Instructions to use hf-internal-testing/tiny-random-MobileNetV2ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileNetV2ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-MobileNetV2ForImageClassification") 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-MobileNetV2ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-MobileNetV2ForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hf-internal-testing/tiny-random-MobileNetV2ForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 1.04 MB
-
https://huggingface.co/hf-internal-testing/tiny-random-MobileNetV2ForImageClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://hf-internal-testing/tiny-random-MobileNetV2ForImageClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hf-internal-testing/tiny-random-MobileNetV2ForImageClassification/resolve/refs%2Fpr%2F1/model.safetensors
1.04 MB
- Xet hash:
- 869c3082ab451bb89c660e13842e400741b5c7ca4295649ba4ff0dc228c35f02
- Size of remote file:
- 1.04 MB
- SHA256:
- 1208846f47411d98ef88edcb199f1826a2780da729a09e40a1ace75a818b14c3
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