Instructions to use mobilint/EfficientNet_B3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/EfficientNet_B3 with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="EfficientNet_B3", 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
Kanybek Asanbekov commited on
Commit ·
6aeba14
1
Parent(s): 4231dbd
Add aries results and configure git lfs
Browse files- .gitattributes +1 -0
- aries/best_result.json +1 -0
- aries/efficientnet_b3_IMAGENET1K_V1.mxq +3 -0
.gitattributes
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aries/best_result.json
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{"acc": 0.81166, "timestamp": 1776595434, "checkpoint_dir_name": null, "done": true, "training_iteration": 1, "trial_id": "ea377d64", "date": "2026-04-19_19-43-54", "time_this_iter_s": 1246.2904686927795, "time_total_s": 1246.2904686927795, "pid": 3792601, "hostname": "ae30e054f296", "node_ip": "172.17.0.3", "config": {"percentile": 0.0012631317321570416, "topk": 0.0019332855154857876}, "time_since_restore": 1246.2904686927795, "iterations_since_restore": 1, "experiment_tag": "7_percentile=0.0013,topk=0.0019"}
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aries/efficientnet_b3_IMAGENET1K_V1.mxq
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version https://git-lfs.github.com/spec/v1
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oid sha256:2b8cebab211b7a1420a50f90495f9d04f96843e4b291901895bb7974a01c3a24
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size 16872322
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