I knew that nowadays, people usually use a pre-trained model to fine-tune and train their own models. There are few famous models that have been trained with the ImageNet dataset is Resnet, Inception, or VGG.
You may all know that the ImageNet dataset comes along with a Multi-class classification problem so that we usually use
CrossEntropy to train the model. I usually use pre-trained Resnet50 or VGG19 in TorchVision models
I just wonder if we have a large dataset of Multi-Label instead (suppose 10M images) of Multi-class classification with more than 10k labels (10x larger than Imagenet labels) so that I can build a CNN model using
BinaryCrossEntroy to train our model with that dataset.
Is that a good idea if we can use that new checkpoint model for transfer learning purposes? If yes, when we should use that scenario when we shouldn’t? or Why not?