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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_00001_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: 0.5166666666666667
---
<!-- 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_00001_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: 0.9877
- Accuracy: 0.5167
## 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: 1e-05
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0889 | 1.0 | 375 | 1.0662 | 0.4217 |
| 1.0663 | 2.0 | 750 | 1.0632 | 0.4233 |
| 1.047 | 3.0 | 1125 | 1.0602 | 0.43 |
| 1.0589 | 4.0 | 1500 | 1.0573 | 0.43 |
| 1.0463 | 5.0 | 1875 | 1.0544 | 0.4317 |
| 1.0388 | 6.0 | 2250 | 1.0516 | 0.4333 |
| 1.0109 | 7.0 | 2625 | 1.0487 | 0.435 |
| 1.0394 | 8.0 | 3000 | 1.0459 | 0.4383 |
| 1.0347 | 9.0 | 3375 | 1.0431 | 0.4417 |
| 1.0355 | 10.0 | 3750 | 1.0404 | 0.4467 |
| 1.059 | 11.0 | 4125 | 1.0377 | 0.4467 |
| 1.0235 | 12.0 | 4500 | 1.0352 | 0.445 |
| 1.0136 | 13.0 | 4875 | 1.0326 | 0.4467 |
| 1.0313 | 14.0 | 5250 | 1.0301 | 0.45 |
| 1.0046 | 15.0 | 5625 | 1.0277 | 0.4567 |
| 1.0138 | 16.0 | 6000 | 1.0253 | 0.4633 |
| 1.0055 | 17.0 | 6375 | 1.0230 | 0.465 |
| 0.998 | 18.0 | 6750 | 1.0207 | 0.4667 |
| 1.0178 | 19.0 | 7125 | 1.0186 | 0.4667 |
| 1.019 | 20.0 | 7500 | 1.0165 | 0.4717 |
| 0.9884 | 21.0 | 7875 | 1.0145 | 0.4783 |
| 1.0226 | 22.0 | 8250 | 1.0125 | 0.48 |
| 1.0239 | 23.0 | 8625 | 1.0106 | 0.4833 |
| 1.0151 | 24.0 | 9000 | 1.0088 | 0.49 |
| 0.997 | 25.0 | 9375 | 1.0071 | 0.49 |
| 0.9698 | 26.0 | 9750 | 1.0054 | 0.4917 |
| 0.958 | 27.0 | 10125 | 1.0038 | 0.495 |
| 1.0132 | 28.0 | 10500 | 1.0023 | 0.4933 |
| 0.9673 | 29.0 | 10875 | 1.0008 | 0.4983 |
| 0.9986 | 30.0 | 11250 | 0.9995 | 0.5 |
| 0.9881 | 31.0 | 11625 | 0.9982 | 0.505 |
| 1.0083 | 32.0 | 12000 | 0.9970 | 0.505 |
| 0.9851 | 33.0 | 12375 | 0.9959 | 0.5067 |
| 0.9949 | 34.0 | 12750 | 0.9949 | 0.5067 |
| 0.988 | 35.0 | 13125 | 0.9939 | 0.5083 |
| 1.0062 | 36.0 | 13500 | 0.9930 | 0.51 |
| 0.9899 | 37.0 | 13875 | 0.9922 | 0.5083 |
| 0.9951 | 38.0 | 14250 | 0.9914 | 0.51 |
| 1.0002 | 39.0 | 14625 | 0.9908 | 0.5133 |
| 0.9573 | 40.0 | 15000 | 0.9902 | 0.5133 |
| 0.9723 | 41.0 | 15375 | 0.9896 | 0.515 |
| 0.977 | 42.0 | 15750 | 0.9892 | 0.515 |
| 0.9762 | 43.0 | 16125 | 0.9888 | 0.515 |
| 0.9976 | 44.0 | 16500 | 0.9885 | 0.5167 |
| 0.965 | 45.0 | 16875 | 0.9882 | 0.5167 |
| 0.9904 | 46.0 | 17250 | 0.9880 | 0.5167 |
| 0.9962 | 47.0 | 17625 | 0.9879 | 0.5167 |
| 0.982 | 48.0 | 18000 | 0.9878 | 0.5167 |
| 0.9851 | 49.0 | 18375 | 0.9877 | 0.5167 |
| 0.9675 | 50.0 | 18750 | 0.9877 | 0.5167 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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