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swin-tiny-patch4-window7-224-finetuned-eurosat

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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: 0.42857142857142855
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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: 0.9799
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- - Accuracy: 0.4286
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  ## Model description
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@@ -60,15 +60,102 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 1 | 1.0887 | 0.1429 |
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- | No log | 2.0 | 2 | 0.9799 | 0.4286 |
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- | No log | 3.0 | 3 | 0.9799 | 0.4286 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.5714285714285714
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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: 1.2931
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+ - Accuracy: 0.5714
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  ## Model description
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 90
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 1 | 0.9799 | 0.4286 |
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+ | No log | 2.0 | 2 | 0.9703 | 0.4286 |
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+ | No log | 3.0 | 3 | 0.9703 | 0.4286 |
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+ | No log | 4.0 | 4 | 0.9699 | 0.4286 |
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+ | No log | 5.0 | 5 | 0.9699 | 0.4286 |
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+ | No log | 6.0 | 6 | 0.9881 | 0.4286 |
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+ | No log | 7.0 | 7 | 0.9881 | 0.4286 |
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+ | No log | 8.0 | 8 | 1.0213 | 0.4286 |
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+ | No log | 9.0 | 9 | 1.0213 | 0.4286 |
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+ | 0.426 | 10.0 | 10 | 1.0291 | 0.5714 |
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+ | 0.426 | 11.0 | 11 | 1.0291 | 0.5714 |
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+ | 0.426 | 12.0 | 12 | 0.9996 | 0.5714 |
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+ | 0.426 | 13.0 | 13 | 0.9996 | 0.5714 |
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+ | 0.426 | 14.0 | 14 | 0.8998 | 0.5714 |
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+ | 0.426 | 15.0 | 15 | 0.8998 | 0.5714 |
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+ | 0.426 | 16.0 | 16 | 0.8356 | 0.5714 |
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+ | 0.426 | 17.0 | 17 | 0.8356 | 0.5714 |
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+ | 0.426 | 18.0 | 18 | 0.8575 | 0.5714 |
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+ | 0.426 | 19.0 | 19 | 0.8575 | 0.5714 |
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+ | 0.324 | 20.0 | 20 | 0.9310 | 0.4286 |
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+ | 0.324 | 21.0 | 21 | 0.9310 | 0.4286 |
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+ | 0.324 | 22.0 | 22 | 1.0029 | 0.4286 |
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+ | 0.324 | 23.0 | 23 | 1.0029 | 0.4286 |
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+ | 0.324 | 24.0 | 24 | 1.0582 | 0.4286 |
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+ | 0.324 | 25.0 | 25 | 1.0582 | 0.4286 |
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+ | 0.324 | 26.0 | 26 | 1.0812 | 0.4286 |
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+ | 0.324 | 27.0 | 27 | 1.0812 | 0.4286 |
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+ | 0.324 | 28.0 | 28 | 1.0345 | 0.4286 |
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+ | 0.324 | 29.0 | 29 | 1.0345 | 0.4286 |
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+ | 0.2536 | 30.0 | 30 | 0.9996 | 0.4286 |
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+ | 0.2536 | 31.0 | 31 | 0.9996 | 0.4286 |
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+ | 0.2536 | 32.0 | 32 | 0.9401 | 0.5714 |
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+ | 0.2536 | 33.0 | 33 | 0.9401 | 0.5714 |
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+ | 0.2536 | 34.0 | 34 | 0.8978 | 0.5714 |
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+ | 0.2536 | 35.0 | 35 | 0.8978 | 0.5714 |
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+ | 0.2536 | 36.0 | 36 | 0.9056 | 0.5714 |
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+ | 0.2536 | 37.0 | 37 | 0.9056 | 0.5714 |
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+ | 0.2536 | 38.0 | 38 | 0.9364 | 0.5714 |
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+ | 0.2536 | 39.0 | 39 | 0.9364 | 0.5714 |
