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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_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: 0.8818635607321131
---
<!-- 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_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: 0.3259
- Accuracy: 0.8819
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8524 | 1.0 | 375 | 0.7691 | 0.6689 |
| 0.5007 | 2.0 | 750 | 0.5539 | 0.7804 |
| 0.4114 | 3.0 | 1125 | 0.4742 | 0.8070 |
| 0.3629 | 4.0 | 1500 | 0.4296 | 0.8286 |
| 0.3623 | 5.0 | 1875 | 0.3981 | 0.8469 |
| 0.3098 | 6.0 | 2250 | 0.3783 | 0.8502 |
| 0.3017 | 7.0 | 2625 | 0.3643 | 0.8453 |
| 0.3224 | 8.0 | 3000 | 0.3602 | 0.8519 |
| 0.2666 | 9.0 | 3375 | 0.3471 | 0.8586 |
| 0.2737 | 10.0 | 3750 | 0.3436 | 0.8552 |
| 0.2547 | 11.0 | 4125 | 0.3356 | 0.8669 |
| 0.2986 | 12.0 | 4500 | 0.3379 | 0.8602 |
| 0.2268 | 13.0 | 4875 | 0.3304 | 0.8669 |
| 0.2538 | 14.0 | 5250 | 0.3304 | 0.8702 |
| 0.2279 | 15.0 | 5625 | 0.3282 | 0.8602 |
| 0.1964 | 16.0 | 6000 | 0.3276 | 0.8719 |
| 0.2475 | 17.0 | 6375 | 0.3297 | 0.8652 |
| 0.2224 | 18.0 | 6750 | 0.3277 | 0.8669 |
| 0.1863 | 19.0 | 7125 | 0.3205 | 0.8686 |
| 0.2493 | 20.0 | 7500 | 0.3208 | 0.8752 |
| 0.1873 | 21.0 | 7875 | 0.3214 | 0.8769 |
| 0.1921 | 22.0 | 8250 | 0.3223 | 0.8735 |
| 0.2083 | 23.0 | 8625 | 0.3204 | 0.8735 |
| 0.1865 | 24.0 | 9000 | 0.3201 | 0.8702 |
| 0.1643 | 25.0 | 9375 | 0.3196 | 0.8802 |
| 0.2115 | 26.0 | 9750 | 0.3209 | 0.8785 |
| 0.2108 | 27.0 | 10125 | 0.3192 | 0.8802 |
| 0.1576 | 28.0 | 10500 | 0.3201 | 0.8802 |
| 0.1807 | 29.0 | 10875 | 0.3220 | 0.8785 |
| 0.1891 | 30.0 | 11250 | 0.3216 | 0.8802 |
| 0.1864 | 31.0 | 11625 | 0.3224 | 0.8835 |
| 0.1759 | 32.0 | 12000 | 0.3215 | 0.8852 |
| 0.1618 | 33.0 | 12375 | 0.3224 | 0.8835 |
| 0.1343 | 34.0 | 12750 | 0.3219 | 0.8835 |
| 0.1642 | 35.0 | 13125 | 0.3213 | 0.8852 |
| 0.1538 | 36.0 | 13500 | 0.3239 | 0.8785 |
| 0.1527 | 37.0 | 13875 | 0.3229 | 0.8852 |
| 0.1581 | 38.0 | 14250 | 0.3248 | 0.8802 |
| 0.135 | 39.0 | 14625 | 0.3238 | 0.8852 |
| 0.1591 | 40.0 | 15000 | 0.3237 | 0.8835 |
| 0.1366 | 41.0 | 15375 | 0.3243 | 0.8819 |
| 0.1361 | 42.0 | 15750 | 0.3249 | 0.8785 |
| 0.1751 | 43.0 | 16125 | 0.3245 | 0.8835 |
| 0.135 | 44.0 | 16500 | 0.3255 | 0.8819 |
| 0.1208 | 45.0 | 16875 | 0.3256 | 0.8819 |
| 0.1748 | 46.0 | 17250 | 0.3261 | 0.8819 |
| 0.1449 | 47.0 | 17625 | 0.3264 | 0.8785 |
| 0.1594 | 48.0 | 18000 | 0.3263 | 0.8785 |
| 0.1892 | 49.0 | 18375 | 0.3260 | 0.8819 |
| 0.1218 | 50.0 | 18750 | 0.3259 | 0.8819 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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