Computer Vision/Object Detection/Abdomen MRIs Object Detection/Abdomen_MRI_Object_Detection_YOLOS.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uEN0vcQiGje"
      },
      "source": [
        "## Abdomen MRI: Object Detection\n",
        "\n",
        "Dataset Source: https://huggingface.co/datasets/Francesco/abdomen-mri"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AFosmDxRiKSP"
      },
      "source": [
        "#### Install Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oLI5IgFciKJn",
        "outputId": "1c303a6e-6192-4be7-b931-d7d8512c7353"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.16.1->albumentations) (2.25.1)\n",
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            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard) (3.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (1.3.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.19.1->qudida>=0.0.4->albumentations) (3.2.0)\n",
            "Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m43.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: tokenizers, transformers\n",
            "Successfully installed tokenizers-0.13.3 transformers-4.31.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mCollecting accelerate\n",
            "  Obtaining dependency information for accelerate from https://files.pythonhosted.org/packages/70/f9/c381bcdd0c3829d723aa14eec8e75c6c377b4ca61ec68b8093d9f35fc7a7/accelerate-0.21.0-py3-none-any.whl.metadata\n",
            "  Downloading accelerate-0.21.0-py3-none-any.whl.metadata (17 kB)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.22.4)\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.12.2)\n",
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            "Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: accelerate\n",
            "Successfully installed accelerate-0.21.0\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "%pip install --upgrade pip\n",
        "%pip install datasets torch evaluate pytorch-lightning timm\n",
        "%pip install transformers albumentations tensorboard\n",
        "%pip install accelerate -U"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WsGTRPXeiGji"
      },
      "source": [
        "#### Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntMceoKDiGjj",
        "outputId": "68035efa-f86b-4d66-ea46-ec8f15bbc9a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Git LFS initialized.\n"
          ]
        }
      ],
      "source": [
        "import os, sys, random, shutil, re, json\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from PIL import Image, ImageDraw\n",
        "import PIL.Image\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "import datasets\n",
        "from datasets import load_dataset\n",
        "\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "import transformers\n",
        "from transformers import AutoImageProcessor, TrainingArguments\n",
        "from transformers import AutoModelForObjectDetection, Trainer\n",
        "\n",
        "import albumentations\n",
        "\n",
        "import evaluate\n",
        "\n",
        "!git lfs install"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FxjFrK11iGjl"
      },
      "source": [
        "#### Display Versions of Relevant Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vS131vH5iGjm",
        "outputId": "771344aa-3558-4fb6-8fa1-c16a0dab0636"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+-------------------------------+\n",
            "|        Library | Version      |\n",
            "|*******************************|\n",
            "|         Python | 3.10.6       |\n",
            "|          NumPy | 1.22.4       |\n",
            "|          Torch | 2.0.1+cu118  |\n",
            "|   Torch Vision | 0.15.2+cu118 |\n",
            "|       Datasets | 2.14.1       |\n",
            "| Albumentations | 1.2.1        |\n",
            "|    Transformer | 4.31.0       |\n",
            "|       Evaluate | 0.4.0        |\n",
            "+-------------------------------+\n"
          ]
        }
      ],
      "source": [
        "library_len = 14\n",
        "version_len = 12\n",
        "\n",
        "print(f\"+{'-' * (library_len + version_len + 5)}+\")\n",
        "print(\"|\", \"Library\".rjust(library_len), \"|\", \"Version\".ljust(version_len), \"|\")\n",
        "print(f\"|{'*' * (library_len + version_len + 5)}|\")\n",
        "print(\"|\", \"Python\".rjust(library_len), \"|\", sys.version[0:6].ljust(version_len), \"|\")\n",
        "print(\"|\", \"NumPy\".rjust(library_len), \"|\", np.__version__.ljust(version_len), \"|\")\n",
        "print(\"|\", \"Torch\".rjust(library_len), \"|\", torch.__version__.ljust(version_len), \"|\")\n",
        "print(\"|\", \"Torch Vision\".rjust(library_len), \"|\", torchvision.__version__.ljust(version_len), \"|\")\n",
        "print(\"|\", \"Datasets\".rjust(library_len), \"|\", datasets.__version__.ljust(version_len), \"|\")\n",
        "print(\"|\", \"Albumentations\".rjust(library_len), \"|\", albumentations.__version__.ljust(version_len), \"|\")\n",
        "print(\"|\", \"Transformer\".rjust(library_len), \"|\", transformers.__version__.ljust(version_len), \"|\")\n",
