Spaces:
Configuration error
Configuration error
MsSaidat25 commited on
Commit ·
d498e1c
1
Parent(s): e91a347
Created using Colab
Browse files- Train_Object_Detection_Simple.ipynb +1873 -0
Train_Object_Detection_Simple.ipynb
ADDED
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@@ -0,0 +1,1873 @@
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"Downloading google_auth_oauthlib-1.0.0-py2.py3-none-any.whl (18 kB)\n",
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"Installing collected packages: typing-extensions, tensorflow-estimator, numpy, keras, gast, google-auth-oauthlib, tensorboard, tensorflow, keras-core, keras-cv\n",
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" Attempting uninstall: typing-extensions\n",
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" Found existing installation: typing_extensions 4.12.2\n",
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" Uninstalling typing_extensions-4.12.2:\n",
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" Attempting uninstall: numpy\n",
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" Found existing installation: numpy 1.26.4\n",
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" Uninstalling numpy-1.26.4:\n",
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" Successfully uninstalled numpy-1.26.4\n",
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" Attempting uninstall: keras\n",
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" Found existing installation: keras 3.5.0\n",
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" Uninstalling keras-3.5.0:\n",
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" Successfully uninstalled keras-3.5.0\n",
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" Attempting uninstall: gast\n",
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" Found existing installation: gast 0.6.0\n",
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" Uninstalling gast-0.6.0:\n",
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" Successfully uninstalled gast-0.6.0\n",
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" Attempting uninstall: google-auth-oauthlib\n",
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| 1189 |
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" Found existing installation: google-auth-oauthlib 1.2.1\n",
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" Uninstalling google-auth-oauthlib-1.2.1:\n",
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" Successfully uninstalled google-auth-oauthlib-1.2.1\n",
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" Attempting uninstall: tensorboard\n",
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| 1193 |
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" Found existing installation: tensorboard 2.17.1\n",
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" Uninstalling tensorboard-2.17.1:\n",
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" Successfully uninstalled tensorboard-2.17.1\n",
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" Attempting uninstall: tensorflow\n",
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| 1197 |
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" Found existing installation: tensorflow 2.17.1\n",
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" Uninstalling tensorflow-2.17.1:\n",
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" Successfully uninstalled tensorflow-2.17.1\n",
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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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"sqlalchemy 2.0.36 requires typing-extensions>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.\n",
