Instructions to use hf-internal-testing/tiny-random-EfficientFormerForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-EfficientFormerForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-EfficientFormerForImageClassification") 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-EfficientFormerForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 91de3276a35b59de6bd3b4305e61ddc6eb7baf56ac93c5cdb5f2aa962e1045e0
- Size of remote file:
- 1.84 MB
- SHA256:
- 773c4c5a6d3705a15c928c7ebac6c12a49475f7843c56bbd4eda4249cb61b500
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.