11-classifier-finetuned-padchest
This model is a fine-tuned version of microsoft/resnet-50 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.1743
- F1: 0.5098
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: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 2.0747 | 1.0 | 18 | 2.0666 | 0.1549 |
| 2.0661 | 2.0 | 36 | 2.0560 | 0.1777 |
| 2.0497 | 3.0 | 54 | 2.0385 | 0.2169 |
| 2.018 | 4.0 | 72 | 2.0047 | 0.2515 |
| 1.9792 | 5.0 | 90 | 1.9773 | 0.2329 |
| 1.9619 | 6.0 | 108 | 1.9421 | 0.2321 |
| 1.9186 | 7.0 | 126 | 1.9145 | 0.2055 |
| 1.8838 | 8.0 | 144 | 1.8976 | 0.2596 |
| 1.8402 | 9.0 | 162 | 1.8444 | 0.2337 |
| 1.7906 | 10.0 | 180 | 1.7951 | 0.2397 |
| 1.7716 | 11.0 | 198 | 1.7695 | 0.3373 |
| 1.7474 | 12.0 | 216 | 1.7940 | 0.3209 |
| 1.6957 | 13.0 | 234 | 1.7425 | 0.3314 |
| 1.6791 | 14.0 | 252 | 1.6727 | 0.3558 |
| 1.6483 | 15.0 | 270 | 1.6638 | 0.3895 |
| 1.614 | 16.0 | 288 | 1.6513 | 0.4186 |
| 1.6166 | 17.0 | 306 | 1.6002 | 0.4406 |
| 1.5654 | 18.0 | 324 | 1.5528 | 0.4627 |
| 1.5145 | 19.0 | 342 | 1.5571 | 0.4676 |
| 1.5049 | 20.0 | 360 | 1.4334 | 0.4364 |
| 1.457 | 21.0 | 378 | 1.4711 | 0.4535 |
| 1.4516 | 22.0 | 396 | 1.5013 | 0.4516 |
| 1.4172 | 23.0 | 414 | 1.3614 | 0.4682 |
| 1.3817 | 24.0 | 432 | 1.3519 | 0.4545 |
| 1.3987 | 25.0 | 450 | 1.3806 | 0.4759 |
| 1.4063 | 26.0 | 468 | 1.2961 | 0.4866 |
| 1.3684 | 27.0 | 486 | 1.3328 | 0.4768 |
| 1.3789 | 28.0 | 504 | 1.2810 | 0.4859 |
| 1.341 | 29.0 | 522 | 1.3227 | 0.4737 |
| 1.3574 | 30.0 | 540 | 1.2406 | 0.5025 |
| 1.357 | 31.0 | 558 | 1.2427 | 0.5033 |
| 1.3204 | 32.0 | 576 | 1.2478 | 0.5053 |
| 1.3122 | 33.0 | 594 | 1.2205 | 0.5133 |
| 1.334 | 34.0 | 612 | 1.2138 | 0.5204 |
| 1.2998 | 35.0 | 630 | 1.2122 | 0.5111 |
| 1.3097 | 36.0 | 648 | 1.2118 | 0.5102 |
| 1.2956 | 37.0 | 666 | 1.2077 | 0.5163 |
| 1.3058 | 38.0 | 684 | 1.2023 | 0.5157 |
| 1.2851 | 39.0 | 702 | 1.1968 | 0.5067 |
| 1.2728 | 40.0 | 720 | 1.1940 | 0.5169 |
| 1.2653 | 41.0 | 738 | 1.1700 | 0.5165 |
| 1.2837 | 42.0 | 756 | 1.1767 | 0.5262 |
| 1.2789 | 43.0 | 774 | 1.1885 | 0.5146 |
| 1.2343 | 44.0 | 792 | 1.1925 | 0.5101 |
| 1.2454 | 45.0 | 810 | 1.1874 | 0.5119 |
| 1.2922 | 46.0 | 828 | 1.1845 | 0.5216 |
| 1.2547 | 47.0 | 846 | 1.1920 | 0.5299 |
| 1.272 | 48.0 | 864 | 1.1732 | 0.5225 |
| 1.2506 | 49.0 | 882 | 1.1722 | 0.5117 |
| 1.2494 | 50.0 | 900 | 1.1743 | 0.5098 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.13.3
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