Projects sharing features with Ordered Neurons

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

Library for automatic retraining and continual learning

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

ECCV 2020 PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer

Tree LSTM implementation in PyTorch

Implementing a Neural Network from Scratch

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

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

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

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

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

Codebase for Image Classification Research, written in PyTorch.

Keras implementation of a res2net module with tf banckend

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

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

Caffe Implementation of MobileNets V3

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