Hugging Face's logo

samitizerxu
/
segformer-b1-improved

Image Segmentation
Transformers
Safetensors
segformer
vision
Generated from Trainer
Inference Endpoints
Model card Files Community
samitizerxu commited on
Commit
f2a6a4c
·
verified ·
1 Parent(s): 73173f5

End of training

Browse files
Files changed (4) hide show
  1. README.md +433 -198
  2. config.json +76 -0
  3. model.safetensors +3 -0
  4. training_args.bin +3 -0
README.md CHANGED
@@ -1,201 +1,436 @@
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/mit-b1
4
+ tags:
5
+ - vision
6
+ - image-segmentation
7
+ - generated_from_trainer
8
+ model-index:
9
+ - name: segformer-b1-improved
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-b1-improved
17
+
18
+ This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the samitizerxu/kelp_data dataset.
19
+ It achieves the following results on the evaluation set:
20
+ - Iou Kelp: 0.0
21
+ - Loss: 0.0024
22
+
23
+ ## Model description
24
+
25
+ More information needed
26
+
27
+ ## Intended uses & limitations
28
+
29
+ More information needed
30
+
31
+ ## Training and evaluation data
32
+
33
+ More information needed
34
+
35
+ ## Training procedure
36
+
37
+ ### Training hyperparameters
38
+
39
+ The following hyperparameters were used during training:
40
+ - learning_rate: 2e-05
41
+ - train_batch_size: 16
42
+ - eval_batch_size: 16
43
+ - seed: 42
44
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
45
+ - lr_scheduler_type: linear
46
+ - lr_scheduler_warmup_ratio: 0.2
47
+ - num_epochs: 40
48
+
49
+ ### Training results
50
+
51
+ | Training Loss | Epoch | Step | Iou Kelp | Validation Loss |
52
+ |:-------------:|:-----:|:-----:|:--------:|:---------------:|
53
+ | 0.0769 | 0.11 | 30 | 0.0 | 0.0650 |
54
+ | 0.075 | 0.21 | 60 | 0.0 | 0.0710 |
55
+ | 0.0701 | 0.32 | 90 | 0.0 | 0.0738 |
56
+ | 0.0704 | 0.43 | 120 | 0.0 | 0.0710 |
57
+ | 0.0669 | 0.53 | 150 | 0.0 | 0.0699 |
58
+ | 0.0694 | 0.64 | 180 | 0.0 | 0.0679 |
59
+ | 0.0635 | 0.74 | 210 | 0.0 | 0.0676 |
60
+ | 0.0584 | 0.85 | 240 | 0.0 | 0.0647 |
61
+ | 0.0552 | 0.96 | 270 | 0.0 | 0.0631 |
62
+ | 0.0525 | 1.06 | 300 | 0.0 | 0.0592 |
63
+ | 0.052 | 1.17 | 330 | 0.0 | 0.0540 |
64
+ | 0.0449 | 1.28 | 360 | 0.0 | 0.0517 |
65
+ | 0.0443 | 1.38 | 390 | 0.0 | 0.0459 |
66
+ | 0.0364 | 1.49 | 420 | 0.0 | 0.0422 |
67
+ | 0.0361 | 1.6 | 450 | 0.0 | 0.0374 |
68
+ | 0.0339 | 1.7 | 480 | 0.0 | 0.0352 |
69
+ | 0.0307 | 1.81 | 510 | 0.0 | 0.0319 |
70
+ | 0.0283 | 1.91 | 540 | 0.0 | 0.0283 |
71
+ | 0.0302 | 2.02 | 570 | 0.0 | 0.0275 |
72
+ | 0.0277 | 2.13 | 600 | 0.0 | 0.0252 |
73
