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# dogv1

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: 0.7833
- F1: 0.7727
- Precision: 0.7821
- Recall: 0.7833
- Loss: 1.0875
- Classification Report:               precision    recall  f1-score   support

           0       0.83      1.00      0.91        20
           1       0.73      0.80      0.76        20
           2       0.79      0.55      0.65        20

    accuracy                           0.78        60
   macro avg       0.78      0.78      0.77        60
weighted avg       0.78      0.78      0.77        60

- Confusion Matrix: [[1.0, 0.0, 0.0], [0.05, 0.8, 0.15], [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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- 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                                        |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------:|:------:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------:|
| 1.024         | 1.0   | 367  | 0.5333   | 0.4617 | 0.5932    | 0.5333 | 1.0337          |               precision    recall  f1-score   support

           0       0.49      1.00      0.66        20
           1       0.62      0.50      0.56        20
           2       0.67      0.10      0.17        20

    accuracy                           0.53        60
   macro avg       0.59      0.53      0.46        60
weighted avg       0.59      0.53      0.46        60
 | [[1.0, 0.0, 0.0], [0.45, 0.5, 0.05], [0.6, 0.3, 0.1]]   |
| 1.0018        | 2.0   | 735  | 0.4833   | 0.4548 | 0.5667    | 0.4833 | 1.1677          |               precision    recall  f1-score   support

           0       0.80      0.20      0.32        20
           1       0.50      0.75      0.60        20
           2       0.40      0.50      0.44        20

    accuracy                           0.48        60
   macro avg       0.57      0.48      0.45        60
weighted avg       0.57      0.48      0.45        60
 | [[0.2, 0.3, 0.5], [0.0, 0.75, 0.25], [0.05, 0.45, 0.5]] |
| 0.9701        | 3.0   | 1102 | 0.4333   | 0.3799 | 0.6641    | 0.4333 | 1.0422          |               precision    recall  f1-score   support

           0       1.00      0.15      0.26        20
           1       0.62      0.25      0.36        20
           2       0.37      0.90      0.52        20

    accuracy                           0.43        60
   macro avg       0.66      0.43      0.38        60
weighted avg       0.66      0.43      0.38        60
 | [[0.15, 0.05, 0.8], [0.0, 0.25, 0.75], [0.0, 0.1, 0.9]] |
| 0.8329        | 4.0   | 1470 | 0.7333   | 0.7249 | 0.7264    | 0.7333 | 0.6789          |               precision    recall  f1-score   support

           0       0.79      0.95      0.86        20
           1       0.70      0.70      0.70        20
           2       0.69      0.55      0.61        20

    accuracy                           0.73        60
   macro avg       0.73      0.73      0.72        60
weighted avg       0.73      0.73      0.72        60
 | [[0.95, 0.0, 0.05], [0.1, 0.7, 0.2], [0.15, 0.3, 0.55]] |
| 0.5928        | 4.99  | 1835 | 0.7833   | 0.7727 | 0.7821    | 0.7833 | 1.0875          |               precision    recall  f1-score   support

           0       0.83      1.00      0.91        20
           1       0.73      0.80      0.76        20
           2       0.79      0.55      0.65        20

    accuracy                           0.78        60
   macro avg       0.78      0.78      0.77        60
weighted avg       0.78      0.78      0.77        60
 | [[1.0, 0.0, 0.0], [0.05, 0.8, 0.15], [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": "dogv1", "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