{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "-tGvKM82qJsO" }, "source": [ "# Object Detection" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wed Apr 3 23:56:37 2024 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 525.89.02 Driver Version: 525.89.02 CUDA Version: 12.0 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 Tesla V100-PCIE... On | 00000000:3B:00.0 Off | 0 |\n", "| N/A 30C P0 25W / 250W | 0MiB / 16384MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", "| 1 Tesla V100-PCIE... On | 00000000:D8:00.0 Off | 0 |\n", "| N/A 32C P0 26W / 250W | 0MiB / 16384MiB | 0% Default |\n", "| | | N/A |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ], "source": [ "!nvidia-smi" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "I1WFGtgTqItg", "outputId": "ef51af7f-2a14-4a21-e6ef-cef04da2d4c7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extraction completed.\n" ] } ], "source": [ "from zipfile import ZipFile\n", "\n", "# Path to your ZIP file\n", "zip_file_path = '/user/charviku/DL_Project/final_data.zip'\n", "# Destination directory where you want to extract the files\n", "extraction_directory = '/user/charviku/DL_Project/in'\n", "\n", "# Extract the ZIP file\n", "with ZipFile(zip_file_path, 'r') as zip_ref:\n", " zip_ref.extractall(extraction_directory)\n", "\n", "print(\"Extraction completed.\")\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "65r28yu_uZUc" }, "outputs": [], "source": [ "import xml.etree.ElementTree as ET\n", "import os\n", "from PIL import Image\n", "\n", "# Load class names and create a mapping to IDs\n", "def load_class_names(class_file_path):\n", " with open(class_file_path, 'r') as file:\n", " class_names = file.read().strip().split('\\n')\n", " return {name: i for i, name in enumerate(class_names)}\n", "\n", "def convert_voc_to_yolo(voc_xml_file, class_mapping, img_width, img_height):\n", " tree = ET.parse(voc_xml_file)\n", " root = tree.getroot()\n", " yolo_format = []\n", "\n", " for member in root.findall('object'):\n", " classname = member.find('name').text\n", " class_id = class_mapping[classname]\n", "\n", " bndbox = member.find('bndbox')\n", " xmin = int(bndbox.find('xmin').text)\n", " ymin = int(bndbox.find('ymin').text)\n", " xmax = int(bndbox.find('xmax').text)\n", " ymax = int(bndbox.find('ymax').text)\n", "\n", " x_center = ((xmin + xmax) / 2) / img_width\n", " y_center = ((ymin + ymax) / 2) / img_height\n", " width = (xmax - xmin) / img_width\n", " height = (ymax - ymin) / img_height\n", "\n", " yolo_format.append(f\"{class_id} {x_center} {y_center} {width} {height}\")\n", "\n", " return yolo_format\n", "\n", "def process_dataset(dataset_directory, class_file_path):\n", " class_mapping = load_class_names(class_file_path)\n", "\n", " for class_dir in os.listdir(dataset_directory):\n", " class_path = os.path.join(dataset_directory, class_dir)\n", " if os.path.isdir(class_path):\n", " for file in os.listdir(class_path):\n", " if file.endswith('.xml'):\n", " img_file = os.path.splitext(file)[0] + '.jpg'\n", " img_path = os.path.join(class_path, img_file)\n", " xml_path = os.path.join(class_path, file)\n", "\n", " # Use PIL to get image dimensions\n", " with Image.open(img_path) as img:\n", " img_width, img_height = img.size\n", "\n", " yolo_annotations = convert_voc_to_yolo(xml_path, class_mapping, img_width, img_height)\n", " yolo_annotation_text = \"\\n\".join(yolo_annotations)\n", "\n", " # Save YOLO annotations to a .txt file\n", " txt_filename = os.path.splitext(xml_path)[0] + '.txt'\n", " with open(txt_filename, 'w') as f:\n", " f.write(yolo_annotation_text)\n", "\n", "# Assuming your class file path and dataset directory are as follows:\n", "class_file_path = '/user/charviku/DL_Project/Final_classes.txt'\n", "dataset_directory = '/user/charviku/DL_Project/in/initial_data_annotated'\n", "process_dataset(dataset_directory, class_file_path)\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Defaulting to user installation because normal site-packages is not writeable\n", "Collecting scikit-learn\n", " Downloading scikit_learn-1.4.1.post1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n", "Requirement already satisfied: numpy<2.0,>=1.19.5 in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/MPI/gcc/11.2.0/openmpi/4.1.1/scipy-bundle/2021.10/lib/python3.9/site-packages (from scikit-learn) (1.21.3)\n", "Requirement already satisfied: scipy>=1.6.0 in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/MPI/gcc/11.2.0/openmpi/4.1.1/scipy-bundle/2021.10/lib/python3.9/site-packages (from scikit-learn) (1.7.1)\n", "Collecting joblib>=1.2.0 (from scikit-learn)\n", " Downloading joblib-1.3.2-py3-none-any.whl.metadata (5.4 kB)\n", "Requirement already satisfied: threadpoolctl>=2.0.0 in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/Compiler/gcccore/11.2.0/python/3.9.6/lib/python3.9/site-packages (from scikit-learn) (2.2.0)\n", "Downloading scikit_learn-1.4.1.post1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.2 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.2/12.2 MB\u001b[0m \u001b[31m94.