README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - cifar10
metrics:
  - accuracy
model-index:
  - name: AML_A2_Q4
    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: 0.9894

AML_A2_Q4

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the cifar10 dataset. It achieves the following results on the evaluation set:

• Loss: 0.0432
• Accuracy: 0.9894

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: 2e-05
• train_batch_size: 20
• eval_batch_size: 4
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.1642	1.0	2250	0.0572	0.9862
0.1503	2.0	4500	0.0591	0.9854
0.1818	3.0	6750	0.0432	0.9894

Framework versions

• Transformers 4.28.0
• Pytorch 2.0.1+cu118
• Datasets 2.12.0
• Tokenizers 0.13.3