FAQ
What is Data Labeling?
Data Labeling consists in assigning specific labels or categories to data (images, texts, videos, audio, etc.) in order to make them understandable for machine learning algorithms. These annotations allow AI models to learn to recognize patterns and make decisions based on that information.
Why is data labeling critical for machine learning?
Data Labeling is critical because machine learning models require annotated data to learn effectively. Without accurate annotations, algorithms cannot correctly identify data characteristics and may produce erroneous results. Quality labeling improves the performance and reliability of AI models.
What types of data can be labeled?
All types of data can be labelled, including:
• Images: classification, object detection, semantic segmentation.
• Videos: object tracking, event annotation.
• Text: sentiment analysis, recognition of named entities.
• Audio: transcription, identification of speakers, detection of specific sounds.
What are the different ways to label data?
The main methods include:
• Manual labeling: done by human annotators.
• AI/semi-automatic labelling: probably the best approach. Teams of annotators use algorithms to pre-annotate data (for example SAM2) and then a human annotator validates or fixes it.
• Automatic labeling: entirely carried out by pre-trained AI models, before being reviewed manually to meet human validation requirements
• Crowdsourcing : use of platforms like Amazon Mechanical Turk for large-scale annotation, with a heterogeneous or low level of quality.
How do you ensure the quality of labelled data?
To ensure the quality of Data Labeling, it is essential to:
• Define clear guidelines for annotators.
• Set up a validation process (multiple annotations, quality control).
• Use advanced annotation tools with correction functions.
• Train annotators and regularly assess their performance.
What are the common challenges of Data Labeling and how can they be overcome?
Challenges include:
• Inconsistencies in the annotation → Use accurate guides and validate annotations with multiple annotators.
• Large volume of data → Automate part of the process and prioritize critical data.
• High cost → Outsource certain tasks or use hybrid solutions (human + AI).
• Bias in annotations → Diversify annotators and apply bias detection techniques.
What is the difference between data labeling and data annotation?
Labeling and annotation are often used interchangeably. However, annotation can include more complex tasks such as segmenting images or identifying relationships in text, while tagging generally refers to applying simple categories (e.g., “cat” or “dog” to an image).
Can Data Labeling be automated?
Yes, in part. Automation is possible thanks to AI models that pre-label data. However, human validation is often required to correct errors and ensure accuracy. Techniques such as active learning and the human-in-the-loop approach make it possible to improve this automation while maintaining a high level of quality and human validation (= all nuances are understood).
Moreover, manual annotation and human data validation are unlikely to disappear completely one day. After all, who would want an AI whose functioning or internal mechanisms cannot be understood? Human intervention remains essential, not only to ensure the quality of training data, but also to validate the results produced by the models, once they are deployed. Moreover, regulations go in this direction and will increasingly require this human supervision.
What is semi-supervised learning and how does it relate to Data Labeling?
Semi-supervised learning is an approach that combines labeled and unlabeled data to train an AI model. It reduces the need for comprehensive labeling by allowing the model to learn from a small set of annotated data and extrapolate that knowledge to unlabeled data.
How is Data Labeling used in Computer Vision models?
In Computer Vision, Data Labeling is used to train AI models to recognize and interpret images and videos. It may include tasks such as:
• The image classification (ex: recognize a cat or a dog).
• The object detection (delimit objects in an image).
• The semantic segmentation (identify each pixel in an image according to its category).
• The Tracking objects in videos (tracking moving elements).
What tools do you recommend for labeling data?
We work with all data annotation platforms on the market. There are several tools depending on the type of data and the level of automation desired. Most allow you to build your personalized and ergonomic annotation interface, to optimize annotation processes. Among the most popular tools:
• Supervise.ly and V7 for annotating images and videos
• Encord for the annotation of medical data
• Labelbox and Amazon SageMaker Ground Truth for versatile solutions with AI integration.
