Hugging Face's logo

AI-Lab-Makerere
/
mit-b0_corm

Image Segmentation
Transformers
Safetensors
segformer
vision
Generated from Trainer
Model card Files
xet
Community
mujerry commited on
Commit
bceb61c
·
verified ·
1 Parent(s): 9ee76c3

End of training

Browse files
Files changed (5) hide show
  1. README.md +233 -0
  2. config.json +80 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +23 -0
  5. training_args.bin +3 -0
README.md ADDED
@@ -0,0 +1,233 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: transformers
3
+ license: other
4
+ base_model: nvidia/mit-b0
5
+ tags:
6
+ - vision
7
+ - image-segmentation
8
+ - generated_from_trainer
9
+ model-index:
10
+ - name: mit-b0_corm
11
+ results: []
12
+ ---
13
+
14
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
15
+ should probably proofread and complete it, then remove this comment. -->
16
+
17
+ # mit-b0_corm
18
+
19
+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
20
+ It achieves the following results on the evaluation set:
21
+ - Loss: 0.0427
22
+ - Mean Iou: 0.5919
23
+ - Mean Accuracy: 0.9341
24
+ - Overall Accuracy: 0.9364
25
+ - Accuracy Background: nan
26
+ - Accuracy Corm: 0.9213
27
+ - Accuracy Damage: 0.9469
28
+ - Iou Background: 0.0
29
+ - Iou Corm: 0.8662
30
+ - Iou Damage: 0.9096
31
+
32
+ ## Model description
33
+
34
+ More information needed
35
+
36
+ ## Intended uses & limitations
37
+
38
+ More information needed
39
+
40
+ ## Training and evaluation data
41
+
42
+ More information needed
43
+
44
+ ## Training procedure
45
+
46
+ ### Training hyperparameters
47
+
48
+ The following hyperparameters were used during training:
49
+ - learning_rate: 6e-05
50
+ - train_batch_size: 8
51
+ - eval_batch_size: 8
52
+ - seed: 42
53
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
54
+ - lr_scheduler_type: linear
55
+ - num_epochs: 100
56
+
57
+ ### Training results
58
+
59
+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Corm | Accuracy Damage | Iou Background | Iou Corm | Iou Damage |
60
+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:---------------:|:--------------:|:--------:|:----------:|
61
+ | 0.5329 | 0.6061 | 20 | 0.7253 | 0.5216 | 0.8712 | 0.8832 | nan | 0.8030 | 0.9393 | 0.0 | 0.7392 | 0.8256 |
62
+ | 0.3886 | 1.2121 | 40 | 0.4067 | 0.5446 | 0.8956 | 0.9023 | nan | 0.8576 | 0.9336 | 0.0 | 0.7842 | 0.8495 |
63
+ | 0.3707 | 1.8182 | 60 | 0.3026 | 0.5527 | 0.8968 | 0.9099 | nan | 0.8225 | 0.9712 | 0.0 | 0.7920 | 0.8661 |
64
+ | 0.3182 | 2.4242 | 80 | 0.2408 | 0.5798 | 0.9260 | 0.9303 | nan | 0.9021 | 0.9500 | 0.0 | 0.8460 | 0.8935 |
65
+ | 0.1806 | 3.0303 | 100 | 0.1803 | 0.5762 | 0.9288 | 0.9252 | nan | 0.9491 | 0.9084 | 0.0 | 0.8456 | 0.8831 |
66
+ | 0.1668 | 3.6364 | 120 | 0.1540 | 0.5853 | 0.9271 | 0.9328 | nan | 0.8945 | 0.9597 | 0.0 | 0.8536 | 0.9024 |
67
+ | 0.1414 | 4.2424 | 140 | 0.1352 | 0.5823 | 0.9226 | 0.9312 | nan | 0.8735 | 0.9717 | 0.0 | 0.8464 | 0.9007 |
68
