swinv2-tiny-patch4-window8-256-DMAE-da2-colab2
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: 0.7389
- Accuracy: 0.7174
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: 1.5e-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
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3473 | 0.9565 | 11 | 1.3763 | 0.2609 |
| 1.2385 | 1.9348 | 22 | 1.2596 | 0.5 |
| 1.1683 | 2.9130 | 33 | 1.2476 | 0.5 |
| 1.0809 | 3.9783 | 45 | 1.1625 | 0.5435 |
| 1.0147 | 4.9565 | 56 | 1.0490 | 0.5435 |
| 0.9755 | 5.9348 | 67 | 0.9155 | 0.6739 |
| 0.89 | 6.9130 | 78 | 0.9343 | 0.6304 |
| 0.832 | 7.9783 | 90 | 0.7973 | 0.6522 |
| 0.7846 | 8.9565 | 101 | 0.8325 | 0.6087 |
| 0.74 | 9.9348 | 112 | 0.8328 | 0.6087 |
| 0.679 | 10.9130 | 123 | 0.7195 | 0.7174 |
| 0.6487 | 11.9783 | 135 | 0.6740 | 0.7826 |
| 0.6774 | 12.9565 | 146 | 0.7157 | 0.6957 |
| 0.5727 | 13.9348 | 157 | 0.6934 | 0.7174 |
| 0.5872 | 14.9130 | 168 | 0.7335 | 0.6739 |
| 0.6116 | 15.9783 | 180 | 0.6892 | 0.7609 |
| 0.5783 | 16.9565 | 191 | 0.6796 | 0.7174 |
| 0.5658 | 17.9348 | 202 | 0.6966 | 0.7609 |
| 0.5447 | 18.9130 | 213 | 0.6708 | 0.7174 |
| 0.5452 | 19.9783 | 225 | 0.7297 | 0.6957 |
| 0.509 | 20.9565 | 236 | 0.7137 | 0.6522 |
| 0.527 | 21.9348 | 247 | 0.7047 | 0.7174 |
| 0.5382 | 22.9130 | 258 | 0.7737 | 0.6739 |
| 0.508 | 23.9783 | 270 | 0.7073 | 0.7174 |
| 0.4579 | 24.9565 | 281 | 0.7355 | 0.6739 |
| 0.492 | 25.9348 | 292 | 0.7152 | 0.7174 |
| 0.4273 | 26.9130 | 303 | 0.7243 | 0.7174 |
| 0.4823 | 27.9783 | 315 | 0.7477 | 0.6739 |
| 0.4659 | 28.9565 | 326 | 0.7193 | 0.6957 |
| 0.4401 | 29.9348 | 337 | 0.7509 | 0.6957 |
| 0.4717 | 30.9130 | 348 | 0.7327 | 0.6957 |
| 0.4139 | 31.9783 | 360 | 0.7041 | 0.7174 |
| 0.4365 | 32.9565 | 371 | 0.7286 | 0.6957 |
| 0.4088 | 33.9348 | 382 | 0.7495 | 0.7174 |
| 0.4505 | 34.9130 | 393 | 0.7399 | 0.7174 |
| 0.4143 | 35.9783 | 405 | 0.7160 | 0.7174 |
| 0.4093 | 36.9565 | 416 | 0.7210 | 0.7174 |
| 0.43 | 37.9348 | 427 | 0.7342 | 0.7174 |
| 0.424 | 38.9130 | 438 | 0.7386 | 0.7174 |
| 0.4169 | 39.1087 | 440 | 0.7389 | 0.7174 |
Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3