--- license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: S2_M1_R2_vit_42499499 results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.998114985862394 --- # S2_M1_R2_vit_42499499 This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0063 - Accuracy: 0.9981 ## 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: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1394 | 0.99 | 66 | 0.0669 | 0.9915 | | 0.0058 | 2.0 | 133 | 0.0206 | 0.9953 | | 0.0118 | 2.99 | 199 | 0.0100 | 0.9981 | | 0.0037 | 4.0 | 266 | 0.0097 | 0.9981 | | 0.002 | 4.96 | 330 | 0.0063 | 0.9981 | ### Framework versions - Transformers 4.36.2 - Pytorch 1.11.0+cu102 - Datasets 2.16.0 - Tokenizers 0.15.0