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# smids_5x_deit_small_rms_001_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: 0.5587
- Accuracy: 0.7571

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1114        | 1.0   | 375   | 1.0890          | 0.3910   |
| 0.8594        | 2.0   | 750   | 0.8338          | 0.5258   |
| 0.7942        | 3.0   | 1125  | 0.8187          | 0.5857   |
| 0.8295        | 4.0   | 1500  | 0.8518          | 0.5441   |
| 0.7679        | 5.0   | 1875  | 0.8037          | 0.5574   |
| 0.78          | 6.0   | 2250  | 0.8086          | 0.5641   |
| 0.8051        | 7.0   | 2625  | 0.8475          | 0.5707   |
| 0.7931        | 8.0   | 3000  | 0.7986          | 0.5740   |
| 0.787         | 9.0   | 3375  | 0.8111          | 0.6273   |
| 0.7865        | 10.0  | 3750  | 0.7854          | 0.5874   |
| 0.7545        | 11.0  | 4125  | 0.7499          | 0.6156   |
| 0.8266        | 12.0  | 4500  | 0.7256          | 0.6373   |
| 0.699         | 13.0  | 4875  | 0.7372          | 0.6456   |
| 0.7725        | 14.0  | 5250  | 0.7195          | 0.6439   |
| 0.7834        | 15.0  | 5625  | 0.7235          | 0.6489   |
| 0.6954        | 16.0  | 6000  | 0.7061          | 0.6589   |
| 0.6911        | 17.0  | 6375  | 0.8229          | 0.5973   |
| 0.667         | 18.0  | 6750  | 0.6746          | 0.6772   |
| 0.6889        | 19.0  | 7125  | 0.6831          | 0.6955   |
| 0.6628        | 20.0  | 7500  | 0.6921          | 0.6922   |
| 0.7228        | 21.0  | 7875  | 0.6764          | 0.6656   |
| 0.7022        | 22.0  | 8250  | 0.6797          | 0.6905   |
| 0.6549        | 23.0  | 8625  | 0.6709          | 0.6772   |
| 0.7183        | 24.0  | 9000  | 0.6429          | 0.6955   |
| 0.6612        | 25.0  | 9375  | 0.6503          | 0.6988   |
| 0.6901        | 26.0  | 9750  | 0.7018          | 0.6739   |
| 0.7038        | 27.0  | 10125 | 0.6168          | 0.7271   |
| 0.6364        | 28.0  | 10500 | 0.6219          | 0.7121   |
| 0.6477        | 29.0  | 10875 | 0.6546          | 0.7188   |
| 0.5753        | 30.0  | 11250 | 0.6252          | 0.7221   |
| 0.6932        | 31.0  | 11625 | 0.6174          | 0.7271   |
| 0.6245        | 32.0  | 12000 | 0.6281          | 0.7255   |
| 0.6083        | 33.0  | 12375 | 0.6211          | 0.7105   |
| 0.6277        | 34.0  | 12750 | 0.5911          | 0.7471   |
| 0.596         | 35.0  | 13125 | 0.5943          | 0.7304   |
| 0.5539        | 36.0  | 13500 | 0.5798          | 0.7121   |
| 0.6231        | 37.0  | 13875 | 0.5842          | 0.7321   |
| 0.5692        | 38.0  | 14250 | 0.5897          | 0.7288   |
| 0.5587        | 39.0  | 14625 | 0.6220          | 0.7155   |
| 0.5891        | 40.0  | 15000 | 0.6063          | 0.7338   |
| 0.561         | 41.0  | 15375 | 0.5930          | 0.7338   |
| 0.5901        | 42.0  | 15750 | 0.5990          | 0.7288   |
| 0.5194        | 43.0  | 16125 | 0.5632          | 0.7488   |
| 0.5311        | 44.0  | 16500 | 0.5715          | 0.7488   |
| 0.5414        | 45.0  | 16875 | 0.5640          | 0.7537   |
| 0.5291        | 46.0  | 17250 | 0.5674          | 0.7504   |
| 0.5724        | 47.0  | 17625 | 0.5765          | 0.7454   |
| 0.4849        | 48.0  | 18000 | 0.5625          | 0.7438   |
| 0.5463        | 49.0  | 18375 | 0.5558          | 0.7571   |
| 0.4844        | 50.0  | 18750 | 0.5587          | 0.7571   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/deit-small-patch16-224", "model-index": [{"name": "smids_5x_deit_small_rms_001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.757071547420965, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-small-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us