Train Ultralytics YOLO models on your 3LC tables with automatic task detection,
per-sample loss tracking, and embedding collection for data debugging and fixing.
Auto-detect
Task type detected from your table schema automatically
Per-sample loss
Track individual sample losses to find mislabeled or hard examples
Embeddings
Collect embeddings for visual clustering in the Dashboard
Collect mode
Run inference-only to collect metrics without training
1 Select tables
2 Configure model
3 Train
Config
1 Input Tables
Select your training data table. The task type (detection, segmentation, classification) is auto-detected from the table schema.
Optional validation set for tracking metrics during training. If empty, validation is skipped (val=False).
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2 Ultralytics YOLO Settings
Choose a YOLO model size. Larger models are more accurate but slower to train. Nano (n) is great for testing.
Train a YOLO model with 3LC metrics collection.
Number of dataloader worker threads. 0 = main thread only. Higher values speed up data loading on multi-core machines.