---
task_categories:
  - text-to-image
language:
  - en
tags:
  - text-to-image
  - diffusion
  - image-caption
  - imagefolder
  - rectified-flow
  - echo-vision
license: apache-2.0
size_categories:
  - 10K<n<100K
---

# EchoVision-45K

**Training data for [Echo-Vision-1](https://huggingface.co/Maxilicious20/echo-vision-1)** — a two-stage coarse-to-fine rectified-flow text-to-image model trained from scratch on a single consumer GPU.

> ~45,000 image–caption pairs · max 768 px · machine-generated captions · copyright-filtered · compressed JPEG

---

## Dataset Summary

EchoVision-45K is the curated dataset used to train **Echo-Vision-1**, a cascade latent-diffusion model (draft 384 px → final 768 px). It consists of square-cropped web images paired with detailed machine-generated captions.

- **Images:** max side 768 px, optimized JPEG (quality 85). Larger originals were downscaled because the model never consumes more than 768 px.
- **Captions:** generated by **Florence-2-large** (`<MORE_DETAILED_CAPTION>`), with ~7 k high-quality single-sentence captions from **Qwen2-VL-2B-Instruct** and BLIP fallbacks.
- **Safety:** a two-layer copyright filter (deterministic franchise/brand/watermark text blacklist + CLIP zero-shot visual flagging) removed logos, watermarks, and licensed-franchise content.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("Maxilicious20/echo-vision-45k")
sample = ds["train"][0]
sample["image"]     # PIL.Image  (<=768px)
sample["caption"]   # str
```

Training-style transform:

```python
from torchvision import transforms
tf = transforms.Compose([
    transforms.Resize((768, 768)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5]),
])
x = tf(sample["image"])   # -> [3,768,768] in [-1,1]
```

## Dataset Structure

- **Config:** `imagefolder` (auto-detected)
- **Features:** `image` (Image), `caption` (string)
- **Split:** `train` (~45 k examples; exact count in the Files/Viewer)

## Data Collection & Processing

1. **Collection:** web images downloaded with associated queries.
2. **Captioning:** Florence-2-large / Qwen2-VL-2B / BLIP.
3. **Filtering:** resolution & sharpness (Laplacian) pre-filter.
4. **Copyright removal:** text blacklist (franchises, brands, "watermark", "fan art", ©/™/® …) + CLIP zero-shot visual flagging → flagged pairs deleted.
5. **Compression:** downscale to ≤768 px + JPEG q85 (no training-relevant information lost).

## Limitations & Biases

- Web-scraped imagery inherits the **biases of the open web** (demographic, cultural, aesthetic).
- Machine captions can be **noisy or incomplete**.
- Despite filtering, residual copyrighted or identifiable content may remain; this dataset is intended for **research only**.

## License & Third-Party Content

The dataset's original contributions — captions, metadata, curation/filtering code, and the
compiled arrangement — are released under the **Apache License 2.0**.

The underlying images were collected from public web sources and **remain the property of
their respective copyright holders**. The Apache-2.0 license does **not** grant rights to
these third-party images. This dataset is provided for **research only**.

If you hold rights to any image and wish it removed, please open a discussion — it will be
taken down promptly.

## Citation

```bibtex
@misc{echovision45k,
  title  = {EchoVision-45K: A Copyright-Filtered Image--Caption Dataset
            for Text-to-Image Diffusion Research},
  author = {<Your Name>},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Maxilicious20/echo-vision-45k}}
}
```

---

*Companion model: [Echo-Vision-1](https://huggingface.co/Maxilicious20/echo-vision-1) (Apache-2.0) · Built on a single RTX 4060.*