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m:01\u001b[0m\n", "\u001b[?25hDownloading joblib-1.3.2-py3-none-any.whl (302 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.2/302.2 kB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h\u001b[33mDEPRECATION: matlabengineforpython R2021b has a non-standard version number. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of matlabengineforpython or contact the author to suggest that they release a version with a conforming version number. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n", "\u001b[0mInstalling collected packages: joblib, scikit-learn\n", "Successfully installed joblib-1.3.2 scikit-learn-1.4.1.post1\n", "\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "pip install scikit-learn" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6wQoeoSzsUZ4", "outputId": "91698654-1b61-4e91-a7f4-4f706d28faad" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training images: 3988\n", "Validation images: 499\n", "Test images: 499\n" ] } ], "source": [ "import os\n", "import random\n", "from sklearn.model_selection import train_test_split\n", "\n", "# Define your dataset directory and output files\n", "dataset_dir = '/user/charviku/DL_Project/in/initial_data_annotated'\n", "output_train = '/user/charviku/DL_Project/train.txt'\n", "output_val = '/user/charviku/DL_Project/valid.txt'\n", "output_test = '/user/charviku/DL_Project/test.txt'\n", "\n", "# Specify the split ratios\n", "train_ratio = 0.8\n", "val_ratio = 0.1\n", "test_ratio = 0.1 # Ensures train + val + test = 1.0\n", "\n", "# Collect all image file paths\n", "image_paths = []\n", "for root, dirs, files in os.walk(dataset_dir):\n", " for file in files:\n", " if file.endswith('.jpg'):\n", " image_paths.append(os.path.join(root, file))\n", "\n", "# Split the data\n", "train_val_paths, test_paths = train_test_split(image_paths, test_size=test_ratio, random_state=42)\n", "train_paths, val_paths = train_test_split(train_val_paths, test_size=val_ratio/(train_ratio+val_ratio), random_state=42)\n", "\n", "# Function to write paths to a file\n", "def write_paths(file_paths, output_file):\n", " with open(output_file, 'w') as f:\n", " for path in file_paths:\n", " f.write(path + '\\n')\n", "\n", "# Write the splits to their respective files\n", "write_paths(train_paths, output_train)\n", "write_paths(val_paths, output_val)\n", "write_paths(test_paths, output_test)\n", "\n", "print(f\"Training images: {len(train_paths)}\")\n", "print(f\"Validation images: {len(val_paths)}\")\n", "print(f\"Test images: {len(test_paths)}\")\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sCJT9Xxx4cnH", "outputId": "77ae092a-83ca-442c-bc36-0d531a5be850" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cloning into 'yolov7'...\n", "remote: Enumerating objects: 1197, done.\u001b[K\n", "remote: Total 1197 (delta 0), reused 0 (delta 0), pack-reused 1197\u001b[K\n", "Receiving objects: 100% (1197/1197), 74.23 MiB | 61.69 MiB/s, done.\n", "Resolving deltas: 100% (519/519), done.\n", "Updating files: 100% (108/108), done.\n", "/user/charviku/DL_Project/yolov7\n" ] } ], "source": [ "!git clone https://github.com/WongKinYiu/yolov7.git\n", "%cd yolov7\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "Pp_80KK95PFq", "outputId": "1d7b7a25-77ef-4233-dad0-b2285f32cb68" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Defaulting to user installation because normal site-packages is not writeable\n", "Requirement already satisfied: matplotlib>=3.2.2 in /user/charviku/.local/lib/python3.9/site-packages (from -r requirements.txt (line 4)) (3.8.2)\n", "Requirement already satisfied: numpy<1.24.0,>=1.18.5 in 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A possible replacement is to upgrade to a newer version of matlabengineforpython or contact the author to suggest that they release a version with a conforming version number. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n", "\u001b[0mInstalling collected packages: tqdm, opencv-python, thop, seaborn\n", "Successfully installed opencv-python-4.9.0.80 seaborn-0.13.2 thop-0.1.1.post2209072238 tqdm-4.66.2\n", "\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "!pip install -r requirements.txt\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "ZvAB0t1a53xr" }, "outputs": [], "source": [ "yaml_content = \"\"\"\n", "train: /user/charviku/DL_Project/train.txt\n", "val: /user/charviku/DL_Project/valid.txt\n", "test: /user/charviku/DL_Project/test.txt\n", "\n", "nc: 100\n", "names: [\n", " 'all_purpose_flour', 'almonds', 'apple', 'apricot', 'asparagus', 'avocado', 'bacon', 'banana', 'barley', 'basil',\n", " 'basmati_rice', 'beans', 'beef', 'beets', 'bell_pepper', 'berries', 'biscuits', 'blackberries', 'black_pepper',\n", " 'blueberries', 'bread', 'bread_crumbs', 'bread_flour', 'broccoli', 'brownie_mix', 'brown_rice', 'butter', 'cabbage',\n", " 'cake', 'cardamom', 'carrot', 'cashews', 'cauliflower', 'celery', 'cereal', 'cheese', 'cherries', 'chicken',\n", " 'chickpeas', 