README.md

---
license: apache-2.0
dataset_info:
  features:
  - name: image
    dtype: image
  - name: prompt
    dtype: string
  - name: word_scores
    dtype: string
  - name: alignment_score_norm
    dtype: float32
  - name: coherence_score_norm
    dtype: float32
  - name: style_score_norm
    dtype: float32
  - name: alignment_heatmap
    sequence:
      sequence: float16
  - name: coherence_heatmap
    sequence:
      sequence: float16
  - name: alignment_score
    dtype: float32
  - name: coherence_score
    dtype: float32
  - name: style_score
    dtype: float32
  splits:
  - name: train
    num_bytes: 13690325760.8
    num_examples: 6550
  download_size: 9034018750
  dataset_size: 13690325760.8
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-to-image
- text-classification
- image-classification
- image-to-text
- image-segmentation
language:
- en
tags:
- t2i
- preferences
- human
- flux
- midjourney
- imagen
- dalle
- heatmap
- coherence
- alignment
- style
- plausiblity
pretty_name: Rich Human Feedback for Text to Image Models
size_categories:
- 1M<n<10M
---
<a href="https://www.rapidata.ai">
<img src="https://cdn-uploads.huggingface.co/production/uploads/66f5624c42b853e73e0738eb/jfxR79bOztqaC6_yNNnGU.jpeg" width="250" alt="Rapidata Logo">
</a>

Building upon Google's research [Rich Human Feedback for Text-to-Image Generation](https://arxiv.org/abs/2312.10240) we have collected over 1.5 million responses from 152'684 individual humans using Rapidata via the [Python API](https://docs.rapidata.ai/)

# Overview
We asked humans to evaluate AI-generated images in style, coherence and prompt alignment. For images that contained flaws, participants were asked to identify specific problematic areas. Additionally, for all images, participants identified words from the prompts that were not accurately represented in the generated images.

# Word Scores
Users identified words from the prompts that were NOT accurately depicted in the generated images. Higher word scores indicate poorer representation in the image. Participants also had the option to select "[No_mistakes]" for prompts where all elements were accurately depicted.

### Examples Results:
| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/lzlWHmLKBvBJhjGWP8xZZ.png" width="500"> | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/b38uskYWaGEgfeJQtKiaO.png" width="500"> |
|---|---|
| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/4uWKVjZBA5aX2YDUYNpdV.png" width="500"> | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/f9JIuwDoNohy7EkDYILFm.png" width="500"> |


# Coherence
The coherence score measures whether the generated image is logically consistent and free from artifacts or visual glitches. Without seeing the original prompt, users were asked: "Look closely, does this image have weird errors, like senseless or malformed objects, incomprehensible details, or visual glitches?" Each image received 21 responses, which were aggregated on a scale of 1-5.

Images scoring below 3.8 in coherence were further evaluated, with participants marking specific errors in the image.

### Example Results:

| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/sc-4ls9X0yO-hGN0VCDSX.png" width="500"> | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/J77EmYp4oyRRakkcRnaF9.png" width="500"> |
|---|---|
| <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/mRDdoQdc4_iy2JcLhdI7J.png" width="500"> | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/2N2KJyz4YOGT6N6tuUX8M.png" width="500"> |


# Alignment
The alignment score quantifies how well an image matches its prompt. Users were asked: "How well does the image match the description?". The final score is calculated on a scale of 1-5 by aggregating 21 responses per prompt-image pair.

For images with an alignment score below 3.2, additional users were asked to highlight areas where the image did not align with the prompt. These responses were then compiled into a heatmap.

As mentioned in the google paper, aligment is harder to annotate consistently, if e.g. an object is missing, it is unclear to the annotators what they need to highlight. 

### Example Results:


<style>
.example-results-grid {
    display: grid;
    grid-template-columns: repeat(2, 450px);
    gap: 20px;
    margin: 20px 0;
    justify-content: left;
}

.result-card {
    background-color: #fff;
    border-radius: 8px;
    box-shadow: 0 2px 4px rgba(0,0,0,0.1);
    padding: 15px;
    width: 450px;
}

.prompt {
    margin-bottom: 10px;
    font-size: 18px;
    line-height: 1.4;
    color: #333;
    background-color: #f8f8f8;
    padding: 10px;
    border-radius: 5px;
}

.image-container img {
    width: 450px;
    height: auto;
    border-radius: 4px;
}

@media (max-width: 1050px) {
    .example-results-grid {
        grid-template-columns: 450px;
    }
}
</style>

<div class="example-results-grid">
    <div class="result-card">
        <div class="prompt">
            <strong>Prompt:</strong> Three cats and one dog sitting on the grass.
        </div>
        <div class="image-container">
            <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/qCNWVSNjPsp8XQ3zliLcp.png" alt="Three cats and one dog">
        </div>
    </div>
    <div class="result-card">
        <div class="prompt">
            <strong>Prompt:</strong> A brown toilet with a white wooden seat.
        </div>
        <div class="image-container">
            <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/M3buzP-5k4pRCxOi_ijxM.png" alt="Brown toilet">
        </div>
    </div>
    <div class="result-card">
        <div class="prompt">
            <strong>Prompt:</strong> Photograph of a pale Asian woman, wearing an oriental costume, sitting in a luxurious white chair. Her head is floating off the chair, with the chin on the table and chin on her knees, her chin on her knees. Closeup
        </div>
        <div class="image-container">
            <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/ggYXUEbGppiTeL84pG-DP.png" alt="Asian woman in costume">
        </div>
    </div>
    <div class="result-card">
        <div class="prompt">
            <strong>Prompt:</strong> A tennis racket underneath a traffic light.
        </div>
        <div class="image-container">
            <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/mT7sAbnO-w6ySXaeEqEki.png" alt="Racket under traffic light">
        </div>
    </div>
</div>


# Style
The style score reflects how visually appealing participants found each image, independent of the prompt. Users were asked: "How much do you like the way this image looks?" Each image received 21 responses, which were aggregated on a scale of 1-5.

# About Rapidata
Rapidata's technology makes collecting human feedback at scale faster and more accessible than ever before. Visit [rapidata.ai](https://www.rapidata.ai/) to learn more about how we're revolutionizing human feedback collection for AI development.