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# dognewvit_v1

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: 0.65
- F1: 0.6541
- Precision: 0.7682
- Recall: 0.65
- Loss: 1.2218
- Classification Report:               precision    recall  f1-score   support

           0       1.00      0.60      0.75        20
           1       0.82      0.45      0.58        20
           2       0.49      0.90      0.63        20

    accuracy                           0.65        60
   macro avg       0.77      0.65      0.65        60
weighted avg       0.77      0.65      0.65        60

- Confusion Matrix: [[0.6, 0.0, 0.4], [0.0, 0.45, 0.55], [0.0, 0.1, 0.9]]

## 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: 1e-05
- 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: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Accuracy | F1     | Precision | Recall | Validation Loss | Classification Report                                                                                                                                                                                                                                                                                                                                                                        | Confusion Matrix                                          |
|:-------------:|:-----:|:-----:|:--------:|:------:|:---------:|:------:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|
| 0.9527        | 1.0   | 893   | 0.3333   | 0.1667 | 0.7778    | 0.3333 | 1.6578          |               precision    recall  f1-score   support

           0       0.00      0.00      0.00        20
           1       0.00      0.00      0.00        20
           2       0.33      1.00      0.50        20

    accuracy                           0.33        60
   macro avg       0.11      0.33      0.17        60
weighted avg       0.11      0.33      0.17        60
 | [[0.0, 0.0, 1.0], [0.0, 0.0, 1.0], [0.0, 0.0, 1.0]]       |
| 0.9697        | 2.0   | 1786  | 0.35     | 0.2282 | 0.3929    | 0.3500 | 1.3920          |               precision    recall  f1-score   support

           0       0.33      0.05      0.09        20
           1       0.50      0.05      0.09        20
           2       0.35      0.95      0.51        20

    accuracy                           0.35        60
   macro avg       0.39      0.35      0.23        60
weighted avg       0.39      0.35      0.23        60
 | [[0.05, 0.05, 0.9], [0.05, 0.05, 0.9], [0.05, 0.0, 0.95]] |
| 0.9553        | 3.0   | 2679  | 0.3667   | 0.2489 | 0.6485    | 0.3667 | 1.8832          |               precision    recall  f1-score   support

           0       0.60      0.15      0.24        20
           1       0.00      0.00      0.00        20
           2       0.35      0.95      0.51        20

    accuracy                           0.37        60
   macro avg       0.32      0.37      0.25        60
weighted avg       0.32      0.37      0.25        60
 | [[0.15, 0.0, 0.85], [0.05, 0.0, 0.95], [0.05, 0.0, 0.95]] |
| 0.9602        | 4.0   | 3572  | 0.4667   | 0.4329 | 0.6806    | 0.4667 | 1.2973          |               precision    recall  f1-score   support

           0       0.83      0.25      0.38        20
           1       0.83      0.25      0.38        20
           2       0.38      0.90      0.53        20

    accuracy                           0.47        60
   macro avg       0.68      0.47      0.43        60
weighted avg       0.68      0.47      0.43        60
 | [[0.25, 0.0, 0.75], [0.0, 0.25, 0.75], [0.05, 0.05, 0.9]] |
| 0.9318        | 5.0   | 4465  | 0.3667   | 0.2500 | 0.6964    | 0.3667 | 1.8812          |               precision    recall  f1-score   support

           0       0.75      0.15      0.25        20
           1       0.00      0.00      0.00        20
           2       0.34      0.95      0.50        20

    accuracy                           0.37        60
   macro avg       0.36      0.37      0.25        60
weighted avg       0.36      0.37      0.25        60
 | [[0.15, 0.0, 0.85], [0.0, 0.0, 1.0], [0.05, 0.0, 0.95]]   |
| 0.9318        | 6.0   | 5358  | 0.45     | 0.4163 | 0.6121    | 0.45   | 1.5292          |               precision    recall  f1-score   support

           0       0.80      0.20      0.32        20
           1       0.67      0.30      0.41        20
           2       0.37      0.85      0.52        20

    accuracy                           0.45        60
   macro avg       0.61      0.45      0.42        60
weighted avg       0.61      0.45      0.42        60
 | [[0.2, 0.05, 0.75], [0.0, 0.3, 0.7], [0.05, 0.1, 0.85]]   |
| 0.9208        | 7.0   | 6251  | 0.4667   | 0.4428 | 0.6510    | 0.4667 | 1.4889          |               precision    recall  f1-score   support

