Hugging Face's logo

Datasets:
ICoM-RWTH
/
3DPE-Crack22

Modalities:
Image
Text
License:
Dataset card Data Studio Files
xet
 Community
thenguyentrong commited on
Commit
3286fe5
·
1 Parent(s): 91fd92a

Update notebooks

Browse files
Files changed (2) hide show
  1. examples/Hyperparameters.ipynb +3 -1030
  2. examples/crack_evaluation.ipynb +3 -927
examples/Hyperparameters.ipynb CHANGED
@@ -1,1030 +1,3 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "markdown",
5
- "metadata": {
6
- "id": "ishDL4poP5AP"
7
- },
8
- "source": [
9
- "## Step 1: Install deps"
10
- ]
11
- },
12
- {
13
- "cell_type": "code",
14
- "execution_count": 1,
15
- "metadata": {
16
- "colab": {
17
- "base_uri": "https://localhost:8080/"
18
- },
19
- "id": "zr9bvajZ1eHV",
20
- "outputId": "e546b5a3-13a1-43e2-ad98-e92bc2163991"
21
- },
22
- "outputs": [
23
- {
24
- "name": "stdout",
25
- "output_type": "stream",
26
- "text": [
27
- "\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",
28
- "\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",
29
- "\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",
30
- "\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",
31
- "\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",
32
- "\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",
33
- "\u001b[?25h"
34
- ]
35
- }
36
- ],
37
- "source": [
38
- "!pip install --quiet ultralytics pyyaml pandas roboflow\n"
39
- ]
40
- },
41
- {
42
- "cell_type": "markdown",
43
- "metadata": {
44
- "id": "-W6ZMnt7P88e"
45
- },
46
- "source": [
47
- "## Step 2: Download dataset"
48
- ]
49
- },
50
- {
51
- "cell_type": "code",
52
- "execution_count": null,
53
- "metadata": {
54
- "colab": {
55
- "base_uri": "https://localhost:8080/"
56
- },
57
- "id": "ZahCnJzk3xXe",
58
- "outputId": "f79309c7-58cb-4305-a95d-da998015c25f"
59
- },
60
- "outputs": [
61
- {
62
- "name": "stdout",
63
- "output_type": "stream",
64
- "text": [
65
- "loading Roboflow workspace...\n",
66
- "loading Roboflow project...\n"
67
- ]
68
- },
69
- {
70
- "name": "stderr",
71
- "output_type": "stream",
72
- "text": [
73
- "Downloading Dataset Version Zip in Clay-Crack-Detection-14 to yolov11:: 100%|██████████| 359480/359480 [00:05<00:00, 61260.38it/s]"
74
- ]
75
- },
76
- {
77
- "name": "stdout",
78
- "output_type": "stream",
79
- "text": [
80
- "\n"
81
- ]
82
- },
83
- {
84
- "name": "stderr",
85
- "output_type": "stream",
86
- "text": [
87
- "\n",
88
- "Extracting Dataset Version Zip to Clay-Crack-Detection-14 in yolov11:: 100%|██████████| 15197/15197 [00:04<00:00, 3417.33it/s]\n"
89
- ]
90
- },
91
- {
92
- "name": "stdout",
93
- "output_type": "stream",
94
- "text": [
95
- "Dataset at: /content/Clay-Crack-Detection-14\n"
96
- ]
97
- }
98
- ],
99
- "source": [
100
- "from roboflow import Roboflow\n",
101
- "rf = Roboflow(api_key=\"ROBOFLOW_API_KEY\") # avoid sharing this key publicly\n",
102
- "project = rf.workspace(\"tv-vloon\").project(\"clay-crack-detection\")\n",
103
- "version = project.version(14)\n",
104
- "dataset = version.download(\"yolov11\")\n",
105
- "\n",
106
- "DATA_DIR = dataset.location # path with data.yaml and roboflow README\n",
107
- "print(\"Dataset at:\", DATA_DIR)\n"
108
- ]
109
- },
110
- {
111
- "cell_type": "markdown",
112
- "metadata": {
113
- "id": "bwCdXcuKQFOF"
114
- },
115
- "source": [
116
- "## Step 3: Upload weight"
117
- ]
118
- },
119
- {
120
- "cell_type": "code",
121
- "execution_count": 4,
122
- "metadata": {
123
- "colab": {
124
- "base_uri": "https://localhost:8080/",
125
- "height": 90
126
- },
127
- "id": "UW3gBrxG339j",
128
- "outputId": "8089f8a5-15bd-4237-f8fa-f953f2f0c054"
129
- },
130
- "outputs": [
131
- {
132
- "name": "stdout",
133
- "output_type": "stream",
134
- "text": [
135
- "Upload: best.pt (or last.pt). Optional: args.yaml, hyp.yaml, training.json, or a runs/ zip.\n"
136
- ]
137
- },
138
- {
139
- "data": {
140
- "text/html": [
141
- "\n",
142
- " <input type=\"file\" id=\"files-13779d1c-4da1-4162-ab3b-283d8da77c70\" name=\"files[]\" multiple disabled\n",
143
- " style=\"border:none\" />\n",
144
- " <output id=\"result-13779d1c-4da1-4162-ab3b-283d8da77c70\">\n",
145
- " Upload widget is only available when the cell has been executed in the\n",
146
- " current browser session. Please rerun this cell to enable.\n",
147
- " </output>\n",
148
- " <script>// Copyright 2017 Google LLC\n",
149
- "//\n",
150
- "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
151
- "// you may not use this file except in compliance with the License.\n",
152
- "// You may obtain a copy of the License at\n",
153
- "//\n",
154
- "// http://www.apache.org/licenses/LICENSE-2.0\n",
155
- "//\n",
156
- "// Unless required by applicable law or agreed to in writing, software\n",
157
- "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
158
- "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
159
- "// See the License for the specific language governing permissions and\n",
160
- "// limitations under the License.\n",
161
- "\n",
162
- "/**\n",
163
- " * @fileoverview Helpers for google.colab Python module.\n",
164
- " */\n",
165
- "(function(scope) {\n",
166
- "function span(text, styleAttributes = {}) {\n",
167
- " const element = document.createElement('span');\n",
168
- " element.textContent = text;\n",
169
- " for (const key of Object.keys(styleAttributes)) {\n",
170
- " element.style[key] = styleAttributes[key];\n",
171
- " }\n",
172
- " return element;\n",
173
- "}\n",
174
- "\n",
175
- "// Max number of bytes which will be uploaded at a time.\n",
176
- "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
177
- "\n",
178
- "function _uploadFiles(inputId, outputId) {\n",
179
- " const steps = uploadFilesStep(inputId, outputId);\n",
180
- " const outputElement = document.getElementById(outputId);\n",
181
- " // Cache steps on the outputElement to make it available for the next call\n",
182
- " // to uploadFilesContinue from Python.\n",
183
- " outputElement.steps = steps;\n",
184
- "\n",
185
- " return _uploadFilesContinue(outputId);\n",
186
- "}\n",
187
- "\n",
188
- "// This is roughly an async generator (not supported in the browser yet),\n",
189
- "// where there are multiple asynchronous steps and the Python side is going\n",
190
- "// to poll for completion of each step.\n",
191
- "// This uses a Promise to block the python side on completion of each step,\n",
192
- "// then passes the result of the previous step as the input to the next step.\n",
193
- "function _uploadFilesContinue(outputId) {\n",
194
- " const outputElement = document.getElementById(outputId);\n",
195
- " const steps = outputElement.steps;\n",
196
- "\n",
197
- " const next = steps.next(outputElement.lastPromiseValue);\n",
198
- " return Promise.resolve(next.value.promise).then((value) => {\n",
199
- " // Cache the last promise value to make it available to the next\n",
200
- " // step of the generator.\n",
201
- " outputElement.lastPromiseValue = value;\n",
202
- " return next.value.response;\n",
203
- " });\n",
204
- "}\n",
205
- "\n",
206
- "/**\n",
207
- " * Generator function which is called between each async step of the upload\n",
208
- " * process.\n",
209
- " * @param {string} inputId Element ID of the input file picker element.\n",
210
- " * @param {string} outputId Element ID of the output display.\n",
211
- " * @return {!Iterable<!Object>} Iterable of next steps.\n",
212
- " */\n",
213
- "function* uploadFilesStep(inputId, outputId) {\n",
214
- " const inputElement = document.getElementById(inputId);\n",
215
- " inputElement.disabled = false;\n",
216
- "\n",
217
- " const outputElement = document.getElementById(outputId);\n",
218
- " outputElement.innerHTML = '';\n",
219
- "\n",
220
- " const pickedPromise = new Promise((resolve) => {\n",
221
- " inputElement.addEventListener('change', (e) => {\n",
222
- " resolve(e.target.files);\n",
223
- " });\n",
224
- " });\n",
225
- "\n",
226
- " const cancel = document.createElement('button');\n",
227
- " inputElement.parentElement.appendChild(cancel);\n",
228
- " cancel.textContent = 'Cancel upload';\n",
229
- " const cancelPromise = new Promise((resolve) => {\n",
230
- " cancel.onclick = () => {\n",
231
- " resolve(null);\n",
232
- " };\n",
233
- " });\n",
234
- "\n",
235
