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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_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: 0.6811352253756261
---
<!-- 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_fold1
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: 0.7301
- Accuracy: 0.6811
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1511 | 1.0 | 76 | 1.0039 | 0.4290 |
| 0.9396 | 2.0 | 152 | 0.9980 | 0.4658 |
| 0.9392 | 3.0 | 228 | 1.1592 | 0.3239 |
| 0.9832 | 4.0 | 304 | 1.0157 | 0.4791 |
| 0.9342 | 5.0 | 380 | 0.9184 | 0.4725 |
| 0.951 | 6.0 | 456 | 0.9262 | 0.4958 |
| 1.1061 | 7.0 | 532 | 1.1999 | 0.3406 |
| 0.8983 | 8.0 | 608 | 1.5626 | 0.4207 |
| 0.8399 | 9.0 | 684 | 0.8862 | 0.5242 |
| 0.7906 | 10.0 | 760 | 2.9194 | 0.3255 |
| 0.9054 | 11.0 | 836 | 0.8409 | 0.5476 |
| 0.8842 | 12.0 | 912 | 0.8563 | 0.5409 |
| 0.8173 | 13.0 | 988 | 0.9009 | 0.4958 |
| 0.8653 | 14.0 | 1064 | 0.8617 | 0.5476 |
| 0.7859 | 15.0 | 1140 | 0.8470 | 0.5109 |
| 0.7904 | 16.0 | 1216 | 0.8290 | 0.6027 |
| 0.8076 | 17.0 | 1292 | 1.0668 | 0.5326 |
| 0.7582 | 18.0 | 1368 | 0.8092 | 0.5776 |
| 0.8375 | 19.0 | 1444 | 0.8034 | 0.5927 |
| 0.817 | 20.0 | 1520 | 0.8094 | 0.5593 |
| 0.7636 | 21.0 | 1596 | 0.8786 | 0.6060 |
| 0.7574 | 22.0 | 1672 | 0.7805 | 0.6093 |
| 0.7196 | 23.0 | 1748 | 0.8013 | 0.6227 |
| 0.746 | 24.0 | 1824 | 0.9940 | 0.5492 |
| 0.698 | 25.0 | 1900 | 0.7894 | 0.6227 |
| 0.7416 | 26.0 | 1976 | 0.7704 | 0.6177 |
| 0.7441 | 27.0 | 2052 | 0.7868 | 0.6110 |
| 0.7488 | 28.0 | 2128 | 0.7854 | 0.6294 |
| 0.6844 | 29.0 | 2204 | 0.7483 | 0.6394 |
| 0.7046 | 30.0 | 2280 | 0.7522 | 0.6144 |
| 0.7612 | 31.0 | 2356 | 0.7237 | 0.6811 |
| 0.7095 | 32.0 | 2432 | 0.7781 | 0.6060 |
| 0.7219 | 33.0 | 2508 | 0.7248 | 0.6477 |
| 0.7697 | 34.0 | 2584 | 0.7404 | 0.6394 |
| 0.7924 | 35.0 | 2660 | 0.7779 | 0.6077 |
| 0.6939 | 36.0 | 2736 | 0.7018 | 0.6628 |
| 0.7175 | 37.0 | 2812 | 0.7115 | 0.6711 |
| 0.663 | 38.0 | 2888 | 0.7095 | 0.6594 |
| 0.7209 | 39.0 | 2964 | 0.7131 | 0.6761 |
| 0.6707 | 40.0 | 3040 | 0.7148 | 0.6745 |
| 0.6033 | 41.0 | 3116 | 0.7278 | 0.6761 |
| 0.6657 | 42.0 | 3192 | 0.7175 | 0.6745 |
| 0.5768 | 43.0 | 3268 | 0.7542 | 0.6611 |
| 0.608 | 44.0 | 3344 | 0.7272 | 0.6811 |
| 0.5917 | 45.0 | 3420 | 0.7194 | 0.6795 |
| 0.6179 | 46.0 | 3496 | 0.7229 | 0.6828 |
| 0.5513 | 47.0 | 3572 | 0.7301 | 0.6861 |
| 0.5669 | 48.0 | 3648 | 0.7286 | 0.6845 |
| 0.4852 | 49.0 | 3724 | 0.7286 | 0.6811 |
| 0.6153 | 50.0 | 3800 | 0.7301 | 0.6811 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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