By Liang Han, Pichao Wang, Zhaozheng Yin, Fan Wang and Hao Li.
This repo is an implementation of ["Context and Structure Mining Network for Video Object Detection"], accepted by IJCV 2021. This repository contains a PyTorch implementation of our approach CSMN based on maskrcnn_benchmark.
Please cite our paper in your publications if it helps your research:
Please follow INSTALL.md for installation instructions.
Please download ILSVRC2015 DET and ILSVRC2015 VID dataset from here. After that, we recommend to symlink the path to the datasets to datasets/. And the path structure should be as follows:
./datasets/ILSVRC2015/
./datasets/ILSVRC2015/Annotations/DET
./datasets/ILSVRC2015/Annotations/VID
./datasets/ILSVRC2015/Data/DET
./datasets/ILSVRC2015/Data/VID
./datasets/ILSVRC2015/ImageSets
Note: We have already provided a list of all images we use to train and test our model as txt files under directory datasets/ILSVRC2015/ImageSets. You do not need to change them.
Note: Cache files will be created at the first time you run this project, this may take some time! Don't worry!
Note: Currently, one GPU could only hold 1 image. Do not put 2 or more images on 1 GPU!
Note We provide template files named BASE_RCNN_{}gpus.yaml which would automatically change the batch size and other relevant settings. This behavior is similar to detectron2. If you want to train model with different number of gpus, please change it by yourself :) But assure 1 GPU only holds 1 image! That is to say, you should always keep SOLVER.IMS_PER_BATCH and TEST.IMS_PER_BATCH equal to the number of GPUs you use.
Please follow demo/README.md to see how to visualize your own images or video.
If you want to use these methods on your own dataset or implement your new method. Please follow CUSTOMIZE.md.
The implementation of this work is built on the official implementation of "Memory Enhanced Global-Local Aggregation for Video Object Detection" (https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Memory_Enhanced_Global-Local_Aggregation_for_Video_Object_Detection_CVPR_2020_paper.pdf), which is publicly available at https://github.com/Scalsol/mega.pytorch
Any pull requests or issues are welcomed.