单阶段通用目标检测器.zip

<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.