#Pytorch

Explore a tutorial focused on applying deep learning in image processing, without overstatements or promotional language. The course targets learners at all levels, offering video sessions on constructing and training networks using PyTorch and TensorFlow. Gain insights into models like LeNet, AlexNet, ResNet, and their application across tasks such as classification, detection, and segmentation. Detailed navigation includes network explanations and coding examples, with resources like downloadable PPTs for an efficient learning path.

Discover an OCR tool capable of recognizing text in over 80 languages such as Latin, Chinese, and Arabic. EasyOCR integrates effortlessly with applications via Huggingface Spaces using Gradio, offering a web demo without any initial setup. Regular updates enhance compatibility and promise future features like handwritten text recognition. Easy to install through pip, it includes detailed tutorials and API documentation to guide usage. The tool facilitates simultaneous multi-language support, backed by comprehensive instructions and command-line options.

Explore Google's Imagen project for efficient text-to-image generation in Pytorch, featuring simplified architecture and tools from Huggingface. Key features include dynamic clipping, noise level conditioning, and multi-GPU support.

Discover advanced non-autoregressive models in this unofficial Pytorch implementation for creating customized neural vocoders. Utilize models such as ParallelWaveGAN, MelGAN, Multiband-MelGAN, HiFi-GAN, and StyleMelGAN for real-time speech and singing synthesis. The repository is compatible with ESPnet and NVIDIA Tacotron2 and offers a variety of language recipes along with pre-trained models. Experience efficient decoding on both CPU and GPU, and explore Tensorflow conversions for enhanced performance. Stay informed on updates including new LIBRITTS-R recipes and singing voice vocoder support.

Enformer provides a PyTorch version of Deepmind's advanced attention network, designed for seamless gene expression prediction and genomic analysis. It offers fine-tuning capabilities on various tasks with pre-trained weights, improving efficiency when transitioning from TensorFlow models. The resource supports extensive training adaptability with specialized wrappers and optimizes data handling techniques for better genomic interventions.

Tutel MoE provides an efficient implementation of Mixture-of-Experts, including 'No-penalty Parallelism' for adaptable training and inference. It is compatible with PyTorch and supports CUDA and ROCm GPUs as well as various CPU formats. Recent updates feature new benchmarks, tensorcore options, and improved communication. Tutel enables seamless configuration changes without additional costs and offers straightforward installation and testing processes. It supports distributed modes across multi-node and multi-GPU setups, making it suitable for developers looking to improve performance and scalability in machine learning frameworks.

This implementation of a State-of-the-Art GAN from Adobe is enhanced for faster convergence and improved stability, leveraging lightweight GAN technologies. It features 1k to 4k upsamplers, skip layer excitation, and auxiliary reconstruction loss in the discriminator for high-resolution image synthesis. The project supports unconditional settings and integrates multi-GPU training via Huggingface's Accelerator, ensuring effective multi-scale input processing and stable training with an efficient gradient penalty application.

Gradient Cache overcomes GPU/TPU memory limits to efficiently scale contrastive learning. Compatible with PyTorch and JAX, it supports dense passage retrieval on single GPUs, lowering hardware costs with high FLOP systems. Suitable for deep learning, it supports mixed precision and distributed training, offering functional and decorator tools for streamlined cache implementation.

Discover the Pytorch implementation of Meta AI's text-to-video generator, incorporating pseudo-3D convolutions and temporal attention for enhanced temporal fusion. This technology builds on SOTA text-to-image models like DALL-E2, offering modifications for efficient computation and precise frame interpolation. Whether applied to images or videos, it supports flexible training for diverse uses. Developed with support from Stability.ai and contributions from leading AI researchers.

This project provides tools for generating photorealistic human avatars in conversations using audio inputs. It includes PyTorch-based resources, with training/testing codes and pretrained models. A demo is available for trial, and code can be run locally for further exploration. This tool is suited for those interested in human-computer interaction, speech processing, and virtual reality, focusing on synthesizing body language and facial expressions.

Discover the Pytorch implementation of Sparsely Gated Mixture of Experts intended to enhance language model capacity by increasing parameters without additional computation. This version adds features to the original TensorFlow model, supporting complex architectures such as hierarchical mixtures, and enables customization of expert networks with various activation functions and gating policies. Suitable for developers who wish to scale models effectively while maintaining performance, it includes setup and usage instructions for easy integration.

Discover how NAFNet transforms image restoration with a simpler and more efficient approach by removing complex nonlinear activations. Achieve exceptional results in deblurring and denoising tasks on established benchmarks like GoPro and SIDD while reducing computational costs.

