openEuler/siglip2_so400m_patch14_384

Model Card for SigLIP2 SO400M Patch14-384 (IB-Robot)

SigLIP2 (Sigmoid Loss for Language Image Pre-training 2) with SO400M backbone, patch size 14, 384x384 input. Produces 1152-dimensional L2-normalized image and text embeddings for vision-language matching. Packaged for the IB-Robot framework.

Repository Structure

• —inference_manifest.json — deployment routing (schema v3)
• —assets/model/ — HuggingFace model snapshot (safetensors, config, tokenizer)
• —assets/adapter.json — deployment adapter config
• —artifacts/ascend_310b/ — vision + text encoder OM
• —artifacts/ascend_310p/ — vision + text encoder OM (aarch64)

Deployment Backends

Each Ascend deployment runs a dual-encoder pipeline: vision + text.

Inputs: masked_images float32 [-1,3,384,384] NCHW, text_tokens int64 [-1,64], text_attention_mask int64 [-1,64] Outputs: image_embeddings float32 [-1,1152], text_embeddings float32 [-1,1152] Embedding space: L2-normalized, dimension 1152, google/siglip2-so400m-patch14-384@main

Source Model

This bundle's torch weights originate from the upstream SigLIP2 model:

• —HuggingFace: google/siglip2-so400m-patch14-384

The Ascend OM artifacts were converted from these torch weights. Download script: scripts/download_perception_models.sh.

Citation

@inproceedings{siglip2, title = {SigLIP 2: Multilingual Vision-Language Pre-Training with Improved Semantic Alignment}, author = {Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas}, booktitle = {arXiv preprint arXiv:2502.14795}, year = {2025} } @software{ibrobot, title = {IB-Robot: Intelligence Boom Robot}, url = {https://gitcode.com/openeuler/IBRobot}, license = {Apache-2.0} }