Ren Tianhe's repositories
pytorch-distributed-training
Simple tutorials on Pytorch DDP training
Learn-Detectron2-From-Scratch
Detectron2 Learning Notes Sharing
knowledge-graph-visualization
knowledge graph system based on Neo4j and Vue
Awesome-Anything
General AI methods for Anything: AnyObject, AnyGeneration, AnyModel, AnyTask, AnyX
rentainhe.github.io
Personal homepage
Caption-Anything
Caption-Anything is a versatile tool combining image segmentation, visual captioning, and ChatGPT, generating tailored captions with diverse controls for user preferences.
detectron2
Detectron2 is FAIR's next-generation platform for object detection, segmentation and other visual recognition tasks.
DiffusionDet
PyTorch implementation of DiffusionDet (https://arxiv.org/abs/2211.09788)
DINO
[ICLR 2023] Official implementation of the paper "DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection"
DiT
Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"
EVA
Exploring the Limits of Masked Visual Representation Learning at Scale (https://arxiv.org/abs/2211.07636)
GroundingDINO
The official implementation of "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
InternImage
[CVPR 2023] InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions
learn-ddim
Denoising Diffusion Implicit Models
learned-guided-diffusion
Learning Guided Diffusion
lixiang007666
index
MIMDet
MIMDet: Unleashing Vanilla Vision Transformer with Masked Image Modeling for Object Detection
object-intrinsics
(CVPR 2023) Seeing a Rose in Five Thousand Ways
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
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.
Segment-Everything-Everywhere-All-At-Once
Official implementation of the paper "Segment Everything Everywhere All at Once"
stable-diffusion-learned
Personal Learning Version