msi-dinat-mini-pretrain
This model is a fine-tuned version of shi-labs/dinat-mini-in1k-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.5378
- Accuracy: 0.8937
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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.1041 | 1.0 | 1562 | 0.4092 | 0.8756 |
| 0.0527 | 2.0 | 3125 | 0.6298 | 0.8765 |
| 0.0611 | 3.0 | 4686 | 0.5378 | 0.8937 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0