Hugging Face's logo

yzsun2025
/
rise_model

Model card Files
xet
Community
yzsun2025 commited on
Commit
a1d68bc
·
verified ·
1 Parent(s): deb8ddd

Upload 9 files

Browse files
Files changed (10) hide show
  1. .gitattributes +1 -0
  2. logs/log_grab_plate_20250820.txt +2752 -0
  3. logs/log_grab_plate_20250821.txt +0 -0
  4. logs/log_grab_plate_20250905.txt +3 -0
  5. logs/log_grab_scanner_20250818.txt +0 -0
  6. logs/log_grab_scanner_20250819.txt +0 -0
  7. logs/log_grab_scanner_20250929.txt +427 -0
  8. logs/log_grab_scanner_20250930.txt +0 -0
  9. logs/log_grab_scanner_20251013.txt +96 -0
  10. logs/log_put_scanner_20251002.txt +390 -0
.gitattributes CHANGED
@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
  log_grab_plate_gripper_20250806.txt filter=lfs diff=lfs merge=lfs -text
 
 
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
  log_grab_plate_gripper_20250806.txt filter=lfs diff=lfs merge=lfs -text
37
+ logs/log_grab_plate_20250905.txt filter=lfs diff=lfs merge=lfs -text
logs/log_grab_plate_20250820.txt ADDED
@@ -0,0 +1,2752 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Loading dataset ...
2
+ Segmentation model loaded
3
+ Connected to /dev/ttyUSB2
4
+ Gripper components initialized!
5
+ Found valid preprocessed data with 30006 samples.
6
+ Using preprocessed data...
7
+ Loading policy ...
8
+ Number of parameters: 51.01M
9
+ Loading optimizer and scheduler ...
10
+ Epoch 0
11
+ Train loss: 1.002109
12
+ Epoch 1
13
+ Train loss: 0.476038
14
+ Epoch 2
15
+ Train loss: 0.157004
16
+ Epoch 3
17
+ Train loss: 0.089041
18
+ Epoch 4
19
+ Train loss: 0.063472
20
+ Epoch 5
21
+ Train loss: 0.051853
22
+ Epoch 6
23
+ Train loss: 0.044463
24
+ Epoch 7
25
+ Train loss: 0.037839
26
+ Epoch 8
27
+ Train loss: 0.033665
28
+ Epoch 9
29
+ Train loss: 0.029251
30
+ Epoch 10
31
+ Train loss: 0.026286
32
+ Epoch 11
33
+ Train loss: 0.023260
34
+ Epoch 12
35
+ Train loss: 0.020451
36
+ Epoch 13
37
+ Train loss: 0.018856
38
+ Epoch 14
39
+ Train loss: 0.017321
40
+ Epoch 15
41
+ Train loss: 0.016659
42
+ Epoch 16
43
+ Train loss: 0.015235
44
+ Epoch 17
45
+ Train loss: 0.014840
46
+ Epoch 18
47
+ Train loss: 0.013064
48
+ Epoch 19
49
+ Train loss: 0.012464
50
+ Epoch 20
51
+ Train loss: 0.011997
52
+ Epoch 21
53
+ Train loss: 0.011670
54
+ Epoch 22
55
+ Train loss: 0.011240
56
+ Epoch 23
57
+ Train loss: 0.011193
58
+ Epoch 24
59
+ Train loss: 0.010550
60
+ Epoch 25
61
+ Train loss: 0.010166
62
+ Epoch 26
63
+ Train loss: 0.010233
64
+ Epoch 27
65
+ Train loss: 0.010031
66
+ Epoch 28
67
+ Train loss: 0.009492
68
+ Epoch 29
69
+ Train loss: 0.009420
70
+ Epoch 30
71
+ Train loss: 0.009245
72
+ Epoch 31
73
+ Train loss: 0.008863
74
+ Epoch 32
75
+ Train loss: 0.009412
76
+ Epoch 33
77
+ Train loss: 0.009440
78
+ Epoch 34
79
+ Train loss: 0.008361
80
+ Epoch 35
81
+ Train loss: 0.008559
82
+ Epoch 36
83
+ Train loss: 0.008493
84
+ Epoch 37
85
+ Train loss: 0.008267
86
+ Epoch 38
87
+ Train loss: 0.008376
88
+ Epoch 39
89
+ Train loss: 0.008195
90
+ Epoch 40
91
+ Train loss: 0.007913
92
+ Epoch 41
93
+ Train loss: 0.007951
94
+ Epoch 42
95
+ Train loss: 0.008071
96
+ Epoch 43
97
+ Train loss: 0.007752
98
+ Epoch 44
99
+ Train loss: 0.007480
100
+ Epoch 45
101
+ Train loss: 0.007585
102
+ Epoch 46
103
+ Train loss: 0.007307
104
+ Epoch 47
105
+ Train loss: 0.007218
106
+ Epoch 48
107
+ Train loss: 0.007445
108
+ Epoch 49
109
+ Train loss: 0.007202
110
+ Epoch 50
111
+ Train loss: 0.007119
112
+ Epoch 51
113
+ Train loss: 0.007068
114
+ Epoch 52
115
+ Train loss: 0.006948
116
+ Epoch 53
117
+ Train loss: 0.007012
118
+ Epoch 54
119
+ Train loss: 0.006985
120
+ Epoch 55
121
+ Train loss: 0.006999
122
+ Epoch 56
123
+ Train loss: 0.006803
124
+ Epoch 57
125
+ Train loss: 0.006848
126
+ Epoch 58
127
+ Train loss: 0.006768
128
+ Epoch 59
129
+ Train loss: 0.006679
130
+ Epoch 60
131
+ Train loss: 0.006834
132
+ Epoch 61
133
+ Train loss: 0.006724
134
+ Epoch 62
135
+ Train loss: 0.006405
136
+ Epoch 63
137
+ Train loss: 0.006666
138
+ Epoch 64
139
+ Train loss: 0.006438
140
+ Epoch 65
141
+ Train loss: 0.006011
142
+ Epoch 66
143
+ Train loss: 0.006381
144
+ Epoch 67
145
+ Train loss: 0.006322
146
+ Epoch 68
147
+ Train loss: 0.006338
148
+ Epoch 69
149
+ Train loss: 0.006199
150
+ Epoch 70
151
+ Train loss: 0.006218
152
+ Epoch 71
153
+ Train loss: 0.006045
154
+ Epoch 72
155
+ Train loss: 0.005935
156
+ Epoch 73
157
+ Train loss: 0.005896
158
+ Epoch 74
159
+ Train loss: 0.005791
160
+ Epoch 75
161
+ Train loss: 0.005961
162
+ Epoch 76
163
+ Train loss: 0.005820
164
+ Epoch 77
165
+ Train loss: 0.006093
166
+ Epoch 78
167
+ Train loss: 0.005873
168
+ Epoch 79
169
+ Train loss: 0.005979
170
+ Epoch 80
171
+ Train loss: 0.005753
172
+ Epoch 81
173
+ Train loss: 0.005659
174
+ Epoch 82
175
+ Train loss: 0.005748
176
+ Epoch 83
177
+ Train loss: 0.005614
178
+ Epoch 84
179
+ Train loss: 0.005463
180
+ Epoch 85
181
+ Train loss: 0.005439
182
+ Epoch 86
183
+ Train loss: 0.005611
184
+ Epoch 87
185
+ Train loss: 0.005507
186
+ Epoch 88
187
+ Train loss: 0.005520
188
+ Epoch 89
189
+ Train loss: 0.005395
190
+ Epoch 90
191
+ Train loss: 0.005530
192
+ Epoch 91
193
+ Train loss: 0.005475
194
+ Epoch 92
195
+ Train loss: 0.005334
196
+ Epoch 93
197
+ Train loss: 0.005311
198
+ Epoch 94
199
+ Train loss: 0.005244
200
+ Epoch 95
201
+ Train loss: 0.005233
202
+ Epoch 96
203
+ Train loss: 0.005225
204
+ Epoch 97
205
+ Train loss: 0.005371
206
+ Epoch 98
207
+ Train loss: 0.005338
208
+ Epoch 99
209
+ Train loss: 0.005051
210
+ Epoch 100
211
+ Train loss: 0.005051
212
+ Epoch 101
213
+ Train loss: 0.005119
214
+ Epoch 102
215
+ Train loss: 0.005026
216
+ Epoch 103
217
+ Train loss: 0.004985
218
+ Epoch 104
219
+ Train loss: 0.005195
220
+ Epoch 105
221
+ Train loss: 0.004994
222
+ Epoch 106
223
+ Train loss: 0.005004
224
+ Epoch 107
225
+ Train loss: 0.004973
226
+ Epoch 108
227
+ Train loss: 0.005199
228
+ Epoch 109
229
+ Train loss: 0.005118
230
+ Epoch 110
231
+ Train loss: 0.004667
232
+ Epoch 111
233
+ Train loss: 0.004801
234
+ Epoch 112
235
+ Train loss: 0.005042
236
+ Epoch 113
237
+ Train loss: 0.004677
238
+ Epoch 114
239
+ Train loss: 0.004636
240
+ Epoch 115
241
+ Train loss: 0.004690
242
+ Epoch 116
243
+ Train loss: 0.004735
244
+ Epoch 117
245
+ Train loss: 0.004528
246
+ Epoch 118
247
+ Train loss: 0.004732
248
+ Epoch 119
249
+ Train loss: 0.004697
250
+ Epoch 120
251
+ Train loss: 0.004718
252
+ Epoch 121
253
+ Train loss: 0.004575
254
+ Epoch 122
255
+ Train loss: 0.004638
256
+ Epoch 123
257
+ Train loss: 0.004549
258
+ Epoch 124
259
+ Train loss: 0.004555
260
+ Epoch 125
261
+ Train loss: 0.004643
262
+ Epoch 126
263
+ Train loss: 0.004392
264
+ Epoch 127
265
+ Train loss: 0.004493
266
+ Epoch 128
267
+ Train loss: 0.004430
268
+ Epoch 129
269
+ Train loss: 0.004393
270
+ Epoch 130
271
+ Train loss: 0.004402
272
+ Epoch 131
273
+ Train loss: 0.004423
274
+ Epoch 132
275
+ Train loss: 0.004382
276
+ Epoch 133
277
+ Train loss: 0.004318
278
+ Epoch 134
279
+ Train loss: 0.004191
280
+ Epoch 135
281
+ Train loss: 0.004371
282
+ Epoch 136
283
+ Train loss: 0.004253
284
+ Epoch 137
285
+ Train loss: 0.004148
286
+ Epoch 138
287
+ Train loss: 0.004112
288
+ Epoch 139
289
+ Train loss: 0.004102
290
+ Epoch 140
291
+ Train loss: 0.004251
292
+ Epoch 141
293
+ Train loss: 0.004293
294
+ Epoch 142
295
+ Train loss: 0.004231
296
+ Epoch 143
297
+ Train loss: 0.004060
298
+ Epoch 144
299
+ Train loss: 0.004071
300
+ Epoch 145
301
+ Train loss: 0.004177
302
+ Epoch 146
303
+ Train loss: 0.004357
304
+ Epoch 147
305
+ Train loss: 0.004092
306
+ Epoch 148
307
+ Train loss: 0.003907
308
+ Epoch 149
309
+ Train loss: 0.004037
310
+ Epoch 150
311
+ Train loss: 0.003997
312
+ Epoch 151
313
+ Train loss: 0.003816
314
+ Epoch 152
315
+ Train loss: 0.003904
316
+ Epoch 153
317
+ Train loss: 0.003789
318
+ Epoch 154
319
+ Train loss: 0.003947
320
+ Epoch 155
321
+ Train loss: 0.003980
322
+ Epoch 156
323
+ Train loss: 0.003829
324
+ Epoch 157
325
+ Train loss: 0.003959
326
+ Epoch 158
327
+ Train loss: 0.003922
328
+ Epoch 159
329
+ Train loss: 0.003880
330
+ Epoch 160
331
+ Train loss: 0.003861
332
+ Epoch 161
333
+ Train loss: 0.003617
334
+ Epoch 162
335
+ Train loss: 0.003820
336
+ Epoch 163
337
+ Train loss: 0.003816
338
+ Epoch 164
339
+ Train loss: 0.003825
340
+ Epoch 165
341
+ Train loss: 0.003872
342
+ Epoch 166
343
+ Train loss: 0.003881
344
+ Epoch 167
345
+ Train loss: 0.003693
346
+ Epoch 168
347
+ Train loss: 0.003717
348
+ Epoch 169
349
+ Train loss: 0.003512
350
+ Epoch 170
351
+ Train loss: 0.003730
352
+ Epoch 171
353
+ Train loss: 0.003703
354
+ Epoch 172
355
+ Train loss: 0.003572
356
+ Epoch 173
357
+ Train loss: 0.003722
358
+ Epoch 174
359
+ Train loss: 0.003582
360
+ Epoch 175
361
+ Train loss: 0.003752
362
+ Epoch 176
363
+ Train loss: 0.003531
364
+ Epoch 177
365
+ Train loss: 0.003680
366
+ Epoch 178
367
+ Train loss: 0.003641
368
+ Epoch 179
369
+ Train loss: 0.003560
370
+ Epoch 180
371
+ Train loss: 0.003574
372
+ Epoch 181
373
+ Train loss: 0.003479
374
+ Epoch 182
375
+ Train loss: 0.003466
376
+ Epoch 183
377
+ Train loss: 0.003303
378
+ Epoch 184
379
+ Train loss: 0.003509
380
+ Epoch 185
381
+ Train loss: 0.003401
382
+ Epoch 186
383
+ Train loss: 0.003566
384
+ Epoch 187
385
+ Train loss: 0.003579
386
+ Epoch 188
387
+ Train loss: 0.003369
388
+ Epoch 189
389
+ Train loss: 0.003222
390
+ Epoch 190
391
+ Train loss: 0.003623
392
+ Epoch 191
393
+ Train loss: 0.005050
394
+ Epoch 192
395
+ Train loss: 0.004048
396
+ Epoch 193
397
+ Train loss: 0.003826
398
+ Epoch 194
399
+ Train loss: 0.003550
400
+ Epoch 195
401
+ Train loss: 0.003450
402
+ Epoch 196
403
+ Train loss: 0.003393
404
+ Epoch 197
405
+ Train loss: 0.003875
406
+ Epoch 198
407
+ Train loss: 0.003478
408
+ Epoch 199
409
+ Train loss: 0.003301
410
+ Epoch 200
411
+ Train loss: 0.003365
412
+ Epoch 201
413
+ Train loss: 0.003390
414
+ Epoch 202
415
+ Train loss: 0.003308
416
+ Epoch 203
417
+ Train loss: 0.003218
418
+ Epoch 204
419
+ Train loss: 0.003257
420
+ Epoch 205
421
+ Train loss: 0.003331
422
+ Epoch 206
423
+ Train loss: 0.003125
424
+ Epoch 207
425
+ Train loss: 0.003226
426
+ Epoch 208
427
+ Train loss: 0.003268
428
+ Epoch 209
429
+ Train loss: 0.003199
430
+ Epoch 210
431
+ Train loss: 0.003188
432
+ Epoch 211
433
+ Train loss: 0.003123
434
+ Epoch 212
435
+ Train loss: 0.003252
436
+ Epoch 213
437
+ Train loss: 0.003134
438
+ Epoch 214
439
+ Train loss: 0.003205
440
+ Epoch 215
441
+ Train loss: 0.003191
442
+ Epoch 216
443
+ Train loss: 0.003260
444
+ Epoch 217
445
+ Train loss: 0.003193
446
+ Epoch 218
447
+ Train loss: 0.003085
448
+ Epoch 219
449
+ Train loss: 0.003061
450
+ Epoch 220
451
+ Train loss: 0.003303
452
+ Epoch 221
453
+ Train loss: 0.003175
454
+ Epoch 222
455
+ Train loss: 0.003068
456
+ Epoch 223
457
+ Train loss: 0.003328
458
+ Epoch 224
459
+ Train loss: 0.003256
460
+ Epoch 225
461
+ Train loss: 0.003111
462
+ Epoch 226
463
+ Train loss: 0.003227
464
+ Epoch 227
465
+ Train loss: 0.003097
466
+ Epoch 228
467
+ Train loss: 0.003005
468
+ Epoch 229
469
+ Train loss: 0.002954
470
+ Epoch 230
471
+ Train loss: 0.003147
472
+ Epoch 231
473
+ Train loss: 0.003116
474
+ Epoch 232
475
+ Train loss: 0.003141
476
+ Epoch 233
477
+ Train loss: 0.003103
478
+ Epoch 234
479
+ Train loss: 0.002919
480
+ Epoch 235
481
+ Train loss: 0.003072
482
+ Epoch 236
483
+ Train loss: 0.003079
484
+ Epoch 237
485
+ Train loss: 0.003112
486
+ Epoch 238
487
+ Train loss: 0.003094
488
+ Epoch 239
489
+ Train loss: 0.002842
490
+ Epoch 240
491
+ Train loss: 0.002795
492
+ Epoch 241
493
+ Train loss: 0.002948
494
+ Epoch 242
495
+ Train loss: 0.002947
496
+ Epoch 243
497
+ Train loss: 0.002874
498
+ Epoch 244
499
+ Train loss: 0.002920
500
+ Epoch 245
501
+ Train loss: 0.002828
502
+ Epoch 246
503
+ Train loss: 0.002885
504
+ Epoch 247
505
+ Train loss: 0.002910
506
+ Epoch 248
507
+ Train loss: 0.002832
508
+ Epoch 249
509
+ Train loss: 0.002869
510
+ Epoch 250
511
+ Train loss: 0.003009
512
+ Epoch 251
513
+ Train loss: 0.002816
514
+ Epoch 252
515
+ Train loss: 0.002983
516
+ Epoch 253
517
+ Train loss: 0.002939
518
+ Epoch 254
519
+ Train loss: 0.002854
520
+ Epoch 255
521
+ Train loss: 0.002847
522
+ Epoch 256
523
+ Train loss: 0.002831
524
+ Epoch 257
525
+ Train loss: 0.002854
526
+ Epoch 258
527
+ Train loss: 0.002978
528
+ Epoch 259
529
+ Train loss: 0.002820
530
+ Epoch 260
531
+ Train loss: 0.002819
532
+ Epoch 261
533
+ Train loss: 0.002926
534
+ Epoch 262
535
+ Train loss: 0.002745
536
+ Epoch 263
537
+ Train loss: 0.002739
538
+ Epoch 264
539
+ Train loss: 0.002744
540
