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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_0001_fold1
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: 0.9115191986644408
---
<!-- 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_0001_fold1
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: 0.7374
- Accuracy: 0.9115
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2308 | 1.0 | 376 | 0.2388 | 0.9115 |
| 0.1317 | 2.0 | 752 | 0.2582 | 0.9098 |
| 0.1686 | 3.0 | 1128 | 0.4270 | 0.8831 |
| 0.0405 | 4.0 | 1504 | 0.4962 | 0.8781 |
| 0.0366 | 5.0 | 1880 | 0.4884 | 0.8965 |
| 0.0181 | 6.0 | 2256 | 0.6032 | 0.8982 |
| 0.0062 | 7.0 | 2632 | 0.6900 | 0.8965 |
| 0.0021 | 8.0 | 3008 | 0.5787 | 0.9115 |
| 0.0022 | 9.0 | 3384 | 0.7270 | 0.8932 |
| 0.0002 | 10.0 | 3760 | 0.6268 | 0.9115 |
| 0.0012 | 11.0 | 4136 | 0.5883 | 0.9082 |
| 0.0001 | 12.0 | 4512 | 0.5732 | 0.9165 |
| 0.0 | 13.0 | 4888 | 0.5583 | 0.9215 |
| 0.011 | 14.0 | 5264 | 0.7089 | 0.9032 |
| 0.0002 | 15.0 | 5640 | 0.7954 | 0.8815 |
| 0.0 | 16.0 | 6016 | 0.6772 | 0.8965 |
| 0.0 | 17.0 | 6392 | 0.6923 | 0.9032 |
| 0.0 | 18.0 | 6768 | 0.7006 | 0.8982 |
| 0.0 | 19.0 | 7144 | 0.6930 | 0.9032 |
| 0.0149 | 20.0 | 7520 | 0.7767 | 0.8948 |
| 0.0 | 21.0 | 7896 | 0.6643 | 0.9132 |
| 0.0 | 22.0 | 8272 | 0.6887 | 0.9015 |
| 0.0 | 23.0 | 8648 | 0.6854 | 0.8998 |
| 0.0001 | 24.0 | 9024 | 0.6666 | 0.9132 |
| 0.0 | 25.0 | 9400 | 0.6680 | 0.9082 |
| 0.0033 | 26.0 | 9776 | 0.6701 | 0.9115 |
| 0.0 | 27.0 | 10152 | 0.6769 | 0.8998 |
| 0.0 | 28.0 | 10528 | 0.6638 | 0.9115 |
| 0.0042 | 29.0 | 10904 | 0.6671 | 0.9165 |
| 0.004 | 30.0 | 11280 | 0.6642 | 0.9115 |
| 0.0 | 31.0 | 11656 | 0.6776 | 0.9098 |
| 0.0 | 32.0 | 12032 | 0.6835 | 0.9098 |
| 0.0 | 33.0 | 12408 | 0.7324 | 0.9048 |
| 0.0 | 34.0 | 12784 | 0.7298 | 0.9048 |
| 0.0 | 35.0 | 13160 | 0.7063 | 0.9098 |
| 0.0 | 36.0 | 13536 | 0.7145 | 0.9098 |
| 0.0 | 37.0 | 13912 | 0.7213 | 0.9082 |
| 0.0 | 38.0 | 14288 | 0.7239 | 0.9098 |
| 0.0 | 39.0 | 14664 | 0.7296 | 0.9065 |
| 0.0 | 40.0 | 15040 | 0.7280 | 0.9082 |
| 0.0 | 41.0 | 15416 | 0.7172 | 0.9098 |
| 0.0 | 42.0 | 15792 | 0.7224 | 0.9098 |
| 0.0 | 43.0 | 16168 | 0.7245 | 0.9115 |
| 0.0 | 44.0 | 16544 | 0.7307 | 0.9098 |
| 0.0 | 45.0 | 16920 | 0.7306 | 0.9098 |
| 0.0026 | 46.0 | 17296 | 0.7273 | 0.9098 |
| 0.0 | 47.0 | 17672 | 0.7340 | 0.9132 |
| 0.0 | 48.0 | 18048 | 0.7346 | 0.9132 |
| 0.0 | 49.0 | 18424 | 0.7359 | 0.9132 |
| 0.0022 | 50.0 | 18800 | 0.7374 | 0.9115 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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