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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_10x_deit_small_sgd_00001_fold3
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: 0.5483333333333333
---
<!-- 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_10x_deit_small_sgd_00001_fold3
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: 0.9545
- Accuracy: 0.5483
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0617 | 1.0 | 750 | 1.0842 | 0.3733 |
| 1.0585 | 2.0 | 1500 | 1.0794 | 0.37 |
| 1.0424 | 3.0 | 2250 | 1.0744 | 0.3733 |
| 1.0456 | 4.0 | 3000 | 1.0693 | 0.3767 |
| 1.0291 | 5.0 | 3750 | 1.0643 | 0.3833 |
| 1.0038 | 6.0 | 4500 | 1.0594 | 0.3917 |
| 1.0218 | 7.0 | 5250 | 1.0545 | 0.4017 |
| 1.0056 | 8.0 | 6000 | 1.0497 | 0.4083 |
| 0.9993 | 9.0 | 6750 | 1.0451 | 0.4133 |
| 0.9987 | 10.0 | 7500 | 1.0406 | 0.4233 |
| 1.005 | 11.0 | 8250 | 1.0361 | 0.43 |
| 0.9768 | 12.0 | 9000 | 1.0318 | 0.4367 |
| 0.9767 | 13.0 | 9750 | 1.0276 | 0.4383 |
| 0.9832 | 14.0 | 10500 | 1.0235 | 0.4417 |
| 0.9795 | 15.0 | 11250 | 1.0196 | 0.4517 |
| 0.9438 | 16.0 | 12000 | 1.0158 | 0.47 |
| 0.9511 | 17.0 | 12750 | 1.0122 | 0.4733 |
| 0.9685 | 18.0 | 13500 | 1.0086 | 0.475 |
| 0.9616 | 19.0 | 14250 | 1.0051 | 0.4833 |
| 0.9593 | 20.0 | 15000 | 1.0018 | 0.485 |
| 0.9173 | 21.0 | 15750 | 0.9985 | 0.49 |
| 0.9516 | 22.0 | 16500 | 0.9954 | 0.5017 |
| 0.9352 | 23.0 | 17250 | 0.9923 | 0.5033 |
| 0.9563 | 24.0 | 18000 | 0.9894 | 0.5083 |
| 0.9134 | 25.0 | 18750 | 0.9866 | 0.5117 |
| 0.9284 | 26.0 | 19500 | 0.9839 | 0.515 |
| 0.8974 | 27.0 | 20250 | 0.9813 | 0.52 |
| 0.9371 | 28.0 | 21000 | 0.9789 | 0.52 |
| 0.8946 | 29.0 | 21750 | 0.9765 | 0.5283 |
| 0.9089 | 30.0 | 22500 | 0.9743 | 0.5317 |
| 0.9026 | 31.0 | 23250 | 0.9722 | 0.5333 |
| 0.9027 | 32.0 | 24000 | 0.9702 | 0.5317 |
| 0.9034 | 33.0 | 24750 | 0.9683 | 0.5333 |
| 0.9095 | 34.0 | 25500 | 0.9666 | 0.5333 |
| 0.8767 | 35.0 | 26250 | 0.9650 | 0.5367 |
| 0.8854 | 36.0 | 27000 | 0.9635 | 0.5367 |
| 0.8862 | 37.0 | 27750 | 0.9621 | 0.5367 |
| 0.9211 | 38.0 | 28500 | 0.9608 | 0.5367 |
| 0.8993 | 39.0 | 29250 | 0.9597 | 0.535 |
| 0.8897 | 40.0 | 30000 | 0.9587 | 0.5383 |
| 0.8933 | 41.0 | 30750 | 0.9578 | 0.5417 |
| 0.8954 | 42.0 | 31500 | 0.9571 | 0.5483 |
| 0.887 | 43.0 | 32250 | 0.9564 | 0.5483 |
| 0.902 | 44.0 | 33000 | 0.9558 | 0.5483 |
| 0.8561 | 45.0 | 33750 | 0.9554 | 0.5483 |
| 0.8814 | 46.0 | 34500 | 0.9551 | 0.5483 |
| 0.8975 | 47.0 | 35250 | 0.9548 | 0.5483 |
| 0.8624 | 48.0 | 36000 | 0.9546 | 0.5483 |
| 0.8832 | 49.0 | 36750 | 0.9546 | 0.5483 |
| 0.8754 | 50.0 | 37500 | 0.9545 | 0.5483 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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