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.
- python 3.6
- pytorch 0.4.0
- Include both Yolov2 and Yolov3
- Good performance
| 544x544 | VOC2007 Test(mAP) | Time per forward (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
-
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/backbonefor details.416x416 VOC2007 Test(mAP) Time per forward
(batch size = 1)TinyYolov2 57.5% 2.4ms TinyYolov3 61.3% 2.3ms The models are trained from scratch with this implementation.
git clone xxxxx/ObjectDetection-OneStageDet
cd ObjectDetection-OneStageDet/
yolo_root=$(pwd)
cd ${yolo_root}/utils/test
make -j32wget https://pjreddie.com/media/files/VOCtrainval_11-May-2012.tar
wget https://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
wget https://pjreddie.com/media/files/VOCtest_06-Nov-2007.tar
tar xf VOCtrainval_11-May-2012.tar
tar xf VOCtrainval_06-Nov-2007.tar
tar xf VOCtest_06-Nov-2007.tar
cd VOCdevkit
VOCdevkit_root=$(pwd)There will now be a VOCdevkit subdirectory with all the VOC training data in it.
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
Download model weights from baidudrive or googledrive.
Or downlowd darknet19_448.conv.23 and 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.
cd ${yolo_root}
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
2.1) 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
The logs and weights will be in ${yolo_root}/outputs.
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.
cd ${yolo_root}
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
2.1) open cfgs/yolov3.yml, let the gpus of test block point to an available gpu id
2.2) run
python examples/test.py Yolov3
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
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.
cd ${yolo_root}
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
2.1) open cfgs/yolov3.yml, let the gpus of speed block point to an available gpu id
2.2) run
python examples/speed.py Yolov3
3.1) 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
4.1) 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
5.1) 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
You can try these like 5) Mobilenet part.
I got a lot of code from lightnet, thanks to EAVISE.
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