This project offers an easy, flexible, modular PyTorch implementation for semantic segmentation to minimize configuration, automate training and deployment, and enable customization of models, encoders, losses and datasets through its modular design.

This is a classification model and a post-processing method of any object detection model for false positive elimination.

A framework to accelerate AI/ML development 🚀

🥈12th place solution on G2Net Detecting Continuous Gravitational Waves🥈

building a multitask training platform based on Pytorch

[WIP] Yet another multimodal video-audio feature extractor based on recent research

An easy classification implement to explain how triton work

A tool to convert pytorch-image-models (a.k.a timm) to TFLite using ai-edge-torch

Low-code machine learning and deep learning

benchmark pytorch models

A general deep learning project that can be easily transferred to other specific tasks.

Implementation of ViT with PyTorch

The AI framework for software engineers

Deep learning based color transfer between images.

This goal of this repository was to minimize the number of code edits by enabling easy configuration of the Image Classifier pipeline using Hydra, Timm & Lightning.

HAT (Hard Attention to the Task) Modules for Continual Learning

Benchmarks for popular neural network models supported by timm

PyTorch implementation of 'ViViT' (Arnab et al., 2021) from scratch.

The code used in research article: <A Vision Transformer Machine Learning Model for COVID-19 Diagnosis Using Chest X-Ray Images>: https://doi.org/10.1016/j.health.2024.100332

Simplified PyTorch implementation of image classification, support CIFAR10, CIFAR100, MNIST, custom dataset, multi-gpu training and validating, automatic mixed precision training, knowledge distillation, hyperparameter optimization using Optuna etc.