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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_small_rms_00001_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: 0.9232053422370617
---
<!-- 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_small_rms_00001_fold1
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: 0.7417
- Accuracy: 0.9232
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2432 | 1.0 | 376 | 0.2964 | 0.8731 |
| 0.1614 | 2.0 | 752 | 0.2849 | 0.8932 |
| 0.1382 | 3.0 | 1128 | 0.2988 | 0.9048 |
| 0.0674 | 4.0 | 1504 | 0.3782 | 0.9082 |
| 0.0238 | 5.0 | 1880 | 0.4767 | 0.9132 |
| 0.0038 | 6.0 | 2256 | 0.4683 | 0.9215 |
| 0.0081 | 7.0 | 2632 | 0.5149 | 0.9015 |
| 0.0311 | 8.0 | 3008 | 0.6215 | 0.9082 |
| 0.0258 | 9.0 | 3384 | 0.6420 | 0.9015 |
| 0.0003 | 10.0 | 3760 | 0.7389 | 0.8982 |
| 0.0016 | 11.0 | 4136 | 0.7097 | 0.9032 |
| 0.0237 | 12.0 | 4512 | 0.7322 | 0.8965 |
| 0.0 | 13.0 | 4888 | 0.6330 | 0.9065 |
| 0.0121 | 14.0 | 5264 | 0.6713 | 0.8998 |
| 0.0128 | 15.0 | 5640 | 0.6959 | 0.9032 |
| 0.0 | 16.0 | 6016 | 0.5921 | 0.9165 |
| 0.0147 | 17.0 | 6392 | 0.7286 | 0.9032 |
| 0.0096 | 18.0 | 6768 | 0.6654 | 0.9115 |
| 0.0001 | 19.0 | 7144 | 0.7241 | 0.9065 |
| 0.026 | 20.0 | 7520 | 0.7595 | 0.9115 |
| 0.0004 | 21.0 | 7896 | 0.7089 | 0.9132 |
| 0.0001 | 22.0 | 8272 | 0.7020 | 0.9132 |
| 0.0189 | 23.0 | 8648 | 0.7064 | 0.9032 |
| 0.0 | 24.0 | 9024 | 0.6953 | 0.9182 |
| 0.0008 | 25.0 | 9400 | 0.6754 | 0.9048 |
| 0.0029 | 26.0 | 9776 | 0.6682 | 0.9149 |
| 0.0039 | 27.0 | 10152 | 0.7036 | 0.9115 |
| 0.0 | 28.0 | 10528 | 0.7901 | 0.9098 |
| 0.0047 | 29.0 | 10904 | 0.7958 | 0.9165 |
| 0.0042 | 30.0 | 11280 | 0.7246 | 0.9115 |
| 0.0 | 31.0 | 11656 | 0.7694 | 0.9132 |
| 0.0 | 32.0 | 12032 | 0.7581 | 0.9082 |
| 0.0 | 33.0 | 12408 | 0.7146 | 0.9149 |
| 0.0 | 34.0 | 12784 | 0.7034 | 0.9165 |
| 0.0 | 35.0 | 13160 | 0.7688 | 0.9115 |
| 0.0 | 36.0 | 13536 | 0.7638 | 0.9132 |
| 0.0 | 37.0 | 13912 | 0.8028 | 0.9115 |
| 0.0 | 38.0 | 14288 | 0.7323 | 0.9215 |
| 0.0 | 39.0 | 14664 | 0.7555 | 0.9199 |
| 0.0 | 40.0 | 15040 | 0.7506 | 0.9215 |
| 0.0 | 41.0 | 15416 | 0.7416 | 0.9215 |
| 0.0 | 42.0 | 15792 | 0.7376 | 0.9199 |
| 0.0 | 43.0 | 16168 | 0.7280 | 0.9215 |
| 0.0 | 44.0 | 16544 | 0.7390 | 0.9215 |
| 0.0 | 45.0 | 16920 | 0.7365 | 0.9232 |
| 0.0028 | 46.0 | 17296 | 0.7367 | 0.9232 |
| 0.0 | 47.0 | 17672 | 0.7395 | 0.9232 |
| 0.0 | 48.0 | 18048 | 0.7408 | 0.9232 |
| 0.0 | 49.0 | 18424 | 0.7418 | 0.9232 |
| 0.0024 | 50.0 | 18800 | 0.7417 | 0.9232 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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