Instructions to use mobilint/SqueezeNet1_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/SqueezeNet1_0 with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="SqueezeNet1_0", model_type="DEFAULT", model_path="", core_mode="global8", ) try: image = model.preprocess("path/to/image.jpg") output = model(image) result = model.postprocess(output) finally: model.dispose() - Notebooks
- Google Colab
- Kaggle
Commit History
3 days ago
-
Publish approved Variant MXQ
Verified signature27b02bc
-
Prepare board-specific Aries Vision artifacts
Verified signaturefa90192 -
Prepare board-specific Aries Vision artifacts
Verified signature88525ba
-
Add library_name: mobilint to model card metadata
Verified signature1a996b4
-
Update ONNX model from torchvision
Verified signature25adc3b
-
Update Mobilint logo URL in README
Kanybek Asanbekov5c38247
-
Move mxq and best_result to aries folder and update gitattributes
Kanybek Asanbekov17e2bf6
-
Update README.md
Verified signaturea35bdff -
initial commit
Verified signature4188248