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A game theoretic approach to explain the output of any machine learning model.
📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程
The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that sho…
AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer (CVPR2025)
This project aims help generate parameter files for blupf90
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"
🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022
Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.
Active Learning in the era of Foundation Models
A topic-centric list of HQ open datasets.
Label-efficient learning in Agriculture
Open3D: A Modern Library for 3D Data Processing
Code release for NeRF (Neural Radiance Fields)
A two-stage coarse-to-fine method for automatic cattle body measurement
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Updated Feb 4, 2025
Torchreid: Deep learning person re-identification in PyTorch.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Toolkits for the synthetic multiview cattle 3D detection and action recognition dataset, MultiviewC.
Segment Anything in High Quality [NeurIPS 2023]
[CVPR 2025 Best Paper Award] VGGT: Visual Geometry Grounded Transformer
Official repository of "SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory"
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.