Hugging Face's logo

jordyvl
/
114-tiny_tobacco3482_kd

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Inference Endpoints
Model card Files Community
jordyvl commited on
Commit
fa983dd
•
1 Parent(s): 56399d0

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +165 -0
README.md ADDED
@@ -0,0 +1,165 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: apache-2.0
3
+ tags:
4
+ - generated_from_trainer
5
+ metrics:
6
+ - accuracy
7
+ model-index:
8
+ - name: 114-tiny_tobacco3482_kd
9
+ results: []
10
+ ---
11
+
12
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
+ should probably proofread and complete it, then remove this comment. -->
14
+
15
+ # 114-tiny_tobacco3482_kd
16
+
17
+ This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-tiny-patch16-224) on the None dataset.
18
+ It achieves the following results on the evaluation set:
19
+ - Loss: 0.1493
20
+ - Accuracy: 0.71
21
+ - Brier Loss: 0.5252
22
+ - Nll: 1.5452
23
+ - F1 Micro: 0.7100
24
+ - F1 Macro: 0.6059
25
+ - Ece: 0.3891
26
+ - Aurc: 0.0888
27
+
28
+ ## Model description
29
+
30
+ More information needed
31
+
32
+ ## Intended uses & limitations
33
+
34
+ More information needed
35
+
36
+ ## Training and evaluation data
37
+
38
+ More information needed
39
+
40
+ ## Training procedure
41
+
42
+ ### Training hyperparameters
43
+
44
+ The following hyperparameters were used during training:
45
+ - learning_rate: 0.0001
46
+ - train_batch_size: 64
47
+ - eval_batch_size: 64
48
+ - seed: 42
49
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
50
+ - lr_scheduler_type: linear
51
+ - lr_scheduler_warmup_ratio: 0.1
52
+ - num_epochs: 100
53
+
54
+ ### Training results
55
+
56
+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
57
+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
58
+ | No log | 1.0 | 13 | 0.9704 | 0.23 | 0.8929 | 7.3757 | 0.23 | 0.1649 | 0.2840 | 0.7790 |
59
+ | No log | 2.0 | 26 | 0.4936 | 0.34 | 0.8279 | 4.5780 | 0.34 | 0.2115 | 0.3215 | 0.5666 |
60
+ | No log | 3.0 | 39 | 0.3759 | 0.455 | 0.7568 | 3.7623 | 0.455 | 0.3073 | 0.3262 | 0.3769 |
61
+ | No log | 4.0 | 52 | 0.3165 | 0.545 | 0.7159 | 2.7078 | 0.545 | 0.4004 | 0.3944 | 0.2710 |
62
+ | No log | 5.0 | 65 | 0.2807 | 0.6 | 0.6537 | 2.7061 | 0.6 | 0.4506 | 0.3539 | 0.2091 |
63
+ | No log | 6.0 | 78 | 0.2714 | 0.575 | 0.6476 | 2.6202 | 0.575 | 0.4324 | 0.3576 | 0.2081 |
64
