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segformer-b0-finetuned-oldapp-oct-1

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  2. config.json +1 -1
  3. model.safetensors +1 -1
  4. training_args.bin +2 -2
README.md CHANGED
@@ -16,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # segformer-b0-finetuned-oldapp-oct-1
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- This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the PushkarA07/oldapptiles5 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0968
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- - Mean Iou: 0.9990
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  - Mean Accuracy: 1.0
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  - Overall Accuracy: 1.0
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@@ -44,7 +44,7 @@ The following hyperparameters were used during training:
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  - train_batch_size: 2
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  - eval_batch_size: 2
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  - seed: 42
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- - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - num_epochs: 100
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@@ -52,151 +52,251 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|
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- | 0.6574 | 0.7143 | 10 | 0.6497 | 0.9990 | 1.0 | 1.0 |
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- | 0.6005 | 1.4286 | 20 | 0.5761 | 0.9990 | 1.0 | 1.0 |
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- | 0.5676 | 2.1429 | 30 | 0.4740 | 0.9990 | 1.0 | 1.0 |
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- | 0.6287 | 2.8571 | 40 | 0.4394 | 0.9990 | 1.0 | 1.0 |
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- | 0.5121 | 3.5714 | 50 | 0.4173 | 0.9990 | 1.0 | 1.0 |
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- | 0.4744 | 4.2857 | 60 | 0.3842 | 0.9990 | 1.0 | 1.0 |
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- | 0.4413 | 5.0 | 70 | 0.4107 | 0.9990 | 1.0 | 1.0 |
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- | 0.4134 | 5.7143 | 80 | 0.3737 | 0.9990 | 1.0 | 1.0 |
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- | 0.4139 | 6.4286 | 90 | 0.3424 | 0.9990 | 1.0 | 1.0 |
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- | 0.4097 | 7.1429 | 100 | 0.3248 | 0.9990 | 1.0 | 1.0 |
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- | 0.3645 | 7.8571 | 110 | 0.3218 | 0.9990 | 1.0 | 1.0 |
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- | 0.3287 | 8.5714 | 120 | 0.2928 | 0.9990 | 1.0 | 1.0 |
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- | 0.3113 | 9.2857 | 130 | 0.3021 | 0.9990 | 1.0 | 1.0 |
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- | 0.3085 | 10.0 | 140 | 0.2962 | 0.9990 | 1.0 | 1.0 |
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- | 0.2879 | 10.7143 | 150 | 0.2596 | 0.9990 | 1.0 | 1.0 |
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- | 0.2958 | 11.4286 | 160 | 0.2409 | 0.9990 | 1.0 | 1.0 |
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- | 0.2788 | 12.1429 | 170 | 0.2396 | 0.9990 | 1.0 | 1.0 |
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- | 0.2565 | 12.8571 | 180 | 0.2076 | 0.9990 | 1.0 | 1.0 |
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- | 0.2457 | 13.5714 | 190 | 0.2184 | 0.9990 | 1.0 | 1.0 |
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- | 0.2328 | 14.2857 | 200 | 0.1962 | 0.9990 | 1.0 | 1.0 |
