<!-- 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. -->
raildefectfft1
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the defect dataset. It achieves the following results on the evaluation set:
- Loss: 0.7259
- Accuracy: 0.7914
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: 0.0002
- train_batch_size: 30
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3927 | 0.67 | 10 | 1.1308 | 0.6429 |
| 0.8111 | 1.33 | 20 | 0.9788 | 0.6629 |
| 0.513 | 2.0 | 30 | 0.7938 | 0.74 |
| 0.2943 | 2.67 | 40 | 0.8517 | 0.7343 |
| 0.2029 | 3.33 | 50 | 0.7300 | 0.7686 |
| 0.1629 | 4.0 | 60 | 0.7259 | 0.7914 |
| 0.1131 | 4.67 | 70 | 0.9103 | 0.7314 |
| 0.0955 | 5.33 | 80 | 0.8504 | 0.7657 |
| 0.0547 | 6.0 | 90 | 1.0702 | 0.72 |
| 0.0489 | 6.67 | 100 | 1.1708 | 0.6971 |
| 0.0382 | 7.33 | 110 | 1.2376 | 0.6943 |
| 0.0356 | 8.0 | 120 | 1.3361 | 0.6857 |
| 0.0311 | 8.67 | 130 | 1.1809 | 0.7229 |
| 0.0346 | 9.33 | 140 | 1.3405 | 0.7086 |
| 0.0378 | 10.0 | 150 | 1.1800 | 0.7171 |
| 0.0326 | 10.67 | 160 | 1.1292 | 0.7343 |
| 0.0319 | 11.33 | 170 | 1.0885 | 0.7371 |
| 0.0347 | 12.0 | 180 | 1.4550 | 0.6771 |
| 0.0283 | 12.67 | 190 | 1.1957 | 0.7314 |
| 0.0336 | 13.33 | 200 | 1.4648 | 0.6743 |
| 0.0175 | 14.0 | 210 | 1.4927 | 0.6771 |
| 0.0167 | 14.67 | 220 | 1.3760 | 0.7057 |
| 0.0149 | 15.33 | 230 | 1.2464 | 0.7229 |
| 0.0154 | 16.0 | 240 | 1.2553 | 0.7257 |
| 0.0135 | 16.67 | 250 | 1.2768 | 0.7314 |
| 0.0133 | 17.33 | 260 | 1.2857 | 0.7343 |
| 0.0122 | 18.0 | 270 | 1.2905 | 0.7314 |
| 0.0121 | 18.67 | 280 | 1.2929 | 0.7314 |
| 0.0115 | 19.33 | 290 | 1.2958 | 0.7314 |
| 0.0111 | 20.0 | 300 | 1.2985 | 0.7314 |
| 0.011 | 20.67 | 310 | 1.3020 | 0.7343 |
| 0.0103 | 21.33 | 320 | 1.3051 | 0.7371 |
| 0.0103 | 22.0 | 330 | 1.3075 | 0.7371 |
| 0.0104 | 22.67 | 340 | 1.3098 | 0.7371 |
| 0.0096 | 23.33 | 350 | 1.3128 | 0.7371 |
| 0.0095 | 24.0 | 360 | 1.3154 | 0.7371 |
| 0.0096 | 24.67 | 370 | 1.3162 | 0.7371 |
| 0.0093 | 25.33 | 380 | 1.3183 | 0.7371 |
| 0.0091 | 26.0 | 390 | 1.3200 | 0.7371 |
| 0.0092 | 26.67 | 400 | 1.3213 | 0.7371 |
| 0.0089 | 27.33 | 410 | 1.3219 | 0.7371 |
| 0.0092 | 28.0 | 420 | 1.3224 | 0.7371 |
| 0.0089 | 28.67 | 430 | 1.3228 | 0.7371 |
| 0.0089 | 29.33 | 440 | 1.3231 | 0.7371 |
| 0.0089 | 30.0 | 450 | 1.3233 | 0.7371 |
Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
