A Pytorch Implementation of FSOD

Requirements

Tested under python3.

• python packages
  • pytorch==0.4.1
  • torchvision>=0.2.0
  • cython
  • matplotlib
  • numpy
  • scipy
  • opencv
  • pyyaml
  • packaging
  • pycocotools — for COCO dataset, also available from pip.
  • tensorboardX — for logging the losses in Tensorboard
• An NVIDAI GPU and CUDA 9.0 are required. (Do not use other versions)
• NOTICE: different versions of Pytorch package have different memory usages.

Compilation

Compile the CUDA code:

cd lib  # please change to this directory
sh make.sh

Data Preparation

Please add data in the fsod directory and the structure is :

Download FSOD:

Download the images and annotations from Google Driver

FSOD Dataset Format and Usage:

The FSOD dataset is in MS COCO format (under debug), so place the FSOD dataset as the COCO dataset. And you can use the FSOD dataset like COCO dataset.

Put the FSOD dataset as the following structure:

YOUR_PATH
    └── your code dir
          ├── your code
          ├── ...
          │ 
          └── datasets
                ├──── fsod
                |       ├── annotations
                │       │       ├── fsod_train.json
                │       │       └── fsod_test.json
                │       └── images
                │             ├── part_1
                │             └── part_2
                │ 
                ├──── coco
                |       ├── annotations
                │       │       ├── instances_train2017.json
                │       │       └── instances_val2017.json
                │       └── images
                │ 
                └── other datasets

Dataset Summary:

	Train	Test
No. Class	800	200
No. Image	52350	14152
No. Box	147489	35102
Avg No. Box / Img	2.82	2.48
Min No. Img / Cls	22	30
Max No. Img / Cls	208	199
Avg No. Img / Cls	75.65	74.31
Box Size	[6, 6828]	[13, 4605]
Box Area Ratio	[0.0009, 1]	[0.0009, 1]
Box W/H Ratio	[0.0216, 89]	[0.0199, 51.5]

Training and evaluation

CUDA_VISIBLE_DEVICES=0,1,2,3 python3 tools/train_net_step.py --save_dir fsod_save_dir --dataset fsod --cfg configs/fsod/voc_e2e_faster_rcnn_R-50-C4_1x_old_1.yaml --bs 4 --iter_size 2 --nw 4 --load_detectron data/pretrained_model/model_final.pkl

CUDA_VISIBLE_DEVICES=0,1,2,3 python3 tools/test_net.py --multi-gpu-testing --dataset fsod --cfg configs/fsod/voc_e2e_faster_rcnn_R-50-C4_1x_old_1.yaml --load_ckpt Outputs/fsod_save_dir/ckpt/model_step59999.pth

This repository is originally built on roytseng-tw/Detectron.pytorch.

Citation

If you use this dataset in your research, please cite this paper.

@inproceedings{fan2020fsod,
  title={Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector},
  author={Fan, Qi and Zhuo, Wei and Tang, Chi-Keung and Tai, Yu-Wing},
  booktitle={CVPR},
  year={2020}
}