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LICENSES.md

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+# Model licenses and attribution
+
+All five bundles are distributed under the Apache License 2.0 terms of their
+respective upstream projects. The archives are byte-for-byte mirrors of the
+pinned source revisions recorded in `MANIFEST.json`.
+
+| Model | Upstream project | License |
+| --- | --- | --- |
+| SAM 2 Hiera-Tiny/Small/Base+/Large | [facebookresearch/sam2](https://github.com/facebookresearch/sam2) | [Apache License 2.0](https://github.com/facebookresearch/sam2/blob/main/LICENSE) |
+| MobileSAM encoder | [ChaoningZhang/MobileSAM](https://github.com/ChaoningZhang/MobileSAM) | [Apache License 2.0](https://github.com/ChaoningZhang/MobileSAM/blob/master/LICENSE) |
+| SAM decoder used by MobileSAM | [facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything) | [Apache License 2.0](https://github.com/facebookresearch/segment-anything/blob/main/LICENSE) |
+
+Copyright remains with the original authors and contributors. AnyLearning and
+Neural Research Lab do not claim ownership of the underlying model research or
+weights.

MANIFEST.json

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+{
+  "schema_version": 1,
+  "created_at": "2026-08-30",
+  "files": {
+    "mobile_sam_20230629.zip": {
+      "archive_members": [
+        "config.yaml",
+        "mobile_sam.encoder.onnx",
+        "sam_vit_h_4b8939.decoder.onnx"
+      ],
+      "sha256": "41aff2660b7531becfee21fb257c49933ddc892c554507bdb775bf504d443942",
+      "size_bytes": 36655105,
+      "source_filename": "mobile_sam_20230629.zip",
+      "source_repo": "vietanhdev/segment-anything-onnx-models",
+      "source_revision": "9effc01a9e135621d710d49159f1ffb0b6f724dc"
+    },
+    "sam2_hiera_base_plus.zip": {
+      "archive_members": [
+        "config.yaml",
+        "sam2_hiera_base_plus.decoder.onnx",
+        "sam2_hiera_base_plus.encoder.onnx"
+      ],
+      "sha256": "c56282103c2bf99bdab07d2dddb1be67c03d71659ade3f9dcf1d7001fda582fb",
+      "size_bytes": 360422777,
+      "source_filename": "sam2_hiera_base_plus.zip",
+      "source_repo": "vietanhdev/segment-anything-2-onnx-models",
+      "source_revision": "071f58077599431edd0e5d2ac52ecca4c78f1cab"
+    },
+    "sam2_hiera_large.zip": {
+      "archive_members": [
+        "config.yaml",
+        "sam2_hiera_large.decoder.onnx",
+        "sam2_hiera_large.encoder.onnx"
+      ],
+      "sha256": "a967ef6e54794e9494c8b71201766532b7b2929fe18f56b8e63940e97dc671ac",
+      "size_bytes": 910003530,
+      "source_filename": "sam2_hiera_large.zip",
+      "source_repo": "vietanhdev/segment-anything-2-onnx-models",
+      "source_revision": "071f58077599431edd0e5d2ac52ecca4c78f1cab"
+    },
+    "sam2_hiera_small.zip": {
+      "archive_members": [
+        "config.yaml",
+        "sam2_hiera_small.decoder.onnx",
+        "sam2_hiera_small.encoder.onnx"
+      ],
+      "sha256": "4ef9047eb7fc7e36041c88a9f968c3c0b1d1640b88ef66d754b7d558a10cee38",
+      "size_bytes": 183345019,
+      "source_filename": "sam2_hiera_small.zip",
+      "source_repo": "vietanhdev/segment-anything-2-onnx-models",
+      "source_revision": "071f58077599431edd0e5d2ac52ecca4c78f1cab"
+    },
+    "sam2_hiera_tiny.zip": {
+      "archive_members": [
+        "config.yaml",
+        "sam2_hiera_tiny.decoder.onnx",
+        "sam2_hiera_tiny.encoder.onnx"
+      ],
+      "sha256": "7454c3afd835b2acaad863afe3acb11f4e4af039c96e886989ad3873a338e1ec",
+      "size_bytes": 154902833,
+      "source_filename": "sam2_hiera_tiny.zip",
+      "source_repo": "vietanhdev/segment-anything-2-onnx-models",
+      "source_revision": "071f58077599431edd0e5d2ac52ecca4c78f1cab"
+    }
+  },
+  "license": "Apache-2.0",
+  "repository": "nrl-ai/anylearning-labeling-models"
+}

