Model save
Browse files
README.md
ADDED
@@ -0,0 +1,99 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
license: other
|
3 |
+
base_model: nvidia/mit-b0
|
4 |
+
tags:
|
5 |
+
- generated_from_trainer
|
6 |
+
model-index:
|
7 |
+
- name: segformer-finetuned-biofilm2
|
8 |
+
results: []
|
9 |
+
---
|
10 |
+
|
11 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
12 |
+
should probably proofread and complete it, then remove this comment. -->
|
13 |
+
|
14 |
+
# segformer-finetuned-biofilm2
|
15 |
+
|
16 |
+
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
|
17 |
+
It achieves the following results on the evaluation set:
|
18 |
+
- Loss: 0.0797
|
19 |
+
- Mean Iou: 0.4786
|
20 |
+
- Mean Accuracy: 0.9572
|
21 |
+
- Overall Accuracy: 0.9572
|
22 |
+
- Accuracy Background: nan
|
23 |
+
- Accuracy Biofilm: 0.9572
|
24 |
+
- Iou Background: 0.0
|
25 |
+
- Iou Biofilm: 0.9572
|
26 |
+
|
27 |
+
## Model description
|
28 |
+
|
29 |
+
More information needed
|
30 |
+
|
31 |
+
## Intended uses & limitations
|
32 |
+
|
33 |
+
More information needed
|
34 |
+
|
35 |
+
## Training and evaluation data
|
36 |
+
|
37 |
+
More information needed
|
38 |
+
|
39 |
+
## Training procedure
|
40 |
+
|
41 |
+
### Training hyperparameters
|
42 |
+
|
43 |
+
The following hyperparameters were used during training:
|
44 |
+
- learning_rate: 6e-05
|
45 |
+
- train_batch_size: 8
|
46 |
+
- eval_batch_size: 8
|
47 |
+
- seed: 1337
|
48 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
|
49 |
+
- lr_scheduler_type: polynomial
|
50 |
+
- training_steps: 10000
|
51 |
+
|
52 |
+
### Training results
|
53 |
+
|
54 |
+
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Biofilm | Iou Background | Iou Biofilm |
|
55 |
+
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:----------------:|:--------------:|:-----------:|
|
56 |
+
| 0.1622 | 1.0 | 280 | 0.1158 | 0.4714 | 0.9428 | 0.9428 | nan | 0.9428 | 0.0 | 0.9428 |
|
57 |
+
| 0.0742 | 2.0 | 560 | 0.0643 | 0.4545 | 0.9090 | 0.9090 | nan | 0.9090 | 0.0 | 0.9090 |
|
58 |
+
| 0.0549 | 3.0 | 840 | 0.0582 | 0.4797 | 0.9594 | 0.9594 | nan | 0.9594 | 0.0 | 0.9594 |
|
59 |
+
| 0.0459 | 4.0 | 1120 | 0.0508 | 0.4737 | 0.9475 | 0.9475 | nan | 0.9475 | 0.0 | 0.9475 |
|
60 |
+
| 0.0506 | 5.0 | 1400 | 0.0405 | 0.4705 | 0.9411 | 0.9411 | nan | 0.9411 | 0.0 | 0.9411 |
|
61 |
+
| 0.0411 | 6.0 | 1680 | 0.0476 | 0.4865 | 0.9729 | 0.9729 | nan | 0.9729 | 0.0 | 0.9729 |
|
62 |
+
| 0.0456 | 7.0 | 1960 | 0.0476 | 0.4754 | 0.9509 | 0.9509 | nan | 0.9509 | 0.0 | 0.9509 |
|
63 |
+
| 0.0381 | 8.0 | 2240 | 0.0554 | 0.4792 | 0.9584 | 0.9584 | nan | 0.9584 | 0.0 | 0.9584 |
|
64 |
+
| 0.0348 | 9.0 | 2520 | 0.0559 | 0.4889 | 0.9779 | 0.9779 | nan | 0.9779 | 0.0 | 0.9779 |
|
65 |
+
| 0.0388 | 10.0 | 2800 | 0.0513 | 0.4757 | 0.9514 | 0.9514 | nan | 0.9514 | 0.0 | 0.9514 |
