Instructions to use HorcruxNo13/beit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HorcruxNo13/beit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="HorcruxNo13/beit-base-patch16-224") 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("HorcruxNo13/beit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("HorcruxNo13/beit-base-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
beit-base-patch16-224 / runs /May04_13-45-02_9f063c349a98 /events.out.tfevents.1714830304.9f063c349a98.602.0
- Xet hash:
- f5f87027237e65fbf99487ecb9cbcf5b31870fdafec38c3f775dd1db77c93fd6
- Size of remote file:
- 28.7 kB
- SHA256:
- 3a8d891af617944e6521c1b58434cfd647677d9cf55fad90a791998f8444a597
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.