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agent9717
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PointCRA

Image Feature Extraction
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PointCloud
PointCloudAnalysis
PointCloudSegmentation
Model card Files
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agent9717 commited on
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  1. best.pt +3 -0
  2. out.log +613 -0
best.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:0c8ddab5b1dc614973fcb0f76a26972caccf6668aab945f8052cdf5b993613b1
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+ size 47230776
out.log ADDED
@@ -0,0 +1,613 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2026-04-06 11:10:10,450 - INFO - Successfully copied delasemseg.py to output/log/20260406_111010/
2
+ 2026-04-06 11:10:10,450 - INFO - base
3
+ 2026-04-06 11:10:10,652 - INFO - ================================================================================
4
+ CONFIG PARAMETERS
5
+ ================================================================================
6
+ Training Parameters:
7
+ - Batch size: 8
8
+ - Learning rate: 0.006
9
+ - Epochs: 110
10
+ - Warmup: 10
11
+ - Label smoothing: 0.2
12
+ S3DIS Dataset Parameters:
13
+ - k: [24, 32, 32, 32]
14
+ - grid_size: [0.04, 0.08, 0.16, 0.32]
15
+ - max_pts: 30000
16
+ S3DIS Warmup Parameters:
17
+ - k: [24, 32, 32, 32]
18
+ - grid_size: [0.04, 3.5, 3.5, 3.5]
19
+ - max_pts: 30000
20
+ Delaunay Segmentation Parameters:
21
+ - ks: [24, 32, 32, 32]
22
+ - depths: [4, 4, 8, 8]
23
+ - dims: [64, 128, 256, 512]
24
+ - head_dim: 256
25
+ - num_classes: 13
26
+ - drop_paths: [[0.0, 0.004347826354205608, 0.008695652708411217, 0.013043479062616825, 0.3], [0.017391305416822433, 0.021739132702350616, 0.02608695812523365, 0.030434783548116684, 0.3], [0.03478261083364487, 0.03913043811917305, 0.04347826540470123, 0.04782608896493912, 0.052173912525177, 0.056521736085414886, 0.06086956337094307, 0.06521739065647125, 0.3], [0.06956521421670914, 0.07391304522752762, 0.0782608687877655, 0.08260869979858398, 0.08695652335882187, 0.09130434691905975, 0.09565217792987823, 0.10000000149011612, 0.3]]
27
+ - head_drops: [0.0, 0.05000000074505806, 0.10000000894069672, 0.15000000596046448]
28
+ - bn_momentum: 0.02
29
+ - act: <class 'torch.nn.modules.activation.GELU'>
30
+ - mlp_ratio: 2
31
+ - use_cp: False
32
+ - cor_std: [1.6, 3.8, 7.6, 15.2]
33
+ - all_dist: tensor([ 0.0160, 0.0255, 0.0490, ..., 11.5967, 12.8736, 14.9626])
34
+ - all_dist0: tensor([ 0.0404, 0.0425, 0.0451, ..., 14.8801, 15.2674, 31.5054])
35
+ - cp_bn_momentum: 0.01005050633883342
36
+ ================================================================================
37
+ 2026-04-06 11:10:10,652 - INFO - MODEL STRUCTURE:
38
+ 2026-04-06 11:10:10,652 - INFO - ================================================================================
39
+ 2026-04-06 11:10:10,652 - INFO - torchsummary not installed, using simple model print
40
+ 2026-04-06 11:10:10,652 - INFO - Raw model structure:
41
+ 2026-04-06 11:10:10,653 - INFO - DelaSemSeg(
42
+ (stage): Stage(
43
+ (nbr_bn): BatchNorm1d(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
44
+ (nbr_proj): Sequential(
45
+ (0): BatchNorm1d(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
46
+ (1): Linear(in_features=64, out_features=128, bias=True)
47
+ (2): GELU(approximate='none')
48
+ (3): Linear(in_features=128, out_features=64, bias=False)
49
+ )
50
+ (blk): Block(
51
+ (lfps): ModuleList(
52
+ (0-3): 4 x LFP(
53
+ (proj): Linear(in_features=64, out_features=64, bias=False)
54
+ (bn): BatchNorm1d(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
55
+ )
56
+ )
57
+ (mlp): Mlp(
58
+ (mlp): Sequential(
59
+ (0): Linear(in_features=64, out_features=128, bias=True)
60
+ (1): GELU(approximate='none')
61
+ (2): Linear(in_features=128, out_features=64, bias=False)
62
+ (3): BatchNorm1d(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
63
+ )
64
+ )
65
+ (mlps): ModuleList(
66
+ (0-1): 2 x Mlp(
67
+ (mlp): Sequential(
68
+ (0): Linear(in_features=64, out_features=128, bias=True)
69
+ (1): GELU(approximate='none')
70
+ (2): Linear(in_features=128, out_features=64, bias=False)
71
+ (3): BatchNorm1d(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
72
+ )
73
+ )
74
+ )
75
+ (drop_paths): ModuleList(
76
+ (0): DropPath(drop_prob=0.000)
77
+ (1): DropPath(drop_prob=0.004)
78
+ (2): DropPath(drop_prob=0.009)
79
+ (3): DropPath(drop_prob=0.013)
80
+ (4): DropPath(drop_prob=0.300)
81
+ )
82
+ (final_ctwc): CTWC_Block(
83
+ (drop): DropPath(drop_prob=0.100)
84
+ (mlp): Sequential(
85
+ (0): Linear(in_features=64, out_features=128, bias=True)
86
+ (1): GELU(approximate='none')
87
+ (2): Linear(in_features=128, out_features=64, bias=False)
88
+ (3): BatchNorm1d(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
89
+ )
90
+ (net): Sequential(
91
+ (0): Linear(in_features=2, out_features=4, bias=True)
92
+ (1): ReLU(inplace=True)
93
+ (2): Linear(in_features=4, out_features=4, bias=True)
