Hugging Face's logo

peldrak
/
segformer-b4-cityscapes-finetuned-coastTrain

Image Segmentation
Transformers
TensorBoard
Safetensors
segformer
vision
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
peldrak commited on
Commit
e48b8ff
•
1 Parent(s): 4da6238

End of training

Browse files
Files changed (5) hide show
  1. README.md +233 -198
  2. config.json +88 -0
  3. model.safetensors +3 -0
  4. runs/Mar02_11-43-19_8377836801c7/events.out.tfevents.1709379802.8377836801c7.27.0 +3 -0
  5. training_args.bin +3 -0
README.md CHANGED
@@ -1,201 +1,236 @@
1
  ---
2
- library_name: transformers
3
- tags: []
 
 
 
 
 
 
 
4
  ---
5
 
6
- # Model Card for Model ID
7
-
8
- <!-- Provide a quick summary of what the model is/does. -->
9
-
10
-
11
-
12
- ## Model Details
13
-
14
- ### Model Description
15
-
16
- <!-- Provide a longer summary of what this model is. -->
17
-
18
- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
19
-
20
- - **Developed by:** [More Information Needed]
21
- - **Funded by [optional]:** [More Information Needed]
22
- - **Shared by [optional]:** [More Information Needed]
23
- - **Model type:** [More Information Needed]
24
- - **Language(s) (NLP):** [More Information Needed]
25
- - **License:** [More Information Needed]
26
- - **Finetuned from model [optional]:** [More Information Needed]
27
-
28
- ### Model Sources [optional]
29
-
30
- <!-- Provide the basic links for the model. -->
31
-
32
- - **Repository:** [More Information Needed]
33
- - **Paper [optional]:** [More Information Needed]
34
- - **Demo [optional]:** [More Information Needed]
35
-
36
- ## Uses
37
-
38
- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
-
40
- ### Direct Use
41
-
42
- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
-
44
- [More Information Needed]
45
-
46
- ### Downstream Use [optional]
47
-
48
- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
-
50
- [More Information Needed]
51
-
52
- ### Out-of-Scope Use
53
-
54
- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
-
56
- [More Information Needed]
57
-
58
- ## Bias, Risks, and Limitations
59
-
60
- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
-
62
- [More Information Needed]
63
-
64
- ### Recommendations
65
-
66
- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
-
68
- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
-
70
- ## How to Get Started with the Model
71
-
72
- Use the code below to get started with the model.
73
-
74
- [More Information Needed]
75
-
76
- ## Training Details
77
-
78
- ### Training Data
79
-
80
- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
-
82
- [More Information Needed]
83
-
84
- ### Training Procedure
85
-
86
- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
-
88
- #### Preprocessing [optional]
89
-
90
- [More Information Needed]
91
-
92
-
93
- #### Training Hyperparameters
94
-
95
- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
-
97
- #### Speeds, Sizes, Times [optional]
98
-
99
- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
-
101
- [More Information Needed]
102
-
103
- ## Evaluation
104
-
105
- <!-- This section describes the evaluation protocols and provides the results. -->
106
-
107
- ### Testing Data, Factors & Metrics
108
-
109
- #### Testing Data
110
-
111
- <!-- This should link to a Dataset Card if possible. -->
112
-
113
- [More Information Needed]
114
-
115
- #### Factors
116
-
117
- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
-
119
- [More Information Needed]
120
-
121
- #### Metrics
122
-
123
- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
-
125
- [More Information Needed]
126
-
127
- ### Results
128
-
129
- [More Information Needed]
130
-
131
- #### Summary
132
-
133
-
134
-
135
- ## Model Examination [optional]
136
-
137
- <!-- Relevant interpretability work for the model goes here -->
138
-
139
- [More Information Needed]
140
-
141
- ## Environmental Impact
142
-
143
- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
-
145
- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
-
147
- - **Hardware Type:** [More Information Needed]
148
- - **Hours used:** [More Information Needed]
149
- - **Cloud Provider:** [More Information Needed]
150
- - **Compute Region:** [More Information Needed]
151
- - **Carbon Emitted:** [More Information Needed]
152
-
153
- ## Technical Specifications [optional]
154
-
155
- ### Model Architecture and Objective
156
-
157
- [More Information Needed]
158
-
159
- ### Compute Infrastructure
160
-
161
- [More Information Needed]
162
-
163
- #### Hardware
164
-
165
- [More Information Needed]
166
-
167
- #### Software
168
-
169
- [More Information Needed]
170
-
171
- ## Citation [optional]
172
-
173
- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
-
175
- **BibTeX:**
176
-
177
- [More Information Needed]
178
-
179
- **APA:**
180
-
181
- [More Information Needed]
182
-
183
- ## Glossary [optional]
184
-
185
- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
