videomae-surf-analytics-sans-wandb
This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5544
- Accuracy: 0.8852
- F1: 0.8840
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1850
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| 1.3096 | 0.2005 | 371 | 1.3267 | 0.4590 | 0.2888 |
| 1.0586 | 1.2005 | 742 | 1.2866 | 0.5820 | 0.5035 |
| 0.9781 | 2.2005 | 1113 | 0.7952 | 0.7459 | 0.7466 |
| 0.0034 | 3.2005 | 1484 | 0.7218 | 0.8361 | 0.8343 |
| 0.1895 | 4.1978 | 1850 | 0.5544 | 0.8852 | 0.8840 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.0+cu118
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for 2nzi/videomae-surf-analytics-v2
Base model
MCG-NJU/videomae-base