Add example notebooks, scripts, and demo videos

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.ipynb filter=lfs diff=lfs merge=lfs -text

examples/Example_3DPE_Crack22_YOLOv11.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7ee27b7a76933490f2c8f9ac23924b83f94d76394c9a94372acb2bb88e60d5bd
+size 73902371

examples/Hyperparameters.ipynb

@@ -0,0 +1,1030 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ishDL4poP5AP"
+      },
+      "source": [
+        "## Step 1: Install deps"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zr9bvajZ1eHV",
+        "outputId": "e546b5a3-13a1-43e2-ad98-e92bc2163991"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m44.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m19.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/94.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.0/94.0 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.8/66.8 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.9/49.9 MB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m59.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.5/5.5 MB\u001b[0m \u001b[31m137.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install --quiet ultralytics pyyaml pandas roboflow\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-W6ZMnt7P88e"
+      },
+      "source": [
+        "## Step 2: Download dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZahCnJzk3xXe",
+        "outputId": "f79309c7-58cb-4305-a95d-da998015c25f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "loading Roboflow workspace...\n",
+            "loading Roboflow project...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Downloading Dataset Version Zip in Clay-Crack-Detection-14 to yolov11:: 100%|██████████| 359480/359480 [00:05<00:00, 61260.38it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Extracting Dataset Version Zip to Clay-Crack-Detection-14 in yolov11:: 100%|██████████| 15197/15197 [00:04<00:00, 3417.33it/s]\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset at: /content/Clay-Crack-Detection-14\n"
+          ]
+        }
+      ],
+      "source": [
+        "from roboflow import Roboflow\n",
+        "rf = Roboflow(api_key=\"ROBOFLOW_API_KEY\")  # avoid sharing this key publicly\n",
+        "project = rf.workspace(\"tv-vloon\").project(\"clay-crack-detection\")\n",
+        "version = project.version(14)\n",
+        "dataset = version.download(\"yolov11\")\n",
+        "\n",
+        "DATA_DIR = dataset.location  # path with data.yaml and roboflow README\n",
+        "print(\"Dataset at:\", DATA_DIR)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bwCdXcuKQFOF"
+      },
+      "source": [
+        "## Step 3: Upload weight"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 90
+        },
+        "id": "UW3gBrxG339j",
+        "outputId": "8089f8a5-15bd-4237-f8fa-f953f2f0c054"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Upload: best.pt (or last.pt). Optional: args.yaml, hyp.yaml, training.json, or a runs/ zip.\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-13779d1c-4da1-4162-ab3b-283d8da77c70\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-13779d1c-4da1-4162-ab3b-283d8da77c70\">\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 crack-seg.pt to crack-seg.pt\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import files\n",
+        "print(\"Upload: best.pt (or last.pt). Optional: args.yaml, hyp.yaml, training.json, or a runs/ zip.\")\n",
+        "uploaded = files.upload()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tOYh9EudQLt5"
+      },
+      "source": [
+        "## Step 4: Parse & merge hyperparameters into a clean table + downloads"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "0g_b1GuC36Rc",
+        "outputId": "febc99ae-4c02-41f7-81f1-500b9619e986"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Creating new Ultralytics Settings v0.0.6 file ✅ \n",
+            "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n",
+            "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n",
+            "✅ Sources: dataset:README.roboflow.txt, dataset:data.yaml, crack-seg.pt:train_args, crack-seg.pt:model.args\n",
+            "✅ Total keys: 111  |  Primary shown: 33\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 33,\n  \"fields\": [\n    {\n      \"column\": \"parameter\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"train.optimizer\",\n          \"data.test\",\n          \"lr.warmup_momentum\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-723fce6a-37d1-4818-b483-a84434f982e8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>parameter</th>\n",
+              "      <th>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>aug.copy_paste</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>aug.degrees</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>aug.erasing</td>\n",
+              "      <td>0.4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>aug.fliplr</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>aug.flipud</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>aug.hsv_h</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>aug.hsv_s</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>aug.hsv_v</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>aug.mixup</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>aug.mosaic</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>aug.perspective</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>aug.scale</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>12</th>\n",
+              "      <td>aug.shear</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13</th>\n",
+              "      <td>aug.translate</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>14</th>\n",
+              "      <td>data.names</td>\n",
+              "      <td>[crack_b, crack_s, crack_shadow]</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>data.test</td>\n",
+              "      <td>../test/images</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>16</th>\n",
+              "      <td>data.train</td>\n",
+              "      <td>../train/images</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>data.val</td>\n",
