---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: canny_images
    dtype: image
  - name: image_caption
    dtype: string
  splits:
  - name: train
    num_bytes: 1568029838.25
    num_examples: 6462
  download_size: 1566706053
  dataset_size: 1568029838.25
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---


from transformers import pipeline
from datasets import load_dataset

dataset_name = "Jieya/only_fractal_canny"
faces = load_dataset(dataset_name)
faces = faces["train"]

captioner = pipeline("image-to-text",model="Salesforce/blip-image-captioning-large", device=0)

def caption_image_data(example):
    image = example["image"]
    image_caption = captioner(image)[0]['generated_text']
    example['image_caption'] = image_caption
    return example

faces_proc = faces.map(caption_image_data)
faces_proc.push_to_hub(f"Jieya/captioned_fractal_canny")