Instructions to use optimum-intel-internal-testing/tiny-random-vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use optimum-intel-internal-testing/tiny-random-vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="optimum-intel-internal-testing/tiny-random-vit") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("optimum-intel-internal-testing/tiny-random-vit") model = AutoModelForImageClassification.from_pretrained("optimum-intel-internal-testing/tiny-random-vit") - Notebooks
- Google Colab
- Kaggle
File size: 227 Bytes
d4dddda | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | {
"do_normalize": true,
"do_resize": true,
"feature_extractor_type": "ViTFeatureExtractor",
"image_mean": [
0.5,
0.5,
0.5
],
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"size": 30
}
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