vit-bmr-tuned
This model is a fine-tuned version of google/vit-large-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5394
- Accuracy: 0.9259
- Precision: 0.9267
- Recall: 0.9259
- F1: 0.9258
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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 160 | 0.5105 | 0.8889 | 0.8905 | 0.8889 | 0.8885 |
| No log | 2.0 | 320 | 0.7906 | 0.8148 | 0.8485 | 0.8148 | 0.8079 |
| No log | 3.0 | 480 | 0.5384 | 0.9012 | 0.9019 | 0.9012 | 0.9010 |
| 0.211 | 4.0 | 640 | 0.5394 | 0.9259 | 0.9267 | 0.9259 | 0.9258 |
Framework versions
- Transformers 4.57.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1