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:
- 1161171994171ee11dfa4b9b174816a79efe1e97fbc661b72c3ec875e9906808
- Size of remote file:
- 687 MB
- SHA256:
- b12c9a85d762309f2411ea3489fd88d125a8682b7a985cd165979a0519782898
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.