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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "-tGvKM82qJsO"
},
"source": [
"# Object Detection"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GPU available: NVIDIA H100 PCIe\n"
]
}
],
"source": [
"import torch\n",
"\n",
"if torch.cuda.is_available():\n",
" print(f\"GPU available: {torch.cuda.get_device_name(0)}\")\n",
"else:\n",
" print(\"GPU not available.\")\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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/bhanucha/final_data.zip'\n",
"# Destination directory where you want to extract the files\n",
"extraction_directory = '/user/bhanucha/input'\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": 3,
"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/bhanucha/Final_classes.txt'\n",
"dataset_directory = '/user/bhanucha/input/initial_data_annotated'\n",
"process_dataset(dataset_directory, class_file_path)\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"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 (12.2 MB)\n",
"\u001b[K |████████████████████████████████| 12.2 MB 3.7 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement 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",
"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",
"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",
"Collecting joblib>=1.2.0\n",
" Downloading joblib-1.3.2-py3-none-any.whl (302 kB)\n",
"\u001b[K |████████████████████████████████| 302 kB 170.2 MB/s eta 0:00:01\n",
"\u001b[?25hInstalling collected packages: joblib, scikit-learn\n",
"Successfully installed joblib-1.3.2 scikit-learn-1.4.1.post1\n",
"\u001b[33mWARNING: You are using pip version 21.2.2; however, version 24.0 is available.\n",
"You should consider upgrading via the '/cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/Compiler/gcccore/11.2.0/python/3.9.6/bin/python -m pip install --upgrade pip' command.\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": 1,
"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/bhanucha/input/initial_data_annotated'\n",
"output_train = '/user/bhanucha/train.txt'\n",
"output_val = '/user/bhanucha/valid.txt'\n",
"output_test = '/user/bhanucha/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": 2,
"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 | 69.03 MiB/s, done.\n",
"Resolving deltas: 100% (519/519), done.\n",
"/user/bhanucha/yolov7\n"
]
}
],
"source": [
"!git clone https://github.com/WongKinYiu/yolov7.git\n",
"%cd yolov7\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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",
"Collecting matplotlib>=3.2.2\n",
" Downloading matplotlib-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.6 MB)\n",
"\u001b[K |████████████████████████████████| 11.6 MB 3.5 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: numpy<1.24.0,>=1.18.5 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 -r requirements.txt (line 5)) (1.21.3)\n",
"Collecting opencv-python>=4.1.1\n",
" Downloading opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (62.2 MB)\n",
"\u001b[K |████████████████████████████████| 62.2 MB 169.7 MB/s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: Pillow>=7.1.2 in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/Compiler/gcccore/11.2.0/pillow/9.2.0/lib/python3.9/site-packages (from -r requirements.txt (line 7)) (9.2.0)\n",
"Requirement already satisfied: PyYAML>=5.3.1 in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/Compiler/gcccore/11.2.0/pyyaml/5.4.1/lib/python3.9/site-packages (from -r requirements.txt (line 8)) (5.4.1)\n",
"Requirement already satisfied: requests>=2.23.0 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 -r requirements.txt (line 9)) (2.26.0)\n",
"Requirement already satisfied: scipy>=1.4.1 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 -r requirements.txt (line 10)) (1.7.1)\n",
"Requirement already satisfied: torch!=1.12.0,>=1.7.0 in /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 (from -r requirements.txt (line 11)) (1.13.1)\n",
"Requirement already satisfied: torchvision!=0.13.0,>=0.8.1 in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/MPI/gcc/11.2.0/openmpi/4.1.1/torchvision/0.14.1-CUDA-11.8.0/lib/python3.9/site-packages (from -r requirements.txt (line 12)) (0.14.1)\n",
"Collecting tqdm>=4.41.0\n",
" Downloading tqdm-4.66.2-py3-none-any.whl (78 kB)\n",
