In this Deep Neural Network project, I trained a ResNet-n (n=9) neural networks architecture with a different layers to classify a diverse set of 250 Bird Species from the Kaggle dataset with over 96% accuracy. For this project, I used the 250 Birds Species dataset, which consists of 250 bird species. 35215 training images, 1250 test images(5 per species) and 12500 validation images(5 per species. All images are 224 X 224 X 3 color images in jpg format. Also includes a “consolidated” image set that combines the training, test and validation images into a single data set.. …
In this post I trained a feed-forward neural network model to identify handwritten digits from the CIFAR10 dataset: https://www.cs.toronto.edu/~kriz/cifar.html with an accuracy of around 50.01%.
However, I also noticed that it is quite challenging to improve the accuracy beyond 50%, due to the model’s limited power.
I created a base model class to start off with, which contains everything except the model architecture (i.e. it will not contain the
__forward__ methods) so that later I extend this class to try out different architectures. In fact, I can extend this model to solve any image classification problem:
Senior Software Engineer