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swin-tiny-patch4-window7-224-finetuned-woody_90epochs
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4351
- Accuracy: 0.8424
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: 90
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.6659 | 1.0 | 58 | 0.6216 | 0.6558 |
| 0.6181 | 2.0 | 116 | 0.5616 | 0.7115 |
| 0.5941 | 3.0 | 174 | 0.5464 | 0.7224 |
| 0.5727 | 4.0 | 232 | 0.5368 | 0.7297 |
| 0.573 | 5.0 | 290 | 0.4971 | 0.7539 |
| 0.5724 | 6.0 | 348 | 0.4920 | 0.7467 |
| 0.5584 | 7.0 | 406 | 0.4949 | 0.7564 |
| 0.5352 | 8.0 | 464 | 0.5255 | 0.7406 |
| 0.5857 | 9.0 | 522 | 0.4954 | 0.7515 |
| 0.5352 | 10.0 | 580 | 0.4888 | 0.7455 |
| 0.5161 | 11.0 | 638 | 0.5306 | 0.7224 |
| 0.5457 | 12.0 | 696 | 0.4856 | 0.76 |
| 0.5309 | 13.0 | 754 | 0.4647 | 0.7612 |
| 0.5357 | 14.0 | 812 | 0.4688 | 0.7697 |
| 0.5183 | 15.0 | 870 | 0.4830 | 0.7527 |
| 0.4837 | 16.0 | 928 | 0.5238 | 0.7370 |
| 0.51 | 17.0 | 986 | 0.4658 | 0.7745 |
| 0.533 | 18.0 | 1044 | 0.4589 | 0.7673 |
| 0.4808 | 19.0 | 1102 | 0.4375 | 0.7794 |
| 0.4854 | 20.0 | 1160 | 0.4574 | 0.7745 |
| 0.4708 | 21.0 | 1218 | 0.4738 | 0.7709 |
| 0.4801 | 22.0 | 1276 | 0.4688 | 0.76 |
| 0.4751 | 23.0 | 1334 | 0.4610 | 0.7648 |
| 0.497 | 24.0 | 1392 | 0.5058 | 0.7624 |
| 0.4767 | 25.0 | 1450 | 0.4709 | 0.7721 |
| 0.4805 | 26.0 | 1508 | 0.4447 | 0.7697 |
| 0.4557 | 27.0 | 1566 | 0.4558 | 0.7721 |
| 0.4636 | 28.0 | 1624 | 0.4325 | 0.8036 |
| 0.4285 | 29.0 | 1682 | 0.4526 | 0.7794 |
| 0.4358 | 30.0 | 1740 | 0.4302 | 0.8048 |
| 0.4257 | 31.0 | 1798 | 0.4373 | 0.7927 |
| 0.4137 | 32.0 | 1856 | 0.4458 | 0.7903 |
| 0.4389 | 33.0 | 1914 | 0.4522 | 0.7988 |
| 0.4537 | 34.0 | 1972 | 0.4395 | 0.7927 |
| 0.4249 | 35.0 | 2030 | 0.4348 | 0.8 |
| 0.4244 | 36.0 | 2088 | 0.4650 | 0.7867 |
| 0.4256 | 37.0 | 2146 | 0.4402 | 0.8012 |
| 0.4118 | 38.0 | 2204 | 0.4394 | 0.7867 |
| 0.4128 | 39.0 | 2262 | 0.4225 | 0.8133 |
| 0.416 | 40.0 | 2320 | 0.4410 | 0.8073 |
| 0.4211 | 41.0 | 2378 | 0.4464 | 0.8024 |
| 0.3838 | 42.0 | 2436 | 0.4440 | 0.7976 |
| 0.374 | 43.0 | 2494 | 0.4175 | 0.7903 |
