I trained my custom data set in the yoloV5s model and I got 80% accuracy on my inference. Now I need to increase the accuracy by adding more images and labels.
My question here is, I already trained 10,000+ labels to reach 80% it took 7 hours for me. Shall I need to include the old 10,000+ data with my new data which is only 1000 to train and improve my accuracy?
Is there any way that I can include the new data only to retrain the model even I add a new class?
How can I save my time and space?
The question you are asking is of topic continual learning, which is an active area of research nowadays. Since you need to add more classes to your model, you need to add the new class with the previous data and retrain the model from start. If you don't do that, i.e., you only train on the new class, your model will forget completely about the previous data (learned feature); this forgetting is known as Catastrophic Forgetting.
Many people have suggested various ways to avoid this Catastrophic forgetting; I personally feel that Progressive Neural Network is highly immune to Forgetting. Apart from it, you can find other methods here
As I told you, this is currently a highly active area of research; There is no full-proof solution. For now, the best way is to add the new data to the previous data and retrain your model.
