Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
huggingpics
Eval Results (legacy)
Instructions to use rizvandwiki/gender-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rizvandwiki/gender-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rizvandwiki/gender-classification") 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("rizvandwiki/gender-classification") model = AutoModelForImageClassification.from_pretrained("rizvandwiki/gender-classification", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 994d79548e0498d9bd336485e6cdb8b0a139d3c735964f11bffd9487db12dbff
- Size of remote file:
- 29.5 kB
- SHA256:
- 802366c93c74e84e7eff1d6b676c2cd5c6a9e2c34907556959ebff7356996e52
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.