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# smids_10x_deit_tiny_rms_001_fold4

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: 2.8355
- Accuracy: 0.8117

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7719        | 1.0   | 750   | 0.7663          | 0.6417   |
| 0.7164        | 2.0   | 1500  | 0.6422          | 0.7167   |
| 0.6166        | 3.0   | 2250  | 0.5992          | 0.7233   |
| 0.5878        | 4.0   | 3000  | 0.6512          | 0.6783   |
| 0.5907        | 5.0   | 3750  | 0.5898          | 0.7183   |
| 0.5259        | 6.0   | 4500  | 0.5668          | 0.7617   |
| 0.4934        | 7.0   | 5250  | 0.5092          | 0.7817   |
| 0.5606        | 8.0   | 6000  | 0.5230          | 0.76     |
| 0.5352        | 9.0   | 6750  | 0.5089          | 0.7733   |
| 0.4475        | 10.0  | 7500  | 0.5880          | 0.745    |
| 0.4921        | 11.0  | 8250  | 0.5315          | 0.765    |
| 0.5457        | 12.0  | 9000  | 0.5773          | 0.7583   |
| 0.4353        | 13.0  | 9750  | 0.5700          | 0.7533   |
| 0.4266        | 14.0  | 10500 | 0.5929          | 0.7633   |
| 0.4011        | 15.0  | 11250 | 0.5510          | 0.7883   |
| 0.4243        | 16.0  | 12000 | 0.5772          | 0.7633   |
| 0.2788        | 17.0  | 12750 | 0.5913          | 0.7817   |
| 0.36          | 18.0  | 13500 | 0.5472          | 0.7767   |
| 0.3727        | 19.0  | 14250 | 0.5501          | 0.7867   |
| 0.2873        | 20.0  | 15000 | 0.6706          | 0.77     |
| 0.3441        | 21.0  | 15750 | 0.5563          | 0.8083   |
| 0.3741        | 22.0  | 16500 | 0.5905          | 0.7767   |
| 0.2914        | 23.0  | 17250 | 0.6313          | 0.785    |
| 0.3593        | 24.0  | 18000 | 0.5992          | 0.7983   |
| 0.2548        | 25.0  | 18750 | 0.6167          | 0.8      |
| 0.2172        | 26.0  | 19500 | 0.6453          | 0.785    |
| 0.1957        | 27.0  | 20250 | 0.6311          | 0.815    |
| 0.2482        | 28.0  | 21000 | 0.7520          | 0.8067   |
| 0.1858        | 29.0  | 21750 | 0.7460          | 0.7917   |
| 0.1724        | 30.0  | 22500 | 0.6735          | 0.8183   |
| 0.1536        | 31.0  | 23250 | 0.8260          | 0.7933   |
| 0.1432        | 32.0  | 24000 | 0.9327          | 0.765    |
| 0.1489        | 33.0  | 24750 | 0.8695          | 0.7967   |
| 0.1215        | 34.0  | 25500 | 0.8392          | 0.8167   |
| 0.1302        | 35.0  | 26250 | 1.0000          | 0.8133   |
| 0.0677        | 36.0  | 27000 | 1.0715          | 0.8083   |
| 0.0727        | 37.0  | 27750 | 1.1501          | 0.7983   |
| 0.1221        | 38.0  | 28500 | 1.3342          | 0.7883   |
| 0.0469        | 39.0  | 29250 | 1.3213          | 0.8      |
| 0.068         | 40.0  | 30000 | 1.4945          | 0.8      |
| 0.0607        | 41.0  | 30750 | 1.4763          | 0.8133   |
| 0.0293        | 42.0  | 31500 | 1.8072          | 0.79     |
| 0.0304        | 43.0  | 32250 | 2.0290          | 0.7817   |
| 0.0187        | 44.0  | 33000 | 2.2554          | 0.7867   |
| 0.0136        | 45.0  | 33750 | 2.3220          | 0.8      |
| 0.0034        | 46.0  | 34500 | 2.4619          | 0.8033   |
| 0.0045        | 47.0  | 35250 | 2.5490          | 0.8      |
| 0.0159        | 48.0  | 36000 | 2.5993          | 0.825    |
| 0.0004        | 49.0  | 36750 | 2.7895          | 0.8083   |
| 0.0001        | 50.0  | 37500 | 2.8355          | 0.8117   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.1.1+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-tiny-patch16-224", "model-index": [{"name": "smids_10x_deit_tiny_rms_001_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8116666666666666, "name": "Accuracy"}]}]}]}
transformers pytorch vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-tiny-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us