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Oxford Pet Segmentation
A PyTorch-based FPN model specifically designed for image segmentation tasks on the Oxford Pet dataset, providing efficient semantic segmentation capabilities.
Downloads 14
Release Time : 5/30/2024
Model Overview
This is a semantic segmentation model using the Feature Pyramid Network architecture, pre-trained on the Oxford Pet dataset, suitable for precise pet image segmentation tasks.
Model Features
Pre-trained Weights
Uses ResNet34 encoder pre-trained on ImageNet to enhance model performance
Efficient Pyramid Structure
Adopts FPN architecture to effectively combine multi-scale feature information
Easy Integration
Can be easily integrated into existing projects via the segmentation_models.pytorch library
Model Capabilities
Image Segmentation
Semantic Segmentation
Pet Image Analysis
Use Cases
Computer Vision
Pet Image Segmentation
Accurately segments pet photos, separating pets from the background
Achieves an IoU score of 0.91 on the Oxford Pet dataset
Medical Image Analysis
Can be adapted for organ or lesion segmentation in medical images
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