metadata
library_name: transformers
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-humeda-DAV22
results: []
swinv2-tiny-patch4-window8-256-dmae-humeda-DAV22
This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0386
- Accuracy: 0.6346
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 3.1723 | 1.0 | 22 | 1.6164 | 0.1923 |
| 2.8734 | 2.0 | 44 | 1.4347 | 0.3846 |
| 2.1553 | 3.0 | 66 | 1.1520 | 0.4808 |
| 1.6674 | 4.0 | 88 | 1.0921 | 0.4808 |
| 1.1204 | 5.0 | 110 | 0.9091 | 0.5962 |
| 1.0373 | 6.0 | 132 | 0.8185 | 0.6923 |
| 0.9181 | 7.0 | 154 | 0.9377 | 0.6731 |
| 0.7475 | 8.0 | 176 | 0.8407 | 0.6731 |
| 0.6679 | 9.0 | 198 | 0.9488 | 0.6923 |
| 0.4914 | 10.0 | 220 | 0.8699 | 0.7115 |
| 0.4421 | 11.0 | 242 | 1.1132 | 0.6538 |
| 0.3759 | 12.0 | 264 | 0.9250 | 0.7115 |
| 0.4317 | 13.0 | 286 | 0.9220 | 0.6731 |
| 0.4137 | 14.0 | 308 | 1.0225 | 0.7115 |
| 0.3451 | 15.0 | 330 | 1.0872 | 0.6538 |
| 0.3482 | 16.0 | 352 | 1.0129 | 0.6731 |
| 0.3346 | 17.0 | 374 | 1.0314 | 0.6538 |
| 0.3105 | 18.0 | 396 | 1.0348 | 0.6538 |
| 0.252 | 19.0 | 418 | 1.0386 | 0.6346 |
| 0.317 | 19.0930 | 420 | 1.0386 | 0.6346 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0