-
187
- [More Information Needed]
188
-
189
- ## More Information [optional]
190
-
191
- [More Information Needed]
192
-
193
- ## Model Card Authors [optional]
194
-
195
- [More Information Needed]
196
-
197
- ## Model Card Contact
198
-
199
- [More Information Needed]
200
-
201
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ license: other
3
+ base_model: nvidia/segformer-b4-finetuned-cityscapes-1024-1024
4
+ tags:
5
+ - vision
6
+ - image-segmentation
7
+ - generated_from_trainer
8
+ model-index:
9
+ - name: segformer-b4-cityscapes-finetuned-coastTrain
10
+ results: []
11
  ---
12
 
13
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
+ should probably proofread and complete it, then remove this comment. -->
15
+
16
+ # segformer-b4-cityscapes-finetuned-coastTrain
17
+
18
+ This model is a fine-tuned version of [nvidia/segformer-b4-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b4-finetuned-cityscapes-1024-1024) on the peldrak/coastTrain dataset.
19
+ It achieves the following results on the evaluation set:
20
+ - Loss: 1.7934
21
+ - Mean Iou: 0.3377
22
+ - Mean Accuracy: 0.4616
23
+ - Overall Accuracy: 0.6149
24
+ - Accuracy Water: 0.7365
25
+ - Accuracy Whitewater: 0.2890
26
+ - Accuracy Sediment: 0.5092
27
+ - Accuracy Other Natural Terrain: 0.0
28
+ - Accuracy Vegetation: 0.6559
29
+ - Accuracy Development: 0.4292
30
+ - Accuracy Unknown: 0.6114
31
+ - Iou Water: 0.5485
32
+ - Iou Whitewater: 0.2007
33
+ - Iou Sediment: 0.3051
34
+ - Iou Other Natural Terrain: 0.0
35
+ - Iou Vegetation: 0.4379
36
+ - Iou Development: 0.3387
37
+ - Iou Unknown: 0.5329
38
+
39
+ ## Model description
40
+
41
+ More information needed
42
+
43
+ ## Intended uses & limitations
44
+
45
+ More information needed
46
+
47
+ ## Training and evaluation data
48
+
49
+ More information needed
50
+
51
+ ## Training procedure
52
+
53
+ ### Training hyperparameters
54
+
55
+ The following hyperparameters were used during training:
56
+ - learning_rate: 6e-05
57
+ - train_batch_size: 4
58
+ - eval_batch_size: 4
59
+ - seed: 42
60
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
61
+ - lr_scheduler_type: linear
62
+ - num_epochs: 25
63
+
64
+ ### Training results
65
+
66
+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown |
67
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|
68
+ | 1.7569 | 0.16 | 20 | 1.7402 | 0.2133 | 0.3152 | 0.4785 | 0.4809 | 0.0009 | 0.3513 | 0.0000 | 0.6367 | 0.1253 | 0.6111 | 0.4036 | 0.0007 | 0.1612 | 0.0000 | 0.3354 | 0.1114 | 0.4809 |
69
+ | 1.63 | 0.31 | 40 | 1.5328 | 0.2365 | 0.3333 | 0.5247 | 0.6080 | 0.0001 | 0.1259 | 0.0 | 0.7204 | 0.2803 | 0.5984 | 0.4654 | 0.0001 | 0.0919 | 0.0 | 0.3556 | 0.2504 | 0.4918 |
70
+ | 1.906 | 0.47 | 60 | 1.3859 | 0.2522 | 0.3529 | 0.5322 | 0.6338 | 0.0 | 0.2417 | 0.0 | 0.6411 | 0.3559 | 0.5981 | 0.4844 | 0.0 | 0.1482 | 0.0 | 0.3473 | 0.3104 | 0.4750 |
71
+ | 1.5334 | 0.62 | 80 | 1.3012 | 0.2615 | 0.3647 | 0.5369 | 0.6174 | 0.0 | 0.3648 | 0.0 | 0.6158 | 0.3414 | 0.6137 | 0.4859 | 0.0 | 0.1798 | 0.0 | 0.3587 | 0.3133 | 0.4928 |
72
+ | 1.6145 | 0.78 | 100 | 1.3025 | 0.2511 | 0.3676 | 0.5121 | 0.7099 | 0.0 | 0.5566 | 0.0 | 0.3362 | 0.3532 | 0.6171 | 0.4548 | 0.0 | 0.2233 | 0.0 | 0.2621 | 0.3183 | 0.4991 |
73
+ | 1.3849 | 0.93 | 120 | 1.2896 | 0.2567 | 0.3629 | 0.5158 | 0.7054 | 0.0 | 0.5193 | 0.0 | 0.4043 | 0.3536 | 0.5577 | 0.4380 | 0.0 | 0.2158 | 0.0 | 0.2987 | 0.3140 | 0.5301 |
74
+ | 1.0822 | 1.09 | 140 | 1.3057 | 0.2466 | 0.3438 | 0.5223 | 0.8529 | 0.0 | 0.1934 | 0.0 | 0.3833 | 0.3679 | 0.6093 | 0.4129 | 0.0 | 0.1447 | 0.0 | 0.2971 | 0.3240 | 0.5473 |
75
+ | 1.194 | 1.24 | 160 | 1.3503 | 0.2351 | 0.3304 | 0.5232 | 0.7353 | 0.0 | 0.0090 | 0.0 | 0.6092 | 0.3841 | 0.5749 | 0.4310 | 0.0 | 0.0089 | 0.0 | 0.3314 | 0.3268 | 0.5478 |
76
+ | 1.0077 | 1.4 | 180 | 1.2997 | 0.2517 | 0.3451 | 0.5519 | 0.6497 | 0.0 | 0.0254 | 0.0 | 0.7971 | 0.3521 | 0.5912 | 0.4773 | 0.0 | 0.0240 | 0.0 | 0.3774 | 0.3211 | 0.5623 |
77
+ | 1.2622 | 1.55 | 200 | 1.2323 | 0.2605 | 0.3577 | 0.5539 | 0.6771 | 0.0 | 0.1258 | 0.0 | 0.7073 | 0.3810 | 0.6129 | 0.4788 | 0.0 | 0.0954 | 0.0 | 0.3793 | 0.3338 | 0.5361 |
78
+ | 1.3585 | 1.71 | 220 | 1.2909 | 0.2403 | 0.3427 | 0.5397 | 0.7068 | 0.0 | 0.0258 | 0.0 | 0.6645 | 0.3737 | 0.6282 | 0.4432 | 0.0 | 0.0249 | 0.0 | 0.3665 | 0.3287 | 0.5187 |
79
+ | 1.658 | 1.86 | 240 | 1.2727 | 0.2445 | 0.3403 | 0.5364 | 0.6112 | 0.0 | 0.0252 | 0.0 | 0.7711 | 0.3758 | 0.5987 | 0.4456 | 0.0 | 0.0243 | 0.0 | 0.3621 | 0.3333 | 0.5458 |
80
+ | 0.8811 | 2.02 | 260 | 1.2903 | 0.2471 | 0.3554 | 0.4984 | 0.7937 | 0.0 | 0.4628 | 0.0 | 0.2324 | 0.3884 | 0.6102 | 0.4343 | 0.0 | 0.1838 | 0.0 | 0.2103 | 0.3240 | 0.5777 |
81
+ | 1.1669 | 2.17 | 280 | 1.2575 | 0.2761 | 0.3778 | 0.5702 | 0.7343 | 0.0 | 0.2741 | 0.0 | 0.6302 | 0.3824 | 0.6235 | 0.4986 | 0.0 | 0.1781 | 0.0 | 0.3861 | 0.3299 | 0.5402 |
82
