Deep learning: Training from COCO/imagenet weights or from a class-specific trained weights when classify 2 similar objects I have a question about the possible outcome of a trained model. Imagine that I would like to classify 2 different models of Ferrari and the dataset of these 2 models is small (for example, a few hundred images per model). 

In the Keras blog, the issue was discussed but in the example, they are classifying dogs and cats, 2 class that are very general, distinct between them, and there are many cats and dogs already included in the original imagenet model (lets say the car/bus/truck are not included in the imagenet output classes).

Will it be better start training the model from Coco/Imagenet weights or start training using the weights of a model previously trained to classify cars only?
If you have a previously trained model to classify only cars, you can use it considering the fact that you have less data. The trained model for cars classification that you have already, would have been fine-tuned on the Imagenet or COCO in the first place. This way I think you would be able to train the model in less amount of time compared to fine tuning on Imagenet weights. Try both the methods once and try to find out the amount of performance increase in terms of time and accuracy over each other.