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* Update clip.md * Update docs/source/en/model_doc/clip.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/clip.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/clip.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Incorporated suggested changes * Update docs/source/en/model_doc/clip.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/clip.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> * Update docs/source/en/model_doc/clip.md Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com> --------- Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
214 lines
8.5 KiB
Markdown
214 lines
8.5 KiB
Markdown
<!--Copyright 2021 The HuggingFace Team. All rights reserved.
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">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# CLIP
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[CLIP](https://huggingface.co/papers/2103.00020) is a is a multimodal vision and language model motivated by overcoming the fixed number of object categories when training a computer vision model. CLIP learns about images directly from raw text by jointly training on 400M (image, text) pairs. Pretraining on this scale enables zero-shot transfer to downstream tasks. CLIP uses an image encoder and text encoder to get visual features and text features. Both features are projected to a latent space with the same number of dimensions and their dot product gives a similarity score.
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You can find all the original CLIP checkpoints under the [OpenAI](https://huggingface.co/openai?search_models=clip) organization.
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> [!TIP]
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> Click on the CLIP models in the right sidebar for more examples of how to apply CLIP to different image and language tasks.
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The example below demonstrates how to calculate similarity scores between multiple text descriptions and an image with [`Pipeline`] or the [`AutoModel`] class.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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clip = pipeline(
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task="zero-shot-image-classification",
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model="openai/clip-vit-base-patch32",
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torch_dtype=torch.bfloat16,
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device=0
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)
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labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"]
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clip("http://images.cocodataset.org/val2017/000000039769.jpg", candidate_labels=labels)
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import requests
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import torch
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from PIL import Image
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from transformers import AutoProcessor, AutoModel
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model = AutoModel.from_pretrained("openai/clip-vit-base-patch32", torch_dtype=torch.bfloat16, attn_implementation="sdpa")
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processor = AutoProcessor.from_pretrained("openai/clip-vit-base-patch32")
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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labels = ["a photo of a cat", "a photo of a dog", "a photo of a car"]
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inputs = processor(text=labels, images=image, return_tensors="pt", padding=True)
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outputs = model(**inputs)
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logits_per_image = outputs.logits_per_image
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probs = logits_per_image.softmax(dim=1)
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most_likely_idx = probs.argmax(dim=1).item()
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most_likely_label = labels[most_likely_idx]
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print(f"Most likely label: {most_likely_label} with probability: {probs[0][most_likely_idx].item():.3f}")
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```
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</hfoption>
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</hfoptions>
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## Notes
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- Use [`CLIPImageProcessor`] to resize (or rescale) and normalizes images for the model.
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## CLIPConfig
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[[autodoc]] CLIPConfig
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- from_text_vision_configs
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## CLIPTextConfig
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[[autodoc]] CLIPTextConfig
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## CLIPVisionConfig
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[[autodoc]] CLIPVisionConfig
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## CLIPTokenizer
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[[autodoc]] CLIPTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## CLIPTokenizerFast
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[[autodoc]] CLIPTokenizerFast
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## CLIPImageProcessor
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[[autodoc]] CLIPImageProcessor
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- preprocess
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## CLIPImageProcessorFast
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[[autodoc]] CLIPImageProcessorFast
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- preprocess
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## CLIPFeatureExtractor
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[[autodoc]] CLIPFeatureExtractor
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## CLIPProcessor
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[[autodoc]] CLIPProcessor
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<frameworkcontent>
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<pt>
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## CLIPModel
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[[autodoc]] CLIPModel
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- forward
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- get_text_features
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- get_image_features
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## CLIPTextModel
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[[autodoc]] CLIPTextModel
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- forward
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## CLIPTextModelWithProjection
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[[autodoc]] CLIPTextModelWithProjection
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- forward
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## CLIPVisionModelWithProjection
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[[autodoc]] CLIPVisionModelWithProjection
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- forward
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## CLIPVisionModel
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[[autodoc]] CLIPVisionModel
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- forward
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## CLIPForImageClassification
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[[autodoc]] CLIPForImageClassification
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- forward
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</pt>
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<tf>
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## TFCLIPModel
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[[autodoc]] TFCLIPModel
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- call
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- get_text_features
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- get_image_features
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## TFCLIPTextModel
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[[autodoc]] TFCLIPTextModel
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- call
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## TFCLIPVisionModel
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[[autodoc]] TFCLIPVisionModel
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- call
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</tf>
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<jax>
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## FlaxCLIPModel
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[[autodoc]] FlaxCLIPModel
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- __call__
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- get_text_features
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- get_image_features
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## FlaxCLIPTextModel
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[[autodoc]] FlaxCLIPTextModel
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- __call__
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## FlaxCLIPTextModelWithProjection
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[[autodoc]] FlaxCLIPTextModelWithProjection
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- __call__
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## FlaxCLIPVisionModel
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[[autodoc]] FlaxCLIPVisionModel
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- __call__
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</jax>
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</frameworkcontent>
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