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Examples

Using Python-API is the most flexible approach: you are not limited by our project & config structures and you can use only the needed part of OML's functionality. You will find code snippets below to train, validate and inference the model on a tiny dataset of figures. Here are more details regarding dataset format.

Schemas, explanations and tips illustrating the code below.

../../../docs/readme/examples_source/extractor/train.md

../../../docs/readme/examples_source/extractor/val.md

../../../docs/readme/examples_source/extractor/retrieval_usage.md

../../../docs/readme/examples_source/extractor/train_val_pl.md

../../../docs/readme/examples_source/extractor/train_val_pl_ddp.md

../../../docs/readme/examples_source/extractor/train_2loaders_val.md

Usage with PyTorch Metric Learning

You can easily access a lot of content from PyTorch Metric Learning. The examples below are different from the basic ones only in a few lines of code:

../../../docs/readme/examples_source/extractor/train_with_pml.md

../../../docs/readme/examples_source/extractor/train_with_pml_advanced.md

To use content from PyTorch Metric Learning with our Pipelines just follow the standard tutorial of adding custom loss.

Note, during the validation process OpenMetricLearning computes L2 distances. Thus, when choosing a distance from PML, we recommend you to pick distances.LpDistance(p=2).

Handling sequences of photos

../../../docs/readme/examples_source/extractor/val_with_sequence.md

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