What are the best practices for training and fine-tuning vision transformers on large-scale datasets?

Great question. Researchers had excellent results with the BERT for NLP. It was only a matter of time before they decided to try and use an architecture very similar to BERT for Image data. So instead of words, you are working with patches of images. This video gets into the details: https://www.dhirubhai.net/learning/advanced-ai-transformers-for-computer-vision/comparing-vision-transformers-to-bert?autoSkip=true&autoplay=true&resume=false&u=2125562

Vision transformers (ViTs) present a promising alternative to traditional CNNs for image classification tasks due to their ability to capture global dependencies and contextual information. When training and fine-tuning ViTs on large-scale datasets, several best practices can enhance performance. 1. Pretraining on a diverse and representative dataset, such as ImageNet, can provide a strong initialization for the model. 2. Data augmentation techniques, such as random cropping, flipping, and color jittering, help improve robustness and generalization. 3. Fine-tuning the pretrained ViT on the target dataset with a suitable learning rate schedule and regularization methods further refines the model's representations for the specific task.

Using a well-known pre-trained model is always a great starting point. This is because most of these models have been trained on a large dataset like ImageNet, with has over 1000 different classes of images and in excess of 1 million images. Using a pre-trained models allows you to you this model as your starting point and you can then fine-tune this model based on your dataset. I get into the details of this here: https://www.dhirubhai.net/learning/advanced-ai-transformers-for-computer-vision/using-a-pretrained-model-without-fine-tuning?autoplay=true&resume=false&u=2125562

You can also use data augmentation techniques with Vision transformers. One of the benefits of working with computer vision is that you can quickly visually confirm if the data augmentation techniques are working as expected. Here is a short example of preprocessing images: https://www.dhirubhai.net/learning/advanced-ai-transformers-for-computer-vision/preprocessing-images?autoplay=true&resume=false&u=2125562

In addition to the general techniques mentioned above, it is important to look at how many ViT layers can be fine-tuned and how many of them can ve kept frozen. When you gradually increase the number of layers for fine-tuning, you will arrive at a point where, adding more layers to tuning could start overfitting/ increasingly hard to train due to the complexity in the data (or) network and may not reach to a local minima too. Hence, experimentation with the number of layers to be fine-tuned would also be important.

Actually, I disagree that smaller patch sizes would reduce sequence length. Sequence length is calculated as: (image_size // patch_size) ** 2, where image_size is resolution of (one side) of the image, and patch_size is the length of one side of the patch. So, reducing the patch size would result in longer sequences, whereas increasing the patch size would result in shorter sequences.

If you are looking at an image classification task, then accuracy is typically your go-to metric. I talk a little more about this here: https://www.dhirubhai.net/learning/advanced-ai-transformers-for-computer-vision/training-arguments?autoplay=true&resume=false&u=2125562

In addition to Classification tasks, Vision Transformers can be used for Regression. When it comes to Regression, there are some additional metrics which are more relevant, for example, normalized mean square error, normalized mean absolute error and R-squared. The first two metrics would give us the average of how close the predicted values are to the actual values, the third metric would tell you how good is your regression fit with 0 being bad fit to 1 being the best fit.