A latent text-to-image diffusion model

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

A collection of various deep learning architectures, models, and tips

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

PyTorch code and models for the DINOv2 self-supervised learning method.

Using Low-rank adaptation to quickly fine-tune diffusion models.

A scikit-learn compatible neural network library that wraps PyTorch

VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.

Unsupervised Semantic Segmentation by Distilling Feature Correspondences

Use evolutionary algorithms instead of gridsearch in scikit-learn

Dino V2 for Classification, PCA Visualization, Instance Retrival: https://arxiv.org/abs/2304.07193

MSE5540/6640 Materials Informatics course at the University of Utah. Learn how data science tools are revolutionizing materials science!

Things that you should (and should not) do in your Materials Informatics research.

Clustering for mixed-type data

A deep learning tabular classification architecture inspired by TabTransformer with integrated gated multilayer perceptron.

Software and instructions for setting up and running a self-driving lab (autonomous experimentation) demo using dimmable RGB LEDs, an 8-channel spectrophotometer, a microcontroller, and an adaptive…

Honegumi (骨組み) is an interactive "skeleton code" generator for API tutorials focusing on optimization packages.

Applied Self Supervised Learning techniques such as Jigsaw as pretext task, SRGAN and SimCLR for fine-grained classification

Official code for 🔥 Unsupervised Wildfire Change Detection based on Contrastive Learning 🔥

RAGSkeleton: A foundational, modular framework for building customizable Retrieval-Augmented Generation (RAG) systems across any domain.

Self-Supervised Representation Learning of Wafer Maps with FastSiam

Self-Supervised Representation Learning of Semiconductor Wafer Maps using PyTorch