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: 0.2081
Accuracy: 0.9296

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
0.385	1.0	370	0.2946	0.9283
0.2121	2.0	740	0.2315	0.9364
0.1545	3.0	1110	0.2084	0.9364
0.1476	4.0	1480	0.2036	0.9391
0.1198	5.0	1850	0.2018	0.9405

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.

halimalm/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

Zero-Shot classification model

Results:

halimalm/
vit-base-oxford-iiit-pets