Instructions to use mobilint/Swin_B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/Swin_B with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="Swin_B", 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
| library_name: mobilint | |
| license: bsd-3-clause | |
| tags: | |
| - mobilint | |
| pipeline_tag: image-classification | |
| base_model_relation: quantized | |
| <div align="center"> | |
| <a href="https://mobilint.com"> | |
| <img src="https://raw.githubusercontent.com/mobilint/.github/main/assets/Mobilint_Logo_Primary.png?raw=true" | |
| width="50%" | |
| alt="mobilint" /> | |
| </a> | |
| </div> | |
| # About | |
| This repository provides a model compiled and optimized for Mobilint NPU hardware. | |
| The model is packaged for deployment on Mobilint’s acceleration stack and is intended to be used within that environment. |