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meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_9

Image Classification
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_9
    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: 0.9439252336448598

meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_9

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1854
  • Accuracy: 0.9439

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: 64
  • eval_batch_size: 1
  • seed: 42
  • 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
1.101 1.0 21 1.0776 0.4206
1.0459 2.0 42 1.0368 0.5109
0.9957 3.0 63 0.9830 0.5296
0.9613 4.0 84 0.8994 0.5857
0.9072 5.0 105 0.8663 0.6231
0.8397 6.0 126 0.7738 0.7196
0.7726 7.0 147 0.7461 0.6978
0.7757 8.0 168 0.7647 0.6636
0.6253 9.0 189 0.6437 0.7445
0.5671 10.0 210 0.6791 0.7072
0.529 11.0 231 0.6623 0.7072
0.4362 12.0 252 0.5876 0.7508
0.552 13.0 273 0.5093 0.7913
0.3779 14.0 294 0.7716 0.6573
0.5052 15.0 315 0.7956 0.6791
0.5263 16.0 336 0.6077 0.7445
0.3712 17.0 357 0.4699 0.8474
0.2958 18.0 378 0.5815 0.7819
0.2775 19.0 399 0.5083 0.8100
0.2467 20.0 420 0.4561 0.8162
0.2541 21.0 441 0.4463 0.8380
0.2689 22.0 462 0.5149 0.7944
0.1893 23.0 483 0.4155 0.8567
0.2263 24.0 504 0.7202 0.7601
0.2646 25.0 525 0.4365 0.8162
0.2416 26.0 546 0.3365 0.8816
0.1788 27.0 567 0.3679 0.8723
0.1947 28.0 588 0.3725 0.8660
0.1802 29.0 609 0.4176 0.8567
0.1695 30.0 630 0.3776 0.8692
0.2089 31.0 651 0.3047 0.8785
0.1727 32.0 672 0.3225 0.8754
0.143 33.0 693 0.2355 0.9128
0.1593 34.0 714 0.3424 0.8847
0.1179 35.0 735 0.2873 0.9065
0.1227 36.0 756 0.3114 0.8941
0.1636 37.0 777 0.4101 0.8660
0.1347 38.0 798 0.2253 0.9190
0.1108 39.0 819 0.3387 0.8847
0.1383 40.0 840 0.4024 0.8754
0.1173 41.0 861 0.2436 0.9034
0.1027 42.0 882 0.2603 0.9065
0.1244 43.0 903 0.2990 0.9128
0.1061 44.0 924 0.3082 0.9065
0.1179 45.0 945 0.2212 0.9128
0.0945 46.0 966 0.3455 0.8816
0.1055 47.0 987 0.2951 0.8847
0.0895 48.0 1008 0.3003 0.9065
0.1298 49.0 1029 0.3123 0.9097
0.1379 50.0 1050 0.2787 0.9128
0.0879 51.0 1071 0.2920 0.9128
0.1011 52.0 1092 0.3667 0.8816
0.1038 53.0 1113 0.2967 0.9034
0.0834 54.0 1134 0.2281 0.9346
0.0998 55.0 1155 0.2835 0.8941
0.0794 56.0 1176 0.2260 0.9221
0.0732 57.0 1197 0.3309 0.9003
0.0612 58.0 1218 0.3392 0.9065
0.0679 59.0 1239 0.2728 0.9252
0.0986 60.0 1260 0.2380 0.9221
0.0639 61.0 1281 0.2672 0.9221
0.061 62.0 1302 0.2898 0.9221
0.0741 63.0 1323 0.3098 0.9097
0.0532 64.0 1344 0.2621 0.9252
0.0642 65.0 1365 0.1643 0.9377
0.0762 66.0 1386 0.2332 0.9346
0.0562 67.0 1407 0.2603 0.9252
0.0481 68.0 1428 0.2363 0.9097
0.0576 69.0 1449 0.2421 0.9377
0.0355 70.0 1470 0.2040 0.9346
0.07 71.0 1491 0.2900 0.9283
0.0631 72.0 1512 0.2531 0.9221
0.046 73.0 1533 0.2054 0.9346
0.0767 74.0 1554 0.1651 0.9408
0.0569 75.0 1575 0.2251 0.9377
0.0454 76.0 1596 0.2003 0.9408
0.0596 77.0 1617 0.1607 0.9564
0.0602 78.0 1638 0.1789 0.9408
0.0512 79.0 1659 0.2390 0.9221
0.0402 80.0 1680 0.1433 0.9564
0.0425 81.0 1701 0.1379 0.9502
0.0428 82.0 1722 0.1422 0.9564
0.0353 83.0 1743 0.1433 0.9502
0.0449 84.0 1764 0.2206 0.9190
0.0402 85.0 1785 0.2194 0.9470
0.041 86.0 1806 0.1564 0.9502
0.057 87.0 1827 0.2352 0.9346
0.0475 88.0 1848 0.1618 0.9502
0.0399 89.0 1869 0.1322 0.9564
0.0324 90.0 1890 0.1803 0.9502
0.0796 91.0 1911 0.1577 0.9408
0.0283 92.0 1932 0.1599 0.9626
0.0325 93.0 1953 0.1741 0.9626
0.0345 94.0 1974 0.1089 0.9533
0.0361 95.0 1995 0.1044 0.9595
0.0346 96.0 2016 0.1416 0.9533
0.0339 97.0 2037 0.2032 0.9346
0.0309 98.0 2058 0.1348 0.9595
0.046 99.0 2079 0.1345 0.9533
0.0285 100.0 2100 0.1854 0.9439

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1
  • Datasets 2.20.0
  • Tokenizers 0.19.1