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README.md

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-license: other
-license_name: aplux-model-farm-license
-license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
----
+---
+license: other
+license_name: aplux-model-farm-license
+license_link: https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf
+pipeline_tag: image-classification
+tags:
+- AIoT
+- QNN
+---
+
+![](https://aiot.aidlux.com/_next/image?url=%2Fapi%2Fv1%2Ffiles%2Fmodel%2Fcover%2F20250320025932_%25E5%259B%25BE1(11).png&w=640&q=75)
+
+## SigLIP-base: lmage Captioning
+
+SigLIP-base is a medium-sized multimodal model developed by Google, built on the SoViT (Shape-optimized Vision Transformer) architecture and trained using Sigmoid Loss instead of the contrastive loss used in CLIP. This training approach improves performance in small-batch settings and enhances robustness to negative samples. SigLIP-base achieves strong results in tasks such as image-text retrieval and zero-shot image classification. With solid inference efficiency and scalability, it is well-suited for multilingual and multitask vision-language applications.
+
+### Source model
+
+- Input shape: [1x3x384x384], [1x64]
+- Number of parameters: 88.86M, 105.16M
+- Model size: 359.10M, 424.01M
+- Output shape: [1x768], [1x768]
+
+The source model can be found [here](https://huggingface.co/google/siglip-base-patch16-384)
+
+## Performance Reference
+
+Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
+
+## Inference & Model Conversion
+
+Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
+
+## License
+
+- Source Model: [APACHE-2.0](https://github.com/google-research/big_vision/blob/main/LICENSE)
+
+- Deployable Model: [APLUX-MODEL-FARM-LICENSE](https://aiot.aidlux.com/api/v1/files/license/model_farm_license_en.pdf)