shashipal95 / analyticsvidhya_gameofdeeplearning

This repository contains my solution for the Computer Vision Contest "Game of Deep Learning" organised by Analytics Vidhya

AnalyticsVidhya_GameOfDeepLearning

This repository contains 5th place solution for the Computer Vision Hackathon Game of Deep Learning organised by Analytics Vidhya.

Problem Statement

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.

Dataset Description

There are 6252 images in train and 2680 images in test data. The categories of ships and their corresponding codes in the dataset are as follows -

There are 5 classes of ships to be detected which are as follows:

• Cargo
• Military
• Carrier
• Cruise
• Tankers

Evaluation Metric

Weighted F1 score

Approach

• Each model is trained in 3 stages using progressive resizing : 128x128 -> 256*256 -> 299x299

• Various combinations of techniques were used like Training on FP16, Data Augmentations(flip left right, random zoom and crop, etc), Mixup with Focal Loss and FlattenedLoss of CrossEntropyLoss.

• Final Submission was generated using Final Blending notebook. Used Avg of predictions of 3 models for final submission as it performed better on Public LB.

Model	Public LB Score	Private LB Score
ResNet50	0.98127	0.97129
ResNeXt50	0.98320	0.97822
SeResNeXt50	0.98031	0.98066
Avg of predictions of 3 models	0.98599	0.98567
Avg of TTA predictions of 3 models	0.98410	0.98815

LeaderBoard

• Public LB : 0.98599 & 8th out of 2083 participants
• Private LB : 0.98567 & 5th out of 2083 participants

Setting up environment

fastai==1.0.52
pretrainedmodels==0.7.4

Models were trained on Colab using Python 3 notebooks, so other necessary packages were already installed.

Steps to Reproduce

• Extract train.zip in data folder and remove _MACOSX and train.zip file.
• Run the notebooks Final_ResNet50, Final_ResNeXt50 and Final_SeResNeXt50.
• Run the Final_Blending notebook on the generated outputs from the three notebooks.

Also predicted probabilities of the three models are provided in PredictedProbabilities folder and the two submission files are provided in FinalSubmission folder