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mit-b0-finetuned-human-parsing-dataset

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  1. README.md +47 -47
  2. model.safetensors +1 -1
  3. runs/Apr13_19-43-32_b0d34c10ec29/events.out.tfevents.1713037417.b0d34c10ec29.1918.0 +3 -0
  4. runs/Apr13_19-50-00_b0d34c10ec29/events.out.tfevents.1713037804.b0d34c10ec29.3940.0 +3 -0
  5. runs/Apr13_20-18-48_b0d34c10ec29/events.out.tfevents.1713039542.b0d34c10ec29.3940.1 +3 -0
  6. runs/Apr13_20-22-59_b0d34c10ec29/events.out.tfevents.1713039799.b0d34c10ec29.3940.2 +3 -0
  7. training_args.bin +1 -1
README.md CHANGED
@@ -15,45 +15,45 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.4607
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- - Mean Iou: 0.1852
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- - Mean Accuracy: 0.2923
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- - Overall Accuracy: 0.5287
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  - Accuracy Background: nan
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- - Accuracy Hat: 0.0
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- - Accuracy Hair: 0.8343
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- - Accuracy Sunglasses: 0.0
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- - Accuracy Upper-clothes: 0.9139
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- - Accuracy Skirt: 0.3524
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- - Accuracy Pants: 0.6638
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- - Accuracy Dress: 0.0044
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- - Accuracy Belt: 0.0
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- - Accuracy Left-shoe: 0.0
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- - Accuracy Right-shoe: 0.0070
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- - Accuracy Face: 0.6567
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- - Accuracy Left-leg: 0.7527
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- - Accuracy Right-leg: 0.4466
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- - Accuracy Left-arm: 0.0418
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- - Accuracy Right-arm: 0.2919
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- - Accuracy Bag: 0.0041
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  - Accuracy Scarf: 0.0
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  - Iou Background: 0.0
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- - Iou Hat: 0.0
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- - Iou Hair: 0.6182
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- - Iou Sunglasses: 0.0
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- - Iou Upper-clothes: 0.4633
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- - Iou Skirt: 0.2299
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- - Iou Pants: 0.4408
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- - Iou Dress: 0.0044
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- - Iou Belt: 0.0
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- - Iou Left-shoe: 0.0
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- - Iou Right-shoe: 0.0069
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- - Iou Face: 0.5668
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- - Iou Left-leg: 0.4302
