Clarify model licensing scope and dataset provenance

Separate implementation licenses from pretrained-weight and training-data terms. Preserve existing valid grants and document unresolved distribution permissions. No weights or access settings changed.

LICENSE-NOTICE.md

@@ -0,0 +1,7 @@
+# facenet — licensing scope notice
+
+This distribution retains its existing `license: mit` metadata, and the [facenet-pytorch implementation](https://github.com/timesler/facenet-pytorch/blob/master/LICENSE.md) is MIT. The shipped file is `facenet_20180402_114759_vggface2.pth`, corresponding to the **VGGFace2-trained 20180402-114759** checkpoint in the [upstream checkpoint table](https://github.com/davidsandberg/facenet#pre-trained-models). CASIA-WebFace is a separate upstream checkpoint; this artifact must not be described as jointly trained on both.
+
+[VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) data/source-image rights and the exact pretrained-weight grant require separate consideration from the implementation license. Selecting FaceNet or reading an MIT badge does not establish unconditional commercial clearance.
+
+This notice clarifies scope; it does not revoke existing valid grants or create a new license for third-party material. Dataset and teacher terms do not automatically relicense every trained artifact or inference output. A software license or model-card badge alone does not establish all checkpoint redistribution or commercial-use permissions.

README.md

@@ -9,6 +9,8 @@ pipeline_tag: image-feature-extraction
 
 # FaceNet
 
+> **Licensing scope:** See the [license and provenance notice](LICENSE-NOTICE.md) before relying on this card's license metadata for pretrained-weight redistribution or commercial use. Existing valid grants are preserved.
+
 ## Model Description
 facenet uses an Inception Residual Masking Network pretrained on VGGFace2 to classify facial identities. Facenet also exposes a 512 latent facial embedding space. 
 
@@ -38,7 +40,7 @@ F. Schroff, D. Kalenichenko, J. Philbin. FaceNet: A Unified Embedding for Face R
 ```
 
 ## Acknowledgements
-We thank Tim Esler and David Sandberg for sharing their code and training weights with a permissive license. 
+We thank Tim Esler and David Sandberg for sharing their implementation and pretrained checkpoints. Their code licenses and checkpoint/data provenance are addressed separately below. 
 
 ## Example Useage
 
@@ -71,4 +73,11 @@ identity_embeddings = identity_detector.forward(extracted_faces)
 
 ```
 
+## License and provenance
+
+This distribution retains its existing `license: mit` metadata, and the [facenet-pytorch implementation](https://github.com/timesler/facenet-pytorch/blob/master/LICENSE.md) is MIT. The shipped file is `facenet_20180402_114759_vggface2.pth`, corresponding to the **VGGFace2-trained 20180402-114759** checkpoint in the [upstream checkpoint table](https://github.com/davidsandberg/facenet#pre-trained-models). CASIA-WebFace is a separate upstream checkpoint; this artifact must not be described as jointly trained on both.
+
+[VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) data/source-image rights and the exact pretrained-weight grant require separate consideration from the implementation license. Selecting FaceNet or reading an MIT badge does not establish unconditional commercial clearance.
+
+This notice clarifies scope; it does not revoke existing valid grants or create a new license for third-party material. Dataset and teacher terms do not automatically relicense every trained artifact or inference output. A software license or model-card badge alone does not establish all checkpoint redistribution or commercial-use permissions.