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---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_deit_base_rms_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: 0.8019966722129783
---
<!-- 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_base_rms_001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6036
- Accuracy: 0.8020
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1186 | 1.0 | 225 | 0.8982 | 0.5075 |
| 0.9141 | 2.0 | 450 | 0.8195 | 0.5557 |
| 0.8927 | 3.0 | 675 | 0.8189 | 0.5441 |
| 0.8323 | 4.0 | 900 | 0.8095 | 0.5541 |
| 0.8947 | 5.0 | 1125 | 0.7623 | 0.5757 |
| 0.7287 | 6.0 | 1350 | 0.8273 | 0.5591 |
| 0.7585 | 7.0 | 1575 | 0.7770 | 0.5973 |
| 0.8103 | 8.0 | 1800 | 0.7290 | 0.6106 |
| 0.7335 | 9.0 | 2025 | 0.7908 | 0.5807 |
| 0.7359 | 10.0 | 2250 | 0.7312 | 0.5874 |
| 0.8194 | 11.0 | 2475 | 0.9398 | 0.5557 |
| 0.7512 | 12.0 | 2700 | 0.7107 | 0.5923 |
| 0.7169 | 13.0 | 2925 | 0.7015 | 0.6639 |
| 0.6759 | 14.0 | 3150 | 0.6767 | 0.6672 |
| 0.7072 | 15.0 | 3375 | 0.6493 | 0.6955 |
| 0.6502 | 16.0 | 3600 | 0.6076 | 0.7404 |
| 0.6691 | 17.0 | 3825 | 0.6396 | 0.6855 |
| 0.6248 | 18.0 | 4050 | 0.5525 | 0.7621 |
| 0.5977 | 19.0 | 4275 | 0.7766 | 0.6373 |
| 0.582 | 20.0 | 4500 | 0.5758 | 0.7438 |
| 0.5383 | 21.0 | 4725 | 0.5521 | 0.7554 |
| 0.6208 | 22.0 | 4950 | 0.5508 | 0.7521 |
| 0.6018 | 23.0 | 5175 | 0.5519 | 0.7604 |
| 0.5417 | 24.0 | 5400 | 0.5813 | 0.7471 |
| 0.6149 | 25.0 | 5625 | 0.5077 | 0.7820 |
| 0.5061 | 26.0 | 5850 | 0.5197 | 0.7804 |
| 0.5327 | 27.0 | 6075 | 0.5610 | 0.7454 |
| 0.487 | 28.0 | 6300 | 0.5448 | 0.7654 |
| 0.5248 | 29.0 | 6525 | 0.5394 | 0.7704 |
| 0.4978 | 30.0 | 6750 | 0.5209 | 0.7804 |
| 0.523 | 31.0 | 6975 | 0.5417 | 0.7604 |
| 0.502 | 32.0 | 7200 | 0.5080 | 0.7770 |
| 0.4674 | 33.0 | 7425 | 0.5071 | 0.7820 |
| 0.4329 | 34.0 | 7650 | 0.4947 | 0.8003 |
| 0.4583 | 35.0 | 7875 | 0.5207 | 0.7854 |
| 0.4868 | 36.0 | 8100 | 0.4819 | 0.8087 |
| 0.4542 | 37.0 | 8325 | 0.4836 | 0.7987 |
| 0.4328 | 38.0 | 8550 | 0.5050 | 0.7937 |
| 0.3395 | 39.0 | 8775 | 0.5073 | 0.7953 |
| 0.339 | 40.0 | 9000 | 0.5849 | 0.7870 |
| 0.3908 | 41.0 | 9225 | 0.5523 | 0.7820 |
| 0.4049 | 42.0 | 9450 | 0.5288 | 0.7920 |
| 0.3295 | 43.0 | 9675 | 0.5405 | 0.8053 |
| 0.3742 | 44.0 | 9900 | 0.5541 | 0.8020 |
| 0.3832 | 45.0 | 10125 | 0.5567 | 0.7953 |
| 0.3742 | 46.0 | 10350 | 0.5578 | 0.7920 |
| 0.3317 | 47.0 | 10575 | 0.5698 | 0.8103 |
| 0.2873 | 48.0 | 10800 | 0.5859 | 0.8037 |
| 0.3255 | 49.0 | 11025 | 0.6015 | 0.7970 |
| 0.3175 | 50.0 | 11250 | 0.6036 | 0.8020 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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