3DL_U-Net model
Model author: Mustapha EL AMMARI
Model description
The model has been designed by transfer tuning then fine tuning 3D U-Net model as autoencoder, using a home made dataset [1] composed of 3D image stack acquired using a confocal microscope. Training and Inference Notebooks are hosted on our Github repo [2].
Stardist Training parameters
- patch size: (64,64,64)
- batch size: 64
- epochs : 200
- image normalization: normalize channel independantly
Training dataset parameters
- split : Train 0.8 / Val 0.2
Inference
- patch size : (64,64,64)
- model : file.h5