--- 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_epoch120_V_0_1 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: 0.8164556962025317 --- # meat_calssify_fresh_crop_fixed_epoch120_V_0_1 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: 0.5969 - Accuracy: 0.8165 ## 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: 64 - 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: 120 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 1.0907 | 1.0 | 10 | 1.0937 | 0.3291 | | 1.0777 | 2.0 | 20 | 1.0915 | 0.3354 | | 1.0596 | 3.0 | 30 | 1.0801 | 0.3544 | | 1.0333 | 4.0 | 40 | 1.0463 | 0.4810 | | 0.9965 | 5.0 | 50 | 0.9945 | 0.5316 | | 0.9588 | 6.0 | 60 | 0.9807 | 0.5127 | | 0.9183 | 7.0 | 70 | 0.9944 | 0.5063 | | 0.8966 | 8.0 | 80 | 1.0074 | 0.5127 | | 0.8199 | 9.0 | 90 | 0.9472 | 0.5506 | | 0.8089 | 10.0 | 100 | 0.9102 | 0.5886 | | 0.7151 | 11.0 | 110 | 0.7697 | 0.6962 | | 0.6578 | 12.0 | 120 | 0.9557 | 0.5570 | | 0.6375 | 13.0 | 130 | 0.7262 | 0.6709 | | 0.5994 | 14.0 | 140 | 0.7609 | 0.6709 | | 0.5086 | 15.0 | 150 | 0.7112 | 0.7089 | | 0.4771 | 16.0 | 160 | 0.6239 | 0.7658 | | 0.4631 | 17.0 | 170 | 0.7229 | 0.7278 | | 0.5158 | 18.0 | 180 | 0.8230 | 0.6835 | | 0.4938 | 19.0 | 190 | 0.6708 | 0.7278 | | 0.4287 | 20.0 | 200 | 0.5675 | 0.7722 | | 0.3256 | 21.0 | 210 | 0.6100 | 0.7468 | | 0.3555 | 22.0 | 220 | 0.6967 | 0.7342 | | 0.3453 | 23.0 | 230 | 0.6273 | 0.7532 | | 0.3391 | 24.0 | 240 | 0.7153 | 0.7468 | | 0.3129 | 25.0 | 250 | 0.7745 | 0.6835 | | 0.3296 | 26.0 | 260 | 0.7254 | 0.7089 | | 0.2695 | 27.0 | 270 | 0.6191 | 0.7658 | | 0.2704 | 28.0 | 280 | 0.6397 | 0.7342 | | 0.2466 | 29.0 | 290 | 0.7419 | 0.7342 | | 0.2655 | 30.0 | 300 | 0.6966 | 0.7532 | | 0.2448 | 31.0 | 310 | 0.6931 | 0.7532 | | 0.2286 | 32.0 | 320 | 0.5090 | 0.7975 | | 0.2419 | 33.0 | 330 | 0.5987 | 0.7405 | | 0.1965 | 34.0 | 340 | 0.7482 | 0.7405 | | 0.197 | 35.0 | 350 | 0.8851 | 0.7089 | | 0.1829 | 36.0 | 360 | 0.5874 | 0.7848 | | 0.187 | 37.0 | 370 | 0.7249 | 0.7405 | | 0.2011 | 38.0 | 380 | 0.6821 | 0.7468 | | 0.1927 | 39.0 | 390 | 0.7133 | 0.7848 | | 0.2043 | 40.0 | 400 | 0.6956 | 0.7468 | | 0.1661 | 41.0 | 410 | 0.6911 | 0.7532 | | 0.1625 | 42.0 | 420 | 0.7424 | 0.7405 | | 0.1876 | 43.0 | 430 | 0.6222 | 0.7722 | | 0.1865 | 44.0 | 440 | 0.6250 | 0.8101 | | 0.1604 | 45.0 | 450 | 0.6950 | 0.7405 | | 0.2072 | 46.0 | 460 | 0.8475 | 0.7025 | | 0.1816 | 47.0 | 470 | 0.5856 | 0.7975 | | 0.1683 | 48.0 | 480 | 0.6331 | 0.7911 | | 0.1698 | 49.0 | 490 | 0.8147 | 0.7278 | | 0.1724 | 50.0 | 500 | 0.5908 | 0.7848 | | 0.1231 | 51.0 | 510 | 0.4522 | 0.8291 | | 0.1121 | 52.0 | 520 | 0.5198 | 0.8291 | | 0.1335 | 53.0 | 530 | 0.6377 | 0.7785 | | 0.1414 | 54.0 | 540 | 0.6638 | 0.7722 | | 0.1263 | 55.0 | 550 | 0.6548 | 0.7722 | | 0.1427 | 56.0 | 560 | 0.6146 | 0.7785 | | 0.1137 | 57.0 | 570 | 0.5344 | 0.8101 | | 0.1504 | 58.0 | 580 | 0.7023 | 0.7785 | | 0.1549 | 59.0 | 590 | 0.8293 | 0.7152 | | 0.1142 | 60.0 | 600 | 0.7865 | 0.7658 | | 0.1271 | 61.0 | 610 | 0.6282 | 0.8038 | | 0.1134 | 62.0 | 620 | 0.7117 | 0.7658 | | 0.0954 | 63.0 | 630 | 0.5500 | 0.8165 | | 0.1011 | 64.0 | 640 | 0.5801 | 0.7911 | | 0.0878 | 65.0 | 650 | 0.4268 | 0.8418 | | 0.1065 | 66.0 | 660 | 0.6277 | 0.8038 | | 0.1298 | 67.0 | 670 | 0.5940 | 0.8038 | | 0.111 | 68.0 | 680 | 0.6945 | 0.7785 | | 0.0955 | 69.0 | 690 | 0.6320 | 0.8038 | | 0.0728 | 70.0 | 700 | 0.6484 | 0.7975 | | 0.0893 | 71.0 | 710 | 0.5842 | 0.8165 | | 0.0962 | 72.0 | 720 | 0.5417 | 0.8354 | | 0.0963 | 73.0 | 730 | 0.7366 | 0.7848 | | 0.1103 | 74.0 | 740 | 0.5413 | 0.8354 | | 0.1145 | 75.0 | 750 | 0.6310 | 0.7911 | | 0.1093 | 76.0 | 760 | 0.5101 | 0.8481 | | 0.0934 | 77.0 | 770 | 0.5049 | 0.8101 | | 0.0914 | 78.0 | 780 | 0.6828 | 0.7975 | | 0.0739 | 79.0 | 790 | 0.8685 | 0.7595 | | 0.1098 | 80.0 | 800 | 0.5542 | 0.7975 | | 0.0748 | 81.0 | 810 | 0.4864 | 0.8354 | | 0.0763 | 82.0 | 820 | 0.6708 | 0.7785 | | 0.0805 | 83.0 | 830 | 0.5730 | 0.8291 | | 0.0936 | 84.0 | 840 | 0.5680 | 0.8228 | | 0.0664 | 85.0 | 850 | 0.5645 | 0.8228 | | 0.0932 | 86.0 | 860 | 0.4601 | 0.8608 | | 0.0846 | 87.0 | 870 | 0.7324 | 0.7911 | | 0.0799 | 88.0 | 880 | 0.6234 | 0.8101 | | 0.0707 | 89.0 | 890 | 0.4808 | 0.8544 | | 0.0626 | 90.0 | 900 | 0.6119 | 0.7848 | | 0.066 | 91.0 | 910 | 0.5173 | 0.8228 | | 0.0701 | 92.0 | 920 | 0.7111 | 0.7722 | | 0.0862 | 93.0 | 930 | 0.6035 | 0.7975 | | 0.0397 | 94.0 | 940 | 0.5329 | 0.8418 | | 0.095 | 95.0 | 950 | 0.6635 | 0.7911 | | 0.0688 | 96.0 | 960 | 0.4878 | 0.8734 | | 0.07 | 97.0 | 970 | 0.5253 | 0.8608 | | 0.0704 | 98.0 | 980 | 0.4443 | 0.8608 | | 0.0883 | 99.0 | 990 | 0.5571 | 0.8165 | | 0.064 | 100.0 | 1000 | 0.7047 | 0.7911 | | 0.0547 | 101.0 | 1010 | 0.6558 | 0.8101 | | 0.0686 | 102.0 | 1020 | 0.6330 | 0.8165 | | 0.0806 | 103.0 | 1030 | 0.5754 | 0.8354 | | 0.0481 | 104.0 | 1040 | 0.5074 | 0.8544 | | 0.0499 | 105.0 | 1050 | 0.6701 | 0.8165 | | 0.0703 | 106.0 | 1060 | 0.6151 | 0.8291 | | 0.0921 | 107.0 | 1070 | 0.5935 | 0.8354 | | 0.0426 | 108.0 | 1080 | 0.6534 | 0.7975 | | 0.0618 | 109.0 | 1090 | 0.5265 | 0.8165 | | 0.0597 | 110.0 | 1100 | 0.5604 | 0.8354 | | 0.0471 | 111.0 | 1110 | 0.5451 | 0.8354 | | 0.0541 | 112.0 | 1120 | 0.5182 | 0.8544 | | 0.0369 | 113.0 | 1130 | 0.5276 | 0.8291 | | 0.0571 | 114.0 | 1140 | 0.4766 | 0.8354 | | 0.0469 | 115.0 | 1150 | 0.6508 | 0.8101 | | 0.0877 | 116.0 | 1160 | 0.5894 | 0.8418 | | 0.0681 | 117.0 | 1170 | 0.4952 | 0.8418 | | 0.0303 | 118.0 | 1180 | 0.5804 | 0.8418 | | 0.0536 | 119.0 | 1190 | 0.7055 | 0.8101 | | 0.0576 | 120.0 | 1200 | 0.5969 | 0.8165 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1 - Datasets 2.20.0 - Tokenizers 0.19.1