Instructions to use mobilint/EfficientNet_B0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/EfficientNet_B0 with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="EfficientNet_B0", 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 ·
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Parent(s): f645358
Add aries results and configure git lfs
Browse files- .gitattributes +1 -0
- aries/best_result.json +1 -0
- aries/efficientnet_b0_IMAGENET1K_V1.mxq +3 -0
.gitattributes
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*.mxq filter=lfs diff=lfs merge=lfs -text
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aries/best_result.json
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{"acc": 0.7566, "timestamp": 1776519936, "checkpoint_dir_name": null, "done": true, "training_iteration": 1, "trial_id": "127c5e04", "date": "2026-04-18_22-45-36", "time_this_iter_s": 765.3710839748383, "time_total_s": 765.3710839748383, "pid": 1255061, "hostname": "ae30e054f296", "node_ip": "172.17.0.3", "config": {"percentile": 0.012916074229647054, "topk": 0.011929267655945553}, "time_since_restore": 765.3710839748383, "iterations_since_restore": 1, "experiment_tag": "12_percentile=0.0129,topk=0.0119"}
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aries/efficientnet_b0_IMAGENET1K_V1.mxq
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version https://git-lfs.github.com/spec/v1
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oid sha256:490eec366cfbd4398ef4a765d2db5b07c039d2ff2bd4b7e096011101856f0a3f
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size 7432894
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