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:
- 4b4a23e7ee6613f3dd41531bf32f6c45af2ae6c239ac5f2800f6fe5cf10bcc4c
- Size of remote file:
- 14 MB
- SHA256:
- 9818f0a2995646e33beeee6fe9925d41297150f35f35d561544b2222f2fb8d9a
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