AqUavplant Dataset: An Aquatic Plant Classification and Segmentation High-Resolution Image Dataset using Unmanned Aerial Vehicle RGB Camera. This repository is for custom data loader and benchmarking all the baselines in PyTorch. This work is the benchmarking code for the AqUavplant dataset. The article is submitted to a journal and it is titled as 'AqUavplant Dataset: A High-Resolution Aquatic Plant Classification and Segmentation Image Dataset Using UAV'
The dataset will be available upon article publication. It is uploaded privately in figshare.
Image directory tree:
locationwise_split/
train/
BAUbotanicalgarden/
frame1/
BAUbotanicalgarden_1_binary.png
BAUbotanicalgarden_1_multiclass.png
frame_1.jpg
frame2/
BAUbotanicalgarden_2_binary.png
....
BAUmuseum/
frame1/
BAUmuseum_1_binary.png
....
validation/
Shaplabil1/
Shaplabil1_1_binary.png
....
test/
Shaplabi2/
Shaplabil2_1_binary.png
....
classwise_split/
train/
...
validation/
...
test/
...
pip install -r requirements.txt
N.B: Install pytorch-gpu from conda if it mismatches.
- For binary semantic segmentation run the notebook 'AqUavplant-binary-segmentation.ipynb'. There are five models in the notebook, one can be used by uncommenting the corresponding block. After running the whole code, all the output results and figures will be saved.
- For multiclass semantic segmentation run the notebook 'aqUavplant-multiclass-segmentation.ipynb'. There are five models in the notebook similar to binary one, one can be used by uncommenting the corresponding block.
The models that are in the codes are:
- U Net
- R2Unet
- Attention U Net
- R2Attention U Net
- DeepLabV3 (resnet 50, resnet101, mobilenet V3)
Thanks to the repository: https://github.com/LeeJunHyun/Image_Segmentation/tree/master, for his model architecture codes in pytorch.