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Codebase for "A Foundation Model for Cell Segmentation"

Layer-Wise Relevance Propagation for Large Language Models and Vision Transformers [ICML 2024]

An official implementation of the Anchor DETR.

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…

OpenMMLab Detection Toolbox and Benchmark

Unsupervised Semantic Correspondence Using Stable Diffusion

RETFound - A foundation model for retinal image

CLIP the gap CVPR 2023

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

Latent Text-to-Image Diffusion

An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) fo…

Text-to-Image Diffusion Models are Zero-Shot Video Generators

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.

Bayesian learning and inference for state space models

A python library for self-supervised learning on images.

OpenMMLab Self-Supervised Learning Toolbox and Benchmark

PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

Label Studio is a multi-type data labeling and annotation tool with standardized output format

Release for Improved Denoising Diffusion Probabilistic Models

Datasets, Transforms and Models specific to Computer Vision

PyTorch implementation of Masked Autoencoder

Python classifier for the PhysioNet/CinC Challenge 2021

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

Code supporting the NeurIPS 2020 publication "Robust Disentanglement of a Few Factors at a Time"

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes