🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
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《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
Learn AI Engineering! Learn it. Build it. Ship it for others.
Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
We write your reusable computer vision tools. 💜
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
⚡️HivisionIDPhotos: a lightweight and efficient AI ID photos tools. 一个轻量级的AI证件照制作算法。
Datasets, Transforms and Models specific to Computer Vision
pix2tex: Using a ViT to convert images of equations into LaTeX code.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
A MNIST-like fashion product database. Benchmark :point_down:
A collaboration friendly studio for NeRFs
🐍 Geometric Computer Vision Library for Spatial AI
Low-code framework for building custom LLMs, neural networks, and other AI models
Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Fast and Accurate ML in 3 Lines of Code
A system for quickly generating training data with weak supervision
:earth_americas: Simple and ready-to-use tutorials for TensorFlow
A python library for self-supervised learning and latent world models on images.
Visual tracking library based on PyTorch.
Accelerated deep learning R&D
An open source library and framework for deep learning on satellite and aerial imagery.
(IROS 2020, ECCVW 2020) Official Python Implementation for "3D Multi-Object Tracking: A Baseline and New Evaluation Metrics"