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---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_base_adamax_001_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: 0.8851913477537438
---
<!-- 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_base_adamax_001_fold2
This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1252
- Accuracy: 0.8852
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3593 | 1.0 | 375 | 0.2868 | 0.8902 |
| 0.3012 | 2.0 | 750 | 0.2473 | 0.9085 |
| 0.3836 | 3.0 | 1125 | 0.3500 | 0.8619 |
| 0.1484 | 4.0 | 1500 | 0.3561 | 0.8819 |
| 0.142 | 5.0 | 1875 | 0.3496 | 0.8619 |
| 0.1054 | 6.0 | 2250 | 0.5030 | 0.8519 |
| 0.1132 | 7.0 | 2625 | 0.4021 | 0.8769 |
| 0.0387 | 8.0 | 3000 | 0.5600 | 0.8752 |
| 0.0412 | 9.0 | 3375 | 0.4804 | 0.8935 |
| 0.049 | 10.0 | 3750 | 0.4670 | 0.8902 |
| 0.0223 | 11.0 | 4125 | 0.5161 | 0.8852 |
| 0.0227 | 12.0 | 4500 | 0.5268 | 0.8802 |
| 0.029 | 13.0 | 4875 | 0.5511 | 0.8819 |
| 0.0101 | 14.0 | 5250 | 0.5655 | 0.8935 |
| 0.0239 | 15.0 | 5625 | 0.5903 | 0.8885 |
| 0.0204 | 16.0 | 6000 | 0.6826 | 0.8869 |
| 0.0387 | 17.0 | 6375 | 0.6581 | 0.8835 |
| 0.0045 | 18.0 | 6750 | 0.5940 | 0.8869 |
| 0.0004 | 19.0 | 7125 | 0.7563 | 0.8885 |
| 0.0271 | 20.0 | 7500 | 0.5791 | 0.9035 |
| 0.0211 | 21.0 | 7875 | 0.5981 | 0.8869 |
| 0.0086 | 22.0 | 8250 | 0.6990 | 0.8869 |
| 0.0146 | 23.0 | 8625 | 0.6527 | 0.8935 |
| 0.0006 | 24.0 | 9000 | 0.5903 | 0.8885 |
| 0.02 | 25.0 | 9375 | 0.6548 | 0.8952 |
| 0.0007 | 26.0 | 9750 | 0.7230 | 0.8952 |
| 0.0 | 27.0 | 10125 | 0.7646 | 0.9002 |
| 0.0 | 28.0 | 10500 | 0.8095 | 0.8852 |
| 0.0 | 29.0 | 10875 | 0.8926 | 0.8835 |
| 0.0 | 30.0 | 11250 | 0.8629 | 0.8819 |
| 0.0041 | 31.0 | 11625 | 0.8782 | 0.8819 |
| 0.0047 | 32.0 | 12000 | 0.8948 | 0.8819 |
| 0.0063 | 33.0 | 12375 | 0.9158 | 0.8752 |
| 0.0001 | 34.0 | 12750 | 0.9726 | 0.8918 |
| 0.0 | 35.0 | 13125 | 1.0164 | 0.8819 |
| 0.0 | 36.0 | 13500 | 1.0004 | 0.8869 |
| 0.0 | 37.0 | 13875 | 1.0193 | 0.8869 |
| 0.0 | 38.0 | 14250 | 1.0151 | 0.8935 |
| 0.0 | 39.0 | 14625 | 1.0231 | 0.8902 |
| 0.0035 | 40.0 | 15000 | 1.0298 | 0.8852 |
| 0.0 | 41.0 | 15375 | 1.0402 | 0.8902 |
| 0.0028 | 42.0 | 15750 | 1.0577 | 0.8869 |
| 0.0026 | 43.0 | 16125 | 1.0687 | 0.8819 |
| 0.0027 | 44.0 | 16500 | 1.0626 | 0.8852 |
| 0.0029 | 45.0 | 16875 | 1.0972 | 0.8835 |
| 0.0 | 46.0 | 17250 | 1.0976 | 0.8819 |
| 0.0055 | 47.0 | 17625 | 1.1056 | 0.8819 |
| 0.0 | 48.0 | 18000 | 1.1143 | 0.8852 |
| 0.0025 | 49.0 | 18375 | 1.1213 | 0.8835 |
| 0.0024 | 50.0 | 18750 | 1.1252 | 0.8852 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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