<div align="center">
<p>
<a align="left" href="https://ultralytics.com/yolov3" target="_blank">
<img width="850" src="https://user-images.githubusercontent.com/26833433/99805965-8f2ca800-2b3d-11eb-8fad-13a96b222a23.jpg"></a>
</p>
<br>
<div>
<a href="https://github.com/ultralytics/yolov3/actions"><img src="https://github.com/ultralytics/yolov3/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="YOLOv3 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://colab.research.google.com/github/ultralytics/yolov3/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/yolov3"><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>
<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>
<img width="2%" />
<a href="https://twitter.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://youtube.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.facebook.com/ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
</a>
<img width="2%" />
<a href="https://www.instagram.com/ultralytics/">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
</a>
</div>
<br>
<p>
YOLOv3 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>
<!--
<a align="center" href="https://ultralytics.com/yolov3" target="_blank">
<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
-->
</div>
## <div align="center">Documentation</div>
See the [YOLOv3 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>
[**Python>=3.6.0**](https://www.python.org/) is required with all
[requirements.txt](https://github.com/ultralytics/yolov3/blob/master/requirements.txt) installed including
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
```bash
$ git clone https://github.com/ultralytics/yolov3
$ cd yolov3
$ pip install -r requirements.txt
```
</details>
<details open>
<summary>Inference</summary>
Inference with YOLOv3 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download
from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases).
```python
import torch
# Model
model = torch.hub.load('ultralytics/yolov3', 'yolov3') # or yolov3-spp, yolov3-tiny, 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 automatically from
the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/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>
<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/yolov3/wiki/Train-Custom-Data) ð RECOMMENDED
* [Tips for Best Training Results](https://github.com/ultralytics/yolov3/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
* [TorchScript, ONNX, CoreML 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 align="center">
<a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
</a>
<a href="https://www.kaggle.com/ultralytics/yolov3">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
</a>
<a href="https://hub.docker.com/r/ultralytics/yolov3">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
</a>
<a href="https://github.com/ultralytics/yolov3/wiki/AWS-Quickstart">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
</a>
<a href="https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
</a>
</div>
## <div align="center">Integrations</div>
<div align="center">
<a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png" width="49%"/>
</a>
<a href="https://roboflow.com/?ref=ultralytics">
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow-long.png" width="49%"/>
</a>
</div>
|Weights and Biases|Roboflow â NEW|
|:-:|:-:|
|Automa
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YOLOv3猫狗检测训练模型+代码+6000张标注好的猫狗数据集
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1、YOLOv3猫狗检测训练模型 ,附有各种训练曲线图,可使用tensorboard打开训练日志 2、classes: cat, dog; 3、包含猫狗检测数据,标签格式为VOC和YOLO两种 4、检测结果和数据集参考:https://blog.csdn.net/weixin_51154380/article/details/127988229
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YOLOv3猫狗检测训练模型+代码+6000张标注好的猫狗数据集 (2000个子文件)
README.md 13KB
README.md 10KB
CONTRIBUTING.md 5KB
datasets.py 44KB
general.py 34KB
train.py 32KB
common.py 27KB
wandb_utils.py 25KB
tf.py 20KB
plots.py 20KB
val.py 17KB
export.py 16KB
yolo.py 15KB
metrics.py 13KB
torch_utils.py 13KB
detect.py 12KB
augmentations.py 11KB
loss.py 9KB
autoanchor.py 7KB
__init__.py 7KB
downloads.py 6KB
experimental.py 4KB
hubconf.py 4KB
activations.py 4KB
callbacks.py 2KB
autobatch.py 2KB
sweep.py 1KB
log_dataset.py 1KB
__init__.py 445B
__init__.py 0B
__init__.py 0B
get_coco.sh 900B
get_coco128.sh 615B
download_weights.sh 474B
requirements.txt 892B
COCO_val2014_000000161505.txt 692B
2008_004590.txt 405B
2008_007057.txt 373B
COCO_val2014_000000357322.txt 346B
008115.txt 334B
COCO_val2014_000000195817.txt 310B
2008_006294.txt 307B
002215.txt 293B
2009_001026.txt 265B
COCO_val2014_000000127313.txt 254B
2010_001247.txt 254B
2008_001420.txt 242B
2010_002067.txt 241B
002587.txt 236B
COCO_val2014_000000431092.txt 235B
COCO_val2014_000000534252.txt 233B
002697.txt 230B
2009_000400.txt 229B
COCO_val2014_000000105120.txt 227B
003681.txt 227B
2010_002907.txt 226B
2010_001376.txt 225B
COCO_val2014_000000289618.txt 224B
003100.txt 222B
2008_007320.txt 219B
001217.txt 215B
006580.txt 214B
2009_001117.txt 213B
2008_000019.txt 207B
2008_003272.txt 206B
COCO_val2014_000000118546.txt 199B
005590.txt 197B
2008_006724.txt 197B
COCO_val2014_000000530913.txt 195B
COCO_val2014_000000483074.txt 194B
2009_004813.txt 188B
2008_006965.txt 188B
2010_002399.txt 188B
COCO_val2014_000000161220.txt 187B
COCO_val2014_000000047916.txt 181B
2010_000948.txt 177B
2010_001407.txt 176B
2010_004007.txt 175B
COCO_val2014_000000387383.txt 175B
2010_001279.txt 175B
005415.txt 175B
2008_005780.txt 174B
2010_004017.txt 174B
2008_002304.txt 174B
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2009_005073.txt 172B
003792.txt 172B
2010_000061.txt 172B
002826.txt 172B
2011_001127.txt 171B
2009_000142.txt 163B
2009_004425.txt 163B
2010_004901.txt 162B
2008_005337.txt 162B
2008_007404.txt 162B
008509.txt 162B
001044.txt 162B
2010_005637.txt 161B
003592.txt 161B
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