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---
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_12
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.9439252336448598
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_12
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.1999
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- Accuracy: 0.9439
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 64
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- eval_batch_size: 1
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 100
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.093 | 1.0 | 21 | 1.0798 | 0.4361 |
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| 1.0819 | 2.0 | 42 | 1.0504 | 0.4704 |
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| 1.0195 | 3.0 | 63 | 1.0107 | 0.4860 |
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| 0.9205 | 4.0 | 84 | 0.9285 | 0.5514 |
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| 0.879 | 5.0 | 105 | 0.8812 | 0.6044 |
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| 0.757 | 6.0 | 126 | 0.8114 | 0.6324 |
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| 0.6884 | 7.0 | 147 | 0.7328 | 0.6885 |
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| 0.6399 | 8.0 | 168 | 0.7187 | 0.6978 |
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| 0.5531 | 9.0 | 189 | 0.6771 | 0.7196 |
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| 0.5187 | 10.0 | 210 | 0.6594 | 0.7134 |
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| 0.5125 | 11.0 | 231 | 0.7660 | 0.6729 |
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| 0.495 | 12.0 | 252 | 0.7215 | 0.7165 |
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| 0.5014 | 13.0 | 273 | 0.5828 | 0.7570 |
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| 0.3638 | 14.0 | 294 | 0.7056 | 0.7134 |
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| 0.4493 | 15.0 | 315 | 0.7061 | 0.7383 |
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| 0.4304 | 16.0 | 336 | 0.5031 | 0.7944 |
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| 0.3223 | 17.0 | 357 | 0.5052 | 0.7975 |
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| 0.3496 | 18.0 | 378 | 0.5136 | 0.8069 |
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| 0.2498 | 19.0 | 399 | 0.5414 | 0.7944 |
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| 0.3783 | 20.0 | 420 | 0.4276 | 0.8380 |
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| 0.2768 | 21.0 | 441 | 0.4990 | 0.8100 |
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| 0.2588 | 22.0 | 462 | 0.5184 | 0.8100 |
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| 0.33 | 23.0 | 483 | 0.4037 | 0.8380 |
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| 0.2418 | 24.0 | 504 | 0.4764 | 0.8100 |
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| 0.2 | 25.0 | 525 | 0.3888 | 0.8505 |
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| 0.1859 | 26.0 | 546 | 0.3868 | 0.8660 |
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| 0.1804 | 27.0 | 567 | 0.5299 | 0.7944 |
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| 0.1891 | 28.0 | 588 | 0.4448 | 0.8411 |
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| 0.1837 | 29.0 | 609 | 0.4972 | 0.8349 |
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| 0.209 | 30.0 | 630 | 0.4709 | 0.8380 |
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| 0.1669 | 31.0 | 651 | 0.4084 | 0.8536 |
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| 0.1474 | 32.0 | 672 | 0.4000 | 0.8785 |
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| 0.1666 | 33.0 | 693 | 0.4109 | 0.8598 |
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| 0.1657 | 34.0 | 714 | 0.3265 | 0.8910 |
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| 0.1454 | 35.0 | 735 | 0.5221 | 0.8162 |
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| 0.2093 | 36.0 | 756 | 0.6376 | 0.7944 |
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| 0.1929 | 37.0 | 777 | 0.4007 | 0.8723 |
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| 0.1393 | 38.0 | 798 | 0.3291 | 0.8879 |
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| 0.1328 | 39.0 | 819 | 0.3766 | 0.8598 |
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| 0.127 | 40.0 | 840 | 0.2965 | 0.9003 |
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| 0.1325 | 41.0 | 861 | 0.3481 | 0.8723 |
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| 0.118 | 42.0 | 882 | 0.3093 | 0.9065 |
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| 0.1001 | 43.0 | 903 | 0.4232 | 0.8692 |
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| 0.124 | 44.0 | 924 | 0.3761 | 0.8723 |
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| 0.1159 | 45.0 | 945 | 0.3523 | 0.8910 |
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| 0.129 | 46.0 | 966 | 0.3309 | 0.8785 |
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| 0.1129 | 47.0 | 987 | 0.2915 | 0.9003 |
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| 0.1043 | 48.0 | 1008 | 0.3259 | 0.8972 |
