vision
YOLO-Pro
Edge Impulse YOLO detector family sized from pico to xlarge for microcontrollers, NPUs and edge GPUs.
Levelmodel
Classvision
Parametersnot in the notes
Contextnot in the notes
Releasednot in the notes
Opennessunknown
Runs onnot in the notes
Statusnot in the notes
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
- CNN
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Note
- Pico to xlarge.
- Context length
- not in the notes
- Input
- image
- Output
- labels and boxes
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
- Edge or toolkit builds listed in: Edge Impulse.
- 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, USAModelYOLO-ProEdge Impulse, vision
YOLO-ProEdge Impulse, visionR&D TopicsReal-Time Object Detection (YOLO and DETR Lines)Efficient Vision Networks for Mobile and Edge Devices
Use CasesSafety (PPE, Hazards, Fatigue)IndustrialVisual Quality Inspection and Defect DetectionIndustrialUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Bukit Asam TbkIn production, 9 Sep 2026
PT Aneka Tambang Tbk (ANTAM), ferronickelIn production, 9 Sep 2026PLN Indonesia Power with Huawei (PLTU Banten 3 Lontar)Research, 26 Nov 2025
PT Kilang Pertamina Internasional (Pertamina refineries)In production, 17 Oct 2025+5 moreDeployments of these use cases; none names this model.
PT Bukit Asam TbkIn production, 9 Sep 2026PT Aneka Tambang Tbk (ANTAM), ferronickelIn production, 9 Sep 2026PLN Indonesia Power with Huawei (PLTU Banten 3 Lontar)Research, 26 Nov 2025PT Kilang Pertamina Internasional (Pertamina refineries)In production, 17 Oct 2025+5 moreDeployments of these use cases; none names this model.Organisations 1
R&D topics 2
Real-Time Object Detection (YOLO and DETR Lines)Detectors that find and box objects in images fast enough for live camera feeds on one GP…produced byEfficient Vision Networks for Mobile and Edge DevicesSmall, fast vision networks, often found by architecture search, that run on phones, came…produced by
Sources
- docs.edgeimpulse.com/studio/projects/learning-blocks/blocks/object... https://docs.edgeimpulse.com/studio/projects/learning-blocks/blocks/object-detection/yolo-pro
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "YOLO-Pro", https://aurion.semai.id/models/edge-impulse-yolo-pro/, records of October 2026.