beit-base-patch16-224-hasta-85-fold1
This model is a fine-tuned version of microsoft/beit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.9074
- Accuracy: 0.7273
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 1 | 1.4041 | 0.2727 |
| No log | 2.0 | 2 | 1.1167 | 0.5455 |
| No log | 3.0 | 3 | 0.7944 | 0.6364 |
| No log | 4.0 | 4 | 0.9074 | 0.7273 |
| No log | 5.0 | 5 | 1.3600 | 0.7273 |
| No log | 6.0 | 6 | 1.6643 | 0.7273 |
| No log | 7.0 | 7 | 1.6410 | 0.7273 |
| No log | 8.0 | 8 | 1.4549 | 0.7273 |
| No log | 9.0 | 9 | 1.4814 | 0.3636 |
| 0.4133 | 10.0 | 10 | 1.2719 | 0.6364 |
| 0.4133 | 11.0 | 11 | 1.0360 | 0.7273 |
| 0.4133 | 12.0 | 12 | 1.0691 | 0.7273 |
| 0.4133 | 13.0 | 13 | 0.9528 | 0.7273 |
| 0.4133 | 14.0 | 14 | 1.0430 | 0.7273 |
| 0.4133 | 15.0 | 15 | 1.3221 | 0.7273 |
| 0.4133 | 16.0 | 16 | 1.5236 | 0.7273 |
| 0.4133 | 17.0 | 17 | 1.5914 | 0.7273 |
| 0.4133 | 18.0 | 18 | 1.5015 | 0.7273 |
| 0.4133 | 19.0 | 19 | 1.3859 | 0.7273 |
| 0.1601 | 20.0 | 20 | 1.4514 | 0.7273 |
| 0.1601 | 21.0 | 21 | 1.6169 | 0.7273 |
| 0.1601 | 22.0 | 22 | 1.5223 | 0.7273 |
| 0.1601 | 23.0 | 23 | 1.4172 | 0.7273 |
| 0.1601 | 24.0 | 24 | 1.4753 | 0.6364 |
| 0.1601 | 25.0 | 25 | 1.5207 | 0.7273 |
| 0.1601 | 26.0 | 26 | 1.7055 | 0.7273 |
| 0.1601 | 27.0 | 27 | 1.7901 | 0.7273 |
| 0.1601 | 28.0 | 28 | 1.9109 | 0.7273 |
| 0.1601 | 29.0 | 29 | 1.8261 | 0.7273 |
| 0.1015 | 30.0 | 30 | 1.5657 | 0.7273 |
| 0.1015 | 31.0 | 31 | 1.3513 | 0.7273 |
| 0.1015 | 32.0 | 32 | 1.3374 | 0.7273 |
| 0.1015 | 33.0 | 33 | 1.4942 | 0.7273 |
| 0.1015 | 34.0 | 34 | 1.7731 | 0.7273 |
| 0.1015 | 35.0 | 35 | 1.8369 | 0.7273 |
| 0.1015 | 36.0 | 36 | 1.7328 | 0.7273 |
| 0.1015 | 37.0 | 37 | 1.5451 | 0.7273 |
| 0.1015 | 38.0 | 38 | 1.4483 | 0.7273 |
| 0.1015 | 39.0 | 39 | 1.4289 | 0.7273 |
| 0.0819 | 40.0 | 40 | 1.3922 | 0.7273 |
| 0.0819 | 41.0 | 41 | 1.4002 | 0.7273 |
| 0.0819 | 42.0 | 42 | 1.3673 | 0.7273 |
| 0.0819 | 43.0 | 43 | 1.2194 | 0.7273 |
| 0.0819 | 44.0 | 44 | 1.2879 | 0.7273 |
| 0.0819 | 45.0 | 45 | 1.4545 | 0.7273 |
| 0.0819 | 46.0 | 46 | 1.6902 | 0.7273 |
| 0.0819 | 47.0 | 47 | 1.8415 | 0.7273 |
| 0.0819 | 48.0 | 48 | 1.8891 | 0.7273 |
