Instructions to use hf-internal-testing/tiny-random-HieraForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-HieraForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-HieraForImageClassification") 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-HieraForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-HieraForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f016753efa93ba16a6308c2201cf56d1a64eaae298ba8589603be6a622fc1b25
- Size of remote file:
- 1.92 MB
- SHA256:
- 68153d36fc2f294e20014d90fabaced13243a12e7284aa9fdcae939ca4e2a5d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.