Stars
An Open Source Machine Learning Framework for Everyone
Cross-platform, customizable ML solutions for live and streaming media.
ncnn is a high-performance neural network inference framework optimized for the mobile platform
MNN: A blazing-fast, lightweight inference engine battle-tested by Alibaba, powering high-performance on-device LLMs and Edge AI.
FlashMLA: Efficient Multi-head Latent Attention Kernels
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.
C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library
(CGCSTCD'2017) An easy, flexible, and accurate plate recognition project for Chinese licenses in unconstrained situations. CGCSTCD = China Graduate Contest on Smart-city Technology and Creative Design
High Performance Chinese License Plate Recognition Framework.
header only, dependency-free deep learning framework in C++14
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
weiliu89 / caffe
Forked from BVLC/caffeCaffe: a fast open framework for deep learning.
TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its…
Tengine is a lite, high performance, modular inference engine for embedded device
The Compute Library is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.
FeatherCNN is a high performance inference engine for convolutional neural networks.
This fork of BVLC/Caffe is dedicated to improving performance of this deep learning framework when running on CPU, in particular Intel® Xeon processors.
🔥 (yolov3 yolov4 yolov5 unet ...)A mini pytorch inference framework which inspired from darknet.
this repository is the implementation of MTCNN with no framework, Just need opencv and openblas, support linux and windows
ppl.cv is a high-performance image processing library of openPPL supporting various platforms.
Supervised Descent Method Apply to Face Alignment, and Head Pose Estimation with Linear Regression. It is cross-platfrom, easily compile in windows, ubuntu, even in Android & iOS.