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# aditnnda/felidae_klasifikasi_fix

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an [Felidae Dataset](https://huggingface.co/datasets/aditnnda/Felidae).
It achieves the following results on the evaluation set:
- Train Loss: 0.2941
- Train Accuracy: 0.9180
- Validation Loss: 0.2691
- Validation Accuracy: 0.9180
- Epoch: 49

## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 9100, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 1.5677     | 0.6885         | 1.4799          | 0.6885              | 0     |
| 1.4926     | 0.9180         | 1.3584          | 0.9180              | 1     |
| 1.3957     | 0.9016         | 1.2332          | 0.9016              | 2     |
| 1.3151     | 0.8361         | 1.1098          | 0.8361              | 3     |
| 1.1756     | 0.8525         | 0.9886          | 0.8525              | 4     |
| 1.1173     | 0.8852         | 0.8775          | 0.8852              | 5     |
| 1.0196     | 0.9016         | 0.8042          | 0.9016              | 6     |
| 0.9451     | 0.9344         | 0.6991          | 0.9344              | 7     |
| 0.9189     | 0.9508         | 0.6396          | 0.9508              | 8     |
| 0.8269     | 0.9672         | 0.5717          | 0.9672              | 9     |
| 0.7581     | 0.9836         | 0.5063          | 0.9836              | 10    |
| 0.7328     | 0.9672         | 0.4772          | 0.9672              | 11    |
| 0.6639     | 0.9508         | 0.4691          | 0.9508              | 12    |
| 0.7082     | 0.9180         | 0.4597          | 0.9180              | 13    |
| 0.6607     | 0.9672         | 0.4006          | 0.9672              | 14    |
| 0.7278     | 0.9508         | 0.3966          | 0.9508              | 15    |
| 0.6285     | 0.9508         | 0.3669          | 0.9508              | 16    |
| 0.5902     | 0.9836         | 0.3321          | 0.9836              | 17    |
| 0.5963     | 0.9344         | 0.3899          | 0.9344              | 18    |
| 0.6273     | 0.9344         | 0.3246          | 0.9344              | 19    |
| 0.5917     | 0.9016         | 0.4248          | 0.9016              | 20    |
| 0.5190     | 0.9180         | 0.3575          | 0.9180              | 21    |
| 0.4991     | 0.9508         | 0.3060          | 0.9508              | 22    |
| 0.4861     | 0.9344         | 0.3244          | 0.9344              | 23    |
| 0.4650     | 0.9344         | 0.3014          | 0.9344              | 24    |
| 0.5044     | 0.9508         | 0.2727          | 0.9508              | 25    |
| 0.4232     | 0.9672         | 0.2439          | 0.9672              | 26    |
| 0.4247     | 0.9672         | 0.2233          | 0.9672              | 27    |
| 0.4732     | 0.9508         | 0.2857          | 0.9508              | 28    |
| 0.4222     | 1.0            | 0.1937          | 1.0                 | 29    |
| 0.4165     | 0.9836         | 0.1996          | 0.9836              | 30    |
| 0.3979     | 0.9508         | 0.2074          | 0.9508              | 31    |
| 0.3691     | 0.9344         | 0.2627          | 0.9344              | 32    |
| 0.3094     | 0.9344         | 0.2641          | 0.9344              | 33    |
| 0.3726     | 0.9508         | 0.2224          | 0.9508              | 34    |
| 0.3503     | 0.9508         | 0.2241          | 0.9508              | 35    |
| 0.3832     | 0.9344         | 0.2274          | 0.9344              | 36    |
| 0.3765     | 0.9508         | 0.2421          | 0.9508              | 37    |
| 0.3927     | 0.9508         | 0.1979          | 0.9508              | 38    |
| 0.2968     | 0.9672         | 0.1857          | 0.9672              | 39    |
| 0.3489     | 0.9508         | 0.2158          | 0.9508              | 40    |
| 0.4102     | 0.9672         | 0.1951          | 0.9672              | 41    |
| 0.3842     | 0.9672         | 0.1971          | 0.9672              | 42    |
| 0.3417     | 0.8852         | 0.3686          | 0.8852              | 43    |
| 0.3219     | 0.9344         | 0.2255          | 0.9344              | 44    |
| 0.3671     | 0.9672         | 0.1570          | 0.9672              | 45    |
| 0.3948     | 0.9344         | 0.2217          | 0.9344              | 46    |
| 0.3201     | 0.9672         | 0.1993          | 0.9672              | 47    |
| 0.3612     | 0.9508         | 0.1936          | 0.9508              | 48    |
| 0.2941     | 0.9180         | 0.2691          | 0.9180              | 49    |


### Framework versions

- Transformers 4.35.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1

cheetah leopard lion puma tiger