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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_11
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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: 0.9314641744548287
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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_11
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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: 0.2049
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- Accuracy: 0.9315
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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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| 1.089 | 1.0 | 21 | 1.0699 | 0.4642 |
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| 1.0499 | 2.0 | 42 | 1.0288 | 0.5514 |
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| 1.012 | 3.0 | 63 | 0.9738 | 0.5234 |
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| 0.9484 | 4.0 | 84 | 0.9146 | 0.5763 |
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| 0.8696 | 5.0 | 105 | 0.8897 | 0.6075 |
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| 0.8194 | 6.0 | 126 | 0.8737 | 0.6168 |
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| 0.7567 | 7.0 | 147 | 0.7329 | 0.6916 |
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| 0.6649 | 8.0 | 168 | 0.7376 | 0.6978 |
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| 0.6515 | 9.0 | 189 | 1.0486 | 0.5202 |
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| 0.7191 | 10.0 | 210 | 0.7651 | 0.6636 |
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| 0.573 | 11.0 | 231 | 0.7393 | 0.6885 |
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| 0.4787 | 12.0 | 252 | 0.7596 | 0.6791 |
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| 0.4838 | 13.0 | 273 | 0.6008 | 0.7788 |
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| 0.4554 | 14.0 | 294 | 0.6622 | 0.7477 |
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| 0.5433 | 15.0 | 315 | 0.6715 | 0.7196 |
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| 0.4842 | 16.0 | 336 | 0.5973 | 0.7414 |
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| 0.4186 | 17.0 | 357 | 0.5679 | 0.7757 |
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| 0.3345 | 18.0 | 378 | 0.4770 | 0.8162 |
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| 0.2651 | 19.0 | 399 | 0.4308 | 0.8442 |
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| 0.2247 | 20.0 | 420 | 0.4637 | 0.8442 |
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| 0.2601 | 21.0 | 441 | 0.3916 | 0.8723 |
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| 0.2419 | 22.0 | 462 | 0.3525 | 0.8785 |
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| 0.2626 | 23.0 | 483 | 0.4901 | 0.8380 |
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| 0.2554 | 24.0 | 504 | 0.6997 | 0.7445 |
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| 0.2352 | 25.0 | 525 | 0.2725 | 0.9159 |
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| 0.2139 | 26.0 | 546 | 0.5544 | 0.8006 |
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| 0.2456 | 27.0 | 567 | 0.3419 | 0.8785 |
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| 0.2336 | 28.0 | 588 | 0.3981 | 0.8349 |
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| 0.1654 | 29.0 | 609 | 0.3819 | 0.8474 |
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| 0.1543 | 30.0 | 630 | 0.2538 | 0.9128 |
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| 0.1744 | 31.0 | 651 | 0.4008 | 0.8536 |
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| 0.1627 | 32.0 | 672 | 0.3453 | 0.8785 |
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| 0.1641 | 33.0 | 693 | 0.2883 | 0.8972 |
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| 0.1816 | 34.0 | 714 | 0.3159 | 0.8910 |
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| 0.3087 | 35.0 | 735 | 0.5607 | 0.8131 |
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| 0.1463 | 36.0 | 756 | 0.2616 | 0.9034 |
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| 0.2832 | 37.0 | 777 | 0.3128 | 0.9003 |
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| 0.1135 | 38.0 | 798 | 0.2374 | 0.9221 |
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| 0.109 | 39.0 | 819 | 0.2972 | 0.9159 |
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| 0.103 | 40.0 | 840 | 0.3414 | 0.8879 |
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| 0.1084 | 41.0 | 861 | 0.5068 | 0.8318 |
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| 0.1464 | 42.0 | 882 | 0.2895 | 0.9034 |
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| 0.0994 | 43.0 | 903 | 0.2374 | 0.9221 |
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| 0.0908 | 44.0 | 924 | 0.2381 | 0.9283 |
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| 0.113 | 45.0 | 945 | 0.2854 | 0.9065 |
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| 0.1415 | 46.0 | 966 | 0.2304 | 0.9283 |
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| 0.0965 | 47.0 | 987 | 0.2900 | 0.9003 |
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| 0.0773 | 48.0 | 1008 | 0.3234 | 0.8972 |
