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HorcruxNo13
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beit-base-patch16-224

Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
beit
Generated from Trainer
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  1. README.md +23 -23
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -24,13 +24,13 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7933333333333333
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  - name: Precision
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  type: precision
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- value: 0.7853286177424108
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  - name: Recall
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  type: recall
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- value: 0.7933333333333333
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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
@@ -40,11 +40,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8531
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- - Accuracy: 0.7933
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- - Precision: 0.7853
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- - Recall: 0.7933
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- - F1 Score: 0.7662
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  ## Model description
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@@ -78,21 +78,21 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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- | No log | 1.0 | 4 | 0.5815 | 0.7292 | 0.6273 | 0.7292 | 0.6259 |
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- | No log | 2.0 | 8 | 0.5493 | 0.7333 | 0.6901 | 0.7333 | 0.6863 |
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- | No log | 3.0 | 12 | 0.5545 | 0.7667 | 0.7575 | 0.7667 | 0.7147 |
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- | 0.5698 | 4.0 | 16 | 0.5706 | 0.7667 | 0.7503 | 0.7667 | 0.7221 |
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- | 0.5698 | 5.0 | 20 | 0.5800 | 0.7667 | 0.7575 | 0.7667 | 0.7147 |
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- | 0.5698 | 6.0 | 24 | 0.5929 | 0.7833 | 0.7772 | 0.7833 | 0.7451 |
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- | 0.5698 | 7.0 | 28 | 0.5783 | 0.7833 | 0.7677 | 0.7833 | 0.7672 |
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- | 0.2938 | 8.0 | 32 | 0.5665 | 0.7875 | 0.7793 | 0.7875 | 0.7821 |
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- | 0.2938 | 9.0 | 36 | 0.7751 | 0.7875 | 0.7770 | 0.7875 | 0.7571 |
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- | 0.2938 | 10.0 | 40 | 0.7088 | 0.7917 | 0.7816 | 0.7917 | 0.7843 |
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- | 0.2938 | 11.0 | 44 | 0.8799 | 0.8042 | 0.7972 | 0.8042 | 0.7808 |
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- | 0.0834 | 12.0 | 48 | 0.8367 | 0.7875 | 0.7793 | 0.7875 | 0.7821 |
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- | 0.0834 | 13.0 | 52 | 0.9200 | 0.7958 | 0.7834 | 0.7958 | 0.7758 |
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- | 0.0834 | 14.0 | 56 | 0.8821 | 0.8 | 0.7879 | 0.8 | 0.7869 |
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- | 0.0358 | 15.0 | 60 | 0.8674 | 0.7875 | 0.7753 | 0.7875 | 0.7777 |
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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: 0.7333333333333333
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  - name: Precision
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  type: precision
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+ value: 0.708216298040535
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  - name: Recall
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  type: recall
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+ value: 0.7333333333333333
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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 [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5490
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+ - Accuracy: 0.7333
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+ - Precision: 0.7082
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+ - Recall: 0.7333
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+ - F1 Score: 0.7050
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
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+ | No log | 1.0 | 4 | 0.6369 | 0.725 | 0.5256 | 0.725 | 0.6094 |
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+ | No log | 2.0 | 8 | 0.6192 | 0.7458 | 0.7215 | 0.7458 | 0.6907 |
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+ | No log | 3.0 | 12 | 0.5699 | 0.725 | 0.5256 | 0.725 | 0.6094 |
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+ | 0.727 | 4.0 | 16 | 0.6237 | 0.6792 | 0.6716 | 0.6792 | 0.6751 |
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+ | 0.727 | 5.0 | 20 | 0.5533 | 0.7292 | 0.8028 | 0.7292 | 0.6191 |
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+ | 0.727 | 6.0 | 24 | 0.5601 | 0.7375 | 0.7200 | 0.7375 | 0.6562 |
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+ | 0.727 | 7.0 | 28 | 0.5901 | 0.7167 | 0.6944 | 0.7167 | 0.7013 |
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+ | 0.5968 | 8.0 | 32 | 0.5543 | 0.7375 | 0.7081 | 0.7375 | 0.7080 |
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+ | 0.5968 | 9.0 | 36 | 0.5780 | 0.7208 | 0.7095 | 0.7208 | 0.7141 |
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+ | 0.5968 | 10.0 | 40 | 0.5389 | 0.7375 | 0.7049 | 0.7375 | 0.6990 |
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+ | 0.5968 | 11.0 | 44 | 0.5438 | 0.7542 | 0.7306 | 0.7542 | 0.7238 |
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+ | 0.5631 | 12.0 | 48 | 0.5426 | 0.7458 | 0.7187 | 0.7458 | 0.7145 |
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+ | 0.5631 | 13.0 | 52 | 0.5383 | 0.7458 | 0.7187 | 0.7458 | 0.7145 |
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+ | 0.5631 | 14.0 | 56 | 0.5432 | 0.7458 | 0.7239 | 0.7458 | 0.7269 |
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+ | 0.541 | 15.0 | 60 | 0.5453 | 0.7417 | 0.7212 | 0.7417 | 0.7256 |
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  ### Framework versions
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