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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51dbf74857f6d6cbf496b72e3648df31583cfb6a23cdfdbd7123f79c14a7793e
- Size of remote file:
- 14.2 kB
- SHA256:
- 402e6a542f06a2c06763558550f9ed42b11607ffb492b43eaaadd2670a31e1fc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.