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--- |
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license: apache-2.0 |
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base_model: microsoft/beit-base-patch16-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: beit-base-patch16-224-hasta-75-fold5 |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 1.0 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# beit-base-patch16-224-hasta-75-fold5 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0761 |
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- Accuracy: 1.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 100 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 1 | 0.9045 | 0.3333 | |
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| No log | 2.0 | 2 | 0.6690 | 0.8333 | |
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| No log | 3.0 | 3 | 0.4176 | 0.9167 | |
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| No log | 4.0 | 4 | 0.3611 | 0.9167 | |
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| No log | 5.0 | 5 | 0.3914 | 0.9167 | |
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| No log | 6.0 | 6 | 0.3954 | 0.9167 | |
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| No log | 7.0 | 7 | 0.3574 | 0.9167 | |
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| No log | 8.0 | 8 | 0.3282 | 0.9167 | |
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| No log | 9.0 | 9 | 0.3443 | 0.9167 | |
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| 0.3098 | 10.0 | 10 | 0.2939 | 0.9167 | |
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| 0.3098 | 11.0 | 11 | 0.2509 | 0.9167 | |
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| 0.3098 | 12.0 | 12 | 0.2714 | 0.9167 | |
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| 0.3098 | 13.0 | 13 | 0.3388 | 0.9167 | |
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| 0.3098 | 14.0 | 14 | 0.4240 | 0.8333 | |
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| 0.3098 | 15.0 | 15 | 0.3483 | 0.8333 | |
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| 0.3098 | 16.0 | 16 | 0.3874 | 0.75 | |
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| 0.3098 | 17.0 | 17 | 0.3755 | 0.8333 | |
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| 0.3098 | 18.0 | 18 | 0.2999 | 0.9167 | |
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| 0.3098 | 19.0 | 19 | 0.3344 | 0.9167 | |
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| 0.1617 | 20.0 | 20 | 0.3177 | 0.9167 | |
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| 0.1617 | 21.0 | 21 | 0.3033 | 0.8333 | |
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| 0.1617 | 22.0 | 22 | 0.2805 | 0.8333 | |
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| 0.1617 | 23.0 | 23 | 0.2428 | 0.8333 | |
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| 0.1617 | 24.0 | 24 | 0.1912 | 0.9167 | |
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| 0.1617 | 25.0 | 25 | 0.1992 | 0.8333 | |
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| 0.1617 | 26.0 | 26 | 0.2689 | 0.9167 | |
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| 0.1617 | 27.0 | 27 | 0.2284 | 0.9167 | |
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| 0.1617 | 28.0 | 28 | 0.1536 | 0.9167 | |
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| 0.1617 | 29.0 | 29 | 0.1593 | 0.9167 | |
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| 0.1003 | 30.0 | 30 | 0.1818 | 0.8333 | |
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| 0.1003 | 31.0 | 31 | 0.2490 | 0.8333 | |
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| 0.1003 | 32.0 | 32 | 0.3354 | 0.9167 | |
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| 0.1003 | 33.0 | 33 | 0.3148 | 0.8333 | |
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| 0.1003 | 34.0 | 34 | 0.3323 | 0.8333 | |
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| 0.1003 | 35.0 | 35 | 0.3582 | 0.9167 | |
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| 0.1003 | 36.0 | 36 | 0.3736 | 0.8333 | |
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| 0.1003 | 37.0 | 37 | 0.4285 | 0.8333 | |
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| 0.1003 | 38.0 | 38 | 0.4383 | 0.8333 | |
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| 0.1003 | 39.0 | 39 | 0.4500 | 0.8333 | |
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| 0.0541 | 40.0 | 40 | 0.3576 | 0.8333 | |
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| 0.0541 | 41.0 | 41 | 0.2811 | 0.8333 | |
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| 0.0541 | 42.0 | 42 | 0.1908 | 0.9167 | |
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| 0.0541 | 43.0 | 43 | 0.1601 | 0.9167 | |
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| 0.0541 | 44.0 | 44 | 0.1516 | 0.9167 | |
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| 0.0541 | 45.0 | 45 | 0.0944 | 0.9167 | |
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| 0.0541 | 46.0 | 46 | 0.1231 | 0.9167 | |
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| 0.0541 | 47.0 | 47 | 0.1886 | 0.9167 | |
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| 0.0541 | 48.0 | 48 | 0.1905 | 0.9167 | |
