20E-affecthq / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: 20E-affecthq
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.7188003869719445
          - name: Precision
            type: precision
            value: 0.7219837313936599
          - name: Recall
            type: recall
            value: 0.7188003869719445
          - name: F1
            type: f1
            value: 0.718989971086903

20E-affecthq

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8271
  • Accuracy: 0.7188
  • Precision: 0.7220
  • Recall: 0.7188
  • F1: 0.7190

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: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 17
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1
1.9149 1.0 194 1.8887 0.3750 0.3413 0.3750 0.3045
1.2903 2.0 388 1.2485 0.5792 0.5726 0.5792 0.5526
1.071 3.0 582 1.0587 0.6321 0.6258 0.6321 0.6228
1.0185 4.0 776 0.9817 0.6617 0.6584 0.6617 0.6553
0.894 5.0 970 0.9293 0.6869 0.6872 0.6869 0.6820
0.8283 6.0 1164 0.8881 0.6936 0.6929 0.6936 0.6905
0.8185 7.0 1358 0.8659 0.6982 0.7011 0.6982 0.6988
0.7499 8.0 1552 0.8558 0.7046 0.7050 0.7046 0.7021
0.7219 9.0 1746 0.8399 0.7124 0.7165 0.7124 0.7127
0.7382 10.0 1940 0.8300 0.7159 0.7184 0.7159 0.7145
0.6392 11.0 2134 0.8329 0.7088 0.7135 0.7088 0.7095
0.6549 12.0 2328 0.8297 0.7133 0.7135 0.7133 0.7120
0.6762 13.0 2522 0.8180 0.7156 0.7162 0.7156 0.7153
0.5937 14.0 2716 0.8271 0.7188 0.7220 0.7188 0.7190
0.569 15.0 2910 0.8245 0.7178 0.7175 0.7178 0.7165
0.5623 16.0 3104 0.8228 0.7165 0.7153 0.7165 0.7157
0.5291 17.0 3298 0.8238 0.7162 0.7165 0.7162 0.7156
0.5775 18.0 3492 0.8246 0.7153 0.7162 0.7153 0.7151
0.545 19.0 3686 0.8257 0.7178 0.7192 0.7178 0.7174
0.5409 20.0 3880 0.8245 0.7178 0.7187 0.7178 0.7177

Framework versions

  • Transformers 4.27.0.dev0
  • Pytorch 1.13.1+cu116
  • Datasets 2.9.0
  • Tokenizers 0.13.2