| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/vit-large-patch16-224 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: vit_itri_downsample_normal_2class |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: test |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.788020919807405 |
| - name: Precision |
| type: precision |
| value: 0.8677060975195995 |
| - name: Recall |
| type: recall |
| value: 0.788020919807405 |
| - name: F1 |
| type: f1 |
| value: 0.8034489412987279 |
| --- |
| |
| <!-- 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. --> |
|
|
| # vit_itri_downsample_normal_2class |
|
|
| This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.6672 |
| - Accuracy: 0.7880 |
| - Precision: 0.8677 |
| - Recall: 0.7880 |
| - F1: 0.8034 |
|
|
| ## 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: 0.0001 |
| - train_batch_size: 24 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.2527 | 1.0 | 342 | 1.4323 | 0.6135 | 0.8417 | 0.6135 | 0.6373 | |
| | 0.1052 | 2.0 | 684 | 1.1099 | 0.6818 | 0.8441 | 0.6818 | 0.7058 | |
| | 0.0722 | 3.0 | 1026 | 0.7571 | 0.8196 | 0.8691 | 0.8196 | 0.8309 | |
| | 0.0364 | 4.0 | 1368 | 1.1982 | 0.7126 | 0.8538 | 0.7126 | 0.7347 | |
| | 0.0211 | 5.0 | 1710 | 1.8288 | 0.6682 | 0.8450 | 0.6682 | 0.6925 | |
| | 0.0154 | 6.0 | 2052 | 1.7574 | 0.7124 | 0.8537 | 0.7124 | 0.7345 | |
| | 0.0126 | 7.0 | 2394 | 2.0744 | 0.7140 | 0.8536 | 0.7140 | 0.7360 | |
| | 0.0027 | 8.0 | 2736 | 1.6455 | 0.7868 | 0.8658 | 0.7868 | 0.8023 | |
| | 0.0024 | 9.0 | 3078 | 1.8174 | 0.7700 | 0.8630 | 0.7700 | 0.7873 | |
| | 0.0016 | 10.0 | 3420 | 1.6672 | 0.7880 | 0.8677 | 0.7880 | 0.8034 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.53.0.dev0 |
| - Pytorch 2.7.1+cu126 |
| - Datasets 3.6.0 |
| - Tokenizers 0.21.1 |
|
|
