| --- |
| license: apache-2.0 |
| base_model: microsoft/swinv2-tiny-patch4-window8-256 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| model-index: |
| - name: swinv2-tiny-patch4-window8-256-finetuned-microbes |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.7051282051282052 |
| --- |
| |
| <!-- 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-finetuned-microbes |
|
|
| 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. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.0939 |
| - Accuracy: 0.7051 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 128 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 20 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 4.1153 | 0.97 | 16 | 3.8048 | 0.1239 | |
| | 3.2047 | 2.0 | 33 | 2.9123 | 0.2949 | |
| | 2.6979 | 2.97 | 49 | 2.2162 | 0.4231 | |
| | 1.9422 | 4.0 | 66 | 1.8476 | 0.5043 | |
| | 1.5677 | 4.97 | 82 | 1.6194 | 0.5684 | |
| | 1.3485 | 6.0 | 99 | 1.4825 | 0.5855 | |
| | 1.146 | 6.97 | 115 | 1.4073 | 0.5983 | |
| | 1.0408 | 8.0 | 132 | 1.2730 | 0.6325 | |
| | 0.9334 | 8.97 | 148 | 1.2782 | 0.6282 | |
| | 0.8702 | 10.0 | 165 | 1.1758 | 0.6752 | |
| | 0.8589 | 10.97 | 181 | 1.1652 | 0.6838 | |
| | 0.7607 | 12.0 | 198 | 1.2129 | 0.6795 | |
| | 0.7676 | 12.97 | 214 | 1.1509 | 0.6795 | |
| | 0.7359 | 14.0 | 231 | 1.1327 | 0.6966 | |
| | 0.7491 | 14.97 | 247 | 1.1059 | 0.6966 | |
| | 0.6664 | 16.0 | 264 | 1.1413 | 0.6923 | |
| | 0.618 | 16.97 | 280 | 1.0954 | 0.7009 | |
| | 0.6504 | 18.0 | 297 | 1.1030 | 0.7009 | |
| | 0.6241 | 18.97 | 313 | 1.0956 | 0.7009 | |
| | 0.6258 | 19.39 | 320 | 1.0939 | 0.7051 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.32.1 |
| - Pytorch 2.0.1+cpu |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.3 |
|
|
