Awesome
Pelee: A Real-Time Object Detection System on Mobile Devices, in PyTorch
A PyTorch implementation of Pelee: A Real-Time Object Detection System on Mobile Devices The official and original Caffe code can be found here.
Description
I train Pelee with pytorch and the result is better than the original paper result,the pretrained model can be downloaded in peleenet.pth.
MAP in VOC2007
| Method | 07+12 | 07+12+coco |
|---|---|---|
| SSD300 | 77.2 | 81.2 |
| SSD+MobileNet | 68 | 72.7 |
| Original Pelee | 70.9 | 76.4 |
| Ours Pelee | 71.76 | --- |
Preparation
the supported version is pytorch-0.4.1 or pytorch-1.0
- tqdm
- opencv
- addict
- pytorch>=0.4
- Clone this repository.
git clone https://github.com/yxlijun/Pelee.Pytorch
- Compile the nms and coco tools:
sh make.sh
- Prepare dataset (e.g., VOC, COCO), refer to ssd.pytorch for detailed instructions.
train
you can train different set according to configs/*,First, you should download the pretrained model peleenet.pth,then,move the file to weights/
python train.py --dataset VOC\COCO --config ./configs/Pelee_VOC.py
if you train with multi gpu
CUDA_VISIBLE_DEVICES=0,1 python train.py --dataset VOC\COCO --config ./configs/Pelee_VOC.py --ngpu 2
eval
you can evaluate your model in voc and coco
python test.py --dataset VOC\COCO --config ./configs/Pelee_VOC.py --trained_model ./weights/Pelee_VOC.pth
demo
you can test your image, First, download the trained model Pelee_VOC.pth file. Then, move the file to weights/.
python demo.py --dataset VOC\COCO --config ./configs/Pelee_VOC.py --trained_model ./weights/Pelee_VOC.pth --show
You can see the image with drawed boxes as:
<div align=center><img src="imgs/VOC/im_res/street_stdngit.jpg" width="450" hegiht="163" align=center /> <div align=left>TODO
the code support:
- Support for the MS COCO dataset and VOC PASCAL dataset
- Support for Pelee304_VOC、Pelee304_COCO training and testing
- Support for mulltigpu training
- Support training and and testing in VOC and COCO