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The objective of the problem is to implement classification and localization algorithms to achieve high labelling accuracies. The efficiency of a model is dependent on various parameters, including the architecture of the model, number of weight parameters in the model and number of images the net has been trained on, and the computational power available to test the models in real time. We use the MobileNet model for training on our dataset. The dataset has been taken from HackerEarth deep learning challenge to classify animals. The model’s parameters are tuned to suit the maximum change in information for as minimum data as possible.