pre_CIDAUTv4
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0177
- Accuracy: 0.9918
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.8889 | 4 | 0.6742 | 0.5679 |
| No log | 2.0 | 9 | 0.3347 | 0.9218 |
| 0.6003 | 2.8889 | 13 | 0.1238 | 0.9753 |
| 0.6003 | 4.0 | 18 | 0.1298 | 0.9465 |
| 0.199 | 4.8889 | 22 | 0.0360 | 0.9877 |
| 0.199 | 6.0 | 27 | 0.1049 | 0.9671 |
| 0.0832 | 6.8889 | 31 | 0.0058 | 1.0 |
| 0.0832 | 8.0 | 36 | 0.0138 | 0.9918 |
| 0.0438 | 8.8889 | 40 | 0.0177 | 0.9918 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0