There is a project I'm currently working on that requires object detection with continuous training. The idea is to train a model beforehand with a standard dataset. When I get new images I want to "teach" the model with theses new images. The procedure is the following:
- Train a neural network with a standard dataset for object detection.
- Create a script that will deploy the model, capture camera images and make inference on them.
- Since all processed images are stored in a computer, I want to use them for training, example: for a true positive the model learns the object, for a false positive, the model learns that the object detected is not from the desired class, for false negatives the user needs to create a label and train the model. Also the model needs to be trained with true negatives.
- The learning should be periodically based on new images (When the user presses a certain button or automatically).
- The model should not forget the features from old images, or at least retain the most possible features from them.
Basically the model should improve continuously as new images are gathered. I tried finding the technique on google, but for this specific scenario I can't find anything. I found some papers about the incremental learning, but they refer on their solution about adding new classes to the model. That is not what I want. For now, I need only one class, hopefully I can add more in the future for the same model.