'chocolate', 'chocolate_chips', 'chocolate_syrup', 'cilantro', 'cinnamon', 'clove', 'cocoa_powder',\n", " 'coconut', 'cookies', 'corn', 'cucumber', 'dates', 'eggplant', 'eggs', 'fish', 'garlic', 'ginger', 'grapes', 'honey',\n", " 'jalapeno', 'kidney_beans', 'lemon', 'mango', 'marshmallows', 'milk', 'mint', 'muffins', 'mushroom', 'noodles',\n", " 'nuts', 'oats', 'okra', 'olive', 'onion', 'orange', 'oreo_cookies', 'pasta', 'pear', 'pepper', 'pineapple',\n", " 'pistachios', 'pork', 'potato', 'pumpkin', 'radishes', 'raisins', 'red_chilies', 'rice', 'rosemary', 'salmon', 'salt',\n", " 'shrimp', 'spinach', 'strawberries', 'sugar', 'sweet_potato', 'tomato', 'vanilla_ice_cream', 'walnuts', 'watermelon',\n", " 'yogurt'\n", "]\n", "\"\"\"\n", "\n", "with open('/user/charviku/DL_Project/ingredients.yaml', 'w') as file:\n", " file.write(yaml_content.strip())\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uzzsNi5Y6fdU", "outputId": "d2702961-cc1a-42dc-ee26-e3bfdc361240" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/user/charviku/DL_Project/yolov7\n" ] } ], "source": [ "%cd /user/charviku/DL_Project/yolov7\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sQbHP-J86FhY", "outputId": "3832631b-f51e-4b7c-9ac1-0b70e7606d0a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "YOLOR 🚀 v0.1-128-ga207844 torch 1.13.1 CUDA:0 (Tesla V100-PCIE-16GB, 16150.875MB)\n", "\n", "Namespace(weights='yolov7.pt', cfg='cfg/training/yolov7.yaml', data='/user/charviku/DL_Project/ingredients.yaml', hyp='data/hyp.scratch.p5.yaml', epochs=100, batch_size=32, img_size=[320, 320], rect=False, resume=False, nosave=False, notest=False, noautoanchor=False, evolve=False, bucket='', cache_images=False, image_weights=False, device='0', multi_scale=False, single_cls=False, adam=False, sync_bn=False, local_rank=-1, workers=4, project='runs/train', entity=None, name='exp', exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias='latest', freeze=[0], v5_metric=False, world_size=1, global_rank=-1, save_dir='runs/train/exp2', total_batch_size=32)\n", "\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.3, cls_pw=1.0, obj=0.7, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.2, scale=0.9, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.15, copy_paste=0.0, paste_in=0.15, loss_ota=1\n", "\u001b[34m\u001b[1mwandb: \u001b[0mInstall Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)\n", "Overriding model.yaml nc=80 with nc=100\n", "\n", " from n params module arguments \n", " 0 -1 1 928 models.common.Conv [3, 32, 3, 1] \n", " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", " 2 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", " 4 -1 1 8320 models.common.Conv [128, 64, 1, 1] \n", " 5 -2 1 8320 models.common.Conv [128, 64, 1, 1] \n", " 6 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", " 7 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", " 8 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", " 9 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n", " 10 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n", " 11 -1 1 66048 models.common.Conv [256, 256, 1, 1] \n", " 12 -1 1 0 models.common.MP [] \n", " 13 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", " 14 -3 1 33024 models.common.Conv [256, 128, 1, 1] \n", " 15 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", " 16 [-1, -3] 1 0 models.common.Concat [1] \n", " 17 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", " 18 -2 1 33024 models.common.Conv [256, 128, 1, 1] \n", " 19 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", " 20 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", " 21 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", " 22 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n", " 23 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n", " 24 -1 1 263168 models.common.Conv [512, 512, 1, 1] \n", " 25 -1 1 0 models.common.MP [] \n", " 26 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", " 27 -3 1 131584 models.common.Conv [512, 256, 1, 1] \n", " 28 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", " 29 [-1, -3] 1 0 models.common.Concat [1] \n", " 30 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", " 31 -2 1 131584 models.common.Conv [512, 256, 1, 1] \n", " 32 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", " 33 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", " 34 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", " 35 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", " 36 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n", " 37 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] \n", " 38 -1 1 0 models.common.MP [] \n", " 39 -1 1 525312 models.common.Conv [1024, 512, 1, 1] \n", " 40 -3 1 525312 models.common.Conv [1024, 512, 1, 1] \n", " 41 -1 1 2360320 models.common.Conv [512, 512, 3, 2] \n", " 42 [-1, -3] 1 0 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models.common.Concat [1] \n", " 94 -1 1 525312 models.common.Conv [1024, 512, 1, 1] \n", " 95 -2 1 525312 models.common.Conv [1024, 