[NeurIPS 2024] Depth Anything V2. A More Capable Foundation Model for Monocular Depth Estimation

Official Repo of "MMBench: Is Your Multi-modal Model an All-around Player?"

DINO-X: The World's Top-Performing Vision Model for Open-World Object Detection and Understanding

An extremely fast Python package and project manager, written in Rust.

An example of using uv in Docker images

This add-on allows you to edit, reshape and animate SMPL-H, SMPL-X, and SUPR bodies ("SMPL Bodies" for short) to your current Blender scene. Each body consists of a mesh, a shape specific skeleton,…

SynthMoCap Datasets

Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.

3D Gaussian Splat Editor

Questions to ask the company during your interview

書籍『深層ニューラルネットワークの高速化』のサポートサイトです。

Cross Stage Partial Networks

YOLOv10: Real-Time End-to-End Object Detection [NeurIPS 2024]

RTMPose series (RTMPose, DWPose, RTMO, RTMW) without mmcv, mmpose, mmdet etc.

Code for "Spatial-Aware Regression for Keypoint Localization", CVPR 2024 Highlight

OpenMMLab Pose Estimation Toolbox and Benchmark.

The official repo for [NeurIPS'22] "ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation" and [TPAMI'23] "ViTPose++: Vision Transformer for Generic Body Pose Estimation"

[ECCV'2022 Oral] PyTorch implementation for: SimCC: a Simple Coordinate Classification Perspective for Human Pose Estimation (http://arxiv.org/abs/2107.03332). Old name: SimDR

OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.

Ultralytics YOLO11 🚀

Efficient neural feature detector and descriptor

A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives

A SAM-based model for instance segmentation of images of grains

A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. All in a modern, AI-native editor.

Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"

[CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation

High-performance, optimized pre-trained template AI application pipelines for systems using Hailo devices

SAM with text prompt

Pocket-Sized Multimodal AI for content understanding and generation across multilingual texts, images, and 🔜 video, up to 5x faster than OpenAI CLIP and LLaVA 🖼️ & 🖋️