"\u001b[K |████████████████████████████████| 78 kB 3.5 MB/s s eta 0:00:01\n",
"\u001b[?25hRequirement already satisfied: protobuf<4.21.3 in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/Compiler/gcccore/11.2.0/protobuf-python/3.17.3/lib/python3.9/site-packages (from -r requirements.txt (line 14)) (3.17.3)\n",
"Requirement already satisfied: tensorboard>=2.4.1 in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/MPI/gcc/11.2.0/openmpi/4.1.1/tensorflow/2.11.0-CUDA-11.8.0/lib/python3.9/site-packages (from -r requirements.txt (line 17)) (2.11.1)\n",
"Requirement already satisfied: pandas>=1.1.4 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 -r requirements.txt (line 21)) (1.3.4)\n",
"Collecting seaborn>=0.11.0\n",
" Downloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
"\u001b[K |████████████████████████████████| 294 kB 165.8 MB/s eta 0:00:01\n",
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"Requirement already satisfied: psutil 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 -r requirements.txt (line 35)) (5.8.0)\n",
"Collecting thop\n",
" Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
"Collecting cycler>=0.10\n",
" Downloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
"Collecting fonttools>=4.22.0\n",
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"Collecting contourpy>=1.0.1\n",
" Downloading contourpy-1.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (304 kB)\n",
"\u001b[K |████████████████████████████████| 304 kB 166.2 MB/s eta 0:00:01\n",
"\u001b[?25hCollecting kiwisolver>=1.3.1\n",
" Downloading kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.6 MB)\n",
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"Requirement already satisfied: python-dateutil>=2.7 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 matplotlib>=3.2.2->-r requirements.txt (line 4)) (2.8.2)\n",
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"Requirement already satisfied: pytz>=2017.3 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 pandas>=1.1.4->-r requirements.txt (line 21)) (2021.1)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
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"Requirement already satisfied: backcall in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/Compiler/gcccore/11.2.0/ipython/7.26.0/lib/python3.9/site-packages (from ipython->-r requirements.txt (line 34)) (0.2.0)\n",
"Requirement already satisfied: decorator 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 ipython->-r requirements.txt (line 34)) (5.0.9)\n",
"Requirement already satisfied: matplotlib-inline in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/Compiler/gcccore/11.2.0/ipython/7.26.0/lib/python3.9/site-packages (from ipython->-r requirements.txt (line 34)) (0.1.2)\n",
"Requirement already satisfied: jedi>=0.16 in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/Compiler/gcccore/11.2.0/ipython/7.26.0/lib/python3.9/site-packages (from ipython->-r requirements.txt (line 34)) (0.18.0)\n",
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"Requirement already satisfied: pexpect>4.3 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 ipython->-r requirements.txt (line 34)) (4.8.0)\n",
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"Requirement already satisfied: importlib-metadata>=4.4 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 markdown>=2.6.8->tensorboard>=2.4.1->-r requirements.txt (line 17)) (4.6.3)\n",
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"Requirement already satisfied: oauthlib>=3.0.0 in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/MPI/gcc/11.2.0/openmpi/4.1.1/tensorflow/2.11.0-CUDA-11.8.0/lib/python3.9/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->-r requirements.txt (line 17)) (3.2.2)\n",
"Requirement already satisfied: ipython-genutils in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/Compiler/gcccore/11.2.0/ipython/7.26.0/lib/python3.9/site-packages (from traitlets>=4.2->ipython->-r requirements.txt (line 34)) (0.2.0)\n",
"Requirement already satisfied: MarkupSafe>=2.1.1 in /cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/MPI/gcc/11.2.0/openmpi/4.1.1/tensorflow/2.11.0-CUDA-11.8.0/lib/python3.9/site-packages (from werkzeug>=1.0.1->tensorboard>=2.4.1->-r requirements.txt (line 17)) (2.1.1)\n",