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| 1202 |
+
"albucore 0.0.19 requires numpy>=1.24.4, but you have numpy 1.24.3 which is incompatible.\n",
|
| 1203 |
+
"albumentations 1.4.20 requires numpy>=1.24.4, but you have numpy 1.24.3 which is incompatible.\n",
|
| 1204 |
+
"langchain-core 0.3.19 requires typing-extensions>=4.7, but you have typing-extensions 4.5.0 which is incompatible.\n",
|
| 1205 |
+
"nibabel 5.3.2 requires typing-extensions>=4.6; python_version < \"3.13\", but you have typing-extensions 4.5.0 which is incompatible.\n",
|
| 1206 |
+
"openai 1.54.4 requires typing-extensions<5,>=4.11, but you have typing-extensions 4.5.0 which is incompatible.\n",
|
| 1207 |
+
"pydantic 2.9.2 requires typing-extensions>=4.6.1; python_version < \"3.13\", but you have typing-extensions 4.5.0 which is incompatible.\n",
|
| 1208 |
+
"pydantic-core 2.23.4 requires typing-extensions!=4.7.0,>=4.6.0, but you have typing-extensions 4.5.0 which is incompatible.\n",
|
| 1209 |
+
"tf-keras 2.17.0 requires tensorflow<2.18,>=2.17, but you have tensorflow 2.13.1 which is incompatible.\n",
|
| 1210 |
+
"torch 2.5.1+cu121 requires typing-extensions>=4.8.0, but you have typing-extensions 4.5.0 which is incompatible.\n",
|
| 1211 |
+
"typeguard 4.4.1 requires typing-extensions>=4.10.0, but you have typing-extensions 4.5.0 which is incompatible.\u001b[0m\u001b[31m\n",
|
| 1212 |
+
"\u001b[0mSuccessfully installed gast-0.4.0 google-auth-oauthlib-1.0.0 keras-2.13.1 keras-core-0.1.0 keras-cv-0.6.1 numpy-1.24.3 tensorboard-2.13.0 tensorflow-2.13.1 tensorflow-estimator-2.13.0 typing-extensions-4.5.0\n"
|
| 1213 |
+
]
|
| 1214 |
+
},
|
| 1215 |
+
{
|
| 1216 |
+
"output_type": "display_data",
|
| 1217 |
+
"data": {
|
| 1218 |
+
"application/vnd.colab-display-data+json": {
|
| 1219 |
+
"pip_warning": {
|
| 1220 |
+
"packages": [
|
| 1221 |
+
"numpy"
|
| 1222 |
+
]
|
| 1223 |
+
},
|
| 1224 |
+
"id": "bda050b6b20243f68edf1e045d560d58"
|
| 1225 |
+
}
|
| 1226 |
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},
|
| 1227 |
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"metadata": {}
|
| 1228 |
+
}
|
| 1229 |
+
],
|
| 1230 |
+
"source": [
|
| 1231 |
+
"!pip install keras-cv==0.6.1 keras-core==0.1.0 tensorflow==2.13.1 tensorflow-datasets==4.9.7"
|
| 1232 |
+
]
|
| 1233 |
+
},
|
| 1234 |
+
{
|
| 1235 |
+
"cell_type": "code",
|
| 1236 |
+
"source": [
|
| 1237 |
+
"import tensorflow as tf\n",
|
| 1238 |
+
"import tensorflow_datasets as tfds\n",
|
| 1239 |
+
"from tensorflow import keras\n",
|
| 1240 |
+
"from tensorflow.keras import optimizers\n",
|
| 1241 |
+
"import keras_cv\n",
|
| 1242 |
+
"import numpy as np\n",
|
| 1243 |
+
"from keras_cv import bounding_box\n",
|
| 1244 |
+
"import os\n",
|
| 1245 |
+
"import resource\n",
|
| 1246 |
+
"from keras_cv import visualization\n",
|
| 1247 |
+
"import tqdm"
|
| 1248 |
+
],
|
| 1249 |
+
"metadata": {
|
| 1250 |
+
"id": "7lU4oWFfCAQq",
|
| 1251 |
+
"colab": {
|
| 1252 |
+
"base_uri": "https://localhost:8080/"
|
| 1253 |
+
},
|
| 1254 |
+
"outputId": "0a42906e-a737-4190-b196-2b1dc72c785a"
|
| 1255 |
+
},
|
| 1256 |
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"execution_count": null,
|
| 1257 |
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"outputs": [
|
| 1258 |
+
{
|
| 1259 |
+
"output_type": "stream",
|
| 1260 |
+
"name": "stdout",
|
| 1261 |
+
"text": [
|
| 1262 |
+
"Using TensorFlow backend\n"
|
| 1263 |
+
]
|
| 1264 |
+
}
|
| 1265 |
+
]
|
| 1266 |
+
},
|
| 1267 |
+
{
|
| 1268 |
+
"cell_type": "code",
|
| 1269 |
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"source": [
|
| 1270 |
+
"# Get a dictionary pointing from int classes to class names\n",
|
| 1271 |
+
"\n",
|
| 1272 |
+
"class_ids = [\n",
|
| 1273 |
+
" \"Aeroplane\",\n",
|
| 1274 |
+
