vit-base-oxford-iiit-pets

Zero-Shot Classification Results (Oxford-IIIT Pets Test Set)

• Model Used: openai/clip-vit-large-patch14
• Accuracy: 0.9039
• Precision (Weighted): 0.9189
• Recall (Weighted): 0.9039
• Precision (Macro): 0.9131
• Recall (Macro): 0.9091

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: 0.2136
• Accuracy: 0.9350

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

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.3667	1.0	370	0.3159	0.9188
0.2091	2.0	740	0.2353	0.9418
0.1749	3.0	1110	0.2184	0.9391
0.1361	4.0	1480	0.2089	0.9432
0.1401	5.0	1850	0.2064	0.9405

Framework versions

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