Comparing the performance of pixel-wise binary classification for road detection, from panchromatic satellite images using a custom model called RoadSegNN (with ResNet and Swin-T backbones) and SegNet.
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Updated
Jun 23, 2024 - Python
Comparing the performance of pixel-wise binary classification for road detection, from panchromatic satellite images using a custom model called RoadSegNN (with ResNet and Swin-T backbones) and SegNet.
Enhancing lane detection systems using deep learning models: U-Net and SegNet for the course ECE-5554 Computer Vision
PyTorch implementation for Semantic Segmentation, include FCN, U-Net, SegNet, GCN, PSPNet, Deeplabv3, Deeplabv3+, Mask R-CNN, DUC, GoogleNet, and more dataset
This project implements semantic image segmentation using two popular convolutional neural network architectures: U-Net and SegNet. Semantic image segmentation involves partitioning an image into multiple segments, each representing a different class.
AI solution for detecting individuals crossing the safety line on the subway platform
The official implementation of "Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting"
Image segmentation implemented using pytorch on a COCO format Dataset of Ingredients with various models including U-NET, U-NET++, SegNet and DeepLabV3+
Performance of various image segmentation models.
A comparative study for skin lesion segmentation and melanoma detection where deep learning methods can perform very well without complex pre-processing techniques except for normalization and augmentation.
A repo for Segnet training from start to finish in two different ways
CNN architectures for multiclass semantic segmentation of esophageal diseases
PyTorch implementation of U-Net and SegNet segmentation models for detecting melanoma, along with pre-trained models and utility functions for evaluation.
Image Segmentation Paper Review and Implementation
Project implementation of land cover classification problem. This repository contains the implementation of models in pytorch lightning and their results.
Just another implementation of SegNet in Tensorflow-2
Lane detection using Semantic Segmentation. To be used in industries by autonomous shuttles in a controlled environment.
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