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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_9
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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_9
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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: 0.1854
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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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| 1.101 | 1.0 | 21 | 1.0776 | 0.4206 |
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| 1.0459 | 2.0 | 42 | 1.0368 | 0.5109 |
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| 0.9957 | 3.0 | 63 | 0.9830 | 0.5296 |
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| 0.9613 | 4.0 | 84 | 0.8994 | 0.5857 |
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| 0.9072 | 5.0 | 105 | 0.8663 | 0.6231 |
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| 0.8397 | 6.0 | 126 | 0.7738 | 0.7196 |
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| 0.7726 | 7.0 | 147 | 0.7461 | 0.6978 |
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| 0.7757 | 8.0 | 168 | 0.7647 | 0.6636 |
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| 0.6253 | 9.0 | 189 | 0.6437 | 0.7445 |
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| 0.5671 | 10.0 | 210 | 0.6791 | 0.7072 |
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| 0.529 | 11.0 | 231 | 0.6623 | 0.7072 |
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| 0.4362 | 12.0 | 252 | 0.5876 | 0.7508 |
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| 0.552 | 13.0 | 273 | 0.5093 | 0.7913 |
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| 0.3779 | 14.0 | 294 | 0.7716 | 0.6573 |
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| 0.5052 | 15.0 | 315 | 0.7956 | 0.6791 |
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| 0.5263 | 16.0 | 336 | 0.6077 | 0.7445 |
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| 0.3712 | 17.0 | 357 | 0.4699 | 0.8474 |
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| 0.2958 | 18.0 | 378 | 0.5815 | 0.7819 |
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| 0.2775 | 19.0 | 399 | 0.5083 | 0.8100 |
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| 0.2467 | 20.0 | 420 | 0.4561 | 0.8162 |
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| 0.2541 | 21.0 | 441 | 0.4463 | 0.8380 |
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| 0.2689 | 22.0 | 462 | 0.5149 | 0.7944 |
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| 0.1893 | 23.0 | 483 | 0.4155 | 0.8567 |
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| 0.2263 | 24.0 | 504 | 0.7202 | 0.7601 |
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| 0.2646 | 25.0 | 525 | 0.4365 | 0.8162 |
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| 0.2416 | 26.0 | 546 | 0.3365 | 0.8816 |
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| 0.1788 | 27.0 | 567 | 0.3679 | 0.8723 |
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| 0.1947 | 28.0 | 588 | 0.3725 | 0.8660 |
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| 0.1802 | 29.0 | 609 | 0.4176 | 0.8567 |
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| 0.1695 | 30.0 | 630 | 0.3776 | 0.8692 |
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| 0.2089 | 31.0 | 651 | 0.3047 | 0.8785 |
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| 0.1727 | 32.0 | 672 | 0.3225 | 0.8754 |
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| 0.143 | 33.0 | 693 | 0.2355 | 0.9128 |
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| 0.1593 | 34.0 | 714 | 0.3424 | 0.8847 |
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| 0.1179 | 35.0 | 735 | 0.2873 | 0.9065 |
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| 0.1227 | 36.0 | 756 | 0.3114 | 0.8941 |
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| 0.1636 | 37.0 | 777 | 0.4101 | 0.8660 |
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| 0.1347 | 38.0 | 798 | 0.2253 | 0.9190 |
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| 0.1108 | 39.0 | 819 | 0.3387 | 0.8847 |
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| 0.1383 | 40.0 | 840 | 0.4024 | 0.8754 |
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| 0.1173 | 41.0 | 861 | 0.2436 | 0.9034 |
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| 0.1027 | 42.0 | 882 | 0.2603 | 0.9065 |
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| 0.1244 | 43.0 | 903 | 0.2990 | 0.9128 |
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| 0.1061 | 44.0 | 924 | 0.3082 | 0.9065 |
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| 0.1179 | 45.0 | 945 | 0.2212 | 0.9128 |
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| 0.0945 | 46.0 | 966 | 0.3455 | 0.8816 |
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| 0.1055 | 47.0 | 987 | 0.2951 | 0.8847 |
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| 0.0895 | 48.0 | 1008 | 0.3003 | 0.9065 |
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| 0.1298 | 49.0 | 1029 | 0.3123 | 0.9097 |
