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: 0.2023
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
0.3878	1.0	370	0.2921	0.9215
0.2188	2.0	740	0.2260	0.9269
0.1832	3.0	1110	0.2136	0.9283
0.14	4.0	1480	0.2050	0.9323
0.1322	5.0	1850	0.2030	0.9323

Framework versions

Transformers 4.50.0
Pytorch 2.6.0+cu124
Datasets 3.4.1
Tokenizers 0.21.1

Zero-Shot Evaluation Model used: ⁠ openai/clip-vit-large-patch14 ⁠ Dataset: Oxford-IIIT-Pets (⁠ pcuenq/oxford-pets ⁠) Accuracy: 0.8800 Precision: 0.8768 Recall: 0.8800 The zero-shot evaluation was done using Hugging Face Transformers and the CLIP model on the Oxford-Pet dataset.

thini77/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

thini77/
vit-base-oxford-iiit-pets