" \"Bicycle\",\n",
|
| 1275 |
+
" \"Bird\",\n",
|
| 1276 |
+
" \"Boat\",\n",
|
| 1277 |
+
" \"Bottle\",\n",
|
| 1278 |
+
" \"Bus\",\n",
|
| 1279 |
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" \"Car\",\n",
|
| 1280 |
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" \"Cat\",\n",
|
| 1281 |
+
" \"Chair\",\n",
|
| 1282 |
+
" \"Cow\",\n",
|
| 1283 |
+
" \"Dining Table\",\n",
|
| 1284 |
+
" \"Dog\",\n",
|
| 1285 |
+
" \"Horse\",\n",
|
| 1286 |
+
" \"Motorbike\",\n",
|
| 1287 |
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" \"Person\",\n",
|
| 1288 |
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" \"Potted Plant\",\n",
|
| 1289 |
+
" \"Sheep\",\n",
|
| 1290 |
+
" \"Sofa\",\n",
|
| 1291 |
+
" \"Train\",\n",
|
| 1292 |
+
" \"Tvmonitor\",\n",
|
| 1293 |
+
" \"Total\",\n",
|
| 1294 |
+
"]\n",
|
| 1295 |
+
"class_mapping = dict(zip(range(len(class_ids)), class_ids))"
|
| 1296 |
+
],
|
| 1297 |
+
"metadata": {
|
| 1298 |
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"id": "CmzbTImk8fwk"
|
| 1299 |
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},
|
| 1300 |
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"execution_count": null,
|
| 1301 |
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"outputs": []
|
| 1302 |
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},
|
| 1303 |
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{
|
| 1304 |
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"cell_type": "code",
|
| 1305 |
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"source": [
|
| 1306 |
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"class_mapping"
|
| 1307 |
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],
|
| 1308 |
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"metadata": {
|
| 1309 |
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"colab": {
|
| 1310 |
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"base_uri": "https://localhost:8080/"
|
| 1311 |
+
},
|
| 1312 |
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"id": "mOcC4P3bUnPb",
|
| 1313 |
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"outputId": "38a25f07-c7c9-4426-cfce-f00760463538"
|
| 1314 |
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},
|
| 1315 |
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"execution_count": null,
|
| 1316 |
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"outputs": [
|
| 1317 |
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{
|
| 1318 |
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"output_type": "execute_result",
|
| 1319 |
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"data": {
|
| 1320 |
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"text/plain": [
|
| 1321 |
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"{0: 'Aeroplane',\n",
|
| 1322 |
+
" 1: 'Bicycle',\n",
|
| 1323 |
+
" 2: 'Bird',\n",
|
| 1324 |
+
" 3: 'Boat',\n",
|
| 1325 |
+
" 4: 'Bottle',\n",
|
| 1326 |
+
" 5: 'Bus',\n",
|
| 1327 |
+
" 6: 'Car',\n",
|
| 1328 |
+
" 7: 'Cat',\n",
|
| 1329 |
+
" 8: 'Chair',\n",
|
| 1330 |
+
" 9: 'Cow',\n",
|
| 1331 |
+
" 10: 'Dining Table',\n",
|
| 1332 |
+
" 11: 'Dog',\n",
|
| 1333 |
+
" 12: 'Horse',\n",
|
| 1334 |
+
" 13: 'Motorbike',\n",
|
| 1335 |
+
" 14: 'Person',\n",
|
| 1336 |
+
" 15: 'Potted Plant',\n",
|
| 1337 |
+
" 16: 'Sheep',\n",
|
| 1338 |
+
" 17: 'Sofa',\n",
|
| 1339 |
+
" 18: 'Train',\n",
|
| 1340 |
+
" 19: 'Tvmonitor',\n",
|
| 1341 |
+
" 20: 'Total'}"
|
| 1342 |
+
]
|
| 1343 |
+
},
|
| 1344 |
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"metadata": {},
|
| 1345 |
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"execution_count": 4
|
| 1346 |
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}
|
| 1347 |
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]
