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< div align = "center" >
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< p >
< a align = "center" href = "https://ultralytics.com/yolov5" target = "_blank" >
< img width = "100%" src = "https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png" > < / a >
< / p >
[English ](README.md ) | [简体中文 ](README.zh-CN.md )
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< br >
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< div >
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< a href = "https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml" > < img src = "https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml/badge.svg" alt = " CI" > < / a >
< a href = "https://zenodo.org/badge/latestdoi/264818686" > < img src = "https://zenodo.org/badge/264818686.svg" alt = " Citation" > < / a >
< a href = "https://hub.docker.com/r/ultralytics/yolov3" > < img src = "https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker" alt = "Docker Pulls" > < / a >
< br >
< a href = "https://bit.ly/yolov5-paperspace-notebook" > < img src = "https://assets.paperspace.io/img/gradient-badge.svg" alt = "Run on Gradient" > < / a >
< a href = "https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb" > < img src = "https://colab.research.google.com/assets/colab-badge.svg" alt = "Open In Colab" > < / a >
< a href = "https://www.kaggle.com/ultralytics/yolov5" > < img src = "https://kaggle.com/static/images/open-in-kaggle.svg" alt = "Open In Kaggle" > < / a >
< / div >
< br >
🚀 is the world's most loved vision AI, representing < a href = "https://ultralytics.com" > Ultralytics< / a > 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 < a href = "https://ultralytics.com/license" > Ultralytics
Licensing< / a > .
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< div align = "center" >
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< a href = "https://github.com/ultralytics" style = "text-decoration:none;" >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width = "2%" alt = "" / > < / a >
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< a href = "https://www.linkedin.com/company/ultralytics" style = "text-decoration:none;" >
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< / div >
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< / div >
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< br >
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## <div align="center">YOLOv8 🚀 NEW</div>
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 ](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 ](https://docs.ultralytics.com ) for details and get started with:
```commandline
pip install ultralytics
```
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< div align = "center" >
< a href = "https://ultralytics.com/yolov8" target = "_blank" >
< img width = "100%" src = "https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png" > < / a >
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< / div >
## <div align="center">Documentation</div>
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See the [ Docs ](https://docs.ultralytics.com ) for full documentation on training, testing and deployment. See below for
quickstart examples.
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< details open >
< summary > Install< / summary >
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Clone repo and install [requirements.txt ](https://github.com/ultralytics/yolov3/blob/master/requirements.txt ) in a
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[**Python>=3.7.0** ](https://www.python.org/ ) environment, including
[**PyTorch>=1.7** ](https://pytorch.org/get-started/locally/ ).
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```bash
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git clone https://github.com/ultralytics/yolov3 # clone
cd yolov3
pip install -r requirements.txt # install
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```
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< / details >
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< details >
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< summary > Inference< / summary >
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[PyTorch Hub ](https://github.com/ultralytics/yolov5/issues/36 )
inference. [Models ](https://github.com/ultralytics/yolov5/tree/master/models ) download automatically from the latest
[release ](https://github.com/ultralytics/yolov5/releases ).
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```python
import torch
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# Model
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model = torch.hub.load(
"ultralytics/yolov3", "yolov3"
) # or yolov3-spp, yolov3-tiny, custom
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# Images
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img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list
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# Inference
results = model(img)
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# Results
results.print() # or .show(), .save(), .crop(), .pandas(), etc.
```
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< / details >
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< details >
< summary > Inference with detect.py< / summary >
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`detect.py` runs inference on a variety of sources,
downloading [models ](https://github.com/ultralytics/yolov5/tree/master/models ) automatically from
the latest [release ](https://github.com/ultralytics/yolov5/releases ) and saving results to `runs/detect` .
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```bash
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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
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```
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< / details >
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< details >
< summary > Training< / summary >
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The commands below reproduce [COCO ](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh )
results. [Models ](https://github.com/ultralytics/yolov5/tree/master/models )
and [datasets ](https://github.com/ultralytics/yolov5/tree/master/data ) download automatically from the latest
[release ](https://github.com/ultralytics/yolov5/releases ). Training times for YOLOv5n/s/m/l/x are
1/2/4/6/8 days on a V100 GPU ([Multi-GPU ](https://github.com/ultralytics/yolov5/issues/475 ) times faster). Use the
largest `--batch-size` possible, or pass `--batch-size -1` for
[AutoBatch ](https://github.com/ultralytics/yolov5/pull/5092 ). Batch sizes shown for V100-16GB.
