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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
README.md CHANGED
@@ -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: 0.8571428571428571
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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
@@ -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: 0.7036
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- - Accuracy: 0.8571
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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 | 1.9875 | 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
 
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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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  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: 0.0240
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+ - Accuracy: 1.0
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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 | 1.6169 | 0.4286 |
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+ | No log | 2.0 | 2 | 1.5622 | 0.5 |
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+ | No log | 3.0 | 3 | 1.4656 | 0.5714 |
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+ | No log | 4.0 | 4 | 1.3434 | 0.7143 |
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+ | No log | 5.0 | 5 | 1.1958 | 0.8571 |
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+ | No log | 6.0 | 6 | 1.0398 | 0.8571 |
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+ | No log | 7.0 | 7 | 0.8839 | 0.8571 |
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+ | No log | 8.0 | 8 | 0.7458 | 0.8571 |
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+ | No log | 9.0 | 9 | 0.6267 | 0.8571 |
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+ | No log | 10.0 | 10 | 0.5253 | 0.8571 |
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+ | No log | 11.0 | 11 | 0.4414 | 0.8571 |
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+ | No log | 12.0 | 12 | 0.3764 | 0.8571 |
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+ | No log | 13.0 | 13 | 0.3250 | 0.8571 |
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+ | No log | 14.0 | 14 | 0.2810 | 0.8571 |
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+ | No log | 15.0 | 15 | 0.2406 | 0.9286 |
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+ | No log | 16.0 | 16 | 0.2027 | 1.0 |
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+ | No log | 17.0 | 17 | 0.1679 | 1.0 |
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+ | No log | 18.0 | 18 | 0.1376 | 1.0 |
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+ | No log | 19.0 | 19 | 0.1119 | 1.0 |
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+ | 1.0444 | 20.0 | 20 | 0.0910 | 1.0 |
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+ | 1.0444 | 21.0 | 21 | 0.0734 | 1.0 |
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+ | 1.0444 | 22.0 | 22 | 0.0616 | 1.0 |
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+ | 1.0444 | 23.0 | 23 | 0.0536 | 1.0 |
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+ | 1.0444 | 24.0 | 24 | 0.0478 | 1.0 |
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+ | 1.0444 | 25.0 | 25 | 0.0437 | 1.0 |
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+ | 1.0444 | 26.0 | 26 | 0.0414 | 1.0 |
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+ | 1.0444 | 27.0 | 27 | 0.0376 | 1.0 |
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+ | 1.0444 | 28.0 | 28 | 0.0342 | 1.0 |
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+ | 1.0444 | 29.0 | 29 | 0.0313 | 1.0 |
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+ | 1.0444 | 30.0 | 30 | 0.0287 | 1.0 |
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+ | 1.0444 | 31.0 | 31 | 0.0274 | 1.0 |
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+ | 1.0444 | 32.0 | 32 | 0.0267 | 1.0 |
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+ | 1.0444 | 33.0 | 33 | 0.0263 | 1.0 |
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+ | 1.0444 | 34.0 | 34 | 0.0260 | 1.0 |
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+ | 1.0444 | 35.0 | 35 | 0.0258 | 1.0 |
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+ | 1.0444 | 36.0 | 36 | 0.0255 | 1.0 |
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+ | 1.0444 | 37.0 | 37 | 0.0249 | 1.0 |
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+ | 1.0444 | 38.0 | 38 | 0.0246 | 1.0 |
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+ | 1.0444 | 39.0 | 39 | 0.0243 | 1.0 |
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+ | 0.3497 | 40.0 | 40 | 0.0240 | 1.0 |
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  ### Framework versions