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---
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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tags:
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- generated_from_trainer
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datasets:
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- imagefolder
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metrics:
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- accuracy
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model-index:
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- name: meat_calssify_fresh_crop_fixed_epoch100_V_0_3
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results:
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- task:
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name: Image Classification
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type: image-classification
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dataset:
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name: imagefolder
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type: imagefolder
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config: default
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split: train
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args: default
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.759493670886076
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# meat_calssify_fresh_crop_fixed_epoch100_V_0_3
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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.
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It achieves the following results on the evaluation set:
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- Loss: 0.8015
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- Accuracy: 0.7595
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size: 64
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- eval_batch_size: 64
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 100
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 1.1038 | 1.0 | 10 | 1.1003 | 0.3165 |
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| 1.0874 | 2.0 | 20 | 1.0824 | 0.4367 |
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| 1.0699 | 3.0 | 30 | 1.0695 | 0.4051 |
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| 1.0363 | 4.0 | 40 | 1.0423 | 0.4620 |
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| 0.9912 | 5.0 | 50 | 0.9911 | 0.5253 |
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| 0.9378 | 6.0 | 60 | 0.9630 | 0.5380 |
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| 0.8829 | 7.0 | 70 | 0.9239 | 0.5696 |
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| 0.8168 | 8.0 | 80 | 0.9127 | 0.5759 |
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| 0.758 | 9.0 | 90 | 0.8583 | 0.6392 |
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| 0.7032 | 10.0 | 100 | 0.7911 | 0.6772 |
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| 0.6348 | 11.0 | 110 | 0.8342 | 0.5949 |
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| 0.6082 | 12.0 | 120 | 0.8187 | 0.6203 |
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| 0.5936 | 13.0 | 130 | 0.6830 | 0.7468 |
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| 0.5726 | 14.0 | 140 | 0.8194 | 0.6962 |
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| 0.5774 | 15.0 | 150 | 0.7164 | 0.6709 |
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| 0.4617 | 16.0 | 160 | 0.8145 | 0.6456 |
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| 0.4399 | 17.0 | 170 | 0.6810 | 0.7215 |
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| 0.4065 | 18.0 | 180 | 0.7049 | 0.7089 |
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| 0.4012 | 19.0 | 190 | 0.7462 | 0.6962 |
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| 0.3553 | 20.0 | 200 | 0.7550 | 0.6835 |
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| 0.419 | 21.0 | 210 | 0.9119 | 0.5886 |
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| 0.3862 | 22.0 | 220 | 0.7467 | 0.6646 |
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| 0.3218 | 23.0 | 230 | 0.7734 | 0.7025 |
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| 0.2856 | 24.0 | 240 | 0.7714 | 0.6772 |
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| 0.2853 | 25.0 | 250 | 0.7505 | 0.7215 |
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| 0.3005 | 26.0 | 260 | 0.8128 | 0.7152 |
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| 0.3128 | 27.0 | 270 | 0.8520 | 0.6456 |
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| 0.3146 | 28.0 | 280 | 0.8034 | 0.6962 |
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| 0.3091 | 29.0 | 290 | 0.8961 | 0.6835 |
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| 0.3128 | 30.0 | 300 | 0.8976 | 0.6772 |
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| 0.2696 | 31.0 | 310 | 0.7489 | 0.7089 |
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| 0.286 | 32.0 | 320 | 0.6697 | 0.7532 |
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| 0.3084 | 33.0 | 330 | 0.8061 | 0.7089 |
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| 0.2527 | 34.0 | 340 | 0.6714 | 0.7658 |
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| 0.2239 | 35.0 | 350 | 0.6858 | 0.7595 |
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| 0.2251 | 36.0 | 360 | 0.7142 | 0.7405 |
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| 0.2049 | 37.0 | 370 | 0.6644 | 0.7785 |
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| 0.2141 | 38.0 | 380 | 0.6722 | 0.7532 |
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| 0.2265 | 39.0 | 390 | 0.8172 | 0.7089 |
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| 0.1847 | 40.0 | 400 | 0.5876 | 0.7848 |
