Open-source semantic segmentation models - Free implementation of precise semantic segmentation for pet images

Semantic Segmentation

Developed by keras-io

Semantic segmentation model based on U-net architecture, specifically designed for pet image segmentation

Image Segmentation #Pet Image Segmentation #U-net Architecture #Pixel-level Classification

Downloads 18

Release Time : 3/2/2022

Model Overview

This model uses the U-net architecture for semantic segmentation of pet images, accurately identifying pet contours and shapes in images.

Model Features

Precise Pixel-level Segmentation

Capable of classifying each pixel in an image to accurately identify pet contours

U-net Architecture

Utilizes the classic U-net architecture, particularly suitable for medical imaging and semantic segmentation tasks

Visualization Output

Provides intuitive segmentation result visualizations, including raw output, masks, and binary masks

Model Capabilities

Image Segmentation

Pixel-level Classification

Pet Recognition

Shape Analysis

Use Cases

Computer Vision

Pet Image Analysis

Used for identifying and segmenting subjects in pet photos

Generates precise pet contour segmentation maps

Image Editing Assistance

Provides accurate pet segmentation results for image editing software

Facilitates editing operations such as background replacement

🚀 Keras semantic segmentation models on the 🤗Hub! 🐶 🐕 🐩

Host your Keras semantic segmentation models on the Hugging Face Hub and perform image segmentation tasks.

🚀 Quick Start

Full credits go to François Chollet.

This repository contains the model from this notebook on segmenting pets using U-net-like architecture. We've changed the inference part to enable segmentation widget on the Hub. (see pipeline.py)

✨ Features

Image Segmentation: This model can segment images and reveal the shapes of objects in the images.
Semantic Segmentation: It classifies pixels and assigns a class (e.g., cat or dog) to each pixel.

📚 Documentation

Background Information

Image classification task tells us about a class assigned to an image, and object detection task creates a boundary box on an object in an image. But what if we want to know about the shape of the image? Segmentation models helps us segment images and reveal their shapes. It has many variants, including, panoptic segmentation, instance segmentation and semantic segmentation. This post is on hosting your Keras semantic segmentation models on Hub.

Semantic segmentation models classify pixels, meaning, they assign a class (can be cat or dog) to each pixel. The output of a model looks like following. Raw Output

We need to get the best prediction for every pixel. Mask

This is still not readable. We have to convert this into different binary masks for each class and convert to a readable format by converting each mask into base64. We will return a list of dicts, and for each dictionary, we have the label itself, the base64 code and a score (semantic segmentation models don't return a score, so we have to return 1.0 for this case). You can find the full implementation in pipeline.py. Binary Mask

Now that you know the expected output by the model, you can host your Keras segmentation models (and other semantic segmentation models) in the similar fashion. Try it yourself and host your segmentation models! Segmented Cat

📄 License

This project is licensed under the CC0 1.0 Universal license.

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