Pytorch
Showing 36 of 259 projects
A zero-shot foundation model for generating universal embeddings from single-cell gene expression data.
A bi-directional equivariant transformer for long-range DNA sequence modeling, enabling reverse-complement aware genomic analysis.
A Python library that simplifies using, finetuning, and deploying state-of-the-art machine learning models for various AI tasks.
Turn Godot projects into OpenAI Gym environments for training reinforcement learning models with PyTorch via shared memory.
A PyTorch-based Python package for deep and machine learning analysis of microscopy data, designed for domain scientists.
A PyTorch frontend for JAX that enables running PyTorch code on TPUs and provides seamless PyTorch-JAX interoperability.
A Python library for building lazy data processing and machine learning workflows that handle datasets larger than memory.
A benchmark dataset and meta self-learning method for multi-source domain adaptation in scene text recognition.
A foundation model for cell segmentation that achieves state-of-the-art performance across diverse cellular targets and imaging modalities.
A PyTorch-based segmentation toolbox for electron microscopy connectomics, enabling neural structure analysis in 3D volumes.
A simple, flexible, and extensible object-oriented template for PyTorch projects.
A Python machine learning and informatics suite for analyzing, mining, and modeling chemical and materials data.
A collection of examples demonstrating how to use Comet.ml for machine learning experiment tracking across various Python frameworks.
A deep learning model for joint perception and motion prediction in autonomous driving using bird's eye view maps.
A Python library that builds neural networks with minimal boilerplate code for PyTorch and TensorFlow.
A modular NLP framework for extracting information from French clinical notes, compatible with spaCy and PyTorch.
A deep learning model using transformer architecture to predict compound-protein interactions from molecular and protein sequences.
Open-source implementation of the winning solution for the 2018 Data Science Bowl Kaggle competition using PyTorch and U-Net.
A PyTorch implementation combining Graph Convolutional Networks with OpenNMT-py for structured data to text generation.
A deep bilinear attention network framework with adversarial domain adaptation for interpretable drug-target interaction prediction.
A PyTorch implementation of the DeepDream algorithm for generating psychedelic, dream-like images from neural network activations.
A PyTorch Geometric extension library for signed and directed graph neural networks, embedding, and clustering methods.
A deep learning-based, threshold-agnostic, subpixel-accurate 2D and 3D spot detection method for fluorescence microscopy and spatial transcriptomics.
An open-source benchmark solution for the Kaggle TGS Salt Identification Challenge using semantic segmentation.
A knowledge-informed cross-species foundation model pre-trained on over 120 million human and mouse single-cell transcriptomes to decipher universal gene regulatory mechanisms.
A 3D object detection method that exploits visibility information from LiDAR point clouds to improve accuracy.
A deep learning framework for predicting chromatin profiles and sequence regulatory activities from DNA sequences and variants.
A PyTorch-based deep learning library for building and training spiking convolutional neural networks with hardware deployment support.
A Python package providing popular computer vision model architectures built with Equinox for JAX.
A toolkit for evaluating and monitoring machine learning models in clinical healthcare settings.
A PyTorch-based toolbox for graph reliability, focusing on adversarial attacks, defenses, and robustness techniques for graph neural networks.
A pre-configured Docker image with deep learning frameworks, data science tools, and GPU support for rapid environment setup.
A Python package and tutorial collection for signal processing and machine learning, built on NumPy and SciPy.
A Python library for building, training, and deploying spiking neural networks with support for multiple simulation backends and neuromorphic hardware.
A curated archive of pre-trained computer vision models for object detection, face recognition, fire detection, and more.
An open-source prompt guard model that detects prompt injection attacks while mitigating over-defense against benign inputs.
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