0.193, kcc: 0.150
+2025-07-18 23:40:54: Epoch: 018 Train Loss: 0.433, mae: 0.528, rse: 0.225, pcc: 0.883, kcc: 0.709
+2025-07-18 23:40:54: Epoch: 018 New best VALIDATION metrics
+2025-07-18 23:40:55: Epoch: 018 Test results, ap: mae: 1.031, rse: 3.423, pcc: 0.217, kcc: 0.160
+2025-07-18 23:41:30: Epoch: 019 Train Loss: 0.376, mae: 0.508, rse: 0.206, pcc: 0.893, kcc: 0.715
+2025-07-18 23:41:30: Epoch: 019 New best VALIDATION metrics
+2025-07-18 23:41:32: Epoch: 019 Test results, ap: mae: 0.877, rse: 2.205, pcc: 0.236, kcc: 0.185
+2025-07-18 23:42:06: Epoch: 020 Train Loss: 0.361, mae: 0.503, rse: 0.202, pcc: 0.894, kcc: 0.718
+2025-07-18 23:42:06: Epoch: 020 New best VALIDATION metrics
+2025-07-18 23:42:08: Epoch: 020 Test results, ap: mae: 0.875, rse: 2.392, pcc: 0.241, kcc: 0.197
+2025-07-18 23:42:42: Epoch: 021 Train Loss: 0.299, mae: 0.455, rse: 0.189, pcc: 0.902, kcc: 0.746
+2025-07-18 23:42:42: Epoch: 021 New best VALIDATION metrics
+2025-07-18 23:42:44: Epoch: 021 Test results, ap: mae: 0.908, rse: 2.554, pcc: 0.229, kcc: 0.168
+2025-07-18 23:43:18: Epoch: 022 Train Loss: 0.296, mae: 0.451, rse: 0.187, pcc: 0.904, kcc: 0.745
+2025-07-18 23:43:18: Epoch: 022 New best VALIDATION metrics
+2025-07-18 23:43:20: Epoch: 022 Test results, ap: mae: 0.894, rse: 2.414, pcc: 0.264, kcc: 0.187
+2025-07-18 23:43:55: Epoch: 023 Train Loss: 0.273, mae: 0.458, rse: 0.188, pcc: 0.903, kcc: 0.743
+2025-07-18 23:43:56: Epoch: 023 Test results, ap: mae: 0.918, rse: 2.450, pcc: 0.283, kcc: 0.214
+2025-07-18 23:44:31: Epoch: 024 Train Loss: 0.247, mae: 0.441, rse: 0.181, pcc: 0.906, kcc: 0.753
+2025-07-18 23:44:31: Epoch: 024 New best VALIDATION metrics
+2025-07-18 23:44:33: Epoch: 024 Test results, ap: mae: 0.931, rse: 2.662, pcc: 0.240, kcc: 0.177
+2025-07-18 23:45:07: Epoch: 025 Train Loss: 0.250, mae: 0.441, rse: 0.185, pcc: 0.904, kcc: 0.751
+2025-07-18 23:45:09: Epoch: 025 Test results, ap: mae: 0.912, rse: 2.453, pcc: 0.240, kcc: 0.180
+2025-07-18 23:45:43: Epoch: 026 Train Loss: 0.241, mae: 0.435, rse: 0.187, pcc: 0.903, kcc: 0.760
+2025-07-18 23:45:43: Epoch: 026 New best VALIDATION metrics
+2025-07-18 23:45:45: Epoch: 026 Test results, ap: mae: 0.920, rse: 2.637, pcc: 0.264, kcc: 0.213
+2025-07-18 23:46:20: Epoch: 027 Train Loss: 0.229, mae: 0.442, rse: 0.172, pcc: 0.912, kcc: 0.766
+2025-07-18 23:46:20: Epoch: 027 New best VALIDATION metrics
+2025-07-18 23:46:21: Epoch: 027 Test results, ap: mae: 1.002, rse: 2.909, pcc: 0.224, kcc: 0.158
+2025-07-18 23:46:56: Epoch: 028 Train Loss: 0.234, mae: 0.481, rse: 0.199, pcc: 0.910, kcc: 0.761
+2025-07-18 23:46:57: Epoch: 028 Test results, ap: mae: 0.932, rse: 2.704, pcc: 0.255, kcc: 0.186
+2025-07-18 23:47:32: Epoch: 029 Train Loss: 0.212, mae: 0.426, rse: 0.177, pcc: 0.913, kcc: 0.768
+2025-07-18 23:47:32: Epoch: 029 New best VALIDATION metrics
+2025-07-18 23:47:34: Epoch: 029 Test results, ap: mae: 0.924, rse: 2.687, pcc: 0.268, kcc: 0.208
+2025-07-18 23:48:08: Epoch: 030 Train Loss: 0.196, mae: 0.406, rse: 0.164, pcc: 0.917, kcc: 0.776
+2025-07-18 23:48:08: Epoch: 030 New best VALIDATION metrics
+2025-07-18 23:48:10: Epoch: 030 Test results, ap: mae: 0.898, rse: 2.444, pcc: 0.240, kcc: 0.176
+2025-07-18 23:48:45: Epoch: 031 Train Loss: 0.185, mae: 0.423, rse: 0.169, pcc: 0.921, kcc: 0.784
+2025-07-18 23:48:46: Epoch: 031 Test results, ap: mae: 0.989, rse: 3.014, pcc: 0.246, kcc: 0.180
+2025-07-18 23:49:21: Epoch: 032 Train Loss: 0.170, mae: 0.381, rse: 0.152, pcc: 0.922, kcc: 0.785
+2025-07-18 23:49:21: Epoch: 032 New best VALIDATION metrics
+2025-07-18 23:49:22: Epoch: 032 Test results, ap: mae: 0.916, rse: 2.620, pcc: 0.249, kcc: 0.183
+2025-07-18 23:49:57: Epoch: 033 Train Loss: 0.163, mae: 0.364, rse: 0.149, pcc: 0.924, kcc: 0.792
+2025-07-18 23:49:57: Epoch: 033 New best VALIDATION metrics
+2025-07-18 23:49:59: Epoch: 033 Test results, ap: mae: 0.960, rse: 2.813, pcc: 0.228, kcc: 0.170
+2025-07-18 23:50:33: Epoch: 034 Train Loss: 0.168, mae: 0.387, rse: 0.156, pcc: 0.920, kcc: 0.787
+2025-07-18 23:50:35: Epoch: 034 Test results, ap: mae: 0.969, rse: 2.822, pcc: 0.250, kcc: 0.178
+2025-07-18 23:51:09: Epoch: 035 Train Loss: 0.155, mae: 0.368, rse: 0.146, pcc: 0.925, kcc: 0.796
+2025-07-18 23:51:09: Epoch: 035 New best VALIDATION metrics
+2025-07-18 23:51:11: Epoch: 035 Test results, ap: mae: 0.939, rse: 2.694, pcc: 0.254, kcc: 0.192
+2025-07-18 23:51:46: Epoch: 036 Train Loss: 0.166, mae: 0.411, rse: 0.159, pcc: 0.925, kcc: 0.798
+2025-07-18 23:51:47: Epoch: 036 Test results, ap: mae: 0.966, rse: 2.795, pcc: 0.214, kcc: 0.162
+2025-07-18 23:52:22: Epoch: 037 Train Loss: 0.154, mae: 0.389, rse: 0.159, pcc: 0.925, kcc: 0.798
+2025-07-18 23:52:23: Epoch: 037 Test results, ap: mae: 0.969, rse: 2.860, pcc: 0.237, kcc: 0.177
+2025-07-18 23:52:58: Epoch: 038 Train Loss: 0.158, mae: 0.353, rse: 0.141, pcc: 0.927, kcc: 0.798
+2025-07-18 23:52:58: Epoch: 038 New best VALIDATION metrics
+2025-07-18 23:53:00: Epoch: 038 Test results, ap: mae: 0.885, rse: 2.354, pcc: 0.241, kcc: 0.179
+2025-07-18 23:53:34: Epoch: 039 Train Loss: 0.148, mae: 0.353, rse: 0.142, pcc: 0.928, kcc: 0.800
+2025-07-18 23:53:34: Epoch: 039 New best VALIDATION metrics
+2025-07-18 23:53:36: Epoch: 039 Test results, ap: mae: 0.901, rse: 2.418, pcc: 0.241, kcc: 0.180
+2025-07-18 23:54:11: Epoch: 040 Train Loss: 0.139, mae: 0.358, rse: 0.143, pcc: 0.927, kcc: 0.799
+2025-07-18 23:54:12: Epoch: 040 Test results, ap: mae: 0.907, rse: 2.488, pcc: 0.248, kcc: 0.178
+2025-07-18 23:54:47: Epoch: 041 Train Loss: 0.139, mae: 0.351, rse: 0.140, pcc: 0.929, kcc: 0.805
+2025-07-18 23:54:47: Epoch: 041 New best VALIDATION metrics
+2025-07-18 23:54:48: Epoch: 041 Test results, ap: mae: 0.933, rse: 2.589, pcc: 0.208, kcc: 0.150
+2025-07-18 23:55:23: Epoch: 042 Train Loss: 0.139, mae: 0.340, rse: 0.137, pcc: 0.931, kcc: 0.810
+2025-07-18 23:55:23: Epoch: 042 New best VALIDATION metrics
+2025-07-18 23:55:25: Epoch: 042 Test results, ap: mae: 0.977, rse: 2.858, pcc: 0.198, kcc: 0.152
+2025-07-18 23:55:59: Epoch: 043 Train Loss: 0.135, mae: 0.333, rse: 0.133, pcc: 0.931, kcc: 0.812
+2025-07-18 23:55:59: Epoch: 043 New best VALIDATION metrics
+2025-07-18 23:56:01: Epoch: 043 Test results, ap: mae: 0.919, rse: 2.554, pcc: 0.187, kcc: 0.142
+2025-07-18 23:56:35: Epoch: 044 Train Loss: 0.122, mae: 0.339, rse: 0.139, pcc: 0.930, kcc: 0.810
+2025-07-18 23:56:37: Epoch: 044 Test results, ap: mae: 0.929, rse: 2.579, pcc: 0.215, kcc: 0.164
+2025-07-18 23:57:11: Epoch: 045 Train Loss: 0.128, mae: 0.337, rse: 0.138, pcc: 0.931, kcc: 0.813
+2025-07-18 23:57:13: Epoch: 045 Test results, ap: mae: 0.919, rse: 2.591, pcc: 0.224, kcc: 0.170
+2025-07-18 23:57:48: Epoch: 046 Train Loss: 0.134, mae: 0.343, rse: 0.137, pcc: 0.930, kcc: 0.807
+2025-07-18 23:57:49: Epoch: 046 Test results, ap: mae: 0.915, rse: 2.512, pcc: 0.204, kcc: 0.153
+2025-07-18 23:58:24: Epoch: 047 Train Loss: 0.127, mae: 0.357, rse: 0.142, pcc: 0.932, kcc: 0.812
+2025-07-18 23:58:25: Epoch: 047 Test results, ap: mae: 0.987, rse: 2.979, pcc: 0.205, kcc: 0.157
+2025-07-18 23:59:00: Epoch: 048 Train Loss: 0.128, mae: 0.334, rse: 0.136, pcc: 0.931, kcc: 0.813
+2025-07-18 23:59:02: Epoch: 048 Test results, ap: mae: 0.952, rse: 2.722, pcc: 0.209, kcc: 0.157
+2025-07-18 23:59:36: Epoch: 049 Train Loss: 0.126, mae: 0.347, rse: 0.139, pcc: 0.933, kcc: 0.819
+2025-07-18 23:59:38: Epoch: 049 Test results, ap: mae: 0.965, rse: 2.846, pcc: 0.221, kcc: 0.169
+2025-07-19 00:00:12: Epoch: 050 Train Loss: 0.121, mae: 0.324, rse: 0.131, pcc: 0.933, kcc: 0.816
+2025-07-19 00:00:12: Epoch: 050 New best VALIDATION metrics
+2025-07-19 00:00:14: Epoch: 050 Test results, ap: mae: 0.935, rse: 2.621, pcc: 0.213, kcc: 0.164
+2025-07-19 00:00:14: used time 0.50h
+2025-07-19 00:00:14: Running Cross Validation 3
+2025-07-19 00:00:14: Fold 3  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 00:00:49: Epoch: 001 Train Loss: 2.616, mae: 1.237, rse: 0.913, pcc: 0.316, kcc: 0.245
+2025-07-19 00:00:49: Epoch: 001 New best VALIDATION metrics
+2025-07-19 00:00:50: Epoch: 001 Test results, ap: mae: 0.661, rse: 1.216, pcc: -0.457, kcc: -0.317
+2025-07-19 00:01:25: Epoch: 002 Train Loss: 2.441, mae: 1.252, rse: 0.924, pcc: 0.287, kcc: 0.200
+2025-07-19 00:01:27: Epoch: 002 Test results, ap: mae: 0.589, rse: 1.027, pcc: -0.074, kcc: -0.289
+2025-07-19 00:02:01: Epoch: 003 Train Loss: 2.382, mae: 1.219, rse: 0.892, pcc: 0.364, kcc: 0.288
+2025-07-19 00:02:01: Epoch: 003 New best VALIDATION metrics
+2025-07-19 00:02:03: Epoch: 003 Test results, ap: mae: 0.663, rse: 1.192, pcc: 0.017, kcc: -0.172
+2025-07-19 00:02:38: Epoch: 004 Train Loss: 2.189, mae: 1.211, rse: 0.873, pcc: 0.362, kcc: 0.232
+2025-07-19 00:02:39: Epoch: 004 Test results, ap: mae: 0.729, rse: 1.573, pcc: -0.274, kcc: -0.197
+2025-07-19 00:03:14: Epoch: 005 Train Loss: 1.971, mae: 1.285, rse: 0.860, pcc: 0.485, kcc: 0.341
+2025-07-19 00:03:14: Epoch: 005 New best VALIDATION metrics
+2025-07-19 00:03:16: Epoch: 005 Test results, ap: mae: 0.707, rse: 1.420, pcc: -0.043, kcc: -0.030
+2025-07-19 00:03:50: Epoch: 006 Train Loss: 1.621, mae: 1.091, rse: 0.724, pcc: 0.648, kcc: 0.442
+2025-07-19 00:03:50: Epoch: 006 New best VALIDATION metrics
+2025-07-19 00:03:52: Epoch: 006 Test results, ap: mae: 1.307, rse: 4.100, pcc: 0.357, kcc: 0.256
+2025-07-19 00:04:26: Epoch: 007 Train Loss: 1.265, mae: 0.824, rse: 0.426, pcc: 0.766, kcc: 0.545
+2025-07-19 00:04:26: Epoch: 007 New best VALIDATION metrics
+2025-07-19 00:04:28: Epoch: 007 Test results, ap: mae: 0.577, rse: 0.969, pcc: 0.264, kcc: 0.157
+2025-07-19 00:05:03: Epoch: 008 Train Loss: 1.070, mae: 0.749, rse: 0.356, pcc: 0.804, kcc: 0.599
+2025-07-19 00:05:03: Epoch: 008 New best VALIDATION metrics
+2025-07-19 00:05:04: Epoch: 008 Test results, ap: mae: 0.828, rse: 2.216, pcc: 0.196, kcc: 0.141
+2025-07-19 00:05:39: Epoch: 009 Train Loss: 0.891, mae: 0.775, rse: 0.379, pcc: 0.796, kcc: 0.597
+2025-07-19 00:05:41: Epoch: 009 Test results, ap: mae: 0.737, rse: 1.767, pcc: 0.072, kcc: 0.069
+2025-07-19 00:06:15: Epoch: 010 Train Loss: 0.787, mae: 0.741, rse: 0.362, pcc: 0.804, kcc: 0.623
+2025-07-19 00:06:15: Epoch: 010 New best VALIDATION metrics
+2025-07-19 00:06:16: Epoch: 010 Test results, ap: mae: 0.938, rse: 2.393, pcc: 0.187, kcc: 0.105
+2025-07-19 00:06:51: Epoch: 011 Train Loss: 0.610, mae: 0.579, rse: 0.247, pcc: 0.870, kcc: 0.691
+2025-07-19 00:06:51: Epoch: 011 New best VALIDATION metrics
+2025-07-19 00:06:53: Epoch: 011 Test results, ap: mae: 0.978, rse: 2.763, pcc: 0.185, kcc: 0.120
+2025-07-19 00:06:53: Epoch: 011 New best TEST metrics
+2025-07-19 00:07:28: Epoch: 012 Train Loss: 0.528, mae: 0.533, rse: 0.213, pcc: 0.887, kcc: 0.718
+2025-07-19 00:07:28: Epoch: 012 New best VALIDATION metrics
+2025-07-19 00:07:29: Epoch: 012 Test results, ap: mae: 0.893, rse: 2.383, pcc: 0.297, kcc: 0.189
+2025-07-19 00:07:29: Epoch: 012 New best TEST metrics
+2025-07-19 00:08:04: Epoch: 013 Train Loss: 0.531, mae: 0.539, rse: 0.225, pcc: 0.881, kcc: 0.715
+2025-07-19 00:08:05: Epoch: 013 Test results, ap: mae: 0.886, rse: 2.274, pcc: 0.285, kcc: 0.203
+2025-07-19 00:08:05: Epoch: 013 New best TEST metrics
+2025-07-19 00:08:40: Epoch: 014 Train Loss: 0.435, mae: 0.521, rse: 0.204, pcc: 0.899, kcc: 0.736
+2025-07-19 00:08:40: Epoch: 014 New best VALIDATION metrics
+2025-07-19 00:08:42: Epoch: 014 Test results, ap: mae: 1.031, rse: 2.904, pcc: 0.204, kcc: 0.144
+2025-07-19 00:09:16: Epoch: 015 Train Loss: 0.446, mae: 0.504, rse: 0.182, pcc: 0.909, kcc: 0.749
+2025-07-19 00:09:16: Epoch: 015 New best VALIDATION metrics
+2025-07-19 00:09:18: Epoch: 015 Test results, ap: mae: 0.936, rse: 2.580, pcc: 0.195, kcc: 0.131
+2025-07-19 00:09:52: Epoch: 016 Train Loss: 0.386, mae: 0.543, rse: 0.210, pcc: 0.895, kcc: 0.729
+2025-07-19 00:09:54: Epoch: 016 Test results, ap: mae: 0.882, rse: 2.383, pcc: 0.035, kcc: 0.031
+2025-07-19 00:10:29: Epoch: 017 Train Loss: 0.368, mae: 0.453, rse: 0.168, pcc: 0.913, kcc: 0.755
+2025-07-19 00:10:29: Epoch: 017 New best VALIDATION metrics
+2025-07-19 00:10:30: Epoch: 017 Test results, ap: mae: 1.055, rse: 3.110, pcc: 0.243, kcc: 0.173
+2025-07-19 00:11:05: Epoch: 018 Train Loss: 0.310, mae: 0.495, rse: 0.181, pcc: 0.912, kcc: 0.745
+2025-07-19 00:11:07: Epoch: 018 Test results, ap: mae: 0.915, rse: 2.373, pcc: 0.247, kcc: 0.189
+2025-07-19 00:11:41: Epoch: 019 Train Loss: 0.319, mae: 0.444, rse: 0.155, pcc: 0.920, kcc: 0.765
+2025-07-19 00:11:41: Epoch: 019 New best VALIDATION metrics
+2025-07-19 00:11:43: Epoch: 019 Test results, ap: mae: 1.077, rse: 3.357, pcc: 0.244, kcc: 0.180
+2025-07-19 00:12:17: Epoch: 020 Train Loss: 0.286, mae: 0.553, rse: 0.229, pcc: 0.885, kcc: 0.708
+2025-07-19 00:12:19: Epoch: 020 Test results, ap: mae: 1.083, rse: 3.450, pcc: 0.225, kcc: 0.170
+2025-07-19 00:12:53: Epoch: 021 Train Loss: 0.220, mae: 0.461, rse: 0.157, pcc: 0.928, kcc: 0.786
+2025-07-19 00:12:53: Epoch: 021 New best VALIDATION metrics
+2025-07-19 00:12:55: Epoch: 021 Test results, ap: mae: 1.072, rse: 3.252, pcc: 0.250, kcc: 0.181
+2025-07-19 00:13:29: Epoch: 022 Train Loss: 0.214, mae: 0.385, rse: 0.130, pcc: 0.934, kcc: 0.802
+2025-07-19 00:13:29: Epoch: 022 New best VALIDATION metrics
+2025-07-19 00:13:31: Epoch: 022 Test results, ap: mae: 1.048, rse: 3.105, pcc: 0.188, kcc: 0.141
+2025-07-19 00:14:05: Epoch: 023 Train Loss: 0.198, mae: 0.378, rse: 0.125, pcc: 0.938, kcc: 0.807
+2025-07-19 00:14:05: Epoch: 023 New best VALIDATION metrics
+2025-07-19 00:14:07: Epoch: 023 Test results, ap: mae: 1.044, rse: 3.073, pcc: 0.226, kcc: 0.166
+2025-07-19 00:14:42: Epoch: 024 Train Loss: 0.196, mae: 0.384, rse: 0.127, pcc: 0.937, kcc: 0.802
+2025-07-19 00:14:43: Epoch: 024 Test results, ap: mae: 0.969, rse: 2.658, pcc: 0.265, kcc: 0.187
+2025-07-19 00:15:18: Epoch: 025 Train Loss: 0.212, mae: 0.376, rse: 0.124, pcc: 0.939, kcc: 0.813
+2025-07-19 00:15:18: Epoch: 025 New best VALIDATION metrics
+2025-07-19 00:15:19: Epoch: 025 Test results, ap: mae: 0.974, rse: 2.678, pcc: 0.209, kcc: 0.151
+2025-07-19 00:15:54: Epoch: 026 Train Loss: 0.191, mae: 0.370, rse: 0.123, pcc: 0.937, kcc: 0.803
+2025-07-19 00:15:56: Epoch: 026 Test results, ap: mae: 1.033, rse: 3.017, pcc: 0.191, kcc: 0.138
+2025-07-19 00:16:30: Epoch: 027 Train Loss: 0.176, mae: 0.382, rse: 0.128, pcc: 0.935, kcc: 0.800
+2025-07-19 00:16:32: Epoch: 027 Test results, ap: mae: 1.124, rse: 3.528, pcc: 0.171, kcc: 0.119
+2025-07-19 00:17:06: Epoch: 028 Train Loss: 0.176, mae: 0.428, rse: 0.138, pcc: 0.939, kcc: 0.811
+2025-07-19 00:17:08: Epoch: 028 Test results, ap: mae: 1.172, rse: 3.721, pcc: 0.188, kcc: 0.145
+2025-07-19 00:17:43: Epoch: 029 Train Loss: 0.183, mae: 0.376, rse: 0.125, pcc: 0.941, kcc: 0.814
+2025-07-19 00:17:43: Epoch: 029 New best VALIDATION metrics
+2025-07-19 00:17:44: Epoch: 029 Test results, ap: mae: 1.009, rse: 2.873, pcc: 0.206, kcc: 0.154
+2025-07-19 00:18:19: Epoch: 030 Train Loss: 0.182, mae: 0.359, rse: 0.113, pcc: 0.944, kcc: 0.818
+2025-07-19 00:18:19: Epoch: 030 New best VALIDATION metrics
+2025-07-19 00:18:20: Epoch: 030 Test results, ap: mae: 1.039, rse: 3.063, pcc: 0.173, kcc: 0.124
+2025-07-19 00:18:55: Epoch: 031 Train Loss: 0.150, mae: 0.341, rse: 0.111, pcc: 0.945, kcc: 0.826
+2025-07-19 00:18:55: Epoch: 031 New best VALIDATION metrics
+2025-07-19 00:18:57: Epoch: 031 Test results, ap: mae: 0.944, rse: 2.582, pcc: 0.188, kcc: 0.140
+2025-07-19 00:19:31: Epoch: 032 Train Loss: 0.139, mae: 0.332, rse: 0.102, pcc: 0.950, kcc: 0.832
+2025-07-19 00:19:31: Epoch: 032 New best VALIDATION metrics
+2025-07-19 00:19:33: Epoch: 032 Test results, ap: mae: 0.902, rse: 2.382, pcc: 0.193, kcc: 0.142
+2025-07-19 00:20:08: Epoch: 033 Train Loss: 0.149, mae: 0.336, rse: 0.107, pcc: 0.948, kcc: 0.828
+2025-07-19 00:20:09: Epoch: 033 Test results, ap: mae: 0.855, rse: 2.127, pcc: 0.191, kcc: 0.151
+2025-07-19 00:20:09: Epoch: 033 New best TEST metrics
+2025-07-19 00:20:44: Epoch: 034 Train Loss: 0.130, mae: 0.324, rse: 0.103, pcc: 0.950, kcc: 0.836
+2025-07-19 00:20:44: Epoch: 034 New best VALIDATION metrics
+2025-07-19 00:20:45: Epoch: 034 Test results, ap: mae: 0.944, rse: 2.543, pcc: 0.205, kcc: 0.156
+2025-07-19 00:21:20: Epoch: 035 Train Loss: 0.131, mae: 0.313, rse: 0.100, pcc: 0.949, kcc: 0.834
+2025-07-19 00:21:20: Epoch: 035 New best VALIDATION metrics
+2025-07-19 00:21:22: Epoch: 035 Test results, ap: mae: 0.954, rse: 2.619, pcc: 0.187, kcc: 0.142
+2025-07-19 00:21:56: Epoch: 036 Train Loss: 0.131, mae: 0.353, rse: 0.109, pcc: 0.948, kcc: 0.830
+2025-07-19 00:21:58: Epoch: 036 Test results, ap: mae: 1.044, rse: 3.023, pcc: 0.207, kcc: 0.152
+2025-07-19 00:22:32: Epoch: 037 Train Loss: 0.144, mae: 0.344, rse: 0.105, pcc: 0.951, kcc: 0.835
+2025-07-19 00:22:34: Epoch: 037 Test results, ap: mae: 0.946, rse: 2.580, pcc: 0.201, kcc: 0.151
+2025-07-19 00:23:09: Epoch: 038 Train Loss: 0.154, mae: 0.336, rse: 0.106, pcc: 0.949, kcc: 0.829
+2025-07-19 00:23:10: Epoch: 038 Test results, ap: mae: 0.917, rse: 2.383, pcc: 0.181, kcc: 0.141
+2025-07-19 00:23:45: Epoch: 039 Train Loss: 0.129, mae: 0.335, rse: 0.101, pcc: 0.951, kcc: 0.831
+2025-07-19 00:23:46: Epoch: 039 Test results, ap: mae: 0.919, rse: 2.421, pcc: 0.194, kcc: 0.144
+2025-07-19 00:24:21: Epoch: 040 Train Loss: 0.132, mae: 0.343, rse: 0.107, pcc: 0.949, kcc: 0.830
+2025-07-19 00:24:23: Epoch: 040 Test results, ap: mae: 0.926, rse: 2.444, pcc: 0.200, kcc: 0.151
+2025-07-19 00:24:57: Epoch: 041 Train Loss: 0.123, mae: 0.325, rse: 0.100, pcc: 0.952, kcc: 0.840
+2025-07-19 00:24:59: Epoch: 041 Test results, ap: mae: 0.920, rse: 2.385, pcc: 0.199, kcc: 0.147
+2025-07-19 00:25:34: Epoch: 042 Train Loss: 0.113, mae: 0.335, rse: 0.101, pcc: 0.953, kcc: 0.841
+2025-07-19 00:25:35: Epoch: 042 Test results, ap: mae: 0.936, rse: 2.463, pcc: 0.210, kcc: 0.151
+2025-07-19 00:26:10: Epoch: 043 Train Loss: 0.114, mae: 0.337, rse: 0.100, pcc: 0.954, kcc: 0.844
+2025-07-19 00:26:11: Epoch: 043 Test results, ap: mae: 0.945, rse: 2.517, pcc: 0.229, kcc: 0.163
+2025-07-19 00:26:46: Epoch: 044 Train Loss: 0.128, mae: 0.298, rse: 0.093, pcc: 0.954, kcc: 0.843
+2025-07-19 00:26:46: Epoch: 044 New best VALIDATION metrics
+2025-07-19 00:26:48: Epoch: 044 Test results, ap: mae: 0.920, rse: 2.403, pcc: 0.189, kcc: 0.139
+2025-07-19 00:27:22: Epoch: 045 Train Loss: 0.123, mae: 0.350, rse: 0.108, pcc: 0.952, kcc: 0.839
+2025-07-19 00:27:24: Epoch: 045 Test results, ap: mae: 0.997, rse: 2.804, pcc: 0.229, kcc: 0.168
+2025-07-19 00:27:59: Epoch: 046 Train Loss: 0.116, mae: 0.337, rse: 0.104, pcc: 0.952, kcc: 0.842
+2025-07-19 00:28:00: Epoch: 046 Test results, ap: mae: 0.962, rse: 2.623, pcc: 0.209, kcc: 0.150
+2025-07-19 00:28:35: Epoch: 047 Train Loss: 0.112, mae: 0.315, rse: 0.098, pcc: 0.953, kcc: 0.842
+2025-07-19 00:28:36: Epoch: 047 Test results, ap: mae: 0.935, rse: 2.506, pcc: 0.184, kcc: 0.138
+2025-07-19 00:29:11: Epoch: 048 Train Loss: 0.114, mae: 0.339, rse: 0.101, pcc: 0.954, kcc: 0.842
+2025-07-19 00:29:12: Epoch: 048 Test results, ap: mae: 0.970, rse: 2.668, pcc: 0.222, kcc: 0.165
+2025-07-19 00:29:47: Epoch: 049 Train Loss: 0.119, mae: 0.342, rse: 0.105, pcc: 0.952, kcc: 0.839
+2025-07-19 00:29:49: Epoch: 049 Test results, ap: mae: 0.949, rse: 2.573, pcc: 0.220, kcc: 0.165
+2025-07-19 00:30:23: Epoch: 050 Train Loss: 0.121, mae: 0.327, rse: 0.100, pcc: 0.953, kcc: 0.843
+2025-07-19 00:30:25: Epoch: 050 Test results, ap: mae: 0.980, rse: 2.697, pcc: 0.233, kcc: 0.175
+2025-07-19 00:30:25: used time 0.50h
+2025-07-19 00:30:25: Running Cross Validation 4
+2025-07-19 00:30:25: Fold 4  Train set:2270, Valid set:567, Test set: 108
+2025-07-19 00:30:59: Epoch: 001 Train Loss: 2.649, mae: 1.191, rse: 0.949, pcc: 0.287, kcc: 0.214
+2025-07-19 00:30:59: Epoch: 001 New best VALIDATION metrics
+2025-07-19 00:31:01: Epoch: 001 Test results, ap: mae: 0.747, rse: 1.663, pcc: -0.445, kcc: -0.357
+2025-07-19 00:31:36: Epoch: 002 Train Loss: 2.544, mae: 1.522, rse: 1.287, pcc: 0.220, kcc: 0.170
+2025-07-19 00:31:37: Epoch: 002 Test results, ap: mae: 0.850, rse: 1.818, pcc: -0.389, kcc: -0.260
+2025-07-19 00:32:12: Epoch: 003 Train Loss: 2.549, mae: 1.185, rse: 0.905, pcc: 0.341, kcc: 0.221
+2025-07-19 00:32:12: Epoch: 003 New best VALIDATION metrics
+2025-07-19 00:32:13: Epoch: 003 Test results, ap: mae: 0.695, rse: 1.358, pcc: -0.370, kcc: -0.321
+2025-07-19 00:32:48: Epoch: 004 Train Loss: 2.510, mae: 1.178, rse: 0.929, pcc: 0.320, kcc: 0.211
+2025-07-19 00:32:49: Epoch: 004 Test results, ap: mae: 0.613, rse: 1.156, pcc: -0.014, kcc: -0.009
+2025-07-19 00:33:24: Epoch: 005 Train Loss: 2.461, mae: 1.157, rse: 0.883, pcc: 0.358, kcc: 0.258
+2025-07-19 00:33:24: Epoch: 005 New best VALIDATION metrics
+2025-07-19 00:33:25: Epoch: 005 Test results, ap: mae: 0.717, rse: 1.351, pcc: -0.182, kcc: -0.190
+2025-07-19 00:34:00: Epoch: 006 Train Loss: 2.378, mae: 1.206, rse: 0.922, pcc: 0.358, kcc: 0.247
+2025-07-19 00:34:02: Epoch: 006 Test results, ap: mae: 1.088, rse: 3.122, pcc: -0.193, kcc: -0.181
+2025-07-19 00:34:36: Epoch: 007 Train Loss: 2.363, mae: 1.162, rse: 0.874, pcc: 0.373, kcc: 0.248
+2025-07-19 00:34:36: Epoch: 007 New best VALIDATION metrics
+2025-07-19 00:34:38: Epoch: 007 Test results, ap: mae: 0.705, rse: 1.317, pcc: -0.112, kcc: -0.102
+2025-07-19 00:35:12: Epoch: 008 Train Loss: 2.191, mae: 1.117, rse: 0.824, pcc: 0.423, kcc: 0.301
+2025-07-19 00:35:12: Epoch: 008 New best VALIDATION metrics
+2025-07-19 00:35:14: Epoch: 008 Test results, ap: mae: 0.905, rse: 2.070, pcc: -0.107, kcc: -0.063
+2025-07-19 00:35:49: Epoch: 009 Train Loss: 2.079, mae: 1.201, rse: 0.915, pcc: 0.437, kcc: 0.304
+2025-07-19 00:35:50: Epoch: 009 Test results, ap: mae: 0.901, rse: 2.051, pcc: -0.271, kcc: -0.209
+2025-07-19 00:36:25: Epoch: 010 Train Loss: 1.838, mae: 1.031, rse: 0.688, pcc: 0.564, kcc: 0.394
+2025-07-19 00:36:25: Epoch: 010 New best VALIDATION metrics
+2025-07-19 00:36:27: Epoch: 010 Test results, ap: mae: 1.026, rse: 2.572, pcc: 0.034, kcc: 0.069
+2025-07-19 00:37:01: Epoch: 011 Train Loss: 1.404, mae: 1.086, rse: 0.733, pcc: 0.590, kcc: 0.433
+2025-07-19 00:37:03: Epoch: 011 Test results, ap: mae: 1.250, rse: 3.846, pcc: -0.058, kcc: -0.024
+2025-07-19 00:37:03: Epoch: 011 New best TEST metrics
+2025-07-19 00:37:38: Epoch: 012 Train Loss: 1.231, mae: 0.797, rse: 0.462, pcc: 0.734, kcc: 0.559
+2025-07-19 00:37:38: Epoch: 012 New best VALIDATION metrics
+2025-07-19 00:37:39: Epoch: 012 Test results, ap: mae: 1.165, rse: 3.596, pcc: 0.209, kcc: 0.125
+2025-07-19 00:37:39: Epoch: 012 New best TEST metrics
+2025-07-19 00:38:14: Epoch: 013 Train Loss: 0.930, mae: 0.757, rse: 0.414, pcc: 0.775, kcc: 0.599
+2025-07-19 00:38:14: Epoch: 013 New best VALIDATION metrics
+2025-07-19 00:38:15: Epoch: 013 Test results, ap: mae: 1.045, rse: 3.001, pcc: -0.058, kcc: -0.065
+2025-07-19 00:38:15: Epoch: 013 New best TEST metrics
+2025-07-19 00:38:50: Epoch: 014 Train Loss: 0.827, mae: 0.745, rse: 0.419, pcc: 0.766, kcc: 0.597
+2025-07-19 00:38:52: Epoch: 014 Test results, ap: mae: 1.185, rse: 3.523, pcc: 0.096, kcc: 0.071
+2025-07-19 00:39:26: Epoch: 015 Train Loss: 0.730, mae: 0.630, rse: 0.309, pcc: 0.831, kcc: 0.647
+2025-07-19 00:39:26: Epoch: 015 New best VALIDATION metrics
+2025-07-19 00:39:28: Epoch: 015 Test results, ap: mae: 1.119, rse: 3.410, pcc: 0.229, kcc: 0.112
+2025-07-19 00:40:03: Epoch: 016 Train Loss: 0.626, mae: 0.750, rse: 0.430, pcc: 0.770, kcc: 0.594
+2025-07-19 00:40:04: Epoch: 016 Test results, ap: mae: 1.279, rse: 4.072, pcc: 0.007, kcc: 0.002
