Hugging Face's logo

JEdward7777
/
delivery_truck_classification

Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
4
JEdward7777 commited on
Commit
3169fac
·
1 Parent(s): 8d3ff80

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +63 -63
README.md CHANGED
@@ -21,7 +21,7 @@ model-index:
21
  metrics:
22
  - name: Accuracy
23
  type: accuracy
24
- value: 0.96875
25
  ---
26
 
27
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
31
 
32
  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
33
  It achieves the following results on the evaluation set:
34
- - Loss: 0.1835
35
- - Accuracy: 0.9688
36
 
37
  ## Model description
38
 
@@ -66,66 +66,66 @@ The following hyperparameters were used during training:
66
 
67
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
68
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
69
- | No log | 0.89 | 4 | 2.0074 | 0.1562 |
70
- | No log | 1.89 | 8 | 1.8896 | 0.25 |
71
- | No log | 2.89 | 12 | 1.7421 | 0.4062 |
72
- | No log | 3.89 | 16 | 1.5892 | 0.4375 |
73
- | 1.973 | 4.89 | 20 | 1.3623 | 0.6094 |
74
- | 1.973 | 5.89 | 24 | 1.1093 | 0.6094 |
75
- | 1.973 | 6.89 | 28 | 0.7901 | 0.7812 |
76
- | 1.973 | 7.89 | 32 | 0.5773 | 0.8438 |
77
- | 1.973 | 8.89 | 36 | 0.3857 | 0.8906 |
78
- | 1.0433 | 9.89 | 40 | 0.3254 | 0.9062 |
79
- | 1.0433 | 10.89 | 44 | 0.2461 | 0.9219 |
80
- | 1.0433 | 11.89 | 48 | 0.2340 | 0.9219 |
81
- | 1.0433 | 12.89 | 52 | 0.1835 | 0.9688 |
82
- | 1.0433 | 13.89 | 56 | 0.1779 | 0.9375 |
83
- | 0.5842 | 14.89 | 60 | 0.1545 | 0.9531 |
84
- | 0.5842 | 15.89 | 64 | 0.1487 | 0.9531 |
85
- | 0.5842 | 16.89 | 68 | 0.1996 | 0.9219 |
86
- | 0.5842 | 17.89 | 72 | 0.1619 | 0.9062 |
87
- | 0.5842 | 18.89 | 76 | 0.1350 | 0.9688 |
88
- | 0.4616 | 19.89 | 80 | 0.1706 | 0.9375 |
89
- | 0.4616 | 20.89 | 84 | 0.1579 | 0.9219 |
90
- | 0.4616 | 21.89 | 88 | 0.1630 | 0.9375 |
