metadata
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224-in22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-base-patch4-window7-224-in22k-finetuned-lora-ISIC-2019
results: []
swin-base-patch4-window7-224-in22k-finetuned-lora-ISIC-2019
This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.4229
- Accuracy: 0.9008
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: 0.001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.858 | 0.99 | 62 | 0.7349 | 0.7339 |
| 0.7403 | 2.0 | 125 | 0.6364 | 0.7762 |
| 0.675 | 2.99 | 187 | 0.5777 | 0.7999 |
| 0.6309 | 4.0 | 250 | 0.5701 | 0.7875 |
| 0.5734 | 4.99 | 312 | 0.5294 | 0.8016 |
| 0.5338 | 6.0 | 375 | 0.5418 | 0.8010 |
| 0.5104 | 6.99 | 437 | 0.5057 | 0.8179 |
| 0.5091 | 8.0 | 500 | 0.5010 | 0.8207 |
| 0.4678 | 8.99 | 562 | 0.4757 | 0.8247 |
| 0.467 | 10.0 | 625 | 0.4579 | 0.8151 |
| 0.4416 | 10.99 | 687 | 0.4650 | 0.8315 |
| 0.4277 | 12.0 | 750 | 0.4405 | 0.8405 |
| 0.4261 | 12.99 | 812 | 0.4414 | 0.8388 |
| 0.4016 | 14.0 | 875 | 0.4392 | 0.8286 |
| 0.3729 | 14.99 | 937 | 0.4471 | 0.8281 |
| 0.3813 | 16.0 | 1000 | 0.4155 | 0.8433 |
| 0.3454 | 16.99 | 1062 | 0.4322 | 0.8365 |
| 0.3639 | 18.0 | 1125 | 0.4332 | 0.8360 |
| 0.3393 | 18.99 | 1187 | 0.4190 | 0.8523 |
| 0.3135 | 20.0 | 1250 | 0.4166 | 0.8534 |
| 0.3094 | 20.99 | 1312 | 0.4005 | 0.8563 |
| 0.3263 | 22.0 | 1375 | 0.4399 | 0.8495 |
| 0.3009 | 22.99 | 1437 | 0.4122 | 0.8523 |
| 0.2804 | 24.0 | 1500 | 0.4293 | 0.8563 |
| 0.2516 | 24.99 | 1562 | 0.4289 | 0.8563 |
| 0.2763 | 26.0 | 1625 | 0.4125 | 0.8647 |
| 0.2707 | 26.99 | 1687 | 0.4231 | 0.8664 |
| 0.2585 | 28.0 | 1750 | 0.4210 | 0.8596 |
| 0.2317 | 28.99 | 1812 | 0.4296 | 0.8602 |
| 0.2118 | 30.0 | 1875 | 0.4440 | 0.8636 |
| 0.2224 | 30.99 | 1937 | 0.3928 | 0.8726 |
| 0.2166 | 32.0 | 2000 | 0.4246 | 0.8602 |
| 0.2038 | 32.99 | 2062 | 0.4146 | 0.8709 |
| 0.2183 | 34.0 | 2125 | 0.4165 | 0.8698 |
| 0.22 | 34.99 | 2187 | 0.4212 | 0.8766 |
| 0.206 | 36.0 | 2250 | 0.4139 | 0.8726 |
| 0.199 | 36.99 | 2312 | 0.3793 | 0.8833 |
| 0.1926 | 38.0 | 2375 | 0.4127 | 0.8839 |
| 0.1648 | 38.99 | 2437 | 0.4296 | 0.8822 |
| 0.1578 | 40.0 | 2500 | 0.4132 | 0.8833 |
| 0.181 | 40.99 | 2562 | 0.4217 | 0.8777 |
| 0.1735 | 42.0 | 2625 | 0.4186 | 0.8715 |
| 0.1603 | 42.99 | 2687 | 0.4117 | 0.8805 |
| 0.1516 | 44.0 | 2750 | 0.4250 | 0.8816 |
| 0.1733 | 44.99 | 2812 | 0.3914 | 0.8844 |
| 0.164 | 46.0 | 2875 | 0.4369 | 0.8828 |
