Anime Eye Detector (YOLOv8)

Also available on Hugging Face🤗: https://huggingface.co/killjoyelite/anime-eye-yolov8

A YOLOv8 object detection model fine-tuned to detect eyes in anime-style character art, intended for use with ComfyUI + Impact Pack for automated eye detailing/inpainting workflows (similar to how face_yolov8n.pt and hand_yolov8n.pt are used).

Model details

• Base model: yolov8n.pt/yolov8s.pt (Ultralytics)

• Task: Object detection, single class (eye)

• Training data: 212 self-generated anime-style images (AI-generated, primarily female characters), manually labeled with bounding boxes around each visible eye

• Training config: 100 epochs, image size 640, batch size 8

Performance (on validation split)

Known limitations

• Trained predominantly on female anime characters — detection on male character eyes is less reliable and may miss detections.

• Struggles with very large, cartoony/chibi-style eyes that deviate significantly from standard anime proportions or sometimes closeup of faces (WIP).

• Trained entirely on a single generation style/checkpoint's output — may generalize less well to very different art styles (e.g. heavily stylized, painterly, or non-anime art) than to mainstream anime/semi-realistic anime styles.

• Small dataset (212 images) — while validation metrics are strong, real-world robustness across the full diversity of anime art is inherently more limited than a larger, more varied dataset would provide.

If you find specific failure cases, feel free to open a discussion — this is a good candidate for community-driven dataset expansion over time.

Examples

Detection preview — the model correctly finds eyes across different poses/styles:

Eye color change/Eye fixing — using the detected eye region with Detailer (SEGS) to redraw eye color/detail from a prompt, while keeping the rest of the image untouched:

Usage (ComfyUI)

1. Download the model file — Civitai renames files automatically (e.g. animeEyeDetector_v10.pt, animeEyeDetector_v11Small.pt).

2. Place it in:

   ComfyUI/models/ultralytics/bbox/
   

3. Restart ComfyUI.

4. In your workflow:

   Load Image → UltralyticsDetectorProvider (select the eye detector model) → BboxDetectorSEGS → Detailer (SEGS)
   

5. Recommended Detailer (SEGS) starting settings for eye detailing:

   • guide_size: 512

   • denoise: 0.5–0.7 (lower = closer to the original eye, higher = more prompt-driven reinterpretation)

   • feather: 5–10

License

Released under the MIT License. Training images were self-generated by the author; users should independently verify licensing terms of any base checkpoint used to generate their own training/inference images if that matters for their use case.