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https://github.com/lyongo/NWPU-MOC
This is a repository about NWPU-MOC dataset and code.
https://github.com/lyongo/NWPU-MOC
Last synced: about 2 months ago
JSON representation
This is a repository about NWPU-MOC dataset and code.
- Host: GitHub
- URL: https://github.com/lyongo/NWPU-MOC
- Owner: lyongo
- Created: 2023-07-22T09:23:51.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-01-24T06:01:13.000Z (8 months ago)
- Last Synced: 2024-07-22T05:20:57.262Z (2 months ago)
- Language: Python
- Size: 3.26 MB
- Stars: 18
- Watchers: 1
- Forks: 2
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# NWPU-MOC dataset and Sample Code
---
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](https://drive.google.com/file/d/1AHOBAzOag0jlH3cLjukXdMYsCJhYaiOi/view?usp=drive_link) or [BaiduNetDisk](https://pan.baidu.com/s/145ajOgWBNF_KRb04mDTtkg?pwd=nwpu).
- 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}
}
```