I just completed the first Lesson in the course which introduces one to Image classification and gives some of the basic code. As per Jeremy’s suggestion I thought the best way to learn anything would be to actually try my hand at training a model on my own Image classification problem.
I have taken part in an ongoing Analytics Vidhya (it’s kind of the Indian counterpart to Kaggle) hackathon on Image classification. Here are the details about the problem:-
Problem Statement- Classify images of ships into 5 categories cargo ship, war ship, cruise vessel etc.
No. of categories- 5
No. of training images- 6580
No. of test images- 2480
What I have tried:- I have basically tried the code shown in the first notebook with a variety of different architectures (Resnet 18, 34, 50 etc). I have tried unfreezing the weights and retraining the model, finding best learning rate etc. But I’m unable to move beyond 95% Fbeta (this is the target measure for the competition) which puts me near rank 100 on the leaderboard. The top results are around 98.7%.
Any tips on things to try out for improving the model would be greatly appreciated :). If more details are required for the question please let me know.
P.S.- This is my first question on the forum so kindly excuse if I selected the wrong thread.