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| 0.0749 | 49.0 | 1029 | 0.3964 | 0.8785 |
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| 0.1094 | 50.0 | 1050 | 0.4835 | 0.8536 |
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| 0.1152 | 51.0 | 1071 | 0.2459 | 0.9159 |
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| 0.1123 | 52.0 | 1092 | 0.2469 | 0.9190 |
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| 0.0837 | 53.0 | 1113 | 0.2169 | 0.9252 |
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| 0.0944 | 54.0 | 1134 | 0.2855 | 0.9003 |
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| 0.0975 | 55.0 | 1155 | 0.2581 | 0.9065 |
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| 0.0738 | 56.0 | 1176 | 0.2912 | 0.8972 |
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| 0.0735 | 57.0 | 1197 | 0.2847 | 0.9003 |
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| 0.0773 | 58.0 | 1218 | 0.2194 | 0.9252 |
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| 0.0917 | 59.0 | 1239 | 0.2202 | 0.9159 |
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| 0.0843 | 60.0 | 1260 | 0.4062 | 0.8629 |
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| 0.0796 | 61.0 | 1281 | 0.2564 | 0.9190 |
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| 0.0592 | 62.0 | 1302 | 0.2795 | 0.9097 |
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| 0.0526 | 63.0 | 1323 | 0.2589 | 0.9252 |
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| 0.072 | 64.0 | 1344 | 0.1720 | 0.9470 |
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| 0.0721 | 65.0 | 1365 | 0.3482 | 0.8972 |
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| 0.0643 | 66.0 | 1386 | 0.2056 | 0.9315 |
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| 0.0632 | 67.0 | 1407 | 0.2368 | 0.9377 |
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| 0.0656 | 68.0 | 1428 | 0.1891 | 0.9346 |
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| 0.0547 | 69.0 | 1449 | 0.2592 | 0.9315 |
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| 0.0613 | 70.0 | 1470 | 0.2446 | 0.9221 |
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| 0.0572 | 71.0 | 1491 | 0.1700 | 0.9439 |
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| 0.0707 | 72.0 | 1512 | 0.1974 | 0.9377 |
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| 0.0462 | 73.0 | 1533 | 0.3013 | 0.9221 |
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| 0.045 | 74.0 | 1554 | 0.2223 | 0.9252 |
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| 0.0729 | 75.0 | 1575 | 0.2085 | 0.9346 |
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| 0.049 | 76.0 | 1596 | 0.2198 | 0.9470 |
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| 0.0531 | 77.0 | 1617 | 0.2064 | 0.9439 |
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| 0.047 | 78.0 | 1638 | 0.3139 | 0.9065 |
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| 0.0484 | 79.0 | 1659 | 0.3167 | 0.9190 |
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| 0.0572 | 80.0 | 1680 | 0.2002 | 0.9408 |
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| 0.0356 | 81.0 | 1701 | 0.2248 | 0.9283 |
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| 0.0405 | 82.0 | 1722 | 0.2738 | 0.9283 |
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| 0.0502 | 83.0 | 1743 | 0.1940 | 0.9315 |
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| 0.0403 | 84.0 | 1764 | 0.2541 | 0.9252 |
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| 0.0334 | 85.0 | 1785 | 0.2284 | 0.9439 |
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| 0.0395 | 86.0 | 1806 | 0.2369 | 0.9315 |
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| 0.0359 | 87.0 | 1827 | 0.1361 | 0.9688 |
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| 0.0412 | 88.0 | 1848 | 0.2190 | 0.9408 |
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| 0.0399 | 89.0 | 1869 | 0.2068 | 0.9408 |
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| 0.047 | 90.0 | 1890 | 0.2655 | 0.9159 |
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| 0.0377 | 91.0 | 1911 | 0.1519 | 0.9377 |
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| 0.0246 | 92.0 | 1932 | 0.2156 | 0.9377 |
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| 0.0285 | 93.0 | 1953 | 0.2732 | 0.9315 |
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| 0.0447 | 94.0 | 1974 | 0.2069 | 0.9315 |
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| 0.0271 | 95.0 | 1995 | 0.2119 | 0.9377 |
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| 0.0316 | 96.0 | 2016 | 0.2199 | 0.9377 |
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| 0.0335 | 97.0 | 2037 | 0.1942 | 0.9439 |
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| 0.0285 | 98.0 | 2058 | 0.1771 | 0.9439 |
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| 0.0262 | 99.0 | 2079 | 0.1745 | 0.9470 |
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| 0.0276 | 100.0 | 2100 | 0.2049 | 0.9315 |
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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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