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| 0.1379 | 50.0 | 1050 | 0.2787 | 0.9128 |
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| 0.0879 | 51.0 | 1071 | 0.2920 | 0.9128 |
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| 0.1011 | 52.0 | 1092 | 0.3667 | 0.8816 |
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| 0.1038 | 53.0 | 1113 | 0.2967 | 0.9034 |
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| 0.0834 | 54.0 | 1134 | 0.2281 | 0.9346 |
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| 0.0998 | 55.0 | 1155 | 0.2835 | 0.8941 |
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| 0.0794 | 56.0 | 1176 | 0.2260 | 0.9221 |
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| 0.0732 | 57.0 | 1197 | 0.3309 | 0.9003 |
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| 0.0612 | 58.0 | 1218 | 0.3392 | 0.9065 |
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| 0.0679 | 59.0 | 1239 | 0.2728 | 0.9252 |
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| 0.0986 | 60.0 | 1260 | 0.2380 | 0.9221 |
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| 0.0639 | 61.0 | 1281 | 0.2672 | 0.9221 |
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| 0.061 | 62.0 | 1302 | 0.2898 | 0.9221 |
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| 0.0741 | 63.0 | 1323 | 0.3098 | 0.9097 |
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| 0.0532 | 64.0 | 1344 | 0.2621 | 0.9252 |
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| 0.0642 | 65.0 | 1365 | 0.1643 | 0.9377 |
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| 0.0762 | 66.0 | 1386 | 0.2332 | 0.9346 |
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| 0.0562 | 67.0 | 1407 | 0.2603 | 0.9252 |
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| 0.0481 | 68.0 | 1428 | 0.2363 | 0.9097 |
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| 0.0576 | 69.0 | 1449 | 0.2421 | 0.9377 |
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| 0.0355 | 70.0 | 1470 | 0.2040 | 0.9346 |
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| 0.07 | 71.0 | 1491 | 0.2900 | 0.9283 |
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| 0.0631 | 72.0 | 1512 | 0.2531 | 0.9221 |
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| 0.046 | 73.0 | 1533 | 0.2054 | 0.9346 |
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| 0.0767 | 74.0 | 1554 | 0.1651 | 0.9408 |
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| 0.0569 | 75.0 | 1575 | 0.2251 | 0.9377 |
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| 0.0454 | 76.0 | 1596 | 0.2003 | 0.9408 |
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| 0.0596 | 77.0 | 1617 | 0.1607 | 0.9564 |
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| 0.0602 | 78.0 | 1638 | 0.1789 | 0.9408 |
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| 0.0512 | 79.0 | 1659 | 0.2390 | 0.9221 |
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| 0.0402 | 80.0 | 1680 | 0.1433 | 0.9564 |
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| 0.0425 | 81.0 | 1701 | 0.1379 | 0.9502 |
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| 0.0428 | 82.0 | 1722 | 0.1422 | 0.9564 |
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| 0.0353 | 83.0 | 1743 | 0.1433 | 0.9502 |
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| 0.0449 | 84.0 | 1764 | 0.2206 | 0.9190 |
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| 0.0402 | 85.0 | 1785 | 0.2194 | 0.9470 |
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| 0.041 | 86.0 | 1806 | 0.1564 | 0.9502 |
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| 0.057 | 87.0 | 1827 | 0.2352 | 0.9346 |
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| 0.0475 | 88.0 | 1848 | 0.1618 | 0.9502 |
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| 0.0399 | 89.0 | 1869 | 0.1322 | 0.9564 |
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| 0.0324 | 90.0 | 1890 | 0.1803 | 0.9502 |
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| 0.0796 | 91.0 | 1911 | 0.1577 | 0.9408 |
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| 0.0283 | 92.0 | 1932 | 0.1599 | 0.9626 |
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| 0.0325 | 93.0 | 1953 | 0.1741 | 0.9626 |
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| 0.0345 | 94.0 | 1974 | 0.1089 | 0.9533 |
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| 0.0361 | 95.0 | 1995 | 0.1044 | 0.9595 |
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| 0.0346 | 96.0 | 2016 | 0.1416 | 0.9533 |
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| 0.0339 | 97.0 | 2037 | 0.2032 | 0.9346 |
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| 0.0309 | 98.0 | 2058 | 0.1348 | 0.9595 |
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| 0.046 | 99.0 | 2079 | 0.1345 | 0.9533 |
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| 0.0285 | 100.0 | 2100 | 0.1854 | 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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