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Oxford Pet Segmentation

Developed by Matiullah2401592
PyTorch-based DeepLabV3Plus image segmentation model supporting multiple encoder architectures
Image Segmentation
Safetensors
Open Source License:MIT#Efficient Semantic Segmentation#Multi-Dilation Rate ASPP#Pet Image Parsing
Downloads 87
Release Time : 4/9/2025

Model Overview

DeepLabV3Plus is an advanced semantic segmentation model combining depthwise separable convolution and ASPP modules, suitable for high-precision pixel-level image segmentation tasks

Model Features

Multi-Encoder Support
Supports various pre-trained encoders (e.g., EfficientNet) for easy transfer learning
ASPP Module
Utilizes Atrous Spatial Pyramid Pooling to effectively capture multi-scale contextual information
High-Precision Segmentation
Achieves 90.7% IoU on the Oxford Pets dataset

Model Capabilities

Image Semantic Segmentation
Pixel-Level Classification
Multi-Scale Feature Extraction

Use Cases

Medical Imaging
Organ Segmentation
Used for organ identification and segmentation in medical imaging
Autonomous Driving
Road Scene Understanding
Segmentation of key elements like roads, vehicles, and pedestrians
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