Instructions to use hf-internal-testing/tiny-random-FocalNetForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FocalNetForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-FocalNetForImageClassification") 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-FocalNetForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-FocalNetForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edea8da6c440387242ab66347a6b81d7dd2170e97253f74f5e91a073d69a84ac
- Size of remote file:
- 303 kB
- SHA256:
- 77e3e9516c27e5d09ba841dab6c90b0d2b138bf5895bcedb052f3695539b271c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.