+2025-07-19 00:40:39: Epoch: 017 Train Loss: 0.573, mae: 0.553, rse: 0.236, pcc: 0.881, kcc: 0.687
+2025-07-19 00:40:39: Epoch: 017 New best VALIDATION metrics
+2025-07-19 00:40:41: Epoch: 017 Test results, ap: mae: 1.317, rse: 4.442, pcc: 0.126, kcc: 0.047
+2025-07-19 00:41:15: Epoch: 018 Train Loss: 0.466, mae: 0.524, rse: 0.232, pcc: 0.878, kcc: 0.697
+2025-07-19 00:41:15: Epoch: 018 New best VALIDATION metrics
+2025-07-19 00:41:17: Epoch: 018 Test results, ap: mae: 1.092, rse: 3.303, pcc: 0.071, kcc: 0.021
+2025-07-19 00:41:52: Epoch: 019 Train Loss: 0.384, mae: 0.567, rse: 0.287, pcc: 0.860, kcc: 0.682
+2025-07-19 00:41:53: Epoch: 019 Test results, ap: mae: 1.027, rse: 3.110, pcc: 0.016, kcc: -0.025
+2025-07-19 00:41:53: Epoch: 019 New best TEST metrics
+2025-07-19 00:42:28: Epoch: 020 Train Loss: 0.428, mae: 0.594, rse: 0.294, pcc: 0.861, kcc: 0.687
+2025-07-19 00:42:30: Epoch: 020 Test results, ap: mae: 1.373, rse: 4.734, pcc: 0.072, kcc: 0.015
+2025-07-19 00:43:04: Epoch: 021 Train Loss: 0.306, mae: 0.463, rse: 0.202, pcc: 0.897, kcc: 0.740
+2025-07-19 00:43:04: Epoch: 021 New best VALIDATION metrics
+2025-07-19 00:43:06: Epoch: 021 Test results, ap: mae: 1.172, rse: 3.958, pcc: -0.009, kcc: -0.052
+2025-07-19 00:43:40: Epoch: 022 Train Loss: 0.271, mae: 0.456, rse: 0.204, pcc: 0.894, kcc: 0.743
+2025-07-19 00:43:40: Epoch: 022 New best VALIDATION metrics
+2025-07-19 00:43:42: Epoch: 022 Test results, ap: mae: 1.247, rse: 4.416, pcc: 0.032, kcc: -0.039
+2025-07-19 00:44:17: Epoch: 023 Train Loss: 0.258, mae: 0.387, rse: 0.167, pcc: 0.914, kcc: 0.763
+2025-07-19 00:44:17: Epoch: 023 New best VALIDATION metrics
+2025-07-19 00:44:18: Epoch: 023 Test results, ap: mae: 1.083, rse: 3.609, pcc: -0.003, kcc: -0.033
+2025-07-19 00:44:53: Epoch: 024 Train Loss: 0.263, mae: 0.416, rse: 0.197, pcc: 0.896, kcc: 0.758
+2025-07-19 00:44:54: Epoch: 024 Test results, ap: mae: 1.047, rse: 3.218, pcc: -0.037, kcc: -0.067
+2025-07-19 00:45:29: Epoch: 025 Train Loss: 0.220, mae: 0.405, rse: 0.176, pcc: 0.909, kcc: 0.760
+2025-07-19 00:45:31: Epoch: 025 Test results, ap: mae: 1.016, rse: 3.120, pcc: 0.035, kcc: -0.034
+2025-07-19 00:45:31: Epoch: 025 New best TEST metrics
+2025-07-19 00:46:05: Epoch: 026 Train Loss: 0.233, mae: 0.405, rse: 0.185, pcc: 0.909, kcc: 0.767
+2025-07-19 00:46:07: Epoch: 026 Test results, ap: mae: 1.154, rse: 3.885, pcc: 0.075, kcc: -0.003
+2025-07-19 00:46:41: Epoch: 027 Train Loss: 0.228, mae: 0.386, rse: 0.188, pcc: 0.904, kcc: 0.771
+2025-07-19 00:46:43: Epoch: 027 Test results, ap: mae: 1.006, rse: 3.062, pcc: -0.018, kcc: -0.059
+2025-07-19 00:47:18: Epoch: 028 Train Loss: 0.199, mae: 0.358, rse: 0.166, pcc: 0.913, kcc: 0.784
+2025-07-19 00:47:18: Epoch: 028 New best VALIDATION metrics
+2025-07-19 00:47:19: Epoch: 028 Test results, ap: mae: 1.029, rse: 3.156, pcc: 0.049, kcc: -0.027
+2025-07-19 00:47:54: Epoch: 029 Train Loss: 0.205, mae: 0.367, rse: 0.166, pcc: 0.913, kcc: 0.790
+2025-07-19 00:47:55: Epoch: 029 Test results, ap: mae: 1.018, rse: 3.140, pcc: 0.025, kcc: -0.044
+2025-07-19 00:48:30: Epoch: 030 Train Loss: 0.193, mae: 0.370, rse: 0.162, pcc: 0.917, kcc: 0.790
+2025-07-19 00:48:30: Epoch: 030 New best VALIDATION metrics
+2025-07-19 00:48:32: Epoch: 030 Test results, ap: mae: 1.025, rse: 3.315, pcc: 0.036, kcc: -0.026
+2025-07-19 00:49:06: Epoch: 031 Train Loss: 0.155, mae: 0.385, rse: 0.165, pcc: 0.921, kcc: 0.805
+2025-07-19 00:49:06: Epoch: 031 New best VALIDATION metrics
+2025-07-19 00:49:08: Epoch: 031 Test results, ap: mae: 1.026, rse: 3.267, pcc: 0.007, kcc: -0.058
+2025-07-19 00:49:42: Epoch: 032 Train Loss: 0.156, mae: 0.321, rse: 0.146, pcc: 0.924, kcc: 0.812
+2025-07-19 00:49:42: Epoch: 032 New best VALIDATION metrics
+2025-07-19 00:49:44: Epoch: 032 Test results, ap: mae: 0.947, rse: 2.860, pcc: 0.013, kcc: -0.056
+2025-07-19 00:49:44: Epoch: 032 New best TEST metrics
+2025-07-19 00:50:19: Epoch: 033 Train Loss: 0.155, mae: 0.315, rse: 0.146, pcc: 0.924, kcc: 0.811
+2025-07-19 00:50:19: Epoch: 033 New best VALIDATION metrics
+2025-07-19 00:50:20: Epoch: 033 Test results, ap: mae: 0.951, rse: 2.853, pcc: -0.006, kcc: -0.060
+2025-07-19 00:50:55: Epoch: 034 Train Loss: 0.147, mae: 0.327, rse: 0.146, pcc: 0.926, kcc: 0.820
+2025-07-19 00:50:57: Epoch: 034 Test results, ap: mae: 0.936, rse: 2.843, pcc: 0.029, kcc: -0.027
+2025-07-19 00:50:57: Epoch: 034 New best TEST metrics
+2025-07-19 00:51:31: Epoch: 035 Train Loss: 0.162, mae: 0.312, rse: 0.148, pcc: 0.923, kcc: 0.816
+2025-07-19 00:51:31: Epoch: 035 New best VALIDATION metrics
+2025-07-19 00:51:33: Epoch: 035 Test results, ap: mae: 0.976, rse: 3.002, pcc: -0.012, kcc: -0.068
+2025-07-19 00:52:08: Epoch: 036 Train Loss: 0.154, mae: 0.338, rse: 0.162, pcc: 0.919, kcc: 0.807
+2025-07-19 00:52:09: Epoch: 036 Test results, ap: mae: 1.074, rse: 3.514, pcc: 0.024, kcc: -0.061
+2025-07-19 00:52:44: Epoch: 037 Train Loss: 0.138, mae: 0.332, rse: 0.153, pcc: 0.924, kcc: 0.825
+2025-07-19 00:52:45: Epoch: 037 Test results, ap: mae: 1.110, rse: 3.727, pcc: 0.014, kcc: -0.057
+2025-07-19 00:53:20: Epoch: 038 Train Loss: 0.130, mae: 0.345, rse: 0.150, pcc: 0.924, kcc: 0.818
+2025-07-19 00:53:21: Epoch: 038 Test results, ap: mae: 0.972, rse: 2.887, pcc: 0.026, kcc: -0.053
+2025-07-19 00:53:56: Epoch: 039 Train Loss: 0.138, mae: 0.320, rse: 0.140, pcc: 0.929, kcc: 0.818
+2025-07-19 00:53:56: Epoch: 039 New best VALIDATION metrics
+2025-07-19 00:53:57: Epoch: 039 Test results, ap: mae: 1.068, rse: 3.457, pcc: 0.010, kcc: -0.050
+2025-07-19 00:54:32: Epoch: 040 Train Loss: 0.128, mae: 0.315, rse: 0.154, pcc: 0.920, kcc: 0.820
+2025-07-19 00:54:34: Epoch: 040 Test results, ap: mae: 0.942, rse: 2.721, pcc: -0.008, kcc: -0.079
+2025-07-19 00:54:34: Epoch: 040 New best TEST metrics
+2025-07-19 00:55:08: Epoch: 041 Train Loss: 0.127, mae: 0.308, rse: 0.147, pcc: 0.925, kcc: 0.823
+2025-07-19 00:55:08: Epoch: 041 New best VALIDATION metrics
+2025-07-19 00:55:10: Epoch: 041 Test results, ap: mae: 0.980, rse: 2.970, pcc: 0.023, kcc: -0.056
+2025-07-19 00:55:45: Epoch: 042 Train Loss: 0.120, mae: 0.299, rse: 0.143, pcc: 0.927, kcc: 0.828
+2025-07-19 00:55:45: Epoch: 042 New best VALIDATION metrics
+2025-07-19 00:55:46: Epoch: 042 Test results, ap: mae: 0.975, rse: 2.902, pcc: 0.012, kcc: -0.063
+2025-07-19 00:56:21: Epoch: 043 Train Loss: 0.121, mae: 0.314, rse: 0.148, pcc: 0.926, kcc: 0.830
+2025-07-19 00:56:22: Epoch: 043 Test results, ap: mae: 1.037, rse: 3.278, pcc: 0.017, kcc: -0.059
+2025-07-19 00:56:57: Epoch: 044 Train Loss: 0.118, mae: 0.326, rse: 0.144, pcc: 0.929, kcc: 0.833
+2025-07-19 00:56:58: Epoch: 044 Test results, ap: mae: 1.021, rse: 3.203, pcc: 0.001, kcc: -0.065
+2025-07-19 00:57:33: Epoch: 045 Train Loss: 0.116, mae: 0.306, rse: 0.141, pcc: 0.929, kcc: 0.831
+2025-07-19 00:57:35: Epoch: 045 Test results, ap: mae: 0.992, rse: 3.061, pcc: 0.015, kcc: -0.055
+2025-07-19 00:58:09: Epoch: 046 Train Loss: 0.116, mae: 0.300, rse: 0.145, pcc: 0.925, kcc: 0.833
+2025-07-19 00:58:09: Epoch: 046 New best VALIDATION metrics
+2025-07-19 00:58:11: Epoch: 046 Test results, ap: mae: 0.980, rse: 2.937, pcc: -0.002, kcc: -0.064
+2025-07-19 00:58:46: Epoch: 047 Train Loss: 0.114, mae: 0.291, rse: 0.142, pcc: 0.927, kcc: 0.837
+2025-07-19 00:58:46: Epoch: 047 New best VALIDATION metrics
+2025-07-19 00:58:47: Epoch: 047 Test results, ap: mae: 0.963, rse: 2.828, pcc: 0.003, kcc: -0.062
+2025-07-19 00:59:22: Epoch: 048 Train Loss: 0.110, mae: 0.329, rse: 0.150, pcc: 0.925, kcc: 0.835
+2025-07-19 00:59:23: Epoch: 048 Test results, ap: mae: 1.017, rse: 3.164, pcc: -0.004, kcc: -0.062
+2025-07-19 00:59:58: Epoch: 049 Train Loss: 0.115, mae: 0.322, rse: 0.151, pcc: 0.925, kcc: 0.829
+2025-07-19 01:00:00: Epoch: 049 Test results, ap: mae: 1.010, rse: 3.067, pcc: 0.015, kcc: -0.056
+2025-07-19 01:00:34: Epoch: 050 Train Loss: 0.109, mae: 0.310, rse: 0.142, pcc: 0.929, kcc: 0.840
+2025-07-19 01:00:36: Epoch: 050 Test results, ap: mae: 0.983, rse: 2.942, pcc: 0.009, kcc: -0.060
+2025-07-19 01:00:36: used time 0.50h
+2025-07-19 01:00:36: Cross Validation Finished!
+2025-07-19 01:00:36: Best validation perform list
+[[0.34716055 0.13033909 0.93478781 0.7968865 ]
+ [0.37160835 0.15123102 0.92143387 0.78144634]
+ [0.32408452 0.1307413  0.93317407 0.81559759]
+ [0.29826987 0.09346266 0.9535386  0.84303826]
+ [0.29144725 0.14203408 0.92719471 0.83673775]]
+2025-07-19 01:00:36: mean: [0.327 0.13  0.934 0.815]
+2025-07-19 01:00:36: std: [0.03  0.02  0.011 0.023]
+2025-07-19 01:00:36: Best test perform list
+[[ 0.64414644  1.2159251   0.15650304  0.13113651]
+ [ 0.66240031  1.25202882  0.06617241 -0.0031223 ]
+ [ 0.81659389  1.92582953  0.06578838  0.04509985]
+ [ 0.85482037  2.12718463  0.19146281  0.15056413]
+ [ 0.94229674  2.72120261 -0.00830921 -0.07909821]]
+2025-07-19 01:00:36: mean: [0.784 1.848 0.094 0.049]
+2025-07-19 01:00:36: std: [0.114 0.566 0.071 0.085]
diff --git a/run.sh b/run.sh
new file mode 100644
index 0000000000000000000000000000000000000000..ec0a6ace50edf2033054613177651b8f109c3aa3
--- /dev/null
+++ b/run.sh
@@ -0,0 +1,60 @@
+#!/bin/bash
+# python main_simple.py --q-encoder lstm --channels 256 --fusion mlp
+# python main_simple.py --q-encoder lstm --channels 256 --fusion att
+# python main_simple.py --q-encoder lstm --channels 256 --fusion diff
+# python main_simple.py --q-encoder mamba --channels 256 --fusion mlp
+# python main_simple.py --q-encoder mamba --channels 256 --fusion att
+# python main_simple.py --q-encoder mamba --channels 256 --fusion diff
+# python main_simple.py --q-encoder mha --channels 256 --fusion mlp
+# python main_simple.py --q-encoder mha --channels 256 --fusion att
+# python main_simple.py --q-encoder mha --channels 256 --fusion diff
+# python main_simple.py --q-encoder gru --channels 256 --fusion mlp
+# python main_simple.py --q-encoder gru --channels 256 --fusion att
+# python main_simple.py --q-encoder gru --channels 256 --fusion diff
+# python main_simple.py --q-encoder rn18 --channels 16 --fusion mlp
+# python main_simple.py --q-encoder rn18 --channels 16 --fusion att
+# python main_simple.py --q-encoder rn18 --channels 16 --fusion diff
+# python main.py --q-encoder rn18 --channels 16 --fusion mlp
+# python main.py --q-encoder rn18 --channels 16 --fusion att
+# python main.py --q-encoder rn18 --channels 16 --fusion diff
+# python uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion mlp --one-way --task cls --loss ce
+# python main_simple.py --q-encoder rn18 --channels 16 --fusion mlp --gpu 1 &
+# python main_simple.py --q-encoder rn18 --channels 16 --fusion mlp --pcs --gpu 0 &
+# wait
+# python main_simple.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion mlp --pcs
+
+# python uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --case r2 --gpu 0 &
+# python uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --case r2
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case r2 --gpu 1 &
+# wait
+# python uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --case Indolicidin --gpu 0 &
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Indolicidin --gpu 1 &
+# wait
+# python uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --case Temporin-A --gpu 0 &
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Temporin-A --gpu 1 &
+# wait
+# python uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --case Anoplin --gpu 0 &
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Anoplin --gpu 1 &
+# wait
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case r2 --gpu 0 &
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Indolicidin --gpu 1 &
+# wait
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Temporin-A --gpu 0 &
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Anoplin --gpu 1 &
+# wait
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case r2
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Indolicidin
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Temporin-A
+# python uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Anoplin
+# python infer_case_uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --task reg --loss mse --case r2
+# python infer_case_uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --task reg --loss mse --case Indolicidin
+# python infer_case_uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --task reg --loss mse --case Temporin-A
+# python infer_case_uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --task reg --loss mse --case Anoplin
+# python infer_case_uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case r2
+# python infer_case_uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Indolicidin
+# python infer_case_uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Temporin-A
+# python infer_case_uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Anoplin
+# python uda.py --task cls --loss ce --q-encoder rn18 --channels 16 --pcs --fusion diff
+# python uda.py --task reg --loss mse --q-encoder rn18 --channels 16 --pcs --fusion diff
+python uda.py --task cls --loss ce --q-encoder mamba --channels 256 --pcs --fusion diff
+python uda.py --task reg --loss mse --q-encoder mamba --channels 256 --pcs --fusion diff
\ No newline at end of file
diff --git a/runthro_cls.sh b/runthro_cls.sh
new file mode 100644
index 0000000000000000000000000000000000000000..19f4e9d8926e22cc8a57ec3285cb735047c90ec4
--- /dev/null
+++ b/runthro_cls.sh
@@ -0,0 +1,20 @@
+#!/bin/bash
+# python main.py --task cls --loss ce --q-encoder lstm --channels 256 --fusion mlp
+# python main.py --task cls --loss ce --q-encoder lstm --channels 256 --fusion att
+# python main.py --task cls --loss ce --q-encoder lstm --channels 256 --fusion diff
+# python main.py --task cls --loss ce --q-encoder mamba --channels 256 --fusion mlp
+# python main.py --task cls --loss ce --q-encoder mamba --channels 256 --fusion att
+# python main.py --task cls --loss ce --q-encoder mamba --channels 256 --fusion diff
+# python main.py --task cls --loss ce --q-encoder mha --channels 256 --fusion mlp
+# python main.py --task cls --loss ce --q-encoder mha --channels 256 --fusion att
+# python main.py --task cls --loss ce --q-encoder mha --channels 256 --fusion diff
+# python main.py --task cls --loss ce --q-encoder gru --channels 256 --fusion mlp
+# python main.py --task cls --loss ce --q-encoder gru --channels 256 --fusion att
+# python main.py --task cls --loss ce --q-encoder gru --channels 256 --fusion diff
+python main.py --task cls --loss ce --q-encoder rn18 --channels 16 --fusion mlp
+python main.py --task cls --loss ce --q-encoder rn18 --channels 16 --fusion att
+python main.py --task cls --loss ce --q-encoder rn18 --channels 16 --fusion diff
+python main.py --task cls --loss ce --q-encoder rn18 --channels 16 --pcs --fusion diff
+python main.py --task cls --loss ce --q-encoder rn18 --channels 16 --pcs --fusion diff --side-enc mamba
+python main.py --task cls --loss ce --q-encoder rn18 --channels 16 --pcs --fusion diff --side-enc mamba --non-siamese
+python uda.py --task cls --loss ce --q-encoder rn18 --channels 16 --pcs --fusion diff --side-enc mamba --case r2
diff --git a/runthro_reg.sh b/runthro_reg.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7b081b7b2cb0f411628e7c674b09d7b036293d66
--- /dev/null
+++ b/runthro_reg.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+# python main.py --q-encoder lstm --channels 256 --fusion mlp
+# python main.py --q-encoder lstm --channels 256 --fusion att
+# python main.py --q-encoder lstm --channels 256 --fusion diff
+# python main.py --q-encoder mamba --channels 256 --fusion mlp
+# python main.py --q-encoder mamba --channels 256 --fusion att
+# python main.py --q-encoder mamba --channels 256 --fusion diff
+# python main.py --q-encoder mha --channels 256 --fusion mlp
+# python main.py --q-encoder mha --channels 256 --fusion att
+# python main.py --q-encoder mha --channels 256 --fusion diff
+# python main.py --q-encoder gru --channels 256 --fusion mlp
+# python main.py --q-encoder gru --channels 256 --fusion att
+# python main.py --q-encoder gru --channels 256 --fusion diff
+# python main.py --q-encoder cnn --channels 16 --fusion mlp
+# python main.py --q-encoder cnn --channels 16 --fusion att
+# python main.py --q-encoder cnn --channels 16 --fusion diff
+# python main.py --q-encoder cnn --channels 16 --pcs --fusion mlp
+# python main.py --q-encoder cnn --channels 16 --pcs --fusion att
+# python main.py --q-encoder cnn --channels 16 --pcs --fusion diff
+python main.py --q-encoder rn18 --channels 16 --fusion mlp
+python main.py --q-encoder rn18 --channels 16 --fusion att
+python main.py --q-encoder rn18 --channels 16 --fusion diff
+python main.py --q-encoder rn18 --channels 16 --pcs --fusion mlp
+python main.py --q-encoder rn18 --channels 16 --pcs --fusion att
+python main.py --q-encoder rn18 --channels 16 --pcs --fusion diff
+# python main.py --q-encoder cnn --channels 16 --side-enc lstm --pcs --fusion att
\ No newline at end of file
diff --git a/sfda.py b/sfda.py
new file mode 100644
index 0000000000000000000000000000000000000000..389d58f8de1b5c165d71bb6385e78544a4334d93
--- /dev/null
+++ b/sfda.py
@@ -0,0 +1,189 @@
+import argparse
+import json
+import logging
+import os
+import time
+from copy import deepcopy
+
+from dataset import PeptidePairPicCaseDataset, PeptidePairPicDataset
+from network import DMutaPeptide, DMutaPeptideCNN
+from sklearn.model_selection import KFold
+from train import move_to_device
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, Subset, RandomSampler
+import numpy as np
+from loss import MLCE, SuperLoss, LogCoshLoss, BMCLoss
+from utils import set_seed
+from torchmetrics import MeanAbsoluteError, RelativeSquaredError, PearsonCorrCoef, KendallRankCorrCoef
+
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='lstm',
+                    help='lstm mamba mla')
+parser.add_argument("--side-enc", dest='side_enc', type=str, default=None,
+                    help="use side features")
+parser.add_argument('--channels', type=int, default=256)
+parser.add_argument('--fusion', type=str, default='att',
+                    help='mlp att diff')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='reg',
+                    help='reg or cls')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=1,
+                    help="Seed (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cleavage site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cleavage site')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+# parser.add_argument('--llm-data', action='store_true', default=False,
+#                     help='Use LLM augmentation data')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='mse',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+parser.add_argument('--case', type=str, default='r2')
+parser.add_argument('--iter-num', dest='iter_num', type=int, default=1000)
+args = parser.parse_args()
+
+
+def noise_and_move(x, intensity: float = 0.05, device=torch.device('cpu')):
+    if isinstance(x, (tuple, list)):
+        return type(x)(noise_and_move(x_i, intensity, device) for x_i in x)
+    return (x + torch.randn_like(x) * intensity).to(device)
+
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+        if args.loss == "mse" or args.loss in ['ce']:
+            args.loss = 'mse'
+        else:
+            raise NotImplementedError("unimplemented regression task loss function")
+    elif args.task == 'cls':
+        args.classes = 2
+        if args.loss == 'ce' or args.loss in ['mse', 'smoothl1', 'super']:
+            args.loss = 'ce'
+        else:
+            raise NotImplementedError("unimplemented classification task loss function")
+    else:
+        raise NotImplementedError("unimplemented task")
+
+    if args.q_encoder in ['cnn', 'rn18']:
+        weight_dir = f'./run-{args.task}/{args.q_encoder}{"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+    else:
+        weight_dir = f'./run-{args.task}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+
+    logging.basicConfig(handlers=[
+    logging.FileHandler(filename=os.path.join(weight_dir, "sfda_tuning.log"), encoding='utf-8', mode='w+'),
+    logging.StreamHandler()],
+    format="%(asctime)s: %(message)s", datefmt="%F %T", level=logging.INFO)
+
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+
+    dataset = PeptidePairPicCaseDataset(case=args.case, pad_length=args.max_length, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize, gf=args.glob_feat)
+    sampler = RandomSampler(dataset, replacement=True, num_samples=args.iter_num * args.batch_size // 2)
+    dataloader = DataLoader(dataset, batch_size=args.batch_size // 2, sampler=sampler, num_workers=16,  pin_memory=True)
+
+    valset = PeptidePairPicDataset(mode='r2_case', pad_length=args.max_length, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize, gf=args.glob_feat)
+    valloader = DataLoader(valset, batch_size=args.batch_size, shuffle=False, num_workers=16, pin_memory=True)
+
+    criterion = torch.nn.MSELoss()
+
+    metric_funcs = {
+        'mae': MeanAbsoluteError().to(device),
+        'rse': RelativeSquaredError().to(device),
+        'pcc': PearsonCorrCoef().to(device),
+        'kcc': KendallRankCorrCoef().to(device)
+    }
+
+    best_perform_list = [[] for _ in range(args.split)]
+    for fold in range(args.split):
+        logging.info(f"Fold {fold}")
+        weights_path = f"{weight_dir}/model_{fold}.pth"
+        model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese)
+        model.load_state_dict(torch.load(weights_path))
+        model = model.to(device)
+
+        optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
+        
+        best_val_metric = -float('inf')
+        for iteration, (x, _) in enumerate(dataloader, 1):
+            x1 = noise_and_move(x, 0.05, device)
+            x2 = noise_and_move(x, 0.2, device)
+
+            y1 = model(x1)
+            y2 = model(x2)
+
+            loss = criterion(y1, y2)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            if iteration % 10 == 0:
+                with torch.no_grad():
+                    val_pred, val_gt = [], []
+                    for x, gt in valloader:
+                        x = move_to_device(x, device, non_blocking=True)
+                        out = model(x)
+                        val_pred.append(out)
+                        val_gt.append(gt.to(device, non_blocking=True))
+                    val_pred = torch.cat(val_pred, dim=0)
+                    val_gt = torch.cat(val_gt, dim=0)
+                    val_mae = metric_funcs['mae'](val_pred, val_gt).item()
+                    val_rse = metric_funcs['rse'](val_pred, val_gt).item()
+                    val_pcc = metric_funcs['pcc'](val_pred, val_gt).item()
+                    val_kcc = metric_funcs['kcc'](val_pred, val_gt).item()
+                    val_metric = val_pcc + val_kcc - val_mae - val_rse
+                    logging.info(f'Iteration {iteration}, Train Loss: {loss.item():.4f}, Val: mae: {val_mae:.4f} rse: {val_rse:.4f} pcc: {val_pcc:.4f} kcc: {val_kcc:.4f}')
+
+                    if val_metric > best_val_metric:
+                        logging.info('NEW best validation iteration')
+                        best_val_metric = val_metric
+                        best_perform_list[fold] = [val_mae, val_rse, val_pcc, val_kcc]
+                        torch.save(model.state_dict(), weights_path.replace('.pth', '_sfda.pth'))
+    
+    logging.info(f'SFDA Tuning Finished!')
