Xclip Base Patch32
MIT-licensed mirror hosted under CondadosAI/ for use with the acaua computer vision library.
Source du modèle
- Type
- Text model
- Taille du fichier
- 750 MB
- Modèle de base
- microsoft/xclip-base-patch32
- Version
- vmodel
- Mis à jour
- 24 oct. 2026
- Téléchargements
- 14
- Source
- Hugging Face
Description de la source
X-CLIP (base, patch 32) — acaua mirror
MIT-licensed mirror hosted under CondadosAI/ for use with the acaua computer vision library.
This is a safetensors-only mirror of the upstream Microsoft weights at the pinned commit shown below. The legacy pytorch_model.bin (pickle format) that upstream ships alongside model.safetensors has been deliberately removed for security hygiene — pickle loads can execute arbitrary code, and transformers auto-prefers safetensors when both are present, so removing it has zero functional impact on downstream users.
X-CLIP is a zero-shot video classification model: you provide a list of candidate text labels at inference time and the model ranks them by similarity to the video clip. It is not a closed-set softmax classifier, and it does not appear in AutoModelForVideoClassification.
Provenance
| Upstream repo | microsoft/xclip-base-patch32 |
| Upstream commit SHA | a2e27a78a2b5d802e894b8a1ef14f3a8ce490963 |
| Upstream commit date | 2024-02-04 |
| Declared license | MIT |
| Paper | Ni et al., "Expanding Language-Image Pretrained Models for General Video Recognition", ECCV 2022, arXiv:2208.02816 |
| Official code | microsoft/VideoX (MIT) |
| Mirrored on | 2026-04-23 |
| Mirrored by | CondadosAI/acaua |
Usage via acaua
import acaua
model = acaua.Model.from_pretrained(
"CondadosAI/xclip_base_patch32",
allow_non_apache=True, # weights are MIT, not Apache-2.0
)
result = model.predict(
"dance.mp4",
labels=["dancing", "cooking", "running", "sleeping", "walking"],
top_k=3,
)
for label, score in zip(result.labels, result.scores.tolist()):
print(f"{label}: {score:.3f}")
Usage via 🤗 Transformers
This mirror is drop-in compatible with the upstream repo.
from transformers import XCLIPModel, XCLIPProcessor
processor = XCLIPProcessor.from_pretrained("CondadosAI/xclip_base_patch32")
model = XCLIPModel.from_pretrained("CondadosAI/xclip_base_patch32")
Expected input
- Frames: 8 uniformly-sampled frames per clip (
vision_config.num_frames=8). - Resolution: 224 × 224 after resize + center-crop.
- Normalization: ImageNet mean/std (handled by
XCLIPProcessor). - Text prompts: supplied at inference time — any natural-language strings.
License and attribution
Redistributed under MIT, consistent with the upstream declaration. See NOTICE for required attribution.
Citation
@inproceedings{ni2022expanding,
title={Expanding language-image pretrained models for general video recognition},
author={Ni, Bolin and Peng, Houwen and Chen, Minghao and Zhang, Songyang and Meng, Gaofeng and Fu, Jianlong and Xiang, Shiming and Ling, Haibin},
booktitle={European Conference on Computer Vision (ECCV)},
pages={1--18},
year={2022},
publisher={Springer}
}
Sources
1 source
Sources
1 sourceVérifié 24 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- license: mit library_name: transformers pipeline_tag: video-classification tags: - video-classification - zero-shot - vision - acaua datasets: - kinetics-400 base_model: microsoft/xclip-base-patch32 --- # X-CLIP (base, patch 32) — acaua mirror MIT-licensed mirror hosted under `CondadosAI/` for use with the [acaua](https://github.com/CondadosAI/acaua) computer vision library. This is a **safetensors-only mirror** of the upstream Microsoft weights at the pinned commit shown below. The legacy `pytorch_model.bin` (pickle format) that upstream ships alongside `model.safetensors` has been deliberately removed for security hygiene — pickle loads can execute arbitrary code, and `transformers` auto-prefers safetensors when both are present, so removing it has zero functional impact on downstream users. X-CLIP is a **zero-shot video classification** model: you provide a list of candidate text labels at inference time and the model ranks them by similarity to the video clip. It is not a closed-set softmax classifier, and it does not appear in `AutoModelForVideoClassification`. ## Provenance | | | |---|---| | Upstream repo | [`microsoft/xclip-base-patch32`](https://huggingface.co/microsoft/xclip-base-patch32) | | Upstream commit SHA | `a2e27a78a2b5d802e894b8a1ef14f3a8ce490963` | | Upstream commit date | 2024-02-04 | | Declared license | MIT | | Paper | Ni et al., *"Expanding Language-Image Pretrained Models for General Video Recognition"*, ECCV 2022, arXiv:[2208.02816](https://arxiv.org/abs/2208.02816) | | Official code | [`microsoft/VideoX`](https://github.com/microsoft/VideoX) (MIT) | | Mirrored on | 2026-04-23 | | Mirrored by | [CondadosAI/acaua](https://github.com/CondadosAI/acaua) | ## Usage via acaua ```python import acaua model = acaua.Model.from_pretrained( "CondadosAI/xclip_base_patch32", allow_non_apache=True, # weights are MIT, not Apache-2.0 ) result = model.predict( "dance.mp4", labels=["dancing", "cooking", "running", "sleeping", "walking"], top_k=3, ) for label, score in zip(result.labels, result.scores.tolist()): print(f"{label}: {score:.3f}") ``` ## Usage via 🤗 Transformers This mirror is drop-in compatible with the upstream repo. ```python from transformers import XCLIPModel, XCLIPProcessor processor = XCLIPProcessor.from_pretrained("CondadosAI/xclip_base_patch32") model = XCLIPModel.from_pretrained("CondadosAI/xclip_bas...
Source context: 14 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo CondadosAI/xclip_base_patch32
Source context: 7 downloads · 0 likes · Pipeline video-classification · Library transformers · Repo CondadosAI/xclip_base_patch32
