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---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_deit_tiny_adamax_001_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: 0.8935108153078203
---
<!-- 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_1x_deit_tiny_adamax_001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8198
- Accuracy: 0.8935
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6145 | 1.0 | 75 | 0.5428 | 0.7970 |
| 0.3766 | 2.0 | 150 | 0.5726 | 0.7720 |
| 0.4048 | 3.0 | 225 | 0.6119 | 0.7920 |
| 0.3699 | 4.0 | 300 | 0.3532 | 0.8619 |
| 0.3283 | 5.0 | 375 | 0.4734 | 0.8270 |
| 0.2617 | 6.0 | 450 | 0.5747 | 0.8053 |
| 0.2131 | 7.0 | 525 | 0.4492 | 0.8486 |
| 0.1731 | 8.0 | 600 | 0.4339 | 0.8686 |
| 0.1832 | 9.0 | 675 | 0.5654 | 0.8336 |
| 0.1286 | 10.0 | 750 | 1.0166 | 0.7704 |
| 0.0921 | 11.0 | 825 | 0.5592 | 0.8519 |
| 0.0818 | 12.0 | 900 | 0.6074 | 0.8486 |
| 0.1315 | 13.0 | 975 | 0.7091 | 0.8369 |
| 0.0851 | 14.0 | 1050 | 0.6304 | 0.8436 |
| 0.0354 | 15.0 | 1125 | 0.8000 | 0.8469 |
| 0.0659 | 16.0 | 1200 | 0.7712 | 0.8586 |
| 0.0297 | 17.0 | 1275 | 0.8136 | 0.8686 |
| 0.058 | 18.0 | 1350 | 0.7968 | 0.8536 |
| 0.0096 | 19.0 | 1425 | 0.7312 | 0.8719 |
| 0.0206 | 20.0 | 1500 | 0.7618 | 0.8453 |
| 0.0111 | 21.0 | 1575 | 1.0098 | 0.8336 |
| 0.0053 | 22.0 | 1650 | 0.8487 | 0.8502 |
| 0.0105 | 23.0 | 1725 | 0.7386 | 0.8702 |
| 0.0094 | 24.0 | 1800 | 0.8515 | 0.8419 |
| 0.0004 | 25.0 | 1875 | 0.8080 | 0.8636 |
| 0.0177 | 26.0 | 1950 | 0.6472 | 0.8819 |
| 0.0321 | 27.0 | 2025 | 0.6905 | 0.8785 |
| 0.0096 | 28.0 | 2100 | 0.6932 | 0.8852 |
| 0.0091 | 29.0 | 2175 | 0.7066 | 0.8869 |
| 0.0059 | 30.0 | 2250 | 0.7159 | 0.8819 |
| 0.0056 | 31.0 | 2325 | 0.7490 | 0.8869 |
| 0.0 | 32.0 | 2400 | 0.7569 | 0.8885 |
| 0.0 | 33.0 | 2475 | 0.7589 | 0.8869 |
| 0.0003 | 34.0 | 2550 | 0.7519 | 0.8935 |
| 0.01 | 35.0 | 2625 | 0.7808 | 0.8902 |
| 0.0 | 36.0 | 2700 | 0.7653 | 0.8918 |
| 0.0001 | 37.0 | 2775 | 0.7709 | 0.8902 |
| 0.0 | 38.0 | 2850 | 0.7835 | 0.8885 |
| 0.0016 | 39.0 | 2925 | 0.7996 | 0.8935 |
| 0.0 | 40.0 | 3000 | 0.7825 | 0.8918 |
| 0.0036 | 41.0 | 3075 | 0.7879 | 0.8918 |
| 0.0 | 42.0 | 3150 | 0.7990 | 0.8935 |
| 0.003 | 43.0 | 3225 | 0.8020 | 0.8935 |
| 0.0034 | 44.0 | 3300 | 0.8080 | 0.8935 |
| 0.0 | 45.0 | 3375 | 0.8073 | 0.8935 |
| 0.0 | 46.0 | 3450 | 0.8161 | 0.8935 |
| 0.0029 | 47.0 | 3525 | 0.8235 | 0.8918 |
| 0.0 | 48.0 | 3600 | 0.8195 | 0.8935 |
| 0.0023 | 49.0 | 3675 | 0.8192 | 0.8935 |
| 0.0022 | 50.0 | 3750 | 0.8198 | 0.8935 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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