swin-tiny-patch4-window7-224-finetuned-ADC-4cls-0922
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: 0.8947
- Accuracy: 0.7
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: 0.0001
- 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
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 200
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 1.0 | 2 | 0.9655 | 0.6714 |
No log | 2.0 | 4 | 0.9654 | 0.6571 |
No log | 3.0 | 6 | 0.9651 | 0.6571 |
No log | 4.0 | 8 | 0.9647 | 0.6571 |
1.0064 | 5.0 | 10 | 0.9641 | 0.6571 |
1.0064 | 6.0 | 12 | 0.9635 | 0.6571 |
1.0064 | 7.0 | 14 | 0.9629 | 0.6571 |
1.0064 | 8.0 | 16 | 0.9623 | 0.6571 |
1.0064 | 9.0 | 18 | 0.9617 | 0.6571 |
0.9821 | 10.0 | 20 | 0.9611 | 0.6571 |
0.9821 | 11.0 | 22 | 0.9607 | 0.6571 |
0.9821 | 12.0 | 24 | 0.9604 | 0.6714 |
0.9821 | 13.0 | 26 | 0.9601 | 0.6714 |
0.9821 | 14.0 | 28 | 0.9597 | 0.6714 |
1.0278 | 15.0 | 30 | 0.9592 | 0.6714 |
1.0278 | 16.0 | 32 | 0.9581 | 0.6714 |
1.0278 | 17.0 | 34 | 0.9567 | 0.6714 |
1.0278 | 18.0 | 36 | 0.9551 | 0.6714 |
1.0278 | 19.0 | 38 | 0.9534 | 0.6714 |
0.9986 | 20.0 | 40 | 0.9514 | 0.6571 |
0.9986 | 21.0 | 42 | 0.9493 | 0.6571 |
0.9986 | 22.0 | 44 | 0.9472 | 0.6429 |
0.9986 | 23.0 | 46 | 0.9452 | 0.6429 |
0.9986 | 24.0 | 48 | 0.9434 | 0.6429 |
0.9973 | 25.0 | 50 | 0.9420 | 0.6429 |
0.9973 | 26.0 | 52 | 0.9405 | 0.6429 |
0.9973 | 27.0 | 54 | 0.9387 | 0.6286 |
0.9973 | 28.0 | 56 | 0.9376 | 0.6286 |
0.9973 | 29.0 | 58 | 0.9368 | 0.6429 |
0.9936 | 30.0 | 60 | 0.9362 | 0.6429 |
0.9936 | 31.0 | 62 | 0.9361 | 0.6571 |
0.9936 | 32.0 | 64 | 0.9364 | 0.6714 |
0.9936 | 33.0 | 66 | 0.9371 | 0.6714 |
0.9936 | 34.0 | 68 | 0.9380 | 0.6429 |
0.9746 | 35.0 | 70 | 0.9380 | 0.6571 |
0.9746 | 36.0 | 72 | 0.9375 | 0.6714 |
0.9746 | 37.0 | 74 | 0.9380 | 0.6714 |
0.9746 | 38.0 | 76 | 0.9375 | 0.6714 |
0.9746 | 39.0 | 78 | 0.9370 | 0.6714 |
1.0113 | 40.0 | 80 | 0.9362 | 0.6714 |
1.0113 | 41.0 | 82 | 0.9341 | 0.6714 |
1.0113 | 42.0 | 84 | 0.9301 | 0.6857 |
1.0113 | 43.0 | 86 | 0.9260 | 0.6714 |
1.0113 | 44.0 | 88 | 0.9224 | 0.6571 |
0.9756 | 45.0 | 90 | 0.9190 | 0.6714 |
0.9756 | 46.0 | 92 | 0.9154 | 0.6714 |
