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)
| Method | Settings |
|---|---|
| Random Flip | True |
| Random Crop | 8% ~ 100% |
| Aspect Ratio | 3/4 ~ 4/3 |
| Random PCA Lighting | 0.1 |
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
| Model | Top-1 | Top-5 | Baidu Yun | Google Cloud |
|---|---|---|---|---|
| FishNet99 | 77.41% | 93.59% | Download | Download |
| FishNet150 | 78.14% | 93.95% | Download | Download |
| FishNet201 | 78.76% | 94.39% | Available Soon | Available Soon |
Models trained with cosine lr schedule (200 epochs) and label smoothing
| Model | Top-1 | Top-5 | Baidu Yun | Google Cloud |
|---|---|---|---|---|
| FishNet99 | - | - | Available Soon | Available Soon |
| FishNet150 | 79.35% | 94.75% | Download | Download |
| FishNet201 | - | - | Available Soon | Available Soon |
- 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].