DSSD in pytorch
This repository implements DSSD : Deconvolutional Single Shot Detector. The code were borrowed heavily from SSD. The things I did was the DSSD network definition, including the backbone of resnet101, deconvolutional module, and the prediction modules. Code for training, distributed training, dataset loading and data augmention is the same as lufficc's SSD. Thanks @lufficc for his great job.
It is worth mentioning that I changed the DSSD321
to DSSD320
and DSSD513
to DSSD512
to fit pytorch convolution and deconvolution modules. The mAP will not be affected at all. In fact, I get a higher mAP than paper!
Installation
Requirements
- Python3
- PyTorch 1.0 or higher
- yacs
- Vizer
- GCC >= 4.9
- OpenCV
Step-by-step installation
git clone https://github.com/ZQPei/DSSD.git
cd DSSD
#Required packages
pip install -r requirements.txt
Build
If your torchvision >= 0.3.0, nms build is not needed! We also provide a python-like nms, but is very slower than build-version.
# For faster inference you need to build nms, this is needed when evaluating. Only training doesn't need this.
cd ext
python build.py build_ext develop
Train
Setting Up Datasets
Pascal VOC
For Pascal VOC dataset, make the folder structure like this:
VOC_ROOT
|__ VOC2007
|_ JPEGImages
|_ Annotations
|_ ImageSets
|_ SegmentationClass
|__ VOC2012
|_ JPEGImages
|_ Annotations
|_ ImageSets
|_ SegmentationClass
|__ ...
Where VOC_ROOT
default is datasets
folder in current project, you can create symlinks to datasets
or export VOC_ROOT="/path/to/voc_root"
.
COCO
For COCO dataset, make the folder structure like this:
COCO_ROOT
|__ annotations
|_ instances_valminusminival2014.json
|_ instances_minival2014.json
|_ instances_train2014.json
|_ instances_val2014.json
|_ ...
|__ train2014
|_ <im-1-name>.jpg
|_ ...
|_ <im-N-name>.jpg
|__ val2014
|_ <im-1-name>.jpg
|_ ...
|_ <im-N-name>.jpg
|__ ...
Where COCO_ROOT
default is datasets
folder in current project, you can create symlinks to datasets
or export COCO_ROOT="/path/to/coco_root"
.
Single GPU training
# edit script file
vi scripts/resnet101_dssd320_voc0712_single_gpu.sh
# change line 2 export VOC_ROOT="/data/pzq/voc/VOCdevkit" to your path of VOC dataset.
# do the same change to the rest scripts file.
# for example, train DSSD320 on VOC:
sh scripts/resnet101_dssd320_voc0712_single_gpu.sh
Multi-GPU training
# for example, train DSSD320 with 4 GPUs:
sh scripts/resnet101_dssd320_voc0712_multi_gpu.sh
Evaluate
Single GPU evaluating
# for example, evaluate DSSD320:
python test.py --config-file configs/resnet101_dssd320_voc0712.yaml
Multi-GPU evaluating
# for example, evaluate DSSD320 with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS test.py --config-file configs/resnet101_dssd320_voc0712.yaml
Demo
Predicting image in a folder is simple:
python demo.py --config-file configs/resnet101_dssd320_voc0712.yaml --images_dir demo --ckpt [ckpt_path]
Develop Guide
If you want to add your custom components, please see DEVELOP_GUIDE.md for more details.