File size: 9,723 Bytes
162639f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 |
---
license: other
base_model: nvidia/mit-b5
tags:
- generated_from_trainer
model-index:
- name: segcrack9k_conglomerate_segformer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segcrack9k_conglomerate_segformer
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0857
- Mean Iou: 0.0010
- Mean Accuracy: 0.0021
- Overall Accuracy: 0.0021
- Accuracy Background: nan
- Accuracy Crack: 0.0021
- Iou Background: 0.0
- Iou Crack: 0.0021
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Crack | Iou Background | Iou Crack |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:--------------:|:--------------:|:---------:|
| 0.0716 | 0.02 | 100 | 0.1132 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
| 0.0708 | 0.04 | 200 | 0.1006 | 0.0001 | 0.0003 | 0.0003 | nan | 0.0003 | 0.0 | 0.0003 |
| 0.1661 | 0.06 | 300 | 0.1042 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
| 0.0601 | 0.08 | 400 | 0.1005 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 |
| 0.1034 | 0.1 | 500 | 0.0980 | 0.0237 | 0.0474 | 0.0474 | nan | 0.0474 | 0.0 | 0.0474 |
| 0.0581 | 0.12 | 600 | 0.0965 | 0.0003 | 0.0005 | 0.0005 | nan | 0.0005 | 0.0 | 0.0005 |
| 0.0561 | 0.14 | 700 | 0.1023 | 0.0038 | 0.0075 | 0.0075 | nan | 0.0075 | 0.0 | 0.0075 |
| 0.1034 | 0.16 | 800 | 0.0956 | 0.0002 | 0.0003 | 0.0003 | nan | 0.0003 | 0.0 | 0.0003 |
| 0.1341 | 0.18 | 900 | 0.0985 | 0.0185 | 0.0369 | 0.0369 | nan | 0.0369 | 0.0 | 0.0369 |
| 0.1988 | 0.2 | 1000 | 0.0946 | 0.0059 | 0.0118 | 0.0118 | nan | 0.0118 | 0.0 | 0.0118 |
| 0.0378 | 0.22 | 1100 | 0.0945 | 0.1402 | 0.2804 | 0.2804 | nan | 0.2804 | 0.0 | 0.2804 |
| 0.0582 | 0.24 | 1200 | 0.0907 | 0.0488 | 0.0976 | 0.0976 | nan | 0.0976 | 0.0 | 0.0976 |
| 0.1464 | 0.26 | 1300 | 0.0971 | 0.1701 | 0.3401 | 0.3401 | nan | 0.3401 | 0.0 | 0.3401 |
| 0.0601 | 0.28 | 1400 | 0.0893 | 0.0222 | 0.0444 | 0.0444 | nan | 0.0444 | 0.0 | 0.0444 |
| 0.0855 | 0.3 | 1500 | 0.0910 | 0.0307 | 0.0613 | 0.0613 | nan | 0.0613 | 0.0 | 0.0613 |
| 0.1167 | 0.32 | 1600 | 0.0895 | 0.0143 | 0.0286 | 0.0286 | nan | 0.0286 | 0.0 | 0.0286 |
| 0.0641 | 0.34 | 1700 | 0.0918 | 0.0073 | 0.0145 | 0.0145 | nan | 0.0145 | 0.0 | 0.0145 |
| 0.0621 | 0.36 | 1800 | 0.0927 | 0.0181 | 0.0363 | 0.0363 | nan | 0.0363 | 0.0 | 0.0363 |
| 0.0364 | 0.38 | 1900 | 0.0884 | 0.1397 | 0.2794 | 0.2794 | nan | 0.2794 | 0.0 | 0.2794 |
| 0.1394 | 0.4 | 2000 | 0.0903 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 |
| 0.0187 | 0.42 | 2100 | 0.0914 | 0.0124 | 0.0248 | 0.0248 | nan | 0.0248 | 0.0 | 0.0248 |
