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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_7
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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: 0.810126582278481
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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_7
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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: 0.6404
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- Accuracy: 0.8101
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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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| 1.098 | 1.0 | 10 | 1.0916 | 0.4114 |
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| 1.0825 | 2.0 | 20 | 1.0892 | 0.3861 |
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| 1.0588 | 3.0 | 30 | 1.0802 | 0.3608 |
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| 1.0298 | 4.0 | 40 | 1.0551 | 0.4430 |
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| 0.981 | 5.0 | 50 | 1.0183 | 0.4620 |
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| 0.9274 | 6.0 | 60 | 0.9723 | 0.5063 |
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| 0.8655 | 7.0 | 70 | 0.9539 | 0.5443 |
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| 0.8275 | 8.0 | 80 | 0.8944 | 0.5696 |
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| 0.815 | 9.0 | 90 | 0.8859 | 0.6013 |
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| 0.7543 | 10.0 | 100 | 0.9931 | 0.5253 |
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| 0.7501 | 11.0 | 110 | 0.9048 | 0.5316 |
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| 0.7036 | 12.0 | 120 | 0.8500 | 0.6329 |
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| 0.6742 | 13.0 | 130 | 0.8228 | 0.6203 |
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| 0.6331 | 14.0 | 140 | 0.8357 | 0.6076 |
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| 0.5479 | 15.0 | 150 | 0.7833 | 0.6203 |
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| 0.5342 | 16.0 | 160 | 0.9080 | 0.5633 |
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| 0.5066 | 17.0 | 170 | 0.9448 | 0.5823 |
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| 0.5077 | 18.0 | 180 | 0.7903 | 0.6772 |
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| 0.3941 | 19.0 | 190 | 0.7109 | 0.7089 |
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| 0.3837 | 20.0 | 200 | 0.7422 | 0.6899 |
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| 0.3622 | 21.0 | 210 | 0.6693 | 0.7152 |
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| 0.3725 | 22.0 | 220 | 0.7556 | 0.6709 |
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| 0.3945 | 23.0 | 230 | 0.7561 | 0.6835 |
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| 0.3325 | 24.0 | 240 | 0.8287 | 0.6203 |
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| 0.3456 | 25.0 | 250 | 1.2373 | 0.5759 |
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| 0.3829 | 26.0 | 260 | 0.8878 | 0.6013 |
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| 0.3148 | 27.0 | 270 | 0.8503 | 0.7025 |
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| 0.3543 | 28.0 | 280 | 0.6707 | 0.7152 |
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| 0.2581 | 29.0 | 290 | 0.6273 | 0.7595 |
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| 0.2237 | 30.0 | 300 | 0.5921 | 0.7722 |
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| 0.1946 | 31.0 | 310 | 0.5894 | 0.7911 |
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| 0.2369 | 32.0 | 320 | 0.7187 | 0.7278 |
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| 0.2564 | 33.0 | 330 | 0.8258 | 0.7025 |
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| 0.1967 | 34.0 | 340 | 0.5263 | 0.7911 |
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| 0.1974 | 35.0 | 350 | 0.7137 | 0.7152 |
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| 0.1656 | 36.0 | 360 | 0.6219 | 0.7722 |
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| 0.1874 | 37.0 | 370 | 0.7103 | 0.7342 |
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| 0.22 | 38.0 | 380 | 0.6303 | 0.7785 |
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| 0.1705 | 39.0 | 390 | 0.6412 | 0.7658 |
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| 0.1848 | 40.0 | 400 | 0.6148 | 0.7785 |
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| 0.1567 | 41.0 | 410 | 0.5199 | 0.8101 |
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| 0.113 | 42.0 | 420 | 0.7023 | 0.7595 |
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| 0.1704 | 43.0 | 430 | 0.6339 | 0.7848 |
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| 0.1829 | 44.0 | 440 | 0.5446 | 0.8165 |
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| 0.1325 | 45.0 | 450 | 0.6403 | 0.7658 |
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| 0.1375 | 46.0 | 460 | 0.6033 | 0.8101 |
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| 0.1425 | 47.0 | 470 | 0.5715 | 0.8101 |
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| 0.16 | 48.0 | 480 | 0.6529 | 0.7911 |