94
+ (3): ReLU(inplace=True)
95
+ (4): Linear(in_features=4, out_features=1, bias=True)
96
+ (5): Sigmoid()
97
+ )
98
+ )
99
+ (final_drop_path): DropPath(drop_prob=0.200)
100
+ )
101
+ (drop): DropPath(drop_prob=0.000)
102
+ (postproj): Sequential(
103
+ (0): BatchNorm1d(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
104
+ (1): Linear(in_features=64, out_features=256, bias=False)
105
+ )
106
+ (cor_head): Sequential(
107
+ (0): Linear(in_features=64, out_features=32, bias=False)
108
+ (1): BatchNorm1d(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
109
+ (2): GELU(approximate='none')
110
+ (3): Linear(in_features=32, out_features=3, bias=False)
111
+ )
112
+ (sub_stage): Stage(
113
+ (nbr_bn): BatchNorm1d(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
114
+ (nbr_proj): Sequential(
115
+ (0): BatchNorm1d(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
116
+ (1): Linear(in_features=64, out_features=128, bias=True)
117
+ (2): GELU(approximate='none')
118
+ (3): Linear(in_features=128, out_features=128, bias=False)
119
+ )
120
+ (lfp): LFP(
121
+ (proj): Linear(in_features=64, out_features=128, bias=False)
122
+ (bn): BatchNorm1d(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
123
+ )
124
+ (skip_proj): Sequential(
125
+ (0): Linear(in_features=64, out_features=128, bias=False)
126
+ (1): BatchNorm1d(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
127
+ )
128
+ (blk): Block(
129
+ (lfps): ModuleList(
130
+ (0-3): 4 x LFP(
131
+ (proj): Linear(in_features=128, out_features=128, bias=False)
132
+ (bn): BatchNorm1d(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
133
+ )
134
+ )
135
+ (mlp): Mlp(
136
+ (mlp): Sequential(
137
+ (0): Linear(in_features=128, out_features=256, bias=True)
138
+ (1): GELU(approximate='none')
139
+ (2): Linear(in_features=256, out_features=128, bias=False)
140
+ (3): BatchNorm1d(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
141
+ )
142
+ )
143
+ (mlps): ModuleList(
144
+ (0-1): 2 x Mlp(
145
+ (mlp): Sequential(
146
+ (0): Linear(in_features=128, out_features=256, bias=True)
147
+ (1): GELU(approximate='none')
148
+ (2): Linear(in_features=256, out_features=128, bias=False)
149
+ (3): BatchNorm1d(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
150
+ )
151
+ )
152
+ )
153
+ (drop_paths): ModuleList(
154
+ (0): DropPath(drop_prob=0.017)
155
+ (1): DropPath(drop_prob=0.022)
156
+ (2): DropPath(drop_prob=0.026)
157
+ (3): DropPath(drop_prob=0.030)
158
+ (4): DropPath(drop_prob=0.300)
159
+ )
160
+ (final_ctwc): CTWC_Block(
161
+ (drop): DropPath(drop_prob=0.100)
162
+ (mlp): Sequential(
163
+ (0): Linear(in_features=128, out_features=256, bias=True)
164
+ (1): GELU(approximate='none')
165
+ (2): Linear(in_features=256, out_features=128, bias=False)
166
+ (3): BatchNorm1d(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
167
+ )
168
+ (net): Sequential(
169
+ (0): Linear(in_features=2, out_features=4, bias=True)
170
+ (1): ReLU(inplace=True)
171
+ (2): Linear(in_features=4, out_features=4, bias=True)
172
+ (3): ReLU(inplace=True)
173
+ (4): Linear(in_features=4, out_features=1, bias=True)
174
+ (5): Sigmoid()
175
+ )
176
+ )
177
+ (final_drop_path): DropPath(drop_prob=0.200)
178
+ )
179
+ (drop): DropPath(drop_prob=0.050)
180
+ (postproj): Sequential(
181
+ (0): BatchNorm1d(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
182
+ (1): Linear(in_features=128, out_features=256, bias=False)
183
+ )
184
+ (cor_head): Sequential(
185
+ (0): Linear(in_features=128, out_features=32, bias=False)
186
+ (1): BatchNorm1d(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
187
+ (2): GELU(approximate='none')
188
+ (3): Linear(in_features=32, out_features=3, bias=False)
189
+ )
190
+ (sub_stage): Stage(
191
+ (nbr_bn): BatchNorm1d(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
192
+ (nbr_proj): Sequential(
193
+ (0): BatchNorm1d(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
194
+ (1): Linear(in_features=64, out_features=128, bias=True)
195
+ (2): GELU(approximate='none')
196
+ (3): Linear(in_features=128, out_features=256, bias=False)
197
+ )
198
+ (lfp): LFP(
199
+ (proj): Linear(in_features=128, out_features=256, bias=False)
200
+ (bn): BatchNorm1d(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
201
+ )
202
+ (skip_proj): Sequential(
203
+ (0): Linear(in_features=128, out_features=256, bias=False)
204
+ (1): BatchNorm1d(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
205
+ )
206
+ (blk): Block(
207
+ (lfps): ModuleList(
208
+ (0-7): 8 x LFP(
209
+ (proj): Linear(in_features=256, out_features=256, bias=False)
210
+ (bn): BatchNorm1d(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
211
+ )
212
+ )
213
+ (mlp): Mlp(
214
+ (mlp): Sequential(
215
+ (0): Linear(in_features=256, out_features=512, bias=True)
216
+ (1): GELU(approximate='none')
217
+ (2): Linear(in_features=512, out_features=256, bias=False)
218
+ (3): BatchNorm1d(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
219
+ )
220
+ )
221
+ (mlps): ModuleList(
222
+ (0-3): 4 x Mlp(