+    best_perform_list = np.asarray(best_perform_list)
+    logging.info('Best validation perform list\n%s', best_perform_list)
+    logging.info('mean: %s', np.round(np.mean(best_perform_list, 0), 4))
+    logging.info('std: %s', np.round(np.std(best_perform_list, 0), 4))
+
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/summary.csv b/summary.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e49615cf8bdfb57edff1bc2ed6003a9f53166858
--- /dev/null
+++ b/summary.csv
@@ -0,0 +1,61 @@
+Valid
+Run,MAE,RSE,PCC,KCC
+gru-att-256-mse-32-0.001-50,114.21$_{\pm{12.10}}$,85.85$_{\pm{13.31}}$,33.81$_{\pm{19.12}}$,26.71$_{\pm{9.12}}$
+gru-diff-256-mse-32-0.001-50,36.78$_{\pm{30.71}}$,21.52$_{\pm{20.98}}$,87.64$_{\pm{13.46}}$,77.58$_{\pm{18.26}}$
+gru-mlp-256-mse-32-0.001-50,36.50$_{\pm{23.21}}$,18.91$_{\pm{12.33}}$,89.89$_{\pm{7.10}}$,79.01$_{\pm{11.93}}$
+lstm-att-256-bmc-32-0.001-35,138.82$_{\pm{6.07}}$,100.78$_{\pm{0.78}}$,26.30$_{\pm{37.01}}$,11.65$_{\pm{5.71}}$
+lstm-att-256-mse-32-0.001-50,123.37$_{\pm{3.60}}$,97.99$_{\pm{4.07}}$,15.88$_{\pm{10.48}}$,12.37$_{\pm{2.74}}$
+lstm-att-256-oneway-bmc-32-0.001-35,125.57$_{\pm{6.74}}$,100.49$_{\pm{0.33}}$,-0.97$_{\pm{14.47}}$,3.65$_{\pm{11.17}}$
+lstm-att-256-oneway-huber-32-0.001-35,121.95$_{\pm{5.64}}$,101.44$_{\pm{0.85}}$,55.23$_{\pm{42.38}}$,8.07$_{\pm{4.09}}$
+lstm-att-256-oneway-mse-32-0.001-35,124.12$_{\pm{5.75}}$,100.36$_{\pm{0.25}}$,12.21$_{\pm{12.60}}$,11.95$_{\pm{3.75}}$
+lstm-att-256-oneway-smoothl1-32-0.001-35,121.86$_{\pm{5.65}}$,101.32$_{\pm{0.63}}$,10.55$_{\pm{2.70}}$,12.34$_{\pm{3.83}}$
+lstm-diff-256-mse-32-0.001-50,109.95$_{\pm{15.44}}$,82.10$_{\pm{19.94}}$,34.75$_{\pm{24.94}}$,27.70$_{\pm{13.35}}$
+lstm-diff-256-oneway-smoothl1-32-0.001-35,119.88$_{\pm{3.99}}$,97.89$_{\pm{2.40}}$,23.09$_{\pm{3.98}}$,16.01$_{\pm{4.03}}$
+lstm-diff-256-smoothl1-32-0.001-35,86.13$_{\pm{30.66}}$,53.58$_{\pm{28.27}}$,66.76$_{\pm{20.19}}$,54.62$_{\pm{16.78}}$
+lstm-mlp-256-mse-32-0.001-50,123.56$_{\pm{2.04}}$,99.05$_{\pm{2.30}}$,12.66$_{\pm{6.52}}$,14.60$_{\pm{4.38}}$
+mamba-att-256-mse-32-0.001-50,118.44$_{\pm{8.76}}$,92.26$_{\pm{10.55}}$,32.27$_{\pm{13.46}}$,23.91$_{\pm{6.47}}$
+mamba-att-256-smoothl1-32-0.001-35,129.46$_{\pm{10.79}}$,90.56$_{\pm{8.38}}$,33.52$_{\pm{10.41}}$,24.72$_{\pm{5.73}}$
+mamba-diff-256-mse-32-0.001-50,20.04$_{\pm{2.10}}$,10.94$_{\pm{2.84}}$,94.45$_{\pm{1.47}}$,87.05$_{\pm{1.67}}$
+mamba-mlp-256-mse-32-0.001-50,31.56$_{\pm{14.15}}$,15.83$_{\pm{7.34}}$,91.72$_{\pm{4.15}}$,80.83$_{\pm{7.68}}$
+mha-att-256-mse-32-0.001-50,94.52$_{\pm{14.58}}$,60.72$_{\pm{14.48}}$,61.95$_{\pm{11.36}}$,40.19$_{\pm{8.81}}$
+mha-diff-256-mse-32-0.001-50,55.98$_{\pm{7.85}}$,25.57$_{\pm{7.45}}$,86.34$_{\pm{4.23}}$,67.83$_{\pm{3.62}}$
+mha-mlp-256-mse-32-0.001-50,73.42$_{\pm{8.72}}$,40.09$_{\pm{8.97}}$,77.29$_{\pm{5.64}}$,58.18$_{\pm{5.95}}$
+non-siamese-rn18-diff-16-mamba-pcs-768-mse-32-0.001-50,38.57$_{\pm{2.37}}$,20.37$_{\pm{5.18}}$,89.44$_{\pm{2.75}}$,76.82$_{\pm{0.86}}$
+random_forest,99.04$_{\pm{2.29}}$,65.56$_{\pm{3.15}}$,59.23$_{\pm{2.54}}$,39.39$_{\pm{2.55}}$
+ridge,119.02$_{\pm{3.31}}$,90.65$_{\pm{5.67}}$,31.95$_{\pm{7.22}}$,24.12$_{\pm{4.45}}$
+rn18-att-16-768-mse-32-0.001-50,109.56$_{\pm{6.16}}$,78.25$_{\pm{6.37}}$,49.77$_{\pm{5.22}}$,34.23$_{\pm{4.18}}$
+rn18-diff-16-768-mse-32-0.001-50,28.97$_{\pm{3.74}}$,10.78$_{\pm{3.28}}$,94.77$_{\pm{1.69}}$,84.32$_{\pm{2.00}}$
+rn18-diff-16-mamba-pcs-768-mse-32-0.001-50,18.72$_{\pm{1.74}}$,8.89$_{\pm{1.71}}$,95.53$_{\pm{0.90}}$,88.89$_{\pm{0.63}}$
+rn18-diff-16-pcs-768-mse-32-0.001-50,27.02$_{\pm{1.85}}$,9.72$_{\pm{2.30}}$,95.24$_{\pm{1.28}}$,84.77$_{\pm{1.09}}$
+rn18-mlp-16-768-mse-32-0.001-50,32.65$_{\pm{3.00}}$,12.96$_{\pm{1.96}}$,93.40$_{\pm{1.08}}$,81.47$_{\pm{2.33}}$
+
+QLX
+Run,MAE,RSE,PCC,KCC
+gru-att-256-mse-32-0.001-50,61.08$_{\pm{0.72}}$,112.93$_{\pm{0.67}}$,33.09$_{\pm{33.72}}$,8.73$_{\pm{5.40}}$
+gru-diff-256-mse-32-0.001-50,71.84$_{\pm{4.52}}$,145.31$_{\pm{19.96}}$,20.48$_{\pm{14.01}}$,11.51$_{\pm{8.38}}$
+gru-mlp-256-mse-32-0.001-50,64.08$_{\pm{6.28}}$,119.36$_{\pm{20.05}}$,28.11$_{\pm{8.47}}$,18.48$_{\pm{5.88}}$
+lstm-att-256-bmc-32-0.001-35,58.28$_{\pm{0.15}}$,101.67$_{\pm{1.11}}$,85.69$_{\pm{28.61}}$,8.44$_{\pm{8.94}}$
+lstm-att-256-mse-32-0.001-50,60.97$_{\pm{0.33}}$,113.85$_{\pm{1.29}}$,78.36$_{\pm{27.27}}$,9.65$_{\pm{8.24}}$
+lstm-att-256-oneway-bmc-32-0.001-35,61.30$_{\pm{0.61}}$,105.08$_{\pm{1.33}}$,75.30$_{\pm{46.07}}$,3.22$_{\pm{7.85}}$
+lstm-att-256-oneway-huber-32-0.001-35,60.19$_{\pm{0.36}}$,110.67$_{\pm{1.52}}$,50.91$_{\pm{41.70}}$,14.50$_{\pm{19.33}}$
+lstm-att-256-oneway-mse-32-0.001-35,58.31$_{\pm{0.13}}$,101.94$_{\pm{0.89}}$,100.00$_{\pm{0.00}}$,-3.49$_{\pm{5.90}}$
+lstm-att-256-oneway-smoothl1-32-0.001-35,60.40$_{\pm{0.69}}$,111.52$_{\pm{2.80}}$,28.34$_{\pm{11.91}}$,8.46$_{\pm{10.42}}$
+lstm-diff-256-mse-32-0.001-50,62.05$_{\pm{0.94}}$,116.30$_{\pm{3.46}}$,23.76$_{\pm{43.56}}$,6.18$_{\pm{15.53}}$
+lstm-diff-256-oneway-smoothl1-32-0.001-35,60.59$_{\pm{1.44}}$,107.87$_{\pm{2.48}}$,11.14$_{\pm{12.65}}$,3.25$_{\pm{14.37}}$
+lstm-diff-256-smoothl1-32-0.001-35,62.79$_{\pm{3.39}}$,118.05$_{\pm{9.34}}$,12.70$_{\pm{7.75}}$,8.40$_{\pm{3.12}}$
+lstm-mlp-256-mse-32-0.001-50,61.14$_{\pm{0.34}}$,114.50$_{\pm{1.33}}$,79.62$_{\pm{28.88}}$,10.74$_{\pm{6.88}}$
+mamba-att-256-mse-32-0.001-50,61.78$_{\pm{1.25}}$,116.14$_{\pm{3.50}}$,19.13$_{\pm{12.54}}$,13.20$_{\pm{7.59}}$
+mamba-att-256-smoothl1-32-0.001-35,61.41$_{\pm{1.28}}$,115.01$_{\pm{5.06}}$,23.13$_{\pm{13.69}}$,15.86$_{\pm{11.27}}$
+mamba-diff-256-mse-32-0.001-50,101.37$_{\pm{7.88}}$,316.52$_{\pm{46.58}}$,12.86$_{\pm{15.11}}$,7.03$_{\pm{8.79}}$
+mamba-mlp-256-mse-32-0.001-50,71.87$_{\pm{17.60}}$,167.14$_{\pm{94.65}}$,14.14$_{\pm{7.25}}$,5.60$_{\pm{5.55}}$
+mha-att-256-mse-32-0.001-50,63.74$_{\pm{3.37}}$,120.66$_{\pm{14.70}}$,9.30$_{\pm{7.01}}$,2.90$_{\pm{6.10}}$
+mha-diff-256-mse-32-0.001-50,84.33$_{\pm{12.38}}$,214.08$_{\pm{50.07}}$,0.04$_{\pm{13.49}}$,0.48$_{\pm{7.78}}$
+mha-mlp-256-mse-32-0.001-50,69.69$_{\pm{5.94}}$,147.59$_{\pm{23.81}}$,10.76$_{\pm{12.50}}$,6.57$_{\pm{5.24}}$
+non-siamese-rn18-diff-16-mamba-pcs-768-mse-32-0.001-50,72.97$_{\pm{7.64}}$,175.41$_{\pm{30.09}}$,17.71$_{\pm{19.17}}$,14.85$_{\pm{12.40}}$
+random_forest,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$
+ridge,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$
+rn18-att-16-768-mse-32-0.001-50,76.06$_{\pm{8.06}}$,155.95$_{\pm{24.65}}$,-14.36$_{\pm{6.34}}$,-13.47$_{\pm{9.06}}$
+rn18-diff-16-768-mse-32-0.001-50,82.00$_{\pm{6.95}}$,195.95$_{\pm{32.25}}$,17.65$_{\pm{5.09}}$,12.39$_{\pm{3.62}}$
+rn18-diff-16-mamba-pcs-768-mse-32-0.001-50,83.88$_{\pm{8.59}}$,204.28$_{\pm{35.47}}$,19.94$_{\pm{11.56}}$,11.09$_{\pm{7.62}}$
+rn18-diff-16-pcs-768-mse-32-0.001-50,68.20$_{\pm{4.56}}$,136.58$_{\pm{20.04}}$,25.06$_{\pm{6.41}}$,16.01$_{\pm{2.37}}$
+rn18-mlp-16-768-mse-32-0.001-50,78.41$_{\pm{11.44}}$,184.84$_{\pm{56.59}}$,9.43$_{\pm{7.13}}$,4.89$_{\pm{8.51}}$
diff --git a/summary.py b/summary.py
new file mode 100644
index 0000000000000000000000000000000000000000..03f88f3a1162e911544063f735bbc8cbaa37f8f4
--- /dev/null
+++ b/summary.py
@@ -0,0 +1,83 @@
+import os
+import csv
+import argparse
+from natsort import natsorted
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--dir', type=str, default='./run-reg')
+parser.add_argument('--output', type=str, default='summary.csv')
+args = parser.parse_args()
+
+# 设置目录路径和输出CSV文件名
+directory_path = args.dir
+output_csv = args.output
+
+# 创建CSV文件并写入数据
+with open(output_csv, mode='w', newline='') as file:
+    writer = csv.writer(file)
+
+    # 写入第一个子表的标题和表头
+    writer.writerow(['Valid'])
+    writer.writerow(['Run', 'MAE', 'RSE', 'PCC', 'KCC'])
+
+    # 遍历指定目录中的子目录
+    for subdirectory in natsorted(os.listdir(directory_path)):
+        subdirectory_path = os.path.join(directory_path, subdirectory)
+        if os.path.isdir(subdirectory_path):
+            result_file_path = os.path.join(subdirectory_path, 'result.txt')
+            if os.path.isfile(result_file_path):
+                with open(result_file_path, 'r') as result_file:
+                    lines = result_file.readlines()
+                    if len(lines) >= 3:
+                        means_5_6 = lines[1].strip().split(',')
+                        std_devs_5_6 = lines[2].strip().split(',')
+                        if len(means_5_6) == 4 and len(std_devs_5_6) == 4:
+                            values_5_6 = [f'{float(mean.strip()) * 100:.2f}$_{{\\pm{{{float(std_dev.strip()) * 100:.2f}}}}}$' 
+                                          for mean, std_dev in zip(means_5_6, std_devs_5_6)]
+                            writer.writerow([subdirectory] + values_5_6)
+
+    # 加入空行以分隔两个子表
+    writer.writerow([])
+
+    # 写入第一个子表的标题和表头
+    writer.writerow(['QLX'])
+    writer.writerow(['Run', 'MAE', 'RSE', 'PCC', 'KCC'])
+
+    # 遍历指定目录中的子目录
+    for subdirectory in natsorted(os.listdir(directory_path)):
+        subdirectory_path = os.path.join(directory_path, subdirectory)
+        if os.path.isdir(subdirectory_path):
+            result_file_path = os.path.join(subdirectory_path, 'result.txt')
+            if os.path.isfile(result_file_path):
+                with open(result_file_path, 'r') as result_file:
+                    lines = result_file.readlines()
+                    if len(lines) >= 6:
+                        means_5_6 = lines[4].strip().split(',')
+                        std_devs_5_6 = lines[5].strip().split(',')
+                        if len(means_5_6) == 4 and len(std_devs_5_6) == 4:
+                            values_5_6 = [f'{float(mean.strip()) * 100:.2f}$_{{\\pm{{{float(std_dev.strip()) * 100:.2f}}}}}$' 
+                                          for mean, std_dev in zip(means_5_6, std_devs_5_6)]
+                            writer.writerow([subdirectory] + values_5_6)
+
+    # # 加入空行以分隔两个子表
+    # writer.writerow([])
+
+    # # 写入第二个子表的标题和表头
+    # writer.writerow(['SAAP'])
+    # writer.writerow(['Run', 'AP', 'F1', 'ACC', 'AUC'])
+
+    # # 再次遍历指定目录中的子目录
+    # for subdirectory in natsorted(os.listdir(directory_path)):
+    #     subdirectory_path = os.path.join(directory_path, subdirectory)
+    #     if os.path.isdir(subdirectory_path):
+    #         result_file_path = os.path.join(subdirectory_path, 'result.txt')
+    #         if os.path.isfile(result_file_path):
+    #             with open(result_file_path, 'r') as result_file:
+    #                 lines = result_file.readlines()
+    #                 if len(lines) >= 9:
+    #                     means_8_9 = lines[7].strip().split(',')
+    #                     std_devs_8_9 = lines[8].strip().split(',')
+    #                     if len(means_8_9) == 4 and len(std_devs_8_9) == 4:
+    #                         values_8_9 = [f'{float(mean.strip()) * 100:.2f}$_{{\\pm{{{float(std_dev.strip()) * 100:.2f}}}}}$' 
+    #                                   for mean, std_dev in zip(means_8_9, std_devs_8_9)]
+    #                         writer.writerow([subdirectory] + values_8_9)
\ No newline at end of file
diff --git a/summary_cls-simple.csv b/summary_cls-simple.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b4eb268c1be9a89afb63d2c7e98b486a889d51c0
--- /dev/null
+++ b/summary_cls-simple.csv
@@ -0,0 +1,45 @@
+Valid
+Run,AUPRC,AUROC,F1,ACC
+_rn18-mlp-16-mamba-simple-768-oneway-ce-32-0.001-50,75.55$_{\pm{6.24}}$,77.74$_{\pm{4.05}}$,65.47$_{\pm{5.85}}$,71.84$_{\pm{1.50}}$
+gru-att-256-simple-oneway-ce-32-0.001-50,67.64$_{\pm{12.24}}$,73.36$_{\pm{7.69}}$,63.64$_{\pm{8.47}}$,65.05$_{\pm{9.77}}$
+gru-diff-256-simple-oneway-ce-32-0.001-50,72.00$_{\pm{7.97}}$,74.84$_{\pm{5.51}}$,60.26$_{\pm{8.49}}$,69.51$_{\pm{3.11}}$
+gru-mlp-256-simple-oneway-ce-32-0.001-50,68.19$_{\pm{9.80}}$,73.34$_{\pm{6.66}}$,54.49$_{\pm{22.57}}$,64.85$_{\pm{4.85}}$
+lstm-att-256-simple-oneway-ce-32-0.001-50,59.27$_{\pm{9.75}}$,65.82$_{\pm{4.13}}$,60.16$_{\pm{4.75}}$,48.16$_{\pm{11.03}}$
+lstm-diff-256-simple-oneway-ce-32-0.001-50,62.59$_{\pm{6.60}}$,67.86$_{\pm{1.59}}$,35.12$_{\pm{28.89}}$,54.37$_{\pm{7.74}}$
+lstm-mlp-256-simple-oneway-ce-32-0.001-50,54.28$_{\pm{3.67}}$,63.06$_{\pm{3.22}}$,58.94$_{\pm{8.79}}$,53.40$_{\pm{10.07}}$
+mamba-att-256-simple-oneway-ce-32-0.001-50,70.45$_{\pm{9.08}}$,74.65$_{\pm{5.25}}$,49.71$_{\pm{25.86}}$,64.66$_{\pm{6.69}}$
+mamba-diff-256-simple-oneway-ce-32-0.001-50,68.38$_{\pm{6.69}}$,72.01$_{\pm{2.56}}$,58.97$_{\pm{5.06}}$,67.38$_{\pm{1.45}}$
+mamba-mlp-256-simple-oneway-ce-32-0.001-50,73.36$_{\pm{9.51}}$,77.63$_{\pm{5.61}}$,66.24$_{\pm{8.77}}$,70.49$_{\pm{4.05}}$
+mha-att-256-simple-oneway-ce-32-0.001-50,72.14$_{\pm{7.30}}$,75.14$_{\pm{4.10}}$,63.98$_{\pm{6.25}}$,68.54$_{\pm{3.71}}$
+mha-diff-256-simple-oneway-ce-32-0.001-50,70.36$_{\pm{5.61}}$,73.85$_{\pm{2.62}}$,62.57$_{\pm{4.97}}$,68.35$_{\pm{2.58}}$
+mha-mlp-256-simple-oneway-ce-32-0.001-50,72.39$_{\pm{7.21}}$,75.79$_{\pm{2.87}}$,64.13$_{\pm{5.09}}$,63.30$_{\pm{11.83}}$
+rn18-att-16-mamba-simple-768-oneway-ce-32-0.001-50,75.89$_{\pm{6.28}}$,77.14$_{\pm{4.42}}$,44.05$_{\pm{23.40}}$,68.93$_{\pm{4.87}}$
+rn18-att-16-pcs-simple-768-oneway-ce-32-0.001-50,74.52$_{\pm{6.59}}$,76.06$_{\pm{2.90}}$,63.30$_{\pm{7.37}}$,70.29$_{\pm{4.70}}$
+rn18-att-16-simple-768-oneway-ce-32-0.001-50,76.39$_{\pm{5.39}}$,79.19$_{\pm{1.84}}$,62.67$_{\pm{10.43}}$,73.01$_{\pm{2.56}}$
+rn18-diff-16-simple-768-oneway-ce-32-0.001-50,67.93$_{\pm{6.88}}$,68.59$_{\pm{3.13}}$,59.69$_{\pm{5.75}}$,46.60$_{\pm{4.25}}$
+rn18-mlp-16-mamba-pcs-simple-768-oneway-ce-32-0.001-50,75.59$_{\pm{6.27}}$,77.95$_{\pm{3.56}}$,61.19$_{\pm{16.24}}$,71.46$_{\pm{2.26}}$
+rn18-mlp-16-pcs-simple-768-oneway-ce-32-0.001-50,75.47$_{\pm{6.64}}$,77.63$_{\pm{3.61}}$,68.10$_{\pm{5.49}}$,69.32$_{\pm{9.04}}$
+rn18-mlp-16-simple-768-oneway-ce-32-0.001-50,75.34$_{\pm{6.07}}$,79.36$_{\pm{3.57}}$,57.70$_{\pm{20.97}}$,70.29$_{\pm{4.90}}$
+
+Test
+Run,AUPRC,AUROC,F1,ACC
+_rn18-mlp-16-mamba-simple-768-oneway-ce-32-0.001-50,30.05$_{\pm{5.30}}$,47.55$_{\pm{6.68}}$,23.50$_{\pm{6.09}}$,59.07$_{\pm{16.96}}$
+gru-att-256-simple-oneway-ce-32-0.001-50,40.79$_{\pm{8.60}}$,65.43$_{\pm{3.25}}$,27.84$_{\pm{16.56}}$,47.04$_{\pm{21.17}}$
+gru-diff-256-simple-oneway-ce-32-0.001-50,32.54$_{\pm{8.96}}$,53.91$_{\pm{7.24}}$,30.68$_{\pm{8.56}}$,60.37$_{\pm{11.83}}$
+gru-mlp-256-simple-oneway-ce-32-0.001-50,38.77$_{\pm{9.46}}$,55.09$_{\pm{7.43}}$,37.33$_{\pm{2.95}}$,30.37$_{\pm{12.59}}$
+lstm-att-256-simple-oneway-ce-32-0.001-50,37.95$_{\pm{6.52}}$,62.97$_{\pm{4.18}}$,0.00$_{\pm{0.00}}$,75.93$_{\pm{0.00}}$
+lstm-diff-256-simple-oneway-ce-32-0.001-50,42.05$_{\pm{8.26}}$,68.08$_{\pm{5.49}}$,15.52$_{\pm{19.01}}$,55.19$_{\pm{25.40}}$
+lstm-mlp-256-simple-oneway-ce-32-0.001-50,33.20$_{\pm{4.30}}$,61.81$_{\pm{2.70}}$,23.28$_{\pm{19.01}}$,44.07$_{\pm{24.52}}$
+mamba-att-256-simple-oneway-ce-32-0.001-50,26.85$_{\pm{5.27}}$,51.89$_{\pm{8.14}}$,20.59$_{\pm{18.10}}$,50.19$_{\pm{19.91}}$
+mamba-diff-256-simple-oneway-ce-32-0.001-50,25.81$_{\pm{2.31}}$,51.01$_{\pm{4.30}}$,26.03$_{\pm{8.89}}$,61.85$_{\pm{3.48}}$
+mamba-mlp-256-simple-oneway-ce-32-0.001-50,23.16$_{\pm{1.30}}$,47.29$_{\pm{3.11}}$,26.81$_{\pm{15.71}}$,44.26$_{\pm{24.72}}$
+mha-att-256-simple-oneway-ce-32-0.001-50,28.99$_{\pm{3.74}}$,54.25$_{\pm{6.59}}$,12.65$_{\pm{15.65}}$,66.30$_{\pm{11.59}}$
+mha-diff-256-simple-oneway-ce-32-0.001-50,24.71$_{\pm{2.81}}$,47.25$_{\pm{4.86}}$,16.34$_{\pm{11.54}}$,53.70$_{\pm{12.99}}$
+mha-mlp-256-simple-oneway-ce-32-0.001-50,25.03$_{\pm{1.58}}$,49.36$_{\pm{2.66}}$,19.91$_{\pm{11.73}}$,62.04$_{\pm{8.73}}$
+rn18-att-16-mamba-simple-768-oneway-ce-32-0.001-50,28.98$_{\pm{3.05}}$,57.86$_{\pm{6.28}}$,17.43$_{\pm{17.41}}$,70.93$_{\pm{7.91}}$
+rn18-att-16-pcs-simple-768-oneway-ce-32-0.001-50,25.69$_{\pm{3.38}}$,48.20$_{\pm{8.09}}$,6.60$_{\pm{8.24}}$,71.11$_{\pm{3.86}}$
+rn18-att-16-simple-768-oneway-ce-32-0.001-50,36.57$_{\pm{8.80}}$,57.95$_{\pm{12.60}}$,12.04$_{\pm{12.17}}$,74.81$_{\pm{1.48}}$
+rn18-diff-16-simple-768-oneway-ce-32-0.001-50,35.91$_{\pm{4.25}}$,56.47$_{\pm{4.37}}$,37.70$_{\pm{2.81}}$,44.26$_{\pm{9.15}}$
+rn18-mlp-16-mamba-pcs-simple-768-oneway-ce-32-0.001-50,27.00$_{\pm{2.18}}$,53.15$_{\pm{3.87}}$,19.58$_{\pm{14.54}}$,67.22$_{\pm{8.52}}$
+rn18-mlp-16-pcs-simple-768-oneway-ce-32-0.001-50,29.33$_{\pm{5.82}}$,52.39$_{\pm{9.03}}$,23.54$_{\pm{14.28}}$,58.52$_{\pm{10.98}}$
+rn18-mlp-16-simple-768-oneway-ce-32-0.001-50,36.71$_{\pm{7.10}}$,61.77$_{\pm{5.90}}$,16.13$_{\pm{19.15}}$,73.70$_{\pm{1.72}}$
diff --git a/summary_cls.csv b/summary_cls.csv
new file mode 100644
index 0000000000000000000000000000000000000000..00422f5006031cb5707294c0f9f9e400a7c08e6e
--- /dev/null
+++ b/summary_cls.csv
@@ -0,0 +1,45 @@
+Valid
+Run,AUPRC,AUROC,F1,ACC
+gru-att-256-ce-32-0.001-50,66.31$_{\pm{8.77}}$,71.11$_{\pm{9.07}}$,52.07$_{\pm{26.72}}$,64.82$_{\pm{8.69}}$
+gru-diff-256-ce-32-0.001-50,93.07$_{\pm{1.87}}$,94.06$_{\pm{1.11}}$,86.78$_{\pm{1.73}}$,88.12$_{\pm{1.47}}$
+gru-mlp-256-ce-32-0.001-50,87.77$_{\pm{8.86}}$,89.35$_{\pm{7.73}}$,81.14$_{\pm{8.71}}$,82.97$_{\pm{7.89}}$
+logistic_regression,63.69$_{\pm{4.24}}$,69.21$_{\pm{2.75}}$,58.27$_{\pm{3.26}}$,64.77$_{\pm{2.89}}$
+lstm-att-256-ce-32-0.001-50,54.97$_{\pm{5.99}}$,60.95$_{\pm{5.73}}$,49.01$_{\pm{24.59}}$,55.59$_{\pm{8.68}}$
+lstm-diff-256-ce-32-0.001-50,60.94$_{\pm{7.57}}$,66.10$_{\pm{6.88}}$,49.46$_{\pm{24.82}}$,56.93$_{\pm{9.90}}$
+lstm-mlp-256-ce-32-0.001-50,50.10$_{\pm{2.64}}$,57.97$_{\pm{1.72}}$,48.71$_{\pm{24.36}}$,45.40$_{\pm{3.30}}$
+mamba-att-256-ce-32-0.001-50,82.78$_{\pm{8.92}}$,85.38$_{\pm{7.61}}$,75.30$_{\pm{8.73}}$,78.22$_{\pm{6.65}}$
+mamba-diff-256-ce-32-0.001-50,94.33$_{\pm{0.64}}$,95.43$_{\pm{0.73}}$,88.35$_{\pm{1.16}}$,89.50$_{\pm{1.18}}$
+mamba-mlp-256-ce-32-0.001-50,93.55$_{\pm{0.71}}$,94.92$_{\pm{0.50}}$,87.23$_{\pm{0.94}}$,88.61$_{\pm{0.73}}$
+mha-att-256-ce-32-0.001-50,71.35$_{\pm{4.97}}$,75.80$_{\pm{4.98}}$,68.26$_{\pm{3.84}}$,69.79$_{\pm{4.28}}$
+mha-diff-256-ce-32-0.001-50,88.94$_{\pm{2.63}}$,90.87$_{\pm{1.87}}$,81.31$_{\pm{1.67}}$,82.87$_{\pm{1.59}}$
+mha-mlp-256-ce-32-0.001-50,88.61$_{\pm{2.80}}$,90.35$_{\pm{2.23}}$,80.63$_{\pm{2.18}}$,82.41$_{\pm{1.96}}$
+non-siamese-rn18-diff-16-mamba-pcs-768-ce-32-0.001-50,88.65$_{\pm{1.53}}$,91.28$_{\pm{1.25}}$,82.77$_{\pm{1.75}}$,84.39$_{\pm{1.34}}$
+random_forest,73.45$_{\pm{1.98}}$,76.28$_{\pm{0.70}}$,64.03$_{\pm{2.38}}$,67.34$_{\pm{1.24}}$
+rn18-att-16-768-ce-32-0.001-50,64.59$_{\pm{4.86}}$,69.71$_{\pm{2.10}}$,61.03$_{\pm{2.57}}$,64.68$_{\pm{2.04}}$
+rn18-diff-16-768-ce-32-0.001-50,92.10$_{\pm{1.55}}$,94.08$_{\pm{1.00}}$,85.78$_{\pm{0.96}}$,87.35$_{\pm{1.00}}$
+rn18-diff-16-mamba-pcs-768-ce-32-0.001-50,93.95$_{\pm{0.84}}$,95.25$_{\pm{0.28}}$,87.95$_{\pm{0.75}}$,88.97$_{\pm{0.51}}$
+rn18-diff-16-pcs-768-ce-32-0.001-50,92.53$_{\pm{1.14}}$,94.17$_{\pm{0.58}}$,84.30$_{\pm{0.84}}$,86.29$_{\pm{0.73}}$
+rn18-mlp-16-768-ce-32-0.001-50,90.85$_{\pm{1.54}}$,93.25$_{\pm{0.92}}$,84.90$_{\pm{1.98}}$,86.82$_{\pm{1.28}}$
+
+Test
+Run,AUPRC,AUROC,F1,ACC
+gru-att-256-ce-32-0.001-50,39.53$_{\pm{9.72}}$,63.97$_{\pm{8.31}}$,29.65$_{\pm{15.90}}$,52.68$_{\pm{20.45}}$
+gru-diff-256-ce-32-0.001-50,36.65$_{\pm{2.59}}$,60.45$_{\pm{2.79}}$,37.68$_{\pm{4.60}}$,60.33$_{\pm{5.55}}$
+gru-mlp-256-ce-32-0.001-50,40.52$_{\pm{5.91}}$,61.81$_{\pm{6.87}}$,41.05$_{\pm{4.72}}$,45.37$_{\pm{9.40}}$
+logistic_regression,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$
+lstm-att-256-ce-32-0.001-50,42.22$_{\pm{12.80}}$,64.48$_{\pm{9.38}}$,0.00$_{\pm{0.00}}$,75.61$_{\pm{0.00}}$
+lstm-diff-256-ce-32-0.001-50,35.48$_{\pm{8.71}}$,61.92$_{\pm{1.81}}$,22.72$_{\pm{18.57}}$,52.03$_{\pm{21.57}}$
+lstm-mlp-256-ce-32-0.001-50,29.73$_{\pm{3.08}}$,59.56$_{\pm{4.62}}$,0.00$_{\pm{0.00}}$,75.61$_{\pm{0.00}}$
+mamba-att-256-ce-32-0.001-50,36.81$_{\pm{7.33}}$,62.06$_{\pm{4.67}}$,30.86$_{\pm{16.50}}$,57.07$_{\pm{16.81}}$
+mamba-diff-256-ce-32-0.001-50,34.33$_{\pm{2.96}}$,58.09$_{\pm{3.74}}$,29.53$_{\pm{4.17}}$,65.04$_{\pm{4.39}}$
+mamba-mlp-256-ce-32-0.001-50,33.88$_{\pm{6.48}}$,55.99$_{\pm{5.40}}$,25.87$_{\pm{7.67}}$,66.99$_{\pm{7.21}}$
+mha-att-256-ce-32-0.001-50,29.52$_{\pm{4.80}}$,53.88$_{\pm{3.48}}$,15.75$_{\pm{16.11}}$,57.72$_{\pm{18.40}}$
+mha-diff-256-ce-32-0.001-50,36.40$_{\pm{3.46}}$,59.47$_{\pm{2.31}}$,35.86$_{\pm{2.04}}$,60.81$_{\pm{7.51}}$
+mha-mlp-256-ce-32-0.001-50,31.67$_{\pm{5.23}}$,55.67$_{\pm{6.92}}$,28.93$_{\pm{11.48}}$,57.24$_{\pm{5.69}}$
+non-siamese-rn18-diff-16-mamba-pcs-768-ce-32-0.001-50,33.29$_{\pm{5.34}}$,58.59$_{\pm{6.32}}$,33.76$_{\pm{11.37}}$,59.51$_{\pm{9.78}}$
+random_forest,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$,0.00$_{\pm{0.00}}$
+rn18-att-16-768-ce-32-0.001-50,31.14$_{\pm{6.42}}$,58.06$_{\pm{5.96}}$,26.31$_{\pm{12.04}}$,62.76$_{\pm{6.28}}$
+rn18-diff-16-768-ce-32-0.001-50,28.89$_{\pm{2.57}}$,59.34$_{\pm{3.27}}$,11.82$_{\pm{7.59}}$,71.06$_{\pm{2.09}}$
+rn18-diff-16-mamba-pcs-768-ce-32-0.001-50,34.60$_{\pm{6.18}}$,57.43$_{\pm{5.53}}$,34.49$_{\pm{6.17}}$,65.53$_{\pm{7.39}}$
+rn18-diff-16-pcs-768-ce-32-0.001-50,31.62$_{\pm{4.70}}$,58.89$_{\pm{5.58}}$,27.46$_{\pm{11.63}}$,69.43$_{\pm{2.44}}$
+rn18-mlp-16-768-ce-32-0.001-50,30.30$_{\pm{5.11}}$,57.29$_{\pm{5.48}}$,9.06$_{\pm{10.52}}$,71.71$_{\pm{4.49}}$
diff --git a/summary_cls.py b/summary_cls.py
new file mode 100644
index 0000000000000000000000000000000000000000..c633e37a2dd9de6f1b8582876ec2cbc31b0d26a8
--- /dev/null
+++ b/summary_cls.py
@@ -0,0 +1,83 @@
+import os
+import csv
+import argparse
+from natsort import natsorted
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--dir', type=str, default='./run-cls')
+parser.add_argument('--output', type=str, default='summary_cls.csv')
+args = parser.parse_args()
+
+# 设置目录路径和输出CSV文件名
+directory_path = args.dir
+output_csv = args.output
+
+# 创建CSV文件并写入数据
+with open(output_csv, mode='w', newline='') as file:
+    writer = csv.writer(file)
+
+    # 写入第一个子表的标题和表头
+    writer.writerow(['Valid'])
+    writer.writerow(['Run', 'AUPRC', 'AUROC', 'F1', 'ACC'])
+
+    # 遍历指定目录中的子目录
+    for subdirectory in natsorted(os.listdir(directory_path)):
+        subdirectory_path = os.path.join(directory_path, subdirectory)
+        if os.path.isdir(subdirectory_path):
+            result_file_path = os.path.join(subdirectory_path, 'result.txt')
+            if os.path.isfile(result_file_path):
+                with open(result_file_path, 'r') as result_file:
+                    lines = result_file.readlines()
+                    if len(lines) >= 3:
+                        means_5_6 = lines[1].strip().split(',')
+                        std_devs_5_6 = lines[2].strip().split(',')
+                        if len(means_5_6) == 4 and len(std_devs_5_6) == 4:
+                            values_5_6 = [f'{float(mean.strip()) * 100:.2f}$_{{\\pm{{{float(std_dev.strip()) * 100:.2f}}}}}$' 
+                                          for mean, std_dev in zip(means_5_6, std_devs_5_6)]
+                            writer.writerow([subdirectory] + values_5_6)
+
+    # 加入空行以分隔两个子表
+    writer.writerow([])
+
+    # 写入第一个子表的标题和表头
+    writer.writerow(['Test'])
+    writer.writerow(['Run', 'AUPRC', 'AUROC', 'F1', 'ACC'])
+
+    # 遍历指定目录中的子目录
+    for subdirectory in natsorted(os.listdir(directory_path)):
+        subdirectory_path = os.path.join(directory_path, subdirectory)
+        if os.path.isdir(subdirectory_path):
+            result_file_path = os.path.join(subdirectory_path, 'result.txt')
+            if os.path.isfile(result_file_path):
+                with open(result_file_path, 'r') as result_file:
+                    lines = result_file.readlines()
+                    if len(lines) >= 6:
+                        means_5_6 = lines[4].strip().split(',')
+                        std_devs_5_6 = lines[5].strip().split(',')
+                        if len(means_5_6) == 4 and len(std_devs_5_6) == 4:
+                            values_5_6 = [f'{float(mean.strip()) * 100:.2f}$_{{\\pm{{{float(std_dev.strip()) * 100:.2f}}}}}$' 
+                                          for mean, std_dev in zip(means_5_6, std_devs_5_6)]
+                            writer.writerow([subdirectory] + values_5_6)
+
+    # # 加入空行以分隔两个子表
+    # writer.writerow([])
+
+    # # 写入第二个子表的标题和表头
+    # writer.writerow(['SAAP'])
+    # writer.writerow(['Run', 'AP', 'F1', 'ACC', 'AUC'])
+
+    # # 再次遍历指定目录中的子目录
+    # for subdirectory in natsorted(os.listdir(directory_path)):
+    #     subdirectory_path = os.path.join(directory_path, subdirectory)
+    #     if os.path.isdir(subdirectory_path):
