--- library_name: transformers license: apache-2.0 base_model: Xrenya/pvt-small-224 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: pvt-small-224-finetuned-papsmear 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: 0.2426470588235294 --- # pvt-small-224-finetuned-papsmear This model is a fine-tuned version of [Xrenya/pvt-small-224](https://huggingface.co/Xrenya/pvt-small-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: nan - Accuracy: 0.2426 ## 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-------:|:----:|:---------------:|:--------:| | 0.0 | 0.9935 | 38 | nan | 0.2426 | | 0.0 | 1.9869 | 76 | nan | 0.2426 | | 0.0 | 2.9804 | 114 | nan | 0.2426 | | 0.0 | 4.0 | 153 | nan | 0.2426 | | 0.0 | 4.9935 | 191 | nan | 0.2426 | | 0.0 | 5.9869 | 229 | nan | 0.2426 | | 0.0 | 6.9804 | 267 | nan | 0.2426 | | 0.0 | 8.0 | 306 | nan | 0.2426 | | 0.0 | 8.9935 | 344 | nan | 0.2426 | | 0.0 | 9.9869 | 382 | nan | 0.2426 | | 0.0 | 10.9804 | 420 | nan | 0.2426 | | 0.0 | 12.0 | 459 | nan | 0.2426 | | 0.0 | 12.9935 | 497 | nan | 0.2426 | | 0.0 | 13.9869 | 535 | nan | 0.2426 | | 0.0 | 14.9020 | 570 | nan | 0.2426 | ### Framework versions - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.1 - Tokenizers 0.19.1