Obtaining Common Public Face Datasets and Creating Custom Face Datasets
Your project is a very interesting attempt, demonstrating the powerful application of deep learning in image processing through the entire process from collecting celebrity photos to conducting facial recognition and feature annotation. Below are some suggestions and improvement ideas for your project: ### 1. Data Collection and Cleaning - **Data Source**: Ensure that all used images are legally sourced and authorized. Avoid using photos with copyright disputes. - **Deduplication and Filtering**: - You can first use a hashing algorithm to deduplicate images (e.g., by calculating the MD5 value of the images). -
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 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 MoreNotes on "My PaddlePaddle Learning Journey" IV — Recognition of Custom Image Datasets
This series of notes mainly introduces how to implement a simple image recognition task using PaddlePaddle, including data preparation, model construction and training, as well as result prediction. The following is a summary of the main content of each part: ### 1. Environment Setup and Initial Configuration - **Environment Configuration**: First, install Python 3 and ensure it runs properly. - **Download Preprocessing Script**: Use the `DownloadImages.py` script to batch-download images to be recognized from Baidu Images. This script can perform downloads based on keywords (the original text is truncated here).
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