Instructions to use MBZUAI/swiftformer-s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MBZUAI/swiftformer-s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="MBZUAI/swiftformer-s") 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("MBZUAI/swiftformer-s") model = AutoModelForImageClassification.from_pretrained("MBZUAI/swiftformer-s") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0441e75a3218adbae2b9453e92353c8c164a08a1efe89f1c5fe439ba987ce5d8
- Size of remote file:
- 24.5 MB
- SHA256:
- 637e2c7e408b307ceaa0dc39d439327b6efe32bf4b330f50c5ab622b7dcdc766
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.