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vit-base-patch16-224-MSC-ARMD-1
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:
- Loss: 0.6451
- Accuracy: 0.95
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 16
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.67 | 1 | 1.3505 | 0.25 |
| No log | 2.0 | 3 | 1.1657 | 0.4 |
| No log | 2.67 | 4 | 1.0703 | 0.55 |
| No log | 4.0 | 6 | 0.8973 | 0.85 |
| No log | 4.67 | 7 | 0.8834 | 0.8 |
| 1.0988 | 6.0 | 9 | 0.7316 | 0.9 |
| 1.0988 | 6.67 | 10 | 0.6451 | 0.95 |
| 1.0988 | 8.0 | 12 | 0.5251 | 0.95 |
| 1.0988 | 8.67 | 13 | 0.4916 | 0.95 |
| 1.0988 | 10.0 | 15 | 0.4606 | 0.85 |
| 0.4896 | 10.67 | 16 | 0.4564 | 0.85 |
Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
