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vit-base-patch16-384-finetuned-humid-binary-2

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metadata
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-384
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-384-finetuned-humid-binary-2
    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: 0.9411764705882353

vit-base-patch16-384-finetuned-humid-binary-2

This model is a fine-tuned version of google/vit-base-patch16-384 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4611
  • Accuracy: 0.9412
  • F1 Macro: 0.9399
  • Precision Macro: 0.9399
  • Recall Macro: 0.9399
  • Precision Dry: 0.9310
  • Recall Dry: 0.9310
  • F1 Dry: 0.9310
  • Precision Humid: 0.9487
  • Recall Humid: 0.9487
  • F1 Humid: 0.9487

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: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Use OptimizerNames.ADAMW_TORCH 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 Humid Recall Humid F1 Humid
No log 1.0 6 0.6149 0.7206 0.7176 0.7170 0.7210 0.6562 0.7241 0.6885 0.7778 0.7179 0.7467
0.6101 2.0 12 0.3865 0.8824 0.8798 0.8798 0.8798 0.8621 0.8621 0.8621 0.8974 0.8974 0.8974
0.6101 3.0 18 0.3597 0.8382 0.8379 0.8438 0.8501 0.75 0.9310 0.8308 0.9375 0.7692 0.8451
0.2613 4.0 24 0.5014 0.8235 0.8235 0.8417 0.8417 0.7179 0.9655 0.8235 0.9655 0.7179 0.8235
0.1505 5.0 30 0.4383 0.8529 0.8524 0.8554 0.8630 0.7714 0.9310 0.8438 0.9394 0.7949 0.8611
0.1505 6.0 36 0.4248 0.8529 0.8528 0.8622 0.8674 0.7568 0.9655 0.8485 0.9677 0.7692 0.8571
0.1298 7.0 42 0.3932 0.8971 0.8932 0.9020 0.8882 0.9231 0.8276 0.8727 0.8810 0.9487 0.9136
0.1298 8.0 48 0.3933 0.8971 0.8960 0.8941 0.9014 0.8438 0.9310 0.8852 0.9444 0.8718 0.9067
0.1033 9.0 54 0.7160 0.7941 0.7934 0.8372 0.8205 0.6744 1.0 0.8056 1.0 0.6410 0.7812
0.1135 10.0 60 0.4036 0.9265 0.9252 0.9237 0.9271 0.9 0.9310 0.9153 0.9474 0.9231 0.9351
0.1135 11.0 66 0.4228 0.9118 0.9089 0.9142 0.9054 0.9259 0.8621 0.8929 0.9024 0.9487 0.925
0.0331 12.0 72 0.4821 0.8824 0.8819 0.8848 0.8930 0.8 0.9655 0.875 0.9697 0.8205 0.8889
0.0331 13.0 78 0.4474 0.9118 0.9105 0.9085 0.9142 0.8710 0.9310 0.9 0.9459 0.8974 0.9211
0.0142 14.0 84 0.4478 0.9118 0.9098 0.9098 0.9098 0.8966 0.8966 0.8966 0.9231 0.9231 0.9231
0.0074 15.0 90 0.6302 0.8824 0.8819 0.8848 0.8930 0.8 0.9655 0.875 0.9697 0.8205 0.8889
0.0074 16.0 96 0.4611 0.9412 0.9399 0.9399 0.9399 0.9310 0.9310 0.9310 0.9487 0.9487 0.9487
0.0114 17.0 102 0.6472 0.8824 0.8819 0.8848 0.8930 0.8 0.9655 0.875 0.9697 0.8205 0.8889
0.0114 18.0 108 0.4690 0.9118 0.9089 0.9142 0.9054 0.9259 0.8621 0.8929 0.9024 0.9487 0.925
0.0054 19.0 114 0.7610 0.8676 0.8674 0.8733 0.8802 0.7778 0.9655 0.8615 0.9688 0.7949 0.8732
0.0024 20.0 120 0.5395 0.9118 0.9105 0.9085 0.9142 0.8710 0.9310 0.9 0.9459 0.8974 0.9211
0.0024 21.0 126 0.6275 0.8971 0.8960 0.8941 0.9014 0.8438 0.9310 0.8852 0.9444 0.8718 0.9067
0.0024 22.0 132 0.7972 0.8676 0.8674 0.8733 0.8802 0.7778 0.9655 0.8615 0.9688 0.7949 0.8732
0.0024 23.0 138 0.5466 0.9118 0.9105 0.9085 0.9142 0.8710 0.9310 0.9 0.9459 0.8974 0.9211
0.0009 24.0 144 0.5304 0.8971 0.8960 0.8941 0.9014 0.8438 0.9310 0.8852 0.9444 0.8718 0.9067
0.0012 25.0 150 0.6568 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0012 26.0 156 0.6134 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0004 27.0 162 0.6094 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0004 28.0 168 0.6153 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0003 29.0 174 0.6319 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0002 30.0 180 0.6457 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0002 31.0 186 0.6530 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0002 32.0 192 0.6580 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0002 33.0 198 0.6650 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0002 34.0 204 0.6774 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0002 35.0 210 0.6827 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0002 36.0 216 0.6850 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 37.0 222 0.6846 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 38.0 228 0.6853 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 39.0 234 0.6854 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 40.0 240 0.6845 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 41.0 246 0.6847 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 42.0 252 0.6860 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 43.0 258 0.6875 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 44.0 264 0.6895 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 45.0 270 0.6903 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 46.0 276 0.6914 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 47.0 282 0.6920 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 48.0 288 0.6921 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 49.0 294 0.6923 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041
0.0001 50.0 300 0.6924 0.8971 0.8965 0.8971 0.9058 0.8235 0.9655 0.8889 0.9706 0.8462 0.9041

Framework versions

  • Transformers 4.56.1
  • Pytorch 2.5.1+cu124
  • Datasets 4.0.0
  • Tokenizers 0.22.0