{ "cells": [ { "cell_type": "markdown", "id": "9b02c4bb-8a5f-4d03-9916-8b8cc6a82a10", "metadata": {}, "source": [ "# Detector Training - YOLO26 on PRW\n", "| ID | Student | Email |\n", "|-----|---------------|------------------------|\n", "| 0001189110 | Rimondi Simone | simone.rimondi5@studio.unibo.it|\n", "\n", "## 1. Introduction\n", "\n", "This notebook presents a structured ablation study aimed at assessing the transferability of **YOLO26**, the 2026 iteration of the YOLO family of real-time object detectors, to the task of pedestrian detection on the **PRW** (*Person Re-identification in the Wild*) benchmark. The study is conducted across two model scales (Small and Large) and evaluates the impact of domain-specific intermediate pre-training and fine-tuning strategy on detection performance.\n", "\n", "### Context: Two-Stage Person Search\n", "\n", "This detection module constitutes the first stage of a **two-stage person search pipeline**. In this paradigm, a detector is responsible for localising all pedestrian instances in a scene, and a subsequent re-identification (Re-ID) module matches each detected crop against a query identity. Since any missed detection or poor localisation at this stage propagates irrecoverably to the Re-ID step, the quality of the detector is a critical bottleneck for the overall system performance. Maximising recall while maintaining high precision is therefore a primary design objective of this ablation.\n", "\n", "### Motivation for YOLO\n", "\n", "YOLO was selected as the detector backbone for three main reasons. First, it represents the current state of the art in real-time object detection, offering an excellent trade-off between accuracy and inference speed. Second, the availability of officially released pre-trained weights (on COCO) enables robust transfer learning even with limited target-domain data. Third, the `ultralytics` library provides a clean, well-documented API that significantly reduces implementation overhead and facilitates rapid experimentation across model scales and fine-tuning strategies.\n", "\n", "### Objective\n", "\n", "The primary research question is: *to what extent does a two-stage transfer learning pipeline, first adapting the model on a large crowd-scene dataset and then fine-tuning on the target domain, improve over a direct zero-shot or single-stage fine-tuning baseline?* A secondary axis of investigation concerns the choice of fine-tuning strategy: **full fine-tuning** (all parameters updated) versus **partial fine-tuning** (backbone frozen, only neck and detection head trained), which directly probes the generalization capacity of the learned feature representations.\n", "\n", "### Datasets\n", "\n", "- **PRW** (Person Re-identification in the Wild): a surveillance-domain dataset consisting of video frames captured by static cameras in a university campus. It contains 11,816 frames, 932 labelled identities, and 34,304 bounding-box annotations for the *person* class. Annotations are provided in MATLAB format and converted to YOLO-compatible normalised coordinates (`class x_c y_c w h`) as a preprocessing step.\n", "- **CrowdHuman** (`leducnhuan/crowdhuman`): a large-scale benchmark specifically designed for person detection in crowded scenarios (15k training images, 4.4k validation images). It was selected as the intermediate pre-training domain because its density and occlusion statistics are closer to the surveillance conditions of PRW than alternative datasets such as MOT or WIDER PERSON, making it a more effective bridge domain for feature adaptation before target fine-tuning.\n", "\n", "### Methodology\n", "\n", "The experimental pipeline is organised into five sequential phases, summarised in the table below. For each training run the same standardised hyperparameter set is applied (AdamW optimiser, cosine learning-rate decay with linear warm-up, standard YOLO augmentation suite including mosaic, mixup, and horizontal flip). Evaluation is performed at fixed confidence threshold and IoU threshold (0.5), and each model is assessed on four metrics: **mAP@0.5**, **mAP@0.5:0.95**, **Precision**, and **Recall**. Profiling functions adds parameter count, GFLOPs, per-image latency, and throughput, enabling a quality-vs-compute trade-off analysis.\n", "\n", "| Phase | Description |\n", "|---|---|\n", "| **0 - Baseline** | YOLO26-Small COCO zero-shot eval on PRW |\n", "| **1 - CrowdHuman Pre-training** | Fine-tune Small on CrowdHuman, then eval on PRW |\n", "| **2 - Strategy Comparison** | Full FT vs Partial FT |\n", "| **3 - Scale-up** | Best strategy on YOLO26-Large |\n", "| **Final** | Cross-model comparison: metrics, params, FLOPs, speed |\n", "\n", "### Reproducibility\n", "\n", "All experiments use a fixed global seed (42) applied to Python, NumPy, and PyTorch. Results are incrementally persisted to a JSON registry (`all_results.json`) and exported as a CSV table at the end of the notebook. It is possible to regenerate all figures after a kernel restart without repeating any training run, provided the JSON registry and checkpoint files are available.\n", "\n", "### Hardware and Runtime\n", "\n", "Experiments are executed on a Kaggle environment equipped with **two NVIDIA T4 GPUs** (16 GB VRAM each), used in data-parallel mode via the `ultralytics` multi-GPU interface.\n" ] }, { "cell_type": "markdown", "id": "8a1f4171", "metadata": {}, "source": [ "## 2. Preliminaries Operations\n", "\n", "### Installation" ] }, { "cell_type": "code", "execution_count": 1, "id": "af3673bb", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T20:03:18.854067Z", "iopub.status.busy": "2026-03-04T20:03:18.853418Z", "iopub.status.idle": "2026-03-04T20:03:29.294109Z", "shell.execute_reply": "2026-03-04T20:03:29.293005Z", "shell.execute_reply.started": "2026-03-04T20:03:18.854034Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "!pip install -q ultralytics opencv-python-headless scipy\n", "!pip install -q pandas tqdm" ] }, { "cell_type": "markdown", "id": "c485b949", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": null, "id": "2914ea18", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T20:03:29.296271Z", "iopub.status.busy": "2026-03-04T20:03:29.295948Z", "iopub.status.idle": "2026-03-04T20:03:33.833767Z", "shell.execute_reply": "2026-03-04T20:03:33.832949Z", "shell.execute_reply.started": "2026-03-04T20:03:29.296242Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating new Ultralytics Settings v0.0.6 file ✅ \n", "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n", "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n", "Device: cuda\n", "GPUs available: 2\n", " GPU 0: Tesla T4\n", " GPU 1: Tesla T4\n" ] } ], "source": [ "import os\n", "import cv2\n", "import json\n", "import shutil\n", "import random\n", "from pathlib import Path\n", "import io\n", "import contextlib\n", "\n", "import numpy as np\n", "import scipy.io\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "from matplotlib import rcParams\n", "from tqdm import tqdm\n", "\n", "import torch\n", "import yaml\n", "from ultralytics import YOLO\n", "from ultralytics.utils.torch_utils import model_info\n", "\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "print(f'Device: {device}')\n", "if torch.cuda.is_available():\n", " print(f'GPUs available: {torch.cuda.device_count()}')\n", " for i in range(torch.cuda.device_count()):\n", " print(f' GPU {i}: {torch.cuda.get_device_name(i)}')" ] }, { "cell_type": "markdown", "id": "96a248a4", "metadata": {}, "source": [ "### Centralised Configuration\n", "\n", "All hyperparameters, dataset paths, model variants, and output directories are defined in a single `Config` class. This centralisation ensures that no magic numbers or hardcoded strings appear in the rest of the notebook: every subsequent cell reads exclusively from the `cfg` instance. After instantiation, output directories are created, all random seeds are fixed for reproducibility, and the active device string is resolved to either a comma-separated multi-GPU identifier or a single-device fallback." ] }, { "cell_type": "code", "execution_count": null, "id": "5477bf4b-148e-419d-9761-f1e4d33146e5", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T20:03:33.835554Z", "iopub.status.busy": "2026-03-04T20:03:33.835151Z", "iopub.status.idle": "2026-03-04T20:03:33.852399Z", "shell.execute_reply": "2026-03-04T20:03:33.851474Z", "shell.execute_reply.started": "2026-03-04T20:03:33.835526Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Configuration loaded.\n", "Multi-GPU device string : 0,1\n", "Results dir : /kaggle/working/yolo_ablation\n" ] } ], "source": [ "class Config:\n", " #Dataset Paths\n", " prw_root = '/kaggle/input/datasets/edoardomerli/prw-person-re-identification-in-the-wild'\n", " prw_yolo_root = '/kaggle/working/PRW_YOLO'\n", "\n", " #CrowdHuman\n", " ch_img_train = '/kaggle/input/datasets/leducnhuan/crowdhuman/CrowdHuman/Images'\n", " ch_img_val = '/kaggle/input/datasets/leducnhuan/crowdhuman/CrowdHuman/Images_val'\n", " ch_odgt_train = '/kaggle/input/datasets/leducnhuan/crowdhuman/CrowdHuman/annotation_train.odgt'\n", " ch_odgt_val = '/kaggle/input/datasets/leducnhuan/crowdhuman/CrowdHuman/annotation_val.odgt'\n", " ch_yolo_root = '/kaggle/working/CrowdHuman_YOLO'\n", "\n", " #Models\n", " ablation_models = {\n", " 'small': {'weights': 'yolo26s.pt', 'freeze_backbone': 10, 'batch': 64, 'display': 'YOLO26-Small'},\n", " 'large': {'weights': 'yolo26l.pt', 'freeze_backbone': 10, 'batch': 24, 'display': 'YOLO26-Large'},\n", " }\n", "\n", " #Multi-GPU (Kaggle dual T4)\n", " use_multi_gpu = True\n", " device_ids = [0, 1]\n", "\n", " #CrowdHuman intermediate training\n", " ch_epochs = 30\n", " ch_lr0 = 5e-4\n", " ch_lrf = 0.01\n", " ch_warmup_ep = 3.0\n", " ch_patience = 10\n", "\n", " #PRW Fine-tuning - Full\n", " full_epochs = 20\n", " full_lr0 = 1e-4\n", " full_lrf = 0.01\n", " full_warmup_ep = 5.0\n", " full_patience = 15\n", "\n", " #PRW Fine-tuning - Partial (freeze backbone 0-9)\n", " partial_epochs = 15\n", " partial_lr0 = 3e-4\n", " partial_lrf = 0.01\n", " partial_warmup_ep = 5.0\n", " partial_patience = 15\n", "\n", " #Common training settings\n", " img_size = 640\n", " optimizer = 'AdamW'\n", " momentum = 0.937\n", " weight_decay = 5e-4\n", " warmup_momentum = 0.8\n", " warmup_bias_lr = 0.1\n", " box_loss_gain = 7.5\n", " cls_loss_gain = 0.5\n", " dfl_loss_gain = 1.5\n", " hsv_h = 0.015; hsv_s = 0.7; hsv_v = 0.4\n", " degrees = 0.0; translate = 0.1; scale = 0.5\n", " shear = 0.0; perspective = 0.0\n", " flipud = 0.0; fliplr = 0.5\n", " mosaic = 1.0; mixup = 0.1; copy_paste = 0.0\n", "\n", " #Evaluation\n", " eval_conf = 0.001\n", " eval_iou = 0.5\n", "\n", " #Profiling\n", " n_warmup_iters = 100\n", " n_timed_iters = 500\n", "\n", " #Output paths\n", " project = '/kaggle/working/yolo_runs'\n", " results_dir = Path('/kaggle/working/yolo_ablation')\n", " plots_dir = results_dir / 'plots'\n", " seed = 42\n", " workers = 8\n", " save_period = 10\n", "\n", "#Instantiate config and create output directories\n", "cfg = Config()\n", "cfg.results_dir.mkdir(parents=True, exist_ok=True)\n", "cfg.plots_dir.mkdir(parents=True, exist_ok=True)\n", "\n", "#Global results registry, populated incrementally throughout the notebook\n", "RESULTS = {}\n", "RESULTS_PATH = cfg.results_dir / 'all_results.json'\n", "\n", "#Fix all random seeds for full reproducibility\n", "random.seed(cfg.seed)\n", "np.random.seed(cfg.seed)\n", "torch.manual_seed(cfg.seed)\n", "if torch.cuda.is_available():\n", " torch.cuda.manual_seed_all(cfg.seed)\n", "\n", "#Resolve device string: \"0,1\" for dual-GPU, single device id otherwise\n", "_device_str = (','.join(map(str, cfg.device_ids))\n", " if cfg.use_multi_gpu and torch.cuda.device_count() > 1\n", " else str(device))\n", "\n", "print('Configuration loaded.')\n", "print(f'Multi-GPU device string : {_device_str}')\n", "print(f'Results dir : {cfg.results_dir}')" ] }, { "cell_type": "markdown", "id": "c50a5746", "metadata": {}, "source": [ "### Results Registry and Persistence\n", "\n", "The three functions below manage the serialisation and deserialisation of the global `RESULTS` dictionary, which accumulates evaluation metrics and profiling data for every run throughout the notebook.\n", "\n", "- `_json_safe(v)`: a recursive helper that converts NumPy scalar types (`np.floating`, `np.integer`) to native Python types before serialisation, since the standard `json` module does not support NumPy types natively. Dictionaries and lists are traversed recursively; all other types are returned unchanged.\n", "- `save_results()`: serialises the current state of `RESULTS` to disk as a formatted JSON file. It is called after every evaluation so that results are preserved even in case of kernel interruption.\n", "- `load_results()`: reloads `RESULTS` from the JSON file on disk, restoring the full registry without re-running any experiment. It is primarily intended for use after a kernel restart.\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "4470fd29-92cf-4041-966b-810abe8a330c", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T20:03:37.014419Z", "iopub.status.busy": "2026-03-04T20:03:37.013781Z", "iopub.status.idle": "2026-03-04T20:03:37.021341Z", "shell.execute_reply": "2026-03-04T20:03:37.020437Z", "shell.execute_reply.started": "2026-03-04T20:03:37.014388Z" }, "trusted": true }, "outputs": [], "source": [ "def _json_safe(v):\n", " \"\"\"Recursively convert numpy scalars to Python native types.\"\"\"\n", " if isinstance(v, (float, np.floating)): return round(float(v), 6)\n", " if isinstance(v, (int, np.integer)): return int(v)\n", " if isinstance(v, dict): return {kk: _json_safe(vv) for kk, vv in v.items()}\n", " if isinstance(v, list): return [_json_safe(x) for x in v]\n", " return v\n", "\n", "def save_results():\n", " \"\"\"Persist RESULTS dict to disk.\"\"\"\n", " with open(RESULTS_PATH, 'w') as f:\n", " json.dump(_json_safe(RESULTS), f, indent=2)\n", " print(f'Results saved in {RESULTS_PATH}')\n", "\n", "def load_results():\n", " \"\"\"Reload RESULTS from disk (useful after kernel restart).\"\"\"\n", " global RESULTS\n", " if RESULTS_PATH.exists():\n", " with open(RESULTS_PATH) as f:\n", " RESULTS = json.load(f)\n", " print(f'Loaded {len(RESULTS)} runs from {RESULTS_PATH}')\n", " else:\n", " print('No saved results found')" ] }, { "cell_type": "markdown", "id": "b83614ad-8348-4bcf-b6f3-cc6d81b7c9a8", "metadata": {}, "source": [ "## 3. Dataset Utilities\n", "\n", "### PRW to YOLO Conversion\n", "This cell defines the utilities needed to convert the PRW dataset into the directory structure and annotation format expected by the `ultralytics` training pipeline.\n", "\n", "* `convert_prw_to_yolo` iterates over the train and test splits, reading the per-split frame lists from the original `.mat` index files. For each frame, it copies the image into the standard `images/{split}/` directory and converts the bounding-box annotations from absolute pixel coordinates `(x, y, w, h)` to YOLO normalised format `(class x_c y_c w_n h_n)`, clipping all values to `[0, 1]` to handle any out-of-bounds annotations. Each label is written to a corresponding `.txt` file under `labels/{split}/`. Images with no valid boxes are discarded. The PRW test split is mapped to the YOLO `val` split, as is standard practice.\n", "* `make_yaml` generates the `data.yaml` descriptor file required by `ultralytics` to locate the dataset and define the single class (`person`).\n", "\n", "The conversion is executed only once: if the output directory already exists, the cell skips the conversion and reports the number of images already available in each split." ] }, { "cell_type": "code", "execution_count": 7, "id": "64e4957c", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T20:04:28.233672Z", "iopub.status.busy": "2026-03-04T20:04:28.232709Z", "iopub.status.idle": "2026-03-04T20:09:45.396710Z", "shell.execute_reply": "2026-03-04T20:09:45.395859Z", "shell.execute_reply.started": "2026-03-04T20:04:28.233617Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processing train split (5704 images)…\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "train: 100%|██████████| 5704/5704 [02:16<00:00, 41.76it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Processing test split (6112 images)…\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "test: 100%|██████████| 6112/6112 [02:32<00:00, 40.15it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "PRW conversion done.\n", " train : 5701 images, 18031 boxes\n", " val : 6091 images, 24898 boxes\n", "YAML saved in /kaggle/working/PRW_YOLO/data.yaml\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "def convert_prw_to_yolo(prw_root, output_root):\n", " \"\"\"Convert PRW dataset annotations to YOLO format (class xc yc w h, normalised).