Feature Extraction
Transformers
Joblib
Safetensors
English
comodo
imu
wearable-sensing
human-activity-recognition
time-series
cross-modal-distillation
svm
custom_code
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
Download core50/processor_config.json from CRUISEResearchGroup/COMODO-AnyMo-Bench: direct link, hf CLI and curl.
- Browser
- Download file 285 Bytes
-
https://huggingface.co/CRUISEResearchGroup/COMODO-AnyMo-Bench/resolve/main/core50/processor_config.json
- Command line
-
hf download hf://CRUISEResearchGroup/COMODO-AnyMo-Bench/core50/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/CRUISEResearchGroup/COMODO-AnyMo-Bench/resolve/main/core50/processor_config.json
285 Bytes
| { | |
| "processor_class": "ComodoProcessor", | |
| "sampling_rate": 60, | |
| "sequence_length": 512, | |
| "input_layout": "[T,18] or [B,T,18]", | |
| "location_order": [ | |
| "head", | |
| "left_wrist", | |
| "right_wrist" | |
| ], | |
| "auto_map": { | |
| "AutoProcessor": "processing_comodo.ComodoProcessor" | |
| } | |
| } | |