{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MCJl-P7XRI9L"
      },
      "outputs": [],
      "source": [
        "!pip install transformers datasets evaluate accelerate -q"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "zme5FpXDTmD0",
        "outputId": "99f188d9-f0d4-4221-a40d-060f5dc64326"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-e02e3d02-7f3f-4f98-b503-639f26dca737\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-e02e3d02-7f3f-4f98-b503-639f26dca737\">\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 imagefolder.zip to imagefolder (1).zip\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "496Tk7e0UJ0p",
        "outputId": "95621bb4-efd2-44bc-d680-816ee3ccc220"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Entpackt!\n"
          ]
        }
      ],
      "source": [
        "import zipfile\n",
        "with zipfile.ZipFile(\"imagefolder.zip\", \"r\") as zip_ref:\n",
        "    zip_ref.extractall(\".\")\n",
        "print(\"Entpackt!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "srgWq5O3WClq",
        "outputId": "edd53bdd-a81a-4cd8-aa72-5a366d6884b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Keys: dict_keys(['image', 'label', 'pixel_values'])\n",
            "Types: {'image': <class 'PIL.JpegImagePlugin.JpegImageFile'>, 'label': <class 'int'>, 'pixel_values': <class 'torch.Tensor'>}\n"
          ]
        }
      ],
      "source": [
        "# Debug – was ist in einem Batch?\n",
        "sample = train_ds[0]\n",
        "print(\"Keys:\", sample.keys())\n",
        "print(\"Types:\", {k: type(v) for k, v in sample.items()})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "cb64c8bf64ae40b88ebedc1ae1f8e9b8"
          ]
        },
        "id": "N4lrS1lbVSeQ",
        "outputId": "ad4de80a-585a-4c75-f47d-530d24f5af9f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e2e70f40251845aeaef6a1faafb42afb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Resolving data files:   0%|          | 0/55 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "✅ Spalten: dict_keys(['image', 'label'])\n",
            "✅ Train: 44, Val: 11\n",
            "🚀 Training startet...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [30/30 02:11, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.841797</td>\n",
              "      <td>1.777965</td>\n",
              "      <td>0.181818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.762858</td>\n",
              "      <td>1.766424</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.731120</td>\n",
              "      <td>1.757724</td>\n",
              "      <td>0.363636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.708415</td>\n",
              "      <td>1.749112</td>\n",
              "      <td>0.363636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>1.717692</td>\n",
              "      <td>1.740234</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>1.659098</td>\n",
              "      <td>1.731889</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>1.593750</td>\n",
              "      <td>1.724876</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>1.669108</td>\n",
              "      <td>1.718661</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>1.613688</td>\n",
              "      <td>1.715643</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>1.692220</td>\n",
              "      <td>1.714489</td>\n",
              "      <td>0.454545</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
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            "/usr/local/lib/python3.12/dist-packages/PIL/Image.py:1047: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
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          "text": [
            "✅ Training fertig!\n"
          ]
        }
      ],
      "source": [
        "from torch.utils.data import Dataset\n",
        "from datasets import load_dataset\n",
        "\n",
        "dataset_fresh = load_dataset(\"imagefolder\", data_dir=\"./imagefolder\")\n",
        "split_fresh = dataset_fresh['train'].train_test_split(test_size=0.2, seed=42)\n",
        "train_raw = split_fresh['train']\n",
        "val_raw = split_fresh['test']\n",
        "print(f\"Spalten: {train_raw.features.keys()}\")\n",
        "\n",
        "class MotorcycleDataset(Dataset):\n",
        "    def __init__(self, dataset, transform):\n",
        "        self.dataset = dataset\n",
        "        self.transform = transform\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.dataset)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        item = self.dataset[idx]\n",
        "        image = item['image'].convert('RGB')\n",
        "        return {\n",
        "            'pixel_values': self.transform(image),\n",
        "            'label': item['label']\n",
        "        }\n",
        "\n",
        "train_dataset = MotorcycleDataset(train_raw, train_transforms)\n",
        "val_dataset = MotorcycleDataset(val_raw, val_transforms)\n",
        "print(f\"Train: {len(train_dataset)}, Val: {len(val_dataset)}\")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=val_dataset,\n",
        "    compute_metrics=compute_metrics,\n",
        "    data_collator=collate_fn,\n",
        ")\n",
        "\n",
        "print(\"Training startet...\")\n",
        "trainer.train()\n",
        "print(\"Training fertig!\")"
      ]
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              "  ...orcycle/model.safetensors:   0%|          | 21.9kB /  343MB            "
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          "text": [
            "🎉 Modell auf HF: https://huggingface.co/durovali/vit-motorcycle\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import login\n",
        "\n",
        "login(token=\"YOUR_HF_TOKEN\")\n",
        "\n",
        "trainer.push_to_hub(\"durovali/vit-motorcycle\")\n",
        "image_processor.push_to_hub(\"durovali/vit-motorcycle\")\n",
        "print(\"Modell auf HF: https://huggingface.co/durovali/vit-motorcycle\")"
      ]
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