Use our image similarity search model to easily find similar images

We offer image similarity search pre-trained model that you can use out-of-the-box without any additional training.

An image similarity search model is used to find visually similar images to one that is uploaded. The retrieved images are sorted by their similarity score, allowing users to efficiently sort large datasets to find duplicates or similar images.

The Image Similarity models come in two forms:

  • 1vN model.
  • NvN model.

1 vs N Image Similarity

The 1 vs N model allows users to find all similar images in your data set to your ‘query’ image.

Use the 1vN Tool in 3 easy steps;

  • Upload your selection of images to use as your data set, to find similar images within
  • Upload your ‘query image’, the image that you would like to find similar images to
  • Start your model to make a prediction!

N vs N Image Similarity

The N vs N model allows users to find all similar image pairs within your data set.

Use the NvN Tool in 2 easy steps;

  • Upload your selection of images to use as your data set, to find similar image pairs within
  • Start your model to make a prediction!

Use Cases of Image Similarity

Features of Platform

Four different ways to use the Image Similarity

You can train and use your own Image Similarity Search models using in a number of ways, depending on your requirements and set-up

Web interface

Web Interface

Using the tools on the Web Interface is the quickest and most straightforward way for trying out your similarity search models or using them if you do not require scalability.

Rest API


Using the REST API to build your image similarity search models offers you a great deal of flexibility and scalability without the need for expensive hardware such as GPUs.

On premise solution

On Premise Models

The image similarity search tool is available to download for offline use, allowing you to use the tool without the need to send data to our server.

Sentisight on mobile

Mobile app

The mobile app enables users to easily use image similarity tool from their phone, as well as uploading images to their projects.

Get Sentisight app on Google Play

Download Sentisight app on the App Store

Image Similarity User Guides

The platform has been bespokely built to offer an extensive amount of features for those delving into the world of image recognition. These features vary in complexity and with our platform welcoming users ranging in knowledge and understanding, we want to ensure everyone can unlock the full potential of using image similarity search models via

Therefore, we have provided in-depth user guides encasing everything related to image similarity search. You will be able to find an explanation of the following:

Using Image Similarity Search

  • Online via web platform: Follow our guide on how to correctly navigate through our online functions to use your image similarity search model.
  • REST API sample codes: We have provided the sample codes for popular programming languages so you can successfully run an image similarity search model using images from your computer or already uploaded to the platform.
  • 1vN and NvN image similarity search: Learn the difference between 1vN and NvN image similarity search and how to use both on the platform.
  • Offline use: Discover how to correctly set up your own REST API server to use the model offline. This includes a free 30 day trial.

Labeling by Similarity

  • Use cases: How to utilize image similarity to assist with different processes like iterative labeling and classification with no model training required.
  • Uploading images: How to upload individual images or in bulk, plus how to upload images using REST API.
  • Using the web platform: Use’s web platform to label by similarity on uploaded images or existing images. Alter the number of images which will be compared, as well as the threshold in order for labels to be automatically assigned.
  • REST API: Our team has provided the sample code for several programming languages to help you use labeling by image similarity with either images on your computer or images already uploaded to the platform.
  • Offline model: Learn how to use our image similarity search model and label by similarity offline.

Take a read of both of the user guides mentioned above for a deeper understanding into how can help bring your image similarity project to life.

Video Tutorials

Image similarity search tutorial

Topics covered:
  • 1vN similarity search by uploading image
  • 1vN similarity search by choosing an image from your data set
  • NvN similarity search
  • Similarity search history
  • Similarity search via REST api

You can download video tutorial here

Video transcription:

Choosing your similarity search tool

To use the similarity search tool, you will need to first choose from either 1 versus N or N versus N.

1 v N finds all similar images in your dataset to your query image

N v N finds all similar image pairs in your dataset

Understanding the different parameters

The ‘number of results’ box allows you to choose how many results you see. If you enter 0, the results will be unlimited, with all images in the dataset sorted by similarity

Users can enter a minimum score threshold (%) , so that only images with similarity prediction above this threshold will be displayed.

1vN similarity search by uploading image

If you are using the 1 vs N model, you will need to start up by uploading your query image. Then, click on start, and this will run a similarity search against all images in the dataset. The results will be filtered by top scoring images by similarity.

You can download these results in CSV format, JSON format, or download the top scoring images by similarity.

On the left hand side, you will be able to see the image similarity search results history. This search history can be easily accessed at any time, by clicking on Image Similarity Search at the top, and then click ‘Show History’.

1vN similarity search by choosing an image from your data set
Rather than uploading a specific query image, you can perform a similarity search on any image already in your dataset. To do this, simply right click on the image, and then select similarity search.
NvN similarity search

To find the most similar image pairs in your dataset, run the N vs N model. Enter the number of results and score threshold as before. Then, click Start.

