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jayanthspratap
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vit-base-patch16-224

Image Classification
Transformers
TensorBoard
Safetensors
swinv2
Generated from Trainer
Eval Results (legacy)
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  1. README.md +16 -14
  2. model.safetensors +1 -1
  3. runs/Aug10_00-26-27_EMIMDGXA100GPU1/events.out.tfevents.1723263989.EMIMDGXA100GPU1.4104023.0 +2 -2
README.md CHANGED
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  ---
 
 
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  tags:
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  - generated_from_trainer
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  datasets:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7441860465116279
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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
@@ -28,10 +30,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # vit-base-patch16-224
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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: 0.5859
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- - Accuracy: 0.7442
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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 | 0.5859 | 0.7442 |
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- | 0.605 | 1.92 | 12 | 0.5842 | 0.7442 |
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- | 0.605 | 2.88 | 18 | 0.5919 | 0.7442 |
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- | 0.5428 | 4.0 | 25 | 0.5885 | 0.7442 |
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- | 0.5584 | 4.96 | 31 | 0.5886 | 0.7442 |
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- | 0.5584 | 5.92 | 37 | 0.5915 | 0.7442 |
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- | 0.5593 | 6.88 | 43 | 0.5935 | 0.7442 |
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- | 0.5097 | 8.0 | 50 | 0.5947 | 0.7442 |
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- | 0.5097 | 8.96 | 56 | 0.5949 | 0.7442 |
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- | 0.5205 | 9.6 | 60 | 0.5949 | 0.7442 |
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  ### Framework versions
 
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  ---
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+ license: apache-2.0
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+ base_model: microsoft/swinv2-base-patch4-window8-256
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.7209302325581395
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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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  # vit-base-patch16-224
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+ This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window8-256](https://huggingface.co/microsoft/swinv2-base-patch4-window8-256) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6046
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+ - Accuracy: 0.7209
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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 | 0.7424 | 0.3488 |
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+ | 0.7374 | 1.92 | 12 | 0.5932 | 0.7209 |
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+ | 0.7374 | 2.88 | 18 | 0.5843 | 0.7209 |
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+ | 0.5783 | 4.0 | 25 | 0.5996 | 0.7209 |
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+ | 0.5358 | 4.96 | 31 | 0.6147 | 0.7209 |
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+ | 0.5358 | 5.92 | 37 | 0.6159 | 0.7209 |
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+ | 0.5745 | 6.88 | 43 | 0.6091 | 0.7209 |
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+ | 0.5325 | 8.0 | 50 | 0.6067 | 0.7209 |
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+ | 0.5325 | 8.96 | 56 | 0.6047 | 0.7209 |
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+ | 0.524 | 9.6 | 60 | 0.6046 | 0.7209 |
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  ### Framework versions
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