+ | 0.0231 | 2.23 | 630 | 0.0 | 0.0223 |
74
+ | 0.0261 | 2.34 | 660 | 0.0 | 0.0219 |
75
+ | 0.025 | 2.45 | 690 | 0.0 | 0.0183 |
76
+ | 0.0262 | 2.55 | 720 | 0.0 | 0.0163 |
77
+ | 0.0182 | 2.66 | 750 | 0.0 | 0.0175 |
78
+ | 0.0186 | 2.77 | 780 | 0.0 | 0.0147 |
79
+ | 0.0156 | 2.87 | 810 | 0.0 | 0.0151 |
80
+ | 0.0171 | 2.98 | 840 | 0.0 | 0.0148 |
81
+ | 0.0164 | 3.09 | 870 | 0.0 | 0.0161 |
82
+ | 0.0155 | 3.19 | 900 | 0.0 | 0.0139 |
83
+ | 0.0127 | 3.3 | 930 | 0.0 | 0.0126 |
84
+ | 0.0111 | 3.4 | 960 | 0.0 | 0.0105 |
85
+ | 0.0139 | 3.51 | 990 | 0.0 | 0.0103 |
86
+ | 0.0113 | 3.62 | 1020 | 0.0 | 0.0096 |
87
+ | 0.0093 | 3.72 | 1050 | 0.0 | 0.0088 |
88
+ | 0.0099 | 3.83 | 1080 | 0.0 | 0.0096 |
89
+ | 0.0092 | 3.94 | 1110 | 0.0 | 0.0096 |
90
+ | 0.0095 | 4.04 | 1140 | 0.0 | 0.0083 |
91
+ | 0.0113 | 4.15 | 1170 | 0.0 | 0.0086 |
92
+ | 0.0084 | 4.26 | 1200 | 0.0 | 0.0073 |
93
+ | 0.0066 | 4.36 | 1230 | 0.0 | 0.0081 |
94
+ | 0.01 | 4.47 | 1260 | 0.0 | 0.0074 |
95
+ | 0.0062 | 4.57 | 1290 | 0.0 | 0.0067 |
96
+ | 0.0053 | 4.68 | 1320 | 0.0 | 0.0065 |
97
+ | 0.0073 | 4.79 | 1350 | 0.0 | 0.0058 |
98
+ | 0.0069 | 4.89 | 1380 | 0.0 | 0.0065 |
99
+ | 0.0063 | 5.0 | 1410 | 0.0 | 0.0060 |
100
+ | 0.0062 | 5.11 | 1440 | 0.0 | 0.0055 |
101
+ | 0.0069 | 5.21 | 1470 | 0.0 | 0.0058 |
102
+ | 0.0049 | 5.32 | 1500 | 0.0 | 0.0055 |
103
+ | 0.0047 | 5.43 | 1530 | 0.0 | 0.0050 |
104
+ | 0.0092 | 5.53 | 1560 | 0.0 | 0.0054 |
105
+ | 0.0041 | 5.64 | 1590 | 0.0 | 0.0049 |
106
+ | 0.0042 | 5.74 | 1620 | 0.0 | 0.0049 |
107
+ | 0.005 | 5.85 | 1650 | 0.0 | 0.0044 |
108
+ | 0.0036 | 5.96 | 1680 | 0.0 | 0.0047 |
109
+ | 0.0059 | 6.06 | 1710 | 0.0 | 0.0048 |
110
+ | 0.0078 | 6.17 | 1740 | 0.0 | 0.0045 |
111
+ | 0.0036 | 6.28 | 1770 | 0.0 | 0.0042 |
112
+ | 0.004 | 6.38 | 1800 | 0.0 | 0.0039 |
113
+ | 0.0038 | 6.49 | 1830 | 0.0 | 0.0041 |
114
+ | 0.0038 | 6.6 | 1860 | 0.0 | 0.0042 |
115
+ | 0.0028 | 6.7 | 1890 | 0.0 | 0.0042 |
116
+ | 0.0036 | 6.81 | 1920 | 0.0 | 0.0036 |
117
+ | 0.0032 | 6.91 | 1950 | 0.0 | 0.0037 |
118
+ | 0.0029 | 7.02 | 1980 | 0.0 | 0.0035 |
119
+ | 0.0022 | 7.13 | 2010 | 0.0 | 0.0036 |
120
+ | 0.0025 | 7.23 | 2040 | 0.0 | 0.0036 |
121
+ | 0.0024 | 7.34 | 2070 | 0.0 | 0.0035 |
122
+ | 0.0022 | 7.45 | 2100 | 0.0 | 0.0032 |
123
+ | 0.0031 | 7.55 | 2130 | 0.0 | 0.0033 |
124
+ | 0.0028 | 7.66 | 2160 | 0.0 | 0.0031 |
125
+ | 0.0022 | 7.77 | 2190 | 0.0 | 0.0032 |
126
+ | 0.0023 | 7.87 | 2220 | 0.0 | 0.0034 |
127
+ | 0.0021 | 7.98 | 2250 | 0.0 | 0.0033 |
128
+ | 0.0018 | 8.09 | 2280 | 0.0 | 0.0031 |
129
+ | 0.0021 | 8.19 | 2310 | 0.0 | 0.0032 |
130