        "print(\"|\", \"Evaluate\".rjust(library_len), \"|\", evaluate.__version__.ljust(version_len), \"|\")\n",
        "print(f\"+{'-' * (library_len + version_len + 5)}+\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4YEaZxdjOfg"
      },
      "source": [
        "#### Enter HuggingFace Access Token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXrzmFesjOTf",
        "outputId": "ae5347df-182a-409d-e555-3dd53ecdb489"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cvc4CiSpkMpT"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CzasuxXUkPN2",
        "outputId": "657b5ffc-8d05-43e1-c391-9e1522fd1b2a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xtY41d0OiGjn"
      },
      "source": [
        "#### Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548,
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            "6b44bb37519448edacafd7e8bd14f89c",
            "73f43cb2126f421f96e5a93dd91448fa",
            "e719520a71d645cb8d474f38fff5135d"
          ]
        },
        "id": "odit9zsziGjn",
        "outputId": "c00900a6-52e4-4697-e816-ebfb1dc2d4ba"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ff50c608a23646289151ccb83ef5b4bb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/3.32k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a8c0350e1c0b47f685f93d3cfc7b2a21",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "257a9e8ac9be4f51a72b60f66b892be3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/62.5M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ed6e368273bc4034b6edc8307cfeb771",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/9.08M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "870afbafa66e44a88e986b2589c3b6af",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/14.9M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c18f8cf8449d40cc847d9e75846eb51c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4cd291041aad4f30b4b40f1b1d0b9bd0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bb0eecefa6a44a3899649013eab8ce62",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a1d8a9d8ef354fc0b93ff8b2ad1f732c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['image_id', 'image', 'width', 'height', 'objects'],\n",
              "        num_rows: 1887\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['image_id', 'image', 'width', 'height', 'objects'],\n",
              "        num_rows: 238\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['image_id', 'image', 'width', 'height', 'objects'],\n",
              "        num_rows: 479\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = load_dataset(\"Francesco/abdomen-mri\")\n",
        "\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E3H6SwpGiGjo"
      },
      "source": [
        "#### Display Features for Sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgVo2cS0iGjo",
        "outputId": "c0a06f2d-27d6-4b99-9c90-fa40ede2588a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'image_id': 912,\n",
              " 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640>,\n",
              " 'width': 640,\n",
              " 'height': 640,\n",
              " 'objects': {'id': [929],\n",
              "  'area': [40953],\n",
              "  'bbox': [[335.0, 172.0, 167.5, 244.5]],\n",
              "  'category': [1]}}"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['train'][12]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wBeuMUlTiGjp"
      },
      "source": [
        "#### Define List of Category Names & Conversion Dictionaries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iO_LeRt_iGjp",
        "outputId": "efa5e9fa-1ee7-4e7c-c0d5-922d51017b14"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "List of Category Names:\n",
            "['abdomen-MRI', '0']\n",
            "Number of Category Names: 2\n",
            "id2label:\n",
            "{0: 'abdomen-MRI', 1: '0'}\n",
            "label2id:\n",
            "{'abdomen-MRI': 0, '0': 1}\n"
          ]
        }
      ],
      "source": [
        "categories = ['abdomen-MRI', '0']\n",
        "\n",
        "NUM_OF_CATEGORIES = len(categories)\n",
        "\n",
        "id2label = {idx: tag for idx, tag in enumerate(categories)}\n",
        "label2id = {tag: idx for idx, tag in enumerate(categories)}\n",
        "\n",
        "print(f\"List of Category Names:\\n{categories}\")\n",
        "print(f\"Number of Category Names: {NUM_OF_CATEGORIES}\")\n",
        "print(f\"id2label:\\n{id2label}\")\n",
        "print(f\"label2id:\\n{label2id}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThldoTw-iGjq"
      },
      "source": [
        "#### Visualize A Sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "u9RgTfzeiGjq",
        "outputId": "0dd3af7d-14df-4f76-bebf-534432f91828"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640>"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "sample = data['train'][24]\n",
        "\n",
        "sample_image = sample['image']\n",
        "annotations = sample['objects']\n",
        "\n",
        "draw = ImageDraw.Draw(sample_image)\n",
        "\n",
        "for i in range(len(annotations[\"id\"])):\n",
        "    box = annotations[\"bbox\"][i - 1]\n",
        "    class_idx = annotations[\"category\"][i - 1]\n",
