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---
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-DAV50
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-humeda-DAV50
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: 0.8354
- Accuracy: 0.7273
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 1.5451 | 0.3864 |
| No log | 2.0 | 10 | 1.5220 | 0.3864 |
| 1.4177 | 3.0 | 15 | 1.4938 | 0.4205 |
| 1.4177 | 4.0 | 20 | 1.4111 | 0.4432 |
| 1.2671 | 5.0 | 25 | 1.2941 | 0.4545 |
| 1.2671 | 6.0 | 30 | 1.2036 | 0.4545 |
| 1.2671 | 7.0 | 35 | 1.0816 | 0.5114 |
| 0.9869 | 8.0 | 40 | 1.0452 | 0.5795 |
| 0.9869 | 9.0 | 45 | 0.9876 | 0.625 |
| 0.8456 | 10.0 | 50 | 0.9791 | 0.5909 |
| 0.8456 | 11.0 | 55 | 0.9662 | 0.6023 |
| 0.7126 | 12.0 | 60 | 0.9302 | 0.6364 |
| 0.7126 | 13.0 | 65 | 0.9379 | 0.625 |
| 0.7126 | 14.0 | 70 | 0.9036 | 0.6705 |
| 0.6561 | 15.0 | 75 | 0.8846 | 0.6591 |
| 0.6561 | 16.0 | 80 | 0.8689 | 0.6591 |
| 0.6367 | 17.0 | 85 | 0.8543 | 0.6591 |
| 0.6367 | 18.0 | 90 | 0.8342 | 0.6932 |
| 0.6367 | 19.0 | 95 | 0.8185 | 0.6705 |
| 0.5463 | 20.0 | 100 | 0.8290 | 0.7159 |
| 0.5463 | 21.0 | 105 | 0.8354 | 0.7273 |
| 0.5504 | 22.0 | 110 | 0.8160 | 0.7159 |
| 0.5504 | 23.0 | 115 | 0.8073 | 0.7159 |
| 0.507 | 24.0 | 120 | 0.8071 | 0.7045 |
| 0.507 | 25.0 | 125 | 0.8071 | 0.6932 |
| 0.507 | 26.0 | 130 | 0.8047 | 0.7045 |
| 0.5226 | 27.0 | 135 | 0.8000 | 0.7045 |
| 0.5226 | 28.0 | 140 | 0.7987 | 0.7159 |
| 0.5144 | 29.0 | 145 | 0.8000 | 0.7159 |
| 0.5144 | 30.0 | 150 | 0.8002 | 0.7159 |
| 0.5144 | 31.0 | 155 | 0.8008 | 0.7159 |
| 0.4862 | 32.0 | 160 | 0.8008 | 0.7159 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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