Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/BobLiu20/YOLOv3_PyTorch

Full implementation of YOLOv3 in PyTorch
https://github.com/BobLiu20/YOLOv3_PyTorch

object-detection pytorch yolo yolov3

Last synced: 12 days ago
JSON representation

Full implementation of YOLOv3 in PyTorch

Lists

README 

        
# YOLOv3

Full implementation of YOLOv3 in PyTorch.



## Overview

#### YOLOv3: An Incremental Improvement

[[Paper]](https://pjreddie.com/media/files/papers/YOLOv3.pdf)   

[[Original Implementation]](https://github.com/pjreddie/darknet)   



#### Why this project

* Implement YOLOv3 and darknet53 without original darknet cfg parser.   

* It is easy to custom your backbone network. Such as resnet, densenet...   



## Installation

##### Environment

* pytorch >= 0.4.0

* python >= 3.6.0

##### Get code

```

git clone https://github.com/BobLiu20/YOLOv3_PyTorch.git

cd YOLOv3_PyTorch

pip3 install -r requirements.txt --user

```

##### Download COCO dataset

```

cd data/

bash get_coco_dataset.sh

```



## Training

##### Download pretrained weights

1. See [weights readme](weights/README.md) for detail.   

2. Download pretrained backbone wegiths from [Google Drive](https://drive.google.com/open?id=1VYwHUznM3jLD7ftmOSCHnpkVpBJcFIOA) or [Baidu Drive](https://pan.baidu.com/s/1axXjz6ct9Rn9GtDTust6DA)   

3. Move downloaded file ```darknet53_weights_pytorch.pth``` to ```wegihts``` folder in this project.   

##### Modify training parameters

1. Review config file ```training/params.py```   

2. Replace ```YOUR_WORKING_DIR``` to your working directory. Use for save model and tmp file.

3. Adjust your GPU device. see parallels.   

4. Adjust other parameters.   

##### Start training

```

cd training

python training.py params.py

```

##### Option: Visualizing training

```

#  please install tensorboard in first

python -m tensorboard.main --logdir=YOUR_WORKING_DIR   

```




## Evaluate

##### Download pretrained weights

1. See [weights readme](weights/README.md) for detail.   

2. Download pretrained yolo3 full wegiths from [Google Drive](https://drive.google.com/file/d/1SnFAlSvsx37J7MDNs3WWLgeKY0iknikP/view?usp=sharing) or [Baidu Drive](https://pan.baidu.com/s/1YCcRLPWPNhsQfn5f8bs_0g)   

3. Move downloaded file ```official_yolov3_weights_pytorch.pth``` to ```wegihts``` folder in this project.   

##### Start evaluate

```

cd evaluate

python eval_coco.py params.py

```



## Quick test

##### pretrained weights

Please download pretrained weights ```official_yolov3_weights_pytorch.pth``` or use yourself checkpoint.   

##### Start test

```

cd test

python test_images.py params.py

```

You can got result images in output folder.   






## Measure FPS

##### pretrained weights

Please download pretrained weights ```official_yolov3_weights_pytorch.pth``` or use yourself checkpoint.   

##### Start test

```

cd test

python test_fps.py params.py

```

##### Results

* Test in TitanX GPU with different input size and batch size.   

* Keep in mind this is a full test in YOLOv3. Not only backbone but also yolo layer and NMS.   



| Imp.	| Backbone | Input Size | Batch Size | Inference Time | FPS |

| ----- |:--------:|:----------:|:----------:|:--------------:|:---:|

| Paper | Darknet53| 320        | 1          | 22ms           | 45  |

| Paper | Darknet53| 416        | 1          | 29ms           | 34  |

| Paper | Darknet53| 608        | 1          | 51ms           | 19  |

| Our   | Darknet53| 416        | 1          | 28ms           | 36  |

| Our   | Darknet53| 416        | 8          | 17ms           | 58  |



## Credit

```

@article{yolov3,

	title={YOLOv3: An Incremental Improvement},

	author={Redmon, Joseph and Farhadi, Ali},

	journal = {arXiv},

	year={2018}

}

```



## Reference

* [darknet](https://github.com/pjreddie/darknet)

* [PyTorch-YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3): Thanks for YOLO loss code