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swin-base-patch4-window12-384-finetuned-humid-classes-1

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metadata 
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-base-patch4-window12-384
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swin-base-patch4-window12-384-finetuned-humid-classes-1
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: validation
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 1

swin-base-patch4-window12-384-finetuned-humid-classes-1

This model is a fine-tuned version of microsoft/swin-base-patch4-window12-384 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0096
  • Accuracy: 1.0
  • F1 Macro: 1.0
  • Precision Macro: 1.0
  • Recall Macro: 1.0
  • Precision Dry: 1.0
  • Recall Dry: 1.0
  • F1 Dry: 1.0
  • Precision Firm: 1.0
  • Recall Firm: 1.0
  • F1 Firm: 1.0
  • Precision Humid: 1.0
  • Recall Humid: 1.0
  • F1 Humid: 1.0
  • Precision Lump: 1.0
  • Recall Lump: 1.0
  • F1 Lump: 1.0
  • Precision Rockies: 1.0
  • Recall Rockies: 1.0
  • F1 Rockies: 1.0

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Macro Precision Macro Recall Macro Precision Dry Recall Dry F1 Dry Precision Firm Recall Firm F1 Firm Precision Humid Recall Humid F1 Humid Precision Lump Recall Lump F1 Lump Precision Rockies Recall Rockies F1 Rockies
1.4441 1.0 10 1.3328 0.5238 0.2903 0.2337 0.4 0.0 0.0 0.0 0.7368 1.0 0.8485 0.0 0.0 0.0 0.4318 1.0 0.6032 0.0 0.0 0.0
1.0125 2.0 20 0.7622 0.6984 0.5484 0.6673 0.5922 1.0 0.8182 0.9 0.7778 1.0 0.875 0.0 0.0 0.0 0.5588 1.0 0.7170 1.0 0.1429 0.25
0.4585 3.0 30 0.3032 0.8571 0.7864 0.9251 0.7723 1.0 1.0 1.0 0.9333 1.0 0.9655 1.0 0.2 0.3333 0.6923 0.9474 0.8 1.0 0.7143 0.8333
0.2434 4.0 40 0.4579 0.8571 0.8227 0.8926 0.8131 1.0 1.0 1.0 0.8235 1.0 0.9032 1.0 0.4 0.5714 0.875 0.7368 0.8 0.7647 0.9286 0.8387
0.171 5.0 50 0.1754 0.9365 0.9152 0.9523 0.8989 1.0 1.0 1.0 0.9333 1.0 0.9655 1.0 0.6 0.75 0.8947 0.8947 0.8947 0.9333 1.0 0.9655
0.0951 6.0 60 0.1278 0.9683 0.9638 0.9533 0.9789 1.0 1.0 1.0 1.0 1.0 1.0 0.8333 1.0 0.9091 1.0 0.8947 0.9444 0.9333 1.0 0.9655
0.2476 7.0 70 0.1856 0.9365 0.8874 0.9581 0.8695 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.4 0.5714 0.8571 0.9474 0.9 0.9333 1.0 0.9655
0.0706 8.0 80 0.2010 0.9206 0.8749 0.9433 0.8589 1.0 1.0 1.0 0.9333 1.0 0.9655 1.0 0.4 0.5714 0.85 0.8947 0.8718 0.9333 1.0 0.9655
0.1555 9.0 90 0.5145 0.8413 0.8034 0.89 0.7875 1.0 1.0 1.0 0.7 1.0 0.8235 1.0 0.4 0.5714 0.85 0.8947 0.8718 0.9 0.6429 0.75
0.0746 10.0 100 0.3848 0.9365 0.8874 0.9581 0.8695 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.4 0.5714 0.8571 0.9474 0.9 0.9333 1.0 0.9655
0.2615 11.0 110 0.5946 0.8571 0.7836 0.9190 0.7723 1.0 1.0 1.0 0.875 1.0 0.9333 1.0 0.2 0.3333 0.72 0.9474 0.8182 1.0 0.7143 0.8333
0.1522 12.0 120 0.2942 0.9206 0.8735 0.9464 0.8552 1.0 1.0 1.0 0.875 1.0 0.9333 1.0 0.4 0.5714 0.8571 0.9474 0.9 1.0 0.9286 0.9630
0.0401 13.0 130 0.1079 0.9524 0.8997 0.9727 0.8800 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.4 0.5714 0.8636 1.0 0.9268 1.0 1.0 1.0
0.0526 14.0 140 0.0778 0.9683 0.9495 0.9495 0.9495 1.0 1.0 1.0 1.0 1.0 1.0 0.8 0.8 0.8 0.9474 0.9474 0.9474 1.0 1.0 1.0
0.0078 15.0 150 0.0907 0.9524 0.9179 0.93 0.9095 1.0 1.0 1.0 1.0 1.0 1.0 0.75 0.6 0.6667 0.9 0.9474 0.9231 1.0 1.0 1.0
