Bird Diversity 250 Species.
Bird Diversity 250 Species.
World Bird Species Diversity

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: 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.

Base Model class definition for training on GPU

I created a base model class to start off with, which contains everything except the model architecture (i.e. it will not contain the __init__ and __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:

I extended…


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