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meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_8

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_8
    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: 0.9376947040498442

meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_8

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: 0.2201
  • Accuracy: 0.9377

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
1.0975 1.0 21 1.0890 0.4174
1.0736 2.0 42 1.0605 0.4673
1.0236 3.0 63 1.0034 0.5202
0.9414 4.0 84 0.9182 0.6044
0.8522 5.0 105 0.8534 0.6417
0.7504 6.0 126 0.8599 0.6044
0.7472 7.0 147 0.6873 0.7040
0.6671 8.0 168 0.6945 0.7165
0.5986 9.0 189 0.6044 0.7664
0.5255 10.0 210 0.6201 0.7383
0.4988 11.0 231 0.6229 0.7570
0.5221 12.0 252 0.6687 0.7321
0.4338 13.0 273 0.5720 0.7757
0.4157 14.0 294 0.5662 0.7757
0.5228 15.0 315 0.6383 0.7383
0.3387 16.0 336 0.4655 0.8069
0.2952 17.0 357 0.4642 0.8006
0.3083 18.0 378 0.5752 0.7726
0.2659 19.0 399 0.5155 0.7913
0.2824 20.0 420 0.4943 0.8162
0.3329 21.0 441 0.5901 0.7757
0.3527 22.0 462 0.4185 0.8380
0.2394 23.0 483 0.3630 0.8723
0.2106 24.0 504 0.4305 0.8474
0.1845 25.0 525 0.3412 0.8629
0.1882 26.0 546 0.3621 0.8816
0.2144 27.0 567 0.3275 0.8754
0.1824 28.0 588 0.3481 0.8723
0.163 29.0 609 0.3861 0.8598
0.1467 30.0 630 0.3590 0.8692
0.2073 31.0 651 0.3481 0.8879
0.1669 32.0 672 0.3134 0.8847
0.167 33.0 693 0.3726 0.8754
0.1624 34.0 714 0.5522 0.7944
0.1812 35.0 735 0.4431 0.8193
0.1172 36.0 756 0.3441 0.8816
0.1515 37.0 777 0.4946 0.8255
0.1612 38.0 798 0.3402 0.8847
0.0937 39.0 819 0.4480 0.8598
0.1453 40.0 840 0.4515 0.8411
0.1259 41.0 861 0.3361 0.8847
0.107 42.0 882 0.3544 0.8598
0.1244 43.0 903 0.3990 0.8567
0.0824 44.0 924 0.3566 0.9034
0.1171 45.0 945 0.3223 0.9003
0.1052 46.0 966 0.3364 0.8660
0.1274 47.0 987 0.3034 0.8941
0.0799 48.0 1008 0.3928 0.8910
0.0814 49.0 1029 0.3428 0.8847
0.091 50.0 1050 0.3141 0.9065
0.0777 51.0 1071 0.4016 0.8785
0.0644 52.0 1092 0.3398 0.8972
0.1019 53.0 1113 0.3559 0.8847
0.076 54.0 1134 0.3503 0.8910
0.067 55.0 1155 0.3245 0.8910
0.0679 56.0 1176 0.3099 0.9034
0.0661 57.0 1197 0.3249 0.8723
0.0716 58.0 1218 0.3016 0.9034
0.075 59.0 1239 0.4144 0.8692
0.0874 60.0 1260 0.3850 0.8723
0.0821 61.0 1281 0.2938 0.9065
0.0735 62.0 1302 0.2518 0.9190
0.0755 63.0 1323 0.4015 0.8972
0.2235 64.0 1344 0.3127 0.8972
0.0631 65.0 1365 0.2518 0.9128
0.0711 66.0 1386 0.3544 0.8941
0.0671 67.0 1407 0.3616 0.8816
0.059 68.0 1428 0.2567 0.9097
0.0558 69.0 1449 0.3696 0.8692
0.0755 70.0 1470 0.3032 0.9065
0.0666 71.0 1491 0.2819 0.9128
0.0519 72.0 1512 0.2179 0.9252
0.0443 73.0 1533 0.2722 0.9159
0.0415 74.0 1554 0.2167 0.9346
0.0632 75.0 1575 0.2115 0.9377
0.067 76.0 1596 0.4024 0.8785
0.0592 77.0 1617 0.2328 0.9283
0.0528 78.0 1638 0.2425 0.9065
0.0462 79.0 1659 0.2385 0.9252
0.0248 80.0 1680 0.2694 0.9159
0.04 81.0 1701 0.2192 0.9283
0.0436 82.0 1722 0.2697 0.9221
0.0415 83.0 1743 0.2855 0.9128
0.0431 84.0 1764 0.1680 0.9502
0.0438 85.0 1785 0.2513 0.9221
0.0385 86.0 1806 0.2609 0.9190
0.0291 87.0 1827 0.2136 0.9439
0.0326 88.0 1848 0.2069 0.9439
0.0347 89.0 1869 0.2450 0.9315
0.0393 90.0 1890 0.2609 0.9377
0.0355 91.0 1911 0.1932 0.9408
0.0423 92.0 1932 0.2481 0.9315
0.0386 93.0 1953 0.1963 0.9377
0.029 94.0 1974 0.2220 0.9377
0.0383 95.0 1995 0.2626 0.9252
0.0205 96.0 2016 0.1894 0.9470
0.0392 97.0 2037 0.1744 0.9502
0.0282 98.0 2058 0.2907 0.9190
0.0416 99.0 2079 0.1868 0.9533
0.0223 100.0 2100 0.2201 0.9377

Framework versions

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