Image Classification
Transformers
TensorBoard
Safetensors
PyTorch
vit
huggingpics
Eval Results (legacy)
Instructions to use musaoc/gender_classfication_finetune450 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use musaoc/gender_classfication_finetune450 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="musaoc/gender_classfication_finetune450") 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_classfication_finetune450") model = AutoModelForImageClassification.from_pretrained("musaoc/gender_classfication_finetune450") - Notebooks
- Google Colab
- Kaggle
gender_classfication_finetune450
Autogenerated by HuggingPics🤗🖼️
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
random man
random things
random woman
- Downloads last month
- 3
Evaluation results
- Accuracyself-reported0.936


