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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: 0.9814814814814815
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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: 0.1169
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- - Accuracy: 0.9815
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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 | 0.8 | 3 | 0.1169 | 0.9815 |
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- | No log | 1.8 | 6 | 0.1169 | 0.9815 |
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- | No log | 2.8 | 9 | 0.1169 | 0.9815 |
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- | No log | 3.8 | 12 | 0.1169 | 0.9815 |
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- | No log | 4.8 | 15 | 0.1169 | 0.9815 |
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- | No log | 5.8 | 18 | 0.1169 | 0.9815 |
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- | 0.3448 | 6.8 | 21 | 0.1169 | 0.9815 |
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- | 0.3448 | 7.8 | 24 | 0.1169 | 0.9815 |
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- | 0.3448 | 8.8 | 27 | 0.1169 | 0.9815 |
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- | 0.3448 | 9.8 | 30 | 0.1169 | 0.9815 |
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- | 0.3448 | 10.8 | 33 | 0.1169 | 0.9815 |
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- | 0.3448 | 11.8 | 36 | 0.1169 | 0.9815 |
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- | 0.3448 | 12.8 | 39 | 0.1169 | 0.9815 |
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- | 0.3344 | 13.8 | 42 | 0.1169 | 0.9815 |
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- | 0.3344 | 14.8 | 45 | 0.1169 | 0.9815 |
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- | 0.3344 | 15.8 | 48 | 0.1169 | 0.9815 |
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- | 0.3344 | 16.8 | 51 | 0.1169 | 0.9815 |
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- | 0.3344 | 17.8 | 54 | 0.1169 | 0.9815 |
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- | 0.3344 | 18.8 | 57 | 0.1169 | 0.9815 |
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- | 0.3274 | 19.8 | 60 | 0.1169 | 0.9815 |
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- | 0.3274 | 20.8 | 63 | 0.1169 | 0.9815 |
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- | 0.3274 | 21.8 | 66 | 0.1169 | 0.9815 |
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- | 0.3274 | 22.8 | 69 | 0.1169 | 0.9815 |
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- | 0.3274 | 23.8 | 72 | 0.1169 | 0.9815 |
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- | 0.3274 | 24.8 | 75 | 0.1169 | 0.9815 |
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- | 0.3274 | 25.8 | 78 | 0.1169 | 0.9815 |
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- | 0.3426 | 26.8 | 81 | 0.1169 | 0.9815 |
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- | 0.3426 | 27.8 | 84 | 0.1169 | 0.9815 |
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- | 0.3426 | 28.8 | 87 | 0.1169 | 0.9815 |
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- | 0.3426 | 29.8 | 90 | 0.1169 | 0.9815 |
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- | 0.3426 | 30.8 | 93 | 0.1169 | 0.9815 |
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- | 0.3426 | 31.8 | 96 | 0.1169 | 0.9815 |
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- | 0.3426 | 32.8 | 99 | 0.1169 | 0.9815 |
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- | 0.3436 | 33.8 | 102 | 0.1169 | 0.9815 |
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- | 0.3436 | 34.8 | 105 | 0.1169 | 0.9815 |
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- | 0.3436 | 35.8 | 108 | 0.1169 | 0.9815 |
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- | 0.3436 | 36.8 | 111 | 0.1169 | 0.9815 |
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- | 0.3436 | 37.8 | 114 | 0.1169 | 0.9815 |
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- | 0.3436 | 38.8 | 117 | 0.1169 | 0.9815 |
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- | 0.3243 | 39.8 | 120 | 0.1169 | 0.9815 |
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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: 0.9259259259259259
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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: 0.2315
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+ - Accuracy: 0.9259
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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 | 1.8673 | 0.2222 |
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+ | No log | 1.8 | 6 | 1.7421 | 0.2593 |
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+ | No log | 2.8 | 9 | 1.5910 | 0.4259 |
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+ | No log | 3.8 | 12 | 1.4371 | 0.5 |
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+ | No log | 4.8 | 15 | 1.2871 | 0.5741 |
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+ | No log | 5.8 | 18 | 1.1511 | 0.5741 |
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+ | 1.8164 | 6.8 | 21 | 0.9363 | 0.7222 |
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+ | 1.8164 | 7.8 | 24 | 0.7903 | 0.7778 |
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+ | 1.8164 | 8.8 | 27 | 0.6839 | 0.7593 |
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+ | 1.8164 | 9.8 | 30 | 0.5661 | 0.7778 |
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+ | 1.8164 | 10.8 | 33 | 0.4638 | 0.8519 |
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+ | 1.8164 | 11.8 | 36 | 0.4015 | 0.8704 |
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+ | 1.8164 | 12.8 | 39 | 0.3809 | 0.8704 |
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+ | 0.8525 | 13.8 | 42 | 0.3214 | 0.9074 |
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+ | 0.8525 | 14.8 | 45 | 0.3114 | 0.8704 |
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+ | 0.8525 | 15.8 | 48 | 0.3026 | 0.8889 |
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+ | 0.8525 | 16.8 | 51 | 0.2970 | 0.8889 |
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+ | 0.8525 | 17.8 | 54 | 0.2597 | 0.8889 |
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+ | 0.8525 | 18.8 | 57 | 0.2792 | 0.8889 |
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+ | 0.4831 | 19.8 | 60 | 0.3209 | 0.8704 |
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+ | 0.4831 | 20.8 | 63 | 0.2929 | 0.9074 |
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+ | 0.4831 | 21.8 | 66 | 0.2419 | 0.9259 |
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+ | 0.4831 | 22.8 | 69 | 0.2496 | 0.9074 |
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+ | 0.4831 | 23.8 | 72 | 0.2953 | 0.9074 |
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+ | 0.4831 | 24.8 | 75 | 0.3094 | 0.8889 |
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+ | 0.4831 | 25.8 | 78 | 0.2792 | 0.9259 |
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+ | 0.3889 | 26.8 | 81 | 0.2522 | 0.9259 |
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+ | 0.3889 | 27.8 | 84 | 0.2451 | 0.9259 |
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+ | 0.3889 | 28.8 | 87 | 0.2541 | 0.9074 |
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+ | 0.3889 | 29.8 | 90 | 0.2718 | 0.9074 |
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+ | 0.3889 | 30.8 | 93 | 0.2738 | 0.9074 |
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+ | 0.3889 | 31.8 | 96 | 0.2639 | 0.9259 |
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+ | 0.3889 | 32.8 | 99 | 0.2561 | 0.9259 |
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+ | 0.3407 | 33.8 | 102 | 0.2497 | 0.9259 |
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+ | 0.3407 | 34.8 | 105 | 0.2501 | 0.9259 |
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+ | 0.3407 | 35.8 | 108 | 0.2455 | 0.9259 |
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+ | 0.3407 | 36.8 | 111 | 0.2381 | 0.9259 |
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+ | 0.3407 | 37.8 | 114 | 0.2340 | 0.9259 |
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+ | 0.3407 | 38.8 | 117 | 0.2321 | 0.9259 |
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+ | 0.3112 | 39.8 | 120 | 0.2315 | 0.9259 |
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  ### Framework versions