```bash
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
yolov5s 64
yolov5m 40
yolov5l 24
yolov5x 16
```
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< img width = "800" src = "https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png" >
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< / details >
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< details open >
< summary > Tutorials< / summary >
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- [Train Custom Data ](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data )🚀 RECOMMENDED
- [Tips for Best Training Results ](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results )☘️
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RECOMMENDED
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- [Multi-GPU Training ](https://github.com/ultralytics/yolov5/issues/475 )
- [PyTorch Hub ](https://github.com/ultralytics/yolov5/issues/36 ) 🌟 NEW
- [TFLite, ONNX, CoreML, TensorRT Export ](https://github.com/ultralytics/yolov5/issues/251 ) 🚀
- [NVIDIA Jetson Nano Deployment ](https://github.com/ultralytics/yolov5/issues/9627 ) 🌟 NEW
- [Test-Time Augmentation (TTA) ](https://github.com/ultralytics/yolov5/issues/303 )
- [Model Ensembling ](https://github.com/ultralytics/yolov5/issues/318 )
- [Model Pruning/Sparsity ](https://github.com/ultralytics/yolov5/issues/304 )
- [Hyperparameter Evolution ](https://github.com/ultralytics/yolov5/issues/607 )
- [Transfer Learning with Frozen Layers ](https://github.com/ultralytics/yolov5/issues/1314 )
- [Architecture Summary ](https://github.com/ultralytics/yolov5/issues/6998 ) 🌟 NEW
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- [Roboflow for Datasets, Labeling, and Active Learning ](https://github.com/ultralytics/yolov5/issues/4975 )🌟 NEW
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- [ClearML Logging ](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml ) 🌟 NEW
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- [ with Neural Magic's Deepsparse ](https://bit.ly/yolov5-neuralmagic ) 🌟 NEW
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- [Comet Logging ](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet ) 🌟 NEW
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< / details >
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## <div align="center">Integrations</div>
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< br >
< a align = "center" href = "https://bit.ly/ultralytics_hub" target = "_blank" >
< img width = "100%" src = "https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png" > < / a >
< br >
< br >
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< div align = "center" >
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< a href = "https://roboflow.com/?ref=ultralytics" >
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< img src = "https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width = "10%" / > < / a >
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< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "15%" height = "0" alt = "" / >
< a href = "https://cutt.ly/yolov5-readme-clearml" >
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< img src = "https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width = "10%" / > < / a >
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< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "15%" height = "0" alt = "" / >
< a href = "https://bit.ly/yolov5-readme-comet" >
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< img src = "https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width = "10%" / > < / a >
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< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "15%" height = "0" alt = "" / >
< a href = "https://bit.ly/yolov5-neuralmagic" >
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< img src = "https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width = "10%" / > < / a >
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< / div >
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| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
| :--------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------: |
| Label and export your custom datasets directly to for training with [Roboflow ](https://roboflow.com/?ref=ultralytics ) | Automatically track, visualize and even remotely train using [ClearML ](https://cutt.ly/yolov5-readme-clearml ) (open-source!) | Free forever, [Comet ](https://bit.ly/yolov5-readme-comet2 ) lets you save models, resume training, and interactively visualise and debug predictions | Run inference up to 6x faster with [Neural Magic DeepSparse ](https://bit.ly/yolov5-neuralmagic ) |
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## <div align="center">Ultralytics HUB</div>
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[Ultralytics HUB ](https://bit.ly/ultralytics_hub ) is our ⭐ **NEW** no-code solution to visualize datasets, train 🚀
models, and deploy to the real world in a seamless experience. Get started for **Free** now!
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< a align = "center" href = "https://bit.ly/ultralytics_hub" target = "_blank" >
< img width = "100%" src = "https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" > < / a >
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## <div align="center">Why YOLO</div>
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has been designed to be super easy to get started and simple to learn. We prioritize real-world results.
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< p align = "left" > < img width = "800" src = "https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png" > < / p >
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< details >
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< summary > YOLOv5-P5 640 Figure< / summary >
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< p align = "left" > < img width = "800" src = "https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png" > < / p >
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< / details >
< details >
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< summary > Figure Notes< / summary >
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- **COCO AP val** denotes mAP@0 .5:0.95 metric measured on the 5000-image [COCO val2017 ](http://cocodataset.org ) dataset
over various inference sizes from 256 to 1536.
