Instructions to use hf-tiny-model-private/tiny-random-ViTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-ViTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/tiny-random-ViTForImageClassification") 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-tiny-model-private/tiny-random-ViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-ViTForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63e51ec63c6fd35f80cb8a4ab1dabd52779e754eb53d8af9f41b16c6fd25ae9c
- Size of remote file:
- 176 kB
- SHA256:
- abed1e4b3cc55b207b715375a91a5cb21d82327936666e2bc457a58cd1eddeae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.