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
CHANGED
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@@ -110,7 +110,7 @@ for epoch in range(1, NUM_EPOCHS + 1):
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print(f"new best: {100 * best_val_acc:.2f}%")
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current_lr = optimizer.param_groups[0]["lr"]
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print("
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print(f"Epoch [{epoch}/{NUM_EPOCHS}]")
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print(f"lr : {current_lr:.6f}")
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print(f"train_loss : {train_loss:.4f}")
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print(f"new best: {100 * best_val_acc:.2f}%")
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current_lr = optimizer.param_groups[0]["lr"]
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+
print(f"{'|'}{'-'*60}{'|'}")
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print(f"Epoch [{epoch}/{NUM_EPOCHS}]")
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print(f"lr : {current_lr:.6f}")
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print(f"train_loss : {train_loss:.4f}")
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