<div align="center">
<p>
<a align="left" href="https://ultralytics.com/yolov5" target="_blank">
<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
</p>
<br>
<div>
<a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
<br>
<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>
<a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
</div>
<br>
<p>
YOLOv5 ð is a family of object detection architectures and models pretrained on the COCO dataset, and represents <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.
</p>
<div align="center">
<a href="https://github.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.linkedin.com/company/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
</a>
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<a href="https://twitter.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
</a>
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<a href="https://www.producthunt.com/@glenn_jocher">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="2%"/>
</a>
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<a href="https://youtube.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
</a>
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<a href="https://www.facebook.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
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<a href="https://www.instagram.com/ultralytics/">
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</div>
## <div align="center">Documentation</div>
See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
## <div align="center">Quick Start Examples</div>
<details open>
<summary>Install</summary>
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
```
</details>
<details open>
<summary>Inference</summary>
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.
```
</details>
<details>
<summary>Inference with detect.py</summary>
`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
```
</details>
<details>
<summary>Training</summary>
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
```
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
</details>
<details open>
<summary>Tutorials</summary>
* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) ð RECOMMENDED
* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) âï¸
RECOMMENDED
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) ð NEW
* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) ð NEW
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) â NEW
* [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) ð
* [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) â NEW
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
</details>
## <div align="center">Environments</div>
Get started in seconds with our verified environments. Click each icon below for details.
<div ali
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YOLOv5:通过真实结果的txt文件与预测结果的txt文件进行结果评估 (129个子文件)
setup.cfg 1KB
Dockerfile 2KB
Dockerfile 846B
.dockerignore 4KB
tutorial.ipynb 56KB
bus.jpg 476KB
zidane.jpg 165KB
000000000034.jpg 103KB
000000000025.jpg 88KB
000000000009.jpg 61KB
000000000030.jpg 29KB
LICENSE 35KB
README.md 16KB
README.md 11KB
CONTRIBUTING.md 5KB
README.md 2KB
datasets.py 64KB
datasets copy.py 46KB
general.py 36KB
train.py 34KB
common.py 33KB
export.py 28KB
wandb_utils.py 27KB
yolo_annotations.py 25KB
val_txt.py 22KB
val.py 22KB
tf.py 21KB
plots.py 21KB
val copy.py 19KB
metrics.py 17KB
metrics copy.py 16KB
yolo.py 15KB
torch_utils.py 14KB
detect.py 13KB
loss_annotations.py 13KB
augmentations.py 12KB
loss.py 10KB
loss copy.py 10KB
__init__.py 8KB
autoanchor.py 7KB
hubconf.py 6KB
downloads.py 6KB
experimental.py 5KB
benchmarks.py 4KB
activations.py 4KB
callbacks.py 2KB
autobatch.py 2KB
resume.py 1KB
sweep.py 1KB
__init__.py 1KB
restapi.py 1KB
log_dataset.py 1KB
example_request.py 312B
None.py 21B
__init__.py 0B
__init__.py 0B
__init__.py 0B
datasets.cpython-38.pyc 44KB
general.cpython-38.pyc 31KB
common.cpython-38.pyc 29KB
plots.cpython-38.pyc 18KB
yolo.cpython-38.pyc 12KB
torch_utils.cpython-38.pyc 12KB
metrics.cpython-38.pyc 11KB
augmentations.cpython-38.pyc 9KB
autoanchor.cpython-38.pyc 6KB
experimental.cpython-38.pyc 5KB
activations.cpython-38.pyc 4KB
downloads.cpython-38.pyc 4KB
callbacks.cpython-38.pyc 2KB
__init__.cpython-38.pyc 1020B
__init__.cpython-38.pyc 130B
userdata.sh 1KB
get_coco.sh 927B
mime.sh 806B
get_coco128.sh 632B
download_weights.sh 543B
requirements.txt 963B
000000000009.txt 312B
additional_requirements.txt 109B
000000000009.txt 97B
000000000025.txt 97B
000000000025.txt 78B
000000000030.txt 72B
000000000030.txt 49B
000000000034.txt 48B
000000000034.txt 39B
Objects365.yaml 8KB
xView.yaml 5KB
VOC.yaml 3KB
anchors.yaml 3KB
VisDrone.yaml 3KB
Argoverse.yaml 3KB
sweep.yaml 3KB
SKU-110K.yaml 2KB
coco.yaml 2KB
yolov5-p7.yaml 2KB
GlobalWheat2020.yaml 2KB
yolov5x6.yaml 2KB
yolov5s6.yaml 2KB
共 129 条
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