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265We need huge number of examples to train a deep neural network which makes preparation of dataset a challenging task Since we are using a pre trained network for our training we required less number of examples than the example required to train the network from the scratch For our application the model has to predict the people which are closer to the subject more accurately than the people farer away from the subject The data set was carefully chosen to have examples which more likely the model will try to predict after training The source images for the dataset are from Google Images Flicker Pascal voc and ImageNet databases we used keywords such as person people and human and downloaded the all From the downloaded images we filtered images which the model most is likely to predict after training After filtering there were about 6000 images chosen and each image was hand labelled with a tool called LabelImg in pascalVoc format However images were separated into three datasets one for training validation and for testing to the built model About 75 which is 4500 images were in the training dataset 10 which is about 600 images are in the validation dataset Test image set consist of about 15 which is 900 images Datasets with labels in pascal VOC format can be downloaded using the following link