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JEdward7777
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delivery_truck_classification

Image Classification
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swin
generated_from_trainer
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update model card README.md

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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 | 0.91 | 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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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.91 | 5 | 0.1787 | 0.9733 |
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+ | No log | 1.91 | 10 | 0.1787 | 0.9733 |
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+ | No log | 2.91 | 15 | 0.1787 | 0.9733 |
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+ | 0.3799 | 3.91 | 20 | 0.1787 | 0.9733 |
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+ | 0.3799 | 4.91 | 25 | 0.1787 | 0.9733 |
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+ | 0.3799 | 5.91 | 30 | 0.1787 | 0.9733 |
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+ | 0.3799 | 6.91 | 35 | 0.1787 | 0.9733 |
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+ | 0.3648 | 7.91 | 40 | 0.1787 | 0.9733 |
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+ | 0.3648 | 8.91 | 45 | 0.1787 | 0.9733 |
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+ | 0.3648 | 9.91 | 50 | 0.1787 | 0.9733 |
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+ | 0.3648 | 10.91 | 55 | 0.1787 | 0.9733 |
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+ | 0.3954 | 11.91 | 60 | 0.1787 | 0.9733 |
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+ | 0.3954 | 12.91 | 65 | 0.1787 | 0.9733 |
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+ | 0.3954 | 13.91 | 70 | 0.1787 | 0.9733 |
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+ | 0.3954 | 14.91 | 75 | 0.1787 | 0.9733 |
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+ | 0.3926 | 15.91 | 80 | 0.1787 | 0.9733 |
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+ | 0.3926 | 16.91 | 85 | 0.1787 | 0.9733 |
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+ | 0.3926 | 17.91 | 90 | 0.1787 | 0.9733 |
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+ | 0.3926 | 18.91 | 95 | 0.1787 | 0.9733 |
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+ | 0.3801 | 19.91 | 100 | 0.1787 | 0.9733 |
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+ | 0.3801 | 20.91 | 105 | 0.1787 | 0.9733 |
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+ | 0.3801 | 21.91 | 110 | 0.1787 | 0.9733 |
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+ | 0.3801 | 22.91 | 115 | 0.1787 | 0.9733 |
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+ | 0.3815 | 23.91 | 120 | 0.1787 | 0.9733 |
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+ | 0.3815 | 24.91 | 125 | 0.1787 | 0.9733 |
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+ | 0.3815 | 25.91 | 130 | 0.1787 | 0.9733 |
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+ | 0.3815 | 26.91 | 135 | 0.1787 | 0.9733 |
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+ | 0.3955 | 27.91 | 140 | 0.1787 | 0.9733 |
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+ | 0.3955 | 28.91 | 145 | 0.1787 | 0.9733 |
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+ | 0.3955 | 29.91 | 150 | 0.1787 | 0.9733 |
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+ | 0.3955 | 30.91 | 155 | 0.1787 | 0.9733 |
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+ | 0.3854 | 31.91 | 160 | 0.1787 | 0.9733 |
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+ | 0.3854 | 32.91 | 165 | 0.1787 | 0.9733 |
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+ | 0.3854 | 33.91 | 170 | 0.1787 | 0.9733 |
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+ | 0.3854 | 34.91 | 175 | 0.1787 | 0.9733 |
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+ | 0.3949 | 35.91 | 180 | 0.1787 | 0.9733 |
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+ | 0.3949 | 36.91 | 185 | 0.1787 | 0.9733 |
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+ | 0.3949 | 37.91 | 190 | 0.1787 | 0.9733 |
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+ | 0.3949 | 38.91 | 195 | 0.1787 | 0.9733 |
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+ | 0.423 | 39.91 | 200 | 0.1787 | 0.9733 |
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+ | 0.423 | 40.91 | 205 | 0.1787 | 0.9733 |
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+ | 0.423 | 41.91 | 210 | 0.1787 | 0.9733 |
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+ | 0.423 | 42.91 | 215 | 0.1787 | 0.9733 |
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+ | 0.3761 | 43.91 | 220 | 0.1787 | 0.9733 |
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+ | 0.3761 | 44.91 | 225 | 0.1787 | 0.9733 |
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+ | 0.3761 | 45.91 | 230 | 0.1787 | 0.9733 |
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+ | 0.3761 | 46.91 | 235 | 0.1787 | 0.9733 |
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+ | 0.3673 | 47.91 | 240 | 0.1787 | 0.9733 |
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+ | 0.3673 | 48.91 | 245 | 0.1787 | 0.9733 |
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+ | 0.3673 | 49.91 | 250 | 0.1787 | 0.9733 |
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+ | 0.3673 | 50.91 | 255 | 0.1787 | 0.9733 |
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+ | 0.3639 | 51.91 | 260 | 0.1787 | 0.9733 |
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+ | 0.3639 | 52.91 | 265 | 0.1787 | 0.9733 |
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+ | 0.3639 | 53.91 | 270 | 0.1787 | 0.9733 |
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+ | 0.3639 | 54.91 | 275 | 0.1787 | 0.9733 |
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+ | 0.4031 | 55.91 | 280 | 0.1787 | 0.9733 |
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+ | 0.4031 | 56.91 | 285 | 0.1787 | 0.9733 |
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+ | 0.4031 | 57.91 | 290 | 0.1787 | 0.9733 |
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+ | 0.4031 | 58.91 | 295 | 0.1787 | 0.9733 |
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+ | 0.3787 | 59.91 | 300 | 0.1787 | 0.9733 |
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  ### Framework versions