metadata
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_V_0_3
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: 0.75
meat_calssify_fresh_crop_fixed_V_0_3
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.6191
- Accuracy: 0.75
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: 1
- 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: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0921 | 1.0 | 621 | 1.0844 | 0.3654 |
| 1.0438 | 2.0 | 1242 | 1.1755 | 0.4423 |
| 1.1558 | 3.0 | 1863 | 1.0148 | 0.4487 |
| 1.1274 | 4.0 | 2484 | 1.0371 | 0.5705 |
| 1.1992 | 5.0 | 3105 | 1.0598 | 0.3718 |
| 1.2195 | 6.0 | 3726 | 1.3422 | 0.5256 |
| 1.2366 | 7.0 | 4347 | 1.8744 | 0.4359 |
| 1.1302 | 8.0 | 4968 | 1.4699 | 0.5705 |
| 1.1137 | 9.0 | 5589 | 1.3480 | 0.6154 |
| 0.9534 | 10.0 | 6210 | 1.4723 | 0.5769 |
| 1.0269 | 11.0 | 6831 | 1.0999 | 0.6538 |
| 1.0545 | 12.0 | 7452 | 1.2980 | 0.6090 |
| 0.8669 | 13.0 | 8073 | 1.3408 | 0.6731 |
| 0.9571 | 14.0 | 8694 | 1.4879 | 0.6474 |
| 0.8345 | 15.0 | 9315 | 1.4030 | 0.6603 |
| 0.6623 | 16.0 | 9936 | 1.0840 | 0.7308 |
| 0.6014 | 17.0 | 10557 | 1.5054 | 0.7115 |
| 0.5599 | 18.0 | 11178 | 1.1956 | 0.7628 |
| 0.7209 | 19.0 | 11799 | 1.9734 | 0.5962 |
| 0.6289 | 20.0 | 12420 | 1.6165 | 0.6923 |
| 0.429 | 21.0 | 13041 | 1.5766 | 0.6987 |
| 0.628 | 22.0 | 13662 | 1.3948 | 0.7179 |
| 0.5427 | 23.0 | 14283 | 1.7663 | 0.6795 |
| 0.3806 | 24.0 | 14904 | 1.6219 | 0.6987 |
| 0.4443 | 25.0 | 15525 | 1.5065 | 0.7051 |
| 0.3648 | 26.0 | 16146 | 1.5225 | 0.7308 |
| 0.3812 | 27.0 | 16767 | 1.3488 | 0.75 |
| 0.3106 | 28.0 | 17388 | 1.7758 | 0.6987 |
| 0.3725 | 29.0 | 18009 | 1.4190 | 0.7372 |
| 0.4284 | 30.0 | 18630 | 1.6205 | 0.7115 |
| 0.2257 | 31.0 | 19251 | 1.5535 | 0.7628 |
| 0.2869 | 32.0 | 19872 | 1.2077 | 0.8013 |
| 0.3128 | 33.0 | 20493 | 1.9065 | 0.7051 |
| 0.2802 | 34.0 | 21114 | 1.2794 | 0.7885 |
| 0.2647 | 35.0 | 21735 | 1.5823 | 0.7436 |
| 0.3054 | 36.0 | 22356 | 1.3412 | 0.7756 |
| 0.2619 | 37.0 | 22977 | 1.4471 | 0.7308 |
| 0.2289 | 38.0 | 23598 | 1.8176 | 0.7244 |
| 0.1554 | 39.0 | 24219 | 1.5014 | 0.7628 |
| 0.1794 | 40.0 | 24840 | 1.4112 | 0.7628 |
| 0.1835 | 41.0 | 25461 | 1.8688 | 0.7244 |
| 0.2177 | 42.0 | 26082 | 1.2748 | 0.7821 |
| 0.1063 | 43.0 | 26703 | 1.4471 | 0.7628 |
| 0.1798 | 44.0 | 27324 | 1.1872 | 0.7949 |
| 0.1511 | 45.0 | 27945 | 1.3028 | 0.8077 |
| 0.1659 | 46.0 | 28566 | 1.7257 | 0.7308 |
| 0.0917 | 47.0 | 29187 | 1.2314 | 0.8205 |
| 0.1554 | 48.0 | 29808 | 1.4090 | 0.7821 |
| 0.0927 | 49.0 | 30429 | 1.0295 | 0.8397 |
| 0.1188 | 50.0 | 31050 | 1.6191 | 0.75 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.2
- Tokenizers 0.19.1