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jayanthspratap
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vit-base-patch16-224

Image Classification
Transformers
TensorBoard
Safetensors
swinv2
Generated from Trainer
Eval Results
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  1. README.md +13 -13
  2. model.safetensors +1 -1
  3. runs/Aug10_00-01-17_EMIMDGXA100GPU1/events.out.tfevents.1723262478.EMIMDGXA100GPU1.4102634.0 +2 -2
README.md CHANGED
@@ -20,7 +20,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8
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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
@@ -30,8 +30,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model was trained from scratch on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5546
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- - Accuracy: 0.8
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  ## Model description
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@@ -65,16 +65,16 @@ 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 | 8 | 0.5546 | 0.8 |
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- | 0.5945 | 2.0 | 16 | 0.5409 | 0.8 |
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- | 0.5832 | 3.0 | 24 | 0.5467 | 0.8 |
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- | 0.5338 | 4.0 | 32 | 0.5518 | 0.8 |
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- | 0.5513 | 5.0 | 40 | 0.5602 | 0.8 |
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- | 0.5513 | 6.0 | 48 | 0.5607 | 0.7333 |
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- | 0.5417 | 7.0 | 56 | 0.5707 | 0.7333 |
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- | 0.5343 | 8.0 | 64 | 0.5748 | 0.7333 |
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- | 0.5379 | 9.0 | 72 | 0.5736 | 0.7333 |
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- | 0.5137 | 10.0 | 80 | 0.5730 | 0.7333 |
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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: 0.813953488372093
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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 was trained from scratch on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5079
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+ - Accuracy: 0.8140
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.96 | 6 | 0.4999 | 0.7907 |
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+ | 0.5993 | 1.92 | 12 | 0.4812 | 0.8140 |
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+ | 0.5993 | 2.88 | 18 | 0.4892 | 0.8140 |
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+ | 0.6031 | 4.0 | 25 | 0.4975 | 0.8140 |
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+ | 0.5946 | 4.96 | 31 | 0.5062 | 0.8140 |
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+ | 0.5946 | 5.92 | 37 | 0.5119 | 0.8140 |
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+ | 0.5557 | 6.88 | 43 | 0.5102 | 0.8140 |
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+ | 0.583 | 8.0 | 50 | 0.5075 | 0.8140 |
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+ | 0.583 | 8.96 | 56 | 0.5080 | 0.8140 |
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+ | 0.6013 | 9.6 | 60 | 0.5079 | 0.8140 |
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  ### Framework versions
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