Streamline Data Labeling with Advanced ML Techniques

Table of Contents:

1. Introduction
2. Challenges in Building Computer Vision Models
3. Major Stages of a Computer Vision Data Preparation Pipeline
4. Data Collection and Ingestion
5. Data Labeling and Auditing
6. Data Delivery
7. Bottlenecks in Computer Vision Data Preparation Pipelines
8. The Pain Points of Data Preparation and Management
9. The Need for Manual Data Labeling and Auditing
10. The Cost and Time Involved in Data Labeling
11. Ways to Leverage AI for Efficient Data Labeling
12. Transfer Learning
13. Viewshot Learning
14. Automated Machine Learning (AutoML)
15. Uncertainty Estimation
16. How Curb AI Uses Advanced Auto Labeling Techniques
17. The ML-First Approach to Data Labeling and Auditing
18. Conclusion

Introduction

Welcome back! In this article, we will discuss the challenges faced in building computer vision models and the major stages involved in the data preparation pipeline. We will also explore the bottlenecks that affect computer vision data preparation and how to leverage AI for efficient data labeling. Additionally, we will examine the advanced auto labeling techniques used by Curb AI to automate the data preparation process. Let's dive in!

Challenges in Building Computer Vision Models

Building computer vision models poses several challenges, especially when it comes to preparing the training data. Collecting raw data, labeling it accurately, and achieving a well-balanced data distribution can be time-consuming and costly. Machine learning engineers and data scientists often find themselves spending a significant amount of time on data preparation and management rather than training the actual models. This inefficiency hampers productivity and limits the progress of projects.

Major Stages of a Computer Vision Data Preparation Pipeline

The computer vision data preparation process involves three major stages: data collection and ingestion, data labeling and auditing, and data delivery.

Data Collection and Ingestion

The first step in creating a training dataset is collecting raw data, such as images and videos, from various sources. This can involve crawling the web or deploying edge devices to capture on-site streaming videos and images. Once collected, the data needs to be reviewed, validated, and loaded into a data platform or system for further processing.

Data Labeling and Auditing

After the raw data is ingested, the next step is data labeling and auditing. This is a crucial stage where project managers and labelers manually annotate the collected data. Sometimes, outsourcing to labeling agencies or crowdsourced workers is necessary. The labeled data set undergoes an audit to ensure its accuracy, sufficient size, and balanced distribution across different cases and scenarios. This step helps train the machine learning model effectively.

Data Delivery

Once the data is labeled and audited, it is delivered to the machine learning team responsible for model creation. The prepared data set provides the necessary inputs for training and testing the model. However, the data preparation pipeline doesn't end here as the machine learning team may require additional data or modifications to enhance the model's performance. This repetitive process continues until the desired performance is achieved.

Bottlenecks in Computer Vision Data Preparation Pipelines

Through extensive research and interviews with data and machine learning engineers, several bottlenecks within computer vision data preparation pipelines have been identified. The most common pain point reported by teams is the significant amount of time and resources spent on data preparation and management. Data labeling and auditing, in particular, Consume a substantial portion of the project timeline and budget. This bottleneck affects both teams starting new projects and those with models in production, impacting their overall efficiency and delaying progress.

The Pain Points of Data Preparation and Management

Data labeling, in particular, is a time-consuming and labor-intensive task. It often requires brute force manual labor, resulting in high costs and potential errors. Auditing the labeled data set to ensure accuracy, balance, and sufficient size adds to the time and effort required. As a result, teams face significant challenges in automating the manual data labeling and auditing process. While the importance of automating these tasks is recognized, practical implementation remains a challenge.

Pros:

• Automated data labeling and auditing can significantly reduce time and costs.
• Using advanced machine learning techniques improves data preparation efficiency.
• Repurposing pre-trained models through transfer learning and viewshot learning accelerates the process.
• Automated machine learning (AutoML) eliminates the need for manual model training and fine-tuning.

Cons:

• Implementation of AI-assisted data labeling and auditing may require specialized knowledge.
• Initial setup and integration of AI Tools and platforms may take time and resources.
• Over-reliance on automated techniques may result in quality control issues and the need for human verification.

The Need for Manual Data Labeling and Auditing

Despite the challenges and pain points, manual data labeling and auditing remain crucial for ensuring the accuracy and quality of training data. Labeling beyond the capabilities of pre-trained models and assessing data distribution and balance require human involvement. Additionally, dealing with rare events or edge cases where model accuracy is low necessitates manual intervention. Teams understand the need for human review to correct errors, validate predictions, and improve model performance.

