--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-railspace results: [] widget: - src: https://huggingface.co/davanstrien/autotrain-mapreader-5000-40830105612/resolve/main/1.png example_title: patch - src: https://huggingface.co/davanstrien/autotrain-mapreader-5000-40830105612/resolve/main/271.png example_title: patch --- # vit-base-beans-demo-v5 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: 0.0292 - Accuracy: 0.9926 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data precision recall f1-score support 0 1.00 1.00 1.00 11315 1 0.92 0.94 0.93 204 2 0.95 0.97 0.96 714 3 0.87 0.98 0.92 171 macro avg 0.93 0.97 0.95 12404 weighted avg 0.99 0.99 0.99 12404 accuracy 0.99 12404 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 64 - 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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0206 | 1.72 | 1000 | 0.0422 | 0.9854 | | 0.0008 | 3.44 | 2000 | 0.0316 | 0.9918 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.10.1 - Tokenizers 0.13.2