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
| { | |
| "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 | |
| } | |
| } | |
| } |