+              "      <td>../valid/images</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18</th>\n",
+              "      <td>env.device</td>\n",
+              "      <td>None</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>env.seed</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>20</th>\n",
+              "      <td>env.workers</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>21</th>\n",
+              "      <td>lr.lr0</td>\n",
+              "      <td>0.01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>lr.lrf</td>\n",
+              "      <td>0.01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>23</th>\n",
+              "      <td>lr.momentum</td>\n",
+              "      <td>0.937</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24</th>\n",
+              "      <td>lr.warmup_bias_lr</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25</th>\n",
+              "      <td>lr.warmup_epochs</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>lr.warmup_momentum</td>\n",
+              "      <td>0.8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>lr.weight_decay</td>\n",
+              "      <td>0.0005</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>train.batch</td>\n",
+              "      <td>14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29</th>\n",
+              "      <td>train.epochs</td>\n",
+              "      <td>300</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>30</th>\n",
+              "      <td>train.imgsz</td>\n",
+              "      <td>640</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>31</th>\n",
+              "      <td>train.optimizer</td>\n",
+              "      <td>auto</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>32</th>\n",
+              "      <td>train.patience</td>\n",
+              "      <td>10</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-723fce6a-37d1-4818-b483-a84434f982e8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-723fce6a-37d1-4818-b483-a84434f982e8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-723fce6a-37d1-4818-b483-a84434f982e8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_3aaba703-05cc-4db8-9426-ae24fc534ac4\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_3aaba703-05cc-4db8-9426-ae24fc534ac4 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "             parameter                             value\n",
+              "0       aug.copy_paste                               0.0\n",
+              "1          aug.degrees                               0.0\n",
+              "2          aug.erasing                               0.4\n",
+              "3           aug.fliplr                               0.0\n",
+              "4           aug.flipud                               0.0\n",
+              "5            aug.hsv_h                               0.0\n",
+              "6            aug.hsv_s                               0.0\n",
+              "7            aug.hsv_v                               0.0\n",
+              "8            aug.mixup                               0.0\n",
+              "9           aug.mosaic                               0.0\n",
+              "10     aug.perspective                               0.0\n",
+              "11           aug.scale                               0.0\n",
+              "12           aug.shear                               0.0\n",
+              "13       aug.translate                               0.0\n",
+              "14          data.names  [crack_b, crack_s, crack_shadow]\n",
+              "15           data.test                    ../test/images\n",
+              "16          data.train                   ../train/images\n",
+              "17            data.val                   ../valid/images\n",
+              "18          env.device                              None\n",
+              "19            env.seed                                 0\n",
+              "20         env.workers                                 1\n",
+              "21              lr.lr0                              0.01\n",
+              "22              lr.lrf                              0.01\n",
+              "23         lr.momentum                             0.937\n",
+              "24   lr.warmup_bias_lr                               0.0\n",
+              "25    lr.warmup_epochs                               3.0\n",
+              "26  lr.warmup_momentum                               0.8\n",
+              "27     lr.weight_decay                            0.0005\n",
+              "28         train.batch                                14\n",
+              "29        train.epochs                               300\n",
+              "30         train.imgsz                               640\n",
+              "31     train.optimizer                              auto\n",
+              "32      train.patience                                10"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import io, os, zipfile, json, yaml, pandas as pd\n",
+        "from ultralytics import YOLO\n",
+        "\n",
+        "workdir = \"/content/hparam_scan\"\n",
+        "os.makedirs(workdir, exist_ok=True)\n",
+        "\n",
+        "def try_read_yaml_bytes(b):\n",
+        "    try: return yaml.safe_load(b.decode(\"utf-8\"))\n",
+        "    except Exception:\n",
+        "        try: return yaml.safe_load(b)\n",
+        "        except Exception: return None\n",
+        "\n",
+        "def flatten(d, parent=\"\"):\n",
+        "    out = {}\n",
+        "    if isinstance(d, dict):\n",
+        "        for k, v in d.items():\n",
+        "            key = f\"{parent}.{k}\" if parent else str(k)\n",
+        "            if isinstance(v, dict):\n",
+        "                out.update(flatten(v, key))\n",
+        "            else:\n",
+        "                out[key] = v\n",
+        "    return out\n",
+        "\n",
+        "def take(d, keys):\n",
+        "    return {k: d[k] for k in keys if k in d}\n",
+        "\n",
+        "params = {}\n",
+        "sources = []\n",
+        "\n",
+        "# A) Read Roboflow dataset metadata (preprocess/augment & class info)\n",
+        "rf_files = [\"README.roboflow.txt\",\"README.dataset.txt\",\"data.yaml\",\"README.txt\"]\n",
