A novel medical large language model family with 13/70B parameters, which have SOTA performances on various medical tasks

For Med-Gemini, we relabeled the MedQA benchmark; this repo includes the annotations and analysis code.

Winner of MICCAI 2024 Challenge CXR-LT task3: zero-shot classification of chest X-ray images.

The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…

Janus-Series: Unified Multimodal Understanding and Generation Models

Lightweight, useful implementation of conformal prediction on real data.

PyTorch implementation of MAE https//arxiv.org/abs/2111.06377

Lightweight Python library for in-memory matrix completion.

Conformal Language Modeling

NeurIPS 2024 (spotlight): A Textbook Remedy for Domain Shifts Knowledge Priors for Medical Image Analysis

Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"

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Repo for lifespan brain region parcellation

Detail-Oriented CLIP for Fine-Grained Tasks

Official code to accompany the paper "Bootstrapping Variational Information Pursuit with Large Language and Vision Models for Interpretable Image Classification (ICLR 2024)."

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

Implementation for "DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations" (NeurIPS 2022))

Official Implementation of Robust Training under Label Noise by Over-parameterization

An automatic and efficient tool to describe functionalities of individual neurons in DNNs

Grounding DINO 1.5: IDEA Research's Most Capable Open-World Object Detection Model Series

Code for the CVPR paper "Interactive and Explainable Region-guided Radiology Report Generation"

[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

Ultralytics YOLO11 🚀

PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO