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 comet_ml # install
import torch
import utils
display = utils.notebook_init() # checks
YOLOv5 🚀 v7.0-2-gc9d47ae 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. Predict
segment/predict.py runs YOLOv5 instance segmentation inference on a variety of sources, downloading models automatically from the latest YOLOv5 release, and saving results to runs/predict. Example inference sources are:
python segment/predict.py --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
'path/*.jpg' # glob
'https://youtu.be/LNwODJXcvt4' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
!python segment/predict.py --weights yolov5s-seg.pt --img 640 --conf 0.25 --source data/images
#display.Image(filename='runs/predict-seg/exp/zidane.jpg', width=600)
[34m[1msegment/predict: [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 YOLOv5 🚀 v7.0-2-gc9d47ae 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-seg.pt to yolov5s-seg.pt... 100% 14.9M/14.9M [00:01<00:00, 12.0MB/s] Fusing layers... YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, 18.2ms image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, 13.4ms Speed: 0.5ms pre-process, 15.8ms inference, 18.5ms NMS per image at shape (1, 3, 640, 640) Results saved to [1mruns/predict-seg/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
!bash data/scripts/get_coco.sh --val --segments # download (780M - 5000 images)
Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip ... Downloading http://images.cocodataset.org/zips/val2017.zip ... ######################################################################## 100.0% ######################################################################## 100.0%
# Validate YOLOv5s-seg on COCO val
!python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 --half
[34m[1msegment/val: [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
YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)
Fusing layers...
YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs
[34m[1mval: [0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1361.31it/s]
[34m[1mval: [0mNew cache created: /content/datasets/coco/val2017.cache
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]
all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319
Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)
Results saved to [1mruns/val-seg/exp[0m
3. Train
Close the active learning loop by sampling images from your inference conditions with the roboflow pip package
Train a YOLOv5s-seg model on the COCO128 dataset with --data coco128-seg.yaml, starting from pretrained --weights yolov5s-seg.pt, or from randomly initialized --weights '' --cfg yolov5s-seg.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-seg/with incrementing run directories, i.e.runs/train-seg/exp2,runs/train-seg/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/train-yolov5-instance-segmentation-custom-dataset/
- Custom Training Notebook:
Label images lightning fast (including with model-assisted labeling)
#@title Select YOLOv5 🚀 logger {run: 'auto'}
logger = 'Comet' #@param ['Comet', 'ClearML', 'TensorBoard']
if logger == 'Comet':
%pip install -q comet_ml
import comet_ml; comet_ml.init()
elif logger == 'ClearML':
%pip install -q clearml
import clearml; clearml.browser_login()
elif logger == 'TensorBoard':
%load_ext tensorboard
%tensorboard --logdir runs/train
# Train YOLOv5s on COCO128 for 3 epochs
!python segment/train.py --img 640 --batch 16 --epochs 3 --data coco128-seg.yaml --weights yolov5s-seg.pt --cache
[34m[1msegment/train: [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
[34m[1mgithub: [0mup to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v7.0-2-gc9d47ae 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[1mTensorBoard: [0mStart with 'tensorboard --logdir runs/train-seg', view at http://localhost:6006/
Dataset not found ⚠️, missing paths ['/content/datasets/coco128-seg/images/train2017']
Downloading https://ultralytics.com/assets/coco128-seg.zip to coco128-seg.zip...
100% 6.79M/6.79M [00:01<00:00, 6.73MB/s]
Dataset download success ✅ (1.9s), 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 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]]
Model summary: 225 layers, 7621277 parameters, 7621277 gradients, 26.6 GFLOPs
Transferred 367/367 items from yolov5s-seg.pt
[34m[1mAMP: [0mchecks passed ✅
[34m[1moptimizer:[0m SGD(lr=0.01) with parameter groups 60 weight(decay=0.0), 63 weight(decay=0.0005), 63 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-seg/labels/train2017... 126 images, 2 backgrounds, 0 corrupt: 100% 128/128 [00:00<00:00, 1389.59it/s]
[34m[1mtrain: [0mNew cache created: /content/datasets/coco128-seg/labels/train2017.cache
[34m[1mtrain: [0mCaching images (0.1GB ram): 100% 128/128 [00:00<00:00, 238.86it/s]
[34m[1mval: [0mScanning /content/datasets/coco128-seg/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, 98.90it/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-seg/exp/labels.jpg...
