This model is a convolutional neural network (CNN) optimized for mushroom species classification based on images. It utilizes transfer learning techniques and includes specific enhancements such as class balancing and data augmentation to improve accuracy across diverse mushroom classes. The model architecture has been fine-tuned for generalization, leveraging GlobalAveragePooling2D to reduce sensitivity to extreme values and increase adaptability across various data distributions.
The model is trained on a curated dataset of mushroom images, consisting of 23 classes, with adjustments for class imbalance to ensure robust classification performance even with underrepresented classes. It includes Grad-CAM visualization capabilities to interpret and explain predictions, providing insights into the model's decision-making process. The model has been developed …
