metadata
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_small_sgd_00001_fold5
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: 0.5033333333333333
smids_3x_deit_small_sgd_00001_fold5
This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0167
- Accuracy: 0.5033
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 |
|---|---|---|---|---|
| 1.0819 | 1.0 | 225 | 1.0751 | 0.425 |
| 1.0811 | 2.0 | 450 | 1.0723 | 0.4283 |
| 1.0651 | 3.0 | 675 | 1.0696 | 0.43 |
| 1.0585 | 4.0 | 900 | 1.0669 | 0.4317 |
| 1.0233 | 5.0 | 1125 | 1.0644 | 0.4367 |
| 1.0543 | 6.0 | 1350 | 1.0620 | 0.4383 |
| 1.0645 | 7.0 | 1575 | 1.0597 | 0.4433 |
| 1.0639 | 8.0 | 1800 | 1.0574 | 0.445 |
| 1.0491 | 9.0 | 2025 | 1.0553 | 0.4467 |
| 1.0536 | 10.0 | 2250 | 1.0531 | 0.4483 |
| 1.0638 | 11.0 | 2475 | 1.0511 | 0.4533 |
| 1.0457 | 12.0 | 2700 | 1.0491 | 0.4583 |
| 1.0693 | 13.0 | 2925 | 1.0472 | 0.4583 |
| 1.043 | 14.0 | 3150 | 1.0454 | 0.4583 |
| 1.0417 | 15.0 | 3375 | 1.0437 | 0.4667 |
| 1.0387 | 16.0 | 3600 | 1.0420 | 0.465 |
| 1.0423 | 17.0 | 3825 | 1.0404 | 0.4667 |
| 1.0457 | 18.0 | 4050 | 1.0388 | 0.465 |
| 1.0201 | 19.0 | 4275 | 1.0373 | 0.4683 |
| 1.0442 | 20.0 | 4500 | 1.0358 | 0.4683 |
| 1.0444 | 21.0 | 4725 | 1.0344 | 0.4717 |
| 1.0357 | 22.0 | 4950 | 1.0331 | 0.475 |
| 1.0413 | 23.0 | 5175 | 1.0318 | 0.4767 |
| 1.0389 | 24.0 | 5400 | 1.0306 | 0.4767 |
| 1.0161 | 25.0 | 5625 | 1.0294 | 0.4833 |
| 1.021 | 26.0 | 5850 | 1.0283 | 0.485 |
| 1.0545 | 27.0 | 6075 | 1.0273 | 0.4867 |
| 1.0129 | 28.0 | 6300 | 1.0263 | 0.4883 |
| 1.0266 | 29.0 | 6525 | 1.0254 | 0.49 |
| 1.0226 | 30.0 | 6750 | 1.0245 | 0.4917 |
| 1.0147 | 31.0 | 6975 | 1.0236 | 0.4933 |
| 1.0284 | 32.0 | 7200 | 1.0228 | 0.495 |
| 1.0418 | 33.0 | 7425 | 1.0221 | 0.495 |
| 1.0168 | 34.0 | 7650 | 1.0214 | 0.4967 |
| 0.9987 | 35.0 | 7875 | 1.0208 | 0.4967 |
| 0.9922 | 36.0 | 8100 | 1.0202 | 0.4983 |
| 1.0184 | 37.0 | 8325 | 1.0197 | 0.5 |
| 1.0229 | 38.0 | 8550 | 1.0192 | 0.5 |
| 0.9957 | 39.0 | 8775 | 1.0187 | 0.5 |
| 0.9899 | 40.0 | 9000 | 1.0183 | 0.5 |
| 1.0292 | 41.0 | 9225 | 1.0180 | 0.5 |
| 1.0309 | 42.0 | 9450 | 1.0177 | 0.5 |
| 1.0287 | 43.0 | 9675 | 1.0174 | 0.5 |
| 1.0138 | 44.0 | 9900 | 1.0172 | 0.5033 |
| 0.9831 | 45.0 | 10125 | 1.0170 | 0.5033 |
| 1.0147 | 46.0 | 10350 | 1.0169 | 0.5033 |
| 1.015 | 47.0 | 10575 | 1.0168 | 0.5033 |
| 1.0202 | 48.0 | 10800 | 1.0167 | 0.5033 |
| 1.015 | 49.0 | 11025 | 1.0167 | 0.5033 |
| 1.0165 | 50.0 | 11250 | 1.0167 | 0.5033 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2