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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_8
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: 0.9376947040498442
---
<!-- 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_8
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: 0.2201
- Accuracy: 0.9377
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0975 | 1.0 | 21 | 1.0890 | 0.4174 |
| 1.0736 | 2.0 | 42 | 1.0605 | 0.4673 |
| 1.0236 | 3.0 | 63 | 1.0034 | 0.5202 |
| 0.9414 | 4.0 | 84 | 0.9182 | 0.6044 |
| 0.8522 | 5.0 | 105 | 0.8534 | 0.6417 |
| 0.7504 | 6.0 | 126 | 0.8599 | 0.6044 |
| 0.7472 | 7.0 | 147 | 0.6873 | 0.7040 |
| 0.6671 | 8.0 | 168 | 0.6945 | 0.7165 |
| 0.5986 | 9.0 | 189 | 0.6044 | 0.7664 |
| 0.5255 | 10.0 | 210 | 0.6201 | 0.7383 |
| 0.4988 | 11.0 | 231 | 0.6229 | 0.7570 |
| 0.5221 | 12.0 | 252 | 0.6687 | 0.7321 |
| 0.4338 | 13.0 | 273 | 0.5720 | 0.7757 |
| 0.4157 | 14.0 | 294 | 0.5662 | 0.7757 |
| 0.5228 | 15.0 | 315 | 0.6383 | 0.7383 |
| 0.3387 | 16.0 | 336 | 0.4655 | 0.8069 |
| 0.2952 | 17.0 | 357 | 0.4642 | 0.8006 |
| 0.3083 | 18.0 | 378 | 0.5752 | 0.7726 |
| 0.2659 | 19.0 | 399 | 0.5155 | 0.7913 |
| 0.2824 | 20.0 | 420 | 0.4943 | 0.8162 |
| 0.3329 | 21.0 | 441 | 0.5901 | 0.7757 |
| 0.3527 | 22.0 | 462 | 0.4185 | 0.8380 |
| 0.2394 | 23.0 | 483 | 0.3630 | 0.8723 |
| 0.2106 | 24.0 | 504 | 0.4305 | 0.8474 |
| 0.1845 | 25.0 | 525 | 0.3412 | 0.8629 |
| 0.1882 | 26.0 | 546 | 0.3621 | 0.8816 |
| 0.2144 | 27.0 | 567 | 0.3275 | 0.8754 |
| 0.1824 | 28.0 | 588 | 0.3481 | 0.8723 |
| 0.163 | 29.0 | 609 | 0.3861 | 0.8598 |
| 0.1467 | 30.0 | 630 | 0.3590 | 0.8692 |
| 0.2073 | 31.0 | 651 | 0.3481 | 0.8879 |
| 0.1669 | 32.0 | 672 | 0.3134 | 0.8847 |
| 0.167 | 33.0 | 693 | 0.3726 | 0.8754 |
| 0.1624 | 34.0 | 714 | 0.5522 | 0.7944 |
| 0.1812 | 35.0 | 735 | 0.4431 | 0.8193 |
| 0.1172 | 36.0 | 756 | 0.3441 | 0.8816 |
| 0.1515 | 37.0 | 777 | 0.4946 | 0.8255 |
| 0.1612 | 38.0 | 798 | 0.3402 | 0.8847 |
| 0.0937 | 39.0 | 819 | 0.4480 | 0.8598 |
| 0.1453 | 40.0 | 840 | 0.4515 | 0.8411 |
| 0.1259 | 41.0 | 861 | 0.3361 | 0.8847 |
| 0.107 | 42.0 | 882 | 0.3544 | 0.8598 |
| 0.1244 | 43.0 | 903 | 0.3990 | 0.8567 |
| 0.0824 | 44.0 | 924 | 0.3566 | 0.9034 |
| 0.1171 | 45.0 | 945 | 0.3223 | 0.9003 |
| 0.1052 | 46.0 | 966 | 0.3364 | 0.8660 |
| 0.1274 | 47.0 | 987 | 0.3034 | 0.8941 |
