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yolo3_pytorch

(★ 104)

Yolo3 Implementation in Pytorch using COCO and BDD100K dataset. Forked from https://github.com/BobLiu20/YOLOv3_PyTorch.git. For my own fun, so the introduction is not accurate.

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- __init__.py
- bdd_dataset.py
- coco_dataset.py
- data_transforms.py
- loss_curve.png
- utils.py
- detect.py
- params.py
- predictions.jpg
- predictions_small.jpg
- coco_index2category.json
- eval.py
- eval_coco.py
- params.py
- - __init__.py
  - darknet.py
- __init__.py
- model_main.py
- yolo_loss.py
- params.py
- test_fps.py
- test_img.py
- params.py
- training.py
- training_gray.py
- README.md
.gitignore
data
README.md
requirements.txt

# Installation Guide

1. Get the code

git clone https://github.com/zhanghanduo/yolo3_pytorch

Downloads the entire project code from GitHub to your computer.

cd yolo3_pytorch

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip3 install -r requirements.txt --user

Installs the Python libraries listed in requirements.txt (or similar).

python -m tensorboard.main --logdir=YOUR_WORKING_DIR

Runs the Python script (or module).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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# YOLO version3 in Pytorch
Full implementation of YOLO version3 in PyTorch, including training, evaluation, simple deployment(developing).

## Overview
#### YOLOv3: An Incremental Improvement
[[Paper]](https://pjreddie.com/media/files/papers/YOLOv3.pdf)   
[[Original Implementation]](https://github.com/pjreddie/darknet)

#### Motivation
Implement YOLOv3 and darknet53 without original darknet cfg parser.   
It is easy to custom your backbone network. Such as resnet, densenet...

Also decide to develop custom structure (like grayscale pretrained model)
## Installation
##### Environment
* pytorch >= 0.4.0
* python >= 3.6.0
##### Get code
```
git clone https://github.com/zhanghanduo/yolo3_pytorch.git
cd YOLOv3_PyTorch
pip3 install -r requirements.txt --user
```
##### Download COCO dataset
```
cd data/
bash get_coco_dataset.sh
```
##### Download BDD dataset
Please visit [BDD100K](https://github.com/ucbdrive/bdd-data) for details.

## Training
##### 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 ```weights``` 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   
```
<p><img src="common/loss_curve.png"\></p>

## 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/open?id=1Bm_CLv9hP3mMQ5cyerKRjvt7_t1duvjI) or [Baidu Drive](https://pan.baidu.com/s/1gx-XRUE1NTfIMKkQ1L0awQ)   
3. Move downloaded file ```yolov3_weights_pytorch.pth``` to ```wegihts``` folder in this project.   
##### Start evaluate
```
cd evaluate
python eval.py params.py
python eval_coco.py params.py
```

<p align="center"><img src="https://pjreddie.com/media/image/Screen_Shot_2018-03-24_at_10.48.42_PM.png" width="480"\></p>

#### Roadmap

- [x] Yolov3 training
- [x] Yolov3 evaluation
- [ ] Add backbone network other than Darknet
- [ ] Able to adapt 3-channel image to 1-channel input

## 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 Evaluate and YOLO loss code