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beit-finetuned-pokemon

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@@ -12,14 +12,14 @@ should probably proofread and complete it, then remove this comment. -->
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  # beit-finetuned-pokemon
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- This model is a fine-tuned version of [microsoft/beit-base-finetuned-ade-640-640](https://huggingface.co/microsoft/beit-base-finetuned-ade-640-640) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0426
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- - Mean Accuracy: 0.9851
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- - Mean Iou: 0.4926
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- - Overall Accuracy: 0.9851
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- - Per Category Accuracy: [nan, 0.9851295328900131]
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- - Per Category Iou: [0.0, 0.9851295328900131]
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  ## Model description
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@@ -44,30 +44,30 @@ The following hyperparameters were used during training:
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  - seed: 42
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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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- - num_epochs: 1
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Mean Accuracy | Mean Iou | Overall Accuracy | Per Category Accuracy | Per Category Iou |
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- |:-------------:|:-----:|:----:|:---------------:|:-------------:|:--------:|:----------------:|:-------------------------:|:-------------------------:|
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- | 0.2845 | 0.05 | 250 | 0.1909 | 0.8750 | 0.4375 | 0.8750 | [nan, 0.8750296526422883] | [0.0, 0.8750296526422883] |
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- | 0.103 | 0.11 | 500 | 0.1987 | 0.9048 | 0.4524 | 0.9048 | [nan, 0.9047505435789185] | [0.0, 0.9047505435789185] |
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- | 0.091 | 0.16 | 750 | 0.2199 | 0.8935 | 0.4468 | 0.8935 | [nan, 0.8935388953867466] | [0.0, 0.8935388953867466] |
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- | 0.0787 | 0.21 | 1000 | 0.0498 | 0.9832 | 0.4916 | 0.9832 | [nan, 0.9832157481853218] | [0.0, 0.9832157481853218] |
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- | 0.0516 | 0.27 | 1250 | 0.0642 | 0.9767 | 0.4884 | 0.9767 | [nan, 0.9767367885585835] | [0.0, 0.9767367885585835] |
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- | 0.051 | 0.32 | 1500 | 0.0907 | 0.9582 | 0.4791 | 0.9582 | [nan, 0.9582013500039326] | [0.0, 0.9582013500039326] |
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- | 0.0518 | 0.37 | 1750 | 0.0813 | 0.9578 | 0.4789 | 0.9578 | [nan, 0.9577983594953152] | [0.0, 0.9577983594953152] |
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- | 0.038 | 0.43 | 2000 | 0.0394 | 0.9875 | 0.4937 | 0.9875 | [nan, 0.9874955917462267] | [0.0, 0.9874955917462267] |
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- | 0.0466 | 0.48 | 2250 | 0.0482 | 0.9831 | 0.4915 | 0.9831 | [nan, 0.9830982793221819] | [0.0, 0.9830982793221819] |
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- | 0.054 | 0.53 | 2500 | 0.0568 | 0.9818 | 0.4909 | 0.9818 | [nan, 0.9818346010498621] | [0.0, 0.9818346010498621] |
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- | 0.0356 | 0.59 | 2750 | 0.0330 | 0.9921 | 0.4961 | 0.9921 | [nan, 0.9921038026421615] | [0.0, 0.9921038026421615] |
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- | 0.0292 | 0.64 | 3000 | 0.0364 | 0.9893 | 0.4947 | 0.9893 | [nan, 0.9893293618878236] | [0.0, 0.9893293618878236] |
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- | 0.0252 | 0.69 | 3250 | 0.0607 | 0.9824 | 0.4912 | 0.9824 | [nan, 0.9823825882221607] | [0.0, 0.9823825882221607] |
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- | 0.0286 | 0.75 | 3500 | 0.0526 | 0.9830 | 0.4915 | 0.9830 | [nan, 0.9830357074898451] | [0.0, 0.9830357074898451] |
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- | 0.0297 | 0.8 | 3750 | 0.0403 | 0.9844 | 0.4922 | 0.9844 | [nan, 0.9843719475221174] | [0.0, 0.9843719475221174] |
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- | 0.0257 | 0.85 | 4000 | 0.0478 | 0.9848 | 0.4924 | 0.9848 | [nan, 0.9847944421751276] | [0.0, 0.9847944421751276] |
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- | 0.0271 | 0.91 | 4250 | 0.0340 | 0.9869 | 0.4935 | 0.9869 | [nan, 0.9869270221516337] | [0.0, 0.9869270221516337] |