        "    x, y, w, h = tuple(box)\n",
        "    draw.rectangle((x, y, x + w, y + h), outline=\"yellow\", width=1)\n",
        "    draw.text((x, y), id2label[class_idx], fill=\"white\")\n",
        "\n",
        "sample_image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGbSZM5liGjq"
      },
      "source": [
        "#### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "6edv0mtWiGjq"
      },
      "outputs": [],
      "source": [
        "MODEL_CKPT = \"hustvl/yolos-small\"\n",
        "MODEL_NAME = f'{MODEL_CKPT.split(\"/\")[-1]}-Abdomen_MRI'\n",
        "\n",
        "LR = 1e-5\n",
        "WEIGHT_DECAY = 1e-4\n",
        "\n",
        "NUM_OF_EPOCHS = 15\n",
        "BATCH_SIZE = 8\n",
        "\n",
        "STRATEGY = \"steps\"\n",
        "REPORTS_TO = \"tensorboard\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eiLjrPOziGjr"
      },
      "source": [
        "#### Instantiate Image Processor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "62de91cf57ec4183b5f44f741a778f20",
            "0e1d03dbb9e849478b389a3135df013b",
            "2f1b666a8cd54053b3ac71ed710dcf8c",
            "6be020e9768f49e097090964cd3cc088",
            "9a2148a6f26a44f8809b11962af76218",
            "5c91d78ad4424bb8bf1d3ac3a9cf335e",
            "f18349effb284381a44127d59058c71b",
            "d9ef9c012f5041e98556ea6a3a72b815",
            "3199366c637f4c7186bd695c13513106",
            "0a5cd063c1e94a7e9070b90a1efe4631",
            "8db0324d8d694b499dd563f8b427b202"
          ]
        },
        "id": "4KBQoxyLiGjr",
        "outputId": "a749bb85-52d4-4cf8-ba35-f0b98c3c4066"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "62de91cf57ec4183b5f44f741a778f20",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)rocessor_config.json:   0%|          | 0.00/275 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.\n",
            "The `max_size` parameter is deprecated and will be removed in v4.26. Please specify in `size['longest_edge'] instead`.\n"
          ]
        }
      ],
      "source": [
        "image_processor = AutoImageProcessor.from_pretrained(MODEL_CKPT)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bajdsq5PiGjr"
      },
      "source": [
        "#### Define Some Transformations for Images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "Bo_L_xFNiGjr"
      },
      "outputs": [],
      "source": [
        "transform = albumentations.Compose(\n",
        "    [\n",
        "        albumentations.Resize(480, 480),\n",
        "        albumentations.HorizontalFlip(p=1.0),\n",
        "        albumentations.RandomBrightnessContrast(p=1.0),\n",
        "    ],\n",
        "    bbox_params=albumentations.BboxParams(format=\"coco\", label_fields=['category']),\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AWlmJRG7iGjs"
      },
      "source": [
        "#### Create Function to Reformat Annotations Correctly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "V8J4pIX2iGjs"
      },
      "outputs": [],
      "source": [
        "def reformat_annotations(image_id, category, area, bbox):\n",
        "    annotations = []\n",
        "    for x in range(0, len(category)):\n",
        "        new_annotation = {\n",
        "            \"image_id\": image_id,\n",
        "            \"category_id\": category[x],\n",
        "            \"isCrowd\": 0,\n",
        "            \"area\": area[x],\n",
        "            \"bbox\": list(bbox[x]),\n",
        "        }\n",
        "        annotations.append(new_annotation)\n",
        "\n",
        "    return annotations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "shuCAUQhiGjs"
      },
      "source": [
        "#### Combine Image & Annotation Transformations For Use On Batch of Samples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "dO0J2CkfiGjs"
      },
      "outputs": [],
      "source": [
        "def transform_image_ann(samples):\n",
        "    image_ids = samples[\"image_id\"]\n",
        "    images, bboxes, area, categories = [], [], [], []\n",
        "    for image, objects in zip(samples[\"image\"], samples[\"objects\"]):\n",
        "        image = np.array(image.convert(\"RGB\"))[:, :, ::-1]\n",
        "        out = transform(image=image, bboxes=objects[\"bbox\"], category=objects[\"category\"])\n",
        "\n",
        "        area.append(objects[\"area\"])\n",
        "        images.append(out[\"image\"])\n",
        "        bboxes.append(out[\"bboxes\"])\n",
        "        categories.append(out[\"category\"])\n",
        "\n",
        "    targets = [\n",
        "        { \"image_id\": id_, \"annotations\": reformat_annotations(id_, cat_, ar_, box_)\n",
        "        } for id_, cat_, ar_, box_ in zip(image_ids, categories, area, bboxes)\n",
        "    ]\n",
        "\n",
        "    return image_processor(images=images,\n",
        "                           annotations=targets,\n",
        "                           return_tensors=\"pt\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXgum9DyiGjs"
      },
      "source": [
        "#### Apply Image Transformations to Training Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YESTz6RiiGjt",
