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 modeling_nula.py
Browse files- modeling_nula.py +1 -1
modeling_nula.py
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@@ -3,7 +3,7 @@ import torch.nn as nn
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import torch.nn.functional as F
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from transformers import PreTrainedModel
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from transformers.modeling_outputs import ImageClassifierOutput
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from configuration_nula import NulaConfig
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class BlurPool2d(nn.Module):
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def __init__(self, channels, stride=2):
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import torch.nn.functional as F
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from transformers import PreTrainedModel
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from transformers.modeling_outputs import ImageClassifierOutput
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from .configuration_nula import NulaConfig
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class BlurPool2d(nn.Module):
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def __init__(self, channels, stride=2):
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