+ | 0.0038 | 8.3 | 2340 | 0.0 | 0.0030 |
131
+ | 0.0016 | 8.4 | 2370 | 0.0 | 0.0031 |
132
+ | 0.0066 | 8.51 | 2400 | 0.0 | 0.0029 |
133
+ | 0.0015 | 8.62 | 2430 | 0.0 | 0.0030 |
134
+ | 0.0022 | 8.72 | 2460 | 0.0 | 0.0028 |
135
+ | 0.0022 | 8.83 | 2490 | 0.0 | 0.0028 |
136
+ | 0.0012 | 8.94 | 2520 | 0.0 | 0.0027 |
137
+ | 0.0017 | 9.04 | 2550 | 0.0 | 0.0028 |
138
+ | 0.0019 | 9.15 | 2580 | 0.0 | 0.0027 |
139
+ | 0.0014 | 9.26 | 2610 | 0.0 | 0.0028 |
140
+ | 0.0014 | 9.36 | 2640 | 0.0 | 0.0028 |
141
+ | 0.0013 | 9.47 | 2670 | 0.0 | 0.0027 |
142
+ | 0.0014 | 9.57 | 2700 | 0.0 | 0.0027 |
143
+ | 0.0015 | 9.68 | 2730 | 0.0 | 0.0028 |
144
+ | 0.0014 | 9.79 | 2760 | 0.0 | 0.0027 |
145
+ | 0.001 | 9.89 | 2790 | 0.0 | 0.0026 |
146
+ | 0.0013 | 10.0 | 2820 | 0.0 | 0.0026 |
147
+ | 0.0014 | 10.11 | 2850 | 0.0 | 0.0026 |
148
+ | 0.0014 | 10.21 | 2880 | 0.0 | 0.0026 |
149
+ | 0.0031 | 10.32 | 2910 | 0.0 | 0.0025 |
150
+ | 0.0013 | 10.43 | 2940 | 0.0 | 0.0026 |
151
+ | 0.0012 | 10.53 | 2970 | 0.0 | 0.0026 |
152
+ | 0.0011 | 10.64 | 3000 | 0.0 | 0.0025 |
153
+ | 0.0009 | 10.74 | 3030 | 0.0 | 0.0025 |
154
+ | 0.0016 | 10.85 | 3060 | 0.0 | 0.0025 |
155
+ | 0.0014 | 10.96 | 3090 | 0.0 | 0.0025 |
156
+ | 0.0008 | 11.06 | 3120 | 0.0 | 0.0025 |
157
+ | 0.0008 | 11.17 | 3150 | 0.0 | 0.0025 |
158
+ | 0.0019 | 11.28 | 3180 | 0.0 | 0.0026 |
159
+ | 0.001 | 11.38 | 3210 | 0.0 | 0.0025 |
160
+ | 0.0009 | 11.49 | 3240 | 0.0 | 0.0025 |
161
+ | 0.0014 | 11.6 | 3270 | 0.0 | 0.0026 |
162
+ | 0.0014 | 11.7 | 3300 | 0.0 | 0.0024 |
163
+ | 0.0008 | 11.81 | 3330 | 0.0 | 0.0025 |
164
+ | 0.001 | 11.91 | 3360 | 0.0 | 0.0024 |
165
+ | 0.0012 | 12.02 | 3390 | 0.0 | 0.0024 |
166
+ | 0.0009 | 12.13 | 3420 | 0.0 | 0.0024 |
167
+ | 0.0007 | 12.23 | 3450 | 0.0 | 0.0025 |
168
+ | 0.0011 | 12.34 | 3480 | 0.0 | 0.0024 |
169
+ | 0.0012 | 12.45 | 3510 | 0.0 | 0.0023 |
170
+ | 0.0008 | 12.55 | 3540 | 0.0 | 0.0023 |
171
+ | 0.0005 | 12.66 | 3570 | 0.0 | 0.0024 |
172
+ | 0.0007 | 12.77 | 3600 | 0.0 | 0.0023 |
173
+ | 0.0006 | 12.87 | 3630 | 0.0 | 0.0024 |
174
+ | 0.0006 | 12.98 | 3660 | 0.0 | 0.0024 |
175
+ | 0.0014 | 13.09 | 3690 | 0.0 | 0.0025 |
176
+ | 0.0008 | 13.19 | 3720 | 0.0 | 0.0023 |
177
+ | 0.0007 | 13.3 | 3750 | 0.0 | 0.0022 |
178
+ | 0.0005 | 13.4 | 3780 | 0.0 | 0.0022 |
179
+ | 0.0006 | 13.51 | 3810 | 0.0 | 0.0022 |
180
+ | 0.0005 | 13.62 | 3840 | 0.0 | 0.0023 |
181
+ | 0.0007 | 13.72 | 3870 | 0.0 | 0.0022 |
182
+ | 0.0009 | 13.83 | 3900 | 0.0 | 0.0023 |
183
+ | 0.0005 | 13.94 | 3930 | 0.0 | 0.0022 |
184
+ | 0.0006 | 14.04 | 3960 | 0.0 | 0.0023 |
185
+ | 0.0005 | 14.15 | 3990 | 0.0 | 0.0023 |
