Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Transformers | 101,817 | 64 | 911 | 9 hours ago | 91 | June 21, 2022 | 719 | apache-2.0 | Python | |
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. | ||||||||||
Pytorch | 67,250 | 146 | 10 hours ago | 23 | August 10, 2022 | 12,004 | other | Python | ||
Tensors and Dynamic neural networks in Python with strong GPU acceleration | ||||||||||
Yolov5 | 38,857 | a day ago | 35 | May 21, 2022 | 346 | 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 | 25,767 | 80 | 199 | 10 hours ago | 76 | June 09, 2022 | 2,839 | 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 | 25,073 | 12 | 15 hours ago | 53 | May 25, 2022 | 864 | 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 | a day ago | 143 | July 04, 2022 | 156 | apache-2.0 | Jupyter Notebook | |
The fastai deep learning library | ||||||||||
Lightning | 23,372 | 7 | 389 | 20 hours ago | 221 | June 01, 2022 | 678 | apache-2.0 | Python | |
Deep learning framework to train, deploy, and ship AI products Lightning fast. | ||||||||||
Netron | 22,896 | 4 | 63 | 13 hours ago | 489 | July 04, 2022 | 26 | mit | JavaScript | |
Visualizer for neural network, deep learning, and machine learning models | ||||||||||
Annotated_deep_learning_paper_implementations | 22,464 | 1 | 16 days 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, ... 🧠 |
Tengine, developed by OPEN AI LAB, is an AI application development platform for AIoT scenarios launched by OPEN AI LAB, which is dedicated to solving the fragmentation problem of aiot industrial chain and accelerating the landing of AI industrialization. Tengine is specially designed for AIoT scenarios, and it has several features, such as cross platform, heterogeneous scheduling, chip bottom acceleration, ultra light weight and independent, and complete development and deployment tool chain. Tengine is compatible with a variety of operating systems and deep learning algorithm framework, which simplifies and accelerates the rapid migration of scene oriented AI algorithm on embedded edge devices, as well as the actual application deployment;
Tengine is composed of five modules: core/operator/serializer/executor/driver.
please refer to Wiki
please visit examples for demos on classification/detection and download models from Tengine model zoo (psw: hhgc)
tengine applications is a project for sharing android/linux applications powered by Tengine
Test on RK3399-1*A72
Model | fp32 | int8-hybrid | int8-e2e |
---|---|---|---|
Squeezenet v1.1 | 55.3ms | 48.6ms | 44.6ms |
Mobilenet v1 | 108.7ms | 74.6ms | 64.2ms |
More Benchmark data to be added.
2020.5 updated