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---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_rms_001_fold5
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: 0.7433333333333333
---
<!-- 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_rms_001_fold5
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6094
- Accuracy: 0.7433
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8822 | 1.0 | 375 | 0.8719 | 0.52 |
| 0.8936 | 2.0 | 750 | 0.8470 | 0.535 |
| 0.8252 | 3.0 | 1125 | 0.8071 | 0.595 |
| 0.8333 | 4.0 | 1500 | 0.7970 | 0.6017 |
| 0.8046 | 5.0 | 1875 | 0.8070 | 0.5633 |
| 0.8082 | 6.0 | 2250 | 0.9208 | 0.5167 |
| 0.7481 | 7.0 | 2625 | 0.7984 | 0.5633 |
| 0.8409 | 8.0 | 3000 | 0.7900 | 0.5783 |
| 0.7673 | 9.0 | 3375 | 0.7551 | 0.62 |
| 0.7321 | 10.0 | 3750 | 0.7485 | 0.6133 |
| 0.8282 | 11.0 | 4125 | 0.7517 | 0.6083 |
| 0.7206 | 12.0 | 4500 | 0.7745 | 0.6 |
| 0.6841 | 13.0 | 4875 | 0.8307 | 0.5917 |
| 0.7738 | 14.0 | 5250 | 0.7274 | 0.6683 |
| 0.8416 | 15.0 | 5625 | 0.7353 | 0.67 |
| 0.704 | 16.0 | 6000 | 0.7258 | 0.65 |
| 0.6873 | 17.0 | 6375 | 0.7174 | 0.68 |
| 0.714 | 18.0 | 6750 | 0.7557 | 0.6483 |
| 0.7105 | 19.0 | 7125 | 0.6868 | 0.6917 |
| 0.6559 | 20.0 | 7500 | 0.6845 | 0.6783 |
| 0.6717 | 21.0 | 7875 | 0.7043 | 0.67 |
| 0.7139 | 22.0 | 8250 | 0.6944 | 0.68 |
| 0.6633 | 23.0 | 8625 | 0.7071 | 0.6667 |
| 0.6888 | 24.0 | 9000 | 0.6979 | 0.6883 |
| 0.6621 | 25.0 | 9375 | 0.6468 | 0.7117 |
| 0.6157 | 26.0 | 9750 | 0.6767 | 0.6833 |
| 0.6777 | 27.0 | 10125 | 0.7097 | 0.67 |
| 0.7108 | 28.0 | 10500 | 0.6811 | 0.6917 |
| 0.8139 | 29.0 | 10875 | 0.6750 | 0.7067 |
| 0.6291 | 30.0 | 11250 | 0.6415 | 0.7133 |
| 0.5725 | 31.0 | 11625 | 0.6769 | 0.6833 |
| 0.6243 | 32.0 | 12000 | 0.6733 | 0.7267 |
| 0.6311 | 33.0 | 12375 | 0.6227 | 0.7217 |
| 0.6254 | 34.0 | 12750 | 0.6222 | 0.72 |
| 0.567 | 35.0 | 13125 | 0.6040 | 0.735 |
| 0.5363 | 36.0 | 13500 | 0.5935 | 0.7533 |
| 0.6308 | 37.0 | 13875 | 0.6047 | 0.7267 |
| 0.5334 | 38.0 | 14250 | 0.6481 | 0.7217 |
| 0.5951 | 39.0 | 14625 | 0.6059 | 0.7317 |
| 0.6325 | 40.0 | 15000 | 0.6172 | 0.735 |
| 0.5905 | 41.0 | 15375 | 0.6255 | 0.7233 |
| 0.6095 | 42.0 | 15750 | 0.5896 | 0.7433 |
| 0.49 | 43.0 | 16125 | 0.5925 | 0.7367 |
| 0.4891 | 44.0 | 16500 | 0.5937 | 0.7367 |
| 0.4867 | 45.0 | 16875 | 0.5918 | 0.7583 |
| 0.5178 | 46.0 | 17250 | 0.6030 | 0.735 |
| 0.561 | 47.0 | 17625 | 0.6183 | 0.74 |
| 0.4632 | 48.0 | 18000 | 0.5943 | 0.7517 |
| 0.4666 | 49.0 | 18375 | 0.6107 | 0.7417 |
| 0.4901 | 50.0 | 18750 | 0.6094 | 0.7433 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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