Background
Machine learning has historically required significant programming expertise to access. During my undergraduate studies, I noticed that many researchers and educators who understood the theory of deep learning were blocked by the engineering barrier — they couldn't experiment with CNNs without writing Python code from scratch.
ConvNet Runner was built to close that gap. The idea was simple: expose the full power of TensorFlow's CNN training pipeline through a clean graphical interface, so that anyone — regardless of coding background — could upload a dataset, configure a network architecture, and train a model.
Features
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Impact
Related research
This project grew alongside my research on computer vision applications. Work on camel detection and road safety from the same period evolved into two published arXiv papers — Spot-the-Camel (12 citations) and Computer Vision for Camel-Vehicle Collision Mitigation — both building on the CNN knowledge developed during ConvNet Runner.