What is Image Embeddings?

Image Embeddings use deep learning models, such as convolutional neural networks, to create numerical representations of images. These representations are complex, high-dimensional vectors that capture the essence of the images.

Developers can use image embeddings to submit images and receive corresponding embeddings, making tasks like identifying similar images, organizing images, and retrieving pictures based on their content easier.

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The API simplifies complex image processing tasks by using pre-trained models allowing you to take advantage of deep learning in different applications without having to train models from scratch.

As of now, dedicated APIs exclusively offering image embeddings are not available. Developers seeking image embeddings can, however, turn to multimodal embeddings APIs which offer a broader spectrum by accommodating diverse data types, allowing developers to handle various types of data (images, text, etc.) in a unified way.

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Image Embeddings use cases

You can use Image Embeddings in numerous fields, here are some examples of common use cases:

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1. Image Search and Retrieval: Users can search for and retrieve images based on their content, making it easier to organize and locate specific visuals.
2. Content Moderation: Image embeddings can be utilized for content moderation, helping to automatically identify and filter out inappropriate or offensive images.
3. E-commerce Product Recommendations: E-commerce platforms can use image embeddings to recommend similar products based on the visual features of the items a user is viewing or has purchased.
4. Medical Image Analysis: Image embeddings can assist in medical image analysis, helping to identify patterns or abnormalities in medical imaging data for diagnostics and research.

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Best Multimodal Embeddings APIs on the market

As mentioned above, developers looking for image embeddings can opt for multimodal embeddings APIs, providing a comprehensive solution that handles diverse data types, such as images and text, in a unified manner. While comparing Multimodal Embeddings APIs, it is crucial to consider different aspects, among others, cost security and privacy.

Image Embeddings experts at Eden AI tested, compared, and used many Multimodal Embeddings APIs of the market. Here are some actors that perform well (in alphabetical order):

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• Amazon Titan Multimodal
• Aleph Alpha
• Google
• Microsoft Azure
• OpenAI
• Replicate

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1. Amazon Titan’s Multimodal Embedding API

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The Titan Multimodal Embeddings API is a programming interface for multimodal embeddings. It can be used to search for images by text, image, or a combination of text and image.

The API converts images and short English text up to 128 tokens into embeddings that capture semantic meaning and relationships between data. The API generates vectors of 1,024 dimensions that can be used to build search experiences with high accuracy and speed.

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2. Aleph Alpha’s Multimodal Embedding API - Available on Eden AI

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Aleph Alpha provides multimodal and multilingual embeddings via its API. This technology enables the creation of text and image embeddings that share the same latent space. The Image Embedding API enhances image processing by integrating advanced capabilities to assist with recognition and classification.

The robust algorithms extract rich visual features, providing versatility for applications in various sectors, including e-commerce and content-driven services.

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3. Google’s Multimodal Embedding API

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Google's Multimodal Embeddings API  generates 1408-dimensional vectors based on input data, which can include images and/or text. These vectors can be used for tasks such as image classification or content moderation.

The image and text vectors are in the same semantic space and have the same dimensionality. Therefore, these vectors can be used interchangeably for tasks such as searching for images using text or searching for text using images.

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4. Microsoft Azure’s Multimodal Embedding API

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Microsoft's Multi-modal embeddings API enables the vectorization of both images and text queries. Images are converted to coordinates in a multi-dimensional vector space, and incoming text queries can also be converted to vectors.

Images can then be matched to the text based on semantic closeness, allowing users to search a set of images using text without the need for image tags or other metadata.

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5. OpenAI’s Multimodal Embedding API

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The OpenAI Contrastive Learning In Pretraining (CLIP) API is capable of comprehending concepts in both text and image formats, and can even establish connections between the two modalities.

This is made possible by the use of two encoder models, one for text inputs and the other for image inputs. These models generate vector representations of the respective inputs, which are then used to identify similar concepts and patterns across both domains using vector search.

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6. Replicate’s Multimodal Embedding API

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Replicate's Multimodal embeddings API is ideal for searching images by text, image, or a combination of text and image. It is designed for high accuracy and fast responses, making it an excellent choice for search and recommendation use cases.

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Performance Variations of Image Embeddings

Image Embeddings' performance can vary depending on several variables, including the technology used by the provider, the underlying algorithms, the amount of the dataset, the server architecture, and network latency. Listed below are a few typical performance discrepancies between several Multimodal Embeddings APIs:

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1. Training Data: The quality and quantity of training data play a crucial role. Models trained on diverse and representative datasets tend to perform better in various scenarios. Pre-training on large-scale datasets (e.g., ImageNet) and fine-tuning on task-specific datasets can be effective.
2. Hyperparameter: Hyperparameters like learning rate, batch size, and optimization algorithms can impact the training process. Fine-tuning these hyperparameters for specific tasks or datasets can improve performance.
3. Data Augmentation: Applying data augmentation techniques during training can improve the model's ability to generalize to different variations of input images.
4. Task-Specific Considerations: The nature of the downstream task for which the embeddings are used matters. Some tasks may require fine-grained details in the embeddings, while others may benefit from more abstract representations.

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Why choose Eden AI to manage your Multimodal Embeddings APIs

Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate Image Embeddings tasks in their cloud-based applications, without having to build their solutions.

Eden AI offers multiple AI APIs on its platform among several technologies: Text-to-Speech, Language Detection, Sentiment Analysis, Face Recognition, Question Answering, Data Anonymization, Speech Recognition, and so forth.

We want our users to have access to multiple Image Embeddings engines and manage them in one place so they can reach high performance, optimize cost, and cover all their needs. There are many reasons for using multiple APIs :

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• Fallback provider is the ABCs: You need to set up a provider API that is requested if and only if the main Multimodal Embeddings API does not perform well (or is down). You can use the confidence score returned or other methods to check provider accuracy.
• Performance optimization: After the testing phase, you will be able to build a mapping of providers’ performance based on the criteria you have chosen (languages, fields, etc.). Each data that you need to process will then be sent to the best Image Embeddings.‍
• Cost - Performance ratio optimization: You can choose the cheapest Image Embeddings provider that performs well for your data.
• Combine multiple AI APIs: This approach is required if you look for extremely high accuracy. The combination leads to higher costs but allows your AI service to be safe and accurate because Multimodal Embeddings APIs will validate and invalidate each other for each piece of data.

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How Eden AI can help you?

Eden AI is the future of AI usage in companies: our app allows you to call multiple AI APIs.

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• Centralized and fully monitored billing on Eden AI for all Multimodal Embeddings APIs.
• Unified API for all providers: simple and standard to use, quick switch between providers, access to the specific features of each provider.
• Standardized response format: the JSON output format is the same for all suppliers thanks to Eden AI's standardization work. The response elements are also standardized thanks to Eden AI's powerful matching algorithms.
• The best Artificial Intelligence APIs in the market are available: big cloud providers (Google, AWS, Microsoft, and more specialized engines).
• Data protection: Eden AI will not store or use any data. Possibility to filter to use only GDPR engines.

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You can see Eden AI documentation here.

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Next step in your project

The Eden AI team can help you with your Image Embeddings integration project. This can be done by:

• Organizing a product demo and a discussion to better understand your needs. You can book a time slot on this link: Contact
• By testing the public version of Eden AI for free: however, not all providers are available on this version. Some are only available on the Enterprise version.
• By benefiting from the support and advice of a team of experts to find the optimal combination of providers according to the specifics of your needs.
• Having the possibility to integrate on a third-party platform: we can quickly develop connectors.

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