metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_deit_tiny_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: 0.8916666666666667
smids_10x_deit_tiny_adamax_0001_fold4
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: 1.1802
- Accuracy: 0.8917
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 |
|---|---|---|---|---|
| 0.2287 | 1.0 | 750 | 0.3453 | 0.8783 |
| 0.1774 | 2.0 | 1500 | 0.3819 | 0.8783 |
| 0.1288 | 3.0 | 2250 | 0.5077 | 0.8617 |
| 0.0591 | 4.0 | 3000 | 0.5258 | 0.8767 |
| 0.013 | 5.0 | 3750 | 0.7962 | 0.87 |
| 0.0441 | 6.0 | 4500 | 0.7798 | 0.875 |
| 0.0022 | 7.0 | 5250 | 0.8865 | 0.8783 |
| 0.0168 | 8.0 | 6000 | 0.9982 | 0.8817 |
| 0.0002 | 9.0 | 6750 | 0.9825 | 0.8833 |
| 0.012 | 10.0 | 7500 | 0.9837 | 0.8883 |
| 0.0139 | 11.0 | 8250 | 1.0185 | 0.88 |
| 0.0283 | 12.0 | 9000 | 1.0469 | 0.8767 |
| 0.0013 | 13.0 | 9750 | 1.1375 | 0.885 |
| 0.0051 | 14.0 | 10500 | 1.1468 | 0.8817 |
| 0.0 | 15.0 | 11250 | 1.1486 | 0.875 |
| 0.0211 | 16.0 | 12000 | 1.0421 | 0.8867 |
| 0.0 | 17.0 | 12750 | 1.1215 | 0.8783 |
| 0.0 | 18.0 | 13500 | 1.1501 | 0.8917 |
| 0.0001 | 19.0 | 14250 | 1.2352 | 0.88 |
| 0.0002 | 20.0 | 15000 | 1.2860 | 0.8883 |
| 0.0 | 21.0 | 15750 | 1.1704 | 0.8833 |
| 0.0 | 22.0 | 16500 | 1.0833 | 0.8933 |
| 0.0 | 23.0 | 17250 | 1.1109 | 0.8933 |
| 0.0 | 24.0 | 18000 | 1.1424 | 0.8933 |
| 0.0 | 25.0 | 18750 | 1.0812 | 0.89 |
| 0.0 | 26.0 | 19500 | 1.1046 | 0.8917 |
| 0.0 | 27.0 | 20250 | 1.1453 | 0.8883 |
| 0.0 | 28.0 | 21000 | 1.1203 | 0.885 |
| 0.0 | 29.0 | 21750 | 1.1015 | 0.8933 |
| 0.0 | 30.0 | 22500 | 1.1212 | 0.8967 |
| 0.0 | 31.0 | 23250 | 1.1480 | 0.8883 |
| 0.0 | 32.0 | 24000 | 1.1454 | 0.8833 |
| 0.0 | 33.0 | 24750 | 1.1314 | 0.8867 |
| 0.0 | 34.0 | 25500 | 1.1208 | 0.885 |
| 0.0 | 35.0 | 26250 | 1.1448 | 0.8833 |
| 0.0 | 36.0 | 27000 | 1.1486 | 0.8833 |
| 0.0 | 37.0 | 27750 | 1.1572 | 0.885 |
| 0.0 | 38.0 | 28500 | 1.1406 | 0.8867 |
| 0.0 | 39.0 | 29250 | 1.1768 | 0.89 |
| 0.0 | 40.0 | 30000 | 1.1690 | 0.885 |
| 0.0 | 41.0 | 30750 | 1.1715 | 0.8883 |
| 0.0 | 42.0 | 31500 | 1.1720 | 0.89 |
| 0.0 | 43.0 | 32250 | 1.1654 | 0.8917 |
| 0.0 | 44.0 | 33000 | 1.1692 | 0.8917 |
| 0.0 | 45.0 | 33750 | 1.1750 | 0.8917 |
| 0.0 | 46.0 | 34500 | 1.1770 | 0.8917 |
| 0.0 | 47.0 | 35250 | 1.1783 | 0.8917 |
| 0.0 | 48.0 | 36000 | 1.1786 | 0.8917 |
| 0.0 | 49.0 | 36750 | 1.1796 | 0.8917 |
| 0.0 | 50.0 | 37500 | 1.1802 | 0.8917 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2