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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: 4 days ago
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Full implementation of YOLOv3 in PyTorch
- Host: GitHub
- URL: https://github.com/BobLiu20/YOLOv3_PyTorch
- Owner: BobLiu20
- Archived: true
- Created: 2018-06-15T02:25:32.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-31T09:54:47.000Z (almost 5 years ago)
- Last Synced: 2024-08-03T01:22:01.413Z (3 months ago)
- Topics: object-detection, pytorch, yolo, yolov3
- Language: Python
- Size: 884 KB
- Stars: 624
- Watchers: 20
- Forks: 190
- Open Issues: 51
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-yolo-object-detection - BobLiu20/YOLOv3_PyTorch
- awesome-yolo-object-detection - BobLiu20/YOLOv3_PyTorch
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