+    #         result_file_path = os.path.join(subdirectory_path, 'result.txt')
+    #         if os.path.isfile(result_file_path):
+    #             with open(result_file_path, 'r') as result_file:
+    #                 lines = result_file.readlines()
+    #                 if len(lines) >= 9:
+    #                     means_8_9 = lines[7].strip().split(',')
+    #                     std_devs_8_9 = lines[8].strip().split(',')
+    #                     if len(means_8_9) == 4 and len(std_devs_8_9) == 4:
+    #                         values_8_9 = [f'{float(mean.strip()) * 100:.2f}$_{{\\pm{{{float(std_dev.strip()) * 100:.2f}}}}}$' 
+    #                                   for mean, std_dev in zip(means_8_9, std_devs_8_9)]
+    #                         writer.writerow([subdirectory] + values_8_9)
\ No newline at end of file
diff --git a/tmp.py b/tmp.py
new file mode 100644
index 0000000000000000000000000000000000000000..af75d86b2512df960621bccf176335904096d800
--- /dev/null
+++ b/tmp.py
@@ -0,0 +1,24 @@
+import torch
+
+# 1. 加载完整的状态字典
+state_dict_path = '/home/auralray/Downloads/ImageMol.pth.tar' 
+pretrained_state_dict = torch.load(state_dict_path)['state_dict']
+# print(pretrained_state_dict)
+
+# 2. 定义源模块的前缀
+# 根据你的实际键名，可能是 'embedding_layer.' 或 'self.embedding_layer.'
+# 我们以 'embedding_layer.' 为例进行演示
+old_prefix = "embedding_layer." 
+
+# 3. 提取并清理键名
+q_encoder_weights = {}
+for key, value in pretrained_state_dict.items():
+    if key.startswith(old_prefix):
+        # 移除前缀，得到模块内部的相对键名（如 '0.weight', '1.bias'）
+        # 新模型 q_encoder 内部的层结构和参数命名与 embedding_layer 内部一致
+        new_key = key[len(old_prefix):]
+        q_encoder_weights[new_key] = value
+
+print(f"成功提取并清理了 {len(q_encoder_weights)} 个键。")
+# print(q_encoder_weights)
+torch.save(q_encoder_weights, './ImageMolEncoder.pth')
\ No newline at end of file
diff --git a/tmp_test.py b/tmp_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..47e7bb3ca5bc725d771a4f4de07122e235a96561
--- /dev/null
+++ b/tmp_test.py
@@ -0,0 +1,245 @@
+import argparse
+import json
+import logging
+import os
+import time
+
+from dataset import PeptidePairDataset, PeptidePairPicDataset, SimplePairClsDataset, AA_to_index
+from network import DMutaPeptide, DMutaPeptideCNN#, DMutaPeptideWiden
+from sklearn.model_selection import KFold
+from train import move_to_device
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader, WeightedRandomSampler, RandomSampler, Subset
+import numpy as np
+from loss import MLCE, SuperLoss, LogCoshLoss, BMCLoss
+from utils import set_seed
+from infer_case import FasterModelForCase, CustomDataset
+
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='rn18',
+                    help='lstm mamba mla')
+parser.add_argument("--side-enc", dest='side_enc', type=str, default='mamba',
+                    help="use side features")
+parser.add_argument('--channels', type=int, default=16)
+parser.add_argument('--fusion', type=str, default='diff',
+                    help='mlp att diff')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+parser.add_argument('--widen', action='store_true', default=False,
+                    help='use widen non-siamese architecture')
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='cls',
+                    help='reg or cls')
+parser.add_argument('--pdb-src', type=str, dest='pdb_src', default='af',
+                    help='af or hf')
+parser.add_argument('--data-ver', type=str, dest='data_ver', default='250228',
+                    help='data version')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--run-folds', type=int, dest='run_folds', nargs='+', default=-1,
+                    help='specify which folds to run')
+parser.add_argument('--seed', type=int, default=1,
+                    help="Seed (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cut site')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+parser.add_argument('--llm-data', action='store_true', default=False,
+                    help='Use LLM augmentation data')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--warm-steps', type=int, dest='warm_steps', default=0,
+                    help='number of warm start steps for learning rate (default: 10)')
+parser.add_argument('--patience', type=int, default=10,
+                    help='patience for early stopping (default: 10)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='ce',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+parser.add_argument('--bias-curri', dest='bias_curri', action='store_true', default=False,
+                    help='directly use loss as the training data (biased) or not (unbiased)')
+parser.add_argument('--anti-curri', dest='anti_curri', action='store_true', default=False,
+                    help='easy to hard (curri), hard to easy (anti)')
+parser.add_argument('--std-coff', dest='std_coff', type=float, default=1,
+                    help='the hyper-parameter of std')
+
+parser.add_argument('--ft-epochs', dest='ft_epochs', type=int, default=15,
+                    help='fine-tune epochs')
+parser.add_argument('--ft-lr', dest='ft_lr', type=float, default=0.0002,
+                    help='fine-tune learning rate')
+
+parser.add_argument('--simple', dest='simple', action='store_true', default=False)
+
+args = parser.parse_args()
+
+if args.llm_data:
+    args.simple = True
+
+if args.simple:
+    args.one_way = True
+
+if args.run_folds == -1:
+    args.run_folds = list(range(args.split))
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+        if args.loss == "mse" or args.loss in ['ce']:
+            args.loss = 'mse'
+            criterion = nn.MSELoss()
+        elif args.loss == "smoothl1":
+            criterion = nn.SmoothL1Loss()
+        elif args.loss == "super":
+            criterion = SuperLoss()
+        elif args.loss in ["bmc", "bmc_ln"]:
+            criterion = BMCLoss()
+        else:
+            raise NotImplementedError("unimplemented regression task loss function")
+    elif args.task == 'cls':
+        args.classes = 2
+        if args.loss == 'ce' or args.loss in ['mse', 'smoothl1', 'super']:
+            args.loss = 'ce'
+            criterion = nn.CrossEntropyLoss()
+        else:
+            raise NotImplementedError("unimplemented classification task loss function")
+    else:
+        raise NotImplementedError("unimplemented task")
+    
+    if args.q_encoder in ['cnn', 'rn18']:
+        weight_dir = f'./run-{args.task}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+    else:
+        weight_dir = f'./run-{args.task}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{"-simple" if args.simple else ""}{"-llm" if args.llm_data else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}'
+    
+    logging.basicConfig(handlers=[
+        # logging.FileHandler(filename=os.path.join(weight_dir, "finetune.log"), encoding='utf-8', mode='w+'),
+        logging.StreamHandler()],
+        format="%(asctime)s: %(message)s", datefmt="%F %T", level=logging.INFO)
+    
+    logging.info(f'Finetuning: {weight_dir}')
+
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+
+    # logging.info(f'Loading Training Dataset')
+    # train_set = SimplePairClsDataset(pad_length=args.max_length, ftr2=True, gf=args.glob_feat, q_encoder=args.q_encoder, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+
+    logging.info('Loading Test Dataset')
+    if args.q_encoder in ['cnn', 'rn18']:
+        test_set = PeptidePairPicDataset(mode='train', pad_length=args.max_length, task=args.task, one_way=True, gf=args.glob_feat, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize)
+    # test_set = CustomDataset(case='r2', pad_length=args.max_length, side_enc=args.side_enc, pcs=True, resize=args.resize, gf=args.glob_feat)
+
+    # train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=8, pin_memory=True)
+    test_loader = DataLoader(test_set, batch_size=args.batch_size, shuffle=False, num_workers=8, pin_memory=True)
+
+    best_perform_list = [[] for i in range(5)]
+
+    # for fold in range(args.split):
+    for fold in [0]:
+        logging.info(f'Finetuning Fold {fold}')
+        logging.info(f'Fold {fold}, Test set: {len(test_set)}')
+        # if args.widen:
+        #     model = DMutaPeptideWiden(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, side_enc=args.side_enc)
+        # else:
+        # model = FasterModelForCase(classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
+        model = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese).to(device).eval()
+        weights_path = f"{weight_dir}/model_{fold}.pth"
+
+        model.load_state_dict(torch.load(weights_path, map_location=device))
+
+        # model.load_state_dict(torch.load(weights_path.replace('.pth', '_test.pth'), map_location=device), strict=False)
+
+
+        if args.task == 'cls':
+            train_cls(args, None, model, None, test_loader, device, criterion, None)
+
+def train_cls(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer):
+    num_labels = model.classes
+
+    model.eval()
+    seqs_t, seqs_d = [], []
+    preds = []
+    gt_list_valid = []
+    with torch.no_grad():
+        for data in valid_loader:
+            x, gt = data
+            seqs_1, seqs_2 = get_seq_from_batched_data(x)
+            seqs_t.extend(seqs_1)
+            seqs_d.extend(seqs_2)
+            # x1, x2 = move_to_device(x, device)
+            x = move_to_device(x, device)
+            # model.cache_temp_vector(x1)
+            gt_list_valid.append(gt.to(device))
+            # out = model(x2)
+            out = model(x)
+            preds.append(out)
+
+    # calculate metrics
+    preds = torch.softmax(torch.cat(preds, dim=0), dim=-1).squeeze()
+    gt_list_valid = torch.cat(gt_list_valid, dim=0).int().squeeze()
+
+    preds = (preds[:, 1] > 0.5).int()
+
+    wrong_preds = (preds != gt_list_valid)
+
+    for i in range(len(wrong_preds)):
+        if wrong_preds[i]:
+            print(f"{seqs_t[i]} {seqs_d[i]} {preds[i]} {gt_list_valid[i]}")
+
+index_to_aa = {v: k for k, v in AA_to_index.items()}
+
+def get_seq_from_batched_data(x):
+    seq_encs_1, seq_encs_2 = x[0][1], x[1][1]
+    seqs_1 = get_seq_from_enc(seq_encs_1)
+    seqs_2 = get_seq_from_enc(seq_encs_2)
+    return seqs_1, seqs_2
+
+def get_seq_from_enc(enc:torch.Tensor):
+    encs = enc.cpu().numpy()
+    seqs = []
+    for enc in encs:
+        seq = ''
+        d_indicator = enc[:, 0].astype(bool)
+        enc[:, 0] = 0.
+        index = np.argmax(enc, axis=-1) - 1
+        for d, i in zip(d_indicator, index):
+            if i < 0:
+                break
+            if d:
+                seq += index_to_aa[i].lower()
+            else:
+                seq += index_to_aa[i]
+        seqs.append(seq)
+    return seqs
+
+if __name__ == "__main__":
+    main()
diff --git a/trad_ml.py b/trad_ml.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c66c1d622199dc2c060cb2a654a2c20f1fe2758
--- /dev/null
+++ b/trad_ml.py
@@ -0,0 +1,346 @@
+import os
+from copy import deepcopy
+from joblib import dump
+from dataset import build_peptide_smiles, load_data, process_label
+from rdkit import Chem
+from rdkit.Chem import AllChem
+from rdkit import DataStructs
+from rdkit.Chem.rdFingerprintGenerator import GetMorganGenerator
+import numpy as np
+from sklearn.base import BaseEstimator, TransformerMixin
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import KFold
+from sklearn.linear_model import LogisticRegression, Ridge
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from sklearn.metrics import (
+    average_precision_score, roc_auc_score, f1_score, accuracy_score, mean_absolute_error
+)
+from scipy.stats import pearsonr, kendalltau
+from typing import List, Tuple, Union, Literal, Optional
+
+
+def encode_sequence(seq):
+    return Chem.MolFromSmiles(build_peptide_smiles(seq))
+
+
+def get_data(mode='train', task="cls", include_reverse=False, include_self=False, one_way=False) :
+    if mode == "train":
+        loader = load_data
+        xlsx_file = os.path.join(os.path.dirname(__file__), 'dataset', 'train.xlsx')
+    elif mode in ["test", "r2_case", 'r2_case_']:
+        one_way = True
+        if mode in ["test", "r2_case", 'r2_case_']:
+            loader = load_data
+            xlsx_file = os.path.join(os.path.dirname(__file__), 'dataset', f'{mode}.xlsx')
+    else:
+        raise ValueError("未知的 mode，请使用 'train' 或 'test'")
+
+    groups_avg = loader(xlsx_file, mode)
+
+    data = []
+    # 针对每个原型，过滤掉长度超过 pad_length 的变种
+    for orig, variant_dict in groups_avg.items():
+        # a = len(data)
+        variants = list(variant_dict.keys())
+        n_variants = len(variants)
+        if n_variants == 0:
+            continue
+
+        # 若启用自组合，则添加 (A, A) 样本，标签为 process_label(1, task) → log2(1)=0（再分类也为 0）
+        if include_self and (not one_way):
+            for variant in variants:
+                encoded_seq = encode_sequence(variant)
+                label = process_label(1.0, task)  # log2(1)=0
+                data.append(((encoded_seq, encoded_seq), label))
+        
+        # 添加不同变种之间的样本
+        for i in [0] if one_way else range(n_variants):
+            for j in range(i + 1, n_variants):
+                var1 = variants[i]
+                var2 = variants[j]
+                mic1 = variant_dict[var1]
+                mic2 = variant_dict[var2]
+                
+                # 正向组合: (var1, var2) 标签为 log₂(mic2/mic1)
+                ratio = mic2 / mic1 if mic1 != 0 else np.nan
+                label = process_label(ratio, task)
+                if np.isnan(label):
+                    continue
+                encoded_var1 = encode_sequence(var1)
+                encoded_var2 = encode_sequence(var2)
+                data.append(((encoded_var1, encoded_var2), label))
+                
+                # 若启用正反组合，则添加 (var2, var1)
+                if include_reverse and (not one_way):
+                    rev_ratio = mic1 / mic2 if mic2 != 0 else np.nan
+                    rev_label = process_label(rev_ratio, task)
+                    data.append(((encoded_var2, encoded_var1), rev_label))
+    
+    return data
+
+
+def _morgan_bitvect_from_mol(mol, radius=2, nBits=2048, useChirality=True):
+    # bool array of size nBits
+    gen = GetMorganGenerator(radius=radius, fpSize=nBits, includeChirality=useChirality)
+    bv = gen.GetFingerprint(mol)  # ExplicitBitVect
+    arr = np.zeros((nBits,), dtype=np.uint8)
+    # ConvertToNumpyArray works, but with generator we can use this:
+    # bv is an ExplicitBitVect; use ToBitString if needed, but converting is fine:
+    # RDKit still supports DataStructs.ConvertToNumpyArray; however to avoid old API you can do:
+    onbits = list(bv.GetOnBits())
+    arr[onbits] = 1
+    return arr
+
+def _morgan_countvect_hashed_from_mol(mol, radius=2, nBits=2048, useChirality=True):
+    # dense float32 vector with hashed counts, consistent with previous behavior
+    gen = GetMorganGenerator(radius=radius, fpSize=nBits, includeChirality=useChirality)
+    sp = gen.GetCountFingerprint(mol)  # SparseIntVect
+    vec = np.zeros((nBits,), dtype=np.float32)
+    for k, v in sp.GetNonzeroElements().items():
+        # The generator already hashed to [0, fpSize). Keys are within range.
+        # So no extra Python hash: just place counts directly.
+        idx = int(k)
+        if 0 <= idx < nBits:
+            vec[idx] += float(v)
+    return vec
+
+class PairwiseMolFeaturizer(BaseEstimator, TransformerMixin):
+    def __init__(self, nBits=2048, radius=2, useChirality=True, mode="count"):
+        """
+        mode:
+          - 'count': 使用 Δcount（回归推荐）
+          - 'count+binary': 使用 [Δcount, XOR(bit)]（分类推荐）
+        """
+        self.nBits = nBits
+        self.radius = radius
+        self.useChirality = useChirality
+        self.mode = mode
+
+    def fit(self, X, y=None):
+        return self
+
+    def transform(self, X):
+        feats = []
+        for (mol1, mol2) in X:
+            c1 = _morgan_countvect_hashed_from_mol(mol1, radius=self.radius, nBits=self.nBits, useChirality=self.useChirality)
+            c2 = _morgan_countvect_hashed_from_mol(mol2, radius=self.radius, nBits=self.nBits, useChirality=self.useChirality)
+            delta_count = c2 - c1
+            if self.mode == "count":
+                feats.append(delta_count)
+            elif self.mode == "count+binary":
+                b1 = _morgan_bitvect_from_mol(mol1, radius=self.radius, nBits=self.nBits, useChirality=self.useChirality)
+                b2 = _morgan_bitvect_from_mol(mol2, radius=self.radius, nBits=self.nBits, useChirality=self.useChirality)
+                xor = np.logical_xor(b2, b1).astype(np.uint8).astype(np.float32)
+                feats.append(np.concatenate([delta_count, xor], axis=0))
+            else:
+                raise ValueError("Unknown featurization mode")
+        return np.vstack(feats)
+
+# ----------------- 数据与工具 -----------------
+def unpack_pairs(data_list: List[Tuple[Tuple[object, object], Union[int, float]]]):
+    X = [pair for (pair, y) in data_list]
+    y = np.array([y for (pair, y) in data_list])
+    return X, y
+
+def _mean_std(vals):
+    arr = np.array(vals, dtype=float)
+    arr = arr[~np.isnan(arr)]
+    if arr.size == 0:
+        return np.nan, np.nan
+    return float(np.mean(arr)), float(np.std(arr))
+
+# ----------------- 主训练-评估入口 -----------------
+def run(
+    get_data_func,
+    task: Literal['cls', 'reg'],
+    model: Literal['rf', 'lr'],
+    n_splits: int = 5,
+    random_state: int = 42,
+    nBits: int = 2048,
+    radius: int = 2,
+    useChirality: bool = True
+):
+    """
+    参数:
+      - task: 'cls' 或 'reg'
+      - model: 'rf' 或 'lr'
+          * 分类: 'rf' -> RandomForestClassifier, 'lr' -> LogisticRegression
+          * 回归: 'rf' -> RandomForestRegressor, 'lr' -> Ridge
+    返回:
+      - 最后一次拟合的 Pipeline 模型
+      - 指标汇总 dict
+    """
+    data = get_data_func(task=task)
+    X, y = unpack_pairs(data)
+    print(f"Total pairs: {len(X)} | Task={task} | Model={model}")
+
+    kf = KFold(n_splits=n_splits, shuffle=True, random_state=random_state)
+
+    if task == 'cls':
+        # 特征
+        fx = PairwiseMolFeaturizer(nBits=nBits, radius=radius, useChirality=useChirality, mode="count+binary")
+        # 模型
+        if model == 'rf':
+            clf = RandomForestClassifier(
+                n_estimators=600, max_features="sqrt", min_samples_leaf=1,
+                class_weight="balanced_subsample", random_state=random_state, n_jobs=-1
+            )
+        elif model == 'lr':
+            clf = LogisticRegression(solver="liblinear", max_iter=2000, class_weight="balanced")
+        else:
+            raise ValueError("model must be 'rf' or 'lr'")
+
+        pipe = Pipeline([("fx", fx), ("clf", clf)])
+
+        auprc_list, auroc_list, f1_list, acc_list, pipes = [], [], [], [], []
+
+        for fold, (tr, te) in enumerate(kf.split(X), 1):
+            X_tr = [X[i] for i in tr]; y_tr = y[tr].astype(int)
+            X_te = [X[i] for i in te]; y_te = y[te].astype(int)
+
+            pipe.fit(X_tr, y_tr)
+            # 概率输出
+            if hasattr(pipe.named_steps['clf'], "predict_proba"):
+                proba = pipe.predict_proba(X_te)[:, 1]
+            else:
+                # 如模型不支持 predict_proba，用 decision_function 做一个近似映射
+                if hasattr(pipe.named_steps['clf'], "decision_function"):
+                    scores = pipe.decision_function(X_te)
+                    # min-max 归一为 [0,1] 近似概率
+                    mn, mx = float(np.min(scores)), float(np.max(scores))
+                    proba = (scores - mn) / (mx - mn + 1e-9)
+                else:
+                    # 退化为 hard label，不用于 AUC，但勉强计算 AUPRC 会失真
+                    pred_hard = pipe.predict(X_te)
+                    proba = pred_hard.astype(float)
+            
+            pipes.append(deepcopy(pipe))
+
+            pred = (proba >= 0.5).astype(int)
+
+            auprc = average_precision_score(y_te, proba)
+            try:
+                auroc = roc_auc_score(y_te, proba)
+            except ValueError:
+                auroc = np.nan
+            f1 = f1_score(y_te, pred)
+            acc = accuracy_score(y_te, pred)
+
+            auprc_list.append(auprc); auroc_list.append(auroc); f1_list.append(f1); acc_list.append(acc)
+            print(f"[Fold {fold}] AUPRC={auprc:.4f} | AUROC={auroc if not np.isnan(auroc) else float('nan'):.4f} | F1={f1:.4f} | ACC={acc:.4f}")
+
+        metrics = {
+            "AUPRC": _mean_std(auprc_list),
+            "AUROC": _mean_std([v for v in auroc_list if not np.isnan(v)]),
+            "F1": _mean_std(f1_list),
+            "ACC": _mean_std(acc_list),
+        }
+
+        print("\nValidation (KFold, mean ± std)")
+        print(f"- AUPRC: {metrics['AUPRC'][0]:.4f} ± {metrics['AUPRC'][1]:.4f}")
+        if not np.isnan(metrics["AUROC"][0]):
+            print(f"- AUROC: {metrics['AUROC'][0]:.4f} ± {metrics['AUROC'][1]:.4f}")
+        else:
+            print("- AUROC: undefined (some folds single-class)")
+        print(f"- F1: {metrics['F1'][0]:.4f} ± {metrics['F1'][1]:.4f}")
+        print(f"- ACC: {metrics['ACC'][0]:.4f} ± {metrics['ACC'][1]:.4f}")
+
+        return pipes, metrics
+
+    elif task == 'reg':
+        fx = PairwiseMolFeaturizer(nBits=nBits, radius=radius, useChirality=useChirality, mode="count")
+
+        if model == 'rf':
+            reg = RandomForestRegressor(
+                n_estimators=800, max_features="sqrt", min_samples_leaf=1,
+                random_state=random_state, n_jobs=-1
+            )
+        elif model == 'lr':
+            # 线性基线：Ridge
+            reg = Ridge(alpha=1.0, random_state=random_state)
+        else:
+            raise ValueError("model must be 'rf' or 'lr'")
+
+        pipe = Pipeline([("fx", fx), ("reg", reg)])
+
+        mae_list, rse_list, pcc_list, kcc_list, pipes = [], [], [], [], []
+
+        for fold, (tr, te) in enumerate(kf.split(X), 1):
+            X_tr = [X[i] for i in tr]; y_tr = y[tr].astype(float)
+            X_te = [X[i] for i in te]; y_te = y[te].astype(float)
+
+            pipe.fit(X_tr, y_tr)
+            pred = pipe.predict(X_te)
+
+            pipes.append(deepcopy(pipe))
+
+            mae = mean_absolute_error(y_te, pred)
+            rss = float(np.sum((y_te - pred) ** 2))
+            tss = float(np.sum((y_te - np.mean(y_te)) ** 2))
+            rse = rss / tss if tss > 0 else np.nan
+
+            if len(np.unique(y_te)) > 1:
+                pcc = pearsonr(y_te, pred)[0]
+                kcc = kendalltau(y_te, pred)[0]
+            else:
+                pcc, kcc = np.nan, np.nan
+
+            mae_list.append(mae); rse_list.append(rse); pcc_list.append(pcc); kcc_list.append(kcc)
+            print(f"[Fold {fold}] MAE={mae:.4f} | RSE={rse if not np.isnan(rse) else float('nan'):.4f} | PCC={pcc if not np.isnan(pcc) else float('nan'):.4f} | KCC={kcc if not np.isnan(kcc) else float('nan'):.4f}")
+
+        metrics = {
+            "MAE": _mean_std(mae_list),
+            "RSE": _mean_std(rse_list),
+            "PCC": _mean_std([v for v in pcc_list if not np.isnan(v)]),
+            "KCC": _mean_std([v for v in kcc_list if not np.isnan(v)]),
+        }
+
+        print("\nValidation (KFold, mean ± std)")
+        print(f"- MAE: {metrics['MAE'][0]:.4f} ± {metrics['MAE'][1]:.4f}")
+        print(f"- RSE: {metrics['RSE'][0]:.4f} ± {metrics['RSE'][1]:.4f}")
+        if not np.isnan(metrics["PCC"][0]):
+            print(f"- PCC: {metrics['PCC'][0]:.4f} ± {metrics['PCC'][1]:.4f}")
+        else:
+            print("- PCC: undefined")
+        if not np.isnan(metrics["KCC"][0]):
+            print(f"- KCC: {metrics['KCC'][0]:.4f} ± {metrics['KCC'][1]:.4f}")
+        else:
+            print("- KCC: undefined")
+
+        return pipes, metrics
+
+    else:
+        raise ValueError("task must be 'cls' or 'reg'")
+
+
+def save_things(models: List[Pipeline], metrics, path):
+    os.makedirs(path, exist_ok=True)
+    for i, pipe in enumerate(models):
+        dump(pipe, os.path.join(path, f"model_{i}.joblib")) 
+    means, stds = [], []
+    for k, v in metrics.items():
+        means.append(f"{v[0]}")
+        stds.append(f"{v[1]}")
+    means = ','.join(means)
+    stds = ','.join(stds)
+    string = '\n'.join(('Valid', means, stds, 'Test', '0.,0.,0.,0.', '0.,0.,0.,0.'))
