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---
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swinv2-tiny-patch4-window8-256-dmae-va-U-40
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swinv2-tiny-patch4-window8-256-dmae-va-U-40
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co./microsoft/swinv2-tiny-patch4-window8-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1175
- Accuracy: 0.9817
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 | 0.9 | 7 | 1.3839 | 0.3028 |
| 1.4343 | 1.94 | 15 | 1.3643 | 0.2844 |
| 1.3413 | 2.97 | 23 | 1.3310 | 0.3578 |
| 1.2473 | 4.0 | 31 | 1.1081 | 0.5138 |
| 1.2473 | 4.9 | 38 | 0.8292 | 0.7064 |
| 1.0532 | 5.94 | 46 | 0.7420 | 0.6239 |
| 0.917 | 6.97 | 54 | 0.6345 | 0.6972 |
| 0.7939 | 8.0 | 62 | 0.4898 | 0.8532 |
| 0.7939 | 8.9 | 69 | 0.5918 | 0.7523 |
| 0.7457 | 9.94 | 77 | 0.5271 | 0.7615 |
| 0.6834 | 10.97 | 85 | 0.3296 | 0.9450 |
| 0.5847 | 12.0 | 93 | 0.2883 | 0.9174 |
| 0.5199 | 12.9 | 100 | 0.2896 | 0.9266 |
| 0.5199 | 13.94 | 108 | 0.2859 | 0.8991 |
| 0.4657 | 14.97 | 116 | 0.2515 | 0.9083 |
| 0.4585 | 16.0 | 124 | 0.2261 | 0.9083 |
| 0.3892 | 16.9 | 131 | 0.2142 | 0.9266 |
| 0.3892 | 17.94 | 139 | 0.1788 | 0.9450 |
| 0.3939 | 18.97 | 147 | 0.1948 | 0.9266 |
| 0.3429 | 20.0 | 155 | 0.1685 | 0.9450 |
| 0.3493 | 20.9 | 162 | 0.1986 | 0.9083 |
| 0.3462 | 21.94 | 170 | 0.1540 | 0.9358 |
| 0.3462 | 22.97 | 178 | 0.1449 | 0.9450 |
| 0.3117 | 24.0 | 186 | 0.1379 | 0.9541 |
| 0.3109 | 24.9 | 193 | 0.1423 | 0.9450 |
| 0.2867 | 25.94 | 201 | 0.1451 | 0.9450 |
| 0.2867 | 26.97 | 209 | 0.1154 | 0.9725 |
| 0.293 | 28.0 | 217 | 0.1152 | 0.9541 |
| 0.2782 | 28.9 | 224 | 0.1261 | 0.9633 |
| 0.2744 | 29.94 | 232 | 0.1175 | 0.9817 |
| 0.2711 | 30.97 | 240 | 0.1292 | 0.9633 |
| 0.2711 | 32.0 | 248 | 0.1101 | 0.9817 |
| 0.2652 | 32.9 | 255 | 0.1202 | 0.9633 |
| 0.2218 | 33.94 | 263 | 0.1119 | 0.9817 |
| 0.2899 | 34.97 | 271 | 0.1071 | 0.9817 |
| 0.2899 | 36.0 | 279 | 0.1077 | 0.9817 |
| 0.2143 | 36.13 | 280 | 0.1077 | 0.9817 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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