0.9756 | 47.0 | 94 | 0.9123 | 0.6714 |
0.9756 | 48.0 | 96 | 0.9091 | 0.6571 |
0.9756 | 49.0 | 98 | 0.9071 | 0.6571 |
0.9721 | 50.0 | 100 | 0.9056 | 0.6571 |
0.9721 | 51.0 | 102 | 0.9047 | 0.6571 |
0.9721 | 52.0 | 104 | 0.9039 | 0.6571 |
0.9721 | 53.0 | 106 | 0.9031 | 0.6714 |
0.9721 | 54.0 | 108 | 0.9025 | 0.6714 |
0.9698 | 55.0 | 110 | 0.9023 | 0.6714 |
0.9698 | 56.0 | 112 | 0.9012 | 0.6714 |
0.9698 | 57.0 | 114 | 0.8997 | 0.6714 |
0.9698 | 58.0 | 116 | 0.8982 | 0.6714 |
0.9698 | 59.0 | 118 | 0.8970 | 0.6714 |
0.9341 | 60.0 | 120 | 0.8957 | 0.6857 |
0.9341 | 61.0 | 122 | 0.8947 | 0.7 |
0.9341 | 62.0 | 124 | 0.8940 | 0.7 |
0.9341 | 63.0 | 126 | 0.8941 | 0.6714 |
0.9341 | 64.0 | 128 | 0.8934 | 0.6714 |
0.9717 | 65.0 | 130 | 0.8917 | 0.6714 |
0.9717 | 66.0 | 132 | 0.8898 | 0.6857 |
0.9717 | 67.0 | 134 | 0.8884 | 0.6857 |
0.9717 | 68.0 | 136 | 0.8870 | 0.6857 |
0.9717 | 69.0 | 138 | 0.8854 | 0.6857 |
0.9655 | 70.0 | 140 | 0.8840 | 0.6857 |
0.9655 | 71.0 | 142 | 0.8827 | 0.6857 |
0.9655 | 72.0 | 144 | 0.8814 | 0.6857 |
0.9655 | 73.0 | 146 | 0.8805 | 0.6857 |
0.9655 | 74.0 | 148 | 0.8803 | 0.6857 |
0.9458 | 75.0 | 150 | 0.8802 | 0.6857 |
0.9458 | 76.0 | 152 | 0.8797 | 0.6714 |
0.9458 | 77.0 | 154 | 0.8794 | 0.6714 |
0.9458 | 78.0 | 156 | 0.8796 | 0.6714 |
0.9458 | 79.0 | 158 | 0.8808 | 0.6714 |
0.9094 | 80.0 | 160 | 0.8817 | 0.6714 |
0.9094 | 81.0 | 162 | 0.8828 | 0.6714 |
0.9094 | 82.0 | 164 | 0.8836 | 0.6714 |
0.9094 | 83.0 | 166 | 0.8830 | 0.6714 |
0.9094 | 84.0 | 168 | 0.8821 | 0.6571 |
0.8719 | 85.0 | 170 | 0.8813 | 0.6571 |
0.8719 | 86.0 | 172 | 0.8804 | 0.6714 |
0.8719 | 87.0 | 174 | 0.8798 | 0.6571 |
0.8719 | 88.0 | 176 | 0.8787 | 0.6571 |
0.8719 | 89.0 | 178 | 0.8770 | 0.6571 |
0.9288 | 90.0 | 180 | 0.8752 | 0.6857 |
0.9288 | 91.0 | 182 | 0.8722 | 0.6857 |
0.9288 | 92.0 | 184 | 0.8694 | 0.6714 |
0.9288 | 93.0 | 186 | 0.8670 | 0.6714 |
0.9288 | 94.0 | 188 | 0.8645 | 0.6857 |
0.9039 | 95.0 | 190 | 0.8624 | 0.6857 |
0.9039 | 96.0 | 192 | 0.8603 | 0.6714 |
0.9039 | 97.0 | 194 | 0.8584 | 0.6857 |
0.9039 | 98.0 | 196 | 0.8566 | 0.6857 |
0.9039 | 99.0 | 198 | 0.8553 | 0.6857 |
0.9081 | 100.0 | 200 | 0.8550 | 0.6857 |