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- | 0.1916 | 15.0 | 210 | 0.2003 | 0.9990 | 1.0 | 1.0 |
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- | 0.3277 | 15.7143 | 220 | 0.1875 | 0.9990 | 1.0 | 1.0 |
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- | 0.2053 | 16.4286 | 230 | 0.1718 | 0.9990 | 1.0 | 1.0 |
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- | 0.2555 | 17.1429 | 240 | 0.1571 | 0.9990 | 1.0 | 1.0 |
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- | 0.1863 | 17.8571 | 250 | 0.1546 | 0.9990 | 1.0 | 1.0 |
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- | 0.1944 | 18.5714 | 260 | 0.1503 | 0.9990 | 1.0 | 1.0 |
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- | 0.2652 | 19.2857 | 270 | 0.1456 | 0.9990 | 1.0 | 1.0 |
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- | 0.1614 | 20.0 | 280 | 0.1442 | 0.9990 | 1.0 | 1.0 |
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- | 0.139 | 20.7143 | 290 | 0.1413 | 0.9990 | 1.0 | 1.0 |
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- | 0.1631 | 21.4286 | 300 | 0.1308 | 0.9990 | 1.0 | 1.0 |
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- | 0.1988 | 22.1429 | 310 | 0.1256 | 0.9990 | 1.0 | 1.0 |
86
- | 0.1294 | 22.8571 | 320 | 0.1190 | 0.9990 | 1.0 | 1.0 |
87
- | 0.1174 | 23.5714 | 330 | 0.1185 | 0.9990 | 1.0 | 1.0 |
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- | 0.1287 | 24.2857 | 340 | 0.1251 | 0.9990 | 1.0 | 1.0 |
89
- | 0.1322 | 25.0 | 350 | 0.1308 | 0.9990 | 1.0 | 1.0 |
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- | 0.1667 | 25.7143 | 360 | 0.1215 | 0.9990 | 1.0 | 1.0 |
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- | 0.1095 | 26.4286 | 370 | 0.1226 | 0.9990 | 1.0 | 1.0 |
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- | 0.1992 | 27.1429 | 380 | 0.1331 | 0.9990 | 1.0 | 1.0 |
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- | 0.1987 | 27.8571 | 390 | 0.1174 | 0.9990 | 1.0 | 1.0 |
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- | 0.1587 | 28.5714 | 400 | 0.1162 | 0.9990 | 1.0 | 1.0 |
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- | 0.1043 | 29.2857 | 410 | 0.1161 | 0.9990 | 1.0 | 1.0 |
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- | 0.1073 | 30.0 | 420 | 0.1112 | 0.9990 | 1.0 | 1.0 |
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- | 0.14 | 30.7143 | 430 | 0.1279 | 0.9990 | 1.0 | 1.0 |
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- | 0.1183 | 31.4286 | 440 | 0.1361 | 0.9990 | 1.0 | 1.0 |
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- | 0.1096 | 32.1429 | 450 | 0.1430 | 0.9990 | 1.0 | 1.0 |
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- | 0.0957 | 32.8571 | 460 | 0.1397 | 0.9990 | 1.0 | 1.0 |
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- | 0.1605 | 33.5714 | 470 | 0.1436 | 0.9990 | 1.0 | 1.0 |
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- | 0.0837 | 34.2857 | 480 | 0.1163 | 0.9990 | 1.0 | 1.0 |
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- | 0.1032 | 35.0 | 490 | 0.1223 | 0.9990 | 1.0 | 1.0 |
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- | 0.1815 | 35.7143 | 500 | 0.0829 | 0.9990 | 1.0 | 1.0 |
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- | 0.0762 | 36.4286 | 510 | 0.1128 | 0.9990 | 1.0 | 1.0 |
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- | 0.0754 | 37.1429 | 520 | 0.1676 | 0.9990 | 1.0 | 1.0 |
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- | 0.0883 | 37.8571 | 530 | 0.1639 | 0.9990 | 1.0 | 1.0 |
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- | 0.0721 | 38.5714 | 540 | 0.1843 | 0.9990 | 1.0 | 1.0 |
