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segformer-b0-crop-detection

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README.md CHANGED
@@ -18,14 +18,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the BigR-Oclock/CropSegmentation dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1657
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- - Mean Iou: 0.4764
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- - Mean Accuracy: 0.9528
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- - Overall Accuracy: 0.9528
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  - Accuracy Background: nan
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- - Accuracy Crop: 0.9528
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  - Iou Background: 0.0
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- - Iou Crop: 0.9528
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  ## Model description
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@@ -50,57 +50,103 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use OptimizerNames.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: 5
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55
  ### Training results
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57
  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crop | Iou Background | Iou Crop |
58
  |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------:|
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- | 0.5411 | 0.1092 | 50 | 0.3889 | 0.4150 | 0.8300 | 0.8300 | nan | 0.8300 | 0.0 | 0.8300 |
60
- | 0.3662 | 0.2183 | 100 | 0.2850 | 0.4310 | 0.8620 | 0.8620 | nan | 0.8620 | 0.0 | 0.8620 |
61
- | 0.3153 | 0.3275 | 150 | 0.2954 | 0.4529 | 0.9058 | 0.9058 | nan | 0.9058 | 0.0 | 0.9058 |
62
- | 0.3091 | 0.4367 | 200 | 0.3252 | 0.4679 | 0.9358 | 0.9358 | nan | 0.9358 | 0.0 | 0.9358 |
63
- | 0.254 | 0.5459 | 250 | 0.2212 | 0.4412 | 0.8823 | 0.8823 | nan | 0.8823 | 0.0 | 0.8823 |
64
- | 0.2448 | 0.6550 | 300 | 0.2553 | 0.4713 | 0.9426 | 0.9426 | nan | 0.9426 | 0.0 | 0.9426 |
65
- | 0.2923 | 0.7642 | 350 | 0.2271 | 0.4232 | 0.8464 | 0.8464 | nan | 0.8464 | 0.0 | 0.8464 |
66
- | 0.2075 | 0.8734 | 400 | 0.2252 | 0.4471 | 0.8942 | 0.8942 | nan | 0.8942 | 0.0 | 0.8942 |
67
- | 0.264 | 0.9825 | 450 | 0.2803 | 0.3986 | 0.7973 | 0.7973 | nan | 0.7973 | 0.0 | 0.7973 |
68
- | 0.1751 | 1.0917 | 500 | 0.1732 | 0.4728 | 0.9455 | 0.9455 | nan | 0.9455 | 0.0 | 0.9455 |
69
- | 0.2092 | 1.2009 | 550 | 0.2124 | 0.4224 | 0.8449 | 0.8449 | nan | 0.8449 | 0.0 | 0.8449 |
70
- | 0.1721 | 1.3100 | 600 | 0.1805 | 0.4559 | 0.9117 | 0.9117 | nan | 0.9117 | 0.0 | 0.9117 |
71
- | 0.178 | 1.4192 | 650 | 0.2127 | 0.4800 | 0.9600 | 0.9600 | nan | 0.9600 | 0.0 | 0.9600 |
72
- | 0.2028 | 1.5284 | 700 | 0.1853 | 0.4630 | 0.9260 | 0.9260 | nan | 0.9260 | 0.0 | 0.9260 |
73
- | 0.181 | 1.6376 | 750 | 0.1809 | 0.4555 | 0.9110 | 0.9110 | nan | 0.9110 | 0.0 | 0.9110 |
74
- | 0.1724 | 1.7467 | 800 | 0.1591 | 0.4579 | 0.9157 | 0.9157 | nan | 0.9157 | 0.0 | 0.9157 |
75
- | 0.1518 | 1.8559 | 850 | 0.2068 | 0.4422 | 0.8843 | 0.8843 | nan | 0.8843 | 0.0 | 0.8843 |
76
- | 0.1698 | 1.9651 | 900 | 0.1948 | 0.4408 | 0.8816 | 0.8816 | nan | 0.8816 | 0.0 | 0.8816 |
77
- | 0.1368 | 2.0742 | 950 | 0.1874 | 0.4628 | 0.9256 | 0.9256 | nan | 0.9256 | 0.0 | 0.9256 |
78
- | 0.1274 | 2.1834 | 1000 | 0.1605 | 0.4634 | 0.9268 | 0.9268 | nan | 0.9268 | 0.0 | 0.9268 |
79
- | 0.1312 | 2.2926 | 1050 | 0.1934 | 0.4489 | 0.8978 | 0.8978 | nan | 0.8978 | 0.0 | 0.8978 |
80
