Computer Vision

Label images in-house for image annotation tasks such as object detection, image segmentation and image classification. Use Prodigy's fully scriptable back-end to build powerful workflows by putting your model in the loop.

Continuously update and evaluate your image models

If you're running a model for object detection, image segmentation or image classification in production, the examples your model is classifying will be changing constantly. Prodigy makes it easy to validate your model's predictions, and correct its mistakes.

Stream in your images from a directory or even a custom Python function, and mark objects or draw polygon shapes using an intuitive, browser-based interface. Your annotations can be exported as a simple JSON file with pixel coordinates, making it easy to integrate the data into the rest of your pipeline.

Try it live and draw bounding boxes!

This live demo requires JavaScript to be enabled.
This live demo requires JavaScript to be enabled.

Try it live and select the category!

This live demo requires JavaScript to be enabled.

Try it live and select the images!

This live demo requires JavaScript to be enabled.
recipe.py@prodigy.recipe("custom-image-recipe")
def custom_image_recipe(dataset, image_dir):
    stream = Images(image_dir)
    stream = fetch_images(stream)
    model = load_your_model()
    return {
        "dataset": dataset,
        "stream": model(stream),
        "update": model.update,
        "view_id": "image"
    }

Plug in your own models

Custom recipes let you integrate machine learning models using any framework of your choice, load in data from different sources, implement your own storage solution or add other hooks and features. No matter how complex your pipeline is – if you can call it from a Python function, you can use it in Prodigy.

View TensorFlow example
View the documentation