Computer Vision/Object Detection/Brain Tumors/Brain_Tumor_m2pbp_Object_Detection_YOLOS.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wWvc9PX1rgnM"
      },
      "source": [
        "## Brain Tumor m2pbp: Object Detection (Fine-Tuning & Evaluation)\n",
        "\n",
        "Dataset Source: https://huggingface.co/datasets/Francesco/brain-tumor-m2pbp"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wEBBcO4yrxoR"
      },
      "source": [
        "#### Install Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDW1EdBKrxfY",
        "outputId": "2da9838e-dc53-4ae8-f7e1-7662a019fadc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.7.1)\n",
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            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.16.1->albumentations) (9.4.0)\n",
            "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",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.16.1->albumentations) (2023.7.18)\n",
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            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard) (2.1.3)\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[31m40.3 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: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
            "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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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n",
            "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[31m4.2 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 pip -U\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": "GNSlyXGSrxWj"
      },
      "source": [
        "#### Mount Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oBCMM2rzr2tq",
        "outputId": "6a443ac3-d0cc-4cce-bdc9-7609c3d8b316"
      },
      "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": "oPtKoOiyrgnR"
      },
      "source": [
        "#### Import Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Rnzqbu_rgnS",
        "outputId": "4468138f-3cca-4ced-bd63-705d50d4e932"
      },
      "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": "vSoKfO95ss0X"
      },
      "source": [
        "#### Enter HuggingFace Access Token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3tjzW_RDsstV",
        "outputId": "8c64827e-480c-4834-a2dd-b2bbed9e9e81"
      },
      "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": "gC_FtsEJrgnU"
      },
      "source": [
        "#### Display Versions of Relevant Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VO-dSqznrgnV",
        "outputId": "a7c5cc4e-986b-4405-d6ea-5e883459d4da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "+-------------------------------+\n",
            "|        Library | Version      |\n",
            "|*******************************|\n",
            "|         Python | 3.10.1       |\n",
            "|          NumPy | 1.22.4       |\n",
            "|          Torch | 2.0.1+cu118  |\n",
            "|   Torch Vision | 0.15.2+cu118 |\n",
            "|       Datasets | 2.14.2       |\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": "mrKjL8rlrgnW"
      },
      "source": [
        "#### Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548,
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            "fbba0ffac701415892673404669c05a1",
            "9fe5704956ea4b309b91f9194e00e7ea",
            "76e0bdf78f3d498b9647b2d08c8433cc"
          ]
        },
        "id": "9auNOaalrgnW",
        "outputId": "64b0d0d8-cd9f-41a9-e412-ff3b65d1d1df"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dd723647e32f4f6ebbf47f2b16baf49a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/3.43k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a08f3f4434b342418dcafd9b2b2901ce",
              "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": "c511ab42aa034921912caec0cba7de1d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/142M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "35a457bcbbad4db389c4a804590968fd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/20.1M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "df18f399e8b14046b5be0cb84aaf8900",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/40.4M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d8a0728ed2f94700b4f74b20d338b311",
              "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": "6103246cabfb4bd393193f141a58218e",
              "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": "d17ff97fa51642429b6986c341c493e7",
              "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": "44764401a3564cddb1990d21150a278b",
              "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: 6930\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['image_id', 'image', 'width', 'height', 'objects'],\n",
              "        num_rows: 990\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['image_id', 'image', 'width', 'height', 'objects'],\n",
              "        num_rows: 1980\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = load_dataset(\"Francesco/brain-tumor-m2pbp\")\n",
        "\n",
        "data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2URKIlorgnX"
      },
      "source": [
        "#### Display Features for Sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S4hvHXprrgnY",
        "outputId": "9d9c0edf-c251-4e9d-cb70-22996291604f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'image_id': 6569,\n",
              " 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640>,\n",
              " 'width': 640,\n",
              " 'height': 640,\n",
              " 'objects': {'id': [14263],\n",
              "  'area': [15218],\n",
              "  'bbox': [[283.0, 379.0, 154.5, 98.5]],\n",
