| --- |
| license: apache-2.0 |
| base_model: google/vit-large-patch16-224 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - recall |
| - f1 |
| - precision |
| model-index: |
| - name: vit-large-patch16-224-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: 0.8420604512558536 |
| - name: Recall |
| type: recall |
| value: 0.8420604512558536 |
| - name: F1 |
| type: f1 |
| value: 0.840458775689156 |
| - name: Precision |
| type: precision |
| value: 0.8450034699086092 |
| --- |
| |
| <!-- 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-large-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask |
|
|
| This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.3294 |
| - Accuracy: 0.8421 |
| - Recall: 0.8421 |
| - F1: 0.8405 |
| - Precision: 0.8450 |
|
|
| ## 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 | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
| | 0.5269 | 0.9974 | 293 | 0.5393 | 0.8029 | 0.8029 | 0.7943 | 0.7941 | |
| | 0.4275 | 1.9983 | 587 | 0.4630 | 0.8182 | 0.8182 | 0.8103 | 0.8255 | |
| | 0.4681 | 2.9991 | 881 | 0.4346 | 0.8408 | 0.8408 | 0.8358 | 0.8557 | |
| | 0.3721 | 4.0 | 1175 | 0.3631 | 0.8450 | 0.8450 | 0.8417 | 0.8541 | |
| | 0.4054 | 4.9974 | 1468 | 0.3536 | 0.8455 | 0.8455 | 0.8445 | 0.8491 | |
| | 0.2519 | 5.9983 | 1762 | 0.3747 | 0.8421 | 0.8421 | 0.8391 | 0.8549 | |
| | 0.2923 | 6.9991 | 2056 | 0.3664 | 0.8395 | 0.8395 | 0.8402 | 0.8467 | |
| | 0.2288 | 8.0 | 2350 | 0.3496 | 0.8382 | 0.8382 | 0.8377 | 0.8442 | |
| | 0.1642 | 8.9974 | 2643 | 0.3455 | 0.8463 | 0.8463 | 0.8444 | 0.8468 | |
| | 0.1783 | 9.9745 | 2930 | 0.3468 | 0.8476 | 0.8476 | 0.8463 | 0.8490 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.40.1 |
| - Pytorch 2.2.0a0+81ea7a4 |
| - Datasets 2.19.0 |
| - Tokenizers 0.19.1 |
|
|
