vit-base-patch16-224-in21k-finetuned-cifar100

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar-100 dataset. It achieves the following results on the evaluation set:

• Loss: 0.7079
• Accuracy: 0.9054

Model description

More information needed

Intended uses & limitations

More information needed

How to Get Started with the Model

from transformers import pipeline

pipe = pipeline("image-classification", "avanishd/vit-base-patch16-224-in21k-finetuned-cifar10")
pipe(image)

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 6
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.9669	1	313	2.7011	0.8221
1.9046	2.992	626	1.6451	0.8779
1.2161	4.987	939	0.8919	0.9023
1.0013	5.986	1252	0.7079	0.9054

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1