+ | 0.8381 | 2.33 | 300 | 1.2328 | 0.2816 | 0.3830 | 0.5752 | 0.8315 | 0.0 | 0.3044 | 0.0 | 0.5289 | 0.4017 | 0.6144 | 0.4768 | 0.0 | 0.2181 | 0.0 | 0.3741 | 0.3265 | 0.5756 |
83
+ | 0.8328 | 2.48 | 320 | 1.2427 | 0.2793 | 0.3874 | 0.5596 | 0.7068 | 0.0 | 0.4709 | 0.0 | 0.5404 | 0.3851 | 0.6083 | 0.4893 | 0.0 | 0.2192 | 0.0 | 0.3758 | 0.3318 | 0.5394 |
84
+ | 1.3779 | 2.64 | 340 | 1.2944 | 0.2589 | 0.3577 | 0.5472 | 0.9167 | 0.0 | 0.2089 | 0.0 | 0.3997 | 0.3867 | 0.5918 | 0.4491 | 0.0 | 0.1514 | 0.0 | 0.3271 | 0.3268 | 0.5581 |
85
+ | 1.1746 | 2.79 | 360 | 1.2871 | 0.2757 | 0.3773 | 0.5525 | 0.7185 | 0.0 | 0.4048 | 0.0 | 0.5429 | 0.3834 | 0.5916 | 0.4750 | 0.0 | 0.1968 | 0.0 | 0.3691 | 0.3301 | 0.5592 |
86
+ | 1.6891 | 2.95 | 380 | 1.3098 | 0.2739 | 0.3691 | 0.5653 | 0.7325 | 0.0 | 0.2323 | 0.0 | 0.6552 | 0.3721 | 0.5916 | 0.4692 | 0.0 | 0.1678 | 0.0 | 0.3868 | 0.3288 | 0.5650 |
87
+ | 1.158 | 3.1 | 400 | 1.3047 | 0.2533 | 0.3689 | 0.5088 | 0.7811 | 0.0 | 0.5898 | 0.0 | 0.2372 | 0.3808 | 0.5937 | 0.4509 | 0.0 | 0.2197 | 0.0 | 0.2144 | 0.3330 | 0.5552 |
88
+ | 0.8941 | 3.26 | 420 | 1.3076 | 0.2574 | 0.3769 | 0.5200 | 0.7011 | 0.0 | 0.5846 | 0.0 | 0.3491 | 0.3835 | 0.6199 | 0.4753 | 0.0 | 0.2108 | 0.0 | 0.2944 | 0.3344 | 0.4867 |
89
+ | 1.4153 | 3.41 | 440 | 1.2622 | 0.2845 | 0.3894 | 0.5681 | 0.7614 | 0.0 | 0.4455 | 0.0 | 0.5232 | 0.3881 | 0.6074 | 0.4802 | 0.0 | 0.2325 | 0.0 | 0.3743 | 0.3359 | 0.5685 |
90
+ | 1.462 | 3.57 | 460 | 1.3032 | 0.2565 | 0.3513 | 0.5377 | 0.8988 | 0.0 | 0.1999 | 0.0 | 0.3895 | 0.3682 | 0.6028 | 0.4252 | 0.0 | 0.1503 | 0.0 | 0.3169 | 0.3342 | 0.5687 |
91
+ | 1.8197 | 3.72 | 480 | 1.2476 | 0.2757 | 0.3889 | 0.5533 | 0.6608 | 0.0 | 0.5232 | 0.0 | 0.5425 | 0.3858 | 0.6103 | 0.4770 | 0.0 | 0.2382 | 0.0 | 0.3699 | 0.3337 | 0.5113 |
92
+ | 1.7337 | 3.88 | 500 | 1.2683 | 0.2698 | 0.3669 | 0.5613 | 0.7260 | 0.0000 | 0.2072 | 0.0 | 0.6517 | 0.3879 | 0.5954 | 0.4473 | 0.0000 | 0.1530 | 0.0 | 0.3995 | 0.3337 | 0.5550 |
93
+ | 1.5127 | 4.03 | 520 | 1.2744 | 0.2899 | 0.3942 | 0.5773 | 0.7202 | 0.0 | 0.4551 | 0.0 | 0.6046 | 0.3887 | 0.5909 | 0.4890 | 0.0 | 0.2363 | 0.0 | 0.4057 | 0.3348 | 0.5638 |
94
+ | 0.9636 | 4.19 | 540 | 1.2881 | 0.2841 | 0.3809 | 0.5821 | 0.8651 | 0.0000 | 0.2381 | 0.0 | 0.5402 | 0.3864 | 0.6362 | 0.4734 | 0.0000 | 0.1821 | 0.0 | 0.3924 | 0.3373 | 0.6038 |
95
+ | 1.4962 | 4.34 | 560 | 1.2440 | 0.2916 | 0.3924 | 0.5951 | 0.7456 | 0.0 | 0.2607 | 0.0 | 0.6835 | 0.3800 | 0.6770 | 0.5150 | 0.0 | 0.1752 | 0.0 | 0.4253 | 0.3402 | 0.5856 |
96
+ | 0.6763 | 4.5 | 580 | 1.2502 | 0.2887 | 0.3909 | 0.5766 | 0.6715 | 0.0003 | 0.4034 | 0.0 | 0.6711 | 0.3861 | 0.6042 | 0.5080 | 0.0003 | 0.2061 | 0.0 | 0.4129 | 0.3394 | 0.5544 |
97
+ | 0.6586 | 4.65 | 600 | 1.3005 | 0.2780 | 0.3744 | 0.5718 | 0.8302 | 0.0000 | 0.2621 | 0.0 | 0.5575 | 0.3786 | 0.5925 | 0.4698 | 0.0000 | 0.1758 | 0.0 | 0.3962 | 0.3382 | 0.5660 |
98
+ | 1.029 | 4.81 | 620 | 1.2937 | 0.2789 | 0.3884 | 0.5524 | 0.7219 | 0.0 | 0.5282 | 0.0 | 0.4734 | 0.3911 | 0.6043 | 0.4880 | 0.0 | 0.2112 | 0.0 | 0.3702 | 0.3368 | 0.5462 |
99
+ | 1.259 | 4.96 | 640 | 1.2884 | 0.2811 | 0.3889 | 0.5607 | 0.7142 | 0.0 | 0.4933 | 0.0 | 0.5310 | 0.3811 | 0.6029 | 0.4795 | 0.0 | 0.2182 | 0.0 | 0.3926 | 0.3381 | 0.5392 |
100
+ | 0.8988 | 5.12 | 660 | 1.2773 | 0.2850 | 0.3966 | 0.5710 | 0.7221 | 0.0033 | 0.5105 | 0.0 | 0.5474 | 0.3875 | 0.6056 | 0.4949 | 0.0033 | 0.2345 | 0.0 | 0.4045 | 0.3407 | 0.5172 |
101
+ | 0.7265 | 5.27 | 680 | 1.3382 | 0.2856 | 0.3984 | 0.5581 | 0.7362 | 0.0039 | 0.6073 | 0.0 | 0.4377 | 0.3912 | 0.6124 | 0.5083 | 0.0039 | 0.2242 | 0.0 | 0.3623 | 0.3338 | 0.5666 |
102
+ | 1.02 | 5.43 | 700 | 1.2442 | 0.2939 | 0.3997 | 0.5842 | 0.7699 | 0.0066 | 0.4562 | 0.0 | 0.5657 | 0.3976 | 0.6019 | 0.5070 | 0.0065 | 0.2239 | 0.0 | 0.4204 | 0.3260 | 0.5738 |
103
+ | 1.1758 | 5.58 | 720 | 1.2318 | 0.2883 | 0.3875 | 0.5870 | 0.7505 | 0.0004 | 0.3176 | 0.0 | 0.6681 | 0.3745 | 0.6011 | 0.4957 | 0.0004 | 0.1892 | 0.0 | 0.4391 | 0.3236 | 0.5698 |
104
+ | 1.3481 | 5.74 | 740 | 1.3314 | 0.2787 | 0.3787 | 0.5601 | 0.8524 | 0.0022 | 0.3845 | 0.0 | 0.4301 | 0.3750 | 0.6070 | 0.4899 | 0.0021 | 0.1818 | 0.0 | 0.3585 | 0.3442 | 0.5743 |
105
+ | 1.3499 | 5.89 | 760 | 1.2573 | 0.2892 | 0.3972 | 0.5804 | 0.6713 | 0.0226 | 0.4002 | 0.0 | 0.6724 | 0.3884 | 0.6255 | 0.4814 | 0.0220 | 0.2374 | 0.0 | 0.4222 | 0.3322 | 0.5289 |
106
+ | 1.344 | 6.05 | 780 | 1.2872 | 0.2926 | 0.3961 | 0.5879 | 0.8401 | 0.0037 | 0.4138 | 0.0 | 0.5255 | 0.3832 | 0.6068 | 0.4901 | 0.0037 | 0.2556 | 0.0 | 0.3952 | 0.3431 | 0.5604 |
107
+ | 1.0147 | 6.2 | 800 | 1.2955 | 0.2866 | 0.3954 | 0.5619 | 0.7667 | 0.0024 | 0.5622 | 0.0 | 0.4424 | 0.3817 | 0.6121 | 0.4868 | 0.0024 | 0.2277 | 0.0 | 0.3686 | 0.3489 | 0.5718 |
108
+ | 0.7702 | 6.36 | 820 | 1.3188 | 0.2974 | 0.4042 | 0.5875 | 0.7072 | 0.0129 | 0.4685 | 0.0 | 0.6311 | 0.3879 | 0.6215 | 0.4961 | 0.0126 | 0.2483 | 0.0 | 0.4215 | 0.3336 | 0.5699 |
109