+ | No log | 7.0 | 91 | 0.2473 | 0.64 | 0.6163 | 2.4990 | 0.64 | 0.5080 | 0.3882 | 0.1616 |
65
+ | No log | 8.0 | 104 | 0.2752 | 0.625 | 0.5837 | 2.5795 | 0.625 | 0.5022 | 0.3291 | 0.1892 |
66
+ | No log | 9.0 | 117 | 0.3128 | 0.59 | 0.6027 | 2.7893 | 0.59 | 0.4859 | 0.2944 | 0.2296 |
67
+ | No log | 10.0 | 130 | 0.2292 | 0.66 | 0.5612 | 2.3152 | 0.66 | 0.5253 | 0.3822 | 0.1155 |
68
+ | No log | 11.0 | 143 | 0.2676 | 0.665 | 0.5632 | 2.6937 | 0.665 | 0.5479 | 0.3608 | 0.1422 |
69
+ | No log | 12.0 | 156 | 0.2512 | 0.65 | 0.5543 | 2.2519 | 0.65 | 0.5533 | 0.3324 | 0.1300 |
70
+ | No log | 13.0 | 169 | 0.2053 | 0.67 | 0.5555 | 1.9904 | 0.67 | 0.5739 | 0.3659 | 0.1162 |
71
+ | No log | 14.0 | 182 | 0.2281 | 0.68 | 0.5613 | 2.2343 | 0.68 | 0.5508 | 0.3683 | 0.1193 |
72
+ | No log | 15.0 | 195 | 0.2029 | 0.705 | 0.5511 | 1.6184 | 0.705 | 0.5984 | 0.4175 | 0.0937 |
73
+ | No log | 16.0 | 208 | 0.2090 | 0.71 | 0.5459 | 1.9750 | 0.7100 | 0.6052 | 0.3911 | 0.0983 |
74
+ | No log | 17.0 | 221 | 0.1828 | 0.705 | 0.5385 | 1.8272 | 0.705 | 0.5973 | 0.3700 | 0.0969 |
75
+ | No log | 18.0 | 234 | 0.1739 | 0.73 | 0.5358 | 1.6202 | 0.7300 | 0.6180 | 0.4115 | 0.0962 |
76
+ | No log | 19.0 | 247 | 0.1847 | 0.685 | 0.5300 | 2.1083 | 0.685 | 0.5717 | 0.3582 | 0.1047 |
77
+ | No log | 20.0 | 260 | 0.1839 | 0.69 | 0.5390 | 1.8560 | 0.69 | 0.5932 | 0.3708 | 0.1090 |
78
+ | No log | 21.0 | 273 | 0.1756 | 0.72 | 0.5417 | 1.7203 | 0.72 | 0.6132 | 0.4000 | 0.0855 |
79
+ | No log | 22.0 | 286 | 0.1727 | 0.69 | 0.5212 | 1.9503 | 0.69 | 0.5853 | 0.3574 | 0.1041 |
80
+ | No log | 23.0 | 299 | 0.1684 | 0.72 | 0.5333 | 1.5951 | 0.72 | 0.6229 | 0.3922 | 0.0943 |
81
+ | No log | 24.0 | 312 | 0.1652 | 0.735 | 0.5263 | 1.6768 | 0.735 | 0.6519 | 0.4001 | 0.0920 |
82
+ | No log | 25.0 | 325 | 0.1637 | 0.735 | 0.5363 | 1.6879 | 0.735 | 0.6514 | 0.4079 | 0.0819 |
83
+ | No log | 26.0 | 338 | 0.1609 | 0.675 | 0.5299 | 1.5660 | 0.675 | 0.5602 | 0.3593 | 0.0989 |
84
+ | No log | 27.0 | 351 | 0.1581 | 0.725 | 0.5210 | 1.5886 | 0.7250 | 0.6206 | 0.3739 | 0.0847 |
85
+ | No log | 28.0 | 364 | 0.1591 | 0.71 | 0.5286 | 1.7728 | 0.7100 | 0.6076 | 0.3868 | 0.0921 |
86
+ | No log | 29.0 | 377 | 0.1544 | 0.715 | 0.5251 | 1.6215 | 0.715 | 0.6201 | 0.3813 | 0.0948 |
87
+ | No log | 30.0 | 390 | 0.1618 | 0.705 | 0.5340 | 1.5824 | 0.705 | 0.6064 | 0.3853 | 0.1000 |
88
