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
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Download README.md from vdaular/ForbiddenVision_Models: direct link, hf CLI and curl.
- Browser
- Download file 939 Bytes
-
https://huggingface.co/vdaular/ForbiddenVision_Models/resolve/main/README.md
- Command line
-
hf download hf://vdaular/ForbiddenVision_Models/README.md
-
curl -L -o README.md https://huggingface.co/vdaular/ForbiddenVision_Models/resolve/main/README.md
939 Bytes
metadata
license: apache-2.0
library_name: ultralytics
base_model:
- timm/tf_efficientnetv2_s.in21k_ft_in1k
- Ultralytics/YOLO11
tags:
- comfyui
- object-detection
- face-detection
- face-segmentation
- pytorch
- image-segmentation
Custom-trained models for face detection and segmentation across realistic, anime, and stylized content.
Made for the Forbidden Vision ComfyUI custom nodes
Contact
- GitHub: ComfyUI-Forbidden-Vision
- Issues: GitHub Issues
- Support: Ko-fi