Image Classification
Transformers
Safetensors
English
clip
zero-shot-image-classification
fairface
vision
Eval Results (legacy)
Instructions to use syntheticbot/gender-classification-clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use syntheticbot/gender-classification-clip with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="syntheticbot/gender-classification-clip") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("syntheticbot/gender-classification-clip") model = AutoModelForZeroShotImageClassification.from_pretrained("syntheticbot/gender-classification-clip", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from syntheticbot/gender-classification-clip: direct link, hf CLI and curl.
- Browser
- Download file 2.22 MB
-
https://huggingface.co/syntheticbot/gender-classification-clip/resolve/main/tokenizer.json
- Command line
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hf download hf://syntheticbot/gender-classification-clip/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/syntheticbot/gender-classification-clip/resolve/main/tokenizer.json
2.22 MB
File too large to display, you can check the raw version instead.