Hugging Face's logo

HorcruxNo13
/
segformer-b0-finetuned-segments-toolwear

Transformers
PyTorch
TensorBoard
Safetensors
segformer
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
HorcruxNo13 commited on
Commit
d344718
•
1 Parent(s): 08ea240

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +25 -23
README.md CHANGED
@@ -1,6 +1,8 @@
1
  ---
2
  license: other
3
  tags:
 
 
4
  - generated_from_trainer
5
  model-index:
6
  - name: segformer-b0-finetuned-segments-toolwear
@@ -12,18 +14,18 @@ should probably proofread and complete it, then remove this comment. -->
12
 
13
  # segformer-b0-finetuned-segments-toolwear
14
 
15
- This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
16
  It achieves the following results on the evaluation set:
17
- - Loss: 0.1338
18
- - Mean Iou: 0.4591
19
- - Mean Accuracy: 0.7164
20
- - Overall Accuracy: 0.9595
21
  - Accuracy Unlabeled: nan
22
- - Accuracy Wear: 0.4489
23
- - Accuracy Tool: 0.9838
24
  - Iou Unlabeled: 0.0
25
- - Iou Wear: 0.4154
26
- - Iou Tool: 0.9618
27
 
28
  ## Model description
29
 
@@ -52,21 +54,21 @@ The following hyperparameters were used during training:
52
 
53
  ### Training results
54
 
55
- | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Wear | Accuracy Tool | Iou Unlabeled | Iou Wear | Iou Tool |
56
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
57
- | 0.5488 | 1.82 | 20 | 0.7199 | 0.3293 | 0.5153 | 0.9405 | nan | 0.0476 | 0.9830 | 0.0 | 0.0475 | 0.9404 |
58
- | 0.5195 | 3.64 | 40 | 0.3507 | 0.3622 | 0.5634 | 0.9239 | nan | 0.1667 | 0.9600 | 0.0 | 0.1629 | 0.9236 |
59
- | 0.2738 | 5.45 | 60 | 0.2569 | 0.4662 | 0.7496 | 0.9435 | nan | 0.5363 | 0.9629 | 0.0 | 0.4547 | 0.9438 |
60
- | 0.2461 | 7.27 | 80 | 0.2220 | 0.4491 | 0.7057 | 0.9482 | nan | 0.4389 | 0.9725 | 0.0 | 0.3982 | 0.9492 |
61
- | 0.1999 | 9.09 | 100 | 0.1962 | 0.4492 | 0.7084 | 0.9597 | nan | 0.4319 | 0.9848 | 0.0 | 0.3860 | 0.9616 |
62
- | 0.2004 | 10.91 | 120 | 0.1890 | 0.4031 | 0.6239 | 0.9537 | nan | 0.2610 | 0.9867 | 0.0 | 0.2539 | 0.9553 |
63
- | 0.4753 | 12.73 | 140 | 0.1704 | 0.4360 | 0.6760 | 0.9494 | nan | 0.3753 | 0.9768 | 0.0 | 0.3562 | 0.9518 |
64
- | 0.1606 | 14.55 | 160 | 0.1579 | 0.4483 | 0.7028 | 0.9580 | nan | 0.4222 | 0.9835 | 0.0 | 0.3822 | 0.9625 |
65
- | 0.1388 | 16.36 | 180 | 0.1519 | 0.4829 | 0.7940 | 0.9565 | nan | 0.6152 | 0.9728 | 0.0 | 0.4900 | 0.9586 |
66
- | 0.138 | 18.18 | 200 | 0.1374 | 0.5120 | 0.8119 | 0.9643 | nan | 0.6443 | 0.9795 | 0.0 | 0.5693 | 0.9668 |
67
- | 0.1078 | 20.0 | 220 | 0.1400 | 0.4541 | 0.7066 | 0.9606 | nan | 0.4271 | 0.9860 | 0.0 | 0.3985 | 0.9638 |
68
- | 0.1426 | 21.82 | 240 | 0.1323 | 0.4530 | 0.7053 | 0.9581 | nan | 0.4272 | 0.9834 | 0.0 | 0.3978 | 0.9611 |
69
- | 0.3498 | 23.64 | 260 | 0.1338 | 0.4591 | 0.7164 | 0.9595 | nan | 0.4489 | 0.9838 | 0.0 | 0.4154 | 0.9618 |
70
 