0.9081 | 101.0 | 202 | 0.8551 | 0.6857 |
0.9081 | 102.0 | 204 | 0.8556 | 0.6857 |
0.9081 | 103.0 | 206 | 0.8558 | 0.6857 |
0.9081 | 104.0 | 208 | 0.8554 | 0.6857 |
0.9142 | 105.0 | 210 | 0.8551 | 0.6857 |
0.9142 | 106.0 | 212 | 0.8553 | 0.6857 |
0.9142 | 107.0 | 214 | 0.8551 | 0.6857 |
0.9142 | 108.0 | 216 | 0.8549 | 0.6857 |
0.9142 | 109.0 | 218 | 0.8549 | 0.6857 |
0.9347 | 110.0 | 220 | 0.8551 | 0.6714 |
0.9347 | 111.0 | 222 | 0.8554 | 0.6714 |
0.9347 | 112.0 | 224 | 0.8548 | 0.6714 |
0.9347 | 113.0 | 226 | 0.8538 | 0.6714 |
0.9347 | 114.0 | 228 | 0.8525 | 0.6714 |
0.8922 | 115.0 | 230 | 0.8512 | 0.6857 |
0.8922 | 116.0 | 232 | 0.8505 | 0.6857 |
0.8922 | 117.0 | 234 | 0.8495 | 0.6857 |
0.8922 | 118.0 | 236 | 0.8484 | 0.6857 |
0.8922 | 119.0 | 238 | 0.8472 | 0.6857 |
0.8897 | 120.0 | 240 | 0.8456 | 0.6857 |
0.8897 | 121.0 | 242 | 0.8440 | 0.6857 |
0.8897 | 122.0 | 244 | 0.8426 | 0.6714 |
0.8897 | 123.0 | 246 | 0.8412 | 0.6857 |
0.8897 | 124.0 | 248 | 0.8396 | 0.6857 |
0.8829 | 125.0 | 250 | 0.8384 | 0.6857 |
0.8829 | 126.0 | 252 | 0.8373 | 0.6857 |
0.8829 | 127.0 | 254 | 0.8365 | 0.6857 |
0.8829 | 128.0 | 256 | 0.8360 | 0.6857 |
0.8829 | 129.0 | 258 | 0.8353 | 0.6857 |
0.8744 | 130.0 | 260 | 0.8344 | 0.6857 |
0.8744 | 131.0 | 262 | 0.8337 | 0.6714 |
0.8744 | 132.0 | 264 | 0.8329 | 0.6857 |
0.8744 | 133.0 | 266 | 0.8325 | 0.6857 |
0.8744 | 134.0 | 268 | 0.8318 | 0.6857 |
0.8657 | 135.0 | 270 | 0.8312 | 0.6857 |
0.8657 | 136.0 | 272 | 0.8306 | 0.6714 |
0.8657 | 137.0 | 274 | 0.8300 | 0.6714 |
0.8657 | 138.0 | 276 | 0.8296 | 0.6714 |
0.8657 | 139.0 | 278 | 0.8294 | 0.6714 |
0.9421 | 140.0 | 280 | 0.8292 | 0.6714 |
0.9421 | 141.0 | 282 | 0.8291 | 0.6714 |
0.9421 | 142.0 | 284 | 0.8290 | 0.6714 |
0.9421 | 143.0 | 286 | 0.8290 | 0.6857 |
0.9421 | 144.0 | 288 | 0.8289 | 0.6857 |
0.9066 | 145.0 | 290 | 0.8287 | 0.6857 |
0.9066 | 146.0 | 292 | 0.8290 | 0.6857 |
0.9066 | 147.0 | 294 | 0.8293 | 0.6857 |
0.9066 | 148.0 | 296 | 0.8294 | 0.6857 |
0.9066 | 149.0 | 298 | 0.8295 | 0.6857 |
0.9068 | 150.0 | 300 | 0.8295 | 0.6857 |
0.9068 | 151.0 | 302 | 0.8294 | 0.6857 |
0.9068 | 152.0 | 304 | 0.8293 | 0.6857 |
