---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-swinv2-tiny
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9253901789113057
    - name: F1
      type: f1
      value: 0.9052377115229654
    - name: Precision
      type: precision
      value: 0.9233171693926194
    - name: Recall
      type: recall
      value: 0.8878526831581444
---

<!-- 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. -->

# msi-swinv2-tiny

This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1768
- Accuracy: 0.9254
- F1: 0.9052
- Precision: 0.9233
- Recall: 0.8879

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3786        | 1.0   | 1970 | 0.3166          | 0.8590   | 0.8184 | 0.8469    | 0.7917 |
| 0.2976        | 2.0   | 3941 | 0.2426          | 0.8952   | 0.8621 | 0.9138    | 0.8159 |
| 0.2525        | 3.0   | 5911 | 0.2015          | 0.9144   | 0.8908 | 0.9132    | 0.8694 |
| 0.2319        | 4.0   | 7882 | 0.1859          | 0.9216   | 0.9026 | 0.8996    | 0.9056 |
| 0.206         | 5.0   | 9850 | 0.1768          | 0.9254   | 0.9052 | 0.9233    | 0.8879 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0