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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-80b
    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: 0.6521739130434783

swiftformer-xs-ve-U13-b-80b

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: 1.2197
  • Accuracy: 0.6522

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: 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 1.3862 0.2174
1.3862 2.0 13 1.3856 0.3261
1.3862 2.92 19 1.3849 0.2826
1.3848 4.0 26 1.3830 0.2609
1.3806 4.92 32 1.3804 0.1739
1.3806 6.0 39 1.3758 0.1957
1.3662 6.92 45 1.3700 0.1739
1.3261 8.0 52 1.3652 0.1739
1.3261 8.92 58 1.3625 0.1522
1.2588 10.0 65 1.3629 0.1304
1.1972 10.92 71 1.3592 0.1304
1.1972 12.0 78 1.3570 0.2174
1.1578 12.92 84 1.3590 0.1957
1.124 14.0 91 1.3731 0.2174
1.124 14.92 97 1.3718 0.1522
1.1045 16.0 104 1.3736 0.1739
1.0703 16.92 110 1.4983 0.2174
1.0703 18.0 117 1.5455 0.1739
1.0663 18.92 123 1.4473 0.1739
1.01 20.0 130 1.4011 0.2609
1.01 20.92 136 1.4053 0.2826
0.9961 22.0 143 1.4186 0.2174
0.9961 22.92 149 1.5168 0.2609
0.9754 24.0 156 1.3873 0.2826
0.9417 24.92 162 1.4656 0.3261
0.9417 26.0 169 1.3499 0.2609
0.9286 26.92 175 1.3902 0.3043
0.9216 28.0 182 1.4819 0.3261
0.9216 28.92 188 1.4133 0.3043
0.8868 30.0 195 1.4124 0.4130
0.8908 30.92 201 1.4421 0.3478
0.8908 32.0 208 1.5085 0.3043
0.8729 32.92 214 1.3854 0.3478
0.8685 34.0 221 1.3264 0.3043
0.8685 34.92 227 1.3947 0.3043
0.8739 36.0 234 1.3455 0.3913
0.8288 36.92 240 1.3621 0.3913
0.8288 38.0 247 1.3875 0.3913
0.8369 38.92 253 1.4274 0.3696
0.8101 40.0 260 1.3251 0.4565
0.8101 40.92 266 1.3039 0.4783
0.8126 42.0 273 1.2523 0.5435
0.8126 42.92 279 1.3060 0.5217
0.7971 44.0 286 1.2678 0.5217
0.7806 44.92 292 1.3332 0.5
0.7806 46.0 299 1.2550 0.5652
0.7899 46.92 305 1.2517 0.5870
0.7602 48.0 312 1.2627 0.5870
0.7602 48.92 318 1.2620 0.6087
0.7748 50.0 325 1.2286 0.5652
0.7613 50.92 331 1.1997 0.6087
0.7613 52.0 338 1.2353 0.5870
0.7514 52.92 344 1.2466 0.5870
0.7581 54.0 351 1.2161 0.5870
0.7581 54.92 357 1.2396 0.5435
0.7401 56.0 364 1.1859 0.6087
0.7421 56.92 370 1.1757 0.6304
0.7421 58.0 377 1.1754 0.5870
0.7261 58.92 383 1.1630 0.6304
0.709 60.0 390 1.2157 0.5870
0.709 60.92 396 1.2124 0.6087
0.7075 62.0 403 1.2095 0.6087
0.7075 62.92 409 1.2543 0.5652
0.7141 64.0 416 1.2210 0.6087
0.6907 64.92 422 1.3190 0.5435
0.6907 66.0 429 1.2197 0.6522
0.7237 66.92 435 1.2365 0.5652
0.6918 68.0 442 1.1570 0.6304
0.6918 68.92 448 1.1790 0.6087
0.7137 70.0 455 1.1968 0.6087
0.6954 70.92 461 1.1959 0.6304
0.6954 72.0 468 1.1782 0.6304
0.6961 72.92 474 1.1935 0.5652
0.6889 73.85 480 1.1835 0.6087

Framework versions

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