| --- |
| license: apache-2.0 |
| tags: |
| - image-classification |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| model-index: |
| - name: model_handwritenNumbers-nesanchezo |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # model_handwritenNumbers-nesanchezo |
| |
| This model is a fine-tuned version of [farleyknight-org-username/vit-base-mnist](https://huggingface.co/farleyknight-org-username/vit-base-mnist) on the handwriten-Numbers dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0807 |
| - Accuracy: 0.9839 |
| |
| ## 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: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 4 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.396 | 0.34 | 500 | 0.1925 | 0.9470 | |
| | 0.2672 | 0.67 | 1000 | 0.2655 | 0.9297 | |
| | 0.2261 | 1.01 | 1500 | 0.1767 | 0.9548 | |
| | 0.1603 | 1.34 | 2000 | 0.1423 | 0.9658 | |
| | 0.1308 | 1.68 | 2500 | 0.1378 | 0.9709 | |
| | 0.1187 | 2.02 | 3000 | 0.1168 | 0.9737 | |
| | 0.0873 | 2.35 | 3500 | 0.0857 | 0.9823 | |
| | 0.0686 | 2.69 | 4000 | 0.1188 | 0.9753 | |
| | 0.0635 | 3.03 | 4500 | 0.0836 | 0.9804 | |
| | 0.034 | 3.36 | 5000 | 0.0807 | 0.9839 | |
| | 0.0155 | 3.7 | 5500 | 0.0898 | 0.9823 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.30.2 |
| - Pytorch 2.0.1 |
| - Datasets 2.12.0 |
| - Tokenizers 0.13.3 |
| |
