UNET for Retina Blood Vessel Segmentation in PyTorch | Image Segmentation | Deep Learning

In this video, we are going to learn about UNET segmentation using the PyTorch framework in the python programming language. Here, we are going to use the DRIVE (Digital Retinal Images for Vessel Extraction) dataset consisting of retina vessel images along with their annotated binary mask. The dataset is consist of 20 images for both training and testing set.

UNET is a U-shaped encoder-decoder network architecture consisting of four encoder blocks and four decoder blocks connected via a bridge. The encoder network (contracting path) half the spatial dimensions and double the number of filters (feature channels) at each encoder block. Likewise, the decoder network doubles the spatial dimensions and half the number of feature channels.
Read more: https://idiotdeveloper.com/what-is-unet/

Code (GitHub): https://github.com/nikhilroxtomar/Ret...

Dataset: https://www.kaggle.com/datasets/zionf...

Blog Post:
UNET Implementation in PyTorch: https://idiotdeveloper.com/unet-imple...
RESUNET Implementation in PyTorch: https://idiotdeveloper.com/resunet-im...
UNET Implementation in TensorFlow using Keras API: https://idiotdeveloper.com/unet-imple...
What is UNET: https://idiotdeveloper.com/what-is-unet/
What is RESUNET: https://idiotdeveloper.com/what-is-re...

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