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JEdward7777
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delivery_truck_classification

Image Classification
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swin
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update model card README.md

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@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 1.0
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  ---
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  <!-- 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. -->
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  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.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0290
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- - Accuracy: 1.0
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  ## Model description
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@@ -66,66 +66,66 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 0.94 | 4 | 0.0290 | 1.0 |
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- | No log | 1.94 | 8 | 0.0290 | 1.0 |
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- | No log | 2.94 | 12 | 0.0290 | 1.0 |
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- | No log | 3.94 | 16 | 0.0290 | 1.0 |
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- | 0.2595 | 4.94 | 20 | 0.0290 | 1.0 |
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- | 0.2595 | 5.94 | 24 | 0.0290 | 1.0 |
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- | 0.2595 | 6.94 | 28 | 0.0290 | 1.0 |
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- | 0.2595 | 7.94 | 32 | 0.0290 | 1.0 |
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- | 0.2595 | 8.94 | 36 | 0.0290 | 1.0 |
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- | 0.2679 | 9.94 | 40 | 0.0290 | 1.0 |
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- | 0.2679 | 10.94 | 44 | 0.0290 | 1.0 |
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- | 0.2679 | 11.94 | 48 | 0.0290 | 1.0 |
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- | 0.2679 | 12.94 | 52 | 0.0290 | 1.0 |
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- | 0.2679 | 13.94 | 56 | 0.0290 | 1.0 |
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- | 0.275 | 14.94 | 60 | 0.0290 | 1.0 |
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- | 0.275 | 15.94 | 64 | 0.0290 | 1.0 |
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- | 0.275 | 16.94 | 68 | 0.0290 | 1.0 |
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- | 0.275 | 17.94 | 72 | 0.0290 | 1.0 |
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- | 0.275 | 18.94 | 76 | 0.0290 | 1.0 |
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- | 0.248 | 19.94 | 80 | 0.0290 | 1.0 |
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- | 0.248 | 20.94 | 84 | 0.0290 | 1.0 |
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- | 0.248 | 21.94 | 88 | 0.0290 | 1.0 |
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- | 0.248 | 22.94 | 92 | 0.0290 | 1.0 |
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- | 0.248 | 23.94 | 96 | 0.0290 | 1.0 |
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- | 0.2669 | 24.94 | 100 | 0.0290 | 1.0 |
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- | 0.2669 | 25.94 | 104 | 0.0290 | 1.0 |
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- | 0.2669 | 26.94 | 108 | 0.0290 | 1.0 |
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- | 0.2669 | 27.94 | 112 | 0.0290 | 1.0 |
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- | 0.2669 | 28.94 | 116 | 0.0290 | 1.0 |
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- | 0.2589 | 29.94 | 120 | 0.0290 | 1.0 |
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- | 0.2589 | 30.94 | 124 | 0.0290 | 1.0 |
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- | 0.2589 | 31.94 | 128 | 0.0290 | 1.0 |
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- | 0.2589 | 32.94 | 132 | 0.0290 | 1.0 |
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- | 0.2589 | 33.94 | 136 | 0.0290 | 1.0 |
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- | 0.278 | 34.94 | 140 | 0.0290 | 1.0 |
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- | 0.278 | 35.94 | 144 | 0.0290 | 1.0 |
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- | 0.278 | 36.94 | 148 | 0.0290 | 1.0 |
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- | 0.278 | 37.94 | 152 | 0.0290 | 1.0 |
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- | 0.278 | 38.94 | 156 | 0.0290 | 1.0 |
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- | 0.273 | 39.94 | 160 | 0.0290 | 1.0 |
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- | 0.273 | 40.94 | 164 | 0.0290 | 1.0 |
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- | 0.273 | 41.94 | 168 | 0.0290 | 1.0 |
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- | 0.273 | 42.94 | 172 | 0.0290 | 1.0 |
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- | 0.273 | 43.94 | 176 | 0.0290 | 1.0 |
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- | 0.2535 | 44.94 | 180 | 0.0290 | 1.0 |
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- | 0.2535 | 45.94 | 184 | 0.0290 | 1.0 |
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- | 0.2535 | 46.94 | 188 | 0.0290 | 1.0 |
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- | 0.2535 | 47.94 | 192 | 0.0290 | 1.0 |
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- | 0.2535 | 48.94 | 196 | 0.0290 | 1.0 |
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- | 0.2762 | 49.94 | 200 | 0.0290 | 1.0 |
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- | 0.2762 | 50.94 | 204 | 0.0290 | 1.0 |
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- | 0.2762 | 51.94 | 208 | 0.0290 | 1.0 |
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- | 0.2762 | 52.94 | 212 | 0.0290 | 1.0 |
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- | 0.2762 | 53.94 | 216 | 0.0290 | 1.0 |
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- | 0.2798 | 54.94 | 220 | 0.0290 | 1.0 |
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- | 0.2798 | 55.94 | 224 | 0.0290 | 1.0 |
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- | 0.2798 | 56.94 | 228 | 0.0290 | 1.0 |
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- | 0.2798 | 57.94 | 232 | 0.0290 | 1.0 |
