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---
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_overlap_epoch100_V_0_4
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: 0.9314641744548287
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# meat_calssify_fresh_crop_fixed_overlap_epoch100_V_0_4
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.
It achieves the following results on the evaluation set:
- Loss: 0.2286
- Accuracy: 0.9315
## 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: 64
- 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 | 21 | 1.0790 | 0.4517 |
| 1.0782 | 2.0 | 42 | 1.0505 | 0.4891 |
| 1.0069 | 3.0 | 63 | 0.9886 | 0.5639 |
| 0.9347 | 4.0 | 84 | 0.9224 | 0.5794 |
| 0.8519 | 5.0 | 105 | 0.8643 | 0.6324 |
| 0.7989 | 6.0 | 126 | 0.7891 | 0.6667 |
| 0.695 | 7.0 | 147 | 0.8411 | 0.5919 |
| 0.6872 | 8.0 | 168 | 0.7448 | 0.6978 |
| 0.5872 | 9.0 | 189 | 0.7257 | 0.6854 |
| 0.6367 | 10.0 | 210 | 0.6716 | 0.7227 |
| 0.5617 | 11.0 | 231 | 0.6554 | 0.7321 |
| 0.5104 | 12.0 | 252 | 0.6906 | 0.7134 |
| 0.4581 | 13.0 | 273 | 0.6179 | 0.7601 |
| 0.5126 | 14.0 | 294 | 0.6726 | 0.7321 |
| 0.5078 | 15.0 | 315 | 0.5767 | 0.7819 |
| 0.3308 | 16.0 | 336 | 0.5843 | 0.7632 |
| 0.3396 | 17.0 | 357 | 0.5064 | 0.8287 |
| 0.3137 | 18.0 | 378 | 0.7024 | 0.7414 |
| 0.2981 | 19.0 | 399 | 0.4692 | 0.8411 |
| 0.2593 | 20.0 | 420 | 0.7424 | 0.7352 |
| 0.5048 | 21.0 | 441 | 0.4293 | 0.8411 |
| 0.2252 | 22.0 | 462 | 0.5090 | 0.7975 |
| 0.261 | 23.0 | 483 | 0.4810 | 0.8505 |
| 0.2575 | 24.0 | 504 | 0.4389 | 0.8442 |
| 0.176 | 25.0 | 525 | 0.4528 | 0.8287 |
| 0.2075 | 26.0 | 546 | 0.4764 | 0.8349 |
| 0.2069 | 27.0 | 567 | 0.5269 | 0.8162 |
| 0.2306 | 28.0 | 588 | 0.4180 | 0.8536 |
| 0.1564 | 29.0 | 609 | 0.3936 | 0.8505 |
| 0.1632 | 30.0 | 630 | 0.4111 | 0.8474 |
| 0.1923 | 31.0 | 651 | 0.3862 | 0.8629 |
| 0.1708 | 32.0 | 672 | 0.4155 | 0.8474 |
| 0.1744 | 33.0 | 693 | 0.4346 | 0.8505 |
| 0.1381 | 34.0 | 714 | 0.3908 | 0.8660 |
| 0.1668 | 35.0 | 735 | 0.5195 | 0.8255 |
| 0.146 | 36.0 | 756 | 0.4954 | 0.8255 |
| 0.1288 | 37.0 | 777 | 0.4273 | 0.8505 |
| 0.1595 | 38.0 | 798 | 0.3274 | 0.9034 |
| 0.107 | 39.0 | 819 | 0.4688 | 0.8380 |
| 0.1437 | 40.0 | 840 | 0.4269 | 0.8692 |
| 0.1432 | 41.0 | 861 | 0.5034 | 0.8224 |
| 0.1512 | 42.0 | 882 | 0.4046 | 0.8629 |
| 0.1156 | 43.0 | 903 | 0.3166 | 0.8941 |
| 0.1173 | 44.0 | 924 | 0.4023 | 0.8598 |
| 0.1366 | 45.0 | 945 | 0.3869 | 0.8692 |
