Blip Vqa Base

--- pipeline_tag: 'visual-question-answering' tags: - visual-question-answering inference: false languages: - en license: bsd-3-clause --- # BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation Model card for BLIP trained on visual question answering- base architecture (with ViT base backbone). |  | |:--:| | <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, BlipForQuestionAnswering processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base") model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-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') question = "how many dogs are in the picture?"...

Source context: 600562 downloads · 195 likes · Pipeline visual-question-answering · Library transformers · Repo Salesforce/blip-vqa-base