--- license: apache-2.0 tags: - image-classification - vision - generated_from_trainer datasets: - beans metrics: - accuracy model-index: - name: vit-base-beans results: - task: name: Image Classification type: image-classification dataset: name: beans type: beans args: default metrics: - name: Accuracy type: accuracy value: 0.9699248120300752 --- # vit-base-beans 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 beans dataset. It achieves the following results on the evaluation set: - Loss: 0.1824 - Accuracy: 0.9699 ## 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: 24 - eval_batch_size: 24 - seed: 1337 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.672 | 1.0 | 44 | 0.5672 | 0.9398 | | 0.411 | 2.0 | 88 | 0.3027 | 0.9699 | | 0.2542 | 3.0 | 132 | 0.2078 | 0.9699 | | 0.1886 | 4.0 | 176 | 0.1882 | 0.9699 | | 0.1931 | 5.0 | 220 | 0.1824 | 0.9699 | ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.2.2 - Tokenizers 0.12.1