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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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  1. README.md +43 -43
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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: 0.5916
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- - Accuracy: 1.0
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  ## Model description
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@@ -66,46 +66,46 @@ 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 | 0.67 | 1 | 1.8688 | 0.1818 |
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- | No log | 1.67 | 2 | 1.7920 | 0.1818 |
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- | No log | 2.67 | 3 | 1.6533 | 0.3636 |
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- | No log | 3.67 | 4 | 1.4775 | 0.4545 |
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- | No log | 4.67 | 5 | 1.2912 | 0.5909 |
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- | No log | 5.67 | 6 | 1.1475 | 0.7273 |
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- | No log | 6.67 | 7 | 1.0266 | 0.7727 |
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- | No log | 7.67 | 8 | 0.9196 | 0.7727 |
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- | No log | 8.67 | 9 | 0.8273 | 0.8182 |
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- | No log | 9.67 | 10 | 0.7492 | 0.8182 |
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- | No log | 10.67 | 11 | 0.6857 | 0.9091 |
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- | No log | 11.67 | 12 | 0.6369 | 0.9091 |
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- | No log | 12.67 | 13 | 0.5916 | 1.0 |
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- | No log | 13.67 | 14 | 0.5462 | 1.0 |
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- | No log | 14.67 | 15 | 0.4927 | 1.0 |
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- | No log | 15.67 | 16 | 0.4390 | 1.0 |
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- | No log | 16.67 | 17 | 0.3914 | 1.0 |
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- | No log | 17.67 | 18 | 0.3446 | 1.0 |
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- | No log | 18.67 | 19 | 0.3019 | 1.0 |
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- | 1.7058 | 19.67 | 20 | 0.2611 | 1.0 |
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- | 1.7058 | 20.67 | 21 | 0.2289 | 1.0 |
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- | 1.7058 | 21.67 | 22 | 0.1960 | 1.0 |
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- | 1.7058 | 22.67 | 23 | 0.1711 | 1.0 |
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- | 1.7058 | 23.67 | 24 | 0.1568 | 1.0 |
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- | 1.7058 | 24.67 | 25 | 0.1463 | 1.0 |
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- | 1.7058 | 25.67 | 26 | 0.1383 | 1.0 |
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- | 1.7058 | 26.67 | 27 | 0.1323 | 1.0 |
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- | 1.7058 | 27.67 | 28 | 0.1268 | 1.0 |
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- | 1.7058 | 28.67 | 29 | 0.1199 | 1.0 |
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- | 1.7058 | 29.67 | 30 | 0.1145 | 1.0 |
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- | 1.7058 | 30.67 | 31 | 0.1129 | 1.0 |
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- | 1.7058 | 31.67 | 32 | 0.1095 | 1.0 |
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- | 1.7058 | 32.67 | 33 | 0.1079 | 1.0 |
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- | 1.7058 | 33.67 | 34 | 0.1053 | 1.0 |
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- | 1.7058 | 34.67 | 35 | 0.1034 | 1.0 |
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- | 1.7058 | 35.67 | 36 | 0.0990 | 1.0 |
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- | 1.7058 | 36.67 | 37 | 0.0963 | 1.0 |
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- | 1.7058 | 37.67 | 38 | 0.0952 | 1.0 |
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- | 1.7058 | 38.67 | 39 | 0.0944 | 1.0 |
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- | 0.6083 | 39.67 | 40 | 0.0942 | 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: 0.8636363636363636
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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: 0.2636
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+ - Accuracy: 0.8636
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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 | 0.57 | 1 | 1.9638 | 0.1364 |
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+ | No log | 1.57 | 2 | 1.9022 | 0.0909 |
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+ | No log | 2.57 | 3 | 1.7954 | 0.0909 |
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+ | No log | 3.57 | 4 | 1.6466 | 0.3636 |
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+ | No log | 4.57 | 5 | 1.5161 | 0.5 |
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+ | No log | 5.57 | 6 | 1.4261 | 0.5455 |
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+ | No log | 6.57 | 7 | 1.3547 | 0.5455 |
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+ | No log | 7.57 | 8 | 1.2798 | 0.6364 |
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+ | No log | 8.57 | 9 | 1.2200 | 0.6364 |
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+ | No log | 9.57 | 10 | 1.1594 | 0.6364 |
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+ | No log | 10.57 | 11 | 1.1154 | 0.6818 |
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+ | No log | 11.57 | 12 | 1.0781 | 0.6818 |
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+ | No log | 12.57 | 13 | 1.0286 | 0.6818 |
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+ | No log | 13.57 | 14 | 0.9623 | 0.6818 |
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+ | No log | 14.57 | 15 | 0.8952 | 0.6818 |
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+ | No log | 15.57 | 16 | 0.8218 | 0.7273 |
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+ | No log | 16.57 | 17 | 0.7331 | 0.7727 |
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+ | No log | 17.57 | 18 | 0.6525 | 0.8182 |
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+ | No log | 18.57 | 19 | 0.5678 | 0.8636 |
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+ | 1.9399 | 19.57 | 20 | 0.4980 | 0.8636 |
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+ | 1.9399 | 20.57 | 21 | 0.4614 | 0.9091 |
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+ | 1.9399 | 21.57 | 22 | 0.4494 | 0.9091 |
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+ | 1.9399 | 22.57 | 23 | 0.4405 | 0.8182 |
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+ | 1.9399 | 23.57 | 24 | 0.4358 | 0.8636 |
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+ | 1.9399 | 24.57 | 25 | 0.4307 | 0.8636 |
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+ | 1.9399 | 25.57 | 26 | 0.4100 | 0.8636 |
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+ | 1.9399 | 26.57 | 27 | 0.3926 | 0.8182 |
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+ | 1.9399 | 27.57 | 28 | 0.3818 | 0.8182 |
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+ | 1.9399 | 28.57 | 29 | 0.3661 | 0.8182 |
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+ | 1.9399 | 29.57 | 30 | 0.3515 | 0.8636 |
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+ | 1.9399 | 30.57 | 31 | 0.3345 | 0.8636 |
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+ | 1.9399 | 31.57 | 32 | 0.3204 | 0.8636 |
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+ | 1.9399 | 32.57 | 33 | 0.3078 | 0.8636 |
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+ | 1.9399 | 33.57 | 34 | 0.2948 | 0.8636 |
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+ | 1.9399 | 34.57 | 35 | 0.2848 | 0.8636 |
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+ | 1.9399 | 35.57 | 36 | 0.2748 | 0.8636 |
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+ | 1.9399 | 36.57 | 37 | 0.2679 | 0.8636 |
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+ | 1.9399 | 37.57 | 38 | 0.2642 | 0.8636 |
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+ | 1.9399 | 38.57 | 39 | 0.2639 | 0.8636 |
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+ | 0.728 | 39.57 | 40 | 0.2636 | 0.8636 |
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  ### Framework versions