metadata
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_deit_base_sgd_00001_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: 0.5833333333333334
smids_10x_deit_base_sgd_00001_fold4
This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0053
- Accuracy: 0.5833
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 |
|---|---|---|---|---|
| 1.1184 | 1.0 | 750 | 1.1087 | 0.3417 |
| 1.0954 | 2.0 | 1500 | 1.1049 | 0.3567 |
| 1.0943 | 3.0 | 2250 | 1.1012 | 0.3733 |
| 1.0965 | 4.0 | 3000 | 1.0976 | 0.3883 |
| 1.097 | 5.0 | 3750 | 1.0941 | 0.3983 |
| 1.0859 | 6.0 | 4500 | 1.0907 | 0.41 |
| 1.0772 | 7.0 | 5250 | 1.0874 | 0.4133 |
| 1.0913 | 8.0 | 6000 | 1.0841 | 0.43 |
| 1.0722 | 9.0 | 6750 | 1.0809 | 0.4383 |
| 1.0755 | 10.0 | 7500 | 1.0778 | 0.445 |
| 1.0725 | 11.0 | 8250 | 1.0746 | 0.4467 |
| 1.0546 | 12.0 | 9000 | 1.0716 | 0.4517 |
| 1.0625 | 13.0 | 9750 | 1.0686 | 0.46 |
| 1.0581 | 14.0 | 10500 | 1.0656 | 0.4683 |
| 1.047 | 15.0 | 11250 | 1.0626 | 0.4733 |
| 1.0434 | 16.0 | 12000 | 1.0597 | 0.48 |
| 1.0251 | 17.0 | 12750 | 1.0568 | 0.4917 |
| 1.0453 | 18.0 | 13500 | 1.0540 | 0.4933 |
| 1.0513 | 19.0 | 14250 | 1.0512 | 0.5 |
| 1.0388 | 20.0 | 15000 | 1.0484 | 0.51 |
| 1.0333 | 21.0 | 15750 | 1.0457 | 0.52 |
| 1.0252 | 22.0 | 16500 | 1.0431 | 0.5267 |
| 1.0295 | 23.0 | 17250 | 1.0405 | 0.5283 |
| 1.0271 | 24.0 | 18000 | 1.0379 | 0.5317 |
| 1.0317 | 25.0 | 18750 | 1.0355 | 0.5333 |
| 1.0277 | 26.0 | 19500 | 1.0331 | 0.5367 |
| 1.0061 | 27.0 | 20250 | 1.0308 | 0.5467 |
| 1.0064 | 28.0 | 21000 | 1.0286 | 0.555 |
| 1.0287 | 29.0 | 21750 | 1.0265 | 0.56 |
| 1.0101 | 30.0 | 22500 | 1.0245 | 0.5633 |
| 1.0026 | 31.0 | 23250 | 1.0225 | 0.5667 |
| 1.0037 | 32.0 | 24000 | 1.0207 | 0.5667 |
| 1.0192 | 33.0 | 24750 | 1.0189 | 0.57 |
| 1.0129 | 34.0 | 25500 | 1.0173 | 0.57 |
| 1.007 | 35.0 | 26250 | 1.0157 | 0.57 |
| 0.9958 | 36.0 | 27000 | 1.0143 | 0.5717 |
| 1.0225 | 37.0 | 27750 | 1.0130 | 0.5733 |
| 1.0064 | 38.0 | 28500 | 1.0118 | 0.575 |
| 0.99 | 39.0 | 29250 | 1.0107 | 0.5733 |
| 0.9987 | 40.0 | 30000 | 1.0097 | 0.5767 |
| 1.0146 | 41.0 | 30750 | 1.0088 | 0.5817 |
| 0.9734 | 42.0 | 31500 | 1.0080 | 0.5817 |
| 1.0086 | 43.0 | 32250 | 1.0073 | 0.5817 |
| 0.9898 | 44.0 | 33000 | 1.0068 | 0.5833 |
| 0.9877 | 45.0 | 33750 | 1.0063 | 0.5833 |
| 0.9974 | 46.0 | 34500 | 1.0059 | 0.5833 |
| 0.9888 | 47.0 | 35250 | 1.0057 | 0.5833 |
| 0.9987 | 48.0 | 36000 | 1.0055 | 0.5833 |
| 1.01 | 49.0 | 36750 | 1.0054 | 0.5833 |
| 0.9819 | 50.0 | 37500 | 1.0053 | 0.5833 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2