• Prodigy, UbiaI and LightTag for natural language processing (NLP).
• Label Studio for audio annotation.
The choice depends on your needs in terms of ergonomics, scalability and integration with your AI models.
How do you deal with biases in Data Labeling?
Biases can be reduced by adopting several strategies:
• Diversifying annotators to avoid homogeneity in the interpretation of the data.
• Define clear guidelines and well-documented to limit subjective errors.
• Perform quality checks with several annotations on the same sample.
• Use data rebalancing techniques (e.g. balancing underrepresented classes in a dataset).
How important is consistency in data labelling?
Consistent annotation is essential for training reliable AI models. If differences occur in the annotation of the same type of data, the algorithm may not learn properly and produce inconsistent results. The implementation of precise standards and the cross-validation between annotators make it possible to ensure this consistency.
How do you train annotators to ensure accurate labeling?
Effective training is based on several elements:
• Explanation of guidelines and best practices with concrete examples.
• Test sessions with correction to ensure that the annotators fully understand the instructions.
• Implementation of continuous feedback to adjust and refine their work.
• Performance monitoring to identify recurring errors and fix them quickly.
What are the costs associated with Data Labeling?
The costs vary according to:
• Of the data type (annotating images is often less expensive than annotating videos).
• The level of precision requested (complex annotations take longer).
• From the annotation mode (manual, automatic or mixed).
• From outsourcing (some providers offer services at a lower cost, but with quality control to ensure).
Annotation rates generally range from a few cents to several euros per piece of data, depending on the level of complexity. Behind each annotation, there is much more than a simple click: a rigorous process, adapted tools, and above all, trained annotators. Even for offshore services, abnormally low prices should arouse vigilance. They are often a symptom of unsustainable working conditions, overloaded teams, and as a result, compromised quality. Reliable AI is based above all on human work carried out in ethical conditions and with attention to detail.
How long does it take to label a data set?
It depends on the volume of data and the type of annotation. For example:
• An image can be annotated in a few seconds (simple classification) or in several minutes (pixel-by-pixel segmentation).
• A video may take several hours if each frame needs to be annotated individually.
• A text of a few sentences can be labeled in a few minutes, while in-depth analysis (e.g. entity recognition) may take longer.
Automation and crowdsourcing help speed up the process.
Which industries benefit the most from Data Labeling?
Data Labeling is used in a variety of industries, including:
• The automobile (autonomous vehicles, obstacle detection).
• Health (annotation of medical images for AI-assisted diagnosis).
• E-commerce (image recognition for product research).
• The safety (facial detection, video surveillance).
• Marketing (analysis of feelings on social networks).
How does Data Labeling contribute to the improvement of AI models?
Without labeled data, AI models can't learn effectively. Good Data Labeling allows:
• One better understanding of the data by the algorithm.
• One improved accuracy predictions.
• One reduction of errors and biases in the results.
• One optimization of convergence time when training the model.
What are the best practices for labeling data?
• Define precise annotation rules to avoid subjective interpretations.
• Partially automate labeling to save time.
• Implement rigorous quality control (cross-validation, human reviews).
• Ensuring a good balance of data to avoid bias in training the model.
• Train annotators regularly to maintain a high level of quality.
How do you manage sensitive data during labeling?
The processing of sensitive data involves specific precautions:
• Anonymization or pseudonymization data to avoid personal identification.
• Use of secure platforms and hosted in Europe/France for customers who request it, to limit access to confidential information.
• Regulatory compliance (GDPR, HIPAA) depending on the type of data processed.
• Strict access control and confidentiality commitment for annotators.
What is the difference between manual and automatic data labeling?
• Manual Data Labeling : carried out by human annotators, it guarantees better precision but takes more time and costs more.
• Automatic data labeling : relies on AI models that pre-annotate data using pattern recognition algorithms. It is faster but requires human corrections in most cases.
• Hybrid solution : a mixed approach where AI pre-tagging and human annotators validate or correct the results.