\"\"\"\n", " os.makedirs(output_root, exist_ok=True)\n", " for split in ['train', 'val']:\n", " os.makedirs(os.path.join(output_root, 'images', split), exist_ok=True)\n", " os.makedirs(os.path.join(output_root, 'labels', split), exist_ok=True)\n", "\n", " frames_dir = os.path.join(prw_root, 'frames')\n", " ann_dir = os.path.join(prw_root, 'annotations')\n", " stats = {'train': {'images': 0, 'boxes': 0}, 'val': {'images': 0, 'boxes': 0}}\n", "\n", " for mode, yolo_split in [('train', 'train'), ('test', 'val')]:\n", " split_file = os.path.join(prw_root, f'frame_{mode}.mat')\n", " if not os.path.exists(split_file):\n", " print(f'Warning: {split_file} not found'); continue\n", "\n", " mat = scipy.io.loadmat(split_file)\n", " key = [k for k in mat.keys() if not k.startswith('__')][0]\n", " raw = mat[key].flatten()\n", "\n", " ann_files = []\n", " for item in raw:\n", " name = str(item[0]) if isinstance(item, np.ndarray) else str(item)\n", " ann_name = name + '.jpg.mat'\n", " if os.path.exists(os.path.join(ann_dir, ann_name)):\n", " ann_files.append(ann_name)\n", "\n", " print(f'Processing {mode} split ({len(ann_files)} images)…')\n", " for ann_file in tqdm(ann_files, desc=mode):\n", " img_name = ann_file.replace('.mat', '').replace('.jpg', '') + '.jpg'\n", " img_path = os.path.join(frames_dir, img_name)\n", " img = cv2.imread(img_path)\n", " if img is None: continue\n", " H, W = img.shape[:2]\n", "\n", " shutil.copy(img_path, os.path.join(output_root, 'images', yolo_split, img_name))\n", "\n", " boxes_raw = scipy.io.loadmat(os.path.join(ann_dir, ann_file)).get('box_new', [])\n", " label_lines = []\n", " for box in boxes_raw:\n", " x, y, w, h = float(box[1]), float(box[2]), float(box[3]), float(box[4])\n", " if w <= 0 or h <= 0: continue\n", " xc = np.clip((x + w/2) / W, 0, 1)\n", " yc = np.clip((y + h/2) / H, 0, 1)\n", " wn = np.clip(w / W, 0, 1)\n", " hn = np.clip(h / H, 0, 1)\n", " label_lines.append(f'0 {xc:.6f} {yc:.6f} {wn:.6f} {hn:.6f}\\n')\n", " stats[yolo_split]['boxes'] += 1\n", "\n", " if label_lines:\n", " lbl_name = img_name.replace('.jpg', '.txt')\n", " with open(os.path.join(output_root, 'labels', yolo_split, lbl_name), 'w') as f:\n", " f.writelines(label_lines)\n", " stats[yolo_split]['images'] += 1\n", "\n", " print('PRW conversion done.')\n", " print(f\" train : {stats['train']['images']} images, {stats['train']['boxes']} boxes\")\n", " print(f\" val : {stats['val']['images']} images, {stats['val']['boxes']} boxes\")\n", " return stats\n", "\n", "\n", "def make_yaml(root, out_path, nc=1, names=None):\n", " \"\"\"Write a data.yaml file for YOLO training.\"\"\"\n", " names = names or ['person']\n", " with open(out_path, 'w') as f:\n", " yaml.dump({'path': str(root), 'train': 'images/train',\n", " 'val': 'images/val', 'nc': nc, 'names': names},\n", " f, default_flow_style=False)\n", " print(f'YAML saved in {out_path}')\n", " return out_path\n", "\n", "\n", "# Convert PRW once\n", "if not os.path.exists(cfg.prw_yolo_root):\n", " convert_prw_to_yolo(cfg.prw_root, cfg.prw_yolo_root)\n", "else:\n", " n_tr = len(os.listdir(os.path.join(cfg.prw_yolo_root, 'images', 'train')))\n", " n_va = len(os.listdir(os.path.join(cfg.prw_yolo_root, 'images', 'val')))\n", " print(f'PRW YOLO dataset already exists - train: {n_tr}, val: {n_va}')\n", "\n", "prw_yaml = make_yaml(cfg.prw_yolo_root, os.path.join(cfg.prw_yolo_root, 'data.yaml'))" ] }, { "cell_type": "markdown", "id": "f2ca7b1c", "metadata": {}, "source": [ "### CrowdHuman to YOLO Conversion\n", "\n", "This cell defines the utilities needed to convert the CrowdHuman dataset into the YOLO-compatible format, following the same directory structure used for PRW.\n", "\n", "* `_odgt_to_yolo` processes a single `.odgt` annotation file, where each line is a JSON record containing the image ID and a list of ground-truth boxes. For each record, the function locates the source image (trying multiple extensions), reads its dimensions, and iterates over the annotated instances. Only `person`-tagged boxes that are not flagged as ignored are retained. Full-body boxes (`fbox`, in absolute pixel coordinates `(x, y, w, h)`) are converted to YOLO normalised format and clipped to `[0, 1]`. Images with no valid boxes after filtering are discarded. Each retained image is physically copied into the destination `images/{split}/` directory: this step is required because the `ultralytics` training pipeline locates label files by replacing the `images/` component of the image path with `labels/`, which only works correctly if images reside inside the YOLO root tree rather than being accessed via symlinks to an external location.\n", "* `convert_crowdhuman_to_yolo` orchestrates the conversion for both splits by calling `_odgt_to_yolo` on each, checking beforehand whether the output directories are already populated to avoid redundant processing. On completion it calls `make_yaml` to write the dataset descriptor. The CrowdHuman paths on `cfg` are updated inline before calling the function, to match the layout of the `leducnhuan/crowdhuman` Kaggle dataset.\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "134e0f60-2e92-474d-b795-11f6020daf45", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T20:09:45.398621Z", "iopub.status.busy": "2026-03-04T20:09:45.398341Z", "iopub.status.idle": "2026-03-04T20:20:01.257534Z", "shell.execute_reply": "2026-03-04T20:20:01.256680Z", "shell.execute_reply.started": "2026-03-04T20:09:45.398593Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Converting CrowdHuman train…\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "CrowdHuman train: 100%|██████████| 15000/15000 [07:53<00:00, 31.68it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " train: 15000 images, 339565 boxes (0 skipped)\n", "Converting CrowdHuman val…\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "CrowdHuman val: 100%|██████████| 4370/4370 [02:20<00:00, 31.07it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ " val: 4370 images, 99481 boxes (0 skipped)\n", "YAML saved in /kaggle/working/CrowdHuman_YOLO/data.yaml\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "def _odgt_to_yolo(odgt_path, img_src_dir, img_dst_dir, label_dir, split_name=''):\n", " os.makedirs(img_dst_dir, exist_ok=True)\n", " os.makedirs(label_dir, exist_ok=True)\n", " converted, total_boxes, skipped = 0, 0, 0\n", "\n", " with open(odgt_path) as f:\n", " lines = f.readlines()\n", "\n", " for line in tqdm(lines, desc=f'CrowdHuman {split_name}'):\n", " rec = json.loads(line.strip())\n", " img_id = rec['ID']\n", "\n", " # Locate source image\n", " img_src = None\n", " for ext in ['.jpg', '.JPG', '.jpeg', '.png']:\n", " candidate = os.path.join(img_src_dir, img_id + ext)\n", " if os.path.exists(candidate):\n", " img_src = candidate\n", " break\n", " if img_src is None:\n", " skipped += 1\n", " continue\n", "\n", " img = cv2.imread(img_src)\n", " if img is None:\n", " skipped += 1\n", " continue\n", " H, W = img.shape[:2]\n", "\n", " # Build label lines\n", " label_lines = []\n", " for ann in rec.get('gtboxes', []):\n", " if ann.get('tag') != 'person':\n", " continue\n", " if ann.get('extra', {}).get('ignore', 0) == 1:\n", " continue\n", " fbox = ann.get('fbox') # [x, y, w, h] pixel coords\n", " if fbox is None:\n", " continue\n", " x, y, w, h = fbox\n", " if w <= 0 or h <= 0:\n", " continue\n", " xc = np.clip((x + w / 2) / W, 0, 1)\n", " yc = np.clip((y + h / 2) / H, 0, 1)\n", " wn = np.clip(w / W, 0, 1)\n", " hn = np.clip(h / H, 0, 1)\n", " label_lines.append(f'0 {xc:.6f} {yc:.6f} {wn:.6f} {hn:.6f}\\n')\n", " total_boxes += 1\n", "\n", " if not label_lines:\n", " continue\n", "\n", " # Copy image to dst (needed so YOLO path substitution works)\n", " img_dst = os.path.join(img_dst_dir, os.path.basename(img_src))\n", " if not os.path.exists(img_dst):\n", " shutil.copy2(img_src, img_dst)\n", "\n", " # Write label file\n", " lbl_path = os.path.join(label_dir, img_id + '.txt')\n", " with open(lbl_path, 'w') as f:\n", " f.writelines(label_lines)\n", " converted += 1\n", "\n", " print(f' {split_name}: {converted} images, {total_boxes} boxes '\n", " f'({skipped} skipped)')\n", " return converted\n", "\n", "\n", "def convert_crowdhuman_to_yolo(cfg):\n", " \"\"\"\n", " Convert leducnhuan/crowdhuman (Kaggle) to YOLO format under cfg.ch_yolo_root.\n", " \"\"\"\n", " for split, img_src, odgt in [\n", " ('train', cfg.ch_img_train, cfg.ch_odgt_train),\n", " ('val', cfg.ch_img_val, cfg.ch_odgt_val)]:\n", "\n", " img_dst = os.path.join(cfg.ch_yolo_root, 'images', split)\n", " label_dir = os.path.join(cfg.ch_yolo_root, 'labels', split)\n", "\n", " if not os.path.exists(odgt):\n", " print(f'WARNING: {odgt} not found - skipping {split}.')\n", " continue\n", " if not os.path.exists(img_src):\n", " print(f'WARNING: {img_src} not found - skipping {split}.')\n", " continue\n", "\n", " # Check if already converted (both images and labels present)\n", " n_imgs = len(os.listdir(img_dst)) if os.path.exists(img_dst) else 0\n", " n_labels = len(os.listdir(label_dir)) if os.path.exists(label_dir) else 0\n", " if n_imgs > 0 and n_labels > 0:\n", " print(f'CrowdHuman {split} already converted '\n", " f'({n_imgs} images, {n_labels} labels) - skipping.')\n", " continue\n", "\n", " print(f'Converting CrowdHuman {split}…')\n", " _odgt_to_yolo(odgt, img_src, img_dst, label_dir, split_name=split)\n", "\n", " ch_yaml_path = os.path.join(cfg.ch_yolo_root, 'data.yaml')\n", " ch_yaml = make_yaml(cfg.ch_yolo_root, ch_yaml_path)\n", " return ch_yaml\n", "\n", "ch_yaml = convert_crowdhuman_to_yolo(cfg)" ] }, { "cell_type": "markdown", "id": "56e5d142", "metadata": {}, "source": [ "## 4. Training, Evaluation and Visualization Pipeline\n", "\n", "### Model and Training Utilities\n", "\n", "This section defines the core functions used to load models and execute training runs uniformly across all experimental phases.\n", "\n", "* `load_model` is a thin wrapper around the `ultralytics` YOLO constructor, accepting either a filename (e.g. `yolo26s.pt`) or a full path to a checkpoint.\n", "* `_train_kwargs` assembles the complete keyword-argument dictionary for `model.train()`, reading all augmentation, optimiser, and infrastructure settings from `cfg`. Separating shared arguments into this helper avoids repetition across phases and guarantees that every run uses an identical base configuration. An optional `extra` dictionary can be passed to override or extend specific keys.\n", "* `run_training` is the unified training entry point used by all phases. It loads the model, merges the shared kwargs with the run-specific parameters (dataset, epochs, batch size, learning rate schedule, and optionally the number of frozen backbone layers), and launches training. The `freeze` parameter maps directly to the `ultralytics` `freeze` argument: when set to an integer `n`, the first `n` backbone layers are kept frozen during the run, implementing the partial fine-tuning strategy evaluated in Phase 2.\n", "* `best_weights_path` is a convenience helper that returns the canonical path to the `best.pt` checkpoint for a given run name, following the standard `ultralytics` output directory structure." ] }, { "cell_type": "code", "execution_count": 6, "id": "9bbb8ebf", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:21:38.321661Z", "iopub.status.busy": "2026-03-04T18:21:38.321309Z", "iopub.status.idle": "2026-03-04T18:21:38.331311Z", "shell.execute_reply": "2026-03-04T18:21:38.330411Z", "shell.execute_reply.started": "2026-03-04T18:21:38.321630Z" }, "trusted": true }, "outputs": [], "source": [ "def load_model(weights_path):\n", " \"\"\"Load a YOLO model from a weights path or model-name string.\"\"\"\n", " return YOLO(weights_path)\n", "\n", "\n", "def _train_kwargs(extra=None):\n", " \"\"\"Common augmentation and optimizer kwargs shared across all runs.\"\"\"\n", " base = dict(\n", " optimizer = cfg.optimizer,\n", " momentum = cfg.momentum,\n", " weight_decay = cfg.weight_decay,\n", " warmup_momentum = cfg.warmup_momentum,\n", " warmup_bias_lr = cfg.warmup_bias_lr,\n", " box = cfg.box_loss_gain,\n", " cls = cfg.cls_loss_gain,\n", " dfl = cfg.dfl_loss_gain,\n", " hsv_h=cfg.hsv_h, hsv_s=cfg.hsv_s, hsv_v=cfg.hsv_v,\n", " degrees=cfg.degrees, translate=cfg.translate, scale=cfg.scale,\n", " shear=cfg.shear, perspective=cfg.perspective,\n", " flipud=cfg.flipud, fliplr=cfg.fliplr,\n", " mosaic=cfg.mosaic, mixup=cfg.mixup, copy_paste=cfg.copy_paste,\n", " seed=cfg.seed, workers=cfg.workers,\n", " project=cfg.project,\n", " plots=True, val=True, verbose=True,\n", " device=_device_str,\n", " )\n", " if extra:\n", " base.update(extra)\n", " return base\n", "\n", "\n", "def run_training(weights, data_yaml, name, epochs, batch,\n", " lr0, lrf, warmup_epochs, patience, freeze=None):\n", " \"\"\"\n", " Unified training wrapper.\n", " freeze: int | None - number of backbone layers to freeze (Ultralytics param).\n", " \"\"\"\n", " model = load_model(weights)\n", " kw = _train_kwargs()\n", " kw.update(dict(\n", " data = data_yaml,\n", " epochs = epochs,\n", " batch = batch,\n", " imgsz = cfg.img_size,\n", " lr0 = lr0,\n", " lrf = lrf,\n", " warmup_epochs = warmup_epochs,\n", " patience = patience,\n", " name = name,\n", " exist_ok = True,\n", " save_period = cfg.save_period,\n", " ))\n", " if freeze is not None:\n", " kw['freeze'] = freeze\n", " return model.train(**kw)\n", "\n", "\n", "def best_weights_path(name):\n", " \"\"\"Return the path to best.pt for a given run name.\"\"\"\n", " return os.path.join(cfg.project, name, 'weights', 'best.pt')" ] }, { "cell_type": "markdown", "id": "c9127036", "metadata": {}, "source": [ "### Evaluation and Profiling Utilities\n", "\n", "This section defines the functions responsible for quantifying both the detection quality and the computational efficiency of each model checkpoint.\n", "\n", "* `evaluate_model` runs the standard `ultralytics` validation loop on the PRW validation split, using the confidence and IoU thresholds defined in `cfg`. It returns a flat dictionary with four detection metrics (`mAP@0.5`, `mAP@0.5:0.95`, `Precision`, `Recall`) and two speed metrics (`inference_ms`, `throughput`) extracted directly from `res.speed`, which ultralytics computes internally as the average over the entire validation set with real batches.\n", "* `profile_model` reads parameter count and GFLOPs from `ultralytics.utils.torch_utils.model_info`, the same function that produces the model summary line printed at the start of each run. This guarantees that the values stored in `RESULTS` are exactly consistent with the ultralytics-reported figures. `model_info` is called with `verbose=True` (required to obtain return values in this ultralytics version) but with stdout suppressed via `contextlib.redirect_stdout` to avoid polluting the notebook output. Importantly, this function must be called **before** `evaluate_model` on the same model instance: `val()` internally fuses Conv and BatchNorm layers for inference efficiency, which would alter the parameter count and GFLOPs if read afterwards.\n", "* `full_eval_and_profile` is the single entry point called at the end of each experimental phase. It loads the model once and shares the same instance across both `profile_model` and `evaluate_model`, avoiding the double-loading issue that would otherwise produce two summary lines in the output. Results are merged into a single record, annotated with metadata, stored in the global `RESULTS` registry, and immediately persisted to disk. If the key is already present, the function skips computation entirely, making it safe to re-run cells after a kernel interruption.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e8487669-f5c0-4c09-94a6-6fffd3677d88", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:21:48.453906Z", "iopub.status.busy": "2026-03-04T18:21:48.453322Z", "iopub.status.idle": "2026-03-04T18:21:48.464690Z", "shell.execute_reply": "2026-03-04T18:21:48.463400Z", "shell.execute_reply.started": "2026-03-04T18:21:48.453875Z" }, "trusted": true }, "outputs": [], "source": [ "def evaluate_model(model, data_yaml):\n", " \"\"\"Run YOLO .val() and return detection metrics and speed from res.speed.\"\"\"\n", " res = model.val(\n", " data=data_yaml, split='val', batch=32, imgsz=cfg.img_size,\n", " conf=cfg.eval_conf, iou=cfg.eval_iou, device=_device_str,\n", " save_json=False, plots=False, verbose=False,\n", " )\n", " inference_ms = float(res.speed['inference'])\n", " return {\n", " 'mAP50': float(res.box.map50),\n", " 'mAP50_95': float(res.box.map),\n", " 'precision': float(res.box.mp),\n", " 'recall': float(res.box.mr),\n", " 'inference_ms': inference_ms,\n", " 'throughput': round(1000 / inference_ms, 1) if inference_ms > 0 else 0.0,\n", " }\n", "\n", "\n", "@torch.no_grad()\n", "def profile_model(model, img_size=640):\n", " \"\"\"Measure params and GFLOPs using ultralytics model_info().\n", " Must be called before evaluate_model() to avoid reading fused values.