fireviewer/litert-models

FireViewer LiteRT Models for Android

Fire/smoke detection and pointing conversions for experimental Android workflows: 6 delivered variants, including 5 with trainable heads or output adapters. Reviewed annotations can update the exposed adaptation parameters while the visual backbones remain frozen.

Usage guide · Conversion and SDK evidence · Kotlin SDK · Checkpoint lifecycle.

Catalogue and recorded qualification

Variant	Synthetic host learning	Standalone Android SDK	Vision Dataset Studio
D-FINE M strict v1, learning	PASS	Not qualified	Not qualified
RT-DETRv2 R50, learning	PASS	Not qualified	Not qualified
YOLO11 M strict v1, inference	N/A	Not qualified	Not qualified
YOLO11 M strict v1, learning	PASS	PASS on emulator	Not qualified
RF-DETR Medium 1.10, learning	PASS	Not qualified	Not qualified
DINOv3 pointing pilot v1, learning	PASS	Not qualified	Not qualified

The retained delivery receipt covers five learning conversions. The YOLO inference-only graph is a sixth delivered variant. No SegFormer graph is delivered because the optional source was unavailable. Detection adapters cannot create proposals absent from the frozen detector.

All six variants remain unqualified inside Vision Dataset Studio. The YOLO learning SDK pass does not qualify other models, application integration or a physical ARM phone. Task accuracy, generalization and forgetting are unmeasured.

Download and integrate

Choose a variant and read its README, runtime contract, artifact manifest and source notices. Retrieve all matching files at the same immutable revision:

from huggingface_hub import hf_hub_download

contract = hf_hub_download(
    repo_id="fireviewer/litert-models",
    revision="bdcd483d5c5aadeff1b531a35b536f3530906e18",
    filename="models/fireviewer_yolo11m_strict_v1_learning/runtime_contract.json",
)

Apply the exact normalization, tensor layout, labels and coordinate transform. Check inference before learning, train only with reviewed targets, keep the original weights, and store checkpoints separately. Evaluate adapted candidates before activation. See the complete usage guide.

SDK and runtime contract

The Kotlin SDK exposes infer, train, save and restore. Checkpoints preserve parameters, SGD momentum and step counters; receipts check model/label identity and file integrity. The integrating application selects the active checkpoint.

YOLO supports image dimensions that are multiples of 32. Other graphs resize to their internal grid. The historical SDK uses TensorFlow Lite and Select TF Ops 2.16.1. Vision Dataset Studio's separate 16 KB Flex fix does not retroactively qualify this SDK. Conversion sources.

Rights and responsible use

Source weights, datasets and software retain their own licences and restrictions. Read each variant's notices; download access alone grants no commercial-use or redistribution permission. In particular, YOLO upstream obligations and the DINOv3 pilot's retained release policy remain applicable.

Synthetic checks establish parameter updates and persistence, not field accuracy or reliable alerts. These models must not be used alone to confirm a fire, trigger an emergency alert, order an evacuation or direct emergency response.

Documentation and revision

Card updated in English on 5 October 2026, from the public repository at bdcd483d5c5aadeff1b531a35b536f3530906e18 and its retained reports. This update checks documentation, repository metadata and small evidence files; it does not rerun training, inference, dataset payload verification or device qualification. Historical receipts keep their original dates, revisions and scope. Earlier README hashes in artifact manifests refer to those earlier releases.

The association FIRE-VIEWER provides administrative and financial stewardship for resources under its control. Technical governance is maintainer-led. Upstream, source-specific and pre-association rights remain separate; repository placement does not transfer ownership or grant additional rights.

Project documentation · Institutional website. Contact: contact@fire-viewer.fr.