+ Epoch 265
541
+ Train loss: 0.002755
542
+ Epoch 266
543
+ Train loss: 0.002784
544
+ Epoch 267
545
+ Train loss: 0.002909
546
+ Epoch 268
547
+ Train loss: 0.003923
548
+ Epoch 269
549
+ Train loss: 0.003082
550
+ Epoch 270
551
+ Train loss: 0.002905
552
+ Epoch 271
553
+ Train loss: 0.002929
554
+ Epoch 272
555
+ Train loss: 0.002647
556
+ Epoch 273
557
+ Train loss: 0.002827
558
+ Epoch 274
559
+ Train loss: 0.002714
560
+ Epoch 275
561
+ Train loss: 0.002549
562
+ Epoch 276
563
+ Train loss: 0.002777
564
+ Epoch 277
565
+ Train loss: 0.002724
566
+ Epoch 278
567
+ Train loss: 0.002609
568
+ Epoch 279
569
+ Train loss: 0.002574
570
+ Epoch 280
571
+ Train loss: 0.002650
572
+ Epoch 281
573
+ Train loss: 0.002595
574
+ Epoch 282
575
+ Train loss: 0.002644
576
+ Epoch 283
577
+ Train loss: 0.002722
578
+ Epoch 284
579
+ Train loss: 0.002658
580
+ Epoch 285
581
+ Train loss: 0.002758
582
+ Epoch 286
583
+ Train loss: 0.002586
584
+ Epoch 287
585
+ Train loss: 0.002561
586
+ Epoch 288
587
+ Train loss: 0.002607
588
+ Epoch 289
589
+ Train loss: 0.002651
590
+ Epoch 290
591
+ Train loss: 0.002659
592
+ Epoch 291
593
+ Train loss: 0.002775
594
+ Epoch 292
595
+ Train loss: 0.002644
596
+ Epoch 293
597
+ Train loss: 0.002613
598
+ Epoch 294
599
+ Train loss: 0.002670
600
+ Epoch 295
601
+ Train loss: 0.002850
602
+ Epoch 296
603
+ Train loss: 0.002620
604
+ Epoch 297
605
+ Train loss: 0.002574
606
+ Epoch 298
607
+ Train loss: 0.002687
608
+ Epoch 299
609
+ Train loss: 0.002698
610
+ Epoch 300
611
+ Train loss: 0.003409
612
+ Epoch 301
613
+ Train loss: 0.004392
614
+ Epoch 302
615
+ Train loss: 0.003990
616
+ Epoch 303
617
+ Train loss: 0.003390
618
+ Epoch 304
619
+ Train loss: 0.003161
620
+ Epoch 305
621
+ Train loss: 0.003036
622
+ Epoch 306
623
+ Train loss: 0.003099
624
+ Epoch 307
625
+ Train loss: 0.002949
626
+ Epoch 308
627
+ Train loss: 0.002834
628
+ Epoch 309
629
+ Train loss: 0.002724
630
+ Epoch 310
631
+ Train loss: 0.003600
632
+ Epoch 311
633
+ Train loss: 0.003131
634
+ Epoch 312
635
+ Train loss: 0.002873
636
+ Epoch 313
637
+ Train loss: 0.002632
638
+ Epoch 314
639
+ Train loss: 0.002849
640
+ Epoch 315
641
+ Train loss: 0.002638
642
+ Epoch 316
643
+ Train loss: 0.002607
644
+ Epoch 317
645
+ Train loss: 0.002598
646
+ Epoch 318
647
+ Train loss: 0.002542
648
+ Epoch 319
649
+ Train loss: 0.002548
650
+ Epoch 320
651
+ Train loss: 0.002611
652
+ Epoch 321
653
+ Train loss: 0.002535
654
+ Epoch 322
655
+ Train loss: 0.002566
656
+ Epoch 323
657
+ Train loss: 0.002528
658
+ Epoch 324
659
+ Train loss: 0.002423
660
+ Epoch 325
661
+ Train loss: 0.002483
662
+ Epoch 326
663
+ Train loss: 0.002708
664
+ Epoch 327
665
+ Train loss: 0.002975
666
+ Epoch 328
667
+ Train loss: 0.002939
668
+ Epoch 329
669
+ Train loss: 0.002933
670
+ Epoch 330
671
+ Train loss: 0.002667
672
+ Epoch 331
673
+ Train loss: 0.002525
674
+ Epoch 332
675
+ Train loss: 0.002872
676
+ Epoch 333
677
+ Train loss: 0.003074
678
+ Epoch 334
679
+ Train loss: 0.002876
680
+ Epoch 335
681
+ Train loss: 0.002731
682
+ Epoch 336
683
+ Train loss: 0.002745
684
+ Epoch 337
685
+ Train loss: 0.002622
686
+ Epoch 338
687
+ Train loss: 0.002542
688
+ Epoch 339
689
+ Train loss: 0.002532
690
+ Epoch 340
691
+ Train loss: 0.002497
692
+ Epoch 341
693
+ Train loss: 0.002888
694
+ Epoch 342
695
+ Train loss: 0.002701
696
+ Epoch 343
697
+ Train loss: 0.002602
698
+ Epoch 344
699
+ Train loss: 0.002641
700
+ Epoch 345
701
+ Train loss: 0.002568
702
+ Epoch 346
703
+ Train loss: 0.002447
704
+ Epoch 347
705
+ Train loss: 0.002484
706
+ Epoch 348
707
+ Train loss: 0.002457
708
+ Epoch 349
709
+ Train loss: 0.002578
710
+ Epoch 350
711
+ Train loss: 0.002436
712
+ Epoch 351
713
+ Train loss: 0.002408
714
+ Epoch 352
715
+ Train loss: 0.002455
716
+ Epoch 353
717
+ Train loss: 0.002469
718
+ Epoch 354
719
+ Train loss: 0.002498
720
+ Epoch 355
721
+ Train loss: 0.002485
722
+ Epoch 356
723
+ Train loss: 0.002561
724
+ Epoch 357
725
+ Train loss: 0.002462
726
+ Epoch 358
727
+ Train loss: 0.002412
728
+ Epoch 359
729
+ Train loss: 0.002428
730
+ Epoch 360
731
+ Train loss: 0.002459
732
+ Epoch 361
733
+ Train loss: 0.002460
734
+ Epoch 362
735
+ Train loss: 0.002471
736
+ Epoch 363
737
+ Train loss: 0.002336
738
+ Epoch 364
739
+ Train loss: 0.002482
740
+ Epoch 365
741
+ Train loss: 0.002437
742
+ Epoch 366
743
+ Train loss: 0.002596
744
+ Epoch 367
745
+ Train loss: 0.002397
746
+ Epoch 368
747
+ Train loss: 0.002553
748
+ Epoch 369
749
+ Train loss: 0.002592
750
+ Epoch 370
751
+ Train loss: 0.002582
752
+ Epoch 371
753
+ Train loss: 0.002601
754
+ Epoch 372
755
+ Train loss: 0.002378
756
+ Epoch 373
757
+ Train loss: 0.002306
758
+ Epoch 374
759
+ Train loss: 0.002383
760
+ Epoch 375
761
+ Train loss: 0.002481
762
+ Epoch 376
763
+ Train loss: 0.002416
764
+ Epoch 377
765
+ Train loss: 0.002301
766
+ Epoch 378
767
+ Train loss: 0.002378
768
+ Epoch 379
769
+ Train loss: 0.002403
770
+ Epoch 380
771
+ Train loss: 0.002234
772
+ Epoch 381
773
+ Train loss: 0.002479
774
+ Epoch 382
775
+ Train loss: 0.002693
776
+ Epoch 383
777
+ Train loss: 0.002757
778
+ Epoch 384
779
+ Train loss: 0.002594
780
+ Epoch 385
781
+ Train loss: 0.002555
782
+ Epoch 386
783
+ Train loss: 0.002376
784
+ Epoch 387
785
+ Train loss: 0.002347
786
+ Epoch 388
787
+ Train loss: 0.002532
788
+ Epoch 389
789
+ Train loss: 0.003229
790
+ Epoch 390
791
+ Train loss: 0.002612
792
+ Epoch 391
793
+ Train loss: 0.002433
794
+ Epoch 392
795
+ Train loss: 0.002564
796
+ Epoch 393
797
+ Train loss: 0.002389
798
+ Epoch 394
799
+ Train loss: 0.002388
800
+ Epoch 395
801
+ Train loss: 0.002253
802
+ Epoch 396
803
+ Train loss: 0.002498
804
+ Epoch 397
805
+ Train loss: 0.002408
806
+ Epoch 398
807
+ Train loss: 0.002434
808
+ Epoch 399
809
+ Train loss: 0.002345
810
+ Epoch 400
811
+ Train loss: 0.003676
812
+ Epoch 401
813
+ Train loss: 0.003897
814
+ Epoch 402
815
+ Train loss: 0.003180
816
+ Epoch 403
817
+ Train loss: 0.002878
818
+ Epoch 404
819
+ Train loss: 0.002836
820
+ Epoch 405
821
+ Train loss: 0.002602
822
+ Epoch 406
823
+ Train loss: 0.002482
824
+ Epoch 407
825
+ Train loss: 0.002447
826
+ Epoch 408
827
+ Train loss: 0.002497
828
+ Epoch 409
829
+ Train loss: 0.002351
830
+ Epoch 410
831
+ Train loss: 0.002424
832
+ Epoch 411
833
+ Train loss: 0.002341
834
+ Epoch 412
835
+ Train loss: 0.002310
836
+ Epoch 413
837
+ Train loss: 0.002281
838
+ Epoch 414
839
+ Train loss: 0.002237
840
+ Epoch 415
841
+ Train loss: 0.002229
842
+ Epoch 416
843
+ Train loss: 0.002315
844
+ Epoch 417
845
+ Train loss: 0.002455
846
+ Epoch 418
847
+ Train loss: 0.002247
848
+ Epoch 419
849
+ Train loss: 0.002294
850
+ Epoch 420
851
+ Train loss: 0.002325
852
+ Epoch 421
853
+ Train loss: 0.002483
854
+ Epoch 422
855
+ Train loss: 0.002484
856
+ Epoch 423
857
+ Train loss: 0.002467
858
+ Epoch 424
859
+ Train loss: 0.002595
860
+ Epoch 425
861
+ Train loss: 0.002633
862
+ Epoch 426
863
+ Train loss: 0.002589
864
+ Epoch 427
865
+ Train loss: 0.002658
866
+ Epoch 428
867
+ Train loss: 0.002488
868
+ Epoch 429
869
+ Train loss: 0.003446
870
+ Epoch 430
871
+ Train loss: 0.003643
872
+ Epoch 431
873
+ Train loss: 0.003573
874
+ Epoch 432
875
+ Train loss: 0.003293
876
+ Epoch 433
877
+ Train loss: 0.003066
878
+ Epoch 434
879
+ Train loss: 0.002859
880
+ Epoch 435
881
+ Train loss: 0.002886
882
+ Epoch 436
883
+ Train loss: 0.002810
884
+ Epoch 437
885
+ Train loss: 0.002527
886
+ Epoch 438
887
+ Train loss: 0.002712
888
+ Epoch 439
889
+ Train loss: 0.002537
890
+ Epoch 440
891
+ Train loss: 0.002549
892
+ Epoch 441
893
+ Train loss: 0.002454
894
+ Epoch 442
895
+ Train loss: 0.002406
896
+ Epoch 443
897
+ Train loss: 0.002443
898
+ Epoch 444
899
+ Train loss: 0.002357
900
+ Epoch 445
901
+ Train loss: 0.002265
902
+ Epoch 446
903
+ Train loss: 0.002337
904
+ Epoch 447
905
+ Train loss: 0.002299
906
+ Epoch 448
907
+ Train loss: 0.002296
908
+ Epoch 449
909
+ Train loss: 0.002489
910
+ Epoch 450
911
+ Train loss: 0.002326
912
+ Epoch 451
913
+ Train loss: 0.002303
914
+ Epoch 452
915
+ Train loss: 0.002282
916
+ Epoch 453
917
+ Train loss: 0.002300
918
+ Epoch 454
919
+ Train loss: 0.002343
920
+ Epoch 455
921
+ Train loss: 0.002234
922
+ Epoch 456
923
+ Train loss: 0.002299
924
+ Epoch 457
925
+ Train loss: 0.002180
926
+ Epoch 458
927
+ Train loss: 0.002188
928
+ Epoch 459
929
+ Train loss: 0.002194
930
+ Epoch 460
931
+ Train loss: 0.002152
932
+ Epoch 461
933
+ Train loss: 0.002062
934
+ Epoch 462
935
+ Train loss: 0.002177
936
+ Epoch 463
937
+ Train loss: 0.002154
938
+ Epoch 464
939
+ Train loss: 0.002089
940
+ Epoch 465
941
+ Train loss: 0.002076
942
+ Epoch 466
943
+ Train loss: 0.002186
944
+ Epoch 467
945
+ Train loss: 0.002226
946
+ Epoch 468
947
+ Train loss: 0.002265
948
+ Epoch 469
949
+ Train loss: 0.002324
950
+ Epoch 470
951
+ Train loss: 0.002369
952
+ Epoch 471
953
+ Train loss: 0.002311
954
+ Epoch 472
955
+ Train loss: 0.002292
956
+ Epoch 473
957
+ Train loss: 0.002261
958
+ Epoch 474
959
+ Train loss: 0.002274
960
+ Epoch 475
961
+ Train loss: 0.002237
962
+ Epoch 476
963
+ Train loss: 0.002242
964
+ Epoch 477
965
+ Train loss: 0.002220
966
+ Epoch 478
967
+ Train loss: 0.002280
968
+ Epoch 479
969
+ Train loss: 0.002235
970
+ Epoch 480
971
+ Train loss: 0.002318
972
+ Epoch 481
973
+ Train loss: 0.002277
974
+ Epoch 482
975
+ Train loss: 0.002219
976
+ Epoch 483
977
+ Train loss: 0.002237
978
+ Epoch 484
979
+ Train loss: 0.002865
980
+ Epoch 485
981
+ Train loss: 0.002981
982
+ Epoch 486
983
+ Train loss: 0.002658
984
+ Epoch 487
985
+ Train loss: 0.002461
986
+ Epoch 488
987
+ Train loss: 0.002264
988
+ Epoch 489
989
+ Train loss: 0.002152
990
+ Epoch 490
991
+ Train loss: 0.002618
992
+ Epoch 491
993
+ Train loss: 0.002895
994
+ Epoch 492
995
+ Train loss: 0.002686
996
+ Epoch 493
997
+ Train loss: 0.002528
998
+ Epoch 494
999
+ Train loss: 0.002425
1000
+ Epoch 495
1001
+ Train loss: 0.002432
1002
+ Epoch 496
1003
+ Train loss: 0.002368
1004
+ Epoch 497
1005
+ Train loss: 0.002445
1006
+ Epoch 498
1007
+ Train loss: 0.002291
1008
+ Epoch 499
1009
+ Train loss: 0.002173
1010
+ Epoch 500
1011
+ Train loss: 0.002232
1012
+ Epoch 501
1013
+ Train loss: 0.002270
1014
+ Epoch 502
1015
+ Train loss: 0.002192
1016
+ Epoch 503
1017
+ Train loss: 0.002306
1018
+ Epoch 504
1019
+ Train loss: 0.002241
1020
+ Epoch 505
1021
+ Train loss: 0.002175
1022
+ Epoch 506
1023
+ Train loss: 0.002315
1024
+ Epoch 507
1025
+ Train loss: 0.002379
1026
+ Epoch 508
1027
+ Train loss: 0.002250
1028
+ Epoch 509
1029
+ Train loss: 0.002251
1030
+ Epoch 510
1031
+ Train loss: 0.002337
1032
+ Epoch 511
1033
+ Train loss: 0.002233
1034
+ Epoch 512
1035
+ Train loss: 0.002387
1036
+ Epoch 513
1037
+ Train loss: 0.003105
1038
+ Epoch 514
1039
+ Train loss: 0.002920
1040
+ Epoch 515
1041
+ Train loss: 0.002634
1042
+ Epoch 516
1043
+ Train loss: 0.002437
1044
+ Epoch 517
1045
+ Train loss: 0.002698
1046
+ Epoch 518
1047
+ Train loss: 0.002911
1048
+ Epoch 519
1049
+ Train loss: 0.002498
1050
+ Epoch 520
1051
+ Train loss: 0.002647
1052
+ Epoch 521
1053
+ Train loss: 0.003162
1054
+ Epoch 522
1055
+ Train loss: 0.002815
1056
+ Epoch 523
1057
+ Train loss: 0.002640
1058
+ Epoch 524
1059
+ Train loss: 0.002622
1060
+ Epoch 525
1061
+ Train loss: 0.002612
1062
+ Epoch 526
1063
+ Train loss: 0.002854
1064
+ Epoch 527
1065
+ Train loss: 0.002509
1066
+ Epoch 528
1067
+ Train loss: 0.002391
1068
+ Epoch 529
1069
+ Train loss: 0.002340
1070
+ Epoch 530
1071
+ Train loss: 0.002426
1072
+ Epoch 531
1073
+ Train loss: 0.002327
1074
+ Epoch 532
1075
+ Train loss: 0.002688
1076
+ Epoch 533
1077
+ Train loss: 0.002581
1078
+ Epoch 534
1079
+ Train loss: 0.002506
1080
+ Epoch 535
1081
+ Train loss: 0.002971
1082
+ Epoch 536
1083
+ Train loss: 0.003647
1084
+ Epoch 537
1085
+ Train loss: 0.003102
1086
+ Epoch 538
1087
+ Train loss: 0.003740
1088
+ Epoch 539
1089
+ Train loss: 0.003408
1090
+ Epoch 540
1091
+ Train loss: 0.003103
1092
+ Epoch 541
1093
+ Train loss: 0.003004
1094
+ Epoch 542
1095
+ Train loss: 0.002927
1096
+ Epoch 543
1097
+ Train loss: 0.002759
1098
+ Epoch 544
1099
+ Train loss: 0.002604
1100
+ Epoch 545
1101
+ Train loss: 0.002483
1102
+ Epoch 546
1103
+ Train loss: 0.002485
1104
+ Epoch 547
1105
+ Train loss: 0.002485
1106
+ Epoch 548
1107
+ Train loss: 0.002544
1108
+ Epoch 549
1109
+ Train loss: 0.002446
1110
+ Epoch 550
1111
+ Train loss: 0.002588
1112
+ Epoch 551
1113
+ Train loss: 0.002406
1114
+ Epoch 552
1115
+ Train loss: 0.002533
1116
+ Epoch 553
1117
+ Train loss: 0.002531
1118
+ Epoch 554
1119
+ Train loss: 0.002476
1120
+ Epoch 555
1121