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| 0.0986 | 49.0 | 1029 | 0.2627 | 0.9097 |
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| 0.083 | 50.0 | 1050 | 0.3035 | 0.9034 |
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| 0.0874 | 51.0 | 1071 | 0.3994 | 0.8629 |
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| 0.0959 | 52.0 | 1092 | 0.2904 | 0.9065 |
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| 0.0883 | 53.0 | 1113 | 0.2771 | 0.9128 |
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| 0.0766 | 54.0 | 1134 | 0.2984 | 0.9128 |
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| 0.0865 | 55.0 | 1155 | 0.3534 | 0.8941 |
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| 0.0907 | 56.0 | 1176 | 0.3874 | 0.8723 |
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| 0.0596 | 57.0 | 1197 | 0.2080 | 0.9283 |
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| 0.0658 | 58.0 | 1218 | 0.3571 | 0.8879 |
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| 0.0806 | 59.0 | 1239 | 0.3444 | 0.9003 |
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| 0.0709 | 60.0 | 1260 | 0.3292 | 0.8972 |
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| 0.0864 | 61.0 | 1281 | 0.3551 | 0.8816 |
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| 0.0773 | 62.0 | 1302 | 0.2930 | 0.9159 |
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| 0.0758 | 63.0 | 1323 | 0.2828 | 0.9221 |
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| 0.0767 | 64.0 | 1344 | 0.2919 | 0.9065 |
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| 0.0686 | 65.0 | 1365 | 0.2971 | 0.9065 |
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| 0.0818 | 66.0 | 1386 | 0.3057 | 0.8972 |
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| 0.0659 | 67.0 | 1407 | 0.2323 | 0.9221 |
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| 0.0627 | 68.0 | 1428 | 0.3991 | 0.8754 |
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| 0.0536 | 69.0 | 1449 | 0.2314 | 0.9221 |
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| 0.2167 | 70.0 | 1470 | 0.2586 | 0.9346 |
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| 0.0706 | 71.0 | 1491 | 0.2813 | 0.9315 |
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| 0.0631 | 72.0 | 1512 | 0.2981 | 0.9034 |
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| 0.0586 | 73.0 | 1533 | 0.2586 | 0.9283 |
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| 0.0597 | 74.0 | 1554 | 0.3115 | 0.9097 |
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| 0.0412 | 75.0 | 1575 | 0.2327 | 0.9315 |
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| 0.0504 | 76.0 | 1596 | 0.2493 | 0.9408 |
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| 0.0515 | 77.0 | 1617 | 0.2861 | 0.9283 |
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| 0.0394 | 78.0 | 1638 | 0.2715 | 0.9128 |
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| 0.0526 | 79.0 | 1659 | 0.2521 | 0.9190 |
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| 0.043 | 80.0 | 1680 | 0.2421 | 0.9283 |
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| 0.0466 | 81.0 | 1701 | 0.2918 | 0.9034 |
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| 0.0418 | 82.0 | 1722 | 0.2956 | 0.9065 |
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| 0.048 | 83.0 | 1743 | 0.2199 | 0.9283 |
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| 0.0311 | 84.0 | 1764 | 0.2732 | 0.9128 |
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| 0.0681 | 85.0 | 1785 | 0.2148 | 0.9346 |
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| 0.0392 | 86.0 | 1806 | 0.2609 | 0.9252 |
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| 0.0447 | 87.0 | 1827 | 0.2791 | 0.9346 |
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| 0.0244 | 88.0 | 1848 | 0.2863 | 0.9221 |
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| 0.0382 | 89.0 | 1869 | 0.2894 | 0.9190 |
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| 0.0524 | 90.0 | 1890 | 0.1708 | 0.9408 |
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| 0.0356 | 91.0 | 1911 | 0.2084 | 0.9221 |
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| 0.0387 | 92.0 | 1932 | 0.2262 | 0.9377 |
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| 0.0345 | 93.0 | 1953 | 0.2441 | 0.9377 |
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| 0.0298 | 94.0 | 1974 | 0.2042 | 0.9408 |
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| 0.0427 | 95.0 | 1995 | 0.1611 | 0.9533 |
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| 0.043 | 96.0 | 2016 | 0.2175 | 0.9533 |
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| 0.0241 | 97.0 | 2037 | 0.2445 | 0.9283 |
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| 0.0416 | 98.0 | 2058 | 0.2236 | 0.9283 |
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| 0.0311 | 99.0 | 2079 | 0.1943 | 0.9502 |
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| 0.0352 | 100.0 | 2100 | 0.1999 | 0.9439 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.1
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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