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- | 0.0703 | 39.2857 | 550 | 0.1493 | 0.9990 | 1.0 | 1.0 |
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- | 0.0766 | 40.0 | 560 | 0.1616 | 0.9990 | 1.0 | 1.0 |
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- | 0.0636 | 40.7143 | 570 | 0.1292 | 0.9990 | 1.0 | 1.0 |
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- | 0.0771 | 41.4286 | 580 | 0.1087 | 0.9990 | 1.0 | 1.0 |
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- | 0.1019 | 42.1429 | 590 | 0.1540 | 0.9990 | 1.0 | 1.0 |
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- | 0.0734 | 42.8571 | 600 | 0.1639 | 0.9990 | 1.0 | 1.0 |
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- | 0.0504 | 43.5714 | 610 | 0.1544 | 0.9990 | 1.0 | 1.0 |
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- | 0.0606 | 44.2857 | 620 | 0.1403 | 0.9990 | 1.0 | 1.0 |
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- | 0.0925 | 45.0 | 630 | 0.1664 | 0.9990 | 1.0 | 1.0 |
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- | 0.0584 | 45.7143 | 640 | 0.1589 | 0.9990 | 1.0 | 1.0 |
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- | 0.0662 | 46.4286 | 650 | 0.1696 | 0.9990 | 1.0 | 1.0 |
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- | 0.0537 | 47.1429 | 660 | 0.1487 | 0.9990 | 1.0 | 1.0 |
121
- | 0.0772 | 47.8571 | 670 | 0.1688 | 0.9990 | 1.0 | 1.0 |
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- | 0.0529 | 48.5714 | 680 | 0.1637 | 0.9990 | 1.0 | 1.0 |
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- | 0.0538 | 49.2857 | 690 | 0.1573 | 0.9990 | 1.0 | 1.0 |
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- | 0.045 | 50.0 | 700 | 0.1661 | 0.9990 | 1.0 | 1.0 |
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- | 0.0588 | 50.7143 | 710 | 0.1824 | 0.9990 | 1.0 | 1.0 |
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- | 0.0482 | 51.4286 | 720 | 0.1653 | 0.9990 | 1.0 | 1.0 |
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- | 0.0811 | 52.1429 | 730 | 0.1579 | 0.9990 | 1.0 | 1.0 |
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- | -0.0544 | 52.8571 | 740 | 0.1355 | 0.9990 | 1.0 | 1.0 |
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- | 0.0463 | 53.5714 | 750 | 0.1514 | 0.9990 | 1.0 | 1.0 |
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- | 0.0465 | 54.2857 | 760 | 0.1259 | 0.9990 | 1.0 | 1.0 |
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- | 0.0798 | 55.0 | 770 | 0.1504 | 0.9990 | 1.0 | 1.0 |
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- | -0.042 | 55.7143 | 780 | 0.1638 | 0.9990 | 1.0 | 1.0 |
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- | -0.2192 | 56.4286 | 790 | 0.1666 | 0.9990 | 1.0 | 1.0 |
134
- | 0.0365 | 57.1429 | 800 | 0.1834 | 0.9990 | 1.0 | 1.0 |
135
- | 0.0765 | 57.8571 | 810 | 0.1456 | 0.9990 | 1.0 | 1.0 |
136
- | 0.0552 | 58.5714 | 820 | 0.1491 | 0.9990 | 1.0 | 1.0 |
137
- | 0.0375 | 59.2857 | 830 | 0.1515 | 0.9990 | 1.0 | 1.0 |
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- | 0.0499 | 60.0 | 840 | 0.1082 | 0.9990 | 1.0 | 1.0 |
139
- | 0.0787 | 60.7143 | 850 | 0.1422 | 0.9990 | 1.0 | 1.0 |
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- | 0.0562 | 61.4286 | 860 | 0.1337 | 0.9990 | 1.0 | 1.0 |
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- | 0.0623 | 62.1429 | 870 | 0.1399 | 0.9990 | 1.0 | 1.0 |
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- | 0.0966 | 62.8571 | 880 | 0.1412 | 0.9990 | 1.0 | 1.0 |
