README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - cifar100
metrics:
  - accuracy
model-index:
  - name: swin-base-finetuned-cifar100
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: cifar100
          type: cifar100
          config: cifar100
          split: train
          args: cifar100
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9201

swin-base-finetuned-cifar100

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224 on the cifar100 dataset. It achieves the following results on the evaluation set:

• Accuracy: 0.9201
• Loss: 0.3670

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: 4e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Accuracy	Validation Loss
0.3536	1.0	781	0.9052	0.3141
0.3254	2.0	1562	0.9117	0.2991
0.0936	3.0	2343	0.9138	0.3322
0.1054	4.0	3124	0.9158	0.3483
0.0269	5.0	3905	0.9201	0.3670

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Datasets 2.8.0
• Tokenizers 0.13.2