"Installing collected packages: kiwisolver, fonttools, cycler, contourpy, matplotlib, tqdm, thop, seaborn, opencv-python\n",
"Successfully installed contourpy-1.2.1 cycler-0.12.1 fonttools-4.50.0 kiwisolver-1.4.5 matplotlib-3.8.4 opencv-python-4.9.0.80 seaborn-0.13.2 thop-0.1.1.post2209072238 tqdm-4.66.2\n",
"\u001b[33mWARNING: You are using pip version 21.2.2; however, version 24.0 is available.\n",
"You should consider upgrading via the '/cvmfs/soft.ccr.buffalo.edu/versions/2023.01/easybuild/software/avx512/Compiler/gcccore/11.2.0/python/3.9.6/bin/python3.9 -m pip install --upgrade pip' command.\u001b[0m\n"
]
}
],
"source": [
"!pip install -r requirements.txt\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "ZvAB0t1a53xr"
},
"outputs": [],
"source": [
"yaml_content = \"\"\"\n",
"train: /user/bhanucha/train.txt\n",
"val: /user/bhanucha/valid.txt\n",
"test: /user/bhanucha/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/bhanucha/ingredients.yaml', 'w') as file:\n",
" file.write(yaml_content.strip())\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uzzsNi5Y6fdU",
"outputId": "d2702961-cc1a-42dc-ee26-e3bfdc361240"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/user/bhanucha/yolov7\n"
]
}
],
"source": [
"%cd /user/bhanucha/yolov7\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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 (NVIDIA H100 PCIe, 81230.375MB)\n",
"\n",
"Namespace(weights='yolov7.pt', cfg='cfg/training/yolov7.yaml', data='/user/bhanucha/ingredients.yaml', hyp='data/hyp.scratch.p5.yaml', epochs=50, batch_size=64, img_size=[640, 640], 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=64)\n",
"\u001b[34m\u001b[1mtensorboard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
"2024-04-04 11:10:34.277310: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX512_VNNI AVX512_BF16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2024-04-04 11:10:40.042222: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\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 models.common.Concat [1] \n",
" 43 -1 1 262656 models.common.Conv [1024, 256, 1, 1] \n",
" 44 -2 1 262656 models.common.Conv [1024, 256, 1, 1] \n",
" 45 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n",
" 46 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n",
" 47 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n",
" 48 -1 1 590336 models.common.Conv [256, 256, 3, 1] \n",
" 49 [-1, -3, -5, -6] 1 0 models.common.Concat [1] \n",
" 50 -1 1 1050624 models.common.Conv [1024, 1024, 1, 1] \n",
" 51 -1 1 7609344 models.common.SPPCSPC [1024, 512, 1] \n",
" 52 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
" 53 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 54 37 1 262656 models.common.Conv [1024, 256, 1, 1] \n",
" 55 [-1, -2] 1 0 models.common.Concat [1] \n",
" 56 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
" 57 -2 1 131584 models.common.Conv [512, 256, 1, 1] \n",
" 58 -1 1 295168 models.common.Conv [256, 128, 3, 1] \n",
" 59 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n",
" 60 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n",
" 61 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n",
" 62[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n",
" 63 -1 1 262656 models.common.Conv [1024, 256, 1, 1] \n",
" 64 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
" 65 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 66 24 1 65792 models.common.Conv [512, 128, 1, 1] \n",
" 67 [-1, -2] 1 0 models.common.Concat [1] \n",
" 68 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
" 69 -2 1 33024 models.common.Conv [256, 128, 1, 1] \n",
" 70 -1 1 73856 models.common.Conv [128, 64, 3, 1] \n",
" 71 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n",
" 72 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n",
" 73 -1 1 36992 models.common.Conv [64, 64, 3, 1] \n",
" 74[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n",
" 75 -1 1 65792 models.common.Conv [512, 128, 1, 1] \n",
" 76 -1 1 0 models.common.MP [] \n",
" 77 -1 1 16640 models.common.Conv [128, 128, 1, 1] \n",
" 78 -3 1 16640 models.common.Conv [128, 128, 1, 1] \n",
" 79 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
" 80 [-1, -3, 63] 1 0 models.common.Concat [1] \n",
" 81 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
" 82 -2 1 131584 models.common.Conv [512, 256, 1, 1] \n",
" 83 -1 1 295168 models.common.Conv [256, 128, 3, 1] \n",