223
+ (mlp): Sequential(
224
+ (0): Linear(in_features=256, out_features=512, bias=True)
225
+ (1): GELU(approximate='none')
226
+ (2): Linear(in_features=512, out_features=256, bias=False)
227
+ (3): BatchNorm1d(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
228
+ )
229
+ )
230
+ )
231
+ (drop_paths): ModuleList(
232
+ (0): DropPath(drop_prob=0.035)
233
+ (1): DropPath(drop_prob=0.039)
234
+ (2): DropPath(drop_prob=0.043)
235
+ (3): DropPath(drop_prob=0.048)
236
+ (4): DropPath(drop_prob=0.052)
237
+ (5): DropPath(drop_prob=0.057)
238
+ (6): DropPath(drop_prob=0.061)
239
+ (7): DropPath(drop_prob=0.065)
240
+ (8): DropPath(drop_prob=0.300)
241
+ )
242
+ (final_ctwc): CTWC_Block(
243
+ (drop): DropPath(drop_prob=0.100)
244
+ (mlp): Sequential(
245
+ (0): Linear(in_features=256, out_features=512, bias=True)
246
+ (1): GELU(approximate='none')
247
+ (2): Linear(in_features=512, out_features=256, bias=False)
248
+ (3): BatchNorm1d(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
249
+ )
250
+ (net): Sequential(
251
+ (0): Linear(in_features=2, out_features=4, bias=True)
252
+ (1): ReLU(inplace=True)
253
+ (2): Linear(in_features=4, out_features=4, bias=True)
254
+ (3): ReLU(inplace=True)
255
+ (4): Linear(in_features=4, out_features=1, bias=True)
256
+ (5): Sigmoid()
257
+ )
258
+ )
259
+ (final_drop_path): DropPath(drop_prob=0.200)
260
+ )
261
+ (drop): DropPath(drop_prob=0.100)
262
+ (postproj): Sequential(
263
+ (0): BatchNorm1d(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
264
+ (1): Linear(in_features=256, out_features=256, bias=False)
265
+ )
266
+ (cor_head): Sequential(
267
+ (0): Linear(in_features=256, out_features=32, bias=False)
268
+ (1): BatchNorm1d(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
269
+ (2): GELU(approximate='none')
270
+ (3): Linear(in_features=32, out_features=3, bias=False)
271
+ )
272
+ (sub_stage): Stage(
273
+ (nbr_bn): BatchNorm1d(512, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
274
+ (nbr_proj): Sequential(
275
+ (0): BatchNorm1d(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
276
+ (1): Linear(in_features=64, out_features=128, bias=True)
277
+ (2): GELU(approximate='none')
278
+ (3): Linear(in_features=128, out_features=512, bias=False)
279
+ )
280
+ (lfp): LFP(
281
+ (proj): Linear(in_features=256, out_features=512, bias=False)
282
+ (bn): BatchNorm1d(512, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
283
+ )
284
+ (skip_proj): Sequential(
285
+ (0): Linear(in_features=256, out_features=512, bias=False)
286
+ (1): BatchNorm1d(512, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
287
+ )
288
+ (blk): Block(
289
+ (lfps): ModuleList(
290
+ (0-7): 8 x LFP(
291
+ (proj): Linear(in_features=512, out_features=512, bias=False)
292
+ (bn): BatchNorm1d(512, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
293
+ )
294
+ )
295
+ (mlp): Mlp(
296
+ (mlp): Sequential(
297
+ (0): Linear(in_features=512, out_features=1024, bias=True)
298
+ (1): GELU(approximate='none')
299
+ (2): Linear(in_features=1024, out_features=512, bias=False)
300
+ (3): BatchNorm1d(512, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
301
+ )
302
+ )
303
+ (mlps): ModuleList(
304
+ (0-3): 4 x Mlp(
305
+ (mlp): Sequential(
306
+ (0): Linear(in_features=512, out_features=1024, bias=True)
307
+ (1): GELU(approximate='none')
308
+ (2): Linear(in_features=1024, out_features=512, bias=False)
309
+ (3): BatchNorm1d(512, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
310
+ )
311
+ )
312
+ )
313
+ (drop_paths): ModuleList(
314
+ (0): DropPath(drop_prob=0.070)
315
+ (1): DropPath(drop_prob=0.074)
316
+ (2): DropPath(drop_prob=0.078)
317
+ (3): DropPath(drop_prob=0.083)
318
+ (4): DropPath(drop_prob=0.087)
319
+ (5): DropPath(drop_prob=0.091)
320
+ (6): DropPath(drop_prob=0.096)
321
+ (7): DropPath(drop_prob=0.100)
322
+ (8): DropPath(drop_prob=0.300)
323
+ )
324
+ (final_ctwc): CTWC_Block(
325
+ (drop): DropPath(drop_prob=0.100)
326
+ (mlp): Sequential(
327
+ (0): Linear(in_features=512, out_features=1024, bias=True)
328
+ (1): GELU(approximate='none')
329
+ (2): Linear(in_features=1024, out_features=512, bias=False)
330
+ (3): BatchNorm1d(512, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
331
+ )
332
+ (net): Sequential(
333
+ (0): Linear(in_features=2, out_features=4, bias=True)
334
+ (1): ReLU(inplace=True)
335
+ (2): Linear(in_features=4, out_features=4, bias=True)
336
+ (3): ReLU(inplace=True)
337
+ (4): Linear(in_features=4, out_features=1, bias=True)
338
+ (5): Sigmoid()
339
+ )
340
+ )
341
+ (final_drop_path): DropPath(drop_prob=0.200)
342
+ )
343
+ (drop): DropPath(drop_prob=0.150)
344
+ (postproj): Sequential(
345
+ (0): BatchNorm1d(512, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
346
+ (1): Linear(in_features=512, out_features=256, bias=False)
347
+ )
348
+ (cor_head): Sequential(
349
+ (0): Linear(in_features=512, out_features=32, bias=False)
350
+ (1): BatchNorm1d(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
351
+ (2): GELU(approximate='none')