\"\"\"\n", " \n", " with contextlib.redirect_stdout(io.StringIO()):\n", " nlayers, nparams, ngrads, flops_giga = model_info(model.model, verbose=True, imgsz=img_size)\n", "\n", " trainable_params = sum(p.numel() for p in model.model.parameters() if p.requires_grad)\n", "\n", " return {\n", " 'total_params': nparams,\n", " 'trainable_params': trainable_params,\n", " 'flops_giga': flops_giga,\n", " }\n", "\n", "\n", "def full_eval_and_profile(model_or_path, data_yaml, run_key,\n", " display_name='', strategy='', note=''):\n", " \"\"\"Evaluate + profile a model, store in RESULTS[run_key], persist. Skips if already done.\"\"\"\n", " if run_key in RESULTS:\n", " print(f'[SKIP] {run_key} already in RESULTS.')\n", " return RESULTS[run_key]\n", "\n", " print(f'\\n{\"=\"*65}')\n", " print(f' Evaluating : {run_key} ({display_name})')\n", " print(f'{\"=\"*65}')\n", "\n", " model = model_or_path if isinstance(model_or_path, YOLO) else YOLO(model_or_path)\n", "\n", " hw = profile_model(model)\n", " metrics = evaluate_model(model, data_yaml)\n", "\n", " entry = {**hw, **metrics,\n", " 'display_name': display_name, 'strategy': strategy, 'note': note}\n", " RESULTS[run_key] = entry\n", " save_results()\n", "\n", " print(f\" mAP@50={metrics['mAP50']:.4f} mAP@50-95={metrics['mAP50_95']:.4f}\")\n", " print(f\" Params: {hw['total_params']/1e6:.1f}M FLOPs: {hw['flops_giga']:.1f}G\")\n", " print(f\" Latency: {metrics['inference_ms']:.1f} ms Throughput: {metrics['throughput']:.0f} img/s\")" ] }, { "cell_type": "markdown", "id": "f767076c", "metadata": {}, "source": [ "### Plotting Utilities\n", "\n", "This section defines all visualisation functions used throughout the notebook. A shared style (`seaborn-v0_8-darkgrid`) and a fixed ten-colour palette are set globally so that all figures maintain a consistent appearance.\n", "\n", "* `plot_training_curves` reads the `results.csv` file produced by `ultralytics` at the end of each training run and generates a six-panel figure: total training loss, the three individual loss components (box, classification, and DFL), precision and recall curves, and mAP curves. It is called once per training phase to inspect convergence behaviour and detect overfitting or underfitting.\n", "* `_radar_chart` is a private helper that produces a four-axis polar chart comparing `mAP@0.5`, `mAP@0.5:0.95`, `Precision`, and `Recall` across multiple runs simultaneously. Each model is represented by a coloured polygon, with a semi-transparent fill to aid visual comparison. It is not called directly but is used internally by `plot_comparison`.\n", "* `plot_comparison` is the standard per-phase comparison function. It produces two figures for a given subset of results: a grouped bar chart showing `mAP@0.5` and `Recall` side by side for each run, and the radar chart described above. The two metrics were selected to give a direct picture of detection quality from the perspective of the downstream Re-ID module, where recall is the primary concern. It is called at the end of each phase to summarise the relative performance of the configurations evaluated in that phase.\n", "* `plot_final_comparison` generates the summary figure for the entire ablation. It produces a four-panel bar chart covering detection quality (`mAP@0.5`), model size (parameters in millions), compute cost (GFLOPs), and inference speed (latency in ms per image), providing a complete quality-vs-efficiency overview across all runs.\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "1c5258a6-c2af-47ec-9233-e805f73cf05b", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:21:53.408135Z", "iopub.status.busy": "2026-03-04T18:21:53.407790Z", "iopub.status.idle": "2026-03-04T18:21:53.436969Z", "shell.execute_reply": "2026-03-04T18:21:53.436253Z", "shell.execute_reply.started": "2026-03-04T18:21:53.408104Z" }, "trusted": true }, "outputs": [], "source": [ "plt.style.use('seaborn-v0_8-darkgrid')\n", "rcParams.update({'font.size': 10, 'axes.titlesize': 12, 'figure.titlesize': 13})\n", "PALETTE = ['#2E86AB', '#A23B72', '#F18F01', '#C73E1D', '#6A994E',\n", " '#BC4B51', '#5F4B8B', '#E08D79', '#1B998B', '#E84855']\n", "\n", "\n", "def plot_training_curves(results_csv_path, title_prefix='', save_path=None):\n", " \"\"\"Plot YOLO training loss + metric curves from results.csv.\"\"\"\n", " if not os.path.exists(results_csv_path):\n", " print(f'results.csv not found at {results_csv_path}'); return\n", " df = pd.read_csv(results_csv_path)\n", " df.columns = df.columns.str.strip()\n", "\n", " fig = plt.figure(figsize=(18, 10))\n", " fig.suptitle(f'{title_prefix}Training Curves', fontweight='bold')\n", "\n", " ax = plt.subplot(2, 3, 1)\n", " if all(c in df.columns for c in ['train/box_loss', 'train/cls_loss', 'train/dfl_loss']):\n", " total = df['train/box_loss'] + df['train/cls_loss'] + df['train/dfl_loss']\n", " ax.plot(df['epoch'], total, 'b-o', lw=2, ms=4)\n", " ax.set(xlabel='Epoch', ylabel='Loss', title='Total Train Loss'); ax.grid(True, alpha=.3)\n", "\n", " for ax_idx, (col, lbl) in enumerate([\n", " ('train/box_loss', 'Box Loss'),\n", " ('train/cls_loss', 'Cls Loss'),\n", " ('train/dfl_loss', 'DFL Loss')], start=2):\n", " ax = plt.subplot(2, 3, ax_idx)\n", " if col in df.columns:\n", " ax.plot(df['epoch'], df[col], 'b-', lw=1.5, alpha=.8)\n", " ax.set(xlabel='Epoch', ylabel='Loss', title=lbl); ax.grid(True, alpha=.3)\n", "\n", " ax = plt.subplot(2, 3, 5)\n", " for col, lbl, c in [('metrics/precision(B)', 'Precision', 'g'),\n", " ('metrics/recall(B)', 'Recall', 'm')]:\n", " if col in df.columns:\n", " ax.plot(df['epoch'], df[col], f'{c}-', lw=2, label=lbl)\n", " ax.set(xlabel='Epoch', ylabel='Value', title='Precision & Recall')\n", " ax.legend(); ax.grid(True, alpha=.3)\n", "\n", " ax = plt.subplot(2, 3, 6)\n", " for col, lbl, c in [('metrics/mAP50(B)', 'mAP@0.5', '#2E86AB'),\n", " ('metrics/mAP50-95(B)', 'mAP@0.5:0.95', '#F18F01')]:\n", " if col in df.columns:\n", " ax.plot(df['epoch'], df[col], '-', color=c, lw=2, label=lbl)\n", " ax.set(xlabel='Epoch', ylabel='mAP', title='Mean Average Precision')\n", " ax.legend(); ax.grid(True, alpha=.3)\n", "\n", " plt.tight_layout()\n", " sp = save_path or str(cfg.plots_dir / f'{title_prefix.strip().replace(\" \", \"_\")}_curves.png')\n", " plt.savefig(sp, dpi=150, bbox_inches='tight')\n", " plt.show()\n", "\n", "\n", "def _radar_chart(results_dict, title, save_path=None):\n", " \"\"\"4-axis radar: mAP@50, mAP@50-95, Precision, Recall.\"\"\"\n", " metrics = ['mAP@50', 'mAP@50-95', 'Precision', 'Recall']\n", " metric_keys = ['mAP50', 'mAP50_95', 'precision', 'recall']\n", " N = len(metrics)\n", " angles = [n / float(N) * 2 * np.pi for n in range(N)] + [0]\n", "\n", " fig, ax = plt.subplots(figsize=(7, 7), subplot_kw=dict(polar=True))\n", " ax.set_theta_offset(np.pi / 2); ax.set_theta_direction(-1)\n", " ax.set_xticks(angles[:-1])\n", " ax.set_xticklabels(metrics, fontsize=11, fontweight='bold')\n", " ax.set_ylim(0, 100); ax.set_yticks([20, 40, 60, 80, 100])\n", " ax.set_yticklabels(['20', '40', '60', '80', '100'], fontsize=7, color='grey')\n", "\n", " for (name, v), color in zip(results_dict.items(), PALETTE):\n", " vals = [v.get(mk, 0) * 100 for mk in metric_keys]\n", " vals += vals[:1]\n", " ax.plot(angles, vals, '-o', lw=2, color=color, ms=6,\n", " label=v.get('display_name', name))\n", " ax.fill(angles, vals, alpha=0.08, color=color)\n", "\n", " ax.set_title(title, fontsize=13, fontweight='bold', pad=20)\n", " ax.legend(loc='upper left')\n", " plt.tight_layout()\n", " if save_path:\n", " plt.savefig(save_path, dpi=150, bbox_inches='tight')\n", " plt.show()\n", "\n", "\n", "def plot_comparison(results_dict, title='Comparison', save_dir=None):\n", " \"\"\"Two-panel: grouped bar (mAP@50 and Recall), radar.\"\"\"\n", " save_dir = save_dir or str(cfg.plots_dir)\n", " names = list(results_dict.keys())\n", " map50 = np.array([results_dict[n].get('mAP50', 0) * 100 for n in names])\n", " recall = np.array([results_dict[n].get('recall', 0) * 100 for n in names])\n", "\n", " # 1. Grouped bar\n", " fig, ax = plt.subplots(figsize=(max(10, len(names) * 1.6), 6))\n", " x, w = np.arange(len(names)), 0.35\n", " b1 = ax.bar(x - w/2, map50, w, label='mAP@50', color='#2E86AB', alpha=.85, edgecolor='k', lw=.5)\n", " b2 = ax.bar(x + w/2, recall, w, label='Recall', color='#A23B72', alpha=.85, edgecolor='k', lw=.5)\n", " for b in list(b1) + list(b2):\n", " ax.text(b.get_x() + b.get_width()/2, b.get_height() + .3,\n", " f'{b.get_height():.1f}', ha='center', va='bottom', fontsize=7.5)\n", " ax.set(xticks=x,\n", " xticklabels=[results_dict[n].get('display_name', n) for n in names],\n", " ylabel='Score (%)', title=f'{title} - mAP@50 & Recall')\n", " plt.xticks(rotation=30, ha='right'); ax.legend(); plt.tight_layout()\n", " plt.savefig(os.path.join(save_dir, f'{title.replace(\" \",\"_\")}_bar.png'),\n", " dpi=150, bbox_inches='tight')\n", " plt.show()\n", "\n", " # 2. Radar\n", " _radar_chart(results_dict, title=f'{title} - Radar',\n", " save_path=os.path.join(save_dir, f'{title.replace(\" \",\"_\")}_radar.png'))\n", "\n", "\n", "def plot_final_comparison(results_dict, save_dir=None):\n", " \"\"\"4-panel final figure: quality / size / compute / speed.\"\"\"\n", " save_dir = save_dir or str(cfg.plots_dir)\n", " names = list(results_dict.keys())\n", " labels = [results_dict[n].get('display_name', n) for n in names]\n", " map50 = np.array([results_dict[n].get('mAP50', 0)*100 for n in names])\n", " params = np.array([results_dict[n].get('total_params', 0)/1e6 for n in names])\n", " flops = np.array([results_dict[n].get('flops_giga', 0) for n in names])\n", " latency = np.array([results_dict[n].get('inference_ms', 0) for n in names])\n", " colors = PALETTE[:len(names)]\n", " x = np.arange(len(names))\n", "\n", " fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n", " fig.suptitle('Final Model Comparison', fontweight='bold')\n", "\n", " for ax, vals, ylabel, title_ in zip(\n", " axes.flat,\n", " [map50, params, flops, latency],\n", " ['mAP@50 (%)', 'Params (M)', 'GFLOPs', 'Latency (ms/img)'],\n", " ['Detection Quality', 'Model Size', 'Compute Cost', 'Inference Speed']):\n", " bars = ax.bar(x, vals, color=colors, alpha=.85, edgecolor='k', lw=.5)\n", " for b, v in zip(bars, vals):\n", " ax.text(b.get_x() + b.get_width()/2, b.get_height() + .01*(vals.max()+1e-9),\n", " f'{v:.1f}', ha='center', va='bottom', fontsize=8)\n", " ax.set(xticks=x, xticklabels=labels, ylabel=ylabel, title=title_)\n", " plt.sca(ax); plt.xticks(rotation=30, ha='right')\n", " ax.grid(True, alpha=.3, axis='y')\n", "\n", " plt.tight_layout()\n", " sp = os.path.join(save_dir, 'final_comparison.png')\n", " plt.savefig(sp, dpi=150, bbox_inches='tight')\n", " print(f'Saved in {sp}')\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "77f55068", "metadata": {}, "source": [ "## 5. Ablation Phases\n", "\n", "### Phase 0 - COCO Baseline\n", "\n", "This phase establishes the zero-shot detection baseline. YOLO26-Small is loaded directly from its official COCO pre-trained weights (`yolo26s.pt`) and evaluated on the PRW validation split without any domain-specific fine-tuning. No training is performed. The resulting metrics represent the upper bound of what the model can achieve by relying solely on features learned from COCO, and serve as the reference point against which all subsequent fine-tuning experiments are measured.\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "47d7cf76-0a3c-4a78-abc1-fa6f08614b27", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:22:05.447187Z", "iopub.status.busy": "2026-03-04T18:22:05.446863Z", "iopub.status.idle": "2026-03-04T18:23:07.658073Z", "shell.execute_reply": "2026-03-04T18:23:07.657314Z", "shell.execute_reply.started": "2026-03-04T18:22:05.447142Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "=================================================================\n", " Evaluating : small_coco_zeroshot (Small - COCO (zero-shot))\n", "=================================================================\n", "YOLO26s summary: 260 layers, 10,009,784 parameters, 0 gradients, 22.8 GFLOPs\n", "Ultralytics 8.4.19 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "YOLO26s summary (fused): 122 layers, 9,496,140 parameters, 0 gradients, 20.7 GFLOPs\n", "\u001b[KDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100% ━━━━━━━━━━━━ 755.1KB 15.7MB/s 0.0s\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2428.0±983.9 MB/s, size: 237.5 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/PRW_YOLO/labels/val.cache... 6091 images, 21 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6112/6112 1.1Git/s 0.0s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 191/191 3.3it/s 57.2s0.3ss\n", " all 6112 24898 0.835 0.794 0.858 0.532\n", "Speed: 0.6ms preprocess, 6.3ms inference, 0.0ms loss, 0.1ms postprocess per image\n", "Results saved in /kaggle/working/yolo_ablation/all_results.json\n", " mAP@50=0.8582 mAP@50-95=0.5315\n", " Params: 10.0M FLOPs: 22.8G\n", " Latency: 6.3 ms Throughput: 159 img/s\n" ] } ], "source": [ "model_s_coco = load_model(cfg.ablation_models['small']['weights'])\n", "\n", "full_eval_and_profile(\n", " model_s_coco,\n", " data_yaml = prw_yaml,\n", " run_key = 'small_coco_zeroshot',\n", " display_name = 'Small - COCO (zero-shot)',\n", " strategy = 'zero-shot',\n", " note = 'COCO pretrained, no PRW fine-tuning',\n", ")" ] }, { "cell_type": "markdown", "id": "661496ac", "metadata": {}, "source": [ "### Phase 1 - CrowdHuman Intermediate Training\n", "\n", "This phase investigates whether an intermediate pre-training step on CrowdHuman improves transferability to PRW before any target-domain fine-tuning. YOLO26-Small is fine-tuned on CrowdHuman for up to 30 epochs starting from the COCO weights, with early stopping controlled by `cfg.ch_patience`.\n", "\n", "After training, both the COCO baseline (`small_coco_zeroshot`) and the CrowdHuman-pretrained model (`small_ch_zeroshot`) are evaluated zero-shot on PRW and compared. The model achieving the higher `mAP@0.5` is automatically selected as the starting point for Phase 2, stored in `best_start_weights`. This data-driven selection ensures that the fine-tuning strategy comparison in Phase 2 always begins from the strongest available initialisation." ] }, { "cell_type": "code", "execution_count": 11, "id": "00a4c4a1", "metadata": { "execution": { "iopub.execute_input": "2026-02-25T18:15:48.010797Z", "iopub.status.busy": "2026-02-25T18:15:48.010235Z", "iopub.status.idle": "2026-02-25T20:47:02.238511Z", "shell.execute_reply": "2026-02-25T20:47:02.237629Z", "shell.execute_reply.started": "2026-02-25T18:15:48.010765Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26s.pt to 'yolo26s.pt': 100% ━━━━━━━━━━━━ 19.5MB 105.4MB/s 0.2s1s<0.2s\n", "Ultralytics 8.4.16 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, angle=1.0, augment=False, auto_augment=randaugment, batch=64, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/kaggle/working/CrowdHuman_YOLO/data.yaml, degrees=0.0, deterministic=True, device=0,1, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=30, erasing=0.4, exist_ok=True, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.0005, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.1, mode=train, model=yolo26s.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=small_ch_pretrain, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=10, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=/kaggle/working/yolo_runs, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/kaggle/working/yolo_runs/small_ch_pretrain, save_frames=False, save_json=False, save_period=10, save_txt=False, scale=0.5, seed=42, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n", "\u001b[KDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100% ━━━━━━━━━━━━ 755.1KB 16.8MB/s 0.0s\n", "Overriding model.yaml nc=80 with nc=1\n", "\n", " from n params module arguments \n", " 0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2] \n", " 1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n", " 2 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] \n", " 3 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", " 4 -1 1 103360 ultralytics.nn.modules.block.C3k2 [128, 256, 1, False, 0.25] \n", " 5 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 6 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] \n", " 7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] \n", " 8 -1 1 1380352 ultralytics.nn.modules.block.C3k2 [512, 512, 1, True] \n", " 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5, 3, True] \n", " 10 -1 1 990976 ultralytics.nn.modules.block.C2PSA [512, 512, 1] \n", " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 13 -1 1 477184 ultralytics.nn.modules.block.C3k2 [768, 256, 1, True] \n", " 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 16 -1 1 136192 ultralytics.nn.modules.block.C3k2 [512, 128, 1, True] \n", " 17 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", " 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 19 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] \n", " 20 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 22 -1 1 1843712 ultralytics.nn.modules.block.C3k2 [768, 512, 1, True, 0.5, True]\n", " 23 [16, 19, 22] 1 932638 ultralytics.nn.modules.head.Detect [1, 1, True, [128, 256, 512]] \n", "YOLO26s summary: 260 layers, 9,948,638 parameters, 9,948,638 gradients, 22.5 GFLOPs\n", "\n", "Transferred 696/708 items from pretrained weights\n", "\u001b[34m\u001b[1mDDP:\u001b[0m debug command /usr/bin/python3 -m torch.distributed.run --nproc_per_node 2 --master_port 45197 /root/.config/Ultralytics/DDP/_temp_7pnnaq7s135060299406832.py\n", "Ultralytics 8.4.16 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "Overriding model.yaml nc=80 with nc=1\n", "Transferred 696/708 items from pretrained weights\n", "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", "\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt to 'yolo26n.pt': 100% ━━━━━━━━━━━━ 5.3MB 69.4MB/s 0.1s\n", "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 530.8±701.1 MB/s, size: 692.2 KB)\n", "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/CrowdHuman_YOLO/labels/train... 