This project was developed as a part of the presentation that I gave on the Programming 2.0 webinar: Autonomous driving.

This is a project on semantic image segmentation using CamVid dataset, implemented through the FastAI framework.

Deep Convolutional Encoder-Decoder Architecture implemented along with max-pooling indices for pixel-wise semantic segmentation using CamVid dataset.

Deep Convolutional Encoder-Decoder Architecture implemented along with max-pooling indices for pixel-wise semantic segmentation using CamVid dataset.

Image Segmentation by Iterative Inference from Conditional Score Estimation

fast semantic segmentation with Enet

MATLAB implementation of popular image segmentation algorithms

A survey of Real time Semantic Segmentation for autonomous driving

Pytorch Implementation of ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation (https://arxiv.org/abs/1606.02147)

CamVid Dataset for Segmentation

Semantic segmentation on CamVid dataset using the U-Net.

Репозиторий для обучения нейросетевых моделей по семантической сегментации + пример использования моделей на практике

Deep learning semantic segmentation on the Camvid dataset using PyTorch FCN ResNet50 neural network.

Adapted representation of synthetic data to real world data.

This is the DL repository for Semantic Segmentation using U-Net model in pytorch library.

Semantic Segmentation using Tensorflow on popular Datasets like Ade20k, Camvid, Coco, PascalVoc

Applying the 100 Layer Tiramisu on the Camvid Dataset

A pytorch-based real-time segmentation model for autonomous driving

Tensorflow 2 implementation of complete pipeline for multiclass image semantic segmentation using UNet, SegNet and FCN32 architectures on Cambridge-driving Labeled Video Database (CamVid) dataset.

This is the official repository for our recent work: PIDNet