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End of training

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  1. README.md +24 -54
  2. model.safetensors +1 -1
  3. runs/Apr04_17-17-17_f7abf00e7cce/events.out.tfevents.1712251038.f7abf00e7cce.273.0 +3 -0
  4. training_args.bin +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: 0.9666666666666667
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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-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3882
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- - Accuracy: 0.9667
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  ## Model description
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@@ -61,62 +61,32 @@ 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: 50
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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 | 2 | 1.7939 | 0.2 |
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- | No log | 2.0 | 4 | 1.7836 | 0.1333 |
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- | No log | 3.0 | 6 | 1.7646 | 0.0667 |
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- | No log | 4.0 | 8 | 1.6917 | 0.1333 |
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- | 1.7382 | 5.0 | 10 | 1.6700 | 0.3333 |
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- | 1.7382 | 6.0 | 12 | 1.5990 | 0.5 |
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- | 1.7382 | 7.0 | 14 | 1.5424 | 0.4667 |
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- | 1.7382 | 8.0 | 16 | 1.4673 | 0.6 |
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- | 1.7382 | 9.0 | 18 | 1.4155 | 0.7333 |
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- | 1.3754 | 10.0 | 20 | 1.3015 | 0.7 |
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- | 1.3754 | 11.0 | 22 | 1.3055 | 0.6667 |
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- | 1.3754 | 12.0 | 24 | 1.2209 | 0.7 |
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- | 1.3754 | 13.0 | 26 | 1.0965 | 0.8 |
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- | 1.3754 | 14.0 | 28 | 1.0976 | 0.7667 |
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- | 0.9947 | 15.0 | 30 | 1.0388 | 0.8667 |
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- | 0.9947 | 16.0 | 32 | 1.0757 | 0.7 |
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- | 0.9947 | 17.0 | 34 | 0.9617 | 0.8 |
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- | 0.9947 | 18.0 | 36 | 0.8713 | 0.8667 |
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- | 0.9947 | 19.0 | 38 | 0.8803 | 0.8667 |
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- | 0.7399 | 20.0 | 40 | 0.8257 | 0.8667 |
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- | 0.7399 | 21.0 | 42 | 0.8740 | 0.8333 |
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- | 0.7399 | 22.0 | 44 | 0.7554 | 0.9667 |
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- | 0.7399 | 23.0 | 46 | 0.7581 | 0.8667 |
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- | 0.7399 | 24.0 | 48 | 0.7983 | 0.8333 |
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- | 0.5797 | 25.0 | 50 | 0.7052 | 0.9333 |
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- | 0.5797 | 26.0 | 52 | 0.7930 | 0.8667 |
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- | 0.5797 | 27.0 | 54 | 0.7511 | 0.8333 |
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- | 0.5797 | 28.0 | 56 | 0.5578 | 0.9667 |
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- | 0.5797 | 29.0 | 58 | 0.5771 | 0.9667 |
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- | 0.4642 | 30.0 | 60 | 0.5641 | 0.9667 |
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- | 0.4642 | 31.0 | 62 | 0.5368 | 0.9667 |
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- | 0.4642 | 32.0 | 64 | 0.5313 | 0.9333 |
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- | 0.4642 | 33.0 | 66 | 0.5521 | 0.9 |
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- | 0.4642 | 34.0 | 68 | 0.5530 | 0.9333 |
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- | 0.3813 | 35.0 | 70 | 0.5416 | 0.9 |
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- | 0.3813 | 36.0 | 72 | 0.4796 | 0.9 |
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- | 0.3813 | 37.0 | 74 | 0.4627 | 0.9667 |
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- | 0.3813 | 38.0 | 76 | 0.4788 | 0.9667 |
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- | 0.3813 | 39.0 | 78 | 0.5044 | 0.9 |
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- | 0.3555 | 40.0 | 80 | 0.5886 | 0.8667 |
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- | 0.3555 | 41.0 | 82 | 0.4892 | 0.9 |
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- | 0.3555 | 42.0 | 84 | 0.5306 | 0.8333 |
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- | 0.3555 | 43.0 | 86 | 0.5294 | 0.8333 |
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- | 0.3555 | 44.0 | 88 | 0.5260 | 0.8667 |
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- | 0.3441 | 45.0 | 90 | 0.4445 | 0.9667 |
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- | 0.3441 | 46.0 | 92 | 0.4579 | 0.9 |
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- | 0.3441 | 47.0 | 94 | 0.4390 | 0.9333 |
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- | 0.3441 | 48.0 | 96 | 0.4139 | 0.9667 |
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- | 0.3441 | 49.0 | 98 | 0.4820 | 0.9667 |
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- | 0.3155 | 50.0 | 100 | 0.3882 | 0.9667 |
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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: 0.9
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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-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5284
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+ - Accuracy: 0.9
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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: 20
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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 | 2 | 1.0120 | 0.7 |
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+ | No log | 2.0 | 4 | 0.9958 | 0.8 |
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+ | No log | 3.0 | 6 | 0.9576 | 0.8333 |
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+ | No log | 4.0 | 8 | 0.8673 | 0.8333 |
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+ | 0.8292 | 5.0 | 10 | 0.8140 | 0.8667 |
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+ | 0.8292 | 6.0 | 12 | 0.7034 | 0.9 |
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+ | 0.8292 | 7.0 | 14 | 0.7036 | 0.9 |
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+ | 0.8292 | 8.0 | 16 | 0.6949 | 0.9333 |
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+ | 0.8292 | 9.0 | 18 | 0.5620 | 0.9667 |
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+ | 0.6112 | 10.0 | 20 | 0.5829 | 0.9333 |
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+ | 0.6112 | 11.0 | 22 | 0.6530 | 0.9 |
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+ | 0.6112 | 12.0 | 24 | 0.5664 | 0.9333 |
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+ | 0.6112 | 13.0 | 26 | 0.5084 | 1.0 |
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+ | 0.6112 | 14.0 | 28 | 0.6490 | 0.8333 |
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+ | 0.4805 | 15.0 | 30 | 0.4700 | 1.0 |
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+ | 0.4805 | 16.0 | 32 | 0.5473 | 0.9333 |
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+ | 0.4805 | 17.0 | 34 | 0.4928 | 0.9667 |
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+ | 0.4805 | 18.0 | 36 | 0.5023 | 0.9667 |
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+ | 0.4805 | 19.0 | 38 | 0.4885 | 0.9333 |
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+ | 0.4145 | 20.0 | 40 | 0.5284 | 0.9 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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