Spaces:
Runtime error
Runtime error
File size: 43,292 Bytes
26ccc67 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "t6MPjfT5NrKQ"
},
"source": [
"<div align=\"center\">\n",
"\n",
" <a href=\"https://ultralytics.com/yolov5\" target=\"_blank\">\n",
" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png\"></a>\n",
"\n",
"\n",
"<br>\n",
" <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a>\n",
" <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
" <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
"<br>\n",
"\n",
"This <a href=\"https://github.com/ultralytics/yolov5\">YOLOv5</a> 🚀 notebook by <a href=\"https://ultralytics.com\">Ultralytics</a> presents simple train, validate and predict examples to help start your AI adventure.<br>See <a href=\"https://github.com/ultralytics/yolov5/issues/new/choose\">GitHub</a> for community support or <a href=\"https://ultralytics.com/contact\">contact us</a> for professional support.\n",
"\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7mGmQbAO5pQb"
},
"source": [
"# Setup\n",
"\n",
"Clone GitHub [repository](https://github.com/ultralytics/yolov5), install [dependencies](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) and check PyTorch and GPU."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wbvMlHd_QwMG",
"outputId": "171b23f0-71b9-4cbf-b666-6fa2ecef70c8"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)\n"
]
}
],
"source": [
"!git clone https://github.com/ultralytics/yolov5 # clone\n",
"%cd yolov5\n",
"%pip install -qr requirements.txt # install\n",
"\n",
"import torch\n",
"import utils\n",
"display = utils.notebook_init() # checks"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4JnkELT0cIJg"
},
"source": [
"# 1. Predict\n",
"\n",
"`segment/predict.py` runs YOLOv5 instance segmentation inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/predict`. Example inference sources are:\n",
"\n",
"```shell\n",
"python segment/predict.py --source 0 # webcam\n",
" img.jpg # image \n",
" vid.mp4 # video\n",
" screen # screenshot\n",
" path/ # directory\n",
" 'path/*.jpg' # glob\n",
" 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n",
" 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zR9ZbuQCH7FX",
"outputId": "3f67f1c7-f15e-4fa5-d251-967c3b77eaad"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[34m\u001b[1msegment/predict: \u001b[0mweights=['yolov5s-seg.pt'], source=data/images, data=data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/predict-seg, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1, retina_masks=False\n",
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"\n",
"Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt to yolov5s-seg.pt...\n",
"100% 14.9M/14.9M [00:01<00:00, 12.0MB/s]\n",
"\n",
"Fusing layers... \n",
"YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
"image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 18.2ms\n",
"image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 13.4ms\n",
"Speed: 0.5ms pre-process, 15.8ms inference, 18.5ms NMS per image at shape (1, 3, 640, 640)\n",
"Results saved to \u001b[1mruns/predict-seg/exp\u001b[0m\n"
]
}
],
"source": [
"!python segment/predict.py --weights yolov5s-seg.pt --img 640 --conf 0.25 --source data/images\n",
"#display.Image(filename='runs/predict-seg/exp/zidane.jpg', width=600)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hkAzDWJ7cWTr"
},
"source": [
" \n",
"<img align=\"left\" src=\"https://user-images.githubusercontent.com/26833433/199030123-08c72f8d-6871-4116-8ed3-c373642cf28e.jpg\" width=\"600\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0eq1SMWl6Sfn"
},
"source": [
"# 2. Validate\n",
"Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "WQPtK1QYVaD_",
"outputId": "9d751d8c-bee8-4339-cf30-9854ca530449"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip ...\n",
"Downloading http://images.cocodataset.org/zips/val2017.zip ...\n",
"######################################################################## 100.0%\n",
