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@ -28,7 +28,7 @@ The abstract from the paper is the following:
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- Usage of SigLIP is similar to [CLIP](clip). The main difference is the training loss, which does not require a global view of all the pairwise similarities of images and texts within a batch. One needs to apply the sigmoid activation function to the logits, rather than the softmax.
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- Training is not yet supported. If you want to fine-tune SigLIP or train from scratch, refer to the loss function from [OpenCLIP](https://github.com/mlfoundations/open_clip/blob/73ad04ae7fb93ede1c02dc9040a828634cb1edf1/src/open_clip/loss.py#L307), which leverages various `torch.distributed` utilities.
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- When using the standalone [`SiglipTokenizer`], make sure to pass `padding="max_length"` as that's how the model was trained. The multimodal [`SiglipProcessor`] takes care of this behind the scenes.
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- When using the standalone [`SiglipTokenizer`] or [`SiglipProcessor`], make sure to pass `padding="max_length"` as that's how the model was trained.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/siglip_table.jpeg"
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alt="drawing" width="600"/>
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@ -82,7 +82,8 @@ If you want to do the pre- and postprocessing yourself, here's how to do that:
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> texts = ["a photo of 2 cats", "a photo of 2 dogs"]
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>>> inputs = processor(text=texts, images=image, return_tensors="pt")
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>>> # important: we pass `padding=max_length` since the model was trained with this
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>>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
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>>> with torch.no_grad():
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... outputs = model(**inputs)
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@ -1123,7 +1123,8 @@ class SiglipModel(SiglipPreTrainedModel):
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> texts = ["a photo of 2 cats", "a photo of 2 dogs"]
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>>> inputs = processor(text=texts, images=image, return_tensors="pt")
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>>> # important: we pass `padding=max_length` since the model was trained with this
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>>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt")
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>>> with torch.no_grad():
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... outputs = model(**inputs)
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@ -50,9 +50,9 @@ class SiglipProcessor(ProcessorMixin):
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self,
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text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
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images: ImageInput = None,
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padding: Union[bool, str, PaddingStrategy] = "max_length",
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padding: Union[bool, str, PaddingStrategy] = False,
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truncation: Union[bool, str, TruncationStrategy] = None,
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max_length=None,
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max_length: int = None,
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return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
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) -> BatchFeature:
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"""
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@ -71,7 +71,7 @@ class SiglipProcessor(ProcessorMixin):
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The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
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tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
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number of channels, H and W are image height and width.
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padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `max_length`):
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padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
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Select a strategy to pad the returned sequences (according to the model's padding side and padding
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index) among:
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- `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
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