I have thousands of images similar to this.
I can classify them using existing metadata to different folders according to gravel product type loaded on the truck.
What would be optimal way to train a model for image classification that would be able to guess the type of stone product on truck from the picture? I can use ML.NET builder that suits me as part of Visual Studio and .NET but perhaps something pre-trained would be better?
Pre-training is usually beneficial if the dataset the model was trained on is to some degree related to your target dataset (i.e. the dataset you wish to fine-tune on.) And is also useful to overcome having little data.
For example, using ImageNet to pretrain the model can help if the target dataset is about natural images too.
Maybe you can try to pre-train on some general dataset like ImageNet (or use a pretrained model), then when fine-tuning you also want to learn one or more last hidden layers, so not only the output layer: doing so ensure more adaptation to your target data.