+ | 0.1263 | 4.8485 | 160 | 0.1218 | 0.5681 | 0.9062 | 0.9195 | nan | 0.8311 | 0.9813 | 0.0 | 0.8166 | 0.8877 |
69
+ | 0.1355 | 5.4545 | 180 | 0.1054 | 0.5835 | 0.9319 | 0.9294 | nan | 0.9464 | 0.9175 | 0.0 | 0.8559 | 0.8945 |
70
+ | 0.1089 | 6.0606 | 200 | 0.0942 | 0.5896 | 0.9345 | 0.9349 | nan | 0.9324 | 0.9367 | 0.0 | 0.8642 | 0.9046 |
71
+ | 0.1132 | 6.6667 | 220 | 0.0893 | 0.5910 | 0.9333 | 0.9365 | nan | 0.9149 | 0.9516 | 0.0 | 0.8648 | 0.9081 |
72
+ | 0.0826 | 7.2727 | 240 | 0.0922 | 0.5694 | 0.9070 | 0.9203 | nan | 0.8320 | 0.9820 | 0.0 | 0.8184 | 0.8898 |
73
+ | 0.0814 | 7.8788 | 260 | 0.0761 | 0.5899 | 0.9314 | 0.9338 | nan | 0.9177 | 0.9451 | 0.0 | 0.8624 | 0.9072 |
74
+ | 0.0725 | 8.4848 | 280 | 0.0744 | 0.5901 | 0.9353 | 0.9358 | nan | 0.9326 | 0.9381 | 0.0 | 0.8644 | 0.9058 |
75
+ | 0.0745 | 9.0909 | 300 | 0.0756 | 0.5762 | 0.9252 | 0.9207 | nan | 0.9508 | 0.8996 | 0.0 | 0.8449 | 0.8838 |
76
+ | 0.0735 | 9.6970 | 320 | 0.0694 | 0.5888 | 0.9337 | 0.9335 | nan | 0.9351 | 0.9323 | 0.0 | 0.8623 | 0.9042 |
77
+ | 0.0565 | 10.3030 | 340 | 0.0661 | 0.5910 | 0.9332 | 0.9346 | nan | 0.9256 | 0.9408 | 0.0 | 0.8654 | 0.9076 |
78
+ | 0.0658 | 10.9091 | 360 | 0.0642 | 0.5883 | 0.9273 | 0.9338 | nan | 0.8904 | 0.9643 | 0.0 | 0.8571 | 0.9079 |
79
+ | 0.068 | 11.5152 | 380 | 0.0606 | 0.5916 | 0.9323 | 0.9351 | nan | 0.9163 | 0.9483 | 0.0 | 0.8652 | 0.9097 |
80
+ | 0.0602 | 12.1212 | 400 | 0.0574 | 0.5919 | 0.9338 | 0.9360 | nan | 0.9211 | 0.9464 | 0.0 | 0.8659 | 0.9097 |
81
+ | 0.0493 | 12.7273 | 420 | 0.0587 | 0.5916 | 0.9321 | 0.9350 | nan | 0.9154 | 0.9488 | 0.0 | 0.8651 | 0.9097 |
82
+ | 0.0611 | 13.3333 | 440 | 0.0580 | 0.5917 | 0.9329 | 0.9373 | nan | 0.9080 | 0.9579 | 0.0 | 0.8648 | 0.9104 |
83
+ | 0.0553 | 13.9394 | 460 | 0.0544 | 0.5919 | 0.9333 | 0.9360 | nan | 0.9181 | 0.9485 | 0.0 | 0.8653 | 0.9104 |
84
+ | 0.045 | 14.5455 | 480 | 0.0609 | 0.5821 | 0.9196 | 0.9287 | nan | 0.8680 | 0.9712 | 0.0 | 0.8438 | 0.9026 |
85
+ | 0.0516 | 15.1515 | 500 | 0.0548 | 0.5906 | 0.9302 | 0.9358 | nan | 0.8981 | 0.9623 | 0.0 | 0.8616 | 0.9102 |
86
+ | 0.051 | 15.7576 | 520 | 0.0541 | 0.5909 | 0.9306 | 0.9361 | nan | 0.8995 | 0.9617 | 0.0 | 0.8622 | 0.9103 |
87
+ | 0.047 | 16.3636 | 540 | 0.0543 | 0.5921 | 0.9327 | 0.9362 | nan | 0.9130 | 0.9525 | 0.0 | 0.8651 | 0.9112 |
88
+ | 0.0487 | 16.9697 | 560 | 0.0513 | 0.5927 | 0.9350 | 0.9367 | nan | 0.9258 | 0.9443 | 0.0 | 0.8680 | 0.9101 |
89
+ | 0.0495 | 17.5758 | 580 | 0.0525 | 0.5910 | 0.9345 | 0.9348 | nan | 0.9325 | 0.9365 | 0.0 | 0.8658 | 0.9072 |
90
+ | 0.0418 | 18.1818 | 600 | 0.0511 | 0.5923 | 0.9326 | 0.9371 | nan | 0.9075 | 0.9578 | 0.0 | 0.8651 | 0.9118 |
91
+ | 0.044 | 18.7879 | 620 | 0.0519 | 0.5893 | 0.9341 | 0.9333 | nan | 0.9386 | 0.9295 | 0.0 | 0.8636 | 0.9044 |
92
+ | 0.0391 | 19.3939 | 640 | 0.0515 | 0.5885 | 0.9269 | 0.9336 | nan | 0.8887 | 0.9651 | 0.0 | 0.8567 | 0.9088 |
93
+ | 0.0459 | 20.0 | 660 | 0.0507 | 0.5913 | 0.9342 | 0.9348 | nan | 0.9310 | 0.9375 | 0.0 | 0.8662 | 0.9076 |
94
