AdaCLIP pretrained prompt weights (mirror)
Byte-identical mirror of caoyunkang/AdaCLIP (weights published through Google Drive links in the project README) for the
Cuvis.AI plugins. No fine-tuning,
conversion or re-serialization: the files below are the upstream release as published,
with the upstream licence text in LICENSE.
Why this mirror exists
Cuvis.AI provisions model weights once into a shared cache (download-model download <name> from cuvis-ai-core) and then runs its pipelines in an offline, token-free
runtime that only reads that cache. Hosting the exact upstream files under the
cubert-gmbh organisation makes that provisioning reproducible (commit-pinned and
sha256-verified) and takes the per-user Hugging Face account and token out of the
users' path.
Files and provenance
| File | Upstream source | Upstream revision / id | sha256 | Size |
|---|---|---|---|---|
pretrained_all.pth |
Google Drive file 1Cgkfx3GAaSYnXPLolx-P7pFqYV0IVzZF |
1Cgkfx3GAaSYnXPLolx-P7pFqYV0IVzZF |
33e8d3db1cb4aab030866b8b70a46e10aa27ebf2c23b5463cb07f2574addd98c |
42.7 MB |
pretrained_mvtec_colondb.pth |
Google Drive file 1xVXANHGuJBRx59rqPRir7iqbkYzq45W0 |
1xVXANHGuJBRx59rqPRir7iqbkYzq45W0 |
be51a42c052bd4cf060e54f503a1f5d0b2a3b899bc8dc2e243042f18b215427e |
42.7 MB |
pretrained_visa_clinicdb.pth |
Google Drive file 1QGmPB0ByPZQ7FucvGODMSz7r5Ke5wx9W |
1QGmPB0ByPZQ7FucvGODMSz7r5Ke5wx9W |
3deabbbaf1e412cfdfcb42923a500b986f4b9ee96ccbc7a735d89dbc87df44c8 |
42.7 MB |
LICENSE |
https://raw.githubusercontent.com/caoyunkang/AdaCLIP/main/LICENSE | n/a (URL; sha256 pinned) | 58bf3cbb252fb8ee158f71b5eefa0f93e24632f587926659eb2638aa0df6c618 |
1.1 kB |
Mirrored on 2026-09-04 by Cubert GmbH from the sources above. The sha256 values are the
upstream values; tools/mirror_weights.py check in cuvis-ai-core re-verifies this
repository against them and against the upstream licence text.
Licence
The AdaCLIP code and these released checkpoints come from the AdaCLIP project
(Yunkang Cao et al., "AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for
Zero-Shot Anomaly Detection", ECCV 2024, https://github.com/caoyunkang/AdaCLIP).
The project publishes its code under the MIT License (LICENSE, verbatim). The
authors published these weights through Google Drive links in the project README
without a separate licence statement; they are redistributed here unchanged, as
released, so that Cuvis.AI can provision them without Google Drive.
The license: unknown tag reflects that no licence statement covers the weights; the
LICENSE file is the project's code licence. The checkpoints were trained on
auxiliary anomaly-detection datasets (MVTec AD, VisA, ClinicDB, ColonDB). MVTec AD is
licensed CC BY-NC-SA 4.0; check the training-data licences for your use. Naming: the
upstream README's weights table labels the Drive file pretrained_mvtec_colondb.pth
as "MVTec AD & ClinicDB" and pretrained_visa_clinicdb.pth as "VisA & ColonDB",
while its Train section pairs MVTec AD with ColonDB and VisA with ClinicDB, matching
the file names. This mirror keeps the upstream file names and renames nothing. The
AdaCLIP authors have been notified of this mirror. If you are a rights holder and
object to this redistribution, open a discussion on this repository and the files
will be taken down.
Usage with Cuvis.AI
uv run download-model download adaclip_all
uv run download-model download adaclip_mvtec_colondb
uv run download-model download adaclip_visa_clinicdb
download-model (from cuvis-ai-core) provisions the file into the shared Hugging
Face cache and verifies its sha256. From cuvis-ai-core 0.16.0 on,
ModelWeights.resolve("<name>") returns the cached path and the cuvis-ai-adaclip nodes
resolve their weights through it, so a pipeline needs no manual step once the
weights are provisioned; earlier plugin releases still fetch from their original
upstream sources.