Discover extensive deep learning resources featuring expert-led video lectures, comprehensive notes, and practical datasets. Perfect for both beginners and advanced learners aiming to enhance AI skills and employability, including in top firms like United Imaging. Join collaborative groups for valuable insights in AI applications.

The project provides a Pytorch implementation of OpenAI's DALL-E 2 that advances text-to-image synthesis through diffusion networks. It focuses on a prior network for predicting image embeddings, enhancing generation accuracy and diversity. This repository supports AI researchers and developers in model replication and training, in collaboration with the LAION community. It integrates neural networks like CLIP and diffusion priors to generate high-quality images from text. Discover the innovative use of pixel shuffle upsamplers and cascading DDPMs for image generation and join the Discord community for contributions and pre-trained models on Hugging Face.

Explore a versatile tool designed to facilitate interpretability analysis for sequence generation models using PyTorch. This guide provides insights into installation, application, and the features offered, catering to Python enthusiasts. Understand feature attribution for multiple models through methods such as Integrated Gradients and Attention Weight Attribution. Visualize results seamlessly in Jupyter notebooks or the console, using custom scores to gain comprehensive insights. Inseq simplifies post-hoc model analysis, fostering enhanced understanding and innovation in sequence generation.

detrex is an open-source toolbox offering cutting-edge Transformer-based detection algorithms. It is built on Detectron2 and features a modular design for custom models and robust baselines. The project is user-friendly and lightweight, incorporating a LazyConfig System and training engine. detrex supports models like Focus-DETR and SQR-DETR and uses PyTorch 1.10+ for integration. Regular updates and comprehensive tutorials enhance usability. Explore detrex's project page for detailed features, documentation, and training techniques.

Explore the Lion optimizer's innovative approach to sign momentum, developed by Google Brain and implemented in Pytorch. Understand how it can potentially outperform Adam with proper tuning, applied in robust model training. Examine Lion's adjustable learning rate strategies and how it performs across different architectures. It is particularly effective in language modeling and significant text-to-image training, especially in large batch sizes. Stay updated with experimental findings to maximize its capabilities, and leverage its Triton compatibility for optimal performance.

Explore a practical implementation of BYOL in Pytorch for self-supervised learning that simplifies the process by removing the need for contrastive learning and negative pairs. Seamlessly integrate with any image-based neural network using unlabelled data. Features include recent updates like group norm and weight standardization for optimization. Delve into augmentation and distributed training to improve network efficiency on supervised tasks, providing a cost-effective solution.

Discover Muse, an open-source project for generating images from text using Masked Generative Transformers in Pytorch. This implementation focuses on VQGanVAE model training and MaskGit Transformer integration, supporting high-resolution image generation and super-resolution features. Engage with LAION's community for collaboration opportunities.

The repository provides resources for training continuous-time neural models such as Liquid Time-Constant Networks, Neural ODEs, and Continuous-time RNNs through backpropagation through time (BPTT). Built with TensorFlow 1.14.0 and tested on Ubuntu, it caters to tasks like gesture segmentation and traffic prediction. The repository includes comprehensive setup scripts and examples per dataset, supporting various RNN models and training configurations for mastering temporal prediction tasks.

Discover the Autoregressive Diffusion Pytorch library, designed for generating images without vector quantization through autoregressive models. This implementation features advanced techniques to synthesize images as token sequences. The library provides clear installation guides and usage examples, compatible with both diffusion and flow matching methods. It serves as a flexible tool for researchers and developers focused on cutting-edge image generation technologies.

A solution for improving Transformer-based models with support for Baichuan, LLaMA, and other large language models through int8 quantization. Suitable for large models on a single GPU, it enables efficient processing of multi-modal and NLP tasks with enhanced performance via CUDA kernel optimization and innovative algorithms, and is easily integrable into Transformers and Fairseq.

FourierKAN is a Pytorch layer that serves as an alternative to traditional Linear + non-linear activations, utilizing 1D Fourier coefficients inspired by Kolmogorov-Arnold Networks. It optimizes computational efficiency and offers periodic function benefits. The layer is usable on both CPU and GPU, with a naive implementation that manages memory proportional to gridsize and plans for advanced fused operations. Training is enhanced with Brownian noise initialization and frequency regularization for function smoothness. Current offerings are MIT licensed, while future versions may include proprietary fused kernels.

