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_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: 0.5540765391014975
smids_10x_deit_base_sgd_00001_fold2
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: 1.0091
- Accuracy: 0.5541
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 |
|---|---|---|---|---|
| 1.1301 | 1.0 | 750 | 1.1044 | 0.3361 |
| 1.0956 | 2.0 | 1500 | 1.1008 | 0.3394 |
| 1.0885 | 3.0 | 2250 | 1.0973 | 0.3527 |
| 1.0813 | 4.0 | 3000 | 1.0939 | 0.3677 |
| 1.0897 | 5.0 | 3750 | 1.0907 | 0.3794 |
| 1.0999 | 6.0 | 4500 | 1.0875 | 0.3794 |
| 1.0837 | 7.0 | 5250 | 1.0844 | 0.3810 |
| 1.0633 | 8.0 | 6000 | 1.0813 | 0.3860 |
| 1.0777 | 9.0 | 6750 | 1.0784 | 0.3977 |
| 1.0672 | 10.0 | 7500 | 1.0755 | 0.4027 |
| 1.0688 | 11.0 | 8250 | 1.0726 | 0.4143 |
| 1.0524 | 12.0 | 9000 | 1.0698 | 0.4193 |
| 1.0588 | 13.0 | 9750 | 1.0670 | 0.4276 |
| 1.0355 | 14.0 | 10500 | 1.0642 | 0.4443 |
| 1.0421 | 15.0 | 11250 | 1.0615 | 0.4526 |
| 1.0517 | 16.0 | 12000 | 1.0588 | 0.4526 |
| 1.0228 | 17.0 | 12750 | 1.0561 | 0.4609 |
| 1.052 | 18.0 | 13500 | 1.0535 | 0.4626 |
| 1.0453 | 19.0 | 14250 | 1.0509 | 0.4742 |
| 1.0307 | 20.0 | 15000 | 1.0483 | 0.4842 |
| 1.0308 | 21.0 | 15750 | 1.0459 | 0.4892 |
| 1.0369 | 22.0 | 16500 | 1.0434 | 0.5008 |
| 1.0173 | 23.0 | 17250 | 1.0411 | 0.5008 |
| 1.0178 | 24.0 | 18000 | 1.0388 | 0.5058 |
| 1.021 | 25.0 | 18750 | 1.0366 | 0.5075 |
| 1.0167 | 26.0 | 19500 | 1.0344 | 0.5075 |
| 1.0247 | 27.0 | 20250 | 1.0323 | 0.5125 |
| 1.0234 | 28.0 | 21000 | 1.0303 | 0.5208 |
| 1.003 | 29.0 | 21750 | 1.0283 | 0.5258 |
| 1.008 | 30.0 | 22500 | 1.0265 | 0.5308 |
| 1.0192 | 31.0 | 23250 | 1.0247 | 0.5341 |
| 1.0098 | 32.0 | 24000 | 1.0231 | 0.5374 |
| 0.9987 | 33.0 | 24750 | 1.0215 | 0.5408 |
| 0.9994 | 34.0 | 25500 | 1.0200 | 0.5408 |
| 1.0171 | 35.0 | 26250 | 1.0186 | 0.5408 |
| 1.013 | 36.0 | 27000 | 1.0173 | 0.5441 |
| 0.988 | 37.0 | 27750 | 1.0161 | 0.5441 |
| 0.9905 | 38.0 | 28500 | 1.0150 | 0.5491 |
| 0.9967 | 39.0 | 29250 | 1.0140 | 0.5491 |
| 0.9901 | 40.0 | 30000 | 1.0131 | 0.5557 |
| 0.9977 | 41.0 | 30750 | 1.0123 | 0.5557 |
| 1.0045 | 42.0 | 31500 | 1.0116 | 0.5557 |
| 0.9983 | 43.0 | 32250 | 1.0109 | 0.5541 |
| 0.9818 | 44.0 | 33000 | 1.0104 | 0.5541 |
| 0.9768 | 45.0 | 33750 | 1.0100 | 0.5541 |
| 0.9827 | 46.0 | 34500 | 1.0096 | 0.5541 |
| 0.9904 | 47.0 | 35250 | 1.0094 | 0.5541 |
| 0.9884 | 48.0 | 36000 | 1.0092 | 0.5541 |
| 0.9851 | 49.0 | 36750 | 1.0091 | 0.5541 |
| 0.9904 | 50.0 | 37500 | 1.0091 | 0.5541 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2