vision
EfficientDet-Lite
Mobile versions of EfficientDet for TensorFlow Lite, MediaPipe and edge NPUs.
Levelvariant
Classvision
Parametersnot in the notes
Contextnot in the notes
Released20 Nov 2019
Opennessunknown
Runs onmobile or NPU
Statusactive
R&D Topics
Identity
- Developer
- Family
- EfficientDet
- Released
- 20 Nov 2019 sourcesannouncement page
- 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
- Lite0 to Lite4.
- 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
- open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- mobile or NPU
- Footprint
- Edge or toolkit builds listed in: NXP eIQ model zoo, MediaPipe.
- 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
DeveloperGooglebig tech, USA
Googlebig tech, USAModelEfficientDet-LiteGoogle, vision
EfficientDet-LiteGoogle, visionR&D TopicsReal-Time Object Detection (YOLO and DETR Lines)Efficient Vision Networks for Mobile and Edge DevicesQuantization and Edge Inference
Use CasesSafety (PPE, Hazards, Fatigue)IndustrialVisual Quality Inspection and Defect DetectionIndustrialIndonesian and Regional-Language LLMsPublic SectorIndustrial Copilots and Maintenance AssistantsIndustrialUse 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 2026
PT Pertamina Geothermal EnergyIn production, 14 Jul 2026
Siemens Erlangen electronics factoryPlanned, 6 Jan 2026+19 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 2026PT Pertamina Geothermal EnergyIn production, 14 Jul 2026Siemens Erlangen electronics factoryPlanned, 6 Jan 2026+19 moreDeployments of these use cases; none names this model.Models 1
Organisations 1
R&D topics 3
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 byQuantization and Edge InferenceLower-bit weights and on-device serving.produced by
Sources
- arxiv.org/abs/1911.09070 https://arxiv.org/abs/1911.09070
- github.com/NXP/eiq-model-zoo/tree/main/tasks/vision/obje... https://github.com/NXP/eiq-model-zoo/tree/main/tasks/vision/object-detection
- ai.google.dev/edge/mediapipe/solutions/vision/object_detector https://ai.google.dev/edge/mediapipe/solutions/vision/object_detector
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "EfficientDet-Lite", https://aurion.semai.id/models/google-efficientdet-lite/, records of October 2026.