Image sizes 640 train, 640 val
Using 2 dataloader workers
Logging results to [1mruns/train-seg/exp[0m
Starting training for 3 epochs...
Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size
0/2 4.92G 0.0417 0.04646 0.06066 0.02126 192 640: 100% 8/8 [00:08<00:00, 1.10s/it]
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]
all 128 929 0.737 0.649 0.715 0.492 0.719 0.617 0.658 0.408
Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size
1/2 6.29G 0.04157 0.04503 0.05772 0.01777 208 640: 100% 8/8 [00:09<00:00, 1.21s/it]
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]
all 128 929 0.756 0.674 0.738 0.506 0.725 0.64 0.68 0.422
Epoch GPU_mem box_loss seg_loss obj_loss cls_loss Instances Size
2/2 6.29G 0.0425 0.04793 0.06784 0.01863 161 640: 100% 8/8 [00:03<00:00, 2.02it/s]
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]
all 128 929 0.736 0.694 0.747 0.522 0.769 0.622 0.683 0.427
3 epochs completed in 0.009 hours.
Optimizer stripped from runs/train-seg/exp/weights/last.pt, 15.6MB
Optimizer stripped from runs/train-seg/exp/weights/best.pt, 15.6MB
Validating runs/train-seg/exp/weights/best.pt...
Fusing layers...
Model summary: 165 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs
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]
all 128 929 0.738 0.694 0.746 0.522 0.759 0.625 0.682 0.426
person 128 254 0.845 0.756 0.836 0.55 0.861 0.669 0.759 0.407
bicycle 128 6 0.475 0.333 0.549 0.341 0.711 0.333 0.526 0.322
car 128 46 0.612 0.565 0.539 0.257 0.555 0.435 0.477 0.171
motorcycle 128 5 0.73 0.8 0.752 0.571 0.747 0.8 0.752 0.42
airplane 128 6 1 0.943 0.995 0.732 0.92 0.833 0.839 0.555
bus 128 7 0.677 0.714 0.722 0.653 0.711 0.714 0.722 0.593
train 128 3 1 0.951 0.995 0.551 1 0.884 0.995 0.781
truck 128 12 0.555 0.417 0.457 0.285 0.624 0.417 0.397 0.277
boat 128 6 0.624 0.5 0.584 0.186 1 0.326 0.412 0.133
traffic light 128 14 0.513 0.302 0.411 0.247 0.435 0.214 0.376 0.251
stop sign 128 2 0.824 1 0.995 0.796 0.906 1 0.995 0.747
bench 128 9 0.75 0.667 0.763 0.367 0.724 0.585 0.698 0.209
bird 128 16 0.961 1 0.995 0.686 0.918 0.938 0.91 0.525
cat 128 4 0.771 0.857 0.945 0.752 0.76 0.8 0.945 0.728
dog 128 9 0.987 0.778 0.963 0.681 1 0.705 0.89 0.574
horse 128 2 0.703 1 0.995 0.697 0.759 1 0.995 0.249
elephant 128 17 0.916 0.882 0.93 0.691 0.811 0.765 0.829 0.537
bear 128 1 0.664 1 0.995 0.995 0.701 1 0.995 0.895
zebra 128 4 0.864 1 0.995 0.921 0.879 1 0.995 0.804
giraffe 128 9 0.883 0.889 0.94 0.683 0.845 0.778 0.78 0.463
backpack 128 6 1 0.59 0.701 0.372 1 0.474 0.52 0.252
umbrella 128 18 0.654 0.839 0.887 0.52 0.517 0.556 0.427 0.229
handbag 128 19 0.54 0.211 0.408 0.221 0.796 0.206 0.396 0.196
tie 128 7 0.864 0.857 0.857 0.577 0.925 0.857 0.857 0.534
suitcase 128 4 0.716 1 0.945 0.647 0.767 1 0.945 0.634