+        "for f in rf_files:\n",
+        "    p = os.path.join(DATA_DIR, f)\n",
+        "    if os.path.exists(p):\n",
+        "        try:\n",
+        "            if f.endswith(\".yaml\"):\n",
+        "                y = try_read_yaml_bytes(open(p,\"rb\").read()) or {}\n",
+        "                params.update({f\"data.{k}\": v for k,v in y.items()})\n",
+        "            else:\n",
+        "                txt = open(p,\"r\",errors=\"ignore\").read()\n",
+        "                params[\"roboflow.readme_present\"] = True\n",
+        "            sources.append(f\"dataset:{f}\")\n",
+        "        except: pass\n",
+        "\n",
+        "# B) Uploaded YAML/JSON\n",
+        "for name, content in uploaded.items():\n",
+        "    low = name.lower()\n",
+        "    if any(low.endswith(ext) for ext in [\".yaml\",\".yml\",\".json\"]):\n",
+        "        try:\n",
+        "            y = json.loads(content.decode(\"utf-8\")) if low.endswith(\".json\") else try_read_yaml_bytes(content)\n",
+        "            if isinstance(y, dict):\n",
+        "                params.update(flatten(y))\n",
+        "                sources.append(name)\n",
+        "        except: pass\n",
+        "\n",
+        "# C) Uploaded ZIP of runs/*\n",
+        "for name, content in uploaded.items():\n",
+        "    if name.lower().endswith(\".zip\"):\n",
+        "        zpath = os.path.join(workdir, name)\n",
+        "        open(zpath,\"wb\").write(content)\n",
+        "        with zipfile.ZipFile(zpath,\"r\") as z: z.extractall(os.path.join(workdir,\"unzipped\"))\n",
+        "        for root, _, files_ in os.walk(os.path.join(workdir,\"unzipped\")):\n",
+        "            for f in files_:\n",
+        "                if f.lower() in {\"args.yaml\",\"hyp.yaml\",\"cfg.yaml\",\"opt.yaml\",\"results.yaml\",\"data.yaml\",\"config.yaml\"}:\n",
+        "                    p = os.path.join(root,f)\n",
+        "                    y = try_read_yaml_bytes(open(p,\"rb\").read())\n",
+        "                    if isinstance(y, dict):\n",
+        "                        params.update(flatten(y))\n",
+        "                        sources.append(f\"zip:{f}\")\n",
+        "\n",
+        "# D) Read .pt\n",
+        "for name, content in uploaded.items():\n",
+        "    if name.endswith(\".pt\"):\n",
+        "        tmp = os.path.join(workdir, name)\n",
+        "        open(tmp,\"wb\").write(content)\n",
+        "        model = YOLO(tmp)\n",
+        "        ckpt = getattr(model, \"ckpt\", {}) or {}\n",
+        "        if isinstance(ckpt, dict):\n",
+        "            train_args = ckpt.get(\"train_args\", {}) or ckpt.get(\"args\", {}) or {}\n",
+        "            if train_args:\n",
+        "                params.update(flatten(train_args))\n",
+        "                sources.append(f\"{name}:train_args\")\n",
+        "        try:\n",
+        "            model_args = getattr(model.model, \"args\", {}) or {}\n",
+        "            if model_args:\n",
+        "                params.update(flatten(model_args))\n",
+        "                sources.append(f\"{name}:model.args\")\n",
+        "        except: pass\n",
+        "\n",
+        "# E) Normalize to friendly keys (common YOLO train hypers)\n",
+        "alias = {\n",
+        "    \"epochs\":\"train.epochs\",\"batch\":\"train.batch\",\"imgsz\":\"train.imgsz\",\"img_size\":\"train.imgsz\",\n",
+        "    \"optimizer\":\"train.optimizer\",\"patience\":\"train.patience\",\n",
+        "    \"lr0\":\"lr.lr0\",\"lrf\":\"lr.lrf\",\"momentum\":\"lr.momentum\",\"weight_decay\":\"lr.weight_decay\",\n",
+        "    \"warmup_epochs\":\"lr.warmup_epochs\",\"warmup_momentum\":\"lr.warmup_momentum\",\"warmup_bias_lr\":\"lr.warmup_bias_lr\",\n",
+        "    \"mosaic\":\"aug.mosaic\",\"hsv_h\":\"aug.hsv_h\",\"hsv_s\":\"aug.hsv_s\",\"hsv_v\":\"aug.hsv_v\",\n",
+        "    \"flipud\":\"aug.flipud\",\"fliplr\":\"aug.fliplr\",\"degrees\":\"aug.degrees\",\"translate\":\"aug.translate\",\n",
+        "    \"scale\":\"aug.scale\",\"shear\":\"aug.shear\",\"perspective\":\"aug.perspective\",\"erasing\":\"aug.erasing\",\n",
+        "    \"mixup\":\"aug.mixup\",\"copy_paste\":\"aug.copy_paste\",\n",
+        "    \"device\":\"env.device\",\"workers\":\"env.workers\",\"seed\":\"env.seed\",\"project\":\"env.project\",\"name\":\"env.name\",\"exist_ok\":\"env.exist_ok\"\n",
+        "}\n",
+        "normalized = {}\n",
+        "for k, v in list(params.items()):\n",
+        "    k0 = k.split(\".\")[-1]\n",
+        "    if k in alias: normalized[alias[k]] = v\n",
+        "    elif k0 in alias: normalized[alias[k0]] = v\n",
+        "    else: normalized[k] = v\n",
+        "params = normalized\n",
+        "\n",
+        "# Short, report-ready subset\n",
+        "primary_keys = [\n",
+        "  \"train.epochs\",\"train.batch\",\"train.imgsz\",\"train.optimizer\",\"train.patience\",\n",
+        "  \"lr.lr0\",\"lr.lrf\",\"lr.momentum\",\"lr.weight_decay\",\"lr.warmup_epochs\",\"lr.warmup_momentum\",\"lr.warmup_bias_lr\",\n",
+        "  \"aug.mosaic\",\"aug.hsv_h\",\"aug.hsv_s\",\"aug.hsv_v\",\"aug.flipud\",\"aug.fliplr\",\"aug.degrees\",\"aug.translate\",\"aug.scale\",\"aug.shear\",\"aug.perspective\",\"aug.erasing\",\"aug.mixup\",\"aug.copy_paste\",\n",
+        "  \"env.device\",\"env.workers\",\"env.seed\",\n",
+        "  \"data.path\",\"data.train\",\"data.val\",\"data.test\",\"data.names\",\"nc\",\"names\"\n",
+        "]\n",
+        "primary = take(params, primary_keys)\n",
+        "\n",
+        "print(\"✅ Sources:\", \", \".join(sources) if sources else \"none\")\n",
+        "print(f\"✅ Total keys: {len(params)}  |  Primary shown: {len(primary)}\")\n",
+        "\n",
+        "import pandas as pd\n",
+        "df = pd.DataFrame(\n",
+        "    [{\"parameter\": k, \"value\": primary.get(k, params.get(k))} for k in sorted(primary or params)]\n",
+        ")\n",
+        "df\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "id": "_F1fJ4dh38JN",