- | 0.1471 | 2.4017 | 1100 | 0.1807 | 0.4518 | 0.9036 | 0.9036 | nan | 0.9036 | 0.0 | 0.9036 |
81
- | 0.1322 | 2.5109 | 1150 | 0.1699 | 0.4743 | 0.9485 | 0.9485 | nan | 0.9485 | 0.0 | 0.9485 |
82
- | 0.1182 | 2.6201 | 1200 | 0.1618 | 0.4506 | 0.9012 | 0.9012 | nan | 0.9012 | 0.0 | 0.9012 |
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- | 0.1285 | 2.7293 | 1250 | 0.1403 | 0.4717 | 0.9433 | 0.9433 | nan | 0.9433 | 0.0 | 0.9433 |
84
- | 0.1191 | 2.8384 | 1300 | 0.1740 | 0.4596 | 0.9192 | 0.9192 | nan | 0.9192 | 0.0 | 0.9192 |
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- | 0.1208 | 2.9476 | 1350 | 0.1796 | 0.4652 | 0.9303 | 0.9303 | nan | 0.9303 | 0.0 | 0.9303 |
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- | 0.144 | 3.0568 | 1400 | 0.1926 | 0.4533 | 0.9067 | 0.9067 | nan | 0.9067 | 0.0 | 0.9067 |
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- | 0.1098 | 3.1659 | 1450 | 0.1790 | 0.4619 | 0.9237 | 0.9237 | nan | 0.9237 | 0.0 | 0.9237 |
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- | 0.1156 | 3.2751 | 1500 | 0.1586 | 0.4793 | 0.9586 | 0.9586 | nan | 0.9586 | 0.0 | 0.9586 |
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- | 0.1096 | 3.3843 | 1550 | 0.1755 | 0.4536 | 0.9072 | 0.9072 | nan | 0.9072 | 0.0 | 0.9072 |
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- | 0.101 | 3.4934 | 1600 | 0.1917 | 0.4789 | 0.9579 | 0.9579 | nan | 0.9579 | 0.0 | 0.9579 |
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- | 0.1008 | 3.6026 | 1650 | 0.1555 | 0.4655 | 0.9311 | 0.9311 | nan | 0.9311 | 0.0 | 0.9311 |
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- | 0.0951 | 3.7118 | 1700 | 0.1974 | 0.4570 | 0.9141 | 0.9141 | nan | 0.9141 | 0.0 | 0.9141 |
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- | 0.1089 | 3.8210 | 1750 | 0.1740 | 0.4587 | 0.9173 | 0.9173 | nan | 0.9173 | 0.0 | 0.9173 |
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- | 0.091 | 3.9301 | 1800 | 0.1747 | 0.4566 | 0.9133 | 0.9133 | nan | 0.9133 | 0.0 | 0.9133 |
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- | 0.0939 | 4.0393 | 1850 | 0.1832 | 0.4649 | 0.9298 | 0.9298 | nan | 0.9298 | 0.0 | 0.9298 |
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- | 0.0883 | 4.1485 | 1900 | 0.1649 | 0.4673 | 0.9346 | 0.9346 | nan | 0.9346 | 0.0 | 0.9346 |
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- | 0.0921 | 4.2576 | 1950 | 0.1610 | 0.4686 | 0.9372 | 0.9372 | nan | 0.9372 | 0.0 | 0.9372 |
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- | 0.1064 | 4.3668 | 2000 | 0.1862 | 0.4522 | 0.9044 | 0.9044 | nan | 0.9044 | 0.0 | 0.9044 |
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- | 0.0847 | 4.4760 | 2050 | 0.1472 | 0.4729 | 0.9459 | 0.9459 | nan | 0.9459 | 0.0 | 0.9459 |
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- | 0.0941 | 4.5852 | 2100 | 0.1991 | 0.4471 | 0.8941 | 0.8941 | nan | 0.8941 | 0.0 | 0.8941 |
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- | 0.0841 | 4.6943 | 2150 | 0.1568 | 0.4735 | 0.9471 | 0.9471 | nan | 0.9471 | 0.0 | 0.9471 |
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- | 0.0923 | 4.8035 | 2200 | 0.1617 | 0.4737 | 0.9474 | 0.9474 | nan | 0.9474 | 0.0 | 0.9474 |
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- | 0.0837 | 4.9127 | 2250 | 0.1657 | 0.4764 | 0.9528 | 0.9528 | nan | 0.9528 | 0.0 | 0.9528 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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19
  This model is a fine-tuned version of [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) on the BigR-Oclock/CropSegmentation dataset.
