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ntnxx2
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vit-base-patch16-224-finetuned-Visual-Emotional

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  1. README.md +32 -7
  2. model.safetensors +1 -1
  3. runs/Nov26_07-42-22_c0788ea2aa74/events.out.tfevents.1700984571.c0788ea2aa74.27844.0 +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: 0.325
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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 [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.9225
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- - Accuracy: 0.325
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  ## Model description
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@@ -61,15 +61,40 @@ 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 | 0.87 | 5 | 2.0775 | 0.25 |
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- | 2.0477 | 1.91 | 11 | 1.9474 | 0.3125 |
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- | 2.0477 | 2.61 | 15 | 1.9225 | 0.325 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.5375
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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 [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.3425
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+ - Accuracy: 0.5375
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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: 32
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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 | 0.87 | 5 | 2.1419 | 0.15 |
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+ | 2.1722 | 1.91 | 11 | 2.0381 | 0.1625 |
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+ | 2.1722 | 2.96 | 17 | 1.8729 | 0.3 |
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+ | 1.8696 | 4.0 | 23 | 1.6683 | 0.3625 |
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+ | 1.8696 | 4.87 | 28 | 1.5172 | 0.4 |
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+ | 1.4531 | 5.91 | 34 | 1.3960 | 0.4625 |
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+ | 1.1483 | 6.96 | 40 | 1.3788 | 0.45 |
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+ | 1.1483 | 8.0 | 46 | 1.3186 | 0.5125 |
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+ | 0.955 | 8.87 | 51 | 1.2741 | 0.475 |
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+ | 0.955 | 9.91 | 57 | 1.2990 | 0.5 |
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+ | 0.7894 | 10.96 | 63 | 1.2462 | 0.475 |
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+ | 0.7894 | 12.0 | 69 | 1.3090 | 0.5375 |
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+ | 0.6769 | 12.87 | 74 | 1.2809 | 0.5125 |
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+ | 0.5958 | 13.91 | 80 | 1.3020 | 0.525 |
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+ | 0.5958 | 14.96 | 86 | 1.3032 | 0.5 |
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+ | 0.5179 | 16.0 | 92 | 1.2624 | 0.5375 |
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+ | 0.5179 | 16.87 | 97 | 1.2776 | 0.525 |
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+ | 0.4808 | 17.91 | 103 | 1.2705 | 0.525 |
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+ | 0.4808 | 18.96 | 109 | 1.2792 | 0.5125 |
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+ | 0.4025 | 20.0 | 115 | 1.2923 | 0.5375 |
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+ | 0.3908 | 20.87 | 120 | 1.3156 | 0.525 |
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+ | 0.3908 | 21.91 | 126 | 1.3290 | 0.5375 |
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+ | 0.3384 | 22.96 | 132 | 1.3141 | 0.5625 |
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+ | 0.3384 | 24.0 | 138 | 1.3253 | 0.55 |
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+ | 0.3428 | 24.87 | 143 | 1.3502 | 0.5375 |
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+ | 0.3428 | 25.91 | 149 | 1.3498 | 0.525 |
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+ | 0.3236 | 26.96 | 155 | 1.3450 | 0.525 |
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+ | 0.2951 | 27.83 | 160 | 1.3425 | 0.5375 |
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  ### Framework versions
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