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meat_calssify_fresh_crop_fixed_epoch100_V_0_6

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meat_calssify_fresh_crop_fixed_epoch100_V_0_6

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5432
  • 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: 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
1.1096 1.0 10 1.1024 0.3291
1.0818 2.0 20 1.0876 0.4241
1.0619 3.0 30 1.0912 0.3987
1.0289 4.0 40 1.0680 0.4494
1.0023 5.0 50 1.0357 0.4241
0.9453 6.0 60 1.0095 0.4937
0.8856 7.0 70 0.9563 0.5443
0.8301 8.0 80 0.9307 0.5506
0.7855 9.0 90 0.9109 0.5696
0.7062 10.0 100 0.8383 0.5949
0.6655 11.0 110 0.7534 0.7089
0.617 12.0 120 0.8160 0.6646
0.5821 13.0 130 0.7361 0.6962
0.5097 14.0 140 0.7594 0.6899
0.4944 15.0 150 0.7679 0.6709
0.4787 16.0 160 0.6902 0.7595
0.4234 17.0 170 0.8019 0.6835
0.451 18.0 180 0.6534 0.7532
0.4402 19.0 190 0.6980 0.7152
0.395 20.0 200 0.7276 0.6962
0.3548 21.0 210 0.7002 0.6899
0.3231 22.0 220 0.7080 0.7089
0.2844 23.0 230 0.6934 0.7152
0.2909 24.0 240 0.6465 0.7468
0.3433 25.0 250 0.5959 0.7595
0.2718 26.0 260 0.6787 0.7405
0.2516 27.0 270 0.5951 0.8165
0.2717 28.0 280 0.6355 0.7405
0.2509 29.0 290 0.6980 0.6899
0.2407 30.0 300 0.5824 0.7722
0.2444 31.0 310 0.6241 0.7342
0.2153 32.0 320 0.5982 0.7658
0.2294 33.0 330 0.6701 0.7532
0.1901 34.0 340 0.8116 0.6772
0.252 35.0 350 0.5960 0.7848
0.1761 36.0 360 0.5993 0.7785
0.1892 37.0 370 0.6046 0.7785
0.15 38.0 380 0.6442 0.8038
0.1705 39.0 390 0.7802 0.7152
0.2171 40.0 400 0.7078 0.7278
0.2233 41.0 410 0.5835 0.7785
0.2252 42.0 420 0.7923 0.7089
0.2304 43.0 430 0.6414 0.7342
0.231 44.0 440 0.5405 0.7911
0.1573 45.0 450 0.7065 0.7468
0.1203 46.0 460 0.7642 0.7278
0.1712 47.0 470 0.6204 0.7658
0.1406 48.0 480 0.6591 0.7658
0.1877 49.0 490 0.7968 0.7152
0.1457 50.0 500 0.6790 0.7658
0.1391 51.0 510 0.6116 0.7658
0.1508 52.0 520 0.7331 0.7722
0.1468 53.0 530 0.6900 0.7722
0.13 54.0 540 0.5799 0.7975
0.1078 55.0 550 0.6568 0.7785
0.0945 56.0 560 0.6073 0.8101
0.1358 57.0 570 0.4966 0.8291
0.1605 58.0 580 0.6678 0.7722
0.1673 59.0 590 0.5742 0.7975
0.1112 60.0 600 0.6181 0.8038
0.1144 61.0 610 0.5031 0.8354
0.1154 62.0 620 0.6085 0.8038
0.1141 63.0 630 0.4470 0.8734
0.1201 64.0 640 0.5089 0.8291
0.0871 65.0 650 0.5447 0.8481
0.0939 66.0 660 0.5763 0.7911
0.1098 67.0 670 0.6186 0.7911
0.1062 68.0 680 0.6349 0.8038
0.0755 69.0 690 0.8513 0.7278
0.1257 70.0 700 0.6852 0.7911
0.0651 71.0 710 0.7073 0.7722
0.1024 72.0 720 0.5794 0.8354
0.0887 73.0 730 0.7889 0.7278
0.1014 74.0 740 0.5774 0.8228
0.0986 75.0 750 0.6864 0.8038
0.098 76.0 760 0.4825 0.8354
0.1049 77.0 770 0.7881 0.7658
0.0997 78.0 780 0.5239 0.8418
0.0939 79.0 790 0.6434 0.8228
0.0851 80.0 800 0.5087 0.8291
0.0683 81.0 810 0.6410 0.7658
0.084 82.0 820 0.6120 0.8101
0.0717 83.0 830 0.6231 0.8038
0.0811 84.0 840 0.4338 0.8544
0.066 85.0 850 0.4633 0.8544
0.0746 86.0 860 0.6960 0.7595
0.0864 87.0 870 0.6154 0.8101
0.0432 88.0 880 0.5864 0.8228
0.0644 89.0 890 0.6045 0.7911
0.0644 90.0 900 0.5924 0.8101
0.0976 91.0 910 0.6515 0.8165
0.0593 92.0 920 0.5491 0.8101
0.0884 93.0 930 0.6618 0.8101
0.0752 94.0 940 0.5612 0.8165
0.0423 95.0 950 0.5914 0.8101
0.0685 96.0 960 0.5502 0.8291
0.0425 97.0 970 0.6455 0.7975
0.0639 98.0 980 0.5402 0.8291
0.056 99.0 990 0.5159 0.8481
0.0663 100.0 1000 0.5432 0.8165

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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google/vit-base-patch16-224-in21k
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Evaluation results

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