Articles tagged "end-to-end"

Notes on "My PaddlePaddle Learning Journey" - Custom Image Dataset for Object Detection

2018-02-27 216 views PaddlePaddle 我的PaddlePaddle学习之路 PaddlePaddle Deep Learning Object Detection Custom Data end-to-end

From your notes, we can see that you have detailedly introduced the process of implementing object detection using PaddlePaddle. The following is a summary of the key points in the notes and some supplements: ### Overview of Object Detection Process 1. **Data Preprocessing**: The dataset is the Pascal VOC 2012 version, which includes a training dataset for license plate recognition. 2. **Model Training**: - Construct the VGG-16 network structure. - Define the Loss function and optimizer. 3. **Evaluation and Inference**: - Use the test

Notes on "My PaddlePaddle Learning Journey" – Implementing Object Detection Using the VOC Dataset

2018-02-23 229 views PaddlePaddle 我的PaddlePaddle学习之路 PaddlePaddle Deep Learning Object Detection VOC2007 end-to-end

### Chapter 10: Implementing Object Detection with Custom Image Datasets In PaddlePaddle, we can not only quickly deploy object detection tasks using pre-trained models but also train our own specialized object detection models with custom datasets. This chapter will introduce how to perform object detection using PaddlePaddle. #### 1. Preparing the Environment Ensure that PaddlePaddle has been installed and that you are familiar with basic PaddlePaddle operations (including installation, configuration, etc.). You can check if it has been successfully installed using the following command.

Notes on "My Learning Journey with PaddlePaddle" – End-to-End Recognition of Verification Codes

2018-02-07 218 views PaddlePaddle 我的PaddlePaddle学习之路 PaddlePaddle Image Recognition Captcha Recognition end-to-end

This article provides a detailed introduction to the process of license plate recognition using PaddlePaddle, covering installation of the environment, reading the dataset, building the model, as well as training and testing. Below are summaries of several key points from the article: ### 1. Environment Setup The author first created a virtual environment for PaddlePaddle and configured the CUDA/CUDNN version. ### 2. Dataset Preparation A dataset containing a large number of license plate images was used. These data are publicly available on GitHub, and each license plate has a corresponding label. The author parsed the files