        "outputId": "cbbc8b71-aa38-4682-a650-189e10eb52a7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'pixel_values': tensor([[[-0.7479, -1.0733, -1.5528,  ..., -2.0837, -1.9638, -1.8782],\n",
              "          [-0.7822, -1.1247, -1.6384,  ..., -2.0494, -1.9809, -1.9295],\n",
              "          [-0.8335, -1.2103, -1.7583,  ..., -1.9980, -2.0152, -2.0152],\n",
              "          ...,\n",
              "          [-0.9020, -1.1932, -1.6384,  ..., -1.9980, -1.9980, -1.9980],\n",
              "          [-0.8335, -1.0733, -1.4500,  ..., -1.9124, -1.9124, -1.9124],\n",
              "          [-0.7822, -1.0048, -1.3302,  ..., -1.8439, -1.8439, -1.8439]],\n",
              " \n",
              "         [[-0.6352, -0.9678, -1.4580,  ..., -2.0007, -1.8782, -1.7906],\n",
              "          [-0.6702, -1.0203, -1.5455,  ..., -1.9657, -1.8957, -1.8431],\n",
              "          [-0.7227, -1.1078, -1.6681,  ..., -1.9132, -1.9307, -1.9307],\n",
              "          ...,\n",
              "          [-0.7052, -1.0203, -1.4755,  ..., -1.9132, -1.9132, -1.9132],\n",
              "          [-0.6352, -0.8978, -1.2829,  ..., -1.8256, -1.8256, -1.8256],\n",
              "          [-0.5826, -0.8102, -1.1429,  ..., -1.7556, -1.7556, -1.7556]],\n",
              " \n",
              "         [[-0.4101, -0.7413, -1.2293,  ..., -1.7696, -1.6476, -1.5604],\n",
              "          [-0.4450, -0.7936, -1.3164,  ..., -1.7347, -1.6650, -1.6127],\n",
              "          [-0.4973, -0.8807, -1.4384,  ..., -1.6824, -1.6999, -1.6999],\n",
              "          ...,\n",
              "          [-0.3753, -0.6890, -1.1421,  ..., -1.6824, -1.6824, -1.6824],\n",
              "          [-0.3055, -0.5670, -0.9504,  ..., -1.5953, -1.5953, -1.5953],\n",
              "          [-0.2532, -0.4798, -0.8284,  ..., -1.5256, -1.5256, -1.5256]]]),\n",
              " 'labels': {'size': tensor([800, 800]), 'image_id': tensor([842]), 'class_labels': tensor([1]), 'boxes': tensor([[0.4863, 0.3145, 0.1273, 0.1633]]), 'area': tensor([23655.5547]), 'iscrowd': tensor([0]), 'orig_size': tensor([480, 480])}}"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['train'] = data['train'].with_transform(transform_image_ann)\n",
        "\n",
        "data['train'][24]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zL_UUtc_iGjt"
      },
      "source": [
        "#### Define Data Collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "p_DdrE0FiGjt"
      },
      "outputs": [],
      "source": [
        "def data_collator_fn(batch):\n",
        "    pixel_values = [item[\"pixel_values\"] for item in batch]\n",
        "    encoding = image_processor.pad(pixel_values, return_tensors=\"pt\")\n",
        "    labels = [item[\"labels\"] for item in batch]\n",
        "    batch = {}\n",
        "    batch[\"pixel_values\"] = encoding[\"pixel_values\"]\n",
        "    batch[\"labels\"] = labels\n",
        "    return batch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "teybXaiciGjt"
      },
      "source": [
        "#### Define Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170,
          "referenced_widgets": [
            "40de1e7d06be4ee2a8647b391b58877a",
            "3e5f5e080cbf4bb1926445689cf74037",
            "4a3446d3ad4e4ceca22f6642a7855493",
            "78a1b26f519849d994a19d3986615a60",
            "7d060ea7ca4349199107f83801d0e38b",
            "8a296f7ea64b452d8124343c75fb63ad",
            "0fee9a2d361e4d20990aab60559d6c26",
            "337ad1787da34de5b46990be13c7857e",
            "0310a5f688b14e9cadc27f9f9da366cf",
            "c009ba2cb90149a0a0f7100f12ceedcf",
            "8385f188358f43a9b03d443dd6874c0c",
            "0453a8933a8e4cbd829e20cdaa829a0d",
            "c94e8ebe7b60446186b7c77e6d981378",
            "8b3a44e95cd841edb200d86da59524b1",
            "d852f6b84ab7447c984b08a6ccba35d9",
            "8bc9696d361844c98b4da085d9c766f0",
            "19231b90a56e49338fdb416b6d405eb8",
            "79d615337e14466b8570c10db9edce10",
            "075cd95eb71e411da2cf8de3239d3616",
            "b763033f4f9a44748e0d9bae652b3285",
            "7b778b59a35b49aa9f34487c76dde6f8",
            "3ad54c82d44344b8853e4e3f2b604ecb"
          ]
        },
        "id": "ew_dHhAMiGjt",
        "outputId": "794d9b30-f71a-48c3-8802-f954a6b6341b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "40de1e7d06be4ee2a8647b391b58877a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/4.13k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0453a8933a8e4cbd829e20cdaa829a0d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/123M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of YolosForObjectDetection were not initialized from the model checkpoint at hustvl/yolos-small and are newly initialized because the shapes did not match:\n",
            "- class_labels_classifier.layers.2.weight: found shape torch.Size([92, 384]) in the checkpoint and torch.Size([3, 384]) in the model instantiated\n",
            "- class_labels_classifier.layers.2.bias: found shape torch.Size([92]) in the checkpoint and torch.Size([3]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ],
      "source": [
        "model = AutoModelForObjectDetection.from_pretrained(\n",
        "    MODEL_CKPT,\n",
        "    id2label=id2label,\n",
