README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - cifar10
metrics:
  - accuracy
model-index:
  - name: vit-base-patch16-224-in21k-finetuned-cifar10
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: cifar10
          type: cifar10
          args: plain_text
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9875
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: cifar10
          type: cifar10
          config: plain_text
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.973
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.9734266055324291
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.973
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.9734266055324291
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.9730000000000001
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.973
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.973
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.9730140713232215
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.973
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.9730140713232215
            verified: true
          - name: loss
            type: loss
            value: 0.09959099441766739
            verified: true

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

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: 0.0503
• Accuracy: 0.9875

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• 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
0.3118	1.0	1562	0.1135	0.9778
0.2717	2.0	3124	0.0619	0.9867
0.1964	3.0	4686	0.0503	0.9875

Framework versions

• Transformers 4.18.0.dev0
• Pytorch 1.11.0
• Datasets 2.0.0
• Tokenizers 0.11.6