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- - Iou Right-leg: 0.3307
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- - Iou Left-arm: 0.0414
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- - Iou Right-arm: 0.1962
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- - Iou Bag: 0.0040
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  - Iou Scarf: 0.0
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  ## Model description
@@ -73,7 +73,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1e-05
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  - train_batch_size: 2
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  - eval_batch_size: 2
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  - seed: 42
@@ -87,16 +87,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Hat | Accuracy Hair | Accuracy Sunglasses | Accuracy Upper-clothes | Accuracy Skirt | Accuracy Pants | Accuracy Dress | Accuracy Belt | Accuracy Left-shoe | Accuracy Right-shoe | Accuracy Face | Accuracy Left-leg | Accuracy Right-leg | Accuracy Left-arm | Accuracy Right-arm | Accuracy Bag | Accuracy Scarf | Iou Background | Iou Hat | Iou Hair | Iou Sunglasses | Iou Upper-clothes | Iou Skirt | Iou Pants | Iou Dress | Iou Belt | Iou Left-shoe | Iou Right-shoe | Iou Face | Iou Left-leg | Iou Right-leg | Iou Left-arm | Iou Right-arm | Iou Bag | Iou Scarf |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:-------------:|:-------------------:|:----------------------:|:--------------:|:--------------:|:--------------:|:-------------:|:------------------:|:-------------------:|:-------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:------------:|:--------------:|:--------------:|:-------:|:--------:|:--------------:|:-----------------:|:---------:|:---------:|:---------:|:--------:|:-------------:|:--------------:|:--------:|:------------:|:-------------:|:------------:|:-------------:|:-------:|:---------:|
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- | 2.3018 | 1.0 | 100 | 2.3397 | 0.0797 | 0.1672 | 0.3305 | nan | 0.0126 | 0.7491 | 0.0 | 0.5958 | 0.0964 | 0.4504 | 0.0 | 0.0 | 0.0 | 0.0639 | 0.0 | 0.1682 | 0.6099 | 0.0006 | 0.0956 | 0.0004 | 0.0 | 0.0 | 0.0075 | 0.4410 | 0.0 | 0.3494 | 0.0795 | 0.2411 | 0.0 | 0.0 | 0.0 | 0.0560 | 0.0 | 0.0878 | 0.1303 | 0.0006 | 0.0418 | 0.0004 | 0.0 |
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- | 1.9921 | 2.0 | 200 | 2.0111 | 0.0912 | 0.1829 | 0.4155 | nan | 0.0009 | 0.8152 | 0.0 | 0.8937 | 0.0626 | 0.5552 | 0.0 | 0.0 | 0.0 | 0.0324 | 0.0 | 0.0359 | 0.5840 | 0.0 | 0.1287 | 0.0002 | 0.0 | 0.0 | 0.0008 | 0.4618 | 0.0 | 0.4121 | 0.0542 | 0.2899 | 0.0 | 0.0 | 0.0 | 0.0304 | 0.0 | 0.0344 | 0.2775 | 0.0 | 0.0806 | 0.0002 | 0.0 |
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- | 1.9356 | 3.0 | 300 | 1.8783 | 0.1275 | 0.2271 | 0.4567 | nan | 0.0001 | 0.8593 | 0.0 | 0.8534 | 0.0847 | 0.6947 | 0.0 | 0.0 | 0.0 | 0.0319 | 0.1338 | 0.3891 | 0.5265 | 0.0009 | 0.2864 | 0.0002 | 0.0 | 0.0 | 0.0001 | 0.4867 | 0.0 | 0.4318 | 0.0737 | 0.3392 | 0.0 | 0.0 | 0.0 | 0.0295 | 0.1321 | 0.3197 | 0.3100 | 0.0009 | 0.1710 | 0.0002 | 0.0 |
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- | 1.7506 | 4.0 | 400 | 1.7309 | 0.1530 | 0.2545 | 0.4868 | nan | 0.0 | 0.8491 | 0.0 | 0.8918 | 0.1117 | 0.6755 | 0.0005 | 0.0 | 0.0 | 0.0156 | 0.3402 | 0.5809 | 0.5198 | 0.0056 | 0.3348 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.5369 | 0.0 | 0.4371 | 0.0944 | 0.3758 | 0.0005 | 0.0 | 0.0 | 0.0148 | 0.3282 | 0.4183 | 0.3503 | 0.0055 | 0.1913 | 0.0006 | 0.0 |