512, 1, 1] \n", " 96 -1 1 1180160 models.common.Conv [512, 256, 3, 1] \n", " 97 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", " 98 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", " 99 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n", "100[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n", "101 -1 1 1049600 models.common.Conv [2048, 512, 1, 1] \n", "102 75 1 328704 models.common.RepConv [128, 256, 3, 1] \n", "103 88 1 1312768 models.common.RepConv [256, 512, 3, 1] \n", "104 101 1 5246976 models.common.RepConv [512, 1024, 3, 1] \n", "105 [102, 103, 104] 1 568162 models.yolo.IDetect [100, [[12, 16, 19, 36, 40, 28], [36, 75, 76, 55, 72, 146], [142, 110, 192, 243, 459, 401]], [256, 512, 1024]]\n", "/cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/MPI/gcc/11.2.0/openmpi/4.1.1/pytorch/1.13.1-CUDA-11.8.0/lib/python3.9/site-packages/torch/functional.py:504: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /var/tmp/aebruno2/easybuild/build/PyTorch/1.13.1/foss-2021b-CUDA-11.8.0/pytorch-v1.13.1/aten/src/ATen/native/TensorShape.cpp:3190.)\n", " return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]\n", "Model Summary: 415 layers, 37730562 parameters, 37730562 gradients, 106.8 GFLOPS\n", "\n", "Transferred 552/566 items from yolov7.pt\n", "Scaled weight_decay = 0.0005\n", "Optimizer groups: 95 .bias, 95 conv.weight, 98 other\n", "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/user/charviku/DL_Project/train.cache' images and labels... 398\u001b[0m\n", "\u001b[34m\u001b[1mval: \u001b[0mScanning '/user/charviku/DL_Project/valid.cache' images and labels... 499 f\u001b[0m\n", "\n", "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 5.21, Best Possible Recall (BPR) = 1.0000\n", "Image sizes 320 train, 320 test\n", "Using 4 dataloader workers\n", "Logging results to runs/train/exp2\n", "Starting training for 100 epochs...\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 0/99 6.42G 0.05893 0.007474 0.06394 0.1304 41 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.355 0.0124 0.00473 0.00262\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 1/99 6.44G 0.03447 0.009123 0.06219 0.1058 55 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.0576 0.103 0.0145 0.0109\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 2/99 6.44G 0.02901 0.009443 0.06186 0.1003 47 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.0207 0.189 0.0201 0.0161\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 3/99 6.44G 0.02664 0.008717 0.06156 0.09691 36 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.0213 0.138 0.0221 0.0172\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 4/99 6.44G 0.02865 0.008369 0.06141 0.09843 43 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.0633 0.142 0.0258 0.0205\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 5/99 6.44G 0.02656 0.008483 0.06107 0.09611 54 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.143 0.089 0.0223 0.0164\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 6/99 6.44G 0.02654 0.008155 0.06052 0.09521 44 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.096 0.131 0.0302 0.0229\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 7/99 6.44G 0.02699 0.0086 0.06021 0.0958 51 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.0198 0.167 0.0195 0.015\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 8/99 6.44G 0.02751 0.008372 0.05972 0.0956 47 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.328 0.0459 0.0194 0.0145\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 9/99 6.44G 0.02637 0.008422 0.05883 0.09362 39 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.28 0.157 0.043 0.0323\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 10/99 6.44G 0.02617 0.008412 0.05738 0.09196 43 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.142 0.252 0.0528 0.0397\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 11/99 6.44G 0.02564 0.008484 0.05597 0.09009 38 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.65 0.0885 0.0517 0.0409\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 12/99 6.44G 0.02605 0.008226 0.05474 0.08902 35 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.415 0.147 0.0681 0.0541\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 13/99 6.44G 0.02549 0.0082 0.05298 0.08668 49 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.165 0.236 0.0767 0.0634\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 14/99 6.44G 0.02555 0.00835 0.05151 0.08541 59 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.372 0.195 0.0821 0.0666\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 15/99 6.44G 0.0253 0.00816 0.05012 0.08358 32 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.418 0.238 0.119 0.0966\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 16/99 6.44G 0.02481 0.008267 0.0483 0.08138 54 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.292 0.261 0.148 0.119\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 17/99 6.44G 0.02521 0.008244 0.04759 0.08104 38 