metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - cifar10
metrics:
  - accuracy
model-index:
  - name: swin-base-patch4-window7-224-in22k-finetuned-cifar10
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: cifar10
          type: cifar10
          config: plain_text
          split: train
          args: plain_text
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.989

swin-base-patch4-window7-224-in22k-finetuned-cifar10

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the cifar10 dataset. It achieves the following results on the evaluation set:

Loss: 0.0365
Accuracy: 0.989

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
0.2666	1.0	390	0.0560	0.9817
0.2169	2.0	780	0.0437	0.9864
0.2105	3.0	1170	0.0365	0.989

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.7.1
Tokenizers 0.13.2