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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1bac4125dc177b43f513796eb8caeef441b39838695407d170f677b851e66f19
- Size of remote file:
- 1.84 MB
- SHA256:
- 9fcfda3052bae1d58b34a47f9ca0dc8ea965a1065ce64697443baa302391e95f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.