20
  It achieves the following results on the evaluation set:
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+ - Loss: 0.2364
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+ - Mean Iou: 0.4754
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+ - Mean Accuracy: 0.9509
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+ - Overall Accuracy: 0.9509
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  - Accuracy Background: nan
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+ - Accuracy Crop: 0.9509
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  - Iou Background: 0.0
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+ - Iou Crop: 0.9509
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  ## Model description
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50
  - seed: 42
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  - optimizer: Use OptimizerNames.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: 10
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55
  ### Training results
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57
  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crop | Iou Background | Iou Crop |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:--------------:|:--------:|
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+ | 0.5159 | 0.1092 | 50 | 0.3885 | 0.4099 | 0.8197 | 0.8197 | nan | 0.8197 | 0.0 | 0.8197 |
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+ | 0.3496 | 0.2183 | 100 | 0.2894 | 0.4077 | 0.8155 | 0.8155 | nan | 0.8155 | 0.0 | 0.8155 |
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+ | 0.3076 | 0.3275 | 150 | 0.2679 | 0.4386 | 0.8773 | 0.8773 | nan | 0.8773 | 0.0 | 0.8773 |
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+ | 0.2953 | 0.4367 | 200 | 0.2906 | 0.4444 | 0.8888 | 0.8888 | nan | 0.8888 | 0.0 | 0.8888 |
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+ | 0.2322 | 0.5459 | 250 | 0.2511 | 0.3949 | 0.7898 | 0.7898 | nan | 0.7898 | 0.0 | 0.7898 |
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+ | 0.2256 | 0.6550 | 300 | 0.2468 | 0.4529 | 0.9058 | 0.9058 | nan | 0.9058 | 0.0 | 0.9058 |
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+ | 0.2706 | 0.7642 | 350 | 0.1816 | 0.4332 | 0.8663 | 0.8663 | nan | 0.8663 | 0.0 | 0.8663 |
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+ | 0.1979 | 0.8734 | 400 | 0.2390 | 0.4521 | 0.9043 | 0.9043 | nan | 0.9043 | 0.0 | 0.9043 |
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+ | 0.2527 | 0.9825 | 450 | 0.2981 | 0.3835 | 0.7670 | 0.7670 | nan | 0.7670 | 0.0 | 0.7670 |
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+ | 0.1658 | 1.0917 | 500 | 0.1473 | 0.4537 | 0.9073 | 0.9073 | nan | 0.9073 | 0.0 | 0.9073 |
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+ | 0.1866 | 1.2009 | 550 | 0.2338 | 0.4246 | 0.8492 | 0.8492 | nan | 0.8492 | 0.0 | 0.8492 |
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+ | 0.1665 | 1.3100 | 600 | 0.1739 | 0.4639 | 0.9278 | 0.9278 | nan | 0.9278 | 0.0 | 0.9278 |
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+ | 0.1692 | 1.4192 | 650 | 0.1808 | 0.4511 | 0.9022 | 0.9022 | nan | 0.9022 | 0.0 | 0.9022 |
