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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!


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Image Processing in Python: Algorithms, Tools, and Methods You Should Know

9 mins read | Author Neetika Khandelwal | Updated May 27th, 2021

Images define the world, each image has its own story, it contains a lot of crucial information that can be useful in many ways. This information can be obtained with the help of the technique known as Image Processing.

It is the core part of computer vision which plays a crucial role in many real-world examples like robotics, self-driving cars, and object detection. Image processing allows us to transform and manipulate thousands of images at a time and extract useful insights from them. It has a wide range of applications in almost every field. 

Python is one of the widely used programming languages for this purpose. Its amazing libraries and tools help in achieving the task of image processing very efficiently. 

Through this article, you will learn about classical algorithms, techniques, and tools to process the image and get the desired output.

Let’s get into it!

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