+ | No log | 31.0 | 403 | 0.1580 | 0.705 | 0.5202 | 1.7228 | 0.705 | 0.5949 | 0.3710 | 0.0963 |
89
+ | No log | 32.0 | 416 | 0.1531 | 0.72 | 0.5257 | 1.6330 | 0.72 | 0.6137 | 0.3857 | 0.0904 |
90
+ | No log | 33.0 | 429 | 0.1521 | 0.72 | 0.5248 | 1.6212 | 0.72 | 0.6349 | 0.3928 | 0.0898 |
91
+ | No log | 34.0 | 442 | 0.1526 | 0.71 | 0.5261 | 1.4652 | 0.7100 | 0.6141 | 0.3829 | 0.0905 |
92
+ | No log | 35.0 | 455 | 0.1529 | 0.7 | 0.5256 | 1.5784 | 0.7 | 0.5926 | 0.3885 | 0.0887 |
93
+ | No log | 36.0 | 468 | 0.1526 | 0.735 | 0.5268 | 1.5163 | 0.735 | 0.6514 | 0.3991 | 0.0878 |
94
+ | No log | 37.0 | 481 | 0.1497 | 0.695 | 0.5222 | 1.6068 | 0.695 | 0.5785 | 0.3918 | 0.0919 |
95
+ | No log | 38.0 | 494 | 0.1488 | 0.72 | 0.5248 | 1.5401 | 0.72 | 0.6115 | 0.3905 | 0.0891 |
96
+ | 0.1554 | 39.0 | 507 | 0.1504 | 0.715 | 0.5208 | 1.5917 | 0.715 | 0.6152 | 0.3730 | 0.0894 |
97
+ | 0.1554 | 40.0 | 520 | 0.1487 | 0.725 | 0.5260 | 1.5258 | 0.7250 | 0.6399 | 0.3998 | 0.0879 |
98
+ | 0.1554 | 41.0 | 533 | 0.1484 | 0.71 | 0.5250 | 1.6093 | 0.7100 | 0.6073 | 0.3908 | 0.0880 |
99
+ | 0.1554 | 42.0 | 546 | 0.1481 | 0.715 | 0.5245 | 1.5711 | 0.715 | 0.6096 | 0.3857 | 0.0860 |
100
+ | 0.1554 | 43.0 | 559 | 0.1493 | 0.705 | 0.5243 | 1.6261 | 0.705 | 0.6000 | 0.3727 | 0.0901 |
101
+ | 0.1554 | 44.0 | 572 | 0.1495 | 0.71 | 0.5242 | 1.5942 | 0.7100 | 0.6080 | 0.3808 | 0.0868 |
102
+ | 0.1554 | 45.0 | 585 | 0.1495 | 0.71 | 0.5242 | 1.5417 | 0.7100 | 0.6059 | 0.3813 | 0.0881 |
103
+ | 0.1554 | 46.0 | 598 | 0.1490 | 0.715 | 0.5239 | 1.5403 | 0.715 | 0.6134 | 0.3826 | 0.0893 |
104
+ | 0.1554 | 47.0 | 611 | 0.1486 | 0.715 | 0.5248 | 1.5387 | 0.715 | 0.6112 | 0.3754 | 0.0883 |
105
+ | 0.1554 | 48.0 | 624 | 0.1491 | 0.71 | 0.5252 | 1.5527 | 0.7100 | 0.6059 | 0.3761 | 0.0889 |
106
+ | 0.1554 | 49.0 | 637 | 0.1491 | 0.71 | 0.5249 | 1.5545 | 0.7100 | 0.6059 | 0.3880 | 0.0885 |
107
+ | 0.1554 | 50.0 | 650 | 0.1489 | 0.71 | 0.5247 | 1.5376 | 0.7100 | 0.6059 | 0.3900 | 0.0895 |
108
+ | 0.1554 | 51.0 | 663 | 0.1492 | 0.71 | 0.5257 | 1.5385 | 0.7100 | 0.6059 | 0.3857 | 0.0890 |
109
+ | 0.1554 | 52.0 | 676 | 0.1491 | 0.71 | 0.5251 | 1.5460 | 0.7100 | 0.6059 | 0.3816 | 0.0888 |
110
+ | 0.1554 | 53.0 | 689 | 0.1491 | 0.71 | 0.5248 | 1.5429 | 0.7100 | 0.6059 | 0.3806 | 0.0886 |
111
+ | 0.1554 | 54.0 | 702 | 0.1489 | 0.71 | 0.5247 | 1.5426 | 0.7100 | 0.6059 | 0.3949 | 0.0887 |
112