" 84 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n",
" 85 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n",
" 86 -1 1 147712 models.common.Conv [128, 128, 3, 1] \n",
" 87[-1, -2, -3, -4, -5, -6] 1 0 models.common.Concat [1] \n",
" 88 -1 1 262656 models.common.Conv [1024, 256, 1, 1] \n",
" 89 -1 1 0 models.common.MP [] \n",
" 90 -1 1 66048 models.common.Conv [256, 256, 1, 1] \n",
" 91 -3 1 66048 models.common.Conv [256, 256, 1, 1] \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 92 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
" 93 [-1, -3, 51] 1 0 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/bhanucha/train.cache' images and labels... 3988 found, 0 \u001b[0m\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning '/user/bhanucha/valid.cache' images and labels... 499 found, 0 mis\u001b[0m\n",
"\n",
"\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 3.83, Best Possible Recall (BPR) = 1.0000\n",
"Image sizes 640 train, 640 test\n",
"Using 4 dataloader workers\n",
"Logging results to runs/train/exp2\n",
"Starting training for 50 epochs...\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 0/49 43.7G 0.05927 0.01208 0.0645 0.1359 42 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.00089 0.0115 0.00104 0.000718\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 1/49 43.7G 0.03925 0.01081 0.06218 0.1122 48 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.00495 0.28 0.0139 0.0107\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 2/49 43.7G 0.03601 0.01014 0.06201 0.1082 47 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.0126 0.156 0.0165 0.0133\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 3/49 43.7G 0.03411 0.009824 0.06196 0.1059 50 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.00946 0.147 0.0199 0.0155\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 4/49 43.7G 0.03276 0.009452 0.06175 0.104 45 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.0803 0.0671 0.0234 0.0164\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 5/49 43.7G 0.03274 0.009439 0.06162 0.1038 60 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.26 0.0682 0.022 0.0164\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 6/49 43.7G 0.03168 0.00919 0.06142 0.1023 55 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.048 0.166 0.0258 0.0189\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 7/49 43.7G 0.02938 0.0095 0.06109 0.09998 43 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.124 0.126 0.0272 0.0224\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 8/49 43.7G 0.02771 0.009402 0.06073 0.09784 50 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.0845 0.152 0.0393 0.0318\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 9/49 43.7G 0.02783 0.009483 0.05996 0.09727 40 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.632 0.0821 0.0637 0.0536\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 10/49 43.7G 0.02585 0.009862 0.05914 0.09485 39 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.248 0.189 0.0822 0.0652\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 11/49 43.7G 0.02348 0.01029 0.05817 0.09195 64 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.711 0.0972 0.0957 0.0743\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 12/49 43.7G 0.02314 0.009896 0.0566 0.08963 54 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.523 0.145 0.115 0.0907\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 13/49 43.7G 0.02371 0.009747 0.0548 0.08825 37 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.586 0.168 0.164 0.137\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 14/49 43.7G 0.02441 0.009791 0.05267 0.08687 44 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.448 0.224 0.165 0.128\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 15/49 43.7G 0.02581 0.009632 0.05134 0.08679 37 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.766 0.152 0.176 0.137\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 16/49 43.7G 0.02668 0.009865 0.0499 0.08644 55 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.6 0.183 0.175 0.143\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 17/49 43.7G 0.02685 0.00988 0.04829 0.08502 44 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.196 0.266 0.138 0.107\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 18/49 