  Recently, Robotic Cooking has been a very promising field. To execute a
recipe, a robot has to recognize different objects and their states. Contrary
to object recognition, state identification has not been explored that much.
But it is very important because different recipe might require different state
of an object. Moreover, robotic grasping depends on the state. Pretrained model
usually perform very well in this type of tests. Our challenge was to handle
this problem without using any pretrained model. In this paper, we have
proposed a CNN and trained it from scratch. The model is trained and tested on
the dataset from cooking state recognition challenge. We have also evaluated
the performance of our network from various perspective. Our model achieves
65.8% accuracy on the unseen test dataset.
