nrl-ai/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:

File	Model	Download size	SHA-256
mobile_sam_20230629.zip	MobileSAM	36,655,105 bytes	41aff2660b7531becfee21fb257c49933ddc892c554507bdb775bf504d443942
efficientvit_sam_l0.zip	EfficientViT-SAM-L0	129,322,579 bytes	ef48c8f4c6d72da3df0690d9f16aa794035978d23a637acbd24a984220020072
efficientvit_sam_l1.zip	EfficientViT-SAM-L1	177,189,481 bytes	e7f6a5a38a00b53c6706ea45e70c5601b2140c57729ad6412c0600fc719634a2
efficientvit_sam_l2.zip	EfficientViT-SAM-L2	228,296,139 bytes	be208e948e445b2d3743d17c2cab83564a403b698e90c9cbdfb1ea6273ab823e
efficientvit_sam_xl0.zip	EfficientViT-SAM-XL0	434,729,250 bytes	98ccd8ca3a4ff339cdaa29f6f8ff6dad259516f38e7fb093c8b5cbd575d604ea
efficientvit_sam_xl1.zip	EfficientViT-SAM-XL1	756,188,273 bytes	316c92d1900b09e102cfbb3585f8da25b6df20c6c6fe0f0ef6552ec3fc0aab6d
sam2_hiera_tiny.zip	SAM 2 Hiera-Tiny	143,293,170 bytes	f96e764cf19ba63e7870f8d588cfbfe772cda40a13180f19e73100e28f8c92da
sam2_hiera_small.zip	SAM 2 Hiera-Small	169,638,840 bytes	90aa65e8b9c98cca73916c0b579d0b63139ef60e8b508946c389204819195261
sam2_hiera_base_plus.zip	SAM 2 Hiera-Base+	302,582,623 bytes	c30ce8030d9be4ee68afb825285d0d50b706571a671847ba0d7cb36e7aab75c3
sam2_hiera_large.zip	SAM 2 Hiera-Large	843,636,551 bytes	97d5841ca0827acb3ae54ed896adedb23cc77a4fa23118402efca3e169d20542
sam2_1_hiera_tiny.zip	SAM 2.1 Hiera-Tiny	116,527,977 bytes	b15c3d3265392901f6dbe45a42e2f64709bf265b15fde899b2439bc04496ba04
sam2_1_hiera_small.zip	SAM 2.1 Hiera-Small	142,921,559 bytes	5802f9e3b05d41b9a5ce03f32d3935fe6cc94294b77956f37c58b7f2581471ce
sam2_1_hiera_base_plus.zip	SAM 2.1 Hiera-Base+	272,032,266 bytes	389aae8f1e552275326e13a10973e822583989dba95aa7c3ffc80022460ac7eb
sam2_1_hiera_large.zip	SAM 2.1 Hiera-Large	805,293,551 bytes	15f74c68530b0bc9f37d2189394a100b81541f55362b34f55a309ba12b9e1fa4
rfdetr_nano_detection_1_9_4.zip	RF-DETR Nano detection	99,769,055 bytes	5b130a1c2eb01be3bfbda703367b5d993821690de26f4abbb14a94ee3660c5fe
rfdetr_nano_segmentation_1_9_4.zip	RF-DETR Segmentation Nano	113,602,026 bytes	133fdb5aed76233a6959addbdb7d64f5132f3cf2b2705a1994c2cbdfc2e93f4d
dfine_n_coco_956d170.zip	D-FINE-N COCO detection	13,979,719 bytes	f753f6e552632ef1696ec53c92ccb1ca374dbf2f014c6e4ab005ab98efa3a6e8

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.