"######################################################################## 100.0%\n"
]
}
],
"source": [
"# Download COCO val\n",
"!bash data/scripts/get_coco.sh --val --segments # download (780M - 5000 images)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "X58w8JLpMnjH",
"outputId": "a140d67a-02da-479e-9ddb-7d54bf9e407a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[34m\u001b[1msegment/val: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s-seg.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val-seg, name=exp, exist_ok=False, half=True, dnn=False\n",
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"\n",
"Fusing layers... \n",
"YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1361.31it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 157/157 [01:54<00:00, 1.37it/s]\n",
" all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319\n",
"Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)\n",
"Results saved to \u001b[1mruns/val-seg/exp\u001b[0m\n"
]
}
],
"source": [
"# Validate YOLOv5s-seg on COCO val\n",
"!python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 --half"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZY2VXXXu74w5"
},
"source": [
"# 3. Train\n",
"\n",
"<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"1000\" src=\"https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png\"/></a></p>\n",
"Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n",
"<br><br>\n",
"\n",
"Train a YOLOv5s-seg model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128-seg.yaml`, starting from pretrained `--weights yolov5s-seg.pt`, or from randomly initialized `--weights '' --cfg yolov5s-seg.yaml`.\n",
"\n",
"- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n",
"automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n",
"- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n",
"- **Training Results** are saved to `runs/train-seg/` with incrementing run directories, i.e. `runs/train-seg/exp2`, `runs/train-seg/exp3` etc.\n",
"<br><br>\n",
"\n",
"A **Mosaic Dataloader** is used for training which combines 4 images into 1 mosaic.\n",
"\n",
"## Train on Custom Data with Roboflow 🌟 NEW\n",
"\n",
"[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n",
"\n",
"- Custom Training Example: [https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/](https://blog.roboflow.com/train-yolov5-instance-segmentation-custom-dataset/?ref=ultralytics)\n",
"- Custom Training Notebook: [](https://colab.research.google.com/drive/1JTz7kpmHsg-5qwVz2d2IH3AaenI1tv0N?usp=sharing)\n",
"<br>\n",
"\n",
"<p align=\"\"><a href=\"https://roboflow.com/?ref=ultralytics\"><img width=\"480\" src=\"https://robflow-public-assets.s3.amazonaws.com/how-to-train-yolov5-segmentation-annotation.gif\"/></a></p>Label images lightning fast (including with model-assisted labeling)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "i3oKtE4g-aNn"
},
"outputs": [],
"source": [
"#@title Select YOLOv5 🚀 logger {run: 'auto'}\n",
"logger = 'TensorBoard' #@param ['TensorBoard', 'Comet', 'ClearML']\n",
"\n",
"if logger == 'TensorBoard':\n",
" %load_ext tensorboard\n",
" %tensorboard --logdir runs/train-seg\n",
"elif logger == 'Comet':\n",
" %pip install -q comet_ml\n",
" import comet_ml; comet_ml.init()\n",
"elif logger == 'ClearML':\n",
" import clearml; clearml.browser_login()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1NcFxRcFdJ_O",
"outputId": "3a3e0cf7-e79c-47a5-c8e7-2d26eeeab988"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[34m\u001b[1msegment/train: \u001b[0mweights=yolov5s-seg.pt, cfg=, data=coco128-seg.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train-seg, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, mask_ratio=4, no_overlap=False\n",
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
"\n",
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/\n",
"\n",
"Dataset not found ⚠️, missing paths ['/content/datasets/coco128-seg/images/train2017']\n",
