Instructions to use wcycn/touhou-ai-yolo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use wcycn/touhou-ai-yolo with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("wcycn/touhou-ai-yolo") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Touhou AI YOLO Detector
An experimental 21-class object detector used by Touhou AI, an unofficial visual-control project for Touhou Koumakyou: the Embodiment of Scarlet Devil.
This is a fan-made technical experiment. It is not affiliated with, endorsed by, or sponsored by Team Shanghai Alice, ZUN, or the Touhou Project.
Demo
The overlay is reconstructed from a recorded AI Control session and shows detections, tracked bullet trajectories, collision risk, planned movement, and the keys held by the controller.
Model details
- Task: object detection
- Framework: Ultralytics YOLO / PyTorch
- Checkpoint format: PyTorch
.pt - Training image size: 640
- Classes: 21
- SHA-256:
78eb395d277bb5f35f27025a7bada7725928d6e7f7b15681f659a43b5bf60ab2
Classes
| ID | Class |
|---|---|
| 0 | boss |
| 1 | boss_occluded |
| 2 | bullet_enemy |
| 3 | bullet_enemy_big_blue |
| 4 | bullet_enemy_big_green |
| 5 | bullet_enemy_big_red |
| 6 | bullet_enemy_big_yellow |
| 7 | bullet_enemy_small_blue |
| 8 | bullet_enemy_small_green |
| 9 | bullet_enemy_small_red |
| 10 | bullet_enemy_small_yellow |
| 11 | bullet_enemy_unique_blue |
| 12 | bullet_enemy_unique_green |
| 13 | bullet_enemy_unique_red |
| 14 | bullet_enemy_unique_yellow |
| 15 | bullet_player |
| 16 | character |
| 17 | enemy_small_blue |
| 18 | enemy_small_red |
| 19 | powerup_blue |
| 20 | powerup_red |
Training data
The repository owner trained this checkpoint on a locally collected and annotated screenshot dataset:
- Total images: 1,834
- Training split: 1,794 images and 1,794 YOLO label files
- Validation split: 40 images and 40 YOLO label files
- Annotation format: YOLO bounding boxes
The raw screenshots are not distributed with this model because they contain
game imagery. The repository owner keeps a
private archival backup
for recovery and reproducibility; it is not a public dataset. A separate
final_train folder found in the historical project was produced later through
automatic annotation and was not used to train this checkpoint.
Usage
from ultralytics import YOLO
model = YOLO("best.pt")
results = model.predict(
source="frame.png",
imgsz=640,
conf=0.15,
)
The checkpoint is designed for the original project's capture pipeline and should not be assumed to generalize to other Touhou games, resolutions, visual mods, scaling settings, or capture methods.
Known limitations
- No independent, manually reviewed benchmark is currently published.
- Detection quality is uneven across the 21 classes.
- Lasers are not represented as a dedicated class.
- Dense boss patterns and partially occluded player sprites remain difficult.
- Some training samples were augmented; reported sample counts are not a substitute for real-world evaluation.
This model should not be presented as a reliable game-clear system or as a general-purpose Touhou detector.
License
The checkpoint is released under the GNU Affero General Public License v3.0, consistent with the Ultralytics YOLO open-source license.
The Touhou Project, game imagery, names, characters, and other third-party materials remain the property of their respective rights holders and are not licensed by this model repository.
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