tzutalin/labelImgArchived
LabelImg
labelImg is a desktop image annotation tool and dataset preparation utility used to create labeled datasets for computer vision training. It provides a graphical interface for drawing bounding boxes around objects in images and assigning them class labels to build ground truth data for machine learning models.
The software specifically supports the Pascal VOC XML annotation format, exporting image coordinates and class names into standard XML or text structures. It allows users to load predefined class lists from text files to standardize naming across an entire project.
Beyond initial labeling, the tool covers image annotation workflows including the visualization of saved annotations and manual dataset verification. This includes the ability to flag images as verified or difficult to maintain dataset quality.
Features
- Computer Vision Tools - Provides an interactive software interface for labeling, annotating, and preparing visual datasets for computer vision model training.
- 2D Object Labeling - Provides a graphical interface for defining rectangular regions of interest and assigning class labels to 2D image datasets.
- Pixel Coordinate Mappings - Translates mouse interactions on the screen into precise pixel coordinates for bounding box definition.
- Interactive Bounding Box Creation - Enables users to manually draw rectangular boxes around objects and assign them category labels.
- Object Detection - Provides a specialized interface for identifying and locating objects within images using bounding boxes for model training.
- Annotation Format Converters - Converts human-created labels into standardized formats like Pascal VOC XML for model training.
- Bounding Box Interfaces - Implements a graphical interface for defining rectangular regions of interest and assigning class labels to image datasets.
- Computer Vision Data Preparation - Facilitates the preparation of image collections for computer vision through manual annotation and formatting.
- Data Exporters - Exports annotation data into multiple industry-standard file formats for compatibility with various ML frameworks.
- Structured Data Exporters - Converts internal coordinate data into structured XML or CSV formats for machine learning pipelines.
- Bounding Box Visualizers - Overlays previously saved annotation coordinates onto images for manual review and verification.
- Dataset Preparation Tools - Provides utilities for collecting, cleaning, and curating image data samples to build ground truth datasets for machine learning.
- Dataset Review and Flagging Tools - Provides mechanisms to flag images as verified or difficult to maintain high training data quality.
- Label Configuration Systems - Allows users to load predefined label lists from text files to ensure naming consistency across datasets.
- Local File Storage - Persists annotation data directly to the local disk as XML or text files for project portability.
- Computer Vision Annotation Formats - Exports image coordinates and class names into the standard Pascal VOC XML format for computer vision training.
- Image Integrity Verification - Provides a process for manually reviewing and verifying the accuracy of bounding box placements in image datasets.
- Annotation - Graphical tool for bounding box image annotation.
- Computer Vision Libraries - Graphical tool for annotating object bounding boxes.
- Computer Vision Tools - A graphical image annotation tool for object detection datasets.
- Data Annotation - Graphical tool for bounding box image annotation.
- Image Annotation - Graphical interface for drawing object bounding boxes.
- Image Annotation Tools - Graphical tool for labeling object bounding boxes in images.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
