Instructions to use mobilint/YOLOv8m-pose with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/YOLOv8m-pose with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="YOLOv8m-pose", 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
File size: 613 Bytes
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library_name: mobilint
license: agpl-3.0
tags:
- mobilint
pipeline_tag: keypoint-detection
base_model_relation: quantized
base_model:
- Ultralytics/YOLOv8
---
<div align="center">
<a href="https://mobilint.com">
<img src="https://github.com/mobilint/mblt-model-zoo/blob/master/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. |