Datasets:
File size: 1,680 Bytes
e429301 e1f9123 e429301 55b822c e75a513 4159606 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
---
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: 37969774373.0
num_examples: 57857
download_size: 37837578517
dataset_size: 37969774373.0
---
It can be captioned by [PaliGemma2](https://huggingface.co/google/paligemma2-3b-ft-docci-448)
```python
from datasets import load_dataset
from tqdm import tqdm
dataset = load_dataset("WeiChow/splash")
for item in dataset:
...
```
caption:
```python
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')
```
|