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")# pip install -U transformers accelerate # 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
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Download README.md from rizvandwiki/gender-classification: direct link, hf CLI and curl.
- Browser
- Download file 747 Bytes
-
https://huggingface.co/rizvandwiki/gender-classification/resolve/main/README.md
- Command line
-
hf download hf://rizvandwiki/gender-classification/README.md
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curl -L -o README.md https://huggingface.co/rizvandwiki/gender-classification/resolve/main/README.md
747 Bytes
metadata
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: gender-classification
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9244444370269775
gender-classification
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.

