| | ---
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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: SW2-RHS-DA
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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: validation
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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: 0.8598130841121495
|
| | ---
|
| |
|
| | <!-- 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. -->
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| |
|
| | # SW2-RHS-DA
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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: 0.4541
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| | - Accuracy: 0.8598
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| |
|
| | ## Model description
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| |
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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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| |
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| | ## Training and evaluation data
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| |
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| | More information needed
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| |
|
| | ## Training procedure
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| |
|
| | ### Training hyperparameters
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| |
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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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| | - num_epochs: 40
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| |
|
| | ### Training results
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| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy |
|
| | |:-------------:|:-----:|:----:|:---------------:|:--------:|
|
| | | 2.0538 | 0.99 | 35 | 1.2866 | 0.4112 |
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| | | 0.7464 | 2.0 | 71 | 0.6780 | 0.5888 |
|
| | | 0.7061 | 2.99 | 106 | 0.6851 | 0.5888 |
|
| | | 0.6951 | 4.0 | 142 | 0.6742 | 0.5888 |
|
| | | 0.6928 | 4.99 | 177 | 0.6917 | 0.4486 |
|
| | | 0.683 | 6.0 | 213 | 0.6531 | 0.5794 |
|
| | | 0.7013 | 6.99 | 248 | 0.7000 | 0.4486 |
|
| | | 0.6921 | 8.0 | 284 | 0.7519 | 0.5514 |
|
| | | 0.6166 | 8.99 | 319 | 0.5947 | 0.6822 |
|
| | | 0.6128 | 10.0 | 355 | 0.5434 | 0.7850 |
|
| | | 0.5737 | 10.99 | 390 | 0.5533 | 0.7570 |
|
| | | 0.5376 | 12.0 | 426 | 0.5347 | 0.7103 |
|
| | | 0.5056 | 12.99 | 461 | 0.4949 | 0.7664 |
|
| | | 0.5396 | 14.0 | 497 | 0.5151 | 0.7477 |
|
| | | 0.4826 | 14.99 | 532 | 0.5669 | 0.7196 |
|
| | | 0.4269 | 16.0 | 568 | 0.4796 | 0.7570 |
|
| | | 0.5004 | 16.99 | 603 | 0.4489 | 0.8037 |
|
| | | 0.4116 | 18.0 | 639 | 0.4362 | 0.8224 |
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| | | 0.3776 | 18.99 | 674 | 0.5300 | 0.7570 |
|
| | | 0.3646 | 20.0 | 710 | 0.4175 | 0.8037 |
|
| | | 0.3683 | 20.99 | 745 | 0.4700 | 0.8224 |
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| | | 0.3277 | 22.0 | 781 | 0.4707 | 0.8131 |
|
| | | 0.3534 | 22.99 | 816 | 0.5240 | 0.8131 |
|
| | | 0.3083 | 24.0 | 852 | 0.5012 | 0.8131 |
|
| | | 0.2829 | 24.99 | 887 | 0.4421 | 0.8318 |
|
| | | 0.2564 | 26.0 | 923 | 0.4548 | 0.8224 |
|
| | | 0.3136 | 26.99 | 958 | 0.4374 | 0.8318 |
|
| | | 0.2443 | 28.0 | 994 | 0.5277 | 0.8131 |
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| | | 0.258 | 28.99 | 1029 | 0.4601 | 0.8224 |
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| | | 0.2673 | 30.0 | 1065 | 0.4520 | 0.8318 |
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| | | 0.2233 | 30.99 | 1100 | 0.4541 | 0.8598 |
|
| | | 0.2276 | 32.0 | 1136 | 0.4247 | 0.8505 |
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| | | 0.2653 | 32.99 | 1171 | 0.4091 | 0.8505 |
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| | | 0.2007 | 34.0 | 1207 | 0.4719 | 0.8505 |
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| | | 0.2082 | 34.99 | 1242 | 0.4624 | 0.8411 |
|
| | | 0.1794 | 36.0 | 1278 | 0.4856 | 0.8318 |
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| | | 0.1987 | 36.99 | 1313 | 0.4904 | 0.8224 |
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| | | 0.2066 | 38.0 | 1349 | 0.4741 | 0.8505 |
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| | | 0.1972 | 38.99 | 1384 | 0.4530 | 0.8505 |
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| | | 0.2319 | 39.44 | 1400 | 0.4536 | 0.8505 |
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| |
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| |
|
| | ### Framework versions
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| |
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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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| | |