+        "outputId": "54ad9e14-a7f6-4810-98fb-e80dbaf17610"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/javascript": "download(\"download_227d7458-1155-429c-8f56-4bb4cbf25697\", \"hyperparams_all.yaml\", 2037)",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/javascript": "download(\"download_02788a7c-4324-4881-b298-49de5a6336f8\", \"hyperparams_primary.yaml\", 633)",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/javascript": "download(\"download_73abe0db-c31a-4a05-aa7f-a5a6e10ccb87\", \"hyperparams_all.csv\", 1912)",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/javascript": "download(\"download_b8f2f1bb-5d09-472a-8f54-1a33642b1657\", \"hyperparams_primary.md\", 872)",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Done.\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import files, output\n",
+        "import yaml, pandas as pd, os\n",
+        "\n",
+        "os.makedirs(workdir, exist_ok=True)\n",
+        "all_yaml = os.path.join(workdir, \"hyperparams_all.yaml\")\n",
+        "primary_yaml = os.path.join(workdir, \"hyperparams_primary.yaml\")\n",
+        "csv_path = os.path.join(workdir, \"hyperparams_all.csv\")\n",
+        "md_path  = os.path.join(workdir, \"hyperparams_primary.md\")\n",
+        "\n",
+        "with open(all_yaml, \"w\") as f: yaml.safe_dump(params, f, sort_keys=True, allow_unicode=True)\n",
+        "with open(primary_yaml, \"w\") as f: yaml.safe_dump({k: params[k] for k in sorted(primary.keys()) if k in params}, f, sort_keys=False, allow_unicode=True)\n",
+        "pd.DataFrame([{\"parameter\": k, \"value\": v} for k, v in sorted(params.items())]).to_csv(csv_path, index=False)\n",
+        "with open(md_path, \"w\") as f:\n",
+        "    f.write(\"# YOLOv11 Hyperparameters (Primary)\\n\\n\")\n",
+        "    for k in sorted(primary.keys()):\n",
+        "        if k in params:\n",
+        "            f.write(f\"- **{k}**: {params[k]}\\n\")\n",
+        "\n",
+        "for p in [all_yaml, primary_yaml, csv_path, md_path]:\n",
+        "    files.download(p)\n",
+        "print(\"Done.\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

examples/Record.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:30036c354c820f7c72f0b9ab6bbb714483b1828fca98fcbb17492351fa1bc816
+size 91906916

examples/crack_evaluation.ipynb

@@ -0,0 +1,927 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZHqQGT0jBqLi"
+      },
+      "source": [
+        "## STEP 1: Install dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LHUcg2ZDjre_",
+        "outputId": "6d4976a5-62ae-4609-9d8d-9b9cbd586997"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m40.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/89.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.9/89.9 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.8/66.8 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.9/49.9 MB\u001b[0m \u001b[31m20.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m78.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m126.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install ultralytics roboflow --upgrade --quiet"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AMz9DvsFBxtA"
+      },
+      "source": [
+        "## STEP 2: Imports"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "h8GE0fXCjsVO",
+        "outputId": "f1fac47c-71e1-4c2e-8a2f-d583d2a86c86"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Creating new Ultralytics Settings v0.0.6 file ✅ \n",
+            "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n",
+            "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from roboflow import Roboflow\n",
+        "from ultralytics import YOLO\n",
+        "from google.colab import files"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tHZrVz8-B4VM"
+      },
+      "source": [
+        "## STEP 3: Get training dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uPmqYYhDj3nf",
+        "outputId": "645a87a0-2f61-445e-e68a-3fe04e232dcf"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "loading Roboflow workspace...\n",
+            "loading Roboflow project...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Downloading Dataset Version Zip in Clay-Crack-Detection-14 to yolov11:: 100%|██████████| 359480/359480 [00:11<00:00, 30777.63it/s]"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Extracting Dataset Version Zip to Clay-Crack-Detection-14 in yolov11:: 100%|██████████| 15197/15197 [00:02<00:00, 6929.67it/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "rf = Roboflow(api_key=\"ROBOFLOW_API_KEY\")\n",
+        "project = rf.workspace(\"tv-vloon\").project(\"clay-crack-detection\")\n",
+        "version = project.version(14)\n",
+        "dataset = version.download(\"yolov11\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 90
+        },
+        "id": "9EPy__E8j4x2",
+        "outputId": "2fdbbb45-1e14-4a21-f402-bdc3fbe5a15a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Upload YOLOv11 model\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-f2bc48c8-d992-4c4d-8f9e-b145346f89db\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-f2bc48c8-d992-4c4d-8f9e-b145346f89db\">\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 crack-seg.pt to crack-seg.pt\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Upload YOLOv11 model\")\n",
+        "uploaded = files.upload()\n",
+        "model_path = list(uploaded.keys())[0]  # Grab the uploaded file name"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-Iv8ULS6j5wq"
+      },
+      "outputs": [],
+      "source": [
+        "model = YOLO(model_path)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ceeSxcFRCOZU"
+      },
+      "source": [
+        "## STEP 4: Run Evaluation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lh4IzW4sj6z_",
+        "outputId": "1b25edb4-4f7b-4b79-de75-81e584d6264e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Ultralytics 8.3.214 🚀 Python-3.12.12 torch-2.8.0+cu126 CUDA:0 (Tesla T4, 15095MiB)\n",
+            "YOLO11s-seg summary (fused): 113 layers, 10,067,977 parameters, 0 gradients, 32.8 GFLOPs\n",
+            "\u001b[KDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100% ━━━━━━━━━━━━ 755.1KB 40.9MB/s 0.0s\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mFast image access ✅ (ping: 0.0±0.0 ms, read: 1600.0±510.3 MB/s, size: 46.1 KB)\n",
