{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FVSemFwCiMf",
        "outputId": "b05de6c5-6f67-45fe-89eb-2d5e94af875f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.1/18.1 MB\u001b[0m \u001b[31m126.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m37.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25htorch: 2.9.0+cu126\n",
            "timm: 1.0.22\n",
            "onnx: 1.20.0\n",
            "onnxruntime: 1.20.0\n",
            "sklearn: 1.6.1\n"
          ]
        }
      ],
      "source": [
        "# Core packages\n",
        "!pip install -q kaggle timm scikit-learn seaborn albumentations matplotlib pillow onnx\n",
        "\n",
        "# Install ONNX Runtime with Python 3.11 support\n",
        "!pip install -q onnxruntime==1.20.0\n",
        "\n",
        "# Verify\n",
        "import torch, timm, onnx, onnxruntime, sklearn\n",
        "print(\"torch:\", torch.__version__)\n",
        "\n",
        "print(\"timm:\", timm.__version__)\n",
        "print(\"onnx:\", onnx.__version__)\n",
        "print(\"onnxruntime:\", onnxruntime.__version__)\n",
        "print(\"sklearn:\", sklearn.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j-aB2VBtCwCo",
        "outputId": "77099b97-aac5-47f1-c564-812924356e31"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CUDA available: True\n",
            "Sat Dec  6 06:55:35 2025       \n",
            "+-----------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |\n",
            "|-----------------------------------------+------------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                        |               MIG M. |\n",
            "|=========================================+========================+======================|\n",
            "|   0  Tesla T4                       Off |   00000000:00:04.0 Off |                    0 |\n",
            "| N/A   43C    P8             10W /   70W |       2MiB /  15360MiB |      0%      Default |\n",
            "|                                         |                        |                  N/A |\n",
            "+-----------------------------------------+------------------------+----------------------+\n",
            "                                                                                         \n",
            "+-----------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                              |\n",
            "|  GPU   GI   CI        PID   Type   Process name                              GPU Memory |\n",
            "|        ID   ID                                                               Usage      |\n",
            "|=========================================================================================|\n",
            "|  No running processes found                                                             |\n",
            "+-----------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "import torch, subprocess, sys\n",
        "print(\"CUDA available:\", torch.cuda.is_available())\n",
        "try:\n",
        "    !nvidia-smi\n",
        "except Exception as e:\n",
        "    print(\"nvidia-smi not found. Make sure you selected GPU runtime. Error:\", e)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "id": "IzTqzZ-1C7mX",
        "outputId": "3fb53cce-2003-4564-d5fc-fcfa6b3474a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please upload kaggle.json (from your Kaggle account -> Create New API Token).\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-4906823b-b4d0-4822-a2b7-f2f7d8b73c38\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-4906823b-b4d0-4822-a2b7-f2f7d8b73c38\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n",
            "kaggle.json saved to /root/.kaggle/kaggle.json\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "import os, json, sys, textwrap\n",
        "\n",
        "print(\"Please upload kaggle.json (from your Kaggle account -> Create New API Token).\")\n",
        "uploaded = files.upload()\n",
        "if \"kaggle.json\" not in uploaded:\n",
        "    # if user uploaded a key string, instruct how to make kaggle.json\n",
        "    print(\"If you uploaded another file, make sure it is named kaggle.json and contains {\\\"username\\\":\\\"...\\\",\\\"key\\\":\\\"...\\\"}\")\n",
        "    raise SystemExit(\"kaggle.json not uploaded.\")\n",
        "\n",
        "# move it to expected location\n",
        "os.makedirs(\"/root/.kaggle\", exist_ok=True)\n",
        "with open(\"/root/.kaggle/kaggle.json\",\"wb\") as f:\n",
        "    f.write(uploaded[\"kaggle.json\"])\n",
        "os.chmod(\"/root/.kaggle/kaggle.json\", 0o600)\n",
        "print(\"kaggle.json saved to /root/.kaggle/kaggle.json\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBadllO7DAxF",
        "outputId": "1c7f36a7-eaa9-4ac7-cf88-5f8837c18d4a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/mexwell/crop-diseases-classification\n",
            "License(s): other\n",
