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windowz_test-020625

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metadata
library_name: transformers
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: windowz_test-020625
    results: []

windowz_test-020625

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

  • Model Preparation Time: 0.001
  • Accuracy: 0.9521
  • F1: 0.9483
  • Iou: 0.9076
  • Contour Dice: 0.9035
  • Per Class Metrics: {0: {'f1': 0.97101, 'iou': 0.94365, 'accuracy': 0.95541, 'contour_dice': 0.97101}, 1: {'f1': 0.90428, 'iou': 0.82528, 'accuracy': 0.95685, 'contour_dice': 0.90428}, 2: {'f1': 0.27674, 'iou': 0.16059, 'accuracy': 0.99191, 'contour_dice': 0.27674}}
  • Loss: 0.4842

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 1

Training results

Training Loss Epoch Step Model Preparation Time Dice Class Metrics Validation Loss
1.3502 0.0501 257 0.001 0.5644 0.0674 {0: {'f1': 0.85985, 'iou': 0.75416, 'accuracy': 0.75632, 'contour_dice': 0.85985}, 1: {'f1': 3e-05, 'iou': 2e-05, 'accuracy': 0.75751, 'contour_dice': 3e-05}, 2: {'f1': 0.03477, 'iou': 0.01769, 'accuracy': 0.98186, 'contour_dice': 0.03477}} 1.0501
1.2779 0.1003 514 0.001 0.5614 0.0110 {0: {'f1': 0.85654, 'iou': 0.74908, 'accuracy': 0.74943, 'contour_dice': 0.85654}, 1: {'f1': 0.0, 'iou': 0.0, 'accuracy': 0.7575, 'contour_dice': 0.0}, 2: {'f1': 0.18607, 'iou': 0.10258, 'accuracy': 0.99115, 'contour_dice': 0.18607}} 0.9869
1.2058 0.1504 771 0.001 0.5601 0.0024 {0: {'f1': 0.85601, 'iou': 0.74826, 'accuracy': 0.74834, 'contour_dice': 0.85601}, 1: {'f1': 0.00043, 'iou': 0.00021, 'accuracy': 0.75755, 'contour_dice': 0.00043}, 2: {'f1': 0.04908, 'iou': 0.02516, 'accuracy': 0.99076, 'contour_dice': 0.04908}} 0.9181
1.1158 0.2005 1028 0.001 0.5610 0.0074 {0: {'f1': 0.85634, 'iou': 0.74877, 'accuracy': 0.74901, 'contour_dice': 0.85634}, 1: {'f1': 0.00103, 'iou': 0.00052, 'accuracy': 0.75763, 'contour_dice': 0.00103}, 2: {'f1': 0.14378, 'iou': 0.07746, 'accuracy': 0.99122, 'contour_dice': 0.14378}} 0.8743
1.0785 0.2507 1285 0.001 0.5614 0.0087 {0: {'f1': 0.85643, 'iou': 0.74892, 'accuracy': 0.74919, 'contour_dice': 0.85643}, 1: {'f1': 0.00618, 'iou': 0.0031, 'accuracy': 0.75825, 'contour_dice': 0.00618}, 2: {'f1': 0.07155, 'iou': 0.0371, 'accuracy': 0.99093, 'contour_dice': 0.07155}} 0.8316
1.0445 0.3008 1542 0.001 0.7385 0.6122 {0: {'f1': 0.9132, 'iou': 0.84027, 'accuracy': 0.85815, 'contour_dice': 0.9132}, 1: {'f1': 0.619, 'iou': 0.44823, 'accuracy': 0.8647, 'contour_dice': 0.619}, 2: {'f1': 0.22291, 'iou': 0.12543, 'accuracy': 0.99173, 'contour_dice': 0.22291}} 0.7703
1.0092 0.3510 1799 0.001 0.7510 0.6389 {0: {'f1': 0.91749, 'iou': 0.84755, 'accuracy': 0.86567, 'contour_dice': 0.91749}, 1: {'f1': 0.64415, 'iou': 0.47509, 'accuracy': 0.87163, 'contour_dice': 0.64415}, 2: {'f1': 0.31734, 'iou': 0.18859, 'accuracy': 0.99231, 'contour_dice': 0.31734}} 0.7823
0.9676 0.4011 2056 0.001 0.7371 0.6147 {0: {'f1': 0.91344, 'iou': 0.84066, 'accuracy': 0.85863, 'contour_dice': 0.91344}, 1: {'f1': 0.60393, 'iou': 0.43259, 'accuracy': 0.86089, 'contour_dice': 0.60393}, 2: {'f1': 0.52376, 'iou': 0.3548, 'accuracy': 0.99253, 'contour_dice': 0.52376}} 0.8004