+            "\u001b[K\u001b[34m\u001b[1mval: \u001b[0mScanning /content/Clay-Crack-Detection-14/test/labels... 198 images, 34 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 198/198 1.2Kit/s 0.2s\n",
+            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/Clay-Crack-Detection-14/test/labels.cache\n",
+            "\u001b[31m\u001b[1mrequirements:\u001b[0m Ultralytics requirement ['faster-coco-eval>=1.6.7'] not found, attempting AutoUpdate...\n",
+            "\n",
+            "\u001b[31m\u001b[1mrequirements:\u001b[0m AutoUpdate success ✅ 0.8s\n",
+            "WARNING ⚠️ \u001b[31m\u001b[1mrequirements:\u001b[0m \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
+            "\n",
+            "\u001b[K                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95)     Mask(P          R      mAP50  mAP50-95): 100% ━━━━━━━━━━━━ 13/13 0.6it/s 20.5s\n",
+            "                   all        198       1911      0.667      0.629      0.622       0.41       0.67      0.632      0.624      0.352\n",
+            "               crack_b        135        811      0.812      0.816      0.819      0.608      0.816      0.819      0.827      0.542\n",
+            "               crack_s        152       1034      0.724      0.648      0.656      0.376      0.713      0.637      0.648      0.311\n",
+            "          crack_shadow         35         66      0.465      0.424      0.392      0.245      0.482      0.439      0.396      0.203\n",
+            "Speed: 1.8ms preprocess, 12.3ms inference, 0.0ms loss, 4.6ms postprocess per image\n",
+            "Saving /content/runs/segment/val/predictions.json...\n",
+            "Results saved to \u001b[1m/content/runs/segment/val\u001b[0m\n"
+          ]
+        }
+      ],
+      "source": [
+        "results = model.val(data=f\"{dataset.location}/data.yaml\", split=\"test\", save_json=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iXFceDUyj738",
+        "outputId": "a39090b8-65b0-4033-d327-c79f7028e692"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Bounding Box (Box) Evaluation:\n",
+            "Precision     : 0.667\n",
+            "Recall        : 0.629\n",
+            "mAP@0.5       : 0.622\n",
+            "mAP@0.5:0.95  : 0.410\n",
+            "\n",
+            "Segmentation Mask (Mask) Evaluation:\n",
+            "Precision     : 0.670\n",
+            "Recall        : 0.632\n",
+            "mAP@0.5       : 0.624\n",
+            "mAP@0.5:0.95  : 0.352\n"
+          ]
+        }
+      ],
+      "source": [
+        "metrics = results.results_dict\n",
+        "\n",
+        "print(\"\\nBounding Box (Box) Evaluation:\")\n",
+        "print(f\"Precision     : {metrics['metrics/precision(B)']:.3f}\")\n",
+        "print(f\"Recall        : {metrics['metrics/recall(B)']:.3f}\")\n",
+        "print(f\"mAP@0.5       : {metrics['metrics/mAP50(B)']:.3f}\")\n",
+        "print(f\"mAP@0.5:0.95  : {metrics['metrics/mAP50-95(B)']:.3f}\")\n",
+        "\n",
+        "print(\"\\nSegmentation Mask (Mask) Evaluation:\")\n",
+        "print(f\"Precision     : {metrics['metrics/precision(M)']:.3f}\")\n",
+        "print(f\"Recall        : {metrics['metrics/recall(M)']:.3f}\")\n",
+        "print(f\"mAP@0.5       : {metrics['metrics/mAP50(M)']:.3f}\")\n",
+        "print(f\"mAP@0.5:0.95  : {metrics['metrics/mAP50-95(M)']:.3f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 196
+        },
+        "id": "D0MGJRRNj8_4",
+        "outputId": "c3abccc8-f308-4495-80fa-65f61d86a902"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Per-Class Evaluation Metrics:\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "summary": "{\n  \"name\": \"df_per_class\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"crack_b\",\n          \"crack_s\",\n          \"crack_shadow\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1803717489527495,\n        \"min\": 0.465232485462606,\n        \"max\": 0.8121673307119629,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8121673307119629,\n          0.7243098568355149,\n          0.465232485462606\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19640824276848068,\n        \"min\": 0.42424242424242425,\n        \"max\": 0.8157276086782503,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8157276086782503,\n          0.6479690522243714,\n          0.42424242424242425\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mAP@0.5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.6224444136770416,\n        \"max\": 0.6224444136770416,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.6224444136770416\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mAP@0.5:0.95\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.40963491067115904,\n        \"max\": 0.40963491067115904,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.40963491067115904\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
+              "type": "dataframe",
+              "variable_name": "df_per_class"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-44d224d4-3865-4201-b84c-e6c961b63e3f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Class</th>\n",
+              "      <th>Precision</th>\n",
+              "      <th>Recall</th>\n",
+              "      <th>mAP@0.5</th>\n",
+              "      <th>mAP@0.5:0.95</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>crack_b</td>\n",
+              "      <td>0.812167</td>\n",
+              "      <td>0.815728</td>\n",
+              "      <td>0.622444</td>\n",
+              "      <td>0.409635</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>crack_s</td>\n",
+              "      <td>0.724310</td>\n",
+              "      <td>0.647969</td>\n",
+              "      <td>0.622444</td>\n",
+              "      <td>0.409635</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>crack_shadow</td>\n",
+              "      <td>0.465232</td>\n",
+              "      <td>0.424242</td>\n",
+              "      <td>0.622444</td>\n",
