metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_tiny_rms_00001_fold2
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: 0.8752079866888519
smids_3x_deit_tiny_rms_00001_fold2
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: 1.0668
- Accuracy: 0.8752
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: 1e-05
- 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 |
|---|---|---|---|---|
| 0.2993 | 1.0 | 225 | 0.3110 | 0.8719 |
| 0.214 | 2.0 | 450 | 0.3099 | 0.8719 |
| 0.1117 | 3.0 | 675 | 0.3255 | 0.8835 |
| 0.1732 | 4.0 | 900 | 0.3969 | 0.8636 |
| 0.1001 | 5.0 | 1125 | 0.4203 | 0.8735 |
| 0.0568 | 6.0 | 1350 | 0.5015 | 0.8735 |
| 0.0354 | 7.0 | 1575 | 0.5359 | 0.8769 |
| 0.0128 | 8.0 | 1800 | 0.6487 | 0.8769 |
| 0.012 | 9.0 | 2025 | 0.7872 | 0.8552 |
| 0.0123 | 10.0 | 2250 | 0.8404 | 0.8752 |
| 0.0004 | 11.0 | 2475 | 0.8481 | 0.8652 |
| 0.0319 | 12.0 | 2700 | 0.9253 | 0.8686 |
| 0.0001 | 13.0 | 2925 | 0.9570 | 0.8636 |
| 0.0029 | 14.0 | 3150 | 0.9176 | 0.8702 |
| 0.0009 | 15.0 | 3375 | 1.0326 | 0.8785 |
| 0.0207 | 16.0 | 3600 | 1.0323 | 0.8719 |
| 0.0113 | 17.0 | 3825 | 1.0675 | 0.8686 |
| 0.0006 | 18.0 | 4050 | 1.0013 | 0.8769 |
| 0.0 | 19.0 | 4275 | 1.1724 | 0.8669 |
| 0.0 | 20.0 | 4500 | 0.9929 | 0.8735 |
| 0.0002 | 21.0 | 4725 | 0.9953 | 0.8719 |
| 0.0 | 22.0 | 4950 | 1.1132 | 0.8735 |
| 0.0425 | 23.0 | 5175 | 1.0471 | 0.8686 |
| 0.0 | 24.0 | 5400 | 1.1403 | 0.8652 |
| 0.0141 | 25.0 | 5625 | 1.1287 | 0.8619 |
| 0.0 | 26.0 | 5850 | 0.9874 | 0.8785 |
| 0.0 | 27.0 | 6075 | 1.0105 | 0.8752 |
| 0.0 | 28.0 | 6300 | 1.0130 | 0.8802 |
| 0.0 | 29.0 | 6525 | 1.0721 | 0.8652 |
| 0.0053 | 30.0 | 6750 | 1.0713 | 0.8819 |
| 0.0 | 31.0 | 6975 | 1.0241 | 0.8835 |
| 0.0 | 32.0 | 7200 | 1.0643 | 0.8869 |
| 0.0 | 33.0 | 7425 | 1.0320 | 0.8669 |
| 0.0 | 34.0 | 7650 | 1.0424 | 0.8802 |
| 0.0 | 35.0 | 7875 | 1.0176 | 0.8802 |
| 0.0044 | 36.0 | 8100 | 0.9778 | 0.8785 |
| 0.0 | 37.0 | 8325 | 0.9990 | 0.8819 |
| 0.0 | 38.0 | 8550 | 1.0176 | 0.8752 |
| 0.0047 | 39.0 | 8775 | 1.0819 | 0.8719 |
| 0.0 | 40.0 | 9000 | 1.0393 | 0.8735 |
| 0.0 | 41.0 | 9225 | 1.0424 | 0.8735 |
| 0.0 | 42.0 | 9450 | 1.0459 | 0.8702 |
| 0.0 | 43.0 | 9675 | 1.0528 | 0.8752 |
| 0.0 | 44.0 | 9900 | 1.0545 | 0.8769 |
| 0.0 | 45.0 | 10125 | 1.0566 | 0.8785 |
| 0.0 | 46.0 | 10350 | 1.0564 | 0.8769 |
| 0.0 | 47.0 | 10575 | 1.0599 | 0.8785 |
| 0.0 | 48.0 | 10800 | 1.0618 | 0.8785 |
| 0.002 | 49.0 | 11025 | 1.0652 | 0.8752 |
| 0.002 | 50.0 | 11250 | 1.0668 | 0.8752 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2