352
+ (3): Linear(in_features=32, out_features=3, bias=False)
353
+ )
354
+ )
355
+ )
356
+ )
357
+ )
358
+ (head): Sequential(
359
+ (0): BatchNorm1d(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
360
+ (1): GELU(approximate='none')
361
+ (2): Linear(in_features=256, out_features=13, bias=True)
362
+ )
363
+ )
364
+ 2026-04-06 11:10:10,653 - INFO - ================================================================================
365
+ 2026-04-06 11:10:10,654 - INFO - MODEL PARAMETERS:
366
+ - Total parameters: 11,730,521
367
+ - Trainable parameters: 11,730,521
368
+ - Non-trainable parameters: 0
369
+ - Model size: 44.75 MB (FP32)
370
+ ================================================================================
371
+ 2026-04-06 11:12:08,580 - INFO - epoch:0/110 || loss: 1.367 || cls: 1.2886
372
+ 2026-04-06 11:12:11,691 - INFO - duration: 1:54 (mm:ss),train miou:0.4739,val miou:0.2725 best miou: 0
373
+ 2026-04-06 11:12:11,691 - INFO - ============================== new best! epoch:0,train:0.4739,val:0.2725 ==============================
374
+ 2026-04-06 11:14:02,388 - INFO - epoch:1/110 || loss: 1.1982 || cls: 0.8202
375
+ 2026-04-06 11:14:04,867 - INFO - duration: 1:53 (mm:ss),train miou:0.6728,val miou:0.0078 best miou: 0.2725
376
+ 2026-04-06 11:15:55,881 - INFO - epoch:2/110 || loss: 1.1538 || cls: 0.7513
377
+ 2026-04-06 11:15:58,179 - INFO - duration: 1:53 (mm:ss),train miou:0.7284,val miou:0.2405 best miou: 0.2725
378
+ 2026-04-06 11:17:48,858 - INFO - epoch:3/110 || loss: 1.13 || cls: 0.6818
379
+ 2026-04-06 11:17:51,416 - INFO - duration: 1:53 (mm:ss),train miou:0.7582,val miou:0.1973 best miou: 0.2725
380
+ 2026-04-06 11:19:42,092 - INFO - epoch:4/110 || loss: 1.1173 || cls: 0.5636
381
+ 2026-04-06 11:19:44,606 - INFO - duration: 1:53 (mm:ss),train miou:0.7734,val miou:0.2793 best miou: 0.2725
382
+ 2026-04-06 11:19:44,606 - INFO - ============================== new best! epoch:4,train:0.7734,val:0.2793 ==============================
383
+ 2026-04-06 11:21:35,344 - INFO - epoch:5/110 || loss: 1.1028 || cls: 0.4514
384
+ 2026-04-06 11:21:37,908 - INFO - duration: 1:53 (mm:ss),train miou:0.7942,val miou:0.6452 best miou: 0.2793
385
+ 2026-04-06 11:21:37,908 - INFO - ============================== new best! epoch:5,train:0.7942,val:0.6452 ==============================
386
+ 2026-04-06 11:23:28,667 - INFO - epoch:6/110 || loss: 1.0968 || cls: 0.4074
387
+ 2026-04-06 11:23:31,199 - INFO - duration: 1:53 (mm:ss),train miou:0.7986,val miou:0.6002 best miou: 0.6452
388
+ 2026-04-06 11:25:21,953 - INFO - epoch:7/110 || loss: 1.0916 || cls: 0.3757
389
+ 2026-04-06 11:25:24,561 - INFO - duration: 1:53 (mm:ss),train miou:0.8054,val miou:0.5855 best miou: 0.6452
390
+ 2026-04-06 11:27:15,506 - INFO - epoch:8/110 || loss: 1.0912 || cls: 0.3647
391
+ 2026-04-06 11:27:17,758 - INFO - duration: 1:53 (mm:ss),train miou:0.8085,val miou:0.0091 best miou: 0.6452
392
+ 2026-04-06 11:29:08,651 - INFO - epoch:9/110 || loss: 1.0845 || cls: 0.3468
393
+ 2026-04-06 11:29:10,810 - INFO - duration: 1:52 (mm:ss),train miou:0.8148,val miou:0.3857 best miou: 0.6452
394
+ 2026-04-06 11:31:01,802 - INFO - epoch:10/110 || loss: 1.0783 || cls: 0.3385
395
+ 2026-04-06 11:31:03,970 - INFO - duration: 1:53 (mm:ss),train miou:0.8236,val miou:0.2654 best miou: 0.6452
396
+ 2026-04-06 11:32:54,917 - INFO - epoch:11/110 || loss: 1.0725 || cls: 0.3264
397
+ 2026-04-06 11:32:57,372 - INFO - duration: 1:53 (mm:ss),train miou:0.8314,val miou:0.143 best miou: 0.6452
398
+ 2026-04-06 11:34:48,193 - INFO - epoch:12/110 || loss: 1.0721 || cls: 0.3232
399
+ 2026-04-06 11:34:50,800 - INFO - duration: 1:53 (mm:ss),train miou:0.8315,val miou:0.1575 best miou: 0.6452
400
+ 2026-04-06 11:36:41,744 - INFO - epoch:13/110 || loss: 1.0676 || cls: 0.3161
401
+ 2026-04-06 11:36:44,526 - INFO - duration: 1:53 (mm:ss),train miou:0.8361,val miou:0.5703 best miou: 0.6452
402
+ 2026-04-06 11:38:35,972 - INFO - epoch:14/110 || loss: 1.0618 || cls: 0.3156
403
+ 2026-04-06 11:38:38,419 - INFO - duration: 1:53 (mm:ss),train miou:0.8431,val miou:0.6907 best miou: 0.6452
404
+ 2026-04-06 11:38:38,419 - INFO - ============================== new best! epoch:14,train:0.8431,val:0.6907 ==============================
405
+ 2026-04-06 11:40:28,477 - INFO - epoch:15/110 || loss: 1.0572 || cls: 0.3096
406
+ 2026-04-06 11:40:31,048 - INFO - duration: 1:52 (mm:ss),train miou:0.8494,val miou:0.6781 best miou: 0.6907
407
+ 2026-04-06 11:42:21,985 - INFO - epoch:16/110 || loss: 1.0556 || cls: 0.3076
408
+ 2026-04-06 11:42:24,396 - INFO - duration: 1:53 (mm:ss),train miou:0.8522,val miou:0.6544 best miou: 0.6907
409
+ 2026-04-06 11:44:15,198 - INFO - epoch:17/110 || loss: 1.0523 || cls: 0.3061
410
+ 2026-04-06 11:44:17,891 - INFO - duration: 1:53 (mm:ss),train miou:0.8564,val miou:0.6781 best miou: 0.6907
411
+ 2026-04-06 11:46:09,150 - INFO - epoch:18/110 || loss: 1.0528 || cls: 0.3115
412
+ 2026-04-06 11:46:11,693 - INFO - duration: 1:53 (mm:ss),train miou:0.8543,val miou:0.69 best miou: 0.6907
