This repo holds the implementation code of the paper:

FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction , Shuyang Sun, Jiangmiao Pang, Jianping Shi, Shuai Yi, Wanli Ouyang, NeurIPS 2018.

FishNet was used as a key component for winning the 1st place in COCO Detection Challenge 2018.

• Python 3.6.x
• PyTorch 0.4.0 (0.3.1 support soon)

Note: We apply weight decay to all weights and biases instead of just the weights of the convolution layers.

To train FishNet-150 with 8 GPUs and batch size 256, simply run

python main.py --config "cfgs/fishnet150.yaml" IMAGENET_ROOT_PATH

Models trained without tricks

Models trained with cosine lr schedule (200 epochs) and label smoothing

• Update our arxiv paper.
• Release pre-train models.
• Train the model with more training tricks.

If you find our research useful, please cite the paper:

@inproceedings{sun2018fishnet,
  title={FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction},
  author={Sun, Shuyang and Pang, Jiangmiao and Shi, Jianping and Yi, Shuai and Ouyang, Wanli},
  booktitle={Advances in Neural Information Processing Systems},
  pages={760--770},
  year={2018}
}

You can contact Shuyang Sun by sending email to [email protected].