+              "      <td>0.409635</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44d224d4-3865-4201-b84c-e6c961b63e3f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-44d224d4-3865-4201-b84c-e6c961b63e3f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-44d224d4-3865-4201-b84c-e6c961b63e3f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-d486e766-8968-4dc8-8476-2627c282dbb7\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d486e766-8968-4dc8-8476-2627c282dbb7')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-d486e766-8968-4dc8-8476-2627c282dbb7 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_2d7fbd5c-457b-4892-8ab7-07b34cb27aff\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_per_class')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_2d7fbd5c-457b-4892-8ab7-07b34cb27aff button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_per_class');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "          Class  Precision    Recall   mAP@0.5  mAP@0.5:0.95\n",
+              "0       crack_b   0.812167  0.815728  0.622444      0.409635\n",
+              "1       crack_s   0.724310  0.647969  0.622444      0.409635\n",
+              "2  crack_shadow   0.465232  0.424242  0.622444      0.409635"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅ Saved to per_class_metrics.csv\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "# Get class names\n",
+        "class_names = list(model.names.values())\n",
+        "\n",
+        "# Get per-class values (already NumPy arrays)\n",
+        "p = results.box.p\n",
+        "r = results.box.r\n",
+        "map50 = results.box.map50\n",
+        "map95 = results.box.map\n",
+        "\n",
+        "# Build DataFrame\n",
+        "df_per_class = pd.DataFrame({\n",
+        "    \"Class\": class_names,\n",
+        "    \"Precision\": p,\n",
+        "    \"Recall\": r,\n",
+        "    \"mAP@0.5\": map50,\n",
+        "    \"mAP@0.5:0.95\": map95\n",
+        "})\n",
+        "\n",
+        "# Show + save\n",
+        "print(\"\\nPer-Class Evaluation Metrics:\")\n",
+        "display(df_per_class)\n",
+        "df_per_class.to_csv(\"per_class_metrics.csv\", index=False)\n",
+        "print(\"✅ Saved to per_class_metrics.csv\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17
+        },
+        "id": "NBQ0qgXrOMbN",
+        "outputId": "23d45da3-9549-4c7c-b95d-6a2c12382871"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/javascript": "download(\"download_8d4b5cea-a8f3-46b8-aef5-606799d3bd61\", \"segment.zip\", 5598780)",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from google.colab import files\n",
+        "import shutil\n",
+        "\n",
+        "# Zip the folder\n",
+        "shutil.make_archive(\"/content/segment\", 'zip', \"/content/runs/segment\")\n",
+        "\n",
+        "# Download the zip\n",
+        "files.download(\"/content/segment.zip\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

examples/example_short.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:33dbd88145905ce71bda8725bdc99e1fbfbd911ea5809419fdf1eb9952424fc1
+size 5734442

examples/extract_preprocess_frames.py

@@ -0,0 +1,399 @@
+import cv2
+import numpy as np
+import os
+from PIL import Image, ExifTags
+import random
+import argparse
+import time
+
+# Increase OpenCV/FFMPEG frame read retry attempts for long or unstable video reads
+os.environ["OPENCV_FFMPEG_READ_ATTEMPTS"] = "50000"
+
+# Default values (can be overridden via command line)
+video_path = "path/to/video.mp4"
+output_dir = "outputs/preprocessed"
+
+frame_interval = 30          # Extract every nth frame
+zoom_level = 5.0             # 5x zoom -> crop 1/5 of original frame, then resize to 640x640
+crops_per_frame = 100        # Number of random crops per extracted frame
+manual_crop = False          # If True, use one fixed crop position instead of random crops
+crop_x_center = 0.5          # Manual crop center X (relative: 0.0 to 1.0)
+crop_y_center = 0.5          # Manual crop center Y (relative: 0.0 to 1.0)
+
+
+def extract_and_preprocess_frames(
+    video_path,
+    output_dir,
+    frame_interval=10,
+    zoom_level=5.0,
+    crops_per_frame=5,
+    manual_crop=False,
+    crop_x=0.5,
+    crop_y=0.5,
+    start_frame=0,
+    end_frame=None,
+    segment_id=0,
+):
+    """
+    Extract frames from a video segment and generate zoomed crops.
+
+    Workflow:
+    1) Read video frames in a specified frame range [start_frame, end_frame)
+    2) Keep every nth frame (frame_interval)
+    3) Optional EXIF-based orientation correction (mostly useful for images, harmless for video frames)
+    4) Crop a zoom window (manual or random position)
+    5) Resize crop to 640x640
+    6) Apply CLAHE contrast enhancement
+    7) Save as JPG with frame and crop position encoded in filename
+
+    Returns:
+        saved_count (int): Number of extracted frames processed (not total crops)
+        total_saved (int): Total number of crops saved
+    """
+    # Create base output directory
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Open video
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise ValueError(f"Could not open video file: {video_path}")
+
+    # Video metadata
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+
+    # Clamp end_frame to video length
+    if end_frame is None or end_frame > total_frames:
+        end_frame = total_frames
+
+    print(f"Video loaded: {video_path}")
+    print(f"Total frames: {total_frames}")
+    print(f"FPS: {fps}")
+    print(f"Processing segment {segment_id + 1}: frames {start_frame} to {end_frame}")
+    print(f"Extracting every {frame_interval} frames")
+    print(f"Zooming {zoom_level * 100}% and cropping to 640x640")
+    print(f"Crops per frame: {crops_per_frame}")
+    print(f"Manual crop: {manual_crop}, Position: ({crop_x}, {crop_y})")
+
+    # CLAHE for local contrast enhancement (helps visibility in crack-like textures)
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+
+    # Seek to start frame
+    cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
+    count = start_frame
+    saved_count = 0  # Counts processed frames (not crops)
+
+    # Save segment outputs in separate subfolder
+    segment_dir = os.path.join(output_dir, f"segment_{segment_id + 1}")
+    os.makedirs(segment_dir, exist_ok=True)
+
+    while count < end_frame:
+        ret, frame = cap.read()
+        if not ret:
+            print(f"Error reading frame at position {count}. Breaking out of loop.")
