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# deit-ena24

This model is a fine-tuned version of [facebook/deit-base-distilled-patch16-224](https://huggingface.co/facebook/deit-base-distilled-patch16-224) on the ena24 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1233
- Accuracy: 0.9809
- F1: 0.9799

## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 1.396         | 0.1302 | 100  | 1.0114          | 0.7107   | 0.6602 |
| 1.0428        | 0.2604 | 200  | 0.7400          | 0.7939   | 0.7694 |
| 0.6952        | 0.3906 | 300  | 0.6129          | 0.8160   | 0.7981 |
| 0.4429        | 0.5208 | 400  | 0.4991          | 0.8618   | 0.8171 |
| 0.5441        | 0.6510 | 500  | 0.4392          | 0.8840   | 0.8631 |
| 0.4533        | 0.7812 | 600  | 0.4120          | 0.8985   | 0.8765 |
| 0.1213        | 0.9115 | 700  | 0.3953          | 0.8916   | 0.8738 |
| 0.1151        | 1.0417 | 800  | 0.3146          | 0.9237   | 0.9141 |
| 0.0953        | 1.1719 | 900  | 0.4656          | 0.9015   | 0.8786 |
| 0.1876        | 1.3021 | 1000 | 0.3164          | 0.9168   | 0.9023 |
| 0.2368        | 1.4323 | 1100 | 0.2997          | 0.9305   | 0.9219 |
| 0.0658        | 1.5625 | 1200 | 0.2324          | 0.9534   | 0.9473 |
| 0.0566        | 1.6927 | 1300 | 0.3444          | 0.9176   | 0.9077 |
| 0.2437        | 1.8229 | 1400 | 0.3033          | 0.9435   | 0.9363 |
| 0.1011        | 1.9531 | 1500 | 0.2740          | 0.9450   | 0.9330 |
| 0.2987        | 2.0833 | 1600 | 0.2715          | 0.9489   | 0.9419 |
| 0.0227        | 2.2135 | 1700 | 0.2050          | 0.9603   | 0.9562 |
| 0.1891        | 2.3438 | 1800 | 0.2055          | 0.9542   | 0.9494 |
| 0.0325        | 2.4740 | 1900 | 0.2070          | 0.9626   | 0.9604 |
| 0.0407        | 2.6042 | 2000 | 0.1876          | 0.9611   | 0.9550 |
| 0.0112        | 2.7344 | 2100 | 0.1702          | 0.9748   | 0.9719 |
| 0.112         | 2.8646 | 2200 | 0.1695          | 0.9656   | 0.9624 |
| 0.184         | 2.9948 | 2300 | 0.2088          | 0.9626   | 0.9590 |
| 0.0464        | 3.125  | 2400 | 0.1805          | 0.9656   | 0.9613 |
| 0.0794        | 3.2552 | 2500 | 0.2089          | 0.9634   | 0.9608 |
| 0.0033        | 3.3854 | 2600 | 0.2128          | 0.9603   | 0.9623 |
| 0.0422        | 3.5156 | 2700 | 0.1378          | 0.9702   | 0.9701 |
| 0.2038        | 3.6458 | 2800 | 0.1674          | 0.9687   | 0.9685 |
| 0.0156        | 3.7760 | 2900 | 0.1383          | 0.9756   | 0.9758 |
| 0.0004        | 3.9062 | 3000 | 0.1544          | 0.9733   | 0.9715 |
| 0.002         | 4.0365 | 3100 | 0.1552          | 0.9710   | 0.9690 |
| 0.0405        | 4.1667 | 3200 | 0.1326          | 0.9763   | 0.9751 |
| 0.0031        | 4.2969 | 3300 | 0.1437          | 0.9756   | 0.9759 |
| 0.0022        | 4.4271 | 3400 | 0.1316          | 0.9794   | 0.9790 |
| 0.0019        | 4.5573 | 3500 | 0.1233          | 0.9809   | 0.9799 |
| 0.0005        | 4.6875 | 3600 | 0.1400          | 0.9771   | 0.9763 |
| 0.0002        | 4.8177 | 3700 | 0.1339          | 0.9794   | 0.9797 |
| 0.0304        | 4.9479 | 3800 | 0.1469          | 0.9794   | 0.9795 |
| 0.0           | 5.0781 | 3900 | 0.1532          | 0.9763   | 0.9760 |
| 0.0           | 5.2083 | 4000 | 0.1530          | 0.9779   | 0.9771 |
| 0.0006        | 5.3385 | 4100 | 0.1434          | 0.9771   | 0.9765 |
| 0.0218        | 5.4688 | 4200 | 0.1468          | 0.9763   | 0.9751 |
| 0.0043        | 5.5990 | 4300 | 0.1568          | 0.9779   | 0.9763 |
| 0.0246        | 5.7292 | 4400 | 0.1582          | 0.9771   | 0.9748 |
| 0.0052        | 5.8594 | 4500 | 0.1489          | 0.9786   | 0.9774 |
| 0.0003        | 5.9896 | 4600 | 0.1499          | 0.9779   | 0.9775 |
| 0.0           | 6.1198 | 4700 | 0.1457          | 0.9794   | 0.9786 |
| 0.0           | 6.25   | 4800 | 0.1437          | 0.9802   | 0.9794 |
| 0.0048        | 6.3802 | 4900 | 0.1440          | 0.9794   | 0.9782 |
| 0.0002        | 6.5104 | 5000 | 0.1417          | 0.9802   | 0.9793 |
| 0.0001        | 6.6406 | 5100 | 0.1427          | 0.9802   | 0.9794 |
| 0.0           | 6.7708 | 5200 | 0.1422          | 0.9802   | 0.9787 |
| 0.0002        | 6.9010 | 5300 | 0.1425          | 0.9802   | 0.9787 |


### Framework versions

- Transformers 4.52.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1

american black bear american crow eastern fox squirrel eastern gray squirrel grey fox horse northern raccoon red fox striped skunk vehicle virginia opossum white_tailed_deer bird wild turkey woodchuck bobcat chicken coyote dog domestic cat eastern chipmunk eastern cottontail