413
+ 2026-04-06 11:48:02,717 - INFO - epoch:19/110 || loss: 1.0516 || cls: 0.3118
414
+ 2026-04-06 11:48:05,509 - INFO - duration: 1:53 (mm:ss),train miou:0.8584,val miou:0.6825 best miou: 0.6907
415
+ 2026-04-06 11:49:56,760 - INFO - epoch:20/110 || loss: 1.0471 || cls: 0.3137
416
+ 2026-04-06 11:49:59,086 - INFO - duration: 1:53 (mm:ss),train miou:0.8613,val miou:0.6854 best miou: 0.6907
417
+ 2026-04-06 11:51:50,678 - INFO - epoch:21/110 || loss: 1.0477 || cls: 0.3175
418
+ 2026-04-06 11:51:53,279 - INFO - duration: 1:54 (mm:ss),train miou:0.8614,val miou:0.6756 best miou: 0.6907
419
+ 2026-04-06 11:53:44,053 - INFO - epoch:22/110 || loss: 1.0421 || cls: 0.3109
420
+ 2026-04-06 11:53:46,791 - INFO - duration: 1:53 (mm:ss),train miou:0.8687,val miou:0.7078 best miou: 0.6907
421
+ 2026-04-06 11:53:46,791 - INFO - ============================== new best! epoch:22,train:0.8687,val:0.7078 ==============================
422
+ 2026-04-06 11:55:37,444 - INFO - epoch:23/110 || loss: 1.0429 || cls: 0.317
423
+ 2026-04-06 11:55:40,058 - INFO - duration: 1:52 (mm:ss),train miou:0.8665,val miou:0.7015 best miou: 0.7078
424
+ 2026-04-06 11:57:30,704 - INFO - epoch:24/110 || loss: 1.0427 || cls: 0.3243
425
+ 2026-04-06 11:57:33,148 - INFO - duration: 1:52 (mm:ss),train miou:0.869,val miou:0.6963 best miou: 0.7078
426
+ 2026-04-06 11:59:23,215 - INFO - epoch:25/110 || loss: 1.0377 || cls: 0.319
427
+ 2026-04-06 11:59:25,567 - INFO - duration: 1:52 (mm:ss),train miou:0.8736,val miou:0.698 best miou: 0.7078
428
+ 2026-04-06 12:01:15,720 - INFO - epoch:26/110 || loss: 1.0406 || cls: 0.3312
429
+ 2026-04-06 12:01:18,305 - INFO - duration: 1:52 (mm:ss),train miou:0.87,val miou:0.7057 best miou: 0.7078
430
+ 2026-04-06 12:03:08,378 - INFO - epoch:27/110 || loss: 1.0338 || cls: 0.328
431
+ 2026-04-06 12:03:10,653 - INFO - duration: 1:52 (mm:ss),train miou:0.8795,val miou:0.689 best miou: 0.7078
432
+ 2026-04-06 12:05:00,509 - INFO - epoch:28/110 || loss: 1.0363 || cls: 0.3371
433
+ 2026-04-06 12:05:02,663 - INFO - duration: 1:51 (mm:ss),train miou:0.8743,val miou:0.6927 best miou: 0.7078
434
+ 2026-04-06 12:06:52,796 - INFO - epoch:29/110 || loss: 1.0326 || cls: 0.336
435
+ 2026-04-06 12:06:55,135 - INFO - duration: 1:52 (mm:ss),train miou:0.8818,val miou:0.724 best miou: 0.7078
436
+ 2026-04-06 12:06:55,136 - INFO - ============================== new best! epoch:29,train:0.8818,val:0.724 ==============================
437
+ 2026-04-06 12:08:45,601 - INFO - epoch:30/110 || loss: 1.0327 || cls: 0.342
438
+ 2026-04-06 12:08:48,244 - INFO - duration: 1:52 (mm:ss),train miou:0.8806,val miou:0.7014 best miou: 0.724
439
+ 2026-04-06 12:10:39,187 - INFO - epoch:31/110 || loss: 1.031 || cls: 0.3484
440
+ 2026-04-06 12:10:41,566 - INFO - duration: 1:53 (mm:ss),train miou:0.8824,val miou:0.7031 best miou: 0.724
441
+ 2026-04-06 12:12:31,662 - INFO - epoch:32/110 || loss: 1.0275 || cls: 0.3426
442
+ 2026-04-06 12:12:34,211 - INFO - duration: 1:52 (mm:ss),train miou:0.8862,val miou:0.6985 best miou: 0.724
443
+ 2026-04-06 12:14:24,520 - INFO - epoch:33/110 || loss: 1.031 || cls: 0.3581
444
+ 2026-04-06 12:14:27,321 - INFO - duration: 1:52 (mm:ss),train miou:0.8826,val miou:0.7174 best miou: 0.724
445
+ 2026-04-06 12:16:17,513 - INFO - epoch:34/110 || loss: 1.0286 || cls: 0.3614
446
+ 2026-04-06 12:16:19,929 - INFO - duration: 1:52 (mm:ss),train miou:0.8852,val miou:0.6857 best miou: 0.724
447
+ 2026-04-06 12:18:10,228 - INFO - epoch:35/110 || loss: 1.0258 || cls: 0.3625
448
+ 2026-04-06 12:18:12,877 - INFO - duration: 1:52 (mm:ss),train miou:0.8882,val miou:0.7039 best miou: 0.724
449
+ 2026-04-06 12:20:03,216 - INFO - epoch:36/110 || loss: 1.0258 || cls: 0.3626
450
+ 2026-04-06 12:20:05,959 - INFO - duration: 1:52 (mm:ss),train miou:0.8888,val miou:0.7037 best miou: 0.724
451
+ 2026-04-06 12:21:56,389 - INFO - epoch:37/110 || loss: 1.0256 || cls: 0.3706
452
+ 2026-04-06 12:21:58,891 - INFO - duration: 1:52 (mm:ss),train miou:0.8892,val miou:0.6918 best miou: 0.724
453
+ 2026-04-06 12:23:49,604 - INFO - epoch:38/110 || loss: 1.0234 || cls: 0.374
454
+ 2026-04-06 12:23:51,917 - INFO - duration: 1:52 (mm:ss),train miou:0.8919,val miou:0.6994 best miou: 0.724
455
+ 2026-04-06 12:25:42,902 - INFO - epoch:39/110 || loss: 1.0233 || cls: 0.3836
456
+ 2026-04-06 12:25:45,244 - INFO - duration: 1:53 (mm:ss),train miou:0.8916,val miou:0.7127 best miou: 0.724
457
+ 2026-04-06 12:27:35,827 - INFO - epoch:40/110 || loss: 1.02 || cls: 0.3803
458
+ 2026-04-06 12:27:37,997 - INFO - duration: 1:52 (mm:ss),train miou:0.8955,val miou:0.7097 best miou: 0.724
459
+ 2026-04-06 12:29:27,934 - INFO - epoch:41/110 || loss: 1.0217 || cls: 0.3896
460
+ 2026-04-06 12:29:30,333 - INFO - duration: 1:52 (mm:ss),train miou:0.8935,val miou:0.6969 best miou: 0.724
461
+ 2026-04-06 12:31:20,979 - INFO - epoch:42/110 || loss: 1.0227 || cls: 0.4025
462
+ 2026-04-06 12:31:23,372 - INFO - duration: 1:52 (mm:ss),train miou:0.8898,val miou:0.6983 best miou: 0.724
463