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.211 0.303 0.146 0.119\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 18/99 6.44G 0.02498 0.007996 0.04597 0.07895 48 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.309 0.29 0.165 0.135\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 19/99 6.44G 0.02481 0.007921 0.04433 0.07706 49 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.347 0.3 0.185 0.155\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 20/99 6.44G 0.02473 0.008011 0.04331 0.07605 51 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.383 0.26 0.205 0.17\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 21/99 6.44G 0.02438 0.007891 0.04166 0.07393 44 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.408 0.247 0.217 0.179\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 22/99 6.44G 0.0242 0.007849 0.04045 0.0725 50 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.438 0.315 0.27 0.224\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 23/99 6.44G 0.02451 0.007912 0.0397 0.07212 47 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.362 0.264 0.231 0.188\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 24/99 6.44G 0.02415 0.007795 0.03856 0.07051 48 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.32 0.326 0.254 0.211\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 25/99 6.44G 0.02429 0.00775 0.03797 0.07002 47 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.465 0.267 0.251 0.208\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 26/99 6.44G 0.02424 0.007821 0.03705 0.06911 51 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.295 0.365 0.257 0.218\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 27/99 6.44G 0.02405 0.007641 0.03622 0.06791 39 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.333 0.383 0.284 0.241\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 28/99 6.44G 0.02384 0.007582 0.03547 0.0669 37 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.479 0.315 0.299 0.25\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 29/99 6.44G 0.02359 0.00759 0.03437 0.06556 42 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.324 0.346 0.295 0.248\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 30/99 6.44G 0.0237 0.007385 0.03351 0.06459 44 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.516 0.303 0.322 0.266\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 31/99 6.44G 0.02376 0.007478 0.033 0.06424 40 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.372 0.386 0.309 0.258\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 32/99 6.44G 0.02344 0.007618 0.03228 0.06334 45 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.263 0.434 0.322 0.273\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 33/99 6.44G 0.02327 0.007463 0.03159 0.06232 61 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.467 0.346 0.324 0.274\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 34/99 6.44G 0.02318 0.007452 0.03093 0.06157 62 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.491 0.377 0.357 0.299\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 35/99 6.44G 0.02308 0.007366 0.03054 0.06099 35 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.474 0.375 0.354 0.299\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 36/99 6.44G 0.02292 0.007254 0.02897 0.05915 45 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.477 0.39 0.359 0.307\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 37/99 6.44G 0.02285 0.007356 0.02905 0.05925 55 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.327 0.424 0.358 0.306\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 38/99 6.44G 0.0226 0.007282 0.02878 0.05866 60 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.476 0.377 0.365 0.311\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 39/99 6.44G 0.02225 0.007231 0.02745 0.05692 44 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.382 0.452 0.39 0.323\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 40/99 6.44G 0.02186 0.007114 0.0268 0.05577 52 320\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Class Images Labels P R mAP@.5\n", " all 499 367 0.362 0.441 0.394 0.341\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 41/99 6.44G 0.0222 0.007117 0.02663 0.05595 37 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.535 0.405 0.421 0.361\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 42/99 6.44G 0.02216 0.007183 0.02588 0.05522 33 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.447 0.417 0.409 0.356\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 43/99 6.44G 0.02219 0.007101 0.02547 0.05476 47 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.323 0.496 0.391 0.338\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 44/99 6.44G 0.02182 0.006971 0.02509 0.05388 41 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.351 0.486 0.426 0.365\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 45/99 6.44G 0.0217 0.006926 0.02502 0.05364 44 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.435 