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beit-finetuned-pokemon

This model is a fine-tuned version of ydmeira/beit-finetuned-pokemon on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0219
  • Mean Iou: 0.4955
  • Mean Accuracy: 0.9910
  • Overall Accuracy: 0.9910
  • Per Category Iou: [0.0, 0.9909617791470107]
  • Per Category Accuracy: [nan, 0.9909617791470107]

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 6e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Per Category Iou Per Category Accuracy
0.0354 0.21 1000 0.0347 0.4978 0.9955 0.9955 [0.0, 0.9955007125868244] [nan, 0.9955007125868244]
0.0273 0.43 2000 0.0277 0.4951 0.9903 0.9903 [0.0, 0.9902709092544748] [nan, 0.9902709092544748]
0.0307 0.64 3000 0.0788 0.4875 0.9751 0.9751 [0.0, 0.9750850921785902] [nan, 0.9750850921785902]
0.0295 0.85 4000 0.0412 0.4939 0.9877 0.9877 [0.0, 0.9877162657609527] [nan, 0.9877162657609527]
0.0255 1.07 5000 0.0842 0.4862 0.9723 0.9723 [0.0, 0.972304346385062] [nan, 0.972304346385062]
0.0253 1.28 6000 0.0325 0.4950 0.9901 0.9901 [0.0, 0.9900621363084688] [nan, 0.9900621363084688]
0.0239 1.49 7000 0.0440 0.4917 0.9835 0.9835 [0.0, 0.9834701005512881] [nan, 0.9834701005512881]
0.0238 1.71 8000 0.0338 0.4950 0.9900 0.9900 [0.0, 0.9899977115151821] [nan, 0.9899977115151821]
0.0223 1.92 9000 0.0319 0.4950 0.9900 0.9900 [0.0, 0.989994712810938] [nan, 0.989994712810938]
0.0231 2.13 10000 0.0382 0.4921 0.9841 0.9841 [0.0, 0.984106425591889] [nan, 0.984106425591889]
0.0205 2.35 11000 0.0450 0.4926 0.9851 0.9851 [0.0, 0.9851146530893756] [nan, 0.9851146530893756]
0.0201 2.56 12000 0.0265 0.4954 0.9908 0.9908 [0.0, 0.9908277212846449] [nan, 0.9908277212846449]
0.0188 2.77 13000 0.0377 0.4933 0.9866 0.9866 [0.0, 0.9865726862234793] [nan, 0.9865726862234793]
0.0181 2.99 14000 0.0219 0.4955 0.9910 0.9910 [0.0, 0.9909617791470107] [nan, 0.9909617791470107]

Framework versions

  • Transformers 4.22.1
  • Pytorch 1.12.1+cu113
  • Datasets 2.4.0
  • Tokenizers 0.12.1
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