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")# pip install -U transformers accelerate # 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
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README.md
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## Training Statistics
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## Usage
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## Training Statistics
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first evaluation on CIFAR-10 test set (10,000 images). All perturbations applied at inference
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the model never saw test distribution during training.
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| Perturbation | Accuracy | Drop |
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| Clean | 91.95% | — |
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| Resize ×0.5 (bilinear) | 59.83% | −32.12% |
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| Resize ×0.25 (bilinear) | 24.82% | −67.13% |
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| Decimate ×2 | 30.03% | −61.92% |
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| Checkerboard ε=0.03 | 75.47% | −16.48% |
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| Checkerboard ε=0.05 | 44.99% | −46.96% |
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## Usage
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