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 train_robust.py
Browse files- train_robust.py +1 -1
train_robust.py
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@@ -5,7 +5,7 @@ from tqdm.auto import tqdm
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from torch.optim.lr_scheduler import LinearLR, CosineAnnealingLR, SequentialLR
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from configuration_nula import NulaConfig
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from modeling_nula import NulaForImageClassification, BlurPool2d
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from
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from augmentations import resize_down_up, decimate
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NUM_EPOCHS = 50
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from torch.optim.lr_scheduler import LinearLR, CosineAnnealingLR, SequentialLR
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from configuration_nula import NulaConfig
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from modeling_nula import NulaForImageClassification, BlurPool2d
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from dataset_nula import get_loaders, get_device
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from augmentations import resize_down_up, decimate
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NUM_EPOCHS = 50
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