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model2

Image Segmentation
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PyTorch
segformer
vision
Generated from Trainer
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metadata
license: other
base_model: nvidia/mit-b2
tags:
  - image-segmentation
  - vision
  - generated_from_trainer
model-index:
  - name: model2
    results: []

model2

This model is a fine-tuned version of nvidia/mit-b2 on the giuseppemartino/isaid_sam_predicted dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2318
  • Mean Iou: 0.2504
  • Mean Accuracy: 0.3019
  • Overall Accuracy: 0.4542
  • Accuracy Background: nan
  • Accuracy Ship: 0.6330
  • Accuracy Small-vehicle: 0.4644
  • Accuracy Tennis-court: 0.0280
  • Accuracy Helicopter: nan
  • Accuracy Basketball-court: 0.0
  • Accuracy Ground-track-field: 0.6010
  • Accuracy Swimming-pool: nan
  • Accuracy Harbor: 0.4575
  • Accuracy Soccer-ball-field: 0.7776
  • Accuracy Plane: nan
  • Accuracy Storage-tank: 0.0
  • Accuracy Baseball-diamond: nan
  • Accuracy Large-vehicle: 0.3594
  • Accuracy Bridge: 0.0
  • Accuracy Roundabout: 0.0
  • Iou Background: 0.0
  • Iou Ship: 0.5194
  • Iou Small-vehicle: 0.4368
  • Iou Tennis-court: 0.0280
  • Iou Helicopter: nan
  • Iou Basketball-court: 0.0
  • Iou Ground-track-field: 0.5492
  • Iou Swimming-pool: nan
  • Iou Harbor: 0.3611
  • Iou Soccer-ball-field: 0.7592
  • Iou Plane: nan
  • Iou Storage-tank: 0.0
  • Iou Baseball-diamond: nan
  • Iou Large-vehicle: 0.3508
  • Iou Bridge: 0.0
  • Iou Roundabout: 0.0

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: 8
  • eval_batch_size: 8
  • seed: 1337
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: polynomial
  • training_steps: 1345

