Anomaly-Detection-D1
Collection
3 items • Updated
Timesformers-d1
This model is a fine-tuned version of google/vivit-b-16x2-kinetics400 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.7694
- Accuracy: 0.7438
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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 12010
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0539 | 0.1 | 1201 | 2.3236 | 0.6307 |
| 0.5697 | 1.1 | 2402 | 1.9547 | 0.6739 |
| 0.5417 | 2.1 | 3603 | 1.7376 | 0.6951 |
| 0.0014 | 3.1 | 4804 | 1.8078 | 0.6920 |
| 1.1162 | 4.1 | 6005 | 1.7942 | 0.6921 |
| 0.0009 | 5.1 | 7206 | 1.4165 | 0.7779 |
| 0.0053 | 6.1 | 8407 | 1.7419 | 0.7540 |
| 1.4804 | 7.1 | 9608 | 1.5797 | 0.7424 |
| 0.6189 | 8.1 | 10809 | 1.9191 | 0.7305 |
| 0.0009 | 9.1 | 12010 | 1.7694 | 0.7438 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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Base model
google/vivit-b-16x2-kinetics400