You can also boost retrieval accuracy of your features extractor by adding a postprocessor (we recommend to check the examples above first). In the example below we train a siamese model to re-rank top retrieval outputs of the original model by performing inference on pairs (query, output_i) where i=1..top_n.
../../../docs/readme/examples_source/postprocessing/train_val.md
../../../docs/readme/examples_source/postprocessing/predict.md
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The documentation for related classes is available via the link.
You can also check the corresponding pipeline analogue.