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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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@@ -7,7 +7,7 @@ datasets:
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  metrics:
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  - accuracy
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  model-index:
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- - name: swin-tiny-patch4-window7-224-finetuned-eurosat
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  results:
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  - task:
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  name: Image Classification
@@ -21,18 +21,18 @@ 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
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  should probably proofread and complete it, then remove this comment. -->
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- # swin-tiny-patch4-window7-224-finetuned-eurosat
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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: 0.0600
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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 | 1.0 | 1 | 1.4318 | 0.8 |
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- | No log | 2.0 | 2 | 1.3863 | 0.8 |
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- | No log | 3.0 | 3 | 1.2880 | 0.8 |
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- | No log | 4.0 | 4 | 1.1589 | 0.8 |
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- | No log | 5.0 | 5 | 0.9954 | 0.8 |
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- | No log | 6.0 | 6 | 0.8942 | 0.8 |
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- | No log | 7.0 | 7 | 0.8269 | 0.8 |
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- | No log | 8.0 | 8 | 0.7702 | 0.8 |
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- | No log | 9.0 | 9 | 0.7138 | 1.0 |
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- | No log | 10.0 | 10 | 0.6602 | 1.0 |
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- | No log | 11.0 | 11 | 0.6255 | 1.0 |
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- | No log | 12.0 | 12 | 0.5900 | 1.0 |
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- | No log | 13.0 | 13 | 0.5367 | 1.0 |
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- | No log | 14.0 | 14 | 0.4790 | 1.0 |
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- | No log | 15.0 | 15 | 0.4158 | 1.0 |
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- | No log | 16.0 | 16 | 0.3573 | 1.0 |
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- | No log | 17.0 | 17 | 0.2964 | 1.0 |
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- | No log | 18.0 | 18 | 0.2439 | 1.0 |
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- | No log | 19.0 | 19 | 0.2028 | 1.0 |
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- | 0.5248 | 20.0 | 20 | 0.1671 | 1.0 |
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- | 0.5248 | 21.0 | 21 | 0.1399 | 1.0 |
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- | 0.5248 | 22.0 | 22 | 0.1182 | 1.0 |
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- | 0.5248 | 23.0 | 23 | 0.1013 | 1.0 |
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- | 0.5248 | 24.0 | 24 | 0.0897 | 1.0 |
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- | 0.5248 | 25.0 | 25 | 0.0824 | 1.0 |
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- | 0.5248 | 26.0 | 26 | 0.0769 | 1.0 |
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- | 0.5248 | 27.0 | 27 | 0.0721 | 1.0 |
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- | 0.5248 | 28.0 | 28 | 0.0701 | 1.0 |
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- | 0.5248 | 29.0 | 29 | 0.0697 | 1.0 |
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- | 0.5248 | 30.0 | 30 | 0.0693 | 1.0 |
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- | 0.5248 | 31.0 | 31 | 0.0672 | 1.0 |
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- | 0.5248 | 32.0 | 32 | 0.0646 | 1.0 |
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- | 0.5248 | 33.0 | 33 | 0.0633 | 1.0 |
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- | 0.5248 | 34.0 | 34 | 0.0628 | 1.0 |
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- | 0.5248 | 35.0 | 35 | 0.0626 | 1.0 |
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- | 0.5248 | 36.0 | 36 | 0.0626 | 1.0 |
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- | 0.5248 | 37.0 | 37 | 0.0617 | 1.0 |
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- | 0.5248 | 38.0 | 38 | 0.0608 | 1.0 |
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- | 0.5248 | 39.0 | 39 | 0.0603 | 1.0 |
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- | 0.2241 | 40.0 | 40 | 0.0600 | 1.0 |
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  ### Framework versions
 
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  metrics:
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  - accuracy
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  model-index:
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+ - name: delivery_truck_classification
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  results:
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  - task:
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  name: Image Classification
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8571428571428571
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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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  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: 0.6936
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+ - Accuracy: 0.8571
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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 | 1.0 | 1 | 1.9875 | 0.1429 |
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+ | No log | 2.0 | 2 | 1.9132 | 0.1429 |
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+ | No log | 3.0 | 3 | 1.7585 | 0.4286 |
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+ | No log | 4.0 | 4 | 1.5935 | 0.4286 |
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+ | No log | 5.0 | 5 | 1.5026 | 0.4286 |
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+ | No log | 6.0 | 6 | 1.4699 | 0.4286 |
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+ | No log | 7.0 | 7 | 1.4361 | 0.4286 |
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+ | No log | 8.0 | 8 | 1.3962 | 0.4286 |
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+ | No log | 9.0 | 9 | 1.3457 | 0.4286 |
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+ | No log | 10.0 | 10 | 1.2874 | 0.4286 |
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+ | No log | 11.0 | 11 | 1.2240 | 0.4286 |
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+ | No log | 12.0 | 12 | 1.1643 | 0.4286 |
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+ | No log | 13.0 | 13 | 1.1016 | 0.5714 |
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+ | No log | 14.0 | 14 | 1.0356 | 0.5714 |
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+ | No log | 15.0 | 15 | 0.9719 | 0.7143 |
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+ | No log | 16.0 | 16 | 0.9120 | 0.7143 |
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+ | No log | 17.0 | 17 | 0.8606 | 0.7143 |
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+ | No log | 18.0 | 18 | 0.8117 | 0.7143 |
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+ | No log | 19.0 | 19 | 0.7707 | 0.7143 |
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+ | 0.5111 | 20.0 | 20 | 0.7367 | 0.7143 |
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+ | 0.5111 | 21.0 | 21 | 0.7157 | 0.7143 |
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+ | 0.5111 | 22.0 | 22 | 0.7067 | 0.7143 |
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+ | 0.5111 | 23.0 | 23 | 0.7012 | 0.7143 |
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+ | 0.5111 | 24.0 | 24 | 0.6977 | 0.7143 |
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+ | 0.5111 | 25.0 | 25 | 0.6974 | 0.7143 |
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+ | 0.5111 | 26.0 | 26 | 0.6977 | 0.7143 |
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+ | 0.5111 | 27.0 | 27 | 0.7036 | 0.8571 |
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+ | 0.5111 | 28.0 | 28 | 0.7074 | 0.8571 |
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+ | 0.5111 | 29.0 | 29 | 0.7062 | 0.8571 |
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+ | 0.5111 | 30.0 | 30 | 0.7056 | 0.8571 |
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+ | 0.5111 | 31.0 | 31 | 0.7050 | 0.8571 |
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+ | 0.5111 | 32.0 | 32 | 0.7050 | 0.8571 |
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+ | 0.5111 | 33.0 | 33 | 0.7031 | 0.8571 |
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+ | 0.5111 | 34.0 | 34 | 0.7016 | 0.8571 |
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+ | 0.5111 | 35.0 | 35 | 0.6996 | 0.8571 |
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+ | 0.5111 | 36.0 | 36 | 0.6971 | 0.8571 |
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+ | 0.5111 | 37.0 | 37 | 0.6953 | 0.8571 |
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+ | 0.5111 | 38.0 | 38 | 0.6939 | 0.8571 |
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+ | 0.5111 | 39.0 | 39 | 0.6938 | 0.8571 |
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+ | 0.1719 | 40.0 | 40 | 0.6936 | 0.8571 |
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  ### Framework versions