Fine-tuning

We provide a predefined fine-tuning configuration for the ViT B/16 model on ImageNet-1K, which can be found in the conf/fine_tuning/imagenet1k.yaml file. The following table highlights the key differences between ViT pretraining and fine-tuning:

Aspect	ViT Pretraining	ViT Fine-tuning
Configuration Folder	`conf/training/vit`	`conf/fine_tuning/vit`
Training Samples Seen	400M	10M
Optimizer	Fused AdamW	SGD
Resolution	224x224	384x384
Classification Head	MLP with one hidden layer	MLP with single linear layer

To enable the fine-tuning stage with a ViT model, configure the configuration files:

In the defaults section of conf/config.yaml, update the fine_tuning field to point to the desired ViT configuration file. For example, if you want to use the vit/imagenet1k configuration, change the fine_tuning field to vit/imagenet1k.
```
defaults:
  - fine_tuning: vit/imagenet1k
  ...
```
In the stages field of conf/config.yaml, make sure the fine_tuning stage is included. For example,
```
stages:
  - fine_tuning
  ...
```
Execute launcher pipeline: python3 main.py

Remarks: To load a pretrained checkpoint for fine-tuning, set the restore_from_path field in the model section to the path of the pretrained checkpoint in .nemo format. By default, this field links to the .nemo format checkpoint located in the training checkpoints folder.