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intelligent-maintenance-distilled-h5
wanfengaodaliya/intelligent-maintenance-distilled-h5
intelligent-maintenance-distilled-h5 is a machine learning model from wanfengaodaliya. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch.
This directory is the self-contained runtime package for the formal edge model.
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Public
Updated Aug 18, 2026
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.pt305 KB · 98%
At a glance
- Library
- pytorch
- Access
- Public
- Created
- Aug 18, 2026
- Updated
- Aug 18, 2026
- SHA
- edf483de
Model card
From the Hugging Face model README
Distilled H5 Edge Model
This directory is the self-contained runtime package for the formal edge model.
best_model.pt is the distilled three-branch H5 checkpoint. Its SHA-256 must
match checkpoint_sha256.txt; the service verifies this before loading.
Runtime inputs are a 50 ms raw packet: 3,200 vibration samples at 64 kHz, three 200-sample operating-condition channels at 4 kHz, and one temperature value. The runner applies the training-time 4x polyphase downsampling only to the CNN branch; physical features use the original vibration data.
Release mirror: wanfengaodaliya/intelligent-maintenance-distilled-h5.
The project copy is the default runtime source. The public Hugging Face mirror contains the same release package for recovery and teammate distribution.
Run the service from the repository root with conda activate moment, then
python cloud_edge_project/edge_service/run_edge_service.py.
- Library
- pytorch
- Created
- Aug 18, 2026
- Updated
- Aug 18, 2026