+ 2026-04-06 12:33:13,814 - INFO - epoch:43/110 || loss: 1.0169 || cls: 0.3927
464
+ 2026-04-06 12:33:16,524 - INFO - duration: 1:53 (mm:ss),train miou:0.9002,val miou:0.7226 best miou: 0.724
465
+ 2026-04-06 12:35:06,906 - INFO - epoch:44/110 || loss: 1.0188 || cls: 0.4065
466
+ 2026-04-06 12:35:09,850 - INFO - duration: 1:53 (mm:ss),train miou:0.8978,val miou:0.711 best miou: 0.724
467
+ 2026-04-06 12:37:00,287 - INFO - epoch:45/110 || loss: 1.0175 || cls: 0.4087
468
+ 2026-04-06 12:37:02,462 - INFO - duration: 1:52 (mm:ss),train miou:0.9014,val miou:0.7323 best miou: 0.724
469
+ 2026-04-06 12:37:02,462 - INFO - ============================== new best! epoch:45,train:0.9014,val:0.7323 ==============================
470
+ 2026-04-06 12:38:53,016 - INFO - epoch:46/110 || loss: 1.0154 || cls: 0.412
471
+ 2026-04-06 12:38:55,577 - INFO - duration: 1:52 (mm:ss),train miou:0.9001,val miou:0.726 best miou: 0.7323
472
+ 2026-04-06 12:40:45,688 - INFO - epoch:47/110 || loss: 1.0135 || cls: 0.4109
473
+ 2026-04-06 12:40:48,044 - INFO - duration: 1:52 (mm:ss),train miou:0.9046,val miou:0.7223 best miou: 0.7323
474
+ 2026-04-06 12:42:38,204 - INFO - epoch:48/110 || loss: 1.0167 || cls: 0.4279
475
+ 2026-04-06 12:42:40,817 - INFO - duration: 1:52 (mm:ss),train miou:0.9012,val miou:0.7349 best miou: 0.7323
476
+ 2026-04-06 12:42:40,817 - INFO - ============================== new best! epoch:48,train:0.9012,val:0.7349 ==============================
477
+ 2026-04-06 12:44:31,079 - INFO - epoch:49/110 || loss: 1.0154 || cls: 0.4263
478
+ 2026-04-06 12:44:33,506 - INFO - duration: 1:52 (mm:ss),train miou:0.9026,val miou:0.7224 best miou: 0.7349
479
+ 2026-04-06 12:46:23,712 - INFO - epoch:50/110 || loss: 1.011 || cls: 0.4275
480
+ 2026-04-06 12:46:26,224 - INFO - duration: 1:52 (mm:ss),train miou:0.9079,val miou:0.7003 best miou: 0.7349
481
+ 2026-04-06 12:48:16,384 - INFO - epoch:51/110 || loss: 1.0105 || cls: 0.4329
482
+ 2026-04-06 12:48:18,766 - INFO - duration: 1:52 (mm:ss),train miou:0.9083,val miou:0.6976 best miou: 0.7349
483
+ 2026-04-06 12:50:09,086 - INFO - epoch:52/110 || loss: 1.0102 || cls: 0.4446
484
+ 2026-04-06 12:50:11,283 - INFO - duration: 1:52 (mm:ss),train miou:0.91,val miou:0.7302 best miou: 0.7349
485
+ 2026-04-06 12:52:01,438 - INFO - epoch:53/110 || loss: 1.0091 || cls: 0.4412
486
+ 2026-04-06 12:52:03,907 - INFO - duration: 1:52 (mm:ss),train miou:0.9103,val miou:0.7193 best miou: 0.7349
487
+ 2026-04-06 12:53:54,208 - INFO - epoch:54/110 || loss: 1.0093 || cls: 0.4552
488
+ 2026-04-06 12:53:56,583 - INFO - duration: 1:52 (mm:ss),train miou:0.9086,val miou:0.7284 best miou: 0.7349
489
+ 2026-04-06 12:55:46,570 - INFO - epoch:55/110 || loss: 1.0053 || cls: 0.4457
490
+ 2026-04-06 12:55:48,966 - INFO - duration: 1:52 (mm:ss),train miou:0.9135,val miou:0.7138 best miou: 0.7349
491
+ 2026-04-06 12:57:39,378 - INFO - epoch:56/110 || loss: 1.0049 || cls: 0.454
492
+ 2026-04-06 12:57:41,854 - INFO - duration: 1:52 (mm:ss),train miou:0.915,val miou:0.7382 best miou: 0.7349
493
+ 2026-04-06 12:57:41,855 - INFO - ============================== new best! epoch:56,train:0.915,val:0.7382 ==============================
494
+ 2026-04-06 12:59:32,131 - INFO - epoch:57/110 || loss: 1.0044 || cls: 0.4581
495
+ 2026-04-06 12:59:34,393 - INFO - duration: 1:52 (mm:ss),train miou:0.9148,val miou:0.7329 best miou: 0.7382
496
+ 2026-04-06 13:01:24,976 - INFO - epoch:58/110 || loss: 1.0028 || cls: 0.4632
497
+ 2026-04-06 13:01:27,286 - INFO - duration: 1:52 (mm:ss),train miou:0.9164,val miou:0.7291 best miou: 0.7382
498
+ 2026-04-06 13:03:17,488 - INFO - epoch:59/110 || loss: 1.0053 || cls: 0.4766
499
+ 2026-04-06 13:03:20,147 - INFO - duration: 1:52 (mm:ss),train miou:0.9157,val miou:0.712 best miou: 0.7382
500
+ 2026-04-06 13:05:10,451 - INFO - epoch:60/110 || loss: 1.0018 || cls: 0.473
501
+ 2026-04-06 13:05:12,838 - INFO - duration: 1:52 (mm:ss),train miou:0.9192,val miou:0.7492 best miou: 0.7382
502
+ 2026-04-06 13:05:12,838 - INFO - ============================== new best! epoch:60,train:0.9192,val:0.7492 ==============================
503
+ 2026-04-06 13:07:03,011 - INFO - epoch:61/110 || loss: 1.0007 || cls: 0.472
504
+ 2026-04-06 13:07:05,297 - INFO - duration: 1:52 (mm:ss),train miou:0.9209,val miou:0.7148 best miou: 0.7492
505
+ 2026-04-06 13:08:55,258 - INFO - epoch:62/110 || loss: 1.0004 || cls: 0.4853
506
+ 2026-04-06 13:08:57,747 - INFO - duration: 1:52 (mm:ss),train miou:0.9195,val miou:0.7215 best miou: 0.7492
507
+ 2026-04-06 13:10:48,047 - INFO - epoch:63/110 || loss: 1.0006 || cls: 0.486
508
+ 2026-04-06 13:10:50,688 - INFO - duration: 1:52 (mm:ss),train miou:0.9206,val miou:0.7215 best miou: 0.7492
509
+ 2026-04-06 13:12:40,777 - INFO - epoch:64/110 || loss: 0.9956 || cls: 0.4706
510
+ 2026-04-06 13:12:43,000 - INFO - duration: 1:52 (mm:ss),train miou:0.9285,val miou:0.7007 best miou: 0.7492
511
+ 2026-04-06 13:14:33,314 - INFO - epoch:65/110 || loss: 0.9969 || cls: 0.498
512
+ 2026-04-06 13:14:35,895 - INFO - duration: 1:52 (mm:ss),train miou:0.9257,val miou:0.735 best miou: 0.7492