+ | 0.0532 | 20.6061 | 680 | 0.0488 | 0.5928 | 0.9339 | 0.9368 | nan | 0.9175 | 0.9503 | 0.0 | 0.8669 | 0.9114 |
95
+ | 0.041 | 21.2121 | 700 | 0.0500 | 0.5903 | 0.9340 | 0.9342 | nan | 0.9329 | 0.9351 | 0.0 | 0.8641 | 0.9069 |
96
+ | 0.0416 | 21.8182 | 720 | 0.0481 | 0.5921 | 0.9316 | 0.9361 | nan | 0.9066 | 0.9567 | 0.0 | 0.8648 | 0.9114 |
97
+ | 0.0421 | 22.4242 | 740 | 0.0482 | 0.5920 | 0.9319 | 0.9365 | nan | 0.9058 | 0.9580 | 0.0 | 0.8642 | 0.9116 |
98
+ | 0.0504 | 23.0303 | 760 | 0.0488 | 0.5879 | 0.9261 | 0.9332 | nan | 0.8861 | 0.9662 | 0.0 | 0.8554 | 0.9082 |
99
+ | 0.0413 | 23.6364 | 780 | 0.0492 | 0.5883 | 0.9274 | 0.9337 | nan | 0.8911 | 0.9636 | 0.0 | 0.8576 | 0.9072 |
100
+ | 0.0385 | 24.2424 | 800 | 0.0479 | 0.5909 | 0.9307 | 0.9352 | nan | 0.9055 | 0.9559 | 0.0 | 0.8614 | 0.9113 |
101
+ | 0.0516 | 24.8485 | 820 | 0.0467 | 0.5922 | 0.9336 | 0.9351 | nan | 0.9256 | 0.9417 | 0.0 | 0.8672 | 0.9094 |
102
+ | 0.0405 | 25.4545 | 840 | 0.0468 | 0.5920 | 0.9335 | 0.9349 | nan | 0.9254 | 0.9416 | 0.0 | 0.8668 | 0.9093 |
103
+ | 0.0388 | 26.0606 | 860 | 0.0461 | 0.5923 | 0.9320 | 0.9366 | nan | 0.9058 | 0.9582 | 0.0 | 0.8649 | 0.9119 |
104
+ | 0.0445 | 26.6667 | 880 | 0.0457 | 0.5926 | 0.9337 | 0.9365 | nan | 0.9183 | 0.9492 | 0.0 | 0.8668 | 0.9110 |
105
+ | 0.0368 | 27.2727 | 900 | 0.0470 | 0.5908 | 0.9303 | 0.9361 | nan | 0.8973 | 0.9632 | 0.0 | 0.8612 | 0.9111 |
106
+ | 0.0378 | 27.8788 | 920 | 0.0460 | 0.5928 | 0.9338 | 0.9364 | nan | 0.9188 | 0.9488 | 0.0 | 0.8671 | 0.9113 |
107
+ | 0.0356 | 28.4848 | 940 | 0.0468 | 0.5889 | 0.9276 | 0.9340 | nan | 0.8910 | 0.9641 | 0.0 | 0.8580 | 0.9088 |
108
+ | 0.0415 | 29.0909 | 960 | 0.0497 | 0.5829 | 0.9209 | 0.9300 | nan | 0.8693 | 0.9725 | 0.0 | 0.8454 | 0.9033 |
109
+ | 0.0439 | 29.6970 | 980 | 0.0459 | 0.5895 | 0.9284 | 0.9347 | nan | 0.8929 | 0.9640 | 0.0 | 0.8594 | 0.9091 |
110
+ | 0.0407 | 30.3030 | 1000 | 0.0449 | 0.5922 | 0.9322 | 0.9358 | nan | 0.9120 | 0.9525 | 0.0 | 0.8652 | 0.9115 |
111
+ | 0.036 | 30.9091 | 1020 | 0.0451 | 0.5917 | 0.9320 | 0.9364 | nan | 0.9070 | 0.9570 | 0.0 | 0.8643 | 0.9107 |
112
+ | 0.0376 | 31.5152 | 1040 | 0.0453 | 0.5912 | 0.9310 | 0.9363 | nan | 0.9011 | 0.9609 | 0.0 | 0.8627 | 0.9110 |
113
+ | 0.0333 | 32.1212 | 1060 | 0.0472 | 0.5866 | 0.9250 | 0.9330 | nan | 0.8792 | 0.9708 | 0.0 | 0.8526 | 0.9070 |
114
+ | 0.0341 | 32.7273 | 1080 | 0.0492 | 0.5820 | 0.9203 | 0.9296 | nan | 0.8675 | 0.9732 | 0.0 | 0.8439 | 0.9020 |
115
+ | 0.0387 | 33.3333 | 1100 | 0.0445 | 0.5925 | 0.9327 | 0.9376 | nan | 0.9051 | 0.9603 | 0.0 | 0.8650 | 0.9124 |
116
+ | 0.037 | 33.9394 | 1120 | 0.0443 | 0.5924 | 0.9354 | 0.9360 | nan | 0.9321 | 0.9388 | 0.0 | 0.8682 | 0.9089 |
117
+ | 0.0325 | 34.5455 | 1140 | 0.0463 | 0.5891 | 0.9334 | 0.9328 | nan | 0.9367 | 0.9301 | 0.0 | 0.8627 | 0.9044 |
118
+ | 0.037 | 35.1515 | 1160 | 0.0441 | 0.5927 | 0.9336 | 0.9370 | nan | 0.9143 | 0.9530 | 0.0 | 0.8667 | 0.9113 |
119
