Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
huggingpics
Eval Results (legacy)
Instructions to use rizvandwiki/gender-classification-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rizvandwiki/gender-classification-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rizvandwiki/gender-classification-2") 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-2") model = AutoModelForImageClassification.from_pretrained("rizvandwiki/gender-classification-2") - Inference
- Notebooks
- Google Colab
- Kaggle
gender-classification-2
Autogenerated by HuggingPicsπ€πΌοΈ
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
female
male
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Spaces using rizvandwiki/gender-classification-2 16
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EdBoy2202/ImageAttributeDetectionandImageGeneration
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Djbahiaiajak/njklludndndjn
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narendra-mall/gender-classification
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bit-guber/Face_Features_Extraction
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gartajackhats1985/custom_nodes
Evaluation results
- Accuracyself-reported0.991

