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dropoff-utcustom-train-SF-RGB-b5_1

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metadata
license: other
tags:
  - generated_from_trainer
model-index:
  - name: dropoff-utcustom-train-SF-RGB-b5_1
    results: []

dropoff-utcustom-train-SF-RGB-b5_1

This model is a fine-tuned version of nvidia/mit-b5 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6279
  • Mean Iou: 0.4054
  • Mean Accuracy: 0.7471
  • Overall Accuracy: 0.8860
  • Accuracy Unlabeled: nan
  • Accuracy Dropoff: 0.5956
  • Accuracy Undropoff: 0.8986
  • Iou Unlabeled: 0.0
  • Iou Dropoff: 0.3318
  • Iou Undropoff: 0.8843

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: 2e-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
1.0071 5.0 10 1.0206 0.1745 0.2748 0.5034 nan 0.0255 0.5241 0.0 0.0147 0.5087
0.9688 10.0 20 0.9873 0.2140 0.3486 0.5771 nan 0.0992 0.5979 0.0 0.0582 0.5838
0.9406 15.0 30 0.9313 0.2613 0.4446 0.6655 nan 0.2038 0.6855 0.0 0.1135 0.6705
0.9278 20.0 40 0.8851 0.2930 0.5149 0.7111 nan 0.3009 0.7289 0.0 0.1648 0.7142
0.8956 25.0 50 0.8563 0.3118 0.5642 0.7358 nan 0.3770 0.7514 0.0 0.1985 0.7370
0.8674 30.0 60 0.8260 0.3303 0.6086 0.7664 nan 0.4366 0.7807 0.0 0.2246 0.7664
0.8438 35.0 70 0.8149 0.3347 0.6355 0.7671 nan 0.4921 0.7790 0.0 0.2381 0.7660
0.8309 40.0 80 0.7881 0.3459 0.6472 0.7847 nan 0.4972 0.7972 0.0 0.2539 0.7839
0.8069 45.0 90 0.7640 0.3567 0.6617 0.8041 nan 0.5063 0.8170 0.0 0.2668 0.8033
0.7779 50.0 100 0.7486 0.3637 0.6792 0.8145 nan 0.5316 0.8268 0.0 0.2778 0.8132
0.7695 55.0 110 0.7354 0.3684 0.6936 0.8214 nan 0.5542 0.8329 0.0 0.2858 0.8195
0.7568 60.0 120 0.7164 0.3757 0.7032 0.8365 nan 0.5577 0.8486 0.0 0.2924 0.8347
0.7285 65.0 130 0.6976 0.3836 0.7119 0.8484 nan 0.5630 0.8608 0.0 0.3042 0.8467
0.7217 70.0 140 0.6922 0.3857 0.7217 0.8499 nan 0.5817 0.8616 0.0 0.3091 0.8480
0.7095 75.0 150 0.6708 0.3926 0.7287 0.8624 nan 0.5828 0.8745 0.0 0.3172 0.8605
0.6944 80.0 160 0.6637 0.3951 0.7320 0.8660 nan 0.5858 0.8781 0.0 0.3212 0.8641
0.6878 85.0 170 0.6632 0.3942 0.7397 0.8673 nan 0.6005 0.8788 0.0 0.3175 0.8652
0.6868 90.0 180 0.6468 0.3998 0.7391 0.8756 nan 0.5902 0.8880 0.0 0.3257 0.8739
0.6581 95.0 190 0.6444 0.4003 0.7421 0.8776 nan 0.5942 0.8899 0.0 0.3249 0.8759
0.6587 100.0 200 0.6383 0.4026 0.7427 0.8814 nan 0.5914 0.8940 0.0 0.3281 0.8797
0.6525 105.0 210 0.6334 0.4032 0.7434 0.8825 nan 0.5918 0.8951 0.0 0.3289 0.8808
0.658 110.0 220 0.6345 0.4026 0.7451 0.8811 nan 0.5968 0.8934 0.0 0.3285 0.8793
0.6575 115.0 230 0.6300 0.4050 0.7463 0.8851 nan 0.5948 0.8977 0.0 0.3314 0.8835
0.6625 120.0 240 0.6279 0.4054 0.7471 0.8860 nan 0.5956 0.8986 0.0 0.3318 0.8843

Framework versions

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