Kaggle #1 Winning Approach for Image Classification Challenge
This post is about the approach I used for the Kaggle competition: Plant Seedlings Classification. I was the #1 in the ranking for a couple of months and finally ending with #5 upon final evaluation. The approach is pretty generic and can be used for other Image Recognition tasks as well.
Kaggle is a platform for predictive modelling and analytics competitions in which statisticians and data miners compete to produce the best models for predicting and describing the datasets uploaded by companies and users. This crowdsourcing approach relies on the fact that there are countless strategies that can be applied to any predictive modelling task and it is impossible to know beforehand which technique or analyst will be most effective.[1]
Also, check out the blog that achieves State of the Art results in Intent Classification task on NLP:
TASK OVERVIEW
Can you differentiate a weed from a crop seedling?
The ability to do so effectively can mean better crop yields and better stewardship of the environment.
The Aarhus University Signal Processing group, in collaboration with University…