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- | 0.2798 | 58.94 | 236 | 0.0290 | 1.0 |
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- | 0.2709 | 59.94 | 240 | 0.0290 | 1.0 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.96875
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  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.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0918
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+ - Accuracy: 0.9688
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.89 | 4 | 2.0074 | 0.1562 |
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+ | No log | 1.89 | 8 | 1.8896 | 0.25 |
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+ | No log | 2.89 | 12 | 1.7421 | 0.4062 |
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+ | No log | 3.89 | 16 | 1.5892 | 0.4375 |
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+ | 1.973 | 4.89 | 20 | 1.3623 | 0.6094 |
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+ | 1.973 | 5.89 | 24 | 1.1093 | 0.6094 |
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+ | 1.973 | 6.89 | 28 | 0.7901 | 0.7812 |
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+ | 1.973 | 7.89 | 32 | 0.5773 | 0.8438 |
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+ | 1.973 | 8.89 | 36 | 0.3857 | 0.8906 |
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+ | 1.0433 | 9.89 | 40 | 0.3254 | 0.9062 |
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+ | 1.0433 | 10.89 | 44 | 0.2461 | 0.9219 |
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+ | 1.0433 | 11.89 | 48 | 0.2340 | 0.9219 |
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+ | 1.0433 | 12.89 | 52 | 0.1835 | 0.9688 |
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+ | 1.0433 | 13.89 | 56 | 0.1779 | 0.9375 |
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+ | 0.5842 | 14.89 | 60 | 0.1545 | 0.9531 |
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+ | 0.5842 | 15.89 | 64 | 0.1487 | 0.9531 |
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+ | 0.5842 | 16.89 | 68 | 0.1996 | 0.9219 |
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+ | 0.5842 | 17.89 | 72 | 0.1619 | 0.9062 |
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+ | 0.5842 | 18.89 | 76 | 0.1350 | 0.9688 |
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+ | 0.4616 | 19.89 | 80 | 0.1706 | 0.9375 |
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+ | 0.4616 | 20.89 | 84 | 0.1579 | 0.9219 |
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+ | 0.4616 | 21.89 | 88 | 0.1630 | 0.9375 |
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+ | 0.4616 | 22.89 | 92 | 0.2080 | 0.9062 |
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+ | 0.4616 | 23.89 | 96 | 0.1463 | 0.9375 |
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+ | 0.3898 | 24.89 | 100 | 0.1185 | 0.9688 |
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+ | 0.3898 | 25.89 | 104 | 0.1445 | 0.9219 |
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+ | 0.3898 | 26.89 | 108 | 0.2051 | 0.9219 |
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+ | 0.3898 | 27.89 | 112 | 0.1928 | 0.9375 |
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+ | 0.3898 | 28.89 | 116 | 0.1365 | 0.9375 |
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+ | 0.3511 | 29.89 | 120 | 0.1057 | 0.9531 |
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+ | 0.3511 | 30.89 | 124 | 0.1091 | 0.9531 |
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+ | 0.3511 | 31.89 | 128 | 0.1894 | 0.9375 |
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+ | 0.3511 | 32.89 | 132 | 0.1208 | 0.9531 |
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+ | 0.3511 | 33.89 | 136 | 0.1101 | 0.9688 |
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+ | 0.3286 | 34.89 | 140 | 0.1409 | 0.9375 |
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+ | 0.3286 | 35.89 | 144 | 0.1830 | 0.9219 |
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+ | 0.3286 | 36.89 | 148 | 0.1519 | 0.9219 |
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+ | 0.3286 | 37.89 | 152 | 0.1031 | 0.9531 |
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+ | 0.3286 | 38.89 | 156 | 0.0962 | 0.9688 |
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+ | 0.3095 | 39.89 | 160 | 0.0903 | 0.9688 |
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+ | 0.3095 | 40.89 | 164 | 0.0886 | 0.9688 |
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+ | 0.3095 | 41.89 | 168 | 0.1033 | 0.9688 |
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+ | 0.3095 | 42.89 | 172 | 0.1117 | 0.9531 |
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+ | 0.3095 | 43.89 | 176 | 0.1192 | 0.9375 |
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+ | 0.3056 | 44.89 | 180 | 0.0984 | 0.9531 |
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+ | 0.3056 | 45.89 | 184 | 0.0820 | 0.9531 |
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+ | 0.3056 | 46.89 | 188 | 0.0857 | 0.9531 |
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+ | 0.3056 | 47.89 | 192 | 0.1058 | 0.9531 |
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+ | 0.3056 | 48.89 | 196 | 0.1163 | 0.9375 |
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+ | 0.255 | 49.89 | 200 | 0.1121 | 0.9531 |
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+ | 0.255 | 50.89 | 204 | 0.1004 | 0.9688 |
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+ | 0.255 | 51.89 | 208 | 0.0954 | 0.9688 |
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+ | 0.255 | 52.89 | 212 | 0.0925 | 0.9688 |
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+ | 0.255 | 53.89 | 216 | 0.0892 | 0.9688 |
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+ | 0.2494 | 54.89 | 220 | 0.0893 | 0.9688 |
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+ | 0.2494 | 55.89 | 224 | 0.0901 | 0.9688 |
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+ | 0.2494 | 56.89 | 228 | 0.0896 | 0.9688 |
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+ | 0.2494 | 57.89 | 232 | 0.0903 | 0.9688 |
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+ | 0.2494 | 58.89 | 236 | 0.0913 | 0.9688 |
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+ | 0.2588 | 59.89 | 240 | 0.0918 | 0.9688 |
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  ### Framework versions