README.md

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+---
+license: apache-2.0
+pipeline_tag: image-segmentation
+library_name: onnx
+tags:
+  - anylearning
+  - onnx
+  - onnxruntime
+  - segment-anything
+  - segment-anything-2
+  - image-segmentation
+  - promptable-segmentation
+  - edge-ai
+authors:
+  - Neural Research Lab
+  - Viet-Anh Nguyen
+---
+
+# AnyLearning labeling models
+
+Versioned ONNX model bundles used by
+[AnyLearning](https://github.com/nrl-ai/anylearning-oss) for local,
+prompt-guided image segmentation.
+
+This repository contains the five models currently offered by AnyLearning:
+
+| File | Model | Download size | SHA-256 |
+| --- | --- | ---: | --- |
+| `mobile_sam_20230629.zip` | MobileSAM | 36,655,105 bytes | `41aff2660b7531becfee21fb257c49933ddc892c554507bdb775bf504d443942` |
+| `sam2_hiera_tiny.zip` | SAM 2 Hiera-Tiny | 154,902,833 bytes | `7454c3afd835b2acaad863afe3acb11f4e4af039c96e886989ad3873a338e1ec` |
+| `sam2_hiera_small.zip` | SAM 2 Hiera-Small | 183,345,019 bytes | `4ef9047eb7fc7e36041c88a9f968c3c0b1d1640b88ef66d754b7d558a10cee38` |
+| `sam2_hiera_base_plus.zip` | SAM 2 Hiera-Base+ | 360,422,777 bytes | `c56282103c2bf99bdab07d2dddb1be67c03d71659ade3f9dcf1d7001fda582fb` |
+| `sam2_hiera_large.zip` | SAM 2 Hiera-Large | 910,003,530 bytes | `a967ef6e54794e9494c8b71201766532b7b2929fe18f56b8e63940e97dc671ac` |
+
+Each ZIP contains a small AnyLearning model configuration and one encoder plus
+one decoder ONNX model. `MANIFEST.json` records the exact source revision,
+archive size, checksum, and expected members.
+
+## Provenance
+
+The files are byte-for-byte mirrors of ONNX exports published by Viet-Anh
+Nguyen:
+
+- SAM 2 bundles: source revision
+  `071f58077599431edd0e5d2ac52ecca4c78f1cab` from
+  `vietanhdev/segment-anything-2-onnx-models`.
+- MobileSAM bundle: source revision
+  `9effc01a9e135621d710d49159f1ffb0b6f724dc` from
+  `vietanhdev/segment-anything-onnx-models`.
+
+Original model projects:
+
+- [Segment Anything 2](https://github.com/facebookresearch/sam2)
+- [Segment Anything](https://github.com/facebookresearch/segment-anything)
+- [MobileSAM](https://github.com/ChaoningZhang/MobileSAM)
+
+These mirrors do not change the weights or model graphs.
+
+## 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, unexpected archive members, and files exceeding their
+configured size limits.
+
+```python
+from hashlib import sha256
+from pathlib import Path
+
+from huggingface_hub import hf_hub_download
+
+path = Path(
+    hf_hub_download(
+        repo_id="nrl-ai/anylearning-labeling-models",
+        filename="sam2_hiera_tiny.zip",
+        revision="v1.0.0",
+    )
+)
+
+expected = "7454c3afd835b2acaad863afe3acb11f4e4af039c96e886989ad3873a338e1ec"
+assert sha256(path.read_bytes()).hexdigest() == expected
+```
+
+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.
+- 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, cite:
+
+```bibtex
+@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:
+
+```bibtex
+@article{kirillov2023segment,
+  title={Segment Anything},
+  author={Kirillov, Alexander and others},
+  journal={arXiv:2304.02643},
+  year={2023}
+}
+```

mobile_sam_20230629.zip

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+size 36655105

sam2_hiera_base_plus.zip

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sam2_hiera_large.zip

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+size 910003530

sam2_hiera_small.zip

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+size 183345019

sam2_hiera_tiny.zip

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