"Downloading https://ultralytics.com/assets/coco128-seg.zip to coco128-seg.zip...\n",
"100% 6.79M/6.79M [00:01<00:00, 6.73MB/s]\n",
"Dataset download success ✅ (1.9s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
" 2 -1 1 18816 models.common.C3 [64, 64, 1] \n",
" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
" 4 -1 2 115712 models.common.C3 [128, 128, 2] \n",
" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
" 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
" 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
" 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n",
" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 12 [-1, 6] 1 0 models.common.Concat [1] \n",
" 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n",
" 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
" 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 16 [-1, 4] 1 0 models.common.Concat [1] \n",
" 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n",
" 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
" 19 [-1, 14] 1 0 models.common.Concat [1] \n",
" 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n",
" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
" 24 [17, 20, 23] 1 615133 models.yolo.Segment [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], 32, 128, [128, 256, 512]]\n",
"Model summary: 225 layers, 7621277 parameters, 7621277 gradients, 26.6 GFLOPs\n",
"\n",
"Transferred 367/367 items from yolov5s-seg.pt\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 bias\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1389.59it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco128-seg/labels/train2017.cache\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 238.86it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco128-seg/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 98.90it/s]\n",
"\n",
"\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
"Plotting labels to runs/train-seg/exp/labels.jpg... \n",
"Image sizes 640 train, 640 val\n",
"Using 2 dataloader workers\n",
"Logging results to \u001b[1mruns/train-seg/exp\u001b[0m\n",
"Starting training for 3 epochs...\n",
"\n",
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
" 0/2 4.92G 0.0417 0.04646 0.06066 0.02126 192 640: 100% 8/8 [00:08<00:00, 1.10s/it]\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.81it/s]\n",
" all 128 929 0.737 0.649 0.715 0.492 0.719 0.617 0.658 0.408\n",
"\n",
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
" 1/2 6.29G 0.04157 0.04503 0.05772 0.01777 208 640: 100% 8/8 [00:09<00:00, 1.21s/it]\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.87it/s]\n",
" all 128 929 0.756 0.674 0.738 0.506 0.725 0.64 0.68 0.422\n",
"\n",
" Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size\n",
" 2/2 6.29G 0.0425 0.04793 0.06784 0.01863 161 640: 100% 8/8 [00:03<00:00, 2.02it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:02<00:00, 1.88it/s]\n",
" all 128 929 0.736 0.694 0.747 0.522 0.769 0.622 0.683 0.427\n",
"\n",
"3 epochs completed in 0.009 hours.\n",
"Optimizer stripped from runs/train-seg/exp/weights/last.pt, 15.6MB\n",
"Optimizer stripped from runs/train-seg/exp/weights/best.pt, 15.6MB\n",
"\n",
"Validating runs/train-seg/exp/weights/best.pt...\n",
"Fusing layers... \n",
"Model summary: 165 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
" Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 4/4 [00:06<00:00, 1.59s/it]\n",
" all 128 929 0.738 0.694 0.746 0.522 0.759 0.625 0.682 0.426\n",
" person 128 254 0.845 0.756 0.836 0.55 0.861 0.669 0.759 0.407\n",
" bicycle 128 6 0.475 0.333 0.549 0.341 0.711 0.333 0.526 0.322\n",
" car 128 46 0.612 0.565 0.539 0.257 0.555 0.435 0.477 0.171\n",
" motorcycle 128 5 0.73 0.8 0.752 0.571 0.747 0.8 0.752 0.42\n",
" airplane 128 6 1 0.943 0.995 0.732 0.92 0.833 0.839 0.555\n",
" bus 128 7 0.677 0.714 0.722 0.653 0.711 0.714 0.722 0.593\n",
" train 128 3 1 0.951 0.995 0.551 1 0.884 0.995 0.781\n",
