similarity

TensorFlow Similarity: Metric Learning for Humans

TensorFlow Similarity is a TensorFlow library for similarity learning which includes techniques such as self-supervised learning, metric learning, similarity learning, and contrastive learning. TensorFlow Similarity is still in beta and we may push breaking changes.

Introduction

Tensorflow Similarity offers state-of-the-art algorithms for metric learning along with all the necessary components to research, train, evaluate, and serve similarity and contrastive based models. These components include models, losses, metrics, samplers, visualizers, and indexing subsystems to make this quick and easy.

With Tensorflow Similarity you can train two main types of models:

1. Self-supervised models: Used to learn general data representations on unlabeled data to boost the accuracy of downstream tasks where you have few labels. For example, you can pre-train a model on a large number of unlabled images using one of the supported contrastive methods supported by TensorFlow Similarity, and then fine-tune it on a small labeled dataset to achieve higher accuracy. To get started training your own self-supervised model see this notebook.

2. Similarity models: Output embeddings that allow you to find and cluster similar examples such as images representing the same object within a large corpus of examples. For instance, as visible above, you can train a similarity model to find and cluster similar looking, unseen cat and dog images from the Oxford IIIT Pet Dataset while only training on a few of the dataset classes. To get started training your own similarity model see this notebook.

What’s new

• [Mar 2023]: 0.17 more losses and metric and massive refactoring
  • Added VicReg Loss to contrastive losses.
  • Added metrics used in retrieval papers such as Precision@K
  • Native support for distributed training e.g SimClr now works correctly with distributed training.
  • Multi-modal embedding initial support (CLIP)

For more details and previous releases information - see the changelog

Getting Started

Installation

Use pip to install the library.

NOTE: The Tensorflow extra_require key can be omitted if you already have tensorflow>=2.4 installed.

pip install --upgrade-strategy=only-if-needed tensorflow_similarity[tensorflow] 

Documentation

The detailed and narrated notebooks are a good way to get started with TensorFlow Similarity. There is likely to be one that is similar to your data or your problem (if not, let us know). You can start working with the examples immediately in Google Colab by clicking the Google Colab icon.

For more information about specific functions, you can check the API documentation

For contributing to the project please check out the contribution guidelines

Minimal Example: MNIST similarity

from tensorflow_similarity.samplers import TFDatasetMultiShotMemorySampler

# Data sampler that generates balanced batches from MNIST dataset
sampler = TFDatasetMultiShotMemorySampler(dataset_name='mnist', classes_per_batch=10)

from tensorflow.keras import layers
from tensorflow_similarity.layers import MetricEmbedding
from tensorflow_similarity.models import SimilarityModel

# Build a Similarity model using standard Keras layers
inputs = layers.Input(shape=(28, 28, 1))
x = layers.experimental.preprocessing.Rescaling(1/255)(inputs)
x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.Flatten()(x)
x = layers.Dense(64, activation='relu')(x)
outputs = MetricEmbedding(64)(x)

# Build a specialized Similarity model
model = SimilarityModel(inputs, outputs)

from tensorflow_similarity.losses import MultiSimilarityLoss

# Train Similarity model using contrastive loss
model.compile('adam', loss=MultiSimilarityLoss())
model.fit(sampler, epochs=5)

from tensorflow_similarity.visualization import viz_neigbors_imgs

# Index 100 embedded MNIST examples to make them searchable
sx, sy = sampler.get_slice(0,100)
model.index(x=sx, y=sy, data=sx)

# Find the top 5 most similar indexed MNIST examples for a given example
qx, qy = sampler.get_slice(3713, 1)
nns = model.single_lookup(qx[0])

# Visualize the query example and its top 5 neighbors
viz_neigbors_imgs(qx[0], qy[0], nns)

Supported Algorithms

Self-Supervised Models

• SimCLR
• SimSiam
• Barlow Twins

Supervised Losses

• Triplet Loss
• PN Loss
• Multi Sim Loss
• Circle Loss
• Soft Nearest Neighbor Loss

Metrics

Tensorflow Similarity offers many of the most common metrics used for classification and retrieval evaluation. Including:

Citing

Please cite this reference if you use any part of TensorFlow similarity in your research:

@article{EBSIM21,
  title={TensorFlow Similarity: A Usable, High-Performance Metric Learning Library},
  author={Elie Bursztein, James Long, Shun Lin, Owen Vallis, Francois Chollet},
  journal={Fixme},
  year={2021}
}

Disclaimer

This is not an official Google product.