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metadata
base_model: MBZUAI/swiftformer-xs
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swiftformer-xs-ve-U13-b-80
    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: 0.8260869565217391

swiftformer-xs-ve-U13-b-80

This model is a fine-tuned version of MBZUAI/swiftformer-xs on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7132
  • Accuracy: 0.8261

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: 0.0002
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • 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: 80

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.92 6 1.3859 0.2391
1.3857 2.0 13 1.3834 0.2826
1.3857 2.92 19 1.3789 0.1957
1.3767 4.0 26 1.3666 0.1522
1.3226 4.92 32 1.3565 0.1522
1.3226 6.0 39 1.3902 0.1087
1.1987 6.92 45 1.3712 0.2174
1.1075 8.0 52 1.3197 0.3478
1.1075 8.92 58 1.3649 0.3696
0.9988 10.0 65 1.2583 0.3696
0.8863 10.92 71 1.2484 0.3696
0.8863 12.0 78 1.2869 0.4130
0.8228 12.92 84 1.1678 0.4783
0.7456 14.0 91 1.0275 0.6739
0.7456 14.92 97 0.9702 0.7174
0.6595 16.0 104 0.9103 0.6957
0.5995 16.92 110 0.8506 0.7391
0.5995 18.0 117 0.8514 0.7174
0.5826 18.92 123 0.8964 0.7391
0.4818 20.0 130 0.8550 0.7609
0.4818 20.92 136 0.7132 0.8261
0.4553 22.0 143 0.6973 0.7826
0.4553 22.92 149 0.7496 0.7391
0.4276 24.0 156 0.9087 0.6957
0.3375 24.92 162 0.7787 0.8261
0.3375 26.0 169 0.7132 0.8043
0.3199 26.92 175 0.7570 0.7391
0.2756 28.0 182 0.7873 0.6957
0.2756 28.92 188 0.7895 0.7609
0.2254 30.0 195 0.7443 0.8043
0.2576 30.92 201 0.9623 0.6739
0.2576 32.0 208 0.7349 0.7826
0.2113 32.92 214 0.7887 0.7609
0.1978 34.0 221 0.8921 0.7391
0.1978 34.92 227 0.8102 0.7391
0.2455 36.0 234 0.8947 0.7391
0.1809 36.92 240 0.8144 0.7826
0.1809 38.0 247 0.8290 0.7174
0.1967 38.92 253 0.8135 0.7391
0.1608 40.0 260 0.8065 0.7609
0.1608 40.92 266 0.7399 0.7609
0.1704 42.0 273 0.7099 0.8043
0.1704 42.92 279 0.7569 0.7826
0.1682 44.0 286 0.8459 0.7826
0.1607 44.92 292 0.7311 0.7609
0.1607 46.0 299 0.7833 0.7174
0.1589 46.92 305 0.8073 0.6957
0.1524 48.0 312 0.7473 0.7609
0.1524 48.92 318 0.6780 0.8043
0.1586 50.0 325 0.7573 0.7174
0.128 50.92 331 0.7614 0.7391
0.128 52.0 338 0.7338 0.7609
0.1254 52.92 344 0.7666 0.7391
0.1206 54.0 351 0.8433 0.7174
0.1206 54.92 357 0.8747 0.6957
0.1398 56.0 364 0.8940 0.7174
0.1536 56.92 370 0.7781 0.7826
0.1536 58.0 377 0.7351 0.7391
0.1281 58.92 383 0.7601 0.7174
0.1156 60.0 390 0.7991 0.7174
0.1156 60.92 396 0.7776 0.7609
0.0852 62.0 403 0.7838 0.7391
0.0852 62.92 409 0.7752 0.7609
0.1106 64.0 416 0.7541 0.7609
0.0817 64.92 422 0.7536 0.7391
0.0817 66.0 429 0.8129 0.7609
0.1211 66.92 435 0.7884 0.7609
0.0944 68.0 442 0.8011 0.7609
0.0944 68.92 448 0.8068 0.7391
0.1187 70.0 455 0.7796 0.7391
0.0935 70.92 461 0.7934 0.7391
0.0935 72.0 468 0.7367 0.7391
0.109 72.92 474 0.7515 0.7391
0.1006 73.85 480 0.7888 0.7174

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.2+cu118
  • Datasets 2.16.1
  • Tokenizers 0.15.0