--- 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: 25257389633.104 num_examples: 13024 download_size: 17856619960 dataset_size: 25257389633.104 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 Rapidata Logo

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/). Collection took roughly 5 days. # 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: |

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| # 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: |

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| # 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:

Prompt: Three cats and one dog sitting on the grass.

Prompt: A brown toilet with a white wooden seat.

Prompt: 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

Prompt: A tennis racket underneath a traffic light.

# 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. In contrast to other prefrence collection methods, such as the huggingface image arena, the preferences were collected from humans from around the world (156 different countries) from all walks of life, creating a more representative score. # 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. # Other Datasets We run a benchmark of the major image generation models, the results can be found on our [website](https://www.rapidata.ai/leaderboard/image-models). We rank the models according to their coherence/plausiblity, their aligment with the given prompt and style prefernce. The underlying 2M+ annotations can be found here: - Link to the [Coherence dataset](https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset) - Link to the [Text-2-Image Alignment dataset](https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Alignment_Dataset) - Link to the [Preference dataset](https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3)