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+ | 0.1803 | 1.5284 | 700 | 0.2468 | 0.4138 | 0.8277 | 0.8277 | nan | 0.8277 | 0.0 | 0.8277 |
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+ | 0.1722 | 1.6376 | 750 | 0.1914 | 0.4345 | 0.8691 | 0.8691 | nan | 0.8691 | 0.0 | 0.8691 |
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+ | 0.1526 | 1.7467 | 800 | 0.2183 | 0.4396 | 0.8792 | 0.8792 | nan | 0.8792 | 0.0 | 0.8792 |
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+ | 0.1409 | 1.8559 | 850 | 0.2273 | 0.4216 | 0.8433 | 0.8433 | nan | 0.8433 | 0.0 | 0.8433 |
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+ | 0.169 | 1.9651 | 900 | 0.2728 | 0.4036 | 0.8072 | 0.8072 | nan | 0.8072 | 0.0 | 0.8072 |
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+ | 0.1302 | 2.0742 | 950 | 0.2208 | 0.4452 | 0.8903 | 0.8903 | nan | 0.8903 | 0.0 | 0.8903 |
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+ | 0.1268 | 2.1834 | 1000 | 0.2283 | 0.4253 | 0.8507 | 0.8507 | nan | 0.8507 | 0.0 | 0.8507 |
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+ | 0.1271 | 2.2926 | 1050 | 0.1984 | 0.4506 | 0.9012 | 0.9012 | nan | 0.9012 | 0.0 | 0.9012 |
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+ | 0.1321 | 2.4017 | 1100 | 0.1618 | 0.4560 | 0.9120 | 0.9120 | nan | 0.9120 | 0.0 | 0.9120 |
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+ | 0.1345 | 2.5109 | 1150 | 0.1725 | 0.4659 | 0.9318 | 0.9318 | nan | 0.9318 | 0.0 | 0.9318 |
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+ | 0.1053 | 2.6201 | 1200 | 0.1550 | 0.4574 | 0.9148 | 0.9148 | nan | 0.9148 | 0.0 | 0.9148 |
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+ | 0.1245 | 2.7293 | 1250 | 0.1696 | 0.4816 | 0.9632 | 0.9632 | nan | 0.9632 | 0.0 | 0.9632 |
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+ | 0.1104 | 2.8384 | 1300 | 0.2519 | 0.4330 | 0.8661 | 0.8661 | nan | 0.8661 | 0.0 | 0.8661 |
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+ | 0.1105 | 2.9476 | 1350 | 0.1830 | 0.4655 | 0.9310 | 0.9310 | nan | 0.9310 | 0.0 | 0.9310 |
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+ | 0.1215 | 3.0568 | 1400 | 0.2102 | 0.4596 | 0.9192 | 0.9192 | nan | 0.9192 | 0.0 | 0.9192 |
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+ | 0.0995 | 3.1659 | 1450 | 0.2363 | 0.4478 | 0.8957 | 0.8957 | nan | 0.8957 | 0.0 | 0.8957 |
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+ | 0.1115 | 3.2751 | 1500 | 0.1730 | 0.4717 | 0.9435 | 0.9435 | nan | 0.9435 | 0.0 | 0.9435 |
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+ | 0.0998 | 3.3843 | 1550 | 0.2067 | 0.4535 | 0.9070 | 0.9070 | nan | 0.9070 | 0.0 | 0.9070 |
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+ | 0.0963 | 3.4934 | 1600 | 0.2127 | 0.4701 | 0.9401 | 0.9401 | nan | 0.9401 | 0.0 | 0.9401 |
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+ | 0.0985 | 3.6026 | 1650 | 0.1695 | 0.4686 | 0.9371 | 0.9371 | nan | 0.9371 | 0.0 | 0.9371 |
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+ | 0.0822 | 3.7118 | 1700 | 0.2069 | 0.4494 | 0.8988 | 0.8988 | nan | 0.8988 | 0.0 | 0.8988 |
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+ | 0.1065 | 3.8210 | 1750 | 0.2140 | 0.4590 | 0.9179 | 0.9179 | nan | 0.9179 | 0.0 | 0.9179 |
