Datasets:
Tasks:
Visual Question Answering
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
medical
License:
| """This script de-duplicates the data provided by the PathVQA authors, | |
| creates an "imagefolder" dataset and pushes it to the Hugging Face Hub. | |
| """ | |
| import re | |
| import os | |
| import shutil | |
| import pickle | |
| import datasets | |
| import pandas as pd | |
| for split in ["train", "val", "test"]: | |
| os.makedirs(f"data/{split}/", exist_ok=True) | |
| # load the image-question-answer triplets | |
| data = pd.DataFrame(pickle.load(open(f"pvqa/qas/{split}/{split}_qa.pkl", "rb"))) | |
| # drop the duplicate image-question-answer triplets | |
| data = data.drop_duplicates(ignore_index=True) | |
| # perform some basic data cleaning/normalization | |
| f = lambda x: re.sub(' +', ' ', str(x).lower()).replace(" ?", "?").strip() | |
| data["question"] = data["question"].apply(f) | |
| data["answer"] = data["answer"].apply(f) | |
| # copy the images using unique file names | |
| data.insert(0, "file_name", "") | |
| for i, row in data.iterrows(): | |
| file_name = f"img_{i}.jpg" | |
| data["file_name"].iloc[i] = file_name | |
| shutil.copyfile(src=f"pvqa/images/{split}/{row['image']}.jpg", dst=f"data/{split}/{file_name}") | |
| _ = data.pop("image") | |
| # save the metadata | |
| data.to_csv(f"data/{split}/metadata.csv", index=False) | |
| # push the dataset to the hub | |
| dataset = datasets.load_dataset("imagefolder", data_dir="data/") | |
| dataset.push_to_hub("flaviagiammarino/path-vqa") | |