Instructions to use hf-internal-testing/tiny-random-SwiftFormerForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-SwiftFormerForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-SwiftFormerForImageClassification") 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-SwiftFormerForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-SwiftFormerForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 31ab5debb21b93376f152bea40f918ed80d54624aace19a39b51d3de2ae4fa8d
- Size of remote file:
- 14 MB
- SHA256:
- 2245bb0c8c5ed798b890cfe92530bdc07cd5fd78cd60a0b8bc0ca635c515232d
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