Instructions to use mobilint/GELANc-seg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Mobilint
How to use mobilint/GELANc-seg with Mobilint:
# pip install mblt-model-zoo from mblt_model_zoo.vision import MBLT_Engine model = MBLT_Engine( model_cls="GELANc-seg", 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 ·
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Parent(s): 9fd9425
feat: Add best_result.json and gelanc-seg.mxq files for model results and configuration
Browse files- aries/best_result.json +1 -0
- aries/gelanc-seg.mxq +3 -0
aries/best_result.json
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{"acc": 0.42149783854473644, "timestamp": 1776153702, "checkpoint_dir_name": null, "done": true, "training_iteration": 1, "trial_id": "8d7b8a6a", "date": "2026-04-14_17-01-42", "time_this_iter_s": 1310.8918352127075, "time_total_s": 1310.8918352127075, "pid": 294365, "hostname": "05a26c61d56d", "node_ip": "172.17.0.3", "config": {"percentile": 0.0005496340464680395, "topk": 0.030037990170711452}, "time_since_restore": 1310.8918352127075, "iterations_since_restore": 1, "experiment_tag": "8_percentile=0.0005,topk=0.0300"}
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aries/gelanc-seg.mxq
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version https://git-lfs.github.com/spec/v1
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oid sha256:82b57317f8626e3b447b7a66dd16d1278626bf6251704e6961b9587f3d1ccbe5
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size 31604207
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