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: 813 Bytes
fde1a86 30d2f28 fde1a86 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | {
"model": "nula-base-cifar10-v0",
"note": "clean_training = standard training, adversarial_training = with resize + decimate + blur_decimate augmentation. adversarial model is the deployment candidate.",
"results": {
"clean": {
"clean_training": 0.9195,
"adversarial_training": 0.8942
},
"resize_0.5": {
"clean_training": 0.5983,
"adversarial_training": 0.8537
},
"resize_0.25": {
"clean_training": 0.2482,
"adversarial_training": 0.7180
},
"decimate_x2": {
"clean_training": 0.3003,
"adversarial_training": 0.8502
},
"checker_eps_0.03": {
"clean_training": 0.7547,
"adversarial_training": 0.8943
},
"checker_eps_0.05": {
"clean_training": 0.4499,
"adversarial_training": 0.8939
}
}
} |