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GitHub – ultralytics/yolov5: YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite

YOLOv8 🚀 NEW

We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model
released at https://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 the YOLOv8 Docs for details and get started with:

pip install ultralytics

Documentation

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 --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/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

Integrations

Roboflow
ClearML ⭐ NEW
Comet ⭐ NEW
Neural Magic ⭐ 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
Run YOLOv5 inference up to 6x faster with Neural Magic DeepSparse

Ultralytics HUB

Experience seamless AI with Ultralytics HUB ⭐, the all-in-one solution for data visualization, YOLOv5 🚀 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-friendly Ultralytics App. Start your journey for Free now!

Why YOLOv5

YOLOv5 has been designed to be super easy to get started and simple to learn. We prioritize real-world results.

YOLOv5-P5 640 Figure

Figure Notes

  • COCO AP val denotes [email protected]: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

Pretrained Checkpoints

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 by python 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 by python val.py --data coco.yaml --img 640 --task speed --batch 1
  • TTA Test Time Augmentation includes reflection and scale augmentations.
    Reproduce by python val.py --data coco.yaml --img 1536 --iou 0.7 --augment

Segmentation

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 and weight_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 by python 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 by python 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 by python export.py --weights yolov5s-seg.pt --include engine --device 0 --half

Segmentation Usage Examples  Open In Colab

Train

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

Val

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

Predict

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)

zidane
bus

Export

Export YOLOv5s-seg model to ONNX and TensorRT:

python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0

Classification

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 and weight_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 by python classify/val.py --data ../datasets/imagenet --img 224
  • Speed averaged over 100 inference images using a Google Colab Pro V100 High-RAM instance.
    Reproduce by python classify/val.py --data ../datasets/imagenet --img 224 --batch 1
  • Export to ONNX at FP32 and TensorRT at FP16 done with export.py.
    Reproduce by python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224

Classification Usage Examples  Open In Colab

Train

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

Val

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

Predict

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

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

Environments

Get started in seconds with our verified environments. Click each icon below for details.

Contribute

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!

License

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.

Contact

For YOLOv5 bug reports and feature requests please visit GitHub Issues or the Ultralytics Community Forum.

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