This is the official github repository of the Met dataset

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Deep Learning met
Overview

The Met dataset

This is the official github repository of the Met dataset. The official webpage of the dataset can be found here.

What is it?

This code provides examples for the following:

  1. How to use the dataset.
  2. How to evaluate your own method.
  3. How to reproduce some of the baselines presented in the NeurIPS paper.

Prerequisites

In order to run the code you will need:

  1. Python3
  2. NumPy
  3. Faiss library for efficient similarity search
  4. PyTorch
  5. The Met dataset from the official website

Embedding models

We provide models for descriptor extraction. You can download them here.

Pre-extracted descriptors

We provide pre-extracted descriptors. You can download them here.

Usage

Navigate (cd) to [YOUR_MET_ROOT]/met. [YOUR_MET_ROOT] is where you have cloned the github repository.

Descriptor extraction

Example script for extracting descriptors for the images of the Met dataset is located in code/examples/extract_descriptors.py

For detailed explanation of the options run:

python3 -m code.examples.extract_descriptors -h
kNN classifier & evaluation

Example evaluation script of pre-extracted descriptors with the non-parametric classifier is located in code/examples/knn_eval.py

For detailed explanation of the options run:

python3 -m code.examples.knn_eval -h
Training with contrastive loss

Example training script for trainng the embedding model with contrastive loss on the Met training set is located in code/examples/train_contrastive.py. The trained network can be used for descriptor extraction and kNN classification.

For detailed explanation of the options run:

python3 -m code.examples.train_contrastive -h

State

Repository is under update...

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