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dmartincc
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vet-sm

Image Classification
Transformers
Safetensors
vit
Generated from Trainer
Eval Results
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  1. README.md +10 -16
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@@ -6,7 +6,6 @@ tags:
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  datasets:
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  - imagefolder
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  metrics:
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- - f1
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  - accuracy
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  model-index:
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  - name: vet-sm
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  split: train
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  args: default
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  metrics:
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- - name: F1
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- type: f1
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- value: 0.47
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  - name: Accuracy
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  type: accuracy
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- value: 0.31
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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
@@ -36,10 +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: 0.2940
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- - F1: 0.47
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- - Roc Auc: 0.66
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- - Accuracy: 0.31
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  ## Model description
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:----:|:-------:|:--------:|
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- | 0.4074 | 0.99 | 67 | 0.3991 | 0.0 | 0.5 | 0.0 |
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- | 0.3597 | 2.0 | 135 | 0.3546 | 0.01 | 0.5 | 0.01 |
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- | 0.3191 | 2.99 | 202 | 0.3307 | 0.18 | 0.55 | 0.1 |
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- | 0.2902 | 4.0 | 270 | 0.3034 | 0.39 | 0.62 | 0.25 |
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- | 0.2749 | 4.96 | 335 | 0.2940 | 0.47 | 0.66 | 0.31 |
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  ### Framework versions
 
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  datasets:
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  - imagefolder
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  metrics:
 
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  - accuracy
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  model-index:
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  - name: vet-sm
 
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  split: train
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  args: default
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  metrics:
 
 
 
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  - name: Accuracy
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  type: accuracy
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+ value: 0.6771653543307087
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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: 1.0058
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+ - Accuracy: 0.6772
 
 
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  ## Model description
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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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+ | 1.7243 | 0.99 | 67 | 1.6282 | 0.4829 |
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+ | 1.283 | 2.0 | 135 | 1.3131 | 0.5722 |
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+ | 0.9453 | 2.99 | 202 | 1.1048 | 0.6430 |
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+ | 0.7164 | 4.0 | 270 | 1.0157 | 0.6719 |
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+ | 0.5591 | 4.96 | 335 | 1.0058 | 0.6772 |
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  ### Framework versions