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image_classification

Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
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End of training

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  1. README.md +54 -54
  2. model.safetensors +1 -1
  3. runs/Feb16_14-58-53_13e690524ef8/events.out.tfevents.1708095546.13e690524ef8.4038.2 +2 -2
  4. runs/Feb16_14-58-53_13e690524ef8/events.out.tfevents.1708098582.13e690524ef8.4038.3 +3 -0
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: 0.4875
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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: 1.4148
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- - Accuracy: 0.4875
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  ## Model description
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@@ -52,7 +52,7 @@ 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: 5e-06
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -64,56 +64,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 40 | 2.0788 | 0.1125 |
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- | No log | 2.0 | 80 | 2.0706 | 0.1688 |
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- | No log | 3.0 | 120 | 2.0465 | 0.2062 |
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- | No log | 4.0 | 160 | 2.0386 | 0.2 |
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- | No log | 5.0 | 200 | 2.0110 | 0.2188 |
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- | No log | 6.0 | 240 | 1.9815 | 0.225 |
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- | No log | 7.0 | 280 | 1.9430 | 0.2313 |
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- | No log | 8.0 | 320 | 1.8889 | 0.3312 |
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- | No log | 9.0 | 360 | 1.8283 | 0.3063 |
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- | No log | 10.0 | 400 | 1.7769 | 0.3438 |
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- | No log | 11.0 | 440 | 1.7292 | 0.325 |
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- | No log | 12.0 | 480 | 1.6966 | 0.3312 |
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- | 1.885 | 13.0 | 520 | 1.6708 | 0.375 |
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- | 1.885 | 14.0 | 560 | 1.6527 | 0.3937 |
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- | 1.885 | 15.0 | 600 | 1.6266 | 0.3937 |
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- | 1.885 | 16.0 | 640 | 1.6116 | 0.3937 |
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- | 1.885 | 17.0 | 680 | 1.5944 | 0.4188 |
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- | 1.885 | 18.0 | 720 | 1.5931 | 0.3688 |
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- | 1.885 | 19.0 | 760 | 1.5645 | 0.3937 |
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- | 1.885 | 20.0 | 800 | 1.5503 | 0.45 |
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- | 1.885 | 21.0 | 840 | 1.5550 | 0.425 |
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- | 1.885 | 22.0 | 880 | 1.5370 | 0.4375 |
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- | 1.885 | 23.0 | 920 | 1.5239 | 0.4688 |
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- | 1.885 | 24.0 | 960 | 1.5240 | 0.4437 |
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- | 1.4797 | 25.0 | 1000 | 1.5031 | 0.4688 |
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- | 1.4797 | 26.0 | 1040 | 1.5183 | 0.4188 |
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- | 1.4797 | 27.0 | 1080 | 1.4949 | 0.4062 |
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- | 1.4797 | 28.0 | 1120 | 1.5014 | 0.4437 |
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- | 1.4797 | 29.0 | 1160 | 1.4766 | 0.4625 |
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- | 1.4797 | 30.0 | 1200 | 1.4892 | 0.4375 |
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- | 1.4797 | 31.0 | 1240 | 1.4812 | 0.4938 |
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- | 1.4797 | 32.0 | 1280 | 1.4472 | 0.4688 |
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- | 1.4797 | 33.0 | 1320 | 1.4744 | 0.425 |
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- | 1.4797 | 34.0 | 1360 | 1.4563 | 0.4562 |
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- | 1.4797 | 35.0 | 1400 | 1.4785 | 0.4313 |
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- | 1.4797 | 36.0 | 1440 | 1.4331 | 0.5125 |
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- | 1.4797 | 37.0 | 1480 | 1.4551 | 0.45 |
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- | 1.293 | 38.0 | 1520 | 1.4470 | 0.4625 |
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- | 1.293 | 39.0 | 1560 | 1.4695 | 0.4375 |
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- | 1.293 | 40.0 | 1600 | 1.4366 | 0.4813 |
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- | 1.293 | 41.0 | 1640 | 1.4350 | 0.5 |
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- | 1.293 | 42.0 | 1680 | 1.4181 | 0.475 |
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- | 1.293 | 43.0 | 1720 | 1.4428 | 0.4875 |
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- | 1.293 | 44.0 | 1760 | 1.4067 | 0.5188 |
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- | 1.293 | 45.0 | 1800 | 1.4058 | 0.475 |
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- | 1.293 | 46.0 | 1840 | 1.4341 | 0.475 |
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- | 1.293 | 47.0 | 1880 | 1.4082 | 0.4813 |
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- | 1.293 | 48.0 | 1920 | 1.4461 | 0.4688 |
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- | 1.293 | 49.0 | 1960 | 1.4136 | 0.5062 |
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- | 1.1998 | 50.0 | 2000 | 1.4226 | 0.4938 |
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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: 0.53125
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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
 