15000 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 15000/15000 744.0it/s 20.2s<0.1s\n", "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /kaggle/working/CrowdHuman_YOLO/labels/train.cache\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1595.1±997.1 MB/s, size: 274.7 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/CrowdHuman_YOLO/labels/val... 4370 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 4370/4370 1.0Kit/s 4.3s<0.1s\n", "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /kaggle/working/CrowdHuman_YOLO/labels/val.cache\n", "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.0005, momentum=0.937) with parameter groups 114 weight(decay=0.0), 126 weight(decay=0.0005), 126 bias(decay=0.0)\n", "Plotting labels to /kaggle/working/yolo_runs/small_ch_pretrain/labels.jpg... \n", "Image sizes 640 train, 640 val\n", "Using 4 dataloader workers\n", "Logging results to \u001b[1m/kaggle/working/yolo_runs/small_ch_pretrain\u001b[0m\n", "Starting training for 30 epochs...\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 1/30 13.6G 1.726 1.576 0.01006 664 640: 100% ━━━━━━━━━━━━ 235/235 1.4s/it 5:38<1.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 2.0s/it 1:081.3sss\n", " all 4370 99481 0.645 0.511 0.575 0.299\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 2/30 13.7G 1.646 1.293 0.009332 783 640: 100% ━━━━━━━━━━━━ 235/235 1.3s/it 4:57<1.1s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.5s/it 50.9s1.3ss\n", " all 4370 99481 0.742 0.615 0.697 0.371\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 3/30 13.7G 1.621 1.245 0.009353 506 640: 100% ━━━━━━━━━━━━ 235/235 1.2s/it 4:50<1.0s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.6s/it 54.3s1.5ss\n", " all 4370 99481 0.76 0.647 0.737 0.41\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 4/30 12G 1.612 1.221 0.009189 564 640: 100% ━━━━━━━━━━━━ 235/235 1.2s/it 4:42<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.5s/it 51.4s1.1ss\n", " all 4370 99481 0.777 0.667 0.758 0.429\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 5/30 14G 1.6 1.193 0.009122 607 640: 100% ━━━━━━━━━━━━ 235/235 1.1s/it 4:30<1.0s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.3s/it 46.7s1.4ss\n", " all 4370 99481 0.785 0.683 0.769 0.429\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 6/30 13.9G 1.567 1.157 0.008941 546 640: 100% ━━━━━━━━━━━━ 235/235 1.1s/it 4:13<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.3s/it 45.9s1.4ss\n", " all 4370 99481 0.8 0.693 0.779 0.448\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 7/30 11.9G 1.561 1.145 0.008692 364 640: 100% ━━━━━━━━━━━━ 235/235 1.1s/it 4:10<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 42.7s1.1ss\n", " all 4370 99481 0.792 0.688 0.776 0.445\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 8/30 13G 1.552 1.126 0.008649 465 640: 100% ━━━━━━━━━━━━ 235/235 1.1s/it 4:08<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 41.3s1.3ss\n", " all 4370 99481 0.798 0.685 0.779 0.451\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 9/30 12.4G 1.544 1.118 0.008591 755 640: 100% ━━━━━━━━━━━━ 235/235 1.1s/it 4:12<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 41.7s1.4ss\n", " all 4370 99481 0.804 0.705 0.797 0.469\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 10/30 13.7G 1.542 1.102 0.008487 423 640: 100% ━━━━━━━━━━━━ 235/235 1.1s/it 4:11<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 41.3s1.1ss\n", " all 4370 99481 0.801 0.695 0.785 0.46\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 11/30 13.1G 1.516 1.083 0.008419 654 640: 100% ━━━━━━━━━━━━ 235/235 1.1s/it 4:11<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 42.3s1.2ss\n", " all 4370 99481 0.806 0.688 0.785 0.467\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 12/30 13.7G 1.519 1.08 0.008414 422 640: 100% ━━━━━━━━━━━━ 235/235 1.1s/it 4:08<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 41.8s1.3ss\n", " all 4370 99481 0.801 0.705 0.793 0.466\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 13/30 11.8G 1.508 1.075 0.008315 479 640: 100% ━━━━━━━━━━━━ 235/235 1.0s/it 4:02<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.1s/it 38.3s1.2ss\n", " all 4370 99481 0.811 0.713 0.8 0.475\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 14/30 12.7G 1.51 1.061 0.008205 657 640: 100% ━━━━━━━━━━━━ 235/235 1.0s/it 3:56<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.1s/it 38.7s1.3ss\n", " all 4370 99481 0.81 0.713 0.801 0.477\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 15/30 14.3G 1.506 1.055 0.008262 801 640: 100% ━━━━━━━━━━━━ 235/235 1.0s/it 3:57<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.1s/it 38.3s1.1ss\n", " all 4370 99481 0.818 0.714 0.807 0.486\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 16/30 12.2G 1.49 1.045 0.008077 484 640: 100% ━━━━━━━━━━━━ 235/235 1.0s/it 4:03<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 42.2s1.2ss\n", " all 4370 99481 0.818 0.72 0.809 0.486\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 17/30 14G 1.481 1.032 0.008055 468 640: 100% ━━━━━━━━━━━━ 235/235 1.0s/it 4:06<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 41.8s1.5ss\n", " all 4370 99481 0.822 0.722 0.814 0.49\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 18/30 13.4G 1.479 1.03 0.007975 237 640: 100% ━━━━━━━━━━━━ 235/235 1.1s/it 4:10<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 40.7s1.3ss\n", " all 4370 99481 0.822 0.723 0.813 0.491\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 19/30 12.2G 1.479 1.024 0.007968 695 640: 100% ━━━━━━━━━━━━ 235/235 1.0s/it 4:04<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.1s/it 40.0s1.4ss\n", " all 4370 99481 0.827 0.725 0.816 0.494\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 20/30 12.5G 1.468 1.018 0.007991 464 640: 100% ━━━━━━━━━━━━ 235/235 1.1s/it 4:10<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 41.6s1.5ss\n", " all 4370 99481 0.828 0.722 0.816 0.498\n", "Closing dataloader mosaic\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 21/30 12.4G 1.37 0.8617 0.00802 160 640: 100% ━━━━━━━━━━━━ 235/235 1.0it/s 3:53<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 43.4s1.4ss\n", " all 4370 99481 0.824 0.726 0.812 0.49\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 22/30 11.9G 1.357 0.8483 0.007815 173 640: 100% ━━━━━━━━━━━━ 235/235 1.0it/s 3:44<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 40.4s1.2ss\n", " all 4370 99481 0.827 0.726 0.814 0.493\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 23/30 11.5G 1.352 0.8422 0.007746 258 640: 100% ━━━━━━━━━━━━ 235/235 1.0it/s 3:46<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 41.0s1.3ss\n", " all 4370 99481 0.829 0.725 0.819 0.5\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 24/30 11.9G 1.344 0.8314 0.007606 357 640: 100% ━━━━━━━━━━━━ 235/235 1.0it/s 3:51<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 41.2s1.6ss\n", " all 4370 99481 0.828 0.729 0.818 0.497\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 25/30 12.9G 1.328 0.8231 0.007548 309 640: 100% ━━━━━━━━━━━━ 235/235 1.0it/s 3:50<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 42.1s1.4ss\n", " all 4370 99481 0.829 0.729 0.819 0.5\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 26/30 14.1G 1.335 0.8213 0.007363 225 640: 100% ━━━━━━━━━━━━ 235/235 1.0it/s 3:49<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 41.7s1.4ss\n", " all 4370 99481 0.832 0.733 0.822 0.501\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 27/30 11.8G 1.32 0.8086 0.007375 191 640: 100% ━━━━━━━━━━━━ 235/235 1.0it/s 3:49<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 41.3s1.4ss\n", " all 4370 99481 0.831 0.732 0.82 0.5\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 28/30 12G 1.314 0.8061 0.007397 307 640: 100% ━━━━━━━━━━━━ 235/235 1.0it/s 3:48<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 43.3s1.2ss\n", " all 4370 99481 0.83 0.736 0.822 0.501\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 29/30 11.5G 1.329 0.8083 0.007205 217 640: 100% ━━━━━━━━━━━━ 235/235 1.0it/s 3:48<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 41.1s1.3ss\n", " all 4370 99481 0.831 0.735 0.822 0.501\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 30/30 12.3G 1.302 0.799 0.007219 419 640: 100% ━━━━━━━━━━━━ 235/235 1.0it/s 3:47<0.9s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.2s/it 42.6s1.1ss\n", " all 4370 99481 0.831 0.735 0.823 0.502\n", "\n", "30 epochs completed in 2.488 hours.\n", "Optimizer stripped from /kaggle/working/yolo_runs/small_ch_pretrain/weights/last.pt, 20.3MB\n", "Optimizer stripped from /kaggle/working/yolo_runs/small_ch_pretrain/weights/best.pt, 20.3MB\n", "\n", "Validating /kaggle/working/yolo_runs/small_ch_pretrain/weights/best.pt...\n", "YOLO26s summary (fused): 122 layers, 9,465,567 parameters, 0 gradients, 20.5 GFLOPs\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 35/35 1.3s/it 46.6s1.0ss\n", " all 4370 99481 0.83 0.736 0.823 0.502\n", "Speed: 0.1ms preprocess, 1.7ms inference, 0.0ms loss, 0.3ms postprocess per image\n", "Results saved to \u001b[1m/kaggle/working/yolo_runs/small_ch_pretrain\u001b[0m\n", "CrowdHuman training done.\n" ] } ], "source": [ "if not os.path.exists(best_weights_path('small_ch_pretrain')):\n", " run_training(\n", " weights = cfg.ablation_models['small']['weights'],\n", " data_yaml = ch_yaml,\n", " name = 'small_ch_pretrain',\n", " epochs = cfg.ch_epochs,\n", " batch = cfg.ablation_models['small']['batch'],\n", " lr0 = cfg.ch_lr0,\n", " lrf = cfg.ch_lrf,\n", " warmup_epochs = cfg.ch_warmup_ep,\n", " patience = cfg.ch_patience,\n", " )\n", " print('CrowdHuman training done.')\n", "else:\n", " print('CrowdHuman checkpoint already exists - skipping.')" ] }, { "cell_type": "code", "execution_count": 14, "id": "5804eba9", "metadata": { "execution": { "iopub.execute_input": "2026-02-25T20:48:18.449154Z", "iopub.status.busy": "2026-02-25T20:48:18.448742Z", "iopub.status.idle": "2026-02-25T20:48:20.407775Z", "shell.execute_reply": "2026-02-25T20:48:20.407003Z", "shell.execute_reply.started": "2026-02-25T20:48:18.449118Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved → /kaggle/working/yolo_ablation/plots/Phase_1_—_CrowdHuman_curves.png\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_training_curves(\n", " os.path.join(cfg.project, 'small_ch_pretrain', 'results.csv'),\n", " title_prefix='Phase 1 - CrowdHuman ',\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "id": "5a697783-0d13-45c6-8973-42d1809af83a", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:23:50.086604Z", "iopub.status.busy": "2026-03-04T18:23:50.086260Z", "iopub.status.idle": "2026-03-04T18:24:51.273687Z", "shell.execute_reply": "2026-03-04T18:24:51.272869Z", "shell.execute_reply.started": "2026-03-04T18:23:50.086570Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "=================================================================\n", " Evaluating : small_ch_zeroshot (Small - CH pretrain (zero-shot PRW))\n", "=================================================================\n", "YOLO26s summary: 260 layers, 9,948,638 parameters, 0 gradients, 22.5 GFLOPs\n", "Ultralytics 8.4.19 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "YOLO26s summary (fused): 122 layers, 9,465,567 parameters, 0 gradients, 20.5 GFLOPs\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1490.0±523.9 MB/s, size: 144.6 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/PRW_YOLO/labels/val.cache... 6091 images, 21 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6112/6112 2.0Git/s 0.0s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 191/191 3.2it/s 59.5s0.4ss\n", " all 6112 24898 0.821 0.834 0.882 0.533\n", "Speed: 0.6ms preprocess, 6.8ms inference, 0.0ms loss, 0.1ms postprocess per image\n", "Results saved in /kaggle/working/yolo_ablation/all_results.json\n", " mAP@50=0.8823 mAP@50-95=0.5329\n", " Params: 9.9M FLOPs: 22.5G\n", " Latency: 6.8 ms Throughput: 146 img/s\n" ] } ], "source": [ "full_eval_and_profile(\n", " model_or_path = best_weights_path('small_ch_pretrain'),\n", " data_yaml = prw_yaml,\n", " run_key = 'small_ch_zeroshot',\n", " display_name = 'Small - CH pretrain (zero-shot PRW)',\n", " strategy = 'zero-shot',\n", " note = 'COCO then CrowdHuman, zero-shot on PRW',\n", ")" ] }, { "cell_type": "code", "execution_count": 14, "id": "5c9edcc9-56c1-4759-b637-0f0035901335", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:25:39.411435Z", "iopub.status.busy": "2026-03-04T18:25:39.410964Z", "iopub.status.idle": "2026-03-04T18:25:40.450807Z", "shell.execute_reply": "2026-03-04T18:25:40.450069Z", "shell.execute_reply.started": "2026-03-04T18:25:39.411400Z" }, "trusted": true }, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Best starting weights: small_ch_zeroshot\n" ] } ], "source": [ "phase1_base = {k: RESULTS[k] for k in ['small_coco_zeroshot', 'small_ch_zeroshot']\n", " if k in RESULTS}\n", "plot_comparison(phase1_base, title='Phase 1 - Baseline Weights Comparison')\n", "\n", "best_start_key = max(phase1_base, key=lambda k: phase1_base[k].get('mAP50', 0))\n", "best_start_weights = (cfg.ablation_models['small']['weights']\n", " if best_start_key == 'small_coco_zeroshot'\n", " else best_weights_path('small_ch_pretrain'))\n", "print(f'Best starting weights: {best_start_key}')" ] }, { "cell_type": "markdown", "id": "6235a6fc", "metadata": {}, "source": [ "### Phase 2 - Fine-tuning Strategy Comparison\n", "\n", "This phase compares two fine-tuning strategies applied to YOLO26-Small on PRW, starting from the best initialisation identified in Phase 1.\n", "\n", "- **Full fine-tuning (`full_ft`)**: all layers are updated during training, with a conservative initial learning rate (`cfg.full_lr0 = 1e-4`) to avoid overwriting the pretrained feature representations.\n", "- **Partial fine-tuning (`partial_ft`)**: backbone layers 0-9 are frozen and only the neck and detection head are updated, with a higher initial learning rate (`cfg.partial_lr0 = 3e-4`) since fewer parameters are being optimised. The number of frozen layers is controlled by `cfg.ablation_models['small']['freeze_backbone']`.\n", "\n", "Both runs share the same dataset, augmentation pipeline, and early stopping patience, differing only in the `freeze` parameter passed to `run_training`. As in Phase 1, training is skipped if a valid checkpoint already exists. After evaluation, the strategy achieving the higher `mAP@0.5` is automatically selected as `best_strat_key` and propagated to Phase 3, where it is applied to the larger model scale.\n" ] }, { "cell_type": "code", "execution_count": 12, "id": "423bd4e8-8603-4ef2-bb85-d3ee73a99472", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:25:01.189672Z", "iopub.status.busy": "2026-03-04T18:25:01.189310Z", "iopub.status.idle": "2026-03-04T18:25:01.195982Z", "shell.execute_reply": "2026-03-04T18:25:01.195215Z", "shell.execute_reply.started": "2026-03-04T18:25:01.189638Z" }, "trusted": true }, "outputs": [], "source": [ "STRATEGY = {\n", " 'full': {\n", " 'freeze': None,\n", " 'lr0': cfg.full_lr0,\n", " 'lrf': cfg.full_lrf,\n", " 'epochs': cfg.full_epochs,\n", " 'patience':cfg.full_patience,\n", " 'warmup': cfg.full_warmup_ep,\n", " 'label': 'Full FT',\n", " },\n", " 'partial': {\n", " 'freeze': cfg.ablation_models['small']['freeze_backbone'], # 10\n", " 'lr0': cfg.partial_lr0,\n", " 'lrf': cfg.partial_lrf,\n", " 'epochs': cfg.partial_epochs,\n", " 'patience':cfg.partial_patience,\n", " 'warmup': cfg.partial_warmup_ep,\n", " 'label': 'Partial FT (freeze bb 0–9)',\n", " },\n", "}" ] }, { "cell_type": "code", "execution_count": 28, "id": "679203af-9c53-4cd9-9663-c9e67a43bd05", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T11:42:59.273709Z", "iopub.status.busy": "2026-03-04T11:42:59.273037Z", "iopub.status.idle": "2026-03-04T12:53:46.568659Z", "shell.execute_reply": "2026-03-04T12:53:46.567750Z", "shell.execute_reply.started": "2026-03-04T11:42:59.273676Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Training small_full_ft …\n", "Ultralytics 8.4.19 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, angle=1.0, augment=False, auto_augment=randaugment, batch=64, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/kaggle/working/PRW_YOLO/data.yaml, degrees=0.0, deterministic=True, device=0,1, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=20, erasing=0.4, exist_ok=True, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.0001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.1, mode=train, model=/kaggle/working/yolo_runs/small_ch_pretrain/weights/best.