513
+ 2026-04-06 13:16:25,972 - INFO - epoch:66/110 || loss: 0.9947 || cls: 0.4834
514
+ 2026-04-06 13:16:28,287 - INFO - duration: 1:52 (mm:ss),train miou:0.9285,val miou:0.7211 best miou: 0.7492
515
+ 2026-04-06 13:18:18,173 - INFO - epoch:67/110 || loss: 0.997 || cls: 0.5033
516
+ 2026-04-06 13:18:20,615 - INFO - duration: 1:52 (mm:ss),train miou:0.9238,val miou:0.7417 best miou: 0.7492
517
+ 2026-04-06 13:20:10,829 - INFO - epoch:68/110 || loss: 0.9929 || cls: 0.4897
518
+ 2026-04-06 13:20:13,249 - INFO - duration: 1:52 (mm:ss),train miou:0.9297,val miou:0.7474 best miou: 0.7492
519
+ 2026-04-06 13:22:03,265 - INFO - epoch:69/110 || loss: 0.9941 || cls: 0.5012
520
+ 2026-04-06 13:22:05,566 - INFO - duration: 1:52 (mm:ss),train miou:0.929,val miou:0.726 best miou: 0.7492
521
+ 2026-04-06 13:23:55,584 - INFO - epoch:70/110 || loss: 0.9912 || cls: 0.4921
522
+ 2026-04-06 13:23:57,936 - INFO - duration: 1:52 (mm:ss),train miou:0.9322,val miou:0.7466 best miou: 0.7492
523
+ 2026-04-06 13:25:48,276 - INFO - epoch:71/110 || loss: 0.9912 || cls: 0.4927
524
+ 2026-04-06 13:25:50,660 - INFO - duration: 1:52 (mm:ss),train miou:0.9325,val miou:0.7388 best miou: 0.7492
525
+ 2026-04-06 13:27:40,603 - INFO - epoch:72/110 || loss: 0.9909 || cls: 0.5017
526
+ 2026-04-06 13:27:43,184 - INFO - duration: 1:52 (mm:ss),train miou:0.9329,val miou:0.7306 best miou: 0.7492
527
+ 2026-04-06 13:29:33,169 - INFO - epoch:73/110 || loss: 0.9895 || cls: 0.5036
528
+ 2026-04-06 13:29:35,719 - INFO - duration: 1:52 (mm:ss),train miou:0.9356,val miou:0.7209 best miou: 0.7492
529
+ 2026-04-06 13:31:25,865 - INFO - epoch:74/110 || loss: 0.9894 || cls: 0.5116
530
+ 2026-04-06 13:31:28,122 - INFO - duration: 1:52 (mm:ss),train miou:0.9346,val miou:0.7415 best miou: 0.7492
531
+ 2026-04-06 13:33:18,033 - INFO - epoch:75/110 || loss: 0.9858 || cls: 0.4985
532
+ 2026-04-06 13:33:20,654 - INFO - duration: 1:52 (mm:ss),train miou:0.9398,val miou:0.7504 best miou: 0.7492
533
+ 2026-04-06 13:33:20,654 - INFO - ============================== new best! epoch:75,train:0.9398,val:0.7504 ==============================
534
+ 2026-04-06 13:35:10,861 - INFO - epoch:76/110 || loss: 0.9868 || cls: 0.5072
535
+ 2026-04-06 13:35:13,318 - INFO - duration: 1:52 (mm:ss),train miou:0.9378,val miou:0.7406 best miou: 0.7504
536
+ 2026-04-06 13:37:03,236 - INFO - epoch:77/110 || loss: 0.9851 || cls: 0.5086
537
+ 2026-04-06 13:37:05,690 - INFO - duration: 1:52 (mm:ss),train miou:0.939,val miou:0.7303 best miou: 0.7504
538
+ 2026-04-06 13:38:55,836 - INFO - epoch:78/110 || loss: 0.9843 || cls: 0.5037
539
+ 2026-04-06 13:38:58,613 - INFO - duration: 1:52 (mm:ss),train miou:0.9415,val miou:0.7352 best miou: 0.7504
540
+ 2026-04-06 13:40:48,628 - INFO - epoch:79/110 || loss: 0.9845 || cls: 0.5109
541
+ 2026-04-06 13:40:51,254 - INFO - duration: 1:52 (mm:ss),train miou:0.9415,val miou:0.7362 best miou: 0.7504
542
+ 2026-04-06 13:42:41,641 - INFO - epoch:80/110 || loss: 0.982 || cls: 0.5003
543
+ 2026-04-06 13:42:44,090 - INFO - duration: 1:52 (mm:ss),train miou:0.9439,val miou:0.7517 best miou: 0.7504
544
+ 2026-04-06 13:42:44,090 - INFO - ============================== new best! epoch:80,train:0.9439,val:0.7517 ==============================
545
+ 2026-04-06 13:44:34,347 - INFO - epoch:81/110 || loss: 0.9825 || cls: 0.5099
546
+ 2026-04-06 13:44:36,622 - INFO - duration: 1:52 (mm:ss),train miou:0.9444,val miou:0.742 best miou: 0.7517
547
+ 2026-04-06 13:46:26,774 - INFO - epoch:82/110 || loss: 0.9803 || cls: 0.5036
548
+ 2026-04-06 13:46:29,118 - INFO - duration: 1:52 (mm:ss),train miou:0.946,val miou:0.7397 best miou: 0.7517
549
+ 2026-04-06 13:48:19,135 - INFO - epoch:83/110 || loss: 0.9784 || cls: 0.4934
550
+ 2026-04-06 13:48:21,552 - INFO - duration: 1:52 (mm:ss),train miou:0.949,val miou:0.7328 best miou: 0.7517
551
+ 2026-04-06 13:50:11,724 - INFO - epoch:84/110 || loss: 0.9793 || cls: 0.5028
552
+ 2026-04-06 13:50:14,166 - INFO - duration: 1:52 (mm:ss),train miou:0.9475,val miou:0.7599 best miou: 0.7517
553
+ 2026-04-06 13:50:14,166 - INFO - ============================== new best! epoch:84,train:0.9475,val:0.7599 ==============================
554
+ 2026-04-06 13:52:04,146 - INFO - epoch:85/110 || loss: 0.9789 || cls: 0.5107
555
+ 2026-04-06 13:52:06,778 - INFO - duration: 1:52 (mm:ss),train miou:0.9479,val miou:0.7416 best miou: 0.7599
556
+ 2026-04-06 13:53:56,972 - INFO - epoch:86/110 || loss: 0.9777 || cls: 0.4988
557
+ 2026-04-06 13:53:59,452 - INFO - duration: 1:52 (mm:ss),train miou:0.9494,val miou:0.7571 best miou: 0.7599
558
+ 2026-04-06 13:55:49,746 - INFO - epoch:87/110 || loss: 0.9766 || cls: 0.4946
559
+ 2026-04-06 13:55:52,315 - INFO - duration: 1:52 (mm:ss),train miou:0.9513,val miou:0.7413 best miou: 0.7599
560
+ 2026-04-06 13:57:42,600 - INFO - epoch:88/110 || loss: 0.9758 || cls: 0.4943
561
+ 2026-04-06 13:57:44,961 - INFO - duration: 1:52 (mm:ss),train miou:0.9518,val miou:0.7495 best miou: 0.7599
562
