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| license: cc-by-sa-4.0 |
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| |
| # syn-vis-v0-base |
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| Mirror of the exact bmcore v24 holdout subset. Source: [retowyss/Syn-Vis-v0](https://huggingface.co/datasets/retowyss/Syn-Vis-v0). |
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| All 480 local basenames match images/base/. Card licenses images CC0 and compilation/documentation CC BY-SA 4.0. |
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| Contains 480 synthetic image files. This mirror repackages the media; it does not grant additional rights. |
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| Source revision reviewed: `100262732989e77f38cd831d70a376a93735006a`. |
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| ## Original source card |
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| --- |
| license: cc-by-sa-4.0 |
| language: |
| - en |
| tags: |
| - synthetic |
| - faces |
| - portraits |
| - face-generation |
| - semi-realistic |
| pretty_name: Syn-Vis-v0 |
| size_categories: |
| - n<1K |
| --- |
|
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| # Syn-Vis-v0: A Dataset of Synthetic Faces |
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| Syn-Vis-v0 (Synthetic Visage Version 0) is a dataset of 480 synthetic faces generated with Qwen-Image and Qwen-Image-Edit-2509. |
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| - **Diversity**: |
| - The dataset is balanced across ethnicities - approximately 60 images per broad category (Asian, Black, Hispanic, White, Indian, Middle Eastern) and 120 ethnically ambiguous images. |
| - Wide range of skin-tones, facial features, hairstyles, hair colors, nose shapes, eye shapes, and eye colors. |
| - **Quality**: |
| - Rendered at 2048x2048 resolution using Qwen-Image-Edit-2509 (BF16) and 50 steps. |
| - Checked for artifacts, defects, and watermarks. |
| - **Style**: semi-realistic, 3d-rendered CGI, with hints of photography and painterly accents. |
| - **Captions**: Natural language descriptions consolidated from multiple caption sources using GPT-OSS-120B. |
| - **Metadata**: Each image is accompanied by ethnicity/race analysis scores (0-100) across six categories (Asian, Indian, Black, White, Middle Eastern, Latino Hispanic) generated using DeepFace. |
| - **Analysis Cards**: Each image has a corresponding analysis card showing similarity to other faces in the dataset. |
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| ## Updates |
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| - **2025-11-01**: Added a second set of images. These images have a higher shot-type consistency (head-and-shoulders portraits). |
| - Directory: `images/headshot/` |
| - Manifest: `metadata_headshot.csv` |
| - Resolution: 1024×1024 (50 steps, CFG 4, Qwen-Image-Edit-2509 BF16) |
| - Metadata: Only filename links to corresponding base image; demographic scores are not re-computed. |
| - **2025-10-24**: Improved dataset structure to better conforms with `datasets` library standards. Added `dataset_info.json` for schema definition. Updated README with more detailed information. |
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| ## Analysis Cards |
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| ## Dataset Structure |
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| ```txt |
| Syn-Vis-v0/ |
| ├── images/ |
| │ └── base/ # Main dataset images |
| │ └── headshot/ # Additional headshot-style images |
| ├── metadata.csv # Root-level metadata file for Hugging Face preview |
| ├── metadata_headshot.csv |
| ├── dataset_info.json # Schema definition for image and metadata fields |
| ├── misc/ # Analysis cards, banners, etc. |
| └── README.md |
| ``` |
|
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| ## Metadata Format |
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| The `metadata.csv` contains the following columns: |
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| - `file_name`: Image filename (e.g., "001-0042.png") |
| - `caption`: Consolidated natural language description |
| - `race_asian`: Asian demographic score (0-100) |
| - `race_indian`: Indian demographic score (0-100) |
| - `race_black`: Black demographic score (0-100) |
| - `race_white`: White demographic score (0-100) |
| - `race_middle_eastern`: Middle Eastern demographic score (0-100) |
| - `race_latino_hispanic`: Latino Hispanic demographic score (0-100) |
| - `dominant_race`: Primary predicted demographic category |
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| ## Caption Quality |
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| Captions are consolidated from multiple JoyCaption-Beta-One outputs using GPT-OSS-120B and feature: |
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| - **Natural language**: Start with "The woman..." or "A woman..." |
| - **Specific descriptions**: Physical features described precisely rather than using broad demographic categories |
| - **Structured order**: Face → hair/physical features → clothing → background → lighting |
| - **Style-neutral**: Remove technical photography terms and medium references |
| - **Flowing narrative**: Natural sentences without section headers |
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| Example caption: |
| > "The woman has a smooth medium‑brown complexion that catches a gentle, even glow. Her eyes are large, dark brown and framed by thick, dark lashes, giving them a calm, slightly serious look as she gazes directly forward..." |
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| ## Use Cases |
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| - Raw training data for small models |
| - Base images for image-to-image generation tasks |
| - Base images for style transfer |
| - Whatever you want! |
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| ## Statistics |