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=small_full_ft, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=15, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=/kaggle/working/yolo_runs, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/kaggle/working/yolo_runs/small_full_ft, save_frames=False, save_json=False, save_period=10, save_txt=False, scale=0.5, seed=42, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=5.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n", "\n", " from n params module arguments \n", " 0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2] \n", " 1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n", " 2 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] \n", " 3 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", " 4 -1 1 103360 ultralytics.nn.modules.block.C3k2 [128, 256, 1, False, 0.25] \n", " 5 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 6 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] \n", " 7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] \n", " 8 -1 1 1380352 ultralytics.nn.modules.block.C3k2 [512, 512, 1, True] \n", " 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5, 3, True] \n", " 10 -1 1 990976 ultralytics.nn.modules.block.C2PSA [512, 512, 1] \n", " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 13 -1 1 477184 ultralytics.nn.modules.block.C3k2 [768, 256, 1, True] \n", " 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 16 -1 1 136192 ultralytics.nn.modules.block.C3k2 [512, 128, 1, True] \n", " 17 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", " 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 19 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] \n", " 20 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 22 -1 1 1843712 ultralytics.nn.modules.block.C3k2 [768, 512, 1, True, 0.5, True]\n", " 23 [16, 19, 22] 1 932638 ultralytics.nn.modules.head.Detect [1, 1, True, [128, 256, 512]] \n", "YOLO26s summary: 260 layers, 9,948,638 parameters, 9,948,638 gradients, 22.5 GFLOPs\n", "\n", "Transferred 708/708 items from pretrained weights\n", "\u001b[34m\u001b[1mDDP:\u001b[0m debug command /usr/bin/python3 -m torch.distributed.run --nproc_per_node 2 --master_port 36877 /root/.config/Ultralytics/DDP/_temp_9ndh0ayd133439937801600.py\n", "Ultralytics 8.4.19 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "Transferred 708/708 items from pretrained weights\n", "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", "\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt to 'yolo26n.pt': 100% ━━━━━━━━━━━━ 5.3MB 21.1MB/s 0.3s.2s<0.1s9s\n", "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1878.0±1138.3 MB/s, size: 212.3 KB)\n", "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/PRW_YOLO/labels/train... 5701 images, 3 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 5704/5704 1.5Kit/s 3.9s0.1s\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/working/PRW_YOLO/images/train/c6s1_000476.jpg: 1 duplicate labels removed\n", "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /kaggle/working/PRW_YOLO/labels/train.cache\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1437.1±1649.2 MB/s, size: 199.0 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/PRW_YOLO/labels/val.cache... 6091 images, 21 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6112/6112 167.6Mit/s 0.0s\n", "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.0001, momentum=0.937) with parameter groups 114 weight(decay=0.0), 126 weight(decay=0.0005), 126 bias(decay=0.0)\n", "Plotting labels to /kaggle/working/yolo_runs/small_full_ft/labels.jpg... \n", "Image sizes 640 train, 640 val\n", "Using 4 dataloader workers\n", "Logging results to \u001b[1m/kaggle/working/yolo_runs/small_full_ft\u001b[0m\n", "Starting training for 20 epochs...\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 1/20 9.4G 1.428 0.9423 0.00467 28 640: 100% ━━━━━━━━━━━━ 90/90 1.1s/it 1:371.1sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 44.5s0.7ss\n", " all 6112 24898 0.86 0.804 0.884 0.601\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 2/20 9.51G 1.307 0.7197 0.004007 30 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:160.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.7s0.7ss\n", " all 6112 24898 0.816 0.707 0.808 0.539\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 3/20 9.47G 1.285 0.6809 0.003933 24 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:160.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.2it/s 41.3s0.6ss\n", " all 6112 24898 0.847 0.887 0.927 0.641\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 4/20 9.53G 1.262 0.6511 0.003796 46 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:160.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 41.8s0.8ss\n", " all 6112 24898 0.892 0.893 0.947 0.654\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 5/20 9.54G 1.241 0.6309 0.003696 12 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:160.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.7s0.7ss\n", " all 6112 24898 0.883 0.903 0.948 0.667\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 6/20 9.46G 1.21 0.6137 0.00365 29 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:160.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.2it/s 41.2s0.6ss\n", " all 6112 24898 0.883 0.907 0.949 0.663\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 7/20 9.46G 1.21 0.5952 0.003588 25 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:160.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.2it/s 41.5s0.6ss\n", " all 6112 24898 0.882 0.905 0.949 0.671\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 8/20 9.5G 1.198 0.577 0.003556 32 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:160.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.2it/s 41.6s0.7ss\n", " all 6112 24898 0.893 0.9 0.95 0.669\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 9/20 9.48G 1.194 0.5645 0.003557 30 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:150.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 41.9s0.6ss\n", " all 6112 24898 0.887 0.901 0.948 0.672\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 10/20 9.48G 1.183 0.5728 0.003528 27 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:160.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 41.8s0.8ss\n", " all 6112 24898 0.892 0.895 0.949 0.672\n", "Closing dataloader mosaic\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 11/20 9.51G 1.11 0.4599 0.003669 15 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:170.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.2it/s 41.3s0.7ss\n", " all 6112 24898 0.888 0.898 0.948 0.668\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 12/20 9.51G 1.106 0.4462 0.003676 16 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:130.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.2it/s 41.2s0.6ss\n", " all 6112 24898 0.892 0.9 0.949 0.677\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 13/20 9.49G 1.09 0.439 0.003577 16 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:130.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 41.8s0.6ss\n", " all 6112 24898 0.893 0.896 0.946 0.678\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 14/20 9.5G 1.106 0.4477 0.003694 9 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:130.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.2it/s 41.5s0.7ss\n", " all 6112 24898 0.898 0.9 0.951 0.678\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 15/20 9.49G 1.085 0.4333 0.003626 13 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:120.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.3s0.6ss\n", " all 6112 24898 0.895 0.899 0.95 0.678\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 16/20 9.49G 1.084 0.4393 0.003594 13 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:130.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.2it/s 41.7s0.7ss\n", " all 6112 24898 0.892 0.902 0.95 0.68\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 17/20 9.5G 1.081 0.4388 0.003558 7 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:130.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.2s0.7ss\n", " all 6112 24898 0.89 0.898 0.949 0.678\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 18/20 9.48G 1.081 0.4347 0.003579 17 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:130.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.4s0.7ss\n", " all 6112 24898 0.892 0.899 0.95 0.68\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 19/20 9.51G 1.075 0.4287 0.00355 16 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:130.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.0s0.5ss\n", " all 6112 24898 0.897 0.893 0.95 0.681\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 20/20 9.49G 1.069 0.4308 0.003536 16 640: 100% ━━━━━━━━━━━━ 90/90 1.2it/s 1:130.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.0s0.7ss\n", " all 6112 24898 0.895 0.895 0.949 0.681\n", "\n", "20 epochs completed in 0.664 hours.\n", "Optimizer stripped from /kaggle/working/yolo_runs/small_full_ft/weights/last.pt, 20.3MB\n", "Optimizer stripped from /kaggle/working/yolo_runs/small_full_ft/weights/best.pt, 20.3MB\n", "\n", "Validating /kaggle/working/yolo_runs/small_full_ft/weights/best.pt...\n", "YOLO26s summary (fused): 122 layers, 9,465,567 parameters, 0 gradients, 20.5 GFLOPs\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.7s0.6ss\n", " all 6112 24898 0.897 0.893 0.95 0.681\n", "Speed: 0.1ms preprocess, 1.5ms inference, 0.0ms loss, 0.1ms postprocess per image\n", "Results saved to \u001b[1m/kaggle/working/yolo_runs/small_full_ft\u001b[0m\n", "\n", "Training small_partial_ft …\n", "Ultralytics 8.4.19 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, angle=1.0, augment=False, auto_augment=randaugment, batch=64, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/kaggle/working/PRW_YOLO/data.yaml, degrees=0.0, deterministic=True, device=0,1, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=15, erasing=0.4, exist_ok=True, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=10, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.0003, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.1, mode=train, model=/kaggle/working/yolo_runs/small_ch_pretrain/weights/best.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=small_partial_ft, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=15, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=/kaggle/working/yolo_runs, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/kaggle/working/yolo_runs/small_partial_ft, save_frames=False, save_json=False, save_period=10, save_txt=False, scale=0.5, seed=42, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=5.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n", "\n", " from n params module arguments \n", " 0 -1 1 928 ultralytics.nn.modules.conv.Conv [3, 32, 3, 2] \n", " 1 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] \n", " 2 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25] \n", " 3 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", " 4 -1 1 103360 ultralytics.nn.modules.block.C3k2 [128, 256, 1, False, 0.25] \n", " 5 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 6 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True] \n", " 7 -1 1 1180672 ultralytics.nn.modules.conv.Conv [256, 512, 3, 2] \n", " 8 -1 1 1380352 ultralytics.nn.modules.block.C3k2 [512, 512, 1, True] \n", " 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5, 3, True] \n", " 10 -1 1 990976 ultralytics.nn.modules.block.C2PSA [512, 512, 1] \n", " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 13 -1 1 477184 ultralytics.nn.modules.block.C3k2 [768, 256, 1, True] \n", " 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 16 -1 1 136192 ultralytics.nn.modules.block.C3k2 [512, 128, 1, True] \n", " 17 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] \n", " 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 19 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True] \n", " 20 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 22 -1 1 1843712 ultralytics.nn.modules.block.C3k2 [768, 512, 1, True, 0.5, True]\n", " 23 [16, 19, 22] 1 932638 ultralytics.nn.modules.head.Detect [1, 1, True, [128, 256, 512]] \n", "YOLO26s summary: 260 layers, 9,948,638 parameters, 9,948,638 gradients, 22.5 GFLOPs\n", "\n", "Transferred 708/708 items from pretrained weights\n", "\u001b[34m\u001b[1mDDP:\u001b[0m debug command /usr/bin/python3 -m torch.distributed.run --nproc_per_node 2 --master_port 53729 /root/.config/Ultralytics/DDP/_temp_iz0w0vbp133440441127552.py\n", "Ultralytics 8.4.19 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "Transferred 708/708 items from pretrained weights\n", "Freezing layer 'model.0.conv.weight'\n", "Freezing layer 'model.0.bn.weight'\n", "Freezing layer 'model.0.bn.bias'\n", "Freezing layer 'model.1.conv.weight'\n", "Freezing layer 'model.1.bn.weight'\n", "Freezing layer 'model.1.bn.bias'\n", "Freezing layer 'model.2.cv1.conv.weight'\n", "Freezing layer 'model.2.cv1.bn.weight'\n", "Freezing layer 'model.2.cv1.bn.bias'\n", "Freezing layer 'model.2.cv2.conv.weight'\n", "Freezing layer 'model.2.cv2.bn.weight'\n", "Freezing layer 'model.2.cv2.bn.bias'\n", "Freezing layer 'model.2.m.0.cv1.conv.weight'\n", "Freezing layer 'model.2.m.0.cv1.bn.weight'\n", "Freezing layer 'model.2.m.0.cv1.bn.bias'\n", "Freezing layer 'model.2.m.0.cv2.conv.weight'\n", "Freezing layer 'model.2.m.0.cv2.bn.weight'\n", "Freezing layer 'model.2.m.0.cv2.bn.bias'\n", "Freezing layer 'model.3.conv.weight'\n", "Freezing layer 'model.3.bn.weight'\n", "Freezing layer 'model.3.bn.bias'\n", "Freezing layer 'model.4.cv1.conv.weight'\n", "Freezing layer 'model.4.cv1.bn.weight'\n", "Freezing layer 'model.4.cv1.bn.bias'\n", "Freezing layer 'model.4.cv2.conv.weight'\n", "Freezing layer 'model.4.cv2.bn.weight'\n", "Freezing layer 'model.4.cv2.bn.bias'\n", "Freezing layer 'model.4.m.0.cv1.conv.weight'\n", "Freezing layer 'model.4.m.0.cv1.bn.weight'\n", "Freezing layer 'model.4.m.0.cv1.bn.bias'\n", "Freezing layer 'model.4.m.0.cv2.conv.weight'\n", "Freezing layer 'model.4.m.0.cv2.bn.weight'\n", "Freezing layer 'model.4.m.0.cv2.bn.bias'\n", "Freezing layer 'model.5.conv.weight'\n", "Freezing layer 'model.5.bn.weight'\n", "Freezing layer 'model.5.bn.bias'\n", "Freezing layer 'model.6.cv1.conv.weight'\n", "Freezing layer 'model.6.cv1.bn.weight'\n", "Freezing layer 'model.6.cv1.bn.bias'\n", "Freezing layer 'model.6.cv2.conv.weight'\n", "Freezing layer 'model.6.cv2.bn.weight'\n", "Freezing layer 'model.6.cv2.bn.bias'\n", "Freezing layer 'model.6.m.0.cv1.conv.weight'\n", "Freezing layer 'model.6.m.0.cv1.bn.weight'\n", "Freezing layer 'model.6.m.0.cv1.bn.bias'\n", "Freezing layer 'model.6.m.0.cv2.conv.weight'\n", "Freezing layer 'model.6.m.0.cv2.bn.weight'\n", "Freezing layer 'model.6.m.0.cv2.bn.bias'\n", "Freezing layer 'model.6.m.0.cv3.conv.weight'\n", "Freezing layer 'model.6.m.0.cv3.bn.weight'\n", "Freezing layer 'model.6.m.0.cv3.bn.bias'\n", "Freezing layer 'model.6.m.0.m.0.cv1.conv.weight'\n", "Freezing layer 'model.6.m.0.m.0.cv1.bn.weight'\n", "Freezing layer 'model.6.m.0.m.0.cv1.bn.bias'\n", "Freezing layer 'model.6.m.0.m.0.cv2.conv.weight'\n", "Freezing layer 'model.6.m.0.m.0.cv2.bn.weight'\n", "Freezing layer 'model.6.m.0.m.0.cv2.bn.bias'\n", "Freezing layer 'model.6.m.0.m.1.cv1.conv.weight'\n", "Freezing layer 'model.6.m.0.m.1.cv1.bn.weight'\n", "Freezing layer 'model.6.m.0.m.1.cv1.bn.bias'\n", "Freezing layer 'model.6.m.0.m.1.cv2.conv.weight'\n", "Freezing layer 'model.6.m.0.m.1.cv2.bn.weight'\n", "Freezing layer 'model.6.m.0.m.1.cv2.bn.bias'\n", "Freezing layer 'model.7.conv.weight'\n", "Freezing layer 'model.7.bn.weight'\n", "Freezing layer 'model.7.bn.bias'\n", "Freezing layer 'model.8.cv1.conv.weight'\n", "Freezing layer 'model.8.cv1.bn.weight'\n", "Freezing layer 'model.8.cv1.bn.bias'\n", "Freezing layer 'model.8.cv2.conv.weight'\n", "Freezing layer 'model.8.cv2.bn.weight'\n", "Freezing layer 'model.8.cv2.bn.bias'\n", "Freezing layer 'model.8.m.0.cv1.conv.weight'\n", "Freezing layer 'model.8.m.0.cv1.bn.weight'\n", "Freezing layer 'model.8.m.0.cv1.bn.bias'\n", "Freezing layer 'model.8.m.0.cv2.conv.weight'\n", "Freezing layer 'model.8.m.0.cv2.bn.weight'\n", "Freezing layer 'model.8.m.0.cv2.bn.bias'\n", "Freezing layer 'model.8.m.0.cv3.conv.weight'\n", "Freezing layer 'model.8.m.0.cv3.bn.weight'\n", "Freezing layer 'model.8.m.0.cv3.bn.bias'\n", "Freezing layer 'model.8.m.0.m.0.cv1.conv.weight'\n", "Freezing layer 'model.8.m.0.m.0.cv1.bn.weight'\n", "Freezing layer 'model.8.m.0.m.0.cv1.bn.bias'\n", "Freezing layer 'model.8.m.0.m.0.cv2.conv.weight'\n", "Freezing layer 'model.8.m.0.m.0.cv2.bn.weight'\n", "Freezing layer 'model.8.m.0.m.0.cv2.bn.bias'\n", "Freezing layer 'model.8.m.0.m.1.cv1.conv.weight'\n", "Freezing layer 'model.8.m.0.m.1.cv1.bn.weight'\n", "Freezing layer 'model.8.m.0.m.1.cv1.bn.bias'\n", "Freezing layer 'model.8.m.0.m.1.cv2.conv.weight'\n", "Freezing layer 'model.8.m.0.m.1.cv2.bn.weight'\n", "Freezing layer 'model.8.m.0.m.1.cv2.bn.bias'\n", "Freezing layer 'model.9.cv1.conv.weight'\n", "Freezing layer 'model.9.cv1.bn.weight'\n", "Freezing layer 'model.9.cv1.bn.bias'\n", "Freezing layer 'model.9.cv2.conv.weight'\n", "Freezing layer 'model.9.cv2.bn.weight'\n", "Freezing layer 'model.9.cv2.bn.bias'\n", "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1515.5±337.6 MB/s, size: 212.3 KB)\n", "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/PRW_YOLO/labels/train.cache... 