metadata
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-R1-40
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7540983606557377
vit-base-patch16-224-R1-40
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.7212
- Accuracy: 0.7541
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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 40
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3233 | 0.99 | 38 | 1.2355 | 0.5574 |
| 0.8643 | 1.99 | 76 | 0.9297 | 0.5902 |
| 0.4464 | 2.98 | 114 | 1.1190 | 0.6393 |
| 0.3092 | 4.0 | 153 | 0.9861 | 0.7049 |
| 0.1628 | 4.99 | 191 | 1.1221 | 0.6721 |
| 0.121 | 5.99 | 229 | 1.1710 | 0.6885 |
| 0.1138 | 6.98 | 267 | 1.1993 | 0.7213 |
| 0.1124 | 8.0 | 306 | 1.2636 | 0.6885 |
| 0.0748 | 8.99 | 344 | 1.3881 | 0.7049 |
| 0.0877 | 9.99 | 382 | 1.2892 | 0.7213 |
| 0.0642 | 10.98 | 420 | 1.3759 | 0.7049 |
| 0.0675 | 12.0 | 459 | 1.4283 | 0.7213 |
| 0.0694 | 12.99 | 497 | 1.3616 | 0.7213 |
| 0.0689 | 13.99 | 535 | 1.3864 | 0.7213 |
| 0.0378 | 14.98 | 573 | 1.4322 | 0.7213 |
| 0.0472 | 16.0 | 612 | 1.6004 | 0.7213 |
| 0.044 | 16.99 | 650 | 1.5810 | 0.7049 |
| 0.0386 | 17.99 | 688 | 1.6404 | 0.6885 |
| 0.0341 | 18.98 | 726 | 1.5698 | 0.7377 |
| 0.0328 | 20.0 | 765 | 1.6720 | 0.6885 |
| 0.0444 | 20.99 | 803 | 1.6269 | 0.7213 |
| 0.0342 | 21.99 | 841 | 1.6345 | 0.7377 |
| 0.0324 | 22.98 | 879 | 1.7916 | 0.7049 |
| 0.023 | 24.0 | 918 | 1.8753 | 0.6885 |
| 0.048 | 24.99 | 956 | 1.7679 | 0.7377 |
| 0.0202 | 25.99 | 994 | 1.7212 | 0.7541 |
| 0.0336 | 26.98 | 1032 | 1.7305 | 0.7377 |
| 0.0163 | 28.0 | 1071 | 1.7576 | 0.7049 |
| 0.0186 | 28.99 | 1109 | 1.7540 | 0.7377 |
| 0.0189 | 29.99 | 1147 | 1.6594 | 0.7541 |
| 0.039 | 30.98 | 1185 | 1.7423 | 0.7213 |
| 0.0194 | 32.0 | 1224 | 1.7148 | 0.7377 |
| 0.0205 | 32.99 | 1262 | 1.6965 | 0.7377 |
| 0.0186 | 33.99 | 1300 | 1.7553 | 0.7541 |
| 0.0177 | 34.98 | 1338 | 1.7476 | 0.7377 |
| 0.0132 | 36.0 | 1377 | 1.7506 | 0.7541 |
| 0.0068 | 36.99 | 1415 | 1.6917 | 0.7377 |
| 0.0121 | 37.99 | 1453 | 1.7276 | 0.7541 |
| 0.0129 | 38.98 | 1491 | 1.7218 | 0.7541 |
| 0.0067 | 39.74 | 1520 | 1.7220 | 0.7541 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0