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+ | 0.2176 | 40.0 | 40 | 1.0523 | 0.5714 |
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+ | 0.2176 | 41.0 | 41 | 1.0523 | 0.5714 |
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+ | 0.2176 | 42.0 | 42 | 1.1687 | 0.4286 |
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+ | 0.2176 | 43.0 | 43 | 1.1687 | 0.4286 |
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+ | 0.2176 | 44.0 | 44 | 1.1968 | 0.4286 |
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+ | 0.2176 | 45.0 | 45 | 1.1968 | 0.4286 |
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+ | 0.2176 | 46.0 | 46 | 1.1604 | 0.4286 |
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+ | 0.2176 | 47.0 | 47 | 1.1604 | 0.4286 |
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+ | 0.2176 | 48.0 | 48 | 1.0505 | 0.4286 |
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+ | 0.2176 | 49.0 | 49 | 1.0505 | 0.4286 |
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+ | 0.1597 | 50.0 | 50 | 0.9059 | 0.5714 |
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+ | 0.1597 | 51.0 | 51 | 0.9059 | 0.5714 |
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+ | 0.1597 | 52.0 | 52 | 0.8606 | 0.5714 |
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+ | 0.1597 | 53.0 | 53 | 0.8606 | 0.5714 |
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+ | 0.1597 | 54.0 | 54 | 0.8946 | 0.5714 |
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+ | 0.1597 | 55.0 | 55 | 0.8946 | 0.5714 |
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+ | 0.1597 | 56.0 | 56 | 0.9643 | 0.5714 |
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+ | 0.1597 | 57.0 | 57 | 0.9643 | 0.5714 |
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+ | 0.1597 | 58.0 | 58 | 1.0598 | 0.5714 |
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+ | 0.1597 | 59.0 | 59 | 1.0598 | 0.5714 |
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+ | 0.1231 | 60.0 | 60 | 1.1833 | 0.5714 |
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+ | 0.1231 | 61.0 | 61 | 1.1833 | 0.5714 |
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+ | 0.1231 | 62.0 | 62 | 1.2730 | 0.5714 |
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+ | 0.1231 | 63.0 | 63 | 1.2730 | 0.5714 |
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+ | 0.1231 | 64.0 | 64 | 1.3132 | 0.4286 |
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+ | 0.1231 | 65.0 | 65 | 1.3132 | 0.4286 |
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+ | 0.1231 | 66.0 | 66 | 1.3025 | 0.4286 |
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+ | 0.1231 | 67.0 | 67 | 1.3025 | 0.4286 |
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+ | 0.1231 | 68.0 | 68 | 1.2702 | 0.4286 |
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+ | 0.1231 | 69.0 | 69 | 1.2702 | 0.4286 |
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+ | 0.1364 | 70.0 | 70 | 1.2411 | 0.4286 |
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+ | 0.1364 | 71.0 | 71 | 1.2411 | 0.4286 |
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+ | 0.1364 | 72.0 | 72 | 1.2222 | 0.4286 |
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+ | 0.1364 | 73.0 | 73 | 1.2222 | 0.4286 |
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+ | 0.1364 | 74.0 | 74 | 1.2257 | 0.4286 |
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+ | 0.1364 | 75.0 | 75 | 1.2257 | 0.4286 |
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+ | 0.1364 | 76.0 | 76 | 1.2552 | 0.4286 |
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+ | 0.1364 | 77.0 | 77 | 1.2552 | 0.4286 |
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+ | 0.1364 | 78.0 | 78 | 1.2701 | 0.5714 |
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+ | 0.1364 | 79.0 | 79 | 1.2701 | 0.5714 |
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+ | 0.0937 | 80.0 | 80 | 1.2753 | 0.5714 |
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+ | 0.0937 | 81.0 | 81 | 1.2753 | 0.5714 |
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+ | 0.0937 | 82.0 | 82 | 1.2797 | 0.5714 |
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+ | 0.0937 | 83.0 | 83 | 1.2797 | 0.5714 |
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+ | 0.0937 | 84.0 | 84 | 1.2840 | 0.5714 |
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+ | 0.0937 | 85.0 | 85 | 1.2840 | 0.5714 |
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+ | 0.0937 | 86.0 | 86 | 1.2895 | 0.5714 |
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+ | 0.0937 | 87.0 | 87 | 1.2895 | 0.5714 |
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+ | 0.0937 | 88.0 | 88 | 1.2917 | 0.5714 |
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+ | 0.0937 | 89.0 | 89 | 1.2917 | 0.5714 |
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+ | 0.1082 | 90.0 | 90 | 1.2931 | 0.5714 |
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  ### Framework versions