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YOLOv5 ๐ is the world's most loved vision AI, representing Ultralytics
open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
To request an Enterprise License please complete the form at Ultralytics Licensing.
We're excited to announce our very first Ultralytics Live Session โจ streaming live at https://youtu.be/KS4weDInJYs on Tuesday, November 29th at 16:00 CET with @thepycoder of ClearML. Glenn and Victor will discuss all things experiment tracking and how you can maximize the benefits of the new YOLOv5 + ClearML integration. ๐ฅ
Our new YOLOv5 release v7.0 instance segmentation models are the fastest and most accurate in the world, beating all current SOTA benchmarks. We've made them super simple to train, validate and deploy. See full details in our Release Notes and visit our YOLOv5 Segmentation Colab Notebook for 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 Google Colab Pro notebooks 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 to https://wandb.ai/glenn-jocher/YOLOv5_v70_official - Accuracy values are for single-model single-scale on COCO dataset.
Reproduce bypython segment/val.py --data coco.yaml --weights yolov5s-seg.pt
- Speed averaged over 100 inference images using a Colab Pro A100 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
- Export to 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 ![Open In Colab](https://camo.githubusercontent.com/f5e0d0538a9c2972b5d413e0ace04cecd8efd828d133133933dfffec282a4e1b/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)
YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with --data coco128-seg.yaml
argument and manual download of COCO-segments dataset with bash data/scripts/get_coco.sh --train --val --segments
and then python 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 --data data/images/bus.jpg
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5m-seg.pt') # load from PyTorch Hub (WARNING: inference not yet supported)
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Export YOLOv5s-seg model to ONNX and TensorRT:
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
See the YOLOv5 Docs for full documentation on training, testing and deployment. See below for quickstart examples.
Install
Clone repo and install requirements.txt in a Python>=3.7.0 environment, including PyTorch>=1.7.
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
Inference
YOLOv5 PyTorch Hub inference. Models download automatically from the latest YOLOv5 release.
import torch
# 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, downloading models automatically from
the latest YOLOv5 release and saving results to runs/detect
.
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
Training
The commands below reproduce YOLOv5 COCO
results. Models
and datasets download automatically from the latest
YOLOv5 release. Training times for YOLOv5n/s/m/l/x are
1/2/4/6/8 days on a V100 GPU (Multi-GPU times faster). Use the
largest --batch-size
possible, or pass --batch-size -1
for
YOLOv5 AutoBatch. 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
![](https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png)
Tutorials
- Train Custom Data ๐ RECOMMENDED
- Tips for Best Training Results โ๏ธ RECOMMENDED
- Multi-GPU Training
- PyTorch Hub ๐ NEW
- TFLite, ONNX, CoreML, TensorRT Export ๐
- NVIDIA Jetson Nano Deployment ๐ NEW
- Test-Time Augmentation (TTA)
- Model Ensembling
- Model Pruning/Sparsity
- Hyperparameter Evolution
- Transfer Learning with Frozen Layers
- Architecture Summary ๐ NEW
- Roboflow for Datasets, Labeling, and Active Learning ๐ NEW
- ClearML Logging ๐ NEW
- Deci Platform ๐ NEW
- Comet Logging ๐ NEW
![](https://github.com/ultralytics/assets/raw/master/im/integrations-loop.png?raw=true)
Roboflow | ClearML โญ NEW | Comet โญ NEW | Deci โญ NEW |
---|---|---|---|
Label and export your custom datasets directly to YOLOv5 for training with Roboflow | Automatically track, visualize and even remotely train YOLOv5 using ClearML (open-source!) | Free forever, Comet lets you save YOLOv5 models, resume training, and interactively visualise and debug predictions | Automatically compile and quantize YOLOv5 for better inference performance in one click at Deci |
Ultralytics HUB is our โญ NEW no-code solution to visualize datasets, train YOLOv5 ๐ models, and deploy to the real world in a seamless experience. Get started for Free now!
![](https://github.com/ultralytics/assets/raw/master/im/ultralytics-hub.png?raw=true)
YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results.
Figure Notes
- COCO AP val denotes mAP@0.5:0.95 metric measured on the 5000-image COCO val2017 dataset over various inference sizes from 256 to 1536.
- GPU Speed measures average inference time per image on COCO val2017 dataset using a AWS p3.2xlarge V100 instance at batch-size 32.
- EfficientDet data from google/automl at batch size 8.
- Reproduce by
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 use hyp.scratch-low.yaml hyps, all others use hyp.scratch-high.yaml.
- mAPval values are for single-model single-scale on COCO val2017 dataset.
Reproduce bypython val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65
- Speed averaged over COCO val images using a AWS p3.2xlarge instance. NMS times (~1 ms/img) not included.
Reproduce bypython val.py --data coco.yaml --img 640 --task speed --batch 1
- TTA Test Time Augmentation includes reflection and scale augmentations.
Reproduce bypython val.py --data coco.yaml --img 1536 --iou 0.7 --augment
YOLOv5 release v6.2 brings support for classification model training, validation and deployment! See full details in our Release Notes and visit our YOLOv5 Classification Colab Notebook for 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 Google Colab Pro for 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 to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2 - Accuracy values are for single-model single-scale on ImageNet-1k dataset.
Reproduce bypython classify/val.py --data ../datasets/imagenet --img 224
- Speed averaged over 100 inference images using a Google Colab Pro V100 High-RAM instance.
Reproduce bypython classify/val.py --data ../datasets/imagenet --img 224 --batch 1
- Export to 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 ![Open In Colab](https://camo.githubusercontent.com/f5e0d0538a9c2972b5d413e0ace04cecd8efd828d133133933dfffec282a4e1b/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)
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 --data 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 our Contributing Guide to get started, and fill out the YOLOv5 Survey to send us feedback on your experiences. Thank you to all our contributors!
YOLOv5 is available under two different licenses:
- GPL-3.0 License: See LICENSE file for details.
- Enterprise License: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at Ultralytics Licensing.
For YOLOv5 bugs and feature requests please visit GitHub Issues. For professional support please Contact Us.