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swiftformer-xs-DMAE

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  1. README.md +41 -41
  2. model.safetensors +1 -1
  3. runs/Dec02_13-05-50_DESKTOP-SKBE9FB/events.out.tfevents.1733166351.DESKTOP-SKBE9FB.16464.0 +2 -2
README.md CHANGED
@@ -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: 0.7391304347826086
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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 [MBZUAI/swiftformer-xs](https://huggingface.co/MBZUAI/swiftformer-xs) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8596
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- - Accuracy: 0.7391
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  ## Model description
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@@ -51,7 +51,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0015
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -64,43 +64,43 @@ The following hyperparameters were used during training:
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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 | 0.86 | 3 | 1.3836 | 0.4565 |
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- | No log | 2.0 | 7 | 1.3327 | 0.6522 |
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- | 1.3567 | 2.86 | 10 | 1.1681 | 0.6522 |
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- | 1.3567 | 4.0 | 14 | 1.0440 | 0.5652 |
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- | 1.3567 | 4.86 | 17 | 1.0462 | 0.6304 |
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- | 1.0903 | 6.0 | 21 | 0.9294 | 0.5870 |
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- | 1.0903 | 6.86 | 24 | 0.9572 | 0.6522 |
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- | 1.0903 | 8.0 | 28 | 0.9286 | 0.6739 |
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- | 1.0969 | 8.86 | 31 | 0.9229 | 0.6304 |
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- | 1.0969 | 10.0 | 35 | 0.9061 | 0.6522 |
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- | 1.0969 | 10.86 | 38 | 0.8341 | 0.6739 |
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- | 0.8923 | 12.0 | 42 | 0.8786 | 0.6739 |
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- | 0.8923 | 12.86 | 45 | 0.8596 | 0.7391 |
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- | 0.8923 | 14.0 | 49 | 0.8902 | 0.7174 |
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- | 0.7289 | 14.86 | 52 | 0.8024 | 0.6739 |
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- | 0.7289 | 16.0 | 56 | 0.9341 | 0.7174 |
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- | 0.7289 | 16.86 | 59 | 1.0464 | 0.7174 |
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- | 0.6609 | 18.0 | 63 | 0.9923 | 0.6087 |
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- | 0.6609 | 18.86 | 66 | 0.8225 | 0.7174 |
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- | 0.6527 | 20.0 | 70 | 0.8748 | 0.6957 |
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- | 0.6527 | 20.86 | 73 | 0.8052 | 0.6739 |
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- | 0.6527 | 22.0 | 77 | 0.8861 | 0.6957 |
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- | 0.493 | 22.86 | 80 | 0.9555 | 0.6957 |
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- | 0.493 | 24.0 | 84 | 1.0336 | 0.6739 |
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- | 0.493 | 24.86 | 87 | 0.9961 | 0.6957 |
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- | 0.4088 | 26.0 | 91 | 1.0400 | 0.6957 |
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- | 0.4088 | 26.86 | 94 | 1.0536 | 0.6957 |
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- | 0.4088 | 28.0 | 98 | 1.1388 | 0.6739 |
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- | 0.4047 | 28.86 | 101 | 1.2295 | 0.6522 |
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- | 0.4047 | 30.0 | 105 | 1.2627 | 0.6522 |
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- | 0.4047 | 30.86 | 108 | 1.2372 | 0.6739 |
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- | 0.3681 | 32.0 | 112 | 1.2919 | 0.6522 |
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- | 0.3681 | 32.86 | 115 | 1.2453 | 0.6522 |
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- | 0.3681 | 34.0 | 119 | 1.2612 | 0.6739 |
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- | 0.353 | 34.29 | 120 | 1.2611 | 0.6957 |
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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: 0.45652173913043476
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  ---
26
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
31
 
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  This model is a fine-tuned version of [MBZUAI/swiftformer-xs](https://huggingface.co/MBZUAI/swiftformer-xs) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 67319515540793508675715072.0000
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+ - Accuracy: 0.4565
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.015
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
 
