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147 lines
4.2 KiB
Markdown
147 lines
4.2 KiB
Markdown
<!--Copyright 2023 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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-->
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*This model was published in HF papers on 2022-01-28 and contributed to Hugging Face Transformers on 2022-12-21.*
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# BLIP
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[BLIP](https://huggingface.co/papers/2201.12086) (Bootstrapped Language-Image Pretraining) is a vision-language pretraining (VLP) framework designed for *both* understanding and generation tasks. Most existing pretrained models are only good at one or the other. It uses a captioner to generate captions and a filter to remove the noisy captions. This increases training data quality and more effectively uses the messy web data.
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You can find all the original BLIP checkpoints under the [BLIP](https://huggingface.co/collections/Salesforce/blip-models-65242f40f1491fbf6a9e9472) collection.
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> [!TIP]
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> This model was contributed by [ybelkada](https://huggingface.co/ybelkada).
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>
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> Click on the BLIP models in the right sidebar for more examples of how to apply BLIP to different vision language tasks.
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The example below demonstrates how to visual question answering with [`Pipeline`] or the [`AutoModel`] class.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```python
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from transformers import pipeline
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pipeline = pipeline(
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task="visual-question-answering",
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model="Salesforce/blip-vqa-base",
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device=0
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)
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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pipeline(question="What is the weather in this image?", image=url)
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```
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</hfoption>
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<hfoption id="AutoModel">
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```python
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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 AutoModelForVisualQuestionAnswering, AutoProcessor
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processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base")
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model = AutoModelForVisualQuestionAnswering.from_pretrained(
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"Salesforce/blip-vqa-base",
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device_map="auto"
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)
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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image = Image.open(requests.get(url, stream=True).raw)
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question = "What is the weather in this image?"
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inputs = processor(images=image, text=question, return_tensors="pt").to(model.device, torch.float16)
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output = model.generate(**inputs)
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processor.batch_decode(output, skip_special_tokens=True)[0]
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```
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</hfoption>
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</hfoptions>
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## Resources
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Refer to this [notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_captioning_blip.ipynb) to learn how to fine-tune BLIP for image captioning on a custom dataset.
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## BlipConfig
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[[autodoc]] BlipConfig
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## BlipTextConfig
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[[autodoc]] BlipTextConfig
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## BlipVisionConfig
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[[autodoc]] BlipVisionConfig
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## BlipProcessor
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[[autodoc]] BlipProcessor
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- __call__
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## BlipImageProcessor
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[[autodoc]] BlipImageProcessor
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- preprocess
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## BlipImageProcessorPil
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[[autodoc]] BlipImageProcessorPil
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- preprocess
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## BlipModel
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`BlipModel` is going to be deprecated in future versions, please use `BlipForConditionalGeneration`, `BlipForImageTextRetrieval` or `BlipForQuestionAnswering` depending on your usecase.
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[[autodoc]] BlipModel
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- forward
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- get_text_features
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- get_image_features
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## BlipTextModel
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[[autodoc]] BlipTextModel
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- forward
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## BlipTextLMHeadModel
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[[autodoc]] BlipTextLMHeadModel
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- forward
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## BlipVisionModel
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[[autodoc]] BlipVisionModel
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- forward
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## BlipForConditionalGeneration
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[[autodoc]] BlipForConditionalGeneration
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- forward
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## BlipForImageTextRetrieval
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[[autodoc]] BlipForImageTextRetrieval
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- forward
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## BlipForQuestionAnswering
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[[autodoc]] BlipForQuestionAnswering
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- forward
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