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

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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vit-base-patch16-224-in21k-finetuned-cifar10
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---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/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