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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_14
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: 0.9470404984423676
---
<!-- 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_14
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: 0.1627
- Accuracy: 0.9470
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0987 | 1.0 | 21 | 1.0847 | 0.4361 |
| 1.0683 | 2.0 | 42 | 1.0508 | 0.5171 |
| 1.0224 | 3.0 | 63 | 1.0214 | 0.4704 |
| 0.9556 | 4.0 | 84 | 0.9954 | 0.4891 |
| 0.9266 | 5.0 | 105 | 0.9473 | 0.5389 |
| 0.9168 | 6.0 | 126 | 0.8557 | 0.5919 |
| 0.7754 | 7.0 | 147 | 0.8971 | 0.5763 |
| 0.7383 | 8.0 | 168 | 0.6777 | 0.7695 |
| 0.6482 | 9.0 | 189 | 0.7117 | 0.7009 |
| 0.5976 | 10.0 | 210 | 0.5923 | 0.7757 |
| 0.6336 | 11.0 | 231 | 0.5497 | 0.7975 |
| 0.5193 | 12.0 | 252 | 0.6389 | 0.7383 |
| 0.4496 | 13.0 | 273 | 0.5799 | 0.7632 |
| 0.4089 | 14.0 | 294 | 0.5227 | 0.8006 |
| 0.3668 | 15.0 | 315 | 0.5907 | 0.7539 |
| 0.3644 | 16.0 | 336 | 0.7197 | 0.7414 |
| 0.3398 | 17.0 | 357 | 0.4430 | 0.8255 |
| 0.2927 | 18.0 | 378 | 0.5855 | 0.7819 |
| 0.3007 | 19.0 | 399 | 0.4378 | 0.8287 |
| 0.252 | 20.0 | 420 | 0.3540 | 0.8816 |
| 0.3041 | 21.0 | 441 | 0.5140 | 0.8162 |
| 0.2773 | 22.0 | 462 | 0.4456 | 0.8287 |
| 0.2474 | 23.0 | 483 | 0.4632 | 0.8100 |
| 0.2469 | 24.0 | 504 | 0.5080 | 0.8131 |
| 0.2201 | 25.0 | 525 | 0.3787 | 0.8660 |
| 0.167 | 26.0 | 546 | 0.3245 | 0.8723 |
| 0.1614 | 27.0 | 567 | 0.5479 | 0.8287 |
| 0.1585 | 28.0 | 588 | 0.3292 | 0.8598 |
| 0.1686 | 29.0 | 609 | 0.5806 | 0.7944 |
| 0.2157 | 30.0 | 630 | 0.4449 | 0.8193 |
| 0.1846 | 31.0 | 651 | 0.6371 | 0.7850 |
| 0.1614 | 32.0 | 672 | 0.3739 | 0.8754 |
| 0.1214 | 33.0 | 693 | 0.3230 | 0.8879 |
| 0.1294 | 34.0 | 714 | 0.4792 | 0.8442 |
| 0.112 | 35.0 | 735 | 0.3600 | 0.8847 |
| 0.1436 | 36.0 | 756 | 0.4445 | 0.8567 |
| 0.121 | 37.0 | 777 | 0.3601 | 0.8785 |
| 0.1524 | 38.0 | 798 | 0.4202 | 0.8567 |
| 0.1221 | 39.0 | 819 | 0.3454 | 0.8754 |
| 0.1397 | 40.0 | 840 | 0.4782 | 0.8536 |
| 0.1608 | 41.0 | 861 | 0.5481 | 0.8224 |
| 0.1207 | 42.0 | 882 | 0.3432 | 0.8660 |
| 0.1176 | 43.0 | 903 | 0.3480 | 0.8816 |
| 0.1072 | 44.0 | 924 | 0.3242 | 0.8785 |
| 0.0989 | 45.0 | 945 | 0.3556 | 0.8847 |
| 0.0946 | 46.0 | 966 | 0.3630 | 0.8723 |
| 0.1087 | 47.0 | 987 | 0.2972 | 0.8910 |
