| | --- |
| | library_name: transformers |
| | license: apache-2.0 |
| | base_model: google/vit-base-patch16-224-in21k |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: dog-emotion-classifier |
| | 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. --> |
| |
|
| | # dog-emotion-classifier |
| |
|
| | This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.7653 |
| | - Accuracy: 0.8612 |
| |
|
| | ## 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: 16 |
| | - eval_batch_size: 8 |
| | - 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: 10 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | | No log | 0.5 | 100 | 0.5477 | 0.795 | |
| | | No log | 1.0 | 200 | 0.4834 | 0.83 | |
| | | No log | 1.5 | 300 | 0.4944 | 0.8263 | |
| | | No log | 2.0 | 400 | 0.5020 | 0.8187 | |
| | | 0.4298 | 2.5 | 500 | 0.5745 | 0.83 | |
| | | 0.4298 | 3.0 | 600 | 0.6207 | 0.8187 | |
| | | 0.4298 | 3.5 | 700 | 0.5745 | 0.85 | |
| | | 0.4298 | 4.0 | 800 | 0.7309 | 0.84 | |
| | | 0.4298 | 4.5 | 900 | 0.7073 | 0.835 | |
| | | 0.0585 | 5.0 | 1000 | 0.6339 | 0.8538 | |
| | | 0.0585 | 5.5 | 1100 | 0.7294 | 0.8413 | |
| | | 0.0585 | 6.0 | 1200 | 0.7083 | 0.8562 | |
| | | 0.0585 | 6.5 | 1300 | 0.7272 | 0.8588 | |
| | | 0.0585 | 7.0 | 1400 | 0.7358 | 0.8588 | |
| | | 0.0042 | 7.5 | 1500 | 0.7447 | 0.86 | |
| | | 0.0042 | 8.0 | 1600 | 0.7517 | 0.86 | |
| | | 0.0042 | 8.5 | 1700 | 0.7581 | 0.8612 | |
| | | 0.0042 | 9.0 | 1800 | 0.7620 | 0.8612 | |
| | | 0.0042 | 9.5 | 1900 | 0.7645 | 0.8612 | |
| | | 0.0012 | 10.0 | 2000 | 0.7653 | 0.8612 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.57.1 |
| | - Pytorch 2.8.0+cu126 |
| | - Datasets 4.0.0 |
| | - Tokenizers 0.22.1 |
| | |
