We provide our dataset and PyTorch implementation for relation network benchmark. Details are in our paper.

• Linux or macOS
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN
• PyTorch 0.4+

• Google drive: download here
• Online Preview: Coming soon

First, download pretrained model here.

python autolabel.py -sd imgs/example/support -td imgs/example/query

• Set option -sd to the support directory and the script will input them as support set.
• Set option -td to the path of your query images.
• Results will be saved under ./results

• Label 5 support images following the format in imgs/example/support/.
• Set your support and query path accordingly.

Arrange the dataset as described in get_oneshot_batch() in training.py, then run

python training.py

If you use this repository, dataset or want to reference our work, please use the following BibTeX entry.

@article{FSS1000,
Author = {Xiang Li and Tianhan Wei and Yau Pun Chen and Yu-Wing Tai and Chi-Keung Tang},
Title = {FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation},
Year = {2020},
Journal = {CVPR},
}