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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": [
      "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",
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      "Collecting seaborn>=0.11.0 (from -r requirements.txt (line 22))\n",
      "  Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n",
      "Requirement already satisfied: ipython 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 -r requirements.txt (line 34)) (7.26.0)\n",
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      "Requirement already satisfied: contourpy>=1.0.1 in /user/charviku/.local/lib/python3.9/site-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (1.2.0)\n",
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     ]
    },
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     "name": "stdout",
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      "Requirement already satisfied: importlib-metadata>=4.4 in /user/charviku/.local/lib/python3.9/site-packages (from markdown>=2.6.8->tensorboard>=2.4.1->-r requirements.txt (line 17)) (7.0.1)\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",
      "Downloading opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (62.2 MB)\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.2/62.2 MB\u001b[0m \u001b[31m51.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hDownloading tqdm-4.66.2-py3-none-any.whl (78 kB)\n",
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      "\u001b[?25hDownloading seaborn-0.13.2-py3-none-any.whl (294 kB)\n",
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      "\u001b[?25hDownloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
      "\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: 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  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",
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     ]
    },
    {
     "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",
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      " 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"
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