swinv2-tiny-patch4-window8-256-DMAE-3e-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: 1.0126
- Accuracy: 0.6739
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: 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 | 1.6337 | 0.1087 |
| No log | 2.0 | 7 | 1.6223 | 0.1087 |
| 1.6298 | 2.86 | 10 | 1.5972 | 0.1087 |
| 1.6298 | 4.0 | 14 | 1.5041 | 0.1087 |
| 1.6298 | 4.86 | 17 | 1.4149 | 0.1087 |
| 1.4858 | 6.0 | 21 | 1.3037 | 0.4565 |
| 1.4858 | 6.86 | 24 | 1.2491 | 0.4565 |
| 1.4858 | 8.0 | 28 | 1.2219 | 0.4565 |
| 1.2337 | 8.86 | 31 | 1.2146 | 0.4565 |
| 1.2337 | 10.0 | 35 | 1.2145 | 0.4565 |
| 1.2337 | 10.86 | 38 | 1.2116 | 0.4565 |
| 1.1726 | 12.0 | 42 | 1.2065 | 0.4565 |
| 1.1726 | 12.86 | 45 | 1.2011 | 0.4565 |
| 1.1726 | 14.0 | 49 | 1.1941 | 0.4565 |
| 1.1953 | 14.86 | 52 | 1.1878 | 0.4565 |
| 1.1953 | 16.0 | 56 | 1.1771 | 0.4565 |
| 1.1953 | 16.86 | 59 | 1.1677 | 0.4565 |
| 1.1602 | 18.0 | 63 | 1.1564 | 0.4565 |
| 1.1602 | 18.86 | 66 | 1.1502 | 0.4565 |
| 1.1415 | 20.0 | 70 | 1.1227 | 0.4565 |
| 1.1415 | 20.86 | 73 | 1.1017 | 0.4783 |
| 1.1415 | 22.0 | 77 | 1.0872 | 0.5870 |
| 1.0949 | 22.86 | 80 | 1.0795 | 0.5870 |
| 1.0949 | 24.0 | 84 | 1.0582 | 0.6087 |
| 1.0949 | 24.86 | 87 | 1.0402 | 0.6087 |
| 1.0559 | 26.0 | 91 | 1.0186 | 0.6522 |
| 1.0559 | 26.86 | 94 | 1.0126 | 0.6739 |
| 1.0559 | 28.0 | 98 | 1.0033 | 0.6739 |
| 1.0423 | 28.86 | 101 | 0.9849 | 0.6739 |
| 1.0423 | 30.0 | 105 | 0.9670 | 0.6739 |
| 1.0423 | 30.86 | 108 | 0.9631 | 0.6739 |
| 1.0002 | 32.0 | 112 | 0.9599 | 0.6522 |
| 1.0002 | 32.86 | 115 | 0.9587 | 0.6522 |
| 1.0002 | 34.0 | 119 | 0.9573 | 0.6522 |
| 1.0077 | 34.29 | 120 | 0.9571 | 0.6522 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0