Image Classification#
Image classification is a foundational task in computer vision, where a model learns to assign labels to images based on their content. In this project, you will begin by collecting and annotating your own multiclass image dataset, then design and train a convolutional neural network from scratch to establish a performance baseline. After you have evaluated its accuracy and generalisation, you will select a pretrained network and replace its classification head to match your dataset’s classes. You will train the classification head on your dataset, and then fine-tune the entire network. Finally, you will compare the performance of the three models: the from-scratch CNN, the pretrained network with a frozen backbone, and the pretrained network with fine-tuning.
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Suggested Tutorials |
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Easy |
