dataset-annotation

Annotate datasets with bounding boxes, SAM2 segmentation, and DINOv3 visual grounding.

3.0k|463|Updated Mar 5, 2019

Search Tags:#machine learning #computer vision #dataset #annotation #yolo #coco

Ready to run

npx skills add https://github.com/SharpAI/DeepCamera --skill dataset-annotation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dataset-annotation
Source: https://github.com/SharpAI/DeepCamera/tree/main/skills/annotation/dataset-annotation
Command: npx skills add https://github.com/SharpAI/DeepCamera --skill dataset-annotation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, opencv-python-headless, Pillow, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the creation of custom datasets for training AI models by providing AI-assisted annotation tools and exporting in standard formats.

Core Features & Use Cases

AI-assisted annotation: Leverage methods like BBox, SAM2, and DINOv3 for efficient object and segmentation annotation.
Multiple export formats: Export annotations in COCO, YOLO, or VOC formats for compatibility with various training frameworks.
Use Case: Train a custom object detection model for a specific type of industrial equipment by annotating images of that equipment using this Skill, then exporting the dataset in COCO format for training a YOLO model.

Quick Start

Use the dataset-annotation skill to annotate frames using the dinov3 method and export the dataset in coco format.

Frequently Asked Questions about dataset-annotation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate image dataset annotation for COCO and YOLO formats?▼

Automate image dataset annotation using AI-assisted methods like BBox, SAM2, and DINOv3. This Skill processes images, generates bounding boxes or segmentation masks, and exports the final annotations directly into COCO, YOLO, or VOC formats for model training.

Can I use SAM2 segmentation to automatically label images for object detection?▼

Yes, you can use SAM2 segmentation to automatically label images. The Skill integrates SAM2 alongside DINOv3 visual grounding and bounding box methods to facilitate efficient object detection and segmentation annotation workflows for custom datasets.

What is the best way to export custom computer vision datasets for training?▼

The best way to export custom computer vision datasets is by using AI-assisted annotation tools that support standard formats. This Skill enables exporting annotated images directly into COCO, YOLO, or VOC formats, ensuring compatibility with various training frameworks.

Does this dataset annotation tool support DINOv3 visual grounding?▼

Yes, this dataset annotation tool supports DINOv3 visual grounding. It leverages DINOv3 alongside BBox and SAM2 methods to provide AI-assisted annotation, allowing automated detection followed by human correction workflows.

How do I annotate industrial equipment images for a custom YOLO model?▼

Annotate industrial equipment images by using AI-assisted tools to generate bounding boxes or segmentation masks. You can then export the annotated dataset in COCO format, which is compatible for training custom YOLO object detection models.

When do I need AI-assisted annotation for computer vision tasks?▼

You need AI-assisted annotation for computer vision tasks when streamlining the creation of custom datasets for training AI models. It accelerates labeling using methods like SAM2 and DINOv3, reducing manual effort before exporting to COCO, YOLO, or VOC formats.