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
Commit ·
fbff38e
1
Parent(s): 8bce5a7
feat: Add ARIES model artifacts
Browse files- aries/best_result.json +1 -0
- yolov8m-pose.mxq → aries/yolov8m-pose.mxq +0 -0
- best_result.json +0 -7
aries/best_result.json
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{"acc": 0.6215340229049843, "timestamp": 1776258694, "checkpoint_dir_name": null, "done": true, "training_iteration": 1, "trial_id": "b86367c7", "date": "2026-04-15_22-11-34", "time_this_iter_s": 361.62191104888916, "time_total_s": 361.62191104888916, "pid": 1268110, "hostname": "0cad359a9af6", "node_ip": "172.17.0.2", "config": {"percentile": 0.0017391936196009922, "topk": 0.014699819111394832}, "time_since_restore": 361.62191104888916, "iterations_since_restore": 1, "experiment_tag": "11_percentile=0.0017,topk=0.0147"}
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yolov8m-pose.mxq → aries/yolov8m-pose.mxq
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best_result.json
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"acc": 0.6215340229049843,
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"config": {
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"percentile": 0.0017391936196009922,
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"topk": 0.014699819111394832
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}
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}
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