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

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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-80d
    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-80d

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: 0.7669
  • 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: 0.0003
  • 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: 0.05
  • num_epochs: 70

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.92 6 1.3848 0.2174
1.3838 2.0 13 1.3723 0.1957
1.3838 2.92 19 1.3540 0.1739
1.3023 4.0 26 1.3327 0.2391
1.1398 4.92 32 1.2555 0.2391
1.1398 6.0 39 1.3010 0.3913
1.0076 6.92 45 1.1957 0.5
0.8823 8.0 52 1.0565 0.5870
0.8823 8.92 58 0.9410 0.7391
0.7637 10.0 65 0.9274 0.7391
0.6688 10.92 71 0.8492 0.7826
0.6688 12.0 78 0.8906 0.6739
0.5855 12.92 84 0.8929 0.6522
0.4921 14.0 91 0.8338 0.7391
0.4921 14.92 97 0.7686 0.7826
0.4318 16.0 104 0.8430 0.7609
0.386 16.92 110 0.8315 0.7826
0.386 18.0 117 0.7669 0.8261
0.3483 18.92 123 0.8347 0.7174
0.3023 20.0 130 1.1037 0.6304
0.3023 20.92 136 0.9024 0.7174
0.2973 22.0 143 0.7760 0.7826
0.2973 22.92 149 0.7400 0.7826
0.2529 24.0 156 1.0058 0.7174
0.2086 24.92 162 0.9260 0.7609
0.2086 26.0 169 0.8370 0.7174
0.2265 26.92 175 0.8060 0.7391
0.1942 28.0 182 0.9812 0.6957
0.1942 28.92 188 0.8996 0.7391
0.1708 30.0 195 0.9630 0.6957
0.1747 30.92 201 0.9691 0.7174
0.1747 32.0 208 1.0017 0.7391
0.1461 32.92 214 0.9965 0.6957
0.1457 34.0 221 0.9506 0.7391
0.1457 34.92 227 0.9834 0.7391
0.1814 36.0 234 1.0191 0.7609
0.1383 36.92 240 0.8807 0.7609
0.1383 38.0 247 0.8724 0.7609
0.1718 38.92 253 0.8090 0.7391
0.1289 40.0 260 0.8709 0.7609
0.1289 40.92 266 0.9704 0.7391
0.1193 42.0 273 1.0518 0.7391
0.1193 42.92 279 1.0157 0.7174
0.1224 44.0 286 1.0794 0.7391
0.1104 44.92 292 1.0402 0.7391
0.1104 46.0 299 0.9837 0.7609
0.1055 46.92 305 1.0323 0.7174
0.1242 48.0 312 0.9907 0.7391
0.1242 48.92 318 1.0436 0.7609
0.1283 50.0 325 0.9829 0.7391
0.1035 50.92 331 1.0400 0.7174
0.1035 52.0 338 1.0414 0.7174
0.1066 52.92 344 1.0958 0.6957
0.0863 54.0 351 1.0914 0.7174
0.0863 54.92 357 1.0816 0.7174
0.1062 56.0 364 1.0087 0.7174
0.1214 56.92 370 1.0454 0.7391
0.1214 58.0 377 1.0324 0.7391
0.0984 58.92 383 1.0591 0.6739
0.0966 60.0 390 1.0037 0.6957
0.0966 60.92 396 0.9887 0.6957
0.0626 62.0 403 1.0294 0.6739
0.0626 62.92 409 0.9939 0.7174
0.085 64.0 416 0.9886 0.6957
0.068 64.62 420 1.0773 0.6739

Framework versions

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