LibreSigLIP2b16 Cls

--- license: apache-2.0 library_name: libreyolo pipeline_tag: zero-shot-image-classification tags: - zero-shot-image-classification - siglip2 --- # LibreSigLIP2b16-cls SigLIP 2 base patch-16 (256 px) dual-tower zero-shot image classifier, repackaged for LibreYOLO. Open vocabulary: define your own class set at runtime with `set_classes`, no training needed. Multilingual (SentencePiece, Gemma vocabulary), with an optional `multi_label=True` sigmoid scoring mode. ## Usage ```python from libreyolo import LibreYOLO model = LibreYOLO("LibreSigLIP2b16-cls.pt") model.set_classes(["a forklift", "an empty aisle", "a spill"]) r = model.predict("frame.jpg")[0] print(model.names[r.probs.top1], float(r.probs.top1conf)) ``` Requires the `libreyolo[siglip2]` extra (SentencePiece tokenizer). ## Source Derived from [google/siglip2-base-patch16-256](https://huggingface.co/google/siglip2-base-patch16-256) at commit `3f9f96cb90da5dbc758b01813f2f6f1aee24c1ab`. Copyright (c) Google LLC. Licensed under the Apache License 2.0. Reference implementations: [google-research/big_vision](https://github.com/google-research/big_vision) and the Hugging Face `transformers` SigLIP code (both Apache-2.0). This fixed-resolution SigLIP 2 checkpoint uses the original SigLIP transformer architecture (`model_type: "siglip"`); the NaFlex variants are not included. ## Modifications State-dict metadata wrap only (LibreYOLO v1.0 checkpoint schema). Learned parameters are unchanged. See `weights/convert_siglip2_weights.py` in the [LibreYOLO source repository](https://github.com/LibreYOLO/libreyolo). Parity vs the reference `transformers` model at fp32 CPU: text tower and vision encoder are bit-identical (max_abs_diff == 0); the attention-pooled image feature differs by < 1e-6 (a torch MultiheadAttention fused-kernel residual, documented in `weights/parity_siglip2.py`). ## Zero-shot accuracy Imagenette (10-class, val, `model.val()` zero-shot, prompt "a photo of a {}."): 99.2 top-1 (160 px source images), 99.5 top-1 (320 px source images). ## License Apache License 2.0. See the [`LICENSE`](./LICENSE) and [`NOTICE`](./NOTICE) files in this repository. Weights were trained by Google on the WebLI dataset, which is not redistributed here.

Source context: 0 downloads · 0 likes · Pipeline zero-shot-image-classification · Library libreyolo · Repo LibreYOLO/LibreSigLIP2b16-cls