Been working with image annotation for a few projects now, and I keep bumping into the same problem: stuff that seems simple at first becomes messy quickly once you get into real, complex data.
Things like:
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ambiguous objects that don’t fit your original label classes
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inconsistent annotation between different reviewers
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images where perspective, blur, or occlusion make labels unclear
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edge cases that keep popping up as you scale
I was trying to think through what makes a good annotation workflow in practice, and one breakdown I found helpful focused on how teams structure their steps and stay consistent even with tough edge cases:
https://aipersonic.com/image-annotation/
It’s not perfect, but it helped me see why some patterns work better than others.
For folks here who’ve done annotation at scale:
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how do you define clear classes for weird images?
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what checks do you use to keep labeling consistent across reviewers?
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do you prefer tools with automated suggestions or full manual control?
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any strategies for handling ambiguous images without slowing the whole team down?
Would love to hear real-world approaches and tricks that actually work.