|
| 1348 |
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},
|
| 1349 |
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{
|
| 1350 |
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"cell_type": "code",
|
| 1351 |
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"source": [
|
| 1352 |
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"BATCH_SIZE = 4"
|
| 1353 |
+
],
|
| 1354 |
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"metadata": {
|
| 1355 |
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"id": "eMm6wJEK5R-h"
|
| 1356 |
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},
|
| 1357 |
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"execution_count": null,
|
| 1358 |
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"outputs": []
|
| 1359 |
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},
|
| 1360 |
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{
|
| 1361 |
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"cell_type": "code",
|
| 1362 |
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"source": [
|
| 1363 |
+
"def visualize_dataset(inputs, value_range, rows, cols, bounding_box_format):\n",
|
| 1364 |
+
" inputs = next(iter(inputs.take(1)))\n",
|
| 1365 |
+
" images, bounding_boxes = inputs[\"images\"], inputs[\"bounding_boxes\"]\n",
|
| 1366 |
+
" visualization.plot_bounding_box_gallery(\n",
|
| 1367 |
+
" images,\n",
|
| 1368 |
+
" value_range=value_range,\n",
|
| 1369 |
+
" rows=rows,\n",
|
| 1370 |
+
" cols=cols,\n",
|
| 1371 |
+
" y_true=bounding_boxes,\n",
|
| 1372 |
+
" scale=5,\n",
|
| 1373 |
+
" font_scale=0.7,\n",
|
| 1374 |
+
" bounding_box_format=bounding_box_format,\n",
|
| 1375 |
+
" class_mapping=class_mapping,\n",
|
| 1376 |
+
" )"
|
| 1377 |
+
],
|
| 1378 |
+
"metadata": {
|
| 1379 |
+
"id": "sDfJCn_o5STH"
|
| 1380 |
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},
|
| 1381 |
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"execution_count": null,
|
| 1382 |
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"outputs": []
|
| 1383 |
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},
|
| 1384 |
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{
|
| 1385 |
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"cell_type": "code",
|
| 1386 |
+
"source": [
|
| 1387 |
+
"# https://keras.io/api/keras_cv/bounding_box/formats/#rel_xyxy-class\n",
|
| 1388 |
+
"def unpackage_raw_tfds_inputs(inputs, bounding_box_format):\n",
|
| 1389 |
+
" image = inputs[\"image\"]\n",
|
| 1390 |
+
" boxes = keras_cv.bounding_box.convert_format(\n",
|
| 1391 |
+
" inputs[\"objects\"][\"bbox\"],\n",
|
| 1392 |
+
" images=image,\n",
|
| 1393 |
+
" source=\"rel_yxyx\",\n",
|
| 1394 |
+
" target=bounding_box_format,\n",
|
| 1395 |
+
" )\n",
|
| 1396 |
+
" bounding_boxes = {\n",
|
| 1397 |
+
" \"classes\": tf.cast(inputs[\"objects\"][\"label\"], dtype=tf.float32),\n",
|
| 1398 |
+
" \"boxes\": tf.cast(boxes, dtype=tf.float32),\n",
|
| 1399 |
+
" }\n",
|
| 1400 |
+
" return {\n",
|
| 1401 |
+
" \"images\": tf.cast(image, tf.float32), \"bounding_boxes\": bounding_boxes\n",
|
| 1402 |
+
" }"
|
| 1403 |
+
],
|
| 1404 |
+
"metadata": {
|
| 1405 |
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"id": "q-6Netbl5egs"
|
| 1406 |
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},
|
| 1407 |
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"execution_count": null,
|
| 1408 |
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"outputs": []
|
| 1409 |
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},
|
| 1410 |
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{
|
| 1411 |
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"cell_type": "code",
|
| 1412 |
