vit-base-oxford-iiit-pets

This model was trained to classify cats and dogs and define it's breed using transfer learning method. It is a fine-tuned version of google/vit-base-patch16-224 on the pcuenq/oxford-pets dataset. It achieves the following results on the evaluation set:

• Loss: 0.2068
• Accuracy: 0.9350

Model description

Since google/vit-base-patch16-224 was used as the base model, the final classification layer was modified to predict 37 classes of cats and dogs from the dataset.

Intended uses & limitations

This model is designed for educational purposes, enabling the classification of cats and dogs and the identification of their breeds. It currently supports 37 distinct breeds, offering a starting point for various learning and experimentation scenarios. Beyond its educational use, the model can serve as a foundation for further development, such as expanding its classification capabilities to include additional breeds, other animal species, or even entirely different tasks. With fine-tuning, this model could be adapted to broader applications in animal recognition, wildlife monitoring, and pet identification systems.

Training and evaluation data

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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Evaluation results

Framework versions

• Transformers 4.46.3
• Pytorch 2.0.1+cu117
• Datasets 3.1.0
• Tokenizers 0.20.3