Magnetic resonance imaging (MRI) is an advanced imaging technique that is used to observe a variety of diseases and parts of the body..neural networks can analyze these images individually (as a radiologist would) or combine them into a single 3D volume to make predictions. At a high level, MRI works by measuring the radio waves emitting by atom…

Deep Learning Project which help us to identify a Person is Covid or non-Covid and Segment the Infection in the Lungs.

Surface Defect Detection as a Tensorflow/Keras model microservice container.

This program detect and identify obstacle on railway. If program detect some obstacle that train must stop, program gives you warning sign. This program Also estimate riskiness of obstacle how it is negligible or not. We provide many models to you to detect railways and obstacles.

Code of kaggle semantic segmentation competition: Steel Defect Detection.

Implementation of the paper "UNet: Convolutional Networks for Biomedical Image Segmentation"

A Python Library for High-Level Semantic Segmentation Models based on TensorFlow and Keras with pretrained backbones.

Segmentation and Classification models for COVID CT scans (COVID, pneumonia, normal) based on Mask R-CNN.

Segment Anything combined with CLIP

A Custom NN architecture to generate a segmentation map of a given set of Images.

MobileNetV3 implementation from scratch using PyTorch.

A deep learning-based road segmentation model for autonomous vehicles, MESNet integrates ResNet-50, VGG-16, and PSPNet to achieve high accuracy and precision in diverse environments. Trained on the KITTI dataset, it handles real-time road segmentation tasks with robust performance metrics.