swinv2-tiny-patch4-window8-256-DMAE-4e-3
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.7507
- Accuracy: 0.7391
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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.8571 | 3 | 1.3959 | 0.3043 |
| No log | 1.7857 | 6 | 1.2662 | 0.3913 |
| No log | 2.7143 | 9 | 1.1960 | 0.4783 |
| 1.3226 | 3.9286 | 13 | 1.1950 | 0.4565 |
| 1.3226 | 4.8571 | 16 | 1.1891 | 0.4783 |
| 1.3226 | 5.7857 | 19 | 1.1898 | 0.4783 |
| 1.1833 | 6.7143 | 22 | 1.1824 | 0.5435 |
| 1.1833 | 7.9286 | 26 | 1.1618 | 0.5217 |
| 1.1833 | 8.8571 | 29 | 1.1359 | 0.5652 |
| 1.1384 | 9.7857 | 32 | 1.0974 | 0.5870 |
| 1.1384 | 10.7143 | 35 | 1.0524 | 0.5870 |
| 1.1384 | 11.9286 | 39 | 1.0083 | 0.6957 |
| 1.0628 | 12.8571 | 42 | 0.9696 | 0.6739 |
| 1.0628 | 13.7857 | 45 | 0.9369 | 0.6739 |
| 1.0628 | 14.7143 | 48 | 0.8825 | 0.7174 |
| 1.0069 | 15.9286 | 52 | 0.8396 | 0.6957 |
| 1.0069 | 16.8571 | 55 | 0.8267 | 0.7174 |
| 1.0069 | 17.7857 | 58 | 0.8275 | 0.7174 |
| 0.9339 | 18.7143 | 61 | 0.8255 | 0.7174 |
| 0.9339 | 19.9286 | 65 | 0.7899 | 0.7174 |
| 0.9339 | 20.8571 | 68 | 0.7604 | 0.7174 |
| 0.905 | 21.7857 | 71 | 0.7442 | 0.6957 |
| 0.905 | 22.7143 | 74 | 0.7361 | 0.7391 |
| 0.905 | 23.9286 | 78 | 0.7598 | 0.6957 |
| 0.8465 | 24.8571 | 81 | 0.7650 | 0.7174 |
| 0.8465 | 25.7857 | 84 | 0.7631 | 0.7391 |
| 0.8465 | 26.7143 | 87 | 0.7561 | 0.7174 |
| 0.8363 | 27.9286 | 91 | 0.7494 | 0.6957 |
| 0.8363 | 28.8571 | 94 | 0.7539 | 0.7174 |
| 0.8363 | 29.7857 | 97 | 0.7497 | 0.7174 |
| 0.7751 | 30.7143 | 100 | 0.7477 | 0.7174 |
| 0.7751 | 31.9286 | 104 | 0.7463 | 0.7609 |
| 0.7751 | 32.8571 | 107 | 0.7507 | 0.7609 |
| 0.7843 | 33.7857 | 110 | 0.7534 | 0.7391 |
| 0.7843 | 34.7143 | 113 | 0.7542 | 0.7391 |
| 0.7843 | 35.9286 | 117 | 0.7519 | 0.7391 |
| 0.7435 | 36.8571 | 120 | 0.7507 | 0.7391 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3