Name		Name	Last commit message	Last commit date
Dog_Breed_Classification.ipynb		Dog_Breed_Classification.ipynb
README.md		README.md
model_architecture.json		model_architecture.json

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Dogs_Breed_Classification

Image Processing techniques such as Resizing, Rescaling, Reading, Standardizing etc. are used in the project.
The model is designed using RESNET50 Deep Learning Architecture.
The target classes to be predicted are: : beagle, chihuahua, doberman,french_bulldog, golden_retriever, malamute, pug, saint_bernard, scottish_deerhound,tibetan_mastiff.
The model is implemneted using Tensorflow and Keras Frameworks.
The implemenetd Deep learning Model is evaluated on measuring metrics such as Accuracy Score, Confusion Matrix, ROC-AUC Score and F1-Score.
Also, a classification report is generated that documents an anaysis of individual classes against various metrics.

This is the Dogs Breed Classification Task.

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