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"source": [
|
| 1413 |
+
"def load_pascal_voc(split, dataset, bounding_box_format):\n",
|
| 1414 |
+
" ds = tfds.load(dataset, split=split, with_info=False, shuffle_files=True)\n",
|
| 1415 |
+
" ds = ds.map(\n",
|
| 1416 |
+
" lambda x: unpackage_raw_tfds_inputs(\n",
|
| 1417 |
+
" x, bounding_box_format=bounding_box_format),\n",
|
| 1418 |
+
" num_parallel_calls=tf.data.AUTOTUNE,\n",
|
| 1419 |
+
" )\n",
|
| 1420 |
+
" return ds"
|
| 1421 |
+
],
|
| 1422 |
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"metadata": {
|
| 1423 |
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"id": "KdgejrK35hJn"
|
| 1424 |
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},
|
| 1425 |
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"execution_count": null,
|
| 1426 |
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"outputs": []
|
| 1427 |
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},
|
| 1428 |
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{
|
| 1429 |
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"cell_type": "code",
|
| 1430 |
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"source": [
|
| 1431 |
+
"train_ds = load_pascal_voc(\n",
|
| 1432 |
+
" split=\"train\", dataset=\"voc/2007\", bounding_box_format=\"xywh\"\n",
|
| 1433 |
+
")\n",
|
| 1434 |
+
"eval_ds = load_pascal_voc(\n",
|
| 1435 |
+
" split=\"test\", dataset=\"voc/2007\", bounding_box_format=\"xywh\"\n",
|
| 1436 |
+
")\n",
|
| 1437 |
+
"\n",
|
| 1438 |
+
"train_ds = train_ds.shuffle(BATCH_SIZE * 4)"
|
| 1439 |
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],
|
| 1440 |
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"metadata": {
|
| 1441 |
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"colab": {
|
| 1442 |
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"height": 131,
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{
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"output_type": "stream",
|
| 1487 |
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"name": "stdout",
|
| 1488 |
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"text": [
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"Downloading and preparing dataset 868.85 MiB (download: 868.85 MiB, generated: Unknown size, total: 868.85 MiB) to /root/tensorflow_datasets/voc/2007/4.0.0...\n"
|
| 1490 |
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]
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},
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| 1492 |
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{
|
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"output_type": "display_data",
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| 1494 |
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"data": {
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| 1495 |
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"text/plain": [
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|
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],
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"output_type": "display_data",
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"data": {
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"text/plain": [
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],
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{
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"data": {
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"text/plain": [
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],
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{
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| 1537 |
+
"cell_type": "code",
|
| 1538 |
+
"source": [
|
| 1539 |
