Full Custom YOLO Detection Model
Model Description
This model is a custom-trained YOLO object detection model for multi-class detection and segmentation tasks on a specialized dataset.
It is trained for fine-grained object detection using bounding box annotations across multiple classes.
NSFW
breast
vulva
butt
penis
anal
vaginal
blowjob
handjob
Custom
face
nipple
mouth
eyes
navel
anus
Intended Use
Object detection on NSFW datasets
Bounding box classification for custom classes
Limitations
Trained on a custom dataset that was boxed with the ten classes by hand.
Performance may degrade on unseen domains or distributions, but in testing on 10k out of database images the error rate was less the 4%
Evaluation Results
Overall Metrics
Precision: 0.858
Recall: 0.808
mAP@50: 0.898
mAP@50-95: 0.6156
Per-Class Evaluation Results
============================
Class | Images | Instances | Precision | Recall | mAP50 | mAP50-95
all | 613 | 1683 | 0.858 | 0.809 | 0.898 | 0.616
person | 233 | 256 | 0.829 | 0.902 | 0.928 | 0.762
breast | 286 | 298 | 0.910 | 0.884 | 0.964 | 0.642
vulva | 165 | 166 | 0.874 | 0.777 | 0.873 | 0.495
butt | 127 | 131 | 0.848 | 0.771 | 0.895 | 0.596
male | 102 | 108 | 0.865 | 0.474 | 0.718 | 0.553
penis | 237 | 269 | 0.824 | 0.855 | 0.903 | 0.577
anal | 145 | 147 | 0.936 | 0.905 | 0.957 | 0.704
vaginal | 182 | 184 | 0.888 | 0.903 | 0.952 | 0.619
blowjob | 36 | 36 | 0.779 | 0.778 | 0.869 | 0.603
handjob | 73 | 88 | 0.830 | 0.841 | 0.925 | 0.603
Description
FAQ
Comments (4)
Is this for ultralytics library yolo? How does one access multiclass in comfy? Afaik impact pack's adetailer doesn't support keywords or something.
Are the person and face models also trained or the standard ones?
@firemanbrakeneck Person is trained, however in the GUI the standard nano face, and general model is used
It is standard ultralytics yolo import, but instead of the standard 30 something triggers it has the ones you see listed in the checkbox