--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base_rvl-cdip results: [] --- # vit-base_rvl-cdip This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5535 - Accuracy: 0.897 - Brier Loss: 0.1768 - Nll: 1.0978 - F1 Micro: 0.897 - F1 Macro: 0.8972 - Ece: 0.0801 - Aurc: 0.0180 ## 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: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 16 - 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:| | 0.676 | 1.0 | 5000 | 0.6451 | 0.8230 | 0.2574 | 1.2627 | 0.8230 | 0.8237 | 0.0458 | 0.0425 | | 0.4207 | 2.0 | 10000 | 0.4251 | 0.8766 | 0.1800 | 1.2821 | 0.8766 | 0.8779 | 0.0154 | 0.0218 | | 0.3335 | 3.0 | 15000 | 0.3914 | 0.8861 | 0.1676 | 1.2589 | 0.8861 | 0.8858 | 0.0252 | 0.0192 | | 0.2447 | 4.0 | 20000 | 0.3687 | 0.8934 | 0.1574 | 1.2243 | 0.8934 | 0.8937 | 0.0331 | 0.0164 | | 0.1623 | 5.0 | 25000 | 0.3843 | 0.8976 | 0.1583 | 1.1553 | 0.8976 | 0.8973 | 0.0461 | 0.0159 | | 0.1083 | 6.0 | 30000 | 0.4131 | 0.8964 | 0.1624 | 1.1514 | 0.8964 | 0.8967 | 0.0581 | 0.0163 | | 0.0652 | 7.0 | 35000 | 0.4633 | 0.8966 | 0.1690 | 1.1300 | 0.8966 | 0.8967 | 0.0692 | 0.0169 | | 0.0361 | 8.0 | 40000 | 0.5068 | 0.8976 | 0.1723 | 1.1161 | 0.8976 | 0.8976 | 0.0737 | 0.0175 | | 0.0192 | 9.0 | 45000 | 0.5418 | 0.8982 | 0.1748 | 1.1015 | 0.8982 | 0.8983 | 0.0779 | 0.0179 | | 0.0111 | 10.0 | 50000 | 0.5535 | 0.897 | 0.1768 | 1.0978 | 0.897 | 0.8972 | 0.0801 | 0.0180 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1.post200 - Datasets 2.9.0 - Tokenizers 0.13.2