anylearning-labeling-models

AnyLearning labeling models

Versioned ONNX model bundles used by AnyLearning for local object detection, instance segmentation, and prompt-guided image segmentation.

This repository contains the seventeen models currently offered by AnyLearning:

Each promptable-segmentation ZIP contains a small AnyLearning model configuration and one encoder plus one decoder ONNX model. Each RF-DETR and D-FINE ZIP contains one static ONNX graph plus its provenance, checksums, and verbatim upstream license. MANIFEST.json records the exact source revision, archive size, checksum, and expected members. Every transformed or exported bundle pins each extracted member's size and SHA-256 as well as its source artifact identity.

Provenance

The source graph pairs originate from checksum-pinned ONNX exports published by Viet-Anh Nguyen:

• SAM 2 bundles: source revision 071f58077599431edd0e5d2ac52ecca4c78f1cab from vietanhdev/segment-anything-2-onnx-models.
• SAM 2.1 bundles: source revision 6a3ac868340a3196a349050a6efae22a5acc0330 from vietanhdev/segment-anything-2.1-onnx-models.
• MobileSAM bundle: source revision 9effc01a9e135621d710d49159f1ffb0b6f724dc from vietanhdev/segment-anything-onnx-models.
• EfficientViT-SAM encoders and source decoders: immutable model revision a2f0c5929196a13ef1ffc9338d3f9e482e1e0e68 from mit-han-lab/efficientvit-sam. The published decoders were transformed by AnyLearning's checksum-gated ONNX-only tool to expose the four native mask tokens before the official graph's lossy single-mask selection. The transform does not load a native checkpoint or alter the learned tensors.
• RF-DETR Nano detection and instance segmentation: official checkpoints and exporter from rfdetr==1.9.4, source revision 9b009fa928d6218320439803d1da01869a85c072. Both graphs use static batch-one float32 contracts at opset 17. Native/export parity evidence is recorded inside each archive.
• D-FINE-N detection: the official COCO-only checkpoint and exporter at source revision 956d1709314c2c6a4df6f34de232054578a7449f. The checkpoint was loaded only by the included restricted weights-only conversion helper. The static opset-16 graph has exact native label parity and less than 0.005-pixel box drift on the retained landscape/portrait corpus. Objects365-derived weights are excluded.

The SAM 2 and SAM 2.1 encoders were transformed by AnyLearning's checksum-gated ONNX-only tool. It repairs exact stale shape metadata, removes only unreachable initializers, requires strict ONNX type/shape inference, and records the source digest. It does not load a native checkpoint or change reachable learned tensors. The decoders are unchanged mirrors.

Original model projects:

• Segment Anything 2
• Segment Anything
• MobileSAM
• EfficientViT-SAM
• RF-DETR
• D-FINE

The SAM and MobileSAM files are unchanged mirrors. EfficientViT-SAM encoders are unchanged mirrors; their decoders preserve the original learned tensors and compute graph while exposing the four pre-selection outputs and recording transform provenance in ONNX metadata. SAM 2 and SAM 2.1 prepared encoders are paired with unchanged source decoders in deterministic archives.

Secure and reproducible download

Pin a repository revision and verify the SHA-256 value from MANIFEST.json before extracting or loading a model. Consumers should reject absolute paths, parent traversal, links, encrypted or duplicate entries, unexpected archive members, and files whose exact sizes or digests differ from the manifest.

AnyLearning performs the full image encoding, prompt conversion, mask decoding, and editable-shape conversion. These archives are not standalone applications.

Intended use and limitations

• Intended for interactive point/rectangle-prompt segmentation in AnyLearning.
• RF-DETR bundles are intended for human-reviewed COCO object detection and instance-segmentation suggestions.
• The D-FINE bundle is intended for human-reviewed COCO object-detection suggestions.
• Results require human review before becoming dataset labels.
• Quality and latency vary with image content, hardware, execution provider, and model size.
• These models can reproduce biases and limitations of their original training data.
• Do not use segmentation output as the sole basis for safety-critical, medical, legal, or similarly consequential decisions.

License

The model code and weights are distributed under Apache License 2.0 by their respective upstream projects. See LICENSES.md for source and attribution links.

Citation

For SAM 2 and SAM 2.1, cite:

@article{ravi2024sam2,
  title={SAM 2: Segment Anything in Images and Videos},
  author={Ravi, Nikhila and others},
  journal={arXiv:2408.00714},
  year={2024}
}

For SAM, cite:

@article{kirillov2023segment,
  title={Segment Anything},
  author={Kirillov, Alexander and others},
  journal={arXiv:2304.02643},
  year={2023}
}

For EfficientViT-SAM, cite:

@inproceedings{cai2023efficientvit,
  title={EfficientViT: Lightweight Multi-Scale Attention for High-Resolution Dense Prediction},
  author={Cai, Han and Li, Junyan and Hu, Muyan and Gan, Chuang and Han, Song},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2023}
}

@article{zhang2024efficientvit,
  title={EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss},
  author={Zhang, Zhuoyang and Cai, Han and Han, Song},
  journal={arXiv preprint arXiv:2402.05008},
  year={2024}
}

For RF-DETR, use the citation requested by the official project.

For D-FINE, use the citation requested by the official project.