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If you've only got a few classes it's faster to transfer learn a model from a few hundred examples, get that model to relabel your entire dataset and then fix a
by joshvm 7y ago
If you've only got a few classes it's faster to transfer learn a model from a few hundred examples, get that model to relabel your entire dataset and then fix any mistakes it's made. This is less than ten lines of python using fast ai.
If you've split your labelled data into folders then you can scan through several thumbnails at once to catch any misclassified pictures.
Rinse and repeat until the model is good enough.
- amelius 7y ago> and then fix any mistakes it's made This might be the most practical way to approach this, but it's not really a fundamental improvement since you still need to consider every image (also the correctly labeled ones).
- pfundstein 7y ago> you still need to consider every image Not necessarily. The classifier will not only tell you whether it sees a rat, but also its confidence. So you can manually classify just the low confidence images, retrain, and repeat until there's none left or you're happy with its detection rate.
- joshvm 7y agoTrue, although as the sibling suggested you can sort by confidence and weed out the edge cases. It's also quicker to go through mostly correct images as you need to make fewer (corrective) interactions/clicks. You can even view eg four thumbnails at once to spot outliers.