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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4f1f075d3366d747061d96d188e206b04c4b29a0af1721ac229e59e9c81fda7b
- Size of remote file:
- 928 kB
- SHA256:
- c64f7b84174e367fc6e1486d2391bf4c6b660621aa509cd3ab853fec697313c7
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