- " // Wait for the user to pick the files.\n",
236
- " const files = yield {\n",
237
- " promise: Promise.race([pickedPromise, cancelPromise]),\n",
238
- " response: {\n",
239
- " action: 'starting',\n",
240
- " }\n",
241
- " };\n",
242
- "\n",
243
- " cancel.remove();\n",
244
- "\n",
245
- " // Disable the input element since further picks are not allowed.\n",
246
- " inputElement.disabled = true;\n",
247
- "\n",
248
- " if (!files) {\n",
249
- " return {\n",
250
- " response: {\n",
251
- " action: 'complete',\n",
252
- " }\n",
253
- " };\n",
254
- " }\n",
255
- "\n",
256
- " for (const file of files) {\n",
257
- " const li = document.createElement('li');\n",
258
- " li.append(span(file.name, {fontWeight: 'bold'}));\n",
259
- " li.append(span(\n",
260
- " `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
261
- " `last modified: ${\n",
262
- " file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
263
- " 'n/a'} - `));\n",
264
- " const percent = span('0% done');\n",
265
- " li.appendChild(percent);\n",
266
- "\n",
267
- " outputElement.appendChild(li);\n",
268
- "\n",
269
- " const fileDataPromise = new Promise((resolve) => {\n",
270
- " const reader = new FileReader();\n",
271
- " reader.onload = (e) => {\n",
272
- " resolve(e.target.result);\n",
273
- " };\n",
274
- " reader.readAsArrayBuffer(file);\n",
275
- " });\n",
276
- " // Wait for the data to be ready.\n",
277
- " let fileData = yield {\n",
278
- " promise: fileDataPromise,\n",
279
- " response: {\n",
280
- " action: 'continue',\n",
281
- " }\n",
282
- " };\n",
283
- "\n",
284
- " // Use a chunked sending to avoid message size limits. See b/62115660.\n",
285
- " let position = 0;\n",
286
- " do {\n",
287
- " const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
288
- " const chunk = new Uint8Array(fileData, position, length);\n",
289
- " position += length;\n",
290
- "\n",
291
- " const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
292
- " yield {\n",
293
- " response: {\n",
294
- " action: 'append',\n",
295
- " file: file.name,\n",
296
- " data: base64,\n",
297
- " },\n",
298
- " };\n",
299
- "\n",
300
- " let percentDone = fileData.byteLength === 0 ?\n",
301
- " 100 :\n",
302
- " Math.round((position / fileData.byteLength) * 100);\n",
303
- " percent.textContent = `${percentDone}% done`;\n",
304
- "\n",
305
- " } while (position < fileData.byteLength);\n",
306
- " }\n",
307
- "\n",
308
- " // All done.\n",
309
- " yield {\n",
310
- " response: {\n",
311
- " action: 'complete',\n",
312
- " }\n",
313
- " };\n",
314
- "}\n",
315
- "\n",
316
- "scope.google = scope.google || {};\n",
317
- "scope.google.colab = scope.google.colab || {};\n",
318
- "scope.google.colab._files = {\n",
319
- " _uploadFiles,\n",
320
- " _uploadFilesContinue,\n",
321
- "};\n",
322
- "})(self);\n",
323
- "</script> "
324
- ],
325
- "text/plain": [
326
- "<IPython.core.display.HTML object>"
327
- ]
328
- },
329
- "metadata": {},
330
- "output_type": "display_data"
331
- },
332
- {
333
- "name": "stdout",
334
- "output_type": "stream",
335
- "text": [
336
- "Saving crack-seg.pt to crack-seg.pt\n"
337
- ]
338
- }
339
- ],
340
- "source": [
341
- "from google.colab import files\n",
342
- "print(\"Upload: best.pt (or last.pt). Optional: args.yaml, hyp.yaml, training.json, or a runs/ zip.\")\n",
343
- "uploaded = files.upload()\n"
344
- ]
345
- },
346
- {
347
- "cell_type": "markdown",
348
- "metadata": {
349
- "id": "tOYh9EudQLt5"
350
- },
351
- "source": [
352
- "## Step 4: Parse & merge hyperparameters into a clean table + downloads"
353
- ]
354
- },
355
- {
356
- "cell_type": "code",
357
- "execution_count": 5,
358
- "metadata": {
359
- "colab": {
360
- "base_uri": "https://localhost:8080/",
361
- "height": 1000
362
- },
363
- "id": "0g_b1GuC36Rc",
364
- "outputId": "febc99ae-4c02-41f7-81f1-500b9619e986"
365
- },
366
- "outputs": [
367
- {
368
- "name": "stdout",
369
- "output_type": "stream",
370
- "text": [
371
- "Creating new Ultralytics Settings v0.0.6 file ✅ \n",
372
- "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n",
373
- "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",
374
- "✅ Sources: dataset:README.roboflow.txt, dataset:data.yaml, crack-seg.pt:train_args, crack-seg.pt:model.args\n",
375
- "✅ Total keys: 111 | Primary shown: 33\n"
376
- ]
377
- },
378
- {
379
- "data": {
380
- "application/vnd.google.colaboratory.intrinsic+json": {
381
- "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}",
382
- "type": "dataframe",
383
- "variable_name": "df"
384
- },
385
- "text/html": [
386
- "\n",
387
- " <div id=\"df-723fce6a-37d1-4818-b483-a84434f982e8\" class=\"colab-df-container\">\n",
388
- " <div>\n",
389
- "<style scoped>\n",
390
- " .dataframe tbody tr th:only-of-type {\n",
391
- " vertical-align: middle;\n",
392
- " }\n",
393
- "\n",
394
- " .dataframe tbody tr th {\n",
395
- " vertical-align: top;\n",
396
- " }\n",
397
- "\n",
398
- " .dataframe thead th {\n",
399
- " text-align: right;\n",
400
- " }\n",
401
- "</style>\n",
402
- "<table border=\"1\" class=\"dataframe\">\n",
403
- " <thead>\n",
404
- " <tr style=\"text-align: right;\">\n",
405
- " <th></th>\n",
406
- " <th>parameter</th>\n",
407
- " <th>value</th>\n",
408
- " </tr>\n",
409
- " </thead>\n",
410
- " <tbody>\n",
411
- " <tr>\n",
412
- " <th>0</th>\n",
413
- " <td>aug.copy_paste</td>\n",
414
- " <td>0.0</td>\n",
415
- " </tr>\n",
416
- " <tr>\n",
417
- " <th>1</th>\n",
418
- " <td>aug.degrees</td>\n",
419
- " <td>0.0</td>\n",
420
- " </tr>\n",
421
- " <tr>\n",
422
- " <th>2</th>\n",
423
- " <td>aug.erasing</td>\n",
424
- " <td>0.4</td>\n",
425
- " </tr>\n",
426
- " <tr>\n",
427
- " <th>3</th>\n",
428
- " <td>aug.fliplr</td>\n",
429
- " <td>0.0</td>\n",
430
- " </tr>\n",
431
- " <tr>\n",
432
- " <th>4</th>\n",
433
- " <td>aug.flipud</td>\n",
434
- " <td>0.0</td>\n",
435
- " </tr>\n",
436
- " <tr>\n",
437
- " <th>5</th>\n",
438
- " <td>aug.hsv_h</td>\n",
439
- " <td>0.0</td>\n",
440
- " </tr>\n",
441
- " <tr>\n",
442
- " <th>6</th>\n",
443
- " <td>aug.hsv_s</td>\n",
444
- " <td>0.0</td>\n",
445
- " </tr>\n",
446
- " <tr>\n",
447
- " <th>7</th>\n",
448
- " <td>aug.hsv_v</td>\n",
449
- " <td>0.0</td>\n",
450
- " </tr>\n",
451
- " <tr>\n",
452
- " <th>8</th>\n",
453
- " <td>aug.mixup</td>\n",
454
- " <td>0.0</td>\n",
455
- " </tr>\n",
456
- " <tr>\n",
457
- " <th>9</th>\n",
458
- " <td>aug.mosaic</td>\n",
459
- " <td>0.0</td>\n",
460
- " </tr>\n",
461
- " <tr>\n",
462
- " <th>10</th>\n",
463
- " <td>aug.perspective</td>\n",
464
- " <td>0.0</td>\n",
465
- " </tr>\n",
466
- " <tr>\n",
467
- " <th>11</th>\n",
468
- " <td>aug.scale</td>\n",
469
- " <td>0.0</td>\n",
470
- " </tr>\n",
471
- " <tr>\n",
472
- " <th>12</th>\n",
473
- " <td>aug.shear</td>\n",
474
- " <td>0.0</td>\n",
475
- " </tr>\n",
476
- " <tr>\n",
477
- " <th>13</th>\n",
478
- " <td>aug.translate</td>\n",
479
- " <td>0.0</td>\n",
480
- " </tr>\n",
481
- " <tr>\n",
482
- " <th>14</th>\n",
483
- " <td>data.names</td>\n",
484
- " <td>[crack_b, crack_s, crack_shadow]</td>\n",
485
- " </tr>\n",
486
- " <tr>\n",
487
- " <th>15</th>\n",
488
- " <td>data.test</td>\n",
489
- " <td>../test/images</td>\n",
490
- " </tr>\n",
491
- " <tr>\n",
492
- " <th>16</th>\n",
493
- " <td>data.train</td>\n",
494
- " <td>../train/images</td>\n",
495
- " </tr>\n",
496
- " <tr>\n",
497
- " <th>17</th>\n",
498
- " <td>data.val</td>\n",
499
- " <td>../valid/images</td>\n",
500
- " </tr>\n",
501
- " <tr>\n",
502
- " <th>18</th>\n",
503
- " <td>env.device</td>\n",
504
- " <td>None</td>\n",
505
- " </tr>\n",
506
- " <tr>\n",
507
- " <th>19</th>\n",
508
- " <td>env.seed</td>\n",
509
- " <td>0</td>\n",
510
- " </tr>\n",
511
- " <tr>\n",
512
- " <th>20</th>\n",
513
- " <td>env.workers</td>\n",
514
- " <td>1</td>\n",
515
- " </tr>\n",
516
- " <tr>\n",
517
- " <th>21</th>\n",
518
- " <td>lr.lr0</td>\n",
519
- " <td>0.01</td>\n",
520
- " </tr>\n",
521
- " <tr>\n",
522
- " <th>22</th>\n",
523
- " <td>lr.lrf</td>\n",
524
- " <td>0.01</td>\n",
525
- " </tr>\n",
526
- " <tr>\n",
527
- " <th>23</th>\n",
528
- " <td>lr.momentum</td>\n",
529
- " <td>0.937</td>\n",
530
- " </tr>\n",
531
- " <tr>\n",
532
- " <th>24</th>\n",
533
- " <td>lr.warmup_bias_lr</td>\n",
534
- " <td>0.0</td>\n",
535
- " </tr>\n",
536
- " <tr>\n",
537
- " <th>25</th>\n",
