enyalien / FSS-1000

FSS-1000, A 1000-class Dataset For Few-shot Segmentation

FSS-1000: A 1000 Class Dataset for Few-shot Segmentation

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

Prerequisites

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

FSS-1000 Dataset

• Google drive: download here
• Online Preview

Getting Started

Testing

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

View our testing results here

Testing your own data

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

Training

Coming soon

Citing

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

@article{FSS1000,
Author = {Tianhan Wei and Xiang Li and Yau Pun Chen and Yu-Wing Tai and Chi-Keung Tang},
Title = {FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation},
Year = {2019},
Eprint = {arXiv:1907.12347},
}