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meat_calssify_fresh_crop_fixed_epoch100_V_0_8

Image Classification
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meat_calssify_fresh_crop_fixed_epoch100_V_0_8 / README.md
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metadata
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_epoch100_V_0_8
    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: 0.8481012658227848

meat_calssify_fresh_crop_fixed_epoch100_V_0_8

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: 0.5407
  • Accuracy: 0.8481

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
1.0983 1.0 10 1.0923 0.3861
1.0837 2.0 20 1.0603 0.4937
1.0593 3.0 30 1.0236 0.5633
1.0323 4.0 40 0.9905 0.5696
0.991 5.0 50 0.9442 0.5759
0.9535 6.0 60 0.9318 0.5506
0.8994 7.0 70 0.8444 0.6139
0.847 8.0 80 0.8550 0.6266
0.8083 9.0 90 0.8252 0.6456
0.7465 10.0 100 0.7622 0.6646
0.6872 11.0 110 0.7461 0.6329
0.6664 12.0 120 0.7210 0.6899
0.5655 13.0 130 0.6896 0.6962
0.5541 14.0 140 0.6891 0.6962
0.5162 15.0 150 0.6646 0.7468
0.531 16.0 160 0.6972 0.6962
0.4662 17.0 170 0.5651 0.7658
0.4806 18.0 180 0.7426 0.6709
0.405 19.0 190 0.6764 0.7089
0.3897 20.0 200 0.5832 0.7658
0.327 21.0 210 0.5591 0.7532
0.3542 22.0 220 0.6793 0.7278
0.3309 23.0 230 0.5890 0.7532
0.3384 24.0 240 0.5595 0.7722
0.2867 25.0 250 0.5669 0.7975
0.2651 26.0 260 0.6369 0.7468
0.2659 27.0 270 0.7925 0.6709
0.3057 28.0 280 0.7006 0.7405
0.2606 29.0 290 0.5800 0.7658
0.2145 30.0 300 0.4187 0.8418
0.1951 31.0 310 0.7022 0.7342
0.2658 32.0 320 0.6902 0.7342
0.2329 33.0 330 0.5709 0.7595
0.1807 34.0 340 0.5226 0.7911
0.1602 35.0 350 0.5418 0.7911
0.2104 36.0 360 0.6453 0.7658
0.2009 37.0 370 0.4814 0.8291
0.2059 38.0 380 0.6135 0.7595
0.2203 39.0 390 0.5581 0.7785
0.1864 40.0 400 0.5939 0.7911
0.1564 41.0 410 0.6002 0.7848
0.1229 42.0 420 0.6470 0.7658
0.1867 43.0 430 0.6545 0.7975
0.1679 44.0 440 0.6079 0.7658
0.1752 45.0 450 0.6666 0.7468
0.1256 46.0 460 0.6651 0.7595
0.188 47.0 470 0.6574 0.7532
0.1695 48.0 480 0.5883 0.7975
0.1797 49.0 490 0.7344 0.7595
0.1913 50.0 500 0.5662 0.8101
0.1483 51.0 510 0.5385 0.8038
0.1502 52.0 520 0.5101 0.8165
0.1142 53.0 530 0.5263 0.8228
0.0839 54.0 540 0.4852 0.8038
0.1432 55.0 550 0.5651 0.8101
0.1327 56.0 560 0.6218 0.7911
0.0948 57.0 570 0.6101 0.7722
0.1387 58.0 580 0.5350 0.8101
0.0957 59.0 590 0.7503 0.7722
0.1243 60.0 600 0.5468 0.7911
0.1179 61.0 610 0.5851 0.8038
0.128 62.0 620 0.5167 0.8291
0.1018 63.0 630 0.5119 0.8481
0.0987 64.0 640 0.6415 0.7911
0.0901 65.0 650 0.6031 0.8038
0.1457 66.0 660 0.6773 0.7848
0.1247 67.0 670 0.5563 0.7975
0.127 68.0 680 0.7763 0.7595
0.0841 69.0 690 0.4934 0.8544
0.0914 70.0 700 0.6510 0.8228
0.0982 71.0 710 0.5742 0.8101
0.0945 72.0 720 0.4954 0.8481
0.077 73.0 730 0.6194 0.8101
0.0936 74.0 740 0.5301 0.8228
0.0641 75.0 750 0.5673 0.8165
0.0646 76.0 760 0.5055 0.8291
0.0794 77.0 770 0.5444 0.8228
0.0774 78.0 780 0.5511 0.8228
0.0674 79.0 790 0.5688 0.8354
0.0731 80.0 800 0.5594 0.8291
0.0839 81.0 810 0.6970 0.7785
0.0857 82.0 820 0.5651 0.7975
0.0729 83.0 830 0.7003 0.7848
0.074 84.0 840 0.5293 0.8165
0.0505 85.0 850 0.5051 0.8544
0.0669 86.0 860 0.6459 0.8101
0.0614 87.0 870 0.5474 0.8291
0.0659 88.0 880 0.4981 0.8291
0.0702 89.0 890 0.5611 0.8291
0.0635 90.0 900 0.6273 0.7975
0.0698 91.0 910 0.4314 0.8734
0.0671 92.0 920 0.5471 0.8291
0.057 93.0 930 0.4922 0.8481
0.0563 94.0 940 0.5463 0.8418
0.0638 95.0 950 0.5177 0.8291
0.0545 96.0 960 0.6183 0.8038
0.0534 97.0 970 0.5460 0.8165
0.0655 98.0 980 0.4196 0.8861
0.0775 99.0 990 0.5088 0.8354
0.0519 100.0 1000 0.5407 0.8481

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1
  • Datasets 2.20.0
  • Tokenizers 0.19.1