- **GPU Speed** measures average inference time per image on [COCO val2017 ](http://cocodataset.org ) dataset using
a [AWS p3.2xlarge ](https://aws.amazon.com/ec2/instance-types/p3/ ) V100 instance at batch-size 32.
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- **EfficientDet** data from [google/automl ](https://github.com/google/automl ) at batch size 8.
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- **Reproduce**
by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
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< / details >
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### Pretrained Checkpoints
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| Model | size< br >< sup > (pixels) | mAP< sup > val< br > 50-95 | mAP< sup > val< br > 50 | Speed< br >< sup > CPU b1< br > (ms) | Speed< br >< sup > V100 b1< br > (ms) | Speed< br >< sup > V100 b32< br > (ms) | params< br >< sup > (M) | FLOPs< br >< sup > @640 (B) |
| ----------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
| [YOLOv5n ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt ) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
| [YOLOv5s ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt ) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
| [YOLOv5m ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt ) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
| [YOLOv5l ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt ) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
| [YOLOv5x ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt ) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
| | | | | | | | | |
| [YOLOv5n6 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt ) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
| [YOLOv5s6 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt ) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
| [YOLOv5m6 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt ) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
| [YOLOv5l6 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt ) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
| [YOLOv5x6 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt )< br > + [TTA] | 1280< br > 1536 | 55.0< br > **55.8** | 72.7< br > **72.7** | 3136< br > - | 26.2< br > - | 19.4< br > - | 140.7< br > - | 209.8< br > - |
< details >
< summary > Table Notes< / summary >
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- All checkpoints are trained to 300 epochs with default settings. Nano and Small models
use [hyp.scratch-low.yaml ](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml ) hyps, all
others use [hyp.scratch-high.yaml ](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml ).
- **mAP< sup > val</ sup > ** values are for single-model single-scale on [COCO val2017 ](http://cocodataset.org ) dataset.< br >
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 ](https://aws.amazon.com/ec2/instance-types/p3/ )
instance. NMS times (~1 ms/img) not included.< br > Reproduce
by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
- **TTA** [Test Time Augmentation ](https://github.com/ultralytics/yolov5/issues/303 ) includes reflection and scale
augmentations.< br > Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
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< / details >
## <div align="center">Segmentation</div>
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Our new YOLOv5 [release v7.0 ](https://github.com/ultralytics/yolov5/releases/v7.0 ) instance segmentation models are the
fastest and most accurate in the world, beating all
current [SOTA benchmarks ](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco ). We've made them
super simple to train, validate and deploy. See full details in
our [Release Notes ](https://github.com/ultralytics/yolov5/releases/v7.0 ) and visit
our [YOLOv5 Segmentation Colab Notebook ](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb ) for
quickstart tutorials.
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< details >
< summary > Segmentation Checkpoints< / summary >
< div align = "center" >
< a align = "center" href = "https://ultralytics.com/yolov5" target = "_blank" >
< img width = "800" src = "https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png" > < / a >
< / div >
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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 ](https://colab.research.google.com/signup ) notebooks for easy reproducibility.
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| Model | size< br >< sup > (pixels) | mAP< sup > box< br > 50-95 | mAP< sup > mask< br > 50-95 | Train time< br >< sup > 300 epochs< br > A100 (hours) | Speed< br >< sup > ONNX CPU< br > (ms) | Speed< br >< sup > TRT A100< br > (ms) | params< br >< sup > (M) | FLOPs< br >< sup > @640 (B) |
| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- |
| [YOLOv5n-seg ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt ) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** |
| [YOLOv5s-seg ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt ) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
| [YOLOv5m-seg ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt ) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
| [YOLOv5l-seg ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt ) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
| [YOLOv5x-seg ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt ) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
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- 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.< br > Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official
- **Accuracy** values are for single-model single-scale on COCO dataset.< br > Reproduce
by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
- **Speed** averaged over 100 inference images using a [Colab Pro ](https://colab.research.google.com/signup ) A100
High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image). < br > 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` . < br > Reproduce
by `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
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< / details >
< details >
< summary > Segmentation Usage Examples < a href = "https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb" > < img src = "https://colab.research.google.com/assets/colab-badge.svg" alt = "Open In Colab" > < / a > < / summary >
### Train
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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` .
