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convnext-tiny-224-finetuned

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  1. README.md +34 -24
  2. model.safetensors +1 -1
README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1657
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- - Logloss: 1.1657
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  - Accuracy: {'accuracy': 0.5808823529411765}
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  ## Model description
@@ -47,31 +47,41 @@ The following hyperparameters were used during training:
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 20
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Logloss | Accuracy |
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- |:-------------:|:-------:|:----:|:---------------:|:-------:|:---------------------------------:|
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- | No log | 0.9412 | 8 | 1.6072 | 1.6072 | {'accuracy': 0.18382352941176472} |
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- | 1.6101 | 2.0 | 17 | 1.5668 | 1.5668 | {'accuracy': 0.31985294117647056} |
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- | 1.5645 | 2.9412 | 25 | 1.5246 | 1.5246 | {'accuracy': 0.33455882352941174} |
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- | 1.4902 | 4.0 | 34 | 1.4774 | 1.4774 | {'accuracy': 0.4007352941176471} |
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- | 1.4243 | 4.9412 | 42 | 1.4283 | 1.4283 | {'accuracy': 0.44485294117647056} |
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- | 1.3502 | 6.0 | 51 | 1.3747 | 1.3747 | {'accuracy': 0.48161764705882354} |
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- | 1.3502 | 6.9412 | 59 | 1.3332 | 1.3332 | {'accuracy': 0.48161764705882354} |
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- | 1.2906 | 8.0 | 68 | 1.2978 | 1.2978 | {'accuracy': 0.5036764705882353} |
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- | 1.2371 | 8.9412 | 76 | 1.2702 | 1.2702 | {'accuracy': 0.5147058823529411} |
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- | 1.1856 | 10.0 | 85 | 1.2434 | 1.2434 | {'accuracy': 0.5404411764705882} |
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- | 1.1506 | 10.9412 | 93 | 1.2300 | 1.2300 | {'accuracy': 0.5477941176470589} |
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- | 1.0987 | 12.0 | 102 | 1.2088 | 1.2088 | {'accuracy': 0.5588235294117647} |
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- | 1.0758 | 12.9412 | 110 | 1.1949 | 1.1949 | {'accuracy': 0.5514705882352942} |
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- | 1.0758 | 14.0 | 119 | 1.1896 | 1.1896 | {'accuracy': 0.5588235294117647} |
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- | 1.0483 | 14.9412 | 127 | 1.1773 | 1.1773 | {'accuracy': 0.5698529411764706} |
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- | 1.0346 | 16.0 | 136 | 1.1719 | 1.1719 | {'accuracy': 0.5735294117647058} |
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- | 1.0215 | 16.9412 | 144 | 1.1702 | 1.1702 | {'accuracy': 0.5698529411764706} |
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- | 1.0177 | 18.0 | 153 | 1.1666 | 1.1666 | {'accuracy': 0.5772058823529411} |
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- | 0.9956 | 18.8235 | 160 | 1.1657 | 1.1657 | {'accuracy': 0.5808823529411765} |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0987
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+ - Logloss: 1.0987
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  - Accuracy: {'accuracy': 0.5808823529411765}
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  ## Model description
 
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 30
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Logloss | Accuracy |
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+ |:-------------:|:-------:|:----:|:---------------:|:-------:|:--------------------------------:|
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+ | No log | 0.9412 | 8 | 1.1618 | 1.1618 | {'accuracy': 0.5882352941176471} |
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+ | 0.9966 | 2.0 | 17 | 1.1464 | 1.1464 | {'accuracy': 0.5845588235294118} |
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+ | 0.9619 | 2.9412 | 25 | 1.1380 | 1.1380 | {'accuracy': 0.5772058823529411} |
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+ | 0.9133 | 4.0 | 34 | 1.1249 | 1.1249 | {'accuracy': 0.5735294117647058} |
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+ | 0.8577 | 4.9412 | 42 | 1.1043 | 1.1043 | {'accuracy': 0.5882352941176471} |
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+ | 0.8092 | 6.0 | 51 | 1.0899 | 1.0899 | {'accuracy': 0.5955882352941176} |
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+ | 0.8092 | 6.9412 | 59 | 1.0927 | 1.0927 | {'accuracy': 0.5845588235294118} |
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+ | 0.772 | 8.0 | 68 | 1.0834 | 1.0834 | {'accuracy': 0.5845588235294118} |
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+ | 0.7128 | 8.9412 | 76 | 1.0730 | 1.0730 | {'accuracy': 0.5845588235294118} |
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+ | 0.6902 | 10.0 | 85 | 1.0788 | 1.0788 | {'accuracy': 0.5882352941176471} |
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+ | 0.645 | 10.9412 | 93 | 1.0649 | 1.0649 | {'accuracy': 0.5808823529411765} |
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+ | 0.591 | 12.0 | 102 | 1.0631 | 1.0631 | {'accuracy': 0.5845588235294118} |
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+ | 0.578 | 12.9412 | 110 | 1.0764 | 1.0764 | {'accuracy': 0.5845588235294118} |
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+ | 0.578 | 14.0 | 119 | 1.0658 | 1.0658 | {'accuracy': 0.5808823529411765} |
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+ | 0.5377 | 14.9412 | 127 | 1.0674 | 1.0674 | {'accuracy': 0.5808823529411765} |
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+ | 0.516 | 16.0 | 136 | 1.0798 | 1.0798 | {'accuracy': 0.5808823529411765} |
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+ | 0.4974 | 16.9412 | 144 | 1.0804 | 1.0804 | {'accuracy': 0.5808823529411765} |
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+ | 0.4649 | 18.0 | 153 | 1.0818 | 1.0818 | {'accuracy': 0.5955882352941176} |
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+ | 0.4422 | 18.9412 | 161 | 1.0742 | 1.0742 | {'accuracy': 0.5808823529411765} |
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+ | 0.4222 | 20.0 | 170 | 1.0862 | 1.0862 | {'accuracy': 0.5735294117647058} |
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+ | 0.4222 | 20.9412 | 178 | 1.0935 | 1.0935 | {'accuracy': 0.5772058823529411} |
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+ | 0.4136 | 22.0 | 187 | 1.0907 | 1.0907 | {'accuracy': 0.5772058823529411} |
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+ | 0.4006 | 22.9412 | 195 | 1.0967 | 1.0967 | {'accuracy': 0.5735294117647058} |
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+ | 0.4032 | 24.0 | 204 | 1.0931 | 1.0931 | {'accuracy': 0.5772058823529411} |
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+ | 0.3805 | 24.9412 | 212 | 1.1000 | 1.1000 | {'accuracy': 0.5845588235294118} |
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+ | 0.3654 | 26.0 | 221 | 1.1078 | 1.1078 | {'accuracy': 0.5661764705882353} |
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+ | 0.3654 | 26.9412 | 229 | 1.0959 | 1.0959 | {'accuracy': 0.5772058823529411} |
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+ | 0.3678 | 28.0 | 238 | 1.0986 | 1.0986 | {'accuracy': 0.5808823529411765} |
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+ | 0.3789 | 28.2353 | 240 | 1.0987 | 1.0987 | {'accuracy': 0.5808823529411765} |
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  ### Framework versions
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