538
- " <td>lr.warmup_epochs</td>\n",
539
- " <td>3.0</td>\n",
540
- " </tr>\n",
541
- " <tr>\n",
542
- " <th>26</th>\n",
543
- " <td>lr.warmup_momentum</td>\n",
544
- " <td>0.8</td>\n",
545
- " </tr>\n",
546
- " <tr>\n",
547
- " <th>27</th>\n",
548
- " <td>lr.weight_decay</td>\n",
549
- " <td>0.0005</td>\n",
550
- " </tr>\n",
551
- " <tr>\n",
552
- " <th>28</th>\n",
553
- " <td>train.batch</td>\n",
554
- " <td>14</td>\n",
555
- " </tr>\n",
556
- " <tr>\n",
557
- " <th>29</th>\n",
558
- " <td>train.epochs</td>\n",
559
- " <td>300</td>\n",
560
- " </tr>\n",
561
- " <tr>\n",
562
- " <th>30</th>\n",
563
- " <td>train.imgsz</td>\n",
564
- " <td>640</td>\n",
565
- " </tr>\n",
566
- " <tr>\n",
567
- " <th>31</th>\n",
568
- " <td>train.optimizer</td>\n",
569
- " <td>auto</td>\n",
570
- " </tr>\n",
571
- " <tr>\n",
572
- " <th>32</th>\n",
573
- " <td>train.patience</td>\n",
574
- " <td>10</td>\n",
575
- " </tr>\n",
576
- " </tbody>\n",
577
- "</table>\n",
578
- "</div>\n",
579
- " <div class=\"colab-df-buttons\">\n",
580
- "\n",
581
- " <div class=\"colab-df-container\">\n",
582
- " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-723fce6a-37d1-4818-b483-a84434f982e8')\"\n",
583
- " title=\"Convert this dataframe to an interactive table.\"\n",
584
- " style=\"display:none;\">\n",
585
- "\n",
586
- " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
587
- " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
588
- " </svg>\n",
589
- " </button>\n",
590
- "\n",
591
- " <style>\n",
592
- " .colab-df-container {\n",
593
- " display:flex;\n",
594
- " gap: 12px;\n",
595
- " }\n",
596
- "\n",
597
- " .colab-df-convert {\n",
598
- " background-color: #E8F0FE;\n",
599
- " border: none;\n",
600
- " border-radius: 50%;\n",
601
- " cursor: pointer;\n",
602
- " display: none;\n",
603
- " fill: #1967D2;\n",
604
- " height: 32px;\n",
605
- " padding: 0 0 0 0;\n",
606
- " width: 32px;\n",
607
- " }\n",
608
- "\n",
609
- " .colab-df-convert:hover {\n",
610
- " background-color: #E2EBFA;\n",
611
- " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
612
- " fill: #174EA6;\n",
613
- " }\n",
614
- "\n",
615
- " .colab-df-buttons div {\n",
616
- " margin-bottom: 4px;\n",
617
- " }\n",
618
- "\n",
619
- " [theme=dark] .colab-df-convert {\n",
620
- " background-color: #3B4455;\n",
621
- " fill: #D2E3FC;\n",
622
- " }\n",
623
- "\n",
624
- " [theme=dark] .colab-df-convert:hover {\n",
625
- " background-color: #434B5C;\n",
626
- " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
627
- " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
628
- " fill: #FFFFFF;\n",
629
- " }\n",
630
- " </style>\n",
631
- "\n",
632
- " <script>\n",
633
- " const buttonEl =\n",
634
- " document.querySelector('#df-723fce6a-37d1-4818-b483-a84434f982e8 button.colab-df-convert');\n",
635
- " buttonEl.style.display =\n",
636
- " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
637
- "\n",
638
- " async function convertToInteractive(key) {\n",
639
- " const element = document.querySelector('#df-723fce6a-37d1-4818-b483-a84434f982e8');\n",
640
- " const dataTable =\n",
641
- " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
642
- " [key], {});\n",
643
- " if (!dataTable) return;\n",
644
- "\n",
645
- " const docLinkHtml = 'Like what you see? Visit the ' +\n",
646
- " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
647
- " + ' to learn more about interactive tables.';\n",
648
- " element.innerHTML = '';\n",
649
- " dataTable['output_type'] = 'display_data';\n",
650
- " await google.colab.output.renderOutput(dataTable, element);\n",
651
- " const docLink = document.createElement('div');\n",
652
- " docLink.innerHTML = docLinkHtml;\n",
653
- " element.appendChild(docLink);\n",
654
- " }\n",
655
- " </script>\n",
656
- " </div>\n",
657
- "\n",
658
- "\n",
659
- " <div id=\"id_3aaba703-05cc-4db8-9426-ae24fc534ac4\">\n",
660
- " <style>\n",
661
- " .colab-df-generate {\n",
662
- " background-color: #E8F0FE;\n",
663
- " border: none;\n",
664
- " border-radius: 50%;\n",
665
- " cursor: pointer;\n",
666
- " display: none;\n",
667
- " fill: #1967D2;\n",
668
- " height: 32px;\n",
669
- " padding: 0 0 0 0;\n",
670
- " width: 32px;\n",
671
- " }\n",
672
- "\n",
673
- " .colab-df-generate:hover {\n",
674
- " background-color: #E2EBFA;\n",
675
- " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
676
- " fill: #174EA6;\n",
677
- " }\n",
678
- "\n",
679
- " [theme=dark] .colab-df-generate {\n",
680
- " background-color: #3B4455;\n",
681
- " fill: #D2E3FC;\n",
682
- " }\n",
683
- "\n",
684
- " [theme=dark] .colab-df-generate:hover {\n",
685
- " background-color: #434B5C;\n",
686
- " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
687
- " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
688
- " fill: #FFFFFF;\n",
689
- " }\n",
690
- " </style>\n",
691
- " <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
692
- " title=\"Generate code using this dataframe.\"\n",
693
- " style=\"display:none;\">\n",
694
- "\n",
695
- " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
696
- " width=\"24px\">\n",
697
- " <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",
698
- " </svg>\n",
699
- " </button>\n",
700
- " <script>\n",
701
- " (() => {\n",
702
- " const buttonEl =\n",
703
- " document.querySelector('#id_3aaba703-05cc-4db8-9426-ae24fc534ac4 button.colab-df-generate');\n",
704
- " buttonEl.style.display =\n",
705
- " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
706
- "\n",
707
- " buttonEl.onclick = () => {\n",
708
- " google.colab.notebook.generateWithVariable('df');\n",
709
- " }\n",
710
- " })();\n",
711
- " </script>\n",
712
- " </div>\n",
713
- "\n",
714
- " </div>\n",
715
- " </div>\n"
716
- ],
717
- "text/plain": [
718
- " parameter value\n",
719
- "0 aug.copy_paste 0.0\n",
720
- "1 aug.degrees 0.0\n",
721
- "2 aug.erasing 0.4\n",
722
- "3 aug.fliplr 0.0\n",
723
- "4 aug.flipud 0.0\n",
724
- "5 aug.hsv_h 0.0\n",
725
- "6 aug.hsv_s 0.0\n",
726
- "7 aug.hsv_v 0.0\n",
727
- "8 aug.mixup 0.0\n",
728
- "9 aug.mosaic 0.0\n",
729
- "10 aug.perspective 0.0\n",
730
- "11 aug.scale 0.0\n",
731
- "12 aug.shear 0.0\n",
732
- "13 aug.translate 0.0\n",
733
- "14 data.names [crack_b, crack_s, crack_shadow]\n",
734
- "15 data.test ../test/images\n",
735
- "16 data.train ../train/images\n",
736
- "17 data.val ../valid/images\n",
737
- "18 env.device None\n",
738
- "19 env.seed 0\n",
739
- "20 env.workers 1\n",
740
- "21 lr.lr0 0.01\n",
741
- "22 lr.lrf 0.01\n",
742
- "23 lr.momentum 0.937\n",
743
- "24 lr.warmup_bias_lr 0.0\n",
744
- "25 lr.warmup_epochs 3.0\n",
745
- "26 lr.warmup_momentum 0.8\n",
746
- "27 lr.weight_decay 0.0005\n",
747
- "28 train.batch 14\n",
748
- "29 train.epochs 300\n",
749
- "30 train.imgsz 640\n",
750
- "31 train.optimizer auto\n",
751
- "32 train.patience 10"
752
- ]
753
- },
754
- "execution_count": 5,
755
- "metadata": {},
756
- "output_type": "execute_result"
757
- }
758
- ],
759
- "source": [
760
- "import io, os, zipfile, json, yaml, pandas as pd\n",
761
- "from ultralytics import YOLO\n",
762
- "\n",
763
- "workdir = \"/content/hparam_scan\"\n",
764
- "os.makedirs(workdir, exist_ok=True)\n",
765
- "\n",
766
- "def try_read_yaml_bytes(b):\n",
767
- " try: return yaml.safe_load(b.decode(\"utf-8\"))\n",
768
- " except Exception:\n",
769
- " try: return yaml.safe_load(b)\n",
770
- " except Exception: return None\n",
771
- "\n",
772
- "def flatten(d, parent=\"\"):\n",
773
- " out = {}\n",
774
- " if isinstance(d, dict):\n",
775
- " for k, v in d.items():\n",
776
- " key = f\"{parent}.{k}\" if parent else str(k)\n",
777
- " if isinstance(v, dict):\n",
778
- " out.update(flatten(v, key))\n",
779
- " else:\n",
780
- " out[key] = v\n",
781
- " return out\n",
782
- "\n",
783
- "def take(d, keys):\n",
784
- " return {k: d[k] for k in keys if k in d}\n",
785
- "\n",
786
- "params = {}\n",
787
- "sources = []\n",
788
- "\n",
789
- "# A) Read Roboflow dataset metadata (preprocess/augment & class info)\n",
790
- "rf_files = [\"README.roboflow.txt\",\"README.dataset.txt\",\"data.yaml\",\"README.txt\"]\n",
791
- "for f in rf_files:\n",
792
- " p = os.path.join(DATA_DIR, f)\n",
793
- " if os.path.exists(p):\n",
794
- " try:\n",
795
- " if f.endswith(\".yaml\"):\n",
796
- " y = try_read_yaml_bytes(open(p,\"rb\").read()) or {}\n",
