- There is no absolute number that anyone can tell you with certainty in how many images are required for a model to train well. Essentially you have to continue adding annotated images until the model performs to the level at which you expect or require.
- Unless you have included all the data from the COCO dataset as part of your training dataset, it won’t perform as well. The model is trained on the data that you provide it during training and anything prior to that is lost when the weights are updated. You might be able to retain some performance but using
freeze=NwhereNis the number of layers to freeze during training, however without including all the COCO data, your model might not perform as well. You’ll need to add more data or use multiple models. - It depends on what you mean by “accuracy” as there are multiple metrics of performance. They’re reported on the final validation after training and you can read more about them in the docs.
- Add more and diverse data. There’s a training argument called
patiencewhich defaults to100and if you’re seeing overfitting during training, you can lower the value to something likepatience=50to help prevent overfitting.