91
- | 0.4616 | 22.89 | 92 | 0.2080 | 0.9062 |
92
- | 0.4616 | 23.89 | 96 | 0.1463 | 0.9375 |
93
- | 0.3898 | 24.89 | 100 | 0.1185 | 0.9688 |
94
- | 0.3898 | 25.89 | 104 | 0.1445 | 0.9219 |
95
- | 0.3898 | 26.89 | 108 | 0.2051 | 0.9219 |
96
- | 0.3898 | 27.89 | 112 | 0.1928 | 0.9375 |
97
- | 0.3898 | 28.89 | 116 | 0.1365 | 0.9375 |
98
- | 0.3511 | 29.89 | 120 | 0.1057 | 0.9531 |
99
- | 0.3511 | 30.89 | 124 | 0.1091 | 0.9531 |
100
- | 0.3511 | 31.89 | 128 | 0.1894 | 0.9375 |
101
- | 0.3511 | 32.89 | 132 | 0.1208 | 0.9531 |
102
- | 0.3511 | 33.89 | 136 | 0.1101 | 0.9688 |
103
- | 0.3286 | 34.89 | 140 | 0.1409 | 0.9375 |
104
- | 0.3286 | 35.89 | 144 | 0.1830 | 0.9219 |
105
- | 0.3286 | 36.89 | 148 | 0.1519 | 0.9219 |
106
- | 0.3286 | 37.89 | 152 | 0.1031 | 0.9531 |
107
- | 0.3286 | 38.89 | 156 | 0.0962 | 0.9688 |
108
- | 0.3095 | 39.89 | 160 | 0.0903 | 0.9688 |
109
- | 0.3095 | 40.89 | 164 | 0.0886 | 0.9688 |
110
- | 0.3095 | 41.89 | 168 | 0.1033 | 0.9688 |
111
- | 0.3095 | 42.89 | 172 | 0.1117 | 0.9531 |
112
- | 0.3095 | 43.89 | 176 | 0.1192 | 0.9375 |
113
- | 0.3056 | 44.89 | 180 | 0.0984 | 0.9531 |
114
- | 0.3056 | 45.89 | 184 | 0.0820 | 0.9531 |
115
- | 0.3056 | 46.89 | 188 | 0.0857 | 0.9531 |
116
- | 0.3056 | 47.89 | 192 | 0.1058 | 0.9531 |
117
- | 0.3056 | 48.89 | 196 | 0.1163 | 0.9375 |
118
- | 0.255 | 49.89 | 200 | 0.1121 | 0.9531 |
119
- | 0.255 | 50.89 | 204 | 0.1004 | 0.9688 |
120
- | 0.255 | 51.89 | 208 | 0.0954 | 0.9688 |
121
- | 0.255 | 52.89 | 212 | 0.0925 | 0.9688 |
122
- | 0.255 | 53.89 | 216 | 0.0892 | 0.9688 |
123
- | 0.2494 | 54.89 | 220 | 0.0893 | 0.9688 |
124
- | 0.2494 | 55.89 | 224 | 0.0901 | 0.9688 |
125
- | 0.2494 | 56.89 | 228 | 0.0896 | 0.9688 |
126
- | 0.2494 | 57.89 | 232 | 0.0903 | 0.9688 |
127
- | 0.2494 | 58.89 | 236 | 0.0913 | 0.9688 |
128
- | 0.2588 | 59.89 | 240 | 0.0918 | 0.9688 |
129
 
