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# vit-orinoquia

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the orinoquia dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1021
- Accuracy: 0.9691

## 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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.9968        | 0.0463 | 100  | 1.8490          | 0.4418   |
| 1.3976        | 0.0927 | 200  | 1.5191          | 0.5054   |
| 1.3472        | 0.1390 | 300  | 1.3085          | 0.6078   |
| 0.9815        | 0.1854 | 400  | 1.1603          | 0.6314   |
| 1.2055        | 0.2317 | 500  | 1.0710          | 0.6709   |
| 1.0358        | 0.2780 | 600  | 1.0229          | 0.6820   |
| 0.8788        | 0.3244 | 700  | 0.8523          | 0.7340   |
| 0.9701        | 0.3707 | 800  | 0.8020          | 0.7497   |
| 0.6715        | 0.4171 | 900  | 0.7216          | 0.7830   |
| 0.851         | 0.4634 | 1000 | 0.7933          | 0.7527   |
| 0.6638        | 0.5097 | 1100 | 0.6775          | 0.8034   |
| 0.6149        | 0.5561 | 1200 | 0.6193          | 0.8183   |
| 0.6763        | 0.6024 | 1300 | 0.5211          | 0.8462   |
| 0.6147        | 0.6487 | 1400 | 0.5817          | 0.8229   |
| 0.6746        | 0.6951 | 1500 | 0.4546          | 0.8700   |
| 0.4658        | 0.7414 | 1600 | 0.4779          | 0.8586   |
| 0.4134        | 0.7878 | 1700 | 0.3890          | 0.8854   |
| 0.4485        | 0.8341 | 1800 | 0.4842          | 0.8518   |
| 0.4662        | 0.8804 | 1900 | 0.3461          | 0.8992   |
| 0.475         | 0.9268 | 2000 | 0.3462          | 0.8968   |
| 0.2374        | 0.9731 | 2100 | 0.3530          | 0.8936   |
| 0.2639        | 1.0195 | 2200 | 0.3032          | 0.9128   |
| 0.2466        | 1.0658 | 2300 | 0.3104          | 0.9120   |
| 0.1393        | 1.1121 | 2400 | 0.2706          | 0.9244   |
| 0.1186        | 1.1585 | 2500 | 0.2955          | 0.9193   |
| 0.121         | 1.2048 | 2600 | 0.2699          | 0.9236   |
| 0.4363        | 1.2512 | 2700 | 0.2491          | 0.9323   |
| 0.3046        | 1.2975 | 2800 | 0.2502          | 0.9290   |
| 0.1064        | 1.3438 | 2900 | 0.2466          | 0.9339   |
| 0.1233        | 1.3902 | 3000 | 0.2184          | 0.9391   |
| 0.1971        | 1.4365 | 3100 | 0.2066          | 0.9426   |
| 0.0741        | 1.4829 | 3200 | 0.1730          | 0.9510   |
| 0.1206        | 1.5292 | 3300 | 0.1964          | 0.9477   |
| 0.045         | 1.5755 | 3400 | 0.1719          | 0.9515   |
| 0.0972        | 1.6219 | 3500 | 0.1527          | 0.9588   |
| 0.1798        | 1.6682 | 3600 | 0.1389          | 0.9613   |
| 0.0468        | 1.7146 | 3700 | 0.1267          | 0.9664   |
| 0.0451        | 1.7609 | 3800 | 0.1337          | 0.9645   |
| 0.0362        | 1.8072 | 3900 | 0.1312          | 0.9648   |
| 0.0546        | 1.8536 | 4000 | 0.1172          | 0.9680   |
| 0.163         | 1.8999 | 4100 | 0.1091          | 0.9694   |
| 0.0625        | 1.9462 | 4200 | 0.1055          | 0.9686   |
| 0.0725        | 1.9926 | 4300 | 0.1021          | 0.9691   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1

agouti bird spec red brocket deer red deer red fox red squirrel roe deer spiny rat white tailed deer white-nosed coati wild boar wood mouse coiban agouti collared peccary common opossum european hare great tinamou mouflon ocelot paca