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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: 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
@@ -29,10 +29,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # delivery_truck_classification
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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: 0.2180
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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 | 0.57 | 1 | 1.7779 | 0.2727 |
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- | No log | 1.57 | 2 | 1.7088 | 0.3182 |
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- | No log | 2.57 | 3 | 1.5921 | 0.5455 |
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- | No log | 3.57 | 4 | 1.4587 | 0.5909 |
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- | No log | 4.57 | 5 | 1.3256 | 0.5455 |
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- | No log | 5.57 | 6 | 1.2211 | 0.5 |
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- | No log | 6.57 | 7 | 1.1066 | 0.6818 |
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- | No log | 7.57 | 8 | 0.9768 | 0.7727 |
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- | No log | 8.57 | 9 | 0.8590 | 0.8636 |
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- | No log | 9.57 | 10 | 0.7718 | 0.9091 |
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- | No log | 10.57 | 11 | 0.6999 | 0.9091 |
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- | No log | 11.57 | 12 | 0.6385 | 0.9091 |
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- | No log | 12.57 | 13 | 0.5761 | 0.9545 |
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- | No log | 13.57 | 14 | 0.5189 | 0.9545 |
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- | No log | 14.57 | 15 | 0.4646 | 0.9545 |
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- | No log | 15.57 | 16 | 0.4137 | 0.9091 |
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- | No log | 16.57 | 17 | 0.3679 | 0.9091 |
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- | No log | 17.57 | 18 | 0.3291 | 0.9091 |
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- | No log | 18.57 | 19 | 0.2937 | 0.9545 |
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- | 1.8863 | 19.57 | 20 | 0.2642 | 0.9545 |
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- | 1.8863 | 20.57 | 21 | 0.2366 | 0.9545 |
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- | 1.8863 | 21.57 | 22 | 0.2180 | 1.0 |
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- | 1.8863 | 22.57 | 23 | 0.2061 | 1.0 |
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- | 1.8863 | 23.57 | 24 | 0.1984 | 1.0 |
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- | 1.8863 | 24.57 | 25 | 0.1918 | 1.0 |
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- | 1.8863 | 25.57 | 26 | 0.1787 | 1.0 |
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- | 1.8863 | 26.57 | 27 | 0.1605 | 1.0 |
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- | 1.8863 | 27.57 | 28 | 0.1412 | 1.0 |
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- | 1.8863 | 28.57 | 29 | 0.1269 | 1.0 |
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- | 1.8863 | 29.57 | 30 | 0.1142 | 1.0 |
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- | 1.8863 | 30.57 | 31 | 0.1051 | 1.0 |
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- | 1.8863 | 31.57 | 32 | 0.0995 | 1.0 |
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- | 1.8863 | 32.57 | 33 | 0.0946 | 1.0 |
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- | 1.8863 | 33.57 | 34 | 0.0911 | 1.0 |
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- | 1.8863 | 34.57 | 35 | 0.0892 | 1.0 |
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- | 1.8863 | 35.57 | 36 | 0.0876 | 1.0 |
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- | 1.8863 | 36.57 | 37 | 0.0865 | 1.0 |
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- | 1.8863 | 37.57 | 38 | 0.0857 | 1.0 |
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- | 1.8863 | 38.57 | 39 | 0.0854 | 1.0 |
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- | 0.6775 | 39.57 | 40 | 0.0853 | 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: 0.9714285714285714
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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
 
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  # delivery_truck_classification
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+ This model is a fine-tuned version of [JEdward7777/delivery_truck_classification](https://huggingface.co/JEdward7777/delivery_truck_classification) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2074
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+ - Accuracy: 0.9714
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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 | 0.8 | 2 | 0.1919 | 0.9429 |
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+ | No log | 1.8 | 4 | 0.1383 | 0.9714 |
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+ | No log | 2.8 | 6 | 0.1930 | 0.9143 |
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+ | No log | 3.8 | 8 | 0.1463 | 0.9714 |
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+ | No log | 4.8 | 10 | 0.1735 | 0.9714 |
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+ | No log | 5.8 | 12 | 0.1692 | 0.9714 |
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+ | No log | 6.8 | 14 | 0.1626 | 0.9714 |
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+ | No log | 7.8 | 16 | 0.1659 | 0.9714 |
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+ | No log | 8.8 | 18 | 0.1622 | 0.9714 |
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+ | 0.2046 | 9.8 | 20 | 0.1598 | 0.9714 |
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+ | 0.2046 | 10.8 | 22 | 0.1668 | 0.9714 |
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+ | 0.2046 | 11.8 | 24 | 0.1747 | 0.9714 |
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+ | 0.2046 | 12.8 | 26 | 0.1804 | 0.9714 |
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+ | 0.2046 | 13.8 | 28 | 0.1837 | 0.9714 |
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+ | 0.2046 | 14.8 | 30 | 0.1837 | 0.9714 |
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+ | 0.2046 | 15.8 | 32 | 0.1811 | 0.9714 |
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+ | 0.2046 | 16.8 | 34 | 0.1801 | 0.9714 |
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+ | 0.2046 | 17.8 | 36 | 0.1841 | 0.9714 |
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+ | 0.2046 | 18.8 | 38 | 0.1899 | 0.9714 |
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+ | 0.1657 | 19.8 | 40 | 0.1960 | 0.9714 |
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+ | 0.1657 | 20.8 | 42 | 0.1993 | 0.9714 |
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+ | 0.1657 | 21.8 | 44 | 0.2017 | 0.9714 |
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+ | 0.1657 | 22.8 | 46 | 0.2004 | 0.9714 |
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+ | 0.1657 | 23.8 | 48 | 0.1922 | 0.9714 |
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+ | 0.1657 | 24.8 | 50 | 0.1856 | 0.9714 |
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+ | 0.1657 | 25.8 | 52 | 0.1834 | 0.9714 |
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+ | 0.1657 | 26.8 | 54 | 0.1846 | 0.9714 |
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+ | 0.1657 | 27.8 | 56 | 0.1898 | 0.9714 |
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+ | 0.1657 | 28.8 | 58 | 0.1951 | 0.9714 |
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+ | 0.1308 | 29.8 | 60 | 0.2019 | 0.9714 |
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+ | 0.1308 | 30.8 | 62 | 0.2095 | 0.9714 |
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+ | 0.1308 | 31.8 | 64 | 0.2145 | 0.9714 |
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+ | 0.1308 | 32.8 | 66 | 0.2154 | 0.9714 |
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+ | 0.1308 | 33.8 | 68 | 0.2137 | 0.9714 |
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+ | 0.1308 | 34.8 | 70 | 0.2116 | 0.9714 |
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+ | 0.1308 | 35.8 | 72 | 0.2096 | 0.9714 |
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+ | 0.1308 | 36.8 | 74 | 0.2084 | 0.9714 |
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+ | 0.1308 | 37.8 | 76 | 0.2078 | 0.9714 |
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+ | 0.1308 | 38.8 | 78 | 0.2075 | 0.9714 |
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+ | 0.1053 | 39.8 | 80 | 0.2074 | 0.9714 |
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  ### Framework versions