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

📊 Evaluation on Oxford Pets Dataset (Zero-Shot Image Classification)

This model was evaluated using the Oxford Pets dataset in a zero-shot image classification setting, where no additional training was performed.

🔍 Model Information

Model used: openai/clip-vit-large-patch14
Task: Zero-Shot Image Classification
Approach: The model was prompted with a list of 37 pet breed labels and asked to classify each image from the dataset without any fine-tuning.

📈 Evaluation Results

Metric	Value
Accuracy	88.00%
Precision	87.68%
Recall	88.00%

norburay/vit-base-oxford-iiit-pets

vit-base-oxford-iiit-pets

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

📊 Evaluation on Oxford Pets Dataset (Zero-Shot Image Classification)

🔍 Model Information

📈 Evaluation Results

norburay/
vit-base-oxford-iiit-pets