yolov1-pytorch

This repo is a pytorch implementation of yolov1.

• 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

Demo

Run in command line

detect.py runs inference on a variety of sources, cd your project path and type:

$ 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:

$ 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 dependencies installed, including torch>=1.2. To install run:

$ 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