--- 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_10 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: 0.9439252336448598 --- # meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_10 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: 0.2318 - Accuracy: 0.9439 ## 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0968 | 1.0 | 21 | 1.0916 | 0.3551 | | 1.0709 | 2.0 | 42 | 1.0570 | 0.4704 | | 1.0215 | 3.0 | 63 | 1.0161 | 0.4891 | | 0.9489 | 4.0 | 84 | 0.9513 | 0.5670 | | 0.8937 | 5.0 | 105 | 0.8632 | 0.6106 | | 0.7992 | 6.0 | 126 | 0.7990 | 0.6449 | | 0.7194 | 7.0 | 147 | 0.7417 | 0.7227 | | 0.6455 | 8.0 | 168 | 0.6491 | 0.7321 | | 0.5874 | 9.0 | 189 | 0.6231 | 0.7072 | | 0.5869 | 10.0 | 210 | 0.7566 | 0.6978 | | 0.5565 | 11.0 | 231 | 0.5440 | 0.7913 | | 0.5238 | 12.0 | 252 | 0.5628 | 0.7726 | | 0.4397 | 13.0 | 273 | 0.5377 | 0.7882 | | 0.3858 | 14.0 | 294 | 0.5161 | 0.7850 | | 0.3587 | 15.0 | 315 | 0.6103 | 0.7726 | | 0.3383 | 16.0 | 336 | 0.5065 | 0.7850 | | 0.2967 | 17.0 | 357 | 0.4653 | 0.8224 | | 0.4375 | 18.0 | 378 | 0.4497 | 0.8255 | | 0.2805 | 19.0 | 399 | 0.5011 | 0.8287 | | 0.2937 | 20.0 | 420 | 0.4294 | 0.8318 | | 0.2917 | 21.0 | 441 | 0.4914 | 0.8224 | | 0.2387 | 22.0 | 462 | 0.5050 | 0.8255 | | 0.219 | 23.0 | 483 | 0.4312 | 0.8692 | | 0.2975 | 24.0 | 504 | 0.4167 | 0.8598 | | 0.2482 | 25.0 | 525 | 0.4272 | 0.8536 | | 0.1913 | 26.0 | 546 | 0.3625 | 0.8660 | | 0.1896 | 27.0 | 567 | 0.5346 | 0.7944 | | 0.1937 | 28.0 | 588 | 0.3983 | 0.8629 | | 0.1517 | 29.0 | 609 | 0.3777 | 0.8629 | | 0.3356 | 30.0 | 630 | 0.3373 | 0.8941 | | 0.1562 | 31.0 | 651 | 0.3154 | 0.8879 | | 0.1494 | 32.0 | 672 | 0.3680 | 0.8692 | | 0.1677 | 33.0 | 693 | 0.3984 | 0.8629 | | 0.2689 | 34.0 | 714 | 0.2916 | 0.8910 | | 0.1302 | 35.0 | 735 | 0.3458 | 0.8754 | | 0.1374 | 36.0 | 756 | 0.2694 | 0.9065 | | 0.2007 | 37.0 | 777 | 0.3715 | 0.8723 | | 0.1404 | 38.0 | 798 | 0.4245 | 0.8723 | | 0.1154 | 39.0 | 819 | 0.4718 | 0.8442 | | 0.2125 | 40.0 | 840 | 0.3549 | 0.8910 | | 0.1087 | 41.0 | 861 | 0.4262 | 0.8567 | | 0.1156 | 42.0 | 882 | 0.3013 | 0.8910 | | 0.1011 | 43.0 | 903 | 0.3019 | 0.9097 | | 0.11 | 44.0 | 924 | 0.2630 | 0.9128 | | 0.1233 | 45.0 | 945 | 0.3000 | 0.8972 | | 0.1236 | 46.0 | 966 | 0.3547 | 0.8660 | | 0.1571 | 47.0 | 987 | 0.3384 | 0.8879 | | 0.0855 | 48.0 | 1008 | 0.3221 | 0.8816 | | 0.1155 | 49.0 | 1029 | 0.4779 | 0.8536 | | 0.1089 | 50.0 | 1050 | 0.3355 | 0.9034 | | 0.0939 | 51.0 | 1071 | 0.2130 | 0.9221 | | 0.0826 | 52.0 | 1092 | 0.3103 | 0.9097 | | 0.0943 | 53.0 | 1113 | 0.3179 | 0.9034 | | 0.0574 | 54.0 | 1134 | 0.3351 | 0.8972 | | 0.0818 | 55.0 | 1155 | 0.2165 | 0.9315 | | 0.0863 | 56.0 | 1176 | 0.3347 | 0.8879 | | 0.0963 | 57.0 | 1197 | 0.3789 | 0.8972 | | 0.0762 | 58.0 | 1218 | 0.3579 | 0.8972 | | 0.0898 | 59.0 | 1239 | 0.2550 | 0.9159 | | 0.0802 | 60.0 | 1260 | 0.2112 | 0.9221 | | 0.0698 | 61.0 | 1281 | 0.3252 | 0.9097 | | 0.0764 | 62.0 | 1302 | 0.4277 | 0.8754 | | 0.0781 | 63.0 | 1323 | 0.3593 | 0.8879 | | 0.0939 | 64.0 | 1344 | 0.3397 | 0.8941 | | 0.0669 | 65.0 | 1365 | 0.3701 | 0.8847 | | 0.0632 | 66.0 | 1386 | 0.2624 | 0.9097 | | 0.0569 | 67.0 | 1407 | 0.2987 | 0.9221 | | 0.0655 | 68.0 | 1428 | 0.3286 | 0.9003 | | 0.0581 | 69.0 | 1449 | 0.2540 | 0.9283 | | 0.0668 | 70.0 | 1470 | 0.2397 | 0.9346 | | 0.0639 | 71.0 | 1491 | 0.2721 | 0.9190 | | 0.0617 | 72.0 | 1512 | 0.2059 | 0.9377 | | 0.0555 | 73.0 | 1533 | 0.4196 | 0.8879 | | 0.0515 | 74.0 | 1554 | 0.2260 | 0.9346 | | 0.0494 | 75.0 | 1575 | 0.3137 | 0.9097 | | 0.0425 | 76.0 | 1596 | 0.3027 | 0.9128 | | 0.0529 | 77.0 | 1617 | 0.2964 | 0.9221 | | 0.0473 | 78.0 | 1638 | 0.2776 | 0.9190 | | 0.0629 | 79.0 | 1659 | 0.2397 | 0.9346 | | 0.0417 | 80.0 | 1680 | 0.2041 | 0.9408 | | 0.0437 | 81.0 | 1701 | 0.2451 | 0.9408 | | 0.0444 | 82.0 | 1722 | 0.2813 | 0.9315 | | 0.0561 | 83.0 | 1743 | 0.2596 | 0.9159 | | 0.0458 | 84.0 | 1764 | 0.2085 | 0.9346 | | 0.0653 | 85.0 | 1785 | 0.3033 | 0.9221 | | 0.0301 | 86.0 | 1806 | 0.1604 | 0.9470 | | 0.0441 | 87.0 | 1827 | 0.3603 | 0.8941 | | 0.0297 | 88.0 | 1848 | 0.2406 | 0.9377 | | 0.0472 | 89.0 | 1869 | 0.3045 | 0.9190 | | 0.0421 | 90.0 | 1890 | 0.2231 | 0.9377 | | 0.0391 | 91.0 | 1911 | 0.2259 | 0.9439 | | 0.0418 | 92.0 | 1932 | 0.2433 | 0.9377 | | 0.0405 | 93.0 | 1953 | 0.2753 | 0.9221 | | 0.0338 | 94.0 | 1974 | 0.1519 | 0.9533 | | 0.0355 | 95.0 | 1995 | 0.2370 | 0.9408 | | 0.028 | 96.0 | 2016 | 0.2116 | 0.9346 | | 0.0248 | 97.0 | 2037 | 0.2833 | 0.9283 | | 0.033 | 98.0 | 2058 | 0.2603 | 0.9221 | | 0.0304 | 99.0 | 2079 | 0.2598 | 0.9252 | | 0.0148 | 100.0 | 2100 | 0.2318 | 0.9439 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1 - Datasets 2.20.0 - Tokenizers 0.19.1