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convnext-tiny-224-finetuned

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  1. README.md +37 -36
  2. model.safetensors +1 -1
  3. runs/Sep04_17-57-52_93e6f912ca7d/events.out.tfevents.1725472680.93e6f912ca7d.1732.0 +2 -2
README.md CHANGED
@@ -1,4 +1,5 @@
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  ---
 
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  license: apache-2.0
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  base_model: facebook/convnext-tiny-224
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  tags:
@@ -17,9 +18,9 @@ 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: 1.0987
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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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@@ -51,42 +52,42 @@ The following hyperparameters were used during training:
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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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- - Transformers 4.42.4
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- - Pytorch 2.3.1+cu121
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  - Datasets 2.21.0
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  - Tokenizers 0.19.1
 
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  ---
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+ library_name: transformers
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  license: apache-2.0
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  base_model: facebook/convnext-tiny-224
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  tags:
 
18
 
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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: 1.0311
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+ - Logloss: 1.0311
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+ - Accuracy: {'accuracy': 0.5626423690205011}
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  ## Model description
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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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+ | 1.6135 | 0.9455 | 13 | 1.5881 | 1.5881 | {'accuracy': 0.22323462414578588} |
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+ | 1.5823 | 1.9636 | 27 | 1.5302 | 1.5302 | {'accuracy': 0.35990888382687924} |
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+ | 1.4988 | 2.9818 | 41 | 1.4480 | 1.4480 | {'accuracy': 0.4874715261958998} |
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+ | 1.4303 | 4.0 | 55 | 1.3424 | 1.3424 | {'accuracy': 0.5034168564920274} |
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+ | 1.3653 | 4.9455 | 68 | 1.2544 | 1.2544 | {'accuracy': 0.5239179954441914} |
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+ | 1.2232 | 5.9636 | 82 | 1.1867 | 1.1867 | {'accuracy': 0.5239179954441914} |
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+ | 1.1734 | 6.9818 | 96 | 1.1330 | 1.1330 | {'accuracy': 0.5466970387243736} |
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+ | 1.0747 | 8.0 | 110 | 1.1197 | 1.1197 | {'accuracy': 0.5626423690205011} |
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+ | 1.0405 | 8.9455 | 123 | 1.0871 | 1.0871 | {'accuracy': 0.5535307517084282} |
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+ | 1.0313 | 9.9636 | 137 | 1.0900 | 1.0900 | {'accuracy': 0.5671981776765376} |
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+ | 0.959 | 10.9818 | 151 | 1.0766 | 1.0766 | {'accuracy': 0.5603644646924829} |
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+ | 0.9314 | 12.0 | 165 | 1.0608 | 1.0608 | {'accuracy': 0.5603644646924829} |
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+ | 0.9102 | 12.9455 | 178 | 1.0388 | 1.0388 | {'accuracy': 0.5649202733485194} |
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+ | 0.8437 | 13.9636 | 192 | 1.0332 | 1.0332 | {'accuracy': 0.5785876993166287} |
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+ | 0.8234 | 14.9818 | 206 | 1.0302 | 1.0302 | {'accuracy': 0.5763097949886105} |
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+ | 0.7883 | 16.0 | 220 | 1.0276 | 1.0276 | {'accuracy': 0.5649202733485194} |
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+ | 0.7364 | 16.9455 | 233 | 1.0278 | 1.0278 | {'accuracy': 0.5649202733485194} |
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+ | 0.7561 | 17.9636 | 247 | 1.0258 | 1.0258 | {'accuracy': 0.5649202733485194} |
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+ | 0.7062 | 18.9818 | 261 | 1.0196 | 1.0196 | {'accuracy': 0.5694760820045558} |
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+ | 0.6897 | 20.0 | 275 | 1.0308 | 1.0308 | {'accuracy': 0.5558086560364465} |
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+ | 0.6511 | 20.9455 | 288 | 1.0247 | 1.0247 | {'accuracy': 0.5626423690205011} |
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+ | 0.6338 | 21.9636 | 302 | 1.0310 | 1.0310 | {'accuracy': 0.5603644646924829} |
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+ | 0.619 | 22.9818 | 316 | 1.0258 | 1.0258 | {'accuracy': 0.5671981776765376} |
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+ | 0.6008 | 24.0 | 330 | 1.0299 | 1.0299 | {'accuracy': 0.5626423690205011} |
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+ | 0.601 | 24.9455 | 343 | 1.0329 | 1.0329 | {'accuracy': 0.5671981776765376} |
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+ | 0.595 | 25.9636 | 357 | 1.0277 | 1.0277 | {'accuracy': 0.5694760820045558} |
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+ | 0.598 | 26.9818 | 371 | 1.0288 | 1.0288 | {'accuracy': 0.5671981776765376} |
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+ | 0.5771 | 28.0 | 385 | 1.0311 | 1.0311 | {'accuracy': 0.5603644646924829} |
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+ | 0.5829 | 28.3636 | 390 | 1.0311 | 1.0311 | {'accuracy': 0.5626423690205011} |
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  ### Framework versions
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+ - Transformers 4.44.2
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+ - Pytorch 2.4.0+cu121
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  - Datasets 2.21.0
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  - Tokenizers 0.19.1
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