PyTorch implementation of the U-Net for image semantic segmentation with high quality images
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Updated
May 29, 2024 - Python
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
Implemented in pure pascal LightNet is an artificial intelligence neural network library Inspired by Darknet and yolo library which can run most of the darknet including YOLO models nativly and self dependently in pascal, tests are provided for both Lazarus and Delphi
Contains information links, articles, research papers, tweets, blog posts, companies etc and everything which is even minutely related to the field of Artificial Intelligence, Distributed Computing, Quantum Computing and Physics, Evolutionary Biology, Crypto-currency, Virual and Augmented Reality etc.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Stringlifier is on Opensource ML Library for detecting random strings in raw text. It can be used in sanitising logs, detecting accidentally exposed credentials and as a pre-processing step in unsupervised ML-based analysis of application text data.
This repository contains labs and tutorials on Convolutional Graph Networks (GCNs).
Learning Based Calibrated Photometric Stereo for Non-Lambertian Surface (ECCV 2018)
TensorFlow implementation of DispNet by Zhijian Jiang.
Make pytorch and tensorflow two become one.
A simple graph neural network for CORA node classification
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
Simple implementation of the LSUV initialization in PyTorch
یادگیری ماشین و شبکه عصبی برای یادگیری بازی آتاری
Collection of must read papers for Data Science, or Machine Learning / Deep Learning Engineer
Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices
ML implementations for me to look up
Modern deep convolutional neural networks implemented with PyTorch <3. ResNet, DenseNet and InceptionNet are trained on the CIFAR-10 dataset and the results are compared.
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
JavaFx Application for Convolutional Network to perfom Image Classification using Softmax Output Layer, Back Propagation, Gradient Descent, Partial Derivatives, Matrix Flattening, Matrix Unfolding, Concurrent Task, Performance Histogram, Confusion Matrix
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