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- | 0.0811 | 63.5714 | 890 | 0.1311 | 0.9990 | 1.0 | 1.0 |
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- | 0.0496 | 64.2857 | 900 | 0.1591 | 0.9990 | 1.0 | 1.0 |
145
- | 0.0447 | 65.0 | 910 | 0.1587 | 0.9990 | 1.0 | 1.0 |
146
- | 0.0345 | 65.7143 | 920 | 0.1637 | 0.9990 | 1.0 | 1.0 |
147
- | 0.0637 | 66.4286 | 930 | 0.1427 | 0.9990 | 1.0 | 1.0 |
148
- | 0.0644 | 67.1429 | 940 | 0.1611 | 0.9990 | 1.0 | 1.0 |
149
- | 0.0779 | 67.8571 | 950 | 0.1566 | 0.9990 | 1.0 | 1.0 |
150
- | 0.0417 | 68.5714 | 960 | 0.1488 | 0.9990 | 1.0 | 1.0 |
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- | 0.0969 | 69.2857 | 970 | 0.1577 | 0.9990 | 1.0 | 1.0 |
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- | 0.0452 | 70.0 | 980 | 0.1166 | 0.9990 | 1.0 | 1.0 |
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- | 0.0373 | 70.7143 | 990 | 0.1429 | 0.9990 | 1.0 | 1.0 |
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- | 0.0438 | 71.4286 | 1000 | 0.1425 | 0.9990 | 1.0 | 1.0 |
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- | 0.0606 | 72.1429 | 1010 | 0.1238 | 0.9990 | 1.0 | 1.0 |
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- | 0.0389 | 72.8571 | 1020 | 0.1284 | 0.9990 | 1.0 | 1.0 |
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- | 0.0402 | 73.5714 | 1030 | 0.1350 | 0.9990 | 1.0 | 1.0 |
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- | 0.0349 | 74.2857 | 1040 | 0.1583 | 0.9990 | 1.0 | 1.0 |
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- | 0.031 | 75.0 | 1050 | 0.1563 | 0.9990 | 1.0 | 1.0 |
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- | 0.0501 | 75.7143 | 1060 | 0.1501 | 0.9990 | 1.0 | 1.0 |
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- | 0.0412 | 76.4286 | 1070 | 0.1417 | 0.9990 | 1.0 | 1.0 |
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- | 0.0532 | 77.1429 | 1080 | 0.1456 | 0.9990 | 1.0 | 1.0 |
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- | 0.0378 | 77.8571 | 1090 | 0.1059 | 0.9990 | 1.0 | 1.0 |
164
- | -0.3927 | 78.5714 | 1100 | 0.1200 | 0.9990 | 1.0 | 1.0 |
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- | 0.0499 | 79.2857 | 1110 | 0.1396 | 0.9990 | 1.0 | 1.0 |
166
- | 0.0501 | 80.0 | 1120 | 0.1277 | 0.9990 | 1.0 | 1.0 |
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- | 0.0408 | 80.7143 | 1130 | 0.1494 | 0.9990 | 1.0 | 1.0 |
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- | 0.0369 | 81.4286 | 1140 | 0.1394 | 0.9990 | 1.0 | 1.0 |
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- | 0.0014 | 82.1429 | 1150 | 0.1306 | 0.9990 | 1.0 | 1.0 |
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- | 0.0359 | 82.8571 | 1160 | 0.1557 | 0.9990 | 1.0 | 1.0 |
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- | -0.4227 | 83.5714 | 1170 | 0.1380 | 0.9990 | 1.0 | 1.0 |
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- | 0.0307 | 84.2857 | 1180 | 0.1351 | 0.9990 | 1.0 | 1.0 |
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- | 0.0433 | 85.0 | 1190 | 0.1379 | 0.9990 | 1.0 | 1.0 |
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- | 0.0407 | 85.7143 | 1200 | 0.1346 | 0.9990 | 1.0 | 1.0 |
175
- | 0.0247 | 86.4286 | 1210 | 0.1572 | 0.9990 | 1.0 | 1.0 |
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- | 0.0498 | 87.1429 | 1220 | 0.1398 | 0.9990 | 1.0 | 1.0 |
177
- | 0.0399 | 87.8571 | 1230 | 0.1261 | 0.9990 | 1.0 | 1.0 |
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- | 0.0354 | 88.5714 | 1240 | 0.0936 | 0.9990 | 1.0 | 1.0 |
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- | 0.0336 | 89.2857 | 1250 | 0.1343 | 0.9990 | 1.0 | 1.0 |
