Image Classification
Transformers
Safetensors
English
siglip
Human
Non-Human
Detection
SigLIP2
Vision-Encoder
Instructions to use prithivMLmods/Human-vs-NonHuman-Detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Human-vs-NonHuman-Detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Human-vs-NonHuman-Detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/Human-vs-NonHuman-Detection") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Human-vs-NonHuman-Detection") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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datasets:
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- prithivMLmods/Human-vs-NonHuman
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license: apache-2.0
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datasets:
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- prithivMLmods/Human-vs-NonHuman
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```py
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Classification Report:
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precision recall f1-score support
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Human 𖨆 0.9939 0.9735 0.9836 6646
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Non Human メ 0.9807 0.9956 0.9881 8989
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accuracy 0.9862 15635
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macro avg 0.9873 0.9845 0.9858 15635
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weighted avg 0.9863 0.9862 0.9862 15635
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```
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