README.md

metadata

license: cc-by-4.0
task_categories:
  - text-to-image
  - image-to-image
language:
  - en
pretty_name: splash
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
dataset_info:
  features:
    - name: IMAGE_ID
      dtype: string
    - name: CAPTION
      dtype: string
    - name: IMG
      dtype: image
  splits:
    - name: train
      num_bytes: 37969774373
      num_examples: 57857
  download_size: 37837578517
  dataset_size: 37969774373

It can be captioned by PaliGemma2

from datasets import load_dataset
from tqdm import tqdm

dataset = load_dataset("WeiChow/splash")

for item in dataset:
    ...

caption:

from transformers import PaliGemmaProcessor, PaliGemmaForConditionalGeneration
import torch
from datasets import load_dataset
from tqdm import tqdm
from termcolor import cprint

dataset = load_dataset("WeiChow/splash")

model_id = "google/paligemma2-3b-ft-docci-448"
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="cuda").eval()
processor = PaliGemmaProcessor.from_pretrained(model_id)

for item in dataset:
    model_inputs = processor(text="caption en", images=item['IMG'], return_tensors="pt").to(torch.bfloat16).to(model.device)
    input_len = model_inputs["input_ids"].shape[-1]

    with torch.inference_mode():
        generation = model.generate(**model_inputs, max_new_tokens=30, do_sample=False)
        generation = generation[0][input_len:]
        decoded = processor.decode(generation, skip_special_tokens=True)
        print(item['IMAGE_ID'])
        cprint(decoded, 'cyan')