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+ | 0.0849 | 3.9301 | 1800 | 0.2108 | 0.4592 | 0.9183 | 0.9183 | nan | 0.9183 | 0.0 | 0.9183 |
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+ | 0.0917 | 4.0393 | 1850 | 0.1940 | 0.4668 | 0.9336 | 0.9336 | nan | 0.9336 | 0.0 | 0.9336 |
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+ | 0.0793 | 4.1485 | 1900 | 0.1795 | 0.4649 | 0.9298 | 0.9298 | nan | 0.9298 | 0.0 | 0.9298 |
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+ | 0.0851 | 4.2576 | 1950 | 0.2118 | 0.4462 | 0.8924 | 0.8924 | nan | 0.8924 | 0.0 | 0.8924 |
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+ | 0.0951 | 4.3668 | 2000 | 0.2864 | 0.4212 | 0.8424 | 0.8424 | nan | 0.8424 | 0.0 | 0.8424 |
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+ | 0.0805 | 4.4760 | 2050 | 0.1498 | 0.4683 | 0.9366 | 0.9366 | nan | 0.9366 | 0.0 | 0.9366 |
100
+ | 0.085 | 4.5852 | 2100 | 0.2223 | 0.4514 | 0.9028 | 0.9028 | nan | 0.9028 | 0.0 | 0.9028 |
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+ | 0.0736 | 4.6943 | 2150 | 0.1860 | 0.4695 | 0.9390 | 0.9390 | nan | 0.9390 | 0.0 | 0.9390 |
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+ | 0.079 | 4.8035 | 2200 | 0.2069 | 0.4653 | 0.9305 | 0.9305 | nan | 0.9305 | 0.0 | 0.9305 |
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+ | 0.0701 | 4.9127 | 2250 | 0.1728 | 0.4724 | 0.9448 | 0.9448 | nan | 0.9448 | 0.0 | 0.9448 |
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+ | 0.0994 | 5.0218 | 2300 | 0.2480 | 0.4602 | 0.9204 | 0.9204 | nan | 0.9204 | 0.0 | 0.9204 |
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+ | 0.0749 | 5.1310 | 2350 | 0.1951 | 0.4663 | 0.9325 | 0.9325 | nan | 0.9325 | 0.0 | 0.9325 |
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+ | 0.0691 | 5.2402 | 2400 | 0.2103 | 0.4568 | 0.9136 | 0.9136 | nan | 0.9136 | 0.0 | 0.9136 |
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+ | 0.0653 | 5.3493 | 2450 | 0.1794 | 0.4570 | 0.9140 | 0.9140 | nan | 0.9140 | 0.0 | 0.9140 |
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+ | 0.0621 | 5.4585 | 2500 | 0.1971 | 0.4715 | 0.9430 | 0.9430 | nan | 0.9430 | 0.0 | 0.9430 |
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+ | 0.073 | 5.5677 | 2550 | 0.1905 | 0.4589 | 0.9179 | 0.9179 | nan | 0.9179 | 0.0 | 0.9179 |
110
+ | 0.0658 | 5.6769 | 2600 | 0.2289 | 0.4791 | 0.9581 | 0.9581 | nan | 0.9581 | 0.0 | 0.9581 |
111
+ | 0.0727 | 5.7860 | 2650 | 0.1976 | 0.4769 | 0.9539 | 0.9539 | nan | 0.9539 | 0.0 | 0.9539 |
112
+ | 0.0756 | 5.8952 | 2700 | 0.1724 | 0.4687 | 0.9373 | 0.9373 | nan | 0.9373 | 0.0 | 0.9373 |
113
+ | 0.0756 | 6.0044 | 2750 | 0.1867 | 0.4566 | 0.9133 | 0.9133 | nan | 0.9133 | 0.0 | 0.9133 |
114
+ | 0.0695 | 6.1135 | 2800 | 0.1944 | 0.4715 | 0.9430 | 0.9430 | nan | 0.9430 | 0.0 | 0.9430 |
115
+ | 0.0683 | 6.2227 | 2850 | 0.2176 | 0.4744 | 0.9488 | 0.9488 | nan | 0.9488 | 0.0 | 0.9488 |
116
+ | 0.061 | 6.3319 | 2900 | 0.1959 | 0.4663 | 0.9326 | 0.9326 | nan | 0.9326 | 0.0 | 0.9326 |
117
+ | 0.06 | 6.4410 | 2950 | 0.2090 | 0.4615 | 0.9230 | 0.9230 | nan | 0.9230 | 0.0 | 0.9230 |
118
+ | 0.0537 | 6.5502 | 3000 | 0.2119 | 0.4735 | 0.9469 | 0.9469 | nan | 0.9469 | 0.0 | 0.9469 |
