Instructions to use hf-tiny-model-private/tiny-random-MobileNetV1ForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-MobileNetV1ForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hf-tiny-model-private/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-tiny-model-private/tiny-random-MobileNetV1ForImageClassification") model = AutoModelForImageClassification.from_pretrained("hf-tiny-model-private/tiny-random-MobileNetV1ForImageClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c44a3cb003e20f4991e73828ffa3b01156ddfcc57ca34ff5085b28fc1458ec8
- Size of remote file:
- 894 kB
- SHA256:
- 68abe711db903d5805cc32026734a5b1e2e2b85de2e5085219a7288b0471e1aa
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