Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use WT-MM/vit-base-blur with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WT-MM/vit-base-blur with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="WT-MM/vit-base-blur") 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("WT-MM/vit-base-blur") model = AutoModelForImageClassification.from_pretrained("WT-MM/vit-base-blur") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
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## Model description
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Model trained for binary classification between 'noisy' (blurry) and clean images, where 'noisy' images are the result of unfinished/insufficient passes from an LDM for image generation
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## Intended uses & limitations
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## Training and evaluation data
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1000ish clean and blurry images using 30 and 10 steps respectively on SD2.1
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## Training procedure
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