Kaggle IEEE Challenge: Need help (Practicing Lesson 2 v2)

Yesterday, In an effort to master the concepts taught in lesson 2. I entered IEEE challenge on Kaggle. Competition is about identifying the camera from which photos were taken.
I have decided to use the pretrained resnet50 model from the fastai library.
I just followed the concepts/steps covered in lesson 2 (part1 v2). To my surprise, I was able to achieve 94% accuracy on a validation set. I, immediately, calculated the prediction on test data and submitted it to Kaggle, my accuracy on test data was only 21% :frowning:
My notebook is here.
Can anyone guide me, how can I get good accuracy on data set?

My guesses about why I am not getting good accuracy on test data set.

  • Different formates of train(JPEG) and test(tiff) data,
  • Not Suitable model: As resnet is used for object detection in image, it might not be good for detection of camera with which photos were taken

Can anyone guide me how can I move on with this challenge?

If any want to join my team please reply

The test set has some specific augmentations in it. It also has photos from different cameras. You would need to try to replicate that in your validation set.


@irshaduetian Can you share your code?..I am not able to see the code on git…I am also working on the same competition using the code that we learnt in lesson2…but I am finding it slightly difficult to work with the data…

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Hello, the link is present in the above post. But pasting here again https://github.com/irshadqemu/fast.ai_DeepLearning-1-MOOC/blob/master/lesson1-rxt50_iee.ipynb,
it is kind of rough but the code until the “Test Prediction” belong to the IEEE competition.
Code from In[77] to the end of the notebook don’t belong to this competition, need to remove that.

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Thanks…I could see the code…It has helped a lot in pre-processing the data for this competition…are you still working on this ?

No, I am not working on it anymore due to time constraints. If you could obtain some good results, do share them with me :slight_smile: Best of luck man.