Visual Embedder
Learn about our visual embedder model type
Input: Images and videos
Output: Embeddings
Visual embedder, also known as visual embedding, is a type of deep fine-tuned model specifically designed to generate meaningful numerical representations (embeddings) from images and video frames.
The primary goal of a visual embedder model is to transform the raw pixel values of images or video frames into a compact and high-dimensional vector. These vectors capture essential features and patterns in the visual content, enabling the model to understand and process the data in a more structured and interpretable way.
These vectors can then be used for a variety of tasks, such as:
- Visual search: This is the task of finding images or videos that are similar to a given query image or video. The visual embedder model can be used to create a similarity metric between images or videos, which can then be used to search for similar visual content in a vector database.
- Training on top of them: The visual embedder model can also be used as a starting point for training other machine learning models. For example, a model that can classify images or videos can be trained on top of the visual embedder model.
The visual embedder model type also comes with various templates that give you the control to choose the specific architecture used by your neural network, as well as define a set of hyperparameters you can use to fine-tune the way your model learns.
You may choose a visual embedder model type in cases where:
- You need a model that can accurately represent images and video frames as vectors. Once the model is trained, you can use it to embed new images or videos into vectors.
- You need an embedding model to learn new features not recognized by the existing Clarifai models. In that case, you may need to "deep fine-tune" your custom model and integrate it directly within your workflows.
- You have a custom-tailored dataset, accurate labels, and the expertise and time to fine-tune models.