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:
- 2a5e11acc78237cadb16dda5024edb86795e1b100f9d9e7c038ac7e494d14e02
- Size of remote file:
- 197 kB
- SHA256:
- 63b23c6ec74439fd4d419597f11b41d58255dcab415f432c79156f7dde14e1b2
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