797
- " params.update({f\"data.{k}\": v for k,v in y.items()})\n",
798
- " else:\n",
799
- " txt = open(p,\"r\",errors=\"ignore\").read()\n",
800
- " params[\"roboflow.readme_present\"] = True\n",
801
- " sources.append(f\"dataset:{f}\")\n",
802
- " except: pass\n",
803
- "\n",
804
- "# B) Uploaded YAML/JSON\n",
805
- "for name, content in uploaded.items():\n",
806
- " low = name.lower()\n",
807
- " if any(low.endswith(ext) for ext in [\".yaml\",\".yml\",\".json\"]):\n",
808
- " try:\n",
809
- " y = json.loads(content.decode(\"utf-8\")) if low.endswith(\".json\") else try_read_yaml_bytes(content)\n",
810
- " if isinstance(y, dict):\n",
811
- " params.update(flatten(y))\n",
812
- " sources.append(name)\n",
813
- " except: pass\n",
814
- "\n",
815
- "# C) Uploaded ZIP of runs/*\n",
816
- "for name, content in uploaded.items():\n",
817
- " if name.lower().endswith(\".zip\"):\n",
818
- " zpath = os.path.join(workdir, name)\n",
819
- " open(zpath,\"wb\").write(content)\n",
820
- " with zipfile.ZipFile(zpath,\"r\") as z: z.extractall(os.path.join(workdir,\"unzipped\"))\n",
821
- " for root, _, files_ in os.walk(os.path.join(workdir,\"unzipped\")):\n",
822
- " for f in files_:\n",
823
- " if f.lower() in {\"args.yaml\",\"hyp.yaml\",\"cfg.yaml\",\"opt.yaml\",\"results.yaml\",\"data.yaml\",\"config.yaml\"}:\n",
824
- " p = os.path.join(root,f)\n",
825
- " y = try_read_yaml_bytes(open(p,\"rb\").read())\n",
826
- " if isinstance(y, dict):\n",
827
- " params.update(flatten(y))\n",
828
- " sources.append(f\"zip:{f}\")\n",
829
- "\n",
830
- "# D) Read .pt\n",
831
- "for name, content in uploaded.items():\n",
832
- " if name.endswith(\".pt\"):\n",
833
- " tmp = os.path.join(workdir, name)\n",
834
- " open(tmp,\"wb\").write(content)\n",
835
- " model = YOLO(tmp)\n",
836
- " ckpt = getattr(model, \"ckpt\", {}) or {}\n",
837
- " if isinstance(ckpt, dict):\n",
838
- " train_args = ckpt.get(\"train_args\", {}) or ckpt.get(\"args\", {}) or {}\n",
839
- " if train_args:\n",
840
- " params.update(flatten(train_args))\n",
841
- " sources.append(f\"{name}:train_args\")\n",
842
- " try:\n",
843
- " model_args = getattr(model.model, \"args\", {}) or {}\n",
844
- " if model_args:\n",
845
- " params.update(flatten(model_args))\n",
846
- " sources.append(f\"{name}:model.args\")\n",
847
- " except: pass\n",
848
- "\n",
849
- "# E) Normalize to friendly keys (common YOLO train hypers)\n",
850
- "alias = {\n",
851
- " \"epochs\":\"train.epochs\",\"batch\":\"train.batch\",\"imgsz\":\"train.imgsz\",\"img_size\":\"train.imgsz\",\n",
852
- " \"optimizer\":\"train.optimizer\",\"patience\":\"train.patience\",\n",
853
- " \"lr0\":\"lr.lr0\",\"lrf\":\"lr.lrf\",\"momentum\":\"lr.momentum\",\"weight_decay\":\"lr.weight_decay\",\n",
854
- " \"warmup_epochs\":\"lr.warmup_epochs\",\"warmup_momentum\":\"lr.warmup_momentum\",\"warmup_bias_lr\":\"lr.warmup_bias_lr\",\n",
855
- " \"mosaic\":\"aug.mosaic\",\"hsv_h\":\"aug.hsv_h\",\"hsv_s\":\"aug.hsv_s\",\"hsv_v\":\"aug.hsv_v\",\n",
856
- " \"flipud\":\"aug.flipud\",\"fliplr\":\"aug.fliplr\",\"degrees\":\"aug.degrees\",\"translate\":\"aug.translate\",\n",
857
- " \"scale\":\"aug.scale\",\"shear\":\"aug.shear\",\"perspective\":\"aug.perspective\",\"erasing\":\"aug.erasing\",\n",
858
- " \"mixup\":\"aug.mixup\",\"copy_paste\":\"aug.copy_paste\",\n",
859
- " \"device\":\"env.device\",\"workers\":\"env.workers\",\"seed\":\"env.seed\",\"project\":\"env.project\",\"name\":\"env.name\",\"exist_ok\":\"env.exist_ok\"\n",
860
- "}\n",
861
- "normalized = {}\n",
862
- "for k, v in list(params.items()):\n",
863
- " k0 = k.split(\".\")[-1]\n",
864
- " if k in alias: normalized[alias[k]] = v\n",
865
- " elif k0 in alias: normalized[alias[k0]] = v\n",
866
- " else: normalized[k] = v\n",
867
- "params = normalized\n",
868
- "\n",
869
- "# Short, report-ready subset\n",
870
- "primary_keys = [\n",
871
- " \"train.epochs\",\"train.batch\",\"train.imgsz\",\"train.optimizer\",\"train.patience\",\n",
872
- " \"lr.lr0\",\"lr.lrf\",\"lr.momentum\",\"lr.weight_decay\",\"lr.warmup_epochs\",\"lr.warmup_momentum\",\"lr.warmup_bias_lr\",\n",
873
- " \"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",
874
- " \"env.device\",\"env.workers\",\"env.seed\",\n",
875
- " \"data.path\",\"data.train\",\"data.val\",\"data.test\",\"data.names\",\"nc\",\"names\"\n",
876
- "]\n",
877
- "primary = take(params, primary_keys)\n",
878
- "\n",
879
- "print(\"✅ Sources:\", \", \".join(sources) if sources else \"none\")\n",
880
- "print(f\"✅ Total keys: {len(params)} | Primary shown: {len(primary)}\")\n",
881
- "\n",
882
- "import pandas as pd\n",
883
- "df = pd.DataFrame(\n",
884
- " [{\"parameter\": k, \"value\": primary.get(k, params.get(k))} for k in sorted(primary or params)]\n",
885
- ")\n",
886
- "df\n"
887
- ]
888
- },
889
- {
890
- "cell_type": "code",
891
- "execution_count": 6,
892
- "metadata": {
893
- "colab": {
894
- "base_uri": "https://localhost:8080/",
895
- "height": 34
896
- },
897
- "id": "_F1fJ4dh38JN",
898
- "outputId": "54ad9e14-a7f6-4810-98fb-e80dbaf17610"
899
- },
900
- "outputs": [
901
- {
902
- "data": {
903
- "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 \"${filename}\": `;\n div.appendChild(label);\n const progress = document.createElement('progress');\n progress.max = size;\n div.appendChild(progress);\n document.body.appendChild(div);\n\n const buffers = [];\n let downloaded = 0;\n\n const channel = await google.colab.kernel.comms.open(id);\n // Send a message to notify the kernel that we're ready.\n channel.send({})\n\n for await (const message of channel.messages) {\n // Send a message to notify the kernel that we're ready.\n channel.send({})\n if (message.buffers) {\n for (const buffer of message.buffers) {\n buffers.push(buffer);\n downloaded += buffer.byteLength;\n progress.value = downloaded;\n }\n }\n }\n const blob = new Blob(buffers, {type: 'application/binary'});\n const a = document.createElement('a');\n a.href = window.URL.createObjectURL(blob);\n a.download = filename;\n div.appendChild(a);\n a.click();\n div.remove();\n }\n ",
904
- "text/plain": [
905
- "<IPython.core.display.Javascript object>"
906
- ]
907
- },
908
- "metadata": {},
909
- "output_type": "display_data"
910
- },
911
- {
912
- "data": {
913
- "application/javascript": "download(\"download_227d7458-1155-429c-8f56-4bb4cbf25697\", \"hyperparams_all.yaml\", 2037)",
914
- "text/plain": [
915
- "<IPython.core.display.Javascript object>"
916
- ]
917
- },
918
- "metadata": {},
919
- "output_type": "display_data"
920
- },
921
- {
922
- "data": {
923
- "application/javascript": "\n async function download(id, filename, size) {\n if (!google.colab.kernel.accessAllowed) {\n return;\n }\n const div = document.createElement('div');\n const label = document.createElement('label');\n label.textContent = `Downloading \"${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 ",
924
- "text/plain": [
925
- "<IPython.core.display.Javascript object>"
926
- ]
927
- },
928
- "metadata": {},
929
- "output_type": "display_data"
930
- },
931
- {
932
- "data": {
933
- "application/javascript": "download(\"download_02788a7c-4324-4881-b298-49de5a6336f8\", \"hyperparams_primary.yaml\", 633)",
934
- "text/plain": [
935
- "<IPython.core.display.Javascript object>"
936
- ]
937
- },
938
- "metadata": {},
939
- "output_type": "display_data"
940
- },
941
- {
942
- "data": {
943
- "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 \"${filename}\": `;\n div.appendChild(label);\n const progress = document.createElement('progress');\n progress.max = size;\n div.appendChild(progress);\n document.body.appendChild(div);\n\n const buffers = [];\n let downloaded = 0;\n\n const channel = await google.colab.kernel.comms.open(id);\n // Send a message to notify the kernel that we're ready.\n channel.send({})\n\n for await (const message of channel.messages) {\n // Send a message to notify the kernel that we're ready.\n channel.send({})\n if (message.buffers) {\n for (const buffer of message.buffers) {\n buffers.push(buffer);\n downloaded += buffer.byteLength;\n progress.value = downloaded;\n }\n }\n }\n const blob = new Blob(buffers, {type: 'application/binary'});\n const a = document.createElement('a');\n a.href = window.URL.createObjectURL(blob);\n a.download = filename;\n div.appendChild(a);\n a.click();\n div.remove();\n }\n ",
