Instructions to use hf-internal-testing/tiny-random-DinatForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-DinatForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-DinatForImageClassification") 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-DinatForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-DinatForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8aff83bc77e570dd3f6e0628bf749a0fd856e079399ee253431cc169426ddf5
- Size of remote file:
- 342 kB
- SHA256:
- 610db2c57bfa9bbe7709425c825ceba4fe5a8b1951693ba4fd3c83efba0f7b31
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