Instructions to use hf-internal-testing/tiny-random-DeiTForImageClassificationWithTeacher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DeiTForImageClassificationWithTeacher with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-DeiTForImageClassificationWithTeacher") 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("hf-internal-testing/tiny-random-DeiTForImageClassificationWithTeacher") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-DeiTForImageClassificationWithTeacher", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 56c8713e1a873626a41951332c9dc41f52e7093486765fe9857baf241fccc74e
- Size of remote file:
- 297 kB
- SHA256:
- c4e5619f385f8059e366f7ee86b4a914c4a76c4ccb241164bd37e94ef401cbe1
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