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| 0.1692 | 41.0 | 410 | 0.7485 | 0.7468 |
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| 0.1759 | 42.0 | 420 | 0.6973 | 0.7468 |
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| 0.2126 | 43.0 | 430 | 0.7180 | 0.7468 |
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| 0.2217 | 44.0 | 440 | 0.8617 | 0.6772 |
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| 0.1662 | 45.0 | 450 | 0.7264 | 0.7468 |
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| 0.1168 | 46.0 | 460 | 0.6226 | 0.7848 |
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| 0.1737 | 47.0 | 470 | 0.7201 | 0.7658 |
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| 0.1673 | 48.0 | 480 | 0.7411 | 0.7658 |
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| 0.1992 | 49.0 | 490 | 0.6667 | 0.7722 |
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| 0.1327 | 50.0 | 500 | 0.8436 | 0.6962 |
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| 0.1409 | 51.0 | 510 | 0.8467 | 0.6899 |
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| 0.1325 | 52.0 | 520 | 0.8331 | 0.7278 |
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| 0.1247 | 53.0 | 530 | 0.6017 | 0.7722 |
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| 0.1215 | 54.0 | 540 | 0.5934 | 0.8038 |
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| 0.1556 | 55.0 | 550 | 0.8121 | 0.7215 |
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| 0.1615 | 56.0 | 560 | 0.5814 | 0.7911 |
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| 0.1268 | 57.0 | 570 | 0.6809 | 0.7468 |
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| 0.1258 | 58.0 | 580 | 0.5749 | 0.7975 |
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| 0.1128 | 59.0 | 590 | 0.6332 | 0.8101 |
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| 0.1519 | 60.0 | 600 | 0.7176 | 0.7785 |
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| 0.1303 | 61.0 | 610 | 0.6800 | 0.7405 |
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| 0.1256 | 62.0 | 620 | 0.7101 | 0.7468 |
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| 0.1139 | 63.0 | 630 | 0.7587 | 0.7532 |
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| 0.0914 | 64.0 | 640 | 0.6320 | 0.8038 |
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| 0.1314 | 65.0 | 650 | 0.7287 | 0.7658 |
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| 0.1402 | 66.0 | 660 | 0.9050 | 0.7025 |
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| 0.0947 | 67.0 | 670 | 0.5996 | 0.7785 |
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| 0.0902 | 68.0 | 680 | 0.6142 | 0.8038 |
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| 0.1101 | 69.0 | 690 | 0.8431 | 0.7405 |
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| 0.1138 | 70.0 | 700 | 0.6796 | 0.7658 |
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| 0.0875 | 71.0 | 710 | 0.8089 | 0.7405 |
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| 0.1006 | 72.0 | 720 | 0.6522 | 0.7532 |
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| 0.0811 | 73.0 | 730 | 0.7060 | 0.7975 |
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| 0.0919 | 74.0 | 740 | 0.7761 | 0.7658 |
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| 0.0717 | 75.0 | 750 | 0.8626 | 0.7089 |
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| 0.0961 | 76.0 | 760 | 0.6235 | 0.7975 |
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| 0.0869 | 77.0 | 770 | 0.6554 | 0.7975 |
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| 0.096 | 78.0 | 780 | 0.6839 | 0.7658 |
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| 0.0926 | 79.0 | 790 | 0.6482 | 0.8038 |
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| 0.0702 | 80.0 | 800 | 0.8970 | 0.7405 |
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| 0.0802 | 81.0 | 810 | 0.7076 | 0.7785 |
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| 0.0631 | 82.0 | 820 | 0.5784 | 0.8228 |
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| 0.0874 | 83.0 | 830 | 0.6042 | 0.8038 |
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| 0.092 | 84.0 | 840 | 0.6569 | 0.8038 |
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| 0.0827 | 85.0 | 850 | 0.7801 | 0.7848 |
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| 0.0796 | 86.0 | 860 | 0.7321 | 0.7975 |
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| 0.0731 | 87.0 | 870 | 0.6231 | 0.7911 |
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| 0.0731 | 88.0 | 880 | 0.6244 | 0.8038 |
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| 0.0694 | 89.0 | 890 | 0.6433 | 0.7975 |
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| 0.0601 | 90.0 | 900 | 0.7026 | 0.7785 |
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| 0.0715 | 91.0 | 910 | 0.5609 | 0.8165 |
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| 0.0782 | 92.0 | 920 | 0.5387 | 0.8481 |
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| 0.0685 | 93.0 | 930 | 0.5740 | 0.8165 |
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| 0.0508 | 94.0 | 940 | 0.6352 | 0.8291 |
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| 0.0871 | 95.0 | 950 | 0.6687 | 0.8038 |
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| 0.0533 | 96.0 | 960 | 0.5791 | 0.8165 |
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| 0.0525 | 97.0 | 970 | 0.8043 | 0.7532 |
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| 0.0884 | 98.0 | 980 | 0.7164 | 0.7911 |
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| 0.0619 | 99.0 | 990 | 0.7417 | 0.7785 |
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| 0.0703 | 100.0 | 1000 | 0.8015 | 0.7595 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.1
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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