+ Train loss: 0.002435
1122
+ Epoch 556
1123
+ Train loss: 0.002453
1124
+ Epoch 557
1125
+ Train loss: 0.002651
1126
+ Epoch 558
1127
+ Train loss: 0.006514
1128
+ Epoch 559
1129
+ Train loss: 0.003368
1130
+ Epoch 560
1131
+ Train loss: 0.003419
1132
+ Epoch 561
1133
+ Train loss: 0.004336
1134
+ Epoch 562
1135
+ Train loss: 0.003524
1136
+ Epoch 563
1137
+ Train loss: 0.003425
1138
+ Epoch 564
1139
+ Train loss: 0.003207
1140
+ Epoch 565
1141
+ Train loss: 0.003167
1142
+ Epoch 566
1143
+ Train loss: 0.003228
1144
+ Epoch 567
1145
+ Train loss: 0.003466
1146
+ Epoch 568
1147
+ Train loss: 0.003537
1148
+ Epoch 569
1149
+ Train loss: 0.003250
1150
+ Epoch 570
1151
+ Train loss: 0.003228
1152
+ Epoch 571
1153
+ Train loss: 0.003084
1154
+ Epoch 572
1155
+ Train loss: 0.003125
1156
+ Epoch 573
1157
+ Train loss: 0.003039
1158
+ Epoch 574
1159
+ Train loss: 0.003211
1160
+ Epoch 575
1161
+ Train loss: 0.003136
1162
+ Epoch 576
1163
+ Train loss: 0.003332
1164
+ Epoch 577
1165
+ Train loss: 0.002842
1166
+ Epoch 578
1167
+ Train loss: 0.002838
1168
+ Epoch 579
1169
+ Train loss: 0.002938
1170
+ Epoch 580
1171
+ Train loss: 0.002817
1172
+ Epoch 581
1173
+ Train loss: 0.002878
1174
+ Epoch 582
1175
+ Train loss: 0.002838
1176
+ Epoch 583
1177
+ Train loss: 0.002806
1178
+ Epoch 584
1179
+ Train loss: 0.003368
1180
+ Epoch 585
1181
+ Train loss: 0.003221
1182
+ Epoch 586
1183
+ Train loss: 0.003368
1184
+ Epoch 587
1185
+ Train loss: 0.003109
1186
+ Epoch 588
1187
+ Train loss: 0.003345
1188
+ Epoch 589
1189
+ Train loss: 0.002929
1190
+ Epoch 590
1191
+ Train loss: 0.003379
1192
+ Epoch 591
1193
+ Train loss: 0.003317
1194
+ Epoch 592
1195
+ Train loss: 0.004388
1196
+ Epoch 593
1197
+ Train loss: 0.004844
1198
+ Epoch 594
1199
+ Train loss: 0.004712
1200
+ Epoch 595
1201
+ Train loss: 0.004147
1202
+ Epoch 596
1203
+ Train loss: 0.004049
1204
+ Epoch 597
1205
+ Train loss: 0.003718
1206
+ Epoch 598
1207
+ Train loss: 0.004085
1208
+ Epoch 599
1209
+ Train loss: 0.003408
1210
+ Epoch 600
1211
+ Train loss: 0.003253
1212
+ Epoch 601
1213
+ Train loss: 0.003269
1214
+ Epoch 602
1215
+ Train loss: 0.003281
1216
+ Epoch 603
1217
+ Train loss: 0.003332
1218
+ Epoch 604
1219
+ Train loss: 0.003052
1220
+ Epoch 605
1221
+ Train loss: 0.003221
1222
+ Epoch 606
1223
+ Train loss: 0.003245
1224
+ Epoch 607
1225
+ Train loss: 0.002961
1226
+ Epoch 608
1227
+ Train loss: 0.003224
1228
+ Epoch 609
1229
+ Train loss: 0.003514
1230
+ Epoch 610
1231
+ Train loss: 0.003583
1232
+ Epoch 611
1233
+ Train loss: 0.003301
1234
+ Epoch 612
1235
+ Train loss: 0.003172
1236
+ Epoch 613
1237
+ Train loss: 0.003033
1238
+ Epoch 614
1239
+ Train loss: 0.002935
1240
+ Epoch 615
1241
+ Train loss: 0.002891
1242
+ Epoch 616
1243
+ Train loss: 0.002804
1244
+ Epoch 617
1245
+ Train loss: 0.002763
1246
+ Epoch 618
1247
+ Train loss: 0.002788
1248
+ Epoch 619
1249
+ Train loss: 0.002647
1250
+ Epoch 620
1251
+ Train loss: 0.002623
1252
+ Epoch 621
1253
+ Train loss: 0.002511
1254
+ Epoch 622
1255
+ Train loss: 0.002666
1256
+ Epoch 623
1257
+ Train loss: 0.002544
1258
+ Epoch 624
1259
+ Train loss: 0.002542
1260
+ Epoch 625
1261
+ Train loss: 0.002642
1262
+ Epoch 626
1263
+ Train loss: 0.002532
1264
+ Epoch 627
1265
+ Train loss: 0.002802
1266
+ Epoch 628
1267
+ Train loss: 0.002808
1268
+ Epoch 629
1269
+ Train loss: 0.002651
1270
+ Epoch 630
1271
+ Train loss: 0.002605
1272
+ Epoch 631
1273
+ Train loss: 0.002660
1274
+ Epoch 632
1275
+ Train loss: 0.003095
1276
+ Epoch 633
1277
+ Train loss: 0.004315
1278
+ Epoch 634
1279
+ Train loss: 0.005250
1280
+ Epoch 635
1281
+ Train loss: 0.004485
1282
+ Epoch 636
1283
+ Train loss: 0.004632
1284
+ Epoch 637
1285
+ Train loss: 0.005032
1286
+ Epoch 638
1287
+ Train loss: 0.004152
1288
+ Epoch 639
1289
+ Train loss: 0.004112
1290
+ Epoch 640
1291
+ Train loss: 0.003889
1292
+ Epoch 641
1293
+ Train loss: 0.003837
1294
+ Epoch 642
1295
+ Train loss: 0.004042
1296
+ Epoch 643
1297
+ Train loss: 0.004064
1298
+ Epoch 644
1299
+ Train loss: 0.003951
1300
+ Epoch 645
1301
+ Train loss: 0.003806
1302
+ Epoch 646
1303
+ Train loss: 0.004102
1304
+ Epoch 647
1305
+ Train loss: 0.003874
1306
+ Epoch 648
1307
+ Train loss: 0.003597
1308
+ Epoch 649
1309
+ Train loss: 0.003663
1310
+ Epoch 650
1311
+ Train loss: 0.003911
1312
+ Epoch 651
1313
+ Train loss: 0.003786
1314
+ Epoch 652
1315
+ Train loss: 0.003957
1316
+ Epoch 653
1317
+ Train loss: 0.004153
1318
+ Epoch 654
1319
+ Train loss: 0.003634
1320
+ Epoch 655
1321
+ Train loss: 0.004080
1322
+ Epoch 656
1323
+ Train loss: 0.003790
1324
+ Epoch 657
1325
+ Train loss: 0.003611
1326
+ Epoch 658
1327
+ Train loss: 0.003499
1328
+ Epoch 659
1329
+ Train loss: 0.003771
1330
+ Epoch 660
1331
+ Train loss: 0.003851
1332
+ Epoch 661
1333
+ Train loss: 0.003710
1334
+ Epoch 662
1335
+ Train loss: 0.003628
1336
+ Epoch 663
1337
+ Train loss: 0.003730
1338
+ Epoch 664
1339
+ Train loss: 0.003843
1340
+ Epoch 665
1341
+ Train loss: 0.004346
1342
+ Epoch 666
1343
+ Train loss: 0.004531
1344
+ Epoch 667
1345
+ Train loss: 0.004241
1346
+ Epoch 668
1347
+ Train loss: 0.004068
1348
+ Epoch 669
1349
+ Train loss: 0.004074
1350
+ Epoch 670
1351
+ Train loss: 0.003896
1352
+ Epoch 671
1353
+ Train loss: 0.003817
1354
+ Epoch 672
1355
+ Train loss: 0.004567
1356
+ Epoch 673
1357
+ Train loss: 0.004276
1358
+ Epoch 674
1359
+ Train loss: 0.004266
1360
+ Epoch 675
1361
+ Train loss: 0.004657
1362
+ Epoch 676
1363
+ Train loss: 0.004813
1364
+ Epoch 677
1365
+ Train loss: 0.004887
1366
+ Epoch 678
1367
+ Train loss: 0.004949
1368
+ Epoch 679
1369
+ Train loss: 0.004799
1370
+ Epoch 680
1371
+ Train loss: 0.004534
1372
+ Epoch 681
1373
+ Train loss: 0.004276
1374
+ Epoch 682
1375
+ Train loss: 0.004360
1376
+ Epoch 683
1377
+ Train loss: 0.004564
1378
+ Epoch 684
1379
+ Train loss: 0.004579
1380
+ Epoch 685
1381
+ Train loss: 0.004219
1382
+ Epoch 686
1383
+ Train loss: 0.004058
1384
+ Epoch 687
1385
+ Train loss: 0.004244
1386
+ Epoch 688
1387
+ Train loss: 0.004000
1388
+ Epoch 689
1389
+ Train loss: 0.004259
1390
+ Epoch 690
1391
+ Train loss: 0.004184
1392
+ Epoch 691
1393
+ Train loss: 0.004019
1394
+ Epoch 692
1395
+ Train loss: 0.004083
1396
+ Epoch 693
1397
+ Train loss: 0.003935
1398
+ Epoch 694
1399
+ Train loss: 0.004185
1400
+ Epoch 695
1401
+ Train loss: 0.004411
1402
+ Epoch 696
1403
+ Train loss: 0.004143
1404
+ Epoch 697
1405
+ Train loss: 0.004235
1406
+ Epoch 698
1407
+ Train loss: 0.004572
1408
+ Epoch 699
1409
+ Train loss: 0.004489
1410
+ Epoch 700
1411
+ Train loss: 0.004467
1412
+ Epoch 701
1413
+ Train loss: 0.004398
1414
+ Epoch 702
1415
+ Train loss: 0.004266
1416
+ Epoch 703
1417
+ Train loss: 0.004412
1418
+ Epoch 704
1419
+ Train loss: 0.004371
1420
+ Epoch 705
1421
+ Train loss: 0.004444
1422
+ Epoch 706
1423
+ Train loss: 0.004577
1424
+ Epoch 707
1425
+ Train loss: 0.004463
1426
+ Epoch 708
1427
+ Train loss: 0.004217
1428
+ Epoch 709
1429
+ Train loss: 0.004371
1430
+ Epoch 710
1431
+ Train loss: 0.004286
1432
+ Epoch 711
1433
+ Train loss: 0.004317
1434
+ Epoch 712
1435
+ Train loss: 0.004181
1436
+ Epoch 713
1437
+ Train loss: 0.004071
1438
+ Epoch 714
1439
+ Train loss: 0.004284
1440
+ Epoch 715
1441
+ Train loss: 0.004208
1442
+ Epoch 716
1443
+ Train loss: 0.004336
1444
+ Epoch 717
1445
+ Train loss: 0.004274
1446
+ Epoch 718
1447
+ Train loss: 0.004305
1448
+ Epoch 719
1449
+ Train loss: 0.004062
1450
+ Epoch 720
1451
+ Train loss: 0.003925
1452
+ Epoch 721
1453
+ Train loss: 0.004023
1454
+ Epoch 722
1455
+ Train loss: 0.004260
1456
+ Epoch 723
1457
+ Train loss: 0.004847
1458
+ Epoch 724
1459
+ Train loss: 0.004672
1460
+ Epoch 725
1461
+ Train loss: 0.004542
1462
+ Epoch 726
1463
+ Train loss: 0.004284
1464
+ Epoch 727
1465
+ Train loss: 0.004195
1466
+ Epoch 728
1467
+ Train loss: 0.004571
1468
+ Epoch 729
1469
+ Train loss: 0.004275
1470
+ Epoch 730
1471
+ Train loss: 0.004341
1472
+ Epoch 731
1473
+ Train loss: 0.004461
1474
+ Epoch 732
1475
+ Train loss: 0.004409
1476
+ Epoch 733
1477
+ Train loss: 0.004380
1478
+ Epoch 734
1479
+ Train loss: 0.004164
1480
+ Epoch 735
1481
+ Train loss: 0.004102
1482
+ Epoch 736
1483
+ Train loss: 0.003914
1484
+ Epoch 737
1485
+ Train loss: 0.003981
1486
+ Epoch 738
1487
+ Train loss: 0.004016
1488
+ Epoch 739
1489
+ Train loss: 0.004198
1490
+ Epoch 740
1491
+ Train loss: 0.003862
1492
+ Epoch 741
1493
+ Train loss: 0.003858
1494
+ Epoch 742
1495
+ Train loss: 0.003981
1496
+ Epoch 743
1497
+ Train loss: 0.003819
1498
+ Epoch 744
1499
+ Train loss: 0.003842
1500
+ Epoch 745
1501
+ Train loss: 0.003821
1502
+ Epoch 746
1503
+ Train loss: 0.003811
1504
+ Epoch 747
1505
+ Train loss: 0.004816
1506
+ Epoch 748
1507
+ Train loss: 0.004588
1508
+ Epoch 749
1509
+ Train loss: 0.004233
1510
+ Epoch 750
1511
+ Train loss: 0.004205
1512
+ Epoch 751
1513
+ Train loss: 0.004096
1514
+ Epoch 752
1515
+ Train loss: 0.004088
1516
+ Epoch 753
1517
+ Train loss: 0.004495
1518
+ Epoch 754
1519
+ Train loss: 0.004418
1520
+ Epoch 755
1521
+ Train loss: 0.004418
1522
+ Epoch 756
1523
+ Train loss: 0.004468
1524
+ Epoch 757
1525
+ Train loss: 0.004368
1526
+ Epoch 758
1527
+ Train loss: 0.004537
1528
+ Epoch 759
1529
+ Train loss: 0.004382
1530
+ Epoch 760
1531
+ Train loss: 0.004353
1532
+ Epoch 761
1533
+ Train loss: 0.004472
1534
+ Epoch 762
1535
+ Train loss: 0.004554
1536
+ Epoch 763
1537
+ Train loss: 0.004323
1538
+ Epoch 764
1539
+ Train loss: 0.004538
1540
+ Epoch 765
1541
+ Train loss: 0.004248
1542
+ Epoch 766
1543
+ Train loss: 0.004146
1544
+ Epoch 767
1545
+ Train loss: 0.004261
1546
+ Epoch 768
1547
+ Train loss: 0.004383
1548
+ Epoch 769
1549
+ Train loss: 0.004280
1550
+ Epoch 770
1551
+ Train loss: 0.004768
1552
+ Epoch 771
1553
+ Train loss: 0.004578
1554
+ Epoch 772
1555
+ Train loss: 0.004654
1556
+ Epoch 773
1557
+ Train loss: 0.004703
1558
+ Epoch 774
1559
+ Train loss: 0.004466
1560
+ Epoch 775
1561
+ Train loss: 0.004435
1562
+ Epoch 776
1563
+ Train loss: 0.004635
1564
+ Epoch 777
1565
+ Train loss: 0.004498
1566
+ Epoch 778
1567
+ Train loss: 0.004412
1568
+ Epoch 779
1569
+ Train loss: 0.004488
1570
+ Epoch 780
1571
+ Train loss: 0.004522
1572
+ Epoch 781
1573
+ Train loss: 0.004423
1574
+ Epoch 782
1575
+ Train loss: 0.004441
1576
+ Epoch 783
1577
+ Train loss: 0.004343
1578
+ Epoch 784
1579
+ Train loss: 0.004392
1580
+ Epoch 785
1581
+ Train loss: 0.004861
1582
+ Epoch 786
1583
+ Train loss: 0.004648
1584
+ Epoch 787
1585
+ Train loss: 0.004572
1586
+ Epoch 788
1587
+ Train loss: 0.004652
1588
+ Epoch 789
1589
+ Train loss: 0.004601
1590
+ Epoch 790
1591
+ Train loss: 0.005237
1592
+ Epoch 791
1593
+ Train loss: 0.004934
1594
+ Epoch 792
1595
+ Train loss: 0.004912
1596
+ Epoch 793
1597
+ Train loss: 0.004781
1598
+ Epoch 794
1599
+ Train loss: 0.004804
1600
+ Epoch 795
1601
+ Train loss: 0.004707
1602
+ Epoch 796
1603
+ Train loss: 0.004904
1604
+ Epoch 797
1605
+ Train loss: 0.004609
1606
+ Epoch 798
1607
+ Train loss: 0.004823
1608
+ Epoch 799
1609
+ Train loss: 0.004665
1610
+ Epoch 800
1611
+ Train loss: 0.004611
1612
+ Epoch 801
1613
+ Train loss: 0.004606
1614
+ Epoch 802
1615
+ Train loss: 0.004753
1616
+ Epoch 803
1617
+ Train loss: 0.004711
1618
+ Epoch 804
1619
+ Train loss: 0.005857
1620
+ Epoch 805
1621
+ Train loss: 0.005250
1622
+ Epoch 806
1623
+ Train loss: 0.004803
1624
+ Epoch 807
1625
+ Train loss: 0.004784
1626
+ Epoch 808
1627
+ Train loss: 0.004706
1628
+ Epoch 809
1629
+ Train loss: 0.004636
1630
+ Epoch 810
1631
+ Train loss: 0.004555
1632
+ Epoch 811
1633
+ Train loss: 0.004300
1634
+ Epoch 812
1635
+ Train loss: 0.004391
1636
+ Epoch 813
1637
+ Train loss: 0.004381
1638
+ Epoch 814
1639
+ Train loss: 0.004241
1640
+ Epoch 815
1641
+ Train loss: 0.004279
1642
+ Epoch 816
1643
+ Train loss: 0.004428
1644
+ Epoch 817
1645
+ Train loss: 0.004421
1646
+ Epoch 818
1647
+ Train loss: 0.004175
1648
+ Epoch 819
1649
+ Train loss: 0.004121
1650
+ Epoch 820
1651
+ Train loss: 0.004238
1652
+ Epoch 821
1653
+ Train loss: 0.004266
1654
+ Epoch 822
1655
+ Train loss: 0.004134
1656
+ Epoch 823
1657
+ Train loss: 0.004211
1658
+ Epoch 824
1659
+ Train loss: 0.004156
1660
+ Epoch 825
1661
+ Train loss: 0.004133
1662
+ Epoch 826
1663
+ Train loss: 0.004344
1664
+ Epoch 827
1665
+ Train loss: 0.004563
1666
+ Epoch 828
1667
+ Train loss: 0.004394
1668
+ Epoch 829
1669
+ Train loss: 0.004335
1670
+ Epoch 830
1671
+ Train loss: 0.004401
1672
+ Epoch 831
1673
+ Train loss: 0.004426
1674
+ Epoch 832
1675
+ Train loss: 0.004106
1676
+ Epoch 833
1677
+ Train loss: 0.004165
1678
+ Epoch 834
1679
+ Train loss: 0.004090
1680
+ Epoch 835
1681
+ Train loss: 0.004083
1682
