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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_adamax_001_fold1
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: 0.8898163606010017
---
<!-- 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_adamax_001_fold1
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: 0.9489
- Accuracy: 0.8898
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3549 | 1.0 | 376 | 0.4844 | 0.8264 |
| 0.2678 | 2.0 | 752 | 0.3259 | 0.8798 |
| 0.3098 | 3.0 | 1128 | 0.3469 | 0.8548 |
| 0.2057 | 4.0 | 1504 | 0.3089 | 0.8831 |
| 0.15 | 5.0 | 1880 | 0.4280 | 0.8748 |
| 0.0947 | 6.0 | 2256 | 0.5773 | 0.8581 |
| 0.1544 | 7.0 | 2632 | 0.3805 | 0.8881 |
| 0.1085 | 8.0 | 3008 | 0.4878 | 0.8731 |
| 0.0399 | 9.0 | 3384 | 0.4495 | 0.8965 |
| 0.0251 | 10.0 | 3760 | 0.5573 | 0.8681 |
| 0.0684 | 11.0 | 4136 | 0.4467 | 0.8648 |
| 0.0506 | 12.0 | 4512 | 0.5126 | 0.8982 |
| 0.0075 | 13.0 | 4888 | 0.8575 | 0.8715 |
| 0.0481 | 14.0 | 5264 | 0.7463 | 0.8664 |
| 0.0077 | 15.0 | 5640 | 0.6816 | 0.8865 |
| 0.0098 | 16.0 | 6016 | 0.6312 | 0.8831 |
| 0.0003 | 17.0 | 6392 | 0.7022 | 0.8965 |
| 0.0075 | 18.0 | 6768 | 0.6976 | 0.8731 |
| 0.0042 | 19.0 | 7144 | 0.6012 | 0.8881 |
| 0.0311 | 20.0 | 7520 | 0.7693 | 0.8932 |
| 0.003 | 21.0 | 7896 | 0.6254 | 0.8915 |
| 0.0101 | 22.0 | 8272 | 0.6004 | 0.8998 |
| 0.0209 | 23.0 | 8648 | 0.7643 | 0.8815 |
| 0.0001 | 24.0 | 9024 | 0.8262 | 0.8848 |
| 0.0007 | 25.0 | 9400 | 0.6944 | 0.8898 |
| 0.0034 | 26.0 | 9776 | 0.7140 | 0.8915 |
| 0.0071 | 27.0 | 10152 | 0.8088 | 0.8798 |
| 0.0001 | 28.0 | 10528 | 0.7766 | 0.9032 |
| 0.0039 | 29.0 | 10904 | 0.8084 | 0.8948 |
| 0.0045 | 30.0 | 11280 | 0.7741 | 0.8831 |
| 0.0006 | 31.0 | 11656 | 0.8264 | 0.8932 |
| 0.0 | 32.0 | 12032 | 0.8432 | 0.8865 |
| 0.0 | 33.0 | 12408 | 0.8641 | 0.8848 |
| 0.0 | 34.0 | 12784 | 0.8447 | 0.8865 |
| 0.0 | 35.0 | 13160 | 0.8402 | 0.8848 |
| 0.0 | 36.0 | 13536 | 0.8232 | 0.8948 |
| 0.0 | 37.0 | 13912 | 0.8382 | 0.8915 |
| 0.0 | 38.0 | 14288 | 0.8652 | 0.8898 |
| 0.0 | 39.0 | 14664 | 0.8733 | 0.8848 |
| 0.0 | 40.0 | 15040 | 0.8254 | 0.8881 |
| 0.0 | 41.0 | 15416 | 0.8627 | 0.8848 |
| 0.0 | 42.0 | 15792 | 0.8799 | 0.8881 |
| 0.0 | 43.0 | 16168 | 0.8887 | 0.8915 |
| 0.0 | 44.0 | 16544 | 0.9046 | 0.8932 |
| 0.0 | 45.0 | 16920 | 0.9092 | 0.8932 |
| 0.0031 | 46.0 | 17296 | 0.9143 | 0.8881 |
| 0.0 | 47.0 | 17672 | 0.9293 | 0.8915 |
| 0.0 | 48.0 | 18048 | 0.9378 | 0.8898 |
| 0.0 | 49.0 | 18424 | 0.9447 | 0.8898 |
| 0.0023 | 50.0 | 18800 | 0.9489 | 0.8898 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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