+ | 0.0549 | 35.7576 | 1180 | 0.0442 | 0.5917 | 0.9315 | 0.9366 | nan | 0.9027 | 0.9603 | 0.0 | 0.8636 | 0.9115 |
120
+ | 0.0365 | 36.3636 | 1200 | 0.0441 | 0.5928 | 0.9337 | 0.9373 | nan | 0.9134 | 0.9541 | 0.0 | 0.8666 | 0.9117 |
121
+ | 0.033 | 36.9697 | 1220 | 0.0439 | 0.5926 | 0.9349 | 0.9374 | nan | 0.9205 | 0.9492 | 0.0 | 0.8673 | 0.9105 |
122
+ | 0.0414 | 37.5758 | 1240 | 0.0445 | 0.5921 | 0.9326 | 0.9375 | nan | 0.9047 | 0.9605 | 0.0 | 0.8644 | 0.9118 |
123
+ | 0.0347 | 38.1818 | 1260 | 0.0436 | 0.5920 | 0.9325 | 0.9360 | nan | 0.9129 | 0.9522 | 0.0 | 0.8654 | 0.9104 |
124
+ | 0.0311 | 38.7879 | 1280 | 0.0457 | 0.5890 | 0.9337 | 0.9327 | nan | 0.9394 | 0.9280 | 0.0 | 0.8631 | 0.9040 |
125
+ | 0.0425 | 39.3939 | 1300 | 0.0446 | 0.5904 | 0.9328 | 0.9330 | nan | 0.9319 | 0.9338 | 0.0 | 0.8646 | 0.9068 |
126
+ | 0.0175 | 40.0 | 1320 | 0.0459 | 0.5879 | 0.9320 | 0.9307 | nan | 0.9390 | 0.9249 | 0.0 | 0.8615 | 0.9023 |
127
+ | 0.0331 | 40.6061 | 1340 | 0.0429 | 0.5932 | 0.9348 | 0.9373 | nan | 0.9210 | 0.9486 | 0.0 | 0.8683 | 0.9114 |
128
+ | 0.0357 | 41.2121 | 1360 | 0.0436 | 0.5920 | 0.9346 | 0.9358 | nan | 0.9281 | 0.9411 | 0.0 | 0.8667 | 0.9094 |
129
+ | 0.0432 | 41.8182 | 1380 | 0.0431 | 0.5926 | 0.9339 | 0.9368 | nan | 0.9171 | 0.9506 | 0.0 | 0.8666 | 0.9112 |
130
+ | 0.0332 | 42.4242 | 1400 | 0.0472 | 0.5858 | 0.9329 | 0.9302 | nan | 0.9479 | 0.9178 | 0.0 | 0.8592 | 0.8983 |
131
+ | 0.0286 | 43.0303 | 1420 | 0.0437 | 0.5910 | 0.9347 | 0.9348 | nan | 0.9342 | 0.9353 | 0.0 | 0.8657 | 0.9073 |
132
+ | 0.0381 | 43.6364 | 1440 | 0.0438 | 0.5912 | 0.9343 | 0.9346 | nan | 0.9327 | 0.9360 | 0.0 | 0.8660 | 0.9075 |
133
+ | 0.0431 | 44.2424 | 1460 | 0.0432 | 0.5916 | 0.9336 | 0.9352 | nan | 0.9246 | 0.9426 | 0.0 | 0.8655 | 0.9092 |
134
+ | 0.0382 | 44.8485 | 1480 | 0.0446 | 0.5899 | 0.9338 | 0.9333 | nan | 0.9369 | 0.9307 | 0.0 | 0.8647 | 0.9051 |
135
+ | 0.0362 | 45.4545 | 1500 | 0.0427 | 0.5927 | 0.9338 | 0.9375 | nan | 0.9130 | 0.9546 | 0.0 | 0.8663 | 0.9117 |
136
+ | 0.0289 | 46.0606 | 1520 | 0.0429 | 0.5927 | 0.9337 | 0.9376 | nan | 0.9115 | 0.9559 | 0.0 | 0.8665 | 0.9117 |
137
+ | 0.0462 | 46.6667 | 1540 | 0.0434 | 0.5898 | 0.9292 | 0.9348 | nan | 0.8972 | 0.9611 | 0.0 | 0.8597 | 0.9098 |
138
+ | 0.0404 | 47.2727 | 1560 | 0.0425 | 0.5927 | 0.9340 | 0.9372 | nan | 0.9159 | 0.9521 | 0.0 | 0.8665 | 0.9115 |
139
+ | 0.0435 | 47.8788 | 1580 | 0.0429 | 0.5920 | 0.9328 | 0.9363 | nan | 0.9130 | 0.9526 | 0.0 | 0.8655 | 0.9104 |
140
+ | 0.0358 | 48.4848 | 1600 | 0.0426 | 0.5924 | 0.9336 | 0.9370 | nan | 0.9142 | 0.9531 | 0.0 | 0.8659 | 0.9112 |
141
+ | 0.0348 | 49.0909 | 1620 | 0.0425 | 0.5924 | 0.9340 | 0.9360 | nan | 0.9225 | 0.9455 | 0.0 | 0.8668 | 0.9103 |
142
+ | 0.0388 | 49.6970 | 1640 | 0.0427 | 0.5915 | 0.9327 | 0.9344 | nan | 0.9232 | 0.9422 | 0.0 | 0.8655 | 0.9090 |
143
+ | 0.0375 | 50.3030 | 1660 | 0.0425 | 0.5924 | 0.9348 | 0.9365 | nan | 0.9250 | 0.9446 | 0.0 | 0.8671 | 0.9101 |
144
