Instructions to use hf-internal-testing/tiny-random-NatForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-NatForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/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-internal-testing/tiny-random-NatForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 131 Bytes
2ff4c9e | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:de8868fb1d7dbf91386e9df3c0d3daebf8b2ddb919963acffc30c2f02b90f2a8
size 341869
|