beit-base-patch16-224-DMAE-5e-5
This model is a fine-tuned version of microsoft/beit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6236
- Accuracy: 0.8043
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.86 | 3 | 1.3816 | 0.4565 |
| No log | 2.0 | 7 | 1.3504 | 0.4565 |
| 1.3654 | 2.86 | 10 | 1.3075 | 0.4565 |
| 1.3654 | 4.0 | 14 | 1.2444 | 0.4565 |
| 1.3654 | 4.86 | 17 | 1.2089 | 0.4565 |
| 1.2386 | 6.0 | 21 | 1.1711 | 0.4565 |
| 1.2386 | 6.86 | 24 | 1.1492 | 0.4565 |
| 1.2386 | 8.0 | 28 | 1.1050 | 0.4783 |
| 1.132 | 8.86 | 31 | 1.0928 | 0.6522 |
| 1.132 | 10.0 | 35 | 1.0230 | 0.6739 |
| 1.132 | 10.86 | 38 | 0.9864 | 0.6522 |
| 1.015 | 12.0 | 42 | 1.0060 | 0.6739 |
| 1.015 | 12.86 | 45 | 0.8790 | 0.7174 |
| 1.015 | 14.0 | 49 | 0.8533 | 0.6739 |
| 0.9217 | 14.86 | 52 | 0.8203 | 0.6957 |
| 0.9217 | 16.0 | 56 | 0.8152 | 0.6739 |
| 0.9217 | 16.86 | 59 | 0.8598 | 0.6522 |
| 0.7989 | 18.0 | 63 | 0.7979 | 0.6957 |
| 0.7989 | 18.86 | 66 | 0.7555 | 0.6739 |
| 0.7309 | 20.0 | 70 | 0.7019 | 0.7609 |
| 0.7309 | 20.86 | 73 | 0.7254 | 0.7174 |
| 0.7309 | 22.0 | 77 | 0.7268 | 0.6739 |
| 0.6267 | 22.86 | 80 | 0.7096 | 0.6739 |
| 0.6267 | 24.0 | 84 | 0.7568 | 0.6739 |
| 0.6267 | 24.86 | 87 | 0.6565 | 0.7391 |
| 0.564 | 26.0 | 91 | 0.7035 | 0.7391 |
| 0.564 | 26.86 | 94 | 0.7081 | 0.7391 |
| 0.564 | 28.0 | 98 | 0.6236 | 0.8043 |
| 0.5625 | 28.86 | 101 | 0.6091 | 0.8043 |
| 0.5625 | 30.0 | 105 | 0.6523 | 0.7609 |
| 0.5625 | 30.86 | 108 | 0.6235 | 0.7826 |
| 0.5185 | 32.0 | 112 | 0.5974 | 0.7826 |
| 0.5185 | 32.86 | 115 | 0.5944 | 0.7826 |
| 0.5185 | 34.0 | 119 | 0.6005 | 0.7826 |
| 0.5005 | 34.29 | 120 | 0.6005 | 0.7826 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0