Keywords - Convolutional Neural Networks, Multi-class image classification, precision, recall, accuracy, F-1 score, confusion matrix, cross-validation
Why is multi-class image classification important?
Object Recognition - recognize and categorize objects within images, allowing systems to understand and respond to diverse visual content.
Medical Diagnosis - for diagnosing diseases or conditions based on visual data such as X-rays, MRIs, or pathology slides.
Retail and E-commerce - to categorize products, making it easier for users to search, browse, and find relevant items. It also enables automated product tagging and recommendation systems.
Human-Computer Interaction - enabling systems to understand and respond to gestures, facial expressions, or objects in images or video streams.
This project aims at testing two Convolutional Neural Networks (CNN) for recognizing 5 different flower species based on images. The models are trained to classify images into specific flower categories.
We first build a simple Sequential CNN model with convolutional layers for feature extraction, max pooling layers for down-sampling, a flatten layer for reshaping, and fully connected layers for classification. The model is compiled with the Adam optimizer, categorical cross-entropy loss, and accuracy as the evaluation metric.
We then add complexity to this model by adding dense layers followed by dropout layers with a dropout rate of 0.5 for regularization and improved generalization.
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