Feature Extraction
Transformers
Safetensors
English
anymo
imu
wearable-sensing
human-activity-recognition
multimodal
motion-language
retrieval
captioning
custom_code
Instructions to use CRUISEResearchGroup/AnyMo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CRUISEResearchGroup/AnyMo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="CRUISEResearchGroup/AnyMo", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CRUISEResearchGroup/AnyMo", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,000 Bytes
5261696 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | {
"auto_map": {
"AutoProcessor": "processing_anymo.AnyMoProcessor"
},
"channel_order": [
"acc_x",
"acc_y",
"acc_z",
"gyro_x",
"gyro_y",
"gyro_z"
],
"location_aliases": {
"chest": "T8",
"head": "Head",
"left ankle": "L_LowerLeg",
"left foot": "L_Foot",
"left forearm": "L_Forearm",
"left hand": "L_Hand",
"left lower leg": "L_LowerLeg",
"left shoulder": "L_Shoulder",
"left thigh": "L_UpperLeg",
"left upper arm": "L_UpperArm",
"left wrist": "L_Forearm",
"lower back": "L5",
"neck": "Neck",
"pelvis": "Pelvis",
"right ankle": "R_LowerLeg",
"right foot": "R_Foot",
"right forearm": "R_Forearm",
"right hand": "R_Hand",
"right lower leg": "R_LowerLeg",
"right shoulder": "R_Shoulder",
"right thigh": "R_UpperLeg",
"right upper arm": "R_UpperArm",
"right wrist": "R_Forearm",
"waist": "Pelvis"
},
"processor_class": "AnyMoProcessor",
"target_sample_rate_hz": 60
}
|