Hugging Face's logo

sam1120
/
dropoff-utcustom-train-SF-RGB-b5_5

Image Segmentation
Transformers
PyTorch
TensorBoard
segformer
vision
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
sam1120 commited on
Commit
9c8fcbd
•
1 Parent(s): 91e7e2d

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +89 -0
README.md ADDED
@@ -0,0 +1,89 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: other
3
+ tags:
4
+ - generated_from_trainer
5
+ model-index:
6
+ - name: dropoff-utcustom-train-SF-RGB-b5_5
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
+ # dropoff-utcustom-train-SF-RGB-b5_5
14
+
15
+ This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the None dataset.
16
+ It achieves the following results on the evaluation set:
17
+ - Loss: 0.1911
18
+ - Mean Iou: 0.4677
19
+ - Mean Accuracy: 0.7472
20
+ - Overall Accuracy: 0.9719
21
+ - Accuracy Unlabeled: nan
22
+ - Accuracy Dropoff: 0.5020
23
+ - Accuracy Undropoff: 0.9923
24
+ - Iou Unlabeled: 0.0
25
+ - Iou Dropoff: 0.4318
26
+ - Iou Undropoff: 0.9713
27
+
28
+ ## Model description
29
+
30
+ More information needed
31
+
32
+ ## Intended uses & limitations
33
+
34
+ More information needed
35
+
36
+ ## Training and evaluation data
37
+
38
+ More information needed
39
+
40
+ ## Training procedure
41
+
42
+ ### Training hyperparameters
43
+
44
+ The following hyperparameters were used during training:
45
+ - learning_rate: 9e-06
46
+ - train_batch_size: 16
47
+ - eval_batch_size: 16
48
+ - seed: 42
49
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
+ - lr_scheduler_type: linear
51
+ - lr_scheduler_warmup_ratio: 0.05
52
+ - num_epochs: 120
53
+
54
+ ### Training results
55
+
56
+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Dropoff | Accuracy Undropoff | Iou Unlabeled | Iou Dropoff | Iou Undropoff |
57
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:----------------:|:------------------:|:-------------:|:-----------:|:-------------:|
58
+ | 1.0685 | 5.0 | 10 | 1.0222 | 0.2189 | 0.3725 | 0.5989 | nan | 0.1256 | 0.6194 | 0.0 | 0.0497 | 0.6070 |
59
+ | 0.9481 | 10.0 | 20 | 0.8419 | 0.3703 | 0.6398 | 0.8451 | nan | 0.4159 | 0.8637 | 0.0 | 0.2633 | 0.8476 |
60
+ | 0.8268 | 15.0 | 30 | 0.7165 | 0.3949 | 0.6938 | 0.8694 | nan | 0.5023 | 0.8853 | 0.0 | 0.3136 | 0.8711 |
61
+ | 0.7573 | 20.0 | 40 | 0.6206 | 0.4084 | 0.7186 | 0.8994 | nan | 0.5214 | 0.9158 | 0.0 | 0.3243 | 0.9010 |
62
+ | 0.636 | 25.0 | 50 | 0.5194 | 0.4239 | 0.7253 | 0.9300 | nan | 0.5020 | 0.9485 | 0.0 | 0.3401 | 0.9316 |
63
+ | 0.5238 | 30.0 | 60 | 0.4507 | 0.4365 | 0.7368 | 0.9461 | nan | 0.5085 | 0.9651 | 0.0 | 0.3618 | 0.9476 |
64
+ | 0.4296 | 35.0 | 70 | 0.4064 | 0.4410 | 0.7422 | 0.9530 | nan | 0.5123 | 0.9721 | 0.0 | 0.3683 | 0.9546 |
65
+ | 0.4105 | 40.0 | 80 | 0.3547 | 0.4502 | 0.7467 | 0.9619 | nan | 0.5120 | 0.9814 | 0.0 | 0.3880 | 0.9627 |
66
+ | 0.3436 | 45.0 | 90 | 0.3304 | 0.4571 | 0.7596 | 0.9644 | nan | 0.5361 | 0.9830 | 0.0 | 0.4066 | 0.9647 |
67
+ | 0.2729 | 50.0 | 100 | 0.2953 | 0.4614 | 0.7552 | 0.9680 | nan | 0.5232 | 0.9873 | 0.0 | 0.4163 | 0.9678 |
68
+ | 0.2546 | 55.0 | 110 | 0.2770 | 0.4629 | 0.7579 | 0.9691 | nan | 0.5276 | 0.9882 | 0.0 | 0.4201 | 0.9686 |
69
+ | 0.2281 | 60.0 | 120 | 0.2591 | 0.4647 | 0.7566 | 0.9702 | nan | 0.5235 | 0.9896 | 0.0 | 0.4245 | 0.9696 |
70
+ | 0.2041 | 65.0 | 130 | 0.2453 | 0.4657 | 0.7556 | 0.9708 | nan | 0.5209 | 0.9903 | 0.0 | 0.4269 | 0.9701 |
71
+ | 0.1772 | 70.0 | 140 | 0.2292 | 0.4676 | 0.7542 | 0.9717 | nan | 0.5171 | 0.9914 | 0.0 | 0.4317 | 0.9711 |
72
+ | 0.169 | 75.0 | 150 | 0.2161 | 0.4681 | 0.7520 | 0.9719 | nan | 0.5122 | 0.9919 | 0.0 | 0.4331 | 0.9713 |
73
+ | 0.1543 | 80.0 | 160 | 0.2111 | 0.4682 | 0.7530 | 0.9715 | nan | 0.5147 | 0.9913 | 0.0 | 0.4336 | 0.9709 |
74
+ | 0.1374 | 85.0 | 170 | 0.1973 | 0.4659 | 0.7450 | 0.9715 | nan | 0.4980 | 0.9921 | 0.0 | 0.4268 | 0.9709 |
75
+ | 0.1523 | 90.0 | 180 | 0.1974 | 0.4681 | 0.7501 | 0.9717 | nan | 0.5085 | 0.9918 | 0.0 | 0.4332 | 0.9711 |
76
+ | 0.1323 | 95.0 | 190 | 0.1928 | 0.4658 | 0.7434 | 0.9717 | nan | 0.4944 | 0.9924 | 0.0 | 0.4263 | 0.9711 |
77
+ | 0.1254 | 100.0 | 200 | 0.1923 | 0.4671 | 0.7467 | 0.9717 | nan | 0.5013 | 0.9921 | 0.0 | 0.4301 | 0.9711 |
78
+ | 0.125 | 105.0 | 210 | 0.1867 | 0.4637 | 0.7380 | 0.9717 | nan | 0.4831 | 0.9929 | 0.0 | 0.4201 | 0.9711 |
79
+ | 0.1239 | 110.0 | 220 | 0.1912 | 0.4694 | 0.7520 | 0.9719 | nan | 0.5121 | 0.9919 | 0.0 | 0.4369 | 0.9713 |
80
+ | 0.1252 | 115.0 | 230 | 0.1913 | 0.4689 | 0.7503 | 0.9720 | nan | 0.5085 | 0.9921 | 0.0 | 0.4354 | 0.9714 |
81
+ | 0.1357 | 120.0 | 240 | 0.1911 | 0.4677 | 0.7472 | 0.9719 | nan | 0.5020 | 0.9923 | 0.0 | 0.4318 | 0.9713 |
82
+
83
+
84
+ ### Framework versions
85
+
86
+ - Transformers 4.30.2
87
+ - Pytorch 2.0.1+cu117
88
+ - Datasets 2.13.1
89
+ - Tokenizers 0.13.3