Instructions to use PSImera/manga_bubbles_detect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use PSImera/manga_bubbles_detect with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("PSImera/manga_bubbles_detect") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| tags: | |
| - object-detection | |
| - manga | |
| - yolov8 | |
| - speech-bubble-detection | |
| - ultralytics | |
| datasets: | |
| - PSImera/manga_bubbles_detect | |
| # manga_bubbles_detect | |
| > [Русская версия](README-RU.md) | |
| YOLOv8 model for detecting speech bubbles in manga pages. Trained to locate bubble bounding boxes (single class `location-of-bubbles`). | |
| **Validation mAP50 ≈ 0.977** | |
| Part of [Manga Translate](https://github.com/PSImera/manga_translate) — a full manga translation pipeline (bubble detection → OCR → inpaint → LLM → render). | |
|  | |
| ## Usage | |
| ```python | |
| from ultralytics import YOLO | |
| model = YOLO("bubbles_detect.pt") | |
| results = model.predict("page.jpg", conf=0.25, iou=0.5, imgsz=1024) | |
| ``` | |
| Or use it automatically via the [Manga Translate](https://github.com/PSImera/manga_translate) app — the model is downloaded from here on first run. | |
| ## Training data | |
| Trained on a combined dataset published at [PSImera/manga_bubbles_detect](https://huggingface.co/datasets/PSImera/manga_bubbles_detect): | |
| - [manga.v4i (Roboflow)](https://universe.roboflow.com/manga-wtdm0/manga-mvbxx) — 1304/189/103 train/valid/test pages | |
| - 1079 additional pages from [DLS Manga Translator](https://github.com/ikefir34/DLS_Manga_Translator), manually corrected in CVAT | |
| Training scripts and reports: [training/](https://github.com/PSImera/manga_translate/tree/main/training) | |