[Solved] Developing a customized caffe-based Convolutional Neural Network from scratch

Question

I just copy the reply from there for those facing similar issues.

You do not need a pre-trained model. Actually, when you are developing your own, custom architecture, you cannot even get a pre-trained model.

1200 images is not much, but without any other information, one cannot tell if it is enough or not. Maybe your problem is simple, your network small and the dataset will be enough. Maybe your problem is tough and even 109 images would not be enough. There is no simple answer to that.

Also, there is no simple answer to which of the two options will be better. I would start training from scratch, using augmented dataset. Ideally, generate the augmented versions on the fly rather than creating a fixed amount (e.g. 18) from each image. If you observe poor performance, pre-train on VOC and then fine-tune on your own data (but still use augmentation). This is the only way to compare which is better.

As final remarks, I would recommend this article summarizing some good tips for neural network development and debugging; and this thread focused on learning problems.

Accepted Answer

I just copy the reply from there for those facing similar issues.

You do not need a pre-trained model. Actually, when you are developing your own, custom architecture, you cannot even get a pre-trained model.

1200 images is not much, but without any other information, one cannot tell if it is enough or not. Maybe your problem is simple, your network small and the dataset will be enough. Maybe your problem is tough and even 109 images would not be enough. There is no simple answer to that.

Also, there is no simple answer to which of the two options will be better. I would start training from scratch, using augmented dataset. Ideally, generate the augmented versions on the fly rather than creating a fixed amount (e.g. 18) from each image. If you observe poor performance, pre-train on VOC and then fine-tune on your own data (but still use augmentation). This is the only way to compare which is better.

As final remarks, I would recommend this article summarizing some good tips for neural network development and debugging; and this thread focused on learning problems.