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")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 53fcc4689a5d10c6b2bd72db6103cf840b170bcb668d43a9a0181656beff5381
- Size of remote file:
- 1.11 MB
- SHA256:
- d36ac5d0867773ffad974ea34200a040b8347590d93ea5e4a57fa37a796f13fe
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