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https://github.com/hlf20010508/pytorch-yolo-v5

基于pytorch的yolov5目标检测框架
https://github.com/hlf20010508/pytorch-yolo-v5
Last synced: about 5 hours ago
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基于pytorch的yolov5目标检测框架
Host: GitHub
URL: https://github.com/hlf20010508/pytorch-yolo-v5
Owner: hlf20010508
License: mit
Created: 2022-04-26T02:39:56.000Z (over 2 years ago)
Default Branch: master
Last Pushed: 2022-07-27T05:51:33.000Z (over 2 years ago)
Last Synced: 2023-03-04T23:24:18.952Z (over 1 year ago)
Language: Python
Homepage:
Size: 10.8 MB
Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project

README

        




   

   








   

   

   

   


   

   

   








YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics

 open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.





   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   

   





## 
Documentation


See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.

## 
Quick Start Examples


Install

Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a

[**Python>=3.7.0**](https://www.python.org/) environment, including

[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).

```bash

git clone https://github.com/ultralytics/yolov5  # clone

cd yolov5

pip install -r requirements.txt  # install

```

Inference

Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)

. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest

YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).

```python

import torch

# Model

model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, 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](https://github.com/ultralytics/yolov5/tree/master/models) automatically from

the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.

```bash

python detect.py --source 0  # webcam

                          img.jpg  # image

                          vid.mp4  # video

                          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](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

YOLOv5 [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

YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.

```bash

python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128

                                       yolov5s                                64

                                       yolov5m                                40

                                       yolov5l                                24

                                       yolov5x                                16

```



Tutorials

- [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)  ☘️

  RECOMMENDED

- [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)  🌟 NEW

- [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)  🌟 NEW

- [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) 🚀

- [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)  ⭐ NEW

- [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998)  ⭐ NEW

## 
Environments


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



    

        

    

    

        

    

    

        

    

    

        

    

    

        

    



## 
Integrations




    

        

    

    

        

    



|Weights and Biases|Roboflow ⭐ NEW|

|:-:|:-:|

|Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |

## 
Why YOLOv5




  YOLOv5-P5 640 Figure (click to expand)



  Figure Notes (click to expand)

- **COCO AP val** denotes [email protected]: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.

- **EfficientDet** data from [google/automl](https://github.com/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) |mAP^{val
0.5:0.95 |mAP^{val
0.5 |Speed
^{CPU b1
(ms) |Speed
^{V100 b1
(ms) |Speed
^{V100 b32
(ms) |params
^{(M) |FLOPs
^{@640 (B)

|---                    |---  |---    |---    |---    |---    |---    |---    |---

|[YOLOv5n][assets]      |640  |28.0   |45.7   |**45** |**6.3**|**0.6**|**1.9**|**4.5**

|[YOLOv5s][assets]      |640  |37.4   |56.8   |98     |6.4    |0.9    |7.2    |16.5

|[YOLOv5m][assets]      |640  |45.4   |64.1   |224    |8.2    |1.7    |21.2   |49.0

|[YOLOv5l][assets]      |640  |49.0   |67.3   |430    |10.1   |2.7    |46.5   |109.1

|[YOLOv5x][assets]      |640  |50.7   |68.9   |766    |12.1   |4.8    |86.7   |205.7

|                       |     |       |       |       |       |       |       |

|[YOLOv5n6][assets]     |1280 |36.0   |54.4   |153    |8.1    |2.1    |3.2    |4.6

|[YOLOv5s6][assets]     |1280 |44.8   |63.7   |385    |8.2    |3.6    |12.6   |16.8

|[YOLOv5m6][assets]     |1280 |51.3   |69.3   |887    |11.1   |6.8    |35.7   |50.0

|[YOLOv5l6][assets]     |1280 |53.7   |71.3   |1784   |15.8   |10.5   |76.8   |111.4

|[YOLOv5x6][assets]
+ [TTA][TTA]|1280
1536 |55.0
**55.8** |72.7
**72.7** |3136
- |26.2
- |19.4
- |140.7
- |209.8
-}}}}}}}}

  Table Notes (click to expand)

- 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^val** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) 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](https://aws.amazon.com/ec2/instance-types/p3/) 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](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`

## 
Contribute


We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 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!



## 
Contact


For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or

professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).






    

        

    

    

    

        

    

    

    

        

    

    

    

    

    

    

    

        

    

    

    

        

    

    

    

        

    



[assets]: https://github.com/ultralytics/yolov5/releases

[tta]: https://github.com/ultralytics/yolov5/issues/303