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/1beb34ae0def113fbb7b75457c25ed5b385c755b/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-EfficientFormerForImageClassification@1beb34ae0def113fbb7b75457c25ed5b385c755b/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-EfficientFormerForImageClassification/resolve/1beb34ae0def113fbb7b75457c25ed5b385c755b/pytorch_model.bin
1.84 MB
- Xet hash:
- 2750a8415f53c97a978ddd09def6f237f52095bd41795baf72be3348850ebfcf
- Size of remote file:
- 1.84 MB
- SHA256:
- 3a760678b586490e10f9e85ce5d48483a66d1ad4f7ff6485a46aa260dabdbeb7
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