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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_10x_deit_small_sgd_00001_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: 0.5590682196339434
---
<!-- 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_10x_deit_small_sgd_00001_fold2
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: 0.9349
- Accuracy: 0.5591
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0693 | 1.0 | 750 | 1.0692 | 0.4293 |
| 1.0584 | 2.0 | 1500 | 1.0648 | 0.4343 |
| 1.0342 | 3.0 | 2250 | 1.0600 | 0.4376 |
| 1.0374 | 4.0 | 3000 | 1.0551 | 0.4443 |
| 1.028 | 5.0 | 3750 | 1.0500 | 0.4459 |
| 1.0131 | 6.0 | 4500 | 1.0451 | 0.4476 |
| 1.022 | 7.0 | 5250 | 1.0402 | 0.4459 |
| 1.0192 | 8.0 | 6000 | 1.0354 | 0.4526 |
| 1.0168 | 9.0 | 6750 | 1.0306 | 0.4576 |
| 0.9985 | 10.0 | 7500 | 1.0259 | 0.4592 |
| 0.9898 | 11.0 | 8250 | 1.0213 | 0.4609 |
| 1.0116 | 12.0 | 9000 | 1.0168 | 0.4642 |
| 0.9986 | 13.0 | 9750 | 1.0125 | 0.4659 |
| 0.9818 | 14.0 | 10500 | 1.0083 | 0.4759 |
| 0.9837 | 15.0 | 11250 | 1.0041 | 0.4809 |
| 0.9601 | 16.0 | 12000 | 1.0001 | 0.4809 |
| 0.9572 | 17.0 | 12750 | 0.9961 | 0.4809 |
| 0.9406 | 18.0 | 13500 | 0.9923 | 0.4859 |
| 0.9621 | 19.0 | 14250 | 0.9887 | 0.4892 |
| 0.9467 | 20.0 | 15000 | 0.9850 | 0.4925 |
| 0.9691 | 21.0 | 15750 | 0.9816 | 0.4992 |
| 0.9406 | 22.0 | 16500 | 0.9782 | 0.5008 |
| 0.9223 | 23.0 | 17250 | 0.9750 | 0.5058 |
| 0.9127 | 24.0 | 18000 | 0.9718 | 0.5075 |
| 0.9371 | 25.0 | 18750 | 0.9688 | 0.5141 |
| 0.9589 | 26.0 | 19500 | 0.9659 | 0.5175 |
| 0.9189 | 27.0 | 20250 | 0.9631 | 0.5208 |
| 0.9249 | 28.0 | 21000 | 0.9605 | 0.5258 |
| 0.927 | 29.0 | 21750 | 0.9580 | 0.5275 |
| 0.9378 | 30.0 | 22500 | 0.9556 | 0.5308 |
| 0.8829 | 31.0 | 23250 | 0.9533 | 0.5308 |
| 0.931 | 32.0 | 24000 | 0.9512 | 0.5341 |
| 0.9197 | 33.0 | 24750 | 0.9492 | 0.5374 |
| 0.9032 | 34.0 | 25500 | 0.9474 | 0.5374 |
| 0.9 | 35.0 | 26250 | 0.9457 | 0.5391 |
| 0.8939 | 36.0 | 27000 | 0.9442 | 0.5441 |
| 0.9276 | 37.0 | 27750 | 0.9427 | 0.5458 |
| 0.8712 | 38.0 | 28500 | 0.9414 | 0.5458 |
| 0.9222 | 39.0 | 29250 | 0.9402 | 0.5458 |
| 0.8913 | 40.0 | 30000 | 0.9392 | 0.5474 |
| 0.8879 | 41.0 | 30750 | 0.9383 | 0.5474 |
| 0.8851 | 42.0 | 31500 | 0.9375 | 0.5541 |
| 0.8777 | 43.0 | 32250 | 0.9368 | 0.5541 |
| 0.8945 | 44.0 | 33000 | 0.9362 | 0.5541 |
| 0.8708 | 45.0 | 33750 | 0.9358 | 0.5574 |
| 0.9082 | 46.0 | 34500 | 0.9354 | 0.5591 |
| 0.9028 | 47.0 | 35250 | 0.9352 | 0.5591 |
| 0.8903 | 48.0 | 36000 | 0.9350 | 0.5591 |
| 0.8994 | 49.0 | 36750 | 0.9349 | 0.5591 |
| 0.9183 | 50.0 | 37500 | 0.9349 | 0.5591 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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