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Veda0718
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swin-tiny-patch4-window7-224-finetuned-fish

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  1. README.md +29 -7
  2. model.safetensors +1 -1
  3. runs/May05_19-43-16_503a5f38d136/events.out.tfevents.1714938204.503a5f38d136.8635.2 +2 -2
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: 0.5
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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: 1.8280
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- - Accuracy: 0.5
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  ## Model description
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@@ -61,15 +61,37 @@ 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 | 1.9845 | 0.0 |
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- | No log | 2.0 | 2 | 1.8803 | 0.5 |
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- | No log | 3.0 | 3 | 1.8280 | 0.5 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.8333333333333334
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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: 0.3933
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+ - Accuracy: 0.8333
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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: 25
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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 | 1.8803 | 0.5 |
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+ | No log | 2.0 | 2 | 1.8439 | 0.5 |
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+ | No log | 3.0 | 3 | 1.7572 | 0.5 |
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+ | No log | 4.0 | 4 | 1.6256 | 0.5 |
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+ | No log | 5.0 | 5 | 1.5082 | 0.5 |
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+ | No log | 6.0 | 6 | 1.4301 | 0.5 |
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+ | No log | 7.0 | 7 | 1.3379 | 0.5 |
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+ | No log | 8.0 | 8 | 1.2260 | 0.5 |
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+ | No log | 9.0 | 9 | 1.1071 | 0.6667 |
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+ | 0.6539 | 10.0 | 10 | 0.9941 | 0.6667 |
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+ | 0.6539 | 11.0 | 11 | 0.8836 | 0.6667 |
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+ | 0.6539 | 12.0 | 12 | 0.7859 | 0.6667 |
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+ | 0.6539 | 13.0 | 13 | 0.7059 | 0.8333 |
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+ | 0.6539 | 14.0 | 14 | 0.6358 | 0.8333 |
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+ | 0.6539 | 15.0 | 15 | 0.5752 | 0.8333 |
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+ | 0.6539 | 16.0 | 16 | 0.5343 | 0.8333 |
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+ | 0.6539 | 17.0 | 17 | 0.4994 | 0.8333 |
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+ | 0.6539 | 18.0 | 18 | 0.4755 | 0.8333 |
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+ | 0.6539 | 19.0 | 19 | 0.4544 | 0.8333 |
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+ | 0.2777 | 20.0 | 20 | 0.4328 | 0.8333 |
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+ | 0.2777 | 21.0 | 21 | 0.4171 | 0.8333 |
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+ | 0.2777 | 22.0 | 22 | 0.4066 | 0.8333 |
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+ | 0.2777 | 23.0 | 23 | 0.3995 | 0.8333 |
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+ | 0.2777 | 24.0 | 24 | 0.3954 | 0.8333 |
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+ | 0.2777 | 25.0 | 25 | 0.3933 | 0.8333 |
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  ### Framework versions
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