nightlight9013

nightlight9013's Stars

• ytongbai/LVM

  Language:Python1.7k54

• lucidrains/vector-quantize-pytorch

  Vector (and Scalar) Quantization, in Pytorch

  Language:Python2.4k196

• Qiyuan-Ge/PaintMind

  Fast and controllable text-to-image model.

  Language:Python405

• yuhangzang/ContextDET

  Contextual Object Detection with Multimodal Large Language Models

  1815

• flyywh/VCM_resources

  184

• MarkMoHR/Awesome-Referring-Image-Segmentation

  :books: A collection of papers about Referring Image Segmentation.

  59656

• MzeroMiko/VMamba

  VMamba: Visual State Space Models，code is based on mamba

  Language:Python2k118

• dome272/VQGAN-pytorch

  Pytorch implementation of VQGAN (Taming Transformers for High-Resolution Image Synthesis) (https://arxiv.org/pdf/2012.09841.pdf)

  Language:Python43372

• vvvm23/vqvae-2

  PyTorch implementation of VQ-VAE-2 from "Generating Diverse High-Fidelity Images with VQ-VAE-2"

  Language:Python13417

• thuanz123/enhancing-transformers

  An unofficial implementation of both ViT-VQGAN and RQ-VAE in Pytorch

  Language:Python27934

• lucidrains/magvit2-pytorch

  Implementation of MagViT2 Tokenizer in Pytorch

  Language:Python53635

• HengLan/VastTrack

  VastTrack: Vast Category Visual Object Tracking

  Language:Python412

• Event-AHU/Mamba_State_Space_Model_Paper_List

  [Mamba-Survey-2024] Paper list for State-Space-Model/Mamba and it's Applications

  57533

• Sygil-Dev/muse-maskgit-pytorch

  Implementation of Muse: Text-to-Image Generation via Masked Generative Transformers, in Pytorch

  Language:Python345

• huggingface/open-muse

  Open reproduction of MUSE for fast text2image generation.

  Language:Python32126

• bo-miao/SgMg

  [ICCV 2023] Spectrum-guided Multi-granularity Referring Video Object Segmentation.

  Language:Python789

• pengzhiliang/G2SD

  Language:Python793

• ihaeyong/WSN

  Winning SubNetwork (WSN)

  Language:Python266

• luanyunteng/pytorch-be-your-own-teacher

  A pytorch implementation of paper 'Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation', https://arxiv.org/abs/1905.08094

  Language:Python15127

• Ashespt/AdvBCT

  The official implementation of AdvBCT

  Language:Python42

• google-research/google-research

  Google Research

  Language:Jupyter Notebook33.8k7.8k

• HobbitLong/SupContrast

  PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)

  Language:Python3.1k526

• pengsida/learning_research

  本人的科研经验

  5.5k334

• microsoft/VideoX

  VideoX: a collection of video cross-modal models

  Language:Python965160

• bhpfelix/segment-anything-finetuner

  Simple Finetuning Starter Code for Segment Anything

  Language:Python12817

• IDEA-Research/GroundingDINO

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

  Language:Python6.2k648

• Vision-Intelligence-and-Robots-Group/count-anything

  an empirical study on few-shot counting using segment anything (SAM)

  Language:Jupyter Notebook778

• czczup/ViT-Adapter

  [ICLR 2023 Spotlight] Vision Transformer Adapter for Dense Predictions

  Language:Python1.2k135

• gaomingqi/Track-Anything

  Track-Anything is a flexible and interactive tool for video object tracking and segmentation, based on Segment Anything, XMem, and E2FGVI.

  Language:Python6.4k472

• jhgan00/image-retrieval-transformers

  (Unofficial) PyTorch implementation of Training Vision Transformers for Image Retrieval(El-Nouby, Alaaeldin, et al. 2021).

  Language:Python446