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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_5x_deit_tiny_sgd_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: 0.8714524207011686
---
<!-- 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_sgd_001_fold1
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: 0.3293
- Accuracy: 0.8715
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7747 | 1.0 | 376 | 0.8081 | 0.6327 |
| 0.5327 | 2.0 | 752 | 0.5949 | 0.7462 |
| 0.4332 | 3.0 | 1128 | 0.5030 | 0.7846 |
| 0.4359 | 4.0 | 1504 | 0.4457 | 0.8097 |
| 0.3937 | 5.0 | 1880 | 0.4107 | 0.8164 |
| 0.3325 | 6.0 | 2256 | 0.3873 | 0.8297 |
| 0.2877 | 7.0 | 2632 | 0.3645 | 0.8347 |
| 0.2962 | 8.0 | 3008 | 0.3585 | 0.8397 |
| 0.3002 | 9.0 | 3384 | 0.3450 | 0.8414 |
| 0.2749 | 10.0 | 3760 | 0.3357 | 0.8514 |
| 0.2826 | 11.0 | 4136 | 0.3303 | 0.8614 |
| 0.2607 | 12.0 | 4512 | 0.3246 | 0.8664 |
| 0.2479 | 13.0 | 4888 | 0.3195 | 0.8731 |
| 0.209 | 14.0 | 5264 | 0.3192 | 0.8698 |
| 0.2492 | 15.0 | 5640 | 0.3190 | 0.8631 |
| 0.2421 | 16.0 | 6016 | 0.3201 | 0.8664 |
| 0.2313 | 17.0 | 6392 | 0.3123 | 0.8731 |
| 0.2635 | 18.0 | 6768 | 0.3189 | 0.8715 |
| 0.22 | 19.0 | 7144 | 0.3169 | 0.8698 |
| 0.1933 | 20.0 | 7520 | 0.3154 | 0.8715 |
| 0.1972 | 21.0 | 7896 | 0.3125 | 0.8748 |
| 0.2184 | 22.0 | 8272 | 0.3238 | 0.8681 |
| 0.2395 | 23.0 | 8648 | 0.3208 | 0.8715 |
| 0.2148 | 24.0 | 9024 | 0.3152 | 0.8681 |
| 0.2046 | 25.0 | 9400 | 0.3215 | 0.8698 |
| 0.2137 | 26.0 | 9776 | 0.3154 | 0.8681 |
| 0.1523 | 27.0 | 10152 | 0.3167 | 0.8731 |
| 0.1766 | 28.0 | 10528 | 0.3160 | 0.8715 |
| 0.1896 | 29.0 | 10904 | 0.3190 | 0.8715 |
| 0.157 | 30.0 | 11280 | 0.3195 | 0.8698 |
| 0.1522 | 31.0 | 11656 | 0.3183 | 0.8731 |
| 0.1888 | 32.0 | 12032 | 0.3211 | 0.8715 |
| 0.1615 | 33.0 | 12408 | 0.3233 | 0.8681 |
| 0.1503 | 34.0 | 12784 | 0.3209 | 0.8731 |
| 0.1481 | 35.0 | 13160 | 0.3244 | 0.8698 |
| 0.1788 | 36.0 | 13536 | 0.3242 | 0.8681 |
| 0.1497 | 37.0 | 13912 | 0.3239 | 0.8748 |
| 0.1343 | 38.0 | 14288 | 0.3226 | 0.8748 |
| 0.1659 | 39.0 | 14664 | 0.3268 | 0.8748 |
| 0.1781 | 40.0 | 15040 | 0.3250 | 0.8698 |
| 0.1644 | 41.0 | 15416 | 0.3283 | 0.8731 |
| 0.1354 | 42.0 | 15792 | 0.3269 | 0.8731 |
| 0.1533 | 43.0 | 16168 | 0.3272 | 0.8731 |
| 0.1541 | 44.0 | 16544 | 0.3272 | 0.8748 |
| 0.2043 | 45.0 | 16920 | 0.3294 | 0.8731 |
| 0.2146 | 46.0 | 17296 | 0.3299 | 0.8731 |
| 0.154 | 47.0 | 17672 | 0.3285 | 0.8715 |
| 0.1593 | 48.0 | 18048 | 0.3296 | 0.8731 |
| 0.1388 | 49.0 | 18424 | 0.3295 | 0.8731 |
| 0.1123 | 50.0 | 18800 | 0.3293 | 0.8715 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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