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model1

Image Segmentation
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segformer
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metadata
license: other
base_model: nvidia/mit-b2
tags:
  - image-segmentation
  - vision
  - generated_from_trainer
model-index:
  - name: model1
    results: []

model1

This model is a fine-tuned version of nvidia/mit-b2 on the giuseppemartino/i-SAID_custom_or_1 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1646
  • Mean Iou: 0.2689
  • Mean Accuracy: 0.3089
  • Overall Accuracy: 0.3928
  • Accuracy Background: nan
  • Accuracy Ship: 0.7889
  • Accuracy Small-vehicle: 0.3939
  • Accuracy Tennis-court: 0.6399
  • Accuracy Helicopter: nan
  • Accuracy Basketball-court: 0.0
  • Accuracy Ground-track-field: 0.4337
  • Accuracy Swimming-pool: 0.6049
  • Accuracy Harbor: 0.3386
  • Accuracy Soccer-ball-field: 0.2551
  • Accuracy Plane: 0.0001
  • Accuracy Storage-tank: 0.0
  • Accuracy Baseball-diamond: 0.5217
  • Accuracy Large-vehicle: 0.3477
  • Accuracy Bridge: 0.0
  • Accuracy Roundabout: 0.0
  • Iou Background: 0.0
  • Iou Ship: 0.6137
  • Iou Small-vehicle: 0.3354
  • Iou Tennis-court: 0.6399
  • Iou Helicopter: nan
  • Iou Basketball-court: 0.0
  • Iou Ground-track-field: 0.4084
  • Iou Swimming-pool: 0.6049
  • Iou Harbor: 0.3165
  • Iou Soccer-ball-field: 0.2514
  • Iou Plane: 0.0001
  • Iou Storage-tank: 0.0
  • Iou Baseball-diamond: 0.5217
  • Iou Large-vehicle: 0.3418
  • 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: 840

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
1.1466 1.0 105 0.3419 0.0260 0.0279 0.0687 nan 0.0068 0.0036 0.3562 nan 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0240 0.0 0.0 0.0 0.0067 0.0036 0.3562 nan 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0240 0.0 0.0
0.3289 2.0 210 0.2301 0.1252 0.1441 0.2674 nan 0.5316 0.1793 0.6775 nan 0.0 0.0324 0.1854 0.1185 0.0 0.0 0.0 0.0 0.2923 0.0 0.0 0.0 0.4189 0.1612 0.6752 nan 0.0 0.0321 0.1854 0.1157 0.0 0.0 0.0 0.0 0.2898 0.0 0.0
0.1819 3.0 315 0.1965 0.1611 0.1937 0.3286 nan 0.7305 0.2842 0.4229 nan 0.0 0.3566 0.2424 0.1707 0.0739 0.0 0.0 0.0 0.4300 0.0 0.0 0.0 0.5605 0.2492 0.4229 nan 0.0 0.2817 0.2424 0.1637 0.0738 0.0 0.0 0.0 0.4223 0.0 0.0
0.1505 4.0 420 0.1760 0.1987 0.2352 0.3689 nan 0.7552 0.3079 0.5796 nan 0.0 0.4515 0.4367 0.2065 0.1437 0.0 0.0 0.0 0.4115 0.0 0.0 0.0 0.5715 0.2762 0.5790 nan 0.0 0.3752 0.4367 0.1957 0.1435 0.0 0.0 0.0 0.4029 0.0 0.0
0.1269 5.0 525 0.1688 0.2239 0.2616 0.3561 nan 0.8249 0.3133 0.5309 nan 0.0 0.3966 0.6398 0.2513 0.1975 0.0003 0.0 0.1336 0.3738 0.0 0.0 0.0 0.6006 0.2833 0.5309 nan 0.0 0.3711 0.6398 0.2378 0.1957 0.0003 0.0 0.1336 0.3661 0.0 0.0
0.1012 6.0 630 0.1763 0.2563 0.3036 0.3830 nan 0.7977 0.4801 0.6774 nan 0.0 0.4913 0.7772 0.2993 0.2702 0.0 0.0 0.2024 0.2541 0.0 0.0 0.0 0.6060 0.3488 0.6774 nan 0.0 0.4359 0.7767 0.2816 0.2638 0.0 0.0 0.2024 0.2515 0.0 0.0
0.0996 7.0 735 0.1687 0.2515 0.2906 0.3644 nan 0.7947 0.3775 0.5884 nan 0.0 0.4452 0.5756 0.2734 0.2140 0.0 0.0 0.4769 0.3225 0.0 0.0 0.0 0.6093 0.3246 0.5884 nan 0.0 0.4081 0.5756 0.2599 0.2128 0.0 0.0 0.4769 0.3174 0.0 0.0
0.0945 8.0 840 0.1646 0.2689 0.3089 0.3928 nan 0.7889 0.3939 0.6399 nan 0.0 0.4337 0.6049 0.3386 0.2551 0.0001 0.0 0.5217 0.3477 0.0 0.0 0.0 0.6137 0.3354 0.6399 nan 0.0 0.4084 0.6049 0.3165 0.2514 0.0001 0.0 0.5217 0.3418 0.0 0.0

Framework versions

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