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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_3x_deit_tiny_sgd_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: 0.7245409015025042
---
<!-- 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_3x_deit_tiny_sgd_0001_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: 0.6481
- Accuracy: 0.7245
## 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: 0.0001
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.233 | 1.0 | 226 | 1.1760 | 0.3823 |
| 1.1228 | 2.0 | 452 | 1.1140 | 0.4023 |
| 1.086 | 3.0 | 678 | 1.0822 | 0.4174 |
| 1.0737 | 4.0 | 904 | 1.0561 | 0.4474 |
| 1.0492 | 5.0 | 1130 | 1.0319 | 0.4558 |
| 1.0539 | 6.0 | 1356 | 1.0095 | 0.4858 |
| 0.9458 | 7.0 | 1582 | 0.9879 | 0.5008 |
| 0.9572 | 8.0 | 1808 | 0.9681 | 0.5092 |
| 0.9484 | 9.0 | 2034 | 0.9485 | 0.5242 |
| 0.9168 | 10.0 | 2260 | 0.9300 | 0.5359 |
| 0.9317 | 11.0 | 2486 | 0.9125 | 0.5476 |
| 0.8523 | 12.0 | 2712 | 0.8952 | 0.5609 |
| 0.828 | 13.0 | 2938 | 0.8797 | 0.5843 |
| 0.8551 | 14.0 | 3164 | 0.8640 | 0.6010 |
| 0.8431 | 15.0 | 3390 | 0.8498 | 0.6127 |
| 0.7665 | 16.0 | 3616 | 0.8360 | 0.6177 |
| 0.7183 | 17.0 | 3842 | 0.8226 | 0.6210 |
| 0.7754 | 18.0 | 4068 | 0.8101 | 0.6344 |
| 0.7132 | 19.0 | 4294 | 0.7985 | 0.6394 |
| 0.7077 | 20.0 | 4520 | 0.7861 | 0.6544 |
| 0.6887 | 21.0 | 4746 | 0.7746 | 0.6628 |
| 0.7156 | 22.0 | 4972 | 0.7640 | 0.6661 |
| 0.7205 | 23.0 | 5198 | 0.7542 | 0.6761 |
| 0.6924 | 24.0 | 5424 | 0.7447 | 0.6811 |
| 0.668 | 25.0 | 5650 | 0.7359 | 0.6811 |
| 0.7303 | 26.0 | 5876 | 0.7276 | 0.6878 |
| 0.6039 | 27.0 | 6102 | 0.7198 | 0.6945 |
| 0.6316 | 28.0 | 6328 | 0.7126 | 0.6962 |
| 0.5808 | 29.0 | 6554 | 0.7060 | 0.6962 |
| 0.7521 | 30.0 | 6780 | 0.6997 | 0.7028 |
| 0.6067 | 31.0 | 7006 | 0.6939 | 0.7045 |
| 0.617 | 32.0 | 7232 | 0.6885 | 0.7062 |
| 0.5752 | 33.0 | 7458 | 0.6837 | 0.7062 |
| 0.5524 | 34.0 | 7684 | 0.6791 | 0.7078 |
| 0.645 | 35.0 | 7910 | 0.6750 | 0.7129 |
| 0.5855 | 36.0 | 8136 | 0.6712 | 0.7145 |
| 0.5981 | 37.0 | 8362 | 0.6677 | 0.7162 |
| 0.6026 | 38.0 | 8588 | 0.6646 | 0.7179 |
| 0.6372 | 39.0 | 8814 | 0.6617 | 0.7195 |
| 0.561 | 40.0 | 9040 | 0.6592 | 0.7179 |
| 0.5719 | 41.0 | 9266 | 0.6570 | 0.7179 |
| 0.5709 | 42.0 | 9492 | 0.6550 | 0.7195 |
| 0.6421 | 43.0 | 9718 | 0.6533 | 0.7212 |
| 0.5531 | 44.0 | 9944 | 0.6518 | 0.7245 |
| 0.6016 | 45.0 | 10170 | 0.6506 | 0.7245 |
| 0.6135 | 46.0 | 10396 | 0.6496 | 0.7245 |
| 0.5923 | 47.0 | 10622 | 0.6489 | 0.7245 |
| 0.5752 | 48.0 | 10848 | 0.6484 | 0.7245 |
| 0.5457 | 49.0 | 11074 | 0.6481 | 0.7245 |
| 0.586 | 50.0 | 11300 | 0.6481 | 0.7245 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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