Create and Train Models

Learn how to easily create and train new models

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The Clarifai platform simplifies the entire process of creating and training AI models, making it incredibly easy and efficient.

With just a single click, your model is not only trained but also automatically deployed, ready to enhance your business solutions instantly.

You can either build custom models tailored to your specific needs or jumpstart your projects with Clarifai's pre-optimized models, which are designed for immediate use.

Custom Models

When you train a custom model, you are telling the system to look at all the inputs with concepts you've provided and learn from them. Then, when the model encounters new inputs, it could correctly generate predictions by applying the learned knowledge.

The train operation is asynchronous. It may take some time for your model to be fully trained and ready. Your model will be trained on all inputs that have been processed, and a new version will be created.

Clarifai Models

Clarifai models are designed to be fast, scalable, and highly flexible, providing the ability to quickly deploy solutions that can adapt to your specific business needs. They can simplify complex tasks, reduce development time, and deliver reliable, accurate results.

Our model library is continually expanding and evolving. You can explore the Community platform to find a model that best fits your needs.

Our models are fully "trainable" machine learning models developed in-house and rigorously tested, ready to make predictions right out of the box.

We offer models across a wide range of categories, including generative models like large language models (LLMs), as well as classification, detection, and segmentation models.

Try our Hosted Models

Before training your own model, we recommend trying the models hosted on our platform to see if they meet your needs.

Tip: Read a comparison of GPT-5 and other models — covering features, pricing, and use cases.

Model Types

Whether you build a custom model or use one of Clarifai’s pre-built models, selecting the right model type is essential for your specific use case.

Different model types are optimized for different tasks and produce distinct outputs based on your input data and desired AI functionality.

Choosing the appropriate model type enables you to fully leverage the Clarifai platform and power your business with advanced AI capabilities.

Notes

• You can use the List Model Types method to view a complete list of available model types suited to your needs.
• To create a model with a specific type, you need to specify the desired model_type_id in the request body.

Broadly, you can create and train different model types on our platform using either of the following techniques:

