| | ---
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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-U3
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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: 0.45652173913043476
|
| | ---
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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-U3
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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: 1.7136
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| | - Accuracy: 0.4565
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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: 5.5e-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: 0.05
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| | - num_epochs: 40
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| |
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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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| | | No log | 0.86 | 3 | 7.9328 | 0.1087 |
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| | | No log | 2.0 | 7 | 7.7596 | 0.1087 |
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| | | 7.8559 | 2.86 | 10 | 7.3241 | 0.1087 |
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| | | 7.8559 | 4.0 | 14 | 6.4450 | 0.1087 |
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| | | 7.8559 | 4.86 | 17 | 5.7723 | 0.1087 |
|
| | | 6.3363 | 6.0 | 21 | 4.8772 | 0.1087 |
|
| | | 6.3363 | 6.86 | 24 | 4.2678 | 0.1087 |
|
| | | 6.3363 | 8.0 | 28 | 3.5000 | 0.1087 |
|
| | | 4.1887 | 8.86 | 31 | 2.9766 | 0.1087 |
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| | | 4.1887 | 10.0 | 35 | 2.3876 | 0.1087 |
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| | | 4.1887 | 10.86 | 38 | 2.0429 | 0.1087 |
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| | | 2.602 | 12.0 | 42 | 1.7136 | 0.4565 |
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| | | 2.602 | 12.86 | 45 | 1.5478 | 0.4565 |
|
| | | 2.602 | 14.0 | 49 | 1.3874 | 0.4565 |
|
| | | 1.6353 | 14.86 | 52 | 1.2968 | 0.4565 |
|
| | | 1.6353 | 16.0 | 56 | 1.2225 | 0.4565 |
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| | | 1.6353 | 16.86 | 59 | 1.2071 | 0.4565 |
|
| | | 1.2533 | 18.0 | 63 | 1.2177 | 0.3261 |
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| | | 1.2533 | 18.86 | 66 | 1.2197 | 0.3261 |
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| | | 1.2088 | 20.0 | 70 | 1.2112 | 0.4565 |
|
| | | 1.2088 | 20.86 | 73 | 1.2101 | 0.4565 |
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| | | 1.2088 | 22.0 | 77 | 1.2092 | 0.4565 |
|
| | | 1.1798 | 22.86 | 80 | 1.2081 | 0.4565 |
|
| | | 1.1798 | 24.0 | 84 | 1.2076 | 0.4565 |
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| | | 1.1798 | 24.86 | 87 | 1.2049 | 0.4565 |
|
| | | 1.1825 | 26.0 | 91 | 1.2045 | 0.4565 |
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| | | 1.1825 | 26.86 | 94 | 1.2029 | 0.4565 |
|
| | | 1.1825 | 28.0 | 98 | 1.2022 | 0.4565 |
|
| | | 1.1943 | 28.86 | 101 | 1.2014 | 0.4565 |
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| | | 1.1943 | 30.0 | 105 | 1.2040 | 0.4565 |
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| | | 1.1943 | 30.86 | 108 | 1.2050 | 0.4565 |
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| | | 1.1772 | 32.0 | 112 | 1.2031 | 0.4565 |
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| | | 1.1772 | 32.86 | 115 | 1.2019 | 0.4565 |
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| | | 1.1772 | 34.0 | 119 | 1.2013 | 0.4565 |
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| | | 1.1945 | 34.29 | 120 | 1.2012 | 0.4565 |
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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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| |
|