+ Epoch 836
1683
+ Train loss: 0.004024
1684
+ Epoch 837
1685
+ Train loss: 0.004566
1686
+ Epoch 838
1687
+ Train loss: 0.005082
1688
+ Epoch 839
1689
+ Train loss: 0.004640
1690
+ Epoch 840
1691
+ Train loss: 0.004489
1692
+ Epoch 841
1693
+ Train loss: 0.004435
1694
+ Epoch 842
1695
+ Train loss: 0.004356
1696
+ Epoch 843
1697
+ Train loss: 0.004188
1698
+ Epoch 844
1699
+ Train loss: 0.004256
1700
+ Epoch 845
1701
+ Train loss: 0.004451
1702
+ Epoch 846
1703
+ Train loss: 0.004191
1704
+ Epoch 847
1705
+ Train loss: 0.004167
1706
+ Epoch 848
1707
+ Train loss: 0.004272
1708
+ Epoch 849
1709
+ Train loss: 0.004418
1710
+ Epoch 850
1711
+ Train loss: 0.004489
1712
+ Epoch 851
1713
+ Train loss: 0.004609
1714
+ Epoch 852
1715
+ Train loss: 0.004579
1716
+ Epoch 853
1717
+ Train loss: 0.004436
1718
+ Epoch 854
1719
+ Train loss: 0.004740
1720
+ Epoch 855
1721
+ Train loss: 0.004836
1722
+ Epoch 856
1723
+ Train loss: 0.004719
1724
+ Epoch 857
1725
+ Train loss: 0.004657
1726
+ Epoch 858
1727
+ Train loss: 0.004379
1728
+ Epoch 859
1729
+ Train loss: 0.004605
1730
+ Epoch 860
1731
+ Train loss: 0.004709
1732
+ Epoch 861
1733
+ Train loss: 0.004529
1734
+ Epoch 862
1735
+ Train loss: 0.004688
1736
+ Epoch 863
1737
+ Train loss: 0.004603
1738
+ Epoch 864
1739
+ Train loss: 0.004622
1740
+ Epoch 865
1741
+ Train loss: 0.004589
1742
+ Epoch 866
1743
+ Train loss: 0.004531
1744
+ Epoch 867
1745
+ Train loss: 0.004697
1746
+ Epoch 868
1747
+ Train loss: 0.004529
1748
+ Epoch 869
1749
+ Train loss: 0.004511
1750
+ Epoch 870
1751
+ Train loss: 0.004454
1752
+ Epoch 871
1753
+ Train loss: 0.005738
1754
+ Epoch 872
1755
+ Train loss: 0.005438
1756
+ Epoch 873
1757
+ Train loss: 0.005231
1758
+ Epoch 874
1759
+ Train loss: 0.005065
1760
+ Epoch 875
1761
+ Train loss: 0.004861
1762
+ Epoch 876
1763
+ Train loss: 0.004862
1764
+ Epoch 877
1765
+ Train loss: 0.005047
1766
+ Epoch 878
1767
+ Train loss: 0.005110
1768
+ Epoch 879
1769
+ Train loss: 0.004906
1770
+ Epoch 880
1771
+ Train loss: 0.004841
1772
+ Epoch 881
1773
+ Train loss: 0.005305
1774
+ Epoch 882
1775
+ Train loss: 0.005327
1776
+ Epoch 883
1777
+ Train loss: 0.005404
1778
+ Epoch 884
1779
+ Train loss: 0.005272
1780
+ Epoch 885
1781
+ Train loss: 0.005322
1782
+ Epoch 886
1783
+ Train loss: 0.005095
1784
+ Epoch 887
1785
+ Train loss: 0.005218
1786
+ Epoch 888
1787
+ Train loss: 0.005102
1788
+ Epoch 889
1789
+ Train loss: 0.005262
1790
+ Epoch 890
1791
+ Train loss: 0.005101
1792
+ Epoch 891
1793
+ Train loss: 0.005057
1794
+ Epoch 892
1795
+ Train loss: 0.005113
1796
+ Epoch 893
1797
+ Train loss: 0.005085
1798
+ Epoch 894
1799
+ Train loss: 0.005025
1800
+ Epoch 895
1801
+ Train loss: 0.004941
1802
+ Epoch 896
1803
+ Train loss: 0.005008
1804
+ Epoch 897
1805
+ Train loss: 0.005044
1806
+ Epoch 898
1807
+ Train loss: 0.005060
1808
+ Epoch 899
1809
+ Train loss: 0.005085
1810
+ Epoch 900
1811
+ Train loss: 0.005012
1812
+ Epoch 901
1813
+ Train loss: 0.005041
1814
+ Epoch 902
1815
+ Train loss: 0.005306
1816
+ Epoch 903
1817
+ Train loss: 0.005256
1818
+ Epoch 904
1819
+ Train loss: 0.005168
1820
+ Epoch 905
1821
+ Train loss: 0.004993
1822
+ Epoch 906
1823
+ Train loss: 0.005145
1824
+ Epoch 907
1825
+ Train loss: 0.005085
1826
+ Epoch 908
1827
+ Train loss: 0.004902
1828
+ Epoch 909
1829
+ Train loss: 0.004939
1830
+ Epoch 910
1831
+ Train loss: 0.004873
1832
+ Epoch 911
1833
+ Train loss: 0.004949
1834
+ Epoch 912
1835
+ Train loss: 0.004973
1836
+ Epoch 913
1837
+ Train loss: 0.004851
1838
+ Epoch 914
1839
+ Train loss: 0.004921
1840
+ Epoch 915
1841
+ Train loss: 0.004853
1842
+ Epoch 916
1843
+ Train loss: 0.004886
1844
+ Epoch 917
1845
+ Train loss: 0.004790
1846
+ Epoch 918
1847
+ Train loss: 0.004770
1848
+ Epoch 919
1849
+ Train loss: 0.004767
1850
+ Epoch 920
1851
+ Train loss: 0.004851
1852
+ Epoch 921
1853
+ Train loss: 0.004958
1854
+ Epoch 922
1855
+ Train loss: 0.004918
1856
+ Epoch 923
1857
+ Train loss: 0.004812
1858
+ Epoch 924
1859
+ Train loss: 0.004807
1860
+ Epoch 925
1861
+ Train loss: 0.004806
1862
+ Epoch 926
1863
+ Train loss: 0.005089
1864
+ Epoch 927
1865
+ Train loss: 0.004967
1866
+ Epoch 928
1867
+ Train loss: 0.004891
1868
+ Epoch 929
1869
+ Train loss: 0.004923
1870
+ Epoch 930
1871
+ Train loss: 0.004912
1872
+ Epoch 931
1873
+ Train loss: 0.005175
1874
+ Epoch 932
1875
+ Train loss: 0.005206
1876
+ Epoch 933
1877
+ Train loss: 0.005023
1878
+ Epoch 934
1879
+ Train loss: 0.005171
1880
+ Epoch 935
1881
+ Train loss: 0.005137
1882
+ Epoch 936
1883
+ Train loss: 0.005268
1884
+ Epoch 937
1885
+ Train loss: 0.005314
1886
+ Epoch 938
1887
+ Train loss: 0.005161
1888
+ Epoch 939
1889
+ Train loss: 0.005134
1890
+ Epoch 940
1891
+ Train loss: 0.005266
1892
+ Epoch 941
1893
+ Train loss: 0.005245
1894
+ Epoch 942
1895
+ Train loss: 0.005559
1896
+ Epoch 943
1897
+ Train loss: 0.005533
1898
+ Epoch 944
1899
+ Train loss: 0.005280
1900
+ Epoch 945
1901
+ Train loss: 0.005516
1902
+ Epoch 946
1903
+ Train loss: 0.005369
1904
+ Epoch 947
1905
+ Train loss: 0.005380
1906
+ Epoch 948
1907
+ Train loss: 0.005325
1908
+ Epoch 949
1909
+ Train loss: 0.005331
1910
+ Epoch 950
1911
+ Train loss: 0.005392
1912
+ Epoch 951
1913
+ Train loss: 0.005256
1914
+ Epoch 952
1915
+ Train loss: 0.005356
1916
+ Epoch 953
1917
+ Train loss: 0.005314
1918
+ Epoch 954
1919
+ Train loss: 0.005223
1920
+ Epoch 955
1921
+ Train loss: 0.005230
1922
+ Epoch 956
1923
+ Train loss: 0.005261
1924
+ Epoch 957
1925
+ Train loss: 0.005351
1926
+ Epoch 958
1927
+ Train loss: 0.005347
1928
+ Epoch 959
1929
+ Train loss: 0.005399
1930
+ Epoch 960
1931
+ Train loss: 0.005454
1932
+ Epoch 961
1933
+ Train loss: 0.005414
1934
+ Epoch 962
1935
+ Train loss: 0.005527
1936
+ Epoch 963
1937
+ Train loss: 0.005340
1938
+ Epoch 964
1939
+ Train loss: 0.005478
1940
+ Epoch 965
1941
+ Train loss: 0.005381
1942
+ Epoch 966
1943
+ Train loss: 0.005492
1944
+ Epoch 967
1945
+ Train loss: 0.005297
1946
+ Epoch 968
1947
+ Train loss: 0.005471
1948
+ Epoch 969
1949
+ Train loss: 0.005438
1950
+ Epoch 970
1951
+ Train loss: 0.005406
1952
+ Epoch 971
1953
+ Train loss: 0.005327
1954
+ Epoch 972
1955
+ Train loss: 0.005395
1956
+ Epoch 973
1957
+ Train loss: 0.005656
1958
+ Epoch 974
1959
+ Train loss: 0.005621
1960
+ Epoch 975
1961
+ Train loss: 0.005769
1962
+ Epoch 976
1963
+ Train loss: 0.005751
1964
+ Epoch 977
1965
+ Train loss: 0.005943
1966
+ Epoch 978
1967
+ Train loss: 0.005977
1968
+ Epoch 979
1969
+ Train loss: 0.005886
1970
+ Epoch 980
1971
+ Train loss: 0.005970
1972
+ Epoch 981
1973
+ Train loss: 0.005850
1974
+ Epoch 982
1975
+ Train loss: 0.005864
1976
+ Epoch 983
1977
+ Train loss: 0.005994
1978
+ Epoch 984
1979
+ Train loss: 0.005871
1980
+ Epoch 985
1981
+ Train loss: 0.005968
1982
+ Epoch 986
1983
+ Train loss: 0.006202
1984
+ Epoch 987
1985
+ Train loss: 0.006100
1986
+ Epoch 988
1987
+ Train loss: 0.006171
1988
+ Epoch 989
1989
+ Train loss: 0.006195
1990
+ Epoch 990
1991
+ Train loss: 0.006161
1992
+ Epoch 991
1993
+ Train loss: 0.006163
1994
+ Epoch 992
1995
+ Train loss: 0.006230
1996
+ Epoch 993
1997
+ Train loss: 0.006134
1998
+ Epoch 994
1999
+ Train loss: 0.006172
2000
+ Epoch 995
2001
+ Train loss: 0.006116
2002
+ Epoch 996
2003
+ Train loss: 0.006255
2004
+ Epoch 997
2005
+ Train loss: 0.006157
2006
+ Epoch 998
2007
+ Train loss: 0.006193
2008
+ Epoch 999
2009
+ Train loss: 0.006169
2010
+ Epoch 1000
2011
+ Train loss: 0.006207
2012
+ Epoch 1001
2013
+ Train loss: 0.006229
2014
+ Epoch 1002
2015
+ Train loss: 0.006045
2016
+ Epoch 1003
2017
+ Train loss: 0.006091
2018
+ Epoch 1004
2019
+ Train loss: 0.006047
2020
+ Epoch 1005
2021
+ Train loss: 0.006181
2022
+ Epoch 1006
2023
+ Train loss: 0.006034
2024
+ Epoch 1007
2025
+ Train loss: 0.006105
2026
+ Epoch 1008
2027
+ Train loss: 0.006100
2028
+ Epoch 1009
2029
+ Train loss: 0.006153
2030
+ Epoch 1010
2031
+ Train loss: 0.006171
2032
+ Epoch 1011
2033
+ Train loss: 0.006079
2034
+ Epoch 1012
2035
+ Train loss: 0.006162
2036
+ Epoch 1013
2037
+ Train loss: 0.006079
2038
+ Epoch 1014
2039
+ Train loss: 0.006147
2040
+ Epoch 1015
2041
+ Train loss: 0.006049
2042
+ Epoch 1016
2043
+ Train loss: 0.005992
2044
+ Epoch 1017
2045
+ Train loss: 0.006070
2046
+ Epoch 1018
2047
+ Train loss: 0.005939
2048
+ Epoch 1019
2049
+ Train loss: 0.005952
2050
+ Epoch 1020
2051
+ Train loss: 0.006120
2052
+ Epoch 1021
2053
+ Train loss: 0.006046
2054
+ Epoch 1022
2055
+ Train loss: 0.005941
2056
+ Epoch 1023
2057
+ Train loss: 0.005938
2058
+ Epoch 1024
2059
+ Train loss: 0.006109
2060
+ Epoch 1025
2061
+ Train loss: 0.006005
2062
+ Epoch 1026
2063
+ Train loss: 0.006074
2064
+ Epoch 1027
2065
+ Train loss: 0.006013
2066
+ Epoch 1028
2067
+ Train loss: 0.006072
2068
+ Epoch 1029
2069
+ Train loss: 0.006056
2070
+ Epoch 1030
2071
+ Train loss: 0.006060
2072
+ Epoch 1031
2073
+ Train loss: 0.005993
2074
+ Epoch 1032
2075
+ Train loss: 0.006017
2076
+ Epoch 1033
2077
+ Train loss: 0.005944
2078
+ Epoch 1034
2079
+ Train loss: 0.005970
2080
+ Epoch 1035
2081
+ Train loss: 0.006018
2082
+ Epoch 1036
2083
+ Train loss: 0.005920
2084
+ Epoch 1037
2085
+ Train loss: 0.006023
2086
+ Epoch 1038
2087
+ Train loss: 0.006086
2088
+ Epoch 1039
2089
+ Train loss: 0.006039
2090
+ Epoch 1040
2091
+ Train loss: 0.005950
2092
+ Epoch 1041
2093
+ Train loss: 0.005992
2094
+ Epoch 1042
2095
+ Train loss: 0.006023
2096
+ Epoch 1043
2097
+ Train loss: 0.005989
2098
+ Epoch 1044
2099
+ Train loss: 0.006072
2100
+ Epoch 1045
2101
+ Train loss: 0.006109
2102
+ Epoch 1046
2103
+ Train loss: 0.006023
2104
+ Epoch 1047
2105
+ Train loss: 0.005998
2106
+ Epoch 1048
2107
+ Train loss: 0.005960
2108
+ Epoch 1049
2109
+ Train loss: 0.006005
2110
+ Epoch 1050
2111
+ Train loss: 0.006035
2112
+ Epoch 1051
2113
+ Train loss: 0.006094
2114
+ Epoch 1052
2115
+ Train loss: 0.005941
2116
+ Epoch 1053
2117
+ Train loss: 0.006025
2118
+ Epoch 1054
2119
+ Train loss: 0.006129
2120
+ Epoch 1055
2121
+ Train loss: 0.006013
2122
+ Epoch 1056
2123
+ Train loss: 0.006000
2124
+ Epoch 1057
2125
+ Train loss: 0.005908
2126
+ Epoch 1058
2127
+ Train loss: 0.005890
2128
+ Epoch 1059
2129
+ Train loss: 0.005926
2130
+ Epoch 1060
2131
+ Train loss: 0.005951
2132
+ Epoch 1061
2133
+ Train loss: 0.005954
2134
+ Epoch 1062
2135
+ Train loss: 0.005828
2136
+ Epoch 1063
2137
+ Train loss: 0.005888
2138
+ Epoch 1064
2139
+ Train loss: 0.005918
2140
+ Epoch 1065
2141
+ Train loss: 0.005810
2142
+ Epoch 1066
2143
+ Train loss: 0.005893
2144
+ Epoch 1067
2145
+ Train loss: 0.005935
2146
+ Epoch 1068
2147
+ Train loss: 0.005807
2148
+ Epoch 1069
2149
+ Train loss: 0.005885
2150
+ Epoch 1070
2151
+ Train loss: 0.005875
2152
+ Epoch 1071
2153
+ Train loss: 0.005748
2154
+ Epoch 1072
2155
+ Train loss: 0.005796
2156
+ Epoch 1073
2157
+ Train loss: 0.005802
2158
+ Epoch 1074
2159
+ Train loss: 0.005813
2160
+ Epoch 1075
2161
+ Train loss: 0.005870
2162
+ Epoch 1076
2163
+ Train loss: 0.005856
2164
+ Epoch 1077
2165
+ Train loss: 0.005804
2166
+ Epoch 1078
2167
+ Train loss: 0.005796
2168
+ Epoch 1079
2169
+ Train loss: 0.005912
2170
+ Epoch 1080
2171
+ Train loss: 0.005926
2172
+ Epoch 1081
2173
+ Train loss: 0.005867
2174
+ Epoch 1082
2175
+ Train loss: 0.005785
2176
+ Epoch 1083
2177
+ Train loss: 0.005780
2178
+ Epoch 1084
2179
+ Train loss: 0.005840
2180
+ Epoch 1085
2181
+ Train loss: 0.005756
2182
+ Epoch 1086
2183
+ Train loss: 0.005804
2184
+ Epoch 1087
2185
+ Train loss: 0.005772
2186
+ Epoch 1088
2187
+ Train loss: 0.005823
2188
+ Epoch 1089
2189
+ Train loss: 0.005700
2190
+ Epoch 1090
2191
+ Train loss: 0.005791
2192
+ Epoch 1091
2193
+ Train loss: 0.005758
2194
+ Epoch 1092
2195
+ Train loss: 0.005771
2196
+ Epoch 1093
2197
+ Train loss: 0.005818
2198
+ Epoch 1094
2199
+ Train loss: 0.005737
2200
+ Epoch 1095
2201
+ Train loss: 0.005761
2202
+ Epoch 1096
2203
+ Train loss: 0.005688
2204
+ Epoch 1097
2205
+ Train loss: 0.005749
2206
+ Epoch 1098
2207
+ Train loss: 0.005801
2208
+ Epoch 1099
2209
+ Train loss: 0.005826
2210
+ Epoch 1100
2211
+ Train loss: 0.005722
2212
+ Epoch 1101
2213
+ Train loss: 0.005738
2214
+ Epoch 1102
2215
+ Train loss: 0.005709
2216
+ Epoch 1103
2217
+ Train loss: 0.005733
2218
+ Epoch 1104
2219
+ Train loss: 0.005603
2220
+ Epoch 1105
2221
+ Train loss: 0.005624
2222
+ Epoch 1106
2223
+ Train loss: 0.005590
2224
+ Epoch 1107
2225
+ Train loss: 0.005739
2226
+ Epoch 1108
2227
+ Train loss: 0.005696
2228
+ Epoch 1109
2229
+ Train loss: 0.005732
2230
+ Epoch 1110
2231
+ Train loss: 0.005619
2232
+ Epoch 1111
2233
+ Train loss: 0.005620
2234
+ Epoch 1112
2235
+ Train loss: 0.005592
2236
+ Epoch 1113
2237
+ Train loss: 0.005686
