🤗 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.

🦜🔗 The platform for reliable agents.

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Models and examples built with TensorFlow

Ultralytics YOLO 🚀

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…

Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.

OpenMMLab Detection Toolbox and Benchmark

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

A Deep Learning based project for colorizing and restoring old images (and video!)

A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)

Asynchronous HTTP client/server framework for asyncio and Python

Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

Twitter for Python!

Build, Manage and Deploy AI/ML Systems

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

Distributed Asynchronous Hyperparameter Optimization in Python

Keras code and weights files for popular deep learning models.

PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.

Data on COVID-19 (coronavirus) cases, deaths, hospitalizations, tests • All countries • Updated daily by Our World in Data

Download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.

Most popular metrics used to evaluate object detection algorithms.

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

[NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"

Keras implementation of RetinaNet object detection.

Visual analysis and diagnostic tools to facilitate machine learning model selection.