            "Downloading crop-diseases-classification.zip to /content\n",
            " 99% 1.98G/2.00G [00:26<00:00, 205MB/s]\n",
            "100% 2.00G/2.00G [00:26<00:00, 81.1MB/s]\n",
            "Extracted to /content/crop_data\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d mexwell/crop-diseases-classification -p /content --force\n",
        "\n",
        "# unzip\n",
        "!unzip -q /content/crop-diseases-classification.zip -d /content/crop_data\n",
        "print(\"Extracted to /content/crop_data\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9BlTLqsnDIy1",
        "outputId": "9b175eda-583f-4b12-f664-83b47a9318e0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/crop_data\n",
            " - subdirs: 1  - files: 0\n",
            "Data\n"
          ]
        }
      ],
      "source": [
        "# Inspect the unzipped dataset to find image folders\n",
        "import os\n",
        "for root, dirs, files in os.walk(\"/content/crop_data\"):\n",
        "    # print top-level dirs\n",
        "    print(root)\n",
        "    print(\" - subdirs:\", len(dirs), \" - files:\", len(files))\n",
        "    # show first level then break\n",
        "    break\n",
        "\n",
        "# List first few subfolders (helps you understand layout)\n",
        "for item in sorted(os.listdir(\"/content/crop_data\"))[:30]:\n",
        "    print(item)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "GhMBAMfaDjdz",
        "outputId": "fa61269d-c1ba-423d-d0e5-efeca1db9835"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total images in CSV: 21397\n",
            "         image_id  label\n",
            "0  1000015157.jpg      0\n",
            "1  1000201771.jpg      3\n",
            "2   100042118.jpg      1\n",
            "3  1000723321.jpg      1\n",
            "4  1000812911.jpg      3\n",
            "Train size: 18187\n",
            "Val size: 3210\n",
            "Detected classes: [np.int64(0), np.int64(1), np.int64(2), np.int64(3), np.int64(4)]\n",
            "Copying training images...\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1325487740.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1368384368.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/104769172.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1117432302.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1239790463.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1167375452.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1238159478.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1352386013.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1175346022.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1549198414.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1019366633.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1291065002.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1209108991.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1274870847.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1466509745.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1736448195.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1161282261.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1137468272.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1097202105.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/126224804.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1037337861.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1261540961.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1451004490.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1563897532.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1401473494.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/136815898.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1377320047.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1020245430.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/140243499.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1562043567.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1029778366.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1275939259.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1454074626.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1080185160.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1178196000.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1108320889.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1381660932.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1477310762.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1354318448.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/104535906.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/13975743.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1320006527.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1356273389.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1111878443.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1198438913.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1249702391.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1497853421.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1336117857.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1288923505.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1544902048.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1478823591.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1362176870.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1362172635.jpg\n",
