efficientnetv2-xl-native-exp2
EfficientNetV2-XL fine-tuned on CIFAR-10 at native 32x32 resolution (adapted stem).
Fine-tuned from ImageNet-pretrained weights (timm name tf_efficientnetv2_xl) on
CIFAR-10 as part of a native-resolution vs upsampling study.
Evaluation results (CIFAR-10 test set, 10,000 images)
| Metric | Value |
|---|---|
| Top-1 accuracy | 97.02% |
| Top-5 accuracy | 99.85% |
| F1 (macro) | 0.9702 |
| AUC (macro) | 0.9976 |
| ECE | 0.1227 |
- Parameters: 206.85M
- Input: native 32x32 CIFAR images (adapted 3x3 stride-1 stem)
- Preprocessing: resize to 32x32, then normalize with
CIFAR-10 mean
[0.4914, 0.4822, 0.4465]and std[0.247, 0.2435, 0.2616].
Usage
from cifar_classifier import CIFAR10Classifier
clf = CIFAR10Classifier.from_pretrained("DeKUT-DSAIL/convnextv2-huge-cifar10-upsample")
label, probs = clf.predict("image.jpg")
print(label, probs)
Get cifar_classifier.py from the hf_deployment/ folder of the training
repository. It only needs torch, torchvision, timm, Pillow and
huggingface_hub.
Classes
airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck
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