53 KiB
53 KiB
This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.
Setup
Clone GitHub repository, install dependencies and check PyTorch and GPU.
!git clone https://github.com/ultralytics/yolov5 # clone
%cd yolov5
%pip install -qr requirements.txt # install
import torch
import utils
display = utils.notebook_init() # checksYOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)
Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)
1. Detect
detect.py runs YOLOv5 inference on a variety of sources, downloading models automatically from the latest YOLOv5 release, and saving results to runs/detect. Example inference sources are:
python detect.py --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images
# display.Image(filename='runs/detect/exp/zidane.jpg', width=600)[34m[1mdetect: [0mweights=['yolov5s.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/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1 YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB) Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt... 100% 14.1M/14.1M [00:00<00:00, 116MB/s] Fusing layers... YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 17.0ms image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, 14.3ms Speed: 0.5ms pre-process, 15.7ms inference, 18.6ms NMS per image at shape (1, 3, 640, 640) Results saved to [1mruns/detect/exp[0m
2. Validate
Validate a model's accuracy on the COCO dataset's val or test splits. Models are downloaded automatically from the latest YOLOv5 release. To show results by class use the --verbose flag.
# Download COCO val
torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)
!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip0%| | 0.00/780M [00:00<?, ?B/s]
# Validate YOLOv5s on COCO val
!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half[34m[1mval: [0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s.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=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB) Fusing layers... YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients [34m[1mval: [0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 1977.30it/s] [34m[1mval: [0mNew cache created: /content/datasets/coco/val2017.cache Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:12<00:00, 2.17it/s] all 5000 36335 0.67 0.521 0.566 0.371 Speed: 0.1ms pre-process, 2.9ms inference, 2.0ms NMS per image at shape (32, 3, 640, 640) Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json... loading annotations into memory... Done (t=0.43s) creating index... index created! Loading and preparing results... DONE (t=5.85s) creating index... index created! Running per image evaluation... Evaluate annotation type *bbox* DONE (t=82.22s). Accumulating evaluation results... DONE (t=14.92s). Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572 Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402 Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211 Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423 Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.516 Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566 Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378 Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625 Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.723 Results saved to [1mruns/val/exp[0m
3. Train
Train a YOLOv5s model on the COCO128 dataset with --data coco128.yaml, starting from pretrained --weights yolov5s.pt, or from randomly initialized --weights '' --cfg yolov5s.yaml.
- Pretrained Models are downloaded automatically from the latest YOLOv5 release
- Datasets available for autodownload include: COCO, COCO128, VOC, Argoverse, VisDrone, GlobalWheat, xView, Objects365, SKU-110K.
- Training Results are saved to
runs/train/with incrementing run directories, i.e.runs/train/exp2,runs/train/exp3etc.
A Mosaic Dataloader is used for training which combines 4 images into 1 mosaic.
Train on Custom Data with Roboflow 🌟 NEW
Roboflow 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.
- Custom Training Example: https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/
- Custom Training Notebook:
#@title Select YOLOv5 🚀 logger {run: 'auto'}
logger = 'ClearML' #@param ['ClearML', 'Comet', 'TensorBoard']
if logger == 'ClearML':
%pip install -q clearml
import clearml; clearml.browser_login()
