FiftyOne

What is FiftyOne?

FiftyOne is the ultimate solution for managing, visualizing, and working with data and models to build successful visual AI applications. With features like dataset exploration, curation, and model evaluation, FiftyOne simplifies and automates the process of refining visual datasets for computer vision models. Trusted by leading companies like Bosch and Allstate, FiftyOne is the go-to platform for improving data quality and accelerating the path to production-ready models. Whether you're a data scientist, AI engineer, or researcher, FiftyOne provides the tools you need to enhance your visual AI workflows and achieve better results.

Using FiftyOne, you can easily visualize your data, curate high-quality datasets, train better models, find annotation mistakes, and leverage the Brain feature for advanced machine learning capabilities. By integrating with popular dataset providers like COCO, Google's Open Images, and ActivityNet, FiftyOne gives you access to a vast array of annotated image and video datasets for evaluation and training. With over 2,10,000 installs, 7,900 GitHub stars, and 2,900 Slack members, the FiftyOne community is thriving and continuously improving the platform's capabilities for data-centric AI workflows.

FiftyOne isn't just a tool; it's a complete ecosystem for advancing the field of visual AI. From exploring and searching datasets to identifying model failure modes and correcting label mistakes, FiftyOne streamlines the entire data refinement process. With easy integration with favorite ML tools and a focus on accelerating production timelines, FiftyOne is the backbone of successful AI projects. Join the growing community of developers, scientists, and researchers who rely on FiftyOne to elevate their data quality, build robust models, and achieve impactful results in the world of computer vision.

FiftyOne Features

Visualize your data

Visualizing data is crucial for understanding patterns and relationships within datasets. With FiftyOne, users can explore, search, and slice their datasets easily. This feature allows for quick identification of specific samples and labels that meet certain criteria, enabling users to efficiently analyze and work with their visual data.

• Users can create custom views in FiftyOne to visualize their data in a way that best suits their needs. By exploring and interacting with the data visually, users can gain deeper insights and make more informed decisions when building visual AI models.

Curate high quality datasets

Curating high-quality datasets is essential for training reliable and effective machine learning models. FiftyOne offers tight integrations with popular public datasets like COCO, Open Images, and ActivityNet, as well as the ability to create custom datasets from scratch. This feature empowers users to access diverse and comprehensive datasets for their visual AI projects.

• By leveraging FiftyOne's dataset management capabilities, users can easily load and organize datasets, ensuring that data is clean, labeled correctly, and ready for model training. Whether using pre-existing datasets or creating new ones, the platform streamlines the process of dataset curation.

Train better models

The quality of data directly impacts the performance of machine learning models. FiftyOne helps users identify, visualize, and address issues in model training by highlighting failure modes and areas for improvement. This feature aids in training more accurate and robust visual AI models.

• With FiftyOne, users can evaluate models, identify performance metrics, and rectify errors in model training. By focusing on improving data quality and model performance, users can enhance the overall efficacy of their visual AI solutions.

Find mistakes

Detecting and correcting annotation mistakes is pivotal in ensuring the accuracy and reliability of machine learning models. FiftyOne simplifies the process of identifying label errors in datasets, helping users find and rectify mistakes efficiently. This feature enhances the overall quality of annotated data for model training.

• FiftyOne automates the process of finding annotation mistakes, allowing users to quickly correct errors and ensure the accuracy of their labeled datasets. By leveraging this feature, users can avoid errors in model training and improve the overall quality of visual data used in AI applications.

How to Use FiftyOne?

• Visit the FiftyOne homepage at voxel51.com.
• Familiarize yourself with the features of FiftyOne by exploring the 'Products' section.

• Install FiftyOne by following the installation instructions on the official FiftyOne GitHub repository.
• Ensure you have Python and necessary dependencies installed on your system.

• Launch FiftyOne using the command `fiftyone` in your terminal.
• Load your dataset by following instructions in the FiftyOne documentation (you can either use public datasets like COCO, or upload your own dataset).
• Use the `Dataset.from_dir` method to load datasets from specific directories.

• Open the FiftyOne app in your web browser (it usually opens automatically after running `fiftyone`).
• Navigate through your dataset using the powerful visual interface provided by FiftyOne.
• Use the filters and search functionalities to slice and examine your dataset based on specific criteria.

• Use FiftyOne's tools to clean and curate your dataset.
• Leverage integrations with annotation tools like CVAT to refine your dataset labels.
• Identify and correct labeling errors using the annotation correction features in FiftyOne.

• Ingest your trained model's predictions into FiftyOne.
• Evaluate model performance by generating various metrics directly within FiftyOne.
• Visualize the performance of your model on different dataset slices to identify failure modes.

• Enable FiftyOne Brain's features for advanced dataset analysis.
• Utilize FiftyOne's machine learning capabilities to identify edge cases and mine new samples for further training.
• Analyze the visualized results to iterate and improve your models.

• If using FiftyOne Teams, set up user roles and permissions for dataset access.
• Share datasets and annotations with your team using FiftyOne's collaboration features.
• Keep track of data versions and changes using FiftyOne’s versioning system.

• Finalize your dataset and model by addressing any highlighted issues in FiftyOne.
• Export the curated dataset and model predictions for deployment.
• Integrate these components into your production pipeline to achieve robust, reliable visual AI applications.

• Join the FiftyOne Slack community for support and collaboration with other users.
• Attend FiftyOne events and meetups to learn from experts and share your experiences.
• Contribute to the FiftyOne GitHub repository if you have enhancements or fixes to share.

FiftyOne Frequently Asked Questions