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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_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: 0.88
---
<!-- 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_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: 0.3364
- Accuracy: 0.88
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7169 | 1.0 | 375 | 0.7100 | 0.7433 |
| 0.5434 | 2.0 | 750 | 0.5311 | 0.79 |
| 0.3914 | 3.0 | 1125 | 0.4593 | 0.8267 |
| 0.3964 | 4.0 | 1500 | 0.4238 | 0.8467 |
| 0.3421 | 5.0 | 1875 | 0.4004 | 0.84 |
| 0.3188 | 6.0 | 2250 | 0.3866 | 0.845 |
| 0.3163 | 7.0 | 2625 | 0.3739 | 0.855 |
| 0.2969 | 8.0 | 3000 | 0.3662 | 0.8533 |
| 0.2771 | 9.0 | 3375 | 0.3587 | 0.8567 |
| 0.2869 | 10.0 | 3750 | 0.3526 | 0.8533 |
| 0.2532 | 11.0 | 4125 | 0.3512 | 0.855 |
| 0.2402 | 12.0 | 4500 | 0.3469 | 0.8617 |
| 0.2625 | 13.0 | 4875 | 0.3448 | 0.855 |
| 0.2773 | 14.0 | 5250 | 0.3426 | 0.8583 |
| 0.1973 | 15.0 | 5625 | 0.3405 | 0.86 |
| 0.1939 | 16.0 | 6000 | 0.3381 | 0.8633 |
| 0.2343 | 17.0 | 6375 | 0.3367 | 0.8633 |
| 0.2253 | 18.0 | 6750 | 0.3355 | 0.8667 |
| 0.2395 | 19.0 | 7125 | 0.3367 | 0.8633 |
| 0.1792 | 20.0 | 7500 | 0.3346 | 0.8667 |
| 0.2141 | 21.0 | 7875 | 0.3352 | 0.865 |
| 0.206 | 22.0 | 8250 | 0.3351 | 0.8683 |
| 0.1902 | 23.0 | 8625 | 0.3337 | 0.87 |
| 0.1953 | 24.0 | 9000 | 0.3324 | 0.8717 |
| 0.2357 | 25.0 | 9375 | 0.3339 | 0.8683 |
| 0.1602 | 26.0 | 9750 | 0.3324 | 0.8717 |
| 0.2058 | 27.0 | 10125 | 0.3335 | 0.8667 |
| 0.1817 | 28.0 | 10500 | 0.3349 | 0.87 |
| 0.1565 | 29.0 | 10875 | 0.3343 | 0.8667 |
| 0.2147 | 30.0 | 11250 | 0.3327 | 0.8717 |
| 0.1942 | 31.0 | 11625 | 0.3340 | 0.87 |
| 0.1633 | 32.0 | 12000 | 0.3333 | 0.8717 |
| 0.1571 | 33.0 | 12375 | 0.3335 | 0.8733 |
| 0.218 | 34.0 | 12750 | 0.3350 | 0.8733 |
| 0.1424 | 35.0 | 13125 | 0.3354 | 0.8783 |
| 0.1796 | 36.0 | 13500 | 0.3353 | 0.8717 |
| 0.1702 | 37.0 | 13875 | 0.3349 | 0.8767 |
| 0.161 | 38.0 | 14250 | 0.3343 | 0.875 |
| 0.1961 | 39.0 | 14625 | 0.3352 | 0.8767 |
| 0.1721 | 40.0 | 15000 | 0.3365 | 0.88 |
| 0.1561 | 41.0 | 15375 | 0.3358 | 0.8783 |
| 0.1604 | 42.0 | 15750 | 0.3354 | 0.8783 |
| 0.1786 | 43.0 | 16125 | 0.3364 | 0.8817 |
| 0.1636 | 44.0 | 16500 | 0.3360 | 0.88 |
| 0.2307 | 45.0 | 16875 | 0.3365 | 0.8783 |
| 0.1578 | 46.0 | 17250 | 0.3360 | 0.8783 |
| 0.232 | 47.0 | 17625 | 0.3366 | 0.8817 |
| 0.1744 | 48.0 | 18000 | 0.3365 | 0.88 |
| 0.1493 | 49.0 | 18375 | 0.3364 | 0.88 |
| 0.1447 | 50.0 | 18750 | 0.3364 | 0.88 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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