LibreCLIPb16-cls

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 (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.

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.