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```bash
# 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
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:
```bash
python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg
```
```python
model = torch.hub.load(
"ultralytics/yolov5", "custom", "yolov5m-seg.pt"
) # load from PyTorch Hub (WARNING: inference not yet supported)
```
|  |  |
| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
### Export
Export YOLOv5s-seg model to ONNX and TensorRT:
```bash
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
```
< / details >
## <div align="center">Classification</div>
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YOLOv5 [release v6.2 ](https://github.com/ultralytics/yolov5/releases ) brings support for classification model training,
validation and deployment! See full details in our [Release Notes ](https://github.com/ultralytics/yolov5/releases/v6.2 )
and visit
our [YOLOv5 Classification Colab Notebook ](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb )
for quickstart tutorials.
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< details >
< summary > Classification Checkpoints< / summary >
< br >
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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 ](https://colab.research.google.com/signup ) for easy reproducibility.
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| Model | size< br >< sup > (pixels) | acc< br >< sup > top1 | acc< br >< sup > top5 | Training< br >< sup > 90 epochs< br > 4xA100 (hours) | Speed< br >< sup > ONNX CPU< br > (ms) | Speed< br >< sup > TensorRT V100< br > (ms) | params< br >< sup > (M) | FLOPs< br >< sup > @224 (B) |
| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- |
| [YOLOv5n-cls ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt ) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
| [YOLOv5s-cls ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt ) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
| [YOLOv5m-cls ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt ) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
| [YOLOv5l-cls ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt ) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
| [YOLOv5x-cls ](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt ) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
| | | | | | | | | |
| [ResNet18 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt ) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
| [ResNet34 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt ) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
| [ResNet50 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt ) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
| [ResNet101 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt ) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
| | | | | | | | | |
| [EfficientNet_b0 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt ) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
| [EfficientNet_b1 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt ) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
| [EfficientNet_b2 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt ) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
| [EfficientNet_b3 ](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt ) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
2020-06-15 12:30:12 -07:00
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< details >
< summary > Table Notes (click to expand)< / summary >
2020-06-10 16:45:09 -07:00
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- 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.< br > Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
- **Accuracy** values are for single-model single-scale on [ImageNet-1k ](https://www.image-net.org/index.php )
dataset.< br > Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`
- **Speed** averaged over 100 inference images using a Google [Colab Pro ](https://colab.research.google.com/signup ) V100
High-RAM instance.< br > 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` . < br > Reproduce
by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
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< / details >
< / details >
< details >
< summary > Classification Usage Examples < a href = "https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb" > < img src = "https://colab.research.google.com/assets/colab-badge.svg" alt = "Open In Colab" > < / a > < / summary >
### Train
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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` .
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```bash
# 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
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:
```bash
python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
```
```python
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:
```bash
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
```
2020-06-10 16:45:09 -07:00
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< / details >
2020-06-10 16:45:09 -07:00
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## <div align="center">Environments</div>
Get started in seconds with our verified environments. Click each icon below for details.
< div align = "center" >
< a href = "https://bit.ly/yolov5-paperspace-notebook" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gradient.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "5%" alt = "" / >
< a href = "https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "5%" alt = "" / >
< a href = "https://www.kaggle.com/ultralytics/yolov5" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "5%" alt = "" / >
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< a href = "https://hub.docker.com/r/ultralytics/yolov3" >
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< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "5%" alt = "" / >
< a href = "https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width = "10%" / > < / a >
< img src = "https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width = "5%" alt = "" / >
< a href = "https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart" >
< img src = "https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width = "10%" / > < / a >
< / div >
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## <div align="center">Contribute</div>
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We love your input! We want to make contributing to as easy and transparent as possible. Please see
our [Contributing Guide ](CONTRIBUTING.md ) to get started, and fill out
the [ Survey ](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey ) to send us
feedback on your experiences. Thank you to all our contributors!
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<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
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< a href = "https://github.com/ultralytics/yolov5/graphs/contributors" >
< img src = "https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" / > < / a >
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## <div align="center">License</div>
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is available under two different licenses:
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- **GPL-3.0 License**: See [LICENSE ](https://github.com/ultralytics/yolov5/blob/master/LICENSE ) file for details.
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- **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 ](https://ultralytics.com/license ).
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## <div align="center">Contact</div>
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For bug reports and feature requests please visit [GitHub Issues ](https://github.com/ultralytics/yolov3/issues ) or
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the [Ultralytics Community Forum ](https://community.ultralytics.com/ ).
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< br >
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< / div >
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[tta]: https://github.com/ultralytics/yolov5/issues/303
