Instructions to use hf-tiny-model-private/tiny-random-NatForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-NatForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-NatForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-NatForImageClassification", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 342 Bytes
8b7ed7f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"crop_size": 64,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "ViTImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 64,
"width": 64
}
}
|