+ | 0.0282 | 50.9091 | 1680 | 0.0430 | 0.5919 | 0.9319 | 0.9367 | nan | 0.9050 | 0.9589 | 0.0 | 0.8641 | 0.9115 |
145
+ | 0.0306 | 51.5152 | 1700 | 0.0425 | 0.5920 | 0.9324 | 0.9360 | nan | 0.9118 | 0.9530 | 0.0 | 0.8649 | 0.9110 |
146
+ | 0.028 | 52.1212 | 1720 | 0.0439 | 0.5883 | 0.9272 | 0.9337 | nan | 0.8901 | 0.9643 | 0.0 | 0.8570 | 0.9080 |
147
+ | 0.0297 | 52.7273 | 1740 | 0.0426 | 0.5920 | 0.9329 | 0.9364 | nan | 0.9134 | 0.9524 | 0.0 | 0.8653 | 0.9108 |
148
+ | 0.0272 | 53.3333 | 1760 | 0.0430 | 0.5912 | 0.9341 | 0.9355 | nan | 0.9265 | 0.9418 | 0.0 | 0.8655 | 0.9083 |
149
+ | 0.0344 | 53.9394 | 1780 | 0.0446 | 0.5885 | 0.9334 | 0.9322 | nan | 0.9400 | 0.9267 | 0.0 | 0.8623 | 0.9032 |
150
+ | 0.0242 | 54.5455 | 1800 | 0.0434 | 0.5916 | 0.9355 | 0.9358 | nan | 0.9339 | 0.9370 | 0.0 | 0.8671 | 0.9078 |
151
+ | 0.0325 | 55.1515 | 1820 | 0.0423 | 0.5922 | 0.9331 | 0.9361 | nan | 0.9162 | 0.9500 | 0.0 | 0.8660 | 0.9105 |
152
+ | 0.0261 | 55.7576 | 1840 | 0.0423 | 0.5925 | 0.9347 | 0.9366 | nan | 0.9238 | 0.9456 | 0.0 | 0.8674 | 0.9101 |
153
+ | 0.0398 | 56.3636 | 1860 | 0.0427 | 0.5920 | 0.9346 | 0.9364 | nan | 0.9243 | 0.9449 | 0.0 | 0.8662 | 0.9098 |
154
+ | 0.0257 | 56.9697 | 1880 | 0.0426 | 0.5904 | 0.9296 | 0.9352 | nan | 0.8980 | 0.9613 | 0.0 | 0.8608 | 0.9102 |
155
+ | 0.0322 | 57.5758 | 1900 | 0.0430 | 0.5905 | 0.9300 | 0.9348 | nan | 0.9029 | 0.9572 | 0.0 | 0.8612 | 0.9102 |
156
+ | 0.0341 | 58.1818 | 1920 | 0.0425 | 0.5922 | 0.9345 | 0.9367 | nan | 0.9217 | 0.9473 | 0.0 | 0.8668 | 0.9099 |
157
+ | 0.0255 | 58.7879 | 1940 | 0.0432 | 0.5917 | 0.9355 | 0.9359 | nan | 0.9332 | 0.9379 | 0.0 | 0.8672 | 0.9078 |
158
+ | 0.0386 | 59.3939 | 1960 | 0.0426 | 0.5926 | 0.9345 | 0.9373 | nan | 0.9182 | 0.9507 | 0.0 | 0.8668 | 0.9110 |
159
+ | 0.0267 | 60.0 | 1980 | 0.0427 | 0.5907 | 0.9306 | 0.9359 | nan | 0.9002 | 0.9609 | 0.0 | 0.8622 | 0.9100 |
160
+ | 0.0347 | 60.6061 | 2000 | 0.0421 | 0.5918 | 0.9323 | 0.9354 | nan | 0.9148 | 0.9498 | 0.0 | 0.8647 | 0.9108 |
161
+ | 0.0234 | 61.2121 | 2020 | 0.0427 | 0.5909 | 0.9305 | 0.9359 | nan | 0.8998 | 0.9611 | 0.0 | 0.8619 | 0.9107 |
162
+ | 0.0231 | 61.8182 | 2040 | 0.0428 | 0.5913 | 0.9312 | 0.9364 | nan | 0.9019 | 0.9605 | 0.0 | 0.8626 | 0.9111 |
163
+ | 0.0317 | 62.4242 | 2060 | 0.0424 | 0.5916 | 0.9319 | 0.9366 | nan | 0.9056 | 0.9582 | 0.0 | 0.8638 | 0.9110 |
164
+ | 0.0292 | 63.0303 | 2080 | 0.0420 | 0.5935 | 0.9356 | 0.9385 | nan | 0.9193 | 0.9519 | 0.0 | 0.8686 | 0.9119 |
165
+ | 0.0274 | 63.6364 | 2100 | 0.0425 | 0.5920 | 0.9349 | 0.9365 | nan | 0.9262 | 0.9436 | 0.0 | 0.8665 | 0.9095 |
166
+ | 0.0293 | 64.2424 | 2120 | 0.0424 | 0.5918 | 0.9334 | 0.9355 | nan | 0.9214 | 0.9454 | 0.0 | 0.8662 | 0.9093 |
167
+ | 0.0357 | 64.8485 | 2140 | 0.0424 | 0.5919 | 0.9333 | 0.9362 | nan | 0.9169 | 0.9497 | 0.0 | 0.8651 | 0.9104 |
168
+ | 0.0257 | 65.4545 | 2160 | 0.0427 | 0.5919 | 0.9331 | 0.9374 | nan | 0.9083 | 0.9578 | 0.0 | 0.8650 | 0.9108 |
169
+ | 0.0315 | 66.0606 | 2180 | 0.0425 | 0.5923 | 0.9338 | 0.9373 | nan | 0.9141 | 0.9535 | 0.0 | 0.8662 | 0.9107 |
