CIDAUTv2
This model is a fine-tuned version of microsoft/beit-base-patch16-224-pt22k-ft22k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2719
- Accuracy: 0.9259
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 4 | 0.7322 | 0.5880 |
| No log | 2.0 | 8 | 0.6585 | 0.5972 |
| 0.7438 | 3.0 | 12 | 0.6115 | 0.7222 |
| 0.7438 | 4.0 | 16 | 0.5726 | 0.7546 |
| 0.5781 | 5.0 | 20 | 0.4803 | 0.7824 |
| 0.5781 | 6.0 | 24 | 0.4627 | 0.8333 |
| 0.5781 | 7.0 | 28 | 0.4060 | 0.8056 |
| 0.4511 | 8.0 | 32 | 0.3512 | 0.8796 |
| 0.4511 | 9.0 | 36 | 0.2725 | 0.9028 |
| 0.296 | 10.0 | 40 | 0.2719 | 0.9259 |
Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0