I have a question about using pre-trained models for object detection and I hope you can help me getting this right
I have looked into the pascal/pascal-multi notebooks of the 2018 course and there the resnet model was used and then trained on the pascal dataset to do the multi object detection task. For the resnet model, I assume this one was a pre-trained model on ImageNet (?).
If so, the pre-trained model was actually trained to do a classification task for one object in an image and was then used with transfer learning to do the multi detection task. Am I getting this right?
So let’s say we have a dataset of labeled images for the classification of 5 classes and use a model for training that was pre-trained on ImageNet. Then (after optimizing) this model is pretty good at classifying those 5 classes. We then have another dataset of images containing these objects and the bounding boxes for those 5 classes.
My question now is: when we save the trained weights from the classification model and use this trained and optimized model as the new pre-trained model for the multi-object detection task, is this possible (and somehow useful for a higher accuracy)? Or am I missing something here?