U-Net · ResNet34 · MARIDA
OverlaysEvery overlay, uncertainty map and metric on this page is the real trained model's output, precomputed offline on the MARIDA held-out patches and bundled — no runtime model download.
Keyboard-fast triage of model detections vs MARIDA ground truth
Region-level, scored against MARIDA ground truth across the detections you've reviewed on 0 patches. Updates live.
Corrections accumulate into a retraining set. Low-confidence detections (< op threshold) route here — never auto-accepted.
Drag the cutoff — P/R/F1/IoU recompute live on bundled GT + probs
Computed over — labeled pixels across the — demo patches. Moving the slider also re-thresholds the map overlay in the center. The dot marks the current cutoff on the real PR curve.
Cleanlab confident-learning · likely-wrong labels, worst first
MC-Dropout epistemic uncertainty · 20 stochastic passes
Dropout is kept active at inference and the model is run 20 times per patch. Where those passes disagree, the per-pixel standard deviation is high — the model is unsure there. High-uncertainty regions are exactly what the active-learning queue prioritises.
Confidence calibration: routing on MC-Dropout std is more trustworthy than raw softmax, which is typically over-confident on rare classes like debris.
Next-to-label queue ranked by MC-Dropout uncertainty + entropy
The ranking above is a real uncertainty computation. The retrain step needs a live GPU backend, so its projected F1 lift is a scripted illustration — no training happens in your browser.
Encoder-feature embedding · click a point to load that patch
Deepest ResNet34 encoder features per patch, reduced to 2D (—). Nearby points look alike to the model; outliers & high-uncertainty (amber-ringed) points are the most valuable to label or double-check.
Consensus labelling · inter-annotator agreement (Cohen's κ)
Three simulated annotators (a marine-ecology expert, a trained analyst, and a crowd worker) label this patch's detections. Agreement is computed live; disagreements route to adjudication.
Throughput, turnaround & auto-calibrated threshold
Active-learning routing means only low-confidence detections need a human — the rest auto-accept. Cost figures assume a $0.12/region annotation rate.
Where this graduates from a browser demo to an operational pipeline
Every box is a named open-source or managed component. The browser demo you're using exercises the Model, Uncertainty, Label QA and Curation stages on real bundled data; the ingest, orchestration and live-retrain lanes are what a full deployment adds.
Data: MARIDA (Kikaki et al. 2022, PLoS ONE) · Sentinel-2 L2A · CC BY 4.0. Model, uncertainty, label-QA & embeddings computed by GIA.
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