| 0.2532 | 48.0 | 1008 | 0.2845 | 0.9097 |
| 0.0912 | 49.0 | 1029 | 0.3424 | 0.8816 |
| 0.1181 | 50.0 | 1050 | 0.2204 | 0.9159 |
| 0.0925 | 51.0 | 1071 | 0.3311 | 0.8785 |
| 0.1092 | 52.0 | 1092 | 0.2445 | 0.9221 |
| 0.0924 | 53.0 | 1113 | 0.3297 | 0.8879 |
| 0.0871 | 54.0 | 1134 | 0.1846 | 0.9315 |
| 0.0799 | 55.0 | 1155 | 0.3486 | 0.9034 |
| 0.1778 | 56.0 | 1176 | 0.3292 | 0.8941 |
| 0.1039 | 57.0 | 1197 | 0.4066 | 0.8567 |
| 0.0732 | 58.0 | 1218 | 0.3245 | 0.9097 |
| 0.0642 | 59.0 | 1239 | 0.2939 | 0.9190 |
| 0.0811 | 60.0 | 1260 | 0.4293 | 0.8847 |
| 0.0679 | 61.0 | 1281 | 0.3204 | 0.8941 |
| 0.0563 | 62.0 | 1302 | 0.3244 | 0.9190 |
| 0.0868 | 63.0 | 1323 | 0.2359 | 0.9315 |
| 0.1067 | 64.0 | 1344 | 0.2720 | 0.9159 |
| 0.0696 | 65.0 | 1365 | 0.3054 | 0.9003 |
| 0.0586 | 66.0 | 1386 | 0.3045 | 0.9003 |
| 0.0612 | 67.0 | 1407 | 0.3321 | 0.8972 |
| 0.059 | 68.0 | 1428 | 0.3224 | 0.9003 |
| 0.0669 | 69.0 | 1449 | 0.3123 | 0.9003 |
| 0.056 | 70.0 | 1470 | 0.2288 | 0.9252 |
| 0.0517 | 71.0 | 1491 | 0.2590 | 0.9221 |
| 0.0496 | 72.0 | 1512 | 0.2533 | 0.9252 |
| 0.0462 | 73.0 | 1533 | 0.2943 | 0.9065 |
| 0.0457 | 74.0 | 1554 | 0.2280 | 0.9377 |
| 0.051 | 75.0 | 1575 | 0.3099 | 0.9128 |
| 0.0395 | 76.0 | 1596 | 0.2711 | 0.9221 |
| 0.0338 | 77.0 | 1617 | 0.1932 | 0.9408 |
| 0.0483 | 78.0 | 1638 | 0.1974 | 0.9533 |
| 0.0506 | 79.0 | 1659 | 0.2310 | 0.9283 |
| 0.0362 | 80.0 | 1680 | 0.2853 | 0.9252 |
| 0.0485 | 81.0 | 1701 | 0.1954 | 0.9408 |
| 0.0448 | 82.0 | 1722 | 0.2609 | 0.9252 |
| 0.0313 | 83.0 | 1743 | 0.2825 | 0.9190 |
| 0.0506 | 84.0 | 1764 | 0.3219 | 0.9065 |
| 0.0379 | 85.0 | 1785 | 0.2786 | 0.9221 |
| 0.0345 | 86.0 | 1806 | 0.3341 | 0.9065 |
| 0.019 | 87.0 | 1827 | 0.2731 | 0.9346 |
| 0.0438 | 88.0 | 1848 | 0.2449 | 0.9252 |
| 0.0321 | 89.0 | 1869 | 0.2719 | 0.9252 |
| 0.0478 | 90.0 | 1890 | 0.2214 | 0.9408 |
| 0.0598 | 91.0 | 1911 | 0.2174 | 0.9315 |
| 0.0372 | 92.0 | 1932 | 0.2075 | 0.9315 |
| 0.0422 | 93.0 | 1953 | 0.1781 | 0.9439 |
| 0.0324 | 94.0 | 1974 | 0.1692 | 0.9470 |
| 0.0325 | 95.0 | 1995 | 0.1999 | 0.9408 |
| 0.0369 | 96.0 | 2016 | 0.1929 | 0.9346 |
| 0.0309 | 97.0 | 2037 | 0.2310 | 0.9315 |
| 0.0347 | 98.0 | 2058 | 0.1347 | 0.9626 |
| 0.0445 | 99.0 | 2079 | 0.1967 | 0.9470 |
| 0.0337 | 100.0 | 2100 | 0.1627 | 0.9470 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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