180
- | 0.0291 | 90.0 | 1260 | 0.1410 | 0.9990 | 1.0 | 1.0 |
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- | 0.0379 | 90.7143 | 1270 | 0.1520 | 0.9990 | 1.0 | 1.0 |
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- | 0.0331 | 91.4286 | 1280 | 0.1490 | 0.9990 | 1.0 | 1.0 |
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- | 0.034 | 92.1429 | 1290 | 0.1449 | 0.9990 | 1.0 | 1.0 |
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- | 0.0315 | 92.8571 | 1300 | 0.1447 | 0.9990 | 1.0 | 1.0 |
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- | 0.0474 | 93.5714 | 1310 | 0.0865 | 0.9990 | 1.0 | 1.0 |
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- | 0.0295 | 94.2857 | 1320 | 0.1292 | 0.9990 | 1.0 | 1.0 |
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- | 0.0347 | 95.0 | 1330 | 0.1097 | 0.9990 | 1.0 | 1.0 |
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- | 0.0315 | 95.7143 | 1340 | 0.1264 | 0.9990 | 1.0 | 1.0 |
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- | 0.0427 | 96.4286 | 1350 | 0.1458 | 0.9990 | 1.0 | 1.0 |
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- | 0.0072 | 97.1429 | 1360 | 0.1381 | 0.9990 | 1.0 | 1.0 |
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- | 0.0481 | 97.8571 | 1370 | 0.1120 | 0.9990 | 1.0 | 1.0 |
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- | 0.0312 | 98.5714 | 1380 | 0.1331 | 0.9990 | 1.0 | 1.0 |
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- | 0.0617 | 99.2857 | 1390 | 0.1368 | 0.9990 | 1.0 | 1.0 |
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- | -0.4803 | 100.0 | 1400 | 0.0968 | 0.9990 | 1.0 | 1.0 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.44.2
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- - Pytorch 2.5.0+cu121
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  - Datasets 3.1.0
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- - Tokenizers 0.19.1
 
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  # segformer-b0-finetuned-oldapp-oct-1
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+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the PushkarA07/batch1-tiles dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: -434.0362
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+ - Mean Iou: 0.0000
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  - Mean Accuracy: 1.0
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  - Overall Accuracy: 1.0
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  - train_batch_size: 2
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  - eval_batch_size: 2
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  - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - num_epochs: 100
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy |
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  |:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|
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+ | 9.1177 | 0.4167 | 10 | 3.1747 | 0.0000 | 1.0 | 1.0 |
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+ | -7.5948 | 0.8333 | 20 | 3.4239 | 0.0000 | 1.0 | 1.0 |
57
+ | -36.4006 | 1.25 | 30 | 4.9370 | 0.0000 | 1.0 | 1.0 |
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+ | -68.5634 | 1.6667 | 40 | -11.8052 | 0.0000 | 1.0 | 1.0 |
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+ | -72.8737 | 2.0833 | 50 | -10.8982 | 0.0000 | 1.0 | 1.0 |
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+ | -60.664 | 2.5 | 60 | -46.7299 | 0.0000 | 1.0 | 1.0 |
