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cifar10_outputs

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cifar10_outputs
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---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- cifar10
metrics:
- accuracy
model-index:
- name: cifar10_outputs
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: cifar10
      type: cifar10
      args: plain_text
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.991421568627451
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: cifar10
      type: cifar10
      config: plain_text
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9674
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.9679512973887299
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.9674
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9679512973887299
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.9673999999999999
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.9674
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.9674
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.9674620969256708
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.9674000000000001
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.967462096925671
      verified: true
    - name: loss
      type: loss
      value: 0.1527363657951355
      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. -->

# cifar10_outputs

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: 0.0806
- Accuracy: 0.9914

## 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: 0.0001
- train_batch_size: 17
- eval_batch_size: 17
- seed: 1337
- distributed_type: IPU
- gradient_accumulation_steps: 128
- total_train_batch_size: 8704
- total_eval_batch_size: 272
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 100.0
- training precision: Mixed Precision

### Training results



### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cpu
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1