| 0.0819 | 49.0 | 49 | 1.8339 | 0.7273 |
| 0.0551 | 50.0 | 50 | 1.8433 | 0.7273 |
| 0.0551 | 51.0 | 51 | 1.8432 | 0.7273 |
| 0.0551 | 52.0 | 52 | 1.8002 | 0.7273 |
| 0.0551 | 53.0 | 53 | 1.7276 | 0.7273 |
| 0.0551 | 54.0 | 54 | 1.5764 | 0.7273 |
| 0.0551 | 55.0 | 55 | 1.3951 | 0.7273 |
| 0.0551 | 56.0 | 56 | 1.4243 | 0.7273 |
| 0.0551 | 57.0 | 57 | 1.5620 | 0.7273 |
| 0.0551 | 58.0 | 58 | 1.6897 | 0.7273 |
| 0.0551 | 59.0 | 59 | 1.7995 | 0.7273 |
| 0.0413 | 60.0 | 60 | 1.8739 | 0.7273 |
| 0.0413 | 61.0 | 61 | 1.8486 | 0.7273 |
| 0.0413 | 62.0 | 62 | 1.8073 | 0.7273 |
| 0.0413 | 63.0 | 63 | 1.8193 | 0.7273 |
| 0.0413 | 64.0 | 64 | 1.7604 | 0.7273 |
| 0.0413 | 65.0 | 65 | 1.6327 | 0.7273 |
| 0.0413 | 66.0 | 66 | 1.5447 | 0.7273 |
| 0.0413 | 67.0 | 67 | 1.4243 | 0.7273 |
| 0.0413 | 68.0 | 68 | 1.3810 | 0.7273 |
| 0.0413 | 69.0 | 69 | 1.3641 | 0.7273 |
| 0.038 | 70.0 | 70 | 1.4688 | 0.7273 |
| 0.038 | 71.0 | 71 | 1.5677 | 0.7273 |
| 0.038 | 72.0 | 72 | 1.7174 | 0.7273 |
| 0.038 | 73.0 | 73 | 1.7920 | 0.7273 |
| 0.038 | 74.0 | 74 | 1.9000 | 0.7273 |
| 0.038 | 75.0 | 75 | 1.9468 | 0.7273 |
| 0.038 | 76.0 | 76 | 1.9872 | 0.7273 |
| 0.038 | 77.0 | 77 | 2.0208 | 0.7273 |
| 0.038 | 78.0 | 78 | 2.0135 | 0.7273 |
| 0.038 | 79.0 | 79 | 1.9762 | 0.7273 |
| 0.0365 | 80.0 | 80 | 1.9576 | 0.7273 |
| 0.0365 | 81.0 | 81 | 1.9310 | 0.7273 |
| 0.0365 | 82.0 | 82 | 1.8495 | 0.7273 |
| 0.0365 | 83.0 | 83 | 1.7683 | 0.7273 |
| 0.0365 | 84.0 | 84 | 1.7109 | 0.7273 |
| 0.0365 | 85.0 | 85 | 1.6438 | 0.7273 |
| 0.0365 | 86.0 | 86 | 1.6154 | 0.7273 |
| 0.0365 | 87.0 | 87 | 1.5715 | 0.7273 |
| 0.0365 | 88.0 | 88 | 1.5428 | 0.7273 |
| 0.0365 | 89.0 | 89 | 1.5164 | 0.7273 |
| 0.038 | 90.0 | 90 | 1.5008 | 0.7273 |
| 0.038 | 91.0 | 91 | 1.4730 | 0.7273 |
| 0.038 | 92.0 | 92 | 1.4493 | 0.7273 |
| 0.038 | 93.0 | 93 | 1.4728 | 0.7273 |
| 0.038 | 94.0 | 94 | 1.5033 | 0.7273 |
| 0.038 | 95.0 | 95 | 1.5346 | 0.7273 |
| 0.038 | 96.0 | 96 | 1.5556 | 0.7273 |
| 0.038 | 97.0 | 97 | 1.5643 | 0.7273 |
| 0.038 | 98.0 | 98 | 1.5759 | 0.7273 |
| 0.038 | 99.0 | 99 | 1.5807 | 0.7273 |
| 0.0191 | 100.0 | 100 | 1.5806 | 0.7273 |
Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1