186
+ | 0.0005 | 14.26 | 4020 | 0.0 | 0.0023 |
187
+ | 0.0008 | 14.36 | 4050 | 0.0 | 0.0022 |
188
+ | 0.0006 | 14.47 | 4080 | 0.0 | 0.0022 |
189
+ | 0.001 | 14.57 | 4110 | 0.0 | 0.0022 |
190
+ | 0.0004 | 14.68 | 4140 | 0.0 | 0.0021 |
191
+ | 0.0005 | 14.79 | 4170 | 0.0 | 0.0022 |
192
+ | 0.0006 | 14.89 | 4200 | 0.0 | 0.0023 |
193
+ | 0.0006 | 15.0 | 4230 | 0.0 | 0.0023 |
194
+ | 0.0007 | 15.11 | 4260 | 0.0 | 0.0022 |
195
+ | 0.0006 | 15.21 | 4290 | 0.0 | 0.0022 |
196
+ | 0.0005 | 15.32 | 4320 | 0.0 | 0.0021 |
197
+ | 0.0004 | 15.43 | 4350 | 0.0 | 0.0021 |
198
+ | 0.0008 | 15.53 | 4380 | 0.0 | 0.0021 |
199
+ | 0.0005 | 15.64 | 4410 | 0.0 | 0.0021 |
200
+ | 0.0005 | 15.74 | 4440 | 0.0 | 0.0022 |
201
+ | 0.0003 | 15.85 | 4470 | 0.0 | 0.0021 |
202
+ | 0.0005 | 15.96 | 4500 | 0.0 | 0.0021 |
203
+ | 0.0004 | 16.06 | 4530 | 0.0 | 0.0023 |
204
+ | 0.0004 | 16.17 | 4560 | 0.0 | 0.0022 |
205
+ | 0.0004 | 16.28 | 4590 | 0.0 | 0.0021 |
206
+ | 0.0005 | 16.38 | 4620 | 0.0 | 0.0020 |
207
+ | 0.0007 | 16.49 | 4650 | 0.0 | 0.0022 |
208
+ | 0.0005 | 16.6 | 4680 | 0.0 | 0.0021 |
209
+ | 0.0011 | 16.7 | 4710 | 0.0 | 0.0020 |
210
+ | 0.0005 | 16.81 | 4740 | 0.0 | 0.0020 |
211
+ | 0.0005 | 16.91 | 4770 | 0.0 | 0.0022 |
212
+ | 0.0004 | 17.02 | 4800 | 0.0 | 0.0021 |
213
+ | 0.0004 | 17.13 | 4830 | 0.0 | 0.0022 |
214
+ | 0.0004 | 17.23 | 4860 | 0.0 | 0.0021 |
215
+ | 0.0007 | 17.34 | 4890 | 0.0 | 0.0021 |
216
+ | 0.0006 | 17.45 | 4920 | 0.0 | 0.0021 |
217
+ | 0.0006 | 17.55 | 4950 | 0.0 | 0.0021 |
218
+ | 0.001 | 17.66 | 4980 | 0.0 | 0.0021 |
219
+ | 0.0003 | 17.77 | 5010 | 0.0 | 0.0020 |
220
+ | 0.0002 | 17.87 | 5040 | 0.0 | 0.0021 |
221
+ | 0.0005 | 17.98 | 5070 | 0.0 | 0.0021 |
222
+ | 0.0005 | 18.09 | 5100 | 0.0 | 0.0021 |
223
+ | 0.0004 | 18.19 | 5130 | 0.0 | 0.0021 |
224
+ | 0.0008 | 18.3 | 5160 | 0.0 | 0.0023 |
225
+ | 0.0004 | 18.4 | 5190 | 0.0 | 0.0020 |
226
+ | 0.0002 | 18.51 | 5220 | 0.0 | 0.0021 |
227
+ | 0.0003 | 18.62 | 5250 | 0.0 | 0.0020 |
228
+ | 0.0006 | 18.72 | 5280 | 0.0 | 0.0021 |
229
+ | 0.0004 | 18.83 | 5310 | 0.0 | 0.0021 |
230
+ | 0.0005 | 18.94 | 5340 | 0.0 | 0.0020 |
231
+ | 0.0004 | 19.04 | 5370 | 0.0 | 0.0020 |
232
+ | 0.0007 | 19.15 | 5400 | 0.0 | 0.0020 |
233
+ | 0.0005 | 19.26 | 5430 | 0.0 | 0.0021 |
234
+ | 0.0006 | 19.36 | 5460 | 0.0 | 0.0020 |
235
+ | 0.0004 | 19.47 | 5490 | 0.0 | 0.0021 |
236
+ | 0.0004 | 19.57 | 5520 | 0.0 | 0.0021 |
237
+ | 0.0002 | 19.68 | 5550 | 0.0 | 0.0021 |
238
+ | 0.0008 | 19.79 | 5580 | 0.0 | 0.0020 |
239
+ | 0.0006 | 19.89 | 5610 | 0.0 | 0.0024 |
240
+ | 0.0005 | 20.0 | 5640 | 0.0 | 0.0022 |
241
