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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_15
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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: 0.956386292834891
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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_15
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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: 0.1718
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- Accuracy: 0.9564
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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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| 1.1001 | 1.0 | 21 | 1.0997 | 0.3614 |
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| 1.0637 | 2.0 | 42 | 1.0850 | 0.3863 |
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| 1.0464 | 3.0 | 63 | 1.0524 | 0.4766 |
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| 0.9569 | 4.0 | 84 | 0.9464 | 0.5763 |
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| 0.8778 | 5.0 | 105 | 0.8916 | 0.6168 |
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| 0.8396 | 6.0 | 126 | 0.8181 | 0.6573 |
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| 0.7752 | 7.0 | 147 | 0.8517 | 0.6168 |
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| 0.7765 | 8.0 | 168 | 1.0433 | 0.5140 |
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| 0.7283 | 9.0 | 189 | 0.9781 | 0.5452 |
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| 0.6714 | 10.0 | 210 | 0.6957 | 0.7165 |
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| 0.5872 | 11.0 | 231 | 0.6338 | 0.7352 |
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| 0.4924 | 12.0 | 252 | 0.5824 | 0.7757 |
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| 0.4441 | 13.0 | 273 | 0.7042 | 0.7040 |
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| 0.4818 | 14.0 | 294 | 0.4985 | 0.8100 |
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| 0.4477 | 15.0 | 315 | 0.5176 | 0.8100 |
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| 0.387 | 16.0 | 336 | 0.5820 | 0.7757 |
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| 0.378 | 17.0 | 357 | 0.4651 | 0.8287 |
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| 0.3353 | 18.0 | 378 | 0.5163 | 0.8037 |
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| 0.3651 | 19.0 | 399 | 0.3980 | 0.8474 |
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| 0.312 | 20.0 | 420 | 0.4217 | 0.8629 |
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| 0.2572 | 21.0 | 441 | 0.4610 | 0.8255 |
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| 0.25 | 22.0 | 462 | 0.4421 | 0.8349 |
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| 0.2325 | 23.0 | 483 | 0.4322 | 0.8193 |
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| 0.2384 | 24.0 | 504 | 0.4207 | 0.8380 |
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| 0.2295 | 25.0 | 525 | 0.4298 | 0.8411 |
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| 0.4004 | 26.0 | 546 | 0.4976 | 0.8224 |
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| 0.2136 | 27.0 | 567 | 0.3272 | 0.8723 |
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| 0.1851 | 28.0 | 588 | 0.3004 | 0.8941 |
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| 0.1513 | 29.0 | 609 | 0.3198 | 0.8785 |
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| 0.2132 | 30.0 | 630 | 0.3403 | 0.8879 |
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| 0.1704 | 31.0 | 651 | 0.4112 | 0.8692 |
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| 0.1639 | 32.0 | 672 | 0.3038 | 0.8941 |
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| 0.2028 | 33.0 | 693 | 0.6632 | 0.7601 |
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| 0.256 | 34.0 | 714 | 0.3475 | 0.8785 |
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| 0.142 | 35.0 | 735 | 0.2709 | 0.9034 |
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| 0.1358 | 36.0 | 756 | 0.2745 | 0.9034 |
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| 0.1543 | 37.0 | 777 | 0.3139 | 0.8816 |
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| 0.1214 | 38.0 | 798 | 0.2518 | 0.9128 |
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| 0.1291 | 39.0 | 819 | 0.4121 | 0.8598 |
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| 0.1423 | 40.0 | 840 | 0.2469 | 0.9128 |
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| 0.1071 | 41.0 | 861 | 0.2351 | 0.9252 |
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| 0.1259 | 42.0 | 882 | 0.3639 | 0.8785 |
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| 0.1114 | 43.0 | 903 | 0.4624 | 0.8567 |
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| 0.123 | 44.0 | 924 | 0.3147 | 0.8941 |
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| 0.0914 | 45.0 | 945 | 0.3599 | 0.8879 |
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| 0.1154 | 46.0 | 966 | 0.2986 | 0.9003 |
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| 0.1001 | 47.0 | 987 | 0.2688 | 0.9034 |
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| 0.0959 | 48.0 | 1008 | 0.2358 | 0.9159 |
