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Pamreth
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deit-ena24

Image Classification
Transformers
TensorBoard
Safetensors
deit
Generated from Trainer
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  1. README.md +18 -19
  2. model.safetensors +1 -1
README.md CHANGED
@@ -3,7 +3,6 @@ library_name: transformers
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  license: apache-2.0
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  base_model: facebook/deit-base-distilled-patch16-224
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  tags:
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- - image-classification
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  - generated_from_trainer
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  metrics:
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  - accuracy
@@ -17,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # deit-ena24
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- This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the ena24 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0999
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- - Accuracy: 0.9763
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  ## Model description
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@@ -52,21 +51,21 @@ 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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- | 1.2545 | 0.1302 | 100 | 0.9921 | 0.7122 |
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- | 0.8766 | 0.2604 | 200 | 0.5768 | 0.8443 |
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- | 0.5148 | 0.3906 | 300 | 0.4472 | 0.8618 |
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- | 0.4511 | 0.5208 | 400 | 0.4786 | 0.8779 |
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- | 0.4874 | 0.6510 | 500 | 0.4083 | 0.8863 |
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- | 0.5794 | 0.7812 | 600 | 0.3513 | 0.8977 |
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- | 0.3324 | 0.9115 | 700 | 0.2395 | 0.9282 |
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- | 0.0975 | 1.0417 | 800 | 0.2091 | 0.9473 |
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- | 0.0579 | 1.1719 | 900 | 0.1919 | 0.9420 |
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- | 0.2113 | 1.3021 | 1000 | 0.1756 | 0.9611 |
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- | 0.0301 | 1.4323 | 1100 | 0.1412 | 0.9664 |
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- | 0.0534 | 1.5625 | 1200 | 0.1346 | 0.9687 |
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- | 0.0868 | 1.6927 | 1300 | 0.1292 | 0.9687 |
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- | 0.0623 | 1.8229 | 1400 | 0.1086 | 0.9763 |
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- | 0.1078 | 1.9531 | 1500 | 0.0999 | 0.9763 |
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  ### Framework versions
 
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  license: apache-2.0
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  base_model: facebook/deit-base-distilled-patch16-224
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  tags:
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
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  # deit-ena24
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+ This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0870
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+ - Accuracy: 0.9794
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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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+ | 1.2994 | 0.1302 | 100 | 1.0314 | 0.7092 |
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+ | 0.8789 | 0.2604 | 200 | 0.6169 | 0.8328 |
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+ | 0.4592 | 0.3906 | 300 | 0.5234 | 0.8298 |
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+ | 0.6806 | 0.5208 | 400 | 0.5431 | 0.8489 |
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+ | 0.4878 | 0.6510 | 500 | 0.3905 | 0.8855 |
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+ | 0.4643 | 0.7812 | 600 | 0.3281 | 0.9092 |
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+ | 0.3765 | 0.9115 | 700 | 0.2398 | 0.9290 |
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+ | 0.1379 | 1.0417 | 800 | 0.1861 | 0.9412 |
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+ | 0.1422 | 1.1719 | 900 | 0.1657 | 0.9527 |
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+ | 0.2655 | 1.3021 | 1000 | 0.1526 | 0.9557 |
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+ | 0.0304 | 1.4323 | 1100 | 0.1578 | 0.9634 |
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+ | 0.072 | 1.5625 | 1200 | 0.1418 | 0.9679 |
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+ | 0.2936 | 1.6927 | 1300 | 0.1003 | 0.9771 |
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+ | 0.0333 | 1.8229 | 1400 | 0.0935 | 0.9794 |
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+ | 0.0844 | 1.9531 | 1500 | 0.0870 | 0.9794 |
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  ### Framework versions
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