France1/unet-multiclass-pytorch
Multiclass semantic segmentation using U-Net architecture combined with strong image augmentation
This project helps researchers and image analysts automatically identify and outline different objects within images, even with limited labeled examples. You provide images and their corresponding masks (outlines of objects), and it produces a model that can then predict detailed segmentation masks for new, unseen images. It's designed for someone who needs precise object boundary detection in complex images.
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Use this if you need to segment multiple types of objects in images, especially when you have a small dataset of labeled examples, and require robust results through advanced augmentation.
Not ideal if you are looking for a simple object classification or detection system, or if you have a very large, well-labeled dataset where simpler models might suffice.
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Last pushed
Feb 08, 2021
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