The purpose of this ODIR challenge is to compare approaches of ophthalmic disease classification in color fundus images. Dataset comprises of images of 5000 patients categried into into eight labels including normal (N), diabetes (D), glaucoma (G), cataract (C), AMD (A), hypertension (H), myopia (M) and other diseases/abnormalities (O)
working_2.ipynb is the final solution that i submitted. In this notebook we used two paralel EfficientNetB3 architecture one for each eye side. Focal loss is used as loss function and adam as optimizers.
tpu-odir.ipynb is the TPU verison which made us capable of training or large image size and batch size. Unlike the working_2.ipynb implementation which used tensorflow < 2 , TPU version is trained with tensorflow>= 2.0. Kaggle TPU is used for this task. To rerun the notebook, fork it on kaggle here .
"kappa": 0.5484336363210259,
"AUC_vlaue": 0.9167666361765955,
"f-1_score": 0.90125,
"Final_Score": 0.7888167574992071
Following are the sources that help me alot while doing this
https://www.kaggle.com/c/human-protein-atlas-image-classification/discussion/75691
https://www.kaggle.com/c/human-protein-atlas-image-classification/discussion/70365
https://www.kaggle.com/c/human-protein-atlas-image-classification/discussion/74374
https://shaoanlu.wordpress.com/2018/03/26/experiment-with-group-normalization/