Best Image Annotation Tools for Python
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Compare the Top Image Annotation Tools that integrate with Python as of July 2025
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This a list of Image 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 Image Annotation Tools for Python?
Image annotation tools are used to automatically process and label digital images using advanced techniques in machine learning, AI, and computer vision. These tools can accurately recognize important features in images, such as objects, characters, or facial expressions. This data can then be used for various purposes such as automatic image tagging and sorting. Image annotation is becoming an increasingly popular tool for organizing large databases of images and videos. Compare and read user reviews of the best Image Annotation tools for Python currently available using the table below. This list is updated regularly.
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1
Vertex AI
Google
Image Annotation in Vertex AI is a powerful tool for preparing visual data for training computer vision models. By labeling and tagging objects, features, or regions of interest in images, businesses can create more accurate and specialized models for tasks like object detection and facial recognition. Vertex AI provides automated and manual annotation tools that can handle large volumes of image data, ensuring high-quality annotations for machine learning models. New customers receive $300 in free credits, enabling them to test the platform’s image annotation capabilities. With this feature, businesses can accelerate the development of visual AI solutions, increasing the accuracy and reliability of their models.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. -
3
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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Segments.ai
Segments.ai
Segments.ai is an advanced data labeling platform that allows users to label data from multiple sensors simultaneously, improving the speed and accuracy of labeling for robotics and autonomous vehicle (AV) applications. It supports 2D and 3D labeling, including point cloud annotation, and enables users to label moving and stationary objects with ease. The platform leverages smart automation tools like batch mode and ML-powered object tracking, streamlining workflows and reducing manual labor. By fusing 2D image data with 3D point cloud data, Segments.ai offers a more efficient and consistent labeling process, ideal for high-volume, multi-sensor projects.
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