---
license: apache-2.0
base_model: microsoft/swinv2-base-patch4-window12-192-22k
tags:
- generated_from_trainer
datasets:
- p1atdev/pvc
metrics:
- accuracy
model-index:
- name: pvc-quality-swinv2-base
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5317220543806647
library_name: transformers
---

<!-- 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. -->

# pvc-quality-swinv2-base

This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window12-192-22k](https://huggingface.co/microsoft/swinv2-base-patch4-window12-192-22k) on the [pvc figure images dataset](https://huggingface.co/datasets/p1atdev/pvc).
It achieves the following results on the evaluation set:
- Loss: 1.2396
- Accuracy: 0.5317

## 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.7254        | 0.98  | 39   | 1.4826          | 0.4109   |
| 1.3316        | 1.99  | 79   | 1.2177          | 0.5136   |
| 1.0864        | 2.99  | 119  | 1.3006          | 0.4653   |
| 0.8572        | 4.0   | 159  | 1.2090          | 0.5015   |
| 0.7466        | 4.98  | 198  | 1.2150          | 0.5378   |
| 0.5986        | 5.99  | 238  | 1.4600          | 0.4955   |
| 0.4784        | 6.99  | 278  | 1.4131          | 0.5196   |
| 0.3525        | 8.0   | 318  | 1.5256          | 0.4985   |
| 0.3472        | 8.98  | 357  | 1.3883          | 0.5166   |
| 0.3281        | 9.81  | 390  | 1.5012          | 0.4955   |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0