        "    label2id=label2id,\n",
        "    ignore_mismatched_sizes=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMa5MejyiGju"
      },
      "source": [
        "#### Define TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "RBujjRYxiGju"
      },
      "outputs": [],
      "source": [
        "args = TrainingArguments(\n",
        "    MODEL_NAME,\n",
        "    auto_find_batch_size=True,\n",
        "    num_train_epochs=NUM_OF_EPOCHS,\n",
        "    logging_first_step=True,\n",
        "    logging_strategy=STRATEGY,\n",
        "    logging_steps=100,\n",
        "    save_strategy=STRATEGY,\n",
        "    save_steps=871,\n",
        "    save_total_limit=12,\n",
        "    remove_unused_columns=False,\n",
        "    weight_decay=WEIGHT_DECAY,\n",
        "    report_to=REPORTS_TO,\n",
        "    hub_private_repo=True,\n",
        "    push_to_hub=True\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6LXVDNGgiGju"
      },
      "source": [
        "#### Define Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPLHNLbKiGju",
        "outputId": "caf0ee51-e634-45da-8e5c-7c3212cf863c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning https://huggingface.co/DunnBC22/yolos-small-Abdomen_MRI into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/DunnBC22/yolos-small-Abdomen_MRI into local empty directory.\n"
          ]
        }
      ],
      "source": [
        "trainer = Trainer(\n",
        "    model,\n",
        "    args=args,\n",
        "    data_collator=data_collator_fn,\n",
        "    train_dataset=data['train'],\n",
        "    tokenizer=image_processor,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJ_IwBfaiGju"
      },
      "source": [
        "#### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "01UaqEHOiGjv",
        "outputId": "51a98966-c0b2-43ad-9897-4cbe913fe60c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='7080' max='7080' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [7080/7080 3:26:48, Epoch 15/15]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.744500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.542400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.038400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>0.971200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>0.940000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>0.938500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>0.888300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>0.905400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.830800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>0.898500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>0.852200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>0.856100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.815100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>0.843100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>0.804000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>0.779000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.743400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>0.829000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>0.820600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>0.809100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.767100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>0.765300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2200</td>\n",
              "      <td>0.795100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2300</td>\n",
              "      <td>0.792800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>0.755700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>0.745300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2600</td>\n",
              "      <td>0.740600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2700</td>\n",
              "      <td>0.702600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2800</td>\n",
              "      <td>0.677300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2900</td>\n",
              "      <td>0.676800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>0.632700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3100</td>\n",
              "      <td>0.679800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3200</td>\n",
              "      <td>0.660500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3300</td>\n",
              "      <td>0.657100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3400</td>\n",
              "      <td>0.600300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>0.615600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3600</td>\n",
              "      <td>0.602700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3700</td>\n",
              "      <td>0.643300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3800</td>\n",
              "      <td>0.612400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3900</td>\n",
              "      <td>0.585500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>0.557500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4100</td>\n",
              "      <td>0.564600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4200</td>\n",