33
  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: 1.3583
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+ - Accuracy: 0.5312
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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: 5e-05
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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 Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 40 | 1.3917 | 0.5 |
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+ | No log | 2.0 | 80 | 1.3327 | 0.525 |
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+ | No log | 3.0 | 120 | 1.2901 | 0.5062 |
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+ | No log | 4.0 | 160 | 1.3720 | 0.45 |
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+ | No log | 5.0 | 200 | 1.4239 | 0.4688 |
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+ | No log | 6.0 | 240 | 1.3587 | 0.5125 |
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+ | No log | 7.0 | 280 | 1.3874 | 0.5 |
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+ | No log | 8.0 | 320 | 1.3341 | 0.5312 |
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+ | No log | 9.0 | 360 | 1.2295 | 0.6 |
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+ | No log | 10.0 | 400 | 1.3267 | 0.5563 |
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+ | No log | 11.0 | 440 | 1.3808 | 0.5375 |
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+ | No log | 12.0 | 480 | 1.3547 | 0.55 |
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+ | 0.6621 | 13.0 | 520 | 1.5197 | 0.5125 |
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+ | 0.6621 | 14.0 | 560 | 1.5709 | 0.525 |
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+ | 0.6621 | 15.0 | 600 | 1.4058 | 0.5875 |
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+ | 0.6621 | 16.0 | 640 | 1.4561 | 0.5375 |
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+ | 0.6621 | 17.0 | 680 | 1.6183 | 0.525 |
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+ | 0.6621 | 18.0 | 720 | 1.6036 | 0.525 |
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+ | 0.6621 | 19.0 | 760 | 1.5561 | 0.5375 |
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+ | 0.6621 | 20.0 | 800 | 1.6527 | 0.5 |
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+ | 0.6621 | 21.0 | 840 | 1.7574 | 0.5188 |
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+ | 0.6621 | 22.0 | 880 | 1.8418 | 0.475 |
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+ | 0.6621 | 23.0 | 920 | 1.5058 | 0.5625 |
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+ | 0.6621 | 24.0 | 960 | 1.8427 | 0.4938 |
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+ | 0.2166 | 25.0 | 1000 | 1.7561 | 0.4938 |
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+ | 0.2166 | 26.0 | 1040 | 1.7327 | 0.525 |
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+ | 0.2166 | 27.0 | 1080 | 1.8137 | 0.5125 |
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+ | 0.2166 | 28.0 | 1120 | 1.8352 | 0.4938 |
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+ | 0.2166 | 29.0 | 1160 | 1.7171 | 0.55 |
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+ | 0.2166 | 30.0 | 1200 | 2.0487 | 0.4688 |
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+ | 0.2166 | 31.0 | 1240 | 1.8911 | 0.4688 |
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+ | 0.2166 | 32.0 | 1280 | 1.5932 | 0.5563 |
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+ | 0.2166 | 33.0 | 1320 | 1.7250 | 0.5062 |
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+ | 0.2166 | 34.0 | 1360 | 1.9414 | 0.5125 |
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+ | 0.2166 | 35.0 | 1400 | 1.9959 | 0.4688 |
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+ | 0.2166 | 36.0 | 1440 | 1.9066 | 0.4938 |
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+ | 0.2166 | 37.0 | 1480 | 1.8892 | 0.5312 |
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+ | 0.1291 | 38.0 | 1520 | 1.8439 | 0.5375 |
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+ | 0.1291 | 39.0 | 1560 | 2.0001 | 0.525 |
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+ | 0.1291 | 40.0 | 1600 | 1.9060 | 0.5 |
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+ | 0.1291 | 41.0 | 1640 | 1.9419 | 0.5375 |
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+ | 0.1291 | 42.0 | 1680 | 1.7496 | 0.5563 |
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+ | 0.1291 | 43.0 | 1720 | 1.9750 | 0.5188 |
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+ | 0.1291 | 44.0 | 1760 | 2.0106 | 0.5188 |
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+ | 0.1291 | 45.0 | 1800 | 1.9180 | 0.55 |
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+ | 0.1291 | 46.0 | 1840 | 1.9644 | 0.525 |
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+ | 0.1291 | 47.0 | 1880 | 1.8182 | 0.5687 |
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+ | 0.1291 | 48.0 | 1920 | 1.9591 | 0.5312 |
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+ | 0.1291 | 49.0 | 1960 | 1.8103 | 0.5687 |
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+ | 0.0866 | 50.0 | 2000 | 2.0038 | 0.5125 |
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  ### Framework versions
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