Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/Chasel-Tsui/mmrotate-dcfl

Official implementation of the CVPR23 paper: Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection
https://github.com/Chasel-Tsui/mmrotate-dcfl

Last synced: 26 days ago
JSON representation

Official implementation of the CVPR23 paper: Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection

Host: GitHub
URL: https://github.com/Chasel-Tsui/mmrotate-dcfl
Owner: Chasel-Tsui
License: apache-2.0
Created: 2023-03-01T01:55:51.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2023-11-23T01:53:46.000Z (7 months ago)
Last Synced: 2024-02-23T06:35:37.942Z (4 months ago)
Language: Python
Homepage:
Size: 29.3 MB
Stars: 75
Watchers: 4
Forks: 1
Open Issues: 20
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff

Lists

awesome-object-detection-in-aerial-images - Code

README

        # mmrotate-dcfl

Official implementation for the CVPR23 paper: Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection. [arxiv](https://arxiv.org/abs/2304.08876)

## Introduction

DCFL is a learning framework for detecting oriented tiny objects.

![demo image](figures/framework_final.png)

## Installation and Get Started

Required environments:

- Linux

- Python 3.7+

- PyTorch 1.10.0+

- CUDA 9.2+

- GCC 5+

- MMdet 2.23.0+

- [MMCV-DCFL](https://github.com/Chasel-Tsui/MMCV-DCFL) 

Install:

Note that this repository is based on the MMRotate. Assume that your environment has satisfied the above requirements, please follow the following steps for installation.

```

git clone https://github.com/Chasel-Tsui/mmrotate-dcfl.git

cd mmrotate-dcfl

pip install -r requirements/build.txt

python setup.py develop

```

## Main Results

DOTA-v2.0

| Method |         Backbone         | AP50  |  Angle | lr schd | Aug  | Batch Size |                           Configs                          | Speed |

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

|RetinaNet-O| ResNet50 (1024,1024,200) | 46.68 |  le135  |   1x    |  Flipping   |     2      | [retinanet_obb_r50_dota2](configs/baselines/retinanet_le135_r50_dota2.py) | 20.8 FPS|

|R3Det w/ KLD| ResNet50 (1024,1024,200) | 47.26 |  le135  |   1x    |  Flipping   |     2      | [r3det_le135_r50_dota2](configs/baselines/r3det_le135_r50_dota2.py) | 16.2 FPS |

|ATSS-O| ResNet50 (1024,1024,200) | 49.57 |  le135  |   1x    |  Flipping   |     2      | [atss_le135_r50_dota2](configs/baselines/atss_le135_r50_dota2.py) | - |

|S2A-Net| ResNet50 (1024,1024,200) | 49.86 |  le135  |   1x    |  Flipping   |     2      | [s2a_le135_r50_dota2](configs/baselines/s2a_le135_r50_dota2.py) | 18.9 FPS|

|DCFL| ResNet50 (1024,1024,200) | 51.57 | le135  |   1x    |  Flipping   |     2      |     [dcfl_r50_dota2](configs/dcfl/dotav2_test_dcfl_r50_1x.py)      | 20.9 FPS |

|DCFL| ResNet101 (1024,1024,200) | **52.54** | le135  |   1x    |  Flipping   |     2      |     [dcfl_r101_dota2](configs/dcfl/dotav2_test_dcfl_r101_1x.py)      | - |

## Visualization

Predictions of the RetinaNet-O are shown in the first row, predictions of the DCFL are shown in the second row. Note that the green box denotes the True Positive, the red box denotes the False Negative and the blue box denotes the False Positive predictions.

![demo_images](figures/vis.png)

## Citation

If you find this work helpful, please consider citing:

```bibtex

@InProceedings{Xu_2023_CVPR,

    author    = {Xu, Chang and Ding, Jian and Wang, Jinwang and Yang, Wen and Yu, Huai and Yu, Lei and Xia, Gui-Song},

    title     = {Dynamic Coarse-To-Fine Learning for Oriented Tiny Object Detection},

    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},

    month     = {June},

    year      = {2023},

    pages     = {7318-7328}

}

```