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- | 0.0235 | 0.96 | 4500 | 0.0426 | 0.9851 | 0.4926 | 0.9851 | [nan, 0.9851295328900131] | [0.0, 0.9851295328900131] |
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  ### Framework versions
 
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  # beit-finetuned-pokemon
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+ This model is a fine-tuned version of [ydmeira/beit-finetuned-pokemon](https://huggingface.co/ydmeira/beit-finetuned-pokemon) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0222
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+ - Mean Iou: 0.4964
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+ - Mean Accuracy: 0.9927
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+ - Overall Accuracy: 0.9927
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+ - Per Category Iou: [0.0, 0.9927382211696605]
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+ - Per Category Accuracy: [nan, 0.9927382211696605]
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  ## Model description
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  - seed: 42
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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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+ - num_epochs: 2
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------------:|:-------------------------:|
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+ | 0.044 | 0.11 | 500 | 0.0430 | 0.4929 | 0.9857 | 0.9857 | [0.0, 0.9857017551704262] | [nan, 0.9857017551704262] |
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+ | 0.0495 | 0.21 | 1000 | 0.0345 | 0.4960 | 0.9920 | 0.9920 | [0.0, 0.9920118130744071] | [nan, 0.9920118130744071] |
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+ | 0.0382 | 0.32 | 1500 | 0.0399 | 0.4947 | 0.9894 | 0.9894 | [0.0, 0.9893992290428889] | [nan, 0.9893992290428889] |
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+ | 0.0361 | 0.43 | 2000 | 0.0311 | 0.4963 | 0.9926 | 0.9926 | [0.0, 0.9925511589842341] | [nan, 0.9925511589842341] |
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+ | 0.04 | 0.53 | 2500 | 0.0722 | 0.4920 | 0.9840 | 0.9840 | [0.0, 0.9839730680037156] | [nan, 0.9839730680037156] |
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+ | 0.0308 | 0.64 | 3000 | 0.0319 | 0.4977 | 0.9954 | 0.9954 | [0.0, 0.9954462252146663] | [nan, 0.9954462252146663] |
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+ | 0.0391 | 0.75 | 3500 | 0.1028 | 0.4837 | 0.9674 | 0.9674 | [0.0, 0.9673708120597321] | [nan, 0.9673708120597321] |
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+ | 0.0425 | 0.85 | 4000 | 0.0330 | 0.4973 | 0.9946 | 0.9946 | [0.0, 0.9946091381677958] | [nan, 0.9946091381677958] |
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+ | 0.0321 | 0.96 | 4500 | 0.0259 | 0.4963 | 0.9925 | 0.9925 | [0.0, 0.9925195785900393] | [nan, 0.9925195785900393] |
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+ | 0.031 | 1.07 | 5000 | 0.0270 | 0.4965 | 0.9930 | 0.9930 | [0.0, 0.9930111407071547] | [nan, 0.9930111407071547] |
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+ | 0.0281 | 1.17 | 5500 | 0.0367 | 0.4933 | 0.9866 | 0.9866 | [0.0, 0.9865881607581373] | [nan, 0.9865881607581373] |
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+ | 0.0325 | 1.28 | 6000 | 0.0327 | 0.4940 | 0.9880 | 0.9880 | [0.0, 0.9879893562856097] | [nan, 0.9879893562856097] |
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+ | 0.0253 | 1.39 | 6500 | 0.0237 | 0.4968 | 0.9937 | 0.9937 | [0.0, 0.9936538460005984] | [nan, 0.9936538460005984] |
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+ | 0.0258 | 1.49 | 7000 | 0.0241 | 0.4964 | 0.9928 | 0.9928 | [0.0, 0.9927783017073394] | [nan, 0.9927783017073394] |
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+ | 0.0266 | 1.6 | 7500 | 0.0234 | 0.4962 | 0.9924 | 0.9924 | [0.0, 0.9923954115635184] | [nan, 0.9923954115635184] |
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+ | 0.0223 | 1.71 | 8000 | 0.0264 | 0.4964 | 0.9928 | 0.9928 | [0.0, 0.9928421413266322] | [nan, 0.9928421413266322] |
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+ | 0.0212 | 1.81 | 8500 | 0.0235 | 0.4960 | 0.9920 | 0.9920 | [0.0, 0.9920402354291824] | [nan, 0.9920402354291824] |
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+ | 0.0196 | 1.92 | 9000 | 0.0222 | 0.4964 | 0.9927 | 0.9927 | [0.0, 0.9927382211696605] | [nan, 0.9927382211696605] |
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  ### Framework versions