vit-base-aihub_model-v2
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: 0.3076
- Accuracy: 0.9639
- Precision: 0.9610
- Recall: 0.9614
- F1: 0.9604
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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 3 | 1.2753 | 0.8373 | 0.8563 | 0.7993 | 0.8022 |
| No log | 2.0 | 6 | 1.1252 | 0.8675 | 0.8895 | 0.8300 | 0.8333 |
| No log | 3.0 | 9 | 0.9427 | 0.8976 | 0.9185 | 0.8696 | 0.8760 |
| 1.1721 | 4.0 | 12 | 0.7995 | 0.9398 | 0.9474 | 0.9195 | 0.9246 |
| 1.1721 | 5.0 | 15 | 0.6820 | 0.9699 | 0.9704 | 0.9613 | 0.9642 |
| 1.1721 | 6.0 | 18 | 0.5927 | 0.9639 | 0.9603 | 0.9583 | 0.9587 |
| 0.7084 | 7.0 | 21 | 0.5239 | 0.9759 | 0.9725 | 0.9729 | 0.9725 |
| 0.7084 | 8.0 | 24 | 0.4743 | 0.9699 | 0.9665 | 0.9671 | 0.9665 |
| 0.7084 | 9.0 | 27 | 0.4436 | 0.9578 | 0.9558 | 0.9556 | 0.9544 |
| 0.4668 | 10.0 | 30 | 0.4070 | 0.9639 | 0.9610 | 0.9614 | 0.9604 |
| 0.4668 | 11.0 | 33 | 0.3817 | 0.9699 | 0.9665 | 0.9671 | 0.9665 |
| 0.4668 | 12.0 | 36 | 0.3625 | 0.9699 | 0.9665 | 0.9671 | 0.9665 |
| 0.4668 | 13.0 | 39 | 0.3536 | 0.9578 | 0.9558 | 0.9556 | 0.9544 |
| 0.3611 | 14.0 | 42 | 0.3384 | 0.9578 | 0.9558 | 0.9556 | 0.9544 |
| 0.3611 | 15.0 | 45 | 0.3249 | 0.9699 | 0.9665 | 0.9671 | 0.9665 |
| 0.3611 | 16.0 | 48 | 0.3164 | 0.9699 | 0.9665 | 0.9671 | 0.9665 |
| 0.3063 | 17.0 | 51 | 0.3142 | 0.9639 | 0.9610 | 0.9614 | 0.9604 |
| 0.3063 | 18.0 | 54 | 0.3122 | 0.9639 | 0.9610 | 0.9614 | 0.9604 |
| 0.3063 | 19.0 | 57 | 0.3093 | 0.9639 | 0.9610 | 0.9614 | 0.9604 |
| 0.294 | 20.0 | 60 | 0.3076 | 0.9639 | 0.9610 | 0.9614 | 0.9604 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3