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 MoreNotes from Baidu Machine Learning Training Camp – Question & Answer
This code uses PaddlePaddle to build a convolutional neural network (CNN) for processing the CIFAR-10 dataset. The network consists of 3 convolutional-pooling layers and 1 fully connected layer, without using Batch Normalization (BN) layers. **Analysis of Network Structure:** 1. The input image size is (128, 3, 32, 32). 2. The first and second layers have convolutional kernels of size 5x5. The first convolutional layer outputs (128, 20, 28, 28), and the second convolutional layer outputs (128, 50, 14, 14). The number of parameters for the convolutional outputs of each layer is 1500 and 25000, respectively.
Read More"PaddlePaddle From Beginner to Alchemist" - Part 11: Custom Image Dataset Recognition
This note mainly introduces how to use PaddlePaddle for training and prediction in image classification tasks, which specifically includes the following parts: ### 1. Dataset Preparation The author extracted 240 images from a dataset containing 6 categories of fruit images as the training set and organized them into CSV file format. ### 2. Model Construction A simple LeNet model structure was defined using PaddlePaddle. The model consists of two convolutional layers, two pooling layers, a fully connected layer, and finally performs classification through Softmax.
Read MoreFrom PaddlePaddle Beginner to Alchemist: Part 9 — Transfer Learning
Thank you for sharing this detailed and comprehensive tutorial. Using pre-trained models can indeed significantly improve the model's performance and convergence speed, especially when the amount of data is small. Below, I will optimize and supplement the explanation based on your code and provide some suggestions. ### Code Optimization 1. **Error handling when loading and saving models**: Add error handling for file operation errors. 2. **Using `paddle.static` API**: It is recommended to use PaddlePaddle's static graph API because it is more...
Read More"PaddlePaddle from Beginner to Expert" X - VisualDL: Training Visualization
This chapter will detail how to use PaddlePaddle's `VisualDL` tool for visualization during model training, which helps better understand the model learning process and optimization effects. The following are the detailed tutorial steps: ### 1. Install VisualDL First, ensure that PaddlePaddle has been installed, and VisualDL is also installed. If not, you can install it using the following command: ```bash pip install paddlepaddle-gp ``` **Note:** The original instruction may have a typo; typically, the correct installation command for VisualDL is `pip install visualdl` after installing PaddlePaddle. The provided code block installs PaddlePaddle, not VisualDL. The translation above preserves the original content as per the user's input.
Read MoreHandwritten Tibetan Character Recognition with PaddlePaddle
This code uses the PaddlePaddle framework for training, prediction, and plotting of a Tibetan MNIST handwritten digit recognition model. The entire process can be divided into the following steps: 1. **Dataset Loading**: First, the dataset is downloaded from the Kesci platform and decompressed, then the original images are converted into normalized grayscale images. 2. **Model Definition and Training**: - A simple CNN network structure is defined. - The optimizer, loss function, and accuracy calculation method are set. - Using Padd
Read MoreImplementing Image Classification with Tencent's ncnn on Android Phones
The content you shared is very detailed, covering the entire process from Caffe model conversion, optimization using the ncnn library, to integration into Android projects. Below is a summary of your answer and some supplementary suggestions: 1. **Model Conversion**: - Use `net Bender` to convert Caffe models to ncnn format; this is a very practical tool. - During the conversion process, pay attention to parameters such as input/output layer names and whether to use BN layer optimization. 2. **ncnn Library Integration**: - Through `C
Read MoreImplementing Image Classification on Android Phones Using MACE
This is a great tutorial on how to integrate the MACE framework for image recognition in an Android application. You have detailed the entire project implementation process, from the addition of dependency libraries to the specific code implementation, and provided necessary images and reference materials. ### Project Structure Your project's `main` module contains the following files: 1. **build.gradle (Module: app)**: Contains dependency configuration. 2. **AndroidManifest.xml**: Contains... (the original text was cut off here)
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