Image Classification
Transformers
Safetensors
English
siglip
Gender
Classification
art
realism
portrait
Male
Female
SigLIP2
Instructions to use prithivMLmods/Realistic-Gender-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Realistic-Gender-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Realistic-Gender-Classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Realistic-Gender-Classification") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Realistic-Gender-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43f565ab8f5c47c045aba83c862ce31fb81dd043283df1a662fb06a6c45db618
- Size of remote file:
- 687 MB
- SHA256:
- 713f988c399b04c41125df910fd92ef75a2d0cb56d19c66ffabeda21d23162ab
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