119
+ | 0.0529 | 6.6594 | 3050 | 0.2043 | 0.4568 | 0.9136 | 0.9136 | nan | 0.9136 | 0.0 | 0.9136 |
120
+ | 0.08 | 6.7686 | 3100 | 0.2130 | 0.4566 | 0.9132 | 0.9132 | nan | 0.9132 | 0.0 | 0.9132 |
121
+ | 0.0632 | 6.8777 | 3150 | 0.1993 | 0.4692 | 0.9384 | 0.9384 | nan | 0.9384 | 0.0 | 0.9384 |
122
+ | 0.0641 | 6.9869 | 3200 | 0.2408 | 0.4454 | 0.8909 | 0.8909 | nan | 0.8909 | 0.0 | 0.8909 |
123
+ | 0.0517 | 7.0961 | 3250 | 0.1836 | 0.4770 | 0.9540 | 0.9540 | nan | 0.9540 | 0.0 | 0.9540 |
124
+ | 0.0584 | 7.2052 | 3300 | 0.1983 | 0.4643 | 0.9285 | 0.9285 | nan | 0.9285 | 0.0 | 0.9285 |
125
+ | 0.0559 | 7.3144 | 3350 | 0.2036 | 0.4609 | 0.9217 | 0.9217 | nan | 0.9217 | 0.0 | 0.9217 |
126
+ | 0.0621 | 7.4236 | 3400 | 0.2058 | 0.4764 | 0.9528 | 0.9528 | nan | 0.9528 | 0.0 | 0.9528 |
127
+ | 0.0641 | 7.5328 | 3450 | 0.2136 | 0.4657 | 0.9314 | 0.9314 | nan | 0.9314 | 0.0 | 0.9314 |
128
+ | 0.0481 | 7.6419 | 3500 | 0.1938 | 0.4699 | 0.9398 | 0.9398 | nan | 0.9398 | 0.0 | 0.9398 |
129
+ | 0.061 | 7.7511 | 3550 | 0.1979 | 0.4772 | 0.9545 | 0.9545 | nan | 0.9545 | 0.0 | 0.9545 |
130
+ | 0.0561 | 7.8603 | 3600 | 0.2271 | 0.4691 | 0.9382 | 0.9382 | nan | 0.9382 | 0.0 | 0.9382 |
131
+ | 0.0629 | 7.9694 | 3650 | 0.2220 | 0.4596 | 0.9192 | 0.9192 | nan | 0.9192 | 0.0 | 0.9192 |
132
+ | 0.0625 | 8.0786 | 3700 | 0.2422 | 0.4547 | 0.9094 | 0.9094 | nan | 0.9094 | 0.0 | 0.9094 |
133
+ | 0.0479 | 8.1878 | 3750 | 0.2360 | 0.4791 | 0.9581 | 0.9581 | nan | 0.9581 | 0.0 | 0.9581 |
134
+ | 0.0471 | 8.2969 | 3800 | 0.1981 | 0.4713 | 0.9427 | 0.9427 | nan | 0.9427 | 0.0 | 0.9427 |
135
+ | 0.0612 | 8.4061 | 3850 | 0.2427 | 0.4740 | 0.9479 | 0.9479 | nan | 0.9479 | 0.0 | 0.9479 |
136
+ | 0.0526 | 8.5153 | 3900 | 0.2516 | 0.4716 | 0.9432 | 0.9432 | nan | 0.9432 | 0.0 | 0.9432 |
137
+ | 0.0573 | 8.6245 | 3950 | 0.2240 | 0.4663 | 0.9325 | 0.9325 | nan | 0.9325 | 0.0 | 0.9325 |
138
+ | 0.0532 | 8.7336 | 4000 | 0.2539 | 0.4830 | 0.9659 | 0.9659 | nan | 0.9659 | 0.0 | 0.9659 |
139
+ | 0.0537 | 8.8428 | 4050 | 0.2202 | 0.4633 | 0.9267 | 0.9267 | nan | 0.9267 | 0.0 | 0.9267 |
140
+ | 0.0481 | 8.9520 | 4100 | 0.2155 | 0.4617 | 0.9234 | 0.9234 | nan | 0.9234 | 0.0 | 0.9234 |
141
+ | 0.0461 | 9.0611 | 4150 | 0.2217 | 0.4590 | 0.9181 | 0.9181 | nan | 0.9181 | 0.0 | 0.9181 |
142
+ | 0.0486 | 9.1703 | 4200 | 0.2748 | 0.4420 | 0.8841 | 0.8841 | nan | 0.8841 | 0.0 | 0.8841 |
143
+ | 0.0485 | 9.2795 | 4250 | 0.2172 | 0.4680 | 0.9360 | 0.9360 | nan | 0.9360 | 0.0 | 0.9360 |
144
+ | 0.0559 | 9.3886 | 4300 | 0.2285 | 0.4717 | 0.9434 | 0.9434 | nan | 0.9434 | 0.0 | 0.9434 |
145
+ | 0.0434 | 9.4978 | 4350 | 0.2288 | 0.4749 | 0.9498 | 0.9498 | nan | 0.9498 | 0.0 | 0.9498 |
146
+ | 0.0522 | 9.6070 | 4400 | 0.2420 | 0.4609 | 0.9218 | 0.9218 | nan | 0.9218 | 0.0 | 0.9218 |
147
+ | 0.0453 | 9.7162 | 4450 | 0.2370 | 0.4741 | 0.9481 | 0.9481 | nan | 0.9481 | 0.0 | 0.9481 |
148
+ | 0.0538 | 9.8253 | 4500 | 0.2464 | 0.4565 | 0.9130 | 0.9130 | nan | 0.9130 | 0.0 | 0.9130 |
149
+ | 0.0513 | 9.9345 | 4550 | 0.2364 | 0.4754 | 0.9509 | 0.9509 | nan | 0.9509 | 0.0 | 0.9509 |
150
 
151
 
152
  ### Framework versions
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