              "      <td>0.560900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4300</td>\n",
              "      <td>0.563600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4400</td>\n",
              "      <td>0.524600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>0.548200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4600</td>\n",
              "      <td>0.515800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4700</td>\n",
              "      <td>0.536900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4800</td>\n",
              "      <td>0.473700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4900</td>\n",
              "      <td>0.508700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>0.475500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5100</td>\n",
              "      <td>0.497800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5200</td>\n",
              "      <td>0.475700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5300</td>\n",
              "      <td>0.427300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5400</td>\n",
              "      <td>0.414600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>0.440300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5600</td>\n",
              "      <td>0.435700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5700</td>\n",
              "      <td>0.416200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5800</td>\n",
              "      <td>0.389900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5900</td>\n",
              "      <td>0.403900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>0.371500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6100</td>\n",
              "      <td>0.397500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6200</td>\n",
              "      <td>0.352400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6300</td>\n",
              "      <td>0.355500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6400</td>\n",
              "      <td>0.372800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>0.336600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6600</td>\n",
              "      <td>0.370400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6700</td>\n",
              "      <td>0.333700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6800</td>\n",
              "      <td>0.324700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6900</td>\n",
              "      <td>0.334600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>0.342200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=7080, training_loss=0.6311976725772276, metrics={'train_runtime': 12411.1917, 'train_samples_per_second': 2.281, 'train_steps_per_second': 0.57, 'total_flos': 9.9943198550208e+18, 'train_loss': 0.6311976725772276, 'epoch': 15.0})"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-FoSDjENiGjv"
      },
      "source": [
        "#### Push Model To Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336,
          "referenced_widgets": [
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            "35f24bec3ab84025b703c8624f691ae0",
            "bb0e2412e7ad4a3cbb86d77ffca1b235",
            "5cc552136e01489e8d06ac4093ce789c",
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            "d7ae7824d72946f1afff4e3c0b4446f9",
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            "0140e0276ed0411cae7d5be7b3a2b781"
          ]
        },
        "id": "ovyNblZhiGjv",
        "outputId": "c4d92562-be55-405c-c825-b5098abc2699"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c47c7cc56f024721be75e8ee034c9564",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 1.00/117M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3ef9ab6e198f4b9d8b74fd7df09fb9bc",
              "version_major": 2,
              "version_minor": 0
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            "text/plain": [
              "Upload file runs/Jul30_21-05-23_923fe502f31c/events.out.tfevents.1690751135.923fe502f31c.602.0:   0%|         …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/yolos-small-Abdomen_MRI\n",
            "   ec8fb6a..8c232f6  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/yolos-small-Abdomen_MRI\n",
            "   ec8fb6a..8c232f6  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/yolos-small-Abdomen_MRI\n",
            "   8c232f6..4b9cc32  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/yolos-small-Abdomen_MRI\n",
            "   8c232f6..4b9cc32  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/DunnBC22/yolos-small-Abdomen_MRI/commit/8c232f6e89f35d455eb15b5963e6716a794eb85f'"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k3h0mqFTiGjv"
      },
      "source": [
        "#### Save Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "9XTHva0hiGjv"
      },
      "outputs": [],
      "source": [
        "trainer.save_model()\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHChUxECiGj6"
      },
      "source": [
        "### Evaluate Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hr2SuDe_iGj7"
      },
      "source": [
