--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_6 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9283489096573209 --- # meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_6 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. It achieves the following results on the evaluation set: - Loss: 0.2352 - Accuracy: 0.9283 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.1152 | 1.0 | 21 | 1.0916 | 0.3801 | | 1.0864 | 2.0 | 42 | 1.0644 | 0.4361 | | 1.0324 | 3.0 | 63 | 1.0130 | 0.4766 | | 0.9613 | 4.0 | 84 | 0.9579 | 0.5607 | | 0.9331 | 5.0 | 105 | 0.8873 | 0.6044 | | 0.8671 | 6.0 | 126 | 0.8661 | 0.5981 | | 0.7318 | 7.0 | 147 | 0.8945 | 0.5545 | | 0.7254 | 8.0 | 168 | 0.6857 | 0.7227 | | 0.7335 | 9.0 | 189 | 0.7473 | 0.6822 | | 0.5766 | 10.0 | 210 | 0.8212 | 0.6760 | | 0.6084 | 11.0 | 231 | 0.6611 | 0.7383 | | 0.4562 | 12.0 | 252 | 0.5661 | 0.7882 | | 0.4669 | 13.0 | 273 | 0.6090 | 0.7664 | | 0.4217 | 14.0 | 294 | 0.8163 | 0.6760 | | 0.4267 | 15.0 | 315 | 0.6919 | 0.7134 | | 0.3741 | 16.0 | 336 | 0.4754 | 0.8255 | | 0.3325 | 17.0 | 357 | 0.5114 | 0.8069 | | 0.298 | 18.0 | 378 | 0.5082 | 0.8069 | | 0.2493 | 19.0 | 399 | 0.4226 | 0.8255 | | 0.2422 | 20.0 | 420 | 0.4855 | 0.8255 | | 0.2086 | 21.0 | 441 | 0.5766 | 0.7913 | | 0.229 | 22.0 | 462 | 0.5744 | 0.7913 | | 0.2352 | 23.0 | 483 | 0.4443 | 0.8318 | | 0.2055 | 24.0 | 504 | 0.4859 | 0.8255 | | 0.1953 | 25.0 | 525 | 0.4014 | 0.8598 | | 0.2064 | 26.0 | 546 | 0.4137 | 0.8474 | | 0.1801 | 27.0 | 567 | 0.4048 | 0.8536 | | 0.1797 | 28.0 | 588 | 0.3656 | 0.8847 | | 0.1676 | 29.0 | 609 | 0.4490 | 0.8505 | | 0.1748 | 30.0 | 630 | 0.4745 | 0.8349 | | 0.1896 | 31.0 | 651 | 0.4727 | 0.8411 | | 0.1507 | 32.0 | 672 | 0.3046 | 0.8660 | | 0.1744 | 33.0 | 693 | 0.4198 | 0.8692 | | 0.122 | 34.0 | 714 | 0.4129 | 0.8474 | | 0.1495 | 35.0 | 735 | 0.4908 | 0.8224 | | 0.164 | 36.0 | 756 | 0.4992 | 0.8411 | | 0.1876 | 37.0 | 777 | 0.3301 | 0.8816 | | 0.287 | 38.0 | 798 | 0.4695 | 0.8349 | | 0.14 | 39.0 | 819 | 0.4662 | 0.8318 | | 0.1043 | 40.0 | 840 | 0.4756 | 0.8474 | | 0.1275 | 41.0 | 861 | 0.3540 | 0.8785 | | 0.1201 | 42.0 | 882 | 0.4236 | 0.8536 | | 0.131 | 43.0 | 903 | 0.4375 | 0.8598 | | 0.0773 | 44.0 | 924 | 0.4115 | 0.8723 | | 0.1095 | 45.0 | 945 | 0.4374 | 0.8598 | | 0.0934 | 46.0 | 966 | 0.3057 | 0.8972 | | 0.2323 | 47.0 | 987 | 0.4779 | 0.8536 | | 0.1005 | 