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🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and…
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
A toolkit for developing and comparing reinforcement learning algorithms.
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
OpenMMLab Detection Toolbox and Benchmark
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.
State-of-the-art 2D and 3D Face Analysis Project
A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
Graph Neural Network Library for PyTorch
Open source platform for the machine learning lifecycle
Industry leading face manipulation platform
Open standard for machine learning interoperability
PyTorch implementations of Generative Adversarial Networks.
Image augmentation for machine learning experiments.
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Convert Machine Learning Code Between Frameworks
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Python package built to ease deep learning on graph, on top of existing DL frameworks.
手写实现李航《统计学习方法》书中全部算法
Ongoing research training transformer models at scale
Python bindings for FFmpeg - with complex filtering support