+ | 0.9859 | 6.51 | 840 | 1.3531 | 0.3085 | 0.4137 | 0.6036 | 0.7595 | 0.0428 | 0.4501 | 0.0 | 0.6467 | 0.3906 | 0.6064 | 0.5082 | 0.0404 | 0.2721 | 0.0 | 0.4375 | 0.3284 | 0.5726 |
110
+ | 0.908 | 6.67 | 860 | 1.3442 | 0.2973 | 0.4000 | 0.5967 | 0.7009 | 0.0147 | 0.3554 | 0.0 | 0.7255 | 0.3920 | 0.6116 | 0.4971 | 0.0144 | 0.2415 | 0.0 | 0.4368 | 0.3279 | 0.5630 |
111
+ | 0.5036 | 6.82 | 880 | 1.3231 | 0.3020 | 0.4121 | 0.5890 | 0.6650 | 0.0197 | 0.5568 | 0.0 | 0.6503 | 0.3886 | 0.6046 | 0.5000 | 0.0192 | 0.2668 | 0.0 | 0.4294 | 0.3381 | 0.5608 |
112
+ | 0.5637 | 6.98 | 900 | 1.3094 | 0.2988 | 0.4067 | 0.5859 | 0.7662 | 0.0405 | 0.4717 | 0.0 | 0.5613 | 0.3916 | 0.6159 | 0.4835 | 0.0382 | 0.2673 | 0.0 | 0.4071 | 0.3302 | 0.5652 |
113
+ | 0.7936 | 7.13 | 920 | 1.3764 | 0.2902 | 0.4074 | 0.5505 | 0.6624 | 0.0396 | 0.6912 | 0.0 | 0.4475 | 0.4012 | 0.6097 | 0.5094 | 0.0375 | 0.2369 | 0.0 | 0.3482 | 0.3230 | 0.5764 |
114
+ | 0.9688 | 7.29 | 940 | 1.2529 | 0.3070 | 0.4169 | 0.6105 | 0.7955 | 0.0303 | 0.4866 | 0.0 | 0.6137 | 0.3659 | 0.6266 | 0.5367 | 0.0290 | 0.2877 | 0.0 | 0.4354 | 0.3363 | 0.5242 |
115
+ | 1.234 | 7.44 | 960 | 1.3743 | 0.3051 | 0.4135 | 0.6046 | 0.7598 | 0.0191 | 0.4445 | 0.0 | 0.6414 | 0.4197 | 0.6101 | 0.5121 | 0.0185 | 0.2820 | 0.0 | 0.4334 | 0.3551 | 0.5344 |
116
+ | 0.6855 | 7.6 | 980 | 1.3298 | 0.3099 | 0.4206 | 0.6054 | 0.7528 | 0.0544 | 0.4060 | 0.0 | 0.6522 | 0.4757 | 0.6029 | 0.5101 | 0.0505 | 0.2776 | 0.0 | 0.4390 | 0.3355 | 0.5568 |
117
+ | 2.4536 | 7.75 | 1000 | 1.3495 | 0.3131 | 0.4196 | 0.6110 | 0.7581 | 0.0534 | 0.3839 | 0.0 | 0.6854 | 0.4595 | 0.5971 | 0.5276 | 0.0495 | 0.2585 | 0.0 | 0.4440 | 0.3455 | 0.5668 |
118
+ | 1.2149 | 7.91 | 1020 | 1.3316 | 0.3012 | 0.4187 | 0.5878 | 0.7543 | 0.0511 | 0.5899 | 0.0 | 0.5283 | 0.3983 | 0.6089 | 0.5245 | 0.0472 | 0.2707 | 0.0 | 0.4070 | 0.3401 | 0.5187 |
119
+ | 1.2683 | 8.06 | 1040 | 1.2985 | 0.3034 | 0.4066 | 0.6049 | 0.7196 | 0.0318 | 0.3674 | 0.0 | 0.7268 | 0.3779 | 0.6228 | 0.5230 | 0.0305 | 0.2322 | 0.0 | 0.4492 | 0.3307 | 0.5578 |
120
+ | 1.4148 | 8.22 | 1060 | 1.3343 | 0.3119 | 0.4222 | 0.6091 | 0.8148 | 0.0617 | 0.4552 | 0.0 | 0.5836 | 0.4088 | 0.6311 | 0.5371 | 0.0580 | 0.2650 | 0.0 | 0.4310 | 0.3363 | 0.5559 |
121
+ | 1.7363 | 8.37 | 1080 | 1.3213 | 0.3163 | 0.4235 | 0.6202 | 0.8201 | 0.0418 | 0.4181 | 0.0 | 0.6371 | 0.4375 | 0.6098 | 0.5496 | 0.0389 | 0.2751 | 0.0 | 0.4445 | 0.3247 | 0.5815 |
122
+ | 0.8656 | 8.53 | 1100 | 1.4479 | 0.3191 | 0.4264 | 0.6094 | 0.7261 | 0.0835 | 0.4083 | 0.0 | 0.6966 | 0.4727 | 0.5973 | 0.5242 | 0.0746 | 0.2640 | 0.0 | 0.4393 | 0.3613 | 0.5700 |
123
+ | 0.4869 | 8.68 | 1120 | 1.3547 | 0.3185 | 0.4248 | 0.6102 | 0.7458 | 0.0818 | 0.4224 | 0.0 | 0.6725 | 0.4274 | 0.6241 | 0.5281 | 0.0726 | 0.2644 | 0.0 | 0.4363 | 0.3418 | 0.5866 |
124
+ | 0.9576 | 8.84 | 1140 | 1.3494 | 0.3140 | 0.4284 | 0.6124 | 0.8385 | 0.0885 | 0.4607 | 0.0 | 0.5625 | 0.4161 | 0.6328 | 0.5517 | 0.0783 | 0.2830 | 0.0 | 0.4225 | 0.3295 | 0.5333 |
125
+ | 0.6199 | 8.99 | 1160 | 1.3917 | 0.3232 | 0.4311 | 0.6168 | 0.7502 | 0.0714 | 0.5376 | 0.0 | 0.6631 | 0.3913 | 0.6044 | 0.5435 | 0.0650 | 0.2921 | 0.0 | 0.4467 | 0.3377 | 0.5773 |
126
+ | 0.4378 | 9.15 | 1180 | 1.3229 | 0.3275 | 0.4467 | 0.6093 | 0.7722 | 0.1503 | 0.6143 | 0.0 | 0.5572 | 0.4080 | 0.6247 | 0.5468 | 0.1267 | 0.3100 | 0.0 | 0.4117 | 0.3443 | 0.5531 |
127
+ | 0.6044 | 9.3 | 1200 | 1.4230 | 0.3258 | 0.4341 | 0.6179 | 0.7490 | 0.1389 | 0.3962 | 0.0 | 0.7085 | 0.4270 | 0.6188 | 0.5398 | 0.1177 | 0.2662 | 0.0 | 0.4518 | 0.3428 | 0.5623 |
128
+ | 1.6689 | 9.46 | 1220 | 1.4361 | 0.3123 | 0.4305 | 0.5922 | 0.7027 | 0.0682 | 0.6768 | 0.0 | 0.5587 | 0.3961 | 0.6108 | 0.5233 | 0.0617 | 0.2909 | 0.0 | 0.4092 | 0.3365 | 0.5644 |
129
+ | 1.0875 | 9.61 | 1240 | 1.3673 | 0.3113 | 0.4271 | 0.5915 | 0.7718 | 0.0877 | 0.5949 | 0.0 | 0.5149 | 0.4139 | 0.6062 | 0.5317 | 0.0770 | 0.2781 | 0.0 | 0.3897 | 0.3300 | 0.5728 |
130
+ | 1.3708 | 9.77 | 1260 | 1.3343 | 0.3230 | 0.4297 | 0.6187 | 0.8402 | 0.1508 | 0.3629 | 0.0 | 0.6410 | 0.4011 | 0.6123 | 0.5312 | 0.1232 | 0.2550 | 0.0 | 0.4557 | 0.3324 | 0.5636 |
131
+ | 1.4742 | 9.92 | 1280 | 1.4037 | 0.3224 | 0.4325 | 0.6111 | 0.7281 | 0.1684 | 0.3640 | 0.0 | 0.7187 | 0.4290 | 0.6196 | 0.5404 | 0.1392 | 0.2504 | 0.0 | 0.4431 | 0.3408 | 0.5429 |
132
+ | 0.7178 | 10.08 | 1300 | 1.4025 | 0.3265 | 0.4382 | 0.6151 | 0.8271 | 0.1650 | 0.4569 | 0.0 | 0.5966 | 0.4123 | 0.6094 | 0.5394 | 0.1379 | 0.3019 | 0.0 | 0.4237 | 0.3322 | 0.5504 |
133
+ | 0.5788 | 10.23 | 1320 | 1.3881 | 0.3298 | 0.4407 | 0.6195 | 0.7793 | 0.1400 | 0.4722 | 0.0 | 0.6496 | 0.4327 | 0.6113 | 0.5448 | 0.1170 | 0.3117 | 0.0 | 0.4318 | 0.3500 | 0.5531 |
134
+ | 0.598 | 10.39 | 1340 | 1.4500 | 0.3238 | 0.4338 | 0.6103 | 0.7835 | 0.1200 | 0.5444 | 0.0 | 0.6015 | 0.3822 | 0.6049 | 0.5337 | 0.0991 | 0.2975 | 0.0 | 0.4241 | 0.3354 | 0.5766 |
135
+ | 1.0882 | 10.54 | 1360 | 1.4197 | 0.3226 | 0.4282 | 0.6184 | 0.7855 | 0.1282 | 0.4497 | 0.0 | 0.6816 | 0.3421 | 0.6102 | 0.5372 | 0.1056 | 0.2936 | 0.0 | 0.4469 | 0.3063 | 0.5689 |
