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:
- 8c5860ab332ae397a6a6ba48912e3006e13ad15859b169d0183bc4b5c5c1cd50
- Size of remote file:
- 1.96 MB
- SHA256:
- d7d3c96aac24d4e6eb965969b352c1c229848b2b40f54b8848d716e7e303b8e1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.