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: 0.9788
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: cifar10
          type: cifar10
          config: plain_text
          split: test
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9507
            verified: true
          - name: Precision Macro
            type: precision
            value: 0.9528091625476993
            verified: true
          - name: Precision Micro
            type: precision
            value: 0.9507
            verified: true
          - name: Precision Weighted
            type: precision
            value: 0.9528091625476993
            verified: true
          - name: Recall Macro
            type: recall
            value: 0.9507
            verified: true
          - name: Recall Micro
            type: recall
            value: 0.9507
            verified: true
          - name: Recall Weighted
            type: recall
            value: 0.9507
            verified: true
          - name: F1 Macro
            type: f1
            value: 0.9507751063124393
            verified: true
          - name: F1 Micro
            type: f1
            value: 0.9507
            verified: true
          - name: F1 Weighted
            type: f1
            value: 0.9507751063124393
            verified: true
          - name: loss
            type: loss
            value: 0.36527958512306213
            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: 0.2564
• Accuracy: 0.9788

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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4291	1.0	390	0.2564	0.9788

Framework versions

• Transformers 4.17.0
• Pytorch 1.10.0+cu111
• Datasets 2.0.0
• Tokenizers 0.11.6