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CRUISEResearchGroup
/
COMODO-AnyMo-Bench

Feature Extraction
Transformers
Joblib
Safetensors
English
comodo
imu
wearable-sensing
human-activity-recognition
time-series
cross-modal-distillation
svm
custom_code
Model card Files Files and versions
xet
Community

Instructions to use CRUISEResearchGroup/COMODO-AnyMo-Bench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use CRUISEResearchGroup/COMODO-AnyMo-Bench with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("feature-extraction", model="CRUISEResearchGroup/COMODO-AnyMo-Bench", trust_remote_code=True)
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("CRUISEResearchGroup/COMODO-AnyMo-Bench", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
COMODO-AnyMo-Bench / core50 /classifier
214 MB
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  • 1 contributor
History: 1 commit
Breezelled's picture
Breezelled
Release COMODO on AnyMo Bench with Core50 and Fine150 inference bundles
1e4dcb8 verified 22 days ago
  • config.json
    1.66 kB
    Release COMODO on AnyMo Bench with Core50 and Fine150 inference bundles 22 days ago
  • scaler.joblib
    106 kB
    xet
    Release COMODO on AnyMo Bench with Core50 and Fine150 inference bundles 22 days ago
  • svm.joblib
    213 MB
    xet
    Release COMODO on AnyMo Bench with Core50 and Fine150 inference bundles 22 days ago