B{Training Trigger?} B – no –> C[Skip Training] B – yes –> D[Fetch Labeled Tasks] D –> E[Process Annotations] E –> F{Ground Truth?} F – yes –> G[Priority Processing] F – no –> H[Standard Processing] G –> I[Generate Samples] H –> I I –> J[Background + Event Labels] J –> K[PyTorch LSTM Training] K –> L[Model Validation] L –> M[Save Model] M –> N[Cache in Memory]

### Prediction Pipeline

```mermaid
flowchart TD
  T[Prediction Request] --> U[Load PyTorch Model]
  U --> V[Read Task CSV]
  V --> W[Extract Features]
  W --> X[Sliding Window LSTM]
  X --> Y[Overlap Averaging]
  Y --> Z[Filter Background]
  Z --> AA[Group Event Segments]
  AA --> BB[Calculate Confidence]
  BB --> CC[Return Segments]

Key Technical Features

• PyTorch-based LSTM: Modern deep learning framework with better performance and flexibility
• Temporal Modeling: Sliding windows capture time dependencies (default 50 timesteps)
• Background Class: Realistic modeling where unlabeled periods are explicit background
• Balanced Learning: Class-weighted loss function and balanced metrics for imbalanced data
• Instant Label Support: Proper handling of point events (instant=true) vs. duration events (instant=false)
• Smart Early Stopping: Dual criteria (balanced accuracy + minimum per-class F1) prevent premature stopping
• Ground Truth Priority: Ensures highest quality annotations are used for training
• Overlap Averaging: Smoother predictions through overlapping window consensus
• Project-Specific Models: Each Label Studio project gets its own trained model for proper multi-tenant isolation

Customize

Edit docker-compose.yml to set environment variables for your specific use case:

Basic Configuration

environment:
  - LABEL_STUDIO_HOST=http://localhost:8080
  - LABEL_STUDIO_API_KEY=your_api_key_here
  - MODEL_DIR=/app/models
  - START_TRAINING_EACH_N_UPDATES=1
  - TRAIN_EPOCHS=1000
  - SEQUENCE_SIZE=50
  - HIDDEN_SIZE=64

Balanced Learning (Recommended for Instant Labels)

environment:
  # ... basic config above ...
  - BALANCED_ACCURACY_THRESHOLD=0.85
  - MIN_CLASS_F1_THRESHOLD=0.70  
  - USE_CLASS_WEIGHTS=true

Common Scenarios

For instant labels (point events):

• Keep balanced learning enabled (USE_CLASS_WEIGHTS=true)
• Consider lower thresholds (MIN_CLASS_F1_THRESHOLD=0.60) for very rare events
• Increase epochs (TRAIN_EPOCHS=2000) for better minority class learning

For range annotations with balanced data:

• Can disable class weights (USE_CLASS_WEIGHTS=false) if classes are roughly equal
• Use standard accuracy thresholds

For short time series:

• Reduce sequence size (SEQUENCE_SIZE=20) for sequences shorter than 50 timesteps
• Reduce hidden size (HIDDEN_SIZE=32) to prevent overfitting