What is the real cost of free data labelling tools?
🤔 Choosing a data annotation platform : what do you think of “free” solutions?
Data labelling is an essential step in the preparation of high quality datasets for training machine learning models, a pillar of AI. This task can be tedious and expensive, especially when opting for paid tools. Fortunately, the market offers a plethora of free data labelling tools which can be of significant help for projects with limited budgets. In this article, we explore the best free data annotation tools, while considering the real costs that may be associated with using them, an important factor in the growth and development of your AI projects.
Label Studio, an open source data annotation tool, is one of the most popular free tools, thanks to its usability and its ability to manage various types of annotations, a fundamental aspect of the quality of annotated data. Although Label Studio is free, it offers quality and precision that makes it possible to manage speech recognition and computer vision, two areas where machine learning has revolutionized technology and the use of data.
VGG Image Annotator (VIA) and RectLabel are other examples of data annotation tools that promote the development of accurate models for the computer, contributing to the development of artificial intelligence. They allow the annotation of data with great precision, including offline versions of the application, which is essential for data sets involving images and videos. These tools offer a way to manipulate objects in a variety of use cases, and thanks to their functionalities, they play a critical role in the annotation process for AI.
An overview of free data labelling tools...
1. Label Studio Community
Label Studio, in its “Community” version, is one of the most popular free data labeling tools. It offers a user-friendly interface that allows annotators to easily add tags to various object categories in images or videos. This labeling software takes care of several types of annotations (including image annotations and texts), such as border rectangles, key points, and masks, offering great flexibility for various types of projects.
Although Label Studio is advertised as free, it's important to note that there are some advanced features that are only available in the paid version. In addition, if your project requires collaboration between multiple annotators or integration with existing systems, you may encounter difficulties associated with a still imperfect management of concurrent accessess (at the time of writing). In addition, some versions of Label Studio featured data extraction problems in multiple formats as well as performance issues.
Nevertheless, Label Studio Community remains the the most efficient Open Source/free data labeling software on the market, and is acclaimed by a large number of Data Scientists.
2. VGG Image Annotator (VIA)
VGG Image Annotator (VIA) Is a Open Source Data Labeling Tool, designed by researchers at the University of Oxford. It can be used for free. It offers a simple but powerful interface for annotating images with Bounding Boxes, masks and key points. VIA is customizable, allowing users to define their own annotation categories based on the specific needs of their project.
However, it is important to note that VIA being an Open Source solution, it pMay require technical knowledge for installation, its configuration and its maintenance. If your team does not have IT expertise, it may be more beneficial to opt for ready-to-use solutions, even if they are expensive. In addition, its interface may seem dated and put off the most reckless Data Labelers.
3. RectLabel
RectLabel Is another free data labeling tool that focuses primarily on image annotation. It offers an intuitive user interface that allows image annotators to draw boundary rectangles around objects of interest in the images. This tool is especially loved by Mac users because it is specially designed for Mac OS systems.
However, although RectLabel is free, it is important to remember that this free version may have limitations in terms of the number of annotations or advanced features. If your project requires a large number of annotations or more advanced features, it could be necessary to upgrade to the paid version of RectLabel or to explore other alternatives. In addition, RectLabel having been designed for offline annotation, its use can be a challenge when it comes to mobilizing large teams of Data Labelers to work on your largest datasets.
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If the data annotation platform is important, it is above all the efficiency and quality of the data annotation process that are critical to ensuring that the data that feeds your machine learning models is of the highest quality. Choosing the right data annotation tool can influence the quality and accuracy of the data sets generated and, as a result, the success of your AI.
For example, for businesses evolving In the field of speech recognition, the quality of annotations is crucial. The precision in the annotation of audio data and the effective management of different dialects and languages can directly influence the performance of natural language processing models. Likewise, computer vision, applied in technologies such as LiDAR or the development of AI for autonomous vehicles, is based on extremely precise annotation data, where every pixel counts.
Free tools can meet these requirements up to a certain point, but the trade-off often comes in terms of advanced features and support for tracking and accurately segmenting objects in videos (for example: for a large number of free or open source platforms, a semantic annotation, pixel by pixel, is not possible).
In the case of projects requiring a large volume of data, such as for computer vision applications, the ability of tools to manage and store large amounts of data and to enable effective collaboration between annotators is becoming a key success factor. The tool V7 Labs (Darwin), for example, although paid, offers advanced image and video recognition capabilities that are worth checking out, as well as a highly efficient collaborative environment.
In the context of machine learning, where data quality is often synonymous with model quality, data annotation tools need to provide a balance between accessibility and sophistication. Tools like Label Studio, VIA, and RectLabel, while they may require technical knowledge for installation and maintenance, have accessibility benefits that are essential for the implementation of a development process and the development of robust AI models.
Analysis of the real cost of free tools
While these data labeling tools are labeled as free, it is important to assess the real costs associated with their use.
1. Labor costs
One of the main real costs associated with free data annotation platforms is the cost of labor (i.e. the work time of annotators or Data Labelers, sourced via a specialized service providers or via a crowdsourcing platform). Even though the tool itself is free, the labelling task requires time and human resources. Depending on the size and complexity of your project, you may need hire qualified annotators, which represents a financial investment.
2. Storage and bandwidth costs
Some free tools may offer a limited storage space for your annotated datas, or limit the bandwidth for downloading or sharing data. If your project requires significant storage or generates high data traffic, you may exceed the allocated quotas and have to pay additional fees to increase these limits.
