My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano
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Updated
Dec 6, 2021 - Python
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.
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"
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.
SegNet-like Autoencoders in TensorFlow
Lots of semantic image segmentation implementations in Tensorflow/Keras
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
Development of Deep Learning algorithms for Drivable Area Segmentation, Lane Segmentation, Traffic Sign Detection and Classification with data collected and labeled by Ford Otosan.
SegNet implementation & experiments in Chainer
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