944
- "text/plain": [
945
- "<IPython.core.display.Javascript object>"
946
- ]
947
- },
948
- "metadata": {},
949
- "output_type": "display_data"
950
- },
951
- {
952
- "data": {
953
- "application/javascript": "download(\"download_73abe0db-c31a-4a05-aa7f-a5a6e10ccb87\", \"hyperparams_all.csv\", 1912)",
954
- "text/plain": [
955
- "<IPython.core.display.Javascript object>"
956
- ]
957
- },
958
- "metadata": {},
959
- "output_type": "display_data"
960
- },
961
- {
962
- "data": {
963
- "application/javascript": "\n async function download(id, filename, size) {\n if (!google.colab.kernel.accessAllowed) {\n return;\n }\n const div = document.createElement('div');\n const label = document.createElement('label');\n label.textContent = `Downloading \"${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 ",
964
- "text/plain": [
965
- "<IPython.core.display.Javascript object>"
966
- ]
967
- },
968
- "metadata": {},
969
- "output_type": "display_data"
970
- },
971
- {
972
- "data": {
973
- "application/javascript": "download(\"download_b8f2f1bb-5d09-472a-8f54-1a33642b1657\", \"hyperparams_primary.md\", 872)",
974
- "text/plain": [
975
- "<IPython.core.display.Javascript object>"
976
- ]
977
- },
978
- "metadata": {},
979
- "output_type": "display_data"
980
- },
981
- {
982
- "name": "stdout",
983
- "output_type": "stream",
984
- "text": [
985
- "Done.\n"
986
- ]
987
- }
988
- ],
989
- "source": [
990
- "from google.colab import files, output\n",
991
- "import yaml, pandas as pd, os\n",
992
- "\n",
993
- "os.makedirs(workdir, exist_ok=True)\n",
994
- "all_yaml = os.path.join(workdir, \"hyperparams_all.yaml\")\n",
995
- "primary_yaml = os.path.join(workdir, \"hyperparams_primary.yaml\")\n",
996
- "csv_path = os.path.join(workdir, \"hyperparams_all.csv\")\n",
997
- "md_path = os.path.join(workdir, \"hyperparams_primary.md\")\n",
998
- "\n",
999
- "with open(all_yaml, \"w\") as f: yaml.safe_dump(params, f, sort_keys=True, allow_unicode=True)\n",
1000
- "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",
1001
- "pd.DataFrame([{\"parameter\": k, \"value\": v} for k, v in sorted(params.items())]).to_csv(csv_path, index=False)\n",
1002
- "with open(md_path, \"w\") as f:\n",
1003
- " f.write(\"# YOLOv11 Hyperparameters (Primary)\\n\\n\")\n",
1004
- " for k in sorted(primary.keys()):\n",
1005
- " if k in params:\n",
1006
- " f.write(f\"- **{k}**: {params[k]}\\n\")\n",
1007
- "\n",
1008
- "for p in [all_yaml, primary_yaml, csv_path, md_path]:\n",
1009
- " files.download(p)\n",
1010
- "print(\"Done.\")\n"
1011
- ]
1012
- }
1013
- ],
1014
- "metadata": {
1015
- "accelerator": "GPU",
1016
- "colab": {
1017
- "gpuType": "T4",
1018
- "provenance": []
1019
- },
1020
- "kernelspec": {
1021
- "display_name": "Python 3",
1022
- "name": "python3"
1023
- },
1024
- "language_info": {
1025
- "name": "python"
1026
- }
1027
- },
1028
- "nbformat": 4,
1029
- "nbformat_minor": 0
1030
- }
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0c910acfc5dbbf0d44de8e60077ebed3572b4242d24a6ca6c648f03bc85201cd
3
+ size 51620
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
examples/crack_evaluation.ipynb CHANGED
@@ -1,927 +1,3 @@
1
- {
2
- "cells": [
3
- {
4
- "cell_type": "markdown",
5
- "metadata": {
6
- "id": "ZHqQGT0jBqLi"
7
- },
8
- "source": [
9
- "## STEP 1: Install dependencies"
10
- ]
11
- },
12
- {
13
- "cell_type": "code",
14
- "execution_count": null,
15
- "metadata": {
16
- "colab": {
17
- "base_uri": "https://localhost:8080/"
18
- },
19
- "id": "LHUcg2ZDjre_",
20
- "outputId": "6d4976a5-62ae-4609-9d8d-9b9cbd586997"
21
- },
22
- "outputs": [
23
- {
24
- "name": "stdout",
25
- "output_type": "stream",
26
- "text": [
27
- "\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",
28
- "\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",
29
- "\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",
30
- "\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",
31
- "\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",
32
- "\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",
33
- "\u001b[?25h"
34
- ]
35
- }
36
- ],
37
- "source": [
38
- "!pip install ultralytics roboflow --upgrade --quiet"
39
- ]
40
- },
41
- {
42
- "cell_type": "markdown",
43
- "metadata": {
44
- "id": "AMz9DvsFBxtA"
45
- },
46
- "source": [
47
- "## STEP 2: Imports"
48
- ]
49
- },
50
- {
51
- "cell_type": "code",
52
- "execution_count": null,
53
- "metadata": {
54
- "colab": {
55
- "base_uri": "https://localhost:8080/"
56
- },
57
- "id": "h8GE0fXCjsVO",
58
- "outputId": "f1fac47c-71e1-4c2e-8a2f-d583d2a86c86"
59
- },
60
- "outputs": [
61
- {
62
- "name": "stdout",
63
- "output_type": "stream",
64
- "text": [
65
- "Creating new Ultralytics Settings v0.0.6 file ✅ \n",
66
- "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n",
67
- "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"
68
- ]
69
- }
70
- ],
71
- "source": [
72
- "import os\n",
73
- "import pandas as pd\n",
74
- "import matplotlib.pyplot as plt\n",
75
- "from roboflow import Roboflow\n",
76
- "from ultralytics import YOLO\n",
77
- "from google.colab import files"
78
- ]
79
- },
80
- {
81
- "cell_type": "markdown",
82
- "metadata": {
83
- "id": "tHZrVz8-B4VM"
84
- },
85
- "source": [
86
- "## STEP 3: Get training dataset"
87
- ]
88
- },
89
- {
90
- "cell_type": "code",
91
- "execution_count": null,
92
- "metadata": {
93
- "colab": {
94
- "base_uri": "https://localhost:8080/"
95
- },
96
- "id": "uPmqYYhDj3nf",
97
- "outputId": "645a87a0-2f61-445e-e68a-3fe04e232dcf"
98
- },
99
- "outputs": [
100
- {
101
- "name": "stdout",
102
- "output_type": "stream",
103
- "text": [
104
- "loading Roboflow workspace...\n",
105
- "loading Roboflow project...\n"
106
- ]
107
- },
108
- {
109
- "name": "stderr",
110
- "output_type": "stream",
111
- "text": [
112
- "Downloading Dataset Version Zip in Clay-Crack-Detection-14 to yolov11:: 100%|██████████| 359480/359480 [00:11<00:00, 30777.63it/s]"
113
- ]
114
- },
115
- {
116
- "name": "stdout",
117
- "output_type": "stream",
118
- "text": [
119
- "\n"
120
- ]
121
- },
122
- {
123
- "name": "stderr",
124
- "output_type": "stream",
125
- "text": [
126
- "\n",
127
- "Extracting Dataset Version Zip to Clay-Crack-Detection-14 in yolov11:: 100%|██████████| 15197/15197 [00:02<00:00, 6929.67it/s]\n"
128
- ]
129
- }
130
- ],
131
- "source": [
132
- "rf = Roboflow(api_key=\"ROBOFLOW_API_KEY\")\n",
133
- "project = rf.workspace(\"tv-vloon\").project(\"clay-crack-detection\")\n",
134
- "version = project.version(14)\n",
135
- "dataset = version.download(\"yolov11\")"
136
- ]
137
- },
138
- {
139
- "cell_type": "code",
140
- "execution_count": null,
141
- "metadata": {
142
- "colab": {
143
- "base_uri": "https://localhost:8080/",
144
- "height": 90
145
- },
146
- "id": "9EPy__E8j4x2",
147
- "outputId": "2fdbbb45-1e14-4a21-f402-bdc3fbe5a15a"
148
- },
149
- "outputs": [
150
- {
151
- "name": "stdout",
152
- "output_type": "stream",
153
- "text": [
154
- "Upload YOLOv11 model\n"
155
- ]
156
- },
157
- {
158
- "data": {
159
- "text/html": [
160
- "\n",
161
- " <input type=\"file\" id=\"files-f2bc48c8-d992-4c4d-8f9e-b145346f89db\" name=\"files[]\" multiple disabled\n",
162
- " style=\"border:none\" />\n",
163
- " <output id=\"result-f2bc48c8-d992-4c4d-8f9e-b145346f89db\">\n",
164
- " Upload widget is only available when the cell has been executed in the\n",
165
- " current browser session. Please rerun this cell to enable.\n",
166
- " </output>\n",
167
- " <script>// Copyright 2017 Google LLC\n",
168
- "//\n",
169
- "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
170
- "// you may not use this file except in compliance with the License.\n",
171
- "// You may obtain a copy of the License at\n",
172
- "//\n",
173
- "// http://www.apache.org/licenses/LICENSE-2.0\n",
174
- "//\n",
175
- "// Unless required by applicable law or agreed to in writing, software\n",
176
- "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
177
- "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
178
- "// See the License for the specific language governing permissions and\n",
179
- "// limitations under the License.\n",
180
- "\n",
181
- "/**\n",
182
