This years challenge was on Object Localization in images.More specifically we had to find the bounding box prediction around an object in in an image.There was only single object in an image.

Given an image we had to predict the coordinates of the bounding box around the object in that image.During testing the Intersection over union (IOU) was calculated to check the performance of the model.

The original size of image was very high so it was scaled down and greyscaled before training.

It consisted of the image and corresponding coordinates of the bounding box around the object. Here is the link to the training images which is saved as pickle file due to its large size. The images were scaled down to size 64X64 before saving to a pickle file https://drive.google.com/open?id=18tYbNC5vOA-nF5uTywTgLS9WxDhH5oRP

Training set csv file containg the coordinates of the images after being rescaled. https://drive.google.com/open?id=1odTu4xMmVppXeNnduTE1oG3FR91V2OGa

The images were saved and uploaded as a pickle file on drive and them later used in code. https://drive.google.com/open?id=1AKWYNF5LNecWAg0ZBo0CVcfVglBejU6k The test images were also resized to 64X64

We have used keras library which uses tenserflow in backend We created a custom neural net having the following layers

Inspired by VGG-16 with some hyperparameter tuning

================================================================= input_1 (InputLayer) (None, 64, 64, 1) 0

conv2d_1 (Conv2D) (None, 64, 64, 32) 320

batch_normalization_1 (Batch (None, 64, 64, 32) 128

conv2d_2 (Conv2D) (None, 64, 64, 64) 18496

batch_normalization_2 (Batch (None, 64, 64, 64) 256

dropout_1 (Dropout) (None, 64, 64, 64) 0

max_pooling2d_1 (MaxPooling2 (None, 32, 32, 64) 0

conv2d_3 (Conv2D) (None, 32, 32, 32) 18464

batch_normalization_3 (Batch (None, 32, 32, 32) 128

conv2d_4 (Conv2D) (None, 32, 32, 64) 18496

batch_normalization_4 (Batch (None, 32, 32, 64) 256

dropout_2 (Dropout) (None, 32, 32, 64) 0

max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64) 0

conv2d_5 (Conv2D) (None, 16, 16, 128) 73856

batch_normalization_5 (Batch (None, 16, 16, 128) 512

conv2d_6 (Conv2D) (None, 16, 16, 64) 73792

batch_normalization_6 (Batch (None, 16, 16, 64) 256

conv2d_7 (Conv2D) (None, 16, 16, 128) 73856

batch_normalization_7 (Batch (None, 16, 16, 128) 512

conv2d_8 (Conv2D) (None, 16, 16, 256) 295168

batch_normalization_8 (Batch (None, 16, 16, 256) 1024

conv2d_9 (Conv2D) (None, 16, 16, 128) 295040

batch_normalization_9 (Batch (None, 16, 16, 128) 512

conv2d_10 (Conv2D) (None, 16, 16, 256) 295168

batch_normalization_10 (Batc (None, 16, 16, 256) 1024

dropout_3 (Dropout) (None, 16, 16, 256) 0

flatten_1 (Flatten) (None, 65536) 0

dense_1 (Dense) (None, 2048) 134219776

batch_normalization_11 (Batc (None, 2048) 8192

dense_2 (Dense) (None, 2048) 4196352

batch_normalization_12 (Batc (None, 2048) 8192

dense_3 (Dense) (None, 1024) 2098176

batch_normalization_13 (Batc (None, 1024) 4096

================================================================= Total params: 141,706,148 Trainable params: 141,693,604 Non-trainable params: 12,544

We used the 'mse'(L2) loss function , optimizer = adadelta and the trained the network for 100 epochs. Finally we saved the results into test.csv file

We got an accuracy of around 87% on the test data All other details are described as comments in the source(ipynb) file