WeiChow/splash

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')