Pinned Repositories
YOSO
Code release for paper "You Only Segment Once: Towards Real-Time Panoptic Segmentation" [CVPR 2023]
detrex
detrex is a research platform for DETR-based object detection, segmentation, pose estimation and other visual recognition tasks.
Grounded-Segment-Anything
Grounded-SAM: Marrying Grounding-DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
GroundingDINO
Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
Stable-DINO
[ICCV 2023] Official implementation of the paper "Detection Transformer with Stable Matching"
LaVIN
[NeurIPS 2023] Official implementations of "Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models"
pytorch-distributed-training
Simple tutorials on Pytorch DDP training
pytorch-pooling
Test different pooling method used in CNN for Computer Vision Task
TRAR-VQA
[ICCV 2021] Official implementation of the paper "TRAR: Routing the Attention Spans in Transformers for Visual Question Answering"
visualization
a collection of visualization function
rentainhe's Repositories
rentainhe/pytorch-distributed-training
Simple tutorials on Pytorch DDP training
rentainhe/Learn-Detectron2-From-Scratch
Detectron2 Learning Notes Sharing
rentainhe/knowledge-graph-visualization
knowledge graph system based on Neo4j and Vue
rentainhe/Awesome-Anything
General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX
rentainhe/rentainhe.github.io
Personal homepage
rentainhe/Caption-Anything
Caption-Anything is a versatile tool combining image segmentation, visual captioning, and ChatGPT, generating tailored captions with diverse controls for user preferences.
rentainhe/dataset-api
The ApolloScape Open Dataset for Autonomous Driving and its Application.
rentainhe/detectron2
Detectron2 is FAIR's next-generation platform for object detection, segmentation and other visual recognition tasks.
rentainhe/detr
End-to-End Object Detection with Transformers
rentainhe/DiffusionDet
PyTorch implementation of DiffusionDet (https://arxiv.org/abs/2211.09788)
rentainhe/DINO
[ICLR 2023] Official implementation of the paper "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection"
rentainhe/DiT
Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"
rentainhe/EVA
Exploring the Limits of Masked Visual Representation Learning at Scale (https://arxiv.org/abs/2211.07636)
rentainhe/GroundingDINO
The official implementation of "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
rentainhe/InternImage
[CVPR 2023] InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
rentainhe/learn-ddim
Denoising Diffusion Implicit Models
rentainhe/learned-guided-diffusion
Learning Guided Diffusion
rentainhe/libai
LiBai(李白): A Toolbox for Large-Scale Distributed Parallel Training
rentainhe/lixiang007666
index
rentainhe/MIMDet
MIMDet: Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection
rentainhe/object-intrinsics
(CVPR 2023) Seeing a Rose in Five Thousand Ways
rentainhe/openmixup
CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark
rentainhe/pytorch-image-models
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more
rentainhe/rentainhe
rentainhe/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.
rentainhe/Segment-Everything-Everywhere-All-At-Once
Official implementation of the paper "Segment Everything Everywhere All at Once"
rentainhe/stable-diffusion
rentainhe/stable-diffusion-learned
Personal Learning Version
rentainhe/T-Rex
Detect and count any objects by visual prompting
rentainhe/vision-1
Datasets, Transforms and Models specific to Computer Vision