+ | 0.0002 | 20.11 | 5670 | 0.0 | 0.0021 |
242
+ | 0.0002 | 20.21 | 5700 | 0.0 | 0.0022 |
243
+ | 0.0002 | 20.32 | 5730 | 0.0 | 0.0022 |
244
+ | 0.0004 | 20.43 | 5760 | 0.0 | 0.0022 |
245
+ | 0.0002 | 20.53 | 5790 | 0.0 | 0.0023 |
246
+ | 0.0008 | 20.64 | 5820 | 0.0 | 0.0022 |
247
+ | 0.0002 | 20.74 | 5850 | 0.0 | 0.0023 |
248
+ | 0.0002 | 20.85 | 5880 | 0.0 | 0.0023 |
249
+ | 0.0021 | 20.96 | 5910 | 0.0 | 0.0023 |
250
+ | 0.0002 | 21.06 | 5940 | 0.0 | 0.0023 |
251
+ | 0.0002 | 21.17 | 5970 | 0.0 | 0.0023 |
252
+ | 0.0003 | 21.28 | 6000 | 0.0 | 0.0022 |
253
+ | 0.0002 | 21.38 | 6030 | 0.0 | 0.0023 |
254
+ | 0.0004 | 21.49 | 6060 | 0.0 | 0.0022 |
255
+ | 0.0002 | 21.6 | 6090 | 0.0 | 0.0023 |
256
+ | 0.0002 | 21.7 | 6120 | 0.0 | 0.0021 |
257
+ | 0.0007 | 21.81 | 6150 | 0.0 | 0.0022 |
258
+ | 0.0004 | 21.91 | 6180 | 0.0 | 0.0021 |
259
+ | 0.0002 | 22.02 | 6210 | 0.0 | 0.0021 |
260
+ | 0.0004 | 22.13 | 6240 | 0.0 | 0.0023 |
261
+ | 0.0004 | 22.23 | 6270 | 0.0 | 0.0023 |
262
+ | 0.0002 | 22.34 | 6300 | 0.0 | 0.0023 |
263
+ | 0.0002 | 22.45 | 6330 | 0.0 | 0.0024 |
264
+ | 0.0006 | 22.55 | 6360 | 0.0 | 0.0023 |
265
+ | 0.0002 | 22.66 | 6390 | 0.0 | 0.0021 |
266
+ | 0.0002 | 22.77 | 6420 | 0.0 | 0.0022 |
267
+ | 0.0002 | 22.87 | 6450 | 0.0 | 0.0023 |
268
+ | 0.0003 | 22.98 | 6480 | 0.0 | 0.0022 |
269
+ | 0.0004 | 23.09 | 6510 | 0.0 | 0.0021 |
270
+ | 0.0001 | 23.19 | 6540 | 0.0 | 0.0021 |
271
+ | 0.0004 | 23.3 | 6570 | 0.0 | 0.0023 |
272
+ | 0.0002 | 23.4 | 6600 | 0.0 | 0.0023 |
273
+ | 0.0002 | 23.51 | 6630 | 0.0 | 0.0021 |
274
+ | 0.0007 | 23.62 | 6660 | 0.0 | 0.0023 |
275
+ | 0.0001 | 23.72 | 6690 | 0.0 | 0.0022 |
276
+ | 0.0005 | 23.83 | 6720 | 0.0 | 0.0022 |
277
+ | 0.0003 | 23.94 | 6750 | 0.0 | 0.0022 |
278
+ | 0.0003 | 24.04 | 6780 | 0.0 | 0.0023 |
279
+ | 0.0013 | 24.15 | 6810 | 0.0 | 0.0022 |
280
+ | 0.0001 | 24.26 | 6840 | 0.0 | 0.0023 |
281
+ | 0.0003 | 24.36 | 6870 | 0.0 | 0.0022 |
282
+ | 0.0003 | 24.47 | 6900 | 0.0 | 0.0019 |
283
+ | 0.0001 | 24.57 | 6930 | 0.0 | 0.0023 |
284
+ | 0.0002 | 24.68 | 6960 | 0.0 | 0.0023 |
285
+ | 0.0002 | 24.79 | 6990 | 0.0 | 0.0021 |
286
+ | 0.0003 | 24.89 | 7020 | 0.0 | 0.0022 |
287
+ | 0.0003 | 25.0 | 7050 | 0.0 | 0.0022 |
288
+ | 0.0001 | 25.11 | 7080 | 0.0 | 0.0023 |
289
+ | 0.0002 | 25.21 | 7110 | 0.0 | 0.0022 |
290
+ | 0.0002 | 25.32 | 7140 | 0.0 | 0.0022 |
291
+ | 0.0002 | 25.43 | 7170 | 0.0 | 0.0022 |
292
+ | 0.0002 | 25.53 | 7200 | 0.0 | 0.0022 |
293
+ | 0.0003 | 25.64 | 7230 | 0.0 | 0.0023 |
294
+ | 0.0001 | 25.74 | 7260 | 0.0 | 0.0024 |
295
+ | 0.0001 | 25.85 | 7290 | 0.0 | 0.0023 |
296
