Verifitas Vit Image Detection

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer metrics: - accuracy model-index: - name: verifitas_vit_image_detection results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # verifitas_vit_image_detection This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0343 - Accuracy: 0.9925 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.0547 | 1.0 | 3600 | 0.0304 | 0.9894 | | 0.0063 | 2.0 | 7200 | 0.0405 | 0.9900 | | 0.0024 | 3.0 | 10800 | 0.0343 | 0.9925 | ### Framework versions - Transformers 5.10.1 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2

Source context: 7 downloads · 0 likes · Pipeline image-classification · Library transformers · Repo ibrahimamar07/verifitas_vit_image_detection