3. Annotator training costs
If your project requires specially trained annotators for complex or specialized labeling tasks (as is the case in medicine, with Data Labelers specializing in medical data), the training of these annotators may involve additional costs.
In addition, theeffectiveness of the annotation platform chosen has a direct influence on the success of machine learning projects. Integrating cloud services like AWS S3 can make it easier to store and share data, while using APIs allows for better interoperability with other systems and software. At the same time, establishing good data management and optimizing workflows are essential to meet growing demands for high quality data.
4. Lack of embedded collaboration capabilities... provide alternatives
Collaboration between team members and platform users is essential, and the annotation tool should support an environment where this synergy is possible. For example, tools like Kili Technology and LabelBox offer a collaborative and personalized interface to meet the needs of businesses and users. These features can enable teamwork to facilitate the recognition of specific shapes like polygons or cuboids in images, or the transcription of audio to text for model training. NLP.
Collaboration on these platforms should allow teams to work together effectively, taking into account time constraints and production goals. Free tools can offer a good starting point, but it's often necessary to complement them with paid solutions to meet the scale and complexity of projects.
In the absence of collaboration functionalities, it becomes necessary to equip yourself with alternatives, whether they are project management tools, scripts to extract the number of labels produced or the time spent by Data Labelers on the platform... and all this of course represents a hidden cost!
5. Lack of video annotation features... an obstacle to scaling
In terms of computer vision, platforms like CVAT can offer valuable assistance, especially in use cases involving autonomous vehicles or more generally all cases of object detection. Precise annotation of video data is an area where the quality of tools can make a significant difference, allowing for in-depth analysis and a better understanding of image sequences. However, some platforms are not efficient enough for video annotation, which can be a barrier for future Computer Vision use cases.
Ability to meet the specific needs of AI projects
The data annotation tool should not only be measured in terms of cost, but also in terms of its ability to meet the specific needs of the project. Businesses looking to develop AI models need to consider all of the features these tools offer, including their flexibility, scalability, and the variety of annotation types they support.
1. Choosing a solution adapted to the global development and certification strategy
In the global context, where the need for automation and precision in data processing is increasing, Open Source and free solutions can offer a cheap and efficient solution. However, it is vital to assess the various options available on the market, taking into account training needs, the functionalities required for natural language processing (NLP), pattern recognition, and the specificities of the industry concerned.
The adoption of data annotation tools should be thoughtful and aligned with the overall business development strategy, taking into account the impact of these tools on data quality and the effectiveness of annotators. Data annotation platforms such as LabelBox, thanks to their user interface, not only offer a better experience for users but also the possibility of integrating advanced functionalities such as object detection and segmentation.
2. Choose a solution adapted to your use case (NLP, Computer Vision, etc.)
Setting up a robust data annotation system can be a challenge, in particular with respect to managing the diversity of languages required for NLP cases and quality control functionalities. The expertise of Machine Learning engineers is often called upon to adapt platforms to specific needs, such as adding capabilities for video annotation or developing specialized AI models. La data security is also a major concern, and businesses need to ensure intellectual property protection as well as data confidentiality.
3. Choose a tool that evolves with the needs of the project... adopted and maintained by a large community
Finally, it is essential to choose a data annotation tool that will evolve with the needs of the project. Businesses need to anticipate increases in volume and ensure that the tool they choose can adapt effectively. The tool should also be able to integrate into the existing data pipeline, making it easy to deploy machine learning models and apply the acquired knowledge to new data sets.
With this in mind, the annotation platform must be evaluated according to its potential to increase the productivity of annotators and the quality of data sets, two factors that are directly linked to the success of machine learning projects. Tools like Label Studio, with their open source approach, offer advantages in terms of flexibility and access to a developer community, which can be a huge asset for businesses looking for customizable solutions.
The addition of specific functionalities, such as speech detection for speech recognition applications or classifying precise objects for computer vision systems can be important to meet the specific demands of a project. In addition, the integration of cutting-edge machine learning methods and the use of advanced algorithms are aspects that can determine the scope and ability of a data annotation tool to provide reliable and accurate results.
In conclusion...
Free data labeling tools can be of great value for projects with limited budgets. However, it is important to carefully consider the real costs that could result from their use. Les labor, storage, bandwidth, and annotator training costs should be taken into account when selecting the appropriate labelling tool for your project.
In short, while taking into account the cost and the functionalities, it is also important to consider the support and resources available for the use of these tools, such as tutorials, user forums, and how-to guides. Businesses should assess whether the chosen tool offers a level of support that is appropriate to their needs, allowing the annotation team to work effectively and without barriers, thereby contributing to the overall quality and effectiveness of the data annotation process.
The perfect solution does not exist (yet), so it is up to AI Directors and Machine Learning Engineers to define the best approach to build a solid AI pipeline!
🔍 The choice of a labelling tool will also depend on the specific needs of your project, the size of your team, and your overall budget. Take the time to carefully analyze the benefits and costs of each option before making an informed decision for your data labeling project. Once you have chosen the appropriate tool and planned the associated costs, you can set up an effective labelling process and of high quality to train your machine learning models successfully.
Additional resources:
- 🔗 https://www.innovatiana.com/post/top-10-image-annotation-platforms-for-ai
- 🔗 https://www.innovatiana.com/post/how-to-choose-your-data-labeling-platform
- 🔗 https://www.innovatiana.com/post/annotation-partner-vs-crowdsourcing
- 🔗 https://www.innovatiana.com/post/what-is-data-labeling
- 🔗 https://www.innovatiana.com/post/bounding-boxes-annotation
- 🔗 https://www.innovatiana.com/post/natural-language-processing-what-is-it