elif logger == 'Comet':
%pip install -q comet_ml
import comet_ml; comet_ml.init()
elif logger == 'TensorBoard':
%load_ext tensorboard
%tensorboard --logdir runs/train# Train YOLOv5s on COCO128 for 3 epochs
!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache[34m[1mtrain: [0mweights=yolov5s.pt, cfg=, data=coco128.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, 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, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest [34m[1mgithub: [0mup to date with https://github.com/ultralytics/yolov5 ✅ YOLOv5 🚀 v7.0-1-gb32f67f Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB) [34m[1mhyperparameters: [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 [34m[1mClearML: [0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML [34m[1mComet: [0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet [34m[1mTensorBoard: [0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/ Dataset not found ⚠️, missing paths ['/content/datasets/coco128/images/train2017'] Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip... 100% 6.66M/6.66M [00:00<00:00, 261MB/s] Dataset download success ✅ (0.3s), saved to [1m/content/datasets[0m from n params module arguments 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] 2 -1 1 18816 models.common.C3 [64, 64, 1] 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] 4 -1 2 115712 models.common.C3 [128, 128, 2] 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] 6 -1 3 625152 models.common.C3 [256, 256, 3] 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] 8 -1 1 1182720 models.common.C3 [512, 512, 1] 9 -1 1 656896 models.common.SPPF [512, 512, 5] 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 12 [-1, 6] 1 0 models.common.Concat [1] 13 -1 1 361984 models.common.C3 [512, 256, 1, False] 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 16 [-1, 4] 1 0 models.common.Concat [1] 17 -1 1 90880 models.common.C3 [256, 128, 1, False] 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] 19 [-1, 14] 1 0 models.common.Concat [1] 20 -1 1 296448 models.common.C3 [256, 256, 1, False] 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] 22 [-1, 10] 1 0 models.common.Concat [1] 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]] Model summary: 214 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs Transferred 349/349 items from yolov5s.pt [34m[1mAMP: [0mchecks passed ✅ [34m[1moptimizer:[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias [34m[1malbumentations: [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)) [34m[1mtrain: [0mScanning /content/datasets/coco128/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1911.57it/s] [34m[1mtrain: [0mNew cache created: /content/datasets/coco128/labels/train2017.cache [34m[1mtrain: [0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 229.69it/s] [34m[1mval: [0mScanning /content/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<?, ?it/s] [34m[1mval: [0mCaching images (0.1GB ram): 100% 128/128 [00:01<00:00, 97.70it/s] [34m[1mAutoAnchor: [0m4.27 anchors/target, 0.994 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅ Plotting labels to runs/train/exp/labels.jpg... Image sizes 640 train, 640 val Using 2 dataloader workers Logging results to [1mruns/train/exp[0m Starting training for 3 epochs... Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 0/2 3.74G 0.04618 0.07207 0.017 232 640: 100% 8/8 [00:07<00:00, 1.10it/s] Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 2.28it/s] all 128 929 0.672 0.594 0.682 0.451 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 1/2 5.36G 0.04623 0.06888 0.01821 201 640: 100% 8/8 [00:02<00:00, 3.29it/s] Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 3.17it/s] all 128 929 0.721 0.639 0.724 0.48 Epoch GPU_mem box_loss obj_loss cls_loss Instances Size 2/2 5.36G 0.04361 0.06479 0.01698 227 640: 100% 8/8 [00:02<00:00, 3.46it/s] Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:01<00:00, 3.11it/s] all 128 929 0.758 0.641 0.731 0.487 3 epochs completed in 0.005 hours. Optimizer stripped from runs/train/exp/weights/last.pt, 14.9MB Optimizer stripped from runs/train/exp/weights/best.pt, 14.9MB Validating runs/train/exp/weights/best.pt... Fusing layers... Model summary: 157 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs Class Images