Sketch Classification with Convolutional Neural Networks¶
A deep learning approach to classifying hand-drawn sketches across 250 object categories, achieving 58.7% top-k accuracy on the TU-Berlin dataset using a custom CNN architecture.
Abstract¶
We present a deep learning model to recognize hand-drawn sketches. This is an image recognition task and part of multi-class classification using deep learning. We show an overall 58.7% top-k categorical accuracy and that the dataset is well suited for image classification using deep learning. Furthermore, we show that tuning the model parameters, i.e., layers, filter size, and preprocessing, significantly impacts the results.
Key Contributions¶
- Designed a 14-layer CNN architecture (8.6M parameters) for 250-class sketch classification on the TU-Berlin dataset of 20,000 hand-drawn greyscale images
- Demonstrated that top-k categorical accuracy is the appropriate metric for hand-drawn sketch classification, where visual ambiguity across classes is inherent
- Showed that image scaling (128x128), normalization, and grayscale preprocessing significantly impact model convergence and accuracy
- Achieved 58.7% top-k test accuracy in just 5 training epochs, outperforming earlier SVM-based approaches (56%) on the same dataset
- Explored the effects of hyperparameter tuning using AutoKeras and KerasTuner, settling on a sequential model with progressively deeper convolutional blocks
Links¶
- PDF: to be hosted
- arXiv / TechRxiv: to be added