+ | 0.0002 | 25.96 | 7320 | 0.0 | 0.0022 |
297
+ | 0.0002 | 26.06 | 7350 | 0.0 | 0.0021 |
298
+ | 0.0004 | 26.17 | 7380 | 0.0 | 0.0023 |
299
+ | 0.0002 | 26.28 | 7410 | 0.0 | 0.0023 |
300
+ | 0.0001 | 26.38 | 7440 | 0.0 | 0.0023 |
301
+ | 0.0002 | 26.49 | 7470 | 0.0 | 0.0020 |
302
+ | 0.0006 | 26.6 | 7500 | 0.0 | 0.0027 |
303
+ | 0.0002 | 26.7 | 7530 | 0.0 | 0.0022 |
304
+ | 0.0002 | 26.81 | 7560 | 0.0 | 0.0022 |
305
+ | 0.0006 | 26.91 | 7590 | 0.0 | 0.0020 |
306
+ | 0.0002 | 27.02 | 7620 | 0.0 | 0.0023 |
307
+ | 0.0004 | 27.13 | 7650 | 0.0 | 0.0022 |
308
+ | 0.0002 | 27.23 | 7680 | 0.0 | 0.0020 |
309
+ | 0.0004 | 27.34 | 7710 | 0.0 | 0.0021 |
310
+ | 0.0002 | 27.45 | 7740 | 0.0 | 0.0023 |
311
+ | 0.0001 | 27.55 | 7770 | 0.0 | 0.0023 |
312
+ | 0.0005 | 27.66 | 7800 | 0.0 | 0.0022 |
313
+ | 0.0002 | 27.77 | 7830 | 0.0 | 0.0023 |
314
+ | 0.0001 | 27.87 | 7860 | 0.0 | 0.0021 |
315
+ | 0.0001 | 27.98 | 7890 | 0.0 | 0.0022 |
316
+ | 0.0001 | 28.09 | 7920 | 0.0 | 0.0022 |
317
+ | 0.0002 | 28.19 | 7950 | 0.0 | 0.0021 |
318
+ | 0.0002 | 28.3 | 7980 | 0.0 | 0.0020 |
319
+ | 0.0001 | 28.4 | 8010 | 0.0 | 0.0023 |
320
+ | 0.0006 | 28.51 | 8040 | 0.0 | 0.0022 |
321
+ | 0.0001 | 28.62 | 8070 | 0.0 | 0.0023 |
322
+ | 0.0001 | 28.72 | 8100 | 0.0 | 0.0022 |
323
+ | 0.0001 | 28.83 | 8130 | 0.0 | 0.0021 |
324
+ | 0.0001 | 28.94 | 8160 | 0.0 | 0.0021 |
325
+ | 0.0002 | 29.04 | 8190 | 0.0 | 0.0023 |
326
+ | 0.0002 | 29.15 | 8220 | 0.0 | 0.0022 |
327
+ | 0.0001 | 29.26 | 8250 | 0.0 | 0.0020 |
328
+ | 0.0003 | 29.36 | 8280 | 0.0 | 0.0023 |
329
+ | 0.0001 | 29.47 | 8310 | 0.0 | 0.0022 |
330
+ | 0.0001 | 29.57 | 8340 | 0.0 | 0.0023 |
331
+ | 0.0002 | 29.68 | 8370 | 0.0 | 0.0023 |
332
+ | 0.0002 | 29.79 | 8400 | 0.0 | 0.0021 |
333
+ | 0.0002 | 29.89 | 8430 | 0.0 | 0.0023 |
334
+ | 0.0001 | 30.0 | 8460 | 0.0 | 0.0023 |
335
+ | 0.0001 | 30.11 | 8490 | 0.0 | 0.0025 |
336
+ | 0.0001 | 30.21 | 8520 | 0.0 | 0.0024 |
337
+ | 0.0001 | 30.32 | 8550 | 0.0 | 0.0024 |
338
+ | 0.0002 | 30.43 | 8580 | 0.0 | 0.0024 |
339
+ | 0.0003 | 30.53 | 8610 | 0.0 | 0.0024 |
340
+ | 0.0004 | 30.64 | 8640 | 0.0 | 0.0023 |
341
+ | 0.0003 | 30.74 | 8670 | 0.0 | 0.0021 |
342
+ | 0.0001 | 30.85 | 8700 | 0.0 | 0.0019 |
343
+ | 0.0001 | 30.96 | 8730 | 0.0 | 0.0021 |
344
+ | 0.0001 | 31.06 | 8760 | 0.0 | 0.0023 |
345
+ | 0.0004 | 31.17 | 8790 | 0.0 | 0.0022 |
346
+ | 0.0002 | 31.28 | 8820 | 0.0 | 0.0019 |
347
+ | 0.0001 | 31.38 | 8850 | 0.0 | 0.0023 |
348
+ | 0.0005 | 31.49 | 8880 | 0.0 | 0.0024 |
349
+ | 0.0001 | 31.6 | 8910 | 0.0 | 0.0024 |
350
+ | 0.0003 | 31.7 | 8940 | 0.0 | 0.0024 |
351
