[b][size=4]Best Dataset for Country Flag Classification (150+ Classes, Clean Labels Needed)

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Hi everyone,

I’m currently working on a computer vision project focused on multi-class image classification, specifically targeting national flags. The goal is to train a model that can accurately recognize and distinguish between 150+ country flags, including those with very similar colors and patterns.

:pushpin: What I’m Looking For

I’m searching for a well-structured dataset that includes:

[list]
[]High-resolution flag images
[]Clean and consistent labeling
[]Coverage of all recognized countries
[]Minimal noise or distortions
[*]Easy preprocessing (uniform size or scalable format)
[/list]

A PDF collection could also work, as I can convert it into labeled data if needed.

:gear: Challenges I’m Facing

[list]
[]Handling visually similar flags
[]Maintaining accuracy across many classes
[*]Avoiding overfitting
[/list]

:light_bulb: Looking for Suggestions On

[list]
[]Data augmentation techniques
[]Handling class imbalance
[]Best model architectures (e.g., transfer learning)
[]Efficient preprocessing workflows
[/list]

If you’ve worked on something similar or know any good dataset sources, I’d really appreciate your help.

Thanks in advance!

2

I have a couple questions:

  • Why post this question again, when you have already asked this before?
  • Why post in site-feedback?
  • Why your format is so broken, with brackets everywhere?
  • Why is your question classified as AI written?

Regardless, here’s my answer, again: you can easily create your own dataset. If you want minimal noise, there are standard pictures of country flags everywhere on the internet. Just search on google for websites that provide country flags. Download the images and apply the techniques the course has taught you to process the images and labels into a nice dataset.