48.0 | 1008 | 0.2861 | 0.9065 | | 0.1041 | 49.0 | 1029 | 0.3541 | 0.8910 | | 0.1067 | 50.0 | 1050 | 0.3192 | 0.8941 | | 0.1206 | 51.0 | 1071 | 0.3898 | 0.8816 | | 0.0798 | 52.0 | 1092 | 0.4014 | 0.8723 | | 0.088 | 53.0 | 1113 | 0.3105 | 0.9097 | | 0.1013 | 54.0 | 1134 | 0.3736 | 0.8785 | | 0.0896 | 55.0 | 1155 | 0.2967 | 0.8972 | | 0.0862 | 56.0 | 1176 | 0.3588 | 0.8879 | | 0.0686 | 57.0 | 1197 | 0.2938 | 0.8941 | | 0.0847 | 58.0 | 1218 | 0.4074 | 0.8910 | | 0.0953 | 59.0 | 1239 | 0.3553 | 0.8910 | | 0.0854 | 60.0 | 1260 | 0.2624 | 0.9097 | | 0.0712 | 61.0 | 1281 | 0.3190 | 0.9065 | | 0.0699 | 62.0 | 1302 | 0.2595 | 0.9190 | | 0.0644 | 63.0 | 1323 | 0.2592 | 0.9190 | | 0.0754 | 64.0 | 1344 | 0.2786 | 0.9003 | | 0.0548 | 65.0 | 1365 | 0.2758 | 0.9159 | | 0.0715 | 66.0 | 1386 | 0.2817 | 0.9003 | | 0.0806 | 67.0 | 1407 | 0.2805 | 0.9065 | | 0.0485 | 68.0 | 1428 | 0.2198 | 0.9159 | | 0.0688 | 69.0 | 1449 | 0.3272 | 0.8941 | | 0.0713 | 70.0 | 1470 | 0.4739 | 0.8754 | | 0.0746 | 71.0 | 1491 | 0.2930 | 0.9065 | | 0.0753 | 72.0 | 1512 | 0.3323 | 0.8972 | | 0.0565 | 73.0 | 1533 | 0.2289 | 0.9190 | | 0.0509 | 74.0 | 1554 | 0.5005 | 0.8536 | | 0.0787 | 75.0 | 1575 | 0.2635 | 0.9128 | | 0.0541 | 76.0 | 1596 | 0.3005 | 0.9128 | | 0.0415 | 77.0 | 1617 | 0.2386 | 0.9315 | | 0.0538 | 78.0 | 1638 | 0.3808 | 0.8972 | | 0.064 | 79.0 | 1659 | 0.1887 | 0.9252 | | 0.0549 | 80.0 | 1680 | 0.3037 | 0.9034 | | 0.0547 | 81.0 | 1701 | 0.2018 | 0.9283 | | 0.0404 | 82.0 | 1722 | 0.2307 | 0.9315 | | 0.0438 | 83.0 | 1743 | 0.2480 | 0.9159 | | 0.0298 | 84.0 | 1764 | 0.2932 | 0.9221 | | 0.0409 | 85.0 | 1785 | 0.2534 | 0.9252 | | 0.028 | 86.0 | 1806 | 0.3352 | 0.9065 | | 0.0421 | 87.0 | 1827 | 0.3456 | 0.8972 | | 0.0376 | 88.0 | 1848 | 0.2733 | 0.9221 | | 0.0413 | 89.0 | 1869 | 0.3693 | 0.8941 | | 0.035 | 90.0 | 1890 | 0.2873 | 0.9159 | | 0.0688 | 91.0 | 1911 | 0.1962 | 0.9439 | | 0.0438 | 92.0 | 1932 | 0.2506 | 0.9252 | | 0.046 | 93.0 | 1953 | 0.2469 | 0.9252 | | 0.0325 | 94.0 | 1974 | 0.2693 | 0.9190 | | 0.0315 | 95.0 | 1995 | 0.1809 | 0.9533 | | 0.0417 | 96.0 | 2016 | 0.2053 | 0.9315 | | 0.0511 | 97.0 | 2037 | 0.2489 | 0.9346 | | 0.0305 | 98.0 | 2058 | 0.2066 | 0.9315 | | 0.0301 | 99.0 | 2079 | 0.2825 | 0.9159 | | 0.0371 | 100.0 | 2100 | 0.2352 | 0.9283 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1 - Datasets 2.20.0 - Tokenizers 0.19.1