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
Create config.json
Browse files- config.json +41 -0
config.json
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{
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"model_type": "NULA",
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"num_classes": 10,
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"in_channels": 3,
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"input_size": [3, 32, 32],
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"block_channels": [64, 128, 256],
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"use_residual": true,
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"use_se": true,
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"use_spatial_attention": false,
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"norm_layer": "batchnorm",
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"activation": "relu",
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"classifier_hidden_dim": 256,
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"init_scheme": "kaiming_normal",
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"dataset": "uoft-cs/cifar10",
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"mean": [0.5, 0.5, 0.5],
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"std": [0.5, 0.5, 0.5],
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"id2label" {
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"0": "airplane",
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"1": "automobile",
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"2": "bird",
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"3": "cat",
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"4": "deer",
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"5": "dog",
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"6": "frog",
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"7": "horse",
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"8":, "ship",
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"9": "truck"
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}
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"label2id": {
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"airplane": 0,
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"automobile": 1,
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"bird": 2,
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"cat": 3,
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"deer": 4,
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"dog": 5,
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"frog": 6,
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"horse": 7,
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"ship:" 8,
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"truck:" 9
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}
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}
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