Projects sharing features with EfficientNets PyTorch

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

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

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

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

Implementing Searching for MobileNetV3 paper using Pytorch

calculate the cnn model flops support MxNet symbol file