| 0.412 | 44.0 | 2552 | 0.4169 | 0.8109 |
| 0.3746 | 45.0 | 2610 | 0.4243 | 0.8012 |
| 0.3719 | 46.0 | 2668 | 0.4132 | 0.8242 |
| 0.381 | 47.0 | 2726 | 0.4485 | 0.7988 |
| 0.3708 | 48.0 | 2784 | 0.4200 | 0.8085 |
| 0.3591 | 49.0 | 2842 | 0.4071 | 0.8279 |
| 0.3762 | 50.0 | 2900 | 0.4428 | 0.8145 |
| 0.3426 | 51.0 | 2958 | 0.4058 | 0.8158 |
| 0.3541 | 52.0 | 3016 | 0.4470 | 0.8182 |
| 0.3373 | 53.0 | 3074 | 0.4252 | 0.8194 |
| 0.3303 | 54.0 | 3132 | 0.4040 | 0.8315 |
| 0.3275 | 55.0 | 3190 | 0.4235 | 0.8291 |
| 0.3151 | 56.0 | 3248 | 0.3984 | 0.8485 |
| 0.324 | 57.0 | 3306 | 0.4283 | 0.8291 |
| 0.3276 | 58.0 | 3364 | 0.4731 | 0.8145 |
| 0.3208 | 59.0 | 3422 | 0.4360 | 0.8255 |
| 0.3355 | 60.0 | 3480 | 0.4143 | 0.8230 |
| 0.3154 | 61.0 | 3538 | 0.4234 | 0.8267 |
| 0.3451 | 62.0 | 3596 | 0.4059 | 0.8242 |
| 0.3071 | 63.0 | 3654 | 0.3991 | 0.8267 |
| 0.3303 | 64.0 | 3712 | 0.4099 | 0.8242 |
| 0.29 | 65.0 | 3770 | 0.4140 | 0.8327 |
| 0.2937 | 66.0 | 3828 | 0.4590 | 0.8218 |
| 0.3322 | 67.0 | 3886 | 0.4111 | 0.8327 |
| 0.3219 | 68.0 | 3944 | 0.4299 | 0.8327 |
| 0.2839 | 69.0 | 4002 | 0.4074 | 0.8424 |
| 0.2903 | 70.0 | 4060 | 0.4366 | 0.8315 |
| 0.2851 | 71.0 | 4118 | 0.4132 | 0.8473 |
| 0.3029 | 72.0 | 4176 | 0.4239 | 0.8473 |
| 0.2693 | 73.0 | 4234 | 0.4194 | 0.8412 |
| 0.2715 | 74.0 | 4292 | 0.4384 | 0.8412 |
| 0.2842 | 75.0 | 4350 | 0.4279 | 0.8448 |
| 0.2733 | 76.0 | 4408 | 0.4174 | 0.84 |
| 0.2694 | 77.0 | 4466 | 0.3966 | 0.8388 |
| 0.2527 | 78.0 | 4524 | 0.4194 | 0.8364 |
| 0.2813 | 79.0 | 4582 | 0.4231 | 0.8436 |
| 0.2618 | 80.0 | 4640 | 0.4494 | 0.8352 |
| 0.2639 | 81.0 | 4698 | 0.4152 | 0.8388 |
| 0.2643 | 82.0 | 4756 | 0.4241 | 0.8448 |
| 0.276 | 83.0 | 4814 | 0.4518 | 0.8327 |
| 0.2761 | 84.0 | 4872 | 0.4349 | 0.8412 |
| 0.2295 | 85.0 | 4930 | 0.4504 | 0.8315 |
| 0.2723 | 86.0 | 4988 | 0.4385 | 0.8388 |
| 0.2559 | 87.0 | 5046 | 0.4362 | 0.8473 |
| 0.2583 | 88.0 | 5104 | 0.4273 | 0.8436 |
| 0.2523 | 89.0 | 5162 | 0.4292 | 0.8424 |
| 0.2563 | 90.0 | 5220 | 0.4351 | 0.8424 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