| 0.1519 | 46.99 | 2937 | 0.4276 | 0.8771 |
| 0.1534 | 48.0 | 3000 | 0.4421 | 0.8822 |
| 0.158 | 48.99 | 3062 | 0.4240 | 0.8873 |
| 0.1531 | 50.0 | 3125 | 0.4250 | 0.8794 |
| 0.1286 | 50.99 | 3187 | 0.4228 | 0.8732 |
| 0.1396 | 52.0 | 3250 | 0.4317 | 0.8782 |
| 0.1436 | 52.99 | 3312 | 0.4361 | 0.8856 |
| 0.1411 | 54.0 | 3375 | 0.4402 | 0.8850 |
| 0.1312 | 54.99 | 3437 | 0.4327 | 0.8884 |
| 0.1359 | 56.0 | 3500 | 0.4144 | 0.8856 |
| 0.1361 | 56.99 | 3562 | 0.4181 | 0.8867 |
| 0.1272 | 58.0 | 3625 | 0.4204 | 0.8878 |
| 0.1222 | 58.99 | 3687 | 0.4137 | 0.8884 |
| 0.1272 | 60.0 | 3750 | 0.4317 | 0.8890 |
| 0.1132 | 60.99 | 3812 | 0.4351 | 0.8918 |
| 0.1239 | 62.0 | 3875 | 0.4348 | 0.8828 |
| 0.1188 | 62.99 | 3937 | 0.4258 | 0.8861 |
| 0.1203 | 64.0 | 4000 | 0.4318 | 0.8912 |
| 0.1204 | 64.99 | 4062 | 0.4055 | 0.8952 |
| 0.1053 | 66.0 | 4125 | 0.4222 | 0.8918 |
| 0.1187 | 66.99 | 4187 | 0.4248 | 0.8946 |
| 0.1129 | 68.0 | 4250 | 0.4302 | 0.8923 |
| 0.1117 | 68.99 | 4312 | 0.4149 | 0.8968 |
| 0.1194 | 70.0 | 4375 | 0.4160 | 0.8895 |
| 0.1003 | 70.99 | 4437 | 0.4256 | 0.8946 |
| 0.1088 | 72.0 | 4500 | 0.4356 | 0.8918 |
| 0.11 | 72.99 | 4562 | 0.4277 | 0.8935 |
| 0.1016 | 74.0 | 4625 | 0.4095 | 0.8952 |
| 0.0906 | 74.99 | 4687 | 0.4262 | 0.8935 |
| 0.0969 | 76.0 | 4750 | 0.4057 | 0.8940 |
| 0.111 | 76.99 | 4812 | 0.4099 | 0.8997 |
| 0.091 | 78.0 | 4875 | 0.4232 | 0.8963 |
| 0.1013 | 78.99 | 4937 | 0.4311 | 0.8884 |
| 0.119 | 80.0 | 5000 | 0.4302 | 0.8929 |
| 0.0877 | 80.99 | 5062 | 0.4369 | 0.8923 |
| 0.0926 | 82.0 | 5125 | 0.4353 | 0.8968 |
| 0.0969 | 82.99 | 5187 | 0.4336 | 0.8952 |
| 0.092 | 84.0 | 5250 | 0.4214 | 0.8935 |
| 0.0914 | 84.99 | 5312 | 0.4403 | 0.8890 |
| 0.0924 | 86.0 | 5375 | 0.4285 | 0.8929 |
| 0.0964 | 86.99 | 5437 | 0.4207 | 0.8968 |
| 0.0916 | 88.0 | 5500 | 0.4254 | 0.8946 |
| 0.0962 | 88.99 | 5562 | 0.4249 | 0.8980 |
| 0.0927 | 90.0 | 5625 | 0.4242 | 0.8935 |
| 0.0993 | 90.99 | 5687 | 0.4230 | 0.8985 |
| 0.0893 | 92.0 | 5750 | 0.4229 | 0.8980 |
| 0.0878 | 92.99 | 5812 | 0.4215 | 0.8985 |
| 0.0882 | 94.0 | 5875 | 0.4262 | 0.8980 |
| 0.0854 | 94.99 | 5937 | 0.4256 | 0.8974 |
| 0.0795 | 96.0 | 6000 | 0.4229 | 0.9008 |
| 0.0931 | 96.99 | 6062 | 0.4218 | 0.8991 |
| 0.0826 | 98.0 | 6125 | 0.4235 | 0.8985 |
| 0.0926 | 98.99 | 6187 | 0.4237 | 0.8985 |
| 0.0829 | 99.2 | 6200 | 0.4238 | 0.8985 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2