samexporter-onnx-models

SAMExporter validated ONNX models

Real, structurally checked ONNX artifacts for samexporter. These files are published by NRL.ai so the AnyLearning and SAMExporter documentation can use stable, revisioned model links.

Available artifacts

Each directory contains its applicable upstream license. This repository uses license: other because SAM3 is distributed under Meta's SAM License while the other five families are Apache-2.0. Use and redistribution remain subject to the license in each directory. No source checkpoints or training datasets are included.

Download

Download only the family you need:

hf download nrl-ai/samexporter-onnx-models \
  --include "mobile_sam/*" \
  --local-dir output_models

Or download every family:

hf download nrl-ai/samexporter-onnx-models --local-dir output_models

SAM3 uses ONNX external data: keep sam3_decoder.onnx and sam3_decoder.onnx.data together in the same directory.

Validation

• Every ONNX graph passes onnx.checker.check_model(path) with external data resolved from its published directory.
• SAM ViT-B, MobileSAM, SAM 2.1 Tiny, and SAM3 were exported from real official checkpoints; EfficientSAM-Ti and EfficientSAM-S are the official upstream split ONNX pairs.
• Point and rectangle results were run with ONNX Runtime CPU on the landscape truck and portrait plant images in the SAMExporter repository.
• Full-resolution overlays and sibling resource logs are retained in visual_results/.

Exact file digests are listed in CHECKSUMS.sha256. The export provenance is listed in PROVENANCE.md.

Runtime

pip install "samexporter[runtime-cpu]"

python -m samexporter.inference \
  --sam_variant sam \
  --encoder_model output_models/mobile_sam/mobile_sam.encoder.onnx \
  --decoder_model output_models/mobile_sam/mobile_sam.decoder.onnx \
  --image image.jpg \
  --prompt prompt.json \
  --output result.png

See the samexporter documentation for family-specific conversion, provider, prompt, and performance guidance.