Hugging Face's logo

dacunaq
/
vit-base-patch16-384-finetuned-humid-classes-9

Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Model card Files
xet
 Metrics Community
vit-base-patch16-384-finetuned-humid-classes-9 / README.md
dacunaq's picture
dacunaq
End of training
c639abc verified
preview code
|
raw
history blame contribute delete
18.7 kB
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-classes-9
    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

vit-base-patch16-384-finetuned-humid-classes-9

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: 0.0156
  • 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: 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 Firm Recall Firm F1 Firm Precision Humid Recall Humid F1 Humid Precision Lump Recall Lump F1 Lump Precision Rockies Recall Rockies F1 Rockies
No log 1.0 5 1.3884 0.4815 0.2533 0.2143 0.3263 0.0 0.0 0.0 0.5 1.0 0.6667 0.0 0.0 0.0 0.5714 0.6316 0.6 0.0 0.0 0.0
1.352 2.0 10 1.0193 0.7963 0.5380 0.4968 0.5895 0.9167 1.0 0.9565 0.875 1.0 0.9333 0.0 0.0 0.0 0.6923 0.9474 0.8 0.0 0.0 0.0
1.352 3.0 15 0.6250 0.8148 0.5583 0.5310 0.6 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.6552 1.0 0.7917 0.0 0.0 0.0
0.6341 4.0 20 0.3860 0.8704 0.7383 0.7367 0.7495 1.0 1.0 1.0 0.9333 1.0 0.9655 0.0 0.0 0.0 0.75 0.9474 0.8372 1.0 0.8 0.8889
0.6341 5.0 25 0.2527 0.9259 0.9140 0.9179 0.9284 1.0 1.0 1.0 0.875 1.0 0.9333 1.0 0.8 0.8889 1.0 0.8421 0.9143 0.7143 1.0 0.8333
0.2197 6.0 30 0.1329 0.9630 0.9604 0.9761 0.9495 1.0 1.0 1.0 0.9333 1.0 0.9655 1.0 0.8 0.8889 0.9474 0.9474 0.9474 1.0 1.0 1.0
0.2197 7.0 35 0.1464 0.9444 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
0.1142 8.0 40 0.0711 0.9815 0.9877 0.9867 0.9895 1.0 1.0 1.0 0.9333 1.0 0.9655 1.0 1.0 1.0 1.0 0.9474 0.9730 1.0 1.0 1.0
0.1142 9.0 45 0.1523 0.9259 0.8888 0.9167 0.8695 1.0 1.0 1.0 0.9333 1.0 0.9655 0.75 0.6 0.6667 0.9 0.9474 0.9231 1.0 0.8 0.8889
0.0468 10.0 50 0.0735 0.9815 0.9596 0.9667 0.96 1.0 1.0 1.0 1.0 1.0 1.0 0.8333 1.0 0.9091 1.0 1.0 1.0 1.0 0.8 0.8889
0.0468 11.0 55 0.1200 0.9630 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
0.0119 12.0 60 0.1084 0.9444 0.9205 0.925 0.9389 1.0 1.0 1.0 1.0 1.0 1.0 0.625 1.0 0.7692 1.0 0.8947 0.9444 1.0 0.8 0.8889
0.0119 13.0 65 0.0984 0.9630 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
0.0105 14.0 70 0.0510 0.9815 0.9877 0.9867 0.9895 1.0 1.0 1.0 0.9333 1.0 0.9655 1.0 1.0 1.0 1.0 0.9474 0.9730 1.0 1.0 1.0
0.0105 15.0 75 0.0485 0.9815 0.9877 0.9867 0.9895 1.0 1.0 1.0 0.9333 1.0 0.9655 1.0 1.0 1.0 1.0 0.9474 0.9730 1.0 1.0 1.0
0.0031 16.0 80 0.1964 0.9259 0.9234 0.9117 0.9579 1.0 1.0 1.0 0.9333 1.0 0.9655 0.625 1.0 0.7692 1.0 0.7895 0.8824 1.0 1.0 1.0
0.0031 17.0 85 0.0908 0.9815 0.9726 0.99 0.96 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.8 0.8889 0.95 1.0 0.9744 1.0 1.0 1.0
0.0032 18.0 90 0.0334 0.9815 0.9726 0.99 0.96 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.8 0.8889 0.95 1.0 0.9744 1.0 1.0 1.0
0.0032 19.0 95 0.0156 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
0.0013 20.0 100 0.0115 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
0.0013 21.0 105 0.0105 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
0.001 22.0 110 0.0129 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
0.001 23.0 115 0.0160 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
0.0009 24.0 120 0.0188 0.9815 0.9726 0.99 0.96 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.8 0.8889 0.95 1.0 0.9744 1.0 1.0 1.0
0.0009 25.0 125 0.0202 0.9815 0.9726 0.99 0.96 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.8 0.8889 0.95 1.0 0.9744 1.0 1.0 1.0
0.0007 26.0 130 0.0196 0.9815 0.9726 0.99 0.96 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.8 0.8889 0.95 1.0 0.9744 1.0 1.0 1.0
0.0007 27.0 135 0.0183 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
0.0006 28.0 140 0.0172 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
0.0006 29.0 145 0.0162 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
0.0007 30.0 150 0.0077 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
0.0007 31.0 155 0.0070 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
0.0006 32.0 160 0.0072 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
0.0006 33.0 165 0.0083 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
0.0006 34.0 170 0.0106 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
0.0006 35.0 175 0.0130 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
0.0005 36.0 180 0.0149 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
0.0005 37.0 185 0.0165 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
0.0005 38.0 190 0.0172 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
0.0005 39.0 195 0.0175 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
0.0005 40.0 200 0.0173 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
0.0005 41.0 205 0.0170 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
0.0005 42.0 210 0.0168 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
0.0005 43.0 215 0.0165 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
0.0004 44.0 220 0.0162 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
0.0004 45.0 225 0.0160 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
0.0004 46.0 230 0.0160 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
0.0004 47.0 235 0.0160 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
0.0004 48.0 240 0.0159 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
0.0004 49.0 245 0.0159 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
0.0004 50.0 250 0.0159 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

Framework versions

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