130
 
131
  ### Framework versions
 
21
  metrics:
22
  - name: Accuracy
23
  type: accuracy
24
+ value: 0.9846153846153847
25
  ---
26
 
27
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
31
 
32
  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
33
  It achieves the following results on the evaluation set:
34
+ - Loss: 0.0874
35
+ - Accuracy: 0.9846
36
 
37
  ## Model description
38
 
 
66
 
67
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
68
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
69
+ | No log | 0.84 | 4 | 1.8199 | 0.3231 |
70
+ | No log | 1.84 | 8 | 1.7275 | 0.4154 |
71
+ | No log | 2.84 | 12 | 1.6281 | 0.4615 |
72
+ | No log | 3.84 | 16 | 1.5272 | 0.4615 |
73
+ | 1.9537 | 4.84 | 20 | 1.3668 | 0.5077 |
74
+ | 1.9537 | 5.84 | 24 | 1.0964 | 0.6 |
75
+ | 1.9537 | 6.84 | 28 | 0.7691 | 0.7846 |
76
+ | 1.9537 | 7.84 | 32 | 0.6370 | 0.8308 |
77
+ | 1.9537 | 8.84 | 36 | 0.4329 | 0.9077 |
78
+ | 1.0682 | 9.84 | 40 | 0.3518 | 0.9077 |
79
+ | 1.0682 | 10.84 | 44 | 0.3229 | 0.8923 |
80
+ | 1.0682 | 11.84 | 48 | 0.2324 | 0.9385 |
81
+ | 1.0682 | 12.84 | 52 | 0.2369 | 0.9385 |
82
+ | 1.0682 | 13.84 | 56 | 0.2119 | 0.9385 |
83
+ | 0.6335 | 14.84 | 60 | 0.1805 | 0.9385 |
84
+ | 0.6335 | 15.84 | 64 | 0.2135 | 0.9077 |
85
+ | 0.6335 | 16.84 | 68 | 0.1889 | 0.9231 |
86
+ | 0.6335 | 17.84 | 72 | 0.1601 | 0.9538 |
87
+ | 0.6335 | 18.84 | 76 | 0.1412 | 0.9692 |
88
+ | 0.5133 | 19.84 | 80 | 0.1497 | 0.9538 |
89
+ | 0.5133 | 20.84 | 84 | 0.1545 | 0.9538 |
90
+ | 0.5133 | 21.84 | 88 | 0.1298 | 0.9538 |
91
+ | 0.5133 | 22.84 | 92 | 0.1415 | 0.9538 |
92
+ | 0.5133 | 23.84 | 96 | 0.1685 | 0.9231 |
93
+ | 0.4383 | 24.84 | 100 | 0.1381 | 0.9385 |
94
+ | 0.4383 | 25.84 | 104 | 0.1296 | 0.9846 |
95
+ | 0.4383 | 26.84 | 108 | 0.1107 | 0.9538 |
96
+ | 0.4383 | 27.84 | 112 | 0.1237 | 0.9385 |
97
+ | 0.4383 | 28.84 | 116 | 0.1366 | 0.9538 |
98
+ | 0.4149 | 29.84 | 120 | 0.1349 | 0.9692 |
99
+ | 0.4149 | 30.84 | 124 | 0.1046 | 0.9846 |
100
+ | 0.4149 | 31.84 | 128 | 0.0882 | 0.9846 |
101
+ | 0.4149 | 32.84 | 132 | 0.1022 | 0.9846 |
102
+ | 0.4149 | 33.84 | 136 | 0.1207 | 0.9692 |
103
+ | 0.3657 | 34.84 | 140 | 0.1168 | 0.9538 |
104
+ | 0.3657 | 35.84 | 144 | 0.0922 | 0.9846 |
105
+ | 0.3657 | 36.84 | 148 | 0.0931 | 0.9846 |
106
+ | 0.3657 | 37.84 | 152 | 0.1006 | 0.9692 |
107
+ | 0.3657 | 38.84 | 156 | 0.0987 | 0.9692 |
108
+ | 0.3294 | 39.84 | 160 | 0.1128 | 0.9692 |
109
+ | 0.3294 | 40.84 | 164 | 0.1152 | 0.9538 |
110
+ | 0.3294 | 41.84 | 168 | 0.0997 | 0.9538 |
111
+ | 0.3294 | 42.84 | 172 | 0.0968 | 0.9692 |
112
+ | 0.3294 | 43.84 | 176 | 0.0819 | 0.9846 |
113
+ | 0.3198 | 44.84 | 180 | 0.0729 | 0.9846 |
114
+ | 0.3198 | 45.84 | 184 | 0.0744 | 0.9846 |
115
+ | 0.3198 | 46.84 | 188 | 0.0951 | 0.9692 |
116
+ | 0.3198 | 47.84 | 192 | 0.0966 | 0.9692 |
117
+ | 0.3198 | 48.84 | 196 | 0.0833 | 0.9846 |
118
+ | 0.2936 | 49.84 | 200 | 0.0694 | 0.9846 |
119
+ | 0.2936 | 50.84 | 204 | 0.0691 | 0.9846 |
120
+ | 0.2936 | 51.84 | 208 | 0.0736 | 0.9846 |
121
+ | 0.2936 | 52.84 | 212 | 0.0805 | 0.9692 |
122
+ | 0.2936 | 53.84 | 216 | 0.0801 | 0.9846 |
123
+ | 0.3127 | 54.84 | 220 | 0.0826 | 0.9846 |
124
+ | 0.3127 | 55.84 | 224 | 0.0857 | 0.9692 |
125
+ | 0.3127 | 56.84 | 228 | 0.0864 | 0.9846 |
126
+ | 0.3127 | 57.84 | 232 | 0.0878 | 0.9846 |
127
+ | 0.3127 | 58.84 | 236 | 0.0877 | 0.9846 |
128
+ | 0.285 | 59.84 | 240 | 0.0874 | 0.9846 |
129
 
130
 
131
  ### Framework versions