metadata

license: openrail
inference: false
pipeline_tag: image-to-text
tags:
  - image-to-text
  - visual-question-answering
  - image-captioning
datasets:
  - coco
  - textvqa
  - VQAv2
  - OK-VQA
  - A-OKVQA
language:
  - en

QuickStart

Installation

pip install promptcap

Two pipelines are included. One is for image captioning, and the other is for visual question answering.

Captioning Pipeline

Please follow the prompt format, which will give the best performance.

Generate a prompt-guided caption by following:

import torch
from promptcap import PromptCap

model = PromptCap("vqascore/promptcap-coco-vqa")  # also support OFA checkpoints. e.g. "OFA-Sys/ofa-large"

if torch.cuda.is_available():
  model.cuda()

prompt = "please describe this image according to the given question: what piece of clothing is this boy putting on?"
image = "glove_boy.jpeg"

print(model.caption(prompt, image))

To try generic captioning, just use "what does the image describe?"

prompt = "what does the image describe?"
image = "glove_boy.jpeg"

print(model.caption(prompt, image))

PromptCap also support taking OCR inputs:

prompt = "please describe this image according to the given question: what year was this taken?"
image = "dvds.jpg"
ocr = "yip AE Mht juor 02/14/2012"

print(model.caption(prompt, image, ocr))

Visual Question Answering Pipeline

Different from typical VQA models, which are doing classification on VQAv2, PromptCap is open-domain and can be paired with arbitrary text-QA models. Here we provide a pipeline for combining PromptCap with UnifiedQA.

import torch
from promptcap import PromptCap_VQA

# QA model support all UnifiedQA variants. e.g. "allenai/unifiedqa-v2-t5-large-1251000"
vqa_model = PromptCap_VQA(promptcap_model="vqascore/promptcap-coco-vqa", qa_model="allenai/unifiedqa-t5-base")

if torch.cuda.is_available():
  vqa_model.cuda()

question = "what piece of clothing is this boy putting on?"
image = "glove_boy.jpeg"

print(vqa_model.vqa(question, image))

Similarly, PromptCap supports OCR inputs

question = "what year was this taken?"
image = "dvds.jpg"
ocr = "yip AE Mht juor 02/14/2012"

print(vqa_model.vqa(question, image, ocr=ocr))

Because of the flexibility of Unifiedqa, PromptCap also supports multiple-choice VQA

question = "what piece of clothing is this boy putting on?"
image = "glove_boy.jpeg"
choices = ["gloves", "socks", "shoes", "coats"]
print(vqa_model.vqa_multiple_choice(question, image, choices))

Bibtex

@article{hu2022promptcap,
  title={PromptCap: Prompt-Guided Image Captioning for VQA with GPT-3},
  author={Hu, Yushi and Hua, Hang and Yang, Zhengyuan and Shi, Weijia and Smith, Noah A and Luo, Jiebo},
  journal={arXiv preprint arXiv:2211.09699},
  year={2022}
}