136
+ | 0.6926 | 10.7 | 1380 | 1.5226 | 0.3226 | 0.4385 | 0.6055 | 0.7111 | 0.1606 | 0.4148 | 0.0 | 0.6734 | 0.4889 | 0.6205 | 0.5277 | 0.1294 | 0.2757 | 0.0 | 0.4303 | 0.3301 | 0.5652 |
137
+ | 0.6806 | 10.85 | 1400 | 1.4856 | 0.3296 | 0.4413 | 0.6184 | 0.7652 | 0.1607 | 0.4134 | 0.0 | 0.6732 | 0.4568 | 0.6200 | 0.5384 | 0.1313 | 0.2852 | 0.0 | 0.4446 | 0.3328 | 0.5745 |
138
+ | 0.6596 | 11.01 | 1420 | 1.6306 | 0.3202 | 0.4452 | 0.5930 | 0.6496 | 0.1800 | 0.5709 | 0.0 | 0.6296 | 0.4594 | 0.6267 | 0.5035 | 0.1453 | 0.2900 | 0.0 | 0.4386 | 0.2992 | 0.5646 |
139
+ | 1.211 | 11.16 | 1440 | 1.5973 | 0.3129 | 0.4299 | 0.5992 | 0.7065 | 0.1615 | 0.4647 | 0.0 | 0.6655 | 0.3840 | 0.6272 | 0.5198 | 0.1321 | 0.2811 | 0.0 | 0.4399 | 0.3276 | 0.4899 |
140
+ | 1.4257 | 11.32 | 1460 | 1.3641 | 0.3247 | 0.4412 | 0.6222 | 0.8198 | 0.1430 | 0.4994 | 0.0 | 0.6109 | 0.3931 | 0.6222 | 0.5633 | 0.1204 | 0.3188 | 0.0 | 0.4429 | 0.3016 | 0.5263 |
141
+ | 0.6568 | 11.47 | 1480 | 1.4287 | 0.3268 | 0.4417 | 0.6142 | 0.7584 | 0.1498 | 0.5644 | 0.0 | 0.6319 | 0.3835 | 0.6039 | 0.5575 | 0.1225 | 0.3037 | 0.0 | 0.4329 | 0.3206 | 0.5501 |
142
+ | 1.4136 | 11.63 | 1500 | 1.4066 | 0.3366 | 0.4581 | 0.6143 | 0.7648 | 0.2378 | 0.5748 | 0.0 | 0.6039 | 0.4253 | 0.5999 | 0.5518 | 0.1757 | 0.3220 | 0.0 | 0.4207 | 0.3261 | 0.5596 |
143
+ | 1.9236 | 11.78 | 1520 | 1.4022 | 0.3319 | 0.4434 | 0.6213 | 0.7650 | 0.1506 | 0.4408 | 0.0 | 0.6769 | 0.4647 | 0.6059 | 0.5474 | 0.1210 | 0.3018 | 0.0 | 0.4409 | 0.3377 | 0.5743 |
144
+ | 1.886 | 11.94 | 1540 | 1.4970 | 0.3334 | 0.4479 | 0.6179 | 0.7624 | 0.1752 | 0.4403 | 0.0 | 0.6564 | 0.4966 | 0.6044 | 0.5405 | 0.1376 | 0.2993 | 0.0 | 0.4341 | 0.3498 | 0.5730 |
145
+ | 1.4138 | 12.09 | 1560 | 1.5100 | 0.3378 | 0.4487 | 0.6250 | 0.7870 | 0.1931 | 0.4071 | 0.0 | 0.6783 | 0.4703 | 0.6050 | 0.5493 | 0.1559 | 0.2777 | 0.0 | 0.4510 | 0.3601 | 0.5708 |
146
+ | 0.9441 | 12.25 | 1580 | 1.5229 | 0.3255 | 0.4470 | 0.6011 | 0.7390 | 0.1625 | 0.6184 | 0.0 | 0.5607 | 0.4475 | 0.6008 | 0.5432 | 0.1356 | 0.3041 | 0.0 | 0.4053 | 0.3211 | 0.5693 |
147
+ | 1.07 | 12.4 | 1600 | 1.5136 | 0.3286 | 0.4537 | 0.6020 | 0.7376 | 0.2263 | 0.5578 | 0.0 | 0.5786 | 0.4676 | 0.6081 | 0.5328 | 0.1733 | 0.3111 | 0.0 | 0.4161 | 0.2985 | 0.5683 |
148
+ | 0.556 | 12.56 | 1620 | 1.4967 | 0.3265 | 0.4499 | 0.6056 | 0.7293 | 0.1947 | 0.5641 | 0.0 | 0.6087 | 0.4519 | 0.6004 | 0.5333 | 0.1533 | 0.3142 | 0.0 | 0.4334 | 0.2965 | 0.5551 |
149
+ | 0.3512 | 12.71 | 1640 | 1.5022 | 0.3242 | 0.4392 | 0.6128 | 0.7530 | 0.1641 | 0.4160 | 0.0 | 0.6733 | 0.4724 | 0.5954 | 0.5334 | 0.1308 | 0.2904 | 0.0 | 0.4453 | 0.3203 | 0.5492 |
150
+ | 0.7539 | 12.87 | 1660 | 1.5132 | 0.3277 | 0.4501 | 0.6127 | 0.8036 | 0.2367 | 0.4500 | 0.0 | 0.5972 | 0.4484 | 0.6148 | 0.5406 | 0.1741 | 0.3061 | 0.0 | 0.4334 | 0.3116 | 0.5278 |
151
+ | 0.5054 | 13.02 | 1680 | 1.6666 | 0.3243 | 0.4549 | 0.6018 | 0.6803 | 0.2619 | 0.5422 | 0.0 | 0.6549 | 0.4350 | 0.6097 | 0.5233 | 0.1883 | 0.3130 | 0.0 | 0.4493 | 0.3105 | 0.4857 |
152
+ | 0.3952 | 13.18 | 1700 | 1.5602 | 0.3173 | 0.4339 | 0.6016 | 0.7175 | 0.2297 | 0.3574 | 0.0 | 0.7077 | 0.4210 | 0.6041 | 0.5361 | 0.1696 | 0.2463 | 0.0 | 0.4372 | 0.3253 | 0.5070 |
153
+ | 0.5871 | 13.33 | 1720 | 1.6450 | 0.3311 | 0.4482 | 0.6181 | 0.7471 | 0.2165 | 0.4575 | 0.0 | 0.6882 | 0.4216 | 0.6066 | 0.5459 | 0.1683 | 0.2989 | 0.0 | 0.4535 | 0.3330 | 0.5184 |
154
+ | 0.8082 | 13.49 | 1740 | 1.6187 | 0.3172 | 0.4525 | 0.5894 | 0.7940 | 0.2182 | 0.6264 | 0.0 | 0.4342 | 0.4753 | 0.6194 | 0.5439 | 0.1625 | 0.3050 | 0.0 | 0.3611 | 0.3144 | 0.5336 |
155
+ | 1.1003 | 13.64 | 1760 | 1.5242 | 0.3337 | 0.4511 | 0.6113 | 0.7180 | 0.2311 | 0.4761 | 0.0 | 0.6759 | 0.4475 | 0.6090 | 0.5416 | 0.1741 | 0.2970 | 0.0 | 0.4293 | 0.3396 | 0.5544 |
156
+ | 0.5952 | 13.8 | 1780 | 1.6698 | 0.3341 | 0.4571 | 0.6072 | 0.7572 | 0.2123 | 0.6677 | 0.0 | 0.5485 | 0.4064 | 0.6079 | 0.5578 | 0.1661 | 0.3149 | 0.0 | 0.3994 | 0.3402 | 0.5603 |
157
+ | 0.4242 | 13.95 | 1800 | 1.5436 | 0.3207 | 0.4417 | 0.6024 | 0.7642 | 0.1844 | 0.5712 | 0.0 | 0.5779 | 0.3953 | 0.5985 | 0.5394 | 0.1448 | 0.3278 | 0.0 | 0.4027 | 0.3144 | 0.5161 |
158
+ | 0.8716 | 14.11 | 1820 | 1.6754 | 0.3327 | 0.4531 | 0.6154 | 0.7282 | 0.2226 | 0.5343 | 0.0 | 0.6695 | 0.4187 | 0.5982 | 0.5513 | 0.1647 | 0.3258 | 0.0 | 0.4356 | 0.3291 | 0.5226 |
159
+ | 0.6068 | 14.26 | 1840 | 1.5659 | 0.3173 | 0.4429 | 0.5970 | 0.7295 | 0.1822 | 0.6099 | 0.0 | 0.5745 | 0.4021 | 0.6019 | 0.5333 | 0.1407 | 0.3197 | 0.0 | 0.4092 | 0.3218 | 0.4968 |
160
+ | 0.7448 | 14.42 | 1860 | 1.6698 | 0.3274 | 0.4510 | 0.6103 | 0.7353 | 0.2167 | 0.5282 | 0.0 | 0.6282 | 0.4154 | 0.6331 | 0.5433 | 0.1672 | 0.3251 | 0.0 | 0.4300 | 0.3305 | 0.4954 |
161
+ | 1.5591 | 14.57 | 1880 | 1.5198 | 0.3316 | 0.4556 | 0.6107 | 0.7487 | 0.2180 | 0.5448 | 0.0 | 0.6021 | 0.4579 | 0.6177 | 0.5478 | 0.1661 | 0.3051 | 0.0 | 0.4275 | 0.3362 | 0.5384 |
162
