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---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_1x_deit_small_rms_0001_fold3
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: 0.7016666666666667
---
<!-- 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_small_rms_0001_fold3
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1553
- Accuracy: 0.7017
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.14 | 1.0 | 75 | 1.1120 | 0.335 |
| 1.2072 | 2.0 | 150 | 1.0986 | 0.3333 |
| 0.9539 | 3.0 | 225 | 0.9334 | 0.4917 |
| 0.9512 | 4.0 | 300 | 0.9203 | 0.4983 |
| 0.911 | 5.0 | 375 | 1.0159 | 0.445 |
| 0.9061 | 6.0 | 450 | 0.9432 | 0.5133 |
| 0.8557 | 7.0 | 525 | 0.9707 | 0.5517 |
| 0.796 | 8.0 | 600 | 0.8853 | 0.5633 |
| 0.837 | 9.0 | 675 | 0.8169 | 0.5667 |
| 0.8343 | 10.0 | 750 | 0.8015 | 0.5867 |
| 0.8478 | 11.0 | 825 | 0.8424 | 0.5533 |
| 0.7471 | 12.0 | 900 | 0.8480 | 0.5733 |
| 0.7041 | 13.0 | 975 | 0.8701 | 0.55 |
| 0.7689 | 14.0 | 1050 | 0.7602 | 0.625 |
| 0.6385 | 15.0 | 1125 | 0.8263 | 0.5933 |
| 0.7131 | 16.0 | 1200 | 0.7809 | 0.595 |
| 0.7152 | 17.0 | 1275 | 0.8940 | 0.565 |
| 0.7023 | 18.0 | 1350 | 0.7651 | 0.66 |
| 0.6514 | 19.0 | 1425 | 0.7331 | 0.6783 |
| 0.7116 | 20.0 | 1500 | 0.7305 | 0.6883 |
| 0.6713 | 21.0 | 1575 | 0.7155 | 0.6733 |
| 0.634 | 22.0 | 1650 | 0.7520 | 0.6883 |
| 0.664 | 23.0 | 1725 | 0.7448 | 0.6767 |
| 0.5579 | 24.0 | 1800 | 0.7383 | 0.6967 |
| 0.6505 | 25.0 | 1875 | 0.7438 | 0.69 |
| 0.6223 | 26.0 | 1950 | 0.7719 | 0.65 |
| 0.5322 | 27.0 | 2025 | 0.7151 | 0.7017 |
| 0.5674 | 28.0 | 2100 | 0.7078 | 0.6817 |
| 0.493 | 29.0 | 2175 | 0.7341 | 0.71 |
| 0.585 | 30.0 | 2250 | 0.7150 | 0.6867 |
| 0.534 | 31.0 | 2325 | 0.7507 | 0.6967 |
| 0.458 | 32.0 | 2400 | 0.7455 | 0.6983 |
| 0.512 | 33.0 | 2475 | 0.6902 | 0.6967 |
| 0.5074 | 34.0 | 2550 | 0.6773 | 0.6983 |
| 0.512 | 35.0 | 2625 | 0.6981 | 0.7083 |
| 0.452 | 36.0 | 2700 | 0.7620 | 0.7083 |
| 0.4013 | 37.0 | 2775 | 0.7597 | 0.7033 |
| 0.4319 | 38.0 | 2850 | 0.7472 | 0.705 |
| 0.4551 | 39.0 | 2925 | 0.8012 | 0.7067 |
| 0.4136 | 40.0 | 3000 | 0.7673 | 0.7133 |
| 0.4092 | 41.0 | 3075 | 0.8184 | 0.7067 |
| 0.412 | 42.0 | 3150 | 0.8145 | 0.7183 |
| 0.4199 | 43.0 | 3225 | 0.8148 | 0.725 |
| 0.3632 | 44.0 | 3300 | 0.8661 | 0.69 |
| 0.2849 | 45.0 | 3375 | 0.9491 | 0.7167 |
| 0.3044 | 46.0 | 3450 | 0.9227 | 0.7017 |
| 0.2713 | 47.0 | 3525 | 0.9951 | 0.6983 |
| 0.22 | 48.0 | 3600 | 1.0641 | 0.7017 |
| 0.2276 | 49.0 | 3675 | 1.1632 | 0.6983 |
| 0.2183 | 50.0 | 3750 | 1.1553 | 0.7017 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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