+ | 0.0005 | 31.81 | 8970 | 0.0 | 0.0024 |
352
+ | 0.0002 | 31.91 | 9000 | 0.0 | 0.0022 |
353
+ | 0.0003 | 32.02 | 9030 | 0.0 | 0.0023 |
354
+ | 0.0001 | 32.13 | 9060 | 0.0 | 0.0023 |
355
+ | 0.0003 | 32.23 | 9090 | 0.0 | 0.0023 |
356
+ | 0.0002 | 32.34 | 9120 | 0.0 | 0.0021 |
357
+ | 0.0002 | 32.45 | 9150 | 0.0 | 0.0023 |
358
+ | 0.0006 | 32.55 | 9180 | 0.0 | 0.0022 |
359
+ | 0.0006 | 32.66 | 9210 | 0.0 | 0.0023 |
360
+ | 0.0014 | 32.77 | 9240 | 0.0 | 0.0023 |
361
+ | 0.0001 | 32.87 | 9270 | 0.0 | 0.0022 |
362
+ | 0.0001 | 32.98 | 9300 | 0.0 | 0.0021 |
363
+ | 0.0001 | 33.09 | 9330 | 0.0 | 0.0021 |
364
+ | 0.0002 | 33.19 | 9360 | 0.0 | 0.0025 |
365
+ | 0.0001 | 33.3 | 9390 | 0.0 | 0.0024 |
366
+ | 0.0001 | 33.4 | 9420 | 0.0 | 0.0022 |
367
+ | 0.0002 | 33.51 | 9450 | 0.0 | 0.0023 |
368
+ | 0.002 | 33.62 | 9480 | 0.0 | 0.0024 |
369
+ | 0.0003 | 33.72 | 9510 | 0.0 | 0.0022 |
370
+ | 0.0003 | 33.83 | 9540 | 0.0 | 0.0023 |
371
+ | 0.0006 | 33.94 | 9570 | 0.0 | 0.0023 |
372
+ | 0.0001 | 34.04 | 9600 | 0.0 | 0.0024 |
373
+ | 0.0001 | 34.15 | 9630 | 0.0 | 0.0023 |
374
+ | 0.0002 | 34.26 | 9660 | 0.0 | 0.0023 |
375
+ | 0.0002 | 34.36 | 9690 | 0.0 | 0.0021 |
376
+ | 0.0001 | 34.47 | 9720 | 0.0 | 0.0023 |
377
+ | 0.0001 | 34.57 | 9750 | 0.0 | 0.0023 |
378
+ | 0.0003 | 34.68 | 9780 | 0.0 | 0.0024 |
379
+ | 0.0002 | 34.79 | 9810 | 0.0 | 0.0023 |
380
+ | 0.0002 | 34.89 | 9840 | 0.0 | 0.0023 |
381
+ | 0.0001 | 35.0 | 9870 | 0.0 | 0.0023 |
382
+ | 0.0003 | 35.11 | 9900 | 0.0 | 0.0024 |
383
+ | 0.0001 | 35.21 | 9930 | 0.0 | 0.0024 |
384
+ | 0.0002 | 35.32 | 9960 | 0.0 | 0.0022 |
385
+ | 0.0001 | 35.43 | 9990 | 0.0 | 0.0024 |
386
+ | 0.0002 | 35.53 | 10020 | 0.0 | 0.0023 |
387
+ | 0.0001 | 35.64 | 10050 | 0.0 | 0.0024 |
388
+ | 0.0001 | 35.74 | 10080 | 0.0 | 0.0024 |
389
+ | 0.0001 | 35.85 | 10110 | 0.0 | 0.0024 |
390
+ | 0.0001 | 35.96 | 10140 | 0.0 | 0.0022 |
391
+ | 0.0002 | 36.06 | 10170 | 0.0 | 0.0023 |
392
+ | 0.0002 | 36.17 | 10200 | 0.0 | 0.0025 |
393
+ | 0.0001 | 36.28 | 10230 | 0.0 | 0.0025 |
394
+ | 0.0001 | 36.38 | 10260 | 0.0 | 0.0027 |
395
+ | 0.0001 | 36.49 | 10290 | 0.0 | 0.0024 |
396
+ | 0.0001 | 36.6 | 10320 | 0.0 | 0.0024 |
397
+ | 0.0004 | 36.7 | 10350 | 0.0 | 0.0024 |
398
+ | 0.0002 | 36.81 | 10380 | 0.0 | 0.0023 |
399
+ | 0.0002 | 36.91 | 10410 | 0.0 | 0.0024 |
400
+ | 0.0003 | 37.02 | 10440 | 0.0 | 0.0024 |
401
+ | 0.0004 | 37.13 | 10470 | 0.0 | 0.0025 |
402
+ | 0.0001 | 37.23 | 10500 | 0.0 | 0.0024 |
403
+ | 0.0001 | 37.34 | 10530 | 0.0 | 0.0024 |
404
+ | 0.0001 | 37.45 | 10560 | 0.0 | 0.0025 |
405
+ | 0.0002 | 37.55 | 10590 | 0.0 | 0.0025 |
