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anirudhmu
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swin-tiny-patch4-window7-224-finetuned-soccer-binary

Image Classification
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swin
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  1. README.md +14 -7
  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9657142857142857
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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
@@ -32,8 +32,8 @@ 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: 0.1168
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- - Accuracy: 0.9657
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  ## Model description
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@@ -61,15 +61,22 @@ 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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- | 0.3683 | 0.96 | 12 | 0.1277 | 0.9657 |
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- | 0.1466 | 2.0 | 25 | 0.1334 | 0.96 |
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- | 0.1313 | 2.88 | 36 | 0.1168 | 0.9657 |
 
 
 
 
 
 
 
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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: 0.96
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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: 0.1077
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+ - Accuracy: 0.96
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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: 10
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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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+ | 0.1286 | 0.96 | 12 | 0.1138 | 0.9714 |
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+ | 0.1267 | 2.0 | 25 | 0.1283 | 0.9657 |
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+ | 0.121 | 2.96 | 37 | 0.1124 | 0.9657 |
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+ | 0.1142 | 4.0 | 50 | 0.1151 | 0.9657 |
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+ | 0.1069 | 4.96 | 62 | 0.1063 | 0.96 |
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+ | 0.1038 | 6.0 | 75 | 0.1210 | 0.96 |
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+ | 0.0935 | 6.96 | 87 | 0.1150 | 0.96 |
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+ | 0.1042 | 8.0 | 100 | 0.1038 | 0.9657 |
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+ | 0.0945 | 8.96 | 112 | 0.1071 | 0.96 |
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+ | 0.0891 | 9.6 | 120 | 0.1077 | 0.96 |
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  ### Framework versions
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