SigLIP 2 shape-optimized 400M patch-14 (384 px) dual-tower zero-shot image classifier, repackaged for LibreYOLO. The accuracy tier of the LibreSigLIP2 family. 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.

Requires the libreyolo[siglip2] extra (SentencePiece tokenizer). Note: this model runs at 384 px with a 27-layer 1152-wide ViT; it is heavy on CPU, prefer a GPU.

Derived from google/siglip2-so400m-patch14-384 at commit e8e487298228002f3d8a82e0cd5c8ea9c567f57f. Copyright (c) Google LLC. Licensed under the Apache License 2.0.

Reference implementations: 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.

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.

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).

Imagenette (10-class, val, model.val() zero-shot, prompt "a photo of a {}."): 99.5 top-1 (160 px source images), 99.6 top-1 (320 px source images). Beats LibreSigLIP2b16-cls on both.

Apache License 2.0. See the LICENSE and NOTICE files in this repository. Weights were trained by Google on the WebLI dataset, which is not redistributed here.