2238
+ Epoch 1114
2239
+ Train loss: 0.005592
2240
+ Epoch 1115
2241
+ Train loss: 0.005701
2242
+ Epoch 1116
2243
+ Train loss: 0.005664
2244
+ Epoch 1117
2245
+ Train loss: 0.005709
2246
+ Epoch 1118
2247
+ Train loss: 0.005700
2248
+ Epoch 1119
2249
+ Train loss: 0.005646
2250
+ Epoch 1120
2251
+ Train loss: 0.005570
2252
+ Epoch 1121
2253
+ Train loss: 0.005570
2254
+ Epoch 1122
2255
+ Train loss: 0.005652
2256
+ Epoch 1123
2257
+ Train loss: 0.005559
2258
+ Epoch 1124
2259
+ Train loss: 0.005526
2260
+ Epoch 1125
2261
+ Train loss: 0.005607
2262
+ Epoch 1126
2263
+ Train loss: 0.005644
2264
+ Epoch 1127
2265
+ Train loss: 0.005645
2266
+ Epoch 1128
2267
+ Train loss: 0.005600
2268
+ Epoch 1129
2269
+ Train loss: 0.005566
2270
+ Epoch 1130
2271
+ Train loss: 0.005629
2272
+ Epoch 1131
2273
+ Train loss: 0.005544
2274
+ Epoch 1132
2275
+ Train loss: 0.005587
2276
+ Epoch 1133
2277
+ Train loss: 0.005557
2278
+ Epoch 1134
2279
+ Train loss: 0.005540
2280
+ Epoch 1135
2281
+ Train loss: 0.005528
2282
+ Epoch 1136
2283
+ Train loss: 0.005499
2284
+ Epoch 1137
2285
+ Train loss: 0.005540
2286
+ Epoch 1138
2287
+ Train loss: 0.005455
2288
+ Epoch 1139
2289
+ Train loss: 0.005547
2290
+ Epoch 1140
2291
+ Train loss: 0.005609
2292
+ Epoch 1141
2293
+ Train loss: 0.005540
2294
+ Epoch 1142
2295
+ Train loss: 0.005455
2296
+ Epoch 1143
2297
+ Train loss: 0.005588
2298
+ Epoch 1144
2299
+ Train loss: 0.005491
2300
+ Epoch 1145
2301
+ Train loss: 0.005470
2302
+ Epoch 1146
2303
+ Train loss: 0.005516
2304
+ Epoch 1147
2305
+ Train loss: 0.005628
2306
+ Epoch 1148
2307
+ Train loss: 0.005615
2308
+ Epoch 1149
2309
+ Train loss: 0.005470
2310
+ Epoch 1150
2311
+ Train loss: 0.005472
2312
+ Epoch 1151
2313
+ Train loss: 0.005470
2314
+ Epoch 1152
2315
+ Train loss: 0.005350
2316
+ Epoch 1153
2317
+ Train loss: 0.005526
2318
+ Epoch 1154
2319
+ Train loss: 0.005465
2320
+ Epoch 1155
2321
+ Train loss: 0.005532
2322
+ Epoch 1156
2323
+ Train loss: 0.005471
2324
+ Epoch 1157
2325
+ Train loss: 0.005504
2326
+ Epoch 1158
2327
+ Train loss: 0.005498
2328
+ Epoch 1159
2329
+ Train loss: 0.005570
2330
+ Epoch 1160
2331
+ Train loss: 0.005408
2332
+ Epoch 1161
2333
+ Train loss: 0.005417
2334
+ Epoch 1162
2335
+ Train loss: 0.005500
2336
+ Epoch 1163
2337
+ Train loss: 0.005534
2338
+ Epoch 1164
2339
+ Train loss: 0.005501
2340
+ Epoch 1165
2341
+ Train loss: 0.005525
2342
+ Epoch 1166
2343
+ Train loss: 0.005471
2344
+ Epoch 1167
2345
+ Train loss: 0.005553
2346
+ Epoch 1168
2347
+ Train loss: 0.005419
2348
+ Epoch 1169
2349
+ Train loss: 0.005412
2350
+ Epoch 1170
2351
+ Train loss: 0.005398
2352
+ Epoch 1171
2353
+ Train loss: 0.005508
2354
+ Epoch 1172
2355
+ Train loss: 0.005476
2356
+ Epoch 1173
2357
+ Train loss: 0.005381
2358
+ Epoch 1174
2359
+ Train loss: 0.005452
2360
+ Epoch 1175
2361
+ Train loss: 0.005519
2362
+ Epoch 1176
2363
+ Train loss: 0.005453
2364
+ Epoch 1177
2365
+ Train loss: 0.005429
2366
+ Epoch 1178
2367
+ Train loss: 0.005456
2368
+ Epoch 1179
2369
+ Train loss: 0.005458
2370
+ Epoch 1180
2371
+ Train loss: 0.005505
2372
+ Epoch 1181
2373
+ Train loss: 0.005343
2374
+ Epoch 1182
2375
+ Train loss: 0.005495
2376
+ Epoch 1183
2377
+ Train loss: 0.005491
2378
+ Epoch 1184
2379
+ Train loss: 0.005383
2380
+ Epoch 1185
2381
+ Train loss: 0.005393
2382
+ Epoch 1186
2383
+ Train loss: 0.005406
2384
+ Epoch 1187
2385
+ Train loss: 0.005433
2386
+ Epoch 1188
2387
+ Train loss: 0.005331
2388
+ Epoch 1189
2389
+ Train loss: 0.005464
2390
+ Epoch 1190
2391
+ Train loss: 0.005506
2392
+ Epoch 1191
2393
+ Train loss: 0.005423
2394
+ Epoch 1192
2395
+ Train loss: 0.005420
2396
+ Epoch 1193
2397
+ Train loss: 0.005349
2398
+ Epoch 1194
2399
+ Train loss: 0.005412
2400
+ Epoch 1195
2401
+ Train loss: 0.005398
2402
+ Epoch 1196
2403
+ Train loss: 0.005392
2404
+ Epoch 1197
2405
+ Train loss: 0.005297
2406
+ Epoch 1198
2407
+ Train loss: 0.005340
2408
+ Epoch 1199
2409
+ Train loss: 0.005380
2410
+ Epoch 1200
2411
+ Train loss: 0.005301
2412
+ Epoch 1201
2413
+ Train loss: 0.005369
2414
+ Epoch 1202
2415
+ Train loss: 0.005340
2416
+ Epoch 1203
2417
+ Train loss: 0.005280
2418
+ Epoch 1204
2419
+ Train loss: 0.005283
2420
+ Epoch 1205
2421
+ Train loss: 0.005372
2422
+ Epoch 1206
2423
+ Train loss: 0.005328
2424
+ Epoch 1207
2425
+ Train loss: 0.005266
2426
+ Epoch 1208
2427
+ Train loss: 0.005351
2428
+ Epoch 1209
2429
+ Train loss: 0.005425
2430
+ Epoch 1210
2431
+ Train loss: 0.005349
2432
+ Epoch 1211
2433
+ Train loss: 0.005324
2434
+ Epoch 1212
2435
+ Train loss: 0.005216
2436
+ Epoch 1213
2437
+ Train loss: 0.005339
2438
+ Epoch 1214
2439
+ Train loss: 0.005346
2440
+ Epoch 1215
2441
+ Train loss: 0.005238
2442
+ Epoch 1216
2443
+ Train loss: 0.005310
2444
+ Epoch 1217
2445
+ Train loss: 0.005376
2446
+ Epoch 1218
2447
+ Train loss: 0.005388
2448
+ Epoch 1219
2449
+ Train loss: 0.005269
2450
+ Epoch 1220
2451
+ Train loss: 0.005295
2452
+ Epoch 1221
2453
+ Train loss: 0.005311
2454
+ Epoch 1222
2455
+ Train loss: 0.005365
2456
+ Epoch 1223
2457
+ Train loss: 0.005366
2458
+ Epoch 1224
2459
+ Train loss: 0.005345
2460
+ Epoch 1225
2461
+ Train loss: 0.005326
2462
+ Epoch 1226
2463
+ Train loss: 0.005301
2464
+ Epoch 1227
2465
+ Train loss: 0.005353
2466
+ Epoch 1228
2467
+ Train loss: 0.005375
2468
+ Epoch 1229
2469
+ Train loss: 0.005364
2470
+ Epoch 1230
2471
+ Train loss: 0.005362
2472
+ Epoch 1231
2473
+ Train loss: 0.005345
2474
+ Epoch 1232
2475
+ Train loss: 0.005281
2476
+ Epoch 1233
2477
+ Train loss: 0.005222
2478
+ Epoch 1234
2479
+ Train loss: 0.005253
2480
+ Epoch 1235
2481
+ Train loss: 0.005287
2482
+ Epoch 1236
2483
+ Train loss: 0.005205
2484
+ Epoch 1237
2485
+ Train loss: 0.005236
2486
+ Epoch 1238
2487
+ Train loss: 0.005265
2488
+ Epoch 1239
2489
+ Train loss: 0.005251
2490
+ Epoch 1240
2491
+ Train loss: 0.005270
2492
+ Epoch 1241
2493
+ Train loss: 0.005315
2494
+ Epoch 1242
2495
+ Train loss: 0.005302
2496
+ Epoch 1243
2497
+ Train loss: 0.005219
2498
+ Epoch 1244
2499
+ Train loss: 0.005251
2500
+ Epoch 1245
2501
+ Train loss: 0.005272
2502
+ Epoch 1246
2503
+ Train loss: 0.005237
2504
+ Epoch 1247
2505
+ Train loss: 0.005308
2506
+ Epoch 1248
2507
+ Train loss: 0.005221
2508
+ Epoch 1249
2509
+ Train loss: 0.005243
2510
+ Epoch 1250
2511
+ Train loss: 0.005183
2512
+ Epoch 1251
2513
+ Train loss: 0.005286
2514
+ Epoch 1252
2515
+ Train loss: 0.005214
2516
+ Epoch 1253
2517
+ Train loss: 0.005143
2518
+ Epoch 1254
2519
+ Train loss: 0.005083
2520
+ Epoch 1255
2521
+ Train loss: 0.005182
2522
+ Epoch 1256
2523
+ Train loss: 0.005277
2524
+ Epoch 1257
2525
+ Train loss: 0.005245
2526
+ Epoch 1258
2527
+ Train loss: 0.005198
2528
+ Epoch 1259
2529
+ Train loss: 0.005262
2530
+ Epoch 1260
2531
+ Train loss: 0.005220
2532
+ Epoch 1261
2533
+ Train loss: 0.005287
2534
+ Epoch 1262
2535
+ Train loss: 0.005190
2536
+ Epoch 1263
2537
+ Train loss: 0.005240
2538
+ Epoch 1264
2539
+ Train loss: 0.005256
2540
+ Epoch 1265
2541
+ Train loss: 0.005207
2542
+ Epoch 1266
2543
+ Train loss: 0.005124
2544
+ Epoch 1267
2545
+ Train loss: 0.005201
2546
+ Epoch 1268
2547
+ Train loss: 0.005234
2548
+ Epoch 1269
2549
+ Train loss: 0.005194
2550
+ Epoch 1270
2551
+ Train loss: 0.005244
2552
+ Epoch 1271
2553
+ Train loss: 0.005301
2554
+ Epoch 1272
2555
+ Train loss: 0.005213
2556
+ Epoch 1273
2557
+ Train loss: 0.005219
2558
+ Epoch 1274
2559
+ Train loss: 0.005172
2560
+ Epoch 1275
2561
+ Train loss: 0.005217
2562
+ Epoch 1276
2563
+ Train loss: 0.005184
2564
+ Epoch 1277
2565
+ Train loss: 0.005152
2566
+ Epoch 1278
2567
+ Train loss: 0.005084
2568
+ Epoch 1279
2569
+ Train loss: 0.005191
2570
+ Epoch 1280
2571
+ Train loss: 0.005201
2572
+ Epoch 1281
2573
+ Train loss: 0.005225
2574
+ Epoch 1282
2575
+ Train loss: 0.005256
2576
+ Epoch 1283
2577
+ Train loss: 0.005212
2578
+ Epoch 1284
2579
+ Train loss: 0.005212
2580
+ Epoch 1285
2581
+ Train loss: 0.005076
2582
+ Epoch 1286
2583
+ Train loss: 0.005136
2584
+ Epoch 1287
2585
+ Train loss: 0.005172
2586
+ Epoch 1288
2587
+ Train loss: 0.005122
2588
+ Epoch 1289
2589
+ Train loss: 0.005158
2590
+ Epoch 1290
2591
+ Train loss: 0.005198
2592
+ Epoch 1291
2593
+ Train loss: 0.005170
2594
+ Epoch 1292
2595
+ Train loss: 0.005180
2596
+ Epoch 1293
2597
+ Train loss: 0.005164
2598
+ Epoch 1294
2599
+ Train loss: 0.005146
2600
+ Epoch 1295
2601
+ Train loss: 0.005073
2602
+ Epoch 1296
2603
+ Train loss: 0.005193
2604
+ Epoch 1297
2605
+ Train loss: 0.005107
2606
+ Epoch 1298
2607
+ Train loss: 0.005201
2608
+ Epoch 1299
2609
+ Train loss: 0.005171
2610
+ Epoch 1300
2611
+ Train loss: 0.005037
2612
+ Epoch 1301
2613
+ Train loss: 0.005062
2614
+ Epoch 1302
2615
+ Train loss: 0.005209
2616
+ Epoch 1303
2617
+ Train loss: 0.005190
2618
+ Epoch 1304
2619
+ Train loss: 0.005156
2620
+ Epoch 1305
2621
+ Train loss: 0.005094
2622
+ Epoch 1306
2623
+ Train loss: 0.005131
2624
+ Epoch 1307
2625
+ Train loss: 0.005019
2626
+ Epoch 1308
2627
+ Train loss: 0.005122
2628
+ Epoch 1309
2629
+ Train loss: 0.005111
2630
+ Epoch 1310
2631
+ Train loss: 0.005152
2632
+ Epoch 1311
2633
+ Train loss: 0.005154
2634
+ Epoch 1312
2635
+ Train loss: 0.005155
2636
+ Epoch 1313
2637
+ Train loss: 0.005073
2638
+ Epoch 1314
2639
+ Train loss: 0.005126
2640
+ Epoch 1315
2641
+ Train loss: 0.005132
2642
+ Epoch 1316
2643
+ Train loss: 0.005060
2644
+ Epoch 1317
2645
+ Train loss: 0.005126
2646
+ Epoch 1318
2647
+ Train loss: 0.005057
2648
+ Epoch 1319
2649
+ Train loss: 0.005092
2650
+ Epoch 1320
2651
+ Train loss: 0.005134
2652
+ Epoch 1321
2653
+ Train loss: 0.005111
2654
+ Epoch 1322
2655
+ Train loss: 0.005128
2656
+ Epoch 1323
2657
+ Train loss: 0.005119
2658
+ Epoch 1324
2659
+ Train loss: 0.005095
2660
+ Epoch 1325
2661
+ Train loss: 0.005118
2662
+ Epoch 1326
2663
+ Train loss: 0.005128
2664
+ Epoch 1327
2665
+ Train loss: 0.005110
2666
+ Epoch 1328
2667
+ Train loss: 0.005099
2668
+ Epoch 1329
2669
+ Train loss: 0.005117
2670
+ Epoch 1330
2671
+ Train loss: 0.005037
2672
+ Epoch 1331
2673
+ Train loss: 0.005071
2674
+ Epoch 1332
2675
+ Train loss: 0.005054
2676
+ Epoch 1333
2677
+ Train loss: 0.005055
2678
+ Epoch 1334
2679
+ Train loss: 0.005007
2680
+ Epoch 1335
2681
+ Train loss: 0.005076
2682
+ Epoch 1336
2683
+ Train loss: 0.005059
2684
+ Epoch 1337
2685
+ Train loss: 0.005039
2686
+ Epoch 1338
2687
+ Train loss: 0.005085
2688
+ Epoch 1339
2689
+ Train loss: 0.005057
2690
+ Epoch 1340
2691
+ Train loss: 0.005082
2692
+ Epoch 1341
2693
+ Train loss: 0.005022
2694
+ Epoch 1342
2695
+ Train loss: 0.005139
2696
+ Epoch 1343
2697
+ Train loss: 0.005162
2698
+ Epoch 1344
2699
+ Train loss: 0.005097
2700
+ Epoch 1345
2701
+ Train loss: 0.005058
2702
+ Epoch 1346
2703
+ Train loss: 0.005091
2704
+ Epoch 1347
2705
+ Train loss: 0.005022
2706
+ Epoch 1348
2707
+ Train loss: 0.004991
2708
+ Epoch 1349
2709
+ Train loss: 0.005060
2710
+ Epoch 1350
2711
+ Train loss: 0.005043
2712
+ Epoch 1351
2713
+ Train loss: 0.005083
2714
+ Epoch 1352
2715
+ Train loss: 0.005075
2716
+ Epoch 1353
2717
+ Train loss: 0.004996
2718
+ Epoch 1354
2719
+ Train loss: 0.005032
2720
+ Epoch 1355
2721
+ Train loss: 0.005043
2722
+ Epoch 1356
2723
+ Train loss: 0.005003
2724
+ Epoch 1357
2725
+ Train loss: 0.005030
2726
+ Epoch 1358
2727
+ Train loss: 0.005021
2728
+ Epoch 1359
2729
+ Train loss: 0.005002
2730
+ Epoch 1360
2731
+ Train loss: 0.005062
2732
+ Epoch 1361
2733
+ Train loss: 0.005075
2734
+ Epoch 1362
2735
+ Train loss: 0.005049
2736
+ Epoch 1363
2737
+ Train loss: 0.004984
2738
+ Epoch 1364
2739
+ Train loss: 0.004980
2740
+ Epoch 1365
2741
+ Train loss: 0.005068
2742
+ Epoch 1366
2743
+ Train loss: 0.005025
2744
+ Epoch 1367
2745
+ Train loss: 0.005009
2746
+ Epoch 1368
2747
+ Train loss: 0.005055
2748
+ Epoch 1369
2749
+ Train loss: 0.004967
2750
+ Epoch 1370
2751
+ Train loss: 0.005008
2752
+ Epoch 1371
logs/log_grab_plate_20250821.txt ADDED
The diff for this file is too large to render. See raw diff
 
logs/log_grab_plate_20250905.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:7dad33620584783a9e425db0f40b3a6aeb89dd1de8e8d6bbb1aadd052c68802f
3
+ size 11737045
logs/log_grab_scanner_20250818.txt ADDED
The diff for this file is too large to render. See raw diff
 
logs/log_grab_scanner_20250819.txt ADDED
The diff for this file is too large to render. See raw diff
 
logs/log_grab_scanner_20250929.txt ADDED
@@ -0,0 +1,427 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Loading dataset ...
2
+ Segmentation model loaded
3
+ Connected to /dev/ttyUSB0
4
+ Gripper components initialized!
5
+ Preprocessed data not found or invalid. Starting preprocessing...