0.44 0.432 0.371\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 46/99 6.44G 0.0217 0.006932 0.02458 0.05322 46 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.583 0.421 0.455 0.389\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 47/99 6.44G 0.02152 0.006853 0.02405 0.05243 45 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.731 0.345 0.449 0.385\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 48/99 6.44G 0.02104 0.006943 0.02369 0.05167 39 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.455 0.449 0.443 0.378\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 49/99 6.44G 0.02078 0.006797 0.0225 0.05007 43 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.427 0.496 0.449 0.382\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 50/99 6.44G 0.02096 0.00671 0.0217 0.04937 37 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.448 0.438 0.454 0.398\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 51/99 6.44G 0.02091 0.006828 0.02204 0.04978 41 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.456 0.495 0.464 0.403\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 52/99 6.44G 0.02067 0.006721 0.0207 0.04809 45 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.521 0.442 0.488 0.427\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 53/99 6.44G 0.0207 0.006638 0.02062 0.04796 49 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.453 0.448 0.464 0.408\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 54/99 6.44G 0.02011 0.006675 0.02031 0.04709 42 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.538 0.407 0.469 0.409\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 55/99 6.44G 0.02013 0.006693 0.01989 0.04671 44 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.56 0.419 0.482 0.421\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 56/99 6.44G 0.01978 0.006548 0.01918 0.04551 50 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.544 0.413 0.477 0.428\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 57/99 6.44G 0.01954 0.006527 0.01855 0.04462 46 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.563 0.456 0.496 0.436\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 58/99 6.44G 0.01977 0.006555 0.0187 0.04502 41 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.398 0.513 0.494 0.437\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 59/99 6.44G 0.01951 0.006455 0.01861 0.04457 45 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.488 0.449 0.476 0.425\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 60/99 6.44G 0.01906 0.006252 0.01783 0.04314 33 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.564 0.431 0.501 0.445\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 61/99 6.44G 0.01925 0.006372 0.01786 0.04348 61 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.487 0.461 0.491 0.439\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 62/99 6.44G 0.0186 0.006425 0.01742 0.04245 53 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.511 0.452 0.504 0.453\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 63/99 6.44G 0.01871 0.006261 0.01705 0.04201 42 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.478 0.497 0.51 0.461\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 64/99 6.44G 0.01825 0.006214 0.01572 0.04018 49 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.536 0.457 0.505 0.45\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 65/99 6.44G 0.01821 0.006159 0.01588 0.04025 40 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.534 0.474 0.501 0.452\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 66/99 6.44G 0.01829 0.006155 0.01586 0.04031 43 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.442 0.504 0.508 0.459\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 67/99 6.44G 0.01856 0.006116 0.0157 0.04038 38 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.418 0.515 0.502 0.449\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 68/99 6.44G 0.01776 0.006024 0.01507 0.03885 47 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.501 0.47 0.518 0.463\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 69/99 6.44G 0.01792 0.00615 0.01507 0.03914 37 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.436 0.54 0.513 0.468\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 70/99 6.44G 0.01757 0.005912 0.01453 0.03801 41 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.438 0.545 0.524 0.472\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 71/99 6.44G 0.01759 0.005946 0.01469 0.03822 36 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.465 0.49 0.524 0.479\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 72/99 6.44G 0.01749 0.005838 0.01451 0.03783 39 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.479 0.507 0.492 0.446\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 73/99 6.44G 0.01716 0.005929 0.01375 0.03684 56 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.446 0.539 0.523 0.472\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 