170
+ | 0.0312 | 66.6667 | 2200 | 0.0424 | 0.5927 | 0.9352 | 0.9374 | nan | 0.9226 | 0.9478 | 0.0 | 0.8675 | 0.9107 |
171
+ | 0.0267 | 67.2727 | 2220 | 0.0421 | 0.5918 | 0.9322 | 0.9361 | nan | 0.9105 | 0.9540 | 0.0 | 0.8644 | 0.9111 |
172
+ | 0.0281 | 67.8788 | 2240 | 0.0423 | 0.5907 | 0.9308 | 0.9347 | nan | 0.9087 | 0.9530 | 0.0 | 0.8623 | 0.9099 |
173
+ | 0.0323 | 68.4848 | 2260 | 0.0422 | 0.5914 | 0.9324 | 0.9363 | nan | 0.9107 | 0.9542 | 0.0 | 0.8640 | 0.9103 |
174
+ | 0.0264 | 69.0909 | 2280 | 0.0428 | 0.5918 | 0.9338 | 0.9365 | nan | 0.9189 | 0.9488 | 0.0 | 0.8656 | 0.9099 |
175
+ | 0.0305 | 69.6970 | 2300 | 0.0425 | 0.5915 | 0.9340 | 0.9356 | nan | 0.9249 | 0.9430 | 0.0 | 0.8657 | 0.9089 |
176
+ | 0.0333 | 70.3030 | 2320 | 0.0433 | 0.5907 | 0.9310 | 0.9357 | nan | 0.9046 | 0.9574 | 0.0 | 0.8620 | 0.9099 |
177
+ | 0.0247 | 70.9091 | 2340 | 0.0427 | 0.5906 | 0.9307 | 0.9356 | nan | 0.9027 | 0.9587 | 0.0 | 0.8620 | 0.9098 |
178
+ | 0.0246 | 71.5152 | 2360 | 0.0431 | 0.5918 | 0.9344 | 0.9359 | nan | 0.9257 | 0.9430 | 0.0 | 0.8667 | 0.9087 |
179
+ | 0.0238 | 72.1212 | 2380 | 0.0424 | 0.5917 | 0.9344 | 0.9358 | nan | 0.9264 | 0.9424 | 0.0 | 0.8664 | 0.9088 |
180
+ | 0.0327 | 72.7273 | 2400 | 0.0424 | 0.5913 | 0.9317 | 0.9347 | nan | 0.9149 | 0.9486 | 0.0 | 0.8641 | 0.9098 |
181
+ | 0.0208 | 73.3333 | 2420 | 0.0428 | 0.5912 | 0.9318 | 0.9359 | nan | 0.9090 | 0.9546 | 0.0 | 0.8636 | 0.9099 |
182
+ | 0.0251 | 73.9394 | 2440 | 0.0424 | 0.5920 | 0.9335 | 0.9364 | nan | 0.9172 | 0.9498 | 0.0 | 0.8655 | 0.9104 |
183
+ | 0.0288 | 74.5455 | 2460 | 0.0427 | 0.5921 | 0.9337 | 0.9367 | nan | 0.9163 | 0.9511 | 0.0 | 0.8660 | 0.9103 |
184
+ | 0.0231 | 75.1515 | 2480 | 0.0426 | 0.5919 | 0.9338 | 0.9359 | nan | 0.9219 | 0.9456 | 0.0 | 0.8662 | 0.9095 |
185
+ | 0.0285 | 75.7576 | 2500 | 0.0429 | 0.5909 | 0.9312 | 0.9360 | nan | 0.9040 | 0.9583 | 0.0 | 0.8625 | 0.9101 |
186
+ | 0.0233 | 76.3636 | 2520 | 0.0425 | 0.5918 | 0.9341 | 0.9359 | nan | 0.9241 | 0.9442 | 0.0 | 0.8661 | 0.9093 |
187
+ | 0.0282 | 76.9697 | 2540 | 0.0428 | 0.5919 | 0.9346 | 0.9362 | nan | 0.9259 | 0.9434 | 0.0 | 0.8666 | 0.9092 |
188
+ | 0.0198 | 77.5758 | 2560 | 0.0429 | 0.5915 | 0.9320 | 0.9365 | nan | 0.9068 | 0.9573 | 0.0 | 0.8639 | 0.9105 |
189
+ | 0.0281 | 78.1818 | 2580 | 0.0426 | 0.5925 | 0.9345 | 0.9373 | nan | 0.9191 | 0.9500 | 0.0 | 0.8670 | 0.9105 |
190
+ | 0.0216 | 78.7879 | 2600 | 0.0423 | 0.5918 | 0.9325 | 0.9357 | nan | 0.9139 | 0.9510 | 0.0 | 0.8649 | 0.9104 |
191
+ | 0.0247 | 79.3939 | 2620 | 0.0428 | 0.5921 | 0.9332 | 0.9368 | nan | 0.9123 | 0.9541 | 0.0 | 0.8656 | 0.9107 |
192
+ | 0.0405 | 80.0 | 2640 | 0.0424 | 0.5917 | 0.9330 | 0.9365 | nan | 0.9130 | 0.9529 | 0.0 | 0.8651 | 0.9101 |
193
+ | 0.0252 | 80.6061 | 2660 | 0.0425 | 0.5918 | 0.9330 | 0.9357 | nan | 0.9176 | 0.9483 | 0.0 | 0.8655 | 0.9099 |
194
+ | 0.0307 | 81.2121 | 2680 | 0.0423 | 0.5921 | 0.9335 | 0.9369 | nan | 0.9145 | 0.9525 | 0.0 | 0.8657 | 0.9106 |