            "WARNING: missing image: /content/crop_data/Data/train_images/1487645324.jpg\n",
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            "Class 0: train=784, val=137\n",
            "Class 1: train=1558, val=273\n",
            "Class 2: train=1687, val=306\n",
            "Class 3: train=9374, val=1653\n",
            "Class 4: train=1840, val=326\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import shutil\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# PATHS\n",
        "SRC = \"/content/crop_data/Data\"\n",
        "IMG_DIR = os.path.join(SRC, \"train_images\")\n",
        "CSV_PATH = os.path.join(SRC, \"train.csv\")\n",
        "\n",
        "OUT = \"/content/dataset_split\"\n",
        "TRAIN_DIR = os.path.join(OUT, \"train\")\n",
        "VAL_DIR = os.path.join(OUT, \"val\")\n",
        "\n",
        "# Remove previous split\n",
        "if os.path.exists(OUT):\n",
        "    print(\"Removing existing split folder:\", OUT)\n",
        "    shutil.rmtree(OUT)\n",
        "\n",
        "os.makedirs(TRAIN_DIR, exist_ok=True)\n",
        "os.makedirs(VAL_DIR, exist_ok=True)\n",
        "\n",
        "# Load labels\n",
        "df = pd.read_csv(CSV_PATH)\n",
        "\n",
        "print(\"Total images in CSV:\", len(df))\n",
        "print(df.head())\n",
        "\n",
        "# Stratified split\n",
        "train_df, val_df = train_test_split(\n",
        "    df,\n",
        "    test_size=0.15,\n",
        "    stratify=df[\"label\"],\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "print(\"Train size:\", len(train_df))\n",
        "print(\"Val size:\", len(val_df))\n",
        "\n",
        "# Create folders per class\n",
        "unique_classes = sorted(df[\"label\"].unique())\n",
        "\n",
        "print(\"Detected classes:\", unique_classes)\n",
        "\n",
        "for cls in unique_classes:\n",
        "    os.makedirs(os.path.join(TRAIN_DIR, str(cls)), exist_ok=True)\n",
        "    os.makedirs(os.path.join(VAL_DIR, str(cls)), exist_ok=True)\n",
        "\n",
        "# Copy images correctly\n",
        "def copy_images(subdf, target_root):\n",
        "    for _, row in subdf.iterrows():\n",
        "        img_name = row.image_id\n",
        "        cls = str(row.label)\n",
        "\n",
        "        src_path = os.path.join(IMG_DIR, img_name)\n",
        "        dst_path = os.path.join(target_root, cls, img_name)\n",
        "\n",
        "        # Safety: skip missing files\n",
        "        if not os.path.isfile(src_path):\n",
        "            print(\"WARNING: missing image:\", src_path)\n",
        "            continue\n",
        "\n",
        "        shutil.copy(src_path, dst_path)\n",
        "\n",
        "print(\"Copying training images...\")\n",
        "copy_images(train_df, TRAIN_DIR)\n",
        "\n",
        "print(\"Copying validation images...\")\n",
        "copy_images(val_df, VAL_DIR)\n",
        "\n",
        "# Summary check\n",
        "for cls in unique_classes:\n",
        "    t = len(os.listdir(os.path.join(TRAIN_DIR, str(cls))))\n",
        "    v = len(os.listdir(os.path.join(VAL_DIR, str(cls))))\n",
        "    print(f\"Class {cls}: train={t}, val={v}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "m3Y3MlLnDn1l"
      },
      "outputs": [],
      "source": [
        "# imports, reproducibility, config\n",
        "import torch, numpy as np, random, logging, time, os\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import DataLoader\n",
        "import torchvision\n",
        "\n",
        "def seed_everything(seed=42):\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)\n",
        "seed_everything(42)\n",
        "\n",
        "logging.basicConfig(level=logging.INFO, format=\"%(asctime)s %(levelname)s %(message)s\")\n",
        "logger = logging.getLogger(\"agricare_notebook\")\n",
        "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "logger.info(f\"Using device: {DEVICE}\")\n",
        "\n",
        "IMG_SIZE = 300\n",
        "BATCH_SIZE = 32\n",
        "NUM_EPOCHS = 8\n",
        "NUM_WORKERS = 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lYhnbTLODwXe"
      },
      "outputs": [],
      "source": [
        "# Data transforms and dataloaders\n",
        "from torchvision.transforms import Compose, Resize, RandomHorizontalFlip, RandomRotation, ColorJitter, ToTensor, Normalize\n",
        "\n",
        "train_transforms = Compose([\n",
        "    Resize((IMG_SIZE, IMG_SIZE)),\n",
