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# dogb

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.6
- F1: 0.5950
- Precision: 0.5935
- Recall: 0.6
- Loss: 0.7738
- Classification Report:               precision    recall  f1-score   support

           0       0.73      0.80      0.76        20
           1       0.53      0.45      0.49        20
           2       0.52      0.55      0.54        20

    accuracy                           0.60        60
   macro avg       0.59      0.60      0.59        60
weighted avg       0.59      0.60      0.59        60

- Confusion Matrix: [[0.8, 0.1, 0.1], [0.15, 0.45, 0.4], [0.15, 0.3, 0.55]]

## 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: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Accuracy | F1     | Precision | Recall | Validation Loss | Classification Report                                                                                                                                                                                                                                                                                                                                                                        | Confusion Matrix                                          |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------:|:------:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|
| 1.1189        | 1.0   | 841  | 0.4667   | 0.4550 | 0.4861    | 0.4667 | 1.0521          |               precision    recall  f1-score   support

           0       0.58      0.35      0.44        20
           1       0.44      0.70      0.54        20
           2       0.44      0.35      0.39        20

    accuracy                           0.47        60
   macro avg       0.49      0.47      0.45        60
weighted avg       0.49      0.47      0.45        60
 | [[0.35, 0.35, 0.3], [0.15, 0.7, 0.15], [0.1, 0.55, 0.35]] |
| 1.0513        | 2.0   | 1682 | 0.5167   | 0.4487 | 0.5133    | 0.5167 | 1.0535          |               precision    recall  f1-score   support

           0       0.51      0.95      0.67        20
           1       0.50      0.10      0.17        20
           2       0.53      0.50      0.51        20

    accuracy                           0.52        60
   macro avg       0.51      0.52      0.45        60
weighted avg       0.51      0.52      0.45        60
 | [[0.95, 0.0, 0.05], [0.5, 0.1, 0.4], [0.4, 0.1, 0.5]]     |
| 1.028         | 3.0   | 2523 | 0.5667   | 0.5554 | 0.5623    | 0.5667 | 1.0046          |               precision    recall  f1-score   support

           0       0.62      0.80      0.70        20
           1       0.50      0.50      0.50        20
           2       0.57      0.40      0.47        20

    accuracy                           0.57        60
   macro avg       0.56      0.57      0.56        60
weighted avg       0.56      0.57      0.56        60
 | [[0.8, 0.15, 0.05], [0.25, 0.5, 0.25], [0.25, 0.35, 0.4]] |
| 0.9835        | 4.0   | 3364 | 0.55     | 0.5418 | 0.6032    | 0.5500 | 0.8709          |               precision    recall  f1-score   support

           0       0.77      0.50      0.61        20
           1       0.58      0.35      0.44        20
           2       0.46      0.80      0.58        20

    accuracy                           0.55        60
   macro avg       0.60      0.55      0.54        60
weighted avg       0.60      0.55      0.54        60
 | [[0.5, 0.1, 0.4], [0.1, 0.35, 0.55], [0.05, 0.15, 0.8]]   |
| 0.9244        | 5.0   | 4205 | 0.6      | 0.5950 | 0.5935    | 0.6    | 0.7738          |               precision    recall  f1-score   support

           0       0.73      0.80      0.76        20
           1       0.53      0.45      0.49        20
           2       0.52      0.55      0.54        20

    accuracy                           0.60        60
   macro avg       0.59      0.60      0.59        60
weighted avg       0.59      0.60      0.59        60
 | [[0.8, 0.1, 0.1], [0.15, 0.45, 0.4], [0.15, 0.3, 0.55]]   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "google/vit-base-patch16-224", "model-index": [{"name": "dogb", "results": []}]}
transformers tensorboard safetensors vit image-classification generated_from_trainer base_model:google/vit-base-patch16-224 license:apache-2.0 autotrain_compatible endpoints_compatible region:us