- " * @fileoverview Helpers for google.colab Python module.\n",
183
- " */\n",
184
- "(function(scope) {\n",
185
- "function span(text, styleAttributes = {}) {\n",
186
- " const element = document.createElement('span');\n",
187
- " element.textContent = text;\n",
188
- " for (const key of Object.keys(styleAttributes)) {\n",
189
- " element.style[key] = styleAttributes[key];\n",
190
- " }\n",
191
- " return element;\n",
192
- "}\n",
193
- "\n",
194
- "// Max number of bytes which will be uploaded at a time.\n",
195
- "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
196
- "\n",
197
- "function _uploadFiles(inputId, outputId) {\n",
198
- " const steps = uploadFilesStep(inputId, outputId);\n",
199
- " const outputElement = document.getElementById(outputId);\n",
200
- " // Cache steps on the outputElement to make it available for the next call\n",
201
- " // to uploadFilesContinue from Python.\n",
202
- " outputElement.steps = steps;\n",
203
- "\n",
204
- " return _uploadFilesContinue(outputId);\n",
205
- "}\n",
206
- "\n",
207
- "// This is roughly an async generator (not supported in the browser yet),\n",
208
- "// where there are multiple asynchronous steps and the Python side is going\n",
209
- "// to poll for completion of each step.\n",
210
- "// This uses a Promise to block the python side on completion of each step,\n",
211
- "// then passes the result of the previous step as the input to the next step.\n",
212
- "function _uploadFilesContinue(outputId) {\n",
213
- " const outputElement = document.getElementById(outputId);\n",
214
- " const steps = outputElement.steps;\n",
215
- "\n",
216
- " const next = steps.next(outputElement.lastPromiseValue);\n",
217
- " return Promise.resolve(next.value.promise).then((value) => {\n",
218
- " // Cache the last promise value to make it available to the next\n",
219
- " // step of the generator.\n",
220
- " outputElement.lastPromiseValue = value;\n",
221
- " return next.value.response;\n",
222
- " });\n",
223
- "}\n",
224
- "\n",
225
- "/**\n",
226
- " * Generator function which is called between each async step of the upload\n",
227
- " * process.\n",
228
- " * @param {string} inputId Element ID of the input file picker element.\n",
229
- " * @param {string} outputId Element ID of the output display.\n",
230
- " * @return {!Iterable<!Object>} Iterable of next steps.\n",
231
- " */\n",
232
- "function* uploadFilesStep(inputId, outputId) {\n",
233
- " const inputElement = document.getElementById(inputId);\n",
234
- " inputElement.disabled = false;\n",
235
- "\n",
236
- " const outputElement = document.getElementById(outputId);\n",
237
- " outputElement.innerHTML = '';\n",
238
- "\n",
239
- " const pickedPromise = new Promise((resolve) => {\n",
240
- " inputElement.addEventListener('change', (e) => {\n",
241
- " resolve(e.target.files);\n",
242
- " });\n",
243
- " });\n",
244
- "\n",
245
- " const cancel = document.createElement('button');\n",
246
- " inputElement.parentElement.appendChild(cancel);\n",
247
- " cancel.textContent = 'Cancel upload';\n",
248
- " const cancelPromise = new Promise((resolve) => {\n",
249
- " cancel.onclick = () => {\n",
250
- " resolve(null);\n",
251
- " };\n",
252
- " });\n",
253
- "\n",
254
- " // Wait for the user to pick the files.\n",
255
- " const files = yield {\n",
256
- " promise: Promise.race([pickedPromise, cancelPromise]),\n",
257
- " response: {\n",
258
- " action: 'starting',\n",
259
- " }\n",
260
- " };\n",
261
- "\n",
262
- " cancel.remove();\n",
263
- "\n",
264
- " // Disable the input element since further picks are not allowed.\n",
265
- " inputElement.disabled = true;\n",
266
- "\n",
267
- " if (!files) {\n",
268
- " return {\n",
269
- " response: {\n",
270
- " action: 'complete',\n",
271
- " }\n",
272
- " };\n",
273
- " }\n",
274
- "\n",
275
- " for (const file of files) {\n",
276
- " const li = document.createElement('li');\n",
277
- " li.append(span(file.name, {fontWeight: 'bold'}));\n",
278
- " li.append(span(\n",
279
- " `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
280
- " `last modified: ${\n",
281
- " file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
282
- " 'n/a'} - `));\n",
283
- " const percent = span('0% done');\n",
284
- " li.appendChild(percent);\n",
285
- "\n",
286
- " outputElement.appendChild(li);\n",
287
- "\n",
288
- " const fileDataPromise = new Promise((resolve) => {\n",
289
- " const reader = new FileReader();\n",
290
- " reader.onload = (e) => {\n",
291
- " resolve(e.target.result);\n",
292
- " };\n",
293
- " reader.readAsArrayBuffer(file);\n",
294
- " });\n",
295
- " // Wait for the data to be ready.\n",
296
- " let fileData = yield {\n",
297
- " promise: fileDataPromise,\n",
298
- " response: {\n",
299
- " action: 'continue',\n",
300
- " }\n",
301
- " };\n",
302
- "\n",
303
- " // Use a chunked sending to avoid message size limits. See b/62115660.\n",
304
- " let position = 0;\n",
305
- " do {\n",
306
- " const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
307
- " const chunk = new Uint8Array(fileData, position, length);\n",
308
- " position += length;\n",
309
- "\n",
310
- " const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
311
- " yield {\n",
312
- " response: {\n",
313
- " action: 'append',\n",
314
- " file: file.name,\n",
315
- " data: base64,\n",
316
- " },\n",
317
- " };\n",
318
- "\n",
319
- " let percentDone = fileData.byteLength === 0 ?\n",
320
- " 100 :\n",
321
- " Math.round((position / fileData.byteLength) * 100);\n",
322
- " percent.textContent = `${percentDone}% done`;\n",
323
- "\n",
324
- " } while (position < fileData.byteLength);\n",
325
- " }\n",
326
- "\n",
327
- " // All done.\n",
328
- " yield {\n",
329
- " response: {\n",
330
- " action: 'complete',\n",
331
- " }\n",
332
- " };\n",
333
- "}\n",
334
- "\n",
335
- "scope.google = scope.google || {};\n",
336
- "scope.google.colab = scope.google.colab || {};\n",
337
- "scope.google.colab._files = {\n",
338
- " _uploadFiles,\n",
339
- " _uploadFilesContinue,\n",
340
- "};\n",
341
- "})(self);\n",
342
- "</script> "
343
- ],
344
- "text/plain": [
345
- "<IPython.core.display.HTML object>"
346
- ]
347
- },
348
- "metadata": {},
349
- "output_type": "display_data"
350
- },
351
- {
352
- "name": "stdout",
353
- "output_type": "stream",
354
- "text": [
355
- "Saving crack-seg.pt to crack-seg.pt\n"
356
- ]
357
- }
358
- ],
359
- "source": [
360
- "print(\"Upload YOLOv11 model\")\n",
361
- "uploaded = files.upload()\n",
362
- "model_path = list(uploaded.keys())[0] # Grab the uploaded file name"
363
- ]
364
- },
365
- {
366
- "cell_type": "code",
367
- "execution_count": null,
368
- "metadata": {
369
- "id": "-Iv8ULS6j5wq"
370
- },
371
- "outputs": [],
372
- "source": [
373
- "model = YOLO(model_path)"
374
- ]
375
- },
376
- {
377
- "cell_type": "markdown",
378
- "metadata": {
379
- "id": "ceeSxcFRCOZU"
380
- },
381
- "source": [
382
- "## STEP 4: Run Evaluation"
383
- ]
384
- },
385
- {
386
- "cell_type": "code",
387
- "execution_count": null,
388
- "metadata": {
389
- "colab": {
390
- "base_uri": "https://localhost:8080/"
391
- },
392
- "id": "lh4IzW4sj6z_",
393
- "outputId": "1b25edb4-4f7b-4b79-de75-81e584d6264e"
394
- },
395
- "outputs": [
396
- {
397
- "name": "stdout",
398
- "output_type": "stream",
399
- "text": [
400
- "Ultralytics 8.3.214 🚀 Python-3.12.12 torch-2.8.0+cu126 CUDA:0 (Tesla T4, 15095MiB)\n",
401
- "YOLO11s-seg summary (fused): 113 layers, 10,067,977 parameters, 0 gradients, 32.8 GFLOPs\n",
402
- "\u001b[KDownloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf': 100% ━━━━━━━━━━━━ 755.1KB 40.9MB/s 0.0s\n",
403
- "\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",
404
- "\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",
405
- "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/Clay-Crack-Detection-14/test/labels.cache\n",
406
- "\u001b[31m\u001b[1mrequirements:\u001b[0m Ultralytics requirement ['faster-coco-eval>=1.6.7'] not found, attempting AutoUpdate...\n",
407
- "\n",
408
- "\u001b[31m\u001b[1mrequirements:\u001b[0m AutoUpdate success ✅ 0.8s\n",
409
- "WARNING ⚠️ \u001b[31m\u001b[1mrequirements:\u001b[0m \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n",
410
- "\n",
411
- "\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",
412