43.7G 0.02572 0.00947 0.04623 0.08143 57 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.348 0.299 0.226 0.181\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 19/49 43.7G 0.02658 0.009464 0.04458 0.08062 50 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.262 0.315 0.21 0.172\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 20/49 43.7G 0.02621 0.009319 0.04289 0.07842 46 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.326 0.375 0.26 0.215\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 21/49 43.7G 0.02617 0.009057 0.04091 0.07614 35 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.522 0.25 0.238 0.187\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 22/49 43.7G 0.02586 0.009198 0.04025 0.0753 44 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.287 0.394 0.303 0.245\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 23/49 43.7G 0.02574 0.009049 0.03859 0.07337 38 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.432 0.281 0.279 0.235\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 24/49 43.7G 0.02544 0.009021 0.03843 0.07289 50 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.32 0.353 0.303 0.244\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 25/49 43.7G 0.02466 0.008962 0.03588 0.0695 59 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.476 0.344 0.35 0.292\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 26/49 43.7G 0.02458 0.008519 0.03484 0.06794 44 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.287 0.384 0.333 0.284\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 27/49 43.7G 0.02477 0.008755 0.03423 0.06776 52 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.373 0.397 0.366 0.314\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 28/49 43.7G 0.02328 0.008634 0.03253 0.06445 32 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.391 0.38 0.391 0.328\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 29/49 43.7G 0.02363 0.008404 0.0319 0.06393 50 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.337 0.405 0.385 0.331\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 30/49 43.7G 0.0229 0.008227 0.03064 0.06177 35 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.475 0.397 0.434 0.376\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 31/49 43.7G 0.02261 0.008141 0.03003 0.06079 53 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.453 0.447 0.423 0.372\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 32/49 43.7G 0.02301 0.008456 0.029 0.06047 48 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.349 0.495 0.413 0.365\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 33/49 43.7G 0.02191 0.008162 0.02761 0.05768 60 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.385 0.491 0.444 0.387\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 34/49 43.7G 0.02187 0.00808 0.02683 0.05678 44 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.435 0.479 0.465 0.419\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 35/49 43.7G 0.02128 0.007955 0.02552 0.05475 44 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.38 0.506 0.459 0.405\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 36/49 43.7G 0.0211 0.007721 0.02477 0.05359 49 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.361 0.5 0.477 0.43\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 37/49 43.7G 0.02061 0.00783 0.02403 0.05247 61 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.473 0.44 0.477 0.432\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 38/49 43.7G 0.01983 0.007664 0.02365 0.05115 57 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.542 0.457 0.501 0.453\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 39/49 43.7G 0.02014 0.00756 0.02243 0.05013 42 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.443 0.482 0.482 0.439\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 40/49 43.7G 0.01994 0.007407 0.02229 0.04964 50 640\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.478 0.497 0.513 0.464\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 41/49 43.7G 0.01893 0.007398 0.02043 0.04676 40 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.469 0.543 0.521 0.476\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 42/49 43.7G 0.01868 0.007207 0.01998 0.04588 39 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.626 0.46 0.541 