Instructions to use dima806/man_woman_face_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/man_woman_face_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/man_woman_face_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("dima806/man_woman_face_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/man_woman_face_image_detection") - Inference
- Notebooks
- Google Colab
- Kaggle
Returns with about 98.7% accuracy whether the face belongs to man or woman based on face image.
See https://www.kaggle.com/code/dima806/man-woman-face-image-detection-vit for more details.
Classification report:
precision recall f1-score support
man 0.9885 0.9857 0.9871 51062
woman 0.9857 0.9885 0.9871 51062
accuracy 0.9871 102124
macro avg 0.9871 0.9871 0.9871 102124
weighted avg 0.9871 0.9871 0.9871 102124
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Model tree for dima806/man_woman_face_image_detection
Base model
google/vit-base-patch16-224-in21k