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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_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: 0.9131886477462438
---
<!-- 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_fold1
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: 0.7497
- Accuracy: 0.9132
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.329 | 1.0 | 376 | 0.4277 | 0.8464 |
| 0.2087 | 2.0 | 752 | 0.3407 | 0.8698 |
| 0.2485 | 3.0 | 1128 | 0.3788 | 0.8598 |
| 0.178 | 4.0 | 1504 | 0.4197 | 0.8531 |
| 0.1258 | 5.0 | 1880 | 0.4173 | 0.8648 |
| 0.1206 | 6.0 | 2256 | 0.3586 | 0.8848 |
| 0.1282 | 7.0 | 2632 | 0.3517 | 0.8865 |
| 0.0583 | 8.0 | 3008 | 0.5359 | 0.8765 |
| 0.0747 | 9.0 | 3384 | 0.5100 | 0.8731 |
| 0.0435 | 10.0 | 3760 | 0.5516 | 0.8781 |
| 0.06 | 11.0 | 4136 | 0.3933 | 0.8998 |
| 0.0257 | 12.0 | 4512 | 0.5267 | 0.8848 |
| 0.0686 | 13.0 | 4888 | 0.4896 | 0.9065 |
| 0.016 | 14.0 | 5264 | 0.5666 | 0.8881 |
| 0.011 | 15.0 | 5640 | 0.5612 | 0.8965 |
| 0.0019 | 16.0 | 6016 | 0.6453 | 0.8848 |
| 0.0015 | 17.0 | 6392 | 0.5726 | 0.8948 |
| 0.0354 | 18.0 | 6768 | 0.5332 | 0.9048 |
| 0.0037 | 19.0 | 7144 | 0.5726 | 0.8965 |
| 0.0094 | 20.0 | 7520 | 0.5926 | 0.9032 |
| 0.0008 | 21.0 | 7896 | 0.5520 | 0.8998 |
| 0.0004 | 22.0 | 8272 | 0.4436 | 0.9165 |
| 0.0006 | 23.0 | 8648 | 0.6077 | 0.8965 |
| 0.001 | 24.0 | 9024 | 0.6248 | 0.9132 |
| 0.0003 | 25.0 | 9400 | 0.6715 | 0.8982 |
| 0.0035 | 26.0 | 9776 | 0.6641 | 0.9082 |
| 0.0 | 27.0 | 10152 | 0.6982 | 0.9048 |
| 0.0 | 28.0 | 10528 | 0.7269 | 0.8982 |
| 0.0054 | 29.0 | 10904 | 0.6756 | 0.9098 |
| 0.0034 | 30.0 | 11280 | 0.6451 | 0.9065 |
| 0.0 | 31.0 | 11656 | 0.6535 | 0.9098 |
| 0.0 | 32.0 | 12032 | 0.6650 | 0.9065 |
| 0.0 | 33.0 | 12408 | 0.6759 | 0.9082 |
| 0.0 | 34.0 | 12784 | 0.6731 | 0.9048 |
| 0.0 | 35.0 | 13160 | 0.6782 | 0.9082 |
| 0.0001 | 36.0 | 13536 | 0.6755 | 0.9032 |
| 0.0 | 37.0 | 13912 | 0.7594 | 0.9098 |
| 0.0 | 38.0 | 14288 | 0.7065 | 0.9115 |
| 0.0 | 39.0 | 14664 | 0.7005 | 0.9082 |
| 0.0 | 40.0 | 15040 | 0.7058 | 0.9149 |
| 0.0 | 41.0 | 15416 | 0.6924 | 0.9115 |
| 0.0 | 42.0 | 15792 | 0.7078 | 0.9149 |
| 0.0 | 43.0 | 16168 | 0.7156 | 0.9149 |
| 0.0 | 44.0 | 16544 | 0.7204 | 0.9165 |
| 0.0 | 45.0 | 16920 | 0.7358 | 0.9149 |
| 0.003 | 46.0 | 17296 | 0.7278 | 0.9165 |
| 0.0 | 47.0 | 17672 | 0.7349 | 0.9149 |
| 0.0 | 48.0 | 18048 | 0.7414 | 0.9149 |
| 0.0 | 49.0 | 18424 | 0.7464 | 0.9149 |
| 0.0023 | 50.0 | 18800 | 0.7497 | 0.9132 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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