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Image Segmentation: Tips and Tricks from 39 Kaggle Competitions

Imagine if you could get all the tips and tricks you need to hammer a Kaggle competition. I have gone over 39 Kaggle competitions including

 – and extracted that knowledge for you. Dig in.

External data

Data exploration and gaining insights

Preprocessing

Data augmentations

Modeling

Architectures

Hardware setups

Loss functions

Training tips

Evaluation and cross-validation

Ensembling methods

Post processing

Final thoughts

Hopefully, this article gave you some background into image segmentation tips and tricks and given you some tools and frameworks that you can use to start competing.

We’ve covered tips on:

  • architectures
  • training tricks,
  • losses,
  • pre-processing,
  • post processing
  • ensembling
  • tools and frameworks.

If you want to go deeper down the rabbit hole, simply follow the links and see how the best image segmentation models are built.

Happy segmenting!


ADDITIONAL RESOURCE
Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names



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ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It

Jakub Czakon | Posted November 26, 2020

Let me share a story that I’ve heard too many times.

”… We were developing an ML model with my team, we ran a lot of experiments and got promising results…

…unfortunately, we couldn’t tell exactly what performed best because we forgot to save some model parameters and dataset versions…

…after a few weeks, we weren’t even sure what we have actually tried and we needed to re-run pretty much everything”

– unfortunate ML researcher.

And the truth is, when you develop ML models you will run a lot of experiments.

Those experiments may:

  • use different models and model hyperparameters
  • use different training or evaluation data, 
  • run different code (including this small change that you wanted to test quickly)
  • run the same code in a different environment (not knowing which PyTorch or Tensorflow version was installed)

And as a result, they can produce completely different evaluation metrics. 

Keeping track of all that information can very quickly become really hard. Especially if you want to organize and compare those experiments and feel confident that you know which setup produced the best result.  

This is where ML experiment tracking comes in. 

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