| --- |
| license: apache-2.0 |
| base_model: google/vit-base-patch16-224 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| model-index: |
| - name: vit-base |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: validation |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.8181818181818182 |
| --- |
| |
| <!-- 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-base |
|
|
| This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7098 |
| - Accuracy: 0.8182 |
|
|
| ## 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: 0.0002 |
| - train_batch_size: 4 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 20 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.0041 | 3.57 | 50 | 0.5183 | 0.7576 | |
| | 0.0006 | 7.14 | 100 | 0.7554 | 0.8182 | |
| | 0.0004 | 10.71 | 150 | 0.7197 | 0.8182 | |
| | 0.0003 | 14.29 | 200 | 0.7126 | 0.8182 | |
| | 0.0003 | 17.86 | 250 | 0.7098 | 0.8182 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.31.0 |
| - Pytorch 2.0.1+cu117 |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.3 |
|
|
