CFPNet-M: A Light-Weight Encoder-Decoder Based Network for Multimodal Biomedical Image Real-Time Segmentation
In this project, we test five datasets:
- Infrared Breast Dataset
- Endoscopy (CVC-ClinicDB)
- Electron Microscopy (ISBI-2012)
- Drive (Digital Retinal Image)
- Dermoscopy (ISIC-2018)
The following dependencies are needed:
- Kearas == 2.2.4
- Opencv == 3.3.1
- Tensorflow == 1.10.0
- Matplotlib == 3.1.3
- Numpy == 1.19.1
You can download the datasets you want to try, and just run: for UNet, DC-UNet, MultiResUNet, ICNet, CFPNet-M, ESPNet and ENet, the code is in the folder network
. For Efficient-b0, MobileNet-v2 and Inception-v3, the code is in the main.py
. Choose the segmentation model you want to test and run:
main.py
The code of test speed and FLOPs are in main.py
, you can run them after training.