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Mini tools
LabelGPT
Auto-label millions of images in minutes
LabelGPT
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Outputs:
Auto-label millions of images in minutes
Most popular alternative: UBIAI (7 saves)
By unverified author Claim this AI
Generated by ChatGPT
LabelGPT is an automated image annotation tool powered by a generative AI model. Its primary function is to generate labels on raw images, thereby aiding the annotation process.
Users can import their data from various sources, including local platforms or cloud sources like AWS, GCP, Azure, and through APIs. The zeroes shot label generation engine is backed by a foundation model that allows Machine Learning teams to generate vast volumes of labeled data.
The tool facilitates the annotation process by taking class or object names as a text prompt and detecting and segmenting the label. It also offers a swift review process.
Users can validate the quality of the labels by filtering the high confidence score, visually verify the results, and directly integrate it into their ML pipeline.
The annotations obtained from LabelGPT can be utilized to propel vision model training, reduce annotation costs, and increase the speed of the labeling process.
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Releases
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May 31, 2023
Initial release of LabelGPT.
Pricing
Pricing model
Freemium
Paid options from
Free tier available
Billing frequency
Monthly
Prompts & Results
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LabelGPT was manually vetted by our editorial team and was first featured on December 22nd 2023.
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35,56525Released 22d agoFree + from $19.99/mo
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838,610636v1.6 released 16d agoFree + from $12/mo
Reducing manual efforts in first-pass during code-review process helps speed up the "final check" before merging PRs
Pros and Cons
Pros
Automated image annotation
Multiple foundation model utilization
Generates voluminous labeled data
Swift review process
Quality validation through high confidence score
Visual result verification
Direct ML pipeline integration
Reduces annotation costs
Speeds up labeling process
Imports from local and cloud (AWS, GCP, Azure)
API for data import
Zero-shot learning
Text prompts for labeling
Image segmentation capabilities
Prompt-based class/object detection
Uses multiple open-source datasets
High-quality label generation
Exports to ML training engine
Multi-platform (cloud, local) data access
Foundation model powered labeling
No manual labeling required
Fast label generation
Saves time in labeling
Visual label validation
Allows dataset selection
Prompt for class/object labeling
Enables data upload
Supports video annotation
Supports text annotation
Data curation functions
Capture and collection features
Pre-labeled dataset availability
Smart feedback loop technology
Flexible pricing options
Offers use case based solutions
Expert discussions and blogs
Dedicated knowledge base
Option to schedule demo
Easy class/object labeling
Validation by filtering high confidences scores
Automates raw image labeling
View 36 more pros
Cons
Limited labeling types
No offline usage
No manual labeling option
No multi-language support
Undefined user access control
Inability to adjust confidence score
No data curation function
Unclear revision history
No free trial mentioned
View 4 more cons
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Q&A
What is LabelGPT?
LabelGPT is an automated image annotation tool powered by a generative AI model. It's primary function is to generate labels on raw images, thereby aiding the annotation process.
How does LabelGPT generate labels on raw images?
LabelGPT generates labels on raw images by taking class or object names as a text prompt. It then uses its generative AI model to detect and segment the label on the related image.
How can I import data into LabelGPT?
Data can be imported into LabelGPT from various sources including local platforms or cloud sources like AWS, GCP, Azure, and also through APIs.
What sources does LabelGPT support for importing data?
LabelGPT supports a wide array of data import sources, including local platforms (like an on-premises server or personal device), and various cloud platforms such as AWS, GCP, Azure. It also supports data importation through APIs.
What is the purpose of the zero-shot label generation engine in LabelGPT?
The zero-shot label generation engine in LabelGPT is responsible for creating automatic labels on images. Its purpose is to maximize efficiency and speed up the label generation process, reducing the need for manual labels and allowing Machine Learning teams to generate large volumes of labeled data.
How does LabelGPT contribute to the Machine Learning pipeline?
LabelGPT directly integrates into a Machine Learning pipeline by allowing users to export the produced labels directly into their ML models. Such contributions aid in the training of these models, accelerating the development process.
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