+ | 0.6154 | 14.73 | 1900 | 1.5842 | 0.3334 | 0.4594 | 0.6113 | 0.7763 | 0.2514 | 0.5785 | 0.0 | 0.5664 | 0.4155 | 0.6278 | 0.5549 | 0.1859 | 0.3161 | 0.0 | 0.4142 | 0.3388 | 0.5237 |
163
+ | 0.5267 | 14.88 | 1920 | 1.6216 | 0.3377 | 0.4614 | 0.6169 | 0.7873 | 0.2627 | 0.5482 | 0.0 | 0.5936 | 0.4275 | 0.6103 | 0.5504 | 0.1890 | 0.3304 | 0.0 | 0.4215 | 0.3391 | 0.5335 |
164
+ | 0.4463 | 15.04 | 1940 | 1.5645 | 0.3336 | 0.4587 | 0.6141 | 0.7394 | 0.2659 | 0.4966 | 0.0 | 0.6520 | 0.4497 | 0.6073 | 0.5505 | 0.1850 | 0.3192 | 0.0 | 0.4336 | 0.3297 | 0.5172 |
165
+ | 0.8863 | 15.19 | 1960 | 1.6356 | 0.3361 | 0.4624 | 0.6116 | 0.7418 | 0.2514 | 0.5871 | 0.0 | 0.5993 | 0.4458 | 0.6118 | 0.5505 | 0.1830 | 0.3279 | 0.0 | 0.4160 | 0.3398 | 0.5357 |
166
+ | 0.6285 | 15.35 | 1980 | 1.5989 | 0.3459 | 0.4681 | 0.6293 | 0.7744 | 0.2751 | 0.5156 | 0.0 | 0.6618 | 0.4421 | 0.6073 | 0.5643 | 0.1968 | 0.3355 | 0.0 | 0.4521 | 0.3170 | 0.5553 |
167
+ | 0.9593 | 15.5 | 2000 | 1.6444 | 0.3383 | 0.4608 | 0.6196 | 0.7627 | 0.2350 | 0.5714 | 0.0 | 0.6231 | 0.4297 | 0.6040 | 0.5531 | 0.1746 | 0.3263 | 0.0 | 0.4396 | 0.3215 | 0.5529 |
168
+ | 1.3486 | 15.66 | 2020 | 1.6766 | 0.3327 | 0.4471 | 0.6180 | 0.7588 | 0.1747 | 0.5269 | 0.0 | 0.6528 | 0.4232 | 0.5936 | 0.5501 | 0.1375 | 0.3100 | 0.0 | 0.4381 | 0.3301 | 0.5630 |
169
+ | 1.0891 | 15.81 | 2040 | 1.6360 | 0.3170 | 0.4392 | 0.5979 | 0.7170 | 0.2107 | 0.5349 | 0.0 | 0.6308 | 0.3772 | 0.6041 | 0.5202 | 0.1596 | 0.3184 | 0.0 | 0.4320 | 0.2581 | 0.5308 |
170
+ | 0.6277 | 15.97 | 2060 | 1.5725 | 0.3334 | 0.4555 | 0.6123 | 0.7371 | 0.2208 | 0.5742 | 0.0 | 0.6226 | 0.4244 | 0.6094 | 0.5485 | 0.1642 | 0.3198 | 0.0 | 0.4262 | 0.3174 | 0.5578 |
171
+ | 0.4244 | 16.12 | 2080 | 1.5871 | 0.3305 | 0.4486 | 0.6134 | 0.7673 | 0.2200 | 0.5159 | 0.0 | 0.6272 | 0.3917 | 0.6180 | 0.5440 | 0.1712 | 0.3070 | 0.0 | 0.4352 | 0.3012 | 0.5550 |
172
+ | 0.8267 | 16.28 | 2100 | 1.6791 | 0.3346 | 0.4555 | 0.6176 | 0.7559 | 0.2433 | 0.5241 | 0.0 | 0.6474 | 0.4040 | 0.6137 | 0.5483 | 0.1880 | 0.3161 | 0.0 | 0.4478 | 0.3058 | 0.5365 |
173
+ | 0.7391 | 16.43 | 2120 | 1.6912 | 0.3310 | 0.4564 | 0.6143 | 0.7247 | 0.2544 | 0.4874 | 0.0 | 0.6703 | 0.4402 | 0.6181 | 0.5503 | 0.1890 | 0.3150 | 0.0 | 0.4524 | 0.2956 | 0.5150 |
174
+ | 0.6529 | 16.59 | 2140 | 1.6165 | 0.3260 | 0.4508 | 0.6123 | 0.7454 | 0.2250 | 0.5196 | 0.0 | 0.6409 | 0.4126 | 0.6121 | 0.5532 | 0.1680 | 0.3189 | 0.0 | 0.4441 | 0.3047 | 0.4933 |
175
+ | 0.4946 | 16.74 | 2160 | 1.6385 | 0.3377 | 0.4596 | 0.6196 | 0.7546 | 0.2660 | 0.5030 | 0.0 | 0.6627 | 0.4307 | 0.6002 | 0.5554 | 0.1842 | 0.3087 | 0.0 | 0.4449 | 0.3294 | 0.5409 |
176
+ | 0.9957 | 16.9 | 2180 | 1.5676 | 0.3350 | 0.4610 | 0.6139 | 0.7460 | 0.2970 | 0.4270 | 0.0 | 0.6590 | 0.4863 | 0.6115 | 0.5476 | 0.2006 | 0.2882 | 0.0 | 0.4391 | 0.3318 | 0.5377 |
177
+ | 0.3648 | 17.05 | 2200 | 1.5738 | 0.3365 | 0.4617 | 0.6164 | 0.7419 | 0.2683 | 0.4867 | 0.0 | 0.6506 | 0.4699 | 0.6147 | 0.5525 | 0.1927 | 0.3075 | 0.0 | 0.4424 | 0.3189 | 0.5419 |
178
+ | 0.7294 | 17.21 | 2220 | 1.6943 | 0.3323 | 0.4582 | 0.6124 | 0.7530 | 0.2427 | 0.5418 | 0.0 | 0.6081 | 0.4428 | 0.6186 | 0.5545 | 0.1807 | 0.3119 | 0.0 | 0.4309 | 0.3219 | 0.5260 |
179
+ | 1.3822 | 17.36 | 2240 | 1.6395 | 0.3354 | 0.4590 | 0.6147 | 0.7827 | 0.2587 | 0.5462 | 0.0 | 0.5953 | 0.4234 | 0.6065 | 0.5607 | 0.1897 | 0.3140 | 0.0 | 0.4212 | 0.3311 | 0.5313 |
180
+ | 0.6101 | 17.52 | 2260 | 1.6983 | 0.3320 | 0.4537 | 0.6164 | 0.7438 | 0.2398 | 0.4938 | 0.0 | 0.6631 | 0.4162 | 0.6190 | 0.5493 | 0.1783 | 0.3102 | 0.0 | 0.4466 | 0.3253 | 0.5145 |
181
+ | 1.1954 | 17.67 | 2280 | 1.8149 | 0.3341 | 0.4571 | 0.6119 | 0.7420 | 0.2520 | 0.5436 | 0.0 | 0.6314 | 0.4301 | 0.6007 | 0.5471 | 0.1784 | 0.3126 | 0.0 | 0.4293 | 0.3222 | 0.5488 |
182
+ | 0.731 | 17.83 | 2300 | 1.7177 | 0.3227 | 0.4415 | 0.6058 | 0.7759 | 0.2099 | 0.4543 | 0.0 | 0.6041 | 0.4155 | 0.6305 | 0.5418 | 0.1626 | 0.2876 | 0.0 | 0.4169 | 0.3303 | 0.5196 |
183
+ | 1.2659 | 17.98 | 2320 | 1.7993 | 0.3285 | 0.4416 | 0.6121 | 0.7725 | 0.2215 | 0.4369 | 0.0 | 0.6592 | 0.3994 | 0.6016 | 0.5345 | 0.1672 | 0.2802 | 0.0 | 0.4392 | 0.3265 | 0.5517 |
184
+ | 0.4587 | 18.14 | 2340 | 1.6811 | 0.3303 | 0.4504 | 0.6102 | 0.7778 | 0.2093 | 0.5379 | 0.0 | 0.5861 | 0.4312 | 0.6102 | 0.5470 | 0.1557 | 0.3059 | 0.0 | 0.4158 | 0.3258 | 0.5620 |
185
+ | 0.5764 | 18.29 | 2360 | 1.6331 | 0.3388 | 0.4627 | 0.6177 | 0.7438 | 0.2568 | 0.5458 | 0.0 | 0.6383 | 0.4432 | 0.6109 | 0.5524 | 0.1895 | 0.3224 | 0.0 | 0.4374 | 0.3234 | 0.5466 |
186
+ | 0.7297 | 18.45 | 2380 | 1.8170 | 0.3305 | 0.4556 | 0.6062 | 0.7491 | 0.2395 | 0.5577 | 0.0 | 0.5865 | 0.4445 | 0.6122 | 0.5347 | 0.1782 | 0.3180 | 0.0 | 0.4174 | 0.3218 | 0.5432 |
187
+ | 0.4377 | 18.6 | 2400 | 1.7875 | 0.3292 | 0.4531 | 0.6068 | 0.7496 | 0.2484 | 0.5223 | 0.0 | 0.6078 | 0.4302 | 0.6133 | 0.5329 | 0.1843 | 0.3133 | 0.0 | 0.4247 | 0.3214 | 0.5277 |
188