Training results

Training Loss Epoch Step Validation Loss Mean Iou Mean Accuracy Overall Accuracy Accuracy Background Accuracy Ship Accuracy Small-vehicle Accuracy Tennis-court Accuracy Helicopter Accuracy Basketball-court Accuracy Ground-track-field Accuracy Swimming-pool Accuracy Harbor Accuracy Soccer-ball-field Accuracy Plane Accuracy Storage-tank Accuracy Baseball-diamond Accuracy Large-vehicle Accuracy Bridge Accuracy Roundabout Iou Background Iou Ship Iou Small-vehicle Iou Tennis-court Iou Helicopter Iou Basketball-court Iou Ground-track-field Iou Swimming-pool Iou Harbor Iou Soccer-ball-field Iou Plane Iou Storage-tank Iou Baseball-diamond Iou Large-vehicle Iou Bridge Iou Roundabout
1.1413 1.0 113 0.5054 0.0431 0.0841 0.0445 nan 0.0611 0.0179 0.0079 nan 0.0 0.0 nan 0.8374 0.0 nan 0.0 nan 0.0010 0.0 0.0 0.0 0.0592 0.0178 0.0079 nan 0.0 0.0 nan 0.4319 0.0 nan 0.0 nan 0.0010 0.0 0.0
0.326 2.0 226 0.3240 0.0756 0.1192 0.2144 nan 0.0433 0.1690 0.0 nan 0.0 0.0 nan 0.8062 0.0 nan 0.0 nan 0.2926 0.0 0.0 0.0 0.0430 0.1614 0.0 nan 0.0 0.0 nan 0.4129 0.0 nan 0.0 nan 0.2904 0.0 0.0
0.1849 3.0 339 0.2807 0.1589 0.2164 0.3238 nan 0.3520 0.3125 0.0 nan 0.0 0.3563 nan 0.6252 0.4509 nan 0.0 nan 0.2835 0.0 0.0 0.0 0.3236 0.2894 0.0 nan 0.0 0.3265 0.0 0.3954 0.4506 nan 0.0 nan 0.2807 0.0 0.0
0.1341 4.0 452 0.2694 0.1618 0.2309 0.3055 nan 0.2089 0.3628 0.0188 nan 0.0 0.4866 nan 0.7552 0.5206 nan 0.0 nan 0.1866 0.0 0.0 0.0 0.2004 0.3303 0.0188 nan 0.0 0.4268 0.0 0.4221 0.5205 nan 0.0 nan 0.1840 0.0 0.0
0.1282 5.0 565 0.2631 0.2057 0.2726 0.3396 nan 0.4061 0.3347 0.0292 nan 0.0 0.6126 nan 0.6152 0.8252 nan 0.0 nan 0.1751 0.0 0.0 0.0 0.3667 0.3169 0.0292 nan 0.0 0.4767 nan 0.3995 0.7049 nan 0.0 nan 0.1745 0.0 0.0
0.1138 6.0 678 0.2418 0.1949 0.2558 0.3865 nan 0.2362 0.3709 0.0122 nan 0.0 0.6128 nan 0.6627 0.5823 nan 0.0 nan 0.3365 0.0 0.0 0.0 0.2249 0.3444 0.0122 nan 0.0 0.4625 nan 0.3921 0.5725 nan 0.0 nan 0.3301 0.0 0.0
0.1049 7.0 791 0.2345 0.2013 0.2623 0.4725 nan 0.3186 0.4071 0.0827 nan 0.0 0.1697 nan 0.7809 0.6140 nan 0.0 nan 0.5118 0.0 0.0 0.0 0.2927 0.3851 0.0827 nan 0.0 0.1679 nan 0.4702 0.5212 nan 0.0 nan 0.4961 0.0 0.0
0.0829 8.0 904 0.2351 0.2194 0.2818 0.4348 nan 0.1689 0.4289 0.0980 nan 0.0 0.5547 nan 0.7050 0.7860 nan 0.0 nan 0.3580 0.0 0.0 0.0 0.1619 0.4048 0.0980 nan 0.0 0.5205 nan 0.3967 0.7023 nan 0.0 nan 0.3490 0.0 0.0
0.0922 9.0 1017 0.2350 0.2549 0.3103 0.5060 nan 0.4729 0.4726 0.0572 nan 0.0 0.5679 nan 0.5794 0.7942 nan 0.0 nan 0.4690 0.0 0.0 0.0 0.4143 0.4398 0.0572 nan 0.0 0.5293 nan 0.4010 0.7613 nan 0.0 nan 0.4563 0.0 0.0
0.0717 10.0 1130 0.2399 0.2344 0.2871 0.4150 nan 0.4512 0.4155 0.0169 nan 0.0 0.5706 nan 0.6279 0.7676 nan 0.0 nan 0.3089 0.0 0.0 0.0 0.3995 0.3949 0.0169 nan 0.0 0.5351 nan 0.4246 0.7393 nan 0.0 nan 0.3023 0.0 0.0
0.0787 11.0 1243 0.2228 0.2578 0.3105 0.4726 nan 0.6679 0.4378 0.0666 nan 0.0 0.5865 nan 0.4684 0.7796 nan 0.0 nan 0.4087 0.0 0.0 0.0 0.5359 0.4172 0.0666 nan 0.0 0.5456 nan 0.3785 0.7528 nan 0.0 nan 0.3975 0.0 0.0
0.0787 11.9 1345 0.2318 0.2504 0.3019 0.4542 nan 0.6330 0.4644 0.0280 nan 0.0 0.6010 nan 0.4575 0.7776 nan 0.0 nan 0.3594 0.0 0.0 0.0 0.5194 0.4368 0.0280 nan 0.0 0.5492 nan 0.3611 0.7592 nan 0.0 nan 0.3508 0.0 0.0

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.5
  • Tokenizers 0.14.1