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mmdet

mmdet

https://github.com/johnran103/mmdet

Science Score: 26.0%

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Repository

mmdet

Basic Info
  • Host: GitHub
  • Owner: johnran103
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 53.9 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 3 years ago · Last pushed over 2 years ago
Metadata Files
Readme License Citation

README.md

UFPMP-Det: Toward Accurate and Efficient Object Detection on Drone Imagery

The repo is the official implementation of UFPMP-Det.

The code of UFP module is at mmdet/core/ufp

The code of MP-Det is at mmdet/models/denseheads/mphead.py

The config of our project is at configs/UFPMP-Det

Install

  1. This repo is implemented based on mmdetection. Please install it according to get_start.md.
  2. shell pip install nltk pip install albumentations ## Quickstart We provide the Dataset(COCO Format) as follows:
  3. VisDrone:https://pan.baidu.com/s/1FfAsAApHZruucO5A2QgQAg qrvs
  4. UAVDT:https://pan.baidu.com/s/1KLmU5BBWwgtFbuZa7QWavw z08x

We provide the checkpoint as follows: - VisDrone Coarse-Det:: https://pan.baidu.com/s/1jK3bqImDGSwqRJGVXinS0w : nab3 - VisDrone MP-Det ResNet50: : https://pan.baidu.com/s/1zOoJVO2fPejnzM9KioZLuQ : m7rj

Training

This repo is only supposed single GPU.

Prepare

Build by yourself: We provide two data set conversion tools.

```shell

conver VisDrone to COCO

python UFPMP-Det-Tools/build_dataset/VisDrone2COCO.py

conver UAVDT to COCO

python UFPMP-Det-Tools/build_dataset/UAVDT2COCO.py

build UFP dataset(VisDrone)

CUDAVISIBLEDEVICES=2 python UFPMP-Det-Tools/builddataset/UFPVisDrone2COCO.py \ ./configs/UFPMP-Det/coarsedet.py \ ./workdirs/coarsedet/epoch12.pth \ xxxxxx/dataset/COCO/images/UAVtrain \ xxxxxx/dataset/COCO/annotations/instancesUAVtrainv1.json \ xxxxxx/dataset/COCO/images/instanceUFPUAVtrain/ \ xxxxxx/dataset/COCO/annotations/instanceUFPUAVtrain.json \ --txtpath pathtoVisDroneannotation_dir ```

Download:

In Quick Start

Train Coarse Detector

shell CUDA_VISIBLE_DEVICES=0 python tools/train.py ./configs/UFPMP-Det/coarse_det.py

Train MP-Det

shell CUDA_VISIBLE_DEVICES=0 python tools/train.py ./configs/UFPMP-Det/mp_det_res50.py

Test

```shell CUDAVISIBLEDEVICES=2 python UFPMP-Det-Tools/evalscript/ufpmpdeteval.py \ ./configs/UFPMP-Det/coarsedet.py \ ./workdirs/coarsedet/epoch12.pth \ ./configs/UFPMP-Det/mpdetres50.py \ ./workdirs/mpdetres50/epoch12.pth \ XXXXX/dataset/COCO/annotations/instancesUAVval_v1.json \ XXXXX/dataset/COCO/images/UAVval

```

Citation

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

@inproceedings{ufpmpdet, title={UFPMP-Det: Toward Accurate and Efficient Object Detection on Drone Imagery}, author={Huang, Yecheng and Chen, Jiaxin and Huang, Di}, booktitle={AAAI Conference on Artificial Intelligence}, year={2022} }

Owner

  • Login: johnran103
  • Kind: user

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Dependencies

docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
requirements/build.txt pypi
  • cython *
  • numpy *
requirements/docs.txt pypi
  • docutils ==0.16.0
  • recommonmark *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
  • sphinx_rtd_theme ==0.5.2
requirements/mminstall.txt pypi
  • mmcv-full >=1.3.17
requirements/optional.txt pypi
  • cityscapesscripts *
  • imagecorruptions *
  • scipy *
  • sklearn *
requirements/readthedocs.txt pypi
  • mmcv *
  • torch *
  • torchvision *
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • pycocotools *
  • pycocotools-windows *
  • six *
  • terminaltables *
requirements/tests.txt pypi
  • asynctest * test
  • codecov * test
  • flake8 * test
  • interrogate * test
  • isort ==4.3.21 test
  • kwarray * test
  • onnx ==1.7.0 test
  • onnxruntime >=1.8.0 test
  • pytest * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements.txt pypi
setup.py pypi