Here is a table depicting the comparison of the tools on various features:
| Tool | Pros | Cons | Cost | Labeling Features Support | Scalability |
| Azure Machine Learning labeling | Rapid data preparation for machine learning projects. Assisted machine learning. | Limited to Microsoft ecosystem. Limited support for custom labeling interfaces. | Azure services may have associated costs depending on the usage | Images, text documents, and audio | Ability to scale labeling tasks with the power of Azure cloud services |
| Label Studio | Open source and multi-type data labeling tool | Limited documentation. Limited support for video data. | Label Studio is available as open source software as well as an Enterprise cloud service | Images, text documents, and video | May require additional configuration for large-scale projects |
| CVAT | Web-based and collaborative. Easy to use with intuitive shortcuts. | Limited support for custom labeling interfaces. Users need to set up and host the tool themselves. | Open source. No direct cost for software; users only pay for hosting and infrastructure. | Images and videos | Large-scale projects may require additional configuration |
| pyOpen Annotate | Supports multiple annotation formats. Supports custom annotation interfaces. | Limited documentation. Limited support for video data. | Free and open source | Images and videos | Large-scale projects may require additional configuration |
Table 12.1 – Comparison of data labeling and annotation tools
The cost of each tool may vary depending on the number of labeling tasks and the features required. It is recommended to evaluate each tool based on your specific requirements before deciding on the labeling tool.
Advanced methods in data labeling
Active learning and semi-automated learning are popular machine learning techniques that help overcome the challenge of data labeling. Both involve presenting uncertain or challenging labels to human annotators for feedback; the key difference lies in the overall strategy and decision-making process. Let’s break down the distinction.
Active learning
Active learning is a machine learning paradigm in which a model is trained on a subset of the data, and then the model actively selects the most informative examples for labeling to improve its performance. The following list discusses various features of this method:
- Workflow: The initial model is trained on a small labeled dataset. The model identifies instances where it is uncertain or likely to make errors. These uncertain or challenging instances are presented to human annotators for labeling. The model is updated with the new labeled data, and the process iterates.
- Benefits: It reduces the amount of labeled data needed for model training and focuses annotation efforts on examples that are challenging for the current model.
- Challenges: It requires an iterative process of model training and annotation. The selection of informative instances is crucial for success.
- Decision-making by the model: In active learning, the model takes an active role in selecting which instances it finds most uncertain or challenging. The model employs specific query strategies to identify instances that, when labeled, are expected to improve its performance the most.
- Iterative process: The initial model is trained on a small labeled dataset. The model selects instances for annotation based on its uncertainty or expected improvement. Human annotators label the selected instances. The model is updated with the new labels, and the process iterates.