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https://github.com/cuogeihong/ceasc

The official implementation of CEASC
https://github.com/cuogeihong/ceasc

Last synced: 12 days ago
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The official implementation of CEASC

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# CEASC: Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images
The repo is the official implementation of CEASC.

Our CEASC module is at [mmdet/models/dense_heads](mmdet/models/dense_heads)

Our Sparse Convolution Implementation is at [Sparse_conv](Sparse_conv)

Our config file is at [configs/UAV](configs/UAV)

## Requirement

Please follow [docs/en/get_started.md](docs/en/get_started.md) and install the [mmdetection](https://github.com/open-mmlab/mmdetection) toolbox.

a. Install [Pytorch 1.10.1](https://pytorch.org/)

b. Install [MMDetection](https://mmdetection.readthedocs.io/en/latest/) toolbox, required mmdet >= 2.7.0, mmcv-full >= 1.4.2.

- Our project utilizes mmdet == 2.24.1, mmcv-full == 1.5.1

c. Install albumentations and other packages.

```shell
pip install nltk
pip install -r requirements/albu.txt
```

d. Install our Sparse Convolution Implementation

```shell
cd ./Sparse_conv
python setup.py install
cd ..
```

## Usage

### 1. Data preparation

You could download VisDrone and UAVDT dataset (COCO Format) from official links or from other repositories like [UFPMP-Det](https://github.com/PuAnysh/UFPMP-Det).

### 2. Training

```shell
% training on a single GPU
python tools/train.py /path/to/config-file --work-dir /path/to/work-dir

% training on multi GPUs
bash tools/dist_train.sh /path/to/config-file num-gpus --work-dir /path/to/work-dir
```

#### Checkpoints:
We provide the following checkpoints:
- GFL v1 baseline, corresponding to [baseline_gfl_res18_visdrone](https://github.com/Cuogeihong/CEASC/blob/main/configs/UAV/baseline_gfl_res18_visdrone.py): [Google Drive](https://drive.google.com/drive/folders/1OaLSG0Y3bz1DH3R7HB4y-1LBRHpgQ55c?usp=sharing)
- GFL v1 CEASC, corresponding to [dynamic_gfl_res18_visdrone](https://github.com/Cuogeihong/CEASC/blob/main/configs/UAV/dynamic_gfl_res18_visdrone.py): [Google Drive](https://drive.google.com/drive/folders/1v7pby3LqmcIdDI52KKUQ43Ra3tBQdusR?usp=sharing)
- RetinaNet baseline, corresponding to [baseline_retinanet_res18_visdrone](https://github.com/Cuogeihong/CEASC/blob/main/configs/UAV/baseline_retinanet_res18_visdrone.py): [Google Drive](https://drive.google.com/drive/folders/1Ws5UQri07GGZo_PUyGjFBQA5cI3pjn2K?usp=sharing)
- RetinaNet CEASC, corresponding to [dynamic_retinanet_res18_visdrone](https://github.com/Cuogeihong/CEASC/blob/main/configs/UAV/dynamic_retinanet_res18_visdrone.py): [Google Drive](https://drive.google.com/drive/folders/1Gu0D5XULRkMEGNTGGKNj7X6-WiZs2a34?usp=sharing)

### 3. Test

```shell
python tools/test.py /path/to/config-file /path/to/work-dir/latest.pth --eval bbox
```

## Citation

If you find our paper or this project helps your research, please kindly consider citing our paper in your publication.

```
@misc{ceasc,
title={Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images},
author={Bowei Du and Yecheng Huang and Jiaxin Chen and Di Huang},
year={2023},
eprint={2303.14488},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```