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| 0.1862 | 49.0 | 490 | 0.7063 | 0.7468 |
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| 0.1583 | 50.0 | 500 | 0.4872 | 0.7975 |
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| 0.1141 | 51.0 | 510 | 0.7283 | 0.7089 |
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| 0.1333 | 52.0 | 520 | 0.6197 | 0.8101 |
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| 0.1062 | 53.0 | 530 | 0.5728 | 0.8291 |
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| 0.1159 | 54.0 | 540 | 0.7551 | 0.7532 |
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| 0.1152 | 55.0 | 550 | 0.7598 | 0.7532 |
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| 0.1339 | 56.0 | 560 | 0.7102 | 0.7658 |
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| 0.1244 | 57.0 | 570 | 0.5283 | 0.8038 |
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| 0.1247 | 58.0 | 580 | 0.6756 | 0.7658 |
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| 0.1269 | 59.0 | 590 | 0.7887 | 0.7468 |
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| 0.1321 | 60.0 | 600 | 0.6724 | 0.7658 |
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| 0.1267 | 61.0 | 610 | 0.6647 | 0.7911 |
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| 0.1066 | 62.0 | 620 | 0.5684 | 0.8038 |
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| 0.1058 | 63.0 | 630 | 0.6389 | 0.7848 |
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| 0.0944 | 64.0 | 640 | 0.5810 | 0.7975 |
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| 0.0751 | 65.0 | 650 | 0.8577 | 0.7215 |
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| 0.1129 | 66.0 | 660 | 0.5848 | 0.8038 |
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| 0.1448 | 67.0 | 670 | 0.5494 | 0.7911 |
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| 0.0962 | 68.0 | 680 | 0.6846 | 0.7722 |
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| 0.0766 | 69.0 | 690 | 0.5374 | 0.8101 |
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| 0.0955 | 70.0 | 700 | 0.6121 | 0.7848 |
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| 0.0917 | 71.0 | 710 | 0.6612 | 0.7848 |
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| 0.0832 | 72.0 | 720 | 0.6200 | 0.7911 |
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| 0.0686 | 73.0 | 730 | 0.6439 | 0.8038 |
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| 0.082 | 74.0 | 740 | 0.5290 | 0.8291 |
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| 0.0853 | 75.0 | 750 | 0.7542 | 0.7595 |
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| 0.0789 | 76.0 | 760 | 0.6179 | 0.8165 |
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| 0.1031 | 77.0 | 770 | 0.5439 | 0.8354 |
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| 0.0724 | 78.0 | 780 | 0.6302 | 0.8165 |
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| 0.0695 | 79.0 | 790 | 0.6113 | 0.7975 |
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| 0.1089 | 80.0 | 800 | 0.7490 | 0.7532 |
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| 0.0714 | 81.0 | 810 | 0.6824 | 0.8038 |
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| 0.09 | 82.0 | 820 | 0.5732 | 0.8165 |
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| 0.0962 | 83.0 | 830 | 0.6818 | 0.7785 |
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| 0.0614 | 84.0 | 840 | 0.5182 | 0.8165 |
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| 0.0685 | 85.0 | 850 | 0.6753 | 0.7532 |
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| 0.0861 | 86.0 | 860 | 0.5541 | 0.8228 |
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| 0.09 | 87.0 | 870 | 0.7829 | 0.7658 |
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| 0.0565 | 88.0 | 880 | 0.7735 | 0.7595 |
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| 0.0574 | 89.0 | 890 | 0.6467 | 0.8038 |
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| 0.0431 | 90.0 | 900 | 0.6314 | 0.8038 |
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| 0.091 | 91.0 | 910 | 0.6226 | 0.8038 |
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| 0.055 | 92.0 | 920 | 0.7533 | 0.7785 |
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| 0.0776 | 93.0 | 930 | 0.6564 | 0.7975 |
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| 0.056 | 94.0 | 940 | 0.6182 | 0.8038 |
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| 0.0683 | 95.0 | 950 | 0.5490 | 0.8228 |
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| 0.0695 | 96.0 | 960 | 0.6460 | 0.7911 |
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| 0.0464 | 97.0 | 970 | 0.6381 | 0.7975 |
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| 0.0483 | 98.0 | 980 | 0.5261 | 0.8608 |
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| 0.0487 | 99.0 | 990 | 0.5322 | 0.8291 |
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| 0.0537 | 100.0 | 1000 | 0.6404 | 0.8101 |
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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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