195
+ | 0.0283 | 81.8182 | 2700 | 0.0429 | 0.5918 | 0.9348 | 0.9363 | nan | 0.9259 | 0.9436 | 0.0 | 0.8665 | 0.9090 |
196
+ | 0.0246 | 82.4242 | 2720 | 0.0429 | 0.5901 | 0.9298 | 0.9343 | nan | 0.9047 | 0.9550 | 0.0 | 0.8609 | 0.9093 |
197
+ | 0.0277 | 83.0303 | 2740 | 0.0426 | 0.5917 | 0.9338 | 0.9359 | nan | 0.9216 | 0.9459 | 0.0 | 0.8656 | 0.9095 |
198
+ | 0.0307 | 83.6364 | 2760 | 0.0428 | 0.5922 | 0.9340 | 0.9371 | nan | 0.9166 | 0.9514 | 0.0 | 0.8664 | 0.9104 |
199
+ | 0.0372 | 84.2424 | 2780 | 0.0427 | 0.5920 | 0.9345 | 0.9363 | nan | 0.9243 | 0.9446 | 0.0 | 0.8665 | 0.9095 |
200
+ | 0.0282 | 84.8485 | 2800 | 0.0425 | 0.5914 | 0.9316 | 0.9358 | nan | 0.9076 | 0.9556 | 0.0 | 0.8636 | 0.9105 |
201
+ | 0.0302 | 85.4545 | 2820 | 0.0427 | 0.5924 | 0.9350 | 0.9368 | nan | 0.9247 | 0.9453 | 0.0 | 0.8674 | 0.9097 |
202
+ | 0.0211 | 86.0606 | 2840 | 0.0433 | 0.5914 | 0.9321 | 0.9365 | nan | 0.9073 | 0.9569 | 0.0 | 0.8636 | 0.9106 |
203
+ | 0.0245 | 86.6667 | 2860 | 0.0427 | 0.5920 | 0.9335 | 0.9367 | nan | 0.9152 | 0.9517 | 0.0 | 0.8655 | 0.9104 |
204
+ | 0.0283 | 87.2727 | 2880 | 0.0426 | 0.5916 | 0.9332 | 0.9359 | nan | 0.9176 | 0.9488 | 0.0 | 0.8652 | 0.9096 |
205
+ | 0.0302 | 87.8788 | 2900 | 0.0427 | 0.5918 | 0.9333 | 0.9365 | nan | 0.9154 | 0.9512 | 0.0 | 0.8651 | 0.9102 |
206
+ | 0.0354 | 88.4848 | 2920 | 0.0431 | 0.5914 | 0.9343 | 0.9355 | nan | 0.9272 | 0.9414 | 0.0 | 0.8660 | 0.9081 |
207
+ | 0.0303 | 89.0909 | 2940 | 0.0426 | 0.5914 | 0.9322 | 0.9359 | nan | 0.9114 | 0.9530 | 0.0 | 0.8642 | 0.9099 |
208
+ | 0.0303 | 89.6970 | 2960 | 0.0430 | 0.5917 | 0.9342 | 0.9360 | nan | 0.9235 | 0.9448 | 0.0 | 0.8657 | 0.9093 |
209
+ | 0.0166 | 90.3030 | 2980 | 0.0435 | 0.5904 | 0.9310 | 0.9357 | nan | 0.9042 | 0.9578 | 0.0 | 0.8617 | 0.9094 |
210
+ | 0.0224 | 90.9091 | 3000 | 0.0429 | 0.5915 | 0.9340 | 0.9359 | nan | 0.9232 | 0.9448 | 0.0 | 0.8658 | 0.9088 |
211
+ | 0.0199 | 91.5152 | 3020 | 0.0428 | 0.5916 | 0.9332 | 0.9361 | nan | 0.9164 | 0.9499 | 0.0 | 0.8650 | 0.9097 |
212
+ | 0.0278 | 92.1212 | 3040 | 0.0425 | 0.5919 | 0.9336 | 0.9364 | nan | 0.9176 | 0.9496 | 0.0 | 0.8657 | 0.9099 |
213
+ | 0.0199 | 92.7273 | 3060 | 0.0427 | 0.5918 | 0.9335 | 0.9362 | nan | 0.9185 | 0.9486 | 0.0 | 0.8657 | 0.9098 |
214
+ | 0.0195 | 93.3333 | 3080 | 0.0425 | 0.5921 | 0.9338 | 0.9367 | nan | 0.9176 | 0.9501 | 0.0 | 0.8659 | 0.9103 |
215
+ | 0.0302 | 93.9394 | 3100 | 0.0426 | 0.5918 | 0.9331 | 0.9362 | nan | 0.9155 | 0.9508 | 0.0 | 0.8653 | 0.9102 |
216
+ | 0.0258 | 94.5455 | 3120 | 0.0427 | 0.5918 | 0.9335 | 0.9362 | nan | 0.9182 | 0.9488 | 0.0 | 0.8655 | 0.9098 |
217
+ | 0.0255 | 95.1515 | 3140 | 0.0427 | 0.5919 | 0.9337 | 0.9363 | nan | 0.9190 | 0.9485 | 0.0 | 0.8659 | 0.9099 |
218
+ | 0.0286 | 95.7576 | 3160 | 0.0427 | 0.5915 | 0.9326 | 0.9359 | nan | 0.9136 | 0.9516 | 0.0 | 0.8645 | 0.9101 |
219
+ | 0.0209 | 96.3636 | 3180 | 0.0426 | 0.5919 | 0.9335 | 0.9365 | nan | 0.9168 | 0.9502 | 0.0 | 0.8656 | 0.9101 |