              "  'category': [2]}}"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data['train'][12]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uv_9RwLVrgnb"
      },
      "source": [
        "#### Define List of Category Names & Conversion Dictionaries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9RVlhbJKrgnc",
        "outputId": "78455748-a2db-472a-eb8e-3a54977796ee"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "List of Category Names:\n",
            "['brain-tumor', 'label0', 'label1', 'label2']\n",
            "Number of Category Names: 4\n",
            "id2label:\n",
            "{0: 'brain-tumor', 1: 'label0', 2: 'label1', 3: 'label2'}\n",
            "label2id:\n",
            "{'brain-tumor': 0, 'label0': 1, 'label1': 2, 'label2': 3}\n"
          ]
        }
      ],
      "source": [
        "categories = data[\"train\"].features[\"objects\"].feature[\"category\"].names\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": "C6B0k2RArgnc"
      },
      "source": [
        "#### Visualize A Sample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "SZLmktEkrgnc",
        "outputId": "eb582d82-fb68-4317-a1c0-6b1118a160d0"
      },
      "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": "LkmY-6ogrgnd"
      },
      "source": [
        "#### Basic Values/Constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "th4FbNuGrgnd"
      },
      "outputs": [],
      "source": [
        "MODEL_CKPT = \"hustvl/yolos-tiny\"\n",
        "MODEL_NAME = f'{MODEL_CKPT.split(\"/\")[-1]}-Brain_Tumor_Detection'\n",
        "\n",
        "LR = 1e-5\n",
        "WEIGHT_DECAY = 1e-4\n",
        "\n",
        "NUM_OF_EPOCHS = 20\n",
        "BATCH_SIZE = 8\n",
        "\n",
        "STRATEGY = \"steps\"\n",
        "REPORTS_TO = \"tensorboard\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9gJH1_AWrgnd"
      },
      "source": [
        "#### Instantiate Image Processor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nuuSM98Krgnd",
        "outputId": "45a10622-a9c1-419a-8ba5-27f2cb73c532"
      },
      "outputs": [
        {
          "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"
          ]
        }
      ],
      "source": [
        "image_processor = AutoImageProcessor.from_pretrained(MODEL_CKPT)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t0uTbokVrgne"
      },
      "source": [
        "#### Define Some Transformations for Images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "S9TkUS8rrgne"
      },
      "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": "ViWwAoSUrgne"
      },
      "source": [
        "#### Create Function to Reformat Annotations Correctly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "idY5HZAZrgne"
      },
      "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": "q43WyF9Orgnf"
      },
      "source": [
        "#### Combine Image & Annotation Transformations For Use On Batch of Samples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "JzTbDkGrrgnf"
      },
      "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": "BORyRQPTrgnf"
      },
      "source": [
        "#### Apply Image Transformations to Training Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHtCu6iArgnf",
        "outputId": "44a577dc-39cb-44e1-c618-d74ae3363993"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'pixel_values': tensor([[[-1.7754, -1.7754, -1.7754,  ..., -1.7754, -1.7754, -1.7754],\n",
              "          [-1.7754, -1.7754, -1.7754,  ..., -1.7754, -1.7754, -1.7754],\n",
              "          [-1.7754, -1.7754, -1.7754,  ..., -1.7754, -1.7754, -1.7754],\n",
              "          ...,\n",
              "          [-1.7754, -1.7754, -1.7754,  ..., -1.7754, -1.7754, -1.7754],\n",
              "          [-1.7754, -1.7754, -1.7754,  ..., -1.7754, -1.7754, -1.7754],\n",
              "          [-1.7754, -1.7754, -1.7754,  ..., -1.7754, -1.7754, -1.7754]],\n",
              " \n",
              "         [[-1.6681, -1.6681, -1.6681,  ..., -1.6681, -1.6681, -1.6681],\n",
              "          [-1.6681, -1.6681, -1.6681,  ..., -1.6681, -1.6681, -1.6681],\n",
              "          [-1.6681, -1.6681, -1.6681,  ..., -1.6681, -1.6681, -1.6681],\n",
              "          ...,\n",
              "          [-1.6681, -1.6681, -1.6681,  ..., -1.6681, -1.6681, -1.6681],\n",
              "          [-1.6681, -1.6681, -1.6681,  ..., -1.6681, -1.6681, -1.6681],\n",
              "          [-1.6681, -1.6681, -1.6681,  ..., -1.6681, -1.6681, -1.6681]],\n",
              " \n",
              "         [[-1.4733, -1.4733, -1.4733,  ..., -1.4733, -1.4733, -1.4733],\n",
              "          [-1.4733, -1.4733, -1.4733,  ..., -1.4733, -1.4733, -1.4733],\n",
              "          [-1.4733, -1.4733, -1.4733,  ..., -1.4733, -1.4733, -1.4733],\n",
              "          ...,\n",
              "          [-1.4733, -1.4733, -1.4733,  ..., -1.4733, -1.4733, -1.4733],\n",
              "          [-1.4733, -1.4733, -1.4733,  ..., -1.4733, -1.4733, -1.4733],\n",
              "          [-1.4733, -1.4733, -1.4733,  ..., -1.4733, -1.4733, -1.4733]]]),\n",
              " 'labels': {'size': tensor([512, 512]), 'image_id': tensor([2734]), 'class_labels': tensor([1, 2, 3]), 'boxes': tensor([[0.3500, 0.3918, 0.1000, 0.0836],\n",
              "         [0.3344, 0.4379, 0.3125, 0.3664],\n",
              "         [0.3328, 0.3859, 0.0750, 0.0875]]), 'area': tensor([ 3895.7512, 53361.7773,  3058.3467]), 'iscrowd': tensor([0, 0, 0]), 'orig_size': tensor([480, 480])}}"
            ]
          },
          "execution_count": 26,
          "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": "yYt-0psArgnf"
      },
      "source": [
