Hi all, I am working on an image classification challenge and currently hit a deadend. My data consist of 800+ images in training and 3 classes. The test dataset doesn’t have labels and we can only get the score after uploading it. The scoring metric is log loss. Here’s the architecture and process which I have followed which got me a score of 0.34 whereas the top score is 0.14. I just want to know what more I can try and since this is my first challenge I want to learn more.
Using fastai with transfer learning and progressive resizing on a resnet50 model I got a score of 0.37
Ran the model for densenet and efficientnetb4 and later ensembled their scores to get 0.34
Progressive resizing I did on size 32 and later went on till 224 image size Also, I have tried to use the albumentation package.
No matter what I do now, I can’t get the score up. Is their any other way which I can follow? I tried using keras and fine-tuning but that got me till 0.6 for a single model and when tried with pytorch and transfer learning the results were pretty bad.
Any help would be really appreciated.
One other approach i tried is cleaning the data by identifying images which are confusing using top losses from fastai and removed some duplicate images.
I am currently totally out of any more ideas
There are folks who got good scores like 0.2 or even 0.14
i just want to know if there is any other approach which i can try