Fast and Improved Data Labeling at Low Cost

In the world of AI and machine learning, certain resources are becoming increasingly affordable: cameras are cheap, storage is cheap, and compute power is cheap. However, one crucial aspect remains costly and time-consuming: data labeling. As data labeling is often the bottleneck in model training, finding ways to streamline and reduce the cost of this process is critical for accelerating AI deployment.

In this blog, we’ll explore how Akridata leverages advanced models like CLIP and DinoV2 to improve the efficiency and accuracy of data labeling, making it faster, more accurate, and cost-effective.

The Labeling Challenge: A Bottleneck in AI Development

While data collection, storage, and computation have become more accessible and affordable, the process of labeling data—especially large datasets—remains labor-intensive and expensive. High-quality labeled data is essential for training effective models, but traditional labeling methods can significantly slow down the development process and drive up costs.

How Akridata Enhances the Labeling Process

Akridata addresses these challenges by integrating and improving upon existing models, such as OpenAI’s CLIP (Contrastive Language-Image Pre-Training) and DinoV2. These enhancements enable a more efficient labeling process, reducing the time and cost associated with preparing training and test datasets.

Image and Text Labeling with CLIP

OpenAI’s CLIP is a powerful model that connects the visual world with the language world, allowing images to be labeled based on class names. One of the key benefits of CLIP is its ability to perform zero-shot operations, meaning it requires no labeled training data. This contrasts with full-shot training, where a fully labeled dataset is necessary to train a model from scratch.

Akridata’s Custom Improvements to CLIP

Akridata improves upon CLIP’s capabilities by introducing the use of class descriptions instead of relying solely on single class names. This refinement helps avoid ambiguities—such as in the case of the word “crane,” which could refer to a bird, a company, or a machine. By providing more detailed descriptions, the risk of misclassification is significantly reduced, leading to more accurate labeling.

Combining CLIP with DinoV2 for Enhanced Accuracy

To further enhance the accuracy of data labeling, Akridata pairs CLIP with another advanced model, DinoV2. The complementary strengths of these two models allow them to work together, reducing the likelihood of errors and improving the overall quality of labeled data. This combination ensures that the labeling process is not only faster but also more reliable, even when working with large and complex datasets.

Results: Improved Accuracy with Akridata’s Flow

Akridata’s approach to data labeling delivers significant improvements in accuracy across various publicly available datasets. Below are some key results demonstrating these enhancements:

Using Class Descriptions and Prompt Refinement

Using DinoV2 and Score Adjustment

These results clearly demonstrate the effectiveness of Akridata’s enhanced labeling flow, which not only improves accuracy but also reduces the time and cost required for preparing datasets.

Summary: Accelerating AI with Fast and Improved Data Labeling

In this blog, we highlighted how Akridata leverages and improves upon advanced models like CLIP and DinoV2 to deliver fast, accurate, and cost-effective data labeling. By refining the labeling process and reducing the associated costs, Akridata helps accelerate the development and deployment of AI models, enabling organizations to bring AI solutions to market more quickly and efficiently.

To learn more about how Akridata can revolutionize your data labeling process, visit us at akridata.ai.