What are the main challenges in tagging audio and video data?
• High data volume : Audio and video files are large and require more time to process.
• Time alignment : the annotation must be precisely synchronized with the audio or video content.
• Background noise : Recordings may contain extraneous sounds that make it difficult to identify relevant items.
• Linguistic variability (for audio): recognition of accents, intonations and homonyms.
• Detecting and tracking moving objects (for the video): requires advanced tracking algorithms and specific labeling methods (Object tracking, interpolation, etc.).
How is Data Labeling evolving with the advances of AI?
AI makes it possible to improve and accelerate Data Labeling thanks to:
• Active learning : the AI selects the most relevant data to be annotated in priority.
• Pre-labelling : the AI generates initial annotations that humans validate.
• Self-supervised models : reduce dependence on human annotations by learning from raw data.
• The increase in data : generation of new data from existing data to enrich the training sets, and human validation to ensure the consistency of the data set.
What is “human-in-the-loop” in the context of Data Labeling?
The Human-in-the-Loop is an approach where human intervention is combined with AI algorithms to improve the quality of annotations. Humans correct or validate AI predictions, thus making it possible to gradually refine the performance of the model.
How do you assess the performance of annotators?
Several indicators make it possible to assess the quality of the work of annotators:
• Inter-Annotator Agreement Rate (IAA for “Inter-Annotator Agreement”) : measures the consistency of annotations between several people, especially in consensus annotation approaches (several annotators annotate the same item).
• Error rate : percentage of incorrect annotations identified during quality checks.
• Average time per annotation : indicator of effectiveness and possible difficulties encountered.
• Feedback from reviewers : qualitative feedback on the annotations made.
What are the key performance indicators for Data Labeling?
• Precision : Percentage of correct annotations.
• Coherence : evaluation of the stability of annotations between different annotators.
• Treatment time : average time to annotate a batch of data.
• Rejection rate : proportion of annotations requiring correction or proofreading.
• Cost per annotation : a measure of the economic efficiency of the labelling process.
How is Data Labeling used in Natural Language Processing (NLP)?
Data Labeling is widely used for NLP and is used in particular to:
• Named Entity Recognition (NER) : identify proper names, places, dates, etc.
• Sentiment analysis : classify a text according to polarity (positive, negative, neutral).
• Text categorization : assign a label to a document (e.g. sport, politics, finance).
• Machine translation : improvement of models by comparing source and translated texts.
• Detecting intentions : understand the intentions of users in chatbots and voice assistants.
What are the risks associated with labeling poor quality data?
Poor Data Labeling can lead to:
• A biased model : annotation errors can lead to erroneous decisions.
• A decrease in model performance : if the data is not well annotated, the AI learns poorly and produces unreliable results.
• An increase in costs : errors require model corrections and re-training, extending development time.
• A lack of confidence in the model : If users see inconsistencies, they may not adopt the AI-based solution.
How can data labeling help reduce errors in AI models?
Good Data Labeling allows you to:
• Provide accurate training data to improve the generalization of the model.
• Correcting biases by balancing annotated data.
• Reducing classification errors thanks to detailed and consistent annotations.
• Improving the understanding of the model by integrating complex annotations and relationships between entities.
What are the current trends in Data Labeling?
• Increased automation with AI to reduce dependence on human work; in reality, it is unrealistic to think that data preparation work can be 100% automated. What is changing: the volumes of data to be processed manually will probably be smaller thanks to automation, with particular attention paid to quality.
• Development of self-supervised models that require less annotated data (but better quality data!).
• Increasing use of human-in-the-loop to combine speed and precision.
• Optimized crowdsourcing with specialized platforms to speed up annotation... useful for accessing experts in certain fields, but does not replace an expert and specialized team for scaling up.
• Multimodal annotation integrating multiple data types (text, image, audio) for more advanced models.
Feed your AI models with high-quality training data!