6
+ Initializing dataset for preprocessing...
7
+ Preloading joint data...
8
+ Joint data preloading completed!
9
+ Preprocessing 15036 samples...
10
+
11
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622197350.jpg: 384x640 1 scanner, 32.7ms
12
+ Speed: 0.6ms preprocess, 32.7ms inference, 83.9ms postprocess per image at shape (1, 3, 384, 640)
13
+
14
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622197751.jpg: 384x640 1 scanner, 6.9ms
15
+ Speed: 0.6ms preprocess, 6.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
16
+
17
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622197901.jpg: 384x640 1 scanner, 6.8ms
18
+ Speed: 0.6ms preprocess, 6.8ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
19
+
20
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622198051.jpg: 384x640 1 scanner, 7.2ms
21
+ Speed: 0.6ms preprocess, 7.2ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
22
+
23
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622198202.jpg: 384x640 1 scanner, 6.9ms
24
+ Speed: 0.6ms preprocess, 6.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
25
+
26
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622198302.jpg: 384x640 1 scanner, 7.1ms
27
+ Speed: 0.6ms preprocess, 7.1ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
28
+
29
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622198402.jpg: 384x640 1 scanner, 6.8ms
30
+ Speed: 0.6ms preprocess, 6.8ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
31
+
32
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622198502.jpg: 384x640 1 scanner, 7.0ms
33
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
34
+
35
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622198602.jpg: 384x640 (no detections), 7.0ms
36
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.2ms postprocess per image at shape (1, 3, 384, 640)
37
+
38
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622198703.jpg: 384x640 1 scanner, 6.9ms
39
+ Speed: 0.6ms preprocess, 6.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
40
+
41
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622198853.jpg: 384x640 (no detections), 6.9ms
42
+ Speed: 0.6ms preprocess, 6.9ms inference, 0.2ms postprocess per image at shape (1, 3, 384, 640)
43
+
44
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622199053.jpg: 384x640 (no detections), 7.0ms
45
+ Speed: 0.7ms preprocess, 7.0ms inference, 0.2ms postprocess per image at shape (1, 3, 384, 640)
46
+
47
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622199153.jpg: 384x640 (no detections), 6.9ms
48
+ Speed: 0.6ms preprocess, 6.9ms inference, 0.2ms postprocess per image at shape (1, 3, 384, 640)
49
+
50
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622199253.jpg: 384x640 (no detections), 7.1ms
51
+ Speed: 0.6ms preprocess, 7.1ms inference, 1.5ms postprocess per image at shape (1, 3, 384, 640)
52
+
53
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622199404.jpg: 384x640 (no detections), 7.0ms
54
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.2ms postprocess per image at shape (1, 3, 384, 640)
55
+
56
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622199554.jpg: 384x640 (no detections), 7.0ms
57
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.2ms postprocess per image at shape (1, 3, 384, 640)
58
+
59
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622199754.jpg: 384x640 (no detections), 7.0ms
60
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.2ms postprocess per image at shape (1, 3, 384, 640)
61
+
62
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622199904.jpg: 384x640 (no detections), 7.3ms
63
+ Speed: 0.7ms preprocess, 7.3ms inference, 0.4ms postprocess per image at shape (1, 3, 384, 640)
64
+
65
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622200055.jpg: 384x640 (no detections), 6.8ms
66
+ Speed: 0.6ms preprocess, 6.8ms inference, 0.2ms postprocess per image at shape (1, 3, 384, 640)
67
+
68
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622200155.jpg: 384x640 1 scanner, 7.0ms
69
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
70
+
71
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622200255.jpg: 384x640 1 scanner, 7.0ms
72
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
73
+
74
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622200355.jpg: 384x640 1 scanner, 6.8ms
75
+ Speed: 0.6ms preprocess, 6.8ms inference, 1.0ms postprocess per image at shape (1, 3, 384, 640)
76
+
77
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622200456.jpg: 384x640 1 scanner, 7.0ms
78
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
79
+
80
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622200556.jpg: 384x640 1 scanner, 7.0ms
81
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
82
+
83
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622200656.jpg: 384x640 1 scanner, 7.0ms
84
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
85
+
86
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622200757.jpg: 384x640 1 scanner, 6.9ms
87
+ Speed: 0.6ms preprocess, 6.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
88
+
89
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622200857.jpg: 384x640 1 scanner, 7.1ms
90
+ Speed: 0.6ms preprocess, 7.1ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
91
+
92
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622200907.jpg: 384x640 1 scanner, 6.9ms
93
+ Speed: 0.6ms preprocess, 6.9ms inference, 0.6ms postprocess per image at shape (1, 3, 384, 640)
94
+
95
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622201008.jpg: 384x640 1 scanner, 6.9ms
96
+ Speed: 0.6ms preprocess, 6.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
97
+
98
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622201108.jpg: 384x640 1 scanner, 7.0ms
99
+ Speed: 0.7ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
100
+
101
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622201208.jpg: 384x640 1 scanner, 7.2ms
102
+ Speed: 0.6ms preprocess, 7.2ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
103
+
104
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622201308.jpg: 384x640 1 scanner, 7.3ms
105
+ Speed: 0.6ms preprocess, 7.3ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
106
+
107
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622201408.jpg: 384x640 1 scanner, 7.0ms
108
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
109
+
110
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622201509.jpg: 384x640 1 scanner, 7.1ms
111
+ Speed: 0.7ms preprocess, 7.1ms inference, 0.9ms postprocess per image at shape (1, 3, 384, 640)
112
+
113
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622201659.jpg: 384x640 1 scanner, 6.9ms
114
+ Speed: 0.6ms preprocess, 6.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
115
+
116
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622201759.jpg: 384x640 1 scanner, 7.0ms
117
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
118
+
119
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808110307/CAMID_1/color/1754622201859.jpg: 384x640 1 scanner, 6.9ms
120
+ Speed: 0.6ms preprocess, 6.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
121
+ Loading dataset ...
122
+ Segmentation model loaded
123
+ Connected to /dev/ttyUSB0
124
+ Gripper components initialized!
125
+ Using raw data with real-time processing...
126
+ Preloading joint data...
127
+ Joint data preloading completed!
128
+ Loading policy ...
129
+ Number of parameters: 51.01M
130
+ Loading optimizer and scheduler ...
131
+ Epoch 0
132
+ Warning: Segmentation failed for data/grab_scanner/20250812144910/CAMID_1/color/1754981363836.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
133
+ Warning: Segmentation failed for data/grab_scanner/20250808164641/CAMID_1/color/1754642829463.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
134
+ Warning: Segmentation failed for data/grab_scanner/20250808110307/CAMID_1/color/1754622206718.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start methodWarning: Segmentation failed for data/grab_scanner/20250812142219/CAMID_1/color/1754979761841.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
135
+
136
+ Warning: Segmentation failed for data/grab_scanner/20250808113634/CAMID_1/color/1754624212656.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
137
+ Warning: Segmentation failed for data/grab_scanner/20250815163953/CAMID_1/color/1755247226967.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
138
+ Warning: Segmentation failed for data/grab_scanner/20250812151955/CAMID_1/color/1754983208283.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start methodWarning: Segmentation failed for data/grab_scanner/20250808171019/CAMID_1/color/1754644232808.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
139
+
140
+ Warning: Segmentation failed for data/grab_scanner/20250815165822/CAMID_1/color/1755248337749.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
141
+ Warning: Segmentation failed for data/grab_scanner/20250812142406/CAMID_1/color/1754979869367.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
142
+ Warning: Segmentation failed for data/grab_scanner/20250815165010/CAMID_1/color/1755247826942.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
143
+ Warning: Segmentation failed for data/grab_scanner/20250812143154/CAMID_1/color/1754980351148.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
144
+ Point range: [ 0.3601 -0.62242 0.20315] [ 1.3 0.64965 0.74757]
145
+ Point range: Point range: [ 0.36007 -0.62653 0.18737] [ 1.3 0.64152 0.74635]
146
+ [ 0.36037 -0.63278 0.20911] [ 1.3 0.60486 0.74684]
147
+ Point range: [ 0.36012 -0.62224 0.21361] [ 1.2999 0.63976 0.75]
148
+ Point range: [ 0.36016 -0.62913 0.19889] [ 1.2999 0.61743 0.74992]
149
+ Point range: [ 0.36028 -0.6269 0.20374] [ 1.3 0.64993 0.74999]
150
+ Point range: [ 0.36011 -0.63944 0.20741] [ 1.3 0.64923 0.74957]
151
+ Point range: [ 0.36026 -0.48389 0.20035] [ 1.2999 0.64983 0.74977]
152
+ Point range: [ 0.36015 -0.62315 0.21023] [ 1.3 0.64437 0.74987]
153
+ Point range: [ 0.36009 -0.6452 0.20566] [ 1.3 0.61857 0.74965]
154
+ Point range: [ 0.36019 -0.62522 0.19698] [ 1.3 0.6019 0.75]
155
+ Point range: [ 0.36021 -0.62614 0.20876] [ 1.3 0.64975 0.74776]
156
+ Point range: [ 0.10244 -0.43862 0.072206] [ 1.3515 0.69347 0.63118]
157
+ Loading dataset ...
158
+ RGBDDepthPredictor initialized with vitl model on cuda
159
+ Segmentation model loaded
160
+ Connected to /dev/ttyUSB0
161
+ Gripper components initialized!
162
+ Using raw data with real-time processing...
163
+ Preloading joint data...
164
+ Joint data preloading completed!
165
+ Loading policy ...
166
+ Number of parameters: 51.01M
167
+ Loading optimizer and scheduler ...
168
+ Epoch 0
169
+ Loading dataset ...
170
+ RGBDDepthPredictor initialized with vitl model on cuda
171
+ Segmentation model loaded
172
+ Connected to /dev/ttyUSB0
173
+ Gripper components initialized!
174
+ Using raw data with real-time processing...
175
+ Preloading joint data...
176
+ Joint data preloading completed!
177
+ Loading policy ...
178
+ Number of parameters: 51.01M
179
+ Loading optimizer and scheduler ...
180
+ Epoch 0
181
+ Loading dataset ...
182
+ RGBDDepthPredictor initialized with vitl model on cuda
183
+ Segmentation model loaded
184
+ Connected to /dev/ttyUSB0
185
+ Gripper components initialized!
186
+ Using raw data with real-time processing...
187
+ Preloading joint data...
188
+ Joint data preloading completed!
189
+ Loading policy ...
190
+ Number of parameters: 51.01M
191
+ Loading optimizer and scheduler ...
192
+ Epoch 0
193
+
194
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808164641/CAMID_1/color/1754642829463.jpg: 384x640 1 scanner, 33.7ms
195
+ Speed: 1.1ms preprocess, 33.7ms inference, 57.6ms postprocess per image at shape (1, 3, 384, 640)
196
+ Point range: [ 0.36 -0.039765 0.16996] [ 1.1723 0.6393 0.56232]
197
+ Point range: [ 0.42474 -0.01399 0.024008] [ 1.0011 0.94282 0.41637]
198
+
199
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250812153627/CAMID_1/color/1754984209661.jpg: 384x640 1 scanner, 7.4ms
200
+ Speed: 0.7ms preprocess, 7.4ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
201
+ Point range: [ 0.36018 -0.039881 0.20345] [ 1.0009 0.64926 0.47503]
202
+ Point range: [ 0.2524 0.17381 0.13935] [ 0.9428 0.81372 0.41093]
203
+
204
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250815165822/CAMID_1/color/1755248328785.jpg: 384x640 1 scanner, 7.0ms
205
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
206
+ Point range: [ 0.36009 -0.039952 0.18132] [ 0.89276 0.64916 0.5394]
207
+ Point range: [ 0.2078 0.13707 0.036689] [ 0.80831 0.79482 0.39477]
208
+
209
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250815170554/CAMID_1/color/1755248776122.jpg: 384x640 1 scanner, 7.1ms
210
+ Speed: 0.6ms preprocess, 7.1ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
211
+ Point range: [ 0.36018 -0.039974 0.21262] [ 0.99828 0.63182 0.50911]
212
+ Point range: [ 0.007278 0.31496 0.16914] [ 0.81728 0.71839 0.46563]
213
+
214
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250812145749/CAMID_1/color/1754981892500.jpg: 384x640 1 scanner, 7.5ms
215
+ Speed: 0.6ms preprocess, 7.5ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
216
+ Point range: [ 0.36001 -0.039933 0.23677] [ 0.94382 0.54595 0.57088]
217
+ Point range: [ 0.25133 0.16334 0.30784] [ 1.0053 0.54178 0.64196]
218
+
219
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250815170403/CAMID_1/color/1755248672974.jpg: 384x640 1 scanner, 7.3ms
220
+ Speed: 0.6ms preprocess, 7.3ms inference, 1.3ms postprocess per image at shape (1, 3, 384, 640)
221
+ Point range: [ 0.36006 -0.039973 0.19882] [ 1.1021 0.64524 0.66336]
222
+ Point range: [ 0.47134 -0.024162 0.030618] [ 1.0595 0.82605 0.49515]
223
+ Loading dataset ...
224
+ RGBDDepthPredictor initialized with vitl model on cuda
225
+ Segmentation model loaded
226
+ Connected to /dev/ttyUSB0
227
+ Gripper components initialized!
228
+ Using raw data with real-time processing...
229
+ Preloading joint data...
230
+ Joint data preloading completed!
231
+ Loading policy ...
232
+ Number of parameters: 51.01M
233
+ Loading optimizer and scheduler ...
234
+ Epoch 0
235
+
236
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808164641/CAMID_1/color/1754642829463.jpg: 384x640 1 scanner, 33.9ms
237
+ Speed: 1.1ms preprocess, 33.9ms inference, 120.7ms postprocess per image at shape (1, 3, 384, 640)
238
+ Point range: [ 0.36 -0.039765 0.16996] [ 1.1723 0.6393 0.56232]
239
+ Loading dataset ...
240
+ RGBDDepthPredictor initialized with vitl model on cuda
241
+ Segmentation model loaded
242
+ Connected to /dev/ttyUSB0
243
+ Gripper components initialized!
244
+ Using raw data with real-time processing...
245
+ Preloading joint data...
246
+ Joint data preloading completed!
247
+ Loading policy ...
248
+ Number of parameters: 51.01M
249
+ Loading optimizer and scheduler ...
250
+ Epoch 0
251
+
252
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808164641/CAMID_1/color/1754642829463.jpg: 384x640 1 scanner, 34.0ms
253
+ Speed: 1.1ms preprocess, 34.0ms inference, 52.7ms postprocess per image at shape (1, 3, 384, 640)
254
+ Point range: [ 0.36 -0.039765 0.16996] [ 1.1723 0.6393 0.56232]
255
+ Loading dataset ...
256
+ RGBDDepthPredictor initialized with vitl model on cuda
257
+ Segmentation model loaded
258
+ Connected to /dev/ttyUSB0
259
+ Gripper components initialized!
260
+ Using raw data with real-time processing...
261
+ Preloading joint data...
262
+ Joint data preloading completed!
263
+ Loading policy ...
264
+ Number of parameters: 51.01M
265
+ Loading optimizer and scheduler ...
266
+ Epoch 0
267
+
268
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808164641/CAMID_1/color/1754642829463.jpg: 384x640 1 scanner, 34.7ms
269
+ Speed: 1.0ms preprocess, 34.7ms inference, 60.5ms postprocess per image at shape (1, 3, 384, 640)
270
+ Point range: [ 0.36011 -0.039854 0.1433] [ 1.1711 0.6393 0.56232]
271
+ Loading dataset ...
272
+ RGBDDepthPredictor initialized with vitl model on cuda
273
+ Segmentation model loaded
274
+ Connected to /dev/ttyUSB0
275
+ Gripper components initialized!
276
+ Using raw data with real-time processing...
277
+ Preloading joint data...
278
+ Joint data preloading completed!
279
+ Loading policy ...
280
+ Number of parameters: 51.01M
281
+ Loading optimizer and scheduler ...
282
+ Epoch 0
283
+
284
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808164641/CAMID_1/color/1754642829463.jpg: 384x640 1 scanner, 33.8ms
285
+ Speed: 1.2ms preprocess, 33.8ms inference, 57.1ms postprocess per image at shape (1, 3, 384, 640)
286
+ Point range: [ 0.36 -0.039765 0.16996] [ 1.1723 0.6393 0.56232]
287
+ Point range: [ 0.4227 -0.0050062 0.024008] [ 0.99902 0.9518 0.41637]
288
+
289
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250812153627/CAMID_1/color/1754984209661.jpg: 384x640 1 scanner, 7.3ms
290
+ Speed: 0.6ms preprocess, 7.3ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
291
+ Point range: [ 0.36018 -0.039881 0.20345] [ 1.0009 0.64926 0.49006]
292
+ Point range: [ 0.25411 0.17238 0.13935] [ 0.94451 0.81229 0.42596]
293
+
294
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250815165822/CAMID_1/color/1755248328785.jpg: 384x640 1 scanner, 7.1ms
295
+ Speed: 0.6ms preprocess, 7.1ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
296
+ Point range: [ 0.36009 -0.039952 0.18132] [ 0.89276 0.64916 0.5394]
297
+ Loading dataset ...
298
+ RGBDDepthPredictor initialized with vitl model on cuda
299
+ Connected to /dev/ttyUSB0
300
+ Gripper components initialized!
301
+ Using raw data with real-time processing...
302
+ Preloading joint data...
303
+ Joint data preloading completed!
304
+ Loading policy ...
305
+ Number of parameters: 51.01M
306
+ Loading optimizer and scheduler ...
307
+ Epoch 0
308
+ Point range: [ 0.36011457 -0.6495617 0.0081773 ] [1.2999717 0.64974016 0.74965936]
309
+ Point range: [ 0.13300496 -0.39893615 -0.13777006] [1.35949156 0.98570231 0.60371201]
310
+ Point range: [ 0.36017877 -0.64973444 0.06065166] [1.29999852 0.64998662 0.74996626]
311
+ Point range: [ 0.2769573 -0.47735777 -0.00344647] [1.26165647 0.85308836 0.68586814]
312
+ Point range: [ 0.36008593 -0.64831012 0.02097718] [1.29995084 0.64959568 0.74970931]
313
+ Loading dataset ...
314
+ RGBDDepthPredictor initialized with vitl model on cuda
315
+ Connected to /dev/ttyUSB0
316
+ Gripper components initialized!
317
+ Using raw data with real-time processing...
318
+ Preloading joint data...
319
+ Joint data preloading completed!
320
+ Loading policy ...
321
+ Number of parameters: 51.01M
322
+ Loading optimizer and scheduler ...
323
+ Epoch 0
324
+ Point range: [ 0.36011457 -0.6495617 0.0081773 ] [1.2999717 0.64974016 0.74965936]
325
+ Point range: [ 0.13367743 -0.3988089 -0.13777006] [1.36016403 0.98582956 0.60371201]
326
+ Loading dataset ...