+            break
+
+        # Process every nth frame inside this segment
+        if (count - start_frame) % frame_interval == 0:
+            # Convert to PIL for optional EXIF orientation correction
+            pil_img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+
+            # EXIF orientation correction (videos usually don't have EXIF, so this often does nothing)
+            try:
+                orientation_tag = None
+                for tag_id, tag_name in ExifTags.TAGS.items():
+                    if tag_name == "Orientation":
+                        orientation_tag = tag_id
+                        break
+
+                exif = dict(pil_img.getexif().items())
+
+                if orientation_tag is not None and orientation_tag in exif:
+                    if exif[orientation_tag] == 3:
+                        pil_img = pil_img.rotate(180, expand=True)
+                    elif exif[orientation_tag] == 6:
+                        pil_img = pil_img.rotate(270, expand=True)
+                    elif exif[orientation_tag] == 8:
+                        pil_img = pil_img.rotate(90, expand=True)
+            except (AttributeError, KeyError, IndexError, TypeError):
+                # No EXIF or EXIF not readable
+                pass
+
+            # Back to OpenCV BGR
+            frame = cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
+
+            # Original frame size
+            height, width = frame.shape[:2]
+
+            # Compute crop window size from zoom factor
+            # Example: zoom=5 -> crop 1/5 width and 1/5 height, then upscale to 640x640
+            crop_width = int(width / zoom_level)
+            crop_height = int(height / zoom_level)
+
+            # Safety check for invalid crop sizes
+            if crop_width <= 0 or crop_height <= 0:
+                print(f"Skipping frame {count}: invalid crop size ({crop_width}, {crop_height})")
+                count += 1
+                continue
+
+            if manual_crop:
+                # Single deterministic crop at user-defined relative position
+                process_crop(
+                    frame, count, 0, crop_x, crop_y,
+                    crop_width, crop_height, width, height,
+                    segment_dir, clahe
+                )
+                saved_count += 1
+            else:
+                # Multiple random crops per frame
+                for i in range(crops_per_frame):
+                    # Avoid edges slightly to reduce out-of-frame crop clipping
+                    random_x = random.uniform(0.1, 0.9)
+                    random_y = random.uniform(0.1, 0.9)
+
+                    process_crop(
+                        frame, count, i, random_x, random_y,
+                        crop_width, crop_height, width, height,
+                        segment_dir, clahe
+                    )
+                saved_count += 1
+
+            if saved_count % 10 == 0:
+                print(f"Segment {segment_id + 1}: Processed {saved_count} extracted frames (current frame={count})")
+
+        count += 1
+
+    cap.release()
+
+    # Total number of crop images written
+    total_saved = saved_count * (1 if manual_crop else crops_per_frame)
+
+    print(
+        f"Segment {segment_id + 1} completed! "
+        f"Processed {saved_count} extracted frames and saved {total_saved} crops."
+    )
+    return saved_count, total_saved
+
+
+def process_crop(
+    frame,
+    frame_count,
+    crop_index,
+    rel_x,
+    rel_y,
+    crop_width,
+    crop_height,
+    width,
+    height,
+    output_dir,
+    clahe,
+):
+    """
+    Create one crop from a frame, resize to 640x640, enhance contrast, and save.
+
+    Args:
+        rel_x, rel_y: Relative crop center coordinates in [0, 1]
+    """
+    # Convert relative center coords to pixel coordinates
+    center_x = int(rel_x * width)
+    center_y = int(rel_y * height)
+
+    # Top-left crop corner
+    start_x = center_x - (crop_width // 2)
+    start_y = center_y - (crop_height // 2)
+
+    # Clamp crop start so crop stays inside frame
+    start_x = max(0, min(start_x, width - crop_width))
+    start_y = max(0, min(start_y, height - crop_height))
+
+    # Compute crop end
+    end_x = min(start_x + crop_width, width)
+    end_y = min(start_y + crop_height, height)
+
+    # Final adjustment if crop was clipped
+    if end_x - start_x < crop_width:
+        start_x = max(0, end_x - crop_width)
+    if end_y - start_y < crop_height:
+        start_y = max(0, end_y - crop_height)
+
+    try:
+        # Crop region from original frame
+        cropped = frame[start_y:end_y, start_x:end_x]
+
+        # Skip empty crops (rare edge case)
+        if cropped.size == 0:
+            print(f"Empty crop at frame {frame_count}, crop {crop_index}")
+            return
+
+        # Resize to model-friendly input size
+        zoomed = cv2.resize(cropped, (640, 640), interpolation=cv2.INTER_LINEAR)
+
+        # Apply CLAHE
+        if len(zoomed.shape) == 3:
+            # Color image: apply CLAHE channel-wise in BGR space
+            # Note: This may shift colors slightly. For more natural results, apply on LAB L-channel.
