QueryDet-PyTorch
This repository is the official implementation of our paper: QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection
Requirement
a. Install Pytorch 1.4
b. Install APEX for mixed precision training
c. Install our Pytorch based sparse convolution toolkit
d. Install the detectron2 toolkit. Note we build our approach based on version 0.2.1, you may follow the instructions to set environment configs
e. Install the Detectron2_Backbone for usage of MobileNet and ShuffleNet
f. Clone our repository and have fun with it!
Usage
1. Data preparation
a. To prepare MS-COCO, you may follow the instructions of Detectron2
b. We provide the data preprocessing code for VisDrone2018. You need to first download dataset from here
c. Check visdrone/data_prepare.py to process the dataset
2. Training
% train coco RetinaNet baseline
python train_coco.py --config-file models/retinanet/configs/coco/train.yaml --num-gpu 8 OUTPUT_DIR /path/to/workdir
% train coco QueryDet
python train_coco.py --config-file models/querydet/configs/coco/train.yaml --num-gpu 8 OUTPUT_DIR /path/to/workdir
% train VisDrone RetinaNet baseline
python train_visdrone.py --config-file models/retinanet/configs/visdrone/train.yaml --num-gpu 8 OUTPUT_DIR /path/to/workdir
% train VisDrone QueryDet
python train_visdrone.py --config-file models/querydet/configs/visdrone/train.yaml --num-gpu 8 OUTPUT_DIR /path/to/workdir
3. Test
% test coco RetinaNet baseline
python infer_coco.py --config-file models/retinanet/configs/coco/test.yaml --num-gpu 8 --eval-only MODEL.WEIGHTS /path/to/workdir/model_final.pth
% test coco QueryDet with Dense Inference
python infer_coco.py --config-file models/querydet/configs/coco/test.yaml --num-gpu 8 --eval-only MODEL.WEIGHTS /path/to/workdir/model_final.pth
% test coco QueryDet with CSQ
python infer_coco.py --config-file models/querydet/configs/coco/test.yaml --num-gpu 8 --eval-only MODEL.WEIGHTS /path/to/workdir/model_final.pth MODEL.QUERY.QUERY_INFER True