        "    RandomHorizontalFlip(),\n",
        "    RandomRotation(10),\n",
        "    ColorJitter(brightness=0.15, contrast=0.15, saturation=0.05),\n",
        "    ToTensor(),\n",
        "    Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])\n",
        "])\n",
        "\n",
        "val_transforms = Compose([\n",
        "    Resize((IMG_SIZE, IMG_SIZE)),\n",
        "    ToTensor(),\n",
        "    Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225])\n",
        "])\n",
        "\n",
        "train_dataset = torchvision.datasets.ImageFolder(TRAIN_DIR, transform=train_transforms)\n",
        "val_dataset = torchvision.datasets.ImageFolder(VAL_DIR, transform=val_transforms)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS, pin_memory=True)\n",
        "val_loader   = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False, num_workers=NUM_WORKERS, pin_memory=True)\n",
        "\n",
        "class_names = train_dataset.classes\n",
        "NUM_CLASSES = len(class_names)\n",
        "logger.info(f\"Found {NUM_CLASSES} classes: {class_names}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
            "126f2fb333874a2ba3029c9b0f6f18f1",
            "94eefa3c5fe64c6493081681e0370db3",
            "1abe786fea354ef3b1a186ad9232c6a1",
            "c438077b1c8c479da551eef52749f8c9",
            "f0f33a1936a047caabc9004f709aaf0f",
            "523d022273a947ec802e34a90316ba97",
            "242eae54c2de446285c102821221ed3a",
            "f04835a06c66469e966f05e584a4e412",
            "286875441091497299a44ae7acf08314",
            "f1bb40456d4d4f1c96449d3b65a2fbc7",
            "65ae4ca23ac24772a874c1de02f32921"
          ]
        },
        "id": "N4oBbeN9Dzot",
        "outputId": "851ecf09-1931-45d8-a3a0-22875c3e3945"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "126f2fb333874a2ba3029c9b0f6f18f1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/49.3M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Model, optimizer, scheduler\n",
        "import timm\n",
        "import torch.nn as nn\n",
        "\n",
        "model = timm.create_model(\"efficientnet_b3\", pretrained=True, num_classes=NUM_CLASSES)\n",
        "model = model.to(DEVICE)\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-5)\n",
        "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=NUM_EPOCHS)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kf2FiSMkD3wZ"
      },
      "outputs": [],
      "source": [
        "# training & validation functions\n",
        "from sklearn.metrics import accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "def train_one_epoch(model, loader, criterion, optimizer, device):\n",
        "    model.train()\n",
        "    losses = []\n",
        "    all_preds, all_labels = [], []\n",
        "    for imgs, labels in loader:\n",
        "        imgs, labels = imgs.to(device), labels.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        out = model(imgs)\n",
        "        loss = criterion(out, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        preds = out.detach().argmax(dim=1).cpu().numpy()\n",
        "        all_preds.extend(preds.tolist())\n",
        "        all_labels.extend(labels.cpu().numpy().tolist())\n",
        "        losses.append(loss.item())\n",
        "    return np.mean(losses), accuracy_score(all_labels, all_preds)\n",
        "\n",
        "def validate(model, loader, criterion, device, collect_low_confidence=False, threshold=0.6):\n",
        "    model.eval()\n",
        "    losses = []\n",
        "    all_preds, all_labels, all_probs = [], [], []\n",
        "    low_confidence = []\n",
        "    with torch.no_grad():\n",
        "        for imgs, labels in loader:\n",
        "            imgs = imgs.to(device)\n",
        "            out = model(imgs)\n",
        "            loss = criterion(out, labels.to(device))\n",
        "            probs = torch.softmax(out, dim=1).cpu().numpy()\n",
        "            preds = probs.argmax(axis=1)\n",
        "            all_preds.extend(preds.tolist())\n",
        "            all_labels.extend(labels.numpy().tolist())\n",
        "            all_probs.extend(probs.tolist())\n",
        "            if collect_low_confidence:\n",
        "                for i,p in enumerate(probs):\n",
        "                    top_conf = float(p.max())\n",
        "                    pred_class = int(p.argmax())\n",
        "                    if top_conf < threshold:\n",
        "                        low_confidence.append({\"index_global\": len(all_labels)-len(probs)+i, \"pred\": pred_class, \"conf\": top_conf})\n",
        "            losses.append(loss.item())\n",
        "    return np.mean(losses), accuracy_score(all_labels, all_preds), all_preds, all_labels, all_probs, low_confidence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "71h4uyDfD-bw"
      },
      "outputs": [],
      "source": [
