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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_5x_deit_tiny_adamax_001_fold4
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: 0.865
---
<!-- 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_adamax_001_fold4
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: 1.5310
- Accuracy: 0.865
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4168 | 1.0 | 375 | 0.3631 | 0.85 |
| 0.2785 | 2.0 | 750 | 0.4582 | 0.82 |
| 0.1977 | 3.0 | 1125 | 0.4757 | 0.845 |
| 0.2154 | 4.0 | 1500 | 0.4151 | 0.8567 |
| 0.2216 | 5.0 | 1875 | 0.4921 | 0.84 |
| 0.1277 | 6.0 | 2250 | 0.5208 | 0.84 |
| 0.1577 | 7.0 | 2625 | 0.6509 | 0.84 |
| 0.1043 | 8.0 | 3000 | 0.6131 | 0.8483 |
| 0.0606 | 9.0 | 3375 | 0.7321 | 0.85 |
| 0.0399 | 10.0 | 3750 | 0.7332 | 0.8483 |
| 0.0878 | 11.0 | 4125 | 0.7794 | 0.86 |
| 0.0753 | 12.0 | 4500 | 0.9361 | 0.855 |
| 0.0315 | 13.0 | 4875 | 0.7541 | 0.87 |
| 0.0322 | 14.0 | 5250 | 0.8827 | 0.855 |
| 0.0291 | 15.0 | 5625 | 0.8552 | 0.8667 |
| 0.0323 | 16.0 | 6000 | 1.0097 | 0.8533 |
| 0.0358 | 17.0 | 6375 | 1.0442 | 0.8367 |
| 0.0726 | 18.0 | 6750 | 1.0675 | 0.8533 |
| 0.0105 | 19.0 | 7125 | 1.0350 | 0.8567 |
| 0.0155 | 20.0 | 7500 | 1.0612 | 0.8467 |
| 0.0001 | 21.0 | 7875 | 1.1933 | 0.8467 |
| 0.001 | 22.0 | 8250 | 0.9964 | 0.86 |
| 0.0061 | 23.0 | 8625 | 1.0207 | 0.86 |
| 0.0139 | 24.0 | 9000 | 1.1598 | 0.8467 |
| 0.0232 | 25.0 | 9375 | 1.1652 | 0.8583 |
| 0.0001 | 26.0 | 9750 | 1.1454 | 0.8583 |
| 0.0011 | 27.0 | 10125 | 1.1331 | 0.865 |
| 0.0 | 28.0 | 10500 | 1.2646 | 0.8667 |
| 0.0 | 29.0 | 10875 | 1.1994 | 0.8683 |
| 0.0001 | 30.0 | 11250 | 1.2306 | 0.8533 |
| 0.004 | 31.0 | 11625 | 1.2452 | 0.8617 |
| 0.0 | 32.0 | 12000 | 1.2904 | 0.8633 |
| 0.0 | 33.0 | 12375 | 1.3971 | 0.86 |
| 0.0001 | 34.0 | 12750 | 1.2738 | 0.8633 |
| 0.0 | 35.0 | 13125 | 1.4099 | 0.865 |
| 0.0 | 36.0 | 13500 | 1.3138 | 0.8633 |
| 0.0 | 37.0 | 13875 | 1.3962 | 0.8617 |
| 0.0037 | 38.0 | 14250 | 1.4247 | 0.8633 |
| 0.0 | 39.0 | 14625 | 1.4177 | 0.865 |
| 0.0 | 40.0 | 15000 | 1.4033 | 0.8633 |
| 0.0 | 41.0 | 15375 | 1.4591 | 0.8633 |
| 0.0 | 42.0 | 15750 | 1.4725 | 0.8617 |
| 0.0 | 43.0 | 16125 | 1.4752 | 0.8633 |
| 0.0 | 44.0 | 16500 | 1.4834 | 0.8633 |
| 0.0 | 45.0 | 16875 | 1.4967 | 0.8633 |
| 0.0 | 46.0 | 17250 | 1.5039 | 0.8633 |
| 0.0 | 47.0 | 17625 | 1.5125 | 0.8633 |
| 0.0 | 48.0 | 18000 | 1.5211 | 0.8633 |
| 0.0 | 49.0 | 18375 | 1.5277 | 0.865 |
| 0.0 | 50.0 | 18750 | 1.5310 | 0.865 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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