Image Classification
Transformers
Safetensors
English
siglip
OpenSDI
Spotting Diffusion-Generated Images in the Open World
SD1.5
AI-vs-Real
SigLIP2
Stable Diffusion v1-5
Instructions to use prithivMLmods/OpenSDI-SD1.5-SigLIP2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/OpenSDI-SD1.5-SigLIP2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/OpenSDI-SD1.5-SigLIP2") 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/OpenSDI-SD1.5-SigLIP2") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/OpenSDI-SD1.5-SigLIP2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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- AI-vs-Real
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- SigLIP2
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- Stable Diffusion v1-5
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- AI-vs-Real
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- SigLIP2
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- Stable Diffusion v1-5
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```py
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Classification Report:
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precision recall f1-score support
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Real_Image 0.9036 0.9323 0.9177 10000
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SD1.5_Generated 0.9301 0.9005 0.9150 10000
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accuracy 0.9164 20000
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macro avg 0.9168 0.9164 0.9164 20000
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weighted avg 0.9168 0.9164 0.9164 20000
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```
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