+    with open(os.path.join(path, 'result.txt'), 'w') as f:
+        f.write(string)
+
+# ----------------- 用法示例 -----------------
+if __name__ == "__main__":
+    # 分类 + 逻辑回归
+    models, metrics = run(get_data, task='cls', model='lr', n_splits=5, random_state=42)
+    save_things(models, metrics, 'run-cls/logistic_regression')
+
+    # 分类 + 随机森林
+    models, metrics = run(get_data, task='cls', model='rf', n_splits=5, random_state=42)
+    save_things(models, metrics, 'run-cls/random_forest')
+
+    # 回归 + Ridge
+    models, metrics = run(get_data, task='reg', model='lr', n_splits=5, random_state=42)
+    save_things(models, metrics, 'run-reg/ridge')
+
+    # 回归 + 随机森林
+    models, metrics = run(get_data, task='reg', model='rf', n_splits=5, random_state=42)
+    save_things(models, metrics, 'run-reg/random_forest')
\ No newline at end of file
diff --git a/train.py b/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c52410683faef03c029b37de6ec61f428c7e3ed
--- /dev/null
+++ b/train.py
@@ -0,0 +1,198 @@
+import torch
+from torchmetrics import MeanAbsoluteError, RelativeSquaredError, PearsonCorrCoef, KendallRankCorrCoef, F1Score, Accuracy, AveragePrecision, AUROC
+
+
+def move_to_device(batch, device, non_blocking=False):
+    if isinstance(batch, (list, tuple)):
+        return type(batch)(move_to_device(item, device, non_blocking) for item in batch)
+    return batch.to(device, non_blocking=non_blocking)
+
+
+def train(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer):
+    train_loss = 0
+    num_labels = model.classes
+    metric_mae = MeanAbsoluteError().to(device)
+    metric_rse = RelativeSquaredError(num_outputs=num_labels).to(device)
+    metric_pcc = PearsonCorrCoef(num_outputs=num_labels).to(device)
+    metric_kcc = KendallRankCorrCoef(num_outputs=num_labels).to(device)
+
+    if args.dir:
+        encodings, labels = [], []
+
+    if train_loader is not None:
+        model.train()
+        for data in train_loader:
+            x, gt = data
+            x = move_to_device(x, device)
+            if args.dir:
+                out, features = model(x,
+                                      gt.to(device),
+                                      epoch)
+                encodings.append(features.detach().cpu())
+                labels.append(gt.cpu())
+            else:
+                out = model(x)
+            loss = criterion(out, gt.to(device))
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+            train_loss += loss.item()
+        train_loss /= len(train_loader)
+
+        if args.dir:
+            encodings, labels = torch.cat(encodings), torch.cat(labels)
+            model.FDS.update_last_epoch_stats(epoch)
+            model.FDS.update_running_stats(encodings, labels, epoch)
+            encodings, labels = [], []
+            
+
+    model.eval()
+    preds = []
+    gt_list_valid = []
+    with torch.no_grad():
+        for data in valid_loader:
+            x, gt = data
+            x = move_to_device(x, device)
+            gt_list_valid.append(gt.to(device))
+            out = model(x)
+            if args.dir:
+                out, _ = out
+            preds.append(out)
+
+    # calculate metrics
+    preds = torch.cat(preds, dim=0)
+    gt_list_valid = torch.cat(gt_list_valid, dim=0)
+
+    mae = metric_mae(preds, gt_list_valid).item()
+    rse = metric_rse(preds, gt_list_valid).item()
+    pcc = metric_pcc(preds.squeeze(), gt_list_valid.squeeze()).mean().item()
+    kcc = metric_kcc(preds.squeeze(), gt_list_valid.squeeze()).mean().item()
+    return train_loss, mae, rse, pcc, kcc
+
+
+def update_ce_loss_weight(loss_fn: torch.nn.CrossEntropyLoss, gt: torch.Tensor, num_classes: int, device):
+    """
+    根据当前 batch 的 ground truth 标签更新 nn.CrossEntropyLoss 对象中的 weight 缓冲区，
+    使用逆频率方法计算新权重，并通过 register_buffer 进行原地更新。
+    
+    参数:
+      loss_fn (nn.CrossEntropyLoss): 已初始化的 nn.CrossEntropyLoss 对象，
+                                      要求在初始化时已经注册了 weight 缓冲区。
+      gt (torch.Tensor): 当前 batch 的 ground truth 标签，1D整数张量，标签取值范围 [0, num_classes-1]。
+    """
+    class_counts = torch.bincount(gt, minlength=num_classes).float()
+    epsilon = 1e-6
+    new_weights = 1.0 / (class_counts + epsilon)
+    new_weights = new_weights / new_weights.sum() * num_classes
+    # 使用 register_buffer 来更新 loss_fn 内部的 weight 缓冲区
+    loss_fn.register_buffer('weight', new_weights.to(device))
+
+def train_cls(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer):
+    train_loss = 0
+    num_labels = model.classes
+    avg = args.metric_avg
+    if num_labels == 1 or num_labels == 2:
+        task = 'binary'
+    else:
+        task = 'multiclass'
+    metric_acc = Accuracy(average=avg, task=task, num_classes=num_labels).to(device)
+    metric_f1 = F1Score(average=avg, task=task, num_classes=num_labels).to(device)
+    metric_ap = AveragePrecision(average=avg, task=task, num_classes=num_labels).to(device)
+    metric_auc = AUROC(average=avg, task=task, num_classes=num_labels).to(device)
+
+    if train_loader is not None:
+        model.train()
+        for data in train_loader:
+            x, gt = data
+            x = move_to_device(x, device)
+            out = model(x)
+            update_ce_loss_weight(criterion, gt, num_classes=num_labels, device=device)
+            loss = criterion(out, gt.to(device))
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+            train_loss += loss.item()
+        train_loss /= len(train_loader)
+
+    model.eval()
+    preds = []
+    gt_list_valid = []
+    with torch.no_grad():
+        for data in valid_loader:
+            x, gt = data
+            x = move_to_device(x, device)
+            gt_list_valid.append(gt.to(device))
+            out = model(x)
+            preds.append(out)
+
+    # calculate metrics
+    preds = torch.softmax(torch.cat(preds, dim=0), dim=-1).squeeze()
+    gt_list_valid = torch.cat(gt_list_valid, dim=0).int().squeeze()
+
+    if num_labels == 2:
+        preds = preds[:, 1]
+
+    ap = metric_ap(preds, gt_list_valid).item()
+    auc = metric_auc(preds, gt_list_valid).item()
+    f1 = metric_f1(preds, gt_list_valid).item()
+    acc = metric_acc(preds, gt_list_valid).item()
+    return train_loss, ap, auc, f1, acc
+
+def train_base(args, epoch, model, train_loader, valid_loader, device, criterion, optimizer):
+    train_loss = 0
+    num_labels = model.classes
+    metric_mae = MeanAbsoluteError().to(device)
+    metric_rse = RelativeSquaredError(num_outputs=num_labels).to(device)
+    metric_pcc = PearsonCorrCoef(num_outputs=num_labels).to(device)
+    metric_kcc = KendallRankCorrCoef(num_outputs=num_labels).to(device)
+
+    if args.dir:
+        encodings, labels = [], []
+
+    if train_loader is not None:
+        model.train()
+        for data in train_loader:
+            seq1, gt = data
+            if args.dir:
+                out, features = model(seq1.to(device),
+                                      gt.to(device),
+                                      epoch)
+                encodings.append(features.detach().cpu())
+                labels.append(gt.cpu())
+            else:
+                out = model(seq1.to(device))
+            loss = criterion(out, gt.to(device))
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+            train_loss += loss.item()
+        train_loss /= len(train_loader)
+
+        if args.dir:
+            encodings, labels = torch.cat(encodings), torch.cat(labels)
+            model.FDS.update_last_epoch_stats(epoch)
+            model.FDS.update_running_stats(encodings, labels, epoch)
+            encodings, labels = [], []
+            
+
+    model.eval()
+    preds = []
+    gt_list_valid = []
+    with torch.no_grad():
+        for data in valid_loader:
+            seq1, gt = data
+            gt_list_valid.append(gt.to(device))
+            out = model(seq1.to(device))
+            if args.dir:
+                out, _ = out
+            preds.append(out)
+
+    # calculate metrics
+    preds = torch.cat(preds, dim=0)
+    gt_list_valid = torch.cat(gt_list_valid, dim=0)
+
+    mae = metric_mae(preds, gt_list_valid).item()
+    rse = metric_rse(preds, gt_list_valid).item()
+    pcc = metric_pcc(preds.squeeze(), gt_list_valid.squeeze()).mean().item()
+    kcc = metric_kcc(preds.squeeze(), gt_list_valid.squeeze()).mean().item()
+    return train_loss, mae, rse, pcc, kcc
\ No newline at end of file
diff --git a/uda.py b/uda.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f10a22c79113438dc41680c17569718c43059a5
--- /dev/null
+++ b/uda.py
@@ -0,0 +1,362 @@
+import argparse
+import json
+import logging
+import os
+import time
+import math
+from copy import deepcopy
+
+from dataset import PeptidePairCaseDataset, PeptidePairDataset, PeptidePairPicCaseDataset, PeptidePairPicDataset
+from network import DMutaPeptide, DMutaPeptideCNN
+from train import move_to_device, update_ce_loss_weight
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms.v2 as T
+from torch.utils.data import DataLoader, Subset, RandomSampler
+import numpy as np
+from loss import MLCE, SuperLoss, LogCoshLoss, BMCLoss
+from utils import set_seed, zip_restart_dataloader as zrd
+from random import shuffle
+from torchmetrics import MeanAbsoluteError, RelativeSquaredError, PearsonCorrCoef, KendallRankCorrCoef, Accuracy, F1Score, AveragePrecision, AUROC
+
+
+parser = argparse.ArgumentParser(description='resnet26')
+# model setting
+parser.add_argument('--model', type=str, default='resnet34',
+                    help='resnet34 resnet50 densenet')
+parser.add_argument('--q-encoder', dest='q_encoder', type=str, default='lstm',
+                    help='lstm mamba mla')
+parser.add_argument("--side-enc", dest='side_enc', type=str, default=None,
+                    help="use side features")
+parser.add_argument('--channels', type=int, default=256)
+parser.add_argument('--fusion', type=str, default='att',
+                    help='mlp att diff')
+parser.add_argument('--glob-feat', dest='glob_feat', action='store_true', default=False,
+                    help="use global features")
+parser.add_argument('--non-siamese', dest='non_siamese', action='store_true', default=False,
+                    help="use non-siamese architecture")
+
+# task & dataset setting
+parser.add_argument('--task', type=str, default='reg',
+                    help='reg or cls')
+parser.add_argument('--one-way', action='store_true', dest='one_way', default=False,
+                    help='use one-way constructed dataset')
+parser.add_argument('--max-length', dest='max_length', type=int, default=30,
+                    help='Max length for sequence filtering')
+parser.add_argument('--split', type=int, default=5,
+                    help="Split k fold in cross validation (default: 5)")
+parser.add_argument('--seed', type=int, default=42,
+                    help="Seed (default: 1)")
+parser.add_argument('--pcs', action='store_true', default=False,
+                    help='Consider protease cleavage site')
+parser.add_argument('--mix-pcs', dest='mix_pcs', action='store_true', default=False,
+                    help='Consider protease cleavage site')
+parser.add_argument('--resize', type=int, default=[768], nargs='+',
+                    help='resize the image')
+# parser.add_argument('--llm-data', action='store_true', default=False,
+#                     help='Use LLM augmentation data')
+
+# training setting
+parser.add_argument('--gpu', type=int, default=0,
+                    help='GPU index to use, -1 for CPU (default: 0)')
+parser.add_argument('--batch-size', type=int, dest='batch_size', default=32,
+                    help='input batch size for training (default: 128)')
+parser.add_argument('--epochs', type=int, default=50,
+                    help='number of epochs to train (default: 100)')
+parser.add_argument('--lr', type=float, default=0.001,
+                    help='learning rate (default: 0.001)')
+parser.add_argument('--decay', type=float, default=0.0005,
+                    help='weight decay (default: 0.0005)')
+parser.add_argument('--pretrain', type=str, dest='pretrain', default='',
+                    help='path of the pretrain model')
+parser.add_argument('--metric-avg', type=str, dest='metric_avg', default='macro',
+                    help='metric average type')
+
+parser.add_argument('--loss', type=str, default='mse',
+                    help='loss function')
+parser.add_argument('--dir', action='store_true', default=False,
+                    help='use DIR')
+
+parser.add_argument('--case', type=str, default='r2')
+parser.add_argument('--simple', action='store_true', default=False)
+parser.add_argument('--pt', action='store_true', default=False)
+parser.add_argument('--uda-epochs', '-ue', dest='uda_epochs', default=50)
+
+args = parser.parse_args()
+
+if args.simple:
+    args.one_way = True
+
+class GaussianNoise(nn.Module):
+    def __init__(self, mean=0., sigma=0.15):
+        super(GaussianNoise, self).__init__()
+        self.mean = mean
+        self.sigma = sigma
+
+    def forward(self, x):
+        return x + torch.randn_like(x) * self.sigma + self.mean
+
+# --------------------
+# 强/弱增强
+# --------------------
+strong_transforms = T.Compose([
+    T.RandomResizedCrop(args.resize, scale=(0.7, 1.0)),
+    T.RandomGrayscale(0.2),
+    GaussianNoise(0., 0.4),
+])
+weak_transforms = T.Compose([
+    T.RandomResizedCrop(args.resize, scale=(0.9, 1.0)),
+    GaussianNoise(0., 0.05),
+])
+
+def strong_aug(x, device=torch.device('cpu')):
+    return aug_and_move(x, strong_transforms, 0.2, device, True)
+
+def weak_aug(x, device=torch.device('cpu')):
+    return aug_and_move(x, weak_transforms, 0.05, device, True)
+
+def aug_and_move(x, transforms: T.Transform, seq_noise=0.05, device=torch.device('cpu'), non_blocking=False):
+    if isinstance(x, (tuple, list)):
+        return type(x)(aug_and_move(x_i, transforms, seq_noise, device, non_blocking) for x_i in x)
+    if len(x.shape) == 3:
+        return (x + torch.randn_like(x) * seq_noise).to(device, non_blocking=non_blocking)
+    else:
+        # return transforms(x.to(device, non_blocking=non_blocking))
+        return torch.stack([transforms(s) for s in x.to(device, non_blocking=non_blocking)], dim=0)
+
+# --------------------
+#  EMA 更新函数
+# --------------------
+def update_ema(student_model: nn.Module, teacher_model: nn.Module, alpha):
+    for s_param, t_param in zip(student_model.parameters(), teacher_model.parameters()):
+        t_param.data = t_param.data.mul_(alpha).add_(s_param.data, alpha=(1 - alpha))
+
+# --------------------
+#  学习率与一致性权重 ramp-up
+# --------------------
+def sigmoid_rampup(current, rampup_length):
+    if rampup_length == 0:
+        return 1.0
+    else:
+        current = max(0.0, min(1.0, current / rampup_length))
+        return float(math.exp(-5.0 * (1.0 - current) * (1.0 - current)))
+    
+# --------------------
+#  交叉熵一致性损失函数
+# --------------------
+def consistency_loss_ce(s_pred, t_pred, threshold=None):
+    """
+    s_pred: 学生模型 logits (B, C)
+    t_pred: 教师模型 logits (B, C)
+    mask: 可选的布尔 mask，True 表示该样本参与 loss 计算
+    """
+    # 1) 计算 teacher softmax 概率
+    probs = F.softmax(t_pred.detach(), dim=1)  # (B, C)
+    # 2) 生成伪标签
+    max_probs, pseudo_labels = probs.max(dim=1)  # (B,), (B,)
+    # 3) 可选：基于置信度做 mask
+    if threshold is None:
+        mask = torch.ones_like(max_probs, dtype=torch.float)
+    else:
+        mask = max_probs.ge(threshold).float()  # (B,) 0/1
+    # 4) 计算交叉熵，并按 mask 加权
+    loss = F.cross_entropy(s_pred, pseudo_labels, reduction='none')  # (B,)
+    loss = (loss * mask).sum() / (mask.sum().clamp(min=1.0))
+    return loss
+
+
+def main():
+    set_seed(args.seed)
+    if args.task == 'reg':
+        args.classes = 1
+        if args.loss == "mse" or args.loss in ['ce']:
+            args.loss = 'mse'
+            criterion = nn.MSELoss()
+            criterion_cons = criterion
+        else:
+            raise NotImplementedError("unimplemented regression task loss function")
+    elif args.task == 'cls':
+        args.classes = 2
+        if args.loss == 'ce' or args.loss in ['mse', 'smoothl1', 'super']:
+            args.loss = 'ce'
+            criterion = nn.CrossEntropyLoss()
+            criterion_cons = consistency_loss_ce
+            # criterion_cons = nn.MSELoss()
+        else:
+            raise NotImplementedError("unimplemented classification task loss function")
+    else:
+        raise NotImplementedError("unimplemented task")
+
+    if args.q_encoder in ['cnn', 'rn18']:
+        weight_dir = f'./run-{args.task}/{args.q_encoder}{"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{f"-{args.side_enc}" if args.side_enc else ""}{"-mixpcs" if args.mix_pcs else ""}{"-pcs" if args.pcs==True else ""}{"-simple" if args.simple else ""}{"-" + "x".join(str(n) for n in args.resize) if args.resize else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}/uda_{args.case}'
+    else:
+        weight_dir = f'./run-{args.task}/{args.q_encoder}{f"-non-siamese" if args.non_siamese else ""}-{args.fusion}-{args.channels}{"-simple" if args.simple else ""}{"-gf" if args.glob_feat else ""}{"-oneway" if args.one_way else ""}-{args.loss + "-dir" if args.dir else args.loss}-{str(args.batch_size)}-{str(args.lr)}-{str(args.epochs)}/uda_{args.case}'
+
+    if not os.path.exists(weight_dir):
+        os.makedirs(weight_dir)
+    
+    logging.basicConfig(handlers=[
+        logging.FileHandler(filename=os.path.join(weight_dir, "training.log"), encoding='utf-8', mode='w+'),
+        logging.StreamHandler()],
+        format="%(asctime)s: %(message)s", datefmt="%F %T", level=logging.INFO)
+
+    logging.info(f'saving_dir: {weight_dir}')
+    
+    with open(os.path.join(weight_dir, "config.json"), "w") as f:
+        f.write(json.dumps(vars(args)))
+
+    device = torch.device("cpu" if args.gpu == -1 or not torch.cuda.is_available() else f"cuda:{args.gpu}")
+
+    logging.info('Loading Training Dataset')
+    if args.q_encoder in ['cnn', 'rn18']:
+        unlabel_set = PeptidePairPicCaseDataset(case=args.case, pad_length=args.max_length, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize, gf=args.glob_feat)
+    else:
+        unlabel_set = PeptidePairCaseDataset(case=args.case, pad_length=args.max_length, gf=args.glob_feat)
+    unlabel_loader = DataLoader(unlabel_set, batch_size=args.batch_size // 2, shuffle=True, drop_last=True, num_workers=16,  pin_memory=True)
+
+    if args.q_encoder in ['cnn', 'rn18']:
+        label_set = PeptidePairPicDataset(mode='train', pad_length=args.max_length, task=args.task, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize, one_way=args.one_way, gf=args.glob_feat)
+    else:
+        label_set = PeptidePairDataset(mode='train', pad_length=args.max_length, task=args.task, one_way=args.one_way, gf=args.glob_feat)
+    label_loader = DataLoader(label_set, batch_size=args.batch_size // 2, shuffle=True, drop_last=True, num_workers=16, pin_memory=True)
+
+    if args.case == 'r2':
+        logging.info('Loading Validation Dataset')
+        if args.q_encoder in ['cnn', 'rn18']:
+            val_set = PeptidePairPicDataset(mode='r2_case', pad_length=args.max_length, task=args.task, side_enc=args.side_enc, pcs=args.pcs, resize=args.resize, gf=args.glob_feat)
+        else:
+            val_set = PeptidePairDataset(mode='r2_case', pad_length=args.max_length, task=args.task, gf=args.glob_feat)
+        val_loader = DataLoader(val_set, batch_size=args.batch_size * 2, shuffle=False, num_workers=16, pin_memory=True)
+        metric_funcs = get_metric_funcs(args.task, device)
+    else:
+        val_loader = None
+
+    best_val_metric = -float('inf')
+    logging.info(f"Start UDA training")
+    weights_path = f"{weight_dir}/model_uda_{'{role}'}.pth"
+
+    if args.q_encoder in ['cnn', 'rn18']:
+        student = DMutaPeptideCNN(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, side_enc=args.side_enc, fusion=args.fusion, non_siamese=args.non_siamese).to(device).train()
+    else:
+        student = DMutaPeptide(q_encoder=args.q_encoder, classes=args.classes, channels=args.channels, dir=args.dir, gf=args.glob_feat, fusion=args.fusion, non_siamese=args.non_siamese).to(device).train()
+    if args.pt:
+        student.load_state_dict(torch.load(os.path.join(os.path.dirname(weight_dir), 'model_0_test.pth'), map_location=device))
+    teacher = deepcopy(student).to(device).eval()
+    for param in teacher.parameters():
+        param.requires_grad = False
+    
+    optimizer = torch.optim.AdamW(student.parameters(), lr=1e-4)
+    
+    global_step = 0
+    rampup_length = 1500
+    for epoch in range(1, args.uda_epochs+1):
+        train_loss = []
+        for (x_l, y_l), (x_u, _) in zrd(label_loader, unlabel_loader):
+            x_l = move_to_device(x_l, device, non_blocking=True)
+            y_l = y_l.to(device, non_blocking=True)
+
+            # -- 1) 有监督损失 -- 
+            pred_l = student(x_l)
+            if args.loss == 'ce':
+                update_ce_loss_weight(criterion, y_l, num_classes=2, device=device)
+            loss_sup = criterion(pred_l, y_l)
+
+            # -- 2) 一致性损失 -- 
+            with torch.no_grad():
+                t_pred = teacher(weak_aug(x_u, device))
+
+            s_pred = student(strong_aug(x_u, device))
+
+            loss_cons = criterion_cons(s_pred, t_pred)
+            
+            # -- 3) 总损失 -- 
+            λ = 1.0 * sigmoid_rampup(global_step, rampup_length)
+            loss = loss_sup + λ * loss_cons
+
+            # -- 4) 学生模型更新 --
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            # -- 5) 教师 EMA 更新 -- 
+            alpha = 0.99
+            update_ema(student, teacher, alpha)
+
+            global_step += 1
+            train_loss.append(loss.item())
+        
+        train_loss = sum(train_loss) / len(train_loss)
+
+        if val_loader:
+            with torch.no_grad():
+                val_pred, val_gt = [], []
+                for x, gt in val_loader:
+                    x = move_to_device(x, device, non_blocking=True)
+                    out = teacher(x)
+                    # out = student(x)
+                    val_pred.append(out)
+                    val_gt.append(gt.to(device, non_blocking=True))
+                val_pred = torch.cat(val_pred, dim=0)
+                val_gt = torch.cat(val_gt, dim=0)
+
+            if args.task == 'cls':
+                val_pred = torch.softmax(val_pred, dim=1)[:, 1]
+                val_ap = metric_funcs['ap'](val_pred, val_gt).item()
+                val_auc = metric_funcs['auc'](val_pred, val_gt).item()
+                val_f1 = metric_funcs['f1'](val_pred, val_gt).item()
+                val_acc = metric_funcs['acc'](val_pred, val_gt).item()
+                val_metric = val_ap + val_auc
+                logging.info(f'Epoch {epoch} Train Loss: {train_loss:.4f} Val: ap: {val_ap:.4f} auc: {val_auc:.4f} f1: {val_f1:.4f} acc: {val_acc:.4f}')
+            elif args.task == 'reg':
+                val_mae = metric_funcs['mae'](val_pred, val_gt).item()
+                val_rse = metric_funcs['rse'](val_pred, val_gt).item()
+                val_pcc = metric_funcs['pcc'](val_pred, val_gt).item()
+                val_kcc = metric_funcs['kcc'](val_pred, val_gt).item()
+                val_metric = val_pcc + val_kcc - val_mae - val_rse
+                logging.info(f'Epoch {epoch} Train Loss: {train_loss:.4f} Val: mae: {val_mae:.4f} rse: {val_rse:.4f} pcc: {val_pcc:.4f} kcc: {val_kcc:.4f}')
+            else:
+                raise NotImplementedError
+
+            if val_metric > best_val_metric:
+                best_val_metric = val_metric
+                logging.info(f'Epoch: {epoch} New best VAL metrics')
+                torch.save(student.state_dict(), weights_path.format(role='student'))
+                torch.save(teacher.state_dict(), weights_path.format(role='teacher'))
+        else:
+            logging.info(f'Epoch {epoch} Train Loss: {train_loss:.4}')
+            if (args.task == 'reg' and train_loss > 0.199) or (args.task == 'cls' and train_loss > 0.259):
+                val_metric = -train_loss
+                if val_metric > best_val_metric:
+                    best_val_metric = val_metric
+                    torch.save(student.state_dict(), weights_path.format(role='student'))
+                    torch.save(teacher.state_dict(), weights_path.format(role='teacher'))
+            else:
+                break
+                
+    logging.info('UDA training finished')
+    torch.save(student.state_dict(), weights_path.format(role='student_last'))
+    torch.save(teacher.state_dict(), weights_path.format(role='teacher_last'))
+
+
+def get_metric_funcs(task, device):
+    if task == 'reg':
+        metric_funcs = {
+            'mae': MeanAbsoluteError().to(device),
+            'rse': RelativeSquaredError().to(device),
+            'pcc': PearsonCorrCoef().to(device),
+            'kcc': KendallRankCorrCoef().to(device)
+        }
+    elif task == 'cls':
+        metric_funcs = {
+            'ap': AveragePrecision(task='binary').to(device),
+            'auc': AUROC(task='binary').to(device),
+            'f1': F1Score(task='binary').to(device),
+            'acc': Accuracy(task='binary').to(device)
+        }
+    else:
+        raise NotImplementedError(f'Task {task} not supported')
+    return metric_funcs
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/uda_infer.sh b/uda_infer.sh
new file mode 100644
index 0000000000000000000000000000000000000000..6f17785dc43febd9e75e3fa02afd825eef03fc33
--- /dev/null
+++ b/uda_infer.sh
@@ -0,0 +1,9 @@
+#!/bin/bash
+python infer_case_uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --task reg --loss mse --case r2
+python infer_case_uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --task reg --loss mse --case Indolicidin
+python infer_case_uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --task reg --loss mse --case Temporin-A
+python infer_case_uda.py --q-encoder rn18 --channels 16 --side-enc mamba --fusion diff --pcs --task reg --loss mse --case Anoplin
+python infer_case_uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case r2
+python infer_case_uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Indolicidin
+python infer_case_uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Temporin-A
+python infer_case_uda.py --q-encoder rn18 --channels 16 --fusion att --simple --task cls --loss ce --case Anoplin
\ No newline at end of file
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..51b90810ba90d2485f1178b75f3884735c15ef5d
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,228 @@
+import random
+import torch
+import numpy as np
+import logging
+import torch.nn as nn
+import torch.nn.functional as F
+from scipy.ndimage import gaussian_filter1d
+from scipy.signal.windows import triang
+from typing import Iterator, Iterable, Tuple, Any
+
+def set_seed(seed):
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+
+def load_pretrain_model(net, weights):
+    net_keys = list(net.state_dict().keys())
+    weights_keys = list(weights.keys())
+    # assert(len(net_keys) <= len(weights_keys))
+    i = 0
+    j = 0
+    while i < len(net_keys) and j < len(weights_keys):
+        name_i = net_keys[i]
+        name_j = weights_keys[j]
+        if net.state_dict()[name_i].shape == weights[name_j].shape:
+            net.state_dict()[name_i].copy_(weights[name_j].cpu())
+            i += 1
+            j += 1
+        else:
+            i += 1
+    # print i, len(net_keys), j, len(weights_keys)
+    return net
+
+
+def calibrate_mean_var(matrix, m1, v1, m2, v2, clip_min=0.1, clip_max=10):
+    if torch.sum(v1) < 1e-10:
+        return matrix
+    if (v1 == 0.).any():
+        valid = (v1 != 0.)
