| --- |
| library_name: transformers |
| license: other |
| base_model: apple/mobilevit-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| model-index: |
| - name: results |
| 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. --> |
|
|
| # results |
|
|
| This model is a fine-tuned version of [apple/mobilevit-small](https://huggingface.co/apple/mobilevit-small) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.9863 |
| - Accuracy: 0.7512 |
|
|
| ## 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: 0.0008 |
| - train_batch_size: 256 |
| - eval_batch_size: 256 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 5 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:------:|:-----:|:---------------:|:--------:| |
| | 1.4609 | 0.2844 | 5000 | 1.3989 | 0.6532 | |
| | 1.3211 | 0.5689 | 10000 | 1.2739 | 0.6803 | |
| | 1.2531 | 0.8533 | 15000 | 1.2132 | 0.6942 | |
| | 1.1875 | 1.1377 | 20000 | 1.1762 | 0.7041 | |
| | 1.157 | 1.4222 | 25000 | 1.1460 | 0.7111 | |
| | 1.144 | 1.7066 | 30000 | 1.1184 | 0.7163 | |
| | 1.1217 | 1.9910 | 35000 | 1.0880 | 0.7247 | |
| | 1.0831 | 2.2754 | 40000 | 1.0729 | 0.7280 | |
| | 1.0761 | 2.5599 | 45000 | 1.0593 | 0.7312 | |
| | 1.0565 | 2.8443 | 50000 | 1.0480 | 0.7346 | |
| | 1.0149 | 3.1287 | 55000 | 1.0356 | 0.7380 | |
| | 1.0102 | 3.4132 | 60000 | 1.0263 | 0.7401 | |
| | 1.0014 | 3.6976 | 65000 | 1.0122 | 0.7437 | |
| | 0.9972 | 3.9820 | 70000 | 1.0028 | 0.7459 | |
| | 0.9556 | 4.2665 | 75000 | 0.9971 | 0.7474 | |
| | 0.9606 | 4.5509 | 80000 | 0.9904 | 0.7496 | |
| | 0.9544 | 4.8353 | 85000 | 0.9842 | 0.7507 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.3 |
| - Pytorch 2.9.0+cu126 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.1 |
| |
