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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_sgd_001_fold4
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: 0.835
---
<!-- 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_sgd_001_fold4
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: 0.4122
- Accuracy: 0.835
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0732 | 1.0 | 75 | 1.0625 | 0.4117 |
| 0.95 | 2.0 | 150 | 0.9470 | 0.52 |
| 0.8388 | 3.0 | 225 | 0.8541 | 0.6133 |
| 0.8107 | 4.0 | 300 | 0.7814 | 0.66 |
| 0.7296 | 5.0 | 375 | 0.7116 | 0.6933 |
| 0.6565 | 6.0 | 450 | 0.6560 | 0.7233 |
| 0.6075 | 7.0 | 525 | 0.6119 | 0.7367 |
| 0.5566 | 8.0 | 600 | 0.5801 | 0.76 |
| 0.5592 | 9.0 | 675 | 0.5568 | 0.7717 |
| 0.4945 | 10.0 | 750 | 0.5396 | 0.785 |
| 0.484 | 11.0 | 825 | 0.5228 | 0.79 |
| 0.4564 | 12.0 | 900 | 0.5098 | 0.7917 |
| 0.4689 | 13.0 | 975 | 0.5015 | 0.7917 |
| 0.4232 | 14.0 | 1050 | 0.4882 | 0.7967 |
| 0.4151 | 15.0 | 1125 | 0.4851 | 0.795 |
| 0.3646 | 16.0 | 1200 | 0.4743 | 0.8017 |
| 0.3676 | 17.0 | 1275 | 0.4658 | 0.8083 |
| 0.3612 | 18.0 | 1350 | 0.4603 | 0.8017 |
| 0.4051 | 19.0 | 1425 | 0.4555 | 0.81 |
| 0.3477 | 20.0 | 1500 | 0.4507 | 0.81 |
| 0.375 | 21.0 | 1575 | 0.4488 | 0.8017 |
| 0.3102 | 22.0 | 1650 | 0.4425 | 0.8083 |
| 0.3203 | 23.0 | 1725 | 0.4393 | 0.8117 |
| 0.3847 | 24.0 | 1800 | 0.4374 | 0.8133 |
| 0.3175 | 25.0 | 1875 | 0.4337 | 0.8133 |
| 0.3275 | 26.0 | 1950 | 0.4305 | 0.8183 |
| 0.2952 | 27.0 | 2025 | 0.4280 | 0.8167 |
| 0.3226 | 28.0 | 2100 | 0.4272 | 0.82 |
| 0.2919 | 29.0 | 2175 | 0.4254 | 0.82 |
| 0.3056 | 30.0 | 2250 | 0.4233 | 0.8233 |
| 0.2391 | 31.0 | 2325 | 0.4233 | 0.8233 |
| 0.3148 | 32.0 | 2400 | 0.4205 | 0.8267 |
| 0.2897 | 33.0 | 2475 | 0.4204 | 0.8267 |
| 0.2561 | 34.0 | 2550 | 0.4195 | 0.8267 |
| 0.2841 | 35.0 | 2625 | 0.4186 | 0.8283 |
| 0.2572 | 36.0 | 2700 | 0.4171 | 0.8267 |
| 0.2531 | 37.0 | 2775 | 0.4160 | 0.8267 |
| 0.2737 | 38.0 | 2850 | 0.4152 | 0.8333 |
| 0.276 | 39.0 | 2925 | 0.4146 | 0.8317 |
| 0.3158 | 40.0 | 3000 | 0.4142 | 0.8317 |
| 0.2611 | 41.0 | 3075 | 0.4144 | 0.8367 |
| 0.2512 | 42.0 | 3150 | 0.4134 | 0.835 |
| 0.2782 | 43.0 | 3225 | 0.4133 | 0.835 |
| 0.2613 | 44.0 | 3300 | 0.4133 | 0.8367 |
| 0.2656 | 45.0 | 3375 | 0.4131 | 0.835 |
| 0.2575 | 46.0 | 3450 | 0.4126 | 0.835 |
| 0.2475 | 47.0 | 3525 | 0.4125 | 0.8367 |
| 0.2893 | 48.0 | 3600 | 0.4124 | 0.835 |
| 0.2785 | 49.0 | 3675 | 0.4123 | 0.835 |
| 0.2483 | 50.0 | 3750 | 0.4122 | 0.835 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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