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/normalization_stats.json from CRUISEResearchGroup/COMODO-AnyMo-Bench: direct link, hf CLI and curl.
- Browser
- Download file 934 Bytes
-
https://huggingface.co/CRUISEResearchGroup/COMODO-AnyMo-Bench/resolve/main/core50/normalization_stats.json
- Command line
-
hf download hf://CRUISEResearchGroup/COMODO-AnyMo-Bench/core50/normalization_stats.json
-
curl -L -o normalization_stats.json https://huggingface.co/CRUISEResearchGroup/COMODO-AnyMo-Bench/resolve/main/core50/normalization_stats.json
934 Bytes
| { | |
| "mean": [ | |
| -8.052331113255715, | |
| 1.2034834133229457, | |
| 3.488412388499796, | |
| 0.002090120324884034, | |
| -0.0025731568072913558, | |
| 0.002667607652325618, | |
| -0.2788385216280735, | |
| -4.639592916156309, | |
| -4.716525503498022, | |
| 0.0029378879738785816, | |
| 0.010520228245777438, | |
| -0.008007955454829125, | |
| -1.2069425602772965, | |
| 4.811643207608252, | |
| -4.573298188323335, | |
| -0.00159757812192767, | |
| 0.0149784624687404, | |
| 0.00825595949860718 | |
| ], | |
| "std": [ | |
| 2.379325429945414, | |
| 2.707410110854913, | |
| 2.776338288176421, | |
| 0.7833971659587226, | |
| 0.3687130034760385, | |
| 0.41378717620148847, | |
| 4.781408427124205, | |
| 4.300047589007626, | |
| 5.414364143517655, | |
| 1.021125397569948, | |
| 0.8516263511948525, | |
| 1.2241616139111862, | |
| 4.660724376281117, | |
| 4.415990486106904, | |
| 5.6005718169582215, | |
| 1.0565962983884163, | |
| 0.9100989796314727, | |
| 1.266563056544128 | |
| ], | |
| "num_samples": 36529357 | |
| } |