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# deit-ena24_MD

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_MD dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8782
- Accuracy: 0.5459
- F1: 0.4851

## 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 OptimizerNames.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     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 1.1949        | 0.1259 | 100  | 2.2116          | 0.4688   | 0.3895 |
| 0.5955        | 0.2519 | 200  | 2.6496          | 0.4443   | 0.4240 |
| 0.6457        | 0.3778 | 300  | 1.8782          | 0.5459   | 0.4851 |
| 0.5871        | 0.5038 | 400  | 1.9099          | 0.6211   | 0.5460 |
| 0.5023        | 0.6297 | 500  | 1.9720          | 0.6328   | 0.5493 |
| 0.4641        | 0.7557 | 600  | 2.2744          | 0.5869   | 0.5344 |
| 0.7986        | 0.8816 | 700  | 2.3082          | 0.5947   | 0.5330 |
| 0.2118        | 1.0076 | 800  | 2.1929          | 0.6182   | 0.5418 |
| 0.3053        | 1.1335 | 900  | 2.2016          | 0.6533   | 0.5738 |
| 0.2434        | 1.2594 | 1000 | 2.4980          | 0.5908   | 0.5520 |
| 0.1593        | 1.3854 | 1100 | 2.3773          | 0.6279   | 0.5581 |
| 0.6254        | 1.5113 | 1200 | 2.4206          | 0.5898   | 0.5226 |
| 0.3366        | 1.6373 | 1300 | 2.8014          | 0.5762   | 0.5195 |
| 0.2613        | 1.7632 | 1400 | 2.6497          | 0.6172   | 0.5472 |
| 0.1136        | 1.8892 | 1500 | 2.4213          | 0.6074   | 0.5433 |
| 0.1071        | 2.0151 | 1600 | 2.5978          | 0.6436   | 0.5991 |
| 0.087         | 2.1411 | 1700 | 2.6259          | 0.6562   | 0.6066 |
| 0.0358        | 2.2670 | 1800 | 3.1009          | 0.5850   | 0.5267 |
| 0.1166        | 2.3929 | 1900 | 2.7449          | 0.6416   | 0.5866 |
| 0.0119        | 2.5189 | 2000 | 3.0153          | 0.625    | 0.5515 |
| 0.0563        | 2.6448 | 2100 | 2.8322          | 0.6387   | 0.5822 |
| 0.0241        | 2.7708 | 2200 | 2.8272          | 0.6318   | 0.5833 |
| 0.0844        | 2.8967 | 2300 | 2.8638          | 0.6416   | 0.5756 |
| 0.1294        | 3.0227 | 2400 | 2.8058          | 0.6484   | 0.5936 |
| 0.0602        | 3.1486 | 2500 | 2.9597          | 0.6279   | 0.5449 |
| 0.0445        | 3.2746 | 2600 | 2.7733          | 0.6602   | 0.5929 |
| 0.0528        | 3.4005 | 2700 | 2.5498          | 0.6826   | 0.6305 |
| 0.0323        | 3.5264 | 2800 | 2.7229          | 0.6719   | 0.6138 |
| 0.007         | 3.6524 | 2900 | 2.9141          | 0.6650   | 0.6025 |
| 0.0012        | 3.7783 | 3000 | 3.1029          | 0.6533   | 0.5968 |
| 0.0005        | 3.9043 | 3100 | 2.8776          | 0.6221   | 0.5639 |
| 0.0056        | 4.0302 | 3200 | 3.0144          | 0.6455   | 0.5882 |
| 0.0033        | 4.1562 | 3300 | 3.0141          | 0.6270   | 0.5702 |
| 0.0998        | 4.2821 | 3400 | 2.8958          | 0.6553   | 0.6073 |
| 0.0136        | 4.4081 | 3500 | 3.0230          | 0.6641   | 0.6053 |
| 0.0002        | 4.5340 | 3600 | 2.8966          | 0.6484   | 0.5750 |
| 0.0024        | 4.6599 | 3700 | 2.8534          | 0.6650   | 0.6010 |
| 0.0022        | 4.7859 | 3800 | 2.6686          | 0.6748   | 0.6247 |
| 0.0408        | 4.9118 | 3900 | 2.6715          | 0.6904   | 0.6327 |
| 0.1765        | 5.0378 | 4000 | 2.8339          | 0.6621   | 0.5940 |
| 0.0001        | 5.1637 | 4100 | 2.8891          | 0.6738   | 0.6109 |
| 0.0008        | 5.2897 | 4200 | 2.8508          | 0.6797   | 0.6183 |
| 0.0006        | 5.4156 | 4300 | 2.8630          | 0.6787   | 0.6190 |
| 0.0553        | 5.5416 | 4400 | 2.8210          | 0.6836   | 0.6239 |
| 0.0           | 5.6675 | 4500 | 2.7912          | 0.6865   | 0.6291 |
| 0.0           | 5.7935 | 4600 | 2.8061          | 0.6797   | 0.6237 |
| 0.0085        | 5.9194 | 4700 | 2.6576          | 0.6904   | 0.6332 |
| 0.0001        | 6.0453 | 4800 | 2.7866          | 0.6748   | 0.6211 |
| 0.0           | 6.1713 | 4900 | 2.8083          | 0.6738   | 0.6197 |
| 0.0           | 6.2972 | 5000 | 2.8078          | 0.6777   | 0.6251 |
| 0.0           | 6.4232 | 5100 | 2.7978          | 0.6797   | 0.6265 |
| 0.0           | 6.5491 | 5200 | 2.7378          | 0.6963   | 0.6421 |
| 0.0           | 6.6751 | 5300 | 2.7390          | 0.6973   | 0.6428 |
| 0.0           | 6.8010 | 5400 | 2.7374          | 0.6963   | 0.6417 |
| 0.0           | 6.9270 | 5500 | 2.7374          | 0.6963   | 0.6415 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- 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 virginia opossum white_tailed_deer wild turkey bird woodchuck bobcat chicken coyote dog domestic cat eastern chipmunk eastern cottontail