71
 
72
  ### Framework versions
 
1
  ---
2
  license: other
3
  tags:
4
+ - vision
5
+ - image-segmentation
6
  - generated_from_trainer
7
  model-index:
8
  - name: segformer-b0-finetuned-segments-toolwear
 
14
 
15
  # segformer-b0-finetuned-segments-toolwear
16
 
17
+ This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the HorcruxNo13/toolwear_segmentsai dataset.
18
  It achieves the following results on the evaluation set:
19
+ - Loss: 0.2940
20
+ - Mean Iou: 0.4104
21
+ - Mean Accuracy: 0.8207
22
+ - Overall Accuracy: 0.8207
23
  - Accuracy Unlabeled: nan
24
+ - Accuracy Tool: nan
25
+ - Accuracy Wear: 0.8207
26
  - Iou Unlabeled: 0.0
27
+ - Iou Tool: nan
28
+ - Iou Wear: 0.8207
29
 
30
  ## Model description
31
 
 
54
 
55
  ### Training results
56
 
57
+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Tool | Accuracy Wear | Iou Unlabeled | Iou Tool | Iou Wear |
58
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:|
59
+ | 0.843 | 1.82 | 20 | 0.8832 | 0.4637 | 0.9274 | 0.9274 | nan | nan | 0.9274 | 0.0 | nan | 0.9274 |
60
+ | 0.6849 | 3.64 | 40 | 0.5914 | 0.4361 | 0.8722 | 0.8722 | nan | nan | 0.8722 | 0.0 | nan | 0.8722 |
61
+ | 0.5085 | 5.45 | 60 | 0.5178 | 0.4628 | 0.9256 | 0.9256 | nan | nan | 0.9256 | 0.0 | nan | 0.9256 |
62
+ | 0.4027 | 7.27 | 80 | 0.5099 | 0.4598 | 0.9195 | 0.9195 | nan | nan | 0.9195 | 0.0 | nan | 0.9195 |
63
+ | 0.3536 | 9.09 | 100 | 0.4262 | 0.4365 | 0.8730 | 0.8730 | nan | nan | 0.8730 | 0.0 | nan | 0.8730 |
64
+ | 0.3657 | 10.91 | 120 | 0.3891 | 0.4228 | 0.8457 | 0.8457 | nan | nan | 0.8457 | 0.0 | nan | 0.8457 |
65
+ | 0.3234 | 12.73 | 140 | 0.4221 | 0.4377 | 0.8754 | 0.8754 | nan | nan | 0.8754 | 0.0 | nan | 0.8754 |
66
+ | 0.2874 | 14.55 | 160 | 0.3355 | 0.4098 | 0.8197 | 0.8197 | nan | nan | 0.8197 | 0.0 | nan | 0.8197 |
67
+ | 0.2335 | 16.36 | 180 | 0.3570 | 0.4266 | 0.8531 | 0.8531 | nan | nan | 0.8531 | 0.0 | nan | 0.8531 |
68
+ | 0.2167 | 18.18 | 200 | 0.3238 | 0.4404 | 0.8808 | 0.8808 | nan | nan | 0.8808 | 0.0 | nan | 0.8808 |
69
+ | 0.2201 | 20.0 | 220 | 0.3103 | 0.4185 | 0.8370 | 0.8370 | nan | nan | 0.8370 | 0.0 | nan | 0.8370 |
70
+ | 0.205 | 21.82 | 240 | 0.2881 | 0.4115 | 0.8230 | 0.8230 | nan | nan | 0.8230 | 0.0 | nan | 0.8230 |
71
+ | 0.241 | 23.64 | 260 | 0.2940 | 0.4104 | 0.8207 | 0.8207 | nan | nan | 0.8207 | 0.0 | nan | 0.8207 |
72
 
73
 
74
  ### Framework versions