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# dogvit

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Accuracy: 1.0
- F1: 1.0
- Precision: 1.0
- Recall: 1.0
- Loss: 0.0230
- Classification Report:               precision    recall  f1-score   support

           0       1.00      1.00      1.00         3
           1       1.00      1.00      1.00         3

    accuracy                           1.00         6
   macro avg       1.00      1.00      1.00         6
weighted avg       1.00      1.00      1.00         6

- Confusion Matrix: [[1.0, 0.0], [0.0, 1.0]]

## 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: 0.0002
- 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                                       |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------:|:------:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------:|
| 0.8032        | 1.0   | 199  | 0.8333   | 0.8286 | 0.875     | 0.8333 | 0.4054          |               precision    recall  f1-score   support

           0       0.75      1.00      0.86         3
           1       1.00      0.67      0.80         3

    accuracy                           0.83         6
   macro avg       0.88      0.83      0.83         6
weighted avg       0.88      0.83      0.83         6
 | [[1.0, 0.0], [0.3333333333333333, 0.6666666666666666]] |
| 0.7593        | 2.0   | 398  | 0.6667   | 0.625  | 0.8       | 0.6667 | 0.7945          |               precision    recall  f1-score   support

           0       1.00      0.33      0.50         3
           1       0.60      1.00      0.75         3

    accuracy                           0.67         6
   macro avg       0.80      0.67      0.62         6
weighted avg       0.80      0.67      0.62         6
 | [[0.3333333333333333, 0.6666666666666666], [0.0, 1.0]] |
| 0.7475        | 3.0   | 597  | 1.0      | 1.0    | 1.0       | 1.0    | 0.0230          |               precision    recall  f1-score   support

           0       1.00      1.00      1.00         3
           1       1.00      1.00      1.00         3

    accuracy                           1.00         6
   macro avg       1.00      1.00      1.00         6
weighted avg       1.00      1.00      1.00         6
 | [[1.0, 0.0], [0.0, 1.0]]                               |
| 0.7073        | 4.0   | 796  | 1.0      | 1.0    | 1.0       | 1.0    | 0.0510          |               precision    recall  f1-score   support

           0       1.00      1.00      1.00         3
           1       1.00      1.00      1.00         3

    accuracy                           1.00         6
   macro avg       1.00      1.00      1.00         6
weighted avg       1.00      1.00      1.00         6
 | [[1.0, 0.0], [0.0, 1.0]]                               |
| 0.6539        | 5.0   | 995  | 1.0      | 1.0    | 1.0       | 1.0    | 0.0321          |               precision    recall  f1-score   support

           0       1.00      1.00      1.00         3
           1       1.00      1.00      1.00         3

    accuracy                           1.00         6
   macro avg       1.00      1.00      1.00         6
weighted avg       1.00      1.00      1.00         6
 | [[1.0, 0.0], [0.0, 1.0]]                               |


### 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"], "datasets": ["imagefolder"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "google/vit-base-patch16-224", "model-index": [{"name": "dogvit", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 1.0, "name": "Accuracy"}, {"type": "f1", "value": 1.0, "name": "F1"}, {"type": "precision", "value": 1.0, "name": "Precision"}, {"type": "recall", "value": 1.0, "name": "Recall"}]}]}]}
transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us