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Starred repositories
Solve Visual Understanding with Reinforced VLMs
[ECCV 2024] Official implementation of the paper "Semantic-SAM: Segment and Recognize Anything at Any Granularity"
OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Language Segment-Anything (with updated dependencies)
[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
Code for Learning Monocular Dense Depth from Events paper (3DV20)
[T-PAMI 2022] Meta-DETR for Few-Shot Object Detection: Official PyTorch Implementation
Virtual whiteboard for sketching hand-drawn like diagrams
《Hello 算法》:动画图解、一键运行的数据结构与算法教程。支持 Python, Java, C++, C, C#, JS, Go, Swift, Rust, Ruby, Kotlin, TS, Dart 代码。简体版和繁体版同步更新,English version ongoing
yolov8 hub,cpp with onnxruntime and opencv
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…
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
GLM-4 series: Open Multilingual Multimodal Chat LMs | 开源多语言多模态对话模型
[CVPR 2024] Real-Time Open-Vocabulary Object Detection
Official Implementation of CVPR24 highligt paper: Matching Anything by Segmenting Anything
A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
[CVPR 2024 Highlight] FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects
[ECCV2024] API code for T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