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Hackernoon logoImage Segmentation: Tips and Tricks from 39 Kaggle Competitions by@jakubczakon

Image Segmentation: Tips and Tricks from 39 Kaggle Competitions

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@jakubczakonjakubczakon

Senior data scientist building experiment tracking tools for ML projects at https://neptune.ai

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.

Contents

  • External Data Preprocessing
  •  Data Augmentations 
  • Modeling 
  • Hardware Setups 
  • Loss Functions 
  • Training Tips
  •  Evaluation and Cross-validation
  •  Ensembling Methods 
  • Post Processing

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
  • ensemblingtools 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!

This article was originally posted by Derrick Mwiti on the Neptune blog. If you liked it, you may like it there :)

You can also find me tweeting @Neptune_ai or posting on Linkedin about ML and Data Science stuff.

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