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: 317 Bytes
133e88b de95b80 133e88b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | {
"do_resize": true,
"size": {
"height": 32,
"width": 32
},
"resample": 3,
"do_center_crop": false,
"do_rescale": true,
"rescale_factor": 0.00392156862745098,
"do_normalize": true,
"image_mean": [0.5, 0.5, 0.5],
"image_std": [0.5, 0.5, 0.5],
"image_processor_type": "ViTImageProcessor"
} |