The CLIP model was not fine-tuned on this dataset. It demonstrates strong generalization but falls short of the ViT model trained via transfer learning.

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: 0.2031
Accuracy: 0.9459

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
0.3727	1.0	370	0.2756	0.9337
0.2145	2.0	740	0.2168	0.9378
0.1835	3.0	1110	0.1918	0.9459
0.147	4.0	1480	0.1857	0.9472
0.1315	5.0	1850	0.1818	0.9472

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

macbaileys/vit-base-oxford-iiit-pets

📊 Zero-Shot Evaluation using CLIP (openai/clip-vit-large-patch14)

vit-base-oxford-iiit-pets

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

macbaileys/
vit-base-oxford-iiit-pets