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metadata
license: apache-2.0
base_model: microsoft/swinv2-tiny-patch4-window8-256
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: swinv2-tiny-patch4-window8-256-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: 0.7391304347826086

swinv2-tiny-patch4-window8-256-ve-U13-b-80

This model is a fine-tuned version of microsoft/swinv2-tiny-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7882
  • Accuracy: 0.7391

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: 5.5e-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: 0.05
  • num_epochs: 80

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.92 6 1.3858 0.1304
1.3856 2.0 13 1.3777 0.3696
1.3856 2.92 19 1.3488 0.2391
1.361 4.0 26 1.2503 0.2826
1.2088 4.92 32 1.1317 0.4130
1.2088 6.0 39 1.0244 0.4565
1.0729 6.92 45 1.0413 0.4565
0.9554 8.0 52 0.9286 0.5652
0.9554 8.92 58 0.9103 0.5652
0.8221 10.0 65 0.8519 0.6522
0.732 10.92 71 0.8300 0.5870
0.732 12.0 78 0.8103 0.6304
0.6491 12.92 84 0.9533 0.5870
0.5724 14.0 91 0.7882 0.7391
0.5724 14.92 97 0.8072 0.6957
0.5305 16.0 104 0.7651 0.7391
0.4879 16.92 110 0.7379 0.7174
0.4879 18.0 117 0.7590 0.6739
0.4346 18.92 123 0.9283 0.6739
0.3671 20.0 130 1.0188 0.6304
0.3671 20.92 136 0.8959 0.7391
0.3725 22.0 143 0.9502 0.6957
0.3725 22.92 149 0.9627 0.6522
0.3321 24.0 156 0.9619 0.6957
0.3376 24.92 162 1.0459 0.6739
0.3376 26.0 169 1.0167 0.6522
0.3699 26.92 175 0.9949 0.6304
0.3098 28.0 182 0.9944 0.6739
0.3098 28.92 188 1.0860 0.6304
0.253 30.0 195 1.1721 0.6522
0.2615 30.92 201 1.1626 0.6739
0.2615 32.0 208 1.2464 0.6304
0.242 32.92 214 1.2179 0.6522
0.2173 34.0 221 1.2407 0.6304
0.2173 34.92 227 1.1585 0.6739
0.2305 36.0 234 1.3048 0.6522
0.2114 36.92 240 1.1776 0.6522
0.2114 38.0 247 1.1460 0.6522
0.2243 38.92 253 1.2424 0.6957
0.1822 40.0 260 1.2804 0.6739
0.1822 40.92 266 1.3472 0.6739
0.2065 42.0 273 1.3632 0.6739
0.2065 42.92 279 1.2832 0.6739
0.1942 44.0 286 1.3500 0.6739
0.1699 44.92 292 1.3242 0.6739
0.1699 46.0 299 1.3189 0.6957
0.1764 46.92 305 1.2840 0.6739
0.1771 48.0 312 1.3069 0.6957
0.1771 48.92 318 1.1585 0.6957
0.2095 50.0 325 1.3702 0.6957
0.1404 50.92 331 1.3539 0.6957
0.1404 52.0 338 1.3723 0.6957
0.1449 52.92 344 1.3877 0.6957
0.1348 54.0 351 1.3381 0.6739
0.1348 54.92 357 1.3700 0.6739
0.1683 56.0 364 1.2871 0.6957
0.1577 56.92 370 1.3214 0.6957
0.1577 58.0 377 1.3992 0.6522
0.1474 58.92 383 1.3800 0.6522
0.1267 60.0 390 1.2535 0.6739
0.1267 60.92 396 1.3200 0.6739
0.1171 62.0 403 1.3730 0.6739
0.1171 62.92 409 1.3678 0.6739
0.1461 64.0 416 1.3788 0.6739
0.1124 64.92 422 1.3944 0.6739
0.1124 66.0 429 1.3724 0.6739
0.1168 66.92 435 1.3553 0.6522
0.1243 68.0 442 1.3829 0.6739
0.1243 68.92 448 1.4040 0.6739
0.1375 70.0 455 1.4127 0.6522
0.1017 70.92 461 1.4070 0.6522
0.1017 72.0 468 1.3989 0.6739
0.1346 72.92 474 1.3995 0.6739
0.1382 73.85 480 1.3988 0.6739

Framework versions

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