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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_10
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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: 0.7974683544303798
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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_10
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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: 0.6095
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- Accuracy: 0.7975
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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: 1
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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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| 1.0941 | 1.0 | 10 | 1.0929 | 0.3418 |
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| 1.0817 | 2.0 | 20 | 1.0775 | 0.4051 |
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| 1.059 | 3.0 | 30 | 1.0631 | 0.4494 |
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| 1.028 | 4.0 | 40 | 1.0305 | 0.4747 |
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| 0.9784 | 5.0 | 50 | 1.0019 | 0.4747 |
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| 0.9123 | 6.0 | 60 | 0.9389 | 0.5759 |
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| 0.8789 | 7.0 | 70 | 0.8875 | 0.5949 |
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| 0.816 | 8.0 | 80 | 0.8561 | 0.6203 |
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| 0.7632 | 9.0 | 90 | 0.8253 | 0.6266 |
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| 0.6857 | 10.0 | 100 | 0.8264 | 0.6203 |
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| 0.6834 | 11.0 | 110 | 0.7056 | 0.6962 |
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| 0.629 | 12.0 | 120 | 0.7708 | 0.6329 |
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| 0.5744 | 13.0 | 130 | 0.6847 | 0.6962 |
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| 0.5661 | 14.0 | 140 | 0.6881 | 0.7215 |
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| 0.5516 | 15.0 | 150 | 0.7477 | 0.6646 |
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| 0.482 | 16.0 | 160 | 0.6717 | 0.7152 |
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| 0.4265 | 17.0 | 170 | 0.6200 | 0.7468 |
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| 0.4074 | 18.0 | 180 | 0.6404 | 0.7278 |
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| 0.3797 | 19.0 | 190 | 0.6577 | 0.7405 |
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| 0.3895 | 20.0 | 200 | 0.6127 | 0.7658 |
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| 0.3244 | 21.0 | 210 | 0.6776 | 0.7658 |
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| 0.3764 | 22.0 | 220 | 0.8015 | 0.6899 |
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| 0.3692 | 23.0 | 230 | 0.6790 | 0.7278 |
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| 0.2687 | 24.0 | 240 | 0.6951 | 0.7215 |
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| 0.3352 | 25.0 | 250 | 0.7140 | 0.7215 |
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| 0.2734 | 26.0 | 260 | 0.6895 | 0.7152 |
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| 0.2857 | 27.0 | 270 | 0.6515 | 0.7089 |
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| 0.2716 | 28.0 | 280 | 0.6171 | 0.7405 |
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| 0.2628 | 29.0 | 290 | 0.5954 | 0.7532 |
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| 0.222 | 30.0 | 300 | 0.6447 | 0.7342 |
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| 0.2458 | 31.0 | 310 | 0.6836 | 0.7532 |
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| 0.2489 | 32.0 | 320 | 0.5701 | 0.7975 |
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| 0.2282 | 33.0 | 330 | 0.6654 | 0.7405 |
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| 0.1824 | 34.0 | 340 | 0.6552 | 0.7468 |
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| 0.2261 | 35.0 | 350 | 0.7548 | 0.7342 |
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| 0.2198 | 36.0 | 360 | 0.6297 | 0.7785 |
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| 0.2118 | 37.0 | 370 | 0.6240 | 0.7911 |
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| 0.1751 | 38.0 | 380 | 0.6787 | 0.7722 |
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| 0.1507 | 39.0 | 390 | 0.5897 | 0.7911 |
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| 0.1647 | 40.0 | 400 | 0.6010 | 0.7975 |
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| 0.2214 | 41.0 | 410 | 0.6143 | 0.7975 |
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| 0.1462 | 42.0 | 420 | 0.8883 | 0.7278 |
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| 0.1841 | 43.0 | 430 | 0.7459 | 0.7532 |
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| 0.2076 | 44.0 | 440 | 0.6125 | 0.8101 |
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| 0.1359 | 45.0 | 450 | 0.5540 | 0.8101 |
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| 0.1315 | 46.0 | 460 | 0.7218 | 0.7532 |
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| 0.1658 | 47.0 | 470 | 0.7927 | 0.7278 |
