Hugging Face's logo

varcoder
/
segformer-b4-crack-segmentation-dataset

Image Segmentation
Transformers
PyTorch
TensorBoard
English
segformer
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
7
varcoder commited on
Commit
77249dc
·
1 Parent(s): e46a79d

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +110 -0
README.md ADDED
@@ -0,0 +1,110 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: other
3
+ tags:
4
+ - generated_from_trainer
5
+ model-index:
6
+ - name: segformer-b4-crack-segmentation-dataset
7
+ results: []
8
+ ---
9
+
10
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
11
+ should probably proofread and complete it, then remove this comment. -->
12
+
13
+ # segformer-b4-crack-segmentation-dataset
14
+
15
+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset.
16
+ It achieves the following results on the evaluation set:
17
+ - Loss: 0.0594
18
+ - Mean Iou: 0.3346
19
+ - Mean Accuracy: 0.6691
20
+ - Overall Accuracy: 0.6691
21
+ - Accuracy Background: nan
22
+ - Accuracy Crack: 0.6691
23
+ - Iou Background: 0.0
24
+ - Iou Crack: 0.6691
25
+
26
+ ## Model description
27
+
28
+ More information needed
29
+
30
+ ## Intended uses & limitations
31
+
32
+ More information needed
33
+
34
+ ## Training and evaluation data
35
+
36
+ More information needed
37
+
38
+ ## Training procedure
39
+
40
+ ### Training hyperparameters
41
+
42
+ The following hyperparameters were used during training:
43
+ - learning_rate: 6e-05
44
+ - train_batch_size: 2
45
+ - eval_batch_size: 2
46
+ - seed: 42
47
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
48
+ - lr_scheduler_type: linear
49
+ - num_epochs: 1
50
+
51
+ ### Training results
52
+
53
+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
54
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
55
+ | 0.2287 | 0.02 | 100 | 0.2515 | 0.1734 | 0.3468 | 0.3468 | nan | 0.3468 | 0.0 | 0.3468 |
56
+ | 0.1792 | 0.04 | 200 | 0.1594 | 0.1671 | 0.3342 | 0.3342 | nan | 0.3342 | 0.0 | 0.3342 |
57
+ | 0.1177 | 0.06 | 300 | 0.1762 | 0.1044 | 0.2088 | 0.2088 | nan | 0.2088 | 0.0 | 0.2088 |
58
+ | 0.0821 | 0.08 | 400 | 0.1706 | 0.2065 | 0.4130 | 0.4130 | nan | 0.4130 | 0.0 | 0.4130 |
59
+ | 0.0666 | 0.1 | 500 | 0.1507 | 0.1931 | 0.3863 | 0.3863 | nan | 0.3863 | 0.0 | 0.3863 |
60
+ | 0.0675 | 0.12 | 600 | 0.1374 | 0.3114 | 0.6227 | 0.6227 | nan | 0.6227 | 0.0 | 0.6227 |
61
+ | 0.0267 | 0.15 | 700 | 0.1400 | 0.2171 | 0.4342 | 0.4342 | nan | 0.4342 | 0.0 | 0.4342 |
62
+ | 0.0192 | 0.17 | 800 | 0.1067 | 0.1594 | 0.3187 | 0.3187 | nan | 0.3187 | 0.0 | 0.3187 |
63
+ | 0.0711 | 0.19 | 900 | 0.1002 | 0.2915 | 0.5830 | 0.5830 | nan | 0.5830 | 0.0 | 0.5830 |
64
+ | 0.0761 | 0.21 | 1000 | 0.0785 | 0.3099 | 0.6199 | 0.6199 | nan | 0.6199 | 0.0 | 0.6199 |
65
+ | 0.0802 | 0.23 | 1100 | 0.0829 | 0.3086 | 0.6173 | 0.6173 | nan | 0.6173 | 0.0 | 0.6173 |
66
+ | 0.1058 | 0.25 | 1200 | 0.0895 | 0.2139 | 0.4278 | 0.4278 | nan | 0.4278 | 0.0 | 0.4278 |
67
+ | 0.0409 | 0.27 | 1300 | 0.0792 | 0.3237 | 0.6475 | 0.6475 | nan | 0.6475 | 0.0 | 0.6475 |
68
+ | 0.063 | 0.29 | 1400 | 0.0739 | 0.3084 | 0.6168 | 0.6168 | nan | 0.6168 | 0.0 | 0.6168 |
69
+ | 0.0669 | 0.31 | 1500 | 0.0747 | 0.3326 | 0.6653 | 0.6653 | nan | 0.6653 | 0.0 | 0.6653 |
70
