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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: 0.9692307692307692
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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: 0.2293
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- - Accuracy: 0.9692
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  ## Model description
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@@ -66,71 +66,71 @@ 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 | 0.84 | 4 | 1.9335 | 0.1846 |
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- | No log | 1.84 | 8 | 1.8364 | 0.2615 |
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- | No log | 2.84 | 12 | 1.7054 | 0.3846 |
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- | No log | 3.84 | 16 | 1.5629 | 0.4154 |
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- | 2.0106 | 4.84 | 20 | 1.3907 | 0.4769 |
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- | 2.0106 | 5.84 | 24 | 1.1984 | 0.5692 |
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- | 2.0106 | 6.84 | 28 | 0.9519 | 0.6615 |
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- | 2.0106 | 7.84 | 32 | 0.7510 | 0.7846 |
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- | 2.0106 | 8.84 | 36 | 0.5749 | 0.8615 |
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- | 1.1009 | 9.84 | 40 | 0.4244 | 0.9385 |
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- | 1.1009 | 10.84 | 44 | 0.3652 | 0.8923 |
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- | 1.1009 | 11.84 | 48 | 0.2735 | 0.9538 |
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- | 1.1009 | 12.84 | 52 | 0.2909 | 0.8923 |
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- | 1.1009 | 13.84 | 56 | 0.2293 | 0.9692 |
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- | 0.6329 | 14.84 | 60 | 0.2563 | 0.9077 |
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- | 0.6329 | 15.84 | 64 | 0.2218 | 0.9231 |
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- | 0.6329 | 16.84 | 68 | 0.2102 | 0.9538 |
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- | 0.6329 | 17.84 | 72 | 0.1829 | 0.9231 |
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- | 0.6329 | 18.84 | 76 | 0.1992 | 0.9231 |
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- | 0.497 | 19.84 | 80 | 0.1814 | 0.9231 |
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- | 0.497 | 20.84 | 84 | 0.1807 | 0.9385 |
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- | 0.497 | 21.84 | 88 | 0.1765 | 0.9538 |
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- | 0.497 | 22.84 | 92 | 0.1868 | 0.9231 |
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- | 0.497 | 23.84 | 96 | 0.2089 | 0.9385 |
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- | 0.4198 | 24.84 | 100 | 0.1898 | 0.9385 |
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- | 0.4198 | 25.84 | 104 | 0.2065 | 0.9231 |
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- | 0.4198 | 26.84 | 108 | 0.1845 | 0.9231 |
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- | 0.4198 | 27.84 | 112 | 0.1724 | 0.9231 |
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- | 0.4198 | 28.84 | 116 | 0.1612 | 0.9385 |
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- | 0.368 | 29.84 | 120 | 0.1538 | 0.9538 |
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- | 0.368 | 30.84 | 124 | 0.1568 | 0.9538 |
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- | 0.368 | 31.84 | 128 | 0.1475 | 0.9692 |
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- | 0.368 | 32.84 | 132 | 0.1453 | 0.9538 |
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- | 0.368 | 33.84 | 136 | 0.1576 | 0.9692 |
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- | 0.3709 | 34.84 | 140 | 0.1430 | 0.9692 |
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- | 0.3709 | 35.84 | 144 | 0.1384 | 0.9692 |
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- | 0.3709 | 36.84 | 148 | 0.1432 | 0.9692 |
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- | 0.3709 | 37.84 | 152 | 0.1347 | 0.9692 |
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- | 0.3709 | 38.84 | 156 | 0.1359 | 0.9538 |
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- | 0.3373 | 39.84 | 160 | 0.1597 | 0.9538 |
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- | 0.3373 | 40.84 | 164 | 0.1522 | 0.9692 |
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- | 0.3373 | 41.84 | 168 | 0.1477 | 0.9538 |
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- | 0.3373 | 42.84 | 172 | 0.1480 | 0.9692 |
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- | 0.3373 | 43.84 | 176 | 0.1472 | 0.9692 |
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- | 0.3342 | 44.84 | 180 | 0.1473 | 0.9692 |
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- | 0.3342 | 45.84 | 184 | 0.1458 | 0.9692 |
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- | 0.3342 | 46.84 | 188 | 0.1529 | 0.9692 |
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- | 0.3342 | 47.84 | 192 | 0.1550 | 0.9692 |
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- | 0.3342 | 48.84 | 196 | 0.1494 | 0.9692 |
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- | 0.2914 | 49.84 | 200 | 0.1470 | 0.9692 |
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- | 0.2914 | 50.84 | 204 | 0.1460 | 0.9692 |
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- | 0.2914 | 51.84 | 208 | 0.1478 | 0.9692 |
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- | 0.2914 | 52.84 | 212 | 0.1481 | 0.9692 |
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- | 0.2914 | 53.84 | 216 | 0.1461 | 0.9692 |
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- | 0.2736 | 54.84 | 220 | 0.1458 | 0.9692 |
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- | 0.2736 | 55.84 | 224 | 0.1438 | 0.9692 |
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- | 0.2736 | 56.84 | 228 | 0.1427 | 0.9692 |
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- | 0.2736 | 57.84 | 232 | 0.1418 | 0.9692 |
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- | 0.2736 | 58.84 | 236 | 0.1401 | 0.9692 |
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- | 0.2589 | 59.84 | 240 | 0.1399 | 0.9692 |
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  ### Framework versions
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  - Transformers 4.25.1