+ 2026-04-06 13:59:35,151 - INFO - epoch:89/110 || loss: 0.9752 || cls: 0.4893
563
+ 2026-04-06 13:59:37,520 - INFO - duration: 1:52 (mm:ss),train miou:0.953,val miou:0.7385 best miou: 0.7599
564
+ 2026-04-06 14:01:28,149 - INFO - epoch:90/110 || loss: 0.9745 || cls: 0.4862
565
+ 2026-04-06 14:01:30,672 - INFO - duration: 1:53 (mm:ss),train miou:0.9534,val miou:0.7524 best miou: 0.7599
566
+ 2026-04-06 14:03:20,970 - INFO - epoch:91/110 || loss: 0.9745 || cls: 0.4924
567
+ 2026-04-06 14:03:23,378 - INFO - duration: 1:52 (mm:ss),train miou:0.9537,val miou:0.752 best miou: 0.7599
568
+ 2026-04-06 14:05:13,750 - INFO - epoch:92/110 || loss: 0.9733 || cls: 0.4858
569
+ 2026-04-06 14:05:16,159 - INFO - duration: 1:52 (mm:ss),train miou:0.9554,val miou:0.7449 best miou: 0.7599
570
+ 2026-04-06 14:07:06,010 - INFO - epoch:93/110 || loss: 0.9724 || cls: 0.4829
571
+ 2026-04-06 14:07:08,273 - INFO - duration: 1:51 (mm:ss),train miou:0.9562,val miou:0.7505 best miou: 0.7599
572
+ 2026-04-06 14:08:58,254 - INFO - epoch:94/110 || loss: 0.9724 || cls: 0.4838
573
+ 2026-04-06 14:09:00,635 - INFO - duration: 1:52 (mm:ss),train miou:0.9564,val miou:0.7519 best miou: 0.7599
574
+ 2026-04-06 14:10:50,622 - INFO - epoch:95/110 || loss: 0.9712 || cls: 0.4835
575
+ 2026-04-06 14:10:53,086 - INFO - duration: 1:52 (mm:ss),train miou:0.958,val miou:0.7495 best miou: 0.7599
576
+ 2026-04-06 14:12:43,127 - INFO - epoch:96/110 || loss: 0.9711 || cls: 0.4805
577
+ 2026-04-06 14:12:45,907 - INFO - duration: 1:52 (mm:ss),train miou:0.9584,val miou:0.7498 best miou: 0.7599
578
+ 2026-04-06 14:14:36,031 - INFO - epoch:97/110 || loss: 0.9706 || cls: 0.4762
579
+ 2026-04-06 14:14:38,657 - INFO - duration: 1:52 (mm:ss),train miou:0.9584,val miou:0.7552 best miou: 0.7599
580
+ 2026-04-06 14:16:28,999 - INFO - epoch:98/110 || loss: 0.9704 || cls: 0.4752
581
+ 2026-04-06 14:16:31,646 - INFO - duration: 1:52 (mm:ss),train miou:0.9587,val miou:0.7565 best miou: 0.7599
582
+ 2026-04-06 14:18:21,852 - INFO - epoch:99/110 || loss: 0.9698 || cls: 0.4748
583
+ 2026-04-06 14:18:24,221 - INFO - duration: 1:52 (mm:ss),train miou:0.9596,val miou:0.7549 best miou: 0.7599
584
+ 2026-04-06 14:20:14,442 - INFO - epoch:100/110 || loss: 0.9696 || cls: 0.4734
585
+ 2026-04-06 14:20:17,018 - INFO - duration: 1:52 (mm:ss),train miou:0.9601,val miou:0.7549 best miou: 0.7599
586
+ 2026-04-06 14:22:07,169 - INFO - epoch:101/110 || loss: 0.9696 || cls: 0.471
587
+ 2026-04-06 14:22:09,902 - INFO - duration: 1:52 (mm:ss),train miou:0.96,val miou:0.7539 best miou: 0.7599
588
+ 2026-04-06 14:23:59,926 - INFO - epoch:102/110 || loss: 0.9693 || cls: 0.4713
589
+ 2026-04-06 14:24:02,352 - INFO - duration: 1:52 (mm:ss),train miou:0.9607,val miou:0.7575 best miou: 0.7599
590
+ 2026-04-06 14:25:52,658 - INFO - epoch:103/110 || loss: 0.9689 || cls: 0.4724
591
+ 2026-04-06 14:25:55,316 - INFO - duration: 1:52 (mm:ss),train miou:0.961,val miou:0.7535 best miou: 0.7599
592
+ 2026-04-06 14:27:45,841 - INFO - epoch:104/110 || loss: 0.9688 || cls: 0.4708
593
+ 2026-04-06 14:27:48,390 - INFO - duration: 1:52 (mm:ss),train miou:0.9606,val miou:0.7579 best miou: 0.7599
594
+ 2026-04-06 14:29:38,685 - INFO - epoch:105/110 || loss: 0.9686 || cls: 0.4694
595
+ 2026-04-06 14:29:41,546 - INFO - duration: 1:53 (mm:ss),train miou:0.961,val miou:0.7547 best miou: 0.7599
596
+ 2026-04-06 14:31:32,226 - INFO - epoch:106/110 || loss: 0.9685 || cls: 0.4693
597
+ 2026-04-06 14:31:34,669 - INFO - duration: 1:52 (mm:ss),train miou:0.9614,val miou:0.7551 best miou: 0.7599
598
+ 2026-04-06 14:33:25,164 - INFO - epoch:107/110 || loss: 0.9684 || cls: 0.4692
599
+ 2026-04-06 14:33:27,732 - INFO - duration: 1:52 (mm:ss),train miou:0.9611,val miou:0.7581 best miou: 0.7599
600
+ 2026-04-06 14:35:17,892 - INFO - epoch:108/110 || loss: 0.9683 || cls: 0.4691
601
+ 2026-04-06 14:35:20,202 - INFO - duration: 1:52 (mm:ss),train miou:0.9617,val miou:0.7566 best miou: 0.7599
602
+ 2026-04-06 14:37:10,594 - INFO - epoch:109/110 || loss: 0.9684 || cls: 0.4674
603
+ 2026-04-06 14:37:12,968 - INFO - duration: 1:52 (mm:ss),train miou:0.9612,val miou:0.7566 best miou: 0.7599
604
+ 2026-04-06 14:37:13,134 - INFO - ================================================================================
605
+ 2026-04-06 14:37:13,134 - INFO - Training completed! Starting automatic testing with best model...
606
+ 2026-04-06 14:37:13,134 - INFO - ================================================================================
607
+ 2026-04-06 14:37:48,404 - INFO - FINAL TEST RESULTS:
608
+ 2026-04-06 14:37:48,404 - INFO - Best model test accuracy: 0.9361
609
+ 2026-04-06 14:37:48,404 - INFO - Best model test mAcc: 0.8222
610
+ 2026-04-06 14:37:48,404 - INFO - Best model test mIoU: 0.773
611
+ 2026-04-06 14:37:48,404 - INFO - ================================================================================
612
+ 2026-04-06 14:37:48,404 - INFO - Training and testing process completed!
613
+ 2026-04-06 14:37:48,404 - INFO - ================================================================================