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 tuned model (val_acc: 0.8310) from Ray Tune
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
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"_name_or_path": "D:/Sanali209/Python/SLM/files_db/annotation_tool/ray_results/image_classification_tune_v2/
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"architectures": [
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"ViTForImageClassification"
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],
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"_name_or_path": "D:/Sanali209/Python/SLM/files_db/annotation_tool/ray_results/image_classification_tune_v2/tune_with_parameters_d1ebf_00000_0_lr=0.0001,optimizer_type=AdamW_2025-05-02_10-26-53/checkpoint_000004\\model",
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"architectures": [
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"ViTForImageClassification"
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],
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model.safetensors
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oid sha256:2033d0b1af7ec553b3fdedcfb8c12d729d0afff1af1c109922cf9fb1ed361624
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size 343230128
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