FluidInference/siglip2-base-patch16-256-coreml

SigLIP 2 base (256 px) for Core ML

Core ML conversion of Google's SigLIP 2 google/siglip2-base-patch16-256, with both towers: an image encoder and a text encoder, each returning an L2-normalized 768-d embedding. Labels are plain text chosen at run time, so it classifies images zero-shot. Google authored the Apache-2.0 source model; Fluid Inference converted it. The image encoder runs entirely on the Apple Neural Engine.

Packages

Package	Input	Output	Size
siglip2-base-patch16-256-image-fp16.mlpackage	pixel_values float32 [1, 3, 256, 256]	image_embeds float32 [1, 768]	176 MB
siglip2-base-patch16-256-text-fp16.mlpackage	input_ids int32 [1, 64]	text_embeds float32 [1, 768]	539 MB

fp16 weights, macOS 14 / iOS 17 or newer. config.json holds the preprocessing and scoring constants:

• Image: resize to 256 × 256 (bilinear, antialiased like PIL), scale to [0, 1], then (x − 0.5) / 0.5.
• Text: lowercase, Gemma tokenizer (tokenizer.json), append <eos>, pad with <pad> to 64 tokens. SigLIP was trained without an attention mask, so the padding must match.
• Scores: cos = image_embeds · text_embeds. sigmoid(logit_scale · cos + logit_bias) (112.90, −16.77) gives an independent probability per label; softmax or argmax over cos picks one label. Embed the labels once and reuse them for every image.

Accuracy

ImageNet-1k zero-shot, all 50,000 test images (clip-benchmark/wds_imagenet1k, prompt this is a photo of {class}.), fp16 on CPU + Neural Engine against the fp32 PyTorch model:

	PyTorch fp32	Core ML fp16
Top-1	76.79%	76.76%
Same prediction as PyTorch	—	99.32%
Image embedding cosine, mean / min	—	0.99990 / 0.975

Google reports 79.1% with its own class names and prompts; the single-prompt protocol here is lower for both backends. Oxford-IIIT Pets test (3,669 photos, 37 breeds): Core ML 94.77%, PyTorch 94.74%, 99.89% identical.

Speed and memory

Apple M5 Pro (24 GB), macOS 27. Image encoder alone, one call at a time: 5.2 ms on CPU + Neural Engine (100% of ops on the ANE), 3.5 ms CPU + GPU, versus 19.1 ms for PyTorch fp32 on MPS.

End to end on 7,349 Oxford-IIIT Pets photos (JPEG decode, resize, encoder, scoring against 37 cached breed prompts), same photos and prompts, measured after model load:

	Core ML (Swift, 4 photos in flight)	PyTorch fp32, MPS, batch 32
Time	36.3 s	102.2 s
Photos per second	202	72
Peak memory (process)	262 MB	4.24 GB
Model on disk	715 MB	1.50 GB
Accuracy	94.26%	94.11%

PyTorch batch 1: 153.5 s, 48 photos/s, 3.64 GB. Reports: reports/.

Use from Swift

FluidUse wraps both packages (SigLIP2Manager, Swift Gemma tokenizer with token ids identical to the Python tokenizer) and includes the photo-sorting demo ImageSortDemo. Conversion scripts: FluidInference/mobius models/emb/siglip2/coreml.