+ | 0.281 | 18.76 | 2420 | 1.7708 | 0.3322 | 0.4600 | 0.6056 | 0.7385 | 0.2727 | 0.5623 | 0.0 | 0.5881 | 0.4316 | 0.6267 | 0.5408 | 0.1966 | 0.3158 | 0.0 | 0.4130 | 0.3279 | 0.5311 |
189
+ | 0.7224 | 18.91 | 2440 | 1.7325 | 0.3282 | 0.4571 | 0.6053 | 0.7518 | 0.2608 | 0.5437 | 0.0 | 0.5811 | 0.4392 | 0.6232 | 0.5426 | 0.1832 | 0.3145 | 0.0 | 0.4159 | 0.3212 | 0.5201 |
190
+ | 0.7736 | 19.07 | 2460 | 1.7377 | 0.3413 | 0.4630 | 0.6210 | 0.7488 | 0.2647 | 0.5380 | 0.0 | 0.6559 | 0.4266 | 0.6071 | 0.5514 | 0.1896 | 0.3179 | 0.0 | 0.4455 | 0.3300 | 0.5545 |
191
+ | 1.0798 | 19.22 | 2480 | 1.7811 | 0.3391 | 0.4608 | 0.6149 | 0.7752 | 0.2590 | 0.5616 | 0.0 | 0.5920 | 0.4199 | 0.6177 | 0.5467 | 0.1946 | 0.3175 | 0.0 | 0.4205 | 0.3404 | 0.5540 |
192
+ | 0.9049 | 19.38 | 2500 | 1.7704 | 0.3349 | 0.4541 | 0.6153 | 0.7431 | 0.2699 | 0.4376 | 0.0 | 0.6773 | 0.4277 | 0.6232 | 0.5453 | 0.1940 | 0.2912 | 0.0 | 0.4381 | 0.3313 | 0.5442 |
193
+ | 0.755 | 19.53 | 2520 | 1.6703 | 0.3287 | 0.4591 | 0.6009 | 0.7374 | 0.2724 | 0.5796 | 0.0 | 0.5538 | 0.4117 | 0.6584 | 0.5485 | 0.1969 | 0.3102 | 0.0 | 0.3965 | 0.3209 | 0.5277 |
194
+ | 0.3202 | 19.69 | 2540 | 1.7747 | 0.3360 | 0.4620 | 0.6057 | 0.7389 | 0.2715 | 0.6069 | 0.0 | 0.5773 | 0.4263 | 0.6133 | 0.5483 | 0.1933 | 0.3127 | 0.0 | 0.4033 | 0.3368 | 0.5576 |
195
+ | 0.5991 | 19.84 | 2560 | 1.7421 | 0.3332 | 0.4551 | 0.6105 | 0.7492 | 0.2548 | 0.5044 | 0.0 | 0.6275 | 0.4404 | 0.6097 | 0.5475 | 0.1837 | 0.3067 | 0.0 | 0.4217 | 0.3208 | 0.5522 |
196
+ | 1.1054 | 20.0 | 2580 | 1.8260 | 0.3302 | 0.4531 | 0.6063 | 0.7691 | 0.2390 | 0.5472 | 0.0 | 0.5772 | 0.4319 | 0.6076 | 0.5451 | 0.1761 | 0.3115 | 0.0 | 0.4071 | 0.3194 | 0.5525 |
197
+ | 0.5427 | 20.16 | 2600 | 1.7024 | 0.3308 | 0.4603 | 0.6076 | 0.7252 | 0.2890 | 0.5094 | 0.0 | 0.6218 | 0.4310 | 0.6459 | 0.5453 | 0.2043 | 0.3116 | 0.0 | 0.4268 | 0.3167 | 0.5108 |
198
+ | 1.0772 | 20.31 | 2620 | 1.7820 | 0.3275 | 0.4541 | 0.6016 | 0.7101 | 0.2480 | 0.5553 | 0.0 | 0.6090 | 0.4286 | 0.6280 | 0.5400 | 0.1844 | 0.3092 | 0.0 | 0.4140 | 0.3297 | 0.5152 |
199
+ | 0.4049 | 20.47 | 2640 | 1.7094 | 0.3280 | 0.4493 | 0.6047 | 0.7690 | 0.2319 | 0.5327 | 0.0 | 0.5820 | 0.4226 | 0.6066 | 0.5412 | 0.1710 | 0.3001 | 0.0 | 0.4094 | 0.3343 | 0.5399 |
200
+ | 1.1701 | 20.62 | 2660 | 1.7772 | 0.3276 | 0.4566 | 0.6001 | 0.7394 | 0.2595 | 0.5895 | 0.0 | 0.5668 | 0.4370 | 0.6042 | 0.5448 | 0.1868 | 0.3098 | 0.0 | 0.4048 | 0.3306 | 0.5160 |
201
+ | 0.9368 | 20.78 | 2680 | 1.7897 | 0.3267 | 0.4525 | 0.6044 | 0.7630 | 0.2430 | 0.5467 | 0.0 | 0.5800 | 0.4341 | 0.6003 | 0.5464 | 0.1808 | 0.3058 | 0.0 | 0.4167 | 0.3252 | 0.5122 |
202
+ | 0.6123 | 20.93 | 2700 | 1.7726 | 0.3330 | 0.4580 | 0.6150 | 0.7424 | 0.2768 | 0.4875 | 0.0 | 0.6616 | 0.4286 | 0.6091 | 0.5513 | 0.1966 | 0.3116 | 0.0 | 0.4444 | 0.3263 | 0.5010 |
203
+ | 0.8703 | 21.09 | 2720 | 1.7163 | 0.3316 | 0.4555 | 0.6137 | 0.7498 | 0.2633 | 0.4849 | 0.0 | 0.6505 | 0.4325 | 0.6079 | 0.5467 | 0.1890 | 0.3111 | 0.0 | 0.4408 | 0.3264 | 0.5072 |
204
+ | 1.1341 | 21.24 | 2740 | 1.7633 | 0.3334 | 0.4577 | 0.6107 | 0.7346 | 0.2647 | 0.5132 | 0.0 | 0.6395 | 0.4447 | 0.6068 | 0.5435 | 0.1890 | 0.3115 | 0.0 | 0.4310 | 0.3170 | 0.5416 |
205
+ | 0.2476 | 21.4 | 2760 | 1.7036 | 0.3299 | 0.4532 | 0.6099 | 0.7377 | 0.2362 | 0.5125 | 0.0 | 0.6271 | 0.4265 | 0.6322 | 0.5446 | 0.1785 | 0.3071 | 0.0 | 0.4288 | 0.3286 | 0.5213 |
206
+ | 0.5096 | 21.55 | 2780 | 1.7595 | 0.3349 | 0.4593 | 0.6110 | 0.7525 | 0.2564 | 0.5616 | 0.0 | 0.6021 | 0.4310 | 0.6115 | 0.5473 | 0.1865 | 0.3084 | 0.0 | 0.4257 | 0.3300 | 0.5466 |
207
+ | 0.4822 | 21.71 | 2800 | 1.8440 | 0.3372 | 0.4600 | 0.6148 | 0.7506 | 0.2497 | 0.5630 | 0.0 | 0.6224 | 0.4328 | 0.6016 | 0.5488 | 0.1850 | 0.3134 | 0.0 | 0.4348 | 0.3294 | 0.5488 |
208
+ | 1.2683 | 21.86 | 2820 | 1.8689 | 0.3381 | 0.4616 | 0.6142 | 0.7525 | 0.2596 | 0.5785 | 0.0 | 0.6124 | 0.4246 | 0.6036 | 0.5519 | 0.1898 | 0.3157 | 0.0 | 0.4274 | 0.3381 | 0.5436 |
209
+ | 0.5363 | 22.02 | 2840 | 1.7173 | 0.3336 | 0.4593 | 0.6104 | 0.7599 | 0.2603 | 0.5590 | 0.0 | 0.5920 | 0.4283 | 0.6154 | 0.5491 | 0.1897 | 0.3106 | 0.0 | 0.4228 | 0.3338 | 0.5292 |
210
+ | 0.4545 | 22.17 | 2860 | 1.7828 | 0.3375 | 0.4617 | 0.6119 | 0.7302 | 0.2713 | 0.5588 | 0.0 | 0.6331 | 0.4350 | 0.6032 | 0.5503 | 0.1944 | 0.3043 | 0.0 | 0.4326 | 0.3286 | 0.5521 |
211
+ | 0.3017 | 22.33 | 2880 | 1.7322 | 0.3370 | 0.4563 | 0.6185 | 0.7672 | 0.2581 | 0.4811 | 0.0 | 0.6543 | 0.4299 | 0.6034 | 0.5538 | 0.1867 | 0.2973 | 0.0 | 0.4419 | 0.3284 | 0.5510 |
212
+ | 0.5573 | 22.48 | 2900 | 1.8035 | 0.3380 | 0.4611 | 0.6132 | 0.7642 | 0.2680 | 0.5638 | 0.0 | 0.6015 | 0.4252 | 0.6053 | 0.5493 | 0.1920 | 0.3096 | 0.0 | 0.4240 | 0.3388 | 0.5523 |
213
+ | 1.2154 | 22.64 | 2920 | 1.7877 | 0.3370 | 0.4627 | 0.6109 | 0.7483 | 0.2675 | 0.5920 | 0.0 | 0.5936 | 0.4259 | 0.6119 | 0.5476 | 0.1916 | 0.3120 | 0.0 | 0.4226 | 0.3395 | 0.5457 |
214
+ | 0.6649 | 22.79 | 2940 | 1.7642 | 0.3367 | 0.4615 | 0.6140 | 0.7413 | 0.2708 | 0.5393 | 0.0 | 0.6336 | 0.4362 | 0.6093 | 0.5501 | 0.1923 | 0.3136 | 0.0 | 0.4332 | 0.3255 | 0.5420 |
