vit-base-oxford-iiit-pets

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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: 0.1893
• Accuracy: 0.9405

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

Zero-Shot classification model

This section compares the performance of a zero-shot model (openai/clip-vit-large-patch14) on the Oxford Pets dataset (pcuenq/oxford-pets).

• Model used: openai/clip-vit-large-patch14
• Dataset: pcuenq/oxford-pets (train split)
• Evaluation Task: Zero-Shot Image Classification
• Candidate Labels: 37 pet breeds from the dataset

Results:

Zero-Shot Evaluation mit CLIP:

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

Evaluated using Hugging Face transformers pipeline and sklearn.metrics on the full training set.