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metadata
license: other
base_model: nvidia/mit-b3
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: segformer-b2-seed63-apr-13-v1
    results: []

segformer-b2-seed63-apr-13-v1

This model is a fine-tuned version of nvidia/mit-b3 on the unreal-hug/REAL_DATASET_SEG_401_6_lbls dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7138
  • Mean Iou: 0.1266
  • Mean Accuracy: 0.2136
  • Overall Accuracy: 0.4273
  • Accuracy Unlabeled: nan
  • Accuracy Lv: 0.6939
  • Accuracy Rv: 0.0982
  • Accuracy Ra: 0.1706
  • Accuracy La: 0.5041
  • Accuracy Vs: 0.0
  • Accuracy As: 0.0
  • Accuracy Mk: 0.0
  • Accuracy Tk: nan
  • Accuracy Asd: 0.0557
  • Accuracy Vsd: 0.2283
  • Accuracy Ak: 0.3849
  • Iou Unlabeled: 0.0
  • Iou Lv: 0.4965
  • Iou Rv: 0.0899
  • Iou Ra: 0.1288
  • Iou La: 0.2845
  • Iou Vs: 0.0
  • Iou As: 0.0
  • Iou Mk: 0.0
  • Iou Tk: 0.0
  • Iou Asd: 0.0462
  • Iou Vsd: 0.1513
  • Iou Ak: 0.3225

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: 1e-06
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.05
  • training_steps: 1000

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Lv Accuracy Rv Accuracy Ra Accuracy La Accuracy Vs Accuracy As Accuracy Mk Accuracy Tk Accuracy Asd Accuracy Vsd Accuracy Ak Iou Unlabeled Iou Lv Iou Rv Iou Ra Iou La Iou Vs Iou As Iou Mk Iou Tk Iou Asd Iou Vsd Iou Ak
2.5423 2.5 100 2.6367 0.0332 0.0976 0.0951 nan 0.0612 0.0642 0.0301 0.1898 0.0 0.0 0.0086 nan 0.0495 0.4697 0.1033 0.0 0.0573 0.0485 0.0262 0.1021 0.0 0.0 0.0019 0.0 0.0204 0.0612 0.0812
2.3042 5.0 200 2.3925 0.0604 0.1412 0.1975 nan 0.2435 0.0655 0.1292 0.2869 0.0 0.0 0.0046 nan 0.0669 0.4894 0.1258 0.0 0.2144 0.0516 0.1074 0.1515 0.0 0.0 0.0017 0.0 0.0243 0.0670 0.1063
2.0869 7.5 300 2.2183 0.0932 0.1839 0.3354 nan 0.5208 0.0717 0.1836 0.4192 0.0 0.0 0.0006 nan 0.0768 0.3608 0.2060 0.0 0.4077 0.0617 0.1436 0.2158 0.0 0.0 0.0003 0.0 0.0358 0.0787 0.1746
2.0559 10.0 400 2.0298 0.1110 0.2055 0.3886 nan 0.6144 0.1027 0.1815 0.4598 0.0 0.0 0.0005 nan 0.0909 0.3011 0.3041 0.0 0.4559 0.0880 0.1400 0.2409 0.0 0.0 0.0003 0.0 0.0534 0.1001 0.2538
1.9554 12.5 500 1.8871 0.1189 0.2111 0.4100 nan 0.6561 0.1004 0.1647 0.4900 0.0 0.0 0.0009 nan 0.0763 0.2611 0.3619 0.0 0.4739 0.0896 0.1263 0.2616 0.0 0.0 0.0007 0.0 0.0531 0.1207 0.3015
2.0181 15.0 600 1.7720 0.1247 0.2139 0.4199 nan 0.6735 0.1008 0.1723 0.4898 0.0 0.0 0.0 nan 0.0706 0.2349 0.3972 0.0 0.4860 0.0912 0.1293 0.2720 0.0 0.0 0.0 0.0 0.0532 0.1386 0.3256
1.6723 17.5 700 1.7386 0.1258 0.2129 0.4251 nan 0.6860 0.1011 0.1724 0.5062 0.0 0.0 0.0 nan 0.0615 0.2167 0.3848 0.0 0.4927 0.0917 0.1304 0.2814 0.0 0.0 0.0 0.0 0.0488 0.1426 0.3221
1.5613 20.0 800 1.7751 0.1269 0.2151 0.4322 nan 0.7050 0.1020 0.1730 0.5066 0.0 0.0 0.0 nan 0.0570 0.2288 0.3788 0.0 0.4990 0.0927 0.1308 0.2841 0.0 0.0 0.0 0.0 0.0465 0.1502 0.3199
1.5653 22.5 900 1.7222 0.1272 0.2142 0.4277 nan 0.6924 0.1003 0.1794 0.5018 0.0 0.0 0.0 nan 0.0568 0.2295 0.3814 0.0 0.4969 0.0914 0.1341 0.2837 0.0 0.0 0.0 0.0 0.0466 0.1523 0.3209
1.5196 25.0 1000 1.7138 0.1266 0.2136 0.4273 nan 0.6939 0.0982 0.1706 0.5041 0.0 0.0 0.0 nan 0.0557 0.2283 0.3849 0.0 0.4965 0.0899 0.1288 0.2845 0.0 0.0 0.0 0.0 0.0462 0.1513 0.3225

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0