Instructions to use mobilint/YOLOv9c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/YOLOv9c with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="YOLOv9c", 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 ·
ea72004
1
Parent(s): bec1715
feat: Add ARIES model artifacts
Browse files- aries/best_result.json +1 -0
- aries/yolov9c.mxq +3 -0
aries/best_result.json
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{"acc": 0.5259797642054045, "timestamp": 1778613623, "checkpoint_dir_name": null, "done": true, "training_iteration": 1, "trial_id": "dbf433d8", "date": "2026-05-13_04-20-23", "time_this_iter_s": 528.8150515556335, "time_total_s": 528.8150515556335, "pid": 115805, "hostname": "285e74beb487", "node_ip": "172.17.0.3", "config": {"percentile": 4.498306996117221e-05, "topk": 0.0445126756866202}, "time_since_restore": 528.8150515556335, "iterations_since_restore": 1, "experiment_tag": "9_percentile=0.0000,topk=0.0445"}
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aries/yolov9c.mxq
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version https://git-lfs.github.com/spec/v1
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oid sha256:d26c6c318e4386590a99475196e72c98916d3598b9fe59675c8cc1e930535ac9
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size 28884638
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