Search code examples
machine-learningdeep-learningcomputer-visionconv-neural-network

Deep learning Training dataset with Caffe

I am working on creating a vehicle classifier for images using Caffe and have a 3-part question:

  1. Are there any best practices in organizing classes for training a CNN? i.e. number of classes and number of samples for each class? For example, would I be better off this way:
  • (a) Vehicles - Car-Sedans/Car-Hatchback/Car-SUV/Truck-18-wheeler/.... (note this could mean several thousand classes), or
  • (b) have a higher level model that classifies between car/truck/2-wheeler and so on... and if car type then query the Car Model to get the car type
    (sedan/hatchback etc)

  1. How many training images per class is a typical best practice? I know there are several other variables that affect the accuracy of the CNN, but what rough number is good to shoot for in each class? Should it be a function of the number of classes in the model? For example, if I have many classes in my model, should I provide more samples per class?

  2. How do we ensure we are not overfitting to class? Is there way to measure heterogeneity in training samples for a class?

Solution

    1. Well, the first choice that you mentioned corresponds to a very challenging task in computer vision community: fine-grained image classification, where you want to classify the subordinates of a base class, say Car! To get more info on this, you may see this paper. According to the literature on image classification, classifying the high-level classes such as car/trucks would be much simpler for CNNs to learn since there may exist more discriminative features. I suggest to follow the second approach, that is classifying all types of cars vs. truck and so on.

    2. Number of training samples is mainly proportional to the number of parameters, that is if you want to train a shallow model, much less samples are required. That also depends on your decision to fine-tune a pre-trained model or train a network from scratch. When sufficient samples are not available, you have to fine-tune a model on your task.

    3. Wrestling with over-fitting has been always a problematic issue in machine learning and even CNNs are not free of them. Within the literature, some practical suggestions have been introduced to reduce the occurrence of over-fitting such as dropout layers and data-augmentation procedures.

    May not included in your questions, but it seems that you should follow the fine-tuning procedure, that is initializing the network with pre-computed weights of a model on another task (say ILSVRC 201X) and adapt the weights according to your new task. This procedure is known as transfer learning (and sometimes domain adaptation) in community.