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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-EfficientFormerForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-EfficientFormerForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 1.84 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-EfficientFormerForImageClassification/resolve/3a0145edeed7698ad4caa12648dc3f05195be03f/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-EfficientFormerForImageClassification@3a0145edeed7698ad4caa12648dc3f05195be03f/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-EfficientFormerForImageClassification/resolve/3a0145edeed7698ad4caa12648dc3f05195be03f/pytorch_model.bin
1.84 MB
- Xet hash:
- 5417c86d4a0ec915e25e2274ffa4834c8a225e579142086358fdbc3437484f35
- Size of remote file:
- 1.84 MB
- SHA256:
- 886ff0a0ba22639b933661bf1403afd90eaf1c4e4c05092bab1c346ab0ba60e8
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