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: 793 Bytes
8ed8ef5 a2c4db7 8ed8ef5 1e7183e 8ed8ef5 6334c1f 8ed8ef5 b58ec66 8ed8ef5 | 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 31 32 33 34 35 36 37 38 39 40 41 | {
"model_type": "NULA",
"num_classes": 10,
"in_channels": 3,
"input_size": [3, 32, 32],
"block_channels": [64, 128, 256],
"use_residual": true,
"use_se": true,
"use_spatial_attention": false,
"norm_layer": "batchnorm",
"activation": "relu",
"classifier_hidden_dim": 256,
"init_scheme": "kaiming_normal",
"dataset": "uoft-cs/cifar10",
"mean": [0.5, 0.5, 0.5],
"std": [0.5, 0.5, 0.5],
"id2label": {
"0": "airplane",
"1": "automobile",
"2": "bird",
"3": "cat",
"4": "deer",
"5": "dog",
"6": "frog",
"7": "horse",
"8": "ship",
"9": "truck"
},
"label2id": {
"airplane": 0,
"automobile": 1,
"bird": 2,
"cat": 3,
"deer": 4,
"dog": 5,
"frog": 6,
"horse": 7,
"ship": 8,
"truck:" 9
}
} |