+            enhanced = cv2.merge([
+                clahe.apply(zoomed[:, :, 0]),
+                clahe.apply(zoomed[:, :, 1]),
+                clahe.apply(zoomed[:, :, 2]),
+            ])
+        else:
+            # Grayscale image
+            enhanced = clahe.apply(zoomed)
+
+        # Filename convention
+        # crop_index == 0 is first crop; in manual mode this is the only crop
+        if crop_index == 0:
+            filename = f"frame5_{frame_count:06d}"
+        else:
+            filename = f"frame5_{frame_count:06d}_crop{crop_index}"
+
+        # Encode crop center position (% of frame) for traceability
+        pos_info = f"_x{int(rel_x * 100):03d}_y{int(rel_y * 100):03d}"
+        frame_filename = os.path.join(output_dir, f"{filename}{pos_info}.jpg")
+
+        cv2.imwrite(frame_filename, enhanced)
+
+    except Exception as e:
+        print(f"Error processing crop at frame {frame_count}, crop {crop_index}: {e}")
+
+
+def process_video_in_segments(
+    video_path,
+    output_dir,
+    frame_interval,
+    zoom_level,
+    crops_per_frame,
+    manual_crop,
+    crop_x,
+    crop_y,
+    segment_size=5000,
+    overlap=100,
+):
+    """
+    Process video in segments to avoid memory/decoder instability on long videos.
+
+    Note:
+    - Overlap can help avoid missing frames near segment boundaries.
+    - But overlap can also create duplicate outputs if the same frame is processed in two segments.
+    """
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise ValueError(f"Could not open video file: {video_path}")
+
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    cap.release()
+
+    print(f"Total frames in video: {total_frames}")
+    print(f"Processing in segments of {segment_size} frames with {overlap} frame overlap")
+
+    # Prevent invalid step size
+    if segment_size <= overlap:
+        raise ValueError("segment_size must be greater than overlap")
+
+    # Segment start indices
+    start_frames = list(range(0, total_frames, segment_size - overlap))
+
+    total_frames_processed = 0  # processed extracted frames
+    total_crops_processed = 0   # saved crop images
+
+    for i, start_frame in enumerate(start_frames):
+        end_frame = min(start_frame + segment_size, total_frames)
+
+        print(f"\n{'=' * 80}")
+        print(f"Processing segment {i + 1}/{len(start_frames)}: frames {start_frame} to {end_frame}")
+        print(f"{'=' * 80}\n")
+
+        # Small pause between segments (helps file handles / decoder stability)
+        if i > 0:
+            time.sleep(2)
+
+        try:
+            frames_processed, crops_processed = extract_and_preprocess_frames(
+                video_path=video_path,
+                output_dir=output_dir,
+                frame_interval=frame_interval,
+                zoom_level=zoom_level,
+                crops_per_frame=crops_per_frame,
+                manual_crop=manual_crop,
+                crop_x=crop_x,
+                crop_y=crop_y,
+                start_frame=start_frame,
+                end_frame=end_frame,
+                segment_id=i,
+            )
+
+            total_frames_processed += frames_processed
+            total_crops_processed += crops_processed
+
+        except Exception as e:
+            print(f"Error processing segment {i + 1}: {e}")
+            print("Continuing with next segment...")
+
+    print(f"\n{'=' * 80}")
+    print(
+        f"Processing complete! "
+        f"Processed {total_frames_processed} extracted frames and saved {total_crops_processed} crops."
+    )
+    print(f"{'=' * 80}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Extract frames from a video and generate zoomed crops with optional CLAHE enhancement"
+    )
+
+    # Input/output
+    parser.add_argument("--video", type=str, default=video_path, help="Path to input video")
+    parser.add_argument("--output", type=str, default=output_dir, help="Directory to save processed crops")
+
+    # Extraction/cropping settings
+    parser.add_argument("--interval", type=int, default=frame_interval, help="Extract every nth frame")
+    parser.add_argument("--zoom", type=float, default=zoom_level, help="Zoom factor (e.g., 5.0 = 500%%)")
+    parser.add_argument("--crops", type=int, default=crops_per_frame, help="Random crops per extracted frame")
+    parser.add_argument("--manual", action="store_true", help="Use one manual crop position instead of random crops")
+    parser.add_argument("--crop_x", type=float, default=crop_x_center, help="Manual crop center X in [0,1]")
+    parser.add_argument("--crop_y", type=float, default=crop_y_center, help="Manual crop center Y in [0,1]")
+
+    # Segmentation settings (for long videos)
+    parser.add_argument("--segment_size", type=int, default=5000, help="Frames per segment")
+    parser.add_argument("--overlap", type=int, default=100, help="Segment overlap in frames")
+
+    args = parser.parse_args()
+
+    # Basic input validation
+    if not args.video or not os.path.isfile(args.video):
+        print(f"Error: Video file '{args.video}' does not exist.")
+        raise SystemExit(1)
+
+    if args.interval <= 0:
+        print("Error: --interval must be > 0")
+        raise SystemExit(1)
+
+    if args.zoom <= 0:
+        print("Error: --zoom must be > 0")
+        raise SystemExit(1)
+
+    if args.crops <= 0 and not args.manual:
+        print("Error: --crops must be > 0 when not using --manual")
+        raise SystemExit(1)
+
+    if not (0.0 <= args.crop_x <= 1.0 and 0.0 <= args.crop_y <= 1.0):
+        print("Error: --crop_x and --crop_y must be in [0,1]")
+        raise SystemExit(1)
+
+    try:
+        process_video_in_segments(
+            video_path=args.video,
+            output_dir=args.output,
+            frame_interval=args.interval,
+            zoom_level=args.zoom,
+            crops_per_frame=args.crops,
+            manual_crop=args.manual,
+            crop_x=args.crop_x,
+            crop_y=args.crop_y,
+            segment_size=args.segment_size,
+            overlap=args.overlap,
+        )
+    except Exception as e:
+        print(f"An error occurred: {e}")

examples/output_with_cracks.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3f68b63e86601fb794a33a5e4717c597ea3645dfaafb8c56f67d71c35a2fb991
+size 83835266