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/efa0b217161c90434c1226431108628a4e9b09c6/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-EfficientFormerForImageClassification@efa0b217161c90434c1226431108628a4e9b09c6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-EfficientFormerForImageClassification/resolve/efa0b217161c90434c1226431108628a4e9b09c6/pytorch_model.bin
1.84 MB
- Xet hash:
- d499120a54a5f70f0c2019f1584a8318a3f9d0ab7eb06a5ed811740716e7ac67
- Size of remote file:
- 1.84 MB
- SHA256:
- fd6750a6772222e6f5707c02b373daad602a379ecb23435c0a73a468b68b8d8e
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