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image_classification

Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Model card Files
xet
Metrics Community
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End of training

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  1. README.md +55 -8
  2. pytorch_model.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: 0.50625
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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: 1.3916
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- - Accuracy: 0.5062
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  ## Model description
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@@ -52,21 +52,68 @@ 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
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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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- - 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 | 1.0 | 40 | 1.4094 | 0.5375 |
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- | No log | 2.0 | 80 | 1.3534 | 0.5188 |
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- | No log | 3.0 | 120 | 1.3492 | 0.5 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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: 0.525
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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: 1.9905
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+ - Accuracy: 0.525
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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: 0.0001
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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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+ - 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 | 40 | 1.7409 | 0.2875 |
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+ | No log | 2.0 | 80 | 1.5124 | 0.4375 |
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+ | No log | 3.0 | 120 | 1.4255 | 0.4437 |
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+ | No log | 4.0 | 160 | 1.4154 | 0.425 |
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+ | No log | 5.0 | 200 | 1.2886 | 0.4625 |
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+ | No log | 6.0 | 240 | 1.2963 | 0.5125 |
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+ | No log | 7.0 | 280 | 1.3139 | 0.55 |
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+ | No log | 8.0 | 320 | 1.2976 | 0.5312 |
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+ | No log | 9.0 | 360 | 1.4368 | 0.5062 |
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+ | No log | 10.0 | 400 | 1.4022 | 0.5062 |
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+ | No log | 11.0 | 440 | 1.2853 | 0.55 |
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+ | No log | 12.0 | 480 | 1.3265 | 0.5563 |
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+ | 0.8831 | 13.0 | 520 | 1.3894 | 0.55 |
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+ | 0.8831 | 14.0 | 560 | 1.4465 | 0.5312 |
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+ | 0.8831 | 15.0 | 600 | 1.7185 | 0.475 |
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+ | 0.8831 | 16.0 | 640 | 1.7408 | 0.4875 |
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+ | 0.8831 | 17.0 | 680 | 1.5199 | 0.5437 |
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+ | 0.8831 | 18.0 | 720 | 1.7238 | 0.525 |
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+ | 0.8831 | 19.0 | 760 | 1.8348 | 0.4875 |
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+ | 0.8831 | 20.0 | 800 | 1.6278 | 0.5125 |
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+ | 0.8831 | 21.0 | 840 | 1.7539 | 0.5 |
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+ | 0.8831 | 22.0 | 880 | 1.9007 | 0.4938 |
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+ | 0.8831 | 23.0 | 920 | 1.6903 | 0.5375 |
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+ | 0.8831 | 24.0 | 960 | 1.7954 | 0.5062 |
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+ | 0.2214 | 25.0 | 1000 | 1.7070 | 0.575 |
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+ | 0.2214 | 26.0 | 1040 | 1.6764 | 0.5625 |
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+ | 0.2214 | 27.0 | 1080 | 1.8590 | 0.5188 |
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+ | 0.2214 | 28.0 | 1120 | 1.7531 | 0.5188 |
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+ | 0.2214 | 29.0 | 1160 | 1.5238 | 0.5875 |
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+ | 0.2214 | 30.0 | 1200 | 1.6463 | 0.6 |
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+ | 0.2214 | 31.0 | 1240 | 1.7955 | 0.5563 |
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+ | 0.2214 | 32.0 | 1280 | 1.9920 | 0.5 |
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+ | 0.2214 | 33.0 | 1320 | 1.8826 | 0.55 |
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+ | 0.2214 | 34.0 | 1360 | 2.0573 | 0.5 |
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+ | 0.2214 | 35.0 | 1400 | 1.8438 | 0.5312 |
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+ | 0.2214 | 36.0 | 1440 | 1.9004 | 0.5312 |
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+ | 0.2214 | 37.0 | 1480 | 1.8215 | 0.5437 |
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+ | 0.1479 | 38.0 | 1520 | 2.0467 | 0.5437 |
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+ | 0.1479 | 39.0 | 1560 | 1.8564 | 0.5687 |
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+ | 0.1479 | 40.0 | 1600 | 1.8381 | 0.5687 |
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+ | 0.1479 | 41.0 | 1640 | 1.8110 | 0.5687 |
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+ | 0.1479 | 42.0 | 1680 | 2.0587 | 0.5375 |
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+ | 0.1479 | 43.0 | 1720 | 1.9597 | 0.5687 |
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+ | 0.1479 | 44.0 | 1760 | 1.9199 | 0.5687 |
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+ | 0.1479 | 45.0 | 1800 | 1.8714 | 0.5312 |
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+ | 0.1479 | 46.0 | 1840 | 1.9463 | 0.575 |
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+ | 0.1479 | 47.0 | 1880 | 2.0449 | 0.5312 |
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+ | 0.1479 | 48.0 | 1920 | 1.9172 | 0.525 |
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+ | 0.1479 | 49.0 | 1960 | 1.9153 | 0.55 |
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+ | 0.0946 | 50.0 | 2000 | 1.9905 | 0.525 |
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  ### Framework versions
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