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--- |
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pipeline_tag: image-to-text |
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tags: |
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- image-captioning |
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languages: |
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- en |
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license: bsd-3-clause |
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--- |
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# BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation |
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Model card for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone). |
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| <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>| |
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## TL;DR |
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Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract: |
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*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.* |
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## Usage |
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You can use this model for conditional and un-conditional image captioning |
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### Using the Pytorch model |
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#### Running the model on CPU |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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import requests |
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from PIL import Image |
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from transformers import BlipProcessor, BlipForConditionalGeneration |
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") |
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") |
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img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' |
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') |
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# conditional image captioning |
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text = "a photography of" |
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inputs = processor(raw_image, text, return_tensors="pt") |
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out = model.generate(**inputs) |
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print(processor.decode(out[0], skip_special_tokens=True)) |
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# unconditional image captioning |
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inputs = processor(raw_image, return_tensors="pt") |
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out = model.generate(**inputs) |
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print(processor.decode(out[0], skip_special_tokens=True)) |
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``` |
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</details> |
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#### Running the model on GPU |
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##### In full precision |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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import requests |
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from PIL import Image |
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from transformers import BlipProcessor, BlipForConditionalGeneration |
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") |
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large").to("cuda") |
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img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' |
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') |
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# conditional image captioning |
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text = "a photography of" |
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inputs = processor(raw_image, text, return_tensors="pt").to("cuda") |
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out = model.generate(**inputs) |
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print(processor.decode(out[0], skip_special_tokens=True)) |
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# unconditional image captioning |
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inputs = processor(raw_image, return_tensors="pt").to("cuda") |
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out = model.generate(**inputs) |
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print(processor.decode(out[0], skip_special_tokens=True)) |
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``` |
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</details> |
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##### In half precision (`float16`) |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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import torch |
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import requests |
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from PIL import Image |
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from transformers import BlipProcessor, BlipForConditionalGeneration |
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processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") |
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model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda") |
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img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' |
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') |
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# conditional image captioning |
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text = "a photography of" |
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inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16) |
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out = model.generate(**inputs) |
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print(processor.decode(out[0], skip_special_tokens=True)) |
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# >>> a photography of a woman and her dog |
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# unconditional image captioning |
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inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16) |
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out = model.generate(**inputs) |
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print(processor.decode(out[0], skip_special_tokens=True)) |
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>>> a woman sitting on the beach with her dog |
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``` |
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</details> |
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## BibTex and citation info |
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``` |
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@misc{https://doi.org/10.48550/arxiv.2201.12086, |
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doi = {10.48550/ARXIV.2201.12086}, |
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url = {https://arxiv.org/abs/2201.12086}, |
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author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven}, |
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keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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``` |