        "#### Define Data Collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "u0ozUKEwrgng"
      },
      "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": "X6AaMw3Irgng"
      },
      "source": [
        "#### Define Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "he4LB4Bsrgng",
        "outputId": "a6120cea-af77-410f-85dc-0f439215f01e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of YolosForObjectDetection were not initialized from the model checkpoint at hustvl/yolos-tiny and are newly initialized because the shapes did not match:\n",
            "- class_labels_classifier.layers.2.bias: found shape torch.Size([92]) in the checkpoint and torch.Size([5]) in the model instantiated\n",
            "- class_labels_classifier.layers.2.weight: found shape torch.Size([92, 192]) in the checkpoint and torch.Size([5, 192]) 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": "HxQNBwIvrgng"
      },
      "source": [
        "#### Define TrainingArguments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "rbad7vZ6rgng"
      },
      "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=500,\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": "9H425pvGrgnh"
      },
      "source": [
        "#### Define Trainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-cYdsDsrgnh",
        "outputId": "04cc0fbe-da0c-44fe-b9e7-66808ee4d698"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/content/yolos-tiny-Brain_Tumor_Detection is already a clone of https://huggingface.co/DunnBC22/yolos-tiny-Brain_Tumor_Detection. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "WARNING:huggingface_hub.repository:/content/yolos-tiny-Brain_Tumor_Detection is already a clone of https://huggingface.co/DunnBC22/yolos-tiny-Brain_Tumor_Detection. Make sure you pull the latest changes with `repo.git_pull()`.\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": "e7sL4-30rgnh"
      },
      "source": [
        "#### Train Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aui79Siergnh",
        "outputId": "0a053525-f2ac-4425-ec8e-420ef7284741"
      },
      "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='17340' max='17340' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [17340/17340 2:53:09, Epoch 20/20]\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>3.865800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>2.060200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.840700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.749600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>1.637300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>500</td>\n",
              "      <td>1.599900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.609700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>700</td>\n",
              "      <td>1.604900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>1.585100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.532500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1000</td>\n",
              "      <td>1.469200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1100</td>\n",
              "      <td>1.490400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>1.423900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1300</td>\n",
              "      <td>1.453700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1400</td>\n",
              "      <td>1.438800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.458200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>1.454700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1700</td>\n",
              "      <td>1.453600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>1.469100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1900</td>\n",
              "      <td>1.413000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>1.392400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>1.395200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2200</td>\n",
              "      <td>1.436300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2300</td>\n",
              "      <td>1.373200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>1.360500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2500</td>\n",
              "      <td>1.328000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2600</td>\n",
              "      <td>1.390400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2700</td>\n",
              "      <td>1.364200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2800</td>\n",
              "      <td>1.366700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2900</td>\n",
              "      <td>1.329600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>1.282100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3100</td>\n",
              "      <td>1.338100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3200</td>\n",
              "      <td>1.331400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3300</td>\n",
              "      <td>1.327500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3400</td>\n",
              "      <td>1.317900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3500</td>\n",
              "      <td>1.327200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3600</td>\n",
              "      <td>1.315600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3700</td>\n",
              "      <td>1.286600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3800</td>\n",
              "      <td>1.305600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3900</td>\n",
              "      <td>1.284500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4000</td>\n",
              "      <td>1.303100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4100</td>\n",
