---
task_categories:
- text-to-image
language:
- en
size_categories:
- 1M<n<10M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: URL
    dtype: string
  - name: TEXT
    dtype: string
  - name: WIDTH
    dtype: float64
  - name: HEIGHT
    dtype: float64
  - name: similarity
    dtype: float64
  - name: punsafe
    dtype: float32
  - name: pwatermark
    dtype: float32
  - name: AESTHETIC_SCORE
    dtype: float32
  - name: hash
    dtype: int64
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 2566541109.0729218
    num_examples: 10770591
  download_size: 2076391246
  dataset_size: 2566541109.0729218
---
~10 million images from LAION Aesthetics v2, with aesthetic score > 5.5, probability of watermark < 0.95, and image megapixels > 1.05

Gathered using the following script

```python
from datasets import load_dataset
import os

def filt(row):
    w = row["WIDTH"]
    h = row["HEIGHT"]
    p = row["pwatermark"]
    a = row["AESTHETIC_SCORE"]

    if w is None or h is None or p is None:
        return False

    return (w * h >= 1024*1024) and p <= 0.95 and a > 5.5


if __name__ == "__main__":
    os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
    ds = load_dataset("laion/aesthetics_v2_4.75", split="train", num_proc=48, cache_dir="data")

    new_ds = ds.filter(lambda x: filt(x), num_proc=48)

    print("New length: ", len(new_ds))

    new_ds.save_to_disk("filtered")
    new_ds.push_to_hub("SwayStar123/laion_aesthetic_5.5_HQ")
```