Instructions to use mobilint/ShuffleNet_V2_X2_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/ShuffleNet_V2_X2_0 with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="ShuffleNet_V2_X2_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
Prepare board-specific Aries Vision artifacts
Browse files
aries-rb/best_result.json
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{"acc": 0.75614, "timestamp": 1778164800, "checkpoint_dir_name": null, "done": true, "training_iteration": 1, "trial_id": "e9e5e97b", "date": "2026-05-07_23-40-00", "time_this_iter_s": 369.7420651912689, "time_total_s": 369.7420651912689, "pid": 1064428, "hostname": "ae30e054f296", "node_ip": "172.17.0.3", "config": {"percentile": 0.0002604786277948382, "topk": 0.009256026158522085}, "time_since_restore": 369.7420651912689, "iterations_since_restore": 1, "experiment_tag": "2_percentile=0.0003,topk=0.0093"}
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