CNN based classifier to classify images of vessels into 5 different categories. This solution was for a hackathon organized by Analytics Vidhya.
Ship or vessel detection has a wide range of applications, in the areas of maritime safety, fisheries management, marine pollution, defence and maritime security, protection from piracy, illegal migration, etc.
Keeping this in mind, a Governmental Maritime and Coastguard Agency is planning to deploy a computer vision based automated system to identify ship type only from the images taken by the survey boats. You have been hired as a consultant to build an efficient model for this project.
There are 5 classes of ships to be detected which are as follows:
- Cargo
- Military
- Tanker
- Carrier
- Cruise
There are 6252 train images and 2680 images in the test set.he categories of ships and their corresponding codes in the dataset are as follows:
'Cargo' -> 1 'Military' -> 2 'Carrier' -> 3 'Cruise' -> 4 'Tankers' -> 5
There are 2 files provided to us along with the dataset:
- train.csv: Train dataset 2 .test_ApKoW4T.csv: Test dataset
Code Files:
- Data_create.py: Divide dataset into train and test folders based on csv files
- Classify.py: CNN architecture to classify the images
The evaluation metrics for this competition was weighted F1 score