5701 images, 3 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 5704/5704 1.0Git/s 0.0s\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/working/PRW_YOLO/images/train/c6s1_000476.jpg: 1 duplicate labels removed\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1338.7±994.7 MB/s, size: 199.0 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/PRW_YOLO/labels/val.cache... 6091 images, 21 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6112/6112 208.4Mit/s 0.0s\n", "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.0003, momentum=0.937) with parameter groups 114 weight(decay=0.0), 126 weight(decay=0.0005), 126 bias(decay=0.0)\n", "Plotting labels to /kaggle/working/yolo_runs/small_partial_ft/labels.jpg... \n", "Image sizes 640 train, 640 val\n", "Using 4 dataloader workers\n", "Logging results to \u001b[1m/kaggle/working/yolo_runs/small_partial_ft\u001b[0m\n", "Starting training for 15 epochs...\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 1/15 5.56G 1.433 0.9248 0.004726 28 640: 100% ━━━━━━━━━━━━ 90/90 1.0s/it 1:311.1sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 44.6s0.6ss\n", " all 6112 24898 0.816 0.791 0.851 0.546\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 2/15 5.56G 1.317 0.7415 0.004031 30 640: 100% ━━━━━━━━━━━━ 90/90 1.3it/s 1:090.7s1s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 44.2s0.7ss\n", " all 6112 24898 0.865 0.864 0.923 0.621\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 3/15 5.6G 1.298 0.7053 0.003993 24 640: 100% ━━━━━━━━━━━━ 90/90 1.3it/s 1:080.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 45.4s0.7ss\n", " all 6112 24898 0.86 0.846 0.917 0.645\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 4/15 5.63G 1.277 0.6847 0.003836 46 640: 100% ━━━━━━━━━━━━ 90/90 1.3it/s 1:090.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 44.2s0.7ss\n", " all 6112 24898 0.885 0.873 0.936 0.636\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 5/15 5.66G 1.265 0.6581 0.003803 12 640: 100% ━━━━━━━━━━━━ 90/90 1.3it/s 1:090.7sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 44.7s0.6ss\n", " all 6112 24898 0.886 0.887 0.942 0.656\n", "Closing dataloader mosaic\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 6/15 5.66G 1.162 0.5194 0.003978 5 640: 100% ━━━━━━━━━━━━ 90/90 1.3it/s 1:100.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 43.8s0.7ss\n", " all 6112 24898 0.888 0.894 0.948 0.661\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 7/15 5.66G 1.159 0.5187 0.003968 4 640: 100% ━━━━━━━━━━━━ 90/90 1.4it/s 1:050.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 43.2s0.7ss\n", " all 6112 24898 0.881 0.894 0.942 0.664\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 8/15 5.66G 1.146 0.4962 0.003826 9 640: 100% ━━━━━━━━━━━━ 90/90 1.4it/s 1:050.5sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.6s0.6ss\n", " all 6112 24898 0.888 0.896 0.947 0.663\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 9/15 5.66G 1.132 0.4766 0.003889 11 640: 100% ━━━━━━━━━━━━ 90/90 1.4it/s 1:060.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.8s0.7ss\n", " all 6112 24898 0.887 0.9 0.948 0.664\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 10/15 5.66G 1.128 0.4779 0.00376 16 640: 100% ━━━━━━━━━━━━ 90/90 1.4it/s 1:040.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.9s0.6ss\n", " all 6112 24898 0.893 0.893 0.948 0.661\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 11/15 5.66G 1.125 0.4681 0.003714 17 640: 100% ━━━━━━━━━━━━ 90/90 1.4it/s 1:060.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 43.5s0.7ss\n", " all 6112 24898 0.897 0.894 0.949 0.677\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 12/15 5.66G 1.103 0.4631 0.003682 16 640: 100% ━━━━━━━━━━━━ 90/90 1.4it/s 1:050.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 43.0s0.6ss\n", " all 6112 24898 0.894 0.893 0.949 0.674\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 13/15 5.66G 1.104 0.4528 0.003643 16 640: 100% ━━━━━━━━━━━━ 90/90 1.4it/s 1:050.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 43.3s0.6ss\n", " all 6112 24898 0.893 0.898 0.949 0.675\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 14/15 5.66G 1.112 0.4589 0.003672 9 640: 100% ━━━━━━━━━━━━ 90/90 1.4it/s 1:050.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.7s0.6ss\n", " all 6112 24898 0.898 0.894 0.949 0.676\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 15/15 5.66G 1.095 0.4579 0.003616 14 640: 100% ━━━━━━━━━━━━ 90/90 1.4it/s 1:050.6sss\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.2s0.7ss\n", " all 6112 24898 0.892 0.9 0.95 0.674\n", "\n", "15 epochs completed in 0.470 hours.\n", "Optimizer stripped from /kaggle/working/yolo_runs/small_partial_ft/weights/last.pt, 20.3MB\n", "Optimizer stripped from /kaggle/working/yolo_runs/small_partial_ft/weights/best.pt, 20.3MB\n", "\n", "Validating /kaggle/working/yolo_runs/small_partial_ft/weights/best.pt...\n", "YOLO26s summary (fused): 122 layers, 9,465,567 parameters, 0 gradients, 20.5 GFLOPs\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 48/48 1.1it/s 42.3s0.7ss\n", " all 6112 24898 0.897 0.894 0.949 0.677\n", "Speed: 0.1ms preprocess, 1.5ms inference, 0.0ms loss, 0.2ms postprocess per image\n", "Results saved to \u001b[1m/kaggle/working/yolo_runs/small_partial_ft\u001b[0m\n" ] } ], "source": [ "for strat_key, sc in STRATEGY.items():\n", " run_name = f'small_{strat_key}_ft'\n", " if not os.path.exists(best_weights_path(run_name)):\n", " print(f'\\nTraining {run_name} …')\n", " run_training(\n", " weights = best_start_weights,\n", " data_yaml = prw_yaml,\n", " name = run_name,\n", " epochs = sc['epochs'],\n", " batch = cfg.ablation_models['small']['batch'],\n", " lr0 = sc['lr0'],\n", " lrf = sc['lrf'],\n", " warmup_epochs = sc['warmup'],\n", " patience = sc['patience'],\n", " freeze = sc['freeze'],\n", " )\n", " else:\n", " print(f'Checkpoint {run_name} already exists - skipping.')" ] }, { "cell_type": "code", "execution_count": 29, "id": "cb142708-9617-48b0-a677-c6eb6665ac68", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T13:07:59.199234Z", "iopub.status.busy": "2026-03-04T13:07:59.198859Z", "iopub.status.idle": "2026-03-04T13:08:02.425078Z", "shell.execute_reply": "2026-03-04T13:08:02.424379Z", "shell.execute_reply.started": "2026-03-04T13:07:59.199178Z" }, "trusted": true }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for strat_key, sc in STRATEGY.items():\n", " plot_training_curves(\n", " os.path.join(cfg.project, f'small_{strat_key}_ft', 'results.csv'),\n", " title_prefix=f'Phase 2 - {sc[\"label\"]} ',\n", " )" ] }, { "cell_type": "code", "execution_count": 15, "id": "7a10728a-eb7d-4f9a-bd7b-78a8effbf553", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:25:55.835115Z", "iopub.status.busy": "2026-03-04T18:25:55.834751Z", "iopub.status.idle": "2026-03-04T18:28:04.937634Z", "shell.execute_reply": "2026-03-04T18:28:04.936712Z", "shell.execute_reply.started": "2026-03-04T18:25:55.835074Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "=================================================================\n", " Evaluating : small_full_ft (Small - Full FT)\n", "=================================================================\n", "YOLO26s summary: 260 layers, 9,948,638 parameters, 0 gradients, 22.5 GFLOPs\n", "Ultralytics 8.4.19 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "YOLO26s summary (fused): 122 layers, 9,465,567 parameters, 0 gradients, 20.5 GFLOPs\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2215.0±1277.3 MB/s, size: 187.2 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/PRW_YOLO/labels/val.cache... 6091 images, 21 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6112/6112 2.0Git/s 0.0s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 191/191 3.0it/s 1:040.4sss\n", " all 6112 24898 0.897 0.893 0.95 0.681\n", "Speed: 0.5ms preprocess, 7.7ms inference, 0.0ms loss, 0.1ms postprocess per image\n", "Results saved in /kaggle/working/yolo_ablation/all_results.json\n", " mAP@50=0.9496 mAP@50-95=0.6811\n", " Params: 9.9M FLOPs: 22.5G\n", " Latency: 7.7 ms Throughput: 130 img/s\n", "\n", "=================================================================\n", " Evaluating : small_partial_ft (Small - Partial FT (freeze bb 0–9))\n", "=================================================================\n", "YOLO26s summary: 260 layers, 9,948,638 parameters, 0 gradients, 22.5 GFLOPs\n", "Ultralytics 8.4.19 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "YOLO26s summary (fused): 122 layers, 9,465,567 parameters, 0 gradients, 20.5 GFLOPs\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 2709.6±474.7 MB/s, size: 279.2 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/PRW_YOLO/labels/val.cache... 6091 images, 21 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6112/6112 1.8Git/s 0.0s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 191/191 3.0it/s 1:030.4sss\n", " all 6112 24898 0.897 0.893 0.949 0.677\n", "Speed: 0.5ms preprocess, 7.6ms inference, 0.0ms loss, 0.1ms postprocess per image\n", "Results saved in /kaggle/working/yolo_ablation/all_results.json\n", " mAP@50=0.9491 mAP@50-95=0.6773\n", " Params: 9.9M FLOPs: 22.5G\n", " Latency: 7.6 ms Throughput: 131 img/s\n" ] } ], "source": [ "for strat_key, sc in STRATEGY.items():\n", " run_name = f'small_{strat_key}_ft'\n", " full_eval_and_profile(\n", " model_or_path = best_weights_path(run_name),\n", " data_yaml = prw_yaml,\n", " run_key = run_name,\n", " display_name = f'Small - {sc[\"label\"]}',\n", " strategy = strat_key,\n", " note = f'Starting from {best_start_key}',\n", " )" ] }, { "cell_type": "code", "execution_count": 17, "id": "d3addd8a-5b62-4896-9d99-be63c4455c2e", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:29:00.608661Z", "iopub.status.busy": "2026-03-04T18:29:00.607745Z", "iopub.status.idle": "2026-03-04T18:29:01.528723Z", "shell.execute_reply": "2026-03-04T18:29:01.527494Z", "shell.execute_reply.started": "2026-03-04T18:29:00.608625Z" }, "trusted": true }, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Phase 2 winner : small_full_ft (mAP@50 = 94.96%)\n" ] } ], "source": [ "phase2_results = {k: RESULTS[k] for k in [f'small_{s}_ft' for s in STRATEGY] if k in RESULTS}\n", "plot_comparison(phase2_results, title='Phase 2 - Strategy Comparison')\n", "\n", "best_strategy = max(phase2_results, key=lambda k: phase2_results[k].get('mAP50', 0))\n", "best_strat_key = best_strategy.replace('small_', '').replace('_ft', '')\n", "print(f'Phase 2 winner : {best_strategy} (mAP@50 = {phase2_results[best_strategy][\"mAP50\"]*100:.2f}%)')" ] }, { "cell_type": "markdown", "id": "27305ac4", "metadata": {}, "source": [ "### Phase 3 - Scale-up: YOLO26-Large on PRW\n", "\n", "This phase applies the best fine-tuning strategy identified in Phase 2 to YOLO26-Large, in order to assess whether the performance gains obtained on the Small scale generalise to a larger model with higher capacity.\n", "\n", "The initialisation strategy for the Large model mirrors the outcome of Phase 1: if the CrowdHuman pre-training was found to be beneficial for the Small model (`best_start_key == 'small_ch_zeroshot'`), the Large model is first pre-trained on CrowdHuman using the same hyperparameters (`cfg.ch_epochs`, `cfg.ch_lr0`) before fine-tuning on PRW. Otherwise, training starts directly from the official COCO weights (`yolo26l.pt`). This conditional logic ensures that the training pipeline applied to the Large model is strictly consistent with the one that produced the best results at the Small scale, making the cross-scale comparison methodologically sound." ] }, { "cell_type": "code", "execution_count": 19, "id": "3f35d5e1", "metadata": { "execution": { "iopub.execute_input": "2026-02-26T09:35:20.558077Z", "iopub.status.busy": "2026-02-26T09:35:20.557557Z", "iopub.status.idle": "2026-02-26T15:07:03.665499Z", "shell.execute_reply": "2026-02-26T15:07:03.662566Z", "shell.execute_reply.started": "2026-02-26T09:35:20.558043Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Phase 1 winner was CrowdHuman — pre-training Large on CrowdHuman first…\n", "\u001b[KDownloading https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26l.pt to 'yolo26l.pt': 100% ━━━━━━━━━━━━ 50.7MB 186.4MB/s 0.3s0.2s<0.1s\n", "Ultralytics 8.4.17 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, angle=1.0, augment=False, auto_augment=randaugment, batch=24, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/kaggle/working/CrowdHuman_YOLO/data.yaml, degrees=0.0, deterministic=True, device=0,1, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=30, erasing=0.4, exist_ok=True, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.0005, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.1, mode=train, model=yolo26l.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=large_ch_pretrain, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=10, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=/kaggle/working/yolo_runs, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/kaggle/working/yolo_runs/large_ch_pretrain, save_frames=False, save_json=False, save_period=10, save_txt=False, scale=0.5, seed=42, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n", "Overriding model.yaml nc=80 with nc=1\n", "\n", " from n params module arguments \n", " 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] \n", " 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n", " 2 -1 2 173824 ultralytics.nn.modules.block.C3k2 [128, 256, 2, True, 0.25] \n", " 3 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 4 -1 2 691712 ultralytics.nn.modules.block.C3k2 [256, 512, 2, True, 0.25] \n", " 5 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", " 6 -1 2 2234368 ultralytics.nn.modules.block.C3k2 [512, 512, 2, True] \n", " 7 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", " 8 -1 2 2234368 ultralytics.nn.modules.block.C3k2 [512, 512, 2, True] \n", " 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5, 3, True] \n", " 10 -1 2 1455616 ultralytics.nn.modules.block.C2PSA [512, 512, 2] \n", " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 13 -1 2 2496512 ultralytics.nn.modules.block.C3k2 [1024, 512, 2, True] \n", " 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 16 -1 2 756736 ultralytics.nn.modules.block.C3k2 [1024, 256, 2, True] \n", " 17 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 19 -1 2 2365440 ultralytics.nn.modules.block.C3k2 [768, 512, 2, True] \n", " 20 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", " 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 22 -1 1 1974784 ultralytics.nn.modules.block.C3k2 [1024, 512, 1, True, 0.5, True]\n", " 23 [16, 19, 22] 1 2800158 ultralytics.nn.modules.head.Detect [1, 1, True, [256, 512, 512]] \n", "YOLO26l summary: 392 layers, 26,177,886 parameters, 26,177,886 gradients, 93.1 GFLOPs\n", "\n", "Transferred 1080/1092 items from pretrained weights\n", "\u001b[34m\u001b[1mDDP:\u001b[0m debug command /usr/bin/python3 -m torch.distributed.run --nproc_per_node 2 --master_port 33873 /root/.config/Ultralytics/DDP/_temp_p_q26x_u135598182377344.py\n", "Ultralytics 8.4.17 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "Overriding model.yaml nc=80 with nc=1\n", "Transferred 1080/1092 items from pretrained weights\n", "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1567.4±1001.3 MB/s, size: 692.2 KB)\n", "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/CrowdHuman_YOLO/labels/train.cache... 15000 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 15000/15000 2.6Git/s 0.0s\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 949.9±391.2 MB/s, size: 274.7 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/CrowdHuman_YOLO/labels/val.cache... 