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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_adamax_00001_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: 0.9033333333333333
---
<!-- 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_adamax_00001_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: 0.6007
- Accuracy: 0.9033
## 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: 1e-05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5827 | 1.0 | 75 | 0.5406 | 0.805 |
| 0.4124 | 2.0 | 150 | 0.3981 | 0.855 |
| 0.3474 | 3.0 | 225 | 0.3546 | 0.8683 |
| 0.2752 | 4.0 | 300 | 0.3424 | 0.8733 |
| 0.1916 | 5.0 | 375 | 0.3180 | 0.8767 |
| 0.1614 | 6.0 | 450 | 0.3047 | 0.8933 |
| 0.176 | 7.0 | 525 | 0.3087 | 0.8967 |
| 0.0932 | 8.0 | 600 | 0.3077 | 0.905 |
| 0.1095 | 9.0 | 675 | 0.3172 | 0.8983 |
| 0.0598 | 10.0 | 750 | 0.3393 | 0.8933 |
| 0.0561 | 11.0 | 825 | 0.3389 | 0.8983 |
| 0.0304 | 12.0 | 900 | 0.3510 | 0.9017 |
| 0.0312 | 13.0 | 975 | 0.3659 | 0.8967 |
| 0.0138 | 14.0 | 1050 | 0.3876 | 0.9033 |
| 0.0066 | 15.0 | 1125 | 0.4169 | 0.895 |
| 0.0031 | 16.0 | 1200 | 0.4314 | 0.8933 |
| 0.0025 | 17.0 | 1275 | 0.4363 | 0.9017 |
| 0.0143 | 18.0 | 1350 | 0.4488 | 0.9017 |
| 0.0193 | 19.0 | 1425 | 0.4765 | 0.9017 |
| 0.0077 | 20.0 | 1500 | 0.5000 | 0.9017 |
| 0.001 | 21.0 | 1575 | 0.4881 | 0.8967 |
| 0.0006 | 22.0 | 1650 | 0.5102 | 0.8967 |
| 0.0114 | 23.0 | 1725 | 0.5087 | 0.9017 |
| 0.0005 | 24.0 | 1800 | 0.5357 | 0.9017 |
| 0.0004 | 25.0 | 1875 | 0.5221 | 0.9017 |
| 0.0091 | 26.0 | 1950 | 0.5331 | 0.8983 |
| 0.0004 | 27.0 | 2025 | 0.5349 | 0.8983 |
| 0.0004 | 28.0 | 2100 | 0.5415 | 0.9017 |
| 0.0003 | 29.0 | 2175 | 0.5413 | 0.9017 |
| 0.0003 | 30.0 | 2250 | 0.5492 | 0.9 |
| 0.0003 | 31.0 | 2325 | 0.5599 | 0.9017 |
| 0.0003 | 32.0 | 2400 | 0.5614 | 0.9 |
| 0.0002 | 33.0 | 2475 | 0.5598 | 0.9 |
| 0.0063 | 34.0 | 2550 | 0.5669 | 0.9033 |
| 0.0068 | 35.0 | 2625 | 0.5658 | 0.905 |
| 0.0129 | 36.0 | 2700 | 0.5827 | 0.9017 |
| 0.002 | 37.0 | 2775 | 0.5827 | 0.9017 |
| 0.0002 | 38.0 | 2850 | 0.5886 | 0.905 |
| 0.0002 | 39.0 | 2925 | 0.5846 | 0.8983 |
| 0.0002 | 40.0 | 3000 | 0.5897 | 0.905 |
| 0.0002 | 41.0 | 3075 | 0.5926 | 0.905 |
| 0.0007 | 42.0 | 3150 | 0.5977 | 0.9017 |
| 0.0036 | 43.0 | 3225 | 0.5932 | 0.905 |
| 0.0002 | 44.0 | 3300 | 0.5979 | 0.9017 |
| 0.01 | 45.0 | 3375 | 0.6035 | 0.9033 |
| 0.0096 | 46.0 | 3450 | 0.6006 | 0.9033 |
| 0.0033 | 47.0 | 3525 | 0.6008 | 0.905 |
| 0.0001 | 48.0 | 3600 | 0.5996 | 0.9033 |
| 0.0001 | 49.0 | 3675 | 0.6014 | 0.9033 |
| 0.003 | 50.0 | 3750 | 0.6007 | 0.9033 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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