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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 | 0.86 | 3 | 67319515540793508675715072.0000 | 0.6739 |
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+ | No log | 2.0 | 7 | 67319515540793508675715072.0000 | 0.1087 |
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+ | 65998362497246039927422976.0000 | 2.86 | 10 | 67319515540793508675715072.0000 | 0.3261 |
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+ | 65998362497246039927422976.0000 | 4.0 | 14 | 67319515540793508675715072.0000 | 0.4565 |
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+ | 65998362497246039927422976.0000 | 4.86 | 17 | 67319515540793508675715072.0000 | 0.3261 |
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+ | 69095061697085437542137856.0000 | 6.0 | 21 | 67319515540793508675715072.0000 | 0.4783 |
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+ | 69095061697085437542137856.0000 | 6.86 | 24 | 67319515540793508675715072.0000 | 0.4565 |
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+ | 69095061697085437542137856.0000 | 8.0 | 28 | 67319515540793508675715072.0000 | 0.4565 |
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+ | 77610986341318196513996800.0000 | 8.86 | 31 | 67319515540793508675715072.0000 | 0.5 |
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+ | 77610986341318196513996800.0000 | 10.0 | 35 | 67319515540793508675715072.0000 | 0.3478 |
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+ | 77610986341318196513996800.0000 | 10.86 | 38 | 67319515540793508675715072.0000 | 0.3478 |
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+ | 57288905682238366283726848.0000 | 12.0 | 42 | 67319515540793508675715072.0000 | 0.3478 |
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+ | 57288905682238366283726848.0000 | 12.86 | 45 | 67319515540793508675715072.0000 | 0.4348 |
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+ | 57288905682238366283726848.0000 | 14.0 | 49 | 67319515540793508675715072.0000 | 0.3696 |
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+ | 74707823001602531749003264.0000 | 14.86 | 52 | 67319515540793508675715072.0000 | 0.3261 |
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+ | 74707823001602531749003264.0000 | 16.0 | 56 | 67319515540793508675715072.0000 | 0.2826 |
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+ | 74707823001602531749003264.0000 | 16.86 | 59 | 67319515540793508675715072.0000 | 0.4565 |
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+ | 70449886504927853738459136.0000 | 18.0 | 63 | 67319515540793508675715072.0000 | 0.4348 |
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+ | 70449886504927853738459136.0000 | 18.86 | 66 | 67319515540793508675715072.0000 | 0.4130 |
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+ | 66191905736067400542978048.0000 | 20.0 | 70 | 67319515540793508675715072.0000 | 0.3478 |
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+ | 66191905736067400542978048.0000 | 20.86 | 73 | 67319515540793508675715072.0000 | 0.4565 |
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+ | 66191905736067400542978048.0000 | 22.0 | 77 | 67319515540793508675715072.0000 | 0.3478 |
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+ | 63869401627606337277919232.0000 | 22.86 | 80 | 67319515540793508675715072.0000 | 0.4130 |
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+ | 63869401627606337277919232.0000 | 24.0 | 84 | 67319515540793508675715072.0000 | 0.3261 |
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+ | 63869401627606337277919232.0000 | 24.86 | 87 | 67319515540793508675715072.0000 | 0.5 |
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+ | 63869386870211073156317184.0000 | 26.0 | 91 | 67319515540793508675715072.0000 | 0.4783 |
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+ | 63869386870211073156317184.0000 | 26.86 | 94 | 67319515540793508675715072.0000 | 0.4565 |
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+ | 63869386870211073156317184.0000 | 28.0 | 98 | 67319515540793508675715072.0000 | 0.4565 |
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+ | 72385304135746204362342400.0000 | 28.86 | 101 | 67319515540793508675715072.0000 | 0.4565 |
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+ | 72385304135746204362342400.0000 | 30.0 | 105 | 67319515540793508675715072.0000 | 0.5 |
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+ | 72385304135746204362342400.0000 | 30.86 | 108 | 67319515540793508675715072.0000 | 0.5 |
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+ | 65030638924441609083813888.0000 | 32.0 | 112 | 67319515540793508675715072.0000 | 0.4565 |
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+ | 65030638924441609083813888.0000 | 32.86 | 115 | 67319515540793508675715072.0000 | 0.4565 |
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+ | 65030638924441609083813888.0000 | 34.0 | 119 | 67319515540793508675715072.0000 | 0.4565 |
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+ | 65998362497246039927422976.0000 | 34.29 | 120 | 67319515540793508675715072.0000 | 0.4565 |
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  ### Framework versions
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