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---
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license: apache-2.0
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base_model: microsoft/swinv2-tiny-patch4-window8-256
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: SWv2-DMAE-H-4-rp-clean-fix-U-40-Cross-5
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.8690476190476191
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# SWv2-DMAE-H-4-rp-clean-fix-U-40-Cross-5
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This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4646
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- Accuracy: 0.8690
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 4e-05
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 64
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 40
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.6088 | 0.98 | 12 | 1.6063 | 0.2024 |
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| 1.6024 | 1.96 | 24 | 1.5854 | 0.2024 |
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| 1.568 | 2.94 | 36 | 1.5534 | 0.2024 |
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| 1.5001 | 4.0 | 49 | 1.4693 | 0.2024 |
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| 1.3811 | 4.98 | 61 | 1.3256 | 0.3690 |
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| 1.27 | 5.96 | 73 | 1.0978 | 0.5476 |
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| 1.0887 | 6.94 | 85 | 0.8237 | 0.7381 |
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| 0.937 | 8.0 | 98 | 0.7746 | 0.7143 |
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| 0.811 | 8.98 | 110 | 0.5772 | 0.7976 |
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| 0.7574 | 9.96 | 122 | 0.6164 | 0.7857 |
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| 0.7118 | 10.94 | 134 | 0.6410 | 0.7976 |
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| 0.6374 | 12.0 | 147 | 0.5243 | 0.8095 |
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| 0.5958 | 12.98 | 159 | 0.4589 | 0.8095 |
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| 0.5446 | 13.96 | 171 | 0.5288 | 0.7738 |
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| 0.5348 | 14.94 | 183 | 0.4989 | 0.7619 |
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| 0.464 | 16.0 | 196 | 0.5408 | 0.7857 |
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| 0.4641 | 16.98 | 208 | 0.4609 | 0.7738 |
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| 0.4471 | 17.96 | 220 | 0.4229 | 0.8333 |
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| 0.4301 | 18.94 | 232 | 0.3962 | 0.8452 |
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| 0.3862 | 20.0 | 245 | 0.4005 | 0.8452 |
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| 0.3659 | 20.98 | 257 | 0.3873 | 0.8452 |
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| 0.3488 | 21.96 | 269 | 0.4196 | 0.8333 |
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| 0.3683 | 22.94 | 281 | 0.4299 | 0.8095 |
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| 0.3477 | 24.0 | 294 | 0.4470 | 0.8214 |
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| 0.3426 | 24.98 | 306 | 0.4478 | 0.8333 |
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| 0.3 | 25.96 | 318 | 0.4604 | 0.8452 |
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| 0.3138 | 26.94 | 330 | 0.4114 | 0.8571 |
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| 0.2569 | 28.0 | 343 | 0.4640 | 0.8452 |
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| 0.2894 | 28.98 | 355 | 0.5187 | 0.7976 |
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| 0.2996 | 29.96 | 367 | 0.4617 | 0.8452 |
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| 0.3046 | 30.94 | 379 | 0.4646 | 0.8690 |
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| 0.2896 | 32.0 | 392 | 0.4492 | 0.8571 |
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| 0.2548 | 32.98 | 404 | 0.4523 | 0.8571 |
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| 0.2137 | 33.96 | 416 | 0.4764 | 0.8333 |
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| 0.2246 | 34.94 | 428 | 0.4474 | 0.8571 |
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| 0.2684 | 36.0 | 441 | 0.4495 | 0.8452 |
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| 0.2413 | 36.98 | 453 | 0.4634 | 0.8452 |
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| 0.2633 | 37.96 | 465 | 0.4558 | 0.8452 |
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| 0.2518 | 38.94 | 477 | 0.4523 | 0.8452 |
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| 0.2428 | 39.18 | 480 | 0.4523 | 0.8452 |
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 2.1.2+cu118
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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