+
"# We use ragged batch since images can be of different sizes\n",
|
| 1540 |
+
"# and each image can have variable number of objects\n",
|
| 1541 |
+
"\n",
|
| 1542 |
+
"train_ds = train_ds.ragged_batch(BATCH_SIZE, drop_remainder=True)\n",
|
| 1543 |
+
"eval_ds = eval_ds.ragged_batch(BATCH_SIZE, drop_remainder=True)"
|
| 1544 |
+
],
|
| 1545 |
+
"metadata": {
|
| 1546 |
+
"id": "FjCLhrhe5o8p"
|
| 1547 |
+
},
|
| 1548 |
+
"execution_count": null,
|
| 1549 |
+
"outputs": []
|
| 1550 |
+
},
|
| 1551 |
+
{
|
| 1552 |
+
"cell_type": "code",
|
| 1553 |
+
"source": [
|
| 1554 |
+
"# Visualize the dataset to ensure bounding boxes are in the right place\n",
|
| 1555 |
+
"# with correct labels. If done incorrectly, bounding boxes will not appear\n",
|
| 1556 |
+
"# or they will be in the wrong place.\n",
|
| 1557 |
+
"\n",
|
| 1558 |
+
"visualize_dataset(\n",
|
| 1559 |
+
" train_ds, bounding_box_format=\"xywh\", value_range=(0, 255), rows=2, cols=2\n",
|
| 1560 |
+
")"
|
| 1561 |
+
],
|
| 1562 |
+
"metadata": {
|
| 1563 |
+
"id": "3VhgWh6k5uWm"
|
| 1564 |
+
},
|
| 1565 |
+
"execution_count": null,
|
| 1566 |
+
"outputs": []
|
| 1567 |
+
},
|
| 1568 |
+
{
|
| 1569 |
+
"cell_type": "code",
|
| 1570 |
+
"source": [
|
| 1571 |
+
"# Visualize validation set\n",
|
| 1572 |
+
"visualize_dataset(\n",
|
| 1573 |
+
" eval_ds,\n",
|
| 1574 |
+
" bounding_box_format=\"xywh\",\n",
|
| 1575 |
+
" value_range=(0, 255),\n",
|
| 1576 |
+
" rows=2,\n",
|
| 1577 |
+
" cols=2,\n",
|
| 1578 |
+
")"
|
| 1579 |
+
],
|
| 1580 |
+
"metadata": {
|
| 1581 |
+
"id": "nee55HMp5ySf"
|
| 1582 |
+
},
|
| 1583 |
+
"execution_count": null,
|
| 1584 |
+
"outputs": []
|
| 1585 |
+
},
|
| 1586 |
+
{
|
| 1587 |
+
"cell_type": "code",
|
| 1588 |
+
"source": [
|
| 1589 |
+
"# Data augmentation is complex since after the image is modified, the bounding\n",
|
| 1590 |
+
"# boxes must also be modified accordingly!\n",
|
| 1591 |
+
"augmenter = keras.Sequential(\n",
|
| 1592 |
+
" layers=[\n",
|
| 1593 |
+
" keras_cv.layers.RandomFlip(\n",
|
| 1594 |
+
" mode=\"horizontal\",\n",
|
| 1595 |
+
" bounding_box_format=\"xywh\"),\n",
|
| 1596 |
+
" keras_cv.layers.JitteredResize(\n",
|
| 1597 |
+
" target_size=(640, 640),\n",
|
| 1598 |
+
" scale_factor=(0.75, 1.3),\n",
|
| 1599 |
+
" bounding_box_format=\"xywh\"\n",
|
| 1600 |
+
" ),\n",
|
| 1601 |
+
" ]\n",
|
| 1602 |
+
")"
|
| 1603 |
+
],
|
| 1604 |
+
"metadata": {
|
| 1605 |
+
"id": "1TS1JnDf50XH"
|
| 1606 |
+
},
|
| 1607 |
+
"execution_count": null,
|
| 1608 |
+
"outputs": []
|
| 1609 |
+
},
|
| 1610 |
+
{
|
| 1611 |
+
"cell_type": "code",
|
| 1612 |
+
"source": [
|
| 1613 |
+
"train_ds = train_ds.map(augmenter, num_parallel_calls=tf.data.AUTOTUNE)\n",
|
| 1614 |
+
"visualize_dataset(\n",
|
| 1615 |
+
" train_ds, bounding_box_format=\"xywh\", value_range=(0, 255), rows=2, cols=2\n",
|
| 1616 |
+
")"
|
| 1617 |
+
],
|
| 1618 |
+
"metadata": {
|
| 1619 |
+
"id": "bPA28xrA58Gr"
|
| 1620 |
+
},
|
| 1621 |
+
"execution_count": null,
|
| 1622 |
+
"outputs": []
|
| 1623 |
+
},
|
| 1624 |
+
{
|
| 1625 |
+
"cell_type": "code",
|
| 1626 |
+
"source": [
|
| 1627 |
+
"# Let's use deterministic resizing for the validation set\n",
|
| 1628 |
+
"\n",
|
| 1629 |
+
"inference_resizing = keras_cv.layers.Resizing(\n",
|
| 1630 |
+
" 640, 640, bounding_box_format=\"xywh\", pad_to_aspect_ratio=True\n",
|
| 1631 |
+
")\n",
|
| 1632 |
+
"eval_ds = eval_ds.map(inference_resizing, num_parallel_calls=tf.data.AUTOTUNE)"
|
| 1633 |
+
],
|
| 1634 |
+
"metadata": {
|
| 1635 |
+
"id": "1kvb--SW5-n5"
|
| 1636 |
+
},
|
| 1637 |
+
"execution_count": null,
|
| 1638 |
+
"outputs": []
|
| 1639 |
+
},
|
| 1640 |
+
{
|
| 1641 |
+
"cell_type": "code",
|
| 1642 |
+
"source": [
|
| 1643 |
+
"# Let's make sure the resizing worked\n",
