swinv2-tiny-patch4-window8-256-RH-6e-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: 0.6666
- Accuracy: 0.6355
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: 6e-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 | 1.0 | 8 | 4.6188 | 0.4112 |
| 4.5343 | 2.0 | 16 | 4.4096 | 0.4112 |
| 4.5396 | 3.0 | 24 | 3.6784 | 0.4112 |
| 3.621 | 4.0 | 32 | 2.4800 | 0.4112 |
| 2.1758 | 5.0 | 40 | 1.2118 | 0.4112 |
| 2.1758 | 6.0 | 48 | 0.6790 | 0.5888 |
| 0.8871 | 7.0 | 56 | 0.7903 | 0.5888 |
| 0.7484 | 8.0 | 64 | 0.7640 | 0.5888 |
| 0.7414 | 9.0 | 72 | 0.6789 | 0.5888 |
| 0.6868 | 10.0 | 80 | 0.6770 | 0.5888 |
| 0.6868 | 11.0 | 88 | 0.6775 | 0.5888 |
| 0.6771 | 12.0 | 96 | 0.6993 | 0.5888 |
| 0.7082 | 13.0 | 104 | 0.6765 | 0.5888 |
| 0.6993 | 14.0 | 112 | 0.6746 | 0.5888 |
| 0.6798 | 15.0 | 120 | 0.6759 | 0.5888 |
| 0.6798 | 16.0 | 128 | 0.6734 | 0.5888 |
| 0.6734 | 17.0 | 136 | 0.6739 | 0.5888 |
| 0.6832 | 18.0 | 144 | 0.7039 | 0.5888 |
| 0.6825 | 19.0 | 152 | 0.6767 | 0.5888 |
| 0.6663 | 20.0 | 160 | 0.6707 | 0.5888 |
| 0.6663 | 21.0 | 168 | 0.6798 | 0.5888 |
| 0.6646 | 22.0 | 176 | 0.6723 | 0.5794 |
| 0.6764 | 23.0 | 184 | 0.6889 | 0.5888 |
| 0.6808 | 24.0 | 192 | 0.6994 | 0.5888 |
| 0.6766 | 25.0 | 200 | 0.6691 | 0.5888 |
| 0.6766 | 26.0 | 208 | 0.6837 | 0.5888 |
| 0.6698 | 27.0 | 216 | 0.6738 | 0.5701 |
| 0.6549 | 28.0 | 224 | 0.6695 | 0.5794 |
| 0.6442 | 29.0 | 232 | 0.7157 | 0.5794 |
| 0.649 | 30.0 | 240 | 0.6726 | 0.6075 |
| 0.649 | 31.0 | 248 | 0.6839 | 0.5794 |
| 0.6388 | 32.0 | 256 | 0.6797 | 0.5888 |
| 0.6416 | 33.0 | 264 | 0.6714 | 0.5981 |
| 0.6398 | 34.0 | 272 | 0.6730 | 0.6075 |
| 0.6522 | 35.0 | 280 | 0.6953 | 0.5794 |
| 0.6522 | 36.0 | 288 | 0.6609 | 0.5701 |
| 0.6376 | 37.0 | 296 | 0.6619 | 0.5794 |
| 0.6441 | 38.0 | 304 | 0.6654 | 0.6262 |
| 0.6149 | 39.0 | 312 | 0.6666 | 0.6355 |
| 0.623 | 40.0 | 320 | 0.6679 | 0.6355 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0