How Does it Work?#

Contrastive metric learning#

From an algorithmic perspective, Finetuner leverages a contrastive metric learning approach to improve your model. How does it work?

Step 1: Convert a model into an embedding model#

Finetuner interprets the backbone model architecture, removes the default head, applies pooling and freezes layers that do not need to be trained. For an image classification task (e.g. cats and dogs), Finetuner is going to remove the classification head (cat-dog classifier) and turn your model into an embedding model.

This embedding model does not make predictions or outputs a probability, but instead outputs a feature vector to represent your data.

Step 2: Triplet construction and training on-the-fly#

Finetuner works on labeled data. It expects either a CSV file or a DocumentArray consisting of Documents where each one contains finetuner_label corresponding to the class of a specific training example. After receiving a CSV file, its contents are parsed and a DocumentArray is constructed.

During the fine-tuning, Finetuner creates Triplets (anchor, positive, negative) on-the-fly. For each anchor, which can be any training example, Finetuner looks for a Document with the same finetuner_label (positive), and a Document with a different finetuner_label (negative). The objective is to pull Documents which belong to the same class together, while pushing the Documents which belong to a different class away from each other.

Cloud-based fine-tuning#

From an engineering perspective, we have hidden all the complexity of machine learning algorithms and resource configuration (such as GPUs). All you need to do is decide on your backbone model and prepare your training data.

Once you have logged in to the Jina Ecosystem with login(), Finetuner will push your training data into our Cloud Artifact Storage (only visible to you). At the same time, we will spin-up an isolated computational resource with proper memory, CPU, GPU dedicated to your fine-tuning job.

Once fine-tuning is done, Finetuner will again push your fine-tuned model to the Cloud Artifact Storage and make it available for you to pull it back to your machine. That’s it!

On the other hand, if you have a certain level of machine learning knowledge, Finetuner gives you enough flexibility to adjust the training parameters. This will be explained in a later section.