- " all 198 1911 0.667 0.629 0.622 0.41 0.67 0.632 0.624 0.352\n",
413
- " crack_b 135 811 0.812 0.816 0.819 0.608 0.816 0.819 0.827 0.542\n",
414
- " crack_s 152 1034 0.724 0.648 0.656 0.376 0.713 0.637 0.648 0.311\n",
415
- " crack_shadow 35 66 0.465 0.424 0.392 0.245 0.482 0.439 0.396 0.203\n",
416
- "Speed: 1.8ms preprocess, 12.3ms inference, 0.0ms loss, 4.6ms postprocess per image\n",
417
- "Saving /content/runs/segment/val/predictions.json...\n",
418
- "Results saved to \u001b[1m/content/runs/segment/val\u001b[0m\n"
419
- ]
420
- }
421
- ],
422
- "source": [
423
- "results = model.val(data=f\"{dataset.location}/data.yaml\", split=\"test\", save_json=True)"
424
- ]
425
- },
426
- {
427
- "cell_type": "code",
428
- "execution_count": null,
429
- "metadata": {
430
- "colab": {
431
- "base_uri": "https://localhost:8080/"
432
- },
433
- "id": "iXFceDUyj738",
434
- "outputId": "a39090b8-65b0-4033-d327-c79f7028e692"
435
- },
436
- "outputs": [
437
- {
438
- "name": "stdout",
439
- "output_type": "stream",
440
- "text": [
441
- "\n",
442
- "Bounding Box (Box) Evaluation:\n",
443
- "Precision : 0.667\n",
444
- "Recall : 0.629\n",
445
- "mAP@0.5 : 0.622\n",
446
- "mAP@0.5:0.95 : 0.410\n",
447
- "\n",
448
- "Segmentation Mask (Mask) Evaluation:\n",
449
- "Precision : 0.670\n",
450
- "Recall : 0.632\n",
451
- "mAP@0.5 : 0.624\n",
452
- "mAP@0.5:0.95 : 0.352\n"
453
- ]
454
- }
455
- ],
456
- "source": [
457
- "metrics = results.results_dict\n",
458
- "\n",
459
- "print(\"\\nBounding Box (Box) Evaluation:\")\n",
460
- "print(f\"Precision : {metrics['metrics/precision(B)']:.3f}\")\n",
461
- "print(f\"Recall : {metrics['metrics/recall(B)']:.3f}\")\n",
462
- "print(f\"mAP@0.5 : {metrics['metrics/mAP50(B)']:.3f}\")\n",
463
- "print(f\"mAP@0.5:0.95 : {metrics['metrics/mAP50-95(B)']:.3f}\")\n",
464
- "\n",
465
- "print(\"\\nSegmentation Mask (Mask) Evaluation:\")\n",
466
- "print(f\"Precision : {metrics['metrics/precision(M)']:.3f}\")\n",
467
- "print(f\"Recall : {metrics['metrics/recall(M)']:.3f}\")\n",
468
- "print(f\"mAP@0.5 : {metrics['metrics/mAP50(M)']:.3f}\")\n",
469
- "print(f\"mAP@0.5:0.95 : {metrics['metrics/mAP50-95(M)']:.3f}\")\n"
470
- ]
471
- },
472
- {
473
- "cell_type": "code",
474
- "execution_count": null,
475
- "metadata": {
476
- "colab": {
477
- "base_uri": "https://localhost:8080/",
478
- "height": 196
479
- },
480
- "id": "D0MGJRRNj8_4",
481
- "outputId": "c3abccc8-f308-4495-80fa-65f61d86a902"
482
- },
483
- "outputs": [
484
- {
485
- "name": "stdout",
486
- "output_type": "stream",
487
- "text": [
488
- "\n",
489
- "Per-Class Evaluation Metrics:\n"
490
- ]
491
- },
492
- {
493
- "data": {
494
- "application/vnd.google.colaboratory.intrinsic+json": {
495
- "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}",
496
- "type": "dataframe",
497
- "variable_name": "df_per_class"
498
- },
499
- "text/html": [
500
- "\n",
501
- " <div id=\"df-44d224d4-3865-4201-b84c-e6c961b63e3f\" class=\"colab-df-container\">\n",
502
- " <div>\n",
503
- "<style scoped>\n",
504
- " .dataframe tbody tr th:only-of-type {\n",
505
- " vertical-align: middle;\n",
506
- " }\n",
507
- "\n",
508
- " .dataframe tbody tr th {\n",
509
- " vertical-align: top;\n",
510
- " }\n",
511
- "\n",
512
- " .dataframe thead th {\n",
513
- " text-align: right;\n",
514
- " }\n",
515
- "</style>\n",
516
- "<table border=\"1\" class=\"dataframe\">\n",
517
- " <thead>\n",
518
- " <tr style=\"text-align: right;\">\n",
519
- " <th></th>\n",
520
- " <th>Class</th>\n",
521
- " <th>Precision</th>\n",
522
- " <th>Recall</th>\n",
523
- " <th>mAP@0.5</th>\n",
524
- " <th>mAP@0.5:0.95</th>\n",
525
- " </tr>\n",
526
- " </thead>\n",
527
- " <tbody>\n",
528
- " <tr>\n",
529
- " <th>0</th>\n",
530
- " <td>crack_b</td>\n",
531
- " <td>0.812167</td>\n",
532
- " <td>0.815728</td>\n",
533
- " <td>0.622444</td>\n",
534
- " <td>0.409635</td>\n",
535
- " </tr>\n",
536
- " <tr>\n",
537
- " <th>1</th>\n",
538
- " <td>crack_s</td>\n",
539
- " <td>0.724310</td>\n",
540
- " <td>0.647969</td>\n",
541
- " <td>0.622444</td>\n",
542
- " <td>0.409635</td>\n",
543
- " </tr>\n",
544
- " <tr>\n",
545
- " <th>2</th>\n",
546
- " <td>crack_shadow</td>\n",
547
- " <td>0.465232</td>\n",
548
- " <td>0.424242</td>\n",
549
- " <td>0.622444</td>\n",
550
- " <td>0.409635</td>\n",
551
- " </tr>\n",
552
- " </tbody>\n",
553
- "</table>\n",
554
- "</div>\n",
555
- " <div class=\"colab-df-buttons\">\n",
556
- "\n",
557
- " <div class=\"colab-df-container\">\n",
558
- " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44d224d4-3865-4201-b84c-e6c961b63e3f')\"\n",
559
- " title=\"Convert this dataframe to an interactive table.\"\n",
560
- " style=\"display:none;\">\n",
561
- "\n",
562
- " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
563
- " <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
564
- " </svg>\n",
565
- " </button>\n",
566
- "\n",
567
- " <style>\n",
568
- " .colab-df-container {\n",
569
- " display:flex;\n",
570
- " gap: 12px;\n",
571
- " }\n",
572
- "\n",
573
- " .colab-df-convert {\n",
574
- " background-color: #E8F0FE;\n",
575
- " border: none;\n",
576
- " border-radius: 50%;\n",
577
- " cursor: pointer;\n",
578
- " display: none;\n",
579
- " fill: #1967D2;\n",
580
- " height: 32px;\n",
581
- " padding: 0 0 0 0;\n",
582
- " width: 32px;\n",
583
- " }\n",
584
- "\n",
585
- " .colab-df-convert:hover {\n",
586
- " background-color: #E2EBFA;\n",
587
- " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
588
- " fill: #174EA6;\n",
589
- " }\n",
590
- "\n",
591
- " .colab-df-buttons div {\n",
592
- " margin-bottom: 4px;\n",
593
- " }\n",
594
- "\n",
595
- " [theme=dark] .colab-df-convert {\n",
596
- " background-color: #3B4455;\n",
597
- " fill: #D2E3FC;\n",
598
- " }\n",
599
- "\n",
600
- " [theme=dark] .colab-df-convert:hover {\n",
601
- " background-color: #434B5C;\n",
602
- " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
603
- " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
604
- " fill: #FFFFFF;\n",
605
- " }\n",
606
- " </style>\n",
607
- "\n",
608
- " <script>\n",
609
- " const buttonEl =\n",
610
- " document.querySelector('#df-44d224d4-3865-4201-b84c-e6c961b63e3f button.colab-df-convert');\n",
611
- " buttonEl.style.display =\n",
612
- " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
613
- "\n",
614
- " async function convertToInteractive(key) {\n",
615
- " const element = document.querySelector('#df-44d224d4-3865-4201-b84c-e6c961b63e3f');\n",
616
- " const dataTable =\n",
617
- " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
618
- " [key], {});\n",
619
- " if (!dataTable) return;\n",
620
- "\n",
621
- " const docLinkHtml = 'Like what you see? Visit the ' +\n",
622
- " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
623
- " + ' to learn more about interactive tables.';\n",
624
- " element.innerHTML = '';\n",
625
- " dataTable['output_type'] = 'display_data';\n",
626
- " await google.colab.output.renderOutput(dataTable, element);\n",
627
- " const docLink = document.createElement('div');\n",
628
- " docLink.innerHTML = docLinkHtml;\n",
629
- " element.appendChild(docLink);\n",
630
- " }\n",
631
- " </script>\n",
632
- " </div>\n",
633
- "\n",
634
- "\n",
635
- " <div id=\"df-d486e766-8968-4dc8-8476-2627c282dbb7\">\n",
636
- " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d486e766-8968-4dc8-8476-2627c282dbb7')\"\n",
637
- " title=\"Suggest charts\"\n",
638
- " style=\"display:none;\">\n",
639
- "\n",
640
- "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
641
- " width=\"24px\">\n",
642
- " <g>\n",
643
- " <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",
644
- " </g>\n",
645
- "</svg>\n",
646
- " </button>\n",
647
- "\n",
648
- "<style>\n",
649
- " .colab-df-quickchart {\n",
650
- " --bg-color: #E8F0FE;\n",
651
- " --fill-color: #1967D2;\n",
652
- " --hover-bg-color: #E2EBFA;\n",
653
- " --hover-fill-color: #174EA6;\n",
654
- " --disabled-fill-color: #AAA;\n",
655
- " --disabled-bg-color: #DDD;\n",
656
- " }\n",
657
- "\n",
658
- " [theme=dark] .colab-df-quickchart {\n",
659
- " --bg-color: #3B4455;\n",
660
- " --fill-color: #D2E3FC;\n",
661
- " --hover-bg-color: #434B5C;\n",
662
- " --hover-fill-color: #FFFFFF;\n",
663
- " --disabled-bg-color: #3B4455;\n",
664
- " --disabled-fill-color: #666;\n",