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- | 1.6655 | 5.0 | 500 | 1.6569 | 0.1699 | 0.2728 | 0.5059 | nan | 0.0 | 0.8412 | 0.0 | 0.9097 | 0.1935 | 0.6488 | 0.0004 | 0.0 | 0.0 | 0.0097 | 0.5260 | 0.6501 | 0.5099 | 0.0142 | 0.3336 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.5798 | 0.0 | 0.4436 | 0.1504 | 0.4018 | 0.0004 | 0.0 | 0.0 | 0.0094 | 0.4803 | 0.4361 | 0.3519 | 0.0141 | 0.1889 | 0.0006 | 0.0 |
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- | 1.5728 | 6.0 | 600 | 1.5539 | 0.1770 | 0.2841 | 0.5175 | nan | 0.0 | 0.8349 | 0.0 | 0.9038 | 0.2631 | 0.6714 | 0.0010 | 0.0 | 0.0 | 0.0101 | 0.6261 | 0.7272 | 0.4377 | 0.0246 | 0.3287 | 0.0007 | 0.0 | 0.0 | 0.0 | 0.6016 | 0.0 | 0.4563 | 0.1912 | 0.4218 | 0.0010 | 0.0 | 0.0 | 0.0099 | 0.5435 | 0.4256 | 0.3130 | 0.0243 | 0.1969 | 0.0007 | 0.0 |
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- | 1.5283 | 7.0 | 700 | 1.4944 | 0.1792 | 0.2817 | 0.5183 | nan | 0.0 | 0.8452 | 0.0 | 0.9137 | 0.2711 | 0.6729 | 0.0019 | 0.0 | 0.0000 | 0.0115 | 0.5787 | 0.6706 | 0.4809 | 0.0360 | 0.3054 | 0.0009 | 0.0 | 0.0 | 0.0 | 0.6007 | 0.0 | 0.4506 | 0.1964 | 0.4194 | 0.0019 | 0.0 | 0.0000 | 0.0112 | 0.5179 | 0.4375 | 0.3582 | 0.0357 | 0.1943 | 0.0009 | 0.0 |
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- | 1.5141 | 8.0 | 800 | 1.4900 | 0.1832 | 0.2877 | 0.5245 | nan | 0.0 | 0.8405 | 0.0 | 0.9183 | 0.3031 | 0.6605 | 0.0040 | 0.0 | 0.0000 | 0.0089 | 0.6319 | 0.7110 | 0.4872 | 0.0420 | 0.2814 | 0.0014 | 0.0 | 0.0 | 0.0 | 0.6120 | 0.0 | 0.4549 | 0.2113 | 0.4309 | 0.0040 | 0.0 | 0.0000 | 0.0087 | 0.5505 | 0.4381 | 0.3541 | 0.0417 | 0.1893 | 0.0014 | 0.0 |
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- | 1.5519 | 9.0 | 900 | 1.4851 | 0.1854 | 0.2928 | 0.5296 | nan | 0.0 | 0.8471 | 0.0 | 0.8987 | 0.3431 | 0.7041 | 0.0047 | 0.0 | 0.0000 | 0.0074 | 0.6376 | 0.7140 | 0.4917 | 0.0490 | 0.2775 | 0.0031 | 0.0 | 0.0 | 0.0 | 0.6136 | 0.0 | 0.4662 | 0.2287 | 0.4365 | 0.0047 | 0.0 | 0.0000 | 0.0073 | 0.5557 | 0.4329 | 0.3514 | 0.0485 | 0.1888 | 0.0031 | 0.0 |
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- | 1.5166 | 10.0 | 1000 | 1.4607 | 0.1852 | 0.2923 | 0.5287 | nan | 0.0 | 0.8343 | 0.0 | 0.9139 | 0.3524 | 0.6638 | 0.0044 | 0.0 | 0.0 | 0.0070 | 0.6567 | 0.7527 | 0.4466 | 0.0418 | 0.2919 | 0.0041 | 0.0 | 0.0 | 0.0 | 0.6182 | 0.0 | 0.4633 | 0.2299 | 0.4408 | 0.0044 | 0.0 | 0.0 | 0.0069 | 0.5668 | 0.4302 | 0.3307 | 0.0414 | 0.1962 | 0.0040 | 0.0 |
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  ### Framework versions
 
15
 
16
  This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 0.2491
19
+ - Mean Iou: 0.4053
20
+ - Mean Accuracy: 0.5247
21
+ - Overall Accuracy: 0.6754
22
  - Accuracy Background: nan
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+ - Accuracy Hat: 0.0490
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+ - Accuracy Hair: 0.8061
25
+ - Accuracy Sunglasses: 0.0853
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+ - Accuracy Upper-clothes: 0.8153
27
+ - Accuracy Skirt: 0.5909
28
+ - Accuracy Pants: 0.7331
29
+ - Accuracy Dress: 0.3565
30
+ - Accuracy Belt: 0.0703
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+ - Accuracy Left-shoe: 0.4865
32
+ - Accuracy Right-shoe: 0.4809
