Image Classification
Transformers
Safetensors
nula
computer-vision
cnn
cifar10
adversarial-robustness
stress-test
downsampling
anti-aliasing
custom_code
Instructions to use MamaPearl/nula-cifar10-robust-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MamaPearl/nula-cifar10-robust-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MamaPearl/nula-cifar10-robust-v0", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("MamaPearl/nula-cifar10-robust-v0", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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[BlurPool: what it does mechanically and why it addresses the problem]
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[SE blocks: what they add]
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- For augmentation functions, see [`augmentations.py`](augmentations.py)
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- For the adversarial training loop, see [`train_robust.py`](train_robust.py)
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### FIRST EVALUATION (Base)
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[BlurPool: what it does mechanically and why it addresses the problem]
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[SE blocks: what they add]
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- For augmentation functions, see [`augmentations.py`](augmentations.py)
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- For the adversarial training loop, see [`train_robust.py`](train_robust.py)
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### FIRST EVALUATION (Base)
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