# yolov1-pytorch
This repo is a pytorch implementation of yolov1.
项目完整链接：[yolov1-pytorch](https://github.com/ivanwhaf/yolov1-pytorch "yolov1-pytorch")
* Deep Learning based yolov1 object detector, using `Pytorch` deep learning framework
* This project can be also transplanted to other edge platforms like `Raspberry Pi`
* Paper: [https://arxiv.org/pdf/1506.02640.pdf](https://arxiv.org/pdf/1506.02640v5.pdf)
# Demo
## Run in command line
`detect.py` runs inference on a variety of sources, cd your project path and type:
```bash
$ python detect.py -c cfg/yolov1.cfg -d cfg/dataset.cfg -w weights/yolov1.pth --source 0 # webcam
file.jpg # image
file.mp4 # video
path/*.jpg # img folder path
path/*.mp4 # video folder path
```
# Usage
## Preparation
* 1.Create an empty folder (in this project was `dataset` folder) as your dataset folder
* 2.Organize directories, you should move images folder and labels folder into dataset folder. Each image must have
corresponding same-name `.txt` label in labels folder. Each label file has several rows, each row represents an
object, each object has `x,y,w,h,c` five values, which represents `center_x, center_y, width, height, class`. The
coord value should be normalized to `0~1`
* 3.Your dataset folder should be like this:
```
dataset/{dataset name}/
├──images
| ├──001.png
| ├──002.png
| └──003.jpg
└──labels
├──001.txt
├──002.txt
└──003.txt
```
* 4.Your label `.txt` file should be like this:
```
0.153262 0.252950 0.185673 0.524723 0
0.467835 0.562623 0.265428 0.113139 1
....
```
## Train
* 1.Edit `cfg/yolov1.yaml` config file, and set **num_classes** according to class number of dataset (this repo
num_classes=10)
* 2.Edit `cfg/dataset.yaml` config file, and set **class_names** as class names, set **images** as dataset images path,
set **labels** as dataset labels path
* 3.Modify **epochs**, **learning rate**, **batch size** and other hyper parameters in `train.py` depending on actual
situations
* 4.Run `train.py` to train your own yolov1 model:
```bash
$ python train.py --cfg cfg/yolov1.cfg --data cfg/dataset.cfg
```
## Caution
* Need plotting model structure? Just install `graphviz` and `torchviz` first
* Validation and test periods are among training process, see train.py for more details
* You can also imitate `models/model.py` customizing your own model
## Program Structure Introduction
* cfg: some yolo and dataset config files
* data: some samples and demo jpgs
* dataset: your own dataset path
* models: some network model structure files
* utils: some util and kernel files
* output: output file folder
* weights: model weights
# Requirements
Python 3.X version with all [requirements.txt](https://github.com/ivanwhaf/yolov1-pytorch/blob/master/requirements.txt)
dependencies installed, including `torch>=1.2`. To install run:
```bash
$ pip install -r requirements.txt
```
# Environment
## PC Ⅰ
* Ubuntu 20.04
* Python 3.7.8
* CUDA 10.0
* cuDNN 7.4
* torch 1.2.0
* Nvidia MX350 2G
## PC Ⅱ
* Windows 10
* Python 3.6.8
* CUDA 10.2
* cuDNN 7.6
* torch 1.6.0
* Nvidia GTX 1060 3G
## PC Ⅲ
* Windows 10
* Python 3.8.6
* CUDA 10.1
* cuDNN 7.6.5
* torch 1.7.0
* Nvidia GTX 1080 TI 11G