406
+ | 0.0002 | 37.66 | 10620 | 0.0 | 0.0024 |
407
+ | 0.0001 | 37.77 | 10650 | 0.0 | 0.0025 |
408
+ | 0.0005 | 37.87 | 10680 | 0.0 | 0.0024 |
409
+ | 0.0001 | 37.98 | 10710 | 0.0 | 0.0025 |
410
+ | 0.0 | 38.09 | 10740 | 0.0 | 0.0025 |
411
+ | 0.0001 | 38.19 | 10770 | 0.0 | 0.0026 |
412
+ | 0.0001 | 38.3 | 10800 | 0.0 | 0.0025 |
413
+ | 0.0001 | 38.4 | 10830 | 0.0 | 0.0025 |
414
+ | 0.0001 | 38.51 | 10860 | 0.0 | 0.0025 |
415
+ | 0.0002 | 38.62 | 10890 | 0.0 | 0.0024 |
416
+ | 0.0001 | 38.72 | 10920 | 0.0 | 0.0025 |
417
+ | 0.0 | 38.83 | 10950 | 0.0 | 0.0025 |
418
+ | 0.0001 | 38.94 | 10980 | 0.0 | 0.0024 |
419
+ | 0.0001 | 39.04 | 11010 | 0.0 | 0.0024 |
420
+ | 0.0003 | 39.15 | 11040 | 0.0 | 0.0024 |
421
+ | 0.0005 | 39.26 | 11070 | 0.0 | 0.0025 |
422
+ | 0.0 | 39.36 | 11100 | 0.0 | 0.0024 |
423
+ | 0.0001 | 39.47 | 11130 | 0.0 | 0.0024 |
424
+ | 0.0002 | 39.57 | 11160 | 0.0 | 0.0024 |
425
+ | 0.0004 | 39.68 | 11190 | 0.0 | 0.0024 |
426
+ | 0.0002 | 39.79 | 11220 | 0.0 | 0.0025 |
427
+ | 0.0003 | 39.89 | 11250 | 0.0 | 0.0024 |
428
+ | 0.0 | 40.0 | 11280 | 0.0 | 0.0024 |
429
+
430
+
431
+ ### Framework versions
432
+
433
+ - Transformers 4.37.1
434
+ - Pytorch 2.1.2
435
+ - Datasets 2.16.1
436
+ - Tokenizers 0.15.1
config.json ADDED
@@ -0,0 +1,76 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "nvidia/mit-b1",
3
+ "architectures": [
4
+ "SegformerForKelpSemanticSegmentation"
5
+ ],
6
+ "attention_probs_dropout_prob": 0.0,
7
+ "classifier_dropout_prob": 0.1,
8
+ "decoder_hidden_size": 256,
9
+ "depths": [
10
+ 2,
11
+ 2,
12
+ 2,
13
+ 2
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
+ "1": "kelp"
32
+ },
33
+ "image_size": 224,
34
+ "initializer_range": 0.02,
35
+ "label2id": {
36
+ "kelp": 1
37
+ },
38
+ "layer_norm_eps": 1e-06,
39
+ "mlp_ratios": [
40
+ 4,
41
+ 4,
42
+ 4,
43
+ 4
44
+ ],
45
+ "model_type": "segformer",
46
+ "num_attention_heads": [
47
+ 1,
48
+ 2,
49
+ 5,
50
+ 8
51
+ ],
52
+ "num_channels": 3,
53
+ "num_encoder_blocks": 4,
54
+ "patch_sizes": [
55
+ 7,
56
+ 3,
57
+ 3,
58
+ 3
59
+ ],
60
+ "reshape_last_stage": true,
61
+ "semantic_loss_ignore_index": 255,
62
+ "sr_ratios": [
63
+ 8,
64
+ 4,
65
+ 2,
66
+ 1
67
+ ],
68
+ "strides": [
69
+ 4,
70
+ 2,
71
+ 2,
72
+ 2
73
+ ],
74
+ "torch_dtype": "float32",
75
+ "transformers_version": "4.37.1"
76
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1a71bb7029fb48c0e39407598826465a82fc629ae38bcdeb8c674f3f18117ec1
3
+ size 54736348
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:beeb93b8feecc3c8213f3cc7dccac7261353d407cf831ab3a2ae822e29fbd7d6
3
+ size 4664