        "# train loop with checkpoints\n",
        "import os, time\n",
        "CHECKPOINT_DIR = \"/content/checkpoints\"\n",
        "os.makedirs(CHECKPOINT_DIR, exist_ok=True)\n",
        "best_val_acc = 0.0\n",
        "\n",
        "for epoch in range(1, NUM_EPOCHS+1):\n",
        "    t0 = time.time()\n",
        "    train_loss, train_acc = train_one_epoch(model, train_loader, criterion, optimizer, DEVICE)\n",
        "    val_loss, val_acc, _, _, _, _ = validate(model, val_loader, criterion, DEVICE)\n",
        "    scheduler.step()\n",
        "    t1 = time.time()\n",
        "    logger.info(f\"Epoch {epoch}/{NUM_EPOCHS} | train_loss={train_loss:.4f} acc={train_acc:.4f} | \"\n",
        "                f\"val_loss={val_loss:.4f} acc={val_acc:.4f} | time={(t1-t0):.1f}s\")\n",
        "    # save checkpoint\n",
        "    ckpt_path = os.path.join(CHECKPOINT_DIR, f\"effb3_epoch{epoch}_valacc{val_acc:.4f}.pth\")\n",
        "    torch.save({\"epoch\": epoch, \"model_state\": model.state_dict(), \"optimizer_state\": optimizer.state_dict()}, ckpt_path)\n",
        "    if val_acc > best_val_acc:\n",
        "        best_val_acc = val_acc\n",
        "        best_path = os.path.join(CHECKPOINT_DIR, \"best_model.pth\")\n",
        "        torch.save(model.state_dict(), best_path)\n",
        "        logger.info(f\"Saved best model to {best_path}\")\n",
        "logger.info(f\"Training finished. Best val acc: {best_val_acc:.4f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 926
        },
        "id": "C0pvxNzAEBmD",
        "outputId": "671f2385-0dc7-4a0d-eb69-aa96892cf994"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.56      0.60      0.58       137\n",
            "           1       0.76      0.69      0.72       273\n",
            "           2       0.77      0.69      0.73       306\n",
            "           3       0.93      0.94      0.94      1653\n",
            "           4       0.62      0.67      0.65       326\n",
            "\n",
            "    accuracy                           0.84      2695\n",
            "   macro avg       0.73      0.72      0.72      2695\n",
            "weighted avg       0.84      0.84      0.84      2695\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# detailed evaluation and confusion matrix\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# load best model\n",
        "best_model_path = os.path.join(CHECKPOINT_DIR, \"best_model.pth\")\n",
        "model.load_state_dict(torch.load(best_model_path))\n",
        "model.to(DEVICE)\n",
        "\n",
        "val_loss, val_acc, preds, labels, probs, low_conf = validate(model, val_loader, criterion, DEVICE, collect_low_confidence=True, threshold=0.6)\n",
        "logger.info(f\"Final validation acc: {val_acc:.4f}\")\n",
        "\n",
        "print(classification_report(labels, preds, target_names=class_names))\n",
        "\n",
        "cm = confusion_matrix(labels, preds)\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(cm, annot=True, fmt=\"d\", xticklabels=class_names, yticklabels=class_names, cmap=\"Blues\")\n",
        "plt.ylabel(\"Actual\")\n",
        "plt.xlabel(\"Predicted\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.show()\n",
        "\n",
        "# Save predictions CSV for audit\n",
        "pred_csv = \"/content/val_predictions.csv\"\n",
        "val_image_paths = [s[0] for s in val_dataset.samples]\n",
        "import csv\n",
        "with open(pred_csv, \"w\", newline=\"\", encoding=\"utf-8\") as f:\n",
        "    writer = csv.writer(f)\n",
        "    writer.writerow([\"image_path\", \"true_label\", \"pred_label\", \"pred_confidence\"])\n",
        "    for path, true, pred, prob in zip(val_image_paths, labels, preds, probs):\n",
        "        writer.writerow([path, true, pred, float(max(prob))])\n",
        "logger.info(\"Saved detailed predictions to \" + pred_csv)\n",
        "logger.info(f\"Low-confidence count: {len(low_conf)} (threshold=0.6)\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQ_-zF-UOJ4e",
        "outputId": "3a57d4da-fd01-4e63-8d38-b7da170a0cb9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipython-input-3714739164.py:13: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).\n",
            "  writer.writerow([datetime.datetime.utcnow().isoformat(), img_path, item[\"pred\"], item[\"conf\"], \"\", \"\"])\n"
          ]
        }
      ],
      "source": [
        "# prepare review queue for agronomists (low-confidence)\n",
        "import datetime, csv, os\n",
        "REVIEW_DIR = \"/content/review_queue\"\n",
        "os.makedirs(REVIEW_DIR, exist_ok=True)\n",
        "REVIEW_CSV = os.path.join(REVIEW_DIR, \"review_queue.csv\")\n",
        "\n",
        "with open(REVIEW_CSV, \"w\", newline=\"\", encoding=\"utf-8\") as f:\n",
        "    writer = csv.writer(f)\n",
        "    writer.writerow([\"timestamp\",\"image_path\",\"pred_label\",\"pred_confidence\",\"expert_label\",\"expert_notes\"])\n",
