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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.*


# BLIP

[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.

You can find all the original BLIP checkpoints under the [BLIP](https://huggingface.co/collections/Salesforce/blip-models-65242f40f1491fbf6a9e9472) collection.

> [!TIP]
> This model was contributed by [ybelkada](https://huggingface.co/ybelkada).
>
> Click on the BLIP models in the right sidebar for more examples of how to apply BLIP to different vision language tasks.

The example below demonstrates how to visual question answering with [`Pipeline`] or the [`AutoModel`] class.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline


pipeline = pipeline(
    task="visual-question-answering",
    model="Salesforce/blip-vqa-base",
    device=0
)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
pipeline(question="What is the weather in this image?", image=url)
```

</hfoption>
<hfoption id="AutoModel">

```python
import requests
import torch
from PIL import Image

from transformers import AutoModelForVisualQuestionAnswering, AutoProcessor


processor = AutoProcessor.from_pretrained("Salesforce/blip-vqa-base")
model = AutoModelForVisualQuestionAnswering.from_pretrained(
    "Salesforce/blip-vqa-base",
    device_map="auto"
)

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
image = Image.open(requests.get(url, stream=True).raw)

question = "What is the weather in this image?"
inputs = processor(images=image, text=question, return_tensors="pt").to(model.device, torch.float16)

output = model.generate(**inputs)
processor.batch_decode(output, skip_special_tokens=True)[0]
```

</hfoption>
</hfoptions>

## Resources

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.

## BlipConfig

[[autodoc]] BlipConfig

## BlipTextConfig

[[autodoc]] BlipTextConfig

## BlipVisionConfig

[[autodoc]] BlipVisionConfig

## BlipProcessor

[[autodoc]] BlipProcessor
    - __call__

## BlipImageProcessor

[[autodoc]] BlipImageProcessor
    - preprocess

## BlipImageProcessorPil

[[autodoc]] BlipImageProcessorPil
    - preprocess

## BlipModel

`BlipModel` is going to be deprecated in future versions, please use `BlipForConditionalGeneration`, `BlipForImageTextRetrieval` or `BlipForQuestionAnswering` depending on your usecase.

[[autodoc]] BlipModel
    - forward
    - get_text_features
    - get_image_features

## BlipTextModel

[[autodoc]] BlipTextModel
    - forward

## BlipTextLMHeadModel

[[autodoc]] BlipTextLMHeadModel
    - forward

## BlipVisionModel

[[autodoc]] BlipVisionModel
    - forward

## BlipForConditionalGeneration

[[autodoc]] BlipForConditionalGeneration
    - forward

## BlipForImageTextRetrieval

[[autodoc]] BlipForImageTextRetrieval
    - forward

## BlipForQuestionAnswering

[[autodoc]] BlipForQuestionAnswering
    - forward