327
+ RGBDDepthPredictor initialized with vitl model on cuda
328
+ Segmentation model loaded
329
+ Connected to /dev/ttyUSB0
330
+ Gripper components initialized!
331
+ Using raw data with real-time processing...
332
+ Preloading joint data...
333
+ Joint data preloading completed!
334
+ Loading policy ...
335
+ Number of parameters: 51.01M
336
+ Loading optimizer and scheduler ...
337
+ Epoch 0
338
+
339
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808164641/CAMID_1/color/1754642829463.jpg: 384x640 1 scanner, 32.9ms
340
+ Speed: 1.1ms preprocess, 32.9ms inference, 52.0ms postprocess per image at shape (1, 3, 384, 640)
341
+ Point range: [ 0.36 -0.039765 0.16996] [ 1.1723 0.6393 0.56232]
342
+ Loading dataset ...
343
+ RGBDDepthPredictor initialized with vitl model on cuda
344
+ Segmentation model loaded
345
+ Connected to /dev/ttyUSB0
346
+ Gripper components initialized!
347
+ Using raw data with real-time processing...
348
+ Preloading joint data...
349
+ Joint data preloading completed!
350
+ Loading policy ...
351
+ Number of parameters: 51.01M
352
+ Loading optimizer and scheduler ...
353
+ Epoch 0
354
+
355
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808164641/CAMID_1/color/1754642829463.jpg: 384x640 1 scanner, 32.9ms
356
+ Speed: 1.2ms preprocess, 32.9ms inference, 74.4ms postprocess per image at shape (1, 3, 384, 640)
357
+ Point range: [ 0.36 -0.039765 0.16996] [ 1.1723 0.6393 0.56232]
358
+ Loading dataset ...
359
+ RGBDDepthPredictor initialized with vitl model on cuda
360
+ Connected to /dev/ttyUSB0
361
+ Gripper components initialized!
362
+ Using raw data with real-time processing...
363
+ Preloading joint data...
364
+ Joint data preloading completed!
365
+ Loading policy ...
366
+ Number of parameters: 51.01M
367
+ Loading optimizer and scheduler ...
368
+ Epoch 0
369
+ Point range: [ 0.36011457 -0.6495617 0.0081773 ] [1.2999717 0.64974016 0.74965936]
370
+ Loading dataset ...
371
+ RGBDDepthPredictor initialized with vitl model on cuda
372
+ Connected to /dev/ttyUSB0
373
+ Gripper components initialized!
374
+ Using raw data with real-time processing...
375
+ Preloading joint data...
376
+ Joint data preloading completed!
377
+ Loading policy ...
378
+ Number of parameters: 51.01M
379
+ Loading optimizer and scheduler ...
380
+ Epoch 0
381
+ Point range: [ 0.36011457 -0.6495617 0.0081773 ] [1.2999717 0.64974016 0.74965936]
382
+ Point range: [ 0.13293408 -0.40011648 -0.13777006] [1.35942069 0.98452198 0.60371201]
383
+ Point range: [ 0.36017877 -0.64973444 0.06065166] [1.29999852 0.64998662 0.74996626]
384
+ Point range: [ 0.27695506 -0.47714212 -0.00344647] [1.26165422 0.85330401 0.68586814]
385
+ Point range: [ 0.36008593 -0.64831012 0.02097718] [1.29995084 0.64959568 0.74970931]
386
+ Point range: [ 0.24085471 -0.52577412 -0.12365087] [1.2457186 0.81653512 0.60508126]
387
+ Point range: [ 0.36018473 -0.64932567 0.00081492] [1.29994082 0.64967489 0.7497915 ]
388
+ Point range: [ 0.0043324 -0.59094412 -0.04266933] [1.30271521 0.81849305 0.70630725]
389
+ Point range: [ 0.36001244 -0.64988476 0.01361879] [1.29998243 0.6487807 0.74988049]
390
+ Point range: [ 0.24345813 -0.66526592 0.08469159] [1.5120163 0.70826113 0.82095329]
391
+ Point range: [ 0.36002854 -0.64981002 0.00296391] [1.29996514 0.64933884 0.74999589]
392
+ Loading dataset ...
393
+ RGBDDepthPredictor initialized with vitl model on cuda
394
+ Connected to /dev/ttyUSB0
395
+ Gripper components initialized!
396
+ Using raw data with real-time processing...
397
+ Preloading joint data...
398
+ Joint data preloading completed!
399
+ Loading policy ...
400
+ Number of parameters: 51.01M
401
+ Loading optimizer and scheduler ...
402
+ Epoch 0
403
+ Point range: [ 0.36000487 -0.63278186 0.20911124] [1.29999912 0.60486311 0.74684101]
404
+ Point range: [ 0.16117949 -0.40555489 0.06316389] [1.23915214 0.97557196 0.60089366]
405
+ Point range: [ 0.36014098 -0.62288368 0.20517752] [1.29997838 0.64978558 0.74995756]
406
+ Point range: [ 0.27533319 -0.42085515 0.14107939] [1.26102361 0.81883244 0.68585943]
407
+ Point range: [ 0.36016181 -0.62738591 0.20768869] [1.29999125 0.64973021 0.74511236]
408
+ Point range: [ 0.24179354 -0.45523217 0.06306064] [1.244868 0.79480537 0.60048431]
409
+ Point range: [ 0.36018473 -0.54951274 0.20080921] [1.29997814 0.6499427 0.74999642]
410
+ Point range: [ 0.02380905 -0.36645858 0.15732496] [1.29123466 0.77512597 0.70651217]
411
+ Point range: [ 0.36015826 -0.62750953 0.20899734] [1.29999638 0.64985204 0.74998528]
412
+ Point range: [ 0.23742549 -0.48223296 0.28007014] [1.49886985 0.66071709 0.82105808]
413
+ Loading dataset ...
414
+ RGBDDepthPredictor initialized with vitl model on cuda
415
+ Connected to /dev/ttyUSB0
416
+ Gripper components initialized!
417
+ Preprocessed data not found or invalid. Starting preprocessing...
418
+ Initializing dataset for preprocessing...
419
+ Preloading joint data...
420
+ Joint data preloading completed!
421
+ Preprocessing 15036 samples...
422
+ Successfully processed 15036/15036 samples
423
+ Using preprocessed data...
424
+ Loading policy ...
425
+ Number of parameters: 51.01M
426
+ Loading optimizer and scheduler ...
427
+ Epoch 0
logs/log_grab_scanner_20250930.txt ADDED
The diff for this file is too large to render. See raw diff
 
logs/log_grab_scanner_20251013.txt ADDED
@@ -0,0 +1,96 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Loading dataset ...
2
+ RGBDDepthPredictor initialized with vitl model on cuda
3
+ Segmentation model loaded
4
+ Connected to /dev/ttyUSB0
5
+ Gripper components initialized!
6
+ Using raw data with real-time processing...
7
+ Preloading joint data...
8
+ Joint data preloading completed!
9
+ Loading policy ...
10
+ Number of parameters: 51.01M
11
+ Loading optimizer and scheduler ...
12
+ Epoch 0
13
+ data/grab_scanner/20250808164641/CAMID_1/depth/1754642829463.png
14
+
15
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808164641/CAMID_1/color/1754642829463.jpg: 384x640 1 scanner, 33.6ms
16
+ Speed: 1.1ms preprocess, 33.6ms inference, 51.2ms postprocess per image at shape (1, 3, 384, 640)
17
+ Point range: [ 0.57107 0.26155 0.16996] [ 1.1723 0.6393 0.56232]
18
+ Point range: [ 0.687 0.083692 0.24695] [ 1.2886 0.4575 0.63932]
19
+ data/grab_scanner/20250812153627/CAMID_1/depth/1754984209661.png
20
+
21
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250812153627/CAMID_1/color/1754984209661.jpg: 384x640 1 scanner, 7.2ms
22
+ Speed: 0.6ms preprocess, 7.2ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
23
+ Point range: [ 0.52143 0.36544 0.20345] [ 1.0009 0.64858 0.47485]
24
+ Point range: [ 0.71645 0.37228 0.11982] [ 1.229 0.62948 0.39122]
25
+ data/grab_scanner/20250815165822/CAMID_1/depth/1755248328785.png
26
+
27
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250815165822/CAMID_1/color/1755248328785.jpg: 384x640 1 scanner, 7.0ms
28
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
29
+ Point range: [ 0.46119 0.40152 0.18132] [ 0.89276 0.64916 0.53938]
30
+ Point range: [ 0.43494 0.49863 0.21323] [ 0.80422 0.816 0.5713]
31
+ data/grab_scanner/20250815170554/CAMID_1/depth/1755248776122.png
32
+
33
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250815170554/CAMID_1/color/1755248776122.jpg: 384x640 1 scanner, 7.6ms
34
+ Speed: 0.7ms preprocess, 7.6ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
35
+ Point range: [ 0.56233 0.27425 0.21262] [ 0.99819 0.63182 0.50626]
36
+ Point range: [ 0.57491 0.15941 0.31032] [ 1.0016 0.47386 0.60396]
37
+ data/grab_scanner/20250812145749/CAMID_1/depth/1754981892500.png
38
+
39
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250812145749/CAMID_1/color/1754981892500.jpg: 384x640 1 scanner, 7.0ms
40
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)
41
+ Point range: [ 0.52411 0.34452 0.23701] [ 0.94319 0.54679 0.57126]
42
+ Point range: [ 0.60097 0.20965 0.35404] [ 1.0073 0.45068 0.68829]
43
+ Loading dataset ...
44
+ RGBDDepthPredictor initialized with vitl model on cuda
45
+ Segmentation model loaded
46
+ Connected to /dev/ttyUSB0
47
+ Gripper components initialized!
48
+ Using raw data with real-time processing...
49
+ Preloading joint data...
50
+ Joint data preloading completed!
51
+ Loading policy ...
52
+ Number of parameters: 51.01M
53
+ Loading optimizer and scheduler ...
54
+ Epoch 0
55
+ [[ 0 0 0 ... 3819 3800 3782]
56
+ [ 0 0 0 ... 3828 3810 3800]
57
+ [ 0 0 0 ... 3847 3838 3828]
58
+ ...
59
+ [ 0 0 0 ... 0 0 0]
60
+ [ 0 0 0 ... 0 0 0]
61
+ [ 0 0 0 ... 0 0 0]]
62
+
63
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808164641/CAMID_1/color/1754642829463.jpg: 384x640 1 scanner, 33.5ms
64
+ Speed: 1.6ms preprocess, 33.5ms inference, 51.8ms postprocess per image at shape (1, 3, 384, 640)
65
+ Point range: [ 0.57107 0.26155 0.16996] [ 1.1723 0.6393 0.56232]
66
+ Loading dataset ...
67
+ RGBDDepthPredictor initialized with vitl model on cuda
68
+ Segmentation model loaded
69
+ Connected to /dev/ttyUSB0
70
+ Gripper components initialized!
71
+ Using raw data with real-time processing...
72
+ Preloading joint data...
73
+ Joint data preloading completed!
74
+ Loading policy ...
75
+ Number of parameters: 51.01M
76
+ Loading optimizer and scheduler ...
77
+ Epoch 0
78
+ [[ 0 0 0 ... 3819 3800 3782]
79
+ [ 0 0 0 ... 3828 3810 3800]
80
+ [ 0 0 0 ... 3847 3838 3828]
81
+ ...
82
+ [ 0 0 0 ... 0 0 0]
83
+ [ 0 0 0 ... 0 0 0]
84
+ [ 0 0 0 ... 0 0 0]]
85
+ [[ 3869 3869 3775.4 ... 4285.4 4232 4266.6]
86
+ [ 3869 3869 3775.4 ... 4285.4 4232 4266.6]
87
+ [ 3814.3 3814.3 3819.6 ... 4262.6 4181.2 4136.2]
88
+ ...
89
+ [ 904.77 904.77 905.01 ... 757.7 758.68 759.72]
90
+ [ 912.18 912.18 901.11 ... 757.04 758.14 759.09]
91
+ [ 900.13 900.13 909.6 ... 756.42 757.43 754.9]]
92
+
93
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/grab_scanner/20250808164641/CAMID_1/color/1754642829463.jpg: 384x640 1 scanner, 33.2ms
94
+ Speed: 1.2ms preprocess, 33.2ms inference, 51.9ms postprocess per image at shape (1, 3, 384, 640)
95
+ Point range: [ 0.57107 0.26155 0.16996] [ 1.1723 0.6393 0.56232]
96
+ Point range: [ 0.687 0.083692 0.24695] [ 1.2886 0.4575 0.63932]
logs/log_put_scanner_20251002.txt ADDED
@@ -0,0 +1,390 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Loading dataset ...
2
+ Segmentation model loaded
3
+ Connected to None
4
+ Gripper components initialized!
5
+ Using raw data with real-time processing...
6
+ Preloading joint data...
7
+ Joint data preloading completed!
8
+ Loading policy ...
9
+ Number of parameters: 51.01M
10
+ Loading optimizer and scheduler ...
11
+ Epoch 0
12
+ Warning: Segmentation failed for data/put_scanner/20250812150559/CAMID_1/color/1754982402288.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
13
+ Warning: Segmentation failed for data/put_scanner/20250815165243/CAMID_1/color/1755247994855.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
14
+ Warning: Segmentation failed for data/put_scanner/20250815172034/CAMID_1/color/1755249656124.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
15
+ Warning: Segmentation failed for data/put_scanner/20250812150559/CAMID_1/color/1754982405243.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
16
+ Warning: Segmentation failed for data/put_scanner/20250812152718/CAMID_1/color/1754983669061.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
17
+ Warning: Segmentation failed for data/put_scanner/20250812144559/CAMID_1/color/1754981188955.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
18
+ Warning: Segmentation failed for data/put_scanner/20250808170712/CAMID_1/color/1754644064961.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
19
+ Warning: Segmentation failed for data/put_scanner/20250812142119/CAMID_1/color/1754979716487.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start methodWarning: Segmentation failed for data/put_scanner/20250812143639/CAMID_1/color/1754980613946.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start methodWarning: Segmentation failed for data/put_scanner/20250812142942/CAMID_1/color/1754980215397.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
20
+
21
+
22
+ Warning: Segmentation failed for data/put_scanner/20250812145002/CAMID_1/color/1754981440382.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
23
+ Warning: Segmentation failed for data/put_scanner/20250808170712/CAMID_1/color/1754644049188.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
24
+ Point range: [ 0.36037 -0.63242 0.20394] [ 1.3 0.45679 0.74989]
25
+ Point range: [ 0.3601 -0.58556 0.20547] [ 1.2999 0.61226 0.74982]
26
+ Point range: [ 0.36019 -0.58816 0.20067] [ 1.3 0.64959 0.74976]
27
+ Point range: [ 0.36016 -0.63778 0.20137] [ 1.3 0.64989 0.74996]
28
+ Point range: [ 0.36027 -0.6164 0.2065] [ 1.3 0.65 0.74977]
29
+ Point range: [ 0.36011 -0.56427 0.21015] [ 1.3 0.64995 0.74992]
30
+ Point range: [ 0.36011 -0.62625 0.19639] [ 1.3 0.6495 0.74976]
31
+ Point range: [ 0.36009 -0.61986 0.20495] [ 1.2999 0.64871 0.74971]
32
+ Point range: Point range: [ 0.36015 -0.62717 0.20911] [ 1.3 0.64976 0.74957]
33
+ [ 0.36028 -0.5877 0.19386] [ 1.3 0.64999 0.74997]
34
+ Point range: [ 0.36007 -0.62829 0.20577] [ 1.3 0.64988 0.74992]
35
+ Point range: [ 0.36017 -0.6265 0.20893] [ 1.3 0.64824 0.74895]
36
+ Loading dataset ...
37
+ Segmentation model loaded
38
+ Connected to None
39
+ Gripper components initialized!
40
+ Using raw data with real-time processing...
41
+ Preloading joint data...
42
+ Joint data preloading completed!
43
+ Loading policy ...
44
+ Number of parameters: 51.01M
45
+ Loading optimizer and scheduler ...
46
+ Epoch 0
47
+ Loading dataset ...
48
+ Segmentation model loaded
49
+ Connected to None
50
+ Gripper components initialized!
51
+ Using raw data with real-time processing...
52
+ Preloading joint data...
53
+ Joint data preloading completed!
54
+ Loading policy ...
55
+ Number of parameters: 51.01M
56
+ Loading optimizer and scheduler ...
57
+ Epoch 0
58
+ Warning: Segmentation failed for data/test/20250808110355/CAMID_1/color/1754622273859.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
59
+ Point range: [ 0.36037 -0.5654 0.21117] [ 1.3 0.63795 0.74998]
60
+ Point range: [ 0.45195 -0.40749 0.17733] [ 1.5433 0.99553 0.71614]
61
+
62
+ Warning: Segmentation failed for data/test/20250808110550/CAMID_1/color/1754622387941.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
63
+ Point range: [ 0.36003 -0.62034 0.22006] [ 1.3 0.59241 0.74992]
64
+ Loading dataset ...
65
+ Segmentation model loaded
66
+ Using raw data with real-time processing...
67
+ Preloading joint data...
68
+ Joint data preloading completed!
69
+ Loading policy ...
70
+ Number of parameters: 51.01M
71
+ Loading optimizer and scheduler ...
72
+ Epoch 0
73
+ Warning: Segmentation failed for data/test/20250808110355/CAMID_1/color/1754622273859.jpg, using original depth. Error: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
74
+ Point range: [ 0.36008 -0.5654 0.21117] [ 1.3 0.63795 0.74998]
75
+ Loading dataset ...
76
+ RGBDDepthPredictor initialized with vitl model on cuda
77
+ Segmentation model loaded
78
+ Using raw data with real-time processing...
79
+ Preloading joint data...
80
+ Joint data preloading completed!
81
+ Loading policy ...
82
+ Number of parameters: 51.01M
83
+ Loading optimizer and scheduler ...
84
+ Epoch 0
85
+ Loading dataset ...
86
+ RGBDDepthPredictor initialized with vitl model on cuda
87
+ Segmentation model loaded
88
+ Using raw data with real-time processing...
89
+ Preloading joint data...
90
+ Joint data preloading completed!
91
+ Loading policy ...
92
+ Number of parameters: 51.01M
93
+ Loading optimizer and scheduler ...
94
+ Epoch 0
95
+
96
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250808110355/CAMID_1/color/1754622273859.jpg: 384x640 1 scanner, 34.6ms
97
+ Speed: 0.7ms preprocess, 34.6ms inference, 75.9ms postprocess per image at shape (1, 3, 384, 640)
98
+ Point range: [ 0.3603 -0.039661 0.24261] [ 0.87999 0.54471 0.6508]
99
+ Point range: [ 0.34727 -0.10712 0.41679] [ 0.78207 0.55787 0.82498]
100
+ Loading dataset ...
101
+ RGBDDepthPredictor initialized with vitl model on cuda
102
+ Using raw data with real-time processing...
103
+ Preloading joint data...
104
+ Joint data preloading completed!
105
+ Loading policy ...
