metadata
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_deit_small_adamax_0001_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: 0.8633333333333333
smids_1x_deit_small_adamax_0001_fold4
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: 1.0934
- Accuracy: 0.8633
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: 0.0001
- 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 |
|---|---|---|---|---|
| 0.4258 | 1.0 | 75 | 0.3716 | 0.85 |
| 0.2443 | 2.0 | 150 | 0.3887 | 0.8583 |
| 0.2372 | 3.0 | 225 | 0.4276 | 0.865 |
| 0.0872 | 4.0 | 300 | 0.5553 | 0.8433 |
| 0.0572 | 5.0 | 375 | 0.6684 | 0.8267 |
| 0.0647 | 6.0 | 450 | 0.6506 | 0.8467 |
| 0.0399 | 7.0 | 525 | 0.6617 | 0.8633 |
| 0.015 | 8.0 | 600 | 0.7633 | 0.8733 |
| 0.0316 | 9.0 | 675 | 0.8713 | 0.855 |
| 0.0011 | 10.0 | 750 | 0.7531 | 0.8717 |
| 0.0167 | 11.0 | 825 | 0.8491 | 0.855 |
| 0.0001 | 12.0 | 900 | 0.8412 | 0.8617 |
| 0.0003 | 13.0 | 975 | 0.9058 | 0.8633 |
| 0.0001 | 14.0 | 1050 | 0.9354 | 0.8733 |
| 0.0001 | 15.0 | 1125 | 0.9281 | 0.86 |
| 0.0001 | 16.0 | 1200 | 0.9960 | 0.8567 |
| 0.0001 | 17.0 | 1275 | 0.9791 | 0.86 |
| 0.0042 | 18.0 | 1350 | 0.9854 | 0.86 |
| 0.0 | 19.0 | 1425 | 0.9815 | 0.855 |
| 0.0 | 20.0 | 1500 | 0.9947 | 0.8617 |
| 0.0 | 21.0 | 1575 | 1.0042 | 0.865 |
| 0.0 | 22.0 | 1650 | 1.0148 | 0.865 |
| 0.0 | 23.0 | 1725 | 1.0209 | 0.8633 |
| 0.0029 | 24.0 | 1800 | 1.0235 | 0.8633 |
| 0.0033 | 25.0 | 1875 | 1.0287 | 0.8633 |
| 0.0 | 26.0 | 1950 | 1.0248 | 0.865 |
| 0.0 | 27.0 | 2025 | 1.0372 | 0.865 |
| 0.0027 | 28.0 | 2100 | 1.0367 | 0.8633 |
| 0.0041 | 29.0 | 2175 | 1.0380 | 0.8617 |
| 0.0 | 30.0 | 2250 | 1.0450 | 0.8633 |
| 0.0 | 31.0 | 2325 | 1.0524 | 0.865 |
| 0.0 | 32.0 | 2400 | 1.0579 | 0.865 |
| 0.0 | 33.0 | 2475 | 1.0579 | 0.8617 |
| 0.0 | 34.0 | 2550 | 1.0595 | 0.8617 |
| 0.0 | 35.0 | 2625 | 1.0612 | 0.8617 |
| 0.0 | 36.0 | 2700 | 1.0672 | 0.8633 |
| 0.0032 | 37.0 | 2775 | 1.0708 | 0.865 |
| 0.0 | 38.0 | 2850 | 1.0762 | 0.865 |
| 0.0 | 39.0 | 2925 | 1.0803 | 0.865 |
| 0.0 | 40.0 | 3000 | 1.0821 | 0.865 |
| 0.0027 | 41.0 | 3075 | 1.0818 | 0.8633 |
| 0.0 | 42.0 | 3150 | 1.0859 | 0.8633 |
| 0.0 | 43.0 | 3225 | 1.0874 | 0.8633 |
| 0.0 | 44.0 | 3300 | 1.0889 | 0.8633 |
| 0.0 | 45.0 | 3375 | 1.0897 | 0.8617 |
| 0.0 | 46.0 | 3450 | 1.0915 | 0.8633 |
| 0.0 | 47.0 | 3525 | 1.0919 | 0.8633 |
| 0.0 | 48.0 | 3600 | 1.0926 | 0.8633 |
| 0.0 | 49.0 | 3675 | 1.0933 | 0.8633 |
| 0.0 | 50.0 | 3750 | 1.0934 | 0.8633 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0