|
66 |
+
| 0.0385 | 11.0 | 3080 | 0.0660 | 0.4883 | 0.9767 | 0.9767 | nan | 0.9767 | 0.0 | 0.9767 |
|
67 |
+
| 0.0309 | 12.0 | 3360 | 0.0589 | 0.4808 | 0.9616 | 0.9616 | nan | 0.9616 | 0.0 | 0.9616 |
|
68 |
+
| 0.0322 | 13.0 | 3640 | 0.0539 | 0.4796 | 0.9592 | 0.9592 | nan | 0.9592 | 0.0 | 0.9592 |
|
69 |
+
| 0.0361 | 14.0 | 3920 | 0.0621 | 0.4812 | 0.9625 | 0.9625 | nan | 0.9625 | 0.0 | 0.9625 |
|
70 |
+
| 0.0277 | 15.0 | 4200 | 0.0576 | 0.4836 | 0.9672 | 0.9672 | nan | 0.9672 | 0.0 | 0.9672 |
|
71 |
+
| 0.0324 | 16.0 | 4480 | 0.0503 | 0.4702 | 0.9404 | 0.9404 | nan | 0.9404 | 0.0 | 0.9404 |
|
72 |
+
| 0.0355 | 17.0 | 4760 | 0.0583 | 0.4801 | 0.9601 | 0.9601 | nan | 0.9601 | 0.0 | 0.9601 |
|
73 |
+
| 0.032 | 18.0 | 5040 | 0.0528 | 0.4679 | 0.9358 | 0.9358 | nan | 0.9358 | 0.0 | 0.9358 |
|
74 |
+
| 0.0275 | 19.0 | 5320 | 0.0682 | 0.4828 | 0.9656 | 0.9656 | nan | 0.9656 | 0.0 | 0.9656 |
|
75 |
+
| 0.0329 | 20.0 | 5600 | 0.0712 | 0.4796 | 0.9591 | 0.9591 | nan | 0.9591 | 0.0 | 0.9591 |
|
76 |
+
| 0.0284 | 21.0 | 5880 | 0.0769 | 0.4868 | 0.9737 | 0.9737 | nan | 0.9737 | 0.0 | 0.9737 |
|
77 |
+
| 0.028 | 22.0 | 6160 | 0.0615 | 0.4826 | 0.9651 | 0.9651 | nan | 0.9651 | 0.0 | 0.9651 |
|
78 |
+
| 0.0275 | 23.0 | 6440 | 0.0640 | 0.4797 | 0.9595 | 0.9595 | nan | 0.9595 | 0.0 | 0.9595 |
|
79 |
+
| 0.0263 | 24.0 | 6720 | 0.0805 | 0.4819 | 0.9639 | 0.9639 | nan | 0.9639 | 0.0 | 0.9639 |
|
80 |
+
| 0.0252 | 25.0 | 7000 | 0.0700 | 0.4830 | 0.9661 | 0.9661 | nan | 0.9661 | 0.0 | 0.9661 |
|
81 |
+
| 0.0309 | 26.0 | 7280 | 0.0747 | 0.4854 | 0.9709 | 0.9709 | nan | 0.9709 | 0.0 | 0.9709 |
|
82 |
+
| 0.0238 | 27.0 | 7560 | 0.0704 | 0.4814 | 0.9628 | 0.9628 | nan | 0.9628 | 0.0 | 0.9628 |
|
83 |
+
| 0.0277 | 28.0 | 7840 | 0.0757 | 0.4858 | 0.9716 | 0.9716 | nan | 0.9716 | 0.0 | 0.9716 |
|
84 |
+
| 0.0281 | 29.0 | 8120 | 0.0847 | 0.4830 | 0.9661 | 0.9661 | nan | 0.9661 | 0.0 | 0.9661 |
|
85 |
+
| 0.0259 | 30.0 | 8400 | 0.0741 | 0.4820 | 0.9640 | 0.9640 | nan | 0.9640 | 0.0 | 0.9640 |
|
86 |
+
| 0.0231 | 31.0 | 8680 | 0.0726 | 0.4794 | 0.9587 | 0.9587 | nan | 0.9587 | 0.0 | 0.9587 |
|
87 |
+
| 0.0234 | 32.0 | 8960 | 0.0739 | 0.4779 | 0.9557 | 0.9557 | nan | 0.9557 | 0.0 | 0.9557 |
|
88 |
+
| 0.0226 | 33.0 | 9240 | 0.0743 | 0.4806 | 0.9613 | 0.9613 | nan | 0.9613 | 0.0 | 0.9613 |
|
89 |
+
| 0.0242 | 34.0 | 9520 | 0.0776 | 0.4792 | 0.9584 | 0.9584 | nan | 0.9584 | 0.0 | 0.9584 |
|
90 |
+
| 0.0211 | 35.0 | 9800 | 0.0775 | 0.4765 | 0.9529 | 0.9529 | nan | 0.9529 | 0.0 | 0.9529 |
|
91 |
+
| 0.0223 | 35.71 | 10000 | 0.0797 | 0.4786 | 0.9572 | 0.9572 | nan | 0.9572 | 0.0 | 0.9572 |
|
92 |
+
|
93 |
+
|
94 |
+
### Framework versions
|
95 |
+
|
96 |
+
- Transformers 4.38.0.dev0
|
97 |
+
- Pytorch 2.1.0
|
98 |
+
- Datasets 2.14.4
|
99 |
+
- Tokenizers 0.15.1
|