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dropoff-utcustom-train-SF-RGB-b5_4

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metadata
license: other
tags:
  - vision
  - image-segmentation
  - generated_from_trainer
model-index:
  - name: dropoff-utcustom-train-SF-RGB-b5_4
    results: []

dropoff-utcustom-train-SF-RGB-b5_4

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: 0.2242
  • Mean Iou: 0.4568
  • Mean Accuracy: 0.7402
  • Overall Accuracy: 0.9696
  • Accuracy Unlabeled: nan
  • Accuracy Dropoff: 0.4899
  • Accuracy Undropoff: 0.9904
  • Iou Unlabeled: 0.0
  • Iou Dropoff: 0.4016
  • Iou Undropoff: 0.9690

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: 7e-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
0.9465 5.0 10 0.9974 0.2695 0.5001 0.6771 nan 0.3071 0.6931 0.0 0.1261 0.6824
0.8558 10.0 20 0.8237 0.3822 0.7119 0.8664 nan 0.5434 0.8804 0.0 0.2787 0.8678
0.7585 15.0 30 0.6801 0.4232 0.7487 0.9194 nan 0.5625 0.9349 0.0 0.3494 0.9202
0.715 20.0 40 0.6076 0.4298 0.7663 0.9232 nan 0.5952 0.9375 0.0 0.3661 0.9233
0.6145 25.0 50 0.5298 0.4398 0.7760 0.9380 nan 0.5994 0.9527 0.0 0.3819 0.9375
0.5355 30.0 60 0.4821 0.4426 0.7749 0.9428 nan 0.5918 0.9581 0.0 0.3857 0.9422
0.4619 35.0 70 0.4266 0.4493 0.7716 0.9524 nan 0.5743 0.9688 0.0 0.3962 0.9517
0.4367 40.0 80 0.3941 0.4519 0.7738 0.9568 nan 0.5742 0.9734 0.0 0.3997 0.9559
0.3839 45.0 90 0.3801 0.4528 0.7796 0.9577 nan 0.5853 0.9738 0.0 0.4017 0.9567
0.3164 50.0 100 0.3549 0.4543 0.7785 0.9608 nan 0.5797 0.9773 0.0 0.4030 0.9599
0.3018 55.0 110 0.3327 0.4573 0.7731 0.9639 nan 0.5650 0.9812 0.0 0.4087 0.9631
0.2646 60.0 120 0.3127 0.4590 0.7703 0.9658 nan 0.5571 0.9835 0.0 0.4121 0.9650
0.2378 65.0 130 0.2958 0.4628 0.7728 0.9673 nan 0.5607 0.9850 0.0 0.4217 0.9666
0.2076 70.0 140 0.2778 0.4675 0.7729 0.9693 nan 0.5586 0.9871 0.0 0.4340 0.9686
0.1951 75.0 150 0.2648 0.4666 0.7719 0.9692 nan 0.5567 0.9871 0.0 0.4314 0.9685
0.1734 80.0 160 0.2522 0.4673 0.7643 0.9703 nan 0.5397 0.9890 0.0 0.4322 0.9696
0.1569 85.0 170 0.2436 0.4660 0.7603 0.9703 nan 0.5312 0.9894 0.0 0.4282 0.9697
0.1691 90.0 180 0.2411 0.4647 0.7624 0.9697 nan 0.5363 0.9885 0.0 0.4250 0.9690
0.1498 95.0 190 0.2335 0.4623 0.7537 0.9699 nan 0.5179 0.9895 0.0 0.4176 0.9692
0.1478 100.0 200 0.2281 0.4585 0.7420 0.9700 nan 0.4934 0.9906 0.0 0.4062 0.9693
0.1407 105.0 210 0.2278 0.4615 0.7501 0.9701 nan 0.5102 0.9900 0.0 0.4151 0.9694
0.1397 110.0 220 0.2305 0.4610 0.7512 0.9698 nan 0.5129 0.9896 0.0 0.4140 0.9691
0.1317 115.0 230 0.2265 0.4576 0.7430 0.9695 nan 0.4959 0.9901 0.0 0.4038 0.9689
0.1548 120.0 240 0.2242 0.4568 0.7402 0.9696 nan 0.4899 0.9904 0.0 0.4016 0.9690

Framework versions

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