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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_16
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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: 0.9158878504672897
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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_16
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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: 0.2644
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- Accuracy: 0.9159
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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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| 1.1034 | 1.0 | 21 | 1.0943 | 0.3676 |
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| 1.0767 | 2.0 | 42 | 1.0823 | 0.4330 |
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| 1.0209 | 3.0 | 63 | 1.0269 | 0.4829 |
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| 0.9495 | 4.0 | 84 | 1.0109 | 0.4953 |
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| 0.9498 | 5.0 | 105 | 0.8882 | 0.6199 |
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| 0.7834 | 6.0 | 126 | 0.8506 | 0.6075 |
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| 0.6988 | 7.0 | 147 | 0.7727 | 0.6480 |
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| 0.6568 | 8.0 | 168 | 0.8098 | 0.6573 |
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| 0.634 | 9.0 | 189 | 0.9338 | 0.5607 |
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| 0.7335 | 10.0 | 210 | 0.7394 | 0.6947 |
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| 0.5521 | 11.0 | 231 | 0.6369 | 0.7539 |
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| 0.5108 | 12.0 | 252 | 0.7480 | 0.7040 |
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| 0.4485 | 13.0 | 273 | 0.8050 | 0.6854 |
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| 0.4928 | 14.0 | 294 | 0.7566 | 0.7040 |
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| 0.5092 | 15.0 | 315 | 0.5191 | 0.7944 |
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| 0.4473 | 16.0 | 336 | 0.6516 | 0.7134 |
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| 0.3521 | 17.0 | 357 | 0.5184 | 0.8069 |
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| 0.2994 | 18.0 | 378 | 0.5233 | 0.8193 |
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| 0.2844 | 19.0 | 399 | 0.5587 | 0.7757 |
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| 0.301 | 20.0 | 420 | 0.5614 | 0.8131 |
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| 0.2898 | 21.0 | 441 | 0.4659 | 0.8287 |
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| 0.2513 | 22.0 | 462 | 0.4748 | 0.8287 |
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| 0.2121 | 23.0 | 483 | 0.4042 | 0.8505 |
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| 0.2302 | 24.0 | 504 | 0.6265 | 0.7757 |
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| 0.2201 | 25.0 | 525 | 0.4746 | 0.8349 |
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| 0.2193 | 26.0 | 546 | 0.3364 | 0.8816 |
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| 0.1852 | 27.0 | 567 | 0.3966 | 0.8567 |
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| 0.2117 | 28.0 | 588 | 0.4427 | 0.8349 |
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| 0.1705 | 29.0 | 609 | 0.4767 | 0.8255 |
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| 0.1756 | 30.0 | 630 | 0.4838 | 0.8380 |
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| 0.1744 | 31.0 | 651 | 0.5400 | 0.8131 |
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| 0.2296 | 32.0 | 672 | 0.4693 | 0.8255 |
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| 0.1517 | 33.0 | 693 | 0.3704 | 0.8660 |
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| 0.3201 | 34.0 | 714 | 0.7578 | 0.7539 |
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| 0.1561 | 35.0 | 735 | 0.3828 | 0.8660 |
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| 0.1458 | 36.0 | 756 | 0.4366 | 0.8692 |
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| 0.2448 | 37.0 | 777 | 0.3000 | 0.8972 |
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| 0.15 | 38.0 | 798 | 0.4457 | 0.8567 |
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| 0.1367 | 39.0 | 819 | 0.2505 | 0.9128 |
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| 0.1167 | 40.0 | 840 | 0.2869 | 0.9003 |
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| 0.0949 | 41.0 | 861 | 0.3303 | 0.8847 |
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| 0.1203 | 42.0 | 882 | 0.3524 | 0.8629 |
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| 0.1429 | 43.0 | 903 | 0.4549 | 0.8318 |
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| 0.11 | 44.0 | 924 | 0.4028 | 0.8754 |
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| 0.1231 | 45.0 | 945 | 0.4290 | 0.8629 |
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| 0.1009 | 46.0 | 966 | 0.4046 | 0.8598 |
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| 0.1132 | 47.0 | 987 | 0.3221 | 0.8972 |
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| 0.1023 | 48.0 | 1008 | 0.2680 | 0.9159 |
