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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: 0.9491525423728814
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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: 0.1790
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- - Accuracy: 0.9492
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  ## Model description
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@@ -60,52 +60,72 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 40
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 0.94 | 4 | 1.8882 | 0.1186 |
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- | No log | 1.94 | 8 | 1.6799 | 0.3559 |
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- | No log | 2.94 | 12 | 1.4260 | 0.5763 |
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- | No log | 3.94 | 16 | 1.1092 | 0.6780 |
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- | 1.7242 | 4.94 | 20 | 0.8653 | 0.7458 |
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- | 1.7242 | 5.94 | 24 | 0.6787 | 0.7797 |
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- | 1.7242 | 6.94 | 28 | 0.5506 | 0.8305 |
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- | 1.7242 | 7.94 | 32 | 0.4174 | 0.8814 |
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- | 1.7242 | 8.94 | 36 | 0.3643 | 0.8814 |
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- | 0.8337 | 9.94 | 40 | 0.2680 | 0.9322 |
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- | 0.8337 | 10.94 | 44 | 0.2705 | 0.8983 |
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- | 0.8337 | 11.94 | 48 | 0.2270 | 0.9153 |
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- | 0.8337 | 12.94 | 52 | 0.1790 | 0.9492 |
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- | 0.8337 | 13.94 | 56 | 0.1694 | 0.9322 |
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- | 0.493 | 14.94 | 60 | 0.1776 | 0.9153 |
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- | 0.493 | 15.94 | 64 | 0.1831 | 0.9322 |
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- | 0.493 | 16.94 | 68 | 0.1765 | 0.9322 |
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- | 0.493 | 17.94 | 72 | 0.1575 | 0.9322 |
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- | 0.493 | 18.94 | 76 | 0.1472 | 0.9322 |
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- | 0.3966 | 19.94 | 80 | 0.1360 | 0.9322 |
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- | 0.3966 | 20.94 | 84 | 0.1448 | 0.9492 |
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- | 0.3966 | 21.94 | 88 | 0.1658 | 0.9322 |
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- | 0.3966 | 22.94 | 92 | 0.1652 | 0.9322 |
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- | 0.3966 | 23.94 | 96 | 0.1565 | 0.9322 |
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- | 0.3645 | 24.94 | 100 | 0.1701 | 0.9322 |
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- | 0.3645 | 25.94 | 104 | 0.1830 | 0.9322 |
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- | 0.3645 | 26.94 | 108 | 0.1682 | 0.9322 |
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- | 0.3645 | 27.94 | 112 | 0.1410 | 0.9492 |
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- | 0.3645 | 28.94 | 116 | 0.1291 | 0.9492 |
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- | 0.3358 | 29.94 | 120 | 0.1248 | 0.9492 |
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- | 0.3358 | 30.94 | 124 | 0.1275 | 0.9492 |
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- | 0.3358 | 31.94 | 128 | 0.1257 | 0.9492 |
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- | 0.3358 | 32.94 | 132 | 0.1288 | 0.9492 |
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- | 0.3358 | 33.94 | 136 | 0.1246 | 0.9492 |
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- | 0.3049 | 34.94 | 140 | 0.1219 | 0.9492 |
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- | 0.3049 | 35.94 | 144 | 0.1224 | 0.9492 |
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- | 0.3049 | 36.94 | 148 | 0.1246 | 0.9492 |
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- | 0.3049 | 37.94 | 152 | 0.1243 | 0.9492 |
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- | 0.3049 | 38.94 | 156 | 0.1248 | 0.9492 |
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- | 0.2962 | 39.94 | 160 | 0.1253 | 0.9492 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.9830508474576272
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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: 0.0375
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+ - Accuracy: 0.9831
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 60
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.94 | 4 | 1.9124 | 0.1864 |
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+ | No log | 1.94 | 8 | 1.8095 | 0.2373 |
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+ | No log | 2.94 | 12 | 1.6757 | 0.3898 |
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+ | No log | 3.94 | 16 | 1.4906 | 0.5254 |
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+ | 1.8286 | 4.94 | 20 | 1.2704 | 0.6441 |
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+ | 1.8286 | 5.94 | 24 | 1.0685 | 0.6780 |
