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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_5x_deit_tiny_adamax_001_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: 0.8968386023294509
---
<!-- 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_5x_deit_tiny_adamax_001_fold2
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: 0.8883
- Accuracy: 0.8968
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3644 | 1.0 | 375 | 0.3398 | 0.8702 |
| 0.2716 | 2.0 | 750 | 0.3172 | 0.8735 |
| 0.3497 | 3.0 | 1125 | 0.3400 | 0.8586 |
| 0.1669 | 4.0 | 1500 | 0.3794 | 0.8669 |
| 0.2114 | 5.0 | 1875 | 0.2911 | 0.8902 |
| 0.1067 | 6.0 | 2250 | 0.4133 | 0.8752 |
| 0.1489 | 7.0 | 2625 | 0.5329 | 0.8419 |
| 0.1233 | 8.0 | 3000 | 0.4750 | 0.8769 |
| 0.121 | 9.0 | 3375 | 0.4209 | 0.8852 |
| 0.0613 | 10.0 | 3750 | 0.3960 | 0.8918 |
| 0.0185 | 11.0 | 4125 | 0.5647 | 0.8769 |
| 0.07 | 12.0 | 4500 | 0.5185 | 0.8586 |
| 0.0467 | 13.0 | 4875 | 0.5032 | 0.8985 |
| 0.0041 | 14.0 | 5250 | 0.5742 | 0.8918 |
| 0.0599 | 15.0 | 5625 | 0.7221 | 0.8652 |
| 0.0363 | 16.0 | 6000 | 0.6853 | 0.8852 |
| 0.0212 | 17.0 | 6375 | 0.5687 | 0.8985 |
| 0.0007 | 18.0 | 6750 | 0.6790 | 0.8702 |
| 0.0025 | 19.0 | 7125 | 0.5146 | 0.8935 |
| 0.0511 | 20.0 | 7500 | 0.4949 | 0.9052 |
| 0.0231 | 21.0 | 7875 | 0.5535 | 0.8952 |
| 0.0 | 22.0 | 8250 | 0.7099 | 0.9002 |
| 0.011 | 23.0 | 8625 | 0.7090 | 0.8902 |
| 0.0118 | 24.0 | 9000 | 0.7009 | 0.9068 |
| 0.0 | 25.0 | 9375 | 0.6598 | 0.8985 |
| 0.0089 | 26.0 | 9750 | 0.7133 | 0.8902 |
| 0.0142 | 27.0 | 10125 | 0.5886 | 0.9052 |
| 0.0 | 28.0 | 10500 | 0.6881 | 0.9018 |
| 0.0001 | 29.0 | 10875 | 0.7679 | 0.8985 |
| 0.0001 | 30.0 | 11250 | 0.7339 | 0.8968 |
| 0.0038 | 31.0 | 11625 | 0.8413 | 0.8918 |
| 0.0044 | 32.0 | 12000 | 0.7669 | 0.9035 |
| 0.0049 | 33.0 | 12375 | 0.7980 | 0.9052 |
| 0.0 | 34.0 | 12750 | 0.7835 | 0.9035 |
| 0.0 | 35.0 | 13125 | 0.8137 | 0.8968 |
| 0.0 | 36.0 | 13500 | 0.8434 | 0.8968 |
| 0.0 | 37.0 | 13875 | 0.8282 | 0.8952 |
| 0.0 | 38.0 | 14250 | 0.8297 | 0.8968 |
| 0.0 | 39.0 | 14625 | 0.8386 | 0.8935 |
| 0.0034 | 40.0 | 15000 | 0.8364 | 0.8952 |
| 0.0 | 41.0 | 15375 | 0.8624 | 0.8985 |
| 0.0031 | 42.0 | 15750 | 0.8414 | 0.8968 |
| 0.0026 | 43.0 | 16125 | 0.9010 | 0.8902 |
| 0.0026 | 44.0 | 16500 | 0.8826 | 0.8952 |
| 0.0029 | 45.0 | 16875 | 0.8702 | 0.8968 |
| 0.0 | 46.0 | 17250 | 0.8727 | 0.8968 |
| 0.0055 | 47.0 | 17625 | 0.8804 | 0.8968 |
| 0.0 | 48.0 | 18000 | 0.8849 | 0.8968 |
| 0.0025 | 49.0 | 18375 | 0.8877 | 0.8968 |
| 0.0023 | 50.0 | 18750 | 0.8883 | 0.8968 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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