Try the easy way first :

Build a model that only learns chairs, with all labeled chairs you have and ignore anything else at first.

Try also image data augmentations and see if it helps.

You are not looking at having the best score, actually you dont care about your score as long as you can label new chairs.

You mostly want to tune the model so that you don't have false positives (and introduce noise in your labels). False negatives are OK, and will occur if you tune the model so that FP are zero. You can tune for instance the threshold on a confidence score or class probability (check the model you're using).

You can also build a basic image validation tool with jupyter notebook widgets, steamlit, or your favorite tool, if you want to validate quickly by hand that they are no false positives. It's a very good exercise.

Good luck !

If you already have some labeled chairs, train a first model with that, then run it on images with chairs and no label. Have a second pass with your enriched dataset, and eventually a third, etc.

You can bootstrap the labelling that way. It should help you label a decent amount of chairs, and you can then label manually the remaining chairs.