Data Labeling Studio
Metadata
- Name: data-labeling-studio
- Display Name: Data Labeling Studio | 数据标注工作室
- Description:
- EN: Intelligent data labeling and annotation toolkit supporting image, text, audio, and video with active learning and quality control. - ZH: 智能数据标注和注释工具包，支持图像、文本、音频和视频，包含主动学习和质量控制。
- Version: 1.0.0
- Author: Kimi Claw
- Tags: data-labeling, annotation, image-annotation, text-annotation, active-learning, quality-control, dataset, ml-training
- Category: Data Processing
- Icon: 🏷️
Capabilities
Actions
image_annotate
Perform image annotation
- image_dir: Image directory path (string, required)
- annotation_type: Type of annotation (string, required) - bounding_box, polygon, keypoint, segmentation
- labels: Label categories (array, required)
- output_format: Output format (string) - coco, pascal_voc, yolo
- active_learning: Enable active learning suggestions (boolean, default: true)
text_annotate
Perform text annotation
- text_data: Text data source (string/object, required)
- annotation_task: Task type (string, required) - classification, ner, sentiment, summarization
- labels: Label categories (array, required)
- output_format: Output format (string) - json, csv, spacy
audio_annotate
Perform audio annotation
- audio_dir: Audio directory path (string, required)
- annotation_type: Type (string, required) - transcription, speaker_id, emotion, event
- segment_duration: Segment duration in seconds (float, default: 5.0)
video_annotate
Perform video annotation
- video_path: Video file path (string, required)
- annotation_type: Type (string, required) - object_tracking, action_recognition, scene_detection
- frame_sample_rate: Frame sampling rate (int, default: 1)
quality_check
Check annotation quality and consistency
- annotations: Annotation file path (string, required)
- ground_truth: Ground truth file path (string, optional)
- metrics: Quality metrics (array) - iou, accuracy, consistency, coverage
dataset_export
Export labeled dataset to ML format
- annotations: Annotation source (string, required)
- format: Target format (string, required) - coco, yolo, tfrecord, huggingface
- output_dir: Output directory (string, required)
- split_ratios: Train/val/test split (object) - {train: 0.8, val: 0.1, test: 0.1}
Requirements
- Python 3.8+
- Pillow >= 10.0.0 (for image processing)
- OpenCV >= 4.8.0 (for image/video annotation)
- NumPy >= 1.24.0
- Pandas >= 2.0.0
- LabelImg >= 1.8.0 (optional)
- Librosa >= 0.10.0 (for audio processing)
- scikit-learn >= 1.3.0 (for active learning)
Examples
Image Annotation
from labeling_studio import ImageAnnotator
# Initialize annotator
annotator = ImageAnnotator(
annotation_type="bounding_box",
labels=["person", "car", "dog", "cat"],
output_format="coco"
)
# Annotate images with active learning
annotator.annotate(
image_dir="./images",
output_file="./annotations/coco.json",
active_learning=True # AI suggests uncertain samples
)
# Export to YOLO format
annotator.export("./annotations", format="yolo")Text Annotation
from labeling_studio import TextAnnotator
# NER annotation
annotator = TextAnnotator(
annotation_task="ner",
labels=["PERSON", "ORG", "LOC", "DATE"]
)
# Annotate from file
annotations = annotator.annotate(
text_data="./data/corpus.txt",
output_file="./annotations/ner.json"
)Quality Check
from labeling_studio import QualityChecker
# Check annotation quality
checker = QualityChecker()
report = checker.check(
annotations="./annotations/coco.json",
ground_truth="./annotations/ground_truth.json",
metrics=["iou", "consistency", "coverage"]
)
print(f"Average IoU: {report['iou']:.2f}")
print(f"Consistency Score: {report['consistency']:.2f}")
print(f"Coverage: {report['coverage']:.2f}")Scripts
scripts/annotate_images.py: 图像标注工具scripts/annotate_text.py: 文本标注工具scripts/annotate_audio.py: 音频标注工具scripts/annotate_video.py: 视频标注工具scripts/quality_check.py: 质量检查工具scripts/export_dataset.py: 数据集导出工具
Installation
pip install -r requirements.txtUsage
# Image annotation with active learning
python scripts/annotate_images.py --input ./images --type bbox --labels person,car --format coco
# Text NER annotation
python scripts/annotate_text.py --input ./texts.txt --task ner --labels PERSON,ORG,LOC
# Quality check
python scripts/quality_check.py --annotations ./coco.json --ground-truth ./gt.json
# Export to YOLO
python scripts/export_dataset.py --input ./coco.json --format yolo --output ./yolo_datasetLicense
MIT License