Instructions to use hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-EfficientFormerForImageClassificationWithTeacher", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e4816a6d5d833125d151d8b98216dce09c2c79187a2719b589a15fc4c6a58d5
- Size of remote file:
- 1.84 MB
- SHA256:
- c04aaf18af06c375d23b8440231ca144aeb7424c8a86ffd3846a50558f7f13f5
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