Explore efficient face recognition in Pytorch with pretrained Inception ResNet (V1) and MTCNN models on VGGFace2 and CASIA-Webface. This repository offers complete pipelines for detection, recognition, and video tracking, supporting easy integration via Docker and Git with automatic pretrained weight downloads.

tsai is a robust open-source deep learning library designed for time series and sequence tasks such as classification, regression, and forecasting. It leverages Pytorch and fastai to integrate innovative models like PatchTST and RNN with Attention. With expanded datasets and Pytorch 2.0 support, tsai offers utilities like walk-forward cross-validation and memory optimization, continually evolving to improve predictive precision. It is well-documented with extensive tutorials, making it a reliable tool for efficient time series data analysis.

Discover WaveRNN, an open-source neural audio synthesis model implemented in Pytorch. Includes Quick Start TTS features, model training using the LJSpeech dataset, and access to pretrained models. Offers customizable scripts for improved text-to-speech processes, benefiting audio researchers and enthusiasts seeking advanced TTS capabilities.

Discover advanced hierarchical transformer modules for tabular data analysis, featuring a synthetic credit card transaction dataset and enhanced Adaptive Softmax for data masking. Utilizing HuggingFace's transformers, the project enables effective modeling of time series with BERT and GPT-2 models, suitable for Python and Pytorch platforms.

Explore a broad range of sophisticated deep reinforcement learning algorithms in Pytorch, emphasizing continuous action spaces. Efficiently train on CPUs or GPUs and straightforwardly evaluate with OpenAI Gym. This repository includes various model-free and model-based RL algorithms, offering techniques like DDPG, PPO, and soft actor-critic, in addition to experimental methods such as prioritized experience replay. Flexible for extensions, it accommodates environments from classic games to complex robotic tasks.

Discover an extensive array of pre-trained AI models designed for seamless multi-platform deployment using ailia SDK. The SDK offers support for various programming environments such as C++, Python, and Unity, utilizing Vulkan and Metal for GPU acceleration to deliver rapid AI inference on platforms like Windows, Mac, Linux, iOS, Android, Jetson, and Raspberry Pi. Access a broad spectrum of models for action recognition, anomaly detection, audio processing, and more, including the latest additions such as whisper-v3-turbo and florence2. Improve your AI initiatives with comprehensive tutorials available on Google Colaboratory and easy PC setup instructions.

NaturalSpeech 2 is an open-source PyTorch model for zero-shot text-to-speech and singing synthesis. It uses a neural audio codec and latent diffusion models to deliver non-autoregressive natural voice synthesis. This project enhances attention mechanisms and transformer components, introducing denoising diffusion techniques. Sponsored by Stability AI and Huggingface, it encourages collaboration from the TTS community. Easily implement with pip and leverage comprehensive coding examples.

Small-Text offers efficient active learning techniques for text classification, featuring pre-implemented query and initialization strategies, along with stopping criteria. Compatible with sklearn, Pytorch, and transformers classifiers, it supports GPU models and lightweight installation suitable for CPU environments. Access detailed documentation and community support for streamlined integration.

Explore state-of-the-art techniques in generalized referring expression segmentation for precise object identification in complex visual environments. Utilizing models like ResNet-50 and Swin-Tiny with technologies such as Detectron2, this project offers enhanced segmentation accuracy. Gain insights into comprehensive configurations and leverage large-scale datasets to advance video segmentation outcomes. Stay informed on recent updates in this dynamic area for optimal performance in referring expression tasks.

Utilize advanced medical image segmentation with DDPM in Pytorch, featuring enhanced Fourier space feature filtering. Includes community-contributed training scripts for datasets such as skin lesions. Offers easy pip installation and detailed scripts for seamless workflow integration. Explore advanced training commands, including self-conditioning for accurate segmentation results. Ideal for researchers seeking top-tier medical imaging capabilities supported by open-source community contributions.

Discover the USB PyTorch-based package for Semi-Supervised Learning, offering a practical framework for creating AI models in computer vision, natural language processing, and audio classification. This package includes 14 algorithms based on Consistency Regularization to optimize small datasets, making advanced AI accessible to smaller teams. The library provides essential resources, from data preparation to algorithm evaluation with comprehensive benchmarking, catering to researchers and developers aiming to improve machine learning projects.

Calflops provides a complete tool for calculating theoretical FLOPs, MACs, and parameters in diverse neural networks such as CNNs, RNNs, and large language models. This tool offers efficient analysis of Pytorch-based models with detailed performance metrics for each submodule, facilitating a deeper understanding of performance costs. The tool's integration with Huggingface enhances usability by enabling computations without full model downloads. Drawing inspiration from libraries like ptflops, deepspeed, and hf accelerate, Calflops improves FLOPs calculations and supports Transformer models, making it a key asset for performance analysis and optimization.

