Recognizor

Project information

A custom convolutional neural network (CNN) was developed from scratch and trained to recognize flower types using pcituires from the Flowers Recognition Dataset. The model’s architecture and hyperparameters were optimized to maximize recognition accuracy.

The custom CNN is compared against MobileNetV2, a pre-trained model fine-tuned using transfer learning. The comparison highlights the trade-offs between custom-built models and transfer learning approaches in terms of accuracy, training efficiency, and computational cost.

Python TensorFlow CNN computer vision

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GNU General Public License v3.0 or later

Created on

April 05, 2026

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