--- license: apache-2.0 tags: - 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 config: default split: validation args: default metrics: - name: Accuracy type: accuracy value: 0.9774436090225563 --- # 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: 0.0886 - Accuracy: 0.9774 ## 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: 8 - eval_batch_size: 8 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2574 | 1.0 | 130 | 0.2307 | 0.9624 | | 0.2785 | 2.0 | 260 | 0.1109 | 0.9925 | | 0.1496 | 3.0 | 390 | 0.1109 | 0.9699 | | 0.0916 | 4.0 | 520 | 0.0875 | 0.9850 | | 0.1489 | 5.0 | 650 | 0.0886 | 0.9774 | ### Framework versions - Transformers 4.28.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.2