Open-source alternatives to GRUV

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

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

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

Library for automatic retraining and continual learning

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

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

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

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

Language Model GRU with Python and Theano

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

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

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

This project is a character-level language modeling system that uses recurrent neural networks to predict and generate text one character at a time. It implements LSTM and GRU architectures to learn sequential patterns and probability distributions from text corpora. The system includes mechanisms for text generation sampling, allowing users to produce new sequences from trained models. It features temperature-based stochasticity to control the randomness and diversity of the generated output. The implementation covers the full model lifecycle, including training, state persistence through c

This repository provides an up-to-date the list of studies addressing imbalance problems in object detection. It follows the taxonomy provided in the following paper (please cite the paper if you benefit from this repository):

A curated list of deep learning resources for computer vision

Recurrent Neural Network - A curated list of resources dedicated to RNN

MobileNetV3 in pytorch and ImageNet pretrained models