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+ - Accuracy Face: 0.8759
34
+ - Accuracy Left-leg: 0.7828
35
+ - Accuracy Right-leg: 0.7800
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+ - Accuracy Left-arm: 0.7422
37
+ - Accuracy Right-arm: 0.7360
38
+ - Accuracy Bag: 0.5289
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  - Accuracy Scarf: 0.0
40
  - Iou Background: 0.0
41
+ - Iou Hat: 0.0475
42
+ - Iou Hair: 0.6885
43
+ - Iou Sunglasses: 0.0848
44
+ - Iou Upper-clothes: 0.6308
45
+ - Iou Skirt: 0.4191
46
+ - Iou Pants: 0.6119
47
+ - Iou Dress: 0.2793
48
+ - Iou Belt: 0.0656
49
+ - Iou Left-shoe: 0.3752
50
+ - Iou Right-shoe: 0.3762
51
+ - Iou Face: 0.7552
52
+ - Iou Left-leg: 0.6406
53
+ - Iou Right-leg: 0.6261
54
+ - Iou Left-arm: 0.6422
55
+ - Iou Right-arm: 0.6335
56
+ - Iou Bag: 0.4188
57
  - Iou Scarf: 0.0
58
 
59
  ## Model description
 
73
  ### Training hyperparameters
74
 
75
  The following hyperparameters were used during training:
76
+ - learning_rate: 7e-05
77
  - train_batch_size: 2
78
  - eval_batch_size: 2
79
  - seed: 42
 
87
 
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  | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Hat | Accuracy Hair | Accuracy Sunglasses | Accuracy Upper-clothes | Accuracy Skirt | Accuracy Pants | Accuracy Dress | Accuracy Belt | Accuracy Left-shoe | Accuracy Right-shoe | Accuracy Face | Accuracy Left-leg | Accuracy Right-leg | Accuracy Left-arm | Accuracy Right-arm | Accuracy Bag | Accuracy Scarf | Iou Background | Iou Hat | Iou Hair | Iou Sunglasses | Iou Upper-clothes | Iou Skirt | Iou Pants | Iou Dress | Iou Belt | Iou Left-shoe | Iou Right-shoe | Iou Face | Iou Left-leg | Iou Right-leg | Iou Left-arm | Iou Right-arm | Iou Bag | Iou Scarf |
89
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------:|:-------------:|:-------------------:|:----------------------:|:--------------:|:--------------:|:--------------:|:-------------:|:------------------:|:-------------------:|:-------------:|:-----------------:|:------------------:|:-----------------:|:------------------:|:------------:|:--------------:|:--------------:|:-------:|:--------:|:--------------:|:-----------------:|:---------:|:---------:|:---------:|:--------:|:-------------:|:--------------:|:--------:|:------------:|:-------------:|:------------:|:-------------:|:-------:|:---------:|
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+ | 0.2651 | 1.0 | 200 | 0.3116 | 0.3393 | 0.4570 | 0.5998 | nan | 0.0129 | 0.7666 | 0.0004 | 0.7934 | 0.3598 | 0.6443 | 0.2453 | 0.0 | 0.4477 | 0.4508 | 0.8653 | 0.7454 | 0.7194 | 0.6835 | 0.6634 | 0.3713 | 0.0 | 0.0 | 0.0127 | 0.6474 | 0.0004 | 0.5567 | 0.2728 | 0.4984 | 0.1899 | 0.0 | 0.3303 | 0.3388 | 0.7288 | 0.5623 | 0.5422 | 0.5631 | 0.5607 | 0.3020 | 0.0 |
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+ | 0.2774 | 2.0 | 400 | 0.2939 | 0.3486 | 0.4677 | 0.6108 | nan | 0.0154 | 0.7829 | 0.0045 | 0.7562 | 0.5055 | 0.6825 | 0.2127 | 0.0002 | 0.4129 | 0.4135 | 0.8785 | 0.7314 | 0.7486 | 0.7046 | 0.6898 | 0.4121 | 0.0 | 0.0 | 0.0151 | 0.6622 | 0.0045 | 0.5746 | 0.3311 | 0.5176 | 0.1684 | 0.0002 | 0.3182 | 0.3247 | 0.7341 | 0.5744 | 0.5603 | 0.5812 | 0.5769 | 0.3317 | 0.0 |