+ | 0.1554 | 55.0 | 715 | 0.1492 | 0.71 | 0.5258 | 1.5550 | 0.7100 | 0.6059 | 0.3921 | 0.0890 |
113
+ | 0.1554 | 56.0 | 728 | 0.1492 | 0.71 | 0.5248 | 1.5470 | 0.7100 | 0.6059 | 0.3859 | 0.0888 |
114
+ | 0.1554 | 57.0 | 741 | 0.1491 | 0.71 | 0.5251 | 1.5447 | 0.7100 | 0.6059 | 0.4035 | 0.0888 |
115
+ | 0.1554 | 58.0 | 754 | 0.1491 | 0.71 | 0.5248 | 1.5440 | 0.7100 | 0.6059 | 0.4033 | 0.0886 |
116
+ | 0.1554 | 59.0 | 767 | 0.1491 | 0.71 | 0.5246 | 1.5561 | 0.7100 | 0.6059 | 0.3920 | 0.0890 |
117
+ | 0.1554 | 60.0 | 780 | 0.1492 | 0.71 | 0.5251 | 1.5461 | 0.7100 | 0.6059 | 0.3847 | 0.0889 |
118
+ | 0.1554 | 61.0 | 793 | 0.1493 | 0.71 | 0.5251 | 1.5455 | 0.7100 | 0.6059 | 0.3931 | 0.0887 |
119
+ | 0.1554 | 62.0 | 806 | 0.1493 | 0.71 | 0.5252 | 1.5443 | 0.7100 | 0.6059 | 0.3912 | 0.0889 |
120
+ | 0.1554 | 63.0 | 819 | 0.1493 | 0.71 | 0.5253 | 1.5441 | 0.7100 | 0.6059 | 0.3944 | 0.0887 |
121
+ | 0.1554 | 64.0 | 832 | 0.1492 | 0.71 | 0.5249 | 1.5444 | 0.7100 | 0.6059 | 0.3891 | 0.0888 |
122
+ | 0.1554 | 65.0 | 845 | 0.1492 | 0.71 | 0.5255 | 1.5430 | 0.7100 | 0.6059 | 0.3995 | 0.0888 |
123
+ | 0.1554 | 66.0 | 858 | 0.1493 | 0.71 | 0.5250 | 1.5435 | 0.7100 | 0.6059 | 0.3991 | 0.0890 |
124
+ | 0.1554 | 67.0 | 871 | 0.1493 | 0.71 | 0.5252 | 1.5449 | 0.7100 | 0.6059 | 0.3991 | 0.0890 |
125
+ | 0.1554 | 68.0 | 884 | 0.1492 | 0.71 | 0.5251 | 1.5458 | 0.7100 | 0.6059 | 0.3968 | 0.0889 |
126
+ | 0.1554 | 69.0 | 897 | 0.1493 | 0.71 | 0.5250 | 1.5468 | 0.7100 | 0.6059 | 0.4036 | 0.0888 |
127
+ | 0.1554 | 70.0 | 910 | 0.1494 | 0.71 | 0.5253 | 1.5464 | 0.7100 | 0.6059 | 0.3889 | 0.0887 |
128
+ | 0.1554 | 71.0 | 923 | 0.1493 | 0.71 | 0.5251 | 1.5452 | 0.7100 | 0.6059 | 0.3888 | 0.0887 |
129
+ | 0.1554 | 72.0 | 936 | 0.1493 | 0.71 | 0.5250 | 1.5457 | 0.7100 | 0.6059 | 0.3928 | 0.0888 |
130
+ | 0.1554 | 73.0 | 949 | 0.1494 | 0.71 | 0.5253 | 1.5455 | 0.7100 | 0.6059 | 0.3946 | 0.0889 |
131
+ | 0.1554 | 74.0 | 962 | 0.1493 | 0.71 | 0.5251 | 1.5441 | 0.7100 | 0.6059 | 0.3928 | 0.0888 |
132
+ | 0.1554 | 75.0 | 975 | 0.1493 | 0.71 | 0.5252 | 1.5455 | 0.7100 | 0.6059 | 0.3929 | 0.0891 |
133
+ | 0.1554 | 76.0 | 988 | 0.1493 | 0.71 | 0.5252 | 1.5449 | 0.7100 | 0.6059 | 0.3940 | 0.0886 |
134
+ | 0.0002 | 77.0 | 1001 | 0.1493 | 0.71 | 0.5253 | 1.5455 | 0.7100 | 0.6059 | 0.3891 | 0.0887 |
135
+ | 0.0002 | 78.0 | 1014 | 0.1493 | 0.71 | 0.5251 | 1.5468 | 0.7100 | 0.6059 | 0.3889 | 0.0887 |
