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 model.safetensors from SpotLab/MobileNetClassification: direct link, hf CLI and curl.
- Browser
- Download file 14.2 MB
-
https://huggingface.co/SpotLab/MobileNetClassification/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://SpotLab/MobileNetClassification@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/SpotLab/MobileNetClassification/resolve/refs%2Fpr%2F1/model.safetensors
14.2 MB
- Xet hash:
- 44bd8232772f329de606ae349758099cacfc2183c83ef8e8c900692156ed666c
- Size of remote file:
- 14.2 MB
- SHA256:
- 94f61c6902b411e6b730cfe2443c8b2d983a2d50d289c4bd7083e0583ccdfe76
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