README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - image_folder
metrics:
  - accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
  - name: vit-base-patch16-224-in21k-finetuned-cifar10
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: image_folder
          type: image_folder
          args: default
        metrics:
          - type: accuracy
            value: 0.9881481481481481
            name: Accuracy

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

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

• Loss: 0.1357
• Accuracy: 0.9881

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2455	1.0	190	0.2227	0.9830
0.1363	2.0	380	0.1357	0.9881
0.0954	3.0	570	0.1194	0.9878

Framework versions

• Transformers 4.18.0
• Pytorch 1.10.0+cu111
• Datasets 2.0.0
• Tokenizers 0.11.6