|
| 1644 |
+
"\n",
|
| 1645 |
+
"visualize_dataset(\n",
|
| 1646 |
+
" eval_ds, bounding_box_format=\"xywh\", value_range=(0, 255), rows=2, cols=2\n",
|
| 1647 |
+
")"
|
| 1648 |
+
],
|
| 1649 |
+
"metadata": {
|
| 1650 |
+
"id": "zDNiu-uo6CQz"
|
| 1651 |
+
},
|
| 1652 |
+
"execution_count": null,
|
| 1653 |
+
"outputs": []
|
| 1654 |
+
},
|
| 1655 |
+
{
|
| 1656 |
+
"cell_type": "code",
|
| 1657 |
+
"source": [
|
| 1658 |
+
"# This is the final form our model expects:\n",
|
| 1659 |
+
"# tuple of (images, bounding_box_dictionary)\n",
|
| 1660 |
+
"# to_dense() makes the batch compatible with TPU\n",
|
| 1661 |
+
"\n",
|
| 1662 |
+
"def dict_to_tuple(inputs):\n",
|
| 1663 |
+
" return inputs[\"images\"], bounding_box.to_dense(\n",
|
| 1664 |
+
" inputs[\"bounding_boxes\"], max_boxes=32\n",
|
| 1665 |
+
" )"
|
| 1666 |
+
],
|
| 1667 |
+
"metadata": {
|
| 1668 |
+
"id": "x6gy0KH_6E3R"
|
| 1669 |
+
},
|
| 1670 |
+
"execution_count": null,
|
| 1671 |
+
"outputs": []
|
| 1672 |
+
},
|
| 1673 |
+
{
|
| 1674 |
+
"cell_type": "code",
|
| 1675 |
+
"source": [
|
| 1676 |
+
"train_ds = train_ds.map(dict_to_tuple, num_parallel_calls=tf.data.AUTOTUNE)\n",
|
| 1677 |
+
"eval_ds = eval_ds.map(dict_to_tuple, num_parallel_calls=tf.data.AUTOTUNE)\n",
|
| 1678 |
+
"\n",
|
| 1679 |
+
"train_ds = train_ds.prefetch(tf.data.AUTOTUNE)\n",
|
| 1680 |
+
"eval_ds = eval_ds.prefetch(tf.data.AUTOTUNE)"
|
| 1681 |
+
],
|
| 1682 |
+
"metadata": {
|
| 1683 |
+
"id": "SjDQ6Kzm6J40"
|
| 1684 |
+
},
|
| 1685 |
+
"execution_count": null,
|
| 1686 |
+
"outputs": []
|
| 1687 |
+
},
|
| 1688 |
+
{
|
| 1689 |
+
"cell_type": "code",
|
| 1690 |
+
"source": [
|
| 1691 |
+
"# Global clipnorm helps to reduce exploding gradient\n",
|
| 1692 |
+
"\n",
|
| 1693 |
+
"base_lr = 0.005\n",
|
| 1694 |
+
"# including a global_clipnorm is extremely important in object detection tasks\n",
|
| 1695 |
+
"optimizer = tf.keras.optimizers.SGD(\n",
|
| 1696 |
+
" learning_rate=base_lr, momentum=0.9, global_clipnorm=10.0\n",
|
| 1697 |
+
")"
|
| 1698 |
+
],
|
| 1699 |
+
"metadata": {
|
| 1700 |
+
"id": "cbpsC9_t6MZK"
|
| 1701 |
+
},
|
| 1702 |
+
"execution_count": null,
|
| 1703 |
+
"outputs": []
|
| 1704 |
+
},
|
| 1705 |
+
{
|
| 1706 |
+
"cell_type": "code",
|
| 1707 |
+
"source": [
|
| 1708 |
+
"# Creates a \"RetinaNet\" from ResNet50 backbone\n",
|
| 1709 |
+
"\n",
|
| 1710 |
+
"model = keras_cv.models.RetinaNet.from_preset(\n",
|
| 1711 |
+
" \"resnet50_imagenet\",\n",
|
| 1712 |
+
" num_classes=len(class_mapping),\n",
|
| 1713 |
+
" bounding_box_format=\"xywh\",\n",
|
| 1714 |
+
")"
|
| 1715 |
+
],
|
| 1716 |
+
"metadata": {
|
| 1717 |
+
"id": "8MRczVBd6t81"
|
| 1718 |
+
},
|
| 1719 |
+
"execution_count": null,
|
| 1720 |
+
"outputs": []
|
| 1721 |
+
},
|
| 1722 |
+
{
|
| 1723 |
+
"cell_type": "code",
|
| 1724 |
+
"source": [
|
| 1725 |
+
"model.compile(\n",
|
| 1726 |
+
" classification_loss=\"focal\",\n",
|
| 1727 |
+
" box_loss=\"smoothl1\",\n",
|
| 1728 |
+
" optimizer=optimizer,\n",
|
| 1729 |
+
")"
|
| 1730 |
+
],
|
| 1731 |
+
"metadata": {
|
| 1732 |
+
"id": "KPhsTs7g6x3-"
|
| 1733 |
+
},
|
| 1734 |
+
"execution_count": null,
|
| 1735 |
+
"outputs": []
|
| 1736 |
+
},
|
| 1737 |
+
{
|
| 1738 |
+
"cell_type": "code",
|
| 1739 |
+
"source": [
|
| 1740 |
+
"# Remove take(20) for full training (takes very long!)\n",
|
| 1741 |
+
"model.fit(\n",
|
| 1742 |
+
" train_ds.take(20),\n",
|
| 1743 |
+
" validation_data=eval_ds.take(20),\n",
|
| 1744 |
+
" epochs=10,\n",
|
| 1745 |
+
")"
|
| 1746 |
+
],
|
| 1747 |
+
"metadata": {
|
| 1748 |
+
"id": "aGoArYns606Q"
|
| 1749 |
+
},
|
| 1750 |
+
"execution_count": null,
|
| 1751 |
+
"outputs": []
|
| 1752 |
+
},
|
| 1753 |
+
{
|
| 1754 |
+
"cell_type": "code",
|
| 1755 |
+
"source": [