              "      <td>1.284200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4200</td>\n",
              "      <td>1.255100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4300</td>\n",
              "      <td>1.293100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4400</td>\n",
              "      <td>1.277900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>1.214700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4600</td>\n",
              "      <td>1.245000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4700</td>\n",
              "      <td>1.231500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4800</td>\n",
              "      <td>1.232700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4900</td>\n",
              "      <td>1.256800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5000</td>\n",
              "      <td>1.284300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5100</td>\n",
              "      <td>1.258300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5200</td>\n",
              "      <td>1.223700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5300</td>\n",
              "      <td>1.202400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5400</td>\n",
              "      <td>1.216600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5500</td>\n",
              "      <td>1.179900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5600</td>\n",
              "      <td>1.210600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5700</td>\n",
              "      <td>1.184600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5800</td>\n",
              "      <td>1.203100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5900</td>\n",
              "      <td>1.199500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>1.239100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6100</td>\n",
              "      <td>1.190900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6200</td>\n",
              "      <td>1.174300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6300</td>\n",
              "      <td>1.146400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6400</td>\n",
              "      <td>1.220100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6500</td>\n",
              "      <td>1.176300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6600</td>\n",
              "      <td>1.173600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6700</td>\n",
              "      <td>1.180300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6800</td>\n",
              "      <td>1.195700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6900</td>\n",
              "      <td>1.189300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7000</td>\n",
              "      <td>1.122300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7100</td>\n",
              "      <td>1.160900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7200</td>\n",
              "      <td>1.158400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7300</td>\n",
              "      <td>1.145200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7400</td>\n",
              "      <td>1.156400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>1.140300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7600</td>\n",
              "      <td>1.134000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7700</td>\n",
              "      <td>1.109800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7800</td>\n",
              "      <td>1.124000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7900</td>\n",
              "      <td>1.128700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8000</td>\n",
              "      <td>1.131400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8100</td>\n",
              "      <td>1.120800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8200</td>\n",
              "      <td>1.047300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8300</td>\n",
              "      <td>1.078400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8400</td>\n",
              "      <td>1.128700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8500</td>\n",
              "      <td>1.113300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8600</td>\n",
              "      <td>1.122900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8700</td>\n",
              "      <td>1.159500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8800</td>\n",
              "      <td>1.047500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8900</td>\n",
              "      <td>1.049500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>1.069400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9100</td>\n",
              "      <td>1.077200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9200</td>\n",
              "      <td>1.043700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9300</td>\n",
              "      <td>1.094800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9400</td>\n",
              "      <td>1.067100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9500</td>\n",
              "      <td>1.100800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9600</td>\n",
              "      <td>1.076500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9700</td>\n",
              "      <td>1.046200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9800</td>\n",
              "      <td>1.042800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9900</td>\n",
              "      <td>1.031400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10000</td>\n",
