# ObjectDetection-OneStageDet

**Repository Path**: yczlab/ObjectDetection-OneStageDet

## Basic Information

- **Project Name**: ObjectDetection-OneStageDet
- **Description**: 单阶段通用目标检测器
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-07-24
- **Last Updated**: 2024-06-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<img src="figures/OSD_logo.PNG" title="Logo" width="300" /> 

## Contents
##### [Introduction](#introduction)
##### [Requirements](#requirements)
##### [Features](#features)
##### [Getting Started](#Getting)
- [Installation](#installation)
- [Training](#training)
- [Evaluation](#evaluation)
- [Benchmarking the speed of network](#benchmarking-the-speed-of-network)

##### [Credits](#credits)

## Introduction
Now we have implemented yolov2 and yolov3 in this repo, which is a generation object detection framework named OneStageDet(OSD), in the future we consider to implement yolo and ssd in a single framework. 

## Requirements
- python 3.6
- pytorch 0.4.0


## Features
- Include both Yolov2 and Yolov3
- Good performance

 |544x544 |VOC2007 Test(mAP)|Time per forward<br/>(batch size = 1)|
  | :-: | :-:|:-:|
  | Yolov2  | 77.6% |11.5ms|
  | Yolov3  | 79.6% |23.1ms|
  
  The models are trained from pretrained weights on imagenet **with this implementation**.

- Train as fast as [darknet](https://github.com/pjreddie/darknet)
- A lot of efficient backbones on hand

  Like tiny yolov2, tiny yolov3, mobilenet, mobilenetv2, shufflenet(g2), shufflenetv2(1x), squeezenext(1.0-SqNxt-23v5), light xception, xception etc. 

  Check folder `vedanet/network/backbone` for details.
  
  |416x416 |VOC2007 Test(mAP)| Time per forward<br/>(batch size = 1)|
  | :-: | :-:| :-: |
  | TinyYolov2  | 57.5% | 2.4ms|
  | TinyYolov3  | 61.3% | 2.3ms|
  
  The models are **trained from scratch with this implementation**.

## Getting Started

### Installation

##### 1) Code

```bash
git clone xxxxx/ObjectDetection-OneStageDet
cd ObjectDetection-OneStageDet/
yolo_root=$(pwd)
cd ${yolo_root}/utils/test
make -j32
```

##### 2) Data

```bash
wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar

tar xf VOCtrainval_11-May-2012.tar
tar xf VOCtrainval_06-Nov-2007.tar
tar xf VOCtest_06-Nov-2007.tar

cd VOCdevkit
VOCdevkit_root=$(pwd)
```

There will now be a VOCdevkit subdirectory with all the VOC training data in it.

```bash
mkdir ${VOCdevkit_root}/onedet_cache
cd ${yolo_root}
```

open examples/labels.py, let the variable `ROOT` point to `${VOCdevkit_root}`

`python examples/labels.py` 

open cfgs/yolov2.yml, let the `data_root_dir` point to `${VOCdevkit_root}/onedet_cache`

open cfgs/yolov3.yml, let the `data_root_dir` point to `${VOCdevkit_root}/onedet_cache`

##### 3) weights
Download model weights from [baidudrive](https://pan.baidu.com/s/1a3Z5IUylBs6rI-GYg3RGbw) or [googledrive](https://drive.google.com/open?id=1nW3u35_5b0ILs2u9TOQ5Nubjx8-1ewwc).

Or downlowd [darknet19_448.conv.23](https://pjreddie.com/media/files/darknet19_448.conv.23) and [darknet53.conv.74](https://pjreddie.com/media/files/darknet53.conv.74) from darknet website:

`wget https://pjreddie.com/media/files/darknet19_448.conv.23`

`wget https://pjreddie.com/media/files/darknet53.conv.74`

Then, move all the model weights to `${yolo_root}/weights` directory.

### Training
`cd ${yolo_root}`

##### 1) Yolov2

1.1) open cfgs/yolov2.yml, let the `weights` of `train` block point to the pretrain weights

1.2) open cfgs/yolov2.yml, let the `gpus` of `train` block point to an available gpu id

1.3) If you want to print log onto screen, make the `stdout` of `train` block `True` in cfgs/yolov2.yml

1.4) run

`python examples/train.py Yolov2`

##### 2) Yolov3
2.1) open cfgs/yolov3.yml, let the `weights` of `train` block point to the pretrain weights

2.2) open cfgs/yolov3.yml, let the `gpus`  of `train` block point to an available gpu id

2.3) If you want to print log onto screen, make the `stdout` of `train` block `True` in cfgs/yolov3.yml

2.4) run

`python examples/train.py Yolov3`

##### 3) Results
The logs and weights will be in `${yolo_root}/outputs`.

##### 4) Other models
There are many other models like tiny yolov2, tiny yolov3, mobilenet, mobilenetv2, shufflenet(g2), shufflenetv2(1x), squeezenext(1.0-SqNxt-23v5), light xception, xception etc. You can try these like `1) Yolov2` part.

---

### Evaluation
`cd ${yolo_root}`

##### 1) Yolov2
1.1) open cfgs/yolov2.yml, let the `gpus` of `test` block point to an available gpu id

1.2) run

`python examples/test.py Yolov2`

##### 2) Yolov3
2.1) open cfgs/yolov3.yml, let the `gpus` of `test` block point to an available gpu id

2.2) run

`python examples/test.py Yolov3`

##### 3) Results
The output bbox will be in `${yolo_root}/results`,  every line of the file in   `${yolo_root}/results` has a format like `img_name confidence xmin ymin xmax ymax`

##### 4) Other models
There are many other models like tiny yolov2, tiny yolov3, mobilenet, mobilenetv2, shufflenet(g2), shufflenetv2(1x), squeezenext(1.0-SqNxt-23v5), light xception, xception etc. You can try these like `1) Yolov2` part.

---

### Benchmarking the speed of network
`cd ${yolo_root}`

##### 1) Yolov2
1.1) open cfgs/yolov2.yml, let the `gpus` of `speed` block point to an available gpu id

1.2) run

`python examples/speed.py Yolov2`

##### 2) Yolov3
2.1) open cfgs/yolov3.yml, let the `gpus` of `speed` block point to an available gpu id

2.2) run

`python examples/speed.py Yolov3`

##### 3) Tiny Yolov2
3.1) open cfgs/tiny_yolov2.yml, let the `gpus` of `speed` block point to an available gpu id

3.2) run

`python examples/speed.py TinyYolov2`

##### 4) Tiny Yolov3
4.1) open cfgs/tiny_yolov3.yml, let the `gpus` of `speed` block point to an available gpu id

4.2) run

`python examples/speed.py TinyYolov3`

##### 5) Mobilenet
5.1) open cfgs/region_mobilenet.yml, let the `gpus` of `speed` block point to an available gpu id

5.2) run

`python examples/speed.py RegionMobilenet`

##### 6) Other backbones with region loss
You can try these like `5) Mobilenet` part.

---

## Credits
I got a lot of code from [lightnet](https://gitlab.com/EAVISE/lightnet), thanks to EAVISE.