|
| 1756 |
+
"# Let's load a fully trained model to test predictions\n",
|
| 1757 |
+
"model = keras_cv.models.RetinaNet.from_preset(\n",
|
| 1758 |
+
" \"retinanet_resnet50_pascalvoc\", bounding_box_format=\"xywh\"\n",
|
| 1759 |
+
")\n",
|
| 1760 |
+
"\n",
|
| 1761 |
+
"# construct a dataset with larger batches:\n",
|
| 1762 |
+
"visualization_ds = eval_ds.unbatch()\n",
|
| 1763 |
+
"visualization_ds = visualization_ds.ragged_batch(16)\n",
|
| 1764 |
+
"visualization_ds = visualization_ds.shuffle(8)"
|
| 1765 |
+
],
|
| 1766 |
+
"metadata": {
|
| 1767 |
+
"id": "w5cZUlDq634K"
|
| 1768 |
+
},
|
| 1769 |
+
"execution_count": null,
|
| 1770 |
+
"outputs": []
|
| 1771 |
+
},
|
| 1772 |
+
{
|
| 1773 |
+
"cell_type": "code",
|
| 1774 |
+
"source": [
|
| 1775 |
+
"def visualize_detections(model, dataset, bounding_box_format):\n",
|
| 1776 |
+
" images, y_true = next(iter(dataset.take(1)))\n",
|
| 1777 |
+
" y_pred = model.predict(images)\n",
|
| 1778 |
+
" y_pred = bounding_box.to_ragged(y_pred)\n",
|
| 1779 |
+
" visualization.plot_bounding_box_gallery(\n",
|
| 1780 |
+
" images,\n",
|
| 1781 |
+
" value_range=(0, 255),\n",
|
| 1782 |
+
" bounding_box_format=bounding_box_format,\n",
|
| 1783 |
+
" y_true=y_true,\n",
|
| 1784 |
+
" y_pred=y_pred,\n",
|
| 1785 |
+
" scale=4,\n",
|
| 1786 |
+
" rows=4,\n",
|
| 1787 |
+
" cols=2,\n",
|
| 1788 |
+
" show=True,\n",
|
| 1789 |
+
" font_scale=0.7,\n",
|
| 1790 |
+
" class_mapping=class_mapping,\n",
|
| 1791 |
+
" )"
|
| 1792 |
+
],
|
| 1793 |
+
"metadata": {
|
| 1794 |
+
"id": "TQ-ovxSfJrrq"
|
| 1795 |
+
},
|
| 1796 |
+
"execution_count": null,
|
| 1797 |
+
"outputs": []
|
| 1798 |
+
},
|
| 1799 |
+
{
|
| 1800 |
+
"cell_type": "code",
|
| 1801 |
+
"source": [
|
| 1802 |
+
"# Set IoU and confidence threshold\n",
|
| 1803 |
+
"model.prediction_decoder = keras_cv.layers.MultiClassNonMaxSuppression(\n",
|
| 1804 |
+
" bounding_box_format=\"xywh\",\n",
|
| 1805 |
+
" from_logits=True,\n",
|
| 1806 |
+
" iou_threshold=0.5,\n",
|
| 1807 |
+
" confidence_threshold=0.5,\n",
|
| 1808 |
+
")"
|
| 1809 |
+
],
|
| 1810 |
+
"metadata": {
|
| 1811 |
+
"id": "UIupgL-fJuzQ"
|
| 1812 |
+
},
|
| 1813 |
+
"execution_count": null,
|
| 1814 |
+
"outputs": []
|
| 1815 |
+
},
|
| 1816 |
+
{
|
| 1817 |
+
"cell_type": "code",
|
| 1818 |
+
"source": [
|
| 1819 |
+
"visualize_detections(model, dataset=visualization_ds, bounding_box_format=\"xywh\")"
|
| 1820 |
+
],
|
| 1821 |
+
"metadata": {
|
| 1822 |
+
"id": "7PdVm8eTJzc4"
|
| 1823 |
+
},
|
| 1824 |
+
"execution_count": null,
|
| 1825 |
+
"outputs": []
|
| 1826 |
+
},
|
| 1827 |
+
{
|
| 1828 |
+
"cell_type": "code",
|
| 1829 |
+
"source": [],
|
| 1830 |
+
"metadata": {
|
| 1831 |
+
"id": "xTlezK2AJ2ye"
|
| 1832 |
+
},
|
| 1833 |
+
"execution_count": null,
|
| 1834 |
+
"outputs": []
|
| 1835 |
+
},
|
| 1836 |
+
{
|
| 1837 |
+
"cell_type": "code",
|
| 1838 |
+
"source": [],
|
| 1839 |
+
"metadata": {
|
| 1840 |
+
"id": "S0acXBWCxiJL"
|
| 1841 |
+
},
|
| 1842 |
+
"execution_count": null,
|
| 1843 |
+
"outputs": []
|
| 1844 |
+
},
|
| 1845 |
+
{
|
| 1846 |
+
"cell_type": "code",
|
| 1847 |
+
"source": [],
|
| 1848 |
+
"metadata": {
|
| 1849 |
+
"id": "oaG3igwFxiQU"
|
| 1850 |
+
},
|
| 1851 |
+
"execution_count": null,
|
| 1852 |
+
"outputs": []
|
| 1853 |
+
},
|
| 1854 |
+
{
|
| 1855 |
+
"cell_type": "code",
|
| 1856 |
+
"source": [],
|
| 1857 |
+
"metadata": {
|
| 1858 |
+
"id": "PzQJleyoxiWi"
|
| 1859 |
+
},
|
| 1860 |
+
"execution_count": null,
|
| 1861 |
+
"outputs": []
|
| 1862 |
+
},
|
| 1863 |
+
{
|
| 1864 |
+
"cell_type": "markdown",
|
| 1865 |
+
"source": [
|
| 1866 |
+
""
|
| 1867 |
+
],
|
| 1868 |
+
"metadata": {
|
| 1869 |
+
"id": "PFJd4PmsxjKb"
|
| 1870 |
+
}
|
| 1871 |
+
}
|
| 1872 |
+
]
|
| 1873 |
+
}
|