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Oxford Pet Segmentation

Developed by SimonLiao
A PyTorch-based FPN image segmentation model supporting various encoder architectures, suitable for semantic segmentation tasks.
Image Segmentation
Safetensors
Open Source License:MIT#High-precision image segmentation#ResNet encoder#Medical image analysis
Downloads 53
Release Time : 4/9/2025

Model Overview

This model is an image segmentation model based on the Feature Pyramid Network architecture, designed for semantic segmentation tasks. It supports multiple pre-trained encoders (e.g., ResNet34) and offers flexible configuration options to meet diverse needs.

Model Features

Multiple Encoder Support
Supports various pre-trained encoders (e.g., ResNet34), allowing flexible selection of backbone networks.
High Performance
Delivers excellent performance on the Oxford Pet dataset, achieving a test IoU of 0.915.
Easy to Use
Provides simple API interfaces for quick loading from pre-trained models.

Model Capabilities

Image segmentation
Semantic segmentation
Feature extraction

Use Cases

Computer Vision
Pet Image Segmentation
Used for segmenting foreground and background in pet images.
Achieved 91.5% IoU on the Oxford Pet dataset.
Medical Image Analysis
Can be used for organ or lesion segmentation in medical images.
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