Skip to content

Top 5% on Kaggle leaderboard using fast.ai library and resnet50 along with transfer learning.

Notifications You must be signed in to change notification settings

aquatiko/Dog-vs-Cat-Redux-Kernel-Edition-Transfer-Learning

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

3 Commits
 
 
 
 

Repository files navigation

Dogs v/s Cats Redux: Kernel Edition- Top 5% Transfer Learning

Transfer Learning approach using fast.ai library which makes implementing it easier. Based on 3 different approaches each with architectures- resnet34, resnet50 and resnet101... got top 5% on Kaggle leaderboard, Accuracy 99.3% and and 0.05605 binary log loss error(evaluation criteria).

Used Diffrential Learning Rates to tune arch , Test Time Augmentation and Learning Rate Anneling to improve model loss.