Trung văn|한국어|Nhật bổn ngữ|Русский|Deutsch|Français|Español|Português|Türkçe|Tiếng Việt|العربية
YOLOv5 🚀 is the world's most loved vision AI, representingUltralyticsopen-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
We hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5Docsfor details, raise an issue onGitHubfor support, and join ourDiscordcommunity for questions and discussions!
To request an Enterprise License please complete the form atUltralytics Licensing.
We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model released athttps://github.com/ultralytics/ultralytics.YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks.
See theYOLOv8 Docsfor details and get started with:
pip install ultralytics
See theYOLOv5 Docsfor full documentation on training, testing and deployment. See below for quickstart examples.
Install
Clone repo and installrequirements.txtin aPython>=3.8.0environment, includingPyTorch>=1.8.
git clone https://github.com/ultralytics/yolov5#clone
cdyolov5
pip install -r requirements.txt#install
Inference
YOLOv5PyTorch Hubinference.Modelsdownload automatically from the latest YOLOv5release.
importtorch
# Model
model=torch.hub.load("ultralytics/yolov5","yolov5s")# or yolov5n - yolov5x6, custom
# Images
img="https://ultralytics.com/images/zidane.jpg"# or file, Path, PIL, OpenCV, numpy, list
# Inference
results=model(img)
# Results
results.print()# or.show(),.save(),.crop(),.pandas(), etc.
Inference with detect.py
detect.py
runs inference on a variety of sources, downloadingmodelsautomatically from the latest YOLOv5releaseand saving results toruns/detect
.
python detect.py --weights yolov5s.pt --source 0#webcam
img.jpg#image
vid.mp4#video
screen#screenshot
path/#directory
list.txt#list of images
list.streams#list of streams
'path/*.jpg'#glob
'https://youtu.be/LNwODJXcvt4'#YouTube
'rtsp://example.com/media.mp4'#RTSP, RTMP, HTTP stream
Training
The commands below reproduce YOLOv5COCOresults.Modelsanddatasetsdownload automatically from the latest YOLOv5release.Training times for YOLOv5n/s/m/l/x are 1/2/4/6/8 days on a V100 GPU (Multi-GPUtimes faster). Use the largest--batch-size
possible, or pass--batch-size -1
for YOLOv5AutoBatch.Batch sizes shown for V100-16GB.
python train.py --data coco.yaml --epochs 300 --weights''--cfg yolov5n.yaml --batch-size 128
yolov5s 64
yolov5m 40
yolov5l 24
yolov5x 16
Tutorials
- Train Custom Data🚀 RECOMMENDED
- Tips for Best Training Results☘️
- Multi-GPU Training
- PyTorch Hub🌟 NEW
- TFLite, ONNX, CoreML, TensorRT Export🚀
- NVIDIA Jetson platform Deployment🌟 NEW
- Test-Time Augmentation (TTA)
- Model Ensembling
- Model Pruning/Sparsity
- Hyperparameter Evolution
- Transfer Learning with Frozen Layers
- Architecture Summary🌟 NEW
- Roboflow for Datasets, Labeling, and Active Learning
- ClearML Logging🌟 NEW
- YOLOv5 with Neural Magic's Deepsparse🌟 NEW
- Comet Logging🌟 NEW
Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration withW&B,Comet,RoboflowandOpenVINO,can optimize your AI workflow.
Ultralytics HUB 🚀 | W&B | Comet ⭐ NEW | Neural Magic |
---|---|---|---|
Streamline YOLO workflows: Label, train, and deploy effortlessly withUltralytics HUB.Try now! | Track experiments, hyperparameters, and results withWeights & Biases | Free forever,Cometlets you save YOLOv5 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster withNeural Magic DeepSparse |
Experience seamless AI withUltralytics HUB⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendlyUltralytics App.Start your journey forFreenow!
YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results.
Figure Notes
- COCO AP valdenotes [email protected]:0.95 metric measured on the 5000-imageCOCO val2017dataset over various inference sizes from 256 to 1536.
- GPU Speedmeasures average inference time per image onCOCO val2017dataset using aAWS p3.2xlargeV100 instance at batch-size 32.
- EfficientDetdata fromgoogle/automlat batch size 8.
- Reproduceby
python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt
Model | size (pixels) |
mAPval 50-95 |
mAPval 50 |
Speed CPU b1 (ms) |
Speed V100 b1 (ms) |
Speed V100 b32 (ms) |
params (M) |
FLOPs @640 (B) |
---|---|---|---|---|---|---|---|---|
YOLOv5n | 640 | 28.0 | 45.7 | 45 | 6.3 | 0.6 | 1.9 | 4.5 |
YOLOv5s | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
YOLOv5m | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
YOLOv5l | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
YOLOv5x | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
YOLOv5n6 | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
YOLOv5s6 | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
YOLOv5m6 | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
YOLOv5l6 | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
YOLOv5x6 +TTA |
1280 1536 |
55.0 55.8 |
72.7 72.7 |
3136 - |
26.2 - |
19.4 - |
140.7 - |
209.8 - |
Table Notes
- All checkpoints are trained to 300 epochs with default settings. Nano and Small models usehyp.scratch-low.yamlhyps, all others usehyp.scratch-high.yaml.
- mAPvalvalues are for single-model single-scale onCOCO val2017dataset.
Reproduce bypython val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
- Speedaveraged over COCO val images using aAWS p3.2xlargeinstance. NMS times (~1 ms/img) not included.
Reproduce bypython val.py --data coco.yaml --img 640 --task speed --batch 1
- TTATest Time Augmentationincludes reflection and scale augmentations.
