NWPU-MOC dataset and Sample Code

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This repo is the official implementation of the paper: NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images.

Getting Started

Preparation

• Installation

  • Clone this repo:

    git clone https://github.com/lyongo/NWPU-MOC.git
    

• Data Preparation

  • Download NWPU-MOC dataset from GoogleDrive or BaiduNetDisk.
  • Unzip *zip files. Finally, the folder tree is below:

    -- NWPU-MOC
      ├── annotations
      │   ├── airplane
      │   ├── boat
      │   ├── car
      │   ├── container
      │   ├── farmland
      │   ├── house
      │   ├── industrial
      │   ├── mansion
      │   ├── other
      │   ├── pool
      │   ├── stadium
      │   ├── tree
      │   ├── truck
      │   └── vessel
      │       └── jsons
      │           ├── A0_2020_orth25_0_8_1.json
      │           ├── A0_2020_orth25_0_8_2.json
      │           ├── ...
      │           └── A7_2020_orth25_9_7_4.json
      ├── gt
      │   ├── A0_2020_orth25_0_8_3.npz
      │   ├── A0_2020_orth25_1_10_2.npz
      │   ├── ...
      │   └── A7_2020_orth25_9_7_4.npz
      ├── gt14
      │   ├── A0_2020_orth25_0_8_1.npz
      │   ├── A0_2020_orth25_0_8_2.npz
      │   ├── ...
      │   └── A7_2020_orth25_9_7_4.npz
      ├── ir
      │   ├── A0_2020_ir_0_8_1.png
      │   ├── A0_2020_ir_0_8_2.png
      │   ├── ...
      │   └── A7_2020_ir_9_7_4.png
      ├── rgb
      │   ├── A0_2020_orth25_0_8_1.png
      │   ├── A0_2020_orth25_0_8_2.png
      │   ├── ..
      │   └── A7_2020_orth25_9_7_4.png
      ├── test.txt
      ├── train.txt
      └── val.txt
  

  

  • Modify __C_MOC_RS.DATA_PATH in ./datasets/setting/MOC.py with the your dataset path.

Training

• Set the parameters in config.py and ./datasets/setting/MOC.py .
• run python train.py.

Testing

We only provide an example to forward the model on the test set. You may need to modify it to test your models.

• Run python test.py.

Pre-trained Models

Performance on the validation set

Citation

If you find this project useful for your research, please cite:

@ARTICLE{10410235,
  author={Gao, Junyu and Zhao, Liangliang and Li, Xuelong},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images}, 
  year={2024},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TGRS.2024.3356492}}

Our code borrows a lot from the C^3 Framework, you may cite:

@article{gao2019c,
  title={C$^3$ Framework: An Open-source PyTorch Code for Crowd Counting},
  author={Gao, Junyu and Lin, Wei and Zhao, Bin and Wang, Dong and Gao, Chenyu and Wen, Jun},
  journal={arXiv preprint arXiv:1907.02724},
  year={2019}
}