Robotics
PyTorch
Cosmos
xperience10m_task_baseline_suite
embodied-ai
multimodal
xperience-10m
baseline
evaluation
qwen3-omni
Instructions to use cy0307/ropedia-xperience-10m-task-baselines with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Cosmos
How to use cy0307/ropedia-xperience-10m-task-baselines with Cosmos:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
File size: 799 Bytes
540e67a a8124a8 540e67a a8124a8 540e67a eeac43c | 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 | {
"micro_f1": 0.1679279279279279,
"macro_f1": 0.048883162556964774,
"exact_match": 0.014367816091954023,
"precision": 0.16431593794076163,
"recall": 0.17170228445099484,
"task": "object_relevance",
"input": "all non-caption modalities -> current relevant object set",
"split": "chronological",
"num_windows": 1161,
"num_train_windows": 813,
"num_test_windows": 348,
"num_objects": 34,
"feature_dim": 7650,
"model": "neural_mlp",
"head": "z-score -> MLP sigmoid multilabel",
"neural_epochs": 80,
"neural_hidden_dim": 128,
"neural_batch_size": 128,
"neural_learning_rate": 0.001,
"neural_weight_decay": 0.0001,
"neural_dropout": 0.1,
"neural_device": "cpu",
"train_final_loss": 0.003651880362182214,
"task_display_name": "Object Relevance Prediction"
}
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