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image_classification

Image Classification
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Safetensors
vit
Generated from Trainer
Eval Results (legacy)
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End of training

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  1. README.md +25 -30
  2. model.safetensors +1 -1
  3. runs/Sep06_02-37-56_bb27b7ff2174/events.out.tfevents.1725590288.bb27b7ff2174.6450.16 +2 -2
  4. runs/Sep06_02-37-56_bb27b7ff2174/events.out.tfevents.1725591071.bb27b7ff2174.6450.17 +3 -0
README.md CHANGED
@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.6125
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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
@@ -33,8 +33,8 @@ 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: 1.1555
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- - Accuracy: 0.6125
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  ## Model description
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@@ -53,7 +53,7 @@ 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-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -62,37 +62,32 @@ 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: 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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- | 1.7108 | 1.0 | 10 | 1.8424 | 0.4188 |
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- | 1.6278 | 2.0 | 20 | 1.7495 | 0.45 |
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- | 1.465 | 3.0 | 30 | 1.6153 | 0.5062 |
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- | 1.2862 | 4.0 | 40 | 1.5099 | 0.55 |
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- | 1.1151 | 5.0 | 50 | 1.4399 | 0.5312 |
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- | 0.9631 | 6.0 | 60 | 1.3803 | 0.5375 |
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- | 0.8242 | 7.0 | 70 | 1.3213 | 0.5875 |
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- | 0.6939 | 8.0 | 80 | 1.2673 | 0.575 |
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- | 0.576 | 9.0 | 90 | 1.2463 | 0.5938 |
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- | 0.4801 | 10.0 | 100 | 1.2108 | 0.6 |
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- | 0.4008 | 11.0 | 110 | 1.2093 | 0.575 |
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- | 0.3426 | 12.0 | 120 | 1.1744 | 0.5687 |
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- | 0.2976 | 13.0 | 130 | 1.1710 | 0.5938 |
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- | 0.2667 | 14.0 | 140 | 1.1545 | 0.5875 |
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- | 0.2434 | 15.0 | 150 | 1.1622 | 0.6 |
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- | 0.2261 | 16.0 | 160 | 1.1522 | 0.5875 |
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- | 0.2119 | 17.0 | 170 | 1.1486 | 0.6062 |
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- | 0.2016 | 18.0 | 180 | 1.1555 | 0.6125 |
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- | 0.1932 | 19.0 | 190 | 1.1487 | 0.6062 |
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- | 0.1857 | 20.0 | 200 | 1.1422 | 0.5938 |
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- | 0.1812 | 21.0 | 210 | 1.1438 | 0.6 |
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- | 0.1772 | 22.0 | 220 | 1.1521 | 0.5687 |
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- | 0.1735 | 23.0 | 230 | 1.1428 | 0.5938 |
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- | 0.1714 | 24.0 | 240 | 1.1487 | 0.6 |
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- | 0.1703 | 25.0 | 250 | 1.1462 | 0.6 |
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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: 0.63125
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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: 1.1383
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+ - Accuracy: 0.6312
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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: 0.0002
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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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  - 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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+ | 0.925 | 1.0 | 10 | 1.3570 | 0.4688 |
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+ | 0.8379 | 2.0 | 20 | 1.1685 | 0.5875 |
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+ | 0.6737 | 3.0 | 30 | 1.1795 | 0.6 |
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+ | 0.4606 | 4.0 | 40 | 1.1383 | 0.6312 |
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+ | 0.3416 | 5.0 | 50 | 1.2393 | 0.5687 |
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+ | 0.2493 | 6.0 | 60 | 1.3971 | 0.5938 |
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+ | 0.2341 | 7.0 | 70 | 1.3546 | 0.6062 |
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+ | 0.1797 | 8.0 | 80 | 1.3681 | 0.5938 |
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+ | 0.1221 | 9.0 | 90 | 1.6936 | 0.525 |
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+ | 0.1077 | 10.0 | 100 | 1.7008 | 0.5375 |
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+ | 0.0966 | 11.0 | 110 | 1.7380 | 0.525 |
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+ | 0.1073 | 12.0 | 120 | 1.5617 | 0.575 |
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+ | 0.0849 | 13.0 | 130 | 1.6178 | 0.6125 |
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+ | 0.0704 | 14.0 | 140 | 1.6144 | 0.6125 |
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+ | 0.0568 | 15.0 | 150 | 1.6111 | 0.6188 |
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+ | 0.0555 | 16.0 | 160 | 1.5946 | 0.6 |
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+ | 0.0498 | 17.0 | 170 | 1.6291 | 0.625 |
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+ | 0.0464 | 18.0 | 180 | 1.6574 | 0.6188 |
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+ | 0.0443 | 19.0 | 190 | 1.6740 | 0.6125 |
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+ | 0.0429 | 20.0 | 200 | 1.6781 | 0.6125 |
 
 
 
 
 
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  ### Framework versions
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