My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano

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Pixel-wise segmentation on VOC2012 dataset using pytorch.

Keras-Semantic-Segmentation

AI Papers

This repository was a fork of BVLC/caffe and includes the upsample, bn, dense_image_data and softmax_with_loss (with class weighting) layers of caffe-segnet (https://github.com/alexgkendall/caffe-segnet) to run SegNet with cuDNN version 5.

Experiments with satellite image data

The official implementation of "Encoder-Decoder Based Convolutional Neural Networks with Multi-Scale-Aware Modules for Crowd Counting"

SegNet-like network implemented in TensorFlow to use for segmenting aerial images

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.

segmentation repo using pytorch

The source code of "A Streamlined Encoder/Decoder Architecture for Melody Extraction"

Lots of semantic image segmentation implementations in Tensorflow/Keras

SegNet-like Autoencoders in TensorFlow

Label-Pixels is the tool for semantic segmentation of remote sensing images using Fully Convolutional Networks. Initially, it is designed for extracting the road network from remote sensing imagery and now, it can be used to extract different features from remote sensing imagery.

A modified SegNet Convolutional Neural Net for segmenting human skin from images

Segmentation of Lungs from Chest X-Rays using Fully Connected Networks

PyTorch implementation for Semantic Segmentation, include FCN, U-Net, SegNet, GCN, PSPNet, Deeplabv3, Deeplabv3+, Mask R-CNN, DUC, GoogleNet, and more dataset

SegNet implementation & experiments in Chainer

Development of Deep Learning algorithms for Drivable Area Segmentation, Lane Segmentation, Traffic Sign Detection and Classification with data collected and labeled by Ford Otosan.