74/99 6.44G 0.01694 0.005841 0.01355 0.03633 50 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.592 0.465 0.527 0.482\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 75/99 6.44G 0.01677 0.00572 0.01317 0.03566 52 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.512 0.499 0.535 0.489\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 76/99 6.44G 0.01654 0.005625 0.01247 0.03463 30 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.579 0.444 0.524 0.474\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 77/99 6.44G 0.01675 0.005614 0.01276 0.03513 57 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.595 0.491 0.54 0.494\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 78/99 6.44G 0.01673 0.005599 0.01259 0.03492 56 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.567 0.474 0.529 0.485\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 79/99 6.44G 0.01606 0.005595 0.01216 0.03382 43 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.466 0.518 0.531 0.477\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 80/99 6.44G 0.01609 0.005451 0.01197 0.03351 48 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.473 0.492 0.517 0.475\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 81/99 6.44G 0.01588 0.00554 0.01182 0.03324 42 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.42 0.558 0.535 0.485\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 82/99 6.44G 0.01603 0.005463 0.01159 0.03308 50 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.558 0.483 0.533 0.486\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 83/99 6.44G 0.0156 0.005384 0.011 0.03199 40 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.518 0.508 0.539 0.497\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 84/99 6.44G 0.01566 0.005527 0.01138 0.03256 58 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.58 0.468 0.535 0.494\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 85/99 6.44G 0.0153 0.005317 0.01069 0.03131 42 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.553 0.501 0.535 0.493\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 86/99 6.44G 0.01562 0.005215 0.01068 0.03151 59 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.525 0.507 0.531 0.488\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 87/99 6.44G 0.01566 0.005309 0.01105 0.03202 46 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.512 0.502 0.538 0.492\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 88/99 6.44G 0.0151 0.005244 0.01063 0.03097 39 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.57 0.458 0.529 0.486\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 89/99 6.44G 0.01505 0.005278 0.01022 0.03055 48 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.476 0.551 0.547 0.502\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 90/99 6.44G 0.01501 0.005181 0.01009 0.03027 55 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.494 0.518 0.537 0.496\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 91/99 6.44G 0.01523 0.005123 0.01002 0.03037 62 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.468 0.533 0.528 0.484\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 92/99 6.44G 0.01521 0.005139 0.01012 0.03047 44 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.502 0.525 0.545 0.502\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 93/99 6.44G 0.01497 0.005197 0.009876 0.03005 62 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.445 0.537 0.519 0.481\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 94/99 6.44G 0.01451 0.005055 0.00956 0.02912 51 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.45 0.562 0.531 0.493\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 95/99 6.44G 0.01485 0.004949 0.009359 0.02915 41 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.493 0.519 0.528 0.488\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 96/99 6.44G 0.01494 0.005117 0.00978 0.02984 38 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.497 0.506 0.521 0.48\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 97/99 6.44G 0.01462 0.005048 0.009439 0.02911 37 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.527 0.484 0.528 0.492\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 98/99 6.44G 0.01449 0.005025 0.00876 0.02828 41 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.536 0.502 0.529 0.491\n", "\n", " Epoch gpu_mem box obj cls total labels img_size\n", " 99/99 6.44G 0.01458 0.005102 0.009102 0.02878 48 320\n", " Class Images Labels P R mAP@.5\n", " all 499 367 0.528 0.504 0.527 0.492\n", "100 epochs completed in 4.077 hours.\n", "\n", "Optimizer stripped from runs/train/exp2/weights/last.pt, 75.8MB\n", "Optimizer stripped from runs/train/exp2/weights/best.pt, 75.8MB\n" ] } ], "source": [ "!python train.py --batch-size 32 --img 320 320 --data /user/charviku/DL_Project/ingredients.yaml --cfg cfg/training/yolov7.yaml --weights 'yolov7.pt' --device 0 --epochs 100 --workers 4" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 1 }