              "      <td>1.088200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10100</td>\n",
              "      <td>1.051800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10200</td>\n",
              "      <td>1.014400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10300</td>\n",
              "      <td>1.058200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10400</td>\n",
              "      <td>1.034400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10500</td>\n",
              "      <td>1.026800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10600</td>\n",
              "      <td>1.025100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10700</td>\n",
              "      <td>1.014500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10800</td>\n",
              "      <td>1.011700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10900</td>\n",
              "      <td>1.015000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11000</td>\n",
              "      <td>1.010700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11100</td>\n",
              "      <td>1.048600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11200</td>\n",
              "      <td>1.004700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11300</td>\n",
              "      <td>1.021500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11400</td>\n",
              "      <td>0.978500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11500</td>\n",
              "      <td>0.982900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11600</td>\n",
              "      <td>0.996200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11700</td>\n",
              "      <td>1.021100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11800</td>\n",
              "      <td>0.983900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11900</td>\n",
              "      <td>0.989500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12000</td>\n",
              "      <td>0.991000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12100</td>\n",
              "      <td>1.015400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12200</td>\n",
              "      <td>0.997500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12300</td>\n",
              "      <td>0.967400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12400</td>\n",
              "      <td>0.974300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12500</td>\n",
              "      <td>0.971300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12600</td>\n",
              "      <td>0.954300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12700</td>\n",
              "      <td>0.981700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12800</td>\n",
              "      <td>0.979700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12900</td>\n",
              "      <td>0.942300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13000</td>\n",
              "      <td>0.966900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13100</td>\n",
              "      <td>0.941800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13200</td>\n",
              "      <td>0.970700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13300</td>\n",
              "      <td>0.956200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13400</td>\n",
              "      <td>0.932000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13500</td>\n",
              "      <td>0.927000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13600</td>\n",
              "      <td>0.915400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13700</td>\n",
              "      <td>0.945000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13800</td>\n",
              "      <td>0.944700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13900</td>\n",
              "      <td>0.902800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14000</td>\n",
              "      <td>0.930000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14100</td>\n",
              "      <td>0.925000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14200</td>\n",
              "      <td>0.939200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14300</td>\n",
              "      <td>0.918400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14400</td>\n",
              "      <td>0.936900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14500</td>\n",
              "      <td>0.928500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14600</td>\n",
              "      <td>0.907500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14700</td>\n",
              "      <td>0.893600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14800</td>\n",
              "      <td>0.876100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14900</td>\n",
              "      <td>0.861800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15000</td>\n",
              "      <td>0.899800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15100</td>\n",
              "      <td>0.932400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15200</td>\n",
              "      <td>0.926600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15300</td>\n",
              "      <td>0.900300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15400</td>\n",
              "      <td>0.882200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15500</td>\n",
              "      <td>0.895000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15600</td>\n",
              "      <td>0.902600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15700</td>\n",
              "      <td>0.919700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15800</td>\n",