136
+ | 0.0002 | 79.0 | 1027 | 0.1494 | 0.71 | 0.5252 | 1.5462 | 0.7100 | 0.6059 | 0.3891 | 0.0888 |
137
+ | 0.0002 | 80.0 | 1040 | 0.1493 | 0.71 | 0.5252 | 1.5443 | 0.7100 | 0.6059 | 0.3994 | 0.0886 |
138
+ | 0.0002 | 81.0 | 1053 | 0.1493 | 0.71 | 0.5251 | 1.5451 | 0.7100 | 0.6059 | 0.3890 | 0.0887 |
139
+ | 0.0002 | 82.0 | 1066 | 0.1493 | 0.71 | 0.5251 | 1.5448 | 0.7100 | 0.6059 | 0.3938 | 0.0886 |
140
+ | 0.0002 | 83.0 | 1079 | 0.1493 | 0.71 | 0.5251 | 1.5453 | 0.7100 | 0.6059 | 0.3890 | 0.0886 |
141
+ | 0.0002 | 84.0 | 1092 | 0.1493 | 0.71 | 0.5252 | 1.5462 | 0.7100 | 0.6059 | 0.3890 | 0.0888 |
142
+ | 0.0002 | 85.0 | 1105 | 0.1493 | 0.71 | 0.5252 | 1.5454 | 0.7100 | 0.6059 | 0.3891 | 0.0887 |
143
+ | 0.0002 | 86.0 | 1118 | 0.1493 | 0.71 | 0.5252 | 1.5452 | 0.7100 | 0.6059 | 0.3890 | 0.0887 |
144
+ | 0.0002 | 87.0 | 1131 | 0.1494 | 0.71 | 0.5252 | 1.5452 | 0.7100 | 0.6059 | 0.3891 | 0.0888 |
145
+ | 0.0002 | 88.0 | 1144 | 0.1494 | 0.71 | 0.5252 | 1.5453 | 0.7100 | 0.6059 | 0.3891 | 0.0886 |
146
+ | 0.0002 | 89.0 | 1157 | 0.1493 | 0.71 | 0.5252 | 1.5451 | 0.7100 | 0.6059 | 0.3891 | 0.0887 |
147
+ | 0.0002 | 90.0 | 1170 | 0.1493 | 0.71 | 0.5252 | 1.5449 | 0.7100 | 0.6059 | 0.3891 | 0.0887 |
148
+ | 0.0002 | 91.0 | 1183 | 0.1493 | 0.71 | 0.5252 | 1.5454 | 0.7100 | 0.6059 | 0.3890 | 0.0887 |
149
+ | 0.0002 | 92.0 | 1196 | 0.1493 | 0.71 | 0.5252 | 1.5450 | 0.7100 | 0.6059 | 0.3891 | 0.0887 |
150
+ | 0.0002 | 93.0 | 1209 | 0.1493 | 0.71 | 0.5252 | 1.5454 | 0.7100 | 0.6059 | 0.3891 | 0.0888 |
151
+ | 0.0002 | 94.0 | 1222 | 0.1493 | 0.71 | 0.5252 | 1.5453 | 0.7100 | 0.6059 | 0.3890 | 0.0886 |
152
+ | 0.0002 | 95.0 | 1235 | 0.1493 | 0.71 | 0.5252 | 1.5454 | 0.7100 | 0.6059 | 0.3891 | 0.0887 |
153
+ | 0.0002 | 96.0 | 1248 | 0.1493 | 0.71 | 0.5252 | 1.5450 | 0.7100 | 0.6059 | 0.3890 | 0.0887 |
154
+ | 0.0002 | 97.0 | 1261 | 0.1493 | 0.71 | 0.5252 | 1.5455 | 0.7100 | 0.6059 | 0.3891 | 0.0888 |
155
+ | 0.0002 | 98.0 | 1274 | 0.1494 | 0.71 | 0.5252 | 1.5453 | 0.7100 | 0.6059 | 0.3891 | 0.0886 |
156
+ | 0.0002 | 99.0 | 1287 | 0.1493 | 0.71 | 0.5252 | 1.5452 | 0.7100 | 0.6059 | 0.3891 | 0.0887 |
157
+ | 0.0002 | 100.0 | 1300 | 0.1493 | 0.71 | 0.5252 | 1.5452 | 0.7100 | 0.6059 | 0.3891 | 0.0888 |
158
+
159
+
160
+ ### Framework versions
161
+
162
+ - Transformers 4.26.1
163
+ - Pytorch 1.13.1.post200
164
+ - Datasets 2.9.0
165
+ - Tokenizers 0.13.2