Instructions to use py-feat/facenet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Py-Feat
How to use py-feat/facenet with Py-Feat:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download LICENSE-NOTICE.md from py-feat/facenet: direct link, hf CLI and curl.
- Browser
- Download file 1.19 kB
-
https://huggingface.co/py-feat/facenet/resolve/main/LICENSE-NOTICE.md
- Command line
-
hf download hf://py-feat/facenet/LICENSE-NOTICE.md
-
curl -L -o LICENSE-NOTICE.md https://huggingface.co/py-feat/facenet/resolve/main/LICENSE-NOTICE.md
facenet — licensing scope notice
This distribution retains its existing license: mit metadata, and the facenet-pytorch implementation is MIT. The shipped file is facenet_20180402_114759_vggface2.pth, corresponding to the VGGFace2-trained 20180402-114759 checkpoint in the upstream checkpoint table. CASIA-WebFace is a separate upstream checkpoint; this artifact must not be described as jointly trained on both.
VGGFace2 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.