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upernet-swin-small-finetuned

This model is a fine-tuned version of openmmlab/upernet-swin-small on the jpodivin/plantorgans dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2914
  • Mean Iou: 0.4182
  • Mean Accuracy: 0.5282
  • Overall Accuracy: 0.7341
  • Accuracy Void: nan
  • Accuracy Fruit: 0.8590
  • Accuracy Leaf: 0.7032
  • Accuracy Flower: 0.0
  • Accuracy Stem: 0.5505
  • Iou Void: 0.0
  • Iou Fruit: 0.8554
  • Iou Leaf: 0.6976
  • Iou Flower: 0.0
  • Iou Stem: 0.5381
  • Median Iou: 0.5381

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: 0.0006
  • train_batch_size: 10
  • eval_batch_size: 10
  • 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 Accuracy Void Accuracy Fruit Accuracy Leaf Accuracy Flower Accuracy Stem Iou Void Iou Fruit Iou Leaf Iou Flower Iou Stem Median Iou
0.8566 1.0 575 0.3365 0.3723 0.4705 0.6560 nan 0.8000 0.6122 0.0 0.4699 0.0 0.7976 0.6041 0.0 0.4598 0.4598
0.3338 2.0 1150 0.3030 0.3922 0.4937 0.7155 nan 0.8558 0.7024 0.0 0.4166 0.0 0.8517 0.6972 0.0 0.4119 0.4119
0.3477 3.0 1725 0.2914 0.4182 0.5282 0.7341 nan 0.8590 0.7032 0.0 0.5505 0.0 0.8554 0.6976 0.0 0.5381 0.5381

Framework versions

  • Transformers 4.38.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0
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