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 config.json
Browse files- config.json +5 -0
config.json
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"architectures": [
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"NulaForImageClassification"
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],
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"block_channels": [
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128,
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256,
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"architectures": [
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"NulaForImageClassification"
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],
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"auto_map": {
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"AutoConfig": "configuration_nula.NulaConfig",
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"AutoModelForImageClassification": "modeling_nula.NulaForImageClassification"
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},
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"model_type": "nula",
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"block_channels": [
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128,
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256,
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