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beit-finetuned-pokemon

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update model card README.md

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@@ -14,12 +14,12 @@ should probably proofread and complete it, then remove this comment. -->
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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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@@ -44,35 +44,31 @@ 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: 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
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- - Transformers 4.21.2
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  - Pytorch 1.12.1+cu113
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  - Datasets 2.4.0
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  - Tokenizers 0.12.1
 
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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: 0.0219
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+ - Mean Iou: 0.4955
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+ - Mean Accuracy: 0.9910
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+ - Overall Accuracy: 0.9910
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+ - Per Category Iou: [0.0, 0.9909617791470107]
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+ - Per Category Accuracy: [nan, 0.9909617791470107]
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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: 3
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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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+ | 0.0354 | 0.21 | 1000 | 0.0347 | 0.4978 | 0.9955 | 0.9955 | [0.0, 0.9955007125868244] | [nan, 0.9955007125868244] |
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+ | 0.0273 | 0.43 | 2000 | 0.0277 | 0.4951 | 0.9903 | 0.9903 | [0.0, 0.9902709092544748] | [nan, 0.9902709092544748] |
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+ | 0.0307 | 0.64 | 3000 | 0.0788 | 0.4875 | 0.9751 | 0.9751 | [0.0, 0.9750850921785902] | [nan, 0.9750850921785902] |
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+ | 0.0295 | 0.85 | 4000 | 0.0412 | 0.4939 | 0.9877 | 0.9877 | [0.0, 0.9877162657609527] | [nan, 0.9877162657609527] |
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+ | 0.0255 | 1.07 | 5000 | 0.0842 | 0.4862 | 0.9723 | 0.9723 | [0.0, 0.972304346385062] | [nan, 0.972304346385062] |
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+ | 0.0253 | 1.28 | 6000 | 0.0325 | 0.4950 | 0.9901 | 0.9901 | [0.0, 0.9900621363084688] | [nan, 0.9900621363084688] |
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+ | 0.0239 | 1.49 | 7000 | 0.0440 | 0.4917 | 0.9835 | 0.9835 | [0.0, 0.9834701005512881] | [nan, 0.9834701005512881] |
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+ | 0.0238 | 1.71 | 8000 | 0.0338 | 0.4950 | 0.9900 | 0.9900 | [0.0, 0.9899977115151821] | [nan, 0.9899977115151821] |
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+ | 0.0223 | 1.92 | 9000 | 0.0319 | 0.4950 | 0.9900 | 0.9900 | [0.0, 0.989994712810938] | [nan, 0.989994712810938] |
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+ | 0.0231 | 2.13 | 10000 | 0.0382 | 0.4921 | 0.9841 | 0.9841 | [0.0, 0.984106425591889] | [nan, 0.984106425591889] |
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+ | 0.0205 | 2.35 | 11000 | 0.0450 | 0.4926 | 0.9851 | 0.9851 | [0.0, 0.9851146530893756] | [nan, 0.9851146530893756] |
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+ | 0.0201 | 2.56 | 12000 | 0.0265 | 0.4954 | 0.9908 | 0.9908 | [0.0, 0.9908277212846449] | [nan, 0.9908277212846449] |
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+ | 0.0188 | 2.77 | 13000 | 0.0377 | 0.4933 | 0.9866 | 0.9866 | [0.0, 0.9865726862234793] | [nan, 0.9865726862234793] |
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+ | 0.0181 | 2.99 | 14000 | 0.0219 | 0.4955 | 0.9910 | 0.9910 | [0.0, 0.9909617791470107] | [nan, 0.9909617791470107] |
 
 
 
 
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  ### Framework versions
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+ - Transformers 4.22.1
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  - Pytorch 1.12.1+cu113
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  - Datasets 2.4.0
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  - Tokenizers 0.12.1