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image-multi-class-classification

Image Classification
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swin
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image-multi-class-classification
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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mnist
metrics:
- accuracy
model-index:
- name: image-classification
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: mnist
      type: mnist
      args: mnist
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9833333333333333
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: mnist
      type: mnist
      config: mnist
      split: test
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9837
      verified: true
    - name: Precision Macro
      type: precision
      value: 0.9836633320435293
      verified: true
    - name: Precision Micro
      type: precision
      value: 0.9837
      verified: true
    - name: Precision Weighted
      type: precision
      value: 0.9837581874425055
      verified: true
    - name: Recall Macro
      type: recall
      value: 0.9831030184134061
      verified: true
    - name: Recall Micro
      type: recall
      value: 0.9837
      verified: true
    - name: Recall Weighted
      type: recall
      value: 0.9837
      verified: true
    - name: F1 Macro
      type: f1
      value: 0.983311507665402
      verified: true
    - name: F1 Micro
      type: f1
      value: 0.9837
      verified: true
    - name: F1 Weighted
      type: f1
      value: 0.9836627364250822
      verified: true
    - name: loss
      type: loss
      value: 0.051053039729595184
      verified: true
    - name: matthews_correlation
      type: matthews_correlation
      value: 0.9818945021449504
      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. -->

# image-classification

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the mnist dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0556
- Accuracy: 0.9833

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3743        | 1.0   | 422  | 0.0556          | 0.9833   |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1