+        factor = torch.clamp(v2[valid] / v1[valid], clip_min, clip_max)
+        matrix[:, valid] = (matrix[:, valid] - m1[valid]) * torch.sqrt(factor) + m2[valid]
+        return matrix
+
+    factor = torch.clamp(v2 / v1, clip_min, clip_max)
+    return (matrix - m1) * torch.sqrt(factor) + m2
+
+
+def zip_restart_dataloader(iter_a: Iterable, dataloader) -> Iterator[Tuple[Any, Any]]:
+    it_a = iter(iter_a)
+
+    def new_it_b():
+        # 每次需要时，重新创建一个 DataLoader 的迭代器（相当于新一轮“epoch”）
+        return iter(dataloader)
+
+    it_b = new_it_b()
+
+    while True:
+        try:
+            a = next(it_a)
+        except StopIteration:
+            return
+
+        try:
+            b = next(it_b)
+        except StopIteration:
+            it_b = new_it_b()
+            try:
+                b = next(it_b)
+            except StopIteration:
+                # DataLoader 为空的情况
+                raise ValueError("DataLoader 为空，无法配对")
+
+        yield a, b
+
+
+class FDS(nn.Module):
+
+    def __init__(self, feature_dim, bucket_num=100, bucket_start=7, start_update=0, start_smooth=1,
+                 kernel='gaussian', ks=5, sigma=2, momentum=0.9):
+        super(FDS, self).__init__()
+        self.feature_dim = feature_dim
+        self.bucket_num = bucket_num
+        self.bucket_start = bucket_start
+        self.kernel_window = self._get_kernel_window(kernel, ks, sigma)
+        self.half_ks = (ks - 1) // 2
+        self.momentum = momentum
+        self.start_update = start_update
+        self.start_smooth = start_smooth
+
+        self.register_buffer('epoch', torch.zeros(1).fill_(start_update))
+        self.register_buffer('running_mean', torch.zeros(bucket_num - bucket_start, feature_dim))
+        self.register_buffer('running_var', torch.ones(bucket_num - bucket_start, feature_dim))
+        self.register_buffer('running_mean_last_epoch', torch.zeros(bucket_num - bucket_start, feature_dim))
+        self.register_buffer('running_var_last_epoch', torch.ones(bucket_num - bucket_start, feature_dim))
+        self.register_buffer('smoothed_mean_last_epoch', torch.zeros(bucket_num - bucket_start, feature_dim))
+        self.register_buffer('smoothed_var_last_epoch', torch.ones(bucket_num - bucket_start, feature_dim))
+        self.register_buffer('num_samples_tracked', torch.zeros(bucket_num - bucket_start))
+
+    @staticmethod
+    def _get_kernel_window(kernel, ks, sigma):
+        assert kernel in ['gaussian', 'triang', 'laplace']
+        half_ks = (ks - 1) // 2
+        if kernel == 'gaussian':
+            base_kernel = [0.] * half_ks + [1.] + [0.] * half_ks
+            base_kernel = np.array(base_kernel, dtype=np.float32)
+            kernel_window = gaussian_filter1d(base_kernel, sigma=sigma) / sum(
+                gaussian_filter1d(base_kernel, sigma=sigma))
+        elif kernel == 'triang':
+            kernel_window = triang(ks) / sum(triang(ks))
+        else:
+            laplace = lambda x: np.exp(-abs(x) / sigma) / (2. * sigma)
+            kernel_window = list(map(laplace, np.arange(-half_ks, half_ks + 1))) / sum(
+                map(laplace, np.arange(-half_ks, half_ks + 1)))
+
+        logging.info(f'Using FDS: [{kernel.upper()}] ({ks}/{sigma})')
+        return torch.tensor(kernel_window, dtype=torch.float32).cuda()
+
+    def _get_bucket_idx(self, label):
+        label = np.float32(label.cpu())
+        return max(min(int(label * np.float32(10)), self.bucket_num - 1), self.bucket_start)
+
+    def _update_last_epoch_stats(self):
+        self.running_mean_last_epoch = self.running_mean
+        self.running_var_last_epoch = self.running_var
+
+        self.smoothed_mean_last_epoch = F.conv1d(
+            input=F.pad(self.running_mean_last_epoch.unsqueeze(1).permute(2, 1, 0),
+                        pad=(self.half_ks, self.half_ks), mode='reflect'),
+            weight=self.kernel_window.view(1, 1, -1), padding=0
+        ).permute(2, 1, 0).squeeze(1)
+        self.smoothed_var_last_epoch = F.conv1d(
+            input=F.pad(self.running_var_last_epoch.unsqueeze(1).permute(2, 1, 0),
+                        pad=(self.half_ks, self.half_ks), mode='reflect'),
+            weight=self.kernel_window.view(1, 1, -1), padding=0
+        ).permute(2, 1, 0).squeeze(1)
+
+        assert self.smoothed_mean_last_epoch.shape == self.running_mean_last_epoch.shape, \
+            "Smoothed shape is not aligned with running shape!"
+
+    def reset(self):
+        self.running_mean.zero_()
+        self.running_var.fill_(1)
+        self.running_mean_last_epoch.zero_()
+        self.running_var_last_epoch.fill_(1)
+        self.smoothed_mean_last_epoch.zero_()
+        self.smoothed_var_last_epoch.fill_(1)
+        self.num_samples_tracked.zero_()
+
+    def update_last_epoch_stats(self, epoch):
+        if epoch == self.epoch + 1:
+            self.epoch += 1
+            self._update_last_epoch_stats()
+            logging.info(f"Updated smoothed statistics of last epoch on Epoch [{epoch}]!")
+
+    def _running_stats_to_device(self, device):
+        if device == 'cpu':
+            self.num_samples_tracked = self.num_samples_tracked.cpu()
+            self.running_mean = self.running_mean.cpu()
+            self.running_var = self.running_var.cpu()
+        else:
+            self.num_samples_tracked = self.num_samples_tracked.cuda()
+            self.running_mean = self.running_mean.cuda()
+            self.running_var = self.running_var.cuda()
+
+    def update_running_stats(self, features, labels, epoch):
+        if epoch < self.epoch:
+            return
+
+        assert self.feature_dim == features.size(1), "Input feature dimension is not aligned!"
+        assert features.size(0) == labels.size(0), "Dimensions of features and labels are not aligned!"
+
+        self._running_stats_to_device('cpu')
+
+        labels = labels.unsqueeze(1).cpu()
+        labels = labels.squeeze(1).view(-1)
+
+        features = features.contiguous().view(-1, self.feature_dim)
+
+        buckets = np.array([self._get_bucket_idx(label) for label in labels])
+        for bucket in np.unique(buckets):
+            curr_feats = features[torch.tensor((buckets == bucket).astype(bool))]
+            curr_num_sample = curr_feats.size(0)
+            curr_mean = torch.mean(curr_feats, 0)
+            curr_var = torch.var(curr_feats, 0, unbiased=True if curr_feats.size(0) != 1 else False)
+
+            self.num_samples_tracked[bucket - self.bucket_start] += curr_num_sample
+            factor = self.momentum if self.momentum is not None else \
+                (1 - curr_num_sample / float(self.num_samples_tracked[bucket - self.bucket_start]))
+            factor = 0 if epoch == self.start_update else factor
+
+            # print(curr_mean.is_cuda)
+            self.running_mean[bucket - self.bucket_start] = \
+                (1 - factor) * curr_mean + factor * self.running_mean[bucket - self.bucket_start]
+            self.running_var[bucket - self.bucket_start] = \
+                (1 - factor) * curr_var + factor * self.running_var[bucket - self.bucket_start]
+
+        self._running_stats_to_device('cuda')
+        logging.info(f"Updated running statistics with Epoch [{epoch}] features!")
+
+    def smooth(self, features, labels, epoch):
+        if epoch < self.start_smooth:
+            return features
+
+        labels = labels.unsqueeze(1)
+        sp = labels.squeeze(1).shape
+
+        labels = labels.squeeze(1).view(-1)
+        features = features.contiguous().view(-1, self.feature_dim)
+
+        buckets = torch.max(torch.stack([torch.min(torch.stack([torch.floor(labels * torch.tensor([10.]).cuda()).int(),
+                                                                torch.zeros(labels.size(0)).fill_(
+                                                                    self.bucket_num - 1).int().cuda()], 0), 0)[0],
+                                         torch.zeros(labels.size(0)).fill_(self.bucket_start).int().cuda()], 0), 0)[0]
+        for bucket in torch.unique(buckets):
+            features[buckets.eq(bucket)] = calibrate_mean_var(
+                features[buckets.eq(bucket)],
+                self.running_mean_last_epoch[bucket.item() - self.bucket_start],
+                self.running_var_last_epoch[bucket.item() - self.bucket_start],
+                self.smoothed_mean_last_epoch[bucket.item() - self.bucket_start],
+                self.smoothed_var_last_epoch[bucket.item() - self.bucket_start]
+            )
+
+        return features.view(*sp, self.feature_dim)
diff --git a/vis/1_kcc_vis.svg b/vis/1_kcc_vis.svg
new file mode 100644
index 0000000000000000000000000000000000000000..66aa98f4e680e107ac61c403cfdf2a8be299f1f6
--- /dev/null
+++ b/vis/1_kcc_vis.svg
@@ -0,0 +1,2118 @@
+<?xml version="1.0" encoding="utf-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+  "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg xmlns:xlink="http://www.w3.org/1999/xlink" width="842.4pt" height="288pt" viewBox="0 0 842.4 288" xmlns="http://www.w3.org/2000/svg" version="1.1">
+ <metadata>
+  <rdf:RDF xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://creativecommons.org/ns#" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#">
+   <cc:Work>
+    <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
+    <dc:date>2025-07-26T13:59:33.139607</dc:date>
+    <dc:format>image/svg+xml</dc:format>
+    <dc:creator>
+     <cc:Agent>
+      <dc:title>Matplotlib v3.10.3, https://matplotlib.org/</dc:title>
+     </cc:Agent>
+    </dc:creator>
+   </cc:Work>
+  </rdf:RDF>
+ </metadata>
+ <defs>
+  <style type="text/css">*{stroke-linejoin: round; stroke-linecap: butt}</style>
+ </defs>
+ <g id="figure_1">
+  <g id="patch_1">
+   <path d="M 0 288 
+L 842.4 288 
+L 842.4 0 
+L 0 0 
+z
+" style="fill: #ffffff"/>
+  </g>
+  <g id="axes_1">
+   <g id="patch_2">
+    <path d="M 59.00625 240.60375 
+L 276.293864 240.60375 
+L 276.293864 29.04 
+L 59.00625 29.04 
+z
+" style="fill: #ffffff"/>
+   </g>
+   <g id="matplotlib.axis_1">
+    <g id="xtick_1">
+     <g id="line2d_1">
+      <path d="M 68.88296 240.60375 
+L 68.88296 29.04 
+" clip-path="url(#paef7272158)" style="fill: none; stroke: #cccccc; stroke-opacity: 0.3; stroke-linecap: round"/>
+     </g>
+     <g id="text_1">
+      <!-- −1.0 -->
+      <g style="fill: #262626" transform="translate(58.025616 257.977344) scale(0.11 -0.11)">
+       <defs>
+        <path id="ArialMT-2212" d="M 3381 1997 
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+      <!-- 0.0 -->
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+       <use xlink:href="#ArialMT-2e" transform="translate(55.615234 0)"/>
+       <use xlink:href="#ArialMT-30" transform="translate(83.398438 0)"/>
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+     <g id="text_24">
+      <!-- 0.5 -->
+      <g style="fill: #262626" transform="translate(422.375472 257.977344) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-30"/>
+       <use xlink:href="#ArialMT-2e" transform="translate(55.615234 0)"/>
+       <use xlink:href="#ArialMT-35" transform="translate(83.398438 0)"/>
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+     <g id="text_25">
+      <!-- 1.0 -->
+      <g style="fill: #262626" transform="translate(450.594642 257.977344) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-31"/>
+       <use xlink:href="#ArialMT-2e" transform="translate(55.615234 0)"/>
+       <use xlink:href="#ArialMT-30" transform="translate(83.398438 0)"/>
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+    <g id="xtick_14">
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+     <g id="text_26">
+      <!-- 1.5 -->
+      <g style="fill: #262626" transform="translate(478.813813 257.977344) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-31"/>
+       <use xlink:href="#ArialMT-2e" transform="translate(55.615234 0)"/>
+       <use xlink:href="#ArialMT-35" transform="translate(83.398438 0)"/>
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+      <!-- 2.0 -->
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+       <use xlink:href="#ArialMT-2e" transform="translate(55.615234 0)"/>
+       <use xlink:href="#ArialMT-30" transform="translate(83.398438 0)"/>
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+      <!-- 2.5 -->
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+       <use xlink:href="#ArialMT-2e" transform="translate(55.615234 0)"/>
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+    <g id="text_29">
+     <!-- True Log2 MIC Ratio -->
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+      <use xlink:href="#ArialMT-72" transform="translate(57.333984 0)"/>
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+      <use xlink:href="#ArialMT-4d" transform="translate(479.892578 0)"/>
+      <use xlink:href="#ArialMT-49" transform="translate(563.193359 0)"/>
+      <use xlink:href="#ArialMT-43" transform="translate(590.976562 0)"/>
+      <use xlink:href="#ArialMT-20" transform="translate(663.193359 0)"/>
+      <use xlink:href="#ArialMT-52" transform="translate(690.976562 0)"/>
+      <use xlink:href="#ArialMT-61" transform="translate(763.193359 0)"/>
+      <use xlink:href="#ArialMT-74" transform="translate(818.808594 0)"/>
+      <use xlink:href="#ArialMT-69" transform="translate(846.591797 0)"/>
+      <use xlink:href="#ArialMT-6f" transform="translate(868.808594 0)"/>
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+     <g id="text_30">
+      <!-- −1.0 -->
+      <g style="fill: #262626" transform="translate(304.271562 234.924013) scale(0.11 -0.11)">
+       <use xlink:href="#ArialMT-2212"/>
+       <use xlink:href="#ArialMT-31" transform="translate(58.398438 0)"/>
+       <use xlink:href="#ArialMT-2e" transform="translate(114.013672 0)"/>
+       <use xlink:href="#ArialMT-30" transform="translate(141.796875 0)"/>
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+      <!-- −0.5 -->
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+       <use xlink:href="#ArialMT-2212"/>
+       <use xlink:href="#ArialMT-30" transform="translate(58.398438 0)"/>
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+       <use xlink:href="#ArialMT-2e" transform="translate(55.615234 0)"/>
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+       <use xlink:href="#ArialMT-2e" transform="translate(55.615234 0)"/>
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+      <!-- 1.5 -->
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+       <use xlink:href="#ArialMT-2e" transform="translate(55.615234 0)"/>
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+      <use xlink:href="#ArialMT-50"/>
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diff --git a/vis/2cls_dataset.py b/vis/2cls_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce6058b3b40ed596631f683da681eec457e95e56
--- /dev/null
+++ b/vis/2cls_dataset.py
@@ -0,0 +1,91 @@
+import sys 
+sys.path.append("..") 
+from dataset import PeptidePairDataset, SimplePairClsDataset
+import matplotlib.pyplot as plt
+from matplotlib.patches import Patch
+import matplotlib.gridspec as gridspec
+import numpy as np
+
+def extract_label(dataset: PeptidePairDataset):
+    labels = []
+    for _, label in dataset:
+        labels.append(label)
+    return labels
+
+train_set = SimplePairClsDataset()
+train_set_llm = SimplePairClsDataset(llm=True)
+test_set = PeptidePairDataset(mode='test', task='cls')
+stablility_set_1 = PeptidePairDataset(mode='125fbs', task='cls')
+stablility_set_2 = PeptidePairDataset(mode='25fbs', task='cls')
+
+train_labels = extract_label(train_set)
+train_labels_llm = extract_label(train_set_llm)
+test_labels = extract_label(test_set)
+stability_labels_1 = extract_label(stablility_set_1)
+stability_labels_2 = extract_label(stablility_set_2)
+
+# 5个数据集和对应的名称
+datasets = [
+    ("Original", train_labels),
+    ("LLM Augmented", train_labels_llm),
+    ("MHB", test_labels),
+    ("12.5% FBS", stability_labels_1),
+    ("25% FBS", stability_labels_2),
+]
+
+# 定义扇区颜色，0为天蓝色，1为橙色
+colors = ["#BDD7EE", "#EBB4B1"]
+legend_labels = ["Not Improved", "Improved"]
+
+plt.rcParams.update({
+    'font.size': 12,
+    'axes.titlesize': 14,
+    'axes.labelsize': 14,
+    'xtick.labelsize': 13,
+    'ytick.labelsize': 13,
+    'legend.fontsize': 14,
+    'figure.titlesize': 18
+})
+
+def make_autopct(total):
+    def my_autopct(pct):
+        absolute = int(round(pct/100. * total))
+        return '{:.1f}%\n({:d})'.format(pct, absolute)
+    return my_autopct
+
+# 使用 GridSpec: 2 行 5 列，第一行用于组标题，第二行用于饼图
+fig = plt.figure(figsize=(10, 4))
+gs = gridspec.GridSpec(2, 5, height_ratios=[0.2, 1], hspace=0)
+
+# 第1行：组标题
+ax_train_title = fig.add_subplot(gs[0, 0:2])
+ax_train_title.axis('off')
+ax_train_title.set_title('Train Set', pad=10, fontsize=16)
+
+ax_test_title = fig.add_subplot(gs[0, 2:5])
+ax_test_title.axis('off')
+ax_test_title.set_title('Test Set', pad=10, fontsize=16)
+
+# 第2行：5 个饼图
+axes = [fig.add_subplot(gs[1, i]) for i in range(5)]
+for ax, (title, data) in zip(axes, datasets):
+    count_0 = data.count(0)
+    count_1 = data.count(1)
+    totals = [count_0, count_1]
+    total_num = sum(totals)
+    ax.pie(totals, colors=colors,
+           autopct=make_autopct(total_num),
+           startangle=90)
+    ax.set_title(title)
+    ax.axis('equal')  # 保证饼图为圆形
+
+# 共用图例
+patches = [Patch(color=colors[i], label=legend_labels[i]) for i in range(2)]
+fig.legend(handles=patches,
+           loc='lower center',
+           ncol=2,
+           bbox_to_anchor=(0.5, 0.01),
+           frameon=False)
+
+plt.tight_layout(rect=[0.015, 0.10, 0.985, 0.95])
+plt.show()
\ No newline at end of file
diff --git a/vis/2cls_dataset_stab.py b/vis/2cls_dataset_stab.py
new file mode 100644
index 0000000000000000000000000000000000000000..a244f259ee10caa1d277282122ddc4eaa67f783b
--- /dev/null
+++ b/vis/2cls_dataset_stab.py
@@ -0,0 +1,89 @@
+import sys 
+sys.path.append("..") 
+from dataset import PeptidePairDataset
+import matplotlib.pyplot as plt
+from matplotlib.patches import Patch
+import numpy as np
+
+def extract_label(dataset: PeptidePairDataset):
+    labels = []
+    for _, label in dataset:
+        labels.append(label)
+    return labels
+
+train_set_split1 = PeptidePairDataset(mode='train', pad_length=30, task='cls')
+train_set_split2 = PeptidePairDataset(mode='r2_case', pad_length=30, task='cls')
+# train_set_split3 = PeptidePairDataset(mode='125fbs', pad_length=30, task='cls')
+# train_set_split4 = PeptidePairDataset(mode='25fbs', pad_length=30, task='cls')
+# train_set_split5 = PeptidePairDataset(mode='val', fold=5, pad_length=30, task='cls')
+# test_set = PeptidePairDataset(mode='test', pad_length=30, task='cls')
+
+train_labels_split1 = extract_label(train_set_split1)
+train_labels_split2 = extract_label(train_set_split2)
+# train_labels_split3 = extract_label(train_set_split3)
+# train_labels_split4 = extract_label(train_set_split4)
+# train_labels_split5 = extract_label(train_set_split5)
+# test_labels = extract_label(test_set)
+
+# 假设以下6个列表均已定义，如同2cls_dataset.py中载入的数据：
+# train_labels_split1, train_labels_split2, train_labels_split3,
+# train_labels_split4, train_labels_split5, test_labels
+
+# 6个数据集和对应的名称
+datasets = [
+    ("Train set", train_labels_split1),
+    ("R2 test set", train_labels_split2),
+    # ("12.5% FBS", train_labels_split3),
+    # ("25% FBS", train_labels_split4),
+    # ("Train Set Split 5", train_labels_split5),
+    # ("Train Set", train_labels_split1 + train_labels_split2 + train_labels_split3 + train_labels_split4 + train_labels_split5),
+    # ("Test Set", test_labels)
+]
+
+# 定义扇区颜色，0为天蓝色，1为橙色
+colors = ["#BDD7EE", "#EBB4B1"]
+
+# 定义图例显示的标签
+legend_labels = ["Negative", "Positive"]
+
+plt.rcParams.update({
+    'font.size': 14,
+    'axes.titlesize': 14,
+    'axes.labelsize': 14,
+    'xtick.labelsize': 13,
+    'ytick.labelsize': 13,
+    'legend.fontsize': 14,
+    'figure.titlesize': 20
+})
+
+# 定制一个autopct函数工厂，可以显示百分比以及对应的绝对数量
+def make_autopct(total):
+    def my_autopct(pct):
+        absolute = int(round(pct/100. * total))
+        return '{:.1f}%\n({:d})'.format(pct, absolute)
+    return my_autopct
+
+# 创建1行6列的子图，调整图像尺寸
+fig, axes = plt.subplots(1, len(datasets), figsize=(len(datasets)*2.4, 3.2))
+
+for ax, (title, data) in zip(axes, datasets):
+    # 统计 0 和 1 的数量
+    count_0 = data.count(0)
+    count_1 = data.count(1)
+    totals = [count_0, count_1]
+    total_num = sum(totals)
+    # 绘制饼图，内部使用自定义的autopct函数显示百分比和数量
+    # startangle设为90，使得起始角度一致
+    ax.pie(totals, colors=colors, autopct=make_autopct(total_num), startangle=90)
+    ax.set_title(title, weight='bold', size=18)
+    ax.axis('equal')  # 保证饼图为圆形
+
+# 调整下方边缘，给图例留足空间
+
+# 创建共用图例：利用 Patch 构造图例句柄，并去除边框 (frameon=False)
+patches = [Patch(color=colors[i], label=legend_labels[i]) for i in range(2)]
+fig.legend(handles=patches, loc='lower center', ncol=2, bbox_to_anchor=(0.5, 0.01), frameon=False, markerscale=0.5)
+plt.tight_layout()
+plt.subplots_adjust(bottom=0.18)
+plt.savefig('cls_dataset_stab.svg')
+plt.show()
\ No newline at end of file
diff --git a/vis/auroc.py b/vis/auroc.py
new file mode 100644
index 0000000000000000000000000000000000000000..980a2a0abf2264a5154df9b3a1678324257671bc
--- /dev/null
+++ b/vis/auroc.py
@@ -0,0 +1,75 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics import roc_curve, precision_recall_curve, auc
+
+# 读取数据，数据包含预测概率（0-1）和真实标签（0或1），真实标签列名为 'gt'
+df = pd.read_csv('auroc_curves.csv')
+
+# 定义子图标题及每个子图需要绘制的列名
+titles = [
+    'Encoder Types', 
+    'Encoder Widths', 
+    'LLM Colabration'
+]
+cols = [
+    ['LSTM 256 MLP', 'LSTM 256 ATT', 'GRU 256 MLP', 'GRU 256 ATT', 'MHA 256 MLP', 'MHA 256 ATT', 'Mamba 256 MLP', 'Mamba 256 ATT'],
+    ['Mamba 128 ATT', 'Mamba 256 ATT', 'Mamba 512 ATT'],
+    ['Mamba 256 ATT', 'DS R1', 'DS R1 Mamba Fusion']
+]
+
+color_map = {
+    "LSTM 256 MLP": "#1f77b4",
+    "LSTM 256 ATT": "#665f88",
+    "GRU 256 MLP": "#1f4494",
+    "GRU 256 ATT": "#1f55a4",
+    "MHA 256 MLP": "#1f6684",
+    "MHA 256 ATT": "#1f88b4",
+    "MLA 256 MLP": "#1f99c4",
+    "MLA 256 ATT": "#1f8F74",
+    "Mamba 256 MLP": "#2ca02c",
+    "Mamba 256 ATT": "#FF5733",       # 突显：鲜艳的橙红色
+    "Mamba 128 ATT": "#9467bd",
+    "Mamba 512 ATT": "#8c564b",
+    "DS R1": "#e377c2",
+    "DS R1 Mamba Fusion": "#FF2222"   # 突显：醒目的深玫红色
+}
+
+# 创建三个子图，调整图形尺寸适合展示
+fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(16, 5))
+
+# 遍历每个子图与对应的标题、方法列数据
+for ax, title, methods in zip(axes, titles, cols):
+    for method in methods:
+        # 获取预测概率及真实标签
+        y_true = df['gt']
+        y_score = df[method]
+        # 计算 ROC 曲线以及 AUROC 值
+        fpr, tpr, thresholds = roc_curve(y_true, y_score, drop_intermediate=False)
+        auroc = auc(fpr, tpr)
+        
+        # 绘制 ROC 曲线，使用已分配的颜色，学术展示的曲线线宽合适
+        ax.plot(fpr, tpr, label=f"{method} AUC: {auroc:.2f}", 
+                color=color_map[method], lw=2, alpha=0.7)
+    
+    # 设置子图标题
+    ax.set_title(title, fontsize=18, weight='bold')
+    # 设置横纵坐标的标签
+    ax.set_xlabel("False Positive Rate", fontsize=14)
+    ax.set_ylabel("True Positive Rate", fontsize=14)
+    ax.yaxis.set_label_position("right")
+    
+    # 仅保留下边框和右边框, 去掉上边框和左边框
+    ax.spines['top'].set_visible(False)
+    ax.spines['left'].set_visible(False)
+    # 仅在底部和右侧显示刻度
+    ax.xaxis.set_ticks_position('bottom')
+    ax.yaxis.set_ticks_position('right')
+    
+    # 设置图例，放置在右下角
+    ax.legend(loc='lower right', fontsize=10, frameon=False, alignment='right', markerfirst=False)
+
+# 调整整个图形的布局，避免子图之间重叠
+plt.tight_layout(w_pad=5)
+plt.savefig('auroc.svg')
+plt.show()
\ No newline at end of file
diff --git a/vis/auroc.svg b/vis/auroc.svg
new file mode 100644
index 0000000000000000000000000000000000000000..4eb85c44eaa79560348694f24e04224ffc47d839
--- /dev/null
+++ b/vis/auroc.svg
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diff --git a/vis/auroc_1.py b/vis/auroc_1.py
new file mode 100644
index 0000000000000000000000000000000000000000..d26094dd73a3eb34d11ae7afcab067147aef70f5
--- /dev/null
+++ b/vis/auroc_1.py
@@ -0,0 +1,66 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics import roc_curve, precision_recall_curve, auc
+
+# 定义子图标题及每个子图需要绘制的列名
+titles = {
+    'R2': 'r2_case',
+    # 'MHB': 'test', 
+    # '12.5% FBS': '125fbs', 
+    # '25% FBS': '25fbs',
+}
+
+methods = {
+    'LSTM': "#665f88", 
+    'GRU': "#658f5f",
+    'MHA': "#5f858f",
+    'Mamba': "#AC9E53",
+    # 'CNN': "#1f6684", 
+    'GAT': "#15A9B1",
+    'GATv2': "#0EA488",
+    'ADAPT': "#FF5733",
+    'ADAPT-UDA': "#FF0000",
+}
+
+# 创建三个子图，调整图形尺寸适合展示
+fig, axes = plt.subplots(nrows=1, ncols=len(titles), figsize=(5*len(titles)+1, 5))
+if len(titles) == 1:
+    axes = [axes]
+
+# 遍历每个子图与对应的标题、方法列数据
+for ax, (title, alias) in zip(axes, titles.items()):
+    for method, color in methods.items():
+        # 获取预测概率及真实标签
+        df = pd.read_csv(f'./roc/{method}/preds_{alias}.csv')
+        y_true = df['gt']
+        y_score = df['fusion']
+        # 计算 ROC 曲线以及 AUROC 值
+        fpr, tpr, thresholds = roc_curve(y_true, y_score)
+        auroc = auc(fpr, tpr)
+        
+        # 绘制 ROC 曲线，使用已分配的颜色，学术展示的曲线线宽合适
+        ax.plot(fpr, tpr, label=f"{method} AUC: {auroc:.2f}", 
+                color=color, lw=2, alpha=0.7)
+    
+    # 设置子图标题
+    ax.set_title(title, fontsize=18, weight='bold')
+    # 设置横纵坐标的标签
+    ax.set_xlabel("False Positive Rate", fontsize=14)
+    ax.set_ylabel("True Positive Rate", fontsize=14)
+    ax.yaxis.set_label_position("right")
+    
+    # 仅保留下边框和右边框, 去掉上边框和左边框
+    ax.spines['top'].set_visible(False)
+    ax.spines['left'].set_visible(False)
+    # 仅在底部和右侧显示刻度
+    ax.xaxis.set_ticks_position('bottom')
+    ax.yaxis.set_ticks_position('right')
+    
+    # 设置图例，放置在右下角
+    ax.legend(loc='upper left', fontsize=10, frameon=False, alignment='right', markerfirst=False)
+
+# 调整整个图形的布局，避免子图之间重叠
+plt.tight_layout(w_pad=5)
+plt.savefig('auroc_1.svg')
+plt.show()
\ No newline at end of file
diff --git a/vis/auroc_1.svg b/vis/auroc_1.svg
new file mode 100644
index 0000000000000000000000000000000000000000..571570c116ff873ca661be21a4df0f13430a2f21
--- /dev/null
+++ b/vis/auroc_1.svg
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diff --git a/vis/auroc_2.py b/vis/auroc_2.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebc6b90267f1f1fa1abc53c9a0a99366944803c6
--- /dev/null
+++ b/vis/auroc_2.py
@@ -0,0 +1,114 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.metrics import roc_curve, precision_recall_curve, roc_auc_score, average_precision_score
+
+# 定义子图标题及每个子图需要绘制的列名
+titles = {
+    'R2': 'r2_case',
+    # 'MHB': 'test', 
+    # '12.5% FBS': '125fbs', 
