| --- |
| license: apache-2.0 |
| base_model: microsoft/beit-base-patch16-224-pt22k-ft22k |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - recall |
| - f1 |
| - precision |
| model-index: |
| - name: beit-base-patch16-224-pt22k-ft22k-finetuned-ind-17-imbalanced-aadhaarmask |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.8450404427415922 |
| - name: Recall |
| type: recall |
| value: 0.8450404427415922 |
| - name: F1 |
| type: f1 |
| value: 0.8442233792705293 |
| - name: Precision |
| type: precision |
| value: 0.8494143266059094 |
| --- |
| |
| <!-- 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-pt22k-ft22k-finetuned-ind-17-imbalanced-aadhaarmask |
|
|
| This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3480 |
| - Accuracy: 0.8450 |
| - Recall: 0.8450 |
| - F1: 0.8442 |
| - Precision: 0.8494 |
|
|
| ## 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: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 10 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1 | Precision | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
| | 0.5859 | 0.9974 | 293 | 0.6117 | 0.8114 | 0.8114 | 0.7891 | 0.8139 | |
| | 0.5281 | 1.9983 | 587 | 0.4362 | 0.8442 | 0.8442 | 0.8375 | 0.8484 | |
| | 0.4214 | 2.9991 | 881 | 0.4228 | 0.8438 | 0.8438 | 0.8392 | 0.8529 | |
| | 0.4221 | 4.0 | 1175 | 0.4121 | 0.8382 | 0.8382 | 0.8331 | 0.8495 | |
| | 0.4127 | 4.9974 | 1468 | 0.3692 | 0.8476 | 0.8476 | 0.8454 | 0.8511 | |
| | 0.3122 | 5.9983 | 1762 | 0.3741 | 0.8408 | 0.8408 | 0.8394 | 0.8462 | |
| | 0.3079 | 6.9991 | 2056 | 0.3628 | 0.8429 | 0.8429 | 0.8403 | 0.8445 | |
| | 0.2851 | 8.0 | 2350 | 0.3635 | 0.8412 | 0.8412 | 0.8389 | 0.8412 | |
| | 0.297 | 8.9974 | 2643 | 0.3407 | 0.8510 | 0.8510 | 0.8497 | 0.8545 | |
| | 0.2109 | 9.9745 | 2930 | 0.3566 | 0.8421 | 0.8421 | 0.8406 | 0.8418 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.40.1 |
| - Pytorch 2.2.0a0+81ea7a4 |
| - Datasets 2.19.0 |
| - Tokenizers 0.19.1 |
|
|
