vit-base-oxford-iiit-pets

Zero-Shot Classification Results on Oxford-Pet Dataset

We evaluated the model using a zero-shot image classification approach on the Oxford-Pet dataset. The openai/clip-vit-large-patch14 model was used for this evaluation.

The following results were obtained on the training split of the dataset:

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

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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: []

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vit-base-oxford-iiit-pets

This model 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: 0.1501
• Accuracy: 0.9499

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 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: 5

Training results

Framework versions

• Transformers 4.50.0
• Pytorch 2.6.0+cu124
• Datasets 3.4.1
• Tokenizers 0.21.1