swinv2-tiny-patch4-window8-256-DMAE-da-4e-5
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: 0.9664
- 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: 4e-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 |
|---|---|---|---|---|
| 1.446 | 0.96 | 11 | 1.6275 | 0.1087 |
| 1.4497 | 2.0 | 23 | 1.5550 | 0.1087 |
| 1.388 | 2.96 | 34 | 1.3769 | 0.3261 |
| 1.2755 | 4.0 | 46 | 1.2483 | 0.4130 |
| 1.1574 | 4.96 | 57 | 1.1545 | 0.4565 |
| 1.0826 | 6.0 | 69 | 1.0429 | 0.5 |
| 0.9124 | 6.96 | 80 | 0.9318 | 0.5652 |
| 0.8228 | 8.0 | 92 | 1.0362 | 0.5217 |
| 0.733 | 8.96 | 103 | 0.9699 | 0.5870 |
| 0.7086 | 10.0 | 115 | 0.8269 | 0.6522 |
| 0.6459 | 10.96 | 126 | 0.8168 | 0.6739 |
| 0.5793 | 12.0 | 138 | 1.0780 | 0.6087 |
| 0.5904 | 12.96 | 149 | 1.0166 | 0.5870 |
| 0.5155 | 14.0 | 161 | 0.8489 | 0.6304 |
| 0.4693 | 14.96 | 172 | 0.8454 | 0.6522 |
| 0.4928 | 16.0 | 184 | 0.8161 | 0.6739 |
| 0.4763 | 16.96 | 195 | 0.7666 | 0.7174 |
| 0.4354 | 18.0 | 207 | 0.8828 | 0.6957 |
| 0.3661 | 18.96 | 218 | 0.8782 | 0.6739 |
| 0.3652 | 20.0 | 230 | 0.9418 | 0.6739 |
| 0.3733 | 20.96 | 241 | 0.8963 | 0.7174 |
| 0.3473 | 22.0 | 253 | 0.9053 | 0.7174 |
| 0.2988 | 22.96 | 264 | 0.8318 | 0.7391 |
| 0.349 | 24.0 | 276 | 1.1129 | 0.6087 |
| 0.2963 | 24.96 | 287 | 1.0557 | 0.6304 |
| 0.3025 | 26.0 | 299 | 0.9567 | 0.7391 |
| 0.2676 | 26.96 | 310 | 1.0131 | 0.6739 |
| 0.2848 | 28.0 | 322 | 0.9576 | 0.6957 |
| 0.2757 | 28.96 | 333 | 0.9821 | 0.7174 |
| 0.2564 | 30.0 | 345 | 1.0166 | 0.6522 |
| 0.2635 | 30.96 | 356 | 0.9664 | 0.7609 |
| 0.2413 | 32.0 | 368 | 0.9894 | 0.7391 |
| 0.2321 | 32.96 | 379 | 1.0272 | 0.7391 |
| 0.2517 | 34.0 | 391 | 1.0312 | 0.7174 |
| 0.2161 | 34.96 | 402 | 1.0433 | 0.7174 |
| 0.2304 | 36.0 | 414 | 1.0158 | 0.7174 |
| 0.2194 | 36.96 | 425 | 1.0120 | 0.6957 |
| 0.2395 | 38.0 | 437 | 1.0153 | 0.6957 |
| 0.2199 | 38.26 | 440 | 1.0149 | 0.6957 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0