metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_deit_tiny_adamax_001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8716666666666667
smids_10x_deit_tiny_adamax_001_fold4
This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.5843
- Accuracy: 0.8717
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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- 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 |
|---|---|---|---|---|
| 0.3385 | 1.0 | 750 | 0.3848 | 0.84 |
| 0.2692 | 2.0 | 1500 | 0.3830 | 0.8633 |
| 0.2345 | 3.0 | 2250 | 0.4255 | 0.8617 |
| 0.1851 | 4.0 | 3000 | 0.4988 | 0.8517 |
| 0.1806 | 5.0 | 3750 | 0.5032 | 0.8433 |
| 0.1568 | 6.0 | 4500 | 0.5429 | 0.8633 |
| 0.0638 | 7.0 | 5250 | 0.6033 | 0.855 |
| 0.1397 | 8.0 | 6000 | 0.6990 | 0.845 |
| 0.1208 | 9.0 | 6750 | 0.6852 | 0.8483 |
| 0.0667 | 10.0 | 7500 | 0.8743 | 0.8383 |
| 0.0482 | 11.0 | 8250 | 0.7516 | 0.8667 |
| 0.0306 | 12.0 | 9000 | 0.8187 | 0.8783 |
| 0.0125 | 13.0 | 9750 | 0.8525 | 0.86 |
| 0.0512 | 14.0 | 10500 | 1.0441 | 0.8483 |
| 0.0023 | 15.0 | 11250 | 1.0562 | 0.85 |
| 0.0353 | 16.0 | 12000 | 1.1914 | 0.8583 |
| 0.0637 | 17.0 | 12750 | 1.1115 | 0.8667 |
| 0.025 | 18.0 | 13500 | 1.1677 | 0.865 |
| 0.0126 | 19.0 | 14250 | 1.0523 | 0.8833 |
| 0.0 | 20.0 | 15000 | 1.0935 | 0.8633 |
| 0.0359 | 21.0 | 15750 | 1.1791 | 0.8733 |
| 0.0003 | 22.0 | 16500 | 1.0630 | 0.87 |
| 0.0003 | 23.0 | 17250 | 1.0996 | 0.8667 |
| 0.0006 | 24.0 | 18000 | 1.0915 | 0.8817 |
| 0.0001 | 25.0 | 18750 | 1.1484 | 0.8617 |
| 0.0 | 26.0 | 19500 | 1.1656 | 0.875 |
| 0.0179 | 27.0 | 20250 | 1.2101 | 0.8717 |
| 0.0 | 28.0 | 21000 | 1.3179 | 0.86 |
| 0.0 | 29.0 | 21750 | 1.2425 | 0.8733 |
| 0.0 | 30.0 | 22500 | 1.3660 | 0.87 |
| 0.0 | 31.0 | 23250 | 1.3781 | 0.87 |
| 0.0 | 32.0 | 24000 | 1.4541 | 0.86 |
| 0.0003 | 33.0 | 24750 | 1.3447 | 0.8717 |
| 0.0 | 34.0 | 25500 | 1.3846 | 0.8633 |
| 0.0 | 35.0 | 26250 | 1.3907 | 0.8733 |
| 0.0 | 36.0 | 27000 | 1.4240 | 0.87 |
| 0.0 | 37.0 | 27750 | 1.3878 | 0.8717 |
| 0.0 | 38.0 | 28500 | 1.4082 | 0.87 |
| 0.0 | 39.0 | 29250 | 1.4530 | 0.8717 |
| 0.0 | 40.0 | 30000 | 1.4653 | 0.8717 |
| 0.0 | 41.0 | 30750 | 1.4878 | 0.87 |
| 0.0 | 42.0 | 31500 | 1.5011 | 0.8717 |
| 0.0 | 43.0 | 32250 | 1.5107 | 0.8717 |
| 0.0 | 44.0 | 33000 | 1.5209 | 0.8717 |
| 0.0 | 45.0 | 33750 | 1.5429 | 0.8717 |
| 0.0 | 46.0 | 34500 | 1.5577 | 0.8717 |
| 0.0 | 47.0 | 35250 | 1.5684 | 0.8717 |
| 0.0 | 48.0 | 36000 | 1.5772 | 0.8717 |
| 0.0 | 49.0 | 36750 | 1.5824 | 0.8717 |
| 0.0 | 50.0 | 37500 | 1.5843 | 0.8717 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2