---
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-merged
  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: 0.7268518518518519
---

<!-- 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-merged

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: 0.8626
- Accuracy: 0.7269

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.8355        | 0.98  | 15   | 2.5831          | 0.3333   |
| 1.9292        | 1.97  | 30   | 1.6850          | 0.5046   |
| 1.4121        | 2.95  | 45   | 1.2324          | 0.5972   |
| 1.0121        | 4.0   | 61   | 1.0345          | 0.6852   |
| 0.854         | 4.98  | 76   | 0.9663          | 0.6806   |
| 0.701         | 5.97  | 91   | 0.9587          | 0.6991   |
| 0.5956        | 6.95  | 106  | 0.8626          | 0.7269   |
| 0.5713        | 7.87  | 120  | 0.8645          | 0.7222   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cpu
- Datasets 2.14.4
- Tokenizers 0.13.3