220
+ | 0.0287 | 96.9697 | 3200 | 0.0427 | 0.5919 | 0.9334 | 0.9363 | nan | 0.9170 | 0.9499 | 0.0 | 0.8655 | 0.9101 |
221
+ | 0.032 | 97.5758 | 3220 | 0.0425 | 0.5919 | 0.9336 | 0.9362 | nan | 0.9186 | 0.9485 | 0.0 | 0.8657 | 0.9099 |
222
+ | 0.0307 | 98.1818 | 3240 | 0.0428 | 0.5920 | 0.9341 | 0.9365 | nan | 0.9202 | 0.9480 | 0.0 | 0.8662 | 0.9098 |
223
+ | 0.025 | 98.7879 | 3260 | 0.0428 | 0.5921 | 0.9342 | 0.9367 | nan | 0.9197 | 0.9487 | 0.0 | 0.8663 | 0.9100 |
224
+ | 0.0307 | 99.3939 | 3280 | 0.0430 | 0.5919 | 0.9330 | 0.9366 | nan | 0.9129 | 0.9532 | 0.0 | 0.8650 | 0.9106 |
225
+ | 0.0252 | 100.0 | 3300 | 0.0427 | 0.5919 | 0.9341 | 0.9364 | nan | 0.9213 | 0.9469 | 0.0 | 0.8662 | 0.9096 |
226
+
227
+
228
+ ### Framework versions
229
+
230
+ - Transformers 4.44.1
231
+ - Pytorch 2.6.0+cpu
232
+ - Datasets 2.21.0
233
+ - Tokenizers 0.19.1
config.json ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "nvidia/mit-b0",
3
+ "architectures": [
4
+ "SegformerForSemanticSegmentation"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.0,
7
+ "classifier_dropout_prob": 0.1,
8
+ "decoder_hidden_size": 256,
9
+ "depths": [
10
+ 2,
11
+ 2,
12
+ 2,
13
+ 2
14
+ ],
15
+ "downsampling_rates": [
16
+ 1,
17
+ 4,
18
+ 8,
19
+ 16
20
+ ],
21
+ "drop_path_rate": 0.1,
22
+ "hidden_act": "gelu",
23
+ "hidden_dropout_prob": 0.0,
24
+ "hidden_sizes": [
25
+ 32,
26
+ 64,
27
+ 160,
28
+ 256
29
+ ],
30
+ "id2label": {
31
+ "0": "background",
32
+ "1": "corm",
33
+ "2": "damage"
34
+ },
35
+ "image_size": 224,
36
+ "initializer_range": 0.02,
37
+ "label2id": {
38
+ "background": 0,
39
+ "corm": 1,
40
+ "damage": 2
41
+ },
42
+ "layer_norm_eps": 1e-06,
43
+ "mlp_ratios": [
44
+ 4,
45
+ 4,
46
+ 4,
47
+ 4
48
+ ],
49
+ "model_type": "segformer",
50
+ "num_attention_heads": [
51
+ 1,
52
+ 2,
53
+ 5,
54
+ 8
55
+ ],
56
+ "num_channels": 3,
57
+ "num_encoder_blocks": 4,
58
+ "patch_sizes": [
59
+ 7,
60
+ 3,
61
+ 3,
62
+ 3
63
+ ],
64
+ "reshape_last_stage": true,
65
+ "semantic_loss_ignore_index": 255,
66
+ "sr_ratios": [
67
+ 8,
68
+ 4,
69
+ 2,
70
+ 1
71
+ ],
72
+ "strides": [
73
+ 4,
74
+ 2,
75
+ 2,
76
+ 2
77
+ ],
78
+ "torch_dtype": "float32",
79
+ "transformers_version": "4.44.1"
80
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b21dec7992cc698a059bbde45fe4171a8b93f8616b8063ddc356314848d3be75
3
+ size 14885804
preprocessor_config.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "do_normalize": true,
3
+ "do_reduce_labels": false,
4
+ "do_rescale": true,
5
+ "do_resize": true,
6
+ "image_mean": [
7
+ 0.485,
8
+ 0.456,
9
+ 0.406
10
+ ],
11
+ "image_processor_type": "SegformerFeatureExtractor",
12
+ "image_std": [
13
+ 0.229,
14
+ 0.224,
15
+ 0.225
16
+ ],
17
+ "resample": 2,
18
+ "rescale_factor": 0.00392156862745098,
19
+ "size": {
20
+ "height": 512,
21
+ "width": 512
22
+ }
23
+ }
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:83e758cbf8fa81c2c8a99bdf49319bdfb41797fffd2379299c4e208b43a5471a
3
+ size 5176