Segments.ai Features
What are the features of Segments.ai?
Quality
- Labeler Quality
- Task Quality
- Data Quality
Image Annotation
- Image Segmentation
- Object Detection
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Segments.ai Categories on G2
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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 | |
Integrations | Can integrate well with other software. | 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 | Gives user a metric to determine the quality of data labelers, based on consistency scores, domain knowledge, dynamic ground truth, and more. 12 reviewers of Segments.ai have provided feedback on this feature. | 88% (Based on 12 reviews) | |
Task Quality | Based on 11 Segments.ai reviews. Ensures that labeling tasks are accurate through consensus, review, anomaly detection, and more. | 86% (Based on 11 reviews) | |
Data Quality | Based on 11 Segments.ai reviews. Ensures the data is of a high quality as compared to benchmark. | 86% (Based on 11 reviews) | |
Human-in-the-Loop | Gives user the ability to review and edit labels. | Not enough data |
Automation
Machine Learning Pre-Labeling | Uses models to predict the correct label for a given input (image, video, audio, text, etc.). | Not enough data |
Image Annotation
Image Segmentation | As reported in 12 Segments.ai reviews. Has the ability to place imaginary boxes or polygons around objects or pixels in an image. | 90% (Based on 12 reviews) | |
Object Detection | Based on 11 Segments.ai reviews. has the ability to detect objects within images. | 85% (Based on 11 reviews) | |
Object Tracking | Track unique object IDs across multiple video frames | Not enough data | |
Data Types | Supports a range of different types of images (satelite, thermal cameras, etc.) | Not enough data |
Natural Language Annotation
Sentiment Detection | Gives user the ability to tag text based on its sentiment. | Not enough data |
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 |
Recognition Type
Object Detection | Provides the ability to recognize various types of objects in various scenarios and settings. | Not enough data | |
Text Detection | Provides the ability to recognize texts. | Not enough data | |
Motion Analysis | Processes video, or image sequences, to track objects or individuals. | Not enough data | |
Video Detection | Provides the ability to detect objects, humans, etc. in video footage. | Not enough data |
Labeling
Model Training | Allows users to train model and provide feedback regarding the model's outputs. | Not enough data | |
Bounding Boxes | Allows users to select given items in an image for the purposes of image recognition. | Not enough data | |
Custom Image Detection | Provides the ability to build custom image detection models. | Not enough data |
