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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_00001_fold5
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: 0.88
---
<!-- 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_00001_fold5
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: 0.6353
- Accuracy: 0.88
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7267 | 1.0 | 75 | 0.6452 | 0.735 |
| 0.4663 | 2.0 | 150 | 0.4479 | 0.8267 |
| 0.3889 | 3.0 | 225 | 0.3768 | 0.8467 |
| 0.4049 | 4.0 | 300 | 0.3517 | 0.8567 |
| 0.3455 | 5.0 | 375 | 0.3282 | 0.8733 |
| 0.2551 | 6.0 | 450 | 0.3120 | 0.8783 |
| 0.253 | 7.0 | 525 | 0.3013 | 0.8733 |
| 0.2149 | 8.0 | 600 | 0.3148 | 0.8733 |
| 0.1593 | 9.0 | 675 | 0.3191 | 0.8733 |
| 0.1223 | 10.0 | 750 | 0.3121 | 0.875 |
| 0.186 | 11.0 | 825 | 0.3224 | 0.875 |
| 0.0958 | 12.0 | 900 | 0.3269 | 0.875 |
| 0.1004 | 13.0 | 975 | 0.3361 | 0.8833 |
| 0.0897 | 14.0 | 1050 | 0.3443 | 0.8833 |
| 0.0668 | 15.0 | 1125 | 0.4038 | 0.8683 |
| 0.0352 | 16.0 | 1200 | 0.3877 | 0.865 |
| 0.026 | 17.0 | 1275 | 0.3941 | 0.88 |
| 0.0445 | 18.0 | 1350 | 0.4318 | 0.8767 |
| 0.0301 | 19.0 | 1425 | 0.4418 | 0.8783 |
| 0.0287 | 20.0 | 1500 | 0.4505 | 0.88 |
| 0.0241 | 21.0 | 1575 | 0.4895 | 0.8817 |
| 0.01 | 22.0 | 1650 | 0.4921 | 0.8833 |
| 0.0214 | 23.0 | 1725 | 0.5270 | 0.88 |
| 0.0229 | 24.0 | 1800 | 0.5245 | 0.8767 |
| 0.0113 | 25.0 | 1875 | 0.5406 | 0.8767 |
| 0.0032 | 26.0 | 1950 | 0.5510 | 0.8817 |
| 0.0224 | 27.0 | 2025 | 0.5698 | 0.8783 |
| 0.029 | 28.0 | 2100 | 0.5630 | 0.8733 |
| 0.0174 | 29.0 | 2175 | 0.5715 | 0.87 |
| 0.0132 | 30.0 | 2250 | 0.5787 | 0.8767 |
| 0.0005 | 31.0 | 2325 | 0.6094 | 0.8783 |
| 0.0004 | 32.0 | 2400 | 0.5908 | 0.875 |
| 0.0169 | 33.0 | 2475 | 0.6294 | 0.8767 |
| 0.0005 | 34.0 | 2550 | 0.6011 | 0.875 |
| 0.0188 | 35.0 | 2625 | 0.6103 | 0.88 |
| 0.0003 | 36.0 | 2700 | 0.6040 | 0.8783 |
| 0.0093 | 37.0 | 2775 | 0.6346 | 0.875 |
| 0.0003 | 38.0 | 2850 | 0.6319 | 0.875 |
| 0.0004 | 39.0 | 2925 | 0.6321 | 0.875 |
| 0.0005 | 40.0 | 3000 | 0.6332 | 0.875 |
| 0.0131 | 41.0 | 3075 | 0.6261 | 0.88 |
| 0.0004 | 42.0 | 3150 | 0.6258 | 0.8783 |
| 0.0183 | 43.0 | 3225 | 0.6307 | 0.8783 |
| 0.0032 | 44.0 | 3300 | 0.6338 | 0.8783 |
| 0.0145 | 45.0 | 3375 | 0.6347 | 0.8817 |
| 0.0002 | 46.0 | 3450 | 0.6408 | 0.88 |
| 0.0126 | 47.0 | 3525 | 0.6363 | 0.8817 |
| 0.0174 | 48.0 | 3600 | 0.6374 | 0.8833 |
| 0.0002 | 49.0 | 3675 | 0.6360 | 0.8817 |
| 0.0129 | 50.0 | 3750 | 0.6353 | 0.88 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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