vit-base-oxford-iiit-pets

This model is a fine-tuned version of google/vit-base-patch16-224 on the Oxford-IIIT Pets dataset. It has been trained to classify 37 breeds of cats and dogs.

It achieves the following results on the validation set:

• Loss: 0.2648
• Accuracy: 0.9459

Model description

This model is based on ViT (Vision Transformer), a transformer-based architecture for image classification that treats image patches as input tokens, enabling the use of pure transformer architectures on vision tasks.

Fine-tuning was done using the transformers Trainer API from Hugging Face.

Intended uses & limitations

You can use this model for:

• Classifying breeds of cats and dogs from the Oxford-IIIT Pets dataset.
• Fine-tuning on other animal classification datasets.
• Serving as a strong vision transformer baseline for academic or benchmarking purposes.

Limitations:

• Performance may degrade on images outside of the pet domain.
• Not optimized for mobile or edge devices.

Training and evaluation data

Dataset used: pcuenq/oxford-pets

• 7,390 training images
• 739 validation images
• 37 breed classes

Training procedure

Hyperparameters

• Learning rate: 5e-05
• Train batch size: 32
• Eval batch size: 8
• Seed: 42
• Optimizer: AdamW
• Scheduler: Linear
• Epochs: 10

Training results

Zero-shot evaluation results

Using a CLIP-based benchmark on the same dataset, the following zero-shot performance was observed:

• Accuracy: 0.8800
• Precision: 0.8768
• Recall: 0.8800

Framework versions

• Transformers: 4.50.3
• PyTorch: 2.5.1+cu121
• Datasets: 3.5.0
• Tokenizers: 0.21.1