665
- " }\n",
666
- "\n",
667
- " .colab-df-quickchart {\n",
668
- " background-color: var(--bg-color);\n",
669
- " border: none;\n",
670
- " border-radius: 50%;\n",
671
- " cursor: pointer;\n",
672
- " display: none;\n",
673
- " fill: var(--fill-color);\n",
674
- " height: 32px;\n",
675
- " padding: 0;\n",
676
- " width: 32px;\n",
677
- " }\n",
678
- "\n",
679
- " .colab-df-quickchart:hover {\n",
680
- " background-color: var(--hover-bg-color);\n",
681
- " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
682
- " fill: var(--button-hover-fill-color);\n",
683
- " }\n",
684
- "\n",
685
- " .colab-df-quickchart-complete:disabled,\n",
686
- " .colab-df-quickchart-complete:disabled:hover {\n",
687
- " background-color: var(--disabled-bg-color);\n",
688
- " fill: var(--disabled-fill-color);\n",
689
- " box-shadow: none;\n",
690
- " }\n",
691
- "\n",
692
- " .colab-df-spinner {\n",
693
- " border: 2px solid var(--fill-color);\n",
694
- " border-color: transparent;\n",
695
- " border-bottom-color: var(--fill-color);\n",
696
- " animation:\n",
697
- " spin 1s steps(1) infinite;\n",
698
- " }\n",
699
- "\n",
700
- " @keyframes spin {\n",
701
- " 0% {\n",
702
- " border-color: transparent;\n",
703
- " border-bottom-color: var(--fill-color);\n",
704
- " border-left-color: var(--fill-color);\n",
705
- " }\n",
706
- " 20% {\n",
707
- " border-color: transparent;\n",
708
- " border-left-color: var(--fill-color);\n",
709
- " border-top-color: var(--fill-color);\n",
710
- " }\n",
711
- " 30% {\n",
712
- " border-color: transparent;\n",
713
- " border-left-color: var(--fill-color);\n",
714
- " border-top-color: var(--fill-color);\n",
715
- " border-right-color: var(--fill-color);\n",
716
- " }\n",
717
- " 40% {\n",
718
- " border-color: transparent;\n",
719
- " border-right-color: var(--fill-color);\n",
720
- " border-top-color: var(--fill-color);\n",
721
- " }\n",
722
- " 60% {\n",
723
- " border-color: transparent;\n",
724
- " border-right-color: var(--fill-color);\n",
725
- " }\n",
726
- " 80% {\n",
727
- " border-color: transparent;\n",
728
- " border-right-color: var(--fill-color);\n",
729
- " border-bottom-color: var(--fill-color);\n",
730
- " }\n",
731
- " 90% {\n",
732
- " border-color: transparent;\n",
733
- " border-bottom-color: var(--fill-color);\n",
734
- " }\n",
735
- " }\n",
736
- "</style>\n",
737
- "\n",
738
- " <script>\n",
739
- " async function quickchart(key) {\n",
740
- " const quickchartButtonEl =\n",
741
- " document.querySelector('#' + key + ' button');\n",
742
- " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
743
- " quickchartButtonEl.classList.add('colab-df-spinner');\n",
744
- " try {\n",
745
- " const charts = await google.colab.kernel.invokeFunction(\n",
746
- " 'suggestCharts', [key], {});\n",
747
- " } catch (error) {\n",
748
- " console.error('Error during call to suggestCharts:', error);\n",
749
- " }\n",
750
- " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
751
- " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
752
- " }\n",
753
- " (() => {\n",
754
- " let quickchartButtonEl =\n",
755
- " document.querySelector('#df-d486e766-8968-4dc8-8476-2627c282dbb7 button');\n",
756
- " quickchartButtonEl.style.display =\n",
757
- " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
758
- " })();\n",
759
- " </script>\n",
760
- " </div>\n",
761
- "\n",
762
- " <div id=\"id_2d7fbd5c-457b-4892-8ab7-07b34cb27aff\">\n",
763
- " <style>\n",
764
- " .colab-df-generate {\n",
765
- " background-color: #E8F0FE;\n",
766
- " border: none;\n",
767
- " border-radius: 50%;\n",
768
- " cursor: pointer;\n",
769
- " display: none;\n",
770
- " fill: #1967D2;\n",
771
- " height: 32px;\n",
772
- " padding: 0 0 0 0;\n",
773
- " width: 32px;\n",
774
- " }\n",
775
- "\n",
776
- " .colab-df-generate:hover {\n",
777
- " background-color: #E2EBFA;\n",
778
- " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
779
- " fill: #174EA6;\n",
780
- " }\n",
781
- "\n",
782
- " [theme=dark] .colab-df-generate {\n",
783
- " background-color: #3B4455;\n",
784
- " fill: #D2E3FC;\n",
785
- " }\n",
786
- "\n",
787
- " [theme=dark] .colab-df-generate:hover {\n",
788
- " background-color: #434B5C;\n",
789
- " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
790
- " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
791
- " fill: #FFFFFF;\n",
792
- " }\n",
793
- " </style>\n",
794
- " <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_per_class')\"\n",
795
- " title=\"Generate code using this dataframe.\"\n",
796
- " style=\"display:none;\">\n",
797
- "\n",
798
- " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
799
- " width=\"24px\">\n",
800
- " <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",
801
- " </svg>\n",
802
- " </button>\n",
803
- " <script>\n",
804
- " (() => {\n",
805
- " const buttonEl =\n",
806
- " document.querySelector('#id_2d7fbd5c-457b-4892-8ab7-07b34cb27aff button.colab-df-generate');\n",
807
- " buttonEl.style.display =\n",
808
- " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
809
- "\n",
810
- " buttonEl.onclick = () => {\n",
811
- " google.colab.notebook.generateWithVariable('df_per_class');\n",
812
- " }\n",
813
- " })();\n",
814
- " </script>\n",
815
- " </div>\n",
816
- "\n",
817
- " </div>\n",
818
- " </div>\n"
819
- ],
820
- "text/plain": [
821
- " Class Precision Recall mAP@0.5 mAP@0.5:0.95\n",
822
- "0 crack_b 0.812167 0.815728 0.622444 0.409635\n",
823
- "1 crack_s 0.724310 0.647969 0.622444 0.409635\n",
824
- "2 crack_shadow 0.465232 0.424242 0.622444 0.409635"
825
- ]
826
- },
827
- "metadata": {},
828
- "output_type": "display_data"
829
- },
830
- {
831
- "name": "stdout",
832
- "output_type": "stream",
833
- "text": [
834
- "✅ Saved to per_class_metrics.csv\n"
835
- ]
836
- }
837
- ],
838
- "source": [
839
- "import pandas as pd\n",
840
- "\n",
841
- "# Get class names\n",
842
- "class_names = list(model.names.values())\n",
843
- "\n",
844
- "# Get per-class values (already NumPy arrays)\n",
845
- "p = results.box.p\n",
846
- "r = results.box.r\n",
847
- "map50 = results.box.map50\n",
848
- "map95 = results.box.map\n",
849
- "\n",
850
- "# Build DataFrame\n",
851
- "df_per_class = pd.DataFrame({\n",
852
- " \"Class\": class_names,\n",
853
- " \"Precision\": p,\n",
854
- " \"Recall\": r,\n",
855
- " \"mAP@0.5\": map50,\n",
856
- " \"mAP@0.5:0.95\": map95\n",
857
- "})\n",
858
- "\n",
859
- "# Show + save\n",
860
- "print(\"\\nPer-Class Evaluation Metrics:\")\n",
861
- "display(df_per_class)\n",
862
- "df_per_class.to_csv(\"per_class_metrics.csv\", index=False)\n",
863
- "print(\"✅ Saved to per_class_metrics.csv\")\n"
864
- ]
865
- },
866
- {
867
- "cell_type": "code",
868
- "execution_count": null,
869
- "metadata": {
870
- "colab": {
871
- "base_uri": "https://localhost:8080/",
872
- "height": 17
873
- },
874
- "id": "NBQ0qgXrOMbN",
875
- "outputId": "23d45da3-9549-4c7c-b95d-6a2c12382871"
876
- },
877
- "outputs": [
878
- {
879
- "data": {
880
- "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 ",
881
- "text/plain": [
882
- "<IPython.core.display.Javascript object>"
883
- ]
884
- },
885
- "metadata": {},
886
- "output_type": "display_data"
887
- },
888
- {
889
- "data": {
890
- "application/javascript": "download(\"download_8d4b5cea-a8f3-46b8-aef5-606799d3bd61\", \"segment.zip\", 5598780)",
891
- "text/plain": [
892
- "<IPython.core.display.Javascript object>"
893
- ]
894
- },
895
- "metadata": {},
896
- "output_type": "display_data"
897
- }
898
- ],
899
- "source": [
900
- "from google.colab import files\n",
901
- "import shutil\n",
902
- "\n",
903
- "# Zip the folder\n",
904
- "shutil.make_archive(\"/content/segment\", 'zip', \"/content/runs/segment\")\n",
905
- "\n",
906
- "# Download the zip\n",
907
- "files.download(\"/content/segment.zip\")\n"
908
- ]
909
- }
910
- ],
911
- "metadata": {
912
- "accelerator": "GPU",
913
- "colab": {
914
- "gpuType": "T4",
915
- "provenance": []
916
- },
917
- "kernelspec": {
918
- "display_name": "Python 3",
919
- "name": "python3"
920
- },
921
- "language_info": {
922
- "name": "python"
923
- }
924
- },
925
- "nbformat": 4,
926
- "nbformat_minor": 0
927
- }
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:70ab54398ec543190e9134feb8f914b950981eeb83564a63a1d50481d4f56a1e
3
+ size 43542