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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_small_sgd_0001_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: 0.8116666666666666
---
<!-- 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_small_sgd_0001_fold5
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: 0.4899
- Accuracy: 0.8117
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0575 | 1.0 | 375 | 1.0409 | 0.4667 |
| 0.9896 | 2.0 | 750 | 1.0031 | 0.5117 |
| 0.9428 | 3.0 | 1125 | 0.9645 | 0.5567 |
| 0.9186 | 4.0 | 1500 | 0.9265 | 0.615 |
| 0.8922 | 5.0 | 1875 | 0.8895 | 0.6483 |
| 0.8541 | 6.0 | 2250 | 0.8539 | 0.6717 |
| 0.7885 | 7.0 | 2625 | 0.8194 | 0.69 |
| 0.7714 | 8.0 | 3000 | 0.7879 | 0.705 |
| 0.758 | 9.0 | 3375 | 0.7592 | 0.7133 |
| 0.7212 | 10.0 | 3750 | 0.7334 | 0.7217 |
| 0.6793 | 11.0 | 4125 | 0.7102 | 0.7333 |
| 0.6484 | 12.0 | 4500 | 0.6895 | 0.7367 |
| 0.6765 | 13.0 | 4875 | 0.6713 | 0.7467 |
| 0.664 | 14.0 | 5250 | 0.6548 | 0.7533 |
| 0.6332 | 15.0 | 5625 | 0.6395 | 0.7617 |
| 0.5983 | 16.0 | 6000 | 0.6261 | 0.77 |
| 0.6122 | 17.0 | 6375 | 0.6142 | 0.77 |
| 0.5912 | 18.0 | 6750 | 0.6024 | 0.7733 |
| 0.5764 | 19.0 | 7125 | 0.5918 | 0.775 |
| 0.5461 | 20.0 | 7500 | 0.5824 | 0.7783 |
| 0.5245 | 21.0 | 7875 | 0.5733 | 0.7833 |
| 0.5339 | 22.0 | 8250 | 0.5654 | 0.7867 |
| 0.5651 | 23.0 | 8625 | 0.5584 | 0.7867 |
| 0.5365 | 24.0 | 9000 | 0.5518 | 0.7933 |
| 0.4982 | 25.0 | 9375 | 0.5457 | 0.795 |
| 0.5274 | 26.0 | 9750 | 0.5402 | 0.7933 |
| 0.5167 | 27.0 | 10125 | 0.5353 | 0.795 |
| 0.53 | 28.0 | 10500 | 0.5303 | 0.7967 |
| 0.5404 | 29.0 | 10875 | 0.5260 | 0.7967 |
| 0.4414 | 30.0 | 11250 | 0.5222 | 0.8017 |
| 0.5269 | 31.0 | 11625 | 0.5183 | 0.8017 |
| 0.5299 | 32.0 | 12000 | 0.5150 | 0.8017 |
| 0.5311 | 33.0 | 12375 | 0.5120 | 0.8033 |
| 0.499 | 34.0 | 12750 | 0.5091 | 0.8033 |
| 0.4712 | 35.0 | 13125 | 0.5065 | 0.8033 |
| 0.4169 | 36.0 | 13500 | 0.5042 | 0.8017 |
| 0.4803 | 37.0 | 13875 | 0.5020 | 0.8017 |
| 0.4796 | 38.0 | 14250 | 0.5001 | 0.805 |
| 0.4865 | 39.0 | 14625 | 0.4984 | 0.8067 |
| 0.5122 | 40.0 | 15000 | 0.4967 | 0.8083 |
| 0.4785 | 41.0 | 15375 | 0.4953 | 0.8067 |
| 0.4562 | 42.0 | 15750 | 0.4941 | 0.8083 |
| 0.5248 | 43.0 | 16125 | 0.4930 | 0.8117 |
| 0.4817 | 44.0 | 16500 | 0.4922 | 0.8117 |
| 0.4662 | 45.0 | 16875 | 0.4914 | 0.8117 |
| 0.4968 | 46.0 | 17250 | 0.4908 | 0.8117 |
| 0.5157 | 47.0 | 17625 | 0.4904 | 0.8117 |
| 0.4378 | 48.0 | 18000 | 0.4901 | 0.8117 |
| 0.4668 | 49.0 | 18375 | 0.4899 | 0.8117 |
| 0.4722 | 50.0 | 18750 | 0.4899 | 0.8117 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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