| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/vit-base-patch16-224-in21k |
| tags: |
| - image-classification |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: test-trainer |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: Chess |
| type: imagefolder |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9107142857142857 |
| - name: F1 |
| type: f1 |
| value: 0.9121670865142396 |
| - name: Precision |
| type: precision |
| value: 0.9171626984126985 |
| - name: Recall |
| type: recall |
| value: 0.9107142857142857 |
| --- |
| |
| <!-- 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. --> |
|
|
| # test-trainer |
|
|
| This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Chess dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7291 |
| - Accuracy: 0.9107 |
| - F1: 0.9122 |
| - Precision: 0.9172 |
| - Recall: 0.9107 |
|
|
| ## 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: 2e-05 |
| - train_batch_size: 10 |
| - 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: 30 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | No log | 1.0 | 50 | 1.6720 | 0.4821 | 0.4134 | 0.3870 | 0.4821 | |
| | No log | 2.0 | 100 | 1.4652 | 0.6429 | 0.6126 | 0.7414 | 0.6429 | |
| | No log | 3.0 | 150 | 1.1742 | 0.7321 | 0.7210 | 0.7792 | 0.7321 | |
| | No log | 4.0 | 200 | 0.9813 | 0.8393 | 0.8433 | 0.8589 | 0.8393 | |
| | No log | 5.0 | 250 | 0.8312 | 0.8214 | 0.8164 | 0.8516 | 0.8214 | |
| | No log | 6.0 | 300 | 0.7291 | 0.9107 | 0.9122 | 0.9172 | 0.9107 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.46.2 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 3.1.0 |
| - Tokenizers 0.20.3 |
|
|