| 0.1361 | 46.0 | 966 | 0.5182 | 0.8349 |
| 0.2102 | 47.0 | 987 | 0.5841 | 0.8069 |
| 0.1504 | 48.0 | 1008 | 0.4403 | 0.8598 |
| 0.1272 | 49.0 | 1029 | 0.3771 | 0.8754 |
| 0.113 | 50.0 | 1050 | 0.3809 | 0.8785 |
| 0.0884 | 51.0 | 1071 | 0.4446 | 0.8629 |
| 0.0951 | 52.0 | 1092 | 0.3689 | 0.8847 |
| 0.0822 | 53.0 | 1113 | 0.4412 | 0.8629 |
| 0.0999 | 54.0 | 1134 | 0.3758 | 0.8785 |
| 0.1321 | 55.0 | 1155 | 0.3982 | 0.8598 |
| 0.0877 | 56.0 | 1176 | 0.3068 | 0.9034 |
| 0.0736 | 57.0 | 1197 | 0.3981 | 0.8910 |
| 0.0903 | 58.0 | 1218 | 0.2888 | 0.8972 |
| 0.0842 | 59.0 | 1239 | 0.3552 | 0.8816 |
| 0.0911 | 60.0 | 1260 | 0.4368 | 0.8536 |
| 0.0847 | 61.0 | 1281 | 0.3188 | 0.9065 |
| 0.066 | 62.0 | 1302 | 0.3727 | 0.8910 |
| 0.059 | 63.0 | 1323 | 0.3373 | 0.8910 |
| 0.0755 | 64.0 | 1344 | 0.3241 | 0.9003 |
| 0.0598 | 65.0 | 1365 | 0.3641 | 0.9003 |
| 0.0561 | 66.0 | 1386 | 0.3889 | 0.8847 |
| 0.0796 | 67.0 | 1407 | 0.3633 | 0.9065 |
| 0.0736 | 68.0 | 1428 | 0.3682 | 0.8816 |
| 0.0723 | 69.0 | 1449 | 0.4165 | 0.8723 |
| 0.0625 | 70.0 | 1470 | 0.2747 | 0.9159 |
| 0.0714 | 71.0 | 1491 | 0.3374 | 0.8972 |
| 0.0723 | 72.0 | 1512 | 0.3534 | 0.9003 |
| 0.0551 | 73.0 | 1533 | 0.3764 | 0.8785 |
| 0.0417 | 74.0 | 1554 | 0.2348 | 0.9252 |
| 0.0513 | 75.0 | 1575 | 0.3214 | 0.9190 |
| 0.0534 | 76.0 | 1596 | 0.2440 | 0.9346 |
| 0.046 | 77.0 | 1617 | 0.3385 | 0.9159 |
| 0.0539 | 78.0 | 1638 | 0.3516 | 0.9003 |
| 0.055 | 79.0 | 1659 | 0.2836 | 0.9221 |
| 0.0686 | 80.0 | 1680 | 0.3542 | 0.8910 |
| 0.0589 | 81.0 | 1701 | 0.2077 | 0.9315 |
| 0.0505 | 82.0 | 1722 | 0.3094 | 0.9034 |
| 0.0332 | 83.0 | 1743 | 0.2678 | 0.9252 |
| 0.0504 | 84.0 | 1764 | 0.3099 | 0.9159 |
| 0.0523 | 85.0 | 1785 | 0.1953 | 0.9315 |
| 0.0291 | 86.0 | 1806 | 0.2377 | 0.9283 |
| 0.0499 | 87.0 | 1827 | 0.2891 | 0.9221 |
| 0.038 | 88.0 | 1848 | 0.2898 | 0.9159 |
| 0.0597 | 89.0 | 1869 | 0.2722 | 0.9190 |
| 0.0367 | 90.0 | 1890 | 0.3110 | 0.9221 |
| 0.0647 | 91.0 | 1911 | 0.2432 | 0.9346 |
| 0.0371 | 92.0 | 1932 | 0.2276 | 0.9377 |
| 0.0286 | 93.0 | 1953 | 0.2281 | 0.9346 |
| 0.0271 | 94.0 | 1974 | 0.2766 | 0.9221 |
| 0.0339 | 95.0 | 1995 | 0.2637 | 0.9283 |
| 0.0211 | 96.0 | 2016 | 0.2434 | 0.9315 |
| 0.0441 | 97.0 | 2037 | 0.3146 | 0.8941 |
| 0.0516 | 98.0 | 2058 | 0.2273 | 0.9439 |
| 0.0311 | 99.0 | 2079 | 0.2151 | 0.9377 |
| 0.0269 | 100.0 | 2100 | 0.2286 | 0.9315 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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