A PyTorch Implementation of Scale-Transferrable Object Detection，the official code is not found,so I trained the model with pytorch,the code support:
- Support for the MS COCO dataset and VOC PASCAL dataset
- Support for stdn300,stdn321,stdn513 training and testing
- Support for mulltigpu training
- Support training and and testing in VOC and COCO
because my GPU is limited,so I only train the stdn300 in VOC0712,if your gpu is enough,you can train other model according to configs/*,the model can be downloaded in stdn300_densenet169。the map is 76.30,the map is lower because I have not pretrained model.
| Original | Ours |
|---|---|
| 78.1 | 76.30 |
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/STDN.pytorch- Compile the nms and coco tools:
sh make.sh- Prepare dataset (e.g., VOC, COCO), refer to ssd.pytorch for detailed instructions.
you can train different set according to configs/*
python train.py --dataset VOC\COCO --config ./configs/stdn300_densenet169.py
if you train with multi gpu
CUDA_VISIBLE_DEVICES=0,1 python train.py --dataset VOC\COCO --config ./configs/stdn300_densenet169.py --ngpu 2
you can evaluate your model in voc and coco
python test.py --dataset VOC\COCO --trained_model ./weights/STDN_VOC_size300_netdensenet_epoch650.pth
you can test your image, First, download the pretrained stdn300_densenet169.pth file. Then, move the file to weights/.
python demo.py --dataset VOC\COCO --trained_model ./weights/STDN_VOC_size300_netdensenet_epoch650.pth --show
You can see the image with drawed boxes as:
