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sam1120
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dropoff-utcustom-train-SF-RGBD-b5_1

Image Segmentation
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segformer
vision
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dropoff-utcustom-train-SF-RGBD-b5_1

This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/dropoff-utcustom-TRAIN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3428
  • Mean Iou: 0.4792
  • Mean Accuracy: 0.5000
  • Overall Accuracy: 0.9583
  • Accuracy Unlabeled: nan
  • Accuracy Dropoff: 0.0001
  • Accuracy Undropoff: 0.9999
  • Iou Unlabeled: nan
  • Iou Dropoff: 0.0001
  • Iou Undropoff: 0.9583

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: 3e-06
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 120

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Unlabeled Accuracy Dropoff Accuracy Undropoff Iou Unlabeled Iou Dropoff Iou Undropoff
0.8047 5.0 10 0.9867 0.2744 0.6315 0.7475 nan 0.5049 0.7581 0.0 0.0812 0.7422
0.7528 10.0 20 0.8526 0.3461 0.5957 0.9213 nan 0.2406 0.9508 0.0 0.1178 0.9205
0.7087 15.0 30 0.7023 0.3450 0.5533 0.9467 nan 0.1243 0.9824 0.0 0.0887 0.9464
0.6601 20.0 40 0.6251 0.3381 0.5390 0.9462 nan 0.0948 0.9832 0.0 0.0684 0.9460
0.6274 25.0 50 0.5828 0.3286 0.5178 0.9486 nan 0.0479 0.9876 0.0 0.0374 0.9485
0.5929 30.0 60 0.5478 0.3257 0.5122 0.9488 nan 0.0359 0.9884 0.0 0.0284 0.9487
0.5672 35.0 70 0.5237 0.3240 0.5088 0.9494 nan 0.0283 0.9893 0.0 0.0227 0.9493
0.5454 40.0 80 0.4966 0.4856 0.5072 0.9529 nan 0.0212 0.9933 nan 0.0183 0.9528
0.5261 45.0 90 0.4700 0.3234 0.5062 0.9553 nan 0.0163 0.9960 0.0 0.0149 0.9552
0.5012 50.0 100 0.4576 0.4832 0.5041 0.9563 nan 0.0107 0.9974 nan 0.0101 0.9563
0.4875 55.0 110 0.4430 0.4811 0.5018 0.9566 nan 0.0058 0.9978 nan 0.0056 0.9565
0.4622 60.0 120 0.4328 0.4800 0.5007 0.9570 nan 0.0031 0.9983 nan 0.0030 0.9570
0.4394 65.0 130 0.4179 0.4796 0.5004 0.9572 nan 0.0021 0.9986 nan 0.0021 0.9572
0.4352 70.0 140 0.4048 0.4795 0.5002 0.9573 nan 0.0016 0.9988 nan 0.0016 0.9573
0.426 75.0 150 0.3881 0.4796 0.5003 0.9577 nan 0.0015 0.9992 nan 0.0014 0.9577
0.4175 80.0 160 0.3794 0.4797 0.5004 0.9579 nan 0.0014 0.9994 nan 0.0014 0.9579
0.4087 85.0 170 0.3742 0.3196 0.5002 0.9577 nan 0.0012 0.9992 0.0 0.0012 0.9577
0.3887 90.0 180 0.3645 0.4792 0.4999 0.9581 nan 0.0003 0.9996 nan 0.0003 0.9581
0.3799 95.0 190 0.3540 0.4791 0.4999 0.9581 nan 0.0001 0.9997 nan 0.0001 0.9581
0.376 100.0 200 0.3511 0.4792 0.4999 0.9582 nan 0.0001 0.9998 nan 0.0001 0.9582
0.3677 105.0 210 0.3452 0.4792 0.4999 0.9582 nan 0.0001 0.9998 nan 0.0001 0.9582
0.358 110.0 220 0.3437 0.4792 0.4999 0.9582 nan 0.0001 0.9998 nan 0.0001 0.9582
0.3997 115.0 230 0.3434 0.4792 0.5000 0.9583 nan 0.0001 0.9999 nan 0.0001 0.9583
0.3769 120.0 240 0.3428 0.4792 0.5000 0.9583 nan 0.0001 0.9999 nan 0.0001 0.9583

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.13.1
  • Tokenizers 0.13.3
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Evaluation results

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