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+ | 1.8286 | 6.94 | 28 | 0.8032 | 0.7458 |
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+ | 1.8286 | 7.94 | 32 | 0.6309 | 0.7627 |
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+ | 1.8286 | 8.94 | 36 | 0.4989 | 0.8475 |
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+ | 0.9342 | 9.94 | 40 | 0.4063 | 0.8475 |
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+ | 0.9342 | 10.94 | 44 | 0.2692 | 0.9153 |
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+ | 0.9342 | 11.94 | 48 | 0.2736 | 0.8983 |
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+ | 0.9342 | 12.94 | 52 | 0.2116 | 0.9322 |
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+ | 0.9342 | 13.94 | 56 | 0.1498 | 0.9831 |
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+ | 0.5151 | 14.94 | 60 | 0.1906 | 0.9153 |
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+ | 0.5151 | 15.94 | 64 | 0.1698 | 0.9492 |
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+ | 0.5151 | 16.94 | 68 | 0.1432 | 0.9492 |
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+ | 0.5151 | 17.94 | 72 | 0.1682 | 0.9322 |
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+ | 0.5151 | 18.94 | 76 | 0.1069 | 0.9831 |
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+ | 0.4009 | 19.94 | 80 | 0.0821 | 0.9831 |
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+ | 0.4009 | 20.94 | 84 | 0.0903 | 0.9831 |
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+ | 0.4009 | 21.94 | 88 | 0.1281 | 0.9661 |
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+ | 0.4009 | 22.94 | 92 | 0.0936 | 0.9831 |
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+ | 0.4009 | 23.94 | 96 | 0.1059 | 0.9661 |
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+ | 0.3482 | 24.94 | 100 | 0.1431 | 0.9492 |
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+ | 0.3482 | 25.94 | 104 | 0.0899 | 0.9661 |
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+ | 0.3482 | 26.94 | 108 | 0.0689 | 0.9661 |
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+ | 0.3482 | 27.94 | 112 | 0.0751 | 0.9661 |
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+ | 0.3482 | 28.94 | 116 | 0.0891 | 0.9661 |
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+ | 0.3306 | 29.94 | 120 | 0.0523 | 0.9831 |
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+ | 0.3306 | 30.94 | 124 | 0.0734 | 0.9831 |
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+ | 0.3306 | 31.94 | 128 | 0.0746 | 0.9831 |
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+ | 0.3306 | 32.94 | 132 | 0.0474 | 0.9661 |
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+ | 0.3306 | 33.94 | 136 | 0.0443 | 0.9831 |
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+ | 0.2871 | 34.94 | 140 | 0.0814 | 0.9831 |
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+ | 0.2871 | 35.94 | 144 | 0.0691 | 0.9831 |
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+ | 0.2871 | 36.94 | 148 | 0.0531 | 0.9831 |
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+ | 0.2871 | 37.94 | 152 | 0.0614 | 0.9831 |
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+ | 0.2871 | 38.94 | 156 | 0.0578 | 0.9831 |
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+ | 0.2754 | 39.94 | 160 | 0.0520 | 0.9831 |
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+ | 0.2754 | 40.94 | 164 | 0.0537 | 0.9831 |
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+ | 0.2754 | 41.94 | 168 | 0.0447 | 0.9831 |
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+ | 0.2754 | 42.94 | 172 | 0.0290 | 1.0 |
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+ | 0.2754 | 43.94 | 176 | 0.0291 | 1.0 |
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+ | 0.269 | 44.94 | 180 | 0.0326 | 0.9831 |
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+ | 0.269 | 45.94 | 184 | 0.0330 | 0.9831 |
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+ | 0.269 | 46.94 | 188 | 0.0348 | 0.9831 |
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+ | 0.269 | 47.94 | 192 | 0.0347 | 0.9831 |
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+ | 0.269 | 48.94 | 196 | 0.0347 | 0.9831 |
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+ | 0.2615 | 49.94 | 200 | 0.0424 | 0.9831 |
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+ | 0.2615 | 50.94 | 204 | 0.0451 | 0.9831 |
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+ | 0.2615 | 51.94 | 208 | 0.0433 | 0.9831 |
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+ | 0.2615 | 52.94 | 212 | 0.0352 | 0.9831 |
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+ | 0.2615 | 53.94 | 216 | 0.0339 | 0.9831 |
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+ | 0.2386 | 54.94 | 220 | 0.0339 | 0.9831 |
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+ | 0.2386 | 55.94 | 224 | 0.0339 | 0.9831 |
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+ | 0.2386 | 56.94 | 228 | 0.0348 | 0.9831 |
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+ | 0.2386 | 57.94 | 232 | 0.0366 | 0.9831 |
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+ | 0.2386 | 58.94 | 236 | 0.0374 | 0.9831 |
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+ | 0.2362 | 59.94 | 240 | 0.0375 | 0.9831 |
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  ### Framework versions