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+ | 0.3388 | 3.0 | 600 | 0.2810 | 0.3644 | 0.4819 | 0.6328 | nan | 0.0192 | 0.8055 | 0.0104 | 0.7898 | 0.4304 | 0.6891 | 0.3331 | 0.0115 | 0.4650 | 0.4417 | 0.8647 | 0.7690 | 0.7301 | 0.6926 | 0.6941 | 0.4468 | 0.0 | 0.0 | 0.0188 | 0.6736 | 0.0104 | 0.5942 | 0.3261 | 0.5421 | 0.2449 | 0.0114 | 0.3468 | 0.3411 | 0.7451 | 0.5877 | 0.5679 | 0.6006 | 0.5987 | 0.3503 | 0.0 |
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+ | 0.3173 | 4.0 | 800 | 0.2736 | 0.3729 | 0.4974 | 0.6442 | nan | 0.0232 | 0.8059 | 0.0313 | 0.8122 | 0.4963 | 0.6829 | 0.2733 | 0.0296 | 0.4650 | 0.4635 | 0.8919 | 0.7654 | 0.7621 | 0.7497 | 0.7358 | 0.4680 | 0.0 | 0.0 | 0.0227 | 0.6783 | 0.0312 | 0.6004 | 0.3611 | 0.5584 | 0.2182 | 0.0291 | 0.3523 | 0.3559 | 0.7374 | 0.5972 | 0.5791 | 0.6120 | 0.6086 | 0.3694 | 0.0 |
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+ | 0.273 | 5.0 | 1000 | 0.2636 | 0.3878 | 0.5086 | 0.6577 | nan | 0.0421 | 0.7981 | 0.0507 | 0.8074 | 0.5790 | 0.7056 | 0.3009 | 0.0393 | 0.4799 | 0.4619 | 0.8786 | 0.7758 | 0.7729 | 0.7369 | 0.7249 | 0.4927 | 0.0 | 0.0 | 0.0408 | 0.6800 | 0.0505 | 0.6128 | 0.3950 | 0.5858 | 0.2359 | 0.0383 | 0.3681 | 0.3620 | 0.7499 | 0.6188 | 0.6004 | 0.6285 | 0.6196 | 0.3936 | 0.0 |
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+ | 0.2328 | 6.0 | 1200 | 0.2595 | 0.3896 | 0.5095 | 0.6618 | nan | 0.0341 | 0.8022 | 0.0527 | 0.8293 | 0.5034 | 0.7043 | 0.3409 | 0.0507 | 0.4628 | 0.4633 | 0.8853 | 0.7842 | 0.7707 | 0.7486 | 0.7431 | 0.4867 | 0.0 | 0.0 | 0.0333 | 0.6842 | 0.0525 | 0.6138 | 0.3798 | 0.5916 | 0.2620 | 0.0487 | 0.3618 | 0.3654 | 0.7476 | 0.6229 | 0.6052 | 0.6286 | 0.6242 | 0.3917 | 0.0 |
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+ | 0.3112 | 7.0 | 1400 | 0.2565 | 0.3943 | 0.5169 | 0.6614 | nan | 0.0368 | 0.8098 | 0.0618 | 0.8047 | 0.5517 | 0.7267 | 0.3032 | 0.0736 | 0.5080 | 0.4878 | 0.8852 | 0.7966 | 0.7721 | 0.7467 | 0.7330 | 0.4891 | 0.0 | 0.0 | 0.0358 | 0.6880 | 0.0615 | 0.6199 | 0.3957 | 0.5843 | 0.2465 | 0.0691 | 0.3810 | 0.3753 | 0.7502 | 0.6302 | 0.6109 | 0.6326 | 0.6260 | 0.3896 | 0.0 |
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+ | 0.2033 | 8.0 | 1600 | 0.2515 | 0.4049 | 0.5239 | 0.6747 | nan | 0.0415 | 0.8093 | 0.0828 | 0.8169 | 0.6027 | 0.7345 | 0.3417 | 0.0758 | 0.4952 | 0.4964 | 0.8688 | 0.7713 | 0.7702 | 0.7326 | 0.7251 | 0.5421 | 0.0 | 0.0 | 0.0404 | 0.6891 | 0.0823 | 0.6298 | 0.4167 | 0.6143 | 0.2670 | 0.0704 | 0.3790 | 0.3824 | 0.7569 | 0.6417 | 0.6271 | 0.6409 | 0.6316 | 0.4185 | 0.0 |
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+ | 0.2127 | 9.0 | 1800 | 0.2500 | 0.4066 | 0.5294 | 0.6776 | nan | 0.0427 | 0.8256 | 0.0871 | 0.8214 | 0.5441 | 0.7349 | 0.3598 | 0.0744 | 0.5160 | 0.5123 | 0.8771 | 0.7875 | 0.7828 | 0.7508 | 0.7444 | 0.5388 | 0.0 | 0.0 | 0.0416 | 0.6935 | 0.0866 | 0.6296 | 0.4039 | 0.6096 | 0.2813 | 0.0694 | 0.3891 | 0.3907 | 0.7538 | 0.6427 | 0.6291 | 0.6441 | 0.6349 | 0.4187 | 0.0 |
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+ | 0.2443 | 10.0 | 2000 | 0.2491 | 0.4053 | 0.5247 | 0.6754 | nan | 0.0490 | 0.8061 | 0.0853 | 0.8153 | 0.5909 | 0.7331 | 0.3565 | 0.0703 | 0.4865 | 0.4809 | 0.8759 | 0.7828 | 0.7800 | 0.7422 | 0.7360 | 0.5289 | 0.0 | 0.0 | 0.0475 | 0.6885 | 0.0848 | 0.6308 | 0.4191 | 0.6119 | 0.2793 | 0.0656 | 0.3752 | 0.3762 | 0.7552 | 0.6406 | 0.6261 | 0.6422 | 0.6335 | 0.4188 | 0.0 |
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  ### Framework versions
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