0.9068 | 153.0 | 306 | 0.8293 | 0.6857 |
0.9068 | 154.0 | 308 | 0.8290 | 0.6857 |
0.8715 | 155.0 | 310 | 0.8287 | 0.6857 |
0.8715 | 156.0 | 312 | 0.8283 | 0.6857 |
0.8715 | 157.0 | 314 | 0.8277 | 0.6857 |
0.8715 | 158.0 | 316 | 0.8274 | 0.6857 |
0.8715 | 159.0 | 318 | 0.8269 | 0.6857 |
0.8921 | 160.0 | 320 | 0.8266 | 0.6857 |
0.8921 | 161.0 | 322 | 0.8264 | 0.6857 |
0.8921 | 162.0 | 324 | 0.8261 | 0.6857 |
0.8921 | 163.0 | 326 | 0.8260 | 0.6857 |
0.8921 | 164.0 | 328 | 0.8258 | 0.6857 |
0.8768 | 165.0 | 330 | 0.8252 | 0.6857 |
0.8768 | 166.0 | 332 | 0.8248 | 0.6857 |
0.8768 | 167.0 | 334 | 0.8243 | 0.6857 |
0.8768 | 168.0 | 336 | 0.8237 | 0.6857 |
0.8768 | 169.0 | 338 | 0.8231 | 0.6857 |
0.8519 | 170.0 | 340 | 0.8227 | 0.6857 |
0.8519 | 171.0 | 342 | 0.8223 | 0.6857 |
0.8519 | 172.0 | 344 | 0.8221 | 0.6857 |
0.8519 | 173.0 | 346 | 0.8220 | 0.6857 |
0.8519 | 174.0 | 348 | 0.8218 | 0.6857 |
0.92 | 175.0 | 350 | 0.8215 | 0.6857 |
0.92 | 176.0 | 352 | 0.8211 | 0.7 |
0.92 | 177.0 | 354 | 0.8207 | 0.7 |
0.92 | 178.0 | 356 | 0.8204 | 0.7 |
0.92 | 179.0 | 358 | 0.8200 | 0.7 |
0.879 | 180.0 | 360 | 0.8197 | 0.7 |
0.879 | 181.0 | 362 | 0.8194 | 0.7 |
0.879 | 182.0 | 364 | 0.8191 | 0.6857 |
0.879 | 183.0 | 366 | 0.8187 | 0.6857 |
0.879 | 184.0 | 368 | 0.8185 | 0.7 |
0.8893 | 185.0 | 370 | 0.8182 | 0.7 |
0.8893 | 186.0 | 372 | 0.8180 | 0.7 |
0.8893 | 187.0 | 374 | 0.8177 | 0.7 |
0.8893 | 188.0 | 376 | 0.8176 | 0.7 |
0.8893 | 189.0 | 378 | 0.8175 | 0.7 |
0.8501 | 190.0 | 380 | 0.8173 | 0.7 |
0.8501 | 191.0 | 382 | 0.8171 | 0.7 |
0.8501 | 192.0 | 384 | 0.8170 | 0.7 |
0.8501 | 193.0 | 386 | 0.8169 | 0.7 |
0.8501 | 194.0 | 388 | 0.8169 | 0.7 |
0.8611 | 195.0 | 390 | 0.8168 | 0.7 |
0.8611 | 196.0 | 392 | 0.8168 | 0.7 |
0.8611 | 197.0 | 394 | 0.8168 | 0.7 |
0.8611 | 198.0 | 396 | 0.8168 | 0.7 |
0.8611 | 199.0 | 398 | 0.8168 | 0.7 |
0.8881 | 200.0 | 400 | 0.8168 | 0.7 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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Model tree for Niraya666/swin-tiny-patch4-window7-224-finetuned-ADC-4cls-0922
Base model
microsoft/swin-tiny-patch4-window7-224