Pytorch
Showing 36 of 259 projects
A PyTorch adaptation of Lucid for visualizing and interpreting neural networks through feature visualization.
Build fully-functioning computer vision and object detection models with PyTorch in just 5 lines of code.
High-resolution de novo protein structure prediction from amino acid sequences using deep learning.
A simplified Keras-like framework for PyTorch that reduces boilerplate code for training neural networks.
A junction tree variational autoencoder for generating valid molecular graphs with desired chemical properties.
A unified deep learning and reinforcement learning framework supporting multiple backends and hardware platforms.
A lightweight encoder-decoder neural network for real-time semantic segmentation on resource-constrained devices.
A sliding window framework for classifying high-resolution whole-slide microscopy and histopathology images using deep neural networks.
An AutoML framework that generates and customizes machine learning pipelines using declarative JSON-AI syntax.
A Python library for translating between 200 languages using Hugging Face transformer models like mBART-50, m2m100, and NLLB-200.
A single-stage 3D object detector for point clouds that improves localization precision by explicitly leveraging structure information.
A Python library for fast, reproducible, and modular Neural Architecture Search (NAS) to generate efficient deep networks.
An AI system that incrementally generates scientific paper drafts by predicting links between concepts and generating text sections.
A PyTorch implementation of TResNet, a high-performance convolutional neural network architecture optimized for GPU training and inference.
A 3D vision library for monocular and stereo 3D human detection, social distancing, and body orientation estimation from 2D keypoints.
A PyTorch library for creating and training autoencoders on sequential data (time series, videos, etc.) in just two lines of code.
An open-source toolkit for scalable, standardized computational pathology analysis, enabling AI and machine learning on large imaging datasets.
A complete pipeline for processing two-photon calcium imaging data, including registration, ROI detection, signal extraction, and spike deconvolution.
OCaml bindings for PyTorch, providing NumPy-like tensor computations with GPU acceleration and automatic differentiation.
A sequence-to-sequence transformer model for predicting chemical reaction pathways (retrosynthesis) with uncertainty calibration.
A lightweight neural network for near-real-time semantic segmentation of LiDAR point clouds using polar coordinate quantization.
A PyTorch reinforcement learning library implementing DQN, DDPG, A2C, PPO, SAC, MADDPG, A3C, APEX, and IMPALA.
A 100M-parameter foundation model for single-cell transcriptomics, enabling gene expression enhancement, drug response prediction, and perturbation analysis.
A PyTorch implementation of the DrQA model for reading comprehension and open-domain question answering.
Convert Torch7 neural network models to Apple CoreML format for deployment on iOS/macOS devices.
A vision transformer-based deep learning model for automated instance segmentation and classification of cell nuclei in histopathology images.
A collection of open-source machine learning and quantitative analysis models implemented in TensorFlow and PyTorch.
An easy-to-use C# deep learning library with support for multiple backends including TensorFlow, PyTorch, and CUDA/OpenCL.
A Python package for training PyTorch neural networks using variational inference for Bayesian deep learning.
Official implementation of a 3D equivariant diffusion model for generating drug-like molecules that bind to specific protein targets and predicting their binding affinity.
An application-oriented Deep Reinforcement Learning framework for real-world decision problems, covering simulation to deployment.
A convolutional neural network for CAPTCHA recognition using Keras and PyTorch.
An integrated framework for training custom generative AI image-to-image models using GANs, Diffusion, and Consistency Models.
A centralized Python framework for agricultural machine learning, providing access to public datasets, benchmarks, pretrained models, and synthetic data generation.
A large-scale image dataset for self-supervised pretraining without humans, designed to reduce privacy concerns.
A PyTorch framework for deep learning on point clouds, providing a modular and reproducible foundation for 3D vision tasks.
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