Example of semantic segmentation in Keras

Last update: Mar 23, 2022

Overview

keras-semantic-segmentation-example

Example of semantic segmentation in Keras

Single class example:

Generated data: random ellipse with random color on random color background and with random noise added.

Result: 1st images is input image, 2nd image is ground truth mask, 3rd image is probability, 4th image is probability thresholded at 0.5.

Multi-class example:

Generated data: first class is random ellipse with random color and second class is random rectangle with random color on random color background and with random noise added.

Result: 1st images is input image, 2nd image is ground truth mask, 3rd image is probability, 4th image is probability thresholded at 0.5.

Owner

GitHub Repository

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Example of semantic segmentation in Keras

Related tags

Overview

keras-semantic-segmentation-example

Single class example:

Multi-class example:

Owner

This example implements the end-to-end MLOps process using Vertex AI platform and Smart Analytics technology capabilities

The Dual Memory is build from a simple CNN for the deep memory and Linear Regression fro the fast Memory

Official Pytorch implementation for 2021 ICCV paper "Learning Motion Priors for 4D Human Body Capture in 3D Scenes" and trained models / data

GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

This is an official implementation of CvT: Introducing Convolutions to Vision Transformers.

The Illinois repository for Climatehack (https://climatehack.ai/). We won 1st place!

This repository introduces a short project about Transfer Learning for Classification of MRI Images.

Python Multi-Agent Reinforcement Learning framework

MGFN: Multi-Graph Fusion Networks for Urban Region Embedding was accepted by IJCAI-2022.

Learning To Have An Ear For Face Super-Resolution

Code for "Multi-Time Attention Networks for Irregularly Sampled Time Series", ICLR 2021.

Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Official code for "Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021".

Code for our paper Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation

Pytorch library for end-to-end transformer models training and serving

LETR: Line Segment Detection Using Transformers without Edges

[CVPR'21] DeepSurfels: Learning Online Appearance Fusion

Reduce end to end training time from days to hours (or hours to minutes), and energy requirements/costs by an order of magnitude using coresets and data selection.

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Anomaly detection in multi-agent trajectories: Code for training, evaluation and the OpenAI highway simulation.