Implementing Image Classification on Android Phones Based on TNN
This project is mainly an image classifier based on TensorFlow Lite, which can achieve real-time image recognition on Android devices. Its main functions and implementation steps are as follows: ### Project Structure - **MainActivity.java**: Implements gallery image selection and real-time camera prediction on the main interface. - **MNNClassification.java**: Integrates and encapsulates MNN model-related operations. ### Implementation Ideas 1. **Initialization**:
Read MoreImage Classification on Android Phones Based on MNN
This is a detailed guide on how to implement image classification in an Android application. You have successfully used TensorFlow Lite for image classification and demonstrated how to obtain input data through two methods: calling the camera and selecting images, and then passing this data to the model for prediction. ### Summary of Main Content 1. **Model Initialization**: First, load the pre-trained `mobilenet_v2_1.0_224.tflite` model and create a classifier instance. 2. **Reading Images and Pro
Read MoreFace Detection, Key Point Detection, and Mask Detection on Android with One Line of Code
This paper introduces the method of implementing face detection, key point detection, and mask detection in Android applications using Paddle Lite. The core code is only one line: calling `FaceDetectionUtil.getInstance().predictImage(bitmap)` can complete multiple functions. Behind this line of code, it involves model training and compilation, including face detection (`pyramidbox.nb`), face key point detection (`facekeypoints.nb`), and mask classification (
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