Best Video Annotation Tools for Python
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Compare the Top Video Annotation Tools that integrate with Python as of July 2025
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This a list of Video Annotation tools that integrate with Python. Use the filters on the left to add additional filters for products that have integrations with Python. View the products that work with Python in the table below.
What are Video Annotation Tools for Python?
Video annotation tools are computer-assisted programs designed to help organize and analyze video data. Most tools are designed for use with machine learning, allowing users to create labels, tags, and other forms of metadata that can be used to train AI models. Annotations can also be used to assist with traditional tasks such as tracking the progress of actors within a scene or counting the number of objects in an image. These applications allow for efficient analysis of large amounts of video data without manual effort. Compare and read user reviews of the best Video Annotation tools for Python currently available using the table below. This list is updated regularly.
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1
Vertex AI
Google
Video Annotation in Vertex AI enables businesses to label and tag objects, actions, and features in video data, which is crucial for training computer vision models that can analyze and interpret dynamic visual content. Vertex AI offers automated and manual annotation tools that support a wide range of video processing tasks, from object detection to activity recognition. By annotating videos, businesses can train more accurate and effective machine learning models for video analytics. New customers receive $300 in free credits to experiment with video annotation tools and apply them to their own datasets. This capability enables businesses to leverage video data for powerful AI applications in industries such as security, entertainment, and sports analysis.Starting Price: Free ($300 in free credits) -
2
Encord
Encord
Achieve peak model performance with the best data. Create & manage training data for any visual modality, debug models and boost performance, and make foundation models your own. Expert review, QA and QC workflows help you deliver higher quality datasets to your artificial intelligence teams, helping improve model performance. Connect your data and models with Encord's Python SDK and API access to create automated pipelines for continuously training ML models. Improve model accuracy by identifying errors and biases in your data, labels and models. -
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Google Cloud Vision AI
Google
Derive insights from your images in the cloud or at the edge with AutoML Vision or use pre-trained Vision API models to detect emotion, understand text, and more. Google Cloud offers two computer vision products that use machine learning to help you understand your images with industry-leading prediction accuracy. Automate the training of your own custom machine learning models. Simply upload images and train custom image models with AutoML Vision’s easy-to-use graphical interface; optimize your models for accuracy, latency, and size; and export them to your application in the cloud, or to an array of devices at the edge. Google Cloud’s Vision API offers powerful pre-trained machine learning models through REST and RPC APIs. Assign labels to images and quickly classify them into millions of predefined categories. Detect objects and faces, read printed and handwritten text, and build valuable metadata into your image catalog.
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