Instructions to use SpotLab/MobileNetClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SpotLab/MobileNetClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SpotLab/MobileNetClassification") 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("SpotLab/MobileNetClassification") model = AutoModelForImageClassification.from_pretrained("SpotLab/MobileNetClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tflite_model.tflite from SpotLab/MobileNetClassification: direct link, hf CLI and curl.
- Browser
- Download file 10.2 MB
-
https://huggingface.co/SpotLab/MobileNetClassification/resolve/main/tflite_model.tflite
- Command line
-
hf download hf://SpotLab/MobileNetClassification/tflite_model.tflite
-
curl -L -o tflite_model.tflite https://huggingface.co/SpotLab/MobileNetClassification/resolve/main/tflite_model.tflite
10.2 MB
- Xet hash:
- 56ffd2e8237cecff913dc29b5d054a167234c9e4048c0b3cf4f2a126336d28f4
- Size of remote file:
- 10.2 MB
- SHA256:
- 9df60af7ab24d287a54668e845ea7da1c854086b828a4a4cf46c55c403095053
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