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convnextv2-tiny-1k-224-finetuned-print

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convnextv2
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metadata
license: apache-2.0
base_model: facebook/convnextv2-tiny-1k-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: convnextv2-tiny-1k-224-finetuned-print
    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: 0.9263157894736842

convnextv2-tiny-1k-224-finetuned-print

This model is a fine-tuned version of facebook/convnextv2-tiny-1k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1811
  • Accuracy: 0.9263

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: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 0.8889 6 1.6653 0.1158
1.6915 1.9259 13 1.5942 0.2316
1.5895 2.9630 20 1.4918 0.3895
1.5895 4.0 27 1.3637 0.5053
1.433 4.8889 33 1.2241 0.6737
1.2158 5.9259 40 1.0279 0.7474
1.2158 6.9630 47 0.8303 0.8105
0.9438 8.0 54 0.6756 0.8211
0.727 8.8889 60 0.5751 0.8421
0.727 9.9259 67 0.4679 0.8632
0.5516 10.9630 74 0.4432 0.8526
0.4337 12.0 81 0.3815 0.8526
0.4337 12.8889 87 0.3641 0.8842
0.3757 13.9259 94 0.3233 0.8737
0.3017 14.9630 101 0.3230 0.9158
0.3017 16.0 108 0.3018 0.8842
0.2495 16.8889 114 0.3445 0.9053
0.2177 17.9259 121 0.2987 0.8947
0.2177 18.9630 128 0.2727 0.8947
0.1738 20.0 135 0.2865 0.8842
0.1572 20.8889 141 0.2646 0.9263
0.1572 21.9259 148 0.3100 0.9053
0.1165 22.9630 155 0.3039 0.9263
0.1057 24.0 162 0.3023 0.9053
0.1057 24.8889 168 0.2254 0.9158
0.0825 25.9259 175 0.3308 0.8737
0.0795 26.9630 182 0.2040 0.9368
0.0795 28.0 189 0.2148 0.9263
0.072 28.8889 195 0.3450 0.8632
0.0701 29.9259 202 0.2418 0.9263
0.0701 30.9630 209 0.2495 0.9263
0.0635 32.0 216 0.3267 0.8947
0.0537 32.8889 222 0.3728 0.9158
0.0537 33.9259 229 0.2852 0.9053
0.0607 34.9630 236 0.2386 0.9474
0.052 36.0 243 0.2070 0.9158
0.052 36.8889 249 0.1860 0.9474
0.049 37.9259 256 0.3069 0.8947
0.0578 38.9630 263 0.4477 0.8737
0.0533 40.0 270 0.2612 0.8947
0.0533 40.8889 276 0.2649 0.8842
0.0505 41.9259 283 0.1950 0.9263
0.0433 42.9630 290 0.2903 0.8842
0.0433 44.0 297 0.2526 0.9368
0.0395 44.8889 303 0.3016 0.8842
0.035 45.9259 310 0.3509 0.8947
0.035 46.9630 317 0.2943 0.8842
0.0335 48.0 324 0.2613 0.8842
0.0408 48.8889 330 0.2165 0.9158
0.0408 49.9259 337 0.2872 0.9263
0.0244 50.9630 344 0.3134 0.8842
0.0323 52.0 351 0.3006 0.9158
0.0323 52.8889 357 0.3758 0.8737
0.0241 53.9259 364 0.3033 0.9263
0.0193 54.9630 371 0.2741 0.9368
0.0193 56.0 378 0.1684 0.9368
0.0273 56.8889 384 0.2403 0.9474
0.0244 57.9259 391 0.1500 0.9474
0.0244 58.9630 398 0.1377 0.9368
0.0268 60.0 405 0.1898 0.9158
0.0405 60.8889 411 0.1756 0.9053
0.0405 61.9259 418 0.1907 0.9263
0.0219 62.9630 425 0.1790 0.9053
0.0329 64.0 432 0.1885 0.9368
0.0329 64.8889 438 0.1550 0.9368
0.019 65.9259 445 0.1811 0.9158
0.0205 66.9630 452 0.2166 0.9263
0.0205 68.0 459 0.1701 0.9053
0.0232 68.8889 465 0.2153 0.9158
0.0269 69.9259 472 0.2229 0.9263
0.0269 70.9630 479 0.2237 0.9263
0.0306 72.0 486 0.1828 0.9368
0.0298 72.8889 492 0.1448 0.9368
0.0298 73.9259 499 0.1948 0.9158
0.0154 74.9630 506 0.2570 0.9158
0.0193 76.0 513 0.2462 0.9263
0.0193 76.8889 519 0.2194 0.9158
0.0188 77.9259 526 0.2254 0.9158
0.0198 78.9630 533 0.1924 0.9263
0.0147 80.0 540 0.1525 0.9368
0.0147 80.8889 546 0.1314 0.9474
0.0282 81.9259 553 0.1381 0.9368
0.0168 82.9630 560 0.1496 0.9158
0.0168 84.0 567 0.1806 0.9263
0.018 84.8889 573 0.2080 0.9263
0.0172 85.9259 580 0.2199 0.9158
0.0172 86.9630 587 0.1939 0.9263
0.0117 88.0 594 0.1815 0.9263
0.0149 88.8889 600 0.1811 0.9263

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1