27 lines
1.4 KiB
Plaintext
27 lines
1.4 KiB
Plaintext
@version 1
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# Label models: image-text encoders that place images and text in one space, so an image can be
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# scored against any phrase. Each [model NAME] names the files it needs from this folder
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# (fetch_models.jai downloads them) and how it is run; labels_create takes the NAME.
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#
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# title shown to people
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# vision, text, tokenizer file names, quoted: the image encoder, the text encoder, tokenizer.json
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# image_size images are resized to this square for the vision model (px)
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# max_tokens text is cut or padded to this many tokens
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# logit_scale, logit_bias a phrase's score for an image is sigmoid(e^logit_scale * cosine + logit_bias),
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# from the trained model (they are not in the ONNX encoders)
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# image_batch images run through the vision model together
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# vision_coreml on macOS, the vision model as a Core ML package, quoted, run on the GPU
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# (tools/export_siglip_coreml.py makes it); without it, onnxruntime runs it
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[model siglip2-base-patch16-384]
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title: "SigLIP 2 base, 384 px"
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vision: "siglip2-base-patch16-384-vision.onnx"
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text: "siglip2-base-patch16-384-text-int8.onnx"
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tokenizer: "siglip2-tokenizer.json"
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image_size: 384
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max_tokens: 64
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logit_scale: 4.726024
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logit_bias: -16.772243
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image_batch: 8
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vision_coreml: "siglip2-base-patch16-384-vision.mlpackage"
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