update model card README.md
Browse files
README.md
ADDED
@@ -0,0 +1,69 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: other
|
3 |
+
tags:
|
4 |
+
- generated_from_trainer
|
5 |
+
model-index:
|
6 |
+
- name: segformer-b0-DeepCrack
|
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-b0-DeepCrack
|
14 |
+
|
15 |
+
This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on the None dataset.
|
16 |
+
It achieves the following results on the evaluation set:
|
17 |
+
- Loss: 0.0145
|
18 |
+
- Mean Iou: 0.0585
|
19 |
+
- Mean Accuracy: 0.1170
|
20 |
+
- Overall Accuracy: 0.1170
|
21 |
+
- Accuracy Background: nan
|
22 |
+
- Accuracy Cracked: 0.1170
|
23 |
+
- Iou Background: 0.0
|
24 |
+
- Iou Cracked: 0.1170
|
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 Cracked | Iou Background | Iou Cracked |
|
54 |
+
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:|
|
55 |
+
| 0.3566 | 0.13 | 20 | 0.2932 | 0.0200 | 0.0401 | 0.0401 | nan | 0.0401 | 0.0 | 0.0401 |
|
56 |
+
| 0.1639 | 0.27 | 40 | 0.1326 | 0.1353 | 0.2706 | 0.2706 | nan | 0.2706 | 0.0 | 0.2706 |
|
57 |
+
| 0.0562 | 0.4 | 60 | 0.0548 | 0.0681 | 0.1363 | 0.1363 | nan | 0.1363 | 0.0 | 0.1363 |
|
58 |
+
| 0.0283 | 0.53 | 80 | 0.0294 | 0.0815 | 0.1630 | 0.1630 | nan | 0.1630 | 0.0 | 0.1630 |
|
59 |
+
| 0.0187 | 0.67 | 100 | 0.0194 | 0.0720 | 0.1439 | 0.1439 | nan | 0.1439 | 0.0 | 0.1439 |
|
60 |
+
| 0.0144 | 0.8 | 120 | 0.0160 | 0.0656 | 0.1313 | 0.1313 | nan | 0.1313 | 0.0 | 0.1313 |
|
61 |
+
| 0.0152 | 0.93 | 140 | 0.0145 | 0.0585 | 0.1170 | 0.1170 | nan | 0.1170 | 0.0 | 0.1170 |
|
62 |
+
|
63 |
+
|
64 |
+
### Framework versions
|
65 |
+
|
66 |
+
- Transformers 4.30.2
|
67 |
+
- Pytorch 2.0.1+cu118
|
68 |
+
- Datasets 2.13.1
|
69 |
+
- Tokenizers 0.13.3
|