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segformer-finetuned-Maize-10k-steps-sem

This model is a fine-tuned version of nvidia/mit-b5 on the koushikn/Maize_sem_seg dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0756
  • Mean Iou: 0.9172
  • Mean Accuracy: 0.9711
  • Overall Accuracy: 0.9804
  • Accuracy Background: 0.9834
  • Accuracy Maize: 0.9588
  • Iou Background: 0.9779
  • Iou Maize: 0.8566

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: 0.001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 1337
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: polynomial
  • training_steps: 10000

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Maize Iou Background Iou Maize
0.0529 1.0 678 69.3785 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.3755 2.0 1356 0.9455 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.0603 3.0 2034 0.0920 0.8356 0.8602 0.9641 0.9976 0.7227 0.9607 0.7106
0.0341 4.0 2712 24.6203 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.0332 5.0 3390 101.5635 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.0331 6.0 4068 9.6824 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.0302 7.0 4746 260.7923 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.0305 8.0 5424 172.8153 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.0313 9.0 6102 304.2714 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.0301 10.0 6780 547.2355 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.03 11.0 7458 224.2607 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.0285 12.0 8136 116.3474 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.0284 13.0 8814 96.8429 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.0281 14.0 9492 54.2593 0.4391 0.5 0.8781 1.0 0.0 0.8781 0.0
0.028 14.75 10000 0.0756 0.9172 0.9711 0.9804 0.9834 0.9588 0.9779 0.8566

Framework versions

  • Transformers 4.21.0.dev0
  • Pytorch 1.10.0+cu102
  • Datasets 2.3.2
  • Tokenizers 0.12.1
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