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| 0.0541 | 49.0 | 49 | 0.2160 | 0.9167 | |
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| 0.0479 | 50.0 | 50 | 0.1523 | 0.9167 | |
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| 0.0479 | 51.0 | 51 | 0.0761 | 1.0 | |
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| 0.0479 | 52.0 | 52 | 0.0611 | 1.0 | |
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| 0.0479 | 53.0 | 53 | 0.0579 | 1.0 | |
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| 0.0479 | 54.0 | 54 | 0.0918 | 0.9167 | |
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| 0.0479 | 55.0 | 55 | 0.2574 | 0.9167 | |
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| 0.0479 | 56.0 | 56 | 0.4721 | 0.9167 | |
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| 0.0479 | 57.0 | 57 | 0.5495 | 0.9167 | |
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| 0.0479 | 58.0 | 58 | 0.5856 | 0.9167 | |
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| 0.0479 | 59.0 | 59 | 0.5852 | 0.9167 | |
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| 0.0579 | 60.0 | 60 | 0.5607 | 0.9167 | |
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| 0.0579 | 61.0 | 61 | 0.4982 | 0.9167 | |
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| 0.0579 | 62.0 | 62 | 0.4343 | 0.9167 | |
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| 0.0579 | 63.0 | 63 | 0.3539 | 0.9167 | |
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| 0.0579 | 64.0 | 64 | 0.2492 | 0.9167 | |
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| 0.0579 | 65.0 | 65 | 0.2232 | 0.9167 | |
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| 0.0579 | 66.0 | 66 | 0.2359 | 0.9167 | |
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| 0.0579 | 67.0 | 67 | 0.2633 | 0.9167 | |
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| 0.0579 | 68.0 | 68 | 0.3047 | 0.9167 | |
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| 0.0579 | 69.0 | 69 | 0.3410 | 0.9167 | |
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| 0.0396 | 70.0 | 70 | 0.3630 | 0.9167 | |
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| 0.0396 | 71.0 | 71 | 0.3515 | 0.9167 | |
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| 0.0396 | 72.0 | 72 | 0.3256 | 0.9167 | |
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| 0.0396 | 73.0 | 73 | 0.2986 | 0.9167 | |
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| 0.0396 | 74.0 | 74 | 0.2439 | 0.9167 | |
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| 0.0396 | 75.0 | 75 | 0.1829 | 0.9167 | |
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| 0.0396 | 76.0 | 76 | 0.1434 | 0.9167 | |
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| 0.0396 | 77.0 | 77 | 0.1233 | 0.9167 | |
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| 0.0396 | 78.0 | 78 | 0.1253 | 0.9167 | |
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| 0.0396 | 79.0 | 79 | 0.1324 | 0.9167 | |
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| 0.0276 | 80.0 | 80 | 0.1492 | 0.9167 | |
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| 0.0276 | 81.0 | 81 | 0.1580 | 0.9167 | |
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| 0.0276 | 82.0 | 82 | 0.1729 | 0.9167 | |
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| 0.0276 | 83.0 | 83 | 0.1670 | 0.9167 | |
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| 0.0276 | 84.0 | 84 | 0.1609 | 0.9167 | |
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| 0.0276 | 85.0 | 85 | 0.1458 | 0.9167 | |
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| 0.0276 | 86.0 | 86 | 0.1262 | 0.9167 | |
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| 0.0276 | 87.0 | 87 | 0.1097 | 0.9167 | |
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| 0.0276 | 88.0 | 88 | 0.0934 | 0.9167 | |
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| 0.0276 | 89.0 | 89 | 0.0820 | 0.9167 | |
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| 0.0324 | 90.0 | 90 | 0.0731 | 1.0 | |
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| 0.0324 | 91.0 | 91 | 0.0676 | 1.0 | |
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| 0.0324 | 92.0 | 92 | 0.0651 | 1.0 | |
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| 0.0324 | 93.0 | 93 | 0.0627 | 1.0 | |
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| 0.0324 | 94.0 | 94 | 0.0610 | 1.0 | |
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| 0.0324 | 95.0 | 95 | 0.0600 | 1.0 | |
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| 0.0324 | 96.0 | 96 | 0.0592 | 1.0 | |
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| 0.0324 | 97.0 | 97 | 0.0586 | 1.0 | |
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| 0.0324 | 98.0 | 98 | 0.0579 | 1.0 | |
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| 0.0324 | 99.0 | 99 | 0.0572 | 1.0 | |
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| 0.0361 | 100.0 | 100 | 0.0569 | 1.0 | |
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### Framework versions |
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- Transformers 4.41.0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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