• Transfer learning

• Deep fine-tuning

List of Model Types

Model ID	Title	Description
embedding-classifier	Transfer Learning Classifier	Classify images or texts based on the embedding model that has indexed them in your app. Transfer learning leverages feature representations from a pre-trained model based on massive amounts of data, so you don’t have to train a new model from scratch and can learn new things very quickly with minimal training data.
audio-embedder	Audio Embedder	Embed audio signal into a vector representing a high level understanding from our AI models. These embeddings enable similarity search and training on top of them.
visual-detector-embedder	Visual Detector + Embedder	Detect bounding box regions in images or video frames where things occur and then embed them into a high level understanding from our AI models to enable visual search and training on top of them.
optical-character-recognizer	Optical Character Recognizer (OCR)	Detect bounding box regions in images or video frames where text is present and then output the text read with the score.
image-to-image	Image to Image	Given an image, apply a transformation on the input and return the post-processed image as output.
image-to-text	Image To Text	Takes in cropped regions with text in them and returns the text it sees.
text-to-image	Text To Image	Takes in a prompt and generates an image.
clusterer	Clusterer	Cluster semantically similar images and video frames together in embedding space. This is the basis for good visual search within your app at scale or for grouping your data together without the need for annotated concepts.
image-color-recognizer	Image Color Recognizer	Recognize standard color formats and the proportion each color that covers an image.
concept-thresholder	Concept Thresholder	Threshold input concepts according to both a threshold and an operator (>, >=, =, <=, or <).
region-thresholder	Region Thresholder	Threshold regions based on the concepts that they contain using a threshold per concept and an overall operator (>, >=, =, <=, or <).
concept-synonym-mapper	Concept Synonym Mapper	Map the input concepts to output concepts by following synonym concept relations in the knowledge graph of your app.
annotation-writer	Annotation Writer	Write the input data to the database in the form of an annotation with a specified status as if a specific user created the annotation.
image-crop	Image Cropper	Crop the input image according to each input region that is present in the input.
random-sample	Random Sampler	Randomly sample allowing the input to pass to the output. This is done with the conditional keep_fraction > rand() where keep_fraction is the fraction to allow through on average.
visual-keypointer	Visual Keypoint	This model detects keypoints in images or video frames.
email	Email Alert	Email alert model will send an email if there are any data fields input to this model.
sms	SMS Alert	SMS alert model will send a SMS if there are any data fields input to this model.
object-counter	Object Counter	Count number of regions that match this model's active concepts frame by frame.
image-align	Image Align	Aligns images using keypoints.
input-searcher	Cross-App Input Searcher	Triggers a visual search in another app based on the model configs if concept(s) are found in images and returns the matched search hits as regions.
input-filter	Input Filter	If the input going through this model does not match those we are filtering for, it will not be passed on in the workflow branch.
text-to-audio	Text to Audio	Given text input, this model produces an audio file containing the spoken version of the input.
regex-based-classifier	Regex Based Classifier	Classifies text using regex. If the regex matches, the text is classified as the provided concepts.
prompter	Prompter	Prompt template where inputted text will be inserted into placeholders marked with {data.text.raw}.
rag-prompter	RAG Prompter	A prompt template where we will perform a semantic search in the app with the incoming text.
image-prompter	Image Prompter	A prompter model, that helps create a MultiModal input with the inputted image and text.
concept-to-text-mapper	Concept To Text Mapper	Maps concepts to text.
mcp	MCP	Process MCP messages with any input and output.
openai	OpenAI	Process Clarifai models with OPENAI Format messages.
any-to-any	Any To Any	Process any input and output with any data type.
image-tiling-operator	Image Tiling Operator	Operator for tiling images into a fixed number of equal sized images.
isolation-operator	Isolation Operator	Operator that computes distance between detections and assigns isolation label.
language-id-operator	Language Identification Operator	Operator for language identification using the langdetect library.
text-aggregation-operator	Text Aggregation Operator	Operator that combines text detections into text body for the whole image.
tiling-region-aggregator-operator	Tiling Region Aggregator Operator	Operator to be used as a follow up to the image-tiling-operator and visual detector. This operator will transform the detections on each of tiles back to the original image and perform non-maximum suppression.
barcode-operator	Barcode Operator	Operator that detects and recognizes barcodes from the image. It assigns regions with barcode text for each detected barcode.
keyword-filter-operator	Keyword Filter Operator	This operator is initialized with a set of words, and then determines which are found in the input text.
raft-operator	RAFT Operator	Calls an LLM to generate questions and answers based on a text input chunk. The output is the chat-formatted instruction for finetuning.
tokens-to-entity-operator	Tokens to Entity Operator	Operator that combines text tokens into entities, e.g. New + York -> New York.
track-representation-operator	Track Representation Operator	The operator takes embedding of each track frame and aggregate them to form a track embedding.
byte-tracker	BYTE Track	A multi-object tracker that aims to keep track of all boxes per frame forming them into tracklets.
centroid-tracker	Centroid Tracker	Relies on the Euclidean distance between centroids of regions in different video frames to assign the same track ID to detections of the same object.
kalman-filter-tracker	Kalman Filter Hungarian Tracker	Relies on the Kalman Filter algorithm to estimate the next position of an object, matched to detections using the Hungarian algorithm.
text-classifier	Text Classifier	Classify text into a set of concepts.
visual-detector	Visual Detector	Detect bounding box regions in images or video frames where things and then classify objects, descriptive words or topics within the boxes.
multimodal-to-text	Multimodal To Text	Generate text from either text or images or both as input, allowing it to understand and respond to questions about those images.
text-embedder	Text Embedder	Embed text into a vector representing a high level understanding from our AI models. These embeddings enable similarity search and training on top of them.
visual-embedder	Visual Embedder	Embed images and videos frames into a vector representing a high level understanding from our AI models. These embeddings enable visual search and training on top of them.
visual-segmenter	Visual Segmenter	Segment a per-pixel mask in images where things are and then classify objects, descriptive words or topics within the masks.
zero-shot-text-classifier	Zero Shot Text Classifier	Classify text into a set of concepts provided by user using a pretrained model.
multimodal-embedder	Multimodal Embedder	Embed text or image into a vector representing a high level understanding from our AI models, e.g. CLIP. These embeddings enable similarity search and training on top of them.
text-to-text	Text Generator	Generate or convert text based on text input, e.g. prompt completion, translation or summarization.
visual-anomaly-heatmap	Visual Anomaly	Visual anomaly detection with image-level score and anomaly heatmap.
zero-shot-image-classifier	Zero Shot Image Classifier	Classify image into a set of concepts provided by user using a pretrained model.
audio-classifier	Audio Classifier	Classify audio into a set of concepts.
text-token-classifier	Text Token Classifier	Classify tokens from a set of entity classes.
visual-classifier	Visual Classifier	Classify images and videos frames into set of concepts.
zero-shot-image-segmenter	Zero Shot Image Segmenter	Dynamically segment a per-pixel mask in images where things are and then classify objects, descriptive words or topics within the masks.
audio-to-text	Audio To Text	Classify audio signal into string of text.

🗃️ Transfer Learning

2 items

🗃️ Deep Fine-Tuning

8 items

🗃️ Training Templates

6 items

📄️ Model Versions

Learn about model version tracking and management

📄️ Manage Models

Learn how to get, update, search, and delete models

🗃️ Evaluations

7 items

📄️ Model Export

Learn how to perform model export using Clarifai SDKs