LibreCLIPb16 Cls

Data provenance

These weights were trained on LAION-2B, which has a documented CSAM-content history (Stanford, December 2023); LAION subsequently released the cleaned Re-LAION. Prefer Re-LAION-derived weights where available. See NOTICE.

Modifications

State-dict key remapping only — LibreCLIP's native towers mirror the OpenCLIP module structure, so the load is 0-missing / 0-unexpected. Learned parameters are unchanged. See weights/convert_clip_weights.py in the LibreYOLO source repository.

Usage

from libreyolo import LibreCLIP

model = LibreCLIP("LibreCLIPb16-cls.pt")                 # autodownloads from this repo
model.set_classes(["a forklift", "an empty aisle", "a spill"])
r = model.predict("warehouse.jpg")[0]
print(model.names[r.probs.top1], float(r.probs.top1conf))

License

MIT License. See the LICENSE and NOTICE files.

--- license: mit library_name: libreyolo pipeline_tag: zero-shot-image-classification tags: - clip - zero-shot-image-classification - open-vocabulary - libreyolo --- # LibreCLIPb16-cls OpenCLIP ViT-B/16 (LAION-2B), repackaged as a native LibreYOLO checkpoint for **zero-shot, open-vocabulary** image classification with `LibreCLIP`. No training and no fixed label set: call `set_classes([...])`, then predict. ## Source Derived from [laion/CLIP-ViT-B-16-laion2B-s34B-b88K](https://huggingface.co/laion/CLIP-ViT-B-16-laion2B-s34B-b88K) (OpenCLIP arch `ViT-B-16`, pretrained tag `laion2b_s34b_b88k`). Copyright (c) 2021 OpenAI; (c) 2012-2021 OpenCLIP authors. Licensed under the MIT License. ## Data provenance These weights were trained on LAION-2B, which has a documented CSAM-content history (Stanford, December 2023); LAION subsequently released the cleaned Re-LAION. Prefer Re-LAION-derived weights where available. See `NOTICE`. ## Modifications State-dict key remapping only — LibreCLIP's native towers mirror the OpenCLIP module structure, so the load is 0-missing / 0-unexpected. Learned parameters are unchanged. See `weights/convert_clip_weights.py` in the [LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo). ## Usage ```python from libreyolo import LibreCLIP model = LibreCLIP("LibreCLIPb16-cls.pt") # autodownloads from this repo model.set_classes(["a forklift", "an empty aisle", "a spill"]) r = model.predict("warehouse.jpg")[0] print(model.names[r.probs.top1], float(r.probs.top1conf)) ``` ## License MIT License. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE) files.