Instructions to use Rizky2Hero/Gender_Identification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rizky2Hero/Gender_Identification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Rizky2Hero/Gender_Identification") 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("Rizky2Hero/Gender_Identification") model = AutoModelForImageClassification.from_pretrained("Rizky2Hero/Gender_Identification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Rizky2Hero/Gender_Identification: direct link, hf CLI and curl.
- Browser
- Download file 308 Bytes
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https://huggingface.co/Rizky2Hero/Gender_Identification/resolve/main/README.md
- Command line
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hf download hf://Rizky2Hero/Gender_Identification/README.md
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curl -L -o README.md https://huggingface.co/Rizky2Hero/Gender_Identification/resolve/main/README.md
308 Bytes
| 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 | |