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bansilp
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Image Classification
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vit
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Files changed (9) hide show
  1. README.md +8 -8
  2. all_results.json +9 -9
  3. eval_results.json +6 -5
  4. model.safetensors +1 -1
  5. runs/Dec15_03-02-53_d2a76fcee09b/events.out.tfevents.1702611081.d2a76fcee09b.2614.15 +3 -0
  6. runs/Dec15_03-35-22_d2a76fcee09b/events.out.tfevents.1702611334.d2a76fcee09b.2614.16 +3 -0
  7. train_results.json +4 -4
  8. trainer_state.json +113 -113
  9. 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: 0.9064814814814814
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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: 0.5586
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- - Accuracy: 0.9065
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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: 0.0004
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  - train_batch_size: 48
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  - eval_batch_size: 8
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  - seed: 42
@@ -65,10 +65,10 @@ 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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- | 0.1059 | 1.11 | 100 | 0.6933 | 0.8546 |
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- | 0.02 | 2.22 | 200 | 0.6565 | 0.875 |
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- | 0.001 | 3.33 | 300 | 0.5975 | 0.8889 |
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- | 0.0027 | 4.44 | 400 | 0.5586 | 0.9065 |
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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: 0.9083333333333333
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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: 0.5528
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+ - Accuracy: 0.9083
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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: 48
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  - eval_batch_size: 8
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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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+ | 0.0009 | 1.11 | 100 | 0.7068 | 0.8713 |
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+ | 0.0039 | 2.22 | 200 | 0.6391 | 0.8991 |
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+ | 0.0014 | 3.33 | 300 | 0.5536 | 0.9056 |
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+ | 0.0018 | 4.44 | 400 | 0.5528 | 0.9083 |
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  ### Framework versions
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