The first part of the project was to build an object detection classifier using YOLO architecture in Keras.
The second part of the project was to build a binary image segmentation model on COCO images that contain just a person, aside from the background. The goal was to build a good enough model to segment out the person from the background in the image.
The model we build by training on 1500 images, each of shape 360*480*3, has an accuracy of 79% on train data, and 78% on validation and test data. The model is successfully able to segment the person in the image, but the borders of the segmentations are slightly off from where they should be. This is due to using a small training set. Considering the number of images used for training, the model did a good job of segmenting.
There are more images available in this dataset that can be used for training, and it might...
