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---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_10x_deit_small_adamax_001_fold3
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: 0.8983333333333333
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_10x_deit_small_adamax_001_fold3
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1653
- Accuracy: 0.8983
## 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: 0.001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2967 | 1.0 | 750 | 0.3026 | 0.8833 |
| 0.2176 | 2.0 | 1500 | 0.3634 | 0.865 |
| 0.1482 | 3.0 | 2250 | 0.3577 | 0.8917 |
| 0.1878 | 4.0 | 3000 | 0.3366 | 0.89 |
| 0.2072 | 5.0 | 3750 | 0.4027 | 0.8783 |
| 0.1159 | 6.0 | 4500 | 0.4215 | 0.9017 |
| 0.1092 | 7.0 | 5250 | 0.4522 | 0.875 |
| 0.1055 | 8.0 | 6000 | 0.5168 | 0.8733 |
| 0.0404 | 9.0 | 6750 | 0.4834 | 0.8867 |
| 0.0353 | 10.0 | 7500 | 0.5506 | 0.895 |
| 0.0316 | 11.0 | 8250 | 0.5770 | 0.88 |
| 0.0204 | 12.0 | 9000 | 0.7013 | 0.88 |
| 0.0769 | 13.0 | 9750 | 0.7049 | 0.8833 |
| 0.0408 | 14.0 | 10500 | 0.5508 | 0.8983 |
| 0.014 | 15.0 | 11250 | 0.6644 | 0.8883 |
| 0.0112 | 16.0 | 12000 | 0.7305 | 0.895 |
| 0.0118 | 17.0 | 12750 | 0.6466 | 0.8967 |
| 0.0015 | 18.0 | 13500 | 0.7382 | 0.89 |
| 0.0022 | 19.0 | 14250 | 0.9099 | 0.8967 |
| 0.0028 | 20.0 | 15000 | 0.8123 | 0.8883 |
| 0.0003 | 21.0 | 15750 | 0.7936 | 0.895 |
| 0.0021 | 22.0 | 16500 | 0.8670 | 0.89 |
| 0.0001 | 23.0 | 17250 | 0.8387 | 0.89 |
| 0.0001 | 24.0 | 18000 | 0.9036 | 0.8867 |
| 0.0071 | 25.0 | 18750 | 0.9933 | 0.8967 |
| 0.0003 | 26.0 | 19500 | 0.9103 | 0.8933 |
| 0.0 | 27.0 | 20250 | 0.9486 | 0.8983 |
| 0.0 | 28.0 | 21000 | 0.9480 | 0.89 |
| 0.0 | 29.0 | 21750 | 1.0149 | 0.8983 |
| 0.0 | 30.0 | 22500 | 0.9710 | 0.89 |
| 0.0 | 31.0 | 23250 | 0.8903 | 0.9017 |
| 0.0 | 32.0 | 24000 | 0.9900 | 0.8983 |
| 0.0 | 33.0 | 24750 | 0.9812 | 0.8967 |
| 0.0 | 34.0 | 25500 | 1.0802 | 0.8917 |
| 0.0 | 35.0 | 26250 | 1.0127 | 0.9 |
| 0.0 | 36.0 | 27000 | 1.0499 | 0.8917 |
| 0.0 | 37.0 | 27750 | 1.0711 | 0.895 |
| 0.0018 | 38.0 | 28500 | 1.1040 | 0.8983 |
| 0.0 | 39.0 | 29250 | 1.0513 | 0.9017 |
| 0.0 | 40.0 | 30000 | 1.1398 | 0.9 |
| 0.0 | 41.0 | 30750 | 1.1537 | 0.9 |
| 0.0 | 42.0 | 31500 | 1.1196 | 0.9 |
| 0.0 | 43.0 | 32250 | 1.1395 | 0.8967 |
| 0.0 | 44.0 | 33000 | 1.1136 | 0.9017 |
| 0.0 | 45.0 | 33750 | 1.1523 | 0.895 |
| 0.0 | 46.0 | 34500 | 1.1446 | 0.9 |
| 0.0 | 47.0 | 35250 | 1.1542 | 0.8967 |
| 0.0 | 48.0 | 36000 | 1.1560 | 0.8983 |
| 0.0 | 49.0 | 36750 | 1.1589 | 0.8983 |
| 0.0 | 50.0 | 37500 | 1.1653 | 0.8983 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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