+    # '25% FBS': '25fbs',
+}
+
+methods = {
+    'LSTM': "#665f88", 
+    'GRU': "#658f5f",
+    'MHA': "#5f858f",
+    'Mamba': "#AC9E53", 
+    'GAT': "#15A9B1",
+    'GATv2': "#0EA488",
+    # 'ADAPT': "#FF5733",
+    'ADAPT': "#FF0000",
+}
+
+folders = {
+    'LSTM': '../run-cls/lstm-diff-256-ce-32-0.001-50', 
+    'GRU': '../run-cls/gru-diff-256-ce-32-0.001-50',
+    'MHA': '../run-cls/mha-diff-256-ce-32-0.001-50',
+    'Mamba': '../run-cls/mamba-diff-256-ce-32-0.001-50',
+    'GAT': '../../DAminoMuta_graph/run-cls/gat-diff-256-ce-32-0.001-35',
+    'GATv2': '../../DAminoMuta_graph/run-cls/gatv2-diff-256-ce-32-0.001-35',
+    # 'ADAPT': '../run-cls/rn18-diff-16-768-ce-32-0.001-50',
+    'ADAPT': '../run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/uda_r2',
+}
+
+# 创建三个子图，调整图形尺寸适合展示
+fig, axes = plt.subplots(nrows=1, ncols=2*len(titles), figsize=(10.66*len(titles), 5))
+
+# 遍历每个子图与对应的标题、方法列数据
+for ax, (title, alias) in zip(axes[0::2], titles.items()):
+    for method, color in methods.items():
+        # 获取预测概率及真实标签
+        df = pd.read_csv(f'{folders[method]}/preds_{alias}.csv')
+        try:
+            y_score = np.concat([df[f'model_{i}_test'].values for i in range(5)], axis=0)
+            y_true = np.concat([df[f'gt'].values for _ in range(5)], axis=0)
+        except:
+            y_score = df[f'model_uda_teacher'].values
+            y_true = df[f'gt'].values
+        # 计算 ROC 曲线以及 AUROC 值
+        fpr, tpr, thresholds = roc_curve(y_true, y_score)
+        auroc = roc_auc_score(y_true, y_score)
+        
+        # 绘制 ROC 曲线，使用已分配的颜色，学术展示的曲线线宽合适
+        ax.plot(fpr, tpr, label=f"{method} AUC: {auroc:.2f}", 
+                color=color, lw=2, alpha=0.7)
+    
+    # 设置子图标题
+    ax.set_title(f'{title} ROC Curve', fontsize=18, weight='bold')
+    # 设置横纵坐标的标签
+    ax.set_xlabel("False Positive Rate", fontsize=14)
+    ax.set_ylabel("True Positive Rate", fontsize=14)
+    ax.yaxis.set_label_position("right")
+    
+    # 仅保留下边框和右边框, 去掉上边框和左边框
+    ax.spines['top'].set_visible(False)
+    ax.spines['left'].set_visible(False)
+    # 仅在底部和右侧显示刻度
+    ax.xaxis.set_ticks_position('bottom')
+    ax.yaxis.set_ticks_position('right')
+    
+    # 设置图例，放置在右下角
+    ax.legend(loc='lower right', fontsize=10, frameon=False, alignment='right', markerfirst=False)
+
+# 遍历每个子图与对应的标题、方法列数据
+for ax, (title, alias) in zip(axes[1::2], titles.items()):
+    for method, color in methods.items():
+        # 获取预测概率及真实标签
+        df = pd.read_csv(f'{folders[method]}/preds_{alias}.csv')
+        try:
+            y_score = np.concat([df[f'model_{i}_test'].values for i in range(5)], axis=0)
+            y_true = np.concat([df[f'gt'].values for _ in range(5)], axis=0)
+        except:
+            y_score = df[f'model_uda_teacher'].values
+            y_true = df[f'gt'].values
+        # 计算 ROC 曲线以及 AUPRC 值
+        pr, rc, thresholds = precision_recall_curve(y_true, y_score, drop_intermediate=True)
+        auprc = average_precision_score(y_true, y_score)
+        
+        # 绘制 PRC 曲线，使用已分配的颜色，学术展示的曲线线宽合适
+        ax.plot(rc, pr, label=f"{method} AUC: {auprc:.2f}", 
+                color=color, lw=2, alpha=0.7)
+    
+    # 设置子图标题
+    ax.set_title(f'{title} PR Curve', fontsize=18, weight='bold')
+    # 设置横纵坐标的标签
+    ax.set_xlabel("Recall", fontsize=14)
+    ax.set_ylabel("Precision", fontsize=14)
+    ax.yaxis.set_label_position("left")
+    
+    # 仅保留下边框和右边框, 去掉上边框和左边框
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    # 仅在底部和右侧显示刻度
+    ax.xaxis.set_ticks_position('bottom')
+    ax.yaxis.set_ticks_position('left')
+    
+    # 设置图例，放置在右下角
+    ax.legend(loc='upper right', fontsize=10, frameon=False, alignment='right', markerfirst=False)
+
+# 调整整个图形的布局，避免子图之间重叠
+plt.tight_layout(w_pad=5)
+plt.savefig('auroc_2.svg')
+plt.show()
\ No newline at end of file
diff --git a/vis/auroc_2.svg b/vis/auroc_2.svg
new file mode 100644
index 0000000000000000000000000000000000000000..ea78c661b270bf01b4947f59deff080ce8b00a42
--- /dev/null
+++ b/vis/auroc_2.svg
@@ -0,0 +1,3164 @@
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diff --git a/vis/auroc_upd.py b/vis/auroc_upd.py
new file mode 100644
index 0000000000000000000000000000000000000000..bec53c3fd498230deaa35b7d5b723c45263902bb
--- /dev/null
+++ b/vis/auroc_upd.py
@@ -0,0 +1,29 @@
+import pandas as pd
+
+df = pd.read_csv('./preds_results.csv')
+
+df['gt'] = pd.read_csv('../run-cls/gru-att-256-ce-256-0.001-50/preds.csv')['gt']
+df['GRU 256 ATT'] = pd.read_csv('../run-cls/gru-att-256-ce-256-0.001-50/preds.csv')['fusion']
+df['GRU 256 ATT GF'] = pd.read_csv('../run-cls/gru-att-256-gf-ce-256-0.001-50/preds.csv')['fusion']
+df['GRU 256 MLP'] = pd.read_csv('../run-cls/gru-mlp-256-ce-256-0.001-50/preds.csv')['fusion']
+df['GRU 256 MLP GF'] = pd.read_csv('../run-cls/gru-mlp-256-gf-ce-256-0.001-50/preds.csv')['fusion']
+df['MHA 256 ATT'] = pd.read_csv('../run-cls/mha-att-256-ce-256-0.001-50/preds.csv')['fusion']
+df['MHA 256 ATT GF'] = pd.read_csv('../run-cls/mha-att-256-gf-ce-256-0.001-50/preds.csv')['fusion']
+df['MHA 256 MLP'] = pd.read_csv('../run-cls/mha-mlp-256-ce-256-0.001-50/preds.csv')['fusion']
+df['MHA 256 MLP GF'] = pd.read_csv('../run-cls/mha-mlp-256-gf-ce-256-0.001-50/preds.csv')['fusion']
+df['LSTM 128 ATT'] = pd.read_csv('../run-cls/lstm-att-128-ce-256-0.001-50/preds.csv')['fusion']
+df['LSTM 256 ATT'] = pd.read_csv('../run-cls/lstm-att-256-ce-256-0.001-50/preds.csv')['fusion']
+df['LSTM 256 ATT GF'] = pd.read_csv('../run-cls/lstm-att-256-gf-ce-256-0.001-50/preds.csv')['fusion']
+df['LSTM 256 MLP'] = pd.read_csv('../run-cls/lstm-mlp-256-ce-256-0.001-50/preds.csv')['fusion']
+df['LSTM 256 MLP GF'] = pd.read_csv('../run-cls/lstm-mlp-256-gf-ce-256-0.001-50/preds.csv')['fusion']
+df['LSTM 512 ATT'] = pd.read_csv('../run-cls/lstm-att-512-ce-256-0.001-50/preds.csv')['fusion']
+df['Mamba 128 ATT'] = pd.read_csv('../run-cls/mamba-att-128-ce-256-0.001-50/preds.csv')['fusion']
+df['Mamba 256 ATT'] = pd.read_csv('../run-cls/mamba-att-256-ce-256-0.001-50/preds.csv')['fusion']
+df['Mamba 256 ATT GF'] = pd.read_csv('../run-cls/mamba-att-256-gf-ce-256-0.001-50/preds.csv')['fusion']
+df['Mamba 512 ATT'] = pd.read_csv('../run-cls/mamba-att-512-ce-256-0.001-50/preds.csv')['fusion']
+df['Mamba 256 MLP'] = pd.read_csv('../run-cls/mamba-mlp-256-ce-256-0.001-50/preds.csv')['fusion']
+df['Mamba 256 MLP GF'] = pd.read_csv('../run-cls/mamba-mlp-256-gf-ce-256-0.001-50/preds.csv')['fusion']
+df['DS R1'] = pd.read_csv('../dsr1/results_1_1.csv')['MIC_change']
+df['DS R1 Mamba Fusion'] = pd.read_csv('../dsr1/results_2_1.csv')['MIC_change']
+
+df.to_csv('./preds_results.csv', index=False)
\ No newline at end of file
diff --git a/vis/case_features.npz b/vis/case_features.npz
new file mode 100644
index 0000000000000000000000000000000000000000..9f0771cfc031acb8bdf0831a5ef8d982883d3b7c
--- /dev/null
+++ b/vis/case_features.npz
@@ -0,0 +1,3 @@
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diff --git a/vis/cls_dataset.svg b/vis/cls_dataset.svg
new file mode 100644
index 0000000000000000000000000000000000000000..99259d124c3a1914aa71b2817b172bed3fa9d842
--- /dev/null
+++ b/vis/cls_dataset.svg
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+     <use xlink:href="#DejaVuSans-38" x="63.623047"/>
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diff --git a/vis/compute_umap_3d.py b/vis/compute_umap_3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ee7e64cf6e31207b5f60057eae380042c0596e7
--- /dev/null
+++ b/vis/compute_umap_3d.py
@@ -0,0 +1,66 @@
+# -*- coding: utf-8 -*-
+import torch
+import numpy as np
+import pandas as pd
+from umap import UMAP
+
+def get_label_dict():
+    """
+    返回一个字典 { 描述(str): 标签(int, 0 或 1) }
+    """
+    df = pd.read_excel('../dataset/r2_case.xlsx')
+    df = df[~pd.isna(df['TAG'])]
+    tags = {}
+    for _, row in df.iterrows():
+        desc = row['SEQUENCE - D-type amino acid substitution']
+        tag = row['TAG']
+        if tag.lower() == 'improved':
+            tags[desc] = 1
+        elif tag.lower() == 'not improved':
+            tags[desc] = 0
+    return tags
+
+def get_score_dict():
+    """
+    返回 { 描述: Float in [0,1] }，用于背景点的颜色映射
+    """
+    df = pd.read_csv('./preds_case.csv')
+    desc = df['seq'].values
+    scores = df['pred'].values
+    mn, mx = scores.min(), scores.max()
+    scores = (scores - mn) / (mx - mn)
+    return { d: s for d, s in zip(desc, scores) }
+
+# —— 1. 加载特征 —— #
+data_dict = torch.load('features.pth', map_location='cpu')
+descs = list(data_dict.keys())
+features = np.vstack([
+    data_dict[d].cpu().numpy() if isinstance(data_dict[d], torch.Tensor)
+    else np.array(data_dict[d])
+    for d in descs
+])
+print(f"共 {features.shape[0]} 个样本，特征维度 {features.shape[1]}")
+
+# —— 2. 计算 label 和 score —— #
+label_dict = get_label_dict()
+score_dict = get_score_dict()
+
+# —— 3. 用 UMAP 降到 3 维 —— #
+umap_3d = UMAP(n_components=3, metric='euclidean')
+points_3d = umap_3d.fit_transform(features)
+
+# —— 4. 构造 DataFrame —— #
+df = pd.DataFrame({
+    'desc': descs,
+    'x': points_3d[:,0],
+    'y': points_3d[:,1],
+    'z': points_3d[:,2],
+    # label: 0, 1，缺失为 NaN
+    'label': [ label_dict.get(d, np.nan) for d in descs ],
+    # score: 背景点着色用，缺失为 NaN
+    'score': [ score_dict.get(d, np.nan) for d in descs ],
+})
+
+# —— 5. 保存 —— #
+df.to_csv('umap_3d_points.csv', index=False)
+print("已保存 umap_3d_points.csv，包含 desc, x, y, z, label, score")
\ No newline at end of file
diff --git a/vis/dataset.pdf b/vis/dataset.pdf
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diff --git a/vis/dataset_more.py b/vis/dataset_more.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a8f2cd34c5c45a5ada90747b4aaad836dff6851
--- /dev/null
+++ b/vis/dataset_more.py
@@ -0,0 +1,213 @@
+import sys
+sys.path.append("..")
+from dataset import load_data
+from Bio.pairwise2 import align
+
+
+def calculate_similarity_score(seq_gen, seq_tem):
+    """
+    对两个序列执行全局比对，并返回归一化得分（得分/序列长度）
+    """
+    score = align.globalxx(seq_gen, seq_tem)[0].score
+    return score
+def read_data():
+    data = load_data("../dataset/train.xlsx")
+    for k, v in data.items():
+        data[k] = [i for i in v.keys() if i is not k]
+    return data
+
+
+# visualize_peptide_dataset.py
+import os
+from collections import Counter, defaultdict
+from joblib import Parallel, delayed
+import numpy as np
+import matplotlib.pyplot as plt
+
+def extract_templates_and_variants(data_dict):
+    """
+    data_dict: {template_L: [variant_with_D1, variant_with_D2, ...]}
+    Returns:
+        templates: list of template sequences (uppercase L-type)
+        variants_map: dict template -> list of variant sequences (may contain lowercase d-residues)
+    """
+    templates = list(data_dict.keys())
+    variants_map = data_dict
+    return templates, variants_map
+
+def compute_length_distribution(templates):
+    lengths = [len(t) for t in templates]
+    return lengths
+
+def compute_amino_acid_composition(templates):
+    """
+    Returns:
+        aa_list_sorted: list of amino acids sorted by frequency desc
+        freqs_sorted: corresponding normalized frequencies
+    Note: only uppercase letters are expected in templates.
+    """
+    counter = Counter()
+    total = 0
+    for t in templates:
+        counter.update(t)
+        total += len(t)
+    if total == 0:
+        return [], []
+
+    # Normalize to frequencies
+    aa_freq = {aa: cnt / total for aa, cnt in counter.items()}
+
+    # Sort by frequency desc, then alphabetically for stability
+    aa_list_sorted = sorted(aa_freq.keys(), key=lambda x: (-aa_freq[x], x))
+    freqs_sorted = [aa_freq[aa] for aa in aa_list_sorted]
+    return aa_list_sorted, freqs_sorted
+
+def compute_d_substitution_counts(templates, variants_map):
+    """
+    For each variant of a template, count number of D-type residues (lowercase letters).
+    Returns:
+        d_counts: list of int counts across all variants of all templates.
+    """
+    d_counts = []
+    for tpl in templates:
+        variants = variants_map.get(tpl, [])
+        for v in variants:
+            d_counts.append(sum(1 for c in v if c.islower()))
+    return d_counts
+
+def compute_intra_dataset_diversity(templates, n_jobs=-1):
+    """
+    For each template, compute the maximum similarity to any other template.
+    Similarity uses calculate_similarity(seq_gen, seq_tem) from dataset_more.py,
+    which normalizes by len(seq_gen). Here we follow your definition and
+    treat seq_gen as the query template and seq_tem as the other template.
+    Returns:
+        max_sims: list of floats, one per template.
+
+    Memory-efficient version with normalization by query length.
+    Maintains consistency with original calculate_similarity behavior.
+    """
+    n = len(templates)
+    if n <= 1:
+        return [1.0] * n
+    
+    # 生成所有需要计算的(i,j)对
+    pairs = [(i, j) for i in range(n) for j in range(i + 1, n)]
+    
+    # 并行计算相似度分数（未归一化）
+    similarities = Parallel(n_jobs=n_jobs)(
+        delayed(calculate_similarity_score)(templates[i], templates[j])
+        for i, j in pairs
+    )
+    
+    # 对每个模板，收集所有相关的相似度分数
+    # 需要分别归一化（因为查询序列不同时分母不同）
+    max_sims = [-np.inf] * n
+    
+    for (i, j), sim_score in zip(pairs, similarities):
+        # 当 i 是查询序列时，归一化
+        sim_i_query = sim_score / len(templates[i])
+        max_sims[i] = max(max_sims[i], sim_i_query)
+        
+        # 当 j 是查询序列时，归一化
+        sim_j_query = sim_score / len(templates[j])
+        max_sims[j] = max(max_sims[j], sim_j_query)
+    
+    return max_sims
+
+def choose_bins(data, kind="auto", min_bins=10, max_bins=50):
+    """
+    Robust bin selection for histograms.
+    kind="auto" uses numpy's auto strategy but clip within [min_bins, max_bins].
+    """
+    if not data:
+        return 10
+    n = len(data)
+    # Freedman–Diaconis rule as a guide
+    data_arr = np.asarray(data)
+    q75, q25 = np.percentile(data_arr, [75, 25])
+    iqr = max(q75 - q25, 1e-9)
+    bin_width = 2 * iqr / (len(data_arr) ** (1 / 3))
+    if bin_width <= 0:
+        bins = min(max_bins, max(min_bins, int(np.sqrt(n))))
+    else:
+        bins = int(np.ceil((data_arr.max() - data_arr.min()) / bin_width))
+        bins = max(min_bins, min(max_bins, bins))
+    return bins
+
+def plot_distributions(templates, variants_map, figsize=(10, 12), save_path=None, show=True):
+    # Compute metrics
+    lengths = compute_length_distribution(templates)
+    aa_list, aa_freqs = compute_amino_acid_composition(templates)
+    d_counts = compute_d_substitution_counts(templates, variants_map)
+    max_sims = compute_intra_dataset_diversity(templates)
+
+    # Prepare figure with 4 vertically stacked subplots
+    plt.figure(figsize=figsize)
+    plt.style.use('seaborn-v0_8-whitegrid')
+    plt.subplots_adjust(hspace=0.5)
+
+    # 1) Length distribution (histogram)
+    plt.subplot(2, 2, 1)
+    if lengths:
+        bins_len = choose_bins(lengths)
+        plt.hist(lengths, bins=bins_len, color="#4C78A8", edgecolor="white")
+    else:
+        plt.hist([], bins=10, color="#4C78A8", edgecolor="white")
+    plt.title("Length Distribution", fontsize=16, fontweight='bold')
+    plt.xlabel("Length", fontsize=14)
+    plt.ylabel("Count", fontsize=14)
+
+    # 2) Amino acid composition distribution (sorted descending)
+    plt.subplot(2, 2, 2)
+    if aa_list:
+        # Use histogram-like bar chart since composition is categorical
+        x = np.arange(len(aa_list))
+        plt.bar(x, aa_freqs, color="#F58518", edgecolor="white")
+        plt.xticks(x, aa_list, fontsize=9)
+    else:
+        plt.bar([], [])
+    plt.title("Amino Acid Composition", fontsize=16, fontweight='bold')
+    plt.xlabel("Amino Acid", fontsize=14)
+    plt.ylabel("Frequency", fontsize=14)
+
+    # 3) D-substitution count distribution (across variants)
+    plt.subplot(2, 2, 3)
+    if d_counts:
+        bins_d = np.arange(0, max(d_counts) + 2) - 0.5  # centered integer bins
+        plt.hist(d_counts, bins=bins_d, color="#E45756", edgecolor="white")
+        plt.xticks(range(0, max(d_counts) + 1, 2))
+    else:
+        plt.hist([], bins=np.arange(-0.5, 1.5), color="#E45756", edgecolor="white")
+        plt.xticks([0])
+    plt.xlabel("Number of D substitutions per parent", fontsize=14)
+    plt.ylabel("Count", fontsize=14)
+    plt.title("D-type Substitution Count", fontsize=16, fontweight='bold')
+
+    # 4) Intra-dataset diversity: distribution of max similarity to others
+    plt.subplot(2, 2, 4)
+    if max_sims:
+        bins_sim = min(40, max(10, int(len(max_sims) ** 0.5)))
+        # Similarity can be negative with mismatch penalties; show full range
+        plt.hist(max_sims, bins=bins_sim, color="#72B7B2", edgecolor="white")
+        plt.xlim(min(max_sims) - 0.05, max(max_sims) + 0.05)
+    else:
+        plt.hist([], bins=10, color="#72B7B2", edgecolor="white")
+    plt.xlabel("Max similarity to other templates", fontsize=14)
+    plt.ylabel("Count", fontsize=14)
+    plt.title("Intra-dataset Diversity", fontsize=16, fontweight='bold')
+
+    if save_path:
+        plt.savefig(save_path, bbox_inches="tight")
+    if show:
+        plt.show()
+    plt.close()
+
+def main():
+    # Read data using your provided function
+    data = read_data()
+    templates, variants_map = extract_templates_and_variants(data)
+    plot_distributions(templates, variants_map, figsize=(14, 7), save_path="dataset_more.svg", show=True)
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/vis/dataset_more.svg b/vis/dataset_more.svg
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diff --git a/vis/dataset_vis.py b/vis/dataset_vis.py
new file mode 100644
index 0000000000000000000000000000000000000000..58ed0618465c98ee1a88d02af8fb568af413971e
--- /dev/null
+++ b/vis/dataset_vis.py
@@ -0,0 +1,183 @@
+import sys 
+sys.path.append("..") 
+from dataset import PeptidePairDataset
+from torch.utils.data import Subset
+from sklearn.model_selection import KFold
+import matplotlib.pyplot as plt
+import numpy as np
+
+def extract_label(dataset: PeptidePairDataset):
+    labels = []
+    for _, label in dataset:
+        labels.append(label)
+    return labels
+
+def extract_labels_cls(dataset: PeptidePairDataset):
+    labels = []
+    for _, label in dataset:
+        if label >= 0.5:
+            labels.append(2)
+        elif label <= -0.5:
+            labels.append(1)
+        else:
+            labels.append(0)
+    return labels
+
+all_set = PeptidePairDataset(mode='train', pad_length=30, task='reg')
+test_set = PeptidePairDataset(mode='r2_case', pad_length=30, task='reg', one_way=True)
+
+# kf = KFold(n_splits=5, shuffle=True, random_state=42)
+# train_idx, val_idx = next(kf.split(all_set))
+# train_set = Subset(all_set, train_idx)
+# valid_set = Subset(all_set, val_idx)
+
+bins = np.arange(-6.5, 6.6, 0.5)
+
+def plot_gradient_bar_histogram(ax, data, bins, cmap_name='Blues'):
+    """使用渐变配色方案绘制直方图，每个柱子颜色不同但连续，并在柱子上添加数值标注"""
+    # 计算直方图数据
+    counts, bin_edges = np.histogram(data, bins=bins)
+    n_bins = len(counts)
+    # 获取 colormap
+    cmap = plt.get_cmap(cmap_name)
+    width = np.diff(bin_edges)[0]  # 本例中每个柱子的宽度均为固定值
+    # 绘制每个柱子，并添加数值标注
+    for i in range(n_bins):
+        # 归一化的颜色比例
+        if n_bins > 1:
+            ratio = (i / (n_bins - 1)) * 0.5 + 0.3
+        else:
+            ratio = 0.5
+        color = cmap(ratio)
+        # 绘制柱子
+        ax.bar(bin_edges[i], counts[i], width=width, align='edge',
+               color=color, zorder=2)
+        # 为非空柱子添加数值标注
+        if counts[i] != 0:
+            x_pos = bin_edges[i] + width / 2
+            y_pos = counts[i]
+            ax.text(
+                x_pos, y_pos, f'{counts[i]}', 
+                ha='center', va='bottom', fontweight='bold', fontsize=9
+                )
+    return counts, bin_edges
+
+def plot_gradient_bar_histogram_cls(ax, data, cmap_name='Blues'):
+    """将类别数据统计后绘制直方图，并添加数值标注，类别固定为 [0, 1, 2]"""
+    # 固定类别顺序
+    classes = np.array([0, 1, 2])
+    counts = np.array([data.count(cls) for cls in classes])
+    num_classes = len(classes)
+    width = 0.6
+    cmap = plt.get_cmap(cmap_name)
+    for i, cls in enumerate(classes):
+        # 渐变配色比例
+        ratio = (i / (num_classes - 1)) * 0.5 + 0.3 if num_classes > 1 else 0.5
+        color = cmap(ratio)
+        ax.bar(cls, counts[i], width=width, color=color, zorder=2)
+        if counts[i] != 0:
+            ax.text(cls, counts[i], f'{counts[i]}', ha='center', va='bottom', fontweight='bold')
+    ax.set_xticks(classes)
+    ax.set_xticklabels(['Unchanged', 'Lower', 'Higher'])
+    return counts
+
+# # 设置全局字体大小
+# plt.rcParams.update({
+#     'font.size': 10,
+#     'axes.titlesize': 14,
+#     'axes.labelsize': 14,
+#     'xtick.labelsize': 13,
+#     'ytick.labelsize': 13,
+#     'legend.fontsize': 14,
+#     'figure.titlesize': 16
+# })
+plt.style.use('seaborn-v0_8-whitegrid')
+
+# 创建 3 行 2 列子图，第一列为回归数据的可视化，第二列为分类数据的可视化
+# fig, axes = plt.subplots(3, 2, figsize=(12, 9), width_ratios=[2.5, 1])
+fig, axes = plt.subplots(1, 2, figsize=(11.4, 3))
+
+# 数据提取：回归数据
+reg_train = extract_label(all_set)
+# reg_val = extract_label(valid_set)
+reg_test = extract_label(test_set)
+
+# 数据提取：分类数据
+# cls_train = extract_labels_cls(all_set)
+# cls_val = extract_labels_cls(valid_set)
+# cls_test = extract_labels_cls(test_set)
+
+# 第一行：训练集
+counts, _ = plot_gradient_bar_histogram(axes[0], reg_train, bins, cmap_name='Blues')
+axes[0].set_xlabel('log2 (MIC ratio)', size=14)
+axes[0].set_xlim(-6.5, 6.5)
+axes[0].set_ylabel('Counts', size=14)
+axes[0].set_ylim(0, max(counts)*1.15)
+axes[0].set_title(f'Train Set ({len(reg_train)})', weight='bold', size=16)
+
+# counts_cls = plot_gradient_bar_histogram_cls(axes[0, 1], cls_train, cmap_name='Blues')
+# axes[0, 1].set_xlabel('Class')
+# axes[0, 1].set_xlim(-0.5, 2.5)
+# axes[0, 1].set_ylabel('Counts')
+# axes[0, 1].set_ylim(0, max(counts_cls)*1.15)
+# axes[0, 1].set_title(f'Train Set (Cls.) ({len(cls_train)})', weight='bold')
+
+# 第二行：验证集
+counts, _ = plot_gradient_bar_histogram(axes[1], reg_test, bins, cmap_name='Blues')
+axes[1].set_xlabel('log2 (MIC ratio)', size=14)
+axes[1].set_xlim(-6.5, 6.5)
+axes[1].set_ylabel('Counts', size=14)
+axes[1].set_ylim(0, max(counts)*1.15)
+axes[1].set_title(f'R2 Set ({len(reg_test)})', weight='bold', size=16)
+
+# counts_cls = plot_gradient_bar_histogram_cls(axes[1, 1], cls_val, cmap_name='Blues')
+# axes[1, 1].set_xlabel('Class')
+# axes[1, 1].set_xlim(-0.5, 2.5)
+# axes[1, 1].set_ylabel('Counts')
+# axes[1, 1].set_ylim(0, max(counts_cls)*1.15)
+# axes[1, 1].set_title(f'Validation Set (Cls.) ({len(cls_val)})', weight='bold')
+
+# # 第三行：测试集
+# counts, _ = plot_gradient_bar_histogram(axes[2, 0], reg_test, bins, cmap_name='Blues')
+# axes[2, 0].set_xlabel('log2 (MIC ratio)')
+# axes[2, 0].set_xlim(-6.5, 6.5)
+# axes[2, 0].set_ylabel('Counts')
+# axes[2, 0].set_ylim(0, max(counts)*1.15)
+# axes[2, 0].set_title(f'Test Set (Reg.) ({len(reg_test)})', weight='bold')
+
+# counts_cls = plot_gradient_bar_histogram_cls(axes[2, 1], cls_test, cmap_name='Blues')
+# axes[2, 1].set_xlabel('Class')
+# axes[2, 1].set_xlim(-0.5, 2.5)
+# axes[2, 1].set_ylabel('Counts')
+# axes[2, 1].set_ylim(0, max(counts_cls)*1.15)
+# axes[2, 1].set_title(f'Test Set (Cls.) ({len(cls_test)})', weight='bold')
+
+plt.tight_layout(w_pad=3.2)
+plt.savefig('dataset.svg')
+plt.show()
+
+# 输出数据集统计信息（回归数据）
+print("\n训练集回归统计信息:")
+print(f"样本总数: {len(reg_train)}")
+print(f"范围: {min(reg_train)} - {max(reg_train)}")
+
+# print("\n验证集回归统计信息:")
+# print(f"样本总数: {len(reg_val)}")
+# print(f"范围: {min(reg_val)} - {max(reg_val)}")
+
+print("\n测试集回归统计信息:")
+print(f"样本总数: {len(reg_test)}")
+print(f"范围: {min(reg_test)} - {max(reg_test)}")
+
+# 输出数据集统计信息（分类数据）
+# print("\n训练集分类统计信息:")
+# print(f"样本总数: {len(cls_train)}")
+# print(f"各类别统计: 0类: {cls_train.count(0)}, 1类: {cls_train.count(1)}, 2类: {cls_train.count(2)}")
+
+# print("\n验证集分类统计信息:")
+# print(f"样本总数: {len(cls_val)}")
+# print(f"各类别统计: 0类: {cls_val.count(0)}, 1类: {cls_val.count(1)}, 2类: {cls_val.count(2)}")
+
+# print("\n测试集分类统计信息:")
+# print(f"样本总数: {len(cls_test)}")
+# print(f"各类别统计: 0类: {cls_test.count(0)}, 1类: {cls_test.count(1)}, 2类: {cls_test.count(2)}")
\ No newline at end of file
diff --git a/vis/features.pth b/vis/features.pth
new file mode 100644
index 0000000000000000000000000000000000000000..5f0f10862d40f9b468f103761161ee1bbedfccbd
--- /dev/null
+++ b/vis/features.pth
@@ -0,0 +1,3 @@
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+size 8988678
diff --git a/vis/kcc/sfda.csv b/vis/kcc/sfda.csv
new file mode 100644
index 0000000000000000000000000000000000000000..a31025efeadee625e7ea727fad16a1814dc2756e
--- /dev/null
+++ b/vis/kcc/sfda.csv
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diff --git a/vis/kcc/uda.csv b/vis/kcc/uda.csv
new file mode 100644
index 0000000000000000000000000000000000000000..e1b78bb39ae00d4ebbf6b87932bb0e6b662ce1cf
--- /dev/null
+++ b/vis/kcc/uda.csv
@@ -0,0 +1,16384 @@
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diff --git a/vis/kcc/vanilla.csv b/vis/kcc/vanilla.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cc22023176e45a7a0a3513e43511f6950b69ab4f
--- /dev/null
+++ b/vis/kcc/vanilla.csv
@@ -0,0 +1,16384 @@
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diff --git a/vis/kcc_upd.py b/vis/kcc_upd.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb40ecdbf96812f13b70a127860627f9d566977b
--- /dev/null
+++ b/vis/kcc_upd.py
@@ -0,0 +1,31 @@
+import pandas as pd
+
+df = pd.DataFrame()
+
+df['gt'] = pd.read_csv('../run-reg/gru-att-256-super-256-0.001-50/preds.csv')['gt']
+df['GRU 256 ATT'] = pd.read_csv('../run-reg/gru-att-256-super-256-0.001-50/preds.csv')['fusion']
+# df['GRU 128 ATT GF'] = pd.read_csv('../run-reg/gru-att-128-gf-super-256-0.001-50/preds.csv')['fusion']
+df['GRU 256 ATT GF'] = pd.read_csv('../run-reg/gru-att-256-gf-super-256-0.001-50/preds.csv')['fusion']
+# df['GRU 512 ATT GF'] = pd.read_csv('../run-reg/gru-att-512-gf-super-256-0.001-50/preds.csv')['fusion']
+df['GRU 256 MLP'] = pd.read_csv('../run-reg/gru-mlp-256-super-256-0.001-50/preds.csv')['fusion']
+df['GRU 256 MLP GF'] = pd.read_csv('../run-reg/gru-mlp-256-gf-super-256-0.001-50/preds.csv')['fusion']
+df['MHA 256 ATT'] = pd.read_csv('../run-reg/mha-att-256-super-256-0.001-50/preds.csv')['fusion']
+df['MHA 256 ATT GF'] = pd.read_csv('../run-reg/mha-att-256-gf-super-256-0.001-50/preds.csv')['fusion']
+df['MHA 256 MLP'] = pd.read_csv('../run-reg/mha-mlp-256-super-256-0.001-50/preds.csv')['fusion']
+df['MHA 256 MLP GF'] = pd.read_csv('../run-reg/mha-mlp-256-gf-super-256-0.001-50/preds.csv')['fusion']