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+ | -106.3157 | 2.9167 | 70 | -44.4029 | 0.0000 | 1.0 | 1.0 |
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+ | -95.8069 | 3.3333 | 80 | -61.6743 | 0.0000 | 1.0 | 1.0 |
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+ | -132.0845 | 3.75 | 90 | -57.1445 | 0.0000 | 1.0 | 1.0 |
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+ | -165.5214 | 4.1667 | 100 | -54.9286 | 0.0000 | 1.0 | 1.0 |
65
+ | -160.2556 | 4.5833 | 110 | -94.0934 | 0.0000 | 1.0 | 1.0 |
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+ | -183.3402 | 5.0 | 120 | -61.7439 | 0.0000 | 1.0 | 1.0 |
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+ | -228.4443 | 5.4167 | 130 | -58.0895 | 0.0000 | 1.0 | 1.0 |
68
+ | -190.4287 | 5.8333 | 140 | -109.6511 | 0.0000 | 1.0 | 1.0 |
69
+ | -212.9903 | 6.25 | 150 | -102.2368 | 0.0000 | 1.0 | 1.0 |
70
+ | -227.9513 | 6.6667 | 160 | -93.2920 | 0.0000 | 1.0 | 1.0 |
71
+ | -225.2955 | 7.0833 | 170 | -86.0104 | 0.0000 | 1.0 | 1.0 |
72
+ | -248.1907 | 7.5 | 180 | -110.2658 | 0.0000 | 1.0 | 1.0 |
73
+ | -237.4253 | 7.9167 | 190 | -112.2953 | 0.0000 | 1.0 | 1.0 |
74
+ | -249.7178 | 8.3333 | 200 | -144.2673 | 0.0000 | 1.0 | 1.0 |
75
+ | -291.5307 | 8.75 | 210 | -117.0896 | 0.0000 | 1.0 | 1.0 |
76
+ | -263.4248 | 9.1667 | 220 | -150.6111 | 0.0000 | 1.0 | 1.0 |
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+ | -290.6562 | 9.5833 | 230 | -186.4456 | 0.0000 | 1.0 | 1.0 |
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+ | -291.933 | 10.0 | 240 | -136.8444 | 0.0000 | 1.0 | 1.0 |
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+ | -330.3426 | 10.4167 | 250 | -134.6306 | 0.0000 | 1.0 | 1.0 |
80
+ | -302.9307 | 10.8333 | 260 | -164.5891 | 0.0000 | 1.0 | 1.0 |
81
+ | -303.2674 | 11.25 | 270 | -184.5806 | 0.0000 | 1.0 | 1.0 |
82
+ | -334.0839 | 11.6667 | 280 | -94.3697 | 0.0000 | 1.0 | 1.0 |
83
+ | -349.2104 | 12.0833 | 290 | -143.0347 | 0.0000 | 1.0 | 1.0 |
84
+ | -367.3026 | 12.5 | 300 | -130.5752 | 0.0000 | 1.0 | 1.0 |
85
+ | -376.3403 | 12.9167 | 310 | -135.2888 | 0.0000 | 1.0 | 1.0 |
86
+ | -369.0916 | 13.3333 | 320 | -121.6049 | 0.0000 | 1.0 | 1.0 |
87
+ | -380.9592 | 13.75 | 330 | -196.7207 | 0.0000 | 1.0 | 1.0 |
88
+ | -379.2241 | 14.1667 | 340 | -150.6844 | 0.0000 | 1.0 | 1.0 |
89
+ | -390.002 | 14.5833 | 350 | -213.3066 | 0.0000 | 1.0 | 1.0 |
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+ | -428.7926 | 15.0 | 360 | -129.6799 | 0.0000 | 1.0 | 1.0 |
91
+ | -399.4067 | 15.4167 | 370 | -172.0849 | 0.0000 | 1.0 | 1.0 |
92
+ | -425.9248 | 15.8333 | 380 | -85.1861 | 0.0000 | 1.0 | 1.0 |
93
+ | -452.1733 | 16.25 | 390 | -116.3002 | 0.0000 | 1.0 | 1.0 |
94
+ | -438.8081 | 16.6667 | 400 | -162.4811 | 0.0000 | 1.0 | 1.0 |
95
+ | -442.2211 | 17.0833 | 410 | -310.0329 | 0.0000 | 1.0 | 1.0 |
96
+ | -481.9792 | 17.5 | 420 | -208.4158 | 0.0000 | 1.0 | 1.0 |
97
+ | -471.0272 | 17.9167 | 430 | -139.1131 | 0.0000 | 1.0 | 1.0 |
98
+ | -480.2365 | 18.3333 | 440 | -138.3438 | 0.0000 | 1.0 | 1.0 |
99
+ | -532.4087 | 18.75 | 450 | -145.5148 | 0.0000 | 1.0 | 1.0 |
100
+ | -459.1265 | 19.1667 | 460 | -151.2145 | 0.0000 | 1.0 | 1.0 |
101
+ | -495.9417 | 19.5833 | 470 | -290.5081 | 0.0000 | 1.0 | 1.0 |
102
+ | -526.197 | 20.0 | 480 | -114.2300 | 0.0000 | 1.0 | 1.0 |
103
+ | -567.6094 | 20.4167 | 490 | -199.9692 | 0.0000 | 1.0 | 1.0 |
104
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297
  ### Framework versions
298
 
299
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300
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301
  - Datasets 3.1.0
302
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