0.9308 0.4512 2313 0.001 0.8564 0.8337 {0: {'f1': 0.95377, 'iou': 0.91162, 'accuracy': 0.92764, 'contour_dice': 0.95377}, 1: {'f1': 0.83301, 'iou': 0.71381, 'accuracy': 0.9292, 'contour_dice': 0.83301}, 2: {'f1': 0.25373, 'iou': 0.1453, 'accuracy': 0.9918, 'contour_dice': 0.25373}} 0.7535
0.9187 0.5014 2570 0.001 0.868 0.8442 {0: {'f1': 0.95642, 'iou': 0.91649, 'accuracy': 0.93189, 'contour_dice': 0.95642}, 1: {'f1': 0.8514, 'iou': 0.74124, 'accuracy': 0.93689, 'contour_dice': 0.8514}, 2: {'f1': 0.43757, 'iou': 0.28006, 'accuracy': 0.99306, 'contour_dice': 0.43757}} 0.7077
0.8916 0.5515 2827 0.001 0.8656 0.8447 {0: {'f1': 0.95636, 'iou': 0.91636, 'accuracy': 0.93186, 'contour_dice': 0.95636}, 1: {'f1': 0.84316, 'iou': 0.72885, 'accuracy': 0.93331, 'contour_dice': 0.84316}, 2: {'f1': 0.5173, 'iou': 0.34889, 'accuracy': 0.9935, 'contour_dice': 0.5173}} 0.6670
0.8723 0.6016 3084 0.001 0.9221 0.9210 {0: {'f1': 0.97564, 'iou': 0.95244, 'accuracy': 0.96276, 'contour_dice': 0.97564}, 1: {'f1': 0.92036, 'iou': 0.85247, 'accuracy': 0.9635, 'contour_dice': 0.92036}, 2: {'f1': 0.46566, 'iou': 0.30349, 'accuracy': 0.99314, 'contour_dice': 0.46566}} 0.6549
0.8761 0.6518 3341 0.001 0.7678 0.7651 {0: {'f1': 0.90349, 'iou': 0.82397, 'accuracy': 0.86319, 'contour_dice': 0.90349}, 1: {'f1': 0.76222, 'iou': 0.6158, 'accuracy': 0.86427, 'contour_dice': 0.76222}, 2: {'f1': 0.35206, 'iou': 0.21364, 'accuracy': 0.99248, 'contour_dice': 0.35206}} 0.7303
0.869 0.7019 3598 0.001 0.9259 0.9263 {0: {'f1': 0.97721, 'iou': 0.95544, 'accuracy': 0.96519, 'contour_dice': 0.97721}, 1: {'f1': 0.92398, 'iou': 0.85871, 'accuracy': 0.96506, 'contour_dice': 0.92398}, 2: {'f1': 0.47308, 'iou': 0.30983, 'accuracy': 0.99326, 'contour_dice': 0.47308}} 0.6818
0.8526 0.7520 3855 0.001 0.9455 0.9507 {0: {'f1': 0.98427, 'iou': 0.96902, 'accuracy': 0.97615, 'contour_dice': 0.98427}, 1: {'f1': 0.94744, 'iou': 0.90013, 'accuracy': 0.97519, 'contour_dice': 0.94744}, 2: {'f1': 0.38871, 'iou': 0.24124, 'accuracy': 0.99266, 'contour_dice': 0.38871}} 0.5164
0.8487 0.8022 4112 0.001 0.8958 0.8869 {0: {'f1': 0.96679, 'iou': 0.93572, 'accuracy': 0.94866, 'contour_dice': 0.96679}, 1: {'f1': 0.88802, 'iou': 0.7986, 'accuracy': 0.95052, 'contour_dice': 0.88802}, 2: {'f1': 0.36537, 'iou': 0.22352, 'accuracy': 0.99239, 'contour_dice': 0.36537}} 0.5553
0.8519 0.8523 4369 0.001 0.9236 0.9231 {0: {'f1': 0.97639, 'iou': 0.95387, 'accuracy': 0.96388, 'contour_dice': 0.97639}, 1: {'f1': 0.92437, 'iou': 0.85937, 'accuracy': 0.9653, 'contour_dice': 0.92437}, 2: {'f1': 0.29738, 'iou': 0.17466, 'accuracy': 0.99214, 'contour_dice': 0.29738}} 0.4860
0.8331 0.9025 4626 0.001 0.9076 0.9035 {0: {'f1': 0.97101, 'iou': 0.94365, 'accuracy': 0.95541, 'contour_dice': 0.97101}, 1: {'f1': 0.90428, 'iou': 0.82528, 'accuracy': 0.95685, 'contour_dice': 0.90428}, 2: {'f1': 0.27674, 'iou': 0.16059, 'accuracy': 0.99191, 'contour_dice': 0.27674}} 0.4842
0.8357 0.9526 4883 0.001 0.8881 0.8770 {0: {'f1': 0.96409, 'iou': 0.93067, 'accuracy': 0.94441, 'contour_dice': 0.96409}, 1: {'f1': 0.87736, 'iou': 0.78151, 'accuracy': 0.94613, 'contour_dice': 0.87736}, 2: {'f1': 0.40324, 'iou': 0.25254, 'accuracy': 0.99246, 'contour_dice': 0.40324}} 0.5096

Framework versions

  • Transformers 4.45.0
  • Pytorch 2.5.1+cu124
  • Datasets 2.21.0
  • Tokenizers 0.20.3