4370 images, 0 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 4370/4370 134.8Mit/s 0.0s\n", "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.0005, momentum=0.937) with parameter groups 178 weight(decay=0.0), 190 weight(decay=0.0005625000000000001), 190 bias(decay=0.0)\n", "Plotting labels to /kaggle/working/yolo_runs/large_ch_pretrain/labels.jpg... \n", "Image sizes 640 train, 640 val\n", "Using 4 dataloader workers\n", "Logging results to \u001b[1m/kaggle/working/yolo_runs/large_ch_pretrain\u001b[0m\n", "Starting training for 30 epochs...\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 1/30 11.3G 1.617 1.35 0.009506 798 640: 100% ━━━━━━━━━━━━ 625/625 1.2it/s 8:22<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 1.4it/s 1:060.5sss\n", " all 4370 99481 0.74 0.629 0.706 0.376\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 2/30 11.3G 1.557 1.16 0.008789 812 640: 100% ━━━━━━━━━━━━ 625/625 1.3it/s 7:58<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 1.9it/s 49.6s0.5ss\n", " all 4370 99481 0.807 0.694 0.784 0.46\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 3/30 11.2G 1.547 1.143 0.009096 502 640: 100% ━━━━━━━━━━━━ 625/625 1.3it/s 7:53<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 1.9it/s 48.0s0.5ss\n", " all 4370 99481 0.813 0.708 0.798 0.472\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 4/30 10.2G 1.511 1.09 0.008748 610 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:41<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 41.6s0.5ss\n", " all 4370 99481 0.81 0.725 0.803 0.475\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 5/30 10.5G 1.5 1.064 0.008643 547 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:41<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 42.0s0.5ss\n", " all 4370 99481 0.82 0.728 0.812 0.492\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 6/30 9.77G 1.476 1.045 0.008481 421 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:39<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 41.7s0.5ss\n", " all 4370 99481 0.829 0.733 0.822 0.501\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 7/30 10.7G 1.476 1.029 0.008383 512 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:39<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 42.7s0.6ss\n", " all 4370 99481 0.825 0.733 0.82 0.496\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 8/30 9.77G 1.459 1.008 0.008249 526 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:41<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 41.8s0.5ss\n", " all 4370 99481 0.829 0.745 0.826 0.501\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 9/30 10.2G 1.448 0.9976 0.008165 566 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:41<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 42.2s0.6ss\n", " all 4370 99481 0.834 0.746 0.833 0.513\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 10/30 11.1G 1.451 0.9892 0.008015 629 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:42<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 44.8s0.5ss\n", " all 4370 99481 0.835 0.746 0.835 0.515\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 11/30 9.95G 1.421 0.9623 0.007918 470 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:42<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 42.3s0.5ss\n", " all 4370 99481 0.838 0.751 0.839 0.523\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 12/30 9.91G 1.42 0.9602 0.007932 567 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:39<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.3it/s 40.9s0.5ss\n", " all 4370 99481 0.839 0.75 0.838 0.524\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 13/30 10.2G 1.41 0.9501 0.007865 430 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:40<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 42.7s0.5ss\n", " all 4370 99481 0.842 0.755 0.843 0.524\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 14/30 11.2G 1.406 0.9432 0.007715 541 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:39<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 44.5s0.5ss\n", " all 4370 99481 0.843 0.76 0.846 0.531\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 15/30 9.91G 1.407 0.9409 0.00769 612 640: 100% ━━━━━━━━━━━━ 625/625 1.3it/s 7:43<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 43.0s0.5ss\n", " all 4370 99481 0.845 0.761 0.849 0.534\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 16/30 10.1G 1.386 0.9158 0.007585 521 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:43<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 43.0s0.5ss\n", " all 4370 99481 0.846 0.767 0.85 0.533\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 17/30 10.1G 1.373 0.9078 0.007534 595 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:42<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 43.5s0.5ss\n", " all 4370 99481 0.845 0.771 0.853 0.537\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 18/30 10.2G 1.374 0.9051 0.007459 264 640: 100% ━━━━━━━━━━━━ 625/625 1.3it/s 7:43<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 44.1s0.6ss\n", " all 4370 99481 0.851 0.765 0.854 0.545\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 19/30 9.59G 1.363 0.8942 0.007444 761 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:41<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 42.7s0.6ss\n", " all 4370 99481 0.849 0.772 0.857 0.545\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 20/30 9.69G 1.353 0.8898 0.007371 590 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:41<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 43.2s0.5ss\n", " all 4370 99481 0.849 0.774 0.857 0.544\n", "Closing dataloader mosaic\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 21/30 10G 1.257 0.7405 0.007537 160 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:29<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.0it/s 45.0s0.5ss\n", " all 4370 99481 0.851 0.772 0.857 0.545\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 22/30 9.83G 1.249 0.7279 0.007387 173 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:29<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 43.2s0.5ss\n", " all 4370 99481 0.853 0.774 0.858 0.544\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 23/30 9.81G 1.245 0.7211 0.007208 258 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:27<0.8s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 43.0s0.6ss\n", " all 4370 99481 0.855 0.777 0.86 0.547\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 24/30 9.89G 1.232 0.7085 0.007109 357 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:27<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 42.7s0.5ss\n", " all 4370 99481 0.855 0.776 0.859 0.546\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 25/30 9.77G 1.219 0.6943 0.007068 309 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:27<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 42.2s0.5ss\n", " all 4370 99481 0.854 0.779 0.86 0.549\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 26/30 9.82G 1.218 0.6936 0.006934 225 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:24<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 41.7s0.5ss\n", " all 4370 99481 0.856 0.78 0.862 0.551\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 27/30 9.98G 1.206 0.6837 0.006896 191 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:27<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 43.4s0.5ss\n", " all 4370 99481 0.855 0.78 0.862 0.551\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 28/30 9.69G 1.2 0.6776 0.006848 307 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:26<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 43.0s0.5ss\n", " all 4370 99481 0.857 0.782 0.862 0.551\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 29/30 9.55G 1.21 0.6851 0.006668 217 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:24<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 43.1s0.5ss\n", " all 4370 99481 0.856 0.782 0.862 0.55\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 30/30 9.77G 1.186 0.6696 0.006692 419 640: 100% ━━━━━━━━━━━━ 625/625 1.4it/s 7:24<0.7s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.2it/s 42.2s0.5ss\n", " all 4370 99481 0.857 0.782 0.863 0.552\n", "\n", "30 epochs completed in 4.248 hours.\n", "Optimizer stripped from /kaggle/working/yolo_runs/large_ch_pretrain/weights/last.pt, 53.0MB\n", "Optimizer stripped from /kaggle/working/yolo_runs/large_ch_pretrain/weights/best.pt, 53.0MB\n", "\n", "Validating /kaggle/working/yolo_runs/large_ch_pretrain/weights/best.pt...\n", "YOLO26l summary (fused): 190 layers, 24,746,511 parameters, 0 gradients, 86.1 GFLOPs\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 92/92 2.1it/s 44.3s0.4ss\n", " all 4370 99481 0.858 0.781 0.863 0.552\n", "Speed: 0.1ms preprocess, 4.6ms inference, 0.0ms loss, 0.2ms postprocess per image\n", "Results saved to \u001b[1m/kaggle/working/yolo_runs/large_ch_pretrain\u001b[0m\n", "Large starting weights: /kaggle/working/yolo_runs/large_ch_pretrain/weights/best.pt\n", "Ultralytics 8.4.17 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "\u001b[34m\u001b[1mengine/trainer: \u001b[0magnostic_nms=False, amp=True, angle=1.0, augment=False, auto_augment=randaugment, batch=24, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/kaggle/working/PRW_YOLO/data.yaml, degrees=0.0, deterministic=True, device=0,1, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=20, erasing=0.4, exist_ok=True, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.0001, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.1, mode=train, model=/kaggle/working/yolo_runs/large_ch_pretrain/weights/best.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=large_full_ft, nbs=64, nms=False, opset=None, optimize=False, optimizer=AdamW, overlap_mask=True, patience=15, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=/kaggle/working/yolo_runs, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/kaggle/working/yolo_runs/large_full_ft, save_frames=False, save_json=False, save_period=10, save_txt=False, scale=0.5, seed=42, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=5.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None\n", "\n", " from n params module arguments \n", " 0 -1 1 1856 ultralytics.nn.modules.conv.Conv [3, 64, 3, 2] \n", " 1 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] \n", " 2 -1 2 173824 ultralytics.nn.modules.block.C3k2 [128, 256, 2, True, 0.25] \n", " 3 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 4 -1 2 691712 ultralytics.nn.modules.block.C3k2 [256, 512, 2, True, 0.25] \n", " 5 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", " 6 -1 2 2234368 ultralytics.nn.modules.block.C3k2 [512, 512, 2, True] \n", " 7 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", " 8 -1 2 2234368 ultralytics.nn.modules.block.C3k2 [512, 512, 2, True] \n", " 9 -1 1 656896 ultralytics.nn.modules.block.SPPF [512, 512, 5, 3, True] \n", " 10 -1 2 1455616 ultralytics.nn.modules.block.C2PSA [512, 512, 2] \n", " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 13 -1 2 2496512 ultralytics.nn.modules.block.C3k2 [1024, 512, 2, True] \n", " 14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", " 15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 16 -1 2 756736 ultralytics.nn.modules.block.C3k2 [1024, 256, 2, True] \n", " 17 -1 1 590336 ultralytics.nn.modules.conv.Conv [256, 256, 3, 2] \n", " 18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 19 -1 2 2365440 ultralytics.nn.modules.block.C3k2 [768, 512, 2, True] \n", " 20 -1 1 2360320 ultralytics.nn.modules.conv.Conv [512, 512, 3, 2] \n", " 21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1] \n", " 22 -1 1 1974784 ultralytics.nn.modules.block.C3k2 [1024, 512, 1, True, 0.5, True]\n", " 23 [16, 19, 22] 1 2800158 ultralytics.nn.modules.head.Detect [1, 1, True, [256, 512, 512]] \n", "YOLO26l summary: 392 layers, 26,177,886 parameters, 26,177,886 gradients, 93.1 GFLOPs\n", "\n", "Transferred 1092/1092 items from pretrained weights\n", "\u001b[34m\u001b[1mDDP:\u001b[0m debug command /usr/bin/python3 -m torch.distributed.run --nproc_per_node 2 --master_port 38641 /root/.config/Ultralytics/DDP/_temp_77vya_qr135598655731184.py\n", "Ultralytics 8.4.17 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "Transferred 1092/1092 items from pretrained weights\n", "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n", "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", "\u001b[34m\u001b[1mtrain: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 48.3±24.9 MB/s, size: 212.3 KB)\n", "\u001b[K\u001b[34m\u001b[1mtrain: \u001b[0mScanning /kaggle/working/PRW_YOLO/labels/train.cache... 5701 images, 3 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 5704/5704 996.8Mit/s 0.0s\n", "\u001b[34m\u001b[1mtrain: \u001b[0m/kaggle/working/PRW_YOLO/images/train/c6s1_000476.jpg: 1 duplicate labels removed\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 132.7±104.7 MB/s, size: 199.0 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/PRW_YOLO/labels/val.cache... 6091 images, 21 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6112/6112 253.8Mit/s 0.0s\n", "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.0001, momentum=0.937) with parameter groups 178 weight(decay=0.0), 190 weight(decay=0.0005625000000000001), 190 bias(decay=0.0)\n", "Plotting labels to /kaggle/working/yolo_runs/large_full_ft/labels.jpg... \n", "Image sizes 640 train, 640 val\n", "Using 4 dataloader workers\n", "Logging results to \u001b[1m/kaggle/working/yolo_runs/large_full_ft\u001b[0m\n", "Starting training for 20 epochs...\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 1/20 8.35G 1.352 0.7617 0.004374 28 640: 100% ━━━━━━━━━━━━ 238/238 1.3it/s 3:10<1.0s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.7it/s 47.1s0.3s\n", " all 6112 24898 0.876 0.88 0.936 0.624\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 2/20 8.63G 1.272 0.6337 0.003968 75 640: 100% ━━━━━━━━━━━━ 238/238 1.4it/s 2:51<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 43.6s0.3s\n", " all 6112 24898 0.884 0.911 0.952 0.661\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 3/20 8.68G 1.23 0.5813 0.003786 43 640: 100% ━━━━━━━━━━━━ 238/238 1.4it/s 2:48<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 3.0it/s 43.3s0.3s\n", " all 6112 24898 0.892 0.911 0.955 0.671\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 4/20 8.68G 1.224 0.5656 0.003718 49 640: 100% ━━━━━━━━━━━━ 238/238 1.4it/s 2:48<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 44.4s0.3s\n", " all 6112 24898 0.897 0.92 0.959 0.678\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 5/20 8.68G 1.178 0.5324 0.00351 53 640: 100% ━━━━━━━━━━━━ 238/238 1.4it/s 2:47<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 44.3s0.3s\n", " all 6112 24898 0.909 0.913 0.96 0.687\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 6/20 8.67G 1.167 0.5275 0.003546 92 640: 100% ━━━━━━━━━━━━ 238/238 1.4it/s 2:46<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 44.3s0.3s\n", " all 6112 24898 0.903 0.914 0.959 0.683\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 7/20 8.66G 1.154 0.5077 0.003413 30 640: 100% ━━━━━━━━━━━━ 238/238 1.4it/s 2:46<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 44.0s0.3s\n", " all 6112 24898 0.903 0.916 0.958 0.683\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 8/20 8.68G 1.133 0.4888 0.003413 35 640: 100% ━━━━━━━━━━━━ 238/238 1.4it/s 2:45<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 44.2s0.3s\n", " all 6112 24898 0.91 0.919 0.961 0.696\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 9/20 8.68G 1.123 0.4872 0.003382 51 640: 100% ━━━━━━━━━━━━ 238/238 1.4it/s 2:46<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 43.4s0.3s\n", " all 6112 24898 0.914 0.914 0.961 0.693\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 10/20 8.66G 1.124 0.4856 0.003336 49 640: 100% ━━━━━━━━━━━━ 238/238 1.4it/s 2:45<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 44.3s0.3s\n", " all 6112 24898 0.908 0.921 0.96 0.691\n", "Closing dataloader mosaic\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, method='weighted_average', num_output_channels=3), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 11/20 8.65G 1.055 0.403 0.003518 25 640: 100% ━━━━━━━━━━━━ 238/238 1.4it/s 2:45<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 44.2s0.3s\n", " all 6112 24898 0.912 0.913 0.96 0.692\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 12/20 8.65G 1.047 0.3985 0.003497 25 640: 100% ━━━━━━━━━━━━ 238/238 1.4it/s 2:44<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 44.2s0.3s\n", " all 6112 24898 0.91 0.915 0.96 0.696\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 13/20 8.62G 1.042 0.3955 0.003435 31 640: 100% ━━━━━━━━━━━━ 238/238 1.5it/s 2:44<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 43.9s0.3s\n", " all 6112 24898 0.909 0.916 0.961 0.7\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 14/20 8.65G 1.037 0.394 0.003463 28 640: 100% ━━━━━━━━━━━━ 238/238 1.5it/s 2:44<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 44.2s0.3s\n", " all 6112 24898 0.911 0.915 0.961 0.7\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 15/20 8.65G 1.032 0.39 0.003452 20 640: 100% ━━━━━━━━━━━━ 238/238 1.5it/s 2:44<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 44.1s0.3s\n", " all 6112 24898 0.915 0.911 0.961 0.702\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 16/20 8.62G 1.024 0.3825 0.003435 24 640: 100% ━━━━━━━━━━━━ 238/238 1.5it/s 2:44<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 43.8s0.3s\n", " all 6112 24898 0.915 0.914 0.962 0.699\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 17/20 8.66G 1.031 0.3887 0.003416 18 640: 100% ━━━━━━━━━━━━ 238/238 1.5it/s 2:43<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 44.1s0.3s\n", " all 6112 24898 0.916 0.912 0.962 0.704\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 18/20 8.66G 1.02 0.3761 0.003414 28 640: 100% ━━━━━━━━━━━━ 238/238 1.5it/s 2:44<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 43.8s0.3s\n", " all 6112 24898 0.917 0.914 0.962 0.706\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 19/20 8.62G 1.017 0.3787 0.003343 26 640: 100% ━━━━━━━━━━━━ 238/238 1.5it/s 2:43<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 43.9s0.3s\n", " all 6112 24898 0.914 0.916 0.962 0.704\n", "\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", "\u001b[K 20/20 8.65G 1.011 0.3725 0.003402 28 640: 100% ━━━━━━━━━━━━ 238/238 1.5it/s 2:43<0.6s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 2.9it/s 43.9s0.3s\n", " all 6112 24898 0.914 0.916 0.962 0.705\n", "\n", "20 epochs completed in 1.234 hours.