Lightweight video player open source android#

Android platform: so size is about 400 KB, OpenCL so is about 400 KB, Vulkan so is about 400 KB.

iOS platform: Static library size for armv7+arm64 platforms is about 5 MB, size increase of linked executables is about 620 KB, and metallib file is about 600 KB.

It can be easily deployed to mobile devices and a variety of embedded devices.

MNN is customized and optimized for mobile devices and has no dependencies.

The GPU op plugin can be loaded dynamically to replace default (CPU) op implementation.

Supports hybrid computing on multiple devices.

Supports iOS 8.0+, Android 4.3+, and embedded devices with POSIX interface.

Supports 86 TensorFlow ops, 34 Caffe ops MNN ops: 71 for CPU, 55 for Metal, 40 for OpenCL, and 35 for Vulkan.

Supports popular frameworks like TensorFlow, Caffe, ONNX, and supports common neural networks such as CNN, RN.

MNN has four advantages: lightweight, versatility, high performance, and ease of use. The inference process consists of loading and parsing models, scheduling computational graphs, and running models efficiently on heterogeneous back-end devices. MNN loads network models, does inference, make predictions and returns relevant results.

Lightweight video player open source download#

Follow the official WeChat account 'Alibaba Technology' and enter 'MNN' in the dialog box to learn more and obtain the GitHub download link to this project. MNN has already been made an open-source project on GitHub. During the 2018 Double 11 event, MNN was used in scenarios like smiley face red envelopes, scans, and a finger-guessing game. In addition, MNN is also applied in IoT devices like Cainiao will-call cabinets. MNN stably runs more than 100 million times per day. Currently, MNN has been adopted in more than 20 apps such as Mobile Taobao, Mobile Tmall, Youku, Juhuasuan, UC, Fliggy, and Qianniu, covering live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control and other scenarios. MNN covers the optimization, conversion, and inference of deep neutral network models. Mobile Neural Network (MNN) is a lightweight mobile-side deep learning inference engine that focuses on the running and inference of deep neutral network model.

How is the technical framework behind MNN designed? What are future plans regarding MNN? Today, let's have a closer look at MNN. Therefore, optimization for inference is especially important.' In large-scale machine learning applications, the number of computations for inference are usually 10+ times more than that for training. This is actually in line with ideas in server-side inference engines like TensorRT. Jia Yangqing, a famous AI scientist, commented that 'compared with general-purpose frameworks like TensorFlow and Caffe2 that cover both training and inference, MNN focuses on the acceleration and optimization of inference and solves efficiency problems during model deployment so that services behind models can be implemented more efficiently on the mobile side.

Lightweight video player open source code#

Recently, Alibaba officially published the source code of its lightweight mobile-side deep learning inference engine - Mobile Neural Network (MNN) - on GitHub.