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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_rms_0001_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: 0.8735440931780366
---
<!-- 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_rms_0001_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: 1.1259
- Accuracy: 0.8735
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3139 | 1.0 | 375 | 0.2920 | 0.8835 |
| 0.213 | 2.0 | 750 | 0.3450 | 0.8785 |
| 0.2004 | 3.0 | 1125 | 0.4306 | 0.8719 |
| 0.1151 | 4.0 | 1500 | 0.4856 | 0.8702 |
| 0.1363 | 5.0 | 1875 | 0.5483 | 0.8752 |
| 0.0415 | 6.0 | 2250 | 0.6014 | 0.8719 |
| 0.0888 | 7.0 | 2625 | 0.6594 | 0.8636 |
| 0.0129 | 8.0 | 3000 | 0.7394 | 0.8702 |
| 0.0606 | 9.0 | 3375 | 0.7551 | 0.8619 |
| 0.0273 | 10.0 | 3750 | 0.7977 | 0.8536 |
| 0.0575 | 11.0 | 4125 | 0.7927 | 0.8702 |
| 0.0142 | 12.0 | 4500 | 0.8285 | 0.8619 |
| 0.006 | 13.0 | 4875 | 0.8594 | 0.8819 |
| 0.0339 | 14.0 | 5250 | 0.8600 | 0.8686 |
| 0.0029 | 15.0 | 5625 | 0.9289 | 0.8719 |
| 0.0348 | 16.0 | 6000 | 0.7828 | 0.8819 |
| 0.0273 | 17.0 | 6375 | 0.7381 | 0.8885 |
| 0.029 | 18.0 | 6750 | 0.9087 | 0.8686 |
| 0.0306 | 19.0 | 7125 | 0.9194 | 0.8785 |
| 0.0034 | 20.0 | 7500 | 1.0978 | 0.8619 |
| 0.0052 | 21.0 | 7875 | 0.9530 | 0.8785 |
| 0.0001 | 22.0 | 8250 | 0.9575 | 0.8752 |
| 0.0447 | 23.0 | 8625 | 0.9869 | 0.8819 |
| 0.0122 | 24.0 | 9000 | 0.8869 | 0.8785 |
| 0.0018 | 25.0 | 9375 | 1.0324 | 0.8669 |
| 0.0117 | 26.0 | 9750 | 0.9387 | 0.8852 |
| 0.0206 | 27.0 | 10125 | 1.0468 | 0.8719 |
| 0.0002 | 28.0 | 10500 | 0.9421 | 0.8785 |
| 0.0001 | 29.0 | 10875 | 0.8621 | 0.8968 |
| 0.0027 | 30.0 | 11250 | 0.9653 | 0.8769 |
| 0.0116 | 31.0 | 11625 | 0.9958 | 0.8785 |
| 0.0019 | 32.0 | 12000 | 1.1300 | 0.8752 |
| 0.0084 | 33.0 | 12375 | 1.0346 | 0.8802 |
| 0.0 | 34.0 | 12750 | 1.0458 | 0.8719 |
| 0.0 | 35.0 | 13125 | 1.0740 | 0.8719 |
| 0.0001 | 36.0 | 13500 | 1.0706 | 0.8719 |
| 0.0 | 37.0 | 13875 | 1.2116 | 0.8735 |
| 0.0 | 38.0 | 14250 | 1.1598 | 0.8735 |
| 0.0 | 39.0 | 14625 | 1.1682 | 0.8785 |
| 0.0029 | 40.0 | 15000 | 1.0573 | 0.8835 |
| 0.0 | 41.0 | 15375 | 1.1307 | 0.8735 |
| 0.0028 | 42.0 | 15750 | 1.1484 | 0.8702 |
| 0.0032 | 43.0 | 16125 | 1.1289 | 0.8752 |
| 0.0031 | 44.0 | 16500 | 1.1224 | 0.8769 |
| 0.0027 | 45.0 | 16875 | 1.1287 | 0.8719 |
| 0.0 | 46.0 | 17250 | 1.1176 | 0.8752 |
| 0.006 | 47.0 | 17625 | 1.1207 | 0.8752 |
| 0.0 | 48.0 | 18000 | 1.1234 | 0.8752 |
| 0.0024 | 49.0 | 18375 | 1.1256 | 0.8752 |
| 0.0022 | 50.0 | 18750 | 1.1259 | 0.8735 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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