Reproduce bypython val.py --data coco.yaml --img 1536 --iou 0.7 --augment
Our new YOLOv5release v7.0instance segmentation models are the fastest and most accurate in the world, beating all currentSOTA benchmarks.We've made them super simple to train, validate and deploy. See full details in ourRelease Notesand visit ourYOLOv5 Segmentation Colab Notebookfor quickstart tutorials.
Segmentation Checkpoints
We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on GoogleColab Pronotebooks for easy reproducibility.
Model | size (pixels) |
mAPbox 50-95 |
mAPmask 50-95 |
Train time 300 epochs A100 (hours) |
Speed ONNX CPU (ms) |
Speed TRT A100 (ms) |
params (M) |
FLOPs @640 (B) |
---|---|---|---|---|---|---|---|---|
YOLOv5n-seg | 640 | 27.6 | 23.4 | 80:17 | 62.7 | 1.2 | 2.0 | 7.1 |
YOLOv5s-seg | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
YOLOv5m-seg | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
YOLOv5l-seg | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
YOLOv5x-seg | 640 | 50.7 | 41.4 | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
- All checkpoints are trained to 300 epochs with SGD optimizer with
lr0=0.01
andweight_decay=5e-5
at image size 640 and all default settings.
Runs logged tohttps://wandb.ai/glenn-jocher/YOLOv5_v70_official - Accuracyvalues are for single-model single-scale on COCO dataset.
Reproduce bypython segment/val.py --data coco.yaml --weights yolov5s-seg.pt
- Speedaveraged over 100 inference images using aColab ProA100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image).
Reproduce bypython segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1
- Exportto ONNX at FP32 and TensorRT at FP16 done with
export.py
.
Reproduce bypython export.py --weights yolov5s-seg.pt --include engine --device 0 --half
Segmentation Usage Examples
YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with--data coco128-seg.yaml
argument and manual download of COCO-segments dataset withbash data/scripts/get_coco.sh --train --val --segments
and thenpython train.py --data coco.yaml
.
#Single-GPU
python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
#Multi-GPU DDP
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
Validate YOLOv5s-seg mask mAP on COCO dataset:
bash data/scripts/get_coco.sh --val --segments#download COCO val segments split (780MB, 5000 images)
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640#validate
Use pretrained YOLOv5m-seg.pt to predict bus.jpg:
python segment/predict.py --weights yolov5m-seg.pt --source data/images/bus.jpg
model=torch.hub.load(
"ultralytics/yolov5","custom","yolov5m-seg.pt"
)# load from PyTorch Hub (WARNING: inference not yet supported)
Export YOLOv5s-seg model to ONNX and TensorRT:
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
YOLOv5release v6.2brings support for classification model training, validation and deployment! See full details in ourRelease Notesand visit ourYOLOv5 Classification Colab Notebookfor quickstart tutorials.
Classification Checkpoints
We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on GoogleColab Profor easy reproducibility.
Model | size (pixels) |
acc top1 |
acc top5 |
Training 90 epochs 4xA100 (hours) |
Speed ONNX CPU (ms) |
Speed TensorRT V100 (ms) |
params (M) |
FLOPs @224 (B) |
---|---|---|---|---|---|---|---|---|
YOLOv5n-cls | 224 | 64.6 | 85.4 | 7:59 | 3.3 | 0.5 | 2.5 | 0.5 |
YOLOv5s-cls | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
YOLOv5m-cls | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
YOLOv5l-cls | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
YOLOv5x-cls | 224 | 79.0 | 94.4 | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
ResNet18 | 224 | 70.3 | 89.5 | 6:47 | 11.2 | 0.5 | 11.7 | 3.7 |
ResNet34 | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
ResNet50 | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
ResNet101 | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
EfficientNet_b0 | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
EfficientNet_b1 | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
EfficientNet_b2 | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
EfficientNet_b3 | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
Table Notes (click to expand)
- All checkpoints are trained to 90 epochs with SGD optimizer with
lr0=0.001
andweight_decay=5e-5
at image size 224 and all default settings.
Runs logged tohttps://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 - Accuracyvalues are for single-model single-scale onImageNet-1kdataset.
Reproduce bypython classify/val.py --data../datasets/imagenet --img 224
- Speedaveraged over 100 inference images using a GoogleColab ProV100 High-RAM instance.
Reproduce bypython classify/val.py --data../datasets/imagenet --img 224 --batch 1
- Exportto ONNX at FP32 and TensorRT at FP16 done with
export.py
.
Reproduce bypython export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224
Classification Usage Examples
YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the--data
argument. To start training on MNIST for example use--data mnist
.
#Single-GPU
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
#Multi-GPU DDP
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
Validate YOLOv5m-cls accuracy on ImageNet-1k dataset:
bash data/scripts/get_imagenet.sh --val#download ImageNet val split (6.3G, 50000 images)
python classify/val.py --weights yolov5m-cls.pt --data../datasets/imagenet --img 224#validate
Use pretrained YOLOv5s-cls.pt to predict bus.jpg:
python classify/predict.py --weights yolov5s-cls.pt --source data/images/bus.jpg
model=torch.hub.load("ultralytics/yolov5","custom","yolov5s-cls.pt")# load from PyTorch Hub
Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
Get started in seconds with our verified environments. Click each icon below for details.
We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see ourContributing Guideto get started, and fill out theYOLOv5 Surveyto send us feedback on your experiences. Thank you to all our contributors!
Ultralytics offers two licensing options to accommodate diverse use cases:
- AGPL-3.0 License:ThisOSI-approvedopen-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See theLICENSEfile for more details.
- Enterprise License:Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out throughUltralytics Licensing.
For YOLOv5 bug reports and feature requests please visitGitHub Issues,and join ourDiscordcommunity for questions and discussions!