Andreaierardi/Fruits-Neural-Networks

Image classification with Machine Learning: recognising fruit and vegetable classes with Neural Networks

Machine-Learning-Project

Project Links.

Contributors 👥

• Andrea Ierardi   LinkedIn   Github   Website

• Emanuele Morales   LinkedIn   Github  

• Gregorio Saporito   LinkedIn   Github  

Assignment

Image classification with Neural Networks: Use Tensorflow 2 to train neural networks for the classification of fruit/vegetable types based on images from this dataset. Images must be transformed from JPG to RGB pixel values and scaled down (e.g., 32x32). Use fruit/vegetable types (as opposed to variety) as labels to predict and consider only the 10 most frequent types (apple, banana, plum, pepper, cherry, grape, tomato, potato, pear, peach). Experiment with different network architectures and training parameters documenting their influence of the final predictive performance. While the training loss can be chosen freely, the reported test errors must be measured according to the zero-one loss for multiclass classification.

If you are not familiar with neural networks for image classification, take one of the many tutorials available in Tensorflow.