        "    for item in low_conf:\n",
        "        idx = item[\"index_global\"]\n",
        "        img_path = val_image_paths[idx]\n",
        "        writer.writerow([datetime.datetime.utcnow().isoformat(), img_path, item[\"pred\"], item[\"conf\"], \"\", \"\"])\n",
        "logger.info(f\"Saved review queue to {REVIEW_CSV} - hand this to agronomists for labeling\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 560
        },
        "id": "c55tXakEOic6",
        "outputId": "593b36ff-6d20-4404-ccea-aecdcc3da25d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting onnxscript\n",
            "  Downloading onnxscript-0.5.6-py3-none-any.whl.metadata (13 kB)\n",
            "Requirement already satisfied: ml_dtypes in /usr/local/lib/python3.12/dist-packages (from onnxscript) (0.5.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.12/dist-packages (from onnxscript) (2.0.2)\n",
            "Collecting onnx_ir<2,>=0.1.12 (from onnxscript)\n",
            "  Downloading onnx_ir-0.1.12-py3-none-any.whl.metadata (3.2 kB)\n",
            "Collecting onnx>=1.16 (from onnxscript)\n",
            "  Downloading onnx-1.20.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl.metadata (8.4 kB)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.12/dist-packages (from onnxscript) (25.0)\n",
            "Requirement already satisfied: typing_extensions>=4.10 in /usr/local/lib/python3.12/dist-packages (from onnxscript) (4.15.0)\n",
            "Requirement already satisfied: protobuf>=4.25.1 in /usr/local/lib/python3.12/dist-packages (from onnx>=1.16->onnxscript) (5.29.5)\n",
            "Downloading onnxscript-0.5.6-py3-none-any.whl (683 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m683.0/683.0 kB\u001b[0m \u001b[31m22.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading onnx-1.20.0-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl (18.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.1/18.1 MB\u001b[0m \u001b[31m103.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading onnx_ir-0.1.12-py3-none-any.whl (129 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.3/129.3 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: onnx, onnx_ir, onnxscript\n",
            "Successfully installed onnx-1.20.0 onnx_ir-0.1.12 onnxscript-0.5.6\n"
          ]
        },
        {
          "ename": "NameError",
          "evalue": "name 'IMG_SIZE' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipython-input-1014614908.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'pip install onnxscript'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0monnx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdummy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIMG_SIZE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIMG_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDEVICE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0monnx_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"/content/cassava_efficientnetb3.onnx\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'IMG_SIZE' is not defined"
          ]
        }
      ],
      "source": [
        "# export to ONNX for production/serving\n",
        "!pip install onnxscript\n",
        "import torch.onnx\n",
        "dummy = torch.randn(1, 3, IMG_SIZE, IMG_SIZE).to(DEVICE)\n",
        "onnx_path = \"/content/cassava_efficientnetb3.onnx\"\n",
        "model.eval()\n",
        "torch.onnx.export(model, dummy, onnx_path, input_names=[\"input\"], output_names=[\"output\"], opset_version=18, dynamic_axes={\"input\":{0:\"batch\"}, \"output\":{0:\"batch\"}})\n",
        "logger.info(\"Exported ONNX model to \" + onnx_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "9ZnwbEXqOwdJ",
        "outputId": "421eb473-218c-4c07-aeb3-6c1cf2a99ff4"
      },
      "outputs": [
        {
          "ename": "FileNotFoundError",
          "evalue": "Cannot find file: /content/cassava_efficientnetb3.onnx",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipython-input-1954250139.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/cassava_efficientnetb3.onnx\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mdownload\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m    231\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_os\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m     \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Cannot find file: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=undefined-variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m   \u001b[0mcomm_manager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_IPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomm_manager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: Cannot find file: /content/cassava_efficientnetb3.onnx"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.download(\"/content/cassava_efficientnetb3.onnx\")"
      ]
    }
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