| | --- |
| | license: apache-2.0 |
| | base_model: google/vit-base-patch16-224 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - imagefolder |
| | metrics: |
| | - accuracy |
| | model-index: |
| | - name: vit-base-patch16-224-brand |
| | 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: 0.8495867768595041 |
| | --- |
| | |
| | <!-- 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-patch16-224-brand |
| |
|
| | 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: 0.4812 |
| | - Accuracy: 0.8496 |
| |
|
| | ## 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: 5e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 64 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_ratio: 0.1 |
| | - num_epochs: 15 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | | 1.4669 | 1.0 | 88 | 1.3067 | 0.5612 | |
| | | 0.8898 | 1.99 | 176 | 0.8380 | 0.7140 | |
| | | 0.7243 | 2.99 | 264 | 0.6559 | 0.7694 | |
| | | 0.5158 | 4.0 | 353 | 0.5982 | 0.7950 | |
| | | 0.4605 | 5.0 | 441 | 0.5856 | 0.8083 | |
| | | 0.332 | 5.99 | 529 | 0.5138 | 0.8355 | |
| | | 0.3375 | 6.99 | 617 | 0.5095 | 0.8264 | |
| | | 0.2188 | 8.0 | 706 | 0.5089 | 0.8322 | |
| | | 0.2112 | 9.0 | 794 | 0.5126 | 0.8380 | |
| | | 0.1895 | 9.99 | 882 | 0.5057 | 0.8364 | |
| | | 0.1593 | 10.99 | 970 | 0.4852 | 0.8529 | |
| | | 0.1463 | 12.0 | 1059 | 0.4934 | 0.8430 | |
| | | 0.1565 | 13.0 | 1147 | 0.4794 | 0.8496 | |
| | | 0.1236 | 13.99 | 1235 | 0.4863 | 0.8463 | |
| | | 0.1407 | 14.96 | 1320 | 0.4812 | 0.8496 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.35.2 |
| | - Pytorch 2.1.0+cu118 |
| | - Datasets 2.15.0 |
| | - Tokenizers 0.15.0 |
| |
|
