Instructions to use averrous/workout_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use averrous/workout_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="averrous/workout_model") 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("averrous/workout_model") model = AutoModelForImageClassification.from_pretrained("averrous/workout_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02f3e24b593401a66e139277a52ea284a7ffd16e552d825ae28d840536ee4040
- Size of remote file:
- 5.11 kB
- SHA256:
- 2774c2df35698025f6f0f89f13db9ca9e90a775339dd6cdf7261d3399d551f2f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.