frisbee 128 5 0.708 0.8 0.761 0.643 0.737 0.8 0.761 0.501
skis 128 1 0.691 1 0.995 0.796 0.761 1 0.995 0.199
snowboard 128 7 0.918 0.857 0.904 0.604 0.32 0.286 0.235 0.137
sports ball 128 6 0.902 0.667 0.701 0.466 0.727 0.5 0.497 0.471
kite 128 10 0.586 0.4 0.511 0.231 0.663 0.394 0.417 0.139
baseball bat 128 4 0.359 0.5 0.401 0.169 0.631 0.5 0.526 0.133
baseball glove 128 7 1 0.519 0.58 0.327 0.687 0.286 0.455 0.328
skateboard 128 5 0.729 0.8 0.862 0.631 0.599 0.6 0.604 0.379
tennis racket 128 7 0.57 0.714 0.645 0.448 0.608 0.714 0.645 0.412
bottle 128 18 0.469 0.393 0.537 0.357 0.661 0.389 0.543 0.349
wine glass 128 16 0.677 0.938 0.866 0.441 0.53 0.625 0.67 0.334
cup 128 36 0.777 0.722 0.812 0.466 0.725 0.583 0.762 0.467
fork 128 6 0.948 0.333 0.425 0.27 0.527 0.167 0.18 0.102
knife 128 16 0.757 0.587 0.669 0.458 0.79 0.5 0.552 0.34
spoon 128 22 0.74 0.364 0.559 0.269 0.925 0.364 0.513 0.213
bowl 128 28 0.766 0.714 0.725 0.559 0.803 0.584 0.665 0.353
banana 128 1 0.408 1 0.995 0.398 0.539 1 0.995 0.497
sandwich 128 2 1 0 0.695 0.536 1 0 0.498 0.448
orange 128 4 0.467 1 0.995 0.693 0.518 1 0.995 0.663
broccoli 128 11 0.462 0.455 0.383 0.259 0.548 0.455 0.384 0.256
carrot 128 24 0.631 0.875 0.77 0.533 0.757 0.909 0.853 0.499
hot dog 128 2 0.555 1 0.995 0.995 0.578 1 0.995 0.796
pizza 128 5 0.89 0.8 0.962 0.796 1 0.778 0.962 0.766
donut 128 14 0.695 1 0.893 0.772 0.704 1 0.893 0.696
cake 128 4 0.826 1 0.995 0.92 0.862 1 0.995 0.846
chair 128 35 0.53 0.571 0.613 0.336 0.67 0.6 0.538 0.271
couch 128 6 0.972 0.667 0.833 0.627 1 0.62 0.696 0.394
potted plant 128 14 0.7 0.857 0.883 0.552 0.836 0.857 0.883 0.473
bed 128 3 0.979 0.667 0.83 0.366 1 0 0.83 0.373
dining table 128 13 0.775 0.308 0.505 0.364 0.644 0.231 0.25 0.0804
toilet 128 2 0.836 1 0.995 0.846 0.887 1 0.995 0.797
tv 128 2 0.6 1 0.995 0.846 0.655 1 0.995 0.896
laptop 128 3 0.822 0.333 0.445 0.307 1 0 0.392 0.12
mouse 128 2 1 0 0 0 1 0 0 0
remote 128 8 0.745 0.5 0.62 0.459 0.821 0.5 0.624 0.449
cell phone 128 8 0.686 0.375 0.502 0.272 0.488 0.25 0.28 0.132
microwave 128 3 0.831 1 0.995 0.722 0.867 1 0.995 0.592
oven 128 5 0.439 0.4 0.435 0.294 0.823 0.6 0.645 0.418
sink 128 6 0.677 0.5 0.565 0.448 0.722 0.5 0.46 0.362
refrigerator 128 5 0.533 0.8 0.783 0.524 0.558 0.8 0.783 0.527
book 128 29 0.732 0.379 0.423 0.196 0.69 0.207 0.38 0.131
clock 128 9 0.889 0.778 0.917 0.677 0.908 0.778 0.875 0.604
vase 128 2 0.375 1 0.995 0.995 0.455 1 0.995 0.796
scissors 128 1 1 0 0.0166 0.00166 1 0 0 0
teddy bear 128 21 0.813 0.829 0.841 0.457 0.826 0.678 0.786 0.422
toothbrush 128 5 0.806 1 0.995 0.733 0.991 1 0.995 0.628
Results saved to [1mruns/train-seg/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-seg', force_reload=True, trust_repo=True) # or 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.