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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

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

Testing your own data

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

Training

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

python training.py

Citing

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},
}