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
File size: 1,031 Bytes
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"architectures": [
"NulaForImageClassification"
],
"auto_map": {
"AutoConfig": "configuration_nula.NulaConfig",
"AutoModelForImageClassification": "modeling_nula.NulaForImageClassification"
},
"model_type": "nula",
"block_channels": [
128,
256,
512
],
"classifier_hidden_dim": 512,
"dtype": "float32",
"id2label": {
"0": "airplane",
"1": "automobile",
"2": "bird",
"3": "cat",
"4": "deer",
"5": "dog",
"6": "frog",
"7": "horse",
"8": "ship",
"9": "truck"
},
"in_channels": 3,
"input_size": [
3,
32,
32
],
"label2id": {
"airplane": 0,
"automobile": 1,
"bird": 2,
"cat": 3,
"deer": 4,
"dog": 5,
"frog": 6,
"horse": 7,
"ship": 8,
"truck": 9
},
"mean": [
0.5,
0.5,
0.5
],
"num_classes": 10,
"se_reduction": 16,
"std": [
0.5,
0.5,
0.5
],
"transformers_version": "5.4.0",
"use_residual": true,
"use_se": true,
"use_spatial_attention": false
}
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