The Cost and Time Involved in Data Labeling

Data labeling entails significant time and financial costs. For example, a project starting with 100,000 images, each with an average of ten objects, would require over a thousand man-hours for labeling alone. Auditing and validating the labels further increase the time and costs involved. As projects progress and models improve, the need for more data arises, resulting in exponential growth in labeling requirements and expenses. The diminishing marginal gain of data as model performance improves also highlights the need for efficient data labeling processes.

Ways to Leverage AI for Efficient Data Labeling

Fortunately, there are ways to leverage AI and advanced machine learning techniques to improve the efficiency and effectiveness of data labeling. The following techniques can assist in automating manual data labeling and auditing processes:

Transfer Learning

Transfer learning involves repurposing pre-trained models by retraining them on new data sets. By leveraging the knowledge learned from previous training, it becomes possible to reduce the amount of data and time required to obtain a trained model. This technique accelerates the process of creating customized models for specific data sets.

Viewshot Learning

Viewshot learning enables training models on a small number of annotated images. Instead of labeling thousands of images, only a few hundred need to be labeled. This approach dramatically reduces the effort and time involved in data preparation. By creating a custom model tailored to the specific data set, viewshot learning streamlines the labeling process.

Automated Machine Learning (AutoML)

AutoML automates the training and fine-tuning of machine learning models, eliminating the need for manual intervention and custom engineering work. Instead of spending hours tuning hyperparameters, machine learning engineers can rely on automated tools to perform these tasks. With just a few clicks, a fully customized model can be trained on the provided data.

Uncertainty Estimation

Uncertainty estimation provides insights into a model's predictions and its level of confidence. By incorporating uncertainty estimation techniques, model outputs can indicate the certainty or uncertainty associated with each prediction. This enables quicker and more targeted auditing of data, focusing on the cases where the model's confidence is low and human review is necessary.

How Curb AI Uses Advanced Auto Labeling Techniques

Curb AI employs advanced auto labeling techniques to help automate the data preparation pipeline. The following techniques are used within their platform, the Superb AI Suite:

1. Transfer Learning: The platform allows users to retrain and repurpose pre-trained models specifically tailored to their data sets. By utilizing the extensive pre-trained models available, the amount of time and labelers required for training a model is significantly reduced.

2. Viewshot Learning: With Curb AI's platform, users can create custom models based on a small number of annotated images. This approach eliminates the need to label thousands of images, making data labeling more efficient and cost-effective.

3. Automated Machine Learning: Curb AI's platform offers automated machine learning capabilities, enabling users to train fully customized models without any human intervention or custom engineering work. This advanced feature saves time and resources in the model creation process.

4. Uncertainty Estimation: Curb AI's platform incorporates uncertainty estimation techniques based on Bayesian deep learning methods. These techniques provide insights into the model's uncertainties, allowing users to focus on reviewing and auditing the cases with uncertain predictions, leading to faster and more efficient data auditing.

By combining these four techniques, Curb AI enables users to achieve high-quality data sets in a fraction of the time. The ML-first approach adopted by Curb AI emphasizes rapid and efficient data labeling and auditing, paving the way for accelerated machine learning development.

The ML-First Approach to Data Labeling and Auditing

Implementing an ML-first approach to data labeling and auditing allows teams to rapidly create and deliver high-quality data sets. This approach involves the following steps:

1. Ingesting the raw data into a data platform or system.
2. Manually labeling a few hundred images to kickstart the process.
3. Training a customized auto labeling model using Curb AI's platform.
4. Applying the trained model to label the remaining data, accompanied by model predictions.
5. Utilizing uncertainty estimation to focus on auditing and reviewing only the uncertain predictions.
6. Delivering the labeled data sets to the machine learning team for model training.
7. Gathering new raw data to augment the existing data sets, repeating the process to enhance model performance.

By iterating through these steps, teams can continuously improve their model's performance by addressing edge cases and fine-tuning their training data. This ML-first approach, combined with the advanced auto labeling techniques offered by Curb AI, streamlines the data preparation and labeling process while ensuring high-quality results.

Conclusion

Automating data labeling and auditing is crucial for efficient computer vision model development. The challenges and bottlenecks faced in the data preparation pipeline can be overcome by leveraging advanced machine learning techniques. Transfer learning, viewshot learning, automated machine learning, and uncertainty estimation are powerful tools for automating data labeling and auditing. Curb AI's platform provides a comprehensive solution that combines these techniques, enabling ML-first approaches to data labeling and auditing. By adopting these techniques and approaches, teams can save time, reduce costs, and accelerate their machine learning projects.

Thank you for reading! If you have further questions or would like to explore Curb AI's ML-first approach to data labeling, please feel free to contact us.

Resources:

• Website: Superb AI
• Website: Curb AI