" truck 128 12 0.555 0.417 0.457 0.285 0.624 0.417 0.397 0.277\n",
" boat 128 6 0.624 0.5 0.584 0.186 1 0.326 0.412 0.133\n",
" traffic light 128 14 0.513 0.302 0.411 0.247 0.435 0.214 0.376 0.251\n",
" stop sign 128 2 0.824 1 0.995 0.796 0.906 1 0.995 0.747\n",
" bench 128 9 0.75 0.667 0.763 0.367 0.724 0.585 0.698 0.209\n",
" bird 128 16 0.961 1 0.995 0.686 0.918 0.938 0.91 0.525\n",
" cat 128 4 0.771 0.857 0.945 0.752 0.76 0.8 0.945 0.728\n",
" dog 128 9 0.987 0.778 0.963 0.681 1 0.705 0.89 0.574\n",
" horse 128 2 0.703 1 0.995 0.697 0.759 1 0.995 0.249\n",
" elephant 128 17 0.916 0.882 0.93 0.691 0.811 0.765 0.829 0.537\n",
" bear 128 1 0.664 1 0.995 0.995 0.701 1 0.995 0.895\n",
" zebra 128 4 0.864 1 0.995 0.921 0.879 1 0.995 0.804\n",
" giraffe 128 9 0.883 0.889 0.94 0.683 0.845 0.778 0.78 0.463\n",
" backpack 128 6 1 0.59 0.701 0.372 1 0.474 0.52 0.252\n",
" umbrella 128 18 0.654 0.839 0.887 0.52 0.517 0.556 0.427 0.229\n",
" handbag 128 19 0.54 0.211 0.408 0.221 0.796 0.206 0.396 0.196\n",
" tie 128 7 0.864 0.857 0.857 0.577 0.925 0.857 0.857 0.534\n",
" suitcase 128 4 0.716 1 0.945 0.647 0.767 1 0.945 0.634\n",
" frisbee 128 5 0.708 0.8 0.761 0.643 0.737 0.8 0.761 0.501\n",
" skis 128 1 0.691 1 0.995 0.796 0.761 1 0.995 0.199\n",
" snowboard 128 7 0.918 0.857 0.904 0.604 0.32 0.286 0.235 0.137\n",
" sports ball 128 6 0.902 0.667 0.701 0.466 0.727 0.5 0.497 0.471\n",
" kite 128 10 0.586 0.4 0.511 0.231 0.663 0.394 0.417 0.139\n",
" baseball bat 128 4 0.359 0.5 0.401 0.169 0.631 0.5 0.526 0.133\n",
" baseball glove 128 7 1 0.519 0.58 0.327 0.687 0.286 0.455 0.328\n",
" skateboard 128 5 0.729 0.8 0.862 0.631 0.599 0.6 0.604 0.379\n",
" tennis racket 128 7 0.57 0.714 0.645 0.448 0.608 0.714 0.645 0.412\n",
" bottle 128 18 0.469 0.393 0.537 0.357 0.661 0.389 0.543 0.349\n",
" wine glass 128 16 0.677 0.938 0.866 0.441 0.53 0.625 0.67 0.334\n",
" cup 128 36 0.777 0.722 0.812 0.466 0.725 0.583 0.762 0.467\n",
" fork 128 6 0.948 0.333 0.425 0.27 0.527 0.167 0.18 0.102\n",
" knife 128 16 0.757 0.587 0.669 0.458 0.79 0.5 0.552 0.34\n",
" spoon 128 22 0.74 0.364 0.559 0.269 0.925 0.364 0.513 0.213\n",
" bowl 128 28 0.766 0.714 0.725 0.559 0.803 0.584 0.665 0.353\n",
" banana 128 1 0.408 1 0.995 0.398 0.539 1 0.995 0.497\n",
" sandwich 128 2 1 0 0.695 0.536 1 0 0.498 0.448\n",
" orange 128 4 0.467 1 0.995 0.693 0.518 1 0.995 0.663\n",
" broccoli 128 11 0.462 0.455 0.383 0.259 0.548 0.455 0.384 0.256\n",
" carrot 128 24 0.631 0.875 0.77 0.533 0.757 0.909 0.853 0.499\n",
" hot dog 128 2 0.555 1 0.995 0.995 0.578 1 0.995 0.796\n",
" pizza 128 5 0.89 0.8 0.962 0.796 1 0.778 0.962 0.766\n",
" donut 128 14 0.695 1 0.893 0.772 0.704 1 0.893 0.696\n",
" cake 128 4 0.826 1 0.995 0.92 0.862 1 0.995 0.846\n",
" chair 128 35 0.53 0.571 0.613 0.336 0.67 0.6 0.538 0.271\n",
" couch 128 6 0.972 0.667 0.833 0.627 1 0.62 0.696 0.394\n",
" potted plant 128 14 0.7 0.857 0.883 0.552 0.836 0.857 0.883 0.473\n",
" bed 128 3 0.979 0.667 0.83 0.366 1 0 0.83 0.373\n",
" dining table 128 13 0.775 0.308 0.505 0.364 0.644 0.231 0.25 0.0804\n",
" toilet 128 2 0.836 1 0.995 0.846 0.887 1 0.995 0.797\n",
" tv 128 2 0.6 1 0.995 0.846 0.655 1 0.995 0.896\n",
" laptop 128 3 0.822 0.333 0.445 0.307 1 0 0.392 0.12\n",
" mouse 128 2 1 0 0 0 1 0 0 0\n",
" remote 128 8 0.745 0.5 0.62 0.459 0.821 0.5 0.624 0.449\n",
" cell phone 128 8 0.686 0.375 0.502 0.272 0.488 0.25 0.28 0.132\n",
" microwave 128 3 0.831 1 0.995 0.722 0.867 1 0.995 0.592\n",
" oven 128 5 0.439 0.4 0.435 0.294 0.823 0.6 0.645 0.418\n",
" sink 128 6 0.677 0.5 0.565 0.448 0.722 0.5 0.46 0.362\n",
" refrigerator 128 5 0.533 0.8 0.783 0.524 0.558 0.8 0.783 0.527\n",
" book 128 29 0.732 0.379 0.423 0.196 0.69 0.207 0.38 0.131\n",
" clock 128 9 0.889 0.778 0.917 0.677 0.908 0.778 0.875 0.604\n",
" vase 128 2 0.375 1 0.995 0.995 0.455 1 0.995 0.796\n",
" scissors 128 1 1 0 0.0166 0.00166 1 0 0 0\n",
" teddy bear 128 21 0.813 0.829 0.841 0.457 0.826 0.678 0.786 0.422\n",
" toothbrush 128 5 0.806 1 0.995 0.733 0.991 1 0.995 0.628\n",
