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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
@@ -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: 0.1871
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- - Accuracy: 1.0
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  ## Model description
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@@ -66,51 +66,51 @@ 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 | 0.8 | 3 | 1.8912 | 0.0392 |
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- | No log | 1.8 | 6 | 1.7519 | 0.2745 |
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- | No log | 2.8 | 9 | 1.5549 | 0.4706 |
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- | No log | 3.8 | 12 | 1.2851 | 0.6667 |
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- | No log | 4.8 | 15 | 0.9968 | 0.7647 |
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- | No log | 5.8 | 18 | 0.7826 | 0.7843 |
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- | 1.787 | 6.8 | 21 | 0.6010 | 0.8824 |
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- | 1.787 | 7.8 | 24 | 0.4301 | 0.9020 |
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- | 1.787 | 8.8 | 27 | 0.3233 | 0.8824 |
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- | 1.787 | 9.8 | 30 | 0.2303 | 0.9412 |
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- | 1.787 | 10.8 | 33 | 0.1871 | 1.0 |
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- | 1.787 | 11.8 | 36 | 0.1600 | 0.9608 |
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- | 1.787 | 12.8 | 39 | 0.1334 | 0.9804 |
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- | 0.7554 | 13.8 | 42 | 0.1025 | 1.0 |
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- | 0.7554 | 14.8 | 45 | 0.0909 | 1.0 |
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- | 0.7554 | 15.8 | 48 | 0.0733 | 1.0 |
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- | 0.7554 | 16.8 | 51 | 0.0625 | 1.0 |
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- | 0.7554 | 17.8 | 54 | 0.0602 | 1.0 |
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- | 0.7554 | 18.8 | 57 | 0.0613 | 1.0 |
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- | 0.4731 | 19.8 | 60 | 0.0506 | 1.0 |
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- | 0.4731 | 20.8 | 63 | 0.0588 | 1.0 |
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- | 0.4731 | 21.8 | 66 | 0.0655 | 0.9804 |
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- | 0.4731 | 22.8 | 69 | 0.0517 | 1.0 |
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- | 0.4731 | 23.8 | 72 | 0.0414 | 1.0 |
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- | 0.4731 | 24.8 | 75 | 0.0408 | 1.0 |
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- | 0.4731 | 25.8 | 78 | 0.0417 | 1.0 |
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- | 0.4248 | 26.8 | 81 | 0.0389 | 1.0 |
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- | 0.4248 | 27.8 | 84 | 0.0376 | 1.0 |
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- | 0.4248 | 28.8 | 87 | 0.0361 | 1.0 |
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- | 0.4248 | 29.8 | 90 | 0.0351 | 1.0 |
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- | 0.4248 | 30.8 | 93 | 0.0299 | 1.0 |
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- | 0.4248 | 31.8 | 96 | 0.0284 | 1.0 |
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- | 0.4248 | 32.8 | 99 | 0.0279 | 1.0 |
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- | 0.3657 | 33.8 | 102 | 0.0275 | 1.0 |
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- | 0.3657 | 34.8 | 105 | 0.0279 | 1.0 |
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- | 0.3657 | 35.8 | 108 | 0.0279 | 1.0 |
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- | 0.3657 | 36.8 | 111 | 0.0278 | 1.0 |
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- | 0.3657 | 37.8 | 114 | 0.0276 | 1.0 |
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- | 0.3657 | 38.8 | 117 | 0.0274 | 1.0 |
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- | 0.3115 | 39.8 | 120 | 0.0274 | 1.0 |
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  ### Framework versions
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  - Transformers 4.24.0
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  - Pytorch 1.12.1+cu113
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- - Datasets 2.6.1
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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: 0.9629629629629629
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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: 0.0807
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+ - Accuracy: 0.9630
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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 | 3 | 1.7556 | 0.2037 |
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+ | No log | 1.8 | 6 | 1.5833 | 0.3704 |
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+ | No log | 2.8 | 9 | 1.3483 | 0.5926 |
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+ | No log | 3.8 | 12 | 1.1101 | 0.6667 |
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+ | No log | 4.8 | 15 | 0.9116 | 0.7222 |
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+ | No log | 5.8 | 18 | 0.7632 | 0.7407 |
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+ | 1.7322 | 6.8 | 21 | 0.6118 | 0.7963 |
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+ | 1.7322 | 7.8 | 24 | 0.5017 | 0.8519 |
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+ | 1.7322 | 8.8 | 27 | 0.4241 | 0.8889 |
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+ | 1.7322 | 9.8 | 30 | 0.3522 | 0.8704 |
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+ | 1.7322 | 10.8 | 33 | 0.2918 | 0.9259 |
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+ | 1.7322 | 11.8 | 36 | 0.2659 | 0.9259 |
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+ | 1.7322 | 12.8 | 39 | 0.2587 | 0.9444 |
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+ | 0.7462 | 13.8 | 42 | 0.2063 | 0.9259 |
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+ | 0.7462 | 14.8 | 45 | 0.1870 | 0.9259 |
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+ | 0.7462 | 15.8 | 48 | 0.1739 | 0.9630 |
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+ | 0.7462 | 16.8 | 51 | 0.2043 | 0.9259 |
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+ | 0.7462 | 17.8 | 54 | 0.1897 | 0.9259 |
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+ | 0.7462 | 18.8 | 57 | 0.1764 | 0.9444 |
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+ | 0.4232 | 19.8 | 60 | 0.1587 | 0.9444 |
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+ | 0.4232 | 20.8 | 63 | 0.1556 | 0.9630 |
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+ | 0.4232 | 21.8 | 66 | 0.1516 | 0.9630 |
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+ | 0.4232 | 22.8 | 69 | 0.1264 | 0.9630 |
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+ | 0.4232 | 23.8 | 72 | 0.1180 | 0.9630 |
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+ | 0.4232 | 24.8 | 75 | 0.1110 | 0.9630 |
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+ | 0.4232 | 25.8 | 78 | 0.1232 | 0.9630 |
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+ | 0.3571 | 26.8 | 81 | 0.1169 | 0.9815 |
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+ | 0.3571 | 27.8 | 84 | 0.1051 | 0.9815 |
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+ | 0.3571 | 28.8 | 87 | 0.0986 | 0.9630 |
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+ | 0.3571 | 29.8 | 90 | 0.0937 | 0.9630 |
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+ | 0.3571 | 30.8 | 93 | 0.0931 | 0.9630 |
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+ | 0.3571 | 31.8 | 96 | 0.0932 | 0.9630 |
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+ | 0.3571 | 32.8 | 99 | 0.0941 | 0.9630 |
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+ | 0.3239 | 33.8 | 102 | 0.0920 | 0.9630 |
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+ | 0.3239 | 34.8 | 105 | 0.0851 | 0.9630 |
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+ | 0.3239 | 35.8 | 108 | 0.0828 | 0.9630 |
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+ | 0.3239 | 36.8 | 111 | 0.0810 | 0.9630 |
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+ | 0.3239 | 37.8 | 114 | 0.0801 | 0.9630 |
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+ | 0.3239 | 38.8 | 117 | 0.0804 | 0.9630 |
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+ | 0.3111 | 39.8 | 120 | 0.0807 | 0.9630 |
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  ### Framework versions
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  - Transformers 4.24.0
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  - Pytorch 1.12.1+cu113
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+ - Datasets 2.7.1
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  - Tokenizers 0.13.2