| 0.0799 | 48.0 | 1008 | 0.3928 | 0.8910 |
| 0.0814 | 49.0 | 1029 | 0.3428 | 0.8847 |
| 0.091 | 50.0 | 1050 | 0.3141 | 0.9065 |
| 0.0777 | 51.0 | 1071 | 0.4016 | 0.8785 |
| 0.0644 | 52.0 | 1092 | 0.3398 | 0.8972 |
| 0.1019 | 53.0 | 1113 | 0.3559 | 0.8847 |
| 0.076 | 54.0 | 1134 | 0.3503 | 0.8910 |
| 0.067 | 55.0 | 1155 | 0.3245 | 0.8910 |
| 0.0679 | 56.0 | 1176 | 0.3099 | 0.9034 |
| 0.0661 | 57.0 | 1197 | 0.3249 | 0.8723 |
| 0.0716 | 58.0 | 1218 | 0.3016 | 0.9034 |
| 0.075 | 59.0 | 1239 | 0.4144 | 0.8692 |
| 0.0874 | 60.0 | 1260 | 0.3850 | 0.8723 |
| 0.0821 | 61.0 | 1281 | 0.2938 | 0.9065 |
| 0.0735 | 62.0 | 1302 | 0.2518 | 0.9190 |
| 0.0755 | 63.0 | 1323 | 0.4015 | 0.8972 |
| 0.2235 | 64.0 | 1344 | 0.3127 | 0.8972 |
| 0.0631 | 65.0 | 1365 | 0.2518 | 0.9128 |
| 0.0711 | 66.0 | 1386 | 0.3544 | 0.8941 |
| 0.0671 | 67.0 | 1407 | 0.3616 | 0.8816 |
| 0.059 | 68.0 | 1428 | 0.2567 | 0.9097 |
| 0.0558 | 69.0 | 1449 | 0.3696 | 0.8692 |
| 0.0755 | 70.0 | 1470 | 0.3032 | 0.9065 |
| 0.0666 | 71.0 | 1491 | 0.2819 | 0.9128 |
| 0.0519 | 72.0 | 1512 | 0.2179 | 0.9252 |
| 0.0443 | 73.0 | 1533 | 0.2722 | 0.9159 |
| 0.0415 | 74.0 | 1554 | 0.2167 | 0.9346 |
| 0.0632 | 75.0 | 1575 | 0.2115 | 0.9377 |
| 0.067 | 76.0 | 1596 | 0.4024 | 0.8785 |
| 0.0592 | 77.0 | 1617 | 0.2328 | 0.9283 |
| 0.0528 | 78.0 | 1638 | 0.2425 | 0.9065 |
| 0.0462 | 79.0 | 1659 | 0.2385 | 0.9252 |
| 0.0248 | 80.0 | 1680 | 0.2694 | 0.9159 |
| 0.04 | 81.0 | 1701 | 0.2192 | 0.9283 |
| 0.0436 | 82.0 | 1722 | 0.2697 | 0.9221 |
| 0.0415 | 83.0 | 1743 | 0.2855 | 0.9128 |
| 0.0431 | 84.0 | 1764 | 0.1680 | 0.9502 |
| 0.0438 | 85.0 | 1785 | 0.2513 | 0.9221 |
| 0.0385 | 86.0 | 1806 | 0.2609 | 0.9190 |
| 0.0291 | 87.0 | 1827 | 0.2136 | 0.9439 |
| 0.0326 | 88.0 | 1848 | 0.2069 | 0.9439 |
| 0.0347 | 89.0 | 1869 | 0.2450 | 0.9315 |
| 0.0393 | 90.0 | 1890 | 0.2609 | 0.9377 |
| 0.0355 | 91.0 | 1911 | 0.1932 | 0.9408 |
| 0.0423 | 92.0 | 1932 | 0.2481 | 0.9315 |
| 0.0386 | 93.0 | 1953 | 0.1963 | 0.9377 |
| 0.029 | 94.0 | 1974 | 0.2220 | 0.9377 |
| 0.0383 | 95.0 | 1995 | 0.2626 | 0.9252 |
| 0.0205 | 96.0 | 2016 | 0.1894 | 0.9470 |
| 0.0392 | 97.0 | 2037 | 0.1744 | 0.9502 |
| 0.0282 | 98.0 | 2058 | 0.2907 | 0.9190 |
| 0.0416 | 99.0 | 2079 | 0.1868 | 0.9533 |
| 0.0223 | 100.0 | 2100 | 0.2201 | 0.9377 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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