Labelbox Features
What are the features of Labelbox?
Quality
- Labeler Quality
- Task Quality
- Data Quality
- Human-in-the-Loop
Automation
- Machine Learning Pre-Labeling
- Automatic Routing of Labeling
Image Annotation
- Image Segmentation
- Object Detection
- Object Tracking
- Data Types
Natural Language Annotation
- Named Entity Recognition
- Sentiment Detection
- OCR
Speech Annotation
- Transcription
- Emotion Recognition
Top Rated Labelbox Alternatives
Labelbox Categories on G2
Filter for Features
Deployment
Language Flexibility | Allows users to input models built in a variety of languages. | Not enough data | |
Framework Flexibility | Allows users to choose the framework or workbench of their preference. | Not enough data | |
Versioning | Records versioning as models are iterated upon. | Not enough data | |
Ease of Deployment | Provides a way to quickly and efficiently deploy machine learning models. | Not enough data | |
Scalability | Offers a way to scale the use of machine learning models across an enterprise. | Not enough data | |
Language Flexibility | Allows users to input models built in a variety of languages. | Not enough data | |
Framework Flexibility | Allows users to choose the framework or workbench of their preference. | Not enough data | |
Versioning | Records versioning as models are iterated upon. | Not enough data | |
Ease of Deployment | Provides a way to quickly and efficiently deploy machine learning models. | Not enough data | |
Scalability | Offers a way to scale the use of machine learning models across an enterprise. | Not enough data |
Management
Cataloging | Records and organizes all machine learning models that have been deployed across the business. | Not enough data | |
Monitoring | Tracks the performance and accuracy of machine learning models. | Not enough data | |
Governing | Provisions users based on authorization to both deploy and iterate upon machine learning models. | Not enough data | |
Model Registry | Allows users to manage model artifacts and tracks which models are deployed in production. | Not enough data | |
Cataloging | Records and organizes all machine learning models that have been deployed across the business. | Not enough data | |
Monitoring | Tracks the performance and accuracy of machine learning models. | Not enough data | |
Governing | Provisions users based on authorization to both deploy and iterate upon machine learning models. | Not enough data |
Quality
Labeler Quality | Based on 23 Labelbox reviews. Gives user a metric to determine the quality of data labelers, based on consistency scores, domain knowledge, dynamic ground truth, and more. | 93% (Based on 23 reviews) | |
Task Quality | As reported in 23 Labelbox reviews. Ensures that labeling tasks are accurate through consensus, review, anomaly detection, and more. | 91% (Based on 23 reviews) | |
Data Quality | As reported in 24 Labelbox reviews. Ensures the data is of a high quality as compared to benchmark. | 94% (Based on 24 reviews) | |
Human-in-the-Loop | As reported in 22 Labelbox reviews. Gives user the ability to review and edit labels. | 89% (Based on 22 reviews) |
Automation
Machine Learning Pre-Labeling | As reported in 21 Labelbox reviews. Uses models to predict the correct label for a given input (image, video, audio, text, etc.). | 87% (Based on 21 reviews) | |
Automatic Routing of Labeling | Automatically route input to the optimal labeler or labeling service based on predicted speed and cost. This feature was mentioned in 20 Labelbox reviews. | 85% (Based on 20 reviews) |
Image Annotation
Image Segmentation | Has the ability to place imaginary boxes or polygons around objects or pixels in an image. This feature was mentioned in 22 Labelbox reviews. | 89% (Based on 22 reviews) | |
Object Detection | has the ability to detect objects within images. This feature was mentioned in 21 Labelbox reviews. | 87% (Based on 21 reviews) | |
Object Tracking | Based on 20 Labelbox reviews. Track unique object IDs across multiple video frames | 89% (Based on 20 reviews) | |
Data Types | As reported in 20 Labelbox reviews. Supports a range of different types of images (satelite, thermal cameras, etc.) | 90% (Based on 20 reviews) |
Natural Language Annotation
Named Entity Recognition | Gives user the ability to extract entities from text (such as locations and names). This feature was mentioned in 19 Labelbox reviews. | 92% (Based on 19 reviews) | |
Sentiment Detection | Gives user the ability to tag text based on its sentiment. 18 reviewers of Labelbox have provided feedback on this feature. | 85% (Based on 18 reviews) | |
OCR | Based on 16 Labelbox reviews. Gives user the ability to label and verify text data in an image. | 90% (Based on 16 reviews) |
Speech Annotation
Transcription | Allows the user to transcribe audio. 15 reviewers of Labelbox have provided feedback on this feature. | 86% (Based on 15 reviews) | |
Emotion Recognition | As reported in 15 Labelbox reviews. Gives user the ability to label emotions in recorded audio. | 80% (Based on 15 reviews) |
Operations
Metrics | Control model usage and performance in production | Not enough data | |
Infrastructure management | Deploy mission-critical ML applications where and when you need them | Not enough data | |
Collaboration | Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. | Not enough data |
Model Training & Optimization - Active Learning Tools
Model Training Efficiency | Enables smart selection of data for annotation to reduce overall training time and costs. | Not enough data | |
Automated Model Retraining | Allows for automatic retraining of models with newly annotated data for continuous improvement. | Not enough data | |
Active Learning Process Implementation | Facilitates the setup of an active learning process tailored to specific AI projects. | Not enough data | |
Iterative Training Loop Creation | Allows users to establish a feedback loop between data annotation and model training. | Not enough data | |
Edge Case Discovery | Provides the ability to identify and address edge cases to enhance model robustness. | Not enough data |
Data Management & Annotation - Active Learning Tools
Smart Data Triage | Enables efficient triaging of training data to identify which data points should be labeled next. | Not enough data | |
Data Labeling Workflow Enhancement | Streamlines the data labeling process with tools designed for efficiency and accuracy. | Not enough data | |
Error and Outlier Identification | Automates the detection of anomalies and outliers in the training data for correction. | Not enough data | |
Data Selection Optimization | Offers tools to optimize the selection of data for labeling based on model uncertainty. | Not enough data | |
Actionable Insights for Data Quality | Provides actionable insights into data quality, enabling targeted improvements in data labeling. | Not enough data |
Model Performance & Analysis - Active Learning Tools
Model Performance Insights | Delivers in-depth insights into factors impacting model performance and suggests enhancements. | Not enough data | |
Cost-Effective Model Improvement | Enables model improvement at the lowest possible cost by focusing on the most impactful data. | Not enough data | |
Edge Case Integration | Integrates the handling of edge cases into the model training loop for continuous performance enhancement. | Not enough data | |
Fine-tuning Model Accuracy | Provides the ability to fine-tune models for increased accuracy and specialization for niche use cases. | Not enough data | |
Label Outlier Analysis | Offers advanced tools to analyze label outliers and errors to inform further model training. | Not enough data |
