- It is a pytorch based Segmentation model inspired by the research paper
T. M. Khan, A. Robles-Kelly and S. S. Naqvi, "T-Net: A Resource-Constrained Tiny Convolutional Neural Network for Medical Image Segmentation," 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2022, pp. 1799-1808, doi: 10.1109/WACV51458.2022.00186.published in CVPR 2022
- run
pip install TNet-Segmentationto install the package
- The model takes three parameters: -
- input_channnel (defaults to 3)
- emb_size (size of the output channel after first downsampling conv layer.Defaults to 512)
- num_classes (the num of segmentation classes, defaults to 3)
- Usage is as simple as below:-
>>> from TNet_Segmentation import TNet >>> net = TNet(input_channel=3, emb_size=256, num_classes=3)
- After creating the model call visualize function as follows:-
>>> from TNet_Segmentation import visualize >>> visualize(net) ================================================================= Layer (type:depth-idx) Param # ================================================================= TNet -- ├─Conv2d: 1-1 7,168 ├─TNetConvBlock: 1-2 -- │ └─ModuleList: 2-1 -- │ │ └─Conv2d: 3-1 590,080 │ │ └─BatchNorm2d: 3-2 512 │ │ └─ReLU: 3-3 -- │ │ └─Conv2d: 3-4 65,792 │ │ └─BatchNorm2d: 3-5 512 │ │ └─Conv2d: 3-6 2,560 │ │ └─BatchNorm2d: 3-7 512 │ │ └─ReLU: 3-8 -- │ │ └─MaxPool2d: 3-9 --
- Dice Loss is implemented in this library and can be used as follows:
>>> from TNet_Segmentation import DiceLoss >>> loss = DiceLoss()