Vit Base Oxford Iiit Pets

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224 tags: - image-classification - generated_from_trainer metrics: - accuracy model-index: - name: vit-base-oxford-iiit-pets 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. --> # vit-base-oxford-iiit-pets This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set: - Loss: 0.2032 - Accuracy: 0.9486 ## 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: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.3736 | 1.0 | 370 | 0.2691 | 0.9432 | | 0.2130 | 2.0 | 740 | 0.2125 | 0.9405 | | 0.1796 | 3.0 | 1110 | 0.1895 | 0.9418 | | 0.1441 | 4.0 | 1480 | 0.1829 | 0.9499 | | 0.1309 | 5.0 | 1850 | 0.1793 | 0.9540 | ### Framework versions - Transformers 5.12.1 - Pytorch 2.11.0+cpu - Datasets 4.0.0 - Tokenizers 0.22.2

Source context: 6 downloads · 0 likes · Pipeline image-classification · Library transformers · Repo Songsuk/vit-base-oxford-iiit-pets

Source context: 91 downloads · 0 likes · Pipeline image-classification · Library transformers · Repo Songsuk/vit-base-oxford-iiit-pets