        "#### Create Function to Reformat Evaluation Annotations Correctly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "wYe9uBQiiGj7"
      },
      "outputs": [],
      "source": [
        "def eval_formatted_annotations(image_id, objects):\n",
        "    annotations = []\n",
        "\n",
        "    for x in range(0, len(objects[\"id\"])):\n",
        "        new_annotation = {\n",
        "            \"id\": objects[\"id\"][x],\n",
        "            \"category_id\": objects[\"category\"][x],\n",
        "            \"iscrowd\": 0,\n",
        "            \"image_id\": image_id,\n",
        "            \"area\": objects[\"area\"][x],\n",
        "            \"bbox\": objects[\"bbox\"][x],\n",
        "        }\n",
        "        annotations.append(new_annotation)\n",
        "\n",
        "    return annotations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5enr78B0iGj7"
      },
      "source": [
        "#### Save Images & Annotations into Proper Files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "UzPSwxcMiGj7"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "Save images & annotations in format that\n",
        "torchvision.datasets.CocoDetection expects\n",
        "\"\"\"\n",
        "\n",
        "def save_images_and_annotation_files(ds):\n",
        "    output_json = {}\n",
        "    path_output_ds = f\"/content/drive/MyDrive/projects/Abdomen MRIs/data\"\n",
        "\n",
        "    if not os.path.exists(path_output_ds):\n",
        "        os.makedirs(path_output_ds)\n",
        "\n",
        "    path_annotation = os.path.join(path_output_ds, \"nfl_annos.json\")\n",
        "    categories_json = [\n",
        "        {\"supercategory\" : \"none\", \"id\": id, \"name\": id2label[id]}\n",
        "        for id in id2label]\n",
        "    output_json['images'] = []\n",
        "    output_json['annotations'] = []\n",
        "\n",
        "    for sample in ds:\n",
        "        anno = eval_formatted_annotations(sample[\"image_id\"], sample[\"objects\"])\n",
        "        output_json[\"images\"].append(\n",
        "            {\n",
        "                \"id\": sample[\"image_id\"],\n",
        "                \"width\": sample[\"image\"].width,\n",
        "                \"height\": sample[\"image\"].height,\n",
        "                \"file_name\": f\"{sample['image_id']}.png\",\n",
        "            }\n",
        "        )\n",
        "        output_json[\"annotations\"].extend(anno)\n",
        "    output_json[\"categories\"] = categories_json\n",
        "\n",
        "    with open(path_annotation, \"w\") as file:\n",
        "        json.dump(output_json, file, ensure_ascii=False, indent=4)\n",
        "\n",
        "    for im, img_id in zip(ds[\"image\"], ds[\"image_id\"]):\n",
        "        path_image = os.path.join(path_output_ds, f\"{img_id}.png\")\n",
        "        im.save(path_image)\n",
        "\n",
        "    return path_output_ds, path_annotation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oe31nK0ViGj8"
      },
      "source": [
        "#### Create Class to Ingest Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "FYGfXQMAiGj8"
      },
      "outputs": [],
      "source": [
        "class CocoDetection(torchvision.datasets.CocoDetection):\n",
        "    def __init__(self, img_folder, image_processor, annot_file):\n",
        "        super().__init__(img_folder, annot_file)\n",
        "        self.image_processor = image_processor\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # read in PIL image and target in COCO format\n",
        "        img, target = super(CocoDetection, self).__getitem__(idx)\n",
        "\n",
        "        # preprocess image and target (converting target to DETR format,\n",
        "        # resizing + normalization of both image and target)\n",
        "        image_id = self.ids[idx]\n",
        "        target = {'image_id': image_id, 'annotations': target}\n",
        "        encoding = self.image_processor(images=img,\n",
        "                                        annotations=target,\n",
        "                                        return_tensors=\"pt\")\n",
        "        pixel_values = encoding[\"pixel_values\"].squeeze()\n",
        "        target = encoding[\"labels\"][0]\n",
        "\n",
        "        return {\"pixel_values\": pixel_values, \"labels\": target}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NUiSLRFEiGj8"
      },
      "source": [
        "#### Apply Above-Defined Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mcrXe-B0iGj8",
        "outputId": "a8735885-6b86-4dae-e045-33fa830a0705"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n"
          ]
        }
      ],
      "source": [
        "EVAL_MODEL_NAME = 'DunnBC22/yolos-small-Abdomen_MRI'\n",
        "\n",
        "eval_image_processor = AutoImageProcessor.from_pretrained(EVAL_MODEL_NAME)\n",
        "\n",
        "path_output_ds, path_annot = save_images_and_annotation_files(data[\"validation\"])\n",
        "eval_ds_coco_format = CocoDetection(path_output_ds,\n",
        "                                    eval_image_processor,\n",
        "                                    path_annot)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iNqm3B5wiGj8"
