Verifitas Vit v3 5epoch

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: verifitas_vit_v3_5epoch results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: None args: default metrics: - name: Accuracy type: accuracy value: 0.993 --- <!-- 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_v3_5epoch 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: 0.0283 - Accuracy: 0.993 - Accuracy Ai: 0.9955 - Accuracy Real: 0.9906 ## 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: 16 - eval_batch_size: 16 - 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 - lr_scheduler_warmup_steps: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy Ai | Accuracy Real | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:-------------:| | 0.0236 | 1.0 | 1000 | 0.0565 | 0.9855 | 0.9960 | 0.9752 | | 0.0069 | 2.0 | 2000 | 0.0221 | 0.9932 | 0.9955 | 0.9911 | | 0.0088 | 3.0 | 3000 | 0.0305 | 0.992 | 0.9929 | 0.9911 | | 0.0001 | 4.0 | 4000 | 0.0368 | 0.992 | 0.9965 | 0.9876 | | 0.0000 | 5.0 | 5000 | 0.0283 | 0.993 | 0.9955 | 0.9906 | ### Framework versions - Transformers 5.10.2 - Pytorch 2.11.0+cu128 - Datasets 4.0.0 - Tokenizers 0.22.2