Neural-Image-Classification

• Developed and trained an initial AlexNet model, and then I modified the AlexNet architecture to enable the classification of three categories: Dogs, Food, and Vehicles. This modification resulted in impressive accuracy rates of 90% and 92.6% for the respective categories. Subsequently, I implemented the VGG-13 model, employing Mixed Precision Training techniques. This approach yielded a remarkable accuracy of 91.4%

• Executed the implementation and training of a customized AlexNet model for the classification of Google Street View House Numbers, resulting in an impressive accuracy of 91.4%.

• Developed and trained a customized AlexNet model for classifying the OCTMNIST dataset, yielding an accuracy of 71%.

• Created and trained a customized AlexNet model to classify a 10-class ImageNet dataset, achieving an accuracy of 68.4%. Then, I implemented the VGG-13 model and employed Mixed Precision Training, which led to a notable accuracy improvement to 71.8%