              "      <td>0.881900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15900</td>\n",
              "      <td>0.844800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16000</td>\n",
              "      <td>0.866700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16100</td>\n",
              "      <td>0.893700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16200</td>\n",
              "      <td>0.869900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16300</td>\n",
              "      <td>0.881800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16400</td>\n",
              "      <td>0.864300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16500</td>\n",
              "      <td>0.913300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16600</td>\n",
              "      <td>0.881900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16700</td>\n",
              "      <td>0.872400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16800</td>\n",
              "      <td>0.872500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16900</td>\n",
              "      <td>0.873300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17000</td>\n",
              "      <td>0.866500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17100</td>\n",
              "      <td>0.871400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17200</td>\n",
              "      <td>0.859500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17300</td>\n",
              "      <td>0.858900</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=17340, training_loss=1.133421648909889, metrics={'train_runtime': 10390.4822, 'train_samples_per_second': 13.339, 'train_steps_per_second': 1.669, 'total_flos': 4.232631651139584e+18, 'train_loss': 1.133421648909889, 'epoch': 20.0})"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KncVC272rgnh"
      },
      "source": [
        "#### Push Model To Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353,
          "referenced_widgets": [
            "2cb5e13c593a4daeb14cae59e6cc94ff",
            "4eb8a6e122484c9ba45f0dc5eb014617",
            "2ce0fc8ac53942998a9c96696bbd2c63",
            "5fc71e9770b54c8e9e6cd54300630647",
            "862921f4393a43ebb6a17c8500262851",
            "905461f6d47547e5be0164b6d5cf3f68",
            "4b56ed3a494541ed8e3d80f211887c5c",
            "bc61f36de68f4697be379a4469316b4e",
            "d6cfc2b9610f4242bacc15b19adfe868",
            "d92cb7ce08be45e997a983508945e197",
            "f07a88274a5b4204b7e26de0b78f07eb",
            "007837bb2ffc4f7ca4d0cea9021e1677",
            "cce8b01af39b49228a0639445ca9a3bc",
            "e380afe75850488c8677e1f17e3c2e55",
            "62fd84db3ed64a06839ba32aca4bb0ae",
            "b39132fbe2814096a7416d46c248379d",
            "e1cf67fcd1f14a90a647caf79ddc27ca",
            "589ab25f48e2463a9ece1907fed6af2b",
            "0bacd38bae9046b78b050bcf9d9f040a",
            "967850159cba46e5be58e66bb95400d3",
            "2287d67f10514026b98e78016a5aa88a",
            "4767d9cc6a7140c5accec1f4070aaeef"
          ]
        },
        "id": "mhWEVNXhrgnh",
        "outputId": "c50473c2-7e62-4cde-cf98-f49770fb8dea"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2cb5e13c593a4daeb14cae59e6cc94ff",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 1.00/24.8M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "007837bb2ffc4f7ca4d0cea9021e1677",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file runs/Aug01_20-23-00_6653943f4cd8/events.out.tfevents.1690921384.6653943f4cd8.324.1:   0%|         …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/DunnBC22/yolos-tiny-Brain_Tumor_Detection\n",
            "   805b06a..0afd663  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/yolos-tiny-Brain_Tumor_Detection\n",
            "   805b06a..0afd663  main -> main\n",
            "\n",
            "To https://huggingface.co/DunnBC22/yolos-tiny-Brain_Tumor_Detection\n",
            "   0afd663..fd1b197  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/DunnBC22/yolos-tiny-Brain_Tumor_Detection\n",
            "   0afd663..fd1b197  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/DunnBC22/yolos-tiny-Brain_Tumor_Detection/commit/0afd663c14d4362f3f7e4d4452de7344406d59da'"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6DAzbGlrgni"
      },
      "source": [
        "#### Save Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "FkB76DtIrgni"
      },
      "outputs": [],
      "source": [
        "trainer.save_model()\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4KFig0gKrgn1"
      },
      "source": [
        "### Evaluate Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OvZCbQYIrgn2"
      },
      "source": [
        "#### Create Function to Reformat Evaluation Annotations Correctly"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "XiLDJ7N-rgn2"
      },
      "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": "s_846kv-tGb9"
      },
      "source": [
        "#### Evaluation Data Collator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "i05CjNR5tGSg"
      },
      "outputs": [],
      "source": [
        "def eval_data_collator_fn(batch):\n",
        "    pixel_values = [item[\"pixel_values\"] for item in batch]\n",
        "    encoding = eval_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": "JqSpbd8prgn2"
      },
      "source": [
        "#### Save Images & Annotations into Proper Files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "F8Epd-Ikrgn2"
      },
      "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/Brain Tumors/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": "QK90t5F2rgn3"
      },
      "source": [
