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segformer-b0-finetuned-segments-greenhouse-oct-23

This model is a fine-tuned version of nvidia/mit-b0 on the MexicanVanGogh/greenhouse dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7058
  • Mean Iou: 0.2227
  • Mean Accuracy: 0.2804
  • Overall Accuracy: 0.9101
  • Accuracy Unlabeled: nan
  • Accuracy Object: nan
  • Accuracy Road: 0.9378
  • Accuracy Plant: 0.9667
  • Accuracy Iron: 0.0
  • Accuracy Wood: 0.0
  • Accuracy Wall: 0.1932
  • Accuracy Raw Road: nan
  • Accuracy Bottom Wall: 0.0
  • Accuracy Roof: 0.1457
  • Accuracy Grass: 0.0
  • Iou Unlabeled: nan
  • Iou Object: nan
  • Iou Road: 0.9039
  • Iou Plant: 0.8421
  • Iou Iron: 0.0
  • Iou Wood: 0.0
  • Iou Wall: 0.1521
  • Iou Raw Road: 0.0
  • Iou Bottom Wall: 0.0
  • Iou Roof: 0.1061
  • Iou Grass: 0.0

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: 30

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Object Accuracy Road Accuracy Plant Accuracy Iron Accuracy Wood Accuracy Wall Accuracy Raw Road Accuracy Bottom Wall Accuracy Roof Accuracy Grass Iou Unlabeled Iou Object Iou Road Iou Plant Iou Iron Iou Wood Iou Wall Iou Raw Road Iou Bottom Wall Iou Roof Iou Grass
1.8756 2.86 20 2.0063 0.1415 0.2269 0.8216 nan nan 0.7882 0.9674 0.0 0.0594 0.0 nan 0.0 0.0 0.0 0.0 0.0 0.7760 0.7552 0.0 0.0256 0.0 0.0 0.0 0.0 0.0
1.3624 5.71 40 1.0910 0.1715 0.2380 0.8991 nan nan 0.9206 0.9757 0.0 0.0077 0.0 nan 0.0 0.0 0.0 0.0 nan 0.8888 0.8220 0.0 0.0045 0.0 0.0 0.0 0.0 0.0
1.4095 8.57 60 0.9033 0.1734 0.2392 0.9068 nan nan 0.9264 0.9873 0.0 0.0 0.0 nan 0.0 0.0 0.0 0.0 nan 0.9000 0.8338 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.8802 11.43 80 0.7784 0.1764 0.2414 0.9165 nan nan 0.9470 0.9823 0.0 0.0022 0.0 nan 0.0 0.0 0.0 0.0 nan 0.9155 0.8463 0.0 0.0021 0.0 0.0 0.0 0.0 0.0
1.0936 14.29 100 0.8060 0.1946 0.2405 0.9132 nan nan 0.9400 0.9839 0.0 0.0 0.0 nan 0.0 0.0 0.0 nan nan 0.9100 0.8418 0.0 0.0 0.0 0.0 0.0 0.0 0.0
0.8086 17.14 120 0.7786 0.1940 0.2402 0.9115 nan nan 0.9361 0.9852 0.0 0.0 0.0 nan 0.0 0.0006 0.0 nan nan 0.9071 0.8380 0.0 0.0 0.0 0.0 0.0 0.0006 0.0
1.0669 20.0 140 0.7462 0.2072 0.2562 0.9088 nan nan 0.9282 0.9853 0.0 0.0 0.0113 nan 0.0 0.1246 0.0 nan nan 0.9010 0.8385 0.0 0.0 0.0102 0.0 0.0 0.1155 0.0
0.7399 22.86 160 0.7328 0.2137 0.2662 0.9080 nan nan 0.9290 0.9788 0.0 0.0 0.0814 nan 0.0 0.1405 0.0 nan nan 0.8997 0.8389 0.0 0.0 0.0663 0.0 0.0 0.1181 0.0
0.808 25.71 180 0.7296 0.2218 0.2797 0.9072 nan nan 0.9277 0.9742 0.0 0.0 0.1840 nan 0.0 0.1515 0.0 nan nan 0.8981 0.8404 0.0 0.0 0.1423 0.0 0.0 0.1155 0.0
0.8494 28.57 200 0.7058 0.2227 0.2804 0.9101 nan nan 0.9378 0.9667 0.0 0.0 0.1932 nan 0.0 0.1457 0.0 nan nan 0.9039 0.8421 0.0 0.0 0.1521 0.0 0.0 0.1061 0.0

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1
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