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| 0.0935 | 49.0 | 1029 | 0.2724 | 0.9159 |
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| 0.104 | 50.0 | 1050 | 0.3857 | 0.8847 |
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| 0.1158 | 51.0 | 1071 | 0.3359 | 0.8910 |
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| 0.0766 | 52.0 | 1092 | 0.3030 | 0.8941 |
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| 0.1048 | 53.0 | 1113 | 0.2648 | 0.9097 |
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| 0.1065 | 54.0 | 1134 | 0.2859 | 0.9128 |
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| 0.0738 | 55.0 | 1155 | 0.3660 | 0.8910 |
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| 0.078 | 56.0 | 1176 | 0.2843 | 0.9221 |
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| 0.0755 | 57.0 | 1197 | 0.4503 | 0.8816 |
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| 0.1193 | 58.0 | 1218 | 0.5647 | 0.8006 |
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| 0.1014 | 59.0 | 1239 | 0.4011 | 0.8660 |
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| 0.0557 | 60.0 | 1260 | 0.3376 | 0.8941 |
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| 0.054 | 61.0 | 1281 | 0.2309 | 0.9283 |
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| 0.0674 | 62.0 | 1302 | 0.3222 | 0.9003 |
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| 0.0845 | 63.0 | 1323 | 0.2429 | 0.9221 |
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| 0.0721 | 64.0 | 1344 | 0.2247 | 0.9283 |
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| 0.0711 | 65.0 | 1365 | 0.3134 | 0.9097 |
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| 0.0881 | 66.0 | 1386 | 0.2918 | 0.9159 |
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| 0.0753 | 67.0 | 1407 | 0.2734 | 0.9065 |
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| 0.059 | 68.0 | 1428 | 0.3353 | 0.8754 |
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| 0.0814 | 69.0 | 1449 | 0.3093 | 0.9159 |
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| 0.1317 | 70.0 | 1470 | 0.1641 | 0.9439 |
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| 0.0539 | 71.0 | 1491 | 0.1988 | 0.9470 |
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| 0.0572 | 72.0 | 1512 | 0.2493 | 0.9159 |
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| 0.0322 | 73.0 | 1533 | 0.2045 | 0.9315 |
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| 0.0473 | 74.0 | 1554 | 0.2380 | 0.9315 |
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| 0.0478 | 75.0 | 1575 | 0.1687 | 0.9377 |
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| 0.0554 | 76.0 | 1596 | 0.2121 | 0.9315 |
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| 0.0444 | 77.0 | 1617 | 0.2172 | 0.9439 |
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| 0.0808 | 78.0 | 1638 | 0.3581 | 0.8910 |
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| 0.0522 | 79.0 | 1659 | 0.2155 | 0.9408 |
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| 0.0402 | 80.0 | 1680 | 0.2204 | 0.9283 |
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| 0.0387 | 81.0 | 1701 | 0.1438 | 0.9564 |
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| 0.0294 | 82.0 | 1722 | 0.3094 | 0.9221 |
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| 0.0449 | 83.0 | 1743 | 0.2850 | 0.9128 |
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| 0.029 | 84.0 | 1764 | 0.3040 | 0.9128 |
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| 0.0419 | 85.0 | 1785 | 0.1831 | 0.9439 |
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| 0.0297 | 86.0 | 1806 | 0.2211 | 0.9221 |
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| 0.0382 | 87.0 | 1827 | 0.2203 | 0.9346 |
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| 0.0524 | 88.0 | 1848 | 0.2093 | 0.9377 |
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| 0.0524 | 89.0 | 1869 | 0.2195 | 0.9252 |
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| 0.0446 | 90.0 | 1890 | 0.2358 | 0.9377 |
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| 0.0423 | 91.0 | 1911 | 0.2129 | 0.9283 |
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| 0.0434 | 92.0 | 1932 | 0.2199 | 0.9315 |
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| 0.0429 | 93.0 | 1953 | 0.1954 | 0.9470 |
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| 0.0302 | 94.0 | 1974 | 0.1379 | 0.9564 |
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| 0.046 | 95.0 | 1995 | 0.1609 | 0.9502 |
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| 0.0247 | 96.0 | 2016 | 0.1978 | 0.9315 |
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| 0.0289 | 97.0 | 2037 | 0.1872 | 0.9439 |
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| 0.0452 | 98.0 | 2058 | 0.2132 | 0.9377 |
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| 0.0308 | 99.0 | 2079 | 0.1592 | 0.9377 |
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| 0.0274 | 100.0 | 2100 | 0.1718 | 0.9564 |
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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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