Deeplabv3p - ResNet50 Open-Source Image Segmentation Model - Free to Implement Pixel-Level Multi-Class Semantic Segmentation Tasks

Deeplabv3p Resnet50

Developed by keras-io

DeepLabV3+ architecture implemented in Keras for pixel-level multi-class semantic segmentation tasks

Image Segmentation #Human body part segmentation #Atrous convolution architecture #Pixel-level annotation

Downloads 175

Release Time : 3/2/2022

Model Overview

This model adopts an encoder-decoder structure, combining atrous convolution and spatial pyramid pooling modules, suitable for fine-grained semantic segmentation scenarios such as human body part segmentation

Model Features

Advanced Atrous Convolution Technology

Utilizes spatial pyramid pooling modules and multi-scale atrous convolution to effectively expand the receptive field

Encoder-Decoder Structure

Combines low-level feature details with high-level semantic information to improve segmentation boundary accuracy

Pre-trained Backbone Network

Uses ImageNet pre-trained ResNet50 as the feature extractor

Model Capabilities

Pixel-level semantic annotation

Multi-class image segmentation

Human body part recognition

Use Cases

Computer Vision

Human Body Part Segmentation

Identify and segment various human body parts in images

Trained on Crowd Instance-level Human Parsing Dataset

Medical Image Analysis

Potentially applicable for organ or lesion area segmentation (requires fine-tuning)

🚀 Multiclass semantic segmentation using DeepLabV3+

This repository provides the model and notebook for multiclass semantic segmentation using DeepLabV3+ in Keras, offering a practical solution for computer vision tasks.

🚀 Quick Start

This repo contains the model and the notebook to this Keras example on Multiclass semantic segmentation using DeepLabV3+.

Full credits to: Soumik Rakshit

The model is trained for demonstrative purposes and does not guarantee the best results in production. For better results, follow & optimize the [Keras example]((https://keras.io/examples/vision/deeplabv3_plus/) as per your need.

✨ Features

Semantic segmentation, with the goal to assign semantic labels to every pixel in an image, is an essential computer vision task. In this example, we implement the DeepLabV3+ model for multi-class semantic segmentation, a fully-convolutional architecture that performs well on semantic segmentation benchmarks.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Training Data

The model is trained on a subset (10,000 images) of Crowd Instance-level Human Parsing Dataset. The Crowd Instance-level Human Parsing (CIHP) dataset has 38,280 diverse human images. Each image in CIHP is labeled with pixel-wise annotations for 20 categories, as well as instance-level identification. This dataset can be used for the "human part segmentation" task.

Model

The model uses ResNet50 pretrained on ImageNet as the backbone model.

References:

🔧 Technical Details

No specific technical implementation details (more than 50 words) are provided in the original document, so this section is skipped.

📄 License

The project is licensed under the cc0-1.0 license.

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