vit-ytr-demo-v2
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the SkinCancerClassification dataset. It achieves the following results on the evaluation set:
- Loss: 0.4299
- Accuracy: 0.8497
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
0.7759 | 0.31 | 100 | 0.7807 | 0.7304 |
0.6829 | 0.62 | 200 | 0.6997 | 0.7459 |
0.8535 | 0.93 | 300 | 0.6830 | 0.7594 |
0.6652 | 1.25 | 400 | 0.6186 | 0.7803 |
0.5537 | 1.56 | 500 | 0.5893 | 0.7973 |
0.5244 | 1.87 | 600 | 0.5723 | 0.7933 |
0.4224 | 2.18 | 700 | 0.5234 | 0.8168 |
0.516 | 2.49 | 800 | 0.5281 | 0.8028 |
0.4097 | 2.8 | 900 | 0.5010 | 0.8293 |
0.2422 | 3.12 | 1000 | 0.4585 | 0.8417 |
0.1393 | 3.43 | 1100 | 0.4508 | 0.8502 |
0.2486 | 3.74 | 1200 | 0.4299 | 0.8497 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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