#annotation tool

CVAT is an interactive annotation solution used globally for video and image processing in computer vision projects. It integrates seamlessly with Data-centric AI, supporting a wide range of annotation formats such as COCO and YOLO, and offers automatic labeling. This enhances productivity significantly. CVAT is available as a free online service or can be self-hosted, catering to diverse annotation needs. With integrations like Roboflow and HuggingFace, along with extensive user support and training, CVAT is essential for automating and streamlining the annotation process, helping organizations and developers effectively tackle real-world challenges.

Screenity provides a robust screen recording and annotation solution, perfect for creating high-quality product demos, tutorials, and presentations. Its unlimited recording capabilities span tabs, applications, or the entire desktop, without usage limits. Key features include audio recording, versatile annotation tools, and an integrated editor for audio and video. Users can export recordings in various formats such as mp4, gif, and webm, or save them to Google Drive. Screenity ensures complete privacy, with no data collection and offline functionality, making it an indispensable tool for professionals and educators.

The repository contains versatile and customizable recipes for Prodigy, enabling scriptable annotations for text and images. A Prodigy license is required to access these scripts, which offer improvements and simplifications over the built-in recipes for easier understanding. It includes installation guidance, usage instructions, and script customization tips for tasks such as Named Entity Recognition and Image Annotation, along with community and tutorial recipes to enhance annotation workflows.

The annotation tool, employing Python and JavaScript technologies, is designed for efficiently annotating the KITTI-360 dataset, which is pivotal for urban scene understanding. It includes features like demo data integration, user-task management, and XML result parsing. The tool's structured setup and compatibility ensure ease of use, making it a valuable resource for researchers. Offered under the MIT license, it is accessible and adaptable for varied research needs.

EfficientSAM employs advanced masked image pretraining to boost segmentation performance across diverse applications. Integrated with popular tools like Labelme, and available on platforms such as Hugging Face Space in ONNX format, it showcases versatile instance segmentation through point, box, and saliency prompts. With accessible checkpoints and comprehensive model examples, EfficientSAM streamlines the implementation of segmentation tasks. Recent enhancements include torchscript and grounded demos, expanding usability and reach. Discover its full capabilities via online demos and Jupyter Notebook illustrations.