Instructions to use hf-internal-testing/tiny-random-BitForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BitForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-BitForImageClassification") 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-BitForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-BitForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b27dd02d8eb3bff59c70fec407b9d5e6c5b4041588a7d8703bea4ccf8b46a473
- Size of remote file:
- 90.5 kB
- SHA256:
- 79b4ae6de8c5065404201528fc41b79836accc7c2bfb35682fe2bf18213949aa
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