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| - **Ethnicities**: (by Dominant Race Counts; some faces show similar scores across multiple categories) |
| - White: 94 images |
| - Latino Hispanic: 93 images |
| - Asian: 90 images |
| - Indian: 70 images |
| - Black: 68 images |
| - Middle Eastern: 65 images |
| - **Skin tones**: Full spectrum from very light to very dark |
| - **Facial features**: Wide variety of eye shapes, nose shapes, lip shapes |
| - **Hair styles**: Various textures, colors, and arrangements |
| - **Backgrounds**: Dark and light, plain and scenic |
| - **Ages**: Almost exclusively 30 ± 5 years according to analysis with DeepFace. |
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| > Anecdotally, Asian, White, and Black, were predicted with a single high score (85+) much more frequently than Latino Hispanic, Indian and Middle Eastern. |
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| ## Ethical Considerations and Other Notes |
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| - The ethnicity/race labels are generated by automated analysis and should not be considered ground truth for real-world applications involving human subjects. Their primary purpose is to ensure coverage of wide range facial features. |
| - Only female-presenting individuals are included. I decided against including male-presenting individuals because **beards** - I didn't know how well the classifiers would handle them (obscured features), so I decided to avoid that complexity. |
| - All faces were explicitly declared female-presenting (in the prompt and the caption), however, DeepFace occasionally suggested some images may be male-presenting. |
| - The dataset has a strong beauty bias and the faces are unusually symmetrical. |
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| ## Creation Process |
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| 1. **Initial Image Generation**: Generated an initial set of 5,500 images at 768x768 using Qwen-Image (FP8). Facial features were randomly selected from lists and then written into natural prompts by Qwen3:30b-a3b. The style prompt was "Photo taken with telephoto lens (130mm), low ISO, high shutter speed". |
| 2. **Initial Analysis & Captioning**: Each of the 5,500 images was captioned three times using JoyCaption-Beta-One. These initial captions were then consolidated using Qwen3:30b-a3b. Concurrently, demographic analysis was run using DeepFace. |
| 3. **Selection**: A balanced subset of 480 images was selected based on the aggregated demographic scores and visual inspection. |
| 4. **Enhancement**: Minor errors like faint watermarks and artifacts were manually corrected using GIMP. |
| 5. **Upscaling & Refinement**: The selected images were upscaled to 2048x2048 using Qwen-Image-Edit-2509 (BF16) with 50 steps at a CFG of 4. The prompt guided the model to transform the style to a high-quality 3d-rendered CGI portrait while maintaining the original likeness and composition. |
| 6. **Final Captioning**: To ensure captions accurately reflected the final, upscaled images and accounted for any minor perspective shifts, the 480 images were fully re-captioned. Each image was captioned three times with JoyCaption-Beta-One, and these were consolidated into a final, high-quality description using GPT-OSS-120B. |
| 7. **Final Analysis**: Each final image was analyzed using DeepFace to generate the demographic scores and similarity analysis cards present in the dataset. |
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| ### Models and Tools Used |
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| - **Qwen-Image**: Image Generation |
| - **Qwen-Image-Edit-2509**: Image Refinement/Upscaling |
| - **JoyCaption-Beta-One**: Captioning |
| - **Qwen3:30b-a3b**: Prompt Writing & Initial Caption Consolidation |
| - **GPT-OSS-120B**: Final Caption Consolidation |
| - **Tools**: vLLM, DeepFace, Python, R, GIMP, ComfyUI |
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| ## Projects That Use Syn-Vis-v0 |
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| - Coming soon! |
| - Your project here? |
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| ## Known Issues |
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| - 001-0309: Appears to be wearing a mask, likely introduced during the image-to-image upscaling step. |
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| ## License |
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| This project is released under a dual-license structure: |
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| - **Images**: All individual images in this dataset are dedicated to the public domain via the **Creative Commons CC0 1.0 Universal Public Domain Dedication**. You are free to copy, modify, distribute, and perform the work, even for commercial purposes, all without asking permission. |
| - **Dataset & Documentation**: The specific compilation of this dataset, its metadata, and all associated documentation (the "Curation") are licensed under the **Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-SA 4.0)**. |
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| Interpretation of the License: |
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| - You can use the images for any project (commercial or non-commercial) with no attribution and no restrictions. |
| - You can use and share the entire dataset/documentation, but you must give credit to us (BY) and share any modifications to the curation itself under the same (SA) license. |
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| ## Citation |
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| ```bibtex |
| @dataset{syn-vis-v0-2025, |
| title={Syn-Vis-v0: A Synthetic Face Dataset}, |
| author={Wyss, Reto}, |
| year={2025}, |
| url={https://huggingface.co/datasets/retowyss/Syn-Vis-v0}, |
| note={Images: CC0 (Public Domain); Dataset compilation and documentation: CC-BY-SA-4.0} |
| } |
| ``` |
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