vision
RetinaNet
One-stage detector trained with focal loss, from the paper "Focal Loss for Dense Object Detection"; a standard dense detector baseline.
Levelmodel
Classvision
Parametersnot in the notes
Contextnot in the notes
Released7 Aug 2017
Opennessunknown
Runs onCPU server
Statusactive
Identity
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
- R50 and R101 FPN in Detectron2, MMDetection, TorchVision, TF Model Garden; RetinaNet-RS.
- 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, cloud marketplace
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- CPU server
- Footprint
- Edge or toolkit builds listed in: OpenVINO.
- 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
DeveloperMetabig tech, USA
Metabig tech, USAModelRetinaNetMeta, vision
RetinaNetMeta, visionUse 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
Sources
- arxiv.org/abs/1708.02002 https://arxiv.org/abs/1708.02002
- jumpstart-cache-prod-us-west-2.s3.us-west-2.amazonaws.com/models_manifest.json https://jumpstart-cache-prod-us-west-2.s3.us-west-2.amazonaws.com/models_manifest.json
- github.com/openvinotoolkit/open_model_zoo/tree/master/mo... https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/retinanet-tf
- modelzoo.co/model/pytorch-retinanet-2 https://modelzoo.co/model/pytorch-retinanet-2
- github.com/yhenon/pytorch-retinanet https://github.com/yhenon/pytorch-retinanet
- kaggle.com/models/tensorflow/retinanet-resnet https://www.kaggle.com/models/tensorflow/retinanet-resnet
- kaggle.com/models/keras/retinanet https://www.kaggle.com/models/keras/retinanet
- arxiv.org/html/1708.02002 https://arxiv.org/html/1708.02002
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "RetinaNet", https://aurion.semai.id/models/retinanet/, records of October 2026.