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# dognewvit

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: 0.4667
- F1: 0.4272
- Precision: 0.6027
- Recall: 0.4667
- Loss: 1.2944
- Classification Report:               precision    recall  f1-score   support

           0       0.75      0.30      0.43        20
           1       0.67      0.20      0.31        20
           2       0.39      0.90      0.55        20

    accuracy                           0.47        60
   macro avg       0.60      0.47      0.43        60
weighted avg       0.60      0.47      0.43        60

- Confusion Matrix: [[0.3, 0.05, 0.65], [0.05, 0.2, 0.75], [0.05, 0.05, 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Accuracy | F1     | Precision | Recall | Validation Loss | Classification Report                                                                                                                                                                                                                                                                                                                                                                        | Confusion Matrix                                          |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------:|:------:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------:|
| 0.9786        | 1.0   | 893  | 0.3333   | 0.1688 | 0.4463    | 0.3333 | 1.6754          |               precision    recall  f1-score   support

           0       0.00      0.00      0.00        20
           1       0.00      0.00      0.00        20
           2       0.34      1.00      0.51        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], [0.05, 0.0, 0.95], [0.0, 0.0, 1.0]]     |
| 0.9473        | 2.0   | 1786 | 0.3833   | 0.2838 | 0.4862    | 0.3833 | 1.4097          |               precision    recall  f1-score   support

           0       0.60      0.15      0.24        20
           1       0.50      0.05      0.09        20
           2       0.36      0.95      0.52        20

    accuracy                           0.38        60
   macro avg       0.49      0.38      0.28        60
weighted avg       0.49      0.38      0.28        60
 | [[0.15, 0.05, 0.8], [0.05, 0.05, 0.9], [0.05, 0.0, 0.95]] |
| 0.9343        | 3.0   | 2679 | 0.4      | 0.2970 | 0.6909    | 0.4000 | 1.6397          |               precision    recall  f1-score   support

           0       0.71      0.25      0.37        20
           1       0.00      0.00      0.00        20
           2       0.36      0.95      0.52        20

    accuracy                           0.40        60
   macro avg       0.36      0.40      0.30        60
weighted avg       0.36      0.40      0.30        60
 | [[0.25, 0.0, 0.75], [0.05, 0.0, 0.95], [0.05, 0.0, 0.95]] |
| 0.9253        | 4.0   | 3572 | 0.45     | 0.3904 | 0.6173    | 0.45   | 1.4890          |               precision    recall  f1-score   support

           0       0.71      0.25      0.37        20
           1       0.75      0.15      0.25        20
           2       0.39      0.95      0.55        20

    accuracy                           0.45        60
   macro avg       0.62      0.45      0.39        60
weighted avg       0.62      0.45      0.39        60
 | [[0.25, 0.05, 0.7], [0.05, 0.15, 0.8], [0.05, 0.0, 0.95]] |
| 0.9084        | 5.0   | 4465 | 0.4667   | 0.4272 | 0.6027    | 0.4667 | 1.2944          |               precision    recall  f1-score   support

           0       0.75      0.30      0.43        20
           1       0.67      0.20      0.31        20
           2       0.39      0.90      0.55        20

    accuracy                           0.47        60
   macro avg       0.60      0.47      0.43        60
weighted avg       0.60      0.47      0.43        60
 | [[0.3, 0.05, 0.65], [0.05, 0.2, 0.75], [0.05, 0.05, 0.9]] |


### 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", "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