metadata

library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - cifar-10
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-in21k-finetuned-cifar10
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: cifar-10
          type: cifar-10
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9877

vit-base-patch16-224-in21k-finetuned-cifar10

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar-10 dataset. It achieves the following results on the evaluation set:

Loss: 0.1126
Accuracy: 0.9877

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

More information needed

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: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4166	1.0	313	0.2324	0.9791
0.3247	2.0	626	0.1320	0.9875
0.2661	2.992	936	0.1126	0.9877

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1