Instructions to use hf-internal-testing/tiny-random-MobileViTV2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileViTV2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-MobileViTV2ForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-MobileViTV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-MobileViTV2ForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-MobileViTV2ForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 1.26 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-MobileViTV2ForImageClassification/resolve/6647b4b5d8a8b1fb11ad60a6b46e1bfbc41d7f19/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-MobileViTV2ForImageClassification@6647b4b5d8a8b1fb11ad60a6b46e1bfbc41d7f19/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-MobileViTV2ForImageClassification/resolve/6647b4b5d8a8b1fb11ad60a6b46e1bfbc41d7f19/pytorch_model.bin
1.26 MB
- Xet hash:
- c4e33d861487107102741a4e54ab31f56d344a6f7c2b28404353e01f0a81a385
- Size of remote file:
- 1.26 MB
- SHA256:
- 8a6a3d2921429e7772a761c06852a686c6c17e287072b7c5dae814a7a5857737
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