0.493\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 43/49 43.7G 0.01885 0.007177 0.01992 0.04595 48 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.462 0.544 0.542 0.494\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 44/49 43.7G 0.0187 0.007042 0.01925 0.04499 55 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.463 0.518 0.513 0.468\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 45/49 43.7G 0.01834 0.007062 0.01923 0.04463 33 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.446 0.509 0.537 0.486\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 46/49 43.7G 0.01818 0.007035 0.01782 0.04304 66 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.42 0.578 0.536 0.487\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 47/49 43.7G 0.01791 0.006884 0.01763 0.04243 48 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.518 0.535 0.553 0.506\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 48/49 43.7G 0.01732 0.006811 0.01735 0.04148 33 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.491 0.524 0.523 0.482\n",
"\n",
" Epoch gpu_mem box obj cls total labels img_size\n",
" 49/49 43.7G 0.01747 0.006579 0.01609 0.04014 41 640\n",
" Class Images Labels P R mAP@.5\n",
" all 499 367 0.526 0.49 0.547 0.503\n",
"50 epochs completed in 1.685 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 64 --img 640 640 --data /user/bhanucha/ingredients.yaml --cfg cfg/training/yolov7.yaml --weights 'yolov7.pt' --device 0 --epochs 50 --workers 4"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_SMQOX8w9rpd",
"outputId": "4cdd6774-189a-417d-8c12-15b6d00b2b0e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Namespace(weights=['/user/bhanucha/yolov7/runs/train/exp2/weights/best.pt'], data='/user/bhanucha/ingredients.yaml', batch_size=64, img_size=640, conf_thres=0.001, iou_thres=0.5, task='test', device='', single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project='runs/test', name='exp', exist_ok=False, no_trace=False, v5_metric=False)\n",
"YOLOR 🚀 v0.1-128-ga207844 torch 1.13.1 CUDA:0 (NVIDIA H100 PCIe, 81230.375MB)\n",
"\n",
"Fusing layers... \n",
"RepConv.fuse_repvgg_block\n",
"RepConv.fuse_repvgg_block\n",
"RepConv.fuse_repvgg_block\n",
"IDetect.fuse\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: 314 layers, 37015778 parameters, 6194944 gradients, 104.9 GFLOPS\n",
" Convert model to Traced-model... \n",
" traced_script_module saved! \n",
" model is traced! \n",
"\n",
"\u001b[34m\u001b[1mtest: \u001b[0mScanning '/user/bhanucha/test.cache' images and labels... 499 found, 0 mis\u001b[0m\n",
" Class Images Labels P R mAP@.5\n",
" all 499 346 0.501 0.546 0.532 0.483\n",
"Speed: 1.6/0.4/1.9 ms inference/NMS/total per 640x640 image at batch-size 64\n",
"Results saved to runs/test/exp4\n"
]
}
],
"source": [
"!python test.py --weights /user/bhanucha/yolov7/runs/train/exp2/weights/best.pt --data /user/bhanucha/ingredients.yaml --img 640 --iou-thres 0.5 --batch-size 64 --task test\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Namespace(weights=['/user/bhanucha/yolov7/runs/train/exp2/weights/best.pt'], data='/user/bhanucha/ingredients.yaml', batch_size=32, img_size=640, conf_thres=0.001, iou_thres=0.25, task='test', device='', single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project='runs/test', name='exp', exist_ok=False, no_trace=False, v5_metric=False)\n",
"YOLOR 🚀 v0.1-128-ga207844 torch 1.13.1 CUDA:0 (NVIDIA H100 PCIe, 81230.375MB)\n",
"\n",
"Fusing layers... \n",
"RepConv.fuse_repvgg_block\n",
"RepConv.fuse_repvgg_block\n",
"RepConv.fuse_repvgg_block\n",
"IDetect.fuse\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: 314 layers, 37015778 parameters, 6194944 gradients, 104.9 GFLOPS\n",
" Convert model to Traced-model... \n",
" traced_script_module saved! \n",
" model is traced! \n",
"\n",
"\u001b[34m\u001b[1mtest: \u001b[0mScanning '/user/bhanucha/test.cache' images and labels... 499 found, 0 mis\u001b[0m\n",
" Class Images Labels P R mAP@.5\n",
" all 499 346 0.54 0.515 0.53 0.481\n",
"Speed: 2.5/0.4/2.9 ms inference/NMS/total per 640x640 image at batch-size 32\n",
"Results saved to runs/test/exp3\n"
]
}
],
"source": [
"!python test.py --weights /user/bhanucha/yolov7/runs/train/exp2/weights/best.pt --data /user/bhanucha/ingredients.yaml --img 640 --iou-thres 0.25 --task test\n"
]
}
],
"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
}
|