Instructions to use BeckerAnas/swept-brook-197 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BeckerAnas/swept-brook-197 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="BeckerAnas/swept-brook-197") 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("BeckerAnas/swept-brook-197") model = AutoModelForImageClassification.from_pretrained("BeckerAnas/swept-brook-197", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6dacbf9fdf27b181f8297acca10454efe7b7912da73faf87c1113856e71d1153
- Size of remote file:
- 5.78 kB
- SHA256:
- 4d9d7aec13f161964c39e528b3a27ea1adb0ff73458e43fdf34687b63bf16e19
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.