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
b9dd126
•
1 Parent(s): 012dba5

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: 0.9846153846153847
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: 0.1296
35
- - Accuracy: 0.9846
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 | 0.84 | 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
 
21
  metrics:
22
  - name: Accuracy
23
  type: accuracy
24
+ value: 0.9692307692307692
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: 0.1399
35
+ - Accuracy: 0.9692
36
 
37
  ## Model description
38
 
 
66
 
67
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
68
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
69
+ | No log | 0.84 | 4 | 1.9335 | 0.1846 |
70
+ | No log | 1.84 | 8 | 1.8364 | 0.2615 |
71
+ | No log | 2.84 | 12 | 1.7054 | 0.3846 |
72
+ | No log | 3.84 | 16 | 1.5629 | 0.4154 |
73
+ | 2.0106 | 4.84 | 20 | 1.3907 | 0.4769 |
74
+ | 2.0106 | 5.84 | 24 | 1.1984 | 0.5692 |
75
+ | 2.0106 | 6.84 | 28 | 0.9519 | 0.6615 |
76
+ | 2.0106 | 7.84 | 32 | 0.7510 | 0.7846 |
77
+ | 2.0106 | 8.84 | 36 | 0.5749 | 0.8615 |
78
+ | 1.1009 | 9.84 | 40 | 0.4244 | 0.9385 |
79
+ | 1.1009 | 10.84 | 44 | 0.3652 | 0.8923 |
80
+ | 1.1009 | 11.84 | 48 | 0.2735 | 0.9538 |
81
+ | 1.1009 | 12.84 | 52 | 0.2909 | 0.8923 |
82
+ | 1.1009 | 13.84 | 56 | 0.2293 | 0.9692 |
83
+ | 0.6329 | 14.84 | 60 | 0.2563 | 0.9077 |
84
+ | 0.6329 | 15.84 | 64 | 0.2218 | 0.9231 |
85
+ | 0.6329 | 16.84 | 68 | 0.2102 | 0.9538 |
86
+ | 0.6329 | 17.84 | 72 | 0.1829 | 0.9231 |
87
+ | 0.6329 | 18.84 | 76 | 0.1992 | 0.9231 |
88
+ | 0.497 | 19.84 | 80 | 0.1814 | 0.9231 |
89
+ | 0.497 | 20.84 | 84 | 0.1807 | 0.9385 |
90
+ | 0.497 | 21.84 | 88 | 0.1765 | 0.9538 |
91
+ | 0.497 | 22.84 | 92 | 0.1868 | 0.9231 |
92
+ | 0.497 | 23.84 | 96 | 0.2089 | 0.9385 |
93
+ | 0.4198 | 24.84 | 100 | 0.1898 | 0.9385 |
94
+ | 0.4198 | 25.84 | 104 | 0.2065 | 0.9231 |
95
+ | 0.4198 | 26.84 | 108 | 0.1845 | 0.9231 |
96
+ | 0.4198 | 27.84 | 112 | 0.1724 | 0.9231 |
97
+ | 0.4198 | 28.84 | 116 | 0.1612 | 0.9385 |
98
+ | 0.368 | 29.84 | 120 | 0.1538 | 0.9538 |
99
+ | 0.368 | 30.84 | 124 | 0.1568 | 0.9538 |
100
+ | 0.368 | 31.84 | 128 | 0.1475 | 0.9692 |
101
+ | 0.368 | 32.84 | 132 | 0.1453 | 0.9538 |
102
+ | 0.368 | 33.84 | 136 | 0.1576 | 0.9692 |
103
+ | 0.3709 | 34.84 | 140 | 0.1430 | 0.9692 |
104
+ | 0.3709 | 35.84 | 144 | 0.1384 | 0.9692 |
105
+ | 0.3709 | 36.84 | 148 | 0.1432 | 0.9692 |
106
+ | 0.3709 | 37.84 | 152 | 0.1347 | 0.9692 |
107
+ | 0.3709 | 38.84 | 156 | 0.1359 | 0.9538 |
108
+ | 0.3373 | 39.84 | 160 | 0.1597 | 0.9538 |
109
+ | 0.3373 | 40.84 | 164 | 0.1522 | 0.9692 |
110
+ | 0.3373 | 41.84 | 168 | 0.1477 | 0.9538 |
111
+ | 0.3373 | 42.84 | 172 | 0.1480 | 0.9692 |
112
+ | 0.3373 | 43.84 | 176 | 0.1472 | 0.9692 |
113
+ | 0.3342 | 44.84 | 180 | 0.1473 | 0.9692 |
114
+ | 0.3342 | 45.84 | 184 | 0.1458 | 0.9692 |
115
+ | 0.3342 | 46.84 | 188 | 0.1529 | 0.9692 |
116
+ | 0.3342 | 47.84 | 192 | 0.1550 | 0.9692 |
117
+ | 0.3342 | 48.84 | 196 | 0.1494 | 0.9692 |
118
+ | 0.2914 | 49.84 | 200 | 0.1470 | 0.9692 |
119
+ | 0.2914 | 50.84 | 204 | 0.1460 | 0.9692 |
120
+ | 0.2914 | 51.84 | 208 | 0.1478 | 0.9692 |
121
+ | 0.2914 | 52.84 | 212 | 0.1481 | 0.9692 |
122
+ | 0.2914 | 53.84 | 216 | 0.1461 | 0.9692 |
123
+ | 0.2736 | 54.84 | 220 | 0.1458 | 0.9692 |
124
+ | 0.2736 | 55.84 | 224 | 0.1438 | 0.9692 |
125
+ | 0.2736 | 56.84 | 228 | 0.1427 | 0.9692 |
126
+ | 0.2736 | 57.84 | 232 | 0.1418 | 0.9692 |
127
+ | 0.2736 | 58.84 | 236 | 0.1401 | 0.9692 |
128
+ | 0.2589 | 59.84 | 240 | 0.1399 | 0.9692 |
129
 
130
 
131
  ### Framework versions