Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use musaoc/gender_vender with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use musaoc/gender_vender with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="musaoc/gender_vender") 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("musaoc/gender_vender") model = AutoModelForImageClassification.from_pretrained("musaoc/gender_vender") - Notebooks
- Google Colab
- Kaggle
gender_vender
The difference between this and usual classifiers is, it is not limited to Man and woman. Rather, if you pass a chart, it would not classify as man or woman unlike other classifiers.
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
Example Images
man
random things
woman
- Downloads last month
- 4
Evaluation results
- Accuracyself-reported0.938


