| | ---
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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: swinv2-tiny-patch4-window8-256-DMAE-da2-4e-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: 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: 0.6521739130434783
|
| | ---
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| |
|
| | <!-- 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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| |
|
| | # swinv2-tiny-patch4-window8-256-DMAE-da2-4e-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: 0.8252
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| | - Accuracy: 0.6522
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| |
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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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| |
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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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| |
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| | ## Training procedure
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| |
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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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| | - lr_scheduler_warmup_ratio: 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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| | | 6.6265 | 0.96 | 11 | 7.9417 | 0.1087 |
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| | | 6.1679 | 2.0 | 23 | 7.9339 | 0.1087 |
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| | | 6.8561 | 2.96 | 34 | 7.9149 | 0.1087 |
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| | | 6.399 | 4.0 | 46 | 7.8629 | 0.1087 |
|
| | | 6.7961 | 4.96 | 57 | 7.7408 | 0.1087 |
|
| | | 6.5019 | 6.0 | 69 | 7.4856 | 0.1087 |
|
| | | 6.085 | 6.96 | 80 | 7.1388 | 0.1087 |
|
| | | 6.014 | 8.0 | 92 | 6.7124 | 0.1087 |
|
| | | 5.6147 | 8.96 | 103 | 6.2444 | 0.1087 |
|
| | | 5.2206 | 10.0 | 115 | 5.6953 | 0.1087 |
|
| | | 4.9027 | 10.96 | 126 | 5.1300 | 0.1087 |
|
| | | 4.4278 | 12.0 | 138 | 4.4629 | 0.1087 |
|
| | | 4.0841 | 12.96 | 149 | 3.8089 | 0.1087 |
|
| | | 3.0004 | 14.0 | 161 | 3.0893 | 0.1087 |
|
| | | 2.5654 | 14.96 | 172 | 2.4630 | 0.1087 |
|
| | | 2.094 | 16.0 | 184 | 1.8911 | 0.1087 |
|
| | | 1.8348 | 16.96 | 195 | 1.5454 | 0.1087 |
|
| | | 1.5383 | 18.0 | 207 | 1.4124 | 0.1087 |
|
| | | 1.4068 | 18.96 | 218 | 1.4348 | 0.1087 |
|
| | | 1.379 | 20.0 | 230 | 1.4207 | 0.1522 |
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| | | 1.3552 | 20.96 | 241 | 1.3786 | 0.3478 |
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| | | 1.262 | 22.0 | 253 | 1.3580 | 0.4783 |
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| | | 1.2235 | 22.96 | 264 | 1.4413 | 0.3696 |
|
| | | 1.2065 | 24.0 | 276 | 1.3013 | 0.1957 |
|
| | | 1.2196 | 24.96 | 287 | 1.3489 | 0.2609 |
|
| | | 1.1815 | 26.0 | 299 | 1.3158 | 0.3043 |
|
| | | 1.0469 | 26.96 | 310 | 1.2959 | 0.4565 |
|
| | | 0.9854 | 28.0 | 322 | 1.4712 | 0.5217 |
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| | | 0.9443 | 28.96 | 333 | 1.2061 | 0.3261 |
|
| | | 0.9168 | 30.0 | 345 | 1.0655 | 0.4348 |
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| | | 0.8138 | 30.96 | 356 | 1.0981 | 0.5435 |
|
| | | 0.8066 | 32.0 | 368 | 1.0023 | 0.6304 |
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| | | 0.8035 | 32.96 | 379 | 0.9529 | 0.5652 |
|
| | | 0.7693 | 34.0 | 391 | 1.0104 | 0.5 |
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| | | 0.6773 | 34.96 | 402 | 1.0684 | 0.5 |
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| | | 0.6726 | 36.0 | 414 | 0.8252 | 0.6522 |
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| | | 0.5859 | 36.96 | 425 | 0.8158 | 0.6304 |
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| | | 0.6204 | 38.0 | 437 | 0.8518 | 0.6087 |
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| | | 0.5921 | 38.26 | 440 | 0.8757 | 0.5870 |
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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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| |
|
