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.

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

Data augmentation for NLP

a generalist algorithm for cellular segmentation with human-in-the-loop capabilities

A dataset of datasets for learning to learn from few examples

Code for Paper: Training Software Engineering Agents and Verifiers with SWE-Gym

Official implementation of the paper "Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models

Repo for "Physion: Evaluating Physical Prediction from Vision in Humans and Machines", presented at NeurIPS 2021 (Datasets & Benchmarks track)

Codes and scripts for "Explainable Semantic Space by Grounding Languageto Vision with Cross-Modal Contrastive Learning"