+df['LSTM 128 ATT'] = pd.read_csv('../run-reg/lstm-att-128-super-256-0.001-50/preds.csv')['fusion']
+df['LSTM 256 ATT'] = pd.read_csv('../run-reg/lstm-att-256-super-256-0.001-50/preds.csv')['fusion']
+df['LSTM 256 ATT GF'] = pd.read_csv('../run-reg/lstm-att-256-gf-super-256-0.001-50/preds.csv')['fusion']
+df['LSTM 256 MLP'] = pd.read_csv('../run-reg/lstm-mlp-256-super-256-0.001-50/preds.csv')['fusion']
+df['LSTM 256 MLP GF'] = pd.read_csv('../run-reg/lstm-mlp-256-gf-super-256-0.001-50/preds.csv')['fusion']
+df['LSTM 512 ATT'] = pd.read_csv('../run-reg/lstm-att-512-super-256-0.001-50/preds.csv')['fusion']
+df['Mamba 128 ATT'] = pd.read_csv('../run-reg/mamba-att-128-super-256-0.001-50/preds.csv')['fusion']
+df['Mamba 256 ATT'] = pd.read_csv('../run-reg/mamba-att-256-super-256-0.001-50/preds.csv')['fusion']
+df['Mamba 256 ATT GF'] = pd.read_csv('../run-reg/mamba-att-256-gf-super-256-0.001-50/preds.csv')['fusion']
+df['Mamba 512 ATT'] = pd.read_csv('../run-reg/mamba-att-512-super-256-0.001-50/preds.csv')['fusion']
+df['Mamba 256 MLP'] = pd.read_csv('../run-reg/mamba-mlp-256-super-256-0.001-50/preds.csv')['fusion']
+df['Mamba 256 MLP GF'] = pd.read_csv('../run-reg/mamba-mlp-256-gf-super-256-0.001-50/preds.csv')['fusion']
+# df['DS R1'] = pd.read_csv('../dsr1/results_1_1.csv')['MIC_change']
+# df['DS R1 Mamba Fusion'] = pd.read_csv('../dsr1/results_2_1.csv')['MIC_change']
+
+df.to_csv('./preds_regs.csv', index=False)
\ No newline at end of file
diff --git a/vis/kcc_vis.py b/vis/kcc_vis.py
new file mode 100644
index 0000000000000000000000000000000000000000..bf76eba54648b72d376967e16e2321b8a54b0ba3
--- /dev/null
+++ b/vis/kcc_vis.py
@@ -0,0 +1,74 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import pearsonr, kendalltau
+import seaborn as sns
+
+
+def get_label_dict():
+    """
+    返回一个字典
+    """
+    df = pd.read_excel('../dataset/r2_case.xlsx')
+    df = df[~pd.isna(df['MIC GM (ug/ml)'])]
+    values = {}
+    for _, row in df.iterrows():
+        desc = row['SEQUENCE - D-type amino acid substitution']
+        value = row['MIC GM (ug/ml)']
+        values[desc] = value
+    template = list(values.keys())[0].upper()
+    t_value = values[template]
+    tags = {}
+    for mutation, value in values.items():
+        if mutation != template:
+            tags[mutation] = np.log2(value / t_value)
+        
+    return tags
+
+# 设置学术风格
+sns.set_theme(style="whitegrid")
+
+predicted_csv_files = ["vanilla.csv", "sfda.csv", "uda.csv"]  # 预测值CSV文件列表
+titles = ["Vanilla", "SFDA", "UDA"]
+
+# 读取真值
+true_data = get_label_dict()
+
+# 创建子图
+fig, axes = plt.subplots(1, len(predicted_csv_files), figsize=(3.9 * len(predicted_csv_files), 4), sharex=True, sharey=True)
+if len(predicted_csv_files) == 1:
+    axes = [axes]  # 确保 axes 是可迭代的
+
+# 遍历预测值文件并绘制子图
+for i, csv_file in enumerate(predicted_csv_files):
+    # 读取当前预测值
+    pred_pd = pd.read_csv(f'./kcc/{csv_file}', index_col='seq')
+    true_values, predicted_values, seqs = [], [], []
+    for seq, true_value in true_data.items():
+        true_values.append(true_value)
+        predicted_values.append(pred_pd.loc[seq]['pred'])
+        seqs.append(seq)
+    
+    # 计算PCC
+    corr_p, _ = pearsonr(true_values, predicted_values)
+    corr_k, _ = kendalltau(true_values, predicted_values)
+
+    
+    # 绘制散点图
+    ax = axes[i]
+    ax.plot([-1, 2.5], [-1, 2.5], color='gray', linestyle='--', linewidth=1)
+    ax.scatter(true_values, predicted_values, alpha=0.7)
+    ax.set_title(titles[i], fontsize=14)
+    ax.set_xlabel("True Log2 MIC Ratio", fontsize=12)
+    ax.set_xticks(np.arange(-1, 2.6, 0.5))
+    ax.set_ylabel("Predicted Log2 MIC Ratio", fontsize=12)
+    ax.set_yticks(np.arange(-1, 2.6, 0.5))
+    ax.tick_params(labelleft=True)
+    ax.text(0.40, 0.03, f'PCC = {corr_p:.3f}\nKCC = {corr_k:.3f}', transform=ax.transAxes, fontsize=12, color='darkred')
+    ax.grid(alpha=0.3)
+
+# 调整布局和显示
+plt.subplots_adjust(wspace=0.5)
+plt.tight_layout()
+plt.savefig('1_kcc_vis.svg')
+plt.show()
\ No newline at end of file
diff --git a/vis/plot_umap_3d.py b/vis/plot_umap_3d.py
new file mode 100644
index 0000000000000000000000000000000000000000..570de2e4bf053f6fcc5799fee168750dd6137841
--- /dev/null
+++ b/vis/plot_umap_3d.py
@@ -0,0 +1,67 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.colors import LinearSegmentedColormap
+from mpl_toolkits.mplot3d import Axes3D  # 注册 3D 投影
+
+# —— 1. 读取坐标和元数据 —— #
+df = pd.read_csv('umap_3d_points.csv')
+
+# —— 2. 划分三类 —— #
+idx1     = df.index[df['label'] == 1].tolist()
+idx0     = df.index[df['label'] == 0].tolist()
+idx_rest = df.index[df['label'].isna()].tolist()
+
+# —— 3. 渐变色 —— #
+cmap = LinearSegmentedColormap.from_list('bg_cmap', ['#87CEEB', '#FFDAB9'])
+bg_scores = df.loc[idx_rest, 'score'].fillna(0.5).values
+bg_colors = cmap(bg_scores)
+
+# —— 4. 绘图 —— #
+fig = plt.figure(figsize=(8,8))
+ax = fig.add_subplot(111, projection='3d')
+
+# 背景点
+ax.scatter(
+    df.loc[idx_rest,'x'],
+    df.loc[idx_rest,'y'],
+    df.loc[idx_rest,'z'],
+    c=bg_colors,
+    s=20,
+    alpha=0.06,
+    label='Background'
+)
+
+# 标签 0
+ax.scatter(
+    df.loc[idx0,'x'],
+    df.loc[idx0,'y'],
+    df.loc[idx0,'z'],
+    c='#006EFF',
+    s=60,
+    alpha=1.0,
+    label='Not Improved'
+)
+
+# 标签 1
+ax.scatter(
+    df.loc[idx1,'x'],
+    df.loc[idx1,'y'],
+    df.loc[idx1,'z'],
+    c='orange',
+    s=60,
+    alpha=1.0,
+    label='Improved'
+)
+
+ax.set_title('3D UMAP Visualization', fontsize=18)
+# ax.set_axis_off()
+ax.set_xticklabels([])
+ax.set_yticklabels([])
+ax.set_zticklabels([])
+ax.legend(loc='upper left', frameon=False, fontsize=12)
+plt.tight_layout()
+plt.show()  # 或者显示
+# plt.savefig('umap_3d.svg')
+# print("已保存 umap_3d.svg")
\ No newline at end of file
diff --git a/vis/preds.csv b/vis/preds.csv
new file mode 100644
index 0000000000000000000000000000000000000000..62fbc21c5b30e37da166ee436d07c24cf932b6f5
--- /dev/null
+++ b/vis/preds.csv
@@ -0,0 +1,93 @@
+,1,2,3,4,5,mean,gt
+0,-0.20641445,0.30561072,-0.14634767,0.45302135,0.87514204,0.2562024,0.067495674
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+3,-0.34278163,1.5180869,0.1239786,-0.53526133,0.4522978,0.24326405,-1.3657606
+4,-0.20837265,0.34230676,0.58524877,-0.14128882,0.5457845,0.22473569,-0.18577212
+5,-1.0231782,-0.861493,1.5785507,0.5396397,-0.19868985,0.0069658696,0.0
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diff --git a/vis/preds_case.csv b/vis/preds_case.csv
new file mode 100644
index 0000000000000000000000000000000000000000..395aa0bca9ebcfa515c541e647fa9fa9efdd6c89
--- /dev/null
+++ b/vis/preds_case.csv
@@ -0,0 +1,16384 @@
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diff --git a/vis/preds_regs.csv b/vis/preds_regs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..23f7a69d7ce1d50759fcab654688f7abca5b628a
--- /dev/null
+++ b/vis/preds_regs.csv
@@ -0,0 +1,93 @@
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diff --git a/vis/preds_results.csv b/vis/preds_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1f13b1fc3c2b4d34330ca8ac493916865c9c8ff5
--- /dev/null
+++ b/vis/preds_results.csv
@@ -0,0 +1,93 @@
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new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..ce775a99f8356690ac6d9072b0502bba8ea555d0
--- /dev/null
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new file mode 100644
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diff --git a/vis/roc/ADAPT/preds_125fbs.csv b/vis/roc/ADAPT/preds_125fbs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ed7596739ecc1a20c2554456de501bf86338355e
--- /dev/null
+++ b/vis/roc/ADAPT/preds_125fbs.csv
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diff --git a/vis/roc/ADAPT/preds_25fbs.csv b/vis/roc/ADAPT/preds_25fbs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..1c288faeca860ab162366d5bc1e819fda07d5e76
--- /dev/null
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diff --git a/vis/roc/ADAPT/preds_r2_case.csv b/vis/roc/ADAPT/preds_r2_case.csv
new file mode 100644
index 0000000000000000000000000000000000000000..607fe4e30494638655de941aa7690df8cf2cc262
--- /dev/null
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diff --git a/vis/roc/ADAPT/preds_test.csv b/vis/roc/ADAPT/preds_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..43d1e177240c8ca7015b7556b0f70813038ee74c
--- /dev/null
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diff --git a/vis/roc/CNN/preds_25fbs.csv b/vis/roc/CNN/preds_25fbs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..bda5987fa6b329c516b71478da8dfb023f60fc46
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diff --git a/vis/roc/CNN/preds_test.csv b/vis/roc/CNN/preds_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2e9c380214c1618a2478123af89ec338b21e1ab8
--- /dev/null
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diff --git a/vis/roc/GAT/preds_125fbs.csv b/vis/roc/GAT/preds_125fbs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..38201dc77f829ea28cce60da34b2e569e189aed7
--- /dev/null
+++ b/vis/roc/GAT/preds_125fbs.csv
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diff --git a/vis/roc/GAT/preds_25fbs.csv b/vis/roc/GAT/preds_25fbs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..2a3db07d7aca53813061000b583b9533e434266a
--- /dev/null
+++ b/vis/roc/GAT/preds_25fbs.csv
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new file mode 100644
index 0000000000000000000000000000000000000000..35da93502ff739994b3f8a6fd8b804418caeffae
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diff --git a/vis/roc/GAT/preds_test.csv b/vis/roc/GAT/preds_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..79e7dffd558283275628d16db1ed7513551aa4b4
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..c30b60125c43771dfef2a94c300b9211405e8c42
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diff --git a/vis/roc/GATv2/preds_25fbs.csv b/vis/roc/GATv2/preds_25fbs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0b6fc194d545e5f347db7f90aeaefc5639cec40a
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..07638adec1641d9911cf32c878a8dbe03f319aed
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..17d14175a5fe5d62b4b7b0d12981af73d936b135
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diff --git a/vis/roc/GRU/preds_125fbs.csv b/vis/roc/GRU/preds_125fbs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..bd712488940fdbcc394920313ac7b810f188fe9c
--- /dev/null
+++ b/vis/roc/GRU/preds_125fbs.csv
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diff --git a/vis/roc/GRU/preds_25fbs.csv b/vis/roc/GRU/preds_25fbs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..21bfb4b9bed51cb68788e9350b8897871c28ad4d
--- /dev/null
+++ b/vis/roc/GRU/preds_25fbs.csv
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diff --git a/vis/roc/GRU/preds_r2_case.csv b/vis/roc/GRU/preds_r2_case.csv
new file mode 100644
index 0000000000000000000000000000000000000000..3834244561cb860556096565bc6adafcc6547cff
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diff --git a/vis/roc/GRU/preds_test.csv b/vis/roc/GRU/preds_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7748e4156fe36e1ac090d0d226ce8ad9e7de6cf3
--- /dev/null
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diff --git a/vis/roc/LSTM/preds_test.csv b/vis/roc/LSTM/preds_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..27a5f6c0ff93d4620ec270474a3992252ef76607
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new file mode 100644
index 0000000000000000000000000000000000000000..59826b642135949507aa5f298b0acf4d4adc77ed
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new file mode 100644
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new file mode 100644
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diff --git a/vis/roc/Mamba/preds_125fbs.csv b/vis/roc/Mamba/preds_125fbs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0360ed272d2a90296b0936208963fdedb45ad73f
--- /dev/null
+++ b/vis/roc/Mamba/preds_125fbs.csv
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diff --git a/vis/roc/Mamba/preds_25fbs.csv b/vis/roc/Mamba/preds_25fbs.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7ffc8904de8b384fe9cc6751848a24bf637bb633
--- /dev/null
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diff --git a/vis/roc/Mamba/preds_r2_case.csv b/vis/roc/Mamba/preds_r2_case.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4e6963101e066254343b5458e9879e309dcf7a66
--- /dev/null
+++ b/vis/roc/Mamba/preds_r2_case.csv
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diff --git a/vis/roc/Mamba/preds_test.csv b/vis/roc/Mamba/preds_test.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6502917635418e7a17a1683a233d4f04d968e952
--- /dev/null
+++ b/vis/roc/Mamba/preds_test.csv
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diff --git a/vis/similarity_r2.py b/vis/similarity_r2.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd09826688d820fe20f06f56a23936286ba59911
--- /dev/null
+++ b/vis/similarity_r2.py
@@ -0,0 +1,80 @@
+import sys
+sys.path.append("..")
+from dataset import load_data
+from Bio.pairwise2 import align
+
+
+def calculate_similarity(seq_gen, seq_tem):
+    """
+    对两个序列执行全局比对，并返回归一化得分（得分/序列长度）
+    """
+    score = align.globalxx(seq_gen, seq_tem)[0].score
+    return score / len(seq_gen)
+
+data = load_data("../dataset/train.xlsx")
+
+sims = {}
+for seq in data.keys():
+    sims[seq] = calculate_similarity(seq, 'KWKIKWPVKWFKML')
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# 将相似度结果转换为数组
+sim_values = np.array(list(sims.values()))
+
+# 基本信息
+print(f"样本数: {len(sim_values)}")
+print(f"均值: {sim_values.mean():.4f}, 中位数: {np.median(sim_values):.4f}, 标准差: {sim_values.std(ddof=1):.4f}")
+print(f"最小值: {sim_values.min():.4f}, 最大值: {sim_values.max():.4f}")
+
+# 找到相似度前10的序列
+sorted_sims = sorted(sims.items(), key=lambda x: x[1], reverse=True)[:10]
+
+# 打印结果
+print("相似度前10的序列:")
+for seq, sim in sorted_sims:
+    print(f"序列: {seq}, 相似度: {sim:.4f}, 样本数: {len(data[seq])}")
+
+# 设定风格
+sns.set_theme(style="whitegrid", context="talk")
+
+# 绘图
+fig, ax = plt.subplots(figsize=(10, 5), dpi=150)
+
+# 直方图 + KDE
+sns.histplot(
+    sim_values,
+    bins=30,              # 可根据样本量调整，如 'auto' 或 20、40
+    kde=True,             # 叠加核密度
+    stat="density",       # y 轴为密度，便于与 KDE 对齐
+    color="#4C78A8",
+    edgecolor="white",
+    alpha=0.8,
+    ax=ax
+)
+
+# 均值与中位数参考线
+mean_val = sim_values.mean()
+median_val = np.median(sim_values)
+# ax.axvline(mean_val, color="#E45756", linestyle="--", linewidth=2, label=f"均值: {mean_val:.3f}")
+# ax.axvline(median_val, color="#72B7B2", linestyle="-.", linewidth=2, label=f"中位数: {median_val:.3f}")
+
+# 轴标签与标题
+ax.set_title("Similarity Distribution to R2", pad=12, fontsize=16, fontweight='bold')
+ax.set_xlabel("Similarity", fontsize=14)
+ax.set_ylabel("Density", fontsize=14)
+
+# x 轴范围（如已知相似度范围约在 [-1, 1] 或 [0, 1] 可手动设置）
+# ax.set_xlim(0, 1)
+
+# 网格与图例
+ax.grid(True, linestyle=":", linewidth=0.8, alpha=0.6)
+# ax.legend()
+
+# 紧凑布局与保存
+plt.tight_layout()
+plt.savefig("similarity_r2.svg")
+plt.show()
\ No newline at end of file
diff --git a/vis/similarity_r2.svg b/vis/similarity_r2.svg
new file mode 100644
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--- /dev/null
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diff --git a/vis/test_features.npz b/vis/test_features.npz
new file mode 100644
index 0000000000000000000000000000000000000000..4215a7e933f8118699ba78e275af51f441cb6298
--- /dev/null
+++ b/vis/test_features.npz
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+version https://git-lfs.github.com/spec/v1
+oid sha256:f189f72e29aa617735f19f65c0da99a86a7d152a23326aa20fce4a676677a634
+size 9484
diff --git a/vis/tsne.py b/vis/tsne.py
new file mode 100644
index 0000000000000000000000000000000000000000..d5480c6bbfa047c829d04f348c7c80b3ac6cbf1d
--- /dev/null
+++ b/vis/tsne.py
@@ -0,0 +1,107 @@
+import torch
+import numpy as np
+import pandas as pd
+from sklearn.manifold import TSNE
+import matplotlib.pyplot as plt
+
+# —— 0. 定义或导入你的标签获取函数 —— #
+def get_label_dict():
+    """
+    返回一个字典 { 描述(str): 标签(int, 0 或 1) }
+    比如:
+      return {
+        "image_0001": 1,
+        "image_0234": 0,
+        # ...
+      }
+    """
+    df = pd.read_excel('../dataset/r2_case.xlsx')
+    df = df[~pd.isna(df['TAG'])]
+
+    tags = {}
+
+    for _, row in df.iterrows():
+        desc = row['SEQUENCE - D-type amino acid substitution']
+        tag = row['TAG']
+        if tag.lower() == 'improved':
+            tags[desc] = 1
+        elif tag.lower() == 'not improved':
+            tags[desc] = 0
+
+    return tags
+
+# —— 1. 加载你的特征字典 —— #
+data_dict = torch.load('features.pth', map_location='cpu')
+descs = list(data_dict.keys())
+features = []
+for d in descs:
+    feat = data_dict[d]
+    if isinstance(feat, torch.Tensor):
+        feat = feat.cpu().numpy()
+    else:
+        feat = np.array(feat)
+    features.append(feat)
+features = np.vstack(features)   # shape = (N, 64)
+print(f"Loaded {features.shape[0]} samples, each dim = {features.shape[1]}")
+
+# —— 2. 拿到标签 —— #
+label_dict = get_label_dict()    # {desc: 0 or 1}
+
+# —— 3. t-SNE 降到 2 维 —— #
+tsne = TSNE(n_components=2,
+            init='pca',
+            random_state=42,
+            learning_rate='auto',
+            perplexity=30,
+            n_iter=1000)
+points_2d = tsne.fit_transform(features)  # shape = (N,2)
+
+# —— 4. 根据标签分组索引 —— #
+idx1 = [i for i,d in enumerate(descs) if d in label_dict and label_dict[d]==1]
+idx0 = [i for i,d in enumerate(descs) if d in label_dict and label_dict[d]==0]
+idx_rest = [i for i,d in enumerate(descs) if d not in label_dict]
+
+# —— 5. 绘图 —— #
+plt.figure(figsize=(8,8))
+
+# 背景点云
+plt.scatter(points_2d[idx_rest,0],
+            points_2d[idx_rest,1],
+            s=5,
+            alpha=0.3,
+            color='skyblue',
+            label='others')
+
+# 标签=0 的点
+plt.scatter(points_2d[idx0,0],
+            points_2d[idx0,1],
+            s=30,
+            alpha=0.8,
+            color='blue',
+            label='label 0')
+
+# 标签=1 的点
+plt.scatter(points_2d[idx1,0],
+            points_2d[idx1,1],
+            s=40,
+            alpha=0.9,
+            color='orange',
+            label='label 1')
+
+# # 对 label=0/1 的点添加文字标注
+# for i in idx0 + idx1:
+#     plt.annotate(
+#         descs[i],
+#         (points_2d[i,0], points_2d[i,1]),
+#         textcoords='offset points',
+#         xytext=(2,2),
+#         fontsize=8,
+#         color='black'
+#     )
+
+plt.title('t-SNE Visualization')
+plt.axis('off')
+plt.legend(loc='upper right')
+plt.tight_layout()
+plt.savefig('tsne_highlight.png', dpi=300)
+plt.show()
\ No newline at end of file
diff --git a/vis/tsne_highlight.png b/vis/tsne_highlight.png
new file mode 100644
index 0000000000000000000000000000000000000000..061fb1b148a5da0d2bc3b54ee8ac194ebe2850da
--- /dev/null
+++ b/vis/tsne_highlight.png
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+oid sha256:27bd1b44496b0c491c74db72c3c7b09bb7ba4c4b2c5aaaa5ded352a231efdbf1
+size 1680632
diff --git a/vis/tsne_pointcloud.png b/vis/tsne_pointcloud.png
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--- /dev/null
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diff --git a/vis/umap_after.png b/vis/umap_after.png
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diff --git a/vis/umap_before.png b/vis/umap_before.png
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index 0000000000000000000000000000000000000000..0858e1f3805e9c93edcf1c19750c2595323a8472
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+++ b/vis/umap_before.png
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+version https://git-lfs.github.com/spec/v1
+oid sha256:89b23450f644b96fcb818bfb7ba8bbf768416bbc54a29ff7cc210128d4d628f1
+size 113737
diff --git a/vis/umap_features.pth b/vis/umap_features.pth
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index 0000000000000000000000000000000000000000..2031fb31923562e70685715864fae6a80ab82b15
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+oid sha256:1d77eaa8c8a7323500aeebcfc2255962b7822f08058a1eb1ed3d1e9dd9180bc0
+size 18324306
diff --git a/vis/umap_highlight.png b/vis/umap_highlight.png
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+++ b/vis/umap_highlight.png
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+oid sha256:389bd91de17f1d89ab5290274abe53d390595727fe732867b0427f7de32c43e9
+size 1783499
diff --git a/vis/umap_plot copy.py b/vis/umap_plot copy.py
new file mode 100644
index 0000000000000000000000000000000000000000..e50630c346d02a33e804540df71f52c8da5992d9
--- /dev/null
+++ b/vis/umap_plot copy.py	
@@ -0,0 +1,85 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import umap
+
+# ---------- 配置参数 ----------
+file1 = "test_features.npz"   # 第一个 npz 文件路径
+file2 = "case_features.npz"   # 第二个 npz 文件路径
+
+titles = ["Before Finetune", "After Finetune"]  # 子图标题，可按需修改
+
+# UMAP 参数
+umap_kwargs = dict(n_components=2, random_state=465, metric="euclidean")
+
+# 颜色与图例标签映射
+color_map = {0: "#81B9E9", 1: "#EBB4B1"}
+label_map = {0: "Not Improved", 1: "Improved"}
+
+# ---------- 加载数据 ----------
+data1 = np.load(file1)
+feats1 = data1["features"]    # shape (n1, d)
+gts1   = data1["gts"].ravel() # shape (n1,)
+
+data2 = np.load(file2)
+feats2 = data2["features"]    # shape (n2, d)
+gts2   = data2["gts"].ravel() # shape (n2,)
+
+# # 合并用于统一 fit
+# feats_all = np.vstack([feats1, feats2])
+# n1 = feats1.shape[0]
+
+# # ---------- 同一空间 UMAP 降维 ----------
+# reducer = umap.UMAP(**umap_kwargs)
+# emb_all = reducer.fit_transform(feats_all)  # shape (n1+n2, 2)
+# emb1 = emb_all[:n1]
+# emb2 = emb_all[n1:]
+
+reducer = umap.UMAP(**umap_kwargs)
+emb1 = reducer.fit_transform(feats1)
+emb2 = reducer.fit_transform(feats2)
+
+# # 确定统一的坐标范围
+# x_min, x_max = emb_all[:, 0].min(), emb_all[:, 0].max()
+# y_min, y_max = emb_all[:, 1].min(), emb_all[:, 1].max()
+
+# ---------- 绘图 ----------
+fig, axes = plt.subplots(1, 2, figsize=(8, 5), sharex=False, sharey=False)
+fig.suptitle(" R2 UMAP Visualization", fontsize=26)
+
+for ax, emb, gts, title in zip(axes, (emb1, emb2), (gts1, gts2), titles):
+    for cls in (0, 1):
+        mask = (gts == cls)
+        ax.scatter(
+            emb[mask, 0], emb[mask, 1],
+            c=color_map[cls],
+            label=label_map[cls],
+            s=50, alpha=0.8
+        )
+    ax.set_title(title, fontsize=20)
+    # ax.set_xlim(x_min, x_max)
+    # ax.set_ylim(y_min, y_max)
+    ax.set_xticks([])
+    ax.set_yticks([])
+
+# ---------- 公用图例放在最下方 ----------
+handles = []
+labels = []
+for cls in (0, 1):
+    handles.append(plt.Line2D([], [], marker="o", color=color_map[cls],
+                              linestyle="", markersize=6))
+    labels.append(label_map[cls])
+
+fig.legend(
+    handles, labels,
+    loc="lower center",
+    ncol=2,
+    frameon=False,
+    bbox_to_anchor=(0.5, -0.02),
+    fontsize=20,
+    markerscale=2.0,
+)
+
+plt.tight_layout()
+plt.subplots_adjust(bottom=0.12, wspace=0.08)
+plt.savefig('umap.svg')
+# plt.show()
\ No newline at end of file
diff --git a/vis/umap_plot.py b/vis/umap_plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..aec98181892b6e1de99023c6e1a412cc88f3a691
--- /dev/null
+++ b/vis/umap_plot.py
@@ -0,0 +1,137 @@
+import torch
+import numpy as np
+import pandas as pd
+from umap import UMAP
+import matplotlib.pyplot as plt
+from matplotlib.colors import LinearSegmentedColormap
+
+# base_path = '../run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50'
+# img_path = 'umap_before.png'
+base_path = '../run-cls/rn18-diff-16-mamba-pcs-768-ce-32-0.001-50/uda_r2'
+img_path = 'umap_after.png'
+
+# —— 0. 你的两个函数 —— #
+def get_label_dict():
+    """
+    返回一个字典 { 描述(str): 标签(int, 0 或 1) }
+    比如:
+      return {
+        "image_0001": 1,
+        "image_0234": 0,
+        # ...
+      }
+    """
+    df = pd.read_excel('../dataset/r2_case.xlsx')
+    df = df[~pd.isna(df['TAG'])]
+
+    tags = {}
+
+    for _, row in df.iterrows():
+        desc = row['SEQUENCE - D-type amino acid substitution']
+        tag = row['TAG']
+        if tag.lower() == 'improved':
+            tags[desc] = 1
+        elif tag.lower() == 'not improved':
+            tags[desc] = 0
+
+    return tags
+
+
+def get_score_dict():
+    """
+    返回 { 描述: Float in [0,1] }，
+    用于背景点的颜色映射。
+    """
+    df = pd.read_csv(f'{base_path}/feature_preds.csv')
+    desc = df['seq'].values
+    scores = df['pred'].values
+    minimum, maximum = np.min(scores), np.max(scores)
+    scores = (scores - minimum) / (maximum - minimum)
+    tags = {
+        desc: score
+        for desc, score in zip(desc, scores)
+    }
+
+    return tags
+
+# —— 1. 加载特征 —— #
+data_dict = torch.load(f'{base_path}/features.pth', map_location='cpu')
+descs = list(data_dict.keys())
+features = np.vstack([
+    data_dict[d].cpu().numpy() if isinstance(data_dict[d], torch.Tensor)
+    else np.array(data_dict[d])
+    for d in descs
+])
+print(f"共 {features.shape[0]} 个样本，特征维度 {features.shape[1]}")
+
+# —— 2. 拿到标签与分数字典 —— #
+label_dict = get_label_dict()
+score_dict = get_score_dict()
+
+# —— 3. 用 UMAP 降到 2 维 —— #
+umap = UMAP(n_components=2, metric='euclidean')
+points_2d = umap.fit_transform(features)
+
+# —— 4. 分别收集索引 —— #
+idx1     = [i for i,d in enumerate(descs) if d in label_dict and label_dict[d]==1]
+idx0     = [i for i,d in enumerate(descs) if d in label_dict and label_dict[d]==0]
+idx_rest = [i for i,d in enumerate(descs) if d not in label_dict]
+
+# —— 5. 构造渐变色 —— #
+# skyblue ('#87CEEB') → peachpuff ('#FFDAB9')
+cmap = LinearSegmentedColormap.from_list('bg_cmap', ["#6EB1EC", "#E69C98"])
+# 背景点用它们对应的 score 决定颜色，缺失则给 0.5
+bg_scores = np.array([ score_dict.get(descs[i], 0.5) for i in idx_rest ])
+bg_colors = cmap(bg_scores)
+
+# —— 6. 绘图 —— #
+plt.figure(figsize=(3,3))
+
+# 背景点（渐变色）
+plt.scatter(
+    points_2d[idx_rest,0],
+    points_2d[idx_rest,1],
+    s=20,
+    c=bg_colors,
+    alpha=0.8,
+    label='Background'
+)
+
+# 标签 0
+plt.scatter(
+    points_2d[idx0,0],
+    points_2d[idx0,1],
+    s=60,
+    c="#218BE7",
+    alpha=1.0,
+    label='Not Improved'
+)
+
+# 标签 1
+plt.scatter(
+    points_2d[idx1,0],
+    points_2d[idx1,1],
+    s=60,
+    c="#E76760",
+    alpha=1.0,
+    label='Improved'
+)
+
+# # 标注被高亮的点
+# for i in idx0 + idx1:
+#     plt.annotate(
+#         descs[i],
+#         (points_2d[i,0], points_2d[i,1]),
+#         textcoords='offset points',
+#         xytext=(2,2),
+#         fontsize=8,
+#         color='black'
+#     )
+
+# plt.title('UMAP Visualization', fontsize=18)
+plt.axis('off')
+# plt.legend(loc='upper left', frameon=False, fontsize=14)
+plt.tight_layout()
+# —— 可选：保存图像 —— #
+plt.savefig(img_path, bbox_inches='tight', pad_inches=0, dpi=300)
+# plt.show()