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Feature Description
Data collection - Gathered a diverse dataset of images containing children and adults.
Data preprocessing - Performed essential preprocessing steps, including resizing, normalization, and augmentation, to prepare the data for training.
Model selection - Chose popular deep learning models, including VGG19, ResNet50, InceptionV3, and MobileNetV2, for the classification task.
Model training - Trained each model using the labelled dataset and appropriate training configurations.
Model evaluation - Evaluated the trained models on a separate test dataset to measure their performance in terms of accuracy and other relevant metrics.
Comparative analysis - Compared the accuracy and results of each model to determine the best-performing model for the task of classifying images into children and adults categories.
Use Case
The main goal of this project is to develop a classification model capable of accurately distinguishing between images of children and adults. The purpose is to explore the performance of different deep learning models specifically tailored for this classification task.
Benefits
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Priority
High
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