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| 0.1807 | 48.0 | 480 | 0.6954 | 0.7911 |
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| 0.1601 | 49.0 | 490 | 0.6399 | 0.7595 |
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| 0.1385 | 50.0 | 500 | 0.6353 | 0.7532 |
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| 0.1387 | 51.0 | 510 | 0.6596 | 0.7658 |
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| 0.1435 | 52.0 | 520 | 0.5697 | 0.8165 |
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| 0.1116 | 53.0 | 530 | 0.6201 | 0.8165 |
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| 0.0899 | 54.0 | 540 | 0.5805 | 0.8101 |
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| 0.1245 | 55.0 | 550 | 0.7132 | 0.7785 |
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| 0.1309 | 56.0 | 560 | 0.6173 | 0.7911 |
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| 0.1176 | 57.0 | 570 | 0.6650 | 0.8038 |
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| 0.1516 | 58.0 | 580 | 0.7006 | 0.7342 |
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| 0.1359 | 59.0 | 590 | 0.7015 | 0.7785 |
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| 0.134 | 60.0 | 600 | 0.6239 | 0.7975 |
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| 0.1167 | 61.0 | 610 | 0.5665 | 0.7848 |
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| 0.127 | 62.0 | 620 | 0.5368 | 0.8038 |
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| 0.1306 | 63.0 | 630 | 0.4862 | 0.8544 |
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| 0.0919 | 64.0 | 640 | 0.6305 | 0.7595 |
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| 0.1082 | 65.0 | 650 | 0.6479 | 0.7848 |
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| 0.1484 | 66.0 | 660 | 0.6687 | 0.7785 |
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| 0.1066 | 67.0 | 670 | 0.5404 | 0.8101 |
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| 0.1011 | 68.0 | 680 | 0.4724 | 0.8797 |
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| 0.0891 | 69.0 | 690 | 0.5482 | 0.8354 |
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| 0.1011 | 70.0 | 700 | 0.7259 | 0.7975 |
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| 0.0819 | 71.0 | 710 | 0.6372 | 0.7911 |
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| 0.0943 | 72.0 | 720 | 0.5851 | 0.7975 |
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| 0.0638 | 73.0 | 730 | 0.5816 | 0.8101 |
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| 0.0875 | 74.0 | 740 | 0.7538 | 0.7595 |
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| 0.1146 | 75.0 | 750 | 0.5902 | 0.8165 |
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| 0.0861 | 76.0 | 760 | 0.5353 | 0.8354 |
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| 0.1031 | 77.0 | 770 | 0.5022 | 0.8101 |
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| 0.0721 | 78.0 | 780 | 0.5100 | 0.8544 |
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| 0.0752 | 79.0 | 790 | 0.6330 | 0.7785 |
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| 0.0753 | 80.0 | 800 | 0.5908 | 0.7848 |
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| 0.0602 | 81.0 | 810 | 0.6954 | 0.7658 |
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| 0.082 | 82.0 | 820 | 0.4405 | 0.8671 |
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| 0.0905 | 83.0 | 830 | 0.5115 | 0.8481 |
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| 0.0597 | 84.0 | 840 | 0.5156 | 0.8608 |
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| 0.0716 | 85.0 | 850 | 0.5273 | 0.8228 |
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| 0.0606 | 86.0 | 860 | 0.6440 | 0.8354 |
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| 0.0736 | 87.0 | 870 | 0.5842 | 0.8354 |
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| 0.0614 | 88.0 | 880 | 0.5470 | 0.8354 |
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| 0.0496 | 89.0 | 890 | 0.5201 | 0.8228 |
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| 0.067 | 90.0 | 900 | 0.5866 | 0.8228 |
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| 0.059 | 91.0 | 910 | 0.5842 | 0.8354 |
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| 0.0525 | 92.0 | 920 | 0.5256 | 0.8418 |
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| 0.0928 | 93.0 | 930 | 0.6557 | 0.8101 |
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| 0.0736 | 94.0 | 940 | 0.6496 | 0.8101 |
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| 0.064 | 95.0 | 950 | 0.5068 | 0.8418 |
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| 0.0654 | 96.0 | 960 | 0.4680 | 0.8291 |
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| 0.0426 | 97.0 | 970 | 0.5116 | 0.8608 |
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| 0.0515 | 98.0 | 980 | 0.4887 | 0.8608 |
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| 0.0466 | 99.0 | 990 | 0.5188 | 0.8228 |
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| 0.0746 | 100.0 | 1000 | 0.6095 | 0.7975 |
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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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