Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
SUMMARY: This project aims to construct a predictive model using a TensorFlow convolutional neural network (CNN) and document the end-to-end steps using a template. The Fruits and Vegetables Image Recognition is a multi-class classification situation where we attempt to predict one of several (more than two) possible outcomes.
INTRODUCTION: The dataset owner collected over 4,300 pieces of fruit and vegetable images and created a dataset that includes 36 classes. The idea was to build an application that recognizes the food items from the captured photo and provides different recipes that can be made using the food items.
ANALYSIS: The EfficientNetV2M model’s performance achieved an accuracy score of 95.44% after 20 epochs using a separate validation dataset. After tuning the learning rate, we improved the accuracy rate to 96.87% using the same validation dataset. When we applied the model to the test dataset, the model achieved an accuracy score of 96.37%.
CONCLUSION: In this iteration, the TensorFlow EfficientNetV2M CNN model appeared suitable for modeling this dataset.
Dataset ML Model: Multi-Class classification with numerical features
Dataset Used: Kritik Seth, “Fruits and Vegetables Image Recognition Dataset,” Kaggle 2020
Dataset Reference: https://www.kaggle.com/datasets/kritikseth/fruit-and-vegetable-image-recognition
One source of potential performance benchmarks: https://www.kaggle.com/datasets/kritikseth/fruit-and-vegetable-image-recognition/code
The HTML formatted report can be found here on GitHub.