| 0.1842 | 0.44 | 2200 | 0.0908 | 0.0045 | 0.0090 | 0.0090 | nan | 0.0090 | 0.0 | 0.0090 |
| 0.0847 | 0.46 | 2300 | 0.0896 | 0.0031 | 0.0062 | 0.0062 | nan | 0.0062 | 0.0 | 0.0062 |
| 0.0556 | 0.48 | 2400 | 0.0871 | 0.0016 | 0.0033 | 0.0033 | nan | 0.0033 | 0.0 | 0.0033 |
| 0.0454 | 0.51 | 2500 | 0.0896 | 0.0005 | 0.0010 | 0.0010 | nan | 0.0010 | 0.0 | 0.0010 |
| 0.1411 | 0.53 | 2600 | 0.0876 | 0.0095 | 0.0190 | 0.0190 | nan | 0.0190 | 0.0 | 0.0190 |
| 0.1044 | 0.55 | 2700 | 0.0936 | 0.0001 | 0.0002 | 0.0002 | nan | 0.0002 | 0.0 | 0.0002 |
| 0.1299 | 0.57 | 2800 | 0.0938 | 0.0008 | 0.0017 | 0.0017 | nan | 0.0017 | 0.0 | 0.0017 |
| 0.0909 | 0.59 | 2900 | 0.0877 | 0.0012 | 0.0024 | 0.0024 | nan | 0.0024 | 0.0 | 0.0024 |
| 0.0981 | 0.61 | 3000 | 0.0914 | 0.0012 | 0.0024 | 0.0024 | nan | 0.0024 | 0.0 | 0.0024 |
| 0.0905 | 0.63 | 3100 | 0.0880 | 0.0077 | 0.0153 | 0.0153 | nan | 0.0153 | 0.0 | 0.0153 |
| 0.2111 | 0.65 | 3200 | 0.0877 | 0.0000 | 0.0001 | 0.0001 | nan | 0.0001 | 0.0 | 0.0001 |
| 0.3218 | 0.67 | 3300 | 0.0860 | 0.0036 | 0.0072 | 0.0072 | nan | 0.0072 | 0.0 | 0.0072 |
| 0.1134 | 0.69 | 3400 | 0.0864 | 0.0075 | 0.0151 | 0.0151 | nan | 0.0151 | 0.0 | 0.0151 |
| 0.2184 | 0.71 | 3500 | 0.0907 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 |
| 0.1779 | 0.73 | 3600 | 0.0877 | 0.0029 | 0.0059 | 0.0059 | nan | 0.0059 | 0.0 | 0.0059 |
| 0.3664 | 0.75 | 3700 | 0.0878 | 0.0001 | 0.0001 | 0.0001 | nan | 0.0001 | 0.0 | 0.0001 |
| 0.0365 | 0.77 | 3800 | 0.0870 | 0.0000 | 0.0000 | 0.0000 | nan | 0.0000 | 0.0 | 0.0000 |
| 0.0591 | 0.79 | 3900 | 0.0877 | 0.0000 | 0.0001 | 0.0001 | nan | 0.0001 | 0.0 | 0.0001 |
| 0.0719 | 0.81 | 4000 | 0.0871 | 0.0004 | 0.0008 | 0.0008 | nan | 0.0008 | 0.0 | 0.0008 |
| 0.0402 | 0.83 | 4100 | 0.0874 | 0.0011 | 0.0022 | 0.0022 | nan | 0.0022 | 0.0 | 0.0022 |
| 0.0814 | 0.85 | 4200 | 0.0887 | 0.0008 | 0.0017 | 0.0017 | nan | 0.0017 | 0.0 | 0.0017 |
| 0.0485 | 0.87 | 4300 | 0.0871 | 0.0025 | 0.0050 | 0.0050 | nan | 0.0050 | 0.0 | 0.0050 |
| 0.0487 | 0.89 | 4400 | 0.0864 | 0.0004 | 0.0007 | 0.0007 | nan | 0.0007 | 0.0 | 0.0007 |
| 0.0689 | 0.91 | 4500 | 0.0859 | 0.0002 | 0.0004 | 0.0004 | nan | 0.0004 | 0.0 | 0.0004 |
| 0.0782 | 0.93 | 4600 | 0.0858 | 0.0018 | 0.0036 | 0.0036 | nan | 0.0036 | 0.0 | 0.0036 |
| 0.2153 | 0.95 | 4700 | 0.0855 | 0.0004 | 0.0008 | 0.0008 | nan | 0.0008 | 0.0 | 0.0008 |
| 0.1974 | 0.97 | 4800 | 0.0860 | 0.0004 | 0.0009 | 0.0009 | nan | 0.0009 | 0.0 | 0.0009 |
| 0.0184 | 0.99 | 4900 | 0.0857 | 0.0010 | 0.0021 | 0.0021 | nan | 0.0021 | 0.0 | 0.0021 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|