beit-base-patch16-224-DMAE
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: 0.6703
- Accuracy: 0.7609
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: 0.00015
- 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 | 1.3117 | 0.4565 |
| No log | 2.0 | 7 | 1.2539 | 0.4565 |
| 1.2875 | 2.86 | 10 | 1.2355 | 0.4565 |
| 1.2875 | 4.0 | 14 | 1.1827 | 0.4783 |
| 1.2875 | 4.86 | 17 | 1.0758 | 0.6522 |
| 1.1369 | 6.0 | 21 | 1.0942 | 0.5 |
| 1.1369 | 6.86 | 24 | 1.1312 | 0.5217 |
| 1.1369 | 8.0 | 28 | 1.0906 | 0.5217 |
| 1.1009 | 8.86 | 31 | 0.8704 | 0.6957 |
| 1.1009 | 10.0 | 35 | 1.0023 | 0.5870 |
| 1.1009 | 10.86 | 38 | 1.0288 | 0.5870 |
| 0.9152 | 12.0 | 42 | 0.7874 | 0.7174 |
| 0.9152 | 12.86 | 45 | 0.7166 | 0.7174 |
| 0.9152 | 14.0 | 49 | 0.7269 | 0.6957 |
| 0.8444 | 14.86 | 52 | 0.8481 | 0.6957 |
| 0.8444 | 16.0 | 56 | 0.7589 | 0.6304 |
| 0.8444 | 16.86 | 59 | 0.7590 | 0.6304 |
| 0.8085 | 18.0 | 63 | 0.8320 | 0.6304 |
| 0.8085 | 18.86 | 66 | 0.7469 | 0.7391 |
| 0.6941 | 20.0 | 70 | 0.8337 | 0.6304 |
| 0.6941 | 20.86 | 73 | 0.7928 | 0.7174 |
| 0.6941 | 22.0 | 77 | 0.8765 | 0.6522 |
| 0.5822 | 22.86 | 80 | 0.7139 | 0.7174 |
| 0.5822 | 24.0 | 84 | 0.7477 | 0.6957 |
| 0.5822 | 24.86 | 87 | 0.6987 | 0.7174 |
| 0.5174 | 26.0 | 91 | 0.6815 | 0.7391 |
| 0.5174 | 26.86 | 94 | 0.7332 | 0.7174 |
| 0.5174 | 28.0 | 98 | 0.6582 | 0.7391 |
| 0.48 | 28.86 | 101 | 0.7273 | 0.7391 |
| 0.48 | 30.0 | 105 | 0.7595 | 0.6957 |
| 0.48 | 30.86 | 108 | 0.7136 | 0.7391 |
| 0.4159 | 32.0 | 112 | 0.6703 | 0.7609 |
| 0.4159 | 32.86 | 115 | 0.6736 | 0.7609 |
| 0.4159 | 34.0 | 119 | 0.6866 | 0.7609 |
| 0.3472 | 34.29 | 120 | 0.6873 | 0.7609 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0