Instances P R mAP50 mAP50-95: 100% 4/4 [00:03<00:00, 1.05it/s] all 128 929 0.757 0.641 0.732 0.487 person 128 254 0.86 0.705 0.804 0.528 bicycle 128 6 0.773 0.578 0.725 0.426 car 128 46 0.658 0.435 0.554 0.239 motorcycle 128 5 0.59 0.8 0.837 0.635 airplane 128 6 1 0.996 0.995 0.696 bus 128 7 0.635 0.714 0.756 0.666 train 128 3 0.691 0.333 0.753 0.511 truck 128 12 0.604 0.333 0.472 0.26 boat 128 6 0.941 0.333 0.46 0.183 traffic light 128 14 0.557 0.183 0.302 0.214 stop sign 128 2 0.827 1 0.995 0.846 bench 128 9 0.79 0.556 0.677 0.318 bird 128 16 0.962 1 0.995 0.663 cat 128 4 0.867 1 0.995 0.754 dog 128 9 1 0.649 0.903 0.654 horse 128 2 0.853 1 0.995 0.622 elephant 128 17 0.908 0.882 0.934 0.698 bear 128 1 0.697 1 0.995 0.995 zebra 128 4 0.867 1 0.995 0.905 giraffe 128 9 0.788 0.829 0.912 0.701 backpack 128 6 0.841 0.5 0.738 0.311 umbrella 128 18 0.786 0.815 0.859 0.48 handbag 128 19 0.772 0.263 0.366 0.216 tie 128 7 0.975 0.714 0.77 0.491 suitcase 128 4 0.643 0.75 0.912 0.563 frisbee 128 5 0.72 0.8 0.76 0.717 skis 128 1 0.748 1 0.995 0.3 snowboard 128 7 0.827 0.686 0.833 0.57 sports ball 128 6 0.637 0.667 0.602 0.311 kite 128 10 0.645 0.6 0.594 0.224 baseball bat 128 4 0.519 0.278 0.468 0.205 baseball glove 128 7 0.483 0.429 0.465 0.278 skateboard 128 5 0.923 0.6 0.687 0.493 tennis racket 128 7 0.774 0.429 0.544 0.333 bottle 128 18 0.577 0.379 0.551 0.275 wine glass 128 16 0.715 0.875 0.893 0.511 cup 128 36 0.843 0.667 0.833 0.531 fork 128 6 0.998 0.333 0.45 0.315 knife 128 16 0.77 0.688 0.695 0.399 spoon 128 22 0.839 0.473 0.638 0.383 bowl 128 28 0.765 0.583 0.715 0.512 banana 128 1 0.903 1 0.995 0.301 sandwich 128 2 1 0 0.359 0.301 orange 128 4 0.718 0.75 0.912 0.581 broccoli 128 11 0.545 0.364 0.43 0.319 carrot 128 24 0.62 0.625 0.724 0.495 hot dog 128 2 0.385 1 0.828 0.762 pizza 128 5 0.833 1 0.962 0.725 donut 128 14 0.631 1 0.96 0.833 cake 128 4 0.871 1 0.995 0.83 chair 128 35 0.583 0.6 0.608 0.318 couch 128 6 0.909 0.667 0.813 0.543 potted plant 128 14 0.745 0.786 0.822 0.48 bed 128 3 0.973 0.333 0.753 0.41 dining table 128 13 0.821 0.356 0.577 0.342 toilet 128 2 1 0.949 0.995 0.797 tv 128 2 0.566 1 0.995 0.796 laptop 128 3 1 0 0.59 0.311 mouse 128 2 1 0 0.105 0.0527 remote 128 8 1 0.623 0.634 0.538 cell phone 128 8 0.565 0.375 0.399 0.179 microwave 128 3 0.709 1 0.995 0.736 oven 128 5 0.328 0.4 0.43 0.282 sink 128 6 0.438 0.333 0.339 0.266 refrigerator 128 5 0.564 0.8 0.798 0.535 book 128 29 0.597 0.256 0.351 0.155 clock 128 9 0.763 0.889 0.934 0.737 vase 128 2 0.331 1 0.995 0.895 scissors 128 1 1 0 0.497 0.0552 teddy bear 128 21 0.857 0.57 0.837 0.544 toothbrush 128 5 0.799 1 0.928 0.556 Results saved to [1mruns/train/exp[0m
4. Visualize
Comet Logging and Visualization 🌟 NEW
Comet 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! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!
Getting started is easy:
pip install comet_ml # 1. install
export COMET_API_KEY=<Your API Key> # 2. paste API key
python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train
To learn more about all of the supported Comet features for this integration, check out the Comet Tutorial. If you'd like to learn more about Comet, head over to our documentation. Get started by trying out the Comet Colab Notebook:
ClearML Logging and Automation 🌟 NEW
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):
pip install clearml- run
clearml-initto connect to a ClearML server (deploy your own open-source server, or use our free hosted server)
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).
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 for details!
Local Logging
Training results are automatically logged with Tensorboard and CSV loggers to runs/train, with a new experiment directory created for each new training as runs/train/exp2, runs/train/exp3, etc.
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.
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), testing (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
Appendix
Additional content below.
# YOLOv5 PyTorch HUB Inference (DetectionModels only)
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True) # yolov5n - yolov5x6 or custom
im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list
results = model(im) # inference
results.print() # or .show(), .save(), .crop(), .pandas(), etc.