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experiment2
This model is a fine-tuned version of google/vit-base-patch16-224 on an unknown dataset. It achieves the following results on the evaluation set:
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Accuracy: 0.975
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F1: 0.9747
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Precision: 0.9792
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Recall: 0.975
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Loss: 0.0697
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Classification Report: precision recall f1-score support
0 1.00 1.00 1.00 5 1 1.00 1.00 1.00 5 2 1.00 1.00 1.00 5 3 0.83 1.00 0.91 5 4 1.00 1.00 1.00 5 5 1.00 1.00 1.00 5 6 1.00 0.80 0.89 5 7 1.00 1.00 1.00 5accuracy 0.97 40 macro avg 0.98 0.97 0.97 40 weighted avg 0.98 0.97 0.97 40
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Confusion Matrix: [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.8, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Precision | Recall | Validation Loss | Classification Report | Confusion Matrix |
|---|---|---|---|---|---|---|---|---|---|
| 0.076 | 1.0 | 174 | 0.95 | 0.9495 | 0.9583 | 0.95 | 0.1352 | precision recall f1-score support |
0 1.00 0.80 0.89 5
1 0.83 1.00 0.91 5
2 1.00 1.00 1.00 5
3 1.00 1.00 1.00 5
4 1.00 1.00 1.00 5
5 1.00 0.80 0.89 5
6 0.83 1.00 0.91 5
7 1.00 1.00 1.00 5
accuracy 0.95 40
macro avg 0.96 0.95 0.95 40 weighted avg 0.96 0.95 0.95 40 | [[0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.2, 0.0, 0.0, 0.0, 0.8, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]] | | 0.0312 | 2.0 | 348 | 0.975 | 0.9747 | 0.9792 | 0.975 | 0.0697 | precision recall f1-score support
0 1.00 1.00 1.00 5
1 1.00 1.00 1.00 5
2 1.00 1.00 1.00 5
3 0.83 1.00 0.91 5
4 1.00 1.00 1.00 5
5 1.00 1.00 1.00 5
6 1.00 0.80 0.89 5
7 1.00 1.00 1.00 5
accuracy 0.97 40
macro avg 0.98 0.97 0.97 40 weighted avg 0.98 0.97 0.97 40 | [[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.2, 0.0, 0.0, 0.8, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]] | | 0.0031 | 3.0 | 522 | 0.975 | 0.9747 | 0.9792 | 0.975 | 0.0720 | precision recall f1-score support
0 1.00 0.80 0.89 5
1 1.00 1.00 1.00 5
2 1.00 1.00 1.00 5
3 1.00 1.00 1.00 5
4 1.00 1.00 1.00 5
5 1.00 1.00 1.00 5
6 0.83 1.00 0.91 5
7 1.00 1.00 1.00 5
accuracy 0.97 40
macro avg 0.98 0.97 0.97 40 weighted avg 0.98 0.97 0.97 40 | [[0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]] |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