"Results saved to \u001b[1mruns/train-seg/exp\u001b[0m\n"
]
}
],
"source": [
"# Train YOLOv5s on COCO128 for 3 epochs\n",
"!python segment/train.py --img 640 --batch 16 --epochs 3 --data coco128-seg.yaml --weights yolov5s-seg.pt --cache"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "15glLzbQx5u0"
},
"source": [
"# 4. Visualize"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nWOsI5wJR1o3"
},
"source": [
"## Comet Logging and Visualization 🌟 NEW\n",
"\n",
"[Comet](https://www.comet.com/site/lp/yolov5-with-comet/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!\n",
"\n",
"Getting started is easy:\n",
"```shell\n",
"pip install comet_ml # 1. install\n",
"export COMET_API_KEY=<Your API Key> # 2. paste API key\n",
"python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train\n",
"```\n",
"To learn more about all of the supported Comet features for this integration, check out the [Comet Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet). If you'd like to learn more about Comet, head over to our [documentation](https://www.comet.com/docs/v2/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=yolov5_colab). Get started by trying out the Comet Colab Notebook:\n",
"[](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)\n",
"\n",
"<a href=\"https://bit.ly/yolov5-readme-comet2\">\n",
"<img alt=\"Comet Dashboard\" src=\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\" width=\"1280\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Lay2WsTjNJzP"
},
"source": [
"## ClearML Logging and Automation 🌟 NEW\n",
"\n",
"[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
"\n",
"- `pip install clearml`\n",
"- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
"\n",
"You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
"\n",
"You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n",
"\n",
"<a href=\"https://cutt.ly/yolov5-notebook-clearml\">\n",
"<img alt=\"ClearML Experiment Management UI\" src=\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\" width=\"1280\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-WPvRbS5Swl6"
},
"source": [
"## Local Logging\n",
"\n",
"Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
"\n",
"This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
"\n",
"<img alt=\"Local logging results\" src=\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\" width=\"1280\"/>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zelyeqbyt3GD"
},
"source": [
"# Environments\n",
"\n",
"YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
"\n",
"- **Notebooks** with free GPU: <a href=\"https://bit.ly/yolov5-paperspace-notebook\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"></a> <a href=\"https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a> <a href=\"https://www.kaggle.com/ultralytics/yolov5\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n",
"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n",
"- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href=\"https://hub.docker.com/r/ultralytics/yolov5\"><img src=\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\" alt=\"Docker Pulls\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6Qu7Iesl0p54"
},
"source": [
"# Status\n",
"\n",
"\n",
"\n",
"If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IEijrePND_2I"
},
"source": [
"# Appendix\n",
"\n",
"Additional content below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GMusP4OAxFu6"
},
"outputs": [],
"source": [
"# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
"import torch\n",
"\n",
"model = torch.hub.load('ultralytics/yolov5', 'yolov5s-seg') # yolov5n - yolov5x6 or custom\n",
"im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n",
"results = model(im) # inference\n",
"results.print() # or .show(), .save(), .crop(), .pandas(), etc."
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"name": "YOLOv5 Segmentation Tutorial",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
} |