+ | 0.1277 | 0.33 | 1600 | 0.0735 | 0.3149 | 0.6297 | 0.6297 | nan | 0.6297 | 0.0 | 0.6297 |
71
+ | 0.0388 | 0.35 | 1700 | 0.0708 | 0.2525 | 0.5050 | 0.5050 | nan | 0.5050 | 0.0 | 0.5050 |
72
+ | 0.0332 | 0.37 | 1800 | 0.0726 | 0.2908 | 0.5816 | 0.5816 | nan | 0.5816 | 0.0 | 0.5816 |
73
+ | 0.0435 | 0.4 | 1900 | 0.0673 | 0.2893 | 0.5786 | 0.5786 | nan | 0.5786 | 0.0 | 0.5786 |
74
+ | 0.1297 | 0.42 | 2000 | 0.0698 | 0.3438 | 0.6877 | 0.6877 | nan | 0.6877 | 0.0 | 0.6877 |
75
+ | 0.1202 | 0.44 | 2100 | 0.0745 | 0.2899 | 0.5798 | 0.5798 | nan | 0.5798 | 0.0 | 0.5798 |
76
+ | 0.0549 | 0.46 | 2200 | 0.0657 | 0.3522 | 0.7044 | 0.7044 | nan | 0.7044 | 0.0 | 0.7044 |
77
+ | 0.0223 | 0.48 | 2300 | 0.0808 | 0.2686 | 0.5372 | 0.5372 | nan | 0.5372 | 0.0 | 0.5372 |
78
+ | 0.0464 | 0.5 | 2400 | 0.0631 | 0.3221 | 0.6442 | 0.6442 | nan | 0.6442 | 0.0 | 0.6442 |
79
+ | 0.0364 | 0.52 | 2500 | 0.0778 | 0.3410 | 0.6820 | 0.6820 | nan | 0.6820 | 0.0 | 0.6820 |
80
+ | 0.047 | 0.54 | 2600 | 0.0689 | 0.3489 | 0.6978 | 0.6978 | nan | 0.6978 | 0.0 | 0.6978 |
81
+ | 0.0322 | 0.56 | 2700 | 0.0640 | 0.2863 | 0.5727 | 0.5727 | nan | 0.5727 | 0.0 | 0.5727 |
82
+ | 0.0453 | 0.58 | 2800 | 0.0574 | 0.3340 | 0.6681 | 0.6681 | nan | 0.6681 | 0.0 | 0.6681 |
83
+ | 0.0347 | 0.6 | 2900 | 0.0611 | 0.3289 | 0.6578 | 0.6578 | nan | 0.6578 | 0.0 | 0.6578 |
84
+ | 0.0916 | 0.62 | 3000 | 0.0609 | 0.3357 | 0.6714 | 0.6714 | nan | 0.6714 | 0.0 | 0.6714 |
85
+ | 0.0523 | 0.65 | 3100 | 0.0557 | 0.3318 | 0.6637 | 0.6637 | nan | 0.6637 | 0.0 | 0.6637 |
86
+ | 0.1246 | 0.67 | 3200 | 0.0558 | 0.3294 | 0.6588 | 0.6588 | nan | 0.6588 | 0.0 | 0.6588 |
87
+ | 0.0501 | 0.69 | 3300 | 0.0697 | 0.2955 | 0.5910 | 0.5910 | nan | 0.5910 | 0.0 | 0.5910 |
88
+ | 0.0312 | 0.71 | 3400 | 0.0604 | 0.3414 | 0.6827 | 0.6827 | nan | 0.6827 | 0.0 | 0.6827 |
89
+ | 0.0449 | 0.73 | 3500 | 0.0612 | 0.3305 | 0.6611 | 0.6611 | nan | 0.6611 | 0.0 | 0.6611 |
90
+ | 0.0111 | 0.75 | 3600 | 0.0617 | 0.2930 | 0.5860 | 0.5860 | nan | 0.5860 | 0.0 | 0.5860 |
91
+ | 0.0206 | 0.77 | 3700 | 0.0627 | 0.3663 | 0.7326 | 0.7326 | nan | 0.7326 | 0.0 | 0.7326 |
92
+ | 0.051 | 0.79 | 3800 | 0.0649 | 0.3159 | 0.6318 | 0.6318 | nan | 0.6318 | 0.0 | 0.6318 |
93
+ | 0.0243 | 0.81 | 3900 | 0.0600 | 0.3370 | 0.6740 | 0.6740 | nan | 0.6740 | 0.0 | 0.6740 |
94
+ | 0.0108 | 0.83 | 4000 | 0.0614 | 0.3595 | 0.7190 | 0.7190 | nan | 0.7190 | 0.0 | 0.7190 |
95
+ | 0.0951 | 0.85 | 4100 | 0.0564 | 0.3571 | 0.7142 | 0.7142 | nan | 0.7142 | 0.0 | 0.7142 |
96
+ | 0.0731 | 0.87 | 4200 | 0.0597 | 0.3497 | 0.6994 | 0.6994 | nan | 0.6994 | 0.0 | 0.6994 |
97
+ | 0.0307 | 0.9 | 4300 | 0.0636 | 0.3468 | 0.6937 | 0.6937 | nan | 0.6937 | 0.0 | 0.6937 |
98
+ | 0.1039 | 0.92 | 4400 | 0.0594 | 0.3397 | 0.6795 | 0.6795 | nan | 0.6795 | 0.0 | 0.6795 |
99
+ | 0.0083 | 0.94 | 4500 | 0.0606 | 0.3512 | 0.7024 | 0.7024 | nan | 0.7024 | 0.0 | 0.7024 |
100
+ | 0.0113 | 0.96 | 4600 | 0.0597 | 0.3288 | 0.6576 | 0.6576 | nan | 0.6576 | 0.0 | 0.6576 |
101
+ | 0.0417 | 0.98 | 4700 | 0.0595 | 0.3405 | 0.6811 | 0.6811 | nan | 0.6811 | 0.0 | 0.6811 |
102
+ | 0.1944 | 1.0 | 4800 | 0.0594 | 0.3346 | 0.6691 | 0.6691 | nan | 0.6691 | 0.0 | 0.6691 |
103
+
104
+
105
+ ### Framework versions
106
+
107
+ - Transformers 4.30.2
108
+ - Pytorch 2.0.1+cu118
109
+ - Datasets 2.13.1
110
+ - Tokenizers 0.13.3