           0       0.83      0.25      0.38        20
           1       0.75      0.30      0.43        20
           2       0.37      0.85      0.52        20

    accuracy                           0.47        60
   macro avg       0.65      0.47      0.44        60
weighted avg       0.65      0.47      0.44        60
 | [[0.25, 0.0, 0.75], [0.0, 0.3, 0.7], [0.05, 0.1, 0.85]]   |
| 0.9065        | 8.0   | 7144  | 0.5167   | 0.4910 | 0.7538    | 0.5167 | 1.6385          |               precision    recall  f1-score   support

           0       1.00      0.30      0.46        20
           1       0.86      0.30      0.44        20
           2       0.40      0.95      0.57        20

    accuracy                           0.52        60
   macro avg       0.75      0.52      0.49        60
weighted avg       0.75      0.52      0.49        60
 | [[0.3, 0.0, 0.7], [0.0, 0.3, 0.7], [0.0, 0.05, 0.95]]     |
| 0.9137        | 9.0   | 8037  | 0.6333   | 0.6288 | 0.7168    | 0.6333 | 0.9756          |               precision    recall  f1-score   support

           0       0.92      0.60      0.73        20
           1       0.73      0.40      0.52        20
           2       0.50      0.90      0.64        20

    accuracy                           0.63        60
   macro avg       0.72      0.63      0.63        60
weighted avg       0.72      0.63      0.63        60
 | [[0.6, 0.05, 0.35], [0.05, 0.4, 0.55], [0.0, 0.1, 0.9]]   |
| 0.8771        | 10.0  | 8930  | 0.6      | 0.5981 | 0.7036    | 0.6    | 1.1206          |               precision    recall  f1-score   support

           0       1.00      0.45      0.62        20
           1       0.62      0.50      0.56        20
           2       0.49      0.85      0.62        20

    accuracy                           0.60        60
   macro avg       0.70      0.60      0.60        60
weighted avg       0.70      0.60      0.60        60
 | [[0.45, 0.15, 0.4], [0.0, 0.5, 0.5], [0.0, 0.15, 0.85]]   |
| 0.9126        | 11.0  | 9823  | 0.6167   | 0.5872 | 0.7816    | 0.6167 | 1.0270          |               precision    recall  f1-score   support

           0       0.86      0.60      0.71        20
           1       1.00      0.25      0.40        20
           2       0.49      1.00      0.66        20

    accuracy                           0.62        60
   macro avg       0.78      0.62      0.59        60
weighted avg       0.78      0.62      0.59        60
 | [[0.6, 0.0, 0.4], [0.1, 0.25, 0.65], [0.0, 0.0, 1.0]]     |
| 0.8493        | 12.0  | 10716 | 0.65     | 0.6541 | 0.7682    | 0.65   | 1.2218          |               precision    recall  f1-score   support

           0       1.00      0.60      0.75        20
           1       0.82      0.45      0.58        20
           2       0.49      0.90      0.63        20

    accuracy                           0.65        60
   macro avg       0.77      0.65      0.65        60
weighted avg       0.77      0.65      0.65        60
 | [[0.6, 0.0, 0.4], [0.0, 0.45, 0.55], [0.0, 0.1, 0.9]]     |
| 0.8645        | 13.0  | 11609 | 0.6167   | 0.6143 | 0.7464    | 0.6167 | 1.3003          |               precision    recall  f1-score   support

           0       1.00      0.60      0.75        20
           1       0.78      0.35      0.48        20
           2       0.46      0.90      0.61        20

    accuracy                           0.62        60
   macro avg       0.75      0.62      0.61        60
weighted avg       0.75      0.62      0.61        60
 | [[0.6, 0.0, 0.4], [0.0, 0.35, 0.65], [0.0, 0.1, 0.9]]     |
| 0.8324        | 14.0  | 12502 | 0.6333   | 0.6347 | 0.7579    | 0.6333 | 1.4515          |               precision    recall  f1-score   support

           0       1.00      0.60      0.75        20
           1       0.80      0.40      0.53        20
           2       0.47      0.90      0.62        20

    accuracy                           0.63        60
   macro avg       0.76      0.63      0.63        60
weighted avg       0.76      0.63      0.63        60
 | [[0.6, 0.0, 0.4], [0.0, 0.4, 0.6], [0.0, 0.1, 0.9]]       |
| 0.8489        | 15.0  | 13395 | 0.6333   | 0.6278 | 0.7833    | 0.6333 | 1.3991          |               precision    recall  f1-score   support

           0       1.00      0.60      0.75        20
           1       0.88      0.35      0.50        20
           2       0.47      0.95      0.63        20

    accuracy                           0.63        60
   macro avg       0.78      0.63      0.63        60
weighted avg       0.78      0.63      0.63        60
 | [[0.6, 0.0, 0.4], [0.0, 0.35, 0.65], [0.0, 0.05, 0.95]]   |


### 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": "dognewvit_v1", "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