Tool Information
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.
F.A.Q (20)
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.
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.
Data can be imported into LabelGPT from various sources including local platforms or cloud sources like AWS, GCP, Azure, and also through APIs.
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.
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.
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.
The process of reviewing labels in LabelGPT involves checking the labeled images that the tool generates. Users can validate the quality of these labels by filtering based on a high-confidence score and visually verifying the results.
In LabelGPT, the quality of labels can be validated by filtering the labels based on confidence scores. Users can then visually verify the results. This allows for review and assurance of accuracy and quality of generated labels.
LabelGPT supports detection and segmentation of labels using generative AI models. By using class or object names as a text prompt, LabelGPT's AI model detects the corresponding objects in the image and segments them to create the labels.
LabelGPT helps increase the speed of the labeling process by automating labeling with its zero-shot labeling engine. In other words, it generates labels instantly without needing annotated examples.
LabelGPT can label raw images. It works by taking class or object names as a text input and generates their corresponding labels on the images.
LabelGPT reduces annotation costs by automating the data labeling process, thus reducing the need for manual intervention. This cuts down on costs associated with human resources and time.
The annotations obtained from LabelGPT can be utilized across numerous areas. Notably, these include propelling vision model training, reducing annotation costs, and increasing the speed of the labeling process.
Yes, the annotations generated by LabelGPT can be utilized to supercharge vision model training - they can be directly integrated into the ML pipeline to enhance the training of vision models.
Yes, LabelGPT supports cloud integration. It can connect to different cloud platforms, which include AWS, GCP, Azure, and allows data importation through APIs.
Auto labeling in LabelGPT works through the process where the AI predicts the label by itself. After receiving a text prompt, it identifies and recognizes the class or object in the images and generates appropriate labels.
The benefit of using LabelGPT's foundation model is the ability to generate large volumes of labeled data through its zero-shot label generation engine, thereby speeding up data processing and improving accuracy of model training.
LabelGPT can be directly integrated into your ML pipeline by exporting the generated labels in the form of output data which can be used to train your models.
The swift review process offered by LabelGPT consists of validation tools that allow users to filter labels based on high confidence scores and visually verify the output. This ensures a quick and precise quality control process.
Yes, LabelGPT has the capability to auto-label a million images in minutes, greatly speeding up the labeling process and making it one of the fastest auto annotation tools in the world.
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
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
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