      },
      "source": [
        "#### Calculate Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P4U6iWIKiGj9",
        "outputId": "bed074b2-387f-4a17-8958-496a07cd466d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  0%|          | 0/30 [00:00<?, ?it/s]`post_process` is deprecated and will be removed in v5 of Transformers, please use `post_process_object_detection` instead, with `threshold=0.` for equivalent results.\n",
            "100%|██████████| 30/30 [38:17<00:00, 76.58s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accumulating evaluation results...\n",
            "DONE (t=0.17s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.453\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.928\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.319\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.426\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.457\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.518\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.645\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.715\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.633\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716\n"
          ]
        }
      ],
      "source": [
        "model = AutoModelForObjectDetection.from_pretrained(EVAL_MODEL_NAME)\n",
        "\n",
        "module = evaluate.load(\"ybelkada/cocoevaluate\",\n",
        "                       coco=eval_ds_coco_format.coco)\n",
        "\n",
        "eval_dataloader = torch.utils.data.DataLoader(\n",
        "    eval_ds_coco_format,\n",
        "    batch_size=8,\n",
        "    shuffle=False,\n",
        "    num_workers=4,\n",
        "    collate_fn=data_collator_fn\n",
        ")\n",
        "\n",
        "with torch.no_grad():\n",
        "    for idx, batch in enumerate(tqdm(eval_dataloader)):\n",
        "        pixel_values = batch[\"pixel_values\"]\n",
        "\n",
        "        labels = [\n",
        "            {k: v for k, v in t.items()} for t in batch[\"labels\"]\n",
        "        ]\n",
        "\n",
        "        # forward pass\n",
        "        outputs = model(pixel_values=pixel_values)\n",
        "\n",
        "        orig_target_sizes = torch.stack(\n",
        "            [\n",
        "                target[\"orig_size\"] for target in labels\n",
        "            ],\n",
        "            dim=0)\n",
        "\n",
        "        results = eval_image_processor.post_process(outputs,\n",
        "                                                    orig_target_sizes)\n",
        "\n",
        "        module.add(prediction=results, reference=labels)\n",
        "        del batch\n",
        "\n",
        "results = module.compute()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vN_gMtRdiGj9"
      },
      "source": [
        "#### Display Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItCRpHYgiGj9",
        "outputId": "98a8ae1f-714b-441e-8efa-98b267c795fb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'iou_bbox': {'AP-IoU=0.50:0.95-area=all-maxDets=100': 0.4527590696540097, 'AP-IoU=0.50-area=all-maxDets=100': 0.9275792923137546, 'AP-IoU=0.75-area=all-maxDets=100': 0.3186136627340522, 'AP-IoU=0.50:0.95-area=small-maxDets=100': -1.0, 'AP-IoU=0.50:0.95-area=medium-maxDets=100': 0.4260655351249411, 'AP-IoU=0.50:0.95-area=large-maxDets=100': 0.4566586008216545, 'AR-IoU=0.50:0.95-area=all-maxDets=1': 0.5175000000000001, 'AR-IoU=0.50:0.95-area=all-maxDets=10': 0.6454166666666666, 'AR-IoU=0.50:0.95-area=all-maxDets=100': 0.7154166666666667, 'AR-IoU=0.50:0.95-area=small-maxDets=100': -1.0, 'AR-IoU=0.50:0.95-area=medium-maxDets=100': 0.6333333333333333, 'AR-IoU=0.50:0.95-area=large-maxDets=100': 0.7164556962025316}}\n"
          ]
        }
      ],
      "source": [
        "print(results)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x7VMrhNjiGkA"
      },
      "source": [
        "### Notes & Other Takeaways\n",
        "****\n",
        "- For the most part, I am pleased with the results.\n",
        "\n",
        "****"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MbIVeWV4iGkA"
      },
      "source": [
        "### Citations\n",
        "\n",
        "- Model Checkpoint\n",
        "    > @article{DBLP:journals/corr/abs-2106-00666, author  = {Yuxin Fang and Bencheng Liao and Xinggang Wang and Jiemin Fang and Jiyang Qi and Rui Wu and Jianwei Niu and Wenyu Liu}, title = {You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection}, journal = {CoRR}, volume = {abs/2106.00666}, year = {2021}, url = {https://arxiv.org/abs/2106.00666}, eprinttype = {arXiv}, eprint = {2106.00666}, timestamp = {Fri, 29 Apr 2022 19:49:16 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib}, bibsource = {dblp computer science bibliography, https://dblp.org}}\n",
        "\n",
        "- Dataset\n",
        "    > @misc{ abdomen-mri, title = { abdomen mri Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \\url{ https://universe.roboflow.com/object-detection/abdomen-mri } }, url = { https://universe.roboflow.com/object-detection/abdomen-mri }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 },}\n"
      ]
    }
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