106
+ Number of parameters: 51.01M
107
+ Loading optimizer and scheduler ...
108
+ Epoch 0
109
+ Point range: [ 0.36030474 -0.64889121 0.00114816] [1.29999852 0.64945602 0.74997544]
110
+ Point range: [ 0.23379904 -0.65298582 0.17532795] [1.32423628 0.77231973 0.92415524]
111
+ Point range: [ 0.36061445 -0.6499387 0.01479402] [1.29999983 0.64981228 0.74999386]
112
+ Point range: [ 0.47380998 -0.72701772 -0.01540195] [1.45496288 0.60269552 0.71979789]
113
+ Point range: [ 0.36016148 -0.64961648 0.03673285] [1.29999602 0.64930969 0.749951 ]
114
+ Point range: [ 0.16347085 -0.5012377 -0.08143704] [1.12409279 0.79467514 0.63178111]
115
+ Point range: [ 0.36012921 -0.64758629 0.10536846] [1.29999745 0.64994913 0.74961472]
116
+ Loading dataset ...
117
+ RGBDDepthPredictor initialized with vitl model on cuda
118
+ Using raw data with real-time processing...
119
+ Preloading joint data...
120
+ Joint data preloading completed!
121
+ Loading policy ...
122
+ Number of parameters: 51.01M
123
+ Loading optimizer and scheduler ...
124
+ Epoch 0
125
+ Point range: [ 0.36030474 -0.64978743 0.09281038] [1.29994512 0.64969891 0.74996901]
126
+ Loading dataset ...
127
+ RGBDDepthPredictor initialized with vitl model on cuda
128
+ Segmentation model loaded
129
+ Using raw data with real-time processing...
130
+ Preloading joint data...
131
+ Joint data preloading completed!
132
+ Loading policy ...
133
+ Number of parameters: 51.01M
134
+ Loading optimizer and scheduler ...
135
+ Epoch 0
136
+
137
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690220578.jpg: 384x640 (no detections), 34.0ms
138
+ Speed: 0.7ms preprocess, 34.0ms inference, 8.3ms postprocess per image at shape (1, 3, 384, 640)
139
+ Point range: [ 0.3603 -0.64979 0.09281] [ 1.2999 0.6497 0.74997]
140
+ Point range: [ 0.21319 -0.60782 0.26699] [ 1.3862 0.77857 0.92415]
141
+
142
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690422100.jpg: 384x640 1 scanner, 7.2ms
143
+ Speed: 0.7ms preprocess, 7.2ms inference, 35.5ms postprocess per image at shape (1, 3, 384, 640)
144
+ Point range: [ 0.36061 -0.039393 0.17526] [ 1.0955 0.64999 0.58985]
145
+ Loading dataset ...
146
+ RGBDDepthPredictor initialized with vitl model on cuda
147
+ Segmentation model loaded
148
+ Using raw data with real-time processing...
149
+ Preloading joint data...
150
+ Joint data preloading completed!
151
+ Loading policy ...
152
+ Number of parameters: 51.01M
153
+ Loading optimizer and scheduler ...
154
+ Epoch 0
155
+
156
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690220578.jpg: 384x640 1 plate, 35.4ms
157
+ Speed: 0.8ms preprocess, 35.4ms inference, 51.2ms postprocess per image at shape (1, 3, 384, 640)
158
+ Point range: [ 0.36033 -0.17669 0.23835] [ 0.81066 0.20085 0.59611]
159
+ Point range: [ 0.29564 -0.18164 0.41253] [ 0.7322 0.21585 0.77029]
160
+
161
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690422100.jpg: 384x640 2 plates, 7.7ms
162
+ Speed: 0.7ms preprocess, 7.7ms inference, 1.2ms postprocess per image at shape (1, 3, 384, 640)
163
+ Point range: [ 0.36061 -0.20522 0.19882] [ 1.1465 0.19886 0.44218]
164
+ Point range: [ 0.47856 -0.26023 0.16862] [ 1.2429 0.12596 0.41198]
165
+
166
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690187875.jpg: 384x640 2 plates, 7.5ms
167
+ Speed: 0.6ms preprocess, 7.5ms inference, 1.1ms postprocess per image at shape (1, 3, 384, 640)
168
+ Point range: [ 0.36013 -0.21789 0.24037] [ 1.1126 0.19985 0.46426]
169
+ Point range: [ 0.16691 -0.077628 0.1222] [ 0.92353 0.3507 0.34609]
170
+
171
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690415991.jpg: 384x640 2 plates, 6.9ms
172
+ Speed: 0.7ms preprocess, 6.9ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
173
+ Point range: [ 0.36013 -0.16828 0.23017] [ 1.061 0.19996 0.43995]
174
+ Loading dataset ...
175
+ RGBDDepthPredictor initialized with vitl model on cuda
176
+ Segmentation model loaded
177
+ Using raw data with real-time processing...
178
+ Preloading joint data...
179
+ Joint data preloading completed!
180
+ Loading policy ...
181
+ Number of parameters: 51.01M
182
+ Loading optimizer and scheduler ...
183
+ Epoch 0
184
+
185
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690220578.jpg: 384x640 1 plate, 35.1ms
186
+ Speed: 0.8ms preprocess, 35.1ms inference, 66.0ms postprocess per image at shape (1, 3, 384, 640)
187
+ Point range: [ 0.36033 -0.17694 0.20548] [ 0.8133 0.20234 0.58371]
188
+ Point range: [ 0.29199 -0.1776 0.37966] [ 0.74201 0.20204 0.75789]
189
+
190
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690422100.jpg: 384x640 2 plates, 7.4ms
191
+ Speed: 0.7ms preprocess, 7.4ms inference, 1.2ms postprocess per image at shape (1, 3, 384, 640)
192
+ Point range: [ 0.36061 -0.20399 5.501e-05] [ 1.1026 0.20006 0.43993]
193
+ Point range: [ 0.47837 -0.26071 -0.030141] [ 1.2153 0.12012 0.40973]
194
+
195
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690187875.jpg: 384x640 2 plates, 7.7ms
196
+ Speed: 0.7ms preprocess, 7.7ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
197
+ Point range: [ 0.36009 -0.21982 0.21992] [ 1.1229 0.20216 0.46203]
198
+ Point range: [ 0.16677 -0.079643 0.10176] [ 0.93455 0.35293 0.34386]
199
+
200
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690415991.jpg: 384x640 2 plates, 7.6ms
201
+ Speed: 0.7ms preprocess, 7.6ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)
202
+ Point range: [ 0.36013 -0.16603 0.00041825] [ 1.0704 0.19996 0.43995]
203
+ Loading dataset ...
204
+ RGBDDepthPredictor initialized with vitl model on cuda
205
+ Segmentation model loaded
206
+ Connected to None
207
+ Gripper components initialized!
208
+ Using raw data with real-time processing...
209
+ Preloading joint data...
210
+ Joint data preloading completed!
211
+ Loading policy ...
212
+ Number of parameters: 51.01M
213
+ Loading optimizer and scheduler ...
214
+ Epoch 0
215
+
216
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690220578.jpg: 384x640 1 plate, 36.7ms
217
+ Speed: 0.8ms preprocess, 36.7ms inference, 64.2ms postprocess per image at shape (1, 3, 384, 640)
218
+ Point range: [ 0.36 -0.17694 0.20548] [ 0.8133 0.24557 0.58371]
219
+ Point range: [ 0.24078 0.028222 0.059532] [ 0.68483 0.41564 0.43777]
220
+
221
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690422100.jpg: 384x640 2 plates, 7.7ms
222
+ Speed: 0.7ms preprocess, 7.7ms inference, 1.1ms postprocess per image at shape (1, 3, 384, 640)
223
+ Point range: [ 0.36018 -0.22334 0.00015438] [ 1.1026 0.2146 0.43984]
224
+ Point range: [ 0.28435 -0.042862 -0.063944] [ 1.0268 0.40405 0.37575]
225
+
226
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690187875.jpg: 384x640 2 plates, 7.2ms
227
+ Speed: 0.7ms preprocess, 7.2ms inference, 1.4ms postprocess per image at shape (1, 3, 384, 640)
228
+ Point range: [ 0.36016 -0.23487 0.21992] [ 1.1229 0.28476 0.48616]
229
+ Point range: [ 0.25384 -0.078899 0.075297] [ 1.0295 0.44971 0.34153]
230
+
231
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690415991.jpg: 384x640 2 plates, 7.4ms
232
+ Speed: 0.7ms preprocess, 7.4ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
233
+ Point range: [ 0.36018 -0.22905 0.00098774] [ 1.0704 0.24704 0.48868]
234
+ Loading dataset ...
235
+ RGBDDepthPredictor initialized with vitl model on cuda
236
+ Segmentation model loaded
237
+ Connected to None
238
+ Gripper components initialized!
239
+ Using raw data with real-time processing...
240
+ Preloading joint data...
241
+ Joint data preloading completed!
242
+ Loading policy ...
243
+ Number of parameters: 51.01M
244
+ Loading optimizer and scheduler ...
245
+ Epoch 0
246
+
247
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690220578.jpg: 384x640 1 plate, 39.1ms
248
+ Speed: 1.0ms preprocess, 39.1ms inference, 58.4ms postprocess per image at shape (1, 3, 384, 640)
249
+ Point range: [ 0.41289 -0.17713 0.20608] [ 0.81431 0.24559 0.58427]
250
+ Point range: [ 0.53031 -0.35569 0.28308] [ 0.93079 0.067388 0.66126]
251
+
252
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690422100.jpg: 384x640 2 plates, 7.8ms
253
+ Speed: 0.7ms preprocess, 7.8ms inference, 1.2ms postprocess per image at shape (1, 3, 384, 640)
254
+ Point range: [ 0.48354 -0.22334 5.501e-05] [ 1.1026 0.21475 0.42849]
255
+ Point range: [ 0.61982 -0.19744 -0.083575] [ 1.2707 0.34569 0.34486]
256
+
257
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690187875.jpg: 384x640 2 plates, 7.2ms
258
+ Speed: 0.7ms preprocess, 7.2ms inference, 1.4ms postprocess per image at shape (1, 3, 384, 640)
259
+ Point range: [ 0.48019 -0.23487 0.21992] [ 1.1229 0.28259 0.48558]
260
+ Point range: [ 0.42033 -0.1322 0.25184] [ 1.071 0.35 0.51749]
261
+
262
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690415991.jpg: 384x640 2 plates, 7.0ms
263
+ Speed: 0.6ms preprocess, 7.0ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
264
+ Point range: [ 0.4869 -0.22905 0.00041825] [ 1.0706 0.24568 0.48802]
265
+ Loading dataset ...
266
+ RGBDDepthPredictor initialized with vitl model on cuda
267
+ Segmentation model loaded
268
+ Connected to None
269
+ Gripper components initialized!
270
+ Using raw data with real-time processing...
271
+ Preloading joint data...
272
+ Joint data preloading completed!
273
+ Loading policy ...
274
+ Number of parameters: 51.01M
275
+ Loading optimizer and scheduler ...
276
+ Epoch 0
277
+
278
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690220578.jpg: 384x640 1 plate, 36.7ms
279
+ Speed: 1.1ms preprocess, 36.7ms inference, 50.3ms postprocess per image at shape (1, 3, 384, 640)
280
+ Point range: [ 0.40174 -0.17669 0.23835] [ 0.81066 0.24566 0.59629]
281
+ Point range: [ 0.51908 -0.35503 0.31535] [ 0.92789 0.067577 0.67329]
282
+
283
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690422100.jpg: 384x640 2 plates, 7.5ms
284
+ Speed: 0.7ms preprocess, 7.5ms inference, 1.0ms postprocess per image at shape (1, 3, 384, 640)
285
+ Point range: [ 0.47764 -0.22334 0.19883] [ 1.1465 0.21524 0.44199]
286
+ Point range: [ 0.60822 -0.21656 0.1152] [ 1.3316 0.3431 0.35836]
287
+
288
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690187875.jpg: 384x640 2 plates, 7.1ms
289
+ Speed: 0.7ms preprocess, 7.1ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)
290
+ Point range: [ 0.48166 -0.23487 0.24028] [ 1.1124 0.28259 0.48558]
291
+ Point range: [ 0.41819 -0.13177 0.2722] [ 1.0594 0.35043 0.51749]
292
+
293
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690415991.jpg: 384x640 2 plates, 7.3ms
294
+ Speed: 0.8ms preprocess, 7.3ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
295
+ Point range: [ 0.47691 -0.22905 0.23033] [ 1.061 0.24648 0.48802]
296
+ Loading dataset ...
297
+ RGBDDepthPredictor initialized with vitl model on cuda
298
+ Segmentation model loaded
299
+ Connected to None
300
+ Gripper components initialized!
301
+ Using raw data with real-time processing...
302
+ Preloading joint data...
303
+ Joint data preloading completed!
304
+ Loading policy ...
305
+ Number of parameters: 51.01M
306
+ Loading optimizer and scheduler ...
307
+ Epoch 0
308
+
309
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690220578.jpg: 384x640 1 plate, 34.3ms
310
+ Speed: 2.0ms preprocess, 34.3ms inference, 56.9ms postprocess per image at shape (1, 3, 384, 640)
311
+ Point range: [ 0.40174 -0.17669 0.23835] [ 0.81066 0.24566 0.59629]
312
+ Point range: [ 0.51908 -0.35503 0.31535] [ 0.92789 0.067577 0.67329]
313
+
314
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690422100.jpg: 384x640 2 plates, 7.3ms
315
+ Speed: 0.7ms preprocess, 7.3ms inference, 1.6ms postprocess per image at shape (1, 3, 384, 640)
316
+ Point range: [ 0.47764 -0.22334 0.19883] [ 1.1465 0.21524 0.44199]
317
+ Point range: [ 0.60822 -0.21656 0.1152] [ 1.3316 0.3431 0.35836]
318
+
319
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690187875.jpg: 384x640 2 plates, 7.5ms
320
+ Speed: 0.7ms preprocess, 7.5ms inference, 1.0ms postprocess per image at shape (1, 3, 384, 640)
321
+ Point range: [ 0.48166 -0.23487 0.24028] [ 1.1124 0.28259 0.48558]
322
+ Point range: [ 0.41819 -0.13177 0.2722] [ 1.0594 0.35043 0.51749]
323
+
324
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690415991.jpg: 384x640 2 plates, 7.5ms
325
+ Speed: 0.7ms preprocess, 7.5ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
326
+ Point range: [ 0.47691 -0.22905 0.23033] [ 1.061 0.24648 0.48802]
327
+ Point range: [ 0.4478 -0.37351 0.32802] [ 1.1035 0.16204 0.58571]
328
+ Loading dataset ...
329
+ RGBDDepthPredictor initialized with vitl model on cuda
330
+ Segmentation model loaded
331
+ Connected to None
332
+ Gripper components initialized!
333
+ Using raw data with real-time processing...
334
+ Preloading joint data...
335
+ Joint data preloading completed!
336
+ Loading policy ...
337
+ Number of parameters: 51.01M
338
+ Loading optimizer and scheduler ...
339
+ Epoch 0
340
+
341
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690220578.jpg: 384x640 1 plate, 34.8ms
342
+ Speed: 2.0ms preprocess, 34.8ms inference, 51.3ms postprocess per image at shape (1, 3, 384, 640)
343
+ Point range: [ 0.40174 -0.17669 0.23835] [ 0.81066 0.24566 0.59629]
344
+ Point range: [ 0.51908 -0.35503 0.31535] [ 0.92789 0.067577 0.67329]
345
+
346
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690422100.jpg: 384x640 2 plates, 7.4ms
347
+ Speed: 0.6ms preprocess, 7.4ms inference, 1.0ms postprocess per image at shape (1, 3, 384, 640)
348
+ Point range: [ 0.47764 -0.22334 0.19883] [ 1.1465 0.21524 0.44199]
349
+ Point range: [ 0.60822 -0.21656 0.1152] [ 1.3316 0.3431 0.35836]
350
+
351
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690187875.jpg: 384x640 2 plates, 7.1ms
352
+ Speed: 0.8ms preprocess, 7.1ms inference, 0.7ms postprocess per image at shape (1, 3, 384, 640)
353
+ Point range: [ 0.48166 -0.23487 0.24028] [ 1.1124 0.28259 0.48558]
354
+ Point range: [ 0.41819 -0.13177 0.2722] [ 1.0594 0.35043 0.51749]
355
+
356
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690415991.jpg: 384x640 2 plates, 7.3ms
357
+ Speed: 0.7ms preprocess, 7.3ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)
358
+ Point range: [ 0.47691 -0.22905 0.23033] [ 1.061 0.24648 0.48802]
359
+ Loading dataset ...
360
+ RGBDDepthPredictor initialized with vitl model on cuda
361
+ Segmentation model loaded
362
+ Connected to None
363
+ Gripper components initialized!
364
+ Using raw data with real-time processing...
365
+ Preloading joint data...
366
+ Joint data preloading completed!
367
+ Loading policy ...
368
+ Number of parameters: 51.01M
369
+ Loading optimizer and scheduler ...
370
+ Epoch 0
371
+
372
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690220578.jpg: 384x640 1 plate, 37.6ms
373
+ Speed: 0.8ms preprocess, 37.6ms inference, 75.6ms postprocess per image at shape (1, 3, 384, 640)
374
+ Point range: [ 0.41289 -0.17713 0.20608] [ 0.81431 0.24559 0.58427]
375
+ Point range: [ 0.53031 -0.35569 0.28308] [ 0.93079 0.067388 0.66126]
376
+
377
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690422100.jpg: 384x640 2 plates, 7.7ms
378
+ Speed: 12.6ms preprocess, 7.7ms inference, 1.2ms postprocess per image at shape (1, 3, 384, 640)
379
+ Point range: [ 0.48354 -0.22334 5.501e-05] [ 1.1026 0.21475 0.42849]
380
+ Point range: [ 0.61982 -0.19744 -0.083575] [ 1.2707 0.34569 0.34486]
381
+
382
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728160934/CAMID_1/color/1753690187875.jpg: 384x640 2 plates, 7.0ms
383
+ Speed: 0.7ms preprocess, 7.0ms inference, 0.8ms postprocess per image at shape (1, 3, 384, 640)
384
+ Point range: [ 0.48019 -0.23487 0.21992] [ 1.1229 0.28259 0.48558]
385
+ Point range: [ 0.42033 -0.1322 0.25184] [ 1.071 0.35 0.51749]
386
+
387
+ image 1/1 /home/admin1/workspace/RISE_TCL/data/test/20250728161314/CAMID_1/color/1753690415991.jpg: 384x640 2 plates, 7.4ms
388
+ Speed: 0.7ms preprocess, 7.4ms inference, 1.2ms postprocess per image at shape (1, 3, 384, 640)
389
+ Point range: [ 0.4869 -0.22905 0.00041825] [ 1.0706 0.24568 0.48802]
390
+ Point range: [ 0.45737 -0.36996 0.098114] [ 1.1133 0.16615 0.58571]