Instructions to use hf-internal-testing/tiny-random-MobileNetV1ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MobileNetV1ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-internal-testing/tiny-random-MobileNetV1ForImageClassification") 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-MobileNetV1ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-internal-testing/tiny-random-MobileNetV1ForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 703ead3f6713c7071ff330f7e4418b53ae3a9faf03addc3f88584f82ce67c670
- Size of remote file:
- 894 kB
- SHA256:
- 161a48e770bcea1f950f9f1ad81c184fa5717115bc6de1b15da2020a8fa578cc
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