# Classification with deeplearning : clean start vs continue training

I trained some weights to identify apples and oranges (using YOLOv3).

If I want to be able to identify peaches, which approach is usually recommended:

1. Start clean and train the 3 classes.
2. Train the peaches over the already-trained weights (with apples and oranges)
1. Only train with peaches images
2. Use all available training data (including apples and oranges)

This is what I have found:

• If I start clean, it will take longer until I can get a good result, but the detection is usually better.
• Every time I add a new class (using 2.2), the detection get worse for the already learned objects, but it takes less time until I can get a good result (however I suspect that apples and oranges become over-fitted?).
• I haven't tested 2.1, as I think that it won't be able to re-adjust the weights for the apples and the oranges.

Is the above expected? What is the recommended course of action?

• Rule of the thumb: when in doubt start clean – mirror2image Nov 20 '19 at 6:41