Label Training Data
See Extended Examples for complete configuration files and templates.
Systematically label data for supervised ML with quality controls and efficient workflows.
When to Use
- Starting supervised ML project that requires labeled training data
- Current model performance limited by insufficient labeled examples
- Need to label text, images, audio, or video data
- Want to measure and improve annotation quality
- Managing team of annotators with different expertise levels
- Implementing active learning to prioritize valuable examples
- Need to track labeling progress and costs
- Ensuring consistent labels across multiple annotators
Inputs
- Required: Unlabeled dataset (images, text, audio, video)
- Required: Label schema (classes, attributes, or annotation types)
- Required: Labeling guidelines document
- Optional: Pre-existing labels (for quality comparison)
- Optional: Model predictions for pre-annotation
- Optional: Budget and timeline constraints
- Optional: Domain expert availability for difficult examples
Procedure
Step 1: Install and Configure Label Studio
Set up Label Studio as the labeling platform.
# Install Label Studio
pip install label-studio
# Or use Docker for production
docker pull heartexlabs/label-studio:latest
# Create project directory
mkdir -p labeling-project/{data,exports,config}
cd labeling-project
# Initialize Label Studio
label-studio init my_project
# Start Label Studio server
label-studio start my_project --port 8080
Access at http://localhost:8080 (default credentials: create on first visit).
For production deployment with Docker:
# docker-compose.yml
version: '3.8'
services:
label-studio:
image: heartexlabs/label-studio:latest
ports:
- "8080:8080"
# ... (see EXAMPLES.md for complete implementation)
docker-compose up -d
Expected: Label Studio running and accessible, PostgreSQL database initialized for production use.
On failure: If port 8080 already in use, change port in config, if Docker fails check Docker daemon is running, ensure sufficient disk space for data volumes, check firewall allows port 8080.
Step 2: Design Labeling Interface and Schema
Create labeling configuration for your task type.
# labeling-project/config/labeling_config.py
"""
Label Studio configuration templates for common tasks.
"""
# Text Classification (single label)
TEXT_CLASSIFICATION = """
<View>
# ... (see EXAMPLES.md for complete implementation)
Expected: Labeling interface configured with appropriate controls for task type, data imported successfully, interface accessible to annotators.
On failure: Validate XML config with Label Studio's config validator, check data file format (JSON or CSV), ensure image/audio URLs are accessible if using external storage, verify API key has correct permissions.
Step 3: Prepare Data and Implement Sampling Strategy
Format data for import and prioritize examples for labeling.
# labeling-project/prepare_data.py
import pandas as pd
import json
import random
from typing import List, Dict
from sklearn.cluster import KMeans
import numpy as np
# ... (see EXAMPLES.md for complete implementation)
Expected: Data formatted correctly for Label Studio import, sampling strategy prioritizes informative examples, tasks include metadata for tracking.
On failure: Verify JSON format with jq or Python json.load(), check that URLs are accessible if using remote images, ensure no special characters break JSON encoding, validate column names match config.
Step 4: Implement Quality Control and IAA Measurement
Set up processes to measure and improve annotation quality.
# labeling-project/quality_control.py
import pandas as pd
import numpy as np
from sklearn.metrics import cohen_kappa_score, confusion_matrix
from typing import Dict, List, Tuple
import logging
logging.basicConfig(level=logging.INFO)
# ... (see EXAMPLES.md for complete implementation)
Expected: Inter-annotator agreement measured (Cohen's Kappa > 0.6 is moderate, >0.8 is good), difficult tasks identified for review, annotator performance tracked.
On failure: If Kappa very low (<0.4), review labeling guidelines for clarity, retrain annotators, simplify label schema, check for ambiguous examples, consider using expert annotators for gold standard.
Step 5: Export and Integrate Labeled Data
Export labels and prepare for ML training.
# labeling-project/export_labels.py
import requests
import pandas as pd
import json
from typing import List, Dict
import logging
logger = logging.getLogger(__name__)
# ... (see EXAMPLES.md for complete implementation)
Expected: Annotations exported in training-ready format, label distribution balanced or documented, data quality validated before training.
On failure: Verify API key permissions, check export format compatibility with your ML framework, handle missing annotations gracefully, validate JSON structure matches expected format.
Step 6: Set Up Continuous Labeling Pipeline
Automate labeling workflow with active learning integration.
# labeling-project/active_learning_pipeline.py
import schedule
import time
import logging
from datetime import datetime
from prepare_data import DataSampler, prepare_label_studio_format
from export_labels import LabelStudioExporter, convert_to_training_format
import pandas as pd
# ... (see EXAMPLES.md for complete implementation)
Expected: Active learning selects informative examples automatically, labeling batches prepared weekly, model retrained when sufficient new labels available.
On failure: If uncertainty sampling doesn't improve model, try diversity sampling, if annotators can't keep up reduce batch size, monitor labeling queue length, implement backpressure if queue grows too large.
Validation
Common Pitfalls
- Unclear guidelines: Ambiguous instructions cause inconsistent labels; invest in detailed guidelines with examples
- Insufficient overlap: Can't measure IAA without multiple annotators per task; use 10-20% overlap
- Ignoring difficult cases: Edge cases often skipped but critical for model robustness; flag for expert review
- Batch effects: Annotator fatigue or learning causes temporal inconsistency; randomize task order
- No quality feedback: Annotators don't improve without feedback; provide regular accuracy reports
- Wrong sampling strategy: Random sampling wastes budget on easy examples; use uncertainty or diversity sampling
- Labeling in isolation: Domain experts needed for complex tasks; pair novices with experts initially
- Not tracking costs: Labeling expensive; monitor time per task and total budget consumption
Related Skills
version-ml-data - Version control for labeled datasetstrack-ml-experiments - Track model performance as labels added