# nateraw /vit-base-beans-demo

 1 --- 2 license: apache-2.0 3 tags: 4 - image-classification 5 - other-image-classification 6 - generated_from_trainer 7 datasets: 8 - beans 9 metrics: 10 - accuracy 11 12 model-index: 13 - name: vit-base-beans-demo 14  results: 15  - task: 16  name: Image Classification 17  type: image-classification 18  dataset: 19  name: beans 20  type: beans 21  args: default 22  metrics: 23  - name: Accuracy 24  type: accuracy 25  value: 0.9774436090225563 26 --- 27 28  30 31 # vit-base-beans-demo 32 33 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. 34 It achieves the following results on the evaluation set: 35 - Loss: 0.0853 36 - Accuracy: 0.9774 37 38 ## Model description 39 40 More information needed 41 42 ## Intended uses & limitations 43 44 More information needed 45 46 ## Training and evaluation data 47 48 More information needed 49 50 ## Training procedure 51 52 ### Training hyperparameters 53 54 The following hyperparameters were used during training: 55 - learning_rate: 0.0002 56 - train_batch_size: 16 57 - eval_batch_size: 8 58 - seed: 42 59 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 60 - lr_scheduler_type: linear 61 - num_epochs: 5 62 - mixed_precision_training: Native AMP 63 64 ### Training results 65 66 | Training Loss | Epoch | Step | Validation Loss | Accuracy | 67 |:-------------:|:-----:|:----:|:---------------:|:--------:| 68 | 0.0545 | 1.54 | 100 | 0.1436 | 0.9624 | 69 | 0.006 | 3.08 | 200 | 0.1058 | 0.9699 | 70 | 0.0038 | 4.62 | 300 | 0.0853 | 0.9774 | 71 72 73 ### Framework versions 74 75 - Transformers 4.9.2 76 - Pytorch 1.9.0+cu102 77 - Datasets 1.11.0 78 - Tokenizers 0.10.3 79