NivAm12

NivAm12's Stars

• facebookresearch/segment-anything

  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.

  Language:Jupyter Notebook48.5k3136835.7k

• facebookresearch/dinov2

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

  Language:Jupyter Notebook9.7k95413862

• tkipf/pygcn

  Graph Convolutional Networks in PyTorch

  Language:Python5.2k55701.2k

• bowang-lab/MedSAM

  Segment Anything in Medical Images

  Language:Jupyter Notebook3.2k24308439

• HiLab-git/SSL4MIS

  Semi Supervised Learning for Medical Image Segmentation, a collection of literature reviews and code implementations.

  Language:Python2.3k3986397

• fudan-zvg/Semantic-Segment-Anything

  Automated dense category annotation engine that serves as the initial semantic labeling for the Segment Anything dataset (SA-1B).

  Language:Python2.2k1958139

• facebookresearch/TimeSformer

  The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

  Language:Python1.6k30129219

• czczup/ViT-Adapter

  [ICLR 2023 Spotlight] Vision Transformer Adapter for Dense Predictions

  Language:Python1.3k18187143

• tianrun-chen/SAM-Adapter-PyTorch

  Adapting Meta AI's Segment Anything to Downstream Tasks with Adapters and Prompts

  Language:Python1.1k810499

• AntonioLonga/PytorchGeometricTutorial

  Pytorch Geometric Tutorials

  Language:Jupyter Notebook1.1k1415310

• omriav/blended-latent-diffusion

  Official implementation for "Blended Latent Diffusion" [SIGGRAPH 2023]

  Language:Jupyter Notebook582481637

• rishikksh20/ViViT-pytorch

  Implementation of ViViT: A Video Vision Transformer

  Language:Python5198967

• hitachinsk/SAMed

  The implementation of the technical report: "Customized Segment Anything Model for Medical Image Segmentation"

  Language:Python50645847

• ShirAmir/dino-vit-features

  Official implementation for the paper "Deep ViT Features as Dense Visual Descriptors".

  Language:Python41042148

• BMEII-AI/RadImageNet

  RadImageNet, a pre-trained convolutional neural networks trained solely from medical imaging to be used as the basis of transfer learning for medical imaging applications.

  Language:Python35881836

• lukemelas/deep-spectral-segmentation

  [CVPR 2022] Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization

  Language:Python23381941

• patrickloeber/pytorch-examples

  Language:Jupyter Notebook23142155

• basiralab/GNNs-in-Network-Neuroscience

  A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.

  2214128

• eliahuhorwitz/Conffusion

  Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.

  Language:Python137515

• simula/kvasir-capsule

  GitHub repository for the Kvasir-Capsule dataset.

  Language:Python396210

• SAMPL-Weizmann/DeepCut

  Language:Python28058

• mezera/mrQ

  mrQ_BoxJoinBox

  Language:MATLAB2117013

• ArielKeslassy/DynamicSoftware

  Dynamic Software lets to any user to customize UI and functionality of any open-source software with natural language using GPT, no coding needed!

  Language:Python5101

• HanaHasan04/CHB_Lab

  Computational Human Behavior Lab - Hana & Michael

  Language:Python1100

• OdedMous/Enhancing-By-Supporting-tasks-Components

  In this project, we propose to deal with the data scarcity problem in a specific NLP task by harnessing existing annotated datasets from related tasks. Our approach involves training a multi-head architecture concurrently on both the main task and these “supporting” tasks. We experimented this approach on medical NLP tasks and on three GLUE tasks.

  Language:Python1000