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Deepak-05-galey
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image-dataset-model

Image Classification
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End of training

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  1. README.md +9 -7
  2. model.safetensors +1 -1
  3. runs/Apr02_09-01-44_505526a8f343/events.out.tfevents.1712048506.505526a8f343.8881.0 +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: 0.975
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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: 0.3925
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- - Accuracy: 0.975
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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: 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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- | 1.0249 | 0.96 | 12 | 0.6935 | 0.96 |
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- | 0.6461 | 2.0 | 25 | 0.4386 | 0.965 |
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- | 0.4566 | 2.88 | 36 | 0.3925 | 0.975 |
 
 
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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: 0.98
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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: 0.3135
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+ - Accuracy: 0.98
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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: 5
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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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+ | 1.4937 | 0.96 | 12 | 1.0681 | 0.935 |
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+ | 0.9543 | 2.0 | 25 | 0.6107 | 0.965 |
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+ | 0.6251 | 2.96 | 37 | 0.4065 | 0.97 |
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+ | 0.3651 | 4.0 | 50 | 0.3438 | 0.97 |
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+ | 0.3225 | 4.8 | 60 | 0.3135 | 0.98 |
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  ### Framework versions
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