        "#### Create Class to Ingest Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "olO2ny3srgn6"
      },
      "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",
        "        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": "3txpeeuyrgn7"
      },
      "source": [
        "#### Apply Above-Defined Functions to Testing Dataset"
      ]
    },
    {
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      "execution_count": 38,
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        "colab": {
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        },
        "id": "drtzNW86rgn7",
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      "outputs": [
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              "model_id": "ac6023af0078461299d7c2a04fd2964b",
              "version_major": 2,
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              "Downloading (…)rocessor_config.json:   0%|          | 0.00/402 [00:00<?, ?B/s]"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=0.04s)\n",
            "creating index...\n",
            "index created!\n"
          ]
        }
      ],
      "source": [
        "EVAL_MODEL_NAME = 'DunnBC22/yolos-tiny-Brain_Tumor_Detection'\n",
        "\n",
        "eval_image_processor = AutoImageProcessor.from_pretrained(EVAL_MODEL_NAME)\n",
        "\n",
        "path_output_ds, path_annot = save_images_and_annotation_files(data[\"test\"])\n",
        "eval_ds_coco_format = CocoDetection(path_output_ds,\n",
        "                                    eval_image_processor,\n",
        "                                    path_annot)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KMbyGboLrgn7"
      },
      "source": [
        "#### Calculate Metrics"
      ]
    },
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              "Downloading (…)lve/main/config.json:   0%|          | 0.00/977 [00:00<?, ?B/s]"
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              "Downloading builder script:   0%|          | 0.00/6.40k [00:00<?, ?B/s]"
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        {
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              "Downloading extra modules:   0%|          | 0.00/14.9k [00:00<?, ?B/s]"
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        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "  0%|          | 0/248 [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%|██████████| 248/248 [25:44<00:00,  6.23s/it]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accumulating evaluation results...\n",
            "DONE (t=3.39s).\n",
            "IoU metric: bbox\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.185\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.448\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.126\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.080\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.296\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.254\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.353\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.407\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.036\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.312\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.565\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=eval_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",
        "        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": "81ZCkm_trgn8"
      },
      "source": [
        "#### Display Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85TUhVRergn8",
        "outputId": "e1c94cf4-7d8e-4231-e49b-f95769bd8986"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'iou_bbox': {'AP-IoU=0.50:0.95-area=all-maxDets=100': 0.1845872711961627, 'AP-IoU=0.50-area=all-maxDets=100': 0.44817116404923796, 'AP-IoU=0.75-area=all-maxDets=100': 0.12629286910455717, 'AP-IoU=0.50:0.95-area=small-maxDets=100': 0.0007021833424583723, 'AP-IoU=0.50:0.95-area=medium-maxDets=100': 0.07962285096690051, 'AP-IoU=0.50:0.95-area=large-maxDets=100': 0.29623542605383424, 'AR-IoU=0.50:0.95-area=all-maxDets=1': 0.2538945582565657, 'AR-IoU=0.50:0.95-area=all-maxDets=10': 0.35253156853278844, 'AR-IoU=0.50:0.95-area=all-maxDets=100': 0.40656168189236347, 'AR-IoU=0.50:0.95-area=small-maxDets=100': 0.03634109689028365, 'AR-IoU=0.50:0.95-area=medium-maxDets=100': 0.31179604417222134, 'AR-IoU=0.50:0.95-area=large-maxDets=100': 0.5646720247447636}}\n"
          ]
        }
      ],
      "source": [
        "print(results)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W-iQUjXhrgn-"
      },
      "source": [
        "### Notes & Other Takeaways\n",
        "****\n",
        "- The main reason that the results of this project were not better is I was unable to use the Yolos-Small checkpoint (which is larger than the tiny checkpoint. Using the larger checkpoint would have provided better results.\n",
        "\n",
        "****"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fw7eM_Q6rgn-"
      },
      "source": [
        "### Citations\n",
        "\n",
        "- Dataset\n",
        "\n",
        "    > @misc{ brain-tumor-m2pbp, title = { brain tumor m2pbp Dataset }, type = { Open Source Dataset }, author = { Roboflow 100 }, howpublished = { \\url{ https://universe.roboflow.com/object-detection/brain-tumor-m2pbp } }, url = { https://universe.roboflow.com/object-detection/brain-tumor-m2pbp }, journal = { Roboflow Universe }, publisher = { Roboflow }, year = { 2022 }, month = { nov }, note = { visited on 2023-03-29 },}\n",
        "\n",
        "- Model Checkpoint (Yolos-Tiny)\n",
        "    \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"
      ]
    }
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