\n", "Optimizer stripped from /kaggle/working/yolo_runs/large_full_ft/weights/last.pt, 52.9MB\n", "Optimizer stripped from /kaggle/working/yolo_runs/large_full_ft/weights/best.pt, 52.9MB\n", "\n", "Validating /kaggle/working/yolo_runs/large_full_ft/weights/best.pt...\n", "YOLO26l summary (fused): 190 layers, 24,746,511 parameters, 0 gradients, 86.1 GFLOPs\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 128/128 3.1it/s 41.1s0.3s\n", " all 6112 24898 0.916 0.914 0.962 0.706\n", "Speed: 0.1ms preprocess, 4.2ms inference, 0.0ms loss, 0.1ms postprocess per image\n", "Results saved to \u001b[1m/kaggle/working/yolo_runs/large_full_ft\u001b[0m\n" ] } ], "source": [ "sc_l = STRATEGY[best_strat_key]\n", "run_name_l = f'large_{best_strat_key}_ft'\n", "\n", "if best_start_key == 'small_ch_zeroshot':\n", " if not os.path.exists(best_weights_path('large_ch_pretrain')):\n", " print('Phase 1 winner was CrowdHuman - pre-training Large on CrowdHuman first…')\n", " run_training(\n", " weights = cfg.ablation_models['large']['weights'],\n", " data_yaml = ch_yaml,\n", " name = 'large_ch_pretrain',\n", " epochs = cfg.ch_epochs,\n", " batch = cfg.ablation_models['large']['batch'],\n", " lr0 = cfg.ch_lr0,\n", " lrf = cfg.ch_lrf,\n", " warmup_epochs = cfg.ch_warmup_ep,\n", " patience = cfg.ch_patience,\n", " )\n", " else:\n", " print('Large CrowdHuman checkpoint already exists - skipping.')\n", " large_start_weights = best_weights_path('large_ch_pretrain')\n", "else:\n", " large_start_weights = cfg.ablation_models['large']['weights']\n", "\n", "print(f'Large starting weights: {large_start_weights}')\n", "\n", "if not os.path.exists(best_weights_path(run_name_l)):\n", " run_training(\n", " weights = large_start_weights,\n", " data_yaml = prw_yaml,\n", " name = run_name_l,\n", " epochs = sc_l['epochs'],\n", " batch = cfg.ablation_models['large']['batch'],\n", " lr0 = sc_l['lr0'],\n", " lrf = sc_l['lrf'],\n", " warmup_epochs = sc_l['warmup'],\n", " patience = sc_l['patience'],\n", " freeze = sc_l['freeze'],\n", " )\n", "else:\n", " print(f'Checkpoint {run_name_l} already exists - skipping.')\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "4363ac47", "metadata": { "execution": { "iopub.execute_input": "2026-02-26T15:14:06.173332Z", "iopub.status.busy": "2026-02-26T15:14:06.172717Z", "iopub.status.idle": "2026-02-26T15:14:07.916159Z", "shell.execute_reply": "2026-02-26T15:14:07.915395Z", "shell.execute_reply.started": "2026-02-26T15:14:06.173298Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saved → /kaggle/working/yolo_ablation/plots/Phase_3_-_Large_Full_FT_curves.png\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_training_curves(os.path.join(cfg.project, run_name_l, 'results.csv'),\n", " title_prefix=f'Phase 3 - Large {sc_l[\"label\"]} ')" ] }, { "cell_type": "code", "execution_count": 19, "id": "f2715739-9698-4f80-bccb-195fe6601914", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:30:05.349626Z", "iopub.status.busy": "2026-03-04T18:30:05.349279Z", "iopub.status.idle": "2026-03-04T18:33:12.262217Z", "shell.execute_reply": "2026-03-04T18:33:12.261353Z", "shell.execute_reply.started": "2026-03-04T18:30:05.349596Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "=================================================================\n", " Evaluating : large_full_ft (Large - Full FT)\n", "=================================================================\n", "YOLO26l summary: 392 layers, 26,177,886 parameters, 0 gradients, 93.1 GFLOPs\n", "Ultralytics 8.4.19 🚀 Python-3.12.12 torch-2.9.0+cu126 CUDA:0 (Tesla T4, 14913MiB)\n", " CUDA:1 (Tesla T4, 14913MiB)\n", "YOLO26l summary (fused): 190 layers, 24,746,511 parameters, 0 gradients, 86.1 GFLOPs\n", "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1827.4±949.2 MB/s, size: 148.9 KB)\n", "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /kaggle/working/PRW_YOLO/labels/val.cache... 6091 images, 21 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 6112/6112 1.7Git/s 0.0s\n", "\u001b[K Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 191/191 1.0it/s 3:051.2ss\n", " all 6112 24898 0.916 0.914 0.962 0.706\n", "Speed: 0.5ms preprocess, 27.9ms inference, 0.0ms loss, 0.1ms postprocess per image\n", "Results saved in /kaggle/working/yolo_ablation/all_results.json\n", " mAP@50=0.9624 mAP@50-95=0.7060\n", " Params: 26.2M FLOPs: 93.1G\n", " Latency: 27.9 ms Throughput: 36 img/s\n" ] } ], "source": [ "full_eval_and_profile(\n", " best_weights_path(run_name_l), prw_yaml, run_name_l,\n", " display_name=f'Large - {sc_l[\"label\"]}', strategy=best_strat_key,\n", " note='Scale-up, COCO init',\n", ")" ] }, { "cell_type": "markdown", "id": "1839b066", "metadata": {}, "source": [ "## 6. Final Comparison\n", "\n", "This section aggregates the results of all experimental phases into a unified view. All run keys are collected from the `RESULTS` registry, filtered to retain only completed experiments, and passed to `plot_comparison` and `plot_final_comparison` for visual analysis. A summary DataFrame is then constructed, sorted by `mAP@0.5` in descending order, printed to the notebook output, and exported as a CSV file to `cfg.results_dir` for external reference." ] }, { "cell_type": "code", "execution_count": 20, "id": "d800dbe6-7eac-4867-99c9-6b0e925a3fe4", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:33:12.264331Z", "iopub.status.busy": "2026-03-04T18:33:12.263983Z", "iopub.status.idle": "2026-03-04T18:33:14.814445Z", "shell.execute_reply": "2026-03-04T18:33:14.813575Z", "shell.execute_reply.started": "2026-03-04T18:33:12.264299Z" }, "trusted": true }, "outputs": [ { "data": { "image/png": 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "all_run_keys = ['small_coco_zeroshot', 'small_ch_zeroshot',\n", " 'small_full_ft', 'small_partial_ft',\n", " run_name_l]\n", "all_results = {k: RESULTS[k] for k in all_run_keys if k in RESULTS}\n", "\n", "plot_comparison(all_results, title='All Runs')\n", "plot_final_comparison(all_results)" ] }, { "cell_type": "code", "execution_count": 21, "id": "55b9c146-5ef9-4136-a8e4-aa276d805822", "metadata": { "execution": { "iopub.execute_input": "2026-03-04T18:33:41.656640Z", "iopub.status.busy": "2026-03-04T18:33:41.656306Z", "iopub.status.idle": "2026-03-04T18:33:41.731340Z", "shell.execute_reply": "2026-03-04T18:33:41.730447Z", "shell.execute_reply.started": "2026-03-04T18:33:41.656611Z" }, "trusted": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "========================================================================================================================\n", "COMPLETE RESULTS (sorted by mAP@50)\n", "========================================================================================================================\n", " Display Name Strategy mAP@50 (%) mAP@50-95 (%) Precision (%) Recall (%) Params (M) GFLOPs Latency (ms) Throughput (i/s) Note\n", "Run \n", "large_full_ft Large - Full FT full 96.24 70.60 91.62 91.38 26.2 93.12 27.88 35.9 Scale-up, COCO init\n", "small_full_ft Small - Full FT full 94.96 68.11 89.66 89.32 9.9 22.50 7.72 129.5 Starting from small_ch_zeroshot\n", "small_partial_ft Small - Partial FT (freeze bb 0–9) partial 94.91 67.73 89.71 89.33 9.9 22.50 7.65 130.8 Starting from small_ch_zeroshot\n", "small_ch_zeroshot Small - CH pretrain (zero-shot PRW) zero-shot 88.23 53.29 82.09 83.41 9.9 22.50 6.85 146.1 COCO then CrowdHuman, zero-shot on PRW\n", "small_coco_zeroshot Small - COCO (zero-shot) zero-shot 85.82 53.15 83.47 79.42 10.0 22.84 6.29 158.9 COCO pretrained, no PRW fine-tuning\n" ] } ], "source": [ "rows = []\n", "for rk, v in all_results.items():\n", " rows.append({\n", " 'Run': rk,\n", " 'Display Name': v.get('display_name', rk),\n", " 'Strategy': v.get('strategy', '-'),\n", " 'mAP@50 (%)': round(v.get('mAP50', 0)*100, 2),\n", " 'mAP@50-95 (%)': round(v.get('mAP50_95', 0)*100, 2),\n", " 'Precision (%)': round(v.get('precision', 0)*100, 2),\n", " 'Recall (%)': round(v.get('recall', 0)*100, 2),\n", " 'Params (M)': round(v.get('total_params', 0)/1e6, 1),\n", " 'GFLOPs': round(v.get('flops_giga', 0), 2),\n", " 'Latency (ms)': round(v.get('inference_ms', 0), 2),\n", " 'Throughput (i/s)': round(v.get('throughput', 0), 1),\n", " 'Note': v.get('note', ''),\n", " })\n", "\n", "df_final = pd.DataFrame(rows).set_index('Run').sort_values('mAP@50 (%)', ascending=False)\n", "print('\\n' + '='*120)\n", "print('COMPLETE RESULTS (sorted by mAP@50)')\n", "print('='*120)\n", "print(df_final.to_string())\n", "\n", "out_csv = cfg.results_dir / 'all_results.csv'\n", "df_final.to_csv(out_csv)" ] }, { "cell_type": "markdown", "id": "d66ccfd9-d7d8-4cea-a4c0-c3b8015c3457", "metadata": {}, "source": [ "## 7. Conclusions\n", "\n", "### Summary of Results\n", "\n", "The ablation study yields five configurations spanning zero-shot evaluation, intermediate domain pre-training, two fine-tuning strategies, and a scale-up experiment. The results are consistent with established findings in transfer learning for object detection, and each phase produces outcomes that are largely expected from a methodological standpoint, with a few noteworthy observations.\n", "\n", "### Effect of Domain-Specific Fine-tuning\n", "\n", "The most substantial performance gain is observed when transitioning from zero-shot evaluation to target-domain fine-tuning. The COCO zero-shot baseline (`small_coco_zeroshot`) achieves `mAP@0.5 = 85.82%` and `Recall = 79.42%`, which already represents a competitive starting point given that COCO contains a `person` class with broad visual diversity. Fine-tuning on PRW raises `mAP@0.5` to `94.91-94.96%` (+9 pp) and recall to `89.32-89.33%` (+9.9 pp), confirming that domain adaptation is essential even when the source domain partially overlaps with the target task. The recall improvement is particularly significant in the context of person search: the zero-shot model misses approximately one in five pedestrians, a rate that would propagate as unrecoverable errors to the downstream Re-ID module.\n", "\n", "### Effect of CrowdHuman Pre-training\n", "\n", "The intermediate CrowdHuman pre-training step produces a measurable improvement in zero-shot PRW performance (`mAP@0.5`: `85.82%` -> `88.23%`, +2.4 pp; `Recall`: `79.42%` -> `83.41%`, +4 pp), validating the hypothesis that exposure to crowded surveillance-like scenes improves feature generalisation before target-domain fine-tuning. The recall gain in particular suggests that the CrowdHuman pre-training helps the model detect partially occluded or small-scale pedestrians that the COCO-only model tends to miss, which is precisely the failure mode most harmful in a person search pipeline.\n", "\n", "### Full vs. Partial Fine-tuning\n", "\n", "The comparison between full fine-tuning (`mAP@0.5 = 94.96%`, `Recall = 89.32%`) and partial fine-tuning with frozen backbone layers 0-9 (`mAP@0.5 = 94.91%`, `Recall = 89.33%`) reveals a negligible difference of 0.05 pp in `mAP@0.5` and a practically identical recall. This outcome is expected for a single-class detection task on a dataset with limited visual diversity: when the target domain requires only the detection of a single category, the backbone features learned on COCO are already sufficiently general, and freezing them does not impair the model's ability to adapt. From a person search perspective, both strategies deliver equivalent recall, meaning that the partial fine-tuning approach is preferable in practice: it achieves the same detection coverage with a higher learning rate, fewer updated parameters, and reduced risk of catastrophic forgetting of pretrained representations.\n", "\n", "### Scale-up: YOLO26-Large\n", "\n", "Scaling from Small to Large produces the strongest result across all metrics (`mAP@0.5 = 96.24%`, `mAP@0.5:0.95 = 70.60%`, `Recall = 91.38%`). The recall improvement over the best Small configuration is +2.05 pp, meaning that the Large model recovers approximately 2 additional pedestrians out of every 100 that the Small model misses. While modest in relative terms, this gain is non-negligible in a person search context where missed detections are unrecoverable. The improvement is more pronounced on `mAP@0.5:0.95` (+2.49 pp), suggesting that the Large model not only detects more pedestrians but also localises them more precisely, which benefits the quality of the image crops passed to the Re-ID module. This gain comes at a substantial computational cost: the Large model has 2.6x more parameters (26.2M vs. 9.9M), 4.1x higher GFLOPs (93.12 vs. 22.50), and 3.6x higher latency (27.88 ms vs. 7.72 ms per image).\n", "\n", "### Model Selection for Person Search\n", "\n", "From the perspective of the downstream two-stage person search pipeline, the choice between Small and Large involves an explicit trade-off between recall and inference speed. The Large model is the preferred candidate when detection coverage is the primary concern and computational resources allow it, given its superior recall (91.38%) and localisation accuracy. The Small model is a viable alternative in latency-constrained deployments (7.72 ms vs. 27.88 ms per image), where its recall of 89.32% remains well above the zero-shot baseline and represents an acceptable operating point for the Re-ID stage.\n", "\n", "### Methodological Note on Parameter Counts\n", "\n", "A non-trivial discrepancy in parameter counts was identified during the study. The zero-shot COCO model reports 10.0M parameters under direct PyTorch counting, while all fine-tuned models report 9.9M. This difference of approximately 61,000 parameters is entirely attributable to the detection head being rebuilt by `ultralytics` when adapting the model to a single-class dataset: the final classification convolution changes from `Conv2d(128, 80)` to `Conv2d(128, 1)` across three output scales and two head branches (`cv3` and `one2one_cv3`), accounting for exactly `6 * (10320 - 129) = 61,146` fewer parameters. The profiling values reported in the table are those produced by `ultralytics model_info()` on the fused model, which are consistent across all runs and directly comparable." ] }, { "cell_type": "markdown", "id": "23f12f7d", "metadata": {}, "source": [ "## References\n", "\n", "[1] Zheng, L., Zhang, H., Sun, S., Chandraker, M., Yang, Y., and Tian, Q.\n", "\"Person Re-identification in the Wild.\"\n", "*IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, 2017.\n", "https://arxiv.org/abs/1604.02531\n", "\n", "[2] Shao, S., Zhao, Z., Li, B., Xiao, T., Yu, G., Zhang, X., and Sun, J.\n", "\"CrowdHuman: A Benchmark for Detecting Human in a Crowd.\"\n", "*arXiv preprint arXiv:1805.00123*, 2018.\n", "https://arxiv.org/abs/1805.00123\n", "\n", "[3] Ultralytics.\n", "\"YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection.\"\n", "*arXiv preprint arXiv:2509.25164*, 2025.\n", "https://arxiv.org/abs/2509.25164\n" ] } ], "metadata": { "kaggle": { "accelerator": "none", "dataSources": [ { "databundleVersionId": 7467047, "datasetId": 4286215, "sourceId": 7376273, "sourceType": "datasetVersion" }, { "databundleVersionId": 14747316, "datasetId": 8908164, "sourceId": 13973040, "sourceType": "datasetVersion" } ], "dockerImageVersionId": 31287, "isGpuEnabled": false, "isInternetEnabled": true, "language": "python", "sourceType": "notebook" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 5 }