Ming Wang, Peng Yue*, Liangcun Jiang*, Dayu Yu, Tianyu Tuo, Jian Li
[Paper] [Project] [Dataset] [BibTeX]
No custom code was developed for this work. However, to replicate the technical validation results, the Swin Transformer source code is available at: https://github.com/SwinTransformer/Swin-Transformer-Object-Detection. The following steps can be referred to for configuring the operating environment of the Swin Transformer model.
Step 1. Create a conda environment and activate it. conda create --name openmmlab python=3.8 -y conda activate openmmlab
Step 2. Install PyTorch following official instructions, e.g.
The code requires python>=3.7, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
Step 3. Install MMEngine and MMCV using MIM. pip install -U openmim mim install mmengine mim install "mmcv>=2.0.0" Note: In MMCV-v2.x, mmcv-full is rename to mmcv, if you want to install mmcv without CUDA ops, you can use mim install "mmcv-lite>=2.0.0rc1" to install the lite version.
Step 4. Install MMDetection. Case a: If you develop and run mmdet directly, install it from source: git clone https://github.com/open-mmlab/mmdetection.git cd mmdetection pip install -v -e .
The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. jupyter is also required to run the example notebooks.
pip install opencv-python pycocotools matplotlib
If you use FASDD in your research, please use the following BibTeX entry.
@article{
title={An Open Flame and Smoke Detection Dataset for Deep Learning in Remote Sensing Based Fire Detection},
author={Ming Wang, Peng Yue, Liangcun Jiang, Dayu Yu, Tianyu Tuo, Jian Li},
journal={Geo-spatial Information Science},
year={2024}
}