| --- |
| license: apache-2.0 |
| base_model: microsoft/beit-base-patch16-224 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| model-index: |
| - name: beit-base-patch16-224 |
| 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: 0.7333333333333333 |
| - name: Precision |
| type: precision |
| value: 0.708216298040535 |
| - name: Recall |
| type: recall |
| value: 0.7333333333333333 |
| --- |
| |
| <!-- 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. --> |
|
|
| # beit-base-patch16-224 |
|
|
| This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5490 |
| - Accuracy: 0.7333 |
| - Precision: 0.7082 |
| - Recall: 0.7333 |
| - F1 Score: 0.7050 |
|
|
| ## 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: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 256 |
| - 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 | Precision | Recall | F1 Score | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:| |
| | No log | 1.0 | 4 | 0.6369 | 0.725 | 0.5256 | 0.725 | 0.6094 | |
| | No log | 2.0 | 8 | 0.6192 | 0.7458 | 0.7215 | 0.7458 | 0.6907 | |
| | No log | 3.0 | 12 | 0.5699 | 0.725 | 0.5256 | 0.725 | 0.6094 | |
| | 0.727 | 4.0 | 16 | 0.6237 | 0.6792 | 0.6716 | 0.6792 | 0.6751 | |
| | 0.727 | 5.0 | 20 | 0.5533 | 0.7292 | 0.8028 | 0.7292 | 0.6191 | |
| | 0.727 | 6.0 | 24 | 0.5601 | 0.7375 | 0.7200 | 0.7375 | 0.6562 | |
| | 0.727 | 7.0 | 28 | 0.5901 | 0.7167 | 0.6944 | 0.7167 | 0.7013 | |
| | 0.5968 | 8.0 | 32 | 0.5543 | 0.7375 | 0.7081 | 0.7375 | 0.7080 | |
| | 0.5968 | 9.0 | 36 | 0.5780 | 0.7208 | 0.7095 | 0.7208 | 0.7141 | |
| | 0.5968 | 10.0 | 40 | 0.5389 | 0.7375 | 0.7049 | 0.7375 | 0.6990 | |
| | 0.5968 | 11.0 | 44 | 0.5438 | 0.7542 | 0.7306 | 0.7542 | 0.7238 | |
| | 0.5631 | 12.0 | 48 | 0.5426 | 0.7458 | 0.7187 | 0.7458 | 0.7145 | |
| | 0.5631 | 13.0 | 52 | 0.5383 | 0.7458 | 0.7187 | 0.7458 | 0.7145 | |
| | 0.5631 | 14.0 | 56 | 0.5432 | 0.7458 | 0.7239 | 0.7458 | 0.7269 | |
| | 0.541 | 15.0 | 60 | 0.5453 | 0.7417 | 0.7212 | 0.7417 | 0.7256 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.33.2 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.5 |
| - Tokenizers 0.13.3 |
|
|
