SIP-Lab/Deep-Learning-Mobile
Implementing Deep Learning Models on smartphones using existing Deep Learning Python Libraries
This project helps app developers and machine learning engineers turn their trained deep learning models into real-time smartphone applications for both Android and iOS devices. It provides a structured workflow and code examples to integrate these models, allowing smartphone apps to use deep learning for tasks like image recognition directly on the device. The input is a pre-trained deep learning model, and the output is a functional mobile app capable of real-time inference.
No commits in the last 6 months.
Use this if you are a mobile app developer or machine learning engineer looking for a clear process and benchmarked examples to deploy deep learning models onto smartphones for real-time mobile applications.
Not ideal if you are looking for a no-code solution or a general guide to developing deep learning models from scratch, as this focuses specifically on deployment to mobile platforms.
Stars
12
Forks
3
Language
Java
License
MIT
Category
Last pushed
Apr 30, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/SIP-Lab/Deep-Learning-Mobile"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
geekwenjie/SmartJavaAI
🔥🔥🔥Java免费离线AI算法工具箱，支持人脸识别，活体检测，表情识别、目标检测、实例分割、行人检测、OCR文字识别、车牌识别、表格识别、ASR+TTS、机器翻译等功能，Maven引用即可使用。...
amitshekhariitbhu/AndroidTensorFlowMachineLearningExample
Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
amitshekhariitbhu/Android-TensorFlow-Lite-Example
Android TensorFlow Lite Machine Learning Example
jenly1314/MLKit
🌝 MLKit是一个强大易用的工具包。通过ML Kit您可以很轻松的实现文字识别、条码识别、图像标记、人脸检测、对象检测等功能。
nihui/ncnn-android-squeezenet
The squeezenet image classification android example