0.0256 16.0 160 0.1176 0.9524 0.9509 0.9400 0.9684 1.0 1.0 1.0 0.9333 1.0 0.9655 0.8333 1.0 0.9091 1.0 0.8421 0.9143 0.9333 1.0 0.9655
0.0168 17.0 170 0.1039 0.9524 0.9298 0.925 0.9571 1.0 1.0 1.0 1.0 1.0 1.0 0.625 1.0 0.7692 1.0 1.0 1.0 1.0 0.7857 0.88
0.0004 18.0 180 0.1826 0.9524 0.8997 0.9727 0.8800 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.4 0.5714 0.8636 1.0 0.9268 1.0 1.0 1.0
0.002 19.0 190 0.1104 0.9683 0.9539 0.9429 0.9752 1.0 1.0 1.0 1.0 1.0 1.0 0.7143 1.0 0.8333 1.0 0.9474 0.9730 1.0 0.9286 0.9630
0.0176 20.0 200 1.0026 0.8889 0.8116 0.9462 0.7971 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.2 0.3333 0.7308 1.0 0.8444 1.0 0.7857 0.88
0.0328 21.0 210 0.0096 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
0.0001 22.0 220 0.1568 0.9524 0.8997 0.9727 0.8800 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.4 0.5714 0.8636 1.0 0.9268 1.0 1.0 1.0
0.0005 23.0 230 0.0403 0.9683 0.9638 0.9533 0.9789 1.0 1.0 1.0 1.0 1.0 1.0 0.8333 1.0 0.9091 1.0 0.8947 0.9444 0.9333 1.0 0.9655
0.0002 24.0 240 0.0450 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0002 25.0 250 0.1338 0.9524 0.8997 0.9727 0.8800 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.4 0.5714 0.8636 1.0 0.9268 1.0 1.0 1.0
0.0 26.0 260 0.0212 0.9841 0.9764 0.9667 0.9895 1.0 1.0 1.0 1.0 1.0 1.0 0.8333 1.0 0.9091 1.0 0.9474 0.9730 1.0 1.0 1.0
0.0 27.0 270 0.0644 0.9683 0.9556 0.9429 0.9789 1.0 1.0 1.0 1.0 1.0 1.0 0.7143 1.0 0.8333 1.0 0.8947 0.9444 1.0 1.0 1.0
0.0001 28.0 280 0.0017 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
0.0 29.0 290 0.0342 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 30.0 300 0.0710 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 31.0 310 0.0844 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 32.0 320 0.0876 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 33.0 330 0.0894 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 34.0 340 0.0896 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 35.0 350 0.0823 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 36.0 360 0.0757 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 37.0 370 0.0721 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 38.0 380 0.0705 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 39.0 390 0.0669 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 40.0 400 0.0660 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0001 41.0 410 0.0691 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 42.0 420 0.0700 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 43.0 430 0.0711 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 44.0 440 0.0716 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0002 45.0 450 0.0804 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 46.0 460 0.0911 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 47.0 470 0.0935 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 48.0 480 0.0938 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0 49.0 490 0.0938 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0
0.0041 50.0 500 0.0941 0.9683 0.9400 0.9810 0.9200 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.6 0.75 0.9048 1.0 0.95 1.0 1.0 1.0

Framework versions

  • Transformers 4.56.1
  • Pytorch 2.9.0+cu126
  • Datasets 4.0.0
  • Tokenizers 0.22.0