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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: hushem_1x_deit_tiny_adamax_lr0001_fold5
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: 0.6585365853658537
---
<!-- 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. -->
# hushem_1x_deit_tiny_adamax_lr0001_fold5
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: 0.8924
- Accuracy: 0.6585
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.67 | 1 | 1.9330 | 0.2439 |
| No log | 2.0 | 3 | 1.4362 | 0.3659 |
| No log | 2.67 | 4 | 1.3806 | 0.3902 |
| No log | 4.0 | 6 | 1.3304 | 0.4634 |
| No log | 4.67 | 7 | 1.3017 | 0.4390 |
| No log | 6.0 | 9 | 1.1836 | 0.4878 |
| 1.2323 | 6.67 | 10 | 1.1688 | 0.5610 |
| 1.2323 | 8.0 | 12 | 1.1361 | 0.5366 |
| 1.2323 | 8.67 | 13 | 1.1291 | 0.5366 |
| 1.2323 | 10.0 | 15 | 1.0782 | 0.6098 |
| 1.2323 | 10.67 | 16 | 1.0358 | 0.6585 |
| 1.2323 | 12.0 | 18 | 1.0020 | 0.6098 |
| 1.2323 | 12.67 | 19 | 1.0059 | 0.6098 |
| 0.3527 | 14.0 | 21 | 0.9293 | 0.6098 |
| 0.3527 | 14.67 | 22 | 0.9162 | 0.6341 |
| 0.3527 | 16.0 | 24 | 0.9233 | 0.6098 |
| 0.3527 | 16.67 | 25 | 0.9213 | 0.6098 |
| 0.3527 | 18.0 | 27 | 0.9193 | 0.6098 |
| 0.3527 | 18.67 | 28 | 0.9345 | 0.6098 |
| 0.04 | 20.0 | 30 | 0.8872 | 0.6585 |
| 0.04 | 20.67 | 31 | 0.8549 | 0.6829 |
| 0.04 | 22.0 | 33 | 0.8221 | 0.6829 |
| 0.04 | 22.67 | 34 | 0.8117 | 0.7073 |
| 0.04 | 24.0 | 36 | 0.8041 | 0.7561 |
| 0.04 | 24.67 | 37 | 0.8128 | 0.7561 |
| 0.04 | 26.0 | 39 | 0.8413 | 0.6829 |
| 0.0062 | 26.67 | 40 | 0.8565 | 0.6585 |
| 0.0062 | 28.0 | 42 | 0.8789 | 0.6585 |
| 0.0062 | 28.67 | 43 | 0.8864 | 0.6585 |
| 0.0062 | 30.0 | 45 | 0.8920 | 0.6585 |
| 0.0062 | 30.67 | 46 | 0.8925 | 0.6585 |
| 0.0062 | 32.0 | 48 | 0.8929 | 0.6585 |
| 0.0062 | 32.67 | 49 | 0.8927 | 0.6585 |
| 0.0031 | 33.33 | 50 | 0.8924 | 0.6585 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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