In N vs N, there is no query image. Instead, the result images are split into pairs, and ranked on their similarity score. This can be useful to remove any duplicates in a dataset

Labeling by image similarity tutorial

Topics covered:
  • Labeling by image similarity feature
  • Changing parameters
  • Adjusting suggested labels manually
  • Performing AI-assisted labeling iteratively
  • Downloading classification labels

You can download video tutorial here

Video transcription:

Welcome to this label by similarity search tutorial. This is a very useful feature if you want to label your images for classification because you can use ai assisted labelling capabilities even without training any model.
What you will need to Label by Similarity
To label images by similarity, first you need a project where some of your images are already labelled. Then you can click image similarity, label by similarity, upload images, and select some unlabeled images from your computer. You can change some of the parameters but i’ll explain them later then click confirm, here you can see the results of image labeling by similarity, on the left side you can see the query images that you uploaded.
Label Thresholds
On the right side, you can see the most similar images in your data set that were found for this image. Since these images are labeled, the suggested label for your query image will depend on the labels of already labeled images, in this case the top 10 images have the same label, so you have only one suggested label for your image. In some other cases, there might be several different labels for the similar images. By default, we use score threshold 30% and all labels that are above 30% are by default checked since none of those labels are above 30%, none of them are checked however you can check one of them manually. The score threshold for images to be labeled by default can be changed here, so if i set this one to 20% for example, both chanterelle and honey fungus will be checked.
Top Scoring Labels
Also, you can click to mark only the top scoring label and always only the top scoring label will be checked in your results. This is useful if you are labeling images for single labeling classification. By default, we use 10 most similar images to suggest a label for the query image, however in some cases it might occur that only the top one or two images have that correct label, as in this case. You can change the number of images that we are using to suggest the score label here, for example if i changed to two, only the top two images will be used for suggesting the label for the query image. You can also change it to only the most useful images will be used to suggest the label for the query image. You can also set it to a higher number, let’s say 20 so in this case even more images will be used to suggest the label for the query image. Please note that changing either the number of results or the score threshold will cancel any manual changes that you have made. For example, if I change it here, all the checkboxes that I checked will be reset.
Adding images to the dataset
Once you have manually reviewed and corrected all the suggested labels, you can click select all images and add to the data set, to add your results to your data set. You will see the labeled images marked by auto labeled mark in your data set. Another way to use a label by similarity tool, is to use it on images that are already uploaded to your data set, for this you can first filter images that do not contain classification labels in your data set, then select either all or some of those images, right click on the image, click ai tools and label by similarity. The rest of the process is similar as I explained before. The beauty of labeling images by similarity, is that it can be done interactively, for example in the previous step I have labeled these images by similarity.
Suggestions for new query images
Now when I label these images by similarity, the previously labeled images will already be used to give the suggestions for the new query images. So our suggestion is just to upload a lot of unlabeled images, filter out the images that do not contain declassification labels and label them in batches. For example, 10 or 20 images each time and that way each subsequent batch will be labeled more and more accurately automatically without any need for manual review. Of course you can also organize batches by uploading images from your PC by clicking here and selecting upload images. Each time you can upload 10 or 20 or any number of images you think is suitable for the batch size in your case.
Downloading the Images
Don’t forget that after you finish labeling your images for classification, you can always select all images and download the classification labels by right clicking on one of the images, clicking image operations, download, and selecting download classification labels. A zip file containing the classification labels in csv and json format will be downloaded. If you want, you can also use our platform to train a classification model and deploy your model on the cloud. So this is all I wanted to share in this tutorial about labeling images by similarity.

Use the Image Similarity tool for yourself

To get started using for comparing datasets of images for similarity, simply register for a account and head over to the dashboard to get started!

Summary of Image Similarity Search Pricing is supported by a pay-as-you-go wallet based system that allows users to pay for only what they use, maximising flexibility and value for money. New users get €20 of free credits when you sign up for a account. Every user receives €5 a month of free credits for use on the platform. There is no need to enter your billing information to receive these free credits. Therefore, the platform can be completely free to use if you do exceed the €5 monthly free credit buffer.

The cost to train and use the Image Similarity Search tool are as follows;

  Pricing Range 1-10,000 Predictions 10,001-100,000 predictions 100,000+ predictions
Prediction 0.0008-0.001 EUR 0.001 EUR / prediction 0.0009 EUR / prediction 0.0008 EUR / prediction
1vN Similarity Search 1 x Predicition Price 1 x 0.001 EUR / prediction 1 x 0.0009 EUR / prediction 1 x 0.0008 EUR / prediction
NvN Similiarity Search N x Prediction Price N x 0.001 EUR / prediction N x 0.0009 EUR / prediction N x 0.0008 EUR / prediction


Dedicated Image Similarity Search Service

Customers with a large selection of images can use a dedicated Image Similarity Search service where the images are loaded and kept in the computer memory so that the search requests are faster. The cost of this service is dependant upon the quantity of images that the customer has.

Image count Price
<50’000 images 100 EUR / month
<100’000 images 200 EUR / month
<200’000 images 300 EUR / month
<500’000 images 400 EUR / month
<1’000’000 images 600 EUR / month
<5’000’000 images 1500 EUR / month
>5’000’000 images contact us


Each user gets 5GB of disk space for free, with additional disk space available for 1 GB for 0.1 EUR / month.

For full details of’s pricing model, including project management features and extra disk space, please visit the Pricing Page.

For more information on how to download an offline version of pre-trained model, click herePlease contact us for a custom quote for a model download license.