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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: 0.9714285714285714
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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: 0.1192
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- - Accuracy: 0.9714
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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 | 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
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- - Transformers 4.25.1
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  - Pytorch 1.13.1+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: 0.9466666666666667
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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: 0.1463
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+ - Accuracy: 0.9467
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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 | 0.91 | 5 | 2.1248 | 0.0667 |
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+ | No log | 1.91 | 10 | 1.9221 | 0.24 |
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+ | No log | 2.91 | 15 | 1.7177 | 0.32 |
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+ | 2.0123 | 3.91 | 20 | 1.5490 | 0.4267 |
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+ | 2.0123 | 4.91 | 25 | 1.3192 | 0.5333 |
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+ | 2.0123 | 5.91 | 30 | 1.0764 | 0.64 |
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+ | 2.0123 | 6.91 | 35 | 0.8421 | 0.76 |
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+ | 1.3539 | 7.91 | 40 | 0.6504 | 0.8267 |
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+ | 1.3539 | 8.91 | 45 | 0.5243 | 0.8667 |
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+ | 1.3539 | 9.91 | 50 | 0.4282 | 0.88 |
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+ | 1.3539 | 10.91 | 55 | 0.3950 | 0.9067 |
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+ | 0.7315 | 11.91 | 60 | 0.3617 | 0.8933 |
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+ | 0.7315 | 12.91 | 65 | 0.3167 | 0.9067 |
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+ | 0.7315 | 13.91 | 70 | 0.3023 | 0.9067 |
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+ | 0.7315 | 14.91 | 75 | 0.2440 | 0.9333 |
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+ | 0.5713 | 15.91 | 80 | 0.2475 | 0.9333 |
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+ | 0.5713 | 16.91 | 85 | 0.2443 | 0.92 |
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+ | 0.5713 | 17.91 | 90 | 0.2093 | 0.96 |
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+ | 0.5713 | 18.91 | 95 | 0.2077 | 0.9467 |
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+ | 0.515 | 19.91 | 100 | 0.2124 | 0.9333 |
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+ | 0.515 | 20.91 | 105 | 0.2166 | 0.96 |
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+ | 0.515 | 21.91 | 110 | 0.1940 | 0.9333 |
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+ | 0.515 | 22.91 | 115 | 0.1984 | 0.9333 |
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+ | 0.4582 | 23.91 | 120 | 0.2395 | 0.9333 |
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+ | 0.4582 | 24.91 | 125 | 0.2480 | 0.92 |
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+ | 0.4582 | 25.91 | 130 | 0.2180 | 0.92 |
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+ | 0.4582 | 26.91 | 135 | 0.2232 | 0.9333 |
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+ | 0.4279 | 27.91 | 140 | 0.1977 | 0.9333 |
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+ | 0.4279 | 28.91 | 145 | 0.1847 | 0.9467 |
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+ | 0.4279 | 29.91 | 150 | 0.1922 | 0.9467 |
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+ | 0.4279 | 30.91 | 155 | 0.1787 | 0.9733 |
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+ | 0.4031 | 31.91 | 160 | 0.1626 | 0.9733 |
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+ | 0.4031 | 32.91 | 165 | 0.1667 | 0.9733 |
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+ | 0.4031 | 33.91 | 170 | 0.1871 | 0.9733 |
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+ | 0.4031 | 34.91 | 175 | 0.2015 | 0.9733 |
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+ | 0.3952 | 35.91 | 180 | 0.1836 | 0.9733 |
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+ | 0.3952 | 36.91 | 185 | 0.1856 | 0.96 |
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+ | 0.3952 | 37.91 | 190 | 0.1952 | 0.9333 |
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+ | 0.3952 | 38.91 | 195 | 0.1721 | 0.96 |
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+ | 0.369 | 39.91 | 200 | 0.1619 | 0.9467 |
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+ | 0.369 | 40.91 | 205 | 0.1659 | 0.96 |
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+ | 0.369 | 41.91 | 210 | 0.1569 | 0.96 |
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+ | 0.369 | 42.91 | 215 | 0.1358 | 0.96 |
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+ | 0.3262 | 43.91 | 220 | 0.1371 | 0.96 |
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+ | 0.3262 | 44.91 | 225 | 0.1337 | 0.9467 |
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+ | 0.3262 | 45.91 | 230 | 0.1374 | 0.9467 |
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+ | 0.3262 | 46.91 | 235 | 0.1789 | 0.96 |
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+ | 0.3616 | 47.91 | 240 | 0.2167 | 0.9467 |
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+ | 0.3616 | 48.91 | 245 | 0.1757 | 0.96 |
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+ | 0.3616 | 49.91 | 250 | 0.1729 | 0.9733 |
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+ | 0.3616 | 50.91 | 255 | 0.1722 | 0.9733 |
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+ | 0.303 | 51.91 | 260 | 0.1601 | 0.9733 |
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+ | 0.303 | 52.91 | 265 | 0.1592 | 0.9733 |
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+ | 0.303 | 53.91 | 270 | 0.1613 | 0.9733 |
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+ | 0.303 | 54.91 | 275 | 0.1575 | 0.9733 |
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+ | 0.305 | 55.91 | 280 | 0.1559 | 0.9733 |
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+ | 0.305 | 56.91 | 285 | 0.1489 | 0.9733 |
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+ | 0.305 | 57.91 | 290 | 0.1464 | 0.96 |
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+ | 0.305 | 58.91 | 295 | 0.1463 | 0.9467 |
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+ | 0.3328 | 59.91 | 300 | 0.1463 | 0.9467 |
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  ### Framework versions
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+ - Transformers 4.26.0
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  - Pytorch 1.13.1+cu116
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+ - Datasets 2.9.0
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  - Tokenizers 0.13.2