Open-source alternatives to PSConv

This repository provides a collection of reference implementations, toolkits, and orchestration tools for training and deploying large-scale AI models on Cloud TPU hardware. It serves as a framework for managing the lifecycle of accelerator clusters, including hardware orchestration and the provisioning of high-performance compute infrastructure for machine learning workloads. The project specifically enables the pre-training of foundation models, large language models, and complex reasoning architectures through distributed training toolkits and multi-host scaling recipes. It further provide

This is a PyTorch implementation of EfficientNet convolutional neural networks. It serves as a computer vision model library providing architectures for image classification and high-level feature extraction, including pre-trained weights for immediate image categorization. The library supports transfer learning by allowing the modification of model architectures and output layers to accommodate a custom number of classes for new datasets. It also includes a model exporter to convert trained PyTorch weights into the ONNX format for production inference. The system covers broader computer vis

This project is a PyTorch library for building and training Kolmogorov-Arnold Networks. It implements a neural network architecture that replaces fixed activation functions with learnable spline-based functions on edges, serving as a tool for interpretable machine learning. The implementation utilizes reformulated matrix operations to reduce memory overhead and increase computation speed. It employs L1 regularization to sparsify network weights, which improves the transparency of the model's internal logic and decisions. The framework covers a range of capabilities including grid-based funct

SlowFast is a PyTorch video understanding framework and spatiotemporal neural network library. It serves as a toolset for video action recognition, enabling the training and evaluation of models designed to classify complex activities and objects within video sequences. The framework is distinguished by its use of dual-pathway spatiotemporal sampling to capture both slow and fast motions. It supports self-supervised video learning for pre-training models on unlabeled data and employs multigrid spatiotemporal training to optimize learning across multiple spatial and temporal resolutions. The

ai-edu is a comprehensive AI education curriculum and machine learning courseware collection. It provides theoretical tutorials, deep learning lab exercises, and project blueprints designed to teach artificial intelligence fundamentals through a combination of study and practical implementation. The project focuses on a learning-by-doing approach, guiding users from Python programming and neural network basics to advanced topics. It includes specialized instructional content on distributed AI training, MLOps educational guides for model quantization and pruning, and detailed frameworks for im

This project is a collection of deep learning research implementations and a reproduction kit designed to translate theoretical AI papers into working code. It provides a library of neural network architectures and reference implementations for reproducing seminal research concepts through interactive notebooks. The repository distinguishes itself through the implementation of AI theory and scaling laws, covering complexity dynamics, information theory, and the simulation of universal AI agents. It also includes a benchmarking suite for synthetic reasoning, allowing for the evaluation of mode

Reimplementation Antol et al 2015 Keras-based LSTM/CNN models for Visual Question Answering

Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

74.3% MobileNetV3-Large and 67.2% MobileNetV3-Small model on ImageNet

PyTorch-style and human-readable RegNet with a spectrum of pre-trained models

Implementing a Neural Network from Scratch

Speed up your Neural Network with Theano and the GPU

PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

Code for Kaggle EEG Detection competition

This repository reproduces the results of the paper: "Fixing the train-test resolution discrepancy" https://arxiv.org/abs/1906.06423

Update (Aug 17, 2021): refactored the code of ACB. The readability has been greatly improved. You may call switchtodeploy of an ACB to convert it to the inference-time structure. If you use ACB in your own model, the conversion is as easy as `` for m in yourmodel.modules(): if hasattr(m,…

Codebase for Image Classification Research, written in PyTorch.

Recurrent Neural Network Tutorial, Part 2 - Implementing a RNN in Python and Theano

Keras implementation of a res2net module with tf banckend

By Songtao Liu, Di Huang, Yunhong Wang

Code for Noisy Student Training. https://arxiv.org/abs/1911.04252

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier

Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier

by Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, Jiaya Jia, details are in project page.

GhostNet provides a set of efficient AI model architectures and neural network design patterns designed to reduce computation and memory overhead. It serves as a computer vision backbone and a lightweight vision transformer, optimizing the balance between predictive accuracy and inference speed. The project focuses on reducing resource consumption for deployment on mobile devices and edge hardware. It achieves this through the use of lightweight vision transformer implementations and architectures that minimize the total number of parameters. The codebase covers a range of capabilities for i

This is the PyTorch implementation of our paper "Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition". (Note that this is the code for image recognition on ImageNet. For semantic image segmentation/parsing refer to this repository:…

Language Model GRU with Python and Theano