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- - Pytorch 1.13.0+cu116
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  - Datasets 2.8.0
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  - Tokenizers 0.13.2
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9571428571428572
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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: 0.1235
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+ - Accuracy: 0.9571
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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 | 1.0 | 5 | 1.9402 | 0.1286 |
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+ | No log | 2.0 | 10 | 1.8379 | 0.2429 |
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+ | No log | 3.0 | 15 | 1.6960 | 0.4 |
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+ | 1.7795 | 4.0 | 20 | 1.4423 | 0.5143 |
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+ | 1.7795 | 5.0 | 25 | 1.1295 | 0.6857 |
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+ | 1.7795 | 6.0 | 30 | 0.8280 | 0.7286 |
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+ | 1.7795 | 7.0 | 35 | 0.5572 | 0.8429 |
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+ | 1.0588 | 8.0 | 40 | 0.3855 | 0.9286 |
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+ | 1.0588 | 9.0 | 45 | 0.3107 | 0.9143 |
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+ | 1.0588 | 10.0 | 50 | 0.2564 | 0.9286 |
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+ | 1.0588 | 11.0 | 55 | 0.2050 | 0.9286 |
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+ | 0.591 | 12.0 | 60 | 0.1900 | 0.9571 |
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+ | 0.591 | 13.0 | 65 | 0.1720 | 0.9286 |
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+ | 0.591 | 14.0 | 70 | 0.1881 | 0.9143 |
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+ | 0.591 | 15.0 | 75 | 0.1789 | 0.9429 |
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+ | 0.4609 | 16.0 | 80 | 0.1999 | 0.9143 |
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+ | 0.4609 | 17.0 | 85 | 0.1492 | 0.9286 |
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+ | 0.4609 | 18.0 | 90 | 0.1648 | 0.9286 |
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+ | 0.4609 | 19.0 | 95 | 0.1195 | 0.9571 |
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+ | 0.3941 | 20.0 | 100 | 0.1395 | 0.9286 |
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+ | 0.3941 | 21.0 | 105 | 0.1476 | 0.9286 |
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+ | 0.3941 | 22.0 | 110 | 0.1113 | 0.9571 |
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+ | 0.3941 | 23.0 | 115 | 0.1328 | 0.9571 |
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+ | 0.3475 | 24.0 | 120 | 0.1192 | 0.9714 |
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+ | 0.3475 | 25.0 | 125 | 0.1200 | 0.9571 |
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+ | 0.3475 | 26.0 | 130 | 0.1360 | 0.9714 |
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+ | 0.3475 | 27.0 | 135 | 0.1425 | 0.9429 |
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+ | 0.3542 | 28.0 | 140 | 0.1103 | 0.9571 |
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+ | 0.3542 | 29.0 | 145 | 0.1244 | 0.9429 |
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+ | 0.3542 | 30.0 | 150 | 0.1176 | 0.9571 |
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+ | 0.3542 | 31.0 | 155 | 0.1028 | 0.9571 |
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+ | 0.317 | 32.0 | 160 | 0.1084 | 0.9571 |
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+ | 0.317 | 33.0 | 165 | 0.1269 | 0.9571 |
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+ | 0.317 | 34.0 | 170 | 0.1295 | 0.9429 |
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+ | 0.317 | 35.0 | 175 | 0.1245 | 0.9571 |
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+ | 0.2947 | 36.0 | 180 | 0.1315 | 0.9429 |
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+ | 0.2947 | 37.0 | 185 | 0.1313 | 0.9571 |
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+ | 0.2947 | 38.0 | 190 | 0.1421 | 0.9429 |
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+ | 0.2947 | 39.0 | 195 | 0.1440 | 0.9571 |
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+ | 0.3124 | 40.0 | 200 | 0.1339 | 0.9571 |
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+ | 0.3124 | 41.0 | 205 | 0.1553 | 0.9429 |
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+ | 0.3124 | 42.0 | 210 | 0.1547 | 0.9429 |
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+ | 0.3124 | 43.0 | 215 | 0.1316 | 0.9571 |
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+ | 0.2843 | 44.0 | 220 | 0.1287 | 0.9571 |
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+ | 0.2843 | 45.0 | 225 | 0.1308 | 0.9571 |
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+ | 0.2843 | 46.0 | 230 | 0.1401 | 0.9571 |
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+ | 0.2843 | 47.0 | 235 | 0.1186 | 0.9571 |
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+ | 0.2655 | 48.0 | 240 | 0.1057 | 0.9571 |
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+ | 0.2655 | 49.0 | 245 | 0.1203 | 0.9571 |
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+ | 0.2655 | 50.0 | 250 | 0.1374 | 0.9571 |
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+ | 0.2655 | 51.0 | 255 | 0.1361 | 0.9571 |
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+ | 0.26 | 52.0 | 260 | 0.1198 | 0.9571 |
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+ | 0.26 | 53.0 | 265 | 0.1175 | 0.9571 |
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+ | 0.26 | 54.0 | 270 | 0.1313 | 0.9571 |
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+ | 0.26 | 55.0 | 275 | 0.1398 | 0.9429 |
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+ | 0.2601 | 56.0 | 280 | 0.1354 | 0.9571 |
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+ | 0.2601 | 57.0 | 285 | 0.1271 | 0.9571 |
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+ | 0.2601 | 58.0 | 290 | 0.1242 | 0.9571 |
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+ | 0.2601 | 59.0 | 295 | 0.1233 | 0.9571 |
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+ | 0.2562 | 60.0 | 300 | 0.1235 | 0.9571 |
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  ### Framework versions
132
 
133
  - Transformers 4.25.1
134
+ - Pytorch 1.13.1+cu116
135
  - Datasets 2.8.0
136
  - Tokenizers 0.13.2