Comic Layout YOLO26s
SafeTensors distribution of a YOLO26s instance-segmentation model for comic-page layout analysis. It detects panels, visible text regions, and speech or narration balloons for OCR and translation workflows.
Labels
| ID | Label | Meaning |
|---|---|---|
| 0 | frame | Panel or frame region |
| 1 | text | Visible text region |
| 2 | balloon | Speech, thought, or narration container |
The class order is checkpoint-sensitive and must not be changed.
Files
model.safetensors: model state dictionaryconfig.json: Koharu model and processor configurationyolo26s-seg.yaml: authoritative Ultralytics architecture configuration extracted from the checkpointexport-manifest.json: conversion hashes and validation results
The model uses a 1280-pixel inference size. Single-image prediction follows Ultralytics letterboxing with stride 32, confidence threshold 0.25, IoU threshold 0.7, and original-resolution masks.
Conversion
The MIT-licensed source checkpoint was published by ShadowB and trained with Ultralytics 8.4.43. This repository converts the original PyTorch archive to SafeTensors; it does not contain the pickle-based source checkpoint.
| Property | Value |
|---|---|
| Source revision | 3a860269ee0beb43ce9f31d82c7851441eb178ae |
| Source checkpoint SHA-256 | 0b4376e426fa96af3976afa6a2602421dacf2dec96ef87b4a44f5e8d4971cb6f |
| SafeTensors SHA-256 | d577a9e66bcf4e3445c0ee799d8407b7f095cb94010745f8eca763c20583de66 |
| Parameters | 11,436,269 |
| Tensors | 844 |
Conversion validation performs all of the following:
- Reloads every serialized tensor and checks exact equality.
- Loads the state dictionary into a freshly constructed YOLO26s segmentation model with strict key and shape checking.
- Compares all forward-output tensors on a deterministic input.
The recorded maximum absolute forward-output difference is 0.0.
Koharu
Koharu resolves config.json and model.safetensors directly from this repository. The public result contains the original image dimensions and a label, confidence, bounding box, area, and original-resolution binary mask for each instance.
License and provenance
The converted model is distributed under the upstream MIT license. Dataset terms remain separate from the model license. Validation metrics above are copied from the upstream checkpoint and have not been independently reproduced during conversion.