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Deeplabv3 Tu Resnet18
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Deeplabv3 Tu Resnet18

Developed by smp-test-models
DeepLabV3 is a semantic segmentation model implemented in PyTorch, suitable for image segmentation tasks.
Image Segmentation
Safetensors
Open Source License:MIT#Semantic Segmentation#Multi-Scale Atrous Convolution#Image Parsing
Downloads 210
Release Time : 12/23/2024

Model Overview

DeepLabV3 is an advanced semantic segmentation model capable of classifying each pixel in an image, widely used in scene understanding, medical image analysis, and other fields.

Model Features

Efficient Encoder-Decoder Structure
Uses ResNet as the encoder combined with ASPP modules to enhance multi-scale feature extraction.
Supports Multiple Pre-trained Weights
Can be initialized with ImageNet pre-trained weights to improve model performance.
Flexible Configuration Options
Supports adjusting encoder depth, output stride, decoder channels, and other parameters to adapt to different task requirements.

Model Capabilities

Image Segmentation
Semantic Segmentation
Pixel-level Classification

Use Cases

Computer Vision
Scene Understanding
Segments roads, vehicles, pedestrians, etc., in street-view images for autonomous driving systems.
Medical Image Analysis
Segments organs or lesion areas in medical images to assist in diagnosis.
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