Object Detection
ultralytics
ONNX
PyTorch
comfyui
face-detection
face-segmentation
image-segmentation
Instructions to use vdaular/ForbiddenVision_Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use vdaular/ForbiddenVision_Models with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("vdaular/ForbiddenVision_Models", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 629 Bytes
f4a16e7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"model_name": "ForbiddenVision Face Detection & Segmentation",
"version": "1.0.0",
"models": {
"face_detection": {
"file": "ForbiddenVision_face_detect_v1.pt",
"architecture": "YOLO11s",
"precision": 0.996,
"recall": 1.0,
"training_data": "14k+ mixed-domain images"
},
"face_segmentation": {
"file": "ForbiddenVision_face_segment_v1.pth",
"architecture": "TimmUniversalEncoder (tf_efficientnetv2_s.in21k_ft_in1k)",
"training_data": "4k+ mixed-domain images"
}
},
"usage": "Designed for ComfyUI via ForbiddenVision node pack",
"license": "Apache-2.0"
} |