Discover the capabilities of Google's new MusicLM model for advanced music generation using Pytorch. This project utilizes text-conditioned AudioLM with MuLan embeddings for enhanced audio generation. Collaborate with the LAION community to contribute to this open-source initiative, and benefit from Stability.ai's support and Huggingface's accelerate library for optimized training.

Designed for those with some experience in machine learning, including familiarity with frameworks like Keras, TensorFlow, or Pytorch, this guide makes Pytorch learning accessible with its optimized, easy-to-follow examples and step-by-step progression. Spend 30 minutes to 2 hours daily over 20 days to effectively incorporate Pytorch into real-world projects. The guide serves as a reliable reference, packed with practical examples, for enhancing application development expertise.

Voice Cloning App is a Python and PyTorch-powered tool designed for synthesizing human voices, featuring capabilities like automatic dataset creation, multilingual support, and both local and remote training. It supports configurations with multiple GPUs, offering flexible train start/stop and data handling functions. This application provides opportunities for live voice training and sharing on platforms such as a dedicated voice hub. Upcoming updates will potentially include Talknet integration, improved batch size prediction, and AMD GPU compatibility, continually enhancing its versatility for various applications.

DataChain enables efficient organization of unstructured data into scalable datasets, integrating seamlessly with AI models without abstraction. Features include Pythonic pipelines, multimodal data support, and metadata generation via AI models, alongside optimized operations through parallelization and vector search. Supports integration with PyTorch and TensorFlow.

Discover a Pytorch-based model that provides quicker training durations in speech synthesis using the Transformer Network. This implementation offers comparative audio quality to traditional seq2seq models like Tacotron, with notably faster training times. By employing the CBHG model for post network learning and the Griffin-Lim algorithm for audio transformation, it leverages the LJSpeech dataset to effectively synthesize speech. This makes it a valuable resource for developers and researchers focused on enhancing performance while preserving quality.

The project provides a complete PyTorch implementation of StyleGAN2, allowing training of generative adversarial networks directly via command line. It features easy setup with multi-GPU support and data-efficient training techniques for generating high-quality synthetic images, including cities and celebrity faces. Additionally, it includes options for model customization and improvements like attention mechanisms and top-k training for enhanced GAN performance. Suitable for developers interested in a straightforward yet effective tool for AI-generated imagery.

This repository provides an implementation of the MetaAI Voicebox model in Pytorch for advanced text-to-speech applications. It features rotary embeddings and adaptive normalization, techniques inspired by successful AI audio projects like Paella. It includes installation instructions, usage examples, and regular updates. This project, supported by community contributions, aims to broaden access to high-quality open-source AI models for academic and commercial use.

ECT offers a sophisticated approach to generative modeling by introducing few-step capabilities with minimal tuning effort. This PyTorch-based framework allows for effective consistency tuning across diverse protocols, such as CIFAR-10 and ImageNet, and is compatible with PyTorch 2.3.0. The implementation includes features like mixed precision training via AMP GradScaler, enhancing computational efficiency. Recent updates demonstrate ECT's superior performance compared to traditional GANs and diffusion models on CIFAR10, achieving high-quality image generation with optimized FID scores.

Discover the Pytorch-based Alphafold 3 for precise biomolecular predictions. Features include Lightning and Hydra integration and a collaborative Discord discussion platform. Visualize molecular data interactively. The project benefits from innovations like Relative Positional Encoding, supporting efficient algorithms and PDB dataset clustering. Offers straightforward installation and comprehensive documentation.

This project benchmarks cutting-edge unbounded Neural Radiance Fields (NeRF) algorithms, offering a streamlined, high-performance code repository. The results highlight comparisons with widely-used methods such as NeRF++, Plenoxels, and DVGO, showcasing notable PSNR improvements. With practical guidelines on installation, data processing, and training, this project is a valuable resource for researchers and developers aiming for optimized neural radiance field performance using public datasets. The project also provides ongoing updates and comprehensive documentation for building custom NeRFs.

This Tab Transformer implements attention-driven architecture for tabular data in PyTorch, close to GBDT performance. It features straightforward setup, supports binary and diverse prediction types, and optimizes deep learning for structured datasets. Compare it with FT Transformer for improved numerical embeddings and explore unsupervised training options. Stay informed on the latest developments in modeling tabular data.

EGNN-Pytorch provides an implementation of E(n)-Equivariant Graph Neural Networks, focusing on invariant features for improved accuracy and performance. It is primarily used in dynamical system models and molecular activity predictions. Key functionalities include handling sparse neighbors via an adjacency matrix or automatic Nth-order neighbor determination, which enhances stability and scalability. The model is versatile due to adjustable parameters such as input dimensions, edge dimensions, and normalization options, suitable for complex computational tasks.