---
license: apache-2.0
base_model: microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7129629629629629
---

<!-- 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. -->

# swinv2-large-patch4-window12to24-192to384-22kto1k-ft-microbes

This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to24-192to384-22kto1k-ft) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0311
- Accuracy: 0.7130

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.8445        | 0.98  | 15   | 2.8535          | 0.3194   |
| 2.1358        | 1.97  | 30   | 1.9654          | 0.4491   |
| 1.5947        | 2.95  | 45   | 1.4172          | 0.6204   |
| 1.045         | 4.0   | 61   | 1.1698          | 0.6806   |
| 0.985         | 4.98  | 76   | 1.1927          | 0.6852   |
| 0.775         | 5.97  | 91   | 1.1012          | 0.6898   |
| 0.7207        | 6.95  | 106  | 1.0311          | 0.7130   |
| 0.6611        | 7.87  | 120  | 1.0311          | 0.6991   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3