Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Transformers | 103,258 | 64 | 911 | an hour ago | 91 | June 21, 2022 | 750 | apache-2.0 | Python | |
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. | ||||||||||
Pytorch | 67,645 | 146 | an hour ago | 23 | August 10, 2022 | 12,240 | other | Python | ||
Tensors and Dynamic neural networks in Python with strong GPU acceleration | ||||||||||
Yolov5 | 39,205 | 4 hours ago | 35 | May 21, 2022 | 257 | agpl-3.0 | Python | |||
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite | ||||||||||
Made With Ml | 33,193 | a month ago | 5 | May 15, 2019 | 11 | mit | Jupyter Notebook | |||
Learn how to responsibly develop, deploy and maintain production machine learning applications. | ||||||||||
Ray | 25,939 | 80 | 199 | 2 hours ago | 76 | June 09, 2022 | 2,925 | apache-2.0 | Python | |
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads. | ||||||||||
Deepspeed | 25,582 | 12 | 2 hours ago | 53 | May 25, 2022 | 891 | apache-2.0 | Python | ||
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. | ||||||||||
Fastai | 23,942 | 184 | 117 | 13 days ago | 143 | July 04, 2022 | 156 | apache-2.0 | Jupyter Notebook | |
The fastai deep learning library | ||||||||||
Lightning | 23,613 | 7 | 389 | 3 hours ago | 221 | June 01, 2022 | 701 | apache-2.0 | Python | |
Deep learning framework to train, deploy, and ship AI products Lightning fast. | ||||||||||
Netron | 23,111 | 4 | 63 | 12 hours ago | 489 | July 04, 2022 | 29 | mit | JavaScript | |
Visualizer for neural network, deep learning, and machine learning models | ||||||||||
Annotated_deep_learning_paper_implementations | 22,464 | 1 | a month ago | 76 | June 27, 2022 | 17 | mit | Jupyter Notebook | ||
🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠 |
Open3D is an open-source library that supports rapid development of software that deals with 3D data. The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python. The backend is highly optimized and is set up for parallelization. We welcome contributions from the open-source community.
Core features of Open3D include:
For more, please visit the Open3D documentation.
Pre-built pip packages support Ubuntu 18.04+, macOS 10.15+ and Windows 10+ (64-bit) with Python 3.6-3.10.
# Install
pip install open3d # or
pip install open3d-cpu # Smaller CPU only wheel on x86_64 Linux (v0.17+)
# Verify installation
python -c "import open3d as o3d; print(o3d.__version__)"
# Python API
python -c "import open3d as o3d; \
mesh = o3d.geometry.TriangleMesh.create_sphere(); \
mesh.compute_vertex_normals(); \
o3d.visualization.draw(mesh, raw_mode=True)"
# Open3D CLI
open3d example visualization/draw
To get the latest features in Open3D, install the development pip package. To compile Open3D from source, refer to compiling from source.
Checkout the following links to get started with Open3D C++ API
To use Open3D in your C++ project, checkout the following examples
Open3D-Viewer is a standalone 3D viewer app available on Ubuntu and macOS. Please stay tuned for Windows. Download Open3D Viewer from the release page.
Open3D-ML is an extension of Open3D for 3D machine learning tasks. It builds on top of the Open3D core library and extends it with machine learning tools for 3D data processing. To try it out, install Open3D with PyTorch or TensorFlow and check out Open3D-ML.
Please cite our work if you use Open3D.
@article{Zhou2018,
author = {Qian-Yi Zhou and Jaesik Park and Vladlen Koltun},
title = {{Open3D}: {A} Modern Library for {3D} Data Processing},
journal = {arXiv:1801.09847},
year = {2018},
}