segformer-b4-finetuned-segments-chargers-full-v4.1

This model is a fine-tuned version of nvidia/mit-b4 on the dskong07/chargers-full-v0.1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4050
  • Mean Iou: 0.7302
  • Mean Accuracy: 0.8405
  • Overall Accuracy: 0.9155
  • Accuracy Unlabeled: nan
  • Accuracy Screen: 0.8874
  • Accuracy Body: 0.9156
  • Accuracy Cable: 0.6425
  • Accuracy Plug: 0.8031
  • Accuracy Void-background: 0.9539
  • Iou Unlabeled: nan
  • Iou Screen: 0.7807
  • Iou Body: 0.8343
  • Iou Cable: 0.5652
  • Iou Plug: 0.5556
  • Iou Void-background: 0.9154

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: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Screen Accuracy Body Accuracy Cable Accuracy Plug Accuracy Void-background Iou Unlabeled Iou Screen Iou Body Iou Cable Iou Plug Iou Void-background
0.5049 2.2222 20 0.8068 0.5018 0.7473 0.8447 nan 0.7238 0.9311 0.4504 0.7647 0.8667 0.0 0.6849 0.7101 0.3604 0.4256 0.8298
0.2822 4.4444 40 0.5160 0.6209 0.7951 0.8640 nan 0.9440 0.7910 0.5499 0.7662 0.9246 nan 0.6072 0.7047 0.4638 0.4484 0.8802
0.1945 6.6667 60 0.4086 0.6900 0.8274 0.8978 nan 0.8896 0.8973 0.6177 0.7982 0.9342 nan 0.7376 0.8019 0.5163 0.4930 0.9009
0.2245 8.8889 80 0.4093 0.7007 0.8260 0.9020 nan 0.8361 0.9279 0.6242 0.8090 0.9328 nan 0.7540 0.8136 0.5345 0.4992 0.9021
0.1808 11.1111 100 0.3741 0.7021 0.8068 0.9051 nan 0.7754 0.9242 0.6154 0.7694 0.9498 nan 0.7222 0.8089 0.5384 0.5339 0.9070
0.1342 13.3333 120 0.3797 0.7106 0.8224 0.9066 nan 0.8693 0.9029 0.6329 0.7564 0.9504 nan 0.7614 0.8113 0.5539 0.5205 0.9061
0.1204 15.5556 140 0.4131 0.7112 0.8367 0.9063 nan 0.8737 0.9160 0.6331 0.8214 0.9392 nan 0.7638 0.8212 0.5585 0.5070 0.9057
0.1042 17.7778 160 0.3944 0.7180 0.8386 0.9096 nan 0.8884 0.9107 0.6271 0.8207 0.9461 nan 0.7674 0.8208 0.5567 0.5350 0.9103
0.093 20.0 180 0.3910 0.7231 0.8400 0.9121 nan 0.9020 0.9020 0.6366 0.8069 0.9526 nan 0.7731 0.8274 0.5605 0.5424 0.9119
0.0989 22.2222 200 0.3632 0.7260 0.8355 0.9142 nan 0.9002 0.9133 0.6173 0.7920 0.9547 nan 0.7818 0.8316 0.5560 0.5466 0.9139
0.0937 24.4444 220 0.3956 0.7241 0.8367 0.9130 nan 0.8941 0.9060 0.6165 0.8118 0.9550 nan 0.7743 0.8286 0.5584 0.5467 0.9128
0.0987 26.6667 240 0.4233 0.7240 0.8409 0.9125 nan 0.9040 0.9013 0.6209 0.8244 0.9537 nan 0.7752 0.8274 0.5574 0.5477 0.9124
0.0935 28.8889 260 0.4249 0.7252 0.8392 0.9124 nan 0.8810 0.9102 0.6346 0.8192 0.9512 nan 0.7748 0.8265 0.5619 0.5515 0.9115
0.0827 31.1111 280 0.4266 0.7241 0.8409 0.9124 nan 0.8800 0.9169 0.6387 0.8208 0.9480 nan 0.7736 0.8301 0.5659 0.5388 0.9121
0.0785 33.3333 300 0.4034 0.7285 0.8386 0.9144 nan 0.8847 0.9154 0.6273 0.8122 0.9533 nan 0.7804 0.8317 0.5631 0.5539 0.9135
0.0733 35.5556 320 0.4061 0.7316 0.8446 0.9150 nan 0.8993 0.9031 0.6489 0.8164 0.9554 nan 0.7853 0.8312 0.5658 0.5614 0.9144
0.0683 37.7778 340 0.4115 0.7266 0.8347 0.9148 nan 0.8581 0.9301 0.6239 0.8105 0.9511 nan 0.7685 0.8340 0.5604 0.5545 0.9154
0.1087 40.0 360 0.4317 0.7297 0.8459 0.9143 nan 0.9070 0.9005 0.6467 0.8211 0.9544 nan 0.7768 0.8286 0.5663 0.5620 0.9146
0.0634 42.2222 380 0.4252 0.7296 0.8426 0.9150 nan 0.8926 0.9141 0.6285 0.8250 0.9529 nan 0.7812 0.8341 0.5644 0.5541 0.9144
0.0786 44.4444 400 0.3969 0.7294 0.8408 0.9151 nan 0.8860 0.9139 0.6511 0.7993 0.9535 nan 0.7787 0.8333 0.5647 0.5551 0.9153
0.0693 46.6667 420 0.4133 0.7292 0.8422 0.9147 nan 0.8853 0.9138 0.6427 0.8168 0.9525 nan 0.7782 0.8334 0.5674 0.5523 0.9145
0.0663 48.8889 440 0.4050 0.7302 0.8405 0.9155 nan 0.8874 0.9156 0.6425 0.8031 0.9539 nan 0.7807 0.8343 0.5652 0.5556 0.9154

Framework versions

  • Transformers 4.48.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0
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