Object Detection
ultralytics
ONNX
PyTorch
comfyui
face-detection
face-segmentation
image-segmentation
Instructions to use luxdelux7/ForbiddenVision_Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use luxdelux7/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("luxdelux7/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
Update README.md
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README.md
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**Purpose:** Precise face mask generation
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**Training Approach:**
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- Dataset prepared using the Forbidden Vision
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- Iterative hardmine training in multiple phases:
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**Features:**
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- Detects and includes facial features other models ignore, like protruding anime eybrows, realistic eyelashes sticking out of the face etc.
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**Purpose:** Precise face mask generation
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**Training Approach:**
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- Dataset prepared using the Forbidden Vision YOLO model at 512px resolution
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- Iterative hardmine training in multiple phases:
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- Train on the initial 700 samples
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- Evaluate on remaining images to find failure cases
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- Correct failed masks and add them to the dataset
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- Retrain with the expanded dataset
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- Repeat until failure cases drop to near-zero
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(final dataset: 4k+ images)
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**Features:**
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- Detects and includes facial features other models ignore, like protruding anime eybrows, realistic eyelashes sticking out of the face etc.
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