How Labellerr Transformed This Maritime Startup's Training Data Pipeline: A Case Study

Table of Contents

1. Introduction
2. About Customer
3. Data Volume and Accuracy Challenge
4. How Labellerr Stepped In
5. Results and Impact
6. Conclusion
7. FAQ

Introduction

In today's fast-paced AI-driven landscape, the demand for accurate and efficient image annotation is more critical than ever.

In this case study, we'll discuss the use case and challenges one of our customers was facing while getting high-quality labels.

The customer's data team faced the task of annotating hundreds of thousands of images.

Traditional methods, which often involve manual labeling, proved to be time-consuming, costly, and prone to inaccuracies.

To overcome these challenges and accelerate their project timelines without compromising on quality.

Labellerr's innovative platform transformed customers' workflow by leveraging advanced techniques to deliver high-precision annotations at a fraction of the cost and time required by conventional methods.

By integrating Labellerr's powerful tools, the ML team was able to annotate its extensive image datasets with remarkable speed and accuracy.

About Customer

This startup is revolutionizing the maritime sector by developing autonomous marine technology. To create a more efficient, sustainable, and resilient future for global commerce and exploration.

To build an autonomous tech stack they need highly accurate models that can detect certain near-shore objects with very high precision.

Founded by the team of ex-Lyft, Uber, and Argo, they are poised to revolutionize the maritime industry.

Data Volume and Accuracy Challenge

The primary challenge customer faced was the sheer volume of data requiring annotation, with more than a hundred thousand images needing precise labeling.

The task was to annotate six distinct classes using bounding boxes: Power Boat, Sail Boat, Large Shipping Boat, Channel Marker, and more. The demand for detailed and accurate annotations for these categories added another layer of complexity to the project.

Moreover, the client required this immense annotation effort to be repeated every quarter, with 200,000 to 300,000 new images added for labeling each time. This recurring influx of data necessitated a solution that could keep pace with the growing dataset while maintaining high-level accuracy.

Achieving this within a limited timeframe presented a significant challenge. Then customer needed a reliable partner capable of delivering precise annotations rapidly and affordably, without compromising on quality, to meet their client’s stringent requirements.

How Labellerr Stepped In

Labellerr emerged as the perfect solution for customer's complex annotation requirements, leveraging its robust platform to address the challenges effectively. The features offered by Labellerr played a pivotal role in ensuring that the annotation process was not only fast but also highly accurate and cost-efficient.

Easy Data Import and Connectivity

• Seamless Integration: Labellerr supports various data sources, allowing customer to import images securely from S3.
• Bulk Upload Capabilities: The platform allows for bulk uploading, significantly reducing the time needed to get started with the annotation process.

Intuitive UI for Faster Annotation

• User-Friendly Design: The simple and intuitive interface minimizes the learning curve for annotators, enabling them to start working productively almost immediately.
• Quick Access to Tools: Essential annotation tools are easily accessible, allowing for faster application of bounding boxes and other annotations.

Efficient Workflow Management

• Task Assignment: Labellerr allows project managers to assign tasks to specific annotators, ensuring a balanced distribution of workload.
• Progress Tracking: The platform provides real-time updates on the progress of annotation tasks, making it easy to monitor and adjust workflows as needed.

Seamless Collaboration for Guideline Clarity

• Easy Communication: Annotators can easily communicate, clarifying and resolving doubts quickly.
• Feedback Mechanism: Annotators can provide and receive feedback on their work, fostering continuous improvement and higher accuracy.

Active Learning for High Quantity of Annotation

• Prioritization of Informative Images: Labellerr's active learning feature identifies the most informative images for annotation, optimizing the use of annotator time.
• Improved Accuracy: Active learning helped improve the accuracy of annotations by ensuring that critical images receive the most attention.

Human-in-the-Loop Verification

• Quality Assurance: Human reviewers validate and correct annotations, ensuring high accuracy and reliability of the dataset.
• Error Reduction: The human-in-the-loop process catches and rectifies mistakes that automated systems might miss. Feedback from human reviewers helps refine and improve the annotation algorithms over time.

Other Features of the Labellerr Platform

• Grid View for Quick Review: The grid view feature allows annotators and quality control teams to quickly review and validate large numbers of images, ensuring high accuracy.
• Quality Control Tools: Built-in quality control tools help detect and correct errors early, ensuring that the final dataset meets the required standards.

These comprehensive features of Labellerr not only streamlined the annotation process for customer but also ensured high accuracy and cost efficiency, meeting their client's stringent requirements and timelines.

Results and Impact

The collaboration between the customer and Labellerr resulted in a transformative impact on the customer's image annotation process. What could have taken months of manual effort was reduced to just a matter of weeks, thanks to Labellerr's advanced features and efficient workflow.

This significant reduction in time allowed customers to accelerate its development timelines, enabling faster iterations and quicker deployment of its machine learning models. The precision of Labellerr's annotation ensured high accuracy in labeling, which is critical for the performance of customer models.

Overall, Labellerr's advanced annotation capabilities played a crucial role in the customer's ability to manage large volumes of data swiftly and accurately. This partnership not only streamlined the customer's operations but also set a new standard for efficiency and quality in the field of image annotation.

Conclusion

The partnership between the customer and Labellerr exemplifies the transformative power of advanced annotation technology in the realm of AI development. Faced with the daunting task of annotating hundreds of thousands of images every quarter, the customer found an ideal solution in Labellerr’s robust platform.

This collaboration not only significantly reduced the time and cost associated with manual labeling but also ensured high-quality, consistent annotations crucial for the performance of the customer’s models.

As a result, the customer could accelerate their development timelines, improve their operational efficiency, and meet their demands with ease. The success of this collaboration highlights the importance of choosing the right technological partner in overcoming data annotation challenges and sets a new benchmark for efficiency and quality in the field.

FAQ

1. How to get training data for object detection at a very large scale?

Human in-the-loop verification along with model-assisted labeling can help to generate high-quality images for object detection use cases. With Labellerr teams can generate hundreds of thousands of labeled data.

2. How did Labellerr address these challenges?

Labellerr addressed these challenges by providing an advanced annotation platform with features like easy data import, an intuitive user interface, efficient workflow management, and active learning. These features collectively streamlined the annotation process, significantly reducing the time and cost while ensuring high accuracy.

3. What are the key features of Labellerr that help teams get high-quality labels at scale?

The key features of Labellerr that benefited the customer include:

• Easy Data Import and Connectivity: Facilitated seamless data integration and bulk uploads.
• Intuitive UI for Faster Annotation: Enabled annotators to work efficiently with minimal training.
• Efficient Workflow Management: Helped in task assignment, progress tracking, and deadline management.
• Seamless Collaboration: Provided tools for clear communication and guideline clarity.
• Active Learning: Optimized the annotation process by prioritizing informative images.
• Extra Features: Included grid view for quick review and batch editing for consistency.