Microsoft Automatic Mixed Precision Library

Scientific Large Language Models: A Survey on Biological & Chemical Domains

Biological foundation modeling from molecular to genome scale

Official implementation for HyenaDNA, a long-range genomic foundation model built with Hyena

TART: A plug-and-play Transformer module for task-agnostic reasoning

Ledidi turns any machine learning model into a biological sequence editor, allowing you to design sequences with desired properties.

Explore and understand your training and validation data.

A character tokenizer for Hugging Face Transformers

GENA-LM is a transformer masked language model trained on human DNA sequence.

FFCV: Fast Forward Computer Vision (and other ML workloads!)

Code for our AAMAS 2020 paper: "A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry".

PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

[CVPR 2021] VirTex: Learning Visual Representations from Textual Annotations

Make quick mechanical turk HTML/Javascript interfaces and launch them with Python functions

Converts Optical Flow files to images and optionally compiles them to a video. Flow viewer GUI is also available. Check out mockup right from Github Pages:

Python optical flow visualization following Baker et al. (ICCV 2007) as used by the MPI-Sintel challenge

A python node to detect planes from depth image by using RANSAC algorithm. Input/Output from/to ROS topics.

A fork of Detectron2 with ResNeSt backbone

Code for "Aligning Linguistic Words and Visual Semantic Units for Image Captioning", ACM MM 2019

Learning UI Similarity using Graph Networks

Implementation of Some Deep Hash Algorithms, Including DPSH、DSH、DHN、HashNet、DSDH、DTSH、DFH、GreedyHash、CSQ.

A library for ML benchmarking. It's powerful.

Record experiment data easily

Fetch and initialize objects in one line, without any if-statements or dictionaries. Merge and override complex config options at the command line.

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet

This repository is the collection of research papers in Deep learning, computer vision and NLP.

Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

Self-Supervised Learning for OOD Detection (NeurIPS 2019)