TinyML
FOMO-AD (Visual Anomaly Detection)
Edge Impulse visual anomaly detection block, previously named FOMO-AD, that flags image regions that do not match normal training images. Meant for defect detection when few defect samples exist.
Levelmodel
ClassTinyML
Parametersnot in the notes
Contextnot in the notes
Releasednot in the notes
Opennessunknown
Runs onnot in the notes
Statusactive
R&D Topics
Identity
- Developer
- Edge Impulse
- Family
- not in the notes
- Released
- – sources
- Weights published
- –
- Record checked
- 12 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- other Selectable backbone with PatchCore or Gaussian Mixture Model anomaly scoring
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Context length
- not in the notes
- Input
- image
- Output
- not in the notes
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- unknown
- Licence
- not in the notes sources
- Access routes
- not in the notes
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- The page says GMM scoring is the default for a low-power device and PatchCore for a high-power device.
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Benchmark Results
The notes record no benchmark result for this model.
Lineage
The notes record no lineage for this model.
Industry Use Cases
No industry use case links this model.
Links
DeveloperEdge Impulsecompany, USA
Edge Impulsecompany, USAModelFOMO-AD (Visual Anomaly Detection)Edge Impulse, TinyML
FOMO-AD (Visual Anomaly Detection)Edge Impulse, TinyMLR&D TopicsIndustrial Visual Anomaly and Defect DetectionAnomaly Detection and Predictive Maintenance on Sensor DataTinyML on Microcontrollers
Use CasesVisual Quality Inspection and Defect DetectionIndustrialAnomaly Detection on Sensor, SCADA and Historian DataIndustrialSafety (PPE, Hazards, Fatigue)IndustrialPharma Manufacturing and GMPHealthcare+1 moreUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Aneka Tambang Tbk (ANTAM), ferronickelIn production, 9 Sep 2026
PT Bukit Asam TbkIn production, 9 Sep 2026
PT Kalbe Farma TbkProof of concept, 20 Jul 2026PLN Indonesia Power with Huawei (PLTU Banten 3 Lontar)Research, 26 Nov 2025+19 moreDeployments of these use cases; none names this model.
PT Aneka Tambang Tbk (ANTAM), ferronickelIn production, 9 Sep 2026PT Bukit Asam TbkIn production, 9 Sep 2026PT Kalbe Farma TbkProof of concept, 20 Jul 2026PLN Indonesia Power with Huawei (PLTU Banten 3 Lontar)Research, 26 Nov 2025+19 moreDeployments of these use cases; none names this model.Organisations 1
R&D topics 3
TinyML on MicrocontrollersDesigning and compressing neural networks and inference engines so that vision, audio and…produced byIndustrial Visual Anomaly and Defect DetectionFinding defects on products and equipment by learning what normal looks like, often with…produced byAnomaly Detection and Predictive Maintenance on Sensor DataDetecting abnormal behaviour in machine and process sensor time series to warn of faults…produced by
Sources
- docs.edgeimpulse.com/studio/projects/learning-blocks/blocks/visual... https://docs.edgeimpulse.com/studio/projects/learning-blocks/blocks/visual-anomaly-detection-fomo-ad
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "FOMO-AD (Visual Anomaly Detection)", https://aurion.semai.id/models/edge-impulse-fomo-ad/, records of October 2026.