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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_3x_deit_small_sgd_001_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: 0.87
---
<!-- 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_3x_deit_small_sgd_001_fold4
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: 0.3235
- Accuracy: 0.87
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.834 | 1.0 | 225 | 0.8192 | 0.67 |
| 0.6262 | 2.0 | 450 | 0.6329 | 0.755 |
| 0.5137 | 3.0 | 675 | 0.5393 | 0.8 |
| 0.4726 | 4.0 | 900 | 0.4881 | 0.8117 |
| 0.4753 | 5.0 | 1125 | 0.4529 | 0.825 |
| 0.3563 | 6.0 | 1350 | 0.4306 | 0.8367 |
| 0.4027 | 7.0 | 1575 | 0.4125 | 0.8317 |
| 0.4582 | 8.0 | 1800 | 0.4008 | 0.8433 |
| 0.378 | 9.0 | 2025 | 0.3888 | 0.8417 |
| 0.3387 | 10.0 | 2250 | 0.3828 | 0.84 |
| 0.366 | 11.0 | 2475 | 0.3744 | 0.8433 |
| 0.3618 | 12.0 | 2700 | 0.3690 | 0.845 |
| 0.2883 | 13.0 | 2925 | 0.3626 | 0.845 |
| 0.2516 | 14.0 | 3150 | 0.3569 | 0.8533 |
| 0.2729 | 15.0 | 3375 | 0.3543 | 0.8517 |
| 0.2661 | 16.0 | 3600 | 0.3505 | 0.8567 |
| 0.2566 | 17.0 | 3825 | 0.3484 | 0.8567 |
| 0.2958 | 18.0 | 4050 | 0.3449 | 0.86 |
| 0.2763 | 19.0 | 4275 | 0.3442 | 0.86 |
| 0.2103 | 20.0 | 4500 | 0.3404 | 0.8633 |
| 0.2473 | 21.0 | 4725 | 0.3384 | 0.8633 |
| 0.246 | 22.0 | 4950 | 0.3367 | 0.865 |
| 0.2436 | 23.0 | 5175 | 0.3379 | 0.8617 |
| 0.2089 | 24.0 | 5400 | 0.3339 | 0.8667 |
| 0.2559 | 25.0 | 5625 | 0.3325 | 0.8667 |
| 0.2143 | 26.0 | 5850 | 0.3311 | 0.8667 |
| 0.2194 | 27.0 | 6075 | 0.3314 | 0.8667 |
| 0.2076 | 28.0 | 6300 | 0.3304 | 0.865 |
| 0.1951 | 29.0 | 6525 | 0.3306 | 0.8633 |
| 0.2173 | 30.0 | 6750 | 0.3289 | 0.87 |
| 0.2138 | 31.0 | 6975 | 0.3276 | 0.8667 |
| 0.1666 | 32.0 | 7200 | 0.3279 | 0.8683 |
| 0.2362 | 33.0 | 7425 | 0.3284 | 0.8667 |
| 0.2048 | 34.0 | 7650 | 0.3267 | 0.8683 |
| 0.1835 | 35.0 | 7875 | 0.3262 | 0.8717 |
| 0.2278 | 36.0 | 8100 | 0.3250 | 0.8683 |
| 0.2162 | 37.0 | 8325 | 0.3259 | 0.8683 |
| 0.2267 | 38.0 | 8550 | 0.3242 | 0.8717 |
| 0.2006 | 39.0 | 8775 | 0.3241 | 0.8683 |
| 0.2205 | 40.0 | 9000 | 0.3240 | 0.87 |
| 0.1797 | 41.0 | 9225 | 0.3251 | 0.8683 |
| 0.1988 | 42.0 | 9450 | 0.3237 | 0.87 |
| 0.2045 | 43.0 | 9675 | 0.3237 | 0.8717 |
| 0.2456 | 44.0 | 9900 | 0.3239 | 0.87 |
| 0.1971 | 45.0 | 10125 | 0.3237 | 0.87 |
| 0.2036 | 46.0 | 10350 | 0.3240 | 0.8683 |
| 0.1749 | 47.0 | 10575 | 0.3238 | 0.8683 |
| 0.1994 | 48.0 | 10800 | 0.3236 | 0.87 |
| 0.2429 | 49.0 | 11025 | 0.3236 | 0.87 |
| 0.2034 | 50.0 | 11250 | 0.3235 | 0.87 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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