Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
huggingpics
Eval Results (legacy)
Instructions to use sanali209/nsfwfilter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sanali209/nsfwfilter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sanali209/nsfwfilter") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("sanali209/nsfwfilter") model = AutoModelForImageClassification.from_pretrained("sanali209/nsfwfilter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload best model after training (val_acc: 0.8347)
Browse files- config.json +1 -1
- label_mappings.pth +3 -0
- model.safetensors +1 -1
config.json
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"ViTForImageClassification"
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"_name_or_path": "google/vit-base-patch16-224",
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label_mappings.pth
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model.safetensors
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size 343230128
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