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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If you use this model or repository in your research, please cite:
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```bibtex
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@misc{
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author = {MamaPearl},
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title = {},
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year = {2026},
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publisher = {
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url = {https://
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}
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```
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## License
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If you use this model or repository in your research, please cite:
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```bibtex
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@misc{mamapearl_nula_2026,
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author = {MamaPearl},
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title = {NULA: Anti-Aliased SE-CNN for CIFAR-10},
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year = {2026},
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publisher = {Hugging Face},
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url = {https://huggingface.co/MamaPearl/nula-base-cifar10-v0}
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}
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```
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## License
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