https://github.com/yqyao/FCOS_PLUS

Some improvements (center sample) about FCOS (FCOS: Fully Convolutional One-Stage Object Detection).
https://github.com/yqyao/FCOS_PLUS

anchor-free computer-vision fcos object-detection one-stage pytorch

Last synced: 3 months ago
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Some improvements (center sample) about FCOS (FCOS: Fully Convolutional One-Stage Object Detection).

• Host: GitHub
• URL: https://github.com/yqyao/FCOS_PLUS
• Owner: yqyao
• License: other
• Created: 2019-07-20T08:34:11.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-08-06T05:43:36.000Z (over 5 years ago)
• Last Synced: 2024-05-15T04:39:49.433Z (6 months ago)
• Topics: anchor-free, computer-vision, fcos, object-detection, one-stage, pytorch
• Language: Python
• Homepage:
• Size: 8.56 MB
• Stars: 316
• Watchers: 8
• Forks: 46
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

        
# FCOS_PLUS

This project contains some improvements about FCOS (Fully Convolutional One-Stage Object Detection).

## Installation

Please check [INSTALL.md](INSTALL.md) (same as original FCOS) for installation instructions. 

**Results**

Model | Total training mem (GB) | Multi-scale training | Testing time / im | AP (minival) | link 

---   |:---:|:---:|:---:|:---:|:---:|

FCOS_R_50_FPN_1x | 29.3 | No | 71ms | 37.0 | [model](https://pan.baidu.com/s/1Xcbx7EfOGvwnexXAuovM0A) |

FCOS_R_50_FPN_1x_center | 30.61 | No | 71ms | 37.8 | [model](https://pan.baidu.com/s/1Gs7AzmJRmeYhXUPDQZuSLA) |

FCOS_R_50_FPN_1x_center_liou | 30.61 | No | 71ms | 38.1 | [model](https://pan.baidu.com/s/1HpYrkAsVXNvXRFTd06SGgA) |

FCOS_R_50_FPN_1x_center_giou | 30.61 | No | 71ms | 38.2 | [model](https://pan.baidu.com/s/13_o6343Ikg4td01kVXxGSw) |

FCOS_R_101_FPN_2x | 44.1 | Yes | 74ms | 41.4 | [model](https://pan.baidu.com/s/1u_5OD5NURYe1EYFWnohgEA) |

FCOS_R_101_FPN_2x_center_giou | 44.1 | Yes | 74ms | 42.5 | [model](https://pan.baidu.com/s/1qhHM067ywwlEXfamaFq23g) |

[1] *1x and 2x mean the model is trained for 90K and 180K iterations, respectively.* \

[2] center means [center sample](fcos.pdf) is used in our training. \

[3] liou means the model use linear iou loss function. (1 - iou) \

[4] giou means the use giou loss function. (1 - giou) 

## Training

The following command line will train FCOS_R_50_FPN_1x on 8 GPUs with Synchronous Stochastic Gradient Descent (SGD):

    python -m torch.distributed.launch \

        --nproc_per_node=8 \

        --master_port=$((RANDOM + 10000)) \

        tools/train_net.py \

        --skip-test \

        --config-file configs/fcos/fcos_R_50_FPN_1x_center_giou.yaml \

        DATALOADER.NUM_WORKERS 2 \

        OUTPUT_DIR training_dir/fcos_R_50_FPN_1x_center_giou

        

Note that:

1) If you want to use fewer GPUs, please change `--nproc_per_node` to the number of GPUs. No other settings need to be changed. The total batch size does not depends on `nproc_per_node`. If you want to change the total batch size, please change `SOLVER.IMS_PER_BATCH` in [configs/fcos/fcos_R_50_FPN_1x_center_giou.yaml](configs/fcos/fcos_R_50_FPN_1x_center_giou.yaml).

2) The models will be saved into `OUTPUT_DIR`.

3) If you want to train FCOS with other backbones, please change `--config-file`.

## Citations

Please consider citing original paper in your publications if the project helps your research. 

```

@article{tian2019fcos,

  title   =  {{FCOS}: Fully Convolutional One-Stage Object Detection},

  author  =  {Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},

  journal =  {arXiv preprint arXiv:1904.01355},

  year    =  {2019}

}

```

## License

For academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact the authors.