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| 0.0906 | 49.0 | 1029 | 0.3685 | 0.8754 |
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| 0.1039 | 50.0 | 1050 | 0.3564 | 0.8785 |
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| 0.0948 | 51.0 | 1071 | 0.4784 | 0.8380 |
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| 0.0881 | 52.0 | 1092 | 0.3369 | 0.8816 |
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| 0.0918 | 53.0 | 1113 | 0.2608 | 0.9159 |
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| 0.0828 | 54.0 | 1134 | 0.2678 | 0.9003 |
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| 0.0819 | 55.0 | 1155 | 0.2618 | 0.9034 |
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| 0.1696 | 56.0 | 1176 | 0.3057 | 0.9034 |
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| 0.0943 | 57.0 | 1197 | 0.3915 | 0.8847 |
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| 0.0718 | 58.0 | 1218 | 0.3162 | 0.9065 |
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| 0.0775 | 59.0 | 1239 | 0.3678 | 0.8847 |
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| 0.0674 | 60.0 | 1260 | 0.3083 | 0.8972 |
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| 0.0666 | 61.0 | 1281 | 0.3120 | 0.9128 |
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| 0.0631 | 62.0 | 1302 | 0.3648 | 0.9003 |
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| 0.0726 | 63.0 | 1323 | 0.3771 | 0.8910 |
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| 0.0619 | 64.0 | 1344 | 0.3278 | 0.8910 |
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| 0.0823 | 65.0 | 1365 | 0.4250 | 0.8692 |
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| 0.0628 | 66.0 | 1386 | 0.3618 | 0.9003 |
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| 0.0714 | 67.0 | 1407 | 0.4590 | 0.8629 |
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| 0.056 | 68.0 | 1428 | 0.4471 | 0.8910 |
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| 0.0613 | 69.0 | 1449 | 0.2702 | 0.9097 |
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| 0.0642 | 70.0 | 1470 | 0.2646 | 0.9190 |
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| 0.0549 | 71.0 | 1491 | 0.3084 | 0.8972 |
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| 0.0534 | 72.0 | 1512 | 0.3388 | 0.9128 |
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| 0.0414 | 73.0 | 1533 | 0.2962 | 0.9190 |
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| 0.0552 | 74.0 | 1554 | 0.3004 | 0.9221 |
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| 0.0502 | 75.0 | 1575 | 0.4007 | 0.8879 |
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| 0.0403 | 76.0 | 1596 | 0.2649 | 0.9065 |
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| 0.0341 | 77.0 | 1617 | 0.1945 | 0.9408 |
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| 0.061 | 78.0 | 1638 | 0.2936 | 0.9221 |
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| 0.059 | 79.0 | 1659 | 0.2938 | 0.9128 |
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| 0.0393 | 80.0 | 1680 | 0.3278 | 0.8941 |
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| 0.0475 | 81.0 | 1701 | 0.2856 | 0.9190 |
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| 0.0404 | 82.0 | 1722 | 0.2679 | 0.9252 |
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| 0.0528 | 83.0 | 1743 | 0.2544 | 0.9283 |
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| 0.05 | 84.0 | 1764 | 0.2992 | 0.9097 |
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| 0.0449 | 85.0 | 1785 | 0.3004 | 0.9128 |
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| 0.0337 | 86.0 | 1806 | 0.2744 | 0.9190 |
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| 0.0406 | 87.0 | 1827 | 0.3380 | 0.9003 |
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| 0.0314 | 88.0 | 1848 | 0.2801 | 0.9221 |
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| 0.0355 | 89.0 | 1869 | 0.2609 | 0.9190 |
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| 0.0313 | 90.0 | 1890 | 0.2507 | 0.9315 |
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| 0.0478 | 91.0 | 1911 | 0.2934 | 0.9128 |
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| 0.0365 | 92.0 | 1932 | 0.2642 | 0.9283 |
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| 0.0486 | 93.0 | 1953 | 0.1662 | 0.9626 |
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| 0.0271 | 94.0 | 1974 | 0.2194 | 0.9377 |
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| 0.0215 | 95.0 | 1995 | 0.2492 | 0.9252 |
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| 0.0365 | 96.0 | 2016 | 0.2006 | 0.9502 |
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| 0.0275 | 97.0 | 2037 | 0.2267 | 0.9159 |
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| 0.0647 | 98.0 | 2058 | 0.3226 | 0.9159 |
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| 0.0222 | 99.0 | 2079 | 0.2469 | 0.9346 |
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| 0.0426 | 100.0 | 2100 | 0.2644 | 0.9159 |
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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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