| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google/vit-base-patch16-224 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - recall |
| model-index: |
| - name: fold_3 |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: None |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9827586206896551 |
| - name: Recall |
| type: recall |
| value: 0.9827835611856683 |
| --- |
| |
| <!-- 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. --> |
|
|
| # fold_3 |
| |
| 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: 1.0408 |
| - Accuracy: 0.9828 |
| - F1 Score: 0.9839 |
| - Recall: 0.9828 |
| |
| ## 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: 1e-05 |
| - train_batch_size: 64 |
| - eval_batch_size: 64 |
| - 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: cosine |
| - lr_scheduler_warmup_steps: 150 |
| - num_epochs: 100 |
| - label_smoothing_factor: 0.15 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:| |
| | 2.8990 | 1.0 | 19 | 2.8243 | 0.3 | 0.1902 | 0.2394 | |
| | 2.6775 | 2.0 | 38 | 2.5749 | 0.3759 | 0.2813 | 0.3098 | |
| | 2.3450 | 3.0 | 57 | 2.2350 | 0.5931 | 0.5678 | 0.5485 | |
| | 1.9329 | 4.0 | 76 | 1.8672 | 0.7828 | 0.7860 | 0.7689 | |
| | 1.5705 | 5.0 | 95 | 1.5231 | 0.8931 | 0.9009 | 0.8963 | |
| | 1.3488 | 6.0 | 114 | 1.3058 | 0.9379 | 0.9421 | 0.9399 | |
| | 1.1881 | 7.0 | 133 | 1.2164 | 0.9379 | 0.9429 | 0.9400 | |
| | 1.1692 | 8.0 | 152 | 1.1655 | 0.9483 | 0.9521 | 0.9498 | |
| | 1.1013 | 9.0 | 171 | 1.1383 | 0.9621 | 0.9654 | 0.9620 | |
| | 1.0939 | 10.0 | 190 | 1.1244 | 0.9586 | 0.9621 | 0.9570 | |
| | 1.0743 | 11.0 | 209 | 1.1158 | 0.9690 | 0.9716 | 0.9682 | |
| | 1.0535 | 12.0 | 228 | 1.0974 | 0.9655 | 0.9687 | 0.9670 | |
| | 1.0468 | 13.0 | 247 | 1.0882 | 0.9655 | 0.9683 | 0.9683 | |
| | 1.0113 | 14.0 | 266 | 1.0735 | 0.9759 | 0.9778 | 0.9766 | |
| | 1.0041 | 15.0 | 285 | 1.0831 | 0.9724 | 0.9753 | 0.9717 | |
| | 1.0087 | 16.0 | 304 | 1.0676 | 0.9724 | 0.9749 | 0.9754 | |
| | 1.0055 | 17.0 | 323 | 1.0714 | 0.9655 | 0.9689 | 0.9705 | |
| | 0.9963 | 18.0 | 342 | 1.0680 | 0.9690 | 0.9719 | 0.9730 | |
| | 0.9857 | 19.0 | 361 | 1.0630 | 0.9759 | 0.9776 | 0.9753 | |
| | 0.9784 | 20.0 | 380 | 1.0617 | 0.9724 | 0.9748 | 0.9754 | |
| | 0.9790 | 21.0 | 399 | 1.0653 | 0.9724 | 0.9748 | 0.9754 | |
| | 0.9791 | 22.0 | 418 | 1.0574 | 0.9724 | 0.9749 | 0.9754 | |
| | 0.9900 | 23.0 | 437 | 1.0858 | 0.9690 | 0.9720 | 0.9768 | |
| | 0.9790 | 24.0 | 456 | 1.0520 | 0.9759 | 0.9776 | 0.9753 | |
| | 0.9708 | 25.0 | 475 | 1.0499 | 0.9724 | 0.9749 | 0.9754 | |
| | 0.9708 | 26.0 | 494 | 1.0448 | 0.9793 | 0.9809 | 0.9803 | |
| | 0.9707 | 27.0 | 513 | 1.0459 | 0.9793 | 0.9809 | 0.9803 | |
| | 0.9729 | 28.0 | 532 | 1.0549 | 0.9690 | 0.9719 | 0.9730 | |
| | 0.9845 | 29.0 | 551 | 1.0451 | 0.9793 | 0.9809 | 0.9803 | |
| | 0.9771 | 30.0 | 570 | 1.0502 | 0.9759 | 0.9778 | 0.9766 | |
| | 0.9716 | 31.0 | 589 | 1.0507 | 0.9759 | 0.9779 | 0.9779 | |
| | 0.9669 | 32.0 | 608 | 1.0457 | 0.9793 | 0.9809 | 0.9803 | |
| | 0.9761 | 33.0 | 627 | 1.0483 | 0.9793 | 0.9808 | 0.9791 | |
| | 0.9627 | 34.0 | 646 | 1.0437 | 0.9724 | 0.9743 | 0.9743 | |
| | 0.9738 | 35.0 | 665 | 1.0431 | 0.9759 | 0.9779 | 0.9779 | |
| | 0.9666 | 36.0 | 684 | 1.0413 | 0.9759 | 0.9779 | 0.9779 | |
| | 0.9657 | 37.0 | 703 | 1.0452 | 0.9724 | 0.9743 | 0.9743 | |
| | 0.9649 | 38.0 | 722 | 1.0440 | 0.9793 | 0.9809 | 0.9803 | |
| | 0.9607 | 39.0 | 741 | 1.0389 | 0.9759 | 0.9774 | 0.9780 | |
| | 0.9693 | 40.0 | 760 | 1.0408 | 0.9828 | 0.9839 | 0.9828 | |
| | 0.9692 | 41.0 | 779 | 1.0374 | 0.9793 | 0.9809 | 0.9803 | |
| | 0.9599 | 42.0 | 798 | 1.0390 | 0.9793 | 0.9809 | 0.9803 | |
| | 0.9601 | 43.0 | 817 | 1.0372 | 0.9793 | 0.9810 | 0.9816 | |
| | 0.9642 | 44.0 | 836 | 1.0432 | 0.9793 | 0.9809 | 0.9803 | |
| | 0.9624 | 45.0 | 855 | 1.0412 | 0.9759 | 0.9779 | 0.9779 | |
| | 0.9598 | 46.0 | 874 | 1.0395 | 0.9793 | 0.9809 | 0.9803 | |
| | 0.9567 | 47.0 | 893 | 1.0414 | 0.9793 | 0.9809 | 0.9803 | |
| | 0.9634 | 48.0 | 912 | 1.0416 | 0.9759 | 0.9779 | 0.9779 | |
| | 0.9662 | 49.0 | 931 | 1.0416 | 0.9759 | 0.9779 | 0.9779 | |
| | 0.9652 | 50.0 | 950 | 1.0457 | 0.9759 | 0.9779 | 0.9779 | |
| | 0.9597 | 51.0 | 969 | 1.0418 | 0.9724 | 0.9748 | 0.9754 | |
| | 0.9675 | 52.0 | 988 | 1.0403 | 0.9724 | 0.9748 | 0.9754 | |
| | 0.9615 | 53.0 | 1007 | 1.0389 | 0.9724 | 0.9749 | 0.9754 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.0.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 5.0.0 |
| - Tokenizers 0.22.2 |
| |
