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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_fold2
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: 0.8036605657237936
---
<!-- 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_fold2
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: 0.5006
- Accuracy: 0.8037
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0629 | 1.0 | 375 | 1.0383 | 0.4592 |
| 1.0151 | 2.0 | 750 | 1.0009 | 0.4925 |
| 0.9588 | 3.0 | 1125 | 0.9619 | 0.5574 |
| 0.924 | 4.0 | 1500 | 0.9255 | 0.5890 |
| 0.8743 | 5.0 | 1875 | 0.8899 | 0.6290 |
| 0.8177 | 6.0 | 2250 | 0.8563 | 0.6522 |
| 0.7888 | 7.0 | 2625 | 0.8262 | 0.6755 |
| 0.7921 | 8.0 | 3000 | 0.7964 | 0.7005 |
| 0.7372 | 9.0 | 3375 | 0.7699 | 0.7138 |
| 0.7291 | 10.0 | 3750 | 0.7453 | 0.7221 |
| 0.7295 | 11.0 | 4125 | 0.7221 | 0.7255 |
| 0.6995 | 12.0 | 4500 | 0.7007 | 0.7288 |
| 0.621 | 13.0 | 4875 | 0.6811 | 0.7388 |
| 0.6398 | 14.0 | 5250 | 0.6638 | 0.7504 |
| 0.6383 | 15.0 | 5625 | 0.6483 | 0.7587 |
| 0.5747 | 16.0 | 6000 | 0.6341 | 0.7587 |
| 0.6097 | 17.0 | 6375 | 0.6214 | 0.7604 |
| 0.594 | 18.0 | 6750 | 0.6099 | 0.7604 |
| 0.5533 | 19.0 | 7125 | 0.5997 | 0.7654 |
| 0.5984 | 20.0 | 7500 | 0.5904 | 0.7687 |
| 0.5406 | 21.0 | 7875 | 0.5822 | 0.7720 |
| 0.525 | 22.0 | 8250 | 0.5743 | 0.7704 |
| 0.5434 | 23.0 | 8625 | 0.5673 | 0.7720 |
| 0.5253 | 24.0 | 9000 | 0.5609 | 0.7737 |
| 0.5143 | 25.0 | 9375 | 0.5549 | 0.7754 |
| 0.5351 | 26.0 | 9750 | 0.5494 | 0.7787 |
| 0.5716 | 27.0 | 10125 | 0.5444 | 0.7787 |
| 0.4849 | 28.0 | 10500 | 0.5399 | 0.7820 |
| 0.4878 | 29.0 | 10875 | 0.5357 | 0.7887 |
| 0.4887 | 30.0 | 11250 | 0.5319 | 0.7920 |
| 0.4866 | 31.0 | 11625 | 0.5283 | 0.7920 |
| 0.5025 | 32.0 | 12000 | 0.5250 | 0.7937 |
| 0.4672 | 33.0 | 12375 | 0.5219 | 0.7903 |
| 0.4395 | 34.0 | 12750 | 0.5192 | 0.7887 |
| 0.473 | 35.0 | 13125 | 0.5166 | 0.7920 |
| 0.4458 | 36.0 | 13500 | 0.5143 | 0.7920 |
| 0.4639 | 37.0 | 13875 | 0.5122 | 0.7937 |
| 0.4488 | 38.0 | 14250 | 0.5103 | 0.7953 |
| 0.4766 | 39.0 | 14625 | 0.5086 | 0.7970 |
| 0.4603 | 40.0 | 15000 | 0.5071 | 0.7987 |
| 0.4461 | 41.0 | 15375 | 0.5058 | 0.8003 |
| 0.4671 | 42.0 | 15750 | 0.5046 | 0.8003 |
| 0.4415 | 43.0 | 16125 | 0.5036 | 0.8020 |
| 0.4496 | 44.0 | 16500 | 0.5027 | 0.8020 |
| 0.4327 | 45.0 | 16875 | 0.5020 | 0.8020 |
| 0.5062 | 46.0 | 17250 | 0.5015 | 0.8020 |
| 0.4692 | 47.0 | 17625 | 0.5010 | 0.8037 |
| 0.426 | 48.0 | 18000 | 0.5008 | 0.8037 |
| 0.518 | 49.0 | 18375 | 0.5006 | 0.8037 |
| 0.4765 | 50.0 | 18750 | 0.5006 | 0.8037 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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