215
+ | 0.8076 | 22.95 | 2960 | 1.7899 | 0.3352 | 0.4596 | 0.6156 | 0.7546 | 0.2869 | 0.4783 | 0.0 | 0.6477 | 0.4273 | 0.6223 | 0.5505 | 0.1966 | 0.3014 | 0.0 | 0.4388 | 0.3308 | 0.5283 |
216
+ | 0.3772 | 23.1 | 2980 | 1.7055 | 0.3318 | 0.4573 | 0.6127 | 0.7595 | 0.2810 | 0.4703 | 0.0 | 0.6259 | 0.4204 | 0.6442 | 0.5483 | 0.1938 | 0.2978 | 0.0 | 0.4324 | 0.3291 | 0.5210 |
217
+ | 0.2888 | 23.26 | 3000 | 1.8259 | 0.3313 | 0.4525 | 0.6137 | 0.7526 | 0.2640 | 0.4676 | 0.0 | 0.6553 | 0.4005 | 0.6277 | 0.5481 | 0.1865 | 0.2948 | 0.0 | 0.4381 | 0.3323 | 0.5193 |
218
+ | 0.4429 | 23.41 | 3020 | 1.8434 | 0.3380 | 0.4627 | 0.6156 | 0.7384 | 0.2855 | 0.5305 | 0.0 | 0.6436 | 0.4160 | 0.6251 | 0.5497 | 0.1998 | 0.3086 | 0.0 | 0.4379 | 0.3390 | 0.5308 |
219
+ | 0.9204 | 23.57 | 3040 | 1.7994 | 0.3395 | 0.4641 | 0.6163 | 0.7342 | 0.2937 | 0.5241 | 0.0 | 0.6550 | 0.4263 | 0.6152 | 0.5501 | 0.2025 | 0.3081 | 0.0 | 0.4389 | 0.3395 | 0.5374 |
220
+ | 0.2447 | 23.72 | 3060 | 1.7386 | 0.3390 | 0.4606 | 0.6168 | 0.7514 | 0.2761 | 0.5172 | 0.0 | 0.6485 | 0.4261 | 0.6048 | 0.5518 | 0.1952 | 0.3048 | 0.0 | 0.4367 | 0.3403 | 0.5440 |
221
+ | 0.4847 | 23.88 | 3080 | 1.7776 | 0.3381 | 0.4616 | 0.6163 | 0.7529 | 0.2800 | 0.5278 | 0.0 | 0.6370 | 0.4196 | 0.6142 | 0.5515 | 0.1966 | 0.3087 | 0.0 | 0.4359 | 0.3402 | 0.5335 |
222
+ | 0.3195 | 24.03 | 3100 | 1.7944 | 0.3368 | 0.4606 | 0.6144 | 0.7591 | 0.2794 | 0.5222 | 0.0 | 0.6261 | 0.4290 | 0.6085 | 0.5508 | 0.1960 | 0.3069 | 0.0 | 0.4310 | 0.3370 | 0.5358 |
223
+ | 0.3886 | 24.19 | 3120 | 1.8694 | 0.3389 | 0.4619 | 0.6156 | 0.7388 | 0.2870 | 0.5241 | 0.0 | 0.6547 | 0.4237 | 0.6048 | 0.5493 | 0.1977 | 0.3070 | 0.0 | 0.4365 | 0.3414 | 0.5403 |
224
+ | 0.4507 | 24.34 | 3140 | 1.7188 | 0.3342 | 0.4562 | 0.6151 | 0.7539 | 0.2767 | 0.4621 | 0.0 | 0.6570 | 0.4254 | 0.6185 | 0.5499 | 0.1962 | 0.2926 | 0.0 | 0.4404 | 0.3372 | 0.5232 |
225
+ | 0.3909 | 24.5 | 3160 | 1.7539 | 0.3338 | 0.4558 | 0.6144 | 0.7579 | 0.2724 | 0.4678 | 0.0 | 0.6509 | 0.4333 | 0.6083 | 0.5495 | 0.1928 | 0.2946 | 0.0 | 0.4383 | 0.3316 | 0.5297 |
226
+ | 1.1122 | 24.65 | 3180 | 1.7175 | 0.3320 | 0.4541 | 0.6140 | 0.7508 | 0.2762 | 0.4395 | 0.0 | 0.6655 | 0.4262 | 0.6208 | 0.5497 | 0.1945 | 0.2872 | 0.0 | 0.4414 | 0.3340 | 0.5171 |
227
+ | 0.7051 | 24.81 | 3200 | 1.7688 | 0.3362 | 0.4597 | 0.6145 | 0.7477 | 0.2836 | 0.4967 | 0.0 | 0.6492 | 0.4321 | 0.6083 | 0.5497 | 0.1975 | 0.3020 | 0.0 | 0.4365 | 0.3348 | 0.5330 |
228
+ | 0.3335 | 24.96 | 3220 | 1.7934 | 0.3377 | 0.4616 | 0.6149 | 0.7365 | 0.2890 | 0.5092 | 0.0 | 0.6559 | 0.4292 | 0.6114 | 0.5485 | 0.2007 | 0.3051 | 0.0 | 0.4379 | 0.3387 | 0.5329 |
229
+
230
+
231
+ ### Framework versions
232
+
233
+ - Transformers 4.37.0
234
+ - Pytorch 2.1.2
235
+ - Datasets 2.18.0
236
+ - Tokenizers 0.15.1
config.json ADDED
@@ -0,0 +1,88 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "nvidia/segformer-b4-finetuned-cityscapes-1024-1024",
3
+ "architectures": [
4
+ "SegformerForSemanticSegmentation"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.0,
7
+ "classifier_dropout_prob": 0.1,
8
+ "decoder_hidden_size": 768,
9
+ "depths": [
10
+ 3,
11
+ 8,
12
+ 27,
13
+ 3
14
+ ],
15
+ "downsampling_rates": [
16
+ 1,
17
+ 4,
18
+ 8,
19
+ 16
20
+ ],
21
+ "drop_path_rate": 0.1,
22
+ "hidden_act": "gelu",
23
+ "hidden_dropout_prob": 0.0,
24
+ "hidden_sizes": [
25
+ 64,
26
+ 128,
27
+ 320,
28
+ 512
29
+ ],
30
+ "id2label": {
31
+ "0": "water",
32
+ "1": "whitewater",
33
+ "2": "sediment",
34
+ "3": "other_natural_terrain",
35
+ "4": "vegetation",
36
+ "5": "development",
37
+ "6": "unknown"
38
+ },
39
+ "image_size": 224,
40
+ "initializer_range": 0.02,
41
+ "label2id": {
42
+ "development": 5,
43
+ "other_natural_terrain": 3,
44
+ "sediment": 2,
45
+ "unknown": 6,
46
+ "vegetation": 4,
47
+ "water": 0,
48
+ "whitewater": 1
49
+ },
50
+ "layer_norm_eps": 1e-06,
51
+ "mlp_ratios": [
52
+ 4,
53
+ 4,
54
+ 4,
55
+ 4
56
+ ],
57
+ "model_type": "segformer",
58
+ "num_attention_heads": [
59
+ 1,
60
+ 2,
61
+ 5,
62
+ 8
63
+ ],
64
+ "num_channels": 3,
65
+ "num_encoder_blocks": 4,
66
+ "patch_sizes": [
67
+ 7,
68
+ 3,
69
+ 3,
70
+ 3
71
+ ],
72
+ "reshape_last_stage": true,
73
+ "semantic_loss_ignore_index": 255,
74
+ "sr_ratios": [
75
+ 8,
76
+ 4,
77
+ 2,
78
+ 1
79
+ ],
80
+ "strides": [
81
+ 4,
82
+ 2,
83
+ 2,
84
+ 2
85
+ ],
86
+ "torch_dtype": "float32",
87
+ "transformers_version": "4.37.0"
88
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f250ed0704d8b853e76359b9887edf26bb43cd1bffdb3643dc8305abc47d7355
3
+ size 256112508
runs/Mar02_11-43-19_8377836801c7/events.out.tfevents.1709379802.8377836801c7.27.0 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:dd02bf5e392ddc07b430b8de31c7bc1a9dce00fa9b602718a0eac8be6b99e07c
3
+ size 719227
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a2938871b4b8073eb9e7ac0763dced65e84ddec779ea8e95700a075109faa4b8
3
+ size 4792