
# BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

Model card for image captioning pretrained on COCO dataset - base architecture (with ViT base backbone) - and fine-tuned on
[football dataset](https://huggingface.co/datasets/ybelkada/football-dataset).

Google Colab notebook for fine-tuning: https://colab.research.google.com/drive/1lbqiSiA0sDF7JDWPeS0tccrM85LloVha?usp=sharing 

| ![BLIP.gif](https://s3.amazonaws.com/moonup/production/uploads/1670928184033-62441d1d9fdefb55a0b7d12c.gif) |
|:--:|
| <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>|

## TL;DR

Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract:

*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*

## Usage

You can use this model for conditional and un-conditional image captioning

### Using the Pytorch model

#### Running the model on CPU

<details>
<summary> Click to expand </summary>

```python
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration

processor = BlipProcessor.from_pretrained("ybelkada/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("ybelkada/blip-image-captioning-base")

img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')

# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt")

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
# >>> a photography of a woman and her dog

# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt")

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
>>> a woman sitting on the beach with her dog
```
</details>

#### Running the model on GPU

##### In full precision 

<details>
<summary> Click to expand </summary>

```python
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration

processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesfoce/blip-image-captioning-base").to("cuda")

img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')

# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt").to("cuda")

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
# >>> a photography of a woman and her dog

# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to("cuda")

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
>>> a woman sitting on the beach with her dog
```
</details>

##### In half precision (`float16`)

<details>
<summary> Click to expand </summary>

```python
import torch
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration

processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16).to("cuda")

img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')

# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16)

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
# >>> a photography of a woman and her dog

# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)

out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
>>> a woman sitting on the beach with her dog
```
</details>

## BibTex and citation info

```
@misc{https://doi.org/10.48550/arxiv.2201.12086,
  doi = {10.48550/ARXIV.2201.12086},
  
  url = {https://arxiv.org/abs/2201.12086},
  
  author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {Creative Commons Attribution 4.0 International}
}
```
{"license": "bsd-3-clause", "tags": ["image-captioning"], "datasets": ["ybelkada/football-dataset"], "pipeline_tag": "other", "inference": false, "languages": ["en"]}
transformers pytorch blip text2text-generation image-captioning other dataset:ybelkada/football-dataset arxiv:2201.12086 license:bsd-3-clause autotrain_compatible region:us
TAGS
#transformers #pytorch #blip #text2text-generation #image-captioning #other #dataset-ybelkada/football-dataset #arxiv-2201.12086 #license-bsd-3-clause #autotrain_compatible #region-us 

BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
========================================================================================================


Model card for image captioning pretrained on COCO dataset - base architecture (with ViT base backbone) - and fine-tuned on
football dataset.


Google Colab notebook for fine-tuning: URL



TL;DR
-----


Authors from the paper write in the abstract:


*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*


Usage
-----


You can use this model for conditional and un-conditional image captioning


### Using the Pytorch model


#### Running the model on CPU



 Click to expand 

#### Running the model on GPU


##### In full precision



 Click to expand 

##### In half precision ('float16')



 Click to expand 

BibTex and citation info
------------------------



### Using the Pytorch model #### Running the model on CPU



 Click to expand #### Running the model on GPU ##### In full precision



 Click to expand ##### In half precision ('float16')



 Click to expand 

BibTex and citation info
------------------------
TAGS
#transformers #pytorch #blip #text2text-generation #image-captioning #other #dataset-ybelkada/football-dataset #arxiv-2201.12086 #license-bsd-3-clause #autotrain_compatible #region-us 
 ### Using the Pytorch model #### Running the model on CPU



 Click to expand #### Running the model on GPU ##### In full precision



 Click to expand ##### In half precision ('float16')



 Click to expand 

BibTex and citation info
------------------------
TAGS
#transformers #pytorch #blip #text2text-generation #image-captioning #other #dataset-ybelkada/football-dataset #arxiv-2201.12086 #license-bsd-3-clause #autotrain_compatible #region-us 
### Using the Pytorch model#### Running the model on CPU



 Click to expand#### Running the model on GPU##### In full precision



 Click to expand##### In half precision ('float16')



 Click to expand 

BibTex and citation info
------------------------