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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: 0.7333333333333333
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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
@@ -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: 0.5490
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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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@@ -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 | 0.6369 | 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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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8966666666666666
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  - name: Precision
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  type: precision
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+ value: 0.891224605606628
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  - name: Recall
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  type: recall
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+ value: 0.8966666666666666
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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: 0.2426
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+ - Accuracy: 0.8967
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+ - Precision: 0.8912
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+ - Recall: 0.8967
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+ - F1 Score: 0.8935
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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 | 0.4160 | 0.8667 | 0.8037 | 0.8667 | 0.8160 |
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+ | No log | 2.0 | 8 | 0.4441 | 0.8375 | 0.7702 | 0.8375 | 0.7998 |
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+ | No log | 3.0 | 12 | 0.4451 | 0.8667 | 0.8559 | 0.8667 | 0.8605 |
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+ | 0.4959 | 4.0 | 16 | 0.3299 | 0.8792 | 0.8545 | 0.8792 | 0.8551 |
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+ | 0.4959 | 5.0 | 20 | 0.3813 | 0.8458 | 0.8776 | 0.8458 | 0.8580 |
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+ | 0.4959 | 6.0 | 24 | 0.2802 | 0.8958 | 0.8851 | 0.8958 | 0.8881 |
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+ | 0.4959 | 7.0 | 28 | 0.2991 | 0.8875 | 0.8830 | 0.8875 | 0.8850 |
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+ | 0.3696 | 8.0 | 32 | 0.2565 | 0.8917 | 0.8792 | 0.8917 | 0.8825 |
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+ | 0.3696 | 9.0 | 36 | 0.2582 | 0.9 | 0.8949 | 0.9 | 0.8970 |
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+ | 0.3696 | 10.0 | 40 | 0.2472 | 0.9 | 0.8927 | 0.9 | 0.8954 |
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+ | 0.3696 | 11.0 | 44 | 0.2463 | 0.9208 | 0.9179 | 0.9208 | 0.9191 |
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+ | 0.3299 | 12.0 | 48 | 0.2474 | 0.9167 | 0.9145 | 0.9167 | 0.9155 |
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+ | 0.3299 | 13.0 | 52 | 0.2826 | 0.8833 | 0.8971 | 0.8833 | 0.8889 |
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+ | 0.3299 | 14.0 | 56 | 0.2720 | 0.8958 | 0.9035 | 0.8958 | 0.8991 |
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+ | 0.3036 | 15.0 | 60 | 0.2629 | 0.9 | 0.9059 | 0.9 | 0.9025 |
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  ### Framework versions
pytorch_model.bin CHANGED
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