metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-Trial006_007_008-YEL_STEM1
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: 1
vit-base-patch16-224-Trial006_007_008-YEL_STEM1
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: 0.0453
- Accuracy: 1.0
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: 30
- eval_batch_size: 30
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 120
- 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 |
|---|---|---|---|---|
| 0.7064 | 1.0 | 5 | 0.6605 | 0.5373 |
| 0.5838 | 2.0 | 10 | 0.5289 | 0.8358 |
| 0.4909 | 3.0 | 15 | 0.3967 | 0.8209 |
| 0.3317 | 4.0 | 20 | 0.2759 | 0.9104 |
| 0.2813 | 5.0 | 25 | 0.1820 | 0.9403 |
| 0.2948 | 6.0 | 30 | 0.1286 | 0.9552 |
| 0.2253 | 7.0 | 35 | 0.0453 | 1.0 |
| 0.2125 | 8.0 | 40 | 0.0408 | 1.0 |
| 0.1288 | 9.0 | 45 | 0.0177 | 1.0 |
| 0.1084 | 10.0 | 50 | 0.0265 | 1.0 |
| 0.1642 | 11.0 | 55 | 0.0994 | 0.9403 |
| 0.149 | 12.0 | 60 | 0.0316 | 0.9851 |
| 0.1315 | 13.0 | 65 | 0.0325 | 0.9851 |
| 0.1101 | 14.0 | 70 | 0.1090 | 0.9701 |
| 0.1101 | 15.0 | 75 | 0.0094 | 1.0 |
| 0.0702 | 16.0 | 80 | 0.0070 | 1.0 |
| 0.1184 | 17.0 | 85 | 0.0634 | 0.9851 |
| 0.1506 | 18.0 | 90 | 0.0104 | 1.0 |
| 0.1027 | 19.0 | 95 | 0.0149 | 1.0 |
| 0.159 | 20.0 | 100 | 0.1021 | 0.9552 |
| 0.1205 | 21.0 | 105 | 0.0085 | 1.0 |
| 0.1511 | 22.0 | 110 | 0.0248 | 0.9851 |
| 0.2228 | 23.0 | 115 | 0.0993 | 0.9552 |
| 0.1431 | 24.0 | 120 | 0.0373 | 0.9851 |
| 0.1489 | 25.0 | 125 | 0.0161 | 1.0 |
| 0.0799 | 26.0 | 130 | 0.0382 | 0.9851 |
| 0.1411 | 27.0 | 135 | 0.0071 | 1.0 |
| 0.1457 | 28.0 | 140 | 0.0047 | 1.0 |
| 0.1434 | 29.0 | 145 | 0.0069 | 1.0 |
| 0.0913 | 30.0 | 150 | 0.0032 | 1.0 |
| 0.1354 | 31.0 | 155 | 0.0042 | 1.0 |
| 0.1253 | 32.0 | 160 | 0.0061 | 1.0 |
| 0.1065 | 33.0 | 165 | 0.0039 | 1.0 |
| 0.1199 | 34.0 | 170 | 0.0023 | 1.0 |
| 0.1274 | 35.0 | 175 | 0.0037 | 1.0 |
| 0.1118 | 36.0 | 180 | 0.0100 | 1.0 |
| 0.1237 | 37.0 | 185 | 0.0053 | 1.0 |
| 0.1311 | 38.0 | 190 | 0.0028 | 1.0 |
| 0.1833 | 39.0 | 195 | 0.0026 | 1.0 |
| 0.0858 | 40.0 | 200 | 0.0033 | 1.0 |
| 0.1503 | 41.0 | 205 | 0.0049 | 1.0 |
| 0.0547 | 42.0 | 210 | 0.0037 | 1.0 |
| 0.1647 | 43.0 | 215 | 0.0037 | 1.0 |
| 0.1066 | 44.0 | 220 | 0.0061 | 1.0 |
| 0.1277 | 45.0 | 225 | 0.0083 | 1.0 |
| 0.0885 | 46.0 | 230 | 0.0083 | 1.0 |
| 0.1339 | 47.0 | 235 | 0.0081 | 1.0 |
| 0.0904 | 48.0 | 240 | 0.0073 | 1.0 |
| 0.079 | 49.0 | 245 | 0.0080 | 1.0 |
| 0.0788 | 50.0 | 250 | 0.0086 | 1.0 |
Framework versions
- Transformers 4.30.0.dev0
- Pytorch 1.12.1
- Datasets 2.12.0
- Tokenizers 0.13.1